diff --git a/.all-contributorsrc b/.all-contributorsrc
index d9b17eb8bf..5cbd6f5ebd 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -442,7 +442,7 @@
       "login": "wigging",
       "name": "Gavin Wiggins",
       "avatar_url": "https://avatars.githubusercontent.com/u/6828967?v=4",
-      "profile": "https://wigging.me",
+      "profile": "https://gavinw.me",
       "contributions": [
         "bug",
         "code"
@@ -531,7 +531,8 @@
       "contributions": [
         "infra",
         "code",
-        "doc"
+        "doc",
+        "review"
       ]
     },
     {
@@ -613,7 +614,8 @@
       "contributions": [
         "infra",
         "code",
-        "doc"
+        "doc",
+        "review"
       ]
     },
     {
@@ -697,6 +699,34 @@
         "code",
         "test"
       ]
+    },
+    {
+      "login": "aitorres",
+      "name": "Andrés Ignacio Torres",
+      "avatar_url": "https://avatars.githubusercontent.com/u/26191851?v=4",
+      "profile": "https://aitorres.com",
+      "contributions": [
+        "infra"
+      ]
+    },
+    {
+      "login": "Agnik7",
+      "name": "Agnik Bakshi",
+      "avatar_url": "https://avatars.githubusercontent.com/u/77234005?v=4",
+      "profile": "https://github.com/Agnik7",
+      "contributions": [
+        "doc"
+      ]
+    },
+    {
+      "login": "RuiheLi",
+      "name": "RuiheLi",
+      "avatar_url": "https://avatars.githubusercontent.com/u/84007676?v=4",
+      "profile": "https://github.com/RuiheLi",
+      "contributions": [
+        "code",
+        "test"
+      ]
     }
   ],
   "contributorsPerLine": 7,
diff --git a/.github/release_reminder.md b/.github/release_reminder.md
index 2515166837..94066e80c8 100644
--- a/.github/release_reminder.md
+++ b/.github/release_reminder.md
@@ -1,9 +1,10 @@
 ---
 title: Create {{ date | date('YY.MM') }} (final or rc0) release
+labels: priority:high
 ---
 Quarterly reminder to create a -
 
 1. pre-release if the month has just started.
 2. non-pre-release if the month is about to end (**before the end of the month**).
 
-See [Release Workflow](./release_workflow.md) for more information.
+See [Release Workflow](https://github.com/pybamm-team/PyBaMM/blob/develop/.github/release_workflow.md) for more information.
diff --git a/.github/release_workflow.md b/.github/release_workflow.md
index 04f0667773..690f7fa407 100644
--- a/.github/release_workflow.md
+++ b/.github/release_workflow.md
@@ -1,21 +1,21 @@
 # Release workflow
 
-This file contains the workflow required to make a `PyBaMM` release on GitHub and PyPI by the maintainers.
+This file contains the workflow required to make a `PyBaMM` release on GitHub, PyPI, and conda-forge by the maintainers.
 
 ## rc0 releases (automated)
 
-1. The `update_version.yml` workflow will run on every 1st of January, May and September, updating incrementing the version to `YY.MMrc0` by running `scripts/update_version.py` in the following files -
+1. The `update_version.yml` workflow will run on every 1st of January, May and September, updating incrementing the version to `vYY.MMrc0` by running `scripts/update_version.py` in the following files -
 
    - `pybamm/version.py`
    - `docs/conf.py`
    - `CITATION.cff`
+   - `pyproject.toml`
    - `vcpkg.json`
-   - `docs/_static/versions.json`
    - `CHANGELOG.md`
 
-      These changes will be automatically pushed to a new branch `YY.MM`.
+      These changes will be automatically pushed to a new branch `vYY.MM` and a PR from `vvYY.MM` to `develop` will be created (to sync the branches).
 
-2. Create a new GitHub _pre-release_ with the tag `YY.MMrc0` from the `YY.MM` branch and a description copied from `CHANGELOG.md`.
+2. Create a new GitHub _pre-release_ with the tag `vYY.MMrc0` from the `vYY.MM` branch and a description copied from `CHANGELOG.md`.
 
 3. This release will automatically trigger `publish_pypi.yml` and create a _pre-release_ on PyPI.
 
@@ -23,22 +23,22 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub an
 
 If a new release candidate is required after the release of `rc0` -
 
-1. Fix a bug in `YY.MM` (no new features should be added to `YY.MM` once `rc0` is released) and `develop` individually.
+1. Fix a bug in `vYY.MM` (no new features should be added to `vYY.MM` once `rc0` is released) and `develop` individually.
 
 2. Run `update_version.yml` manually while using `append_to_tag` to specify the release candidate version number (`rc1`, `rc2`, ...).
 
-3. This will increment the version to `YY.MMrcX` by running `scripts/update_version.py` in the following files -
+3. This will increment the version to `vYY.MMrcX` by running `scripts/update_version.py` in the following files -
 
    - `pybamm/version.py`
    - `docs/conf.py`
    - `CITATION.cff`
+   - `pyproject.toml`
    - `vcpkg.json`
-   - `docs/_static/versions.json`
    - `CHANGELOG.md`
 
-      These changes will be automatically pushed to the existing branch `YY.MM`.
+      These changes will be automatically pushed to the existing `vYY.MM` branch and a PR from `vvYY.MM` to `develop` will be created (to sync the branches).
 
-4. Create a new GitHub _pre-release_ with the same tag (`YY.MMrcX`) from the `YY.MM` branch and a description copied from `CHANGELOG.md`.
+4. Create a new GitHub _pre-release_ with the same tag (`vYY.MMrcX`) from the `vYY.MM` branch and a description copied from `CHANGELOG.md`.
 
 5. This release will automatically trigger `publish_pypi.yml` and create a _pre-release_ on PyPI.
 
@@ -48,18 +48,18 @@ Once satisfied with the release candidates -
 
 1. Run `update_version.yml` manually, leaving the `append_to_tag` field blank ("") for an actual release.
 
-2. This will increment the version to `YY.MMrcX` by running `scripts/update_version.py` in the following files -
+2. This will increment the version to `vYY.MMrcX` by running `scripts/update_version.py` in the following files -
 
    - `pybamm/version.py`
    - `docs/conf.py`
    - `CITATION.cff`
+   - `pyproject.toml`
    - `vcpkg.json`
-   - `docs/_static/versions.json`
    - `CHANGELOG.md`
 
-      These changes will be automatically pushed to the existing branch `YY.MM`.
+      These changes will be automatically pushed to the existing `vYY.MM` branch and a PR from `vvYY.MM` to `develop` will be created (to sync the branches).
 
-3. Next, a PR from `YY.MM` to `main` will be generated that should be merged once all the tests pass.
+3. Next, a PR from `vYY.MM` to `main` will be generated that should be merged once all the tests pass.
 
 4. Create a new GitHub _release_ with the same tag from the `main` branch and a description copied from `CHANGELOG.md`.
 
@@ -70,5 +70,11 @@ Once satisfied with the release candidates -
 Some other essential things to check throughout the release process -
 
 - If updating our custom vcpkg registory entries [pybamm-team/sundials-vcpkg-registry](https://github.com/pybamm-team/sundials-vcpkg-registry) or [pybamm-team/casadi-vcpkg-registry](https://github.com/pybamm-team/casadi-vcpkg-registry) (used to build Windows wheels), make sure to update the baseline of the registories in vcpkg-configuration.json to the latest commit id.
-- Update jax and jaxlib to the latest version in `pybamm.util` and `setup.py`, fixing any bugs that arise
-- Make sure the URLs in `docs/_static/versions.json` are valid
+- Update jax and jaxlib to the latest version in `pybamm.util` and `pyproject.toml`, fixing any bugs that arise
+- As the release workflow is initiated by the `release` event, it's important to note that the default `GITHUB_REF` used by `actions/checkout` during the checkout process will correspond to the tag created during the release process. Consequently, the workflows will consistently build PyBaMM based on the commit associated with this tag. Should new commits be introduced to the `vYY.MM` branch, such as those addressing build issues, it becomes necessary to manually update this tag to point to the most recent commit -
+  ```
+  git tag -f <tag_name> <commit_hash>
+  git push -f <pybamm-team/PyBaMM_remote_name> <tag_name>  # can only be carried out by the maintainers
+  ```
+- If changes are made to the API, console scripts, entry points, new optional dependencies are added, support for major Python versions is dropped or added, or core project information and metadata are modified at the time of the release, make sure to update the `meta.yaml` file in the `recipe/` folder of the [conda-forge/pybamm-feedstock](https://github.com/conda-forge/pybamm-feedstock) repository accordingly by following the instructions in the [conda-forge documentation](https://conda-forge.org/docs/maintainer/updating_pkgs.html#updating-the-feedstock-repository) and re-rendering the recipe
+- The conda-forge release workflow will automatically be triggered following a stable PyPI release, and the aforementioned updates should be carried out directly in the main repository by pushing changes to the automated PR created by the conda-forge-bot. A manual PR can also be created if the updates are not included in the automated PR for some reason. This manual PR **must** bump the build number in `meta.yaml` and **must** be from a personal fork of the repository.
diff --git a/.github/wheel_failure.md b/.github/wheel_failure.md
new file mode 100644
index 0000000000..107b4dd6d6
--- /dev/null
+++ b/.github/wheel_failure.md
@@ -0,0 +1,6 @@
+---
+title: Fortnightly build for wheels failed
+labels: priority:high, bug
+---
+
+The build is failing with the following logs - {{ env.LOGS }}
diff --git a/.github/workflows/benchmark_on_push.yml b/.github/workflows/benchmark_on_push.yml
index 8be4af8741..11ed419572 100644
--- a/.github/workflows/benchmark_on_push.yml
+++ b/.github/workflows/benchmark_on_push.yml
@@ -18,16 +18,20 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.8
+
       - name: Install Linux system dependencies
         run: |
           sudo apt-get update
           sudo apt install gfortran gcc libopenblas-dev
+
       - name: Install python dependencies
-      # Pin asv==0.5.1 to fix failing benchmarks. Related to https://github.com/airspeed-velocity/asv/issues/1323
         run: |
-          python -m pip install --upgrade pip wheel setuptools virtualenv asv==0.5.1 wget cmake casadi numpy
-      - name: Install SuiteSparse and Sundials
+          python -m pip install --upgrade pip wheel setuptools wget cmake casadi numpy
+          python -m pip install asv[virtualenv]
+
+      - name: Install SuiteSparse and SUNDIALS
         run: python scripts/install_KLU_Sundials.py
+
       - name: Fetch base branch
         run: |
           # This workflow also runs for merge commits
@@ -49,7 +53,8 @@ jobs:
           HEAD_COMMIT=$(git rev-parse HEAD)
           echo $BASE_COMMIT | tee commits_to_compare.txt
           echo $HEAD_COMMIT | tee -a commits_to_compare.txt
-          asv run HASHFILE:commits_to_compare.txt --m "GitHubRunner" --show-stderr --strict -v
+          asv run HASHFILE:commits_to_compare.txt --m "GitHubRunner" --show-stderr -v
+
       - name: Compare commits' benchmark results
         run: |
           BASE_COMMIT=$(head -1 commits_to_compare.txt)
diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
new file mode 100644
index 0000000000..b6994795d6
--- /dev/null
+++ b/.github/workflows/docker.yml
@@ -0,0 +1,86 @@
+name: Build and push Docker images to Docker Hub
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+    - develop
+
+jobs:
+  build_docker_images:
+    # This workflow is only of value to PyBaMM and would always be skipped in forks
+    if: github.repository_owner == 'pybamm-team'
+    name: Image (${{ matrix.build-args }})
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        build-args: ["No solvers", "JAX", "ODES", "IDAKLU", "ALL"]
+      fail-fast: true
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v3
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Login to Docker Hub
+        uses: docker/login-action@v3
+        with:
+            username: ${{ secrets.DOCKERHUB_USERNAME }}
+            password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: Create tags for Docker images based on build-time arguments
+        id: tags
+        run: |
+          if [ "${{ matrix.build-args }}" = "No solvers" ]; then
+            echo "tag=latest" >> "$GITHUB_OUTPUT"
+          elif [ "${{ matrix.build-args }}" = "JAX" ]; then
+            echo "tag=jax" >> "$GITHUB_OUTPUT"
+          elif [ "${{ matrix.build-args }}" = "ODES" ]; then
+            echo "tag=odes" >> "$GITHUB_OUTPUT"
+          elif [ "${{ matrix.build-args }}" = "IDAKLU" ]; then
+            echo "tag=idaklu" >> "$GITHUB_OUTPUT"
+          elif [ "${{ matrix.build-args }}" = "ALL" ]; then
+            echo "tag=all" >> "$GITHUB_OUTPUT"
+          fi
+
+      - name: Build and push Docker image to Docker Hub (no solvers)
+        if: matrix.build-args == 'No solvers'
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          file: scripts/Dockerfile
+          tags: pybamm/pybamm:${{ steps.tags.outputs.tag }}
+          push: true
+          platforms: linux/amd64, linux/arm64
+
+      - name: Build and push Docker image to Docker Hub (with ODES and IDAKLU solvers)
+        if: matrix.build-args == 'ODES' || matrix.build-args == 'IDAKLU'
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          file: scripts/Dockerfile
+          tags: pybamm/pybamm:${{ steps.tags.outputs.tag }}
+          push: true
+          build-args: ${{ matrix.build-args }}=true
+          platforms: linux/amd64, linux/arm64
+
+      - name: Build and push Docker image to Docker Hub (with ALL and JAX solvers)
+        if: matrix.build-args == 'ALL' || matrix.build-args == 'JAX'
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          file: scripts/Dockerfile
+          tags: pybamm/pybamm:${{ steps.tags.outputs.tag }}
+          push: true
+          build-args: ${{ matrix.build-args }}=true
+          # exclude arm64 for JAX and ALL builds for now, see
+          # https://github.com/google/jax/issues/13608
+          platforms: linux/amd64
+
+      - name: List built image(s)
+        run: docker images
diff --git a/.github/workflows/lychee_url_checker.yml b/.github/workflows/lychee_url_checker.yml
index 4282b8f83d..93dde63845 100644
--- a/.github/workflows/lychee_url_checker.yml
+++ b/.github/workflows/lychee_url_checker.yml
@@ -45,13 +45,18 @@ jobs:
             --accept 200,429
             --exclude-path ./CHANGELOG.md
             --exclude-path ./scripts/update_version.py
+            --exclude-path asv.conf.json
             --exclude-path docs/conf.py
             './**/*.rst'
             './**/*.md'
             './**/*.py'
             './**/*.ipynb'
+            './**/*.json'
+            './**/*.toml'
           # fail the action on broken links
           fail: true
+          jobSummary: true
+          format: markdown
         env:
           # to be used in case rate limits are surpassed
           GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}}
diff --git a/.github/workflows/periodic_benchmarks.yml b/.github/workflows/periodic_benchmarks.yml
index ce0ad37cd2..b0b27d0fe3 100644
--- a/.github/workflows/periodic_benchmarks.yml
+++ b/.github/workflows/periodic_benchmarks.yml
@@ -20,26 +20,33 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+
       - name: Set up Python 3.8
         uses: actions/setup-python@v4
         with:
           python-version: 3.8
+
       - name: Install Linux system dependencies
         run: |
           sudo apt-get update
           sudo apt-get install gfortran gcc libopenblas-dev
+
       - name: Install python dependencies
         run: |
-          python -m pip install --upgrade pip wheel setuptools virtualenv asv wget cmake casadi numpy
-      - name: Install SuiteSparse and Sundials
+          python -m pip install --upgrade pip wheel setuptools wget cmake casadi numpy
+          python -m pip install asv[virtualenv]
+
+      - name: Install SuiteSparse and SUNDIALS
         run: python scripts/install_KLU_Sundials.py
+
       - name: Run benchmarks
         run: |
           asv machine --machine "GitHubRunner"
-          asv run --machine "GitHubRunner" NEW --show-stderr --strict -v
+          asv run --machine "GitHubRunner" NEW --show-stderr -v
         env:
           SUNDIALS_INST: $HOME/.local
           LD_LIBRARY_PATH: $HOME/.local/lib
+
       - name: Upload results as artifact
         uses: actions/upload-artifact@v3
         with:
@@ -55,18 +62,22 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.8
+
       - name: Install asv
         run: pip install asv
+
       - name: Checkout pybamm-bench repo
         uses: actions/checkout@v4
         with:
           repository: pybamm-team/pybamm-bench
           token: ${{ secrets.BENCH_PAT }}
+
       - name: Download results artifact
         uses: actions/download-artifact@v3
         with:
           name: asv_new_results
           path: new_results
+
       - name: Copy new results and push to pybamm-bench repo
         env:
           PUSH_BENCH_EMAIL: ${{ secrets.PUSH_BENCH_EMAIL }}
@@ -78,6 +89,7 @@ jobs:
           git add results
           git commit -am "Add new results"
           git push
+
       - name: Publish results
         run: |
           asv publish
diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 919a00d6ef..3073c95f09 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -1,13 +1,15 @@
 name: Build and publish package to PyPI
-
 on:
   release:
     types: [published]
+  schedule:
+    # Run at 10 am UTC on day-of-month 1 and 15.
+    - cron: "0 10 1,15 * *"
   workflow_dispatch:
     inputs:
       target:
         description: 'Deployment target. Can be "pypi" or "testpypi"'
-        default: "pypi"
+        default: "testpypi"
       debug_enabled:
         type: boolean
         description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'
@@ -24,14 +26,10 @@ jobs:
         with:
           python-version: 3.8
 
-      - name: Install cibuildwheel
-        run: python -m pip install cibuildwheel==2.12.3
-
       - name: Clone pybind11 repo (no history)
-        run: git clone --depth 1 --branch v2.10.4 https://github.com/pybind/pybind11.git
+        run: git clone --depth 1 --branch v2.11.1 https://github.com/pybind/pybind11.git
 
-      # remove when a new vcpkg version is released
-      - name: Install the latest commit of vcpkg on windows
+      - name: Install vcpkg on Windows
         run: |
           cd C:\
           rm -r -fo 'C:\vcpkg'
@@ -39,7 +37,7 @@ jobs:
           cd vcpkg
           .\bootstrap-vcpkg.bat
 
-      - name: Cache packages installed through vcpkg on windows
+      - name: Cache packages installed through vcpkg on Windows
         uses: actions/cache@v3
         env:
           cache-name: vckpg_binary_cache
@@ -52,14 +50,13 @@ jobs:
         uses: mxschmitt/action-tmate@v3
         if: ${{ github.event_name == 'workflow_dispatch' && inputs.debug_enabled }}
 
-      - name: Build 64 bits wheels on Windows
-        run: |
-          python -m cibuildwheel --output-dir wheelhouse
+      - name: Build 64-bit wheels on Windows
+        run: pipx run cibuildwheel --output-dir wheelhouse
         env:
           CIBW_ENVIRONMENT: 'PYBAMM_USE_VCPKG=ON VCPKG_ROOT_DIR=C:\vcpkg VCPKG_DEFAULT_TRIPLET=x64-windows-static-md VCPKG_FEATURE_FLAGS=manifests,registries CMAKE_GENERATOR="Visual Studio 17 2022" CMAKE_GENERATOR_PLATFORM=x64'
           CIBW_ARCHS: "AMD64"
 
-      - name: Upload windows wheels
+      - name: Upload Windows wheels
         uses: actions/upload-artifact@v3
         with:
           name: windows_wheels
@@ -79,42 +76,34 @@ jobs:
         with:
           python-version: 3.8
 
-      - name: Install cibuildwheel
-        run: python -m pip install cibuildwheel==2.12.3
-
       - name: Clone pybind11 repo (no history)
-        run: git clone --depth 1 --branch v2.10.4 https://github.com/pybind/pybind11.git
+        run: git clone --depth 1 --branch v2.11.1 https://github.com/pybind/pybind11.git
 
-      - name: Install SUNDIALS on macOS
+      # sometimes gfortran cannot be found, so reinstall gcc just to be sure
+      - name: Install SuiteSparse and SUNDIALS on macOS
         if: matrix.os == 'macos-latest'
         run: |
-          # https://github.com/actions/virtual-environments/issues/1280
-          rm -f /usr/local/bin/2to3*
-          rm -f /usr/local/bin/idle3*
-          rm -f /usr/local/bin/pydoc3*
-          rm -f /usr/local/bin/python3*
-          brew update
+          brew install graphviz openblas libomp
           brew reinstall gcc
-          brew install libomp
           python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
 
-      - name: Build wheels on Linux and MacOS
-        run: python -m cibuildwheel --output-dir wheelhouse
+      - name: Build wheels on ${{ matrix.os }}
+        run: pipx run cibuildwheel --output-dir wheelhouse
         env:
-          # TODO: openblas no longer available on centos 7 i686 image, use blas instead for now
+          CIBW_ARCHS_LINUX: x86_64
           CIBW_BEFORE_ALL_LINUX: >
-            yum -y install blas-devel lapack-devel &&
-            bash build_manylinux_wheels/install_sundials.sh 5.8.1 6.5.0
-
-          CIBW_BEFORE_BUILD_LINUX: "python -m pip install cmake casadi numpy"
+            yum -y install openblas-devel lapack-devel &&
+            bash scripts/install_sundials.sh 6.0.3 6.5.0
+          CIBW_BEFORE_BUILD_LINUX: >
+            python -m pip install cmake casadi numpy
+          # override; point to casadi install path so that it can be found by the repair command
+          CIBW_REPAIR_WHEEL_COMMAND_LINUX: >
+            LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:$(python -c 'import casadi; print(casadi.__path__[0])')" auditwheel repair -w {dest_dir} {wheel}
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
             install cmake casadi numpy &&
-            python scripts/fix_casadi_rpath_mac.py &&
-            scripts/fix_suitesparse_rpath_mac.sh
-          # got error "re.error: multiple repeat at position 104" on python 3.7 when --require-archs added, so remove
-          # it for mac
+            python scripts/fix_casadi_rpath_mac.py && scripts/fix_suitesparse_rpath_mac.sh
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: >
             delocate-listdeps {wheel} &&
             delocate-wheel -v -w {dest_dir} {wheel}
@@ -128,22 +117,22 @@ jobs:
           if-no-files-found: error
 
   build_sdist:
-    name: Build sdist
+    name: Build SDist
     runs-on: ubuntu-latest
 
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.8
+          python-version: 3.11
 
       - name: Install dependencies
-        run: pip install wheel
+        run: pip install --upgrade pip setuptools wheel
 
-      - name: Build sdist
-        run: python setup.py sdist --formats=gztar
+      - name: Build SDist
+        run: pipx run build --sdist
 
-      - name: Upload sdist
+      - name: Upload SDist
         uses: actions/upload-artifact@v3
         with:
           name: sdist
@@ -151,6 +140,7 @@ jobs:
           if-no-files-found: error
 
   publish_pypi:
+    if: github.event_name != 'schedule'
     name: Upload package to PyPI
     needs: [build_wheels, build_windows_wheels, build_sdist]
     runs-on: ubuntu-latest
@@ -164,14 +154,12 @@ jobs:
           mv windows_wheels/* wheels/* sdist/* files/
 
       - name: Publish on PyPI
-        if: |
-          github.event.inputs.target == 'pypi' ||
-          (github.event_name == 'push' && github.ref == 'refs/heads/main')
+        if: github.event.inputs.target == 'pypi' || github.event_name == 'release'
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
           user: __token__
           password: ${{ secrets.PYPI_TOKEN }}
-          packages_dir: files/
+          packages-dir: files/
 
       - name: Publish on TestPyPI
         if: github.event.inputs.target == 'testpypi'
@@ -179,5 +167,19 @@ jobs:
         with:
           user: __token__
           password: ${{ secrets.TESTPYPI_TOKEN }}
-          packages_dir: files/
-          repository_url: https://test.pypi.org/legacy/
+          packages-dir: files/
+          repository-url: https://test.pypi.org/legacy/
+
+  open_failure_issue:
+    needs: [build_windows_wheels, build_wheels, build_sdist]
+    name: Open an issue if build fails
+    if: ${{ always() && contains(needs.*.result, 'failure') }}
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+    - uses: JasonEtco/create-an-issue@v2
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        LOGS: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}
+      with:
+        filename: .github/wheel_failure.md
diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index b403630db5..4545dc26df 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -12,24 +12,19 @@ on:
   schedule:
     - cron: "0 3 * * *"
 
-jobs:
-  pre_job:
-    runs-on: ubuntu-latest
-    # Map a step output to a job output
-    outputs:
-      should_skip: ${{ steps.skip_check.outputs.should_skip }}
-    steps:
-      - id: skip_check
-        uses: fkirc/skip-duplicate-actions@master
-        with:
-          # All of these options are optional, so you can remove them if you are happy with the defaults
-          concurrent_skipping: "never"
-          cancel_others: "true"
-          paths_ignore: '["**/README.md"]'
+env:
+  FORCE_COLOR: 3
+
+concurrency:
+  # github.workflow: name of the workflow, so that we don't cancel other workflows
+  # github.event.pull_request.number || github.ref: pull request number or branch name if not a pull request
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  # Cancel in-progress runs when a new workflow with the same group name is triggered
+  # This avoids workflow runs on both pushes and PRs
+  cancel-in-progress: true
 
+jobs:
   style:
-    needs: pre_job
-    if: ${{ needs.pre_job.outputs.should_skip != 'true' }}
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
@@ -66,58 +61,54 @@ jobs:
           sudo apt install gfortran gcc libopenblas-dev graphviz pandoc
           sudo apt install texlive-full
 
-      # Added fixes to homebrew installs:
-      # rm -f /usr/local/bin/2to3
-      # (see https://github.com/actions/virtual-environments/issues/2322)
-      - name: Install MacOS system dependencies
+      - name: Install macOS system dependencies
         if: matrix.os == 'macos-latest'
         run: |
-          rm -f /usr/local/bin/2to3*
-          rm -f /usr/local/bin/idle3*
-          rm -f /usr/local/bin/pydoc3*
-          rm -f /usr/local/bin/python3*
-          brew update
-          brew install graphviz
-          brew install openblas
+          brew analytics off
+          brew install graphviz openblas libomp
+          brew reinstall gcc
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
         run: choco install graphviz --version=2.38.0.20190211
 
-      - name: Install standard python dependencies
-        run: |
-          python -m pip install --upgrade pip wheel setuptools nox
+      - name: Install nox
+        run: python -m pip install nox
 
-      - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        if: matrix.os == 'ubuntu-latest'
-        run: nox -s pybamm-requires
+      - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
+        if: matrix.os != 'windows-latest'
+        run: python -m nox -s pybamm-requires
 
       - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, and 3.10, and for macOS and Windows with all Python versions
         if: (matrix.os == 'ubuntu-latest' && matrix.python-version != 3.11) || (matrix.os != 'ubuntu-latest')
-        run: nox -s unit
+        run: python -m nox -s unit
 
       - name: Run unit tests for GNU/Linux with Python 3.11 and generate coverage report
         if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11
-        run: nox -s coverage
+        run: python -m nox -s coverage
 
       - name: Upload coverage report
         if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11
         uses: codecov/codecov-action@v3.1.4
 
       - name: Run integration tests
-        run: nox -s integration
+        run: python -m nox -s integration
 
       - name: Install docs dependencies and run doctests
         if: matrix.os == 'ubuntu-latest'
-        run: nox -s doctests
+        run: python -m nox -s doctests
+
+      - name: Check if the documentation can be built
+        if: matrix.os == 'ubuntu-latest'
+        run: python -m nox -s docs
 
       - name: Install dev dependencies and run example tests
         if: matrix.os == 'ubuntu-latest'
-        run: nox -s examples
+        run: python -m nox -s examples
 
       - name: Run example scripts tests
         if: matrix.os == 'ubuntu-latest'
-        run: nox -s scripts
+        run: python -m nox -s scripts
 
   #M-series Mac Mini
   build-apple-mseries:
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 839d53306f..2f7f94c9bc 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -4,6 +4,9 @@ on:
   workflow_dispatch:
   pull_request:
 
+env:
+  FORCE_COLOR: 3
+
 concurrency:
   # github.workflow: name of the workflow, so that we don't cancel other workflows
   # github.event.pull_request.number || github.ref: pull request number or branch name if not a pull request
@@ -47,7 +50,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         if: matrix.os == 'ubuntu-latest'
         with:
           packages: gfortran gcc graphviz pandoc
@@ -70,10 +73,11 @@ jobs:
           HOMEBREW_NO_COLOR: 1
           # Speed up CI
           NONINTERACTIVE: 1
+        # sometimes gfortran cannot be found, so reinstall gcc just to be sure
         run: |
           brew analytics off
-          brew update
-          brew install graphviz openblas
+          brew install graphviz openblas libomp
+          brew reinstall gcc
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -85,16 +89,13 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
-          cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
+      - name: Install nox
+        run: python -m pip install nox
 
-      - name: Cache pybamm-requires nox environment for GNU/Linux
+      - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
-        if: matrix.os == 'ubuntu-latest'
+        if: matrix.os != 'windows-latest'
         with:
           path: |
             # Repository files
@@ -104,14 +105,14 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
 
-      - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        if: matrix.os == 'ubuntu-latest'
-        run: nox -s pybamm-requires
+      - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
+        if: matrix.os != 'windows-latest'
+        run: python -m nox -s pybamm-requires
 
       - name: Run unit tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
-        run: nox -s unit
+        run: python -m nox -s unit
 
   # Runs only on Ubuntu with Python 3.11
   check_coverage:
@@ -127,7 +128,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         with:
           packages: gfortran gcc graphviz pandoc
           execute_install_scripts: true
@@ -145,12 +146,9 @@ jobs:
         with:
           python-version: 3.11
           cache: 'pip'
-          cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
+      - name: Install nox
+        run: python -m pip install nox
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -163,13 +161,13 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Run unit tests for Ubuntu with Python 3.11 and generate coverage report
-        run: nox -s coverage
+        run: python -m nox -s coverage
 
       - name: Upload coverage report
         uses: codecov/codecov-action@v3.1.4
@@ -190,7 +188,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         if: matrix.os == 'ubuntu-latest'
         with:
           packages: gfortran gcc graphviz pandoc
@@ -213,10 +211,11 @@ jobs:
           HOMEBREW_NO_COLOR: 1
           # Speed up CI
           NONINTERACTIVE: 1
+        # sometimes gfortran cannot be found, so reinstall gcc just to be sure
         run: |
           brew analytics off
-          brew update
-          brew install graphviz openblas
+          brew install graphviz openblas libomp
+          brew reinstall gcc
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -228,16 +227,13 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
-          cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
+      - name: Install nox
+        run: python -m pip install nox
 
-      - name: Cache pybamm-requires nox environment for GNU/Linux
+      - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
-        if: matrix.os == 'ubuntu-latest'
+        if: matrix.os != 'windows-latest'
         with:
           path: |
             # Repository files
@@ -247,22 +243,23 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
 
-      - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        if: matrix.os == 'ubuntu-latest'
-        run: nox -s pybamm-requires
+      - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
+        if: matrix.os != 'windows-latest'
+        run: python -m nox -s pybamm-requires
 
       - name: Run integration tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
-        run: nox -s integration
+        run: python -m nox -s integration
 
-  # Runs only on Ubuntu with Python 3.11
-  run_doctests_and_example_tests:
+# Runs only on Ubuntu with Python 3.11. Skips IDAKLU module compilation
+# for speedups, which is already tested in other jobs.
+  run_doctests:
     needs: style
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-    name: Doctests and notebooks (ubuntu-latest / Python 3.11)
+    name: Doctests (ubuntu-latest / Python 3.11)
 
     steps:
       - name: Check out PyBaMM repository
@@ -270,7 +267,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         with:
           packages: gfortran gcc graphviz pandoc
           execute_install_scripts: true
@@ -288,12 +285,51 @@ jobs:
         with:
           python-version: 3.11
           cache: 'pip'
-          cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install nox
+        run: python -m pip install nox
+
+      - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
+        run: python -m nox -s doctests
+
+      - name: Check if the documentation can be built for GNU/Linux with Python 3.11
+        run: python -m nox -s docs
+
+  # Runs only on Ubuntu with Python 3.11
+  run_example_tests:
+    needs: style
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+    name: Example notebooks (ubuntu-latest / Python 3.11)
+
+    steps:
+      - name: Check out PyBaMM repository
+        uses: actions/checkout@v4
+
+      # Install and cache apt packages
+      - name: Install Linux system dependencies
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
+        with:
+          packages: gfortran gcc graphviz pandoc
+          execute_install_scripts: true
+
+      # dot -c is for registering graphviz fonts and plugins
+      - name: Install OpenBLAS and TeXLive for Linux
         run: |
-          pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
+          sudo apt-get update
+          sudo dot -c
+          sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
+
+      - name: Set up Python 3.11
+        id: setup-python
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.11
+          cache: 'pip'
+
+      - name: Install nox
+        run: python -m pip install nox
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -306,16 +342,13 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
-      - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
-        run: nox -s doctests
-
-      - name: Install dev dependencies and run example tests for GNU/Linux with Python 3.11
-        run: nox -s examples
+      - name: Run example notebooks tests for GNU/Linux with Python 3.11
+        run: python -m nox -s examples
 
   # Runs only on Ubuntu with Python 3.11
   run_scripts_tests:
@@ -331,7 +364,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         with:
           packages: gfortran gcc graphviz
           execute_install_scripts: true
@@ -349,12 +382,9 @@ jobs:
         with:
           python-version: 3.11
           cache: 'pip'
-          cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
+      - name: Install nox
+        run: python -m pip install nox
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -367,10 +397,10 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
-      - name: Install dev dependencies and run example scripts tests for GNU/Linux with Python 3.11
-        run: nox -s scripts
+      - name: Run example scripts tests for GNU/Linux with Python 3.11
+        run: python -m nox -s scripts
diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml
index 472de06f0e..0d63e68007 100644
--- a/.github/workflows/update_version.yml
+++ b/.github/workflows/update_version.yml
@@ -63,7 +63,17 @@ jobs:
         with:
           message: 'Bump to ${{ env.VERSION }}'
 
+      - name: Make a PR from ${{ env.NON_RC_VERSION }} to develop
+        uses: repo-sync/pull-request@v2
+        with:
+          source_branch: '${{ env.NON_RC_VERSION }}'
+          destination_branch: "develop"
+          pr_title: "Sync ${{ env.NON_RC_VERSION }} and develop"
+          pr_body: "**Merge as soon as possible to avoid potential conflicts.**"
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+
       - name: Make a PR from ${{ env.NON_RC_VERSION }} to main
+        id: release_pr
         if: github.event_name == 'workflow_dispatch' && !startsWith(github.event.inputs.append_to_tag, 'rc')
         uses: repo-sync/pull-request@v2
         with:
diff --git a/.gitignore b/.gitignore
index 3e01fcac83..612dc777b1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -138,6 +138,7 @@ results/
 # do not ignore images in _static folder in docs
 !docs/_static/favicon/favicon.png
 !docs/_static/pybamm_logo.png
+!docs/_static/pybamm_logo_whitetext.png
 
 # tests
 test_callback.log
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 64ab531521..ed837e6fdb 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,17 +4,11 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.0.291"
+    rev: "v0.1.6"
     hooks:
       - id: ruff
-        args: [--fix, --ignore=E741, --exclude=__init__.py]
-
-  - repo: https://github.com/nbQA-dev/nbQA
-    rev: 1.7.0
-    hooks:
-      - id: nbqa-ruff
-        additional_dependencies: [ruff==0.0.284]
-        args: ["--fix","--ignore=E501,E402"]
+        args: [--fix, --show-fixes]
+        types_or: [python, pyi, jupyter]
 
   - repo: https://github.com/adamchainz/blacken-docs
     rev: "1.16.0"
@@ -23,7 +17,7 @@ repos:
          additional_dependencies: [black==22.12.0]
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
         - id: check-added-large-files
         - id: check-case-conflict
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 412b1b642e..3c644e4037 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,15 +1,35 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
+## Bug fixes
+
+- Fixed a bug where simulations using the CasADi-based solvers would fail randomly with the half-cell model ([#3494](https://github.com/pybamm-team/PyBaMM/pull/3494))
+- Fixed bug that made identical Experiment steps with different end times crash ([#3516](https://github.com/pybamm-team/PyBaMM/pull/3516))
+- Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
+- The irreversible plating model now increments `f"{Domain} dead lithium concentration [mol.m-3]"`, not `f"{Domain} lithium plating concentration [mol.m-3]"` as it did previously. ([#3485](https://github.com/pybamm-team/PyBaMM/pull/3485))
+
+# [v23.9](https://github.com/pybamm-team/PyBaMM/tree/v23.9) - 2023-10-31
+
 ## Features
+
 - The parameter "Ambient temperature [K]" can now be given as a function of position `(y,z)` and time `t`. The "edge" and "current collector" heat transfer coefficient parameters can also depend on `(y,z)` ([#3257](https://github.com/pybamm-team/PyBaMM/pull/3257))
 - Spherical and cylindrical shell domains can now be solved with any boundary conditions ([#3237](https://github.com/pybamm-team/PyBaMM/pull/3237))
 - Processed variables now get the spatial variables automatically, allowing plotting of more generic models ([#3234](https://github.com/pybamm-team/PyBaMM/pull/3234))
 - Numpy functions now work with PyBaMM symbols (e.g. `np.exp(pybamm.Symbol("a"))` returns `pybamm.Exp(pybamm.Symbol("a"))`). This means that parameter functions can be specified using numpy functions instead of pybamm functions. Additionally, combining numpy arrays with pybamm objects now works (the numpy array is converted to a pybamm array) ([#3205](https://github.com/pybamm-team/PyBaMM/pull/3205))
+- Half-cell models where graphite - or other negative electrode material of choice - is treated as the positive electrode ([#3198](https://github.com/pybamm-team/PyBaMM/pull/3198))
+- Degradation mechanisms `SEI`, `SEI on cracks` and `lithium plating` can be made to work on the positive electrode by specifying the relevant options as a 2-tuple. If a tuple is not given and `working electrode` is set to `both`, they will be applied on the negative electrode only. ([#3198](https://github.com/pybamm-team/PyBaMM/pull/3198))
+- Added an example notebook to demonstrate how to use half-cell models ([#3198](https://github.com/pybamm-team/PyBaMM/pull/3198))
+- Added option to use an empirical hysteresis model for the diffusivity and exchange-current density ([#3194](https://github.com/pybamm-team/PyBaMM/pull/3194))
+- Double-layer capacity can now be provided as a function of temperature ([#3174](https://github.com/pybamm-team/PyBaMM/pull/3174))
+- `pybamm_install_jax` is deprecated. It is now replaced with `pip install pybamm[jax]` ([#3163](https://github.com/pybamm-team/PyBaMM/pull/3163))
 - Implement the MSMR model ([#3116](https://github.com/pybamm-team/PyBaMM/pull/3116))
+- Added new example notebook `rpt-experiment` to demonstrate how to set up degradation experiments with RPTs ([#2851](https://github.com/pybamm-team/PyBaMM/pull/2851))
 
 ## Bug fixes
 
 - Fixed a bug where coordinate systems of variables do not get checked against known ([#3394](https://github.com/pybamm-team/PyBaMM/pull/3394))
+- Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
+- Make pybamm importable with minimal dependencies ([#3044](https://github.com/pybamm-team/PyBaMM/pull/3044), [#3475](https://github.com/pybamm-team/PyBaMM/pull/3475))
+- Fixed a bug where supplying an initial soc did not work with half cell models ([#3456](https://github.com/pybamm-team/PyBaMM/pull/3456))
 - Fixed a bug where empty lists passed to QuickPlot resulted in an IndexError and did not return a meaningful error message ([#3359](https://github.com/pybamm-team/PyBaMM/pull/3359))
 - Fixed a bug where there was a missing thermal conductivity in the thermal pouch cell models ([#3330](https://github.com/pybamm-team/PyBaMM/pull/3330))
 - Fixed a bug that caused incorrect results of “{Domain} electrode thickness change [m]” due to the absence of dimension for the variable `electrode_thickness_change`([#3329](https://github.com/pybamm-team/PyBaMM/pull/3329)).
@@ -19,6 +39,7 @@
 - Fixed bug causing incorrect activation energies using `create_from_bpx()` ([#3242](https://github.com/pybamm-team/PyBaMM/pull/3242))
 - Fixed a bug where the "basic" lithium-ion models gave incorrect results when using nonlinear particle diffusivity ([#3207](https://github.com/pybamm-team/PyBaMM/pull/3207))
 - Particle size distributions now work with SPMe and NewmanTobias models ([#3207](https://github.com/pybamm-team/PyBaMM/pull/3207))
+- Attempting to set `working electrode` to `negative` now triggers an `OptionError`. Instead, set it to `positive` and use what would normally be the negative electrode as the positive electrode. ([#3198](https://github.com/pybamm-team/PyBaMM/pull/3198))
 - Fix to simulate c_rate steps with drive cycles ([#3186](https://github.com/pybamm-team/PyBaMM/pull/3186))
 - Always save last cycle in experiment, to fix issues with `starting_solution` and `last_state` ([#3177](https://github.com/pybamm-team/PyBaMM/pull/3177))
 - Fix simulations with `starting_solution` to work with `start_time` experiments ([#3177](https://github.com/pybamm-team/PyBaMM/pull/3177))
@@ -35,18 +56,24 @@
 
 ## Breaking changes
 
+- The parameter "Exchange-current density for lithium plating [A.m-2]" has been renamed to "Exchange-current density for lithium metal electrode [A.m-2]" when referring to the lithium plating reaction on the surface of a lithium metal electrode ([#3445](https://github.com/pybamm-team/PyBaMM/pull/3445))
+- Dropped support for i686 (32-bit) architectures on GNU/Linux distributions ([#3412](https://github.com/pybamm-team/PyBaMM/pull/3412))
 - The class `pybamm.thermal.OneDimensionalX` has been moved to `pybamm.thermal.pouch_cell.OneDimensionalX` to reflect the fact that the model formulation implicitly assumes a pouch cell geometry ([#3257](https://github.com/pybamm-team/PyBaMM/pull/3257))
 - The "lumped" thermal option now always used the parameters "Cell cooling surface area [m2]", "Cell volume [m3]" and "Total heat transfer coefficient [W.m-2.K-1]" to compute the cell cooling regardless of the chosen "cell geometry" option. The user must now specify the correct values for these parameters instead of them being calculated based on e.g. a pouch cell. An `OptionWarning` is raised to let users know to update their parameters ([#3257](https://github.com/pybamm-team/PyBaMM/pull/3257))
 - Numpy functions now work with PyBaMM symbols (e.g. `np.exp(pybamm.Symbol("a"))` returns `pybamm.Exp(pybamm.Symbol("a"))`). This means that parameter functions can be specified using numpy functions instead of pybamm functions. Additionally, combining numpy arrays with pybamm objects now works (the numpy array is converted to a pybamm array) ([#3205](https://github.com/pybamm-team/PyBaMM/pull/3205))
+- The `SEI`, `SEI on cracks` and `lithium plating` submodels can now be used on either electrode, which means the `__init__` functions for the relevant classes now have `domain` as a required argument ([#3198](https://github.com/pybamm-team/PyBaMM/pull/3198))
+- Likewise, the names of all variables corresponding to those submodels now have domains. For example, instead of `SEI thickness [m]`, use `Negative SEI thickness [m]` or `Positive SEI thickness [m]`. ([#3198](https://github.com/pybamm-team/PyBaMM/pull/3198))
+- If `options["working electrode"] == "both"` and either `SEI`, `SEI on cracks` or `lithium plating` are not provided as tuples, they are automatically made into tuples. This directly modifies `extra_options`, not `default_options` to ensure the other changes to `default_options` still happen when required. ([#3198](https://github.com/pybamm-team/PyBaMM/pull/3198))
 - Added option to use an empirical hysteresis model for the diffusivity and exchange-current density ([#3194](https://github.com/pybamm-team/PyBaMM/pull/3194))
 - Double-layer capacity can now be provided as a function of temperature ([#3174](https://github.com/pybamm-team/PyBaMM/pull/3174))
 - `pybamm_install_jax` is deprecated. It is now replaced with `pip install pybamm[jax]` ([#3163](https://github.com/pybamm-team/PyBaMM/pull/3163))
-- PyBaMM now has optional dependencies that can be installed with the pattern `pip install pybamm[option]` e.g. `pybamm[plot]` ([#3044](https://github.com/pybamm-team/PyBaMM/pull/3044))
+- PyBaMM now has optional dependencies that can be installed with the pattern `pip install pybamm[option]` e.g. `pybamm[plot]` ([#3044](https://github.com/pybamm-team/PyBaMM/pull/3044), [#3475](https://github.com/pybamm-team/PyBaMM/pull/3475))
 
 # [v23.5](https://github.com/pybamm-team/PyBaMM/tree/v23.5) - 2023-06-18
 
 ## Features
 
+- Idaklu solver can be given a list of variables to calculate during the solve ([#3217](https://github.com/pybamm-team/PyBaMM/pull/3217))
 - Enable multithreading in IDAKLU solver ([#2947](https://github.com/pybamm-team/PyBaMM/pull/2947))
 - If a solution contains cycles and steps, the cycle number and step number are now saved when `solution.save_data()` is called ([#2931](https://github.com/pybamm-team/PyBaMM/pull/2931))
 - Experiments can now be given a `start_time` to define when each step should be triggered ([#2616](https://github.com/pybamm-team/PyBaMM/pull/2616))
diff --git a/CITATION.cff b/CITATION.cff
index f5d6fe4911..44f1c5d407 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -24,6 +24,6 @@ keywords:
   - "expression tree"
   - "python"
   - "symbolic differentiation"
-version: "23.5"
+version: "23.9"
 repository-code: "https://github.com/pybamm-team/PyBaMM"
 title: "Python Battery Mathematical Modelling (PyBaMM)"
diff --git a/CMakeBuild.py b/CMakeBuild.py
deleted file mode 100644
index 5b34bb27df..0000000000
--- a/CMakeBuild.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import os
-import sys
-import subprocess
-from pathlib import Path
-from platform import system
-
-try:
-    from setuptools.command.build_ext import build_ext
-except ImportError:
-    from distutils.command.build_ext import build_ext
-
-default_lib_dir = (
-    "" if system() == "Windows" else os.path.join(os.getenv("HOME"), ".local")
-)
-
-
-def set_vcpkg_environment_variables():
-    if not os.getenv("VCPKG_ROOT_DIR"):
-        raise EnvironmentError("Environment variable 'VCPKG_ROOT_DIR' is undefined.")
-    if not os.getenv("VCPKG_DEFAULT_TRIPLET"):
-        raise EnvironmentError(
-            "Environment variable 'VCPKG_DEFAULT_TRIPLET' is undefined."
-        )
-    if not os.getenv("VCPKG_FEATURE_FLAGS"):
-        raise EnvironmentError(
-            "Environment variable 'VCPKG_FEATURE_FLAGS' is undefined."
-        )
-    return (
-        os.getenv("VCPKG_ROOT_DIR"),
-        os.getenv("VCPKG_DEFAULT_TRIPLET"),
-        os.getenv("VCPKG_FEATURE_FLAGS"),
-    )
-
-
-class CMakeBuild(build_ext):
-    user_options = build_ext.user_options + [
-        ("suitesparse-root=", None, "suitesparse source location"),
-        ("sundials-root=", None, "sundials source location"),
-    ]
-
-    def initialize_options(self):
-        build_ext.initialize_options(self)
-        self.suitesparse_root = None
-        self.sundials_root = None
-
-    def finalize_options(self):
-        build_ext.finalize_options(self)
-        # Determine the calling command to get the
-        # undefined options from.
-        # If build_ext was called directly then this
-        # doesn't matter.
-        try:
-            self.get_finalized_command("install", create=0)
-            calling_cmd = "install"
-        except AttributeError:
-            calling_cmd = "bdist_wheel"
-        self.set_undefined_options(
-            calling_cmd,
-            ("suitesparse_root", "suitesparse_root"),
-            ("sundials_root", "sundials_root"),
-        )
-        if not self.suitesparse_root:
-            self.suitesparse_root = os.path.join(default_lib_dir)
-        if not self.sundials_root:
-            self.sundials_root = os.path.join(default_lib_dir)
-
-    def get_build_directory(self):
-        # distutils outputs object files in directory self.build_temp
-        # (typically build/temp.*). This is our CMake build directory.
-        # On Windows, distutils is too smart and appends "Release" or
-        # "Debug" to self.build_temp. So in this case we want the
-        # build directory to be the parent directory.
-        if system() == "Windows":
-            return Path(self.build_temp).parents[0]
-        return self.build_temp
-
-    def run(self):
-        if not self.extensions:
-            return
-
-        if system() == "Windows":
-            use_python_casadi = False
-        else:
-            use_python_casadi = True
-
-        build_type = os.getenv("PYBAMM_CPP_BUILD_TYPE", "RELEASE")
-        cmake_args = [
-            "-DCMAKE_BUILD_TYPE={}".format(build_type),
-            "-DPYTHON_EXECUTABLE={}".format(sys.executable),
-            "-DUSE_PYTHON_CASADI={}".format("TRUE" if use_python_casadi else "FALSE"),
-        ]
-        if self.suitesparse_root:
-            cmake_args.append(
-                "-DSuiteSparse_ROOT={}".format(os.path.abspath(self.suitesparse_root))
-            )
-        if self.sundials_root:
-            cmake_args.append(
-                "-DSUNDIALS_ROOT={}".format(os.path.abspath(self.sundials_root))
-            )
-
-        build_dir = self.get_build_directory()
-        if not os.path.exists(build_dir):
-            os.makedirs(build_dir)
-
-        # The CMakeError.log file is generated by cmake is the configure step
-        # encounters error. In the following the existence of this file is used
-        # to determine whether or not the cmake configure step went smoothly.
-        # So must make sure this file does not remain from a previous failed build.
-        if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
-            os.remove(os.path.join(build_dir, "CMakeError.log"))
-
-        build_env = os.environ
-        if os.getenv("PYBAMM_USE_VCPKG"):
-            (
-                vcpkg_root_dir,
-                vcpkg_default_triplet,
-                vcpkg_feature_flags,
-            ) = set_vcpkg_environment_variables()
-            build_env["vcpkg_root_dir"] = vcpkg_root_dir
-            build_env["vcpkg_default_triplet"] = vcpkg_default_triplet
-            build_env["vcpkg_feature_flags"] = vcpkg_feature_flags
-
-        cmake_list_dir = os.path.abspath(os.path.dirname(__file__))
-        print("-" * 10, "Running CMake for idaklu solver", "-" * 40)
-        subprocess.run(
-            ["cmake", cmake_list_dir] + cmake_args, cwd=build_dir, env=build_env
-        )
-
-        if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
-            msg = (
-                "cmake configuration steps encountered errors, and the idaklu module"
-                " could not be built. Make sure dependencies are correctly "
-                "installed. See "
-                "https://github.com/pybamm-team/PyBaMM/tree/develop"
-                "INSTALL-LINUX-MAC.md"
-            )
-            raise RuntimeError(msg)
-        else:
-            print("-" * 10, "Building idaklu module", "-" * 40)
-            subprocess.run(
-                ["cmake", "--build", ".", "--config", "Release"],
-                cwd=build_dir,
-                env=build_env,
-            )
-
-            # Move from build temp to final position
-            for ext in self.extensions:
-                self.move_output(ext)
-
-    def move_output(self, ext):
-        # Copy built module to dist/ directory
-        build_temp = Path(self.build_temp).resolve()
-        # Get destination location
-        # self.get_ext_fullpath(ext.name) -->
-        # build/lib.linux-x86_64-3.5/idaklu.cpython-37m-x86_64-linux-gnu.so
-        # using resolve() with python < 3.6 will result in a FileNotFoundError
-        # since the location does not yet exists.
-        dest_path = Path(self.get_ext_fullpath(ext.name)).resolve()
-        source_path = build_temp / os.path.basename(self.get_ext_filename(ext.name))
-        dest_directory = dest_path.parents[0]
-        dest_directory.mkdir(parents=True, exist_ok=True)
-        self.copy_file(source_path, dest_path)
diff --git a/CMakeLists.txt b/CMakeLists.txt
index c3c5141d4f..182fd489f3 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -24,7 +24,10 @@ set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_CXX_EXTENSIONS OFF)
 set(CMAKE_EXPORT_COMPILE_COMMANDS 1)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
-
+if(NOT MSVC)
+    # MSVC does not support variable length arrays (vla)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Werror=vla")
+endif()
 
 # casadi seems to compile without the newer versions of std::string
 add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
@@ -39,8 +42,14 @@ pybind11_add_module(idaklu
   pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp
   pybamm/solvers/c_solvers/idaklu/casadi_solver.cpp
   pybamm/solvers/c_solvers/idaklu/casadi_solver.hpp
-  pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.hpp
+  pybamm/solvers/c_solvers/idaklu/CasadiSolver.cpp
+  pybamm/solvers/c_solvers/idaklu/CasadiSolver.hpp
+  pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.cpp
+  pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.hpp
+  pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP_solvers.cpp
+  pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP_solvers.hpp
   pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp
+  pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.hpp
   pybamm/solvers/c_solvers/idaklu/common.hpp
   pybamm/solvers/c_solvers/idaklu/python.hpp
   pybamm/solvers/c_solvers/idaklu/python.cpp
@@ -63,8 +72,8 @@ execute_process(
 
 if (CASADI_DIR)
   file(TO_CMAKE_PATH ${CASADI_DIR} CASADI_DIR)
+  message("Found python casadi path: ${CASADI_DIR}")
 endif()
-message("Found python casadi path: ${CASADI_DIR}")
 
 if(${USE_PYTHON_CASADI})
   message("Trying to link against python casadi package")
@@ -78,7 +87,7 @@ endif()
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PROJECT_SOURCE_DIR})
 # Sundials
 find_package(SUNDIALS REQUIRED)
-message("sundials ${SUNDIALS_INCLUDE_DIR} ${SUNDIALS_LIBRARIES}")
+message("SUNDIALS found in ${SUNDIALS_INCLUDE_DIR}: ${SUNDIALS_LIBRARIES}")
 target_include_directories(idaklu PRIVATE ${SUNDIALS_INCLUDE_DIR})
 target_link_libraries(idaklu PRIVATE ${SUNDIALS_LIBRARIES} casadi)
 
@@ -89,6 +98,7 @@ if(DEFINED VCPKG_ROOT_DIR)
   find_package(SuiteSparse CONFIG REQUIRED)
 else()
   find_package(SuiteSparse REQUIRED)
+  message("SuiteSparse found in ${SuiteSparse_INCLUDE_DIRS}: ${SuiteSparse_LIBRARIES}")
 endif()
 include_directories(${SuiteSparse_INCLUDE_DIRS})
 target_link_libraries(idaklu PRIVATE ${SuiteSparse_LIBRARIES})
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 577dbd67c6..b9800dcd61 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -44,7 +44,7 @@ You now have everything you need to start making changes!
 
 ### B. Writing your code
 
-6. PyBaMM is developed in [Python](https://en.wikipedia.org/wiki/Python_(programming_language)), and makes heavy use of [NumPy](https://en.wikipedia.org/wiki/NumPy) (see also [NumPy for MatLab users](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html) and [Python for R users](http://blog.hackerearth.com/how-can-r-users-learn-python-for-data-science)).
+6. PyBaMM is developed in [Python](https://en.wikipedia.org/wiki/Python_(programming_language)), and makes heavy use of [NumPy](https://en.wikipedia.org/wiki/NumPy) (see also [NumPy for MatLab users](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html) and [Python for R users](https://www.rebeccabarter.com/blog/2023-09-11-from_r_to_python)).
 7. Make sure to follow our [coding style guidelines](#coding-style-guidelines).
 8. Commit your changes to your branch with [useful, descriptive commit messages](https://chris.beams.io/posts/git-commit/): Remember these are publicly visible and should still make sense a few months ahead in time. While developing, you can keep using the GitHub issue you're working on as a place for discussion. [Refer to your commits](https://stackoverflow.com/questions/8910271/how-can-i-reference-a-commit-in-an-issue-comment-on-github) when discussing specific lines of code.
 9. If you want to add a dependency on another library, or re-use code you found somewhere else, have a look at [these guidelines](#dependencies-and-reusing-code).
@@ -72,7 +72,7 @@ python -m pip install pre-commit
 pre-commit run ruff
 ```
 
-ruff is configured inside the file `pre-commit-config.yaml`, allowing us to ignore some errors. If you think this should be added or removed, please submit an [issue](#issues)
+ruff is configured inside the file `pre-commit-config.yaml`, allowing us to ignore some errors. If you think this should be added or removed, please submit an [issue](https://github.com/pybamm-team/PyBaMM/issues)
 
 When you commit your changes they will be checked against ruff automatically (see [Pre-commit checks](#pre-commit-checks)).
 
@@ -100,21 +100,52 @@ On the other hand... We _do_ want to compare several tools, to generate document
 
 Only 'core pybamm' is installed by default. The others have to be specified explicitly when running the installation command.
 
-### Matplotlib
+### Managing Optional Dependencies and Their Imports
 
-We use Matplotlib in PyBaMM, but with two caveats:
+PyBaMM utilizes optional dependencies to allow users to choose which additional libraries they want to use. Managing these optional dependencies and their imports is essential to provide flexibility to PyBaMM users.
 
-First, Matplotlib should only be used in plotting methods, and these should _never_ be called by other PyBaMM methods. So users who don't like Matplotlib will not be forced to use it in any way. Use in notebooks is OK and encouraged.
+PyBaMM provides a utility function `have_optional_dependency`, to check for the availability of optional dependencies within methods. This function can be used to conditionally import optional dependencies only if they are available. Here's how to use it:
 
-Second, Matplotlib should never be imported at the module level, but always inside methods. For example:
+Optional dependencies should never be imported at the module level, but always inside methods. For example:
 
 ```
-def plot_great_things(self, x, y, z):
-    import matplotlib.pyplot as pl
+def use_pybtex(x,y,z):
+    pybtex = have_optional_dependency("pybtex")
     ...
 ```
 
-This allows people to (1) use PyBaMM without ever importing Matplotlib and (2) configure Matplotlib's back-end in their scripts, which _must_ be done before e.g. `pyplot` is first imported.
+While importing a specific module instead of an entire package/library:
+
+```python
+def use_parse_file(x, y, z):
+    parse_file = have_optional_dependency("pybtex.database", "parse_file")
+    ...
+```
+
+This allows people to (1) use PyBaMM without importing optional dependencies by default and (2) configure module-dependent functionalities in their scripts, which _must_ be done before e.g. `print_citations` method is first imported.
+
+**Writing Tests for Optional Dependencies**
+
+Whenever a new optional dependency is added for optional functionality, it is recommended to write a corresponding unit test in `test_util.py`. This ensures that an error is raised upon the absence of said dependency. Here's an example:
+
+```python
+from tests import TestCase
+import pybamm
+
+
+class TestUtil(TestCase):
+    def test_optional_dependency(self):
+        # Test that an error is raised when pybtex is not available
+        with self.assertRaisesRegex(
+            ModuleNotFoundError, "Optional dependency pybtex is not available"
+        ):
+            sys.modules["pybtex"] = None
+            pybamm.function_using_pybtex(x, y, z)
+
+        # Test that the function works when pybtex is available
+        sys.modules["pybtex"] = pybamm.util.have_optional_dependency("pybtex")
+        pybamm.function_using_pybtex(x, y, z)
+```
 
 ## Testing
 
@@ -185,7 +216,7 @@ You may also test multiple notebooks this way. Passing the path to a folder will
 nox -s examples -- docs/source/examples/notebooks/models/
 ```
 
-You may also use an appropriate [glob pattern](https://www.malikbrowne.com/blog/a-beginners-guide-glob-patterns) to run all notebooks matching a particular folder or name pattern.
+You may also use an appropriate [glob pattern](https://docs.python.org/3/library/glob.html) to run all notebooks matching a particular folder or name pattern.
 
 To edit the structure and how the Jupyter notebooks get rendered in the Sphinx documentation (using `nbsphinx`), install [Pandoc](https://pandoc.org/installing.html) on your system, either using `conda` (through the `conda-forge` channel)
 
@@ -266,7 +297,6 @@ This also means that, if you can't fix the bug yourself, it will be much easier
       ```
 
       This will start the debugger at the point where the `ValueError` was raised, and allow you to investigate further. Sometimes, it is more informative to put the try-except block further up the call stack than exactly where the error is raised.
-
    2. Warnings. If functions are raising warnings instead of errors, it can be hard to pinpoint where this is coming from. Here, you can use the `warnings` module to convert warnings to errors:
 
       ```python
@@ -276,7 +306,6 @@ This also means that, if you can't fix the bug yourself, it will be much easier
       ```
 
       Then you can use a try-except block, as in a., but with, for example, `RuntimeWarning` instead of `ValueError`.
-
    3. Stepping through the expression tree. Most calls in PyBaMM are operations on [expression trees](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb). To view an expression tree in ipython, you can use the `render` command:
 
       ```python
@@ -284,11 +313,8 @@ This also means that, if you can't fix the bug yourself, it will be much easier
       ```
 
       You can then step through the expression tree, using the `children` attribute, to pinpoint exactly where a bug is coming from. For example, if `expression_tree.jac(y)` is failing, you can check `expression_tree.children[0].jac(y)`, then `expression_tree.children[0].children[0].jac(y)`, etc.
-
 3. To isolate whether a bug is in a model, its Jacobian or its simplified version, you can set the `use_jacobian` and/or `use_simplify` attributes of the model to `False` (they are both `True` by default for most models).
-
 4. If a model isn't giving the answer you expect, you can try comparing it to other models. For example, you can investigate parameter limits in which two models should give the same answer by setting some parameters to be small or zero. The `StandardOutputComparison` class can be used to compare some standard outputs from battery models.
-
 5. To get more information about what is going on under the hood, and hence understand what is causing the bug, you can set the [logging](https://realpython.com/python-logging/) level to `DEBUG` by adding the following line to your test or script:
 
    ```python3
@@ -347,17 +373,17 @@ Using [Sphinx](http://www.sphinx-doc.org/en/stable/) the documentation in `docs`
 
 ### Building the documentation
 
-To test and debug the documentation, it's best to build it locally. To do this, navigate to your PyBaMM directory in a console, and then type:
+To test and debug the documentation, it's best to build it locally. To do this, navigate to your PyBaMM directory in a console, and then type (on GNU/Linux, macOS, and Windows):
 
 ```
-nox -s docs (GNU/Linux, MacOS and Windows)
+nox -s docs
 ```
 
-And then visit the webpage served at http://127.0.0.1:8000. Each time a change to the documentation source is detected, the HTML is rebuilt and the browser automatically reloaded.
+And then visit the webpage served at `http://127.0.0.1:8000`. Each time a change to the documentation source is detected, the HTML is rebuilt and the browser automatically reloaded. In CI, the docs are built and tested using the `docs` session in the `noxfile.py` file with warnings turned into errors, to fail the build. The warnings can be removed or ignored by adding the appropriate warning identifier to the `suppress_warnings` list in `docs/conf.py`.
 
 ### Example notebooks
 
-Major PyBaMM features are showcased in [Jupyter notebooks](https://jupyter.org/) stored in the [docs/source/examples directory](docs/source/examples/notebooks). Which features are "major" is of course wholly subjective, so please discuss on GitHub first!
+Major PyBaMM features are showcased in [Jupyter notebooks](https://jupyter.org/) stored in the [docs/source/examples directory](https://github.com/pybamm-team/PyBaMM/tree/develop/docs/source/examples). Which features are "major" is of course wholly subjective, so please discuss on GitHub first!
 
 All example notebooks should be listed in [docs/source/examples/index.rst](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/index.rst). Please follow the (naming and writing) style of existing notebooks where possible.
 
@@ -375,7 +401,7 @@ pybamm.print_citations()
 
 to the end of a script will print all citations that were used by that script. This will print BibTeX information to the terminal; passing a filename to `print_citations` will print the BibTeX information to the specified file instead.
 
-When you contribute code to PyBaMM, you can add your own papers that you would like to be cited if that code is used. First, add the BibTeX for your paper to [CITATIONS.bib](pybamm/CITATIONS.bib). Then, add the line
+When you contribute code to PyBaMM, you can add your own papers that you would like to be cited if that code is used. First, add the BibTeX for your paper to [CITATIONS.bib](https://github.com/pybamm-team/PyBaMM/blob/develop/pybamm/CITATIONS.bib). Then, add the line
 
 ```python3
 pybamm.citations.register("your_paper_bibtex_identifier")
@@ -385,21 +411,23 @@ wherever code is called that uses that citation (for example, in functions or in
 
 ## Infrastructure
 
-### Setuptools
+### Installation
 
-Installation of PyBaMM _and dependencies_ is handled via [setuptools](http://setuptools.readthedocs.io/)
+Installation of PyBaMM and its dependencies is handled via [pip](https://pip.pypa.io/en/stable/) and [setuptools](http://setuptools.readthedocs.io/). It uses `CMake` to compile C++ extensions using [`pybind11`](https://pybind11.readthedocs.io/en/stable/) and [`casadi`](https://web.casadi.org/). The installation process is described in detail in the [source installation](https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html) page and is configured through the `CMakeLists.txt` file.
 
 Configuration files:
 
 ```
 setup.py
+pyproject.toml
+MANIFEST.in
 ```
 
-Note that this file must be kept in sync with the version number in [pybamm/**init**.py](pybamm/__init__.py).
+Note: `MANIFEST.in` is used to include and exclude non-Python files and auxiliary package data for PyBaMM when distributing it. If a file is not included in `MANIFEST.in`, it will not be included in the source distribution (SDist) and subsequently not be included in the binary distribution (wheel).
 
-### Continuous Integration using GitHub actions
+### Continuous Integration using GitHub Actions
 
-Each change pushed to the PyBaMM GitHub repository will trigger the test and benchmark suites to be run, using [GitHub actions](https://github.com/features/actions).
+Each change pushed to the PyBaMM GitHub repository will trigger the test and benchmark suites to be run, using [GitHub Actions](https://github.com/features/actions).
 
 Tests are run for different operating systems, and for all Python versions officially supported by PyBaMM. If you opened a Pull Request, feedback is directly available on the corresponding page. If all tests pass, a green tick will be displayed next to the corresponding test run. If one or more test(s) fail, a red cross will be displayed instead.
 
@@ -431,9 +459,9 @@ Editable notebooks are made available using [Google Colab](https://colab.researc
 
 GitHub does some magic with particular filenames. In particular:
 
-- The first page people see when they go to [our GitHub page](https://github.com/pybamm-team/PyBaMM) displays the contents of [README.md](README.md), which is written in the [Markdown](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet) format. Some guidelines can be found [here](https://help.github.com/articles/about-readmes/).
-- The license for using PyBaMM is stored in [LICENSE](LICENSE.txt), and [automatically](https://help.github.com/articles/adding-a-license-to-a-repository/) linked to by GitHub.
-- This file, [CONTRIBUTING.md](CONTRIBUTING.md) is recognised as the contribution guidelines and a link is [automatically](https://github.com/blog/1184-contributing-guidelines) displayed when new issues or pull requests are created.
+- The first page people see when they go to [our GitHub page](https://github.com/pybamm-team/PyBaMM) displays the contents of [README.md](https://github.com/pybamm-team/PyBaMM/blob/develop/README.md), which is written in the [Markdown](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet) format. Some guidelines can be found [here](https://help.github.com/articles/about-readmes/).
+- The license for using PyBaMM is stored in [LICENSE](https://github.com/pybamm-team/PyBaMM/blob/develop/LICENSE.txt), and [automatically](https://help.github.com/articles/adding-a-license-to-a-repository/) linked to by GitHub.
+- This file, [CONTRIBUTING.md](https://github.com/pybamm-team/PyBaMM/blob/develop/CONTRIBUTING.md) is recognised as the contribution guidelines and a link is [automatically](https://github.com/blog/1184-contributing-guidelines) displayed when new issues or pull requests are created.
 
 ## Acknowledgements
 
diff --git a/GOVERNANCE.md b/GOVERNANCE.md
deleted file mode 100644
index f11b785106..0000000000
--- a/GOVERNANCE.md
+++ /dev/null
@@ -1,139 +0,0 @@
-# PyBaMM Governance
-
-The following contains the formal governance structure of the PyBaMM
-project. This document clarifies how decisions are made with respect
-to community interactions, including the relationship between
-open source development and work that may be funded by for-profit
-and non-profit entities.
-
-## Code of Conduct
-
-The PyBaMM community strongly values inclusivity and diversity. Everyone
-should treat others with the utmost respect. Everyone in the community
-must adhere to the
-[Code of Conduct](https://github.com/pybamm-team/PyBaMM/blob/develop/CODE-OF-CONDUCT.md) which
-reflects the values of our community. Violations of the code should be
-reported to members of the steering council, where the offenses will be
-handled on a case-by-case basis.
-
-## Current Steering Council
-
-- [Ferran Brosa Planella](https://www.brosaplanella.xyz)
-- [Saransh Chopra](https://saransh-cpp.github.io)
-- Scott Marquis
-- [Gregory Offer](https://www.imperial.ac.uk/people/gregory.offer)
-- [Valentin Sulzer](https://sites.google.com/view/valentinsulzer)
-
-## Advisory Committee
-
-TBA
-
-# Governing Rules and Duties
-
-## Steering Council
-
-The Project has a Steering Council that consists of Project
-Contributors who have produced contributions that are substantial in
-quality and quantity, and sustained over at least one year. The role
-of the Council is to provide active leadership for the Project in
-making everyday decisions on technical and administrative issues,
-through working with and taking input from the Community.
-
-During the everyday project activities, Council Members participate in
-all discussions, code review and other project activities as peers
-with all other Contributors and the Community. In these everyday
-activities, Council Members do not have any special power or privilege
-through their membership on the Council. However, it is expected that
-because of the quality and quantity of their contributions and their
-expert knowledge of the Project Software and Services that Council
-Members will provide useful guidance, both technical and in terms of
-project direction, to potentially less experienced Contributors.
-
-The Steering Council and its Members play a special role in certain
-situations. In particular, the Council may:
-
-- Make decisions about the overall scope, vision and direction of
-  the project.
-- Make decisions about strategic collaborations with other
-  organizations or individuals.
-- Make decisions about specific technical issues, features, bugs and
-  pull requests. They are the primary mechanism of guiding the code
-  review process and merging pull requests.
-- Make decisions about the Services that are run by the Project and
-  manage those Services for the benefit of the Project and Community.
-- Make decisions when regular community discussion does not produce
-  consensus on an issue in a reasonable time frame.
-
-Steering Council decisions are taken by simple majority, with the
-exception of changes to the Governance Documents which follow the
-procedure in the section 'Changing the Governance Documents'.
-
-### Steering Council membership
-
-To become eligible for being a Steering Council Member, an individual
-must be a Project Contributor who has produced contributions that are
-substantial in quality and quantity, and sustained over at least one
-year. Potential Council Members are nominated by existing Council
-Members or by the Community and voted upon by the existing Council
-after asking if the potential Member is interested and willing to
-serve in that capacity.
-
-When considering potential Members, the Council will look at
-candidates with a comprehensive view of their contributions. This will
-include but is not limited to code, code review, infrastructure work,
-mailing list and chat participation, community help/building,
-education and outreach, design work, etc. We deliberately do not
-set arbitrary quantitative metrics to avoid encouraging behavior
-that plays to the metrics rather than the project's overall well-being.
-We want to encourage a diverse array of backgrounds, viewpoints and
-talents in our team, which is why we explicitly do not define code as
-the sole metric on which Council membership will be evaluated.
-
-If a Council Member becomes inactive in the project for a period of
-one year, they will be considered for removal from the Council. Before
-removal, the inactive Member will be approached by another Council
-member to ask if they plan on returning to active participation. If
-not they will be removed immediately upon a Council vote. If they plan
-on returning to active participation soon, they will be given a grace
-period of one year. If they do not return to active participation
-within that time period they will be removed by vote of the Council
-without further grace period. All former Council members can be
-considered for membership again at any time in the future, like any
-other Project Contributor. Retired Council members will be listed on
-the project website, acknowledging the period during which they were
-active in the Council.
-
-The Council reserves the right to eject current Members if they are
-deemed to be actively harmful to the Project's well-being, and
-attempts at communication and conflict resolution have failed.
-
-## Fiscal Decisions
-
-All fiscal decisions are made by the steering council to ensure any
-funds are spent in a manner that furthers the mission of the Project.
-Fiscal decisions require majority approval by acting steering council
-members.
-
-## Advisory Committee
-
-The Project will consider setting up an Advisory Committee that works to ensure the long-term
-well-being of the Project. The role of the Committee will be to advise the Steering Council.
-
-## Conflict of interest
-
-It is expected that Steering Council and Advisory Committee Members
-will be employed at a wide range of companies, universities and non-profit
-organizations. Because of this, it is possible that Members will have
-conflicts of interest. Such conflicts of interest include, but are not
-limited to:
-
-- Financial interests, such as investments, employment or contracting
-  work, outside of the Project that may influence their work on the
-  Project.
-- Access to proprietary information of their employer that could
-  potentially leak into their work with the Project.
-
-All members of the Council and Committee shall disclose any conflict of
-interest they may have. Members with a conflict of interest in a
-particular issue may participate in Council discussions on that issue,
-but must recuse themselves from voting on the issue.
diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000000..0d05e9f158
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,7 @@
+graft pybamm
+include CITATION.cff
+prune tests
+
+exclude CHANGELOG.md CODE-OF-CONDUCT.md CONTRIBUTING.md CMakeLists.txt
+
+global-exclude __pycache__ *.py[cod] .venv
diff --git a/README.md b/README.md
index c5673ec711..474b528bb6 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-63-orange.svg)](#-contributors)
+[![All Contributors](https://img.shields.io/badge/all_contributors-66-orange.svg)](#-contributors)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 </div>
@@ -34,7 +34,7 @@ explore the effect of different battery designs and modeling assumptions under a
 
 [//]: # "numfocus-fiscal-sponsor-attribution"
 
-PyBaMM uses an [open governance model](./GOVERNANCE.md)
+PyBaMM uses an [open governance model](https://pybamm.org/governance/)
 and is fiscally sponsored by [NumFOCUS](https://numfocus.org/). Consider making
 a [tax-deductible donation](https://numfocus.org/donate-for-pybamm) to help the project
 pay for developer time, professional services, travel, workshops, and a variety of other needs.
@@ -233,7 +233,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
     <tr>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/chuckliu1979"><img src="https://avatars.githubusercontent.com/u/13491954?v=4?s=100" width="100px;" alt="Chuck Liu"/><br /><sub><b>Chuck Liu</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Achuckliu1979" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=chuckliu1979" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/partben"><img src="https://avatars.githubusercontent.com/u/88316576?v=4?s=100" width="100px;" alt="partben"/><br /><sub><b>partben</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=partben" title="Documentation">📖</a></td>
-      <td align="center" valign="top" width="14.28%"><a href="https://wigging.me"><img src="https://avatars.githubusercontent.com/u/6828967?v=4?s=100" width="100px;" alt="Gavin Wiggins"/><br /><sub><b>Gavin Wiggins</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Awigging" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=wigging" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://gavinw.me"><img src="https://avatars.githubusercontent.com/u/6828967?v=4?s=100" width="100px;" alt="Gavin Wiggins"/><br /><sub><b>Gavin Wiggins</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Awigging" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=wigging" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/dion-w"><img src="https://avatars.githubusercontent.com/u/91852142?v=4?s=100" width="100px;" alt="Dion Wilde"/><br /><sub><b>Dion Wilde</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Adion-w" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=dion-w" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://www.ehtec.co"><img src="https://avatars.githubusercontent.com/u/48386220?v=4?s=100" width="100px;" alt="Elias Hohl"/><br /><sub><b>Elias Hohl</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=ehtec" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/KAschad"><img src="https://avatars.githubusercontent.com/u/93784399?v=4?s=100" width="100px;" alt="KAschad"/><br /><sub><b>KAschad</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3AKAschad" title="Bug reports">🐛</a></td>
@@ -244,7 +244,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/ndrewwang"><img src="https://avatars.githubusercontent.com/u/56122552?v=4?s=100" width="100px;" alt="ndrewwang"/><br /><sub><b>ndrewwang</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Andrewwang" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=ndrewwang" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/MichaPhilipp"><img src="https://avatars.githubusercontent.com/u/58085966?v=4?s=100" width="100px;" alt="MichaPhilipp"/><br /><sub><b>MichaPhilipp</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3AMichaPhilipp" title="Bug reports">🐛</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/abillscmu"><img src="https://avatars.githubusercontent.com/u/48105066?v=4?s=100" width="100px;" alt="Alec Bills"/><br /><sub><b>Alec Bills</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=abillscmu" title="Code">💻</a></td>
-      <td align="center" valign="top" width="14.28%"><a href="https://github.com/agriyakhetarpal"><img src="https://avatars.githubusercontent.com/u/74401230?v=4?s=100" width="100px;" alt="Agriya Khetarpal"/><br /><sub><b>Agriya Khetarpal</b></sub></a><br /><a href="#infra-agriyakhetarpal" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=agriyakhetarpal" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=agriyakhetarpal" title="Documentation">📖</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/agriyakhetarpal"><img src="https://avatars.githubusercontent.com/u/74401230?v=4?s=100" width="100px;" alt="Agriya Khetarpal"/><br /><sub><b>Agriya Khetarpal</b></sub></a><br /><a href="#infra-agriyakhetarpal" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=agriyakhetarpal" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=agriyakhetarpal" title="Documentation">📖</a> <a href="https://github.com/pybamm-team/PyBaMM/pulls?q=is%3Apr+reviewed-by%3Aagriyakhetarpal" title="Reviewed Pull Requests">👀</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/awadell1"><img src="https://avatars.githubusercontent.com/u/5857298?v=4?s=100" width="100px;" alt="Alex Wadell"/><br /><sub><b>Alex Wadell</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=awadell1" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=awadell1" title="Tests">⚠️</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=awadell1" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/iatzak"><img src="https://avatars.githubusercontent.com/u/112731474?v=4?s=100" width="100px;" alt="iatzak"/><br /><sub><b>iatzak</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=iatzak" title="Documentation">📖</a> <a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Aiatzak" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=iatzak" title="Code">💻</a></td>
     </tr>
@@ -254,7 +254,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/jeromtom"><img src="https://avatars.githubusercontent.com/u/83979298?v=4?s=100" width="100px;" alt="Jerom Palimattom Tom"/><br /><sub><b>Jerom Palimattom Tom</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=jeromtom" title="Documentation">📖</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=jeromtom" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=jeromtom" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="14.28%"><a href="http://bradyplanden.github.io"><img src="https://avatars.githubusercontent.com/u/55357039?v=4?s=100" width="100px;" alt="Brady Planden"/><br /><sub><b>Brady Planden</b></sub></a><br /><a href="#example-BradyPlanden" title="Examples">💡</a></td>
       <td align="center" valign="top" width="14.28%"><a href="http://www.jsbrittain.com/"><img src="https://avatars.githubusercontent.com/u/98161205?v=4?s=100" width="100px;" alt="jsbrittain"/><br /><sub><b>jsbrittain</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=jsbrittain" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=jsbrittain" title="Tests">⚠️</a></td>
-      <td align="center" valign="top" width="14.28%"><a href="https://github.com/arjxn-py"><img src="https://avatars.githubusercontent.com/u/104268427?v=4?s=100" width="100px;" alt="Arjun"/><br /><sub><b>Arjun</b></sub></a><br /><a href="#infra-arjxn-py" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=arjxn-py" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=arjxn-py" title="Documentation">📖</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/arjxn-py"><img src="https://avatars.githubusercontent.com/u/104268427?v=4?s=100" width="100px;" alt="Arjun"/><br /><sub><b>Arjun</b></sub></a><br /><a href="#infra-arjxn-py" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=arjxn-py" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=arjxn-py" title="Documentation">📖</a> <a href="https://github.com/pybamm-team/PyBaMM/pulls?q=is%3Apr+reviewed-by%3Aarjxn-py" title="Reviewed Pull Requests">👀</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/chenzhao-py"><img src="https://avatars.githubusercontent.com/u/75906533?v=4?s=100" width="100px;" alt="CHEN ZHAO"/><br /><sub><b>CHEN ZHAO</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Achenzhao-py" title="Bug reports">🐛</a></td>
     </tr>
     <tr>
@@ -266,6 +266,11 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/bobonice"><img src="https://avatars.githubusercontent.com/u/22030806?v=4?s=100" width="100px;" alt="bobonice"/><br /><sub><b>bobonice</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Abobonice" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=bobonice" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/kratman"><img src="https://avatars.githubusercontent.com/u/10170302?v=4?s=100" width="100px;" alt="Eric G. Kratz"/><br /><sub><b>Eric G. Kratz</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=kratman" title="Documentation">📖</a> <a href="#infra-kratman" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Akratman" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=kratman" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=kratman" title="Tests">⚠️</a></td>
     </tr>
+    <tr>
+      <td align="center" valign="top" width="14.28%"><a href="https://aitorres.com"><img src="https://avatars.githubusercontent.com/u/26191851?v=4?s=100" width="100px;" alt="Andrés Ignacio Torres"/><br /><sub><b>Andrés Ignacio Torres</b></sub></a><br /><a href="#infra-aitorres" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/Agnik7"><img src="https://avatars.githubusercontent.com/u/77234005?v=4?s=100" width="100px;" alt="Agnik Bakshi"/><br /><sub><b>Agnik Bakshi</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=Agnik7" title="Documentation">📖</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/RuiheLi"><img src="https://avatars.githubusercontent.com/u/84007676?v=4?s=100" width="100px;" alt="RuiheLi"/><br /><sub><b>RuiheLi</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=RuiheLi" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=RuiheLi" title="Tests">⚠️</a></td>
+    </tr>
   </tbody>
 </table>
 
diff --git a/benchmarks/benchmark_utils.py b/benchmarks/benchmark_utils.py
new file mode 100644
index 0000000000..e5431ff4ea
--- /dev/null
+++ b/benchmarks/benchmark_utils.py
@@ -0,0 +1,5 @@
+import numpy as np
+
+
+def set_random_seed(seed_value=42):
+    np.random.seed(seed_value)
diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py
index 0e9e7bc23b..a4cf787ad9 100644
--- a/benchmarks/different_model_options.py
+++ b/benchmarks/different_model_options.py
@@ -1,4 +1,5 @@
 import pybamm
+from benchmarks.benchmark_utils import set_random_seed
 import numpy as np
 
 
@@ -30,6 +31,10 @@ def build_model(parameter, model_, option, value):
 
 
 class SolveModel:
+    solver: pybamm.BaseSolver
+    model: pybamm.BaseModel
+    t_eval: np.ndarray
+
     def solve_setup(self, parameter, model_, option, value, solver_class):
         import importlib
 
@@ -71,7 +76,7 @@ def solve_setup(self, parameter, model_, option, value, solver_class):
         disc = pybamm.Discretisation(mesh, self.model.default_spatial_methods)
         disc.process_model(self.model)
 
-    def solve_model(self, model, params):
+    def solve_model(self, _model, _params):
         self.solver.solve(self.model, t_eval=self.t_eval)
 
 
@@ -82,11 +87,14 @@ class TimeBuildModelLossActiveMaterial:
         ["none", "stress-driven", "reaction-driven", "stress and reaction-driven"],
     )
 
+    def setup(self, _model, _params):
+        set_random_seed()
+
     def time_setup_model(self, model, params):
         build_model("Ai2020", model, "loss of active material", params)
 
 
-class TimeSolveLossActiveMaterial:
+class TimeSolveLossActiveMaterial(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -95,11 +103,12 @@ class TimeSolveLossActiveMaterial:
     )
 
     def setup(self, model, params, solver_class):
+        set_random_seed()
         SolveModel.solve_setup(
             self, "Ai2020", model, "loss of active material", params, solver_class
         )
 
-    def time_solve_model(self, model, params, solver_class):
+    def time_solve_model(self, _model, _params, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
 
 
@@ -110,11 +119,14 @@ class TimeBuildModelLithiumPlating:
         ["none", "irreversible", "reversible", "partially reversible"],
     )
 
+    def setup(self, _model, _params):
+        set_random_seed()
+
     def time_setup_model(self, model, params):
         build_model("OKane2022", model, "lithium plating", params)
 
 
-class TimeSolveLithiumPlating:
+class TimeSolveLithiumPlating(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -123,11 +135,12 @@ class TimeSolveLithiumPlating:
     )
 
     def setup(self, model, params, solver_class):
+        set_random_seed()
         SolveModel.solve_setup(
             self, "OKane2022", model, "lithium plating", params, solver_class
         )
 
-    def time_solve_model(self, model, params, solver_class):
+    def time_solve_model(self, _model, _params, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
 
 
@@ -146,11 +159,14 @@ class TimeBuildModelSEI:
         ],
     )
 
+    def setup(self, _model, _params):
+        set_random_seed()
+
     def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "SEI", params)
 
 
-class TimeSolveSEI:
+class TimeSolveSEI(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -167,9 +183,10 @@ class TimeSolveSEI:
     )
 
     def setup(self, model, params, solver_class):
+        set_random_seed()
         SolveModel.solve_setup(self, "Marquis2019", model, "SEI", params, solver_class)
 
-    def time_solve_model(self, model, params, solver_class):
+    def time_solve_model(self, _model, _params, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
 
 
@@ -185,11 +202,14 @@ class TimeBuildModelParticle:
         ],
     )
 
+    def setup(self, _model, _params):
+        set_random_seed()
+
     def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "particle", params)
 
 
-class TimeSolveParticle:
+class TimeSolveParticle(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -203,11 +223,12 @@ class TimeSolveParticle:
     )
 
     def setup(self, model, params, solver_class):
+        set_random_seed()
         SolveModel.solve_setup(
             self, "Marquis2019", model, "particle", params, solver_class
         )
 
-    def time_solve_model(self, model, params, solver_class):
+    def time_solve_model(self, _model, _params, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
 
 
@@ -218,11 +239,14 @@ class TimeBuildModelThermal:
         ["isothermal", "lumped", "x-full"],
     )
 
+    def setup(self, _model, _params):
+        set_random_seed()
+
     def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "thermal", params)
 
 
-class TimeSolveThermal:
+class TimeSolveThermal(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -231,11 +255,12 @@ class TimeSolveThermal:
     )
 
     def setup(self, model, params, solver_class):
+        set_random_seed()
         SolveModel.solve_setup(
             self, "Marquis2019", model, "thermal", params, solver_class
         )
 
-    def time_solve_model(self, model, params, solver_class):
+    def time_solve_model(self, _model, _params, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
 
 
@@ -246,11 +271,14 @@ class TimeBuildModelSurfaceForm:
         ["false", "differential", "algebraic"],
     )
 
+    def setup(self, _model, _params):
+        set_random_seed()
+
     def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "surface form", params)
 
 
-class TimeSolveSurfaceForm:
+class TimeSolveSurfaceForm(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -259,6 +287,7 @@ class TimeSolveSurfaceForm:
     )
 
     def setup(self, model, params, solver_class):
+        set_random_seed()
         if (model, params, solver_class) == (
             pybamm.lithium_ion.SPM,
             "differential",
@@ -269,5 +298,5 @@ def setup(self, model, params, solver_class):
             self, "Marquis2019", model, "surface form", params, solver_class
         )
 
-    def time_solve_model(self, model, params, solver_class):
+    def time_solve_model(self, _model, _params, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
diff --git a/benchmarks/memory_sims.py b/benchmarks/memory_sims.py
index 1857873476..45d3e41834 100644
--- a/benchmarks/memory_sims.py
+++ b/benchmarks/memory_sims.py
@@ -1,4 +1,5 @@
 import pybamm
+from benchmarks.benchmark_utils import set_random_seed
 
 parameters = ["Marquis2019", "Chen2020"]
 
@@ -6,9 +7,15 @@
 class MemSPMSimulationCCCV:
     param_names = ["parameter"]
     params = parameters
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
+    sim: pybamm.Simulation
 
-    def mem_setup_SPM_simulationCCCV(self, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def setup(self, _params):
+        set_random_seed()
+
+    def mem_setup_SPM_simulationCCCV(self, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.SPM()
         exp = pybamm.Experiment(
             [
@@ -28,9 +35,15 @@ def mem_setup_SPM_simulationCCCV(self, parameters):
 class MemDFNSimulationCCCV:
     param_names = ["parameter"]
     params = parameters
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
+    sim: pybamm.Simulation
+
+    def setup(self, _params):
+        set_random_seed()
 
-    def mem_setup_DFN_simulationCCCV(self, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def mem_setup_DFN_simulationCCCV(self, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.DFN()
         exp = pybamm.Experiment(
             [
@@ -50,9 +63,15 @@ def mem_setup_DFN_simulationCCCV(self, parameters):
 class MemSPMSimulationGITT:
     param_names = ["parameter"]
     params = parameters
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
+    sim: pybamm.Simulation
 
-    def mem_setup_SPM_simulationGITT(self, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def setup(self, _params):
+        set_random_seed()
+
+    def mem_setup_SPM_simulationGITT(self, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.SPM()
         exp = pybamm.Experiment(
             [("Discharge at C/20 for 1 hour", "Rest for 1 hour")] * 20
@@ -66,9 +85,15 @@ def mem_setup_SPM_simulationGITT(self, parameters):
 class MemDFNSimulationGITT:
     param_names = ["parameter"]
     params = parameters
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
+    sim: pybamm.Simulation
+
+    def setup(self, _params):
+        set_random_seed()
 
-    def mem_setup_DFN_simulationGITT(self, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def mem_setup_DFN_simulationGITT(self, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.SPM()
         exp = pybamm.Experiment(
             [("Discharge at C/20 for 1 hour", "Rest for 1 hour")] * 20
diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py
index 4b20996b75..79970c70ef 100644
--- a/benchmarks/memory_unit_benchmarks.py
+++ b/benchmarks/memory_unit_benchmarks.py
@@ -1,8 +1,15 @@
 import pybamm
+from benchmarks.benchmark_utils import set_random_seed
 import numpy as np
 
 
 class MemCreateExpression:
+    R: pybamm.Parameter
+    model: pybamm.BaseModel
+
+    def setup(self):
+        set_random_seed()
+
     def mem_create_expression(self):
         self.R = pybamm.Parameter("Particle radius [m]")
         D = pybamm.Parameter("Diffusion coefficient [m2.s-1]")
@@ -31,8 +38,12 @@ def mem_create_expression(self):
         return self.model
 
 
-class MemParameteriseModel:
+class MemParameteriseModel(MemCreateExpression):
+    r: pybamm.SpatialVariable
+    geometry: dict
+
     def setup(self):
+        set_random_seed()
         MemCreateExpression.mem_create_expression(self)
 
     def mem_parameterise(self):
@@ -58,8 +69,9 @@ def mem_parameterise(self):
         return param
 
 
-class MemDiscretiseModel:
+class MemDiscretiseModel(MemParameteriseModel):
     def setup(self):
+        set_random_seed()
         MemCreateExpression.mem_create_expression(self)
         MemParameteriseModel.mem_parameterise(self)
 
@@ -76,8 +88,9 @@ def mem_discretise(self):
         return disc
 
 
-class MemSolveModel:
+class MemSolveModel(MemDiscretiseModel):
     def setup(self):
+        set_random_seed()
         MemCreateExpression.mem_create_expression(self)
         MemParameteriseModel.mem_parameterise(self)
         MemDiscretiseModel.mem_discretise(self)
diff --git a/benchmarks/time_setup_models_and_sims.py b/benchmarks/time_setup_models_and_sims.py
index 4e9b2423a1..2677c9936c 100644
--- a/benchmarks/time_setup_models_and_sims.py
+++ b/benchmarks/time_setup_models_and_sims.py
@@ -1,4 +1,5 @@
 import pybamm
+from benchmarks.benchmark_utils import set_random_seed
 
 parameters = [
     "Marquis2019",
@@ -33,9 +34,14 @@ def compute_discretisation(model, param):
 class TimeBuildSPM:
     param_names = ["parameter"]
     params = parameters
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
 
-    def time_setup_SPM(self, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def setup(self, _params):
+        set_random_seed()
+
+    def time_setup_SPM(self, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.SPM()
         self.param.process_model(self.model)
         compute_discretisation(self.model, self.param).process_model(self.model)
@@ -45,8 +51,11 @@ class TimeBuildSPMe:
     param_names = ["parameter"]
     params = parameters
 
-    def time_setup_SPMe(self, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def setup(self, _params):
+        set_random_seed()
+
+    def time_setup_SPMe(self, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.SPMe()
         self.param.process_model(self.model)
         compute_discretisation(self.model, self.param).process_model(self.model)
@@ -55,9 +64,14 @@ def time_setup_SPMe(self, parameters):
 class TimeBuildDFN:
     param_names = ["parameter"]
     params = parameters
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
+
+    def setup(self, _params):
+        set_random_seed()
 
-    def time_setup_DFN(self, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def time_setup_DFN(self, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.DFN()
         self.param.process_model(self.model)
         compute_discretisation(self.model, self.param).process_model(self.model)
@@ -66,9 +80,14 @@ def time_setup_DFN(self, parameters):
 class TimeBuildSPMSimulation:
     param_names = ["with experiment", "parameter"]
     params = ([False, True], parameters)
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
 
-    def time_setup_SPM_simulation(self, with_experiment, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def setup(self, _with_experiment, _params):
+        set_random_seed()
+
+    def time_setup_SPM_simulation(self, with_experiment, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.SPM()
         if with_experiment:
             exp = pybamm.Experiment(
@@ -84,9 +103,14 @@ def time_setup_SPM_simulation(self, with_experiment, parameters):
 class TimeBuildSPMeSimulation:
     param_names = ["with experiment", "parameter"]
     params = ([False, True], parameters)
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
+
+    def setup(self, _with_experiment, _params):
+        set_random_seed()
 
-    def time_setup_SPMe_simulation(self, with_experiment, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def time_setup_SPMe_simulation(self, with_experiment, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.SPMe()
         if with_experiment:
             exp = pybamm.Experiment(
@@ -102,9 +126,14 @@ def time_setup_SPMe_simulation(self, with_experiment, parameters):
 class TimeBuildDFNSimulation:
     param_names = ["with experiment", "parameter"]
     params = ([False, True], parameters)
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
+
+    def setup(self, _with_experiment, _params):
+        set_random_seed()
 
-    def time_setup_DFN_simulation(self, with_experiment, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def time_setup_DFN_simulation(self, with_experiment, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.DFN()
         if with_experiment:
             exp = pybamm.Experiment(
diff --git a/benchmarks/time_sims_experiments.py b/benchmarks/time_sims_experiments.py
index 5e05470734..bcd3e71f2f 100644
--- a/benchmarks/time_sims_experiments.py
+++ b/benchmarks/time_sims_experiments.py
@@ -1,4 +1,5 @@
 import pybamm
+from benchmarks.benchmark_utils import set_random_seed
 
 
 class TimeSimulation:
@@ -19,8 +20,14 @@ class TimeSimulation:
         ],
         "GITT": [("Discharge at C/20 for 1 hour", "Rest for 1 hour")] * 10,
     }
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
+    solver: pybamm.BaseSolver
+    exp: pybamm.Experiment
+    sim: pybamm.Simulation
 
     def setup(self, experiment, parameters, model_class, solver_class):
+        set_random_seed()
         if (experiment, parameters, model_class, solver_class) == (
             "GITT",
             "Marquis2019",
@@ -46,5 +53,5 @@ def time_setup(self, experiment, parameters, model_class, solver_class):
         exp = pybamm.Experiment(self.experiment_descriptions[experiment])
         pybamm.Simulation(model, parameter_values=param, experiment=exp, solver=solver)
 
-    def time_solve(self, experiment, parameters, model_class, solver_class):
+    def time_solve(self, _experiment, _parameters, _model_class, _solver_class):
         self.sim.solve()
diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py
index f277769497..e41a7ccd16 100644
--- a/benchmarks/time_solve_models.py
+++ b/benchmarks/time_solve_models.py
@@ -2,6 +2,7 @@
 # See "Writing benchmarks" in the asv docs for more information.
 
 import pybamm
+from benchmarks.benchmark_utils import set_random_seed
 import numpy as np
 
 
@@ -18,9 +19,7 @@ class TimeSolveSPM:
             "ORegan2022",
             "NCA_Kim2011",
             "Prada2013",
-            # "Ai2020",
             "Ramadass2004",
-            # "Mohtat2020",
             "Chen2020",
             "Ecker2015",
         ],
@@ -29,8 +28,12 @@ class TimeSolveSPM:
             pybamm.IDAKLUSolver,
         ],
     )
+    model: pybamm.BaseModel
+    solver: pybamm.BaseSolver
+    t_eval: np.ndarray
 
     def setup(self, solve_first, parameters, solver_class):
+        set_random_seed()
         self.solver = solver_class()
         self.model = pybamm.lithium_ion.SPM()
         c_rate = 1
@@ -62,7 +65,7 @@ def setup(self, solve_first, parameters, solver_class):
         if solve_first:
             solve_model_once(self.model, self.solver, self.t_eval)
 
-    def time_solve_model(self, solve_first, parameters, solver_class):
+    def time_solve_model(self, _solve_first, _parameters, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
 
 
@@ -75,9 +78,7 @@ class TimeSolveSPMe:
             "ORegan2022",
             "NCA_Kim2011",
             "Prada2013",
-            # "Ai2020",
             "Ramadass2004",
-            # "Mohtat2020",
             "Chen2020",
             "Ecker2015",
         ],
@@ -86,8 +87,12 @@ class TimeSolveSPMe:
             pybamm.IDAKLUSolver,
         ],
     )
+    model: pybamm.BaseModel
+    solver: pybamm.BaseSolver
+    t_eval: np.ndarray
 
     def setup(self, solve_first, parameters, solver_class):
+        set_random_seed()
         self.solver = solver_class()
         self.model = pybamm.lithium_ion.SPMe()
         c_rate = 1
@@ -119,7 +124,7 @@ def setup(self, solve_first, parameters, solver_class):
         if solve_first:
             solve_model_once(self.model, self.solver, self.t_eval)
 
-    def time_solve_model(self, solve_first, parameters, solver_class):
+    def time_solve_model(self, _solve_first, _parameters, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
 
 
@@ -130,11 +135,9 @@ class TimeSolveDFN:
         [
             "Marquis2019",
             "ORegan2022",
-            # "NCA_Kim2011",
             "Prada2013",
             "Ai2020",
             "Ramadass2004",
-            # "Mohtat2020",
             "Chen2020",
             "Ecker2015",
         ],
@@ -143,8 +146,12 @@ class TimeSolveDFN:
             pybamm.IDAKLUSolver,
         ],
     )
+    model: pybamm.BaseModel
+    solver: pybamm.BaseSolver
+    t_eval: np.ndarray
 
     def setup(self, solve_first, parameters, solver_class):
+        set_random_seed()
         if (parameters, solver_class) == (
             "ORegan2022",
             pybamm.CasadiSolver,
@@ -181,5 +188,5 @@ def setup(self, solve_first, parameters, solver_class):
         if solve_first:
             solve_model_once(self.model, self.solver, self.t_eval)
 
-    def time_solve_model(self, solve_first, parameters, solver_class):
+    def time_solve_model(self, _solve_first, _parameters, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py
index acee9c210a..73af4dda26 100644
--- a/benchmarks/unit_benchmarks.py
+++ b/benchmarks/unit_benchmarks.py
@@ -1,8 +1,15 @@
 import pybamm
 import numpy as np
+from benchmarks.benchmark_utils import set_random_seed
 
 
 class TimeCreateExpression:
+    R: pybamm.Parameter
+    model: pybamm.BaseModel
+
+    def setup(self):
+        set_random_seed()
+
     def time_create_expression(self):
         self.R = pybamm.Parameter("Particle radius [m]")
         D = pybamm.Parameter("Diffusion coefficient [m2.s-1]")
@@ -30,8 +37,12 @@ def time_create_expression(self):
         }
 
 
-class TimeParameteriseModel:
+class TimeParameteriseModel(TimeCreateExpression):
+    r: pybamm.SpatialVariable
+    geometry: dict
+
     def setup(self):
+        set_random_seed()
         TimeCreateExpression.time_create_expression(self)
 
     def time_parameterise(self):
@@ -56,8 +67,9 @@ def time_parameterise(self):
         param.process_geometry(self.geometry)
 
 
-class TimeDiscretiseModel:
+class TimeDiscretiseModel(TimeParameteriseModel):
     def setup(self):
+        set_random_seed()
         TimeCreateExpression.time_create_expression(self)
         TimeParameteriseModel.time_parameterise(self)
 
@@ -73,8 +85,9 @@ def time_discretise(self):
         disc.process_model(self.model)
 
 
-class TimeSolveModel:
+class TimeSolveModel(TimeDiscretiseModel):
     def setup(self):
+        set_random_seed()
         TimeCreateExpression.time_create_expression(self)
         TimeParameteriseModel.time_parameterise(self)
         TimeDiscretiseModel.time_discretise(self)
diff --git a/benchmarks/work_precision_sets/time_vs_dt_max.py b/benchmarks/work_precision_sets/time_vs_dt_max.py
index 1368dce350..3926a4bcd6 100644
--- a/benchmarks/work_precision_sets/time_vs_dt_max.py
+++ b/benchmarks/work_precision_sets/time_vs_dt_max.py
@@ -41,7 +41,6 @@
 ):
     for params in parameters:
         time_points = []
-        # solver = pybamm.CasadiSolver()
 
         model = model_.new_copy()
         c_rate = 10
diff --git a/build_manylinux_wheels/Dockerfile b/build_manylinux_wheels/Dockerfile
deleted file mode 100644
index a6c2dcc41c..0000000000
--- a/build_manylinux_wheels/Dockerfile
+++ /dev/null
@@ -1,18 +0,0 @@
-FROM quay.io/pypa/manylinux2014_x86_64:2020-11-11-bc8ce45
-
-ENV PLAT manylinux2014_x86_64
-
-RUN yum -y update
-RUN yum -y remove cmake
-RUN yum -y install wget openblas-devel
-RUN /opt/python/cp37-cp37m/bin/pip install --upgrade pip cmake
-RUN ln -s /opt/python/cp37-cp37m/bin/cmake /usr/bin/cmake
-
-COPY install_sundials.sh /install_sundials.sh
-RUN chmod +x /install_sundials.sh
-COPY entrypoint.sh /entrypoint.sh
-RUN chmod +x /entrypoint.sh
-
-RUN ./install_sundials.sh
-
-ENTRYPOINT ["/entrypoint.sh"]
diff --git a/build_manylinux_wheels/action.yml b/build_manylinux_wheels/action.yml
deleted file mode 100644
index 7264606b30..0000000000
--- a/build_manylinux_wheels/action.yml
+++ /dev/null
@@ -1,17 +0,0 @@
-# action.yml
-# Based on  RalfG/python-wheels-manylinux-build/action.yml by Ralf Gabriels
-
-name: "Python wheels manylinux build"
-author: "Thibault Lestang"
-description: "Build manylinux wheels for PyBaMM"
-inputs:
-  python-versions:
-    description: "Python versions to target, space-separated"
-    required: true
-    default: "cp36-cp36m cp37-cp37m"
-
-runs:
-  using: "docker"
-  image: "Dockerfile"
-  args:
-    - ${{ inputs.python-versions }}
diff --git a/build_manylinux_wheels/entrypoint.sh b/build_manylinux_wheels/entrypoint.sh
deleted file mode 100644
index 203e5471d3..0000000000
--- a/build_manylinux_wheels/entrypoint.sh
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/bin/bash
-set -e -x
-
-# GitHub runners add "-e LD_LIBRARY_PATH" option to "docker run",
-# overriding default value of LD_LIBRARY_PATH in manylinux image. This
-# causes libcrypt.so.2 to be missing (it lives in /usr/local/lib)
-export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
-
-# CLI arguments
-PY_VERSIONS=$1
-
-git clone https://github.com/pybind/pybind11.git /github/workspace/pybind11
-# Compile wheels
-arrPY_VERSIONS=(${PY_VERSIONS// / })
-for PY_VER in "${arrPY_VERSIONS[@]}"; do
-    # Update pip
-    /opt/python/"${PY_VER}"/bin/pip install --upgrade --no-cache-dir pip
-
-    # Build wheels
-    /opt/python/"${PY_VER}"/bin/pip wheel /github/workspace/ -w /github/workspace/wheelhouse/ --no-deps || { echo "Building wheels failed."; exit 1; }
-done
-ls -l /github/workspace/wheelhouse/
-
-# Bundle external shared libraries into the wheels
-for whl in /github/workspace/wheelhouse/*-linux*.whl; do
-    auditwheel repair "$whl" --plat "${PLAT}" -w /github/workspace/dist/ || { echo "Repairing wheels failed."; auditwheel show "$whl"; exit 1; }
-done
-
-echo "Succesfully built wheels:"
-ls -l /github/workspace/dist/
diff --git a/docs/_static/pybamm.css b/docs/_static/pybamm.css
index a795e50f23..614716d24a 100644
--- a/docs/_static/pybamm.css
+++ b/docs/_static/pybamm.css
@@ -152,6 +152,11 @@ html[data-theme="dark"] .DocSearch-Commands-Key {
 .DocSearch-Hit-source {
   background: var(--pst-color-background);
 }
+
+.DocSearch-Hit-icon {
+  height: 30px;
+}
+
 .DocSearch-Button {
   border-radius: 6px;
 }
@@ -187,5 +192,5 @@ html[data-theme="light"] .DocSearch-Logo svg rect.cls-1 {
 /* Search field dark theme corrections */
 
 html[data-theme="dark"] .DocSearch-Button {
-  background: var(--pst-color-on-surface);
+  background: var(--pst-color-background);
 }
diff --git a/docs/_static/pybamm_logo_whitetext.png b/docs/_static/pybamm_logo_whitetext.png
new file mode 100644
index 0000000000..3ee7159ed4
Binary files /dev/null and b/docs/_static/pybamm_logo_whitetext.png differ
diff --git a/docs/_static/versions.json b/docs/_static/versions.json
deleted file mode 100644
index 5c9bba7c17..0000000000
--- a/docs/_static/versions.json
+++ /dev/null
@@ -1,167 +0,0 @@
-[
-    {
-        "name": "latest",
-        "version": "latest",
-        "url": "https://docs.pybamm.org/en/latest/"
-    },
-    {
-        "name": "stable",
-        "version": "stable",
-        "url": "https://docs.pybamm.org/en/stable/"
-    },
-    {
-        "name": "v23.5",
-        "version": "23.5",
-        "url": "https://docs.pybamm.org/en/v23.5_a/"
-    },
-    {
-        "name": "v23.4.1",
-        "version": "23.4.1",
-        "url": "https://docs.pybamm.org/en/v23.4.1/"
-    },
-    {
-        "name": "v23.4",
-        "version": "23.4",
-        "url": "https://docs.pybamm.org/en/v23.4/"
-    },
-    {
-        "name": "v23.3",
-        "version": "23.3",
-        "url": "https://docs.pybamm.org/en/v23.3/"
-    },
-    {
-        "name": "v23.2",
-        "version": "23.2",
-        "url": "https://docs.pybamm.org/en/v23.2/"
-    },
-    {
-        "name": "v23.1",
-        "version": "23.1",
-        "url": "https://docs.pybamm.org/en/v23.1/"
-    },
-    {
-        "name": "v22.12",
-        "version": "22.12",
-        "url": "https://docs.pybamm.org/en/v22.12/"
-    },
-    {
-        "name": "v22.11.1",
-        "version": "22.11.1",
-        "url": "https://docs.pybamm.org/en/v22.11.1/"
-    },
-    {
-        "name": "v22.11",
-        "version": "22.11",
-        "url": "https://docs.pybamm.org/en/v22.11/"
-    },
-    {
-        "name": "v22.10",
-        "version": "22.10",
-        "url": "https://docs.pybamm.org/en/v22.10/"
-    },
-    {
-        "name": "v22.9",
-        "version": "22.9",
-        "url": "https://docs.pybamm.org/en/v22.9/"
-    },
-    {
-        "name": "v22.8",
-        "version": "22.8",
-        "url": "https://docs.pybamm.org/en/v22.8/"
-    },
-    {
-        "name": "v22.7",
-        "version": "22.7",
-        "url": "https://docs.pybamm.org/en/v22.7/"
-    },
-    {
-        "name": "v22.6",
-        "version": "22.6",
-        "url": "https://docs.pybamm.org/en/v22.6/"
-    },
-    {
-        "name": "v22.5",
-        "version": "22.5",
-        "url": "https://docs.pybamm.org/en/v22.5/"
-    },
-    {
-        "name": "v22.4",
-        "version": "22.4",
-        "url": "https://docs.pybamm.org/en/v22.4/"
-    },
-    {
-        "name": "v22.3",
-        "version": "22.3",
-        "url": "https://docs.pybamm.org/en/v22.3/"
-    },
-    {
-        "name": "v22.2",
-        "version": "22.2",
-        "url": "https://docs.pybamm.org/en/v22.3/"
-    },
-    {
-        "name": "v22.1",
-        "version": "22.1",
-        "url": "https://docs.pybamm.org/en/v22.1/"
-    },
-    {
-        "name": "v21.12",
-        "version": "21.12",
-        "url": "https://docs.pybamm.org/en/v21.12/"
-    },
-    {
-        "name": "v21.11",
-        "version": "21.11",
-        "url": "https://docs.pybamm.org/en/v21.11/"
-    },
-    {
-        "name": "v21.10",
-        "version": "21.10",
-        "url": "https://docs.pybamm.org/en/v21.10/"
-    },
-    {
-        "name": "v21.9",
-        "version": "21.9",
-        "url": "https://docs.pybamm.org/en/v21.9/"
-    },
-    {
-        "name": "v21.08",
-        "version": "21.08",
-        "url": "https://docs.pybamm.org/en/v21.08/"
-    },
-    {
-        "name": "v0.4.0",
-        "version": "0.4.0",
-        "url": "https://docs.pybamm.org/en/v0.4.0/"
-    },
-    {
-        "name": "v0.3.0",
-        "version": "0.3.0",
-        "url": "https://docs.pybamm.org/en/v0.3.0/"
-    },
-    {
-        "name": "v0.2.3",
-        "version": "0.2.3",
-        "url": "https://docs.pybamm.org/en/v0.2.3/"
-    },
-    {
-        "name": "v0.2.2",
-        "version": "0.2.2",
-        "url": "https://docs.pybamm.org/en/v0.2.2/"
-    },
-    {
-        "name": "v0.2.1",
-        "version": "0.2.1",
-        "url": "https://docs.pybamm.org/en/v0.2.1/"
-    },
-    {
-        "name": "v0.2.0",
-        "version": "0.2.0",
-        "url": "https://docs.pybamm.org/en/v0.2.0/"
-    },
-    {
-        "name": "v0.1.0",
-        "version": "0.1.0",
-        "url": "https://docs.pybamm.org/en/v0.1.0/"
-    }
-]
diff --git a/docs/conf.py b/docs/conf.py
index 1cb1a521ae..8e86dcc48d 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -122,7 +122,7 @@
 html_theme_options = {
     "logo": {
         "image_light": "pybamm_logo.png",
-        "image_dark": "pybamm_logo.png",
+        "image_dark": "pybamm_logo_whitetext.png",
     },
     "icon_links": [
         {
@@ -142,17 +142,6 @@
         },
     ],
     "collapse_navigation": True,
-    "external_links": [
-        {
-            "name": "Contributing",
-            "url": "https://github.com/pybamm-team/PyBaMM/tree/develop/CONTRIBUTING.md",
-        },
-    ],
-    # should be kept versioned to use for the version warning bar
-    "switcher": {
-        "version_match": version,
-        "json_url": "https://docs.pybamm.org/en/latest/_static/versions.json",
-    },
     # turn to False to not fail build if json_url is not found
     "check_switcher": True,
     # for dark mode toggle and social media links
@@ -160,7 +149,12 @@
     "navbar_end": ["theme-switcher", "navbar-icon-links"],
     # add Algolia to the persistent navbar, this removes the default search icon
     "navbar_persistent": "algolia-searchbox",
+    "navigation_with_keys": False,
     "use_edit_page_button": True,
+    "analytics": {
+        "plausible_analytics_domain": "docs.pybamm.org",
+        "plausible_analytics_url": "https://plausible.io/js/script.js",
+    },
     "pygment_light_style": "xcode",
     "pygment_dark_style": "monokai",
     "footer_start": [
diff --git a/docs/index.rst b/docs/index.rst
index 3e5d54ecb5..bf0d34e1a0 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -16,6 +16,7 @@ PyBaMM documentation
    User Guide <source/user_guide/index>
    source/api/index
    source/examples/index
+   Contributing <source/user_guide/contributing>
 
 **Version**: |version|
 
@@ -106,7 +107,7 @@ explore the effect of different battery designs and modeling assumptions under a
 
       +++
 
-      .. button-link:: https://github.com/pybamm-team/PyBaMM/blob/develop/CONTRIBUTING.md
+      .. button-link:: source/user_guide/contributing.html
          :expand:
          :color: secondary
          :click-parent:
diff --git a/docs/source/api/parameters/parameter_sets.rst b/docs/source/api/parameters/parameter_sets.rst
index 5bb0ce842d..575087f415 100644
--- a/docs/source/api/parameters/parameter_sets.rst
+++ b/docs/source/api/parameters/parameter_sets.rst
@@ -99,7 +99,7 @@ Lithium-ion Parameter Sets
 ==========================
 {% for k,v in parameter_sets.items() if v.chemistry == "lithium_ion" %}
 {{k}}
-----------------------------
+--------------------------------
 {{ parameter_sets.get_docstring(k) }}
 {% endfor %}
 
diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst
index 4bab430032..7c17cfc4aa 100644
--- a/docs/source/examples/index.rst
+++ b/docs/source/examples/index.rst
@@ -54,6 +54,7 @@ The notebooks are organised into subfolders, and can be viewed in the galleries
     notebooks/models/DFN-with-particle-size-distributions.ipynb
     notebooks/models/DFN.ipynb
     notebooks/models/electrode-state-of-health.ipynb
+    notebooks/models/half-cell.ipynb
     notebooks/models/jelly-roll-model.ipynb
     notebooks/models/latexify.ipynb
     notebooks/models/lead-acid.ipynb
@@ -111,6 +112,7 @@ The notebooks are organised into subfolders, and can be viewed in the galleries
     notebooks/callbacks.ipynb
     notebooks/change-settings.ipynb
     notebooks/initialize-model-with-solution.ipynb
+    notebooks/rpt-experiment.ipynb
     notebooks/simulating-long-experiments.ipynb
     notebooks/simulation-class.ipynb
     notebooks/solution-data-and-processed-variables.ipynb
diff --git a/docs/source/examples/notebooks/batch_study.ipynb b/docs/source/examples/notebooks/batch_study.ipynb
index 2b9b7b2615..807a368fcc 100644
--- a/docs/source/examples/notebooks/batch_study.ipynb
+++ b/docs/source/examples/notebooks/batch_study.ipynb
@@ -31,7 +31,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "\n",
     "# loading up 3 models to compare\n",
@@ -224,7 +224,7 @@
    "source": [
     "# using less number of images in the example\n",
     "# for a smoother GIF use more images\n",
-    "batch_study.create_gif(number_of_images=5, duration=0.2)"
+    "batch_study.create_gif(number_of_images=5, duration=0.2, output_filename=\"batch.gif\")"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/callbacks.ipynb b/docs/source/examples/notebooks/callbacks.ipynb
index 7bcf75f8b2..e4c4295ce1 100644
--- a/docs/source/examples/notebooks/callbacks.ipynb
+++ b/docs/source/examples/notebooks/callbacks.ipynb
@@ -48,7 +48,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q\n",
+    "%pip install \"pybamm[plot,cite]\" -q\n",
     "import pybamm\n",
     "\n",
     "model = pybamm.lithium_ion.DFN()\n",
diff --git a/docs/source/examples/notebooks/change-settings.ipynb b/docs/source/examples/notebooks/change-settings.ipynb
index a7ca997a91..5b21f4dd6b 100644
--- a/docs/source/examples/notebooks/change-settings.ipynb
+++ b/docs/source/examples/notebooks/change-settings.ipynb
@@ -43,7 +43,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import os\n",
diff --git a/docs/source/examples/notebooks/creating_models/1-an-ode-model.ipynb b/docs/source/examples/notebooks/creating_models/1-an-ode-model.ipynb
index fa5d4f9e09..a610700887 100644
--- a/docs/source/examples/notebooks/creating_models/1-an-ode-model.ipynb
+++ b/docs/source/examples/notebooks/creating_models/1-an-ode-model.ipynb
@@ -36,7 +36,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt"
diff --git a/docs/source/examples/notebooks/creating_models/2-a-pde-model.ipynb b/docs/source/examples/notebooks/creating_models/2-a-pde-model.ipynb
index 2c58e0cb01..c427fd4fe6 100644
--- a/docs/source/examples/notebooks/creating_models/2-a-pde-model.ipynb
+++ b/docs/source/examples/notebooks/creating_models/2-a-pde-model.ipynb
@@ -41,7 +41,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt"
diff --git a/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb b/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb
index 87735951d7..2c338149e7 100644
--- a/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb
+++ b/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb
@@ -58,7 +58,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
diff --git a/docs/source/examples/notebooks/creating_models/4-comparing-full-and-reduced-order-models.ipynb b/docs/source/examples/notebooks/creating_models/4-comparing-full-and-reduced-order-models.ipynb
index 20eb4abced..15d9e8e027 100644
--- a/docs/source/examples/notebooks/creating_models/4-comparing-full-and-reduced-order-models.ipynb
+++ b/docs/source/examples/notebooks/creating_models/4-comparing-full-and-reduced-order-models.ipynb
@@ -62,7 +62,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
diff --git a/docs/source/examples/notebooks/creating_models/5-half-cell-model.ipynb b/docs/source/examples/notebooks/creating_models/5-half-cell-model.ipynb
index 02b9fda40b..b28d6add1a 100644
--- a/docs/source/examples/notebooks/creating_models/5-half-cell-model.ipynb
+++ b/docs/source/examples/notebooks/creating_models/5-half-cell-model.ipynb
@@ -71,7 +71,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "\n",
diff --git a/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb b/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb
index ac34142fab..e383498065 100644
--- a/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb
+++ b/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb
@@ -123,7 +123,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import os\n",
diff --git a/docs/source/examples/notebooks/experiments-start-time.ipynb b/docs/source/examples/notebooks/experiments-start-time.ipynb
index 1b87c48cef..4af1bd6201 100644
--- a/docs/source/examples/notebooks/experiments-start-time.ipynb
+++ b/docs/source/examples/notebooks/experiments-start-time.ipynb
@@ -36,7 +36,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "from datetime import datetime"
    ]
diff --git a/docs/source/examples/notebooks/expression_tree/broadcasts.ipynb b/docs/source/examples/notebooks/expression_tree/broadcasts.ipynb
index aac3bd2995..035fe77ed7 100644
--- a/docs/source/examples/notebooks/expression_tree/broadcasts.ipynb
+++ b/docs/source/examples/notebooks/expression_tree/broadcasts.ipynb
@@ -24,7 +24,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np"
    ]
diff --git a/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb b/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb
index c860198501..b15c8b1d32 100644
--- a/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb
+++ b/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb
@@ -35,7 +35,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "\n",
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-1-how-to-run-a-model.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-1-how-to-run-a-model.ipynb
index aae433eb78..aa50147343 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-1-how-to-run-a-model.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-1-how-to-run-a-model.ipynb
@@ -34,7 +34,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm"
    ]
   },
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-10-creating-a-model.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-10-creating-a-model.ipynb
index cb0d30a510..8744e94f7e 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-10-creating-a-model.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-10-creating-a-model.ipynb
@@ -40,7 +40,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm"
    ]
   },
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb
index 5e68225f7d..a38c0c90ee 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb
@@ -32,7 +32,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm"
    ]
   },
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-2-compare-models.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-2-compare-models.ipynb
index 0ff4f2902c..aa957be1b3 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-2-compare-models.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-2-compare-models.ipynb
@@ -32,7 +32,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm"
    ]
   },
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-3-basic-plotting.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-3-basic-plotting.ipynb
index 583fb99613..40a02f682a 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-3-basic-plotting.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-3-basic-plotting.ipynb
@@ -40,7 +40,7 @@
         }
       ],
       "source": [
-        "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+        "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
         "import pybamm\n",
         "import matplotlib.pyplot as plt\n",
         "\n",
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb
index b3c9e256f5..64a345c312 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb
@@ -32,7 +32,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import os\n",
     "os.chdir(pybamm.__path__[0]+'/..')"
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-5-run-experiments.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-5-run-experiments.ipynb
index 831dc0404c..3aad616445 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-5-run-experiments.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-5-run-experiments.ipynb
@@ -33,7 +33,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np"
    ]
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb
index bea655f2b5..3599c37abb 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb
@@ -42,7 +42,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "model = pybamm.lithium_ion.SPMe()\n",
     "sim = pybamm.Simulation(model)\n",
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-7-model-options.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-7-model-options.ipynb
index 338b13f555..96f6e203f2 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-7-model-options.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-7-model-options.ipynb
@@ -24,7 +24,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm"
    ]
   },
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-8-solver-options.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-8-solver-options.ipynb
index b76cfd1f9d..46a7b24346 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-8-solver-options.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-8-solver-options.ipynb
@@ -28,7 +28,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm"
    ]
   },
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb
index 0e71b218e4..ee4cdc7f63 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb
@@ -28,7 +28,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm"
    ]
   },
diff --git a/docs/source/examples/notebooks/initialize-model-with-solution.ipynb b/docs/source/examples/notebooks/initialize-model-with-solution.ipynb
index 758257071e..8691439334 100644
--- a/docs/source/examples/notebooks/initialize-model-with-solution.ipynb
+++ b/docs/source/examples/notebooks/initialize-model-with-solution.ipynb
@@ -23,14 +23,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1 is available.\n",
-      "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
+      "\u001B[33mWARNING: You are using pip version 21.0.1; however, version 21.1 is available.\n",
+      "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001B[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "\n",
     "import pybamm\n",
     "import pandas as pd\n",
@@ -311,7 +311,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pybamm",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -325,7 +325,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.8.10"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/source/examples/notebooks/models/DFN-with-particle-size-distributions.ipynb b/docs/source/examples/notebooks/models/DFN-with-particle-size-distributions.ipynb
index be9084ef96..59e1e47e97 100644
--- a/docs/source/examples/notebooks/models/DFN-with-particle-size-distributions.ipynb
+++ b/docs/source/examples/notebooks/models/DFN-with-particle-size-distributions.ipynb
@@ -42,7 +42,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import matplotlib.pyplot as plt"
    ]
diff --git a/docs/source/examples/notebooks/models/DFN.ipynb b/docs/source/examples/notebooks/models/DFN.ipynb
index 25b79ec260..682adc8c21 100644
--- a/docs/source/examples/notebooks/models/DFN.ipynb
+++ b/docs/source/examples/notebooks/models/DFN.ipynb
@@ -128,7 +128,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np"
    ]
diff --git a/docs/source/examples/notebooks/models/MPM.ipynb b/docs/source/examples/notebooks/models/MPM.ipynb
index 26365ab37b..c7e1068dc2 100644
--- a/docs/source/examples/notebooks/models/MPM.ipynb
+++ b/docs/source/examples/notebooks/models/MPM.ipynb
@@ -103,7 +103,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import matplotlib.pyplot as plt"
    ]
diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb
index 7413339f5b..6dbe14f484 100644
--- a/docs/source/examples/notebooks/models/MSMR.ipynb
+++ b/docs/source/examples/notebooks/models/MSMR.ipynb
@@ -23,7 +23,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import matplotlib.pyplot as plt"
    ]
diff --git a/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb b/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb
index dd5f353413..d70c7032a3 100644
--- a/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb
+++ b/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Modelling SEI growth on particle cracks\n",
     "\n",
-    "This notebook provides a short demonsration of how the SEI and particle mechanics submodels can be combined to simulate SEi growth on particle cracks."
+    "This notebook provides a short demonsration of how the SEI and particle mechanics submodels can be combined to simulate SEI growth on particle cracks."
    ]
   },
   {
@@ -21,14 +21,14 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip available: \u001B[0m\u001B[31;49m22.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.0.1\u001B[0m\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import matplotlib.pyplot as plt"
    ]
@@ -38,7 +38,7 @@
    "id": "c46a0904",
    "metadata": {},
    "source": [
-    "Define two models. In model1, the only degradation mechanism is solvent-diffusion limited SEI growth. model2 includes the same SEI growth mechanism but also includes particle cracking and SEI growth on the cracks. The SEI model is run twice: once on the initial surface and once on the cracks. The equations for SEI on cracks are reported by O'Kane et al. [8]"
+    "Define two models. In model1, the only degradation mechanism is solvent-diffusion limited SEI growth. model2 includes the same SEI growth mechanism but also includes particle cracking and SEI growth on the cracks. The SEI model is run twice: once on the initial surface and once on the cracks. The equations for SEI on cracks are reported by O'Kane et al. [9] To ensure a fair experiment, particle swelling is enabled in both models."
    ]
   },
   {
@@ -48,7 +48,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model1 = pybamm.lithium_ion.DFN({\"SEI\": \"solvent-diffusion limited\"})\n",
+    "model1 = pybamm.lithium_ion.DFN({\"SEI\": \"solvent-diffusion limited\", \"particle mechanics\": \"swelling only\"})\n",
     "model2 = pybamm.lithium_ion.DFN({\n",
     "    \"particle mechanics\": \"swelling and cracking\",\n",
     "    \"SEI\": \"solvent-diffusion limited\",\n",
@@ -76,8 +76,8 @@
     "    \"x_n\": 20,  # negative electrode\n",
     "    \"x_s\": 20,  # separator \n",
     "    \"x_p\": 20,  # positive electrode\n",
-    "    \"r_n\": 30,  # negative particle\n",
-    "    \"r_p\": 30,  # positive particle\n",
+    "    \"r_n\": 26,  # negative particle\n",
+    "    \"r_p\": 26,  # positive particle\n",
     "}"
    ]
   },
@@ -99,8 +99,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "At t = 486.155, , mxstep steps taken before reaching tout.\n",
-      "At t = 490.579, , mxstep steps taken before reaching tout.\n"
+      "At t = 426.174, , mxstep steps taken before reaching tout.\n",
+      "At t = 186.174, , mxstep steps taken before reaching tout.\n",
+      "At t = 430.603, , mxstep steps taken before reaching tout.\n",
+      "At t = 190.603, , mxstep steps taken before reaching tout.\n"
      ]
     }
    ],
@@ -121,12 +123,12 @@
    "source": [
     "t1 = sol1[\"Time [s]\"].entries\n",
     "V1 = sol1[\"Voltage [V]\"].entries\n",
-    "SEI1 = sol1[\"Loss of lithium to SEI [mol]\"].entries\n",
+    "SEI1 = sol1[\"Loss of lithium to negative SEI [mol]\"].entries\n",
     "lithium_neg1 = sol1[\"Total lithium in negative electrode [mol]\"].entries\n",
     "lithium_pos1 = sol1[\"Total lithium in positive electrode [mol]\"].entries\n",
     "t2 = sol2[\"Time [s]\"].entries\n",
     "V2 = sol2[\"Voltage [V]\"].entries\n",
-    "SEI2 = sol2[\"Loss of lithium to SEI [mol]\"].entries + sol2[\"Loss of lithium to SEI on cracks [mol]\"].entries\n",
+    "SEI2 = sol2[\"Loss of lithium to negative SEI [mol]\"].entries + sol2[\"Loss of lithium to negative SEI on cracks [mol]\"].entries\n",
     "lithium_neg2 = sol2[\"Total lithium in negative electrode [mol]\"].entries\n",
     "lithium_pos2 = sol2[\"Total lithium in positive electrode [mol]\"].entries"
    ]
@@ -139,9 +141,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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QWPjxygElJkQk7tWvlkZGlrNxTwJ1qjZUYkJERERE4kdWJqyeCYvHwpJxsGIKZO6FhGRofAQMvi0YoLJRb0hKCTvauKTEhIjEvXpVgylD125Lp46mDBURERGRsG1aCksiiYglX0L6lqC8Xhc48ipoOSRoHZFSKcQgSw8lJkQk7tWPJCbWbE2nc43mQTZaRERERKSk7NoUzJixeGyQkMh+UFa1EbQ/CVoNDcaJKOUzZ4QlIewAREQKU79aJDGxLR2qN4PtP8G+9JCjklgbMWIEW7ZsYcuWLTz++OP7y8eNG8dJJ50USkx33303Dz300M/K77zzTj7//PMQIhKJDt1vIiK5ZOyBpRNgzL3w5FB4sBW8/guY9SbU7QgnPAjXTYVfz4FTH4MuZyopUQxqMSEica925VQSEyyYMrR+86Bwy49Qp22ocUlsffjhhwAsW7aMxx9/nGuvvTaqx8/IyCApKTp/Bu+9996oHEckLLrfRKTcc4d1cw+ME7H8K9i3CyzxwDgRrYZCo16QmBx2tGWOWkyISNxLTDDqVE4NWkzknDJUSq0HH3yQRx55BIBf//rXDBs2DIAvvviCCy64AIDmzZuzYcMGbr/9dhYvXkz37t259dZbAdixYwdnnnkm7du354ILLsDdf3aORYsWcfTRR9OtWzd69uzJ4sWLGTduHIMGDWLkyJF07NgRgFNPPZVevXrRqVMnnnzyyf37f/zxx/Ts2ZNu3boxfPjwnx3/qaee4oQTTmD37t1ccsklvPnmm/vjvuuuu+jZsyddunRh/vxgSrD169dzzDHH0KlTJy6//HKaNWvGhg0bovVPKpIv3W+630QkH9tWw8xXYNSV8HA7+Hd/+PT3QT2zx4Vw7qvw22Vw2Scw9A5o2ldJiRhRiwkRKRXqVQumDFViIgY+uh3WzIruMet3gRPuz3f1oEGDePjhh7nhhhuYNm0ae/bsYd++fUyYMIHBgwcftO3999/P7NmzmTlzJhA0Lf/222+ZM2cODRs2ZMCAAXz11VcMHDjwoP0uuOACbr/9dk477TTS09PJyspixYoVzJgxg9mzZ9OiRQsAnn32WWrWrMnu3bs54ogjOOOMM8jKyuKKK65g/PjxtGjRgk2bNh107EcffZTPPvuMd955h9TU1J9dX+3atZkxYwaPP/44Dz30EE8//TT33HMPw4YN44477uDjjz/mmWeeOZx/WSntdL/pfhOR8OzdCcsmHhgnYn2QzKRi7WCwylZDg/dqjcOMslxSYkJESoX6VVNZsn4nVK4LSRVgy/KwQ5Ji6NWrF9OnT2fbtm2kpqbSs2dPpk2bxoQJE/Y/2S1Inz59aNw4qDR0796dZcuWHfRFafv27axatYrTTjsNgLS0tIP2zf6SBPDII4/w9ttvA7BixQp++OEH1q9fz+DBg/dvV7Nmzf3b//e//6VJkya88847JCfn/dTk9NNP33+do0aNAmDixIn7z3P88cdTo0aNQq9TJBp0v+l+Eym33GHtbFg0BhaPgR8nB9N4JqVBs/7Q/YIgEVGvMySoM0GYYp6YMLNEYBqwyt1PyrUuFfgv0AvYCJzj7ssi6+4ALgMygRvc/ZNYxyoi8at+1TQmLd4IZkGrCbWYiJ4CnrTGSnJyMi1atOD555+nf//+dO3albFjx7Jo0SI6dOhQ6P45n5omJiaSkZFR5HNXqnRg2q5x48bx+eef8/XXX1OxYkWGDBlCenrBA6t26dKFmTNnsnLlyoO+cOUV36HGJuWA7jfdbyISWzs3BC0iFo+BxV/AjrVBed1OwTSerYYH03gmpxV8HClRJZEWuhGYl8+6y4DN7t4a+DvwVwAz6wicC3QCjgcejyQ4RKScqlctje3pGezam6HERBkxaNAgHnroIQYPHsygQYN44okn6NGjB2Z20HZVqlRh+/bth3TsKlWq0LhxY9555x0A9uzZw65du3623datW6lRowYVK1Zk/vz5TJ48GYC+ffsyfvx4li5dCnBQ0/IePXrwn//8h5EjR/LTTz8VOaYBAwbw+uuvA/Dpp5+yefPmQ7omkeLQ/ab7TaTMytwHyyfBmPvgySHwYGsYdTks/BiaDYBTHoeb58O1k+DYPwbdNZSUiDsxTUyYWWPgRODpfDY5BXgh8vlNYLgFfyFPAV5z9z3uvhRYBPSJZawiEt/qV41MGbo1/UBiIo8B2KT0GDRoEKtXr6Zfv37Uq1ePtLQ0Bg0a9LPtatWqxYABA+jcufP+wfiK4sUXX+SRRx6ha9eu9O/fnzVr1vxsm+OPP56MjAw6dOjA7bffTt++fQGoU6cOTz75JKeffjrdunXjnHPOOWi/gQMH8tBDD3HiiScWeUC9u+66i08//ZTOnTvzxhtvUL9+fapUqVLk6xEpDt1vut9EypTNy2DqM/DaBfBAS3juBJj4d0hMhaG/g8u/gFsXw1nPQY8LoGqDsCOWQlheIytH7eBmbwJ/AaoAt+TRlWM2cLy7r4wsLwaOBO4GJrv7S5HyZ4CP3P3NPM5xJXAlQNOmTXstX65+5yJl0aRFGzj/6Sm8csWR9F//Jnz82yD7rT80h2XevHlFasIt0bNnzx4SExNJSkri66+/5pprrtk/wGA05fWzNbPp7t476ieTn+ndu7dPmzbtoDLdbyWvpO430M9XpERkD1qZPVbExkVBebUm0GoYtD4aWgyGCtVDDVMKVlB9JGZjTJjZScA6d59uZkNidR53fxJ4EoLKQKzOIyLhqlctaDGxdls6NOsHCUnw2vlw4VtQsWYhe4uE78cff+Tss88mKyuLlJQUnnrqqbBDEimzdL+JlHL5DlpZAZoPhCMuD8aKqN0mGH9MSr1YDn45ABhpZiOANKCqmb3k7hfm2GYV0ARYaWZJQDWCQTCzy7M1jpSJSDl1oCvHHujRDc5+Ed64GJ4/CX7xTjBbh0gca9OmDd9++23YYYiUC7rfREqhXZuCwSqzkxH7B63sqEEry4GYJSbc/Q7gDoBIi4lbciUlAN4DLga+Bs4EvnB3N7P3gFfM7G9AQ6AN8E2sYhWR+FcpNYkqqUlBiwmA9iPg/NeDVhPPHg/nvgx11ZT2ULj7zwa+k9Itlt0zpXh0v5VNuudEiiErC9Z8Dz98Bj98CqumgWdBhRrQcii0Hh5006jaMOxIpQTEfLrQ3MzsXmCau78HPAO8aGaLgE0EM3Hg7nPM7HVgLpABXOfumSUdq4jEl3rV0oLBL7O1GgoXvQ2vngdPDIJBv4FBN0NSav4HEQDS0tLYuHEjtWrV0pelMsLd2bhxI2lpepIUbWZWnWAg786AA5e6+9dF3V/3W9mke07kMOzeEmkV8XmQkNi5Lihv2BMG3wqtj4FGPSFBEzKWNyWSmHD3ccC4yOc7c5SnA2fls8+fgD+VQHgiUkrUr5rGmm255rxv2hd+NRU+vgO+vB/mvA1H3QbtT1JTvwI0btyYlStXsn79+rBDkShKS0ujcePGYYdRFv0T+NjdzzSzFKDioeys+63s0j0nUojssSJ++BR++BxWTAHPhLTqQYuINscGXTQq1wk7UglZibeYEBE5XPWqprF4cR5TxVWqDWc8BV3Phg9vhbcuC5oBdj0H2hwD9btqDIpckpOTadGiRdhhiMQ9M6sGDAYuAXD3vcDeQzmG7jcRKVfSt8GScUEyYtHnsH11UF6/Kwz8dZCMaNQLEvVVVA7Qb4OIlBqt61bmrRkrOePfk/jlgOYc16k+yYkJBzZocwxcPwOWjoMZL8K0Z2HKE8G6yvWDkZur1IcqDYJX1ch7tcZQtZFGdRaRvLQA1gPPmVk3YDpwo7vvzN4g19TloQQpIhIad1g3DxZ9FnTP+PFryMqA1GpBt9s2xwTTeVapH3akEsesLA3ak9fc4SJSduzJyOSVKT/y/KRlLN+4i7pVUjm6Yz2GtavLgNa1qZCSqz9i+lZY/X0wsNLq72HLctj2E2xfA5l7Dt42pQrUaQf1OkKzAcFUVNXUPFfKhoLmDZeCmVlvYDIwwN2nmNk/gW3u/oe8tlddRETKhT07YOmXkYErP4NtK4Pyep0jiYhjoEkfSEwON06JKwXVR5SYEJFSJzPLGTt/Ha9PW8HERRvYtTeTtOQETunWiEsGNKdDg6oFH8Addm8OmhZuWw2bl8KGhbB+Aaz+DtK3BNvVbAWdTgu6hNRpG/PrEokVJSYOn5nVBya7e/PI8iDgdnc/Ma/tVRcRkTJr8zJY+Cks/AiWTYTMvcGDnZZHBd0zWh8N1RqFHaXEsYLqI+rKISKlTmKCcXTHehzdsR57MjKZunQzH8z6ibe/XcX/pq2gb8ua3HR0W/q2rJX3AcygYs3gVa/TweuysmDdnOAP7g+fwsS/wYSHoGEP6HMVdD4DklJif5EiEhfcfY2ZrTCzdu6+ABhOMGuYiEjZlpkBK6fCwo9h4Sewfl5QXqsN9LkS2h4HTfqqXiRRoRYTIlJmbNm1l/9NXcFzXy1jzbZ0jutUjztO6EDz2pUO/6Db18Lst+DbF2Hd3GAsir7XQu9fQkoxjitSgtRionjMrDvBdKEpwBLgl+6+Oa9tVRcRkVJt9xZYPCZIRPzwadDCNCEp6Oba9vggGVGrVdhRSimlrhwiUq7s3pvJMxOX8Pi4xezLzOKKQS25YXgb0pKLMSe2OywaA1/9A5ZNgMr1YMjt0OMXGlVa4l55T0yY2fdF2Gy9uw8v7rlUFxGRUmfDDwdaRSyfFEznWbFW0D2j7XHQahikVQs7SikDlJgQkXJp3fZ0/vrRAt6asZJWdSrxwJnd6NWsRvEP/OMU+PyuYNTpWm3g6Lug/Uma1UPilhITNgcYUdAmwHvu3rW451JdRETiXsbeoA6z8JMgIbFpcVBet1OQiGh3QjCdZ0IxHuiI5EGJCREp18YvXM8do2bx09bd/LJ/C245ri0VU4rZysEdFnwIn98dDJzZ5Eg45l5o2jcqMYtEkxITNtDdJxZ3m6JQXURE4tLODcHsGQs/hsVfwJ5tkJgKLQYHyYi2x0F1TXcssaXEhIiUezv2ZPDAx/P579fLaVqzIvef0YX+rWoX/8CZGTDzZRj7Z9ixBjqeCif8VXN1S1wp74mJkqS6iIjEBfdgtrEFHwQtI1Z8A3jQFbXtccF4ES2OgtTKYUcq5YgSEyIiEVOWbOS3b33Pso27OK9PU+4Y0Z6qaVGYY3vvTvj6MRj/ECSlwtF3Q69fQkJC8Y8tUkzlPTFhZrOAvCo8Bng0unBkU11EREKTlQkrpsD8D4JWnZuWBOUNukHbE6Dd8VC/m+omEholJkREckjfl8nfP1vIUxOWUK9qGn8+rQtD29eNzsE3LobRN8HS8UH3jpP/CXU7ROfYIodJiQlrVtB6d18erXOpLiIiJWrvLlgyFuZ/CAs/gl0bISE56KLRfkSQkKjWKOwoRQAlJkRE8jRzxRZue/M7Fq7dwSndG/K7ER2oVzWt+Ad2h+9eg09+B3u2w8CbYNAtkByFY4schvKemMjJzOoBR0QWv3H3ddE8vuoiIhJzO9YHY0Us+BAWj4WM3ZBaDdoeC+1GQOujIa1q2FGK/IwSEyIi+diTkcljYxfzxLjFJCUa1w1tzWUDWxRvatFsOzfCp7+H716Fmq3g5H8ETzBESpgSEwEzOxt4EBhH0I1jEHCru78ZrXOoLiIiMbFhUTBexPwPg+4aOFRtHLSKaDcCmg+ExCh0TRWJISUmREQK8ePGXfz5w3l8PGcNjWtU4P9O7MBxnepj0ZgCdPHYoHvH5mXQ/QI49o9QsWbxjytSREpMBMzsO+CY7FYSZlYH+Nzdu0XrHKqLiEhUZGXBqmkHxovYsDAor98F2p0YJCTqd9VU5VKqFFQfKeZ8eSIiZUPTWhV54qJeTFq0gXven8vVL82gX8ta3HlyRzo0KGZzyFZD4ZqvYfwDMOlfQfPL4/4CXc9WhUKkZCXk6rqxEdAocCISH/alw9IvI8mIj2DnOkhIgmYD4IjLod0JmtJTyiy1mBARySUjM4tXp67g4U8XsHX3Pk7p1pAbhrehZZ0oTKm1Zja8f2PwFKTlUDjp71CzRfGPK1IAtZgImNmDQFfg1UjROcAsd78tWudQXUREDkn6Vlj4Kcx7DxaNgX07IaUKtDk6aBnR5hioUD3sKEWiQl05REQOw5Zde/n3l4v576Tl7MnI5PSejblhWBua1qpYvANnZcLUZ2DMvZCVAUN+C/1+pb6hEjNKTBxgZmcAAyKLE9z97WgeX3URESnUzg1Bq4h578OScZC1DyrXh/aRLhrNBwVTj4uUMUpMiIgUw/rte3jiy8W8NHk5mVnOWb0bc93Q1jSuUcwExdZV8NFtMH801OsMJz8CjXtFJ2iRHJSYOJiZVSVHd1Z33xStY6suIiJ52roS5o0OkhE/TgLPgurNoONI6DASGvWGBPUsk7JNiQkRkShYuy2dx8cu4tVvVuA45xzRhF8NbUP9asWcBnTe+/DhrbB9DfS6GIb9ASrVjk7QIigxkc3MrgLuAdKBLIKZOdzdW0brHKqLiMh+GxbB/PeDv/OrpgdldTpEkhEnBw8lNNaUlCOhJCbMLA0YD6QSPJV4093vyrXN34GhkcWKQF13rx5ZlwnMiqz70d1HFnZOVQZEpCT8tGU3j45dxOtTV5CQYFx4ZDOuHtKSulWKkaBI3wbj/gLfPAnJlWDI7dDnCnXvkKhQYiJgZj8A/dx9Q6zOobqISDnmDmtnB4mIee/DurlBecOeQSKiw8lQu024MYqEKKzEhAGV3H2HmSUDE4Eb3X1yPttfD/Rw90sjyzvc/ZBGmlNlQERK0opNu3hkzA+M+nYVyYnGxf2ac9VRrahZKeXwD7p+AXx8Oyz+Amq3DWbvaHN09IKWckmJiYCZfQyc7u67YnUO1UVEypnsaT3nvRckIzYvA0uApv2DRET7E6F6k7CjFIkLhzVdqJm9V4Rjb3L3S/Ja4UHGY0dkMTnyKigLch5wVwHrRUTiSpOaFXnwrG5cO7Q1j4z5gScnLOGlycv55YAWXDGoJdUqHkZrhzrt4MJRsPAT+OQOePkMaHMcHPcnPWURKb47gElmNgXYk13o7jeEF5KIlDpZmfDjZJj7TpCM2L4aEpKh5RAYeDO0GwGV64QdpUipkm+LiUhzx8sL2hd4zN075buBWSIwHWgd2fa3+WzXDJgMNHb3zEhZBjATyADud/d3CrsYPaUQkTAtWredv3/+Ax98v5oqaUlcPrAllw5sTpW0w+yOkbEHpvwHvnwA9u0Kxp846naoUi+6gUuZpxYTATP7hqAF5yyCMSYAcPcXonUO1UVEyqisTPjxa5jzTtA6YsdaSEqD1kdDx1OgzbGa1lOkEIfVlcPMznb31ws5cKHbRLarDrwNXO/us/NY/1uCpMT1OcoaufsqM2sJfAEMd/fFeex7JXAlQNOmTXstX768sHBERGJq3upt/P2zhXw6dy21K6fwm2PbcXbvJiQmHOYAVzvWw5d/henPQWIq9L8+eKUeUm83KceUmAiY2bfu3iOW51BiQqQMycqE5ZMOtIzYsRaSKkCbY6DTqUGLRv0tFimyw01MnAW87+7pUQriTmCXuz+Ux7pvgevcfVI++z4PjHb3Nws6hyoDIhJPvl+5hftGz2Xqss10bFCVO0/uSN+WtQ7/gBsXw5h7YO67UKkuDPkt9LxYA2RKoZSYCJjZn4FlwPsc3JVD04WKSCA7GTHn7SAZsXNdkIxoeyx0PDVoGaFkhMhhOdzExNvAAOAT4FXgk+xuFkU8aR1gn7tvMbMKwKfAX919dK7t2gMfAy0i41JgZjUIkhh7zKw28DVwirvPLeicqgyISLxxd0Z/v5r7P5rPqi27GdGlPnec0IEmNSse/kFXTIXP7gzmQa/VGobfFQywpSnHJB9KTATMbGkexYVOF2pmy4DtQCaQUdC/peoiIqVQViYs/yrSTSOPZETb4yClUthRipR6hzX4pbufZmZVgdOA64FnzOxd4FV3/7II520AvBAZZyIBeN3dR5vZvcA0d88eXPNc4DU/OEPSAfiPmWVF9r2/sKSEiEg8MjNO7taQYzrW48nxS/j3uMV8Pm8dVwxqwa+GtqFCSuKhH7TJEfDLD2Hhx/DZXfD6RdC4Dxx7HzTtG/2LECkj3L1FMXYfGstpRkWkhGVmBMmI7G4aO9dDcsWgRUSnU4N3JSNESkyRpws1s1rAmcC1QE13j7t5b/SUQkTi3eqtu/nrR/N5Z+ZPNKlZgT+e2oWj2hZj5O7MDJj5Moz9M+xYA+1PClpQ1GkbvaCl1CvvLSbMrKe7zzjcbSItJnoXJTGhuohIHMvKClobzn7r4GRE2+Mi3TSOUTJCJIYOqytHrgPUIEhKnAe0Ad50919HNcooUGVAREqLSYs38H9vz2bJhp2c3K0hfzipA3WrpB3+AffuhMmPw8R/BjN49LwIBv0GqjeNXtBSaikxYd8BQwhmFMvPmPwGxox0AdlMMO35f9z9yVzrNRC3SLxyh1UzgmTEnFHB1J7JFaHt8UHLiNbHQEoxuleKSJEd7hgTlQm6cZwH9ADeA14DxnlRm1mUMCUmRKQ02ZORyb/HLebxsYtJTU7g9hPac94RTUk43Nk7AHZuCKYXnfYs4NDtPBh0M9QssAu9lHFKTNgygulBC7q51rt7n3z2z54prC7wGcEsY+Pz2lZ1EZE4sXYuzH4zSEhsXgaJKUH3jM6nB0kJtYwQKXGHm5jYQDAo5WsEA1/ui12I0aHKgIiURovX7+D3b89i8pJN9GpWg7+e0YXWdasU76BbV8JX/4TpL0BWBnQ5CwbfArXbRCdoKVXKe2IimszsbmBHXrOMgeoiIqHatCRIRMweBevmgiVCy6Og8xlBV8cK1cOOUKRcO9zERAV33x3TyKJMlQERKa3cnVEzVnHfB3PZtTeTW49tx6UDW5BYnNYTANvXwKR/wdRnICMdOp0GQ+7QGBTljBITh8/MKgEJ7r498vkz4F53/ziv7VUXESlh234Kpvac9Sb8FBkmpmm/IBnR8VSoXIxxnEQkqg43MfGku19ZyIEL3aYkqTIgIqXduu3p/P7t2Xw2dy29mtXgobO60aJ2FJqb7lgPXz8K3zwFGbuh2/kw5Lcag6KcUGLi8JlZS+DtyGIS8Iq7/ym/7VUXESkBOzcGs2nMHhXMrIFDg27Q+cwgAV897sboFxEOPzGxjqAbR77HBY5397hpF6zKgIiUBe7OOzNXcde7c9ibmcVtx7Xnkv7Nizf2RLadG2DC32Dq04DDkVfB4FshrVrxjy1xS4mJkqO6iEiMpG+D+R8EXTWWjA26KdZuGyQjOp8BtVuHHaGIFOJwExMXF+HYu9399eIEF02qDIhIWbJ2Wzq3v/U9Yxesp0+Lmjx8Vjea1IzSyOFbVwZTjM58BSrVhuF3QvcLICExOseXuFLeExNm1rOg9YVNJXooVBcRiaLMfbBoDHz/Giz4KOiSWK1pMIBllzOhXmewKCTtRaREFHu60NJClQERKWvcnTemr+S+9+fiwN0jO3FGz0ZYtCpiq2bAx7fDiilBM9jj/wrN+kXn2BI3lJiwsQWsdncfFq1zqS4iUkzusGo6fPdaML3nro1QoWbQKqLr2dD4CCUjREqpguojSSUdjIiIFJ2ZcXbvJvRvVYub//cdt7zxHWPnr+NPp3WmesWU4p+gUU+49JOgaexnd8Jzx0On0+GYe9VHV8oMdx8adgwiUohNS+D7N+D7/8GmxZCUBu1OgK7nQuvhkJgcdoQiEkMJYQcgIiKFa1yjIq9e2Zfbjm/HJ3PWcPw/JjBp0YboHNwsaBL7q6lw1O2w4EN4rA9M/EfQjFaklDOz23J8PivXuj+XfEQiAsCuTcGYR08fA4/0gHF/gaoNYeSjcMtCOOt5aHe8khIi5UCRu3KYWUV33xXjeIpFzSdFpDyYtXIrN/7vW5as38kVg1pwy3HtSE2K4tgQW36Ej26HBR9AnQ5w4sPQfED0ji8lTl05bIa798z9Oa/l4lJdRKQQ+9Jh4cdBy4gfPoOsfcHfmm7nQJezoFrjsCMUkRgpVlcOM+sPPA1UBpqaWTfgKne/NrphiohIUXRpXI0Prh/EHz+Yy1MTljJ+4QYeOLMr3ZpUj84JqjeF814JBhr78DZ4fkQwveix9wUDZYqUPpbP57yWRSTa3OHHr+G7V2HOu7BnK1SuH8wM1fUcqN9F40aIlHNFGWPi78BxwHsA7v6dmQ2OaVQiIlKgCimJ/Om0LgzvUJffjZrNaY9/xWUDW3DzMe2okBKl1hPtToAWg2H8gzDpX0EXj6Pvgp6XQIJ6Akqp4vl8zmtZRKJly4/BIJYzX4bNyyC5EnQcGQxi2eIozQQlIvsVafBLd1+RawT4zNiEIyIih2JY+3p8enNN7v9oPk9NWMpHs9dwy7HtGNmtIQkJUXj6lFIJjr47GHzsw1tg9K/h25fgpL8Hs3iIlA7dzGwbQeuICpHPRJbTwgtLpAzauwvmvR8kI5aOBxyaDwrGMOpwMqRWDjtCEYlDRUlMrIh053AzSwZuBObFNiwRESmqqmnJ/Pm0Lozs1pD7Rs/lpv/N5KkJS7j9hPYMalMnOiep2x4ufh++fx0+/T08OQT6XAlDfw9pVaNzDpEYcXc9lhWJJfdg2umZL8Pst2HvdqjeDIbcDt3OgxrNwo5QROJcoYNfmllt4J/A0QRPFj4FbnT3jbEP79BowCkRKe+yspz3vvuJhz5dwMrNuxnUpja/Pb49nRtVi95Jdm+GL/4IU5+ByvXg+D8HU4yqf3Dc0uCXVhHY5+77IsvtgBHAMnd/O5rnUl1EypWtKyNdNV4JpvhMrgSdToXu50PT/ur2JyIHKag+UuRZOUoDVQZERAJ7MjJ58evlPDp2EVt27eOU7g25cXgbWtaJYhPaVdNh9M2weia0HAIjHobaraN3fIkaJSZsPHCZu/9gZq2Bb4CXgY7AVHe/PVrnUl1Eyrx9u2H+B0HriMVjAYdmA4NkRMdT1FVDRPJVrMSEmT2SR/FWYJq7vxuF+KJGlQERkYNt3b2PJ75czHNfLWVvRhYjuzXkV8Pa0LpulCqOWZkw7VkYcy9kpMOAm2DQzZBcITrHl6hQYsJmuXuXyOf7gJrufp2ZpQDTs9dFg+oiUia5w0/fwoz/wuxRwawa1ZpC9/Og27lQs2XYEYpIKVCs6UIJBoVqD7wRWT4DWEowkNRQd78pKlGKiEjUVauQzG+Pb8+lA1rw1IQlvPj1ct797ieO7ViPywa25IjmNbDidMFISIQ+V0CHkcHYE+MfgFmvw7F/hPYnqXuHxIucT2GGAQ8CuPteM8sKJySRUmD3Zvj+jSAhsXYWJFUIWkV0Pz8Y0FJdNUQkSorSYmIyMMDdMyPLScAEYCAwy907xjzKItJTChGRgm3YsYfnvlrKy1N+ZMuufXRtXI3LBrZgRJcGJCdGoYK55Ev48FbYsACa9A0SFE2OKP5xpVjUYsJeAtYAq4DbgRbuvsvMqgNfunvUpphRXURKPXdY/lWQjJj7btAarkF36PkL6HImpEVxzCIRKVcKqo8UpRZaA8jZ5rcSQRPITGBPASdNM7NvzOw7M5tjZvfksc0lZrbezGZGXpfnWHexmf0QeV1chDhFRKQQtSuncutx7fn69uH88dTO7EjP4MbXZjL4gbE88eVituzaW7wTtDwKrpkEJ/0DNi2BZ46G138BGxdHJX6Rw3QFsAFoDhzr7rsi5R2Bh8IKSiSu7FgHE/8O/+oFz58ICz6GHhfCVePhqi/hiMuUlBCRmClKi4nLgP8DxhHMyjEY+DPwKnC3u9+az34GVHL3HZFpRicSzOYxOcc2lwC93f1XufatCUwDehM0v5wO9HL3zQXFqqcUIiKHJivLGbtgHc9MXMqkxRtJTUrg5G4NuahvM7o1qV68g+/ZAV8/Cl89Apl7g0rt4NugUq2oxC5FV95bTJQk1UWkVMnKhEVjYMYLsPBjyMoIZtPodXHQRS+lYtgRikgZUqwxJtz9GTP7EOgTKfqdu/8U+ZxnUiKynwM7IovJkVdRpwA5DvjM3TcBmNlnwPEEyRAREYmShARjeId6DO9Qj7k/beOlKct559tVvDl9JV0aVePCvk0Z2a0RFVISD/3gqZWDOex7XQLj/gLfPAnfvgz9fwX9roPUKlG/HhERKYKtq4KuGt++CNtWQcXa0PfaoLtG7TZhRyci5VCRpgs1sxpAG4KBMAFw9/FF2C+RoLVDa+Axd/9trvWXAH8B1gMLgV+7+wozuwVIc/c/Rrb7A7Db3QtsbqmnFCIixbc9fR9vf7uKlyYvZ+HaHVRJS+LMXo254MhmxZvNY/0C+OI+mPc+VKwFg26B3pdCclrh+0qxqMVEyVFdROJWVhYsGRvMpLTgI/AsaDUsaB3R9gRISgk7QhEp44o7XejlwI1AY2Am0Bf42t2HHUIA1YG3gevdfXaO8lrADnffY2ZXAee4+7BDSUyY2ZXAlQBNmzbttXz58qKGJSIiBXB3pi7bzEuTl/PR7NXsy3T6tazFhX2bcWyneoc/WOaq6cH0okvGQdXGQauKbudBYlEmipLDocTEwcysMoC77yhs20OlxITEnZ0bYebLQUJi89IgMdzjoqA1W80WYUcnIuVIcRMTs4AjgMnu3t3M2gN/dvfTDzGIO4Fd+bV6iLSu2OTu1czsPGCIu18VWfcfYJy7F9iVQ5UBEZHYWL99D69PW8ErU35k1Zbd1K2SyrlHNOG8I5vSoFqFwzvoknHw+T3w0wyo3RaG/V/Qp1lTjEadEhMBM+sC/BeoSTBu1nrg4pwPTQrYN5Fg/KtV7n5SftupLiJxwR1WfAPTnoE570DmHmjaD3pfBh1HQlJq2BGKSDlUrDEmgHR3TzczzCzV3eebWbsinLQOsM/dt5hZBeAY4K+5tmng7qsjiyOBeZHPnwB/jnQhATgWuKMIsYqISAzUqZLKdUNbc/VRrfhy4Tpemvwj/xq7iEfHLmJ4h3pc1LcZA1vXJiHhEJIKLYfAFUfB/NEw5r5g9o6GPWH4ndBqaMyuRcq1/wA3u/tYADMbAjwJ9C/CvjcS1FOqxio4kWLbsx2+fz1oHbF2NqRUgZ4XBd3m6nUKOzoRkXwVJTGxMtIV4x3gMzPbDBSlv0QD4IXIE4YE4HV3H21m9wLT3P094AYzGwlkAJuASwDcfZOZ3QdMjRzr3uyBMEVEJDyJCcaw9vUY1r4eKzbt4pVvfuT1qSv4bO5amteqyAVHNuOs3o2pXrGIfZXNoMPJ0G4EfPdaMEjmi6dCi8Ew/G5o3CuWlyPlT6XspASAu48zs0qF7WRmjYETgT8BN8cwPpHDs2Z2kIz4/n+wdwfU7xJM29zlrGAgYhGROFekwS/3b2x2FFAN+Mjd98UsqsOk5pMiIiVvT0YmH89ew0uTlzN12WbSkhMY2a0hv+jXnM6NDnHO+4w9MO05GP8g7NoQJC2OuRdqtoxN8OWEunIEzOxtYAbwYqToQoLpyE8rZL83CQbrrgLckrsrh8a7klBk7Am6aUx7BlZMgaQ06HR60DqicW91ixORuFPcMSZedPeLCiuLB0pMiIiEa+5P23hxcjDl6O59mfRoWp2L+jbjxK4NSE06hClH92yHyf+Gr/4JmXuh369g0G/05O8wKTERiHQRvQcYSDCF+QTgbnffUsA+JwEj3P3aSNePnyUmclJdRGJu+5qgdcS0Z2HneqjZKkhGdD8fKtYMOzoRkXwVNzExw9175lhOBGa5e8fohll8qgyIiMSHrbv38db0lbw0eTlLNuykbpVULhnQnAuObEa1CslFP9C21TDmHvjuVajSAI6+B7qerSeBh0iJiYCZneXubxRWlmv9X4CLCLqdphGMMTHK3S/Ma3vVRSRmVk6DKU/AnLchKxPaHg9HXgkthkDCYc6SJCJSgg4rMWFmdwC/AyoAu7KLgb3Ak+4ed4NRqjIgIhJf3J3xP2zgqfFLmLhoA5VSEjm3T1MuHdiCRtUPYTaPFVPho9uCGTwa94ET/gqNeha+nwBKTGTL/bAlv7IC9h+CWkxIScrYC3PfCRISq6ZDatVgqs8+l6uLm4iUOoc1K4e7/wX4i5n9JR6TECIiEv/MjKPa1uGotnWY89NWnhq/hOcnLeP5Scs4qWsDrhrcio4NizDJQZMj4PIx8N0rwRSjTw2DXhfD8LvUdFkKZWYnACOARmb2SI5VVQlaQojElx3rDnTX2LEWarWBEQ9Bt3MhtUrY0YmIRF1BLSYKfHrg7jNiElEx6CmFiEj8W7VlN89OXMpr3/zIzr2ZDG9fl+uGtaZn0xqF7wyQvg3G3R88QaxQPRgcs9v5aspcgPLeYsLMugHdgXuBO3Os2g6MdffN0TqX6iJSLKumw5QnYfZbkLUP2hwLR14FLYfp/zgRKfUOtyvH2DxXBNzdh0UjuGhSZUBEpPTYumsfL3y9jGe/WsqWXfsY1KY2tx3Xni6NiziTx5rZ8MFvYMVkaNIXTnwY6neObdClVHlPTGQzs+RYzyqmuogcsqxMmD8avn4smF0jpQr0uAD6XAm1WoUdnYhI1BRr8MvSRJUBEZHSZ+eeDF6espx/j1vM5l37OKlrA245th3Na1cqfOesrGBgzM/+ALu3QN9rYMjtauqcixITJUd1ESmyvTvh25dh8mOweRnUaA5HXhPMrpFWhC5uIiKlTHFn5UgGrgEGR4rGAf+J9ROHw6HKgIhI6bUtfR9PjV/C0xOWsi8zi/P6NOX64a2pWyWt8J13bQpm75j+AlSpD8f9GTqdptk7IpSYKDmqi0ihtq+Bb56Eqc9A+pZgQN/+10P7EyHhEKZVFhEpZYqbmHgaSAZeiBRdBGS6++VRjTIKVBkQESn91m1P55ExP/DaNytITkzgikEtuHpIKyqm5Dte8wErp8HoX8Oa76Hl0GCwuNqtYx90nFNiouSoLiL5WjcPvn4Uvn8dMvdBh5Og3/XQ9MiwIxMRKRHFTUx85+7dCiuLB6oMiIiUHcs27OTBTxfwwferaVgtjT+c1JHjO9fHCmsFkZUZPIn84j7ISIcBN8GgmyH5EKYnLWOUmAiYWW/g90AzgpnJjGDcrK7ROofqInIQd1j6JUx6FBZ9BkkVoMeFQbczjR8hIuXMYU0XmkOmmbVy98WRg7UEMqMZoIiISG7Na1fisfN7ckn/Tfzhndlc8/IMBrWpzT0jO9GyTuX8d0xIhCOvhI6nwKf/B+MfgO//F7SeaHtsyV2AxKOXgVuBWUBWyLFIWZa5D2aPgq//BWtmQaW6MOz/oPdlmuJYRCQPRWkxMRx4DlhC8GShGfBLdy9o1o5Q6CmFiEjZlJGZxUuTl/PwpwtJz8jk8kEtuX5Y66J171g6Ppi9Y8NCaH8SHH8/VG8S+6DjiFpMBMxsorsPjOU5VBcp5/bugm9fhEn/gq0roHY76P8r6HI2JBdhvBwRkTKs2LNymFkq0C6yuMDd90QxvqhRZUBEpGxbv30P9380n7dmrKRBpHvHCUXp3pGxN+jb/eUDwYCYR/0W+l0HicklE3jIlJgIRB62nAeMAfbXZdx9VLTOobpIObV7C0x9CiY/Abs2BFMYD/w1tDkWEhLCjk5EJC4Ud4yJ74FXgdezu3PEK1UGRETKh+nLN/GHd+Ywd/U2Brauzd0jO9G6bgHdO7Jt+RE+uh0WfAD1usDIf0KjXrEPOGRKTATM7CWgPTCHA1053N0vjdY5VBcpZ7avDab7nPos7N0OrY8JxrRp1j/syERE4k5xExPNgHMiryzgfwRJih+jHWhxqTIgIlJ+ZGY5L09ZzkOfLGD3vkwuG9iSG4YXsXvHvPfhw1thx1o48moY+ntILUJio5RSYiJgZgvcvV3hWx4+1UXKiU1L4at/wsxXIGsfdDw1aCHRIGrjqIqIlDkF1UcKbVvm7svd/QF37wWcD3QFlkY5RhERkUOSmGD8ol9zvrhlCKd2b8QTXy7mmL+N55M5ayi0m2KHk+G6KdDrlzD5cXi8Lyz8tGQClzBNMrOOYQchpdj6BfDWFfCvnjDzZeh+HvxqGpz1nJISIiLFUKROb2bWzMxuA14jaAJ5W0yjEhERKaLalVN58KxuvHl1P6qkJXHVi9O59Pmp/LhxV8E7plWDk/4Gl34CyRXhlbPgzUth58aSCVzC0BeYaWYLzOx7M5sV6bIqUrB184P/Hx47EuaPhr7Xwo3fw8n/1LSfIiJRUJSuHFOAZOB1gi4cS0oisMOh5pMiIuVbRmYWz09axt8/W8i+LOe6Ia256qiWpCUnFrLjHpj4Dxj/IFSoAac8Cm2PK5GYS4K6cgQi3VN/xt2XR+scqouUMWvnBIPmzn0XUipBnyug36+gUu2wIxMRKXUKqo8UoSMuv3D3BVGOSUREJOqSEhO4fFBLTu7WkD9+MI+/f76QUd+u5J6RnRjSrm4BO6bCkN9C+xEw6ip45WzodQkc9xdIqVhi8UvMFT4VmQjAmlnw5V+D8WhSqsCg3wQz+VSsGXZkIiJlUqGJCSUlRESktKlXNY1/ndeDc3o34c53Z3PJc1M5oXN9/nBSRxpWr5D/jvW7wJVjYeyf4KtH4Kdv4dxXoVqjkgteYukDguSEAWlAC2AB0CnMoCSOrP4uaCExfzSkVoXBt0Hfa5SQEBGJsaK0mBARESmVBrapzUc3DeLpCUv51xc/MH7hem49rh0X9WtOYoLlvVNSKhxzLzQbAG9eBk8Ng3NfgcZlf1rRss7du+RcNrOewLUhhSPxZN28ICE57/1g/JkhdwQz9lSoHnZkIiLlQpEGvzwcZpZmZt+Y2XdmNsfM7sljm5vNbG5kAKoxOft+mlmmmc2MvN6LVZwiIlK2pSYlct3Q1nz266Po1bwmd78/lzP+PYn5a7YVvGPb4+Dyz4JExfMjNGtHGeTuM4Ajw45DQrRpCYy6Eh7vB4vHBQmJm2bBkNuVlBARKUH5Dn5pZqcXtKO7jyrwwGYGVHL3HWaWDEwEbnT3yTm2GQpMcfddZnYNMMTdz4ms2+HuhzSpvAacEhGRgrg77878iXtHz2Xb7n1cfVQrfjWsdcGDY+7cAC+dDhsXw+WfQ90OJRdwlGjwy4CZ3ZxjMQHoCdRy96iNdKq6SCmxdRWMfwC+fQkSkuHIK2HATeqyISISQ4c7+OXJBaxzoMDEhAcZjx2RxeTIy3NtMzbH4mTgwoKOKSIiUhxmxqk9GjG4bR3++MFcHh27iA9nrebPp3ehb8taee9UqTac9xo8OQRePRcu/wIq5bOtxLsqOT5nEIw58VZIsUgYdqyHiX+Dqc+AZ0HvS4OBLavUDzsyEZFyrdDpQot1cLNEYDrQGnjM3X9bwLaPAmvc/Y+R5QxgJkHF4X53fyef/a4ErgRo2rRpr+XLozbjl4iIlHETfljP796exYpNu7liUAtuOa4dqUn5tJ5YOQ2eGwFN+sBFb0NicskGWwxqMXH4zCwNGA+kEjzQedPd78pve7WYiFO7t8CkR2DyE5CxG7qfD0f9Fqo3DTsyEZFyo6D6SJESE2Z2IsGI1WnZZe5+7yEEUB14G7je3Wfnsf5C4FfAUe6+J1LWyN1XmVlL4AtguLsvLug8qgyIiMih2rU3gz99MI+Xp/xIp4ZV+ee5PWhdN5+ehN/9D96+EvpfD8f+sWQDLYbynpgws3+4+01m9j55TBnq7iML2LfQrqk5qS4SZzL2wNSnYfyDsHszdD4DhvwOarcOOzIRkXLncLtyZO/8BFARGAo8DZwJfHMoAbj7FjMbCxwPHJSYMLOjgd+TIykR2WdV5H2JmY0DegAFJiZEREQOVcWUJP50WheOaluH3771PSf9awKPnNuDYzvl0bS72znw4yT4+nHocjY06FryAcvheDHy/tCh7liUrqkSh7KyYM4oGHMvbFkOrYbB0ffonhURiVNFmZWjv7v/Atjs7vcA/YC2he1kZnUiLSUwswrAMcD8XNv0AP4DjHT3dTnKa5hZauRzbWAAMLdIVyQiInIYju1Un09uGky7+lW56qXpvDBpWd4bHn03VKgBo38dfPmRuOfu0yPvX+b1Kmx/M0s0s5nAOuAzd5+Sa/2VZjbNzKatX78+Jtcgh2DZRHh6GLx1GaRWhQtHBd2vlJQQEYlbRUlM7I687zKzhsA+oEER9msAjDWz74GpBH/IR5vZvWaW3WTyQaAy8EauaUE7ANPM7DtgLMEYE0pMiIhITNWtmsZrV/Tl6A71uOu9Ofz5w3lkZeV6OF6hBhz3Z1g1DWY8H0qccnjMbICZfWZmC81siZktNbMlhe3n7pnu3h1oDPQxs8651j/p7r3dvXedOnViFL0Uat18eOUceP7EYJDLU5+Aq76E1sPDjkxERApRaFcOYHSk5cODwAyC5otPF7aTu39P0P0id/mdOT4fnc++k4AuRYhNREQkqiqkJPLEhb245/05PDl+CZVTk7hheJuDN+p6Nnz7Inx+N7Q/CSrXDSVWOWTPAL8mGJg781B3LqhrqoRo+xoY++fgnkypHLRqOvJqSK4QdmQiIlJEhSYm3P2+yMe3zGw0kObuW2MbloiISHgSE4x7RnZiR3oGf/98IT2b1mBgm9oHNjCDE/8G/+4Pn90Jpz0RXrByKLa6+0eHsoOZ1QH2RZIS2V1T/xqT6OTQZOyBrx+F8Q9D5t4gGTHoFk3nKyJSChWlxQRm1h9onr29meHu/41hXCIiIqEyM/54WmdmrdrKja99ywc3DKJ+tbQDG9RpC32vhkmPwoCboG770GKVgplZz8jHsWb2IDAKyDng9owCdm8AvBCZAj0BeN3dR8csWCmcOyz4CD75HWxeGrRaOvY+qNky7MhEROQwFWVWjheBVsBMDjR7dECJCRERKdMqpiTx7wt7MvLRr7j+1Rm8ckVfkhNzDM804Ncw7TkY92c4W38W49jDuZZzTlXmwLD8dsyva6qEZP1C+Ph2WDwGarcLBrVsle+PT0RESomitJjoDXSMTJclIiJSrrSuW4W/nN6FG1+byRPjFnN9zvEmKtWCvtfC+Adg9fca9T9OuftQADNr6e4HDXZpZnrMXhqkb4UvH4ApT0ByJTj+fjjickhMDjsyERGJgqLMyjEbyGMydxERkfLhlO6NGNGlPo+OXcSKTbsOXtnvOkirFgy+J/HuzTzK3ijxKKTosrJgxn/hX73g68eg+wVwwwzoe42SEiIiZUhRWkzUBuaa2Tcc3B9zZP67iIiIlC3/d2JHxs5fz32j5/LkL3L0BKhQHfrfAF/cByunQePe+R5DwmFm7YFOQDUzOz3HqqpAWt57SehWTYcPfgM/fQtNjoQL3oCG6lUjIlIWFSUxcXesgxAREYl3DatX4PrhrXng4wWMW7COIe1yTBF65NUw+d8w9k9Bn3eJN+2Ak4DqwMk5yrcDV4QRkBQgfSuMuQ+mPg2V68HpT0OXM4PZcEREpEwqynShX5ZEICIiIvHu8oEteXPaSu5+bw6f/LoWqUmJwYrUytD/V/D53bBmNtTvHGqccjB3fxd418z6ufvXYccj+XCHOaPg4ztg53rocyUM+z9Iqxp2ZCIiEmP5jjFhZhMj79vNbFuO13Yz21ZyIYqIiMSHlKQE7h7ZiWUbd/HsxGUHr+x1CSRXhMmPhxGaFMDMbot8PN/MHsn9CjU4CWxcDC+dDm9eClUawOVjYMQDSkqIiJQT+baYcPeBkfcqJReOiIhIfBvctg5D29XhP+MXc1G/ZlROjfwprVADelwI05+H4XdBlXqhxikHmRd5nxZqFPJzmftg0iMw7q+QmAInPBDMtpGQGHZkIiJSgooyxgRmVgNoknN7d58Rq6BERETi2U1Ht+WUx77ihUnLuG5o6wMrjrwavnkq6Bs/7PfhBSgHcff3I+8vhB2L5LD6O3j3OlgzCzqMhBP+ClUbhh2ViIiEoNDEhJndB1wCLAGyIsUODItdWCIiIvGrW5PqDG1Xh6cnLOHi/s0PtJqo1QrajQgSE4NuhuQK4QYqAJjZ+wR1lzxpprESti8dxj8AE/8BFWvB2S9CR/0IRETKs6K0mDgbaOXue2MdjIiISGlx49FtOfWxr/jv18u4dkiOVhP9roMFH8B3r0HvX4YXoOT0UNgBSMSKb4JWEhsWQvcL4Ng/QsWaYUclIiIhK0piYjbB9FrrYhuKiIhI6dG9SXWGtKvDU+OXcHG/5lTKbjXRrD806BZMH9rzYkjId5xpKSGaYSwO7NsdTAE6+XGo1hgufAtaHx12VCIiEieKUlv6C/CtmX1iZu9lv2IdmIiISLy7cXgbNu/ax3+/Xn6g0Az6/Qo2LIDFY8ILTiRe/DQT/nMUTH4Mel8K136tpISIiBykKC0mXgD+CsziwBgTIiIi5V6PpjUY1KY2z361lEsHNic1KTKTQMdT4bO74OvHoM0xocYoEprMDPjq7zDufqhUBy4cBa2Hhx2ViIjEoaK0mNjl7o+4+1h3/zL7FfPIRERESoErBrVk/fY9jP5u9YHCpBTocwUsGQtr54QXnABgZi9G3m8MO5ZyY+NieO54+OKP0PEUuGaSkhIiIpKvoiQmJpjZX8ysn5n1zH7FPDIREZFSYFCb2rStV5mnJy7FPcfED70ugeSK8PXjocUm+/Uys4bApWZWw8xq5nyFHVyZ4g7TnoMnBgYDXJ7xDJz5rAa4FBGRAhWlK0ePyHvfHGWaLlRERAQwMy4f2JLb3vqerxdvpH/r2sGKijWDWQdmvABH3wWV64YbaPn2BDAGaAlMByzHOo+US3Ht3gLv3wBz34WWQ+CUx6Fao7CjEhGRUqDAFhNmlgi85+5Dc72UlBAREYkY2b0htSun8PTEpQev6HsNZO6DqU+HE5gAEOmS2gF41t1bunuLHC8lJaJh1XT4z2CY/wEccy9c+LaSEiIiUmQFJibcPRM473AObGZpZvaNmX1nZnPM7J48tkk1s/+Z2SIzm2JmzXOsuyNSvsDMjjucGEREREpCWnIiF/Ztxhfz17Fo3Y4DK2q1gnYnBImJfenhBSgAuPs1ZtbNzH4VeXUNO6ZSzz3orvTMceBZ8MuPYMCNmiZXREQOSVH+anxlZo+a2aBDHGNiDzDM3bsB3YHjzaxvrm0uAza7e2vg7wSzf2BmHYFzgU7A8cDjkdYbIiIicenCvs1ISUrgua9ytZo48mrYtRHmaabtsJnZDcDLQN3I62Uzuz7cqEqxXZvgtfPhkzuC2WeuGg9N+oQdlYiIlEJFSUx0J0gQ3As8HHk9VNhOHsh+bJQceXmuzU4hmI4U4E1guJlZpPw1d9/j7kuBRYD+0omISNyqXTmV03s04q0ZK9m0c++BFS0GQ82WwYCAErbLgSPd/U53v5Ng/KwrCtrBzJqY2VgzmxtpAaqZPQBWfBN03fjhMzj+fjj3FQ1wKSIih63QxEQe40sUeYwJM0s0s5nAOuAzd5+Sa5NGwIrIeTKArUCtnOURKyNleZ3jSjObZmbT1q9fX5SwREREYuLSgS1I35fFK1OWHyg0C2bo+HESrF8QWmwCBINeZuZYzuTggTDzkgH8xt07EiQyrou07Cy/pj4Dz50AlgCXfRKMpWKF/TOKiIjkr9DEhJnVM7NnzOyjyHJHM7usKAd390x37w40BvqYWediRZv3OZ50997u3rtOnTrRPryIiEiRta1XhaPa1uGFr5ezJyPH999u50NCMkx/PrTYBIDngClmdreZ3Q1MBp4paAd3X+3uMyKftwPzyOdhSZmXsRfevwk+uBlaDQ+6bjTqFXZUIiJSBhSlK8fzwCdAw8jyQuCmQzmJu28BxhKMF5HTKqAJgJklAdWAjTnLIxpHykREROLa5YNasH77Ht7/bvWBwsp1oMPJMPMVDYIZInf/G/BLYFPk9Ut3/0dR948M0t0DmJKrvOy33tyxDv47EqY/BwNvhvNehQrVw45KRETKiHwTE5FEAUBtd38dyIL9XS4y89svx/51zKx65HMF4Bhgfq7N3gMujnw+E/jC3T1Sfm5k1o4WQBvgm6JelIiISFgGtq5Nu3pVeGbiUoI/aRG9LoH0LTD33bBCE8DdZ0SmD33E3b8t6n5mVhl4C7jJ3bflOmbZbr3507fw5BD4aSac+SwcfRckaExyERGJnoJaTGQnAnaaWS0iA1dGZtbYWoRjNwDGmtn3wFSCMSZGm9m9ZjYyss0zQC0zWwTcDNwO4O5zgNeBucDHwHWRqUtFRETimplx2cAWzFu9ja8XbzywInsQTHXnKHXMLJkgKfGyu48KO54S9f0b8OzxB8aT6HxG2BGJiEgZlFTAuuxRjG4maMHQysy+AuoQtG4okLt/T9DcMXf5nTk+pwNn5bP/n4A/FXYeERGReDOye0Me+GQ+T01YQv/WtYPC7EEwP7sT1i+EOm1DjVGKJjJb2DPAvEhXkPLBHcbdD1/eD037w9n/DbokiYiIxEBBLSbqmNnNwBDgbeAB4CPgKeDo2IcmIiJSOqUlJ3L+kc0Yt3A9KzbtOrCiy9nB+7z3wgmsnDOzSmaWEPnc1sxGRlpDFGQAcBEwzMxmRl4jYh5smDIz4P0bgqRE9wvgF+8qKSEiIjFVUGIiEagMVAEqEbSuSAQqRspEREQkH+ccEYzh/Mb0lQcKqzaARr1h/gchRVXujQfSzKwR8ClBwuH5gnZw94nubu7e1d27R14flkCs4di7C/53Acz4Lwy6BU55DJJSwo5KRETKuIK6cqx293tLLBIREZEypFH1CgxuU4c3pq3gxuFtSEyI9JBsfyKMuQe2roJq5XPWyRCZu++KTHv+uLs/YGYzww4qbuzcCK+eAyunwYiHoM8VYUckIiLlREEtJqyAdSIiIlKIc49owuqt6Yz/IccUku1PCt4XlN2H7nHMzKwfcAGQ3WxF00sAbF4Ozx4Lq78PxpNQUkJEREpQQYmJ4SUWhYiISBk0vEM9alVK4X/frDhQWKct1Gqj7hzhuAm4A3jb3eeYWUtgbLghxYE1s+CZY2DnevjFO9BxZKG7iIiIRFO+iQl331SSgYiIiJQ1KUkJnNGrMZ/PW8v67XsOrGh/IiybALu3hBZbeeTuX7r7SHf/a2QQzA3ufkPYcYVq1Qx4/iRISIJLP4Fm/cOOSEREyqGCWkyIiIhIMZ3duwkZWc6oGTkGwWx/EmRlwA+fhhdYOWRmr5hZVTOrBMwG5prZrWHHFZqV0+C/p0JaVfjlh1C3Q9gRiYhIOaXEhIiISAy1rluZI5rX4H9TV+DuQWGjXlC5HswfHW5w5U9Hd98GnEowBXoLgpk5yp8fpwRJiYo14ZIPoUbzsCMSEZFyTIkJERGRGDujZ2OWbNjJvNXbg4KEBGg3An74HPalhxtc+ZJsZskEiYn33H0f4OGGFIJV0+GlM6By3aClRPUmYUckIiLlnBITIiIiMTasfV0Axi5Yd6Cw/Umwb2cw1oSUlP8Ay4BKwHgzawZsCzWikrZmNrx4eqSlxGio2jDsiERERJSYEBERibW6VdPo0qgaY+atPVDY5Ijgfe3scIIqh9z9EXdv5O4jPLAcGBp2XCVmww/w4qmQXBEufk9JCRERiRtKTIiIiJSAYe3r8u2KLWzauTcoSKsGlerCxkXhBlaOmFk1M/ubmU2LvB4maD1R9m1bHYwp4Q6/eFdjSoiISFxRYkJERKQEDGtfF3cYl7M7R63WsEGJiRL0LLAdODvy2gY8F2pEJSF9G7x8FqRvgYtGQZ22YUckIiJyECUmRERESkCXRtWoXTmVL+bnSEzUbq0WEyWrlbvf5e5LIq97gJZhBxVTmfvg9V/Aurlw9gvQoFvYEYmIiPyMEhMiIiIlICHBGNquDl8uXM++zKygsFYb2LUBdm8ON7jyY7eZDcxeMLMBwO4Q44ktd3j/JlgyFk7+J7Q+OuyIRERE8qTEhIiISAkZ3qEu29MzmL48koio1Tp437g4vKDKl6uBx8xsmZktAx4Frgo3pBia8h+Y+RIMvhV6XhR2NCIiIvlSYkJERKSEDGxTh+REY2x2d47abYL3DT+EF1Q54u7fuXs3oCvQ1d17AMNCDis2lk2ET34H7UbAkN+FHY2IiEiBlJgQEREpIZVTk+jToiZjshMT1ZuBJcJGJSZKkrtvc/dtkcWbQw0mFrauhNcvhpot4bQnIEHVPRERiW/6SyUiIlKChrWvx6J1O1i1ZTckpQTTNmoAzDBZ2AFEVcbeYLDLjD1w7ivBtLQiIiJxTokJERGREtS1cfBF8Ye124MCTRkaNg87gKga92dYNR1OfVzTgoqISKmRFKsDm1kT4L9APYI/+k+6+z9zbXMrcEGOWDoAddx9U2RQqu1AJpDh7r1jFauIiEhJaVarIgDLN+4KCmq3gaVfQlaWmtzHiJltJ+8EhAEVSjic2Fn2FUz8B/S8GDqODDsaERGRIotZYgLIAH7j7jPMrAow3cw+c/e52Ru4+4PAgwBmdjLwa3fflOMYQ919QwxjFBERKVF1KqdSMSWRpRt2BgW1WkFGOmxbCdWbhhtcGeXuVQ53XzN7FjgJWOfunaMXVZSlb4W3r4KaLeC4P4cdjYiIyCGJ2aMZd1/t7jMin7cD84BGBexyHvBqrOIRERGJB2ZGs1qVWL4xOzERmZlD40zEq+eB48MOolAf3grbfoLTn4LUymFHIyIickhKpM2omTUHegBT8llfkeCP/ls5ih341Mymm9mVBRz7SjObZmbT1q9fH8WoRUREYqN5rYoHd+UAjTMRp9x9PLCp0A3DtOAj+P5/cNRt0Fg9X0VEpPSJeWLCzCoTJBxuyjE1V24nA1/l6sYx0N17AicA15nZ4Lx2dPcn3b23u/euU6dOVGMXERGJhWa1KrFi8y4yMrOgcj1IqawWE6VYqA9J9qXDR7+F2u1g0G9K9twiIiJREtPEhJklEyQlXnb3UQVsei65unG4+6rI+zrgbaBPrOIUEREpSc1rVWRfprN6azqYBTNzbPwh7LDkMIX6kGTSI7BlOYx4EBKTS/bcIiIiURKzxISZGfAMMM/d/1bAdtWAo4B3c5RVigyYiZlVAo4FZscqVhERkZLUrFYlAJbtH2eitVpMyKHbvBwmPAydToOWR4UdjYiIyGGLZYuJAcBFwDAzmxl5jTCzq83s6hzbnQZ86u47c5TVAyaa2XfAN8AH7v5xDGMVEREpMc1rB1OGLss5zsSWFbBvd4hRSanzye/AEuDYP4YdiYiISLHEbLpQd59IMD94Yds9TzDidc6yJUC3mAQmIiISsnpV0khNSmD5hhwtJnDYtBTqdQw1NjmYmb0KDAFqm9lK4C53fybcqICl42H+aBh+J1RrHHY0IiIixRKzxISIiIjkLSHBaFar4oEWE7VaB+8bf1BiIs64+3lhx5CniX8PBk7te13YkYiIiBRbiUwXKiIiIgdrVqsSy/ePMdEqeN+0JLyApPRYMwsWfwFHXgXJaWFHIyIiUmxKTIiIiISgRe1KLN+0i6wsh9QqkFwRdm4IOywpDSb9C5IrQe9Lw45EREQkKpSYEBERCUGzWhXZm5HFmm3pQUGFmrBrU7hBSfzbsgJmvQm9LoYKNcKORkREJCqUmBAREQlB89xThlasCbs2hhiRlAqT/x28970m3DhERESiSIkJERGREDSrFUwZujx7AMyKtWC3WkxIAXZvgRkvQOfToXrTsKMRERGJGiUmREREQtCgWgVSEhNytJiopRYTUrBZb8DeHdD/+rAjERERiSolJkREREKQmGA0qVmB5RuyW0yoK4cUYtX0YIrQBt3CjkRERCSqlJgQEREJSfNalQ5uMZG+FTIzwg1K4tfq76F+17CjEBERiTolJkRERELSrFYllm/chbsHiQmA3ZvDDUri0750WD8fGigxISIiZY8SEyIiIiFpXrsiu/dlsn77nqArB6g7h+Rt3VzwTLWYEBGRMkmJCRERkZA0i0wZunTDTqgQSUxoZg7Jy5rvg3e1mBARkTIoKewAREREyquG1dIAWLt9D9SNdOVQiwnJy+rvILUa1GgRdiQiIhISdycjy9mXmcW+DGdvZlbwOfLam+EHPmdmsS/T2ZeRa3n/trmWI8f8vxM7kJBgJX5tSkyIiIiEpEalFAA279x7YIwJJSYkL6u/h/pdwEq+sigiUtZlZTn7svL/Ir83I/vLv+f4Ep91IDHwsySB59gnshzZ56DlQ9k/cr5YMIOUxARSEhO47fh2pCUkxuQ8BVFiQkREJCTVKyRjBpt27oWK9YLCXerKIblkZcLaOdD7l2FHIiJySDIjT/f3ZBT0JfzgJ/3BF3/P8SU+spzji/3eTM93/705Ewb5JBZytxjIyPKYXH9yopGcmLD/lZJoJCflWo58rpqSfNBycmICKUl57J+YsP8YKYlGSo7j5b1PAsmRspT95UEc2cuJIbSQyE2JCRERkZAkJSZQrUJykJhIrgDJFdViQn5uww+QsVsDX4oIEDTn/1kT/Pya7Gfk34R/b0bmz5/cH8aT/gPnPBBH9nKMvu+Tsv9LteX4Qp5rOTGBtOQEqqQl7V/+2TZ57JOcI3mQ3xf57H0OJAXswLocsZlauRWZEhMiIiIhqlkxhU279gYLFWupxYT8nAa+FCkRWVme75P27P73sXzSn73+oMRAxs/32ZcZm2/7iQm2/wt36kFP4X/+JbxyatJBX8QP+mIeeUJ/0PLPnuwfWJ9dXtCT/pxJg8QEfeEvi5SYEBERCVHNSils2pGdmKipWTnk51Z/B4mpULtt2JGIHLKYDtaXq4//z57c5zc2QD7bZ8bo8X7eT+FzfAlPOtBEv2JKwU34D3yJz+NJ/8++xBftSX/2PvHQnF/KLyUmREREQlSjUgorNu0KFirUVFcO+bk130O9jpCYHHYkEkcOebC+g/rdx26wvgNf/A/sEws5B+tLzvEl/eCn8EFZpdSkg7+U53iqf/CX+EN40p/ji31q7j7+OY6dpKf7IkWixISIiEiIalVKYeaKLcFCxVqwZXmo8UiccQ9m5Oh4StiRlBsZRfoSHp0m/HsPWl/2B+tL2f9e2D6lY7A+EYmemCUmzKwJ8F+gHuDAk+7+z1zbDAHeBZZGika5+72RdccD/wQSgafd/f5YxSoiIhKWGpVS2LxzL+6OVaylFhNxKNQ6ydYVkL6l1I8vUdzB+vaP6J+RR9KgDA7WVzUyWF9yEfcpzmB9uVsD6Om+iIQhli0mMoDfuPsMM6sCTDezz9x9bq7tJrj7STkLzCwReAw4BlgJTDWz9/LYV0REpFSrVSmFjCxnW3oG1SrWgvStkJkBiWrUGA9Cr5Osjgx8Wb9bnquzp+LL/QU9Zz/6WE/L97NtSniwvuwv5rm/YEd7sL6UQ3zSr8H6RESKLma1HndfDayOfN5uZvOARkBR/pD3ARa5+xIAM3sNOKWI+4qIiJQaNSqmALB5516qVawZFO7eDJXrhBiV5BBqnWTq5HH0JIHjXtnAjswxcTdYX0rSgf77Ob+kF7V1gAbrExERKKExJsysOdADmJLH6n5m9h3wE3CLu88hSGCsyLHNSuDIfI59JXAlQNOmTaMYtYiISOy1qluZE7s2CL5g1WkPnU4Hj81gcXJYCq2TxLIu4tWb8s3mY+naqEGxB+s7+Ol//k/6NVifiIiUtJgnJsysMvAWcJO7b8u1egbQzN13mNkI4B2gzaEc392fBJ4E6N27d4x6BYqIiMRG9ybVeez8nsFCzUHQYlC4Ackhi2VdpM9pNwA30C+aBxUREYkzCbE8uJklEyQlXnb3UbnXu/s2d98R+fwhkGxmtYFVQJMcmzaOlImIiIiUJNVJREREYixmiQkL2gA+A8xz97/ls039yHaYWZ9IPBuBqUAbM2thZinAucB7sYpVREREJB+qk4iIiMRYLLtyDAAuAmaZ2cxI2e+ApgDu/gRwJnCNmWUAu4Fz3d2BDDP7FfAJwdRcz0bGnhAREREpMe6uOomIiEiMxXJWjolAgSMnufujwKP5rPsQ+DAGoYmIiIgUmeokIiIisRXTMSZERERERERERAqixISIiIiIiIiIhEaJCREREREREREJjRITIiIiIiIiIhIaCybBKBvMbD2wPIqHrA1siOLxwlbWrgfK3jXpeuKbrie+6Xry18zd60TpWFKAGNRFQL/b8U7XE//K2jXpeuKbrid/+dZHylRiItrMbJq79w47jmgpa9cDZe+adD3xTdcT33Q9UlaVtd8FXU98K2vXA2XvmnQ98U3Xc3jUlUNEREREREREQqPEhIiIiIiIiIiERomJgj0ZdgBRVtauB8reNel64puuJ77peqSsKmu/C7qe+FbWrgfK3jXpeuKbrucwaIwJEREREREREQmNWkyIiIiIiIiISGiUmBARERERERGR0CgxkQ8zO97MFpjZIjO7Pex48mNmz5rZOjObnaOsppl9ZmY/RN5rRMrNzB6JXNP3ZtYzxz4XR7b/wcwuDuNaInE0MbOxZjbXzOaY2Y2l+ZrMLM3MvjGz7yLXc0+kvIWZTYnE/T8zS4mUp0aWF0XWN89xrDsi5QvM7LgwridHLIlm9q2ZjY4sl9rrMbNlZjbLzGaa2bRIWan8fYvEUd3M3jSz+WY2z8z6ldbrMbN2kZ9L9mubmd1UWq8nEsevI/8XzDazVyP/R5Ta+0diz1QfCYWpPlIq/v8x1Ufi8vctEofqI3F6PZE4fm3xVh9xd71yvYBEYDHQEkgBvgM6hh1XPrEOBnoCs3OUPQDcHvl8O/DXyOcRwEeAAX2BKZHymsCSyHuNyOcaIV1PA6Bn5HMVYCHQsbReUySuypHPycCUSJyvA+dGyp8Arol8vhZ4IvL5XOB/kc8dI7+HqUCLyO9nYoi/dzcDrwCjI8ul9nqAZUDtXGWl8vctEssLwOWRzylA9dJ8PTmuKxFYAzQrrdcDNAKWAhUiy68Dl5Tm+0evmP/OqD6i+ki0rkf1kTi/HlQfievryXFdqo/E6P4J7Ycazy+gH/BJjuU7gDvCjquAeJtzcEVgAdAg8rkBsCDy+T/Aebm3A84D/pOj/KDtQr62d4FjysI1ARWBGcCRwAYgKffvG/AJ0C/yOSmyneX+Hcy5XQjX0RgYAwwDRkfiK83Xs4yfVwRK5e8bUI3gD42VhevJdQ3HAl+V5ushqAisIKiQJEXun+NK8/2jV8x/Z1QfUX0kFtei+kh8Xs8yVB+Jy+vJdQ2qj8To/lFXjrxl/7CyrYyUlRb13H115PMaoF7kc37XFZfXG2km1IMgq19qrynSzHAmsA74jCCbuMXdM/KIbX/ckfVbgVrE0fUA/wBuA7Iiy7Uo3dfjwKdmNt3MroyUldbftxbAeuC5SNPWp82sEqX3enI6F3g18rlUXo+7rwIeAn4EVhPcD9Mp3fePxFZp/1mXyns1N9VH4vb/n3+g+ki2ePt9U30kjq8nXusjSkyUcR6krzzsOA6VmVUG3gJucvdtOdeVtmty90x3706Q2e8DtA83osNnZicB69x9etixRNFAd+8JnABcZ2aDc64sZb9vSQRNqf/t7j2AnQRNC/crZdcDQKSP40jgjdzrStP1RPqenkJQYWsIVAKODzUokRJSmu7VnFQfiU+qj8Q91UfiWLzWR5SYyNsqoEmO5caRstJirZk1AIi8r4uU53ddcXW9ZpZMUAl42d1HRYpL9TUBuPsWYCxB06jqZpYUWZUztv1xR9ZXAzYSP9czABhpZsuA1wiaT/6T0ns92Vlj3H0d8DZBZa20/r6tBFa6+5TI8psEFYPSej3ZTgBmuPvayHJpvZ6jgaXuvt7d9wGjCO6pUnv/SMyV9p91ab1XAdVHiO//f1Qfie/fN9VH4vt64rI+osRE3qYCbSIjk6YQNNl5L+SYDsV7wMWRzxcT9IvMLv9FZKTYvsDWSPOjT4BjzaxGJIN2bKSsxJmZAc8A89z9bzlWlcprMrM6ZlY98rkCQf/UeQQVgjMjm+W+nuzrPBP4IpKBfQ84NzIqbgugDfBNiVxEDu5+h7s3dvfmBPfFF+5+AaX0esyskplVyf5M8Hsym1L6++bua4AVZtYuUjQcmEspvZ4czuNAs0kovdfzI9DXzCpG/q/L/vmUyvtHSoTqI6qPRIXqI/F9PaqPxPf15KD6SCzvn8MdnKKsvwhGU11I0P/u92HHU0CcrxL0DdpHkJ28jKDPzxjgB+BzoGZkWwMei1zTLKB3juNcCiyKvH4Z4vUMJGgG9T0wM/IaUVqvCegKfBu5ntnAnZHylpEbdxFBc7DUSHlaZHlRZH3LHMf6feQ6FwAnxMHv3hAOjIJdKq8nEvd3kdec7Hu9tP6+ReLoDkyL/M69QzDqc2m+nkoEWflqOcpK8/XcA8yP/H/wIsFI1qXy/tGrxH5nVB8J53pUHykl//+g+kjc/b5F4uiO6iPxfD1xVx+xyAFFREREREREREqcunKIiIiIiIiISGiUmBARERERERGR0CgxISIiIiIiIiKhUWJCREREREREREKjxISIiIiIiIiIhEaJCREREREREREJjRITIoKZ1TKzmZHXGjNbFfm8w8wej8H5njezpWZ2dQHbDDKzuWY2O9rnFxERkfij+ohI+WXuHnYMIhJHzOxuYIe7PxTDczwPjHb3NwvZrnlku86xikVERETij+ojIuWLWkyISL7MbIiZjY58vtvMXjCzCWa23MxON7MHzGyWmX1sZsmR7XqZ2ZdmNt3MPjGzBkU4z1lmNtvMvjOz8bG+LhERESk9VB8RKfuUmBCRQ9EKGAaMBF4Cxrp7F2A3cGKkMvAv4Ex37wU8C/ypCMe9EzjO3btFji0iIiKSH9VHRMqYpLADEJFS5SN332dms4BE4ONI+SygOdAO6Ax8ZmZEtlldhON+BTxvZq8Do6IdtIiIiJQpqo+IlDFKTIjIodgD4O5ZZrbPDwxSk0Xw/4kBc9y936Ec1N2vNrMjgROB6WbWy903RjNwERERKTNUHxEpY9SVQ0SiaQFQx8z6AZhZspl1KmwnM2vl7lPc/U5gPdAkxnGKiIhI2aX6iEgpoxYTIhI17r7XzM4EHjGzagT/x/wDmFPIrg+aWRuCJxxjgO9iGqiIiIiUWaqPiJQ+mi5UREqcpucSERGRsKk+IhI/1JVDRMKwFbjPzK7ObwMzGwS8D2wosahERESkPFF9RCROqMWEiIiIiIiIiIRGLSZEREREREREJDRKTIiIiIiIiIhIaJSYEBEREREREZHQKDEhIiIiIiIiIqH5f1IGWEkH5/OTAAAAAElFTkSuQmCC\n",
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       "text/plain": [
-       "<Figure size 1296x288 with 2 Axes>"
+       "<Figure size 1800x400 with 2 Axes>"
       ]
      },
      "metadata": {
@@ -181,9 +183,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -217,7 +219,7 @@
       "[4] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n",
       "[5] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
       "[6] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
-      "[7] SCOTT G Marquis. Long-term degradation of lithium-ion batteries. PhD thesis, University of Oxford, 2020.\n",
+      "[7] Scott G. Marquis. Long-term degradation of lithium-ion batteries. PhD thesis, University of Oxford, 2020.\n",
       "[8] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n",
       "[9] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n",
       "[10] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
@@ -246,7 +248,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.18"
   },
   "vscode": {
    "interpreter": {
diff --git a/docs/source/examples/notebooks/models/SPM.ipynb b/docs/source/examples/notebooks/models/SPM.ipynb
index 587c1413bb..91a09a11b6 100644
--- a/docs/source/examples/notebooks/models/SPM.ipynb
+++ b/docs/source/examples/notebooks/models/SPM.ipynb
@@ -73,7 +73,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import os\n",
diff --git a/docs/source/examples/notebooks/models/SPMe.ipynb b/docs/source/examples/notebooks/models/SPMe.ipynb
index 1548caa623..a9542d89ec 100644
--- a/docs/source/examples/notebooks/models/SPMe.ipynb
+++ b/docs/source/examples/notebooks/models/SPMe.ipynb
@@ -126,7 +126,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm"
    ]
   },
diff --git a/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb b/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb
index bc04f92fbc..8bdfa76f60 100644
--- a/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb
+++ b/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb
@@ -23,7 +23,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import os\n",
     "import matplotlib.pyplot as plt\n",
diff --git a/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb b/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb
index e23e1ee15f..90611a91a0 100644
--- a/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb
+++ b/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb
@@ -32,7 +32,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import os\n",
diff --git a/docs/source/examples/notebooks/models/compare-ecker-data.ipynb b/docs/source/examples/notebooks/models/compare-ecker-data.ipynb
index 4fb7960c9d..05a375fa45 100644
--- a/docs/source/examples/notebooks/models/compare-ecker-data.ipynb
+++ b/docs/source/examples/notebooks/models/compare-ecker-data.ipynb
@@ -32,7 +32,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import os\n",
     "import pandas as pd\n",
diff --git a/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb b/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb
index ed7f55f897..f194a62d02 100644
--- a/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb
+++ b/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb
@@ -48,7 +48,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import os\n",
     "os.chdir(pybamm.__path__[0]+'/..')\n",
diff --git a/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb b/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb
index 22e72eafb0..6bd9f4cf63 100644
--- a/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb
+++ b/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb
@@ -35,7 +35,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import os\n",
     "import numpy as np\n",
diff --git a/docs/source/examples/notebooks/models/composite_particle.ipynb b/docs/source/examples/notebooks/models/composite_particle.ipynb
index 8d279f959c..59fa9c957e 100644
--- a/docs/source/examples/notebooks/models/composite_particle.ipynb
+++ b/docs/source/examples/notebooks/models/composite_particle.ipynb
@@ -36,7 +36,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "#%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import os\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
diff --git a/docs/source/examples/notebooks/models/coupled-degradation.ipynb b/docs/source/examples/notebooks/models/coupled-degradation.ipynb
index c7e651d268..00b524c041 100644
--- a/docs/source/examples/notebooks/models/coupled-degradation.ipynb
+++ b/docs/source/examples/notebooks/models/coupled-degradation.ipynb
@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "7008f034",
    "metadata": {},
@@ -22,20 +21,19 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
       "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import matplotlib.pyplot as plt"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "a484509e",
    "metadata": {},
@@ -65,7 +63,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "5d85aaac",
    "metadata": {},
@@ -91,7 +88,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "03273e06",
    "metadata": {},
@@ -124,7 +120,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "ff476a16",
    "metadata": {},
@@ -140,20 +135,22 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "Qt = sol[\"Throughput capacity [A.h]\"].entries\n",
-    "Q_SEI = sol[\"Loss of capacity to SEI [A.h]\"].entries\n",
-    "Q_SEI_cr = sol[\"Loss of capacity to SEI on cracks [A.h]\"].entries\n",
-    "Q_plating = sol[\"Loss of capacity to lithium plating [A.h]\"].entries\n",
+    "Q_SEI = sol[\"Loss of capacity to negative SEI [A.h]\"].entries\n",
+    "Q_SEI_cr = sol[\"Loss of capacity to negative SEI on cracks [A.h]\"].entries\n",
+    "Q_plating = sol[\"Loss of capacity to negative lithium plating [A.h]\"].entries\n",
     "Q_side = sol[\"Total capacity lost to side reactions [A.h]\"].entries\n",
     "Q_LLI = sol[\"Total lithium lost [mol]\"].entries * 96485.3 / 3600  # convert from mol to A.h\n",
     "plt.figure()\n",
@@ -169,7 +166,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "498f26f1",
    "metadata": {},
@@ -178,7 +174,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "8becb1ba",
    "metadata": {},
@@ -194,12 +189,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -219,7 +216,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "2a7849de",
    "metadata": {},
@@ -228,7 +224,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "ddfb75d0",
    "metadata": {},
@@ -244,12 +239,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -268,7 +265,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "74354b2a",
    "metadata": {},
@@ -325,7 +321,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb b/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb
index 4d32f6a40e..54b71157f7 100644
--- a/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb
+++ b/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb
@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -9,7 +8,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -33,14 +31,13 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -98,7 +95,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -106,7 +102,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -229,7 +224,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -265,7 +259,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -273,7 +266,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -327,7 +319,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -335,7 +326,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -351,7 +341,9 @@
     "all_parameter_sets = [\n",
     "    k\n",
     "    for k, v in pybamm.parameter_sets.items()\n",
-    "    if v[\"chemistry\"] == \"lithium_ion\" and k not in [\"Xu2019\", \"Chen2020_composite\"]\n",
+    "    if v[\"chemistry\"] == \"lithium_ion\" and k not in [\n",
+    "        \"Xu2019\", \"Chen2020_composite\", \"Ecker2015_graphite_halfcell\", \"OKane2022_graphite_SiOx_halfcell\"\n",
+    "    ]\n",
     "]\n",
     "\n",
     "\n",
@@ -574,7 +566,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -640,7 +631,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.8.10"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/source/examples/notebooks/models/half-cell.ipynb b/docs/source/examples/notebooks/models/half-cell.ipynb
new file mode 100644
index 0000000000..7eda7e2491
--- /dev/null
+++ b/docs/source/examples/notebooks/models/half-cell.ipynb
@@ -0,0 +1,377 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1d1e2d4b",
+   "metadata": {},
+   "source": [
+    "# Half-cell models in PyBaMM\n",
+    "\n",
+    "PyBaMM supports both negative and positive half-cells. In both cases, the working electrode is considered to be the positive electrode and lithium metal the negative electrode. The difference is solely down to the material the positive electrode is made of. This notebook demonstrates how to simulate half-cells made of a range of materials."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a29a7b0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install pybamm -q  # install PyBaMM if it is not installed\n",
+    "import pybamm\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f372aa29",
+   "metadata": {},
+   "source": [
+    "To simulate a half-cell, pass `{\"working electrode\": \"positive\"}` to the options dictionary. This deletes the negative electrode and replaces it with lithium metal, which has a fixed open-circuit voltage of zero. First, we load the NMC-based positive half-cell studied by Xu _et al._ [12]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "917bbc26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pybamm.lithium_ion.DFN({\"working electrode\": \"positive\"})\n",
+    "param_nmc = pybamm.ParameterValues(\"Xu2019\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9453d7a",
+   "metadata": {},
+   "source": [
+    "Start by simulating a pseudo-OCV cycle:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "84d7eeea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "exp_slow = pybamm.Experiment([\"Discharge at C/25 until 3.5 V\", \"Charge at C/25 until 4.2 V\"])\n",
+    "sim1 = pybamm.Simulation(model, parameter_values=param_nmc, experiment=exp_slow)\n",
+    "sol1 = sim1.solve()\n",
+    "t = sol1[\"Time [s]\"].entries\n",
+    "V = sol1[\"Voltage [V]\"].entries\n",
+    "plt.figure()\n",
+    "plt.plot(t,V)\n",
+    "plt.xlabel(\"Time [s]\")\n",
+    "plt.ylabel(\"Voltage [V]\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "201272b5",
+   "metadata": {},
+   "source": [
+    "The charge and discharge curves are the same, as expected. This is not the case for faster cycles:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f0778bb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "At t = 285.669 and h = 7.17426e-14, the corrector convergence failed repeatedly or with |h| = hmin.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "exp_fast = pybamm.Experiment([\"Discharge at 1C until 3.5 V\", \"Charge at 1C until 4.2 V\"])\n",
+    "sim2 = pybamm.Simulation(model, parameter_values=param_nmc, experiment=exp_fast)\n",
+    "sol2 = sim2.solve()\n",
+    "t = sol2[\"Time [s]\"].entries\n",
+    "V = sol2[\"Voltage [V]\"].entries\n",
+    "plt.figure()\n",
+    "plt.plot(t,V)\n",
+    "plt.xlabel(\"Time [s]\")\n",
+    "plt.ylabel(\"Voltage [V]\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "523a222f",
+   "metadata": {},
+   "source": [
+    "Next, load a negative half-cell with a graphite-silicon composite as the positive electrode. This is the negative half of the full cell studied by O'Kane _et al._ [9] and therefore supports all the degradation mechanisms included in that paper. Just like in the [coupled degradation notebook](https://docs.pybamm.org/en/latest/source/examples/notebooks/models/coupled-degradation.html), use the options dictionary to switch the mechanisms on and off. Unlike for a full cell, the `SEI` option applies to the lithium metal electrode as well as the positive electrode. To set different options for each, use a 2-tuple. The `SEI on cracks` and `lithium plating` options do not work on the lithium metal electrode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "af6e5dfd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model_with_degradation = pybamm.lithium_ion.DFN({\n",
+    "    \"working electrode\": \"positive\",\n",
+    "    \"SEI\": \"reaction limited\",  # SEI on both electrodes\n",
+    "    \"SEI porosity change\": \"true\",\n",
+    "    \"particle mechanics\": \"swelling and cracking\",\n",
+    "    \"SEI on cracks\": \"true\",\n",
+    "    \"lithium plating\": \"partially reversible\",\n",
+    "    \"lithium plating porosity change\": \"true\",  # alias for \"SEI porosity change\"\n",
+    "})\n",
+    "param_GrSi = pybamm.ParameterValues(\"OKane2022_graphite_SiOx_halfcell\")\n",
+    "param_GrSi.update({\"SEI reaction exchange current density [A.m-2]\": 1.5e-07})\n",
+    "var_pts = {\"x_n\": 1, \"x_s\": 5, \"x_p\": 7, \"r_n\": 1, \"r_p\": 30}\n",
+    "exp_degradation = pybamm.Experiment([\"Charge at 0.3C until 1.5 V\", \"Discharge at 0.3C until 0.005 V\"])\n",
+    "sim3 = pybamm.Simulation(model_with_degradation, parameter_values=param_GrSi, experiment=exp_degradation, var_pts=var_pts)\n",
+    "sol3 = sim3.solve()\n",
+    "t = sol3[\"Time [s]\"].entries\n",
+    "V = sol3[\"Voltage [V]\"].entries\n",
+    "plt.figure()\n",
+    "plt.plot(t,V)\n",
+    "plt.xlabel(\"Time [s]\")\n",
+    "plt.ylabel(\"Voltage [V]\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21bab951",
+   "metadata": {},
+   "source": [
+    "In order to get SEI growth to work on both electrodes, we had to add domains to the names of the degradation variables. Bear this in mind when writing your code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e66f0384",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Q_SEI_n = sol3[\"Loss of capacity to negative SEI [A.h]\"].entries\n",
+    "Q_SEI_p = sol3[\"Loss of capacity to positive SEI [A.h]\"].entries\n",
+    "Q_SEI_cr = sol3[\"Loss of capacity to positive SEI on cracks [A.h]\"].entries\n",
+    "Q_pl = sol3[\"Loss of capacity to positive lithium plating [A.h]\"].entries\n",
+    "plt.figure()\n",
+    "plt.plot(t,Q_SEI_n,label=\"Negative SEI\")\n",
+    "plt.plot(t,Q_SEI_p,label=\"Positive SEI\")\n",
+    "plt.plot(t,Q_SEI_cr,label=\"SEI on cracks\")\n",
+    "plt.plot(t,Q_pl,label=\"Lithium plating\")\n",
+    "plt.xlabel(\"Time [s]\")\n",
+    "plt.ylabel(\"Loss of lithium inventory [A.h]\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbc7da60",
+   "metadata": {},
+   "source": [
+    "The SEI growth is slow compared to the reversible component of the lithium plating. What happens if the SEI growth rate is increased?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "71ec63ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "param_GrSi.update({\"SEI reaction exchange current density [A.m-2]\": 6e-07})\n",
+    "sim4 = pybamm.Simulation(model_with_degradation, parameter_values=param_GrSi, experiment=exp_degradation, var_pts=var_pts)\n",
+    "sol4 = sim4.solve()\n",
+    "t = sol4[\"Time [s]\"].entries\n",
+    "V = sol4[\"Voltage [V]\"].entries\n",
+    "plt.figure()\n",
+    "plt.plot(t,V)\n",
+    "plt.xlabel(\"Time [s]\")\n",
+    "plt.ylabel(\"Voltage [V]\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "123f46f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2228vbTt9+nSuuOIK5syZw86dOxkwYAA9e/bkyiuv5MiRIwAUFxfz2GOP0a1bN6Kjo1m6dGm5vgsKChgzZgzvvfceeXl5jB07lh49etCtWzc+++yzWn1ehe2QUmJMTnHYPURQtft2L/PpJiHEq8CXQGlo35IEdQqFI/Dyzpc5nH5pLp660CmoE4/3e/yy10eOHMlzzz1Hhw4dGD58OJMmTWLIkCGl16dMmYKXlxcAI0aM4NVXX71sXw8++CB33nknd955JytXruShhx7i66+/BjSD9/vvv3P48GGuv/760mm+Eg4cOMALL7zAn3/+SXBwMOnp6aXXkpKS+PPPP9HpdGRnZ7Nt2zZcXV359ddfefLJJ/niiy9Yvnw58fHxxMXF4erqWq59bm4ukydP5o477uCOO+7giy++ICwsjO+//x6ArKwsyx+owi6YsrMx5ec7rMccVD0191qF92XnGCRwdf3LUSicB19fX2JjY9m2bRubNm1i0qRJLFiwgKlTpwI1m5rbvn07X36pZTW5/fbbmTNnTum1CRMm4OLiQpcuXUhNTb2k7W+//cZNN91EcHAwoEXaLuGmm25Cp9MBmtG48847OXbsGEIIDAYDAL/++ivTp08vnbor2378+PHMmTOHKVOmANC9e3ceffRRHn/8ccaNG6dyFzkBpQnxHDTOHFRhiKSUw2wpRKGoC1WNXKyJTqdj6NChDB06lO7du/PBBx+UGqL6omTqD7Rplprg4+NTev5///d/DBs2jK+++or4+HiGDh1abfuBAweyYcMGbr31VoQQdOjQgd27d/PDDz/w9NNPc8011zBv3rxq+1HYj1LXbQeNqgC1DPFTZtpOoWi0HDlyhGPHjpW+j4uLIzIyslZ9XXnllXz66aeANpKqyUjj6quvZu3ataSlaUu3ZafWypKVlUV4eDhwMfsraNOG7777Lkaj8ZL2zz33HIGBgcycOROA5ORkvL29ue222/jPf/7D7t1qht7RMSQ7dlQFqH2suQfqVYVC4YTk5uZy55130qVLF6Kjozl48CDz588vvT5lypRS9+3hw4dX2dfSpUtZtWoV0dHRfPTRRyxevNhiHV27duWpp55iyJAh9OjRg9mzZ1dab86cOTzxxBP07Nmz1OgA3HvvvURERJQ6O3zyySfl2i1evJiCggLmzJnDP//8Q79+/YiJieHZZ5/l6aeftlinwj4YUpIdNiFeCbVKAyGEcJNSGqygx6aoNBDOjUol4LyofzvbcWb2bAoOHKDdTz/VW592SwMhNK4RQqwAkupLgEKhUCishyE5xaEdFcCyyAr9hRBLgATgG2Ar0MnawhQKhUJRdwwpjr2HCKowREKIl4QQx9DSde8DeqKlffhASplhK4EKhUKhqB2lCfHKOCoYTUZW719NviHfjsrKU9WI6F4gFVgGfGSOptB484orFAqFk1FZQrzVB1bzWuxrbE/ebkdl5alqQ2soMAK4BXhDCLEJ8BJCuEopjVW0UzgZxgsXKNj3D/r4eHSBgbg2b4Zbixa4t2mDcFFJfBUKZ8WQfAa46Lp9NOMob8W9xYjIEVwd4TgxCara0FoMbAA2CCE8gHGAF3BGCLFRSnmrjTQqrICpqIicDRvI+N+nFMTFVVrHrWVLmtx0E4GTbkbXpIlN9SkUirpjLJMQz2Ay8PTvT+Pv7s/T/Z9GCGFndRex6OeulLJISvmFlHIi0B7NQCmclJxNmzg5dhzJj8+lOCODZrNnE7nmYzrs2E7bn38i8uOPCH3xBdzCwji/aBGnJt5E0UmV+aMyfH19Lyl75513+PDDDwFt42iyeUMhXD61wvr161mwYIH1hFYgPj6ebt26VVun7J6iXbt28dBDD1lbmqIeKbuZ9f1973Mo/RDz+s8jyNPB9hRJKSs9gHGXu1aTOo589O7dWzYmjDk5MvHhR+TBjp3k8WvHypwtW6SpuLjKNnm7d8sjVw6Uh/v2k3k7d9pIqWUcPHjQ3hKkj49PldeHDBki//7779L3kZGR8vz589aWVS2nTp2SXbt2rbLOpk2b5NixY61yf0f4t2sMnHnySXnkqqvk/gv7ZcwHMfLxrY/XS7/ALlmP38VVjYheFUL0FEL0utwBvGQDW6moBwxnzxI/eTI5v/xCs0ceoc1XX+I7eHC1a0DePXsS9dmnuAYHkzjrQQznztlIsfNSkvxu3bp17Nq1qzTCQkFBAaBFUejVqxfdu3fn8GEtYvjq1auZNWsWoKVvKJvuoWTUtXnzZoYMGcL48eNp06YNc+fOZc2aNfTr14/u3btz4sSJSrXcfvvtDBgwgPbt2/Pee+9dUic+Pp5BgwbRq1cvevXqxZ9//gnA3Llz2bZtGzExMSxatIjNmzczbty40n7vvvtuhg4dSps2bViyZElpf88//zwdO3bkqquu4pZbbnGaRIANEUPSGXShoTy+9XGaejXliX5P2FtSpVTlrJAKvF5N+2PVXFc4APqEBE7fdTfFWVlErFiBT/8ratTevWVLWr75Jqf+9S9Snn6aVu+843BODGdfeomiQ/WbBsKjcydCnnyy1u0nTpzIm2++ycKFC8tF4Q4ODmb37t28/fbbLFy4kPfff9/iPvfu3cuhQ4cICgqiTZs23HvvvezcuZPFixezdOlS3njjjUva7Nu3jx07dpCXl0fPnj0ZO3ZsuevNmzfnl19+wdPTk2PHjnHLLbewa9cuFixYwMKFC/nuu+8AzRCW5fDhw2zatImcnBw6duzIAw88QFxcHF988QV79+7FYDDQq1cvevfubflDU9QrhqQkjoW7cDr7LCtGrSDAI8DekiqlKmeFoTbUobASBfv2kThjJhQXE/HBB3h161qrfjzatKb543NIfe55zi96g+aPVh7PTFE9N9xwAwC9e/cuTf1gKX379iXU7AHVtm1bRo4cCWjpGTZt2lRpm/Hjx+Pl5YWXlxfDhg1j586dxMTElF43GAzMmjWLuLg4dDodR48etUjL2LFj8fDwwMPDg+bNm5Oamsoff/zB+PHj8fT0xNPTk+uuu65Gn09Rf0ijEX1KCrsjJdOiZ9A3pK+9JV0WlTu4AZP9ww8kP/EkrsHBtFr+Lh5t29apv8BbbqHo0GHS3n8f/3Hj8OzYoZ6U1p26jFxsTUlaB51OVy74aAmurq6YTCYATCZTufTjZVNCuLi4lL53cXGptC/gEu+oiu8XLVpEixYt2Lt3LyaTCU9Pzxp9jqo+i8J+JJ6IQ5hMeLSMZHqP6faWUyWONb+iqBekycT5pW9yZvajeHbrRtTaz+tshED7Amv+6GxcfHw4X2ZNQHF5/Pz8yMnJqVGbqKgoYmNjAc2briSBXW355ptvKCwsJC0tjc2bN9O3b/lfxllZWYSGhuLi4sJHH31EcXFxrbUPHDiQb7/9lsLCQnJzc0un9RS2pdBYyJs/aHmibh46C1cXxx5zKEPUwJAGAylPPMmFt94i4F//ImLVSlzrMfy7rkkTgu6aSu7GjRQeOlRv/Tor+fn5tGzZsvR4/fXyy6pTp05l+vTp5ZwVquO+++5jy5Yt9OjRg+3bt5dLblcboqOjGTZsGP379+f//u//CKsQd2zGjBl88MEH9OjRg8OHD5feLzo6Gp1OR48ePVi0aJFF9+rbty/XX3890dHRjBkzhu7duxMQ4JjrEg0VKSXP73iewsQEAELa97CzIguozq0OiAVmAoH16a7nCEdDc98uzs+Xp6fdLw927CTPvfWWNJlMVrmPMTNTHu7ZSybNftQq/VuKcgGunmeeeUa++uqrNr1nTk6OlFLKvLw82bt3bxkbG3tJHfVvZz3WHFwju63uJn+Ye7s82LmLNOn19X4PbOi+XcIkIAz4WwjxqRBilHCkLbkKAEyFhSQ+MIPcrVsJmT+fZjNmWG3ntC4ggCaTJ5P944/oExOtcg+F8zJt2jRiYmLo1asXN954I716qYTOtiI2NZZX/36VIS2H0N3YAreQEISbm71lVYvFifGEEC5oYX6WAcXAKmCxlLLyvMROQENJjGfS60maNYu8bb8T9vICAq6/3ur3NKSmcnz4CAJvmkjIvHlWv19lqORqzov6t6t/EnMSue2H2/Bz9+OTsZ+Qfud0hJsbkR9+UO/3sktiPCFENPAa8CrwBXATkA38Vl9CFLXDVFTEmYcfIW/rNkKee9YmRgjArUULAsZfT+YXX2JMt99vEUt/SCkcB/VvVv9kFWUxc+NMDCYDS65egr+7P4akJNxatrS3NIuwJDFeLLAI+BuIllI+JKX8S0r5GqACkNkRaTCQNHMWuZs2EfLMPAJvusmm9w+6/XZkURE5P/9i0/uW4OnpSVpamvpicyKklKSlpVnsIq6oHn2xnn9v/jeJOYksHraYNgFtMBUWYjx/HreW4faWZxFV+vSZp+O+kFJWGspHSnmDVVQpLCL1vwvI+/13Qp57lsCbb7b5/T06dMAtMoKcX34hcPIkm9+/ZcuWJCUlcf78eZvfW1F7PD09aekkv9QdHSkl8/+cz99n/+a/g/5bumm1JNipu5M85yoNkZTSJIS4ARVTzuHI/PprMj75hKC77rKLEQJtX5H/iBGkrf6A4uxsdP7+Nr2/m5sbrVu3tuk9FQpHQUrJK3+/wrcnv2VmzEzGtRlXes2QlATQcKbmgF+FEI8JIVoJIYJKDks6F0KMFkIcEUIcF0LMreS6hxDiM/P1v4QQUWWuPWEuPyKEGFWmfKUQ4pwQYn+FvuYLIc4IIeLMx7WWaHRGCo8c4ez8Z/Hu18/uoXb8hg8Ho5HcLVvsqkOhaGy8FfcWHx/6mNs638b90feXu6YvMUThDccQTULbR7QVbU9RLFCtq5kQQge8BYwBugC3CCG6VKh2D5AhpWyHtg71srltF2Ay0BUYDbxt7g9gtbmsMhZJKWPMxw8WfDanw5SXR9JDD6Hz8yP8tYUIV/vumPaMjsa1eXO7rRMpFI2RVftX8e6+d7mh/Q3M6Tvnkq0ahqQzCHd3XJsF20lhzajWEEkpW1dytLGg737AcSnlSSmlHvgUGF+hznigxLdwHXCNeY/SeOBTqSXkOwUcN/eHlHIr4LQu43Xl3OLFGBJOE7ZwIa7NmtlbDsLFBb/h15D7+++YLIwcoFAoas+HBz7k9djXGR01mnn951W6X9CQmIhbeLjDRcm/HJZ4zbkJIR4SQqwzH7OEEJbskAoHyu52TDKXVVpHSmkEsoCmFratjFlCiH3m6bvAy3yeaUKIXUKIXc62yJ2/Zw8ZH31M4K234HNFP3vLKcVvxAhkQQF5f/xhbykKRYNFSsnbcW/z6q5XGRE5gpcGvYTORVdpXf3p07hHRNhYYe2xxFwuA3oDb5uP3uYyR2MZ0BaIAVLQ9j1dgpRyuZSyj5SyTzMHGFFYikmvJ+Xp/8M1NIRmsx+1t5xyePfpg4ufHzkV8tUoFIr6ocQxYdneZYxvO55XBr+Cm0vl4wEpJfrTp3GLdB5DZMkCQ18pZdmoeb8JIfZa0O4M0KrM+5bmssrqJAkhXIEAIM3CtuWQUqaWnAsh3gMaVNjf9BUr0J84Qav3lqPzrVsQzPpGuLnhM3AgeVu2IqW0WmghhaIxUlRcxPw/5/Pdye+Y0nkKc/rOwUVcfgxhPH8eWVCAe2SkDVXWDUtGRMVCiNIcAkKINmghfqrjb6C9EKK1EMIdzflgfYU664E7zecTgd/MAfXWA5PNXnWtgfbAzqpuJoQILfP2X8D+y9V1NvRJZ7jwzrv4jR6N76BB9pZTKb5DhmA8f54iFZFboag3LhRc4O6f7ua7k98xK2YWj/d9vEojBGA4fRoA9wjnMUSWjIj+A2wSQpwEBBAJ3F1dIymlUQgxC/gJ0AErpZQHhBDPoUVuXQ+sAD4SQhxHc0CYbG57QAjxOXAQMAIzpZTFAEKI/wFDgWAhRBLwjJRyBfCKECIGkEA8UN6f0YlJ/e9/QaejxdzH7S3lsvgOugqA3K1b8exS0TlSoVDUlJ0pO5m7bS65hlxeH/o6IyJHWNROn2A2RA1sau53tBFJR/P7I5Z2bnah/qFC2bwy54Vocesqa/si8GIl5bdcpv7tlupyJnI2byZ340aaP/YobiEh9pZzWVyDg/Ho3Jm8P7cTPN2xs0EqFI6ModjAu/veZfm+5UT6R7Js+DI6BnWsvqEZ/enT4OqKW4W8U46MJVNz281u1PvMRxGw3drCFFpA09QXX8K9TRuC7rjD3nKqxad/fwr27FFu3ApFLTmYdpDJ30/m3X3vcl3b6/hs3Gc1MkIA+tMJuIWH2X2PYU24rFIhRAiay7SXEKIn2rQcgD/gbQNtjZ6099/HkJhIxKqVCHd3e8upFp8B/UlftYr83bvxHTjQ3nIUCqchozCDt+LeYu3RtTT1bMqSYUsYFjGsVn0ZEk471foQVD01NwqYiuaxVjb/cQ7wpBU1KQB9YiJpy9/D/9ox+AwYYG85FuHduze4uZG/Y4cyRAqFBWTrs/nk0Cd8ePBD8g35TOo4iZkxMwnwqF169RLX7YCePetZqXW5rCGSUn4AfCCEuFFK+YUNNSmA1BdfQuh0NH/ccR0UKuLi44NXly7k795jbykKhUOTUZjBmkNrWHNoDbmGXIa2HMrDvR6mXWC7OvVbnJGBKTfXqRwVwDJnhe+EELcCUWXrSymfs5aoxk7Ob5vI3byZ5nPm4Naihb3l1Aivnj3J+OQTpF7vFNOJCoUtOZx+mE8OfcL3J79Hb9IzInIE06Kn0SmoU730r09IAMDNiaIqgGWG6Bu00DuxQJF15ShMhYWkvvgi7u3aEnT7bfaWU2O8evYkffVqCg8exCsmxt5yFAq7Yyg28Fvib3xy6BN2n9uNl6sXE9pN4NbOt9K2SdvqO6jJvZxwDxFYZohaSikvF+1aUc+kLX8Pw5kzRHzwAcLNkpB+joVXzxgA8vfEKUOkaNQkZifyxbEv+Or4V6QXphPuG85jfR5jQrsJtV4Dqg59QgK4uODuJJlZS7DEEP0phOgupfzH6moaOcYLF0hbtUpzUHCgoKY1wa15c9xatqRg9264a6q95SgUNsVgMrDp9CbWHV3H9pTt6ISOwS0HM7HDRAaGDbxskNL6Qp9wGrewMKebFrfEEF0FTBVCnEKbmhOAlFJGW1VZIyTtvfeRRUUEP/igvaXUCa+ePcnbsV3FnVM0GhJzEvni6Bd8ffxr0grTCPEJYWbMTP7V7l+08LHdOq+zRd0uwRJDNMbqKhQYUs+R8emnBIwfj4eTp7/27tWT7G+/xXDmDO5OkqpYoagpufpcfkn4he9Pfs9fZ//CRbgwuOVgbupwk01GP5WhP30a/2ud7yu7WkMkpUwQQlwFtJdSrhJCNAN8rS+tcZH27rvI4mKCZ86wt5Q642Xew1Cwe7cyRIoGxdm8s2xP3s62M9vYmrSVouIiIvwi7DL6qUhxZiamrCync1QACwyREOIZoA9arLlVgBvwMaB2LNYThuRkMteupckNNzSIL26P9u1x8fEhf88eAq6/3t5yFIoaUVRcREZhBsm5ySTnJZOYncjBtIMcSDvA+QItmWZzr+ZMaDeB69peR3RwtENMQetPO1+w0xIsmZr7F9AT2A0gpUwWQvhZVVUj48KydwAIfqBhBAsVOh2e0d0p3Kf8W5wJg8lAVlEWmYWZZBRlkFmUSa4+l8LiQgqNhaWvRcVFFBoLMUkTJmlCIi+eS4mJMufm6xKpxcWH0vdSmsvNZdr/qygvKZMX25fWKVte8V5lykvaVLxWaCwk15BLjj4Hg8lQ7rkIBFEBUfQP7U/X4K70DelL+ybtHcL4lKU06nYDXSPSSymlEEICCCEcKyubk2NISSHzq68IvPlm3EJDq2/gJHh27ETG//6HNBqdKvhiQyPfkM/5gvOczz/PhYILXCi4wPmC86QVpJFZZDY4ZsOTo8+ptj83Fzc8dZ6469zRuehwES7ohA6BwEW44CJcEELggvYqhKD0f+bzEspeK/te+38l5eZz0K67uLhcrGfuttJ7icrLy97LU+eJr7svvu6++Ln5EeARQJhvGGE+YYT6huLl6lUv/x7WRH86AYTArVWr6is7GJZ8Q3wuhHgXaCKEuA8tF9F71pXVeMhYswZMJoLurjbFk1Ph0bEjsqgI/enTeLRpY285DRIpJVlFWSTmJJY7knKTOJ9/nvMF5ykwXhoJ3dXFlaaeTQnyDKKJRxPCg8MJ9AikiWeT8q8eTfBz98PT1RNPnSceOg+7LMArLMNw+jSuISG4eHjYW0qNscRZYaEQYgSQjbZONE9K+YvVlTUCTPn5ZHy+Fr8RI5xuA1p1eHbsAEDR4cPKENUDUkpOZZ/iUNohjqQf4XD6YY5kHCG9ML1cveZezQn3C6dL0y4EewUT7BVMM+9m2qtXM5p5NcPfw7/aLJ8K50Of4Jyu22CZs8Js4DNlfOqf7A0/YcrOdspQPtXh3q4d6HQUHjmK/7XX2luO02E0GTmQdoA9qXuIPRdL3Lk4MosyAW16rF2TdgxpOYS2TdoS4RdBK79WhPuFO8UUksI66BMS8Bs+3N4yaoUlU3N+wM9CiHTgM2CtlDLVurIaB5lr1+LeujVevXvbW0q94+Lujkeb1hQdsTihb6MnOTeZP5L/4M8zf/JXyl/kGLQ1m0j/SIa2Gkqv5r3oGtyV1gGtcXNxvvBPCuthzMigOCMDdyfdg2jJ1NyzwLNCiGhgErBFCJEkpXRO0+sgFB0/TsGePTT/z38czvumvvDo2In83bH2luHQnMg8wc8JP/NLwi8cyzgGQAvvFoyMGsmAsAH0btGbYK9gO6tUODr6U/EAuLdpoIaoDOeAs0Aa0Nw6choPmWvXgZsbARPG21uK1fDo2IHs776jOCsLXYB1gjw6I8cyjvFzws/8HP8zJ7NOIhD0bN6Tx/o8xlXhV9EmoE2D/XGisA76UycBnHY91pI1ohnAzUAzYC1wn5TyoLWFNWRMej1Z33yD39VX49q0qb3lWA3Pjh0BKDp6FO++fe2sxr5kFWXx46kf+er4VxxMO4iLcKF3i95M7jSZayKuobm3+m2nqD36U6cQbm64hTun05MlI6JWwCNSyjgra2k05P72G8WZmTSZONHeUqyKR0ct2VfhkcZpiEzSxF8pf/HV8a/YmLARvUlPp6BOzO03l1FRo9SUm6LeKDp5CveoSITOOd3rLVkjekIIoRNChFE+Q+tpqyprwGR+9RWuISH4XDnA3lKsimvzZuiaNKHoyGF7S7Epufpcvj7+NZ8c/oTEnET83f2Z2GEiE9pNoHPTzvaWp2iA6E+dwqNd3dKM2xNLpuZmAfOBVMBkLpaASgNRCwznzpG37Xea3nef0/56sRQhBB4dO1J45Ki9pdiEhOwE/nf4f3x9/GvyDHnENIthVswsrom8Bg+d820yVDgH0mBAn5iI38iR9pZSayyZmnsE6CilTLOylkZB9rffgsnUoJ0UyuLZqSMZn69FFhc3SMMrpWR7ynbWHFrDtqRt6Fx0jIkaw5TOU+ga3NXe8hSNAH1iEhiNuLeOsreUWmOJIUoEsqwtpDEgpSTzq6/w6tnT6XMOWYpHh47IggIt1E8D+szFpmI2nt7I+/+8z6H0QwR5BjG9x3Ru7nizWvtR2BRn95gDywzRSWCzEOJ7tAytAEgpX7eaqgZK4f796I+fIOS5Z+0txWZ4lHjOHTnaIAyRodjAdye/Y+X+lcRnxxPpH8mzVz7LuDbjcNc5V3pmRcNAf+oUgNNuZgXLDNFp8+FuPhS1JPv7HxBubviPcb4MirXFo307cHGh6OgRGD3K3nJqTb4hny+PfcnqA6tJzU+lU1AnFg5ZyPCI4SoQqMKuFJ08ha5ZMDo/583OY2lkBUU9kLt5M95XXOHUfzA1xcXDA/fWrZ3WYSHPkMcnhz7ho4MfkVGUQe8WvZl/5XwGhg1Um04VDoH+5Ek8WjvvtBxUYYiEEG9IKR8RQnxLaZqpi0gpVerNGlB06hT6+HgCb2t4AU6rw7NjBwr27rO3jBqRZ8jjf4f/x+oDq8kqymJQ+CDui76Pns172luaQlGKlJKiU6fwHz3a3lLqRFUjoo/MrwttIaShk7tlCwC+Q4fYWYnt8ejQkewffqQ4Nw+dr2PnVcw35JcaoMyiTAaFD2JGzAy6BXeztzSF4hKKMzIwZWXh4aQx5kq4rCGSUsaaX7fYTk7DJXfzFjzat8O9ZUt7S7E5Hu21jXb6E8fx6tHDzmoqJ9+Qz6dHPmX1/tVkFGUwMHwgM3rMILqZ2i6ncFwagqMC1CzoqaKWFOfkkL9rF03vmmpvKXbBo317AIqOHXM4Q1RgLOCzw5+x6sAq0gvTGRg2kOk9phPTPMbe0hSKaik6qbluuzux6zYoQ2QT8n7/HYxGfIcNs7cUu+DWsiXC05OiY8ftLaWUAmMBnx/5nJX7V5JemM6A0AHMiJmhDJDCqdCfike4u+MWGmpvKXVCGSIbkLtlK7qAAIcbDdgK4eKCR5s2FB23vyEqKi5i7ZG1vP/P+6QVpnFF6BXM6DGDXi162VuaQlFj9CdP4h4V5fRRSyyJNdcHeAqINNcXgJRSqslzC8nfuRPvfv2c/o+lLni0b0/ejh12u7/BZGD98fUs27uM1PxU+ob05bWY1+jdouFlx1U0HvSnTuHRqZO9ZdQZFwvqrAFWATcC1wHjzK/VIoQYLYQ4IoQ4LoSYW8l1DyHEZ+brfwkhospce8JcfkQIMapM+UohxDkhxP4KfQUJIX4RQhwzvwZaotHaGM6cwZCc3CjTIJTFo307jKmpFGdn2/S+Jmnih5M/MOHrCczfPp8W3i14b+R7rBy1UhkhhVMj9Xr0SUlOm5W1LJYYovNSyvVSylNSyoSSo7pGQggd8BYwBugC3CKE6FKh2j1AhpSyHbAIeNnctgswGegKjAbeNvcHsNpcVpG5wEYpZXtgo/m93cn7+28AvK/oZ2cl9sXdHKLeVtNzUko2nd7ExG8n8vi2x/Fw9WDp1Uv5+NqP6R/a3yYaFAprok9MhOJip44xV4Ila0TPCCHeR/tyLxtr7stq2vUDjkspTwIIIT4FxgNls7uOR0sxAbAOeFNo29XHA59KKYuAU0KI4+b+tkspt5YdOVXoa6j5/ANgM/C4BZ/PquT//Te6gIBSz7HGike7Es+543j3su56zI6UHSzZvYR/LvxDpH8krwx+hVFRo3ARlvzuUiicg1KPuSjnHxFZYojuAjoBbpTPR1SdIQpHi9xdQhJwxeXqSCmNQogsoKm5fEeFttXlwG0hpUwxn58FWlRWSQgxDZgGEBERUU2XdadgTxxePXsiXBr3l6BbWCgu3t4UHTtmtXvEnYtj6Z6l7Dy7kxCfEJ698lmub3s9ri7KJ0fR8NCfbBh7iMAyQ9RXStnR6krqESmlFEJcEpbIfG05sBygT58+ldapL4qzstCfPEnA9SoaknBxwb1dO6tMzR1JP8LSPUvZkrSFIM8g5vaby8QOE1UyOkWDRn/qFK7Nmzt8tBJLsMQQ/SmE6CKlPFh91XKcAVqVed/SXFZZnSQhhCsQAKRZ2LYiqUKIUCllihAiFDhXQ731TsG+fwDwimmcbtsV8WjXjtytW+utv1NZp3g77m02xG/Az92Ph3s9zK2dbsXbzbve7qFQOCpFp046/UbWEiyZL+oPxJm91/YJIf4RQlgSwfJvoL0QorUQwh3N+WB9hTrrgTvN5xOB36SU0lw+2exV1xpoD+ys5n5l+7oT+MYCjValYO9eEALPbt3tLcUh8GjXjuILFzBmZNSpn+TcZOb9MY8J30xgS9IW7ut+Hxtu3MC93e9VRkjRKJBSoj8V79RZWctiyYioVmFdzWs+s4CfAB2wUkp5QAjxHLBLSrkeWAF8ZHZGSEczVpjrfY7m2GAEZkopiwGEEP9Dc0oIFkIkAc9IKVcAC4DPhRD3AAnAzbXRXZ8U7N2LR7t2DWLoXB+UDfXj2q/mXoQXCi7w3r73WHt0LQC3drqVe7vfS1OvpvWqU6FwdIrT0jBlZzt9+ocSLDFEtV5HkVL+APxQoWxemfNC4KbLtH0ReLGS8lsuUz8NuKa2WusbKSWFBw7gO3SovaU4DCXBT4uOH8enBoYoqyiLVftX8cnhT9AX65nQbgLTe0wnxCfEWlIVCoemoQQ7LcESQ/Q9mjESgCfQGjiCtsdHcRmMqakUp6fj2bXi1qnGi2uLFrj4+qK30GGh0FjI/w7/j/f/eZ8cfQ5jWo9hZsxMIvyt7+2oUDgyRWaPuVqnf5ASHCixoyUZWsstcAghegEzrKaogVB4UPPt8OysDFEJQgg82rWrNvhpsamY9SfW81bcW6TmpzIofBAP93qYjkFO5bypUFgN/alTCE9PXGsT7PT8EVj/IPzrXQhyjBFVjTdYSCl3CyEq7gdSVKDwwEHNUaGT+vIsi0f79uT88gtSyktSbUsp2Zy4mSV7lnA88zjdg7vz30H/pW9I4w6PpFBUpOiUOdhpTfcn7v8SvpkF7t6Qd955DJEQYnaZty5ALyDZaooaCIWHDuHepg0u3sqLqywe7duRuXYtxWlpuAYHl5bvObeHRbGL2HNuD1H+Ubw+9HWGRwy/xFgpFAptM6tX9xpkDTYUwE9Pwa4V0LIf3PwB+IdZT2ANsWRE5Ffm3Ii2ZvSFdeQ0HAoPHcK7twqqWRGPkphzx47hGhzMicwTLN69mE2Jm2jm1Yx5A+Yxod0E3Fzc7KxUoXBMTEVFGM6cIeA6i2JPQ+pBWHc3nD8EA2bBNc+Aq7t1RdYQS9aInrWFkIZEcXY2xpQUPDp2sLcUh8OjozZVmbE/jkViI+uOrsPL1YuHej7ElM5T1D4ghaIa9AkJYDJVv5lVSm0E9NNT4OEPt30B7YbbRmQNuawhEkK8IaV8RAjxLZW4cEspVdyay1AST62xBzqtDBnoj76JDxt+fYd1HoKbO97MAz0eINDTIbJ2KBQOj/5UPEDVm1mzkuC72XDsJ2h7DfzrHfBtbhN9taGqEdFH5teFthDSkCgxRJ4d1IioLDtTdvL8jue5NTCf9mnerLvuU9oFtrO3LIXCqSg6fgyEqDz9g8kEsSvhl/kgi2H0Auh3Pzh40OXLGiIpZaz5dYvt5DQMio4excXXt3aulQ2QXH0ur8e+ztqja2nl14oO/Ubg8+Um2vpG2luaQuF06E+cwC08HBcvr/IXLhyD9Q/B6T+hzVC4bjEERtlDYo2xxGtuIFrOoIqpwhtGbAkrUHT0GB7t2yuPL2B78nb+74//43zBeaZ2ncqMmBno3TaS/NnPFJ08hadaR1MoakTR8ROlTj8AFGbB1ldhxzuaW/b4tyBmikNtWK0OS7zmVgD/BmKBYuvKcX6klBQdO4bfqFHVV27AFJuKWbZ3Gcv3Lad1QGteH/o60c2iAXAx760qOnxIGSKFogZIoxH9qVP4Dh4EpmLY/SH89gLkp2nG55p54FdpKjaHxhJDlCWl/NHqShoIxRkZFGdl4dGurb2l2I0LBRd4fOvj7Dy7k/Ftx/PkFU+W84Zzb90a4eVFwYEDBIwfb0elCoVzoT+diDQYcPcthHeHQOo/EDEARq+DsJ72lldrqvKaK8nnvEkI8SpaRtayqcJ3W1mbU1IajDAqyr5C7MSBtAM8uPFBcvQ5PD/weSa0m3BJHeHqimfnzhTuP2B7gQqFsyIlRb9/BYDHwTegTShMXAVd/+VU03CVUdWI6LUK7/uUOZfA1fUvx/lpaFFxa8KvCb/yxLYnCPIM4uNrP64yNpxnt65kfr4WaTQiXFUqb4XishQb4NB62P42+p8PA/543Pwi9L/L4Tam1paqvOaGAQgh2kgpT5a9JoRQjgqXQR8fj3Bzwy3MccJnWBspJasOrGJR7CKig6NZfPVigr2Cq2zj1b07GR9+RNGJk2qdSKGojKwzsPd/sGslZJ+BoLYU+fTFLTwHl6vut7e6esWSn6Lr0OLLlWUtoOLXVELRqXjcIiMQOp29pdgEkzTx6t+v8vGhjxkVNYoXBr6Ap6tnte08u2pxsgr3/6MMkUJRQkEmHP1JM0AnNwMSogbB2Neg/SiK/nUD7u0a3t67qtaIOqHlHAoQQtxQ5pI/Wl4iRSXoT53Co23jGDAaTUae+fMZ1p9Yz5TOU5jTdw4uwrKNc+5RkegCAsjfvZsmN95oZaUKhYMiJVw4qhmdw99Dwh9gMkJABAyZAz0mQ5D2fSKNRvQnT+Jz1UD7arYCVY2IOgLjgCZA2eh6OcB9VtTktEijEX1iIn7XOEyiWKtRVFzEf7b8h02Jm5gRM4Pp0dNrtG9KuLjg1acP+bt2WVGlQuFgFBvh/GFI3AHxv2tH3nntWtP2WlDSTmMhvM8l0RD0iZrHnEfbmo+Iik2SUxfyOJiSzaGUbA4mZ/PktZ3pGOJXfWMbUNUa0TfAN0KIAVLK7TbU5LQYzpwBg6HBe8zlGfJ46LeH2Hl2J3P7zWVK5ym16se7Tx9yN27EkHoOtxaOGwdLoagVUkJWIpyJhaRdcGY3pMSBIV+77h8Oba+GyIEQdRU0rXrLh/7ECUBLpVIVuUVGDqdklzM6R1JzKDSYAHB1EbRr7ktmvr7OH7G+qGpqbo6U8hXgViHELRWvSykfsqoyJ6SoEXjM5RvymfHrDPae38tLV73EdW0tDEVfCd59tGXGgthduF17bX1JVCjsQ346JO/WDM6ZWO0oGe3oPCA0GnrdAeG9oWVfLfxODWYRio5rmY3dW5un6qQkOauQg8kXDc6hs9kkpOWXtmni7UbnEH9u7RdJlzB/Oof60a65Lx6ujrWGXdXU3CHzq5o7sRB9fDxQTVRcJ6aouIhHNj1C3Pk4Xh70MqNbj65Tf56dO+Pi40Pejr/wV4ZI4SyUjHRSD0Dqfu01ZS+klzgXC2jWEdqPhPBemuFp3rVOrtZFxmLO7f4HffNQXtgUX2p8sguNpXWimnrTNcyfib1amo2OP6EBnk4Raqyqqblvza8f2E6Oc6M/FY8uIADXwIaX0sBgMvDY5sfYnrKd5wc+X2cjBNrGVu/+/cn7449KU4crHBApzYepmkMC8tLXkj4quyZLss1UUlan9hXvX1UdoLgICrOhKFt7LczU3Kezzlx8NeRdfCaBUdCi28XRTmgMePrX+hGn5RZxKCVHG+WYp9eOn8vl3di9nAwI49OdiXQM8WNcjzA6h/rTJdSfTiF++Hg47368qqbmKs1DVILKR3Qp+lOnGuS0XLGpmCe3PcnmpM08dcVTlUZLqC0+A68kd+NG9PHxeDTAZ2dzpNS+QItyoCgX9HmgLzk3H0VlX3O0OoZCMJoPQ0GZ80IwFoCxSDsu/5XQgBHg2wICwqFZJy25XNN2mvFp0QU8arfgX2ySxKflXZxaMxud1OzSADa08PegS6g/IyN9CPsyjVa33szd/x6FzqVh/WiryoSqPEQ1RB8fj8/AhuVaaZImnvnzGTbEb2B279lM7jS5Xvv3veoqUoG83/9QhuhySAkFGeV/lWefgdxz2rpEQXr5V2lJbGIB7r7g4QvuPuDmBa6e2uHbHFw9wNUL3Dy1V1cP7RA6EC7mQ5Q5r3gI7R4lo9yK70vPK3m9pH7FV6quY1H7smUV3uvctIymnv7aq4c/6Oo22sgtMnLkrLaOc9A82jlyNocCg/ZvVeJAMLBtsDbKMU+tBflo03l5O3dyGgjr06PBGSGoempO5SGqAcW5eRjPnWtQHnNSSl766yW+OfEND/R4gLu63VXv93CPiMA9MpLcTb8RdPtt9d6/02AyacYl7Tikn4C0E+bzk5rxMRaUry90msHwCgLvIGje6eK5V6D25enuo/1aLzU4vhfP3bydPj6ZIyKlJKWsA4F5lBNfxoEgwMuNzqF+TO7Xii6hmsFp36JqB4KiQ9qSvWeXLlb/DPbAeScVHQxD4mkA3CMbRrI3KSWLYhfx2ZHPmNp1Kg/0eMBq9/IbNYq0FSswpqfjGhRktfs4DHlpWtTks/vNi937taRmxsKLddy8IagtNO8CHUZrrr7+YRDQUjv3bQ4ujuX51NjQG00cO5fDoZSccoYnq8BQWieyqTddQv25oVdLzeiE+RNWCweCwoOH0DULxrVZs/r+GA6BMkT1hOHMGQDcWra0s5L64Z2977DqwComdZzE7N6zrepI4D9mNGnLl5Pzy68ETrrZavexC3lpZlde8z6S1P2Qk3Lxum8Lba2h9RBtH0nTdtrhF6pGLA5Eep6eQ2X25Rw0OxAYTdqamaebCx1D/Lm2eyhdQv3oHOpPp1B/fOvJgaDw0CE8O3eul74ckaqcFT6SUt4uhHhYSrnYlqKcEUNyMgBu4c4f7HT1/tW8vfft0lxC1vZm8+jUCffISLJ/+MG5DVGxEc7uhcS/NcOTtAsytL1lCBdtobv1EAjpZl7o7ga+DfMXrrNiMjsQHErJ4WBKVulo52z2xdFqcz8PuoT5M6xT89KptdbBPlZbuzEVFVF04gS+w4ZZpX9HoCpz3VsIEQbcLYT4kNJVQA0pZbpVlTkZhjNnEN7e6Jo0sbeUOvHp4U95LfY1RkWN4tkrn7U4dlxdEELgP/56LixZiuHMGdzCw61+z3rBqNc2MCb8AfF/QOJfmjcagF8YtOwNvadCyz6aS6+Hrz3VKiqQV2Tk8NnybtKHUy46EOhcBO2a+TKgbVM6m0c5nUP9Cfb1sKnOoqPHoLi4cY6IgHeAjUAbtDThZQ2RNJcrzOjPnME9PMyp98J8ffxrXvzrRYa2HMp/B/0XnQ3XIJqMH8+FJUvJ/Pprms2cabP71ghDgTbKSfhDOxL/vuhE0KyzFqAy8kpo1V9z9VU4BFJKzmYXXow+kJLDwZRs4tPySrcO+Xu60jnUn0l9W9ElTNub0665L55u9l+HKzx0EADPLo3QEEkplwBLhBDLpJTWW6luIBjOJOPqxDmINsRv4Jk/n2FA6AAWDl2Im4ubTe/vFh6O94D+ZK77guBp0xButr1/pRQbtdhgJzfBic2QtBOK9YDQptd6T4WogVqqZp+q8y8pbIPeaOL4udxyo5yDKdlk5l90IIgI0hwIJsSEl4a9CW/i5bA/IgsPHcLF17fBrD9XRrUraVLKB4QQPYBB5qKtUsp91pXlfBiSk/HuGWNvGbVic+Jmntj6BDHNYnhj2Bt46Gw79VBC0J13kjT9AbJ//JGA6+2wX1pKzW365CYtLP+pbVCUpV0LiYZ+07TcMBH9wauJ7fUpypGZr+dghVHO8XM5GIq1YY6HqwudQvwY3TWkdF9OpxA//Dwd4EdODSj8Zz+eXbogXKw/TW4vqjVEQoiHgGnAl+aiNUKI5VLKpVZV5kQU5+RgyspynrWNMmxP3s7szbPpGNSRt655C283b7tp8R08GI/27Uh77z38x42zzX94uefh1JaLo57sJK08IAK6joc2QzUHAzXisSsmk+TYuVxiEzLMR3q5vTnN/LQIBEM6NDNPrfkR1dQHV51zf3mb8vMpPHSIpvfea28pVsUS38J7gSuklHkAQoiXge2AMkRmSj3mnGxqbnfqbh7e9DBRAVG8O+JdfN3tu5guXFxoOn06yY8+RvZ331lnVKTPg4TtF0c9qfu1cs8m0HowDJoNbYdBYGvlPm1HcouM7E3MJDYhg10JGew5nUGOOcBnUx93ekcGMqlvBF3NI51mfvYZxVubgv37obgYLyedbbEUSwyRAMrGDCmmggddY8eQou0LcQsNtbMSy9l/YT8zNs6ghXcLlo9YToBHgL0lAeA/ZgxpK1Zw/o3F+I0YgYuXV906NJm0dZ4TG+HkFs2zrVgPOndtiu2aedqoJzRGbRC1E1JKkjIK2H06g13x2ojn8NlsTFL7LdCxhR/X9Qijd0QgvSMDiWzq7bDrOfVNwZ44ALx69LCvECtjiSFaBfwlhPjK/H4CsMJqipwQ49lUAFydxBAdST/C/b/cTxOPJrw38j2CvRxn2km4uNBi7lxO33EnF956i+aPPVbzTnLOwonf4PhGbeSTn6aVt+gOV9wPbYZpDgbu9puGbMzojSYOJGeVmWbL4FyOFujTx11Hz4hAZl3dnt6RgcS0akKAl3Ot6dQnBXv24N6mTYOM6F8WS5wVXhdCbAauMhfdJaXcY0nnQojRwGJAB7wvpVxQ4boH8CHQG0gDJkkp483XngDuQRuBPSSl/KmqPoUQq4EhgHl1malSyjhLdNYVQ+pZ0OlwDXacL/TLcTLrJNN+mYanqyfvj3yfEJ8Qe0u6BJ9+/QiYeCNpq1bjO2wY3r17V93AWASnt2uG58RvF6fbfJppkZLbXqNNt/mqLLD24EJuEbsTMog9ncHuhAz2JmWhN2rZQlsFeXFl26b0jgykd2QQHUP8GmRQz9ogpaQgLg7fa662txSrY1H8CSnlbmB3TToWQuiAt4ARQBLwtxBivZTyYJlq9wAZUsp2QojJwMvAJCFEF2Ay0BUIA34VQnQwt6mqz/9IKdfVRGd9YDybimuzZgidY0/tJOUkcd/P9wHw/sj3aennuO6gLebOJX/n35x59DFar1tb3shLqcVmO7FRMz7xv2v7eVzczNNtz0C7a7QRUAP2NHJEKjoV7D6dwakLWu4eN52gW3gAd/SPpE9UIL0iAmnu72lnxY6LPj6e4sxMvGJi7C3F6lgz1lw/4LiU8iSAEOJTYDxQ1hCNB+abz9cBbwpt8nc88KmUsgg4JYQ4bu4PC/q0OcbUs7i1aGFPCdVyNu8s9/58L4XGQlaOWknrAMdOuaDz9aXlG4uIn3IbiQ/MIOLtRejO7zKPejZBlhZklqA20PM2zfBEDVLRC2xMWaeCEsNT1qmgV2Qgk/u2ondkIN3CAxxig6izULI+5N2zp32F2ABrGqJwILHM+yTgisvVkVIahRBZQFNz+Y4KbUt8o6vq80UhxDy0iBBzzYasHEKIaWju6ERERNTwI1WO4WwqHu3b10tf1uBCwQXu+/k+MosyeX/k+3QM6mhvSdVjKsbTP5/wqQNIeuc3Eq4fQqvBabgF+ECbIXDVI9D2aghybIPakCjrVBCboDkWlHUq6NC88ToVWIOCPXtw8ffHvU3DD2JjyT4iH6BASmkyT491An6UUhqqaWprngDOAu7AcuBx4LmKlaSUy83X6dOnT53TTUopMZw9i++gq6qvbAcyCzOZ9ss0UvNTeWf4O3QL7mZvSZcnLw2O/wrHftLWegoy8EPQakJnkr7PJWFHV8KXvoVXdMP2IHIUlFOBfcnfsxuvHj0a9EbWEiwZEW0FBgkhAoGfgb+BScCUatqdAVqVed/SXFZZnSQhhCsQgOa0UFXbSsullCWx9YuEEKuAWrhb1RxTTg4yPx/XFo636J+rz2X6r9NJyErgzWvepFeLXvaWVB4pIWUvHPtFMz5JuwCpORl0GK05GrQZiq9PMJG37idpxgziJ99K0J130mzWTFx8fOz9CRoUablF7D6dya6EdHYnZLAvKYuiSpwKekUG0rGFn9NvFnVkjOfPoz9+goDx4+0txSZYtI9ISpkvhLgHeFtK+YoQIs6Cdn8D7YUQrdGMxWTg1gp11gN3om2QnQj8JqWUQoj1wCdCiNfRnBXaAzvR9i9V2qcQIlRKmWJeY5oA7LdAY50xnD0LgFuIY60R5RvymblxJkfSj/DGsDcYEDbA3pI09Pmak8HRnzQDlKs9P8J6wZDHocNICO15iZOBV/dutPn+O8699jrpq1aR+eWXBE6aROAtk51q/5ajYDJJjp/PLd23U5lTwe39I0sNTwvlVGBT8nb8BYBPfwf579bKWGSIhBAD0EZA95jLql1xNK/5zAJ+MtdfKaU8IIR4DtglpVyPth/pI7MzQjqaYcFc73M0JwQjMFNKWWwWc0mf5luuEUI0QzNWccB0Cz5bnTGmmvcQhTjOiKiouIiHNz1M3Pk4Xhn8CkNaDbGvIEOhNuV24Es4sgEMeVoq67ZXQ4dR2sjHAtdqnb8/oc/Op8mNN5D2/grS3nuPtOXL8erRA99hw/Dq2ROvbl3VSKkS8oqMxFXjVDDJ7FTQXTkV2J28Hdtx8fdv0BG3y2KJIXoEbf3lK7OBaANssqRzKeUPwA8VyuaVOS8EbrpM2xeBFy3p01xuF2d747nzALg2d4w9KgaTgcc2P8aOlB28MPAFRkWNso8QKbWMpLGr4MDXoM8BryCIvgm6/gsiB4KudmsKXtHRtFyyGH1iItk//Ej2Txs4/8Yb2kUXFzw6dMC7Vy882rfDPSoK96goXFu0aBRz7XCpU0FsQgaHUso7FYyLDqNPpHIqcESklORv34HPFf0cfktIfWHJhtYtwBYAIYQLcEFK+ZC1hTkLxvNmQ+QAm1mNJiNzt85lc9Jmnr7iaca3s8P8sqEA9v4P/l4Jqf+Am49meLrdoMVyq6XxqQz3Vq0Ivn8awfdPw5iRQeE//1AQt5eCuD1kfv01Mv9iUEzh4YF7ZKR2REXhHhVZaqR0QUFO/UVc1qmgxPikZl90KoiJaKKcCpwI/cmTGJKTaTrtPntLsRmWeM19gjbNVYy27uMvhFgspXzV2uKcAeOFC7j4+eHiad85dJM0Me+Pefyc8DOP9XmMSZ0m2VZA3gXY+R78/Z4WUickGsYtgm4TwdPf6rd3DQzEd/BgfAcPBkCaTBjPnUMfH48+PkF7TUig6PhxcjZvBsNFp08Xf388O3bENTQE12bN8IqJwWfAleh8HXOKr8SpIDahJFJBZqlTQctALwa0UU4Fzkzu5i0ApX/LjQFLpua6SCmzhRBTgB+BuWgZW5UhQhsR2Xs0JKXk+R3P8+3Jb5kVM4s7u95pu5tnnobf34C4NWAshA5j4MoHtUyldhxlCBcX3EJCcAsJwad//3LXpNGIITkZfUIC+lPxFJ04QdGRIxTE7sZ4/jzpK1aCqyvevXrhO3gQPoMG49GhvV1GTSVOBWVdqJVTQcMmd+tWPNq3d7po/nXBEkPkJoRwQ/NEe1NKaRBC1Hn/TUPBeOECrs2a2e3+Ukpe/vtl1h1dx33d7+P+Hvfb5sbZybB1Iez+UDM4PSbDgFnQzPE3ywpXV9wjInCPiIBBg8pdkwYD+Xv2kLdtG7lbt3Fu4Wuw8DVcQ0LwHTIE/1Ej8e7XD+Fqnb3geeZIBbuUU0GjpDg7m/zYWJreNdXeUmyKJf81vQvEA3uBrUKISCDbmqKcCeOF83h1tc8mUSkli3cvZs2hNdzW+TYe7Pmg9W+akwq/L4JdK0EWQ8/bYfBjEOC4cetqgnBzw6dfP3z69aP5o49iSE0tNUpZ335L5mefoQsMxG/0KJrecw/udUjfLKXkTGZBudFOZU4Fvc1OBVHKqaDBk/Pbb2A04jd8uL2l2BRLnBWWAEvKFCUIIYZZT5JzYTx/Addm9pmae2ffO6zYv4KbO9zMnL5zrPsllXcB/ngDdr6v5fOJuQUGz4HASOvd0wFwa9GCJhMn0mTiREyFheRu3UrOhp/I+vIrstZ9QZNbJhM8fTquQUHV9qU3mjiYks2u+PRLnAq83XX0jGjCrGHt6BUZSM+IQOVU0AjJ+fkXXEND8YyOtrcUm2KJs0IA8AxQsnK2BS10TtZlGzUSTHl5WlQFO0zNrdq/irfj3ub6ttfzVP+nrGeEDAWwYxlse13b/xM9CQb/B5q2tc79HBgXT0/8R47Ef+RIDKmpXHjzTTI+XkPWF18S/OAsgm67rdyUXXqe/uK+nUqcCvq3aVo62lFOBYri3Fzyfv+dwFtuaXQjX0um5laiRSm42fz+drRkeTdYS5SzYLxwAQCdjZ0VPjn0Ca/Hvs7oqNE8d+VzuAgrfIGZTPDP57DxechOgo7XwvD5TrEGZAvcWrQg9PnnCZo6ldRXXuHcgpc599U3HL79If6QQexOyOBkGaeCrmHKqUBRNdnf/4DU6/Efe629pdgcSwxRWynljWXeP2thiJ8Gz8U9RLYbEX157Ev+u/O/DGs1jJcGvYTOGumtT22Fn5/W4sCFxsC/3oHWg6pt1pjIK01/YCL2irtxN7Tmztgvaft/D/JP99G0u/YmblZOBYoakPnFF3i0b49n9+72lmJzLDFEBUKIq6SUvwMIIQYCBdaV5RyUjIhsNTX33cnvmP/nfAaGD2ThkIW4udTzGsL5I/DLPDi6AQJawQ3vafuAGklEgstR1qmgJNPooZQcik2y1Kmg17ixnLvjeoLXvstNG7/HSyQRvmQxbi2qXztSKAoPH6Zw3z6az3280U3LgWWGaDrwoXmtCCADLVBpo8d43myIgpta/V6/JPzC078/Td+Qvrwx9A3cde7113nuOdj8X4j9ANx9tCm4K6aDm1f93cOJKHEqKDE8uxLSL3EqmDm0beVOBUMXk/X996T83zzib7qZiFUr8Wjb+NbTFDUjbeVKhLc3TSZMsLcUu2CJ19xeoIcQwt/8PlsI8Qiwz8raHJ7ijHQQAl2TJla9z+bEzczZMofuwd1ZevVSPF3raX1Bnw873tI2pBoLoe89WgRsH/uHK7Il6Xn60pFObPzlnQp6RQTSKaR6p4KAsWPxaNee0/feQ8JttxOx4n08u3SxxUdROCH6pDNkf/8DQbfdZvXvEkfF4l15Usqye4dmA2/Uuxonw5ieji4gwKqBCTcnbubfm/9Np6BOvD38bbzdvOveqckE+z7VHBFykqHTOG0UFOy4WWbrC5NJcsIcqWCXecRT0angNrNTQe86OBV4duxA1EcfkXD33STcOZVW776Ddy8HywelcAjOvbYQ4epKUCPbxFqW2m4Pb3yTmJVQnJ6Brqn1puW2JG7RjFBgJ94d+S5+7n517/TkFvj5KTj7j5YD6Mb3IWpg3ft1UPKKjOxNyiQ2Xhvx7E7IINscqSDIx51eEYHc1EdzKohuWb9OBe5RUUR9/DGn77qb0/fcS8s3l+I7sOE+a0XNyd32Ozk/biD4wVm4OVAqGVtTW0OkQvwAxenpuAYGWqXvEiPUMbAj7458F3/3OgYOPX8Efv4/LRNqQATc8D50u7FBOSJU5VQA0KGFL2OjQ+kdGWSzSAVuYWFErvmY03ffQ9L0Bwh/YxF+11xj1XsqnAN9UhLJjz2GR/t2NL3nnuobNGAua4iEEDlUbnAE0DhXsStgzMiwykL01qSt/Hvzv+kQ2IHlI5fXzQjlnjc7Iqwu44jwALg5/z6Wik4FsQkZnM0uBDSngphWZZwKWgUS4G2fSAWuwcFEfvgBp6fdT9JDDxO24L8EXHedXbQoHAN9YiKnp96FlJKWS5faPXq/vbmsIZJS1sM8UMOmOD0dXd8+9drn1qStPLLpEdoHtufdEXUYCRkKYMfbsG0RGPKhz90wdK5TOyKUcypIyGBv4kWngvAmXlzRJqhGTgW2RNekCRErV5I0YwbJcx7HlJdP4GQbp+pQ2B0pJTk//8LZefOQQMTKlbhHRdlblt2xTgjhRoAsLqY4M9OiGGOWUtYILR+xnACPgOobVcRkgv3rYONzkJWopWUY8Rw061BvOm1BWaeCkuNyTgW9IgIJCXD8X5Q6Xx9aLX+XpIcf5uz8+Zjy8mh6z932lqWwAVJKCnbv5tyiRRTsisWjc2davrEI98iGHavRUpQhqiXFWVkgJbrA+jFE25K21d0IJfwJPz0Fybu1xHQT3tayojoB+XojcYmZ5n07tnUqsCUunp60WrqUM3Me59yrr2IqKKDZrJn2lqWwEqa8PDI+X0vm2rXoT55E17QpIfPn02TijVZLJeKMqCdRS4rT0wHQBdXdWWHT6U08uuVR2jVpVzsjlHZCi4hw+DvwC4MJ72jBSR3UEUFKSXJWoTbSiU+/rFNBrwjNhbp1sE+D2m0u3N0Jf20hKZ6eXHjzTZCSZg/OsrcsRT1i0utJX/0B6StXUpyZiVdMDKEvPI//mDG4+Dhm5l97ogxRLTGaDVFdp+Z+iv+JuVvn0rlpZ5YNX1YzI5R3Aba+Cn+/DzoPGPY0DJgJ7vWw16geMRSbOJicXTrSqcypYIbZqaCXHZ0KbInQ6Qh98QUQggtvvQWgjFEDofDoUZLnPE7R4cP4DBlMswcewCsmxt6yHBpliGpJcXoGALo6GKJvT3zL0388TUyzGN665i183X0ta6jPg+1vwx+LtdQMPW+HYU+BX4taa6lPMkrSH5idCvYlZVJouOhU0K91UOmGUUdzKrAlQqcj9IXnlTFqQGRv2EDynMdx8fOj5dtv43e1St1mCcoQ1ZLiDPPUXC33Ea09upbntz9Pv9B+LBm2xLKICcVG2PMhbF4AuanQcSwMf8auqRkucSo4ncHJ85pTgauLoGt4AFOucC6nAltSaoxAGSMnJ3vDBs7MfhSvnj1puWQxrlbc7N7QUIaolpROzdXCEK05tIYFOxcwKHwQi4YtwkPnUXUDKbX1n1+fhbRj0OoKuPlDiOhfG+l1oqxTQWxCBrtPZ5JVYADKOBX0dn6nAlsiXFzKGyNpIvjBBxvUulhDJ3/3bpL/MwevmBgi3luOi7djTY87OsoQ1ZLirCxcfH0RbjVbz1jxzwre2P0GwyOG88rgV3DTVdM+YbvmiJC0E4I7wKQ10Gks2OhLqlykgoQMDqZkl3MquLZ7SIN1KrAlpcZIwIW3l1GcnUOLJ59AOKjDieIixrQ0zjzyb1zDQmn19lvKCNUCZYhqiSkrC12A5Y4FUkqW7V3Gsr3LGNN6DC9d9RKuLlU8/nOHYeOzcOQH8A2B6xZDzG2gs94/WYlTQckU2+6EDFKyGrdTgS0RLi6EPv88Oj9/0levpjg9nbAF/0W412PKD0W9c3b+fIozM4la/lmjjZ5dV5QhqiXFmZYbIikli3YvYtX+VUxoN4H5A+ZfPrNq5mnY8jLEfQJuPnD109B/hhaep57JyNOz+7S2b6cyp4K+UcqpwNYIFxeaPz4H1+CmnFv4GsWZmYQvWYzO10JHFoVNydm8mZxffqXZ7Nl4dupkbzlOizJEtaQ4MxNdk+oNkUmaWLBzAf87/D8mdZzEk1c8iYuo5As95yxsXajFhBMC+t0Pg/8DPvWz4GkySU5eMKc/iK/cqeDWfhfTHyinAvshhKDpvfeiCwwiZd484idNpuWbS/Fo3dre0hRlMBUWkvrCi7i3aUPTqSpXaF1QhqiWFGdl4RYeVmUdg8nAU78/xY+nfmRq16nM7j370jWUvDT4YxHsfA9MRuh5m2aAAlrWSV++3sjexCxiE9IvcSoI9Hajd2QgE3u3pE9kkHIqcFCa3HgDbuHhnHnkEeJvnkT4wlfxHTLE3rIUZtKWL8eQlETE6tVq+rSOKENUS4qzsnCpYmquwFjA7M2z+f3M7zzS6xHu6V4hzHthFmx/S9sPpM+F6Ju1oKRBbWqlJzmzoNyG0bJOBe2b+zKmW0jpaEc5FTgPPv2vIGrdOpIefJDE6Q8QPGsmwdOnWzUZo6J6ik6dIu299/G/7jp8+l9hbzlOjzJEtUCaTBRX4ayQrc9m1sZZ7D2/l2cGPMPEDhMvXizKhZ3Ltc2ohZnQZTwMfRKaWz6/rJwKGhfuLcOJ+mQNKc88w4Wlb5K/82/CXnkFtxbN7S2tUSJNJs4++xzC05MWc/5jbzkNAmWIaoEpLw9MJnQBTS65dj7/PNN/nc6prFO8OvhVRkaN1C4UZsPf78Gfb0JBOrQfqUVDCIup9n4lTgUlm0b3KqeCRoeLlxdhL7+MzxX9OfvCC5yaMIGwlxfgO9g5gto2JDLWfEL+jh2EzJ+Pa7Nm9pbTIFCGqBYUZ2UBXDIiSsxJZNrP00grTOOta95iQNgAKMiEv97VcgMVZmoGaPAcaNW30r7LOhWUHCcu41TQK7IJoQEqR2FjQQhBkxtvwCumB2f+PZvEafcTdOcdBD/4EDpfFUjTFuTt+Itzr7yCz5DBNJl0s73lNBiUIaoFxRmZAOW85o5mHOX+X+7HYDKwYuQKuvuEw28vwl/vQFE2dLxWc0II71WurxKngpIRz+7TGWTml3cquLF3S3pHBBLdsgle7mptoLHj0bYtUZ9/xrlXXiH9gw/J+uEHmj/6KAHXXafWjqxI3p9/kvTgQ7hHRRL+yitqnbUeUYaoFpSOiMyb1+LOxTFj4wy8XL34YMhi2u79UlsH0udC5+s0AxTaA9CcCsqOdio6FYzuGkKvyED6KKcCRRW4eHoSMm8eAePHc/aFF0mZ+wQXli2j6b33EjB2rNrdX89kfvElKc88g0ebNrR6b3mNNrMrqkdIKe2twW706dNH7tq1q8btsr7/nuRHH6PN99+x0yOZ2ZtnE+IVzLtenQnb86mWmrvrvzAMnM0hUytt705CeacCLzfNqaB3ZCC9o5RTgaL2SJOJnF9/5cI771B08BDC2xv/EcPxueoqvGJicGvZUv2gqSXGjAzOvbqQrC+/xOfKK9XmYjNCiFgpZZ/66s+qIyIhxGhgMaAD3pdSLqhw3QP4EOgNpAGTpJTx5mtPAPcAxcBDUsqfqupTCNEa+BRoCsQCt0sp9db4XCUjok2Zu3jiwH9pr/Nl2ZE4ggw7SIkYx/dNbuWXc03YuyyJQsNpQHMq6BMVRO+IJvSJClJOBYp6Q7i44D9yJH4jRlCwaxdZ69eTveEnsr5Zr1338MAtJARdUBAuXl4Iby9cvLxx8fbGxcsLFx8fXHx9cfHxRufri4uPD7qAAHRBQVobn8Y1Mpd6PflxceRs+Ims777DlJtL02nTaPbgrBrHllRYhtVGREIIHXAUGAEkAX8Dt0gpD5apMwOIllJOF0JMBv4lpZwkhOgC/A/oB4QBvwIdzM0q7VMI8TnwpZTyUyHEO8BeKeWyqjTWdkR0Ydkyzi9ewq1zdPQwGFh89gLbdYNZkDeWeBmqORWE+Zun2IKUU4HC5sjiYoqOHaMgLg59wmkMKSkUZ2Viys9H5hdgKjAf+fnIgoIq+xLu7majFIhroGacdIFNEG5uWlBWFx1CV+a1xGiV+W4p9z1T7pzLlJe7UA/9VN5OSokpLw9TdjbFWdkYkpPRJyaC0Yjw9MR32FCazZiBR/v2FR9Lo8aZRkT9gONSypMAQohPgfHAwTJ1xgPzzefrgDeF9tNrPPCplLIIOCWEOG7uj8r6FEIcAq4GbjXX+cDcb5WGqLZs++NjIt1hYGEhV6R0YYrr04S06sTNkYHKqUDhEAidDs9OnSyKfyaLizHl52tfyLm5mHJzKc7KwpieQXF6Osb0NIpLzjMy0MfHU5yZiTQawWRCmkxQXGzlDyRqfS6que7i7Y3O3x+XgAA82rXFb+RIPDt3xnfQVSqtt42wpiEKBxLLvE8CKm5BLq0jpTQKIbLQptbCgR0V2oabzyvrsymQKaU0VlK/HEKIacA0gIiIiJp9IjMyPIyThfmM6LqCbjdFc5tyKlA4MUKnQ+fnh87Pr079SJMJSo6S/x5qajTUf0eNkkbnNSelXA4sB21qrjZ93PDy2nrVpFA0BLRpOrXuqag51vyrOQO0KvO+pbms0jpCCFcgAM1p4XJtL1eeBjQx93G5eykUCoXCAbGmIfobaC+EaC2EcAcmA+sr1FkPlMRPnwj8JrXVyPXAZCGEh9kbrj2w83J9mttsMveBuc9vrPjZFAqFQlFPWG1qzrzmMwv4Cc3VeqWU8oAQ4jlgl5RyPbAC+MjsjJCOZlgw1/sczbHBCMyUUhYDVNan+ZaPA58KIV4A9pj7VigUCoWDoza01sJ9W6FQKBoz9e2+rVYWFQqFQmFXlCFSKBQKhV1RhkihUCgUdkUZIoVCoVDYlUbtrCCEOA8k1LJ5MHChHuXYAmfUDM6pW2m2Hc6o29k1R0op6y09baM2RHVBCLGrPr1GbIEzagbn1K002w5n1K00l0dNzSkUCoXCrihDpFAoFAq7ogxR7VlubwG1wBk1g3PqVppthzPqVprLoNaIFAqFQmFX1IhIoVAoFHZFGSKFQqFQ2BVliGqBEGK0EOKIEOK4EGKunbW0EkJsEkIcFEIcEEI8bC6fL4Q4I4SIMx/XlmnzhFn7ESHEqDLlNvtcQoh4IcQ/Zm27zGVBQohfhBDHzK+B5nIhhFhi1rVPCNGrTD93musfE0Lcebn71YPejmWeZZwQIlsI8YgjPmchxEohxDkhxP4yZfX2bIUQvc3/dsfNbeucVvUyml8VQhw26/pKCNHEXB4lhCgo88zfqU7b5T6/FTTX29+D0NLd/GUu/0xoqW+sofmzMnrjhRBx5nLbPWcppTpqcKClnzgBtAHcgb1AFzvqCQV6mc/9gKNAF2A+8Fgl9buYNXsArc2fRWfrzwXEA8EVyl4B5prP5wIvm8+vBX4EBNAf+MtcHgScNL8Gms8DbfQ3cBaIdMTnDAwGegH7rfFs0XKD9Te3+REYYyXNIwFX8/nLZTRHla1XoZ9KtV3u81tBc739PQCfA5PN5+8AD1hDc4XrrwHzbP2c1Yio5vQDjkspT0op9cCnwHh7iZFSpkgpd5vPc4BDQHgVTcYDn0opi6SUp4DjaJ/JET7XeOAD8/kHwIQy5R9KjR1o2XhDgVHAL1LKdCllBvALMNoGOq8BTkgpq4rKYbfnLKXcipbfq6KeOj9b8zV/KeUOqX3bfFimr3rVLKX8WUppNL/dgZZ5+bJUo+1yn79eNVdBjf4ezCOMq4F1ttJsvufNwP+q6sMaz1kZopoTDiSWeZ9E1V/8NkMIEQX0BP4yF80yT2usLDNEvpx+W38uCfwshIgVQkwzl7WQUqaYz88CLcznjqK5hMmU/4/VkZ9zCfX1bMPN5xXLrc3daL+8S2gthNgjhNgihBhkLqtK2+U+vzWoj7+HpkBmGUNsi+c8CEiVUh4rU2aT56wMUQNBCOELfAE8IqXMBpYBbYEYIAVtyO1IXCWl7AWMAWYKIQaXvWj+peVwewvM8/TXA2vNRY7+nC/BUZ/t5RBCPIWWqXmNuSgFiJBS9gRmA58IIfwt7c/Kn9/p/h7KcAvlf2DZ7DkrQ1RzzgCtyrxvaS6zG0IINzQjtEZK+SWAlDJVSlkspTQB76FNAcDl9dv0c0kpz5hfzwFfmfWlmof9JcP/c46k2cwYYLeUMhUc/zmXob6e7RnKT5FZVb8QYiowDphi/mLDPL2VZj6PRVtj6VCNtst9/nqlHv8e0tCmSV0r+Sz1jvk+NwCflZTZ8jkrQ1Rz/gbamz1a3NGmadbbS4x5XncFcEhK+XqZ8tAy1f4FlHjJrAcmCyE8hBCtgfZoC482+1xCCB8hhF/JOdqi9H7z/Uq8s+4Evimj+Q6h0R/IMg//fwJGCiECzVMgI81l1qTcr0ZHfs4VqJdna76WLYTob/7bu6NMX/WKEGI0MAe4XkqZX6a8mRBCZz5vg/ZsT1aj7XKfv74118vfg9nobgImWluzmeHAYSll6ZSbTZ+zpd4W6ijnMXItmnfaCeApO2u5Cm34uw+IMx/XAh8B/5jL1wOhZdo8ZdZ+hDIeT7b6XGgeQnvNx4GSe6HNi28EjgG/AkHmcgG8Zdb1D9CnTF93oy38HgfusvKz9kH7pRpQpszhnjOaoUwBDGjz9/fU57MF+qB9wZ4A3sQcocUKmo+jrZ+U/F2/Y657o/nvJg7YDVxXnbbLfX4raK63vwfzfyc7zc9hLeBhDc3m8tXA9Ap1bfacVYgfhUKhUNgVNTWnUCgUCruiDJFCoVAo7IoyRAqFQqGwK8oQKRQKhcKuKEOkUCgUCruiDJFCoVAo7IoyRApFPSCEaFomXP5ZcTEVQK4Q4m0r3G+1EOKUEGJ6FXUGCS09yP7L1VEoHAG1j0ihqGeEEPOBXCnlQiveYzXwnZRyXTX1osz1ullLi0JRV9SISKGwIkKIoUKI78zn84UQHwghtgkhEoQQNwghXhFagrEN5piBJUnHtpgjk/9UIWzM5e5zkxBivxBirxBiq7U/l0JRnyhDpFDYlrZoeWauBz4GNkkpuwMFwFizMVoKTJRS9gZWAi9a0O88YJSUsoe5b4XCaXCtvopCoahHfpRSGoQQ/6Bl59xgLv8HLSNmR6Ab8IsWTxIdWmyw6vgDWC2E+Bz4sr5FKxTWRBkihcK2FAFIKU1CCIO8uEhrQvvvUQAHpJQDatKplHK6EOIKYCwQK4ToLc0h/BUKR0dNzSkUjsURoJkQYgBouaaEEF2raySEaCul/EtKOQ84T/kcNwqFQ6NGRAqFAyGl1AshJgJLhBABaP+NvoEWjr8qXhVCtEcbUW1ES7GhUDgFyn1boXBClPu2oiGhpuYUCuckC3i+ug2twLfABZupUihqgRoRKRQKhcKuqBGRQqFQKOyKMkQKhUKhsCvKECkUCoXCrihDpFAoFAq78v97Jqotx7tJdwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Q_SEI_n = sol4[\"Loss of capacity to negative SEI [A.h]\"].entries\n",
+    "Q_SEI_p = sol4[\"Loss of capacity to positive SEI [A.h]\"].entries\n",
+    "Q_SEI_cr = sol4[\"Loss of capacity to positive SEI on cracks [A.h]\"].entries\n",
+    "Q_pl = sol4[\"Loss of capacity to positive lithium plating [A.h]\"].entries\n",
+    "plt.figure()\n",
+    "plt.plot(t,Q_SEI_n,label=\"Negative SEI\")\n",
+    "plt.plot(t,Q_SEI_p,label=\"Positive SEI\")\n",
+    "plt.plot(t,Q_SEI_cr,label=\"SEI on cracks\")\n",
+    "plt.plot(t,Q_pl,label=\"Lithium plating\")\n",
+    "plt.xlabel(\"Time [s]\")\n",
+    "plt.ylabel(\"Loss of lithium inventory [A.h]\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e900be5",
+   "metadata": {},
+   "source": [
+    "The additional SEI increases the cell resistance, preventing the graphite-silicon composite from being fully lithiated, so there is less plating than before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "faa82d38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n",
+      "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n",
+      "[4] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n",
+      "[5] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
+      "[6] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[7] Scott G. Marquis. Long-term degradation of lithium-ion batteries. PhD thesis, University of Oxford, 2020.\n",
+      "[8] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n",
+      "[9] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n",
+      "[10] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "[11] Lars Ole Valøen and Jan N Reimers. Transport properties of lipf6-based li-ion battery electrolytes. Journal of The Electrochemical Society, 152(5):A882, 2005.\n",
+      "[12] Shanshan Xu, Kuan-Hung Chen, Neil P Dasgupta, Jason B Siegel, and Anna G Stefanopoulou. Evolution of dead lithium growth in lithium metal batteries: experimentally validated model of the apparent capacity loss. Journal of The Electrochemical Society, 166(14):A3456, 2019.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5d2ea51",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
index 933d27aa78..fe6173f1ce 100644
--- a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
+++ b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
@@ -56,7 +56,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np \n",
     "from numpy import pi\n",
diff --git a/docs/source/examples/notebooks/models/latexify.ipynb b/docs/source/examples/notebooks/models/latexify.ipynb
index 401b3108d5..63e7c0d519 100644
--- a/docs/source/examples/notebooks/models/latexify.ipynb
+++ b/docs/source/examples/notebooks/models/latexify.ipynb
@@ -31,7 +31,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm"
    ]
   },
diff --git a/docs/source/examples/notebooks/models/lead-acid.ipynb b/docs/source/examples/notebooks/models/lead-acid.ipynb
index db43a642ba..f550540182 100644
--- a/docs/source/examples/notebooks/models/lead-acid.ipynb
+++ b/docs/source/examples/notebooks/models/lead-acid.ipynb
@@ -32,7 +32,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import os\n",
diff --git a/docs/source/examples/notebooks/models/lithium-plating.ipynb b/docs/source/examples/notebooks/models/lithium-plating.ipynb
index 051e215ca1..1e14513620 100644
--- a/docs/source/examples/notebooks/models/lithium-plating.ipynb
+++ b/docs/source/examples/notebooks/models/lithium-plating.ipynb
@@ -1,42 +1,31 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Modelling lithium plating in PyBaMM\n",
     "\n",
-    "This notebook shows how PyBaMM [7] can be used to model both reversible and irreversible lithium plating."
+    "This notebook shows how PyBaMM [8] can be used to model both reversible and irreversible lithium plating."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import os\n",
-    "import matplotlib.pyplot as plt\n",
     "os.chdir(pybamm.__path__[0]+'/..')"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The Doyle-Fuller-Newman model [3] is upgraded with three different lithium plating models. Model 1 contains the reversible lithium plating model of O'Kane et al. [6]. Model 2 contains the same model but with the lithium stripping capability removed, making the plating irreversible. Model 3 contains the updated partially reversible plating of O'Kane et al. [7]. The parameters are taken from Chen et al.'s investigation [2] of an LG M50 cell."
+    "The Doyle-Fuller-Newman model [3] is upgraded with three different lithium plating models. Model 1 contains the reversible lithium plating model of O'Kane et al. [5]. Model 2 contains the same model but with the lithium stripping capability removed, making the plating irreversible. Model 3 contains the updated partially reversible plating of O'Kane et al. [6]. The parameters are taken from Chen et al.'s investigation [2] of an LG M50 cell."
    ]
   },
   {
@@ -60,11 +49,10 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A series of simple fast charging experiments based on those of Ren et al. [8] is defined here. We first initialise the model at 0% SoC by performing a C/20 discharge (see more details on how to initialise a model from a simulation in [this notebook](../initialize-model-with-solution.ipynb))."
+    "A series of simple fast charging experiments based on those of Ren et al. [7] is defined here. We first initialise the model at 0% SoC by performing a C/20 discharge (see more details on how to initialise a model from a simulation in [this notebook](../initialize-model-with-solution.ipynb))."
    ]
   },
   {
@@ -93,7 +81,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -119,7 +106,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -152,7 +138,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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eREQE7du3t1R4Vd61DWBat25NYWEh//vf/+jVqxcnT57E3t4egClTprBhwwZ+/vlnnJ2dmTBhAo8++qhp+o5er6dfv374+Piwd+9e4uLiGDp0KFqt1vS0Pioqin79+vH888/z3XffsW3bNkaPHo2vr69pnaAff/yRqVOn8umnn9K2bVsWLVpE7969OX36NF5eXpb5gMop/nw6GMFoMEqCUAhx16nIPcHtqltZp06dIijo6rJiWVlZ+Pr6FltODcpehkSr1ZodK4pyw12Ts7KyeO6555g4cWKx964l1ADT79prbG3LXsaif//+1KpVi2XLluHn54fBYKBx48albsyxatUqpk+fzoIFC2jfvj2Ojo68//777N+/v8zzVFZFPqsbXeu10YnXP/i9thbkf/33cyypLCsri/79+zNv3rxidX19fU1/rszf9+0gSUILCfYMZm9qOLlWGaRfycHNt3hWWQghhBBVi9Fo5IUXXmDNmjXs3LnT9AXgml69euHh4cF7773HmjVrirVPS0uTdQm5ugD69b788ku8vLw4fPgw9913H+np6axYsYLvv/+ebt2urhG1cuVKGjRowN9//027du3YsmULJ0+e5I8//sDb25vmzZvz9ttv8/LLL/PGG29gZWXFp59+SlBQEAsWLACgQYMG/PXXXyxcuNCUJPzggw8YM2YMI0aMAODTTz9lw4YNfPHFF8yYMaPE+PPz88nPzzcdZ2RYZiORFr1r4RPsjHuNovtIg/7qFwqVWh5ACyGEJWzfvp1jx44xZcoUAFq0aEF8fDwajeaWbjhhZWVlWjPwmhYtWnDy5Enq1KlTob6aNm3Ktm3bzKYIX5OcnMzp06dZtmwZnTtfHbF5/SyKkuzZs4cOHTowbtw4U9l/R02WFP9/NWjQgNWrV5uNzNyzZw+Ojo5mD2krwtHRkcDAQLZt28b9999f7P1rU3vj4uIIDQ0FMNvEpKJatGjB6tWrCQwMvKldm7Va7Q0/r1tB7h4spFHA1SG56bb5nL+cbuFohBBCCFEe48eP59tvv+X777/H0dGR+Ph44uPjyc3NBa4+PV6+fDkbNmzgoYce4o8//uDChQscOnSIl156ieeff97CV1A1/XcDmMOHD6PT6ejRo4epTv369alZsyb79u0DYN++fTRp0sRs+nHv3r3JyMjgxIkTpjrX93GtzrU+CgoKOHz4sFkdlUpFjx49THVKMnfuXJydnU2vgICAm7n8m+IX4oK1bdGXjr9+PstvS8LJyy551IMQQohbJz8/n/j4eGJjYzly5AjvvPMODz/8MA8++CBDhw4FoEePHrRv354BAwawZcsWLly4wN69e3nllVc4dOhQpc8dGBjIn3/+SWxsrGmn3Jdffpm9e/cyYcIEwsLCiIyMZN26daade0szc+ZMDh48yLhx4wgPDyciIoKlS5eSlJSEq6sr7u7ufP7555w9e5bt27czderUMvsLCQnh0KFDbN68mTNnzvDaa69x8ODBYvGHh4dz+vRpkpKSShytN27cOGJiYnjhhReIiIhg3bp1vP7660ydOrXEDVPK64033mDBggV89NFHREZGcuTIET7++GPg6kjDdu3a8e6773Lq1Cl27drFq6++WulzjR8/npSUFJ588kkOHjzIuXPn2Lx5MyNGjKhQ0u9aYjM+Pt606/TtIElCCylQh1CQ2pZYBw+iIi9YOhwhhBBClMPSpUtJT0+na9eu+Pr6ml4//vijqc7DDz/M3r170Wq1PPXUU9SvX58nn3yS9PR0Zs+ebcHoq6aSNoCJj4/Hysqq2KhLb29v4uPjTXWuTxBee//ae2XVycjIIDc3l6SkJPR6fYl1rvVRkpkzZ5Kenm56xcTEVPzCb4OMpFxO7b1MzMkUEqIsM7pRCCHuJZs2bcLX15fAwED69OnDjh07+Oijj1i3bp1pPUFFUfj999+57777GDFiBHXr1mXw4MFcvHix2O+finjrrbe4cOECtWvXNo1+a9q0Kbt27eLMmTN07tyZ0NBQZs2ahZ+fX5l91a1bly1btnD06FHatGlD+/btWbduHRqNBpVKxapVqzh8+DCNGzdmypQpvP/++2X299xzz/Hoo4/yxBNP0LZtW5KTk81GFQKMGTOGevXq0apVKzw9PUvcXdjf35/ff/+dAwcO0KxZM55//nlGjRp1U0k7gGHDhrFo0SI++eQTGjVqxIMPPmjafA6ubixSWFhIy5YtmTx58k3dv/n5+bFnzx70ej29evWiSZMmTJ48GRcXlwolOhcsWMDWrVsJCAgwjXC8HRSjrLBdTEZGBs7OzqSnp+Pk5HRbzjF85d/sPJ2Mtc8axuvaM/HFUbflPEIIIURVlJeXR1RUFEFBQaad9oS5sj6jO3GvcqeMHTuWjRs38tdff5mmDn3//feMGDHCbEovQJs2bbj//vuZN28ezz77LBcvXmTz5s2m93NycrC3t+f333+nb9++1K1blxEjRjBz5kxTnd9//51+/fqRk5NDamoq/v7+7N2712wNyZdeeoldu3aVe+2kqvT3cSUmk/hz6TTpWrlpWEIIcafJPYEQt8atuHeUkYQWEuxx9S/FUOBBUvoZC0cjhBBCCHHnXdsAZseOHWZrC/n4+FBQUEBaWppZ/YSEBHx8fEx1EhISir1/7b2y6jg5OWFra4uHhwdqtbrEOtf6qEou5uaTWVj21CTPAEezBGF+biEndsfKzttCCCGEuCFJElpILXc7AIwFbmTnV40pKkIIIYQQd4LRaGTChAmsWbOG7du3F9sApmXLlmi1WrZt22YqO336NNHR0aYRf+3bt+fYsWMkJiaa6mzduhUnJycaNmxoqnN9H9fqXOvDysqKli1bmtUxGAxs27atyu1OnaM3MPRYFD0PnSYiO7dcbYwGI1tXnGDnd6fZ9+u5GzcQQgghxD1Ndje2kJr/JgkNOnfylUT0hQbUGsnZCiGEEOLuN378eL7//nvWrVtn2gAGwNnZGVtbW5ydnRk1ahRTp07Fzc0NJycnXnjhBdq3b0+7du2AqztJN2zYkGeeeYb33nuP+Ph4Xn31VcaPH4+1tTUAzz//PIsXL+all15i5MiRbN++nZ9++okNGzaYYpk6dSrDhg2jVatWtGnThkWLFpGdnW3a7biquJxfQFahngKjEXdtOW/hFajZyI3LZ9Oo08rr9gYohBBCiGpPkoQWUsvt3yRhgRs56nQyknJx9bG3cFRCCCGEELff0qVLAejatatZ+cqVKxk+fDgACxcuRKVSMXDgQPLz8+nduzeffPKJqa5area3335j7NixtG/fHnt7e4YNG8Zbb71lqhMUFMSGDRuYMmUKH374ITVq1GD58uX07t3bVOeJJ57gypUrzJo1i/j4eJo3b86mTZtuajH526GOnQ3bWtcjOq8ATyutqTzfYMC6lIXPFUWh6f0BhLT2xtbBylRu0BtQqeXhtBBCCCHMSZLQQmq42qFgxGi0IsVWy8W4DEkSCiGEEOKeUJ718WxsbFiyZAlLliwptU6tWrX4/fffy+yna9eu/PPPP2XWmTBhAhMmTLhhTJbmotXgct0owv1pWYw9eZFF9Wtyn5tjqe2uTxBmJOWy/sMwOj9Rl1qN3W9rvEIIIYSoXuQRooVYaVR4Ol29yYu3d+PcuWgLRySEEEIIIaqTDy8mcDlfx0/xKeVuc2TzRdKv5LJ//XmMBtnMRAghhBBFZCShBQW6O5CYkU6a1p34i5FAW0uHJIQQQgghqonljYP4+GIC42uWf73Bzk/URWutpln3migq5TZGJ4QQQojqRkYSWlBtTycADAXuJKWetnA0QgghhBCiKjPk5GA0GEzHdmoVLwf74qBRm8qWRCdyJD271D7UGhUdHwvBwdXaVBZ3No1Cnf72BC2EEEKIakOShBZUy/3qGoQGnTtZ+RctHI0QQgghhKiqjIWFXHphIrGTp2DIzS2xzoYrabx97jKPhp3lUl5BufqNO5fOukVhrFsYRn5u4a0MWQghRAV07dqVyZMn39I+L1y4gKIohIWF3dJ+xd1LkoQWdP0Ox3kkotcbbtBCCCGEEJb2559/0r9/f/z8/FAUhbVr1xarc/bsWUaMGEGNGjWwtrYmKCiIJ598kkOHDt35gMVdIe/4cbIPHCBr924KomNKrNPV1ZEe7k48F+BFDRurEuv8l6HQgFqrwsZBi9ZafeMGQgghALhy5Qpjx46lZs2aWFtb4+PjQ+/evdmzZ4+pTmn3CSX59ddfefvtt29pjAEBAcTFxdG4ceNb2u/dYPjw4QwYMMDSYVQ5siahBdV0v5okNOrcydOkkpmch4uXnYWjEkIIIURZsrOzadasGSNHjuTRRx8t9v6hQ4fo3r07jRs35rPPPqN+/fpkZmaybt06pk2bxq5duywQtajubJs3p9bKL9BnZGJTr26Jdew1ar5qEsT1Kw3m6Q2oFQVtKesP+tdzZdCMVti7WKOSNQqFEKLcBg4cSEFBAV999RXBwcEkJCSwbds2kpOTK9RPQUEBVlZWuLm53fIY1Wo1Pj4+t7zfO+Ha53I9vV6PoiioVDLe7XaRT9aCrk03NurtSbfScyk+y8IRCSGEEOJG+vbty+zZs3nkkUeKvWc0Ghk+fDghISHs3r2bfv36Ubt2bZo3b87rr7/OunXrLBCxufXr11f4lVvK9FZxZ9m1aoVjt/tNxwWXLpG5fYdZHbWioFKuJvsMRiMvnIpmSPg50nWlTyV28bYzG0V4ZMtFzh5OvMXRCyHE3SMtLY3du3czb9487r//fmrVqkWbNm2YOXMmDz30EACBgYEAPPLIIyiKYjp+4403aN68OcuXLycoKAgbGxug+HTjwMBA3n77bZ588kns7e3x9/dnyZIlZnEoisLSpUvp27cvtra2BAcH88svv5je/+904507d6IoCtu2baNVq1bY2dnRoUMHTp823yNh9uzZeHl54ejoyOjRo5kxYwbNmzcv8zM5ceIEDz74IE5OTjg6OtK5c2fOnTtX4rUBDBgwgOHDhxe73qFDh+Lk5MSzzz7Ll19+iYuLC+vXr6dhw4ZYW1sTHR1Nfn4+06dPx9/fH3t7e9q2bcvOnTtNfV1rt3nzZho0aICDgwN9+vQhLi7O9Hfw1VdfsW7dOhRFQVEUs/b3MhlJaEEO1hqcbRXSc43E2btx9nwMjZuWf3c6IYQQ4m5iNBrJMdz5pTfsVCoU5daMoAoLC+PEiRN8//33JT7ldnFxuSXnuRkVnVqjKAqRkZEEBwffnoBEpeizsoh5/nkKzp3Hb967OP/7pfR6kTn5bEvJQGcwciYnn9bON771vxSRwr5fz4ECLt6t8ajheDvCF0KIG9LlX91QSWNV9HtaX2jAoDeiUimotaridbUq087ter0BQ6ERRQUarfqGddXq8o+hcnBwwMHBgbVr19KuXTusra2L1Tl48CBeXl6sXLmSPn36oFYXxXD27FlWr17Nr7/+alb+X++//z7/+9//ePPNN9m8eTOTJk2ibt269OzZ01Tntdde49133+XDDz/km2++YfDgwRw7dowGDRqU2u8rr7zCggUL8PT05Pnnn2fkyJGmadLfffcdc+bM4ZNPPqFjx46sWrWKBQsWEBQUVGp/sbGx3HfffXTt2pXt27fj5OTEnj17KCys2Fq38+fPZ9asWbz++usA7N69m5ycHObNm8fy5ctxd3fHy8uLCRMmcPLkSVatWoWfnx9r1qyhT58+HDt2jJCQEABycnKYP38+33zzDSqViqeffprp06fz3XffMX36dE6dOkVGRgYrV64EuC0jOasjSRJaWE13O45dyibZ2oP4C+eAlpYOSQghhLCIHIOB2n8eu+PnPXdfE+zLuEGviMjISADq169/S/q7XeLj4/HyKt+DSUdHSRJVRSobG+xat8aQkYld27Yl1qlnb8O60Dqcz82ntbN9ufr1q+tKk641UGtVkiAUQljU55OuLs8x8v1O2DpenXb6z5Zo9q8/T8OOvtz/TFES7IsXd1NYYOCZ2e1x8rAF4PjOWP76OZKQ1t70GtXIVPfrV/aSl6Vj8Kw2uPs5ABCxN45Gnf3LHZtGo+HLL79kzJgxfPrpp7Ro0YIuXbowePBgmjZtCoCnpydw9QHhf6f8FhQU8PXXX5vqlKZjx47MmDEDgLp167Jnzx4WLlxoliQcNGgQo0ePBuDtt99m69atfPzxx3zyySel9jtnzhy6dOkCwIwZM+jXrx95eXnY2Njw8ccfM2rUKEaMGAHArFmz2LJlC1lZpc98XLJkCc7OzqxatQqtVmuKt6K6devGtGnTTMe7d+9Gp9PxySef0KxZMwCio6NZuXIl0dHR+Pn5ATB9+nQ2bdrEypUreeeddwDQ6XR8+umn1K5dG4AJEybw1ltvAVeTvLa2tuTn51fb6di3i0w3trDaHs4AFBa6cyX59A1qCyGEEKIqMxqNlg7hhoYNG4atrW256z/99NM4OTndxohEZSgaDT6zZhG0dg1ab29TufE/o3GbONrxsJer6fhKgY4/kjNK7VelUuj8RAgdHq1tKtPrDOgK9LcweiGEqP4GDhzI5cuXWb9+PX369GHnzp20aNGCL7/88oZta9WqdcMEIUD79u2LHZ86darCdf7rWiITwNfXF4DExKvLTJw+fZo2bdqY1f/v8X+FhYXRuXNnU4Kwslq1alWszMrKyizeY8eOodfrqVu3rmlEp4ODA7t27TJNbwaws7MzJQjh6nVeu0ZROhlJaGGmdQkL3MnKu2DZYIQQQggLslOpOHdfE4uc91a59tQ8IiKC0NDQW9bvrXRtWk15LV269DZFIirqQNwB3GzcqONaB7g6FVxz3fSonH/+IeHt2dT4+CO0/sVHxGTr9QwJP8/xzFw+alCTx3xKnlp1/fR7o9HItq9PkZGUS79xTU2jeYQQ4nZ79sOrI900VkW/p0N71aRZ94BiGy2NfL/z1brXTUFu3NWfhp38UP7za37onA7F6tbv4FupGG1sbOjZsyc9e/bktddeY/To0bz++utma+2VxN6+fKO7b5frk3nX/p9vuIklX2708FGlUhV7kKrT6YrVK+lzsbW1Nfu9lJWVhVqt5vDhw8Wmajs4OJj+/N+EpaIo1eJhrqXJSEILq/XvDscGnRv5xngM+ju/FpMQQghRFSiKgr1afcdft2o9QoDmzZvTsGFDFixYUOLNdlpa2i07l7i3RKZGMmnHJIZuHMrplOKzT4xGI/FvvU3eyZNcWVLyFDNrRUVTBztctGpaOJXvC2pmch7RJ5O5cjGT5FjZZE8IcedordVorc1/T6s1KrTWarP1CM3qXpc8VKuv1r1+PcKy6t4KDRs2JDs7u+hcWi16feVHYv/999/Fjv+71mB56lREvXr1OHjwoFnZf4//q2nTpqapwSXx9PQ0bRoCV3cpPn78eKXiCw0NRa/Xk5iYSJ06dcxeFZk6bGVldVN/N3crSRJa2LWRhIYCd/LVqWSm5Fk4IiGEEEKUJSsri7CwMNNOgVFRUYSFhREdHY2iKKxcuZIzZ87QuXNnfv/9d86fP094eDhz5szh4Ycftmzw11m+fDnDhg0zjSz88ccfadCgAcHBwaYFw0XV4WHrQYhrCPXc6hHkXHzxeEVRCFj6Cc4DBuDz6isl9qFRKbxfrwZbW9Uj2K74IvslcfKwZeCLLekxsiE16sui7kIIAZCcnEy3bt349ttvCQ8PJyoqip9//pn33nvP7Hd9YGAg27ZtIz4+ntTU1AqfZ8+ePbz33nucOXOGJUuW8PPPPzNp0iSzOj///DNffPEFZ86c4fXXX+fAgQNMmDCh0tf2wgsvsGLFCr766isiIyOZPXs24eHhZT5UnTBhAhkZGQwePJhDhw4RGRnJN998Y9o1uVu3bmzYsIENGzYQERHB2LFjK/3gtG7dugwZMoShQ4fy66+/EhUVxYEDB5g7dy4bNmwodz+BgYGEh4dz+vRpkpKSSk1w3mtkurGFXRtJaCx0IsMqn7j4bJw97SwclRBCCCFKc+jQIe6//37T8dSpU4Gra/19+eWXtGnThkOHDjFnzhzGjBlDUlISvr6+dOjQgUWLFlkoanOLFi3i1VdfpXfv3rzyyitcvnyZhQsXMmXKFPR6PQsWLMDf359nn33W0qGKf7nauLKs1zLy9flYqYum/BqNRtMXN62PD37vzjVrV3DhAlaBgaZjRVHwtylqfyorl/kX4llUvyaOmpI38HH1scfVp2jkYW5mAfFRGQQ19bgVlyaEENWOg4MDbdu2ZeHChZw7dw6dTkdAQABjxozhf//7n6neggULmDp1KsuWLcPf358LFy5U6DzTpk3j0KFDvPnmmzg5OfHBBx/Qu3dvszpvvvkmq1atYty4cfj6+vLDDz/QsGHDSl/bkCFDOH/+PNOnTycvL4/HH3+c4cOHc+DAgVLbuLu7s337dl588UW6dOmCWq2mefPmdOzYEYCRI0dy9OhRhg4dikajYcqUKWb3UhW1cuVKZs+ezbRp04iNjcXDw4N27drx4IMPlruPMWPGsHPnTlq1akVWVhY7duyga9eulY7pbqEYZVJ2MRkZGTg7O5Oenn7bF+o2Go00mPU7eTqo7byASU0+5KEH7/x6TEIIIcSdlJeXR1RUFEFBQdjY2Fg6nCqprM/oZu9VGjRowGuvvcZTTz3FP//8Q5s2bfj0008ZNWoUACtWrGDp0qUcOnTollzL3e5O3jte78eIHzmWdIzX27+OVl18sfj09eu5PGMm3jNn4vbM08Xe1xuNdDkQwdmcfIb6ufNevYAbnrOwQM+6Rf8QH5XB/UPq07CT3y25FiHEvUvuCUoWGBjI5MmTmTx5cql1FEVhzZo1DBgw4LbG0rNnT3x8fPjmm29u63nEzbkV944y3djCFEWhhuvVv7xEOzfio87doIUQQgghxM25ePEinTp1Aq6u7aNWq2nXrp3p/S5dupjtECiqnoTsBOYdnMe6c+vYdGFTiXXyTpwAgwFdbGyJ76sVhSUNa9HJxYH/BZdv0X6VRoVnTSesbTX41HaudPxCCCGqppycHD744ANOnDhBREQEr7/+On/88QfDhg2zdGjiDpDpxlVAsKcTZxPzKDB4kJR0xtLhCCGEEOIuZ2dnZ7awuqenp9mOgACFhYV3OixRAd723nx4/4fsubyHB4NLnl7lNWMGdq1b49CtW6n9NHO045fQOmZlSQWFeFiV/DVBpVLo/EQILXrXxMFVRvwIIcTdRlEUfv/9d+bMmUNeXh716tVj9erV9OjRw9KhiTtAkoRVQJC7A5CIocCNzNzzlg5HCCGEEHe5+vXrEx4ebtr9MCYmxuz9iIgIAq9bx05UTZ1rdKZzjc6mY71BT0xmDIHOgcDVL3qO132pMxqNpH79NS6PPYbKvuTdjTcnpfP8iYt81KAm/b1cSqyjKIpZgjDlcja7fzpDjxENsXcu34YoQgghylae9Qtvx+pxtra2/PHHH7e8X1E9yHTjKuD6HY7zjHEY9AYLRySEEEKIu9m8efOoV69eqe9HR0fz3HPP3cGIxM0yGo3MOziPwRsGcyCu5MXlkz7+mIS57xI9ajRGvb7EOusT08g1GNiZklHu82776iSXIlLZ88vZSscvhBBCCMuTkYRVgGmHY50bBapkslLzcfKwtXBUQgghhLhbXdttsDTjxo27Q5GIW0Vn0HE27Sw5uhyS85JLrOPQpQupP6zCZdAgFHXJOxl/1KAmbZztecrXvVznVRSFnqMa8dfPkdz3RN1Kxy+EEEIIy5ORhFVATberSUJDgRvZ2mwSErJv0EIIIYQQ4tYaN24cSUlJlg5DVJKV2oqlPZayuPti+gb1LbGObbNm1N60EZeBj5baj1pRGObvgValAFdHCv4Un0K+ofSZLi5edjw4vhk2DkU7LKdfyanklQghhBDCUiRJWAX4udiiVhkBDYkO9py5mGDpkIQQQghxj/n222/JyCjfFFNRNVmrrbmvxn2m42xdNmsi15itWaV2LtqR2JCXR8zzY8k5dKjUPpfGXGHiqWiePHoefTnXvoo+kcz3r+9n//+dvy3rZQkhhBDi9pDpxlWAWqXg42xFbKqOOFt34i5cABpZOiwhhBBC3EMkmXN30Rv0TN4xmb/j/iYuO45xzYtPIU/67DOydu4k78QJam/dgsqm+G7FTRxscVSr6OXuhFpRynXuhAsZGAxGMpPzbvo6hBBCCHHnSJKwigj2cCQ2NYUcxZ3kBFn0WQghhBBCVJ5apea+GvdxLOkYnf07l1jH47nn0F28iOtTT5WYIATo7ObI7rYN8LHWlvh+SVr3C8Ld34Fajd1RyplYFEIIIYTlyXTjKiLYwxEAo86djGxJEgohhBDizsrMzCQ4ONjSYYhb6JmGz7DhkQ008WxS4vsqGxv8P/gAu1atTGWFqanFRpVenyDUGYyMPh7FrpTMMs8d3NwTtaboq8aRzRdJS5R1CoUQ4mYoisLatWstHYa4i0mSsIqo6W4PXN28JM9wGYNBpvwIIYQQ4s5ITEzk+PHjhIeHm71E9eduW7RLcWxWLFN3TiU9P73EurqERC4MfIyEt2dj1OtLrLPs0hV+u5LOmBNRpOsKyxXDyT2X2bfmHL++f5i8bF3FL0IIIaoYRVHKfL3xxhultr1w4QKKohAWFnbH4q2q5LOoemS6cRVRy7TDsTsFqiSyUvJw8rC1cFRCCCGEKEl8fDxz5sxhw4YNxMbG4uXlRfPmzZk8eTLdu3c31QsKCmLZsmVoNBoWLlzIgQMHyMjIICQkhBdffJEhQ4ZY8Crg8OHDDBs2jFOnTplGjymKgtFoRFEU9KUkioRlRMRn4GpnhbdTyVODy2I0Gnlp10uEJ4WjUlTM7zK/WJ2cA/vRxcWRvWcPhqwss01OrhlVw4OTWbk85OWCs7Z8XyVqNXbHq5YjwaGe2NiXf9qyEEJUVXFxcaY///jjj8yaNYvTp0+byhwcHCwR1i2n1+tRFAWVynx8WUFBAVZWVhaKStxOMpKwiqjl/m+SUOdOjiaTJJmOIYQQQlRJFy5coGXLlmzfvp3333+fY8eOsWnTJu6//37Gjx9vqhceHk5qaipdunRh7969NG3alNWrVxMeHs6IESMYOnQov/32mwWvBEaOHEndunXZu3cv58+fJyoqyuy/ouqIScnhmRUHePSTvVxIyq5we0VReL3D6zTzbMaLrV4ssY5z//74L1pEwIrlJSYIAaxVKhY3rEUvj6L3E/J1FBgMpZ7b3tmaR6a3oEXvWqayQp1eNssRQlRbPj4+ppezszOKopiOvby8+OCDD6hRowbW1tY0b96cTZs2mdoGBQUBEBoaiqIodO3aFYCDBw/Ss2dPPDw8cHZ2pkuXLhw5cqRCcRkMBt577z3q1KmDtbU1NWvWZM6cOQDs3LkTRVFIS0sz1Q8LC0NRFC5cuADAl19+iYuLC+vXr6dhw4ZYW1sTHR1NYGAgb7/9NkOHDsXJyYlnn30WgL/++ovOnTtja2tLQEAAEydOJDu76HdUYGAg77zzDiNHjsTR0ZGaNWvy+eef3/CzEJYjIwmriIB/RxJisCHZXktkbDLBDd3LbiSEEELcRYxGI8bc3Dt+XsXWtkKbK4wbNw5FUThw4AD29vam8kaNGjFy5EjT8bp16+jTpw9arZb//e9/Zn1MmjSJLVu28Ouvv/Lggw/e/EVU0vnz51m9ejV16tSxWAyifIxGcLDWYK1R4eZQudEbdV3r8k3fb8z+vecW5mKrKZq94tS7l1mbnIMH0dasidbbu8Q+03SFPBZ2Fl9rLcsbB+GkUZdYT6MtKjfoDWz67DjW9hq6Pd0AtVbGLQghihiNRgoLSn/wcDtprFQ3veHShx9+yIIFC/jss88IDQ3liy++4KGHHuLEiROEhIRw4MAB2rRpwx9//EGjRo1MI/IyMzMZNmwYH3/8MUajkQULFvDAAw8QGRmJo6Njuc49c+ZMli1bxsKFC+nUqRNxcXFERERUKP6cnBzmzZvH8uXLcXd3x8vLC4D58+cza9YsXn/9dQDOnTtHnz59mD17Nl988QVXrlxhwoQJTJgwgZUrV5r6W7BgAW+//Tb/+9//+OWXXxg7dixdunShXr16pX4WwnIkSVhF2GjVuDuoSc7Sc9nenUvno4C6lg5LCCGEuGOMubmcbtHyjp+33pHDKHZ25aqbkpLCpk2bmDNnjlmC8BoXFxfTn9evX8/UqVNL7Ss9PZ0GDRpUON5bqXv37hw9elSShNVATXc7Vo/tQEGhASebyk/Zvf7L75GEI0zdOZX3u7xPa5/WxermHj9BzHPPo3JxJvCbb9D6+xerczo7j9h8Hdl6A9l6falJwuslRGUQfTIFtVohtGctPGrcHdPyhBC3RmGBgc8n7bLIuZ/9sAta6xv/f6ws8+fP5+WXX2bw4MEAzJs3jx07drBo0SKWLFmCp6cnAO7u7vj4+JjadevWzayfzz//HBcXF3bt2lWuB4qZmZl8+OGHLF68mGHDhgFQu3ZtOnXqVKH4dTodn3zyCc2aNTMr79atG9OmTTMdjx49miFDhjB58mQAQkJC+Oijj+jSpQtLly7Fxubq0hgPPPAA48aNA+Dll19m4cKF7Nixg3r16pX6WQjLkSRhFRLkYU9yVgYZGndS4qIsHY4QQggh/uPs2bMYjUbq169fZr3Y2FjCw8Pp27dvie//9NNPHDx4kM8+++x2hFluy5cvZ9iwYRw/fpzGjRuj1Zonnx566CELRSZK4mZvPsJi84l4EjPyeKZ9YKX6++rEVyTnJfNDxA8lJgnVLi5ovLzQ+Pqg/veL3H+1dXFgXWgdNIqCr3X5RoD41nGh/wvN0OXpJUEohLirZGRkcPnyZTp27GhW3rFjR44ePVpm24SEBF599VV27txJYmIier2enJwcoqOjy3XuU6dOkZ+fb7Y2cmVYWVnRtGnTYuWtWrUyOz569Cjh4eF89913pjKj0YjBYCAqKsr0IPT6vq5Ny05MTLypGMXtI0nCKiTYw4lDFzIwFLiTnn3G0uEIIYQQd5Ria0u9I4ctct7yKu8aauvXr6dTp05mIwuv2bFjByNGjGDZsmU0atSo3Oe+Hfbt28eePXvYuHFjsfdk45KqLSopm4k//EN+oQEvJxt6N6r4CIz3urzH5+GfM7rJ6BLft6rhT60fvkfRalGVMQWsiaP5SNwDaVlk6Q10c3cqtU1AAzez48yUPNIScwio71ZKCyHEvUJjpeLZD7tY7NyWMmzYMJKTk/nwww+pVasW1tbWtG/fnoKCgnK1t73B/cy1zUeuv5fR6YrvOG9byjIs/51BkZWVxXPPPcfEiROL1a1Zs6bpz/99AKkoCoYy1rEVliVJwiqklvvVHzqDzo28whMYDUYU1c2thyCEEEJUF4qilHvar6WEhISgKMoN1/dZv359iaPwdu3aRf/+/Vm4cCFDhw69XWGW2wsvvMDTTz/Na6+9hncpa86JqinQ3Y4J99fh6KU0utf3qlQf1mprXgh9wazseNJxGns0Nh1rXF3N3k/5/nsUjQbXxx8vsc/o3HyGH48iTafnm6bBdC8jUXhNQV4hG5YcJSUuh54jGxLSSv4tCnEvUxTlpqf8WoqTkxN+fn7s2bOHLl2KEp179uyhTZs2AKZ19/77IG7Pnj188sknPPDAAwDExMSQlJRU7nOHhIRga2vLtm3bGD26+MOfa1N74+LicP33/+1hYWHlv7j/aNGiBSdPnrypJUtK+yyE5cgqwVVIzX83LzEUuKNTXSErLd/CEQkhhBDiem5ubvTu3ZslS5aY7d53TVpaGllZWezYsYOHH37Y7L2dO3fSr18/5s2bZ9oV0NKSk5OZMmWKJAirIUVReKF7CJ890wqNumh0SJ6u8l+01p5dy5MbnmT+wfkljprNDQ8n4e3ZxM96nZxDh0rsw8daS093Z5o62tHepXxTidVqFe41HLB10OITXPKuykIIUV28+OKLzJs3jx9//JHTp08zY8YMwsLCmDRpEgBeXl7Y2tqyadMmEhISSE9PB64m+b755htOnTrF/v37GTJkyA1HB17PxsaGl19+mZdeeomvv/6ac+fO8ffff7NixQoA6tSpQ0BAAG+88QaRkZFs2LCBBQsWVPo6X375Zfbu3cuECRMICwsjMjKSdevWMWHChHL3UdpnISxHkoRVSC33q0lCY4E7eZp0khOKf/kQQgghhGUtWbIEvV5PmzZtWL16NZGRkZw6dYqPPvqI9u3bs2nTJurWrUtgYKCpzY4dO+jXrx8TJ05k4MCBxMfHEx8fT0pKiuUuBHj00UfZsWOHRWMQN0d93ayTpbvO8dine7mSWbkHzRn5GQColJJ397Rp0gT3557F5cnB2LYseZMhK5WKRfUD+KV5bezURV81Cg2lT9VXa1X0GN6QQTNb4+hmYyov7/R+IYSoSiZOnMjUqVOZNm0aTZo0YdOmTaxfv56QkBAANBoNH330EZ999hl+fn6mh4orVqwgNTWVFi1a8MwzzzBx4kTTzsLl9dprrzFt2jRmzZpFgwYNeOKJJ0zr/2m1Wn744QciIiJo2rQp8+bNY/bs2ZW+zqZNm7Jr1y7OnDlD586dCQ0NZdasWfj5+ZW7j9I+C2E5ilF++xaTkZGBs7Mz6enpODndeIrErZKeo6PZW1sAaGP9GmPu+5me3WvfsfMLIYQQd0peXh5RUVEEBQWZdr+rTuLi4pgzZw6//fYbcXFxeHp60rJlS6ZMmcKKFSuoVauW2Y338OHD+eqrr4r106VLF3bu3FniOcr6jG7VvcqcOXNYtGgR/fr1o0mTJsXWDSppnSFRnKXuHa+XnqOj+wc7ScoqYN7AJjzRuuaNG5XgcMJhQr1CUSmljyUwGo2mJKKxsBBjfj6qEnb7vubn+BSWXbrC102C8bEu387M8VHp7F51ht5jGuPkUf6RNEKI6qe63xMIUVXcinvHKjOS8N1330VRFNP22SU5ceIEAwcOJDAwEEVRWLRoUbE6197772v8+PG3L/hbxNlOi4PN1RuuWEc3LkVdsGxAQgghhCiRr68vixcv5sKFC+Tn53Pp0iXWrVtHp06d+P3334utR/jll19iNBqLvUpLEN4py5cvx8HBgV27drF48WIWLlxoepV0nyWqLmc7Lb8834FXHmhQ6QQhQEvvlqYEocFo4LOjn5GSZz7i1ZQgNBqJf3s2F555Bl0pO1Xm6g3MPR9HeGYuP8aVb+Ss0Wjkzx/OkHgxk4O/RVX6WoQQQghRMVVi45KDBw/y2WeflbjN9vVycnIIDg5m0KBBTJkypdS+rl/08vjx4/Ts2ZNBgwbd0phvl5pudpy8nE2KlTuply9YOhwhhBBCVEBKSgpTpkyhdevWlg6lXKKiJAFzNwn0sGfMfcGm44JCAycupxNa07WMVqX79OinLD26lI1RG/m5/89o1eajAAsTr5C5dSv61FTyTpxAW8K0OFu1ijWhdfgqNpkXapVv2pyiKDwwtil/rz1H58F1KxW7EEIIISrO4iMJs7KyGDJkCMuWLTPtsFOa1q1b8/777zN48GCsra1LrOPp6YmPj4/p9dtvv1G7dm2znYX+Kz8/n4yMDLOXpdT2uDrs01DoTnpmpMXiEEIIIUTFeXl58eqrr5a4npsQd5LBYOSlX44y6NN9rAuLrVQffYP64u/gz4jGI4olCAG03l4ErvoBv3fn4nj//aX2U8vWmll1/FD9+3NhMBrZmpRe5pqDDq7W9BjRECubojEN0SeTMZSxtqEQQgghbo7Fk4Tjx4+nX79+9OjR45b3XVBQwLfffsvIkSPLvFmfO3cuzs7OpldAQMAtj6W8arlfXc/FUOBOXuEljHIjJIQQQog7bN26dXz99deWDkPcBL3RyLXbSGfb8q0D+F9BzkGseXgND9cpWkj+v4k9q5o1cb5uoXl9ZiZZu3aV2e/7UfE8cyyKWWfLn7w8eziR//voKBuWhKPXG8rdTgghhBDlZ9Ek4apVqzhy5Ahz5869Lf2vXbuWtLQ0hg8fXma9mTNnkp6ebnrFxMTclnjKo+a/OxwbdO4UkEBWWuV2pxNCCCGEqKyXX36ZESNGWDoMcRO0ahWLnmjOL2M70LVexXbHvJ6tpmjTkNzCXEZvGc2WC1tKrGssKODShBeIeX4sqT/+VGqfDho1CtDYwa5CsWi0Klx97FCrLT7OQQhxG8ieqkLcnFvxM2SxNQljYmKYNGkSW7duvW07GK1YsYK+ffvecAtua2vrUqcv32mBppGEbuRr00lNzMHRTXZ4EkIIIcSdExERYekQxC2gUik0D3AxHadkF7Bs93mm9KiLlabiibZVEas4EH+AyNRIOvh1wMHKwbyCWo11nTrkHjuGbdMmpfYzvqYXPd2dqGtf/nvcOi29cPOzx8W7KLF4/S7LQojqS6u9Oto5JycHW1vZzVyIysrJyQGKfqYqw2JJwsOHD5OYmEiLFi1MZXq9nj///JPFixeTn5+PWq2udP8XL17kjz/+4Ndff70V4d4xtf4dSWjUuZBunce5+HRq1nezcFRCCCGEuJekpaXx7bffMmHCBEuHIm4Ro9HI898c5sCFFBIz8lnweLMK9zG04VAScxLpWatn8QQhoKjVeL/6Cm4jhmNVo0aZfV2fIMzVGxh5PIrxNb3o5OpYahs3X3uz69n+1Sk8aznSpGsNSRYKUY2p1WpcXFxI/HeXdDs7O/mZFqICjEYjOTk5JCYm4uLiclO5NIslCbt3786xY8fMykaMGEH9+vV5+eWXb+qiAFauXImXlxf9+vW7qX7uNC9Ha6w0UFCoJs7RlYtRMdA1yNJhCSGEEOIesG3bNlasWMGaNWuws7OTJOFdRFEUxnerQ+yvxxjbNfjGDUqgVql5uc3LZmWXMi/haeeJtdradJ7rE4QF0dHEv/EmvnPfQevtXWK/i6MT2JGSyYmsXP5u1xC7ckwnvngsmYi/4zlzIIGABm64+tjfsI0Qoury8fEBMCUKhRAV5+LiYvpZqiyLJQkdHR1p3LixWZm9vT3u7u6m8qFDh+Lv729as7CgoICTJ0+a/hwbG0tYWBgODg7UqVPH1I/BYGDlypUMGzYMjcZil1gpiqLg72pD1JU8Em3cSb18AbjP0mEJIYQQ4i4VExPDypUrWblyJdHR0QwePJg1a9bQvXt3S4cmbrEudT3ZPr0L1pqih/E6vQFtJdf4S8xJZMTmEfg7+PPh/R/ibO1crE7cK6+Sc/Ag8W+8ScDST0rs54Wa3lzK0/GEj1u5EoQAtZq402FgHbRWKkkQCnEXUBQFX19fvLy80Ol0lg5HiGpHq9Xe9GA7sGCSsDyio6NRqYpuFC5fvkxoaKjpeP78+cyfP58uXbqwc+dOU/kff/xBdHQ0I0eOvJPh3jK1PZyIupKHTu9OeupZS4cjhBBCiLuMTqdj7dq1LF++nN27d9OnTx/ef/99nnzySV555RUaNmxo6RDFbXJ9gjAyIZNRXx1i/qBmtAmq+PI2sVmxZBVkkZybXOpi6b5z5xL/5pv4vPlGqf3YqFV82KCmWdmZ7Dy8rTQ4a0v+uqIoCqE9zdvkZBSQlZqHVy2nil2IEKLKUKvVtyTRIYSonCqVJLw+0VfScWBgYLl2a+nVq1e13hmp1rXNS3Tu5OmiyUzJk81LhBBCCHHL+Pv7U79+fZ5++mlWrVqFq6srAE8++aSFIxN30kfbzxKdksPCrWf4fkzbCq8BFuoVypd9vsRea4+LjUuJdaxq+FNz2edmZQUXL2JVq1ap/V4p0DH46DlsVCp+aBZMLdsbbzCoLzSw6bNjJF7MpNfoRgQ396zQtQghhBACKje3QNxWgdc2LylwR6dKIOZkioUjEkIIIcT14uPjeeGFFwgODsba2pqAgAD69+/Ptm3bzOoFBQXxxx9/mJWdPXsWR0dHXFxc7mDE5goLC1EUBUVRZMTGPez9x5oyomMgS4a0qPQmAfXc6lHDsWgNwj2xe9h7eW+p9XMOHeL8g/1JeHceRr2+xDrJukIUQKWAaykjCf9LX2jA2l6LWqPg6mN34wZCCCGEKKZKjSQUV9U0jSR0Q6ckEx6RRMNOfhaOSgghhLi9jEYjuYW5d/y8thrbCiVILly4QMeOHXFxceH999+nSZMm6HQ6Nm/ezPjx44mIiAAgPDyc1NRUunTpYmqr0+l48skn6dy5M3v3lp5Iud0uX77M6tWrWbFiBZMmTaJv3748/fTTspvkPcZGq+b1/o3Myv6JTqVZDRdUqor/WzibepYpO6eg0+tY3ns5Lb1bFquTe+w4Rp0OXVwclPLvrb69LZta1SWr0ICTpnxJbCsbDQ8834TUhByzNQqNBiNKJa5FCCGEuBdJkrAKquV29emnocCNbKtsjlyO4XF9Y1SVXFRaCCGEqA5yC3Np+33bO37e/U/tx05b/pFH48aNQ1EUDhw4gL19UTKiUaNGZushr1u3jj59+qDVak1lr776KvXr16d79+4WTRLa2NgwZMgQhgwZwrlz51i5ciUTJ06ksLCQOXPmMHz4cLp16yajDO8xu85cYeSXB+nV0JsPB4dipanYvWdNp5p0DehKSl4KTT2blljHfcRwrIICsW/bFkVVev+eVlo8rYqOtySlsy4xjffrBZS6uYmiUnDzLfqZTLmczZYVJ+gxogEeNRwrdC1CCCHEvUiyTlWQv6stKgUwWhHt6oQ29SQJURmWDksIIYS456WkpLBp0ybGjx9vliC85vopxOvXr+fhhx82HW/fvp2ff/6ZJUuW3IlQy6127drMnj2bixcvsmHDBvLz83nwwQfx9va2dGjiDkvP1aFSwFarRquu+Og7K7UV73Z+l4/u/wit6mpy3Gg0ojOY71Tq2LUrKltb03Hy8uXkHj1aar9ZhXomnYpmdUIqKy5dKXc8e1ZHkhybxf515yt4JUIIIcS9SUYSVkFatQpfZ2ti0/K55ORJ35NHuXAyGd86LpYOTQghhLhtbDW27H9qv0XOW15nz57FaDRSv379MuvFxsYSHh5O3759AUhOTmb48OF8++23ODlVzZ1XVSoVffv2pW/fvly5coVvvvnG0iGJO+yhZn4EuttRz8ex0lPPVYrKbGTut6e+ZcuFLXzU7SNcbVyL1c/84w8S5y9Asbam9u8b0Pr7F6vjoFGzonEQX8Re4fkAr3LH0nNkI/asPkuHR2pX6lqEEEKIe40kCauoIA9HYtPyMRa4oS4IZ+/5ZNojNzhCCCHuXoqiVGjaryUYjcZy1Vu/fj2dOnUyjSwcM2YMTz31FPfdd99tjO7W8fT0ZOrUqZYOQ1hA0xouZscfbD1D+2B32td2r3Bf6fnpfBb+Gen56Wy9uJXH6z1erI5du/Y43H8/1iEhJSYIr+ng6kAHVwfTsdFoZE9aFp1cS59GbGOvpfvQBmZlZw8n4l/XBVtHq1JaCSGEEPcumW5cRdVyv7YuoTtn/fKJvRJJblaBhaMSQggh7m0hISEoimLanKQ069ev56GHHjIdb9++nfnz56PRaNBoNIwaNYr09HQ0Gg1ffPHF7Q7bjJubG0lJSeWuX7NmTS5evHgbIxJV1W/hl/loWyTDvjhAbFrFNxVytnbm6z5fM67ZOAbVHVRiHbWDPTUWf4zn5EmmMkNeHoa8vDL7XhGbxGNh53jpdEy5k/eXIlLYsuIEP71zkJwMua8WQggh/ktGElZRpiShzo2jQQo9Tp4i5lQKdVv7WDgyIYQQ4t7l5uZG7969WbJkCRMnTiy2LmFaWhoajYYdO3awdOlSU/m+ffvQ6/Wm43Xr1jFv3jz27t2Lfxmjp26HtLQ0Nm7ciLOzc7nqJycnm8Uu7h09GnjzQBMfGvk54+9S/mn51wt2CWZs87GmY51Bx8G4g3Tw72AqU67bIMdoMHD55Rno4uOo8fHHaL1Knl5cYDCiAoJtrcs9NdrOyRonDxt8gpyxddTeuIEQQghxj5EkYRVV0+3qlw5jgSepjgquqf9w+mSyJAmFEEIIC1uyZAkdO3akTZs2vPXWWzRt2pTCwkK2bt3K0qVLefvtt6lbty6BgYGmNg0amE95PHToECqVisaNG9/h6K8aNmyYRc4rqhcbrZrFT7bg+hxcTkEhGpWqwjsfw9UpwnP3z+XnMz8zqcUkRjcZXayO7tIlsv/+G0NODrpLsaUmCcfV9OI+VwcaORQlLw1GI6oyEoZufvYMmtkatVoxJRb1hQYwglorE6yEEEIISRJWUY39ry5qbsjzxai3Jld7kb2xCTxobFjphaSFEEIIcfOCg4M5cuQIc+bMYdq0acTFxeHp6UnLli1ZunQpK1asMJtqXNUYDAZLhyCqEZWq6L5TbzDywvf/kJVfyKdPt8TVvuLr+jlaOaKgUMelTonvW9WsSdBPP5J3KgK7FqFl9tXYsWgN00KDkafCz9HD3YkxNTxLvV+2tjX/+rNn9VkSzqfT+9nGOLlXbrSkEEIIcbeQR2ZVVA1XO4I87DGiojCnNscDjRRmnCE5NsvSoQkhhBD3PF9fXxYvXsyFCxfIz8/n0qVLrFu3jk6dOvH777/fMEk4fPhw0tLS7kywQtwiZxOz2B+VQlhMGjGpORVurygKU1pO4deHfqVrQNdS61nVqoVTn96mY11iIlc++QRjGdPe1yWm8mdqFvOi4onL15UrnpyMAs4ciCfxYiYpl7PLfR1CCCHE3UpGElZhnep4EJWUjT67DicDTtB4/3GiT6TgUaP0XdyEEEIIYTkpKSlMmTKF1q1bWzoUIW65ej6OrB7bgeiUnGK7IFdEHdeiUYQZBRlM3TGVyS0n09ij+PR7o15P7AsTyT16FH1KKj6vvlJin496u5JaqMfXWoufTflGONo5WfH4/1oTczKFwCYelbsYIYQQ4i4iIwmrsE4hV29WjNn1yLdSsM8M49DZZAtHJYQQQojSeHl58eqrr8rSIOKuVc/HkZ4NvU3HsWm5/HAgutL9fXzkY/bH72fG7hkUGgqLva+o1bgOfQaNtzduzzxdaj+KojC6hif9PF1MZWey89iSlF7m+Z3cbWnUuWjzoPwcHTu+jSA3S3Y/FkIIce+RkYRVWPva7qhVCoUF7hh0zsS7pJJ55RIFeU2xspG/OiGEEEIIYTl5Oj2jvzrEqbgM0nN1PN+ldoX7mNxyMqn5qYxqPAqNquT7W+d+/XDs0QOVtbWprDA1FY2ra6n9ZhfqGXU8isicfBbVD2Cwr3u54tn1wxkiDyaQlpDDI9NaVOxihBBCiGpORhJWYU42WprVcAagMDuE8CAF+4xTxJ5Js2xgQgghhBDinmetUfFwcz+8HK3p38yvUn3Ya+2Z32U+DdyLdgA/nXKaHJ35mofXJwjzTp/mXM9eJC9fjtFoLLFfrUqhs6sjPlZaurs7lTueln1q4eZnT8fHSt5YRQghhLibSZKwiusU4gmAPjuEKB/wSgzn/IkkC0clhBBCiLtBly5d+Prrr8nNzbV0KKIaUhSF57vUZtu0Lvi7FO0MnKcrfYORG4nJjGH0ltEM3zScpNyS73kzNm3CkJVF9t59UMpu3VYqFe/UrcH2NvXwtNKayhNvsKmJu78Dg19tg1etosRi4sUMCnKLT4UWQggh7jaSJKziOl9blzCnLgZUqHUn+etSioWjEkIIIcTdIDQ0lOnTp+Pj48OYMWP4+++/LR2SqIYcbYqScGExaXR5fwe7I69Uqq/UvFRUigqVosJB61BiHc+JE/F95x38FsxHUavL7M9NWzSFeV9aFm3+PsnS6MRSRyACKKqiNUWzUvP4v4+P8uM7B0m/Isl0IYQQdzdJElZxzQNccLDWoC+0xZDnS6RfASkZ50lLzLlxYyGEEEKIMixatIjLly+zcuVKEhMTue+++2jYsCHz588nISHB0uGJamjpzrMkZOTz7d8XK9W+qWdTfuj3A4vuX4SNxqbEOoqi4PLoI2ZrEqZ8/Q2ZO3aU2ffGK+nkGYwczyp/si8vW4fGSoWVjRp7l/LtmiyEEEJUV5IkrOK0ahXtgt2Aq+sSHg1ScEs5ScxJGU0ohBBCiJun0Wh49NFHWbduHZcuXeKpp57itddeIyAggAEDBrB9+3ZLhyiqkQ8Hh/JCtzrMH9Ss0n34OfjhY+9jOl57di2v7XmNfH1+ifVzjvxDwrvvcmnsOHKPHS+13zfr+PFxg5q8V69GuXcg96jhyBOvtKHv803QaItGLRYWVH5KtRBCCFFVSZKwGuhU5+qUY0N2CMnOCp7JYZyIkHUJhRBCCHHrHDhwgNdff50FCxbg5eXFzJkz8fDw4MEHH2T69OmWDk9UEzZaNdN61TObgrzqQDSX0yo3VTclL4V39r/D2rNrWXd2XYl1bBs3wnXwEzgPfBSbxo1K7UtRFAb5uGF/3RTlN8/GsjI2qczpxzb2Wpzci9ZcPLU3jlWzD5B0KbMSVySEEEJUXZobVxGWdm3zEkNuEEaDhhzNRSKTrjBIZ0CtlTyvEEIIISonMTGRb775hpUrVxIZGUn//v354Ycf6N27t2mk1fDhw+nTpw/z58+3cLSiOtp8Ip4Zvx7Dw8GKzZPvw93B+saNruNm48ai+xexMWojj9V9rMQ6ipUVPrNmYdTrTf9uDQUF6C7FYh0cVGrf+9OyWBpzde3EUEc7mjvZ3TAeg97Akc0XSU/M5cKxZDxqOFboeoQQQoiqTDJM1UBtT3t8nW0wGNXoc4I4FgjqrEjizqVZOjQhhBDinhQfH88LL7xAcHAw1tbWBAQE0L9/f7Zt22ZWLygoiD/++AOAzZs3065dOxwdHfH09GTgwIFcuHDBAtEXqVGjBsuXL2fYsGFcunSJX375hT59+phNxWzatCmtW7e2YJSiOmvo60QDXyf6N/OrcILwmg5+HXi749uolKtfXQxGA39e+rNYves3MUl45x2iBg4kY+PGUvtt42zPG7X9mFTLu1wJQgCVWsXAF1vS+sEgWvauVcErEUIIIao2GUlYDSiKQqc6Hvx8+BKF2SGcrHmGQfuPc/FEMjXqu1k6PCGEEOKWMBqN5Oru/Dpftlp1udcnA7hw4QIdO3bExcWF999/nyZNmqDT6di8eTPjx48nIiICgPDwcFJTU+nSpQtRUVE8/PDDTJ06le+++4709HSmTJnCo48+ypEjR27Xpd3Qtm3b6Ny5c5l1nJyc2HGDDSGEKE2Amx2rx7bHSl00NiFPp0elKFhpKjdeYenRpXx69FOeqv8UM9vOLPa+oaAAXXQ0xrw8VA4l75AMV++xn6/pZVaWUahnXWIqT/u6l/r/BRsHLW0eLBqhaDQY2fFtBA06+uFb27lS1ySEEEJUBZIkrCY61/Xk58OXILseed6/45wext8X0+ho6cCEEEKIWyRXp6fhrM13/Lwn3+qNnVX5b4nGjRuHoigcOHAAe3t7U3mjRo0YOXKk6XjdunX06dMHrVbL4cOH0ev1zJ49G5XqamJk+vTpPPzww+h0OrRabbHz3Amvv/46v/76Ky4uLmblGRkZsmmJuGWu//kyGo3M/PUYF5Kz+WRIC3ydbctoWTJrtTUKCo09Gpf4vsrKioBly8g5eBD7du2Kzm0woKhKT0wajUamRkTz25V0TmblMbdujXLFc/zPWE7tjePskUSGvdMBazvL/DwLIYQQN0umG1cTHWu7A6DL98ZQ6MBlt3SiMmPJTit5lzchhBBC3HopKSls2rSJ8ePHmyUIr7k+2bZ+/XoefvhhAFq2bIlKpWLlypXo9XrS09P55ptv6NGjh8UShAC7du2ioKCgWHleXh67d++2QETibncpNZdtpxIIv5ROdHJOpfoY3WQ0ax9eS//a/U1lBXrzf8eKWm2WINSnpxP12GNk3iDx3dHVEXu1ikE+ruWOp15bH0Jae9PpsRBJEAohhKjWZCRhNeHuYE0jPydOXM5An12H8KB/qH8lguiTbWnQwdfS4QkhhBA3zVar5uRbvS1y3vI6e/YsRqOR+vXrl1kvNjaW8PBw+vbtC1xdm3DLli08/vjjPPfcc+j1etq3b8/vv/9+U7FXVnh4OHB15NTJkyeJj483vafX69m0aRP+/v4WiU3c3QLc7Pjthc4cjk6hbbB7pfsJdgk2/TmvMI+hG4dyX437GNtsLGpV8Z/p5BVfkH/yFAlz38W+UydUVlbF6iiKwgh/DwZ4ueCqLfqaFJmdR5CtNRpVydOPrWw19BzZ0KwsLTGHtPgcApt6VPYShRBCiDtORhJWI51Crt5kFGaHcM4X/BKOEnkqycJRCSGEELeGoijYWWnu+Ksi6xEajcZy1Vu/fj2dOnUyjSyMj49nzJgxDBs2jIMHD7Jr1y6srKx47LHHyt3nrdS8eXNCQ0NRFIVu3brRvHlz06tly5bMnj2bWbNm3dYY/vzzT/r374+fnx+KorB27Vqz941GI7NmzcLX1xdbW1t69OhBZGSkWZ2UlBSGDBmCk5MTLi4ujBo1iqysLLM64eHhdO7cGRsbGwICAnjvvfeKxfLzzz9Tv359bGxsaNKkicWSt/eKmu52PBJaNJX3SmY+M38NJyNPV6n+tkdv51TKKX46/ROp+akl1vGcMB634cPxX7iwxATh9a5PEMbn63j4n0geDTvLlYLS41MUxfT/En2hga0rTrDhk3CO77pUiSsSQgghLENGElYjnet48tmu85BTDwMKmoJT/JWYyoMGI6pSnmwKIYQQ4tYJCQlBURTT5iSlWb9+PQ899JDpeMmSJTg7O5slqL799lsCAgLYv38/7a6bFnknREVFYTQaCQ4O5sCBA3h6epres7KywsvLC7W6/CMsKyM7O5tmzZoxcuRIHn300WLvv/fee3z00Ud89dVXBAUF8dprr9G7d29OnjyJjY0NAEOGDCEuLo6tW7ei0+kYMWIEzz77LN9//z1wdW3FXr160aNHDz799FOOHTvGyJEjcXFx4dlnnwVg7969PPnkk8ydO5cHH3yQ77//ngEDBnDkyBEaNy55zTtxa03/+Si7zlzhSmY+y4dVfCftB4IfwIABD1sPPGxLHrmnWFnhPeNls7KsPXtQFAX7Dh1K7ftMdh4FBiM5egNOmnL+TBjBt7YLGUl5MpJQCCFEtaIYLfH4uorLyMjA2dmZ9PR0nJycLB2OSZ5OT7M3t5BfaMAu+AN6nYxH7zSDmc/0xy/ExdLhCSGEEOWWl5dHVFQUQUFBpoRPddG3b1+OHTvG6dOni61LmJaWhkajwcPDg4iICAIDAwGYNm0af/31F/v37zfVjYuLw8/Pjz179tChhCRFWZ9RVb1XqSxFUVizZg0DBgwAro4i9PPzY9q0aUyfPh2A9PR0vL29+fLLLxk8eDCnTp2iYcOGHDx4kFatWgGwadMmHnjgAS5duoSfnx9Lly7llVdeIT4+Hqt/R4/NmDGDtWvXmhK9TzzxBNnZ2fz222+meNq1a0fz5s359NNPS4w3Pz+f/PyidaEzMjIICAi4a/4+7rSjMWm8+MtRPhnSgjpejrekz+NJxzmedJwn6j1R4mhhXVwcUQMeQZ+RQcCnS3Ho0qXUvi7m5qM3QrCdNXD132eewYituuxJWblZBdg6FI1aTLyYgWeAI4o83BdCCHGHlffeUaYbVyM2WjVtgtwA0GeHEB6k4JF8glN7L1s4MiGEEOLesWTJEvR6PW3atGH16tVERkZy6tQpPvroI9q3b8+mTZuoW7euKUEI0K9fPw4ePMhbb71FZGQkR44cYcSIEdSqVYvQ0NA7Gv/69evR6XSmP5f1spSoqCji4+Pp0aOHqczZ2Zm2bduyb98+APbt24eLi4spQQjQo0cPVCqVKRm7b98+7rvvPlOCEKB3796cPn2a1NRUU53rz3OtzrXzlGTu3Lk4OzubXgEBATd/0fewZgEubJp0n1mC8NildPIL9ZXqL7Mgk6k7pzJn/xy+j/i+xDpqFxcce/fGpmFD7Nq3L7O/WrbWpgQhwI/xKXQ/eJqwjLI3Xrk+QXglOpPV7x1m/Udh6PIrd11CCCHE7SbTjauZTnU82B2ZhCE7hCsBe/BNCOPgyQfolKOT3dSEEEKIOyA4OJgjR44wZ84cpk2bRlxcHJ6enrRs2ZKlS5eyYsUKs6nGAN26deP777/nvffe47333sPOzs6UULS1tb2j8Q8YMID4+Hi8vLxMI/dKoigKer1lkhnXNlLx9vY2K/f29ja9d+0arqfRaHBzczOrExQUVKyPa++5uroSHx9f5nlKMnPmTKZOnWo6vjaSUFTe9UvnnLuSxZPL/ibQw46Vw9vg6WhdRsviHLQOPN3gaVZHrubh2g+XfD5bW3zfehNDTo5pjUKj0UheeDi2zZqV2rfeaGRJdCLnc/P5MzWT5k525YopLTEHlUrBylaDxkrGaQghhKiaJElYzXQK8YCNYMipjdGoJssqhgj3PE7vT6Dp/TVu3IEQQgghbpqvry+LFy9m8eLFZuWFhYUMHDiQjRs3FmszePBgBg8efKdCLJXBYCjxz6L8rK2tsbauWOJKlF9iRj5WGhV2VhpcK/EQXFEUhjYaypP1n0SrLmp/KP4QLb1bmk0/VtkVJfnSf/2VuFdexfWZZ/B55X8l9q1WFP6vRQhfxCYxvqZXiXVKEtLKG48aDtg4aM02ONEXGrCyka9kQgghqgZ5jFXNNPBxwt3eCr1Biz63JseCwD7zGMf/irXI7ohCCCGEKJKSksKUKVNo3brimy9UFWlpaZYOAR8fHwASEhLMyhMSEkzv+fj4kJiYaPZ+YWEhKSkpZnVK6uP6c5RW59r74s5rX9ud3yd25uMnQ9H8u+6f0WgkT1exka3XJwh3xexixOYRTNwxkUJDYYn1dbFXl/DRXLeRT0lctBqmBvqg/jfZZzAaGXEsiu/jksu8H3f1sTebgnzg/87z4+wDxJ9PL/c1CSGEELeTJAmrGZVKoWOdq7uk6bPqcKKmwn2Hd3KoMJ+EqAwLRyeEEELc27y8vHj11VdL3CihKpo3bx4//vij6XjQoEG4ubnh7+/P0aNHLRZXUFAQPj4+bNu2zVSWkZHB/v37af/v+nHt27cnLS2Nw4cPm+ps374dg8FA27ZtTXX+/PNP0xqMAFu3bqVevXq4urqa6lx/nmt12t9gnTpxe/k42+DtVLRhz4q/ohiwZA/nrmRVqr+UvBS0Ki01HGqgUZU8cs9z4gsErvoB99GjTGWGnJwbPohfn5jGxqR0Xjlzibh8XZl1r9EV6Dl7OJGMpDxyMgrKfyFCCCHEbSRJwmqoU8jVJCG59cm1UVDpzxHvlMSJv2QDEyGEEEKU36effmpaS2/r1q388ccfbNq0ib59+/Liiy/e1nNnZWURFhZGWFgYcHWzkrCwMKKjo1EUhcmTJzN79mzWr1/PsWPHGDp0KH5+fqZ1FBs0aECfPn0YM2YMBw4cYM+ePUyYMIHBgwfj5+cHwFNPPYWVlRWjRo3ixIkT/Pjjj3z44Ydm6wlOmjSJTZs2sWDBAiIiInjjjTc4dOgQEyZMuK3XL8ovT6dnxV9RRMRnsvdccqX6eCTkEX588EemtJxS1G9hHgaj+ZR72+bNUVT/jl40GIgZP57YiZMo/Hejm5L093Lhtdp+vBXij5+NVan1rqe1UvP4K224/+n6BDcvGrmo18kSAEIIISxHFsCohjr/myTU5fhhpbdlR9NsGpz9i7A4dzrlhmBtK3+tQgghqgdZKqN0d+KziY+PNyUJf/vtNx5//HF69epFYGCgaTTe7XLo0CHuv/9+0/G1xN2wYcP48ssveemll8jOzubZZ58lLS2NTp06sWnTJmxsikaXfffdd0yYMIHu3bujUqkYOHAgH330kel9Z2dntmzZwvjx42nZsiUeHh7MmjWLZ5991lSnQ4cOfP/997z66qv873//IyQkhLVr19K4cePbev2i/Gy0ataN78hPh2J4um3NSvcT4hpi+rPRaOT1va+TlJvEnE5z8LEvPr0878QJcg4eQtFq0aemofl39Ol/qRWl2PqE53Ly+CzmCq/V9sNRoy6xnbWthoad/EzHunw9P889SO2WXrTqG4haI+M5hBBC3FmSTaqGfJ1tqe1pz7kr2RRmB/N3vePMX/EnXw58mDP742nSVTYwEUIIUbVptVfXCsvJybnju/tWFzk5OUDRZ3U7uLq6EhMTQ0BAAJs2bWL27NnA1QTK7d7ZuGvXrmUmQhVF4a233uKtt94qtY6bmxvff/99medp2rQpu3fvLrPOoEGDGDRoUNkBC4vycrJhQreiJJ9Ob2D6z0cZ3SmYJjWcK9xfTGYMO2J2UKAvICEnocQkoW2TJgT+uApddDTWwUW7ZBuNxjKXFDAajUw6Fc2hjBxyDQY+blCrXDGdPZxAanwOEXvjaN6jpiQJhRBC3HGSJKymOod4cu5KNva6lhQ4neBYYA7a3BMc321L4y7+1WYtJCGEEPcmtVqNi4uLaeMJOzs7+d31L6PRSE5ODomJibi4uKBWlzwK6VZ49NFHeeqppwgJCSE5OZm+ffsC8M8//1CnTp3bdl4hbtbnf55nXdhl9pxN4q+Xu2GjrdjPSU2nmvz04E/8k/gPzTybmcr/mwC0bdQI20aNTMf5UVHEzZiJz1tvYVOvbol9K4rCq7X9eC0ylhlBvuWOqUEHPzRWamwctGYzg26UlBRCCCFuFUkSVlOdQzz4cu8FDDn1wB22N1MxcP8u/mncmG4XMvEOcrJ0iEIIIUSZru0e+98dasVVLi4ut32H3YULFxIYGEhMTAzvvfceDg4OAMTFxTFu3Ljbem4hbsZTbWpy7FI6/Zv5VThBeE2gcyCBzoGm46TcJCbvmMyLrV80SxxeL/HdeeQePUrigvnU/PzzUvtu5+LAllZ1zZJ7X8cmEWxnTSdXx1LbhbTyNjuOOZXCgf87z/3PNMDN176cVyaEEEJUjiQJq6m2we5oVAqpWWqcdF6c903EI+UYF9wzOPFXrCQJhRBCVHmKouDr64uXl5fZ7rPi6hTj2zmC8PrzTJ8+vVj5lClTSqgtRNXham/F0qdbmCXhjsemE52SwwNNyj9673ofHfmIo1eOMufvOfz44I8ljt7znTObhHfn4TVtagk9mLu+/amsXF6JjEVnNPJ7yxBaON044Wc0GtnzSyTJsdmc2B1L58dLHrkohBBC3CqSJKymHKw1tA12Y8/ZZPx5mEssY0dThdoX93E0xpFOj4VgJRuYCCGEqAbUavUdSYiJkkVGRrJjxw4SExMxGMx3Vp01a5aFohLixq5PwuUW6Jm46h/OX8nm7QGNeaZd+dYBvN701tMxGA0MbzS81Om9Gg8P/Oe/b1aW8vXXqOwdcH70kVLbBdhY8ZSvGwkFOkId7coVj6Io9BvfjIMbomj7ULCpXKYfCyGEuF0ki1SNDWoZwJ6zySQl1sHop7C7kcLb3+7k24f7cOZgAo3v87d0iEIIIYSowpYtW8bYsWPx8PDAx8fHLPGgKIokCUW1oVEr9G3sw69HYulXyZGETlZOzO4026zs/879H7mFuQyqO6jExFz++fMkvD8fdDo0Pt44dOxYYt8OGjXz6gVQaChK8OUbDHx0MYHnA7xK3QHZ0c2Gbs80MCvb/k0Edo5WtO4XiMZKHrAIIYS4dSRJWI31aeyD4zoNVzIK8fduSYbtIS56pqI3RHJ8tzWNOvvJU0YhhBBClGr27NnMmTOHl19+2dKhCHFTtGoVL/auz7iudbC3LvqKsyMikQ513LEuJQlXlsScRObsn0O2LhsHrQMPBD9QrI5VrVp4TZpI7okT2HfocMM+Naqie/NFFxJYeDGBbcmZbGwZUq779ivRmUTsjUNRIDjUE+9AWWJICCHEraOydACi8my0ah5u7geAU34fALY3U+gUtoujOXlcic60ZHhCCCGEqOJSU1MZNGiQpcMQ4pa5PkF4ICqFkV8d5OHFe8gpKKxwXx62HoxvPp62vm3pHdi7xDqKWo376NH4f/CBKclnLCgg4d15FCYlldl/Z1dHatlYMa6mV7kf7HvWdKTv801o+3CwWYLQaDSW86qEEEKI0kmSsJp7vFUAAOdiHTHqbTkWpCLkwmHOeOdzYvdlC0cnhBBCiKps0KBBbNmyxdJhCHFbZBcU4mpnRdMazthZVXwClUpR8UzDZ1jWcxlq1dWRiAajgcX/LOZKzhWzutcn+a4sXUrKl19ycdhwjP9Z5/N6HVwd2NWmPv09nU1lB9Ky+OZyEoYykn7BzT1p2SfQdJyTUcCPcw5yPuxKqW2EEEKI8pDpxtVcE39n6vs4EhGfSRAPc5lV/NXYSMDl/YRftKLjY3WwspG/ZiGEEEIUV6dOHV577TX+/vtvmjRpglarNXt/4sSJFopMiJt3fz0vtky5D2tN0biIrPxC4tJyCfF2LHc/1ycAV0Ws4rPwz1h3bh0bHtmAldqqWH2n3r3J2r4Dj3HjUFRlj8mwURe9n28wMO10DJE5+aTr9Eyo5V2u+I5suUjypSwO/BZFYFMPVCpZbkgIIUTlSPaomlMUhcdbBfDWbyfJTWkOnqvY0UThpTXb+aVvVyIPJtCos2xgIoQQQojiPv/8cxwcHNi1axe7du0ye09RFEkSimrPw8Ha7Pid30/xy6FLvD2gEU+0rlnh/lr7tKaBWwMeCXmkxAQhgE39+gSt/gVFU/RVK+fIEfLPncPlscdK3zlZUXjGz52VsUk87ede7pjaPhSMWqMiuJmnKUFoNBoxGoyo1DJxTAghRPlJkvAuMCDUn7kbTxGdZMTLtTbJzudIt4knUxPNid3WkiQUQgghRImioqIsHYIQd4xObyA+PY8CvYEAN7tK9RHiGsJ3/b5DrRRthHIu7RzhV8IZUGeAKQF4fYLQkJ9P3KuvUXD+PIaMDNxHjSqxb7Wi8GyAFyP8PdFeNxrwwwsJtHGxp72LQ4nttFZq2g+obVYWsS+e8B0xdH2qPt5BsrmJEEKI8qkyj5beffddFEVh8uTJpdY5ceIEAwcOJDAwEEVRWLRoUYn1YmNjefrpp3F3d8fW1pYmTZpw6NCh2xN4FeBmb0Wvhj4AeBkGAFc3MGl/7E/CM3NIvJhhweiEEEIIUdUVFBRw+vRpCgsrvrmDENWFVq1ixbBWrB7bgQ61PUzlZxMzKSgsfe3AYv2otKiUq1+jDEYDr+99nVl7Z/HJ0U9KrK9oNLgMGoQ2IACXxx4rR/9FCcK/07KYGxXHo/+cJSonv1zxGQ1Gjmy+SFJMFrFnUsvVRgghhIAqkiQ8ePAgn332GU2bNi2zXk5ODsHBwbz77rv4+PiUWCc1NZWOHTui1WrZuHEjJ0+eZMGCBbi6ut6O0KuMQa1qAHAx1hujQc3BugotT+7jpL+B47tiLRydEEIIIaqinJwcRo0ahZ2dHY0aNSI6OhqAF154gXfffdfC0Qlx6ymKQstaRd8L0nN1PLVsP/0//ouYlJwK92c0GulRswfuNu4MDBlY8jnVatxHDKf27xtQOxdtUpK8YgW5x0+U2X89exuG+rnzlK8bQXbWZdY1nU+l8Oj0FrTsU4tmPQJM5XnZOtkFWQghRJksniTMyspiyJAhLFu27IaJvNatW/P+++8zePBgrK1L/iU5b948AgICWLlyJW3atCEoKIhevXpRu3btEusD5Ofnk5GRYfaqbjqHeOLrbENmngFveqJXKxysq8PzymGOHYwnIznX0iEKIYQQooqZOXMmR48eZefOndjY2JjKe/TowY8//mjByIS4M85dyaLQYESnN+DpWL4k3PXUKjXDGw9n08BN+NgXDWJYE7mGiJQIs7rKdRsD5Rw6ROL787nwxBMUXLpUav+uWg3v1Qtgfr2iZF+arpDBYec4nJ5dajtbRyvaDaiN+t81CY1GI5s+P8aa+UdIjS+9nRBCiHubxZOE48ePp1+/fvTo0eOW9Ld+/XpatWrFoEGD8PLyIjQ0lGXLlpXZZu7cuTg7O5teAQEBZdavitQqhcdaXh1NqMrsBMD2pip67d/B8RpajmyOtmR4QgghhKiC1q5dy+LFi+nUqZPZZgqNGjXi3LlzFoxMiDujRU1X/pjahaVPt8RGW7TO4LFL6RXqx0ZTlGQ/m3qWt/5+i8G/DeZM6pkS61sFBeHUrx8ujz6CVY0aN+z/+p/PhRcS2JmaydTTMRjKOTIwLSGHhKgMEqMzUWss/hVQCCFEFWXR3xCrVq3iyJEjzJ0795b1ef78eZYuXUpISAibN29m7NixTJw4ka+++qrUNjNnziQ9Pd30iomJuWXx3EmDWl5Nbp6N06Ap9CTGS8Eq/zxxjomc3HOZzJQ8C0cohBBCiKrkypUreHl5FSvPzs4udQdWIe42bvZW1PNxNB1vPBZH/8V/Mf3no5Wanuti40K3gG509u9MiEtIiXU07u74L5iPz+uvm8r06enETptOwcWLZfb/Qi1vBvu4MSfEH5VStJtxoaH0WF197BnyZjt6jmyIk4etqTzxYgaGMtoJIYS4t1gsSRgTE8OkSZP47rvvzKa33CyDwUCLFi145513CA0N5dlnn2XMmDF8+umnpbaxtrbGycnJ7FUd1XS3o32wO0Yj+CtXF0Xe3kxFh6O7OOWj4cjmsm84hBBCCHFvadWqFRs2bDAdX0sMLl++nPbt21sqLCEs6tyVLFQK+DrbVCpZ7mHrwYKuC5jfdb6pfb4+n7f3vc2lTPOpxdfvgnzlo4/J2LCBS1OmlJmc9LDSsKhBTTq5FiU21yam0fPQafakZpbazsHVhtqhRQ8F0q/ksPr9w/zy7iHysnUVvk4hhBB3H82Nq9wehw8fJjExkRYtWpjK9Ho9f/75J4sXLyY/Px+1Wl1GDyXz9fWlYcOGZmUNGjRg9erVNx1zdfB46xrsO59MQnwQRj+FPQ0VPvp0Oy91eIgGey7Tsk8gDq4VX29FCCGEEHefd955h759+3Ly5EkKCwv58MMPOXnyJHv37mXXrl2WDk8Ii5jQLYQudb0I8XYwlcWm5ZKclU/TGi7l7sdaXXTP/cXxL/jpzE/8Hfc36wesR60q/j3H7ZmnKbh4EfcxYyqUnDQajSyOTuBUdh4H07PpeF3ysCypcTlotGps7DVY21nsa6EQQogqxGIjCbt3786xY8cICwszvVq1asWQIUMICwurVIIQoGPHjpw+fdqs7MyZM9SqVetWhF3l9W3si6ONhisZBtyMbcm1Vvintp6uhzdz2kvDkS0ymlAIIYQQV3Xq1ImwsDAKCwtp0qQJW7ZswcvLi3379tGyZUtLhyeExTSp4Wy2RuGb60/w8JI9fLX3QqX661WrF628W/FC6AslJggBrAIDqbl8GfZt25jKMjZuJGbChDI3N1EUhZ+b12FyLW+eCygaKRifryOzUF9qu8CmHgx5sx1dn65vSkoW6vQc2XyRgrzCil6iEEKIu4DFHhk5OjrSuHFjszJ7e3vc3d1N5UOHDsXf39+0ZmFBQQEnT540/Tk2NpawsDAcHByoU6cOAFOmTKFDhw688847PP744xw4cIDPP/+czz///A5eneXYaNU81MyP7/ZH45jXi1S7v9nYSsWs77cwZXpf6u++TMvetbB3kdGEQgghhIDatWvfcJM3Ie5lBYUG7KzUqBWF9rXdK9VHbZfafNH7C7OyA3EH+OnMT7zY6kW87b2LtTEWFpL4/nx0ly9j27gxHs8/X2r/bloNM4J9zcqmRcQQnpXD4ga16OJW8uhCOycrs+PwHZfYt+YcZw8nMmhmK1mbVAgh7jFVemur6Oho4uLiTMeXL18mNDSU0NBQ4uLimD9/PqGhoYwePdpUp3Xr1qxZs4YffviBxo0b8/bbb7No0SKGDBliiUuwiCdaX93AJPKSPVaKC1E+Cmf8C+j4zx+c8VDzzxbZ6VgIIYS4V2VkZJT7JYQAK42KRYND2TG9K3W9i5Jtvx+L41Rc+X9OFEUxJd2MRiPvHnyXzRc28/XJr0uur9FQ49OlOD3UH7fhw03l+vR0jAZDmedK0RUSlZtPmk5PDRttuWN087HHydOWJl1rSIJQCCHuQYqxMlt23eUyMjJwdnYmPT29Wm5iYjQa6fvhbiLiM7mveRz/5H9IvUtGXvzFmmlTFzLkgIGhs9tj7yyjCYUQQojq6GbuVVQqVbm//Ov1pU9VFEWq+72jqLj49Dy6L9hJrk7P6rEdCK3pWuE+IlIi+Pifj3mn0zs4WzsDkFuYi4267A1Top97Dn1yCr5zZmNTr16p9QoMhmJrFK6OT6GBgy0NHWxLbacvNKBSKSiqqzHEnk7lnz+i6fBoHdx87St6mUIIIaqA8t6ryAq1dyFFUXi8VQBv/XaSxITaWLlZcbpGATGeubQ5vp2zbt34Z2s0nR4LsXSoQgghhLjDduzYYfrzhQsXmDFjBsOHDzftZrxv3z6++uor03IvQojiVAp0qedJbFoezSqwmcn16rvVZ0n3JWZlc/6ew8WMi7za7lXquRVPAOouXyb38BEM+fkoVlbF3r+elUplliCMz9cx/fQl8gwGNrasS3MnuxLbqTXmk832rT1HQlQGTh623PdE3fJenhBCiGpIkoR3qQGh/szdeIqIuBweqfcUfyR8ya8dFF747XemT+5ByK5YWvSqVWwdEiGEEELc3bp06WL681tvvcUHH3zAk08+aSp76KGHaNKkCZ9//jnDhg2zRIhCVHleTjZ8MqQlOQWFqP4dcac3GBn/3REebeFPz4beFZ6um5KXwtaLW8kpzCFfn19iHa2fH7U3bSTn0GGsg4JM5Vl79mDbtClqx9J3NjYYjfT0cCIuT0czx6KRhEajscxYewxvyP7/O0+rvoGmstzMAlDA1kG+SwghxN2kSq9JKCrPzd6KXg19AFAyO6FRNBwLUnHFKZPQiD8556IibKusTSiEEELcy/bt20erVq2Klbdq1YoDBw5YICIhqhc7q6IxF6uPXGLTiXim/3yUjErsDuxm48b6AeuZ1X4WTT2bmspPJJ8gR5djOtZ4eODUp7fpWJeQyKXxEzjXsxcFl2JL7d/PxorPGwXyc/PaRbsZG4z0PxLJJ9GJ5JeyzqGLtx29Rzc2G1zw97rzfPPqPk7vj6/wdQohhKi6JEl4FxvStiYAG46m0M1/AAC/dlDxxNb/Y19dNcd2Xbr6FFAIIYQQ96SAgIASdzZevnw5AQEBFohIiOrrgSa+jL+/Ni/2roezbdFmIZl5unL34W3vzaC6g0zHGQUZjN06ln5r+nE+/XyJbQqvXEHr54dVUBBaf78bnsNGXfQVcF1iKocyclgSnUihoXxL1ev1BpJiMtHl6XHyKH1tQyGEENWPTDe+i7Wv7U6zABeOxqRhldkbhdUcDlExeFcaDc/t5bxTW8L+iKH9I7UtHaoQQgghLGDhwoUMHDiQjRs30rZtWwAOHDhAZGQkq1evtnB0QlQvDtYaXuxd36zseGw6T3y2j1GdgpjSs26FpyBfyryEndYOK7UVNR1rlljHtnEjgtevozAlpWj3ZJ2O2GnTcRn0GPadOpV63gHerhT8u4+lvUZtKt+ZkkEnF0c0quLt1GoVj73cisuRafjWdjaVnzkQT6HOQL12PqjVMhZFCCGqI/m/911MURQm3F8HgP/7J537/fsBsKaDiie3rGdvPS3Hdl4iL6v8TzeFEEIIcfd44IEHiIyM5KGHHiIlJYWUlBT69+/PmTNneOCBBywdnhDV3q9HYsku0HMxJafCCUKAhu4N+b8B/8fibovRqK6O7zAajbyx9w32Xd5nqqdoNGi9vEzHab+uIXPLFi6/PANjbm6p/asVhSd93XnS191UdiQjm8FHz3P/wQjy9CVPQVZUCv71inZ01uXr+euXs+z4JoLIgwkVvk4hhBBVg4wkvMt1r+9FfR9HIuIzccp9CPiNfQ1UPL47idrR+4lyCCVsWzTtHpbRhEIIIcS9qEaNGsyZM8fSYQhxV3rtwQa0CXKloW/RiLv0HB1bTyXwSKg/6hJG6v2XVq2lplPRKMJt0dtYHbma36N+Z+tjW3G2di7WxrFXTwqiorAKDERlV7SLccHFi1jVqlXm+eLzdbhp1TR3sjObmlwWRQUtetUk8lAiIa28TeXZafnYOGplZKEQQlQT8n/ru5xKpTDeNJowk06+3TEqsLa9iiGb1vFXfWvCd1wiJ0PWJhRCCCGEEOJWUhSFPo19qelelKhbuusc038+ygs/HKlUn6FeoQxpMISRjUeaJQgTsotG8GlcXfGe8TKug58wleWGhXGudx8uTZ6CsZRNSgAe8HThYLuGzKpdtL5hckEh/Q9HsjYhFYOx+NqFGq2a5j1q8tjLLVFrrn7FNBqNbF5+nO/f2E/8+fRKXasQQog7S5KE94AHmvgS7GFPWo4On8KrCyH/2ViFVUE8AXGHuWgLB/6v5IWQhRBCCCGEELeOr7MNzrZaHg2tYSozGIwYS0i+lcTd1p0ZbWbwfLPnTWUX0i/QZ3Ufpu6cis5Q8lJCOYePgKKgsrNDUZX9NdBeo8bTqmjzlc9iEjmYkc3SmETKGvt4/ZTq7LQC0hJyyE7Nx9HNplzXJoQQwrIkSXgPUKsUnu96dTrx/x3JpbVXBwwqWN9OxdMb1/JXQ2tO/nWZpEtZFo5UCCGEEEKIu9uwDoH89fL9dG9QtIbg/4Vf5pFP9vJXZFKl+twXtw+9UY9Or0Or0pZYx33USIL/bz2eL0wwlRUmJxP74kvknz1bZv/janrxYqAPLwX5mhKBhQYj319OJreUdQsdXK15ZnYHHpzQFHsXa1P5/vXnCfsjmoK8wopephBCiNtMkoT3iEdC/fF3sSUpK58g1VMA7GiqwjnzEp5Jx4h207Dnl8hyP8EUQgghhBBCVI6jjdZs1N2y3ecJi0kjLCa1Uv09Wf9JVj+0mimtppjKcnQ5vPrXq5xJPWMqs65TB62vr+k4efkKMv7v/7j8yitl9u+i1TAtyIfu7k6msrWJqUw9HUPfw2dK/Q6htVZTo76b6TgrNZ8jmy+y55ezJMdmV/g6hRBC3F6SJLxHaNUqnusSDMDGfwpp5tECnQZ+a6Pimd/XsDXUlpiIVC6EV+7ppRBCCCGqn27dupGWllasPCMjg27dut35gIS4R60c3oaxXWszrEOgqexMQibbIxLK/RA/xDWEYOdg0/Gq06tYd24d03ZOw2AsebSf84CHcezZE8/x401lxoICsg8cuOF5rVQqathoecTL1SzhmaIrfYSgrYOWLk/Wo357H3xrF62neOl0KpkpeTe8RiGEELeXJAnvIY+3CsDDwZrLaXk00A4FYGsLFT5JUdSNOsSxWlbs+eUs+sLSFzIWQgghxN1j586dFBQU37wsLy+P3bt3WyAiIe5Nno7WvNynPo42RVOFF2w5zcgvDzF/y+lK9Xmf/330DuzNs02fRaUUbSZyKP6QKQFoU68eNT7+CIf77jO1S9/wO9FDh3HphRfK7P8hLxf2tW3IswGeprKjmTk033OCl07HlJhkVGtVNOzkR/dhDU1lep2BP744wbev7uNyZOVGUgohhLg1JEl4D7HRqhnTOQiArUdV1HdtSL4Wfm+tYszaH/irkYYrKbkc23nJwpEKIYQQ4nYKDw8nPDwcgJMnT5qOw8PD+eeff1ixYgX+/v4WjlKIe5fRaCTQwx57KzWPhBb9LGblF5Kn05erjzqudZjfZT79a/c3le2O3c2IzSMYuXlkqSMFC5OuoFhZYde8uXlM+uLn1aoUbNVFXym3JKVTYDSSozeYjS4sS25WAS7edtg6WeEdWDS6MCejAKNBlkISQog7SWPpAMSdNaRdLZbuOseF5Bx62Y8kInU6m1qr6XfwCj33bWFf/R44bLhAvbY+2DpaWTpcIYQQQtwGzZs3R1EUFEUpcVqxra0tH3/8sQUiE0LA1V2CZ/ZtwMRuIdhbF31l+3TnOVYdjOGVfvV55LrdkcsrLisOW40tjT0amyXxCg2FaFRXz+MxZgwujzyCYmNrej/36FEuTZ6C+4jhuA0dWmr/Lwb50s3NCRet2lR2pUDHsGNRjPD3YKC3K6r/JA8dXG0YMLUFuZkFqLVFCcdNnx0jJ7OAHiMa4hPkjBBCiNtPRhLeYxysNYzocHU04Z/HbKnrUo8cKyOrO6oYsnEtp2oUkISeA/8XZeFIhRBCCHG7REVFce7cOYxGIwcOHCAqKsr0io2NJSMjg5EjR1o6TCHuedcnCI1GI9siEknKysf2uiRcRTxR/wm2DNzCqMajTGUXMy7S85eeLD261DS6UOPhgdrB3lQn9YdVFMbFkXf6xlOfWzrbU9vOxnT8dWwyRzJyWH7pCmWNLbx+gEJ2ej7Jl7PJTM7D0a2oL9lkUQghbi8ZSXgPGt4hkGW7zxMRn8n0Vi9wJm0Cm1uq6HUklyEbV7Ot/dM4746lcRd/3P0dLB2uEEIIIW6xWrVqAWAwyDrEQlQXiqKwfkJHtpxIoGdDH1P5+qOX2RGRyKhOQTT2v/GIOxcbF7Pj1ZGrScpN4mTSyVKnCPu8+QZ2bdpg27SJqUyXmEj8a7Nwffpp7Dt1LLXtyBoeaFUK9e1tTHV0BiMzz1xioI8r7Zzti7W1d7Zm2NwOxJ9Px97Z2lS+6/vT5GXpaP1gkHxPEUKI20CShPcgZzstT7erxae7zvHHUQ2d693H7tg/+fZ+FdPWbGdN197Eujry18+RPDSpebnXExFCCCFE9RMZGcmOHTtITEwsljScNWuWhaISQpREq1bRr6mv6dhoNPLJjrNExGdS19uxXEnC/3oh9AUauDXA36Fo7cNsXTZTdkzhkZBH6FWrF2pra1wefcSsXdqqVWTt2oU+MxOHzp1K7d9Vq2FiLW+zso1J6Xwbl8zm5HQOt2+IVQnfN6xsNNRs6G46zs/REbEvHn2hgeY9a1b4OoUQQtyYJAnvUaM6BbFyTxRhMWk83fF59l7ew6G6EFFDz/O/fscnT0zDd1sqF48lE9jUw9LhCiGEEOI2WLZsGWPHjsXDwwMfHx+zB4OKokiSUIgqTlEU3nusKV/vu8jg1gGm8rCYNHZEJDKkbU28nGzK6AG0Ki19g/qala09u5Z9cfuIz4mnT2CfEts5DxiAITsbu7ZtTWWGggKufLAQ50cewaZe3VLP2cDehmf83KlpY4WVqmgFrM9iErnP1ZEGDrbF2ljbaRk0sxVRR6/gHeRUdK1/RJMcm0XzHjVldKEQQtykciUJ3dzcKtSpoigcOXLENJVFVD2ejtY82aYmX+69wC8Hsnis6WP8ePpHvu6hZu4XYfzS7SQnA4JxXX2WgIZuqDWyfKUQQghxt5k9ezZz5szh5ZdftnQoQohKalrDhfmDXMzKlu8+z2/hcSRk5PHuwKYV7rN3YG8y8jOo4VjD9PDAYDTw7oF36V6zO619WmNVsybeM2eatcvcvIWUL78kY9Mm6mz7A0Vd8tqJIfY2vF8vwKzseGYOr5+9jFZRONaxES7a4l9V3f0dzBKBBoORo9tjyErJx7+eqyQJhRDiJpUrSZiWlsaiRYtwdr7x8HWj0ci4cePQ6/U3HZy4vZ69L5jv9l/k7/MpPNPhKTZoNxDlncWfTRTGrv6Wlya/Q91NGRzfFUuz7gE37lAIIYQQ1UpqaiqDBg2ydBhCiFvsgSa+xKXnMbR9oKksNi2XrSfieSS0Bs522jLbe9h6MLb5WLOy/XH7+SHiB/7v3P+x/fHt2GqKj/azCqyFY+/e2DRsaJYgTF7xBQ733491cFCp51QpCg94OGOtUswShN9dTqa+vQ0tnOyKLYOkKNB7dGNO7YujTgsvU/m5I4lEHU2icRd/fIJlZ2QhhCgvxViOLaJUKhXx8fF4eXndqCoAjo6OHD16lODg4JsO0BIyMjJwdnYmPT0dJyenGzeoxt7+7SQr/oqinrcjj3c/x6J/PsAtS2HRpzo+fnwMuU7t6H6xkCFvtcPWwerGHQohhBDitrtV9yqjRo2idevWPP/887cwunvPvXTvKKqvD7ae4aNtkdxX15OvR7apcPsL6Rf45uQ3OFg5MKXlFFP55+Gf09ijMe1826FSis8+yj12nAuDBqFYW1N37x5U9vbF6lzPaDSakoFpukKa7T1BvsHIH63q0tjRrlyxrlv0D5ciUmnVL5C2/avnd1IhhLiVynuvUq6RhBXd+S4zM7NC9YXlTOwWwuojlzidkIk2+z78HX4kllh+a6Mw6v9+YtRr7WgWpWffmnN0e6aBpcMVQgghxC1Up04dXnvtNf7++2+aNGmCVms+umjixIkWikwIcasFe9hT38eRgS2KNijJ0+n59u+LDAj1x8PBuozWEOgcyGvtXzMru5R5iY//n737Do+qWAM4/DvbSza990oNJfSOCAqCImAXARE7Ciqi1y427P2qCCqiIig2RAURpPfeQoCEENLrJtnsZuu5fyxuyKUIioYy7/NwQ2bnzPmSXOKcb2fm2/oOAL+M+IVYU+wx10kqJX79+qHw82uUIKz87HO0zZph6NwJ6ahzCY9eLWh1exgaHshBq53WR51TOK+4EpUkcWloAAblsYnJrkOT8Q8romX3hiIvpYdqWPtdNul9YkjpcGqLXwRBEC40p7SSEGDBggUMHjwYheL8P5vuQns3+JPVB5ny4x5C/TQ8dq2LJ9c+hNYl8db7Tn7qNYwtbYZxxcY6hj2QQUyzoKYOVxAEQRAueGdqrpKUdOKtf5IkkZOT85fHvpBcaHNH4dwlyzKyDAqFNxH3/dYC7pu7jZQwI0smXXTa4xXXFfPxro8pt5Xz+kWv+9o/3f0pRrWRgYkDMWlM3nu73b4tyK6qKvb36QtOJ8k/zkeblnbS+3hkGYXvbESZzmv3UGB3Mq11AleGn9rzyfLZWexaUUCzrhFcMra1r132yEiKY6srC4IgnE/O6EpCgGHDhhEREcHNN9/M2LFjSU1NPSOBCk3vpm4JfLb2EDnldezPSaFdWDu2l21nTh8F4xb/xIJeAygKUrHsiyyue7wzKvXxDyAWBEEQBOHccvDgwaYOQRCEf5EkSRx9rF+AXk272AD6Nm9YWSfLMq/9uo++zcPolBB0zDmAR4s0RvJo10cbtdnddqZtn0ats5Z4Uzxdorxbm48+o1B2OAgcNgzH4cONEoRVX30FHg+mgQNRBTUk/xRHxWD3yFwbGcyvFdVcGtJw3uBPZWY2VddxQ1QIzYzHVnTOuDQevUlNTPOGca01DuY8t4GU9mH0vi4NxXFWJQqCIFxITvm34MGDB7njjjuYM2cOzZs3p2/fvnz22WfYbLZ/Mj7hX6BWKnhksHcr8YxVBxnT3HvGyPK2CoqCnIybP4eFXfyoLLWyeeGhpgxVEARBEIR/gMPhICsrC5fL1dShCILwL+rXIpwf7unFhIsbFoDsyK/m3d8PMOqj9Vjsp/87we1xc3vb2+kZ3ZNOkZ187d/s+4YX1r9AVmUW6ogIop59hvhPPva9LrvdlL/7X4qfnoJt69YTjq9XKng4OYolnVugPyqpN7OgnPcPl7GwvLphTFnmj41z/qF6ulyR3GhnVM62Mmw1DkoP1TRKEFqq7JzihjtBEITzyiknCePi4njyySfJzs7mt99+IzExkbvuuouoqCjuvPNONm7c+E/GKfzDBrQMp3tyCA6Xhx83KLgs8TJkCWb1V3LJhlX4mQ+yKVXLloWHqCysa+pwBUEQBEE4A6xWK+PGjcNgMNC6dWvy8vIAuPfee3nxxRebODpBEP4tqqMSZEatkqs7xjI8IwaTruGc0mcX7OGD5dlUWOwnHcugNnBz+s18cMkHjQqZzN47my/3fsmu8l2+No/swSN7z7+XnU6Cx4zB0Lkzxl69fH3M33zD4XvuwbJ69UnvOy4mjCFhAVwRFuhrW1ddR4/1mbxzqOS417TqGcUVE9rR9cqG4iYet4e5z2/g8yfWUl1mPek9BUEQzjd/aT11v379+PTTTykqKuKVV15h586ddOvWjXbt2p3p+IR/iSRJPDakJZIE87cX0j/iVjQKDbsSJTanStw/+2NWtNFRpZVY9sVeZI94Z00QBEEQznWPPPII27dvZ9myZeh0DdvzBgwYwNy5c5swMkEQmkpquIlXr2nH1BFtfW1mq4NP1+Ty4i97qbY5fe0u96kVuJRlmYkdJjI0ZSj94/v72n8//DsDvxnIRzs/QqHTETLuFhI+m4VCo/H1qf7+Byy/LcG+N6thPJcL9/8VyxwUFsBH6UkkGRoKsPxUZuagzcF+a32jvpkWm/ecQ6WC+FYhxLcK8b1WVWzFZXdjt7kwBTf8XszfW0lBVhUe8RwkCMJ57G8dumAymejfvz/9+vUjMDCQPXv2nKm4hCaQHhPA1R28FcmmLS3jppajAPh8gIrkgoMMXrGIhZ2NFGZXs3tVYVOGKgiCIAjCGfD999/z7rvv0qtXr0bnjrVu3Zrs7OwmjEwQhLOJSqng6aGtub5zHMlhfr7253/OZPBbK/ltz/FX6v1BkiT6xPbh+V7PE6gL9LUvzVtKcV0xZrvZ1ybLMhuLN+L0eJOREY8/Rsgdd+A/+DJfH+uGDezr0ZOChx466X0fSYrig1YJ3BIT5msrtjvptzGLDmv2UOd2H3NNSIwft7zam6ET2jfagrx+fg7fv7GVPeI5SBCE89hfShLabDZmzZrFRRddRFpaGnPmzOGBBx4gNzf3DIcn/NseHNgcvVrJljwzcYqhBOuCKQySWdRRYtyPX2HWVJMZq2bttweoM598q4EgCIIgCGe3srIywsPDj2mvq6s7abECQRAuLH5aFTd1S+DFqxpWF8qyzOI9JewpquHotXUVFjubcitxn8KKu6d6PMWb/d5kRNoIX9sB8wFuWXQLl867FJfHha55c8Lvvw91VJSvj3XzFnA6kY5acQhg/vY77AcP+s4TNKqUDIsIor2/wdcnq64eP6WCaJ0a41HFVGbkl/FlUQVVThdqrZLwhIbqnx6PTFCUEZ2fmqS2ob723J3l/PjOdvZvPHmSVBAE4VxxWknCdevWcfvtt/vOIYyNjeW3337jwIEDPPbYY8TExPxTcQr/kgh/HXf2TQHgzcW53NnmXgDmXqSmTmtnwpxPWNzJSI3bw8qv9jVlqIIgCIIg/E2dOnXip59+8n3+R2JwxowZdO/evanCEgThHCBJEvPv6cVLV7Whd1pD4uznXcVc/cFabv30z8+s1yq19I/vT1JAkq+t0FJIsC6YViGtUClUvvb/bvsvc/bOodpeTdi995D880+E3nqr73VncTFFjz5KzuAhuM3mE96zb7CJPb3S+aBVgq/N5ZF5PbeY+/ceJtPSsDXZ6vbglmUUComLR7Vk7Mu9MAY2bGc+uK2MvN0VFOdUN7pH6aEasS1ZEIRzkurPu3i1atWKrKwsMjIymDp1KjfeeCMBAQF/fqFwzrmtTxJfbsgjv8qGubQ9GeEZbC3dykcDVTz09Rba797I0nbt0W0q4+D2MpLahf35oIIgCIIgnHVeeOEFLrvsMvbs2YPL5eKtt95iz549rFmzhuXLlzd1eIIgnOWCjRqu6xzfqM1qd+GvU9E1ueGcP6fbw72zt9ItOZgbusajVSn/fyifvnF9WXrNUqodDYk3q9PKxzs/xuFx0CWyCwHaALTJyZjrzXhc9ehUOtzVNRh7dEd2OFEFNVQwLnriCVylZYTedSf69u0B0CgUxOsbkn122cO4mDBWmy10CTD62qcfLuP9w6VMSIjg7vhwFIrGK6zbXxJPQLihUcVkc4mVr6duQu+vYczUHiiVf+uEL0EQhH/VKf/GGjBgAFu2bGHTpk3cddddIkF4HjNoVEwe2ByA937PZkLbx1ApVGxOhfXNJe79aiZ7o93khapYMWcfjnpXE0csCIIgCMJf0atXL7Zv347L5aJNmzb8+uuvhIeHs3btWjp27NjU4QmCcA66o28Km5+4hNHdG1bqbcqtYuHuYt5ZegC1ouER9EBpbaNCKH9QKpQE64J9n7tlN+MzxjMgfkCjVYfTd06n15xefLr7U3TNmxH/8cfEz/rU97rsdlP762Isy5cjH3X+oCMvj9olS3zFT4xKJZOSIvk2IxXVUYnA9dUWzC43hqMSfXUuN88cKGRJRQ2BEQY6DEwgIqlha7K51IrWoCI4ytAoQbh8dhbLZmdRVVx36t9MQRCEf9kpJwnffvttUb34AjI8I4b0GH9q7S6+2+DglvRbAPhkkBpDvZlxP8zll25+mKvtrP8hp4mjFQRBEAThdDmdTm655RYkSWL69Ols2LCBPXv28Pnnn9OmTZumDk8QhHOYWqnAoGnYtJYUauQ/l7Xgll5JjVbjTfpqOx2eXcySzJOf6WfSmLgl/Rbe6PdGo/NSs6qysLvtRBgjfG0ltlIeW/UYP2b/CAoF8bNmET55MvqjnmVrfv6Z/PH3UPToY43uIzsbJyxntUnmxw5pDAlrWCCz2mzhvcOlPL4/v1EsB6z12NweEtuEcssrvbjklta+19wuD3vXF7N7RQEuR0NF6KriOg7trhCLLgRBOGucUpKwQ4cOVFVVnfKgvXr1oqCg4C8HJTQ9hULi8SGtAPhyQx4XRdxIgn8CVQYPs/squHLFYkJKslnTQseOZfmUHKxp4ogFQRAEQTgdarWab775pqnDEAThAhAZ4D33fHy/VF+b3eXG6nDj9sikxzQk4X7ZWcTojzfw7Zb8Px13+iXT+W7od/SO6e1rW1+0nvnZ85mzdw6SJKFr3oyQcbewuXwbpdZSABRGPzSJiRi6d/Nd57ZY2Ne1G4fGjsVj9xZoVCkkOgcYCdOoff3CNWpuiApmeETDFmOAUTtyaL5yJxvMFhRKBcaAhu3MSHDpuNa0vySe0NiG6tB71xWz4J3trJzT+Kx3p/3YqsuCIAj/hlM6k3Dbtm1s376d4ODgP+98pL/dLirfnuu6JYcwsHUEi3aX8Mz8LB4b+ji3L76NxR0U9NntYtLsGYz/z/O0Ouxg6WeZXPtIZ5RqceaGIAiCIJwrhg0bxvfff8/999/f1KEIgnCB0aqULH6gLyU19UT463zty/eVsWJfGS0iTb42WZb5aNVBOiYE0TY2EOWR1YiSJJEalNpo3JYhLbm1za1EGRuqIXtkD/ctu49qezVzhsyh9aibCB51E3ZnPbIsI0kStq1b8VitOAsKUWgbEnwVH32E21xNwLAr0aak0N7fQHv/xucw1rjc1HtkXLJMCz+9r/3TgnJmF1VwU3QIo9qGNqqMDKDRKfEP1RHdLNDXZq1xMPM/qwlPMDHiwQ4oxJmGgiD8i065cEn//v19peT/zNHLroVz2xOXt2Ll/nI25lZx8HAbrky5kh+yf+DDIRpenJHH8KU/80u3wYQsrmHDghy6D0/980EFQRAEQTgrpKWl8cwzz7B69Wo6duyI0Whs9PqECROaKDJBEC4URycIAW7tnUTzSBMd4htW6mWXWXjup0z0aiU7nr4UJd7nzZwyCwF6NSF+DUm9ZkHNaBbUrNGYlfWVxPjF4Pa4aRbc8NrHuz9hTtYcxqWPY1SvUST/tABXaWmja81fz8ORm4s+IwNtSgoAzoICrJs3o8/IQBMXh79KyZburci3O/E/qijLGrOF7bU2BjkathPbPR4eysqng7+BkZcm0HFQIvJRlZBLcmuQPTIuh7tRgnDlV/uw1jhoPyCeiMSGMxAFQRDOpFNKEh48ePC0B46NjT3ta4SzT2yQgUmXNufZBXuY+ksm346fwIr8FeSFVLGgi8SYBd+wrEM3tiUbUfyaR2KbUKJSA5s6bEEQBEEQTsFHH31EYGAgmzdvZvPmzY1ekyRJJAkFQfjXpYabSA03NWpzeWQGto5AQkJ9VOLsqfm7Wbm/nNevbceIDt7nT6fbgwSojuoXqg9l7uVzsblsqBUNW4d3lO+gsr4SjVKDJEloU1Kojw1l9C+jSQ9NZ1LHSYTcfjvWTZswdMjwXWdZuZLip6dg7NGD+I8/Ary/M0OzMnEnJqI8UuTzyZRoBoYG0Oao1YU7a23MLa7kt4oaRkd7K0BLComfysw4PTI9WwQwZmoP6qodjb4HOVvLsFTZad07xtdWdriWrHXFxDQPOmaVoiAIwl9xSknChISEP+8knLdu7pHID9sK2JFfzRu/HmZyt8k8uupR5vVR0W2vg/u//Iin7/4PyUVOfvs0k+se64xGd8qLVAVBEARBaAKyLLNs2TLCw8PR6/V/foEgCEITaRHpz7RRnY5pt9hdvtf/sCyrjHu/3MLgNlG8fm17X7ssy+hVjX/XvdXvLfZU7CHW1LDAZXvZdraWbqWqvoqHOj9E4IjhBI4Yzkc7P6Iup47LUy4nxM+Evn17DF06N4zvdHJozM3I9fWk/LoITXw8MToNQ+tqkdwOwLtiMkyj4v4Eb6GVo3fgvXuolK21Vj5olcCwiCD8gnSU2J1sqK6jrZ+O/je3oji7mvCEhgRqQVYV25ccpqbc1ihJuH3JYfyCtMS1ChbPZYIgnBZxwIHwp5QKiRdHtEWpkPh5ZzHa+s50jeqKQykz/TIVnffsoNuWtfzc00R1mY2132Y3dciCIAiCIPwJWZZJS0sjP//PiwMIgiCcjb67uyfbn7yU5kedX7i7sJp6pwfF/x2BddlbK7lu2loOVdT52jRKDe3D2xOqb0iwtQppxQu9XuD2trc3uv7b/d8yfed0iixFBFw+hMQ5X1J1XX9e3/w6yw8vx1Vaiio8HIW/P+qjdtVVTJ/Bvi5dKXv7bQAS9FoeSopkkr+60fjdAo1kmAxk+Bt8bauqarltdy73ZB4mtnkQnQYnotGp+L2ihkyLjeB4P9pcFEtyRpjvGpfDzepvDrDww13YrQ3bnIuyq8lcU4S5xHpa32NBEC4s4m0F4ZS0ivbntt7JfLA8m6fm7+bjWx9jVOk17Ey0s7K1xISvP2Xs463ZmKZFWlFAUrtQ4luHNHXYgiAIgiCcgEKhIC0tjYqKCtLS0po6HEEQhL8kwNA42Tbh4jSuaBfN0SnCCoudvcW1AAQZNb72j1cd5OvN+dzQJY7R3RMB79bkK1KuaDSmLMuMaT2GXeW7aB3S2te+sXgjn+z6hJzYHPr2f5fUXxfhttQxfdcMTBoTlyVehqu0BAB1TEPi0FVYyIH+A1BFRpK6dAmSQsFTqTE4S0pR4vH1UysUtDXp6RhgaBTLXXsOYXa5+a1TM/pc7z1jcX9dPQdtdlIlFS17RFFTbsMvqOGsxn3ri9m1ooCMS+PpMcJ7jrzH7WHbksMERxmJbx2CQiFqCwjChU6sJBRO2cT+acQHGyiqrmfuWit3tL0DgFkD1ShdNUyaPYOl7Q2UmxQsnZVJfZ2ziSMWBEEQBOFkXnzxRSZPnsyuXbuaOhRBEIQzQqGQSAnzIznMz9cWaNCw6L4+/PfGDvjrjjqTMN9MZlEN1daG5xarw0XXF35j5Ix11DvdgHdb8PDUq3mm5zME6gJ9fdOC0ri22bVcFHeRr81j0PDB9g94Yf0LWJwWYl5/nWbr1rK9nYmp66eyIn8F9pwckCSUgYFIioZH8uKnniKrQ0fM330PwNDwQH5pEcvD9WY8Vu8KQIvbQwujjiCVklRDQ9GXH0rNjN55kLdLK+h3UwuuvC8DSZKYkV/Gr+XVGKP0xDQLJDI5wHfNH7vAFs3Y3Sipum9jMVt+PURlYR2CIFxYxEpC4ZTpNUqeH57OqI828OnaXOa2GUZq4M8cMB9g1gAV43/cxMXrV7CgV29GLTSzYs4+Lh3X+s8HFgRBEAShSYwePRqr1Uq7du3QaDTHnE1YWVnZRJEJgiCcOUqFRPNIU6NtyQAPDWrB4DZRpIQ3JBSzS+soqbHjcsvo1A2Vip/4fhe/ZZbw0MAWXNs5DoD2YR0JU7UiNqjhd6fdZWd0q9Hk1uQS7RftvX9gIOv2bWH23tkoFUr69H6I5ps34ago59ZFtxJjimFyp8m4yspAllHHRPvGs23bxuFbb0WTkkLKTwswqZR83yGN2pUrcW/ahLt1K5R+fgSqlbT209HW1BBLldPF4/sLvF9X7zZ06hcPwE9lZnbU2uiCmtRO4UiShHTUKsK9a4o4nFmFzqgmONpb9b6mwsaaeQcIjfOj0+Ckv/cDEQThrPWXkoRms5l58+aRnZ3N5MmTCQ4OZsuWLURERBATE/PnAwjnrN5pYYzoEMO3Wwp44vtMnr/+SW75dQzL02W6Zkrc+/Wn3NK8NatbGVBuLCG5fRipHcObOmxBEARBEI7jzTffbOoQBEEQmkx0oJ7owMZvjqRF+PH9+J5U1TWuLry/1EK5xYFO05A4PFBq4bK3VhLqp2XT4wMA8NP40SN4NJ0DPFRbXb7tzb1jeqOUlHSP7g6AwmCgwqNkffF61KVqnuj2BEnfzMNVXs57uZ+z+LtnGd16NAMtJpRBQaiTE8mpziHWLxaNUkPpC1NxHDxI/CcfY+zenVtjw7jJUkH1lzOpadMW/0EDsbk9XBkeSLXTjVHVEPcvZdXMK6ni0eQoJtyaDkC108W127NJ0mu5vU0IWoOa8AQTVrcHvUKisrCO7K1lmEttjZKEiz/ZTU2ZjW5XphDTPAjwnotot7kw+GsaFWcRBOHsd9pJwh07djBgwAACAgLIzc3ltttuIzg4mG+//Za8vDxmzZr1T8QpnEUeH9KKZVllZJXUsn5vNGNaj2Hm7pl8OFTDq+/beHjWBzx076OkFjpZPjuLqNQAjAHaPx9YEARBEIR/1ZgxY5o6BEEQhLOKTq2kfVzgMe2fjevCgVILcUEN5wOW1trRqhTEBTdONL6+OIt1OZW8eV17hmV4F9HE6NpRdsiPfQ4TPY+sq/HX+DO114tUO8yoFN5Hc1VoKPt3ZJNbk4vT7cR/0CD8Bw2isCqPId8PQaPQsP7G9WiSkpA9brb4lVOVPZ+MsAz8tm6l8qOP8bvoIvwHDSRap2Fa60Tybr2NPI+biMcfR5uczIAQfwLqbXR125FlGUmSyK13sL3WRpHdyQcXp8PF3hjv2XOIBWVm/hMZTs+rU1FrldS53SwuryFer6E4p4aaMlujr79gv5kF72wnPMHENY80VIA+sLkUSYLotED0Jg2CIJx9TvtMwgceeICbb76Z/fv3o9M1nIEwePBgVqxYcUaDE85OwUYNT1zeEoC3luzn8rhbSA1Mxax1M+1yNRlZu7ly+a8s6OVPbb2T3z/fiyzLTRy1IAiCIAj/Ly8v76R/BEEQBC+DRkXb2MBGhU/6Ngsj85lBzLy5S6O+sUEGUsKMJIYafW17i2uZu+kw328t8LWZNCbmLgvlgx+jWLW/3Nc+Pv1hxjd7l2RDd19bpbsGg8pApDESlVJF3Hv/JXXRIr4tX8pjqx5jRcEKtGlpBI0aha1fJ25eeDNPr3ka2ePBumEDdWvWUuappqq+iivDA3lw3zb8Bw+kYMJEABJ1GmamJ/H2lpVUzf0Kt9kMQH69g3qPTGSgjvYD4mndO4Ycq5079xxi9I6DDLo9nYG3pRMS68dbuSXcs+cQ66osSBIYA7U4PTKVTheyLLN+fg4LP9xFxVFnHRYeMPPj29vY9PPBRt/DmnIb9XVO8RwpCP+y015JuHHjRqZNm3ZMe0xMDMXFxWckKOHsN6y9d8vxyv3lPP1DFlOvmsoNP9/AxlQXy9tI3PH9bDa2asPv7UJQb6kgc3URrXpF//nAgiAIgiD8axITE0+6Fcztdv+L0QiCIJx7FArpmArLr17T7ph+yaFG7huQRoix8Qq63Io6Dlfa0Kkb1u/klal48QcL7eIK+WG8d2tvemg6l5k+psxiJbOohpZR/gAkmVrSJsBDrDEFQ8tOGDp1oqBkM5sXvkmptRRkmbgZ03HmF/D0oU9Ysvp3Huv6GAPMZpAkqiNNfLnxFWL8YrihxQ1kzZhGsd2OulsnjAEBfN0+lbwv5+B+8T+UDr6M8IkTccvQNcBIp20bMerqCUlPR6FT83tWDeuq6xjQKoE73rkIZ72bzDobl27aR7xOw9SUAHRGFf6hOuYWVVLqcJJcUE/enkoUKgUeWUYGlJLE/Le3UV1qY9gDGcQ0825jLj1Uw74NJYTFm2jeNdL3/XLa3ag0CrG1WRDOgNNeSajVaqmpqTmmfd++fYSFhZ2RoISznyRJPD+sDTq1grU5Few4aGR8+/EAfDJIg9ng5NGZ77EpWcXBcBWrvt5PdZm1iaMWBEEQBOFoW7duZcuWLb4/69ev54MPPqBZs2Z8/fXXTR2eIAjCeSMtwsR9A5oxqntio/a5t3dn3p3dfUk/8D5rNY8wkXZUQRWAJXtL+GlHKXV2l6+tme4K1qy7lLcWNLypk+ifSE/di4TW3U1OpQ1jly4EjhhOrcODxx6KSRlO6J130nz7NqwjL2PWnll8nvk5ssNBwPBh+PXty6P7X6fLF134NfdnTKUluA8dosxSwttb3uZA8c98n5HKNW++xKGbRlFbko/L4+Lu+HDezd5Bi/F3YJ75MTo/NSV2b+XoPts30i3NzPAJ6fiH6PmyqILnc4owR2npN6oFrXtHs63WStyy7Vy6MQu30wOAX5CWr4srefdQCRtzq9i+5DAHNpfi9MjUuNzIssyc5zYwbcJySnIb8hSlh2pY+102+zeVNPoe2iwO39iCIBzrtJOEQ4cO5ZlnnsHp9P5jlySJvLw8Hn74Ya666qq/HMiLL76IJEncd999J+yze/durrrqKt+73sc7bPvpp5/2Vmc66k+LFi3+clzCicWHGLh/QDMAnl2wh4Ex15MRnoFN5ea9KzU0y8th5MIf+KmniVqPh19n7MbtEr+QBUEQBOFs0a5du0Z/OnXqxG233carr77K22+/3dThCYIgnPeiA/V0SgzGqG3Y5HdJqwgW3d/nmBWJ/xnUkkcua0FyWEPy0Opwo1crCTM1nAEfog9h9yE9K3ZLmK0NBViujn2MupwH+fg3b1+FRkNYUCwJ9Q/jLhpLVqWdqKefJm7aBxTWWrGYEykzawi+aSQJn82idlA3pu+czie7PkGur0ffti3q+Hge3fcaGZ9lYDMvo09tJfKunRSUZvP4qsc5kD+bg73bMO7dV8kbewv7Dm8lsyKTiwKUPLV9DRn3jCFsy7cktgml3OHCA/T75XuuaHOIW5/LwD9EzxeFFby65yBmTT3t+8eS3D6UHbVWmq3cSY/1mVhrvIk/rV7Fu4dKmJx1mBUHK9iy6BD7N5ZgcblZb7awv66eeS9u4oN7l1F8sNr3fSk8YOa3mXvYuSy/0fe7OKea0kM1OO1iVb1w4Tjt7cavvfYaV199NeHh4dhsNvr27UtxcTHdu3fn+eef/0tB/LGFuW3btiftZ7VaSU5O5pprruH+++8/Yb/WrVvz22+/+T5Xqf5SEWfhFIzrlcTPu4rZftjMQ/N28eJ1z3HNgqvZE2NjQWeJUb98x7r0DBZ3juWKNbWs+yGHnlelNnXYgiAIgiCcRPPmzdm4cWNThyEIgiAcZUjbqGPaRnSIZUSHWBz/txjjzr4pFFTZiAtuKLRid3kwaVWE+jUkFBMDEqmuzuFwpQ2ro2GF4sikp7h//S5+XK/n5g5hqMLCCDfnYFj6OIdydWxsb6Xr558BUPD13dQXD2V9lo7LRgxH3yadHX5mvt32EZGGIm5IHIWhfXtc5ipeO/gpK9es5pkez9BTdlCRl0eOJY+bvx1CjF8s2/q8Q9W9oyhxuchq4aGooJYMbVsu376eDjOnoxk6mLqLxrC3qgKA+955me6BfpgmTsYUquO37QWU7MnkorJSOqSEEdCmBZl19Vy59QDpDhsjaywgK9EZ1UzIPMSWGiujbBpc64px1rsJ6x7B9PwyIjVqtDP2U1tZz1UPd6Q2XIvd48G1r5ad32UT2zKYfiMbFiNt/TUPp8NNyx5RmIK9tRtsFgc1ZfXoTWr8QxsXtxGEs9VpZ88CAgJYvHgxq1atYseOHVgsFjp06MCAAQP+UgAWi4WRI0cyffp0nnvuuZP27dy5M507e6sj/ec//zlhP5VKRWRk5Alf/392ux273e77/HjbqYXjUykVvHldewa/tZK1ORX8uj2chzo/xJS1U5jTT0X7g95tx7c/8gLJcRpYnEds8yAS0kOaOnRBEARBuOD9/5xHlmWKiop4+umnSUtLa6KoBEEQhNOlUTXeJDiya8IxfYZlxDAsIwa3p3ExkMcGt6Skxk5iSEOhFX+tjjYxATSLCPC1JQcmo5FzKbHZUCkb7jc27VEmbt5OVp4RzZAENAkJJFoK8F+uIidXzdauVvodSSjWffMilr2D+LBGy+U33ohfr57sUBaxf4lMvhxLcVo1EcOH4TZX80P+JtZk13N96yiuNGooB0qCJW797j4CNMFsv/oLKu/dTJ3LxYfD9KzduYWeqeNoV1xO8oz3cQ+7lJnu1dRvN5Cov5YXJ9+O0VJL8cfPsMS8kANVgUSv3U/H339BkdwOc5shbKnI5b95ddz76w/0zrGRH9UbnVHNQ9mFbNufwwOF5Ui5DupjM9hXV88VW/bT3GXn2t9qqK72kNA6hNl1NeystdGzGio/zSameSAX39uOb0uqCFAp8XyaQ22FjYG3peOKNWBze1CU2Mj5vZCQGCMdByX6vrfZW0px1LuJaxmMX5A3ueuod2GpsqPRqXxtgnCm/OUldr169aJXr15/O4Dx48czZMgQBgwY8KdJwlO1f/9+oqOj0el0dO/enalTpxIfH3/C/lOnTmXKlCln5N4XoqRQI09c3opHv9vJK4uy+H78JfSNXcby/OW8O1zD8x8VMG7+XGaOGEVUpXcp9/WPd8EYKH6hCYIgCEJTCgwMPOagd1mWiYuLY86cOU0UVdP473//yyuvvEJxcTHt2rXjnXfeoUuXLn9+oSAIwjlGqWj8e39Q+rErFPu3jKB/y4hj2mff2g2z1UlyWENCsVmEP/f0SyUqUOdri/GLIT2yELVUS6C+obDLdc3GsHzjFvzUAagjwlFHhJPusBC7TEtOqYsqu0z7Z58FIGXpN/xeomO5R82USfcQcsftZJbtQp6ZRlFtLBsOlNPnxRdx11STbd5F9paJWHMlHmwbQnXfvhS3iOO79RUo7FF8cG0ofi4nMjCvbCO/rtDRPro1D+vBcCALa5sYxm97GcWmMK7tPokhS35BW1PN4jHw6nezCQ7rzcX77HT99AMcPbvxbOhy3Iv0VPuNZdKzkwgvK2XxnTfxYf4GNkvNUG4yk/btbGoNSSzvmk5R1gGmlCTzwLdf0iOrgrykTuysdTM3U8+OvWU8vH0r8l4lRRd1RttNx+Xbiui1bw83bqom3xXNoAd7M7PGzMqCYi4vNuP4oRT/FokMntyBKdmFGB0O2i0sp7zQxoBb0imK07Gvrp6EOpnK+XkERhjoe1MLVldZ0CgktFsrqSu307xbJOpIPTaPB6XVTdmuSnRGNSkdwn0/s4oCC456N4HhevQmb+Edt9uDvc6FUiWh/b/CPcK57bSThCc6n0aSJHQ6HampqfTp0welUvmnY82ZM4ctW7ac0e0sXbt2ZebMmTRv3pyioiKmTJlC79692bVrFyaT6bjXPPLIIzzwwAO+z2tqaoiLiztjMV0IbugSx5LMEpbsLeWBr7bz0bgn2V52FbkhZr7upeD6pb+wPj2D+X1bMWqhmcWf7GHoxPYoFKIClSAIgiA0laVLlzZKEioUCsLCwkhNTb2gjmuZO3cuDzzwAB988AFdu3blzTffZODAgWRlZREeHv7nAwiCIFwg4oINxAU3bmsZ5d+o8MofZozpfEzbxS3CWflQv0Ztfho/Hrq0LcXV9TSLbHhmH9Z8ACUlOUQFeJOPCo2G1jEd6B3nYUueGZNeTcDlQwC4cdc+1m7ej0Gpx3/QxfgPGojBVkHctJXkVKqpsTm5aOsW5Pp6Itd9jdMcwkG3RJsxvbC3bEaBnxMW76auphndZA0xN92I21xNucNB5cEbqS/08G13DRUbW+FqHse2fRpcNRncd4mJcI93q3deopV5P3VDrZZ5OU6JsbQIw8VJzC4vwDk3iDZtPfTevY3AokLKbgnjrh9ldNoQuqgttP7+Cxxp6VynyMT1XhrOxF7c8PXnJB3OZcnYi3j9pw0YDIlEuzV0eu8lLP7xTOgcwyvToskP68ebs96h+YEsNl/Sn1vX/I7bLx7qTLw+/TW0ijBevbElH80L53d1R+79+XsuzcxmTcv2zHIGU+kfTU5tAC/Mn4uqWMO3l6UQb43m3eo4rty8iet35rHVPw7bFRGURUaw0+zPbauWotlqY2OXRLqPTGVGRSAtsrIZsCmPAw5/AofHUt8sgm1mA322bUG1xkxRy3j6jW3O1xVKTAWlZOwuobJaTfjF8XhSg9hdI5F06BDqTVXUR0XS45o0fq+qR662kHioBlslRLePQZVoYp+lniBLHdKeGiSDkda9Y9hrrcfpduNf6cRZ4SA4yoguQk+504VOknDmWpAUElEpAdR4PMiAVOfCZXGi99OgD9DglkEpgaWiHkkhYQzU+nIWLqcbt0tGqZJQqRvyXbJHRjqP8hqSLMvyn3drkJSURFlZGVarlaAgbynyqqoqDAYDfn5+lJaWkpyczO+//37SRNvhw4fp1KkTixcv9p1FeNFFF9G+ffvjFiT5f4mJidx3330nLXQCYDabSUhI4PXXX2fcuHGn9DXW1NQQEBBAdXU1/v7H/rITjq/cYmfQmysotzi4tVcS3doUcv+y+1HI8PRnLiJqAxj32Iu0KNUzYHMdXYcm02lwYlOHLQiCIAjnHDFXObO6du1K586deffddwHweDzExcVx7733nvSImz/8Gz+Pm195DYvD+Y+MLQiCcK7zAC5ZBUhopIbflRa3Hqesxqi0+dodsppyRxBKyU2EpsLXt8gRSp3bQLi6An9VHQB1soocawIqyU1LQ673XpKH/fVR1DpCidcWE66pQJI9lEkecmszkHDRybALpcuBSwk7PTHY7THEaItIcGQjud0cClKRX90T8NBVswltbTVOjYoNhnjcthTCNSU0r9mOyunkQIyBwzXexGp75TpCyotwa7SsDE/BXdecQE0JbSs2oLXXsz8xkFzLJQC0VG4ktiQXl0rLyphUXHWtMKpLaV+1EUO9lew4f3Js/QEViaotpBVn41KqWRWXgtPSBp26nPbVGzBZ6zgY7c8B50Uga4nT7KBFYRYeScnKhBQclnZoVRW0r9mIv7WWQxH+7JN7g8dAtHYPrQt2I0sKViUkUW9pi0ZVTbu6TQTW1pAfZmKvqjO4jIRrcmlbsBOA1UnxWK3paBU20i1bCa41UxhiIlPXBtnlT6i6iPb52wFYlxSLxdYCjcJJq7odhFZXURpoYpepOTgDCFRX0jZ/JyCxNS6CWmcaKjw0s+8m3FxFlcnITv8UPK5AApS1tCjNQvJ42BsVgtmVjAqZVOdewsxV1Oh17A5Mwu0OxCTZSKnMRun2kB0WgFmORylLJHn2ElpdjVWtYU9QAm5XECbJTlz1IdRON3khRqqIQ+lRkCBnEWSpxqPQYIpN5KNJJ6698Xed6lzltJOEX375JR9++CEzZswgJSUFgAMHDnDHHXdw++2307NnT66//noiIyOZN2/eCcf5/vvvGT58eKMVh263G0mSUCgU2O32k65GPNUkIXjPMhwwYABTp049pa9RTLz/uiWZJYz7dBMAX9zalZ+L32B+9nwiLSpemlbP7uR0Hrr3Ea5dXUfzYhfDHsggOjWwaYMWBEEQhHPMmZqrTJ06lYiICG655ZZG7R9//DFlZWU8/PDDfzfUs57D4cBgMDBv3jyGDRvmax8zZgxms5kffvjhmGuOd551XFzcPzp3THtsJk532D8ytiAIgiAITUurKiLruVv/sfFPde542vtIHn/8cb755htfghAgNTWVV199lauuuoqcnBxefvllrrrqqpOO079/f3bu3NmobezYsbRo0YKHH374lLYrnwqLxUJ2djajRo06I+MJJ9e/ZQQ3do1n9vo8Jn21nXnjJ7GxeCNFFPHhEDUTvtvFjYvm88Olw4j4yczij3Zz3eNd0BnFOQaCIAiC8G+bNm0as2fPPqa9devWXH/99RdEkrC8vBy3201EROOztyIiIti7d+9xr2mK86xDFUVYpcp/9Z7CmSABp7UmQxCEM+6PraDykf9V4JE1gISMdOSjwvt3WUIp2VFK9Uf6qrB7Ao+8dqQP+K5TKyyoFTUggVtWYXPFgCwdNXZDX5XSjEZdCoBHVlFvP3GBMIWyCrW2EBmQZSVOW4sT9pVU1Sh1+b57uS2tTtxXWYNSf9j3ucvSElAcv7PSgkp/6Ki+LYAT5GkUVlSGg0f1bQac4BlfqkdlzG7oW5cGsuYEfR2ojPuP6psK8olqG7hQ+WUd1TcFZN0J+npQ+WU29LUmg+f/K1A3/O5W+e05qm8ieAxHvf7HR+/PW+m3F+lIm8sWD27DkVflo3of6WvchyR5ULohzN1QXbwpnXaSsKioCJfr2OBdLhfFxcUAREdHU1tbe9JxTCYT6enpjdqMRiMhISG+9tGjRxMTE+NbAehwONizZ4/v7wUFBWzbtg0/Pz9SU1MBePDBB7niiitISEigsLCQp556CqVSyQ033HC6X6rwFz0+pCVrsys4WF7HSz8f4qX+LzF24VhWt3DTup3ELT9+zY7UFszvk8bIX6tZOiuTy+5sc8zB6YIgCIIg/LOKi4uJijr2wPqwsDCKioqaIKJzQ1OcZ7322Uf+0fEFQRCagscjY3O6qXO4sDncBBk1+Ou8yaXi6nrWZJdT53Bjc7iwOTxYnS7qHW5sTjfD2sfQIzUUgG2HzTzy7U7sTu9r9Uc+2p3es+fG9PGjf1sJm8vGnoJ63vrxxKmQ1NidxEZupt5VT7UrhMxd15ywrxSwBVXEL95PHEF4sk/85prkvw9lpHd1uuTyg/2P+15TeNyoZA8OtRskN0r/TDSR8wGQPWrcB+9FodCidhuILS1G5XGzL1YGyYPSkI02bKm3rwz1BSNR4ofGE0OXzB0oZA9rW4JH4UGjLiLAsA6lBzxKI5XKS/EoglAqUhiyeikSsKaVB4dKRieZCfXsQuORsGvCKDJZsGoDsRlaMnTlb8iSxJ54C06VB7VkI8h5mGCnApsUQqHBTpXBRFVgOpet+x0ZicOhVbh1MmrJTYBURYQNbLZAio0KzCY9FQHpdN+xBkmSKDNVogqU0Cgl/NR2ourAbfWjMtRMpVZNhSmNFllbkSSJam0Vhkg1arUCo0ZFZB1IFh31sVoqVVCuiyMqNxOFQkGdshb/GDVanRadOpmQOg9Kiwo5OIoaBVSqQzEV56FUKrEp6giONaDTqVEoehNglVFZJJSBIViBSoUeTY0ZhULCIzkJjDSgM2pQSO0w2GWUtW4URhNOwCyDyu1CIYFSocAUqkenVyNJA1E5PCicMgGhhj/51/LvOO0kYb9+/bjjjjuYMWMGGRkZAGzdupW77rqLiy++GICdO3eSlJT0t4PLy8tDoWjIahcWFvruCfDqq6/y6quv0rdvX5YtWwZAfn4+N9xwAxUVFYSFhdGrVy/WrVtHWJjYnvFvMWhUvHFde656fw0/bi9kQMv2TOgwgTc2v8Eng9SkFjl48uN3GPfYVFa00aPYXs6u5QW0uSi2qUMXBEEQhAtKXFwcq1evPmbetnr1aqKjo5soqn9XaGgoSqWSkpKSRu0lJSVERkYe9xqtVotWe6KVDIIgCBcWs9VBdlkdtfVOaupd1NY7qT3q49UdY2kbGwjAin1lTPlxNxa7izq7Nzl49AFozwxLo3+6EYvDwvKsMl74ofqE991du5jwfTux1NdQVhtMbtGIE/b9MvMbvi37HQB3fQSSaiz+OgOhTgWK4iKQnOREu0HhIt+zi5I670pyj6scdfAqwk1JJJUbSdm4BpfSzbw+MkguVJpCdHYZjQtUOgVBSfOp16YRVRDAuB+/Rla4+eJiGY3Ljb7cir5URu1RYglMwBa4lJyIRPzsIdzx/ZfY1So2pdaBpEKuV+IpSyOlRkmVJo16Qz0bm/lT4a+nzeZiXAoVUdhQaXUopGgU5nH0ypGprg3FoQ1nW7KB/GAPbnVzXJKSLkoVKZ2iUKk1qNQTSN1US12BG4VeR56/gmKjAnnEWFQKiQFKBZ0viUejkFBJEoY91TjK7CjVCiwqqFdJaFq1QqtScJVKSYuO4agVCtSShLnIgsPmRqlUoFBKKFQSyit6eD8qFRj8Nb4iH7JHBon/WyzU9zT+n9ftNPq2PY2+J17heawz8Abh/y9gbGKnfSZhcXExo0aNYsmSJajV3gy/y+Wif//+fPbZZ0RERPD777/jdDq59NJL/5Gg/2niTMIz463f9vPGb/sw6VT8PLEXUzdPZmXBSqJrVEydXs+2Zhk8dtdkblxRS2qFm6sf7kRY3PErUAuCIAiC0OBMzVVefvllXn75ZV555RXfm71LlizhoYceYtKkSTzyyIWxeq1r16506dKFd955B/AWLomPj+eee+45awqXCIIg/BNkWcZid2G2Ogk2ajBqveuI9hTWsHBXEWabk+r/+1Njc/LSVW3p39J7TMMP2wqYOGfbCe8x5iIF6ckWqu3VbM918+PqEywOkexoIxagCdoIgLs+CnvpZaQGx5NWr0VxKAcbFpbHlYPCicq4H6W+wPt1uHW4bXH0T7qEi/eqMXz5OTVGJy9d40bvdqJ3OtE7PeicENCyDabQKPbRmsQVVYyf/RE2DSzsoEAhq0FSo0BNuKzH1XYQLv8U5oSaUFZU0X/jamxaDXVaBU6VAYdWT71Gy/A9MmXqeBzaQL7raqQo0EmY2YwsaVBqtYQnhqDRa9Gr1fSYV4ylyArAvmg1FSYlGreMTqEg0E9DvxGp6JUK9AoFtYsKqDc7UGsVuLUKNFoleo0SjVqF1qiiVc+GN/TK8mpxuzyoNApUaiVKtQKVRoFSpUClUfqq9AoXpn+scMkf9u7dy759+wBo3rw5zZs3/2uRnoXERO/McLk9XDNtLVvzzHRNCua/o5pz/U/XUmItoWcmTPjexftX3cSii4Yw7qcqYkw6rnm0M1r9aS9wFQRBEIQLypmaq8iyzH/+8x/efvttHA4HADqdjocffpgnn3zyTIV71ps7dy5jxoxh2rRpdOnShTfffJOvvvqKvXv3HnNW4fGIuaMgCGcTq8NFWa2dslo7qeF+BBq8572ty6ngi/V5VNbZqbA4qKhzYLY6cLq9KYHpoztxSSvv77zvtxZw39xtJ7zHld2riY0uoLK+kv2FSnbuSSfcZCLGPxiTRonNUcb6qtVIinpUpt0o9YUAyC4DbnskI1uNYPBOF/YvZlGkN/PkGBlJ8sahdsoY7BAYGoO/XwidYq+g05JSwj78gBo9LOykQG+X0dvB4ACDW0XCg48Qkt6BCftqMKzfwaC1K6jT67HoDVh1eur0BtwGA9dXGqj0T8XiMjAtXc1hfxdapwOrTodSUhFi1OKnUhKgUjJsbgm2au9/G3fGazD7KdA6ZTQumWCTlsuub46fUolRqWD/zCzsFhdavRKNXoVGd+SPXokxUEvr3jG+7115fi0godYq0eiUqLXehJ44fkv4J/3jScLzmZjonTm55XUMfnslVoebBy5pRu82FsYuHItbdnP7L2767VRwz4NP4zIlcd2SalLahnLZHW18S5AFQRAEQTjWmZ6rWCwWMjMz0ev1pKWlXZBbad99911eeeUViouLad++PW+//TZdu3Y9pWvF3FEQhH+aLMvU2l2UVNcTGaDDdOTcvjXZ5XyxLo/S2npfYrDO4fZdd3Ti72Qr/tRKmaHda4mMyKfMVsbBUifZeVF0jm5Fp+hW+OtVFFVnMW3fu0hKGwp1JZLS3miMiR0mMnSJhYoPpnEoDJ67QYnJCn714GeT8auH6EuuIDgqiSD/dkR+v4Gwae/jUEJpoAKl0ohbbcJlMJEeHUn4g5PQpqVx2aZ9VO3NIjU/F4vBSK3BD4vBQK3BiNto4tVMFbVVdmw1Dj5rp6UwRInOIWOwywSpVaSnhxKoUhKoVmF4bz9WszfuKqP3aDOdSyZApSIy0Z8r7m3n+3o2L8zF7ZLRGVVoDWq0BhU6oxqN3vvR4H+CYhuCcBb6R5OE+fn5zJ8/n7y8PN+7zn94/fXXTz/as4yY6J1Z8zbn8+DX25Ek+OyWruyzz+eNzW+g9ih4/hMHOncYtz46lc4HFfTeZaPbsGQ6Dkps6rAFQRAE4awl5ipnF/HzEATh75Jl2beSbFdBNT/tLKK4up7i6npKauoprqnHeiT5dyqJP71aQaBRwXU91cREVFBSV8L+0mr2HlbSJ7EDveMzCDZqyLXs4j9r7kFSHL+y6oSMCQxfK1M+bRqH9VbeGarE3yoTUIf3j1Umeex4wlPSiTQl4Zn9M7z9Fh6lEntgELWBQVT7mbD6B9AvJYGgm0aiiY1l8OZ95OQXYay3Yvbzx6rTIx+pR6BXSMw4pKa2wk6d2c7ceIkSP+/qQaPdQ7hBQ8++cYSoVYSoVeS8soO6qoaEpSSBzqTBYFITFm+i/5iGar8HNpeiUEjo/NTo/NToTWq0BrXYiiuc9051rnLa+zqXLFnC0KFDSU5OZu/evaSnp5Obm4ssy3To0OFvBS2cn67uGMum3ErmbDzMhDlb+fHe69hcspkV+St44xotL04vY/LnHzLltvuILnch/ZBDeKI/cS2Cmzp0QRAEQRAEQRCEv63O7iK7zMLhSht5lVYKzFYKqmwUmuspNNt48/r2vjP+csrreH9Z9nHH8depsDndVNurKbAUUKcoYHDnOtpEJNAxJo0wk5aDlm1MXH4nFuCjHCDnqAHUEBIWSPsDLszffIvJkkdsNxeBNR6C6iDQAkEWmbRxE4lplkFSQBLSpgXIViuxVnjxaz2OkFAsQcHUhYSS0TaewNQr0cTGMmjTPvYntkX5yofUGoy+pB+AViHxqy6cwzvqqf09E39dPcEqPwx2PbFVHqKMcNGAOMI0asI0KrYs3OpL/A08UldKo1NiCNASnqDlktiGwqQJI1ugUEkYTBoM/hq0xhMn/VI7hv/ln6EgXAhOO0n4yCOP8OCDDzJlyhRMJhPffPMN4eHhjBw5kkGDBv0TMQrngaeHtmZHfjV7imqY8OV2Phj9LDf8fC1FlPDhYBUTvt/Alct/5YfeAxn7i5lfZ+zm2kc7YwrWNXXogiAIgiAIgiAIJ+XxyBRW2zhYXsfB8jryKqxc2T6GNrEBAPyWWXLS4h6FZpvv762iTIzpnkC4v4aYQCMR/jrcinLmHfyIIushXtxzmMe21Ta6vk3q3XQwpVCfuZXq3C0A+DtVhFgUBFc5CTa7Sbn8BmKT2tAmtA3OrSuoW72acOCN7d4xFEFByJGRGKOjCYvoiy6qBY/vz2dLXAvMz7xBoX8g9dqG5zONJJHbty1uh4eKAguB9R4UOiNBLki0yIShoGv7CCK1aqI0ata8sgPrkcRf76ODlyA8Xsk1USENY1+ZjCRJ+AVqMQZqMQZpUWuUx/3eJaSHHLddEITTd9pJwszMTL788kvvxSoVNpsNPz8/nnnmGa688kruuuuuMx6kcO7TqZW8f1MHLn9nFZsPVTHt92Je7fsqNy+8mdUt3bTOlRg/73MOxCXy3UVpjFpoZuGHuxgxqQNKteLPbyAIgiAIgiAIgvAP83hk3yq1XQXVvLN0PwfL68itsOJweRr1jQ8x+JKE8cEGwk1a4oINxAcbiA3SExOoJ9BPxqMoxyrt4O0t88mtyeVg9UEO1x7mtsTbGNbuDgAOVtewZMUvjcYPcuuICUogNjiJlIAUqn+YT+krr6CWYJYKdM6jthBLErFX9sSU2o88m53t6e0xT3qY3MBg9hoD2WHw45CsQKuQyOnTFsWRrc9FdidbVHoI06MAIpVKEv10xOk1xOk0fPHCRmoOWwDoKUHvow4z8wvSMubKtg0N7cNw2N2YgnWYgnX4h+gwhegwBmlRKhs/87XoFvWXf0aCIPx1p50kNBqNvnMIo6KiyM7OpnXr1gCUl5ef2eiE80pCiJFXr2nHHZ9tZsaqg3RK7MDEDhN5ffPrfDJQTWqhg2emv8lt/3mehV39uHxNDau+3k/fG8+fytmCIAiCIAiCIJzdZFmmos7BvpJaDpRa2F9iYX+p9+8PXNKcG7vGA2B3eVi0u8R3nVopkRBiJDHESEKIgZZRDed+xYd5ePcWf/y1/rQIbgFAjjmHK3+48oRxHK49jHXrVsxzv8J56ABjjCrCS+xEVMmEV4POaSHmzdvw7zsQgNqUZWjTUlHHJyDFxVEZEUVBaBjZgaHc1aUtyiNFqZ46UMgvtUBq+6O+aO8HlSSRlWtGKq6nsrCO9mYLsTUOtIU2/OrcBAbqGP1CG99l3+mKqAG0BhUBYXr8w/QEhB75GKZv9PX0vq7Zaf4kBEH4t512krBbt26sWrWKli1bMnjwYCZNmsTOnTv59ttv6dat2z8Ro3AeGdg6ktv7JPPhihwmf72D7++5mk2xm1iRv4LXr9PxwnQzU6a/yf33PUFkmhZpRQERyf7inSRBEARBOEPmz59/yn2HDh36D0YiCILQ9OqdbuwuDwF6b7XgXQXVjPpoPVVW53H77y9t2OabFuHHU1e0IinUSHKoH9GBOmTc7CjfwYGqnfxa/B3v7T1AtjmbKnsVACPSRjClxxQAYkwxKFAQrDQR6/In2qwgqsBK6wHX0qL7EKL8orD+tpTq778HYAiAQoEmLh5N2xQ0iYlo4uMAWFVVy4q4Zux9/i0y6+o5XH9UgVEXXI2CyCOfpvvpybPZiZNUhNfLhNXLXNQlhkS9hlC1im9e3kzJwRoAFMAfp/ipNAp0fmo8bg+KIyv/+t/cEo3OW+1XEIRz32knCV9//XUsFu9y4ilTpmCxWJg7dy5paWnnRWVj4Z83eWBztuZVsTG3ivFfbGXmuGe42TySAgp482oNj3yxn3u+/pS3b7iVyCo3y77IIiTGj7A4U1OHLgiCIAjnvGHDhp1SP0mScLvd/2wwgiAI/6Jyi52dBdVkFtWQWVRLZlENOWUWbu+Twn8u867uiwzQUWV1IkkQF2QgLdyPtAjTkY9+pIT5Ad7VhlZ3BcnxeagVauJDegBgcVi5eeHNx9xbQiLWFEuANgDbzp2UvvY69n37mFnrQOesACp8fcNSFYQO9Cb/9G3aEDrhXrTJKVjjE9gbHMpOu5vdFhtTm8WiU3sf6X+rqOGDw2WN7hmuUZFq0JFq0FJyqIai3DrK82uJzrdwVbEV2eNdPihJ0OGiVN8xT9GpgajUCoKjjARFGQmMNBAUYcAYqPVVYP6Df0jj1YKCIJzbTjtJmJyc7Pu70Wjkgw8+OKMBCec/tVLBuzd2YMjbK9lbXMsbiwp46+K3GPXLKHbE2fjiYgWjlyxhX3wS3/Xpz9ifq1g4bSfXPNJZvEMlCIIgCH+Tx+P5806CIAjnOJfbg+rIardyi52h76yisLr+uH3zKut8fw8xalhwby9SwvzQHymUIcsyxXXF7KrYyJJdu9hTsYesyizf6sBOEZ3oEeNNEvpp/OgU3hGt3UOCVU9skZOorEpaX3wNUSNGAlCflYV13ToAdJKEJjEBbbNmR/6koW/XDoAdtVZ+scGuXoPYZbFRVFIPJfm+WMfEhNI90Ju07B1kwur2kCSpCK92419cz4AhKUhHzk9c/PFu9m1o2BoNoDWqCIn2IzjaiNPh9iUJe1yV+le/7YIgnOP+UpJw48aNhIQ0riBkNpvp0KEDOTk5J7hSEBpE+Ot46/oMbvpoPXM3HaZjYlue6/kck5ZPYkEXBYklMhPnziQnOp7v+yZz4+Jqfpu5hyF3tfX9h04QBEEQBEEQBMHucrO7sIYth6rYdtjMjvxq2sQE8N+RHQBv4s9idyFJkBxqpFV0AC2jTLSM8qdVlD/hJq1vLEmSiA2Rya3dR8uQlr726xZc50sK/kEpKUkKSCI1MBVXRQXl06Zh276dh/dkIjsbb1eWQ3bD9d6/a5OTiXr+ObTNmqNNTcGsUrOtxsrWGitXRQaRqPfGs7nGyhuHGif2kvVa0k162vjpCXVLHNpVQemhGuoP1dLyUA3WagfFQDHQqXMUQZFGAGJbBOF2yYTG+hEa50dorN9xVwYKgnBhO+0kYW5u7nG3ntjtdgoKCs5IUMKFoWdqKA8MaMZri/fxxPe7+O7untzW5jam75zOtCFqoiscPDv9DW77zwv8lmFk4OYK1s/PoduwlKYOXRAEQRDOG3V1dSxfvpy8vDxfcbo/TJgwoYmiEgRBODlZlnl5URYbDlays6D6mMrCMg1ldiVJYu4d3YkLNuCnbfwI7JE9ZJuz2Vq2lW2l29hWuo282jzCDeEsuWaJ7/p24e0oqSuhVUAz0ix+JObZSQ1tTsSV3syf22Kh6rPPQfbeVxEQgK5lS3StWqFr1Qp9O2+VX4fHw06rg81derO1xsrW7QfJtTX87o3Sqn1Jwq4BRq6PDCbdpKe1QUdUrYfoCCManfdrWD8/h00/5zb6eiQJgqONhCf4N0oAtuwRTcse0X/5+y0IwoXhlJOERx9yvWjRIgICAnyfu91ulixZQmJi4hkNTjj/je+Xyua8KpZllXHH55v47u7b2Ve1j+X5y3ntWi1TZ1Tx9PQ3mXTf40SVa2DhIYJjjDTrHPnngwuCIAiCcFJbt25l8ODBWK1W6urqCA4Opry8HIPBQHh4uEgSCoLQ5GRZ5lCFlXU5FRSabTxwaXPAm7hbtb+cnQXVgHe1YEZ8EBnxgbSLDaRNTECjcY6uNPyHVza+wvcHvqfGUXPMa3qVHovDgq6mHuumzTy6ORLb5gLse7+DI8c22Dp0gKu9SUKlnx9hEyegjolF364t6rg4JEnC7HThkGUCNN5jk1ZUWbhpx7G771L0WjL8DcTrNQDU1zkx5lq5IcdFUXY+O3Jr2GJ3M2R8WxLbhAIQkehPYISB8AQT4Qn+hCeYCI03oT6yTVoQBOF0nXKS8I9DriVJYsyYMY1eU6vVJCYm8tprr53R4ITzn0Ih8ca17bnyv6vJq7Ry9xdb+WDU84xZdBO55PL61Wqe/CyLu775jGnXjiW0tpqls/YSGG4gPOHY/9ALgiAIgnDq7r//fq644go++OADAgICWLduHWq1mptuuomJEyc2dXiCIFyAjk4Kev9UUlzjPUtQpZC466JU31mBt/dJxuHy0DEhiIQQw3G3ztrddraXbmdD8Qa2lm7lvQHvoVV6V+q5ZTc1jhp0Sh1twtrQPqw97YzNaJ/YnQBtALIss3/oQNyVlY3GVMfGom/XDkPnTo3aQ++8k8J6B4vNFjbsy2d9dR1ZdfVMiA/n0RTvKr5O/gZC1Co6+hvo4G8gw99IO5OewCMFSIoOmJnz7noqCur4fxqdEmtNw6rDxLahJLYN/avfakEQhGOccpLwj0Ouk5KS2LhxI6Gh4peRcGYEGTV8NKYTI95bw4aDlbz8yyHe6v8WI38eyd5oCx9fquD2hb+yLz6ZeRf15eZfqvj5/Z1c80gnjAHaP7+BIAiCIAjHtW3bNqZNm4ZCoUCpVGK320lOTubll19mzJgxjBgxoqlDFAThAvPwNzv4alN+oza1UiIjLoiuycE43B70eJOEV7Q7dvus0+1kV8Uu1hetZ2PxRraVbsPhaUisbS/dTpeoLgBc3/x6LgvpTWxmBfaVG7CunY/scOC/YjngXSBj6NQJx6FDGDp2xNCpI/qOnVBHhDe6Z63LzTPZhayqquWgrfGxDQD59obzCQPVKnb1bI2t1knhfjOF64ooTwsksKN3TJ2f2pcgDIwwEJnkT2RKAJHJAQRFGVGI89kFQfgHnfaZhAcPHvwn4hAucGkRJt6+MYNxMzcyZ+NhmkW04qU+L3HPknv4LcNbyGTSlx9xf3gU3/RLZeQiMz+/v5PhkzJQqcVyekEQBEH4K9RqNQqFt5pleHg4eXl5tGzZkoCAAA4fPtzE0QmCcL6qd7pZf7CSlfvKWLG/jI/GdCYu2AB4twX/kRTslhxMt+QQMuKDfKsH/58sy3hkD0qF9/XZe2fz6qZXG/UJ04fRObIzXSK7kBKYgnXTJmoXL8azdh2affsoPbqzSoWrtBR1RAQAMW+8jqRsuLfZ6WJVqRmrx8O1kcEAGJUKFpSaqXK5UQBtTQa6BRrpGmCkU4CRMI0ap91N4X4zhzMrOZxZSWVhw0pBm8VB6pEkYWCEgUF3pBOVEojBX/N3vs2CIAin7ZSShG+//fYpDyjOrhH+qn7Nw3l0cEue+ymT537aw8c3d2ZChwm8teUtPrlURVyZk+envcadDz3Lgh5BDF9Zw7LPs+h/c0tRlUsQBEEQ/oKMjAw2btxIWloaffv25cknn6S8vJzPPvuM9PT0pg5PEITzhCzL7C+1sDzLmxRcf7CyUaGRlfvLubFrPADXdorj+s7xJ0wKAlgcFtYXr2d1wWpWF6zmnox7uCLlCgC6RHYhWBdMp4hOdInsQufIzkSVONElJSFpvEm3ksUfUvnpLN942hYtMHbvjrF7NwwdO6IwGn2veRQKtlfXsbSylmWVNWypseIBorVqrokIQpIkFJLEk6nRhKhVdAv0w1/VOHanw83HD67E5WxcXCUkxkh0syAS0kN8bZIkkZLReKWiIAjCv0WSZVn+s05JSUmnNpgkkZNz7CGs55qamhoCAgKorq7G31+ce/dvkmXZt8XApFXx3d09+GDvFBblLiLAruT5j+zY9DGMnzyFDrkKLtppo/uIFDpcmtDUoQuCIAjCv+ZMzVU2bdpEbW0t/fr1o7S0lNGjR7NmzRrS0tL4+OOPadeu3RmM+vwl5o6CcHILdxVz5+ebG7VF+uvo0yyUPs3C6J0aRoBBfcLrZVkm25zNsvxlrC5YzbbSbbhkl+/1y5MvZ2rvqb6+nro6rOvXY1m+AsuKFbiKi4mfORNjt64A1K3fQM2CBRh7dMfQtSuq4ODj3ve57EK+KKygyuVu1J5m0NI7yMSTKdHolApfu83iIG9XBbm7KnA5PAy5u63vtXkvbaKu2k58y2BiWwYT2yIIvZ9YKSgIwr/jVOcqp7SSUGwxFv4tkiTx7LB0DpbXsTG3iltnbWL27U9wqOYQeyv38uINWp6ZWcBTM97ikfEPEVLthu+yCY4y+qp8CYIgCIJwajp1ajh0Pzw8nIULFzZhNIIgnOuqrU6W7Stl8Z4SMuKDGNfLu9ikR2oIfloVHRKC6JMWSt9mYaSG+53ybqByWznD5w9v1Jbgn0DP6J70jOlJp4hOuCoqqFmwgNply7Bu2gzOhnMAJa0Wx+E8X5LQ2LULxq5dfK97ZJkdtTYWV1QzMSECzZFjGBwemSqXG3+Vgj5BJvoF+3NRsIkYnTe5J8syFQUWcneWk7ujguKD1XBkCY6kkLDbXGj13kfuK+5th0avEjugBEE4q53SSsIT+ePS8+0XnXg3uOlVWOxc+d/V5FfZ6JESwivXJTB60U2UWktpfVjisdlOfu7Zn3evH8dNS2tIqoOrH+5EcJTxzwcXBEEQhHOcmKucXcTPQ7iQFVfX88uuIhbtLmZjbhVuj/cZsV1cID+M7+nr53B50KgUJxoGAKvTyprCNfx++HdcHhcv9XnJ99qYX8ZgVBvpE9uHntE9iTXFItfXo9DrAbDt2k3u1Vf7+qvj4vDr2xe/vn0wdO6MQqdrdK96t4dVZgu/llfza3kNxQ5vUnFuuxT6BpsAyLbWU+Fw0cHfiOo4BUOWzMpk75qiRm0hsX4kpocQnx5CZJI/CuXJv2ZBEIR/wxldSfj/Zs2axSuvvML+/fsBaNasGZMnT2bUqFF/LVpB+D8hflpmjOnEVe+tYU12BR8s9eO9/u8xZuEYdsfV8cEQBeN/XEJ+eBTf9B3MzQur+em9HVzzn07ojCfeqiAIgiAIF7oOHTqwZMkSgoKCyMjIOOmbvVu2bPkXIxME4VwiyzKjP97Ayv3ljdqbR5jo3zKcAa0iGrWfKEFocVhYlr+MRbmLWFOwxleJWKVQ8YTjCfw0fgB8MugTJBls27ZR+/6XZP/2G4ZOnYie+gIAutatMF02CH27dvj17YsmMfG4v992W2y8nlvM75W1WN0NZwQalQr6BZswHZXUSzHoSDGA0+7mwK4KcraW0n1EKqZgb8IxMsmf/RtLiG0RRGKbUBLSQ3yvCYIgnItOO0n4+uuv88QTT3DPPffQs6f3naFVq1Zx5513Ul5ezv3333/GgxQuTC0i/Xnr+gxu+2wTn607RLOI1rzW9zXGLxnPinQIr4I7v/2CwtBwvr6oI6N/NfPLBzsZOqE9SrV4x04QBEEQjufKK69Eq9UCMGzYsKYNRhCEc0aB2caq/WVc19lbYESSJPz13jfnOyYEcVl6JANbR/qqFJ+Kd7e+yye7PvElBgFi/WLpF9+PfnH90Kv0yB4Ptm3bqPnpZ2p+XYS7rCEpWedwIMsykiQhSRKxb7xxzD0qnS7q3B7idA3n//1UVg1AlFbNpSH+DAwNoGeQH1pFwzOE3eYid0c52VtKydtTiftI0ZGI5ADaXRwHQLMukTTrGon6JEVWBEEQziWnvd04KSmJKVOmMHr06Ebtn376KU8//fR5cX6h2DJydvlgeTYv/rIXpUJi+uiOVCpWMmXtFADuXuCmW5aGiQ88iUITx9UramneJYIBY1udd9vgBUEQBOEPYq5ydhE/D+F8VVnnYMGOQr7bWsDWPDMAi+/vQ1qEdzvuwfI6dGoFUQH6Px3L6rSyomAFXSO7EqQLAuDLvV/ywvoXSPRPZGDiQC5NvJS0wLRG8/hDN4/Fum6d73OFyYTfRRdhGjAAv149G1Ui9sXtdPFLWTU/lppZZa5lWHgQ77byFjqUZZn/5pXSJ9hEGz/9Mc8M1WU2VszZR/7eSjzuhkdl/1AdyRnhtOgWSUiM3yl+BwVBEM4O/9h246KiInr06HFMe48ePSgqKjrOFYLw99zRJ5nsUgtfb87n7i+28OVt/bm1TQEzds5g2mAVITUOXnj/Ve58+Fl+a29E2lCCf6ierkOTmzp0QRAEQTirbdy4EY/HQ9euXRu1r1+/HqVS2aiwiSAIF4Z6p5slmaV8tzWfZVlluDx/nEMPnRODqXM0VPpNCj35eeBuj5sNxRv4MftHfsv7DZvLxpPdn+SaZtcAMDhpMB3CO9AsqBmSJGE/cIDyL94j5LZbURxZ8WzokEH9zp2YBgzAf8hgjN26IWmOrQpc43KzoMzM/BIzK821HJXfI7++8YrDexIatkK7HG4sVXYCI7wrIPV+avKzvAnCoEgDKR3CSc4IIzT21AutCIIgnKtOO0mYmprKV199xaOPPtqofe7cuaSlpZ2xwAThD5Ik8cKINpRZ7CzLKuOWmRv5+s5bKKgt4JfcX3j9ahXPfFrF1Pde4d5JT+Nv1cLPuZhCdLTqGd3U4QuCIAjCWWv8+PE89NBDxyQJCwoKeOmll1i/fn0TRSYIQlNZsa+M8bMbziNNj/FneEYsV7SNItz/1M7bO1B1gB9zfmRBzgJKraW+9li/WFRSwyNogDYAY52bqs8+w/zd99gzMwHQtWiOacAAAILHjiXkjjt8ScMTGbH1ALsstoa4/fRcERbIFeGBJBsaX+txe8jfW8W+jSXkbC0jKNLANY90BkCjVzFgTCtCYv1EUURBEC44p50knDJlCtdddx0rVqzwnUm4evVqlixZwldffXXGAxQEALVSwXsjO3DDh+vYnl/NzZ9s5MvbH6fEWsKW0i1MvV7N858cYsr0N3jsrsmYrB4UX2RhCtIR1yq4qcMXBEEQhLPSnj176NChwzHtGRkZ7NmzpwkiEgTh31RgtvH1psMEGTSM6ZEIwEXNw2kV5U/f5mGMyIjxbS0+VVX1VVz949W4Ze+KQ3+NP5clXcYVKVfQNrQtkiQhO51YVqzA/N13WJYtB5fLe7FajV+vXiiDgnzjKU2N7y/LMptrrHxfWsVjydHojxQaGRIWgMMjc1VE0HETg7IsU5pbS9b6Yg5sLsFW6/S9Zqt1Yrc60Rq8ZyymdW5cdEUQBOFCccpnEu7atYv09HQANm/ezBtvvEHmkXd6WrZsyaRJk8jIyPjnIv0XiXNlzl4VFjtXvb+G3AorLaP8mT6mJXf9PpbcmlySSmDKZy5WZvTk1dF3c8MyCykWmasmdxTnhgiCIAjnlTM1VwkJCWHBggV07969UfuaNWsYMmQIVVVVfzfUC4KYOwrnEqfbw5LMUuZszGP5vjJkGeKC9ayY3O+0t9O6PW7WFK5hd8Vu7mx3p699wtIJAAxNGUqf2D5olI23B9sPHCDn8it8n+vS0wkYPgz/wYNRHZUgPFqezc7c4kq+Kaki1+YtdPJBqwSGRXj7Oz0yKokTfg0rvsxi5/KChnv6qUnrGE5a5wgikwOQFGIrsSAI569TnauccpJQoVDQuXNnbr31Vq6//npMptN7R+lcIiZ6Z7e8Cisj3l9DucVOz9QQnhkRxdhfR1Flr6LdQXj4Kxff9RvMzCtHMvq3GpKUaq5+uBPGwJNvURAEQRCEc8WZmqvccMMNFBUV8cMPPxAQEACA2Wxm2LBhhIeHi10ip0jMHYVzwaGKOuZsPMy8zfmU1dp97d2Sg7mhSzyXt41GeYqJspK6Er478B3f7v+WoroiJCR+ueoXYvxiAPDIHhSSd4Wf21JHzYIfcZaUED5xom+Mw3fdjSYpiYBhV6Jr1uy497F7PCwsr2Z2YSUrqmr548HVoFQwODSA2+LCaGc6tpqy2+3h0M4KwuJNmIK9W6Rztpbx68e7SW4fRvNukcS2CEKpVBxzrSAIwvnojCcJV65cySeffMK8efPweDxcffXVjBs3jt69e5+xoM8WYqJ39ttVUM1109ZS53BzRbtobukvccfi27G5bHTf42HifA/Th93Aoj5XMGaRmaQwI8MndUCjO+0d9oIgCIJw1jlTc5WCggL69OlDRUWFb0fItm3biIiIYPHixcTFxZ2pkM9rYu4onAsmf72drzfnAxDqp+GqjrFc3zn+T4uP/MHlcbG6YDXz9s1jRcEKPLIH8G4nHpoylJtb30yEsWGbbv2ePVTNmUv1ggXIViuSRkPaqpUoT+PfyAFrPb3W7/V93ifIj+sigxkUFoBRqTymf2VhHZlrCslaX4yt1knnIYl0ucJbzNDt9uCyu31bigVBEC4kZzxJ+Ie6ujq++uorZs6cycqVK0lNTWXcuHGMGTOGyMjIvx342UBM9M4NK/eXMfaTjbg8Mrf2SuLiDDPjl47H5XExYKuH2xZ6eGn0nexo3YtRi6tJax7CkLvboBDvGAqCIAjnuDM5V6mrq+OLL75g+/bt6PV62rZtyw033IBaLR6kT5WYOwpnmwqLnS835HFp60iaHTlTcEteFW/9tp8busTRv2UE6tOcE8/Pns9jqx7zfd4xoiNXN7uaSxIuQav07tjx2GzU/PwLVXPnUr9jh6+vJimJoOuvI/Dqq1EYj5+UrHO5+aHUTL7dwUNJUb72O3fnkqTXcn1UMAn6Y3cGuZ0esreVsntFIYX7zb52vb+GjAHxZFwaf1pfpyAIwvnoH0sSHu3AgQN88sknfPbZZxQXFzNo0CDmz5//V4c7a4iJ3rnj+60F3Dd3GwCPDW5JQvwBJq+YjEf2MGyNh+tWwuN3TqI0vA3Xr6ilTc9oLrqx+WmftyIIgiAIZxMxVzm7iJ+HcLbYmV/NzDW5/LijEIfLww1d4pk6os1fGmtX+S7qnHV0jfJWP7c6rVw1/yr6x/dnRLMRJAckH3NNxYwZlL76mvcTtRr/SwYQeN31GLp0PuH8+4C1nk/yy/mquJJatwe1JLG1R2tCNX++A8jjkfniybXUlNcDICkkEtuE0LJnNPGtg8V2YkEQhCP+lSQhNLz7/Mgjj2A2m3G73X9nuLOCmOidWz5ckc0LP3u3Ibx2TTswrefptU8DMHKpm4Fb1Dw48TEUygSuXF9Hl6O2HQiCIAjCuehMz1X27NlDXl4eDoejUfvQoUP/9tgXAjF3FJqS0+3hl13FfLoml82HGooNtY0N4PY+yVzeNvrUx/I4WZy7mC/2fsGOsh2kBqby7dBvfQk+WZYb/b1uzRoUegOGDt7jClzl5Ry6aRQBI0YQOGI4qtDQ497HLcssLq/hk4JyllfV+tqT9VpGRocwKjoEf9Wx24k9HpnC/WZimgX64vj9870c2llOq17RtOoVg1+QOIdcEATh/53qXOUvH9C2YsUKPv74Y7755hsUCgXXXnst48aN+6vDCcJfdlvvZEpq7Hy06iCT523n7Ru680DHB3h98+t8cbESY72TF957mYmTnmJpm1Ckn3Ix+GtI7xvb1KELgiAIQpPKyclh+PDh7Ny5E0mS+OO94z8evs+HN38F4XwmyzJD311NZlENACqFxJC2UYzpkUhGXOAp756psFUwb988vsr6ilJb6ZGxVLQMbonVZcWo9m4RliQJj81G9Y8/UvXZZ9j3H8DQuTMJn83yXhMaSvIvP//pfWcWlPPYfm+lYQm4NNSfW2LC6B3kh+I419qtTvasKmLnsnxqK+u5+uFORCR5H3K7D0+hzw3NxKpBQRCEM+C0koSFhYXMnDmTmTNncuDAAXr06MHbb7/Ntddei/EEZ0sIwj9NkiQeG9ySOruLORsPM3HONt4beRnj0qv5aNdHTL9MidFu5aV3X+SeB5/B4NDDnH3o/DSkdgxv6vAFQRAEoclMnDiRpKQklixZQlJSEhs2bKCiooJJkybx6quvNnV4giAcR2ltPaFGLQqFhCRJDGwdQVmtnZFd4xnZNZ5wf91pjfdF5he8vul1HB7vSuJQfSjXNr+Wa5pdQ6i+YSWgs7iYqtlfYp47F3d1NQAKgwFtixbILheSyvtoebwE4W6LDbvHQwd/7zPj8Igg/ptXyoiIIEZHhxB/nLMGAcylVnYszSdzbREuu/dNC61BRU2FzZck1BnF+amCIAhnyilvN77sssv47bffCA0NZfTo0dxyyy00b978n46vSYgtI+cmt0dm8tfb+XZrAWqlxLSbOrLS/AHz9s1D5YaHv3YTZI3ivgeeoOdeFR0PO7ninnbEtghu6tAFQRAE4bScqblKaGgoS5cupW3btgQEBLBhwwaaN2/O0qVLmTRpElu3bj2DUZ+/xNxR+Ddkl1mYviKHb7cU8P5NHejf0ltJ2OpwoVRIaI+zPfdE3B43SoW3/5rCNdyx+A7SQ9IZ2WokAxMGolY2TryVvf0O5R9+CC4XAOrYWIJH3UTAiBEoTabj3kOWZZZV1vL+4VJWVFno5G9gQcdmDTHIMsoTrDi01jj4/fO95O4shyNPq8HRRtr1j6NZ5whUmlP/WgVBEIR/YLuxWq1m3rx5XH755SiPU25eEJqaUiHx8tVtsbs8/LSziDu/2MKM0XdRm1jLotxFvHqVkie+LOSVt19g0sQn0DpVKN/fyfBJHQiLP/7kRhAEQRDOZ263G9ORB/zQ0FAKCwtp3rw5CQkJZGVlNXF0giAAbD5UxbTl2SzOLOGP5R0r9pX5koSGUyjwAd6k3drCtXy862PahrVlQocJAHSP6s6XQ76kdUjrRqsAZY8HSeHdwqtJSgKXC0PnzgSPGY1fv35IJ3gmtHs8fFdSxQeHy9hb5y0oopQgRqfB5vagP7It+EQJQgCdUUV5fi3IkJAeQrv+ccS2CBLFBwVBEP5hp5wkPB+qFgvnP5VSwZvXt8fh9rB4Twm3f7aZj29+CIvTwuqC1bxwnZrHvzzMy+9MZfKER9FuVvDjO9sYMbkjgeGGpg5fEARBEP5V6enpbN++naSkJLp27crLL7+MRqPhww8/JDlZFPkShKa0LqeCt37bz9qcCl/bgJYR3Nk3mU6Jp74TxuVxsfjQYj7Z9QmZlZkA7Kvax13t7kKtVCNJEumh6YA3kWhZvpyKD6fjP/BSgseMAcD/skFoU1PQtWx50nvNK67kmexCSh3eFYdGpYKRUSHcGht6wi3FjnoXu1cUkrOtlGGTOqBUKlAoFVw8qiV+QVqCIsWxVoIgCP+Wv13d+Hwktoyc++wuN7fP2szyfWUYNUpm3NyOD7L+w5bSLRjs8MRsF/X6NB4d/x9GrHPRGhUjJnfEGCCqoQmCIAhnvzM1V1m0aBF1dXWMGDGCAwcOcPnll7Nv3z5CQkKYO3cuF1988RmM+vwl5o7CmebxyFz21kqySmpRKyWGtY/hjr7JpIaf+u4Xl8fFzwd/Ztr2aeTV5gGgV+m5Ku0qRrcaTZRflK+v7HJR88tCKqZPx75vHwDq+HhSFi08rdV780vN3L47lyitmltjw7gpKpgA9fHXpdhqHez4PZ+dy/KxW71JxUvGtaJZ58hTvp8gCIJwak51riKShMchJnrnh3qnm1tmbmRNdgUmnYpPxrbnnT0PsaV0C8Z6b6Kw1r8FT93xMNeustPaT8/wSR3Q6v9y0W9BEARB+Ff8nbnKjh07SE9PR6E4fiXQyspKgoLEtr7TIeaOwt8lyzLrcippFxfg2z68cFcxqw6UcddFqcQE6k97zDc2v8HHuz4GIFAbyI0tb+SG5jcQqAtsuK/TSfUPP1A+7UOchw8D3mIkgddfT/CYMagjTlzkL9dm591DpbTw03FrbBgALo/MgjIzg8MC0Jzgd0xtZT1bF+eRuaoQl9PjjS/CQMal8TTvGolSJaoUC4IgnGkiSfg3iIne+cPqcDHm4w1szK0i0KDmk7HteH3ng2wr24axHp6c7aIiKJ0Xxk3ihuX1tIny54p724nDkAVBEISz2t+ZqyiVSoqKiggPDyc5OZmNGzcSEhLyD0V6YRBzR+GvkmWZtdkVvPnbfjbkVvLY4Jbc1uevbfV3eVxYHBZfEvBw7WHG/DKGm1rdxPXNr8egPvZonaInn8L81VcAKIOCCB49iqAbb0QZEHDC++yts/HOoVK+K6nCA4RrVGzs3grtCZKCR6spt/HFk+vweLyPoGHxJjoOSiCpfRgKhXhjQhAE4Z9yxguXCMK5yKBR8fHNnRn10Qa2HTYzbuYOPhz9Cm/ufpDtZdt59gYVT87exeRP3+a1UfehWlaDevouLrujjXgXUxAEQTgvBQYGcvDgQcLDw8nNzcXj8TR1SIJwQdqUW8nLC7PYkFsJgEalwGJ3nfY4Lo+Ln3J+YtqOabQMbslrF70GQJwpjkVXL0KtaKhULLtceOrtKP285/wFXncttUuXEjJuHEHXXYvCcOIzurfXWnkrt4Sfy6t9bRcHm7gvIeKkCcJ6ixOdnzcG/1A9UakBSAqJDgMTRDESQRCEs4xYSXgc4t3g80+1zcmoj9azI78af52KaaPb8O7eyewo24Gfzbui8FBMF967djw3LaujbeswLh3XCoVSJAoFQRCEs8/fmavcfvvtzJo1i6ioKPLy8oiNjUV5giqlOTk5ZyLc856YOwqnY29xDa8uyuK3zFLAmxy8sUs8d/ZNITJAd8rjeGQPCw8u5L/b/us7czBYF8yPw3/EX9P4/4ey203Nzz9T/t/3MPboTuSTTza85nAgaTQnvdcbucW8dLAYAAkYHBbAxIQI2ppOnFSsKLSw6adcDu2q4KZnu2Pw997D6XCjFrt2BEEQ/lViJaEgHCVAr+bzW7sy9pONbD5UxW2f7uTdkS8xXX6YHeU7jqwo3MBt36qZMfwOWF6G6rO99B/dEklsfRAEQRDOIx9++KGvUMmECRO47bbbMJlOvRiCIAh/z+u/7uO3zFKUColrO8UxsX/aaSUHZVlmbeFa3tzypq9acZA2iJvTbz5mW7Hs8VC7aBFl77yL40jS31NXR/hDD6HQee95ogShR5ZRHFnld2loAK/nljAsIpB74yNoZjxxvBWFFjb9nMuBzaVwZDlK3p4KWnTzFkoRCUJBEISzl1hJeBzi3eDzV53dxa2fbmJtTgV6tZK3bmzFzJz/sLN8JyYrPDXbxb7Ennx85W3cuMJKl27R9LmhmdgGIQiCIJxVztRcZezYsbz99tsiSfg3ibmjcDIVFjsyEOqnBWBfSS1v/bafBy5tRkqY32mP92P2jzy66lEAjGojY1uPZVSrUcecOVi3bh2lr75G/a5dACgCAggZO5agm27ybTc+nr11Nl45WEyoWsVLzeN87WUOJ2Ea9QmvqyysY+PPBxslB1Mywug0JInQ2NP/OgVBEIQzRxQu+RvERO/8Vu90c+fnm1mWVYZGpeD161ry+aH/sLtiNyYrPPGli9zYrkwffic3rrTRvW8cPUakiEShIAiCcNY4E3MVp9OJXq9n27ZtpKenn+EILyxi7igcT53dxYyVB5m+Mocr2kUxdUTbvzyW0+P0nS1odVoZ/sNwLo6/mNvb3k6QLuiY/pWzZ1PyzLOAt1px8C23EHzzGJR+J07WZVvreS23hO9KqpABrUJia4/WBKv/fPNZvcXJzEdW4z5SrVgkBwVBEM4uYruxIJyATq1k2qiOTPhyK4t2l3Dfl3t48ernkXmUPexhykgVj8xdz13z3EwbcTcsO4xaq6TL5UlNHbogCIIgnDFqtZr4+HjcbndThyII5xWPR+bbrQW8vHAvpbV2APYU1eJ0e1Cf5nnXZdYy3tv+HpkVmcweMhuFpMCgNvDj8B/RKBtvE5Y9HqQjBUT8Bw2i/O138L/8ckLvuhPVSSqYH6538OrBYr4uruSPMkZDwgKYnBR50gShw+ZCo/e+rvNT06pXNHVmO52HJBIaK1YnC4IgnIvESsLjEO8GXxicbg+TvtrO/O2FKCR4bkRzFpY/w9bSrWgd8NA8N3V+7Xj/mnu5brWdi69IIePS+KYOWxAEQRDO2Fzlo48+4ttvv+Wzzz4jODj4DEZ4YRFzR+EPG3MreXbBHnbkeysAxwXreWhgC4a0iUJxGudc17vqmbVnFjN2zsDmsgHw0aUf0SWqyzF9XVVVlL//Ps5DecRN+8DX7qmrQ2E88bZigEXl1dy2KxfHkUfCS0L8eSgpkjYnKUhitzrZ+mseO37PZ/ikDoTFexOCskcWZ3kLgiCcpcRKQkH4E2qlgjeua49OreCrTfk89m0WU4Y+iU75MmuL1jL1WiUPfLede+e8wX+vvQ9+zEalUdDmotimDl0QBEEQzoh3332XAwcOEB0dTUJCAsb/Syhs2bKliSIThHPP15sOM3neDgD8tCruvTiVm3smolWdeqEOWZZZdGgRb2x6g8K6QgDahrZlUqdJdIjo0Kivx+Gg6rPPKH//AzwWCwC2HTvQt/Vua/6zBCFAJ38jWoVEZ5ORx5Kj6BBw4mtcDjc7luWzZeEh7FYXAFnri31JQpEgFARBOPedNUnCF198kUceeYSJEyfy5ptvHrfP7t27efLJJ9m8eTOHDh3ijTfe4L777vtbYwoXNqVC4sURbdGplcxae4gnf8hi8sAH0ce9x9LDS3j1KiX3/LibiV++xjvXPYA0bx8qjZKWPaKaOnRBEARB+NuGDRvW1CEIwnnjklYRhBg1XNo6kgcuaUaYSXta11fVVzHx94lsLd0KQIQhgvs73s/gpMGNzsaWZRnLkiWUvPwKzrw8ALStWhI+aZIvQXg8blnmm5IqVlXV8laLeCRJIkSjYknn5sTpNCc8f9vj9pC5poiNCw5SV+0AICjKSLcrk0lqF3paX6MgCIJwdjsrkoQbN25k2rRptD3Jf9QArFYrycnJXHPNNdx///1nZExBUCgkpgxtjV6jZNryHF5ZtJ8x3cdxebKRBTnzeWeoknGL9nL/7Fd45/oH4MtMAJEoFARBEM55Tz31VFOHIAjnJI9H5pst+azcX85b17dHkiQCDRqWTb4Ik+7EFYBPJkAbgNPtRK/SMzZ9LDe3vhm9St+oj6usjIKHHsK6dh0AqrAwwh54gIArh/rOI/x/sizze2Utz2UXsqeuHoCrIoLpG+xdARivP3kyc/7b2ynIqgLAL1hL1yuSadY18rS2TwuCIAjnhtM7OfcfYLFYGDlyJNOnTyco6NjKXEfr3Lkzr7zyCtdffz1a7Yn/Y3Y6YwoCgCRJPHJZSx4f0hKAT9fmYT40gmvSbkSWYMYgJQfC93PfF6/wQycFP8/ZS+aawiaOWhAEQRD+PrPZzIwZM3jkkUeorKwEvNuMCwoKmjgyQTg77Sms4Zppa5k8bwfztxfye1ap77XTSRDa3XZm7pqJ1WkFQCEpeK7Xc8wfNp+72t11TIIQQBkQgLOwEEmjIeSOO0hZ+AuBw4edMEGYabFx/fYcbtyRw566evxVCh5PjqLLSbYV/7+UjDC0BhU9r07lpindadH99M5XFARBEM4dTb6ScPz48QwZMoQBAwbw3HPPNcmYdrsdu93u+7ympuaMxCGce27tnUyEv45JX23nl13FdKm7iFHtTHyWNY3Z/ZQMX32QB2e9wOs3PYQ8NwuAlj2imzhqQRAEQfhrduzYwYABAwgICCA3N5fbbruN4OBgvv32W/Ly8pg1a1ZThygIZ43aeievL97Hp2ty8chg0CiZ2D+Nnqmnv+V2VcEqpq6fSl5tHrXOWu7NuBeAlMCURv1khwPz998TOHw4klqNpNEQ8/LLKENC0MSe+Jxsi8vNczlFzCooxwNoJImxsaFMTIg4acVia42DjQsOEtsyiJSMcABa944mrXMEOuNfWyEpCIIgnDuaNEk4Z84ctmzZwsaNG5t0zKlTpzJlypQzFoNwbruiXTQhfhrumLWZDQcrMVvbMK7HJD7a+xrf9VRQpy/g0Y+e4eUxD+P+KgtZhlY9RaJQEARBOPc88MAD3Hzzzbz88suYTCZf++DBg7nxxhubMDJBOHvIssz87YU891MmZbXehQVD2kTx+OUtiQo4drXfyRTXFfPyxpdZfGgxAGH6MNKC0o7bt27deoqfeQZHTg5yvZ3g0aMA0Ldr96f30SgkVlTW4gGGhAXwZEo0CSfZVuxyutmxNJ/Nv+TiqHeTt6eCpLahKJQKFEoFOmOTb0ATBEEQ/gVNliQ8fPgwEydOZPHixeh0uiYd85FHHuGBBx7wfV5TU0NcXNwZiUk4N/VICeWrO7tz8ycb2FdioXZpHLf1e5IZWc/yawcFlX4VPPrxFF4d9TCeb7wrCkWiUBAEQTjX/HGG8/+LiYmhuLi4CSIShLOP0y3z1pL9lNXaSQo1MmVoa/o0Czu9MTxOPt/zOe9vfx+by4ZSUnJjyxu5u93d+Gn8GvctLaX05VeoWbAAAGVICMpTOEJpZWUt3QL9UCskNAoFrzT3rjTsGWQ64TWyLJOzrYzV8w5QW+E9rzAs3kSva1JRKEViUBAE4ULTZEnCzZs3U1paSocOHXxtbrebFStW8O6772K321Eqlf/KmFqt9qRnHAoXppZR/nx7d0/GfLyBA6UWZiz0546Br/DJgUfZ1MxBtdHKpM+e47/XPYD7W++5LCJRKAiCIJxLtFrtcY9Z2bdvH2Fhp5cEEYTzic3hRq2UUCkVaFQKnrsync2Hqri9bzJa1ek9owC8sfkNPtvzGQDtw9rzeLfHaR7cvFEf2eWiavaXlL39Nh6LBSSJoBtuIOy+iSj9/U849v66ep46UMDSylqeS4vh1ljvv92TJQcBqorrWDFnH/l7vUVJjIFaug9LplmXSCRx5qAgCMIFqcmShP3792fnzp2N2saOHUuLFi14+OGHTztB+E+NKVzYYgL1zLuzO7d+uolNh6p4a4GCCYPeYU7+ZPbH1PDCdU4mz3uZL4bcw9ffwzUytOolEoWCIAjCuWHo0KE888wzfPXVV4C3kFdeXh4PP/wwV111VRNHJwhNY+X+Mh79bidjuidya+9kAHqkhtLjL5w9+IfRrUazNG8pd7S9gytTr0QhHbtKr/iZZzEf+beoa9OGyKeeQp/e+oRj1rncvJZbwof5pbhkUEsS1U73KcdUU1FP/t4qlCoFGZfG02FgAmqteF4SBEG4kDVZktBkMpGent6ozWg0EhIS4msfPXo0MTExTJ06FQCHw8GePXt8fy8oKGDbtm34+fmRmpp6SmMKwukKNGj4/NauTJyzlUW7S3j1pypu7vUGa21PUkgBU0bKTJ73Fot73MKcHyWuk2Va945p6rAFQRAE4U+99tprXH311YSHh2Oz2ejbty/FxcV0796d559/vqnDE4R/VVWdg+d+yuSbLfkAzN6Qx809ElGd5rZbWZaZnz2fvZV7ebjLwwBEGiNZMHwBKsWJH7+CRo6k9rffCJswgcBrrkY6wQIHWZZZUFbNUwcKKLQ7AbgkxJ8pqTEkG068O0qWZcwlVoIivZWNE1qH0G1YMqkdwwkIM5zW1ygIgiCcn5q8uvHJ5OXloVA0/Ee5sLCQjIwM3+evvvoqr776Kn379mXZsmVNEKFwodCplbw3siMv/JzJR6sOMnNVGZe0fgr/gHfYy06evUHBxB8+JjOthtk/yVznkmnb78QV5wRBEAThbBAQEMDixYtZvXo127dvx2Kx0KFDBwYMGNDUoQnCv0aWZRbsKGLKj7sptziQJBjdLYHJg1qcdoIwvzafZ9Y+w9qitQD0j+9Pp8hOAI0ShLIsU/PjjzgLiwi98w4AdM2bkfr7UhR/cgzSCzlFvJNXCkC8TsPzaTFcEhpw0mvK8mpZOXcf5QUWRk7phjHAe4+OgxJP6+sTBEEQzm+SLMtyUwdxtqmpqSEgIIDq6mr8T3L+h3Bh+mrjYR77fidOt0yraD8ik+ezseJXJFlm7K8e6v0upSp8GNf1ShQTL0EQBOEfcabmKrNmzeK666475mxmh8PBnDlzGD169N8N9YIg5o7nrqJqG49/t4sle71Jt9RwP166qg0dE4JPaxyXx8UXmV/w323/xeayoVVqubv93YxuNfqY1YOO/AKKn36aulWrQKEg6dtv0LVoccr3yrTYGLJlP3fGhXFvfAT6kyQy7TYX677PZveKAmQZVBoFA29LJ7HNX986LQiCIJx7TnWuIpKExyEmesKf2XCwkjs/30xlnYMwk4au7bezvPxTAK5Y5yGpoj27W43lunZJdBuWjCSJw58FQRCEM+dMzVWUSiVFRUWEh4c3aq+oqCA8PBy3+9TPN7uQibnjuSuruJbL31kJwPh+qdx1UcppFybJqsziqTVPsbtiNwCdIzvzVPenSPBPaNRPdrup+vxzSt98C9lmQ9JoCB0/npBbxiKp1Scc/9fyarLq6rk3IcLXZna6CFSfeFOYLMsc2FzKqq/2Y61xAJDWKZweV6XiF6Q7ra9PEARBOPed6lzlrN5uLAhnqy5Jwfwwvie3frqJrJJafl3bmss6P8xS80v82E1Bx/3bGLThVWZzL067m97XpokqcYIgCMJZR5bl476RlZ+fT0DAybcvCsK5ymJ34af1PgY1jzTxwvA2tI8LJC3i5NWAj8fpcXLP0nsorivGpDbxYOcHGZ46/Jh/V/VZWRQ9/gT1R4osGjp1IvLZZ9AmJZ1w7MJ6B4/tL+CX8mqUEvQP8aeVnx7g5AlCj8xP7+/g0M4Kb98IA31vaEZsi9NbHSkIgiBceESSUBD+orhgA9/c3YP75mzjt8wSflgbxKXtX2ST/Uk2pzkoDcxn3K/P8bV8H47PnFx8U0sUp3mujSAIgiD8EzIyMpAkCUmS6N+/PypVw5TQ7XZz8OBBBg0a1IQRCsKZ5/HIfLH+EC8vyuLL27qRHuNNhF/TKe4vj6lWqHmo80P8cvAXHunyCGGGsGPva7ORN3oM7upqFH5+hE+e7C1Mojj+vNAty3xaUM4LOUVY3B5UEtwZF06CXnNKMUkKicAwA4dVlXQclEjHgQko1WIOKgiCIPw5sd34OMSWEeF0eDwyr/yaxfvLsgHomqKnWD+VSncxfjaZ8QuUbGlzNwOSujPwlnSUKjFJEwRBEP6evztXmTJliu/jpEmT8PPz872m0WhITEzkqquuQqM5taTEhU7MHc9+BWYbD83bzuoD3tV1I7vG8/zwNqc9jsPt4P3t79M8qDmDkk49kV45ezZ1a9YQ+cSTqCPCT9gv02LjwazD/2PvvsOjqtIHjn+nl0x6D0kgofcuIBYQFBX3p65rRXHtulhQdy1r39XFtmsXWV3F3bV3cW2IgA2R3kuAkJCQ3mYyfebe3x8TBmJCSMhAEvJ+nuc+t505c+YCycs7p7DK7gJgTIyVJ/pnMbChB+HB7N1Ri8VmCK9c7PMEcNX5iEuVVYuFEELInITtIoGeOBwfrSnizg824AsoZCWYiMt8j93+n9AqKpcvVKlLmsHwfmfyf9cORW9s21w3QgghxIEiFau8/vrrXHjhhZjNMkdZe0js2Hmpqsp7K4v4y2ebqfcGMBu03H3GQC4b3xNtG6eC2Vy1mXt+uIcdtTuINcXy+W8/J8bY9M9b8XqpePZZbCecQNSECeF2HGqOaldQYcyyTVT7g9h0Wv6cm87veyShbeF1Hqefnz7cwZYfS0jvE8u5t42SKW6EEEI0IXMSCnGUnTsyk5wkG7PeWM2eajcVjnMYPqAfmwPzeW2ahlNXv8HuVSW88/zlnP+HERjN8s9PCCFEx7r88suB0GrG5eXlKIrS6H52dnZHNEuIiCize7jrg/Us3lYBwOie8Tx5/nBykqLaVI9f8fPK+lf45/p/ElADJJgTuH/8/c0mCN3r17P3rrvx7dqF44svyf3ic7QmU6sWsbPqtNzeK43vaxz8rW8mGeaWe/LuXFPOd29tDy9MEp9qJRhQ5MtoIYQQh016EjZDvg0W7VHj9DH7nbUs3R4KSEf2drFD9zfQBRhUoHLKpkHoh9/ExTeOwxojw7iEEEK0XaRilby8PK688kp++umnRtf39XqS1Y1bR2LHzunfy3Zz/yebMOq1/PG0flx1Qi66Nvayy6vJ454f7mFL9RYATu15KveOv5cEc+NFQBSfKhjqsAABAABJREFUj8oXXqTqlVcgGESXnET6Q38h+pTJB63bHgjy0I5i/i8lnpMTQoum7PuvWUtJRZfdx3dvb2Pn6lCsGZdqZfKlA8joG9emzyaEEKL7kOHG7SCBnmgvRVF5fvEOnvpmO6oKPZO11Mf8A5+xlORalRlLknANvI2Zt04lNlnmihFCCNE2kYpVJk6ciF6v56677iI9Pb1JYmL48OHtbWq3ILFj56QoKn/5bDMzxmUf1srFpc5Spn84HZ/iI9YUyz3j7uH0Xqc3Xbl482b23nU33u3bAYg56yxS7/kz+vj4g9a9qMrOH7ftocTrp5fFyA/HDUTfigRmVXE9H/1jNV5nAI1Ww6jTshkzvRd6g/QeFEIIcXCSJGwHCfREpHyfV8Etb6+l2unDZtISnf4h9ZafMfhVLl1iQJdwA5f88WKSs9seuAohhOi+IhWrREVFsWrVKgYMGBDB1nU/Ejt2Dr/kV/Pct3nMu2w0VmNkpnV5+OeHKXOWcf+E+5tdudibl8euc38LgQC6hATSHniAmGmnHbS+On+AB3bs5e3SagByLEaeGpDN+DjbQV9zoGBQ4b05K9Fo4JSZA0nOkhhSCCHEoUmSsB0k0BORtLfWzaw3V7OmsBaAXplbqbT9G41GYdI6hf415/B/f7qN7EGJHdtQIYQQXUakYpWxY8fy1FNPccIJJ0Swdd2PxI4dyx9UeOabPF5csgNFhT9M6s0dpx9e4vvzXZ8zMmUk6bb0UN2KH71Gf9Dhv6qqUnzzLaDVkvbA/egTEpotB7Cwso4/bSui1OdHA1ybmcyduelYddqDvkZVVLb/UkqfMano9KFy9TVeLDEGdC28TgghhDiQJAnbQQI9EWm+gMKcL7bw2o+7AeiR6KUu9mk0php6lqmcuXYwk294lIEn5nRsQ4UQQnQJkYpVvv32W+69917+9re/MXToUAwGQ6P7Ege1jsSOHSe/0snst9ewrqgOgN+NzuTB/xuMzdS2noR2n51Hfn6Ez/M/Z0zqGF457RV02qZDeFVVpe6DD4ieOhVdXBwQmo9QYzC0OI/gyjonZ63OA6C3xcTTA7MZG9vyAip1FW6+/fcW9ubVMnZ6L477TW6bPpMQQgixj6xuLEQnYtRreeA3gxndM567PthAcRVYHHdhTPyAgtRfmH/yJlz/nIl975OMu3BcRzdXCCFENzF16lQApkyZ0ui6LFwiOjtVVXlvZREPLtiEyxckxqznb78dylnDMtpc16qyVdz9/d2UOEvQaXQcl34cKk37UQQqKym5517qly6l/vsf6PH0U2g0GrTGQy9ENzrGylnJsWSZjdyRk46lpd6Dqsqm7/fy4wc7CHiD6E06rLGmNn8uIYQQoq0kSSjEUXTWsAyGZ8Yx+521rCqowV3yW+LcQ3Gl/JfXp9ZS8uN11BbeyWl/vKjFb6OFEEKISFi8eHFHN0GIwzJ36U4e/3IbAONyEnjqwhFkxFnaVIdf8TN37Vz+tfFfKKpCpi2TR096lOHJTRfscSxeTMk99xKsrkZjNGIdNbLFuu2BII/nl3BbrzQSDKHhyv8c3AvtIeI7R7WHxf/dyp7NoTkLM/rGccrMgcQmt+2zCSGEEIdDhhs3Q4aMiCMtEFR4cclOnlmUR1BRsZq9qKmvorcW0H+Pyu8qz+Z3T/xFVqoTQgjRLIlVOhf58zj6yuwefvPcD/x+Yi+uO6k3ulasDNzo9c4yZi+ezcaqjQCc3fts7h53N1GGxkOAFbebssceo/btdwAw9etHxhNPYO7f76B1/1jj4OYthRR7/ZydEse8wb1a1aaCjVV8/a9N+NwBdAYtE87pzbDJmWja+NmEEEKIX5PhxkJ0Ynqdlpun9OWEvknMfnsthdWgKbwec/wStmYu5NnETyj5wzqufWg+URlJHd1cIYQQx5D169czZMgQtFot69evb7HssGHDjlKrhGiZoqj8tLOKE/qG4qLUGDNL/jTpsFcxjjHF4Al6iDHGcP+E+5nWa1qTMt5d+RTNmoUvPx+AhMsvJ/m2W9Gamh/66wkqzMkvYd6eCgB6mo1c1aP1cVxsigUlqJCaE8OUywcSn9bynIVCCCFEpElPwmbIt8HiaKr3Bnjgk018sLoIAKNpL4bMN9Aaqzh5o5E/nvYPep16cge3UgghRGfSnlhFq9VSWlpKSkoKWq0WjUZDc+GgzEnYehI7HlnVTh+3v7uWxdsqmDtjFGcMTT+sejwBD0adEa0mNB9ggb0Ak85EWlRas+WDdXXsOudcCAbJeHQOUccff9C6Nzhc3LilkG1ODwCXpifyUJ8MovQtjwqpLHKQlBkdPi8vsJOUaUMrKxcLIYSIIOlJKEQXYTPp+fsFw5nUP5l7PtqA3ZNBIP9WDMmfs2TwMvI2zGL2+t9x+m0PyDyFQggh2i0/P5/k5OTwsRCd2S/51dz81hpK7R5Mei1O3+ElrnfW7uSPS//ImTlncs2wawDoGdOzSbmgw4HWZkOj0aCLjSXrxRfQp6Whj48/aN1fV9Zx1cbd+FWVZKOef/TP4tSk2Bbb43UH+O7tbWz/pYxzbxtJRt9Q/Sk9JckshBCi48hXVEJ0Er8ZnsGXs09iQm4iiqLHW/Z/ePOvp8iWzN3x7/PIn8/EX1Pd0c0UQgjRxfXs2TP8pVPPnj1b3I6URx55hOOPPx6r1UpcXFyzZQoLC5k+fTpWq5WUlBT+9Kc/EQgEGpVZsmQJo0aNwmQy0adPH+bPn9+knhdeeIFevXphNpsZN24cv/zyS6P7Ho+HWbNmkZiYiM1m47zzzqOsrCxSH1UcJkVRef7bPC765zJK7R5yk6P4eNZEfjc6s031qKrKR3kfcdFnF7GjdgfvbHsHl9/VbFnnzz+z68zp1H34YfiaeeDAFhOEAONio0g26pmeHMuSsQMOmSDcm1fD239dzvblZWiAyqL6Nn0mIYQQ4kiRJKEQnUhGnIU3rh7Hw+cMIcqow+/tiSt/Nu7ak3m7fzGXvTiFgl9kJUohhBBdm8/n4/zzz+eGG25o9n4wGGT69On4fD5++uknXn/9debPn8/9998fLpOfn8/06dOZPHkya9euZfbs2Vx99dV89dVX4TLvvPMOt912Gw888ACrV69m+PDhTJs2jfLy8nCZW2+9lQULFvDee++xdOlS9u7dy29/+9sj9+HFIVU4vFz+2i88+fV2FBV+O7IHC248gYHpbetl5/Q7+fMPf+b+n+7HE/QwIX0Cb5/1NlaDtVE5NRCg/JlnKLziSgIVFdS8/Q6qohy0XlVV+b7aER6mH2vQ8+XofrwyuBeJLcyRGAwoLPt4Jx/9Yw311V5iksz89k+jGTY5q02fSwghhDhSZE7CZsi8MqIzKKpxcfeHG/g+rxIAnakQU4/3iVXK+JP+N5x75SNotJLnF0KI7uhYiVXmz5/P7Nmzqa2tbXT9iy++4KyzzmLv3r2kpqYC8NJLL3HnnXdSUVGB0Wjkzjvv5H//+x8bN24Mv+6iiy6itraWL7/8EoBx48YxduxYnn/+eQAURSErK4ubbrqJu+66i7q6OpKTk3nzzTf53e9+B8DWrVsZOHAgy5YtY/z48a36HMfKn0dn8dWmUq77zyrMBi1/OXsI54/ObPOUK1urt/KnpX9it303Oo2OG0feyJVDrgzPR7iPv6SE4j/+CfeqVQDEnf87Uv/8Z7QWS7P11vkD3LG9iE/Ka/l7/yxmZCS2qj01pU4WvrqZikIHAAOOT+fEC/piNMvsT0IIIY681sYqkmEQopPKjLfy7yuP4/HzhhFt1hP0ZuPadQtVzincr/+cmx8/haqinR3dTCGEECLili1bxtChQ8MJQoBp06Zht9vZtGlTuMzUqVMbvW7atGksW7YMCPVWXLVqVaMyWq2WqVOnhsusWrUKv9/fqMyAAQPIzs4Ol2mO1+vFbrc32kTkTBucxr3TB7LgxhO4YExWmxOEdp+dK768gt323aRaU3l12qtcPfTqJglCx6JF7DrnXNyrVqGNiiLj70+S/te/HjRBuLy2nlNWbOOT8lp0GnAEWj8/YukuOxWFDkxRek6/dghTZg6UBKEQQohOR5KEQnRiGo2GC8ZmsfDWk5kyIAXQ4as4DVf+jSyKs3Dup+fy+Sf/6OhmCiGEEBFVWlraKEEIhM9LS0tbLGO323G73VRWVhIMBpstc2AdRqOxybyIB5Zpzpw5c4iNjQ1vWVkyXLQ9nN4A93y0gdI6T/ja1Sfm0jc1uoVXHVyMMYbZo2ZzUuZJvP+b9xmVOqpJGe+ufIpuuhmlrg7zkCHkfPQhsdOnN1tfQFF5Ir+Ec9fsoNjrp6fZyIKRfbk+O6XFdhw4YGvAhDTGn5PLxfeNo/eoll8nhBBCdBRJEgrRBaTFmnnl8jE8feEIokx6FG8Grt1/oMR+NndUvc3sZ0+nurq4o5sphBCii1mxYgXLly9vcn358uWsXLmyTXXdddddaDSaFretW7dGqukd6u6776auri687dmzp6Ob1GXtKHdw9gs/8sbyQm55ew2HOxPSHscettdsD59f0P8CnjvlOeLMcc2WN+XmkPSHP5BwxRX0evMNjNnZzZYrdHs5d80O/r67DAU4Py2eRWP7Myo2qsX2FG6u4v1HV+Jx+oHQF7+jT+9FVJzpsD6fEEIIcTRIH3chugiNRsM5I3swsU8S932ykS83luKvHU/AMYQvU75g5Ttnck//WZxxyrUd3VQhhBBdxKxZs7jjjjsYN25co+vFxcU89thjzSYQD+b222/n97//fYtlcnNzW1VXWlpak1WI9604nJaWFt7/ehXisrIyYmJisFgs6HQ6dDpds2UOrMPn81FbW9uoN+GBZZpjMpkwmSTZ017/W1/CHe+vw+kLkhpj4k/T+rd5aDHA0j1LufuHu4kxxvDOWe8Qa4oNJaZpXFf99z9g7JkdTggm3zjrkHUXe/2ssjuJ1ml5vH8W56a2vNJxMKCw/JNdrFlYCMCqL3Yz8Xd92/yZhBBCiI4gSUIhupjkaBMvXTqaH/Iq+MNba7C7bHhKzqfEMobbte/yZd7/eOCSf5IQnXroyoQQQnRrmzdvZtSopkMxR44cyebNm9tUV3JyMsnJyRFp14QJE3jkkUcoLy8nJSU0NHPhwoXExMQwaNCgcJnPP/+80esWLlzIhAkTADAajYwePZpFixZxzjnnAKGFSxYtWsSNN94IwOjRozEYDCxatIjzzjsPgG3btlFYWBiuR0SeP6jw6Bdb+dcP+QCMz03guYtHkRzdtsRrUAnywtoXeHnDywDkxObgDXqblFODQSpfeIHKuS9hGjiAXm+9hbaFJK+qquFk5YQ4G08OyOKEOBvZlpbbV1fh4utXNlFeEFqcZMjJPRj3f61LjAshhBCdgQw3FqKLOqFvMiv/PJUzB6WBFoLuHFz5N/N53UDO+u9v+N/Prx/2kB0hhBDdg8lkatLTDqCkpAS9/sh9l1xYWMjatWspLCwkGAyydu1a1q5dS319PQCnnXYagwYN4rLLLmPdunV89dVX3HvvvcyaNSvcg+/6669n165d3HHHHWzdupUXX3yRd999l1tvvTX8Prfddhsvv/wyr7/+Olu2bOGGG27A6XRyxRVXABAbG8tVV13FbbfdxuLFi1m1ahVXXHEFEyZMaPXKxqJtquq9zHh5eThBeP3JvfnvVePanCCs9lRz3TfXhROElwy4hPnT5pNibTzfX6CqisKrr6byxbmgqliGDWux3q1ON9NX57HTtX9+xEvSEw+ZINy2vJR3HllBeYEDk1XPGdcN5eSL+6M36tr0uYQQQoiOpFEli9BEa5eGFqKzWLq+lOu+2IinJvTtuUZfhynlc46PquHBc58jO0G+xRZCiGNJpGKViy++mJKSEj755BNiY2MBqK2t5ZxzziElJYV33303Uk1u5Pe//z2vv/56k+uLFy9m0qRJABQUFHDDDTewZMkSoqKiuPzyy3n00UcbJS+XLFnCrbfeyubNm8nMzOS+++5rMuT5+eef54knnqC0tJQRI0bw7LPPNhpe7fF4uP3223nrrbfwer1MmzaNF198scXhxr8msWPr1XsD/PbFHymp9fDkBcOZNrj1z3mfDRUbuHXJrZS5yrDoLTww4QGm5zZddMS1ejXFs28lUF6OxmIh/S8PEfub3zRbp6qqvFVazT3bi3ArKpMTonlreO9WtWfj0iKWvhWaDzG9TyynXjmY6ARzmz+XEEIIcaS0NlaRJGEzJNATXVG93cslb65mQ0k1asNIG62lAFvS51zffzxXT74Do87YsY0UQggREZGKVYqLiznppJOoqqpi5MiRAKxdu5bU1FQWLlwoq/a2ksSObVNQ5SSoqOQm2w7r9TcuupGlRUvpFdOLpyY9RZ/4Po3uq6pK9fzXKX/ySQgGMebmkvnsM5j69Gm2vvpAkDu3F/FBWQ0Ak+KjeW5QNslGQ6va43H6eW/OCvqPT2fMGT3R6mSwlhBCiM5FkoTtIIGe6KqCQYWnP9rC8xWVaArqUAgNcdFHr6NX0o88fMafGZ89sYNbKYQQor0iGas4nU7eeOMN1q1bh8ViYdiwYVx88cUYDK1LkAiJHVsSCCrM+WIrKdEmrju5dT3zDqXWU8vza59n9qjZ2IxNE42Kz0fBJTPwbNxIzPTppP/lIbRRza9GvKnezbUbd7PT7UWngTtz0rkxOwVtCwuoqKrKni3VZA1MCM9d6PcFMcjQYiGEEJ2UJAnbQQI90dX9uKyI23cUUVFeS7A8ABoNaAIY4n9kem8n9575EEmWpI5uphBCiMMksUrnIn8ezatx+pj15mp+2lmFTqvh29tPpmdi88m6llS5q/hy95fMGDij1a/x791L/XffE3fhBQddMXllnZPz1u7Aq6ikmwy8NKgn4+Ja7t3odQdY/J+t7FxdzuRLBzDohIw2fRYhhBCiI7Q2VpHVjYU4Bk2ckMmCrFju/Xwrn+WqJKwpwOGz4q8+mU/q6vm24F7unHICFw29GJ1WvvUWQoju5NNPP+WMM87AYDDw6aeftlj2//7v/45Sq8SxZkuJnWv+vZKiGjdWo45/XDD8sBKEm6o2MXvxbEqdpZh1Zs7rd16z5RyLF+PN20HStdcAYMjIIP6iC1use3i0laE2C3EGPc8MyCbR2PJ/jSoKHXz58kbsFW60Wg0Bv9LmzyOEEEJ0ZtKTsBnybbA4Vvg8AV5/ZwvPxHhwO+qJ2liCXRuamF5rLCc7bSVzzrmMCRmygqMQQnQl7YlVtFotpaWlpKSkoNUefO40jUZDMBhsb1O7BYkdG/vf+hL++N463P4g2QlWXp45hv5p0W2uZ8HOBTy07CG8QS+9YnrxzCnPkBvbeDE2VVGofHEulc8/D0D2/NeIamFl6l0uL1lmIwZtqHdhnT9AtF53yOHFm77fyw/v5hEMKNgSTEy7eghpubFt/kxCCCFER5CehEIIjGY9V88cwoglRfy1ophfpg0kZ8VWaqp1eHwp7C48kxnzNjC69/944uyr6BXbq6ObLIQQ4ghTFKXZYyEi4amF23lmUR4AJ/ZN4rmLRxJnbdvCaQElwFOrnuLfm/8NwEmZJ/HoiY8SbWycaAza7ey9407qlywBIP6SS7COGnXQej8oreaP24q4vEciD/bpAUCsoeX/DvncARa/sZUdK8sB6DUsiSmXD8QcJXN2CiGEOPbI0ltCHOM0Gg1jJ2fxz1MHcsU6L+XD++M5MZMhgZ3oVQ+KN4MVm8cz5akvuOnT56jz1nV0k4UQQhwFfr+fKVOmkJeX19FNEceQRFsoIXjtSbm89vuxbU4Q1nnruOGbG8IJwmuGXsNzpzzXJEHozctj9/kXUL9kCRqjkfS//Y20++9DY2z6fl5F4c5te5i1pRC3orCl3oNfad1gqsoiBztWlaPVajj+vD6cecNQSRAKIYQ4ZklPQiG6ibScWB64djRD/r2Jl2L0rJx+EtmFe+jzyxp+jh5M0JPNgp/g67VvcMXJyfzxxHPRa+VHhBBCHKsMBgPr16/v6GaIY8zMCb0Y0iOWUdnxh/X6jZUbWV6yHIvewsMTH+a0Xqc1KWP/+mv23nU3qsuFPj2dzGefxTJ0SLP17fH4uGbjbtY6XADc2jOVP+akoWthePGBMvrGc+IFfUnpGSPDi4UQQhzzpCehEN2I2WbgkuuH84/EZM7+xUlZWg++PW8642JLGV+3HA0BvK6evPSFlZGPvcyrKxcj05YKIcSx69JLL+Vf//pXRzdDdGHr9tRy8T9/ps7lD1873AQhwMQeE7lvwn3854z/NJsgBFCcLlSXC+v48eR88P5BE4TfVNk5dcU21jpcxOt1vDEslztz01tMEAZ8Qb57ezu15a7wtWGTsyRBKIQQoluQhUuaIZNPi+6gdFcd77+xmXdydeRlGIlyu7j2q8/4Pqhjo20sEFr1OC62jFumDuD3YyagaeW37kIIIY6sSMUqN910E//+97/p27cvo0ePJiqq8eqz//jHP9rb1G6hu8aOC9bt5Y/vrcMbULh0fDYPnzP0sOp5f/v7TMiYQA9bj1a/xrFoEbaTT0ajb37UQ40/wNhlm6kPKoyItvLykF5kmVse+lxX4eLLf26kck89SVk2Lrh7LBqtxD5CCCG6vtbGKpIkbEZ3DfRE9+NzB1jy1lbeL6/l65FWPEYt/YsKuO6LD3glLZddxlHsSxbGxtQwe8pALh87Cq0EzEII0aEiFatMnjy5xfuLFy8+7Lq7k+4WO6qqytPf5IUXKDllQArPXDSCaHPb5urzK34e++Ux3tn2Dn3j+/LGmW9g0VualPMVFVP28MOkP/Iw+sTEVtf/cVkNP9c5eahPBqYWVvIGyF9XwTfzt+BzB7BEGzj1qsFkDUho0+cRQgghOitJErZDdwv0hNi2vJQFH23nkyFm8noY0SgKV274hbHfLuC5fsPYaTgO1NC37zE2Jzef0p/fjxuKXiczFgghREeQWKVz6U5/Hh5/kNvfW8f/1pcAcM2JOdx1xkB0bfwCsc5bx+1Lb2d5yXI0aJg9ejZXDL6iyagF5/JfKL7lFoK1tURPm0bmM08ftM5N9W7cQYUxsVEHLfNrSlBh+YJ8Vn9ZAEBabgzTrhmCLd7cps8jhBBCdGatjVXkf/hCCPqPS+Oa28dwc7GWs3+uxxSAfw0fz22z7uf6+HieWfF3ctVFoPVgr4/i4U+LGPHIx7y4dAMuX6Cjmy+EEOIwXXnllTgcjibXnU4nV155ZQe0SHRm5Q4PF85bxv/Wl6DXanjsvKHcM31QmxOE+XX5zPh8BstLlmPVW3lm8jNcOeTKJgnCmrfeovCqqwjW1mIePJjUu+86aJ0fldVw1qrtXLExnxKvr1Xt8Dj9fPrsunCCcNgpmZxz2yhJEAohhOi2pCdhM7rTt8FCHCgYVFjxWT5LlhTy5cgotmaFeg8OcTt49NsFFKz+hufGH0+B5kTUoA0AkyHABWN7cMNJg8mIazpESAghRORFKlbR6XSUlJSQkpLS6HplZSVpaWkEAvJFUGt0l9ix3OHh3Bd+wukL8NKloxmf2/qhv/v8XPIzty25DYfPQXpUOs+d8hz9E/o3KqP6fJQ+8jdq33kHgJizziL94b+iNTdN3gUUlb/u3Mu8ogoAJidE8+KgnsQbmp+r8EDBgMJHf19N1V4np1w2gL5jUtv8eYQQQoiuQIYbt0N3CfSEOJjibTV88/pmVpsUvhxtpS4qNC/hFfWVXPnuv1ldtpYXxo9nj3oCqj/0HwSNRmXygDhumjyYke1Y1VAIIcShtTdWsdvtqKpKfHw8eXl5JCcnh+8Fg0EWLFjAXXfdxd69eyPZ7GNWd4od88ocGHRaeiW1fkjvPqqqcunnl7K+cj0jkkfw9OSnSbQ0TjQGamoovulmXCtXgkZDyu23kXDVVc0unlbh83PdpgJ+qq0H4JaeqdyRk9bi6sWqqoJKeEESR7UHvzdIQnrbP48QQgjRVUiSsB26U6AnxMH43AF+eD+PdT+X8N0QC8v7m1E0GmxaDU+U5zP0X/NYEyzgX+MHk2c4kaCrd/i1gzIs3HDyAE4fkoZB5i0UQoiIa2+sotVqW1yxXqPR8NBDD3HPPfe0p5ndxrEcO767Yg96nYbfjsqMSH3lrnJe2/gas0fPxqQzNbkfrKtj94UXEaisJOPJJ4ieNKnZelbbnVy9cTd7vX6idFqeHZjN9OS4Ft874A/y3VvbsUQbmXBu7xbLCiGEEMcSSRK2w7Ec6AnRVrs3VLL4v1vZpQny+ZgoihNDw3dGWU08nrcG079eYbO2hNcn9GB91IkE7CNADZVJitYzc3wuF47NIjVG5vcRQohIaW+ssnTpUlRV5ZRTTuGDDz4gIWH/Kq5Go5GePXuSkZERySYf047F2FFVVf7+9XaeX7wDg07DZzedSP+06DbX4w16+aH4B6ZkT2n1a3y7d6MGApj69DlomRs3F/B+WQ29LSZeHZpD/6iW4wxHtYcv522gvMCBRqvhkgfHEZdibXWbhBBCiK5MkoTtcCwGekK0h8fp57u3t7NtZRmre5tYPNyKR69BC1yeFM0fVv6A+5WXyTNU8daEGH5JPB5/zTjUYOg/E1oNTB2YyiXjsjmpbzLaNk5wLoQQorFIxSoFBQVkZ2e32KtQHNqxFjt6A0H+9N56Pl0XGm5+8yl9uPXUfm3+e1LtqeaWb29hbcVaHj/pcc7IOaP5cm++iUarJf6ii1pdtzMQ5PH8Um7PSSNGr2uxbPH2Gr56eSNuhx9TlJ5pVw8ha2BCi68RQgghjiWSJGyHYy3QEyJSdq4uZ8mb26gIBPhmZBQbs0MLm8TqddyRGsNZ335J7auvkm+y897xRn7MGIa/dhxBd064jsx4Mxcf15Pzx2SSEi29C4UQ4nBEMlb5/vvvmTdvHrt27eK9996jR48e/Oc//yEnJ4cTTjghQi0+th1LsWON08e1/1nJit016LUa/vbboVwwJqvN9eTX5fOHb/5AUX0R0cZonp70NMelH9eojBoIUPboY9T897+g05Hz4QeY+/dvtr5qf4A39lZxY3ZKq5OVqqqy/tsifvxgB6qikpRl44zrhhKTJAutCSGE6F5aG6scetkvIYRo0HtUCul94lj61jail1UwaoeeRcfZKLbBPcU1zB95Eg+/O51RH71Pr//8h0uNK/l87GoWDkzH6TgOf90oimrgia+28dTC7Zw6KJULx2ZxYt9kdNK7UAghjroPPviAyy67jBkzZrB69Wq8Xi8AdXV1/O1vf+Pzzz/v4BaKo2l3pZMr5q8gv9JJtFnPS5eOZmKfpDbXs6J0BbMXz8bus9PD1oMXp75IbmxuozJBh4PiW2/D+cMPACTffDOmfv2arW9LvZvfb8inwOMD4KaerVuFeMmb29j8fag3ZL9xqUyaMQCDseVeh0IIIUR3Jj0Jm3EsfRssxJGgqip5K8v44d08nPV+1uaa+G5UFI6GNUqmJcXwQJyZqLffoOatt3EoLhaO1PD52Cgq/YPw1Y5DcfcM15cSbeLckT04b3Qm/VLbPt+REEJ0N5GKVUaOHMmtt97KzJkziY6OZt26deTm5rJmzRrOOOMMSktLI9jqY9exEjs+/20eT369nR5xFuZfMZa+h/E7ecHOBdz/0/0ElADDk4fz7CnPkmBuPLTXV1TEnuuvx7djJxqzmYzHHiNm2mnN1vdlRR2zthTgDCpkm428PjSHgbbW9QTcvqKURa9tYeL5fRg6KVOG1QshhOi2WhurdJplRx999FE0Gg2zZ88+aJlNmzZx3nnn0atXLzQaDU8//XSTMnPnzmXYsGHExMQQExPDhAkT+OKLL45cw4XohjQaDf3GpnHJA+MZND6NUTu9XPdxDRN3+9EBX1XambSzgpd/O4PUr7+m15V/4LxN0cx9vp5bfljBoNiXseY8jSH+JzQ6F+UOL/O+28VpT33HWc99z2s/5lNV7+3ojymEEMe8bdu2cdJJJzW5HhsbS21t7dFvkOhQsyb34bZT+/HRrOMPK0G4pWoLf/7hzwSUANN6TeOV015pkiB0rV7D7vMvwLdjJ/qUFHr+97/NJghVVeUfu0v5/cZ8nEGFiXE2vhjd75AJwoA/GD7uNzaNSx4az7DJWZIgFEIIIVqhUyQJV6xYwbx58xg2bFiL5VwuF7m5uTz66KOkpaU1WyYzM5NHH32UVatWsXLlSk455RTOPvtsNm3adCSaLkS3ZrYZmHL5IP5v9ghS48ycstzBNZ/XMsQJPlXl+cJyJm7dy0fnXkj2NwtJn30bU4rjefwlDw98VMzx/i+w9XkEc4//oLdtAk2QjcV2HlqwmXF/W8TVr6/kiw0leA4I+IUQQkROWloaO3bsaHL9hx9+IDc3t5lXiGPNlxv3/57VaDTcPKXvYc8ZPDBxIFcNuYorBl/B4yc9jlnftB73mtUEa2owDxpEr/fexTJkcJMyzmCQazbt5vH8UE/WK3sk8fbw3iQaDz5TkqqqbFhSxJsPLsdZt/+LxthkmX9QCCGEaK0OH25cX1/PqFGjePHFF3n44YcZMWJEsz0Ef61Xr17Mnj27xZ6H+yQkJPDEE09w1VVXtapNx8qQESGOpoAvyIrPd7P260KCikp+LzPfjYtmD6H/eGSZjdyVk8bZMWbs779P1b9eJVBWRmkcLDrOxLcjddQpegL2EQTqRhH0ZIbrjjLqOHVQKtOHZXBSvyRMh1jFUAghjnWRilXmzJnDf//7X1599VVOPfVUPv/8cwoKCrj11lu57777uOmmmyLY6mNXV4wdVVXlHwu389y3O5g2OJUXZ4w+rPmBXX4XfsVPrCk2XG9LvfZUVaX2nXeJ/b/foLVamy2z2u7k7NWh5PWj/TKZkZHYYhuCAYXv3tkenn9w3P/lMObMnBZfI4QQQnQnXWbhklmzZjF9+nSmTp3Kww8/HNG6g8Eg7733Hk6nkwkTJhy0nNfrDU/UDaGHJ4RoG71Rx4RzetN3TCqL/7MFzW4HvQo87BwTy6K+RvZ4fMzaUsiLNjP3Tj+Hky68EPunn2Kc/zozvt7J+d/CsoFaFk3ayNacnwh6UwjUjUJ1jMbpi+bjtXv5eO1eos16ThuUxlnD0pnYJwmjvlN0iBZCiC7prrvuQlEUpkyZgsvl4qSTTsJkMvHHP/5REoTHMH9Q4a4PNvDB6iIABqTFcDjrh1W5q7jp25vQa/W8fNrLmHSmJglC1e+n6pVXiL9sJjpbFBqNhviLLmyx3lExUfxjQBbZZiPj4mwtlnXZfXw5bwMlO+tAAxPO6c3I07Lb/mGEEEII0bFJwrfffpvVq1ezYsWKiNa7YcMGJkyYgMfjwWaz8dFHHzFo0KCDlp8zZw4PPfRQRNsgRHeVlGnjvDvHsGFxEcsX7KLvijp6rYH8qSl8nqCwqd7Dxet3cUKcjXunTWf4eefh/PFHql+bz8k//cTJG6rZlQrfTvWxNHsRnuSvMLizCDpGoHGOwuEx88HqIj5YXUSsxcDpg9M4fWgax/dOlB6GQgjRRhqNhnvuuYc//elP7Nixg/r6egYNGoTN1nJiRnRd9d4AN/x3Fd/nVaLTanjknCFcdFzbk2oF9gJu+OYG9jj2EGuKZY99D33i+zQqE3Q4KJ59K84ff8S9bj2Zc188aC/Dj8pqGGgzMyAqNDz4/LSEZssdqLzAzhcvbaC+xovRoufUKwfRa2jbV2MWQgghREiHDTfes2cPY8aMYeHCheG5CCdNmhSR4cY+n4/CwkLq6up4//33eeWVV1i6dOlBE4XN9STMysrqUkNGhOiMnHVeln24k23LQ3MKBWMNbDs9mQUaL76GHz3Tk2P5Y680BtoseLZtp/rfr2P/dAGq34/TBMuOj2PpcVa2actRVQ1Bd0/0zuMIOobh8u7/niPKqGNS/xROHZTK5P4pxFoNHfKZhRDiaOiKw1uPZV3lz6Pc7uGK+SvYtNeOxaDjxRmjmDwgpc31rKtYx02LbqLGW0MPWw/mTp1LTmzj4b3+vXvZc931ePPy0Fgs9Pj734k+ZXKTuhRV5fH8Up4uKCPbbOTrMf2IMxy6H0PxthoWPL+OoF8hLtXKmTcMJT4tqs2fRQghhOgOWhurdFiS8OOPP+bcc89Fp9vf8ycYDKLRaNBqtXi93kb3fq0tcxJOnTqV3r17M2/evFa1rasEekJ0FXt31PLd29upKqoHQNPHxqoTE/jcVc++H0C/SY7jtl6pDLRZCFRWUvPmW9S89RbBmhoACtP0/DQ9m8XpNdQEHaGEoSuHGN8k3PY+2F37hx3rtRrG5SZw2qA0Th2USkacTFouhDi2RCpW8Xg8PPfccyxevJjy8nIURWl0f/Xq1e1tarfQFWJHVVU5+4UfWV9UR2KUkVd/P5bhWXFtrmdx4WLu+O4OPEEPgxIH8cKUF0iyNO695960iaLrbyBQUYEuOYmsuS8ddIGSm7cU8r+KOgBuzE7hz7npaFuxErHPHeD9x1YSk2Th1KsGY7J0+CxKQgghRKfV6ZOEDoeDgoKCRteuuOIKBgwYwJ133smQIUNafH1bkoSnnHIK2dnZzJ8/v1Vt6wqBnhBdjRJU2PT9XpZ/uguvKwCA9cQUvh9k4fNaR7jcgclCxePB/uWX1L71Nu516wAIaGH9xDS+PyGOnzX5BNUgqqoBbzYp6hRctX0prW38n4tB6TFM6p/MpP4pjMqOQ6+TeQyFEF1bpGKVGTNm8PXXX/O73/2O1NTUJkNBH3jggfY2tVvoKrHjqoIa7vloA/MuG03PxLb3uvts12fc88M9KKrCiT1O5MmTn8RqaLz4iGPxYopv/yOqy4Wpb1+y5r2EISOjSV0lXh+Xr89nfb0bg0bDk/2zuDC95SHGAV8QnUEb/nvqsvsw2wxoD2dCRSGEEKIb6fRJwub8erjxzJkz6dGjB3PmzAFCw4g3b94MwJlnnsmMGTOYMWMGNpuNPn1Cc6DcfffdnHHGGWRnZ+NwOHjzzTd57LHH+Oqrrzj11FNb1Y6uEugJ0RW5HT5+/ngnm38qARX0Jh0J03rwVaaOz6rqwuUOTBYCeLZsoebtd6hbsADV5QKgNsHEynMH8H1PF1vc+eHX6vxpZOqm46nrx84ylQN/ykWb9ZzYN4lJ/VI4uX8yqTHmo/PBhRAigiIVq8TGxvL5558zceLECLau++nMsaPd4yfGvH8KDkVRDzuptqtuF5d/cTmnZJ/CfePvQ69t3HtP8XjYefoZBEpLiTr+eHo88zS66Ogm9ay1u7h8wy7KfAESDDpeG5JzyAVK7FVuPn9xPQMmpDNiqixMIoQQQrTFMZEknDRpEr169Qr3ANy9ezc5OTlNXnfyySezZMkSAK666ioWLVpESUkJsbGxDBs2jDvvvLPVCULo3IGeEMeKsnw7372znfLdodXEo2KNJJ2VzSfxQRY0DDvSAGclx3FTzxSGRYd6KgTr66n79FNq33obb15euL7KoZn8cnpPlsSWsNtZGL5u1iTTx/AbFNdAthZpqHMHGrVjYHoMJ/dL5sS+SYzuGY/ZIIufCCE6v0jFKoMGDeLtt98Ozw8tDk9njR3fX1XEXz/bzBtXj2NIj9iI1FlSX0JaVNpBFyDxbN5M7QcfknrXnWgMzc8PfOn6XXxTZad/lJl/D82hp8XU8nvuqOWLeRtwO/xExRq55KHxGM0yvFgIIYRorS6ZJOwsOmugJ8SxRlVVdqwqZ9lHO3FUeQBI7BFFyv9l85bOy4KK2nDZk+JtzMpO5aR4GxqNBlVVca9ZQ+0772D/6mtUT+j1qlZL2WkjWH58PIs0Wyl1lYXrMOusDLadgdU3lsKyaDbtdTTqZWjUaxmdHc/EPokc3yeJYT1iZWiyEKJTilSs8sUXX/Dss8/y0ksv0bNnzwi2sHvpbLGjqqq8tHQXj325FYArJvbigd80nRPwULxBL/f9cB/n9j2XCRkTmn8vnw/P1q1Y2pBorvEHmLOrhPt6ZxCtb/nLuS0/lbDkja0oQZWkLBtn3jCM6AQZBSCEEEK0hSQJ26GzBXpCHOuCfoX1S4pY9cXu8HyF2YMSSDori/966vm4vIZgw0+qoTYLs7JTOCs5Dn3DcKlgfT2Or76i9sOPcK9atb/imGiKfjuOX4aZWepaR4mzJHzLoDUwMvEk0jSTqK1JZ+VuB2X2/aucA0Sb9IzLTeD43kmMz02kf1o0Opn3SAjRCUQqVqmoqOCCCy7gu+++w2q1YvhVz6/q6ur2NrVb6Eyxo6Ko/PV/m3ntx90AXHtSLnedPqDNQ4ztPjs3f3szq8pWEWeK46vzvmoy/2Cwvp7im2/GtWo12fNfwzpyZLN1+RWVzytrOTslvk2fY9lHO1m7MDQ6IHdkMlN/PwiDSXr8CyGEEG0lScJ26EyBnhDdicfpZ+Xnu9mwpAglqKLRwIAJ6WSclsl/HXW8sbcad8PKm9lmI9dnJXNReiLWA3r7+QoKqP34Y+o+/oRAyf6koD6zB2W/Gccvg/Qsca4hv27/HIZajZahicMYHHMKeu8gdpboWLarmjq3v1H7os16xvSMZ0yvBI7LSWBYZiymQ/SAEEKIIyFSscrUqVMpLCzkqquuanbhkssvv7y9Te0WOkvs6A0Euf3ddXy2PvT7797pA7n6xNw211PuKueGb25ge812bAYbz0x+huPSj2tUxl9Wxp5rr8O7bRsaq5XMZ57BduIJTeqq8we4auNufqit5299e3BlZvIh319VVb54aQP56yoBGHNmL447KweNfFEnhBBCHBZJErZDZwn0hOiu6ipcLPtoFztXlwOg1WsYfEIPck/twTt2B/8qrqDaHwQgwaDj8owkLu+RRJppfw8YNRjEtXw5tR9+hGPRIlS3O3zP2Kc3NdMnsGKQnsX2VWyp3tLo/VOtqUzscSK9jCdQb89gxW4Hq3ZX4/QFG5Uz6rUMz4xlbK8ExvZKYGR2HHFW45F6LEIIERapWMVqtbJs2TKGDx8ewdZ1P50hdnR6A1z7n5X8uKMKg07Dk+cP5+wRPdpcz+663Vy38Dr2OveSZEli7tS5DEgY0KiMNy+PwmuvI1BSgi4piayXXsIypOlw5gK3l0vX7yLP5SVKp+WlQT05Nal1cyOuX7yHnz7cyZSZA+k7NrXNn0MIIYQQ+0mSsB06Q6AnhIDSXXX8/MkuirfVAKAzaBl6cg8GTs3i43oHL+2poNDjA0CvCS1ycnVmMqNjrI16wyguF/VLllD3+ec4l36H6t/fQ9A8eDCeM09k7WALPzk38HPJz3iCnvB9o9bI2PSxTEg7gRT9SEoqLazYXcOK3dVU1vuatDk3KYoR2XGMzI5nZFYcA9KiZV5DIUTERSpWGTVqFC+++CLjx4+PYOu6n84QO/qDCle9vpJVu6t56bLRnNj30D32fm1j5Ub+8M0fqPHWkB2dzbxT55EZndmojHP5LxTdeCOKw4ExJ4esl/+JMTOzSV2r6pzM3JBPlT9AusnAf4flMthmafH9D1x5WVVVHFUeYpJafo0QQgghDk2ShO3QGQI9IcR+RVurWf5pPqW7Qqse6006hk/OZMjULL51uXi1qIKf65zh8sOiLVydmczZKXGYtI0TdEG7Hcc3i7D/7384f/4Zgvt7B5oGDMB46iS2jU7lZ3byffH3FNcXN3p9iiWF8RnjGZc2ngzTcHaVavhldzWrCmrIr3Tya2aDlmE94hiZHcewzDiGZcaSGW856KqQQgjRGpGKVb7++mseeughHnnkEYYOHdpkTkKJg1qns8SOTm+AwmoXA9MPrw0PLXuI97e/z+DEwbww5QUSLYmN7rs3bKTgkktQ/X4so0aR+cLz6OObzjP4aXktN28pwKOoDLVZ+PewHNJNLfe0376ilDVfF3LOrSMxWZtfFVkIIYQQh0eShO3QWQI9IcR+qqpSuLma5Z/soqLQAYDRomfE1CyGnZLF9oCPV4sr+bCsBq8S+rGWaNAzMyORmT0Sm/3PSaCqCvtXX+H48itcK1dCw3yHAIbsbGxTp1B5wiBWRFewrGQZq8tX4w02XtykT1wfxqePZ0LGBHJtQ8kr87OmsJY1hTWs3VOLwxNo8r5xVgNDe8Tu3zJj6REniUMhROtFKlbRNnyR8uufP6qqotFoCAaDzb1M/EpHxY4rd1fzzZZy7jy9f0R+h/gVP6+sf4WZg2cSZYhqcl8NBimefSsAGU88jtbcdJXhXS4vJyzfggKclhjD3EE9iWph/l5VVVnxv92s+Cw0V/Bxv8lh7PScdn8WIYQQQuwnScJ2kCShEJ2Xqqrkr6vklwW7qCoO9dwzWvQMndSD4adk4TJpebOkiteKK9nrDQ0r1mlgamIMM9ITOSUhJrwq8oEC1dXUL16MY+E3OH/6CdW3fyixLjmJ6EmTMZw0gbzeFn6uWcOyvcvYWr0Vlf0/QrUaLQMSBjAmdQxj08YyInkklXYdawprWLOnlg1FdWwtteMPNv2xG281MKRHLIPSYxiUEcPA9Bhyk6JkqLIQolmRilWWLl3a4v2TTz75sOvuTjoidvxyYym3vL0Gb0Dh8d8N44IxWYdVz4/FPzI+fTw6bfOJPFVVIRhEo9cDoPh8aHQ6NLqDJ/5eKCyn1OvjwT490LWQvAz4g3z7763krSgDYMSp2Uw4t3ebV2IWQgghRMskSdgOkiQUovNTFZUdq8tZ8Vk+NaUuAPQGLYNOzGDkqdmYY018UVnHv341FDndZOCitAQuTk8g22Jqtu5gvRPnD9/jWPgN9UuXotTXh+9pDAasY8cQddJJBCaMZI2xhJ9LfmZ5yXKK6osa1aNBQ/+E/oxJHcOY1DGMTB1JlD6W7aX1rC+uZWNxHeuL6thW6iCgNP1RbNRr6Z8azcD0aAalhxKH/dOiZXEUIYTEKp3M0f7z+Pey3Tzw6SZUFaYOTOG5i0dhMR48adccVVWZt34eL6x9gQv6XcC94+9t2qPU76fk3vsASH90zkF7KzoDQeqDCqkNC4jt64naEpfdxxcvrad0lx2tVsPJl/Rn0AkZbfoMQgghhGgdSRK2gwTeQnQdqqKya10Fq74oCA9D1uo0DBifxshpPYlLsZLn9PBGSRXvllaHV0XWACfHRzMjI5FpSTEYtc332FN8PlzLl1O/9Dvqly7Fv2dPo/uGrCxsJ52E7eSTcAzKYpV9EytLV7KqbBW77bub1Jcdnc2IlBEMTx7OiJQR9I7tjT8I20odbNprZ0uJnc0ldraW2JusprxPSrSJfqnR9E210T81mr6p0fRLtRFtljmchOguIhWrfPfddy3eP+mkkw677u7kaMWOqqry+FfbmLtkJwAXH5fFX88e0uZe54qq8Ngvj/Hm1jcBuG7YdcwaMavxol9OJ0Wzb8X5/feg09Hr7bewDB3apK4yr5/L1u/Cr6p8MqovMS0MLd6nptTJgmfX4aj2YLLqOf3aIWQOSGjTZxBCCCFE60mSsB0kSShE16OqKnu2VLPqiwL25tUCoNFAn9EpjDq9J0mZ0XgVhS8r63hjbxXf1ezvHZho0PPb1Dh+l5bAMNvB5wZUVRVf/m7qv1uK87vvcK5YCQeslIzBgHXECKKOn0DUhAk4eqeyunIdK8tWsrJ0JTvrdjap02awMTRpaDhxODhxMHHmOBRFpbDaxZaS/YnDLSUOimvdB30GGbFm+qRG0zs5it7JtoYtiuRok8x3KMQxJtJzEh7owJ8XMidh6xyN2NEXULjrg/V8uCa0oNZtp/bjplP6tPnnuz/o594f7+Xz/M8BuOu4u5gxcEajMoHqavZcdz2eDRvQmM30ePopoidNalLXVqebGet2Uez1k2jQ8/6I3gw8xArGEOpF+N6jK9DptEyfNYz4tKbzHwohhBAiciRJ2A6SJBSiayvZUcuqrwoo2FAVvtajfzwjpmTRc0giGq2GAreXN0uqebukijLf/sVF+lpNnJ+WwG9T48k0tzysV3E6cf78M/VLllL/4w8E9pY0uq+12bCOG0fUhAlEHT8BT0YiG6o2sLZ8LWsr1rKhYgOugKtJvZm2TIYkDWFI0hAGJw5mUOIgrAYrAPXeAHllDraXOdheVt+wd1Bm9zapZ59ok57cFFs4edgrMYpeSVZ6JUYRZdK36pkKITqXSMUqdXV1jc79fj9r1qzhvvvu45FHHmHKlCntbWq3cDRixxW7q7lw3jI0Gg1zzh3KBWPbPgehy+/itqW38WPxj+g1eh4+4WGm505vVMa3Zw+FV1+Nv6AQXVwcWfNewjJ8eJO6fqhxcOXGfOwBhT5WE28My6XnQabxaE5NqROLzYjZJr3ghRBCiCNNkoTtIElCIY4NFXscrP6qgJ2rK1Ab5vyLS7UybHImAyakYzDpCCgq31bb+aCshq8q6/AcMDfghLgozk9N4KyUuEMOn1JVFX9hIc5ly3D+tAzn8uUov/rPty45CeuYMVjHjsU6Zgz63Bx22nextnwtayrWsLFyIwX2giZ1azVacmNzGZw4mIGJAxmQMID+8f2xGW3hMnUuP9vLHewsr2dnRT07K5zsrKhnT7WLZqY7DEuONtEr0dqQOIyiV2IUPROtZMVbibXKf9yE6KyOdKyydOlSbrvtNlatWhXxuo9FRyt2fGdFISnRZiYPSGnza1VV5Zqvr2F56XLMOjP/mPQPTsw8sVEZz+bNFF57HcHKSgw9epD18suYcpuuNPx+aTW3bt2DX1UZFxvF/KE5xBsO/qWTqqgs+3gniRlR9B+f3ua2CyGEEKJ9JEnYDpIkFOLY4qj2sH5xEZt/2IvPHeo1aLLqGXRCBkMnZRKdYAbAHgjyWUUt75fW8FPt/uHIZq2GKYkx/CY5jlMTY4hqxXxLajCIZ/OWUNJw2U+4V61utGIygC42FsuYMeHEoXlAf+xBJ5urNrOpahMbKzeysXIjZa6yZt8jKzqLAQkDGm3JluRGQ8+8gSAFVa5w8nBXhZPdVU52V7modvqarXefGLOe7IaEYXaClcyE0D4r3kJGnAWzoW2T5AshIudIxypbt25lzJgx1B+wcJM4uK4SOy7Zs4QHfnqAZyY/w4iUEU3uO3/6icLrrsfUuzdZ/5yHIaVpMvKNvVXcvi00P+/ZKXE8MyAbcwtzIgZ8Qb55bTM711Sg1Wu49C8Twr93hRBCCHF0SJKwHbpKoCeEaBufJ8DWZaWs/3YPdRWhuf00Wg19RiUzZFIm6b1jwwm2Io+PD8tqeK+0mjzX/qG8Fq2GU9qYMARQvF4869fjXLEC98qVuNasRXU3nl9QY7ViGToUy4gRWEYMxzJiBPr4eCpcFWyq2sSmqk1srdrK1pqtlDpLm32feFM8feL70Deu7/59XJ9GvQ73qXP7KWhIGO6ubEgeVjoprHZTWX/w4cv7JNmM9Iiz0CPeEtrHhZKHPeItZMRaiLMaZC5EIY6QSMUq69evb3SuqiolJSU8+uijBAIBfvjhh/Y2tVvozLGjoipoNfuTeC6/KzyFRXOcP/2EeehQdNHRzd7f6/ExfXUev02N557cdLQt/Jx32X18Pnc9Zfl2tHoNp1w2kP7j0g7/wwghhBDisEiSsB06c6AnhGg/RVEp2FDJukV7KN5eG76ekBHFkJN60G9cGiZLaNiUqqpsrHfzaXktCypq2e3e3/vucHoY7qP6/Xg2bcK1ciWuFStxrV6N4nA0KWfs2TOUNBw5AsuIEZj69EGj11PrqWVrzVa2VW9jS/UWtlVvI78un6Da/CID6VHp9I0PJQxzY3PJic0hJzaHaGPz/wl0+QIU1bgprHKxp8ZFYbWLPdVuCqudFNW4cR1k5eUDmQ1a0mMtpMWYSY81kxYb2qfHWkiLNZMSYyIxyoROK4lEIdoqkguXaDQafh0Ojh8/nldffZUBAwa0t6ndQmeNHTdVbeL+H+/nmcnPkBmd2WyZmnffxTpmbLPDivcJqiq6A5KBNf5Ai8OLAar3OvnshXU4qjyYovScef1QMvrGH94HEUIIIUS7SJKwHTproCeEiLyKQgcblhSRt6KMgF8BQG/U0m9sKoNP6kFKz/0/A/YlDBc0JAzzD0gYmrQaToyPZlpSDKclxpJqatt8fmowiHfHTtxr14Y3X35+k3IasxnzwIGYhwzBMmQw5iFDMPbqhUanwxPwsKtuFztqd5BXk0debR55NXmUu8oP+r7JluRGScPcuFx6xfQixZrSqOdJo7aqKnVuP0U1bopr3RT/ar+31k3VIYYy76PTakiJNpESYyY12kRqjJnUmNB5SrSJ5IZNkolCNBapWKWgoPE8qFqtluTkZMxmGQ7aFp0xdlxespybv70ZV8DFaT1P4++T/t7ovqqqVD73HJUvzkWfkU7uhx+ii4trUk+Fz8/lG/K5ISuF36Q0vd+cPVur+XLeRnzuALHJFs66cThxqQfvvSiEEEKII0uShO3QGQM9IcSR5XX52ba8lI3f7aWmxBm+ntIzmsEn9aDvmFQMpv09BVVVZVO9mwUVdSwor2WXu/Hw3JHRVqYlxTAtKZYBUebDGnYbrK3FvX79/sTh+g0ozcwPprVaMQ8ahHnwYMyDB2EaMABTTg4aQyhRWeetY0ftDnbU7CCvNo/ddbvZVbeLCnfFQd/brDOTFZNFz+ie9IwJbdkx2fSM6UmiOfGQn8fjD1Jm91BS56G0LrQvqXOH92V2L5X1Xlr7G0irgUSbiWRbKGmYEm0iKdpEYpSRJJuJJJuJRFvoON5qQN/C/FhCHAskVulcOtufx8KChdz53Z34FT/j0sbx9OSnG007oQaDlD70F2rffReApBtvJGnWH5r8bN/l8nLxup0UeHykmwz8NG4gllb8fF2+YBcr/7eb9N6xnHHDUCw2Y2Q/oBBCCCHaRJKE7dDZAj0hxNGjqiolO+rY+F0xO9eUowRCPyINJh19RqcwYEIa6X3iGv1HSlVVtrk8fF1p58vKOlbbXY3qzDYbmZYUw9TEWMbFRrU4wXuLbVMUfAUFeDZuwrNxI+5NG/Fs3oLqcjUpqzEYMPXti2nAAMwD+jfsB6A74Geaw+cgvy6fXXW7Gu2LHcUE1MBB22HVW8mMziTTlhnaR2eSFZ1Fpi2TDFsGRl3r/jMYCCpU1vsos3sO2LyhvcNLRcNW5Wx9MhFAo4F4q5HEKCMJh9jiraHNYpRFWETX0p5Y5dlnn2112ZtvvrmtTeuWOlPs+N729/jrsr+iojI1eyqPnvQoJp0pfF/xetn7xz/iWPgNaDSkPXA/8Rdd1KSe1XVOLt2wi2p/kGyzkbeG59Lb2roepqqqsnFpMQMnpqOXRa6EEEKIDidJwnboTIGeEKLjuB0+tiwrYdP3e7FX7F9kJCbJzIAJ6fQfl0ZMkqXJ68q9fhZWhRKG39c48Cj7f8xatBomxNk4JTGGyQnR5FpM7VrcQw0G8eXn4964Ec+GjXi2bsW7dSuK09lseX1Geih52KdPw74vpt65aC37P4df8VNSX0KBvSC8FToKKbAXsLd+LyoH/7WhQUOKNYUeth6k29LJiMpotE+PSseib/rMWhIIKlQ7fZQ7vFTU708eVtZ7qaz3UVXvpareR2W9l2qXr00JxX1Mei0JUUbirEbirQbirUbiDtjHWAzEWQzENZzHWkKbrPAsOkp7YpWcnIPPPXcgjUbDrl27Dqd53U5niB1VVeXlDS/z3JrnAPhdv99x77h70Wn3/5wK2u0U/WEWrpUr0RgMZDz5JDHTTmtS11eVdVy/aTduRWVYtIU3huWSbDz4NBrBgMKarwsZMTULvXzpIoQQQnQ6kiRsh84Q6AkhOo99vQu3/lzCjlXl+D37F+3o0S+O/uPT6T0qGaO56STuzmCQ76vr+bqqjsXVDkq8/kb3s8xGJidEMzkhmhPio4luw+InB22vouAvLg4lDLdsxbNtG94tW/Dv3dv8CzQaDFlZocRhnz6Y+vTGmJODMScHna3xqsi+oI/i+mKKHEUU1RdR5Chij2NP+NgdcDf/HgdIMCeQFpVGqjWVtKi0JscplhQMurbN6bhPUFGpcfn2Jw2dvoNuVU4ftS4fAeXwfw2a9NpwwjDGYiDGrA8fx1oMxJgNRJv1xFhC+2jzvr2eGLMBk14rK0CLwyKxSufSGf48fEEfl39xORurNnLN0Gu4aeRNTX6+lNz/ALXvvovWZiPzhReIGndck3peL67k7u1FKMApCdG8PLhXiwtzeV1+vpi3geJttfQdk8JpVw+J9EcTQgghRDtJkrAdOkOgJ4TonPzeILvWVrB1WQlF22rY16lOb9SSMyyJPmNS6Tk4EZ2h6ZBiVVXZ6vSwuNrBkmo7P9c68R3wI1ingRHRVk6Ij2ZinI0xsVFYIzi3XrCuDu/27Xh37MCbt6Nhn0ewpuagr9GnpIQShrk5mHJyw8lDQ3oaGl3j/zSqqkq1p5qi+iJK6kvY69zL3vq9lDhLwnunv/kejgfSoCHRkkiKNYUUSwrJ1uTQccOWbEkm1ZpKrCm23Qk2VVWp9waodfmpcYWSh/uOa1x+6lw+at1+6tx+al2hfejYRztyi2EGnYZoswGbSR/azHqiG/YHnkft24x6okw6bA3ntvA9HaYIJJhF13EkYpV9IaEkrtuus8SOle5Klu5Zynn9zmv2frCujuJbbyXljjswH2Tl6gd2FDNvTwUXpyfweL8sDC0sGmWvcvPZ8+upKXFiMOk4/dohZA9OjMhnEUIIIUTkSJKwHTpLoCeE6Nwc1R62LS9l67IS6sr396AzWvTkjkii75hUMgfEoz1Ios8ZDPJTTT2Lqx0srrY3Wi0ZwKjRMDrWysS4aE6ItzEqxopRG/kFOQJVVXjz8kKJw7w8fPn5ePPzCVZWHvQ1GoMBQ1YWxuxsjD2zMWRnY8zuGTrOyECjb9qrUlVV7D47Jc4SSp2llDpLKXOVNTouc5bhU1q3MrJeqyfJkkSSOYkka1Lo+IDzRHMiiZZEEs2JWA2RXVVTUVTqfQHqGhKHdo8fu9uP3R3A7mm41pBQdHhC1xyeQPi43hs4rGHRLTHoNFiNeqKMOqymhn1DUnHf3mLQYzXqsJp0WA2hclajjiijHrNBF7pn1IWPLUYdZr0Oraws3elEMlb597//zRNPPEFeXh4A/fr1409/+hOXXXZZJJraLXRU7FjhquC7ou8OmhQE8O/diyEjo9V1KqrKZxV1/Ca55S9iygvs/O+F9bjsPqLiTJx14zCSMqPb1H4hhBBCHB2SJGwHSRIKIdpCVVXKCxzkrSxjx8pynLX7Vzo22wz0GZVC37EppPeOQ9NCsmWPx8ePNQ5+qKnnx9r6JkOTLVoNo2KiOC42inFxUYyOiYrI8OSDCdrt4YShb1d+w/Eu/AWFqH7/wV+o12NIS8OQmYkhswfGzEwMPULHhh490CcnH/Q/nqqqUuOtodRZSoWrgnJ3OeWu8tCxq+HYXUG1p7pNn8Wit5BgTiDBnECiOZEES+g43hRPvHn/lmBKIM4c1+Z5E9tqX5IxlDj04/SGjuu9Aeob9o3OfQGc3gAub5B6bwBnw3m9N4DHrxzRtgJYDKGEocWgw2zQHnCs+9W9fZs2tNeHypoNoZ6O4esGHSa9Nlw2fE8Skq0WqVjlH//4B/fddx833ngjEydOBOCHH37ghRde4OGHH+bWW2+NVJOPaR0RO+6o2cEfFv2BEmcJc06cw1m5ZzUp4/j2W4pvu52U228n4bJLm62nPhDk2YIybs9Jw9TKL6J2r6/kq1c2EvApJPawcdaNw7DFt25REyGEEEIcfZIkbAdJEgohDpeqqJTsrCNvZRk7V5fjduxPpllijOQMTyJ3RDKZ/ePR6Q/+nzFVVcl3+/ihxsGPtfX8UFNPlb/xisNaYLDNwnGxURwXF8W4WBtppsOby68t1GCQQGkpvsJCfAWFDfsC/IUF+Ar3oHq9Lb5eYzJhyMho2NLRp6djSN9/bkhNRWNseYVkf9BPlaeKSnclFa4KKj2VVLorqXQ17Bu2ak81nqCnzZ/RorcQZ4prtMWaYokzH3C877oxlhhTDNHGaLSayPf0PJRAUMHlD+LyBsPJQ6c3iMsXwOkLNpwHcPuCOH1B3L4ALl+w4TUNx74gbn8Qty/0Orc/eFSSj80x6DSY9PuTiCa9FqNei6nheN9m1Gsx6kIJRuOB1xrdO/Bc1+jegXuDTvOr89Ber9V02qG3kYpVcnJyeOihh5g5c2aj66+//joPPvgg+fn57W1qt3C0Y8efS37m1sW3Uu+vp1dML16c8iJZMVmNytS8+y6lDz4EioJt0iQyX3wBza+SgGVePzPW72JjvZuL0hJ4emD2Id/b7w3yn/uW4bb7yBoYz+nXDsVoadp7XAghhBCdhyQJ20GShEKISFCCCsXbaslbWcautRV4XfuTfEazjp5DEskZkUzPIYnNLnpyIFVV2ebysKLOyfJaJ7/UOSn0NB2Wm2k2MDI6ilExVkbFWBkabY3ovIaHoioKgfJy/EVF+IqK8BcV4y8uDp0XFxEoLQPlEMknjQZ9UhL6tDQMaanoU/ftQ5shLQ19aipak+nQ7VFV3AE3Ve4qqjxVVHuqQ3t3aF/rqaXaW02tp5YaTw3V3moCSuCQ9TbbbDREG6OJMcYQa4olxhhDjCkmtDeGkoj77h+4txltRBujMekO/XmOJkVRQ4nDcPIwiGffuT+IxxdsdN/tC+IJhJKL+8p5G449gYb7fgVvYP/e61fwBIL4g503FDHqQklEQ0MS0XBAYtGg06LXaTE2HBvC9zXotVqeunAEuiPUMzJSsYrZbGbjxo306dOn0fW8vDyGDh2Kx9P2JHt3dDRjx092fMKDPz1IQA0wKmUUz0x+hjhzXPi+qqpUvvgilc89D0Dsb39L+kMPojE0/hIpz+nh4vU7KfL4STTo+e+wXEbGtG5qhpKddWxfXsoJF/ZFdxR/xwghhBDi8EiSsB0kSSiEiLRgQKF4ew35ayvZta4CV93+BJ9OryVzYDw5w5LoOSQJW3zrkkUlXh+/1Dn5pdbJijonG+vd/Dr9ptPAwCgLo2KsjGzY+lrN6Dqod5Tq9+MvKcG/twT/3r34S/biLykhED4vOWRPxH10sbHoU1LQJycfZJ+EPikJraX1Q4dVVcXpd4YThnXeOmq9tdR6aqn11obPw9e9tdh99lat6nwoBq0hlDQ02LAZbaH9AcdRhiiiDFHYDDasBmv4ntVgJUofFb5v1ps7pEdjewQVtUny0BtoOA4oDeeh+75g6L4vqOzfN5T1BZT92wH3913zNhz7f7X3HbCPZFSUP+fMI9YTMVKxypAhQ7jkkkv485//3Oj6ww8/zDvvvMOGDRva29Ru4WjEjqqqMnfdXOaumwvAGb3O4K8n/LXRFwxqIEDpX/5K7bvvApB4/XUk33JLk7+Hv9TWc/mGfGoCQXItJt4cnksvy8F/9wT8QWpKXCRny5yDQgghRFckScJ2kCShEOJIUhWVst12dq2tYNfaikaLngAkZtroOSSRnkMSScuJOejCJ79WHwiy1uFijd3FaruL1XYnZb6mveIsWi2DbWaGRlsZGm1hmM1CvyjzEVkUpa1UVSVYU4O/eC+B8jL8ZWUESssIlJXiLy0jUFqKv6wMtQ29m7RWK7qkUMJQn5iILimx4TgJXWIC+oQEdPEJ6BMT0MbEHFZSxx/0U+erw+61N9rXeetw+Bw4fA7sPnuzx/X++ja/X0s0aLDoLeGk4b5jq8GKVW9tdm/RW7DqQ3uL3oLFYGlyzaA1dNqht5EUCIaShf6gGk4kHphM3Hf9wHL7yhx4HAiqXHlCzhFrZ6RilQ8++IALL7yQqVOnhuck/PHHH1m0aBHvvvsu5557bqSafEw7GrHjytKVXPHVFQBcPfRqbhp5U6MvBFRFoejmm6n/ZhFoNKTedy8Jl1zSpJ7/VdTyh80FeBWVUTFW/j00lyTjwXuze5x+Pp+7nso99Zz7x1EkZ0miUAghhOhqJEnYDpIkFEIcLaqqUl3iJH9tBbs3VFG22w4H/FQ2WfVkDUqg55BEsgclYo1pea6+X9e91+tvlDRcX+/GFWw63Neo0TDAZmaYzcogm5lBNgsDo8zEGjrfPFOqqqLU1RGoqMBfXk6gooJAeUXDvrzRvrW9EsP0enTxcegTEtElxKOPT0AXF4cuPj60xcWhi49DFxeHvuFcY7G0K3kWVII4A06cPicOv4N6Xz31/vrw3uFz4PQ7cfqd1Pvrcfld1Pvr91/z1eMKuHD6nagcuV/pOo0Oi96CWW9utLfoGl8z6UxNj3VmTHpTaK8zhe+Z9eYm14w6Y5frCdkR2hurbNy4kSFDhgCwatUqnnrqKbZs2QLAwIEDuf322xk5cmRE23wsO1qx49x1c0myJHF+v/ObvV/16mtUPP00GU8+QcxppzVtZyDI+J83U+0PMi0phrmDerU4JYW90s2C59ZRW+bCaNZx5g3D6NE/PmKfRwghhBBHhyQJ20GShEKIjuKu91G4qZqCjVUUbq7C62zcEzApy0bmgAQyB8ST0ScOg6ltqxsHVZVdLi8b692sd7jY4HCzod5NXSDYbPkeJgMDGxKGg2wWBtrM9LaYMXSBFWhVVUVxOglWVhKorCRQWUWgqpJgVRWBikoCVVUEq6sJVFcTrK5GqT+8Hn0aoxFdbCy6uFi0sbHoYuNC57Gx6GJj0MbEoIuJRRcTjS5m33lorz3EAi1t/bzugBtXwIXL7wonEfeduwKu0P2G4wP3zoATt9+NO9B08ystrGR9hBi1Rkw6Eya9KbT/1WbQGcIJRZPOhFFr3H+sCx3vu2bUGTFo95c3ao0YdIbGZQ64ZtDu33fmZGV7YxWtVsvYsWO5+uqrueiii4iOlt5h7dGRsaOqqo2+qPDt2YMxK+ug5ZfV1vNZeS0P9emBvoWf5eUFdj57fh1uhx9bvImzbhxOYg9bRNsuhBBCiKNDkoTtIElCIURnoCgq5bvtFGysomBjFRWFjkb3tToNabmxZA6IJ2tgAik9o1s9NPlAqqpS6PGFE4abG7Zib/PJIYNGQ47FRL8oE/2izPSzmukXZSbXYsLchSewV3w+gg0Jw0BVNcGaaoI1NQRqagjW1hKsqSUYPq4hUFsL/vYl0DQmE9qYaHS2aLTR0ehsNrTR0WijbQ3XbOiio9FG2dDabGhtUeiiohqObWijotBarWh0bUsWt4Vf8YcShn43nqAHd8CNJ+AJJxEbHQc9eAIevEEv7oAbb9CLJxC6duA9T9CDN9CwD3rxBrwE1MNbMOZI0mv0GHSGRonDXx/rtfpG5/vKG7QGHpjwADrtkfmzaW+s8v333/Paa6/x/vvvoygKv/vd77jqqqs48cQTj0Brj30dFTs6vl1M5byXyH7lFXQHSfQ6A0HyXF5GtHJREoDd6yv56pWNBHwKiZk2zpo1vNXz5QohhBCi85EkYTtIklAI0Rm57D6KtlVTtLWGPVuqqa9uPJTWYNaR0TeOjD5xZPSNIzk7Gp3+8JN2df4AW50eNjs9bKl3s6Xew2anG2czw5UBtEAvi4m+USZ6W8zkWk3kWkzkWk2kGvXH3Hx2oZ6KLpS6WoJ2O8G6OoK1daF9XR3BulqCtbUodgdBh4OgvS50bLejOBxEcoUMjdWKNsqKzhqFpmGvjdq3WdFa9+2tobLhLaphb0FrsaCxWELnFssRTTw2J6AEQgnDhqRh+PjALeDFp/jwBr34gr7w9X3HB+59ig9/0I9P8YXOgz78ih9v0Itf8YfOD7yv+A57ZeuDWT9zfadfuMTpdPLuu+8yf/58vv/+e/r06cNVV13F5ZdfTlpaWgRbfGw72rGjqqpUvzaf8ieeAFUl6Q83kHzzzU3KlXh9zFyfz263lwWj+zIg6tALORVtq+HTp9egqpA9KIFp1w7BaO58U08IIYQQovUkSdgOkiQUQnR2qqpSV+GmaGsNRVurKdpW02Rost6gJTU3Jpw0TM2NxWBsX+JHaZjncLvTw3anhzyXh+1OL9tdnoMOWQaI0mnJtZjIsZrobTHRy2Kil8VIT8uxmUA8FFVRQkOh6+woDjtBhwOlvh7F4SDoqEepDyUWlX3HTidKvTNUpmELOp0QOHK97zRGYyhZ2JA0DCURzWgtVrRmc+jYfMB1swWtxYzGZEZjNqE1Wxr25lB5sznUc7LhWGsyoTGZjnoysiWKquxPICp+/EF/6Lwh4ehX/M3eb7Q1XAsoAa4aetURa+uRiFV27NjBa6+9xn/+8x9KS0s5/fTT+fTTTyNS97HuaMaOqt8fWsH4vfcAiLvwQtLuvQeNwdCo3EaHi8s25FPi9ZNo0POfYTmMiok6ZP3BoMLnL6wnKs7EyTP6o+vCPcSFEEIIESJJwnaQJKEQoqtRFZXKonr25tWGN4+z8VBYrVZDcs9o0nJiSc2NIS03Flu8KSIJOlVVqfAF2O7ysM3pId/tZafLS77bS6HbR/N9D0MsWg1Z5n1Jw1DisKfZSLbFRKbZQFQnSiJ1Jqqqovp8KA4HisuF4nTu3w4833fscqG43A37hs3tRnE5UZwu1IbzSPZwbBWDIZQwPDBxaDKhNRrDxxqTEa2x4dhobDg3ho6N+69pjAdeb9gM+/aGhmuG0FyQhtBeYzCEkiuGrrV685GKVZxOJ2+88QZ33303tbW1BIMHT/6L/Y5W7Bisq6Poltm4fv4ZtFpS77yD+Jkzm/zdXVhZx3WbC3AFFfpaTfx3WC49LQcfLhzwB9FqNeEpKwL+IDq9tkv9mxBCCCHEwbU2VpGxA0IIcQzQaDUkZ0eTnB3N8ClZqKpKTYmLvTtCCcOSHbXU13gpy7dTlm+Hb0Ovs8Ya9ycNc2JJ7hl9WL0NNRoNKSYDKSYDJ8Q3nhfLpygUuH3hxOEut5fdbi+73T6KPT7cisp2l4ftLk+zdScYdGSZjWSZjWQ27PcdZ5gMxOp13fI/shqNJpRMM0VunjBVVVG9XhS3O5w0DCUS3ShuF6rHg+L2oHrcKG4PiseN6m44drtQPV4UrwfV7QntPd5QGY8XxeMJvd7rbTyXo9+P4vdDfT0dnY7alzAMJxTDxw1JxAM3/YHH+gPuhY7R60m57bZO1VOyJd999x2vvvoqH3zwAVqtlgsuuICrrjpyPSFF2/kKC9lz7XX4du9Ga7WS8Y+/Ez1pUpNyrxRVcH9eMQpwYryNVwb3anGlene9j89f3EBipo2TL+6HRqNBb+gaf2+FEEIIEVnSk7AZ0pNQCHGsUVUVR5WHkp11lO2qozTfTmVRParS+FeAVqshPiOKlIaEY0rPGBIzo47Yfxj9ikqRx0eBJ5Q0LHB7KXCHzos8/haHMO9j1WnpYTKQbjKQYTKSYTbQwxRKIKY1bHHdNJHYWanBYCgZ6fWGEoceD6rPF04iql4fqs/bUMaH6vWi+rwoHm+onLdh7/eh+HwN5Ztuin/fsb/xPX/o/Ej3mhywZXOnnpNw7969zJ8/n/nz57Njxw6OP/54rrrqKi644AKiog49LFXsdzRiR395ObsvuBC0GrLmzsXcv3+TMh+V1XDD5gIALklP4LF+WS2uRl9b5uKz59dRV+HGZNVz4b3HEZ1gPiLtF0IIIUTHkZ6EQgghwjQaDTFJFmKSLPQfF1qMwO8LUlHgoDS/jrJ8O6W76nDV+agqqqeqqJ4tP5UATROHydnRJPawYTC1P3Fo0GrIsYbmKmxOnT9AkddPkcfHHo+PPW4fRV4fhW4fxV4f1f4grqBCnstLnsvbbB0AZq2GVGMoYZhqMpBuDO1TjXpSjAaSTaF9vCQTjwqNThdeQKUjqYFAOGGo+v37j32+/fcabQeUDwRQ/aE9++4HAqGEZCCAGgx06r9LZ5xxBt988w1JSUnMnDmTK6+8kv7NJJ1E52FISSH75X+ii4tDn5zcbJnpybGcEGfj5IRobsxOafHv4N4dtXw+dz1eZ4DoRDNn3ThcEoRCCCFENydJQiGE6KYMxobVkPvGAaHehvU1XioKHJQX2qkodFBR6MDt8DdJHKKB2GQLST1sJGbaSOxhIynTRnSiOaKJkViDnliDnsG25lfkdAcVSrx+9np9FHtC+71eP8UeHyVeP2U+P9X+IB5FpcDjo8Dja/mZaDQkG/Uk70seGvUkGfQkGfUkGQ37jw16Egx69C300BGdn0avR6PXg+XQK74eawwGA++//z5nnXUWui4yJFqAqW/fJte+q3YwPi4Ko1aLUavlnRG90R3i53DeyjK+mb8ZJaCS0jOa6bOGY40xHqlmCyGEEKKLkOHGzZDhxkIIEdJc4rByTz0ue/PJNqNZR2KmjYT0KBIyoohPjyIhPQprjLHDelV5ggrlPj+lXj+lvgBlXn84gVju81PuC1DRkExsq3i9jkRjKGGYYNA17PefJxr0xBv0xBt0xOn1xOp1klgUESGxSufSEX8enqDCX3bu5dXiSq7PSubBPj1a9bq13xTy4/s7AMgZnsSpVw0+rLlohRBCCNF1yHBjIYQQ7abRaIhOMBOdYCZ35P7hbS57aFhyZXGoh2FlUT01pU58niAlO+oo2VHXqB6TVU98WhQJ6Vbi06OIT4siLtVKdKIZ7RFOmpl1WrItJrJbWNkTQgusVPgC4aRhmc9PpS8Q2vyN99X+ACpQEwhSEwgCBx/q/Gsxei3xej1xBl14H6PXEacP7WMNOmIbEor7tmi9jhh9qJeQEELkOT1cv3k3m+pDCz5pCH2p05ovY+JSrGg0MGxyFsf/rs8R/xkshBBCiK5DehI2Q76dF0KItgsGFGpKXVQV11NT4qS6YbNXuA+6PoRWryE22UpcioX4NCuxKVbiU63EpVox2wyddk63oKpS4w9S4fNT4w9S7Q8csIXOqxrOa/xBagMB7AGl3e9r0WoaEoYNm25/AtGm1xGt0xGt1xKt0zWch67bDtzrdC0uZCC6BolVOpej9eehqipvlVZzz/Zi3IpCokHPswOzmZLY8nv+OoFYVVxPYg/bEWunEEIIIToX6UkohBDiqNLptSRlhuYmPFDAH6S2zB1OHNaUOKkpc1FX7g4lFhuu5a9rXJ/BrCM22UJskoWYZAuxyQ37JAu2hCPfA7ElOo2mYZ7C1v8aDSgqtYFQwrDWH6TGH6AmEKTWH6AuENy/+YPYA0FqA/v3rmAowehWVNwNvR3bw6jRYNNridLtTxxG6bRE6bVYtNrQsU6HVbfvWIv1gM2ibf7YoNF02sSuEF2dPRDkjm17+Li8FoAT4208P7AnqSZDi6+rLKrn239vYdo1Q4hNDs2/KQlCIYQQQjRHkoRCCCGOKL1B12zyUFFU6qs91Ja5qC13UVsa2teUuaiv9uL3BKncU0/lnvomdWp1DcOgE83EJJqJTrQ0Oo6KNaLpZL3l9Nq2Jxb3CSgqjmAoabhvcwQU7MF9x6Hz+mCQ+qDScH7AcTCIM6jgVUJdOn2q2tDjse3zMLZEpwGLVotZq8XSkEC06DQN+9B1s1aD+cDj8L3QsUmrxdRwbNZpGp2btBqMWi0mjWb/sVaDVhKTohuo9gf4psqOTgN35qRzY3bKIf/u71pTwcL5mwl4g/z4fh5n3jDsKLVWCCGEEF2RJAmFEEJ0CK1WQ0yShZgkC9mDExvdC/iD2Cs92Cvc1FW4qat0h4/tVW6UgBq6XuFuvm69Blu8meh4E7Z4M7Z4E7aEhn3DNZNV32V6vem1GuK1oUVQ2sOvqOFEojMYxBlQqA+GkovOoIIrqOBsuOc64NzVcM0dVHEp+++5ldA+0DCcPKjSUJ8C/gh88FbSawgnE40aLUatJrRpNBi0oUSjQRO6ZtJqMDSU2Xdt//H+coaG1zbaN3Os/9V1/QHXe5g675B50fX0sph4bmA2yUYDY2KjWiyrKiorv9jNLwvyAcgcEM8pMwcejWYKIYQQoguTJKEQQohOR2/QhVZITm/6H2FFUXHWenFUubFXeXBUeRr2bhxVHhzVXpSAir0ilFg86HuYdETFGrHFmbDGmrDFmYjat8UaiYozYY01ojccO6t+GsLJxsjW62tIFnoUFXdQwaMouIMKroa9R1Fxh48VPEE1VEYJ3fMqCp6Gch4l1OPRe8A+XEZR8CkqHqXxJJcBFQJBBWcQILK9I9tj76ThSIqwqd27d/PXv/6Vb7/9ltLSUjIyMrj00ku55557MBqN4XLr169n1qxZrFixguTkZG666SbuuOOORnW999573HfffezevZu+ffvy2GOPceaZZ4bvq6rKAw88wMsvv0xtbS0TJ05k7ty59O3bN1ymurqam266iQULFqDVajnvvPN45plnsNk635DcM5LjDlnG7w2y6PXN7FxdAcCwUzKZeF4ftDpZ+EgIIYQQLZMkoRBCiC5Fq92/4nJG36b3laCCs86Ho8pDfY2H+hov9dUeHDXe0Hm1F4/TT8AbpK7cTV35wROJEFqZ2RpjxBJtxBprxBqzbzM1XDdgiQ7tj6WEYlsYtUd35WVVVfGrKj5FDScSfWro2NeQSPQ13Pc13PM3lPWpoft+ZX8d+/bhe+r++032DWUDDceBhvcKNJTZd11RVRkGfRBbt25FURTmzZtHnz592LhxI9dccw1Op5Mnn3wSCE2ufdpppzF16lReeuklNmzYwJVXXklcXBzXXnstAD/99BMXX3wxc+bM4ayzzuLNN9/knHPOYfXq1QwZMgSAxx9/nGeffZbXX3+dnJwc7rvvPqZNm8bmzZsxm80AzJgxg5KSEhYuXIjf7+eKK67g2muv5c033+yYB9QOzjovC55bR1VRPVqdhpMv6c+giRkd3SwhhBBCdBGyunEzZMVAIYQ4tvl9QZw1Xpy1Xpx1Xpy1voa9d/++1kewjSsSG8y6UMLQFkocWqMNmG1GzDYD5igDFpshfGy2GTBZ9J1u7kTRNRxrscoTTzzB3Llz2bVrFwBz587lnnvuobS0NNy78K677uLjjz9m69atAFx44YU4nU4+++yzcD3jx49nxIgRvPTSS6iqSkZGBrfffjt//OMfAairqyM1NZX58+dz0UUXsWXLFgYNGsSKFSsYM2YMAF9++SVnnnkmRUVFZGQ0n2Dzer14vd7wud1uJysrq8P/PAL+IB/9fQ2OKjdnXDeU9D5xHdYWIYQQQnQesrqxEEIIcRAGo464VCtxqdaDllFVFZ87gMvuw1XnC+3tPlx2b/jcaffhcfhwO/woiorfE8TvaXmY84E0Wg3mKD0mqwGTVY85yoApSo+54dwUFUoomix6TFY9RqsekyV0T2/Uynx34phRV1dHQkJC+HzZsmWcdNJJjYYfT5s2jccee4yamhri4+NZtmwZt912W6N6pk2bxscffwxAfn4+paWlTJ06NXw/NjaWcePGsWzZMi666CKWLVtGXFxcOEEIMHXqVLRaLcuXL+fcc89ttr1z5szhoYceisRHbzevO4BOr0Fv0KE36Djz+qEoikp0grmjmyaEEEKILkaShEIIIUQzNBpNQ/LOQHzaIRYJUFW8rgCeej8uhw+PI7R3O3x46v14nH489X7cBxz7vUFURcXt8ON2tH2VD61WE0oaWvWYLHqMFj1Gsx6jRRc6bjjff0+HwbxvrwuVNetknjLR4Xbs2MFzzz0XHmoMUFpaSk5OTqNyqamp4Xvx8fGUlpaGrx1YprS0NFzuwNcdrExKSkqj+3q9noSEhHCZ5tx9992NEpT7ehIebYWbq1j8n630HZPK8ef1ASAqznTU2yGEEEKIY0OnSRI++uij3H333dxyyy08/fTTzZbZtGkT999/P6tWraKgoICnnnqK2bNnNyozZ84cPvzwQ7Zu3YrFYuH444/nscceo3///kf+QwghhOiWNBpNaAhxlKHF3okHCvoVPM5Q4tDr8oeSjE4/XmcAr8uPxxXA6wwlFX3uAF5XAK87gM8VQFFUFEUNJSDr27eMsN6gxWDRYzSFkocG075N/6tzHQbj/mO9SYfBqG3YN1wz6tAbtRiMOhlG3Q3dddddPPbYYy2W2bJlCwMGDAifFxcXc/rpp3P++edzzTXXHOkmRozJZMJk6rhknM8d4McPdrD5h70A5K+v5Ljf5KA3ds95UYUQQggRGZ0iSbhixQrmzZvHsGHDWizncrnIzc3l/PPP59Zbb222zNKlS5k1axZjx44lEAjw5z//mdNOO43NmzcTFdVyTxAhhBDiaNEZtOHVlNtCVVUCPiWUNGxILvrcoQSi39OQSHQHw9d8ntB9nyeI37NvHwzPtxjwKwT8Plo3QLptn29fwnBf8lBv2LfXojOEkow6ow59Q1m9QRd6nUEb3u+7Fr6uDx3r9L8+10ivyA52++238/vf/77FMrm5ueHjvXv3MnnyZI4//nj++c9/NiqXlpZGWVlZo2v7ztPS0losc+D9fdfS09MblRkxYkS4THl5eaM6AoEA1dXV4dd3Nnu2VPPtf7ZQXx2aE3HY5EzGn9NbEoRCCCGEaLcOTxLW19czY8YMXn75ZR5++OEWy44dO5axY8cCoW+rm/Pll182Op8/fz4pKSmsWrWKk046KTKNFkIIITqIRqMJ9+azxR9+T6ZgQMHvCYaSiJ4gfm8QvzcQmlfRu3/zeQLh44A3iN+nhMp5FQK+huvh/f6FXoJ+haBfwesMROJjt4pGq0Gn14QSh3ot2gOO92+ahnv7E4uNruk0jfZanQZdQ5kDz7U6TcPWcE2//1ir06DVaolPt3areSOTk5NJTk5uVdni4mImT57M6NGjee2119D+anXsCRMmcM899+D3+zEYDAAsXLiQ/v37Ex8fHy6zaNGiRqNKFi5cyIQJEwDIyckhLS2NRYsWhZOCdrud5cuXc8MNN4TrqK2tZdWqVYwePRqAb7/9FkVRGDdu3GE/iyPB5wnw0wc72PR9qPdgTJKZU2YOpEe/+A5umRBCCCGOFR2eJJw1axbTp09n6tSph0wSHo66ujqARpNh/1pzK9QJIYQQxzKdXovOpsVsM0SsTlVVQz0TfaGE4YHJw/A1/6/3Dff8CkFfkEBAIehTQnu/QsCnEAyEygX9wdC1gEIwoBL0K6iKuv/9FZWAT22UrOxIf3hxMnSfHGGrFRcXM2nSJHr27MmTTz5JRUVF+N6+3nuXXHIJDz30EFdddRV33nknGzdu5JlnnuGpp54Kl73llls4+eST+fvf/8706dN5++23WblyZbhXokajYfbs2Tz88MP07duXnJwc7rvvPjIyMjjnnHMAGDhwIKeffjrXXHMNL730En6/nxtvvJGLLrrooCsbdxSvK8D2FaGek0NP7sH4c3tjNHd4KC+EEEKIY0iHRhZvv/02q1evZsWKFUekfkVRmD17NhMnTmTIkCEHLdeZVqgTQgghuiqNRhOan/AoDntUgg0Jw4akYjBwwOZXCQaV8D1lX7ngAccBBSWo7t/7919TggrBoIqy717DcTC47756QNlQ+X3lVEWVeRkPYuHChezYsYMdO3aQmZnZ6J6qhpK+sbGxfP3118yaNYvRo0eTlJTE/fffz7XXXhsue/zxx/Pmm29y77338uc//5m+ffvy8ccfN4r57rjjDpxOJ9deey21tbWccMIJfPnll5jN+1f+feONN7jxxhuZMmUKWq2W8847j2efffYIP4W2i04wM/nSAViijWT2l96DQgghhIg8jbovGjvK9uzZw5gxY1i4cGF4LsJJkyYxYsSIgy5ccqBevXoxe/bsJguXHOiGG27giy++4IcffmgShB6ouZ6EWVlZ1NXVERMT0+rPJIQQQghxNNjtdmJjYyVW6STkz0MIIYQQnVlrY5UO60m4atUqysvLGTVqVPhaMBjku+++4/nnn8fr9aLTHX5PhBtvvJHPPvuM7777rsUEIXT8CnVCCCGEEEIIIYQQQnSkDksSTpkyhQ0bNjS6dsUVVzBgwADuvPPOw04QqqrKTTfdxEcffcSSJUvIycmJRHOFEEIIIYQQQgghhDhmdViSMDo6usk8gVFRUSQmJoavz5w5kx49ejBnzhwAfD4fmzdvDh8XFxezdu1abDYbffr0AUILobz55pt88sknREdHU1paCoTmtrFYLEfr4wkhhBBCCCGEEEII0WVoO7oBLSksLKSkpCR8vnfvXkaOHMnIkSMpKSnhySefZOTIkVx99dXhMnPnzqWuro5JkyaRnp4e3t55552O+AhCCCGEEEIIIYQQQnR6Hbq68a8tWbKkxfNevXpxqHVWOmgdFiGEEEIIIYQQQgghuqxO3ZNQCCGEEEIIIYQQQghx5EmSUAghhBBCCCGEEEKIbk6ShEIIIYQQQgghhBBCdHOSJBRCCCGEEEIIIYQQopuTJKEQQgghhBBCCCGEEN2cJAmFEEIIIYQQQgghhOjmJEkohBBCCCGEEEIIIUQ3J0lCIYQQQgghhBBCCCG6OUkSCiGEEEIIIYQQQgjRzek7ugGdkaqqANjt9g5uiRBCCCFEU/tilH0xi+hYEjsKIYQQojNrbewoScJmOBwOALKysjq4JUIIIYQQB+dwOIiNje3oZnR7EjsKIYQQois4VOyoUeUr6CYURWHv3r1ER0ej0WiOyHvY7XaysrLYs2cPMTExR+Q9uhN5npEnzzSy5HlGljzPyJLnGVlH43mqqorD4SAjIwOtVmaP6WgSO3Y98jwjS55nZMnzjDx5ppElzzOyOlPsKD0Jm6HVasnMzDwq7xUTEyP/qCJInmfkyTONLHmekSXPM7LkeUbWkX6e0oOw85DYseuS5xlZ8jwjS55n5MkzjSx5npHVGWJH+epZCCGEEEIIIYQQQohuTpKEQgghhBBCCCGEEEJ0c5Ik7CAmk4kHHngAk8nU0U05JsjzjDx5ppElzzOy5HlGljzPyJLnKY4E+XsVWfI8I0ueZ2TJ84w8eaaRJc8zsjrT85SFS4QQQgghhBBCCCGE6OakJ6EQQgghhBBCCCGEEN2cJAmFEEIIIYQQQgghhOjmJEkohBBCCCGEEEIIIUQ3J0lCIYQQQgghhBBCCCG6OUkSCiGEEEIIIYQQQgjRzUmSsIO88MIL9OrVC7PZzLhx4/jll186ukldwnfffcdvfvMbMjIy0Gg0fPzxx43uq6rK/fffT3p6OhaLhalTp5KXl9cxje0C5syZw9ixY4mOjiYlJYVzzjmHbdu2NSrj8XiYNWsWiYmJ2Gw2zjvvPMrKyjqoxZ3b3LlzGTZsGDExMcTExDBhwgS++OKL8H15lu3z6KOPotFomD17dviaPNPWe/DBB9FoNI22AQMGhO/Ls2y74uJiLr30UhITE7FYLAwdOpSVK1eG78vvJBFJEjseHokdI0tix8iS2PHIktixfSR2jLyuEDtKkrADvPPOO9x222088MADrF69muHDhzNt2jTKy8s7ummdntPpZPjw4bzwwgvN3n/88cd59tlneemll1i+fDlRUVFMmzYNj8dzlFvaNSxdupRZs2bx888/s3DhQvx+P6eddhpOpzNc5tZbb2XBggW89957LF26lL179/Lb3/62A1vdeWVmZvLoo4+yatUqVq5cySmnnMLZZ5/Npk2bAHmW7bFixQrmzZvHsGHDGl2XZ9o2gwcPpqSkJLz98MMP4XvyLNumpqaGiRMnYjAY+OKLL9i8eTN///vfiY+PD5eR30kiUiR2PHwSO0aWxI6RJbHjkSOxY2RI7Bg5XSZ2VMVRd9xxx6mzZs0KnweDQTUjI0OdM2dOB7aq6wHUjz76KHyuKIqalpamPvHEE+FrtbW1qslkUt96660OaGHXU15ergLq0qVLVVUNPT+DwaC+99574TJbtmxRAXXZsmUd1cwuJT4+Xn3llVfkWbaDw+FQ+/btqy5cuFA9+eST1VtuuUVVVfn72VYPPPCAOnz48GbvybNsuzvvvFM94YQTDnpffieJSJLYMTIkdow8iR0jT2LH9pPYMTIkdoysrhI7Sk/Co8zn87Fq1SqmTp0avqbVapk6dSrLli3rwJZ1ffn5+ZSWljZ6trGxsYwbN06ebSvV1dUBkJCQAMCqVavw+/2NnumAAQPIzs6WZ3oIwWCQt99+G6fTyYQJE+RZtsOsWbOYPn16o2cH8vfzcOTl5ZGRkUFubi4zZsygsLAQkGd5OD799FPGjBnD+eefT0pKCiNHjuTll18O35ffSSJSJHY8cuTfaftJ7Bg5EjtGjsSOkSOxY+R0ldhRkoRHWWVlJcFgkNTU1EbXU1NTKS0t7aBWHRv2PT95todHURRmz57NxIkTGTJkCBB6pkajkbi4uEZl5Zke3IYNG7DZbJhMJq6//no++ugjBg0aJM/yML399tusXr2aOXPmNLknz7Rtxo0bx/z58/nyyy+ZO3cu+fn5nHjiiTgcDnmWh2HXrl3MnTuXvn378tVXX3HDDTdw88038/rrrwPyO0lEjsSOR478O20fiR0jQ2LHyJLYMXIkdoysrhI76o/aOwkhOrVZs2axcePGRvNMiLbr378/a9eupa6ujvfff5/LL7+cpUuXdnSzuqQ9e/Zwyy23sHDhQsxmc0c3p8s744wzwsfDhg1j3Lhx9OzZk3fffReLxdKBLeuaFEVhzJgx/O1vfwNg5MiRbNy4kZdeeonLL7+8g1snhBBHnsSOkSGxY+RI7BhZEjtGVleJHaUn4VGWlJSETqdrsupPWVkZaWlpHdSqY8O+5yfPtu1uvPFGPvvsMxYvXkxmZmb4elpaGj6fj9ra2kbl5ZkenNFopE+fPowePZo5c+YwfPhwnnnmGXmWh2HVqlWUl5czatQo9Ho9er2epUuX8uyzz6LX60lNTZVn2g5xcXH069ePHTt2yN/Pw5Cens6gQYMaXRs4cGB4GI78ThKRIrHjkSP/Tg+fxI6RI7Fj5EjseGRJ7Ng+XSV2lCThUWY0Ghk9ejSLFi0KX1MUhUWLFjFhwoQObFnXl5OTQ1paWqNna7fbWb58uTzbg1BVlRtvvJGPPvqIb7/9lpycnEb3R48ejcFgaPRMt23bRmFhoTzTVlIUBa/XK8/yMEyZMoUNGzawdu3a8DZmzBhmzJgRPpZnevjq6+vZuXMn6enp8vfzMEycOJFt27Y1urZ9+3Z69uwJyO8kETkSOx458u+07SR2PPIkdjx8EjseWRI7tk+XiR2P2hIpIuztt99WTSaTOn/+fHXz5s3qtddeq8bFxamlpaUd3bROz+FwqGvWrFHXrFmjAuo//vEPdc2aNWpBQYGqqqr66KOPqnFxceonn3yirl+/Xj377LPVnJwc1e12d3DLO6cbbrhBjY2NVZcsWaKWlJSEN5fLFS5z/fXXq9nZ2eq3336rrly5Up0wYYI6YcKEDmx153XXXXepS5cuVfPz89X169erd911l6rRaNSvv/5aVVV5lpFw4Ap1qirPtC1uv/12dcmSJWp+fr76448/qlOnTlWTkpLU8vJyVVXlWbbVL7/8our1evWRRx5R8/Ly1DfeeEO1Wq3qf//733AZ+Z0kIkVix8MnsWNkSewYWRI7HnkSOx4+iR0jq6vEjpIk7CDPPfecmp2drRqNRvW4445Tf/75545uUpewePFiFWiyXX755aqqhpYNv++++9TU1FTVZDKpU6ZMUbdt29axje7EmnuWgPraa6+Fy7jdbvUPf/iDGh8fr1qtVvXcc89VS0pKOq7RndiVV16p9uzZUzUajWpycrI6ZcqUcJCnqvIsI+HXgZ4809a78MIL1fT0dNVoNKo9evRQL7zwQnXHjh3h+/Is227BggXqkCFDVJPJpA4YMED95z//2ei+/E4SkSSx4+GR2DGyJHaMLIkdjzyJHQ+fxI6R1xViR42qqurR67cohBBCCCGEEEIIIYTobGROQiGEEEIIIYQQQgghujlJEgohhBBCCCGEEEII0c1JklAIIYQQQgghhBBCiG5OkoRCCCGEEEIIIYQQQnRzkiQUQgghhBBCCCGEEKKbkyShEEIIIYQQQgghhBDdnCQJhRBCCCGEEEIIIYTo5iRJKIQQQgghhBBCCCFENydJQiFEl/X73/+ec84556i/7/z589FoNGg0GmbPnt3uuuLi4iLSriNt0qRJ4c+9du3ajm6OEEIIIUSbSOx4dEnsKETXo+/oBgghRHM0Gk2L9x944AGeeeYZVFU9Si1qLCYmhm3bthEVFdWuei688ELOPPPMCLVqP41Gw0cffRTRQPjDDz9k586dHHfccRGrUwghhBAiEiR2bB+JHYUQIElCIUQnVVJSEj5+5513uP/++9m2bVv4ms1mw2azdUTTgFAglZaW1u56LBYLFoslAi068hISErDb7R3dDCGEEEKIJiR27HwkdhSi65HhxkKITiktLS28xcbGhgOrfZvNZmsyZGTSpEncdNNNzJ49m/j4eFJTU3n55ZdxOp1cccUVREdH06dPH7744otG77Vx40bOOOMMbDYbqampXHbZZVRWVra5zb169eLhhx9m5syZ2Gw2evbsyaeffkpFRQVnn302NpuNYcOGsXLlyvBrfj1k5MEHH2TEiBH85z//oVevXsTGxnLRRRfhcDgavc/TTz/d6L1HjBjBgw8+GL4PcO6556LRaMLnAJ988gmjRo3CbDaTm5vLQw89RCAQAEBVVR588EGys7MxmUxkZGRw8803t/k5CCGEEEIcbRI7SuwohGg/SRIKIY4pr7/+OklJSfzyyy/cdNNN3HDDDZx//vkcf/zxrF69mtNOO43LLrsMl8sFQG1tLaeccgojR45k5cqVfPnll5SVlXHBBRcc1vs/9dRTTJw4kTVr1jB9+nQuu+wyZs6cyaWXXsrq1avp3bs3M2fObHGoy86dO/n444/57LPP+Oyzz1i6dCmPPvpoq9uwYsUKAF577TVKSkrC599//z0zZ87klltuYfPmzcybN4/58+fzyCOPAPDBBx/w1FNPMW/ePPLy8vj4448ZOnToYT0HIYQQQoiuQGJHiR2FEPtJklAIcUwZPnw49957L3379uXuu+/GbDaTlJTENddcQ9++fbn//vupqqpi/fr1ADz//POMHDmSv/3tbwwYMICRI0fy6quvsnjxYrZv397m9z/zzDO57rrrwu9lt9sZO3Ys559/Pv369ePOO+9ky5YtlJWVHbQORVGYP38+Q4YM4cQTT+Syyy5j0aJFrW5DcnIyAHFxcaSlpYXPH3roIe666y4uv/xycnNzOfXUU/nrX//KvHnzACgsLCQtLY2pU6eSnZ3NcccdxzXXXNPmZyCEEEII0VVI7CixoxBiP0kSCiGOKcOGDQsf63Q6EhMTG32jmZqaCkB5eTkA69atY/HixeF5amw2GwMGDABC38q25/33vVdL79+cXr16ER0dHT5PT09vsXxrrVu3jr/85S+NPus111xDSUkJLpeL888/H7fbTW5uLtdccw0fffRReDiJEEIIIcSxSGLHg5PYUYjuRxYuEUIcUwwGQ6NzjUbT6Nr/s3ff8VGU+QPHP7N903slJKFIC72DIqgnKqKooKAiYjkVFRH1FOtZTs7fnRycBSwUEbEDJyoC0iz03kOoIb1nU7fO749NQiKBBAQ25ft+Oc7MM88z+W4WyOS7T6lc+c7lcgFQXFzM8OHDeeutt067V2Rk5J/6+pVf62xfv657VLapXl+j0Zw25MRut9cZW3FxMa+++iq33nrraddMJhMxMTEkJiby888/s3LlSiZMmMC//vUv1q1bd1pMQgghhBBNgTw7npk8OwrR/EiSUAjRrPXo0YNvv/2WuLg4dLrG8U9iaGhojRX8LBYLx44dq1FHr9fjdDprlPXo0YPExETatGlzxnubzWaGDx/O8OHDefTRR2nfvj179uyhR48eF/ZFCCGEEEI0QvLsWJM8OwrRtMhwYyFEs/boo4+Sl5fHmDFj2LJlC0eOHGH58uWMHz/+tAelhuKqq67i008/5ddff2XPnj2MGzcOrVZbo05cXByrVq0iIyOD/Px8AF5++WXmz5/Pq6++yr59+zhw4ABffPEFL774IuBeLW/27Nns3buXo0ePsmDBAsxmM7GxsZf8NQohhBBCNETy7CjPjkI0ZZIkFEI0a1FRUfz+++84nU6uvfZaOnfuzKRJkwgICECjaZj/RE6ZMoUrr7ySG2+8kWHDhjFixAhat25do87bb7/NypUriYmJoXv37gAMHTqU77//nhUrVtC7d2/69evHf/7zn6oHuYCAAD766CMGDhxIly5d+Pnnn1m6dCnBwcGX/DUKIYQQQjRE8uwoz45CNGWKera11IUQQpxm3rx5TJo0iYKCAk+HcskdP36c+Ph4duzYQbdu3TwdjhBCCCFEgyfPjvLsKERj0TA/6hBCiAausLAQHx8fnn32WU+Hcslcf/31dOrUydNhCCGEEEI0OvLsKIRoDKQnoRBCnKOioiIyMzMB9zCLkJAQD0d0aaSmplJWVgZAy5YtMRgMHo5ICCGEEKLhk2dHeXYUorGQJKEQQgghhBBCCCGEEM2cDDcWQgghhBBCCCGEEKKZkyShEEIIIYQQQgghhBDNnCQJhRBCCCGEEEIIIYRo5iRJKIQQQgghhBBCCCFEMydJQiGEEEIIIYQQQgghmjlJEgohhBBCCCGEEEII0cxJklAIIYQQQgghhBBCiGZOkoRCCCGEEEIIIYQQQjRzkiQUQgghhBBCCCGEEKKZkyShEEIIIYQQQgghhBDNnCQJhRBCCCGEEEIIIYRo5iRJKIQQQgghhBBCCCFEMydJQiGEEEIIIYQQQgghmjmdpwNoiFwuF2lpafj6+qIoiqfDEUIIIYSoQVVVioqKiIqKQqORz3w9TZ4dhRBCCNGQ1ffZUZKEtUhLSyMmJsbTYQghhBBCnNXJkydp0aKFp8No9uTZUQghhBCNQV3PjpIkrIWvry/g/ub5+fl5OBohhBBCiJosFgsxMTFVzyzNRWpqKs8++yzLli2jtLSUNm3aMHfuXHr16lVr/XvvvZdPPvnktPKOHTuyb98+AKZOncqiRYs4ePAgZrOZAQMG8NZbb9GuXbt6xyXPjkIIIYRoyOr77ChJwlpUDhPx8/OTBz0hhBBCNFjNaWhrfn4+AwcOZMiQISxbtozQ0FCSkpIIDAw8Y5sZM2bwz3/+s+rc4XDQtWtXRo0aVVW2bt06Hn30UXr37o3D4eD555/n2muvZf/+/Xh7e9crNnl2FEIIIURjUNezoyQJhRBCCCFEg/fWW28RExPD3Llzq8ri4+PP2sbf3x9/f/+q8yVLlpCfn8/48eOryn766acabebNm0dYWBjbtm1j0KBBFyh6IYQQQoiGT2a6FkIIIYQQDd53331Hr169GDVqFGFhYXTv3p2PPvronO4xe/ZsrrnmGmJjY89Yp7CwEICgoKAz1rFarVgslhqbEEIIIURjJ0lCIYQQQgjR4B09epSZM2fStm1bli9fziOPPMLEiRNrnXOwNmlpaSxbtowHHnjgjHVcLheTJk1i4MCBJCQknLHe1KlTq3op+vv7y6IlQgghhGgSFFVVVU8H0dBYLBb8/f0pLCyUeWWEEEII0eA0x2cVg8FAr169WL9+fVXZxIkT2bJlCxs2bKiz/dSpU3n77bdJS0vDYDDUWueRRx5h2bJl/Pbbb2dd+c9qtWK1WqvOKycDb07vhxBCCCEaj/o+O0pPQiGEEEII0eBFRkbSsWPHGmUdOnQgOTm5zraqqjJnzhzGjh17xgThY489xvfff8+aNWvOmiAEMBqNVYuUyGIlQgghhGgqZOESIYQQQgjR4A0cOJDExMQaZYcOHTrr/IKV1q1bx+HDh7n//vtPu6aqKo8//jiLFy9m7dq1dS6GIoQQQgjRVElPQiGEEEII0eA9+eSTbNy4kTfffJPDhw+zcOFCPvzwQx599NGqOlOmTOGee+45re3s2bPp27dvrfMMPvrooyxYsICFCxfi6+tLRkYGGRkZlJWVXdTXI4QQQgjR0EiSUAghhBBCNHi9e/dm8eLFfP755yQkJPD6668zffp07rrrrqo66enppw0/Liws5Ntvv621FyHAzJkzKSwsZPDgwURGRlZtX3755UV9PUIIIYQQDY0sXFKL5jgZuBBCCCEaD3lWaVjk/RBCCCFEQyYLlwghhBBCCCGEEEIIIepFkoRCCCGEEEIIIYQQQjRzkiQUQgghhBBCCCGEEKKZkyShEEIIIYQQQgghhBDNnM7TAQC89957/Otf/yIjI4OuXbvyzjvv0KdPnzPW//rrr3nppZc4fvw4bdu25a233uKGG26oun7vvffyySef1GgzdOhQfvrpp4v2Gs6V0+HixN5cYjoEoTdqPR2OEEIIIYQQQgghRIOgqiou1b1XAVUFFdW9r35MzTqc5ZpKRSGcfs9qX7f68r5V7aqOq9Wr/JoVd1RruTfV6py6XrMcwKTX0CbM989+2/40jycJv/zySyZPnsysWbPo27cv06dPZ+jQoSQmJhIWFnZa/fXr1zNmzBimTp3KjTfeyMKFCxkxYgTbt28nISGhqt51113H3Llzq86NRuMleT31tfjt7WQes3DN+I606xvh6XCEEEIIIYQQQghRwelSsTtdOFwqjqq9isPlqrimVtVxulQcLrViX+3cqeJU1WrXXThd/GGv4lT/WAZOVcXlcrd3VR67wFVxP6eqolYeV5RXXlPVU/UqE20u1f111Kr7uetU1nWp1evyh/NqCbtq1yrbVyX0OL2eCrhcpxJxrj9co9px5X2aow6Rfix74gpPh+H5JOG0adN48MEHGT9+PACzZs3ihx9+YM6cOTz33HOn1Z8xYwbXXXcdzzzzDACvv/46K1eu5N1332XWrFlV9YxGIxERDTf5FpsQTOYxCwc3pEuSUAghhBBCCCFEs6OqKlaHq2JzYrVXO3a4Ks6d2Crq2BwubE5Xxbm73OZwYXW6sDtUbE4ndoc7cVdZz+50YXeq2JzuY4fz1PUa545TiUC7y1Wjl5doOhQFFEBRFJQaZe4LSsU5uMuq13eXVf6v2n2qnVPLvavudlq9iq8LhPgYLuTLPG8eTRLabDa2bdvGlClTqso0Gg3XXHMNGzZsqLXNhg0bmDx5co2yoUOHsmTJkhpla9euJSwsjMDAQK666ireeOMNgoODa72n1WrFarVWnVsslvN8RfXXrm8Em5ceIyUxn6K8cnyDTBf9awohhBBCCCGEEPVld7ootTkptTkosZ7al9lPnZfZnJTZXZTZHJTZnZTanJTZnZTbnRXX3NetFWXldhfljlPHjYlWo6DVKOgr91oNWo2CTqOg1SroNZqqOjqtglajcV+rrFO5KQqairLK/R/LtIqCRqHqWFu9/A/XNYqCVgMapfK45jWNcuqaplq9ymtK9Toad+JKozlVVpkMq7ynQrV9xX0UxZ30qjrm1L2VGvdxH0Pt9VFOJeeqt1FQUDQ1E3xKtbpQ/V5/qHMqUyfq4NEkYU5ODk6nk/Dw8Brl4eHhHDx4sNY2GRkZtdbPyMioOr/uuuu49dZbiY+P58iRIzz//PNcf/31bNiwAa329Pn/pk6dyquvvnoBXlH9+YWYiWobQFpSAYc2Z9DzurhL+vWFEEIIIYQQQjRNNocLS7mdonIHRdX2lnIHReUOLGV2iq0OissdFNsclFQeWx2UVCQCi60ObI5Ll8RTFDDqNBh1Wvde7z42aCuPNRgqz3WV5xWb1r3XV+0VDFoN+soyrXuv1yoVe/exruKaTqu4zzWVx+7knq6ynsa9l2STaOo8Ptz4Yhg9enTVcefOnenSpQutW7dm7dq1XH311afVnzJlSo3eiRaLhZiYmIseZ7t+EaQlFZC4MYMeQ2PlHxwhhBBCCCGEEIB7KG6pzUl+qY2CUjv5pTbyS+0UlNrIL7FTWHZqs5TVPC+zOy9oLAatBrNBi7dB694bdXgZtHgZdJgNWsx6LV4Ve1PlscF9XFlm0mvce50Ws8GdADTptRj1Gkw6rSThhGgAPJokDAkJQavVkpmZWaM8MzPzjPMJRkREnFN9gFatWhESEsLhw4drTRIajUaPLGzSpkcYv35xiPyMUrKOFxEe73fJYxBCCCGEEEIIcfGpqkqJzUl2kZWcYiu5xTbySmzkFlvJLXEf55XYyCm2uhOCJXZszj/Xk8/HqMPXVLnp/7DX4WvU4W3U4VOxeRt1+JhOHXtXJAINOs0F+i4IIRoyjyYJDQYDPXv2ZNWqVYwYMQIAl8vFqlWreOyxx2pt079/f1atWsWkSZOqylauXEn//v3P+HVSUlLIzc0lMjLyQob/pxnMOlp1D+XQ5kwObkyXJKEQQgghhBBCNDKqqpJXYiPDUk6mpZyMQivZRVayi8vd+yIr2cVWcops59XDz6DVEOClJ9DLULUP9Nbjbzbgb9aftvmZdfib9fia9Gg10jNPCFF/Hh9uPHnyZMaNG0evXr3o06cP06dPp6SkpGq143vuuYfo6GimTp0KwBNPPMGVV17J22+/zbBhw/jiiy/YunUrH374IQDFxcW8+uqr3HbbbURERHDkyBH+9re/0aZNG4YOHeqx13km7fpFcGhzJklbM7l8ZFu0evmERgghhBBCCCEagsoEYGpBGWkFZaQWlJNWUOZOCBaWk2EpJ8tiPacef94GLcE+RoJ9DAR7GwjyNrjPqx0HVSQCA70MeBm0MgxXCHFJeDxJeMcdd5Cdnc3LL79MRkYG3bp146effqpanCQ5ORmN5lTibMCAASxcuJAXX3yR559/nrZt27JkyRISEhIA0Gq17N69m08++YSCggKioqK49tpref311z0ypLguLdoH4R1gpKTAyvG9ObTuHubpkIQQQgghhBCiWahMAp7IK+VkXinJuaWk5JeRVlhWlRis7wq8IT4Gwv1MRPiZCPMzEupjJNTXSEjFvvLY2+jxX8OFEKJWiqqqqqeDaGgsFgv+/v4UFhbi53fxhwBvWHyY7cuTiesSwrAJXS761xNCCCFE43apn1XE2cn7IUTD5nKppFvKOZZdwrGcYpLzSjmRW0pyRWKwxFb3EOAwXyNRAWaiA81E+ZuI8DcT4Wciwt9IuJ+JMF+TzNsnhGiw6vusIh9hNADt+kayfXkyyXtzKSuyYfY1eDokIYQQQgghhGhUCsvsHMku5lh2CUdzijmWU8LR7BKO55bU2Rsw0t9ETJAXMYFexASZiQ6o2ALNRPibMOq0l+hVCCGE50iSsAEIivImLNaXrBNFHNqSSderYjwdkhBCCCGEEEI0SJZyO0mZxSRlFnEos5ikrCIOZRaRabGesY1eq9AyyIv4EG9ig71pGeRFyyAvYoK8aBFoxqSXJKAQQkiSsIFo1y+SrBNFJG7MkCShEEIIIYQQotlzulSO55awP83CvjQLB9ItHMosIr2w/IxtIvxMtAr1Jj7EvbUO9SE+xJsWgWZ0WhkOLIQQZyNJwgaibe8wfv8miezkInJTiwmO9vF0SEIIIYQQQghxSZTbnRzMKGJfWiH70yzsT7dwML2IMnvt8wWG+xm5LNyXtmG+tIvwoW24L23DfPA16S9x5EII0XRIkrCBMPsYiE0I5tiuHBI3ZjDgtjaeDkkIIYQQQgghLjinS+VwVjG7ThawM6WAXScLSMwowuE6fU1Ns15L+0hfOkb60SHSj/YRvrQN98XfLMlAIYS40CRJ2IC07x/pThJuzqDfiFZopDu8EEIIIYQQopHLspSz9UQ+O08WsPNkAXtTCymtZUXhEB8DHaP86RjpR8coPzpG+hEf4o1Wo3ggaiGEaH4kSdiAxCYEY/LWU1po4+TBfGI7BXs6JCGEEEIIIYSoN1VVOZJdwtbjeWw5ns/WE3mcyC09rZ63QUvnFv50jQmgW4sAusYEEOlvQlEkISiEEJ4iScIGRKvT0LZ3OHvWppC4IV2ShEIIIYQQQogGzelS2Z9mYePRXDYfz2PbiXzySmw16igKtI/wo3vLALrFuLfWoT7SQ1AIIRoYSRI2MO37R7BnbQpHd+VgLXNgNMtbJIQQQgghhGgY3D0Fi/n9cC7rj+Sw8WgehWX2GnWMOg3dYgLoHRdEr7hAesQG4icLigghRIMnGagGJrSlL4GR3uSnl3BkWxYdL4/ydEhCCCGEEEKIZiytoIzfDuew/nAO64/kklVkrXHd16ijb6sg+sQH0SsuiIQofww6mV9dCCEaG0kSNjCKotC+XwQbFh/h4MZ0SRIKIYQQQgghLim708X2E/msScxmzcEsEjOLalw36jT0igtkQOsQBrYJISHKD50suiiEEI2eJAkboMv6RLBxyRHSDxdSmF2Kf6iXp0MSQgghhBBCNGHZRVbWHXInBX9Jyqao3FF1TaNA15gABrYOYUCbYHq0DMSk13owWiGEEBeDJAkbIJ9AIy06BHFyfx6JGzPoM7yVp0MSQgghhBBCNDGHs4pZvi+DFfsy2JVSWONaoJeewe3CGNwulCsvCyXAy+ChKIUQQlwqkiRsoNr3i+Dk/jwObsyg97B4FFn5SwghhBBCCPEnqKrK/nQLP+3N4Ke9GSRlFde4nhDtx1XtwhjcPoyuLQJk9WEhhGhmJEnYQMV3C8Vg0lKUW07qoXxatA/ydEhCCCGEEEKIRsblUtlxsoDl+9yJweS80qpreq3CgNYhXJcQwdXtwwjzM3kwUiGEEJ4mScIGSm/Q0rZPBPt+SeXA+nRJEgohhBBCCCHq7WCGhSU70vhuZyppheVV5Sa9hisvC+X6hEiGtA/D36z3YJRCCCEaEkkSNmAdBkSy75dUjuzIZlCpHaOX/AAXQgghhBBC1C6toIzvdqWxZEcqBzNOrUjsY9RxVfswrk+I4Mp2oXgZ5NdAIYQQp5OfDg1YWKwvwdHe5KaWkLQlk4QrW3g6JCGEEEIIIUQDUlhmZ9medJbsTGXTsTxU1V1u0GoY3C6UW7pHM6R9mKxGLIQQok6SJGzAFEWhw4Aofvs6if2/p0uSUAghhBBCCIGqqmw8mscXW5JZtjcDm8NVda1vfBAjukdzQ0Ik/jISSQghxDmQJGEDd1nfcNYvOkx2chE5KUWEtPD1dEhCCCGEEEIID8gusvLNthS+3JLM8dxTC5C0C/dlRPdobuoWRXSA2YMRCiGEaMwkSdjAmX0MxHcN4cj2bA6sT+eK2yVJKIQQQgghRHPhdKn8mpTNF5tP8vOBTBwu93hib4OWm7pFM6ZPDJ2j/VEUxcORCiGEaOwkSdgIdBgQxZHt2SRuymDALW3Q6jWeDkkIIYQQQghxEeWX2Fi4OZmFm5JJLSirKu8WE8CYPjHc2CUKb6P8OieEEOLCkZ8qjUBMxyC8A4yUFFg5tjuHNj3DPB2SEEIIIYQQ4iJIzChi7u/HWLwjFWvFXIN+Jh239mjB6D4xtI/w83CEQgghmipJEjYCGo1C+/4RbFt2ggO/p0mSUAghhBBCiCbE5VJZfTCLueuP8fvh3KryTlF+jB8Yz41dImV1YiGEEBedJAkbifb9I9m27ATJB/IoyivHN8jk6ZCEEEIIIYQQf0Kx1cHXW0/yyfrjVQuRaBQY2imC8QPj6R0XKHMNCiGEuGQkSeghS7MK+DQth7cuiyHey1hn/YAwL6LaBpCWVEDixnR63RB/CaIUQgghhBBCXGh5JTbm/HaMTzYcp6jcAYCvSceYPi0Z2y+WmCAvD0cohBB/jqqqqKi4VBeqquLCVXXsVJ24VPd0Ci61orxa3crjyvIa96o8r7hn5bXKc1RO1avWtvo9qsdX/X5/bFOjXrXr7v/OUl69fcV5Xcf+Bn+ujr36kr5HtZEkoYcsTM/ll/xiPk/P5fnWUfVq03FgJGlJBRxYn07P6+JQNPKpohBCCCGEEI1FRmE5H/5ylM83J1NmdwLQKsSb8QPjuLVHC1mIRIhGyOlyYnfZqzaHy1G1VT+vOlYdOF3OU/XUU/WdqrPm3uWsql/9WuX5H49dqguHy4FLdVVdqzx2qS4casU1lxOVimSdy329+nllQu+P9SrLK++pqqcSd06XsyppV1km6q9dYDtJEjZnd0UGsyaviC8y8ngmPhJ9PRJ+rXqEof/iEJacclKTCmjRLvASRCqEEEIIIYT4M07kljBr3RG+2ZaC3en+xTkh2o/HhrTh2o4RaOTDfyHOmdPlpNxZTrmjnHJnOVanFavD6t5X28od5dicNqxOKzanDZvLfWx32qvq2F32qjKby3ba3ua01UgCVl132at6pok/R0FBo2hQFAUNFXtFg4JSVYZCVZlG0QDUqFN1j4rz2q5X7avVqf41FU6VVa+noOD+7w9lFXUrX0P1dmeqX728sn0LnxaX7Ht9NpIk9JBrQ/wI0evIsjn4ObeQ60MD6myjN2hp2zuc/b+mcWB9miQJhRBCCCGEaMASM4qYufYw3+1Kw1XRqaZPfBCPDmnDoLYhMt+gaPIcLgeljlJK7RWb49S+xF5SdV7mKKPMUUa5o7zquEaZ072vTAiWO8qxu+yefnm10igadIoOvVaPTqNDr3HvdYrOva8o0yraqnOtRlujjlbRusuUU9cr61fev7Kssm5luUbRVJ1XXv9jWfV95Va9XFGUGterl1Um1DQaDRpOXat+r8oEXuU9qtooNZN/lXWrJ+yEZ0mS0EMMGg13RAbxXnIWC9Ly6pUkBOg4IIr9v6ZxZHs2g+6wY/TSX9xAhRBCCCGEEOfkSHYx01Ye4ofd6VVlg9uF8uiQNvSOC/JgZELUn91px2KzUGwvpshW5D62uY+LbEUU2d37EnsJxbZi995ec1/mKLsksRq1RoxaIyatCYPWgElnqiqr3AxaQ9W+8liv0dco02v07mONAb1WX/teo0ev1bv3lZv2VCJQq5GVyEXjJUlCD7qzIkm4Js9CarmNaJOhzjZhcb4ERXmTl1ZC0tYsEgZFX4JIhRBCCCGEEHVJLSjjvz8n8c32FJwuFUWB6xMimDC4DQnR/p4OTzRTLtVFka2I/PJ8CqwFp/bWfAqthRRaC7HYLFisFgpthVVlpY7SCxaDTtHhpffCW++Nl84LL33FVnFs1pkx68yYtKaqc5PW5N7rTKeuVyT/Kq9XnksvNCEuDEkSelBrLxP9A7zZUFDCF+l5PBUfUWcbRVHoMCCS3785zIHf0yRJKIQQQgghhIflFFt5b81hPtuYjM3pnp/smg7hPHXtZXSI9PNwdKIpKrWXkluWS255Lnnlee59Wd6p87Jc8svzqxKBTtV53l/LW++Nr8HXvel98TP44WPwwdfgi4/evffWe+Oj98HH4FN17K33rjrWa2UEnBCNgSQJPezuyGA2FJSwMD2XSXHhaOvxCUi7vhFsWHSErBNF5KQUE9LC5xJEKoQQQgghhKjOUm7no1+OMvu3Y5Ta3EmYfq2CeGZoe3rGyvzh4tyVO8rJLM0ksyST7LJsskuz3fuK45yyHLLLsimxl5zzvX30PgQYAwg0BRJgDCDAGIC/0R9/oz9+Br+qY3+DP35GP/wN/vgYfNBpJG0gRHMhf9s9bFhoAC8kpZJqtfNLXhFDguv+pNHsayCuawhHd2RzcH06l9/e9hJEKoQQQgghhACwOpx8sv447605QmGZe/GEri38eWZoewa2CZahj6JWdpedzJJM0kvSSS9JJ6Mkg8ySTDJLM93HpZkUWAvqfT+T1kSwOZhgUzBBpiCCzTX3QaYgAk2BBBrdSUHpzSeEqIskCT3MpNVwW3ggs1NzWJCeW68kIUCHAZEc3ZFN4qYM+t/SGq1ec5EjFUIIIYQQonlTVZWV+zP5x48HOJHrnq+tbZgPT13bjqGdwiU52Mw5XU4ySzM5WXSStOI0UotTq/bpJelklmbiUl113sesMxPuFU6oVyih5orN69Q+xBxCqDkUH4OMKBNCXFiSJGwA7o4KZnZqDstzCsm22Qk11P0JT8uOQXj7GygptHF0VzZte4VfgkiFEEIIIYRong5mWHj9+/38fjgXgFBfI88MbcdtPVqg1UhysLmwu+ykFaeRbEnmZNFJThadJLkomWRLMqnFqdhd9rO2N2gMRPpEEuEdQYRXBBHeEYR7hxPuFe4+9grHz+AnCWchhEdIkrAB6OBjpoefF9stpXyZnsdjsXUn/DRaDR0GRrH1x+Ps+zVNkoRCCCGEEEJcBHklNqatTGThpmRcKhh0Gh64PJ4JQ9rgY5Rfp5oqi83C8cLjHCs8dmqzHOOk5SQO1XHGdjqNjhY+LYj2jSbKO4oonyiifaKJ9I4k2ieaYHMwGkVGgQkhGib5qdZA3BUZzHZLKQvT83i0ZVi9PjnqMDCSrcuOk5qYT0FmKQHhXpcgUiGEEEIIIZo+u9PF/A0nmPHzISzl7qTQ9QkRPH9DB2KC5Lm7qSixl5CUn0RSQRKH8w9zuOAwRwuPklOWc8Y2Zp2ZFr4taOnbkpa+Ld3Hfi2J8Y0hwisCrUZ7CV+BEEJcOJIkbCBGhAXw8uFUjpZZ2VBQwoDAuueX8As207JjMMn7ctn/WxoDbmtzCSIVQgghhBCiaVt/JIeXluzlSLZ7BdkOkX68Mrwj/VoFezgycb6cLicnik5wMPcgSQVJJOUncbjgMKnFqWdsE+YVRrx/PPF+8cT5xxHvH08r/1aEeYVJb0AhRJMkScIGwlun5ZawQBak5/JZem69koQAna6IInlfLgc2pNP3playgIkQQgghhBDnKa/Exj9+OMC321MACPEx8PS17RjVK0bmHWxE7C47RwuOsj93PwfyDnAg9wCJ+YmUOcpqrR9mDqNtYFvaBLShbWBbWge0Js4vThYGEUI0O5IkbEDuigpmQXou32cX8IY9mkB93W9PXOdgvAOMlBRYObozm7a9ZW5CIYQQQgghzoWqqnyzLYU3fzxAfqkdRYGx/WJ5emg7/Ex1LyooPMelujheeJzdObvZnb2bfbn7SMpPqnUBEbPOzGWBl3FZ4GW0DWxL24C2tA1si7/R3wORCyFEwyNJwgakm6+Zjt4m9peU821mPg+0CK2zjXsBk0i2/nCcfb+mSpJQCCGEEEKIc3Aku5jnF+1h07E8ANpH+DL11s50bxno4chEbQrKC6oSgnty9rAnew9F9qLT6vnqfekQ3IEOQR2q9rF+sTJfoBBCnIUkCRsQRVG4KyqYF5JS+Swtl/ujQ+q1gEnHgVFs+/E4qYcKyM8oITDC+xJEK4QQQgghRONVbncyc+0RZq49gs3pwqzX8uRf2jJ+YDx6rUzh0xCoqkpqcSrbMrexLXMb27O2c8Jy4rR6Jq2JjsEd6RLahYSQBDoGd6SFT4t6/S4lhBDiFEkSNjC3hQfy+pE0DpSUs8NSSg//uhN+vkEmYhOCOb7HvYDJwJFtL0GkQgghhBBCNE6bjuYyZdEejua4FyYZ0i6U125OkFWLPUxVVY4VHmNr5taqxGBmaeZp9eL84ugS2oUuIV3oEtqFNoFt0GtkWLgQQvxZkiRsYAL0Om4MDeCbzHwWpOfWK0kI0PGKaI7vyeXghgz63twKnV660QshhBBCCFFdud3Jv5YnMuf3Y6gqhPoa+fvwTtzQOUJ6nXlIanEqG9I2sCFtA1szt5JXnlfjuk7R0SmkEz3De9IzvCddQ7vKHIJCCHGRSJKwAborKphvMvNZklXAa22i8dHVnfCL7RSET6CR4nwrR3dkc1mfiEsQqRBCCCGEEI3DrpMFTP5qJ0ey3b0HR/eO4flhHWRhkkvMYrOwJX0LG9LdicHkouQa141aI11Du1YlBTuHdMZLLz08hRDiUpAkYQPUz9+bNl5GDpdaWZJVwN1RwXW2cS9gEsWW74+x79c0SRIKIYQQQggB2Bwu3lmdxPtrj+B0qYT5Gnnrti4MaR/m6dCaBZfq4kDuAdalrGN92nr25uzFqTqrrmsVLZ1DOtM/qj/9IvuREJKAQWvwYMRCCNF8yYy8DZCiKNwZ6U4MfpqWU+92HQdGoiiQllRAXnrJxQpPCCGEEMIjUlNTufvuuwkODsZsNtO5c2e2bt16xvr33nsviqKctnXq1KlGvffee4+4uDhMJhN9+/Zl8+bNF/uliEskMaOIW97/nXdWH8bpUrmpaxQrnhwkCcKLrNReyurk1fx9/d+5+uurGf3DaGbumsmu7F04VSdxfnGMbjeaGUNm8OvoX/n0hk+Z0G0CPcJ7SIJQCCE8SHoSNlC3RwTxz6Pp7CoqY1dRKV196+5i7xNoIrZzCMd357D/tzQuHyULmAghhBCiacjPz2fgwIEMGTKEZcuWERoaSlJSEoGBgWdsM2PGDP75z39WnTscDrp27cqoUaOqyr788ksmT57MrFmz6Nu3L9OnT2fo0KEkJiYSFiaJpMbK6VL56NejTFtxCJvTRaCXnjdGdGZYl0hPh9ZkZZRksO7kOtamrGVz+mZsLlvVNS+dFwOjB3J59OX0j+xPpI+8D0J4mqqq4HSiOp3uvct1au9w1Dx3OlGdLnCdvsflctdxuU5dqzivcc3lAqcLVNcfrqvu46pytfZjlWr1/tCm8hrq2c9VFVWtOFfV0+uoqntDdX9/XOqpsurl1eueV7n7XKWyDAwxMUS8/NKl/UNQC0kSNlAhBh03hgWwKDOfT1JzmNa+Zb3adboiiuO7czi4MZ1+I2QBEyGEEEI0DW+99RYxMTHMnTu3qiw+Pv6sbfz9/fH3P7XAwZIlS8jPz2f8+PFVZdOmTePBBx+sKps1axY//PADc+bM4bnnnrvAr0JcCin5pUz6YidbT+QDcHX7MKbe1pkwX5OHI2t6TlpOsuLEClaeWMm+3H01rkX7RHNliyu5MuZKeoX3kh6CoslQVRXsdlw2O6rNimqzndrsdvd2puOqMjuqw+E+dpwqx+FAtTvc1xwOVMcfypwO97mjIrnncKBWJvoc9prlLldF/T+WuY+rklWiQXC0b+/pEABJEjZo46KCWZSZz+LMfF5uHUWAvu63q2WnYHyCjBTnWTmyPZt2fWVuQiGEEEI0ft999x1Dhw5l1KhRrFu3jujoaCZMmMCDDz5Y73vMnj2ba665htjYWABsNhvbtm1jypQpVXU0Gg3XXHMNGzZsOON9rFYrVqu16txisZzHKxIXw097M/jbN7uwlDvwMep4eXhHRvVsISsXX0AnLCdYeWIlK46v4EDegapyjaKha2hXBrUYxOAWg2kd0Fq+7+KSUJ1OXGXlqGWluMrLcZWVoZaX4yotw1VecVxWjlpehqvcimotx1VejlpuxWUtRy0rd++tNlSrFdVqxWWrfmw9da0iGdjkKQpotSgazel7jQa0GhSNtuZeqVkXjeK+ptG4/y2obKdU3KPadTRKzfKzHVfeS+OeQgSl8l4a4A/XKs8VBUWjuNtWlblfZ9U9FOUMZe7yM5dVlFd+32rco+Lr1VpOtWsK2mofanqSJAkbsD7+3rT3NnGwpJyvM/J5MCa0zjYajULHgVFsXnqMfb+mSpJQCCGEEJfc7t27z7lNx44d0enO/Gh69OhRZs6cyeTJk3n++efZsmULEydOxGAwMG7cuDrvn5aWxrJly1i4cGFVWU5ODk6nk/Dw8Bp1w8PDOXjw4BnvNXXqVF599dV6vCpxqZTbnUz98QCfbDgBQLeYAN4Z052YIFkV90I4Xnic5ceXs+LECg7lH6oq1ypaekf05i+xf+GqllcRYg7xYJSisVDtdpzFxbiKinAVF7uPS0pwlZRW7KttpaWn9mVluEpL3cnAkmrn1T608QidDo3BgFK56fWnturnBgOKTodicJ+j07nLdXp3uV6Pote5y3W6auUVZVpd1bmi04FWh6LTuhNzOj2KToui1f6hXFeRtKso02hRtJo/XKts94dkoGiWJEnYgCmKwrjoEKYcSmF+Wg4PtAip16dxHQdGseWH46QfLiQ3rZjgKJ9LEK0QQgghhFu3bt1QFMU9JKseNBoNhw4dolWrVmes43K56NWrF2+++SYA3bt3Z+/evcyaNateScJPPvmEgIAARowYUa+YzmbKlClMnjy56txisRATE/On7yvOz7GcEh5buJ19ae4enQ8NasXTQ9uh18ovuX9GQXkBy44vY+mRpezJ2VNVrlW09I3sy7Wx1zKk5RCCTEEejFJ4gup04rRYcBYU4CosdB9binBaCnFZinAWWXBVlLmKKvdFOEtKcBUVXbyknqKgmM1oTCY0JpP7uOJcMZnQmE0oRhOKyYjGZEZjMp46N5pQjMaKMiOKwYhiNKAxVjs36N3n1ROCBoMk1ESTIknCBm5keCBvHEkjqdTK7wXFXB7oW2cb7wAjcZ2DObbLvYDJFbdfdgkiFUIIIYQ4ZdOmTYSG1j0KQlVVEhIS6qwXGRlJx44da5R16NCBb7/9tl5fY86cOYwdOxaD4dS8aCEhIWi1WjIzM2vUz8zMJCLizKMxjEYjRqOxzq8rLr7/7Uzl+UV7KLE5CfI28PbtXRnSThacOV82p41fUn5h6ZGl/JL6Cw6XA3AnBvtF9WNo7FCGxAwhwBTg2UDFBaO6XLgsFhx5+Tjz83Dk5eGsflxQULEV4ix0710WywWZz07x8kLr44PGxweNt3e1zcu993Lvtd7eKF5e7nOzFxovc8Wxe69UHCtGowxxF+JPkiRhA+er03JbeCDz03L5JDW3XklCgE6Dojm2K4fEjRn0H9EanUEWMBFCCCHEpXHllVfSpk0bAgIC6lV/0KBBmM3ms9YZOHAgiYmJNcoOHTpUNb/g2axbt47Dhw9z//331yg3GAz07NmTVatWVfUwdLlcrFq1iscee6xesQvPKLM5+ft3+/hy60kA+sYHMWN0dyL8ZXGSc6WqKrtzdrP0yFKWHVuGxXZqjs0OQR0Y3no418dfL0OJGxFVVXEVFmLPysKZm4sjJwdHTi6OnGycORXnFeXO/HxwOs/r62h8fND6+6Px90Pr64fWzw+Nny9aP3+0fr5o/CrKfHzQ+vqi8fVF4+2D1tedFFTOMsWEEMIz5G9lI3BvdAjz03JZllNAptVOuFFfZ5uWHYLwDTJRlFfO4e1ZtO8XeQkiFUIIIYSANWvWnFP9H3/8sc46Tz75JAMGDODNN9/k9ttvZ/PmzXz44Yd8+OGHVXWmTJlCamoq8+fPr9F29uzZ9O3bt9Yei5MnT2bcuHH06tWLPn36MH36dEpKSmqsgCwalsNZRTyyYDtJWcUoCky8qi0Tr26LViM9iM6FxWZh6ZGlfHPoGw4XHK4qDzOHMaz1MIa3Gk7bwLYejFDURrXbsWdm4chIx56RiSMrC0dmJo7sLHd5lns71yG9Gl9ftEGB6AIC0QYFuY+DgtAGBKINCEAbGIDW3999HBCA1s/PPa+eEKJJkSRhI9DRx0xvP2+2WEpYmJ7Lk3F1L0aiaBQ6Xh7Fpu+OsnddqiQJhRBCCNGo9e7dm8WLFzNlyhRee+014uPjmT59OnfddVdVnfT0dJKTk2u0Kyws5Ntvv2XGjBm13veOO+4gOzubl19+mYyMDLp168ZPP/102mImomFYvi+DyV/upMTmJMzXyPTR3RjQWnq41ZeqquzJ2cNXiV+x/Phyyp3lAJi0Jv4S+xeGtx5On4g+aDUyCslTnMXF2FNS3FtaOvaMDOzpaTgqjh1ZWfUe6qv190cbGoIuJBRdcDC6kBC0IcHu85AQdCHBaIOC0QUGoFSbikEI0Xwpan1nlG5GLBYL/v7+FBYW4ufnd1G+RvG6deR//gURL7+EPiqqzvrfZOTx2IFkoox6NvfriK4en5SWWmx8MuV3XE6VUVN6ERZ7cV6LEEIIIS6tS/GscqE4nU7mzZvHqlWryMrKwuVy1bi+evVqD0V24TSm96OxcrlUpv98iP+udvd4698qmHfu7E6Ij8wNWR/FtmJ+OPoDXx/6msT8U8P22wS0YdRlo7ix9Y34GeTP7qWgOhzY09KwJZ+sSAaexJaS6j4+eRJnYWGd91D0enSRkejDw9GFh6MLC0MfHoYuLKzqXBcaikbmThVCVKjvs4r0JPSQ3LnzKN24kfz27QibNKnO+jeGBvDy4VTSrHZ+zrVwXah/nW28/Ay06RnGoc2Z7FmXytX3yA9+IYQQQlxaTzzxBPPmzWPYsGEkJCTIpPLinFnK7Tz5xU5WHcwC4L6B8Tx/Q3t0snpxnY4VHmPB/gUsPbqUMkcZAEatkaFxQxl12Si6hnaVv5MXgepy4UhPx3bihHs7fgLb8ePu49RUsNvP2l4bGIi+RQv0UVHoIyLQR0W6k4IVmzYoSFbUFUJcFJIk9JDAMWMo3biRgq+/IXTChDq7d5u0GsZEBvNechafpOXUK0kI0HlwCw5tziRpcyYDb22DyUfmjRBCCCHEpfPFF1/w1VdfccMNN3g6FNEIHc4q4q/zt3E0pwSjTsPUWztza48Wng6rQVNVlc0Zm5m/fz6/pPxSVR7vH8+oy0ZxU+ub8DfW73cJcXaq3Y4tORnr4SNYjxzGduQo1iNHsB07dtY5ARWjEX1MCwwtYtC3aIEhpoU7KdgiBn10NFof70v4KoQQ4hRJEnqI71VD0IWF4cjKwrJyJf7DhtXZ5p6oYN5PzmJNXhHHy6zEmevuPh4e70doS1+yk4vY/3saPYbWvQKgEEIIIcSFYjAYaNOmjafDEI3Qyv2ZPPnlToqtDqL8TXwwthedW0hy60xsThvLji1j/v75HMo/BICCwpUxV3JPx3voFd5Leg2eJ9Xlwp6SQnliItaDiViTktzJwBMnwOGovZFejyEmBkNsrHuLi6061kVESE9AIUSDJElCD1H0egJGjSLnvffI//zzeiUJY81GBgf5siaviPmpubzcpu65DBVFofPgaFbPP8jedal0+0tLNLLymxBCCCEukaeeeooZM2bw7rvvSoJC1IvLpTJjVRIzViUB0Dc+iPfu6iHzD55Bfnk+Xx/6ms8Pfk5OWQ4AZp2Zm1vfzN0d7ybWTzoJnAtXSQnliYewHkqk/OBBrImHsCYm4iotrbW+xssLQ+vWGFu3xtimNYZWrTG2boU+OhpFJ79uCyEaF/lXy4MCbh9FzqxZlG3dRnniIUztLquzzb3RIazJK+KLjFz+Fh+BqR5zsbTtFc7v3x6mKK+cE3tyiO8aeiHCF0IIIYSo1a233lrjfPXq1SxbtoxOnTqh19ec+mTRokWXMjTRwJXaHDzxxU5W7s8E4N4BcbwwrAN6mX/wNFmlWczdO5dvDn1TtUpxmDmMMR3GMOqyUTKkuB5cZWWUHzhI+d69lO/bS9nefdiOHq119WBFr8fQtg2mdu0xXnYZxjZtMLZp7e4VKB+ACCGaCEkSepA+PBzfq6+maMUKCr78goiXX66zzTXBfkQb9aRa7XyfXcDIiKA62+gMWjoOjGLHimT2rEuVJKEQQgghLip//5rJiVtuucVDkYjGJMtSzv2fbGVPaiEGnYapt3Tmtp4y/+AfpRenM3vvbBYnLcbmsgHQIagD93S6h6GxQ9FrZQ7y2qgOB+WJiZTt2kX53n2U792L9fBh+MOK6wC60FCM7dtjat8O42XtMLVvhyEuDkUv31shRNMmSUIPC7xzDEUrVlC45H+ETn6qzklqtYrC2Khg/nksg3mpOfVKEgIkDIpmx8pkTu7PIz+jhMAImQxXCCGEEBfH3LlzPR2CaGQOZRYxfu4WUgvKCPY28NG4XvRoGejpsBqUk5aTzN47m/8d+R8Ol3sevO5h3Xmoy0MMiBogvdn+wJGfT9muXZTt2EnZjh2U7dmDWlZ2Wj1taAjmTgmYEhIwJXTC3KkTulDpVCGEaJ4kSehhXn37YoiPx3bsGJal3xE4Zkydbe6MDObfxzPYaillX3EZnXzMdbbxCzET1zmE47tz2LsulSvuqHtosxBCCCHExWCxWPjss8+YPXs2W7du9XQ4wsN+S8rhkQXbKLI6aBXizdzxvYkNlg+0Kx0rPMZHuz/ix2M/4lSdAPSN6MtDXR+SxUgqqKqKPTWV0k2bKd2+jbIdO93Dhv9A4+uLuWtXzF06u5OCnTqhCwuT76EQQlRoEJN7vPfee8TFxWEymejbty+bN28+a/2vv/6a9u3bYzKZ6Ny5Mz/++OMZ6z788MMoisL06dMvcNQXhqIoVYnB/IWfo9Yy/8UfhRn13BAaAMAnqTn1/lqdB0cDcHBDOrbyM6zCJYQQQghxkaxZs4axY8cSGRnJ66+/Tt++fT0dkvCwr7ae5N65mymyOugTF8SiCQMkQVghpSiFKb9O4eYlN7P06FKcqpOB0QP59PpP+Xjox/SO6N2sk1v21FQKFi0m7bkpHL76ao5c8xfSX3iBwm8XVSUIDfHx+N9yCxGvvUqrpd9x2aaNtPz4I0InTsT3qqvQh4c36++hEEL8kcd7En755ZdMnjyZWbNm0bdvX6ZPn87QoUNJTEwkLCzstPrr169nzJgxTJ06lRtvvJGFCxcyYsQItm/fTkJCQo26ixcvZuPGjURF1b0KsCf5j7iZrP/8B2tSEmXbtuHVq1edbcZFBfNdVgHfZObzUusofHXaOtvEtA8iINyLgsxSDm3KIOFKmeNFCCGEEBdXamoq8+bNY+7cuRQUFJCfn8/ChQu5/fbb5ZfzZkxVVaatPMQ7qw8DcHO3KP5vZBeM9Ximbepyy3L5cPeHfHXoq6phxYNjBvNQl4dICEmoo3XT5cjOpmT9eko2baZ00ybsqak1K+h0mDt3xqtXL8w9umPu1g1doAxZF0KIc6Go9em6dhH17duX3r178+677wLgcrmIiYnh8ccf57nnnjut/h133EFJSQnff/99VVm/fv3o1q0bs2bNqipLTU2lb9++LF++nGHDhjFp0iQmTZpUawxWqxWr1Vp1brFYiImJobCwED8/vwv0Ss8u/aWXKPj6G/xuuIHoaW/XWV9VVQZtPkhSqZU32kbzQIv6zZuxa/VJfvsqiaAob0a/1EcezoUQQohGyGKx4O/vf0mfVc7Vt99+y+zZs/nll1+4/vrrufvuu7n++uvx9vZm165ddOzY0dMhXjCN4f1oSKwOJ89+s5slO9MAePyqNkz+y2XN/rm0xF7CJ/s+4ZN9n1DqKAWgf2R/nuj5BJ2CO3k4uktPtdko3bGTkt9+pfjX37AePFizglaLOSEBrz598OrbF6/u3dB4Sy9UIYSoTX2fVTzak9Bms7Ft2zamTJlSVabRaLjmmmvYsGFDrW02bNjA5MmTa5QNHTqUJUuWVJ27XC7Gjh3LM888Q6dOdf9AnTp1Kq+++ur5vYgLJGD0aAq+/gbLypWE5+SgCwk5a31FUbi/RSjPHUphTkoO90WHoKnHg1X7/pFs/N9R8tJKSDtUQHQ7+XRNCCGEEBfeHXfcwbPPPsuXX36Jr6+vp8MRDURhqZ0HP93K5mN56DQKb97Smdt7x3g6LI+yOW18fehrPtz9IXnleQB0Cu7EpJ6T6BfZz8PRXVq2kycp/vVXSn77ndKNG3GVlta4burYEe8B/fHq2xdz9x51LvoohBDi3Hg0SZiTk4PT6SQ8PLxGeXh4OAf/+ElRhYyMjFrrZ2RkVJ2/9dZb6HQ6Jk6cWK84pkyZUiPxWNmT8FIyd+qEuWtXynbtouCbbwl5+KE624wKD+TNo2kcLbOyOq+Ia4Lr/uTaaNbRrm8E+35JZc/aFEkSCiGEEOKiuP/++3nvvfdYu3YtY8eO5Y477iBQhv41a1lF5dwzezMHM4rwNeqYeXdPLm979g/GmzKX6uKHoz/w3s73SC12D52N9Yvl8e6Pc23stc2iZ6XqclG2axfFq1dTtGr1aYuNaIOD8R44AJ8rrsB7wAB0wcEeilQIIZqHeiUJLRbLOd/YU0Mttm3bxowZM9i+fXu9f7AajUaMRuNFjqxugXeOoWzXLvK//JLgBx9A0Z59ThZvnZY7I4OZdTKbj09m1ytJCND5ymj2/ZLK0V05FOWV4xtkuhDhCyGEEEJU+eCDD5g+fTpfffUVc+bMYdKkSQwdOhRVVXG5XJ4OT1xiJ/NKuXv2Jk7klhLma2T+/X1oH9F8h2bvzdnL1M1T2Z29G4BQcygPd32YW9regl6j93B0F5fLaqV040aKfl5F0do1OLOrLcSo0+HVrRveV1yB9+UDMXXogKJpEGttCiFEs1CvJGFAQMA5fZKlKAqHDh2iVatWZ60XEhKCVqslMzOzRnlmZiYRERG1tomIiDhr/V9//ZWsrCxatmxZdd3pdPLUU08xffp0jh8/Xu/Xcan5Xncd2qn/xJGeTvG6dfhedVWdbcZHh/DhyWzW5hdxqKScy7zrTvgFR/sQfVkAqYcK2PdrKv1ubn0hwhdCCCGEqMFsNjNu3DjGjRtHUlISc+fOZevWrQwcOJBhw4YxcuRIbr31Vk+HKS6ypMwi7p69iUyLlZggM5/d34+WwV6eDssjcspy+O/2/7Lk8BJUVMw6Mw92fpC7O96NWWf2dHgXjbO4mOI1ayj6eRXFv/6KWm0YscbHB59BV+Bz9dX4DBqEVqYnEEIIj6n3cONvvvmGoKCgOuupqsoNN9xQr3saDAZ69uzJqlWrGDFiBOCeT3DVqlU89thjtbbp378/q1atqrEIycqVK+nfvz8AY8eO5ZprrqnRZujQoYwdO5bx48fXKy5P0RiN+N92K3mz55C/8PN6JQljzUaGhvizLKeQ2SnZvNWufsOkE65sQeqhAvb/lkbvG+LR6uUTOiGEEEJcPG3btuXNN9/kjTfe4IcffmD27NmMGTOmxuJxounZdbKAcXM3U1Bqp124L/Pv70O4X/MbxWJ32fn8wOfM3DWTYnsxAMNbDWdSz0mEeYV5OLqLw1VWRvG6dVh++JHiX35BrfZ3XRcejs9VQ/C9+hq8+/RGMRg8GKkQQohK9UoSxsbGMmjQIILrOQdEq1at0Ovr101+8uTJjBs3jl69etGnTx+mT59OSUlJVULvnnvuITo6mqlTpwLwxBNPcOWVV/L2228zbNgwvvjiC7Zu3cqHH34IQHBw8Glx6vV6IiIiaNeuXb1i8qTAO+4gb85cSn77DVtyMoZqPSLP5P4WISzLKeSrjHyebxWJv77utzW+WwjeAUZKCqwc3p5Fu76199wUQgghhLiQNBoNw4cPZ/jw4WRlZXk6HHERrT+Sw4OfbKXE5qRbTADzxvcmwKv5JYPWp67nn1v+ybHCYwB0DO7IlD5T6BbWzbOBXQQum42S337D8sOPFK1ZU6PHoCE+Ht+h1+J79TWYEjo1izkXhRCisalXkvDYsWPndNO9e/fWu+4dd9xBdnY2L7/8MhkZGXTr1o2ffvqpanGS5ORkNNXmoRgwYAALFy7kxRdf5Pnnn6dt27YsWbKEhISEc4qxoTK0bIn3FZdT8suv5H/xJeF/e6bONgMDfGjvbeJgSTmfp+fxcMu6P43UajUkDIpi03fH2LM2RZKEQgghhLhgvvvuO66//vo6PzQOC3M/s/z4448MGTIEs7npDrdsblbsy+Cxz3dgc7gY2CaYD8f2wtvo0TUTL7nU4lTe2vwWa06uASDIFMQTPZ5gRJsRaJSmM4pHdbko3byZwu+WUrRyJa6ioqpr+uho/G64Ab8brsfYvr0kBoUQooFTVFVVPR1EQ2OxWPD396ewsNAjC7AUrVlDyiMT0Pr702bdWjSmuodkfJaWy1OJJ4kxGdjYrwPaevwALrXY+GTK77icKiOf7UV4fPOdPFoIIYRoTDz9rFIXrVZLRkYGoaGh9arv5+fHzp0765zPuqFq6O/HpfbtthT+9u1unC6VoZ3C+e+Y7hh1Z1+QrylxuBx8duAz3tv5HmWOMnSKjjEdxvBw14fxMzSdPx+2kycpXLyEwiVLsKelVZXrwsLwu/56/G64HlOXLpIYFEKIBqC+zyrn9XHeqlWrWLVqFVlZWaetTjdnzpzzuaWoxmfQIPRRUdjT0rAs+4mAW0bU2eaW8EDeOJLGyXIbK3MsXBfqX2cbLz8DbXuHk7gxg12rkrn2gabRG1MIIYQQnqWqKvfeey9Go7Fe9cvLyy9yROJSmb/hOC//bx8AI3u24J+3dkanbTq95upyMO8gr6x/hf25+wHoGd6Tl/q9ROuAprFQoKukBMvyFRQuWkTp1q1V5RpfX/yuvx7/4Tdi7tlTViQWQohG6pyThK+++iqvvfYavXr1IjIyUj4ZuggUrZaA0aPJnjaN/M8+w3/EzXV+n720Gu6OCuad5Cw+SsmuV5IQoOvVMSRuzODw9mz655XjG9T8JpIWQgghxIU1bty4c6p/1113SQ+8JmDe78f4+1J3cuy+gfG8OKwDGk3z+F2hzFHGzF0zmb9vPk7Via/el8m9JnNr21sb/dBiVVUp27aNgm8XYVm+/NQ8g4qC94AB+N96C75XX12v0U9CCCEatnNOEs6aNYt58+YxduzYixGPqBAw8jZy3n2X8r17Kdu5E6/u3etsMy46hPdPZvF7QTEHisvo4FP3vD6hMb5EtwsgNbGAPWtSGHBbmwsRvhBCCCGasblz53o6BHGJVU8QThjcmmeGtms2nQk2pG3gtQ2vkVKcAsC1sdfyXJ/nCPWq33D7hspZXIJl6XfkL/wca1JSVbkhNhb/W27Bf8TN6CNkXnMhhGhKzjlJaLPZGDBgwMWIRVSjCwrC78YbKVy0iPxPP61XkrCFycD1If58n13IxynZvN2+7pWRAbpe3ZLUxAL2/ZZGr2FxGEzNa1JpIYQQQghx/pprgrCgvIB/bf0X3x35DoBwr3Be7Pcig2MGezawP8l6+DD5Cz+n8H//w1VSAoBiNuM37AYCbr0Vc/fuzeL9FUKI5uic+74/8MADLFy48GLEIv4g6B53b03L8hXYMzLq1ebBFu5PLL/NzCfP7qhXm7iEYPzDzNjKHBzckH5+wQohhBBCiGbnk/XHqxKEjzSjBOG6k+sY8b8RfHfkOxQUxrQfw5KblzTaBKFqt2P5aTkn7hnH0RuHk79wIa6SEgxxcYQ/P4W269YS9cYbePXo0SzeXyGEaK7q1WVs8uTJVccul4sPP/yQn3/+mS5duqDX62vUnTZt2oWNsIlacngJC/Yv4N9X/ps4/7ha65jat8erd29Kt2whf+HnhE1+ss779vH3prOPmT3FZXyWlsvjseF1tlE0Cl2viuGXLw6xa3UKCVe2aDbzxwghhBBCiPPzyfrjvPKde5GSRwa35m/NIEFYYi/hX1v+xbdJ3wLQ2r81fx/wd7qFdfNsYOfJabGQ/8WX5C9YgCMry12o0eBz1RCC7rwTr/79m/x7KoQQ4pR6JQl37NhR47xbt24A7N2794IH1Fz8fOJnEvMTWXhwIc/3ff6M9QLvGUvpli0UfPUVIRMeqXNCYEVReKBFKE8cTGZeag6PxIShq0fCr33/SDZ9dxRLdhnHd+fQqlvjnkNFCCGEEEJcPNUThA9f2TwShDuydvD8r8+TUpyCgsLYjmOZ2GMiRm39VvFuSOxpaeR9Mp+Cr7/GVbEQiTY4mIBRIwm84w70kZEejlAIIYQn1CtJuGbNmosdR7NzV4e7WJeyjiWHl/BY98fwM9S+op/vVVehj47GnppK4dKlBI4aVee9bw4L4LUjaaRa7SzLKWR4WECdbfRGLZ2uiGb78hPsWnVSkoRCCCGEuCCOHj1Kq1atPB2GuIDmb6iZIHz2uqadILQ77by/633m7J2DS3UR6R3JPy7/B70jens6tHNWfvAgubPnYPnxR3A6ATC2bUvQfffhN+wGNAaDhyMUQgjhSec8J2FtVFVl2bJljBw58kLcrlnoF9mPNgFtKHOUsThp8RnrKVotgXfdBUD+/E9RVbXOe5u0Gu6JCgbg45TsesfUebB7mHFaUgFZJyz1bieEEEIIcSZt2rRhyJAhLFiwgPLyck+HI/6k+RuO8/L/3AnCh65s1eQThEn5Sdz54518vOdjXKqLm1rfxLc3fduoEoSqqlL8++8k33c/x0bcgmXpUnA68erbl5gPPyD+u/8RcMsISRAKIYT4c0nCY8eO8dJLL9GyZUtuueUWefA7B4qicFcHd/Lv84Of43Q5z1g3YORtKF5eWJOSKN24sV73Hxcdgk6BTYUl7C4qrVcbn0AjrXuGAbBr1cl6tRFCCCGEOJvt27fTpUsXJk+eTEREBA899BCbN2/2dFjiPHy15WSNBOFz17VvsglCl+rik32fcMf3d3Aw7yABxgCmDZ7GPy7/B74GX0+HVy+qqlK0eg3HR47i5P0PULJ+PWg0+N1wA3HffEPsJ/PwGTSoyb6HQgghzt05JwmtViufffYZV111Fe3atePNN99k8uTJZGVl8f3331+MGJusYa2G4W/0J7U4lbUpa89YT+vnR8CIEQDkzf+0XveOMOoZHhoAwEfn0Juw2zUxABzemkVxvrXe7YQQQgghatOtWzdmzJhBWloac+bMIT09ncsvv5yEhASmTZtGdnb9n1OE5/y4J53nFu0G4IHL45t0gjCvPI8Jqybw763/xu6yc0X0FSy+eTF/if2Lp0OrF1VVKVrjTg6mTJhA+b59KGYzgWPH0nrFCqKnvY05oZOnwxRCCNEA1TtJuG3bNiZMmEBERATTp09nxIgRnDx5Eo1Gw9ChQ/Hzq31OPXFmZp2ZkW3dQ7QX7F9w1rqBd98NQPHatdiSk+t1/7/GuHsFLsksIN1qq1ebsFg/Itv443Kp7FmXUq82QgghhBB10el03HrrrXz99de89dZbHD58mKeffpqYmBjuuece0tPTPR2iOINfk7J54osduFQY3TuGF4Z1aLIJwm2Z2xj13Sh+T/0do9bIS/1e4r2r3yPEHOLp0OqkqipFa9dyfNTtpDxSkRz08iL4wQdos+pnIl54HkOLaE+HKYQQogGrd5Kwb9++GI1GNm7cyJYtW5g4cSLh4eEXM7ZmYXT70WgVLVszt3Iw7+AZ6xlbxeM96ApQVfIWnD2hWKm7nxf9/L2xqyofp+TUO6ZuV7cEYN8vqditZx4GLYQQQghRX1u3bmXChAlERkYybdo0nn76aY4cOcLKlStJS0vj5ptv9nSIohbbTuTz1/nbsDtVhnWO5B+3dG6SCUKX6uLjPR9z//L7ySrLIt4/noXDFnJ7u9sb/OutkRx8+BHK9+49lRz8eSVhTz2FLijI02EKIYRoBOq1ujHA1VdfzezZs8nKymLs2LEMHTq0wf/AbAwivCP4S+xf+On4T3x24DNeH/j6GesGjb2Hkl9+pfDbRYROnIjWx6fO+09oGcbGPceYn5rDpNhwfHXaOtvEdQ3BL8SEJaecxI3pJFzZ4pxekxBCCCFEpWnTpjF37lwSExO54YYbmD9/PjfccAMajfuz6vj4eObNm0dcXJxnAxWnOZBuYfzczZTZnVzRNoRpd3RFq2l6z/955Xk8/+vz/J72OwA3trqRl/q9hJfey8OR1a1k4yaypk2jfLd7KLhiNhN0150E3XefJAaFEA2Cqqo4VXDi3rtUFaeq4gScqopa7ZpTVVEryp0quFBxVbRxAU7VfT9XRR0X7msquOtV1q9RXlmPqvMa+2rHamWdivtUr3fq+qmvV1nuqlhgtnodV7X7VbapXIf21P0q6gERBj0PxoRe3DejHuqdJFy+fDknT55k7ty5PPLII5SVlXHHHXcASLLwT7qrw138dPwnfjj6A5N6TCLYHFxrPe/LB2Jo1Qrb0aMULlpM0D1j67z3NcF+tPUyklRq5bO0XB5uGVZnG41GoctVMfz2VRK7VqfQ6YpolCb4QCiEEEKIi2/mzJncd9993HvvvURGRtZaJywsjNmzZ1/iyMTZHM8p4Z45m7GUO+gZG8gHY3tirMeHzY3Ntsxt/G3d38gqy8KoNfJC3xcY0WZEg//9xpqURNa/36Z43TpAkoNCNAWqqmJTVewuFatLxa6qWF0u7KqKzeXe3MfuMnvFeY3jir2j4tihVp7zh/Nqm8t9vXqZs6KNs0YZ1a65r7uqXassr0z+VSb6VE9/YxuJTj6mBpEkVFRVPa/3bOXKlcydO5fFixcTExPDyJEjGTlyJD169LjQMV5yFosFf39/CgsLL8lci6qqctePd7EnZw+PdnuUh7s+fMa6+Z9/Tsarr6GPbUnrZctQNHWPGP8sLZenEk8SbdSzsV9H9PVI+NnKHXzy3O/Yyp0Mm9CFuC4Nfx4WIYQQorm41M8qf8bx48dp2bJlVc/BSqqqcvLkSVq2bOmhyC6cxvR+1EdGYTkjZ60nJb+M9hG+fPnX/vh76T0d1gXlUl3M2TuHd3a8g0t1Ee8fz7+v/DeXBV7m6dDOyp6ZSfY771C4aDG4XKDTEXjHHYRMeARdcO0dDYQQ56YyWVfmdFHqdFHmqtg7XZS7VMpclcfu8/Jqx2UuF1aXO7lXuS9z/rFMxaaeKqtMANrOLzXT6GkAjQJaRUGDglapOEdBoyhoFHcdraKgVOzdZX/c464PKBXlStV9QKlWXnleWUepaKtQeQxKRT3+WA93R7nKp5qqr1VxfOoe7jKo/Nqn7q/8oR2KuyfhI/Xo1HW+6vusct5Jwkr5+fksWLCAOXPmsHv3bpzOxj+HnSce9H44+gPP/focIeYQVty2Ar229gcxV2kpSYOH4LJYaDHzfXyHDKnz3uVOF7037ifb5uC9Di25LaJ+ny7+/u1hdq5MJrpdICOe7H5Or0cIIYQQF09jSkpptVrS09MJC6v54Jubm0tYWJg8OzYw+SU2bv9gA0lZxcQFe/HVw/0J8zV5OqwLqshWxHO/PscvKb8AjWN4sbO4mNyPPyZv3ieo5eUA+A4dStiTkzDIUH3RzJU7XRQ5nZQ4XRQ7nBRV7EucLoorjoudLkoq6pRWbCVVeyelLvd5WUVS0NkA8nVaBQyKBoNGQa8oGDRK1bFeUdBrFAyKBp3GXU9XUUdXcU2ngF6pfq6cdu7eqHasoNMoaAFdtTraavW0SsV1xZ3E01Uk+NzbqXItpxJ61dtpKuppqxJ6DbvndlNR32eVeg83PpPAwEAef/xxHn/8cbZv3/5nb9dsXRt7LW9vfZvssmx+Ov4Tw1sPr7WexsuLgJEjyZszh7z58+uVJDRpNdwfHcI/j2Uw82Q2t4YH1usvYpchLdi16iSpifnkpBQR0sL3nF+XEEIIIZq3M30eXVxcjMnUtJJPjV2x1cG9czeTlFVMhJ+JT+/v2+QShMcKjzFx9USOW443iuHFqt1O/ldfkfPe+zjz8gAw9+hB2DNP49VdPsQXjZ+qqpQ4XRQ4nFgcTgrsFXuHg0KHk8KK8kKHk2KHC4vDSZHDicXppMjhosjhvKg98HQKeGk1mDUazBV7k0aDSatg0pwqN2mUinINxopjo0bBqKn93KAoGLUa917jTgRWXjdUJPG0DfTfJdG01StJuHv3bhISEk4bJvJHlUON9+3bR7t27dDp/nQOstnQa/WMbj+ad3a8w2cHPuPGVjee8WEl6K47yZs3j9INGyk/dAjTZXUPi7gnOoQZJ7LYW1zGb/nFXBFUd8LPN8hE6+6hHN6WxY6VyfxlfKdzfl1CCCGEaJ4mT54MuHsIvPzyy3h5neql5XQ62bRpE926dfNQdOKP7E4XjyzYxq6UQgK99Hx6fx9ighpuz7rz8UvKLzz7y7MU24sJ9wpnxlUz6BTccJ9vSzZuJOP1N7AdOQKAIS6OsKefwufqqxtsUlM0by5VpdDhJNfuINfmINfuIM/uJM/uIN/uIN/uJN/hoKCqzJ0MdFygHJ+XVoOPVoOPVouPrmKv1eCjc++9tBq8tRq8tdpqx5Xl7jKviqRfZWKwPlN1CdGU1CuL1717dzIyMggNrd8kiv3792fnzp20atXqTwXX3Iy8bCQf7PqAfbn72JW9i25h3Wqtp4+OxveaayhasYL8TxcQ+fprdd47SK9jTGQQc1JzeP9kVr2ShADdr23J4W1ZJG3Jou9NrfALNp/LSxJCCCFEM7Vjxw7A3Utkz549GAyGqmsGg4GuXbvy9NNPeyo8UY2qqryweA+/JuVg1muZN74PbcObzggSVVWZvXc2/93+X1RUeoT14O3BbxNibphzbtszMsh86y2Klv0EgDYoiNDHHyNg5EgUfdOaG1I0fA6XSq7dQbbNTpbNQZbNTrbNUbG5y3LtlQlBx3kP09UrCv46LQF6LX46Lf7VNr8/bD5aTdWxr06Lb0UiUHreCfHn1StJqKoqL730Uo1PgM/GZrP9qaCaqyBTEMNaDWPx4cV8uv/TMyYJAYLuGUvRihUUfvcdoZOfRBcYWOf9H4oJZV5qDmvyithfXEZHn7oTfmGxfrRoH0jKwXx2/nySQXc07MmchRBCCNEwrFmzBoDx48czY8aMRj9XX1P27urDfLU1BY0C797Zna4xAZ4O6YIpc5Tx8u8v89Nxd8Jt1GWjmNJnyhnn//Yk1WYjb/58st+fiVpaChoNgWPGEDrxcbT+/p4OTzQxqqqSa3eSabOTbrWTaa3Y2+xkWCs2m50cm+OcV6f10WoI1usINugI1usI0usI1GsJ0usI0GkJrDgP1OsI1GkJ0OswaxTpIStEA1CvJOGgQYNITEys90379++P2Sw9zs7HXR3uYvHhxaxKXkVGSQYR3hG11jP37ImpY0fK9+8n//PPCZ0woc57x5qNDAsNYGl2AbNOZvHfDrH1iqnH0FhSDuZz4Lc0eg+Lw+xjqLuREEIIIQQwd+5cT4cgzmLxjhTeXnkIgFdvTuDqDuEejujCSStO44k1T3Aw7yA6RceUvlO4vd3tng6rViXr15Pxxj+wHT0KuOcdjHjpRUwdOng4MtFYlTldpFptpJbbSS23nTq22kir2Je76pf+0wDBBh1hBh2hej2hRvc+zKAj1HAqGViZGDTWMU2ZEKLhqleScO3atRc5DFGpXVA7+kT0YXPGZj4/+DlP9nyy1nqKohB0332kPf00+Qs+I/i++9DUY/LvR1qGsjS7gMWZBUxpFUmkse6EX4v2gYS29CU7uYg9a1LoM1yGkQshhBDizG699VbmzZuHn58ft95661nrLlq06BJFJf5o/ZEc/vbNbgAeGtSKsf3q9wFyY7AlYwtPrX2KfGs+QaYgpg2eRs/wnp4O6zT29HQy//kWRcuXA6ANDibsmafxv/lm6VUlzsrhUkmz2kgud28nyyqOy2ycKLeSZXPU6z7Beh2RRj3hBj0RRh0RRr17M7j34QY9wQadDOUVopmQlUUaoLs63MXmjM18c+gbHuryEF762od5+103lOxp07CnpVG45H8Ejr6jznv38POmn783GwtL+Dglh5daR9XZRlEUul/bkhUf72P32hS6XxuL3qg959clhBBCiObB39+/KsHhL8MkG6RDmUU89Ok27E6VYV0iefa69p4O6YL57sh3vPL7KzhUBx2COjBjyAwifSI9HVYNqstF/sLPyZo27dTQ4rvuIvTxx9DK0HxRwaWqpFrtHCu1crTMWrU/WmoludyGvY5Vfb21GlqYDEQZ9bQwGYg26omq2EebDEQa9dLrTwhRgyQJG6ArW1xJC58WpBSn8P3R7884LELR6Qi6dxyZb04lb+5cAkaNRNHWnbyb0DKMjXuOMT81h0mx4fjq6m7TukcYfqFHsWSXsf+3NLpeHXPOr0sIIYQQzUP1IcYy3LjhybKUM37uForKHfSKDeTtUV3RNIEVPFVVZdbuWby/830Arou7jtcGvoZZ17CmQbIePUr6iy9Rtn074J5GKOLllzC1a+fhyISnlDtdHCmzklRSTmJJOYdKyzlcauV4mRXrWYYEGxSFFiYDLU0GWpor98aq80CdVnqkCiHOiSQJGyCtRsudHe7k/7b8HwsOLGDkZSPRKLV/whNw221kv/c+thMnKFq1Cr9rr63z/tcE+9HWy0hSqZXP0nJ5uGVYnW00GoXuf2nJuoWJ7Pw5mYTB0Wi18qmTEEIIIc7u2LFjOBwO2rZtW6M8KSkJvV5PXFycZwJrpkqsDu77ZAupBWXEh3jz0T29MOkb/wgRu8vOaxteY8nhJQDcn3A/E3tMPOMztCeodju5c+aS8957qDYbGi8vwp55moA77kCR3lzNgtXl4nCplf3FZRwsKSepIiGYXGbDdYY2ekUh1mwg3mykldlIvNepfZRRL8OAhRAXlCQJG6hb2tzC+zvf51jhMX5J+YXBMYNrrafx9iZwzGhyZ31A3uw5+P7lL3V+WqRRFB6OCeOpxJN8lJLN/S1C0dfj0+P2/SPY/P0xivOtJG3JpH2/hjVsQwghhBANz7333st99913WpJw06ZNfPzxxzL39SXkcLp4/PMd7E21EOxtYN743gR6N/4F6YptxUxeO5kN6RvQKBpe6PtCg1ugpHz/ftJefBHr/gMAeA+6gsi//x19VN1T/4jGR1VVMmx29heXs7+4jAMl7v3h0nIcZ+gYGKDTcpm3ibZexoq9idZeRqKNBnRNoKevEKJxOOckYUlJCd7e3hcjFlGNj8GHUe1GMXfvXObunXvGJCFA0N13kzdnLmW7dlG2fTtePeuelPm28ED+eSydVKudpdkF3BoeWGcbnV5L16tasHHJUXasSKZdnwgU+YElhBBCiLPYsWMHAwcOPK28X79+PPbYYx6IqHlSVZW/L93H6oNZGHUaPhrXi9jgxv9Mn1mSyYRVEziUfwizzsy/r/w3g1oM8nRYVVxWKznvvU/u7NngdKL19yf8hefxGz5choE2EaqqcrLcxu6iMnYXlbKrqIw9xaXk2Z211vfXaenoY6K9t5nLvE1cVpEUDNHr5M+EEMLjzjlJGB4ezu233859993H5ZdffjFiEhXu7nA3n+7/lO1Z29mVvYuuoV1rracLCcH/5psp+PprcmfPqVeS0KTVcH90CP88lsH7yVncEhZQrx9KCYOi2fbTCfLSSji+N5f4LiHn/LqEEEII0XwoikJRUdFp5YWFhTidtf8SLS68TzeeYMHGZBQFZozuTo+WdX9A3NAdyj/EhJ8nkFmaSbApmPeufo9OIZ08HVaVsl27SHtuCrZjxwDwve46Il58AV2IPD83ZulWGzss7mTgLkspu8+QENQq0NpsoqOPiY4+Zjp4m+jkYybSqJdkoBCiwTrnJOGCBQuYN28eV111FXFxcdx3333cc889RElX+QsuzCuMG1vdyJLDS5i3dx7/GfKfM9YNGj+egm++oXj1aqxHjmBs3brO+98THcKME1nsLS5jbV4RQ4LrXknN6KUnYVA0O1Yks2P5CUkSCiGEEOKsBg0axNSpU/n888/RViyw5nQ6mTp1qnzgfImsP5zDq0v3A/Dsde25LiHCwxH9eRvTN/LkmicpthcT7x/PzGtmEu0T7emwAFAdDnI++ICc92e6ew+GhhDx8sv4/eUvng5NnCOby8XeojK2WkrYaillW2EJqVb7afX0ikIHbxNdfL3o4mumi68X7b1NmGQOdyFEI6Ooah3rpp9BdnY2n376KfPmzePAgQMMHTqU++67j5tuugmdrnFPdWixWPD396ewsBA/v7oTZxfTkYIjjPjfCBQUvhvxHXH+cWese/LRxyhetQr/kbcR9cYb9br/K0mpfJCSTT9/b5b0aFt3A6Ck0Mr8F9bjcqjc8nQPotoE1KudEEIIIS6MhvSsUpf9+/czaNAgAgICuOKKKwD49ddfsVgsrF69moSEBA9H+Oc15PcjObeUm977jYJSO7d0j2ba7V0bfS+mH47+wIu/vYhDddAzvCczhszA3+jv6bAAsJ08Sdozf6Ns504A/G64gYhXXkbr3zDiE2eXZ3ewsaCYLYUlbLOUsquo9LTVhbUKdPA209XXTFdfL7r4etHBx4RRFp8RQjRg9X1WOe8kYXXvvPMOzzzzDDabjZCQEB5++GGee+45vLy8/uytPaKhPeg9tuox1qWsY9Rlo3i5/8tnrFe6fQcn7rwTRa+n9aqf0YfVvWpxhtVOnw37sakqS7q3oV+AT71iWvPpAfb/nk5c52CGPVr7MGghhBBCXBwN7VmlLmlpabz77rvs2rULs9lMly5deOyxxwgKCvJ0aBdEQ30/iq0Obnt/PYmZRXRt4c+XD/Vv9CsZf3nwS/6x6R+oqFwfdz1vXP4GBq3nF19RVZXCxUvIfOMNXKWlaHx8iHjlZfyHD/d0aOIscmzupOCGgmLWFxRzoKT8tDpBei09/bzp5edNL38vuvl64a1r3H+PhBDNT32fVc67y19mZiaffPIJ8+bN48SJE4wcOZL777+flJQU3nrrLTZu3MiKFSvO9/aimns73cu6lHX87/D/mNBtAiHm2of4evXojrl7d8p27CB/wWeETX6yzntHGPWMjgxiflouM05k1jtJ2P3aWPavT+f4nlxyU4sJjq5fOyGEEEI0P1FRUbz55pueDqNZcblUJn+5k8TMIsJ8jXwwtlejTxB+vOdjZmyfAcDodqOZ0ncKGsXzvbecBQWkv/J3ipYvB8DcqydR/3wLQ4uGMfxZnJJnd/B7vjshuL6gmMRakoKXeZnoF+BNTz9vevt7E282NPret0IIUV/nnCRctGgRc+fOZfny5XTs2JEJEyZw9913ExAQUFVnwIABdOjQ4ULG2az1DO9Jl5Au7M7ZzecHP+fx7o+fsW7w/feR8tjj5H/+OcF//Stan7pXrXu0ZRifpeeyJq+InZZSuvnV3QM0INyL1t1CObIjmx0rkrlmfMdzek1CCCGEaF5KS0tJTk7GZrPVKO/SpYuHImrapv98iBX7MzHoNHwwticR/iZPh3TeVFVlxvYZzN47G4AHOz/I490fbxCJm5ING0h7bgqOzEzQ6Qh9/HGCH7gfRdu4E7JNhd2lstVSwrq8ItbmFbGrqJQ/DqNr721iQIAP/QN86BfgTahB75FYhRCiITjnJOH48eMZPXo0v//+O7179661TlRUFC+88MKfDk64KYrCvQn3MnntZL44+AX3J9yPl772RJ7PVVdhiIvDdvw4Bd98TfC999Z5/1izkVvCAvkmM5//nshkTuf4esXVfWgsR3Zkk7Qlk743t8I3qPE+fAohhBDi4sjOzmb8+PEsW7as1uuywvGF98PudP67+jAAU2/pTPdGvJKxS3Xx5qY3+TLxSwCe7Pkk9yXc5+GoQLXbyZo+nbzZcwAwxMUR9a9/Ye7c+OfYbMxUVeVYmY21eRbW5RfxW34xJU5XjTrtvE1cHuDDgEAf+vn7EGxo3PPpCyHEhXTO/yKmp6fXOdeg2WzmlVdeOe+gxOmuirmKlr4tSS5KZvHhxdzV4a5a6ykaDUH3jSfj5VfI+2Q+QXfdhaKv+9Owx2PD+SYznx9zCkksKaedd90Jv/A4P6LbBZKamM/Olclcccdl5/y6hBBCCNG0TZo0iYKCAjZt2sTgwYNZvHgxmZmZvPHGG7z99tueDq/J2ZdWyNNf7wLgwSviua1nCw9HdP4cLgcv//4yS48uRUHhxX4vcnu72z0dFvbMTFKfnEzZ9u0ABIy+g/C//Q1NI52PvbGzu1Q2FRazIsfCitxCjpfV7K0cpNdyZaAvg4P8uDLIlwij9BQUQogzOedJPHx9fcnKyjqtPDc3F610q79otBot4zqNA2D+vvk4XI4z1vW/+Wa0ISE40tOx/PRTve7fztvEsFD3qmvvnMisd1w9hrYEYP9vaZRabHXUFkIIIURzs3r1aqZNm0avXr3QaDTExsZy991383//939MnTrV0+E1KTnFVv46fxtldieDLgvluesb7/Q/NqeNp9Y+xdKjS9EqWqZeMbVBJAhL1q/n2C23UrZ9OxofH6L/O4PIv/9dEoSXmMXhZElmPhP2nyDh972M3HmED1OyOV5mQ68oDAjw4flWkSzvdRl7ByYws1Mcd0QGSYJQCCHqcM5JwjMthmy1WjEYPL+yWFN2U+ubCDIFkVaSxorjZ14URmM0EnS3u6dh7sezz/ie/dHE2HAAFmflc6LMWq82MR2CCIv1xWF3sfPn5Hq1EUIIIUTzUVJSQlhYGACBgYFkZ2cD0LlzZ7ZX9MQSf57N4eKRBdtILSijVYg374zpjlbj+Tn7zkepvZRHVz3K6pOrMWgM/GfwfxjWaphHY1JdLrLfe4/k+x/AmZeHsUMH4r/9Br9rr/VoXM1JutXGxynZ3L7zMB1/28PD+0+wKDOfQoeTIL2W2yMCmZ0Qx4HLE1jUvQ0TY8Pp6uuFpgHMXSmEEI1FvYcb//e//wXc8+N9/PHH+PicWs3W6XTyyy+/0L59+wsfoahi0pkY034M7+18j7n75nJ9/PVnnLA5cPRocj78CGtiIiW/r8fn8oF13r+rrxdDgnxZk1fEu8lZ/KtdTJ1tFEWh97B4fnh/N3vWpdL92paYfSRZLIQQQgi3du3akZiYSFxcHF27duWDDz4gLi6OWbNmERkZ6enwmox//LCfLcfz8TXp+GhcL/zNjbPHVIm9hEd+foQdWTsw68z896r/0i+yn0djcuTnk/bM3yj57TcAAkaNJPyFF9CYZD7uiy3Dauf77AKWZhWwqbCkxrW2XkauDfFnaLAfPf290UoyUAgh/rR6Jwn/85//AO6ehLNmzaoxtNhgMFQ97ImLa3S70czZO4eDeQfZmL6R/lH9a62nDQggYORt5M//lNyPPqpXkhDgidhw1uQV8WV6HpPjwok01p3wi+0cTGhLX7KTi9j180n6jWh9Tq9JCCGEEE3XE088QXp6OgCvvPIK1113HZ999hkGg4F58+Z5Nrgm4n87U/lkwwkAZozuRutQnzpaNEzVE4S+Bl9mXjOTrqFdPRpT6Y4dpD45GUdGBorJRMQrrxBwywiPxtTUZVUkBr+rSAxWHxPV28+bG0L9uTbEj9ZekqQVQogLrd5JwmPHjgEwZMgQFi1aRGBg410lrTELMAVwS5tbWHhwIXP3zj1jkhAg+N57yf/8C0o3baJ0+w68enSv8/79Anzo5+/NxsISZiVn82rb6DrbKIpCrxviWDZrD7vXptDtLy0xeTfOT6+FEEIIcWHdfffdVcc9e/bkxIkTHDx4kJYtWxISEuLByJqGpMwinvt2DwCPX9WGq9qHezii81NqL2XCzxPcCUK9Lx9d+xGdgjt5LB5VVcmfP5/Mf/0bHA4McXFEz5iBqZ0s1HcxFNodfJddwOLMAjYUFNdIDPby8+KmsABuDA0gyiQjloRorFRVxeVSUV0qLqd7r7o4VVaxV9WKcqda1Qb1VL3KOi4XFXVVVJU/7N33UNU/HFfsUSuvVWv3x/I/nFNVfuZrVWUALlA5VV7VnopjAFfFXgWfQCN9hrfyyHtT3TmvbrxmzZqLEYc4B2M7juWLxC/YkL6Bg3kHaR9U+zBvfVQUASNupuDrb8iZNZOWH35Yr/s/ERvOxt1HmZ+Wy8TYcIINdf8xie8aQnALH3JTitm1+iR9G8AfbiGEEEI0LKqqYjab6dGjh6dDaRKKrQ4eXuBeqOTyNiFMuqZxJrAq5yDcnrUdH70PH177oUcThC6rlfSXXsLy3VIAfK+/jsjX30Dr4+2xmJoih0tlbX4RX2XksTynEKvrVGqwh58XN4UGcGNYAC0kMSgE4E6SOe0unI5Tm8uhVjtXK8pcOJ1qxf5UHZfzVF2XU63YKupW1K9e7nKqOJ2VSb1q11xqjeMa16slAV0uFdVZPfnn6e9gwxbcwqfxJAknT57M66+/jre3N5MnTz5r3WnTpl2QwMSZtfBtwdDYoSw7voy5e+fy1qC3zlg3+MEHKfh2ESW//ErZ3n2YE+p+4Boc5EsXXzO7i8r4KCWb51rVPV+Qoij0viGOnz7cy+7VKXS7Ogajl/QmFEIIIQTMnj2b//znPyQlJQHQtm1bJk2axAMPPODhyBovVVV57tvdHMkuIdLfxIzR3RrlQiVljjIeX/04WzO34qP34YO/fEBCSILH4rFnZpHy+OOU794NWi3hzz5L4Ni7zzgPuDh3+4vL+DIjj0WZ+WTbHFXl7bxNjAoP5KawAFqajR6MUIj6cblUHDYndquzYu/CYXNWbC7sFfvKc4e94prdhcPuwmmr2NudFXvXqWsO97nT7sLhcOGyu9w96pooRaOgaECjKCgaBY1WQVHcZYrm1LGm6rhiU9zXNdWOT+3/UOb+HxoNUP0ap+pUlVfsqbxe47hmXaruXXENUDQAtVyn+teAykJFAbNvw/hApF5Jwh07dmC326uOz0R+eF469ybcy7Ljy1h+fDkTe0wk2qf2YcGGli3xu3EYlu+WkvvBLFq8806d91YUhUmx4dy39zhzUrOZ0DIMP522znatuoUSFOVNXloJu9ek0HtY/Dm/LiGEEEI0LS+//DLTpk3j8ccfp39/9zQpGzZs4MknnyQ5OZnXXnut3vdKTU3l2WefZdmyZZSWltKmTRvmzp1Lr169ztjGarXy2muvsWDBAjIyMoiMjOTll1/mvvvuq6ozffp0Zs6cSXJyMiEhIYwcOZKpU6diasALU8xbf5zvd6ej0yi8e2cPgn0aX1Kl3FHO46sfZ3PGZrx0Xsy8ZiZdQrt4LJ6yPXtJefRRHFlZaPz9aTFjOt79PLtoSlORb3fwdUYeX2Xks7e4rKo8SK/l1vBAbo8IorOPWX6fFBeV6lKxW53Yyh3Yyir3Dmzlp47tVif2cic2qxO71YG93FnRxr23Wx04rO4EoNPu8tyLUUCr06DVKmj1GrQ6DRqde6/VKe5zrYJG6z6vsdcqaPQatBr3uUan1KyrqWxbfatWpql2rlFQqsoqknuVSTvNqfrVE3/Vy2ok8ESDUK8kYfUhxjLcuGHoGNyRvpF92ZS+ibl75/JivxfPWDfkr3/FsvR7ilb+TPmhQ5guq3soynUh/lzmZeJQaTlzU3J4Iq7u+W0UjXtuwhUf72PXqpN0vSoGg/mcR7QLIYQQogmZOXMmH330EWPGjKkqu+mmm+jSpQuPP/54vZOE+fn5DBw4kCFDhrBs2TJCQ0NJSkqqc57s22+/nczMTGbPnk2bNm1IT0/H5Tr1i93ChQt57rnnmDNnDgMGDODQoUPce++9KIrSYEfIbDuRzz9+OADAC8M60DO28c0VXu4oZ+LqiWxK34RZZ2bWX2bRLaybx+Ip/P4H0l94AdVqxdC6NTEz38fQsqXH4mkKVFVlm6WUT9Jy+C6roGo4sUFR+EuIH7dHBHFVkB/6RtgDVniO0+6ivNROeYkda4nDvS+1Yy11uLcyB7aKvbXUjq3MibXMjq3Ugc3qhIvRGU8BvUGLzqhFb9CgM2jdm959fKpMg06vRWvQoNNr0Ord5zqDO7l3aq917yuSf1q9glanrdi721X2qBPiQjvnDE5hYSFOp5OgoKAa5Xl5eeh0Ovz8/C5YcOLsHuryEJvSN7EoaRF/7fJXwrzCaq1nbNMG32uvpWj5cnI/+JDot/9d5701isLE2DAeO5DMBylZPBgTipdWU2e71j3CCIw4Rn5GKbvXptDr+rhzfVlCCCGEaELsdnutPf169uyJw+GopUXt3nrrLWJiYpg7d25VWXz82Uct/PTTT6xbt46jR49WPbvGxcXVqLN+/XoGDhzInXfeWXV9zJgxbNq0qd6xXUq5xVYe/Ww7DpfKsC6R3DsgztMhnTOr08oTa55gQ/oGzDozM6+ZSfewuhfYuxhUl4vsGf8l94MPAPC58kqi3v43Wp/GuUJ0Q1DkcPJtZj7zU3PYX1JeVZ7gY+auqGBGhAUQqJeOBMKd8CsrtlFWZKes2EZ5sZ2yYjvlxe4kYPVza0Vi0GH78733NBoFg1mHwaxFb9JhNOswmNzHepMWvVGLwahFb3SfGyrK9BVlOqMGveFUmVavkYSdaDLO+V/n0aNHM3z4cCZMmFCj/KuvvuK7777jxx9/vGDBibPrFd6LHmE92J61nbl75/Jsn2fPWDfk4YcoWr4cy7JlhDz2KMY6HqoBRoQF8q9jGZwot/FpWg4PxdSehKxOo1HoeX0cP8/dz86fk+kypAUGkzwECCGEEM3V2LFjmTlz5mm98j788EPuuuuuet/nu+++Y+jQoYwaNYp169YRHR3NhAkTePDBB8/aplevXvzf//0fn376Kd7e3tx00028/vrrmM1mAAYMGMCCBQvYvHkzffr04ejRo/z444+MHTv2jPe1Wq1Yrdaqc4vFUu/X8Wc4XSoTv9hBhqWc1qHevHVbl0b3i6nNaWPSmkmsT1uPWWfm/avfp2d4T4/E4iwuIe3ZZyletQqA4AfuJ/TJJ1G0dU+zI063p6iU+Wm5fJuZT6nTncgxaRRGhAVyT3Qw3X29Gt2fV3HuXE4XZUV2SgqtlBTaKC20UmqxUWaxUVpkcx8X2SkrsmEtrf8HRdUpChi99Zi89Ri9dBi93HuTlw6Dlw6D2Z34M3rp3QlAr4pEYEViUKuTpJ4QZ3LO2ZtNmzbVOvRi8ODBvPDCCxckqObgeE4JH/16lL9d1x5/8/kt8KEoCn/t8lce/vlhvjn0DQ90foBgc3CtdU0dOuAzeDDFa9eS+9HHRL35jzrvr9MoTIwN56nEk7xzIou7o4LxrsdDU9teYWz54RiFWWXsXZdKj6Gx5/zahBBCCNF0zJ49mxUrVtCvYn63TZs2kZyczD333FNjUbyzDe89evQoM2fOZPLkyTz//PNs2bKFiRMnYjAYGDdu3Bnb/Pbbb5hMJhYvXkxOTg4TJkwgNze3qkfinXfeSU5ODpdffjmqquJwOHj44Yd5/vnnzxjL1KlTefXVV8/nW/GnTP/5EL8fzsXLoGXW3T3xMTauD2IdLgfP/vIsv6X+hklr4r2r36NXxJnnk7yYbCkppDwyAWtSEorBQOQbr+N/000eiaUxc6oqP+UU8sHJbDYXllSVt/UyMi46hJHhgQRIr8Emw1bmoDjfSnF+OcUFVorzrZTkl1NSaKtKCpYV2c5pSK9Go2Dy1WP2MWDy0WP20WOq2KqOvU9tRm89BqMWRYapC3FRKKp6bgtRe3t7s3HjRjp37lyjfM+ePfTt25fS0tILGqAnWCwW/P39KSwsvCjDp1VV5Yb//saBdAuT/3IZE69u+6fuNeaHMezL3cf9CfczqeekM9Yt27mT46PHgE5H659+wtCi9sVOqrO7VAZuOkByuY2XW0cxoWXdvQkBDm5IZ9UnBzD76hn7xgD0RvlEVgghhLhQLvazyoU0ZMiQetVTFIXVq1ef8brBYKBXr16sX7++qmzixIls2bKFDRs21Nrm2muv5ddffyUjIwN/f38AFi1axMiRIykpKcFsNrN27VpGjx7NG2+8Qd++fTl8+DBPPPEEDz74IC+99FKt962tJ2FMTMxFfT/WHMxi/LwtAMwY3Y2bu9X9HNeQqKrK3zf8nUVJi9Br9Lx39Xv0j+rvkVjK9u7j5EMP4czNRRsaQsy772Lu2tUjsTRWxQ4nn6fn8VFKNsnlNgD0isKwUH/uiQqhf4C39NRqZFSXSqnFhiW3nKLcsop9OcV55RRVJANt5c563UtRwMvPgJe/Ee8AI16+esx+Brz8DJh9DXj5GtznvgaMXjpJ+AlxCdT32fGcP9bp06cPH374Ie/8YZXcWbNm0bOnZ4YKNDaKovDI4NZM/HwHs387xviBcfiazr834UNdHmLimol8fvBzxieMx9/oX2tdc7dueA/oT8n6DeTO/pjIV16p8/56jcLkuHAmHTzJu8mZjIsKxrseKx1f1iecLT8cw5JTzr5fU+l2jUz8LIQQQjRHF2rRu8jISDp27FijrEOHDnz77bdnbRMdHV2VIKxso6oqKSkptG3blpdeeomxY8fywAMPANC5c2dKSkr461//ygsvvIBGc/qczEajEaPx0q0mnF9iY9KXOwG4p39so0sQAvxn+39YlLQIjaLh/wb9n8cShMW//ELKpCdRS0sxtm9PzKyZ6CMiPBJLY5RSbmN2SjYL0nIpqhhSHKTXck9UCOOjQwg3nt/vNOLSsJU7sOSUUZhVRmG2e6tKCOaV43LU3X/IYNbhE2is2Ez4BBrx9jfi5W+o2pt9DWgk8SdEo3TOScI33niDa665hl27dnH11VcDsGrVKrZs2cKKFSsueIBN1bDOkUz/+RBHs0v4dOMJJgxuc973GhwzmMsCL+NQ/iE+O/AZE7pNOGPd4IcfpmT9Bgq/+ZaQhx9BH153z8CR4UHMOJHJsTIbc1JzeDy27pWONVoNPa+PY82nB9m+IpmEQdHoDNKbUAghhBDnZ+DAgSQmJtYoO3ToELGxZ57WZODAgXz99dcUFxfjU7EQxaFDh9BoNLRo0QKA0tLS0xKB2orpVc5xwM1FE+ht4KUbO/LNtpO8MKyDp8M5Z3P2zmHuXvfw7lf6v8I1sdd4JI6CbxeR/vLL4HTiPWAA0f+dIQuU1NNOSymzTmaxNLsAZ8VfizZeRv7aIpSREUH1WuBQXBoOm5OCrDLyM0oozCqtkRAstdjO2lZRwCfQhG+wCb9g994nyIRvoAmfIHevQJlvXoim7ZyHGwPs3LmTf/3rX+zcuROz2UyXLl2YMmUKbdue/7DZhuRSDeH5dlsKT329iyBvA789OwQvw/n/g7v8+HKeXvc0vgZfVty2Ah9D7Q88qqpy4u6xlG3bRtC4cYRPea5e9/8mI4/HDiQTqNOyuX9HfOvRm9DpdPHZSxspyivn8tvb0vWqmHN6TUIIIYSoXWMabgywdetWvvrqK5KTk7HZav6SumjRonrdY8uWLQwYMIBXX32V22+/nc2bN/Pggw/WWABlypQppKamMn/+fACKi4vp0KED/fr149VXXyUnJ4cHHniAK6+8ko8++giAv//970ybNo0PP/ywarjxI488Qs+ePfnyyy/rFdulej9UVW10Qzi/PfQtf9/wdwAm95zM+ITxlzwGVVXJef99ct55FwD/m28i8vXXUQyGSx5LY7OpoJj/HM9kbX5RVdnlAT48FBPK1cF+aBrZn8empKzIRn5GCfkZpVVbQWYJltzys84JaPLW4xdqxr9i8wsxVyUEvQONaCXhK0STdNGGGwN069aNzz777LyDE243d4tixqokkvNK+WxjMg8OanXe97qm5TXE+8dzrPAYXyR+wQOdH6i1nqIohDz8MCcffJD8L78k+KG/ogsKqvP+t4QHMv1EJodLrXycks2TcXUPy9BqNfS8Ppa1nyWyY/kJOl0RhU4vvQmFEEKI5uSLL77gnnvuYejQoaxYsYJrr72WQ4cOkZmZyS233FLv+/Tu3ZvFixczZcoUXnvtNeLj45k+fXqNFZLT09NJTk6uOvfx8WHlypU8/vjj9OrVi+DgYG6//XbeeOONqjovvvgiiqLw4osvkpqaSmhoKMOHD+cf/6h7kbdLrbElCFeeWMlrG18DYHzCeM8kCB0OMl59jYKvvwYg+K9/JfTJSY3ue3kpqarKb/nFTDuRwYYC92IkWgVuCQvkkZZhdPIxezjC5sVudZKXVkJuWjF5qe59bloJZWfpFWj00hEQ7kVguBf+YV74h51KCJq8ZUi4EOLMzqsnYaXy8vLTPg1uDJ9m1+VSfjr/5ZZknv12DyE+Rn57dgimP5FEW3pkKc//9jyBxkB+uu0nvPRetdZTVZXjo26nfO9egv/6V8ImP1mv+y/JzOfh/Sfw12nZ3K8D/vVYqczpcLHgpQ0U51u5fFRbul4tvQmFEEKIP6sx9STs0qULDz30EI8++ii+vr7s2rWL+Ph4HnroISIjIz2ySvCF1pjej0tlQ9oGHl31KHaXndva3sYr/V+55Ik5V2kpqU9OpnjdOtBoiHjpRQLHjLmkMTQmqqqyOq+I/xzPYKvFvRilXlEYHRnEYy3DiDVfunk4myNVVSkpsJKdXET2yWJyThaRm1p85p6BCvgGmQiM8CYwwqtqCwj3xuyrl0S4EKKG+j6rnHOSsLS0lL/97W989dVX5Obmnnbd6azfikcN2aV80LM5XAz591pSC8r4+/CO3Dsw/rzv5XA5GL54OCnFKTzd62nGdRp3xrpFq1aR8uhjaLy9abN6FVr/2hc7qc6pqly1JZHEknKeigvnmfjIesW179dU1n6WKCsdCyGEEBdIY0pKeXt7s2/fPuLi4ggODmbt2rV07tyZAwcOcNVVV5Genu7pEP+0xvR+XAp7svdw/4r7KXOUcU3La/j3lf9Gq7m0z3+O3FxOPvwI5Xv2oBiNRL/9b3yv8cxciA2dqqr8lFPIf05ksruoDACTRuGuyGAmtAwj2iTDsi80VVWx5JSRnVxMdnIROSeLyD5ZRFmRvdb6Zj8DwVHeBEf5EBRdsY/ylt+rhBD1dtGGGz/zzDOsWbOGmTNnMnbsWN577z1SU1P54IMP+Oc///mngm6ODDoNjwxuzYtL9jJr3VHG9G2JsR7z/dVGp9HxQOcH+PuGvzNv3zxGtx+NUVv7J34+Q4ZgvOwyrIcOkbdgAaGPPlrn/bWKwtNxETy47zgfnszmgRahBNajN2H7AZFsX5GMJbuM3WtO0vO6uHN9aUIIIYRopAIDAykqcs9nFh0dzd69e+ncuTMFBQWUlpZ6ODpxoR0tOMojqx6hzFFG38i+vDXorUueILSlpJJ8/33YTySj9fenxcyZePXofkljaCx+ySviH0fT2FWRHDRrNIyLDmZCTBhhslLxBVNWbCPzmMW9HbeQddyCtdRxWj1FoxAU6UVIjC+hMb4ER3sTHO2D2VcStUKIS+Ock4RLly5l/vz5DB48mPHjx3PFFVfQpk0bYmNj+eyzz2rMCyPqZ1SvFry7+jAZlnK+3prC3f3OvEpfXW5qfROzds8ioySDRUmLGNO+9iEVikZDyMMPkTr5KfLmf0rQuHH1Wt1tWKg/Hb1N7C8p54OT2TzXqu7ehFqthj43xvPz3P3sqFjp2OglDx1CCCFEczBo0CBWrlxJ586dGTVqFE888QSrV69m5cqVXH311Z4OT1xAWaVZPPTzQxRaC0kITmDGkBkYtJc2uWE9dozk8ffhyMhAHx1NzEcfYWx1/iN1mqodllLePJrGr/nFAHhrNdwfHcJfY8II+ROLKQr34o05ycVkHCusSgpasstOq6fRKYRE+xDS0p0QrEwK6gzSO1AI4Tnn/BMgLy+PVq3cC2z4+fmRl5cHwOWXX84jjzxyYaNrJow6LQ9d2YpXl+5n5toj3N4rBoPu/FaV0mv13JdwH29uepM5e+cwsu1I9NraE3K+Q4diaPUetqNHyfvkk3r1JtQoCs/ERzB+73E+SsnmwRahBNfjQaJt73C2Lz9BXloJO1Ym0+/m1uf82oQQQgjR+Lz77ruUl5cD8MILL6DX61m/fj233XYbL774ooejExdKqb2Ux1Y9RkZJBnF+cbx/zft4670vaQzliYdIvv9+nDk5GFq1ouXcOejDwy9pDA1dUkk5/zyWzg/ZhQAYFIVx0cFMjA0n1CAf4p8Pu81J5jELaUkFpB8uIONoIQ6b67R6AeFehMf7ER7nR3i8H8EtfGQlYSFEg3POScJWrVpx7NgxWrZsSfv27fnqq6/o06cPS5cuJSAg4CKE2DyM6dOS99YcIbWgjMU7Urijd8vzvtetbW/lw90fklGSwdKjS7m17a211lO0WkIff4zUJyeTN3ceQXfdhbYe7+F1If509jGzp7iMmSezeLF1VJ1tNBqFvsNbseyDPexanUKX/2fvvsOjqrYGDv+mpPdAeg+9995EEBFpogIqgogNpIMCIiKoIEqRIiCCCgqKFEWKKAZEmrTQSSCQ3ntvk5n5/siV73IlZBJIJmW9zzOP5mSXNUw0h3X23qu3F5a2smxeCCGEqOkcHR3v/LtSqWT27NlGjEZUBK1Oy9t/vU1QahAOZg6s7bMWB3OHSo0h78oVIl95FV1GBmZNmuC9aSPq//rZq+1i8wtZGh7PD3Gp6AAF8KyrAzN9XfGWgiRlUpBXRNyt9DtJwcSILHTau4/5N7NS4+pnV5wU9LPF2cdWqgoLIaqFMicJx44dy6VLl+jVqxdK5xAAAM+qSURBVBezZ89m0KBBrFmzBo1Gw/LlyysixlrB3ETF6z39+ehAEJ8fuc3TbT1Rl/PJkpnKjJeavcTSc0v58vKXDK43GLXy3h+1zeOPY9aoEQU3bpDy9Tc4T5ta6viK/6wmHH0ljE3Rybzu5WTQk0e/1nVx9rEhMSKLwN8i6P5sg7K+NSGEEEJUMwcOHEClUvH444/fdf33339Hq9XyxBNPGCky8TDo9XqWnF3C0eijmKnMWPXoKrxsvSo1htxz54h6/Q10OTlYtGqF14YvDCrKVxtkFWn5LCKBjdFJFOiKE1mP17Vltp8bTawtjBxd9aAt0pEQlkFUUBpRQakkRmSh192dFLSyN8O9gT3u9e1wa2CPo6sVCqVUFxZCVD9lrm78v8LDwwkMDKR+/fq0bNnyYcVlVMaqUJdbWET3JUdIzSlk+fBWDGvrWf6xNLn039WftII0FnVfxKB6g0ps+0+lY4WlJfX/OGTQU1e9Xs8T50O4mJXLG15OvF/fw6C4Iq+nsHfVJVRqJaM+6Iy1g7nB70kIIYQQxapTNd2WLVvy8ccfM2DAgLuuHzx4kFmzZnHp0iUjRfbwVKfP42H79vq3fHL2EwCW9VpGP99+lTp/9okTRL85EX1+PpYdO+K5di0q68rd5lwV6fR6tsensig0jqTC4gIZne2smFvPnQ528udzP3qdnpTYHKKDU4kKSiM2JO1f24ftnCxwb2j/n8SgPTZ1zFEoJCkohKi6Kqy68f/y9fXF19f3QYcRgKWpmnHd/fj0txusOXyLIa09UJXzCZSliSWjm41mZeBKvrj8BU/4PVHiakLrRx/FvHlz8q9eJeXLjbjMervU8RUKBW/7ufL85VC+iUlmvJczLgZUQPNq4oh7A3tiQ9I5eyCc3i80LvN7E0IIIUT1ERISQtOmTf91vXHjxty6dcsIEYmHJSAigE/PfgrAjHYzKj1BmBUQQMzUaeg1Gqx69cRz5UqU5vIA+mxGDu+GRN+pWOxnYcqC+h48VsdWElklKMwrIvJ6KhFXk4m4lkpeZuFd37ewMcGzsSNeTRzwbOyIjaP8nAkhaqZy7WcNCAhg4MCB1KtXj3r16jFw4ED++OOPhx1brTS6iw92FiaEJuew/0rcA431XOPncDBzICIzgr2395bYTqFQ4DRlMgBp27ahSUg0aPzejja0t7UkX6dndWSCQX0UCgWdhhQXvgk+EUd6Yq5B/YQQQghRPdnZ2REaGvqv67du3cLKSlY0VVdXkq4w+9hs9OgZ3nA4Y5qNqdT5M/btJ3ryFPQaDTb9+uG1enWtTxDG5hcy4XoEgwJDuJSVh7VKyXv13DnasTH96tpJgvB/pCfkcvGPSH5ecYFNM4/x25dXCT4VT15mIWozFT7N69DtmfqMnNeRsZ90p9+4ZjTp6i4JQiFEjVbmJOHatWvp378/NjY2TJkyhSlTpmBra8uAAQP4/PPPKyLGWsXG3ISXu/kBsOZwCDpd+XeDW5lYMa7FOADWX1qPRqspuW337li0aYO+oICUDRsMGr94NaEbAFtiUojKLyylRzH3+vZ4N6uDTqfn7L4wg/oIIYQQonoaMmQIU6dO5fbt23eu3bp1ixkzZjB48GAjRibKKzormomHJ5Kvzae7R3fmdJpTqQmo9N0/EfvWW6DVYjdkMB7Ll6Ewrb0F8fK0OlaEx9PtdDC7E9JQAM+7OXKqcxMmeDtjqpQKugA6nZ6Ym2kc/zGE7947xdb5f3Ni5y1ibqSh0+qxd7GkVV8vhkxtzSvLejBwYita9/Wmjoe1JFiFELVGmc8k9PT0ZPbs2UycOPGu659//jmLFi0iJibmoQZoDMY+VyYjT0P3jw+TVVDEuhfa8kQLt3KPlVeUx5O7nyQpL4l3O73LiMYjSmyb8/dpIl96CUxMqH/wV0w8Sj9nUK/X8+zF2xxPz2a4qwOrmvgYFFdSZBY/LjoLChj5bkfqeFgb+paEEEKIWs/Y9yplkZGRQf/+/Tl37hyensXnLUdHR9OjRw92796Nvb29cQN8CKrT5/GgMgoyGP3raEIzQmns2Jhv+n+DlUnlrQjN+OUXYmfNBr0e+5EjcH3vPRS1OAl2KDmDd0Ji7jys72hnxQcNPGhlY2nkyKoGrVZH7I10bl9IJPRiEnlZ/79oQqlS4N7AHt8WdfFpUQd7Z/kzE0LUXIbeq5T5N2p6ejr9+/f/1/V+/fqRkZFR1uGA4gSjr68v5ubmdOrUiTNnzty3/Y4dO2jcuDHm5ua0aNGCAwcO3PX9999/n8aNG2NlZYWDgwN9+/bl9OnT5YrNGOwsTBjT1ReAlQEPtprQQm3Bqy1fBWDD5Q3kF+WX2NaqcycsO3UCjYbk9esNGl+hUDC3njsAO+LTCMrOM6ifk7cN9do4gR7O7JXVhEIIIURNZWdnx8mTJ9m/fz8TJkxgxowZBAQEcPjw4RqRIKxNNFoN0/+cTmhGKM6Wzqx5dE3lJgj37yd29pziBOFzI3GdP7/WJggTCjS8ejWcF6+EEZVfiLuZCeub+rCnTf1anyDUanSEX0kmYEsQX799nF9WXeTasVjysjSYWapp3NmVJ15vwbhlPRgytQ2t+nhJglAIIf6jzL9VBw8ezE8//fSv63v27GHgwIFlDmD79u1Mnz6d+fPnExgYSKtWrXj88cdJTLz3uXgnT57kueeeY9y4cVy4cIGhQ4cydOhQrl69eqdNw4YNWbNmDVeuXOH48eP4+vrSr18/kpKSyhyfsYzr7oe1mZrg+CwOXH2wswmfbvA0blZuJOYlsv3G9vu2/edswvTdP1EYEWHQ+G1sLRnkZI8e+CjU8Fg7DvJHoYDQi0kkhGca3E8IIYQQ1YtCoaBfv3689dZbTJw4kZ49exo7JFEOH5/5mDPxZ7AysWJtn7W4WLlU2tyZB38j9u1ZoNNh/+wzuM6bVyu3gOr0ejbHJNPjTBB7k9JRKWC8lxPHOjVmqItDrfwzAdBpdUReS+GPr6/z1VvH2P/5ZYJPxlGQU4SFjQlNe7gzeHJrxn7anT4vNcW/jROm5g9cw1MIIWqcMm83/vDDD1m6dCndunWjS5cuAPz999+cOHGCGTNm3LVscfLkyaWO16lTJzp06MCaNWsA0Ol0eHl5MWnSJGbPnv2v9iNGjCAnJ4d9+/bduda5c2dat27N+hJWv/2zrPKPP/6gT58+pcZUVbaMrDh0k5UBIfg7WfH71J6oVeV/UvpTyE+8d/I9HMwcOPj0QSxNSn5aFvnqa+QcO4bdkMG4L1li0PihuQX0OBOEVg+7W9enq4Nh24f/+OY6N/6Ox6upI4MntzaojxBCCFHbVZV7FVGsNnweP974kQ/+/gAFCtb0WUNPz8pL9GYFBBA9ZSoUFWE3dChuiz6qlSsIg7LzePtGNGczcwBoZWPBskZeNK+lKwf1ej1JkVncPJ3AzXMJd1UktrIzxb+NM/XaOuFW3x6lsnYmT4UQ4h+G3quU+fHJpk2bcHBw4Pr161y/fv3OdXt7ezZt2nTna4VCUWqSsLCwkPPnzzNnzpw715RKJX379uXUqVP37HPq1CmmT59+17XHH3+cn3/+ucQ5NmzYgJ2dHa1atbpnm4KCAgoKCu58nZlZNVa1vdLDj82nwglNyuGnCzE8296r3GMNqjeIjVc2EpkVydagrXe2IN+L0+TJ5Bw7RsbefdR57TXM6tUrdXx/SzNGudVhc2wKH4bGsr9tA4OeZHZ40o+QMwlEXU8l5mYaHg0dyvS+hBBCCCFExTqfcJ7FpxcDMLnt5MpNEP75J9FTp0FREbaDBuH20Ye1LkH4T2GStVGJFOnBSqVkjr8bYz3qoqqFKwczk/O4eSaBm2fiSYvPvXPd3MqEBu2dadDRFVc/WxSSGBRCiDIrc5IwLOzhnR+XnJyMVqvFxeXurQouLi4EBwffs098fPw928fHx991bd++fYwcOZLc3Fzc3Nw4dOgQdevWveeYixcvZsGCBQ/wTiqGjbkJ43vVY/GvwawMCGFIaw9M1eW7KVIr1UxoPYHZx2bz9bWvGdF4BLam984eW7RojnWfPmQHBJC0Zg2eK1YYNMcMX1d+jE8jMDOXA8kZPOlkX2ofOycLmnZ35+pfMZz66TZPv92u1m6TEEIIIYSoauKy45j+53SK9EX09+3PuObjKm3u7GPHiZk0GTQabAc8gfviRShUqkqbvyo4npbFzBtRhOcVr5LrX9eWjxp44mFeu6o5awq03DqfQNDJOOJu/f85+CoTJX6t6tKooytezRxRPcDOKyGEEOU4k7C66N27NxcvXuTkyZP079+f4cOHl3jO4Zw5c8jIyLjzioqKquRoSza6iy9ONmZEp+Wx/WzkA43V37c/9e3rk1WYxZZrW+7b1mnyJACyfj1IfgkJ2//lbGbCG15OACwOjaPIwIIr7Z/0RW2qJCEsk9uB1efcSCGEEEKImiyvKI8pR6aQmp9KE8cmLOy2sNIe5uacOkX0xInoNRpsHnsM9yVLUKhrzxlyuVodc29G88zF24TnFeJmZsJXzX35poV/rUoQJkVm8ee2G3wz6ziHtwQXJwgV4NnYgUdHN+HlT7rz+CvN8W1ZVxKEQgjxEJTrN210dDS//PILkZGRFBYW3vW95cuXGzxO3bp1UalUJCQk3HU9ISEBV1fXe/ZxdXU1qL2VlRX169enfv36dO7cmQYNGrBp06a7tjb/w8zMDDMzM4PjrkwWpiomPVqf9/ZcY/XhWzzTzgsL0/I9QVUpVbzZ+k2m/TmNb69/ywtNXsDB/N7be80bNcLmif5k/XqQpNVr8Pp8jUFzTPB2ZnNsMrdyC/ghPpVR7nVK7WNlZ0abx7w5uz+cUz/fxq9VXVTlXDEphBBCiKopMTGRxMREdDrdXddbtmxppIjE/ej1euafmE9QahCO5o6s7L0SC7VFpcydc/oMUeMnoC8owLp3bzyWLUVhYlIpc1cF5zJymBwUSWhe8XFIL7rX4b167tioa8cqysK8Im6eTeD68ViSIrPuXLeta07T7u406uSGtUPV/LubEEJUd2VOEgYEBDB48GD8/f0JDg6mefPmhIeHo9fradu2bZnGMjU1pV27dgQEBDB06FCguHBJQEAAEydOvGefLl26EBAQwNSpU+9cO3To0J0iKiXR6XR3nTtYnYzs4M0XR0OJSc9jy6lwXu9V+hmBJenj3Ycmjk0ISg3i66tfM7399BLbOk2cSNZvv5MdEEDelStYtGhR6vg2ahXTfV15NySGT8PiGObigKUBT/VaP+bNtWOxZCblcfVoDK36lP/8RSGEEEJUHefPn2fMmDEEBQXxT708hUKBXq9HoVCg1WqNHKG4l6+ufsWv4b+iVqhZ1msZbtZulTJv3pWrRI8fjz4/H6tePfFY+RkK09qxcq5Ap2NpWDyfRyaiA9zMTFjeyIvedWpmMZz/lRCeydW/Yrh1LoGiwuKHCUq1Av/WTjTt7o5nQwc5Z1AIISpYmZdrzZkzh5kzZ3LlyhXMzc3ZtWsXUVFR9OrVi2effbbMAUyfPp0vv/ySzZs3ExQUxPjx48nJyWHs2LEAjB49+q7Vf1OmTOHgwYMsW7aM4OBg3n//fc6dO3cnqZiTk8M777zD33//TUREBOfPn+fll18mJiamXPFVBaZqJVP6NgBg3dHbZOVryj2WQqFgUpvircTfB39PUm7J23vN6tXDbtAgABKXLsPQQtgvutfBy9yUhMIiNkYbtn3Y1FxNx0F+AJw9EEZBbvnfoxBCCCGqjpdffpmGDRty8uRJQkNDCQsLu+ufour5K/ovVgauBGBOpzm0d21fKfMWhIYS9dpr6HJzsezSGc9Vq1DWkgTh1axc+p+7yer/JAifcXHgSIdGNT5BqNXqCDmbwM4l59j58TmCT8ZRVKjDwdWSbs/U56WPu/H4K83xauwoCUIhhKgEZV5JGBQUxPfff1/cWa0mLy8Pa2trFi5cyJAhQxg/fnyZxhsxYgRJSUm89957xMfH07p1aw4ePHinOElkZCTK/6pg1rVrV7Zt28a7777LO++8Q4MGDfj5559p3rw5ACqViuDgYDZv3kxycjJ16tShQ4cOHDt2jGbNmpX17VYZw9p4sP7obUKTcth0PIypfRuWe6zuHt1p7dSai0kX+fLKl7zT6Z0S29adNInMAwfIPX2anGPHsO5ZejU7M6WS2X6uvBkUyeqIBEa518HRpPQftSZd3bh0OJq0uBzOH4yg67D6ZXpfQgghhKh6QkND2bVrF/Xry+/16iAsI4xZf81Cj55nGz7L8EbDK2VeTXw8keNeQZuWhnnz5niuXoOyih4H9DAV6fSsjkxgWXg8RXqoY6Lmk0aeBhUArM7ysgu5diyWq0djyEkv3u2lVCmo396ZZj08cKtnJ8UMhRDCCBR6Q5eH/YerqytHjhyhSZMmNG3alI8//pjBgwdz6dIlunXrRnZ2dkXFWmkyMzOxs7MjIyMDW9uq8/Ru76VYJn1/AWszNcfe7o2DVfmfrJ6JO8O438ehVqrZ/9R+3K3dS2ybsOQTUr/+GrMGDfD7+SeDqsrp9HoeO3eDa9n5vO7lxIL6HgbFFX4lmf2fX0alVvL8gk7Y1qmcs2+EEEKI6qSq3qvcy9ChQ3nxxRd5+umnjR1KhalOn8f9ZBZm8sL+FwjPDKetc1s29tuIiarizwIsSksjYtSLFN6+jamfHz5bv0Pt6Fjh8xpbeF4B469FcCErF4ABde1Y0sgTJ9Oae/5icnQ2l49EcfNMAlpN8ZZiC1tTmvf0oFkPd6zsan5iWAghjMHQe5UyryTs3Lkzx48fp0mTJgwYMIAZM2Zw5coVdu/eTefOnR8oaHF/T7ZwY+2ftwmKy2T9X7eZ80STco/V0a0jnVw7cTr+NBsub+D9ru+X2Lbu66+RvmsXBSEhZPy8B/unh5U6vlKh4F1/d567HMrX0cm84umElwGV2Hya18GjkT0xN9I5vSeUx16uvqs/hRBCCAEbN25kzJgxXL16lebNm2PyPwUoBg8ebKTIxH/T6XXMPTaX8MxwXK1cWfbIskpJEOpyc4l+YzyFt2+jdnHBe9PGWpEg/DkhjZk3osjW6rBVK1nUwJOnXRxq5Oo5vV5P1PVUAn+PIOZG+p3rTt42tHrUk/rtXFCZSNFCIYSoCsq8kjA0NJTs7GxatmxJTk4OM2bM4OTJkzRo0IDly5fj4+NTUbFWmqr8NPiP6wm8suUc5iZK/nqrN8625uUe62LiRV789UVUChW7h+zG386/xLYpm74i8dNPUbu4UO+3gyjNS59Xr9fz7MXbHE/PZrirA6uaGPazkRSZxY+LzgLw7Jz2OPtUrc9ACCGEMLaqfK/yv/bu3cuLL75IZmbmv75XUwqXVKfPoySbrmzis8DPMFWasmXAFprVqfgHtfrCQqImvEnO8eMo7ezw3fodZjV8W3qOVsu8kBi2xaUC0MnOis+b+uBpwMP06kan0xN6IYnA3yLuVClWKBXUa+NEy96euMqWYiGEqDSG3quU+ZGNv78/LVu2BMDKyor169dz+fJldu3aVSMShFVdnybOtPayJ1+j4/Mjtx5orNbOrXnE6xG0ei0rz6+8b1uHUS+gdnejKCGB1C3fGjS+QqFgbr3ibcw74tO4np1nUD8nbxsadio+k/LkrlsGF0wRQgghRNUzadIkRo0aRVxcHDqd7q5XTUgQ1gRn48+y6sIqoLhQSaUkCHU6Yue8Q87x4ygsLPD+Yn2NTxAGZefR/9xNtsWlogCm+biwq3X9Gpcg1BbpuH4ilu8XnOa3L6+SFJmF2lRJq0e9ePHDLjz+anPc6ttLglAIIaqgMicJz549y+nTp/91/fTp05w7d+6hBCVKplAoeOvxRgBsOxNJdFruA403re00VAoVh6MOcz7hfIntlGZmOE+dCkDKhg0UpaUZNH4bW0uGONujB94LiTE44ddpsD8qtZKYm+lEXEkxqI8QQgghqp6UlBSmTZt2pyidqFqS85J56+hb6PQ6BtcbzNMNKv7sSL1eT8KixWTu3w9qNZ6rVmLRunWFz2sser2eLTHJPHH+JiG5BTibqtnRuh6z/N1Q16CKvZoCLZcCovhu3imOfBtMekIuZpZq2j/py+hFXek+vAE2juXfBSWEEKLilTlJ+OabbxIVFfWv6zExMbz55psPJShxf93q16VrvTpotHpWBYQ80Fj+9v4Ma1B8xuDyc8vvm8SzHTgQsyZN0GVnk7xuncFzzPV3w0yp4Hh6Nr8l/3ur0T3nqmNBqz6eAJzcfQudVmfwfEIIIYSoOoYNG8aRI0eMHYa4hyJdEW//9TYp+SnUt6/P3E5zK2V1V/K6daR99x0A7h9/jHWPHhU+p7FkaIp47VoEb9+MJl+n51FHGwI6NKK7g42xQ3toCvOLOH8wnC3vnOT4jhCy0wqwtDOl69P1Gb2oK50G+WNhXbNWSwohRE1V5sIl169fp23btv+63qZNG65fv/5QghKlm/l4I4atPcmuwBhe71WPek7W5R5rQusJ7Avdx+Xky/wW8Rv9ffvfs51CqcTlrZlEvjyOtO9/wHHUKEy9vUsd39vCjDe8nFkZkcD7t2PoXccGM2Xp+em2/X25fjyOtPhcgk7G0ayHYRWShRBCCFF1NGzYkDlz5nD8+HFatGjxr8IlkydPNlJk4vOLn3M2/iyWakuWP7IcSxPLCp8zfedOkletBsBl7lzsBj5Z4XMay8XMXF69Fk5UfiFqBcz1d+d1LyeUNWSbbZFGy9WjMQT+FkFelgYAWycL2vbzpnFnNylGIoQQ1VCZk4RmZmYkJCTg7393kYu4uDjU6jIPJ8qprbcDfZs480dQIkt+DWbD6PblHquuRV3GNhvL2ktrWXl+JY96PYqp6t5P+6y6dsWqe3dyjh8n6bPP8Fi+3KA5Jns7831cCuF5hWyKTmaCt3OpfcwsircnHP8xhNN7w2jQwQVTc/kZE0IIIaqTjRs3Ym1tzdGjRzl69Ohd31MoFJIkNJKjUUfZeGUjAAu6LsDPzq/C58w+cYK4+e8DUOeN13F8cVSFz2ks2+JSmHMzmgKdHm9zU9Y386GtrZWxw3ootEU6gk7Gce5AODnpBUBxcrDjQD8atHdGqZLkoBBCVFdl/j94v379mDNnDhkZGXeupaen88477/DYY4891ODE/c1+ojEqpYLfrydwOvTBzu0b02wMdS3qEp0dzY83frxvW+eZM0ChIPPAr+RduWLQ+FZqFe/4uwGwIjyepEKNQf2a9/TAzsmCvMxCLhyKNKiPEEIIIaqOsLCwEl+hoaHGDq9Wis6KZs7xOQA83/h5+vvdexfJw1QQEkLMlKmg1WI7eBBOU6ZU+JzGUKjT8faNKKYHR1Gg0/N4XVv+6NCoRiQIddri5ODW+X9zdNsNctILsHY0o/eLjXn+/U406uQqCUIhhKjmyvx/8aVLlxIVFYWPjw+9e/emd+/e+Pn5ER8fz7JlyyoiRlGC+s42jOzgBcCiA0HodOWvAmxpYsmbrYvPlFx/eT2ZhSWfHWjeuDF2Q4YAkPjJpwYXIxnu6khLGwuytDo+CYs3qI9KraTLU/UAuHgokuy0fIP6CSGEEEKIfyvUFjLj6AyyCrNoWbclM9vPrPA5i5KSiHz9dXTZ2Vi0b4fbhx/WyMq28QUahl24xZbYFBTA236ufN3cD1u1ytihPRC9Xk/IuQS+X3iGw1uCyErJx9LWlJ4jGzJqQReadnNHJclBIYSoERR6QzM8/yUnJ4etW7dy6dIlLCwsaNmyJc8999y/zpiprjIzM7GzsyMjIwNbW1tjh3NfSVkFPPLpEXIKtawc2Zohrct/bl+RrohnfnmG2xm3Gdt8LNPbTS+xrSYujtuP90dfWIjn+nXYPPKIQXOcTs9myIVbKIFDHRrRzNqi1D56vZ6flgUSdyuDBh1c6DeumYHvSAghhKiZqtO9yssvv3zf73/11VeVFEnFqU6fx4d/f8j2G9uxM7Njx8AduFm7Veh8urw8Il4cTf7Vq5j6+ODzw/eoHRwqdE5jOJ2ezavXwkksLMJWrWRtU1/61qnaPwuGiA/N4PiOEBLCihcQmFuZ0PZxH5o/4oGJafVOfgohRG1i6L1KuR75WFlZ8dprr/H555+zdOlSRo8eXWMShNWNk40Z4x8pXmn3ycEb5Gu05R5LrVQzrd00ALZe30psdmyJbU3c3HAc/SIAiUuXoi8qMmiOTvbWDHa2Rwe8FxJj0CpEhUJBj+ENQQEhZxOIDUk3aC4hhBBCGF9aWtpdr8TERA4fPszu3btJT083dni1yr7QfWy/sR0FCj7u8XGFJwj1Wi2xb79N/tWrqOzt8drwRY1LEOr1er6OSebpi7dILCyisZU5v7VrVO0ThJnJefy28Sq7PjlPQlgmajMVHQf58eJHXWjTz1sShEIIUUPJuvAaYFx3f1xtzYlJz2PzyfAHGqunZ086uHagUFfI6gur79u2zmuvobKzo/DWbTJ+/tngOebVc8dMqeBEejYHkzNK7wA4edvQrLs7AH9tv/lAW6uFEEIIUXl++umnu1779u0jNDSUESNG0LlzZ2OHV2uEZ4Sz8NRCAF5r+RrdPbpX+JyJS5eRdegPFCYmeH6+BlMfnwqfszLlaXVMDY5izs1oivQw2Nme/W0b4GdpZuzQyq0gr4hTP91i2/unuXUuERTQtJsboxZ2psOTflJEUAghajhJEtYAFqYqZj7eCIA1R26RllNY7rEUCgUz2s0Aip82B6UEldhWZWtLnfFvAJC0chXa7ByD5vAyN2W8V3F14wW3YynQ6Qzq12mIP2aWalKis7l+vORVjkIIIYSo2pRKJdOnT2fFihXGDqVWKNQW8vZfb5NXlEcH1w6MbzW+wudM+/57Ur/+GgC3xYuxbNeuwuesTAkFGoZeCGF7fCpK4L167nzR1Aeranr+oE6r4+pfMWx97xSBv0WiLdLh2diBEXM70PvFJljZVd/EpxBCCMNJkrCGeKqNB03cbMnKL2LV4ZAHGqtZ3WY84fcEAMvOL7vvlmCH55/HxMeboqQkUr74wuA5Jnk742KqJjyvkI3RyQb1sbA2peMgfwD+3nOb/BzDKiQLIYQQouq5ffs2RQYeVyIezKrAVQSlBmFnZsfi7otRKSs2kZX911/Ef/AhAE5Tp2A38MkKna+yXcvOY8D5m1zKysPRRMX2VvWY4O1cbYuxRAensv2jsxzddoO8LA32LpY8+WZLBk9pTV1PG2OHJ4QQohLJevEaQqVUMHdAE0ZtOs23pyIY3cUXv7pW5R5vStsp/BHxB6fjTnM85jg9PHvcs53S1BSXWbOJnjCB1G++wf6Zpw3aSmKlVvGOvztTgiNZER7PcFcHnExLP9eyeU93rh2LITU2h9O/hNLruUZlfm9CCCGEqDzTp99dCE2v1xMXF8f+/fsZM2aMkaKqPU7EnGDz9c0ALOy6EBcrlwqdLz84mJip00Cnw+6pp6jz+usVOl9l+yMlk9evhZOj1dHA0oxvW/rja1E9V9nlZBRwYuctQs4mAMVFSToM9KNZT6lWLIQQtdUD/d8/OzubzMzMu17CeLo3qMsjjZwo0un55GDwA43lYe3B842fB2D5+eVodSUXRLHu/QhW3buj12hIWPKJwXM86+pAKxsLsrU6loTGG9RHqVLSc0RDAK79FUNydJbB8wkhhBCi8l24cOGu1+XLlwFYtmwZn332mXGDq+FS8lKYe3wuACMajeBR70crdL6ilBSiJkxAl5uLZefOuC14v9qurruXjdFJjL4cSo5WR3d7a/a2bVAtE4Q6nZ7LR6LZNv/v4gShAlr08uCFhZ1p2dtTEoRCCFGLKfSGlJf9L2FhYUycOJE///yT/Pz8O9f1ej0KhQKttvzVdasKQ0tDV0U34rN4YuVf6PSw840utPd1LPdYGQUZDNg9gMzCTOZ3mc8zDZ8psW1BaCihg4dAURFeX36JdQ/DDsM+k57N4Au3UACH2jekuY2lQf1++/Iqt84n4t7AnqHT29SoG1AhhBCiNNX5XqUmqoqfh16v582ANzkWc4z69vX5/snvMVebV9x8Gg2RY18m99w5TH188P1xOyo7uwqbrzIV6fS8dyuGr2KKj8h53s2RJQ29MFFWv/vPhLBMjn5/g6TI4gftzj429Hq+Ec4+VePnVgghRMUw9F6lzI+JRo0aRVpaGl999RUBAQEcPnyYw4cPc+TIEQ4fPvxAQYsH18jVhhEdvAD4cH/Qfc8TLI2dmR2vtyzeIrL6wmoyC0teKWrm74/jCy8AkLB4MXqNYecFdrS3ZoizPXrg3ZAYg+Pt+nR91CZKYkPSuXU+0aA+QgghhBC1xdagrRyLOYap0pQlPZdUaIIQiu//cs+dQ2llhefaz2tMgjCrSMvoK6F3EoTv+ruxrFH1SxDm52j4c9sNdn5yjqTILEwt1PR6riFPz2ovCUIhhBB3lPlMwkuXLnH+/HkaNZKz4KqqaX0bsudiLBej0tl/JY6BLd3LPdZzjZ9jZ8hOwjLCWHdxHbM6ziqxbd03J5Cxdy+FoaGkbt1KnZdeMmiOefXc+T05k78zctiRkMZw19JXP9o4mtO2vw9n9oZxctctfFvUxcSselaTE0IIIWqatm3bEhAQgIODA23a3H/Ff2BgYCVGVjsEpwaz/PxyAGZ2mElDh4YVOl/ajh2kbfseFArcP/0Us3r1KnS+yhKdX8iLl0MJysnHQqlgTVMfnnSyN3ZYZaLX67l5JoETO0PIyyp+iN+okytdn66Ppa2pkaMTQghR1ZQ5SdihQweioqIkSViFOdua81pPfz77I4RPDt7gsaYumKnLl0AzUZkwu+NsXj/0Ot8Hf8/TDZ6mvkP9e7ZV2driNG0q8fPeI3nN59gNGoS6Tp1S5/A0N2WGrwsfhsax4FYs/erYYm9S+o9mm8e8CToZR1ZKPucPhtN5SM24IRVCCCGquyFDhmBmVnxW29ChQ40bTC2Tq8nl7b/eRqPT8IjnI4xsNLJi5wu8QPzCDwBwmjwJm0d7V+h8leVKVi4vXA4lsbAIZ1M1m1v408bWsGNxqoqc9AL+3BpM+JUUABxcLen1XCM8GjkYOTIhhBBVVZnPJLx9+zZvvPEGo0aNonnz5piY3F2RtmXLlg81QGOoiufKlFVuYRGPfPoniVkFzB3QhFd7+j/QeFMOT+Fw1GE6uXXiy8e+LHFFgF6rJfzZ4eRfv479s8/g9sEHBo1fqNPR9+xNbubmM8a9DksaeRnUL/RCEr9+cQWlWsHz8zth51S9bt6EEEKI8qgJ9yo1SVX6PN4/+T67QnbhZOHErsG7cDCvuISQJj6esGeeRZucjE2/fnis/KxGnBN9PC2Ll66Eka3V0cTKnG9b+uNpXn1W3en1em78Hc/xHSEU5BahVCno8KQvbfr5oFJLURIhhKiNKuxMwqSkJG7fvs3YsWPp0KEDrVu3pk2bNnf+KaoGS1M1M/oVby1ZFRBCUlbBA403s8NMTJWmnI47zR+Rf5TYTqFS4fJucRW99J27yLt6zaDxTZVKPm7oCcCW2BQuZOYa1M+vdV08GzugK9JzYuctg/oIIYQQovIVFhYSHR1NZGTkXS/x8Pwe/ju7QnahQMGiHosqNEGoKyggetJktMnJmDVsiPviRTUiQbg/KZ3nL4WSrdXR1d6aPW0bVKsEYXZaPvvWXCZgcxAFuUU4+9gw/J0OtB/gJwlCIYQQpSrzb4qXX36ZNm3acOrUKUJDQwkLC7vrn6LqeKadFy097cgqKGLJweAHGsvLxouxzccC8OnZT8kryiuxrWXbttgOHAh6PQmLFhlejMTBmmdcHNADs25GoTWgn0KhoMeIhiiVCsIuJRNxLcWguYQQQghROW7evEmPHj2wsLDAx8cHPz8//Pz88PX1xc/Pz9jh1RjxOfG8f+p9AMY2H0tnt84VNpderyf+vfnkX7mCys4Oz7Wfo7SyqrD5Ksu3scm8ejWcQr2eAXXt2NbSH9tyHtlT2fR6PddPxPL9gtNEXktBqVbQ5al6PP12O+p4WBs7PCGEENVEmZOEERERLFmyhE6dOuHr64uPj89dL1F1qJQKFgxuBsDO89Gcj0h7oPHGtRiHq5UrcTlxfHP1m/u2dZ45A4WFBXmBgWTu22/wHPPru2OrVnI5K48tsYYl/BzdrGjRu3gV4l/f36CoUGvwfEIIIYSoWGPHjkWpVLJv3z7Onz9PYGAggYGBXLhwQYqWPCQ6vY53T7xLVmEWzes0Z2KbiRU6X9qWLWTs2QMqFR6frcDU07NC56toer2eFeHxvHUjGh0wyq0OXzb3xVxVPVbeZaXms3f1JY58G0xhvhYXP1tGvNORto/7oKwm70EIIUTVUObfGo8++iiXLl2qiFhEBWjj7cCz7Ypv3N7/5RpaXZmOoLyLhdqCme1nArDp6iZismNKbGvi6krd118DIHHpUnS5hm0fdjI1YY5/cTXmxaGxJBVqDOrXcaAfVvZmZCbnc+5AuEF9hBBCCFHxLl68yBdffMETTzxB69atadWq1V0v8eC+D/6e03GnMVeZ83HPjzFRmpTeqZxyTp0i4ZNPAXB5+y2sunSpsLkqg06vZ25IDEvC4gGY5uPCp408UVWDrdPFZw/G8f3C00RdT0WlVtJ1WH2GvdUOR/fqv7JTCCFE5StzknDQoEFMmzaN999/n127dvHLL7/c9RJVz9v9G2NjruZKTAbbz0Y90Fj9fPrRwbUDBdoClp1bdt+2jmPHYuLpSVFCAskbNhg8x2j3OrS0sSCzSMeCW7EG9TG1UNNzZPEZjBd+jyQlNtvg+YQQQghRcZo2bUpycrKxw6ixwjPC+ez8ZwBMazcNH9uK29lTGB1DzNRpoNViN3QoDqNHV9hclaFQp2PC9Qi+iin++fywgQez/N2qxdmKBXlFHPrqOn98E4QmX4urvy0j3u1Am37eKJVVP34hhBBVU5mrGyuVJecVFQoFWm313+pZlSrUPSxfHQ9j4b7rOFiacGTmI9hblv8A5ptpNxm+dzhavZYv+3153zNvMg8dImbSZBSmpvjv24upt7dBc1zIzGXA+Zvogd2t69PVwbCzVA6su0zYpWRc/e0YNrMtCrlJEkIIUQNV9XuVzMzMO/9+7tw53n33XRYtWkSLFi0wMbl7lVtVjL+sjPV5FOmKGPPrGC4nX6azW2e+eOwLlIqK2V6qKywk4oVR5F+5gnnz5vhs/Q6lmVmFzFUZcoq0jLsazp9pWZgoFKxq4s1TLhVX6OVhiruVzqGvr5OVko9CqaDjQF/a9veV5KAQQogSVVh1Y51OV+KrJiQIa6rRXXxo5GJDWq6GZb/ffKCxGjo0ZESjEQB8fPpjNLqStwTb9O2LVdcu6AsLiV/4gcFFTNrYWjLavQ4As29GU6jTGdSvx4iGmJipiA/N4PoJw1YhCiGEEOLhsre3x8HBAQcHBx577DH+/vtv+vTpg7Oz853r/7QR5ff11a+5nHwZGxMbPuj2QYUlCAESP15C/pUrKO3s8Fz5WbVOEGZoihh+6TZ/pmVhqVLybUu/apEg1Gl1nNkbyk/LAslKyce2rjnDZral/QA/SRAKIYR4KNTGDkBUDrVKyfuDm/Hcl3+z9XQEIzt60czdrtzjTWg9gQNhB7idcZvtwdsZ1XTUPdspFApc5s0jbPAQco4fJ+vXX7EdMMCgOeb4u7EvKYObuflsiEpioo9LqX1sHM3pNNif4ztCOPXTbXxb1sXKrvrexAohhBDV0ZEjR4wdQo0XnBrM2ktrAZjdaTauVq4VNlfmgQOkbdsGgPuSjzHx8KiwuSpamqaIkZducykrDwe1iq0t/WlrV/XP78tMzuPQV9eJD80AoFEnV3qObIiphfx1TgghxMNT5u3GCxcuvO/333vvvQcKqCqo6lt4HsTEbYHsuxxHex8HdrzR5YHOXNlxcwcLTy3ExsSGvU/tpY5FnRLbJn3+Ocmr16CqW5d6B/ajMvDP9cf4VCYHRWKhVHKsU2M8zUvfJq3T6dn58TmSIrNo0N6Zfq80N/g9CSGEENVBdbpXiYyMxMvL61/3HHq9nqioKLwNPIqkKqvsz6NQW8iIfSO4lX6LPt59WPHIigo7R68gNIzwZ55Bl5tLnddfx3na1AqZpzKkFBYx4tJtrmbn4WiiYkfr+jSztjB2WKW6eSaeo9tuUJivxdRcRa/nG9GwY8UlhYUQQtQ8ht6rlDlJ2KZNm7u+1mg0hIWFoVarqVevHoGBgeWLuAqpTjfeZRWXkcejS4+Sp9GyYkQrnmrjWe6xtDotz+1/jqDUIJ6q/xQLu5WcQNYVFhI2ZCiFYWHYPzcSt/nzDZpDr9fz1IVb/J2RwxN17fi6hZ9B/ZIis9ix+Cx6PQyc1AqfZiUnMIUQQojqpjrdq6hUKuLi4nB2dr7rekpKCs7OzjXiuJrK/jxWnF/BV1e/wtHckZ+G/ISjuWOFzKPLzSV8xAgKQm5h2bEj3l9tQqGunivXkgo1PHvxNsE5+TiZqtnRuh6Nrap2glBTqOWv728QfKq48rKrvx2PvdwU27pVO24hhBBVT4WdSXjhwoW7XlevXiUuLo4+ffowbdq0BwpaVDw3OwsmPlofgEUHgsnKL/k8wdKolCre6fQOAD/d+onzCedLbKs0NcX1P4nB9B+2k3fxokFzKBQKFjf0RK2AX5Mz+DUp3aB+Tt42tHzUC4C/vr+BprD6/wVECCGEqI70ev09V7llZ2djbm5uhIiqtwuJF/jm2jcAvNflvQpLEOr1euIXLKQg5BYqp7p4LFtabROECQUahl24RXBOPi6mana3rl/lE4TpibnsWnKe4FPxKBTQ4UlfnprRRhKEQgghKtRD+U1va2vLggULGDRoEC+++OLDGFJUoFd6+LHjXBThKbmsPnyLdwY0KfdYrZ1bM6zBMHaH7GbhqYXsHLQTE5XJPdtade6E3ZAhZOzZQ9z89/HbuQOFyb3b/rcm1hZM8HJmVWQis25G08XeGnuT0n90Ow7y43ZgIpnJ+ZzbH0aXp+qX+f0JIYQQonymT58OFD/wmzdvHpaWlne+p9VqOX36NK1btzZSdNVTriaXucfnotPrGFxvMH28+1TYXOk7d5KxZw8olXgsXYbayanC5qpIsfmFPHPxNqF5BbibmbCzdX38Lav2edWhF5MI+OY6hflaLGxM6PdKczwbVf3CKkIIIaq/h1YCLSMjg4yMjIc1nKhAZmoV8wc1A+Cr42HcSsx+oPGmt5uOo7kjoRmhfHX1q/u2dZ71Nio7Owpu3CB1y7eGz+HrSn1LMxILi1hw27CqxabmanqObAjAxUNRpMQ82PsUQgghhOH+2XWi1+u5cuXKXTtRgoODadWqFd98842xw6xWlp9fTlRWFK5WrszuOLvC5skPCiLhgw8BcJo6FatOHStsrooUlV/IUxduEZpXgKe5CT+1qdoJQp1Wx8ndt/h1/RUK87W4+tsx/J2OkiAUQghRacp8JuGqVavu+lqv1xMXF8e3335Lr1692PafymfVWXU65+dBjPvmLAHBiXSvX5dvx3V8oAOv94fuZ/ax2ZgqTdk1eBe+dr4ltk3ftYu4ue+isLCg3r69BlfIO5OezZALt9ADP7aqR09HG4P6/frFFUIvJOHiZ8vTb7VDoayYg72FEEKIylKd7lXGjh3LypUrq3ycD6IyPo8TMSd44483APiy35d0dutcIfNos7IIe/oZNJGRWD/yCJ5rP0ehfGjrCipNRF4BT1+8RXS+Bh9zU3a2qY+XAQXwjCUno4BDm64RczMdgFZ9vOgyrB4qVfX7sxdCCFH1VFjhEj+/uwtHKJVKnJycePTRR5kzZw42NoYlbqqy6nTj/SAiUnJ4bMVfFBbpWDmyNUNaG5asuxe9Xs8bf7zBydiTdHTtyMZ+G0tMOur1eiJefJG8c+eLbz7XrTU4QfnOzWi+iknGy9yUPzs0wkqtKrVPdloB2xb8jSZfS6/nG9G8Z/nfpxBCCFEV1JZ7leqioj+PzMJMntrzFIm5iTzf+HnmdJrz0OeA4nu0mMmTyTr0Bybu7vjt3oXK3r5C5qpIEXkFDLtwi5gCDf4WZuxsXQ/3KpwgjL2Vzm9fXiU3oxATMxWPjm5C/XbOpXcUQgghDFRhScLaoDbdeK8KCGH5oZvUsTIlYEYv7C3LfwMVlRXFsD3DyNfm82G3DxlSf0iJbQtu3SL0qWGg0eCxaiW2/foZNEdOkZaeZ4KJKdDwqmddPmhgWHXmy0eiOLY9BBNzFc+91wkbRzkoXQghRPVV1e9Vhg0bxjfffIOtrS3Dhg27b9vdu3dXUlQVp6I/D61Oy5brW9gbupetA7Zioa6Y4hWpW7aQsGgxmJjgu20rFi1aVMg8FSk2v5ChF24RmV9IA0szdrauj4tZ6WdgG4Ner+fy4WhO7LqFXqfHwc2KJ15vjoOrlbFDE0IIUcNUWHVjUbO80aseDZytSckpZNGBoAcay8vGizdaFW+DWXpuKWn5aSW2NatfnzrjXgYg4aNFaLMNOy/QSq1iWePiqsUbo5M5l5FjUL/mvTxx8bNFk6/lz++Ckdy4EEIIUXHs7Ozu7BKws7O770uUTqVUMbb5WH4c+GOFJQjzr18n4dOlALjMmlUtE4RJhRqGX7pNZH4hvham7KjCCUKtRsfhzUEc3xGCXqenQQcXnpnVThKEQgghjMqglYTyNLhmOx+RytPrTgGw7dVOdK1Xt9xjaXQaRu4byc20mwyuN5iPun9UYltdfj6hg4egiYzE4cUXcZ37jsHzTAmKZHt8Kg0szfijQyPMDDgrJy0+h+0fnkVbpKP3i41p2s3d4PmEEEKIqqS23atUddX989Dl5hL29DMUhoVh3acPnmtWP9BZ1caQqini6Qu3CMrJx8PMhJ/bNqiyZxDmZhby6/orxIdmoFAq6PZMfVr29qx2f+ZCCCGqj4e6klCeBtds7XwcGdXZG4B3dl8hX6Mt91gmShPmd5mPAgW/3P6Fv+P+LrGt0twc1/feAyBt61byrl4zeJ7367vjZKomJLeAz8ITDOrj4GpFp8H+AJzYEUJWar7B8wkhhBBC1FQJiz+mMCwMtbMzbh9+UO2SVZlFWkZeuk1QTj4upmp2tq66RUqSo7PZ8fFZ4kMzMLVQM2hiK1o96lXt/syFEELUTHIm4T1U96fB5ZGZr+Gx5UdJyCxgYu/6zHy80QON99HfH/HDjR/wtvFm95DdmKnMSmwbM2Mmmfv3Y9a4MX47fkRhYti2kH2J6bxyLRy1An5r34hm1qVvv9Hp9Oz+9DwJYZl4N3Vk4KRWclMmhBCi2qnq9ypt2rQx+PdrYGCgwePGxMQwa9Ysfv31V3Jzc6lfvz5ff/017du3L7FPQUEBCxcu5LvvviM+Ph43Nzfee+89Xn755Ttt0tPTmTt3Lrt37yY1NRUfHx8+++wzBgwYYFBcVf3zuJ/Mg78RM3UqKBR4f/0VVp0rpmpyRckp0jLyUihnM3NwNFHxU5sGNLKqmmdPh15M4tDX1ykq0GLnbMGTE1rK9mIhhBCVwtB7FXUlxiSqMFtzExYMbs4b351n/dHbDGzlRmPX8t/kTm47mYDIACKzItlweQOT2kwqsa3LnNnkHD9OQXAwyV9swGnimwbNMdDZnicT7diflMG0oEgOtGuIWnn/v5AolQr6jGnC9g/PEnk9laCTcbLtWAghhHjIhg4d+tDHTEtLo1u3bvTu3Ztff/0VJycnQkJCcHBwuG+/4cOHk5CQwKZNm6hfvz5xcXHodLo73y8sLOSxxx7D2dmZnTt34uHhQUREBPbVsKpvWWni4oj7z66OOq+8Uu0ShHlaHWOuhHE2Mwc7tYrtrepVyQShXq8n8LcI/t4TCnrwbOzA4682x9yqap6XKIQQovYq80rChIQEZs6cSUBAAImJif8qAKHVln+ralVRnZ8GP6jXtpzj9+sJtPayZ9f4rqhKSbrdz6GIQ0z/czpqpZqdg3ZSz75eiW0z9u0nduZMUKvx27kD88aNDZojsUBDzzPBpBdpmevvxiQfF4P6Xfg9kpO7b2FqrmKkVDsWQghRzdTGe5XZs2dz4sQJjh07ZnCfgwcPMnLkSEJDQ3F0dLxnm/Xr1/Ppp58SHByMiYG7Gf5Xdfw89FotkWNeIvfcOcxbtMB321aDd3NUBYU6HWOvhBOQmomVSsmOVvVoa1f1VuUVabQc+TaYm2eKj8dp0cuDbsMboFJJ/UghhBCVp8KqG7/00ksEBgYyb948du7cye7du+96ieptwZBmWJupuRiVztbTEQ80Vl/vvjzi+QhFuiIWnFqATq8rsa3tkwOweawvFBURO+cd9BqNQXM4m5mwoL4HAEvD47mVa9g5g636euHiZ0uhVDsWQgghqoVffvmF9u3b8+yzz+Ls7EybNm348ssvDerzySef4OHhQcOGDZk5cyZ5eXl3tenSpQtvvvkmLi4uNG/enEWLFt33wXdBQQGZmZl3vaqblA0byD13DqWlJR7LllarBGGRTs/46xEEpGZioVTwbQv/KpkgzMko4OflF7h5JgGFUkGv5xrS87lGkiAUQghRZZX5N9Tx48fZunUr48ePZ+jQoQwZMuSul6je3OwseLt/8XmEnxy8QVxGXik9SqZQKHin0ztYqC24kHiB7Te237et63vvobKzoyAoiOQNGwyeZ7irA70dbSjQ6ZkcFEmRrvSE3z/bjlVqZfG24xNxBs8nhBBCiMoXGhrKunXraNCgAb/99hvjx49n8uTJbN68+b59jh8/ztWrV/npp5/47LPP2LlzJxMmTLirzc6dO9FqtRw4cIB58+axbNkyPvzwwxLHXbx48V2F+7y8vB7qe61ouRcukLTmcwBc57+Hqbe3kSMynF6vZ/qNSPYnZWCqUPB1Cz+6OlgbO6x/SU/IZdcnxedgm1mqGTy5Fc17eRo7LCGEEOK+ypwk9PLyklVXNdyoTj609bYnu6CIeT9fe6DP283ajaltpwKw4vwKIjMjS2yrdnLCZd48AJLXrSc/ONigORQKBUsbeWGrVhKYmcuqyLJXOz6+U6odCyGEEFWZTqejbdu2LFq0iDZt2vDaa6/x6quvsn79+vv2USgUbN26lY4dOzJgwACWL1/O5s2b76wm1Ol0ODs7s2HDBtq1a8eIESOYO3fufcedM2cOGRkZd15RUVEP/f1WFG1WFrEz3wKtFttBg7CrZg/5F4XG8WN8GioFbGjmyyOOVW97d3xoBrs+OU9WSj62ThY8M6s9no3vvd1dCCGEqErKnCT87LPPmD17NuHh4RUQjqgKlEoFi4e1RK1U8EdQAr9di3+g8UY2HklH147kFeUx78S8Ctl27GFuyscNi5/iLwuP50JmrkH9/tl2rJFtx0IIIUSV5ubmRtOmTe+61qRJEyIjS34A6ebmhoeHB3Z2dnf10ev1REdH32nTsGFDVCrVXW3i4+MpLCy857hmZmbY2tre9aoO9Ho98fPfRxMTg4mnJ67z3zN2SGWyMTqJ1ZGJAHza0Iv+Tnal9Kh8YZeS2LPiAvk5Gpx9bHj6rXbYu1gaOywhhBDCIAYlCR0cHHB0dMTR0ZGRI0fy559/Uq9ePWxsbO5c/+claoZGrja80au40Mh7e66RkWdYsu5elAolC7stxFJtSWBiIN9d/67Etg+y7fgpZ3uGONuj1cPE6xHkaktORt6JTbYdCyGEENVCt27duHHjxl3Xbt68iY+Pz337xMbGkp2dfVcfpVKJp6fnnTa3bt26q+LxzZs3cXNzw9TU9CG/C+PK+HkPmQcOgEqFx9JPUVlXvW26JdmTmMa8kBgAZvu58rx7HSNH9G9X/4rh1/VXKNLo8GlehyHT2mBpW7N+hoQQQtRsBlU3vt9ZL/9rzJgxDxRQVVAdK9RVhHyNlidWHiMsOYfh7T355JlWDzTejps7WHhqIWYqM3YM2oGfnV+Jbctb7ThNU0TvMzeIL9Qw1qMuixsadvbLP9WOTcxVPCfVjoUQQlRxVf1eZfr06Qa3Xb58uUHtzp49S9euXVmwYAHDhw/nzJkzvPrqq2zYsIEXXngBKN4GHBMTw5YtWwDIzs6mSZMmdO7cmQULFpCcnMwrr7xCr1697hQ9iYqKolmzZowZM4ZJkyYREhLCyy+/zOTJk5k7d65BsVX1zwOgMDqasMFD0OXm4jR1CnXfeMPYIRnseFoWz18KpVCvZ6xHXRY18EChUBg7rDv0ej1n9oZx7kA4AE26uvHIC41QSoESIYQQVYSh9yoGJQlrm+pwo1dZzoSlMmLDKfR6+Oql9jza2KXcY+n1et744w1Oxp6kZd2WbH5iM2qlusS20ZMmkf1HAGZNmuD343aDq+4dTc1ixKXbAGxr6c+jdUr/DHU6PT8tPU98aCbuDewZMq0NSmXVufkUQggh/ltVv1fp3bv3XV8HBgZSVFREo0bFxdFu3ryJSqWiXbt2HD582OBx9+3bx5w5cwgJCcHPz4/p06fz6quv3vn+Sy+9RHh4OH/++eeda8HBwUyaNIkTJ05Qp04dhg8fzocffoiFhcWdNqdOnWLatGlcvHgRDw8Pxo0bx6xZs+7agnw/Vf3z0Ot0RI4eQ+65c1i0b4fP5s0oDHxvxnY1K5ehF26RrdXxpJMdG5r5oqpCCUKtVsefW28QfLJ4N0r7J33pONCvSiUxhRBCiApLEqpUKuLi4nB2dr7rekpKCs7Ozmi12vJFXIVU9Ru9yvbhvutsPB6Gk40Zv0/tiYNV+bdNxOfE89Sep8jWZDO17VTGtRhXYtuipCRCBw5Cm5FB3UkTcXrzTYPnmXszmk0xybiYqjnSsTGOJvdORv639MRcfvzoLJoCLZ2G+NP+CV+D5xNCCCEqU3W6V1m+fDl//vknmzdvxsHBAYC0tDTGjh1Ljx49mDFjhpEjfHBV/fNI+fobEpcsQWFpif+enzGtJtWYI/IKGBQYQmJhEV3srfi+ZT3Mq9DqvML8In778hqR11JQKKDX841o1sPD2GEJIYQQ/2LovUqZf8uWlFMsKCiocee2iGIzH29EfWdrkrIKmLfn6gON5WrlyqyOswD4/OLnhKSFlNi2vNWOAebWc6eBpRkJhUW8fSPKoIIk9s6W9BjREICze8NICMs0eD4hhBBC3NuyZctYvHjxnQQhFJ93/eGHH7Js2TIjRlY7FNy6RdKKFQC4zJpVbRKEyYVFPHcplMTCIppYmfNNc78qlSDMz9GwZ8UFIq+loDZR8sT4lpIgFEIIUe2VvrzqP1atWgUUF5bYuHEj1v910LFWq+Wvv/6isYHnxonqxdxExfLhrXhq7Un2XY7j8WaxDGrlXu7xhtQbwh8Rf3A0+ihzj89l65NbMVHeeyux7ZMDyDz4K9l/BBA75x38tv+AwoBktKVKyZqmPjx5/ib7kjLYmZDGs66lF9Zp3MWViKsp3A5M5NBX1xg+twOm5gb/ZyKEEEKI/5GZmUlSUtK/riclJZGVlWWEiGoPvUZD7KzZ6AsLserZA/vhzxo7JIPkFGkZdTmU0LwCPMxM+L5VPewM2BVSWXIzC/ll5QVSYnIwtzLhyTdb4upf9SotCyGEEGVl8OO4FStWsGLFCvR6PevXr7/z9YoVK1i/fj25ubmsX7++ImMVRtTS0543e9cHYN6eqyRm5pd7LIVCwfwu87E1tSUoNYhNVzbdt63b/Pl3qh0nrV5t8DytbCyZ6esKwDs3o4nKLzQotkdeaIS1gxkZSXkc/7HklY5CCCGEKN1TTz3F2LFj2b17N9HR0URHR7Nr1y7GjRvHsGHDjB1ejZb8xQbyr11DaWeH2wcfVotz8op0el67FsHFrFwcTVT80KoermaGnUtdGbJS8/lpWSApMTlY2pkydEYbSRAKIYSoMQxOEoaFhREWFkavXr24dOnSna/DwsK4ceMGv/32G506darIWIWRTexdn2butqTnapiz+4pBW3hL4mTpxDud3gHgi0tfEJxa8lZitZMTrh9+AEDKxk3k/P234TF7u9De1pIsrY7JQRHoDIjZ3MqEvmObggKCTsZx63yiwfMJIYQQ4m7r16/niSee4Pnnn8fHxwcfHx+ef/55+vfvz9q1a40dXo2Vd/Uayf95gO86bx4mLs6l9Kga5t2KISA1Ewulgm9b+NPAytzYId2RkZTLT0sDSU/IxdrRjKdmtKWOu3XpHYUQQohqoswHexw5cuSuM2VE7WGqVrJ8eGtMVUoCghPZcS76gcYb4DeAvt59KdIXMff4XDRaTYltbR97DPtnnwW9nti3Z1GUlmbQHGqlgtVNfLBUKTmVnsP6qH9vd7oXj4YOtH3cB4A/twaTlVr+lZNCCCFEbWZpacnatWtJSUnhwoULXLhwgdTUVNauXYuVlZWxw6uRdAUFxM6eBUVF2PTvj+2TA4wdkkE2RifxdUwyAGua+tDOrur8fKTG5rB7aSBZqfnYOVswbGY77J0tjR2WEEII8VAZVN14+vTpfPDBB1hZWTF9+vT7tl2+fPlDC85YqnqFOmNbf/Q2H/8ajLWZmoNTe+DpUP4bpJS8FJ7a8xRpBWm80uIVprSdUmJbXW4uYU8/Q2FYGDaP9cVj1SqDt818F5vCzBtRmCgU7G3bgNa2pces1erY/cl5EiOy8Ghoz+CpbVAqq/42HSGEEDVfdbxXuXXrFrdv36Znz55YWFig1+urxfZXQ1S1zyNhySekfv01qrp18d/7C+pq8ID/j5RMRl8ORQfM9Xdjko+LsUO6Iykyi19WXSQ/W4OjuxWDp7TGys7M2GEJIYQQBnuo1Y0vXLiARqO58+8lvS5evPhQghdV26s9/Gnn40B2QRFv7biMTlf+bcd1LOowr0txBeNNVzZxNv5siW2Vlpa4L/0UTEzIOvQH6Tt2GDzPC26ODKhrh0av5/Vr4WQWaUvto1IpeezlZqjNVMTcTOfioUiD5xNCCCFEsZSUFPr06UPDhg0ZMGAAcXFxAIwbN44ZM2YYObqaJ/fsWVK/+QYAtw8WVosEYVB2Hm9cC0cHPOfmyETvqrM1Oj40g59XXCA/W4Ozjw1PTW8rCUIhhBA1lkFJwiNHjmBvb3/n30t6HT58uCJjFVWESqlg2bOtsDBRcSo0hS2nwh9ovMd8HmNYg2Ho0TP7r9mk5Ze8ldiiWTOcpxavNkxY/DEFoWEGzaFQKFje2Asvc1Mi8guZHhxp0JmK9i6W9BjeAIDTe0JJjMg0aD4hhBBCFJs2bRomJiZERkZiafn/K/lHjBjBwYMHjRhZzaPNziF2zjug12P39DBsevc2dkilSizQMOpyKNlaHV3trVnS0LPKrDCNDk5lz8qLFOYV4VbfjiFT22BuXXWKqAghhBAPW5nPJBQCwLeuFXMGNAbg44PBhCZlP9B4szrMwtfWl8S8RN47+d59E3iOY8di2aUz+rw8YmbOQFdYetViAHsTNV809UGtgH1JGXwTm2JQvyZd3ajXxgmdTs+hr66jKSh9FaIQQgghiv3+++8sWbIET0/Pu643aNCAiIgII0VVMyV++ima6GhM3N1xmTPH2OGUKk+r46WrYcQUaPC3MGNTc19MlVXjryeR11PYt+YyRQVavJo4MGhSa0wt1MYOSwghhKhQBv2mGzZsmMED7t69u9zBiOplVCcffr+WwPFbyUz78RI73+iCiap8N3aWJpZ80vMTXjjwAn9G/ckPN37gucbP3bOtQqnE/eMlhA0ZQsH1IJI+W4nL228ZNE9bOyve9Xfn/duxzA+Job2tJS1s7n8+oUKh4JFRjYkPyyQ9IZdj22/y6OgmZX2LQgghRK2Uk5Nz1wrCf6SmpmJmJts2H5bsY8dJ374dALdFi1BZV+2quzq9ninBkQRm5mKvVvFdS38cTKpGEi4qKJUD666gLdLh27Iu/V9tjsqkaiQvhRBCiIpk0G87Ozs7g1+i9lAqFSx5piU25mouRaWz7PebDzRekzpNmN6uuDDO0rNLuZlW8ngmLs64ffQhAKlffUXOyZMGz/O6lxP96thSqNfz2rVwsg04n9DcyoS+Y5uCAoJOxhF0Ms7g+YQQQojarEePHmzZsuXO1wqFAp1OxyeffELvarAdtjrQZucQN/89ABxefBGrzp2MHFHpPg2L55fEdEwUCjY198XfsmokjKODUzmw9jJazX8ShK9JglAIIUTtYVB149qmqlWoq+oOXIljwtZAALa83JGeDZ3KPZZer2fi4Yn8Ff0X9ezq8f3A77FQW5TYPm7++6Rv347ayQm/X/YYfDh3mqaIvmdvEFOg4Slne9Y29THo/JtzB8I4/UsYahMlT89qT13Pqv2UXgghRM1Une5Vrl69Sp8+fWjbti2HDx9m8ODBXLt2jdTUVE6cOEG9evWMHeIDM/bnEb/wA9K2bcPE0xP/X/agvMfKzapkR3wqk4KKC8KtaOzFc251jBxRsZibaexbc4miQh0+LerwxGstJEEohBCiRnio1Y2FuJ8BLdx4oZM3ANN/vEhiVn65x1IoFHzQ7QPqWtTldsZtPj376X3bu8yeham/P0VJScS9O8+gYiQADiZq1jfzRaWAnxLT2RqXalC/dv198W7mSJFGx8ENVyjMKzKonxBCCFFbNW/enJs3b9K9e3eGDBlCTk4Ow4YN48KFCzUiQWhsuefOkbZtG1BczbiqJwjPZuQwIzgKgInezlUmQRh7K519n1+mqFCHdzNHWUEohBCiVpLffOKhmDewKY1cbEjOLmTGj5fQ6cq/QNXR3JFF3RehQMGOmzs4FHGoxLZKCws8ln4KJiZkBwTcuUk2RAc7K2b7uQHwbkg017PzSu2jUCroO7Yp1g5mZCTmcfjbIIMTk0IIIURtFBkZia2tLXPnzuXHH3/kwIEDfPjhh7i5uREZGWns8Ko1XX4+cXPfBcD+2Wew6tLFyBHdX3yBhnFXwyjU6xlQ1453/N2MHRIAcbcz2Lf60p0iJU+80QK1icrYYQkhhBCVTpKE4qEwN1Gx5vk2mJsoORaSzBd/hT7QeF3cuzC2+VgA5p+cT1x2yWcAmjdtivOM4rMMEz5eQt7lywbP86a3M4862pCvKz6fMMeA8wktrE15/NXmKFUKbgcmcflwtMHzCSGEELWNn58fSUlJ/7qekpKCn5+fESKqOZI//5zCiAjUzs44v2VYETdjKdDpGHc1jMTCIhpbmbO6iTdKA456qWjxYRnsXX0RTYEWj0YODBjfUhKEQgghaq0qkST8/PPP8fX1xdzcnE6dOnHmzJn7tt+xYweNGzfG3NycFi1acODAgTvf02g0zJo1ixYtWmBlZYW7uzujR48mNja2ot9GrdfAxYYFg5sBsPT3G5yPSHug8Sa2mUiLui3IKsxi9rHZFOlK3trrOGYMNo/1BY2G6KlTKUozbG6lQsGqJj64mppwK7eAWTejDVoZ6OpvR7dn6gNwctct4kMzDHtTQgghRC2j1+vvee5vdnY25ubmRoioZsi7eo2Ur74GwPX9+aiq+NmUc2/GcD4zFzu1im9a+GGlNn4iLiE8k70rL6LJ1+LR0J4n32yJ2tT4cQkhhBDGYvQk4fbt25k+fTrz588nMDCQVq1a8fjjj5OYmHjP9idPnuS5555j3LhxXLhwgaFDhzJ06FCuXr0KQG5uLoGBgcybN4/AwEB2797NjRs3GDx4cGW+rVpreHsvBrVyR6vTM/n7C2Tkaco9lonShCU9lmBlYkVgYiBfXv6yxLYKhQK3RYsw8fGmKDaO2LfeRq8tfVUgQF1TNeua+aAEdiak8V1cikH9WjziSf12zuh0en778ip5WYUG9RNCCCFqg+nTpzN9+nQUCgXz5s278/X06dOZMmUKI0aMoHXr1sYOs1rSazTEvfsuaLXYDhiAzaOPGjuk+9oSk8x3cSkogHVNffC1MH4l46TILPauukhhvha3+nY8+WYrTCRBKIQQopYzqLrxqlWrDB5w8uTJZQqgU6dOdOjQgTVr1gCg0+nw8vJi0qRJzJ49+1/tR4wYQU5ODvv27btzrXPnzrRu3Zr169ffc46zZ8/SsWNHIiIi8Pb2/tf3CwoKKCgouPN1ZmYmXl5e1aJiYFWUma9h4KrjRKbm8mQLN9Y838agysEl2Re6jznH5qBUKFnfdz1d3Es+byf/xg3CR4xEn59P3YkTcZr4psHzrIpIYFFoHCYKBbvb1KeDnVWpfQrzi9ix+BzpCbl4NXVk4MRWKJXG3zojhBCiZjN2NV1D9O7dG4CjR4/SpUsXTE1N73zP1NQUX19fZs6cSYMGDYwV4kNT2Z9H8rp1JK1chcreHv8D+1E7Olb4nOV1NiOHYRduodHrecffjck+LsYOibT4HHYvDSQ/W4NbPTsGTmqFqbna2GEJIYQQFcbQexWDfhuuWLHirq+TkpLIzc3F3t4egPT0dCwtLXF2di5TkrCwsJDz588zZ86cO9eUSiV9+/bl1KlT9+xz6tQppk+ffte1xx9/nJ9//rnEeTIyMlAoFHfi/V+LFy9mwYIFBsct7s/W3ITVz7Xh6XUn2X8ljm5n6vJ8p38nZw010H8gp+NO8/Otn5n11yy2D9yOm/W9D7o2b9QI1/fnEzd7Dsmff45Fq1ZY9+hu0DyTvJ25lJXL/qQMxl0N4/f2jXA1M7lvH1NzNf1fa87Oj88RdT2VcwfC6ThQzlcSQgghjhw5AsDYsWNZuXJllU1mVjcFt26RvHYdAC5z51bpBOE/hUo0ej0DneyY5O1s7JDISs3nl5UXyc/W4Oxjw8CJkiAUQggh/mHQduOwsLA7r48++ojWrVsTFBREamoqqampBAUF0bZtWz744IMyTZ6cnIxWq8XF5e4nii4uLsTHx9+zT3x8fJna5+fnM2vWLJ577rkSb07nzJlDRkbGnVdUVFSZ3of4t1Ze9szq3xiABXuvcSM+64HGm9tpLk0cm5BWkMb0P6dToC0osa390KHYDx8Oej2xb72FxsDzKBUKBasae9PIypzEwiLGXQ2jQKcrtV8dD2seeaERAGf3hxF53bDtykIIIURt8PXXX0uC8CHRa7XEzX0XvUaDda9e2A580tghlei/C5U0sjJnZWPvB9pZ8jDkZRXyy8qLZKcV4OBqWbyC0EIShEIIIcQ/ynwm4bx581i9ejWNGjW6c61Ro0asWLGCd99996EG96A0Gg3Dhw9Hr9ezbt26EtuZmZlha2t710s8uHHd/XikkRMFRTombgskt7DkwiOlMVebs6L3CuzM7LiacpWPz3x83/Yuc9/BvGlTtOnpRE+dhq7QsPMCrdQqvmnuh51axfnMXObejDGoX6PObjTt4Q56OLTpOpnJeQb1E0IIIWqDc+fO8fbbbzNy5EiGDRt210sYLm3rVvIuXUJpZYXrgveNnnS7n3dD/qtQSXPjFyopzC9i7+pLpCfkYu1gxqDJrbGwNi29oxBCCFGLlDlJGBcXR1HRv5M9Wq2WhISEMo1Vt25dVCrVv/olJCTg6up6zz6urq4Gtf8nQRgREcGhQ4ck8WcESqWCpc+2wtnGjJDEbObsvmJQ5eCSeFh7sKTHEhQo2HlzJz+F/FTy3GZmeKxaidLOjvzLl0n8eInB8/hZmrGuqQ8K4Lu4FLbEJBvUr8fwBjh525Cfo+HAuisU5pc/KSqEEELUFD/88ANdu3YlKCiIn376CY1Gw7Vr1zh8+DB2dnbGDq/aKIyKInHFZwA4v/UWJiXcK1cF38Ym823s/xcq8bM0bqGSIo2WA+uukBSZhbm1CYOntMbGUSprCyGEEP+rzEnCPn368PrrrxMYGHjn2vnz5xk/fjx9+/Yt01impqa0a9eOgICAO9d0Oh0BAQF06XLv4hRdunS5qz3AoUOH7mr/T4IwJCSEP/74gzp16pQpLvHw1LU2Y/VzbVArFey5GMtXJ8IfaLxuHt14s3VxMZIP//6QaynXSmxr6umJ+5LiFYdp27aRsXdfiW3/16N1bHnHv/jcw7khMZxJzy61j9pExRNvtMDCxoSUmGwCNgeh15U/KSqEEELUBIsWLWLFihXs3bsXU1NTVq5cSXBwMMOHD79nQTnxb3q9nrj33kOfl4dlx47YD3/W2CGV6FxGDu/8ZyfGHH83Hq1j3Af1Oq2OQ5uuE3MjDRMzFYMmtcLBtfTidEIIIURtVOYk4VdffYWrqyvt27fHzMwMMzMzOnbsiIuLCxs3bixzANOnT+fLL79k8+bNBAUFMX78eHJychg7diwAo0ePvquwyZQpUzh48CDLli0jODiY999/n3PnzjFx4kSgOEH4zDPPcO7cObZu3YpWqyU+Pp74+HgKDdxyKh6uTv51mPtkEwAWHQji79AHO7Pv1Zav8ojnIxTqCpl+ZDrp+ekltrV55BHqjH8DgLj33qMgJMTgeSZ6OzPIyR6NXs+4a+HEFZT+82PjaM4Tb7REqVYQeiGJs/vDDJ5PCCGEqIlu377Nk08Wn51nampKTk4OCoWCadOmsWHDBiNHVz3oMjPR5xegMDfH7YOFKJRlvoWvFEmFGl65Go5Gr+fJKlCoRK/X8+fWG4ReTEKpVjBgQkucfWR3kRBCCFGSMt9hODk5ceDAAYKDg9mxYwc7duwgKCiIAwcO4Oxc9huBESNGsHTpUt577z1at27NxYsXOXjw4J3iJJGRkcTFxd1p37VrV7Zt28aGDRto1aoVO3fu5Oeff6Z58+YAxMTE8MsvvxAdHU3r1q1xc3O78zp58mSZ4xMPx0tdfXmqjQdanZ6J2wKJyyj/mX1KhZKPenyEt403sTmxzDo2C61OW2J7p4kTseraBX1eHtGTJqPNyDBoHoVCwWeNvWhiZU5SYRHjroYbVMjErZ4djzz/TyGTcG6dTzTsjQkhhBA1kIODA1lZxQXMPDw8uHr1KgDp6enk5uYaM7RqQ2Vnh8/W7/DdthVTHx9jh3NPOr2eSdcjiS/U0MDSrEoUKjm1+zZBJ+NQKODxV5rj2cjBqPEIIYQQVZ1CX85D4goLCwkLC6NevXqo1TWrKlhmZiZ2dnZkZGTIWYYPUV6hlqfXneR6XCatvOz58fXOmD3AIdY3027ywv4XyNfm81rL15jUZlKJbYtSUwl7+hmK4uKw6tYNry/WozDw5zY8r4D+526SXqTleTdHljXyMuim9/iOEC4FRKE2UTLsrXY4edsY/N6EEEKI+6lO9yrPP/887du3Z/r06XzwwQesXr2aIUOGcOjQIdq2bcvu3buNHeIDq06fR0VZER7PkrB4LJQKDrRrSBNrC6PGE/hbBKd+ug3Ao6Mb06Sru1HjEUIIIYzJ0HuVMq8kzM3NZdy4cVhaWtKsWTMiIyMBmDRpEh9/fP+Ks6J2szBV8cWL7bC3NOFSVDrz95R8nqAhGjo05P2u7wOw4fIGjkQeKbGt2tERr7Wfo7CwIOfECRLKUMjE18KM9c18UALb4lL5Jtaw7dJdh9XDu6kjRRodB9ZdJjdTtrsLIYSofdasWcPIkSMBmDt3LtOnTychIYGnn36aTZs2GTk68TAcT8vi07B4ABY39DR6gvDG6fg7CcKuw+pLglAIIYQwUJmThHPmzOHSpUv8+eefmJv/f1Wwvn37sn379ocanKh5vBwtWTWyDUoF/HA2im2nIx9ovCf9n+SFJi8A8M7xd4jIjCixrXmTJv9fyOS770j7wfCf10cc/7+Qybsh0RxOySy1j1KlpN8rzbB3sSQ7rYCDX1xBqyl9u7IQQghRkzg6OuLuXpykUSqVzJ49m19++YVly5bh4CDbP6u7xAIN469HoANGuDoy0s24BQOjb6RxeEsQAK0f86ZNPymOI4QQQhiqzEnCn3/+mTVr1tC9e/e7tlw2a9aM27dvP9TgRM3Us6ETMx8vPrNv/i9XCYxMe6DxZrSbQRvnNmRrspkYMJGMgpLPHLTt1w+nqVMAiP/wQ3L+Pm3wPG96OzPC1RGtHl69Fs617NLPVTSzNGHA+BaYWqiJu53B0e9vUM4d/kIIIUS1kZmZafBLVF9avZ7x1yNIKiyisZU5ixt6GjWe1Ngcfl1/BZ1WT/12znR9qp5R4xFCCCGqmzInCZOSku5ZoOSfSnVCGGJ8r3o80dwVjVbPhO8CScoqKPdYJioTlj+yHFcrV8Izw5nx5ww0Ok2J7eu8/jq2AwdCURHRU6ZQGFHy6sP/plAo+LSRJ93trcnR6hh1OdSgiscOrlY8/kozFAoIOhnH5cPRBr83IYQQojqyt7fHwcHhvq9/2ojqa2lYPCfSs7FUKfmymS+WKuNVXc7JKGDfmksU5hXh6m9Hn5eaoFDK302EEEKIsihzxZH27duzf/9+Jk0qLhLxT2Jw48aNdOnS5eFGJ2oshULBp8+2IiQxm1uJ2by5NZCtr3bCpJw3l3Ut6rLm0TWM/nU0p+NP89HfHzG/y/x7Jq4VCgVuH35AYWQk+ZcvEzV+Ar7bf0BlU3phEVOlkk3NfRkYGEJIbgEvXg7j5zb1sS6lAIt3szp0fbo+J3be4sTOEBxcLfFuZtztOEIIIURFOXKk5HOCRc3wZ2omn0UkAPBpQ08aWJmX0qPiaAq0HFh7mazUfOycLRgwoQVqk/IXxxNCCCFqqzJXNz5+/DhPPPEEo0aN4ptvvuH111/n+vXrnDx5kqNHj9KuXbuKirXSSIW6ynM7KZuha06QVVDEi519WDik2QOtSP0r+i8mHZ6ETq9jRrsZvNT8pRLbahITCX92OEUJCVj16IHXurUGVzyOzCtgwPkQkjVFPOpow5YW/qhLeVqt1+s5/G0wwSfjMDFT8dSMtlLxWAghRLnIvUrVUts+j7iCQvqevUmKpogX3evwaSMvo8Wi0+n5df0Vwi8nY25lwtOz2mHvbGm0eIQQQoiqqMKqG3fv3p2LFy9SVFREixYt+P3333F2dubUqVM1IkEoKlc9J2uWj2iNQgHf/h3B1yfCH2i8np49eav9WwAsP7+cgMiAEtuaODvjufZzFObm5Bw7RuKnnxo8j7eFGVta+mGhVHA4NYu5IdGlnjWoUCh45PlGeDRyQFOgZd+aS2Qml36uoRBCCFET5ObmEhwczOXLl+96ieqlSKdn/LUIUjRFNLM2Z2F9D6PFotfrOf5jCOGXk1GplQyY0FIShEIIIcQDKPNKwtqgtj0Nrgo2/HWbRQeCUShgw4vteaypS7nH0uv1fHT6I7bf2I6F2oJv+n9D0zpNS2yfefA3YqZOBcD1g4U4PPuswXMdSEpn3NVw9MD8eu6M9/73eZ3/qyCviJ+WBpISk429iyVPv9UOc2sTg+cUQgghqtO9SlJSEmPHjuXXX3+95/e1Wm0lR/TwVafP40F9dDuW1ZGJWKuU/N6+Ef6WZkaL5eIfkZzYeQuAx19tTv12pd+HCSGEELVRha0kVKlUJCYm/ut6SkoKKpWc/SHK59Ue/jzfyRu9HiZ/f4GrMSVXKC6NQqFgdsfZdHXvSl5RHpMCJpGQk1Bie9v+j1N30kQA4hcsJOfUKYPnGuBkz/x67gAsvB3LvsT0UvuYWagZOLEV1g5mpCfksn/tZYoKq/9fkIQQQoh7mTp1Kunp6Zw+fRoLCwsOHjzI5s2badCgAb/88ouxwxNl8GdqJqsji/8esLyxt1EThLcvJHJiV3GCsOuw+pIgFEIIIR6CMicJS1p4WFBQgKmp6QMHJGonhULBgsHN6NGgLnkaLS9/c5bY9PJvxVUr1SzttZR6dvVIzEtk0uFJ5GpyS2xfd8IEbAcMKK54PHES+devGzzX615OvORRFz0wMSiCwIycUvtYO5gxcFIrzCzVxIdmcOir6+h0sqhXCCFEzXP48GGWL19O+/btUSqV+Pj4MGrUKD755BMWL15s7PCEgZILi5gcFAnAGPc6DHa2N1os8WHF907ooXkvD1o/ZrwzEYUQQoiaxOAk4apVq1i1ahUKhYKNGzfe+XrVqlWsWLGCN998k8aNG1dkrKKGM1Ep+fyFtjRysSExq4CXvzlLdkFRucezMbVhdZ/VOJg5EJQaxJxjc9Dpdfdsq1AocFu8CMuOHdHl5BD52usURkcbNI9CoeDD+h70cbQlX6fnxSthhOYWlNqvjrs1T7zRAqVaQejFJI7/GFLquYZCCCFEdZOTk4Ozc/EqLwcHB5KSkgBo0aIFgYGBxgxNGEiv1zMtOJLEwiIaWprzvhHPIcxOK+DXdVfQanT4tKhDj+ENHqjonRBCCCH+n8FJwhUrVrBixQr0ej3r16+/8/WKFStYv349ubm5rF+/viJjFbWArbkJX43tgJONGcHxWUzcFkiR9t6JPUN42Xix8tGVmChNOBx1mM/Of1ZiW6WZGZ6fr8GsUSO0yclEjXuFotRUg+ZRKxVsaOZDC2sLUjRFPHvxFrH5haX282joQN+Xis9LvPJnNBd+jzRoPiGEEKK6aNSoETdu3ACgVatWfPHFF8TExLB+/Xrc3NyMHJ0wxDexKRxKycRUoWB9Mx8sVGXejPRQFBVq+XX9ZXIzC6njYUW/cc1QGikWIYQQoiYy+LdqWFgYYWFh9OrVi0uXLt35OiwsjBs3bvDbb7/RqVOnioxV1BIe9hZsGtMecxMlf95IYsHe6w+0wq6NcxsWdlsIwNfXvmbLtS0ltlXZ2OC1YQMm7u4URkQQ9fob6HJK3z4MYKVWsa2VP/4WZsQUaBhx6TbJhaWvhGzQ3oVuz9QH4NRPt7l5Jt6g+YQQQojqYMqUKcTFxQEwf/58fv31V7y9vVm1ahWLFi0ycnSiNME5eSy4FQPAu/XcaGptYZQ49Ho9h78NJjEiC3MrEwaMb4mpudoosQghhBA1lVQ3vofaVKGuKjt4NZ7xW8+j18O8gU0Z193vgcbbeGUjKwNXAvBR948YXG9wiW0LQsOIeP55tOnpWPXogdfaz1GYGFaBODq/kMGBIcQWaGhpY8Gu1vWxUZde1Of4jhAuBUShVCkYOKkVXo0dDXtjQgghap3qfK+Sm5tLcHAw3t7e1K1b19jhPBTV+fO4n3ytjgHnb3I9J5/ejjZsbemP0khbewN/i+DUT7dRKhUMntIaj0YORolDCCGEqI4MvVcpV5IwOjqaX375hcjISAoL795SuXz58rJHW8XU1Bu96mjjsVA+3B+EQgFfjGpHv2au5R5Lr9fz6blP+fb6t6gUKlb2Xkkvr14lts+7eJGIl8aiz8/HbsgQ3D5ebPCZNyE5+Qy5EEKqRksXeyu2taxX6tYcvU7P75uucet8IiZmKoZMbYOLn/z8CSGE+De5V6laaurn8V5IDBuik6hjoubPjo1wMjXsgenDFn45mf3rLoMeej3fiOY9jXcmohBCCFEdGXqvUuZDPAICAmjUqBHr1q1j2bJlHDlyhK+//pqvvvqKixcvPkjMQvzLuO5+jOrsjV4Pk3+4wLlww84IvBeFQsHM9jMZ5D8IrV7LjKMzCEwo+cB0i9at8fhsBahUZOzZQ1IZEuANrMz5oVU9bFRKTqXn8Nq1cDSlVC9WKBX0eakJHo0c0BRo2bv6IklRWQbPKYQQQlRFTz/9NEuWLPnX9U8++YRnn33WCBEJQxxOyWRDdHGRmc8aexktQZgam8PvX10rrmTc00MShEIIIUQFKnOScM6cOcycOZMrV65gbm7Orl27iIqKolevXnKjJx46hULB+4Oa0buRE/kaHWO/Ocv12Mxyj6dUKFnQbQE9PXtSoC1gYsBEbqTeKLG9zSOP4Law+DzDlC83krql5PMM/1dLG0u2tPTHXKngUEomU4Ij0ZWycFdtomLA+Ba4+ttRkFvELysvkhpn2JmIQgghRFX0119/MWDAgH9df+KJJ/jrr7+MEJEoTVKhhinBxcXUXvaoy2N17YwSR36Ohv3rLqPJ1+LR0J7uIxoYJQ4hhBCitihzkjAoKIjRo0cDoFarycvLw9ramoULF97zKbEQD0qtUrL2hXZ08HUgK7+I0V+dISy5/IkzE6UJS3stpa1zW7I0WbzxxxtEZUWV2N7+6WE4TZ0KQMLij8nYv9/gubrYW7OxuR9qBexOSOOdkJhSi7CYmqsZOKkVTt425Gdr2PPZBTKScg2eUwghhKhKsrOzMTU1/dd1ExMTMjPL/+BPVAy9Xs/04CiSCotobGXOvHruRolDp9Xx25dXyUzKw6aOOY+/1hyVVDIWQgghKlSZf9NaWVndOYfQzc2N27dv3/lecnLyw4tMiP9iYapi45gONHWzJTm7gFEbTxOfkV/+8dQWrO6zmgYODUjOS+b1Q6+TnFfyz2+d11/D4fnnQa8n9u1ZZB46ZPBcfevYsqaJDwrgm5hkloSVXr3YzELN4MmtcXS3IjejkD0rLpKVWv73K4QQQhhLixYt2L59+7+u//DDDzRt2tQIEYn7+TommUMpmZgpFaxr6lPqmcoV5cTOW0QHp6E2UzFgfEssrP+daBZCCCHEw1Xm3/qdO3fm+PHjAAwYMIAZM2bw0Ucf8fLLL9O5c+eHHqAQ/7CzMGHzyx3xq2tFTHoeozadJjWnsPSOJbA1teWLvl/gYe1BVFYUbxx6g6zCe58BqFAocJn7DraDB4FWS8z0GWQdOWLwXENdHPi4oScAn0UksDoiodQ+5tYmDJ7SGnsXS7JS89mz4gI5GQUGzymEEEJUBfPmzeODDz5gzJgxbN68mc2bNzN69Gg++ugj5s2bZ+zwxH8Jzslj4e1YAObVc6eJtYVR4rh+PJbLR6IBeOylptT1tDZKHEIIIURtU+Yk4fLly+nUqRMACxYsoE+fPmzfvh1fX182bdr00AMU4r852Zjx7biOuNmZcysxm5e+PkNWvqb841k6seGxDTiaO3Ij7QaTDk8iv+jeK/YUKhXuixZhO+AJ0GiImTyF7GPHDZ5rjEdd5vq7AfBRaBwrw0tPFFrZmTF4Smts6piTkZTHns8ukpdd/sSoEEIIUdkGDRrEzz//zK1bt5gwYQIzZswgOjqaP/74g6FDhxo7PPEfhTodE69Hkq/T86ijDeM86holjoSwTI5+X3xedMdBfvi3cTJKHEIIIURtpNCXdkBaLWRoaWhhPLcSsxn+xSlScwrp7O/IN2M7Ym6iKvd4QSlBvPzby2Rrsuni1oVVj67CXG1+z7Z6jaZ4JeGhQyjMzPBavw6rLl0Mnuuz8Hg+/s+W47f9XJnu61pqn4ykPH5aFkhOegF1vawZOq0NZpbGqTIohBDC+KrLvUpRURGLFi3i5ZdfxtPT09jhVJjq8nncz5LQOFZEJOBoouLPDo1xNqv8+4y87EJ+/Ogs2WkF+Ld2ov/rzVEoFJUehxBCCFHTGHqvIqf/imqpvrM1m8d2xNpMzd+hqUzcdgGNVlfu8ZrUacKaPmuwUFtwKu4Ukw9PLnlFoYkJHsuWYt27N/qCAqLGTyDnzBmD55rq63pnReEnYfEsNeCMQjsnC4ZMbY2FjQnJUdnsXX2Jwvwig+cUQgghjEGtVvPJJ59QVCS/s6qyC5m5rIos3uHwcUMvoyQIdTo9hzZdIzutADtnCx4d00QShEIIIUQlMyhJ6ODggKOjo0EvISpLC087No5pj6layR9BCby98zI6XfkXxrZzacfaPmvvJAqnHJlScqLQ1BSPlZ9h1aMH+vx8ot4YT25goMFzTfJx4d3/JAqXhsezJDSu1KrHDq5WDJ7SBjNLNQlhmfyy8iIFueXfai2EEEJUhj59+nD06FFjhyFKkK/VMTkoAq0ehjrbM9jZ3ihxnN0XRlRQGmpTJU+83gIzC7VR4hBCCCFqM4O2G2/evNngAceMGfNAAVUFNWHLSG3yx/UEXv/uPFqdnuc6evHR0BYoleV/8nwu/hwTAiaQV5RHN/durHx0JWYqs3u21eXnEz1hAjknT6G0ssL766+waNnS4LnWRSay4D8HhE/1cWGWn2upT80TIzL5ZdVFCnKKqOtlzeApraXinxBC1DLV6V5l/fr1LFiwgBdeeIF27dphZWV11/cHDx5spMgenur0efyvBbdiWBeVhLOpmj87NsbRpPKTc+FXktn/+WUA+o5tSqNOpR/FIoQQQgjDGXqvImcS3kN1vtGrrfZcjGHq9ovo9TC8vScfD2tZeYnCvDyiXn+D3DNnUNrY4P3N11g0a2bwXF9EJTL/VnGicJK3M+/4u5WaKEyOzuaXlRfIy9Lg6G7F4CmtsbK7d3xCCCFqnup0r6JUlrxxRaFQoNVqKzGailGdPo//djo9m6EXbqEHtrTwo19du0qPITM5jx8XnaUgt4gWvTzo+VyjSo9BCCGEqOke+pmEOp2OJUuW0K1bNzp06MDs2bPJy8t7KMEK8aCGtPZgxfDWKBXw47lo3tp5Ge0DbD1u79qez/t8joXaghOxJ5hyeAoF2oJ7tlVaWOC1bi0Wbduiy8oi8uVx5F25YvBcr3s582EDDwBWRybywe3Stx7X9bTmqRltsbIzJTU2h5+WBZKVeu+t0UIIIYQx6XS6El81IUFYXeUUaZkcFIkeGOnqaJQEYZFGy69fXKEgtwgXP1u6PdOg0mMQQgghxP8zOEn40Ucf8c4772BtbY2HhwcrV67kzTffrMjYhCiToW08WDmyDSqlgl2B0cz48SJFD1DMpINrh7sThUfukyi0ssJrwxdYtGqFLiODyDEvlamYySueTiz6T6JwbVQi79+KNeiMwqdmtsXG0ZyMxOLqx5nJkrgXQghRdeXnywOtquKD0Dgi8gvxMDNh4X/uQSrbXz/cJDkqG3NrEx5/tTkqE6mpKIQQQhiTwb+Jt2zZwtq1a/ntt9/4+eef2bt3L1u3bkWnK38SRoiHbVArd1Y/1wa1UsHPF2OZ9uOlh5cojDnB1CNTS0wUqqyt8dq0CctOndDl5hL16mtkl+Gg9pc9nfi4oScAX0QnMTU4iqJSVkPaOVny1My22DlZkJWSz+6lgaQn5Br+BoUQQogKptVq+eCDD/Dw8MDa2prQ0FAA5s2bx6ZNm4wcXe10NDWLb2KSAVjR2BtbtarSY7h+IpagE3GggH7jmmHjaF7pMQghhBDibgYnCSMjIxkwYMCdr/v27YtCoSA2NrZCAhOivAa0cGPN821RKxXsvRTLlB8uonkIiUJzlTnHY44zKWASuZp7J+JU1sUrCq1790ZfUEDUmxPJPHDA4Lle8qjLZ429UClge3wqY6+GkVtK7DaO5jw1sy0OblbkpBewe1kgKTHZZXqPQgghREX56KOP+Oabb/jkk08wNf3/QlvNmzdn48aNRoysdsos0jItOBKAsR516eloU+kxJEVm8df3NwHoNMgfryaOlR6DEEIIIf7N4CRhUVER5uZ3P+EzMTFBo9E89KCEeFD9m7uyblQ7TFQK9l+JY9K2CxQWPViicG3ftVioLTgVd4pXf3+V9Pz0e7ZVmpnhuWoltk8+CUVFxMyYSdqOHQbPNdKtDl8398NcqeBQSiYjLt4mTVN03z5WdmY8Nb0NdTytycss5OflF0iMyCzLWxRCCCEqxJYtW9iwYQMvvPACKtX/r1hr1aoVwcHBRoysdpoXEkNsgQZfC1PeredW6fPn52j49YsraIt0+LaoQ7v+PpUegxBCCCHuzeAkoV6v56WXXmLYsGF3Xvn5+bzxxht3XROiqnisqQtfvNgOU5WSg9fieXNb4AMnCjf224idmR2Xky8z5uAY4nPi79lWYWKC+ydLsB8xAvR64ue9R8rX3xg8V7+6dvzYqh52ahVnM3MYeuEWsfmF9+1jYWPK0GltcPa1JT9Hw8/LLxB1PbUsb1EIIYR46GJiYqhfv/6/rut0OnnYXMl+S85ge3wqCmBVY2+sVJW7zViv13Pk22CyUvKxrWtOn5eaolAqKjUGIYQQQpTM4CThmDFjcHZ2xs7O7s5r1KhRuLu733VNiKrk0cYubBjdDlO1kkPXE3j923PkFZa/kmJLp5Zs7r8ZZ0tnQjNCGf3raMIywu7ZVqFS4fr+fOq8Mg6AxCVLSFq1utSCJP/oaG/Nz23q42pqwo2cfAYFhhCSc/8D382tTBgypTUejezRFGjZt+YSwX/Hle1NCiGEEA9R06ZNOXbs2L+u79y5kzZt2hghotopTVPEzBtRALzh5URHe+tKj+Hq0RhCLyahVCl4/NXmmFuZVHoMQgghhCiZQm9oxqIWyczMxM7OjoyMDGxtbY0djngIjoUk8crmcxQU6Wjrbc9XL3XA3tK09I4liM2O5fVDrxOeGY6DmQPrHltHszrN7tlWr9eT8sUGkj77DACH0S/iMns2CqVhOfqo/EJGXrzN7bwCHE1UfNfSn7a2Vvfto9XoCNgSRMjZBAA6DfGnXX8fFAp5Wi+EEDVBdbpX2bNnD2PGjGHOnDksXLiQBQsWcOPGDbZs2cK+fft47LHHjB3iA6sOn8eUoEi2x6fSwNKMQ+0bYa6q3ErCydFZ7Pz4PNoiHd2fbUCrPl6VOr8QQghRmxl6r1K5dwdCGEmPBk5sfaUTdhYmBEam88z6U8Sm55V7PHdrdzY/sZmmdZqSVpDGywdf5kzcmXu2VSgU1H3jdVzefReAtC3fEjtrNrrC+28f/oeXuSl72jaglY0FqRotz1y8zZ+p9z9vUGWi5LGxTWnTzxuA03tCObrtBroHKOAihBBClMeQIUPYu3cvf/zxB1ZWVrz33nsEBQWxd+/eGpEgrA7+TM28s814eWPvSk8Qagq0/PbltTvnELZ81LNS5xdCCCGEYWQl4T1Uh6fBonxuJmQx5qszxGXk42ZnzuaXO9LQpfxV/bILs5l6ZCqn409jojThk56f0Nenb4nt03/+mbi574JWi2X79niuWY3K3t6wuYq0vHw1jL/SslErYGkjL0a61Sm13+Uj0Rz78SbowbdlXfqNa4aJWeWeQSSEEOLhknuVqqUqfx45RVoeOXuDqPxCxnnU5aOGlZ+gC9gSRPDJOKzsTBkxryMW1uXfzSGEEEKIspOVhELcQ0MXG3aN70p9Z2viMvJ5Zt1JzoWXv7iHtak1n/f9nL7efdHoNMw4OoNdN3eV2N5+6FC8NnyB0tqa3HPnCB/5HIWRkYbNpS7eajzU2Z4iPUwNjuKD27FoS8nzt+ztSf/XmqMyURJ+OZmfV1wgL8uwVYxCCCHEg/L39yclJeVf19PT0/H39y/TWDExMYwaNYo6depgYWFBixYtOHfu3H37FBQUMHfuXHx8fDAzM8PX15evvvrqnm1/+OEHFAoFQ4cOLVNcVdnHYXFE5RfiYWbCO/6VX834xul4gk/GoVDAY+OaSYJQCCGEqMIkSShqHXd7C3a+0YW23vZk5hfxwsbTHLqeUO7xzFRmLO21lKcbPI1Or+P9U++zMnAlOv29t/Zad+uGz7atqN3dKAwPJ3zESHIvXDBoLlOlkrVNfZjm4wLA55GJvHw1jJyi+xdjqdfGmSFTWmNmpSYxPJNdn5wnIym3bG9UCCGEKIfw8HC02n//niooKCAmJsbgcdLS0ujWrRsmJib8+uuvXL9+nWXLluHg4HDffsOHDycgIIBNmzZx48YNvv/+exo1anTPOGfOnEmPHj0MjqmqO5+Rw8boZKB4B4KVunJ3EqQn5nJ02w0A2g/wxaPh/T8rIYQQQhiXbDe+h6q8ZUQ8PHmFWiZuCyQgOBGlAhYPa8GIDt7lHk+v17P6wmq+vPIlAH29+/JR94+wNLG8Z3tNYiLR4yeQf+0aClNT3D9Zgm3//gbPtzshjWnBkRTo9DS1MmdLS388ze//dD4tPoe9qy+RlZKPhY0J/V9rgXsDe4PnFEIIUTVUh3uVX375BYChQ4eyefNm7Ozs7nxPq9USEBDAoUOHuHHjhkHjzZ49mxMnTtyzUnJJDh48yMiRIwkNDcXR0bHEdlqtlp49e/Lyyy9z7Ngx0tPT+fnnnw2epyp+HgU6HY+dvcnN3HyedXVgdROfSp1fq9Gx69PzJEVm4d7AniFTW6Os5LMQhRBCCFHM0HsVSRLeQ1W80RMVo0ir452frvDjuWgAZvZryJu96z9QFeBfbv/C+yffR6PT0MSxCasfXY2Llcs92+pyc4mZ+RbZhw8D4DRjOnVeecXg+c9n5PDS1TCSCotwMlXzTXM/2tndv/JxTkYB+z+/TFJkFkqlgh4jG9K8p0fZ3qQQQgijqg73KkplcUJIoVDwv7ebJiYm+Pr6smzZMgYOHGjQeE2bNuXxxx8nOjqao0eP4uHhwYQJE3j11VdL7DNhwgRu3rxJ+/bt+fbbb7GysmLw4MF88MEHWFhY3Gk3f/58Ll++zE8//cRLL71UapKwoKCAgoKCO19nZmbi5eVVpT6PT8PiWBaeQB0TNcc6NcbRRF2p8x/fEcKlgCjMrNSMfLcj1g7mlTq/EEIIIf6fnEkohAHUKiVLnm7Jm73rAbD095u8vfMyBaVs372fwfUGs7HfRhzMHAhKDeL5/c9zLeXaPdsqLS3xXL0Kh9EvApC0bDnx781Hr9EYNFc7Oyt+bdeQZtbmJBUWMeziLXYnpN23j5WdGU/NaEv9ds7odHqObrvBn9tuoC2SysdCCCEeHp1Oh06nw9vbm8TExDtf63Q6CgoKuHHjhsEJQoDQ0FDWrVtHgwYN+O233xg/fjyTJ09m8+bN9+1z/Phxrl69yk8//cRnn33Gzp07mTBhwp02x48fZ9OmTXz55ZcGx7J48WLs7OzuvLy8vAzuWxmCsvNYFZEIwKKGHpWeIAy/nMylgCgA+oxpKglCIYQQopqQlYT3UB2ezouHb8upcN7/5Ro6PbT3cWD9i+2oa21W7vGisqKYFDCJ2xm3MVeZs7jH4vtWPk799jsSFi8GnQ6rrl1xX7YUdSnnLP0jp0jLhKAIfkvOBGCajwtv+bmivM+KRL1eT+BvEfy9JxT04Fbfjv6vtcDSVg4UF0KIqq423quYmprSvn17Tp48eefa5MmTOXv2LKdOnbpnn379+nHs2DHi4+PvbHfevXs3zzzzDDk5ORQVFdGyZUvWrl3LE088AVDtVxJq9XoGng/hQlYuj9e15Zvmfg+0Q6KsstMK2P7hGfJzNLR81JMewxtW2txCCCGEuDdD7x0r97GiEFXY6C6++Nax4s1tgZyLSGPImhN8Obo9Td3Ld7PvZePFtwO+5a2jb3Ei9gTT/pzGlLZTGNd83D1v1h1fHIWJhwcxM2aQc/Ik4c88i+fqVZg3bVrqXFZqFV8192NRaByfRyayIiKBGzn5fNbEG9sSDilXKBS06+9LHQ9rDm26RtytDHYsPsuA8S1x8rYp13sWQggh7iUgIICAgIA7Kwr/W0mVhv+Xm5sbTf/nd2KTJk3YtWvXfft4eHjcdR5ikyZN0Ov1REdHk5OTQ3h4OIMGDbrz/X/iU6vV3Lhxg3r16v1rXDMzM8zMyv8gsSJtjE7iQlYuNiolHzf0rNQEoV6n549vrpGfo6GulzVdn6pfaXMLIYQQ4sHJdmMh/kvPhk78/GY3/OtaEZOex9PrTnLwany5x7MxtWFNnzU83/h5AFYGruTdE+9SqC28d/tHe+P7w/eYeHujiYkh/Lnnydizx6C5VAoF8+q581ljL0wUCg4kZ/D4uRtcy867bz/fFnV5ZnZ77F0syU4rYPen5wk5W/5qz0IIIcR/W7BgAf369SMgIIDk5GTS0tLuehmqW7du/ypycvPmTXx8Si7I0a1bN2JjY8nOzr6rj1KpxNPTk8aNG3PlyhUuXrx45zV48GB69+7NxYsXq9w24tJE5BXwcWjxfcv8+h64mVXu7oBLh6OIuZGO2lTJ4680R2Uif9UQQgghqhPZbnwPtXELj7hbRq6Gid8HciwkGYDpjzVk0qMPVtDkh+Af+PjMx2j1WprXac6yR5bhbu1+z7bajAxi3n6bnKN/AeDwwgu4zHobhalhN/uBmTm8ejWc/2vvPsPjqM6/j39n+0orrXqzZcm94N7BdAwOBocSahwgkJBmIKQ8fyChE2pCCSEhQAIECJhqikMzzRhs3I17L7LVZfWVtHWeF7KFhWQjS7K1sn+f65pLu2fOzJw9Y0u3bp0zJ98fxGUxuHdATy7NTD7gMf66IB/+ey15a3YDMHpKDhPO6YPFcvhGIIiISNt0p1glMzOTBx54gMsuu6xD51m8eDHHHXccd9xxBxdddBGLFi3i6quv5sknn2T69OkA3HTTTeTn5/Pcc88BUFtby+DBg5k4cSJ33HEHZWVl/PSnP+Wkk07a7zMI2zLd+Nui4X6YpslFX29hXkUtxyV4eH1k38M6inB3fi2v3LuYSMjk5OkDOeYELYomIiISLbRwiUgHeGPsPPPjcVw5KReAh+Zs5NqXllMfaP+CJpcMuoR/nPYP4h3xrN69motmX8S8XfNarWv1esl+/HFSZswAoOK//2XHFT8mWFzSpmuNjo9lzriBnJoUR0PE5Dfrd/Kb9XnUh/e/OIkzxs5ZM4YzekovAJZ9sIN3Hl1BXXXrox5FRETaIhAIcNxxx3X4POPGjWPWrFm89NJLDB06lLvuuotHHnmkKUEIUFhYSF5eXtN7j8fDnDlzqKysZOzYsUyfPp1p06bx6KOPdrg90eblonLmVdTishg8ODD7sCYIw8EIc55eSyRkkjssmSHHt/5HUBEREYluGknYimj4a7BEj5cW5XHLm6sJRUyG9fDy5OVjyPS6232+/Np8fvfZ75pWPP7Z8J/xqxG/wmpp/dmBNZ9+SsH/3UCkpgZrago9H3mEmDFj2nStiGny6I5iHthWRAQ4xuPiX8f0pnfMgZ+jtHFxEZ8+v55QIEJMvIPTrxpCz0FJB/U5RUTk0OlOscoNN9yAx+Phlltu6eqmHDJdfT92B0KcsGgd5cEwN/fJ5Jqc9MN6/flvbGb5h3m4PHYuvXWCFkETERGJMm2NVZQkbEVXB3oSfRZu3c0v/7uMcl+A5FgHD188khMHpLb7fIFwgAcWP8DLG14GYGLmRO4/8X6SXK0n4gLbt7Pr2uvwb9oENhvpN95I4vQftnmUwLzyGn6xdge7gyHirBb+OrgXU1MTDnhMeYGPD/61mvICHxgwdmou487qrenHIiJRoDvFKr/+9a957rnnGD58OMOHD8dutzfb/9BDD3VRyzpPV9+P69flMbOonMGxLj4cOxD7YfxZnb+xgjcfXg4mnPmLYfQZ2f74SERERA4NJQk7oKsDPYlOO8vr+PnzS1lbWI1hwDWn9OP6yQOwdiAQn711NncuuJP6UD1pMWk8eNKDjEwb2WrdiM9H4S23UP3uewDETZlC5p13YN1nxcYDKfQH+PmaHSyq8gHwi+xU/tAnE4dl/08dCAbCzHt5I+u+LASgx4AETv/JMcR6o3NFRxGRo0V3ilVOOeWU/e4zDINPPvnkMLbm0OjK+7Ggspbzlm8G4J3R/RnnjT1s1/bXh5h510Jqy/0MnpTJqZcNPmzXFhERkbZTkrADulPgLYdXQzDMnbPX8uLCxucdTeyTxKOXjCIt3tXuc26u2MxvPvsN26u3YzNs/Hbsb/nR4B+1OkrQNE3Kn/0PJQ8+CKEQtsxMevz5AWLGjm3TtYIRkz9tLeCJnaUADPO4eWxIDgNjD9z+DQuL+OzFDYT8Ydxxdk6/8hiyh2j6sYhIV1GsEl266n4EIhFOW7yBTXV+LstK5s8DD+9qzB89u5YNXxURn+Li4pvH43DZDuv1RUREpG20cInIIeCyW7nnvGH89ZKRxDqsfLW1nKmPfsGXm8vafc5+if2YefZMvpf7PUJmiAcWP8BvP/stlQ2VLeoahkHylT8m96WXsOf0IlRYyI7Lr6D00UcxQ6HvvJbdYnBHvx48MzSXJLuVVbX1TFmygX/tKiVygL8XDJyQwUU3jSW5h4f6miBv/20FX721hcgBFkIRERGRQ+ufO0vZVOcn2W7jD30yD+u1Ny8tYcNXRRgGTP7xECUIRUREjgAaSdgK/XVe2mJLaS0z/ruM9UU1GAb8+rT+XHtq/3ZPPzZNkxfXv8hflvyFUCREqjuVP036E8f1aH1FyIjPR9Hd91D1xhsAuEeOJOsvf8bRs2ebrlfsD3L9+jw+La8B4KTEOB4ZnE2mc/8PGw8Fwnzx6ibWzCsAILOvl9N+PARvavsXchERkYPXHWKV888/v0313tjzc6w764r7saPez0mL1tMQMXlscC8uyDh8I/x9VX5eunMhfl+IMd/LYeK5fQ/btUVEROTgaSShyCHWN9XDmzMmccm4bEwTHvloE5c/vZDSGn+7zmcYBtMHT+eFM18gNz6X0vpSfv7Rz7l34b00hBpa1LfExpJ1z91kPfgXLB4P9StWsO3c86ia/b82XS/daefF4X24p38PXBaDuRU1nLJoA2+XVO73GJvDysnTB3HGT4/B7rJSuKWKmX9axOrP89HfG0REZF9er7dNmxw80zS5ceMuGiImxyd4+EF64mG99ifPrcPvC5HaK45xZ/c+bNcWERGRQ0sjCVvRHf46L9Fl1vJd/OGN1dQHw6R4nDxwwTBOHZTe7vPVh+p5aMlDzNwwE4A+3j7ce8K9DEke0mr9wK58Cv7f/6N++XIAvOecQ/ott2D1tO3h5Zt8DcxYt4OVNfUAXJCeyD0DehJvs+73mOqyej7+zzoKNlUC0GtIEqdcNhhPohY1ERE51BSrRJfDfT/eLqnkZ2u24zAMPhk/kH4x7X828sFa9dkuPp+5EavdwkU3jSMp6/AtlCIiIiLto5GEIofReaN68s61kxiQ7qGs1s9Vzy7hxtdXUuv/7ucEtsZtc/PHiX/k8cmPk+JOYWvVVqa/O51/rfoX4Ui4RX1Hzx7kPP8cKTNmgMVC1Vtvse2cc/DNn9+m6/WPdTF7dH+uz0nHArxWXMGpi9fzRUXNfo+JT3Fz7m9GMemCflhtFvLWljPzroVsWFikUYUiIiKHSE0ozC2bdgFwbU7aYU0QVhbXMf/1xpWUjz2vrxKEIiIiRxglCUU6Sb+0ON6+5nh+enxvDANmLt7J9x75nK+27m73OY/vcTxvfP8NJveaTCgS4q/L/spVH1zFrppdLeoaNhup115DzvPPYc/KIpifT95VP6Hg5psJV1d/57UcFgs39snkzVH96OVysKshyAUrtvCb9XlUBltPdhoWg5GTe3HRH8eRlhOHvy7ER8+s5f0nV1NfE2j35xYREZHW3be1kOJAiD5uJ9f2av+shYMViTROMw4FI/QclMjwk9v2DGQRERHpPpQkFOlELruVm88ewktXT6RnoptdFfVc+tRX/Gn2WhqCLUcAtkWiK5GHTn6IuybdRYwthmUly7jgnQt4ef3LRMyWqwvHjBlDn3feJnH6dACqXnudrWdPo+bTT9t0vfEJHj4ZN5Af90gB4KXCck5YtJ63Syr3O0IwKTOWH/zfGCZ8vzcWi8HW5aW8dOdCtq4obddnFhERkZZWVNfxTH4ZAPcN6InLevhC+VWf7qJwSxV2p5VTLhuE0c6F2kRERCR66ZmErdBzfqQz1PpD/Gn2WmYu3glAvzQPD100guE9E9p9zp01O/njF39keUnjswdHpY3itmNvo29C66sK1i1ZQuEfbyawYwcA8dOmkf6Hm7Altu0B5wsra/n9hp1sqmtcjGVKSjz39u9Jlmv/KyCX5tXw0bNrKS/wAdB3dBonXNSf2AQ9q1BEpLMoVokuh+N+hE2TM5duZGVNPeenJ/KPITmH5DqtqSyp4+W7FhEKRjjphwMZemKPw3ZtERER6bi2xipKErZCgbd0pk/WF3PD66sorfFjtRhcc0o/rjm1H/Z2/vU/HAkzc8NM/rrsr9SH6rFZbFw97Gp+OuynOKwtk3eRhgZK//Y3yp95FiIRrElJZNx6C3FTpmAY3z0KwB+J8NcdxfxtRwlB08RjtfDHvllckZWMZT/Hh4MRFs3eyvI5OzEjJg6XlQnn9GXoST2waOSBiEiHKVaJLofjfvxrVyk3b8rHa7PyxYRBpDrsh+Q632ZGTGY9tIzCzVX0GJjIOdePbFP8ICIiItFDScIOUOAtna3CF+Dmt1bzv5WFAAzKiOPu84YyJiep3ecsrC3k7oV3M3fXXAB6e3tz+7G3Mzp9dKv161eupPCPf8S/qfGB43GnTyb9D3/AnpnZpuutq63ndxt2sqy6DoDx3lj+PDCbgbH7f2B66c4aPvvvBkq2Nz4TMS0njpOnDyK1V1ybP6eIiLSkWCW6HOr7URkMMXbBWmrDER4Y0JPL9zwS5HBY+elO5r28CZvTyqW3jCc+xX3Yri0iIiKdQ0nCDlDgLYfK218XcNtbq6moCwJw6fhe3Pi9QXhj2jcawDRNPtjxAfctvI/dDY0LpFw44EKuH3M98Y6W/3YjgQC7//kEZU8+CaEQhttNys9/TtJVV2Jx7H8K8V5h0+SZ/DLu2VpIXTiCzYCf9kzlt7kZxNusrR4TiZis+Tyfr97cQqAhjGHA8FOyGf/93jhctnZ9bhGRo51ilehyOO7HZ+XVvFJUwWODe+13JH9nqyqtZ+ZdCwkFIpx4yQCGabESERGRbklJwg5Q4C2HUrkvwH3vreOVJY0rFKd4HNx81hDOGZnV7uk7Vf4qHl76MK9veh2AVHcq/zfu/5iS2/qU4oYNGyi66y7qlywFwJ7Ti4w//AHPSSe16Xq7GgLctHEXc3Y3jhBMsdv4Q99MLslI2u8vLr4qP1+8uonNS0oA8CQ6OeGiAfQemaJpSyIiB0mxSnQ5Eu+HGTF565Hl5G+spMeABM65fpQWKxEREemmlCTsgCMx0JPos3Drbv745mo2l9QCcHy/FO46dyi9U2Lbfc7FRYu5c8GdbK/eDsDotNHcOP5GBicPblHXNE2qZ/+PkgceIFTauAqx55RTSL/pRhy9erXpeh/vrubWTflsqW9c2GREnJu7+/dkrHf/nyFvzW7mvrSB6rIGAHodk8ykC/qRlNn+zy0icrRRrBJdjsT7seqzXXw+cyM2h4VLbpmAN1XTjEVERLorJQk74EgM9CQ6BUIRnpq3lUc/3oQ/FMFhszDj5H784uQ+OPczffe7+MN+nl79NE+vepqGcAMGBuf3P59rRl1DirvlM4zCtT7KHv8H5f95rnEKssNB8k9/QvLVV2Nxf/cvBIFIhH/vKuPB7UXUhiMAXJCeyM19s8hwtj6NOhQIs+Td7Syfk0ckbGJYDIaekMW4ab1xe7572rOIyNFOsUp0OdLuR3VZPS/dtYiQP8wJF/dn+CnZXd0kERER6QAlCTvgSAv0JPrt2O3j5jdXM29TGQC5yTHcNHUwZwxJb/dU3CJfEQ8vfZh3t70LQKw9ll8M/wXTB0/Hbm2ZvPNv3Urxn+7GN38+ALasTNJ+9zvizzwTw/LdKzGXBoLcs7WQlwrLAYixWvhNTjpX90zFtZ+VnCuKfMx/YwvbVzZ+bofbxrizchl2ck+stvat/iwicjRQrBJdjqT7YZombz2ygvwNFWT283Leb0drmrGIiEg319ZYJSp+C//73/9Obm4uLpeLCRMmsGjRogPWf/XVVxk0aBAul4thw4bx7rvvNtv/xhtvcMYZZ5CcnIxhGKxYseIQtl6k43KSY3nuqvH87dJRpMY52b67jp8/v5RLnvyK1flV7TpnRmwG9594P8+f+TzHJB+DL+jjwaUPcu5b5/LZzs/49t8HnH36kP3vf9Hjb49iy8okVFBIwe9+z/YLLmxKHB5IqsPOw4N68d6YAYyJj6EuHOHurYUct3Ad/y3YTSjS8u8RiRmxnPWr4Xz/+pEk9/QQqA/x5WubefGOhWxdXtqijSIiInJorZlXQP6GCmx2C6dePlgJQhERkaNIlycJX375ZX77299y2223sWzZMkaMGMGUKVMoKSlptf78+fO59NJL+clPfsLy5cs599xzOffcc1m9enVTHZ/Px/HHH8/9999/uD6GSIcZhsG0EVl8+vuTueaUfjhtFhZuK2faY1/w21dWUFhV367zjkwbyYtnvchdk+4ixZ1CXk0e135yLT+b8zPW7F7Tog3xp59O3//9j9RfX4clNpaGtWvJu+on5F31E+rXrNnPVb4xKj6Gd0b352+De9HDaafAH+R3G3Zy8uL1vF1SSaSVxF/2oCQu+sM4TrlsEO54B9Wl9bz3xCrefGg5pXk17frcIiIicnCqd9cz//XNAEw8ty8JaTFd3CIRERE5nLp8uvGECRMYN24cjz32GACRSITs7GyuvfZabrzxxhb1L774Ynw+H7Nnz24qmzhxIiNHjuSf//xns7rbt2+nd+/eLF++nJEjR7a5TUfSlBHpvvIr6/nz++t5c0UBAC67hZ+d2Jefn9iHWKetXef0BX08tfIpnlv7HMFIEIDJvSYzY+QM+iX2a1E/VF5O2T//ScVLMyHYWD/+rLNIvf7XOLK/+/lEDeEIzxWU8ciOYsqDYQCGe9zc1CeTk5PiWp1KHWgIseyDHaz4aCfhYOMzDvuOTmP8tN5a3EREZA/FKtHlSLgfpmky+7GvyVtTTmZfL+f9TtOMRUREjhTdYrpxIBBg6dKlTJ48uanMYrEwefJkFixY0OoxCxYsaFYfYMqUKfut3xZ+v5/q6upmm0hX65Hg5pFLRvHmjEmMzUmkIRjh0Y83ccpfPuOVxTsJtzJ997vE2mO5fsz1vHXuW0zrMw0Dg4/yPuL8t8/npnk3kVed16y+LSmJjD/8gb7vvUv8tGkAVP/vf2yZehZFf7qb0O7dB7yey2rhZ9lpLJo4hN/nZuCxWlhZW8+lK7fygxVbWFrla3GMw2Vj4jl9mX7HRPqPSwdgy7ISZt65kDnPrKGypO6gP7eIiIgc2KYlxeStKcdqs3DKZYOUIBQRETkKdWmSsKysjHA4THp6erPy9PR0ioqKWj2mqKjooOq3xb333ovX623astswQkrkcBmZncCrvziWx6ePpldSDCU1fv7v9ZWc/vBc3lqR365kYXZcNveccA+zzpnF6TmnY2Iye+tsvv/m97l9/u0U+Zr/f3L07EmPPz9A7zdeJ/b44yEYpOKFF9g8+XSK73+AUGnpAa/nsVn5fe8Mvpo4hJ/3TMVpMZhfWctZyzbxo5VbWdJKsjAuycUZPzmGi28eT+8RKZgmbFxYzIu3L+ST59dRvbt9069FRESkuQZfkC9e2QTA2Kk5JGZo5L6IiMjRqMufSRgNbrrpJqqqqpq2nTt3dnWTRJoxDIMzh2Uy57cn8sepg0mIsbO11MevZ65gyiOfM3tlAZF2JAv7JvTloZMfYubZMzm+x/GEzTCvb3qds944i/sX3U9ZfVmz+q4hQ+j1r6fo9czTuIYOxayvp/yZZ9g8+XSK/nQ3we9I1qc4bNzRvwfzJwzmh5lJWICPdldz9rJN/GD5Zj4vr2mxWElKTw9TfzmcC28aS87QZMyIybovC/nvrV8x98UN1Fb4D/pzi4iIyDfmv76Z+pogiZmxjDojp6ubIyIiIl2kS5OEKSkpWK1WiouLm5UXFxeTkZHR6jEZGRkHVb8tnE4n8fHxzTaRaOS0Wbn6xD7M+79T+P0ZA4h32dhcUss1Ly7nzL/O4/3Vhe1KFh6TfAyPT36c5858jrHpYwlEAryw7gWmvDaFOxfcyc7q5onz2GOPJffVV8h+4p+4R4zA9PupeOEFtpx+BoW33U5gV/4Br9fD5eChQb34Yk+y0G4YfFlZy0Vfb2Hq0k28X1rVYoGTtJx4zr5mBD/4vzH0HJRIJGyy+vN8XrhlAXNf2kBVqUYWioiIHKz8DRWsm18IwCnTB2K1aQyBiIjI0SoqFi4ZP348f/vb34DGhUt69erFNddcs9+FS+rq6njnnXeayo477jiGDx+uhUvkqFPdEOTpL7bx73nbqPGHABiSGc9vTh/A5MFprS4M8l1M0+Srwq/4x4p/sKJ0BQAWw8KUnCn8ZNhPGJg0sEX9ugULKPvH49QtWdJYaLPh/f73Sfn5z3DkfPeIhPyGAI/vLOG/Bbup35PkHBTr4rqcdL6fmoCtleci5W+sYOHbWyncXAWAYUC/MWmMOiOH1F5xB/25RUS6E8Uq0aW73o9QMMzLf1pMZXEdx5zYg5N/OPC7DxIREZFup62xSpcnCV9++WWuuOIKnnjiCcaPH88jjzzCK6+8wvr160lPT+fyyy+nR48e3HvvvQDMnz+fk046ifvuu4+zzjqLmTNncs8997Bs2TKGDh0KQHl5OXl5eRQUFDTVGThwIBkZGW0acdhdAz05elXVBfn3F1t5+svt1O5JFg7OjOdnJ/bm7OFZ2K0HPyrANE2WlSzjX6v+xRf5XzSVn9DjBH4y7CeMSR/T4pi6xYspe/xxfPP3LCRksRB32mkk/fgK3KNHf2fSsjQQ5KmdpTyTX0ZNuHFl4xyXg5/2TOWSzCTibNYWbczfWMnyD3aQt7a8qTx7cCKjzsih56DEdiVKRUSinWKV6NJd78fCt7ey5N3txHgd/PC2CThj7F3dJBERETkEuk2SEOCxxx7jz3/+M0VFRYwcOZJHH32UCRMmAHDyySeTm5vLs88+21T/1Vdf5eabb2b79u3079+fBx54gKlTpzbtf/bZZ7nyyitbXOe2227j9ttv/872dNdAT6TCF+CpeVt5dv526gJhALK8Lq46vjeXjO+Fx2lr13nXl6/n6VVP88GOD4iYjcm7UWmjuPKYKzmx54lYLc2Td/UrVlD6+OP45n7eVOYaOpSkK64g/ntTMOwH/iWkKhjimfwyntxVSnmw8XPEWi1ckpHET3qm0ifG2eKYsl01LPsgj81LSzD3jEZM7RXHqDN60XdUKpZ2JEpFRKKVYpXo0h3vR3mBj5fvXkQkbPK9nw2l7+i0rm6SiIiIHCLdKkkYbbpjoCeyr8q6AC98tYNn5++grLZxYY84l43pE3K4clIu6fGudp03rzqPZ9Y8w1ub3yIYCQLQw9ODSwddyrn9zsXr9Dar37BxIxXPP0/VW29jBgIA2NLTSZw+ncSLLsSakHDA6/nCYV4rquBfu0rZVNf4OQzgtOR4ftozhZMS41qMFKwuq2fFxztZ90UBoWBjQjMu2cXQk3ow5LgsXB6NkhCR7k+xSnTpbvfDjJjMenAZhVuqyB2ewtRfDtPIexERkSOYkoQd0N0CPZH9aQiGmbU8n6fmbWVrqQ8Au9Xg3JE9uPrEPgxIb9+z+0rrSnl+3fO8vvF1qgPVALisLs7qcxaXDrq0xXMLQ+XlVMycScWLLxEua1wx2XC58J57Dkk/+hHOfv0OeD3TNPm8opandpXy0e7qpvL+MU5+2jOVC9ITif3WVOT62gCrPstn1ae7aPA1JjStdgv9x6Yx7OSepOXo/7aIdF+KVaJLd7sfqz/PZ+6LG7A7rVx62wTiktr3x0MRERHpHpQk7IDuFuiJfJdIxOSjdcU8+flWluyoaCqf2CeJH03M4YwhGTjasZphfaied7e+y4vrX2Rjxcam8jHpY/jhoB9yaq9TsVm+meIcCQSofvddyv/zHP5165rK3aNHk3DhhcR/bwoWt/uA19xa5+ffu0qZWVSOb89zC2OtFs5LS+SHWUmMiotpNhoiGAizaXExq+fmU5pX01Se3jueYSf1oO+YNGx2a4vriIhEM8Uq0aU73Q9fpZ8Xb/+KQEOY4y/qz4hTs7u6SSIiInKIKUnYAd0p0BM5WEt3VPDk51uYs7aYPY/uI8Xj5JJx2VwyPpueiTEHfc69i5y8uO5FPs77mLDZ+BzBtJg0zut3Huf0O4fsuOxm9esWL6bi+eep+eRTCDfWt8TF4Z02jYSLLsQ1aNABr1kdCvNyYTlP55eyrT7QVD441sX0rGR+kJ5Iov2bBKVpmhRvq2bVZ7vYvKyESKjxw7s8doZMymLwpEwS0g7+s4uIdAXFKtGlO92P959cxZZlpaTlxPGDG8ZisWiasYiIyJFOScIO6E6Bnkh75VfWM3NRHjMX76S0pvF5fxYDThmYxo8m5nDigFSs7fjFodhXzCsbX+G1ja9R3vDNisPjMsZxXr/zmJwzGbftm9GCwZISqt6YReVrrxHctaup3DV8OAkXXoB36lQssbH7vZ5pmiyo9PFi4W5ml1bSsCfz6bQYTE3xMj0rmeMSPFj2GV1YVx1g7ZcFrPk8n9oKf1N5Zj8vg4/LpO/oNByu9i3yIiJyOChWiS7d5X5sW1nGu/9YiWExuOgPY0np2b7HjoiIiEj3oiRhB3SXQE+kMwTDEeasLeaFr3Ywf8vupvKeiW4uHJPN+aN7kJ108CPsAuEAn+R9wpub32R+wXxMGr/VxNpj+V7u9ziv/3kMTxneNDXYjETwLVhA5auvUfPxxxBsfI6gERND3OTT8E77PrHHTsSw7T95VxkM8UZxBf8t3M2a2oam8myXg/PTEzkvPYFBsd8kKCPhCNtX7mbNF/nsXFvO3u+GNqeVfqNSGXRsJln9EzA0ykJEooxilejSHe5HoCHES3cspLbCz+gpvTj2vAM/D1hERESOHEoSdkB3CPREDoUtpbW8uDCP15buoqo+2FQ+oXcSPxjdkzOHZRDnOvjVgYt8Rby95W1mbZrFrtpvRgv29vbm+32/z5TcKc2mI4d276bqzTepfOVVAjt2NJVbk5OJnzoV77SzcQ3b/0qMpmmysrae/xbsZlZxBTV7nl0IMCTWxXnpiZybnki2y9FUXlvhZ8PCQtYvKKKyuK6pPD7FxcCJmQyckIE39cDPSxQROVwUq0SX7nA/5r+xmeUf5hGf4uKSWydgd+h5vCIiIkcLJQk7oDsEeiKHUkMwzLurCnljWT5fbilrGmHnslv43jEZnD+6J5P6pRz0dOSIGWFp8VLe3Pwmc3bMoT5U37RvWMowpuROYUruFDJiM4DGZF/98hVUz36H6vfeJ1zxzaIrjpwc4s8+G++0s3Hk5u73mnXhCB+WVTGrpIJPdtcQ3Odb3gRvLOelJzItNYFkh63pmkVbq1m/oJBNS4oJNoSb6qflxNFvTDr9xqZpJUgR6VKKVaJLtN+P8gIfL/9pEZGIyVm/Gk7u8JSubpKIiIgcRkoSdkC0B3oih1NBZT2zlufz+rJdbC31NZVnxLs4Z2QWU4dlMrynd7+j+vanNlDLhzs+5L1t77GoaBER85vRfqPTRvO93t/j9JzTSXE3/iJjBoPUfvkl1e/MpubjjzEbvplO7BwymPjTTyfu9NNx9O2737ZUBEP8r7SKN4orWFBZy95vflYDjkvwcGaKlzNTvWQ6G0cYBgNhti4vZf2CQvI3VLDvd8uMPvGNCcMxacQmOA/qs4uIdJRilegSzffDNE3eeng5+RsryR2ewlm/Gt7VTRIREZHDTEnCDojmQE+kq5imyde7qnh96S7e/rqg2XTkHglupg7LYOqwTEZmJxx0wrCsvoyPdnzEe9veY1nJsqZyi2FhXPo4Tul1Cqdkn0KWJwuAiM9HzccfU/XObHzz5zetjgzg6N2buD0JQ9fQY/bbloKGAG+VVDKruIKVtfXN9o2Ki2FqqpepqV76xjSOGKyrDrBlWQmbl5ZQsLmSpgyjAVn9Eug3Jo3eI1LwJGqEoYgceopVoks034+Ni4qY8/RabHYLl942gfgUPTpDRETkaKMkYQdEc6AnEg38oTCfri9l9soCPllfQl3gmyRdltfFmcMymTosk1HZCVgOckpyka+ID7d/yPvb32dV2apm+wYmDuSUXqdwcvbJDEkagmEYhCoqqP3kE2o+nINv/nzM4DfJS1tmJnGTJxN32mnEjBmNYW/9eYrb6vy8W1bFe6WVLKmua7ZvQIyLqalezkiJZ0RcDFbDwFfpZ/OyEjYvKaFoa1Wz+qm94sgdnkLv4SmkZHsOOmEqItIWilWiS7Tej0B9iP/e9hV11QEmfL8PY6fmdnWTREREpAsoSdgB0RroiUSj+kCYuRtLeHdVER+vK8a3T8Iw0+vitMFpnDYonWP7JuOyH9xD0nfW7OSTvE/4dOenLC9Z3mxKclpMGqdkN44wHJsxFqfVSbi2Ft/nn1M9Zw61cz/HrPsm4WeJjSX2uGOJPeEEPCeeiD0jo9VrFvuDvF9WxXulVXxRWUNon++QSXYrJyfFc2pSHCclxZHqsFNT3sDmpSVsW1FK4daqb0YYAp5EJ72Hp5A7IoUeAxKx2iwH9flFRPZHsUp0idb78cUrm/j6k51409xcessErHb9HBIRETkaKUnYAdEa6IlEu4ZgmLkbS3l3VSEfryuh1h9q2ueyWzi+XwqnDkrn1EFpZHgPblpuRUMFn+/6nM92fsaXBV82W/TEZXUxJmMMk7ImcVzWcfTx9sH0+/HNX0DNnDnUfv454d27m53POWAAnhNPIPaEE4kZParVUYZVwRAf7a7mvbIq5pbXNFslGWBEnJtTk+I5NTmeUXExBGqD7Fhdxravy9i5rpxQ4Jv6dpeV7EFJZA9JoteQJE33EpEOUawSXaLxfpTtquWVexZjRkymXTeCXkOSu7pJIiIi0kWUJOyAaAz0RLqbhmCY+VvK+GR9CZ+sK6GgqqHZ/iGZ8Zw2OI2TBqQyIjsBu7Xtoxv8YT8LCxfy6c5PmbtzLqX1pc32p8ekc1zWcRyXdRwTMyfidcTTsGYttfM+x/f5POq//pp9VyGxxMYSM24cMRMnEDthAs6BAzEszdsTjJgsrfbxye5qPi2vYdW3nmPotVmZmBDL8QlxTEr00M9up2BDJdtWlrF9ZRl11YHm9VPdTQnDHgMScbhtbf78IiKKVaJLtN0PM2Iy68FlFG6pou/oVL73s2Fd3SQRERHpQkoSdkC0BXoi3Z1pmqwvquGT9SV8vK6Y5Tsrm60UHOuwMrFPMpP6pTCpXwoD0tv+LD/TNNlUuYkFBQuYXzCfpcVL8Yf9TfsNDAYnD2Zs+ljGpo9ldPpoYusi+L6cT+3nc/HN+4JwRUWzc1q9XmLGj29MGk6ciKNPnxbtKfYH+ay8hk/Kq5lbXkNlKNxsf5LdynEJHiYlxnFcfCzesgC71pWTt7ac4q3VRCL7JCktBul94skenESPAQmk5cZjO8ip2SJydFGsEl2i7X6sm1/IJ8+tw+a0Mv32CVpUS0RE5CinJGEHRFugJ3Kk2V3rZ+7GUj5eX8L8zWVU1AWb7U+Nc3Jc32+Shj0S2j41tyHUwLLiZcwvmM+XBV+yuXJzs/0GBgMSBzA2Yyxj0scwOnUUMVuLqVu4EN/Cr6hfspRIXfPFS6ypKcSMHUvMqFG4R43GNWhgs+nJoYjJqtp6vqioYX5lLQurfNR9a2pymsPGBK+Hcd4YRrpcJOY3ULCugp1ry6kqbT4q0WqzkN47nqz+CWQNSCCjjxe7Q0lDEfmGYpXoEk33o8EX5MXbv6K+Jsix5/dl9Bk5XdoeERER6XpKEnZANAV6Ike6SMRkbWE1X24u48stu1m0bTcNweYJth4JbsbmJjIuN4lxuUn0T/O0edXkkroSFhctZknxEpYWL2Vb1bYWdfp6+zIybSTDU4czLGEImTt9NCxajG/hQuqXLcf0+5vVN9xu3MOG4R49qjFxOHIkVq+3aX8wYrKipo4vK2r4srKWxVU+GiLNv9W6LQYj4mIY741lCHZSdzVQu7GK/E2V1H9rarLFapCWE0/WgAQy+3hJ7x2PO87Rps8vIkcmxSrRJZrux9wXN7D683wSM2O5+OZxWA/icR4iIiJyZFKSsAOiKdATOdr4Q2GW7ajky81lfLG5jFX5VYS/lWDzuu2MzUlkXO8kxuUmckyWt80rJ5fVl7G0eClLipawpHhJi5GGAB67h6EpQ5uShv0LwLlqE3XLllG/4msi1dUtjnH064t76DBcQ4fiHjYU56BBWJzOxs8UibCsuo7FVT4WV/lYUuWj4lvTkwH6up2MiHMzwLCRWRYiZnMtuzdW4av0t6jrTXWT3ieejN5eMvp4SeoRq18ERY4iilWiS7Tcj5Id1bx63xIw4dzfjKLHwMQua4uIiIhEDyUJOyBaAj0RAZ8/xPK8ShZvL2fJjnKW7aikPtg8wWa3GgzKiGdEtpcRPRMYmZ1An1QP1jaMNqxoqGB5yXJWla3i69KvWV22utnKyXv18PRgSPIQBiUOon8ggZytPlwrNlG/bBmBHTtanthmwzmgP+5jhuIaNhT30KE4+/XDcDgwTZPNdX4WV3+TNNxU1zIRaAEGxLoYYnfQsyZCcn4Dzk21+ArqWl7ObiE1J4703HhSe8WR2iuOhLQYjDaOuBSR7kWxSnSJhvsRiZi8fv8SSnbU0H9cOmf85JguaYeIiIhEHyUJOyAaAj0RaV0wHGFtQTWLt5ezeHs5S3dUUFYbaFEv1mFlWE8vI7ITGN4jgSFZ8eQkxXznNOVQJMSWyi18Xfo1K0tX8nXp12yv3t5q3bSYNIYkDWFgTC69y2303ObDu2o7gVVrCJeXtzzAbsfZpw/OgQNwDRyEc9BAXAMHYktJYXcgxNc1dXxdU8eKmjq+rq6nKBBscQoLkOty0Nu0klkTIaHAj2tzDa7dQb79yexOKynZHlKz45oSh4kZMVg04lCk21OsEl2i4X6smZfPZ//dgMNl5Yd3TCTW6+ySdoiIiEj0UZKwA6Ih0BORtjFNk/zKer7eWcXXuypZsbOS1flV1AVaTueNcVgZnBnPkMx4hmQ1fh2YEfedU5Wr/FWsL1/Put3rWFu+lnW717GjegcmLb99um1u+iX0o6+jBzm1TrJ3+slcU4Rz+cZWpykDWFNScA0ciHPAAJz9+uLs2xdH376UOlyNScPquj0JxHp2B0OtniPeYiHXtJLpM0ko8ePOqyexPEis32yWPLTaLCRmxpDcw0NyloekHrEkZ3mITXC0eUVpEel6ilWiS1ffjwZfkP/e9hUNtUGOv7A/I07LPuxtEBERkeilJGEHdHWgJyIdE46YbC6p5eudlSzfWcnagirWF9XgD0Va1LUY0CfVw8D0OPqleRiQHseAdA+5KbHYDzDizhf0saF8A+vK17Fu9zo2VGxgS+UWgpGWo/8AklxJ9HH3JDsQR1YFZOT5SFtTiHddAZb9fBu2paXh7NcXR99+OPv2wdGnLzU9erLOGcPaOj9ra+tZU1vP5roGQvv5Th6HQY+gQXJVmLjCBhLLQyTXhPHWRbDsc4wzxrYncRhLYmYsiRkxJGbEEuNV8lAkGilWiS5dfT/mvbKRlZ/sIjEjhotvGa9n1IqIiEgzShJ2QFcHeiLS+ULhCNvKfKwtrGZtQTVrC6tZU1BNua/lVGUAm8Wgd0osA/YkD/uleeidEktuSiwep631a0RC5FXnsalyE5sq9myVm9hVs6vVUYcATouTXo50egY8ZFUYpBXWkbKpjNRtFXh9tJhCDGCJicGem4Mjp3Gz9OpFUVom65PTWGd3sbHOz6a6BnbUB/ZzVbCZkBKEpJowcbuDJFWHSa4Jk1QbIa7+mwSi3WUlMb0xYZiQEUNiRgwJ6TF4U9zYHG1bLEZEOp9ilejSlfejvMDHzD8twoyYfP+6kWQPSTqs1xcREZHopyRhByjwFjk6mKZJSY2ftYXVbCquYVNxLRtLatlcXIOvlenKe6XGOemdEkvv5Fh6p8aSmxxL75RYspPcxDhaJhDrgnVsq9rG1qqtbKvaxvbq7Wyr2saO6h37HXkI4LY4ySKBzDonabtDpO6sJTmvkrSKCCnV4Ghl5rElNhZ7z57Ye/bEkpVFZVo6BclpbEpIZmVcAmsjFnY0+PFH9v+t32pCYn2E+OowCb4wCbUREnyRxte+CDF7pjDHJjjxprobtzQ38SluEtJiiE9143S3nkgVkc6hWCW6dNX9ME2Tdx5dwc51FfQekcLUXw4/bNcWERGR7kNJwg5Q4C1ydDNNk4Kqhm8Sh8U1bCvzsa3Mx+79jDzcK8XjJDvJTXZiDL2SYhpfJ8WQnRhDpteFbZ8pYOFImILaArZVb2tKGubV5LGrZheFvkIiZsvp0ftKNGNI8ztJq4bkkgaS832kVJskV5sk1UBcfcuRiNbERGwZGYTS06lNSaU0MZld3iQ2xyewJjaelS4PDZYDjxC0h0zi6yJ46yLE14X3eR1peu1x2ohLdhGX5Gr6Gp/sbnrtjLVpGrNIByhWiS5ddT+2rijlvX+uwmIz+OFtE/Cmxhy2a4uIiEj3oSRhByjwFpH9qaoPsr3Mx/bdPraWNn7dVuZje5mP6obWFxXZy2JAeryLrAT3ns1FjwQ3Wd7G95leFwkxdgzDIBgOkl+bz86anU2Jw7yaPApqCyioLaAuVPedbbWbFlL8TpJrIbE8QNLuIEk1Jom1kFBrkuiDhFpw7ttsiwUjOZlgcgq+pCQqEpIojk9gZ5yXLTHxbIqNozw+gaq4eCKW/T/zyhWI4KmPEFdvEle/53XDN2UJEUh3OUjyOvEkOIlNdOJJdO3z2okr1q5Eosh+KFaJLl1xP0LBMC/dsZDqsgZGfy+HY8/te1iuKyIiIt1PW2MVzQcTETkIXredEdkJjMhOaLGvqi7Izoo6dpbXsbOijrzyOnaW17OzvI5dFfUEwhEKqxoorGpg6Y6KVs/vtFlIj3eRHu8kPd5FRnwy6fE9GOx1cUqqi9Q4J8mxdiJGHQW+AgprC8mvzafQ1/i1yFdEcV0x5Q3lBI0Iha56Cl1ACkDrSb2YoIXEOoOEqjDe2ggJtbvx1pXhrYX4EkiqM+ntg7PrwLVndrRpGIS9XuoTEqmOT6A8Lp5iTzw7Y+Mo9sRT5YmjyhNPpSeO4ow46lxuaCXh5wiaxDbU42nwEbvdJNYfIbYhgqfBJDZokmSzkuy0k+a2k+5x4vE6ifU6ifE6iIl34I5z4I6zY/uOFapFRI40X3+8k+qyBmK9DsZ8L6ermyMiIiJHACUJRUQ6iTfGjjfGy9Ae3hb7IhGTMp+fgsoGCirrKaisJ3+fr/kV9VTUBfGHIuSVNyYYD8Rlt5Aa5yTF4yLVM4SUuFH09jgZl+IgKdZJvNsAWy1BKqiPlFJaX0JxXTHFdcWU1ZdRWldKWX0ZDeEG6uwR6ryQ74X9JRL3cgYhvs7EUwfx9dV46quJq9/ROGKwDAbkwZh6iG0wiW0ATwPE+MG02qiPi6c6Lp6KWA+73R4qY2OpifFQHeuhJjaW6tg4qmM87E6KpSamcV/Abt+TXDTBrCfGX4d7t0lsfgR3wMQdiOD2m3gi4LVaSbRZSXLYSXbZSI5xkBLrICHW3phM9Dhweey4PHbsWnRFRLqx2go/S97bAcCx5/fD4VJILyIiIh2niEJE5DCwWAzS4lykxbkY2cooRICGYJiSaj/FNQ0UVTVQXN24FVX7m16X1fjxBcI0BCN7RinWf+e1DQO87iySYnNJjnWQEONgqNtOQqKdGKeJ1eYHq48w1QSooIEK6sK7qQmVUtFQTnlDObsbduMP+/HbodRrUOqF1tdebl1Mg0lsQ9WerTGBGONv3JJLINvfvCzGb+IOgD1sI2KJocEegy/GQ21MDLXuWGpjYvC5Y/C53PjcMdS53BS63GxxufHZY/Dhpi7ipj7gIhBx4CoHV9DEFTBxBU3cIZNY08BjWIi1GMRbrcTZrMQ7bHidVhJddhLcdhLddhJjHMTH2HDFOnC4rRq1KCJdbsGbmwn5w2T0iWfA+PSubo6IiIgcIZQkFBGJEi67lV7JMfRKPvCD5+sCIcpqApTW+imt8VO252tprZ/y2gDlvgC7fX7KfQEq64OYJlTWBamsC7K11PcdrUjcs/XBYkC82068y06Gy4bHZcXpiOCwhbBYA1isfkyjnrDhI2TUEDCraYhUUBcupy5Sji9cTkOkGsOIUOcyqHNBKXAwycU9nxgj4sMdKMXtB3dg72biCoDLB6mVkB2gMQkYaJwW7QyCM7BnFWjDQQQnptVFxOIkZIshaI0h6HDR4HDR4HTS4HCS73Sxxemk3uFsKmtwOAnYHYQtdiJWJxh2DKsDw+7EZnXgtlhwGxZiLAZuq4VYq4VYmxWP3Uqs3YrHYcPjsOJxWolz2vG4bMS7bMS57XgcNuwWPXdRRNquaGsVGxcWgwEnXDxAz24VERGRTqMkoYhINxPjsNEr2fadyUSAUDhCZX2wMXFYG6CiLkBlXZCKugBV9UEq9iQSK5vKg1TXBwmEI0T2SS62zgLE7tnS9tsGp83A7bDgtIPDZmK3hbFZw1isQbAEwAgQMRqIUE8IHyHTh59agpFa/JFasAQwLEFqjAC17iDEBDEsAbCEgHBrjzpsRRio27Pt8wkiJs5gYyLRsc9XZwgcdSbeKkgNgX3P5tiz2cMmjiDYw2CYVhp/nNrAsAF2TIsdExs1FgdVFgemxU7Y6iBscez56iRkcxK0OQhb7UQsNkyrnYjVhmltfG3Y7Bg2G4bNgcVmx2K3YbHbsdoc2Bx27A47doejaXO4HDiddlx2Gy67BZfNitthxWW3EGO34rRasFsMHMberwaOPV/tFgO7YSjZIBLlzIjJ5zM3AjD42EzScrRojYiIiHQeJQlFRI5gNquFFI+TFI8T2jgjzTRN/KEI1fVBqhuCVNUHqa4PUd3QmECsqg9S6w9T0xCk1h+itiFETUOIGn+oqcznDxEMmwD4Qyb+UHifKxg0JdVwd+jzGYaJzQo2awSrJYLFEsZiDWEYITCCjQlIAphmgIjhJ0xjQtI0gmCEwQjRYITACIERxtibCTTCe87RWMcwwt/xOgI0tDFh2ZwtZGKNgC0Mtr1fwzSWhcDqbyy3RsAaNvd5vafOPq+tETBMo3HDgmFaMDAAK4ZpAcMCe8obX1sx95SZWMGwYGAFLGCxNdY1rE0bhrXxvBZb43uLFYthxbDYsFisWCw2DMOGxdr42mK1YbVYsVjtWCxWbDYbFosNq82G1dq4GTYrVpsVq82GYW18bbFZsFqtjeexWjEMA4vVitXaWI5hwWK1YLVYsFgsGBZL036LYcGwNpZbDQOLdc9+ixWLxWhsj8VoOqfFMBrPY7VgGBasFgMLYDEavxoGWDCw7Plq0LhSuQFKqspht25BIaV5NThcViZqNWMRERHpZEoSiohIM4Zh4LJbcdmtpMW72n2eQCiCzx/CFwjh84ep9YeoCzQmEH3+MHXBMPV79tUHw9QFQtQFwtTt2dewZ6sPNO7f+7ouGMZszD9imgbBEARDVsAK2DulD9rLILIncRj5JoHI3kRiGNMIA5E9ScjIPgnGvcdE9uxv3Iw95Vj2ZAAdJo2jJyOAuefYfet/q4wIhmHu895s9rV5fbOxnEhjApTgnrK99c1m9Yymsn3OuXfbU9cwvl3W8rUlEMESaPyIFrNxM/Zsrb6PNPb1t8v3fd1sP9/sM75VZjFN+NYx0JhoZU8d9iQG2XscRmPT90ybt+zzeu+/gr31/3nXYmw2hVrSOfz1Ib56cwsA487uTUy8o4tbJCIiIkcaRa4iInJIOGwWHDYHibGd+4vs3pGO/mCEhtDeZGKEhmAYfyjSlFxsCEUIhCL4Q2H8wUjjMaHwnrLGesFwY51AONJUvu/7YDhCMGw2vQ6EIwRDe8rCkZZtw4JpWvYkkeTg7JNoNPZ24LeSi3veG3v38e16+5QbND8O9hy7bz2+dRzNj7V86xrfqm80O67luSyWA68WLnIwFv9vG/U1QRLSYxh2cs+ubo6IiIgcgZQkFBGRbmXfkY7eLhw5aJomwbBJKNKYNAyGI4T2fA2GI4QijcnFcOSbOqE99UP7HBeKRAhHIBxpPCYcaTzv3vehcGNZxDSb9u+7NZY1niNifmv/Pu8jrb02IbJP2d5y06TpWNOk6VymSVO9yD71Gt+DiUlkT/2Dt09Cbd/DWzlVR3KwhzN/qyRh58vPz+eGG27gvffeo66ujn79+vHMM88wduzY/R7j9/u58847eeGFFygqKiIzM5Nbb72Vq666CoCnnnqK5557jtWrVwMwZswY7rnnHsaPH39YPlNb+OuCrJ1XAMDxF/XHatO/LREREel8ShKKiIi0g2EYOGwGDvTLemtMszFhuDepuDfZaNJYZu6TVNy3jrmf9yZ7k497yveex6RZXROz2TGNU9P3OSbyTblJY+G+782m9+Y3X5vOvW9543lh333NzyGdq6KigkmTJnHKKafw3nvvkZqayqZNm0hMTDzgcRdddBHFxcX8+9//pl+/fhQWFhKJfDMS+LPPPuPSSy/luOOOw+Vycf/993PGGWewZs0aevTocag/Vps4Y+xcfPN4Ni8tJueY5K5ujoiIiByhDNM0Fcd+S3V1NV6vl6qqKuLjtWqciIiIRJejMVa58cYb+fLLL5k3b16bj3n//fe55JJL2Lp1K0lJSW06JhwOk5iYyGOPPcbll1/epmOOxvshIiIi3UdbYxUNfxARERGRqPf2228zduxYLrzwQtLS0hg1ahRPPfVUm4554IEH6NGjBwMGDOD3v/899fX1+z2mrq6OYDB4wKSi3++nurq62SYiIiLS3SlJKCIiIiJRb+vWrTz++OP079+fDz74gF/+8pdcd911/Oc//zngMV988QWrV69m1qxZPPLII7z22mv86le/2u8xN9xwA1lZWUyePHm/de699168Xm/Tlp2d3aHPJiIiIhINNN24FZoyIiIiItHsaIxVHA4HY8eOZf78+U1l1113HYsXL2bBggWtHnPGGWcwb948ioqK8Hq9ALzxxhtccMEF+Hw+3G53s/r33XcfDzzwAJ999hnDhw/fb1v8fj9+v7/pfXV1NdnZ2UfV/RAREZHuQ9ONRUREROSIkZmZyZAhQ5qVDR48mLy8vAMe06NHj6YE4d5jTNNk165dzer+5S9/4b777uPDDz88YIIQwOl0Eh8f32wTERER6e6UJBQRERGRqDdp0iQ2bNjQrGzjxo3k5OQc8JiCggJqa2ubHWOxWOjZs2dT2QMPPMBdd93F+++/z9ixYzu/8SIiIiLdgJKEIiIiIhL1fvOb3/DVV19xzz33sHnzZl588UWefPJJZsyY0VTnpptuarYi8Q9/+EOSk5O58sorWbt2LZ9//jn/7//9P6666qqmqcb3338/t9xyC08//TS5ubkUFRVRVFTULLEoIiIicjRQklBEREREot64ceOYNWsWL730EkOHDuWuu+7ikUceYfr06U11CgsLm00/9ng8zJkzh8rKSsaOHcv06dOZNm0ajz76aFOdxx9/nEAgwAUXXEBmZmbT9pe//OWwfj4RERGRrqaFS1pxND4MXERERLoPxSrRRfdDREREopkWLhEREREREREREZE2UZJQRERERERERETkKKckoYiIiIiIiIiIyFFOSUIREREREREREZGjnJKEIiIiIiIiIiIiRzklCUVERERERERERI5ytq5uQDQyTRNoXCJaREREJNrsjVH2xizStRQ7ioiISDRra+yoJGErampqAMjOzu7iloiIiIjsX01NDV6vt6ubcdRT7CgiIiLdwXfFjoapP0G3EIlEKCgoIC4uDsMwDsk1qquryc7OZufOncTHxx+SaxxN1J+dT33audSfnUv92bnUn53rcPSnaZrU1NSQlZWFxaKnx3Q1xY7dj/qzc6k/O5f6s/OpTzuX+rNzRVPsqJGErbBYLPTs2fOwXCs+Pl7/qTqR+rPzqU87l/qzc6k/O5f6s3Md6v7UCMLoodix+1J/di71Z+dSf3Y+9WnnUn92rmiIHfWnZxERERERERERkaOckoQiIiIiIiIiIiJHOSUJu4jT6eS2227D6XR2dVOOCOrPzqc+7Vzqz86l/uxc6s/Opf6UQ0H/rjqX+rNzqT87l/qz86lPO5f6s3NFU39q4RIREREREREREZGjnEYSioiIiIiIiIiIHOWUJBQRERERERERETnKKUkoIiIiIiIiIiJylFOSUERERERERERE5CinJGEX+fvf/05ubi4ul4sJEyawaNGirm5St/D5558zbdo0srKyMAyDN998s9l+0zS59dZbyczMxO12M3nyZDZt2tQ1je0G7r33XsaNG0dcXBxpaWmce+65bNiwoVmdhoYGZsyYQXJyMh6Phx/84AcUFxd3UYuj2+OPP87w4cOJj48nPj6eY489lvfee69pv/qyY+677z4Mw+D6669vKlOftt3tt9+OYRjNtkGDBjXtV18evPz8fH70ox+RnJyM2+1m2LBhLFmypGm/fiZJZ1Ls2D6KHTuXYsfOpdjx0FLs2DGKHTtfd4gdlSTsAi+//DK//e1vue2221i2bBkjRoxgypQplJSUdHXTop7P52PEiBH8/e9/b3X/Aw88wKOPPso///lPFi5cSGxsLFOmTKGhoeEwt7R7mDt3LjNmzOCrr75izpw5BINBzjjjDHw+X1Od3/zmN7zzzju8+uqrzJ07l4KCAs4///wubHX06tmzJ/fddx9Lly5lyZIlnHrqqZxzzjmsWbMGUF92xOLFi3niiScYPnx4s3L16cE55phjKCwsbNq++OKLpn3qy4NTUVHBpEmTsNvtvPfee6xdu5YHH3yQxMTEpjr6mSSdRbFj+yl27FyKHTuXYsdDR7Fj51Ds2Hm6TexoymE3fvx4c8aMGU3vw+GwmZWVZd57771d2KruBzBnzZrV9D4SiZgZGRnmn//856ayyspK0+l0mi+99FIXtLD7KSkpMQFz7ty5pmk29p/dbjdfffXVpjrr1q0zAXPBggVd1cxuJTEx0fzXv/6lvuyAmpoas3///uacOXPMk046yfz1r39tmqb+fR6s2267zRwxYkSr+9SXB++GG24wjz/++P3u188k6UyKHTuHYsfOp9ix8yl27DjFjp1DsWPn6i6xo0YSHmaBQIClS5cyefLkpjKLxcLkyZNZsGBBF7as+9u2bRtFRUXN+tbr9TJhwgT1bRtVVVUBkJSUBMDSpUsJBoPN+nTQoEH06tVLffodwuEwM2fOxOfzceyxx6ovO2DGjBmcddZZzfoO9O+zPTZt2kRWVhZ9+vRh+vTp5OXlAerL9nj77bcZO3YsF154IWlpaYwaNYqnnnqqab9+JklnUex46Oj/accpduw8ih07j2LHzqPYsfN0l9hRScLDrKysjHA4THp6erPy9PR0ioqKuqhVR4a9/ae+bZ9IJML111/PpEmTGDp0KNDYpw6Hg4SEhGZ11af7t2rVKjweD06nk1/84hfMmjWLIUOGqC/baebMmSxbtox77723xT716cGZMGECzz77LO+//z6PP/4427Zt44QTTqCmpkZ92Q5bt27l8ccfp3///nzwwQf88pe/5LrrruM///kPoJ9J0nkUOx46+n/aMYodO4dix86l2LHzKHbsXN0ldrQdtiuJSFSbMWMGq1evbvacCTl4AwcOZMWKFVRVVfHaa69xxRVXMHfu3K5uVre0c+dOfv3rXzNnzhxcLldXN6fbO/PMM5teDx8+nAkTJpCTk8Mrr7yC2+3uwpZ1T5FIhLFjx3LPPfcAMGrUKFavXs0///lPrrjiii5unYjIoafYsXModuw8ih07l2LHztVdYkeNJDzMUlJSsFqtLVb9KS4uJiMjo4tadWTY23/q24N3zTXXMHv2bD799FN69uzZVJ6RkUEgEKCysrJZffXp/jkcDvr168eYMWO49957GTFiBH/961/Vl+2wdOlSSkpKGD16NDabDZvNxty5c3n00Uex2Wykp6erTzsgISGBAQMGsHnzZv37bIfMzEyGDBnSrGzw4MFN03D0M0k6i2LHQ0f/T9tPsWPnUezYeRQ7HlqKHTumu8SOShIeZg6HgzFjxvDxxx83lUUiET7++GOOPfbYLmxZ99e7d28yMjKa9W11dTULFy5U3+6HaZpcc801zJo1i08++YTevXs32z9mzBjsdnuzPt2wYQN5eXnq0zaKRCL4/X71ZTucdtpprFq1ihUrVjRtY8eOZfr06U2v1aftV1tby5YtW8jMzNS/z3aYNGkSGzZsaFa2ceNGcnJyAP1Mks6j2PHQ0f/Tg6fY8dBT7Nh+ih0PLcWOHdNtYsfDtkSKNJk5c6bpdDrNZ5991ly7dq35s5/9zExISDCLioq6umlRr6amxly+fLm5fPlyEzAfeughc/ny5eaOHTtM0zTN++67z0xISDDfeustc+XKleY555xj9u7d26yvr+/ilkenX/7yl6bX6zU/++wzs7CwsGmrq6trqvOLX/zC7NWrl/nJJ5+YS5YsMY899ljz2GOP7cJWR68bb7zRnDt3rrlt2zZz5cqV5o033mgahmF++OGHpmmqLzvDvivUmab69GD87ne/Mz/77DNz27Zt5pdffmlOnjzZTElJMUtKSkzTVF8erEWLFpk2m828++67zU2bNpn//e9/zZiYGPOFF15oqqOfSdJZFDu2n2LHzqXYsXMpdjz0FDu2n2LHztVdYkclCbvI3/72N7NXr16mw+Ewx48fb3711Vdd3aRu4dNPPzWBFtsVV1xhmmbjsuG33HKLmZ6ebjqdTvO0004zN2zY0LWNjmKt9SVgPvPMM0116uvrzV/96ldmYmKiGRMTY5533nlmYWFh1zU6il111VVmTk6O6XA4zNTUVPO0005rCvJMU33ZGb4d6KlP2+7iiy82MzMzTYfDYfbo0cO8+OKLzc2bNzftV18evHfeecccOnSo6XQ6zUGDBplPPvlks/36mSSdSbFj+yh27FyKHTuXYsdDT7Fj+yl27HzdIXY0TNM0D9+4RREREREREREREYk2eiahiIiIiIiIiIjIUU5JQhERERERERERkaOckoQiIiIiIiIiIiJHOSUJRUREREREREREjnJKEoqIiIiIiIiIiBzllCQUERERERERERE5yilJKCIiIiIiIiIicpRTklBEREREREREROQopyShiHRbP/7xjzn33HMP+3WfffZZDMPAMAyuv/76Dp8rISGhU9p1qJ188slNn3vFihVd3RwRERGRg6LY8fBS7CjS/di6ugEiIq0xDOOA+2+77Tb++te/YprmYWpRc/Hx8WzYsIHY2NgOnefiiy9m6tSpndSqbxiGwaxZszo1EH7jjTfYsmUL48eP77RzioiIiHQGxY4do9hRREBJQhGJUoWFhU2vX375ZW699VY2bNjQVObxePB4PF3RNKAxkMrIyOjwedxuN263uxNadOglJSVRXV3d1c0QERERaUGxY/RR7CjS/Wi6sYhEpYyMjKbN6/U2BVZ7N4/H02LKyMknn8y1117L9ddfT2JiIunp6Tz11FP4fD6uvPJK4uLi6NevH++9916za61evZozzzwTj8dDeno6l112GWVlZQfd5tzcXP70pz9x+eWX4/F4yMnJ4e2336a0tJRzzjkHj8fD8OHDWbJkSdMx354ycvvttzNy5Eief/55cnNz8Xq9XHLJJdTU1DS7ziOPPNLs2iNHjuT2229v2g9w3nnnYRhG03uAt956i9GjR+NyuejTpw933HEHoVAIANM0uf322+nVqxdOp5OsrCyuu+66g+4HERERkcNNsaNiRxHpOCUJReSI8p///IeUlBQWLVrEtddeyy9/+UsuvPBCjjvuOJYtW8YZZ5zBZZddRl1dHQCVlZWceuqpjBo1iiVLlvD+++9TXFzMRRdd1K7rP/zww0yaNInly5dz1llncdlll3H55Zfzox/9iGXLltG3b18uv/zyA0512bJlC2+++SazZ89m9uzZzJ07l/vuu6/NbVi8eDEAzzzzDIWFhU3v582bx+WXX86vf/1r1q5dyxNPPMGzzz7L3XffDcDrr7/Oww8/zBNPPMGmTZt48803GTZsWLv6QURERKQ7UOyo2FFEvqEkoYgcUUaMGMHNN99M//79uemmm3C5XKSkpHD11VfTv39/br31Vnbv3s3KlSsBeOyxxxg1ahT33HMPgwYNYtSoUTz99NN8+umnbNy48aCvP3XqVH7+8583Xau6uppx48Zx4YUXMmDAAG644QbWrVtHcXHxfs8RiUR49tlnGTp0KCeccAKXXXYZH3/8cZvbkJqaCkBCQgIZGRlN7++44w5uvPFGrrjiCvr06cPpp5/OXXfdxRNPPAFAXl4eGRkZTJ48mV69ejF+/Hiuvvrqg+4DERERke5CsaNiRxH5hpKEInJEGT58eNNrq9VKcnJys79opqenA1BSUgLA119/zaefftr0nBqPx8OgQYOAxr/KduT6e691oOu3Jjc3l7i4uKb3mZmZB6zfVl9//TV33nlns8969dVXU1hYSF1dHRdeeCH19fX06dOHq6++mlmzZjVNJxERERE5Eil23D/FjiJHHy1cIiJHFLvd3uy9YRjNyvaufBeJRACora1l2rRp3H///S3OlZmZ2aHr773Wga7/XefYe8y+9S0WS4spJ8Fg8DvbVltbyx133MH555/fYp/L5SI7O5sNGzbw0UcfMWfOHH71q1/x5z//mblz57Zok4iIiMiRQLHj/il2FDn6KEkoIke10aNH8/rrr5Obm4vN1j2+JaampjZbwa+6uppt27Y1q2O32wmHw83KRo8ezYYNG+jXr99+z+12u5k2bRrTpk1jxowZDBo0iFWrVjF69OjO/RAiIiIi3ZBix+YUO4ocWTTdWESOajNmzKC8vJxLL72UxYsXs2XLFj744AOuvPLKFoFStDj11FN5/vnnmTdvHqtWreKKK67AarU2q5Obm8vHH39MUVERFRUVANx6660899xz3HHHHaxZs4Z169Yxc+ZMbr75ZqBxtbx///vfrF69mq1bt/LCCy/gdrvJyck57J9RREREJBopdlTsKHIkU5JQRI5qWVlZfPnll4TDYc444wyGDRvG9ddfT0JCAhZLdH6LvOmmmzjppJM4++yzOeusszj33HPp27dvszoPPvggc+bMITs7m1GjRgEwZcoUZs+ezYcffsi4ceOYOHEiDz/8cFMgl5CQwFNPPcWkSZMYPnw4H330Ee+88w7JycmH/TOKiIiIRCPFjoodRY5khnmgtdRFRKSFZ599luuvv57Kysqubspht337dnr37s3y5csZOXJkVzdHREREJOopdlTsKNJdROefOkREolxVVRUej4cbbrihq5ty2Jx55pkcc8wxXd0MERERkW5HsaOIdAcaSSgicpBqamooLi4GGqdZpKSkdHGLDo/8/Hzq6+sB6NWrFw6Ho4tbJCIiIhL9FDsqdhTpLpQkFBEREREREREROcppurGIiIiIiIiIiMhRTklCERERERERERGRo5yShCIiIiIiIiIiIkc5JQlFRERERERERESOckoSioiIiIiIiIiIHOWUJBQRERERERERETnKKUkoIiIiIiIiIiJylFOSUERERERERERE5Cj3/wEGxVTxcbn/pAAAAABJRU5ErkJggg==",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1300x900 with 4 Axes>"
       ]
@@ -162,6 +148,7 @@
     }
    ],
    "source": [
+    "\n",
     "colors = [\"tab:purple\", \"tab:cyan\", \"tab:red\", \"tab:green\", \"tab:blue\"]\n",
     "linestyles = [\"dashed\", \"dotted\", \"solid\"]\n",
     "\n",
@@ -172,12 +159,13 @@
     "\n",
     "currents = [\n",
     "    \"X-averaged negative electrode volumetric interfacial current density [A.m-3]\",\n",
-    "    \"X-averaged lithium plating volumetric interfacial current density [A.m-3]\",\n",
+    "    \"X-averaged negative electrode lithium plating volumetric interfacial current density [A.m-3]\",\n",
     "    \"Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\"\n",
     "]\n",
     "\n",
     "\n",
     "def plot(sims):\n",
+    "    import matplotlib.pyplot as plt\n",
     "    fig, axs = plt.subplots(2, 2, figsize=(13,9))\n",
     "    for (C_rate,sim), color in zip(sims.items(),colors):\n",
     "        # Isolate final equilibration phase\n",
@@ -195,7 +183,7 @@
     "            axs[0,1].plot(t, j, color=color, linestyle=ls)\n",
     "\n",
     "        # Plated lithium capacity\n",
-    "        Q_Li = sol[\"Loss of capacity to lithium plating [A.h]\"].entries\n",
+    "        Q_Li = sol[\"Loss of capacity to negative lithium plating [A.h]\"].entries\n",
     "        axs[1,0].plot(t, Q_Li, color=color, linestyle='solid')\n",
     "\n",
     "        # Capacity vs time\n",
@@ -206,8 +194,8 @@
     "    axs[0,0].set_ylabel(\"Voltage [V]\")\n",
     "    axs[0,1].set_ylabel(\"Volumetric interfacial current density [A.m-3]\")\n",
     "    axs[0,1].legend(('Deintercalation current','Stripping current','Total current'))\n",
-    "    axs[1,0].set_ylabel(\"Plated lithium capacity [Ah]\")\n",
-    "    axs[1,1].set_ylabel(\"Intercalated lithium capacity [Ah]\")\n",
+    "    axs[1,0].set_ylabel(\"Plated lithium capacity [A.h]\")\n",
+    "    axs[1,1].set_ylabel(\"Intercalated lithium capacity [A.h]\")\n",
     "\n",
     "    for ax in axs.flat:\n",
     "        ax.set_xlabel(\"Time [minutes]\")\n",
@@ -221,15 +209,13 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The results show both similarities and differences with those of Ren et al. [8]. Notably, unlike Ren et al., this model uses equations [6] that result in a small but finite amount of plated lithium being present in the steady state."
+    "The results show both similarities and differences with those of Ren et al. [7]. Notably, unlike Ren et al., this model uses equations [5] that result in a small but finite amount of plated lithium being present in the steady state."
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -252,7 +238,22 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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gh73u3ML31nv8a2BXVhYAe9x5bC3IYbe78PvkgTFjeHj0I3Ru3Z6YqMKfJ+9+9BHXDryRd7/8HIDfDyTT+9//pt/1A5j5xKNEOFw8OfpJrr32WqZ/8AE5eMjzuHn33XcZMWIEv/32G0uWLGHw4MF06dKFSy65hOXLlxMVFcWMGTPo2bMn1qNF9fLgtyLhihUrSElJoW3btr5lHo+Hn3/+mcmTJ5OXl/ePTnzXrl3MnTuXL7744m9jnU5niWMclpeQwMJvol1/8cmHiIjIucyd7+XN+xf65dh3vNwNu/PU70O8Xi/Dhg2jS5cuvvF0tm7dCkDjxo3LNEcRgMScPP63K5lD6/7A+sc6am7bTMzBLTgC0kivbrAmaCgX1r8TgK/3b+Xr7rF4vM0w3SFHH8GYXgemaedWc6Nvvx+mRrI29KGSDkuD0OOtoxYfCWWL2a/wRTE1juYRK33P1+QFsdtzSYn7Ta/6m+/5hjwnSZ7zS4w9FHG8ddSWfBspnvNKjN1f5fjPkoQ8Cwc97UqMTQpP9z3fk2ch1dOqxNh94cdbyyXneEnztCgxdm9Ysu/5wWw3ad6mJcdW3ed7npadTyqNwVNCcJXjsZk5BaTSoMRYb9jx2CP5blKpV2KsO/R4vrkFXlKJLzG2ICTl+HOPl1TiSozNCz7eQsvtNTlEnZJj7Yd9z03gELElx7pyfa04AQ6aNTHdxf8cz3N68Z5QkNlPDTxuR7GxuXYbHsvx/SZTnTx38UXNXOc+CuzHR/RKMqLJzi++sGrYD5J7wu+ZfUSSXRBSfKwtk+yA43+67yWS7PziY7HkcDjwePFut1m15Fg8ZAbt9b1KpArZeSU3lkkP2nQ81gwjO696ibGHglb7nu/0hJCdW/L3xqHADb7n+aYFr7fw+v767MoStthBneZVaTkgDq+3sBi9eOJqvMUWThOo0SCcjjc38MUufWUt7uyTi4ANXq8LQJ7XwOMtvsgNUGA9/rXjBTzek9/juHqN2LppAwUWK7/++ivLli1j847dpOQU5jjisWf5afaPzP5mLtfcMBjTdPg+NAHwmBAaFs4jT0/CarVSp25rzr/4a5b8spR/X38HgREmVquVkJAQqkZGkWkE4fHCW6++yeX/7s9/bh0GQK04eGTsUwy++lqmTp2Kx7ABBh3+dQE33fGA73iP3HsbTVu25r/PPkuzmMKvgboNGrE1JQuPF7r3uq7I+T054XUubFWfDRs20Lx5czxe82jeQXi8IViscPeIJ33xNevEsWvjDj755BP+ffU12AKjcThDsOd5qVKtni/O7S0sJh/7jntt6mtE16jFI09PwjAMatdtw91J6Uwa/xS33/84VnseJtCyZUtGP/EEf+zNoHd8Cz6c8QGLf1lOx669ffs2LLl4TBM7MHbsWAbecS/X3zLMt75Jy/Nxe8HtDTmay/HzzTEDfefn9obiMQvf83seeIyOF/SgVtUQQp2FX7e14+sx7NFnfdu++fIEGjVrxb0jx2IYBdSMcPH2228TGxvL+u17qB3fEC8GLVu25IknngCgQYMGTJ48mXnz5nHJJZcQebQFZHh4eImT+JYVvxUJL774YtYdbUp7zM0330zjxo0ZOXLkP66Mzpgxg6ioKHr37v33wX6wLvUXIJrtJTTDFxERkTPPkCFD+OOPP/j1hK5dxz4NFykrHtPEevSPyeTvv6LG20+TH+1lS4yVue1jyTRb486Ow5tbiwcDjnfN3OZpSO7BkovV7Vpc4HteK6YGG1MPYbeaWC2FD4sBVqPw/x7xx8cT79y4LinZ27AYhW3XLIaBYYDlaDfiPvGX+2IvbtGAQ0fWYRxdZ3C896UBDKh3hS/2ktYN2J+5wtcd2Nc+7+iTaxr18cX2aFWfXalLiqw/sT3f5U36+mIvaB7P+qTEIjEnbndRveP77di4Jr12bcY0zROOf+yZSec6x/NtUy+aPs1W4/We3IUYoF3N49ehWe0q9GvpxuM5oeJ1wjbNo3r6njeMDuG6tiYFJXQhblTtUt+yuGpB3NjOILeg+NZgdcIv9D2vFR7IoPNsZOf+uTVYoeohXX3PI0Oc3PovJ4ezjxQbGxXcCa/XxGIxCHPZub2Li4zsYwW+ohcjIrAddq8JVnDZrdx1QQhpWRnF1ZcJC2hJ8NE/1K2GwT0XhnEoM/2kOBMIDWhKtHG80HfvRZHsz0z1rT8xiyBHAxo4w32v7+9ekz1pxf9dFuiIo3VIzeOxPeJJOJBSbKzTVovOkQ19r4dd0pAtycm+czsxD4cthh41Wh/f76XN+GPfvqNBRa+ZzVqV3nWPx957SQtW79lbJOZYgclqOLi6UdcTYtvwW2Ji0dgTErmuxfHvjbsubssvCbuKntQJqdzU4XgX+Nt6tGPe1p3H91nkHTS4qcONvleDLmpL1JbtJ73Hx9K4tvXVkFs4dltEcCCGLZfSqBLswrTlgVlsD+0iwoICKDAKv9YtfxMc7HKQfbSIXNzXZYirmu+51WIhONByUqzVChaLQZWQasxb839kZWVRv06NIjG5OTkk791JoMuC3WZgtVhP2K9Bw8ZNCAm2+7aJqRHDlg3rcbksuJzHJ311WC0EuiyYpsnWTX+wecN6vv/yM996ExOv10tCQgL1GjTEMAxatW1DQMDxgRy2bPyDy/r0JdAV7tvOZrHgCrDgxWTn9u288twzrF35O2mHUjGPTsySmJhI8+bNsR69qHY7OJyFzz+c/hZf/O8DkvbuIS83l4L8fFq3bo1hAVeABYsVDAvYncffEPufavWbNm6ibYeO2AOOx7T713lkH8ni4MG91IqrDRQWCQ0K91vg9RIVE0166kFsJ+zPsAZgNSykpKSwb98+Luh+EdZiPhuw2o+/t8e4XIXvjcUGVkfh/wAt2rfG6nBgtx1vidyiTZsi+926+Q+WL/mFTo0Ke5Ce+PV3IGkPcY0aYrFYadmyZZE8qlevTkpK8T9rypPfioQhISEnzVgTFBRE1apVfcsHDhxIzZo1GT9+PFA46OOGDRt8z/fu3cvq1asJDg6mfv36vv14vV5mzJjBoEGDKu2Al7ERhc3gs3IrZ34iIiL+ZnNYuOPlbn479qm69957+eabb/j555+pVev4UCINj47JtGnTJjp37lxmOcq5Z/ORXF79fR3RhsnjFxR2313tNJlw4SV4suPx5MTC/qJ/8dSJPt4S7/w68eTtTySkwIYrzyDIbRJoGjhMA7sJVRPqwNHGbWO6taLZLyWPGx6YEAGFQzUxuFVDrJ8eKjE2ICYYOhQ+79M4jkNv7ykx1hkSDJ0Kn/eoX4uEyTtKjA2whsC/Cp93rVedtROrlBjrzA+Go3WTtrWr0W51VTwldNcMSA+Goz96mlYPp/26SHKPFBQfmxwChT2eia8WRLuN0WSlFl+gc9YIgosLn9eqEki7LdGkJWcXG+uoGgBHa3/RoQG02xbNgcTDxcbaQuxQ2GOVasFO2iXGsG9revGxTiscrVWGBdppvyeaxPUlj73G0RposNNGh6Rotq86UGKo91IvFoeVALuV8w7FsLnkIfMouMiDzW7FYbPQKT2a9T+X3LUzr4sbZ4Adi8WgS1Y0q38qeSKAgg5eONqoq2t+NL/PK74AClDQ2gNHG9h19USzZF5WibH5TbwQfjTWjMacV/KYeQUNvFA42gRdLFHkzyv5+ubV9sLRIdK6OqLInlvy9c2v7oGowucXBEZzeO7+kmOruuFoY6OuIZEcmrO35NgQNxxtENi1ShT73yx+zEeAfIfb9zXRNSKKPT/uPGFt0cqbmwI42iixS1QUCa9uL3G/ePKhcPQCguw2co/WRf79QJuTQp1BdkKrujAsYLFayNlfWFDsM/RPLX4NcLrshFZzYRhgtVvI2V+Y4xX3tPzzbnGc0FIzyGkn9GAeZtG6p+8M7V7gaGNSq8WgSr4Vr/fEQQ4Mdm7eQt3YOFzZBllZWVSvXp0FCxZw+FCOLxYgLDSMqgVWXF4D6wk/kqwWgyC7g2pum+/4gaYFqxciPTYsecevt9NuJcbiwOM2yT+Szc0Db+Wu2+45nrdhEFzFSe3atXHYrFiB6KBQYk4oqocEuAjBRjXP8WKX3WahljMAd4GXK2+8ntjY2kx9aSox1atjer107NLONzGHzVp43xRtd1DLHsBnn3/ChKce55mnn+O8jucRHBzC5NcnsWzZMmwWC7UDXQRZrLgtVmo7jg8hYNqP9vA8eokMA4JtVuKcx1t2ZjoKL36s00Ws3VU4jqvdjmEY1AkOxFPgJchmI8hiId5VtEWo1QSXq3D7aJeTuoEntxi1BwX6Yo+p7iy8LjUDAqgXFMTeo0PmNa0WSbgrCPsJcwJHh4dRL+iE1qW5ufTu1ZtxTz9TeK0cVqy2wvioatE4HAE4rRbs9uPXvvDcjVOeJbssVOoKVWJiIpYTqrf79u3zDRwJMGHCBCZMmEC3bt1YcMIAq3PnziUxMZFbbrmFyqpWRibgwu22k53vJtBRqd8KERGRCmcYxml1+a1opmly33338eWXX7JgwQLi4+OLrL/00kupVq0azz//fJGJS45JT0/XuITyl/bk5vPShgSsH75LvaRv2F+tJoc6/R9VHTYS8xqQf/B4V0Kn4aVWvoVabhu13FYiDx7vlnRjmzrYvy3aKsEwwGqzYLVbfH/kQeEfzNVig7HaLFisBsbR1oEc/T8s8nh3S6vdQlyLP83KeUJLqIiax8e8s9oM4lpWoyTVah0/F4vFKLJf84QnpgnVYouOpRfb5HiR8M8NeKvVKhpbs2E4HndxbYRMqtUs2jUzpl4YBbnuYvdbJaboH5jRcWGEVi2+iBVStehYepG1QwgIthcbGxhatNhbrVYwthO6sZ6YhzOo6N8REdWD8HqK/8PSai/6AUiV6CDyiul2WZywKBdRcaElB5xQIwqtGkBUnZK6uBa+t8eERDiJrF1y7LE/pgGCwouPPdZi+8RrFBTq8H2NFNeg+8RrERBsp2rN4JODjjrxd1FAkI2IGsV3ITbNorHOQNtJXyMncgQcj7U7rYRHF409sSW6w3n8fbY5LIRGuk46sWMv7ScUvKw2C8FVnEVjjgaagP2EHCwWA1eI/YS4E1oImkWvL8bxczWPbWAee170PT4WX2yzvGPrimFznHwPYHdYfcc9sdhWXKzthFjTNH3X88SvqWP+/L3h8ZjFf+EAFtufCqIFXswTcvll8UI2bFzPHTffg7vAS9u2bUlOTsZmsxFbM/6k78+CPA9et3nS4bxek/zc49+fHreJ6THJz3FjtVlwOBy+1sj5uR7c+R6aN23Jxo0bqBlT+3i+FoNqsce/b0yz8Ji5Wcc//GjcsBnzF/zEA/c9QkjE8Z9VedlukpNS2LptCxOeeZmObQs/mflt+ZJir03u4QKyM/L45Zdfad+2Izf0H+xbt+OEiYTyst0YWMnLK+BI+vEPVrIz848nCTRp0oRPP/mMw6k5vlbcC+cvJDg4hLDAamSl5ha5bnnZbnIO5+PO91KQ5+FwatFWqXZnECEhIcTFxTFnzlxaNe540jk4LYXXau/efbRrXzg0xe/LCycVOpKRR+bBHI5kFOZ8+FAOFreT8OhArEd/h3rcXjIPHv+AoknD5nz7/SyqBEVjs9kIiwrE6Sr8Hs3JyifzQA7eEsYjLZK73V609Xk5qVSVqRMLfcW9jouLK1WXnUsvvbTSd+2plrAPjCpgOkjJzCOuWqV6K0RERKSUhgwZwkcffcTXX39NSEgIyUdnFwwLC8PlchEUFMS0adPo378/ffr0YejQodSvX5+DBw/yySefkJiYyMyZM/18FlIZHcp38+qOfez97DNar/sfszrkMqt2O8Kte3gsOx0c1fhX9UhW5B+glttCLbeFql4DA4PAMAeRtUOIrHG88BEWGciVQ1sREGT3PewB1mIn6Qmt5uK6/578x1NxgsKc9B5S8rh9J3IG2uldTCue4tgc1lLv17AY9Ln/5FZHJbnyvtalji1tvgA972j+90FHXXJLs1LHdh/YpNSx3f7TqNSxXa9tUOrYzv+u//dBR3W8si4dr6xbqth2PeNo1zOuVLGte9SmdY/afx8INO9Wi+bdSjdBZNMuNWja5a8nuzymYccYGnYs3Zhg9dpEUa9NVKli41pUI65FyQX0E9VqHMFNT5euZXr1+uEMGt+lVLFRdUK55YWSxwE9UdUawaVu7R8W6WLI1O6+12aRgqJJbl4eu3btBMBiNYiMDSm+nmgWnQ7cMKDqsQ8Aitngzz/aii3uHmux9qeiZpWYwD/t8/iLE39m5uXlkePJwOP2kJKynx/n/MjzE57j8l69uf3uW7HZrPTo0YPOnTtz1VVXMfbp8TSoX5+kpCS+n/0dfa68inZt2+EMtGH50+RENrulyIcLDpcVq91CSEQAhsUgLi6On3/+mQEDBmC6LURUqcrDD4/koh7nM3rcSAYNvJmgoCA2bd7Iz78uYPLkyb5zdbpsBIUfb7U3cuQjnNelHY+OeZD7hg7B4XAwf/58rujVlxqxUUREVOV/n71Hnbqx7Nmzm6eeeezka0lhS09XiINGjRry2RczWbRsIXVqxzHz0w9Zvny574NUu9NKfHw8C3/5id1JCURUiSA0NAxnoL3Im3fPPfcwadIkHn96JLffehdbt23hhUnPMuSu+3AFF+Z/4lWzOSw4XYXX0mIrPM8iDUKPvs9PPvkkd911F1WrVKNH90vJyjrMb8uWcsdtd+MICKFDu468MOF5GjSsT0pKCmPGPVWYt8OKI8CG/WhR2h5gK/w9ajnx67Loh9x33HYXH858l7uH3crQIcOpeSSaXbt3MnPmTKa8MhW7s/jfw38WFxfHvHnz6NKlC06nkypVSm49/0+oMuUnMdVqY+zOxCyoRlJGNnHViv80SkRERCq3qVOnAnDhhRcWWT5jxgwGDx4MQN++fVm8eDHjx4/nP//5D5mZmcTGxtK9e3fGjh1bwRnLmWBh6mGmzPySbgvf5o+WB5nSvQa5yX3x5sRhC83GHlzY0uGyjrVI/X4vVWsEE1k7mGqxIUTWDiEo7ORJ+exOK7WbVj1puYhIRTKODkp6rGuu5U8FlhO79v5py5P2Yy1h1u/ijmmzl753gr2YlonFmT17NnXiY7HZbFSpUoVWrVrxyiuvMGjQoCK9Ir/77jv++9//cuddt3HgwAFiYmK44IILqB1Xk4AgOzaH9aRCpcVqwRV8vFWxzW7FYjVwhRQuGzNmDHfeeSf16tUjLy8P0zQ571/tWLhwIf/973+5tNfFmKZJvXr1uO664xOPGAY4XLYivydat2/Ojz/+yKOPPkrHjh1xuVycd955XH/99YSGB/LxxzMZOnQo53VpR6NGjXjllVdOuu+Bwg+NQiICuH/EvWzc8gc3334ThmFw/fXXc8899/D9998D4ApxcN+we1i6fBEXXtKVrKws5s+fT1xcXOG5H70WNWvW5LvvvuOhhx6i60XnERERwW233crYsU8dH1ruhMvmCnbgCnZgdxYW88Kiim/JO2jQIHJzc3nppZd4/KlHqVatGtdcc42vNe+777/DrbfeSrt2hef7/PPPc+mllxIcEUB4dCDBR1tbhkcFEh5etJZjs1uoEnN8WZWY+ixespiRI0dyzfV9ycvLo06dOvTs2RNXsJPAEOOkAnFxJk6cyIgRI3jrrbeoWbMmO3fu/NttTodhVvYmd36QmZlJWFgYGRkZhIb+RZP6fyD9159pN28Nnpy6PNOvPv/pUPpP/ERERM42ubm5JCQkEB8fT0BAwN9vcI76q+tUEfcvUqgirvWyr99l6rLnWV7PRd7BSyhI7QxYsZtwvtvBK6MvILiYQqCIyJlG9wAiZaMs7hPVktBPAqJicBq/kA3sTP2LwYJFRERE5Jzj7NCCJbtak7fjCkx34c18w3wLVziC6fHverhKGMtORERE5HSpSOgnU3NNAj2ZZAO7UzP9nY6IiIiIVCKr1oWSu+8/AIR7DC43XAy4sj7NutY8aYB9ERERkbKgIqGfhEdEEOw+zEEgOfWwv9MRERERkUrkytbVeW3uFppg5+6L6tPu4tpnxGzfIiIicuZSkdBPop12QjzZABw8XPA30SIiIiJyLqkaHcT/buxArYZVcLp0yy4iIiLlT3ccfhLjsBPozQcLZOSoy4iIiIiIFFWvVaS/UxAREZFziKpTfhLltOOicGLpIwWOv4kWEREREREREREpPyoS+kmUw4bdUlgc9HgdZOW5/ZyRiIiIiIiIiIicq1Qk9BMz14M9IBwsuQDsz8z1b0IiIiIiIiIiInLOUpHQT9bM200BkVhsmQAkZ+T4OSMRERERERERETlXqUjoJ4FhTrzWaIyjRcKE1FQ/ZyQiIiIiIiIi/nDhhRcybNiwMt3nzp07MQyD1atXl+l+5eylIqGfBIU5cDuq4KSwSLjzUJqfMxIREZHT8fPPP3PllVdSo0YNDMPgq6++Oilm27Zt3HzzzdSqVQun00l8fDzXX389v//+e8UnfAZ49tlnMQyjyB9Lubm5DBkyhKpVqxIcHEy/fv3Yv39/ke0SExPp3bs3gYGBREVF8dBDD+F2Fx33ecGCBbRt2xan00n9+vV55513KuCMRETkbHPgwAHuvvtuateujdPpJCYmhssuu4xFixb5Ykq6LyjOF198wdNPP12mOcbGxpKUlETz5s3LdL9ng8GDB3PVVVf5O41KR0VCPwkMc+KxhhHoLSwS7k7N8HNGIiIicjqOHDlCq1atmDJlSrHrf//9d9q1a8eWLVt444032LBhA19++SWNGzfmgQceqOBsK7/ly5fzxhtv0LJlyyLLhw8fzv/93//x6aefsnDhQvbt28fVV1/tW+/xeOjduzf5+fksXryYd999l3feeYfRo0f7YhISEujduzcXXXQRq1evZtiwYdx222388MMPFXZ+IiJydujXrx+rVq3i3XffZcuWLcyaNYsLL7yQQ4cOndJ+8vPzAYiIiCAkJKRMc7RarcTExGCz2cp0vxXh2HU5kcfjwev1+iGbc4gpJ8nIyDABMyMjo9yOcTg1xxz4wi/mhY/eadYZ+Y3Z59XPyu1YIiIilV1OTo65YcMGMycnx9+p/COA+eWXX/pee71es1mzZma7du1Mj8dzUnxaWtop7f+vrtM/uX/5+uuvT/mRnZ19ysf5O4cPHzYbNGhgzpkzx+zWrZt5//33m6Zpmunp6abdbjc//fRTX+zGjRtNwFyyZIlpmqb53XffmRaLxUxOTvbFTJ061QwNDTXz8vJM0zTNhx9+2GzWrFmRY1533XXmZZdddkp5VsS9oojIueJMvAdIS0szAXPBggUlxtSpU8cEfI86deqYpmmaTzzxhNmqVSvzrbfeMuPi4kzDMEzTNIv83ju2/ZgxY8wBAwaYgYGBZo0aNczJkycXOQZgvvbaa2bPnj3NgIAAMz4+vsjvyoSEBBMwV61aZZqmac6fP98EzLlz55rt2rUzXS6X2blzZ3PTpk1F9vv000+bkZGRZnBwsHnrrbeaI0eONFu1avWX1+SPP/4we/fubYaEhJjBwcFm165dzW3bthV7bqZpmn379jUHDRp00vnedNNNZkhIiDlo0CBzxowZZlhYmPn111+bTZo0Ma1Wq5mQkGDm5uaaDzzwgFmjRg0zMDDQ7Nixozl//nzfvo5tN3v2bLNx48ZmUFCQedlll5n79u3zvQcnvjdAke3PVGVxn3jmlZPPEq5QB8F5JsGebAAOHS7wc0YiIiKVi2maZPvp0+JAiwXDMP7xflavXs369ev56KOPsFhO7sARHh7+j49RFk61u41hGGzdupW6deuWaR5Dhgyhd+/e9OjRg7Fjx/qWr1ixgoKCAnr06OFb1rhxY2rXrs2SJUvo1KkTS5YsoUWLFkRHR/tiLrvsMu6++27Wr19PmzZtWLJkSZF9HIv5uzGg8vLyyMvL873OzMz8h2cqIiJ/pyDPA4DNcfx3ssftxesxsVgMrHbLybF2C4blaKzHi9dtYljAZrf+bazVWvqOlsHBwQQHB/PVV1/RqVMnnE7nSTHLly8nKiqKGTNm0LNnT6zW4zls27aNzz//nC+++KLI8j974YUXePTRR3nqqaf44YcfuP/++2nYsCGXXHKJL+bxxx/n2Wef5eWXX+b9999nwIABrFu3jiZNmpS43//+979MnDiRyMhI7rrrLm655RZfN+kPP/yQcePG8dprr9GlSxdmzpzJxIkTiY+PL3F/e/fu5YILLuDCCy/kp59+IjQ0lEWLFp005MffmTBhAqNHj+aJJ54A4JdffiE7O5vnnnuOadOmUbVqVaKiorj33nvZsGEDM2fOpEaNGnz55Zf07NmTdevW0aBBAwCys7OZMGEC77//PhaLhRtvvJEHH3yQDz/8kAcffJCNGzeSmZnJjBkzgMKWnAIqEvqJ1WqhGhYOefPBAhm56vktIiJyomyvl3o/r/PLsbdf0IKgv7hpL62tW7cChQWtyi45OZmoqKhSxZZ1dyiAmTNnsnLlSpYvX37SuuTkZBwOx0lF1ejoaJKTk30xJxYIj60/tu6vYjIzM8nJycHlchWb2/jx43nqqadO67xEROT0vHn/QgBueaErrhAHAKt+TOS3WTto2qU6F910vAj29kO/4M73ctPYzoRWK/xZ/seCvfz66VYadIjm0lub+WLf++9icrMKGDC6I1VrBAOwaXESzc6vWercbDYb77zzDrfffjuvv/46bdu2pVu3bgwYMMA3XEZkZCRQ+IFgTExMke3z8/N57733fDEl6dKlC4888ggADRs2ZNGiRbz00ktFioT9+/fntttuA+Dpp59mzpw5vPrqq7z22msl7nfcuHF069YNgEceeYTevXuTm5tLQEAAr776Krfeeis333wzAKNHj+bHH38kKyurxP1NmTKFsLAwZs6cid1u9+V7qrp3715kKJZffvmFgoICXnvtNVq1agUUjj88Y8YMEhMTqVGjBgAPPvggs2fPZsaMGTzzzDMAFBQU8Prrr1OvXj0A7r33XsaMGQMUFnldLhd5eXknvTfnOlWm/CjKbufYreiRfCemafo1HxERESlbZ8rv9kGDBpVYICvOjTfeSGhoaJkdf/fu3dx///18+OGHBAQElNl+y8qoUaPIyMjwPXbv3u3vlERExM/69evHvn37mDVrFj179vRNjFWaCbHq1KnztwVCgM6dO5/0euPGjacc82cnjvtbvXp1AFJSUgDYvHkzHTt2LBL/59d/tnr1as4//3xfgfB0tW/f/qRlDoejSL7r1q3D4/HQsGFDX4vO4OBgFi5cyPbt231xgYGBvgIhFJ7nsXOUkqkloR9VdzmwWwq/ibymjcxcN2Guf/ZNJSIicrYItFjYfkELvx27LBz7FH3Tpk20adOmTPZZHo51tSmtqVOnlunxV6xYQUpKCm3btvUt83g8/Pzzz0yePJkffviB/Px80tPTi7Qm3L9/v68FQExMDMuWLSuy32OzH58Y8+cZkffv309oaOhfFkmdTmexXclERKT83PFyYUs3m+P47+Q2l9am1cWxWCxFhwS55YXzC2NP6ILc/MKaNO1aA+NPv9IHjvvXSbGN/1X9tHIMCAjgkksu4ZJLLuHxxx/ntttu44knnmDw4MF/uV1QUNBpHa+snFjMO9aV+59MCPJ3HzRaLJaTPjgtKDh5yLXirovL5SoyBExWVhZWq5UVK1ac1FU7ODjY9/zPBUvDMM6YD2/9SS0J/SgszIlhCwdL4biEKZm5/k1IRESkEjEMgyCr1S+PshiPEKB169Y0bdqUiRMnFnvznZ6eXibHOdNdfPHFrFu3jtWrV/se7du354YbbvA9t9vtzJs3z7fN5s2bSUxM9LWg6Ny5M+vWrSvSSmDOnDmEhobStGlTX8yJ+zgW8+dWGCIi4n92pxW7s+jvZKvNgt1pLTIeYZHYE4qHVmth7InjEf5VbFlo2rQpR44cOX4sux2Px3Pa+1u6dOlJr/881mBpYk5Fo0aNThr6o7ihQE7UsmVLX9fg4kRGRpKUlOR77fF4+OOPP04rvzZt2uDxeEhJSaF+/fpFHqfSddjhcPyj9+ZspSKhHwWFOvDYIrHYCwe/TlaRUERE5IyTlZXlK2wBJCQksHr1ahITEzEMgxkzZrBlyxbOP/98vvvuO3bs2MHatWsZN24cffv29W/yfzJt2jQGDRrka1n48ccf06RJE+rWresbRLw8hISE0Lx58yKPoKAgqlatSvPmzQkLC+PWW29lxIgRzJ8/nxUrVnDzzTfTuXNnOnXqBMCll15K06ZNuemmm1izZg0//PADjz32GEOGDPG1ArzrrrvYsWMHDz/8MJs2beK1117jk08+Yfjw4eV2biIicvY5dOgQ3bt354MPPmDt2rUkJCTw6aef8vzzzxf53R4XF8e8efNITk4mLS3tlI+zaNEinn/+ebZs2cKUKVP49NNPuf/++4vEfPrpp7z99tts2bKFJ554gmXLlnHvvfee9rndd999TJ8+nXfffZetW7cyduxY1q5d+5cfoN57771kZmYyYMAAfv/9d7Zu3cr777/P5s2bgcKxBr/99lu+/fZbNm3axN13333aH5Q2bNiQG264gYEDB/LFF1+QkJDAsmXLGD9+PN9++22p9xMXF8fatWvZvHkzBw8eLLHAea5RkdCPAsOceK0xGLbCImHCoVQ/ZyQiIiKn6vfff6dNmza+7sQjRoygTZs2jB49Gigcx+f333+nfv363H777TRp0oQ+ffqwfv16Jk2a5MfMi5o0aRLDhg0jKyuL//73v4wbN44hQ4Zw4403MnjwYCZNmsSbb77pt/xeeuklrrjiCvr168cFF1xATEwMX3zxhW+91Wrlm2++wWq10rlzZ2688UYGDhzoG6QcID4+nm+//ZY5c+bQqlUrJk6cyLRp07jsssv8cUoiInKGCg4O5rzzzuOll17iggsuoHnz5jz++OPcfvvtTJ482Rc3ceJE5syZQ2xs7GkNO/LAAw/47jPGjh3Liy++eNLvrKeeeoqZM2fSsmVL3nvvPf73v//5WtCfjhtuuIFRo0bx4IMP0rZtWxISEhg8ePBfjhlctWpVfvrpJ7KysujWrRvt2rXjrbfe8nX5veWWWxg0aBADBw6kW7du1K1bl4suuui0c5wxYwYDBw7kgQceoFGjRlx11VUsX76c2rVrl3oft99+O40aNaJ9+/ZERkb6Znc+1xmmOmWfJDMzk7CwMDIyMsp0UO4/274yhTFzfmZZ/npyjrTnlm5VGd2rU7kdT0REpLLKzc0lISGB+Pj4SjlxRWXxV9fpn96/NGnShMcff5z//Oc/rFq1io4dO/L6669z6623AjB9+nSmTp3K77//XibnciarqHtFEZFzge4BihcXF8ewYcMYNmxYiTGGYfDll19y1VVXlWsul1xyCTExMbz//vvlehz5Z8riPlEtCf0oMMyJ2xZKkOcwAHtSM/2ckYiIiJyrdu3aRdeuXYHC8X6sVquvKy9At27diswaKCIiImef7OxsXnzxRdavX8+mTZt44oknmDt3LoMGDfJ3alIBNLuxHwWFObASSIg7k4NAcmqWv1MSERGRc1RgYGCRwdYjIyOLzBII4Ha7KzotERERqUCGYfDdd98xbtw4cnNzadSoEZ9//jk9evTwd2pSAVQk9KPAMAfBuRDsyQHg4GENlCkiIiL+0bhxY9auXeubEXH37t1F1m/atIm4uDg/ZCYiInLu2blz59/GlMfocS6Xi7lz55b5fuXMoCKhH9nsViJMgzRPPlggM1e9v0VERMQ/nnvuOYKCgkpcn5iYyJ133lmBGYmIiIhIRVKR0M8ibTb2Ulj9zy5wYprmX04tLiIiIlIeunTp8pfr77nnngrKRERERET8QU3X/CzG5cBuKazVek0r6dnqciwiIiKVwz333MPBgwf9nYaIiIiIVAAVCf0sItQB9ioY1sJJS/YfzvVzRiIiIiKFPvjgAzIzM/2dhoiIiIhUABUJ/Swo1InXGoVhOwxAcoaKhCIiIlI5lMeA6CIiIiJSOalI6GeBYQ681hgMW+Gn9LvS0v2bkIiIiIiIiIiInHNUJPSzoDAnbkcEDqOwSLjzUJqfMxIREREpdPjwYerWrevvNERERAQwDIOvvvrK32nIWUxFQj8LDHPgsYUS5CksEu5Jy/BzRiIiIiKQkpLCH3/8wdq1a4s8REREznWGYfzl48knnyxx2507d2IYBqtXr66wfCsrXYvKR0VCPwsKc2IYQYQWFBYJk1OP+DkjEREROR3Jycncd9991K1bF6fTSWxsLFdeeSXz5s0rEhcfH8/cuXNZsGABffv2pXr16gQFBdG6dWs+/PBDP2V/3IoVK2jevDnVq1enZcuWtG7dmjZt2vj+FxEROdclJSX5HpMmTSI0NLTIsgcffNDfKZYJj8eD1+s9aXl+fr4fspGKoCKhnwWGOQjOhWBPNgAHswr8nJGIiIicqp07d9KuXTt++uknXnjhBdatW8fs2bO56KKLGDJkiC9u7dq1pKWl0a1bNxYvXkzLli35/PPPWbt2LTfffDMDBw7km2++8eOZwC233ELDhg1ZvHgxO3bsICEhocj/IiIi57qYmBjfIywsDMMwfK+joqJ48cUXqVWrFk6nk9atWzN79mzftvHx8QC0adMGwzC48MILAVi+fDmXXHIJ1apVIywsjG7durFy5cpTysvr9fL8889Tv359nE4ntWvXZty4cQAsWLAAwzBIT0/3xa9evRrDMNi5cycA77zzDuHh4cyaNYumTZvidDpJTEwkLi6Op59+moEDBxIaGsodd9wBwK+//sr555+Py+UiNjaWoUOHcuTI8YZPcXFxPPPMM9xyyy2EhIRQu3Zt3nzzzb+9FuI/Nn8ncK5zBNgI90CGNx8MyMxR3VZERAQKZ9Y1c3L8cmzD5cIwjFLH33PPPRiGwbJlywgKCvItb9asGbfccovv9ddff03Pnj2x2+08+uijRfZx//338+OPP/LFF19wxRVX/POTOE07duzg888/p379+n7LQUREzl2maeLOP7n1WkWwOSyn9Pu/OC+//DITJ07kjTfeoE2bNrz99tv06dOH9evX06BBA5YtW0bHjh2ZO3cuzZo1w+FwAIXjAA8aNIhXX30V0zSZOHEil19+OVu3biUkJKRUxx41ahRvvfUWL730El27diUpKYlNmzadUv7Z2dk899xzTJs2japVqxIVFQXAhAkTGD16NE888QQA27dvp2fPnowdO5a3336bAwcOcO+993LvvfcyY8YM3/4mTpzI008/zaOPPspnn33G3XffTbdu3WjUqFGJ10L8R0XCSiDKZiPFNMGA7AInXq+JxfLPfjCJiIic6cycHDa3beeXYzdauQIjMLBUsampqcyePZtx48YVKRAeEx4e7ns+a9YsRowYUeK+MjIyaNKkySnnW5Yuvvhi1qxZoyKhiIj4hTvfy5v3L/TLse94uRt2p/Uf7WPChAmMHDmSAQMGAPDcc88xf/58Jk2axJQpU4iMjASgatWqxMTE+Lbr3r17kf28+eabhIeHs3DhwlJ9eHj48GFefvllJk+ezKBBgwCoV68eXbt2PaX8CwoKeO2112jVqlWR5d27d+eBBx7wvb7tttu44YYbGDZsGAANGjTglVdeoVu3bkydOpWAgAAALr/8cu655x4ARo4cyUsvvcT8+fNp1KhRiddC/EdFwkog2mlni8UOeDGxcOhIPpEhTn+nJSIiIqWwbds2TNOkcePGfxm3d+9e1q5dS69evYpd/8knn7B8+XLeeOON8kiz1KZNm8agQYP4448/aN68OXa7vcj6Pn36+CkzERGRyi0zM5N9+/bRpUuXIsu7dOnCmjVr/nLb/fv389hjj7FgwQJSUlLweDxkZ2eTmJhYqmNv3LiRvLw8Lr744tPOH8DhcNCyZcuTlrdv377I6zVr1rB27doi4ymbponX6yUhIcH3oeeJ+zrWLTslJeUf5SjlR0XCSqBqqANPWlUM7xFMTwj7M3NVJBQRkXOe4XLRaOUKvx27tEzTLFXcrFmz6Nq1a5GWhcfMnz+fm2++mbfeeotmzZqV+tjlYcmSJSxatIjvv//+pHWGYeDxePyQlYiInCtsDgt3vNzNb8f2l0GDBnHo0CFefvll6tSpg9PppHPnzqWeJMT1N/cuFkvhuZ1431JQcPKcCK4Shlz5c2+JrKws7rzzToYOHXpSbO3atX3P//xho2EYxU6GIpWDioSVQHBoAKY1EsOS6SsSNq8Z5u+0RERE/MowjFJ3+fWnBg0aYBjG3475M2vWrGJb4S1cuJArr7ySl156iYEDB5ZXmqV23333ceONN/L4448THR3t73REROQcYxjGP+7y6y+hoaHUqFGDRYsW0a3b8ULnokWL6NixI4Bv3L0/f+i2aNEiXnvtNS6//HIAdu/ezcGDB0t97AYNGuByuZg3bx633XbbSeuPde1NSkqiSpUqQOHEJaerbdu2bNiw4R8NT1LStRD/0SwZlUBgmAOvPQbDnglAYnqGnzMSERGR0oqIiOCyyy5jypQpRWb0OyY9PZ2srCzmz59P3759i6xbsGABvXv35rnnnvPNFOhvhw4dYvjw4SoQioiInIaHHnqI5557jo8//pjNmzfzyCOPsHr1au6//34AoqKicLlczJ49m/3795ORUfj3f4MGDXj//ffZuHEjv/32GzfccMPftg48UUBAACNHjuThhx/mvffeY/v27SxdupTp06cDUL9+fWJjY3nyySfZunUr3377LRMnTjzt8xw5ciSLFy/m3nvvZfXq1WzdupWvv/6ae++9t9T7KOlaiP+oSFgJBIU5cNsjcFBYJNx1KM3PGYmIiMipmDJlCh6Ph44dO/L555+zdetWNm7cyCuvvELnzp2ZPXs2DRs2JC4uzrfN/Pnz6d27N0OHDqVfv34kJyeTnJxMamqq/04EuPrqq5k/f75fcxARETlTDR06lBEjRvDAAw/QokULZs+ezaxZs2jQoAEANpuNV155hTfeeIMaNWr4PkCcPn06aWlptG3blptuuomhQ4f6ZhYurccff5wHHniA0aNH06RJE6677jrf+H92u53//e9/bNq0iZYtW/Lcc88xduzY0z7Pli1bsnDhQrZs2cL5559PmzZtGD16NDVq1Cj1Pkq6FuI/hlnagXTOIZmZmYSFhZGRkUFoaGi5H2/3xlRGfv87Ww5/x8GcS7i4mYPpN11S7scVERGpLHJzc0lISCA+Pt43G96ZJikpiXHjxvHNN9+QlJREZGQk7dq1Y/jw4UyfPp06deoUuRkfPHgw77777kn76datGwsWLCj2GH91ncrq/mXcuHFMmjSJ3r1706JFi5PGEipu7KFzTUXfK4qInM3OhnsAkcqgLO4TNSZhJRAY5sAwAglxZ3IQSE47uauSiIiIVG7Vq1dn8uTJTJ48uchyt9tNv379TpoI5J133uGdd96pwAxLZ9q0aQQHB7Nw4UIWLlxYZJ1hGCoSioiIiJylVCSsBILCnATnQognG4BDh0+eYUhERETOTKmpqQwfPpwOHTr4O5VSSUhI8HcKIiIiIuIHGpOwEnAG2gjLhyBP4dTmmbl6W0RERM4WUVFRPPbYYxiG4e9URERERERKpGpUJWAYBpE2K4EUDg+ZXeDA7fH6OSsRERGR477++mvee+89f6chIiIiIuVERcJKItppx244AQ9g4dCRfH+nJCIiIuIzcuRIbr75Zn+nISIiZynNqSryz5TF95CKhJVEtRAHHnsVDFsWAPszc/2ckYiIiMhxmzZtwuPx+DsNERE5y9jtdgCys7P9nInIme3Y99Cx76nToYlLKongUCdeWySGmYnpDiM5I5eWtfydlYiIiEih9PR0PvjgA+69915/pyIiImcRq9VKeHg4KSkpAAQGBmocX5FTYJom2dnZpKSkEB4ejtVqPe19qUhYSQSGOfHYYrCYmXiB3emZQIy/0xIREZFz3Lx585g+fTpffvklgYGBKhKKiEiZi4kp/Nv3WKFQRE5deHi473vpdKlIWEkEhjnw2COxF+zDDexMTfN3SiIiInKO2r17NzNmzGDGjBkkJiYyYMAAvvzySy6++GJ/pyYiImchwzCoXr06UVFRFBQU+DsdkTOO3W7/Ry0Ij1GRsJIICnPitoUSlHuYHGBP2mF/pyQiIiLnkIKCAr766iumTZvGL7/8Qs+ePXnhhRe4/vrr+e9//0vTpk39naKIiJzlrFZrmRQ6ROT0qEhYSQSGOTCMIELcmRwE9qdl+TslEREROYfUrFmTxo0bc+ONNzJz5kyqVKkCwPXXX+/nzERERESkImh240oiKMxJcB6Euo8AcOiw288ZiYiIyKlITk7mvvvuo27dujidTmJjY7nyyiuZN29ekbj4+Hjmzp1bZNm2bdsICQkhPDy8AjMuyu12YxgGhmGoFYeIiIjIOUgtCSsJV7CdkDyTLE8+GJCZq/qtiIic20zTJMed45dju2yuU5pZcefOnXTp0oXw8HBeeOEFWrRoQUFBAT/88ANDhgxh06ZNAKxdu5a0tDS6devm27agoIDrr7+e888/n8WLF5f5uZTWvn37+Pzzz5k+fTr3338/vXr14sYbb9QMkyIiIiLnCBUJKwnDYhBpsZJummBAjttJgceL3apioYiInJty3Dmc99F5fjn2b//5jUB7YKnj77nnHgzDYNmyZQQFBfmWN2vWjFtuucX3+uuvv6Znz57Y7Xbfsscee4zGjRtz8cUX+7VIGBAQwA033MANN9zA9u3bmTFjBkOHDsXtdjNu3DgGDx5M9+7d1cpQRERE5CylClQlEuW0Y7c4gcKuxgcO5/k3IREREflbqampzJ49myFDhhQpEB5zYhfiWbNm0bdvX9/rn376iU8//ZQpU6ZURKqlVq9ePcaOHcuuXbv49ttvycvL44orriA6OtrfqYmIiIhIOVFLwkokKtiB+3AEhnkY012F/Zm51Ah3+TstERERv3DZXPz2n9/8duzS2rZtG6Zp0rhx47+M27t3L2vXrqVXr14AHDp0iMGDB/PBBx8QGhr6j/ItLxaLhV69etGrVy8OHDjA+++/7++URERERKScqEhYiQSFOfEeiMIg82iRUC0JRUTk3GUYxil1+fUX0zRLFTdr1iy6du3qa1l4++2385///IcLLrigHLMrO5GRkYwYMcLfaYiIiIhIOVF340okKNSB1xaNxXYYgN3pmX7OSERERP5OgwYNMAzDNzlJSWbNmkWfPn18r3/66ScmTJiAzWbDZrNx6623kpGRgc1m4+233y7vtIuIiIjg4MGDpY6vXbs2u3btKseMRERERKSiqSVhJRIY5sRtj8Tm3osb2JWa5u+URERE5G9ERERw2WWXMWXKFIYOHXrSuITp6enYbDbmz5/P1KlTfcuXLFmCx+Pxvf7666957rnnWLx4MTVr1qyw/I/l+P333xMWFlaq+EOHDhXJXURERETOfCoSViJBYQ48tlCC8zLJBfakHfZ3SiIiIlIKU6ZMoUuXLnTs2JExY8bQsmVL3G43c+bMYerUqTz99NM0bNiQuLg43zZNmjQpso/ff/8di8VC8+bNKzj7QoMGDfLLcUVERESkclCRsBIJDHOCJZiQgkwOAslpR/ydkoiIiJRC3bp1WblyJePGjeOBBx4gKSmJyMhI2rVrx9SpU5k+fXqRrsaVjdfr9XcKIiIiIuJnKhJWIkFhDoLyINSdDcChLLefMxIREZHSql69OpMnT2by5MlFlrvdbvr168f333//l9sPHjyYwYMHl2OGIiIiIiIlU5GwEnGFOgjJNcn25gOQmaN5ZURERM50qampDB8+nA4dOvg7FRERERGREqkKVYlYrRaqGhYCTROAPI+D3AINCi4iInImi4qK4rHHHsMwDH+nIiIiIiJSIhUJK5lohx2bEQBGAQAHDuf5OSMRERERERERETnbqUhYyUQFOXDbIzBsmQCkHM71c0YiIiIiIiIiInK2U5GwkgkOc+K1RWGxHQZgf6ZaEoqIiEjF6datG++99x45OTn+TkVEREREKpCKhJVMUKgDry3a15Jwd9phP2ckIiIi55I2bdrw4IMPEhMTw+23387SpUv9nZKIiIiIVAAVCSuZwDAnBY5q2CyFRcJdaen+TUhERETOKZMmTWLfvn3MmDGDlJQULrjgApo2bcqECRPYv3+/v9MTERERkXJSaYqEzz77LIZhMGzYsBJj1q9fT79+/YiLi8MwDCZNmlRs3N69e7nxxhupWrUqLpeLFi1a8Pvvv5dP4mUsKMyBxxZGkKewSLhHLQlFRESkgtlsNq6++mq+/vpr9uzZw3/+8x8ef/xxYmNjueqqq/jpp5/8naKIiIiIlLFKUSRcvnw5b7zxBi1btvzLuOzsbOrWrcuzzz5LTExMsTFpaWl06dIFu93O999/z4YNG5g4cSJVqlQpj9TLXGCYEyzBhBYUFgn3px3xc0YiIiJyrlq2bBlPPPEEEydOJCoqilGjRlGtWjWuuOIKHnzwQX+nJyIiIiJlyObvBLKysrjhhht46623GDt27F/GdujQgQ4dOgDwyCOPFBvz3HPPERsby4wZM3zL4uPjyy7hchYU5iAoz0JoQTYAh7Lcfs5IREREziUpKSm8//77zJgxg61bt3LllVfyv//9j8suuwzDMAAYPHgwPXv2ZMKECX7OVkRERETKit9bEg4ZMoTevXvTo0ePMtnfrFmzaN++Pf379ycqKoo2bdrw1ltv/eU2eXl5ZGZmFnn4S2CYg+AcL8HewlmNM3P9/haJiIhIKSQnJ3PfffdRt25dnE4nsbGxXHnllcybN69IXHx8PHPnzgXghx9+oFOnToSEhBAZGUm/fv3YuXOnH7I/rlatWkybNo1BgwaxZ88ePvvsM3r27OkrEAK0bNnS98GtiIiIiJwd/NqScObMmaxcuZLly5eX2T537NjB1KlTGTFiBI8++ijLly9n6NChOBwOBg0aVOw248eP56mnniqzHP4Jm91KBAbZpgkG5HvsZOe7CXT4vdGniIhIhTJNk5wCj1+O7bJbixTF/s7OnTvp0qUL4eHhvPDCC7Ro0YKCggJ++OEHhgwZwqZNmwBYu3YtaWlpdOvWjYSEBPr27cuIESP48MMPycjIYPjw4Vx99dWsXLmyvE7tb82bN4/zzz//L2NCQ0OZP39+BWUkIiIiIhXBb5Wn3bt3c//99zNnzhwCAgLKbL9er5f27dvzzDPPANCmTRv++OMPXn/99RKLhKNGjWLEiBG+15mZmcTGxpZZTqcqym4jyXCB9Qh4gtiecoQWtcL8lo+IiIg/5BR4aDr6B78ce8OYy07pA7p77rkHwzBYtmwZQUFBvuXNmjXjlltu8b3++uuv6dmzJ3a7nRUrVuDxeBg7diwWS2HPgQcffJC+fftSUFCA3W4vuxM6BU888QRffPEF4eHhRZZnZmZq0hIRERGRs5jf+rKuWLGClJQU2rZti81mw2azsXDhQl555RVsNhsez+m1HKhevTpNmzYtsqxJkyYkJiaWuI3T6SQ0NLTIw5+iA5247RFYA/YAsGp3ml/zERERkZKlpqYye/ZshgwZUqRAeMyJxbZZs2bRt29fANq1a4fFYmHGjBl4PB4yMjJ4//336dGjh98KhAALFy4kPz//pOW5ubn88ssvfshIRERERCqC31oSXnzxxaxbt67IsptvvpnGjRszcuRIrFbrae23S5cubN68uciyLVu2UKdOndPOtaKFhDnwZERidSXiOdKI1YnpDOzs76xEREQqlstuZcOYy/x27NLatm0bpmnSuHHjv4zbu3cva9eupVevXkDh2IQ//vgj1157LXfeeScej4fOnTvz3Xff/aPcT9fatWuBwm7eGzZsIDk52bfO4/Ewe/Zsatas6ZfcRERERKT8+a1IGBISQvPmzYssCwoKomrVqr7lAwcOpGbNmowfPx6A/Px8NmzY4Hu+d+9eVq9eTXBwMPXr1wdg+PDh/Otf/+KZZ57h2muvZdmyZbz55pu8+eabFXh2/0xQqBOvPQarawEAKxJT/ZuQiIiIHxiGcUaMyWuaZqniZs2aRdeuXX0tC5OTk7n99tsZNGgQ119/PYcPH2b06NFcc801zJkz55TGRCwLrVu3xjAMDMOge/fuJ613uVy8+uqrFZqTiIiIiFScSn3nnZiY6BujB2Dfvn20adPG93rChAlMmDCBbt26sWDBAgA6dOjAl19+yahRoxgzZgzx8fFMmjSJG264oaLTP22BYQ7c9kiCjURygF2Hckg7kk+VIIe/UxMREZE/adCgAYZh+CYnKcmsWbPo06eP7/WUKVMICwvj+eef9y374IMPiI2N5bfffqNTp07llnNxEhISME2TunXrsmzZMiIjI33rHA4HUVFRp93TQ0REREQqv0pVJDxW6CvpdVxcXKk+rb/iiiu44ooryjCzihUU5sRjC6NBSjaHHCl486NYvTudixpH+Ts1ERER+ZOIiAguu+wypkyZwtChQ08alzA9PR2bzcb8+fOZOnWqb3l2dnaRD0MBXxHO6/WWf+J/cmxoFn8cW0RERET8r1IVCaVQYJgD0xpCvSRYFrcbb34UqxLTVCQUERGppKZMmUKXLl3o2LEjY8aMoWXLlrjdbubMmcPUqVN5+umnadiwIXFxcb5tevfuzUsvvcSYMWN83Y0fffRR6tSpU6TnREWYNWsWvXr1wm63M2vWrL+MPbE1pIiIiIicPVQkrISCwpwE5RmE5URgdSXizmjHqt3p/k5LRERESlC3bl1WrlzJuHHjeOCBB0hKSiIyMpJ27doxdepUpk+fflJxrXv37nz00Uc8//zzPP/88wQGBtK5c2dmz56Ny+Wq0PyvuuoqkpOTiYqK4qqrrioxzjAMPB5PxSUmIiIiIhVGRcJKKDDMQXCOlxxXQ6yu3QCs3p2G12tisVTsIOYiIiJSOtWrV2fy5MlMnjy5yHK3202/fv34/vvvT9pmwIABDBgwoKJSLNGJXYzV3VhERETk3GT5+xCpaI4AG2Eeg101mxKVmwRGPodzPew4mOXv1EREROQUpaamMnz4cDp06ODvVE5benq6v1MQERERkXKmImElFW23sTG+Pg2SPFhdewBYmZju36RERETklEVFRfHYY49hGGdGb4DnnnuOjz/+2Pe6f//+REREULNmTdasWePHzERERESkPKlIWEnFuBwkxNSkTooVS8CxLsfp/k1KREREznqvv/46sbGxAMyZM4e5c+cye/ZsevXqxUMPPVRuxx0/fjwdOnQgJCTENzbi5s2bi8Tk5uYyZMgQqlatSnBwMP369WP//v1FYhITE+nduzeBgYFERUXx0EMP4Xa7i8QsWLCAtm3b4nQ6qV+/Pu+88065nZeIiIjImUJFwkoqJMxBlWwDh7cGVlciAKvUklBERM5ypmn6O4VKrSKuT3Jysq9I+M0333Dttddy6aWX8vDDD7N8+fJyO+7ChQsZMmQIS5cuZc6cORQUFHDppZdy5MgRX8zw4cP5v//7Pz799FMWLlzIvn37uPrqq33rPR4PvXv3Jj8/n8WLF/Puu+/yzjvvMHr0aF9MQkICvXv35qKLLmL16tUMGzaM2267jR9++KHczk1ERETkTKCJSyqpoFAnNQ8dISO0CfaApeQCm5MzOZLnJsipt01ERM4udrsdgOzs7Aqf2fdMkp2dDRy/XuWhSpUq7N69m9jYWGbPns3YsWOBwgJlec5sPHv27CKv33nnHaKiolixYgUXXHABGRkZTJ8+nY8++oju3bsDMGPGDJo0acLSpUvp1KkTP/74Ixs2bGDu3LlER0fTunVrnn76aUaOHMmTTz6Jw+Hg9ddfJz4+nokTJwLQpEkTfv31V1566SUuu+yyYnPLy8sjLy/P9zozM7OcroKIiIiI/6jaVEkFhjmokehma53G1Embw3pbOl53OGv3ZNC5XlV/pyciIlKmrFYr4eHhpKSkABAYGHjGjOFXEUzTJDs7m5SUFMLDw7FareV2rKuvvpr//Oc/NGjQgEOHDtGrVy8AVq1aRf369cvtuH+WkZEBQEREBAArVqygoKCAHj16+GIaN25M7dq1WbJkCZ06dWLJkiW0aNGC6OhoX8xll13G3Xffzfr162nTpg1Lliwpso9jMcOGDSsxl/Hjx/PUU0+V4dmJiIiIVD4qElZSQWEOaqa6mdWuPj1+MdkUmYj7cDird6erSCgiImelmJgYAF+hUE4WHh7uu07l5aWXXiIuLo7du3fz/PPPExwcDEBSUhL33HNPuR77GK/Xy7Bhw+jSpQvNmzcHCrtBOxwOwsPDi8RGR0eTnJzsizmxQHhs/bF1fxWTmZlJTk5OsS1ZR40axYgRI3yvMzMzfV2yRURERM4WKhJWUoFhTqLSPaSGVSE63YW19m7ch1uyKjHN36mJiIiUC8MwqF69OlFRURQUFPg7nUrHbreXawvCE4/z4IMPnrR8+PDh5X7sY4YMGcIff/zBr7/+WmHH/CtOpxOn0+nvNERERETKlYqElVRgmAObF6pnmmCNw3Js8pLd6ZimqS5YIiJy1rJarRVSDJOSbd26lfnz55OSkoLX6y2y7sRJQMrDvffeyzfffMPPP/9MrVq1fMtjYmLIz88nPT29SGvC/fv3+1pXxsTEsGzZsiL7Ozb78Ykxf54Ref/+/YSGhmo8TBERETmnqUhYSQWFFX5aXf1gAfurNSPI+JocPBw4nMfe9BxqVQn0c4YiIiJyNnrrrbe4++67qVatGjExMUU+mDQMo9yKhKZpct999/Hll1+yYMEC4uPji6xv164ddrudefPm0a9fPwA2b95MYmIinTt3BqBz586MGzeOlJQUoqKiAJgzZw6hoaE0bdrUF/Pdd98V2fecOXN8+xARERE5V6lIWEk5A23YnFZqHHKzKa4B9VMKSA3Yhzc3ltW701UkFBERkXIxduxYxo0bx8iRIyv0uEOGDOGjjz7i66+/JiQkxDeGYFhYGC6Xi7CwMG699VZGjBhBREQEoaGh3HfffXTu3JlOnToBcOmll9K0aVNuuukmnn/+eZKTk3nssccYMmSIr7vwXXfdxeTJk3n44Ye55ZZb+Omnn/jkk0/49ttvK/R8RURERCobi78TkOIZhkFkbDA1Ut1srlOXevvA6toNwKrEdP8mJyIiImettLQ0+vfvX+HHnTp1KhkZGVx44YVUr17d9/j44499MS+99BJXXHEF/fr144ILLiAmJoYvvvjCt95qtfLNN99gtVrp3LkzN954IwMHDmTMmDG+mPj4eL799lvmzJlDq1atmDhxItOmTeOyyy6r0PMVERERqWzUkrASi44LZd+2DHAGEpYTgdWVSEHavzR5iYiIiJSb/v378+OPP3LXXXdV6HFN0/zbmICAAKZMmcKUKVNKjKlTp85J3Yn/7MILL2TVqlWnnKOIiIjI2UxFwkosKi4UA4jN9JLraoDVtQOAP/Zlku/24rCpIaiIiIiUrfr16/P444+zdOlSWrRogd1uL7J+6NChfspMRERERMqTioSVWHR8KABR+/LYXaMpVXOWkW09Qr47iI1JmbSKDfdvgiIiInLWefPNNwkODmbhwoUsXLiwyDrDMFQkFBERETlLqUhYiYVEBOAKsVP9kJsVcfVpvMlkr2s3niONWZWYpiKhiIiIlLmEhAR/pyAiIiIifqD+qpWYYRhEx4VSI9VNQo1Y4vZbsboSAVi1O92/yYmIiMhZLT8/n82bN+N2u/2dioiIiIhUABUJK7mouFBCck2qmFYc3urHi4Sa4VhERETKQXZ2NrfeeiuBgYE0a9aMxMTCe4/77ruPZ5991s/ZiYiIiEh5UZGwkouOKxyXsEaqh4yQptgCdgNeElOzOZSV59/kRERE5KwzatQo1qxZw4IFCwgICPAt79GjBx9//LEfMxMRERGR8qQiYSUXdbRIGJWUx/bajYhNzcXiOADAanU5FhERkTL21VdfMXnyZLp27YphGL7lzZo1Y/v27X7MTERERETKk4qElVxAkJ2wSBc1DrnZGF+fBvtMLK7dgLoci4iISNk7cOAAUVFRJy0/cuRIkaKhiIiIiJxdVCQ8A0TFhVI9zc2B8KrEpLlOmLwkzc+ZiYiIyNmmffv2fPvtt77XxwqD06ZNo3Pnzv5KS0RERETKmc3fCcjfi44LZevy/dTINzBtcb4i4Zrd6Xi8JlaLPtUXERGRsvHMM8/Qq1cvNmzYgNvt5uWXX2bDhg0sXryYhQsX+js9ERERESknakl4BoiOLxyXMOZAPilVm+IyksHIIyvPw/YDWX7OTkRERM4mXbt2ZfXq1bjdblq0aMGPP/5IVFQUS5YsoV27dv5OT0RERETKiVoSngGqxQZjsRhE7y9gS1xD6u03SXftwZNdj1WJaTSMDvF3iiIiInIWqVevHm+99Za/0xARERGRCqSWhGcAm91K1VrB1DjkZlOdutRLMo+PS6jJS0REROQfyszMLPVDRERERM5Oakl4hoiOCyXql8O4A8IIz66iGY5FRESkzISHh5d65mKPx1PO2YiIiIiIP6hIeIaIigvF+vNeah0xyQtogNW1CYAtKYfJynMT7NRbKSIiIqdn/vz5vuc7d+7kkUceYfDgwb7ZjJcsWcK7777L+PHj/ZWiiIiIiJQzVZbOENFxhZOXRCXnkVijKRG5y8m2p2EWVGHt7nT+Vb+anzMUERGRM1W3bt18z8eMGcOLL77I9ddf71vWp08fWrRowZtvvsmgQYP8kaKIiIiIlDONSXiGCI8JxB5gpfoBNxvjGlB/3/FxCVcmpvk5OxERETlbLFmyhPbt25+0vH379ixbtswPGYmIiIhIRVCR8AxhsRhE1QmhxiE3CTViidtvxepKAGDephQ/ZyciIiJni9jY2GJnNp42bRqxsbF+yEhEREREKoK6G59BouNC2bM5nUCsOD0x2ELWk7e/L6sS09mTlk2tKoH+TlFERETOcC+99BL9+vXj+++/57zzzgNg2bJlbN26lc8//9zP2YmIiIhIeVFLwjNIVFwoBhCb6eVwaBOstkysgYWtCb9fl+zf5EREROSscPnll7N161b69OlDamoqqampXHnllWzZsoXLL7/c3+mJiIiISDlRS8IzyLHJSyL35bEttgk1Ds0jIXQNnuy6fLMuidsvqOvnDEVERORsUKtWLcaNG+fvNERERESkAqkl4RkkuEoAQWEOahxyszG+Pg32mdhC1mNgsmZ3OrtTs/2dooiIiIiIiIiInIFUJDzDRMWFUiPVTUqVqtQ+EIDFlkVQ8F4Avl2X5OfsRERERERERETkTKQi4RkmOj6UoDyTSLeBy9MAq8ekIHgZAN+uVZFQREREREREREROnYqEZ5ioo+MS1kh1sy22Kc13He1ybJis25vBrkNH/JyhiIiIiIiIiIicaVQkPMNE1SksEkbty+fX1h3ovNHEYjtCcOg+QF2ORURE5J/p3r076enpJy3PzMyke/fuFZ+QiIiIiFQIFQnPME6XjSoxgdRMdbMnujoxR2pi8Zrku5YC6nIsIiIi/8yCBQvIz88/aXlubi6//PKLHzISERERkYpg83cCcuqi4kKJWZ6MxYSlTTvTfNfnrIldT/7+q1m/L5OEg0eIrxbk7zRFRETkDLJ27Vrf8w0bNpCcnOx77fF4mD17NjVr1vRHaiIiIiJSAVQkPANFx4WyeWkyNXNhQdvzGPj1Z6yNzyY0dC8ZGbX4du0+7u3ewN9pioiIyBmkdevWGIaBYRjFdit2uVy8+uqrfshMRERERCqCioRnoGOTl8Sk5LO8Tk0aZdfC8O4jL3ApZFzDN2uTVCQUERGRU5KQkIBpmtStW5dly5YRGRnpW+dwOIiKisJqtfoxQxEREREpTyoSnoGq1QrGYjOotTef5XUcrGjWhWaJn7Audj2W5H5sSj7MtpQs6kcF+ztVEREROUPUqVMHAK/X6+dMRERERMQfVCQ8A1ltFiJjQ2iwOxMH8GWL9tzwfx/zR1wOoWF7SU+vxXfrkhh6sVoTioiIyKnbunUr8+fPJyUl5aSi4ejRo/2UlYiIiIiUJxUJz1BRcaHsT8ikVZ6V5dVr0iS7Foa5j1zXYki/lm/XqkgoIiIip+6tt97i7rvvplq1asTExGAYhm+dYRgqEoqIiIicpVQkPENFx4WyDmi6t4DldS2sbtKFJomfsr7WBqz7TTbvP8zW/YdpEB3i71RFRETkDDJ27FjGjRvHyJEj/Z2KiIiIiFQgS2mCIiIiTulRtWpVdu3aVd65n9Oij05eUn39YeyGwect29FpkxfDmkto2F4Avlmb5M8URURE5AyUlpZG//79/Z2GiIiIiFSwUrUkTE9PZ9KkSYSFhf1trGma3HPPPXg8nn+cnJQsLNKFM9AG2W46uVz8Ur0WzbJqYphJ5LgWQdp1fLsuiWE9GhTpJiQiIiLyV/r378+PP/7IXXfd5e9URERERKQClbq78YABA4iKiipV7H333XfaCUnpGBaDqDoh7N6YRqdcK78A65r+i8a7P2dDzQ1YLSbbUrLYsj+LRjHqciwiIiKlU79+fR5//HGWLl1KixYtsNvtRdYPHTrUT5mJiIiISHkqVZHwz7Pa/Z3Dhw+fVjJyaqLjw9i9MY247dnY6sKnzdvTf/anbKydR1j4HlJTY/l27T4axTTyd6oiIiJyhnjzzTcJDg5m4cKFLFy4sMg6wzBUJBQRERE5S5VqTEKAb7755pSLhVK+6rSoCsChNal0CQtmZ41atMysAUB2wK8AfLMuCdM0/ZajiIiInFkSEhJKfOzYscPf6YmIiIhIOSl1kfCqq64iNjaW//73v2zbtq08c5JSio4LJbiKk4I8D+fl28Aw+KNpFxrtMbEFb8RmMdlx4Agbk9SyU0RERE5Nfn4+mzdvxu12+zsVEREREakApS4SJiQkcOeddzJz5kwaNWpEt27deP/998nJySnP/OQvGIZBvbaF40TW3pSFBfik+bFZjvMJr7IHgG/X7fNjliIiInImyc7O5tZbbyUwMJBmzZqRmJgIFI45/eyzz/o5OxEREREpL6UuEsbGxjJ69Gi2b9/O3LlziYuL4+6776Z69ercddddLF++vDzzlBIcKxKmrTpEp7AgEmrE0jKjsMvxEefRLsdr1eVYRERESmfUqFGsWbOGBQsWEBAQ4Fveo0cPPv74Yz9mJiIiIiLlqdRFwhNddNFFvPvuuyQlJfHCCy+wbt06OnXqRKtWrco6P/kbMfGhBIU5yM/18C+3HQyDTU270GCviTVkIw6bya5D2SzadsjfqYqIiMgZ4KuvvmLy5Ml07doVwzB8y5s1a8b27dv9mJmIiIiIlKfTKhIeExISwsUXX8xFF11EeHg4GzZsKKu8pJQMi0Hdo60J4zYfwQBmNmtb2OXYkk9k1FYA3vpFA42LiIjI3ztw4ABRUVEnLT9y5EiRoqGIiIiInF1Oq0iYk5PDe++9x4UXXkiDBg2YOXMmI0aMYOfOnWWcnpRG/baRAKSvOkSH0EB21KxNq4wYADJcX2ExYOGWA2xO1gQmIiIi8tfat2/Pt99+63t9rDA4bdo0Onfu7K+0RERERKSc2U4leOnSpbz99tt88skn5Ofnc/XVVzN37lwuuuii8spPSiGmXjiBoQ6yM/Pp4nWwzMhme+Ou1Nv3BdtrpNKsTgHrdtp565cdTOivLuEiIiJSsmeeeYZevXqxYcMG3G43L7/8Mhs2bGDx4sUsXLjQ3+mJiIiISDkpdUvCpk2b0qVLF1auXMn48eNJSkrigw8+UIGwErBYDOq2KWxNWHdLNgAfNWtL501eAJzhvwDw9eq97M/M9U+SIiIickbo2rUra9aswe1206JFC3788UeioqJYsmQJ7dq183d6IiIiIlJOSl0k7NGjBytXruT333/n7rvvJiwsrDzzklN0bJbjjJUHaRsSyLZadWidHg3A1rw5tI4NpsBj8s7inX7MUkRERCqzgoICbrnlFgzD4K233mLZsmVs2LCBDz74gBYtWvg7PREREREpR6UuEr7yyiuavbgSq1E/DFeInbwjbrqaDjAMEhqfT7OdXryY1IzdDMCHS3eRlef2c7YiIiJSGdntdj7//HN/pyEiIiIiflCqImHbtm1JS0sr9U67du3K3r17TzspOXUWq4X41oVdjuttP9bluA1XLDcBWHF4BnWqusjMdfPJ8t1+y1NEREQqt6uuuoqvvvrK32mIiIiISAUr1cQlq1evZs2aNURERJRqp6tXryYvL+8fJSanrn6bKDb8so8jKw7R8too1taKo5E3juqHEkmqmsV5DQ6x61Ag039NYGDnOtispzW5tYiIiJzFGjRowJgxY1i0aBHt2rUjKCioyPqhQ4f6KTMRERERKU+lnt344osvxjTNUsUahnHaCcnpq9EonIAgOzmHC7jAcLDWyGHupX3p/dvLTOtpZWP+O0QE3s/e9Bxmr0/mipY1/J2yiIiIVDLTp08nPDycFStWsGLFiiLrDMNQkVBERETkLFWqImFCQsIp77hWrVqnvI38M1arhfjW1di4KIn623MhCt5o1pZZX0YwMyedJBK5qKnJrN/hrZ930LtFdRV0RURExMc0TRYsWEBUVBQul8vf6YiIiIhIBSpVkbBOnTrlnYeUkXpto9i4KInsFYdoek01NhyBg1dczSUrp/NlF4MU+2c4bVezZk8GyxJSOa9uVX+nLCIiIpWEaZo0aNCA9evX06BBA3+nIyIiIiIVSIPSnWVqNaqCM9BGTmY+3axOAD7q3J2e6+1YPSbr05fSvVkgAG/9ssOfqYqIiEglY7FYaNCgAYcOHfJ3KiIiIiJSwVQkPMtYbRbiW1YDoEFC4eQxP7gNql/cly4bjo4pGfYThgFzN6awLSXLX6mKiIhIJfTss8/y0EMP8ccff/g7FRERERGpQCoSnoXqtYsCIH/5QdqEBFJgmvxwyRVcsbywSLjkwFec3yAMgOm/qjWhiIiIHDdw4ECWLVtGq1atcLlcREREFHmIiIiIyNmp0hQJn332WQzDYNiwYSXGrF+/nn79+hEXF4dhGEyaNOmkmCeffBLDMIo8GjduXH6JV0KxjSNwBFg5kpHPtY7CrsWTDRdNm15Is51ePHiJqrEOgM9X7uXA4Tx/pisiIiKVyKRJk3jzzTd5++23ef3113nppZeKPERERETk7FSqiUv+LD09nc8++4zt27fz0EMPERERwcqVK4mOjqZmzZqnvL/ly5fzxhtv0LJly7+My87Opm7duvTv35/hw4eXGNesWTPmzp3re22zndZpnrGsdgtxraqx5bf9xG/KpkYtO/vyCljT52qumLKA9XHw68H3aFnredbuyeT9JTsZcWkjf6ctIiIilcCgQYP8nYKIiIiI+MEptyRcu3YtDRs25LnnnmPChAmkp6cD8MUXXzBq1KhTTiArK4sbbriBt956iypVqvxlbIcOHXjhhRcYMGAATqezxDibzUZMTIzvUa1atVPO60xXr01hl+Ndqw5wS43C838poiadHI2pfsjkiCeLZvWSAHh/6S5y8j1+y1VEREQqj8TExL98iIiIiMjZ6ZSLhCNGjGDw4MFs3bqVgIAA3/LLL7+cn3/++ZQTGDJkCL1796ZHjx6nvG1Jtm7dSo0aNahbty433HDD397Q5uXlkZmZWeRxpqvdNAK700pWWh6XFthxWSxszM4j9drr6b3cC8CqIzOIreIiLbuAj5frpl9EREQgLi6O+Pj4Eh8iIiIicnY65SLh8uXLufPOO09aXrNmTZKTk09pXzNnzmTlypWMHz/+VNMo0Xnnncc777zD7NmzmTp1KgkJCZx//vkcPny4xG3Gjx9PWFiY7xEbG1tm+fiLzWElrkVVAA6sOcSA6oUDjU9u2IruB6IIzjHZe2QP3ZoXAPDKT9vIzC3wW74iIiJSOaxatYqVK1f6Hr/99huvv/46DRs25NNPP/V3eiIiIiJSTk65SOh0OottabdlyxYiIyNLvZ/du3dz//338+GHHxZpkfhP9erVi/79+9OyZUsuu+wyvvvuO9LT0/nkk09K3GbUqFFkZGT4Hrt37y6zfPzp2CzH21ce4LYa1TCAHzOycfS7nktWFs50vJOPqBcZROqRfKb8tM2P2YqIiEhl0KpVqyKP9u3bc/vttzNhwgReeeUVf6cnIiIiIuXklIuEffr0YcyYMRQUFLY6MwyDxMRERo4cSb9+/Uq9nxUrVpCSkkLbtm2x2WzYbDYWLlzIK6+8gs1mw+MpmzHywsPDadiwIdu2lVwAczqdhIaGFnmcDWo3q4o9wMrh1Fxsu45wSdXC8/qg4wX03OjE6jFZc3AlN3YtnAF5xqKdJB7K9mfKIiIiUkk1atSI5cuX+zsNERERESknp1wknDhxIllZWURFRZGTk0O3bt2oX78+ISEhjBs3rtT7ufjii1m3bh2rV6/2Pdq3b88NN9zA6tWrsVqtp5pasbKysti+fTvVq1cvk/2dSewOK03/VQOANfN2c0dsYUvPD7IKqHnp1XTZUNiacEPuZ3StX418j5fnZm/yW74iIiLif38epzkjI4NNmzbx2GOP0aBBA3+nJyIiIiLlxHaqG4SFhTFnzhx+/fVX1q5dS1ZWFm3btj3liUdCQkJo3rx5kWVBQUFUrVrVt3zgwIHUrFnTN2Zhfn4+GzZs8D3fu3cvq1evJjg4mPr16wPw4IMPcuWVV1KnTh327dvHE088gdVq5frrrz/VUz0rtOxei7Xzd7N7Qyr/OlKPZsEBrM/KZc4lven9yP/4uQXM2fkDr3a/i8XbD/LtuiQG70ylQ1yEv1MXERERPwgPD8cwjCLLTNMkNjaWmTNn+ikrERERESlvp1wkPKZr16507dq1LHM5SWJiIhbL8caO+/bto02bNr7XEyZMYMKECXTr1o0FCxYAsGfPHq6//noOHTpEZGQkXbt2ZenSpac0XuLZJLSai7ptItm+8gBrf9rDHZdEc/+mRKZ4HXzdsgfNds5lfRz8euBjruvQm/8t283Ybzbw5T1dsFiMv92/iIiInF1++umnIkVCi8VCZGQk9evXx2Y77VvHSmfKlCm88MILJCcn06pVK1599VU6duzo77RERERE/MYwTdM8lQ1KGrDaMAwCAgKoX78+F1xwQZl1F/aHzMxMwsLCyMjIOCvGJ0zekcHnz6/AYjMYMLYzF6zfxoF8NzO8mRx+/i6e/o8Vm2Fleo/PuOn1bRzJ9zDputZc1aamv1MXERGRUjrb7l/K08cff8zAgQN5/fXXOe+885g0aRKffvopmzdvJioq6m+317UWERGRM0lp711OuUgYHx/PgQMHyM7OpkqVKgCkpaURGBhIcHAwKSkp1K1bl/nz5xMbG/vPzsJPzsYbv8+e+539CZl06B3Hry0CeT4hmVYhLl5/4QmeaLSOVfUsXFLnEuqZ9/DCD5upHhbATw9ciMtx5hZ7RUREziVldf8yfvx4oqOjueWWW4osf/vttzlw4AAjR478p6n63XnnnUeHDh2YPHkyAF6vl9jYWO677z4eeeSRk+Lz8vLIy8vzvc7MzCQ2Nrbc7hWXrF3PwI+XAQYmlqP/G4AF0zz6/9HXZeOU/hwQERGRMlbDto1FY0aV2/5Le594yn1GnnnmGd58802mTZtGvXr1ANi2bRt33nknd9xxB126dGHAgAEMHz6czz777PTPQMpUq4tj+XHaev74eS83dO/Iy7v2s+ZwDqk3DuSG5x5gdbyFObvmcG2PG6n5m4u96TlM+2UH912sAcpFRETOJW+88QYfffTRScubNWvGgAEDzvgiYX5+PitWrGDUqOM34haLhR49erBkyZJitxk/fjxPPfVURaWI2+OhwPP3LRpFRETk7FBgVo7h3k65SPjYY4/x+eef+wqEAPXr12fChAn069ePHTt28Pzzz9OvX78yTVT+mXptIgmOcJKVmsehVYe4JroKHyalMqVOY56o25GL1v7OT60tvLpmIiN7Ps/QmauZunA713WIJSo0wN/pi4iISAVJTk6mevXqJy2PjIwkKSnJDxmVrYMHD+LxeIiOji6yPDo6mk2bNhW7zahRoxgxYoTv9bGWhOWlaXwdrqm9BJvFUviwWrHZrNhtduwWK3a7FbvVhs1qxVrCGNJ/XlpSW0HTPDm23JX2gMXF/VWjRzWIFBGRM1Tt6Ev8nQJwGkXCpKQk3G73ScvdbjfJyckA1KhRg8OHD//z7KTMWKwWWnWPZdFn21g9bze3P9CSD5NS+f5QJo8PG851t1/PoqYGaw+s5aYm62hTO5xVielM+HEzz1/Tyt/pi4iISAWJjY1l0aJFxMfHF1m+aNEiatSo4aes/MvpdOJ0OivseFXDw5hwz50VdjwREREROI2BTC666CLuvPNOVq1a5Vu2atUq7r77brp37w7AunXrTrqxFP9r0qUG9gAraUlHCNyVzUURIZjA2yHViOvRhz6/eQF4eeXLPNKrsJvxpyv2sH5fhh+zFhERkYp0++23M2zYMGbMmMGuXbvYtWsXb7/9NsOHD+f222/3d3r/WLVq1bBarezfv7/I8v379xMTE+OnrERERET875SLhNOnTyciIoJ27dr5PlVt3749ERERTJ8+HYDg4GAmTpxY5snKP+N02WjapbAFwOp5u7mjViQAHyWlEnDvfVy52k54lsmerD1syfmBK1vVwDRh3LcbOcX5bUREROQM9dBDD3Hrrbdyzz33ULduXerWrct9993H0KFDi4zjd6ZyOBy0a9eOefPm+ZZ5vV7mzZtH586d/ZiZiIiIiH+d8uzGx2zatIktW7YA0KhRIxo1alSmifnT2Ti78TGZB3P44PElmCZc91gHrt67ly3ZuTxWtzrXfvk/PlnyJm9cbiXUEcq07l/Q99WV5Lu9vDWwPZc0jf77A4iIiIhflPX9S1ZWFhs3bsTlctGgQYMK7W5b3j7++GMGDRrEG2+8QceOHZk0aRKffPIJmzZtOmmswuKczfeKIiIicvYp7b3LKbckPKZx48b06dOHPn36nFUFwrNdaDUXddsUtiBc+9Me7qld+PzVxBSsg2/m4r1ViE0xyczP5NvE97mta2G38We+20hugcdveYuIiEjFCg4OpkOHDjRv3vysKhACXHfddUyYMIHRo0fTunVrVq9ezezZs0tVIBQRERE5W51WS8I9e/Ywa9YsEhMTyc/PL7LuxRdfLLPk/OVs/3Q4eUcGnz+/AqvNwg3jOtNncwIbj+RyZ61Ihq34le/fe4rx11mxW+z8r9eX3PTGVlIO53HHBXV59PIm/k5fREREinG2379UJrrWIiIiciYpt5aE8+bNo1GjRkydOpWJEycyf/58ZsyYwdtvv83q1av/Sc5SQWLqhhEdH4rH7WXjz3t5vF7hOIVv7z1IRu8r6GiJp0WClwJvAdPXT2H81S0AeOuXHazYlerP1EVEREREREREpByccpFw1KhRPPjgg6xbt46AgAA+//xzdu/eTbdu3ejfv3955CjloHWP2gD88fNezg8OpFuVEApMk/GJB4h5eCQ3/eTFMOH7nd8TWW0/17SrhWnCg5+uJSdf3Y5FRERERERERM4mp1wk3LhxIwMHDgTAZrORk5NDcHAwY8aM4bnnnivzBKV81G1djZCIAHIOF7B1eQqj69fAAL5OSWdLy7Y0q9+ZC9Z5AZj4+0Qe792EmNAAEg4e4YUfNvs3eRERERERERERKVO2U90gKCjINw5h9erV2b59O82aNQPg4MGDZZudlBuL1ULL7rVY9Nk2Vs/bzfVdqtM/pgqfJKfx1I4kZj70EAMG9WNxE5OVKSv5/eAvPNuvBYNnLGfG4gQuaxbNeXWr+vs0REREpAzMmjWr1LF9+vQpx0xERERExF9OuUjYqVMnfv31V5o0acLll1/OAw88wLp16/jiiy/o1KlTeeQo5aRJlxos+yaBtKQjJG5I5ZF61ZmVks5vGUdYEBtHq0v+zZXLvuKLLgYv/v4iX/T9ggEdYpm5fDcPfbaW2cPOJ9Bxyl9CIiIiUslcddVVpYozDAOPR8OOiIiIiJyNTrm78Ysvvsh5550HwFNPPcXFF1/Mxx9/TFxcHNOnTy/zBKX8OF02mnYtnLTkt693UN1h587YKADGbk+iyn1DuWq1g/Ask8TDiby2+jX+27sJNcICSEzN5rnvN/kzfRERESkjXq+3VA8VCEVERETOXqdcJKxbty4tW7YECrsev/7666xdu5bPP/+cOnXqlHmCUr7aXloHe4CVA4mH2bJ8P/fWjqKq3cb2nDz+57ZQ66ZbuW124diE76x/h11Zm3n+mlYAvLtkF4u3qYu5iIiIiIiIiMiZ7rSKhIcOHTppeXp6OnXr1i2TpKTiBIY6aNezsLi79KvtuLzwYHwMABN37scxaBD/OhzNvzZ48ZpeHl/0OB3rhnLDeYWzIz/02Vqy8tx+y19ERETK3pEjR/juu+94/fXXeeWVV4o8REREROTsdMoDyu3cubPYriZ5eXns3bu3TJKSitWqeyx/LNxLVloea37azY2X1mHa7gNsz8njtYNZ3DfmKW65/07+qGOwjW28ufZNRl1+Fwu3HGBPWg7PfLeRZ/7dwt+nISIiImVg1apVXH755WRnZ3PkyBEiIiI4ePAggYGBREVFMXToUH+nKCIiIiLloNQtCWfNmuWb+e6HH37wvZ41axZffvklTz/9NHFxceWVp5Qjm8NKp6vqAbBi9i4Ksgp4vF7hWIVv7E4ho2MnYntdza0/FnY7nr5uGnuObOP5awq7nX/0WyI/bzngn+RFRESkTA0fPpwrr7yStLQ0XC4XS5cuZdeuXbRr144JEyb4Oz0RERERKSeGaZpmaQItlsJ6omEY/HkTu91OXFwcEydO5Iorrij7LCtYZmYmYWFhZGRkEBoa6u90KoTpNfn02d85kHiY5t1qcsGAhvx71TaWZhzh2pgqvFSzCjuuuJLnuhzkt8YWGkc05qPeHzHumy28s3gnNcICmD38AkID7P4+FRERkXNSWd2/hIeH89tvv9GoUSPCw8NZsmQJTZo04bfffmPQoEFs2qSJy87Fe0URERE5c5X23qXULQmPzWpXu3ZtUlJSisx0l5eXx+bNm8+KAuG5yrAYdLmmPgDrf9lHWnI2o+sXtib8NDmNjYaNmDFPcesPXkKyTTalbmL6uuk83LMRdaoGsi8jl1GfrzupgCwiIiJnFrvd7vtwOCoqisTERADCwsLYvXu3P1MTERERkXJ0yhOXJCQkUK1atfLIRfysZsMqxLeqhuk1WfLFNtqGBnFVVDgm8NT2fQR360adS6/i5jmF3Y7fWPsGe47s4KXrWmO3Gny7Lonpvyb49yRERETkH2nTpg3Lly8HoFu3bowePZoPP/yQYcOG0bx5cz9nJyIiIiLlpVTdjU9lJruzYTDrc7kLSVryEWaOWYbXa9J3WGs8dYI4/7dN5JsmrzapzdUuK9uvvJLx5x/i94YWmlZtyoeXf8iHS/fwxKz1WC0GH912HufVrervUxERETmnlNX9y++//87hw4e56KKLSElJYeDAgSxevJgGDRrw9ttv06pVqzLM+sx0Lt8rioiIyJmntPcupSoSxsfHl+qghmGwY8eO0mdZSZ3rN34/z9zCugV7qBYbzLWjOvBKYgrjE5IIt1lZ2LExgUsWse6Buxlxh5UjAQb3t72fW5vfyvCPV/PV6n1Ehjj59r6uRIUG+PtUREREzhnn+v1LRdK1FhERkTNJae9dbKXZWUKCupCeSzr0jmPz0iQO7s5i87Jk7ukYw7cH0lmblcMjW/bwdrduxPXoy6C5s3jtCiuvrX6N7rHdeebqFmxKPsym5MPc8+FK/ndHJ+zWU+7RLiIiIiIiIiIiFewfVXBM09REFWchV4iDdr3iAFj61Q5we3mpSW1sBnx/MIOvU9KJfnQUFydXpc12LwXeAh5f/DhOm8HUG9sR4rTx+640nvluo39PREREREqlbdu2pKWlAYVjErZt27bEh4iIiIicnU6rSPjee+/RokULXC4XLpeLli1b8v7775d1buJHLbvXIiQigCPpeayZu5tmwS7urxMNwKNb95DmCqL6U2O443svrjyTtQfW8vYfbxNfLYiJ1xaOVTRj0U5mrdnnz9MQERGRUujbty9OpxOAq666ir59+5b4EBEREZGzU6nGJDzRiy++yOOPP869995Lly5dAPj111+ZMmUKY8eOZfjw4eWSaEXSODOFtixPZs70DdidVm4Y0wl7iJ2ev29hw5Fc+kaF80azOPY+/DCzEr7htSusWAwL0y6dRoeYDjw/exOvLdiOy27l63u70DA6xN+nIyIiclbT/UvF0bUWERGRM0lp711OuSXhq6++ytSpU3nuuefo06cPffr04fnnn+e11147pVmQpfJr0C6aqDohFOR5WPZNAg6LhZea1MZqwNcp6Xz3/+zdd3Qc1d3G8e9sr1r13m2594IxBkwxPRB6CQklJKQQQg2hBBJICIGQAuQNLaEGQu8xxRiwKca4967e+/a+8/6x0krCsrEt2bLM73POnJmduTt7V7Lku49uaekk+9ZbOa4hlWPWxoipMX616Fe0+Fq44cTRHDkyHX84yk+fXYE7EB7qtyOEEEKIPbBs2TKWLl260/mlS5eyfPnyIaiREEIIIYQ4EPY6JGxoaOCII47Y6fwRRxxBQ0PDoFRKHBwUjcKcc8sA2PhZPY3lTibbLVxVkAnAr7fW4rTayP3DH7ji/RiFzSptgTZ+tfhXqER54MIp5DpMlLd6ufHlNTJ/pRBCCDEMXHXVVdTU1Ox0vq6ujquuumoIaiSEEEIIIQ6EvQ4JR44cyUsvvbTT+RdffJGysrJBqZQ4eOSWJTP68GxQYeHTm4iEolxfnE2ZxUhLKMId2+qwH3MMuT+8kutfj2IOwoqmFTy06iHSbEb++f3pGLQa3t/QxCOLyof67QghhBDiG2zcuLHfBUqmTp3Kxo0bh6BGQgghhBDiQNjrkPDOO+/kjjvu4OSTT+b3v/89v//97zn55JO58847ueuuu/ZHHcUQO/K8MiwOA51NPr56uwKTVsMDYwrRAK80dbCg1UnGL69mZNksfjo/CsAT65/g4+qPmVKQzG/PGAfAfe9v5t110ttUCCGEOJgZjUaampp2Ot/Q0IBOpxuCGgkhhBBCiANhj0PC9evXA3DOOeewdOlS0tPTeeONN3jjjTdIT0/nq6++4qyzztpvFRVDx2TVc8zFYwBY/WE1jeVOpjmsXFmQAcCvttTiUiHvL/dzVFs6py6LAXDb57dR467he4cV8v3DC1FVuObF1SyrbB+y9yKEEEKI3TvxxBO55ZZbcDqdiXOdnZ3ceuutnHDCCUNYMyGEEEIIsT/t8erGGo2GmTNn8qMf/YgLL7wQu/3QXa1WVqzr34dPbmTL0kaSsyxccNtMwlqF45dtodwf5Hs5qfx1TCG+FSvYcdml/PYi2JanMDZ1LM+e+iw6xcBP/7OCBRubcJj1vPqz2YzMPHT/DQkhhBAH2mC1X+rq6jj66KNpa2tj6tSpAKxevZqsrCwWLFhAQUHBYFV52JK2ohBCCCGGk0Ff3XjRokWMHz+eG264gZycHC677DI+/fTTQamsGB6OPL8MS1LXsON3KjBrNfxtTAEK8HxDOwvbXFimTyf3+hu57vUodh9sat/EvV/di1aj8OCFU5lamIzTH+bSJ5bR7AoM9VsSQgghxNfk5eWxdu1a7rvvPsaNG8f06dN54IEHWLdunQSEQgghhBCHsD3uSdjN6/Xy0ksv8dRTT/Hpp58ycuRIrrjiCi699FKys7P3Vz0PKPnr8K5VrGlh/sPrUBQ4+1fTyS518JtttfyrtpVUvZYFM0aTa9RTd821fLptAfdcoEVV4I9H/pHTR5xOuzfEOQ9/QUWrl3E5Sbz4k8Oxm/RD/baEEEKIYU/aLweOfK2FEEIIMZzsadtlr0PC3rZv386TTz7Js88+S2NjIyeffDJvvfXWvt7uoCENv91b8OQGti5tIiXbwvm3zSSiUThj5TbWevzMSLLw2tSRaH0+Ks85l/8U1vDKkRpMWhPPn/Y8ZSllVLf5OPvhz2n1hDhyZDpPXDYTg26v19ARQgghRC+D3X7ZuHEj1dXVhEKhPufPOOOMAd97uJO2ohBCCCGGkwMSEkK8Z+Fzzz3HLbfcQmdnJ9FodCC3OyhIw2/3At4w/71zKT5XiKknFnLE2SOp8gc5YfkWXJEYV+ZncFdZHoEtW9lx4fnc/d0w60o0FCUV8dypz+EwOlhX6+SCx5bgC0U5e2oefzl/MoqiDPVbE0IIIYatwWq/lJeXc9ZZZ7Fu3ToURaG7qdj9//Sh0NYbKGkrCiGEEGI4GfQ5Cb9u8eLFXHbZZWRnZ/OrX/2Ks88+m88//3xfbyeGkfhqx6MBWL2gmsYKJ0VmIw+NLQLgsdoW3mnuxDR6FPm/u5Nr3oyR5lSpclXxy49+STAaZGK+g/+7eBpajcJrq+r48/tbhvItCSGEEKLLNddcQ0lJCc3NzVgsFjZs2MDixYuZMWMGn3zyyVBXTwghhBBC7Cd7FRLW19fzxz/+kVGjRnHMMcewfft2HnzwQerr63n88cc5/PDD91c9xUGmZHIGow7LQlXho6c3EQlHOSndwc8LMgG4dnM15b4gju9+l8IzLuCWl6JYgrCyeSW3f3Y7MTXGsaMzuefsiQD885MdPPtl1VC+JSGEEEIAS5Ys4a677iI9PR2NRoNGo+HII4/knnvu4Ze//OVQV08IIYQQQuwnexwSnnLKKRQVFfHQQw9x1llnsWnTJj777DMuv/xyrFbr/qyjOEgddf4ozEkGOhp9LHunAoBbSnM43GHFE43xo/UV+KMxsm69hdF5k7nh1SjaGLxb+S4PrnwQgPNnFHD9CaMA+O2b63lnbf2QvR8hhBBCxIcT2+12ANLT06mvj//fXFRUxJYt0vNfCCGEEOJQtcchoV6v55VXXqG2tpZ7772X0aNH7896iWHAZNNzzPfi/w5WfVBNY7kTvUbhkfHFpOt1bPQGuHVbLRqjkfyH/8k0tYCfzI/PY/Tv9f/m5a0vA3D1cSO56LBCYipc88Jq/re2YcjekxBCCPFtN2HCBNasWQPArFmzuO+++/j888+56667KC0tHeLaCSGEEEKI/WWPQ8K33nqL7373u2i12v1ZHzHMlE7JoGxmfNjx+/9aT8ATJtuo55HxRWiA/za083xDG7rUVAofe5Tja5M599MYAHd/eTef1n6Koij84cwJnDMtn2hM5ZcvrJKgUAghhBgiv/nNb4jF4v9X33XXXVRUVHDUUUcxf/58HnzwwSGunRBCCCGE2F/2eeESIbrN/d5oHBlmPO1BFjyxgVhM5cgUOzeVZANw69ZaNnj8GIqLyf/n/3H+Mh1Hr4sRVaPcuOhGNrdvRqtRuO/cSZw9LS8RFM5fJ0GhEEIIcSCsXbs2EQyedNJJnH322QCMHDmSzZs309raSnNzM8cdd9xQVlMIIYQQQuxHEhKKATOadZz8k4no9BqqN7azfH4lAL8syuK4VDuBmMqP1lfgikSxTJ1K3p//zE/fVZlQGcMX8XHVh1fR6G1Eq1H487mTE0Hh1f9dxbsSFAohhBD73dSpU2ltbQWgtLSUtra2PtdTU1NRFGUoqiaEEEIIIQ4QCQnFoEjPtzH34vj8hMv+V0HVhjY0isI/xhWRZ9RT4Q9x7aZqYqpK0oknkvfrm7nhtRgFLSrN/mZ+9uHPcIfcPUHh1HhQ+AsJCoUQQoj9Ljk5mYqK+CJklZWViV6FQgghhBDi20NCQjFoxhyew/ijckGFBU9swNXmJ1Wv4/HxxegVhfmtTu4ujwd+qZdcQsGFl3LzS1GSPbC9czvXf3I94Wg4HhSe1xMUSo9CIYQQYv8655xzmDt3LiUlJSiKwowZMygtLe13E0IIIYQQhyZFVVV1qCtxsHG5XDgcDpxOJ0lJSUNdnWElEo7y+v0raa5yk1lk5+wbp6PVa3i5sZ2rN1UDcO+ofC7NS0eNxai79jrWrPmA3/1AR0APJxadyL1H34tOoyMaU7nx5TW8vqoOnUbhH9+byskTcob4HQohhBAHp4G2X9577z22b9/OL3/5S+666y7sdnu/5a655pqBVnXYk7aiEEIIIYaTPW27SEjYD2n4DYyr1c9L9ywj6I0w4eg85n4vPgz5r5WN3FfRiAZ4emIJJ6Q7iAUCVF/+Q5Y4V3PfuVoiWji15FT+eOQf0Wq0EhQKIYQQe2iw2i+XX345Dz744C5DQiFtRSGEEEIML3vadpHhxmLQJaWbOeHy8aDA+sV1bFnaCMB1RVlclJNKDPjJxirWun1oTCby//l/HBYt5PrXomhjML9iPnd8cQfRWBStRuH+8yZz5pRcIjGVnz+3khe+qh7aNyiEEEIcosLhMM8++yxVVVVDXRUhhBBCCHGASUgo9ouiCWnMOLUYgE/+s5m2Og+KonDfqAKOTrHhi8b4/tpyagMhdCkpFPz7XxzuzeaaN6JoYvDWjre468u7iKkxtBqFv5w/hfNn5BNT4ebX1vHgwm1IJ1ghhBBicOn1egoLC4lGo0NdFSGEEEIIcYBJSCj2m5mnlVAwLpVIOMa7j64j5I+g1yj8a0IJY60mmkMRLl5bjjMcwZCfT9FTT3JkZya/fCuKRoXXtr3G3V/ejaqqaDUK954ziV8cOxKAvy7Yym/eWE80JkGhEEIIMZhuu+02br31Vtrb24e6KkIIIYQQ4gCSkFDsNxqNwgk/HIctxYiz2c/CpzehxlSSdFr+M6mUbIOeLd4AV6yvJBSLYSgqoujppzi6NZ2fvx1FUeGlrS9xz1f3oKoqiqJw40mjueu741EUeG5pNT9/bgWBsPR2EEIIIQbLP/7xDxYvXkxubi6jR49m2rRpfTYhhBBCCHFo0g11BcShzWwzcNKVE3j9/pWUr27hi9e2M+fcMvJMBv4zqYTvrtrOZ50ebthSw4NjCjEUF1P49NMce8klxP7Xxj+/o+W/m/+LTqPjVzN+haIoXDK7mHSbkWtfWM37G5q45N9f8fglM3BY9EP9doUQQohh78wzzxzqKgghhBBCiCEgqxv3Q1asG3xbljby4ZMbATjy/DImH1cAwEdtLn6wrpyoCjcWZ3NjSTYAwe3bqbr0Mt7Pb+exU7UAXD7hcq6bdh2KogDwZXkbP356Oe5ghNFZdp764UxyHOYheHdCCCHE0JP2y4EjX2shhBBCDCeyurE4qIyelc3hZ5YC8NnL29ixqhmA49KSuHdUPDC8v7KRZ+paATCOHEnhk09wUnUyV7wfH0785PoneXDVg4kFSw4vTeOln84m025kS5Obc/75Bdub3Qf6rQkhhBCHnM7OTv71r39xyy23JOYmXLlyJXV1dUNcMyGEEEIIsb9ISCgOmGknFTHh6DxQYcETG2nY4QTg+7lpXFuUBcBNW2t5vqENANOoURQ+9SSn7kjisgXxoPBf6/7F3UvvJhqLPx6bk8RrPz+C0gwr9c4A5zy8hCU72obg3QkhhBCHhrVr1zJq1Cjuvfde7r//fjo7OwF47bXXuOWWW4a2ckIIIYQQYr+RkFAcMIqicNQFZRRPTCMajjH/n2vpbPIB8OuSbH6cnw7ADZtreLkx3mvBNHo0hU8+wXe2JXHF+/HFTF7c8iK//vTXhKIhAPJTLLz60yOYWpiM0x/m+/9eyjNLKpGR9EIIIcTeu/7667nsssvYtm0bJpMpcf7UU09l8eLFQ1gzIYQQQgixP0lIKA4ojVbDiT+aQGaRnYA3zNsPrcbnCqEoCneNzOOyvHRU4JpN1bze1AGAadw4Cv/9b07ZbueaN6PoYvB+5fv8YuEv8IXjIWOK1cB/f3w4352SSzSmcsebG7jltXUEI7LysRBCCLE3li1bxk9+8pOdzufl5dHY2DgENRJCCCGEEAeChITigNMbtZx21WSS0k24WgP87//WEA5GURSFP5bl8f2cNGLALzZV8XZzJwDmCeMp+s+zHN2Wwa9fimIKw5KGJfzogx/REegKE/Va/n7BFG49dQwaBV5YVsP3Hl9KszswdG9WCCGEGGaMRiMul2un81u3biUjI2MIaiSEEEIIIQ4ECQnFkLAkGTj96imYrHqaq9x88K/1xKIxNIrCfaPzuTA7lagKP9tYyXst8bkLTaNGUfT888xUi7j9+Qi2AKxrXcel711Kozfes0FRFK48egRPXDYTu0nHiqoOznjoc9bWdg7huxVCCCGGjzPOOIO77rqLcDgMxP9vra6u5te//jXnnHPOENdOCCGEEELsLxISiiGTnGXh1J9PQqvXULmujcUvbkNVVTSKwl/GFHBuVgoRFX68oZIPWuNBoSE/j6Lnn2Ni6gTueiZCmhsqnBX84N0fUO4sT9z7mNGZvHnVHEZkWGl0BTj3kSW8vqp2qN6qEEIIMWz85S9/wePxkJmZid/vZ+7cuYwcORK73c7dd9891NUTQgghhBD7iaLK6g47cblcOBwOnE4nSUlJQ12dQ175qhbefWwdqDDj1GJmnVEKQCSm8otNVbzR3IlBUXh6YgnHpsW/H1GPl7pfXk3V+iX84UId9WmQbEzmn8f/k4kZExP3dgfCXPfiaj7c1AzAlUeX8uuTx6DVKAf+jQohhBD70WC3Xz7//HPWrFmDx+Nh2rRpzJs3bxBqeWiQtqIQQgghhpM9bbtIT0Ix5EqnZnD0BaMAWD6/kq/eqQBAp1H4x9giTstwEFJVLl9fweJ2NwBam5X8Rx6hZM7J3PVshBH1Kp3BTq744AoW1/asvGg36XnsBzO4+riRADy2uJxLn/iKFnfwAL9LIYQQYnh45plnCAaDzJkzh5///OfcdNNNzJs3j1AoxDPPPDPU1RNCCCGEEPuJhITioDDxmHzmnBsP8pa9U8Gy//UEhY+MK+bk9CQCMZUfrCtPzFGoMRjIu/9+is66iN8+H2VSRQx/xM8vFv6CJ9c/SXcnWY1G4YYTR/N/35uGWa/ls+2tnPLAp3y2rXVo3qwQQghxELv88stxOp07nXe73Vx++eVDUCMhhBBCCHEgSEgoDhpT5hUy++wRAHz1dgXL360EQK9ReHR8MaekOwjGVH64voLnG9oAULRasm6/nfyf/IKbX4oxb2UMFZW/rvgrv/n8NwSjPT0GT5uUw9tXz2F0lp1WT5AfPLGUv3ywhUg0dsDfqxBCCHGwUlUVRdl5Wo7a2locDscQ1EgIIYQQQhwIuqGugBC9TTuxCFRY8voOlr5ZjkajMO2kIowaDY+PL+amrTU839DO9ZtraA9FuKowE0VRyPjFVejS0/nx739PYUuUp07Q8taOt6h0VfLAsQ+Qbk4HYGSmnTd/MYc7397If7+q5qGPtrO0vJ0HLppCjsM8xO9eCCGEGDpTp05FURQUReH4449Hp+tpJkajUSoqKjj55JOHsIZCCCGEEGJ/kpBQHHSmnVRELKay9M1ylry+A5R4eKjTKPxldAFpeh0PVTfzh/IGWsMR7hiRi0ZRSLnwAgxFhZx67XXktrv429k61ras5cJ3LuSh4x5ibNpYAEx6LfecPZHZI9K49bV1fFXZzqkPfMpfzp/McWOyhvjdCyGEEEPjzDPPBGD16tWcdNJJ2Gy2xDWDwUBxcTHnnHPOENVOCCGEEELsb7K6cT9kxbqDw/L5FSx9Kz434ZxzRzJlXmHi2iPVzfxuRz0A52Wn8NfRhei7ViwOVVVR8/OrqGrfwb3n66hPBZPWxN1H3s2JxSf2eY3KVi+/+O9K1te5APjxUSX86qQxGHQyEl8IIcTwMljtl6effpoLLrgAk8k0iLU7tEhbUQghhBDDyZ62XSQk7Ic0/A4eX71TwbKu1Y6PPK+MyccXJK691NjOdZuriapwQloSj44vxqKNh3tRt5u6G2+k+cvF/P1MDWtK4+d/Pvnn/GTyT9AoPSFgMBLlnvmbeeqLSgAmFyTz9wumUJJuPUDvUgghhBi4wW6/hEIhmpubicX6zt1bWFi4i2d8e0hbUQghhBDDyZ62XaS7lDioHfadEmacVgzAZy9vY83CmsS187NTeXJCCSaNwoI2Fxeu2UFnOAKA1m6n4J//pPAHV3DzSzFO+yr+Aeefa/7JDZ/cgDvkTtzHqNPyuzPG8+gPppNk0rGmppNTHljMU59XEItJhi6EEOLbZdu2bRx11FGYzWaKioooKSmhpKSE4uJiSkpKhrp6QgghhBBiP5GehP2Qvw4fXFRVZelb5ax4twqAGacWc9jpJYmVF5d2erhkXQXOSJQxVhPPTyol12RIPN/55ps03H4HC8eE+dcpWiIaKLAXcP/c+xmXNq7Pa9V1+vnVy2v4Ykd89eTZpWncd+4kClItB+jdCiGEEPtmsNovc+bMQafTcfPNN5OTk7PTSseTJ08eaFWHPWkrCiGEEGI4keHGAyANv4OPqqosn1/JV2/Hhx6POzKXuReNQtM1vHijx89Fa3bQFIqQadDx5IQSpjt6hgv716yh5he/YIuulb+draclSUWv0XPzYTdz3qjz+nwAisVU/rO0invmb8YfjmI1aPnNd8Zx4cyCnT4oCSGEEAeLwWq/WK1WVqxYwZgxYwaxdocWaSsKIYQQYjiR4cbikKIoCjNPK+GYi0ejKLDxs3ree2w9kXAUgHE2M29PK2Os1URzKMLZq7fzcmN74vnmyZMpeeUVJmRM4t5/h5m+LUY4Fub3X/6eXy/+Nd6wN1FWo1G4ZHYx715zFDOLU/CGotzy2joue3IZjc7AAX/vQgghxIE0btw4Wltbh7oaQgghhBDiADtoQsI//elPKIrCtddeu8syGzZs4JxzzqG4uBhFUfj73/8+4HuK4WX8UXmcdOUEtDoNFWtaeeuB1QR9YQAKzUbenlbGyelJBGMqV2+q5vc76ol2dZbVZ2VR/J9nKbzgUm56JcYPFkbRxODdyne58J0L2dK+pc9rFadbeeHK2fzmtLEYdBoWbW3hxL8t4vVVtUgHXCGEEIeqe++9l5tuuolPPvmEtrY2XC5Xn21/qKys5IorrqCkpASz2cyIESP47W9/SygU6lNu7dq1HHXUUZhMJgoKCrjvvvt2utfLL7/MmDFjMJlMTJw4kfnz5/e5rqoqd9xxBzk5OZjNZubNm8e2bdv2y/sSQgghhBhODoqQcNmyZTz66KNMmjRpt+V8Ph+lpaX86U9/Ijs7e1DuKYafEVMzOf2XkzGYtDRsd/L6X1bi7QwCYNNpeWJCCdcUZQHwf9XNXLquAnck3uNQMRjIuuVmCv7xEN/dbOfO/0RI8yhUuiq5eP7FvLr11T4BoFaj8KOjSpn/yyOZnO/AFYhw3Ytr+PEzy6nt8B34Ny+EEELsZ/PmzePLL7/k+OOPJzMzk5SUFFJSUkhOTiYlJWW/vObmzZuJxWI8+uijbNiwgb/97W888sgj3HrrrYkyLpeLE088kaKiIlasWMGf//xnfve73/HYY48lynzxxRdcdNFFXHHFFaxatYozzzyTM888k/Xr1yfK3HfffTz44IM88sgjLF26FKvVykknnUQgIKMFhBBCCPHtNuRzEno8HqZNm8Y///lP/vCHPzBlypRv7CEIUFxczLXXXttvL8G9vWcwGCQYDCYeu1wuCgoKZJ6Zg1xrrZu3H1yDzxXCnmri9F9OJiW7Zx7C15o6uH5zNYGYyiiLiWcnlVBkNiauh2prqbvuepq3r+P/TtewakQ8Mz+99HRuO/w2rHprn9eLRGM8uricv3+4lXBUxazXct0JZVw+pwS99qDI24UQQnyLDdY8eYsWLdrt9blz5+7zvffGn//8Zx5++GHKy8sBePjhh7nttttobGzEYIgvUHbzzTfzxhtvsHnzZgAuuOACvF4v77zzTuI+hx9+OFOmTOGRRx5BVVVyc3O54YYbuPHGGwFwOp1kZWXx1FNPceGFF+5R3WROQiGEEEIMJ8NmTsKrrrqK0047jXnz5g3ZPe+55x4cDkdiKygoGLS6iP0nPd/OOTdNx5Fpxt0e4LU/r6SpomcY1NlZKbw+tYwsg46tvgAnL9/KZx3uxHVDfj7Fz/2H4vMv4dcvxfjex/Hhx2+Xv805b53DiqYVfV5Pp9Vw1bEjmf/LozisOBV/OMof52/m9Ic+Y2V1xwF730IIIcT+NHfu3N1uB4rT6SQ1NTXxeMmSJRx99NGJgBDgpJNOYsuWLXR0dCTKfL39d9JJJ7FkyRIAKioqaGxs7FPG4XAwa9asRJn+BIPBAzLsWgghhBBiKA1pSPjCCy+wcuVK7rnnniG95y233ILT6UxsNTU1g1YfsX8lpZs551fTySyyE/CGeeNvK6lY05K4PjXJwvszRjPFbqEjEuXCNTt4sq41MaQ4Pvz4Fgr/8RBnb7Dx2+ciZLgU6jx1XP7e5fx1+V8JRfvOh1SWZeeFKw/nvnMmkWzRs7nRzTkPf8Ftr6/D2TU/ohBCCDGcrF27llgsljje3XYgbN++nYceeoif/OQniXONjY1kZWX1Kdf9uLGxcbdlel/v/bz+yvRH/qAshBBCiG+DIQsJa2pquOaaa3juuecwmUxDek+j0UhSUlKfTQwfZruB7143lYKxKURCMeY/so7l8ysTQWC2Uc/rU0dyVmYyERVu2VrLzzZW4emapxDAPm8eJa+/xtSUifz58TDHrImhovLkhie54J0LdlrURKNROH9mAQuvn8s50/JRVXhuaTXH//UT3lxdJwubCCGEGFamTJmSWNF4ypQpTJ06lSlTpuy0TZ06da/ue/PNN6Moym637qHC3erq6jj55JM577zz+PGPfzxo73Eg5A/KQgghhPg20A3VC69YsYLm5mamTZuWOBeNRlm8eDH/+Mc/CAaDaLXaIb+nGB4MJh2n/WIyn7+0jXWL6lj6VjltdR6Ou2QseqMWs1bDP8cVMclu4e7yet5o7mSt28/jE4oZbzPH79E1/Nj68MP8/LHHmbktyqOn6djOdi7834VcNeUqLh9/OVpNz7+hNJuRv5w/mXOn5/ObN9axo8XLNS+s5uXltfz29HGUZdmH6ksihBBC7LGKigoyMjISx4Plhhtu4LLLLtttmdLS0sRxfX09xx57LEcccUSfBUkAsrOzaWpq6nOu+3H3gna7KtP7eve5nJycPmWmTJmyyzoajUaMRuMurwshhBBCHAqGLCQ8/vjjWbduXZ9zl19+OWPGjOHXv/71PoV5++OeYvjQajUcfdFo0vJtLP7vVravaKaz2cepP5uEPdWEoij8rDCTmQ4rV26opNwf5LQVW/lDWT4X56TGezQYDGRecw32Y47B8OubGfVYJY+drGHZ6AgPrHyARTWLuPvIuylMKuzz2rNHpDH/mqN4bFE5D328nc+2t3LyA5/y/VmFXDtvFClWwy5qLYQQQgy9oqKifo8HKiMjIxE+fpO6ujqOPfZYpk+fzpNPPolG03fAy+zZs7ntttsIh8Po9XoAFixYwOjRoxOrLs+ePZuFCxf2WdhuwYIFzJ49G4CSkhKys7NZuHBhIhR0uVwsXbqUn/3sZwN8t0IIIYQQw9uQDTe22+1MmDChz2a1WklLS2PChAkAXHLJJdxyyy2J54RCIVavXs3q1asJhULU1dWxevVqtm/fvsf3FIe+8Ufl8d3rpmK262mt8fDyPcuo39aZuD7DYeXDmaM5PjWJQEzlxi01/GJTNd5ew4/NkydT8vprFJ/9fW58LcbP34liDiusblnNuW+fywubXyCmxvq8rlGn5erjy/jg2qM5YVwW0ZjK00uqOOb+T3jiswrC0b7lhRBCCBFXV1fHMcccQ2FhIffffz8tLS00Njb2mSfwe9/7HgaDgSuuuIINGzbw4osv8sADD3D99dcnylxzzTW89957/OUvf2Hz5s387ne/Y/ny5fziF78AQFEUrr32Wv7whz/w1ltvsW7dOi655BJyc3M588wzD/TbFkIIIYQ4qAz56sa7U11dTUNDQ+JxfX09U6dOZerUqTQ0NHD//fczdepUfvSjHw1hLcXBKLcsmfNumUl6gQ2/O8ybf1/Fhk/rEtdT9TqenVTCbaU5aBV4tamDk1dsZZPHnyijMZvJ/s1tFD35BPNasrj/sTDjq1T8ET93L72bS9+9lG0d23Z67eJ0K49fMoPnfjSLMdl2nP4wd72zkZP+vpiPNzfLfIVCCCHE1yxYsIDt27ezcOFC8vPzycnJSWzdHA4HH3zwARUVFUyfPp0bbriBO+64gyuvvDJR5ogjjuD555/nscceY/Lkybzyyiu88cYbff5YfNNNN3H11Vdz5ZVXMnPmTDweD++9996gzZEthBBCCDFcKaokFjtxuVw4HA6cTqcsYjLMhYNRPnpmE9tXNAMwcW4ec84vQ6vtyceXdnr4yYYqGkNhzBqFP47K58Ls+PDjblGXi6a7/0jHm2/ywXSF54/VEdCr6BQdl0+4nJ9M/glG7c5zFUVjKi8uq+EvH2yhzRtfJfnoURn85rSxjJL5CoUQQgwiab8cOPK1FkIIIcRwsqdtFwkJ+yENv0OLqqqseK+KpW+WA/FehideMR5rck+o1xqKcPWmKj5udwNwekYyfxqVT5qh77Sdrg8+oPF3d9IUbueJEzUsHxUPGwvthdwx+w5m5czqtw6uQJh/fLSdJz+vIBxV0WoULphZwDXHl5GVJD0XhBBCDJy0Xw4c+VoLIYQQYjiRkHAApOF3aKpY08KCJzcSDkQx2fTMu3wcRePTEtdjqspDVc38ubKBiAqZBh1/GV3ACemOPveJdHTQ/Of76XztNb4apfDEyTo6rPEfo++O+C43zriRZFNyv3WobPXyx/mb+GBjfOVFo07DZUcU89O5I2RxEyGEEAMyWO2XZcuWEYvFmDWr7x++li5dilarZcaMGQOt6rAnbUUhhBBCDCd72nY5qOckFGIwlUzO4PyueQoDnjDvPLSGJa/vINq1oIhGUbimOIt3po2izGKkORThB+squHFzDZ5ei5roUlLI/ePdFD39NEeFivjbo2FOXBFDUeHNHW9yxhtn8PaOt/ude7A43cpjl8zgxSsPZ3pRCsFIjEcXl3P0fR/z0MJteIORA/b1EEIIIfpz1VVXUVNTs9P5uro6rrrqqiGokRBCCCGEOBCkJ2E/5K/Dh7ZIOMrnr2xn/aL4QibZpQ5O/NF47Kk9w3790Rh/Km/gsdoWVKDQZODBsYUcnmzrc69YMEjbo4/R+vjjbMkM89ipemrS4z9S0zKncfNhNzM2bWy/9VBVlY+3NHPfe1vY3Bgf5pxmNXDVsSO5+PBCjDrtfnj3QgghDlWD1X6x2WysXbuW0tLSPucrKiqYNGkSbrd7oFUd9qStKIQQQojhRHoSCrELOr2WuReN5qQfT8Bg0tJY7uTFu7+icm1rooxZq+HOsjxemTKCPKOe6kCIs1Zt567t9QS6eh4CaIxGMn55NaVvvM6UnOnc++8wFy6KYogorGxeyQXvXMDvvvgd7YH2neqhKArHjcli/i+P4oELp1CcZqHNG+KudzZy3P2LeGlZDeFeryWEEEIcCEajkaampp3ONzQ0oNPp+nmGEEIIIYQ4FEhPwn7IX4e/PZwtPt5/fAMt1fFeEZPnFTD7zBFodT35uTsS5fZtdbzQGA/6xlhNPDS2kIl2S597qbEYna+8QvP9f6FZdfHcsRo+Hx+/j11v56eTf8pFYy9Cr9H3W5dwNMbLy2t5YOFWmlxBAApSzfxs7kjOmZ4nPQuFEELs1mC1Xy666CIaGhp48803cTji8/J2dnZy5plnkpmZyUsvvTRYVR62pK0ohBBCiOFEFi4ZAGn4fbtEwzG+eG07az+uBSCrJIl5l48jObNvCPhei5Mbt9TQGo6gVeDK/AxuLMnGqu0b3kVaW2n++99xvvoam/NUnjxJR0Vm/FqJo4SbZt7EkXlH7rI+gXCUZ5ZU8tjiclo9IQByHCZ+cnQpFx5WiEkvYaEQQoidDVb7pa6ujqOPPpq2tjamTp0KwOrVq8nKymLBggUUFBQMVpWHLWkrCiGEEGI4kZBwAKTh9+1UvrqFj57ZRNAXQWfQcMTZI5lwdB6KRkmUaQ1FuGVrLW+3dAJQYDJw76h8jkvb+d+Jf/0Gmv74R7yrVvLxJIX/HqvDZY7/uM3Nn8uNM26k2FG8y/r4Q1Ge/6qaRxftoNkd71mYYTdy5VGlXHx4IRaDDPkSQgjRYzDbL16vl+eee441a9ZgNpuZNGkSF110EXp9/73hv22krSiEEEKI4URCwgGQht+3l6vNz0dPb6JuaycA+WNSOO6SsX0WNQH4oNXJLVtrqQuGATgzM5nfl+WRYej74UlVVVz/m0/z/ffj7Gjk1Tka3p2pIaoBraLlrLKz+Nnkn5FpydxlnQLhKC+vqOWRT3ZQ1+kHINVq4IojS/jB7CKSTPKBTQghhLRfDiT5WgshhBBiOJGQcACk4fftpsZU1n5Sy5ev7yASjmEwaTnqglGMPjwbRenpVeiNRLmvopHHa1uIAQ6dljtG5HJRTiqaXuUAYj4fbf/6N23//jd11iDPHK9l5ch4GZPWxPfHfZ/LJ1xOkmHX/95CkRhvrKrj/z7ZTlWbDwCbUcdFhxVw+ZwScpPNg//FEEIIMWwMpP3y1ltvccopp6DX63nrrbd2W/aMM84YSDUPCdJWFEIIIcRwIiHhAEjDTwB0Nvn48KmNNFW4ACielM4xF4/G6jD2KbfG7ePGzTWs88R7+R3usHLf6AJGWU073TNcV0fT/ffjfvc9NuXDc8fr2Jobv5ZkSOLHE3/MRWMvwqg17vTcbpFojLfX1vPPj3ewrdkDgFaj8J1JOfz4qFIm5DkG4+0LIYQYZgbSftFoNDQ2NpKZmYlGo9llOUVRiEajA63qsCdtRSGEEEIMJxISDoA0/ES3WDTGqgXVfPV2BbGoismqZ+73RjNyet/hwZGYyr9qW7i3ohF/LIZeUbiyIINri7Kw97MqsW/lSprv/wu+lStZXqbw3+N01KbGfxSzLFlcNeUqTh9xOjrNrucdjMVUFm1t4fFPy/liR1vi/OzSNK48upS5ozLQaJRdPl8IIcShRdovB458rYUQQggxnEhIOADS8BNf11rrYeHTG2mtiffcGzEtk6MuKNupV2G1P8gtW+tY2B7vfZhh0HFraQ4XZO88BFlVVTyffELLX/+Gf/s2Fk1QePkYPa22GACljlJ+OvmnnFh0IlrN7lc0Xl/n5PFPy3lnbQPRWPxHemSmjR8dWcJ3p+RhNsiKyEIIcagbjPZLOBzm5JNP5pFHHqGsrGyQa3jokLaiEEIIIYYTCQkHQBp+oj/RSIzl8ytZ8V4VakzFYNIy+6wRjD+q7wrIqqqyoM3Fb7fXUeEPATDZbubusnxmOKw73VeNRnG98w4tDzyIt6me96crvHGkHrcxHhaWOEq4ctKVnFJ8yjeGhXWdfp76vIL/flWDJxgBwGHWc/6MfL5/eBFFaTu/vhBCiEPDYLVfMjIy+OKLLyQk3A1pKwohhBBiOJGQcACk4Sd2p6XazSfPbaa5yg1AVkkSx1w8mvR8e59yoViMf9W28tfKRjzReOB3TlYKt5XmkGsy7HTfWChE5wsv0vrII7i97cyfoTD/cD0eQ/y5xUnF8bCw5JTdDkMGcAXCvPhVDc98WUlNe3yuREWBY0ZlcMkRxcwtk6HIQghxqBms9st1112H0WjkT3/60yDW7tAibUUhhBBCDCcSEg6ANPzEN4nFVNYvquPLN3cQDkRRNApTji9g5ndK0Bv79vZrCYX5Y3kDLzS0owJmjYZrijL5SUEmZu3Ok8NHPV7an3yS9qefxhPy8N50hf/N7ulZWGgv5MeTfsx3Sr/zjWFhNKbyyZZmnllSxaKtLYnzRWkWvj+riPNm5JNs2TmwFEIIMfwMVvvl6quv5plnnqGsrIzp06djtfbthf7Xv/51oFUd9qStKIQQQojhRELCAZCGn9hTno4gn728lR0r4wGcPdXE0ReNonhi+k5lV7t83L6tjmUuLwC5Rj03lmRzflYqun569UVdLtqfeZb2Z57BG3Dx/jSFd2brcZniYWG+LZ8fTvwhZ4w4Y7erIXeraPXyny+reHl5Da5AfCiySa/htIm5XHhYATOKUlAU6V0ohBDD1WC1X4499tjdXv/444/3+d6HCmkrCiGEEGI4kZBwAKThJ/ZW5dpWFr2wBU97EIARUzOYc14Z9lRTn3KqqvJGcye/31FPfTAMQJnFyK2lOZyc7ug3pIu63XQ89xztTz6F1+fkg6kKbx+hx2mOh4WpplS+N+Z7XDjmQhxGxzfW1ReK8Obqep5ZUsWmBlfifGmGlQtmFHD2tHwy7N8cOgohhDi4SPvlwJGvtRBCCCGGEwkJB0AafmJfhINRvnqngjULa1BjKlq9hqknFDLtpKKdhiD7ozGerGvlwaomOiNRAGYkWbhtRC6zk2393j/q8dLx/PO0P/EEPk8nC6fEhyG3dK2GbNaZObvsbH4w7gfk2fK+sb6qqrKyuoMXl9XwztoGfKF4PXQahePGZHLhYQUcXZaBrp8h0UIIIQ4+g9V++eEPf8gDDzyA3d53rl2v18vVV1/NE088MdCqDnvSVhRCCCHEcCIh4QBIw08MRGutm09f3Eb9tk4ArMlGZp81glEzs/qsggzgDEf4Z00Lj9W04I/Fw77jUu3cNiKX8TZzv/ePeb10vPACbU88SbCjjS/HKLx1hJ7KjPjztYqWE4tP5PLxlzM2bewe1dkTjPDOmnpeXF7DqurOxPmsJCPnTs/nrKn5jMzsP7wUQghxcBis9otWq6WhoYHMzMw+51tbW8nOziYSiQy0qsOetBWFEEIIMZxISDgA0vATA6WqKuWrWvj81e242wJAfBXkI88rI7t05yHBTcEwf61s5LmGNiIqKMBZWSlcX5zFSItpp/IAsUAA55tv0f7EEwSrqlhXrPDWbC1ri3vKzMqZxcVjLubo/KPRarT93ufrtja5eXFZDa+trKXDF06cn5zv4KypeZw+OZc0mwxHFkKIg81A2y8ulwtVVUlJSWHbtm1kZGQkrkWjUd5++21uvvlm6uvrB7Paw5K0FYUQQggxnEhIOADS8BODJRKOsmZhDSverSIcjA/nHXVYFrPPGoEtZefwr8IX5N6KBt5o7gRAA5yZlcK1RVmMsvYfFqrRKO6PPqL930/gX72aiix4e5aGL8ZpiHV1XMyz5XHh6As5q+ysPZq3ECAYifLhxmZeW1nLJ1tbiMbivyp0GoW5ozI4e1o+x4/NxKTfs/BRCCHE/jXQ9otGo9ntAlaKonDnnXdy2223DaSahwRpKwohhBBiOJGQcACk4ScGm9cZ5Ms3y9m8pAFU0Bk0TDmhkKnzCjGYdTuVX+f28ZfKRt5rjS8sogBnZCZzbVEWY3cxDBnAt3Ilbf/6N56PPqIlCd6fpuHj6TrchvhQZJPWxGmlp/G9sd9jVMqoPa5/qyfI22vqeX1VHWtrnYnzdpOO0ybmcMaUXGaVpKHtZ5VmIYQQB8ZA2y+LFi1CVVWOO+44Xn31VVJTUxPXDAYDRUVF5ObmDmaVhy1pKwohhBBiOJGQcACk4Sf2l+YqF5+9vI2G7fGgzWTVM/2UIibMzUPXT4+89W4ff6tq4n8tPcHcaRkObijOZtxuwsJgeTntTz6J8623CUSDfD5O4b1ZeirTY4kyM7JmcNGYiziu8Dh0mp2Dyl3Z3uzmtZV1vLGqjnpnIHE+w27k1AnZnD45l2mFKWgkMBRCiANqsNovVVVVFBYW7rZX4bedtBWFEEIIMZxISDgA0vAT+1P3fIVL3yqno9EHgC3FyMzvlDDm8Gw0/awmvNHj52+VTbzT0kn3D+wp6Q6uKcpiSpJll68V6eig85VX6PjvfwnXN7A5H96dqeWr0RpiSvxOGeYMvjvyu5w98mwKkgr2+H3EYipLK9p5Y1Ud721oxOnvmb8wx2HitIk5fGdyLpPzHfJBUwghDoDBbL98+umnPProo5SXl/Pyyy+Tl5fHs88+S0lJCUceeeQg1Xj4kraiEEIIIYYTCQkHQBp+4kCIRWNs/rKRZe9U4OkIApCcZWHWGaWMmJbRb7C22evn75VNvNncExYekWzj54WZHJ9q32UYp0YiuD/+mI7nnsf35Ze02+CDaRoWTtfhNPX0LpyVM4tzys7h+MLjMWgNe/xeQpEYn29v5e219SzY0IQ72LPyZUGqmVMn5nDy+Gwm5ydLD0MhhNhPBqv98uqrr/KDH/yAiy++mGeffZaNGzdSWlrKP/7xD+bPn8/8+fMHsdbDk7QVhRBCCDGcSEg4ANLwEwdSJBxlw+J6lr9bScAT742XUWjn8DNLKRib2m/wt80b4MHqJl5v6iDS9RM82mriZwUZnJ2VgkGzc2/EbsFt22h/7jmcb75FOOhneZnCR1N1rClWUbteymF0cHrp6ZxTdg4jU0bu1fsJhKMs2trCO2sb+HBjE/5wNHEtO8nEieOzOHl8NoeVpKLrp9ekEEKIfTNY7ZepU6dy3XXXcckll2C321mzZg2lpaWsWrWKU045hcbGxkGs9fAkbUUhhBBCDCcSEg6ANPzEUAj5I6xeWMPqBdWJlZCzS5OYcWoJheP7DwvrAiEer23hP/VteKLxHoHZBj0/yk/nkrx0knS7Xnk46nLhfONNOl9+meC2bbQkwceTNXw8TU+bpSfYm5Qxie+O+C4nFZ+0xysjd/OHony0uZl31zfw8eZmvKGe+yZb9MwbGw8MjyxLl1WShRBigAar/WKxWNi4cSPFxcV9QsLy8nLGjRtHIBD45psc4qStKIQQQojhRELCAZCGnxhKfneIFe9VsX5xHdFwPPjLLLIz/ZRiSian9xsWuiJRnqlr5V+1rTSG4r0RbVoNF+em8cO8dIrMxl2+nqqqBNasoeOVV3DNf5eo38fqUoWFU7WsGKkk5i7Ua/QcU3AMZ4w4gzl5c9Br9Hv1vgLhKF/saOX99U0s2NREuzeUuGYxaDmqLJ3jx2Zx7OhMMuy7rq8QQoj+DVb7pbS0lMcee4x58+b1CQmfeeYZ/vSnP7Fx48ZBrPXwJG1FIYQQQgwnEhIOgDT8xMHA6wyy+sMa1i+qJRKKh4VpeTZmnFrMiKkZKP3M7ReKxXitqYOHa1rY4o339FCAE9KSuCI/g6NTbLtdRCTq8eD633w6X3mFwLp1dFjh0/EKi6caqE7t6QWYakrl1JJTOX3E6YxNHbvXC5NEojGWV3Xw3vpGPtjQ2GeVZIDJBckcPyaT48dmMi4nSRY+EUKIPTBY7Zd77rmH//znPzzxxBOccMIJzJ8/n6qqKq677jpuv/12rr766kGs9fAkbUUhhBBCDCcSEg6ANPzEwcTvDrF6YQ3rPqklHIgHdSk5VmacUsTIGVn9LgQSU1U+anfzr5oWPulwJ86PtBi5PC+dC7JTse1mKDJAYPNmOl96Gef//kfU6aQqExZN1PDZZD1OY09gODJ5JKeVnsZJxSdRYN/z1ZG7qarK+joXH21uZuHmJtbWOvtcz04ycdzYTI4bncnsEWlYjbq9fg0hhPg2GKz2i6qq/PGPf+See+7B5/MBYDQaufHGG/n9738/WNUd1qStKIQQQojhRELCAZCGnzgYBbxh1n5Uw9qPawn64qsHJ6WbmHx8IWOPyEFv7D/02+4L8GRtKy82tifmLbRpNZyfncoP89MZaTHt9nVjoRCeTz7B+eZbeBYtIhKLsLZEYdEkLctHKYQ1Pb9CJqVP4uSSkzmx6ESyrFn79D6bXQE+3tLMh5ua+Wxba5+FT/RahRlFqcwdncHRZRmMzdn1is5CCPFtM9jtl1AoxPbt2/F4PIwbNw6bzTYItTw0SFtRCCGEEMOJhIQDIA0/cTAL+iOs+7iWNQtrCHjj8w8arTomzs1n4jH5WJIM/T7PHYnyUmM7T9a1st0XTJw/MtnGxblpnJLuwPQNqw1H2ttx/W8+zjfeILBhA14jfDlG4YuJejbkx4h15XUKCtOzpnNKySnMK5pHqil1n95rIBzly/I2Fm5q5pOtzdS0+/tcz7AbObosg6NHpXNUWQap1v7fuxBCfBtI++XAka+1EEIIIYYTCQkHQBp+YjgIh6JsWdLAqg9rcLXEwzOtTsPoWVlMnldIao613+epqsriDg//rm1hQZuL7l8AKTot52ancHFuGmOs5m98/eC2bTjfegvnW28TaWqi09odGBrYnNPT+0+raJmVM4t5RfM4tuBY0s3p+/R+VVWlss3H4q0tLNrawpIdbX16GSoKjMtJYs7IdI4YkcZhJalYDDI0WQjx7TFY7ZdAIMBDDz3Exx9/THNzM7FYrM/1lStXDrSqw560FYUQQggxnEhIOADS8BPDSSymUrGmhdULqmksdyXOF09MY8q8QnJHJe9ySG5NIMR/G9p4oaGd+mA4cX56koWLc9P4bmYyVu3u5y5UYzH8K1fimj8f13vvE21vpzUJvhijsGSygR3pvYI8FKZmTmVe0TyOLzyeXFvuPr/vYCTKisoOFnWFhpsb3X2u67UKUwtSOGJkGnNGpjOlIBn9N/SUFEKI4Wyw2i8XX3wxH3zwAeeeey5ZWVk7/R/y29/+dqBVHfakrSiEEEKI4URCwgGQhp8Yrhp2OFn9YTXlq1vo7iKYlmdlwtx8Rh2WhcHUf8+6qKrycbub5+vb+KDNSaTruTathjMzUzg/O4WZDus3zv+nRiJ4v1yKa/583AsWEHO7aUiBpaMVvppgZHtGpE/58WnjE4FhiaNkQO+92RXgix1tfL69lS92tFHX2XdossWgZUZxKrNKUjm8NJWJeckYdBIaCiEOHYPVfnE4HMyfP585c+YMYu0OLdJWFEIIIcRwIiHhAEjDTwx3nU0+1iysYfOXDURC8WFiBrOOsbNzmDA3j+Qsyy6f2xwM82JjO883tFHhDyXOF5sNnJeVyrnZKRSZjd9Yh1gohPezz3D9bz6ejz8m5vPRmgRfjVJYNt7AppxoYg5DgOKkYubmz2VuwVymZk5Fp9n3ocKqqlLd7uPz7W18vqOVJTvaaPeG+pQx6TVML0phVkl8aPKUgmRM+t33mhRCiIPZYLVfxo0bxwsvvMCkSZMGsXaHFmkrCiGEEGI4kZBwAKThJw4VQV+YzUsaWbeoFmdzT8+6wnGpTDwmn8IJaWg0/fcOVFWVLzo9vNTYwTstnXijPXNSHe6wcl52KqdnJpOk++ZgLRYM4v3iC9wfLMD90UfEnE6cFlhWpvDVeD3rC1QivVZJthvsHJl3JMfkH8OcvDk4jI4BfBXiQ7I3N7pZWtHG0vJ2vqps3yk0NOg0TClIZkZRCjOKU5hWmEKyRRZCEUIMH4PVfnn33Xd58MEHeeSRRygqKhrEGh46pK0ohBBCiOFEQsIBkIafONSoMZWaTe2s+6SWyvVtiaHISekmxh2Zy5jZOVgdu+4d6I1GebfFycuNHSzucCcWOzFpFE5Od3B2VgrHpNoxaL55+K4aDuNbtgzXggW4P/yQaEsrPgOsLVFYPlrL6lE6XPqeYclaRcvUzKnMzZ/LnLw5jEwe+Y3Dnr+xDqrK9mYPX1a0s7S8jaUV7bS4gzuVG5lpY0ZRCtOLUphRnEpxmmXAry2EEPvLYLVfWlpaOP/881m8eDEWiwW9Xt/nent7+0CrOuxJW1EIIYQQw4mEhAMgDT9xKHO2+Fm/uI5Nn9cT9MXDOEWjUDQhjXFH5lI0PhXNbhb4aAiGeLWxg5caO9jqCyTOO3RaTs1wcGZmCnOSbeh20UOxNzUWw796Ne4FH+L5+GNClZXEFNiaCytHalg53ki1I9znOZmWTI7MO5I5uXOYlTNrwL0MoWfl5K8q2lhR1cHyqg7KW7w7lUuzGphamMLUwmSmFCQzKd+B3aTv545CCHHgDVb7Zd68eVRXV3PFFVf0u3DJpZdeOtCqDnvSVhRCCCHEcCIh4QBIw098G4RDUbYvb2LT5w007HAmzlscBsbMzmHsETkkZ+567kJVVVnn8fNKYwdvNnfQFOrp/Zem13F6ZjJnZiZzmMOKZg973wUrKvB8/Amejz/Gt3IlRKM0JcOKkQprRhnYUKAS0vQMe9YoGialT2JO3hzm5M5hXNo4tJrBmVew3RvqCgzbWVHZwdo6J6FIrE8ZRYGRGTamFCQzuSAeHI7JtqOTVZSFEENgsNovFouFJUuWMHny5EGs3aFF2opCCCGEGE4kJBwAafiJb5v2Bi+bvmhgy5cN+N09PffyRiUzdk4upVMy0Bt3Hb5FVZWlnV7eaI7PX9gejiau5Rj1nJGRzGkZDmbsRWAY7ezE8+lneD7+GM+nnxJzuwnpYFOBwupShbVjTdTY+84raDfYmZk1k1k5szg853BKHCWDNjw4GImyvs7JqupOVtfEt9oO/07lTHoN43MdTMxzMCk/vpWk29DuQc9KIYQYiMFqv0ybNo1//vOfHH744YNYu0OLtBWFEEIIMZxISDgA0vAT31bRSIzKta1s/LyB6o09cxfqjFpGTM1g9OHZ5I1K2eViJwDhmMpnHW7eaO5kfksn7l4LnmQadJyc7uC0jGSOSLah38PgTA2H8a9di2fxp3g//ZTAxo0AtCbB6hKFNaP0rCtW8OmifZ6Xac5kVs6sxJZtzd7Lr8jutbiDrKnpCQ3X1HTiDkZ2Kmc1aBmf52BSnoOJ+Q4m5DkoSbPu9usohBB7a7DaLx988AF33nknd999NxMnTtxpTkJpG0lbUQghhBDDi4SEAyANPyHA3R5g85IGNi9pwNXaM/egNdnI6FlZjJqVTVqubbf3CERjfNLu5p2WTj5oc+LqNVw3WaflxPQkTstI5ugUO+a9GKIbaW3F+/nneD79DO9nnxHt7CSqQHkOrCtS2DDKxObsKGFN3+HBhfZCZmTPYEZWfMux5ezxa+6JWEylvNXL+jona2udrKvrZH2dC384ulNZi0HL2Jwkxud2bw7KsmwY92C1aCGE6M9gtV80XYtQfb0ntqqqKIpCNLrz77RvG2krCiGEEGI4kZBwAKThJ0QPVVVpLHex5csGtq9oTix2ApBeYGPM4TmMnJG529WRAUKxGJ93eJjf6uTdFiet4Z77WLQajkmxc0J6EvPSksgw7PliIGo0SmDjRryffYZ3yZf4V61CDYcJ6WBLnsK6YoUNY0zsSAkTU/r+usuz5TE9a3oiNMy35w/66sXRmMqOFk88NKztZG2dk00NLgLh2E5ldRqFsiw743KSGJtjZ2xOEmOy7aTZdv+1FUIIGLz2y6JFi3Z7fe7cuft870OFtBWFEEIIMZxISDgA0vATon/RcIzKda1sWdpI1fo2YtGuXx8K5JUlM3JGFiOmZmC2G3Z/H1VlmdPL/1o6md/ipC7YMw+iAkxLsnBimoMT05MYYzXtVXAX8/vxrViJ78sleL9YQmDTJlBVvEbYkq+wsVBh0ygzO1JCO4WGmZZMpmZOZWrmVKZkTmF0ymh0Gt0ev/aeisZUyls8bKh3sbHBxYZ6JxvqXXT6wv2Wz7AbGZPdExqOyU5iRKZVeh0KIfqQ9suBI19rIYQQQgwnEhIOgDT8hPhmfk+I7cub2bK0kaYKV+K8olHIH5PCyOmZlE7JwGTdfa9AVVVZ6/HzQauTBa0u1nr6LgaSb9JzQpqDE9KSmJ1s26thyQCRjg58Xy3Du+QLfF8tI1ReHq+/Id7TcFOBwubRZralhYh8LTQ068xMTJ/IlMwpTM2cyqSMSSQZ9s/vBFVVqXcG2FDnZGODi80NbjY3uqhq99Hfb2mtRqE4zcLobDtlmXZGZdkZnW2jKM2KXlZXFuJbabDaL4sXL97t9aOPPnqf732okLaiEEIIIYYTCQkHQBp+QuwdV6uf7Sua2b6imZZqd+K8RqtQOC6VkdMzKZ6UjtHyzcOIG4IhPmxz8UGri0873ARiPb+iTBqF2ck2jk21c2xqEiMtxr0eHhxpacG3fDm+ZcvwLVtGcNt2AII62JarsDUfto4wszUnhkfbdxESBYURySOYmD6RCekTmJQxiZHJI/dLb8Nu3mCErU1uNje62dzgYlPX3hXYeYEUAL1WoTTdRlmWjZGZPVtxmhWTXnoeCnEoG+w5CXvr/btW5iSUtqIQQgghhhcJCQdAGn5C7LvOJh/bVzSxbXkz7fXexHmNRiFvTAqlUzIomZz+jXMYAviiMT7rcPNBq4uP2l3UB/sOx8036TkuNYljU+0cmWLHvg/DbyPt7fiWdYWGy5cT3LIFVJUYUJsOW/MVtpQY2Fqko8Ec3On5Zp2ZsaljmZQxKR4cpk8i25o96HMb9qaqKk2uIFub3IltS5OH7U1uvKH+P7xrFChItTAyIx4ajsi0MSLDSmm6jRTr7oeHCyGGh8Fqvzidzj6Pw+Ewq1at4vbbb+fuu+/m+OOPH2hVhz1pKwohhBBiOJGQcACk4SfE4Giv97JtRRPlq1r6BIYokF3ioHRqBqVTMnBkmL/xXqqqssUX4JM2Nx+3u1nS6SHU69eXToFpSVaOTLFxdIqdaUkWDP30hvkmUY8H/+o1+FeuxLdqJf41a1F9PgA6LbA9V2FbnsKOERa2p0fwaXfu0ZdqSmVc2jjGp41P7DMtmfs1OIT46sr1Tj9bm9xsa/KwvdnD9pb43r2LnocAyRY9pelWStJtlGZYKU23UpphoyjNIr0PhRhG9nf7ZdGiRVx//fWsWLFi0O893EhbUQghhBDDiYSEAyANPyEGX2eTj/LVLexY1UJzpavPtbR8GyWT0imelE5moR1F881hmjcaZUmnl4/bXHzc7qbc37eXn0WrYZbDytEpdo5KsTHOZkazDyGdGokQ2LylV2i4hkh9AwAxoD6tKzgs1FNebKTCHthpQRSAdHN6IjAcmzqWsWljybJk7ffgEOIBa4snyPZmDzuae8LDihYv9c7ALp+nKJDrMFOUZqEozUpxmoXidCvFaVYKUy2YDRIgCnEw2d/tl82bNzNjxgw8Hs+g33u4kbaiEEIIIYYTCQkHQBp+Quxfno4A5atbKV/dTP02J2qveQfNSQaKJ6RRPDGd/LEpGEx7Nt9flT/IZx0eFne4+azDQ1u4b8+5VL2WOcl2ZidbmZ1sY7TVtE+hIcTnNfSvXYt/zVr8a9cSWLeOmDfeUzKkg6pM2JGtUFlkpLzAQLXF329wmGxMZnTqaMakjGFM2hjGpo6lKKlov85x+HW+UITKVh/lrfHQsLy1a2vZfe9DgOwkE4VpFgpTLRSlWihMs1CQGn+cZjUckABUCNFjsNova9eu7fNYVVUaGhr405/+RCQS4bPPPhtoVYc9aSsKIYQQYjiRkHAApOEnxIHj94SoXNtG1fpWqje2Ew70zKmn0Snkj0qhaGI6xRPTSEr/5mHJADFVZZM3wKftbj7t8LDE6cEXjfUpk6rXcrjDxuxkG7OTrfvc0xBAjUYJlZcngsPAhg0Etm6FcHwOxaAOqrKgPFuhvNhEVZ6eaoufaD/BoVFrpCy5jFGpoxiVEt/KkstINiXvU932laqqtHlDVLX5qGz1UtXmpbLNR1Wbl4pW7y4XTulmMWgpTI2HhvkpZvJTuvfxY4f5mxexEULsncFcuERRFL7eRDz88MN54oknGDNmzECrOuxJW1EIIYQQw4mEhAMgDT8hhkY0EqN+WyeV61qpXNuKq7XvUNjkLAsF41IpHJdK3qgU9MY9G+4aisVY5fLxRaeHJZ0eljl9+GN9Q0OHTsssh5XDHFZmJduYZDdj3Ic5DbvFQiGCW7YSWL8O//r1BNZvILh9O3StChrSxhdGqcxSqCowUFVooiIpiF/Tf/iWac6kLKUsHhqmlFGWUkaJowSj9psXgNkfOn0hKlq9VLf7qGn3Ud3uo6otftzgCvBN/7PYTbpEcJiXHN9yk83kpZjJTTaRbjWi2YNh50KIHoPVfqmqqurzWKPRkJGRgclkGmgVDxnSVhRCCCHEcCIh4QBIw0+IoaeqKp1NPirXtlG5rpWGHX2HJWt0Crkjk7tCwzTS8qx7PLw1FIux1u1PhIZfOb14v9bT0KhRmGy3MLMrOJyRZCXNMLBhwDG/n8DmzQQ2biSwaRPBjZsIbtuG2tXjMAY0pcSDw+psLbVFVqrTVRoMvn7vp1E05NvyGZE8gpHJIxP7YkfxkIWHAMFIlLoOfyJArO30U9sR3+o6fLR6Qt94D4NOQ67DRG5XeJjrMJHtMJOTbCLXYSbbYSLJpJMhzUL0Iu2XA0e+1kIIIYQYTiQkHABp+Alx8An6I9Rt7qB6YxvVG9txt/XtZWhxGMgfk0L+6FTyx6RgT93zHi+RmMpaj48vO70sc3r5yundaU5DgJEWIzOSrExLsjAtycIYqxndAHu7qaEQwfJyAhs3Edi0icCmjQQ3byHWa2EAvwFqMqA6Q6G6wERNvpHqpBBuTf9hm0bRUGAvoMRRQomjhFJHaeI4yTD0v9N8oQj1nX5qOvzUtvuo6wxQ3+mnrtNPfaefJleA2B78z2Q1aMlJNpPjMJGdZCLbYSIrqec422Ei1WKQHoniW2Mg7ZcHH3xwj8v+8pe/3NuqHXKkrSiEEEKI4URCwgGQhp8QB7fuXobVG9up2dhO3ZYOIuGvDR/ONJM/JpX80SnkjU7GbDPs1f0r/CG+csZ7GS5zetnmC+5UzqzRMNluZlqSlemOeHCYY9zz19nd64fr6glu3UJwyxYCW7YS3LKFUFUVdA2TVgGnFWrSFWoyoL7QSl22nmp7cJfhIcRXWS5xlFCSVEKxo5iipCKKk4rJteUe0AVTdiccjdHo7BscNjgDvTY/nb7wHt1Lr1XItHcHiEYy7SYyu/ZZSUaykkxk2o04zHrplSiGvYG0X0pKSvaonKIolJeX70v1DinSVhRCCCHEcCIh4QBIw0+I4SUSjtKww0nt5g5qN3fQUuXaaU689AIbeaNSyC1LJrcsGZN17xbOaA9HWO70ssLlY6XLyyqXD8/XhigD5Bj1TLFbmGw3MyXJwiS7hVT94IRvMb+f4PYdBLdsJrhtO8Ht2wlu20akuTlRRgU6rVCbrlCXBg0FVupzDNTZw7Tq/Lu8t07RkW/PpzgpHhwWOYooshdRmFRIpiUTjbLv8zPuD75QhMau0LC792GjK0CjM5g4bvUEv3FuxG4GnYYMm5HMJCMZNiMZ9p4t024iw24k3WYg3WbEpN+zuTCFONCk/XLgyNdaCCGEEMOJhIQDIA0/IYa3oD9C/dZ4YFi7pYP2em/fAgqk5doSgWFuWTKWpL3rARhTVbb5gqzoCgxXurxs8gTYOTaEQpOByb2Cw4k2M45BCg4Bok4nwR07+gSHwR3biba09innN0B9KtSlK9TlGGnKM9OQAvVGP0Fl16sVGzQG8u35FNoL4/ukQgrthRTYC8ix5aDXHJwrFYejMZrdQRqdAZpdAZrd8QCxyRWk2R2g2RWkyR3Y416J3exGHem9QsPuLa3rcZrNQJrVQJrNKPMmigNqf7RfupuJ8u+4L2krCiGEEGI4kZBwAKThJ8ShxesMUrelg7ptndRv7aSzaeeFQFKyLeSWJZMzwkH2iGSS0k17/aHYG4my1uNnrdvHapePNW4/5f6dhylDPDicaDcz0WZmgj0eHGYZBzdsizqdBMvLCZWXx/c74vtwbW1i2DLEF0xpt0NDqkJDKjQWWGnMNlKfFKFJ5yeq9Bd9xmkUDdmWbPLt+eTZ8vrs8235pJpSD/pwIRCO0uIO0uIJ0uyK71vcvbd4wNjqCRKO7t1/mXqtQqrVQJq1JzxMsX5tbzGQZovvky0GtDKHothHg9l+eeaZZ/jzn//Mtm3bABg1ahS/+tWv+MEPfjAYVR32pK0ohBBCiOFEQsIBkIafEIc2nytE/bbOrq2DtjrvTmUsSQayRzjioWGpg4xCO1rd3g+5dYYjrPP4We3ysdodDw5rAv3PGZhh0DHBFg8Ox3VtpWbjgBdH+bpYMEiospJQRWV832uLdnb2KRtVoNUBTckKjSnQlG+lJctIQ1KMBoOPkBLd7WuZdWZyrDnk2HLIs+bF97Y8cqzxfZo57aAbyrwrqqri8kdo8cQDw1ZPkFZ3kFZPKPG4zRui3RuizRPCE9x178xdURRIMulJsehJthhIsegT4WGKRU+ytWtvNpBs0eMw63FY9NiN0mNRDF775a9//Su33347v/jFL5gzZw4An332Gf/3f//HH/7wB6677rrBqvKwJW1FIYQQQgwnEhIOgDT8hPh2CXjD8cBweyeNO5y0VLuJfa3HmFavIbPITnapg6ySJLKKk7Cl7PkKyr11hCNs8PhZ5/azzuNnndvHDl+w36HKJo3CKKuJcVYz421mxtpMjLOZB22ew6+LdHR0BYZVhCorCddUE6qqJlRV1WfFZYj3QOy0QbMDmlMUWjKNtOSYaU7R0mQJ0ar18U3/weg1erKt2eRYc3badx9b9Jb98l73t0A4mggM27xB2jzxALHdF6LdE993eHvO7e2w5960GgWHWU+yWU+SuSs87GdL6nOsI8msx2bQyQrQh4jBar+UlJRw5513cskll/Q5//TTT/O73/2OioqKgVZ12JO2ohBCCCGGEwkJB0AafkJ8u0VCUZqr3TTucNKww0njDicB784BjtVhIKukJzTMKLJjMO1beOeNRtnsCbDW42eD289Gr59NngD+WP9DfbMMOsZYzYy2mhhjNTG6a7Pp9s+iGqqqEu3sJFxVRai6KzisriZcU0Oornan+Q8BwlpoTYIWh0JrErTmWGjLNNHiUGi2hGnV+oh9Y4wISYYksqxZZFniW7Y1O35szSLbkk2WNQur3ro/3vYBFYnG6PSH6fSF6PCF6fCG6Og+9oXo9Hbt/WGcvjCd/hBOf5hAeNfDwfeEosTnWXRY9CSZ4pvdFA8Q7SYddpOeJJMufs6kx27qPq/D1nXOqNNIT8aDwGC1X0wmE+vXr2fkyJF9zm/bto2JEycSCAQGWtVhT9qKQgghhBhOJCQcAGn4CSF6U1WVziYfDTucNFW6aKpw0V7n2WnlXEWBlBwrmcVJZBbaySxKIi3fim4fV8ONqSqV/hAbPfHQcKPHz0ZPgOpdDFcGyDfpGW0xM8ZmosxiZJTFRJnVhH0/hYeJuvp8hOvqCNXUEq6tJVRbQ7jrOFxXR8y38zyQUSU+F2JrErQ6FNrSjbRnmWlL0dFqi9KsD+BVdv1ee7PqrWSYM8iyZJFhySDD0nVsziDTkkmmJZN0czoG7d4tUDMcBMJRnP4wnb54wNjpD+P0h3F17fvbXP4ILn+YUD8rdO8LnUbBZtJhM8ZDRbsxHiBajfFzNqO213HX+a7yFoM2cc5q0GHSS+C4rwar/TJhwgS+973vceutt/Y5/4c//IEXX3yRdevWDbSqw560FYUQQggxnEhIOADS8BNCfJNwMEpLtYumCjdNlU6aKlx4OnZepESjUUjNs5JZaCejKInMIjtpuTa0+n2fh88dibLVG2CzN8AWb4DNXj+bvQGaQ7ueAy/boKfMaqSsKzQss8SPMw37fy47VVWJOZ2E6+sJ1dUR6dqH6+oJ19URaWgg6nT2+1yfEVrt0G5XaLdDR5aFjnQjbcla2ixRWvVBPEr/i8P0x2F0kGHOIN2cHt9b4vsMcwZp5jTSzemkmdOw6+3fiqAqEI7iCnSFhoGeYNEViOAOhHH32ceDxcS5YARPMLJTWD5QGgWshq7Q0KjFYogHiVajDrNBi9UQP9d9zazXYjFoMRt6ysaPtVj08eeYDVrMeu0hvyjMYLVfXn31VS644ALmzZuXmJPw888/Z+HChbz00kucddZZg1XlYUvaikIIIYQYTiQkHABp+Akh9oXXGaSpwkVLtZvmKjct1S787p2HKWu0Cik5VjIKbKQX2MkosJOeb8NgHtg8g+3hCFu6gsMt3gDbvAG2+QI07SY8tGs1lFqMjLCYGGE2MsJiZKTFSInFiFW7f3sf9hbzegk3NhKubyDc2ECkoSF+3BDfIk1NqMH+w8CAPt4jsd2m0GGHjlQDnZkWOpJ1dNigzRimXesnzO4XWenNoDH0CQ3TTGmJfao5Nf7YlEaqKZUkY9KwWXxlsMViKr5wFE8ggifYEyZ6gvEg0ROM4g1G8AYjuLv2nq7rnq7H3lC8jC+059+ffWXQajDpNYnQ0KTX9j3u2pv0mj7XjTpN1/n4NZOu13HX3qjTYuze6zRDMgR7oO2X9evXM2HCBABWrFjB3/72NzZt2gTA2LFjueGGG5g6deqg1nm4kraiEEIIIYYTCQkHQBp+QojBoKoqno4gLVVumqtcNFfH90Fv/6GdI8NMeoGd9AIb6fnxzZpsHHDQ4AxH2O4LstUXYJs3yDZfPDys9of6XSylW65RT7HZSKnZSLHZQKnFSInZSJHZiEV7YEOx7jkRI01NidAw3NhIpLFr39xMpKmJmHfnlaoBVMBjhg4rdNoUOmzQmWbEmW7G6YiHiZ3GCB3a4B4Pce6mU3SkmFJINaWSYkrpOTbGj9NMaX3O2w32b22ouDvdgWN3qOgNRvEEI/jD8WN/KIo3FA8TfaH4OV/XY38oGt+Hu47DkT7nhqql0x0WmvTxAPGBC6cyrTBlv73eQNsvGo2GmTNn8qMf/YgLL7wQu92+H2r5zYLBILNmzWLNmjWsWrWKKVOmJK6tXbuWq666imXLlpGRkcHVV1/NTTfd1Of5L7/8MrfffjuVlZWUlZVx7733cuqppyauq6rKb3/7Wx5//HE6OzuZM2cODz/8MGVlZXtcR2krCiGEEGI42dO2y/5ZHlMIIQSKomBPNWFPNVE6NQOIfzh1twdorfHQUuOmtcZDa40bT0cQZ4sfZ4ufHSubE/cwWnWk59lIy7OR1hUcpuZY0Rn2vJefQ69jukPHdEffxT2CsRiV/hA7fAF2+II9mz9AezhKfTBMfTDMF52ene6ZY9RT0hUeFpuNFJri+yKzgZT9sPKyoijoUlLQpaRgGjNml+WiHm88MGxuItLcTLipiUhTM5HmZiwtLaS2tBBpaOnqlRjo2voK6aDTCk4rdFoVnDYFZ4YFV7IBV5IWlwU6DVE6dUHcSpCIGqHF30KLv2WP3otG0ZBsTMZhdJBiTCHZmEyKKb7vPu8wOvocOwwO9Fr9Pn71hgeNRknMWTiYVFUlGIkRCPcNEgPh+LGv6zi+xfCHe64FwzH8Xc8JhKMEuu4TjMQI9npOIBIvG4j0DSSDkRjBSAxXIJKoy8Fs0aJFPPnkk9xwww1cd911nHvuuVxxxRUcddRRB7QeN910E7m5uaxZs6bPeZfLxYknnsi8efN45JFHWLduHT/84Q9JTk7myiuvBOCLL77goosu4p577uE73/kOzz//PGeeeSYrV65M9JK87777ePDBB3n66acpKSnh9ttv56STTmLjxo2YTPu2ar0QQgghxKFAehL2Q/46LIQ40PyeEK3VXcFhrYe2Og8djT7U2M6/ohUFHJkWUnOtpOZaScu1kZprxZFpRjtIPfzawxHKfUEq/F2bL0iFP0SFP4gzsvthoQ6dliKTgaKu0LDAZKDQZKDAbCDfaMB0gHshfp2qqsTcbiItLT1bcwuRtjYirS1EW9uItLYSaWsj2t7O7rqhRTTgssQDRadVwWUBV5IeT6oJd5Iel02Dy6ziMkTp3Ideir1ZdJZEcJhkSCLJmESSISnx+OvnkwxJ2A126bl4AKmqSjiqEozEw8NgJJoIKIORGKOy7IMegvY2WO0Xr9fLSy+9xFNPPcWnn37KyJEjueKKK7j00kvJzs4exBrv7N133+X666/n1VdfZfz48X16Ej788MPcdtttNDY2YjDEFyG6+eabeeONN9i8eTMAF1xwAV6vl3feeSdxz8MPP5wpU6bwyCOPoKoqubm53HDDDdx4440AOJ1OsrKyeOqpp7jwwgv3qJ7SVhRCCCHEcCLDjQdAGn5CiINBNByjvcFLW52H1joPbbUeWms9BDw7z3MIXXMdZltIzY33NkzNsZKSY8GRYUYzSMGcqqp0RKJU+oKU+4NU+kNUBYJU+UNU+YO7nf+wW5ZBR6HJSIE5Hh7mmwzkm/TkmwzkGQ2YhzhE7E2NRIh2dMSDxLZ2ou1tRNraibS1Em1rJ9LeFg8V29uJtrWhhvv/3nSLaOLDnl1mcFsU3F17T6oJj8OI267Fa9bgNsVw66O4tSHcSpCB/EetoGDT20gyxkPD3uGhTW9LPLYZbInrNr0t/lgfP6/TyMCD4WB/tF+2b9/Ok08+ybPPPktjYyMnn3wyb7311qDc++uampqYPn06b7zxBunp6ZSUlPQJCS+55BJcLhdvvPFG4jkff/wxxx13HO3t7aSkpFBYWMj111/Ptddemyjz29/+ljfeeIM1a9ZQXl7OiBEjdhrGPHfuXKZMmcIDDzzQb92CwSDBXvOiulwuCgoKpK0ohBBCiGFBhhsLIcQwp9VryCi0k1HYMy+Yqqr4XCHa672013tpq/ckjsPBKG11Xtrq+s7Lp9EqJGdZSMm2kppjIaUrQHRkmtHp925xEkVRSNXrSHXomPa14csAvmiM6l6hYaU/RE0gvlUHQviiMZpCEZpCEZa5+p8/MF2v6xMc5pviPRBzTXryjAZS9doDtiCEotOhy8hAl5HxjWVVVSXm8RDt6CDa3k6kvYNoR3s8QGzvOtfZga2jk4yODqLtHcQ8HuIzJvq6tp3FAJ8pHi66zeAxKXhN4LFq8TmMeO16vDYtHosGrxE8+igeXQSvEiKgRFBRcYfduMPuff46mHVmbHobVr01ES7aDLbEuT7Hvc5ZdBZsBhtWnRWr3nrID5k+FI0cOZJbb72VoqIibrnlFv73v//tl9dRVZXLLruMn/70p8yYMYPKysqdyjQ2NlJSUtLnXFZWVuJaSkoKjY2NiXO9yzQ2NibK9X5ef2X6c88993DnnXfu9fsSQgghhBhOJCQUQohhRFEUrA4jVoeRgrGpifNqLD7XYXtDT3jY0eCjo9FLJBRLBIk7+twL7GkmkrOspGRbuoLE+N6SZNinIM6i1TDGamaM1bzTNVVVaQ9Hqe4ODf1BqgMhagMhagNhaoPxELE1HKE1HGH1LjIts0Yh12ggz6Qnz2Qg1xgPD3OMenJMenKNBuzaA7+yrKIoaO12tHY7FBbu0XPUUIio00mko4NoR2c8YHQ6iXZ2xvfOTqKdTqzOTlI748exFmdXj8VI17ZrYS34jOA1dW8KHhP4jAp+hxGfXY/PqsNn1uAzKfgMKh59DK82jFcJE1Ti9/dH/Pgj/j2ed3FXDBoDVr0Vi96SCBPNejMWnSV+vnuvt2DWmRPnzDozFr0Fi86S2Jt1Zsw6M1rNgVuF+9tm8eLFPPHEE7z66qtoNBrOP/98rrjiir26x80338y999672zKbNm3igw8+wO12c8sttwykyvvNLbfcwvXXX5943N2TUAghhBDiUCIhoRBCHAIUjUJSupmkdDPFE9MT57vDw45GH+0NXjoavHQ0emlv8BHyR3C1BnC1Bqje0NbnfgazjuQsC8lZZpIzLSRnWnBkxo8N5n37r0NRFNIMOtIMOqYmWXa63j2UuTYRHMbDw7pgiLqufUsogj+mssMfZIc/2M+rxFm1GnKNenKM8dAwx6gnu9eWY9CTZtChPcBB4tcpBsMe91TspqoqaiDQFSK6iLmcieOoy0XU2UnM5Y4fu10kdZ93u4i1ulBDIeK9F/1d265FNPGQ0W+M731GpWsPfouWgM2A36onYNHhN2vwG+NBo1+v4tNF8WsieJUwoa4wMxQLEQqG6Ah27PsX7WuMWmMiMNzVZtKZ+hybtKY+x93Xez/u3hu1A19hfDipr6/nqaee4qmnnmL79u0cccQRPPjgg5x//vlYrTv3Hv4mN9xwA5dddtluy5SWlvLRRx+xZMkSjEZjn2szZszg4osv5umnnyY7O5umpqY+17sfd8+VuKsyva93n8vJyelTpvfw468zGo071U0IIYQQ4lBz0ISEf/rTn7jlllu45ppr+Pvf/95vmQ0bNnDHHXewYsUKqqqq+Nvf/tZnzhmIT2r98MMPJ4apjB8/njvuuINTTjll/74BIYQ4CPUOD4smpCXOdw9b7mz00dHk69k3eXG1BQj5IzRXumiudO10T3OSgeSuwNCRacaREZ/30JFh3ucAEXoNZdbrmGTfOUSE+IrMDcEwtYEQ9cEwdYF4gFgfDNEQDNMQDNMZieKNxtjmC7LNt+sgUatAlqErODToyeraZxp1ifOZBj2pei2agygkUhQFxWxGYzaj34dFJGKBADG3m6jbTczlIur2EHO7iLrcxDxuoi53PFB0e4i53dg98X3U4ybW4YkPkVZV9qQnY7eIBgKG+OY3xENHv0EhoI8fBy16AlY9QauegFlH0BQPHAMGCOghoI3h10YJaqL4lQh+QsS6ZmoMRoMEo0E6g517/bXYUyatCaPO2Cc47O9cYus6b9AaEuWMWiNH5B5Bujn9m19wiJxyyil8+OGHpKenc8kll/DDH/6Q0aNHD+ieGRkZZOxBCP7ggw/yhz/8IfG4vr6ek046iRdffJFZs2YBMHv2bG677TbC4TB6fXzo+oIFCxg9ejQpKSmJMgsXLuzTPlywYAGzZ88GoKSkhOzsbBYuXJgIBV0uF0uXLuVnP/vZgN6rEEIIIcRwd1CEhMuWLePRRx9l0qRJuy3n8/koLS3lvPPO47rrruu3TH5+Pn/6058oKytDVVWefvppvvvd77Jq1SrGjx+/P6ovhBDDTu9hy3mjU/pci4SjOJv9dDT6cLb46Gz242zy0dnsw+8O43eF8LtCNGx37nRfs11PUro5Hh6mm3FkWkhKM5GUYd7nIcy9GTUais1Gis277tHjjUZp7AoM64NhGrpCxKZQ/FxjMExLKEJUhfquMrujVxQyDToyuwLETIO+53HXPqNrP9QrN+8JjcmExmTaq96LvamxGDGfj5gnHhhG3W5iHi8xb/djT9c1N1Gvl1jXZvd0HXs8xLxeos1eSCz0Eura9rAOQFjXFSAaIKjv3hSC+vj5kEVH0GIgZNERMukImbSEDBqCRiVRLqSLB5AhTYyAJkpQiRIkQpAwYXpW8Q5EAwSiAZzs/G9+bzx50pMHdUio1+t55ZVX+M53voNWe2CHcRd+bYi+zWYDYMSIEeTn5wPwve99jzvvvJMrrriCX//616xfv54HHniAv/3tb4nnXXPNNcydO5e//OUvnHbaabzwwgssX76cxx57DIj/7rv22mv5wx/+QFlZGSUlJdx+++3k5uZy5plnHpg3K4QQQghxkBrykNDj8XDxxRfz+OOP9/kLcn9mzpzJzJkzgfgcN/05/fTT+zy+++67efjhh/nyyy93GRL2t2KdEEJ8W+n0WtLybKTl2Xa6FvRHcDbHA8POJj/OFh+uFj/OFn88QOzamip2/j2q02uwdwWGSenmeHiY3nM8kF6IvVm1WkZYtIywmHZZJhJTaQn3hIYNwTDNoQhNwTBNoXDXPkJbOEJYVakLhqkLhuEb1v6wazVkdIWG6QZd/FivI8PQvelJN+hI1+uwDsG8iYNB0WjQ2mxobTv/+9hbsVAoESLGvL5ex702n6/v1uuc3esl5o8fqy4/MZ+vq5cjQLhr2zdRBUJ6COri+1DvvU4hZNYRshgIm3WETTrCRh1ho5awUUvIoBDSawjp42FmSAshnYq9Mwh73/nzgNlfqxYPFofDwQcffMBVV13F9OnTSU9P54477uDKK69MlDniiCN4/vnn+c1vfsOtt95KWVkZb7zxBhMmTEiUuemmm/B6vVx55ZV0dnZy5JFH8t5772Ey7fp3hhBCCCHEt8GQh4RXXXUVp512GvPmzfvGkHBvRaNRXn75Zbxeb2KYSX9kxTohhNgzRrOOzKIkMouSdroW8kdwdgWGifCw1Y+rNYCnPUAkHKOj0UdHY/+r+BotuniImGbGnmbqOjZh73psHKQQEUCnUcgxGsgxGnZbLhSL0dIVHjaHIjSHeu97jltCEYIxFXc0htsfpHw38yV2M2kU0vTxORrTe+/1OlIN8X1a1/DrNL2WJN2BW9X5QNEYDGgMBkhJ+ebCe0BVVdRgkJjfT8zrQ+0KEGN+PzG/H7VrH/N17f0+VJ+fWCCAGug6HwjEz/sDmP1+YgF/vIwviOrvnsNRZV9CyIKpEkLtqeLiYtRE4Ntj0qRJfPrpp7t97nnnncd55523y+uKonDXXXdx1113DbieQgghhBCHkiENCV944QVWrlzJsmXLBvW+69atY/bs2QQCAWw2G6+//jrjxo3bZXlZsU4IIQbOYNaRUWgno9C+07VoNIanPdC1UIofV6sfZ0v82N0eIOAJE/RFCPo8tNZ4+r+/SYs9zYQt1YS995ZmwpZixOIwotEMbohm0GjIMxnIM+0+TFRVFWckSksoEt/C8eCwNRShpStEbAnFV21uC0fwRWMEYr16KO4BnQKpeh0peh2pem38WBc/TtHrSOk6l9p1nKzTkazXDvniLAeSoigoXcOpByt47K13CKkGAsT8XeGivyto7H0tEOgpE4zvdVkHcTdCIYQQQgjxrTdkIWFNTQ3XXHMNCxYsGPThHaNHj2b16tU4nU5eeeUVLr30UhYtWrTLoFBWrBNCiP1Lq9V0LXDS/4IkoUAEd3sAd2sAV1sAd5sfd1v3cYCAN0woEKWtzktbnbffeygaBavDgC3FhC3VGN+nGLF3PbYmG7HYDSiDHCRCPJxK1utI1uso24MFYL3RKG1doWFr17Dm1q7H7eEIbaEI7eEobV2PvdEYEZWunot7tlhIN4dOS4peS0pXaJiq15Gs0+LodS5Zp43XX6fteqxDvx++TsNdnxBSCCGEEEKIQ8yQhYQrVqygubmZadOmJc5Fo1EWL17MP/7xD4LB4D5Pmm0wGBg5ciQA06dPZ9myZTzwwAM8+uijg1J3IYQQg8tg0pGWayMtt/957sLBaDxEbI8PXXa3BXB3dO3bA3g7Q6gxFU9HEE9HEMr7fx2NRsGaHA8MbSlGrClGbN2Pu/ZWhxGtfv8uQGLVarGatRTuZgGW3gLRWDw8DEfoCEdp7woPu487IlE6el3vDEdwR2MAOCNRnJEolXuxMAiARashWRcf5twdKjp08QAxqStMTNLFz319b9VqDqoVoYUYrsKhKHrDgV1ERgghhBDfXkMWEh5//PGsW7euz7nLL7+cMWPG8Otf/3pQV9WLxWJ9FiYRQggxvOiNWlJzrKTm9N9NLxZT8TlDeDoCXUFhAE97fO/ueuxzhYjF1ETYuDsmqx5rsiERGsb3BiwOIxaHAUuSAWvS/g8TE/XRasjVGsj9hmHPvYVjKp2RntCwO0jsCEfpjMTPxfdROiORrn08UATwRWP4orFvXP25PwqQpNNi12lI0mq7juP77s2u1STO2752bNdpsWu16KQ3o/gWi4Zj/OiOTziqKIWLfzABs23Pf/6FEEIIIfbFkIWEdru9z0pzAFarlbS0tMT5Sy65hLy8PO655x4AQqEQGzduTBzX1dWxevVqbDZboufgLbfcwimnnEJhYSFut5vnn3+eTz75hPfff/8AvjshhBAHkkajYEuJ9w7clVg0hs8VSvQ29HYG8XQG8XYE4vvOIF5niGg4RsAbJuAN73JoczejVYfVYYyHhl377hDRnNQTJhqtugO+6Iheo3SttKzfq+dFVRVXV1jYGY4meiI6u4LE7seurs3Za+8MRwmpKio9PRgHssKwWaPBptNg18bDQ5vua3utFptOg02riffO7Lpm1WoS161aDVatBrP0bhTDzKsLy1lEgM+qGjg9PBbzUFdICCGEEIe8IV/deHeqq6vRaHp6adTX1zN16tTE4/vvv5/777+fuXPn8sknnwDQ3NzMJZdcQkNDAw6Hg0mTJvH+++9zwgknHOjqCyGEOIhotJqueQp3PZ+cqqoEfZGuwDCItzMUP+567HOFEvtYRCXojRD0Rmiv332YqNEqmO1d4aHdgMWux9x9nGTAbNcnjk02PVrtgemh2B+tonQthKJjX1KJQDSGKxLvlejuDhOjUdyR+Hl3r1DR03XeHY2fd0dieKJRArH4qrb+WAx/KEYLezcP465YugJDi0bTFR7GQ0RLr83cq0zPeW18r4lft2g1mDUKFq0Ws1bBrJEAUgy+ydNsGFeswaaPoDEeRvcP5Ir3KhkxNZPkrP7neBVCCCGE2FcHVUjYHfTt6nFxcTGqqu72Hv/+978HuVZCCCG+LRRFwWTVY7LqScvrf35E6AoTvZF4YOgM4XXF9z5X9xbE5wrjcwUJeiPEomoibNwTRosOk02P2dYVINr0mOwGzLZ4mGiyxetotukx2fTojdoD3lNxV0xaDSathkzj3vVg7C0UiyUCQ080hjsS33t67d3RKJ5IDG80Xi6x/9o5bzRGd8uhewj1/mDWKJi1GsxdQWLffTxIvKY4izFW6Q8m9kz9uvmkpD1PZqcGg/PHYIE1q5u4bsEmjnl3B7fdczRGy77/nAkhhBBCfN1BFRIKIYQQw4GiKPGgzqYnLW/3ZaORGH53CK8zhN8dDxH97hB+Vxifu+u463zAE0ZVIeiLEPRFcDb796g+Gp2C2arHZDNgsukSQafJqk8EisbEuXgAaTTr0Axhj8XdMWg0pBk0pA1CMyWmqvhjsURA2B0cxo/joaPv61ssfq37sb/rnL/7cdexP9bzh0t/TMUfiwLRXdbl8rz0Ab8f8e0RypvLKS9/RdAGpGcA8MTaGqr1MT5KCnCnuefnY9HzW7CnmRh/VK4Eh0IIIYTYZxISCiGEEPuRVvfNw5y7xWIqQV8YvztMwBPC7w7j94TjQaInTKB77w0T8MSvRcMxYhEVrzMeRO4Ng1kX77Vo1ffsu46NFh0mS8+xsdexwaRDGSaLimgUpWtY8eCvENs7gOwODf19QsS+++I9XM1aCIATRpfiv/MefNEoDn28yT5t5Gb+V7eDQPIK/P4jsVgcbKru4JkvqyiIaBh/dM9fLSrWtNBa66Fkcjrp+XYA1JhKOBhFbzp4eh8LIcRwoqoqqPRpB0XDMVRVRavTJM5HozEiwSiKRsFg6oldAp4w0WgMk0WfWAAvEoric4fQ6jRYHT1tBXd7gEgoijXZmLhHKBDB3RZAq9P0mXais8lH0B/BkW7GZNMnyrbVetDqNWQWJSXKttV58LtDpGRbsSYbE2UbdjjRahXyx6QmyjZXufB2BknNteHIiI+GCAejVK1vQ6NVKJ2SkSjbWOHE1eInLd9GWq4t8d62LW8GYOwROT1ly5201XnIKLQn6haNxNj4WT0A44/KTfwxu2GHk+YqFxkFNnLLUhLfhzULawCYcHQeOoM2UbZheyfp+TYKx6clXm/l+1XEYioTj8nH2PVHtsZyJzWb2knLtVE6NaNP2UgoysRj8jHb44uGNVW6qFzbSkq2hVGHZfcpG/RHmDg3L9HWb6l2s31FE45MC+Pm5CbKrvqgGr87xPij8xJfy7Y6D5u/bCRnhKPP13IoSUgohBBCHCQ0GiU+xNhmAPpfybk3VVWJhGK9QsN4b8TuxwFvhIA3TLBrIZbu86FAvLdbyB8h5I83NveGovQEjAazDqNZl3hsNOsxWLrPaTGYe5Ux9RwfqJWh96f9GUAKoVEUzsxK6XOucFUtJ7W9hKUlg5gh/jvi463NLLSEKbB7uNXQM5z+o89r8KztQKfXJkJCrzPE07d8jlav4ScPzk0EhasWVFO7uZ2xR+QycnomEP/9sOT1HegMGuacW5a4b8XaVporXeSNSk58kIyEo6xeEP+wNu3kIjRdH5JrN7fTVOkiqzgpUTYWU1n5fhUAU+YVoNPHf37qtnbQsL2TjKIkinp9sFs+vwJVhcnHFyQ+JDds76RmUzvpBfY+H6qWv1tJNBJj8rEFiQ/JjeVOqta3kZprpWxGVqLsyverCAUiTDwmP/GhvLnKxY5VLaRkWRgzu+fD7KoPqgl4w4w/OpektPgHu9ZaD9uWNZKUbmb8UT3h7JqFNXidQcbNyU18gG9v8LL5iwZsqUYmHVuQKLvuk1rcbQFGz85OfKDubPax4dN6LHYDU08sTJTd8GkdnU0+Rh2WTUZh/Pvpbg+w9qMajFY9M04pTpTd+Hk97Q1eRk7LJLvU0fW9D7Lqg2r0Ri2zzihNlN3yZQMt1R5KpqSTNyr+7y3gCbN8fiUancIRZ49MlN22rInGCifFE9IpGBf/foYCEZa+WQ7AUReMSpTdsbKZ+m2dFIxNpXhSvBd1NBzj89e2AzDnnJFodfH/ByrWtlKzsZ28UcmMmBb/96eqKp++sBWAw88akfjeV21oo3JtKzkjHH0+qH/60lZiEZVZZ5Qmvvc1m9spX9lCZrGdsUf0fFD//JVthEMxZp5WnPje12/rZOtXjaQX2JnQK2xf8voOAr4w008qIik9/r1vLHey6fN6UnKsTJnX8z1a+lY5XmeQqScUkpId//lsqXaz7pNaHJlmpp/c8z366p0K3G1+Jh9fSHp+/HvfVudh9YJqbKmmPt+j5fMr6WzyMfGYfLJK4mFKR6OXZf+rxOIwcGSvn8+V71fRWuNmwty8RJjiavXzxWvbMVr1HHvxmJ6yH1TRVO5i3FG5iZ85T0eARf/dit6g4cQf9Swy2v07Ytyc3MT3yO8O8cG/N6DRajj96smJsqs/rKZiTStjj8hJ/ByF/BHefmg1AGfdMC0R/qxZWMOWpY2MPjybycfFfzaikRgv37McUDn7V9MT3/u1H9ew7pM6Rh2WxczTShKv9587lhCLqpz76xlYkuKBzrpPalnxbiUjZ2Rx5Hk9X59nf/MFQX+E82+Zmfh+rl9cxxevbqd0agbzLhuXKPv0LZ/jc4Y479aZie/Rxs/r+fjZzRRNTOM7V/W85+fv/BJXa4Bzbpqe+JkrX9nCB//eQN6oZM68flqi7Bt/W0lbnZczrplCwdj4z1HN5g7m/3MtmcVJnHfzjETZ9x9fT1OFi1N+OjHxu6650sWbf19Naq6Vi+6YlSj7yfNbqNvSwQlXjGPUzPjPRnu9l9fuX4k9zcQldx+RKPvlm+VUrm3l2O+PYdyR8Z8Nd1uAdx5ag9mu54d/PqrP93778maOPK+MycfHv0c+V4j3H1+PzqjlJw/MTZTd+Gk9m75oYNZ3SxO/00KBKB89swnoGxJuXdbEuo9rmX5KUZ+QcHHXz/3YI3LQdDWvKte2svL9KiYfV5D4d40Kn78S/30y+vDsREhYu7mdr96uYNxRuX1Cwq/eqSAajjHqsKw+IeFXb1cw6rCsPiFh9+/8kTOyEiFhS7Wb5fMrKZ2S0ed3z7pPavF0BBkxNSMRErY3eFn5fjUF41L7hISbvqino9FH0cS0REjobPazekE1sUi+hIRCCCGEGBhFUdAbteiNWuyp39xTsVs0GiPUNaS5OzzsPg76IgS7976u872O438t7xkSva+0Ok08RDTp0Ju0iQDRkDjWojfFH+uNXfuua3pjr2OTdkgXehHiQIpe8DN8L2aRnp+LTRdvxutoRGvdQodpK6hnAxAIR/ljTT2kwOGpvsTzP93SzEpDhFKjoU9PwrZaD9Ub2skf3dN7JBSIsH5xHRqt0ickrF7fxvrFdSiakp6QMBRj6VvxoGjaSYVA/N5V69tY/WENU08oTJRVY2oiVJp4TD66rtHRtVs6WP6/SibOzesTEn71TiVqTGXcnNyekHCHk2X/q2TM7Ow+H6pWvV9FKBBl9KzsRFDUXBX/YDdiWmafkHDNRzX4nCFGTs9MBEVtdR5WvldF0cS0PiHh+k/rcLX4KZ6UnggJOxrjHwLzRiX3CQk3fdFAW52HgjGpiZDQ1epn1YJqMovsfULCrV810ljuInuEI/GB2tMRZPWCalJyrH1Cwu0rmqnd3EFGoT0REvqcIVZ/WIM9zdQnJKxY0xrv8ZJlSQQWAW+YNQtrMNv1fQKoqg3tbFvWhD3NlAgJQ4EIaz6qQWfQ9AkJaze3s/HzBsx2QyIkjIRirP24FugbEtZv62Ttx7XojdpESBiLqazrKjv7rBF0/3mlsdzJuk9q0WiURAAFsG5RHQAzv1MCXf/FtVS5Wb+ojlhU7fNBfeOn9UTCMaaeVJj43rfVeli/uI4yf1afkHDzkkYC3jCTegXEHY1eNnxaT8nk9D4h4dZljXjag4w/MjcRKjlb/Gz8vIGCsSl9QsIdK5vpaPQxelZ2IiR0twXY9EUDOSMcfULCyrWttFS7GTEtMxFAeZ1BNn/ZSHqBrc/3qGZTO/XbOimelJ4ICf2eMNuWNeHINPcJCeu2dlK9oY3C8Wnkdp0O+iLsWNmS6DHWrbnCRfnqFvLH9PwxIhyMUrm2FaOlb0zQ/Tsib3RP2WgkRu3mDjTavr2SO5v91G/r7FM2FlNpLHcB0HuFAU9HgJZqN/m9yqLGfxYBei9H4HeH6Wzy4XP1HTXhag2gxlTUXlOAhINRvM4QQV+4T9nuP5TGoj1lY9F4D+tIqO+cxbGoSiym9lkTIfFr8+vLJHRd6LN8wq46aytK/FqvshqNglavQavr+yS9Mf7H1t5fY61Og9mu3+l7ZEkyYEsxJv7wAqAzaEjKMGNP6fu9t6cYSc21Yug1bYXOoCG9wIbJ2nfKiuRMC1klSVgchj5lc0Y6EsFct5QcK/ljUkhK62mTanUKheNTd/qCpOVaKZ6UnvhZgfhCf6VTM1Cgz/9RaflWRs7IJL2w13zhCpTNzELR0KcdmJ5vY8zh2WSXOPq83pjZOcSiMfTGnjqn5dkYd1QuWcVJfcqOPSKHcCja52uclmtl4rH5pH9tzvKxR+QQ9EcSYSJASraFyccX7LTA2JjZOfjdIWy9vh+OTDNTTygke0Tf+g4lCQmFEEKIbxmtVoPZbujToNlTkXCUoC/eAzHoixD0R+KBoz8eJPY5748Q8kcJBbrK+yOEu3oxxudqjOF3h7/hFb+ZRqdgMPaEh93BafemM2rRG3o9NmjRGzVd+17nDFp0Bk3iOTq9RoZkioPKvHQHR/z0UrzRnrkvZwQbubTqX7Q4kggrGgxAbYcPjcZHWNVSHf6SWcR7D72+dRsLLWEqi9ZxG8cA0OoJcuXWcnTpUWYWdgDx0OOpFdt4tdDJlPSen4FAOMpTzhp8I/3Mzun5I8HiikZWjwpSYtElPixHYyoLQ620jw0zK6/nHuvq2qkeFyXLqO3zwXdFtIPGCSozCno+7G1vdtI6USVFH//Z7LZeddI5RcFc3PNhr6bdQ8cEFZtG0+dD4HbFjW+GBuuInnNNLj+e8Sr6mBZDrw+BlYqH8EwN9qKeD8kd3hC+sTEMI/WYknruUavxwuE67Dk9v0ddgTD+sTGMRXpMyT1lG/Ghm63HkdHzwdkbjBAoUzHnGrGk99ShWfVjOsJAWq8//ATCUUIjVWzpJmyZPa/XpvqxHGkiI6lnQaRgJEqkVMWRbCYpt+eDaEcsiP0oM6mWnvuGozGiRZBstpJc0HPfjkiA5LlWkgw9X4dINEa0QEPqXBtpxT336AgHSJ1r69OjOhZTieYrpB9jJ31ET906AwEyjk3CrNUmepuqqko0WyHzWAcZo3sNnfQFyTo2GaNW0yeIiGYpZB+XTNaIng/qrkCIzGNS0KtqoocQQCRdQ87xaeT0CgA8gTCZR6WgicUw2XrK/j979x0eZ3Xm//89TV2jrlGxbMlFrtiWi4wxYJqxNRLB8jdANgRMWXYTWgi7vwBJNsBudknCJiEJJJBsgGSzCQRwARUX3DFgucgNF9lYbrI1kmxJo15mnt8fMjN2aAZJo/Z5XZcv0DnPzNw6kmbuueeUjhgzKdfFkzos6oJrEy6LIaHD65uhBtBuN5E6L4G0ZP/9Nrd3EjcrmqjWSMJj/de2RsCw6x0MS/DH29LuIWZGNBETIohy+MeyJQTSFiSREusvmrR2eLBPiyZ4dDhxqf72JhsMz0khKerC35OIKVGMTA/1FZIBGi1e0nOHERfh/31o6/QQOimaUWlhJI/xf88NJoNRN44gLvy877fTS/DEKEalhFxQwK/1eBi1KIOY88fc48U6NpJRiRmMHO/fh7emtZ1RN48kOsSK+dxzhNdr4B0dwcj4kYwa7b9fV0Mr6V8fRXSIDYvtvOef9DDSvzGKMRn+DzMq61tIu20UUSE2gsP9cTSkhZC2eDRj0v3FR5e7lYRbR2IPshJ+XpHmbEoQSXePJjPtvCW27lYib0knJcSGPdn/e+mKtxL/T5mMOe/3pLqhjaD/l8bwECvRI/w/55PRZqK+OYZRw6L949DYhucrKSQGWYg9b9yPhBpY7xxJ6nnXnm1q5+zceGzWBGLG+dsP0EnTzWmkp/pvX9fczpEpEXiyIrBP8H/Pe9raqMx1EHXe76q7tYNdI4NpGZnA3In++y11N3Ngbiyjk/y/O01tnWxOAHdsNDPOv/ZsI9tmRjIi8cLf1bfDOqiZGs7Yif7YdlS5WT8plJQ4/zh2eLwUmFo5PTaIpLH+x9t52k3RKCvxsSEsOLfixOs1eKO1kRMjzPzD6HA+mgu7+5SbN9IgOiqEa0L8zxGvNrg5lOzlpowQxn80ZpVu/hTXQWRkMLPP+319pbaOPXGd3DgiiI/mkH5Y3cjz4a2EJtrIOu936pWaWrbZ21gwLMp37fEzzTxrbcaSaOGnERY++k5edZ3lnfAWrk6J5KP5uBV1LfySBryxZp6KtPLRCL1WeYa3Q5rIi4zE/9FA31KRUERERC6a1WbBGmW5YM+ceB+7QQABAABJREFUL8LrNeho9RcM21v9RcT2lk7f1x0fFRdbPXS0+a/taPNf4+3s+hje22nQ2tk1I7Kn+YqG54oU1o8KiUEW3/9bg7oKitYgM5aPrvP919+fmG7/2Cf0Il9UmMVM2HmzJiIvW0BtYxi25mZfoWZ0YiRfbXiafXHBVFTf4bs2vOUo1shq2ut2+9qa2zx4DRPtHi87KraQPa5rCd2Ww0cpdwdRG7QRWNh1bbuHdcfaADObWzYwja6pSkt37Gd1lZfIuPV8zzwP6CosvLj7LACJLauYwbcA+MuWD/jbqXZCY7fwcNA1vjh++v5pwETb6EJm8RAAf31vHy8eayEoajv/HOJf/vbkxnI8XivHk5dzGd8FYPm2w/zmcBNW+05uDZvju/bRtw/Q3hnELvsbXMEPAFi5+zg/2+fGErGPnOAp2OkqZP3Lij00t4WQFfw35vIEABv3n+ap3XVYwg4xyzKSGLqKAA8V7KC+OZRxrOJafgTAe4eqeXLbGcyhxxhLAgnn3gY+8FYJ1e5QMtqLuZ5LACg9Xsf3t1RhDj6N47JgculaYvjAm+9ScTaUlOEFzKfrZ/HBKTf/3zunMdlqCJ7eyde4suvaZZv5sCqY+NTl5JAFwJHqJr69/iQmi5vvTazlHpxd8S7dxN4KGzFJG8k7d21FbQvfevsomFt5aMxxHuImAP6/5e9QUm4mMvEdbqBrmeTZpnb+ccVhwMM/Dt/PD1gMwPff3Mz6g17C4jfzFaYD0NTeyR1vdS0ZvCVhKz/hXgD+veB93trTSkjse9xg7YrB4zX4xrKupYjOiA38ZsrDAPxsxTb+XFpPUHQJ822TfT/Pr72+C8OwcKV1NX+a/ggAv1u7i2e3VmG172DHwkv46NXpa69tpdMTxEzvm7w289+6fqfePch/vn8Ma+Qe1i0YRdi5q2959V3aOkKY1PwGBZc+CUDRzhN8951yLOEHWXZ1Ipecm874D69uorE1lDGZm1l92X8BsO6D09y34UMsoeW8fOkCrqBrBuatr6yntimM4Rmb2HjFjwF4/0gN96wtwxxykl9PuZTccz/n215dS1V9GEnDN/D+lT8FYNeJOu5euR9zUBX/kTmGW5MuB+Cuv73NiTOhxKWuZ/tVTwNQ5mrgzqIPMFnreDQtnn9OnQ/AP//tbcpcIUQnbWDn1V33e+JsM4vf2o3J0sR98Vb+NbVrFvL9r73N7pM2IhI3sPdcDNWNbdy2ZCeYOrgjrIEnUm8D4LtL1vHeERNh8ZvYd+57c7d28o3XSgH4quk4/53a9Xf/ZME7rN7fSUjsuxy47D8BaPd4ue2VHQAsuLyM54d9p+tnv2oLb+xsJih6CwdnPun72S/+yzbAxOXZe/nzonM/+3WlvPh+Lbaobey85Ae+4sadf34fr9fKjKxSXr+l6+/+tff3899rT2GN3MXmcf+C49xc1nv+9x06OoOZOLGEwtu6Hm/FznJ+WHQES8R+Ch68i/H2rgLit/68kZb2UEZnvsvbd3V9H5sPVvLI63uxhB3mz9/MZ3Zi1wctD/7fehpawhgx6h023PMUANvLz/D9v+zCHHKcyLvmsmB4V7npX19Zx5mGMJKHb+S9e38CwL5Tbp58dTfm4NOE3noJt2R2LS3+3uvrOF0XRsKwDWy9v+tn9GF1Ez9+fS8m2xmCbcP4x0u6nlufXLaBo9UhxCRv4Cvf7rq2oraFny39AJPFjc2yl+/M+AoAPynYyP7TwUQ6NnDrd8797Bva+NXy/WBuxWreyg8u+wcAfrFiM9uPWQiP38g/j++K193SwXNvHQA8WNjEf111NwC/XbOFjYcMQuM28y+Tu/5eWjs8/LbgAACejtVMX/AAAC9v3EbB3naCY97j8Wldz6tew+CFwoNdt2su5Iob/z8A/vbeLlZuayAoqoR/n/k4lnPF5xcKP8AwLDS5l7Pgpu8D8Oa2/ax9pwarfQdPznyM0HOvoS8U7qLTE4S75jX+3ze6fvZr95Szcf1xrJF7OTvtPuzWruf85wt30NYRwpnKV7jtzq6f/fsHT7FxwzEs4QepmHorCUGOc9dupaktjKqK/+Obo7vGZ2d5DRvWHcMSWs7hcTaGnZvq+3zhFuqaw2k4+Tp3jPoP+gMVCUVERCRgzGbTuUNQul8s83R66WjzdP1rPffftk5fW/u5ts72c//96Np2Dx1tXn+7779eOto9eDr8S446273nliB1vwC58OEs33I+kZ4yJiaKX9xywwVthmEwfMb/R/TuXWSOutLXfkukjate/ROHR4/1taVEh3BT06+ot7Tgbbrb1z47vIaKoHXYG+t9bcFWMyMtG6kL7qSuwr/MN82oJSx8P5Edx31tJhMkWA7gDu6k9pjb1x7fUU9Q6GFCvJUYhuGbrRttOUmjrYPGY/77iOlswhp8HJu5hg6vwUeTCe0mF+5g6DjhvzaisxlL0Gms5jpavV7fLI0I4wz1NgueigrftaGdbZht1VjNbpo9/r/3ME8trdYgzJWnfG0hRidmay0WcyNN7f7Zk6EddTRY27C5Kv3jYwazxY3F1Exzs3+v1+C2ekyWToJqqn1tVosJs7kRs7mFJnejr93W4sZk9hJSf8bXZjGbMJtaMJnbaKk9629vrgdzJCHuOl+b2WTCbGoFczut1TX+9sZ6MEUS3OT/WXStemwHUwdtlf5rTQ31YAonqLWJ85nowDB56TzlHx+T2w2mYGztrX93bSeGycB76vTf3a8Za2f7J1wLptP+a40GN5g6MXs7L1jZaTY8eEwG1vN+RjQ1gakTE1685633NHs7uh6v0n+/tDSDqQNMXjznXWvxtIHJQnCNyx9XeyuYOjCZPJwfhaWjBUwWQs74f55mrwdM7WDuxNPh/z2xtjeDyUponf/naTaBydSGydSBp6XF125rbQSzhbD6Wv9jmU2YTK1gbsNo8P+eBLW4wQzhjXXn3W/X7wmWVqjzt9ua3WD2Etpcf8G1FlMTXnML5jNtvvbgZjcmSzChrQ0XxGs1NeKxdGA973c4pLkek8VCSMeF19pMDXRaDIKrqs671o3J2kGQx/89AASZ3HRYvISeN+4hLQ2YrA3YuPDaEFM9bVaD8Gr/tWHtTZhsZ7CYL7w2jFqabCbCqyvPu7YFk60Gk7XhgmXBEcZZ6oKs2M/4rw03OjAHVWG21nG+SOMMbUE2omr931uEyYM5+DRmWy2m8+432ltDU7CNqDp/vJFBZqxBFRBcg7XD/3cQ21lNbYiF6Eb/tREhVmxBJzCCzxDc6l9SHt9ZjSvERHST/9rwYAvBtuN4QtyEN0X72hM6qjke6iW6xR9vqM1CqO0oHcHN2Bv82yo4Os9QFtpGdNt5PzebhXBrOa3B7cTU+2cFJ3WexRLWQKTHf63NYibSeoTmIA/xtf7vLdlThyWsmnDDP74Ws4ko6xEagzwknfe7muRxYwk/Qaj5vL9Dk4kYaznu4A5S6vzPU0lGE5bwMmxWF+bzljLHW49yNsjDsPOvNbViCS/DElRJkNn/IVui+TiuEC/DGvzXJpg7sISXYQ4+Tfh5H8glmU9wMtxgWKP/byAhyOi635AKoiz+0lqK6RRHwr0Ma/Tfb2yQGWtYGaYQF3HnrVJJNVXSEO4lucE/Pn1NRUIREREZkCxWMxarucdn53m9Bp3tnnMFQn8BsbO9q8DY2e6ls8Pf5r/2XHuH94JrPOfaeqIwKnIxTCYT37r9ZuDmC9on3fx1WHAj8a3+go7VYua6K79JdVUNEy652td+wyXTid9ZRrPDv+F+eLCV71nCaf7wOPZ5X/O133XJWKYXLMOd7N/3LcRm4WedZ+jcexDTpff52u+ZMopZr75EY3wiBv5dqn7LKTq37cL7T//su/bOKRlkvfRrWqOjfbNEAP4npJr2LSV4Ft/ha/v65BGM+82P6QwLJ+K8N2svRdXTunEjnn/4B19b/qQU0n/+Q7wWCymh/mV4f3S00by6AE/+Il/bdeMSee0/XscARv7Lv/qvHW6ioeB1vM5cX9vcMfG88cFreA2D4Q/+k//aUcHUv/Eqnmuv87VdOjKOpQdew+P1kvrNW/zXTozi7J//D+8V/uLu1LRolh1bhqelGcddv/R/b1MTqf7Di3iys31tY5MiWV5ZhKeujvjbfuxr/92MVCp/+zzeKf7DFkbEhfOWew2eykpiv+afsfXcrHQqnvkl3nHjfW2J9hAK2zbSefwEUf/vMV/7zy4bxcmf/BRPhv8gicgQG0WmLbQfOoQ972Ff+39cNpq71vwIb4r/98RqMVMcupO2vR8QMe+bvvbHLh3FTcU/xBsfj+28n31R9EGad+4k/Mo7fG33Z2dw3RvfxbBHEnHesucCxymaSt4n/FL/z37x9BFM/7+fQ3AwSSH+osdbw+twb3qL0Gn5vrb8S5IZ/bsfYZhgXIT/+1gyuoP6tX8jbJL/Zz9/YhJLf/kaXgwy4/0/+9fHWzmz4jWCx13ra7tyTAJvnHoDA4NRyf7Y/jolkqq33iA043Jf24z0WN6oeQtvZwcjh33F1/6n6QmceuMVQlP9P/tJqVEsaVxNZ1MTGen+GUn/MyuVir/+ieBE/89+ZEIESzs20V5dy4hRP/C1/2b2CI7+6feEZPp/9slRoSw1b6XVVUnaGP/fwH9fNoojv3+OoHD/zz46LIhloXtoOXmcYfkP+Np/NGc0i5/7OVabf7/HEJuF5VFlNJUfJjXP/3f/6KWjyd/yFFYuPMThrcQTuA9+QPK8O3xt35qRwdVr/4SlPeqCGdZvptZSt7eU5Ln+8b112giyCp/H3ByGI9i/nHpZRhtntm8k6VL/3/1XJqWS8cp/Y7JaGBsZ7Wt/Y7SF6vfXkzjN/7O/emwir1dtBGByrH+fzFfGR1C1YTVxE+f52mZnxPJ69TsYwESHf3HpHyfH4VpVSMycC//uX6t5H8PwMi7V/zv1P9NSOfXWG0TNvNTXNi7JzusNpXhqWskccauv/bczRnDyjb8SOcX/PJ4eH87rrQfoqKln9Mj/52v/2YzRnPjrS4RPmOhrS4oK4Q3jKK0VVYy85VFf+49mjuGel18gbLS/eBkTHsTrQadpPV1B+qLv+Nq/P3MsX//dSkKGp/vawoKsvBFxlqbj5QzPu9fX/tDMsXxlSwFBDv84Wswm3ohtpPHDg6Red5ev/a5pY5i74W9YY+OwnXfS9GtJHhr27yFp7jd8bTdNzmBa8f9iigy/4Pfk1fQg6nduJ3H2V31tzgmpjF66H1NQEPHB/ueI/xsdRd3294if4f9Q7ooxiSz/vwOAiRER/g+B/zQxgTPvbSJuqv/3ZNaoOJa5D4MbxsX4/w5enDKM6o1riJ3of33oaybDuGB7TQHcbjdRUVHU19djt9s//wYiIiIifUz5S+BorEVERGQgudjcRccBioiIiIiIiIiIDHEqEoqIiIiIiIiIiAxxKhKKiIiIiIiIiIgMcSoSioiIiIiIiIiIDHEqEoqIiIiIiIiIiAxxKhKKiIiIiIiIiIgMcSoSioiIiIiIiIiIDHEqEoqIiIiIiIiIiAxxKhKKiIiIiIiIiIgMcSoSioiIiIiIiIiIDHEqEoqIiIiIiIiIiAxxKhKKiIiIiIiIiIgMcSoSioiIiIiIiIiIDHEqEoqIiIiIiIiIiAxx1r4OoD8yDAMAt9vdx5GIiIiIXJyP8paP8hjpPcoVRUREZCC52DxRRcJP0NDQAEBaWlofRyIiIiLyxTQ0NBAVFdXXYQxqyhVFRERkIPq8PNFk6OPmj/F6vZw6dYrIyEhMJlOvPY7b7SYtLY0TJ05gt9t77XGGCo1nz9OY9jyNac/TmPYsjWfPC9SYGoZBQ0MDKSkpmM3aUaY3BSJX1N9iz9OY9iyNZ8/TmPY8jWnP05j2vECM6cXmiZpJ+AnMZjPDhg0L2OPZ7Xb9cfUgjWfP05j2PI1pz9OY9iyNZ88LxJhqBmFgBDJX1N9iz9OY9iyNZ8/TmPY8jWnP05j2vN4e04vJE/Uxs4iIiIiIiIiIyBCnIqGIiIiIiIiIiMgQpyJhHwoODubxxx8nODi4r0MZFDSePU9j2vM0pj1PY9qzNJ49T2MqX4Z+b3qexrRnaTx7nsa052lMe57GtOf1pzHVwSUiIiIiIiIiIiJDnGYSioiIiIiIiIiIDHEqEoqIiIiIiIiIiAxxKhKKiIiIiIiIiIgMcSoSioiIiIiIiIiIDHEqEoqIiIiIiIiIiAxxKhL2keeee4709HRCQkKYNWsWJSUlfR3SgLFx40ZuuOEGUlJSMJlMLFu27IJ+wzD44Q9/SHJyMqGhoVx33XUcOnSob4IdAJ566ilmzpxJZGQkiYmJLFy4kIMHD15wTWtrK/fddx9xcXFERETw//7f/8PlcvVRxP3fb3/7WyZPnozdbsdutzN79myKi4t9/RrP7vvxj3+MyWTioYce8rVpXL+YJ554ApPJdMG/cePG+fo1nl9cRUUF3/jGN4iLiyM0NJRLLrmEbdu2+fr1+iQXS3nil6c8secpV+x5yhV7l/LE7lOe2DsGQq6oImEfePXVV3n44Yd5/PHH2bFjB1OmTGH+/PlUVVX1dWgDQlNTE1OmTOG55577xP6f/vSn/OpXv+L5559ny5YthIeHM3/+fFpbWwMc6cCwYcMG7rvvPt5//31Wr15NR0cH119/PU1NTb5rvvOd7/DWW2/x2muvsWHDBk6dOsWiRYv6MOr+bdiwYfz4xz9m+/btbNu2jWuuuYYbb7yRDz74ANB4dtfWrVt54YUXmDx58gXtGtcvbuLEiZw+fdr375133vH1aTy/mNraWubMmYPNZqO4uJh9+/bxs5/9jJiYGN81en2Si6E8sXuUJ/Y85Yo9T7li71Ge2HOUJ/asAZMrGhJw2dnZxn333ef72uPxGCkpKcZTTz3Vh1ENTICxdOlS39der9dISkoynn76aV9bXV2dERwcbPz1r3/tgwgHnqqqKgMwNmzYYBhG1/jZbDbjtdde812zf/9+AzDee++9vgpzwImJiTH+53/+R+PZTQ0NDcaYMWOM1atXG3PnzjW+/e1vG4ah39Mv4/HHHzemTJnyiX0azy/ukUceMS6//PJP7dfrk1ws5Yk9R3li71Cu2DuUK3af8sSeozyx5w2UXFEzCQOsvb2d7du3c9111/nazGYz1113He+9914fRjY4lJeXU1lZecH4RkVFMWvWLI3vRaqvrwcgNjYWgO3bt9PR0XHBmI4bN47hw4drTC+Cx+PhlVdeoampidmzZ2s8u+m+++4jNzf3gvED/Z5+WYcOHSIlJYWRI0dy6623cvz4cUDj+WW8+eabzJgxg5tuuonExESysrL4/e9/7+vX65NcDOWJvUt/hz1DuWLPUq7Yc5Qn9izliT1roOSKKhIGWE1NDR6PB4fDcUG7w+GgsrKyj6IaPD4aQ43vl+P1ennooYeYM2cOkyZNArrGNCgoiOjo6Auu1Zh+tj179hAREUFwcDDf/OY3Wbp0KRMmTNB4dsMrr7zCjh07eOqppz7Wp3H94mbNmsXLL7/MihUr+O1vf0t5eTlXXHEFDQ0NGs8v4ciRI/z2t79lzJgxrFy5km9961s8+OCD/PGPfwT0+iQXR3li79LfYfcpV+w5yhV7lvLEnqU8secNlFzRGrBHEpF+77777mPv3r0X7DchX87YsWPZuXMn9fX1vP766yxevJgNGzb0dVgD1okTJ/j2t7/N6tWrCQkJ6etwBoWcnBzf/0+ePJlZs2YxYsQI/va3vxEaGtqHkQ1MXq+XGTNm8F//9V8AZGVlsXfvXp5//nkWL17cx9GJiPQM5Yo9R7liz1Ge2POUJ/a8gZIraiZhgMXHx2OxWD528o/L5SIpKamPoho8PhpDje8Xd//991NQUMC6desYNmyYrz0pKYn29nbq6uouuF5j+tmCgoIYPXo006dP56mnnmLKlCn88pe/1Hh+Sdu3b6eqqopp06ZhtVqxWq1s2LCBX/3qV1itVhwOh8a1m6Kjo8nMzOTw4cP6Pf0SkpOTmTBhwgVt48eP9y3N0euTXAzlib1Lf4fdo1yxZylX7DnKE3uf8sTuGyi5ooqEARYUFMT06dNZs2aNr83r9bJmzRpmz57dh5ENDhkZGSQlJV0wvm63my1btmh8P4VhGNx///0sXbqUtWvXkpGRcUH/9OnTsdlsF4zpwYMHOX78uMb0C/B6vbS1tWk8v6Rrr72WPXv2sHPnTt+/GTNmcOutt/r+X+PaPY2NjXz44YckJyfr9/RLmDNnDgcPHrygraysjBEjRgB6fZKLozyxd+nv8MtRrhgYyhW/POWJvU95YvcNmFwxYEekiM8rr7xiBAcHGy+//LKxb98+45/+6Z+M6Ohoo7Kysq9DGxAaGhqM0tJSo7S01ACMn//850Zpaalx7NgxwzAM48c//rERHR1tLF++3Ni9e7dx4403GhkZGUZLS0sfR94/fetb3zKioqKM9evXG6dPn/b9a25u9l3zzW9+0xg+fLixdu1aY9u2bcbs2bON2bNn92HU/dujjz5qbNiwwSgvLzd2795tPProo4bJZDJWrVplGIbGs6ecf2qdYWhcv6h/+Zd/MdavX2+Ul5cbmzdvNq677jojPj7eqKqqMgxD4/lFlZSUGFar1fjP//xP49ChQ8b//d//GWFhYcaf//xn3zV6fZKLoTyxe5Qn9jzlij1PuWLvU57YPcoTe95AyRVVJOwjv/71r43hw4cbQUFBRnZ2tvH+++/3dUgDxrp16wzgY/8WL15sGEbX0eH/9m//ZjgcDiM4ONi49tprjYMHD/Zt0P3YJ40lYLz00ku+a1paWox7773XiImJMcLCwoz8/Hzj9OnTfRd0P3fXXXcZI0aMMIKCgoyEhATj2muv9SV9hqHx7Cl/n/xpXL+YW265xUhOTjaCgoKM1NRU45ZbbjEOHz7s69d4fnFvvfWWMWnSJCM4ONgYN26c8bvf/e6Cfr0+ycVSnvjlKU/secoVe55yxd6nPLF7lCf2joGQK5oMwzACN29RRERERERERERE+hvtSSgiIiIiIiIiIjLEqUgoIiIiIiIiIiIyxKlIKCIiIiIiIiIiMsSpSCgiIiIiIiIiIjLEqUgoIiIiIiIiIiIyxKlIKCIiIiIiIiIiMsSpSCgiIiIiIiIiIjLEqUgoIiIiIiIiIiIyxKlIKCID3h133MHChQsD/rgvv/wyJpMJk8nEQw891O37io6O7pG4ettVV13l+7537tzZ1+GIiIiIfCblioGlXFFk4LL2dQAiIp/FZDJ9Zv/jjz/OL3/5SwzDCFBEF7Lb7Rw8eJDw8PBu3c8tt9yC0+nsoaj8TCYTS5cu7dHEeMmSJXz44YdkZ2f32H2KiIiIfBnKFbtHuaKInE9FQhHp106fPu37/1dffZUf/vCHHDx40NcWERFBREREX4QGdCVWSUlJ3b6f0NBQQkNDeyCi3hcbG4vb7e7rMERERESUK/ZDyhVFBi4tNxaRfi0pKcn3LyoqypdoffQvIiLiY0tIrrrqKh544AEeeughYmJicDgc/P73v6epqYk777yTyMhIRo8eTXFx8QWPtXfvXnJycoiIiMDhcHDbbbdRU1PzhWNOT0/nRz/6EbfffjsRERGMGDGCN998k+rqam688UYiIiKYPHky27Zt893m75eQPPHEE0ydOpX//d//JT09naioKL72ta/R0NBwweM888wzFzz21KlTeeKJJ3z9APn5+ZhMJt/XAMuXL2fatGmEhIQwcuRInnzySTo7OwEwDIMnnniC4cOHExwcTEpKCg8++OAXHgcRERGR3qZcUbmiiPQcFQlFZFD64x//SHx8PCUlJTzwwAN861vf4qabbuKyyy5jx44dXH/99dx22200NzcDUFdXxzXXXENWVhbbtm1jxYoVuFwubr755i/1+L/4xS+YM2cOpaWl5Obmctttt3H77bfzjW98gx07djBq1Chuv/32z1z68uGHH7Js2TIKCgooKChgw4YN/PjHP77oGLZu3QrASy+9xOnTp31fb9q0idtvv51vf/vb7Nu3jxdeeIGXX36Z//zP/wTgjTfe4Be/+AUvvPAChw4dYtmyZVxyySVfahxERERE+iPlisoVReTjVCQUkUFpypQp/OAHP2DMmDE89thjhISEEB8fzz333MOYMWP44Q9/yJkzZ9i9ezcAzz77LFlZWfzXf/0X48aNIysrixdffJF169ZRVlb2hR/f6XTyz//8z77HcrvdzJw5k5tuuonMzEweeeQR9u/fj8vl+tT78Hq9vPzyy0yaNIkrrriC2267jTVr1lx0DAkJCQBER0eTlJTk+/rJJ5/k0UcfZfHixYwcOZJ58+bxH//xH7zwwgsAHD9+nKSkJK677jqGDx9OdnY299xzzxceAxEREZH+SrmickUR+TgVCUVkUJo8ebLv/y0WC3FxcRd8wulwOACoqqoCYNeuXaxbt863b01ERATjxo0Duj6l7c7jf/RYn/X4nyQ9PZ3IyEjf18nJyZ95/cXatWsX//7v/37B93rPPfdw+vRpmpubuemmm2hpaWHkyJHcc889LF261Le8RERERGQwUK746ZQrigxdOrhERAYlm812wdcmk+mCto9OwvN6vQA0NjZyww038JOf/ORj95WcnNytx//osT7r8T/vPj66zfnXm83mjy1B6ejo+NzYGhsbefLJJ1m0aNHH+kJCQkhLS+PgwYO8/fbbrF69mnvvvZenn36aDRs2fCwmERERkYFIueKnU64oMnSpSCgiAkybNo033niD9PR0rNaB8dSYkJBwwYl+breb8vLyC66x2Wx4PJ4L2qZNm8bBgwcZPXr0p953aGgoN9xwAzfccAP33Xcf48aNY8+ePUybNq1nvwkRERGRAUC54oWUK4oMTlpuLCIC3HfffZw9e5Z/+Id/YOvWrXz44YesXLmSO++882OJU39xzTXX8L//+79s2rSJPXv2sHjxYiwWywXXpKens2bNGiorK6mtrQXghz/8IX/605948skn+eCDD9i/fz+vvPIKP/jBD4Cu0/P+8Ic/sHfvXo4cOcKf//xnQkNDGTFiRMC/RxEREZH+QLmickWRoUBFQhERICUlhc2bN+PxeLj++uu55JJLeOihh4iOjsZs7p9PlY899hhz584lLy+P3NxcFi5cyKhRoy645mc/+xmrV68mLS2NrKwsAObPn09BQQGrVq1i5syZXHrppfziF7/wJXbR0dH8/ve/Z86cOUyePJm3336bt956i7i4uIB/jyIiIiL9gXJF5YoiQ4HJ+Kwz1UVE5FO9/PLLPPTQQ9TV1fV1KAF39OhRMjIyKC0tZerUqX0djoiIiEi/o1xRuaLIQNM/P/IQERkg6uvriYiI4JFHHunrUAImJyeHiRMn9nUYIiIiIv2eckURGUg0k1BE5EtqaGjA5XIBXcsu4uPj+ziiwKioqKClpQWA4cOHExQU1McRiYiIiPQ/yhWVK4oMNCoSioiIiIiIiIiIDHFabiwiIiIiIiIiIjLEqUgoIiIiIiIiIiIyxKlIKCIiIiIiIiIiMsSpSCgiIiIiIiIiIjLEqUgoIiIiIiIiIiIyxKlIKCIiIiIiIiIiMsSpSCgiIiIiIiIiIjLEqUgoIiIiIiIiIiIyxKlIKCIiIiIiIiIiMsSpSCgiIiIiIiIiIjLEqUgoIiIiIiIiIiIyxKlIKCIiIiIiIiIiMsRZ+zqA/sjr9XLq1CkiIyMxmUx9HY6IiIjI5zIMg4aGBlJSUjCb9Tlwb1KuKCIiIgPJxeaJKhJ+glOnTpGWltbXYYiIiIh8YSdOnGDYsGF9HcagplxRREREBqLPyxNVJPwEkZGRQNfg2e32Po5GRERE5PO53W7S0tJ8eYz0HuWKIiIiMpBcbJ6oIuEn+GjZiN1uV+InIiIiA4qWv/Y+5YoiIiIyEH1enqgNa0RERERERERERIY4FQlFRERERERERESGOBUJRUREREREREREhjgVCUVERERERERERIY4FQlFRERERERERESGOBUJRUREREREREREhjgVCUVERERERERERIY4FQlFRERERERERESGOBUJRUREREREREREhjgVCUVERERERERERIY4FQlFRERERERERESGOBUJRUREREREREREhjgVCUVERER6keE1cB1193UYMsC07t+P0d7e12GIiIjIEGLt6wBEREREBhvDMKg50UhZSSWHtlXRVNfGN/7jUqISwvo6NBkAvC0tHL31G5hsNiKvuxZ7jpPwS2dhsip1FxERkd6jTENERESkh7hrWigrcVFWUkltZbOvPTjMSu3pZhUJ5aK0HzuGJTyczupq6t9YQv0bS7DExhJ5/TzsOU7CZkzHZLH0dZgiIiIyyKhIKCIiItINLQ3tHN5eRVmJi8oj9b52i9VM+uQ4MmcmMWJSHBabdnmRixMybhyj16+jedt23MVFNKxchefsWepeeZW6V17FmpBA5Pz52J05hE6disms3y0RERHpPpNhGEZfB9HfuN1uoqKiqK+vx26393U4IiIi0s90tHko31VNWYmLE/vO4vWeS6dMMGxsDJnZDkZmJRIcGrjPY5W/BE6gx9ro7KRpyxbcxcU0rFqN1+3f49Kakow9J4eo3FyCx4/HZDL1ejwiIiIysFxs7qIi4SdQki0iIiJ/z+PxcmLfWcpKXJTvqqaz3evrSxgeSWa2gzEzHIRHB/dJfMpfAqcvx9pob6fx3XdpKC6m4e01eJuafH1BGRnYc3Ox5zoJzsgIaFwiIiLSf6lI2A1KskVERAS6DiBxlbsp21LJoe1VtDZ2+PrsCaFkznSQme0gJim8D6PsovwlcPrLWHtbW2ncuBF3YRGN69djtLX5+oInjCcqNxe704ktObnPYhQREZG+pyJhN/SXxE9ERET6xtnTTV0nE2914a5p9bWHRtoYPaOrMOhIt/erpZ3KXwKnP461p7GRxjVrqC8spOnd96Cz09cXOmM6Ubm5RC5YgDUmpg+jFBERkb6gImE39MfET0RERHpXY20bh7a6KNtaSc2JRl+7NdjCqKkJjMl2kDYuBrOlfx4SofwlcPr7WHfW1tKwchXuwkKat22Dj9J9q5Xwy2YTlZtLxLXXYYno+xmwIiIi0vsuNnfR6cYiIiIyZLU1d/BhaTVlJZVUlNXBuVqK2Wxi+MRYMrOTSJ8Sjy3I0qdxinwR1pgYYr52CzFfu4UOlwt3UTHuggJaP/iApo2baNq4CVNwMBFXX40910nElVdiDu6bvTRFRESk/+gXH4U/99xzpKenExISwqxZsygpKfnM6+vq6rjvvvtITk4mODiYzMxMioqKfP1PPPEEJpPpgn/jxo3r7W9DREREBoDODg8f7qii+Pk9vPjdd1j3vweoOFgHBiSPjmLu18dy508vJ/e+KYyZ6VCBUAY0m8NB3J13kPHG64wsLiL+/vsJysjAaGujYcUKKh54kEOXX8Gp73+fpnffxfB4+jpkERER6SN9PpPw1Vdf5eGHH+b5559n1qxZPPPMM8yfP5+DBw+SmJj4sevb29uZN28eiYmJvP7666SmpnLs2DGio6MvuG7ixIm8/fbbvq+t1j7/VkVERKSPeL0GFWW1lJW4OLKjivZWfyEkNiXcdzKxPT60D6MU6V3BGRkk3H8f8ffdS+u+fbgLi3AXFdFZWUn9G0uof2MJloR47AtyiMrLJWTy5H6176aIiIj0rj7fk3DWrFnMnDmTZ599FgCv10taWhoPPPAAjz766Meuf/7553n66ac5cOAANpvtE+/ziSeeYNmyZezcufOiYmhra6PtvNPg3G43aWlp/XafGREREfl8hmFQc6KRgyWVHN7qoqm+3dcXERPMmJkOMrOTiEsNHxSFkP6+T95gMpjG2vB6adm+nfqCQhpWrMBTX+/rs6WlYc91EpWXR/Do0X0YpYiIiHTHgNiTsL29ne3bt/PYY4/52sxmM9dddx3vvffeJ97mzTffZPbs2dx3330sX76chIQEvv71r/PII49gsfiXAx06dIiUlBRCQkKYPXs2Tz31FMOHD//E+3zqqad48skne/abExERkT5RX93Coa2VlJW4qK1s9rUHh1kZNS2RzGwHKaOjMZkHfmFQBq+zrWeJDYnt9ccxmc2EzZxJ2MyZJH3/ezS++y7ugkIa1qyh48QJzjz/Ameef4HgsWOx5+US5XRiS03t9bhEREQk8Pq0SFhTU4PH48HhcFzQ7nA4OHDgwCfe5siRI6xdu5Zbb72VoqIiDh8+zL333ktHRwePP/440DU78eWXX2bs2LGcPn2aJ598kiuuuIK9e/cSGRn5sft87LHHePjhh31ffzSTUERERAaGZnc7h7dXUVZSiavc7Wu3WM2kT44nM9vBiIlxWGz9Yjtmkc/U4e3gK8u+QmJYIvNHzGdBxgJG2Ef0+uOagoKIvOoqIq+6Cm9zMw3r1uF+q4DGzZtpO3iQ6oMHqf7ZzwmdPp2ovFwi58/HGtv7hUwREREJjAG3UZ/X6yUxMZHf/e53WCwWpk+fTkVFBU8//bSvSJiTk+O7fvLkycyaNYsRI0bwt7/9jbvvvvtj9xkcHEywTnQTEREZUNpbOynfVUNZiYsT+89ieLt2UDGZIHVsDJnZSYzKSiAodMClOzLElZ0to6mjiUO1hzhUe4hndz7L+NjxzE+fz/z0+QyLHNbrMZjDwojKzSUqNxdPXR3uVatwFxTSvHUrLdu307J9O5U/+k/C51xGVF4eEddciyUivNfjEhERkd7Tp1lzfHw8FosFl8t1QbvL5SIpKekTb5OcnIzNZrtgafH48eOprKykvb2doKCgj90mOjqazMxMDh8+3LPfgIiIiASUx+PlxL6zlJW4KN9VTWe719eXOCKSMTMdjJnpIDxKH/7JwDUxfiLrb17P2uNrWXF0BVtOb2H/2f3sP7ufZ3Y8wyXxl/gKhknhn5wz9yRLdDQxN99MzM0301FZibuoGHdhIa0ffEDTxk00bdyEKSSEiKuvIiovj/ArrsD8CTm5iIiI9G99WiQMCgpi+vTprFmzhoULFwJdMwXXrFnD/fff/4m3mTNnDn/5y1/wer2YzV1LhsrKykhOTv7EAiFAY2MjH374IbfddluvfB8iIiLSewzDoPKIm7KSSg5vr6K1scPXZ08IJTPbQeZMBzFJmsUkg0dUcBT5Y/LJH5PP2dazvH3sbVYdXcVW11b21OxhT80e/nvbfzMtcRoLMhZw/YjriQuN6/W4bElJxN11J3F33UlbeTnugkLchYW0Hz1KQ/EKGopXYLbbsc+/HntuHmEzZ2A678N9ERER6b/6/HTjV199lcWLF/PCCy+QnZ3NM888w9/+9jcOHDiAw+Hg9ttvJzU1laeeegqAEydOMHHiRBYvXswDDzzAoUOHuOuuu3jwwQf5/ve/D8C//uu/csMNNzBixAhOnTrF448/zs6dO9m3bx8JCQmfG9NgOrFORERkoDp7qomykkrKtrpoONPqaw+NtDFmRtfJxInpkYPiZOKeoPwlcPpyrGtaalh9bDUrylewo2qHr91sMjMraRY5GTlcO+Ja7EGBi8swDFo/2Ie7oAB3URGdVVW+PmtCAnanE3teHiGTJurvVUREpA9cbO7S50VCgGeffZann36ayspKpk6dyq9+9StmzZoFwFVXXUV6ejovv/yy7/r33nuP73znO+zcuZPU1FTuvvvuC043/trXvsbGjRs5c+YMCQkJXH755fznf/4no0aNuqh4lGSLiIj0jcbaNg5tdVG2tZKaE42+dluwhZFTE8jMdjBsXAxmiw4g+XvKXwKnv4x1ZVMlK4+uZEX5Cvae2etrt5ltzEmdgzPDydxhcwmzhQUsJsPjoXnb9q6C4apVeOvrfX1BI0Zgz83FnpdH8MiMgMUkIiIy1A2oImF/018SPxERkaGgrbmDD3dUU1ZSScWhOjiXmZjNJoZPiiMz20H65HhsQVqy+FmUvwROfxzr4+7jrDi6guLyYg7X+ffhDrWGctWwq8jJyGFO6hyCLIHbK9Bob6fxnc24C96iYe06jFb/jOCQCROw5+Vhd+Zg+5S9yEVERKRnqEjYDf0x8RMRERlMOjs8HNtzhrISF0f31uDt9KcjyaOjyMxOYvS0REIibH0Y5cAyGPOXiooKHnnkEYqLi2lubmb06NG89NJLzJgx41Nvs379eh5++GE++OAD0tLS+MEPfsAdd9zh63/qqadYsmQJBw4cIDQ0lMsuu4yf/OQnjB079qLj6u9jfaj2EMXlxRSXF3Oy8aSvPTIoknkj5pGTkcNMx0ws5sAV3r1NTTSsXYe7oIDGzZuhs7Orw2QibMaMroLh/OuxREcHLCYREZGhQkXCbujviZ+IiMhA5PUaVJTVcqjExYel1bS3dPr6YlPCyczuOpnYHhfah1EOXIMtf6mtrSUrK4urr76ab33rWyQkJHDo0CFGjRr1qVvIlJeXM2nSJL75zW/yj//4j6xZs4aHHnqIwsJC5s+fD8CCBQv42te+xsyZM+ns7OR73/see/fuZd++fYSHX9zhNwNlrA3DYG/NXorKi1h5dCXVLdW+vvjQeOanzycnI4fJ8ZMDuldgZ20tDStXUl9QQMu27f4Om42Iyy/HnptL5DVXYw4L3DJpERGRwUxFwm4YKImfiIhIf2cYBjUnGjlYUsnhrS6a6tt9fRExwYyZ2XUASfywiD6McnAYbPnLo48+yubNm9m0adNF3+aRRx6hsLCQvXv9+/N97Wtfo66ujhUrVnzibaqrq0lMTGTDhg1ceeWVn3hNW1sbbW1tvq/dbjdpaWkDaqw9Xg/bXdspKi9i9bHVuNvdvr7UiFRyMnLIycghMyYzoHF1nDqFu6iI+sIi2vbv97WbQkOJvPZa7Hm5RMyZg8mmWcUiIiJfloqE3TDYkmwREZFAq69uoaykkkNbXdRWNvvag8OsjJqeSOZMBymjozGZddJpTxls+cuECROYP38+J0+eZMOGDaSmpnLvvfdyzz33fOptrrzySqZNm8Yzzzzja3vppZd46KGHqD/vAI3zHT58mDFjxrBnzx4mTZr0idc88cQTPPnkkx9rH6hj3eHp4N1T71J8tJi1x9fS0tni6xsdPRpnhpMFGQtIi0wLaFxtH36Iu7CQ+oJCOo4f97VboqKIXLCAqLxcQqdPx2TWwUUiIiJfhIqE3TDYkmwREZFAaHa3c3h7FWUllbjK/bOULDYz6ZfEk5ntYMTEOCw2vcHvDYMtfwkJCQHg4Ycf5qabbmLr1q18+9vf5vnnn2fx4sWfeJvMzEzuvPNOHnvsMV9bUVERubm5NDc3Exp64VJ2r9fLV77yFerq6njnnXc+NZbBMJPw07R0trDhxAaKyot4p+IdOrwdvr7J8ZPJychhQcYC4kPjAxaTYRi07tlDfUEB7qJiPDU1vj5rUhL2XCdRubkEjx8f0GXSIiIiA5WKhN0w2JJsERGR3tLe2kn5rhrKSlyc2H8Ww9uVVphMMGxcDGNmJjEqK4GgUGsfRzr4Dbb8JSgoiBkzZvDuu+/62h588EG2bt3Ke++994m3+aJFwm9961sUFxfzzjvvMGzYsIuObbCN9Ufc7W7WHFtDUXkRJZUleA0vAGaTmZlJM3FmOLl2+LVEBUcFLCbD46G5pIT6twpoWL0ab0ODry9o5EjseblE5eYSNGJEwGISEREZaC42d1HGLiIiIl+Ix+PlxL6zlJW4KN9VTWe719eXOCKy62TiGYmERwX3YZQy0CUnJzNhwoQL2saPH88bb7zxqbdJSkrC5XJd0OZyubDb7R8rEN5///0UFBSwcePGL1QgHMzsQXbyx+STPyafmpYaVh5dSVF5Eburd7Pl9Ba2nN7Cj97/EZenXo4zw8nctLmEWnv3oCGTxUL47NmEz56N9/Ef0rhxI+7CIhrXraP9yBFqfvVran71a0IuuYSovFwic3KwJSb2akwiIiKDlYqEIiIi8rkMw6DyiJuykkoOb6+itdG/JNGeEEpmtoPMmQ5iki7udFiRzzNnzhwOHjx4QVtZWRkjPmPG2OzZsykqKrqgbfXq1cyePdv3tWEYPPDAAyxdupT169eTkZHRs4EPEvGh8dw6/lZuHX8rJxpOsKJ8BUXlRRyuO8y6E+tYd2IdodZQrk67GmeGk8tSLsNm6d3DRczBwdjnzcM+bx6exkYa3n4bd0EhTe+9R+uePbTu2YPrJz8lbFY2UXl5RM6bh2UQzfQUERHpbVpu/AkG6xISERGRL+rs6SbfASTumlZfe2ikjTEzuk4mTkyP1L5g/cBgy1+2bt3KZZddxpNPPsnNN99MSUkJ99xzD7/73e+49dZbAXjssceoqKjgT3/6EwDl5eVMmjSJ++67j7vuuou1a9fy4IMPUlhYyPz58wG49957+ctf/sLy5csZO3as7/GioqI+Ntvw0wy2sf4iDtUeori8mKLyIioaK3ztUcFRzBsxD2eGk+mO6ZhNgdt7tPPMGdzFK3AXFtJSWuprN9lshM+9kqi8PCKuugrzuX0uRUREhhrtSdgNQznxExERaaxt49A2F2UlldScaPS124ItjJyaQGa2g2HjYjBbdABJfzIY85eCggIee+wxDh06REZGBg8//PAFpxvfcccdHD16lPXr1/va1q9fz3e+8x327dvHsGHD+Ld/+zfuuOMOX/+nFbRfeumlC677LINxrL8owzDYXbOb4vJiVpSv4EzrGV9fYlgiC9IX4BzpZELshIB+iNB+8iTuwiLchYW0lZX52s1hYUTOuw57Xh7hs2djsmpBlYiIDB0qEnaDEj8RERlq2po7+LC0mrISFxVltXAuOzCbTQyfFEdmtoP0yfHYgix9G6h8KuUvgaOxvlCnt5OtlVspLi/m7WNv09DhP1xkhH0EORk55GTkMDJqZEDjaj1YhruwEHdBAR2nTvnaLbGx2BcswJ6XR2jWVM2EFhGRQU9Fwm5Q4iciIkNBZ4eHY3vPUFbi4tieM3g6/QeQJI+O6jqAZFoiIRG9u8+Y9AzlL4Gjsf507Z52NlVsori8mPUn1tPmafP1jY8d7ysYJoUnBSwmwzBo2bkT91sFuFeswHP2rK/PlpKCPTcXe14eIWMzAxaTiIhIIKlI2A1K/EREZLDyeg1OldVSVuLiw9Jq2ls6fX2xKeFkZjsYM8OBPb53TywdKhpaOyjeW8nqfS6e+/o0gqy9t0Rb+UvgaKwvTlNHE2uPr6W4vJj3Tr1Hp+F/vpmWOA1nhpPr068nJiQmYDEZnZ00vfc+7oICGt5+G29Tk68veMxo7Ll52PNyCdKJ1yIiMoioSNgNSvxERGQwMQyDmhONXQeQbKuiqc4/syciJrjrAJJZDuJSI7Tsrgd0eLxsOlTNkh0VrN7nou3cDM3f3z6DeRMcvfa4yl8CR2P9xdW21rL62GqKyovY7trua7earFyacinODCfXDL+GcFvgTkj3trbSuH4D7sICGtdvwOjwn9oeOnUq9rw87AvmY42PD1hMIiIivUFFwm5Q4iciIoOBu6aFspKuA0hqK5t97cFhVkZNSyRzpoOUMdGYzCoMdpdhGOw6Wc+y0gre2nWKM03tvr6RCeEsykrlq9PTSIrqvdNVlb8Ejsa6eyqbKllRvoKi8iL2n93vaw+xhHDlsCtxjnRyReoVBFmCAhaTx+2mYfVq3IWFNL2/Bbzntl+wWAi/9FLseXlEzrsOS0REwGISERHpKSoSdoMSPxERGahaGts5vK2KshIXlUfqfe0Wq5n0yXFkZicxYmIcFptOJu4JJ842s6y0gqWlFRyp8S9bjI8I4oYpKSzKGsakVHtAZmgqfwkcjXXPKa8vp7i8mOLyYo66j/raI22RXDviWnIycshOysZqDtxpxB1VVTSsWEF9QSGtu3f72k1BQURcdRX2vFwi5s7FHBwcsJhERES6Q0XCblDiJyIiA0lHm4fy3V0nE5/44Cxe77mXdhMMGxtDZraDkVmJBIcG7k32YFbX3E7hntMs3VHBtmO1vvYQm5n5E5NYmJXKFaPjsVoCW4hV/hI4GuueZxgG+8/up+hIEcVHi6lqrvL1xYXEMT99Ps6RTibHTw7otgjtx47hLiqi/q0C2o8c8bWbIyKIvP567LlOwmfNwmTV86uIiPRfKhJ2gxI/ERHp77weLycO1FJWUsmRnTV0tnl8fQnDI30HkIRHa6ZLT2jr9LDuQBVLSytYd6Cadk/XUkSzCeaMjufGqaksmJRERHDfFQqUvwSOxrp3eQ0v213bKS4vZtWxVdS3+WdFp0akkpORgzPDyZiYMQGLyTAM2g4coL6gAHdRMZ2nT/v6LPHx2HNyiMrLJWRyYIuYIiIiF0NFwm5Q4iciIv2RYRi4yt2Ulbg4vN1FS4N/k317fAiZ2UmMmekgNjlwG/8PZoZhsO1YLUt2VFC4+xTuVv/JrOOT7SzKSuUrU1Nw2Htvn8EvQvlL4GisA6fD08F7p9+j8Egh606so6Wzxdc3Ono0zgwnORk5DIsM3GnEhtdLy44d1BcU0LBiJZ66Ol+fLS0Ne66TqLw8gkePDlhMIiIin0VFwm5Q4iciIv1JbWVT1wEkW124q/1vkEMibIyZnkjmrCQcGYHZ924o+LC60bfP4Mla/3gn2UO4MSuF/KxUxiX1v/xA+UvgaKz7RnNHMxtPbqSovIhNFZvo9PoL95MTJuPMcDI/fT7xoYE7jdjo6KDp3XepLyikYc0ajObzDokaN46ovFzsTie2lJSAxSQiIvL3VCTsBiV+IiLS15rq2zi01UVZiYvq4w2+dmuQmYwpCWRmO0ibEIslwPveDVY1jW28tesUy0or2HXSv7QxItjKgklJLMpKZdbIOCz9+CRo5S+Bo7Hue/Vt9aw5voai8iK2Vm7Fa3y0BYCZ7KRsnBlOrh1xLfagwP18vM3NNKxbh7uwiMZNm6DDP9s7dPp0ovJyiVywAGtMTMBiEhERARUJu0WJn4iI9IX2lk4+LK2mrKSSioO1fPQKbTKbGD4hljEzHWRMiScoRBvk94SWdg+r97tYuuMkGw/V4Dl34IvFbGJuZgL5WalcN95BaJCljyO9OMpfAkdj3b9UN1ez8uhKisuL2V3jP43YZrZxReoVOEc6mTtsLiHWwG0N4Kmrw71qFe6CQpq3bsX3hG61En7ZbKLy8oi45losEdoeQkREep+KhN2gxE9ERALF0+nl2N4zlJW4OLqnBk+H19eXNNJOZnYSo6cnEhoZ1IdRDh4er8H7R86wtLSCFXsraWzzL1eckhZN/tQU8qakEB8x8A58Uf4SOBrr/uuE+wTFR4spLi/mcN1hX3uYNYxrhl+DM8PJpSmXYjPbAhZTh8uFu6gYd0EBrR984Gs3hYQQec3V2PPyCL/8csxBep4XEZHeoSJhNyjxExGR3mR4DU5/WMfBEhcfbq+irdlfqIpJCus6mXhmElEJoX0Y5eByoNLN0h0VLN95ikp3q689LTaUhVNTWZiVyqiEiD6MsPuUvwSOxnpgKKsto7i8q2BY0Vjha48Ojub6EdeTk5HDNMc0zKbAbdvQdqQcd2Eh7oIC2o8d87Wb7Xbs86/HnptH2MwZmCwDYwaziIgMDCoSdoMSPxER6Q1nKhopK6mkbKuLxrNtvvawqCDGzHQwNjuJ+LQIHUDSQ1zuVpbvrGDJjgoOVPr3dYwKtZE7OZlFWalMHxEzaMZb+UvgBGKsny4/zeTIMK6KjSTYrL1Hu8MwDHZV76KovIiVR1dytvWsr88R5iAnIwdnhpNxseMC9nxgGAatH+zDXVCAu6iIzqoqX581MRF7Tg72vDxCJk0cNM9RIiLSd1Qk7AYl2SIi0lMazraeO4CkkjMVTb72oBALI6clkpntIDUzBnM/PhBjIGls62TF3kqWlVaw+cMa3zZgQRYz14xLZGFWKlePSyDYOvhm6Sh/CZzeHuuK1namv7cPALvVTE58NDcmRnNFTCQ2PVd0S6e3k5LKEoqOFLHm+BoaOxp9fen2dJwZTnIyckiPSg9YTIbHQ/O27V0Fw5Ur8brdvr6gESOw5+Vhz80leGRGwGISEZHBRUXCblCSLSIi3dHa1MGHO6ooK3Fx6lCdr91sNTFiYhyZ2UmkXxKHdYAciNHfdXi8vHOohqWlFazaV0nrefs6zkyPIT9rGLmXJBMVFrg9yPqC8pfA6e2xPt3WzvMnqnmzqo7Tbf4TcmNtFvISovlKYjSzoyOwaIZZt7R52th0chNF5UVsPLmRNo9/hveEuAk4M5zMT59PUnhSwGLytrfTtGkT7sJCGtauw2j1b48QMmFCV8HQmYMtKXAxiYjIwKciYTcoyRYRkS+qs93D0T1nKCup5NjeM3g9/pfXlDHRZGY7GDUtkZDwwV2oChTDMNh9sp6lpRW8tesUZ5rafX0j48PJz+raZzAtNqwPowws5S+BE6ix9hoGJfVNLKuqo6CqjpoO//6liUFWbkiIJt8Rw3R7mJakdlNjeyNrT6ylqLyI90+9j8fwAGDCxHTHdJwjncwbPo/okOiAxeRtaqJh7VrqCwpoemczeLpiwmQibMaMroLh/OuxRAcuJhERGZhUJOwGJdkiInIxvF6DioO1lJVUcqS0mvZWj68vLjXi3AEkDiJjQ/owysHlxNlmlpVWsHRnBUeq/cu348KDuGFKCvlZqUweFjUkCybKXwKnL8a602vwbl0jy6tqKayup67T/3yTFhLEwsSuguH48JAh+fvfk862nmXV0VUUlxezo2qHr91qsnJZ6mU4M5xcnXY1YbbAfQjRWVtLw8qV1BcU0LJtu7/DZiPi8sux5+USefXVmMOGzgcjIiJy8VQk7AYl2SIi8mkMw6D6eANlJS4ObXPRXO+fwRYRG0zmzCQysx3EpQ7sk3L7k/rmDgr3nGZp6Um2Hq31tQdbzVw/MYlFWalcPiYem2VoH+6g/CVw+nqs271eNpxtYHlVHcU19TR5/Evsx4QFk++IIT8xhoyw4IDHNticbjxN8dGuE5IPnD3gaw+1hjJ32FycGU7mpM4hyBIUsJg6Tp2ivrAQd2ERbQf8MZnCwoi85hrseblEzJmDyaaZ6yIi0kVFwm7o68RPRET6n/rqFg5treTgFhd1rmZfe3CYlVHTExmb7SB5VDQmHSrQI9o6Paw7UM2y0grWHqii/VwRxGSCy0bFkZ81jPkTHUSG6E3wR5S/BE5/Gutmj5e3z7hZ5qplzVk3bV5/aj8lMpT8xBhudESTHBy4ItZgdaTuCEXlRRSXF3O84bivPTIoknkj5uHMcDLDMQOLOXD7zbYdPtxVMCwopOPECV+7JTqayAXzicrNJXT6dEw6IVtEZEhTkbAb+lPiJyIifaeloZ3D26soK6mk8oj/tEmLzUzG5Hgysx0MnxiHxao3Xz3BMAy2HatlaWkFhbtPU9/iP7BhXFIk+Vmp3Dg1laQoLd/+JMpfAqe/jrW700NRdR3LXHVsqmvgo61RTcDs6AgWOWLITYgixmbt0zgHOsMw+ODMBxSVF7GyfCVVLVW+voTQBOanz8eZ4WRS/KSALf02DIPW3bu7CoZFxXhqanx91qQk7LlOovLyCB43TsvRRUSGIBUJu6G/Jn4iItL7Oto8lO+qpqzExfF9ZzHOzcoxmWDYuBgys5MYOTWBoFC9ye4pR6obWVpawbKdFZw42+Jrd9iDuXFqKvlZqYxP1uvx51H+EjgDYayr2zt4q6qOZVV1lNT79++0mUxcHRtJviOG6+PthFt0ynp3eLwetru2U1RexOpjq3G3+z9QSotMIycjB2eGk1HRowIWk9HZSdOWLbgLi2hYtQpvY6OvL2jkSOx5uUTl5hI0YkTAYhIRkb6lImE3DITET0REeo7X4+XE/nMHkOyqobPNfyBA4ohIMrOTGD0jkfAo7e/VU840tvHWrlMs3XmKXSfqfO3hQRYWTEpm0bRULh0Zh0XLty+a8pfAGWhjfaK1nWWuWpa6atnX1OprDzWbWRBvJ98Rw1WxkQRpSWq3dHg62HxqM0XlRaw/sZ6WTv+HHpkxmTgznORk5JASkRKwmLxtbTRu3Ii7oJDGdesw2v376IZMnkxUXi6RCxZgS0wMWEwiIhJ4KhJ2w0BL/ERE5IszDANXuZuyEheHt7toafAvbbUnhJKZ7SBzpoOYpPA+jHJwae3wsGqfi2WlFWwoq8ZzbpamxWziyjHxLMxK5foJSYQGaWbTl6H8JXAG8lgfbGplmauWJa5ajrX6C0YxVgt5idHkJ8ZwaXQ4Zi1J7ZbmjmbWn1hPcXkx75x6h05vp68vKzGLnIwcrh9xPXGhcQGLydPYSMPqt3EXFtL07rvgPXfgjdlM2KxsovLyiJw3D8sA+50WEZHPN6CKhM899xxPP/00lZWVTJkyhV//+tdkZ2d/6vV1dXV8//vfZ8mSJZw9e5YRI0bwzDPP4HQ6P3btj3/8Yx577DG+/e1v88wzz1xUPAM58RMRkc9W52rmYEklZSUu3NX+WR6hkTZGT3eQOcuBI92uPZt6iMdrsOXIGZaUVrBibyWNbf43ypOHRZGflUre5BQSIjVLs7uUvwTOYBhrwzAobWhmqauW5VV1VLX7/zaTg23cmBjNIkcMl0SE6vmwm+rb6ll9bDXF5cVsrdyKwbkPSEwWLk2+lJyMHK4dfi0RQREBi6mzpgZ38QrchYW07NzpazfZbITPvZKovDwirroKc4j2gBURGQwGTJHw1Vdf5fbbb+f5559n1qxZPPPMM7z22mscPHiQxE+Y9t7e3s6cOXNITEzke9/7HqmpqRw7dozo6GimTJlywbVbt27l5ptvxm63c/XVV6tIKCIyRDXVt3F4W9cBJFXHGnzt1mALI6fEk5mdxLDxMVgsWmrXUw5UullaWsHy0lNUuv3LG1OjQ8nPSmVhViqjEwP3hngoUP4SOINtrD2Gwbu1jSypqqWwug53p9fXNzosmIWJMSxyxDAyTMX87nI1uVh5dCXF5cXsPbPX1x5kDmJu2lxyMnK4IvUKQqyBK861nzyJu7AId8FbtB067Gs3h4cTed112PPyCJ99KSar9uIVERmoBkyRcNasWcycOZNnn30WAK/XS1paGg888ACPPvrox65//vnnefrppzlw4AA2m+1T77exsZFp06bxm9/8hh/96EdMnTpVRUIRkSGkvaWTIzurKSup5OSBWj56tTOZTQyfEEtmtoOMKQnYgrW0tae43K0s31nB0tJT7D/t37zfHmIld3IK+VmpzBgRg1n7DPYK5S+BM5jHutXjZe1ZN0tddaw+U0+r1/9WYUpkKIscMdyYGENS8Kfn4XJxjrmPUVxeTFF5EeX15b72cFs41w6/FmeGk1nJs7CaA1ecaz1YhrugAHdhIR2nTvnaLbGx2BcswJ6XR2jWVM0uFREZYAZEkbC9vZ2wsDBef/11Fi5c6GtfvHgxdXV1LF++/GO3cTqdxMbGEhYWxvLly0lISODrX/86jzzyCJbzTmdbvHgxsbGx/OIXv+Cqq676zCJhW1sbbW1tvq/dbjdpaWmDMvETERnMPJ1ejn9whrISF+W7a/B0+GfDJI20dx1AMj2R0MigPoxycGls62Tl3kqWllaw+cMaXzHWZjFx9dhEFk1L5aqxiYTYVIztbYO5cNXfDJWxbuj0UFRdz7KqWjbWNuD56MMW4LLoCBY5YshNiCLaphlm3WEYBgdrD1JUXsSK8hWcbjrt64sNiWXeiHnkjsxlSsIUzKbAzHg3vF5adu7EXVCIe8UKPGfP+vpsqanYnU7seXmEjM0MSDwiItI9F5u79Okrek1NDR6PB4fDcUG7w+HgwIEDn3ibI0eOsHbtWm699VaKioo4fPgw9957Lx0dHTz++OMAvPLKK+zYsYOtW7deVBxPPfUUTz75ZPe+GRER6ROG1+D0h/WUlVRyeEcVbU3+fbWiHWFdB5BkO4hKCOvDKAeXTo+XTYdrWFZawcoPKmk9rxg7Y0QMC7NSyZucTHSYirEiA1mk1cItybHckhxLdXsHb1XVsayqjpL6JjbXNbK5rpFHy05ybVwkCxNjuD4+ijBt2/CFmUwmxsWOY1zsOB6a9hA7q3ZSVF7EqqOrONt6llcPvsqrB18lOTyZBRkLyM3IJTMms1dn85nMZsKmTSNs2jQcjz1K0/vv4y4opGH1ajoqKjjz+99z5ve/J3jMGOx5edhznQQNG9Zr8YiISGD06UzCU6dOkZqayrvvvsvs2bN97d/97nfZsGEDW7Zs+dhtMjMzaW1tpby83Ddz8Oc//zlPP/00p0+f5sSJE8yYMYPVq1czefJkAM0kFBEZhM6caqSsxMWhEhcNZ/173oXZgxgzs6swmDA8UkuieohhGOytcLOk9CRv7TpFTaP/VNSM+PCufQanpjI8TsXYvjJUZrf1B0N9rI+3tLG8qo6lrlr2Nfmff8MtZnLio1joiGFuTCQ2bS3QLR3eDrac3kJxeTFvH3ub5s5mX9/IqJHkZOTgzHAy3D48YDF5W1tpXL+e+oICmjZsxOjo8PWFTp3aVTDMWYA1LnCnNouIyOcbtMuN586di81m4+233/a1FRcX43Q6aWtro6ioiPz8/AuWHns8HkwmE2azmba2tgv6PslQT/xERPqrxtpWyra6KCtxceZko6/dFmJh1NQEMmclkTpWe971pBNnm3lz1ymW7DjJh9VNvvbY8CBumJxM/rRhTBkWpWJsP9BX+cvu3bu/8G0mTJiAdQAfgqBc0W9/YwvLzhUMj7f6PzyItVm4ISGafEcM2VHhmPUc0S2tna1sPLmR4vJiNp7cSLvXP9aT4ibhHOlkQfoCEsISAhaTx+2mYdUq6gsLaX5/C779JiwWwmfPxp6bS+S867BE6JAqEZG+NiCKhNB1cEl2dja//vWvga6DS4YPH87999//iQeXfO973+Mvf/kLR44cwWzuWs7wy1/+kp/85CecOnWKhoYGjh07dsFt7rzzTsaNG8cjjzzCpEmTPjcmJX4iIv1HW3MHH5Z2HUBSUVYH5161zBYTwyfGkZntIH1yPLYg7XnXU+qbOyjae5qlOyooOerfhyrYambeBAf5WalcmZmATcsK+5W+yl/MZjMmk4mLTSnNZjNlZWWMHDmylyPrPcoVP84wDHa4m1niqmV5VR01Hf6tH1KDbSx0xJCfGM3EiFB9qNBNDe0NrDm+huLyYt4//T5eo2vLBxMmspOyycnI4boR1xEVHBWwmDqqqmhYsYL6gkJaz/vgwBQcTMRVV2HPyyXiyisxB+uEbBGRvjBgioSvvvoqixcv5oUXXiA7O5tnnnmGv/3tbxw4cACHw8Htt99OamoqTz31FAAnTpxg4sSJLF68mAceeIBDhw5x11138eCDD/L973//Ex/j85Yb/z0lfiIifcvT4eXo3hrKSlwc23MGT6d/z7vk0VFdB5BMSyQkQqdr9pT2Ti/rDlaxrLSCNfuraPece9Npgksz4siflsqCSUnYQzTm/VVfFglLSkpISPj8GUyGYTBp0iR2796tIuEg1uk12FzXyBJXLUXVdTR4/M/hY8KCWeSIId8RQ3qoCkbdVdNSw6qjqyguL2Zn9U5fu9Vs5fLUy3FmOJk7bC5htsBtBdF+7Bj1hYW4CwppP3LE126OjCRy3jyi8nIJmzUL0+es7hIRkZ4zYIqEAM8++yxPP/00lZWVTJ06lV/96lfMmjUL6Crwpaen8/LLL/uuf++99/jOd77Dzp07SU1N5e677/7Y6cbnU5FQRKT/M7wGFYfqKCup5MMd1bS3+GehxKaEk5ntYMxMB/a40D6McnAxDIMdx2tZsqOCwj2nqWv27y2V6YggP2sYN05NISVaYz4Q9FX+cvXVV7N06VKio6Mv6nqn08kf/vAHkpOTezewXqRc8eK1ery8fcbN0qpa3j7jps3rf+uRFRnGIkcMX0mMxhGsDyC6q6KxghXlKygqL6KstszXHmoN5eq0q3FmOLks5TJslsCMtWEYtO3f31UwLCyis7LS12dJiMeek0NUbi4hkydrdqmISC8bUEXC/kaJn4hIYBiGwZmKRsq2uCjb6qKpzn+IVERMMGNmOMic5SAuNUJvIHpQeU0TS0srWFZawfGz/o3wEyODuXFqCguzUpmQbNeYDzDKXwJHY/3luDs9FFfXs9RVy8baBj6aX2gG5sREkO+IITc+iijbwN2vsr84XHuY4qPFFB0p4mTjSV97VHAU1w2/jtyRuUxLnIbFHJjZfIbXS8v27dQXFtJQvAJPfb2vzzZ8OPZcJ1F5eQSPGhWQeEREhhoVCbtBiZ+ISO9yn2nh0LkDSM6e8h+GERRqZfS0BDKzk0gZE41JB5D0mDONbRTsPs3S0gp2nqjztYcFWVgwKYn8rFQuGxWPRWM+YCl/CRyNdfdVt3ewvKqOZa5atrn9H1YEmUxcG2cn3xHDvDg7odr7tFsMw2BvzV6KyotYcXQFNS01vr7E0ETmZ8wnNyOXCXETAvbBkNHeTuO77+IuKKRhzRqMlhZfX/C4cUTl5WJ3OrGlpAQkHhGRoUBFwm5Q4ici0vNamzo4vL2KspJKTh/2zyAwW02kXxJPZraDEZPisNq0R1FPae3w8PZ+F0t3VLChrJrOc8v8zCa4YkwC+VmpXD/RQViQZu0MBv0hf/F4PLz88susWbOGqqoqvF7vBf1r167tk7h6Wn8Y68HkWEsby1x1LKmq5WBTq6893GImJz6KRY4YroiJxKYPMbrF4/Ww1bWV4vJiVh9bTUN7g69veORwnCOd5GTkMDIqcPuFepubaVi7DndhIY2bNkGnf6uR0BnTicrLI3L+fKwxMQGLSURkMFKRsBuU+ImI9IzOdg/lu7sOIDn+wRm8nnMvOSZIHRNN5qwkRmUlEBymvah6itdr8H75GZaVVlC8p5KGNv8brktSo1iYlcoNU5JJjAzpwyilN/SH/OX+++/n5ZdfJjc3l+Tk5I/NTPrFL37RJ3H1tP4w1oPV/sYWlrhqWVpVy8lW/z6psTYLX0nsOiF5ZlQ4Zm2H0C3tnnbeqXiH4vJi1p9YT6vHX5wdFzsOZ0ZXwTApPClgMXXW1tKwajXuwkKat26Fj96mWq2Ez7msq2B4zTWYw8MDFpOIyGChImE3KPETEfnyvF6DioO1XQeQlFbT0erx9cUNiyAz20HmTAcRMSpS9aQyVwNLdlTw5s4KTtX73+ylRody49QU8rNSGeOI7MMIpbf1h/wlPj6eP/3pTzidzj55/EDpD2M92BmGwTZ3M0tctbxZVceZDv8HHqnBNvLPnZA8ITxE+6d2U3NHM2tPrKW4vJh3K96l0/CP9bTEaTgznFyffj0xIYGbzddRWYm7qBh3QQGt+/b52k0hIUReczX2vDwiLr8cU1BQwGISERnIVCTsBiV+IiJfjGEYVB9voKzExaFtLprr2319EbHBZM5MIjO76wAS6TlV7lbe3HWKJTsq2Hfa7WuPDLGSe0kyC7NSyU6PxawlekNCf8hfUlJSWL9+PZmZmX3y+IHSH8Z6KOn0GmysbWBpVS3F1fU0evzL2DPDQljkiCbfEcOI0OA+jHJwqGutY9WxVRSXF7PdtR2DrreKVpOVS1MuxZnh5Jrh1xBuC9xsvrYj5bgLC3EXFNB+7Jiv3RwVhf3667Hn5RE2Yzomi7YrERH5NCoSdoMSPxGRi1Nf3cKhrZUc3OKizuXfeD44zMro6YlkZieRPCpKB5D0oKa2TlZ+UMnS0go2H67h3DaD2CwmrhqbyMKpqVw7PpEQ7e045PSH/OVnP/sZR44c4dlnnx3Us7v6w1gPVS0eL2+fcbOsqpbVNW7az3srM80exiJHDF9JiCYxWNtYdFdlUyUrj66kqLyIfWf8s/mCLcHMHTYX50gnV6ReQZAlMLP5DMOgde8HuAsKcBcV0Vld7euzJiZidzqx5+URMjFwh7CIiAwUKhJ2gxI/EZFP19LQ7juApPKIf/aaxWYmY3LXASTDJ8ZhsepEyp7S6fGy+cMzLN1xkpUfuGjp8C/hnjY8mvxpw8i7JJmYcC27Gsr6Kn9ZtGjRBV+vXbuW2NhYJk6ciM12YaFmyZIlAYurNylX7B/qOzopqqlnmauOTbUNfDS/0AxcHhNBviMGZ3wUUTYdztRdR+uPUlxeTFF5EUfdR33tkbZIrh1xLTkZOWQnZWM1B2asDY+H5q3bcBcW4F65Cq/bn48Epadjz83FnptL8MiMgMQjItLfqUjYDUr8REQu1NHmoXx3NWVbXJzYdxbvuelrJhOkjo0hM7vrAJKgUL0R6ymGYfDBKTdLSyt4c9cpqhvafH3pcWHkZw1jYVYKI+K0gbt06av85c4777zoa1966aVejCRwlCv2P1VtHbxZXcdSVy3b3f6Z7UEmE9fF2cl3xHBdnJ1Qiz7A6g7DMNh/dr+vYFjVXOXriwuJY376fHIycpiSMCVgs/m87e00bdqEu7CQhrXrMFr9+/KGTJzYVTB05mBLCtwhLCIi/Y2KhN2gxE9EBLweLycOdB1AcmRnDZ1t/tlrCcMjycx2MGaGg/Bo7QHVkyrqWli+s4KlOyo4VNXoa48Js3HDlBQWZqWSlRatpVTyMcpfAkdj3b8da2ljmauOJVW1HGzyF4wiLGZyEqJYlBjDFTGRWLUVRrd4DS87XDsoLi9m1bFV1LXV+fpSI1LJycjBmeFkTMyYgMXkaWyice0a6gsKaNr8LnjO5S4mE2EzZmDPy8M+/3os0dEBi0lEpD9QkbAblPiJyFBlGAZVRxsoK6nk0DYXLQ0dvj57fAiZ2V0HkMQkafZaT3K3drBiTyVLSk+ypfwsH70yB1nNzJvgIH9qKldmJhCkJdzyGfpr/uJ2u/m///s//vCHP7Bt27a+DqdH9Nexlo/b39jCElctS6tqOdnqf02Ls1n5SmI0+YnRzIgKx6wPXrqlw9vBe6feo6i8iLXH19LS2eLrGx09GmeGk5yMHIZFDgtYTJ1nz9KwciX1BYW0bN/u77DZiLj8cux5uURefTXmsLCAxSQi0ldUJOwGJX4iMtTUuZopK6mkrMRFfbU/sQ+JsDFmeiKZs5JwZNg1e60HtXd62VhWzdLSClbvd9He6T+t89KRsSzKGsaCS5Kwh2jzfbk4/S1/WbduHS+++CJLliwhKiqK/Px8nnvuub4Oq0f0t7GWz+c1DLbVN7G0qo43q+o409Hp6xsWYmNhYgyLHDGMDw/Ra103tXS2sOHkBoqOFLGpYhOdXv9YT06YjDPDyfz0+cSHxgcspo6KCtzFxdQXFNJ24ICv3RQWRuQ112DPyyVizhxMNr3misjgpCJhNyjxE5GhoNndzqFtLspKXFQd9W/4bbWZyZiaQGa2g7QJsVi0f1OPMQyD0hN1LCut4K1dp6ht9s9qGZMYwcKsVBZmpZIaHdqHUcpA1R/yl4qKCl5++WVeeukl6urqqK2t5S9/+Qs333zzoCq89Iexli+v02uwqbaBJVW1FFXX0+Txf0iTGRbCIkc0+Y4YRoRqO43uqm+rZ83xNRSVF7G1citeo2uszSYz2UnZODOcXDviWuxBgfs7ajt0iPrCQtwFhXScPOlrt0RFEblgAVF5uYROn47JrPxHRAYPFQm7QYmfiAxW7a2dlO+qoaykkhP7azHOO4AkbXwsmbOSyJgST1CIDiDpScfONLGs9BTLdlZQXtPka0+IDObGc/sMTkzRTE3pnr7MX9544w3+8Ic/sHHjRnJycvjGN75BTk4O4eHh7Nq1iwkTJgQ0nt6mXHHwaPF4efuMm6WuWt4+46b9vLdG0+xh5CfGcGNiNInBmmHWXdXN1aw8upLi8mJ21+z2tdvMNq5IvQLnSCdzh80lxBoSkHgMw6B1927qCwpxFxfjqanx9VmTkrDnOonKzSV4/Hi9PovIgKciYTco8RORwcTj8XJi31nKSlyU76qms90/YyIx3e47gCTMHtSHUQ4+tU3tFOw5zdIdJ9lxvM7XHmqzsGBSEvlZqVw2Kg6rZmpKD+nL/MVqtfLII4/w6KOPEhkZ6Wu32WwqEsqAUd/RSVFNPUtdtbxT28hHr5Zm4PKYCBY6YsiNjyLKpg/SuuuE+wTFR4spLi/mcN1hX3uYNYxrhl+DM8PJpSmXYjMHpjhrdHbStGUL7sIiGlatwtvoPzgsaORI7Hm5ROXmEjRiREDiERHpaSoSdoMSPxEZ6AzDwFXupmxLJYe2V9Ha6F/WGpUQSma2g8zsJKId2qy7J7V2eFh7oIqlpRWsP1hFh6frJdZsgjmj41k0LZXrJyQRHqw3mNLz+jJ/+ed//mdeffVVJk6cyG233cYtt9xCTEyMioQyYFW1dfBmdR1LXbVsdzf72oNMJq6Ns5PviGFenJ1QfdDTbQfPHqS4vKtgeKrplK89Ojia60dcT05GDtMc0zCbAjPW3rY2GjdswF1YROO6dRjt7b6+kEsuISovl8icHGyJiQGJR0SkJ/RokdDtdn/eJR8zkBMmJX4iMlDVVjZRVuKirKQSd02rrz000saYGV2FwcT0SC2b6UFer8HWo2dZtrOCgt2naWj1b9A+McVOflYqX5mSQqI9MMunZOjq6/ylpaWFv/3tb7z44ots2bKF+fPnU1hYyM6dO5k0aVLA4+lNfT3WEljHWtpY5qrjDVctZc3+19Zwi5mc+CgWOWK4IiYSm1mvrd1hGAa7qndRVF7EyqMrOdt61tfnCHOQk5GDM8PJuNhxActjPI2NNKx+G3dhIU3vvQceT1eH2UzYrGyi8vKInDcPi54HRKSf69Eiodls/kJPxCaTibKyMkaOHHnRt+lPlPiJyEDSVN/G4W1VHNxSSfXxBl+7NdjCyKnxjM1OYti4GMya7dCjDlc1sLS0gmWlp6io858InRIVwo1ZqeRnpZLpiPyMexDpWf0pfzl06BAvvfQSf/zjH2lsbCQ3N5evfvWrLFq0qE/j6in9aawlcAzDYH9TK0tdtSytquVkq3+WfpzNyg2J0SxKjGZGVDhmfRjXLZ3eTkpOl1BUXsSa42to7PAv/023p+Mc6cSZ4WSEPXDLfzvPnMG9YgXugkJaSkt97SabjfC5VxKVl0fEVVdhDtGHgiLS//R4kfCNN94gNjb2cx/YMAycTid79+5VkVBEpJe0t3ZyZGc1ZVsqOXmglo+eyU1mE8MnxpKZ7SBjcgK2YEvfBjrIVDe08eauUywrrWBPRb2vPTLY2rXP4LRULs2Iw6zZJNIH+mP+4vV6KSws5A9/+APFxcW0tbX1dUg9oj+OtQSWYRhsczezxFXLm1V1nOnwzyJPDbaR74gh3xHDhPAQzd7vpjZPG5tObqKovIgNJzbQ7vUv/50YN5GcjBwWpC/AEe4IWEztJ0/iLizCXVBA26FDvnZzWBiR867DnpdH+OzZmKzaXkRE+oceLRJmZGSwbds24uLiLurBJ02aRHFxMWlpaRcfcT+ixE9E+iNPp5fj+85SVlLJ0V01dHb4DyBJGmknMzuJ0dMTCY3UASQ9qbm9k9X7XCzZUcE7h2vwnDsR2mo2MTczgfxpqVw33kGITQVZ6Vv9PX+pqqoicZDs4dXfx1oCq9NrsKm2gaVVtRRV19Po8b8+Z4aFsMgRTb4jhhGhwX0Y5eDQ2N7I2hNrKSov4v1T7+Mxupb/mjAxI2kGORk5XD/ieqKCowIWU+vBMtyFhbgLCug45d9T0RIbi33BAux5eYRmTVWxWET6lA4u6QYlfiLSXxiGQeWH9ZSVuDi8vYrWJv/SpmhH2LkDSBxEJegAkp7k8Rq89+EZlpSeZOXeSpraPb6+qWnR5Gelkjc5mbgIveGT/qOv8pc333yTnJwcbLaLO4W0qKiIq6++mtDQ0F6OrPcoV5RP0+LxsuaMm6VVtbx9xk2b1/9Wa5o9jPzEGL6SGI0jODCn9g5mZ1vPsuroKorLi9lRtcPXbjVbmZMyh5yMHK5Ou5owW2ByJMMwaCnd2VUwLC7Gc9a/p6ItJQV7bi72vDxCxmYGJB4RkfOpSNgNSvxEpK+dPdVEWUklZVtdNJzxb5IeZg/qOoBkloOE4TqApKftO+Vm2c4Klu+swOX2L4scHhvGwqkpLMxKZWRCRB9GKPLp+ip/sVgsVFZWkpCQcFHX2+12du7cOWC3pQHlinJx3J0eiqrrWOqqY1NtAx/NLzQDc2IiyHfEkBsfRZRNS1K763TjaYqPdp2QfODsAV97qDWUq4ZdhXOkkzkpc7BZAlOcNTo7aXrvfdwFBTSsXo232X9CdvCY0dhz87Dn5RI0bFhA4hER6dUi4Zo1a1izZg1VVVV4vd4L+l588cUvHm0/o8RPRPpCU10bZVu7TiauOeHfoNsWbGFkVgJjs5NIHRutA0h62On6Ft7ceYqlpRUcqPQf/BIVaiNvcjKLpqUybXiMCrLS7/VV/mI2m8nJySE4+OJm1hYUFHDgwIGLKhJWVFTwyCOPUFxcTHNzM6NHj+all15ixowZn3qb9evX8/DDD/PBBx+QlpbGD37wA+64444Lrnnuued4+umnqaysZMqUKfz6178mOzv7ouIH5YryxVW3d7C8qo5lrlq2uf0FoyCTiWvj7OQ7YpgXZydUr/HddqTuCEXlRRSXF3O84biv3R5kZ96IeTgznEx3TMdiDsw2Id7WVhrXb8BdWEDj+g0YHf5VIaFTp2LPy8O+YD7W+PiAxCMiQ1OvFQmffPJJ/v3f/50ZM2aQnJz8sTdNS5cu/XIR9yNK/EQkUNpaOjlSWkVZiYuTB2vh3DOy+aMDSGYlkT45HluQ9rvrSY1tnRTvOc2ynRW8++EZ38EvQRYz145PZGFWKlePTSTIqjdrMnD0Vf5y5513fuHbPP3008R/zhvi2tpasrKyuPrqq/nWt75FQkIChw4dYtSoUYwaNeoTb1NeXs6kSZP45je/yT/+4z+yZs0aHnroIQoLC5k/fz4Ar776KrfffjvPP/88s2bN4plnnuG1117j4MGDF71nonJF6Y5jLW0sr6pjiauWA03+1QLhFjM58VHkO2K4MiYSmw7B6hbDMPjgzAcUlRexonwF1S3Vvr6E0ATmp8/HmeFkUvykgH0Q6HG7aVi9mvqCApq3lMBHE24sFsIvvRR7Xh6R867DEqFVCyLSs3qtSJicnMxPf/pTbrvttm4H2V8p8ROR3uTp9HJs7xnKSlwc3V2Dp/P8A0iiyMx2MHpGIqEROoCkJ3V4vLxzqIYlpRWs3ldJ63kHv2Snx5I/LRXnpGSiwrRPlAxMgy1/efTRR9m8eTObNm266Ns88sgjFBYWsnfvXl/b1772Nerq6lixYgUAs2bNYubMmTz77LNA1wnMaWlpPPDAAzz66KOfeL9tbW0XnMzsdrtJS0sbNGMtfWd/YwtLXbUsrarjRKv/1N5Ym4UbEroOPMmOCses2ezd4vF62O7aTlF5EauOraKh3b9yIC0yjZyMHJwZTkZFf/IHEL2ho6qKhhUrqC8opHX3bl+7KSiIiKuuwp6XS8TcuZgvcpa2iMhn6bUiYVxcHCUlJZ/6Ce5gMNiSbBHpe4bX4PSH9ZSVVHJ4exVtzZ2+vpikMDKzk8jMdmCPH7gb+fdHhmGw+2Q9S0sreGvXKc40+d+AjUwIZ1FWKjdOTSUtVge/yMA32PKXCRMmMH/+fE6ePMmGDRtITU3l3nvv5Z577vnU21x55ZVMmzaNZ555xtf20ksv8dBDD1FfX097ezthYWG8/vrrLFy40HfN4sWLqaurY/ny5Z94v0888QRPPvnkx9oHy1hL3zMMg+3uZpa4anmzqo6aDn+ekBpsY6EjhvzEaCZGhGr7i25q97SzuWIzxeXFrD+5npbOFl/f2JixOEc6yUnPITkiOXAxHTuGu6iI+rcKaD9yxNdujoggct487Hm5hM+ahcmq/StF5MvptSLhI488QkREBP/2b//W7SD7q8GWZItI3zlT0UhZiYuyrZU0nvXPQgmLCmLMTAdjs5OIT4tQwt/DTpxtZllpBUt3VnCkusnXHhcexA1TUlg0LZVLUqM07jKoDLb8JSQkBICHH36Ym266ia1bt/Ltb3+b559/nsWLF3/ibTIzM7nzzjt57LHHfG1FRUXk5ubS3NxMbW0tqampvPvuu8yePdt3zXe/+102bNjAli1bPvF+NZNQAqnTa/BOXQNLXXUUVdfR4PHPfB8TFky+I4b8xBgywjTDrLuaO5pZd2IdxeXFbK7YTKfhL85mJWbhzHByffr1xIbEBiQewzBoO3CA+oIC3IVFdFZW+vos8fHYFywgKi+XkClTlMOIyBdysXniRX0U8fDDD/v+3+v18rvf/Y63336byZMnY7NduCzr5z//+ZcMWURkcGisbe06gGSLizMV/gNIgkIsjJyWSGa2g9TMGMzaa6hH1Td3ULjnNEtLT7L1aK2vPdhq5vqJSSzKSuXyMfHYtCm8yIDg9XqZMWMG//Vf/wVAVlYWe/fu/cwiYW8JDg6+6INZRLrLajZxVaydq2Lt/CRzGGvOulniquXtM24ONbfx0/JKflpeydTIMBY5orkxMQZHsLbK+DLCbGHkjswld2Quda11rD6+mqIjRWx3bae0qpTSqlJ+XPJjLk2+FOdIJ9ekXUNEUO/tF2gymQgZP56Q8eNJ/Jd/oWXHDuoLCmgoXoGnpobaP/+Z2j//GduwYdjzconKzSV4zJhei0dEhp6LKhKWlpZe8PXUqVMBLtjvRURkKGtr7uDDHdWUlVRScajOfwCJxcSISXFkZieRfkkcVh1A0qPaO72sO1jF0h0VrD1QRfu52RYmE1w2Ko78rGHMn+ggMkRvnkQGmuTkZCZMmHBB2/jx43njjTc+9TZJSUm4XK4L2lwuF3a7ndDQUCwWCxaL5ROvSUpK6rngRXpIiMVMbkI0uQnRuDs9FFfXs6yqlo21DexsaGZnQzOPHz7FnOgI8h0x5CZEEW3TktQvIzokmpsyb+KmzJtwNblYcXQFReVF7Duzj82nNrP51GaCLcFcOexKnBlOrhh2BcGW3vvwwGQ2EzZjBmEzZpD0ve/R+O67uAuLaFizho6TJznz/Aucef4FgseO7SoYOp3YUlN7LR4RGRq+8HLjoWCwLdcRkd7h6fBydG9N1wEke2rwdvqfTpNHR5GZncTo6YmEhKtA1ZMMw2DH8VqW7KigcM9p6po7fH3jkiLJP7fPYFJUSB9GKRJ4/SF/OXLkCCNHjuyR+/r617/OiRMnLji45Dvf+Q5btmzh3Xff/cTbPPLIIxQVFbFnz54L7ufs2bMXHFySnZ3Nr3/9a6BrxuLw4cO5//77P/Xgkr/XH8Zahrbq9g7eqqpjqauOrW7/tho2k4lr4iLJT4zh+vgowjR7vtuO1h+luLyYovIijrqP+tojbBFcO/xanBlOspOzsZoDU5z1NjfTsG4d7oJCGt95Bzr8eVDotGnY83KxL1iANTYwS6RFZGDotT0JP4lhGKxYsYI//OEPvP766929uz6nxE9EPo3hNTh1qI6ykko+LK2+4ACS2JRwMrMdjJnpwB6nA0h6WnlNE0t3nGTZzlMcP9vsa3fYg7lxair5WamMT9Zztgxd/SF/MZvNzJ07l7vvvpuvfvWrvn0Fv4ytW7dy2WWX8eSTT3LzzTdTUlLCPffcw+9+9ztuvfVWAB577DEqKir405/+BEB5eTmTJk3ivvvu46677mLt2rU8+OCDFBYWMn/+fABeffVVFi9ezAsvvEB2djbPPPMMf/vb3zhw4AAOh+OiYusPYy3ykeMtbSyvqmOJq5b9Ta2+9jCLmZz4KBYmRnNVrB2btjnpFsMwOHD2AEXlRRSXF+Nq9s9Ijg2JZX76fJwZTqYkBG6/QE9dHe5Vq3AXFNK8dSt89NbeYiF8zmVE5eURcc21WCLCAxKPiPRfASkSlpeX8+KLL/Lyyy9TXV3NddddR0FBwZe9u35DiZ+I/L2ak42UlVRyaKuLxlr/5vXh0cFdB5DMchCXqgNIetqZxjYKdp9maWkFO0/U+drDgywsmJRMflYqs0fFYdEbH5F+kb/s3LmTl156ib/+9a+0t7dzyy23cPfdd5Odnf2l7q+goIDHHnuMQ4cOkZGRwcMPP3zB6cZ33HEHR48eZf369b629evX853vfId9+/YxbNgw/u3f/o077rjjgvt99tlnefrpp6msrGTq1Kn86le/YtasWRcdV38Ya5FPsr+xhWVVdSx11XK8td3XHmuzkJcQTb4jhllR4ZiVr3SL1/BSWlVK0ZEiVh1bRV1bna8vNSKVnIwccjJyyIzJDFhMHS4X7qJi3AUFtH7wga/dFBJCxNVXEZWXR/gVV2AOCgpYTCLSf/RakbCtrY3XX3+dP/zhD7zzzjt4PB7++7//m7vvvnvQJElK/EQEoOFsK4e2uji4pZKzp/xLeYJCLIyankhmdhIpY6J1AEkPa+3w8PZ+F0t3VLChrJpOb9fLlMVs4oox8eRnpTJvgoOwIO25JHK+/pS/dHZ28uabb/Lyyy+zYsUKMjMzueuuu7jttttISEjo09h6Qn8aa5FPYhgGpe5mllTVsryqjup2/8qHlGAbNyZGs8gRw6SIUH3A2U0d3g7eP/U+ReVFrD2+luZO/2qH0dGjcWY4ycnIYVjksIDF1FZejruwCHdBAe1Hj/razXY7kdfPIyovj7CZMzFZtFe2yFDR40XC7du384c//IG//vWvjB49mttuu41bbrmFYcOGsWvXro9tLD2QKfETGbpamzr4cEcVZSUuTh2q87WbrSbSJ8WTme1gxCVxWG1KqnqS12vwfvkZlpVWULynkoY2/5uZS1KjyM9K5YYpKSRE6nRRkU/TH/OXtrY2fvOb3/DYY4/R3t5OUFAQN998Mz/5yU9ITk7u6/C+tP441iKfptNr8G5dI0tctRTV1OHu9Pr6RocFk58YQ74jhpFheo3trpbOFjae3EjRkSI2VWyiw+vfL3BywmScGU7mp88nPjQ+IPEYhkHrvn24CwpxFxbSWVXl67MmJGB3OrHn5RIyaZKKxSKDXI8XCa1WKw888ADf/OY3GTt2rK/dZrOpSCgiA1pnh4dje85wcEslxz44c8EBJCljosnMdjBqmg4g6Q1lrgaW7Khg+c4KTtf791FKjQ5lYVYK+VmpjE6M7MMIRQaO/pS/bNu2jRdffJFXXnmF8PBwFi9ezN13383Jkyd58skncbvdlJSU9GmM3dGfxlrki2j1eFl71s1SVx2rz9TT6vXnPFMiQ8lPjOFGRzTJwVqS2l3udjdrjq2hqLyIksoSvEZXcdZsMpOdlI0zw8m1I67FHhSY5xDD46F523bchYW4V67EW1/v67ONGE5Ubh72vFyCe+jwKRHpX3q8SDh//nzee+89brjhBm677Tbmz5+PyWRSkVBEBiTDa1BxqI6yLV0HkLS3+GeuxaWGk5mdxJiZDiJjdUJuT6tyt/LmrlMs2VHBvtNuX3tkiJW8ycksnJrKzPRYLeMW+YL6Q/7y85//nJdeeomDBw/idDr5x3/8R5xOJ2az/4TVkydPkp6eTmdn52fcU//WH8ZapLsaOj2sqKlniauWjbUNeM69KzQBs6MjyHdEk5cQTYxN23t0V01LDSuPrqSovIjd1bt97TazjStSr8A50sncYXMJsQYm7zTa22l8ZzPuggIa1q3DaGnx9YVMmIA9Nxd7rhNbUlJA4hGR3tcrexKeOHGCl156iZdeeomWlhZuueUWfvOb37B7927Gjx/fI4H3B0r8RAYnwzDOHUDi4tBWF011/gNIImKCycx2kJmdRFxqRB9GOTg1tXWy8oNKlpZWsPlwDR9NXLBZTFw1NpFFWalcPS6REC3jFvnS+kP+MmbMGO666y7uuOOOT11O3N7ezl//+lcWL14c4Oh6Tn8Ya5GeVNPeyVvVXQeelNT792G2mUxcFRvJIkcM18fbCdcedt12ouEEK8pXUFRexOG6w772MGsY1wy/BmeGk0tTLsVmDswKFm9TEw1r1+EuKKBx82b46AMck4mwGTOw5+YSOf96rDExAYlHRHpHr59uvHr1al566SWWLl1KWloaX/3qV/nqV7/KtGnTvvB9Pffcc74T5qZMmcKvf/3rzzwFr66uju9///ssWbKEs2fPMmLECJ555hmcTicAv/3tb/ntb3/L0XObtE6cOJEf/vCH5OTkXFQ8SvxEBhf3mRYObXVRVuK64ACS4DAro6YlkpntIGV0NCbNXOtRnR4vmz88w9IdJ1n5gYuWDo+vb9rwaPKnDSPvkmRiwrWkSaQn9If85ejRowwfPvyCmYPQ9SHNiRMnGD58eJ/E1dN6e6wNw+Drv9/ChBQ7uZOTyUqL1n5hEjAnWttZ5qplqauWfU3+rUBCzWYWxNvJd8RwVWwkQX/3dy5fXFltGcXlxRSXF1PRWOFrjwmO4fr068nJyCErMQuzKTBj3VlbS8PKVdQXvEXLtu3+DquViMsv7yoYXnM15vDwgMQjIj2n14uEH6mtreXPf/4zL774Irt378bj8Xz+jc7z6quvcvvtt/P8888za9YsnnnmGV577TUOHjxIYmLix65vb29nzpw5JCYm8r3vfY/U1FSOHTtGdHQ0U6ZMAeCtt97CYrEwZswYDMPgj3/8I08//TSlpaVMnDjxc2PqD0m2iHRPa1MHh7dXUVZSyenD/j1XLFYz6ZfEkTkriRET47DYlOD2JMMw+OCUm6WlFby56xTVDf7ZmulxYSzMSiU/K5URcUouRXpaf8hfLBYLp0+f/lgOd+bMGRITE79wnthf9fZY762oJ+/X7/i+To0OJXdyMrmXJDN5WJQKhhIwB5taWeaqZYmrlmOt7b72aKuFvIRo8h3RzI6OwKzfyW4xDINd1bsoLi9mxdEVnG096+tLCk9iQfoCcjJyGB87PmB//x2nT+MuKqK+oJC2/ft97abQUCKvuQZ7bi4Rl8/BFKQPe0UGgoAVCc+3Y8eOLzyTcNasWcycOZNnn30WAK/XS1paGg888ACPPvrox65//vnnefrppzlw4AA228VPwY6NjeXpp5/m7rvv/lhfW1sbbW3+N7Jut5u0tDQVCUUGmM52D0f3nKGspJJje8/gPW9zndTMaDKzkxiVlUBwmA4g6WkVdS0s31nB0h0VHKpq9LVHh9m4YXIK+dNSNRNGpJf1hyKh2WymsrLyY0XCY8eOMWHCBJqamj7llgNLb491a4eHTYdqKNh9irf3uWhq9xdX02JDyb0khbzJyUxMset5VQLCMAxKG5pZ6qpleVUdVe3+PUWTgmzc6IhmkSOGyRGh+p3spk5vJyWVJRQdKWLN8TU0dvjzqnR7Os4MJzkZOaRHpQcsprYPP8RdWEh9QSEdx4/72s1RUdjnz8eel0vYjBmYNLtUpN/q0SLh7t27mTRp0seWjnyaDz74gLFjx2K1fvYmt+3t7YSFhfH666+zcOFCX/vixYupq6tj+fLlH7uN0+kkNjaWsLAwli9fTkJCAl//+td55JFHsHzCHhkej4fXXnuNxYsXU1pa+okHrDzxxBM8+eSTH2tXkVCk//N6DSrKaikrcXFkRxXtrf43UnHDIhibncSYmYlExOgAkp7mbu1gxZ5KlpSeZEv5WT56NQmympk33sHCrFTmZiYQZFXCKBIIfVkkfPjhhwH45S9/yT333ENYWJivz+PxsGXLFiwWC5s3bw5oXL0lkGPd2uFh/cFqCnafYs3+qgu2bhgRF8YNk1O4YUoKY5N0ErwEhscweLe2kaVVtRRU1+Hu9Pr6RoYGk++IJt8Rw+gw5V7d1eZpY9PJTRSVF7Hx5EbaPP6JLRPiJuDMcDI/fT5J4YE5YMQwDFr37OkqGBYV4amu8fVZHQ7sTif2vFxCJkxQsVikn+nRIqHFYqGyspKEhISLenC73c7OnTsZ+TnHp586dYrU1FTeffddZs+e7Wv/7ne/y4YNG9iyZcvHbjNu3DiOHj3Krbfeyr333svhw4e59957efDBB3n88cd91+3Zs4fZs2fT2tpKREQEf/nLX3x7Fv49zSQUGVgMw6DmRCMHSyo5vNVFU71/+UtEbDCZ2UlkZjuIS9EBJD2tw+NlY1k1S0oreHufi7bz3hjMyoglPyuVnEuSiQrVbE2RQOvLIuHVV18NwIYNG5g9ezZB5y0/CwoKIj09nX/9139lzJgxAY2rt/TVWLe0e1h7oIrCPadYe6CK1g7/c3CmI4IbJqeQNyWFjHht6SCB0eb1su5MA0uqalldU0+L1//WcnJEKAsdMSxMjCYlREtSu6uxvZG1J9ZSVF7E+6fex2N0fWBgwsR0x3ScI53MGz6P6JDogMRjeDw0l5RQX1BAw6rVeBsafH1BGRnYc3OJysslKD09IPGIyGfr0SKh2Wzmn/7pny74VPiz/OY3v2Hfvn29UiTMzMyktbWV8vJy38zBn//85zz99NOcPn3ad117ezvHjx+nvr6e119/nf/5n/9hw4YNnziT8O/1h+U6IvJx7poWykpclJVUUlvZ7GsPDrMyenoimdlJJI+K0gEkPcwwDHaeqGNZaQVv7T7N2SZ/UXZUQjiLpg3jxqkpDIu5uNcIEekd/SF/ufPOO/nlL3856POn/jDWTW2dvL3fRcHu02w4WE27x18wvCQ1ihumJJM7OYXU6NA+iU+GnsZODytq6lnqqmNDrZtO/64vzIoKZ5EjhrzEaGJtn73aTD7f2dazrDq6iuLyYnZU7fC1W01WLku9DGeGk6vTribMFpjczNveTtPGjdQXFNK4bh3GeRNwQiZNwp6Xiz3Hic3x8TMHRCQwerRIeNVVV33h6cJ/+ctfSE5O/sxrvsxy47lz52Kz2Xj77bd9bcXFxTidTtra2i745Pp81113HaNGjeKFF1743Nj7Q+InIl1aGts5vK2KshIXlUfOO4DEZib9kngysx2MmBSHRUtae9z/z96dh8d91Yf+f8+iXZpFy4w0Y8vaLO+W5EV24sT7ptWW4y5AwRDaX2+B0MKlQKEt0NJSSG9IE7gP9LaX0HJpAceytXqPncTxbkmOV9mW5GVGM6NlFu2zfX9/yJ6RSOwkyDMayef1PDwPPt+RdPgKzXy+n3PO53OnZ5DqJhN7m020dwfriKUmxlBZYKCqyMhCo6iHJQiRQsQv4RNp99o55OHgZQu1Fzs5cbMb35jdXMtmaakoMFC6KIO0pJhJnKXwJOlxe6nrclBttXPKGYwhlDJYm6yiSqdha6qaBOV7y0UJH42538z+jv00tDVw3X49MB6njGPtjLWUZJfwjPEZohThOeXh6x+g/8hhnHX1DLzzDjxoWCWTEV9cPJow3LwZhVodlvkIgjBqUhqX/C5WrFhBcXExr776KjDauCQzM5MvfOEL79u45Bvf+Aa//OUvaWtrC9RI/Jd/+Re+//3vYzabH/pz1q9fT2ZmJq+99toHzinSAj9BeNJ43D46WrppPWPhzuVe/P7gUvSMOVryi/XkFOmIiRMr0Y+bY9BN3cVO9jaZOHfbHhiPjZKzZUE6VUVGnslLRakQSVlBiDSTFb/s2LGD1157DZVKxY4dOx752j179oRpVqEVybFiT/8IjZcs1LaYOdMRrBcrl8HTualUFhjYsjBdlIUQwsY07GavzcFeq513+4cC43FyGZtT1VTptKxLSSJGNL2YsDZHG/Xt9TS2N3K3725gPCk6ic2zNlOSXcIy/TIU8vAkZ729vbj278dVV8/QheCOR6KiSFy9GnVZKYnr1iGPEzueBSHUpkyS8Fe/+hW7du3ipz/9KcXFxbz88sv8+te/5tq1a+j1ej71qU9hNBr53ve+B8Ddu3dZsGABu3bt4oUXXuDGjRs8//zzfPGLX+Sb3/wmAH/1V39FSUkJmZmZ9PX18ctf/pLvf//7HDhwgE2bNn3gnCI58BOE6crv83Pv+v0GJE1deEaChdlTZyaSX5zO7GV6ErViF8TjNuL18cY1G9VNJt64FjyyJpfBqrxUthca2bIwncQYkZQVhEg2WfHLZz7zGV555RWSkpL4zGc+88jX/uxnPwvTrEJrqsSKFucw9e92UtNipuWuIzAerZCzZk4aFQUGNs7TER8t3t+F8LgxMEy1zU611U77ULB8iVqpoCxNzQ69lqc0iSjEKYUJkSSJyz2XqW+r50DHAbqGugLX0uLS2JK1hdLsUhamLgzbiRD3PROuhgZc9fWMXA/ueJTHx5O4cQPq8nISnnoKWZRYwBCEUJgySUKAH/3oR7z44otYLBYKCwt55ZVXWLFiBTB61DkrK2vcDsCTJ0/ypS99iebmZoxGI5/97GfHdTf+7Gc/y5EjR+js7EStVrN48WK+9rWvfagEIUydwE8QpjpJkui600fraSut56wMuYLBYlJKLPnFevKL00nOEAXYHzdJkjh3286eCybqL5pxDXsD1+ZlqNhRZKSy0IBeJToTCsJUIeKX8JmK9/p2zwC1LWZqWsy0WvsD43FRCjbN11NZYGC16EgvhIkkSbT0DVFttbPXZsfqDsYh+mgl23Ratus1FCXFi7ImE+Tz+zhvPU9DewMHbx+kzx1sMDIzaSal2aWUZpeSo3l0P4HHabi1FVf9aMLQc+9eYFyh1ZK0dQvq8nLiioqQid2lgvDYTKkkYaSZioGfIEwlzq7B+w1IrDisYxqQJCiZvVRPfrGe9Fy1CApD4FZXP3vv1xm82xs88pOuimVb0Widwbnp4n1PEKaiSIhf2tvb8Xq97+lifOPGDaKiosiaJl0uI+FeT8R1Sx81LSZqWzq50xv8HFbHRVGyMJ3KAgMrclJQiEZgQhj4JImTjn72Wh3UdTlweIOnSbLioqnSaanSa8lPEAuXE+X2uTlhOkFjeyPH7h1jyBuMBedo51CaU0pJVgkZiY/uLfC4SJLEcEsLzrp6XI2N+Hp6AteUhgzUZWWoysqImTNHPBcIwgSJJOEETPXATxAi0VCfmxvnbLSesWBtdwXGFVFyshenkr8incz5yaIBSQh0949Q12KmuslEy71g85eEaAUlizLYUWQUD4OCMA1EQvyyZs0ann/+eXbt2jVu/Be/+AX/9m//xrFjxyZlXo9bJNzrx0GSJFruOalpNlN30YytL9iRVJcUQ9niDCoLDBTO1IgHdCEs3H4/x3r72GO1c6DbxZA/2LV7YWIc23Uatuu1zIh9/2aVwoc36Bnkjbtv0NjeyAnTCbxScDdnka6I0uxSNmdtJjk2OSzzkbxeBk6dxlVXR9+hQ/gHgg1vovNyUZeXoyorI3rmzLDMRxCmm5AlCQcGBkhImN5H/6ZL4CcIk80z4qO9pYvWM1buXOlFut+ARCaDGXO15Benk1OYRrRoQPLYDbl9HLpqZW+TieOtXYFOlwq5jNWzU6laMoNN8/TERYuugoIwXURC/KJSqbhw4QJ5eXnjxm/evMmyZctwOByTMq/HLRLu9ePm80ucbu+htsVMw7sWnEOewLXM5HgqCjLYVmgkX580ibMUniQDXh8Helzssdo51uvCO+apdYU6gSq9lvI0DamipuaEOYYdHLpziIa2Bs5bzyNxP26UKVhpWElpdinrZ64nMToxLPPxDw/Tf/xNXHV19B8/juQOliSKLViMuqwcVclWlGlpYZmPIEwHIUsSJiYm8vu///s8//zzPPPMMxOeaCSajoGfIISL3+fn7jU7rWcstDV34x3TgCQtM4n8Yj2zl+tJUIsGJI+bzy9xuq2HPU0m9l+y0D8SXBEumKFme5GRigIDqYni3gvCdBQJ8YtarebYsWMUFRWNGz9//jxr166lr6/vIV85tUTCvQ4lt9fPWze6qGkxc+iKlUF38LN8bnoSFQUGKgsMzEyOn8RZCk+SXo+X+i4He6x2TjkGePAAq5DBGm0SVXotJalqEpVi8XOiLAMWDnQcoKG9gSs9VwLjMYoYVs9YTVl2Gc/MeIYYRXjiSV9fH32HDuOqq2Pg1Cl4sLtULidh5QpUZeUkbdqIYhq+FwvC4xSyJOHevXt57bXXaGhoICsri+eff55PfepTGAyGCU86Ukz3wE8QHjdJkrB19NF6xsKNc1aG+oK7D1SpseQXp5NfrEebPr13IU+WaxYX1U0m9jWZsbiGA+MztHFUFRnZVmgkTxeelV9BECZPJMQvFRUVxMXF8V//9V+BhnI+n48/+IM/YGBggMbGxkmZ1+MWCfc6XAbdXo5ctVHTYubYdRseX/DRoShTQ2WBgbLFGeiSRL04ITzMw2722RxU2+xc7AvW1IuVy9iUomaHXsP6FBUxounFhHU4O2hsb6ShvYEOV0dgPDEqkQ2ZGyjNKaU4vRilPDy7Ob1dXbga9+Osr2O45WJgXBYVReLaNajKyklcuwZ5rHg/EoTfFvKahF1dXfznf/4nr732GlevXmXLli08//zzVFZWolRO7S3fT1LgJwgT4bA9aEBiwWkbE6QlRjF7qY78Fenos1WijlEIWF3D1DSb2dNk4mpnsMajKlZJ2eLRBiTLZmmRizqDgvDEiIT45cqVK6xevRqNRsOzzz4LwFtvvYXL5eLo0aMsXLhwUub1uEXCvZ4MzkEP+y93UtNi5uStHu5XskAug6dyU6gsMLB1QQbq+KjJnajwxLg1OEy11UG11c6toWBNTZVSTmmqhh16Lau0iShELDohkiRxtfdqIGFoG7QFriXHJrM1aysl2SUUpBWELe5337mDq6EBZ10d7pu3AuPyhASSNm5EVV5OwlMrkU3x3IQgPC5hbVzy6quv8pd/+Ze43W5SU1P5H//jf/D1r3+d+PipeQThSQ38BOHDGHS5uXneyvXTVmwdweSUMkpOdmEa+cV6Zs5PRqEQq7eP28CIlwOXLVQ3mThxszvwcBalkLFujo4dS4ysnaMjNkoctRGEJ1GkxC9ms5kf/ehHtLS0EBcXx+LFi/nCF75AcnJ4it+HQ6Tc68lk6xum/uJowrDpjiMwHq2Qs2ZOGpUFBjaK2rdCmEiSxLv9Q+yx2tlnc9A5EjzVkhatZJtOQ5VOyxJVvFi8niC/5OeC9QIN7Q0cun0Ix4gjcM2YaKQku4SS7BLytflhmY8kSYy0tuKqq8NZX4/X3Bm4pkhJQbV1K6qyMuKKCsXvXniihTxJaLVa+fnPf85rr73G7du3qaqq4rOf/Sz37t3j+9//PgaDgYMHD/7O/wMmkwj8BGE897CX9pZuWs9YuHvVPq4Bycx5yeSvSCe7IJXoWLFS97h5fX7evtnN3iYTBy5bGfIE60ItnaWlqshI2aIMtAmiy58gPOlE/BI+4l6Pd6dnkNqLZmqazVy3ButOxkcr2DRfT2WBgWdnpxGtFAuIQuj5JYlTjgH22uzU2hzYvcHYaVZsNNv1Wqr0GuYmxE3iLKcHj9/DSfNJGtsbOXLnCEPe4MmiPE0epdmllGSXMCNpRljmI/n9DDU14aqvx9W4H5/dHrgWZTSiKitDVV5GbH54EpiCEElCliTcs2cPP/vZzzhw4ADz58/nj//4j/mjP/ojNBpN4DW3bt1i3rx5uMd0IZpKROAnCPcbkFy1c/20hfaWLrxuf+CaLks12oBkmZ54lUhOPW6SJHHZfL/OYLOZ7v7g8ZmslHiqimawvcjArBRR41EQhKBIil8GBwe5c+fOe2LBxYsXT9KMHq9IuteR5rqlj5oWEzUtZu72BhMGmvgoShZmUFlgYEV2siiHIYSF2+/neG8f1TYH+7udDPqC8ey8hFh26LVs02nIjBNN3SZqyDvE8XvHaWxr5C3TW3j8wd2ci9MWU5pdypasLaTGpYZlPpLHw8CpUzhra+k/fAT/4GDgWkx+/mjCsKyM6BnGsMxHECZbyJKEarWaP/zDP+SP//iPWb58+fu+ZmhoiB/84Ad861vf+mizjhAi8BOeVJIkYe1w0Xrays3z4xuQqNPiyC/Wk1+cjkY/NUsJRDqTY4h9zSaqL5i4YesPjGvjo6goGK0zWDhTI45KCILwviIhfunq6uIzn/nMQxuU+Hy+9x2faiLhXkc6SZJouuugptlM3cXOcQteelUMFYsNVBYaWGRUi881ISwGfD4Odbuottk52tOHZ8xj8HJVAtv1Gip1GtKiRU3NiXK5XRy5fYSG9gbOWM7gl0aTs3KZnOL0YkqzS9kwawOq6PC8f/qHhug/dgxnfT0Dx99E8gSfceKKikYThiVbUaakhGU+gjAZQpYkHBwcnLK1Bj8sEfgJTxqHdZDrZyy0nrHi6gqu+sclRZG3TM+c4nR0WUkiiA8B17CHxnc7qW4ycaqtNzAerZSzaZ6eqiIjq/PFES1BED5YJMQvn/jEJ7h9+zYvv/wya9eupbq6GqvVyne/+13+1//6X5SVlU3KvB63SLjXU4nPL3GqrYd9zSYaL1noG/YGrmWlxFNZYKCy0EieLnESZyk8SeweLw1dTqqtdk44+nnwQKyQwbOaJKr0WkrT1CQpRU3Nieoe6uZAxwEa2hq42B3sSBwlj+JZ47OU5pSyZsYaYpXh6UjsczrpO3QIZ109g6dPw4N0iEJBwlNPoSorI2nTRhSJ4v1ImF5CliRUKBR0dnai0+nGjff09KDT6abFCrEI/IQnwaDLzY2zo52JbbeD9YOU0XJyCtPIL05nxjytaEASAh6fn+PXu6huMnHoqhW3N3j0ZWVOMlVFRkoWZaCKFSvZgiB8eJEQv2RkZLBv3z6Ki4tRqVScO3eO/Px8ampq+MEPfsDbb789KfN63CLhXk9VI14fx693UdNi5vBVK8Oe4Gfg/AwVlYUGKgoMGDWiXpwQHpYRDzU2O3usDpr7xhxJlcvYmKKiSqdlY4qKWBETT9jdvrvsb99PQ3sDNx03A+Pxyng2ZG6gJLuElYaVRMnDEwN7rDb69jfirK1j+NKlwLgsJobEtWtRlZWSuGYN8hhxHF2Y+kKWJJTL5VgslvckCc1mM7m5uQwNDT3kK6cOEfgJ05V72Et7cxetZ6zcvdobWDiTyWXMnJfMnBV6sgvSiIoRq6aPmyRJNN91UN1kou5iJ70DwTpds3WJVC0xsq3QKB6KBEH4nUVC/KJSqbh48SJZWVnMmjWLX/7yl6xatYr29nYWLFjA4JiaUFNZJNzr6WBgxMvhq1b2NZt5s7ULrz/4WLJslpZthQZKF2WQkige0IXwaB8codpmp9pq58Zg8Ih8okJOaZqaKp2WZ7VJKEVNzQlrtbfS0NZAY3sj5gFzYFwbo2Vz1mZKskso0hUhl4UnOevu6MBZX4+rrh53e3tgXJ6YSNLmzajLy4hfsQKZQjwnCVPTY08SvvLKKwB86Utf4u///u9JHLP91ufz8eabb9LR0UFTU9MEpz75ROAnTCc+n5+7V3ppPWOlvbkL75gVe322ivzidPKW6kQDkhC50zNIdZOJvc0m2rsHAuOpiTFsKxytM7jAoBJHuQVBmLBIiF+WL1/Od7/7XbZs2UJlZSUajYbvfe97vPLKK+zevZtbt25Nyrwet0i419ONfcBN4yULNS0mTrcHFzIVchmr8lKpLDCwZYGeJLHLXggDSZK43D9Etc3BXqsd00iwhl1qlJJKnYYqvZZlqngRw02QJEm0dLXQ0N7AgY4D9A4Hy++kJ6RTklVCaU4pc7RzwnKvJUli5OpVnHX1uOrr8VqtgWuK1FRUJSWoy8uIXbxY/O6FKeWxJwmzs7MBuH37NjNmzEAxJoMeHR1NVlYWf/d3f8eKFSsmOPXJJwI/YaqTJAlru4vW0xZunLcx3D+mAYkujjkr0pm9XI9GN73ri04Wx6CbuoujdQbP37YHxuOiFGxdmM72IiOrclNQimMrgiA8RpEQv/ziF7/A6/Xy6U9/mvPnz7N161Z6e3uJjo7mtdde4w/+4A8mZV6PWyTc6+nM4hym7qKZmhYzF+85A+PRSjkb5uqoLDCwbq6O2Cixo0cIPb8kcdY5wB6rndouB72eYHmtmbHRbNdp2KHXMi9RnAaZKK/fy5nOMzS0N3DkzhH6PcFGftnqbEqzSynNLiVTlRmW+Uh+P0Pnz+OsraPvwAF8zuD7UdTMmajKy1CXlRGTlxeW+QjCRITsuPG6devYs2cPWq12wpOMVCLwE6Yqu2WA1jOjdQZd3cOB8bikKGYvH+1MrJslGpCEwojXxxvXbOy5YOKN6zY8vtG3VrkMVuWlUlVkZMuCdBJilJM8U0EQpqtIjF8GBwe5du0amZmZpKamTvZ0HptIvNfTVVtXP7UtnexrMdHWFdyRnxijZMuCdCoLDWLhTQgbj1/iTXsf1VY7jd1OBnzBEzpzEmLZodOyXa9hVpw4Ij9RI74R3rr3Fg3tDRy/exy3P1iqZ0HKAkqzS9mavRVdvO4R3+Xxkdxu+k+cwFXfQN+RI0hjyqzFzJ2LurwMVWkpUQZDWOYjCB9VyJKETwIR+AlTyYBz5H4DEitdd8Y0IIlRkFuYRn6xnhlztchF8PzY+f0S5+/Y2XPBRP1FM64x3RrnZ6jYscRIRYEBvSo83doEQXiyRVr88iDEnI4LU5F2r58EkiRx2eyitsVMbYsZszO4GJqSEE3Z4gwqCwwsydQiF/XihDAY9Pk53ONir9XO4R4X7jGP1UtU8ezQa6lM06CLEUfkJ6rf3c/Ru0dpaGvgVOcpfNLobk4ZMpanL6cku4RNszahjlGHZT7+wUH6jr6Bq76e/rfeAm/wGSBu6VLU5WUkbd2KchpvrBKmnseaJPzyl7/M3//935OQkMCXv/zlR772pZde+uizjTAi8BMinXvYS1tTF61nLNy7Zg/U7ZHLZcxckEx+sZ7sxaIBSajc6upnb5OJ6iYT9+zBVcQMdSyVhQZ2FM1gTnrSJM5QEIQnUaTEL//+7//OD3/4Q27cuAHA7Nmz+Yu/+Av++I//eNLm9LhFyr1+Uvn9Eudu26lpMdHwrmVcMzCjJo7yggy2FRiZlyFOTwjh4fR4qe92stdq5217Pw/2F8qBZ7SJbNdrKUtVo44SJ0omqmeoh4O3D9LY3kiTLdgPQSlX8ozhGUpzSlkzYw3xUeEpq+RzOHAdPIirto7Bc+cIPJgplSSsehp1eTlJ69cjT0gIy3wE4WEea5Jw3bp1VFdXo9FoWLdu3cO/mUzG0aNHf7cZRxAR+AmRyOf1c+dKL61nLHS0dI9rQJKeE2xAEpckGpCEQnf/CHUtZqqbTLSMqY+UGKOkZGE6VUuMrMxOEbsXBEGYNJEQv/zt3/4tL730Ei+88AJPPfUUACdPnuRHP/oRX/rSl/i7v/u7SZnX4xYJ91oY5fH5OXGzm5pmMwcuWxhwB+vF5ekSqSwwUFlgICtVPKAL4WEb8VDT5aDaaue8K9jRPVomY0OKiiq9lk0pKuLEKZ8JM/ebaWxvpLG9kev264HxOGUca2eupSy7jKcNTxOlCM9uTo/FgquhEVd9PcOXLwfGZbGxJK1fh6q8nMRnnkEWLZ7XhPATx40nQAR+QqSQJAnLLSetZ6zcPG9jeCDYgESjjye/WE9+sR51mmhAEgrDHh8Hr1jZ22TieGsXPv/o26VCLmNNfhpVRUY2ztMTFy12bAqC8HAe/2jR+6e1iSH9OZEQv6SlpfHKK6/wsY99bNz4f/3Xf/HCCy/Q3d09KfN63CLhXgvvNezxcfSajZpmM0ev23B7gwuqBTPUVBQYRBkQIaxuD42w1+qg2mbn2kDwiHyCQk5JqpoqvZbV2iSixCLzhN1y3KKhvYGGtgbu9d8LjKtj1GzM3EhZThlL9UuRy8KTnB1pa8dVX4+rrg737duBcblajWrzJlRl5cQvX4ZMIZ4jhPAIWZLQ6XTi8/lITk4eN97b24tSqZwWgZII/ITJ1ts5QOsZCzfOWsc1IIlXRd9vQKInLVMcoQkFv1/iVFsP1U0mGi9Z6B8J1hgpmKFme9FoncHURFGQWhCEh5MkiSbXILutdvba7PR6fLy9Yi558aFLTkRC/KLRaDh79iyzZ88eN97a2kpxcTEOh2NS5vW4RcK9Fh7NNezhwCULNS1m3rnVE1jok8lgZXYKlYUGShamo4kXO3qE8LjaP0S11U61zcHd4eAR+eQoBeVpox2Si9UJyEV8PyGSJHGp+xIN7Q3s79hP91BwcUoXp2Nr9lZKs0uZnzI/LM9SkiQxfOkyrro6XI2NeG22wDWlToeqtBRVeTmxC8IzH+HJFbIkYUlJCRUVFXzuc58bN/6Tn/yEmpoaGhoafrcZRxAR+AmTYcAxwo1zVq6fttB9tz8wHhWjILcojfzidIxzRTHuULlu6aO6ycS+ZhOdY4qhz9DGUVVkZHuRkdy00O4CEgRh6usYGuF1i53XrXbahkYC42nRSl6em8mGlNDFFZEQv7zwwgtERUW9p0b1V77yFYaGhvjxj388KfN63CLhXgsfXnf/CA3vdrKv2cz52/bAeJRi9GRARYGBTfP1xEeLenFC6EmSxHnXINVWO/tsDro9wQVpY0wUlbrRhOHCxDiRNJogn9/HOes5GtsbOXj7IH3uYJPHWapZlGSXUJJdQo46JyzzkXw+Bs+ew1Vfh+vAQfwuV+Ba9KxZqMrLUZWVEZOTHZb5CE+WkCUJk5OTOXHiBPPmzRs3fu3aNVatWkVPT8/vNuMIIgI/IVzcQ15uPWhAct0OYxqQZC5IJn9FOlmLU4kSx1lDwuYaZl/zaJ3BK53BD2lVrJKyxQZ2LDGyVHRJFAThA/R6vNTYHOy29HJuTP2pOLmc0jQ1z90/TqYM8XtJJMQvL7zwAv/xH//BzJkzWblyJQCnT5/mzp07fOpTnyIqKlgXaio3u4uEey38bu7ZB6lt6aSmxczVMZ/9cVEKNs3XU1lgYHV+GtFKUS9OCD2vX+KEo589VjsNXQ76fMEj8nnxMVTptFTpteTEixMsE+X2uTlhOkFDewPH7h5j2BfcFDAveV4gYZiekB6W+fjdbgbefhtXXR19R99AGg7OJ3bBAlRlZahKS4hKD898hOkvZEnChIQETp06xaJFi8aNv/vuu6xYsYLBwcGHfOXUIQI/IZR8Xj93Lvdw/bSVjne78Y1pQJKRqya/WE/uUh1xieL4SygMjHg5cNlCdZOJEze7uX/6iCiFjHVzdOxYYmTdXB0xSpGYFQTh4YZ9fg71uHjd2suRnj4898MpObBam8Rz6VpKU9UkhPG9JBLil0c1uBtrqje7i4R7LUzcDWsfNS1malrM3O4JPsOo46IoXZRORYGBFdkpKMRioRAGwz4/R3pdVFvtHOpxMeIPPqYXJMVRpdOyTa8hI0Y8I0zUoGeQo3eP0tjeyDumd/BKwd2cS3RLKM0uZXPWZrSx2rDMx9c/QP8bR3HW1jJw4h3w3W/AJJMRv2zZ6A7DLZtRaDRhmY8wPYUsSbhu3ToWLlzIq6++Om7885//PBcvXuStt9763WYcQUTgJzxukl+is81J62kLNy/YGBkIfhBp0+PJL04nv1iPKjVuEmc5fXl9fk7c6qH6wj0OXLYy5Al2Plw2S8v2IiPlizNEXSJBEB7JL0mccgzwurWX2i4HrjFNERYmxrFTP7rjQx8Tni6Kv03EL+Ej7vX0IkkSLfec1DSbqbtoxtYXLBWgS4qh4n6H5MUz1OL4pxAWfV4fjd1Oqq123rT34bv/xC4DntIkUqXXUJ6mQRsljshPlGPYwcHbB2lsb+S89TzS/aNdSpmSlYaVlGaXsj5zPQlR4emQ7u3tpe/AAZx19QydPx+8EBVF4qpVqMrLSVq/Dnm8aFwpfDQhSxKeOHGCjRs3snz5cjZs2ADAkSNHOHv2LAcPHuTZZ5+d2MwjgAj8hMelx9xP6xkrN85Y6esd04BEPdqAZE5xOqkzE0XAGQKSJHHZ7KK6yURNi5muMQF/dmoC2wuNVBUZyUwRH7CCIDza9YFhXrf08rrVjmkk2GXeGBPFDr2W59K1zE2Y/EUeEb+Ej7jX05fPL3G6vYeaZjONlyw4h4J/81kp8VQUGNhWaCBPlzSJsxSeJF1uD7U2B3ttDs44BwLjUTIZ65KTqNJr2ZyqIkF0yZ0wy4CFAx0HaGhv4ErPlcB4rCKWNTPXUJJdwrPGZ4lWhGdjgcdsxtXQgLOunpFr1wLjsrg4kjZsQFVWSuKqVciixUYH4YOFLEkI0NzczIsvvkhzczNxcXEsXryYv/qrv3pPJ7upSgR+wkT020e4cdZK69nfakASO6YByRxR5y5UTI4h9jWbqL5g4oYteP+18VFUFBioKjJSOFMjErOCIDySbcRDtc3O6xY7F/uHAuNJCjkVOg079cms1ERWF8pIiV/OnTvHr3/9a+7cuYPb7R53bc+ePZM0q8crUu61EFpur583W7vY12Lm8JXxJxHmZajYVmigosCAUTP5iwTCk+HusJu9Vjt7bXYu9wc3IMTJ5WxNVVGl17I2OYlouaipOVEdzg4a2xtpaG+gw9URGE+KSmLjrI2UZJdQnF6MQh6e5OzIzZs46+pw1TfguXs3MK5Qq0nauhV1eRlxS5ciE7974SFCmiSc7kTgJ3xUI0Nebl2w0XrGiql1TAMShYzMBSnMWZFO1qIUlKIBSUi4hj3sf9fCnqZ7nG7v5cG7WrRSzqb5eqoKjayZk0aUQnxoCoLwcAP3j3e9brVzvLePB4eJlTLYkKJipz6ZTSkqYiP0vSQS4pf//u//5lOf+hRbtmzh4MGDbN68mdbWVqxWK1VVVfzsZz+blHk9bpFwr4XwGhjxcviqlZpmM8dbu/COqRe3bJaWykIDpYsySE0UDSaE8Lg+MMxeq509Vju3h4MLMlqlgnKdhu06DU9pEiNqMWsqkiSJq71XAwlD26AtcC01LpUtWVsozS5lUeqisGxCkCSJ4YsXcdbX42poxNfdHbimTE9HVVqKuryMmHnzxKYIYZywJAmHh4ffs0I8HQIlEfgJH4bP4+f25R5az1jouNiDb0xtqow8NfnF6eQt0RGbODm1qaY7j8/P8etdVDebOHzFysiY+78yJ5kdRTPYuigdVay4/4IgPJzXL/GWvY/dVjsNXU6G/MH3kmWqeJ5LT6YyTUNKdOTXfYqE+GXx4sX86Z/+KZ///OdJSkqipaWF7Oxs/vRP/5SMjAy+853vTMq8HrdIuNfC5HEMumm8ZGFfs2nc4qRCLmNVXiqVBQa2LNCTJGIQIQwkSaKpb5Bqq519Ngc2d7D2eUZMFJU6DTv0WhYnxomk0QT5JT/nredpbG/k4O2DOEecgWszEmdQkl1CaXYpedq8sMxH8vkYPH0aZ309fQcP4e/rC1yLzslBVV6GuqyM6FmzwjIfIbKFLEk4ODjIV7/6VX7961/T09Pznus+n+99vmpqEYGf8DCSX6LzloPrZ6zcOm9jZHBMA5KMBOas0DN7mWhAEiqSJNF810F1k4m6i530DgQXKfJ0iexYYmRboVEc+xEE4ZEkSeLd/iF2W+xU2+x0jXmgyo6L5jl9MjvTtWTFTa0dQZEQvyQkJHD58mWysrJISUnh2LFjLFq0iKtXr7J+/Xo6OzsnZV6PWyTcayEyWJzD1F0c7ZB88V4wYRCtlLNhro7KAgPr5uqIjRKnSYTQ80kS79j7qbbZqe9y4vQGn81z4mLYrtdQpdMyOyF2Emc5PXh8Hk52nqS+rZ437r7BkDdYmmS2djal2aWUZJdgTDSGZT7+kRH633wTV109/ceOIY0E67HHLlqEuryMpJISonS6sMxHiDwhSxJ+/vOf54033uDv//7v+eQnP8mPf/xjTCYTP/3pT/mnf/onPvGJT0x48pNNBH7Cb+sxjTYgaT1rob83+IaboI5m9v3OxKkzRAOSULnTM0h1k4m9zSbau4MFm1MTY9hWOFpncIFBJe6/IAiPdHfYTbXVzm8svdwYDL6XJ0cp2KbT8nt6LUWq+Cn7XhIJ8cuMGTNobGxk0aJFgZrVH/vYxzh58iRbt27F6XR+8DeZAiLhXguRp717gJpmM/taTLR1BeOVxBglWxakU1loYFVuCsoILVkgTC8jfj9v9PRRbbNzsNvJ0Jgj8osS46jSa9mm02CMFU0vJmrQM8jxe8dpaG/gbdPbeP3BxceCtAJKs0vZnLWZ1LjUsMzH199P3+HDuOrqGTh5Eh5s5JLJiF+xYjRhuGkTCrU6LPMRIkPIkoSZmZn8x3/8B2vXrkWlUnHhwgXy8vL4z//8T/7rv/6LhoaGCU9+sonATwDotw/TetZK62krPaZgA4zoWAU5S3TMKdZjyBcNSELFMeim7mIne5tMnLttD4zHRSnYskBP1ZIZItAWBOEDOT1e6rqc7Lb2ctIRfGiPlcvYnKpmZxiKvHv9Xlq6WliqXxqynwGREb98/OMfZ9myZXz5y1/m7//+73n11VfZtm0bhw4dYsmSJaJxifBEkCSJy2YXtS1malvMmJ3BBhMpCdGULc6gssDAkkwRRwrhMeD1sb/bSbXNwbFeF94xGYCV6gSq9FrKp0hpjUjnHHFy+PZhGtsbOWM5g3S/WL1cJmdF+gpKc0rZkLmBpOjwdEj39vTg2r8fV109Q01NgXFZVBQJa1ajLisjce1a5HHiJNZ0F7IkYWJiIleuXCEzM5MZM2awZ88eiouLaW9vZ9GiRfT393/wN4lwIvB7co0MerjV1EXraQumG45xDUhmLUwhv1g0IAmlEa+PN67Z2HPBxBvXbXh8Dz5UYVVeKlVFRrYsSCchRgQwgiA8nNvv543ePn5j6eVQj4uR+7snZMDTmkSeSx99GFIpQ/deLkkSV3qvUHerjob2BnqHe6ndXkuWOitkPzMS4pfe3l6Gh4cxGAz4/X5+8IMf8M477zB79mz++q//Gq1WOynzetwi4V4LU4PfL3Hutp2aFhMN71rGlUoxauKoKDBQWWBgXkbSlN3FLEwtPW4vdV0Oqq12TjmDi2dKGazRqqjSa9iaqiYxhJ+RTwrboI0DHQdoaGvgUs+lwHi0PJrVM1ZTkl3C6hmriVWG5/i3+54JV309rvp6RlpbA+Py+HiSNm1EVVZGwlNPIYsS9VSno5AlCRcvXsyrr77KmjVr2LhxI4WFhfzzP/8zr7zyCj/4wQ+4d+/ehCc/2UTg92Txefx0XOqm9YyVjne78Y9ZWsvIUzNnRTq5S3TEJog3y1CQpNHgec8FE/UXzbiGg9vz52Wo2FFkpLLQgF4laqcIgvBwkiRx3jXIbyy91Ngc2MfUYZqTEMtOvZYdem3Ij1WZ+83Ut9VT21ZLu7M9MK6N0fLdZ77L6hmrQ/azRfwSPuJeC78Lj8/PiZvd1DSbOXDZwoA7+D6Vp0tkW4GBykIDs1ISJnGWwpPEPOxmn200YXixP1hTL04uY1OqmiqdhvUpKmJCuNv+SXHHdYeG9gYa2hvGxQcJUQlsyNxAaXYpKzJWoJSHZzPE8PXW0YRhXR0eszkwrtBqSdq6BXV5OXFFRcjE737aCFmS8Ic//CEKhYIvfvGLHD58mIqKCiRJwuPx8NJLL/Hnf/7nE578ZBOB3/Qn+SXMNxy0nrFwq6nr/RuQLNejShHbrkPlVlc/e5tMVDeZuGcPBiXpqli2FY3WGZybLv7+BEF4tLbBEXZbe3ndYuf2cHCHjj5aSZVey069lgUh7ujocrs41HGI2rZazlvPB8ZjFDGsm7mOitwKnjI8RZQ8tItNkRC/NDQ0oFAo2LJly7jxgwcP4vP5KCkpmZR5PW6RcK+FqW3Y4+PoNRv7mk28cb0LtzfYWb1ghpqKAgMVBWKRVAifm4PDVFvt7LU6uDUUrNurUsopS9OwQ6flaW0iCrHjdUIkSeK6/ToN7Q3sb99P50CwoVdybDKbZm2iLKeMgrQC5LLQJ+gkSWKouRlXXT2uxkZ8vb2Ba0pDBuqyMlRlZcTMmSN2O09xIUsS/raOjo5AXcLFixdP5FtFDBH4TV89pn6un7Zw46yVfvuYBiSaGGYv1zNnhZ4Uo2hAEird/SPUtZipbjLRMqYDYEK0gpJFGewoMrIiJwWFqM8jCMIjdLu91Njs7LbaueAaDIzHK+SUpanZqU/mmRA/yHh8Ht42vU1tWy3H7x7H7R9NUMqQsTx9OeU55WyctTFsNYcgMuKXxYsX80//9E+UlpaOG9+/fz9f+9rXaGlpmZR5PW6RcK+F6cM17OHAJQs1LWZO3OzmQX8JmQxWZqewrdBAycIM1PHiVIsQepIkcbF/iGqrnX02B50jnsA1XbSSbbrRDslTudFXpPBLfpptzTS0N3Cw4yD2kWAd9oyEDEqySyjNLiVfmx+Wey15vQycPIWrvp6+Q4fwDwSPo0fn5aIuL0dVVkb0zJkhn4vw+IUtSfg4/PjHP+bFF1/EYrFQUFDAq6++SnFx8UNf73A4+OY3v8mePXvo7e1l1qxZvPzyy4GA9Hvf+x579uzh2rVrxMXF8fTTT/P973+fOXPmfKj5iMBveunrHebGWSvXT1voNY95o4tVkLtUR35xOobZGlE4OkSGPT4OXbFS3WTieGsXvvuRr0IuY/XsVKqWzGDTPD1xos6jIAiPMOTzc7DHyW6LnTfGFF1XyGCNNomd6clsSVWRoAhtncGL3RepvVXLgY4DOEYcgWt5mjzKc8opyykjPSE9ZHN4lEiIX+Li4rh69SpZWVnjxjs6OliwYAEDYx44prJIuNfC9NTVN0LDu53UtJg5P6ZxW5RCxpp8HZWFBjbO0xEvGkwIYeCXJE45Bthrs1P7W6U8ZsVGU6XXsl2vYW6COH01UR6/h9Odp2lsb+Tw7cMMeoOLoLnq3EDCcKYqPAk6//Aw/ceO46qvp//YMSRPMFkcV1CAqqwMVclWlGlpYZmPMHEhTRIeOXKEH/7wh1y9ehWAefPm8Rd/8Rds3LjxI0/0V7/6FZ/61Kf4yU9+wooVK3j55Zf5zW9+w/Xr19HpdO95vdvtZtWqVeh0Or7xjW9gNBq5ffs2Go2GgoICALZu3cof/uEfsnz5crxeL9/4xje4dOkSV65cISHhg2t8iMBv6hse8HDrgo3WM1bMNxyBcblSxqwFKcxZkc6sRSkoo0RiKhT8folTbT1UN5lovGShfyR4nLtghprtRUYqCgykJsZM4iwFQYh0fkniHUc/r1vt1Nkc9PnGHMdLimOnPpnteg1p0aHdXXPXdZe69jrq2+q57bodGE+NS6U0u5SK3ArmaCf/GE4kxC/p6en88pe/ZP369ePGDx8+zMc//nFsNtukzOtxi4R7LUx/d3sHqb1opqbZzDVLX2A8PlrBpvl6KgsMPDs7jWilqBkmhJ7b7+d4bx/VNgf7u50MjvlMnp8QS5Veyzadhsw4Ed9P1LB3mDfvvUlDewNv3nsTjz+YoFuUuoiS7BK2Zm0lLT48CTqfy0XfocO46usZOHUK/Pd/93I5CStXoiovJ2nTRhRJ4Ts9IXx0IUsS/u///b/58z//c3bu3MlTTz0FwKlTp9i9ezc//OEP+fznP/+RJrpixQqWL1/Oj370IwD8fj8zZ87khRde4Otf//p7Xv+Tn/yEF198kWvXrhH1IbvudHV1odPpOH78OKtXf3DBcBH4TU1ej4/b7/Zw/bSF25d7xjUgMczWkF+sFw1IQuy6pY/qJhP7mk10OocD40ZNHFVFRrYXGcnTJU7iDAVBmAqu9g/xutXOHqsd85hjTjNio9ipT2aHXkt+QmjrdDlHnBzoOEDtrVqau5oD43HKODZkbqAip4LijOKwFRj/MCIhfvnTP/1TTp48SXV1Nbm5uQDcvHmT5557juXLl/Nv//ZvkzKvxy0S7rXwZGm19lHTbKamxcyd3uAOI018FCULM6gsMLAiO1mcjBHCYsDn41C3i2qbnaM9fXjGpBSWqxLYrtdQqQv9It6TwOV2ceT2ERrbGzltOY1fGk3QyZBRnF5MSXYJG2dtRB2jDst8vF1duBr346qvZ2hMCRFZdDSJa9agKi8nce0a5DEiWRxpQpYknDFjBl//+tf5whe+MG78xz/+Mf/4j/+IyWT60N/L7XYTHx/P7t272b59e2B8165dOBwO9u3b956vKS0tJTk5mfj4ePbt20daWhof//jH+drXvobiIUeMbt68yezZs3n33XdZuHDhe66PjIwwMhKsT+dyuZg5c6YI/KYAyS9huuGg9fRoAxL3UHDHWooxgfzidGYv15OULIo+h4rNNcy+5tE6g1c6XYFxVaySssWjDUiWzdKKoFUQhEeyjHiottrZbe3lcn9wkUGllFOZpmVnupZidQLyEO7Wc/vcvHnvTWpv1fKm6U28/tHPFLlMzsqMlZTnlLMhcwPxUfEhm8NERELiyul0snXrVs6dO8eMGTMAuHfvHs8++yx79uxBo9FMyrwet0i418KTSZIkWu452ddsou5iJ119wWcYvSqGisWjHZIXGdWTvrtZeDI4PF7qu5xUW+2ccPTzILmgkMGzmiSq9FpK09QkKcUJronqHurmQMcBGtsbaekKJuiUciXPGJ+hLLuMNTPXEKcMz/Fv9507uBoacNbW4b51KzAuT0wkadMmVGVlJKxcgUwZOQuqT7KQJQkTExNpbm4mLy9v3PiNGzcoKiqiv7//Q38vs9mM0WjknXfeCexKBPjqV7/K8ePHOX369Hu+Zu7cuXR0dPCJT3yCz33uc9y8eZPPfe5zfPGLX+Rb3/rWe17v9/uprKzE4XDw9ttvv+88vv3tb/Od73znPeMi8ItMkiTdb0Bi5cZZKwOOYHCUqB1tQJJfnE7qDLFjLVQGRrwcuGyhusk0rsB2lELGujk6qoqMrJurI1Yc5xYE4RH6vT4aup3stvTylj34YBElk7ExRcVzei0bU1TEKkJ3lE6SJJpsTdS2jdYZ7HMHj/TNTZ5LeU45pdmlYTvSMxGRkriSJIlDhw7R0tJCXFwcixcv/lAnOaaSSLnXwpPNd7+8S02zmcZLnbiGg4vl2akJVBQYqCwwiFMcQthYRjzU2OxUWx009QV3vMbIRz/Xq3RaNqSoiAvh5/qT4l7fPfZ37KehvYEb9huB8ThlHOsz11OaXcpThqeIkod+N6ckSYxcv46rvh5nfT1ec7BjsyIlBVVJCeryMmILCsTixSQKWZLw4x//OEVFRfzlX/7luPF//ud/5ty5c/z3f//3h/5ev0uSMD8/n+HhYdrb2wM7B1966SVefPFFOjs73/P6P/uzP6OxsZG33347sKL928ROwqmhr3eY1jMWWs9YxzcgiVOStyQt0IBEJnashYTX5+fErR72NpnYf8nCkCdYuHjpLC1VRUbKFmWgTYiexFkKghDpvH6J4/Y+XrfaaexyMOQPhiHF6gSe02up0GlIjgrtqnOHs4Patlrq2+ox9QdPQejj9ZTllFGeU85s7eyQzuFxE4mr8BH3Wog0I14fx693UdNi5vBVK8OeMfXiMlRUFhqoKDBg1IgGE0J4tA+OsNc2WjrkxuCYTR0KOaVpaqp0Wp7VJqEUz24TdsN+g8b2RhraG8bFNJoYDZtmbaIku4Sl+qXIZaFPzkp+P0NNTbjq63E17sdnH9OAacYMVGVlqMvLiJk9tWKs6SBkScLvfve7/PM//zOrVq0aV5PwxIkT/M//+T/H/bAvfvGLj/xev8tx4zVr1hAVFcXhw4cDY42NjZSWljIyMkJ0dDBB8YUvfIF9+/bx5ptvkp2d/aH/N4rAL3I8aEBy/bSFzpvOwLhcKSNrUSr5xXpmLRQNSEJFkiQum11UN5moaTGPO9KSlRJPVdEMthcZmJXywQ2BBEF4ckmSREvfEK9be6m2Ouj2BHe75MTFsDNdy3N6LbNCXOy8d7iXxvZG6tvqebf73cB4QlQCm2ZtojynnGX6ZSjkU/MzZbrFL+930mPOnDlcu3btfV/v8Xj43ve+x89//nNMJhNz5szh+9//Plu3bg28xufz8e1vf5tf/OIXWCwWDAYDn/70p/nrv/7rj7S7Ybrda2F6GRjxcuiKlZoWM2+2duEdsxizPEtLZYGB0kUZpIgGckIYSJLElYFhqq12qq12TGNqDadEKanUadih17JMFS92mU2QJEm0dLXQ2N7IgY4D9Az3BK7p4/VszdpKaU4p85LnheVeSx4PAydP4qyro+/wEaTBMbtL58xBVV6GurSUKKMx5HMRQpgk/LDJNplMRltb2we+bsWKFRQXF/Pqq68Co8eDMzMz+cIXvvC+jUu+8Y1v8Mtf/pK2tjbk8tFM+L/8y7/w/e9/H7PZDIz+cbzwwgtUV1dz7NgxZn/ELLUI/CaX1+Oj42IPrWcs3L7Ug993//+iMjDO1pC/Ip3cojRi4kUh3FAxO4bY22xib5OJVmuwhIA2PoqKgtE6g4UzNeKDXBCER7ozNMIeq53Xf2sXQXKUgiqdlufStRQlhfahYNg7zLG7x6htq+WE6QQ+aXQXtEKm4GnD01TkVrB25tqw1e8JpekWv3z7299m9+7d4xaGlUolqamp7/v6r33ta/ziF7/g//yf/8PcuXM5cOAAX/7yl3nnnXcoKioC4B//8R956aWX+PnPf86CBQs4d+4cn/nMZ/iHf/iHD1zcHmu63Wth+uodcNN4qZOaZjNnOnp58OSnkMt4Ji+VygIDmxfoSYoVcbUQen5J4qxzgGqbgxqbnd4xJ5NmxEaxXadlh17LvIRY8ZwxQV6/lzOWMzS2N3Lk9hH6PMFyKlmqLEqySyjJLiFb/eE3U02Ef2iI/jfewFnfQP+bb4InmCyOW7IEVXkZqq1bUSYnh2U+T6KQJQkft1/96lfs2rWLn/70pxQXF/Pyyy/z61//mmvXrqHX6/nUpz6F0Wjke9/7HgB3795lwYIF7Nq1ixdeeIEbN27w/PPP88UvfpFvfvObAHzuc5/jl7/8Jfv27WPOnDmBn6VWq4mL++CHABH4hZ/fL2FutXP9jJW2Czbcw8EPjBRjIvkr9OQv15OoFQ1IQsU17GH/uxb2NN3jdHswiIxWytk0T09VkZHV+WlEK0UNEUEQHs7h8VLXNVpn8JQzWBoiVi5jS6qanXota5NVRIXweJFf8nPOco7atloO3T7EgCc4jwUpC6jIrWBr1lZS4lJCNofJMN3il29/+9vs3buX5ubmD/V6g8HAN7/5TT7/+c8Hxp577jni4uL4xS9+AUB5eTl6vZ5///d/f+hr3o8oTSNMB53OIepaOqlpMfOuKXhCJ0YpZ8M8HZUFRtbOSRM1pYWw8Pgl3rL3UW2z09jlpN8XPCKfHx/LDr2GqjCcMngSjPhGePve2zS0N3D83nFGfMHPs3nJ8yjLKWNL1hbSE9LDMh+f04nrwAFc9Q0MnjlDcPVCQcLTT6MuLyNxw0YUieK02uM0ZZKEAD/60Y948cUXsVgsFBYW8sorr7BixQoA1q5dS1ZWFq+99lrg9SdPnuRLX/oSzc3NGI1GPvvZz47rbvywVYef/exnfPrTn/7A+Uy3IDtSSZJE991+Ws9YRhuQON2Ba4naGPKL08kv1pNiFMWWQ8Xj8/Nmaxd7mkwcvmJlxBv8cF6Zk0xVkZGtCzNQx4nVZUEQHs7t93Okx8Vuq51D3S7c90MLGbBKk8hz6VrK0zQh72x4034zUGfQOmgNjBsSDKN1BnPLyVHnhHQOk2m6xS/f/va3efHFF1Gr1cTGxvLUU0/xve99j8zMzPd9fUpKCj/4wQ/47Gc/Gxj7oz/6I95++206OjqA0Z2E//qv/8rBgwfJz8+npaWFzZs389JLL/GJT3zikXMRTe6E6aStq5+aFjM1LWbauoILKUkxSrYsTGdboYGnclJQigYTQhgM+fwc7nGx12bncI+LkTFH5IuS4tmh11Kp06CPEc8kEzXgGeDonaM0tDdw0nwycMJChowl+iWUZpeyedZmNLGasMzHY7XiamzEVVfP8KVLgXFZTAyJ69ahKislcfVq5DEiWTxRIU0S3rt3j5qaGu7cuYPb7R537aWXXvros40w0y3IjjSu7iFaz1ppPWPF3hkMSmLileQu1TGnWE9GrmhAEiqSJNFyz0n1hXvUXuykdyD4N5ynS6SqyMj2IqMobC0IwiNJksQ51yC/sfRSa3Ng9wZ3gM9NiOU5/eiRIWNsaJsZdQ9109DWQF1bHVd7rwbGk6KS2Jy1mfKccpbol4SlWPdki6T4xWazYbPZ8Pv948YXL178ob9HY2Mj/f39zJkzh87OTr7zne9gMpm4dOkSSUlJ73n9xz/+cVpaWti7dy+5ubkcOXKEbdu24fP5ArsA/X4/3/jGN/jBD36AQqHA5/PxD//wD/zVX/3VI+cidhIK09WD+tM1LWZqW8x0OocD11IToylblEFloZElmaLMjBAeLq+Phi4H1VYHb9n7ePApIgee1iRSpddSlqZGE+IGZ0+C3uFeDnUcoqG9gQu2C4FxpUzJ08anKckuYf3M9cRHxYdlPiPt7bjqG3DV1eG+v7gHIE9KImnzJtTl5cQXFyNTiN3Ov4uQJQmPHDlCZWUlOTk5XLt2jYULF9LR0YEkSSxZsoSjR49OePKTLZKC7OliuN/DzQs2Ws+Mb0CiUMrJWpxCfnE6sxakoIia/g9xk+VOz2CgzmBbdzA5m5oYw7bC0TqDCwwqEQAKgvBIbYMj7Lb28rrFzu3h4CJDenQUVXoNO9OTmR/iWkKDnkGO3j1K3a06TnaexC+NPkIo5UqeNT5LRW4Fq2esJkbxZK06R0L8cv78eXbt2sXVq1d5EGLKZDIkSUImk+Hz+T7gOzycw+Fg1qxZvPTSS+N2Cz7Q1dXFn/zJn1BbW4tMJiM3N5eNGzfyf//v/2VoaAiA//7v/+Yv//IvefHFF1mwYAHNzc38xV/8BS+99BK7du360HOJhHstCI+b3y9x7radfc0mGt7txD4YrBk2QxtHZYGBykIDc9PF/+eF8Ohye6ixOai22jnnCja9iJLJWJ+SRJVOy6ZUFQkiaTRhnf2d7O/YT0N7A9d6gw3CYhWxrJ25lpLsEp4xPkO0IrSLvzC6eDF8+cpoh+SGBrzW4OkQRVoqqpIS1OXlxC5aJJ5dP4KQJQmLi4spKSnhO9/5DklJSbS0tKDT6fjEJz7B1q1b+bM/+7MJT36yicDv8fC6fbRf7Kb1jJU7l3+rAUm+lvxiPblLdMTEiVWgUHEMuql/t5PqCybO3Q62n4+LUrBlgZ7tRUaeyUsVR0kEQXikbreXfbbRBiQXxgTpCQo5ZWlqduqTWaVNRBHCQM3n93Hacpq6W3UcvnOYIe9Q4FpBWgEVORVsydoStuMxkSgS4peCggJyc3P52te+hl6vf0/wPmvWrAl9/+XLl7Nx48ZArer3Mzw8TE9PDwaDga9//evU1dVx+fJlAGbOnMnXv/71cXULv/vd7/KLX/zioV2T308k3GtBCCWPz8/bN7upbTZz4LKFAXcwwZ+vTxxNGBYYyUwJzw4jQbg9NMK++wnDqwPBHa/xCjlbU9Vs12lYm5xEtFw810xUm6ONhvYGGtsbudN3JzCeFJ3EplmbKM0uZZl+GQp56JOzkt/P4LlzuOrq6TtwAJ8zuOEoKjMTdXkZqrIyYnJzQz6XqS5kScKkpCSam5vJzc1Fq9Xy9ttvs2DBAlpaWti2bVug5stUJgK/353fL2FqtdN62sKtpi48YxuQzEhkTnE6s5frRAOSEBrx+njjWhfVTfd441oX7vtFgOUyWJWXSlWRkc0L0kmMEclZQRAebsjn50C3k9etdt7odeF9UFNaBmu0SexMT2ZLiFfvJUniuv06dbfqaGhvoGuoK3BtZtJMKnIqKMspI1P1/jXqnjSREL8kJSXR1NREXl7eY//e/f39ZGZm8u1vf/tDdSL2eDzMmzeP3//93+cf//EfgdG6hd/97nfHLWp/73vf42c/+xmtra0fei6RcK8FIVyG3D6OXrOxr9nEsevB2BKgcKaGygID5QUZ6JJEfC+Ex9X+IfbeTxjeGXOqQatUUK7TUKXTslKTgFzsMpsQSZK43HOZhvYG9rfvHxeHpcWlsSVrC6XZpSxMXRiWHX2S203/iROjCcOjR5GGggvGMfPmjSYMS0uJysgI+VymopAlCdPT03njjTeYN28e8+fP55/+6Z+orKykpaWFVatW0d/fP+HJTzYR+H00j2xAkjymAYlBNCAJFUkaPR5S3WSi/mInzqHg8ZB5GSp2FBmpLDSgV4ngTRCEh/NJEicd/ey22KnrcozrNFiQFMdOfTLb9RrSokNbONwyYKGhvYHaW7XcdNwMjKtj1GzN2kp5TjkFaQXiiMlviYT4Zfv27Xzyk5/kueeem/D3+spXvkJFRQWzZs3CbDbzrW99i+bmZq5cuUJaWhqf+tSnMBqNgV2Fp0+fxmQyUVhYiMlk4tvf/jbt7e1cuHABjUYDwKc//WkOHz7MT3/6UxYsWEBTUxP/3//3//H888/z/e9//0PPLRLutSBMBueQhwOXLNS0mHnnVjcP+kvIZfBUbgrbCoxsWZgumt4JYSFJEhdcg1Tb7OyzOehyewPXMmKi2KYb7ZC8ODFOxAwT5PP7OG89T0N7A4duH8LldgWuzUyaSUl2CaXZpeRqwrOjzz84SN+Ro7jq6+l/+23wBn/38cuWoSovI2nLFpRabVjmMxWELEm4fft2ysrK+JM/+RO+8pWvsG/fPj796U+zZ88etFothw8fnvDkJ5sI/D6cQAOS0xbsluDxs5h4JXlLdeQXp5ORqxYNSEKoraufvU0mqptN3O0NrqSkq2LZVjRaZ1DUjREE4YNc7R9it9XOHqudzpExNahio9ipT+Y5vZbZCaFdZOh393P4zmHqbtVxxnIGidHwJEoexdqZaynPKedZ47NEKcSD58NEQvzS3d3Nrl27KC4uZuHChURFjf99VVZWfujv9Yd/+Ie8+eab9PT0kJaWxjPPPMM//MM/kHv/SNHatWvJysritddeA+D48eP82Z/9GW1tbSQmJlJaWso//dM/YTAYAt+zr6+Pv/mbv6G6uhqbzYbBYOBjH/sYf/u3f0t09IevsxQJ91oQJputb5j6i53UtJhpuuMIjEcr5Kydk0ZloYENc/XERYt6cULoef0S7zj6qbbZqe9y4PIGFzpz42LYrh9NGObFi00TE+XxeThhPkFDWwPH7h0bVwJmjnYOJdkllGSXYEg0POK7PD5eu52+g4dw1dUxePZs8IJSSeKqVaMJw/XrkSckhGU+kSpkScK2tjb6+/tZvHgxAwMD/M//+T955513mD17Ni+99NKEa81EAhH4Pdxwv4eb50c7E3fe+u0GJKnkF+uZtTAFhVLUggiVnv4R6i52sqfJRMtdR2A8IVpByaIMdhQZWZGTgkIkZwVBeATLiIc9VjuvW3u53B+s7aNSyqlM07IzXUuxOrRHdTx+DyfNJ6m7Vccbd99g2BecxxLdEipyK9ictRlV9NT9LJZ8PgZOnaL/yFH03/xGSDvyRUL8Ultbyyc/+UlcLtd7rk20cUkkiYR7LQiR5E7PILUXzdQ0m7lu7QuMJ0Qr2LwgncoCA8/MTiVK1MEWwmDE7+doj4tqm4ND3U6G/MGUx6LEOKr0WrbpNBhjQ9+EY7ob9Axy7O4xGtsbedv8Nl5/cEdfka6IkuwSNs/aTEpcSljm4+nsxNXQiKu+nuErVwLjsthYktavR1VeRuIzzyD7CAuD00XIkoRPAhH4jTeuAcmlHvz+YAOSGXNGG5DkFIkGJKE07PFx6IqVvU0mjrd24b3/O1DIZayenUrVkhlsmidWagVBeLR+r4+Gbie7Lb28Ze/nQQAQJZOxMUXFc3otG1NUxIbwIU6SJK70XKG2rZbG9kZ6h3sD17JUWVTkjtYZNCYaQzaHcBi+fh3nvhpcdXV4bTYAZv77v5G4alXIfmYkxC9ZWVmUl5fzN3/zN+j1+kmZQzhEwr0WhEh1zeKiptlMTYuZe/bgDiNtfBSlizKoLDCwPCsZuVjQFsKg3+vjQLeTPVYHx+3BGssAK9UJVOm1lKdpSIkWz7IT5Rxxcuj2IRrbGzlrORs4FaKQKViZsZLSnFLWz1xPYnR4ypCNtLXhqqvHWV+H53awAYtcrUa1eTOqsjLily8L6QJuJAlZkvDs2bP4/X5WrFgxbvz06dMoFAqWLVv2u804gojA734Dkmt2Ws9YuNU8vgFJ6sxE8ovTmb1MT6I2ZhJnOb35/RKn2nuovmCi8ZKF/pHgqsziGWqqioxUFBhITRS/A0EQHs7rlzhu7+N1q53GLse41fTlqgR2pmup0GlIjgptcGzqN1HfVk/trVo6XB2B8eTYZEqyS6jIqWB+yvwpXTPIY7XhqqvDWVPDyPXrgXGFWk1SaQnJn/wkMTk5Ifv5kRC/jG1wN51Fwr0WhEgnSRIX7jiobTFTd7GT7v6RwLUMdSwVBQYqCwwsMKim9Hu/MHX0uL3UdY02PDnlHAiMK2WwWpvEDr2WralqEpVPRtIolKwDVg50HKCxvZFLPZcC4zGKGFbPWE1pdinPzniWGEXon2UlSWL40mVcdXW4GhrwdgUbsCh1OlSlpajKy4ldMLXj0A8SsiRhcXExX/3qV9m5c+e48T179vD973+f06dP/24zjiBPauAnSRJdd/poPWPlxjkrg2MakCSlxJK/XE9+cTrJhif7LH+otVr72HPBxL5mE53O4NE7oyaOqiIj24uM5OlEExhBEB5OkiQu9g+x29JLtdVBtye4yJATF8Nz+tHjxLPiQhuYPVhRrr1VywXbhcB4jCKG9TPXU55bzlOGp4iST906g/6BAfoOH8a5r4aBU6fAP1oDSRYVReLatai3VZK4enVYjrVEQvyya9cunn32Wf74j/94Un5+uETCvRaEqcTr83OyrYeaZjP7L1voGx7zuZSWQMViA5WFBnLTRIwrhIdp2M2++x2S3+0P7niNk8vYlKqmSqdhfYqKGLk4Ij9Rt123aWxvpKG9gXZne2A8MSqRDZkbKM0upTijGKU89Ls5JZ+PwbPncNXX4TpwEP+Y8ijRs2ahKi9HVVZGTE52yOcSbiFLEiYmJnLx4kVyfmslvL29ncWLF9PX1/eQr5w6nrTAz9k1xI2zFq6ftuKwjmlAkqBk9lI9+cV60nPV0zqrPtlsrmFqWszsuWDiSmfwjUoVq6Rs8WgDkmWztOJYhiAIj3R32M0ei53d1l5uDAZ3bCRHKdiu07JTr6VIFR/S93OPz8Nbpreoa6vj2N1jePyjjVBkyChOL6Y8t5yNmRvDdtQkFCSfj4GTp3DW7KPv8BGkweBnZ9ySJagrK1Ft3YLifkfdcImE+OUf/uEfePnllykrK2PRokXvaVzyxS9+cVLm9bhFwr0WhKlq2OPj2PUualvMHL5qZWRMg4mFRhWVBQYqCgxkqOMmcZbCk+Tm4DDVVjt7rQ5uDQXjJ5VSTlmahiqdllXaRBTieXhCJEniuv06De0NNLY3YhmwBK4lxyazJWsLpdmlFKQVhCX34He7GXjrLVz19fQdfQNpOLhBJ3bBAlRlZajKSomaJuVTQpYkTElJoa6ujqeeemrc+DvvvENZWRl2u/13m3EEeRICv6F+NzfP2Wg9Y8XSNqYBSZSc7MWp5K9IJ3N+smhAEkIDI14OXLZQ3WTixM1uHpwAjFLIWDtHx44iI+vm6oiNEtvdBUF4OKfHS23XaJ3BsUdnYuUytqSqeU6vZV2yiqgQLjJIkkRLVwt1bXXs79iPcyT4uZKnyaMit4LS7FLSE9JDNodwGL52LVhncMxRlahZmagrK1FXVhI9c+akzS8S4pfs7IevvMtkMtra2sI4m9CJhHstCNNB37CHQ1es1LSYeetGN777AbFMBsuzkqksMFC6KIPkhCevyYAQfg9OYlRb7eyzOegc8QSu6aKVVOpGE4ZLQrzg+iTwS36abE00tjdysOMg9pFgHsmYaGRr1lZKc0rJ1+aHZT6+/gH63ziKs66OgbdPwINGazIZ8cuXjyYMt2wO+wLw4xSyJOHHPvYxOjs72bdvH2q1GgCHw8H27dvR6XT8+te/ntjMI8B0Dfw8bh8dLd20nrFw53JvoAGJTAYz5mrJL04npzCNaNGAJGS8Pj8nbvVQfeEeBy5bGfIEaz0unaVle5GR8kUZaEUgJAjCI7j9fo729LHb2suhHhcjD97Pgac0iexMHy3CrQpxTZ07rjvUtdVR11bH3b67gfG0uLTROoO5FczRzpnSgbTHah2tM7ivhpHW1sC4Qq1GVVaKurKS2ILwrHh/kOkav0Qica8F4fHr6R+h4ZKF2mYzZzqCTa2UchnPzk6lstDApvnpJMaIZxUh9PySxCnHAHttduq6HPSOeW6bFRvNdr2W7ToN8xLFjteJ8vg9nDKforG9kSN3jjDoDZ7QyNPkUZpdytbsrcxMCs9CrLe3l74DB3DW1TN0/nzwQlQUic88g6qsjKT165DHx4dlPo9LyJKEJpOJ1atX09PTQ1FREQDNzc3o9XoOHTrEzElcQX9cplPg5/f5uXfdTutpK23NXXhGgm9uaZlJ5Bfrmb1cT4JaNL8IFUmSuGx2Ud1koqbFTFdfcAt7Vko8VUUz2F5kYFaKqPUoCMLDSZLEedcgv7H0UmNzYPcG38/nJMSyU69lh16LMTa0iwyOYQf7O/ZT21bLxa6LgfE4ZRwbMzdSnlPOiowVKORTdxe0f2AA16FDuGpqGDh5Cu6HSpNRZ/CjmE7xS6QT91oQQsvkGKKuZbRD8mVzsBRPbJScDfP0bCswsGZOGjGiwYQQBm6/n+O9fey1OWjsdjLoCx6Rn5sQyw69lm06TchrPT8JhrxDvHnvTRraGnjL9FagbA3A4tTFlOaUsiVrC6lxqWGZj8dkwtXYiLOunpFr1wLjsrg4kjZsQFVWSuKqVREXE76fkCUJAQYGBvh//+//0dLSQlxcHIsXL+ZjH/vYe+rOTFVTPfCTJAnb7T5az1i4cc7GkCvYgESVGkt+cTr5xXq06SIpFUomxxD7mk1UXzBxw9YfGNfGR1FRYGB7kZGimZqI2H0iCELkah8cYbe1l9etdjqGgu/n+mglVfrROoMLEuNC+l4y4hvh+N3j1LXV8ZbpLbz+0YLzcpmcpzKeojy3nPUz1xMfNbVWVMeSvN77dQZr6Dt8GGloTBHzB3UGS7aiuH+KIhJFQvzy/PPPP/L6//2//zdMMwmtSLjXgvCkuGnrp6bFTG2LmfbuYFkNVaySrQvT2VZoZGVOCgpRu1sIg0Gfn4PdTvba7Bzp6cMzJp2yVBVP1f2EYVr09MiNTCaX28WR20doaG/gjOUMfmk0OSuXySlOL6Y0u5QNszagig7P5/DIzZs46+tx1dXjuRs8QaNQq0nauhV1eRlxS5cii9BmNyFNEk53UzXwc1gHaT1rpfWMBact+HATmxBF3jId+cXppOeoRFIqhFzDHva/a2FP0z1Ot/c+2HxCtFLOpnl6thcZWZOfRrSo9SgIwiP0uL3ss9l53WrnvCt45CJeIac0Vc3vpSfzTIgLaPslPxesF6hrq+Ngx0H6PMHGZPOS51GWU0Zpdilp8Wkhm0OoSZLEyP06g876Onxd3YFrkVJn8KOIhPilqqpq3L89Hg+XLl3C4XCwfv169uzZMynzetwi4V4LwpNGkiQumVzsazZRe9GM1RU8nZOWFEP54gwqCwwUikV4IUwcHi8NXU6qbXbetvfzILEiB57RJlKl11KaqkYdJY7IT1T3UDcHOg7Q0N4w7iRLlDyKZ43PUpJTwpoZa4hThv74tyRJDF+8iLOuHldjI77uYPyoTE9HVVqKuryMmHnzIuq9SCQJJ2AqBX6DLjc3zllpPW3Bdjv4AKeMkpNdmEZ+sZ6Z85NRKERSKlQ8Pj/Hr3dR3Wzi8JXxHdpWZCezY4mRrQszUMeJ1SRBEB5u2OfnYI+L3ZZejva68N7/dJYDa5KT2KnXsjVNTYIitEer2pxt1N2qo76tHvOAOTCenpBOWXYZ5Tnl5GnzQjqHUPNYLME6gzduBMYjsc7gRxGp8Yvf7+fP/uzPyM3N5atf/epkT+exCMe9PlPbRlpmEjPnJ6MUTcwEYRyfX+JMey81LWYaL3XiGAweScxMjqeywEBloYF8fdIkzlJ4klhHPNTYHFTb7FwYs8AbLZOxIUXFdr2GTSlq4sVz+YTd7bvL/vb9NLQ3cNNxMzAer4xnfeZ6SrNLWWlYSZQ89M/fktfLwOnTuOob6Dt4EH9/8ARhdE4OqvIy1GVlRM+aFfK5fBCRJJyASA2yH3APeWlr6aL1jJV7V4O71WQymDkvmfwV6WQXpBIdK1YsQkWSJJrvOtjbZKL2Yie9A8EjgHm6RKqKjGwrNDBDO3WP3gmCEHp+SeKko5/XrXZqbQ76xtS4WZwUx069lu06LbqY0AY5PUM9NLY3UtdWx+Wey4HxhKgENs3aREVOBcvSlyGXTd3A1tc/QN+hQzhr9jF46vT4OoPr1o3WGXz22SlRU+ZhIjl+uX79OmvXrqWzs3Oyp/JYhPpe9/UO8x/feAeAqFgF2QWp5C3VkzkvGUXU1P07FIRQcHv9vHWji5oWM4euWBl0B2v2zk1PorLQQMViAzOTRVwuhEfH0Ah7rXb2WB20Dg4HxhMUckpS1VTptazWJhEljshPWKu9lcb2RhrbGzH1mwLjmhgNm2dtpjSnlCJdUVhiWP/ICP1vvomrrp7+N95AcgdzBLGLFqEuLyOppIQonS7kc3k/Ikk4AZEYZPu8fm5f6uHGWSvtF7vxeYIPkros1WgDkmV64lVT9+FmKrjTM8jeZhN7m0y0jamJkpoYQ2WBgR1LjCwwiCPdgiA82vWBYXZbetljtWMaCe5+MMZE8Zxey3PpycxJiA3pHIa8Qxy7e4zaW7W8Y34HnzT6UKWQKVhlXEVFTgVrZ64lVhnaeYTSaJ3Bkzj33a8zOBwM1OOWLh2tM7h1S0TXGfwoIjF+eaChoYFdu3bR1dU12VN5LEJ9r/vtwzQfvsvN8zYGHMEjldGxCrIL08hbqmPmvGQUonyJIIwz6PZy+KqNmmYzx1tteHzBR90lmRq2FRopXZRBWpJoMCGEniRJXB0Yptpqp9pm595wMOZLjlJQnqahSq9lhToBuXh+nBBJkmjpaqGxvZH9HfvpHQ52SE9PSKckq4SS7BLmJs8Ny7O6r7+fvsOHcdXVM3DyJPjuL17IZMSvWDGaMNy0KawxqEgSTkCkBNmSX8J800HrGSu3LtgYGfQGrmn08YHEoEYvVsVCyTHopu5iJ3ubTJy7bQ+Mx0bJ2bIgnaoiI8/kpaIUW8cFQXgE24iHapud1y12LvYH68aqlHIq0jQ8p09mpSa0QaLP7+Oc9Ry1t2o5fOcwA57gYsfClIWU55azNWsrKXEpIZtDqEmSxMjVq/frDNaPqxMTPWsWqm336wzOmDGJswyNSIhfvvzlL4/7tyRJdHZ2Ul9fz65du/jRj340KfN63MJ1ryW/hKXdxc3zVm6dtzHgDO5KiI5TklOQSt4yPTPmaUVpGUH4LY5BN/svWahpMXOyrSdw+koug1V5qVQWGNiyMB1VrCgJJISeJEmccw1SbbVTY3PQ7Qk+2xtiotimG00YLgpxM7ongdfv5YzlDA1tDRy5c4R+T/AIcLY6m5LsEkqzS5mlCs8RYG9PD679+3HV1TPU1BS8EBVF4urVqMvLSFy7FnlcaOsphiVJ2N/fj9/vHzcWaSvXv4vJDLIlSaLrTh83zlq5ed5Gvz24ehyvjmb2cj35y/WkZSaJN48QGvH6eOOajeomE29c68Lte9BJaTSo2F5oZMvCdBJjxJFuQRAebsDnY3+Xk91WO8d7+3jwiamUwYYUFTv1yWxKUREb4of7VnsrdW2jdQZtg7bAuDHRSFnOaJ3BbHV2SOcQah6LBWdtLa6aGkZuBOvTKDQaVGVlqLdVErto0bT+7IyEJOG6devG/Vsul5OWlsb69et5/vnnUSqnx+fmZNxryS/R2ebk5nkbt87bGHQFE4YxCUpyi3TkLdNhzNciF0fYBGEcq2uYuoud1LSYabnrCIxHK+Wsn6OjstDA+rk6YkX9TyEMvH6Jtx19VFsdNHSNLzeTFx/Ddp2WKr2G3Pipe5ojUoz4Rnj73tvUt9fz5r03GfEF8ysLUhZQkl3C1qyt6BP0YZmP+949XPUNuOrrGWltDYzL4+PR/MEfoP9a6Go3hyxJ2N7ezhe+8AWOHTvG8JhjO5IkIZPJ8Pl8j/jqqWEyAj+7ZYDWs1ZunLWO60wcHackt2i0AYlBBH0hJUkS527b2XPBRP1FM67h4OrOvAwVO4qMVBYa0KvEm7UgCA/nkyTetvfzG0svDd1OBscEfktV8exMT6YyTUNKdGiTJbZBG43tjdTequW6/XpgPCk6iS1ZW6jIqaBIVzSlk2a+/n76Dh7CWVPD4OkxdQajo0lcvx51ZSWJzz6DLOrJ2CUSCUnCJ8Vk32u/X8Jyy8GNczZuXbAx1Bc8whaniiavKI28ZXoyctXIROwoCON0dA9Q22JmX4uZm7bgDqPEGCWbF+ipLDCwKi+VKLE7VwiDYZ+fo70u9ljtHO5xMewPpmcWJ8ZRpdeyTafBECvKik1Uv7ufN+6+QX17PafMpwKldmTIWJa+jNLsUjbN2oQ6JjxHgIevt+Kqr8dVV4fHbCb5M5+ZmknCVatWIUkSf/7nf45er3/Pw8WaNWt+txlHkHAFfn29w9w4a+XGOSvdd4MfUIooOVmLUslfridzoehoF2q3uvrZ22SiusnEPXswQZuuimVbkYGqIiNz08XDliAIDydJEpf7h9httVNttWN1BxcZsuKiR+sM6pPJiQ9tDaRBzyBH7hyh9lYtpy2n8UujCUqlXMlq42oqcitYPWM10YqpG2hKXi8DJ07grKml78iRcXUG45ctQ7WtEtWWLSiewCTZZCeuniSRdK/9Pj+mGw5unrVyq7mLkYHg+0+CJoa8ZTpmL9OjmyVOoQjCWJIkcbWzj5oWM7UtZkyO4HNAckI0ZYsyqCw0sDRTbNQQwqPP62N/t5M9Vjtv2vt4UFJTBqxQJ1Cl11IehoXmJ0HvcC8HOw7S2N7IBduFwLhSrmSVYRWl2aWsnbmW+KjQl3aTJImh5maUqalEz5wZsp8TsiRhYmIi58+fZ86cOROeZKQKR+B3au8tzu+/Hfi3XC5j5vxkZi/Xi87EYdDdP0Jti5m9TSZa7jkD44kxSrYuTGdHkZEVOSkoREAgCMIjmIfdvG6187rVzrWBYLJKq1RQqdPwe+nJLFXFh/TB3Ov3crrzNLVttRy9c5Qhb/AhpzCtkIrcCrZkbQnbqmgoSJLE8OUrOGv24apvwNfTE7gWnZ2NelslqvIKomcYJ3GWk2+yEldLlizhyJEjaLVaiooevTv1woULD702lURSknAsn9fP3au93Dxvo725C/dw8ISPKjWWvGWj9axTjAkiYSgIY/j9Ehfu2KlpMVN/sZOegeBxfqMmjvKCDCoLDMzPEA0KhfDodnup63Kw12rnlDNYQ1opgzVaFVV6DVtT1SQqxYaiiers76SxY7RD8rXea4HxOGUca2eupTS7lFWGVUQppvbJlJAlCdetW8c3v/lNNm7cOOFJRqpwBH43zlk5+O+XMeRpmL1cT+6SNOISp+7OjqlgyO3j0FUr1Rfu8eaNbnz3t3Ir5DLW5KexvcjIpnl64qLFG60gCA/X5/VR1+XgdYudE45+HnyIRstkbEpV8Xv6ZNanJBEtD90xJUmSuNZ7jdq2WhrbG+keCjbnyEzKpDynnPKccmaqQrcaGQ4esxlnbR3Omhrct24FxhVabbDO4MKF4oHtvslKXH3nO9/hL//yL4mPj+c73/nOI1/7rW99K0yzCq1ITRKO5fX4uHO5l5vnrLRf7MbrDpY+0KbHM3u5nrylOrTpCZM4S0GIPF6fnxO3eqhpNnPgsoX+keDu3Ny0BLYVGqksMJCVKv52hPC4N+xmn81BtdXOpTHN7+LkMjalqqnSaVifoiImhLHnk6LN0UZDewON7Y3c6bsTGFdFq9g0axOl2aUs1S9FIZ96OYOQJQlv3brF//gf/4M/+qM/YuHChUT9Vp2fxYsX/24zjiDhCPy8bh/DA14StaE9evak8/klTrX1UN1kYv+l8R/yBTPUbC8yUlFgIDVR/B4EQXg4j1/iWK+L16129nc7x9WLWalOYGd6MhVpatRRod0FbhmwBBqQ3HQEm3NoYjRszdpKeW45i1MXT+mkma+/n74DB3Duq2Hw7NnxdQY33K8z+MyTU2fwo5gKiavpYqrda8+Ij453u7l5zsbtSz34vMGEYerMRGYvG00YqlJD21lREKaaYc9oM8N9zWaOXrfhHvO3UzBDTUWBgYoCUbNcCJ8bA8NU20bL27QPBXe8qpRyytI0VOm0rNImopjCsWAkkCSJyz2XqW+r50DHAbqGugLXdHE6tmRvoSy7jPkp86dM3B2yJOGpU6f4+Mc/TkdHR/CbyGSicYkQUa5ZXFQ3mdjXZMbiCh4BnKGNY3uhke1FRvJ0iZM4Q0EQIp0kSTT3DbHb0stem4MeT3CRYXZ8DDv1yVTpNWTGhXaRod/dz6Hbh6hrq+Os5SzS/b2L0fJo1s5cS3lOOc8Yn5nSRyACdQb31YzWGRwJdp6LX74c9bZKkrZsQZGUNImzjHyRFL+43W5sNht+v3/ceGZm5iTN6PGKpHv9UY0MeWlv6eLGWRv3rvbiH7PokZ6jIu9+wjBBLRZQBWEs17CHg5et7Gs28c6tnsCpJJkMVmanUFlooGRhOpp4cTpMCD1JkmjpG6LaZmef1YHFHWxglRatZJtuNGG4JMRlb54EPr+P89bzNLQ3cPD2QfrcfYFrmUmZlGSXUJpdSo4mZxJn+cFCliScP38+8+bN46tf/er7Ni6ZNWvW7zbjCDKVA78nmdU1zL5mE9VNZq52ugLjqlglZYsN7FhiFIWHBUH4QHeGRgJ1Bm8OBpNVqVFKqvQadqYnszgxLqQBl8fv4aT5JLW3annj7huM+ILzWKZfRkVuBRtnbUQVPXU/ox5ZZzAnB3VlJeqKcqKMT3adwY8iEuKX1tZWPvvZz/LOO++MG59Oi8kQGff6cRju93CrycaNczZMrXYC9RNkYMzXMHuZnpwiURJHEH5bd/8IDe92UtNs5txte2A8SjFaxqiiwMCm+XriRYMJIQx8ksQpRz97bQ7qbA7s3uBnbWZsNFV6Ldt1GuYlit3iE+X2uTlhOkFjeyPH7h0bVwt8bvJcSrJLKMkqISMxYxJn+f5CliRMSEigpaWFvLy8CU8yUk2XwO9J0D/i5cAlC9VNJk7c6n5wKo0ohYz1c3VUFRlZN1dHjCjoKgjCIzg8Xmq7HOy22Dk9pjh0nFxGSZqG5/Ra1miTUIZwkeHBsYbaW7Xs79hP73Bv4FqOOoeK3ApKs0sxJBpCNodweGidweTk0TqDlZXELlwgVr1/B5EQv6xatQqlUsnXv/51MjIy3vN7LCgomJR5PW6RcK8ftwHnCDfP27h5zoqlLbjYKpfLmDEvmdnLdWQXpBETJ5IegjDWPfsgtS2d1LSM36gQF6Vg03w9lQUGVuenEa0U9eKE0HP7/Rzv7aPa5mB/t5NBX3BH/9yEWKp0WrbrNcwK8UmYJ8GgZ5A37r5BQ3sD75jewSsFTx0t0S2hJLuEzVmbSY5NnsRZBoUsSVhRUcGnP/1pnnvuuQlPMlJNx8BvOvH6/Lx1s5u9TSYOXrYy5AmulCybpaVqiZGyRRliq78gCI/k9vs50uNit9XOoW4X7ge174BntYk8p0+mLC30XeNM/SbqbtVR11ZHh6sjMJ4cm0xpdinlueXMT5469U7ez7g6g2fOBMZlMTEkbViPqrKSxFWrRJ3BCYqE+CUhIYHz588zd+7cSfn54RIJ9zqUXN1D3Dxv48Y5K913+wPjCqWcWQtTyFumI2txKlGi2ZsgjHPD2kdNi5maFjO3ewYD4+q4KEoXpVNRYGBFdgoKcbJJCIMBn49D3S722uwc7ekLxLoAS1XxVOm1VKZp0MWI+GuiHMMODt05RENbA+et5wPlgRQyBSsNKynNLmX9zPUkRk9eybOQJQn/9V//le9+97s8//zzLFq06D2NSyorK3+3GUeQ6R74TUWSJHHJ5GJP0z1qW8x09weLtGanJlBVZGR7oZHMlPhJnKUgCJFOkiTOuQb5jaWX2t86jjE/IZad6aN1BjNiQrvI4BxxcvD2Qepu1XHBdiEwHquIZV3mOipyKnjK8BRK+dTdsSN5PPSfOIGrpoa+I0fH1xksLh6tM7h5s6gz+BhFQvyyfPlyfvjDH/LMM89Mys8Pl0i41+FitwyMJgzPWrFbgkkPZYyC7MWpzF6mI3N+CooosUtKEB6QJImWe05qms3UXjTT1Rf8DNSrYihfbKCywMDiGeopvQgoTB0Oj5eGbifVVjsn7P082F8oB57RJrJdr6UsNfRN+J4E1gEr+zv209DewJWeK4HxGEUMq2espjS7lGdnPEuMIry7OUOWJJQ/oq32dKk18yQFfpHunn2Qfc1mqptM3LQFV7KTE6KpWJxB1ZIZFIgPV0EQPkDb4Ai7rb28brFzezi4yJAeHcUOvZad6Vrmh7hOi8fn4U3Tm9TdquP4veN4/KMFpmXIKM4opiJntM5gQlRCSOcRSpIkMXzpMs6aGlz19fh6g0emo3Nzg3UGDVP7yHSkmqz4xeUKHq87d+4cf/3Xf80//uM/vu9i8nSJq57EWFGSJHpM/dw4O7rDsK8n2BguOk5JTlEa+cv0GOdokCtEwlAQHvD5JU639VDTYqbh3U5cw8EjiVkp8VQWGKgsNJCnE4tmQnjYRjzUdDnYY7VzwRVc/ImWyVifksR2nZbNqWrixXv5hHU4O2hsb6ShvWHciaHEqEQ2ZG6gNKeU4vTisGwMCFmS8EnwJAZ+kcQ55KHh3U6qm0ycaQ8+YMYo5Wycr2dHkZHV+WlEiTctQRAeocftZZ/Nzu7fCoASFHLK0tT8nj6Zp7WJKEK4yCBJEi1dLYE6gy53MJkyWzubipzROoP6BH3I5hAOHpMJZ20tzppa3G1tgXFFSgqqslLUlduIXTC1j0xPBZMVv8jl8nG/2wdNSsYSjUumF0mSsHa4uHnWxo3zVgadwcWXuKQocpfomL1MT0auGpk4VikIASNeH2+2drOv2cThq1aGPcF6cfMyVFQWGKgoyGCGVpyOEsLj9tAIe60O9tjsXB8ILv7EK+SUpKrZrtOwNllFlHgvnxBJkrjWe42G9gYa2xuxDloD15Jjk9matZWynDIWpy0O2RxEknACnvTAbzK4vX6OXbdR3WTiyFUb7vsFVmUyWJmdQlWRka2L0lHFinoJgiA83JDPz6EeF7stvRztdeG9/wmnkMEabRK/l57MljCsjN523aaurY66W3Xc678XGE+LS6Msp4zynHLmJM8J6RxCzdfXh2v/flz7ahg8dy4wPlpncAPqbZUkrFqFTCmOrYTLZMUvx48f/9CvXbNmTQhnEj4iVgzy+yU6bzi4cc7KrQtdDA94AtcSNDHkLdORv1xPWmaSWCgQhDEGRrwcvmqlptnM8dYuvP7gY/myWVq2FRooXZRBSqJoMCGEx9X+IaqtdqptDu6OOXmjVSqo0GnYrtOyUpOAXLyXT4hf8nPBeoHG9kYO3j6IY8QBwNqZa3l1/ash+7khSxL+3d/93SOv/+3f/u1H+XYRSQR+4SFJEhfu2KluMlF3sRPHYDCozNcnUlU0g22FBgwa0apdEISH80sSJx39vG61U2tz0Demi9vipDh+T5/Mdr2GtOjQLjLYh+3s79hPXVsdF7suBsbjlHFsmrWJ8pxyitOLUcinbqF/yeOh/623cdbU0H/0KJL7fgApk43WGaysJGnLZhSJk1eU+UkWCfHLnTt3mDlz5vvuJLx79y6ZmZmTMq/HLRLudSTy+fzcu2bn5lkrbc1duIeDO0dVaXHMXja6wzDFKN4jBGEs+4CbxksW9jWbONPRy4MndIVcxjN5qVQWGNi8QE+S2DAhhIEkSZx3DVJttVPT5aDLHTwinxETxTadhiq9lsWJcWLxZ4I8fg8nzSdpbG9kY+ZGNszaELKfFbIkYVFR0bh/ezwe2tvbUSqV5ObmcuHChYd85dQhAr/Qau8eoLrJxN4mE3d6g0cA05Ji2FZgoGqJkfkZKvGGIwjCI10fGOZ1Sy+vW+2YRoKLDMaYKHamJ/OcXkt+QmxI5zDiG+H43ePUttXy9r238UqjQZRcJucpw1NU5FSwbuY64qOm7rGh0TqDl3Duq8HV0DC+zmBeLurKbaN1BjMyJnGWAkRG/KJQKOjs7ESn040b7+npQafTiePGTxCvx8edy73cOGelo6Ub75hjlcmGBGYv05O3TIdGN3XfHwUhFCzOYeoujnZIvnjPGRiPUcrZME9HZYGBtXN0xEZN3UVHYerw+iXecfRTbbNT3+XA5Q2+l+fExbBdr6FKp2V2iGNuYeLCetzY5XLx6U9/mqqqKj75yU9O9NtNOhH4PX69A27qLprZc8FE811HYDw+WsHWBelULTHydG4qClHrQBCER7CNeKi22XndYudi/1BgXKWUU5GmYWd6MivUoT0G4Zf8nLeep76tnoMdB+nz9AWuzUueR0VuBSXZJaTGpYZsDuEQqDO4rwZ3e3tgXJGSgrq8DFVlJbHzRZ3BSBIJ8YtcLsdqtZKWljZu/Pbt28yfP5+BgYFJmdfjFgn3eipxD3vpeLebG2dt3Lncg98XfPzQzUoib5mevKU6kpLFQ6YgjNXePUBNs5maFhO3uoLvn0kxSrYsTKeywMDTuSkoRa12IQxG/H6O9riotjk41O1kaMwR+UWJcWzXa9mu02CMjZ7EWQoPE/aahO+++y4VFRV0dHR8pK/78Y9/zIsvvojFYqGgoIBXX32V4uLih77e4XDwzW9+kz179tDb28usWbN4+eWXKS0tBeDNN9/kxRdf5Pz583R2dlJdXc327ds/0pxE4Pd4DHt8HL5qZW+TiWPXg3U25DJ4dnYaVUVGNi/QEx8t6lUJgvBwAz4f+7uc7LbaOd7bx4P1S6UMNqSo2KlPZlOKitgQB8htjjbq2uqob6vHPGAOjKcnpFOeU055Tjm5mtyQziHUfC4XrgMH3ltnMDY2WGfw6adFncEINZnxy5e//GUA/uVf/oU/+ZM/IT4+uDvM5/Nx+vRpFAoFJ06cCOu8QkXEir+74QEP7S1d3Dhn4941O9KYh8yMPDWzl+nJXaIjXiUeMgXhAUmSuNLpoqbZTG2LGbMz2GAiNTGaskUZVBYaWJKpFYt3Qlj0e33s73ZSbXVw3B6sAw6wQp3Adr2WijQNqeJZP2J82Njlsf3GnE4nTqfzg184xq9+9Su+/OUv85Of/IQVK1bw8ssvs2XLFq5fv/6eYyoAbrebTZs2odPp2L17N0ajkdu3b6PRaAKvGRgYoKCggOeff54dO3ZM9H+W8BH5/RKn23upbrpH47sW+kaC9QsWGlVUFc2goiADXZJYKRYE4eF8ksTb9n5+Y+mlodvJ4Jg6g0tV8exMT6YyTUNKiAOP7qFu9rfvp7atlis9VwLjiVGJbM7aTHlOOUv1S5HLpu4K/iPrDK5YMVpncPMmUWdQeKSmpiZg9EH23XffJTo6mOCJjo6moKCAr3zlK5M1PSGCxCZEMe9pA/OeNjDoctPWZKP1rJXOm87Af976VSsz5mrJW6YnpzCN2ARRh014sslkMhYY1CwwqPna1rmcu22npsVEw7sWuvvd/PzkbX5+8jZGTRwVBQa2FRqYmy6aBQmhk6hUsDM9mZ3pyfS4vdR3OdhjtXPKOcDp+//56xv3WK1NokqvpSRVTZJSHJGfCj7yTsJXXnll3L8lSaKzs5P//M//ZM2aNfzyl7/80N9rxYoVLF++nB/96EcA+P1+Zs6cyQsvvMDXv/7197z+Jz/5CS+++CLXrl0jKuqDgwWZTPahdhKOjIwwMjIS+LfL5WLmzJlidfgjaLX2Ud1kYl+TadzKllETx7ZCA1VFRmbrkyZxhoIgRDpJkrgyMMxvLL1UW+1YxxRJnhUbzc50Lc/pk8mJD22XvyHvEG/ceYPatlpOmk/ik0ZrqCllSp4xPkNZbhlrZ6wlVjl1FzskSWL43XeDdQbt9sC1mNl5qCorUZeLOoNTTSTsbvvMZz7Dv/zLv0z7+CkS7vV0028f5uZ5GzfOWrHdDpZxkCtkZC5IYfYyHVmLU4mOFbtSBOEBj8/P2ze7qW02c+CyhQF3sO7rbF0ilQUGKgsNzEpJmMRZCk8S87CbfTYH1TY7F/uCpYFi5TI2pKjYodeyITn0J4CE9wrZcePs7Oxx/5bL5aSlpbF+/Xr+6q/+iqSkD5cIcrvdxMfHs3v37nFJvF27duFwONi3b997vqa0tJTk5GTi4+PZt28faWlpfPzjH+drX/saCsV7s9IfNkn47W9/m+985zvvGReB36PZXMPUtIzWGbzS6QqMJ8UqKVuUwfYiI8VZychFnUFBEB7BPOxmj9XObqudawPBRQaNUkGlTsPvpSezTBUf0tVwn9/HWetZam/Vcvj2YQa9waZKi1IXUZ5TztbsrSTHJodsDuHgvmfCVVszWmdwTHkQRWoq6rIy1NsqiZk3T+w8mKJE4ip8xL0OLWfXIDfO2bh5zkqPKViHTRklJ2txKrOX6clcmIxSNG4QhIAht4+j12zsax4t9eQecwqjYKaGygIDFYsz0Kmm7iKnMLXcGhym2upgr83OzcHgpqwkhZySNDVVOi3PapNQinxBWIS9JuFHZTabMRqNvPPOOzz11FOB8a9+9ascP36c06dPv+dr5s6dS0dHB5/4xCf43Oc+x82bN/nc5z7HF7/4Rb71rW+95/ViJ+HjNzDi5eAVC3sumDhxs5sHZWSUchlr5+jYscTI+rmi25YgCI/W5/VR3+Vgt8XOCUc/Dz6IomUyNqWq2KnXsiFFRbQ8tKuMrfZW6m7VUd9ej23QFhg3JhopzymnLKeMbHX2I75D5PO5XLj278dZU8PQufOBcVlsLEkbN47WGXzqKVFncBqYrMTVjh07eO2111CpVB9Y6mXPnj1hmlVoiSRh+PSY+7l5bnSHobMruCslKlZBTmEas5fpmTFPi0LsShGEAOeQhwOXLdS2mMc9s8lk8FROCpUFBkoWZqCOF0f5hdCTJIlL/UPssdrZZ3NgHvEErqVEKanQadih07AsxM0Hn3Rhr0kYDn6/H51Ox7/+67+iUChYunQpJpOJF1988X2ThB9WTEwMMTGhPb42lXl9fk7c6mFvk4n9lywMeYLb2JdkaqgqMlK22EBygigwLQjCw3n8Esftfey29HLgtzqirVQn8Fz6aIFjTVRoP5psgzYa2hqoa6vjuv16YDwpOomtWVspzymnSFc0pXfTSW43/W+/jXNfDf1vvDG+zuDKFagrKknavBlFojh+JEycWq0O/L2o1epJno0w3aQYEkmpTKS4IpuuO32BHYb99hGun7Jw/ZSF2IQocpakkb9MT8ZsjTjFIjzx1HFR/P6ymfz+spnY+oZpuNhJTYuZC3ccvHOrh3du9fA3+y6xJl9HZaGBjfN0opmkEDIymYxFSfEsSornb3INnHEOUG21U9vloMfj5TVTN6+ZujHGRLFdr6VKp2FBYtyUjsWnsg/1ThCKFeLU1FQUCgVWq3XcuNVqJT09/X2/JiMjg6ioqHFHi+fNm4fFYsHtdo8rki1MjCRJXDa7qG4yUdNipqsvuNNyVko8VUVGqoqMor6FIAiPJEkSLX1D7Lb2Um0dDQQeyIuPYadeS5Vey6y40C7UDHoGOXznMHW36jhtOY1fGj2Co5QrWTNjDeU55ayesZpoxdT9HJEkieGLF4N1Bh2OwLWY2bNRb6tEVV5O1EM+YwXhd/Wzn/3sff+7IDxOMpkM3SwVulkqnq7KxdLmHE0Ynrcy1OfhyltmrrxlJl4dTd5SHbOX6dFnq8RDpvDE0yXF8ulV2Xx6VTZ3ewepaRntkHzN0sfhq1YOX7USH61g03w9lQUGnp2dRrRS7MwVQkMuk7FSk8hKTSLfnT2Dt+x97LHaaex2Yhrx8OM7Nn58x8bs+Biq9FqqdFqyQ1yPXBjvQyUJQ7FCHB0dzdKlSzly5EjgOLDf7+fIkSN84QtfeN+vWbVqFb/85S/x+/3I7x9Ba21tJSMjQyQIH5N79kH2NZvZ22Tihq0/MK6Nj6KiwMD2IiNFMzUi4BIE4ZHuDI1QbXWw29rLjTE1SFKilFTpNTynT6YwKbQrhF6/l9Odp6ltq+XonaMMeYPH1Ip0RZTnlLMlawvqmKm988l97x7OmhpcNbXvrTNYXj5aZ3DuXPG+LQjCtCGTy8jI05CRp+GZ38vDdMPBjbNW2pq6GHS6uXj0HheP3iMpJZbZy3TkLdOTOiNRvA8KT7yZyfF8fl0en1+Xx3VLHzUtoxtC7vYOsa/ZzL5mM5r4KEoWZlBZYGBFtqgvL4ROlFzG+hQV61NUDPn8HO5xsddm53CPixuDI/yg3cIP2i0UJMVRpdOyTa8hI0bkfUJt0moSAvzqV79i165d/PSnP6W4uJiXX36ZX//611y7dg29Xs+nPvUpjEYj3/ve9wC4e/cuCxYsYNeuXbzwwgvcuHGD559/ni9+8Yt885vfBKC/v5+bN28CUFRUxEsvvcS6detITk4mMzPzQ83rSasz4xzy0PhuJ9VNJk639wbGo5VyNs3TU1VkZHW+WFESBOHRnB4vtV1Odlt6OeUMFpqPlcvYmqpmZ3oya7RJRIUw2JQkiau9V6lrq6OxvZHuoe7AtVmqWYE6gzOTZoZsDuHgczpx7T8wWmfw/Jg6g3FxJG3YIOoMPqEmK34pKvrwx/MvXLgQ4tmEx5MWK04VPq+fu1d6uXHOSltLN96RYIkcbXo8ecv0zF6mQ5suTsIIwgOSJNF010FNs5m6i5109wcXd/WqGCoWj3ZIXmRUi0S7EBYur4/GLid7bXbetPfhe1BTE3hKk0iVXkNZmobkEJcomm4ivnHJAz/60Y948cUXsVgsFBYW8sorr7BixQoA1q5dS1ZWFq+99lrg9SdPnuRLX/oSzc3NGI1GPvvZz47rbnzs2DHWrVv3np+za9eucd/nUZ6EwM/t9XPsuo29zSYOX7Xh9ga7X63MSWZH0Qy2LkpHFSuK2QqC8HBuv5+jPX3stvZyqMfFyP06gzJglSaR59K1lKdpSFKGtplRZ38n9e311N2q45bzVmBcG6Nla/ZoncFFqYumdHArud30v/VWsM6g537RZ5mMhKdWoqqsJGnjJlFn8Ak2WfHLd77znQ/92onUkI4kT0KsONV53D5uv9vDzXNWOt7twTcm1k2dmcjsZXrylupQpcZN4iwFIbL4/BKn2nrY12yi8ZKFvuFgmZjs1AQqCgxUFhjI0yVO4iyFJ0mX20OtzcFem4MzYzYhKGWwNllFlU7D1lQ1CSF+1pgOQpYktFqtfOUrX+HIkSPYbDZ++8t9Pt9DvnLqmK6BnyRJXLhjp7rJRN3FThyDwa5Cs3WJVC0xsq3QiFEjgiVBEB5OkiTOuwb5jaWXGpsDuzf4vj83IZadei079FoMsaE9DtDn7uPw7cPUttVyznIO6X6P5Gh5NGtnrqUit4JVxlVEyafuYockSQy3tIweJ25oHF9nMD8/WGdQr5+8SQoRY7rGL5FI3OupxT3kpf1iNzfOWrl7pRf/mMZZ+mxVIGGYoBF1rwThgRGvj+PXu9jXYubIVSvDnmCifYFBRWWBgYoCAwbx7CiEyd1hN/usdqptdi73DwfG4+QyNqeqqdJpWZeSRIxcnIB8PyFLEpaUlHDnzh2+8IUvkJGR8Z5dGdu2bfvdZhxBplvg19bVz977dQbv9A4GxnVJMWwrNLCt0MgCgyjsLAjCo7UPjrDb2svrVjsdQ+7AuC5aSZVey+/ptSHvRObxezhhOkFdWx3H7h5jxBc8ErNMv4yK3Ao2zdpEUnRSyOYQDu67d4N1Bm/fDowr09JQ3a8zGDt37iTOUIhE0y1+iWTiXk9dw/0ebjXZuHHOhqnVDg+ehGRgzNcwe5me3CIdsYlTd4FJEB63/hEvh69Y2dds4q0b3XjHJNqLs5KpKDRQtiiD5ARRL04Ij9aBYaqtdvba7LSPeS5RKxWUpqnZodPytDYRhchxBIQsSZiUlMRbb71FYWHhROcYsaZD4NfdP0Jdi5nqZjMtdx2B8fhoBVsXplNVZOTp3FQUohCtIAiP0OvxUmNzsNvSyzlXcJEhTi6nLE3NznQtz2qTQvoBLEkSl7ovUdtWy/72/dhH7IFrOeocKnIrKM0uxZBoCNkcwuGRdQY3bURduY2Ep1YiU4jjFML7mw7xy1Qh7vX0MOAc4dYFGzfO2rC0OQPjcrmMGfOSmb1cR05BGtFxou6VIDzQO+Cm8VIn+5rNnO3o5UE2QSGX8ezsVCoLDGxekE5ijPi7EUJPkiRa+oaottmpsTnoHAmeltRFK6nUaajSaVmiin/iN0WFLEk4f/58/t//+38UFRVNeJKRaqoGfkNuHwevWNjbZOLNG9347q/wPHjDrioysmm+nvho8YYtCMLDDd/vLrbb2suRnj489z8m5MBqbRI707WUhKH2x72+e9S11VHfVk+HqyMwnhKbQkl2CRW5FcxLnjelP/A/qM6gets2kjZuRJ4g6gwKH2yqxi9TkbjX04+rZ4ib52zcOGel+25/YFyhlDNrYQp5y3RkLU4lKlos1AjCA53OIepaOqlpMfOuKZhoj1HK2ThPT0WBgbVz0oiNEn83Quj5JYlTjgH22uzU/lZJpMzYaLbrNFTptcxLfDKPyIcsSXjw4EH+1//6X/z0pz8lKytrovOMSFMp8PP5Jd651U11k4kDlywMuIN/CAUz1GwvMlK+2EBakqixIgjCw/klidPOAV632KntcuAc86G6KDGO5/RaqvRa9DGhPX7lHHFyoOMAdW11NNmaAuOxiljWZ66nIreClRkrUcqn7mKHJEkMNTfjqq0VdQaFx2oqxS9TnbjX05vdMsDN8zZunLVitwR30StjFGQvTmX2cj2Z85JRRIm6V4LwQFtXPzUtZmpazLR1BRtMJMUq2bogncpCA0/lpKBUiL8bIfQ8fonj9j72Wu00dDsZ9AVras5JiKXqfsJwVtyTkyd5rElCrVY7bqfGwMAAXq+X+Ph4oqLGPzD29vZOYNqRIdIDP0mSuGRysbfZRE2Lma6+YE2uzOR4thca2FZkJDdNdJ0SBOHRbgwM87rVzm5rL/eGg9vzDTFR7NBr2ZmuZW5CaFfb3D43b917i9q2Wt689yYe/+g8ZMhYkbGCitwKNmRuICFqau+mc9+9i3NfDc7aGjy37wTGlWlpqCoqRusMzpkziTMUprpIj1+mE3GvnwySJNFjGuDGOSs3z1lxdQcL5cfEK8kpTGP2Mj3GORrkIvEhCMDo381ls4uaFjO1LWY6ncG/m9TEaMoXjzY8WZKpmdKnQYSpY9Dn51CPk71WB0d6XLjHpMCKkuLZoddSqdOEfDPEZHusScKf//znH/oH79q160O/NlJFauB3p2eQvc0m9jabxq3OaOOjKF9sYHuRUbzZCoLwgbrcHvbZHPzG0ktL31BgPFEhpzxNw850LU9rEpGHuM5gc1cztbdqOdBxAJfbFbiWr82nIqeCkuwS9AlTezedz+HAtX8/zn01DDUFd0bK4uNRbdqIqrKShJWizqDweExW/PLlL3/5Q7/2pZdeCuFMwidSY0UhdCRJwtrh4sZZK8Q6kaQAAIYFSURBVDfP2xh0BgvlxyVFkbdER95yPRk5amSi5rcgAOD3S5zt6KWmxUzDu53YB4ML0jO0cVQUGNhWaGBuungfFcLD6fHS0O2k2mrnbXs/D/YXyoGnNYlU6bWUpanRRE3dU0sPE7Ljxk+CSAr8evpHqH+3k71NJi7ccQTGY5RyNs3Xs73QyOr8NKKVYvVSEISHG/T5Odjt5DcWO8fsLnwPikzLYF2yip16LVtS1cSFeCfEbddtam/VUtdWh6nfFBjXxekoyymjLKeMOclTezed3+2m//hxXDU19B87HqwzKJeTsHIl6u3bSNqwQdQZFB67yYpf1q1bN+7fFy5cwOv1Muf+ztjW1lYUCgVLly7l6NGjYZtXKEVSrCiEn98v0XnTwY1zNm6dtzE8EEx8JGpjyFuqY/ZyPWmZ/z97dx4fZXnv//81S/ZkJpNlJgshySRhh7AEEBBFQUCyoBbr8Xiqx/Z4WmutPbTnqKeta4+09Whtbb/Vtsfqqb8erQiShE12RARJgLBJyAaBJDOTZTLZt5n790dwQioiSyYzEz7PxyOPh7nuycwnlyT5zHXf9/uKkJP3QpzX63Sxp7yB/MO1fHh8cEzWGFM4eZkJ5GUmMjo61ItViuuJrbuX/PpmPrDaB23QGKBScUtUBHeZDNwWoyNshJzM99gioUajoa6uDqPROGi8sbERo9GI0+n8kq/0H95u/Nq7+9hykS3m1SqYlx7D8qmJLJloIiJ4ZF8OK4S4Ni5FYW9zG+9Z7Kyvb6btgiyOqRGhrIgzsNwYSWygZ3+X2LvsbKzayPrK9RxpOOIeD9GGcFvybeSYc5gVNwuN2n//ACuKQuehwzjy19GycRMuxwXh3WPHos/7PGfQeIlnEeLaeLt/gf4rBXfu3Mlbb72FwWAAwG638+CDDzJ//nx++MMfeqWuoeYLcy18g9PpouaknbIiK5WH6unpGngvpIsNISPLSEaWiehEiQES4nOdPU62n7Sx7nANO0vr6bmwR02KJC8zgZwp8Rh1wV6sUlxPznR2s87WzFqrnc/aB26RD1GrWRqj406TgQVREQSq/ffiLI8tEqrVaiwWyxcWCWtra0lLS6Ozs/NLvtJ/eKPx6+5zsqu0nvySWrZ+ZqWrd+AX5eREPcunJpCXmSC/KIUQX+mztk5WW+2stdqp7b7gto7gAFaYoviayUBGmGd/l3T1dbHr3C4KKwrZU7OHPqUPALVKzZyEOeSac7kl6RZCA/z7bHFPdfX5nMECeqslZ1B4ly8sXCUmJvLhhx8yceLEQePHjh1j8eLF1NbWeqWuoeYLcy18T1+vk+rjTZQVWTld0kDfBf18VEIYGVlG0meYiDT5998+IYaSo7OXzcctFJTU8nF5A+evj0Gtgjlp0eRlJrB0Yjz6ULlARgyPk+2dfGDtXzA80zUQLRGp1ZAdq+dOk4E5keFo/OxK8SFfJPzNb34DwL/927/x/PPPEx4+cDbM6XSye/duTp8+zaELMpf81XA1fk6Xwv7KRtYdrmXjsTpauvrcx1JjwsjN7F8YTDfKmUchxKVZu3tZY7XzvtXOsbaBkzU6rZq82P4NSGbpwzyaM+hSXBRbiymsLOTD0x/S1tvmPjY+ajy5af05gzEhMR6rYTg4m5tp2bixP2fw8GH3uOQMCm/zhYWriIgICgoKWLBgwaDxHTt2kJeXR2trq1fqGmq+MNfCt/V09XHmaCNlRVbOHG/E1Tfwlit2dAQZWSbSs4xERMkFAEJ8ztbaxYYjdawrqeXQBVFbgRo1N4+NJS8zgUXjTYQESo8lPE9RFA61dLDWZmedrRlbz8B6jSlQS54xkjuNBqbpQv0iWmLIFwlTU1MBOHPmDKNGjUJzwZufwMBAUlJSeO6555g9e/Y1lu59w9H4/WXfGV7dVobtgp2JTbogcqcksHxqIpMSdX7xD00I4T3tTicb6x2sttjZbW91B+8GqFQsjI5ghSmKRdE6gj2cM1jZXElBZQHrK9dT117nHo8LiyM7NZvctFzSItM8WoOnuXp6aNu5E0d+Pm27dsOFOYNz5qBfnkfEokWoQ+XqEOE9vrBwdf/99/PRRx/x0ksvMWvWLAD279/Pv//7vzN//vwr2gzPl/nCXAv/0d3RS+XhBsqLrJw9aUdxDbz9ik/Tk55lIn2GkVBdoBerFMK3nG3qcO+QfNIycIIpNFDD4gkm8qYmMD8jlgDZWVwMA6ei8ElzG2utdtbXO2juG4iWSA4O5E6TgTtMkYwLC/FilZfmsduNb7nlFtasWePOmRmJhqPxe3vfGX7ywTH0IQEsmxxPXmYCs1Kj0MhuaEKIS3AqCh/ZW1ltsbOhwUHHBRkuWbpQVsRFkWeMJMrDO3I1dDawsWojBRUFfNb0mXs8PCCcxSmLyTHnMMM0A7XKfxu3/pzBQzjW5dOy6e9yBseNO58zmE2AUXIGhW/whYWrjo4OfvSjH/HGG2/Qe34xXavV8q1vfYsXX3yRsBGyYY8vzLXwT52tPVQctFFWZKO2vBnOvxNTqSBxrIGMmSbMU2MJDpNbK4X4XKmllfySGtYdruWcfeCOmcjQC95Lp0ShlvfSYhj0uFzsbGplrdXOpoYWOl0D78fGhwVzp6k/9z05JMiLVX6R7G58DYaj8Wvu6KH4jJ35GbIzsRDi0hRF4XhbJ+9Z7XxgtWO94FL31JBAvmaKYkWcgRQP/yHq7Otke/V2CioL2Fe7D6fSfwZNq9JyY+KN5KTlcPOomwnW+vetUz1nzgzkDJ496x7XGo3ocnPQ5y0neOwYL1YoxMX50sJVe3s7FRUVAKSlpY2YxcHP+dJcC//VZu+ivLh/wdB2usU9rtaoGD0hivQsE6mZMQQGe/bEnxD+QlEUDp1tJv9wLYVH6mhoG7grL04XTG5mPHmZcleeGD7tTidbGlpYa7OzvbGV3guW17J0odxhGp6NIi/HkC4Srly5kueff56wsDBWrlx5yce+/PLLV16tj5HGTwjhC2q7enj/fM7gyQt22YoK0JBnNHC3ycB0D2dgOF1OPrV8SmFlIVvPbKWjr8N9bErMFLLN2SxNXUpUcJTHahgOfXY7LRs30rIun86SEve4OjSUiMWL0eflEjp7tuQMCp/mS/1LeXk5FRUV3HTTTYSEhKAoyoh6w+ZLcy1GBkd9J+XFVsoO2GisGcj01QSoSZkcTcZME8kTo9FKFpsQAPQ5XeyrbGLd4Ro2HbfQKvn+wsuae/vYUO9gjdXOx81tn18ojhq40RDOHSYD2TF69B6+4+vLDOki4S233MLatWuJjIzklltu+fInU6nYvn371VXsQ6TxE0J4S2ufk/X1zay2DP7jEqRWcVu0jrvjorglKoJAtWevQC5tKqWwspANlRuwddrc44nhieSm5ZKdmk2KPsWjNXiaq6eHth3ncwZ3/13O4Lx56PPyiFh4q+QMCr/hC/1LY2MjX//619mxYwcqlYqysjLMZjPf/OY3MRgMvPTSS16pa6j5wlyLkauptp2yIitlRVYctoFbKwOCNZgzY0nPMpI0IQqNZLEJAUBXr5Ndp+rJP1zL1s+sdPcN3P45MUFHXmYCuZkJJET6bl6cGFms3b3k25pZa7NzsGXgIotAlYpboyO4w2hgcYye0GH8PS63G18DafyEEMOpz6Ww097KaksTmxscdF4QaH6DPoy746LIifX8WSdru5UNVRsorCzklP2Ue1wXqGNJyhJy03KZGjvVr68GcucMfrCuP2ewZeD2rqDx49Evz0OfnY02NtaLVQpxdXyhf7n//vux2Wz86U9/Yvz48ZSUlGA2m9m8eTMrV67k+PHjl/1czzzzDM8+++ygsbFjx3Ly5MmLPr63t5dVq1bx1ltvUVNTw9ixY/nFL37B0qVLBz2upqaGxx9/nI0bN9LR0UF6ejp//vOfycrKuuzafGGuxcinKAoNZ9soO2ClrNhKW9PArZVBYVrSphnJyDKSMMYgWWxCnNfW3ceWExbyD9fyUVkDfRf01bNSosidmkD25HiiwmSjIDE8znR284G1f8HwwrvDQjVqbo/Rc4cxkgVROgI8/HtcFgmvgTR+QghPUxSFktZOVlub+MDaTEPvwC0SGaFBrDBFcacpktEezhls721n65mtFFYWsr9uP8r5axe1ai03j7qZXHMu80fNJ1Dj341Uz+nTOPILvpgzaDKhz81Bl5dH8BjJGRT+zRf6l7i4ODZv3kxmZiYRERHuRcLKykqmTJlCW1vbVz/Jec888wyrV69m69at7jGtVktMTMxFH//444/z9ttv88c//pFx48a5Fyb37t3LtGnTALDb7UybNo1bbrmFhx9+mNjYWMrKykhLSyMt7fJ3YfeFuRbXF8WlYKlqoazISnmxjc6WHvexUF0g6TOMZMw0YUqVLDYhPtfU3sPGY3XkH67l09NNfL7yoVWruDEjhrzMBBZPjCM8SHI/xfD4rK2TD2zNrLXaqe4a+D1u0Gp4IDGGJ8zxHnvty+1dLuun4a677rrsF16zZs1lP1YIIa43Z7t6WGOxs9raRFnHwBUBMQFa7jRF8jVTFJkRIR5t8Ptcfeyr20dBRQHbq7fT5Rw4ozXdOJ1sczZLUpagD9J7rIbh8JU5g8vzCJ01S3IGhRhC7e3thF7kFv2mpiaCgq78pIdWqyUuLu6yHvuXv/yFH//4xyxbtgyAhx9+mK1bt/LSSy/x9ttvA/CLX/yCpKQk/vznP7u/LjU19YrrEmK4qdQq4tP0xKfpufHuDGpP2SkrslFx0EZHSw9HdpzjyI5zREQFk55lJCPLRExSuCwYiutaVFgg981O5r7ZydQ5OiksqSO/pJajNQ52ltazs7SeIO1RFo03kZuZwIKxsQQHSF8oPGd8eAjjw0N4IjWOgy0drLXZybc1Y+vpo93p9HZ5wGUuEur1/v1GUQghvMnR20dBvYPVlib2Odrd48FqFUtj9KyIi+JmQ4RHLzFXFIUTTScorChkY9VGGrsa3cdSdClkm7PJNmeTFJHksRqGw2XlDC5aiDpEMmmE8IT58+fzv//7vzz//PNAf161y+Xil7/85SVzrb9MWVkZCQkJBAcHM2fOHFatWsXo0aMv+tju7m6Cgwfvrh4SEsKePXvcn+fn57NkyRLuvvtudu3aRWJiIt/97nd56KGHLllHd3c33d0DJ3ZaLogqEGK4qdUqRo2LYtS4KG76hzGc/ayJ8iIblYfraW3q4tCH1Rz6sJpIU6h7wTAqfmTtMC7ElYrXh/DQTWYeuslMZX0b+SW15JfUUlnfzvqjdaw/WkdEsJalE+PIm5rAHHM0Wsn9FB6iUqmYoQ9jhj6MZ9MT2WtvIzHYN+7cktuNL0JuIRFCXKsel4vtja2stjaxpbGF7vN5KCpgXmQ4K+IMZMdGEqH17NnK2rZaNlRtoKCigEpHpXvcEGRgaepScs25TIqZ5NdXGiiKQmdxMY78gi/mDE4Yjz5PcgbF9cEX+pdjx46xcOFCpk+fzvbt28nLy+P48eM0NTXx8ccfX9EtvRs3bqStrY2xY8dSV1fHs88+S01NDceOHSMiIuILj//Hf/xHSkpK+OCDD0hLS2Pbtm0sX74cp9PpXuD7fBFx5cqV3H333Rw4cIDHHnuM1157jQceeOBLa7lYPiIgvaLwKX09Ts4cb6TsgJXTRxtx9g5s3hA9KpyM8wuGuhg5USYE9PeQx2tbyC+ppaCkljrHwN01MeGB5Ezp3/Bk+uhIv+6VhQDJJLwmvtBkCyH8j6IoFLd0sNpqJ99mp6l34JLxcWHBrDAZuMtkIMHDZ4lae1rZcmYLBRUFFFmL3ONBmiAWJC0g15zL3MS5BKgDPFqHp3VXVeHIz6eloJDec+fc49q4OPS5uejzcgnKyPBihUIML1/pXxwOB7/97W8pKSmhra2N6dOn88gjjxAff205O83NzSQnJ/Pyyy/zrW996wvH6+vreeihhygoKEClUpGWlsaiRYt444036Ozs3yE2MDCQrKws9u7d6/6673//+xw4cIBPPvnkS1/7YlcSJiUleX2uhfgyPV19VJU0UF5kpfpEEy7nwFs+U6qOjCwT6TOMhEV6NvtYCH/hcikUnbGz7nANG47WYe/odR8bZQghLzOBvKkJjIuT3/nCPw1pJqEQQogvV9XRzWprE+9b7ZzuHAigNQVqudNkYIXJwMRwz+YM9jp72VOzh8LKQnae3UmPa6COmXEzyTXnsih5ERGBX7z6xp/02e20rN+AIz+friNH3OPqsDAilixBn5dH6KyZqNRye4gQ3lBdXU1SUhI//vGPL3rsy24VvhyRkZGMGTOG8vLyix6PjY3lgw8+oKuri8bGRhISEnjiiScwm83ux8THxzNhwoRBXzd+/Hjef//9S752UFDQVWUqCuEtgcFaxs6OY+zsOLrae6k8XE/ZASs1pXasVS1Yq1rYs7qMhPRIMmaaSJseS0i4b9zqJoQ3qNUqZqVGMSs1imfyJrKnvIGCw7VsPm7hnL2T/7ezgv+3s4IxpnCWT00kd0oCo6O/mMErhL+TRUIhhLgKTb195NuaWW1poqilwz0eolaTHatnRZyB+YYINB5cGFQUhaMNRymoKGDT6U00dze7j6Xp08hJyyE7NZv4cM/tkjUcXN3dtO3YgWNdPm0ffQR953eC1mgImzcXfd5yIhbeKjmDQviA1NRU6urqMBqNg8YbGxtJTU3FeQ2h3G1tbVRUVPCNb3zjko8LDg4mMTGR3t5e3n//fb7+9a+7j82bN4/S0tJBjz916hTJyclXXZcQvi44LIAJ8xKYMC+Bdkc3FQfrKS+yUlfhoLasmdqyZna/c4qk8QYyskykTo0lKETeJorrV4BGzS1jjdwy1khnj5PtJ23kl9Sw42Q9p6xtvLi5lBc3lzI1KZLlUxPInhKPMSL4q59YCD8gtxtfhK/criOE8C3dLhdbGlpYbW1iW2Mrved/faqBmwwRrIgzcHuMnjAP5wyebT1LYWUh6yvXc6bljHs8OjiaZeZl5JpzGRc1zq+zUxSX63zOYD4tmzbjam11HwueMAH98jx02dloY2K8WKUQvsUX+he1Wo3VaiX27zJAz5w5w4QJE2hvb/+Sr/yiH/3oR+Tm5pKcnExtbS1PP/00hw8f5sSJE8TGxnL//feTmJjIqlWrANi/fz81NTVMnTqVmpoannnmGaqqqjh48CCRkZEAHDhwgLlz5/Lss8/y9a9/nU8//ZSHHnqIP/zhD9x3332XXZsvzLUQ16q1qYvyIhtlRVbqqwf+zqq1KpInRpMx00TK5BgCgmS3VyEAHJ29bD5uoaCklo/LGzgfOY5aBXPSosnLTGDpxHj0of4d6SNGJrndWAghhoBLUfjU0c5qi52C+mYcfQNXwUwKD2GFycCdJgOmIM82A45uB5tPb6awspBDtkPu8RBtCLeOvpVccy6z42ejVfv3r/Xuyioc+etoyS+gt7bWPa6Nj0efk4N+eR5B6elerFAIcTErV64E+nfr++lPf0po6MAtWE6nk/379zN16tQres5z585x77330tjYSGxsLDfeeCP79u1zL0BWV1ejviBaoKuri5/85CdUVlYSHh7OsmXL+Mtf/uJeIASYOXMma9eu5cknn+S5554jNTWVV1555YoWCIUYKSKigpm2eDTTFo+m2dpBebGVUwds2OvaqSppoKqkAW2gmtQpMaRnmUieGI0mQOI8xPVLHxLA17OS+HpWErbWLjYcqSO/pJaD1c18XN7Ix+WN/PSD49w8Npa8zAQWjTcREiiL7MK/XNaVhL/5zW8u+wm///3vX1NBvkDODgshyju6WG2x877VztmugXy/+KAA7jqfMzg+3LO3t/Y4e9h9bjcFFQXsrtlNn6v/Nlu1Ss3suNnkpuWycPRCQgP8Ow+lr6lpIGfw6FH3uOQMCnFlvNm/3HLLLQDs2rWLOXPmEBg4kG0WGBhISkoKP/rRj8gYIZsJSa8oRipFUWiqbafsgJWyIistDQO7vQaGaDFPjSEjy0TiOAMajfxdFgLgbFOHe4fkk5aBq3JDAzXcNsHE8qkJ3JgeS6BWfmaE9wzp7sapqamDPq+vr6ejo8N9Zra5uZnQ0FCMRiOVlZXXVrkPkMZPiOtTfU8v62zNrLbYOdw6kDMYrlGTExvJijgDcyLDPZ4zeMh2iILKAjaf3kxrz0CjMdYwlty0XG5PvR1jqPESz+L7LpkzeOM89Hl5RNwqOYNCXAlf6F8efPBBfv3rX4/4/skX5loIT1MUBduZVsqKrJQX2WhvHtjhOzg8gLTpRjKyjCSkR6JS+2/EiRBDqdTSSn5JDfkltZxt6nSPR4YGcPukePIyE5idGoVafmbEMBvSRcIL/fWvf+X//b//x//8z/8wduxYAEpLS3nooYf49re/PSJu15DGT4jrR6fTxeYGB6utdnY2tdB3/jeiRgULDDrujjOwOEZPqIfPlp92nKagsoD1leupaatxjxtDjWSbs8kx5zDGMMajNXjaJXMGJ07szxlctkxyBoW4StK/DB+Za3G9UVwKdRUOyg5YKT9oo6ut130sTB9I+gwT6TONmFJ0fp2JLMRQURSFw2ebWXe4lsIjdTS0DSyym3RB5E5JIG9qApMT9fIzI4aFxxYJ09LSWL16NdOmTRs0XlxczIoVK6iqqrq6in2INH5CjGwuRWFvcxurLXYK65tpc7rcxzIjQrg7LorlxkhiAz2bM9jU1cTGqo2sr1zP0YaB22xDtaHclnwbOWk5zDTNRKP27yyTL80ZTIhHn5PbnzOYlubFCoUYGXylfykqKuJvf/sb1dXV9PT0DDq2Zs0aL1U1tHxlroXwBpfTxblSO2VFNioP1dPT2ec+posJJj3LREaWiejEMFn8EAJwuhQ+qWgkv6SGjccstHYN/MykxoSRm5lAXmYC6cZwL1YpRjqPLRKGhoaya9cuZs6cOWj8008/ZcGCBXR0dHzJV/oPafyEGJlOtney2mJnjdVObffAGfBRwQGsMEVxl8nAmLBgj9bQ1dfFznM7Kawo5OOaj+lT+psEjUrD3IS55KblsiBpASFa/77Ntq+xkZYNG7+YMxgeTsTSJehz8widmSU5g0IMIV/oX9555x3uv/9+lixZwocffsjixYs5deoUVquVO++8kz//+c9eqWuo+cJcC+ELnL0uqk80UlZko6qknr6egROvhrhQMmb2LxhGmvw7P1mIodLd52RXaT3rSmrZ9pmVrt6Bn5kJ8TqWT00gNzOBhEj/fi8gfI/HFglzc3OpqanhT3/6E9OnTwf6ryL813/9VxITE8nPz7+2yn2ANH5CjBy27l7WWPs3IDnaNpALotOqyYs18LU4A7P1Yag9eKbbpbgothZTUFHAljNbaOttcx+bGD2R3LRclqQsISbEv2+zdXV1Dc4ZdJ7fCVqrJfzGG9EvzyP8lltQB3t2IVaI65Uv9C9Tpkzh29/+No888ggRERGUlJSQmprKt7/9beLj43n22We9UtdQ84W5FsLX9HY7OX20gfIiG2eONeLsG1j8iEkKJyPLRHqWEV20LH4IAdDW3cfWE1bWHa7ho7IG+lwDSzMzUwzkZSawbHI80eFBXqxSjBQeWySsr6/ngQceYNOmTQQE9N+K19fXx5IlS3jzzTcxGv07TB+k8RPC37U7nWyq788Z3NXUyuctqlYFC6N1rDBFcVu0jmAP5wyW28sprCxkfdV6LO0W93hCWEJ/zmBaDma92aM1eJrictFRVIQjP5/WTZtxtQ0sgAZPmoQ+Lw9d9jK00dFerFKI64Mv9C9hYWEcP36clJQUoqOj2blzJ5MnT+azzz7j1ltvpa6uzit1DTVfmGshfFl3Zx9VJfWUHbBy7jM7rgsWP+LMejJmGkmbbiRML4sfQgA0tfew8Vgd+Ydr+fR0E5+v0mjUKm5MjyEvM4HFE01EBHs2DkmMXJfbu2iv9IljY2PZsGEDp06d4uTJkwCMGzeOMWP8O1BfCOHfnIrCHnsb71ma2NDgoOOCnMEZulBWxEWRFxtJdOAV/9q7Ig2dDWyo3EBhZSGfNX3mHo8IiGBxymJy03KZZpyGWuXft9l2V1biWJdPS8FFcgZz8/pzBs3+vQAqhLhyBoOB1vObEiUmJnLs2DEmT55Mc3PziIikEUJcnqAQLeNuiGfcDfF0tvVQeaiesiIrNaeasVQ6sFQ62PO3MhLGGMjIMpI2zUhwuCx+iOtXVFgg981O5r7ZydQ5OiksqSO/pJajNQ52napn16l6gtaqWTjeSF5mAgvGGgkO8O/ccuGbrvhKws/19PRQVVVFWloaWq1n33QPNzk7LIT/ONHWyXuWJtZam7H0DOQMJgcHsiLOwNdMUZhDPXuWuqO3g23V21hfuZ5P6j7BpfQvUGrVWuYnzic3LZebRt1EkMa/z5b3NTbSsn5Df87gsWPucXfOYF4eoVmSMyiEt/hC//KP//iPZGVlsXLlSp5//nleffVVli9fzpYtW5g+fbpsXCLEda69uZvyYhtlRVasVS3ucbVaRdKEKDKyjKRmxhIYMrLeXwpxtSrr2ygoqWNdSQ2V9e3u8YggLYsnxpE3NYF5adFoPXyHlPB/HrvduKOjg0cffZS33noLgFOnTmE2m3n00UdJTEzkiSeeuLbKfYA0fkL4trruHtZYm3nf0sSJ9i73uEGrIc8YyYq4KLJ0oR7dUc/pcrK/bj+FlYVsrd5KZ99A3mFmbCa55v6cwcjgSI/VMBxcXV20bd/enzO4Z4/kDArhw3yhf2lqaqKrq4uEhARcLhe//OUv2bt3LxkZGfzkJz/BYDB4pa6h5gtzLYS/a2nopLzYxqkDVhrPDcSVaLRqkidHk5FlInlyNAGBcrWUEIqicLy2hYKSWgpKaql1DLwHigkPZNnkePIyE5g+2oBaLbuKiy/y2CLhY489xscff8wrr7zC0qVLOXLkCGazmXXr1vHMM89w6NChKy72d7/7HS+++CIWi4XMzExeffVVZs2a9aWPb25u5sc//jFr1qyhqamJ5ORkXnnlFZYtW3bVz3khafyE8D1tfU7W1zt439rER/Y2Pv/FFahScVuMjhUmAwujdQR68Co2RVEotZdSUFHAxqqN1HfWu48lRSSRa84lx5xDki7JYzUMB8XlouNAEY78dbRu/nBwzuDkyQM5g1FRXqxSCPH3pH8ZPjLXQgwtu6WdsiIbZQesNFsHogm0QRpSp8SQMdPE6AlRaLRytZQQLpdC0Rk7+SU1bDhqoam9x30sMTKEnMx4lmcmMj4+wqMXTQj/4rFFwuTkZN59911uuOEG9651ZrOZ8vJypk+fTktLy1c/yQXeffdd7r//fl577TVmz57NK6+8wnvvvUdpaelFN0Hp6elh3rx5GI1G/vM//5PExETOnDlDZGQkmZmZV/Wcf08aPyF8Q59LYbe9ldVWOxvrm+m8IPR6tj6MFXEGcmMjiQzw7C0plnYL6yvXU1hZSHlzuXtcH6RnacpSctNymRIzxe//CHdXVOBYl4+jsIC+2oHNBQISEtDl5aLPk5xBIXyZt/qXK+n9RkpfJb2iEJ6hKAoN59ooL7JSdsBGa9PA1VJBoVrM02LJyDKROCYStdxeKQS9ThcflzeQf7iWzccttPc43cfSjeHkZSaQl5lASkyYF6sUvsBji4ShoaEcO3YMs9k8aJGwpKSEm266CYfDcUWFzp49m5kzZ/Lb3/4WAJfLRVJSEo8++uhFb11+7bXXePHFFzl58qR7d+Vrfc6/J42fEN6jKApH2zpZbbGz1manvqfPfSwtJIgVcQbuMhlIDvFsvl9bTxtbq7dSWFHIp5ZPUc5fuxigDmBB0gJyzDnMT5xPgMa/Q7b7cwbX41iXT9fx4+5xdUQEuvM5gyEzZkjOoBB+wFv9i1qt/sqTJIqioFKpcDqdl3ycv5BeUQjPUxQFa1ULZQeslB+00eEYuFoqJCKA9OlG0meaiDfrUcntlULQ1etk+0kb+Ydr2V5qo6dvYCPHzFF6cjMTyM1MwKSTmKDrkcd2N87KymL9+vU8+uijAO6m8E9/+hNz5sy5oufq6emhuLiYJ5980j2mVqtZtGgRn3zyyUW/Jj8/nzlz5vDII4+wbt06YmNj+cd//Ecef/xxNBrNVT1nd3c33d3d7s+v9GpIIcS1O9fVwxqrndUWO6c6Bs4aRwVouMNoYEWcgWkRns0Z7HX18kntJxRWFLLj7A66nAN1zDDNIMecw23Jt6EP0nushuHg6uykdft2HPn5tO/5eHDO4Pz5AzmDQf690YoQYnjs2LHD2yUIIUYglUpFnFlPnFnPvLszqCtrpqzISsXBejpbezm6q4aju2oINwSRPsNIxkwTsaPl9kpx/QoO0LBscjzLJsfT0tXL5mMW8ktq+bi8gZJzDkrOOfivDZ9xQ2o0eVMTuH1SHJGhgd4uW/iYK14kfOGFF7j99ts5ceIEfX19/PrXv+bEiRPs3buXXbt2XdFzNTQ04HQ6MZlMg8ZNJhMnT5686NdUVlayfft27rvvPjZs2EB5eTnf/e536e3t5emnn76q51y1ahXPPvvsFdUuhLh2LX1OCuubWW2xs7f5gtw7tYrFMXpWmAzcEqUjwINnhxVF4UTjCQoq+3MGm7qa3MdSdCnkpuWSbc4mMTzRYzUMB8XlouPTAzjy82ndvBlX+8DuaMFTpvTnDC67XXIGhRBX7Oabb/Z2CUKIEU6tVpE41kDiWAPz/2EM507aKT9gpfJwPW32bg5vPcvhrWfRx4aQMdNEepaR6IRwb5cthNfoggO4OyuJu7OSqG/tZsPROvJLaik+Y+eTykY+qWzkqXXHuCkjlrypCdw2wURooOwqLq5ikfDGG2/k8OHD/PznP2fy5Ml8+OGHTJ8+nU8++YTJkyd7osZBXC4XRqORP/zhD2g0GmbMmEFNTQ0vvvgiTz/99FU955NPPsnKlSvdn7e0tJCU5N8bDwjhq3pdCjuaWlhttfNhg4OuC3IG50aGsyLOQE5sJDqtZ3eyq2mrcecMVjmq3ONRwVEsS11GjjmHCdET/P5sdHd5+fmcwUL66i6WM7icIHOqFysUQoxEHR0dVFdX09PTM2h8ypQpXqpICDFSaDRqkidGkzwxmpt7nVQfb6LsgJXTRxpw1HdStOE0RRtOE5UQRkZW/4JhpDHU22UL4TWxEUE8MDeFB+amcLapg4IjteQfruWkpZVtJ21sO2kjJEDDogkm8jITuHlMLIGySdB166qWitPS0vjjH/94zS8eExODRqPBarUOGrdarcTFxV30a+Lj4wkICECjGVhAGD9+PBaLhZ6enqt6zqCgIILktjohPEZRFA61drDaYucDm52m3oFMqjGhwe6cwVHBnr3c3dHtYMuZLRRUFHDQdtA9HqwJ5pbRt5BjzmFOwhwC1H6eM9jQMJAzeOKEe7w/Z3Ap+uV5hEyfLjmDQoghV19fz4MPPsjGjRsvenykZBIKIXyDNkCDeWos5qmx9HT1ceZoI6cOWKk+3khTbTv78yvZn1+JMTmC9CwT6TOMRERJHpu4fiVFhfLdBel8d0E6p6yt5B+uJb+kluqmDgpKaikoqUUXrGXZ5HjyMhOYbY5GI5mf15UrXiTUaDTU1dV9YZfgxsZGjEbjFTV/gYGBzJgxg23btnHHHXcA/VcKbtu2je9973sX/Zp58+bx17/+FZfLhfr8G9xTp04RHx9PYGD/AsOVPqcQwjPOdHbzvtXO+xY7FZ0DuZ8xAVruMvXnDE4OD/FszqCzl49qPqKwspCdZ3fS6+oFQIWKWfGzyDHnsGj0IsID/fuWFFdnJ63btuPIX0f7x3sH5wzedBP6vDzCb1kgOYNCCI/6wQ9+QHNzM/v372fBggWsXbsWq9XKz372M1566SVvlyeEGMECg7VkzDSRMdNEV3svVSX1lBXZOHfSju1MK7Yzrex9v5z4dD0ZWSbSphsJ1Ukem7h+jTFF8KMlY/nh4jGUnHOQf7iWwiO12Fq7eefAWd45cBZjRBDZU+JZPjWRzFF6v7/LSny1K97dWK1WY7FYvrBIWFtbS1paGp2dnVdUwLvvvssDDzzA66+/zqxZs3jllVf429/+xsmTJzGZTNx///0kJiayatUqAM6ePcvEiRN54IEHePTRRykrK+Ob3/wm3//+9/nxj398Wc/5VWTHOiGuXnNvH/m2Zt632tnvGMi9C1GrWBqjZ0VcFDcbItB6OGewpL6EwspCNp3ehKN7YNf19Mh0ctNyWZa6jLiwi19d7C/6cwY/xbEun9YPPxycM5j5ec7gMrQGgxerFEIMF1/oX+Lj41m3bh2zZs1Cp9NRVFTEmDFjyM/P55e//CV79uzxSl1DzRfmWghxeTpaeqg4aKO82EZtWbN7XKWCUeMMpGeZME+NJTjMv+8kEWIoOF0K+6sayT9cy8ZjFhydve5jydGh5E5JYPnUBDJMEV6sUlyNId/d+De/+Q3Qv8vUn/70J8LDB666cTqd7N69m3Hjxl1xoffccw/19fU89dRTWCwWpk6dyqZNm9yLedXV1e4rBgGSkpLYvHkz//Zv/8aUKVNITEzkscce4/HHH7/s5xRCDK1ul4ttjS28b7WzpaGFnvPnHlTAjYZwvmbqzxkM93DO4JmWMxRWFlJYUci5tnPu8diQ2P6cwbQcxhrG+v0ZsO6yMhz5+TgKCumzWNzjAaNGoc/LRZebS1Cq5AwKIYZfe3u7+0SywWCgvr6eMWPGMHnyZA4ePPgVXy2EEEMvVBfI5AWjmLxgFG32LsqLbZQdsGI708rZz+yc/czOrr+WMnpCFOlZJlIzYwgMlg0cxPVJo1YxNy2GuWkxPLd8ErtP1bOupJatJ6ycaezgtzvK+e2OcsbFRZA3NYHcKQkkRUnm50hy2VcSpp5/w3nmzBlGjRo1KBMwMDCQlJQUnnvuOWbPnu2ZSoeRnB0W4qspikJRSwfvWZrItzXT3DcQNTA+LJgVcVHcaYwkwcM5g/YuO5tOb6KwopAjDUfc4yHaEBaNXkROWg6z42ajUXt2gdLT+urrcaxfjyM/n+4Tn7nH1RER6G6/fSBn0M8XQIUQV88X+peZM2fys5/9jCVLlpCXl0dkZCSrVq3iN7/5DatXr6aiosIrdQ01X5hrIcS1cdR3UFZko7zISmPNwN0Y2gA1yZNjyJhpJHliNNpA/+4hhRgKHT19bDlhpaCkll2n6ul1DiwjzUg2kJeZwLLJ8cRGSLSRr7rc3uWKbze+5ZZbWLNmDYYRfPuaNH5CfLmqjm7eszbxvsXOma6BXStNgVruNBm4Oy6KieEhHq2h29nNzrM7KawoZE/NHvqUPgDUKjVz4ueQk5bDrUm3Ehrg32e1XJ2dtG7dhiM/n/a9kjMohLg0X+hf3n77bfr6+vjnf/5niouLWbp0KU1NTQQGBvLmm29yzz33eKWuoeYLcy2EGDqNtW2UF/VfYeioH4jPCgjWYM6MJT3LSNKEKDQa2fRNiOaOHjYes5B/uJZ9VY18vqKkVsG89BhyMxNYOikOXbDcwu9LPLZIeD2Qxk+IwZp6+1hna2a1pYnilg73eKhGzbIYPXfHRXGjIRyNB69icykuiq3FrK9cz4enP6S1t9V9bHzUeHLMOSwzLyMmJMZjNQyHQTmDmzfj6hiYb8kZFEJcii/2Lx0dHZw8eZLRo0cTE+Pfv58v5ItzLYS4doqi0HC2jbIDVsqKrLTZBzbeCwrTkjbNSMZMEwkZkahlx1chsDi6KDzSvytyybmBHPhAjZpbxsWSl5nIwvFGggPkilxv8+gi4blz58jPz6e6upqenp5Bx15++eUrr9bHSOMnBHQ5XWxtbGG1tYltja30nv9VoQZujopghcnA0lg9YRrP/sKvdFRSWFHI+sr11LbXusfjwuLITs0mx5xDuiHdozUMh65Tp2j5PGfQanWPS86gEOJySf8yfGSuhRj5FJeCpaqFsiIr5cU2OlsG3veG6gJJn9G/YGhK1UncixDA6YZ2CkpqyS+ppczW5h4PC9SwZGIcuVMTuDE9hgC5ItcrPLZIuG3bNvLy8jCbzZw8eZJJkyZx+vRpFEVh+vTpbN++/ZqL9zZp/MT1yqUofOpoZ7XFTkF9M44LcgYnh4ewIs7AnUYDxiDPXjre0NnApqpNFFQWcKLxhHs8PCCc25JvIzctlxmmGahV/v0Hpq++Hkfh+ZzBzy7IGdTp0C1dKjmDQogr4gv9y9e+9jVmzZo1aEM5gF/+8pccOHCA9957zyt1DTVfmGshxPBxuRRqTtkpL7JRcdBGd0ef+1hEVDDpWUYyskzEJIVL3yaue4qi8FldK/kl/VcY1jQP3MIfFRbIsslx5GUmkpVskCtyh5HHFglnzZrF7bffzrPPPktERAQlJSUYjUbuu+8+li5dysMPP3zNxXubNH7ielPe0cVqi533rXbOXpAzmBAUwF0mAyviDIwL82zOYGdfJzuqd1BQWcAntZ/gVPoXKLUqLTcm3kh2WjYLRi0gWBvs0To8zdXRQeu2bTjWnc8ZdLn6DwQEDM4ZDPTshi9CiJHHF/qX2NhYtm/fzuTJkweNHz16lEWLFmG94Eppf+YLcy2E8A5nn4uznzVRVmSl6nADvd0DJ9UjTaHuBcOo+DAvVimEb1AUhYPVdvIP17L+aB0NbRe819QHk5OZQF5mAhMT5IpcT/PYImFERASHDx8mLS0Ng8HAnj17mDhxIiUlJSxfvpzTp09fa+1eJ42fuB7U9/Sezxm0c7h1IPcuXKMmJzaSFXEG5kaGo/bgL2uny8kB6wEKKgrYemYrHX0DdUyJmUK2OZulqUuJCo7yWA3DQXE66di/vz9ncMuWQTmDIZmZ6Jbnobv9dskZFEJcE1/oX0JCQjh8+DBjx44dNH7y5EmmTZtGZ2fnl3ylf/GFuRZCeF9fj5MzxxopK7Jy+mgjzl6X+1j0qHAyzi8Y6mI8e7JdCH/Q53Sxt6KR/JJaNh+z0No9cEWuOTaMvPMLhubYcC9WOXJdbu+ivdInDgsLc+cQxsfHU1FRwcSJEwFoaGi4ynKFEMOh0+lic4OD1VY7O5pa+Hzneo0KbonSscJkYHGMnlAP50SUNpWyvnI96yvXY+u0uccTwxPJTcslOzWbFH2KR2sYDl2lp3Dkr6OloJA+28D32Z8zmIc+L5fAlBTvFSiEEENs8uTJvPvuuzz11FODxt955x0mTJjgpaqEEMIztIEa0qYbSZtupKerj6qSBsqLrFSfaKLxXBuN59rY90ElplQdGVkm0qYbCTcEebtsIbxCq1Fz05hYbhoTy8/umMTO0nryS2rY9pmNyvp2Xtlaxitby5iUqCMvM4HczATi9bLAPtyueJHwhhtuYM+ePYwfP55ly5bxwx/+kKNHj7JmzRpuuOEGT9QohLgGLkVhb3Mbqy12CuubaXMOnOHMjAjh7rgolhsjiQ30bM6gtd3KxqqNFFQWcMp+yj2uC9SxJGUJuWm5TI2d6veXmffabLR8njN48qR7XK3Tobv99v6cwWnT/P77FEKIi/npT3/KXXfdRUVFBbfeeivQn2f9f//3fyMmj1AIIS4mMFjL2NlxjJ0dR1d7L5WH6yk7YKWm1I61qgVrVQt7VpeRkB5JxkwTadNiCYmQeBlxfQoO0LB0UhxLJ8XR2tXLlhNW1h2uZU95A8dqWjhW08KqjSeZmRJFXmYCyybHExUmPy/D4YpvN66srKStrY0pU6bQ3t7OD3/4Q/bu3UtGRgYvv/wyycnJnqp12MgtJGIkKG3vYrWliTVWOzXdve7xUcEBrDBF8TWTgYwwz+b7tfe2s/XMVgorC9lftx+F/l83WrWWm0fdTK45l/mj5hOo8e9f+K6ODlq3bsWRX/DFnMGbz+cMLpCcQSGEZ/lK/7J+/XpeeOEFDh8+TEhICFOmTOHpp5/m5ptv9lpNQ81X5loI4fs6WnqoOGijrMhKXbnDPa5Sq0gaZyA9y4R5agxBoZ49YS+EP2hs62bDMQv5h2s4cNruHteqVczPiCFvagK3TYgjPOiKr3e77nksk/B6II2f8Fe27l7W2uy8b7FzpG0g90mnVZMbG8mKuChm68M8mjPY5+rjk9pPKKgsYEf1DrqcXe5j04zTyDHnsCRlCfogvcdqGA6K00n7vn205OfTsmUryoU5g1Onol+eR8TSpZIzKIQYNt7uX/r6+njhhRf45je/yahRo4b99YeTt+daCOGfWpu6KC+2UXbASn11q3tcrVWRPDGajJkmUibHEBCk8WKVQviGmuZOCktqyS+p5Xhti3s8OEDNwvEm8jITWDA2liCt/LxcDlkkvAbS+Al/0u50srmhhfcsTexqauXzm4m1KlgYrWOFKYrbonUEezBnUFEUTjSdoLCikA1VG2jqanIfS9Ylk23OJsecQ1JEksdqGC5dpaU41uXTUvh3OYNJSQM5gyPgimohhP/xhf4lPDycY8eOkTLC81Z9Ya6FEP6t2dpBebGVUwds2Ova3ePaQDWpU2JIzzKRPDEaTYBns8KF8AfltjYKzi8YVjUM/LxEBGu5fVIceZmJzEmLRqOWSKcvM6SLhAaD4bLzs5qamr76QT5OGj/h65yKwsf2Nt6zNrGh3kH7BTmDM3ShrIiLIi82kuhAz16GXdtWy/rK9RRWFlLpqHSPG4IMLE1dSq45l0kxk/w+f6/XaqOlsLA/Z7C01D2u1uvR3b4Ufd5yQqb5f56iEMK/+UL/snz5cu666y4eeOABr7z+cPGFuRZCjByNNW2UFVkpK7LRUj9wN1BgiBbz1BgyskwkjjOg8fDmgkL4OkVROFbTQn5JDQUldVhaBu5aiwkPImdKPLmZCUwfHSnvzf7OkC4SvvXWW5f9wiOhKZTGT/iqE22dvGdpYq21GUvPQM5gcnAgK+IMfM0UhTnUszumtfS0sOX0FgoqCyi2FrvHgzRBLEhaQK45l7mJcwlQ+3euiqu9vT9ncF0+7fv2DcoZjFhwM7q8PMJvvllyBoUQPsMX+pfXXnuNZ599lvvuu48ZM2YQFhY26HheXp5X6hpqvjDXQoiRR1EU6qtbKTtgpbzYRpu9230sODyAtOlGMrKMxKdHopYrpsR1zuVSOHC6iXUltWw8Woe944IcfkMIeZkJ5E1NYFyc/J0Gud34mkjjJ3yJpbuXNVY7qy1NnGgfOFNi0GrINUZyd1wUWbpQj54p6XX2sqdmDwWVBew6u4seV4/72My4meSac1mUvIiIwAiP1TAcLpkzOG0a+uV56JYuRRMZ6b0ihRDiS/hC/6JWf/lVLiqVCqfTOYzVeI4vzLUQYmRTXAp1lQ7KD1gpP2ijs3VgASRMH0j6DBPpM42YUnRyxZS47vU6XewpayC/pJbNxy109Az0G2NM4f0LhpmJjI4O9WKV3jXki4Qul4sXX3yR/Px8enp6WLhwIU8//TQhISFDVrSvkMZPeFt7n5P1DQ5WW5r4yN7G5z+kgSoVt8XoWGEysDBaR+Al3oxdK0VRONJwhIKKAjaf3kxzd7P7WJo+jZy0HLJTs4kPj/dYDcPlS3MGR48eyBkcPdqLFQohxFeT/mX4yFwLIYaTy+mi5lQzZUVWKg/V093R5z4WER1MRpaJjJlGohPDZcFQXPc6e5xsO2kl/3AtO0vr6bkgmmtqUiR5mQnkTInHqAv2YpXDb8gXCZ9//nmeeeYZFi1aREhICJs3b+bee+/ljTfeGLKifYU0fsIb+lwKH9lbWW21s6HeQadr4JfZbH0YXzMZyDNGEhng2ZzBsy1nKawspLCykOrWavd4dHA0y8zLyDXnMi5qnN83IF+WM6jR64lYdjv6vDxCpkrOoBDCf/ha/9LV1UVw8MhswH1troUQ1w9nn4uzJ5o4dcBK1ZEG+roHrpgyxIWSMdNE+gwjhriwSzyLENcHR2cvm49ZyC+pZW9FA67zq19qFdxgjiYvM4HbJ8WjD/XvqKzLMeSLhBkZGfzoRz/i29/+NgBbt24lOzubzs7OS95a4o+k8RPDRVEUjrV1stpiZ63Njq1n4KygOSTofM6ggeQQz+YMNnc1s/n0ZgorCzlcf9g9HqIN4dbRt5JrzmV2/Gy0as8uUHqaq72dli1baMnPp/2TfXD+158qIIDwBQvQL88j/KabUEnOoBDCD/lC/+J0OnnhhRd47bXXsFqtnDp1CrPZzE9/+lNSUlL41re+5ZW6hpovzLUQQvT2ODlztJGyIitnjjbi7Bu4yCAmKZyMLBPpWUZ00SPv7j8hrpSttYsNR+rIL6nlYHWzezxAo+LmMUbypiawaLyRUA9v/uktQ75IGBQURHl5OUlJSe6x4OBgysvLGTVq1LVX7EOk8ROeVtPV058zaLVTekHOYFSAhjuMBlaYDEzzcM5gj7OH3ed2U1BRwO6a3fS5+hco1So1N8TfQI45h4WjFxIa4N+5DYrTSfsn+3Dkr6N1y1aUzoEd40KmT0efl4du6RLJGRRC+D1f6F+ee+453nrrLZ577jkeeughjh07htls5t133+WVV17hk08+8UpdQ80X5loIIS7U09lHVUk9pw7YOPdZEy7XwNv8OLOO9Kz+KwzD9J69+EAIf3C2qYP8kloKSmo5aWl1j4cGarhtgom8zATmZ8QSqB05F8QN+SKhRqPBYrEQGxvrHouIiODIkSOkpqZee8U+RBo/4QmtfU4K65tZbbGzt3kgZzBIrWJxtJ674wzcEqUjwIM7lbkUF4dshyisLGTz6c209gz8QhwXNY4ccw63p96OMdTosRqGS9fJkwM5g/X17nHJGRRCjFS+0L+kp6fz+uuvs3DhQiIiIigpKcFsNnPy5EnmzJmD3W73Sl1DzRfmWgghvkxXWy8Vh2yUFdmoOWXH/cZDBYljIsnIMpE2zUhw+Mi/xVKIr1JqaSW/pIb8klrONg1cUBIZGsDtk+LJy0xgdmqU3+8oPuSLhGq1mttvv52goIEzDwUFBdx6662EhQ3kHaxZs+YayvYN0viJodLrUtjZ1MJqq53NDQ66Ljijd4M+jLvjosiJ1aP3cM5glaOKgooCNlRtoKatxj1uCjWRbc4mx5xDhiHDozUMh16rtT9ncF0+3adOucclZ1AIcT3whf4lJCSEkydPkpycPGiR8MSJE8yaNYu2tjav1DXUfGGuhRDicrQ7uqk4aKPsgA1LpcM9rlarGDU+ioyZRlIzYwkKGZm3WApxuRRF4fDZZvJLaik8Ukd9a7f7mEkXRM6UBJZPTWByot4v309ebu9y2b8JHnjggS+M/dM//dPVVSfECKYoCiWtnay2NrHW2kxj70DOYEZoECtMUdwVZyAp2LO5d42djWw6vYnCikKONR5zj4cFhHFb8m3kmnPJistCrfLvS6gvmTN4yy3o83IlZ1AIIYbJhAkT+Oijj0hOTh40vnr1aqZNm+alqoQQ4voVpg9iyi1JTLkliZbGTsqLbZQdsNJwto3q441UH29Eoy0leVI06VlGUibHEBCk8XbZQgw7lUrFtNEGpo028JPsCeyvbGTd4Vo2HqvD2tLN/+yp4n/2VJESHUpeZgJ5UxNIN0Z4u+whd9lXEl5P5OywuBpnu3p439LE+1Y7ZR0DZx1iArTcaYrka6YoMiNCPHrWobOvk51nd1JYWcjHNR/jVPp3O9OqtMxLnEeOOYcFSQsI1vr3bpOXzBmcMWMgZ1Cv92KVQggxvHyhf1m3bh0PPPAATz75JM899xzPPvsspaWl/O///i+FhYXcdtttXqlrqPnCXAshxLVotnZQVmSl7IAVu6XDPa4N0pA6OZr0LBPJE6PRBPj3BQVCXKvuPie7TzWQX1LLlhMWunoHNggaH68jLzOB3Mx4Rhl8O8t/yG83vp5I4ycul6O3j8J6B+9ZmtjnaHePB6tVLI3RsyIuipsNER7PGTxgOUBBRQFbq7fS3jtQx6ToSeSk9ecMRgVHeayG4fJlOYOBycnoluehz80l8ILNlYQQ4nriK/3LRx99xHPPPUdJSQltbW1Mnz6dp556isWLF3utpqHmK3MthBDXSlEUGmvaKSuyUl5kpaVhYFPFwBAt5mmxZGQZGTXWgFojC4bi+tbe3cfWz6zkH65l16l6+i6IE8tKNpA3NYFlk+OJCfe9DYJkkfAaSOMnLqXH5WJHUyvvWZrY0thC9/lfDCpgXmQ4K+IMZMdGEqH17GX6ZfYyCioL2FC5AWuH1T2eGJ7ozhlM1fv/pkJfmjMYGYlu2TL0y/MInjLFL3MhhBBiKEn/MnxkroUQI5GiKNjOtFJ2wEp5sY325oG7o4LDA0ifbiRjppH4tEhUfr6JgxDXyt7ew8ZjFtYdruHT002fp16hUauYlx5DXmYCSyaaiAj2jQ2CZJHwGkjjJ/6eoigcaungPauddTY7Tb1O97FxYcGsMBm4y2QgwcM5g/Ud9Wyo2kBBRQGl9lL3eERgBEtTlpJjzmGacZrfL5h9Zc7g8jzC58+XnEEhhLiAL/QvZrOZAwcOEB0dPWi8ubmZ6dOnU1lZ6ZW6hpovzLUQQniS4lKoq2im7ICN8oM2utp63cfC9IGkZ5nIyDJhTInw+/ceQlwri6OLwiO15JfUcuTcwAZBgVo1t441kjc1gVvHGQkO8F7epywSXgNp/MTnznR2s9pi532rncrOgTNpxkAtd5oM3G0yMDHcszmDHb0dbKveRkFFAfst+3Ep/RkIWrWWmxJvIjctl5tG3USgxr8XzJS+vvM5g/m0bpWcQSGEuFK+0L+o1WosFgtGo3HQuNVqZfTo0XR3d3/JV/oXX5hrIYQYLi6ni3OldsqKbFQeqqenc2BjRl1MsHvBMDoxTBYMxXWvqqGdgpJa1h2uoaJ+IAosPEjL4okm8jITuDE9Bu0w374vi4TXQBq/65u9t498WzOrLXYOtAz8UIeo1SyL1bPCZGC+IQKtBy+x73P1sb9uPwWVBWyv3k5n38CC2TTjNHLMOSxJWYI+yL8XzBRFoft8zqBjfSHO+gb3MXfOYF4egaNGebFKIYTwD97sX/Lz8wG44447eOutt9BfcELH6XSybds2tmzZQmlp6Zc9hV+RXlEIcb1y9rqoPtFI2QErVUca6OsZ2MTBEBdKxkwT6TOMGOLCvFilEN6nKAon6lrIL6mlsKSOmuaB9/TRYYEsmxxP3tQEZow2oB6G2/dlkfAaSON3/el2udjW2MJqi52tjS30nP+xUAPzDRGsiDOwLEZPmAdzBhVF4bOmzyisLGRj1UYaOgcWzJJ1yeSYc8g2Z5MU4f8bc/RaLAM5g2Vl7nHJGRRCiKvnzf5Fre4/G65Sqfj71jIgIICUlBReeuklcnJyhrUuT5FeUQghoLfbyemjDZQX2ThzrBFn38CCYUxSOBlZJtKzjOiiQ7xYpRDe53IpHKy2k19Sy/ojdTS297iPJUaGkJMZz/LMRCYkeK6nkEXCayCN3/VBURQOONpZbbWTb2umuW8gZ3BCWDAr4qK4y2QgLsizQaN1bXWsr1pPYUUhFY4K97ghyMDS1P6cwckxk/1+wczZ1k7rli048tfRsW//QM5gYOBAzuCNN0rOoBBCXCVf6F9SU1M5cOAAMTExXnn94eILcy2EEL6ku7OPqsP1lBVZOfuZHeWCXV/jzDrSs/qvMAzT+96ur0IMpz6ni48rGsk/XMvm4xbauvtv3791nJE3/nmmx15XFgmvgTR+I1tlRzerrU28b7FzpmtgBT8uMIC7TAZWxBmYEO7Zs12tPa1sPbOVgsoCiixFKPT/GAaqA7ll9C3kmnOZmziXALVv7IR0tfpzBj/Bse58zmBXl/tYSNbnOYNL0cjPmRBCXDPpX4aPzLUQQny5zrYeKg7WU15kpaasGT5fcVBB4phIMrJMmKfFEhIuFweI61tXr5OdpTbWHa4lZ0oC2VPiPfZaskh4DaTxG3kae/pYZ7Oz2mrnYEuHezxUoyY7Vs/dpijmGcLRePBqvV5XLx/XfExhZSE7z+6k2zkQ3j4zbia55lwWJS8iIjDCYzUMB0VR6P7ss/6cwQ3rB+cMpqSgX56HLjdXcgaFEGKI+Ur/sm3bNrZt24bNZsPlcg069sYbb3ipqqHlK3MthBC+rr25m/KDNsqLrFgqW9zjarWKUeOjyJhpJDUzlqAQrRerFGLku9zeRX4SxYjV5XSxpbGF1dYmtjW20Hd+OVwN3BwVwQqTgaWxesI0ns0ZPNpwlMLKQjZVbcLebXcfS9OnkZOWQ3ZqNvHhnjtjMFx6LRYcBQW05OfTXVbuHtcYDAM5g5P9/7ZpIYQQX+7ZZ5/lueeeIysri/j4ePmdL4QQ17mwyCAyb00i89YkWho6KS+2UVZkpeFsG9XHG6k+3ohGW0rypGjSs4ykTIkhINBz78+EEJcmVxJehJwd9l8uReFTRzurLXYK6ptxXJAzODk8hBVxBu4wGjB5OGfwbOtZCisLWV+5njMtZ9zj0cHRLDMvI9ecy7iocX7/5snZ1k7rhx/iyM+nY//FcgaXEz7/RlQB/n3btBBC+ANf6F/i4+P55S9/yTe+8Q2vvP5w8YW5FkIIf2a3tPcvGB6wYrcM3OmlDdKQOiWGjCwjoydEowlQe7FKIUYOuZJQXFcqOrpYbem/nfjsBTmDCUEDOYPjwjybM+jodrD59GYKKws5ZDvkHg/RhnDr6FvJNecyO342WrV//9gpfX20793bnzO4bZvkDAohhHDr6elh7ty53i5DCCGEjzPEhTEzO5WsZSk01rRTVmSlvMhKS0MXZQeslB2wEhiixTwtlowsI6PGGlBrZMFQCE+TKwkvQs4O+4eGnj4+sNl532LnUOvA2adwjZqc2EhWxBmYGxmO2oNX6/U4e9h9bjcFFQXsrtlNn6t/ZyK1Ss3suNnkpuWycPRCQgNCPVbDcFAUha4TJ2jJz8exfgPOBskZFEIIX+ML/cvjjz9OeHg4P/3pT73y+sPFF+ZaCCFGGkVRsJ1udS8YtjsGLv4IDg8gbbqRjCwjCemRqNT+fUeWEMNNriQUI1Kn08WHjQ5WW+zsaBrIGdSoYIFBx91xBhbH6An14FkmRVE4ZDtEQWUBm09vprWn1X1sXNQ4csw53J56O8ZQo8dqGC6f5ww61q2jp7zCPS45g0IIIS6mq6uLP/zhD2zdupUpU6YQ8HdxEy+//LKXKhNCCOHrVCoVplQdplQd876WTm15M+VFNsoP2uhq6+X47hqO764hTB9I+gwT6TONmFJ08l5EiCHkE4uEv/vd73jxxRexWCxkZmby6quvMmvWrIs+9s033+TBBx8cNBYUFETXBbc8Wq1WHn/8cT788EOam5u56aabePXVV8nIyPDo9yE8w6Uo7GtuZ7W1iQJbM63OgZ0SMyNCWGGK4g5TJLGBns29q3JUuXMGa9pq3OOmUBPZ5mxyzDlkGPz/39glcwZvvRV9Xp7kDAohhLioI0eOMHXqVACOHTs26NiVvol75plnePbZZweNjR07lpMnT1708b29vaxatYq33nqLmpoaxo4dyy9+8QuWLl160cf//Oc/58knn+Sxxx7jlVdeuaLahBBCeJZKrSJxjIHEMQbm35PBuVI7ZUU2Kg/V0+7ooWT7WUq2n0UXE0z6DBMZM41EJ4bLgqEQ18jri4TvvvsuK1eu5LXXXmP27Nm88sorLFmyhNLSUozGi1+JpdPpKC0tdX9+4S8CRVG44447CAgIYN26deh0Ol5++WUWLVrEiRMnCAsL8/j3JIbGqfYuVluaeN9qp6a71z2eGBTAirgovmYyMCYs2KM1NHU1sbFqI4UVhRxrHHizExYQxm3Jt5FjziHLlIVG7d87cF0qZzA0Kwvd8jx0S5ZIzqAQQohL2rFjx5A+38SJE9m6dav7c632y1vXn/zkJ7z99tv88Y9/ZNy4cWzevJk777yTvXv3Mm3atEGPPXDgAK+//jpTpkwZ0nqFEEIMPbVGzegJ0YyeEM2Ce8dSfaKRsiIbVUcaaGno4uDmMxzcfAZDXCjpWSYysowY4uR9vxBXw+uLhC+//DIPPfSQ++rA1157jfXr1/PGG2/wxBNPXPRrVCoVcXFxFz1WVlbGvn37OHbsGBMnTgTg97//PXFxcfzf//0f//Iv//KFr+nu7qa7u9v9eUtLy7V+W+Iq1ff08oG1mfesTRxp7XSPR2jU5BojWWGK4obIMI/mDHb1dbHz7E4KKgv4uOZjnEr/DskalYZ5ifPIMeewIGkBIVrPboTiaZfMGUxN7c8ZzMklcFSiF6sUQghxPdNqtV/a8/29v/zlL/z4xz9m2bJlADz88MNs3bqVl156ibffftv9uLa2Nu677z7++Mc/8rOf/cwjdQshhPAMTYCa1MxYUjNj6e1xcvpIA+XFNs4cbcRu6eBAYRUHCquISQonI8tE+gwjuhj/ft8mxHDy6iJhT08PxcXFPPnkk+4xtVrNokWL+OSTT77069ra2khOTsblcjF9+nReeOEF94Lg54t9wcEDV5ip1WqCgoLYs2fPRRcJV61a9YXbWcTw6XC62NzQnzO4096C83zOoFYFt0TpWBFnYHG0nhAP5gy6FBdFliIKKgvYcmYL7b3t7mOToieRk5bD0pSlRIdEe6yG4dJbV4ejoBBH/kVyBrOz+3MGJ02SS/WFEEJctrvuuuuyHrdmzZoret6ysjISEhIIDg5mzpw5rFq1itGjR1/0sd3d3YP6P4CQkBD27NkzaOyRRx4hOzubRYsWXfYioZxQFkII3xMQqCEjy0RGlomezj6qSuopK7Jx9kQTDWfbaDjbxidrKzCl6twLhmGRQd4uWwif5tVFwoaGBpxOJyaTadC4yWT60ryZsWPH8sYbbzBlyhQcDgf//d//zdy5czl+/DijRo1i3LhxjB49mieffJLXX3+dsLAwfvWrX3Hu3Dnq6uou+pxPPvkkK1eudH/e0tJCUlLS0H2j4gtcisLe5jbes9hZX99M2wU5g1MjQlkRZ+AOo4GYQM/+Ey2zl7lzBq0dVvd4Yngi2eZsss3ZmPVmj9YwHJxtbbRuPp8z+Omng3MGF57PGbxRcgaFEEJcHb1eP+TPOXv2bN58803Gjh1LXV0dzz77LPPnz+fYsWNERER84fFLlizh5Zdf5qabbiItLY1t27axZs0anE6n+zHvvPMOBw8e5MCBA1dUi5xQFkII3xYYomXsDfGMvSGerrZeKg7ZKCuyUXvKjrWqBWtVC3tWl5GYEUl6lom0abGERAR6u2whfI5KUc6vFnhBbW0tiYmJ7N27lzlz5rjH/+M//oNdu3axf//+r3yO3t5exo8fz7333svzzz8PQHFxMd/61rcoKSlBo9GwaNEi1Go1iqKwcePGr3zOy90aWly5k+2drLbYWWO1U3tBzmBScCBfMxlYEWcgPdSzOYP1HfVsqNpAYWUhJ5sGFqMjAiNYkrKEHHMO04zTUKs8d+XicFD6+mj/+OP+nMHt2wfnDM6ciX55HhFLlqC5yBstIYQQ/mek9y/Nzc0kJyfz8ssv861vfesLx+vr63nooYcoKChApVKRlpbGokWLeOONN+js7OTs2bNkZWWxZcsWdxbhggULmDp16lduXHKxKwmTkpJG7FwLIcRI0e7opuJgPeVFVuoqHO5xlVpF0jgD6VkmzFNjCAqViyXEyHa5faJXrySMiYlBo9FgtVoHjVut1svOnwkICGDatGmUl5e7x2bMmMHhw4dxOBz09PQQGxvL7NmzycrKGtL6xeWxdfey1mZntcXO0baBnEGdVk1ebP/C4Cy9Z3MGO3o72Fa9jcLKQvbV7cOl9F+5qFVruSnxJnLTcpk/aj5BGv++/FxRFLqOn8CRv46W9RtwNja6j0nOoBBCCH8WGRnJmDFjBvV8F4qNjeWDDz6gq6uLxsZGEhISeOKJJzCb++8IKC4uxmazMX36dPfXOJ1Odu/ezW9/+1u6u7vRaC6+EVlQUBBBQf7dIwghxPUoTB/ElFtGMeWWUbQ2dVFeZKOsyEp9dSvVJ5qoPtHEzr+qSJ4YTUaWiZQpMQQE+femlEJcC68uEgYGBjJjxgy2bdvGHXfcAYDL5WLbtm1873vfu6zncDqdHD161B1SfaHPb30pKyujqKjIfaWh8LwOp4tNDQ7eszSx2946KGdwYbSOFaYobovWEezBnME+Vx/76/ZTUFnA9urtdPYNLFBOjZ1KblouS1KWoA8a+lukhltvbe35nMF8eiokZ1AIIcTI09bWRkVFBd/4xjcu+bjg4GASExPp7e3l/fff5+tf/zoACxcu5OjRo4Me++CDDzJu3Dgef/zxL10gFEIIMTJERAUzbfFopi0eTbO1g/JiK2VFNppq26kqaaCqpAFtoJqUKTFkzDAxelIU2gD52yCuL17f3XjlypU88MADZGVlMWvWLF555RXa29vdux3ff//9JCYmsmrVKgCee+45brjhBtLT02lububFF1/kzJkzgzYkee+994iNjWX06NEcPXqUxx57jDvuuIPFixd75Xu8XjgVhb32Nt6zNrG+3kH7BTmDM3ShrIiLIi82kmgP5gwqisLJppMUVBawsWojDZ0DO/aOjhhNTloOOak5JOn8P3NScgaFEEKMZD/60Y/Izc0lOTmZ2tpann76aTQaDffeey/wxR5x//791NTUMHXqVGpqanjmmWdwuVz8x3/8BwARERFMmjRp0GuEhYURHR39hXEhhBAjW6QplKxlqWQtS6Wxpo3yYhunDlhpqe+kvMhGeZGNwGANqVNjycgyMWq8AY0HL3ARwld4fZHwnnvuob6+nqeeegqLxcLUqVPZtGmTezOT6upq1OqBH0a73c5DDz2ExWLBYDAwY8YM9u7dy4QJE9yPqaurY+XKlVitVuLj47n//vv56U9/Ouzf2/Xis7ZOVlv7cwbrLsgZTA4O5GtxBlaYojCHevYWHUu7hcLKQgorCqlwDFxJFxkUydKUpeSm5TI5ZrLfX0knOYNCCCGuF+fOnePee++lsbGR2NhYbrzxRvbt20dsbCzwxR6xq6uLn/zkJ1RWVhIeHs6yZcv4y1/+QmRkpJe+AyGEEP4gOjGc6MRwZuWmUl/dSlmRjfIiK232bkr3WSjdZyEoTEvadCMZWSYSMiJRq/37faUQX8arG5f4qpEe/D0UrN29rLXaWW21c+yCnMFIrYY8YyQrTAZm6sM8uijX2tPK1jNbKagsoMhShEL/P+VAdSALkhaQm5bLvMR5BKj9+0q6S+YMms3o8/LQ5+YQkCg5g0IIcT2T/mX4yFwLIcTIprgULJWO/gXDgzY6W3rcx0J1gaTPMJKeZSIuVYdKFgyFH/CLjUuEf2l3OtlU72C11c6uplY+v5k4QKViUbSOFXEGFkXrCFJ77jLsXlcve2v2UlBZwM6zO+l2Duw0ODNuJjnmHG5Lvo2IQP+/ku5LcwajovpzBvPyCJ400e+vjhRCCCGEEEIIX6JSq4hPjyQ+PZIb706npqyZ8iIbFQdtdLT0cGTHOY7sOEd4VBAZM0xkzDQRkxQu782E35NFQnFJTkXh4wtyBjsuyBnM+jxn0BhJVIBncwaPNRyjoLKATVWbsHfb3cfMejO5ablkp2YTHx7vsRqGS3/O4GYc687nDJ6nCgoiYuGt6PLyCJ83T3IGhRBCCCGEEGIYqDVqksZFkTQuipv+YQxnP2uivMhGZUk9bU3dHNpSzaEt1eiNIWRkmcjIMhGVEObtsoW4KrJIKC7qRFsn71maWGttxtIzkDOYEhLIClMUK+IMpIR4NmfwbOtZ1leuZ33lek63nHaPRwdHc3vq7eSm5TI+arzfn61Rentp+/hjWvLzad22HaV74OrI0Fmz+nMGFy+WnEEhhBBCCCGE8CKNVk3K5BhSJsfQ1+PkzPFGyg7YOHO0AYetk6INpynacJqohDAyskykZxmJNIZ6u2whLpssEgo3S3cva6x2VluaONE+sCHG5zmDX4+LYoYu1KOLco5uB5tPb6awspBDtkPu8RBtCLeOvpUccw43xN+AVu3f/3QVRaHr2DEc6/Jp2bABZ1OT+5jkDAohhBBCCCGEb9MGakibZiRtmpGerj5OH2mgrMhG9fFGmmrb2Z9fyf78SmJHR7gXDCOigr1dthCX5N8rLeKatfc52dDgYLXFzkf2gZzBQJWK22J0rDAZWBitI9CDOYM9zh52n9tNYWUhu8/tptfVf+WiWqVmdtxsctNyuXX0rYQF+P8l2701NQM5g5WV7nHJGRRCCCGEEEII/xQYrGXMrDjGzIqjq72XqpJ6yopsnDtpp766lfrqVvauKSc+TU96lom06bGE6T17Z54QV0MWCa9DTkXhI3srqy12NjQMzhmcpQ9jhclArjESg4dzBg/ZDlFQWcDm05tp7Wl1HxtrGEtuWi63p96OMdTosRqGi7O1dSBn8MAB97jkDAohhBBCCCHEyBIcFsD4uQmMn5tAR0sPlYdsnDpgpa7C4f7Y87dTJI41kJFlwjwtluAweS8ofIMsEl5HjrtzBu1Ye/rc46kX5Awmezhn8LTjNAWVBayvXE9NW4173BhqJDs1m5y0HMYYxni0huGg9PbStmcPjvx82rbvkJxBIYQQQgghhLjOhOoCmXTzKCbdPIo2ezcVB22UFVmxVrVw7qSdcyft7PprKUkTosjIMpKaGUtgiCzTCO+Rf30jXF13D2uszay2NPHZBTmDBq2G5SYDd5sMTPdwzmBTVxMbqzayvnI9RxuOusdDtaHclnwbuWm5ZJmy0Kg1HqthOFwyZzAtbSBnMCHBi1UKIYQQQgghhBhu4YYgMhcmkbkwiZaGTsqKrJQV2Wg818aZY42cOdaIRltK8uRoMrJMJE+OJiDQv98jC/8ji4QjUFufk/X1Dt63NvGRvQ3l/PjnOYN3m6K4NTrCozmDXX1d7Dy7k4LKAj6u+Rin4gRAo9IwN2EuuWm5LEhaQIg2xGM1DJcvzRmMjkafk40uL4/gCRMkZ1AIIYQQQgghBLqYEGYsTWHG0hTslnbKimyUHbDSbO2g8lA9lYfq0QZpSJ0SQ0aWkdETotEEeO79uxCfk0XCEaLPpbDL3sr7Vjsb65vpdCnuY7P1YayIM5AbG0mkB3MGXYqLIksRBZUFbDmzhfbedvexidETyTHnsDR1KTEhMR6rYbhcOmdwIfrleYTNm4dKKz9iQgghhBBCCCEuzhAXxqycVGZmp9BY00ZZkY3yIistDV2UHbBSdsBKYIgW89QYMrJMJI4zoNHIgqHwDFnB8GOKonC0rZPVFjtrbXbqL8gZTAsJYkWcgbtMns8ZLLeXu3MGrR1W93hCWALZ5v6cQbPe7NEahsOX5gyqVP05g3l5RCxZjCY83LuFCiGEEEIIIYTwKyqViphREcSMiuCG5WZsp1spK7JSXmyjvbmbk59YOPmJheDwANKmG8nIMhKfHolaLXesiaEji4R+6GxXD2utdt6zNFHWMbAhRlSAhjuMBlbEGZgW4dmcwfqOejZUbaCwspCTTSfd4xGBESxJWUKOOYdpxmmoVf59huOSOYPpaejzlvfnDMbHe7FKIYQQQgghhBAjhUqlwpSqw5SqY97X0qmrcFB2wErFIRudrb0c313D8d01hOoDSZ9hJCPLhClVJxFX4prJIqGfcPT2UVDvYLWliX2Ogdt4g9QqlsToWWEycEuUjgAPnkXo6O1gW/U2CioK2G/Zj0txAaBVa5mfOJ/ctFxuGnUTQRrPXrk4HCRnUAghhBBCCCGEt6nUKhIyIknIiGT+PRnUlDZTVmyl8lA9HY4ejmw/x5Ht54iICiY9q3/BMCYpXN6riqsii4Q+rMflYltjC6utdrY0tNCj9OcMqoC5keF8Lc5ATmwkOq3ndjzqc/Wxv24/BZUFbK/eTmdfp/vY1Nip5Kblsjh5MZHBkR6rYbh8ac5gcPBAzuDcuZIzKIQQQgghhBBi2Kk1apImRJE0IYqb7x3L2RNNlBVZqSppoLWpi0MfVnPow2oiTaH9C4YzTEQlhHm7bOFHZLXDx7gUhf2OdtZY7RTYmmnuc7qPjQsLZoXJwJ0mA4nBgR6rQVEUPmv6jMLKQjZWbaShs8F9bHTEaHLScshJzSFJl+SxGoaL0ttL20d7cBRcJGdw9uz+nMHFt0nOoBBCCCGEEEIIn6HRqkmZEkPKlBj6epycOdZI2QErp4810mztoGj9aYrWnyY6MYz0LBMZWUb0saHeLlv4OFkk9BEn2jp532rnA6udmu5e93hcYAB3miJZERfFhLBgj14yXNdWx/qq9RRWFFLhqHCPRwZFsiRlCblpuUyJmeL3ly0rikLX0aMDOYN2u/uY5AwKIYQQQgghhPAn2kANadONpE030tPVx+kjDZQV2ag+3khjTTuNNZXsX1eJMTmC9CwT6TOMREQFe7ts4YNkkdCLPt+AZI3Vzsn2Lvd4hEZNjjGSr5kMzIkMR+PBRbnWnla2nNlCYWUhBywDt9gGqgO5ZfQt5JhzmJcwjwBNgMdqGC4952poKcjHkV9AT1WVe1xyBoUQQgghhBBCjASBwVrGzIpjzKw4utp7qTxcT3mRlXOlzdjOtGI708re98uJT9eTPsNE2vRYwvT+v6+AGBqySOglL1TU8ptqm/vzQJWKRdE67jIZWBStI1jjuV2Be529fFz7MQUVBew8u5MeV4/72My4meSac1mUvIiIwAiP1TBcnC0ttGzahCM/n86iYve45AwKIYQQQgghhBjJgsMCmDAvgQnzEuho6aHioI3yYhu15c3UlTuoK3ew52+nSBhjICPLSNo0I8Hh/n+BkLh6sjLiJRMjQgY2IDEZyI7Vow/w3P8ORVE40nCEwopCNp3eRHN3s/tYmj6NnLQcslOziQ/3/1ts3TmD+fm0bd+O0nN+EVSlInTWrP6cwSWLJWdQCCGEEEIIIcR1IVQXyOQFo5i8YBRt9m4qDtooK7JirWqhptROTamd3f93ilHjo8iYaSQ1M5agEFkyut6oFOX8lrnCraWlBb1ej8PhQKfTeeQ1upwumnr7SPDgBiQAZ1vOUlhZSGFlIdWt1e7xmJAYlqUuI8ecw7iocX5/i+2lcgaDMtLR5eWhz5GcQSGEECPXcPQvop/MtRBCiJGipaGTsiIr5cU2Gs62ucc1WjXJk6JJzzKSMjmGgCCNF6sU1+pyexdZFvaSYI2aBI1nFgjtXXY2n95MYWUhJfUl7vEQbQgLRy8k15zLrPhZaNX+/7/fnTO4Lp+e06fd45qYGPTZ2eiX5xE0frzfL4IKIYQQQgghhBBDTRcTwoylKcxYmoLd0k5ZkY3yIit2SweVh+upPFyPNlBN6pQY0rNMJE+MRhPguXg04V3+v0okAOjq62LXuV0UVhay59we+pQ+ANQqNTfE30COOYeFoxcSGuD/W55fMmdw0aL+nME5cyRnUAghhBBCCCGEuEyGuDBm5aQyMzuFxpr2/isMi6y0NHRRVmSjrMhGYIgWc2YM6TNNjBpnQOPB/RTE8JNVFD/mUlwUWYoorCxky5kttPUOXBo8Pmo8OeYclpmXERMS48Uqh8YlcwZvmI0+bzkRt92GJjzMu4UKIYQQQgghhBB+TKVSETMqnJhR4dyw3IztTCtlRVYqim202bs5uc/CyX0WgsMCME+PJSPLREJGJGq13MHn72SR0A+VNpWyvnI9G6o2YO2wusfjw+LJMeeQbc4mLTLNixUOjcvKGczNJSAuzotVCiGEEEIIIYQQI5NKpcKUosOUomPeXenUVTgoL7JSftBGZ2svJz6q5cRHtYTqAkmbYSRjhpE4sx6VLBj6JVkk9BOWdgvrK9ezvmo9ZfYy93hEQASLUxaTY85humk6apX/X+orOYNCCCGEEEIIIYRvUalVJGREkpARyY1fz6CmrJnyA1YqDtXT0dLD0R3nOLrjHOGGINKzTGRkGYkdHSHv3f2ILBL6sJaeFrac3kJhZSHF1mIU+jeiDlAHcNOom8gx5zB/1HyCNEFervTaSc6gEEIIIYQQQgjhH9QaNUnjokgaF8VN947l7GdNlBfZqCypp83ezeEt1RzeUo0uNoSMLCMZWSaiE8O9Xbb4CrLi4mM+34BkQ+UGPqr5iF5Xr/tYlimLbHM2tyXfhj5I78Uqh4bS00Pbnj041uXTtmOH5AwKIYQQQgghhBB+RqNVkzI5hpTJMfT1ODlzvJHyIhunjzTQUt9J8cYzFG88Q1RCGBlZRtJnmIg0+f+mqiORLBL6gD5XH5/Wfcr6qvVsq95Ge2+7+1h6ZDrZ5myyU7OJD4/3YpVDQ1EUuo4cGcgZbG52H5OcQSGEEEIIIYQQwn9pAzWkTTOSNs1IT1cfp482UF5k48zxRppq29mfX8X+/CpiR0eQnmUkfYYRXXSIt8sW58kioZcoisKRhiNsqNzAptObaOpqch+LD4tnWeoylpmXMcYwxotVDp2ec+dw5OfTkl/wxZzBnJz+nMFx4ySrQAghhBBCCCGEGAECg7WMmRnHmJlxdHf0Unm4gfJiK2c/s1Nf3Up9dSufrKkgzqwjPctE+gwjYXr/j1PzZ7JI6CU///Tn/PXkX92fG4IMLE5ZTLY5m8zYzBGxAYnT4aBl0+b+nMFiyRkUQgghhBBCCCGuR0GhAYyfG8/4ufF0tvVQcbCe8mIrNaeasVS2YKlsYc97ZSRmRJKeZSJteiwh4YHeLvu6I6szXjI3YS5ry9eycPRClqUu44aEGwhQB3i7rGs2KGdw+3aU3vOZipIzKIQQQgghhBBCXPdCwgOZdFMik25KpN3RTXmxjfIiG5ZKBzWnmqk51czud06RNM5AepYJ89QYgkL9f73EH6gURVG8XYSvaWlpQa/X43A40Ol0HnmNXlcvvc5eQgP8P6zz0jmDGeiX56HLyZGcQSGEEMKDhqN/Ef1kroUQQoih19LY6V4wrK9udY+rtSpGT4gmY6aRlMkxBAbL9W5X6nJ7F5lZLwlQB/j9lYPunMF1+fScOeMe18TGoM+WnEEhhBBCCCGEEEJcHl10CNMXJzN9cTLN1g7Ki62UFdloqm3n9JEGTh9pQBugJnlyDBlZRpInRaMN1Hi77BFFFgnFFfnSnMGQkP6cwbw8wubcIDmDQgghhBBCCCGEuCqRplCylqWStSyVxpo2yottlB2w4qjvpOKgjYqDNgKCNKROjSEjy0TS+Cg0Wv/f28HbZCVHfCWlp4e2jz7qzxncsWNQzmDYnBvQ5eWhu+021GGSMyiEEEIIIYQQQoihE50YTnRiOLNyU2k420bZAStlxVbamro5td/Kqf1WgkK1mKfFkpFlInFMJGqNLBheDZ9YJPzd737Hiy++iMViITMzk1dffZVZs2Zd9LFvvvkmDz744KCxoKAgurq63J+3tbXxxBNP8MEHH9DY2Ehqairf//73+c53vuPR72MkURSFrpKS/tuJN2wcnDM4ZsxAzqDJ5L0ihRBCCCGEEEIIcV1QqVTEjo4gdnQEc+5Kw1rVQlmRlfJiGx2OHj77uI7PPq4jJCKAtOlGMrKMxKdFolJLBNrl8voi4bvvvsvKlSt57bXXmD17Nq+88gpLliyhtLQUo9F40a/R6XSUlpa6P//7zLuVK1eyfft23n77bVJSUvjwww/57ne/S0JCAnl5eR79fvxdz9mz/QuD+QVfzBnMyUW/PI/gceO8WKEQQgghhBBCCCGuZyqVijiznjiznnkrMqgra6asuP825M7WXo7tquHYrhrCIoNIn24kfaYRU4pO9kz4Cl7f3Xj27NnMnDmT3/72twC4XC6SkpJ49NFHeeKJJ77w+DfffJMf/OAHNF9wZdvfmzRpEvfccw8//elP3WMzZszg9ttv52c/+9kXHt/d3U13d7f785aWFpKSkq6bHeucDgctGzf15wwePOgeV4WEEHHbIvR5y/tzBjUSCCqEEEL4Ktlxd/jIXAshhBC+yel0UXPSTlmRlcrDDfR09rmPRUQHk5FlJD3LRMyo8OtqwdAvdjfu6emhuLiYJ5980j2mVqtZtGgRn3zyyZd+XVtbG8nJybhcLqZPn84LL7zAxIkT3cfnzp1Lfn4+3/zmN0lISGDnzp2cOnWKX/3qVxd9vlWrVvHss88O3TfmB5SeHtp27+7PGdy5cyBnUK0m7IYb0C/PI2LRIskZFEIIIYQQQgghhF/QaNSMnhjN6InRLPhHF9UnGikrslF1pIHWxi4Obq7m4OZqIk2hpGcZyZhhIipB1j0+59VFwoaGBpxOJ6a/y7UzmUycPHnyol8zduxY3njjDaZMmYLD4eC///u/mTt3LsePH2fUqFEAvPrqq/zrv/4ro0aNQqvVolar+eMf/8hNN9100ed88sknWblypfvzz68kHGkURaHz8GEc+fm0btiI0+FwHwsaOxZ93uc5gxe/zVsIIYQQQgghhBDCH2gC1KRmxpKaGUtvj5MzRxspL7Jy+lgjzdYOitafpmj9aaITw0jPMpGRZUQfG+rtsr3K65mEV2rOnDnMmTPH/fncuXMZP348r7/+Os8//zzQv0i4b98+8vPzSU5OZvfu3TzyyCMkJCSwaNGiLzxnUFAQQUFBw/Y9DLeeM2dw5BfgKCigt7raPa41GtHl5PTnDI4d68UKhRBCCCGEEEIIITwjIFBD+gwj6TOM9HT1UVXSQFmRlbMnmmisaaexppL96yoxJkeQnmUifYaRiKhgb5c97Ly6SBgTE4NGo8FqtQ4at1qtxMXFXdZzBAQEMG3aNMrLywHo7OzkP//zP1m7di3Z2dkATJkyhcOHD/Pf//3fF10kHIn67HZaN23CsS6fzsOH3eOq0FB0t92GfnkeobNnS86gEEIIIYQQQgghrhuBwVrGzo5j7Ow4utp7qTxcT3mRlXMn7djOtGI708re98uJT9OTnmUkbbqRMP3IvbDsQl5dJAwMDGTGjBls27aNO+64A+jfuGTbtm1873vfu6zncDqdHD16lGXLlgHQ29tLb28varV60OM0Gg0ul2tI6/c1ru5u2nbsxFFQQNvu3XBhzuDcuf05gwsXog69vi+fFUIIIYQQQgghhAgOC2DCvAQmzEugo6WHykM2yops1JY3U1fhoK7CwZ6/lZEwxkBGlpG0aUaCwwO8XbbHeP1245UrV/LAAw+QlZXFrFmzeOWVV2hvb+fBBx8E4P777ycxMZFVq1YB8Nxzz3HDDTeQnp5Oc3MzL774ImfOnOFf/uVfANDpdNx88838+7//OyEhISQnJ7Nr1y7+93//l5dfftlr36enKC4XncXFOPILaNm0CVdrq/tY0ITx/TmDy5YRYJScQSGEEEIIIYQQQoiLCdUFMunmUUy6eRRt9m7Ki62UF9uwVrVQU2qnptTO7v87xajxUWTMNJKaGUtQiNeX1YaU17+be+65h/r6ep566iksFgtTp05l06ZN7s1MqqurB10VaLfbeeihh7BYLBgMBmbMmMHevXuZMGGC+zHvvPMOTz75JPfddx9NTU0kJyfzX//1X3znO98Z9u/PU7orKvoXBgsK6K2tdY9r4+PR5+Sgz8slKCPDixUKIYQQQgghhBBC+J9wQxBTF41m6qLRtDR0Ul5so6zISsPZNqqPN1J9vBG19iTJE6PJyDKRMiWGgCD/j3NTKYqieLsIX9PS0oJer8fhcKDT6bxdjluvzUbLhg205BfQdeKEe1wdFkbE0iXoc/MInTUT1d/dai2EEEKIkc9X+5eRSOZaCCGEuD7ZLe39C4YHrNgtHe5xbaCalMkxZGSZGD0pCm2Aby0YXm7v4vUrCcWludrbad26FUd+Ae2ffAKf5ypqtYTPn48+N4fwW29FHXz97bojhBBCCCGEEEIIMVwMcWHMzE4la1kKTbXtlB2wUlZso6W+/2rD8mIbAcEazJmxpGcZSRofhUbrPxdyySKhD1J6e2nfuxdHQSGt27ahdHa6j4VMnYouLxfd7bejNRi8WKUQQgghhBBCCCHE9UelUhGdGE50Yjizl5upr26lrMhGeZGVNns3pfstlO63EBSqJW1aLOlZJhLHRKLW+PaCoSwS+gjF5aLz0CEchYW0btyEs7nZfSwwORldXi763FwCR4/2XpFCCCGEEEIIIYQQwk2lUmFM1mFM1jH3zjQslQ7Kzl9V2NnSw4mP6zjxcR0hEQGkTTeSkWUiPk2PSq3ydulfIIuEXtZVeoqWwkJa1q8ftAGJJjoa3bJl6HOyCZ4yBZXK9/7xCCGEEEIIIYQQQoh+KrWK+PRI4tMjufHuDGpP2SkrtlFx0EZnay/HdtVwbFcNYZFBpM/oXzA0pkT4zJqPLBJ6iSM/n8Y//Q/dp065x9RhYUTcdhu6nBzCbpiNSiv/e4QQQgghhBBCCCH8jVqtYtS4KEaNi+KmfxjDuZN2yg9YqTxcT3tzNyXbzlKy7Sy6mGAm3pTI9MXJ3i5ZFgm9xWm3033qFKqAAMJuvgl9Tg7hCxbIBiRCCCGEEEIIIYQQI4hGoyZ5YjTJE6O5uddJ9fEmyousVB1poKWhi9aGLm+XCMgiodfoli1DFRqKbvFiNHq9t8sRQgghhBBCCCGEEB6mDdBgnhqLeWosvd1OTh9tICo+zNtlAbJI6DXa2FgMd9/t7TKEEEIIIYQQQgghhBcEBGnIyDJ5uww33957WQghhBBCCCGEEEII4XGySCiEEEIIIYQQQgghxHVOFgmFEEIIIYTPeeaZZ1CpVIM+xo0b96WP7+3t5bnnniMtLY3g4GAyMzPZtGnToMesWrWKmTNnEhERgdFo5I477qC0tNTT34oQQgghhF+QRUIhhBBCCOGTJk6cSF1dnftjz549X/rYn/zkJ7z++uu8+uqrnDhxgu985zvceeedHDp0yP2YXbt28cgjj7Bv3z62bNlCb28vixcvpr29fTi+HSGEEEIInyYblwghhBBCCJ+k1WqJi4u7rMf+5S9/4cc//jHLli0D4OGHH2br1q289NJLvP322wBfuLLwzTffxGg0UlxczE033TS0xQshhBBC+Bm5klAIIYQQQviksrIyEhISMJvN3HfffVRXV3/pY7u7uwkODh40FhIScsmrDx0OBwBRUVGXrKO7u5uWlpZBH0IIIYQQI40sEgohhBBCCJ8ze/Zs3nzzTTZt2sTvf/97qqqqmD9/Pq2trRd9/JIlS3j55ZcpKyvD5XKxZcsW1qxZQ11d3UUf73K5+MEPfsC8efOYNGnSJWtZtWoVer3e/ZGUlHTN358QQgghhK9RKYqieLsIX9PS0oJer8fhcKDT6bxdjhBCCCHEVxrp/UtzczPJycm8/PLLfOtb3/rC8fr6eh566CEKCgpQqVSkpaWxaNEi3njjDTo7O7/w+IcffpiNGzeyZ88eRo0adcnX7u7upru72/15S0sLSUlJI3auhRBCCDGyXG6fKFcSCiGEEEIInxcZGcmYMWMoLy+/6PHY2Fg++OAD2tvbOXPmDCdPniQ8PByz2fyFx37ve9+jsLCQHTt2fOUCIUBQUBA6nW7QhxBCCCHESCOLhEIIIYQQwue1tbVRUVFBfHz8JR8XHBxMYmIifX19vP/++yxfvtx9TFEUvve977F27Vq2b99Oamqqp8sWQgghhPAbskgohBBCCCF8zo9+9CN27drF6dOn2bt3L3feeScajYZ7770XgPvvv58nn3zS/fj9+/ezZs0aKisr+eijj1i6dCkul4v/+I//cD/mkUce4e233+avf/0rERERWCwWLBbLRW9HFkIIIYS43mi9XYAQQgghhBB/79y5c9x77700NjYSGxvLjTfeyL59+4iNjQWguroatXrgfHdXVxc/+clPqKysJDw8nGXLlvGXv/yFyMhI92N+//vfA7BgwYJBr/XnP/+Zf/7nf/b0tySEEEII4dNk45KLGOnB30IIIYQYeaR/GT4y10IIIYTwJ7JxiRBCCCGEEEIIIYQQ4rLI7cYX8fnFlS0tLV6uRAghhBDi8nzet8hNIp4nvaIQQggh/Mnl9omySHgRra2tACQlJXm5EiGEEEKIK9Pa2oper/d2GSOa9IpCCCGE8Edf1SdKJuFFuFwuamtriYiIQKVSeex1WlpaSEpK4uzZs5JnMwRkPoeezOnQkzkdejKnQ0vmc+gN15wqikJraysJCQmDNvQQQ284ekX5WRx6MqdDS+Zz6MmcDj2Z06Enczr0hmNOL7dPlCsJL0KtVjNq1Khhez2dTic/XENI5nPoyZwOPZnToSdzOrRkPofecMypXEE4PIazV5SfxaEnczq0ZD6Hnszp0JM5HXoyp0PP03N6OX2inGYWQgghhBBCCCGEEOI6J4uEQgghhBBCCCGEEEJc52SR0IuCgoJ4+umnCQoK8nYpI4LM59CTOR16MqdDT+Z0aMl8Dj2ZU3E15N/N0JM5HVoyn0NP5nToyZwOPZnToedLcyoblwghhBBCCCGEEEIIcZ2TKwmFEEIIIYQQQgghhLjOySKhEEIIIYQQQgghhBDXOVkkFEIIIYQQQgghhBDiOieLhEIIIYQQQgghhBBCXOdkkdBLfve735GSkkJwcDCzZ8/m008/9XZJfmP37t3k5uaSkJCASqXigw8+GHRcURSeeuop4uPjCQkJYdGiRZSVlXmnWD+watUqZs6cSUREBEajkTvuuIPS0tJBj+nq6uKRRx4hOjqa8PBwvva1r2G1Wr1Use/7/e9/z5QpU9DpdOh0OubMmcPGjRvdx2U+r93Pf/5zVCoVP/jBD9xjMq9X5plnnkGlUg36GDdunPu4zOeVq6mp4Z/+6Z+Ijo4mJCSEyZMnU1RU5D4uf5/E5ZI+8epJnzj0pFccetIrepb0iddO+kTP8IdeURYJveDdd99l5cqVPP300xw8eJDMzEyWLFmCzWbzdml+ob29nczMTH73u99d9Pgvf/lLfvOb3/Daa6+xf/9+wsLCWLJkCV1dXcNcqX/YtWsXjzzyCPv27WPLli309vayePFi2tvb3Y/5t3/7NwoKCnjvvffYtWsXtbW13HXXXV6s2reNGjWKn//85xQXF1NUVMStt97K8uXLOX78OCDzea0OHDjA66+/zpQpUwaNy7xeuYkTJ1JXV+f+2LNnj/uYzOeVsdvtzJs3j4CAADZu3MiJEyd46aWXMBgM7sfI3ydxOaRPvDbSJw496RWHnvSKniN94tCRPnFo+U2vqIhhN2vWLOWRRx5xf+50OpWEhARl1apVXqzKPwHK2rVr3Z+7XC4lLi5OefHFF91jzc3NSlBQkPJ///d/XqjQ/9hsNgVQdu3apShK//wFBAQo7733nvsxn332mQIon3zyibfK9DsGg0H505/+JPN5jVpbW5WMjAxly5Ytys0336w89thjiqLIv9Or8fTTTyuZmZkXPSbzeeUef/xx5cYbb/zS4/L3SVwu6ROHjvSJniG9omdIr3jtpE8cOtInDj1/6RXlSsJh1tPTQ3FxMYsWLXKPqdVqFi1axCeffOLFykaGqqoqLBbLoPnV6/XMnj1b5vcyORwOAKKiogAoLi6mt7d30JyOGzeO0aNHy5xeBqfTyTvvvEN7eztz5syR+bxGjzzyCNnZ2YPmD+Tf6dUqKysjISEBs9nMfffdR3V1NSDzeTXy8/PJysri7rvvxmg0Mm3aNP74xz+6j8vfJ3E5pE/0LPk5HBrSKw4t6RWHjvSJQ0v6xKHlL72iLBIOs4aGBpxOJyaTadC4yWTCYrF4qaqR4/M5lPm9Oi6Xix/84AfMmzePSZMmAf1zGhgYSGRk5KDHypxe2tGjRwkPDycoKIjvfOc7rF27lgkTJsh8XoN33nmHgwcPsmrVqi8ck3m9crNnz+bNN99k06ZN/P73v6eqqor58+fT2toq83kVKisr+f3vf09GRgabN2/m4Ycf5vvf/z5vvfUWIH+fxOWRPtGz5Ofw2kmvOHSkVxxa0icOLekTh56/9IraYXslIYTPe+SRRzh27NigvAlxdcaOHcvhw4dxOBysXr2aBx54gF27dnm7LL919uxZHnvsMbZs2UJwcLC3yxkRbr/9dvd/T5kyhdmzZ5OcnMzf/vY3QkJCvFiZf3K5XGRlZfHCCy8AMG3aNI4dO8Zrr73GAw884OXqhBBiaEivOHSkVxw60icOPekTh56/9IpyJeEwi4mJQaPRfGHnH6vVSlxcnJeqGjk+n0OZ3yv3ve99j8LCQnbs2MGoUaPc43FxcfT09NDc3Dzo8TKnlxYYGEh6ejozZsxg1apVZGZm8utf/1rm8yoVFxdjs9mYPn06Wq0WrVbLrl27+M1vfoNWq8VkMsm8XqPIyEjGjBlDeXm5/Du9CvHx8UyYMGHQ2Pjx49235sjfJ3E5pE/0LPk5vDbSKw4t6RWHjvSJnid94rXzl15RFgmHWWBgIDNmzGDbtm3uMZfLxbZt25gzZ44XKxsZUlNTiYuLGzS/LS0t7N+/X+b3SyiKwve+9z3Wrl3L9u3bSU1NHXR8xowZBAQEDJrT0tJSqqurZU6vgMvloru7W+bzKi1cuJCjR49y+PBh90dWVhb33Xef+79lXq9NW1sbFRUVxMfHy7/TqzBv3jxKS0sHjZ06dYrk5GRA/j6JyyN9omfJz+HVkV5xeEivePWkT/Q86ROvnd/0isO2RYpwe+edd5SgoCDlzTffVE6cOKH867/+qxIZGalYLBZvl+YXWltblUOHDimHDh1SAOXll19WDh06pJw5c0ZRFEX5+c9/rkRGRirr1q1Tjhw5oixfvlxJTU1VOjs7vVy5b3r44YcVvV6v7Ny5U6mrq3N/dHR0uB/zne98Rxk9erSyfft2paioSJkzZ44yZ84cL1bt25544gll165dSlVVlXLkyBHliSeeUFQqlfLhhx8qiiLzOVQu3LVOUWRer9QPf/hDZefOnUpVVZXy8ccfK4sWLVJiYmIUm82mKIrM55X69NNPFa1Wq/zXf/2XUlZWpvx//9//p4SGhipvv/22+zHy90lcDukTr430iUNPesWhJ72i50mfeG2kTxx6/tIryiKhl7z66qvK6NGjlcDAQGXWrFnKvn37vF2S39ixY4cCfOHjgQceUBSlf+vwn/70p4rJZFKCgoKUhQsXKqWlpd4t2oddbC4B5c9//rP7MZ2dncp3v/tdxWAwKKGhocqdd96p1NXVea9oH/fNb35TSU5OVgIDA5XY2Fhl4cKF7qZPUWQ+h8rfN38yr1fmnnvuUeLj45XAwEAlMTFRueeee5Ty8nL3cZnPK1dQUKBMmjRJCQoKUsaNG6f84Q9/GHRc/j6JyyV94tWTPnHoSa849KRX9DzpE6+N9Ime4Q+9okpRFGX4rlsUQgghhBBCCCGEEEL4GskkFEIIIYQQQgghhBDiOieLhEIIIYQQQgghhBBCXOdkkVAIIYQQQgghhBBCiOucLBIKIYQQQgghhBBCCHGdk0VCIYQQQgghhBBCCCGuc7JIKIQQQgghhBBCCCHEdU4WCYUQQgghhBBCCCGEuM7JIqEQQgghhBBCCCGEENc5WSQUQvi9f/7nf+aOO+4Y9td98803UalUqFQqfvCDH1zzc0VGRg5JXZ62YMEC9/d9+PBhb5cjhBBCCHFJ0isOL+kVhfBfWm8XIIQQl6JSqS55/Omnn+bXv/41iqIMU0WD6XQ6SktLCQsLu6bnueeee1i2bNkQVTVApVKxdu3aIW2M16xZQ0VFBbNmzRqy5xRCCCGEuBrSK14b6RWFEBeSRUIhhE+rq6tz//e7777LU089RWlpqXssPDyc8PBwb5QG9DdWcXFx1/w8ISEhhISEDEFFnhcVFUVLS4u3yxBCCCGEkF7RB0mvKIT/ktuNhRA+LS4uzv2h1+vdjdbnH+Hh4V+4hWTBggU8+uij/OAHP8BgMGAymfjjH/9Ie3s7Dz74IBEREaSnp7Nx48ZBr3Xs2DFuv/12wsPDMZlMfOMb36ChoeGKa05JSeFnP/sZ999/P+Hh4SQnJ5Ofn099fT3Lly8nPDycKVOmUFRU5P6av7+F5JlnnmHq1Kn85S9/ISUlBb1ezz/8wz/Q2to66HVeeeWVQa89depUnnnmGfdxgDvvvBOVSuX+HGDdunVMnz6d4OBgzGYzzz77LH19fQAoisIzzzzD6NGjCQoKIiEhge9///tXPA9CCCGEEJ4mvaL0ikKIoSOLhEKIEemtt94iJiaGTz/9lEcffZSHH36Yu+++m7lz53Lw4EEWL17MN77xDTo6OgBobm7m1ltvZdq0aRQVFbFp0yasVitf//rXr+r1f/WrXzFv3jwOHTpEdnY23/jGN7j//vv5p3/6Jw4ePEhaWhr333//JW99qaio4IMPPqCwsJDCwkJ27drFz3/+88uu4cCBAwD8+c9/pq6uzv35Rx99xP33389jjz3GiRMneP3113nzzTf5r//6LwDef/99fvWrX/H6669TVlbGBx98wOTJk69qHoQQQgghfJH0itIrCiG+SBYJhRAjUmZmJj/5yU/IyMjgySefJDg4mJiYGB566CEyMjJ46qmnaGxs5MiRIwD89re/Zdq0abzwwguMGzeOadOm8cYbb7Bjxw5OnTp1xa+/bNkyvv3tb7tfq6WlhZkzZ3L33XczZswYHn/8cT777DOsVuuXPofL5eLNN99k0qRJzJ8/n2984xts27btsmuIjY0FIDIykri4OPfnzz77LE888QQPPPAAZrOZ2267jeeff57XX38dgOrqauLi4li0aBGjR49m1qxZPPTQQ1c8B0IIIYQQvkp6RekVhRBfJIuEQogRacqUKe7/1mg0REdHDzrDaTKZALDZbACUlJSwY8cOd25NeHg448aNA/rP0l7L63/+Wpd6/YtJSUkhIiLC/Xl8fPwlH3+5SkpKeO655wZ9rw899BB1dXV0dHRw991309nZidls5qGHHmLt2rXu20uEEEIIIUYC6RW/nPSKQly/ZOMSIcSIFBAQMOhzlUo1aOzznfBcLhcAbW1t5Obm8otf/OILzxUfH39Nr//5a13q9b/qOT7/mgsfr1arv3ALSm9v71fW1tbWxrPPPstdd/3/7d29S1tRHMbxb2sVAw4BdRGiER0EUfSCIDgIDopoQLo5iatZsokggrOIf4AIog4uTrpo4iDi5OAQRTL4sgi6+Do4hXYoFILYoZS89H4/Y3IIv7NcHp6bw/n+4bva2lpisRi5XI5MJkM6nWZmZoalpSWOjo4+zCRJklSJzIqfMytK4WVJKElAEATs7OwQj8f59q0yHo2NjY0FN/q9vr5yc3NTsKa6upp8Pl/wWRAE5HI52tvbP/3tSCRCIpEgkUiQTCbp6Oggm80SBMG/3YQkSVIFMCsWMitK/yePG0sSkEwmeXx8ZHJyktPTU66urtjf32d6evpDcCoXQ0NDbG5ucnx8TDabZWpqiqqqqoI18Xicw8ND7u/veXp6AmBhYYGNjQ0WFxe5uLjg8vKS7e1t5ufngV+3562trXF+fs719TVbW1tEIhFaWlqKvkdJkqRyYFY0K0phYEkoSUBTUxMnJyfk83mGh4fp6uoilUoRjUb5+rU8H5Vzc3MMDg4yPj7O2NgYExMTtLW1FaxZXl4mnU4Ti8Xo7e0FYGRkhL29PQ4ODujr66O/v5+VlZXfwS4ajbK6usrAwADd3d1kMhl2d3epr68v+h4lSZLKgVnRrCiFwZcff7pTXZL0qfX1dVKpFM/Pz6Uepehub29pbW3l7OyMnp6eUo8jSZJUdsyKZkWp0pTnKw9JqhAvLy/U1dUxOztb6lGKZnR0lM7OzlKPIUmSVPbMipIqif8klKS/9Pb2xsPDA/Dr2EVDQ0OJJyqOu7s73t/fAWhubqampqbEE0mSJJUfs6JZUao0loSSJEmSJElSyHncWJIkSZIkSQo5S0JJkiRJkiQp5CwJJUmSJEmSpJCzJJQkSZIkSZJCzpJQkiRJkiRJCjlLQkmSJEmSJCnkLAklSZIkSZKkkLMklCRJkiRJkkLuJ79zqDI6WAHTAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "(<Figure size 1300x900 with 4 Axes>,\n",
+       " array([[<Axes: xlabel='Time [minutes]', ylabel='Voltage [V]'>,\n",
+       "         <Axes: xlabel='Time [minutes]', ylabel='Volumetric interfacial current density [A.m-3]'>],\n",
+       "        [<Axes: xlabel='Time [minutes]', ylabel='Plated lithium capacity [Ah]'>,\n",
+       "         <Axes: xlabel='Time [minutes]', ylabel='Intercalated lithium capacity [Ah]'>]],\n",
+       "       dtype=object))"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1300x900 with 4 Axes>"
       ]
@@ -266,7 +267,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -289,7 +289,22 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "text/plain": [
+       "(<Figure size 1300x900 with 4 Axes>,\n",
+       " array([[<Axes: xlabel='Time [minutes]', ylabel='Voltage [V]'>,\n",
+       "         <Axes: xlabel='Time [minutes]', ylabel='Volumetric interfacial current density [A.m-3]'>],\n",
+       "        [<Axes: xlabel='Time [minutes]', ylabel='Plated lithium capacity [Ah]'>,\n",
+       "         <Axes: xlabel='Time [minutes]', ylabel='Intercalated lithium capacity [Ah]'>]],\n",
+       "       dtype=object))"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1300x900 with 4 Axes>"
       ]
@@ -303,7 +318,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -311,7 +325,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -360,7 +373,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.11.5"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb
index 7eae36e725..4ec9f4cc65 100644
--- a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb
+++ b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb
@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -33,7 +32,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "\n",
     "model = pybamm.lithium_ion.DFN(\n",
@@ -257,8 +256,8 @@
     "    \"Current [A]\",\n",
     "    \"Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\",\n",
     "    \"X-averaged negative electrode active material volume fraction\",\n",
-    "    \"Total SEI thickness [m]\",\n",
-    "    \"X-averaged total SEI thickness [m]\",\n",
+    "    \"Negative total SEI thickness [m]\",\n",
+    "    \"X-averaged negative total SEI thickness [m]\",\n",
     "])"
    ]
   },
@@ -350,7 +349,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -388,7 +386,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pybamm",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -402,7 +400,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.8.10"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
index d5d291d5b4..a9431211af 100644
--- a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
+++ b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
@@ -49,13 +49,17 @@
                               "name": "stdout",
                               "output_type": "stream",
                               "text": [
-                                    "zsh:1: no matches found: pybamm[plot,cite]\n",
+                                    "\u001b[33mWARNING: pybamm 23.5 does not provide the extra 'cite'\u001b[0m\u001b[33m\n",
+                                    "\u001b[0m\u001b[33mWARNING: pybamm 23.5 does not provide the extra 'plot'\u001b[0m\u001b[33m\n",
+                                    "\u001b[0m\n",
+                                    "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
+                                    "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
                                     "Note: you may need to restart the kernel to use updated packages.\n"
                               ]
                         }
                   ],
                   "source": [
-                        "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+                        "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
                         "import pybamm\n",
                         "import pickle\n",
                         "import matplotlib.pyplot as plt\n",
@@ -82,7 +86,7 @@
                               "name": "stderr",
                               "output_type": "stream",
                               "text": [
-                                    "/Users/robertwtimms/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:835: OptionWarning: The 'lumped' thermal option with 'dimensionality' 0 now uses the parameters 'Cell cooling surface area [m2]', 'Cell volume [m3]' and 'Total heat transfer coefficient [W.m-2.K-1]' to compute the cell cooling term, regardless of the value of the the 'cell geometry' option. Please update your parameters accordingly.\n",
+                                    "/Users/robertwtimms/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:910: OptionWarning: The 'lumped' thermal option with 'dimensionality' 0 now uses the parameters 'Cell cooling surface area [m2]', 'Cell volume [m3]' and 'Total heat transfer coefficient [W.m-2.K-1]' to compute the cell cooling term, regardless of the value of the the 'cell geometry' option. Please update your parameters accordingly.\n",
                                     "  options = BatteryModelOptions(extra_options)\n"
                               ]
                         }
@@ -619,7 +623,7 @@
                   "outputs": [
                         {
                               "data": {
-                                    "image/png": 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",
+                                    "image/png": 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",
                                     "text/plain": [
                                           "<Figure size 1300x700 with 5 Axes>"
                                     ]
@@ -683,7 +687,7 @@
                   "outputs": [
                         {
                               "data": {
-                                    "image/png": 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",
                                     "text/plain": [
                                           "<Figure size 1300x700 with 5 Axes>"
                                     ]
diff --git a/docs/source/examples/notebooks/models/rate-capability.ipynb b/docs/source/examples/notebooks/models/rate-capability.ipynb
index 27942e7cd3..fa01342f1d 100644
--- a/docs/source/examples/notebooks/models/rate-capability.ipynb
+++ b/docs/source/examples/notebooks/models/rate-capability.ipynb
@@ -30,7 +30,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt"
diff --git a/docs/source/examples/notebooks/models/simulating-ORegan-2022-parameter-set.ipynb b/docs/source/examples/notebooks/models/simulating-ORegan-2022-parameter-set.ipynb
index 19c548f8fc..7eb647fc97 100644
--- a/docs/source/examples/notebooks/models/simulating-ORegan-2022-parameter-set.ipynb
+++ b/docs/source/examples/notebooks/models/simulating-ORegan-2022-parameter-set.ipynb
@@ -25,7 +25,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm"
    ]
   },
diff --git a/docs/source/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb b/docs/source/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb
index 28ee46f6de..b3725fd36f 100644
--- a/docs/source/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb
+++ b/docs/source/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb
@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -12,18 +11,15 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-09-26T23:15:29.863147Z",
+     "start_time": "2023-09-26T23:15:29.848113Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import os\n",
     "import matplotlib.pyplot as plt\n",
@@ -31,7 +27,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -43,7 +38,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-09-26T23:15:29.896170Z",
+     "start_time": "2023-09-26T23:15:29.885253Z"
+    }
+   },
    "outputs": [],
    "source": [
     "model = pybamm.lithium_ion.DFN(\n",
@@ -55,7 +55,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -65,16 +64,20 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-09-26T23:15:29.896383Z",
+     "start_time": "2023-09-26T23:15:29.887700Z"
+    }
+   },
    "outputs": [],
    "source": [
     "param = pybamm.ParameterValues(\"Ai2020\")\n",
     "## It can update the speed of crack propagation using the commands below:\n",
-    "# param.update({\"Negative electrode Cracking rate\":3.9e-20*10})"
+    "# param.update({\"Negative electrode Cracking rate\":3.9e-20*10}, check_already_exists=False)"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -82,15 +85,49 @@
     "Now the model can be processed and solved in the usual way, and we still have access to model defaults such as the default geometry and default spatial methods"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Depending on the parameter set being used, the particle cracking model can require a large number of mesh points inside the particles to be numerically stable."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-09-26T23:15:29.901590Z",
+     "start_time": "2023-09-26T23:15:29.893646Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "var_pts = {\n",
+    "    \"x_n\": 20,  # negative electrode\n",
+    "    \"x_s\": 20,  # separator \n",
+    "    \"x_p\": 20,  # positive electrode\n",
+    "    \"r_n\": 26,  # negative particle\n",
+    "    \"r_p\": 26,  # positive particle\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-09-26T23:15:31.412965Z",
+     "start_time": "2023-09-26T23:15:30.060671Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "58a6cbde1d01456aba4d5b4977c28720",
+       "model_id": "70c1339268f44ef19ee852fe5bd01653",
        "version_major": 2,
        "version_minor": 0
       },
@@ -107,6 +144,7 @@
     "    model,\n",
     "    parameter_values=param,\n",
     "    solver=pybamm.CasadiSolver(dt_max=600),\n",
+    "    var_pts=var_pts,\n",
     ")\n",
     "solution = sim.solve(t_eval=[0, 3600], inputs={\"C-rate\": 1})\n",
     "# plot\n",
@@ -115,7 +153,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -124,13 +161,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-09-26T23:15:31.665271Z",
+     "start_time": "2023-09-26T23:15:31.432275Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "508090de19594b48ad2a202c4df9b12e",
+       "model_id": "51b97221822843719c6bea4506288ed3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -187,7 +229,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -196,13 +237,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-09-26T23:15:31.932322Z",
+     "start_time": "2023-09-26T23:15:31.685338Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4046d2be6c1a45f28233574c852cf6ca",
+       "model_id": "3a5afe2d4d7a4987af6c97d9f57b5872",
        "version_major": 2,
        "version_minor": 0
       },
@@ -227,7 +273,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -238,8 +283,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-09-26T23:15:31.959902Z",
+     "start_time": "2023-09-26T23:15:31.946562Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -250,8 +300,7 @@
       "[3] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n",
       "[4] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
       "[5] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
-      "[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
-      "\n"
+      "[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n"
      ]
     }
    ],
@@ -262,7 +311,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pybamm",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -276,7 +325,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.9.18"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/source/examples/notebooks/models/thermal-models.ipynb b/docs/source/examples/notebooks/models/thermal-models.ipynb
index dcf1a761e5..8bcc504af0 100644
--- a/docs/source/examples/notebooks/models/thermal-models.ipynb
+++ b/docs/source/examples/notebooks/models/thermal-models.ipynb
@@ -27,7 +27,7 @@
         }
       ],
       "source": [
-        "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+        "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
         "import pybamm"
       ]
     },
diff --git a/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb b/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb
index 423e4fc800..2de30eedfe 100644
--- a/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb
+++ b/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb
@@ -45,7 +45,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt"
diff --git a/docs/source/examples/notebooks/models/using-model-options_thermal-example.ipynb b/docs/source/examples/notebooks/models/using-model-options_thermal-example.ipynb
index 158f0bdd6d..0c97752792 100644
--- a/docs/source/examples/notebooks/models/using-model-options_thermal-example.ipynb
+++ b/docs/source/examples/notebooks/models/using-model-options_thermal-example.ipynb
@@ -32,7 +32,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import os\n",
     "os.chdir(pybamm.__path__[0]+'/..')"
diff --git a/docs/source/examples/notebooks/models/using-submodels.ipynb b/docs/source/examples/notebooks/models/using-submodels.ipynb
index 221492e012..211e3346d8 100644
--- a/docs/source/examples/notebooks/models/using-submodels.ipynb
+++ b/docs/source/examples/notebooks/models/using-submodels.ipynb
@@ -1,679 +1,666 @@
 {
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Using submodels in PyBaMM\n",
-        "In this notebook we show how to modify existing models by swapping out submodels, and how to build your own model from scratch using existing submodels. To see all of the models and submodels available in PyBaMM, please take a look at the documentation [here](https://docs.pybamm.org/en/latest/source/api/models/index.html)."
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Changing a submodel in an existing battery model\n",
-        "PyBaMM is designed to be a flexible modelling package that allows users to easily compare different models and numerical techniques within a common framework. Battery models within PyBaMM are built up using a number of submodels that describe different physics included within the model, such as mass conservation in the electrolyte or charge conservation in the solid. For ease of use, a number of popular battery models are pre-configured in PyBaMM. As an example, we look at the Single Particle Model (for more information see [here](SPM.ipynb)). \n",
-        "\n",
-        "First we import pybamm"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Note: you may need to restart the kernel to use updated packages.\n"
-          ]
-        }
-      ],
-      "source": [
-        "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
-        "import pybamm"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Then we load the SPM"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model = pybamm.lithium_ion.SPM()"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can look at the submodels that make up the SPM by accessing `model.submodels`, which is a dictionary of the submodel's name (i.e. the physics it represents) and the submodel that is selected"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "external circuit <pybamm.models.submodels.external_circuit.explicit_control_external_circuit.ExplicitCurrentControl object at 0x1514783d0>\n",
-            "porosity <pybamm.models.submodels.porosity.constant_porosity.Constant object at 0x151478790>\n",
-            "Negative interface utilisation <pybamm.models.submodels.interface.interface_utilisation.full_utilisation.Full object at 0x151478940>\n",
-            "Positive interface utilisation <pybamm.models.submodels.interface.interface_utilisation.full_utilisation.Full object at 0x1514789d0>\n",
-            "negative particle mechanics <pybamm.models.submodels.particle_mechanics.no_mechanics.NoMechanics object at 0x151478a00>\n",
-            "positive particle mechanics <pybamm.models.submodels.particle_mechanics.no_mechanics.NoMechanics object at 0x151478a60>\n",
-            "negative primary active material <pybamm.models.submodels.active_material.constant_active_material.Constant object at 0x1514787f0>\n",
-            "positive primary active material <pybamm.models.submodels.active_material.constant_active_material.Constant object at 0x151478a90>\n",
-            "electrolyte transport efficiency <pybamm.models.submodels.transport_efficiency.bruggeman_transport_efficiency.Bruggeman object at 0x1514788e0>\n",
-            "electrode transport efficiency <pybamm.models.submodels.transport_efficiency.bruggeman_transport_efficiency.Bruggeman object at 0x151478ac0>\n",
-            "transverse convection <pybamm.models.submodels.convection.transverse.no_convection.NoConvection object at 0x151478af0>\n",
-            "through-cell convection <pybamm.models.submodels.convection.through_cell.no_convection.NoConvection object at 0x151478a30>\n",
-            "negative primary open-circuit potential <pybamm.models.submodels.interface.open_circuit_potential.single_ocp.SingleOpenCircuitPotential object at 0x151478bb0>\n",
-            "positive primary open-circuit potential <pybamm.models.submodels.interface.open_circuit_potential.single_ocp.SingleOpenCircuitPotential object at 0x151478b50>\n",
-            "negative interface <pybamm.models.submodels.interface.kinetics.inverse_kinetics.inverse_butler_volmer.InverseButlerVolmer object at 0x151478b20>\n",
-            "negative interface current <pybamm.models.submodels.interface.kinetics.inverse_kinetics.inverse_butler_volmer.CurrentForInverseButlerVolmer object at 0x151478c40>\n",
-            "positive interface <pybamm.models.submodels.interface.kinetics.inverse_kinetics.inverse_butler_volmer.InverseButlerVolmer object at 0x151478c70>\n",
-            "positive interface current <pybamm.models.submodels.interface.kinetics.inverse_kinetics.inverse_butler_volmer.CurrentForInverseButlerVolmer object at 0x151478c10>\n",
-            "negative primary particle <pybamm.models.submodels.particle.fickian_diffusion.FickianDiffusion object at 0x151478ca0>\n",
-            "negative primary total particle concentration <pybamm.models.submodels.particle.total_particle_concentration.TotalConcentration object at 0x151478cd0>\n",
-            "positive primary particle <pybamm.models.submodels.particle.fickian_diffusion.FickianDiffusion object at 0x151478b80>\n",
-            "positive primary total particle concentration <pybamm.models.submodels.particle.total_particle_concentration.TotalConcentration object at 0x151478d30>\n",
-            "negative electrode potential <pybamm.models.submodels.electrode.ohm.leading_ohm.LeadingOrder object at 0x151478d00>\n",
-            "positive electrode potential <pybamm.models.submodels.electrode.ohm.leading_ohm.LeadingOrder object at 0x151478d60>\n",
-            "electrolyte diffusion <pybamm.models.submodels.electrolyte_diffusion.constant_concentration.ConstantConcentration object at 0x151478be0>\n",
-            "leading-order electrolyte conductivity <pybamm.models.submodels.electrolyte_conductivity.leading_order_conductivity.LeadingOrder object at 0x151478d90>\n",
-            "negative surface potential difference <pybamm.models.submodels.electrolyte_conductivity.surface_potential_form.explicit_surface_form_conductivity.Explicit object at 0x151478df0>\n",
-            "positive surface potential difference <pybamm.models.submodels.electrolyte_conductivity.surface_potential_form.explicit_surface_form_conductivity.Explicit object at 0x151478e20>\n",
-            "thermal <pybamm.models.submodels.thermal.isothermal.Isothermal object at 0x151478dc0>\n",
-            "current collector <pybamm.models.submodels.current_collector.homogeneous_current_collector.Uniform object at 0x151478e50>\n",
-            "primary sei <pybamm.models.submodels.interface.sei.no_sei.NoSEI object at 0x151478ee0>\n",
-            "primary sei on cracks <pybamm.models.submodels.interface.sei.no_sei.NoSEI object at 0x151478f40>\n",
-            "lithium plating <pybamm.models.submodels.interface.lithium_plating.no_plating.NoPlating object at 0x151478eb0>\n",
-            "total interface <pybamm.models.submodels.interface.total_interfacial_current.TotalInterfacialCurrent object at 0x151454070>\n"
-          ]
-        }
-      ],
-      "source": [
-        "for name, submodel in model.submodels.items():\n",
-        "    print(name, submodel)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "When you load a model in PyBaMM it builds by default. Building the model sets all of the model variables and sets up any variables which are coupled between different submodels: this is the process which couples the submodels together and allows one submodel to access variables from another. If you would like to swap out a submodel in an existing battery model you need to load it without building it by passing the keyword `build=False`"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model = pybamm.lithium_ion.SPM(build=False)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This collects all of the submodels which make up the SPM, but doesn't build the model. Now you are free to swap out one submodel for another. For instance, you may want to assume that diffusion within the negative particles is infinitely fast, so that the PDE describing diffusion is replaced with an ODE for the uniform particle concentration. To change a submodel you simply update the dictionary entry, in this case to the `XAveragedPolynomialProfile` submodel"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model.submodels[\"negative primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(\n",
-        "    model.param, \"negative\", options={**model.options, \"particle\": \"uniform profile\"}\n",
-        ")"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "where we pass in the model parameters, the electrode (negative or positive) the submodel corresponds to, and the name of the polynomial we want to use. In the example we assume uniform concentration within the particle, corresponding to a zero-order polynomial."
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now if we look at the submodels again we see that the model for the negative particle has been changed"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "external circuit <pybamm.models.submodels.external_circuit.explicit_control_external_circuit.ExplicitCurrentControl object at 0x151594940>\n",
-            "porosity <pybamm.models.submodels.porosity.constant_porosity.Constant object at 0x151594d00>\n",
-            "Negative interface utilisation <pybamm.models.submodels.interface.interface_utilisation.full_utilisation.Full object at 0x151594eb0>\n",
-            "Positive interface utilisation <pybamm.models.submodels.interface.interface_utilisation.full_utilisation.Full object at 0x151594f40>\n",
-            "negative particle mechanics <pybamm.models.submodels.particle_mechanics.no_mechanics.NoMechanics object at 0x151594f70>\n",
-            "positive particle mechanics <pybamm.models.submodels.particle_mechanics.no_mechanics.NoMechanics object at 0x151594fd0>\n",
-            "negative primary active material <pybamm.models.submodels.active_material.constant_active_material.Constant object at 0x151594d60>\n",
-            "positive primary active material <pybamm.models.submodels.active_material.constant_active_material.Constant object at 0x1515a2070>\n",
-            "electrolyte transport efficiency <pybamm.models.submodels.transport_efficiency.bruggeman_transport_efficiency.Bruggeman object at 0x151594e50>\n",
-            "electrode transport efficiency <pybamm.models.submodels.transport_efficiency.bruggeman_transport_efficiency.Bruggeman object at 0x151594fa0>\n",
-            "transverse convection <pybamm.models.submodels.convection.transverse.no_convection.NoConvection object at 0x1515a20a0>\n",
-            "through-cell convection <pybamm.models.submodels.convection.through_cell.no_convection.NoConvection object at 0x1515a2040>\n",
-            "negative primary open-circuit potential <pybamm.models.submodels.interface.open_circuit_potential.single_ocp.SingleOpenCircuitPotential object at 0x1515a2160>\n",
-            "positive primary open-circuit potential <pybamm.models.submodels.interface.open_circuit_potential.single_ocp.SingleOpenCircuitPotential object at 0x1515a2100>\n",
-            "negative interface <pybamm.models.submodels.interface.kinetics.inverse_kinetics.inverse_butler_volmer.InverseButlerVolmer object at 0x1515a20d0>\n",
-            "negative interface current <pybamm.models.submodels.interface.kinetics.inverse_kinetics.inverse_butler_volmer.CurrentForInverseButlerVolmer object at 0x1515a21f0>\n",
-            "positive interface <pybamm.models.submodels.interface.kinetics.inverse_kinetics.inverse_butler_volmer.InverseButlerVolmer object at 0x1515a2220>\n",
-            "positive interface current <pybamm.models.submodels.interface.kinetics.inverse_kinetics.inverse_butler_volmer.CurrentForInverseButlerVolmer object at 0x1515a21c0>\n",
-            "negative primary particle <pybamm.models.submodels.particle.x_averaged_polynomial_profile.XAveragedPolynomialProfile object at 0x15154bb50>\n",
-            "negative primary total particle concentration <pybamm.models.submodels.particle.total_particle_concentration.TotalConcentration object at 0x1515a2280>\n",
-            "positive primary particle <pybamm.models.submodels.particle.fickian_diffusion.FickianDiffusion object at 0x1515a2130>\n",
-            "positive primary total particle concentration <pybamm.models.submodels.particle.total_particle_concentration.TotalConcentration object at 0x1515a22e0>\n",
-            "negative electrode potential <pybamm.models.submodels.electrode.ohm.leading_ohm.LeadingOrder object at 0x1515a22b0>\n",
-            "positive electrode potential <pybamm.models.submodels.electrode.ohm.leading_ohm.LeadingOrder object at 0x1515a2310>\n",
-            "electrolyte diffusion <pybamm.models.submodels.electrolyte_diffusion.constant_concentration.ConstantConcentration object at 0x1515a2190>\n",
-            "leading-order electrolyte conductivity <pybamm.models.submodels.electrolyte_conductivity.leading_order_conductivity.LeadingOrder object at 0x1515a2340>\n",
-            "negative surface potential difference <pybamm.models.submodels.electrolyte_conductivity.surface_potential_form.explicit_surface_form_conductivity.Explicit object at 0x1515a23a0>\n",
-            "positive surface potential difference <pybamm.models.submodels.electrolyte_conductivity.surface_potential_form.explicit_surface_form_conductivity.Explicit object at 0x1515a23d0>\n",
-            "thermal <pybamm.models.submodels.thermal.isothermal.Isothermal object at 0x1515a2370>\n",
-            "current collector <pybamm.models.submodels.current_collector.homogeneous_current_collector.Uniform object at 0x1515a2400>\n",
-            "primary sei <pybamm.models.submodels.interface.sei.no_sei.NoSEI object at 0x1515a2490>\n",
-            "primary sei on cracks <pybamm.models.submodels.interface.sei.no_sei.NoSEI object at 0x1515a24f0>\n",
-            "lithium plating <pybamm.models.submodels.interface.lithium_plating.no_plating.NoPlating object at 0x1515a2460>\n",
-            "total interface <pybamm.models.submodels.interface.total_interfacial_current.TotalInterfacialCurrent object at 0x1515a2550>\n"
-          ]
-        }
-      ],
-      "source": [
-        "for name, submodel in model.submodels.items():\n",
-        "    print(name, submodel)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Building the model also sets up the equations, boundary and initial conditions for the model. For example, if we look at `model.rhs` before building we see that it is empty "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "{}"
-            ]
-          },
-          "execution_count": 7,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "model.rhs"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If we try to use this empty model, PyBaMM will give an error. So, before proceeding we must build the model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model.build_model()"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now if we look at `model.rhs` we see that it contains an entry relating to the concentration in each particle, as expected for the SPM"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "{Variable(0x620af1e84efc93fa, Discharge capacity [A.h], children=[], domains={}): Multiplication(0x3098e50eb9cc5275, *, children=['0.0002777777777777778', 'Current function [A]'], domains={}),\n",
-              " Variable(-0x5e5303cde5e32a1d, Average negative particle concentration [mol.m-3], children=[], domains={'primary': ['current collector']}): MatrixMultiplication(0x26225f38ea92e2a0, @, children=['mass(Average negative particle concentration [mol.m-3])', '-3.109280896985319e-05 * Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / x-average(Negative particle radius [m])'], domains={'primary': ['current collector']}),\n",
-              " Variable(0x2e6e9aee084a77f, X-averaged positive particle concentration [mol.m-3], children=[], domains={'primary': ['positive particle'], 'secondary': ['current collector']}): Divergence(-0x6a7c97412b8b9861, div, children=['Positive electrode diffusivity [m2.s-1] * grad(X-averaged positive particle concentration [mol.m-3])'], domains={'primary': ['positive particle'], 'secondary': ['current collector']})}"
-            ]
-          },
-          "execution_count": 9,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "model.rhs"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now the model can be used in a simulation and solved in the usual way, and we still have access to model defaults such as the default geometry and default spatial methods which are used in the simulation"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "8b215ef0f04f4adfba0107a12f3c0a80",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": [
-              "<pybamm.plotting.quick_plot.QuickPlot at 0x1515c5a00>"
-            ]
-          },
-          "execution_count": 10,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "simulation = pybamm.Simulation(model)\n",
-        "simulation.solve([0, 3600])\n",
-        "simulation.plot()"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Building a custom model from submodels\n",
-        "Instead of editing a pre-existing model, you may wish to build your own model from scratch by combining existing submodels of you choice. In this section, we build a Single Particle Model in which the diffusion is assumed infinitely fast in both particles. \n",
-        "\n",
-        "To begin, we load a base lithium-ion model. This sets up the basic model structure behind the scenes, and also sets the default parameters to be those corresponding to a lithium-ion battery. Note that the base model does not select any default submodels, so there is no need to pass `build=False`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model = pybamm.lithium_ion.BaseModel()"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Submodels can be added to the `model.submodels` dictionary in the same way that we changed the submodels earlier. \n",
-        "\n",
-        "We use the simplest model for the external circuit, which is the explicit \"current control\" submodel"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model.submodels[\"external circuit\"] = pybamm.external_circuit.ExplicitCurrentControl(model.param, model.options)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We want to build a 1D model, so select the `Uniform` current collector model (if the current collectors are behaving uniformly, then a 1D model is appropriate). We also want the model to be isothermal, so select the thermal model accordingly. Further, we assume that the porosity and active material are constant in space and time."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model.submodels[\"current collector\"] = pybamm.current_collector.Uniform(model.param)\n",
-        "model.submodels[\"thermal\"] = pybamm.thermal.isothermal.Isothermal(model.param)\n",
-        "model.submodels[\"porosity\"] = pybamm.porosity.Constant(model.param, model.options)\n",
-        "model.submodels[\"negative active material\"] = pybamm.active_material.Constant(\n",
-        "    model.param, \"negative\", model.options\n",
-        ")\n",
-        "model.submodels[\"positive active material\"] = pybamm.active_material.Constant(\n",
-        "    model.param, \"positive\", model.options\n",
-        ")"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We assume that the current density varies linearly in the electrodes. This corresponds the the leading-order terms in Ohm's law in the limit in which the SPM is derived in [[3]](#References)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model.submodels[\"negative electrode potentials\"] = pybamm.electrode.ohm.LeadingOrder(\n",
-        "    model.param, \"negative\"\n",
-        ")\n",
-        "model.submodels[\"positive electrode potentials\"] = pybamm.electrode.ohm.LeadingOrder(\n",
-        "    model.param, \"positive\"\n",
-        ")"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We assume uniform concentration in both the negative and positive particles. We also have to separately specify a model for the total concentration in each electrode, which is calculated from the concentration in the particles (not a separate ODE)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 22,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "options = {**model.options, \"particle\": \"uniform profile\"}\n",
-        "model.submodels[\"negative primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"negative\", options)\n",
-        "model.submodels[\"positive primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"positive\", options)\n",
-        "\n",
-        "model.submodels[\"negative total particle concentration\"] = pybamm.particle.TotalConcentration(model.param, \"negative\", options)\n",
-        "model.submodels[\"positive total particle concentration\"] = pybamm.particle.TotalConcentration(model.param, \"positive\", options)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In the Single Particle Model, the overpotential can be obtained by inverting the Butler-Volmer relation, so we choose the `InverseButlerVolmer` submodel for the interface, with the \"main\" lithium-ion reaction (and default lithium ion options). Because of how the current is implemented, we also need to separately specify the `CurrentForInverseButlerVolmer` submodel. We also need to specify the submodel for open-circuit potential."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 16,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model.submodels[\n",
-        "    \"negative open-circuit potential\"\n",
-        "] = pybamm.open_circuit_potential.SingleOpenCircuitPotential(\n",
-        "    model.param, \"negative\", \"lithium-ion main\", options=model.options\n",
-        ")\n",
-        "model.submodels[\n",
-        "    \"positive open-circuit potential\"\n",
-        "] = pybamm.open_circuit_potential.SingleOpenCircuitPotential(\n",
-        "    model.param, \"positive\", \"lithium-ion main\", options=model.options\n",
-        ")\n",
-        "model.submodels[\n",
-        "    \"negative interface\"\n",
-        "] = pybamm.kinetics.InverseButlerVolmer(\n",
-        "    model.param, \"negative\", \"lithium-ion main\", options=model.options\n",
-        ")\n",
-        "model.submodels[\n",
-        "    \"positive interface\"\n",
-        "] = pybamm.kinetics.InverseButlerVolmer(\n",
-        "    model.param, \"positive\", \"lithium-ion main\", options=model.options\n",
-        ")\n",
-        "model.submodels[\n",
-        "    \"negative interface current\"\n",
-        "] = pybamm.kinetics.CurrentForInverseButlerVolmer(\n",
-        "    model.param, \"negative\", \"lithium-ion main\"\n",
-        ")\n",
-        "model.submodels[\n",
-        "    \"positive interface current\"\n",
-        "] = pybamm.kinetics.CurrentForInverseButlerVolmer(\n",
-        "    model.param, \"positive\", \"lithium-ion main\"\n",
-        ")\n",
-        "model.submodels[\"negative interface utilisation\"] = pybamm.interface_utilisation.Full(\n",
-        "    model.param, \"negative\", model.options\n",
-        ")\n",
-        "model.submodels[\"positive interface utilisation\"] = pybamm.interface_utilisation.Full(\n",
-        "    model.param, \"positive\", model.options\n",
-        ")"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We don't want any particle mechanics, SEI formation or lithium plating in this model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 17,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model.submodels[\n",
-        "    \"Negative particle mechanics\"\n",
-        "] = pybamm.particle_mechanics.NoMechanics(model.param, \"negative\", model.options)\n",
-        "model.submodels[\n",
-        "    \"Positive particle mechanics\"\n",
-        "] = pybamm.particle_mechanics.NoMechanics(model.param, \"positive\", model.options)\n",
-        "model.submodels[\"sei\"] = pybamm.sei.NoSEI(model.param, model.options)\n",
-        "model.submodels[\"sei on cracks\"] = pybamm.sei.NoSEI(model.param, model.options, cracks=True)\n",
-        "model.submodels[\"lithium plating\"] = pybamm.lithium_plating.NoPlating(model.param)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Finally, for the electrolyte we assume that diffusion is infinitely fast so that the concentration is uniform, and also use the leading-order model for charge conservation, which leads to a linear variation in ionic current in the electrodes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 18,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model.submodels[\"electrolyte diffusion\"] = pybamm.electrolyte_diffusion.ConstantConcentration(\n",
-        "    model.param\n",
-        ")\n",
-        "model.submodels[\"electrolyte conductivity\"] = pybamm.electrolyte_conductivity.LeadingOrder(\n",
-        "    model.param\n",
-        ")"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now that we have set all of the submodels we can build the model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 19,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model.build_model()"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can then use the model in a simulation in the usual way"
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using submodels in PyBaMM\n",
+    "In this notebook we show how to modify existing models by swapping out submodels, and how to build your own model from scratch using existing submodels. To see all of the models and submodels available in PyBaMM, please take a look at the documentation [here](https://docs.pybamm.org/en/latest/source/api/models/index.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Changing a submodel in an existing battery model\n",
+    "PyBaMM is designed to be a flexible modelling package that allows users to easily compare different models and numerical techniques within a common framework. Battery models within PyBaMM are built up using a number of submodels that describe different physics included within the model, such as mass conservation in the electrolyte or charge conservation in the solid. For ease of use, a number of popular battery models are pre-configured in PyBaMM. As an example, we look at the Single Particle Model (for more information see [here](SPM.ipynb)). \n",
+    "\n",
+    "First we import pybamm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
+    "import pybamm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we load the SPM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pybamm.lithium_ion.SPM()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can look at the submodels that make up the SPM by accessing `model.submodels`, which is a dictionary of the submodel's name (i.e. the physics it represents) and the submodel that is selected"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "external circuit <pybamm.models.submodels.external_circuit.explicit_control_external_circuit.ExplicitCurrentControl object at 0x7f9cd479ea00>\n",
+      "porosity <pybamm.models.submodels.porosity.constant_porosity.Constant object at 0x7f9cd479ecd0>\n",
+      "Negative interface utilisation <pybamm.models.submodels.interface.interface_utilisation.full_utilisation.Full object at 0x7f9cd479ee80>\n",
+      "Positive interface utilisation <pybamm.models.submodels.interface.interface_utilisation.full_utilisation.Full object at 0x7f9cd479eee0>\n",
+      "negative particle mechanics <pybamm.models.submodels.particle_mechanics.no_mechanics.NoMechanics object at 0x7f9cd479ef40>\n",
+      "positive particle mechanics <pybamm.models.submodels.particle_mechanics.no_mechanics.NoMechanics object at 0x7f9cd479efd0>\n",
+      "negative primary active material <pybamm.models.submodels.active_material.constant_active_material.Constant object at 0x7f9cd47a60a0>\n",
+      "positive primary active material <pybamm.models.submodels.active_material.constant_active_material.Constant object at 0x7f9cd47a6160>\n",
+      "electrolyte transport efficiency <pybamm.models.submodels.transport_efficiency.bruggeman_transport_efficiency.Bruggeman object at 0x7f9cd479ed30>\n",
+      "electrode transport efficiency <pybamm.models.submodels.transport_efficiency.bruggeman_transport_efficiency.Bruggeman object at 0x7f9cd47a61f0>\n",
+      "transverse convection <pybamm.models.submodels.convection.transverse.no_convection.NoConvection object at 0x7f9cd47a6220>\n",
+      "through-cell convection <pybamm.models.submodels.convection.through_cell.no_convection.NoConvection object at 0x7f9cd47a62b0>\n",
+      "negative primary open-circuit potential <pybamm.models.submodels.interface.open_circuit_potential.single_ocp.SingleOpenCircuitPotential object at 0x7f9cd479ec40>\n",
+      "positive primary open-circuit potential <pybamm.models.submodels.interface.open_circuit_potential.single_ocp.SingleOpenCircuitPotential object at 0x7f9cd479ed60>\n",
+      "negative interface <pybamm.models.submodels.interface.kinetics.inverse_kinetics.inverse_butler_volmer.InverseButlerVolmer object at 0x7f9cd479ea90>\n",
+      "negative interface current <pybamm.models.submodels.interface.kinetics.inverse_kinetics.inverse_butler_volmer.CurrentForInverseButlerVolmer object at 0x7f9cd479e880>\n",
+      "positive interface <pybamm.models.submodels.interface.kinetics.inverse_kinetics.inverse_butler_volmer.InverseButlerVolmer object at 0x7f9cd479e610>\n",
+      "positive interface current <pybamm.models.submodels.interface.kinetics.inverse_kinetics.inverse_butler_volmer.CurrentForInverseButlerVolmer object at 0x7f9cd479e6d0>\n",
+      "negative primary particle <pybamm.models.submodels.particle.fickian_diffusion.FickianDiffusion object at 0x7f9cd479e3a0>\n",
+      "negative primary total particle concentration <pybamm.models.submodels.particle.total_particle_concentration.TotalConcentration object at 0x7f9cd479e490>\n",
+      "positive primary particle <pybamm.models.submodels.particle.fickian_diffusion.FickianDiffusion object at 0x7f9cd479e460>\n",
+      "positive primary total particle concentration <pybamm.models.submodels.particle.total_particle_concentration.TotalConcentration object at 0x7f9cd479e2e0>\n",
+      "negative electrode potential <pybamm.models.submodels.electrode.ohm.leading_ohm.LeadingOrder object at 0x7f9cd479e310>\n",
+      "positive electrode potential <pybamm.models.submodels.electrode.ohm.leading_ohm.LeadingOrder object at 0x7f9cd49119a0>\n",
+      "electrolyte diffusion <pybamm.models.submodels.electrolyte_diffusion.constant_concentration.ConstantConcentration object at 0x7f9cd4816d30>\n",
+      "leading-order electrolyte conductivity <pybamm.models.submodels.electrolyte_conductivity.leading_order_conductivity.LeadingOrder object at 0x7f9cd4911fd0>\n",
+      "negative surface potential difference <pybamm.models.submodels.electrolyte_conductivity.surface_potential_form.explicit_surface_form_conductivity.Explicit object at 0x7f9cd4911e50>\n",
+      "positive surface potential difference <pybamm.models.submodels.electrolyte_conductivity.surface_potential_form.explicit_surface_form_conductivity.Explicit object at 0x7f9cd4911cd0>\n",
+      "thermal <pybamm.models.submodels.thermal.isothermal.Isothermal object at 0x7f9cd4911bb0>\n",
+      "current collector <pybamm.models.submodels.current_collector.homogeneous_current_collector.Uniform object at 0x7f9cd4911ac0>\n",
+      "negative primary sei <pybamm.models.submodels.interface.sei.no_sei.NoSEI object at 0x7f9cd4907220>\n",
+      "positive primary sei <pybamm.models.submodels.interface.sei.no_sei.NoSEI object at 0x7f9cd49078b0>\n",
+      "negative primary sei on cracks <pybamm.models.submodels.interface.sei.no_sei.NoSEI object at 0x7f9cd4819880>\n",
+      "positive primary sei on cracks <pybamm.models.submodels.interface.sei.no_sei.NoSEI object at 0x7f9cd4819610>\n",
+      "negative lithium plating <pybamm.models.submodels.interface.lithium_plating.no_plating.NoPlating object at 0x7f9cd48e41c0>\n",
+      "positive lithium plating <pybamm.models.submodels.interface.lithium_plating.no_plating.NoPlating object at 0x7f9cd48f66a0>\n",
+      "total interface <pybamm.models.submodels.interface.total_interfacial_current.TotalInterfacialCurrent object at 0x7f9cd4911700>\n"
+     ]
+    }
+   ],
+   "source": [
+    "for name, submodel in model.submodels.items():\n",
+    "    print(name, submodel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you load a model in PyBaMM it builds by default. Building the model sets all of the model variables and sets up any variables which are coupled between different submodels: this is the process which couples the submodels together and allows one submodel to access variables from another. If you would like to swap out a submodel in an existing battery model you need to load it without building it by passing the keyword `build=False`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pybamm.lithium_ion.SPM(build=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This collects all of the submodels which make up the SPM, but doesn't build the model. Now you are free to swap out one submodel for another. For instance, you may want to assume that diffusion within the negative particles is infinitely fast, so that the PDE describing diffusion is replaced with an ODE for the uniform particle concentration. To change a submodel you simply update the dictionary entry, in this case to the `XAveragedPolynomialProfile` submodel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.submodels[\"negative primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(\n",
+    "    model.param, \"negative\", options={**model.options, \"particle\": \"uniform profile\"}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "where we pass in the model parameters, the electrode (negative or positive) the submodel corresponds to, and the name of the polynomial we want to use. In the example we assume uniform concentration within the particle, corresponding to a zero-order polynomial."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now if we look at the submodels again we see that the model for the negative particle has been changed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "external circuit <pybamm.models.submodels.external_circuit.explicit_control_external_circuit.ExplicitCurrentControl object at 0x7f9cd48d94c0>\n",
+      "porosity <pybamm.models.submodels.porosity.constant_porosity.Constant object at 0x7f9cd48cde80>\n",
+      "Negative interface utilisation <pybamm.models.submodels.interface.interface_utilisation.full_utilisation.Full object at 0x7f9cd48cd970>\n",
+      "Positive interface utilisation <pybamm.models.submodels.interface.interface_utilisation.full_utilisation.Full object at 0x7f9cd48cd850>\n",
+      "negative particle mechanics <pybamm.models.submodels.particle_mechanics.no_mechanics.NoMechanics object at 0x7f9cd48cd790>\n",
+      "positive particle mechanics <pybamm.models.submodels.particle_mechanics.no_mechanics.NoMechanics object at 0x7f9cd48cd520>\n",
+      "negative primary active material <pybamm.models.submodels.active_material.constant_active_material.Constant object at 0x7f9cd48cd7c0>\n",
+      "positive primary active material <pybamm.models.submodels.active_material.constant_active_material.Constant object at 0x7f9cd48cd460>\n",
+      "electrolyte transport efficiency <pybamm.models.submodels.transport_efficiency.bruggeman_transport_efficiency.Bruggeman object at 0x7f9cd48cdb50>\n",
+      "electrode transport efficiency <pybamm.models.submodels.transport_efficiency.bruggeman_transport_efficiency.Bruggeman object at 0x7f9cd491dc70>\n",
+      "transverse convection <pybamm.models.submodels.convection.transverse.no_convection.NoConvection object at 0x7f9cd48b0ac0>\n",
+      "through-cell convection <pybamm.models.submodels.convection.through_cell.no_convection.NoConvection object at 0x7f9cd48c48e0>\n",
+      "negative primary open-circuit potential <pybamm.models.submodels.interface.open_circuit_potential.single_ocp.SingleOpenCircuitPotential object at 0x7f9cd481fd30>\n",
+      "positive primary open-circuit potential <pybamm.models.submodels.interface.open_circuit_potential.single_ocp.SingleOpenCircuitPotential object at 0x7f9cd481f8e0>\n",
+      "negative interface <pybamm.models.submodels.interface.kinetics.inverse_kinetics.inverse_butler_volmer.InverseButlerVolmer object at 0x7f9cd481fd00>\n",
+      "negative interface current <pybamm.models.submodels.interface.kinetics.inverse_kinetics.inverse_butler_volmer.CurrentForInverseButlerVolmer object at 0x7f9cd481f9d0>\n",
+      "positive interface <pybamm.models.submodels.interface.kinetics.inverse_kinetics.inverse_butler_volmer.InverseButlerVolmer object at 0x7f9cd481fa60>\n",
+      "positive interface current <pybamm.models.submodels.interface.kinetics.inverse_kinetics.inverse_butler_volmer.CurrentForInverseButlerVolmer object at 0x7f9cd481f850>\n",
+      "negative primary particle <pybamm.models.submodels.particle.x_averaged_polynomial_profile.XAveragedPolynomialProfile object at 0x7f9cd4952160>\n",
+      "negative primary total particle concentration <pybamm.models.submodels.particle.total_particle_concentration.TotalConcentration object at 0x7f9cd481f6a0>\n",
+      "positive primary particle <pybamm.models.submodels.particle.fickian_diffusion.FickianDiffusion object at 0x7f9cd481f7c0>\n",
+      "positive primary total particle concentration <pybamm.models.submodels.particle.total_particle_concentration.TotalConcentration object at 0x7f9cd481f4c0>\n",
+      "negative electrode potential <pybamm.models.submodels.electrode.ohm.leading_ohm.LeadingOrder object at 0x7f9cd481f550>\n",
+      "positive electrode potential <pybamm.models.submodels.electrode.ohm.leading_ohm.LeadingOrder object at 0x7f9cd481f1c0>\n",
+      "electrolyte diffusion <pybamm.models.submodels.electrolyte_diffusion.constant_concentration.ConstantConcentration object at 0x7f9cd48c4a60>\n",
+      "leading-order electrolyte conductivity <pybamm.models.submodels.electrolyte_conductivity.leading_order_conductivity.LeadingOrder object at 0x7f9cd481fd90>\n",
+      "negative surface potential difference <pybamm.models.submodels.electrolyte_conductivity.surface_potential_form.explicit_surface_form_conductivity.Explicit object at 0x7f9cd481fe20>\n",
+      "positive surface potential difference <pybamm.models.submodels.electrolyte_conductivity.surface_potential_form.explicit_surface_form_conductivity.Explicit object at 0x7f9cd481fe80>\n",
+      "thermal <pybamm.models.submodels.thermal.isothermal.Isothermal object at 0x7f9cd481fdf0>\n",
+      "current collector <pybamm.models.submodels.current_collector.homogeneous_current_collector.Uniform object at 0x7f9cd481ff10>\n",
+      "negative primary sei <pybamm.models.submodels.interface.sei.no_sei.NoSEI object at 0x7f9cd48330d0>\n",
+      "positive primary sei <pybamm.models.submodels.interface.sei.no_sei.NoSEI object at 0x7f9cd4833100>\n",
+      "negative primary sei on cracks <pybamm.models.submodels.interface.sei.no_sei.NoSEI object at 0x7f9cd48331f0>\n",
+      "positive primary sei on cracks <pybamm.models.submodels.interface.sei.no_sei.NoSEI object at 0x7f9cd48331c0>\n",
+      "negative lithium plating <pybamm.models.submodels.interface.lithium_plating.no_plating.NoPlating object at 0x7f9cd4833280>\n",
+      "positive lithium plating <pybamm.models.submodels.interface.lithium_plating.no_plating.NoPlating object at 0x7f9cd48332e0>\n",
+      "total interface <pybamm.models.submodels.interface.total_interfacial_current.TotalInterfacialCurrent object at 0x7f9cd481ff70>\n"
+     ]
+    }
+   ],
+   "source": [
+    "for name, submodel in model.submodels.items():\n",
+    "    print(name, submodel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Building the model also sets up the equations, boundary and initial conditions for the model. For example, if we look at `model.rhs` before building we see that it is empty "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.rhs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we try to use this empty model, PyBaMM will give an error. So, before proceeding we must build the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.build_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now if we look at `model.rhs` we see that it contains an entry relating to the concentration in each particle, as expected for the SPM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{Variable(0x3825da4a5fc4eb0b, Discharge capacity [A.h], children=[], domains={}): Multiplication(0x7678edd47e530eec, *, children=['0.0002777777777777778', 'Current function [A]'], domains={}),\n",
+       " Variable(-0x7fb8d0e6e9632372, Throughput capacity [A.h], children=[], domains={}): Multiplication(-0x7c65e8600b424661, *, children=['0.0002777777777777778', 'abs(Current function [A])'], domains={}),\n",
+       " Variable(0x69f725db1a464db8, Average negative particle concentration [mol.m-3], children=[], domains={'primary': ['current collector']}): MatrixMultiplication(0xf98a766c86b2483, @, children=['mass(Average negative particle concentration [mol.m-3])', '-3.0 * Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / Faraday constant [C.mol-1] / x-average(Negative particle radius [m])'], domains={'primary': ['current collector']}),\n",
+       " Variable(0x48143b39c7603013, X-averaged positive particle concentration [mol.m-3], children=[], domains={'primary': ['positive particle'], 'secondary': ['current collector']}): Divergence(0x17c75a81711ad510, div, children=['Positive electrode diffusivity [m2.s-1] * grad(X-averaged positive particle concentration [mol.m-3])'], domains={'primary': ['positive particle'], 'secondary': ['current collector']})}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.rhs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now the model can be used in a simulation and solved in the usual way, and we still have access to model defaults such as the default geometry and default spatial methods which are used in the simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a57346794d5451683e61d62b66d92fc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 20,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "2faddad136964e19a1e5d623a90ee507",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": [
-              "<pybamm.plotting.quick_plot.QuickPlot at 0x1518a5df0>"
-            ]
-          },
-          "execution_count": 20,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "simulation = pybamm.Simulation(model)\n",
-        "simulation.solve([0, 3600])\n",
-        "simulation.plot()"
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x7f9cd496abe0>"
       ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## References\n",
-        "\n",
-        "The relevant papers for this notebook are:"
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simulation = pybamm.Simulation(model)\n",
+    "simulation.solve([0, 3600])\n",
+    "simulation.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Building a custom model from submodels\n",
+    "Instead of editing a pre-existing model, you may wish to build your own model from scratch by combining existing submodels of you choice. In this section, we build a Single Particle Model in which the diffusion is assumed infinitely fast in both particles. \n",
+    "\n",
+    "To begin, we load a base lithium-ion model. This sets up the basic model structure behind the scenes, and also sets the default parameters to be those corresponding to a lithium-ion battery. Note that the base model does not select any default submodels, so there is no need to pass `build=False`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pybamm.lithium_ion.BaseModel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Submodels can be added to the `model.submodels` dictionary in the same way that we changed the submodels earlier. \n",
+    "\n",
+    "We use the simplest model for the external circuit, which is the explicit \"current control\" submodel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.submodels[\"external circuit\"] = pybamm.external_circuit.ExplicitCurrentControl(model.param, model.options)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to build a 1D model, so select the `Uniform` current collector model (if the current collectors are behaving uniformly, then a 1D model is appropriate). We also want the model to be isothermal, so select the thermal model accordingly. Further, we assume that the porosity and active material are constant in space and time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.submodels[\"current collector\"] = pybamm.current_collector.Uniform(model.param)\n",
+    "model.submodels[\"thermal\"] = pybamm.thermal.isothermal.Isothermal(model.param)\n",
+    "model.submodels[\"porosity\"] = pybamm.porosity.Constant(model.param, model.options)\n",
+    "model.submodels[\"negative active material\"] = pybamm.active_material.Constant(\n",
+    "    model.param, \"negative\", model.options\n",
+    ")\n",
+    "model.submodels[\"positive active material\"] = pybamm.active_material.Constant(\n",
+    "    model.param, \"positive\", model.options\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We assume that the current density varies linearly in the electrodes. This corresponds the the leading-order terms in Ohm's law in the limit in which the SPM is derived in [[3]](#References)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.submodels[\"negative electrode potentials\"] = pybamm.electrode.ohm.LeadingOrder(\n",
+    "    model.param, \"negative\"\n",
+    ")\n",
+    "model.submodels[\"positive electrode potentials\"] = pybamm.electrode.ohm.LeadingOrder(\n",
+    "    model.param, \"positive\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We assume uniform concentration in both the negative and positive particles. We also have to separately specify a model for the total concentration in each electrode, which is calculated from the concentration in the particles (not a separate ODE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "options = {**model.options, \"particle\": \"uniform profile\"}\n",
+    "model.submodels[\"negative primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"negative\", options)\n",
+    "model.submodels[\"positive primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"positive\", options)\n",
+    "\n",
+    "model.submodels[\"negative total particle concentration\"] = pybamm.particle.TotalConcentration(model.param, \"negative\", options)\n",
+    "model.submodels[\"positive total particle concentration\"] = pybamm.particle.TotalConcentration(model.param, \"positive\", options)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the Single Particle Model, the overpotential can be obtained by inverting the Butler-Volmer relation, so we choose the `InverseButlerVolmer` submodel for the interface, with the \"main\" lithium-ion reaction (and default lithium ion options). Because of how the current is implemented, we also need to separately specify the `CurrentForInverseButlerVolmer` submodel. We also need to specify the submodel for open-circuit potential."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.submodels[\n",
+    "    \"negative open-circuit potential\"\n",
+    "] = pybamm.open_circuit_potential.SingleOpenCircuitPotential(\n",
+    "    model.param, \"negative\", \"lithium-ion main\", options=model.options\n",
+    ")\n",
+    "model.submodels[\n",
+    "    \"positive open-circuit potential\"\n",
+    "] = pybamm.open_circuit_potential.SingleOpenCircuitPotential(\n",
+    "    model.param, \"positive\", \"lithium-ion main\", options=model.options\n",
+    ")\n",
+    "model.submodels[\n",
+    "    \"negative interface\"\n",
+    "] = pybamm.kinetics.InverseButlerVolmer(\n",
+    "    model.param, \"negative\", \"lithium-ion main\", options=model.options\n",
+    ")\n",
+    "model.submodels[\n",
+    "    \"positive interface\"\n",
+    "] = pybamm.kinetics.InverseButlerVolmer(\n",
+    "    model.param, \"positive\", \"lithium-ion main\", options=model.options\n",
+    ")\n",
+    "model.submodels[\n",
+    "    \"negative interface current\"\n",
+    "] = pybamm.kinetics.CurrentForInverseButlerVolmer(\n",
+    "    model.param, \"negative\", \"lithium-ion main\"\n",
+    ")\n",
+    "model.submodels[\n",
+    "    \"positive interface current\"\n",
+    "] = pybamm.kinetics.CurrentForInverseButlerVolmer(\n",
+    "    model.param, \"positive\", \"lithium-ion main\"\n",
+    ")\n",
+    "model.submodels[\"negative interface utilisation\"] = pybamm.interface_utilisation.Full(\n",
+    "    model.param, \"negative\", model.options\n",
+    ")\n",
+    "model.submodels[\"positive interface utilisation\"] = pybamm.interface_utilisation.Full(\n",
+    "    model.param, \"positive\", model.options\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We don't want any particle mechanics, SEI formation or lithium plating in this model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.submodels[\n",
+    "    \"Negative particle mechanics\"\n",
+    "] = pybamm.particle_mechanics.NoMechanics(model.param, \"negative\", model.options)\n",
+    "model.submodels[\n",
+    "    \"Positive particle mechanics\"\n",
+    "] = pybamm.particle_mechanics.NoMechanics(model.param, \"positive\", model.options)\n",
+    "model.submodels[\"Negative sei\"] = pybamm.sei.NoSEI(model.param, \"negative\", model.options)\n",
+    "model.submodels[\"Positive sei\"] = pybamm.sei.NoSEI(model.param, \"positive\", model.options)\n",
+    "model.submodels[\"Negative sei on cracks\"] = pybamm.sei.NoSEI(model.param, \"negative\", model.options, cracks=True)\n",
+    "model.submodels[\"Positive sei on cracks\"] = pybamm.sei.NoSEI(model.param, \"positive\", model.options, cracks=True)\n",
+    "model.submodels[\"Negative lithium plating\"] = pybamm.lithium_plating.NoPlating(model.param, \"Negative\")\n",
+    "model.submodels[\"Positive lithium plating\"] = pybamm.lithium_plating.NoPlating(model.param, \"Positive\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, for the electrolyte we assume that diffusion is infinitely fast so that the concentration is uniform, and also use the leading-order model for charge conservation, which leads to a linear variation in ionic current in the electrodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.submodels[\"electrolyte diffusion\"] = pybamm.electrolyte_diffusion.ConstantConcentration(\n",
+    "    model.param\n",
+    ")\n",
+    "model.submodels[\"electrolyte conductivity\"] = pybamm.electrolyte_conductivity.LeadingOrder(\n",
+    "    model.param\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have set all of the submodels we can build the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.build_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then use the model in a simulation in the usual way"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36ae60e068de4c6a9a61697535abb080",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 21,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
-            "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
-            "[3] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
-            "[4] Venkat R. Subramanian, Vinten D. Diwakar, and Deepak Tapriyal. Efficient macro-micro scale coupled modeling of batteries. Journal of The Electrochemical Society, 152(10):A2002, 2005. doi:10.1149/1.2032427.\n",
-            "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "pybamm.print_citations()"
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x7f9ccfd7ba30>"
       ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "pybamm",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.16"
-    },
-    "toc": {
-      "base_numbering": 1,
-      "nav_menu": {},
-      "number_sections": true,
-      "sideBar": true,
-      "skip_h1_title": false,
-      "title_cell": "Table of Contents",
-      "title_sidebar": "Contents",
-      "toc_cell": false,
-      "toc_position": {},
-      "toc_section_display": true,
-      "toc_window_display": true
-    },
-    "vscode": {
-      "interpreter": {
-        "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c"
-      }
+   ],
+   "source": [
+    "simulation = pybamm.Simulation(model)\n",
+    "simulation.solve([0, 3600])\n",
+    "simulation.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "The relevant papers for this notebook are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[3] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
+      "[4] Venkat R. Subramanian, Vinten D. Diwakar, and Deepak Tapriyal. Efficient macro-micro scale coupled modeling of batteries. Journal of The Electrochemical Society, 152(10):A2002, 2005. doi:10.1149/1.2032427.\n",
+      "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "\n"
+     ]
     }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
+  "vscode": {
+   "interpreter": {
+    "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }
diff --git a/docs/source/examples/notebooks/parameterization/change-input-current.ipynb b/docs/source/examples/notebooks/parameterization/change-input-current.ipynb
index 52ed915327..0285ab69dd 100644
--- a/docs/source/examples/notebooks/parameterization/change-input-current.ipynb
+++ b/docs/source/examples/notebooks/parameterization/change-input-current.ipynb
@@ -41,7 +41,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import os\n",
diff --git a/docs/source/examples/notebooks/parameterization/parameter-values.ipynb b/docs/source/examples/notebooks/parameterization/parameter-values.ipynb
index c0d9464dcf..f0a770af08 100644
--- a/docs/source/examples/notebooks/parameterization/parameter-values.ipynb
+++ b/docs/source/examples/notebooks/parameterization/parameter-values.ipynb
@@ -31,7 +31,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import os\n",
diff --git a/docs/source/examples/notebooks/parameterization/parameterization.ipynb b/docs/source/examples/notebooks/parameterization/parameterization.ipynb
index 35226ed89f..f3db45aa44 100644
--- a/docs/source/examples/notebooks/parameterization/parameterization.ipynb
+++ b/docs/source/examples/notebooks/parameterization/parameterization.ipynb
@@ -41,7 +41,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt"
diff --git a/docs/source/examples/notebooks/plotting/customize-quick-plot.ipynb b/docs/source/examples/notebooks/plotting/customize-quick-plot.ipynb
index dbe6888a15..d7a5fda2da 100644
--- a/docs/source/examples/notebooks/plotting/customize-quick-plot.ipynb
+++ b/docs/source/examples/notebooks/plotting/customize-quick-plot.ipynb
@@ -45,7 +45,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "\n",
     "models = [pybamm.lithium_ion.SPM(), pybamm.lithium_ion.SPMe(), pybamm.lithium_ion.DFN()]\n",
diff --git a/docs/source/examples/notebooks/plotting/plot-voltage-components.ipynb b/docs/source/examples/notebooks/plotting/plot-voltage-components.ipynb
index 8740981d04..bab1b8093e 100644
--- a/docs/source/examples/notebooks/plotting/plot-voltage-components.ipynb
+++ b/docs/source/examples/notebooks/plotting/plot-voltage-components.ipynb
@@ -116,7 +116,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "\n",
     "model = pybamm.lithium_ion.DFN()\n",
diff --git a/docs/source/examples/notebooks/rpt-experiment.ipynb b/docs/source/examples/notebooks/rpt-experiment.ipynb
new file mode 100644
index 0000000000..cbe07e3a55
--- /dev/null
+++ b/docs/source/examples/notebooks/rpt-experiment.ipynb
@@ -0,0 +1,429 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ed85baef",
+   "metadata": {},
+   "source": [
+    "# Degradation experiments with reference performance tests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0854463d",
+   "metadata": {},
+   "source": [
+    "When running degradation experiments in the lab, it is important to use reference performance tests (RPTs) to measure capacity and other key metrics, otherwise the experimental contitions will interfere with the measurement! In PyBaMM, you can run simulations with RPTs using the `Experiment` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ee358ae8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install pybamm[plot,cite] -q  # install PyBaMM if it is not installed\n",
+    "\n",
+    "import pybamm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "\n",
+    "os.chdir(pybamm.__path__[0]+'/..')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ee2a95e",
+   "metadata": {},
+   "source": [
+    "Load a simple degradation model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "89901d6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pybamm.lithium_ion.SPM({\"SEI\": \"ec reaction limited\"})\n",
+    "parameter_values = pybamm.ParameterValues(\"Mohtat2020\")\n",
+    "parameter_values.update({\"SEI kinetic rate constant [m.s-1]\": 1e-14})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b5de491",
+   "metadata": {},
+   "source": [
+    "Define three different experiments using the Experiment class:\n",
+    "* cccv_experiment is a cycle ageing experiment\n",
+    "* charge_experiment charges to full after N ageing cycles\n",
+    "* rpt_experiment is a C/3 discharge in this case, but can also contain a charge, GITT, EIS and other procedures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5f50d944",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = 10\n",
+    "cccv_experiment = pybamm.Experiment([\n",
+    "    (\"Charge at 1C until 4.2V\", \n",
+    "     \"Hold at 4.2V until C/50\",\n",
+    "     \"Discharge at 1C until 3V\",\n",
+    "     \"Rest for 1 hour\",\n",
+    "    )\n",
+    "] * N)\n",
+    "charge_experiment = pybamm.Experiment([\n",
+    "    (\"Charge at 1C until 4.2V\", \n",
+    "     \"Hold at 4.2V until C/50\",\n",
+    "    )\n",
+    "])\n",
+    "rpt_experiment = pybamm.Experiment([\n",
+    "    (\"Discharge at C/3 until 3V\",)\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0159eb0",
+   "metadata": {},
+   "source": [
+    "Run the ageing, charge and RPT experiments in order by feeding the previous solution into the solve command:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3fcb0940",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim = pybamm.Simulation(model, experiment=cccv_experiment, parameter_values=parameter_values)\n",
+    "cccv_sol = sim.solve()\n",
+    "sim = pybamm.Simulation(model, experiment=charge_experiment, parameter_values=parameter_values)\n",
+    "charge_sol = sim.solve(starting_solution=cccv_sol)\n",
+    "sim = pybamm.Simulation(model, experiment=rpt_experiment, parameter_values=parameter_values)\n",
+    "rpt_sol = sim.solve(starting_solution=charge_sol)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9474391c",
+   "metadata": {},
+   "source": [
+    "Plot detailed current/voltage data for the RPT cycle only:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a7c53c87",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fcfcd6773a1649f6ad6890ca9d6610e6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=32.16021213978805, description='t', max=34.910964151192324, min=32.160…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x7ff13da91760>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pybamm.dynamic_plot(rpt_sol.cycles[-1], [\"Current [A]\", \"Voltage [V]\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b44afad3",
+   "metadata": {},
+   "source": [
+    "PyBaMM's summary variables track important cell-level degradation veriables at the end of each cycle. The charge and RPT cycles are also counted, making a total of 12 cycles:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d2cee8d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pybamm.plot_summary_variables(rpt_sol);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf87ccc0",
+   "metadata": {},
+   "source": [
+    "Repeat the procedure four times:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0a53f14d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cccv_sols = []\n",
+    "charge_sols = []\n",
+    "rpt_sols = []\n",
+    "M = 5\n",
+    "for i in range(M):\n",
+    "    if i != 0:  # skip the first set of ageing cycles because it's already been done\n",
+    "        sim = pybamm.Simulation(model, experiment=cccv_experiment, parameter_values=parameter_values)\n",
+    "        cccv_sol = sim.solve(starting_solution=rpt_sol)\n",
+    "        sim = pybamm.Simulation(model, experiment=charge_experiment, parameter_values=parameter_values)\n",
+    "        charge_sol = sim.solve(starting_solution=cccv_sol)\n",
+    "        sim = pybamm.Simulation(model, experiment=rpt_experiment, parameter_values=parameter_values)\n",
+    "        rpt_sol = sim.solve(starting_solution=charge_sol)\n",
+    "    cccv_sols.append(cccv_sol)\n",
+    "    charge_sols.append(charge_sol)\n",
+    "    rpt_sols.append(rpt_sol)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9bbb981",
+   "metadata": {},
+   "source": [
+    "You can plot any RPT cycle. The last one is chosen here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "744a06c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36de09f82f3849c982f98497b4cbd5cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=161.47100901868143, description='t', max=163.71508382969813, min=161.4…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x7ff12d79b610>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pybamm.dynamic_plot(rpt_sols[-1].cycles[-1], [\"Current [A]\", \"Voltage [V]\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a95f6ae",
+   "metadata": {},
+   "source": [
+    "One way of demonstrating how useful RPTs are is to plot the discharge capacity for each cycle. It is convenient to use the final `rpt_sol` because it also contains all previous simulations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "89401a9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7ff12d616bb0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cccv_cycles = []\n",
+    "cccv_capacities = []\n",
+    "rpt_cycles = []\n",
+    "rpt_capacities = []\n",
+    "for i in range (M):\n",
+    "    for j in range(N):\n",
+    "        cccv_cycles.append(i*(N+2)+j+1)\n",
+    "        start_capacity = rpt_sol.cycles[i*(N+2)+j].steps[2][\"Discharge capacity [A.h]\"].entries[0]\n",
+    "        end_capacity = rpt_sol.cycles[i*(N+2)+j].steps[2][\"Discharge capacity [A.h]\"].entries[-1]\n",
+    "        cccv_capacities.append(end_capacity-start_capacity)\n",
+    "    rpt_cycles.append((i+1)*(N+2))\n",
+    "    start_capacity = rpt_sol.cycles[(i+1)*(N+2)-1][\"Discharge capacity [A.h]\"].entries[0]\n",
+    "    end_capacity = rpt_sol.cycles[(i+1)*(N+2)-1][\"Discharge capacity [A.h]\"].entries[-1]\n",
+    "    rpt_capacities.append(end_capacity-start_capacity)\n",
+    "plt.scatter(cccv_cycles,cccv_capacities,label=\"Ageing cycles\")\n",
+    "plt.scatter(rpt_cycles,rpt_capacities,label=\"RPT cycles\")\n",
+    "plt.xlabel(\"Cycle number\")\n",
+    "plt.ylabel(\"Discharge capacity [A.h]\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c46698cf",
+   "metadata": {},
+   "source": [
+    "The ageing cycles have a higher discharge rate than the RPT cycles and therefore have a slightly lower discharge capacity. (The charge cycles are not included because they have no discharge capacity.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4cf20ccb",
+   "metadata": {},
+   "source": [
+    "Finally, plot the summary variables for the entire experiment run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3b0346d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pybamm.plot_summary_variables(rpt_sol);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "87b38fc7",
+   "metadata": {},
+   "source": [
+    "# References\n",
+    "\n",
+    "The relevant papers for this notebook are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e60c96be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[2] Ferran Brosa Planella and W. Dhammika Widanage. Systematic derivation of a Single Particle Model with Electrolyte and Side Reactions (SPMe+SR) for degradation of lithium-ion batteries. Submitted for publication, ():, 2022. doi:.\n",
+      "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
+      "[5] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n",
+      "[6] Peyman Mohtat, Suhak Lee, Valentin Sulzer, Jason B. Siegel, and Anna G. Stefanopoulou. Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates. Journal of The Electrochemical Society, 167(11):110561, 2020. doi:10.1149/1945-7111/aba5d1.\n",
+      "[7] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "[8] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
+      "[9] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n",
+      "[10] Xiao Guang Yang, Yongjun Leng, Guangsheng Zhang, Shanhai Ge, and Chao Yang Wang. Modeling of lithium plating induced aging of lithium-ion batteries: transition from linear to nonlinear aging. Journal of Power Sources, 360:28–40, 2017. doi:10.1016/j.jpowsour.2017.05.110.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/examples/notebooks/simulating-long-experiments.ipynb b/docs/source/examples/notebooks/simulating-long-experiments.ipynb
index bfa0321e3a..890107e421 100644
--- a/docs/source/examples/notebooks/simulating-long-experiments.ipynb
+++ b/docs/source/examples/notebooks/simulating-long-experiments.ipynb
@@ -33,7 +33,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import matplotlib.pyplot as plt"
    ]
diff --git a/docs/source/examples/notebooks/simulation-class.ipynb b/docs/source/examples/notebooks/simulation-class.ipynb
index 6797e85e61..bb93ec207a 100644
--- a/docs/source/examples/notebooks/simulation-class.ipynb
+++ b/docs/source/examples/notebooks/simulation-class.ipynb
@@ -17,7 +17,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm"
    ]
   },
@@ -134,7 +134,7 @@
    "source": [
     "# using less number of images in the example\n",
     "# for a smoother GIF use more images\n",
-    "simulation.create_gif(number_of_images=5, duration=0.2)"
+    "simulation.create_gif(number_of_images=5, duration=0.2, output_filename=\"simulation.gif\")"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb b/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb
index ff6ab7cc4d..2849fca58c 100644
--- a/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb
+++ b/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb
@@ -1,536 +1,557 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# A look at solution data and processed variables"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once you have run a simulation the first thing you want to do is have a look at the data. Most of the examples so far have made use of PyBaMM's handy QuickPlot function but there are other ways to access the data and this notebook will explore them. First off we will generate a standard SPMe model and use QuickPlot to view the default variables."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Note: you may need to restart the kernel to use updated packages.\n"
-          ]
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "9ad1d544145646a3ae6c71a74f1dd41d",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "interactive(children=(FloatSlider(value=0.0, description='t', max=3510.0, step=35.1), Output()), _dom_classes=…"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
-        "import pybamm\n",
-        "import numpy as np\n",
-        "import os\n",
-        "import matplotlib.pyplot as plt\n",
-        "os.chdir(pybamm.__path__[0]+'/..')\n",
-        "\n",
-        "# load model\n",
-        "model = pybamm.lithium_ion.SPMe()\n",
-        "\n",
-        "# set up and solve simulation\n",
-        "simulation = pybamm.Simulation(model)\n",
-        "dt = 90\n",
-        "t_eval = np.arange(0, 3600, dt)  # time in seconds\n",
-        "solution = simulation.solve(t_eval)\n",
-        "\n",
-        "quick_plot = pybamm.QuickPlot(solution)\n",
-        "quick_plot.dynamic_plot();"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Behind the scenes the QuickPlot classed has created some processed variables which can interpolate the model variables for our solution and has also stored the results for the solution steps"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "dict_keys(['Negative particle surface concentration [mol.m-3]', 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Current [A]', 'Negative electrode potential [V]', 'Electrolyte potential [V]', 'Positive electrode potential [V]', 'Voltage [V]'])"
-            ]
-          },
-          "execution_count": 2,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "solution.data.keys()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(20, 40)"
-            ]
-          },
-          "execution_count": 3,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "solution.data['Negative particle surface concentration [mol.m-3]'].shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(40,)"
-            ]
-          },
-          "execution_count": 4,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "solution.t.shape"
-      ]
-    },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A look at solution data and processed variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have run a simulation the first thing you want to do is have a look at the data. Most of the examples so far have made use of PyBaMM's handy QuickPlot function but there are other ways to access the data and this notebook will explore them. First off we will generate a standard SPMe model and use QuickPlot to view the default variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Notice that the dictionary keys are in the same order as the subplots in the QuickPlot figure. We can add new processed variables to the solution by simply using it like a dictionary. First let's find a few more variables to look at. As you will see there are quite a few:"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "tags": [
-          "outputPrepend"
-        ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ad1d544145646a3ae6c71a74f1dd41d",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "['Ambient temperature', 'Ambient temperature [K]', 'Average negative particle concentration', 'Average negative particle concentration [mol.m-3]', 'Average positive particle concentration', 'Average positive particle concentration [mol.m-3]', 'Battery voltage [V]', 'C-rate', 'Cell temperature', 'Cell temperature [K]', 'Change in measured open-circuit voltage', 'Change in measured open-circuit voltage [V]', 'Current [A]', 'Current collector current density', 'Current collector current density [A.m-2]', 'Discharge capacity [A.h]', 'Electrode current density', 'Electrode tortuosity', 'Electrolyte concentration', 'Electrolyte concentration [Molar]', 'Electrolyte concentration [mol.m-3]', 'Electrolyte current density', 'Electrolyte current density [A.m-2]', 'Electrolyte flux', 'Electrolyte flux [mol.m-2.s-1]', 'Electrolyte potential', 'Electrolyte potential [V]', 'Electrolyte tortuosity', 'Exchange current density', 'Exchange current density [A.m-2]', 'Exchange current density per volume [A.m-3]', 'Gradient of electrolyte potential', 'Gradient of negative electrode potential', 'Gradient of negative electrolyte potential', 'Gradient of positive electrode potential', 'Gradient of positive electrolyte potential', 'Gradient of separator electrolyte potential', 'Inner SEI concentration [mol.m-3]', 'Inner SEI interfacial current density', 'Inner SEI interfacial current density [A.m-2]', 'Inner SEI thickness', 'Inner SEI thickness [m]', 'Inner positive electrode SEI concentration [mol.m-3]', 'Inner positive electrode SEI interfacial current density', 'Inner positive electrode SEI interfacial current density [A.m-2]', 'Inner positive electrode SEI thickness', 'Inner positive electrode SEI thickness [m]', 'Interfacial current density', 'Interfacial current density [A.m-2]', 'Interfacial current density per volume [A.m-3]', 'Irreversible electrochemical heating', 'Irreversible electrochemical heating [W.m-3]', 'Leading-order current collector current density', 'Leading-order electrode tortuosity', 'Leading-order electrolyte tortuosity', 'Leading-order negative electrode porosity', 'Leading-order negative electrode tortuosity', 'Leading-order negative electrolyte tortuosity', 'Leading-order porosity', 'Leading-order positive electrode porosity', 'Leading-order positive electrode tortuosity', 'Leading-order positive electrolyte tortuosity', 'Leading-order separator porosity', 'Leading-order separator tortuosity', 'Leading-order x-averaged negative electrode porosity', 'Leading-order x-averaged negative electrode porosity change', 'Leading-order x-averaged negative electrode tortuosity', 'Leading-order x-averaged negative electrolyte tortuosity', 'Leading-order x-averaged positive electrode porosity', 'Leading-order x-averaged positive electrode porosity change', 'Leading-order x-averaged positive electrode tortuosity', 'Leading-order x-averaged positive electrolyte tortuosity', 'Leading-order x-averaged separator porosity', 'Leading-order x-averaged separator porosity change', 'Leading-order x-averaged separator tortuosity', 'Local ECM resistance', 'Local ECM resistance [Ohm]', 'Local voltage', 'Local voltage [V]', 'Loss of lithium to SEI [mol]', 'Loss of lithium to positive electrode SEI [mol]', 'Maximum negative particle concentration', 'Maximum negative particle concentration [mol.m-3]', 'Maximum negative particle surface concentration', 'Maximum negative particle surface concentration [mol.m-3]', 'Maximum positive particle concentration', 'Maximum positive particle concentration [mol.m-3]', 'Maximum positive particle surface concentration', 'Maximum positive particle surface concentration [mol.m-3]', 'Measured battery open-circuit voltage [V]', 'Measured open-circuit voltage', 'Measured open-circuit voltage [V]', 'Minimum negative particle concentration', 'Minimum negative particle concentration [mol.m-3]', 'Minimum negative particle surface concentration', 'Minimum negative particle surface concentration [mol.m-3]', 'Minimum positive particle concentration', 'Minimum positive particle concentration [mol.m-3]', 'Minimum positive particle surface concentration', 'Minimum positive particle surface concentration [mol.m-3]', 'Negative current collector potential', 'Negative current collector potential [V]', 'Negative current collector temperature', 'Negative current collector temperature [K]', 'Negative electrode active material volume fraction', 'Negative electrode active material volume fraction change', 'Negative electrode current density', 'Negative electrode current density [A.m-2]', 'Negative electrode entropic change', 'Negative electrode exchange current density', 'Negative electrode exchange current density [A.m-2]', 'Negative electrode exchange current density per volume [A.m-3]', 'Negative electrode extent of lithiation', 'Negative electrode interfacial current density', 'Negative electrode interfacial current density [A.m-2]', 'Negative electrode interfacial current density per volume [A.m-3]', 'Negative electrode ohmic losses', 'Negative electrode ohmic losses [V]', 'Negative electrode open-circuit potential', 'Negative electrode open-circuit potential [V]', 'Negative electrode oxygen exchange current density', 'Negative electrode oxygen exchange current density [A.m-2]', 'Negative electrode oxygen exchange current density per volume [A.m-3]', 'Negative electrode oxygen interfacial current density', 'Negative electrode oxygen interfacial current density [A.m-2]', 'Negative electrode oxygen interfacial current density per volume [A.m-3]', 'Negative electrode oxygen open-circuit potential', 'Negative electrode oxygen open-circuit potential [V]', 'Negative electrode oxygen reaction overpotential', 'Negative electrode oxygen reaction overpotential [V]', 'Negative electrode porosity', 'Negative electrode porosity change', 'Negative electrode potential', 'Negative electrode potential [V]', 'Negative electrode pressure', 'Negative electrode reaction overpotential', 'Negative electrode reaction overpotential [V]', 'SEI film overpotential', 'SEI film overpotential [V]', 'SEI interfacial current density', 'SEI interfacial current density [A.m-2]', 'Negative electrode surface area to volume ratio', 'Negative electrode surface area to volume ratio [m-1]', 'Negative electrode surface potential difference', 'Negative electrode surface potential difference [V]', 'Negative electrode temperature', 'Negative electrode temperature [K]', 'Negative electrode tortuosity', 'Negative electrode transverse volume-averaged acceleration', 'Negative electrode transverse volume-averaged acceleration [m.s-2]', 'Negative electrode transverse volume-averaged velocity', 'Negative electrode transverse volume-averaged velocity [m.s-2]', 'Negative electrode volume-averaged acceleration', 'Negative electrode volume-averaged acceleration [m.s-1]', 'Negative electrode volume-averaged concentration', 'Negative electrode volume-averaged concentration [mol.m-3]', 'Negative electrode volume-averaged velocity', 'Negative electrode volume-averaged velocity [m.s-1]', 'Negative electrolyte concentration', 'Negative electrolyte concentration [Molar]', 'Negative electrolyte concentration [mol.m-3]', 'Negative electrolyte current density', 'Negative electrolyte current density [A.m-2]', 'Negative electrolyte potential', 'Negative electrolyte potential [V]', 'Negative electrolyte tortuosity', 'Negative particle concentration', 'Negative particle concentration [mol.m-3]', 'Negative particle flux', 'Negative particle radius', 'Negative particle radius [m]', 'Negative particle surface concentration', 'Negative particle surface concentration [mol.m-3]', 'Negative SEI concentration [mol.m-3]', 'Ohmic heating', 'Ohmic heating [W.m-3]', 'Outer SEI concentration [mol.m-3]', 'Outer SEI interfacial current density', 'Outer SEI interfacial current density [A.m-2]', 'Outer SEI thickness', 'Outer SEI thickness [m]', 'Outer positive electrode SEI concentration [mol.m-3]', 'Outer positive electrode SEI interfacial current density', 'Outer positive electrode SEI interfacial current density [A.m-2]', 'Outer positive electrode SEI thickness', 'Outer positive electrode SEI thickness [m]', 'Oxygen exchange current density', 'Oxygen exchange current density [A.m-2]', 'Oxygen exchange current density per volume [A.m-3]', 'Oxygen interfacial current density', 'Oxygen interfacial current density [A.m-2]', 'Oxygen interfacial current density per volume [A.m-3]', 'Porosity', 'Porosity change', 'Positive current collector potential', 'Positive current collector potential [V]', 'Positive current collector temperature', 'Positive current collector temperature [K]', 'Positive electrode active material volume fraction', 'Positive electrode active material volume fraction change', 'Positive electrode current density', 'Positive electrode current density [A.m-2]', 'Positive electrode entropic change', 'Positive electrode exchange current density', 'Positive electrode exchange current density [A.m-2]', 'Positive electrode exchange current density per volume [A.m-3]', 'Positive electrode extent of lithiation', 'Positive electrode interfacial current density', 'Positive electrode interfacial current density [A.m-2]', 'Positive electrode interfacial current density per volume [A.m-3]', 'Positive electrode ohmic losses', 'Positive electrode ohmic losses [V]', 'Positive electrode open-circuit potential', 'Positive electrode open-circuit potential [V]', 'Positive electrode oxygen exchange current density', 'Positive electrode oxygen exchange current density [A.m-2]', 'Positive electrode oxygen exchange current density per volume [A.m-3]', 'Positive electrode oxygen interfacial current density', 'Positive electrode oxygen interfacial current density [A.m-2]', 'Positive electrode oxygen interfacial current density per volume [A.m-3]', 'Positive electrode oxygen open-circuit potential', 'Positive electrode oxygen open-circuit potential [V]', 'Positive electrode oxygen reaction overpotential', 'Positive electrode oxygen reaction overpotential [V]', 'Positive electrode porosity', 'Positive electrode porosity change', 'Positive electrode potential', 'Positive electrode potential [V]', 'Positive electrode pressure', 'Positive electrode reaction overpotential', 'Positive electrode reaction overpotential [V]', 'Positive electrode SEI film overpotential', 'Positive electrode SEI film overpotential [V]', 'Positive electrode SEI interfacial current density', 'Positive electrode SEI interfacial current density [A.m-2]', 'Positive electrode surface area to volume ratio', 'Positive electrode surface area to volume ratio [m-1]', 'Positive electrode surface potential difference', 'Positive electrode surface potential difference [V]', 'Positive electrode temperature', 'Positive electrode temperature [K]', 'Positive electrode tortuosity', 'Positive electrode transverse volume-averaged acceleration', 'Positive electrode transverse volume-averaged acceleration [m.s-2]', 'Positive electrode transverse volume-averaged velocity', 'Positive electrode transverse volume-averaged velocity [m.s-2]', 'Positive electrode volume-averaged acceleration', 'Positive electrode volume-averaged acceleration [m.s-1]', 'Positive electrode volume-averaged concentration', 'Positive electrode volume-averaged concentration [mol.m-3]', 'Positive electrode volume-averaged velocity', 'Positive electrode volume-averaged velocity [m.s-1]', 'Positive electrolyte concentration', 'Positive electrolyte concentration [Molar]', 'Positive electrolyte concentration [mol.m-3]', 'Positive electrolyte current density', 'Positive electrolyte current density [A.m-2]', 'Positive electrolyte potential', 'Positive electrolyte potential [V]', 'Positive electrolyte tortuosity', 'Positive particle concentration', 'Positive particle concentration [mol.m-3]', 'Positive particle flux', 'Positive particle radius', 'Positive particle radius [m]', 'Positive particle surface concentration', 'Positive particle surface concentration [mol.m-3]', 'Positive SEI concentration [mol.m-3]', 'Pressure', 'R-averaged negative particle concentration', 'R-averaged negative particle concentration [mol.m-3]', 'R-averaged positive particle concentration', 'R-averaged positive particle concentration [mol.m-3]', 'Reversible heating', 'Reversible heating [W.m-3]', 'Sei interfacial current density', 'Sei interfacial current density [A.m-2]', 'Sei interfacial current density per volume [A.m-3]', 'Separator electrolyte concentration', 'Separator electrolyte concentration [Molar]', 'Separator electrolyte concentration [mol.m-3]', 'Separator electrolyte potential', 'Separator electrolyte potential [V]', 'Separator porosity', 'Separator porosity change', 'Separator pressure', 'Separator temperature', 'Separator temperature [K]', 'Separator tortuosity', 'Separator transverse volume-averaged acceleration', 'Separator transverse volume-averaged acceleration [m.s-2]', 'Separator transverse volume-averaged velocity', 'Separator transverse volume-averaged velocity [m.s-2]', 'Separator volume-averaged acceleration', 'Separator volume-averaged acceleration [m.s-1]', 'Separator volume-averaged velocity', 'Separator volume-averaged velocity [m.s-1]', 'Sum of electrolyte reaction source terms', 'Sum of interfacial current densities', 'Sum of negative electrode electrolyte reaction source terms', 'Sum of negative electrode interfacial current densities', 'Sum of positive electrode electrolyte reaction source terms', 'Sum of positive electrode interfacial current densities', 'Sum of x-averaged negative electrode electrolyte reaction source terms', 'Sum of x-averaged negative electrode interfacial current densities', 'Sum of x-averaged positive electrode electrolyte reaction source terms', 'Sum of x-averaged positive electrode interfacial current densities', 'Terminal power [W]', 'Voltage', 'Voltage [V]', 'Time', 'Time [h]', 'Time [min]', 'Time [s]', 'Total concentration in electrolyte [mol]', 'Total current density', 'Total current density [A.m-2]', 'Total heating', 'Total heating [W.m-3]', 'Total lithium in negative electrode [mol]', 'Total lithium in positive electrode [mol]', 'Total SEI thickness', 'Total SEI thickness [m]', 'Total positive electrode SEI thickness', 'Total positive electrode SEI thickness [m]', 'Transverse volume-averaged acceleration', 'Transverse volume-averaged acceleration [m.s-2]', 'Transverse volume-averaged velocity', 'Transverse volume-averaged velocity [m.s-2]', 'Volume-averaged Ohmic heating', 'Volume-averaged Ohmic heating [W.m-3]', 'Volume-averaged acceleration', 'Volume-averaged acceleration [m.s-1]', 'Volume-averaged cell temperature', 'Volume-averaged cell temperature [K]', 'Volume-averaged irreversible electrochemical heating', 'Volume-averaged irreversible electrochemical heating[W.m-3]', 'Volume-averaged reversible heating', 'Volume-averaged reversible heating [W.m-3]', 'Volume-averaged total heating', 'Volume-averaged total heating [W.m-3]', 'Volume-averaged velocity', 'Volume-averaged velocity [m.s-1]', 'X-averaged Ohmic heating', 'X-averaged Ohmic heating [W.m-3]', 'X-averaged battery concentration overpotential [V]', 'X-averaged battery electrolyte ohmic losses [V]', 'X-averaged battery open-circuit voltage [V]', 'X-averaged battery reaction overpotential [V]', 'X-averaged battery solid phase ohmic losses [V]', 'X-averaged cell temperature', 'X-averaged cell temperature [K]', 'X-averaged concentration overpotential', 'X-averaged concentration overpotential [V]', 'X-averaged electrolyte concentration', 'X-averaged electrolyte concentration [Molar]', 'X-averaged electrolyte concentration [mol.m-3]', 'X-averaged electrolyte ohmic losses', 'X-averaged electrolyte ohmic losses [V]', 'X-averaged electrolyte overpotential', 'X-averaged electrolyte overpotential [V]', 'X-averaged electrolyte potential', 'X-averaged electrolyte potential [V]', 'X-averaged inner SEI concentration [mol.m-3]', 'X-averaged inner SEI interfacial current density', 'X-averaged inner SEI interfacial current density [A.m-2]', 'X-averaged inner SEI thickness', 'X-averaged inner SEI thickness [m]', 'X-averaged inner positive electrode SEI concentration [mol.m-3]', 'X-averaged inner positive electrode SEI interfacial current density', 'X-averaged inner positive electrode SEI interfacial current density [A.m-2]', 'X-averaged inner positive electrode SEI thickness', 'X-averaged inner positive electrode SEI thickness [m]', 'X-averaged irreversible electrochemical heating', 'X-averaged irreversible electrochemical heating [W.m-3]', 'X-averaged negative electrode active material volume fraction', 'X-averaged negative electrode active material volume fraction change', 'X-averaged negative electrode entropic change', 'X-averaged negative electrode exchange current density', 'X-averaged negative electrode exchange current density [A.m-2]', 'X-averaged negative electrode exchange current density per volume [A.m-3]', 'X-averaged negative electrode extent of lithiation', 'X-averaged negative electrode interfacial current density', 'X-averaged negative electrode interfacial current density [A.m-2]', 'X-averaged negative electrode interfacial current density per volume [A.m-3]', 'X-averaged negative electrode ohmic losses', 'X-averaged negative electrode ohmic losses [V]', 'X-averaged negative electrode open-circuit potential', 'X-averaged negative electrode open-circuit potential [V]', 'X-averaged negative electrode oxygen exchange current density', 'X-averaged negative electrode oxygen exchange current density [A.m-2]', 'X-averaged negative electrode oxygen exchange current density per volume [A.m-3]', 'X-averaged negative electrode oxygen interfacial current density', 'X-averaged negative electrode oxygen interfacial current density [A.m-2]', 'X-averaged negative electrode oxygen interfacial current density per volume [A.m-3]', 'X-averaged negative electrode oxygen open-circuit potential', 'X-averaged negative electrode oxygen open-circuit potential [V]', 'X-averaged negative electrode oxygen reaction overpotential', 'X-averaged negative electrode oxygen reaction overpotential [V]', 'X-averaged negative electrode porosity', 'X-averaged negative electrode porosity change', 'X-averaged negative electrode potential', 'X-averaged negative electrode potential [V]', 'X-averaged negative electrode pressure', 'X-averaged negative electrode reaction overpotential', 'X-averaged negative electrode reaction overpotential [V]', 'X-averaged negative electrode resistance [Ohm.m2]', 'X-averaged SEI concentration [mol.m-3]', 'X-averaged SEI film overpotential', 'X-averaged SEI film overpotential [V]', 'X-averaged SEI interfacial current density', 'X-averaged SEI interfacial current density [A.m-2]', 'X-averaged negative electrode surface area to volume ratio', 'X-averaged negative electrode surface area to volume ratio [m-1]', 'X-averaged negative electrode surface potential difference', 'X-averaged negative electrode surface potential difference [V]', 'X-averaged negative electrode temperature', 'X-averaged negative electrode temperature [K]', 'X-averaged negative electrode tortuosity', 'X-averaged negative electrode total interfacial current density', 'X-averaged negative electrode total interfacial current density [A.m-2]', 'X-averaged negative electrode total interfacial current density per volume [A.m-3]', 'X-averaged negative electrode transverse volume-averaged acceleration', 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]', 'X-averaged negative electrode transverse volume-averaged velocity', 'X-averaged negative electrode transverse volume-averaged velocity [m.s-2]', 'X-averaged negative electrode volume-averaged acceleration', 'X-averaged negative electrode volume-averaged acceleration [m.s-1]', 'X-averaged negative electrolyte concentration', 'X-averaged negative electrolyte concentration [mol.m-3]', 'X-averaged negative electrolyte potential', 'X-averaged negative electrolyte potential [V]', 'X-averaged negative electrolyte tortuosity', 'X-averaged negative particle concentration', 'X-averaged negative particle concentration [mol.m-3]', 'X-averaged negative particle flux', 'X-averaged negative particle surface concentration', 'X-averaged negative particle surface concentration [mol.m-3]', 'X-averaged open-circuit voltage', 'X-averaged open-circuit voltage [V]', 'X-averaged outer SEI concentration [mol.m-3]', 'X-averaged outer SEI interfacial current density', 'X-averaged outer SEI interfacial current density [A.m-2]', 'X-averaged outer SEI thickness', 'X-averaged outer SEI thickness [m]', 'X-averaged outer positive electrode SEI concentration [mol.m-3]', 'X-averaged outer positive electrode SEI interfacial current density', 'X-averaged outer positive electrode SEI interfacial current density [A.m-2]', 'X-averaged outer positive electrode SEI thickness', 'X-averaged outer positive electrode SEI thickness [m]', 'X-averaged positive electrode active material volume fraction', 'X-averaged positive electrode active material volume fraction change', 'X-averaged positive electrode entropic change', 'X-averaged positive electrode exchange current density', 'X-averaged positive electrode exchange current density [A.m-2]', 'X-averaged positive electrode exchange current density per volume [A.m-3]', 'X-averaged positive electrode extent of lithiation', 'X-averaged positive electrode interfacial current density', 'X-averaged positive electrode interfacial current density [A.m-2]', 'X-averaged positive electrode interfacial current density per volume [A.m-3]', 'X-averaged positive electrode ohmic losses', 'X-averaged positive electrode ohmic losses [V]', 'X-averaged positive electrode open-circuit potential', 'X-averaged positive electrode open-circuit potential [V]', 'X-averaged positive electrode oxygen exchange current density', 'X-averaged positive electrode oxygen exchange current density [A.m-2]', 'X-averaged positive electrode oxygen exchange current density per volume [A.m-3]', 'X-averaged positive electrode oxygen interfacial current density', 'X-averaged positive electrode oxygen interfacial current density [A.m-2]', 'X-averaged positive electrode oxygen interfacial current density per volume [A.m-3]', 'X-averaged positive electrode oxygen open-circuit potential', 'X-averaged positive electrode oxygen open-circuit potential [V]', 'X-averaged positive electrode oxygen reaction overpotential', 'X-averaged positive electrode oxygen reaction overpotential [V]', 'X-averaged positive electrode porosity', 'X-averaged positive electrode porosity change', 'X-averaged positive electrode potential', 'X-averaged positive electrode potential [V]', 'X-averaged positive electrode pressure', 'X-averaged positive electrode reaction overpotential', 'X-averaged positive electrode reaction overpotential [V]', 'X-averaged positive electrode resistance [Ohm.m2]', 'X-averaged positive electrode SEI concentration [mol.m-3]', 'X-averaged positive electrode SEI film overpotential', 'X-averaged positive electrode SEI film overpotential [V]', 'X-averaged positive electrode SEI interfacial current density', 'X-averaged positive electrode SEI interfacial current density [A.m-2]', 'X-averaged positive electrode surface area to volume ratio', 'X-averaged positive electrode surface area to volume ratio [m-1]', 'X-averaged positive electrode surface potential difference', 'X-averaged positive electrode surface potential difference [V]', 'X-averaged positive electrode temperature', 'X-averaged positive electrode temperature [K]', 'X-averaged positive electrode tortuosity', 'X-averaged positive electrode total interfacial current density', 'X-averaged positive electrode total interfacial current density [A.m-2]', 'X-averaged positive electrode total interfacial current density per volume [A.m-3]', 'X-averaged positive electrode transverse volume-averaged acceleration', 'X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]', 'X-averaged positive electrode transverse volume-averaged velocity', 'X-averaged positive electrode transverse volume-averaged velocity [m.s-2]', 'X-averaged positive electrode volume-averaged acceleration', 'X-averaged positive electrode volume-averaged acceleration [m.s-1]', 'X-averaged positive electrolyte concentration', 'X-averaged positive electrolyte concentration [mol.m-3]', 'X-averaged positive electrolyte potential', 'X-averaged positive electrolyte potential [V]', 'X-averaged positive electrolyte tortuosity', 'X-averaged positive particle concentration', 'X-averaged positive particle concentration [mol.m-3]', 'X-averaged positive particle flux', 'X-averaged positive particle surface concentration', 'X-averaged positive particle surface concentration [mol.m-3]', 'X-averaged reaction overpotential', 'X-averaged reaction overpotential [V]', 'X-averaged reversible heating', 'X-averaged reversible heating [W.m-3]', 'X-averaged SEI film overpotential', 'X-averaged SEI film overpotential [V]', 'X-averaged separator electrolyte concentration', 'X-averaged separator electrolyte concentration [mol.m-3]', 'X-averaged separator electrolyte potential', 'X-averaged separator electrolyte potential [V]', 'X-averaged separator porosity', 'X-averaged separator porosity change', 'X-averaged separator pressure', 'X-averaged separator temperature', 'X-averaged separator temperature [K]', 'X-averaged separator tortuosity', 'X-averaged separator transverse volume-averaged acceleration', 'X-averaged separator transverse volume-averaged acceleration [m.s-2]', 'X-averaged separator transverse volume-averaged velocity', 'X-averaged separator transverse volume-averaged velocity [m.s-2]', 'X-averaged separator volume-averaged acceleration', 'X-averaged separator volume-averaged acceleration [m.s-1]', 'X-averaged solid phase ohmic losses', 'X-averaged solid phase ohmic losses [V]', 'X-averaged total heating', 'X-averaged total heating [W.m-3]', 'X-averaged total SEI thickness', 'X-averaged total SEI thickness [m]', 'X-averaged total positive electrode SEI thickness', 'X-averaged total positive electrode SEI thickness [m]', 'X-averaged volume-averaged acceleration', 'X-averaged volume-averaged acceleration [m.s-1]', 'r_n', 'r_n [m]', 'r_p', 'r_p [m]', 'x', 'x [m]', 'x_n', 'x_n [m]', 'x_p', 'x_p [m]', 'x_s', 'x_s [m]']\n"
-          ]
-        }
-      ],
-      "source": [
-        "keys = list(model.variables.keys())\n",
-        "keys.sort()\n",
-        "print(keys)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If you want to find a particular variable you can search the variables dictionary"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Time\n",
-            "Time [h]\n",
-            "Time [min]\n",
-            "Time [s]\n"
-          ]
-        }
-      ],
-      "source": [
-        "model.variables.search(\"time\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We'll use the time in hours"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<pybamm.solvers.processed_variable.ProcessedVariable at 0x7f69966b6210>"
-            ]
-          },
-          "execution_count": 7,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "solution['Time [h]']"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This created a new processed variable and stored it on the solution object"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "dict_keys(['Negative particle surface concentration [mol.m-3]', 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Current [A]', 'Negative electrode potential [V]', 'Electrolyte potential [V]', 'Positive electrode potential [V]', 'Voltage [V]', 'Time [h]'])"
-            ]
-          },
-          "execution_count": 8,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "solution.data.keys()"
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=3510.0, step=35.1), Output()), _dom_classes=…"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can see the data by simply accessing the entries attribute of the processed variable"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "array([0.   , 0.025, 0.05 , 0.075, 0.1  , 0.125, 0.15 , 0.175, 0.2  ,\n",
-              "       0.225, 0.25 , 0.275, 0.3  , 0.325, 0.35 , 0.375, 0.4  , 0.425,\n",
-              "       0.45 , 0.475, 0.5  , 0.525, 0.55 , 0.575, 0.6  , 0.625, 0.65 ,\n",
-              "       0.675, 0.7  , 0.725, 0.75 , 0.775, 0.8  , 0.825, 0.85 , 0.875,\n",
-              "       0.9  , 0.925, 0.95 , 0.975])"
-            ]
-          },
-          "execution_count": 9,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "solution['Time [h]'].entries"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can also call the method with specified time(s) in SI units of seconds"
-      ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
+    "import pybamm\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "os.chdir(pybamm.__path__[0]+'/..')\n",
+    "\n",
+    "# load model\n",
+    "model = pybamm.lithium_ion.SPMe()\n",
+    "\n",
+    "# set up and solve simulation\n",
+    "simulation = pybamm.Simulation(model)\n",
+    "dt = 90\n",
+    "t_eval = np.arange(0, 3600, dt)  # time in seconds\n",
+    "solution = simulation.solve(t_eval)\n",
+    "\n",
+    "quick_plot = pybamm.QuickPlot(solution)\n",
+    "quick_plot.dynamic_plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Behind the scenes the QuickPlot classed has created some processed variables which can interpolate the model variables for our solution and has also stored the results for the solution steps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "time_in_seconds = np.array([0, 600, 900, 1700, 3000 ])"
+     "data": {
+      "text/plain": [
+       "dict_keys(['Negative particle surface concentration [mol.m-3]', 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Current [A]', 'Negative electrode potential [V]', 'Electrolyte potential [V]', 'Positive electrode potential [V]', 'Voltage [V]'])"
       ]
-    },
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution.data.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "array([0.        , 0.16666667, 0.25      , 0.47222222, 0.83333333])"
-            ]
-          },
-          "execution_count": 11,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "solution['Time [h]'](time_in_seconds)"
+     "data": {
+      "text/plain": [
+       "(20, 40)"
       ]
-    },
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution.data['Negative particle surface concentration [mol.m-3]'].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If the variable has not already been processed it will be created behind the scenes"
+     "data": {
+      "text/plain": [
+       "(40,)"
       ]
-    },
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution.t.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the dictionary keys are in the same order as the subplots in the QuickPlot figure. We can add new processed variables to the solution by simply using it like a dictionary. First let's find a few more variables to look at. As you will see there are quite a few:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": [
+     "outputPrepend"
+    ]
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "array([298.15, 298.15, 298.15, 298.15, 298.15])"
-            ]
-          },
-          "execution_count": 12,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "var = 'X-averaged negative electrode temperature [K]'\n",
-        "solution[var](time_in_seconds)"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Ambient temperature', 'Ambient temperature [K]', 'Average negative particle concentration', 'Average negative particle concentration [mol.m-3]', 'Average positive particle concentration', 'Average positive particle concentration [mol.m-3]', 'Battery voltage [V]', 'C-rate', 'Cell temperature', 'Cell temperature [K]', 'Change in measured open-circuit voltage', 'Change in measured open-circuit voltage [V]', 'Current [A]', 'Current collector current density', 'Current collector current density [A.m-2]', 'Discharge capacity [A.h]', 'Electrode current density', 'Electrode tortuosity', 'Electrolyte concentration', 'Electrolyte concentration [Molar]', 'Electrolyte concentration [mol.m-3]', 'Electrolyte current density', 'Electrolyte current density [A.m-2]', 'Electrolyte flux', 'Electrolyte flux [mol.m-2.s-1]', 'Electrolyte potential', 'Electrolyte potential [V]', 'Electrolyte tortuosity', 'Exchange current density', 'Exchange current density [A.m-2]', 'Exchange current density per volume [A.m-3]', 'Gradient of electrolyte potential', 'Gradient of negative electrode potential', 'Gradient of negative electrolyte potential', 'Gradient of positive electrode potential', 'Gradient of positive electrolyte potential', 'Gradient of separator electrolyte potential', 'Inner SEI concentration [mol.m-3]', 'Inner SEI interfacial current density', 'Inner SEI interfacial current density [A.m-2]', 'Inner SEI thickness', 'Inner SEI thickness [m]', 'Inner positive electrode SEI concentration [mol.m-3]', 'Inner positive electrode SEI interfacial current density', 'Inner positive electrode SEI interfacial current density [A.m-2]', 'Inner positive electrode SEI thickness', 'Inner positive electrode SEI thickness [m]', 'Interfacial current density', 'Interfacial current density [A.m-2]', 'Interfacial current density per volume [A.m-3]', 'Irreversible electrochemical heating', 'Irreversible electrochemical heating [W.m-3]', 'Leading-order current collector current density', 'Leading-order electrode tortuosity', 'Leading-order electrolyte tortuosity', 'Leading-order negative electrode porosity', 'Leading-order negative electrode tortuosity', 'Leading-order negative electrolyte tortuosity', 'Leading-order porosity', 'Leading-order positive electrode porosity', 'Leading-order positive electrode tortuosity', 'Leading-order positive electrolyte tortuosity', 'Leading-order separator porosity', 'Leading-order separator tortuosity', 'Leading-order x-averaged negative electrode porosity', 'Leading-order x-averaged negative electrode porosity change', 'Leading-order x-averaged negative electrode tortuosity', 'Leading-order x-averaged negative electrolyte tortuosity', 'Leading-order x-averaged positive electrode porosity', 'Leading-order x-averaged positive electrode porosity change', 'Leading-order x-averaged positive electrode tortuosity', 'Leading-order x-averaged positive electrolyte tortuosity', 'Leading-order x-averaged separator porosity', 'Leading-order x-averaged separator porosity change', 'Leading-order x-averaged separator tortuosity', 'Local ECM resistance', 'Local ECM resistance [Ohm]', 'Local voltage', 'Local voltage [V]', 'Loss of lithium to SEI [mol]', 'Loss of lithium to positive electrode SEI [mol]', 'Maximum negative particle concentration', 'Maximum negative particle concentration [mol.m-3]', 'Maximum negative particle surface concentration', 'Maximum negative particle surface concentration [mol.m-3]', 'Maximum positive particle concentration', 'Maximum positive particle concentration [mol.m-3]', 'Maximum positive particle surface concentration', 'Maximum positive particle surface concentration [mol.m-3]', 'Measured battery open-circuit voltage [V]', 'Measured open-circuit voltage', 'Measured open-circuit voltage [V]', 'Minimum negative particle concentration', 'Minimum negative particle concentration [mol.m-3]', 'Minimum negative particle surface concentration', 'Minimum negative particle surface concentration [mol.m-3]', 'Minimum positive particle concentration', 'Minimum positive particle concentration [mol.m-3]', 'Minimum positive particle surface concentration', 'Minimum positive particle surface concentration [mol.m-3]', 'Negative current collector potential', 'Negative current collector potential [V]', 'Negative current collector temperature', 'Negative current collector temperature [K]', 'Negative electrode active material volume fraction', 'Negative electrode active material volume fraction change', 'Negative electrode current density', 'Negative electrode current density [A.m-2]', 'Negative electrode entropic change', 'Negative electrode exchange current density', 'Negative electrode exchange current density [A.m-2]', 'Negative electrode exchange current density per volume [A.m-3]', 'Negative electrode extent of lithiation', 'Negative electrode interfacial current density', 'Negative electrode interfacial current density [A.m-2]', 'Negative electrode interfacial current density per volume [A.m-3]', 'Negative electrode ohmic losses', 'Negative electrode ohmic losses [V]', 'Negative electrode open-circuit potential', 'Negative electrode open-circuit potential [V]', 'Negative electrode oxygen exchange current density', 'Negative electrode oxygen exchange current density [A.m-2]', 'Negative electrode oxygen exchange current density per volume [A.m-3]', 'Negative electrode oxygen interfacial current density', 'Negative electrode oxygen interfacial current density [A.m-2]', 'Negative electrode oxygen interfacial current density per volume [A.m-3]', 'Negative electrode oxygen open-circuit potential', 'Negative electrode oxygen open-circuit potential [V]', 'Negative electrode oxygen reaction overpotential', 'Negative electrode oxygen reaction overpotential [V]', 'Negative electrode porosity', 'Negative electrode porosity change', 'Negative electrode potential', 'Negative electrode potential [V]', 'Negative electrode pressure', 'Negative electrode reaction overpotential', 'Negative electrode reaction overpotential [V]', 'SEI film overpotential', 'SEI film overpotential [V]', 'SEI interfacial current density', 'SEI interfacial current density [A.m-2]', 'Negative electrode surface area to volume ratio', 'Negative electrode surface area to volume ratio [m-1]', 'Negative electrode surface potential difference', 'Negative electrode surface potential difference [V]', 'Negative electrode temperature', 'Negative electrode temperature [K]', 'Negative electrode tortuosity', 'Negative electrode transverse volume-averaged acceleration', 'Negative electrode transverse volume-averaged acceleration [m.s-2]', 'Negative electrode transverse volume-averaged velocity', 'Negative electrode transverse volume-averaged velocity [m.s-2]', 'Negative electrode volume-averaged acceleration', 'Negative electrode volume-averaged acceleration [m.s-1]', 'Negative electrode volume-averaged concentration', 'Negative electrode volume-averaged concentration [mol.m-3]', 'Negative electrode volume-averaged velocity', 'Negative electrode volume-averaged velocity [m.s-1]', 'Negative electrolyte concentration', 'Negative electrolyte concentration [Molar]', 'Negative electrolyte concentration [mol.m-3]', 'Negative electrolyte current density', 'Negative electrolyte current density [A.m-2]', 'Negative electrolyte potential', 'Negative electrolyte potential [V]', 'Negative electrolyte tortuosity', 'Negative particle concentration', 'Negative particle concentration [mol.m-3]', 'Negative particle flux', 'Negative particle radius', 'Negative particle radius [m]', 'Negative particle surface concentration', 'Negative particle surface concentration [mol.m-3]', 'Negative SEI concentration [mol.m-3]', 'Ohmic heating', 'Ohmic heating [W.m-3]', 'Outer SEI concentration [mol.m-3]', 'Outer SEI interfacial current density', 'Outer SEI interfacial current density [A.m-2]', 'Outer SEI thickness', 'Outer SEI thickness [m]', 'Outer positive electrode SEI concentration [mol.m-3]', 'Outer positive electrode SEI interfacial current density', 'Outer positive electrode SEI interfacial current density [A.m-2]', 'Outer positive electrode SEI thickness', 'Outer positive electrode SEI thickness [m]', 'Oxygen exchange current density', 'Oxygen exchange current density [A.m-2]', 'Oxygen exchange current density per volume [A.m-3]', 'Oxygen interfacial current density', 'Oxygen interfacial current density [A.m-2]', 'Oxygen interfacial current density per volume [A.m-3]', 'Porosity', 'Porosity change', 'Positive current collector potential', 'Positive current collector potential [V]', 'Positive current collector temperature', 'Positive current collector temperature [K]', 'Positive electrode active material volume fraction', 'Positive electrode active material volume fraction change', 'Positive electrode current density', 'Positive electrode current density [A.m-2]', 'Positive electrode entropic change', 'Positive electrode exchange current density', 'Positive electrode exchange current density [A.m-2]', 'Positive electrode exchange current density per volume [A.m-3]', 'Positive electrode extent of lithiation', 'Positive electrode interfacial current density', 'Positive electrode interfacial current density [A.m-2]', 'Positive electrode interfacial current density per volume [A.m-3]', 'Positive electrode ohmic losses', 'Positive electrode ohmic losses [V]', 'Positive electrode open-circuit potential', 'Positive electrode open-circuit potential [V]', 'Positive electrode oxygen exchange current density', 'Positive electrode oxygen exchange current density [A.m-2]', 'Positive electrode oxygen exchange current density per volume [A.m-3]', 'Positive electrode oxygen interfacial current density', 'Positive electrode oxygen interfacial current density [A.m-2]', 'Positive electrode oxygen interfacial current density per volume [A.m-3]', 'Positive electrode oxygen open-circuit potential', 'Positive electrode oxygen open-circuit potential [V]', 'Positive electrode oxygen reaction overpotential', 'Positive electrode oxygen reaction overpotential [V]', 'Positive electrode porosity', 'Positive electrode porosity change', 'Positive electrode potential', 'Positive electrode potential [V]', 'Positive electrode pressure', 'Positive electrode reaction overpotential', 'Positive electrode reaction overpotential [V]', 'Positive electrode SEI film overpotential', 'Positive electrode SEI film overpotential [V]', 'Positive electrode SEI interfacial current density', 'Positive electrode SEI interfacial current density [A.m-2]', 'Positive electrode surface area to volume ratio', 'Positive electrode surface area to volume ratio [m-1]', 'Positive electrode surface potential difference', 'Positive electrode surface potential difference [V]', 'Positive electrode temperature', 'Positive electrode temperature [K]', 'Positive electrode tortuosity', 'Positive electrode transverse volume-averaged acceleration', 'Positive electrode transverse volume-averaged acceleration [m.s-2]', 'Positive electrode transverse volume-averaged velocity', 'Positive electrode transverse volume-averaged velocity [m.s-2]', 'Positive electrode volume-averaged acceleration', 'Positive electrode volume-averaged acceleration [m.s-1]', 'Positive electrode volume-averaged concentration', 'Positive electrode volume-averaged concentration [mol.m-3]', 'Positive electrode volume-averaged velocity', 'Positive electrode volume-averaged velocity [m.s-1]', 'Positive electrolyte concentration', 'Positive electrolyte concentration [Molar]', 'Positive electrolyte concentration [mol.m-3]', 'Positive electrolyte current density', 'Positive electrolyte current density [A.m-2]', 'Positive electrolyte potential', 'Positive electrolyte potential [V]', 'Positive electrolyte tortuosity', 'Positive particle concentration', 'Positive particle concentration [mol.m-3]', 'Positive particle flux', 'Positive particle radius', 'Positive particle radius [m]', 'Positive particle surface concentration', 'Positive particle surface concentration [mol.m-3]', 'Positive SEI concentration [mol.m-3]', 'Pressure', 'R-averaged negative particle concentration', 'R-averaged negative particle concentration [mol.m-3]', 'R-averaged positive particle concentration', 'R-averaged positive particle concentration [mol.m-3]', 'Reversible heating', 'Reversible heating [W.m-3]', 'Sei interfacial current density', 'Sei interfacial current density [A.m-2]', 'Sei interfacial current density per volume [A.m-3]', 'Separator electrolyte concentration', 'Separator electrolyte concentration [Molar]', 'Separator electrolyte concentration [mol.m-3]', 'Separator electrolyte potential', 'Separator electrolyte potential [V]', 'Separator porosity', 'Separator porosity change', 'Separator pressure', 'Separator temperature', 'Separator temperature [K]', 'Separator tortuosity', 'Separator transverse volume-averaged acceleration', 'Separator transverse volume-averaged acceleration [m.s-2]', 'Separator transverse volume-averaged velocity', 'Separator transverse volume-averaged velocity [m.s-2]', 'Separator volume-averaged acceleration', 'Separator volume-averaged acceleration [m.s-1]', 'Separator volume-averaged velocity', 'Separator volume-averaged velocity [m.s-1]', 'Sum of electrolyte reaction source terms', 'Sum of interfacial current densities', 'Sum of negative electrode electrolyte reaction source terms', 'Sum of negative electrode interfacial current densities', 'Sum of positive electrode electrolyte reaction source terms', 'Sum of positive electrode interfacial current densities', 'Sum of x-averaged negative electrode electrolyte reaction source terms', 'Sum of x-averaged negative electrode interfacial current densities', 'Sum of x-averaged positive electrode electrolyte reaction source terms', 'Sum of x-averaged positive electrode interfacial current densities', 'Terminal power [W]', 'Voltage', 'Voltage [V]', 'Time', 'Time [h]', 'Time [min]', 'Time [s]', 'Total concentration in electrolyte [mol]', 'Total current density', 'Total current density [A.m-2]', 'Total heating', 'Total heating [W.m-3]', 'Total lithium in negative electrode [mol]', 'Total lithium in positive electrode [mol]', 'Total SEI thickness', 'Total SEI thickness [m]', 'Total positive electrode SEI thickness', 'Total positive electrode SEI thickness [m]', 'Transverse volume-averaged acceleration', 'Transverse volume-averaged acceleration [m.s-2]', 'Transverse volume-averaged velocity', 'Transverse volume-averaged velocity [m.s-2]', 'Volume-averaged Ohmic heating', 'Volume-averaged Ohmic heating [W.m-3]', 'Volume-averaged acceleration', 'Volume-averaged acceleration [m.s-1]', 'Volume-averaged cell temperature', 'Volume-averaged cell temperature [K]', 'Volume-averaged irreversible electrochemical heating', 'Volume-averaged irreversible electrochemical heating[W.m-3]', 'Volume-averaged reversible heating', 'Volume-averaged reversible heating [W.m-3]', 'Volume-averaged total heating', 'Volume-averaged total heating [W.m-3]', 'Volume-averaged velocity', 'Volume-averaged velocity [m.s-1]', 'X-averaged Ohmic heating', 'X-averaged Ohmic heating [W.m-3]', 'X-averaged battery concentration overpotential [V]', 'X-averaged battery electrolyte ohmic losses [V]', 'X-averaged battery open-circuit voltage [V]', 'X-averaged battery reaction overpotential [V]', 'X-averaged battery solid phase ohmic losses [V]', 'X-averaged cell temperature', 'X-averaged cell temperature [K]', 'X-averaged concentration overpotential', 'X-averaged concentration overpotential [V]', 'X-averaged electrolyte concentration', 'X-averaged electrolyte concentration [Molar]', 'X-averaged electrolyte concentration [mol.m-3]', 'X-averaged electrolyte ohmic losses', 'X-averaged electrolyte ohmic losses [V]', 'X-averaged electrolyte overpotential', 'X-averaged electrolyte overpotential [V]', 'X-averaged electrolyte potential', 'X-averaged electrolyte potential [V]', 'X-averaged inner SEI concentration [mol.m-3]', 'X-averaged inner SEI interfacial current density', 'X-averaged inner SEI interfacial current density [A.m-2]', 'X-averaged inner SEI thickness', 'X-averaged inner SEI thickness [m]', 'X-averaged inner positive electrode SEI concentration [mol.m-3]', 'X-averaged inner positive electrode SEI interfacial current density', 'X-averaged inner positive electrode SEI interfacial current density [A.m-2]', 'X-averaged inner positive electrode SEI thickness', 'X-averaged inner positive electrode SEI thickness [m]', 'X-averaged irreversible electrochemical heating', 'X-averaged irreversible electrochemical heating [W.m-3]', 'X-averaged negative electrode active material volume fraction', 'X-averaged negative electrode active material volume fraction change', 'X-averaged negative electrode entropic change', 'X-averaged negative electrode exchange current density', 'X-averaged negative electrode exchange current density [A.m-2]', 'X-averaged negative electrode exchange current density per volume [A.m-3]', 'X-averaged negative electrode extent of lithiation', 'X-averaged negative electrode interfacial current density', 'X-averaged negative electrode interfacial current density [A.m-2]', 'X-averaged negative electrode interfacial current density per volume [A.m-3]', 'X-averaged negative electrode ohmic losses', 'X-averaged negative electrode ohmic losses [V]', 'X-averaged negative electrode open-circuit potential', 'X-averaged negative electrode open-circuit potential [V]', 'X-averaged negative electrode oxygen exchange current density', 'X-averaged negative electrode oxygen exchange current density [A.m-2]', 'X-averaged negative electrode oxygen exchange current density per volume [A.m-3]', 'X-averaged negative electrode oxygen interfacial current density', 'X-averaged negative electrode oxygen interfacial current density [A.m-2]', 'X-averaged negative electrode oxygen interfacial current density per volume [A.m-3]', 'X-averaged negative electrode oxygen open-circuit potential', 'X-averaged negative electrode oxygen open-circuit potential [V]', 'X-averaged negative electrode oxygen reaction overpotential', 'X-averaged negative electrode oxygen reaction overpotential [V]', 'X-averaged negative electrode porosity', 'X-averaged negative electrode porosity change', 'X-averaged negative electrode potential', 'X-averaged negative electrode potential [V]', 'X-averaged negative electrode pressure', 'X-averaged negative electrode reaction overpotential', 'X-averaged negative electrode reaction overpotential [V]', 'X-averaged negative electrode resistance [Ohm.m2]', 'X-averaged SEI concentration [mol.m-3]', 'X-averaged SEI film overpotential', 'X-averaged SEI film overpotential [V]', 'X-averaged SEI interfacial current density', 'X-averaged SEI interfacial current density [A.m-2]', 'X-averaged negative electrode surface area to volume ratio', 'X-averaged negative electrode surface area to volume ratio [m-1]', 'X-averaged negative electrode surface potential difference', 'X-averaged negative electrode surface potential difference [V]', 'X-averaged negative electrode temperature', 'X-averaged negative electrode temperature [K]', 'X-averaged negative electrode tortuosity', 'X-averaged negative electrode total interfacial current density', 'X-averaged negative electrode total interfacial current density [A.m-2]', 'X-averaged negative electrode total interfacial current density per volume [A.m-3]', 'X-averaged negative electrode transverse volume-averaged acceleration', 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]', 'X-averaged negative electrode transverse volume-averaged velocity', 'X-averaged negative electrode transverse volume-averaged velocity [m.s-2]', 'X-averaged negative electrode volume-averaged acceleration', 'X-averaged negative electrode volume-averaged acceleration [m.s-1]', 'X-averaged negative electrolyte concentration', 'X-averaged negative electrolyte concentration [mol.m-3]', 'X-averaged negative electrolyte potential', 'X-averaged negative electrolyte potential [V]', 'X-averaged negative electrolyte tortuosity', 'X-averaged negative particle concentration', 'X-averaged negative particle concentration [mol.m-3]', 'X-averaged negative particle flux', 'X-averaged negative particle surface concentration', 'X-averaged negative particle surface concentration [mol.m-3]', 'X-averaged open-circuit voltage', 'X-averaged open-circuit voltage [V]', 'X-averaged outer SEI concentration [mol.m-3]', 'X-averaged outer SEI interfacial current density', 'X-averaged outer SEI interfacial current density [A.m-2]', 'X-averaged outer SEI thickness', 'X-averaged outer SEI thickness [m]', 'X-averaged outer positive electrode SEI concentration [mol.m-3]', 'X-averaged outer positive electrode SEI interfacial current density', 'X-averaged outer positive electrode SEI interfacial current density [A.m-2]', 'X-averaged outer positive electrode SEI thickness', 'X-averaged outer positive electrode SEI thickness [m]', 'X-averaged positive electrode active material volume fraction', 'X-averaged positive electrode active material volume fraction change', 'X-averaged positive electrode entropic change', 'X-averaged positive electrode exchange current density', 'X-averaged positive electrode exchange current density [A.m-2]', 'X-averaged positive electrode exchange current density per volume [A.m-3]', 'X-averaged positive electrode extent of lithiation', 'X-averaged positive electrode interfacial current density', 'X-averaged positive electrode interfacial current density [A.m-2]', 'X-averaged positive electrode interfacial current density per volume [A.m-3]', 'X-averaged positive electrode ohmic losses', 'X-averaged positive electrode ohmic losses [V]', 'X-averaged positive electrode open-circuit potential', 'X-averaged positive electrode open-circuit potential [V]', 'X-averaged positive electrode oxygen exchange current density', 'X-averaged positive electrode oxygen exchange current density [A.m-2]', 'X-averaged positive electrode oxygen exchange current density per volume [A.m-3]', 'X-averaged positive electrode oxygen interfacial current density', 'X-averaged positive electrode oxygen interfacial current density [A.m-2]', 'X-averaged positive electrode oxygen interfacial current density per volume [A.m-3]', 'X-averaged positive electrode oxygen open-circuit potential', 'X-averaged positive electrode oxygen open-circuit potential [V]', 'X-averaged positive electrode oxygen reaction overpotential', 'X-averaged positive electrode oxygen reaction overpotential [V]', 'X-averaged positive electrode porosity', 'X-averaged positive electrode porosity change', 'X-averaged positive electrode potential', 'X-averaged positive electrode potential [V]', 'X-averaged positive electrode pressure', 'X-averaged positive electrode reaction overpotential', 'X-averaged positive electrode reaction overpotential [V]', 'X-averaged positive electrode resistance [Ohm.m2]', 'X-averaged positive electrode SEI concentration [mol.m-3]', 'X-averaged positive electrode SEI film overpotential', 'X-averaged positive electrode SEI film overpotential [V]', 'X-averaged positive electrode SEI interfacial current density', 'X-averaged positive electrode SEI interfacial current density [A.m-2]', 'X-averaged positive electrode surface area to volume ratio', 'X-averaged positive electrode surface area to volume ratio [m-1]', 'X-averaged positive electrode surface potential difference', 'X-averaged positive electrode surface potential difference [V]', 'X-averaged positive electrode temperature', 'X-averaged positive electrode temperature [K]', 'X-averaged positive electrode tortuosity', 'X-averaged positive electrode total interfacial current density', 'X-averaged positive electrode total interfacial current density [A.m-2]', 'X-averaged positive electrode total interfacial current density per volume [A.m-3]', 'X-averaged positive electrode transverse volume-averaged acceleration', 'X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]', 'X-averaged positive electrode transverse volume-averaged velocity', 'X-averaged positive electrode transverse volume-averaged velocity [m.s-2]', 'X-averaged positive electrode volume-averaged acceleration', 'X-averaged positive electrode volume-averaged acceleration [m.s-1]', 'X-averaged positive electrolyte concentration', 'X-averaged positive electrolyte concentration [mol.m-3]', 'X-averaged positive electrolyte potential', 'X-averaged positive electrolyte potential [V]', 'X-averaged positive electrolyte tortuosity', 'X-averaged positive particle concentration', 'X-averaged positive particle concentration [mol.m-3]', 'X-averaged positive particle flux', 'X-averaged positive particle surface concentration', 'X-averaged positive particle surface concentration [mol.m-3]', 'X-averaged reaction overpotential', 'X-averaged reaction overpotential [V]', 'X-averaged reversible heating', 'X-averaged reversible heating [W.m-3]', 'X-averaged SEI film overpotential', 'X-averaged SEI film overpotential [V]', 'X-averaged separator electrolyte concentration', 'X-averaged separator electrolyte concentration [mol.m-3]', 'X-averaged separator electrolyte potential', 'X-averaged separator electrolyte potential [V]', 'X-averaged separator porosity', 'X-averaged separator porosity change', 'X-averaged separator pressure', 'X-averaged separator temperature', 'X-averaged separator temperature [K]', 'X-averaged separator tortuosity', 'X-averaged separator transverse volume-averaged acceleration', 'X-averaged separator transverse volume-averaged acceleration [m.s-2]', 'X-averaged separator transverse volume-averaged velocity', 'X-averaged separator transverse volume-averaged velocity [m.s-2]', 'X-averaged separator volume-averaged acceleration', 'X-averaged separator volume-averaged acceleration [m.s-1]', 'X-averaged solid phase ohmic losses', 'X-averaged solid phase ohmic losses [V]', 'X-averaged total heating', 'X-averaged total heating [W.m-3]', 'X-averaged total SEI thickness', 'X-averaged total SEI thickness [m]', 'X-averaged total positive electrode SEI thickness', 'X-averaged total positive electrode SEI thickness [m]', 'X-averaged volume-averaged acceleration', 'X-averaged volume-averaged acceleration [m.s-1]', 'r_n', 'r_n [m]', 'r_p', 'r_p [m]', 'x', 'x [m]', 'x_n', 'x_n [m]', 'x_p', 'x_p [m]', 'x_s', 'x_s [m]']\n"
+     ]
+    }
+   ],
+   "source": [
+    "keys = list(model.variables.keys())\n",
+    "keys.sort()\n",
+    "print(keys)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want to find a particular variable you can search the variables dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In this example the simulation was isothermal, so the temperature remains unchanged."
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time\n",
+      "Time [h]\n",
+      "Time [min]\n",
+      "Time [s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.variables.search(\"time\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll use the time in hours"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Saving the solution\n",
-        "\n",
-        "The solution can be saved in a number of ways:"
+     "data": {
+      "text/plain": [
+       "<pybamm.solvers.processed_variable.ProcessedVariable at 0x7f69966b6210>"
       ]
-    },
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution['Time [h]']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This created a new processed variable and stored it on the solution object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# to a pickle file (default)\n",
-        "solution.save_data(\n",
-        "    \"outputs.pickle\", [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"]\n",
-        ")\n",
-        "# to a matlab file\n",
-        "# need to give variable names without space\n",
-        "solution.save_data(\n",
-        "    \"outputs.mat\", \n",
-        "    [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"], \n",
-        "    to_format=\"matlab\",\n",
-        "    short_names={\n",
-        "        \"Time [h]\": \"t\", \"Current [A]\": \"I\", \"Voltage [V]\": \"V\", \"Electrolyte concentration [mol.m-3]\": \"c_e\",\n",
-        "    }\n",
-        ")\n",
-        "# to a csv file (time-dependent outputs only, no spatial dependence allowed)\n",
-        "solution.save_data(\n",
-        "    \"outputs.csv\", [\"Time [h]\", \"Current [A]\", \"Voltage [V]\"], to_format=\"csv\"\n",
-        ")"
+     "data": {
+      "text/plain": [
+       "dict_keys(['Negative particle surface concentration [mol.m-3]', 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Current [A]', 'Negative electrode potential [V]', 'Electrolyte potential [V]', 'Positive electrode potential [V]', 'Voltage [V]', 'Time [h]'])"
       ]
-    },
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution.data.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see the data by simply accessing the entries attribute of the processed variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Stepping the solver\n",
-        "\n",
-        "The previous solution was created in one go with the solve method, but it is also possible to step the solution and look at the results as we go. In doing so, the results are automatically updated at each step."
+     "data": {
+      "text/plain": [
+       "array([0.   , 0.025, 0.05 , 0.075, 0.1  , 0.125, 0.15 , 0.175, 0.2  ,\n",
+       "       0.225, 0.25 , 0.275, 0.3  , 0.325, 0.35 , 0.375, 0.4  , 0.425,\n",
+       "       0.45 , 0.475, 0.5  , 0.525, 0.55 , 0.575, 0.6  , 0.625, 0.65 ,\n",
+       "       0.675, 0.7  , 0.725, 0.75 , 0.775, 0.8  , 0.825, 0.85 , 0.875,\n",
+       "       0.9  , 0.925, 0.95 , 0.975])"
       ]
-    },
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution['Time [h]'].entries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also call the method with specified time(s) in SI units of seconds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_in_seconds = np.array([0, 600, 900, 1700, 3000 ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Time 0\n",
-            "[3.77047806 3.71250693]\n",
-            "Time 360\n",
-            "[3.77047806 3.71250693 3.68215218]\n",
-            "Time 720\n",
-            "[3.77047806 3.71250693 3.68215218 3.66125574]\n",
-            "Time 1080\n",
-            "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942]\n",
-            "Time 1440\n",
-            "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857]\n",
-            "Time 1800\n",
-            "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
-            " 3.59709451]\n",
-            "Time 2160\n",
-            "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
-            " 3.59709451 3.58821334]\n",
-            "Time 2520\n",
-            "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
-            " 3.59709451 3.58821334 3.58056055]\n",
-            "Time 2880\n",
-            "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
-            " 3.59709451 3.58821334 3.58056055 3.55158694]\n",
-            "Time 3240\n",
-            "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
-            " 3.59709451 3.58821334 3.58056055 3.55158694 3.16842636]\n"
-          ]
-        }
-      ],
-      "source": [
-        "dt = 360\n",
-        "time = 0\n",
-        "end_time = solution[\"Time [s]\"].entries[-1]\n",
-        "step_simulation = pybamm.Simulation(model)\n",
-        "while time < end_time:\n",
-        "    step_solution = step_simulation.step(dt)\n",
-        "    print('Time', time)\n",
-        "    print(step_solution[\"Voltage [V]\"].entries)\n",
-        "    time += dt"
+     "data": {
+      "text/plain": [
+       "array([0.        , 0.16666667, 0.25      , 0.47222222, 0.83333333])"
       ]
-    },
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution['Time [h]'](time_in_seconds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the variable has not already been processed it will be created behind the scenes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can plot the voltages and see that the solutions are the same"
+     "data": {
+      "text/plain": [
+       "array([298.15, 298.15, 298.15, 298.15, 298.15])"
       ]
-    },
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "var = 'X-averaged negative electrode temperature [K]'\n",
+    "solution[var](time_in_seconds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example the simulation was isothermal, so the temperature remains unchanged."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving the solution\n",
+    "\n",
+    "The solution can be saved in a number of ways:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# to a pickle file (default)\n",
+    "solution.save_data(\n",
+    "    \"outputs.pickle\", [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"]\n",
+    ")\n",
+    "# to a matlab file\n",
+    "# need to give variable names without space\n",
+    "solution.save_data(\n",
+    "    \"outputs.mat\", \n",
+    "    [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"], \n",
+    "    to_format=\"matlab\",\n",
+    "    short_names={\n",
+    "        \"Time [h]\": \"t\", \"Current [A]\": \"I\", \"Voltage [V]\": \"V\", \"Electrolyte concentration [mol.m-3]\": \"c_e\",\n",
+    "    }\n",
+    ")\n",
+    "# to a csv file (time-dependent outputs only, no spatial dependence allowed)\n",
+    "solution.save_data(\n",
+    "    \"outputs.csv\", [\"Time [h]\", \"Current [A]\", \"Voltage [V]\"], to_format=\"csv\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stepping the solver\n",
+    "\n",
+    "The previous solution was created in one go with the solve method, but it is also possible to step the solution and look at the results as we go. In doing so, the results are automatically updated at each step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 15,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<matplotlib.legend.Legend at 0x7f69964178d0>"
-            ]
-          },
-          "execution_count": 15,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          },
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "voltage = solution[\"Voltage [V]\"].entries\n",
-        "step_voltage = step_solution[\"Voltage [V]\"].entries\n",
-        "plt.figure()\n",
-        "plt.plot(solution[\"Time [h]\"].entries, voltage, \"b-\", label=\"SPMe (continuous solve)\")\n",
-        "plt.plot(\n",
-        "    step_solution[\"Time [h]\"].entries, step_voltage, \"ro\", label=\"SPMe (stepped solve)\"\n",
-        ")\n",
-        "plt.legend()"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time 0\n",
+      "[3.77047806 3.71250693]\n",
+      "Time 360\n",
+      "[3.77047806 3.71250693 3.68215218]\n",
+      "Time 720\n",
+      "[3.77047806 3.71250693 3.68215218 3.66125574]\n",
+      "Time 1080\n",
+      "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942]\n",
+      "Time 1440\n",
+      "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857]\n",
+      "Time 1800\n",
+      "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
+      " 3.59709451]\n",
+      "Time 2160\n",
+      "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
+      " 3.59709451 3.58821334]\n",
+      "Time 2520\n",
+      "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
+      " 3.59709451 3.58821334 3.58056055]\n",
+      "Time 2880\n",
+      "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
+      " 3.59709451 3.58821334 3.58056055 3.55158694]\n",
+      "Time 3240\n",
+      "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
+      " 3.59709451 3.58821334 3.58056055 3.55158694 3.16842636]\n"
+     ]
+    }
+   ],
+   "source": [
+    "dt = 360\n",
+    "time = 0\n",
+    "end_time = solution[\"Time [s]\"].entries[-1]\n",
+    "step_simulation = pybamm.Simulation(model)\n",
+    "while time < end_time:\n",
+    "    step_solution = step_simulation.step(dt)\n",
+    "    print('Time', time)\n",
+    "    print(step_solution[\"Voltage [V]\"].entries)\n",
+    "    time += dt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the voltages and see that the solutions are the same"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## References\n",
-        "\n",
-        "The relevant papers for this notebook are:"
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f69964178d0>"
       ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": 16,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
-            "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
-            "[3] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
-            "[4] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "pybamm.print_citations()"
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
       ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.0"
+   ],
+   "source": [
+    "voltage = solution[\"Voltage [V]\"].entries\n",
+    "step_voltage = step_solution[\"Voltage [V]\"].entries\n",
+    "plt.figure()\n",
+    "plt.plot(solution[\"Time [h]\"].entries, voltage, \"b-\", label=\"SPMe (continuous solve)\")\n",
+    "plt.plot(\n",
+    "    step_solution[\"Time [h]\"].entries, step_voltage, \"ro\", label=\"SPMe (stepped solve)\"\n",
+    ")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "As a final step, we will clean up the output files created by this notebook:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "os.remove(\"outputs.csv\")\n",
+    "os.remove(\"outputs.mat\")\n",
+    "os.remove(\"outputs.pickle\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "The relevant papers for this notebook are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[3] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
+      "[4] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n"
+     ]
     }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }
diff --git a/docs/source/examples/notebooks/solvers/dae-solver.ipynb b/docs/source/examples/notebooks/solvers/dae-solver.ipynb
index b9531ed54a..324d500df3 100644
--- a/docs/source/examples/notebooks/solvers/dae-solver.ipynb
+++ b/docs/source/examples/notebooks/solvers/dae-solver.ipynb
@@ -25,7 +25,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import os\n",
diff --git a/docs/source/examples/notebooks/solvers/ode-solver.ipynb b/docs/source/examples/notebooks/solvers/ode-solver.ipynb
index ae981ed597..992dae5980 100644
--- a/docs/source/examples/notebooks/solvers/ode-solver.ipynb
+++ b/docs/source/examples/notebooks/solvers/ode-solver.ipynb
@@ -25,7 +25,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import os\n",
diff --git a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
index 258c37c885..2bd7f47ae1 100644
--- a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
+++ b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
@@ -29,7 +29,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np"
diff --git a/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb b/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb
index fd05496570..7afd4da6f9 100644
--- a/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb
+++ b/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb
@@ -62,7 +62,7 @@
     }
    ],
    "source": [
-    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import numpy as np\n",
     "import os\n",
diff --git a/docs/source/user_guide/contributing.md b/docs/source/user_guide/contributing.md
new file mode 100644
index 0000000000..3f967fa93f
--- /dev/null
+++ b/docs/source/user_guide/contributing.md
@@ -0,0 +1,5 @@
+<!-- a stub file to add CONTRIBUTING.md from the root directory
+to the hosted docs -->
+
+```{include} ../../../CONTRIBUTING.md
+```
diff --git a/docs/source/user_guide/index.md b/docs/source/user_guide/index.md
index e288a67e28..8b28fc6636 100644
--- a/docs/source/user_guide/index.md
+++ b/docs/source/user_guide/index.md
@@ -24,6 +24,14 @@ fundamentals/index
 fundamentals/battery_models
 ```
 
+```{toctree}
+---
+caption: Contributing guide
+maxdepth: 1
+---
+contributing
+```
+
 # Example notebooks
 
 PyBaMM ships with example notebooks that demonstrate how to use it and reveal some of its
diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst
index e66c3c2291..ca95bbe1b5 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/GNU-linux.rst
@@ -6,7 +6,7 @@ GNU-Linux & MacOS
 Prerequisites
 -------------
 
-To use and/or contribute to PyBaMM, you must have Python 3.8, 3.9, 3.10, or 3.11 installed.
+To use PyBaMM, you must have Python 3.8, 3.9, 3.10, or 3.11 installed.
 
 .. tab:: Debian-based distributions (Debian, Ubuntu, Linux Mint)
 
diff --git a/docs/source/user_guide/installation/index.rst b/docs/source/user_guide/installation/index.rst
index 9710a3593a..983f66842e 100644
--- a/docs/source/user_guide/installation/index.rst
+++ b/docs/source/user_guide/installation/index.rst
@@ -66,6 +66,7 @@ Package                                                          Minimum support
 `SciPy <https://docs.scipy.org/doc/scipy/>`__                    2.8.2
 `CasADi <https://web.casadi.org/docs/>`__                        3.6.0
 `Xarray <https://docs.xarray.dev/en/stable/>`__                  2023.04.0
+`Anytree <https://anytree.readthedocs.io/en/stable/>`__          2.4.3
 ================================================================ ==========================
 
 .. _install.optional_dependencies:
@@ -76,7 +77,7 @@ Optional Dependencies
 PyBaMM has a number of optional dependencies for different functionalities.
 If the optional dependency is not installed, PyBaMM will raise an ImportError when the method requiring that dependency is called.
 
-If using pip, optional PyBaMM dependencies can be installed or managed in a file (e.g. requirements.txt or setup.py)
+If you are using ``pip``, optional PyBaMM dependencies can be installed or managed in a file (e.g., setup.py, or pyproject.toml)
 as optional extras (e.g.,``pybamm[dev,plot]``). All optional dependencies can be installed with ``pybamm[all]``,
 and specific sets of dependencies are listed in the sections below.
 
@@ -117,7 +118,7 @@ Installable with ``pip install "pybamm[docs]"``
 ================================================================================================= ================== ================== =======================================================================
 Dependency                                                                                        Minimum Version    pip extra          Notes
 ================================================================================================= ================== ================== =======================================================================
-`sphinx <https://www.sphinx-doc.org/en/master/>`__                                                1.5.0              docs               Sphinx makes it easy to create intelligent and beautiful documentation.
+`sphinx <https://www.sphinx-doc.org/en/master/>`__                                                \-                 docs               Sphinx makes it easy to create intelligent and beautiful documentation.
 `pydata-sphinx-theme <https://pydata-sphinx-theme.readthedocs.io/en/stable/>`__                   \-                 docs               A clean, Bootstrap-based Sphinx theme.
 `sphinx_design <https://sphinx-design.readthedocs.io/en/latest/>`__                               \-                 docs               A sphinx extension for designing.
 `sphinx-copybutton <https://sphinx-copybutton.readthedocs.io/en/latest/>`__                       \-                 docs               To copy codeblocks.
diff --git a/docs/source/user_guide/installation/install-from-docker.rst b/docs/source/user_guide/installation/install-from-docker.rst
index 8024e68fb3..61f99817c7 100644
--- a/docs/source/user_guide/installation/install-from-docker.rst
+++ b/docs/source/user_guide/installation/install-from-docker.rst
@@ -3,12 +3,13 @@ Install from source (Docker)
 
 .. contents::
 
-This page describes the build and installation of PyBaMM from the source code, available on GitHub. Note that this is **not the recommended approach for most users** and should be reserved to people wanting to participate in the development of PyBaMM, or people who really need to use bleeding-edge feature(s) not yet available in the latest released version. If you do not fall in the two previous categories, you would be better off installing PyBaMM using pip or conda.
+This page describes the build and installation of PyBaMM using a Dockerfile, available on GitHub. Note that this is **not the recommended approach for most users** and should be reserved to people wanting to participate in the development of PyBaMM, or people who really need to use bleeding-edge feature(s) not yet available in the latest released version. If you do not fall in the two previous categories, you would be better off installing PyBaMM using ``pip`` or ``conda``.
 
 Prerequisites
 -------------
+
 Before you begin, make sure you have Docker installed on your system. You can download and install Docker from the official `Docker website <https://www.docker.com/get-started/>`_.
-Ensure Docker installation by running :
+Ensure Docker installation by running:
 
 .. code:: bash
 
@@ -16,6 +17,7 @@ Ensure Docker installation by running :
 
 Pulling the Docker image
 ------------------------
+
 Use the following command to pull the PyBaMM Docker image from Docker Hub:
 
 .. tab:: No optional solver
@@ -135,8 +137,8 @@ If you want to build the PyBaMM Docker image locally from the PyBaMM source code
 
       conda activate pybamm
 
-Building Docker images with optional args
------------------------------------------
+Building Docker images with optional arguments
+----------------------------------------------
 
 When building the PyBaMM Docker images locally, you have the option to include specific solvers by using optional arguments. These solvers include:
 
@@ -190,7 +192,7 @@ If you want to exit the Docker container's shell, you can simply type:
       exit
 
 
-Using Git Inside a Running Docker Container
+Using Git inside a running Docker container
 -------------------------------------------
 
 .. note::
@@ -215,7 +217,7 @@ Using Git Inside a Running Docker Container
 
       git fetch --all
 
-Using Visual Studio Code Inside a Running Docker Container
+Using Visual Studio Code inside a running Docker container
 ----------------------------------------------------------
 
 You can easily use Visual Studio Code inside a running Docker container by attaching it directly. This provides a seamless development environment within the container. Here's how:
diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst
index f8aa4968d9..003c7f143a 100644
--- a/docs/source/user_guide/installation/install-from-source.rst
+++ b/docs/source/user_guide/installation/install-from-source.rst
@@ -105,8 +105,8 @@ Installing PyBaMM
 
 You should now have everything ready to build and install PyBaMM successfully.
 
-Using Nox (recommended)
-~~~~~~~~~~~~~~~~~~~~~~~
+Using ``Nox`` (recommended)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code:: bash
 
@@ -116,7 +116,7 @@ Using Nox (recommended)
 .. note::
 	It is recommended to use ``--verbose`` or ``-v`` to see outputs of all commands run.
 
-This creates a virtual environment ``.nox/dev`` inside the ``PyBaMM/`` directory.
+This creates a virtual environment ``venv/`` inside the ``PyBaMM/`` directory.
 It comes ready with PyBaMM and some useful development tools like `pre-commit <https://pre-commit.com/>`_ and `ruff <https://beta.ruff.rs/docs/>`_.
 
 You can now activate the environment with
@@ -125,13 +125,13 @@ You can now activate the environment with
 
 	.. code:: bash
 
-		source .nox/dev/bin/activate
+		source venv/bin/activate
 
 .. tab:: Windows
 
 	.. code:: bash
 
-		.nox\dev\Scripts\activate.bat
+		venv\Scripts\activate.bat
 
 and run the tests to check your installation.
 
@@ -167,7 +167,7 @@ Running the tests
 Using Nox (recommended)
 ~~~~~~~~~~~~~~~~~~~~~~~
 
-You can use Nox to run the unit tests and example notebooks in isolated virtual environments.
+You can use ``Nox`` to run the unit tests and example notebooks in isolated virtual environments.
 
 The default command
 
@@ -175,7 +175,7 @@ The default command
 
 	nox
 
-will run pre-commit, install ``Linux`` dependencies, and run the unit tests.
+will run pre-commit, install ``Linux`` and ``macOS`` dependencies, and run the unit tests.
 This can take several minutes.
 
 To just run the unit tests, use
@@ -246,8 +246,9 @@ Doctests, examples, and coverage
 - ``nox -s coverage``: Measure current test coverage and generate a coverage report.
 - ``nox -s quick``: Run integration tests, unit tests, and doctests sequentially.
 
-Extra tips while using Nox
---------------------------
+Extra tips while using ``Nox``
+------------------------------
+
 Here are some additional useful commands you can run with ``Nox``:
 
 - ``--verbose or -v``: Enables verbose mode, providing more detailed output during the execution of Nox sessions.
@@ -257,11 +258,12 @@ Here are some additional useful commands you can run with ``Nox``:
 - ``--install-only``: Skips the test execution and only performs the installation step defined in the Nox sessions.
 - ``--nocolor``: Disables the color output in the console during the execution of Nox sessions.
 - ``--report output.json``: Generates a JSON report of the Nox session execution and saves it to the specified file, in this case, "output.json".
+- ``nox -s docs --non-interactive``: Builds the documentation without serving it locally (using ``sphinx-build`` instead of ``sphinx-autobuild``).
 
 Troubleshooting
-===============
+---------------
 
-**Problem:** I’ve made edits to source files in PyBaMM, but these are
+**Problem:** I have made edits to source files in PyBaMM, but these are
 not being used when I run my Python script.
 
 **Solution:** Make sure you have installed PyBaMM using the ``-e`` flag,
@@ -279,11 +281,11 @@ sure each command was successful.
 One possibility is that you have not set your ``LD_LIBRARY_PATH`` to
 point to the sundials library, type ``echo $LD_LIBRARY_PATH`` and make
 sure one of the directories printed out corresponds to where the
-sundials libraries are located.
+SUNDIALS libraries are located.
 
 Another common reason is that you forget to install a BLAS library such
-as OpenBLAS before installing sundials. Check the cmake output when you
-configured Sundials, it might say:
+as OpenBLAS before installing SUNDIALS. Check the cmake output when you
+configured SUNDIALS, it might say:
 
 ::
 
@@ -292,5 +294,5 @@ configured Sundials, it might say:
 
 If this is the case, on a Debian or Ubuntu system you can install
 OpenBLAS using ``sudo apt-get install libopenblas-dev`` (or
-``brew install openblas`` for Mac OS) and then re-install sundials using
+``brew install openblas`` for Mac OS) and then re-install SUNDIALS using
 the instructions above.
diff --git a/docs/source/user_guide/installation/windows.rst b/docs/source/user_guide/installation/windows.rst
index 6ff48293bd..5b104e91bd 100644
--- a/docs/source/user_guide/installation/windows.rst
+++ b/docs/source/user_guide/installation/windows.rst
@@ -6,7 +6,7 @@ Windows
 Prerequisites
 -------------
 
-To use and/or contribute to PyBaMM, you must have Python 3.8, 3.9, 3.10, or 3.11 installed.
+To use PyBaMM, you must have Python 3.8, 3.9, 3.10, or 3.11 installed.
 
 To install Python 3 download the installation files from `Python’s
 website <https://www.python.org/downloads/windows/>`__. Make sure to
@@ -27,7 +27,7 @@ install PyBaMM. You can find a reminder of how to navigate the terminal
 We recommend to install PyBaMM within a virtual environment, in order
 not to alter any distribution python files.
 
-To install virtualenv type:
+To install ``virtualenv``, type:
 
 .. code:: bash
 
diff --git a/examples/scripts/calendar_ageing.py b/examples/scripts/calendar_ageing.py
index 10ffb8d7ca..7714ab5ad4 100644
--- a/examples/scripts/calendar_ageing.py
+++ b/examples/scripts/calendar_ageing.py
@@ -45,12 +45,12 @@
         "Negative particle surface concentration",
         "X-averaged negative particle surface concentration",
         "Electrolyte concentration [mol.m-3]",
-        "Total SEI thickness [m]",
-        "X-averaged total SEI thickness [m]",
-        "X-averaged SEI concentration [mol.m-3]",
+        "Negative total SEI thickness [m]",
+        "X-averaged negative total SEI thickness [m]",
+        "X-averaged negative SEI concentration [mol.m-3]",
         "Sum of x-averaged negative electrode volumetric "
         "interfacial current densities [A.m-3]",
         "Loss of lithium inventory [%]",
-        ["Total lithium lost [mol]", "Loss of lithium to SEI [mol]"],
+        ["Total lithium lost [mol]", "Loss of lithium to negative SEI [mol]"],
     ],
 )
diff --git a/examples/scripts/custom_model.py b/examples/scripts/custom_model.py
index ed5e6c94bf..4c6dcf3fad 100644
--- a/examples/scripts/custom_model.py
+++ b/examples/scripts/custom_model.py
@@ -24,12 +24,6 @@
     "electrolyte conductivity"
 ] = pybamm.electrolyte_conductivity.LeadingOrder(model.param)
 
-model.submodels["sei"] = pybamm.sei.NoSEI(model.param, model.options)
-model.submodels["sei on cracks"] = pybamm.sei.NoSEI(
-    model.param, model.options, cracks=True
-)
-model.submodels["lithium plating"] = pybamm.lithium_plating.NoPlating(model.param)
-
 # Loop over negative and positive electrode domains for some submodels
 for domain in ["negative", "positive"]:
     model.submodels[f"{domain} active material"] = pybamm.active_material.Constant(
@@ -78,6 +72,15 @@
     model.submodels[
         f"{domain} particle mechanics"
     ] = pybamm.particle_mechanics.NoMechanics(model.param, domain, model.options)
+    model.submodels[f"{domain} sei"] = pybamm.sei.NoSEI(
+        model.param, domain, model.options
+    )
+    model.submodels[f"{domain} sei on cracks"] = pybamm.sei.NoSEI(
+        model.param, domain, model.options, cracks=True
+    )
+    model.submodels[f"{domain} lithium plating"] = pybamm.lithium_plating.NoPlating(
+        model.param, domain
+    )
 
 # build model
 model.build_model()
diff --git a/examples/scripts/cycling_ageing.py b/examples/scripts/cycling_ageing.py
index 29e2c0d16b..66f23cd900 100644
--- a/examples/scripts/cycling_ageing.py
+++ b/examples/scripts/cycling_ageing.py
@@ -66,14 +66,14 @@
         "Discharge capacity [A.h]",
         "Electrolyte potential [V]",
         "Electrolyte concentration [mol.m-3]",
-        "X-averaged total SEI thickness [m]",
+        "Negative total SEI thickness [m]",
         "Negative electrode porosity",
         "X-averaged negative electrode porosity",
-        "X-averaged SEI interfacial current density [A.m-2]",
-        "X-averaged total SEI thickness [m]",
+        "X-averaged negative electrode SEI interfacial current density [A.m-2]",
+        "X-averaged negative total SEI thickness [m]",
         [
             "Total lithium lost [mol]",
-            "Loss of lithium to SEI [mol]",
+            "Loss of lithium to negative SEI [mol]",
         ],
     ]
 )
diff --git a/noxfile.py b/noxfile.py
index 96a0e82809..4019935ac1 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -1,11 +1,12 @@
 import nox
 import os
 import sys
+from pathlib import Path
 
 
 # Options to modify nox behaviour
 nox.options.reuse_existing_virtualenvs = True
-if sys.platform == "linux":
+if sys.platform != "win32":
     nox.options.sessions = ["pre-commit", "pybamm-requires", "unit"]
 else:
     nox.options.sessions = ["pre-commit", "unit"]
@@ -14,15 +15,9 @@
 homedir = os.getenv("HOME")
 PYBAMM_ENV = {
     "SUNDIALS_INST": f"{homedir}/.local",
-    "LD_LIBRARY_PATH": f"{homedir}/.local/lib:",
+    "LD_LIBRARY_PATH": f"{homedir}/.local/lib",
 }
-# Do not stdout ANSI colours on GitHub Actions
-if os.getenv("CI") == "true":
-    os.environ["NO_COLOR"] = "1"
-    # The setup-python action installs and caches dependencies by default, so we skip
-    # installing them again in nox environments. The dev and docs sessions will still
-    # require a virtual environment, but we don't run them in the CI
-    nox.options.default_venv_backend = "none"
+VENV_DIR = Path("./venv").resolve()
 
 
 def set_environment_variables(env_dict, session):
@@ -46,7 +41,7 @@ def run_pybamm_requires(session):
     """Download, compile, and install the build-time requirements for Linux and macOS: the SuiteSparse and SUNDIALS libraries."""  # noqa: E501
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
-        session.run_always("pip", "install", "wget", "cmake")
+        session.install("wget", "cmake", silent=False)
         session.run("python", "scripts/install_KLU_Sundials.py")
         if not os.path.exists("./pybind11"):
             session.run(
@@ -57,18 +52,18 @@ def run_pybamm_requires(session):
                 external=True,
             )
     else:
-        session.error("nox -s pybamm-requires is only available on Linux & MacOS.")
+        session.error("nox -s pybamm-requires is only available on Linux & macOS.")
 
 
 @nox.session(name="coverage")
 def run_coverage(session):
     """Run the coverage tests and generate an XML report."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.run_always("pip", "install", "coverage")
-    session.run_always("pip", "install", "-e", ".[all]")
+    session.install("coverage", silent=False)
     if sys.platform != "win32":
-        session.run_always("pip", "install", "-e", ".[odes]")
-        session.run_always("pip", "install", "-e", ".[jax]")
+        session.install("-e", ".[all,odes,jax]", silent=False)
+    else:
+        session.install("-e", ".[all]", silent=False)
     session.run("coverage", "run", "--rcfile=.coveragerc", "run-tests.py", "--nosub")
     session.run("coverage", "combine")
     session.run("coverage", "xml")
@@ -78,16 +73,17 @@ def run_coverage(session):
 def run_integration(session):
     """Run the integration tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.run_always("pip", "install", "-e", ".[all]")
-    if sys.platform == "linux":
-        session.run_always("pip", "install", "-e", ".[odes]")
+    if sys.platform != "win32":
+        session.install("-e", ".[all,odes,jax]", silent=False)
+    else:
+        session.install("-e", ".[all]", silent=False)
     session.run("python", "run-tests.py", "--integration")
 
 
 @nox.session(name="doctests")
 def run_doctests(session):
     """Run the doctests and generate the output(s) in the docs/build/ directory."""
-    session.run_always("pip", "install", "-e", ".[all,docs]")
+    session.install("-e", ".[all,docs]", silent=False)
     session.run("python", "run-tests.py", "--doctest")
 
 
@@ -95,10 +91,10 @@ def run_doctests(session):
 def run_unit(session):
     """Run the unit tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.run_always("pip", "install", "-e", ".[all]")
-    if sys.platform == "linux":
-        session.run_always("pip", "install", "-e", ".[odes]")
-        session.run_always("pip", "install", "-e", ".[jax]")
+    if sys.platform != "win32":
+        session.install("-e", ".[all,odes,jax]", silent=False)
+    else:
+        session.install("-e", ".[all]", silent=False)
     session.run("python", "run-tests.py", "--unit")
 
 
@@ -106,8 +102,8 @@ def run_unit(session):
 def run_examples(session):
     """Run the examples tests for Jupyter notebooks."""
     set_environment_variables(PYBAMM_ENV, session=session)
+    session.install("-e", ".[all,dev]", silent=False)
     notebooks_to_test = session.posargs if session.posargs else []
-    session.run_always("pip", "install", "-e", ".[all,dev]")
     session.run("pytest", "--nbmake", *notebooks_to_test, external=True)
 
 
@@ -115,7 +111,7 @@ def run_examples(session):
 def run_scripts(session):
     """Run the scripts tests for Python scripts."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.run_always("pip", "install", "-e", ".[all]")
+    session.install("-e", ".[all]", silent=False)
     session.run("python", "run-tests.py", "--scripts")
 
 
@@ -123,28 +119,42 @@ def run_scripts(session):
 def set_dev(session):
     """Install PyBaMM in editable mode."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    envbindir = session.bin
-    session.install("-e", ".[all]")
-    session.install("cmake")
-    if sys.platform == "linux" or sys.platform == "darwin":
+    session.install("virtualenv", "cmake")
+    session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
+    python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
+    session.run(
+        python,
+        "-m",
+        "pip",
+        "install",
+        "--upgrade",
+        "pip",
+        "setuptools",
+        "wheel",
+        external=True,
+    )
+    if sys.platform == "linux":
         session.run(
-            "echo",
-            "export",
-            f"LD_LIBRARY_PATH={PYBAMM_ENV['LD_LIBRARY_PATH']}",
-            ">>",
-            f"{envbindir}/activate",
-            external=True,  # silence warning about echo being an external command
+            python,
+            "-m",
+            "pip",
+            "install",
+            "-e",
+            ".[all,dev,jax,odes]",
+            external=True,
         )
+    else:
+        session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", external=True)
 
 
 @nox.session(name="tests")
 def run_tests(session):
     """Run the unit tests and integration tests sequentially."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.run_always("pip", "install", "-e", ".[all]")
-    if sys.platform == "linux" or sys.platform == "darwin":
-        session.run_always("pip", "install", "-e", ".[odes]")
-        session.run_always("pip", "install", "-e", ".[jax]")
+    if sys.platform != "win32":
+            session.install("-e", ".[all,odes,jax]", silent=False)
+    else:
+        session.install("-e", ".[all]", silent=False)
     session.run("python", "run-tests.py", "--all")
 
 
@@ -152,23 +162,38 @@ def run_tests(session):
 def build_docs(session):
     """Build the documentation and load it in a browser tab, rebuilding on changes."""
     envbindir = session.bin
-    session.install("-e", ".[all,docs]")
+    session.install("-e", ".[all,docs]", silent=False)
     session.chdir("docs")
-    session.run(
-        "sphinx-autobuild",
-        "-j",
-        "auto",
-        "--open-browser",
-        "-qT",
-        ".",
-        f"{envbindir}/../tmp/html",
-    )
+    # Local development
+    if session.interactive:
+        session.run(
+            "sphinx-autobuild",
+            "-j",
+            "auto",
+            "--open-browser",
+            "-qT",
+            ".",
+            f"{envbindir}/../tmp/html",
+        )
+    # Runs in CI only, treating warnings as errors
+    else:
+        session.run(
+            "sphinx-build",
+            "-j",
+            "auto",
+            "-b",
+            "html",
+            "-W",
+            "--keep-going",
+            ".",
+            f"{envbindir}/../tmp/html",
+        )
 
 
 @nox.session(name="pre-commit")
 def lint(session):
     """Check all files against the defined pre-commit hooks."""
-    session.install("pre-commit")
+    session.install("pre-commit", silent=False)
     session.run("pre-commit", "run", "--all-files")
 
 
diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index 6c2636ba51..07d8a1c0ea 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -47,13 +47,13 @@
     get_parameters_filepath,
     have_jax,
     install_jax,
+    have_optional_dependency,
     is_jax_compatible,
     get_git_commit_info,
 )
 from .logger import logger, set_logging_level, get_new_logger
 from .settings import settings
 from .citations import Citations, citations, print_citations
-
 #
 # Classes for the Expression Tree
 #
@@ -202,6 +202,7 @@
 #
 from .solvers.solution import Solution, EmptySolution, make_cycle_solution
 from .solvers.processed_variable import ProcessedVariable
+from .solvers.processed_variable_computed import ProcessedVariableComputed
 from .solvers.base_solver import BaseSolver
 from .solvers.dummy_solver import DummySolver
 from .solvers.algebraic_solver import AlgebraicSolver
diff --git a/pybamm/citations.py b/pybamm/citations.py
index da619062e0..b72262989b 100644
--- a/pybamm/citations.py
+++ b/pybamm/citations.py
@@ -6,10 +6,8 @@
 import pybamm
 import os
 import warnings
-import pybtex
 from sys import _getframe
-from pybtex.database import parse_file, parse_string, Entry
-from pybtex.scanner import PybtexError
+from pybamm.util import have_optional_dependency
 
 
 class Citations:
@@ -76,6 +74,7 @@ def read_citations(self):
         """Reads the citations in `pybamm.CITATIONS.bib`. Other works can be cited
         by passing a BibTeX citation to :meth:`register`.
         """
+        parse_file = have_optional_dependency("pybtex.database", "parse_file")
         citations_file = os.path.join(pybamm.root_dir(), "pybamm", "CITATIONS.bib")
         bib_data = parse_file(citations_file, bib_format="bibtex")
         for key, entry in bib_data.entries.items():
@@ -86,6 +85,7 @@ def _add_citation(self, key, entry):
         previous entry is overwritten
         """
 
+        Entry = have_optional_dependency("pybtex.database", "Entry")
         # Check input types are correct
         if not isinstance(key, str) or not isinstance(entry, Entry):
             raise TypeError()
@@ -151,6 +151,8 @@ def _parse_citation(self, key):
         key: str
             A BibTeX formatted citation
         """
+        PybtexError = have_optional_dependency("pybtex.scanner", "PybtexError")
+        parse_string = have_optional_dependency("pybtex.database", "parse_string")
         try:
             # Parse string as a bibtex citation, and check that a citation was found
             bib_data = parse_string(key, bib_format="bibtex")
@@ -217,6 +219,7 @@ def print(self, filename=None, output_format="text", verbose=False):
         """
         # Parse citations that were not known keys at registration, but do not
         # fail if they cannot be parsed
+        pybtex = have_optional_dependency("pybtex")
         try:
             for key in self._unknown_citations:
                 self._parse_citation(key)
diff --git a/pybamm/experiment/experiment.py b/pybamm/experiment/experiment.py
index d1c45015b6..9b02e3a20f 100644
--- a/pybamm/experiment/experiment.py
+++ b/pybamm/experiment/experiment.py
@@ -78,7 +78,7 @@ def __init__(
         self.operating_conditions_cycles = operating_conditions_cycles
         self.cycle_lengths = [len(cycle) for cycle in operating_conditions_cycles]
 
-        operating_conditions_steps_unprocessed = self._set_next_start_time(
+        self.operating_conditions_steps_unprocessed = self._set_next_start_time(
             [cond for cycle in operating_conditions_cycles for cond in cycle]
         )
 
@@ -89,7 +89,7 @@ def __init__(
         self.temperature = _convert_temperature_to_kelvin(temperature)
 
         processed_steps = {}
-        for step in operating_conditions_steps_unprocessed:
+        for step in self.operating_conditions_steps_unprocessed:
             if repr(step) in processed_steps:
                 continue
             elif isinstance(step, str):
@@ -106,7 +106,7 @@ def __init__(
 
         self.operating_conditions_steps = [
             processed_steps[repr(step)]
-            for step in operating_conditions_steps_unprocessed
+            for step in self.operating_conditions_steps_unprocessed
         ]
 
         # Save the processed unique steps and the processed operating conditions
diff --git a/pybamm/expression_tree/array.py b/pybamm/expression_tree/array.py
index a9141041b3..2736886d95 100644
--- a/pybamm/expression_tree/array.py
+++ b/pybamm/expression_tree/array.py
@@ -2,10 +2,10 @@
 # NumpyArray class
 #
 import numpy as np
-import sympy
 from scipy.sparse import csr_matrix, issparse
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class Array(pybamm.Symbol):
@@ -125,6 +125,7 @@ def is_constant(self):
 
     def to_equation(self):
         """Returns the value returned by the node when evaluated."""
+        sympy = have_optional_dependency("sympy")
         entries_list = self.entries.tolist()
         return sympy.Array(entries_list)
 
diff --git a/pybamm/expression_tree/averages.py b/pybamm/expression_tree/averages.py
index 6ada30d47a..e063b16c2a 100644
--- a/pybamm/expression_tree/averages.py
+++ b/pybamm/expression_tree/averages.py
@@ -273,7 +273,7 @@ def r_average(symbol):
     # "positive electrode", take the r-average of the child then broadcast back
     elif isinstance(symbol, pybamm.SecondaryBroadcast) and symbol.domains[
         "secondary"
-    ] in [["positive electrode"], ["negative electrode"], ["working electrode"]]:
+    ] in [["positive electrode"], ["negative electrode"]]:
         child = symbol.orphans[0]
         child_av = pybamm.r_average(child)
         return pybamm.PrimaryBroadcast(child_av, symbol.domains["secondary"])
diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index 749384e9bc..bfb31596e6 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -4,22 +4,22 @@
 import numbers
 
 import numpy as np
-import sympy
 from scipy.sparse import csr_matrix, issparse
 import functools
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 def _preprocess_binary(left, right):
     if isinstance(left, numbers.Number):
         left = pybamm.Scalar(left)
-    if isinstance(right, numbers.Number):
-        right = pybamm.Scalar(right)
     elif isinstance(left, np.ndarray):
         if left.ndim > 1:
             raise ValueError("left must be a 1D array")
         left = pybamm.Vector(left)
+    if isinstance(right, numbers.Number):
+        right = pybamm.Scalar(right)
     elif isinstance(right, np.ndarray):
         if right.ndim > 1:
             raise ValueError("right must be a 1D array")
@@ -147,6 +147,7 @@ def _sympy_operator(self, left, right):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
@@ -323,6 +324,7 @@ def _binary_evaluate(self, left, right):
 
     def _sympy_operator(self, left, right):
         """Override :meth:`pybamm.BinaryOperator._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         left = sympy.Matrix(left)
         right = sympy.Matrix(right)
         return left * right
@@ -626,6 +628,7 @@ def _binary_new_copy(self, left, right):
 
     def _sympy_operator(self, left, right):
         """Override :meth:`pybamm.BinaryOperator._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         return sympy.Min(left, right)
 
 
@@ -662,6 +665,7 @@ def _binary_new_copy(self, left, right):
 
     def _sympy_operator(self, left, right):
         """Override :meth:`pybamm.BinaryOperator._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         return sympy.Max(left, right)
 
 
diff --git a/pybamm/expression_tree/broadcasts.py b/pybamm/expression_tree/broadcasts.py
index 32cf2c002b..d30762ad70 100644
--- a/pybamm/expression_tree/broadcasts.py
+++ b/pybamm/expression_tree/broadcasts.py
@@ -546,8 +546,10 @@ def full_like(symbols, fill_value):
         return array_type(entries, domains=sum_symbol.domains)
 
     except NotImplementedError:
-        if sum_symbol.shape_for_testing == (1, 1) or sum_symbol.shape_for_testing == (
-            1,
+        if (
+            sum_symbol.shape_for_testing == (1, 1)
+            or sum_symbol.shape_for_testing == (1,)
+            or sum_symbol.domain == []
         ):
             return pybamm.Scalar(fill_value)
         if sum_symbol.evaluates_on_edges("primary"):
diff --git a/pybamm/expression_tree/concatenations.py b/pybamm/expression_tree/concatenations.py
index 2185a0fad6..1c82aff122 100644
--- a/pybamm/expression_tree/concatenations.py
+++ b/pybamm/expression_tree/concatenations.py
@@ -5,10 +5,10 @@
 from collections import defaultdict
 
 import numpy as np
-import sympy
 from scipy.sparse import issparse, vstack
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class Concatenation(pybamm.Symbol):
@@ -135,6 +135,7 @@ def is_constant(self):
 
     def _sympy_operator(self, *children):
         """Apply appropriate SymPy operators."""
+        sympy = have_optional_dependency("sympy")
         self.concat_latex = tuple(map(sympy.latex, children))
 
         if self.print_name is not None:
diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py
index 80c2848ad9..0c7e98b508 100644
--- a/pybamm/expression_tree/functions.py
+++ b/pybamm/expression_tree/functions.py
@@ -3,13 +3,11 @@
 #
 import numbers
 
-import autograd
 import numpy as np
-import sympy
 from scipy import special
 
 import pybamm
-
+from pybamm.util import have_optional_dependency
 
 class Function(pybamm.Symbol):
     """
@@ -96,6 +94,7 @@ def _function_diff(self, children, idx):
         Derivative with respect to child number 'idx'.
         See :meth:`pybamm.Symbol._diff()`.
         """
+        autograd = have_optional_dependency("autograd")
         # Store differentiated function, needed in case we want to convert to CasADi
         if self.derivative == "autograd":
             return Function(
@@ -202,6 +201,7 @@ def _sympy_operator(self, child):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
@@ -250,6 +250,7 @@ def _function_new_copy(self, children):
 
     def _sympy_operator(self, child):
         """Apply appropriate SymPy operators."""
+        sympy = have_optional_dependency("sympy")
         class_name = self.__class__.__name__.lower()
         sympy_function = getattr(sympy, class_name)
         return sympy_function(child)
@@ -267,6 +268,7 @@ def _function_diff(self, children, idx):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.Function._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         return sympy.asinh(child)
 
 
@@ -287,6 +289,7 @@ def _function_diff(self, children, idx):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.Function._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         return sympy.atan(child)
 
 
diff --git a/pybamm/expression_tree/independent_variable.py b/pybamm/expression_tree/independent_variable.py
index 18c4bd0a4d..4b887a82a7 100644
--- a/pybamm/expression_tree/independent_variable.py
+++ b/pybamm/expression_tree/independent_variable.py
@@ -1,9 +1,8 @@
 #
 # IndependentVariable class
 #
-import sympy
-
 import pybamm
+from pybamm.util import have_optional_dependency
 
 KNOWN_COORD_SYS = ["cartesian", "cylindrical polar", "spherical polar"]
 
@@ -44,6 +43,7 @@ def _jac(self, variable):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
@@ -77,6 +77,7 @@ def _evaluate_for_shape(self):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         return sympy.Symbol("t")
 
 
diff --git a/pybamm/expression_tree/operations/evaluate_python.py b/pybamm/expression_tree/operations/evaluate_python.py
index ae17a333ec..1f44a69784 100644
--- a/pybamm/expression_tree/operations/evaluate_python.py
+++ b/pybamm/expression_tree/operations/evaluate_python.py
@@ -13,7 +13,9 @@
     import jax
     from jax.config import config
 
-    config.update("jax_enable_x64", True)
+    platform = jax.lib.xla_bridge.get_backend().platform.casefold()
+    if platform != "metal":
+        config.update("jax_enable_x64", True)
 
 
 class JaxCooMatrix:
diff --git a/pybamm/expression_tree/operations/latexify.py b/pybamm/expression_tree/operations/latexify.py
index 67e0199656..9f2949069e 100644
--- a/pybamm/expression_tree/operations/latexify.py
+++ b/pybamm/expression_tree/operations/latexify.py
@@ -5,10 +5,9 @@
 import re
 import warnings
 
-import sympy
-
 import pybamm
 from pybamm.expression_tree.printing.sympy_overrides import custom_print_func
+from pybamm.util import have_optional_dependency
 
 
 def get_rng_min_max_name(rng, min_or_max):
@@ -88,6 +87,7 @@ def _get_bcs_displays(self, var):
         Returns a list of boundary condition equations with ranges in front of
         the equations.
         """
+        sympy = have_optional_dependency("sympy")
         bcs_eqn_list = []
         bcs = self.model.boundary_conditions.get(var, None)
 
@@ -118,6 +118,7 @@ def _get_bcs_displays(self, var):
 
     def _get_param_var(self, node):
         """Returns a list of parameters and a list of variables."""
+        sympy = have_optional_dependency("sympy")
         param_list = []
         var_list = []
         dfs_nodes = [node]
@@ -160,6 +161,7 @@ def _get_param_var(self, node):
         return param_list, var_list
 
     def latexify(self, output_variables=None):
+        sympy = have_optional_dependency("sympy")
         # Voltage is the default output variable if it exists
         if output_variables is None:
             if "Voltage [V]" in self.model.variables:
diff --git a/pybamm/expression_tree/parameter.py b/pybamm/expression_tree/parameter.py
index 10addae464..eebe77ad2f 100644
--- a/pybamm/expression_tree/parameter.py
+++ b/pybamm/expression_tree/parameter.py
@@ -5,9 +5,9 @@
 import sys
 
 import numpy as np
-import sympy
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class Parameter(pybamm.Symbol):
@@ -44,6 +44,7 @@ def is_constant(self):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
@@ -217,6 +218,7 @@ def _evaluate_for_shape(self):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index a96aa19729..1898822ea8 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -8,11 +8,9 @@
 
 class CustomPrint(LatexPrinter):
     """Override SymPy methods to match PyBaMM's requirements"""
-
     def _print_Derivative(self, expr):
         """Override :meth:`sympy.printing.latex.LatexPrinter._print_Derivative`"""
         eqn = super()._print_Derivative(expr)
-
         if getattr(expr, "force_partial", False) and "partial" not in eqn:
             var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", eqn)[0]
             eqn = eqn.replace(var1, "\partial").replace(var2, "\partial")
diff --git a/pybamm/expression_tree/scalar.py b/pybamm/expression_tree/scalar.py
index 3149bf7bee..0209c02a8e 100644
--- a/pybamm/expression_tree/scalar.py
+++ b/pybamm/expression_tree/scalar.py
@@ -2,10 +2,9 @@
 # Scalar class
 #
 import numpy as np
-import sympy
 
 import pybamm
-
+from pybamm.util import have_optional_dependency
 
 class Scalar(pybamm.Symbol):
     """
@@ -70,6 +69,7 @@ def is_constant(self):
 
     def to_equation(self):
         """Returns the value returned by the node when evaluated."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 5d28884ed5..8f1608e7ba 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -3,14 +3,12 @@
 #
 import numbers
 
-import anytree
 import numpy as np
-import sympy
-from anytree.exporter import DotExporter
 from scipy.sparse import csr_matrix, issparse
 from functools import lru_cache, cached_property
 
 import pybamm
+from pybamm.util import have_optional_dependency
 from pybamm.expression_tree.printing.print_name import prettify_print_name
 
 DOMAIN_LEVELS = ["primary", "secondary", "tertiary", "quaternary"]
@@ -442,6 +440,7 @@ def render(self):  # pragma: no cover
         """
         Print out a visual representation of the tree (this node and its children)
         """
+        anytree = have_optional_dependency("anytree")
         for pre, _, node in anytree.RenderTree(self):
             if isinstance(node, pybamm.Scalar) and node.name != str(node.value):
                 print("{}{} = {}".format(pre, node.name, node.value))
@@ -460,6 +459,7 @@ def visualise(self, filename):
             filename to output, must end in ".png"
         """
 
+        DotExporter = have_optional_dependency("anytree.exporter", "DotExporter")
         # check that filename ends in .png.
         if filename[-4:] != ".png":
             raise ValueError("filename should end in .png")
@@ -479,6 +479,7 @@ def relabel_tree(self, symbol, counter):
         Finds all children of a symbol and assigns them a new id so that they can be
         visualised properly using the graphviz output
         """
+        anytree = have_optional_dependency("anytree")
         name = symbol.name
         if name == "div":
             name = "&nabla;&sdot;"
@@ -522,6 +523,7 @@ def pre_order(self):
         a
         b
         """
+        anytree = have_optional_dependency("anytree")
         return anytree.PreOrderIter(self)
 
     def __str__(self):
@@ -984,4 +986,5 @@ def print_name(self, name):
         self._print_name = prettify_print_name(name)
 
     def to_equation(self):
+        sympy = have_optional_dependency("sympy")
         return sympy.Symbol(str(self.name))
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index 7f9c45775c..81c3dc28c2 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -4,11 +4,9 @@
 import numbers
 
 import numpy as np
-import sympy
 from scipy.sparse import csr_matrix, issparse
-from sympy.vector.operators import Divergence as sympy_Divergence
-from sympy.vector.operators import Gradient as sympy_Gradient
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class UnaryOperator(pybamm.Symbol):
@@ -83,6 +81,7 @@ def _sympy_operator(self, child):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
@@ -368,6 +367,7 @@ def _unary_new_copy(self, child):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
+        sympy_Gradient = have_optional_dependency("sympy.vector.operators", "Gradient")
         return sympy_Gradient(child)
 
 
@@ -403,6 +403,7 @@ def _unary_new_copy(self, child):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
+        sympy_Divergence = have_optional_dependency("sympy.vector.operators", "Divergence")
         return sympy_Divergence(child)
 
 
@@ -579,6 +580,7 @@ def _evaluates_on_edges(self, dimension):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         return sympy.Integral(child, sympy.Symbol("xn"))
 
 
@@ -889,6 +891,7 @@ def _unary_new_copy(self, child):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         if (
             self.child.domain[0] in ["negative particle", "positive particle"]
             and self.side == "right"
diff --git a/pybamm/expression_tree/variable.py b/pybamm/expression_tree/variable.py
index f9f7d94efc..0d1e1fd424 100644
--- a/pybamm/expression_tree/variable.py
+++ b/pybamm/expression_tree/variable.py
@@ -3,9 +3,9 @@
 #
 
 import numpy as np
-import sympy
 import numbers
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class VariableBase(pybamm.Symbol):
@@ -124,6 +124,7 @@ def _evaluate_for_shape(self):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
diff --git a/pybamm/input/parameters/lithium_ion/Chen2020_composite.py b/pybamm/input/parameters/lithium_ion/Chen2020_composite.py
index bbc7b1990e..f7e27c8d52 100644
--- a/pybamm/input/parameters/lithium_ion/Chen2020_composite.py
+++ b/pybamm/input/parameters/lithium_ion/Chen2020_composite.py
@@ -334,9 +334,18 @@ def get_parameter_values():
         "chemistry": "lithium_ion",
         # sei
         "Primary: Ratio of lithium moles to SEI moles": 2.0,
+        "Primary: Inner SEI reaction proportion": 0.5,
         "Primary: Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
         "Primary: Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Primary: SEI reaction exchange current density [A.m-2]": 1.5e-07,
         "Primary: SEI resistivity [Ohm.m]": 200000.0,
+        "Primary: Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Primary: Bulk solvent concentration [mol.m-3]": 2636.0,
+        "Primary: Inner SEI open-circuit potential [V]": 0.1,
+        "Primary: Outer SEI open-circuit potential [V]": 0.8,
+        "Primary: Inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Primary: Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Primary: Lithium interstitial reference concentration [mol.m-3]": 15.0,
         "Primary: Initial inner SEI thickness [m]": 2.5e-09,
         "Primary: Initial outer SEI thickness [m]": 2.5e-09,
         "Primary: EC initial concentration in electrolyte [mol.m-3]": 4541.0,
@@ -345,9 +354,18 @@ def get_parameter_values():
         "Primary: SEI open-circuit potential [V]": 0.4,
         "Primary: SEI growth activation energy [J.mol-1]": 0.0,
         "Secondary: Ratio of lithium moles to SEI moles": 2.0,
+        "Secondary: Inner SEI reaction proportion": 0.5,
         "Secondary: Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
         "Secondary: Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Secondary: SEI reaction exchange current density [A.m-2]": 1.5e-07,
         "Secondary: SEI resistivity [Ohm.m]": 200000.0,
+        "Secondary: Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Secondary: Bulk solvent concentration [mol.m-3]": 2636.0,
+        "Secondary: Inner SEI open-circuit potential [V]": 0.1,
+        "Secondary: Outer SEI open-circuit potential [V]": 0.8,
+        "Secondary: Inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Secondary: Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Secondary: Lithium interstitial reference concentration [mol.m-3]": 15.0,
         "Secondary: Initial inner SEI thickness [m]": 2.5e-09,
         "Secondary: Initial outer SEI thickness [m]": 2.5e-09,
         "Secondary: EC initial concentration in electrolyte [mol.m-3]": 4541.0,
@@ -355,6 +373,7 @@ def get_parameter_values():
         "Secondary: SEI kinetic rate constant [m.s-1]": 1e-12,
         "Secondary: SEI open-circuit potential [V]": 0.4,
         "Secondary: SEI growth activation energy [J.mol-1]": 0.0,
+        "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
         # cell
         "Negative current collector thickness [m]": 1.2e-05,
         "Negative electrode thickness [m]": 8.52e-05,
diff --git a/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py b/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py
new file mode 100644
index 0000000000..f6bc8e4d93
--- /dev/null
+++ b/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py
@@ -0,0 +1,441 @@
+import pybamm
+
+
+def li_metal_electrolyte_exchange_current_density_Xu2019(c_e, c_Li, T):
+    """
+    Exchange-current density for Butler-Volmer reactions between li metal and LiPF6 in
+    EC:DMC.
+
+    References
+    ----------
+    .. [1] Xu, Shanshan, Chen, Kuan-Hung, Dasgupta, Neil P., Siegel, Jason B. and
+    Stefanopoulou, Anna G. "Evolution of Dead Lithium Growth in Lithium Metal Batteries:
+    Experimentally Validated Model of the Apparent Capacity Loss." Journal of The
+    Electrochemical Society 166.14 (2019): A3456-A3463.
+
+    Parameters
+    ----------
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_Li : :class:`pybamm.Symbol`
+        Pure metal lithium concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+    m_ref = 3.5e-8 * pybamm.constants.F  # (A/m2)(mol/m3) - includes ref concentrations
+
+    return m_ref * c_Li**0.7 * c_e**0.3
+
+
+def graphite_diffusivity_Ecker2015(sto, T):
+    """
+    Graphite diffusivity as a function of stochiometry [1, 2, 3].
+
+    References
+    ----------
+     .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery ii. model validation." Journal of The Electrochemical
+    Society 162.9 (2015): A1849-A1857.
+    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
+    high-rate operation of graded lithium-ion electrodes: Systematic derivation
+    and validation." Electrochemica Acta 339 (2020): 135862
+
+    Parameters
+    ----------
+    sto: :class:`pybamm.Symbol`
+        Electrode stochiometry
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    D_ref = 8.4e-13 * pybamm.exp(-11.3 * sto) + 8.2e-15
+    E_D_s = 3.03e4
+    arrhenius = pybamm.exp(-E_D_s / (pybamm.constants.R * T)) * pybamm.exp(
+        E_D_s / (pybamm.constants.R * 296)
+    )
+
+    return D_ref * arrhenius
+
+
+def graphite_ocp_Ecker2015(sto):
+    """
+    Graphite OCP as a function of stochiometry [1, 2, 3].
+
+    References
+    ----------
+     .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery ii. model validation." Journal of The Electrochemical
+    Society 162.9 (2015): A1849-A1857.
+    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
+    high-rate operation of graded lithium-ion electrodes: Systematic derivation
+    and validation." Electrochemica Acta 339 (2020): 135862
+
+    Parameters
+    ----------
+    sto: :class:`pybamm.Symbol`
+        Electrode stochiometry
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Open-circuit potential
+    """
+
+    # Graphite electrode from Ecker, Kabitz, Laresgoiti et al.
+    # Analytical fit (WebPlotDigitizer + gnuplot)
+    a = 0.716502
+    b = 369.028
+    c = 0.12193
+    d = 35.6478
+    e = 0.0530947
+    g = 0.0169644
+    h = 27.1365
+    i = 0.312832
+    j = 0.0199313
+    k = 28.5697
+    m = 0.614221
+    n = 0.931153
+    o = 36.328
+    p = 1.10743
+    q = 0.140031
+    r = 0.0189193
+    s = 21.1967
+    t = 0.196176
+
+    u_eq = (
+        a * pybamm.exp(-b * sto)
+        + c * pybamm.exp(-d * (sto - e))
+        - r * pybamm.tanh(s * (sto - t))
+        - g * pybamm.tanh(h * (sto - i))
+        - j * pybamm.tanh(k * (sto - m))
+        - n * pybamm.exp(o * (sto - p))
+        + q
+    )
+
+    return u_eq
+
+
+def graphite_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, c_s_max, T):
+    """
+    Exchange-current density for Butler-Volmer reactions between graphite and LiPF6 in
+    EC:DMC.
+
+    References
+    ----------
+    .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery ii. model validation." Journal of The Electrochemical
+    Society 162.9 (2015): A1849-A1857.
+    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
+    high-rate operation of graded lithium-ion electrodes: Systematic derivation
+    and validation." Electrochemica Acta 339 (2020): 135862
+
+    Parameters
+    ----------
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_s_surf : :class:`pybamm.Symbol`
+        Particle concentration [mol.m-3]
+    c_s_max : :class:`pybamm.Symbol`
+        Maximum particle concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+
+    k_ref = 1.11 * 1e-10
+
+    # multiply by Faraday's constant to get correct units
+    m_ref = (
+        pybamm.constants.F * k_ref
+    )  # (A/m2)(m3/mol)**1.5 - includes ref concentrations
+    E_r = 53400
+
+    arrhenius = pybamm.exp(-E_r / (pybamm.constants.R * T)) * pybamm.exp(
+        E_r / (pybamm.constants.R * 296.15)
+    )
+
+    return (
+        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
+    )
+
+
+def electrolyte_diffusivity_Ecker2015(c_e, T):
+    """
+    Diffusivity of LiPF6 in EC:DMC as a function of ion concentration [1, 2, 3].
+
+    References
+    ----------
+    .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery ii. model validation." Journal of The Electrochemical
+    Society 162.9 (2015): A1849-A1857.
+    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
+    high-rate operation of graded lithium-ion electrodes: Systematic derivation
+    and validation." Electrochemica Acta 339 (2020): 135862
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    # The diffusivity epends on the electrolyte conductivity
+    inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T}
+    sigma_e = pybamm.FunctionParameter("Electrolyte conductivity [S.m-1]", inputs)
+
+    D_c_e = (
+        (pybamm.constants.k_b / (pybamm.constants.F * pybamm.constants.q_e))
+        * sigma_e
+        * T
+        / c_e
+    )
+
+    return D_c_e
+
+
+def electrolyte_conductivity_Ecker2015(c_e, T):
+    """
+    Conductivity of LiPF6 in EC:DMC as a function of ion concentration [1, 2, 3].
+
+    References
+    ----------
+    .. [1] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery ii. model validation." Journal of The Electrochemical
+    Society 162.9 (2015): A1849-A1857.
+    .. [3] Richardson, Giles, et. al. "Generalised single particle models for
+    high-rate operation of graded lithium-ion electrodes: Systematic derivation
+    and validation." Electrochemica Acta 339 (2020): 135862
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    # mol/m^3 to mol/l
+    cm = 1e-3 * c_e
+
+    # value at T = 296K
+    sigma_e_296 = 0.2667 * cm**3 - 1.2983 * cm**2 + 1.7919 * cm + 0.1726
+
+    # add temperature dependence
+    E_k_e = 1.71e4
+    C = 296 * pybamm.exp(E_k_e / (pybamm.constants.R * 296))
+    sigma_e = C * sigma_e_296 * pybamm.exp(-E_k_e / (pybamm.constants.R * T)) / T
+
+    return sigma_e
+
+
+# Call dict via a function to avoid errors when editing in place
+def get_parameter_values():
+    """
+    Parameters for a graphite half-cell based on a Kokam SLPB 75106100 cell, from papers
+
+        Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a
+        lithium-ion battery I. determination of parameters." Journal of the
+        Electrochemical Society 162.9 (2015): A1836-A1848.
+
+        Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of a
+        lithium-ion battery II. Model validation." Journal of The Electrochemical
+        Society 162.9 (2015): A1849-A1857.
+
+    The tab placement parameters are taken from measurements in
+
+        Hales, Alastair, et al. "The cell cooling coefficient: a standard to define heat
+        rejection from lithium-ion batteries." Journal of The Electrochemical Society
+        166.12 (2019): A2383.
+
+    The thermal material properties are for a 5 Ah power pouch cell by Kokam. The data
+    are extracted from
+
+        Zhao, Y., et al. "Modeling the effects of thermal gradients induced by tab and
+        surface cooling on lithium ion cell performance."" Journal of The
+        Electrochemical Society, 165.13 (2018): A3169-A3178.
+
+    Graphite electrode parameters
+    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+    The fits to data for the electrode and electrolyte properties are those provided
+    by Dr. Simon O'Kane in the paper:
+
+        Richardson, Giles, et. al. "Generalised single particle models for high-rate
+        operation of graded lithium-ion electrodes: Systematic derivation and
+        validation." Electrochemica Acta 339 (2020): 135862
+
+    SEI parameters are example parameters for SEI growth from the papers:
+
+
+        Ramadass, P., Haran, B., Gomadam, P. M., White, R., & Popov, B. N. (2004).
+        Development of first principles capacity fade model for Li-ion cells. Journal of
+        the Electrochemical Society, 151(2), A196-A203.
+
+        Ploehn, H. J., Ramadass, P., & White, R. E. (2004). Solvent diffusion model for
+        aging of lithium-ion battery cells. Journal of The Electrochemical Society,
+        151(3), A456-A462.
+
+        Single, F., Latz, A., & Horstmann, B. (2018). Identifying the mechanism of
+        continued growth of the solid-electrolyte interphase. ChemSusChem, 11(12),
+        1950-1955.
+
+        Safari, M., Morcrette, M., Teyssot, A., & Delacour, C. (2009). Multimodal
+        Physics- Based Aging Model for Life Prediction of Li-Ion Batteries. Journal of
+        The Electrochemical Society, 156(3),
+
+        Yang, X., Leng, Y., Zhang, G., Ge, S., Wang, C. (2017). Modeling of lithium
+        plating induced aging of lithium-ion batteries: Transition from linear to
+        nonlinear aging. Journal of Power Sources, 360, 28-40.
+
+    Note: this parameter set does not claim to be representative of the true parameter
+    values. Instead these are parameter values that were used to fit SEI models to
+    observed experimental data in the referenced papers.
+    """
+
+    return {
+        "chemistry": "lithium_ion",
+        # sei
+        "Ratio of lithium moles to SEI moles": 2.0,
+        "Inner SEI reaction proportion": 0.5,
+        "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "SEI resistivity [Ohm.m]": 200000.0,
+        "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Bulk solvent concentration [mol.m-3]": 2636.0,
+        "Inner SEI open-circuit potential [V]": 0.1,
+        "Outer SEI open-circuit potential [V]": 0.8,
+        "Inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Lithium interstitial reference concentration [mol.m-3]": 15.0,
+        "Initial inner SEI thickness [m]": 2.5e-09,
+        "Initial outer SEI thickness [m]": 2.5e-09,
+        "EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "EC diffusivity [m2.s-1]": 2e-18,
+        "SEI kinetic rate constant [m.s-1]": 1e-12,
+        "SEI open-circuit potential [V]": 0.4,
+        "SEI growth activation energy [J.mol-1]": 0.0,
+        "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
+        # cell
+        "Negative current collector thickness [m]": 1.4e-05,
+        "Negative electrode thickness [m]": 0.0007,
+        "Positive current collector thickness [m]": 1.4e-05,
+        "Positive electrode thickness [m]": 7.4e-05,
+        "Separator thickness [m]": 2e-05,
+        "Electrode height [m]": 0.101,
+        "Electrode width [m]": 0.085,
+        "Positive tab width [m]": 0.007,
+        "Positive tab centre y-coordinate [m]": 0.0045,
+        "Positive tab centre z-coordinate [m]": 0.101,
+        "Cell cooling surface area [m2]": 0.0172,
+        "Cell volume [m3]": 1.52e-06,
+        "Positive current collector conductivity [S.m-1]": 58411000.0,
+        "Positive current collector density [kg.m-3]": 8933.0,
+        "Positive current collector specific heat capacity [J.kg-1.K-1]": 385.0,
+        "Positive current collector thermal conductivity [W.m-1.K-1]": 398.0,
+        "Nominal cell capacity [A.h]": 0.15625,
+        "Current function [A]": 0.15652,
+        "Contact resistance [Ohm]": 0,
+        # negative electrode
+        "Negative electrode OCP [V]": 0.0,
+        "Negative electrode conductivity [S.m-1]": 10776000.0,
+        "Negative electrode OCP entropic change [V.K-1]": 0.0,
+        "Lithium metal partial molar volume [m3.mol-1]": 1.3e-05,
+        "Exchange-current density for lithium metal electrode [A.m-2]"
+        "": li_metal_electrolyte_exchange_current_density_Xu2019,
+        "Negative electrode charge transfer coefficient": 0.5,
+        "Negative electrode double-layer capacity [F.m-2]": 0.2,
+        # positive electrode
+        "Positive electrode conductivity [S.m-1]": 14.0,
+        "Maximum concentration in positive electrode [mol.m-3]": 31920.0,
+        "Positive electrode diffusivity [m2.s-1]": graphite_diffusivity_Ecker2015,
+        "Positive electrode OCP [V]": graphite_ocp_Ecker2015,
+        "Positive electrode porosity": 0.329,
+        "Positive electrode active material volume fraction": 0.372403,
+        "Positive particle radius [m]": 1.37e-05,
+        "Positive electrode Bruggeman coefficient (electrolyte)": 1.6372789338386007,
+        "Positive electrode Bruggeman coefficient (electrode)": 0.0,
+        "Positive electrode exchange-current density [A.m-2]"
+        "": graphite_electrolyte_exchange_current_density_Ecker2015,
+        "Positive electrode density [kg.m-3]": 1555.0,
+        "Positive electrode specific heat capacity [J.kg-1.K-1]": 1437.0,
+        "Positive electrode thermal conductivity [W.m-1.K-1]": 1.58,
+        "Positive electrode OCP entropic change [V.K-1]": 0.0,
+        # separator
+        "Separator porosity": 0.508,
+        "Separator Bruggeman coefficient (electrolyte)": 1.9804586773134945,
+        "Separator density [kg.m-3]": 1017.0,
+        "Separator specific heat capacity [J.kg-1.K-1]": 1978.0,
+        "Separator thermal conductivity [W.m-1.K-1]": 0.34,
+        # electrolyte
+        "Initial concentration in electrolyte [mol.m-3]": 1000.0,
+        "Cation transference number": 0.26,
+        "Thermodynamic factor": 1.0,
+        "Electrolyte diffusivity [m2.s-1]": electrolyte_diffusivity_Ecker2015,
+        "Electrolyte conductivity [S.m-1]": electrolyte_conductivity_Ecker2015,
+        # experiment
+        "Reference temperature [K]": 296.15,
+        "Positive current collector surface heat transfer coefficient [W.m-2.K-1]"
+        "": 10.0,
+        "Positive tab heat transfer coefficient [W.m-2.K-1]": 10.0,
+        "Edge heat transfer coefficient [W.m-2.K-1]": 10.0,
+        "Total heat transfer coefficient [W.m-2.K-1]": 10.0,
+        "Ambient temperature [K]": 298.15,
+        "Number of electrodes connected in parallel to make a cell": 1.0,
+        "Number of cells connected in series to make a battery": 1.0,
+        "Lower voltage cut-off [V]": 0,
+        "Upper voltage cut-off [V]": 1.5,
+        "Open-circuit voltage at 0% SOC [V]": 0,
+        "Open-circuit voltage at 100% SOC [V]": 1.5,
+        "Initial concentration in positive electrode [mol.m-3]": 26120.05,
+        "Initial temperature [K]": 298.15,
+        # citations
+        "citations": [
+            "Ecker2015i",
+            "Ecker2015ii",
+            "Zhao2018",
+            "Hales2019",
+            "Xu2019",
+            "Richardson2020",
+        ],
+    }
diff --git a/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py b/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py
new file mode 100644
index 0000000000..e13d27fad0
--- /dev/null
+++ b/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py
@@ -0,0 +1,521 @@
+import pybamm
+import os
+
+
+def li_metal_electrolyte_exchange_current_density_Xu2019(c_e, c_Li, T):
+    """
+    Exchange-current density for Butler-Volmer reactions between li metal and LiPF6 in
+    EC:DMC.
+
+    References
+    ----------
+    .. [1] Xu, Shanshan, Chen, Kuan-Hung, Dasgupta, Neil P., Siegel, Jason B. and
+    Stefanopoulou, Anna G. "Evolution of Dead Lithium Growth in Lithium Metal Batteries:
+    Experimentally Validated Model of the Apparent Capacity Loss." Journal of The
+    Electrochemical Society 166.14 (2019): A3456-A3463.
+
+    Parameters
+    ----------
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_Li : :class:`pybamm.Symbol`
+        Pure metal lithium concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+    m_ref = 3.5e-8 * pybamm.constants.F  # (A/m2)(mol/m3) - includes ref concentrations
+
+    return m_ref * c_Li**0.7 * c_e**0.3
+
+
+def plating_exchange_current_density_OKane2020(c_e, c_Li, T):
+    """
+    Exchange-current density for Li plating reaction [A.m-2].
+    References
+    ----------
+    .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and
+    Monica Marinescu. "Physical origin of the differential voltage minimum associated
+    with lithium plating in Li-ion batteries." Journal of The Electrochemical Society
+    167, no. 9 (2020): 090540.
+    Parameters
+    ----------
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_Li : :class:`pybamm.Symbol`
+        Plated lithium concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+
+    k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]")
+
+    return pybamm.constants.F * k_plating * c_e
+
+
+def stripping_exchange_current_density_OKane2020(c_e, c_Li, T):
+    """
+    Exchange-current density for Li stripping reaction [A.m-2].
+
+    References
+    ----------
+
+    .. [1] O’Kane, Simon EJ, Ian D. Campbell, Mohamed WJ Marzook, Gregory J. Offer, and
+    Monica Marinescu. "Physical origin of the differential voltage minimum associated
+    with lithium plating in Li-ion batteries." Journal of The Electrochemical Society
+    167, no. 9 (2020): 090540.
+
+    Parameters
+    ----------
+
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_Li : :class:`pybamm.Symbol`
+        Plated lithium concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+
+    Returns
+    -------
+
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+
+    k_plating = pybamm.Parameter("Lithium plating kinetic rate constant [m.s-1]")
+
+    return pybamm.constants.F * k_plating * c_Li
+
+
+def SEI_limited_dead_lithium_OKane2022(L_sei):
+    """
+    Decay rate for dead lithium formation [s-1].
+    References
+    ----------
+    .. [1] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diega Alonso-Alvarez,
+    Robert Timms, Valentin Sulzer, Jaqueline Sophie Edge, Billy Wu, Gregory J. Offer
+    and Monica Marinescu. "Lithium-ion battery degradation: how to model it."
+    Physical Chemistry: Chemical Physics 24, no. 13 (2022): 7909-7922.
+    Parameters
+    ----------
+    L_sei : :class:`pybamm.Symbol`
+        Total SEI thickness [m]
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Dead lithium decay rate [s-1]
+    """
+
+    gamma_0 = pybamm.Parameter("Dead lithium decay constant [s-1]")
+    L_inner_0 = pybamm.Parameter("Initial inner SEI thickness [m]")
+    L_outer_0 = pybamm.Parameter("Initial outer SEI thickness [m]")
+    L_sei_0 = L_inner_0 + L_outer_0
+
+    gamma = gamma_0 * L_sei_0 / L_sei
+
+    return gamma
+
+
+def graphite_LGM50_diffusivity_Chen2020(sto, T):
+    """
+    LG M50 Graphite diffusivity as a function of stochiometry, in this case the
+    diffusivity is taken to be a constant. The value is taken from [1].
+
+    References
+    ----------
+    .. [1] Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W.
+    Dhammika Widanage, and Emma Kendrick. "Development of Experimental Techniques for
+    Parameterization of Multi-scale Lithium-ion Battery Models." Journal of the
+    Electrochemical Society 167 (2020): 080534.
+
+    Parameters
+    ----------
+    sto: :class:`pybamm.Symbol`
+       Electrode stochiometry
+    T: :class:`pybamm.Symbol`
+       Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+       Solid diffusivity
+    """
+
+    D_ref = 3.3e-14
+    E_D_s = 3.03e4
+    # E_D_s not given by Chen et al (2020), so taken from Ecker et al. (2015) instead
+    arrhenius = pybamm.exp(E_D_s / pybamm.constants.R * (1 / 298.15 - 1 / T))
+
+    return D_ref * arrhenius
+
+
+def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(
+    c_e, c_s_surf, c_s_max, T
+):
+    """
+    Exchange-current density for Butler-Volmer reactions between graphite and LiPF6 in
+    EC:DMC.
+
+    References
+    ----------
+    .. [1] Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W.
+    Dhammika Widanage, and Emma Kendrick. "Development of Experimental Techniques for
+    Parameterization of Multi-scale Lithium-ion Battery Models." Journal of the
+    Electrochemical Society 167 (2020): 080534.
+
+    Parameters
+    ----------
+    c_e : :class:`pybamm.Symbol`
+        Electrolyte concentration [mol.m-3]
+    c_s_surf : :class:`pybamm.Symbol`
+        Particle concentration [mol.m-3]
+    c_s_max : :class:`pybamm.Symbol`
+        Maximum particle concentration [mol.m-3]
+    T : :class:`pybamm.Symbol`
+        Temperature [K]
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Exchange-current density [A.m-2]
+    """
+
+    m_ref = 6.48e-7  # (A/m2)(m3/mol)**1.5 - includes ref concentrations
+    E_r = 35000
+    arrhenius = pybamm.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
+
+    return (
+        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
+    )
+
+
+def graphite_volume_change_Ai2020(sto, c_s_max):
+    """
+    Graphite particle volume change as a function of stochiometry [1, 2].
+
+    References
+    ----------
+     .. [1] Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020).
+     Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity in
+     Lithium-Ion Pouch Cells. Journal of The Electrochemical Society, 167(1), 013512
+      DOI: 10.1149/2.0122001JES.
+     .. [2] Rieger, B., Erhard, S. V., Rumpf, K., & Jossen, A. (2016).
+     A new method to model the thickness change of a commercial pouch cell
+     during discharge. Journal of The Electrochemical Society, 163(8), A1566-A1575.
+
+    Parameters
+    ----------
+    sto: :class:`pybamm.Symbol`
+        Electrode stochiometry, dimensionless
+        should be R-averaged particle concentration
+    Returns
+    -------
+    t_change:class:`pybamm.Symbol`
+        volume change, dimensionless, normalised by particle volume
+    """
+    p1 = 145.907
+    p2 = -681.229
+    p3 = 1334.442
+    p4 = -1415.710
+    p5 = 873.906
+    p6 = -312.528
+    p7 = 60.641
+    p8 = -5.706
+    p9 = 0.386
+    p10 = -4.966e-05
+    t_change = (
+        p1 * sto**9
+        + p2 * sto**8
+        + p3 * sto**7
+        + p4 * sto**6
+        + p5 * sto**5
+        + p6 * sto**4
+        + p7 * sto**3
+        + p8 * sto**2
+        + p9 * sto
+        + p10
+    )
+    return t_change
+
+
+def graphite_cracking_rate_Ai2020(T_dim):
+    """
+    Graphite particle cracking rate as a function of temperature [1, 2].
+
+    References
+    ----------
+     .. [1] Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020).
+     Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity in
+     Lithium-Ion Pouch Cells. Journal of The Electrochemical Society, 167(1), 013512
+      DOI: 10.1149/2.0122001JES.
+     .. [2] Deshpande, R., Verbrugge, M., Cheng, Y. T., Wang, J., & Liu, P. (2012).
+     Battery cycle life prediction with coupled chemical degradation and fatigue
+     mechanics. Journal of the Electrochemical Society, 159(10), A1730.
+
+    Parameters
+    ----------
+    T_dim: :class:`pybamm.Symbol`
+        temperature, [K]
+
+    Returns
+    -------
+    k_cr: :class:`pybamm.Symbol`
+        cracking rate, [m/(Pa.m0.5)^m_cr]
+        where m_cr is another Paris' law constant
+    """
+    k_cr = 3.9e-20
+    Eac_cr = 0  # to be implemented
+    arrhenius = pybamm.exp(Eac_cr / pybamm.constants.R * (1 / T_dim - 1 / 298.15))
+    return k_cr * arrhenius
+
+
+def electrolyte_diffusivity_Nyman2008_arrhenius(c_e, T):
+    """
+    Diffusivity of LiPF6 in EC:EMC (3:7) as a function of ion concentration. The data
+    comes from [1], with Arrhenius temperature dependence added from [2].
+
+    References
+    ----------
+    .. [1] A. Nyman, M. Behm, and G. Lindbergh, "Electrochemical characterisation and
+    modelling of the mass transport phenomena in LiPF6-EC-EMC electrolyte,"
+    Electrochim. Acta, vol. 53, no. 22, pp. 6356–6365, 2008.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    D_c_e = 8.794e-11 * (c_e / 1000) ** 2 - 3.972e-10 * (c_e / 1000) + 4.862e-10
+
+    # Nyman et al. (2008) does not provide temperature dependence
+    # So use temperature dependence from Ecker et al. (2015) instead
+
+    E_D_c_e = 17000
+    arrhenius = pybamm.exp(E_D_c_e / pybamm.constants.R * (1 / 298.15 - 1 / T))
+
+    return D_c_e * arrhenius
+
+
+def electrolyte_conductivity_Nyman2008_arrhenius(c_e, T):
+    """
+    Conductivity of LiPF6 in EC:EMC (3:7) as a function of ion concentration. The data
+    comes from [1], with Arrhenius temperature dependence added from [2].
+
+    References
+    ----------
+    .. [1] A. Nyman, M. Behm, and G. Lindbergh, "Electrochemical characterisation and
+    modelling of the mass transport phenomena in LiPF6-EC-EMC electrolyte,"
+    Electrochim. Acta, vol. 53, no. 22, pp. 6356–6365, 2008.
+    .. [2] Ecker, Madeleine, et al. "Parameterization of a physico-chemical model of
+    a lithium-ion battery i. determination of parameters." Journal of the
+    Electrochemical Society 162.9 (2015): A1836-A1848.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    sigma_e = (
+        0.1297 * (c_e / 1000) ** 3 - 2.51 * (c_e / 1000) ** 1.5 + 3.329 * (c_e / 1000)
+    )
+
+    # Nyman et al. (2008) does not provide temperature dependence
+    # So use temperature dependence from Ecker et al. (2015) instead
+
+    E_sigma_e = 17000
+    arrhenius = pybamm.exp(E_sigma_e / pybamm.constants.R * (1 / 298.15 - 1 / T))
+
+    return sigma_e * arrhenius
+
+
+# Load data in the appropriate format
+path, _ = os.path.split(os.path.abspath(__file__))
+graphite_LGM50_ocp_Chen2020_data = pybamm.parameters.process_1D_data(
+    "graphite_LGM50_ocp_Chen2020.csv", path=path
+)
+
+
+def graphite_LGM50_ocp_Chen2020(sto):
+    name, (x, y) = graphite_LGM50_ocp_Chen2020_data
+    return pybamm.Interpolant(x, y, sto, name=name, interpolator="cubic")
+
+
+# Call dict via a function to avoid errors when editing in place
+def get_parameter_values():
+    """
+    Parameters for the graphite+SiOx negative electrode of a LG M50 cell, from the paper
+
+        Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert
+        Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and
+        Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem.
+        Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H,
+        doi:10.1039/D2CP00417H.
+
+
+    based on the paper
+
+        Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W.
+        Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for
+        Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The
+        Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.
+
+
+    and references therein.
+
+    Note: the SEI, plating and mechanical parameters do not claim to be representative
+    of the true parameter values. These are merely the parameter values that were used
+    in the referenced papers.
+    """
+
+    return {
+        "chemistry": "lithium_ion",
+        # lithium plating
+        "Lithium metal partial molar volume [m3.mol-1]": 1.3e-05,
+        "Lithium plating kinetic rate constant [m.s-1]": 1e-09,
+        "Exchange-current density for plating [A.m-2]"
+        "": plating_exchange_current_density_OKane2020,
+        "Exchange-current density for stripping [A.m-2]"
+        "": stripping_exchange_current_density_OKane2020,
+        "Initial plated lithium concentration [mol.m-3]": 0.0,
+        "Typical plated lithium concentration [mol.m-3]": 1000.0,
+        "Lithium plating transfer coefficient": 0.65,
+        "Dead lithium decay constant [s-1]": 1e-06,
+        "Dead lithium decay rate [s-1]": SEI_limited_dead_lithium_OKane2022,
+        # sei
+        "Ratio of lithium moles to SEI moles": 1.0,
+        "Inner SEI reaction proportion": 0.0,
+        "Inner SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "Outer SEI partial molar volume [m3.mol-1]": 9.585e-05,
+        "SEI reaction exchange current density [A.m-2]": 1.5e-07,
+        "SEI resistivity [Ohm.m]": 200000.0,
+        "Outer SEI solvent diffusivity [m2.s-1]": 2.5000000000000002e-22,
+        "Bulk solvent concentration [mol.m-3]": 2636.0,
+        "Inner SEI open-circuit potential [V]": 0.1,
+        "Outer SEI open-circuit potential [V]": 0.8,
+        "Inner SEI electron conductivity [S.m-1]": 8.95e-14,
+        "Inner SEI lithium interstitial diffusivity [m2.s-1]": 1e-20,
+        "Lithium interstitial reference concentration [mol.m-3]": 15.0,
+        "Initial inner SEI thickness [m]": 0.0,
+        "Initial outer SEI thickness [m]": 5e-09,
+        "EC initial concentration in electrolyte [mol.m-3]": 4541.0,
+        "EC diffusivity [m2.s-1]": 2e-18,
+        "SEI kinetic rate constant [m.s-1]": 1e-12,
+        "SEI open-circuit potential [V]": 0.4,
+        "SEI growth activation energy [J.mol-1]": 38000.0,
+        "Negative electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
+        "Positive electrode reaction-driven LAM factor [m3.mol-1]": 0.0,
+        # cell
+        "Negative current collector thickness [m]": 1.2e-05,
+        "Negative electrode thickness [m]": 0.0007,
+        "Positive current collector thickness [m]": 1.2e-05,
+        "Positive electrode thickness [m]": 8.52e-05,
+        "Separator thickness [m]": 1.2e-05,
+        "Electrode height [m]": 0.065,
+        "Electrode width [m]": 1.58,
+        "Cell cooling surface area [m2]": 0.00531,
+        "Cell volume [m3]": 2.42e-05,
+        "Cell thermal expansion coefficient [m.K-1]": 1.1e-06,
+        "Positive current collector conductivity [S.m-1]": 58411000.0,
+        "Positive current collector density [kg.m-3]": 8960.0,
+        "Positive current collector specific heat capacity [J.kg-1.K-1]": 385.0,
+        "Positive current collector thermal conductivity [W.m-1.K-1]": 401.0,
+        "Nominal cell capacity [A.h]": 5.0,
+        "Current function [A]": 5.0,
+        "Contact resistance [Ohm]": 0,
+        # negative electrode
+        "Negative electrode OCP [V]": 0.0,
+        "Negative electrode conductivity [S.m-1]": 10776000.0,
+        "Negative electrode OCP entropic change [V.K-1]": 0.0,
+        "Exchange-current density for lithium metal electrode [A.m-2]"
+        "": li_metal_electrolyte_exchange_current_density_Xu2019,
+        "Negative electrode charge transfer coefficient": 0.5,
+        "Negative electrode double-layer capacity [F.m-2]": 0.2,
+        # positive electrode
+        "Positive electrode conductivity [S.m-1]": 215.0,
+        "Maximum concentration in positive electrode [mol.m-3]": 33133.0,
+        "Positive electrode diffusivity [m2.s-1]": graphite_LGM50_diffusivity_Chen2020,
+        "Positive electrode OCP [V]": graphite_LGM50_ocp_Chen2020,
+        "Positive electrode porosity": 0.25,
+        "Positive electrode active material volume fraction": 0.75,
+        "Positive particle radius [m]": 5.86e-06,
+        "Positive electrode Bruggeman coefficient (electrolyte)": 1.5,
+        "Positive electrode Bruggeman coefficient (electrode)": 1.5,
+        "Positive electrode charge transfer coefficient": 0.5,
+        "Positive electrode double-layer capacity [F.m-2]": 0.2,
+        "Positive electrode exchange-current density [A.m-2]"
+        "": graphite_LGM50_electrolyte_exchange_current_density_Chen2020,
+        "Positive electrode density [kg.m-3]": 1657.0,
+        "Positive electrode specific heat capacity [J.kg-1.K-1]": 700.0,
+        "Positive electrode thermal conductivity [W.m-1.K-1]": 1.7,
+        "Positive electrode OCP entropic change [V.K-1]": 0.0,
+        "Positive electrode Poisson's ratio": 0.3,
+        "Positive electrode Young's modulus [Pa]": 15000000000.0,
+        "Positive electrode reference concentration for free of deformation [mol.m-3]"
+        "": 0.0,
+        "Positive electrode partial molar volume [m3.mol-1]": 3.1e-06,
+        "Positive electrode volume change": graphite_volume_change_Ai2020,
+        "Positive electrode initial crack length [m]": 2e-08,
+        "Positive electrode initial crack width [m]": 1.5e-08,
+        "Positive electrode number of cracks per unit area [m-2]": 3180000000000000.0,
+        "Positive electrode Paris' law constant b": 1.12,
+        "Positive electrode Paris' law constant m": 2.2,
+        "Positive electrode cracking rate": graphite_cracking_rate_Ai2020,
+        "Positive electrode LAM constant proportional term [s-1]": 2.7778e-07,
+        "Positive electrode LAM constant exponential term": 2.0,
+        "Positive electrode critical stress [Pa]": 60000000.0,
+        # separator
+        "Separator porosity": 0.47,
+        "Separator Bruggeman coefficient (electrolyte)": 1.5,
+        "Separator density [kg.m-3]": 397.0,
+        "Separator specific heat capacity [J.kg-1.K-1]": 700.0,
+        "Separator thermal conductivity [W.m-1.K-1]": 0.16,
+        # electrolyte
+        "Initial concentration in electrolyte [mol.m-3]": 1000.0,
+        "Cation transference number": 0.2594,
+        "Thermodynamic factor": 1.0,
+        "Electrolyte diffusivity [m2.s-1]"
+        "": electrolyte_diffusivity_Nyman2008_arrhenius,
+        "Electrolyte conductivity [S.m-1]"
+        "": electrolyte_conductivity_Nyman2008_arrhenius,
+        # experiment
+        "Reference temperature [K]": 298.15,
+        "Total heat transfer coefficient [W.m-2.K-1]": 10.0,
+        "Ambient temperature [K]": 298.15,
+        "Number of electrodes connected in parallel to make a cell": 1.0,
+        "Number of cells connected in series to make a battery": 1.0,
+        "Lower voltage cut-off [V]": 0.005,
+        "Upper voltage cut-off [V]": 1.5,
+        "Open-circuit voltage at 0% SOC [V]": 0.005,
+        "Open-circuit voltage at 100% SOC [V]": 1.5,
+        "Initial concentration in positive electrode [mol.m-3]": 29866.0,
+        "Initial temperature [K]": 298.15,
+        # citations
+        "citations": ["OKane2022", "OKane2020", "Chen2020", "Xu2019"],
+    }
diff --git a/pybamm/input/parameters/lithium_ion/Xu2019.py b/pybamm/input/parameters/lithium_ion/Xu2019.py
index d6d3383b35..d96afc3f04 100644
--- a/pybamm/input/parameters/lithium_ion/Xu2019.py
+++ b/pybamm/input/parameters/lithium_ion/Xu2019.py
@@ -249,8 +249,8 @@ def get_parameter_values():
         "Negative electrode OCP [V]": 0.0,
         "Negative electrode conductivity [S.m-1]": 10776000.0,
         "Negative electrode OCP entropic change [V.K-1]": 0.0,
-        "Typical plated lithium concentration [mol.m-3]": 76900.0,
-        "Exchange-current density for plating [A.m-2]"
+        "Lithium metal partial molar volume [m3.mol-1]": 1.3e-05,
+        "Exchange-current density for lithium metal electrode [A.m-2]"
         "": li_metal_electrolyte_exchange_current_density_Xu2019,
         "Negative electrode charge transfer coefficient": 0.5,
         "Negative electrode double-layer capacity [F.m-2]": 0.2,
diff --git a/pybamm/meshes/scikit_fem_submeshes.py b/pybamm/meshes/scikit_fem_submeshes.py
index f25dce80b1..23c024dbbb 100644
--- a/pybamm/meshes/scikit_fem_submeshes.py
+++ b/pybamm/meshes/scikit_fem_submeshes.py
@@ -3,10 +3,10 @@
 #
 import pybamm
 from .meshes import SubMesh
-
-import skfem
 import numpy as np
 
+from pybamm.util import have_optional_dependency
+
 
 class ScikitSubMesh2D(SubMesh):
     """
@@ -27,6 +27,7 @@ class ScikitSubMesh2D(SubMesh):
     """
 
     def __init__(self, edges, coord_sys, tabs):
+        skfem = have_optional_dependency("skfem")
         self.edges = edges
         self.nodes = dict.fromkeys(["y", "z"])
         for var in self.nodes.keys():
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 41192dbe1f..08890757b7 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -9,7 +9,7 @@
 import numpy as np
 
 import pybamm
-from pybamm.expression_tree.operations.latexify import Latexify
+from pybamm.util import have_optional_dependency
 
 
 class BaseModel:
@@ -1055,14 +1055,43 @@ def generate(
         C.generate()
 
     def latexify(self, filename=None, newline=True, output_variables=None):
-        # For docstring, see pybamm.expression_tree.operations.latexify.Latexify
+        """
+        Converts all model equations in latex.
+
+        Parameters
+        ----------
+        filename: str (optional)
+            Accepted file formats - any image format, pdf and tex
+            Default is None, When None returns all model equations in latex
+            If not None, returns all model equations in given file format.
+
+        newline: bool (optional)
+            Default is True, If True, returns every equation in a new line.
+            If False, returns the list of all the equations.
+
+        Load model
+        >>> model = pybamm.lithium_ion.SPM()
+
+        This will returns all model equations in png
+        >>> model.latexify("equations.png")
+
+        This will return all the model equations in latex
+        >>> model.latexify()
+
+        This will return the list of all the model equations
+        >>> model.latexify(newline=False)
+
+        This will return first five model equations
+        >>> model.latexify(newline=False)[1:5]
+        """
+        sympy = have_optional_dependency("sympy")
+        if sympy:
+            from pybamm.expression_tree.operations.latexify import Latexify
+
         return Latexify(self, filename, newline).latexify(
             output_variables=output_variables
         )
 
-    # Set :meth:`latexify` docstring from :class:`Latexify`
-    latexify.__doc__ = Latexify.__doc__
-
     def process_parameters_and_discretise(self, symbol, parameter_values, disc):
         """
         Process parameters and discretise a symbol using supplied parameter values
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index ad36786381..ee3e0b5c6f 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -204,10 +204,10 @@ class BatteryModelOptions(pybamm.FuzzyDict):
                 solve an algebraic equation for it. Default is "false", unless "SEI film
                 resistance" is distributed in which case it is automatically set to
                 "true".
-            * "working electrode": str
-                Which electrode(s) intercalates and which is counter. If "both"
-                (default), the model is a standard battery. Otherwise can be "negative"
-                or "positive" to indicate a half-cell model.
+            * "working electrode" : str
+                Can be "both" (default) for a standard battery or "positive" for a
+                half-cell where the negative electrode is replaced with a lithium metal
+                counter electrode.
             * "x-average side reactions": str
                 Whether to average the side reactions (SEI growth, lithium plating and
                 the respective porosity change) over the x-axis in Single Particle
@@ -304,7 +304,7 @@ def __init__(self, extra_options):
             "surface form": ["false", "differential", "algebraic"],
             "thermal": ["isothermal", "lumped", "x-lumped", "x-full"],
             "total interfacial current density as a state": ["false", "true"],
-            "working electrode": ["both", "negative", "positive"],
+            "working electrode": ["both", "positive"],
             "x-average side reactions": ["false", "true"],
         }
 
@@ -313,6 +313,22 @@ def __init__(self, extra_options):
         }
         extra_options = extra_options or {}
 
+        working_electrode_option = extra_options.get("working electrode", "both")
+        SEI_option = extra_options.get("SEI", "none")  # return "none" if not given
+        SEI_cr_option = extra_options.get("SEI on cracks", "false")
+        plating_option = extra_options.get("lithium plating", "none")
+        # For the full cell model, if "SEI", "SEI on cracks" and "lithium plating"
+        # options are not provided as tuples, change them to tuples with "none" or
+        # "false" on the positive electrode. To use these options on the positive
+        # electrode of a full cell, the tuple must be provided by the user
+        if working_electrode_option == "both":
+            if not (isinstance(SEI_option, tuple)) and SEI_option != "none":
+                extra_options["SEI"] = (SEI_option, "none")
+            if not (isinstance(SEI_cr_option, tuple)) and SEI_cr_option != "false":
+                extra_options["SEI on cracks"] = (SEI_cr_option, "false")
+            if not (isinstance(plating_option, tuple)) and plating_option != "none":
+                extra_options["lithium plating"] = (plating_option, "none")
+
         # Change the default for cell geometry based on the current collector
         # dimensionality
         # return "none" if option not given
@@ -341,12 +357,14 @@ def __init__(self, extra_options):
         # The "SEI film resistance" option will still be overridden by extra_options if
         # provided
 
-        # Change the default for particle mechanics based on which SEI on cracks and LAM
-        # options are provided
-        # return "false" and "none" respectively if options not given
+        # Change the default for particle mechanics based on which half-cell,
+        # SEI on cracks and LAM options are provided
+        # return "false", "false" and "none" respectively if options not given
         SEI_cracks_option = extra_options.get("SEI on cracks", "false")
         LAM_opt = extra_options.get("loss of active material", "none")
         if SEI_cracks_option == "true":
+            default_options["particle mechanics"] = "swelling and cracking"
+        elif SEI_cracks_option == ("true", "false"):
             if "stress-driven" in LAM_opt or "stress and reaction-driven" in LAM_opt:
                 default_options["particle mechanics"] = (
                     "swelling and cracking",
@@ -391,11 +409,14 @@ def __init__(self, extra_options):
             default_options["surface form"] = "algebraic"
         # The "surface form" option will still be overridden by
         # extra_options if provided
+
         # Change default SEI model based on which lithium plating option is provided
         # return "none" if option not given
         plating_option = extra_options.get("lithium plating", "none")
         if plating_option == "partially reversible":
             default_options["SEI"] = "constant"
+        elif plating_option == ("partially reversible", "none"):
+            default_options["SEI"] = ("constant", "none")
         else:
             default_options["SEI"] = "none"
         # The "SEI" option will still be overridden by extra_options if provided
@@ -528,6 +549,13 @@ def __init__(self, extra_options):
                 "SEI porosity change must now be given in string format "
                 "('true' or 'false')"
             )
+        if options["working electrode"] == "negative":
+            raise pybamm.OptionError(
+                "The 'negative' working electrode option has been removed because "
+                "the voltage - and therefore the energy stored - would be negative."
+                "Use the 'positive' working electrode option instead and set whatever "
+                "would normally be the negative electrode as the positive electrode."
+            )
 
         # Some standard checks to make sure options are compatible
         if options["dimensionality"] == 0:
@@ -574,12 +602,8 @@ def __init__(self, extra_options):
                     f"X-lumped thermal submodels do not yet support {n}D "
                     "current collectors in a half-cell configuration"
                 )
-            elif options["SEI on cracks"] == "true":
-                raise NotImplementedError(
-                    "SEI on cracks not yet implemented for half-cell models"
-                )
 
-        if options["particle phases"] != "1":
+        if options["particle phases"] not in ["1", ("1", "1")]:
             if not (
                 options["surface form"] != "false"
                 and options["particle size"] == "single"
@@ -598,66 +622,66 @@ def __init__(self, extra_options):
 
         # Check options are valid
         for option, value in options.items():
-            if option in ["working electrode"]:
-                pass
+            if isinstance(value, str) or option in [
+                "dimensionality",
+                "operating mode",
+            ]:  # some options accept non-strings
+                value = (value,)
             else:
-                if isinstance(value, str) or option in [
-                    "dimensionality",
-                    "operating mode",
-                ]:  # some options accept non-strings
-                    value = (value,)
+                if not (
+                    (
+                        option
+                        in [
+                            "diffusivity",
+                            "exchange-current density",
+                            "intercalation kinetics",
+                            "interface utilisation",
+                            "lithium plating",
+                            "loss of active material",
+                            "number of MSMR reactions",
+                            "open-circuit potential",
+                            "particle",
+                            "particle mechanics",
+                            "particle phases",
+                            "particle size",
+                            "SEI",
+                            "SEI on cracks",
+                            "stress-induced diffusion",
+                        ]
+                        and isinstance(value, tuple)
+                        and len(value) == 2
+                    )
+                ):
+                    # more possible options that can take 2-tuples to be added
+                    # as they come
+                    raise pybamm.OptionError(
+                        f"\n'{value}' is not recognized in option '{option}'. "
+                        "Values must be strings or (in some cases) "
+                        "2-tuples of strings"
+                    )
+            # flatten value
+            value_list = []
+            for val in value:
+                if isinstance(val, tuple):
+                    value_list.extend(list(val))
                 else:
-                    if not (
-                        (
-                            option
-                            in [
-                                "diffusivity",
-                                "exchange-current density",
-                                "intercalation kinetics",
-                                "interface utilisation",
-                                "loss of active material",
-                                "number of MSMR reactions",
-                                "open-circuit potential",
-                                "particle",
-                                "particle mechanics",
-                                "particle phases",
-                                "particle size",
-                                "stress-induced diffusion",
-                            ]
-                            and isinstance(value, tuple)
-                            and len(value) == 2
-                        )
+                    value_list.append(val)
+            for val in value_list:
+                if val not in self.possible_options[option]:
+                    if option == "operating mode" and callable(val):
+                        # "operating mode" can be a function
+                        pass
+                    elif (
+                        option == "number of MSMR reactions"
+                        and represents_positive_integer(val)
                     ):
-                        # more possible options that can take 2-tuples to be added
-                        # as they come
+                        # "number of MSMR reactions" can be a positive integer
+                        pass
+                    else:
                         raise pybamm.OptionError(
-                            f"\n'{value}' is not recognized in option '{option}'. "
-                            "Values must be strings or (in some cases) "
-                            "2-tuples of strings"
+                            f"\n'{val}' is not recognized in option '{option}'. "
+                            f"Possible values are {self.possible_options[option]}"
                         )
-                # flatten value
-                value_list = []
-                for val in value:
-                    if isinstance(val, tuple):
-                        value_list.extend(list(val))
-                    else:
-                        value_list.append(val)
-                for val in value_list:
-                    if val not in self.possible_options[option]:
-                        if option == "operating mode" and callable(val):
-                            # "operating mode" can be a function
-                            pass
-                        elif (
-                            option == "number of MSMR reactions"
-                            and represents_positive_integer(val)
-                        ):
-                            # "number of MSMR reactions" can be a positive integer
-                            pass
-                        else:
-                            raise pybamm.OptionError(
-                                f"\n'{val}' is not recognized in option '{option}'. "
-                                f"Possible values are {self.possible_options[option]}"
-                            )
 
         # Issue a warning to let users know that the 'lumped' thermal option (or
         # equivalently 'x-lumped' with 0D current collectors) now uses the total heat
@@ -697,10 +721,10 @@ def phases(self):
     def whole_cell_domains(self):
         if self["working electrode"] == "positive":
             return ["separator", "positive electrode"]
-        elif self["working electrode"] == "negative":
-            return ["negative electrode", "separator"]
         elif self["working electrode"] == "both":
             return ["negative electrode", "separator", "positive electrode"]
+        else:
+            raise NotImplementedError  # future proofing
 
     @property
     def electrode_types(self):
@@ -1242,11 +1266,17 @@ def set_voltage_variables(self):
 
         # SEI film overpotential
         if self.options.electrode_types["negative"] == "planar":
-            eta_sei_av = self.variables["SEI film overpotential [V]"]
+            eta_sei_n_av = self.variables[
+                "Negative electrode SEI film overpotential [V]"
+            ]
         else:
-            eta_sei_av = self.variables[
-                f"X-averaged {phase_n}SEI film overpotential [V]"
+            eta_sei_n_av = self.variables[
+                f"X-averaged negative electrode {phase_n}SEI film overpotential [V]"
             ]
+        eta_sei_p_av = self.variables[
+            f"X-averaged positive electrode {phase_p}SEI film overpotential [V]"
+        ]
+        eta_sei_av = eta_sei_n_av + eta_sei_p_av
 
         # TODO: add current collector losses to the voltage in 3D
 
diff --git a/pybamm/models/full_battery_models/lead_acid/base_lead_acid_model.py b/pybamm/models/full_battery_models/lead_acid/base_lead_acid_model.py
index ecc173f97e..c0b5d1935c 100644
--- a/pybamm/models/full_battery_models/lead_acid/base_lead_acid_model.py
+++ b/pybamm/models/full_battery_models/lead_acid/base_lead_acid_model.py
@@ -97,10 +97,16 @@ def set_active_material_submodel(self):
             )
 
     def set_sei_submodel(self):
-        self.submodels["sei"] = pybamm.sei.NoSEI(self.param, self.options)
+        for domain in ["negative", "positive"]:
+            self.submodels[f"{domain} sei"] = pybamm.sei.NoSEI(
+                self.param, domain, self.options
+            )
 
     def set_lithium_plating_submodel(self):
-        self.submodels["lithium plating"] = pybamm.lithium_plating.NoPlating(self.param)
+        for domain in ["negative", "positive"]:
+            self.submodels[
+                f"{domain} lithium plating"
+            ] = pybamm.lithium_plating.NoPlating(self.param, domain)
 
     def set_total_interface_submodel(self):
         self.submodels["total interface"] = pybamm.interface.TotalInterfacialCurrent(
diff --git a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
index f55df43972..01c17b22f7 100644
--- a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
+++ b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py
@@ -5,10 +5,10 @@
 class Yang2017(DFN):
     def __init__(self, options=None, name="Yang2017", build=True):
         options = {
-            "SEI": "ec reaction limited",
+            "SEI": ("ec reaction limited", "none"),
             "SEI film resistance": "distributed",
             "SEI porosity change": "true",
-            "lithium plating": "irreversible",
+            "lithium plating": ("irreversible", "none"),
             "lithium plating porosity change": "true",
         }
         super().__init__(options=options, name=name)
diff --git a/pybamm/models/full_battery_models/lithium_ion/__init__.py b/pybamm/models/full_battery_models/lithium_ion/__init__.py
index 95a5059f5a..4afb23f493 100644
--- a/pybamm/models/full_battery_models/lithium_ion/__init__.py
+++ b/pybamm/models/full_battery_models/lithium_ion/__init__.py
@@ -9,7 +9,10 @@
     get_initial_ocps,
     get_min_max_ocps,
 )
-from .electrode_soh_half_cell import ElectrodeSOHHalfCell
+from .electrode_soh_half_cell import (
+    ElectrodeSOHHalfCell,
+    get_initial_stoichiometry_half_cell
+)
 from .spm import SPM
 from .spme import SPMe
 from .dfn import DFN
diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py
index 41e4670cf7..cc736d6d04 100644
--- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py
+++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py
@@ -49,8 +49,8 @@ def set_submodels(self, build):
         self.set_electrolyte_potential_submodel()
         self.set_thermal_submodel()
         self.set_current_collector_submodel()
-
         self.set_sei_submodel()
+        self.set_sei_on_cracks_submodel()
         self.set_lithium_plating_submodel()
         self.set_li_metal_counter_electrode_submodels()
         self.set_total_interface_submodel()
@@ -159,13 +159,17 @@ def set_degradation_variables(self):
 
         # Lithium lost to side reactions
         # Different way of measuring LLI but should give same value
-        n_Li_lost_sei = self.variables["Loss of lithium to SEI [mol]"]
-        n_Li_lost_reactions = n_Li_lost_sei
-        if "negative electrode" in domains:
+        n_Li_lost_neg_sei = self.variables["Loss of lithium to negative SEI [mol]"]
+        n_Li_lost_pos_sei = self.variables["Loss of lithium to positive SEI [mol]"]
+        n_Li_lost_reactions = n_Li_lost_neg_sei + n_Li_lost_pos_sei
+        for domain in domains:
+            dom = domain.split()[0].lower()
             n_Li_lost_sei_cracks = self.variables[
-                "Loss of lithium to SEI on cracks [mol]"
+                f"Loss of lithium to {dom} SEI on cracks [mol]"
+            ]
+            n_Li_lost_pl = self.variables[
+                f"Loss of lithium to {dom} lithium plating [mol]"
             ]
-            n_Li_lost_pl = self.variables["Loss of lithium to lithium plating [mol]"]
             n_Li_lost_reactions += n_Li_lost_sei_cracks + n_Li_lost_pl
 
         self.variables.update(
@@ -197,8 +201,10 @@ def set_summary_variables(self):
             "Total lithium lost [mol]",
             "Total lithium lost from particles [mol]",
             "Total lithium lost from electrolyte [mol]",
-            "Loss of lithium to SEI [mol]",
-            "Loss of capacity to SEI [A.h]",
+            "Loss of lithium to negative SEI [mol]",
+            "Loss of capacity to negative SEI [A.h]",
+            "Loss of lithium to positive SEI [mol]",
+            "Loss of capacity to positive SEI [A.h]",
             "Total lithium lost to side reactions [mol]",
             "Total capacity lost to side reactions [A.h]",
             # Resistance
@@ -210,16 +216,20 @@ def set_summary_variables(self):
                 "Negative electrode capacity [A.h]",
                 "Loss of active material in negative electrode [%]",
                 "Total lithium in negative electrode [mol]",
-                "Loss of lithium to lithium plating [mol]",
-                "Loss of capacity to lithium plating [A.h]",
-                "Loss of lithium to SEI on cracks [mol]",
-                "Loss of capacity to SEI on cracks [A.h]",
+                "Loss of lithium to negative lithium plating [mol]",
+                "Loss of capacity to negative lithium plating [A.h]",
+                "Loss of lithium to negative SEI on cracks [mol]",
+                "Loss of capacity to negative SEI on cracks [A.h]",
             ]
         if self.options.electrode_types["positive"] == "porous":
             summary_variables += [
                 "Positive electrode capacity [A.h]",
                 "Loss of active material in positive electrode [%]",
                 "Total lithium in positive electrode [mol]",
+                "Loss of lithium to positive lithium plating [mol]",
+                "Loss of capacity to positive lithium plating [A.h]",
+                "Loss of lithium to positive SEI on cracks [mol]",
+                "Loss of capacity to positive SEI on cracks [A.h]",
             ]
 
         self.summary_variables = summary_variables
@@ -245,56 +255,95 @@ def set_open_circuit_potential_submodel(self):
                 )
 
     def set_sei_submodel(self):
-        if self.options.electrode_types["negative"] == "planar":
-            reaction_loc = "interface"
-        elif self.options["x-average side reactions"] == "true":
-            reaction_loc = "x-average"
-        else:
-            reaction_loc = "full electrode"
-
-        phases = self.options.phases["negative"]
-        for phase in phases:
-            if self.options["SEI"] == "none":
-                submodel = pybamm.sei.NoSEI(self.param, self.options, phase)
-            elif self.options["SEI"] == "constant":
-                submodel = pybamm.sei.ConstantSEI(self.param, self.options, phase)
+        for domain in ["negative", "positive"]:
+            if self.options.electrode_types[domain] == "planar":
+                reaction_loc = "interface"
+            elif self.options["x-average side reactions"] == "true":
+                reaction_loc = "x-average"
             else:
-                submodel = pybamm.sei.SEIGrowth(
-                    self.param, reaction_loc, self.options, phase, cracks=False
-                )
-            self.submodels[f"{phase} sei"] = submodel
-            # Do not set "sei on cracks" submodel for half-cells
-            # For full cells, "sei on cracks" submodel must be set, even if it is zero
-            if reaction_loc != "interface":
-                if (
-                    self.options["SEI"] in ["none", "constant"]
-                    or self.options["SEI on cracks"] == "false"
-                ):
-                    submodel = pybamm.sei.NoSEI(
-                        self.param, self.options, phase, cracks=True
+                reaction_loc = "full electrode"
+            sei_option = getattr(self.options, domain)["SEI"]
+            phases = self.options.phases[domain]
+            for phase in phases:
+                if sei_option == "none":
+                    submodel = pybamm.sei.NoSEI(self.param, domain, self.options, phase)
+                elif sei_option == "constant":
+                    submodel = pybamm.sei.ConstantSEI(
+                        self.param, domain, self.options, phase
                     )
                 else:
                     submodel = pybamm.sei.SEIGrowth(
-                        self.param, reaction_loc, self.options, phase, cracks=True
+                        self.param,
+                        domain,
+                        reaction_loc,
+                        self.options,
+                        phase,
+                        cracks=False,
                     )
-                self.submodels[f"{phase} sei on cracks"] = submodel
+                self.submodels[f"{domain} {phase} sei"] = submodel
+            if len(phases) > 1:
+                self.submodels[f"{domain} total sei"] = pybamm.sei.TotalSEI(
+                    self.param, domain, self.options
+                )
 
-        if len(phases) > 1:
-            self.submodels["total sei"] = pybamm.sei.TotalSEI(self.param, self.options)
-            self.submodels["total sei on cracks"] = pybamm.sei.TotalSEI(
-                self.param, self.options, cracks=True
-            )
+    def set_sei_on_cracks_submodel(self):
+        # Do not set "sei on cracks" submodel for a planar electrode. For porous
+        # electrodes, "sei on cracks" submodel must be set, even if it is zero
+        for domain in self.options.whole_cell_domains:
+            if domain != "separator":
+                domain = domain.split()[0].lower()
+                sei_option = getattr(self.options, domain)["SEI"]
+                sei_on_cracks_option = getattr(self.options, domain)["SEI on cracks"]
+                phases = self.options.phases[domain]
+                for phase in phases:
+                    if (
+                        sei_option in ["none", "constant"]
+                        or sei_on_cracks_option == "false"
+                    ):
+                        submodel = pybamm.sei.NoSEI(
+                            self.param, domain, self.options, phase, cracks=True
+                        )
+                    else:
+                        if self.options["x-average side reactions"] == "true":
+                            reaction_loc = "x-average"
+                        else:
+                            reaction_loc = "full electrode"
+                        submodel = pybamm.sei.SEIGrowth(
+                            self.param,
+                            domain,
+                            reaction_loc,
+                            self.options,
+                            phase,
+                            cracks=True,
+                        )
+                    self.submodels[f"{domain} {phase} sei on cracks"] = submodel
+                if len(phases) > 1:
+                    self.submodels[
+                        f"{domain} total sei on cracks"
+                    ] = pybamm.sei.TotalSEI(
+                        self.param, domain, self.options, cracks=True
+                    )
 
     def set_lithium_plating_submodel(self):
-        if self.options["lithium plating"] == "none":
-            self.submodels["lithium plating"] = pybamm.lithium_plating.NoPlating(
-                self.param, self.options
-            )
-        else:
-            x_average = self.options["x-average side reactions"] == "true"
-            self.submodels["lithium plating"] = pybamm.lithium_plating.Plating(
-                self.param, x_average, self.options
-            )
+        # Do not set "lithium plating" submodel for a planar electrode. For porous
+        # electrodes, "lithium plating" submodel must be set, even if it is zero
+        for domain in self.options.whole_cell_domains:
+            if domain != "separator":
+                domain = domain.split()[0].lower()
+                lithium_plating_opt = getattr(self.options, domain)["lithium plating"]
+                if lithium_plating_opt == "none":
+                    self.submodels[
+                        f"{domain} lithium plating"
+                    ] = pybamm.lithium_plating.NoPlating(
+                        self.param, domain, self.options
+                    )
+                else:
+                    x_average = self.options["x-average side reactions"] == "true"
+                    self.submodels[
+                        f"{domain} lithium plating"
+                    ] = pybamm.lithium_plating.Plating(
+                        self.param, domain, x_average, self.options
+                    )
 
     def set_total_interface_submodel(self):
         self.submodels["total interface"] = pybamm.interface.TotalInterfacialCurrent(
diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
index 6586a4518c..f8379fffec 100644
--- a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
+++ b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
@@ -16,9 +16,7 @@ class BasicDFNHalfCell(BaseModel):
     the full functionality.
 
     The electrode labeled "positive electrode" is the working electrode, and the
-    electrode labeled "negative electrode" is the counter electrode. If the "negative
-    electrode" is the working electrode, then the parameters for the "negative
-    electrode" are used to define the "positive electrode".
+    electrode labeled "negative electrode" is the counter electrode.
     This facilitates compatibility with the full-cell models.
 
     Parameters
@@ -33,11 +31,6 @@ class BasicDFNHalfCell(BaseModel):
 
     def __init__(self, options=None, name="Doyle-Fuller-Newman half cell model"):
         super().__init__(options, name)
-        if self.options["working electrode"] not in ["negative", "positive"]:
-            raise ValueError(
-                "The option 'working electrode' should be either 'positive'"
-                " or 'negative'"
-            )
         pybamm.citations.register("Marquis2019")
         # `param` is a class containing all the relevant parameters and functions for
         # this model. These are purely symbolic at this stage, and will be set by the
@@ -230,7 +223,7 @@ def __init__(self, options=None, name="Doyle-Fuller-Newman half cell model"):
         # reference potential
         L_Li = param.p.L
         sigma_Li = param.p.sigma
-        j_Li = param.j0_plating(pybamm.boundary_value(c_e, "left"), c_w_max, T)
+        j_Li = param.j0_Li_metal(pybamm.boundary_value(c_e, "left"), c_w_max, T)
         eta_Li = 2 * RT_F * pybamm.arcsinh(i_cell / (2 * j_Li))
 
         phi_s_cn = 0
diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py
index c6a445f316..d975de859c 100644
--- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py
+++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py
@@ -410,10 +410,7 @@ def solve(self, inputs):
         # Calculate theoretical energy
         # TODO: energy calc for MSMR
         if self.options["open-circuit potential"] != "MSMR":
-            energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral(
-                self.parameter_values,
-                sol_dict,
-            )
+            energy = self.theoretical_energy_integral(sol_dict)
             sol_dict.update({"Maximum theoretical energy [W.h]": energy})
         return sol_dict
 
@@ -829,6 +826,27 @@ def get_min_max_ocps(self):
         sol = self.solve(inputs)
         return [sol["Un(x_0)"], sol["Un(x_100)"], sol["Up(y_100)"], sol["Up(y_0)"]]
 
+    def theoretical_energy_integral(self, inputs, points=1000):
+        x_0 = inputs["x_0"]
+        y_0 = inputs["y_0"]
+        x_100 = inputs["x_100"]
+        y_100 = inputs["y_100"]
+        Q_p = inputs["Q_p"]
+        x_vals = np.linspace(x_100, x_0, num=points)
+        y_vals = np.linspace(y_100, y_0, num=points)
+        # Calculate OCV at each stoichiometry
+        param = self.param
+        T = param.T_amb_av(0)
+        Vs = self.parameter_values.evaluate(
+            param.p.prim.U(y_vals, T) - param.n.prim.U(x_vals, T)
+        ).flatten()
+        # Calculate dQ
+        Q = Q_p * (y_0 - y_100)
+        dQ = Q / (points - 1)
+        # Integrate and convert to W-h
+        E = np.trapz(Vs, dx=dQ)
+        return E
+
 
 def get_initial_stoichiometries(
     initial_value,
@@ -972,7 +990,7 @@ def get_min_max_ocps(
     return esoh_solver.get_min_max_ocps()
 
 
-def theoretical_energy_integral(parameter_values, inputs, points=100):
+def theoretical_energy_integral(parameter_values, param, inputs, points=100):
     """
     Calculate maximum energy possible from a cell given OCV, initial soc, and final soc
     given voltage limits, open-circuit potentials, etc defined by parameter_values
@@ -991,30 +1009,8 @@ def theoretical_energy_integral(parameter_values, inputs, points=100):
     E
         The total energy of the cell in Wh
     """
-    x_0 = inputs["x_0"]
-    y_0 = inputs["y_0"]
-    x_100 = inputs["x_100"]
-    y_100 = inputs["y_100"]
-    Q_p = inputs["Q_p"]
-    x_vals = np.linspace(x_100, x_0, num=points)
-    y_vals = np.linspace(y_100, y_0, num=points)
-    # Calculate OCV at each stoichiometry
-    param = pybamm.LithiumIonParameters()
-    y = pybamm.standard_spatial_vars.y
-    z = pybamm.standard_spatial_vars.z
-    T = pybamm.yz_average(param.T_amb(y, z, 0))
-    Vs = np.empty(x_vals.shape)
-    for i in range(x_vals.size):
-        Vs[i] = (
-            parameter_values.evaluate(param.p.prim.U(y_vals[i], T)).item()
-            - parameter_values.evaluate(param.n.prim.U(x_vals[i], T)).item()
-        )
-    # Calculate dQ
-    Q = Q_p * (y_0 - y_100)
-    dQ = Q / (points - 1)
-    # Integrate and convert to W-h
-    E = np.trapz(Vs, dx=dQ)
-    return E
+    esoh_solver = ElectrodeSOHSolver(parameter_values, param)
+    return esoh_solver.theoretical_energy_integral(inputs, points=points)
 
 
 def calculate_theoretical_energy(
@@ -1045,6 +1041,7 @@ def calculate_theoretical_energy(
     Q_p = parameter_values.evaluate(pybamm.LithiumIonParameters().p.prim.Q_init)
     E = theoretical_energy_integral(
         parameter_values,
+        pybamm.LithiumIonParameters(),
         {"x_100": x_100, "x_0": x_0, "y_100": y_100, "y_0": y_0, "Q_p": Q_p},
         points=points,
     )
diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py
index 39aad1c896..8c22cf2ada 100644
--- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py
+++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py
@@ -21,21 +21,17 @@ class ElectrodeSOHHalfCell(pybamm.BaseModel):
 
     """
 
-    def __init__(self, working_electrode, name="Electrode-specific SOH model"):
-        self.working_electrode = working_electrode
+    def __init__(self, name="ElectrodeSOH model"):
         pybamm.citations.register("Mohtat2019")
         super().__init__(name)
-        param = pybamm.LithiumIonParameters({"working electrode": working_electrode})
+        param = pybamm.LithiumIonParameters({"working electrode": "positive"})
 
         x_100 = pybamm.Variable("x_100", bounds=(0, 1))
         x_0 = pybamm.Variable("x_0", bounds=(0, 1))
         Q_w = pybamm.InputParameter("Q_w")
         T_ref = param.T_ref
-        if working_electrode == "negative":  # pragma: no cover
-            raise NotImplementedError
-        elif working_electrode == "positive":
-            U_w = param.p.prim.U
-            Q = Q_w * (x_100 - x_0)
+        U_w = param.p.prim.U
+        Q = Q_w * (x_100 - x_0)
 
         V_max = param.ocp_soc_100_dimensional
         V_min = param.ocp_soc_0_dimensional
@@ -60,3 +56,94 @@ def __init__(self, working_electrode, name="Electrode-specific SOH model"):
     def default_solver(self):
         # Use AlgebraicSolver as CasadiAlgebraicSolver gives unnecessary warnings
         return pybamm.AlgebraicSolver()
+
+
+def get_initial_stoichiometry_half_cell(
+    initial_value,
+    parameter_values,
+    param=None,
+    known_value="cyclable lithium capacity",
+    options=None,
+):
+    """
+    Calculate initial stoichiometry to start off the simulation at a particular
+    state of charge, given voltage limits, open-circuit potential, etc defined by
+    parameter_values
+
+    Parameters
+    ----------
+    initial_value : float
+        Target initial value.
+        If integer, interpreted as SOC, must be between 0 and 1.
+        If string e.g. "4 V", interpreted as voltage,
+        must be between V_min and V_max.
+    parameter_values : pybamm.ParameterValues
+        The parameter values to use in the calculation
+
+    Returns
+    -------
+    x
+        The initial stoichiometry that give the desired initial state of charge
+    """
+    param = pybamm.LithiumIonParameters(options)
+    x_0, x_100 = get_min_max_stoichiometries(parameter_values)
+
+    if isinstance(initial_value, str) and initial_value.endswith("V"):
+        V_init = float(initial_value[:-1])
+        V_min = parameter_values.evaluate(param.voltage_low_cut)
+        V_max = parameter_values.evaluate(param.voltage_high_cut)
+
+        if not V_min < V_init < V_max:
+            raise ValueError(
+                f"Initial voltage {V_init}V is outside the voltage limits "
+                f"({V_min}, {V_max})"
+            )
+
+        # Solve simple model for initial soc based on target voltage
+        soc_model = pybamm.BaseModel()
+        soc = pybamm.Variable("soc")
+        Up = param.p.prim.U
+        T_ref = parameter_values["Reference temperature [K]"]
+        x = x_0 + soc * (x_100 - x_0)
+
+        soc_model.algebraic[soc] = Up(x, T_ref) - V_init
+        # initial guess for soc linearly interpolates between 0 and 1
+        # based on V linearly interpolating between V_max and V_min
+        soc_model.initial_conditions[soc] = (V_init - V_min) / (V_max - V_min)
+        soc_model.variables["soc"] = soc
+        parameter_values.process_model(soc_model)
+        initial_soc = pybamm.AlgebraicSolver().solve(soc_model, [0])["soc"].data[0]
+    elif isinstance(initial_value, (int, float)):
+        initial_soc = initial_value
+        if not 0 <= initial_soc <= 1:
+            raise ValueError("Initial SOC should be between 0 and 1")
+
+    else:
+        raise ValueError(
+            "Initial value must be a float between 0 and 1, "
+            "or a string ending in 'V'"
+        )
+
+    x = x_0 + initial_soc * (x_100 - x_0)
+
+    return x
+
+
+def get_min_max_stoichiometries(
+    parameter_values, options={"working electrode": "positive"}
+):
+    """
+    Get the minimum and maximum stoichiometries from the parameter values
+
+    Parameters
+    ----------
+    parameter_values : pybamm.ParameterValues
+        The parameter values to use in the calculation
+    """
+    esoh_model = pybamm.lithium_ion.ElectrodeSOHHalfCell("ElectrodeSOH")
+    param = pybamm.LithiumIonParameters(options)
+    esoh_sim = pybamm.Simulation(esoh_model, parameter_values=parameter_values)
+    Q_w = parameter_values.evaluate(param.p.Q_init)
+    esoh_sol = esoh_sim.solve([0], inputs={"Q_w": Q_w})
+    x_0, x_100 = esoh_sol["x_0"].data[0], esoh_sol["x_100"].data[0]
+    return x_0, x_100
diff --git a/pybamm/models/full_battery_models/lithium_ion/mpm.py b/pybamm/models/full_battery_models/lithium_ion/mpm.py
index 4ad0d58eb8..fa2062a95c 100644
--- a/pybamm/models/full_battery_models/lithium_ion/mpm.py
+++ b/pybamm/models/full_battery_models/lithium_ion/mpm.py
@@ -47,6 +47,6 @@ def __init__(self, options=None, name="Many-Particle Model", build=True):
     def default_parameter_values(self):
         default_params = super().default_parameter_values
         default_params = pybamm.get_size_distribution_parameters(
-            default_params, electrode=self.options["working electrode"]
+            default_params, working_electrode=self.options["working electrode"]
         )
         return default_params
diff --git a/pybamm/models/full_battery_models/lithium_ion/spm.py b/pybamm/models/full_battery_models/lithium_ion/spm.py
index e54a7ec646..bdebf12aef 100644
--- a/pybamm/models/full_battery_models/lithium_ion/spm.py
+++ b/pybamm/models/full_battery_models/lithium_ion/spm.py
@@ -48,7 +48,7 @@ def __init__(self, options=None, name="Single Particle Model", build=True):
             pybamm.citations.register("Marquis2019")
 
         if (
-            self.options["SEI"] not in ["none", "constant"]
+            self.options["SEI"] not in ["none", "constant", ("constant", "none")]
             or self.options["lithium plating"] != "none"
         ):
             pybamm.citations.register("BrosaPlanella2022")
diff --git a/pybamm/models/submodels/active_material/loss_active_material.py b/pybamm/models/submodels/active_material/loss_active_material.py
index 7dfa4f9049..7816122e07 100644
--- a/pybamm/models/submodels/active_material/loss_active_material.py
+++ b/pybamm/models/submodels/active_material/loss_active_material.py
@@ -96,21 +96,16 @@ def get_coupled_variables(self, variables):
 
         if "reaction" in lam_option:
             beta_LAM_sei = self.domain_param.beta_LAM_sei
-            if self.domain == "negative":
-                if self.x_average is True:
-                    a_j_sei = variables[
-                        "X-averaged negative electrode SEI "
-                        "volumetric interfacial current density [A.m-3]"
-                    ]
-                else:
-                    a_j_sei = variables[
-                        "Negative electrode SEI volumetric "
-                        "interfacial current density [A.m-3]"
-                    ]
+            if self.x_average is True:
+                a_j_sei = variables[
+                    f"X-averaged {domain} electrode SEI "
+                    "volumetric interfacial current density [A.m-3]"
+                ]
             else:
-                # No SEI in the positive electrode so no reaction-driven LAM
-                # until other reactions are implemented
-                a_j_sei = 0
+                a_j_sei = variables[
+                    f"{Domain} electrode SEI volumetric "
+                    "interfacial current density [A.m-3]"
+                ]
 
             j_stress_reaction = beta_LAM_sei * a_j_sei / self.param.F
             deps_solid_dt += j_stress_reaction
diff --git a/pybamm/models/submodels/electrode/ohm/li_metal.py b/pybamm/models/submodels/electrode/ohm/li_metal.py
index 49de25231a..6f73d40620 100644
--- a/pybamm/models/submodels/electrode/ohm/li_metal.py
+++ b/pybamm/models/submodels/electrode/ohm/li_metal.py
@@ -76,9 +76,12 @@ def set_initial_conditions(self, variables):
         self.initial_conditions = {delta_phi: delta_phi_init}
 
     def set_rhs(self, variables):
+        Domain = self.domain.capitalize()
         if self.options["surface form"] == "differential":
             j_pl = variables["Lithium metal plating current density [A.m-2]"]
-            j_sei = variables["SEI interfacial current density [A.m-2]"]
+            j_sei = variables[
+                f"{Domain} electrode SEI interfacial current density [A.m-2]"
+            ]
             sum_j = j_pl + j_sei
 
             i_cc = variables["Current collector current density [A.m-2]"]
@@ -95,9 +98,12 @@ def set_rhs(self, variables):
             self.rhs[delta_phi] = 1 / C_dl * (i_cc - sum_j)
 
     def set_algebraic(self, variables):
+        Domain = self.domain.capitalize()
         if self.options["surface form"] != "differential":  # also catches "false"
             j_pl = variables["Lithium metal plating current density [A.m-2]"]
-            j_sei = variables["SEI interfacial current density [A.m-2]"]
+            j_sei = variables[
+                f"{Domain} electrode SEI interfacial current density [A.m-2]"
+            ]
             sum_j = j_pl + j_sei
 
             i_cc = variables["Current collector current density [A.m-2]"]
diff --git a/pybamm/models/submodels/interface/base_interface.py b/pybamm/models/submodels/interface/base_interface.py
index 080c8f54a0..b7e160ee2f 100644
--- a/pybamm/models/submodels/interface/base_interface.py
+++ b/pybamm/models/submodels/interface/base_interface.py
@@ -110,9 +110,10 @@ def _get_exchange_current_density(self, variables):
                     c_e = c_e.orphans[0]
                     T = T.orphans[0]
             # Get main reaction exchange-current density (may have empirical hysteresis)
-            if domain_options["exchange-current density"] == "single":
+            j0_option = getattr(domain_options, self.phase)["exchange-current density"]
+            if j0_option == "single":
                 j0 = phase_param.j0(c_e, c_s_surf, T)
-            elif domain_options["exchange-current density"] == "current sigmoid":
+            elif j0_option == "current sigmoid":
                 current = variables["Total current density [A.m-2]"]
                 k = 100
                 if Domain == "Positive":
@@ -130,8 +131,8 @@ def _get_exchange_current_density(self, variables):
         elif self.reaction == "lithium metal plating":
             # compute T on the surface of the anode (interface with separator)
             T = pybamm.boundary_value(T, "right")
-            c_Li_typ = param.c_Li_typ
-            j0 = param.j0_plating(c_e, c_Li_typ, T)
+            c_Li_metal = 1 / param.V_bar_Li
+            j0 = param.j0_Li_metal(c_e, c_Li_metal, T)
 
         elif self.reaction == "lead-acid main":
             # If variable was broadcast, take only the orphan
@@ -305,9 +306,9 @@ def _get_standard_volumetric_current_density_variables(self, variables):
         a_j_av = pybamm.x_average(a_j)
 
         if reaction_name == "SEI on cracks ":
-            roughness = variables["Negative electrode roughness ratio"] - 1
+            roughness = variables[f"{Domain} electrode roughness ratio"] - 1
             roughness_av = (
-                variables["X-averaged negative electrode roughness ratio"] - 1
+                variables[f"X-averaged {domain} electrode roughness ratio"] - 1
             )
         else:
             roughness = 1
@@ -351,14 +352,14 @@ def _get_standard_overpotential_variables(self, eta_r):
         return variables
 
     def _get_standard_sei_film_overpotential_variables(self, eta_sei):
-        domain = self.domain
+        domain, Domain = self.domain_Domain
         phase_name = self.phase_name
         Phase_name = phase_name.capitalize()
 
-        if self.options.electrode_types["negative"] == "planar":
+        if self.options.electrode_types[domain] == "planar":
             # half-cell domain
             variables = {
-                f"{Phase_name}SEI film overpotential [V]": eta_sei,
+                f"{Domain} electrode {Phase_name}SEI film overpotential [V]": eta_sei,
             }
             return variables
 
@@ -372,8 +373,9 @@ def _get_standard_sei_film_overpotential_variables(self, eta_sei):
             eta_sei = pybamm.PrimaryBroadcast(eta_sei, f"{domain} electrode")
 
         variables = {
-            f"{Phase_name}SEI film overpotential [V]": eta_sei,
-            f"X-averaged {phase_name}SEI film overpotential [V]": eta_sei_av,
+            f"{Domain} electrode {phase_name}SEI film overpotential [V]": eta_sei,
+            f"X-averaged {domain} electrode {phase_name}SEI"
+            " film overpotential [V]": eta_sei_av,
         }
 
         return variables
diff --git a/pybamm/models/submodels/interface/kinetics/base_kinetics.py b/pybamm/models/submodels/interface/kinetics/base_kinetics.py
index c6cdc94ec3..dd5ee76340 100644
--- a/pybamm/models/submodels/interface/kinetics/base_kinetics.py
+++ b/pybamm/models/submodels/interface/kinetics/base_kinetics.py
@@ -124,33 +124,34 @@ def get_coupled_variables(self, variables):
         j_tot_av, a_j_tot_av = self._get_average_total_interfacial_current_density(
             variables
         )
-        # Add SEI resistance in the negative electrode
-        if self.domain == "negative":
-            if self.options.electrode_types["negative"] == "planar":
-                R_sei = self.phase_param.R_sei
-                L_sei = variables[
-                    f"Total {phase_name}SEI thickness [m]"
-                ]  # on interface
-                eta_sei = -j_tot_av * L_sei * R_sei
-            elif self.options["SEI film resistance"] == "average":
-                R_sei = self.phase_param.R_sei
-                L_sei_av = variables[f"X-averaged total {phase_name}SEI thickness [m]"]
-                eta_sei = -j_tot_av * L_sei_av * R_sei
-            elif self.options["SEI film resistance"] == "distributed":
-                R_sei = self.phase_param.R_sei
-                L_sei = variables[f"Total {phase_name}SEI thickness [m]"]
-                j_tot = variables[
-                    f"Total negative electrode {phase_name}"
-                    "interfacial current density variable [A.m-2]"
-                ]
-
-                # Override print_name
-                j_tot.print_name = "j_tot"
-
-                eta_sei = -j_tot * L_sei * R_sei
-            else:
-                eta_sei = pybamm.Scalar(0)
-            eta_r += eta_sei
+        # Add SEI resistance
+        if self.options.electrode_types[domain] == "planar":
+            R_sei = self.phase_param.R_sei
+            L_sei = variables[
+                f"{Domain} total {phase_name}SEI thickness [m]"
+            ]  # on interface
+            eta_sei = -j_tot_av * L_sei * R_sei
+        elif self.options["SEI film resistance"] == "average":
+            R_sei = self.phase_param.R_sei
+            L_sei_av = variables[
+                f"X-averaged {domain} total {phase_name}SEI thickness [m]"
+            ]
+            eta_sei = -j_tot_av * L_sei_av * R_sei
+        elif self.options["SEI film resistance"] == "distributed":
+            R_sei = self.phase_param.R_sei
+            L_sei = variables[f"{Domain} total {phase_name}SEI thickness [m]"]
+            j_tot = variables[
+                f"Total {domain} electrode {phase_name}"
+                "interfacial current density variable [A.m-2]"
+            ]
+
+            # Override print_name
+            j_tot.print_name = "j_tot"
+
+            eta_sei = -j_tot * L_sei * R_sei
+        else:
+            eta_sei = pybamm.Scalar(0)
+        eta_r += eta_sei
 
         # Broadcast j0 to match eta_r's domain, if necessary
         if j0.secondary_domain == ["current collector"] and eta_r.secondary_domain == [
@@ -222,7 +223,7 @@ def get_coupled_variables(self, variables):
             self._get_standard_volumetric_current_density_variables(variables)
         )
 
-        if self.domain == "negative" and self.reaction in [
+        if self.reaction in [
             "lithium-ion main",
             "lithium metal plating",
             "lead-acid main",
diff --git a/pybamm/models/submodels/interface/kinetics/inverse_kinetics/inverse_butler_volmer.py b/pybamm/models/submodels/interface/kinetics/inverse_kinetics/inverse_butler_volmer.py
index c89cd8da69..88e1793263 100644
--- a/pybamm/models/submodels/interface/kinetics/inverse_kinetics/inverse_butler_volmer.py
+++ b/pybamm/models/submodels/interface/kinetics/inverse_kinetics/inverse_butler_volmer.py
@@ -68,22 +68,17 @@ def get_coupled_variables(self, variables):
         eta_r = self._get_overpotential(j_tot, j0, ne, T, u)
 
         # With SEI resistance (distributed and averaged have the same effect here)
-        if self.domain == "negative":
-            if self.options["SEI film resistance"] != "none":
-                R_sei = self.phase_param.R_sei
-                if self.options.electrode_types["negative"] == "planar":
-                    L_sei = variables["Total SEI thickness [m]"]
-                else:
-                    L_sei = variables["X-averaged total SEI thickness [m]"]
-                eta_sei = -j_tot * L_sei * R_sei
-            # Without SEI resistance
+        if self.options["SEI film resistance"] != "none":
+            R_sei = self.phase_param.R_sei
+            if self.options.electrode_types[domain] == "planar":
+                L_sei = variables[f"{Domain} total SEI thickness [m]"]
             else:
-                eta_sei = pybamm.Scalar(0)
-            variables.update(
-                self._get_standard_sei_film_overpotential_variables(eta_sei)
-            )
+                L_sei = variables[f"X-averaged {domain} total SEI thickness [m]"]
+            eta_sei = -j_tot * L_sei * R_sei
+        # Without SEI resistance
         else:
             eta_sei = pybamm.Scalar(0)
+        variables.update(self._get_standard_sei_film_overpotential_variables(eta_sei))
 
         delta_phi = eta_r + ocp - eta_sei  # = phi_s - phi_e
 
@@ -136,19 +131,16 @@ def __init__(self, param, domain, reaction, options=None):
         super().__init__(param, domain, reaction, options=options)
 
     def get_coupled_variables(self, variables):
-        domain = self.domain
+        domain, Domain = self.domain_Domain
 
         j_tot = variables[
             f"X-averaged {domain} electrode total interfacial current density [A.m-2]"
         ]
-        if self.domain == "negative":
-            j_sei = variables["SEI interfacial current density [A.m-2]"]
-            j_stripping = variables[
-                "Lithium plating interfacial current density [A.m-2]"
-            ]
-            j = j_tot - j_sei - j_stripping
-        else:
-            j = j_tot
+        j_sei = variables[f"{Domain} electrode SEI interfacial current density [A.m-2]"]
+        j_stripping = variables[
+            f"{Domain} electrode lithium plating interfacial current density [A.m-2]"
+        ]
+        j = j_tot - j_sei - j_stripping
 
         variables.update(self._get_standard_interfacial_current_variables(j))
         variables.update(
diff --git a/pybamm/models/submodels/interface/lithium_plating/base_plating.py b/pybamm/models/submodels/interface/lithium_plating/base_plating.py
index 61119b3a0f..5b7a7a5b7f 100644
--- a/pybamm/models/submodels/interface/lithium_plating/base_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/base_plating.py
@@ -17,56 +17,45 @@ class BasePlating(BaseInterface):
         A dictionary of options to be passed to the model.
     """
 
-    def __init__(self, param, options=None):
+    def __init__(self, param, domain, options=None):
         reaction = "lithium plating"
-        domain = "negative"
         super().__init__(param, domain, reaction, options=options)
 
     def get_coupled_variables(self, variables):
         # Update some common variables
+        domain, Domain = self.domain_Domain
 
-        if self.options.electrode_types["negative"] == "porous":
-            j_plating = variables["Lithium plating interfacial current density [A.m-2]"]
+        if self.options.electrode_types[domain] == "porous":
+            j_plating = variables[
+                f"{Domain} lithium plating interfacial current density [A.m-2]"
+            ]
             j_plating_av = variables[
-                "X-averaged lithium plating interfacial current density [A.m-2]"
+                f"X-averaged {domain} lithium plating "
+                "interfacial current density [A.m-2]"
             ]
-            if self.options.negative["particle phases"] == "1":
-                a = variables["Negative electrode surface area to volume ratio [m-1]"]
+            particle_phases_option = getattr(self.options, domain)["particle phases"]
+            if particle_phases_option == "1":
+                a = variables[f"{Domain} electrode surface area to volume ratio [m-1]"]
             else:
                 a = variables[
-                    "Negative electrode primary surface area to volume ratio [m-1]"
+                    f"{Domain} electrode primary surface area to volume ratio [m-1]"
                 ]
             a_j_plating = a * j_plating
             a_j_plating_av = pybamm.x_average(a_j_plating)
 
             variables.update(
                 {
-                    "Negative electrode lithium plating interfacial current "
+                    f"{Domain} electrode lithium plating interfacial current "
                     "density [A.m-2]": j_plating,
-                    "X-averaged negative electrode lithium plating "
+                    f"X-averaged {domain} electrode lithium plating "
                     "interfacial current density [A.m-2]": j_plating_av,
-                    "Lithium plating volumetric "
+                    f"{Domain} lithium plating volumetric "
                     "interfacial current density [A.m-3]": a_j_plating,
-                    "X-averaged lithium plating volumetric "
+                    f"X-averaged {domain} lithium plating volumetric "
                     "interfacial current density [A.m-3]": a_j_plating_av,
                 }
             )
 
-        zero_av = pybamm.PrimaryBroadcast(0, "current collector")
-        zero = pybamm.FullBroadcast(0, "positive electrode", "current collector")
-        variables.update(
-            {
-                "X-averaged positive electrode lithium plating "
-                "interfacial current density [A.m-2]": zero_av,
-                "X-averaged positive electrode lithium plating volumetric "
-                "interfacial current density [A.m-3]": zero_av,
-                "Positive electrode lithium plating "
-                "interfacial current density [A.m-2]": zero,
-                "Positive electrode lithium plating volumetric "
-                "interfacial current density [A.m-3]": zero,
-            }
-        )
-
         variables.update(
             self._get_standard_volumetric_current_density_variables(variables)
         )
@@ -87,38 +76,45 @@ def _get_standard_concentration_variables(self, c_plated_Li, c_dead_Li):
             The variables which can be derived from the plated lithium thickness.
         """
         param = self.param
+        domain, Domain = self.domain_Domain
 
         # Set scales to one for the "no plating" model so that they are not required
         # by parameter values in general
         if isinstance(self, pybamm.lithium_plating.NoPlating):
             c_to_L = 1
-        else:
-            c_to_L = param.V_bar_plated_Li / param.n.prim.a_typ
+            L_k = 1
+        elif domain == "negative":
+            c_to_L = param.V_bar_Li / param.n.prim.a_typ
+            L_k = param.n.L
+        elif domain == "positive":
+            c_to_L = param.V_bar_Li / param.p.prim.a_typ
+            L_k = param.p.L
 
         c_plated_Li_av = pybamm.x_average(c_plated_Li)
         L_plated_Li = c_plated_Li * c_to_L  # plated Li thickness
         L_plated_Li_av = pybamm.x_average(L_plated_Li)
-        Q_plated_Li = c_plated_Li_av * param.n.L * param.L_y * param.L_z
+        Q_plated_Li = c_plated_Li_av * L_k * param.L_y * param.L_z
 
         c_dead_Li_av = pybamm.x_average(c_dead_Li)
         # dead Li "thickness", required by porosity submodel
         L_dead_Li = c_dead_Li * c_to_L
         L_dead_Li_av = pybamm.x_average(L_dead_Li)
-        Q_dead_Li = c_dead_Li_av * param.n.L * param.L_y * param.L_z
+        Q_dead_Li = c_dead_Li_av * L_k * param.L_y * param.L_z
 
         variables = {
-            "Lithium plating concentration [mol.m-3]": c_plated_Li,
-            "X-averaged lithium plating concentration [mol.m-3]": c_plated_Li_av,
-            "Dead lithium concentration [mol.m-3]": c_dead_Li,
-            "X-averaged dead lithium concentration [mol.m-3]": c_dead_Li_av,
-            "Lithium plating thickness [m]": L_plated_Li,
-            "X-averaged lithium plating thickness [m]": L_plated_Li_av,
-            "Dead lithium thickness [m]": L_dead_Li,
-            "X-averaged dead lithium thickness [m]": L_dead_Li_av,
-            "Loss of lithium to lithium plating [mol]": (Q_plated_Li + Q_dead_Li),
-            "Loss of capacity to lithium plating [A.h]": (Q_plated_Li + Q_dead_Li)
-            * param.F
-            / 3600,
+            f"{Domain} lithium plating concentration [mol.m-3]": c_plated_Li,
+            f"X-averaged {domain} lithium plating "
+            "concentration [mol.m-3]": c_plated_Li_av,
+            f"{Domain} dead lithium concentration [mol.m-3]": c_dead_Li,
+            f"X-averaged {domain} dead lithium concentration [mol.m-3]": c_dead_Li_av,
+            f"{Domain} lithium plating thickness [m]": L_plated_Li,
+            f"X-averaged {domain} lithium plating thickness [m]": L_plated_Li_av,
+            f"{Domain} dead lithium thickness [m]": L_dead_Li,
+            f"X-averaged {domain} dead lithium thickness [m]": L_dead_Li_av,
+            f"Loss of lithium to {domain} lithium plating "
+            "[mol]": (Q_plated_Li + Q_dead_Li),
+            f"Loss of capacity to {domain} lithium plating "
+            "[A.h]": (Q_plated_Li + Q_dead_Li) * param.F / 3600,
         }
 
         return variables
@@ -136,13 +132,13 @@ def _get_standard_reaction_variables(self, j_stripping):
         variables : dict
             The variables which can be derived from the plated lithium thickness.
         """
-        # Set scales to one for the "no plating" model so that they are not required
-        # by parameter values in general
+        domain, Domain = self.domain_Domain
         j_stripping_av = pybamm.x_average(j_stripping)
 
         variables = {
-            "Lithium plating interfacial current density [A.m-2]": j_stripping,
-            "X-averaged lithium plating "
+            f"{Domain} lithium plating interfacial current density "
+            "[A.m-2]": j_stripping,
+            f"X-averaged {domain} lithium plating "
             "interfacial current density [A.m-2]": j_stripping_av,
         }
 
diff --git a/pybamm/models/submodels/interface/lithium_plating/no_plating.py b/pybamm/models/submodels/interface/lithium_plating/no_plating.py
index 1a2f59808a..94697fdd89 100644
--- a/pybamm/models/submodels/interface/lithium_plating/no_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/no_plating.py
@@ -16,12 +16,12 @@ class NoPlating(BasePlating):
         A dictionary of options to be passed to the model.
     """
 
-    def __init__(self, param, options=None):
-        super().__init__(param, options=options)
+    def __init__(self, param, domain, options=None):
+        super().__init__(param, domain, options=options)
 
     def get_fundamental_variables(self):
         zero = pybamm.FullBroadcast(
-            pybamm.Scalar(0), "negative electrode", "current collector"
+            pybamm.Scalar(0), f"{self.domain} electrode", "current collector"
         )
         variables = self._get_standard_concentration_variables(zero, zero)
         variables.update(self._get_standard_overpotential_variables(zero))
diff --git a/pybamm/models/submodels/interface/lithium_plating/plating.py b/pybamm/models/submodels/interface/lithium_plating/plating.py
index d4d3980153..9f4de08d2f 100644
--- a/pybamm/models/submodels/interface/lithium_plating/plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/plating.py
@@ -19,35 +19,36 @@ class Plating(BasePlating):
         A dictionary of options to be passed to the model.
     """
 
-    def __init__(self, param, x_average, options):
-        super().__init__(param, options)
+    def __init__(self, param, domain, x_average, options):
+        super().__init__(param, domain, options=options)
         self.x_average = x_average
         pybamm.citations.register("OKane2020")
         pybamm.citations.register("OKane2022")
 
     def get_fundamental_variables(self):
+        domain, Domain = self.domain_Domain
         if self.x_average is True:
             c_plated_Li_av = pybamm.Variable(
-                "X-averaged lithium plating concentration [mol.m-3]",
+                f"X-averaged {domain} lithium plating concentration [mol.m-3]",
                 domain="current collector",
                 scale=self.param.c_Li_typ,
             )
-            c_plated_Li = pybamm.PrimaryBroadcast(c_plated_Li_av, "negative electrode")
+            c_plated_Li = pybamm.PrimaryBroadcast(c_plated_Li_av, f"{domain} electrode")
             c_dead_Li_av = pybamm.Variable(
-                "X-averaged dead lithium concentration [mol.m-3]",
+                f"X-averaged {domain} dead lithium concentration [mol.m-3]",
                 domain="current collector",
             )
-            c_dead_Li = pybamm.PrimaryBroadcast(c_dead_Li_av, "negative electrode")
+            c_dead_Li = pybamm.PrimaryBroadcast(c_dead_Li_av, f"{domain} electrode")
         else:
             c_plated_Li = pybamm.Variable(
-                "Lithium plating concentration [mol.m-3]",
-                domain="negative electrode",
+                f"{Domain} lithium plating concentration [mol.m-3]",
+                domain=f"{domain} electrode",
                 auxiliary_domains={"secondary": "current collector"},
                 scale=self.param.c_Li_typ,
             )
             c_dead_Li = pybamm.Variable(
-                "Dead lithium concentration [mol.m-3]",
-                domain="negative electrode",
+                f"{Domain} dead lithium concentration [mol.m-3]",
+                domain=f"{domain} electrode",
                 auxiliary_domains={"secondary": "current collector"},
             )
 
@@ -57,11 +58,12 @@ def get_fundamental_variables(self):
 
     def get_coupled_variables(self, variables):
         param = self.param
-        delta_phi = variables["Negative electrode surface potential difference [V]"]
-        c_e_n = variables["Negative electrolyte concentration [mol.m-3]"]
-        T = variables["Negative electrode temperature [K]"]
-        eta_sei = variables["SEI film overpotential [V]"]
-        c_plated_Li = variables["Lithium plating concentration [mol.m-3]"]
+        domain, Domain = self.domain_Domain
+        delta_phi = variables[f"{Domain} electrode surface potential difference [V]"]
+        c_e_n = variables[f"{Domain} electrolyte concentration [mol.m-3]"]
+        T = variables[f"{Domain} electrode temperature [K]"]
+        eta_sei = variables[f"{Domain} electrode SEI film overpotential [V]"]
+        c_plated_Li = variables[f"{Domain} lithium plating concentration [mol.m-3]"]
         j0_stripping = param.j0_stripping(c_e_n, c_plated_Li, T)
         j0_plating = param.j0_plating(c_e_n, c_plated_Li, T)
 
@@ -72,11 +74,12 @@ def get_coupled_variables(self, variables):
         alpha_stripping = self.param.alpha_stripping
         alpha_plating = self.param.alpha_plating
 
-        if self.options["lithium plating"] in ["reversible", "partially reversible"]:
+        lithium_plating_option = getattr(self.options, domain)["lithium plating"]
+        if lithium_plating_option in ["reversible", "partially reversible"]:
             j_stripping = j0_stripping * pybamm.exp(
                 F_RT * alpha_stripping * eta_stripping
             ) - j0_plating * pybamm.exp(F_RT * alpha_plating * eta_plating)
-        elif self.options["lithium plating"] == "irreversible":
+        elif lithium_plating_option == "irreversible":
             # j_stripping is always negative, because there is no stripping, only
             # plating
             j_stripping = -j0_plating * pybamm.exp(F_RT * alpha_plating * eta_plating)
@@ -90,46 +93,62 @@ def get_coupled_variables(self, variables):
         return variables
 
     def set_rhs(self, variables):
+        domain, Domain = self.domain_Domain
         if self.x_average is True:
             c_plated_Li = variables[
-                "X-averaged lithium plating concentration [mol.m-3]"
+                f"X-averaged {domain} lithium plating concentration [mol.m-3]"
+            ]
+            c_dead_Li = variables[
+                f"X-averaged {domain} dead lithium concentration [mol.m-3]"
             ]
-            c_dead_Li = variables["X-averaged dead lithium concentration [mol.m-3]"]
             a_j_stripping = variables[
-                "X-averaged lithium plating volumetric "
+                f"X-averaged {domain} lithium plating volumetric "
                 "interfacial current density [A.m-3]"
             ]
-            L_sei = variables["X-averaged total SEI thickness [m]"]
+            L_sei = variables[f"X-averaged {domain} total SEI thickness [m]"]
         else:
-            c_plated_Li = variables["Lithium plating concentration [mol.m-3]"]
-            c_dead_Li = variables["Dead lithium concentration [mol.m-3]"]
+            c_plated_Li = variables[f"{Domain} lithium plating concentration [mol.m-3]"]
+            c_dead_Li = variables[f"{Domain} dead lithium concentration [mol.m-3]"]
             a_j_stripping = variables[
-                "Lithium plating volumetric interfacial current density [A.m-3]"
+                f"{Domain} lithium plating volumetric "
+                "interfacial current density [A.m-3]"
             ]
-            L_sei = variables["Total SEI thickness [m]"]
-
-        # In the partially reversible plating model, coupling term turns reversible
-        # lithium into dead lithium. In other plating models, it is zero.
-        if self.options["lithium plating"] == "partially reversible":
+            L_sei = variables[f"{Domain} total SEI thickness [m]"]
+
+        lithium_plating_option = getattr(self.options, domain)["lithium plating"]
+        if lithium_plating_option == "reversible":
+            # In the reversible plating model, there is no dead lithium
+            dc_plated_Li = -a_j_stripping / self.param.F
+            dc_dead_Li = pybamm.Scalar(0)
+        elif lithium_plating_option == "irreversible":
+            # In the irreversible plating model, all plated lithium is dead lithium
+            dc_plated_Li = pybamm.Scalar(0)
+            dc_dead_Li = -a_j_stripping / self.param.F
+        elif lithium_plating_option == "partially reversible":
+            # In the partially reversible plating model, the coupling term turns
+            # reversible lithium into dead lithium over time.
             dead_lithium_decay_rate = self.param.dead_lithium_decay_rate(L_sei)
             coupling_term = dead_lithium_decay_rate * c_plated_Li
-        else:
-            coupling_term = pybamm.Scalar(0)
+            dc_plated_Li = -a_j_stripping / self.param.F - coupling_term
+            dc_dead_Li = coupling_term
 
         self.rhs = {
-            c_plated_Li: -a_j_stripping / self.param.F - coupling_term,
-            c_dead_Li: coupling_term,
+            c_plated_Li: dc_plated_Li,
+            c_dead_Li: dc_dead_Li,
         }
 
     def set_initial_conditions(self, variables):
+        domain, Domain = self.domain_Domain
         if self.x_average is True:
             c_plated_Li = variables[
-                "X-averaged lithium plating concentration [mol.m-3]"
+                f"X-averaged {domain} lithium plating concentration [mol.m-3]"
+            ]
+            c_dead_Li = variables[
+                f"X-averaged {domain} dead lithium concentration [mol.m-3]"
             ]
-            c_dead_Li = variables["X-averaged dead lithium concentration [mol.m-3]"]
         else:
-            c_plated_Li = variables["Lithium plating concentration [mol.m-3]"]
-            c_dead_Li = variables["Dead lithium concentration [mol.m-3]"]
+            c_plated_Li = variables[f"{Domain} lithium plating concentration [mol.m-3]"]
+            c_dead_Li = variables[f"{Domain} dead lithium concentration [mol.m-3]"]
         c_plated_Li_0 = self.param.c_plated_Li_0
         zero = pybamm.Scalar(0)
 
diff --git a/pybamm/models/submodels/interface/sei/base_sei.py b/pybamm/models/submodels/interface/sei/base_sei.py
index 479bc44e70..b0e8db56c6 100644
--- a/pybamm/models/submodels/interface/sei/base_sei.py
+++ b/pybamm/models/submodels/interface/sei/base_sei.py
@@ -20,46 +20,35 @@ class BaseModel(BaseInterface):
         Whether this is a submodel for standard SEI or SEI on cracks
     """
 
-    def __init__(self, param, options, phase="primary", cracks=False):
+    def __init__(self, param, domain, options, phase="primary", cracks=False):
         if cracks is True:
             reaction = "SEI on cracks"
         else:
             reaction = "SEI"
-        domain = "negative"
         super().__init__(param, domain, reaction, options=options, phase=phase)
 
     def get_coupled_variables(self, variables):
         # Update some common variables
+        domain, Domain = self.domain_Domain
 
         if self.reaction_loc != "interface":
             j_sei_av = variables[
-                f"X-averaged {self.reaction_name}interfacial current density [A.m-2]"
+                f"X-averaged {domain} electrode {self.reaction_name}interfacial"
+                " current density [A.m-2]"
             ]
             j_sei = variables[
-                f"{self.reaction_name}interfacial current density [A.m-2]"
+                f"{Domain} electrode {self.reaction_name}interfacial current"
+                " density [A.m-2]"
             ]
             variables.update(
                 {
-                    f"X-averaged negative electrode {self.reaction_name}interfacial "
+                    f"X-averaged {domain} electrode {self.reaction_name}interfacial "
                     "current density [A.m-2]": j_sei_av,
-                    f"Negative electrode {self.reaction_name}interfacial current "
+                    f"{Domain} electrode {self.reaction_name}interfacial current "
                     "density [A.m-2]": j_sei,
                 }
             )
 
-        zero_av = pybamm.PrimaryBroadcast(0, "current collector")
-        zero = pybamm.FullBroadcast(0, "positive electrode", "current collector")
-        variables.update(
-            {
-                f"Positive electrode {self.reaction} "
-                "interfacial current density [A.m-2]": zero,
-                f"X-averaged positive electrode {self.reaction} "
-                "volumetric interfacial current density [A.m-2]": zero_av,
-                f"Positive electrode {self.reaction} "
-                "volumetric interfacial current density [A.m-3]": zero,
-            }
-        )
-
         variables.update(
             self._get_standard_volumetric_current_density_variables(variables)
         )
@@ -83,9 +72,10 @@ def _get_standard_thickness_variables(self, L_inner, L_outer):
         variables : dict
             The variables which can be derived from the SEI thicknesses.
         """
+        domain, Domain = self.domain_Domain
         variables = {
-            f"Inner {self.reaction_name}thickness [m]": L_inner,
-            f"Outer {self.reaction_name}thickness [m]": L_outer,
+            f"{Domain} inner {self.reaction_name}thickness [m]": L_inner,
+            f"{Domain} outer {self.reaction_name}thickness [m]": L_outer,
         }
 
         if self.reaction_loc != "interface":
@@ -93,8 +83,10 @@ def _get_standard_thickness_variables(self, L_inner, L_outer):
             L_outer_av = pybamm.x_average(L_outer)
             variables.update(
                 {
-                    f"X-averaged inner {self.reaction_name}thickness [m]": L_inner_av,
-                    f"X-averaged outer {self.reaction_name}thickness [m]": L_outer_av,
+                    f"X-averaged {domain} inner {self.reaction_name}"
+                    "thickness [m]": L_inner_av,
+                    f"X-averaged {domain} outer {self.reaction_name}"
+                    "thickness [m]": L_outer_av,
                 }
             )
         # Get variables related to the total thickness
@@ -105,7 +97,7 @@ def _get_standard_thickness_variables(self, L_inner, L_outer):
 
     def _get_standard_total_thickness_variables(self, L_sei):
         """Update variables related to total SEI thickness."""
-        domain = self.domain
+        domain, Domain = self.domain_Domain
 
         if isinstance(self, pybamm.sei.NoSEI):
             R_sei = 1
@@ -113,15 +105,16 @@ def _get_standard_total_thickness_variables(self, L_sei):
             R_sei = self.phase_param.R_sei
 
         variables = {
-            f"{self.reaction_name}[m]": L_sei,
-            f"Total {self.reaction_name}thickness [m]": L_sei,
+            f"{Domain} {self.reaction_name}[m]": L_sei,
+            f"{Domain} total {self.reaction_name}thickness [m]": L_sei,
         }
         if self.reaction_loc != "interface":
             L_sei_av = pybamm.x_average(L_sei)
             variables.update(
                 {
-                    f"X-averaged {self.reaction_name}thickness [m]": L_sei_av,
-                    f"X-averaged total {self.reaction_name}thickness [m]": L_sei_av,
+                    f"X-averaged {domain} {self.reaction_name}thickness [m]": L_sei_av,
+                    f"X-averaged {domain} total {self.reaction_name}"
+                    "thickness [m]": L_sei_av,
                 }
             )
             if self.reaction == "SEI":
@@ -135,7 +128,7 @@ def _get_standard_total_thickness_variables(self, L_sei):
 
     def _get_standard_concentration_variables(self, variables):
         """Update variables related to the SEI concentration."""
-        Domain = self.domain.capitalize()
+        domain, Domain = self.domain_Domain
         phase_param = self.phase_param
         reaction_name = self.reaction_name
 
@@ -157,7 +150,7 @@ def _get_standard_concentration_variables(self, variables):
             else:
                 # m * (mol/m4) = mol/m3 (n is a bulk quantity)
                 a = variables[
-                    f"Negative electrode {self.phase_name}"
+                    f"{Domain} electrode {self.phase_name}"
                     "surface area to volume ratio [m-1]"
                 ]
                 L_to_n_inner = a / phase_param.V_bar_inner
@@ -175,8 +168,8 @@ def _get_standard_concentration_variables(self, variables):
             )
 
         if self.reaction == "SEI":
-            L_inner = variables[f"Inner {reaction_name}thickness [m]"]
-            L_outer = variables[f"Outer {reaction_name}thickness [m]"]
+            L_inner = variables[f"{Domain} inner {reaction_name}thickness [m]"]
+            L_outer = variables[f"{Domain} outer {reaction_name}thickness [m]"]
 
             n_inner = L_inner * L_to_n_inner  # inner SEI concentration
             n_outer = L_outer * L_to_n_outer  # outer SEI concentration
@@ -193,35 +186,38 @@ def _get_standard_concentration_variables(self, variables):
 
             # Q_sei in mol
             if self.reaction_loc == "interface":
-                L_n = 1
-            else:
-                L_n = self.param.n.L
+                L_k = 1
+            elif domain == "negative":
+                L_k = self.param.n.L
+            elif domain == "positive":
+                L_k = self.param.p.L
 
-            # Multiply delta_n_SEI by V_n to get total moles of SEI formed
+            # Multiply delta_n_SEI by V_k to get total moles of SEI formed
             # multiply by z_sei to get total lithium moles consumed by SEI
-            V_n = L_n * self.param.L_y * self.param.L_z
-            Q_sei = z_sei * delta_n_SEI * V_n
+            V_k = L_k * self.param.L_y * self.param.L_z
+            Q_sei = z_sei * delta_n_SEI * V_k
 
             variables.update(
                 {
-                    f"Inner {reaction_name}concentration [mol.m-3]": n_inner,
-                    f"X-averaged inner {reaction_name}"
+                    f"{Domain} inner {reaction_name}concentration [mol.m-3]": n_inner,
+                    f"X-averaged {domain} inner {reaction_name}"
                     "concentration [mol.m-3]": n_inner_av,
-                    f"Outer {reaction_name}concentration [mol.m-3]": n_outer,
-                    f"X-averaged outer {reaction_name}"
+                    f"{Domain} outer {reaction_name}concentration [mol.m-3]": n_outer,
+                    f"X-averaged {domain} outer {reaction_name}"
                     "concentration [mol.m-3]": n_outer_av,
-                    f"{reaction_name}concentration [mol.m-3]": n_SEI,
-                    f"X-averaged {reaction_name}concentration [mol.m-3]": n_SEI_xav,
-                    f"Loss of lithium to {reaction_name}[mol]": Q_sei,
-                    f"Loss of capacity to {reaction_name}[A.h]": Q_sei
+                    f"{Domain} {reaction_name}concentration [mol.m-3]": n_SEI,
+                    f"X-averaged {domain} {reaction_name}"
+                    "concentration [mol.m-3]": n_SEI_xav,
+                    f"Loss of lithium to {domain} {reaction_name}[mol]": Q_sei,
+                    f"Loss of capacity to {domain} {reaction_name}[A.h]": Q_sei
                     * self.param.F
                     / 3600,
                 }
             )
         # Concentration variables are handled slightly differently for SEI on cracks
         elif self.reaction == "SEI on cracks":
-            L_inner_cr = variables[f"Inner {reaction_name}thickness [m]"]
-            L_outer_cr = variables[f"Outer {reaction_name}thickness [m]"]
+            L_inner_cr = variables[f"{Domain} inner {reaction_name}thickness [m]"]
+            L_outer_cr = variables[f"{Domain} outer {reaction_name}thickness [m]"]
             roughness = variables[f"{Domain} electrode roughness ratio"]
 
             n_inner_cr = L_inner_cr * L_to_n_inner * (roughness - 1)
@@ -242,28 +238,29 @@ def _get_standard_concentration_variables(self, variables):
             n_SEI_cr_init = n_crack_0 * (roughness_av - 1)
             delta_n_SEI_cr = n_SEI_cr_av - n_SEI_cr_init
 
+            if domain == "negative":
+                L_k = self.param.n.L
+            elif domain == "positive":
+                L_k = self.param.p.L
+
             # Q_sei_cr in mol
-            Q_sei_cr = (
-                z_sei
-                * delta_n_SEI_cr
-                * self.param.n.L
-                * self.param.L_y
-                * self.param.L_z
-            )
+            Q_sei_cr = z_sei * delta_n_SEI_cr * L_k * self.param.L_y * self.param.L_z
 
             variables.update(
                 {
-                    f"Inner {reaction_name}" "concentration [mol.m-3]": n_inner_cr,
-                    f"X-averaged inner {reaction_name}"
+                    f"{Domain} inner {reaction_name}"
+                    "concentration [mol.m-3]": n_inner_cr,
+                    f"X-averaged {domain} inner {reaction_name}"
                     "concentration [mol.m-3]": n_inner_cr_av,
-                    f"Outer {reaction_name}concentration [mol.m-3]": n_outer_cr,
-                    f"X-averaged outer {reaction_name}"
+                    f"{Domain} outer {reaction_name}"
+                    "concentration [mol.m-3]": n_outer_cr,
+                    f"X-averaged {domain} outer {reaction_name}"
                     "concentration [mol.m-3]": n_outer_cr_av,
-                    f"{reaction_name}" "concentration [mol.m-3]": n_SEI_cr,
-                    f"X-averaged {reaction_name}"
+                    f"{Domain} {reaction_name}" "concentration [mol.m-3]": n_SEI_cr,
+                    f"X-averaged {domain} {reaction_name}"
                     "concentration [mol.m-3]": n_SEI_cr_xav,
-                    f"Loss of lithium to {reaction_name}[mol]": Q_sei_cr,
-                    f"Loss of capacity to {reaction_name}[A.h]": Q_sei_cr
+                    f"Loss of lithium to {domain} {reaction_name}[mol]": Q_sei_cr,
+                    f"Loss of capacity to {domain} {reaction_name}[A.h]": Q_sei_cr
                     * self.param.F
                     / 3600,
                 }
@@ -288,25 +285,29 @@ def _get_standard_reaction_variables(self, j_inner, j_outer):
         variables : dict
             The variables which can be derived from the SEI currents.
         """
+        domain, Domain = self.domain_Domain
         j_inner_av = pybamm.x_average(j_inner)
         j_outer_av = pybamm.x_average(j_outer)
         j_sei = j_inner + j_outer
 
         variables = {
-            f"Inner {self.reaction_name}interfacial current density [A.m-2]": j_inner,
-            f"X-averaged inner {self.reaction_name}"
+            f"{Domain} electrode inner {self.reaction_name}"
+            "interfacial current density [A.m-2]": j_inner,
+            f"X-averaged {domain} electrode inner {self.reaction_name}"
             "interfacial current density [A.m-2]": j_inner_av,
-            f"Outer {self.reaction_name}interfacial current density [A.m-2]": j_outer,
-            f"X-averaged outer {self.reaction_name}"
+            f"{Domain} electrode outer {self.reaction_name}"
+            "interfacial current density [A.m-2]": j_outer,
+            f"X-averaged {domain} electrode outer {self.reaction_name}"
             "interfacial current density [A.m-2]": j_outer_av,
-            f"{self.reaction_name}interfacial current density [A.m-2]": j_sei,
+            f"{Domain} electrode {self.reaction_name}"
+            "interfacial current density [A.m-2]": j_sei,
         }
 
         if self.reaction_loc != "interface":
             j_sei_av = pybamm.x_average(j_sei)
             variables.update(
                 {
-                    f"X-averaged {self.reaction_name}"
+                    f"X-averaged {domain} electrode {self.reaction_name}"
                     "interfacial current density [A.m-2]": j_sei_av,
                 }
             )
diff --git a/pybamm/models/submodels/interface/sei/constant_sei.py b/pybamm/models/submodels/interface/sei/constant_sei.py
index becaa511da..4c507eec3a 100644
--- a/pybamm/models/submodels/interface/sei/constant_sei.py
+++ b/pybamm/models/submodels/interface/sei/constant_sei.py
@@ -23,14 +23,15 @@ class ConstantSEI(BaseModel):
         Phase of the particle (default is "primary")
     """
 
-    def __init__(self, param, options, phase="primary"):
-        super().__init__(param, options=options, phase=phase)
-        if self.options.electrode_types["negative"] == "planar":
+    def __init__(self, param, domain, options, phase="primary"):
+        super().__init__(param, domain, options=options, phase=phase)
+        if self.options.electrode_types[domain] == "planar":
             self.reaction_loc = "interface"
         else:
             self.reaction_loc = "full electrode"
 
     def get_fundamental_variables(self):
+        domain = self.domain.lower()
         # Constant thicknesses
         L_inner = self.phase_param.L_inner_0
         L_outer = self.phase_param.L_outer_0
@@ -41,7 +42,7 @@ def get_fundamental_variables(self):
             zero = pybamm.PrimaryBroadcast(pybamm.Scalar(0), "current collector")
         else:
             zero = pybamm.FullBroadcast(
-                pybamm.Scalar(0), "negative electrode", "current collector"
+                pybamm.Scalar(0), f"{domain} electrode", "current collector"
             )
         variables.update(self._get_standard_reaction_variables(zero, zero))
 
diff --git a/pybamm/models/submodels/interface/sei/no_sei.py b/pybamm/models/submodels/interface/sei/no_sei.py
index 463b58bac4..49d11a35e5 100644
--- a/pybamm/models/submodels/interface/sei/no_sei.py
+++ b/pybamm/models/submodels/interface/sei/no_sei.py
@@ -21,19 +21,20 @@ class NoSEI(BaseModel):
         Whether this is a submodel for standard SEI or SEI on cracks
     """
 
-    def __init__(self, param, options, phase="primary", cracks=False):
-        super().__init__(param, options=options, phase=phase, cracks=cracks)
-        if self.options.electrode_types[self.domain] == "planar":
+    def __init__(self, param, domain, options, phase="primary", cracks=False):
+        super().__init__(param, domain, options=options, phase=phase, cracks=cracks)
+        if self.options.electrode_types[domain] == "planar":
             self.reaction_loc = "interface"
         else:
             self.reaction_loc = "full electrode"
 
     def get_fundamental_variables(self):
+        domain = self.domain.lower()
         if self.reaction_loc == "interface":
             zero = pybamm.PrimaryBroadcast(pybamm.Scalar(0), "current collector")
         else:
             zero = pybamm.FullBroadcast(
-                pybamm.Scalar(0), "negative electrode", "current collector"
+                pybamm.Scalar(0), f"{domain} electrode", "current collector"
             )
         variables = self._get_standard_thickness_variables(zero, zero)
         variables.update(self._get_standard_reaction_variables(zero, zero))
diff --git a/pybamm/models/submodels/interface/sei/sei_growth.py b/pybamm/models/submodels/interface/sei/sei_growth.py
index 8fd07a286a..7f6e2771cc 100644
--- a/pybamm/models/submodels/interface/sei/sei_growth.py
+++ b/pybamm/models/submodels/interface/sei/sei_growth.py
@@ -29,37 +29,40 @@ class SEIGrowth(BaseModel):
         Whether this is a submodel for standard SEI or SEI on cracks
     """
 
-    def __init__(self, param, reaction_loc, options, phase="primary", cracks=False):
-        super().__init__(param, options=options, phase=phase, cracks=cracks)
+    def __init__(
+        self, param, domain, reaction_loc, options, phase="primary", cracks=False
+    ):
+        super().__init__(param, domain, options=options, phase=phase, cracks=cracks)
         self.reaction_loc = reaction_loc
-        if self.options["SEI"] == "ec reaction limited":
+        SEI_option = getattr(self.options, domain)["SEI"]
+        if SEI_option == "ec reaction limited":
             pybamm.citations.register("Yang2017")
         else:
             pybamm.citations.register("Marquis2020")
 
     def get_fundamental_variables(self):
+        domain, Domain = self.domain_Domain
         Ls = []
         for pos in ["inner", "outer"]:
-            Pos = pos.capitalize()
             scale = self.phase_param.L_sei_0
             if self.reaction_loc == "x-average":
                 L_av = pybamm.Variable(
-                    f"X-averaged {pos} {self.reaction_name}thickness [m]",
+                    f"X-averaged {domain} {pos} {self.reaction_name}thickness [m]",
                     domain="current collector",
                     scale=scale,
                 )
                 L_av.print_name = f"L_{pos}_av"
-                L = pybamm.PrimaryBroadcast(L_av, "negative electrode")
+                L = pybamm.PrimaryBroadcast(L_av, f"{domain} electrode")
             elif self.reaction_loc == "full electrode":
                 L = pybamm.Variable(
-                    f"{Pos} {self.reaction_name}thickness [m]",
-                    domain="negative electrode",
+                    f"{Domain} {pos} {self.reaction_name}thickness [m]",
+                    domain=f"{domain} electrode",
                     auxiliary_domains={"secondary": "current collector"},
                     scale=scale,
                 )
             elif self.reaction_loc == "interface":
                 L = pybamm.Variable(
-                    f"{Pos} {self.reaction_name}thickness [m]",
+                    f"{Domain} {pos} {self.reaction_name}thickness [m]",
                     domain="current collector",
                     scale=scale,
                 )
@@ -68,7 +71,8 @@ def get_fundamental_variables(self):
 
         L_inner, L_outer = Ls
 
-        if self.options["SEI"].startswith("ec reaction limited"):
+        SEI_option = getattr(self.options, domain)["SEI"]
+        if SEI_option.startswith("ec reaction limited"):
             L_inner = 0 * L_inner  # Set L_inner to zero, copying domains
 
         variables = self._get_standard_thickness_variables(L_inner, L_outer)
@@ -78,34 +82,38 @@ def get_fundamental_variables(self):
     def get_coupled_variables(self, variables):
         param = self.param
         phase_param = self.phase_param
+        domain, Domain = self.domain_Domain
+        SEI_option = getattr(self.options, domain)["SEI"]
+        T = variables[f"{Domain} electrode temperature [K]"]
         # delta_phi = phi_s - phi_e
-        T = variables["Negative electrode temperature [K]"]
         if self.reaction_loc == "interface":
             delta_phi = variables[
                 "Lithium metal interface surface potential difference [V]"
             ]
             T = pybamm.boundary_value(T, "right")
         else:
-            delta_phi = variables["Negative electrode surface potential difference [V]"]
+            delta_phi = variables[
+                f"{Domain} electrode surface potential difference [V]"
+            ]
 
         # Look for current that contributes to the -IR drop
         # If we can't find the interfacial current density from the main reaction, j,
         # it's ok to fall back on the total interfacial current density, j_tot
         # This should only happen when the interface submodel is "InverseButlerVolmer"
         # in which case j = j_tot (uniform) anyway
-        if "Negative electrode interfacial current density [A.m-2]" in variables:
-            j = variables["Negative electrode interfacial current density [A.m-2]"]
+        if f"{Domain} electrode interfacial current density [A.m-2]" in variables:
+            j = variables[f"{Domain} electrode interfacial current density [A.m-2]"]
         elif self.reaction_loc == "interface":
             j = variables["Lithium metal total interfacial current density [A.m-2]"]
         else:
             j = variables[
-                "X-averaged negative electrode total "
+                f"X-averaged {domain} electrode total "
                 "interfacial current density [A.m-2]"
             ]
 
-        L_sei_inner = variables[f"Inner {self.reaction_name}thickness [m]"]
-        L_sei_outer = variables[f"Outer {self.reaction_name}thickness [m]"]
-        L_sei = variables[f"Total {self.reaction_name}thickness [m]"]
+        L_sei_inner = variables[f"{Domain} inner {self.reaction_name}thickness [m]"]
+        L_sei_outer = variables[f"{Domain} outer {self.reaction_name}thickness [m]"]
+        L_sei = variables[f"{Domain} total {self.reaction_name}thickness [m]"]
 
         R_sei = phase_param.R_sei
         eta_SEI = delta_phi - phase_param.U_sei - j * L_sei * R_sei
@@ -114,31 +122,31 @@ def get_coupled_variables(self, variables):
 
         # Define alpha_SEI depending on whether it is symmetric or asymmetric. This
         # applies to "reaction limited" and "EC reaction limited"
-        if self.options["SEI"].endswith("(asymmetric)"):
+        if SEI_option.endswith("(asymmetric)"):
             alpha_SEI = phase_param.alpha_SEI
         else:
             alpha_SEI = 0.5
 
-        if self.options["SEI"].startswith("reaction limited"):
+        if SEI_option.startswith("reaction limited"):
             # Scott Marquis thesis (eq. 5.92)
             j_sei = -phase_param.j0_sei * pybamm.exp(-alpha_SEI * F_RT * eta_SEI)
 
-        elif self.options["SEI"] == "electron-migration limited":
+        elif SEI_option == "electron-migration limited":
             # Scott Marquis thesis (eq. 5.94)
             eta_inner = delta_phi - phase_param.U_inner
             j_sei = phase_param.kappa_inner * eta_inner / L_sei_inner
 
-        elif self.options["SEI"] == "interstitial-diffusion limited":
+        elif SEI_option == "interstitial-diffusion limited":
             # Scott Marquis thesis (eq. 5.96)
             j_sei = -(
                 phase_param.D_li * phase_param.c_li_0 * param.F / L_sei_outer
             ) * pybamm.exp(-F_RT * delta_phi)
 
-        elif self.options["SEI"] == "solvent-diffusion limited":
+        elif SEI_option == "solvent-diffusion limited":
             # Scott Marquis thesis (eq. 5.91)
             j_sei = -phase_param.D_sol * phase_param.c_sol * param.F / L_sei_outer
 
-        elif self.options["SEI"].startswith("ec reaction limited"):
+        elif SEI_option.startswith("ec reaction limited"):
             # we have a linear system for j and c
             #  c = c_0 + j * L / F / D          [1] (eq 11 in the Yang2017 paper)
             #  j = - F * c * k_exp()            [2] (eq 10 in the Yang2017 paper, factor
@@ -159,12 +167,12 @@ def get_coupled_variables(self, variables):
             c_ec_av = pybamm.x_average(c_ec)
 
             if self.reaction == "SEI on cracks":
-                name = "EC concentration on cracks [mol.m-3]"
+                name = f"{Domain} EC concentration on cracks [mol.m-3]"
             else:
-                name = "EC surface concentration [mol.m-3]"
+                name = f"{Domain} EC surface concentration [mol.m-3]"
             variables.update({name: c_ec, f"X-averaged {name}": c_ec_av})
 
-        if self.options["SEI"].startswith("ec reaction limited"):
+        if SEI_option.startswith("ec reaction limited"):
             inner_sei_proportion = 0
         else:
             inner_sei_proportion = phase_param.inner_sei_proportion
@@ -186,38 +194,45 @@ def get_coupled_variables(self, variables):
     def set_rhs(self, variables):
         phase_param = self.phase_param
         param = self.param
+        domain, Domain = self.domain_Domain
 
         if self.reaction_loc == "x-average":
-            L_inner = variables[f"X-averaged inner {self.reaction_name}thickness [m]"]
-            L_outer = variables[f"X-averaged outer {self.reaction_name}thickness [m]"]
+            L_inner = variables[
+                f"X-averaged {domain} inner {self.reaction_name}thickness [m]"
+            ]
+            L_outer = variables[
+                f"X-averaged {domain} outer {self.reaction_name}thickness [m]"
+            ]
             j_inner = variables[
-                f"X-averaged inner {self.reaction_name}"
+                f"X-averaged {domain} electrode inner {self.reaction_name}"
                 "interfacial current density [A.m-2]"
             ]
             j_outer = variables[
-                f"X-averaged outer {self.reaction_name}"
+                f"X-averaged {domain} electrode outer {self.reaction_name}"
                 "interfacial current density [A.m-2]"
             ]
 
         else:
-            L_inner = variables[f"Inner {self.reaction_name}thickness [m]"]
-            L_outer = variables[f"Outer {self.reaction_name}thickness [m]"]
+            L_inner = variables[f"{Domain} inner {self.reaction_name}thickness [m]"]
+            L_outer = variables[f"{Domain} outer {self.reaction_name}thickness [m]"]
             j_inner = variables[
-                f"Inner {self.reaction_name}interfacial current density [A.m-2]"
+                f"{Domain} electrode inner {self.reaction_name}"
+                "interfacial current density [A.m-2]"
             ]
             j_outer = variables[
-                f"Outer {self.reaction_name}interfacial current density [A.m-2]"
+                f"{Domain} electrode outer {self.reaction_name}"
+                "interfacial current density [A.m-2]"
             ]
 
         # The spreading term acts to spread out SEI along the cracks as they grow.
         # For SEI on initial surface (as opposed to cracks), it is zero.
         if self.reaction == "SEI on cracks":
             if self.reaction_loc == "x-average":
-                l_cr = variables["X-averaged negative particle crack length [m]"]
-                dl_cr = variables["X-averaged negative particle cracking rate [m.s-1]"]
+                l_cr = variables[f"X-averaged {domain} particle crack length [m]"]
+                dl_cr = variables[f"X-averaged {domain} particle cracking rate [m.s-1]"]
             else:
-                l_cr = variables["Negative particle crack length [m]"]
-                dl_cr = variables["Negative particle cracking rate [m.s-1]"]
+                l_cr = variables[f"{Domain} particle crack length [m]"]
+                dl_cr = variables[f"{Domain} particle cracking rate [m.s-1]"]
             spreading_outer = (
                 dl_cr / l_cr * (self.phase_param.L_outer_crack_0 - L_outer)
             )
@@ -242,7 +257,8 @@ def set_rhs(self, variables):
         )
 
         # we have to add the spreading rate to account for cracking
-        if self.options["SEI"].startswith("ec reaction limited"):
+        SEI_option = getattr(self.options, domain)["SEI"]
+        if SEI_option.startswith("ec reaction limited"):
             self.rhs = {L_outer: -dLdt_SEI_outer + spreading_outer}
         else:
             self.rhs = {
@@ -251,12 +267,17 @@ def set_rhs(self, variables):
             }
 
     def set_initial_conditions(self, variables):
+        domain, Domain = self.domain_Domain
         if self.reaction_loc == "x-average":
-            L_inner = variables[f"X-averaged inner {self.reaction_name}thickness [m]"]
-            L_outer = variables[f"X-averaged outer {self.reaction_name}thickness [m]"]
+            L_inner = variables[
+                f"X-averaged {domain} inner {self.reaction_name}thickness [m]"
+            ]
+            L_outer = variables[
+                f"X-averaged {domain} outer {self.reaction_name}thickness [m]"
+            ]
         else:
-            L_inner = variables[f"Inner {self.reaction_name}thickness [m]"]
-            L_outer = variables[f"Outer {self.reaction_name}thickness [m]"]
+            L_inner = variables[f"{Domain} inner {self.reaction_name}thickness [m]"]
+            L_outer = variables[f"{Domain} outer {self.reaction_name}thickness [m]"]
 
         if self.reaction == "SEI on cracks":
             L_inner_0 = self.phase_param.L_inner_crack_0
@@ -264,7 +285,8 @@ def set_initial_conditions(self, variables):
         else:
             L_inner_0 = self.phase_param.L_inner_0
             L_outer_0 = self.phase_param.L_outer_0
-        if self.options["SEI"].startswith("ec reaction limited"):
+        SEI_option = getattr(self.options, domain)["SEI"]
+        if SEI_option.startswith("ec reaction limited"):
             self.initial_conditions = {L_outer: L_inner_0 + L_outer_0}
         else:
             self.initial_conditions = {L_inner: L_inner_0, L_outer: L_outer_0}
diff --git a/pybamm/models/submodels/interface/sei/total_sei.py b/pybamm/models/submodels/interface/sei/total_sei.py
index a3ebffa6a6..2a017b94a0 100644
--- a/pybamm/models/submodels/interface/sei/total_sei.py
+++ b/pybamm/models/submodels/interface/sei/total_sei.py
@@ -19,24 +19,25 @@ class TotalSEI(pybamm.BaseSubModel):
         See :class:`pybamm.BaseBatteryModel`
     """
 
-    def __init__(self, param, options, cracks=False):
+    def __init__(self, param, domain, options, cracks=False):
         if cracks is True:
             self.reaction = "SEI on cracks"
         else:
             self.reaction = "SEI"
-        super().__init__(param, options=options)
+        super().__init__(param, domain, options=options)
 
     def get_coupled_variables(self, variables):
-        phases = self.options.phases["negative"]
+        domain, Domain = self.domain_Domain
+        phases = self.options.phases[domain]
         # For each of the variables, the variable name without the phase name
         # is constructed by summing all of the variable names with the phases
         for variable_template in [
-            f"Negative electrode {{}}{self.reaction} volumetric "
+            f"{Domain} electrode {{}}{self.reaction} volumetric "
             "interfacial current density [A.m-3]",
-            f"X-averaged negative electrode {{}}{self.reaction} volumetric "
+            f"X-averaged {domain} electrode {{}}{self.reaction} volumetric "
             "interfacial current density [A.m-3]",
-            f"Loss of lithium to {{}}{self.reaction} [mol]",
-            f"Loss of capacity to {{}}{self.reaction} [A.h]",
+            f"Loss of lithium to {domain} {{}}{self.reaction} [mol]",
+            f"Loss of capacity to {domain} {{}}{self.reaction} [A.h]",
         ]:
             sumvar = sum(
                 variables[variable_template.format(phase + " ")] for phase in phases
diff --git a/pybamm/models/submodels/interface/total_interfacial_current.py b/pybamm/models/submodels/interface/total_interfacial_current.py
index 1eb074baae..a9094c4448 100644
--- a/pybamm/models/submodels/interface/total_interfacial_current.py
+++ b/pybamm/models/submodels/interface/total_interfacial_current.py
@@ -62,10 +62,10 @@ def _get_coupled_variables_by_phase_and_domain(self, variables, domain, phase_na
             reaction_names = [""]
             if phase_name == "":
                 reaction_names += ["SEI "]
-                if self.options.electrode_types["negative"] == "porous":
-                    # separate plating reaction only if the negative electrode is
-                    # porous, since plating is the main reaction
-                    # SEI on cracks only in a porous negative electrode
+                if self.options.electrode_types[domain] == "porous":
+                    # separate plating reaction only if the electrode is porous,
+                    # since plating is the main reaction otherwise.
+                    # Likewise, SEI on cracks only in a porous electrode
                     reaction_names.extend(["lithium plating ", "SEI on cracks "])
         elif self.chemistry == "lead-acid":
             reaction_names = ["", "oxygen "]
diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py
index ad751c3911..dd5a94afc6 100644
--- a/pybamm/models/submodels/particle/base_particle.py
+++ b/pybamm/models/submodels/particle/base_particle.py
@@ -35,9 +35,10 @@ def _get_effective_diffusivity(self, c, T, current):
         domain_options = getattr(self.options, domain)
 
         # Get diffusivity (may have empirical hysteresis)
-        if domain_options["diffusivity"] == "single":
+        diffusivity_option = getattr(domain_options, self.phase)["diffusivity"]
+        if diffusivity_option == "single":
             D = phase_param.D(c, T)
-        elif domain_options["diffusivity"] == "current sigmoid":
+        elif diffusivity_option == "current sigmoid":
             k = 100
             if Domain == "Positive":
                 lithiation_current = current
diff --git a/pybamm/models/submodels/porosity/reaction_driven_porosity.py b/pybamm/models/submodels/porosity/reaction_driven_porosity.py
index 15d5441c0c..fc69d0f1fd 100644
--- a/pybamm/models/submodels/porosity/reaction_driven_porosity.py
+++ b/pybamm/models/submodels/porosity/reaction_driven_porosity.py
@@ -25,31 +25,36 @@ def __init__(self, param, options, x_average):
     def get_coupled_variables(self, variables):
         eps_dict = {}
         for domain in self.options.whole_cell_domains:
-            if domain == "negative electrode":
-                # Only the negative electrode porosity changes
-                L_sei_n = variables["Total SEI thickness [m]"]
-                L_sei_0 = self.param.n.prim.L_inner_0 + self.param.n.prim.L_outer_0
-                L_pl_n = variables["Lithium plating thickness [m]"]
-                L_dead_n = variables["Dead lithium thickness [m]"]
-                L_sei_cr_n = variables["Total SEI on cracks thickness [m]"]
-                roughness_n = variables["Negative electrode roughness ratio"]
+            if domain == "separator":
+                delta_eps_k = 0  # separator porosity does not change
+            else:
+                Domain = domain.split()[0].capitalize()
+                L_sei_k = variables[f"{Domain} total SEI thickness [m]"]
+                if Domain == "Negative":
+                    L_sei_0 = self.param.n.prim.L_inner_0 + self.param.n.prim.L_outer_0
+                elif Domain == "Positive":
+                    L_sei_0 = self.param.p.prim.L_inner_0 + self.param.p.prim.L_outer_0
+                L_pl_k = variables[f"{Domain} lithium plating thickness [m]"]
+                L_dead_k = variables[f"{Domain} dead lithium thickness [m]"]
+                L_sei_cr_k = variables[f"{Domain} total SEI on cracks thickness [m]"]
+                roughness_k = variables[f"{Domain} electrode roughness ratio"]
 
                 L_tot = (
-                    (L_sei_n - L_sei_0)
-                    + L_pl_n
-                    + L_dead_n
-                    + L_sei_cr_n * (roughness_n - 1)
+                    (L_sei_k - L_sei_0)
+                    + L_pl_k
+                    + L_dead_k
+                    + L_sei_cr_k * (roughness_k - 1)
                 )
 
-                a_n = variables["Negative electrode surface area to volume ratio [m-1]"]
+                a_k = variables[
+                    f"{Domain} electrode surface area to volume ratio [m-1]"
+                ]
 
                 # This assumes a thin film so curvature effects are neglected.
                 # They could be included (e.g. for a sphere it is
                 # a_n * (L_tot + L_tot ** 2 / R_n + L_tot ** # 3 / (3 * R_n ** 2)))
                 # but it is not clear if it is relevant or not.
-                delta_eps_k = -a_n * L_tot
-            else:
-                delta_eps_k = 0
+                delta_eps_k = -a_k * L_tot
 
             domain_param = self.param.domain_params[domain.split()[0]]
             eps_k = domain_param.epsilon_init + delta_eps_k
@@ -60,6 +65,21 @@ def get_coupled_variables(self, variables):
         return variables
 
     def set_events(self, variables):
+        eps_p = variables["Positive electrode porosity"]
+        self.events.append(
+            pybamm.Event(
+                "Zero positive electrode porosity cut-off",
+                pybamm.min(eps_p),
+                pybamm.EventType.TERMINATION,
+            )
+        )
+        self.events.append(
+            pybamm.Event(
+                "Max positive electrode porosity cut-off",
+                1 - pybamm.max(eps_p),
+                pybamm.EventType.TERMINATION,
+            )
+        )
         if "negative electrode" in self.options.whole_cell_domains:
             eps_n = variables["Negative electrode porosity"]
             self.events.append(
diff --git a/pybamm/models/submodels/thermal/lumped.py b/pybamm/models/submodels/thermal/lumped.py
index 62c147755b..0f396a3f77 100644
--- a/pybamm/models/submodels/thermal/lumped.py
+++ b/pybamm/models/submodels/thermal/lumped.py
@@ -56,10 +56,9 @@ def set_rhs(self, variables):
         # Newton cooling, accounting for surface area to volume ratio
         cell_surface_area = self.param.A_cooling
         cell_volume = self.param.V_cell
-        total_cooling_coefficient = (
-            -self.param.h_total * cell_surface_area / cell_volume
+        Q_cool_vol_av = (
+            -self.param.h_total * (T_vol_av - T_amb) * cell_surface_area / cell_volume
         )
-        Q_cool_vol_av = total_cooling_coefficient * (T_vol_av - T_amb)
 
         self.rhs = {
             T_vol_av: (Q_vol_av + Q_cool_vol_av) / self.param.rho_c_p_eff(T_vol_av)
diff --git a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py
index a6555170fc..2611dbafdc 100644
--- a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py
+++ b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py
@@ -58,33 +58,29 @@ def set_rhs(self, variables):
         y = pybamm.standard_spatial_vars.y
         z = pybamm.standard_spatial_vars.z
 
-        # Account for surface area to volume ratio of pouch cell in surface and side
-        # cooling terms
-        cell_volume = self.param.L * self.param.L_y * self.param.L_z
-
+        # Calculate cooling, accounting for surface area to volume ratio of pouch cell
+        edge_area = self.param.L_z * self.param.L
         yz_surface_area = self.param.L_y * self.param.L_z
-        yz_surface_cooling_coefficient = (
+        cell_volume = self.param.L * self.param.L_y * self.param.L_z
+        Q_yz_surface = (
             -(self.param.n.h_cc(y, z) + self.param.p.h_cc(y, z))
+            * (T_av - T_amb)
             * yz_surface_area
             / cell_volume
         )
-
-        side_edge_area = self.param.L_z * self.param.L
-        side_edge_cooling_coefficient = (
+        Q_edge = (
             -(self.param.h_edge(0, z) + self.param.h_edge(self.param.L_y, z))
-            * side_edge_area
+            * (T_av - T_amb)
+            * edge_area
             / cell_volume
         )
-
-        total_cooling_coefficient = (
-            yz_surface_cooling_coefficient + side_edge_cooling_coefficient
-        )
+        Q_cool_total = Q_yz_surface + Q_edge
 
         self.rhs = {
             T_av: (
                 pybamm.div(self.param.lambda_eff(T_av) * pybamm.grad(T_av))
                 + Q_av
-                + total_cooling_coefficient * (T_av - T_amb)
+                + Q_cool_total
             )
             / self.param.rho_c_p_eff(T_av)
         }
@@ -94,7 +90,7 @@ def set_boundary_conditions(self, variables):
         T_amb = variables["Ambient temperature [K]"]
         T_av = variables["X-averaged cell temperature [K]"]
 
-        # find tab locations (top vs bottom)
+        # Find tab locations (top vs bottom)
         L_y = param.L_y
         L_z = param.L_z
         neg_tab_z = param.n.centre_z_tab
@@ -104,11 +100,10 @@ def set_boundary_conditions(self, variables):
         pos_tab_top_bool = pybamm.Equality(pos_tab_z, L_z)
         pos_tab_bottom_bool = pybamm.Equality(pos_tab_z, 0)
 
-        # calculate tab vs non-tab area on top and bottom
+        # Calculate tab vs non-tab area on top and bottom
         neg_tab_area = param.n.L_tab * param.n.L_cc
         pos_tab_area = param.p.L_tab * param.p.L_cc
         total_area = param.L * param.L_y
-
         non_tab_top_area = (
             total_area
             - neg_tab_area * neg_tab_top_bool
@@ -120,18 +115,22 @@ def set_boundary_conditions(self, variables):
             - pos_tab_area * pos_tab_bottom_bool
         )
 
-        # calculate effective cooling coefficients
+        # Calculate heat fluxes weighted by area
         # Note: can't do y-average of h_edge here since y isn't meshed. Evaluate at
         # midpoint.
-        top_cooling_coefficient = (
-            param.n.h_tab * neg_tab_area * neg_tab_top_bool
-            + param.p.h_tab * pos_tab_area * pos_tab_top_bool
-            + param.h_edge(L_y / 2, L_z) * non_tab_top_area
+        q_tab_n = -param.n.h_tab * (T_av - T_amb)
+        q_tab_p = -param.p.h_tab * (T_av - T_amb)
+        q_edge_top = -param.h_edge(L_y / 2, L_z) * (T_av - T_amb)
+        q_edge_bottom = -param.h_edge(L_y / 2, 0) * (T_av - T_amb)
+        q_top = (
+            q_tab_n * neg_tab_area * neg_tab_top_bool
+            + q_tab_p * pos_tab_area * pos_tab_top_bool
+            + q_edge_top * non_tab_top_area
         ) / total_area
-        bottom_cooling_coefficient = (
-            param.n.h_tab * neg_tab_area * neg_tab_bottom_bool
-            + param.p.h_tab * pos_tab_area * pos_tab_bottom_bool
-            + param.h_edge(L_y / 2, 0) * non_tab_bottom_area
+        q_bottom = (
+            q_tab_n * neg_tab_area * neg_tab_bottom_bool
+            + q_tab_p * pos_tab_area * pos_tab_bottom_bool
+            + q_edge_bottom * non_tab_bottom_area
         ) / total_area
 
         # just use left and right for clarity
@@ -141,21 +140,14 @@ def set_boundary_conditions(self, variables):
         self.boundary_conditions = {
             T_av: {
                 "left": (
-                    pybamm.boundary_value(
-                        bottom_cooling_coefficient * (T_av - T_amb),
-                        "left",
-                    )
-                    / pybamm.boundary_value(lambda_eff, "left"),
+                    pybamm.boundary_value(-q_bottom / lambda_eff, "left"),
                     "Neumann",
                 ),
                 "right": (
-                    pybamm.boundary_value(
-                        -top_cooling_coefficient * (T_av - T_amb), "right"
-                    )
-                    / pybamm.boundary_value(lambda_eff, "right"),
+                    pybamm.boundary_value(q_top / lambda_eff, "right"),
                     "Neumann",
                 ),
-            }
+            },
         }
 
     def set_initial_conditions(self, variables):
diff --git a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py
index eb8e1b7e49..a5c7c42b17 100644
--- a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py
+++ b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py
@@ -58,20 +58,22 @@ def set_rhs(self, variables):
         y = pybamm.standard_spatial_vars.y
         z = pybamm.standard_spatial_vars.z
 
+        # Calculate cooling
+        Q_yz_surface_W_per_m2 = -(self.param.n.h_cc(y, z) + self.param.p.h_cc(y, z)) * (
+            T_av - T_amb
+        )
+        Q_edge_W_per_m2 = -self.param.h_edge(y, z) * (T_av - T_amb)
+
         # Account for surface area to volume ratio of pouch cell in surface cooling
         # term
-        cell_volume = self.param.L * self.param.L_y * self.param.L_z
-
         yz_surface_area = self.param.L_y * self.param.L_z
-        yz_surface_cooling_coefficient = (
-            -(self.param.n.h_cc(y, z) + self.param.p.h_cc(y, z))
-            * yz_surface_area
-            / cell_volume
+        cell_volume = self.param.L * self.param.L_y * self.param.L_z
+        Q_yz_surface = pybamm.source(
+            Q_yz_surface_W_per_m2 * yz_surface_area / cell_volume, T_av
         )
-
         # Edge cooling appears as a boundary term, so no need to account for surface
         # area to volume ratio
-        edge_cooling_coefficient = -self.param.h_edge(y, z)
+        Q_edge = pybamm.source(Q_edge_W_per_m2, T_av, boundary=True)
 
         # Governing equations contain:
         #   - source term for y-z surface cooling
@@ -88,10 +90,8 @@ def set_rhs(self, variables):
             T_av: (
                 self.param.lambda_eff(T_av) * pybamm.laplacian(T_av)
                 + pybamm.source(Q_av, T_av)
-                + pybamm.source(yz_surface_cooling_coefficient * (T_av - T_amb), T_av)
-                + pybamm.source(
-                    edge_cooling_coefficient * (T_av - T_amb), T_av, boundary=True
-                )
+                + Q_yz_surface
+                + Q_edge
             )
             / self.param.rho_c_p_eff(T_av)
         }
@@ -102,24 +102,21 @@ def set_boundary_conditions(self, variables):
         y = pybamm.standard_spatial_vars.y
         z = pybamm.standard_spatial_vars.z
 
+        # Calculate heat fluxes
+        q_tab_n = -self.param.n.h_tab * (T_av - T_amb)
+        q_tab_p = -self.param.p.h_tab * (T_av - T_amb)
+        q_edge = -self.param.h_edge(y, z) * (T_av - T_amb)
+
         # Subtract the edge cooling from the tab portion so as to not double count
         # Note: tab cooling is also only applied on the current collector hence
-        # the (l_cn / l) and (l_cp / l) prefactors. We also still have edge cooling
+        # the (l_cn / l) and (l_cp / l) prefactors. We still have edge cooling
         # in the region: x in (0, 1)
-        h_tab_n_corrected = (self.param.n.L_cc / self.param.L) * (
-            self.param.n.h_tab - self.param.h_edge(y, z)
-        )
-        h_tab_p_corrected = (self.param.p.L_cc / self.param.L) * (
-            self.param.p.h_tab - self.param.h_edge(y, z)
-        )
-
-        negative_tab_bc = pybamm.boundary_value(
-            -h_tab_n_corrected * (T_av - T_amb) / self.param.n.lambda_cc(T_av),
+        negative_tab_bc = (self.param.n.L_cc / self.param.L) * pybamm.boundary_value(
+            (q_tab_n - q_edge) / self.param.n.lambda_cc(T_av),
             "negative tab",
         )
-        positive_tab_bc = pybamm.boundary_value(
-            -h_tab_p_corrected * (T_av - T_amb) / self.param.p.lambda_cc(T_av),
-            "positive tab",
+        positive_tab_bc = (self.param.p.L_cc / self.param.L) * pybamm.boundary_value(
+            (q_tab_p - q_edge) / self.param.p.lambda_cc(T_av), "positive tab"
         )
 
         self.boundary_conditions = {
diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index 7de1054c9e..c459a4ef1e 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -50,6 +50,7 @@ def _set_parameters(self):
         self.T_ref = self.therm.T_ref
         self.T_init = self.therm.T_init
         self.T_amb = self.therm.T_amb
+        self.T_amb_av = self.therm.T_amb_av
         self.h_edge = self.therm.h_edge
         self.h_total = self.therm.h_total
         self.rho_c_p_eff = self.therm.rho_c_p_eff
@@ -91,7 +92,7 @@ def _set_parameters(self):
         )
 
         # Lithium plating parameters
-        self.V_bar_plated_Li = pybamm.Parameter(
+        self.V_bar_Li = pybamm.Parameter(
             "Lithium metal partial molar volume [m3.mol-1]"
         )
         self.c_Li_typ = pybamm.Parameter(
@@ -176,6 +177,17 @@ def kappa_e(self, c_e, T):
         inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T}
         return pybamm.FunctionParameter("Electrolyte conductivity [S.m-1]", inputs)
 
+    def j0_Li_metal(self, c_e, c_Li, T):
+        """Dimensional exchange-current density for lithium metal electrode [A.m-2]"""
+        inputs = {
+            "Electrolyte concentration [mol.m-3]": c_e,
+            "Lithium metal concentration [mol.m-3]": c_Li,
+            "Temperature [K]": T,
+        }
+        return pybamm.FunctionParameter(
+            "Exchange-current density for lithium metal electrode [A.m-2]", inputs
+        )
+
     def j0_stripping(self, c_e, c_Li, T):
         """Dimensional exchange-current density for stripping [A.m-2]"""
         inputs = {
@@ -396,64 +408,55 @@ def _set_parameters(self):
             f"{pref}{Domain} electrode Butler-Volmer transfer coefficient"
         )
 
-        if self.domain == "negative":
-            # SEI parameters
-            self.V_bar_inner = pybamm.Parameter(
-                f"{pref}Inner SEI partial molar volume [m3.mol-1]"
-            )
-            self.V_bar_outer = pybamm.Parameter(
-                f"{pref}Outer SEI partial molar volume [m3.mol-1]"
-            )
+        # SEI parameters
+        self.V_bar_inner = pybamm.Parameter(
+            f"{pref}Inner SEI partial molar volume [m3.mol-1]"
+        )
+        self.V_bar_outer = pybamm.Parameter(
+            f"{pref}Outer SEI partial molar volume [m3.mol-1]"
+        )
 
-            self.j0_sei = pybamm.Parameter(
-                f"{pref}SEI reaction exchange current density [A.m-2]"
-            )
+        self.j0_sei = pybamm.Parameter(
+            f"{pref}SEI reaction exchange current density [A.m-2]"
+        )
 
-            self.R_sei = pybamm.Parameter(f"{pref}SEI resistivity [Ohm.m]")
-            self.D_sol = pybamm.Parameter(
-                f"{pref}Outer SEI solvent diffusivity [m2.s-1]"
-            )
-            self.c_sol = pybamm.Parameter(f"{pref}Bulk solvent concentration [mol.m-3]")
-            self.U_inner = pybamm.Parameter(
-                f"{pref}Inner SEI open-circuit potential [V]"
-            )
-            self.U_outer = pybamm.Parameter(
-                f"{pref}Outer SEI open-circuit potential [V]"
-            )
-            self.kappa_inner = pybamm.Parameter(
-                f"{pref}Inner SEI electron conductivity [S.m-1]"
-            )
-            self.D_li = pybamm.Parameter(
-                f"{pref}Inner SEI lithium interstitial diffusivity [m2.s-1]"
-            )
-            self.c_li_0 = pybamm.Parameter(
-                f"{pref}Lithium interstitial reference concentration [mol.m-3]"
-            )
-            self.L_inner_0 = pybamm.Parameter(f"{pref}Initial inner SEI thickness [m]")
-            self.L_outer_0 = pybamm.Parameter(f"{pref}Initial outer SEI thickness [m]")
+        self.R_sei = pybamm.Parameter(f"{pref}SEI resistivity [Ohm.m]")
+        self.D_sol = pybamm.Parameter(f"{pref}Outer SEI solvent diffusivity [m2.s-1]")
+        self.c_sol = pybamm.Parameter(f"{pref}Bulk solvent concentration [mol.m-3]")
+        self.U_inner = pybamm.Parameter(f"{pref}Inner SEI open-circuit potential [V]")
+        self.U_outer = pybamm.Parameter(f"{pref}Outer SEI open-circuit potential [V]")
+        self.kappa_inner = pybamm.Parameter(
+            f"{pref}Inner SEI electron conductivity [S.m-1]"
+        )
+        self.D_li = pybamm.Parameter(
+            f"{pref}Inner SEI lithium interstitial diffusivity [m2.s-1]"
+        )
+        self.c_li_0 = pybamm.Parameter(
+            f"{pref}Lithium interstitial reference concentration [mol.m-3]"
+        )
+        self.L_inner_0 = pybamm.Parameter(f"{pref}Initial inner SEI thickness [m]")
+        self.L_outer_0 = pybamm.Parameter(f"{pref}Initial outer SEI thickness [m]")
 
-            # Dividing by 10000 makes initial condition effectively zero
-            # without triggering division by zero errors
-            self.L_inner_crack_0 = self.L_inner_0 / 10000
-            self.L_outer_crack_0 = self.L_outer_0 / 10000
+        # Dividing by 10000 makes initial condition effectively zero
+        # without triggering division by zero errors
+        self.L_inner_crack_0 = self.L_inner_0 / 10000
+        self.L_outer_crack_0 = self.L_outer_0 / 10000
 
-            self.L_sei_0 = self.L_inner_0 + self.L_outer_0
-            self.E_sei = pybamm.Parameter(
-                f"{pref}SEI growth activation energy [J.mol-1]"
-            )
-            self.alpha_SEI = pybamm.Parameter(f"{pref}SEI growth transfer coefficient")
-            self.inner_sei_proportion = pybamm.Parameter(
-                f"{pref}Inner SEI reaction proportion"
-            )
-            self.z_sei = pybamm.Parameter(f"{pref}Ratio of lithium moles to SEI moles")
+        self.L_sei_0 = self.L_inner_0 + self.L_outer_0
+        self.E_sei = pybamm.Parameter(f"{pref}SEI growth activation energy [J.mol-1]")
+        self.alpha_SEI = pybamm.Parameter(f"{pref}SEI growth transfer coefficient")
+        self.inner_sei_proportion = pybamm.Parameter(
+            f"{pref}Inner SEI reaction proportion"
+        )
+        self.z_sei = pybamm.Parameter(f"{pref}Ratio of lithium moles to SEI moles")
 
-            # EC reaction
-            self.c_ec_0 = pybamm.Parameter(
-                f"{pref}EC initial concentration in electrolyte [mol.m-3]"
-            )
-            self.D_ec = pybamm.Parameter(f"{pref}EC diffusivity [m2.s-1]")
-            self.k_sei = pybamm.Parameter(f"{pref}SEI kinetic rate constant [m.s-1]")
-            self.U_sei = pybamm.Parameter(f"{pref}SEI open-circuit potential [V]")
+        # EC reaction
+        self.c_ec_0 = pybamm.Parameter(
+            f"{pref}EC initial concentration in electrolyte [mol.m-3]"
+        )
+        self.D_ec = pybamm.Parameter(f"{pref}EC diffusivity [m2.s-1]")
+        self.k_sei = pybamm.Parameter(f"{pref}SEI kinetic rate constant [m.s-1]")
+        self.U_sei = pybamm.Parameter(f"{pref}SEI open-circuit potential [V]")
 
         if main.options.electrode_types[domain] == "planar":
             self.n_Li_init = pybamm.Scalar(0)
diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index 136d9737aa..d5f12f362f 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -119,7 +119,25 @@ def create_from_bpx(filename, target_soc=1):
         return pybamm.ParameterValues(pybamm_dict)
 
     def __getitem__(self, key):
-        return self._dict_items[key]
+        try:
+            return self._dict_items[key]
+        except KeyError as err:
+            if (
+                "Exchange-current density for lithium metal electrode [A.m-2]"
+                in err.args[0]
+                and "Exchange-current density for plating [A.m-2]" in self._dict_items
+            ):
+                raise KeyError(
+                    "'Exchange-current density for plating [A.m-2]' has been renamed "
+                    "to 'Exchange-current density for lithium metal electrode [A.m-2]' "
+                    "when referring to the reaction at the surface of a lithium metal "
+                    "electrode. This is to avoid confusion with the exchange-current "
+                    "density for the lithium plating reaction in a porous negative "
+                    "electrode. To avoid this error, change your parameter file to use "
+                    "the new name."
+                )
+            else:
+                raise err
 
     def get(self, key, default=None):
         """Return item corresponding to key if it exists, otherwise return default"""
@@ -253,6 +271,39 @@ def update(self, values, check_conflict=False, check_already_exists=True, path="
         # reset processed symbols
         self._processed_symbols = {}
 
+    def set_initial_stoichiometry_half_cell(
+        self,
+        initial_value,
+        param=None,
+        known_value="cyclable lithium capacity",
+        inplace=True,
+        options=None,
+    ):
+        """
+        Set the initial stoichiometry of the working electrode, based on the initial
+        SOC or voltage
+        """
+        param = param or pybamm.LithiumIonParameters(options)
+        x = pybamm.lithium_ion.get_initial_stoichiometry_half_cell(
+            initial_value, self, param=param, known_value=known_value, options=options
+        )
+        if inplace:
+            parameter_values = self
+        else:
+            parameter_values = self.copy()
+
+        c_max = self.evaluate(param.p.prim.c_max)
+
+        parameter_values.update(
+            {
+                "Initial concentration in {} electrode [mol.m-3]".format(
+                    options["working electrode"]
+                ): x
+                * c_max
+            }
+        )
+        return parameter_values
+
     def set_initial_stoichiometries(
         self,
         initial_value,
diff --git a/pybamm/parameters/size_distribution_parameters.py b/pybamm/parameters/size_distribution_parameters.py
index c089be964a..60383fff50 100644
--- a/pybamm/parameters/size_distribution_parameters.py
+++ b/pybamm/parameters/size_distribution_parameters.py
@@ -16,7 +16,7 @@ def get_size_distribution_parameters(
     R_min_p=None,
     R_max_n=None,
     R_max_p=None,
-    electrode="both",
+    working_electrode="both",
 ):
     """
     A convenience method to add standard area-weighted particle-size distribution
@@ -60,7 +60,7 @@ def get_size_distribution_parameters(
         "positive" to indicate a half-cell model, in which case size distribution
         parameters are only added for a single electrode.
     """
-    if electrode in ["both", "negative"]:
+    if working_electrode == "both":
         # Radii from given parameter set
         R_n_typ = param["Negative particle radius [m]"]
 
@@ -86,32 +86,30 @@ def f_a_dist_n(R):
             },
             check_already_exists=False,
         )
-    if electrode in ["both", "positive"]:
-        # Radii from given parameter set
-        R_p_typ = param["Positive particle radius [m]"]
+    # Radii from given parameter set
+    R_p_typ = param["Positive particle radius [m]"]
 
-        # Set the mean particle radii
-        R_p_av = R_p_av or R_p_typ
+    # Set the mean particle radii
+    R_p_av = R_p_av or R_p_typ
 
-        # Minimum radii
-        R_min_p = R_min_p or np.max([0, 1 - sd_p * 5])
+    # Minimum radii
+    R_min_p = R_min_p or np.max([0, 1 - sd_p * 5])
 
-        # Max radii
-        R_max_p = R_max_p or (1 + sd_p * 5)
+    # Max radii
+    R_max_p = R_max_p or (1 + sd_p * 5)
 
-        # Area-weighted particle-size distribution
-        def f_a_dist_p(R):
-            return lognormal(R, R_p_av, sd_p * R_p_av)
+    # Area-weighted particle-size distribution
+    def f_a_dist_p(R):
+        return lognormal(R, R_p_av, sd_p * R_p_av)
 
-        param.update(
-            {
-                "Positive minimum particle radius [m]": R_min_p * R_p_av,
-                "Positive maximum particle radius [m]": R_max_p * R_p_av,
-                "Positive area-weighted "
-                + "particle-size distribution [m-1]": f_a_dist_p,
-            },
-            check_already_exists=False,
-        )
+    param.update(
+        {
+            "Positive minimum particle radius [m]": R_min_p * R_p_av,
+            "Positive maximum particle radius [m]": R_max_p * R_p_av,
+            "Positive area-weighted " + "particle-size distribution [m-1]": f_a_dist_p,
+        },
+        check_already_exists=False,
+    )
     return param
 
 
diff --git a/pybamm/parameters/thermal_parameters.py b/pybamm/parameters/thermal_parameters.py
index ea1dd12065..8e92ff8d34 100644
--- a/pybamm/parameters/thermal_parameters.py
+++ b/pybamm/parameters/thermal_parameters.py
@@ -51,6 +51,12 @@ def T_amb(self, y, z, t):
             },
         )
 
+    def T_amb_av(self, t):
+        """YZ-averaged ambient temperature [K]"""
+        y = pybamm.standard_spatial_vars.y
+        z = pybamm.standard_spatial_vars.z
+        return pybamm.yz_average(self.T_amb(y, z, t))
+
     def h_edge(self, y, z):
         """Cell edge heat transfer coefficient [W.m-2.K-1]"""
         inputs = {
diff --git a/pybamm/plotting/plot.py b/pybamm/plotting/plot.py
index 19aa9dc5e0..88c8dfe442 100644
--- a/pybamm/plotting/plot.py
+++ b/pybamm/plotting/plot.py
@@ -3,6 +3,7 @@
 #
 import pybamm
 from .quick_plot import ax_min, ax_max
+from pybamm.util import have_optional_dependency
 
 
 def plot(x, y, ax=None, testing=False, **kwargs):
@@ -25,7 +26,7 @@ def plot(x, y, ax=None, testing=False, **kwargs):
         Keyword arguments, passed to plt.plot
 
     """
-    import matplotlib.pyplot as plt
+    plt = have_optional_dependency("matplotlib.pyplot")
 
     if not isinstance(x, pybamm.Array):
         raise TypeError("x must be 'pybamm.Array'")
diff --git a/pybamm/plotting/plot2D.py b/pybamm/plotting/plot2D.py
index 80bb5d0ee2..d4f6d31e3a 100644
--- a/pybamm/plotting/plot2D.py
+++ b/pybamm/plotting/plot2D.py
@@ -3,6 +3,7 @@
 #
 import pybamm
 from .quick_plot import ax_min, ax_max
+from pybamm.util import have_optional_dependency
 
 
 def plot2D(x, y, z, ax=None, testing=False, **kwargs):
@@ -25,7 +26,7 @@ def plot2D(x, y, z, ax=None, testing=False, **kwargs):
         Whether to actually make the plot (turned off for unit tests)
 
     """
-    import matplotlib.pyplot as plt
+    plt = have_optional_dependency("matplotlib.pyplot")
 
     if not isinstance(x, pybamm.Array):
         raise TypeError("x must be 'pybamm.Array'")
diff --git a/pybamm/plotting/plot_summary_variables.py b/pybamm/plotting/plot_summary_variables.py
index 7a30d2ec0b..e50f38fddf 100644
--- a/pybamm/plotting/plot_summary_variables.py
+++ b/pybamm/plotting/plot_summary_variables.py
@@ -3,6 +3,7 @@
 #
 import numpy as np
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 def plot_summary_variables(
@@ -25,7 +26,7 @@ def plot_summary_variables(
         Keyword arguments, passed to plt.subplots.
 
     """
-    import matplotlib.pyplot as plt
+    plt = have_optional_dependency("matplotlib.pyplot")
 
     if isinstance(solutions, pybamm.Solution):
         solutions = [solutions]
@@ -37,7 +38,7 @@ def plot_summary_variables(
         output_variables = [
             "Capacity [A.h]",
             "Loss of lithium inventory [%]",
-            "Loss of capacity to SEI [A.h]",
+            "Total capacity lost to side reactions [A.h]",
             "Loss of active material in negative electrode [%]",
             "Loss of active material in positive electrode [%]",
             "x_100",
diff --git a/pybamm/plotting/plot_voltage_components.py b/pybamm/plotting/plot_voltage_components.py
index ad0e9a8b71..a681094bea 100644
--- a/pybamm/plotting/plot_voltage_components.py
+++ b/pybamm/plotting/plot_voltage_components.py
@@ -3,6 +3,8 @@
 #
 import numpy as np
 
+from pybamm.util import have_optional_dependency
+
 
 def plot_voltage_components(
     solution,
@@ -32,7 +34,7 @@ def plot_voltage_components(
         Keyword arguments, passed to ax.fill_between
 
     """
-    import matplotlib.pyplot as plt
+    plt = have_optional_dependency("matplotlib.pyplot")
 
     # Set a default value for alpha, the opacity
     kwargs_fill = {"alpha": 0.6, **kwargs_fill}
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index d6828ce18a..ff657ee375 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -5,6 +5,7 @@
 import numpy as np
 import pybamm
 from collections import defaultdict
+from pybamm.util import have_optional_dependency
 
 
 class LoopList(list):
@@ -46,7 +47,7 @@ def split_long_string(title, max_words=None):
 
 def close_plots():
     """Close all open figures"""
-    import matplotlib.pyplot as plt
+    plt = have_optional_dependency("matplotlib.pyplot")
 
     plt.close("all")
 
@@ -469,9 +470,10 @@ def plot(self, t, dynamic=False):
             Dimensional time (in 'time_units') at which to plot.
         """
 
-        import matplotlib.pyplot as plt
-        import matplotlib.gridspec as gridspec
-        from matplotlib import cm, colors
+        plt = have_optional_dependency("matplotlib.pyplot")
+        gridspec = have_optional_dependency("matplotlib.gridspec")
+        cm = have_optional_dependency("matplotlib", "cm")
+        colors = have_optional_dependency("matplotlib", "colors")
 
         t_in_seconds = t * self.time_scaling_factor
         self.fig = plt.figure(figsize=self.figsize)
@@ -668,8 +670,8 @@ def dynamic_plot(self, testing=False, step=None):
                 continuous_update=False,
             )
         else:
-            import matplotlib.pyplot as plt
-            from matplotlib.widgets import Slider
+            plt = have_optional_dependency("matplotlib.pyplot")
+            Slider = have_optional_dependency("matplotlib.widgets", "Slider")
 
             # create an initial plot at time self.min_t
             self.plot(self.min_t, dynamic=True)
@@ -773,18 +775,20 @@ def create_gif(self, number_of_images=80, duration=0.1, output_filename="plot.gi
             Name of the generated GIF file.
 
         """
-        import imageio.v2 as imageio
-        import matplotlib.pyplot as plt
+        imageio = have_optional_dependency("imageio.v2")
+        plt = have_optional_dependency("matplotlib.pyplot")
 
         # time stamps at which the images/plots will be created
         time_array = np.linspace(self.min_t, self.max_t, num=number_of_images)
         images = []
 
         # create images/plots
+        stub_name = output_filename.split(".")[0]
         for val in time_array:
             self.plot(val)
-            images.append("plot" + str(val) + ".png")
-            self.fig.savefig("plot" + str(val) + ".png", dpi=300)
+            temp_name = f"{stub_name}{val}.png"
+            images.append(temp_name)
+            self.fig.savefig(temp_name, dpi=300)
             plt.close()
 
         # compile the images/plots to create a GIF
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 04a373b436..f743f4fc0f 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -4,11 +4,12 @@
 import pickle
 import pybamm
 import numpy as np
+import hashlib
 import warnings
 import sys
 from functools import lru_cache
 from datetime import timedelta
-import tqdm
+from pybamm.util import have_optional_dependency
 
 
 def is_notebook():
@@ -133,6 +134,9 @@ def __init__(
         self._solution = None
         self.quick_plot = None
 
+        # Initialise instances of Simulation class with the same random seed
+        self._set_random_seed()
+
         # ignore runtime warnings in notebooks
         if is_notebook():  # pragma: no cover
             import warnings
@@ -156,6 +160,18 @@ def __setstate__(self, state):
         self.__dict__ = state
         self.get_esoh_solver = lru_cache()(self._get_esoh_solver)
 
+    # If the solver being used is CasadiSolver or its variants, set a fixed
+    # random seed during class initialization to the SHA-256 hash of the class
+    # name for purposes of reproducibility.
+    def _set_random_seed(self):
+        if isinstance(self._solver, pybamm.CasadiSolver) or isinstance(
+            self._solver, pybamm.CasadiAlgebraicSolver
+        ):
+            np.random.seed(
+                int(hashlib.sha256(self.__class__.__name__.encode()).hexdigest(), 16)
+                % (2**32)
+            )
+
     def set_up_and_parameterise_experiment(self):
         """
         Set up a simulation to run with an experiment. This creates a dictionary of
@@ -290,9 +306,10 @@ def update_new_model_events(self, new_model, op):
                 # figure out whether the voltage event is greater than the starting
                 # voltage (charge) or less (discharge) and set the sign of the
                 # event accordingly
-                if (isinstance(op.value, pybamm.Interpolant) or
-                    isinstance(op.value, pybamm.Multiplication)):
-                    inpt = {"start time":0}
+                if isinstance(op.value, pybamm.Interpolant) or isinstance(
+                    op.value, pybamm.Multiplication
+                ):
+                    inpt = {"start time": 0}
                     init_curr = op.value.evaluate(t=0, inputs=inpt).flatten()[0]
                     sign = np.sign(init_curr)
                 else:
@@ -373,8 +390,16 @@ def set_initial_soc(self, initial_soc):
         options = self.model.options
         param = self._model.param
         if options["open-circuit potential"] == "MSMR":
-            self._parameter_values = self._unprocessed_parameter_values.set_initial_ocps(  # noqa: E501
-                initial_soc, param=param, inplace=False, options=options
+            self._parameter_values = (
+                self._unprocessed_parameter_values.set_initial_ocps(  # noqa: E501
+                    initial_soc, param=param, inplace=False, options=options
+                )
+            )
+        elif options["working electrode"] == "positive":
+            self._parameter_values = (
+                self._unprocessed_parameter_values.set_initial_stoichiometry_half_cell(
+                    initial_soc, param=param, inplace=False, options=options
+                )
             )
         else:
             self._parameter_values = (
@@ -543,7 +568,7 @@ def solve(
                 )
             if (
                 self.operating_mode == "without experiment"
-                or self._model.name == "ElectrodeSOH model"
+                or "ElectrodeSOH" in self._model.name
             ):
                 if t_eval is None:
                     raise pybamm.SolverError(
@@ -717,13 +742,18 @@ def solve(
                         # Update _solution
                         self._solution = current_solution
 
-            for cycle_num, cycle_length in enumerate(
-                # tqdm is the progress bar.
-                tqdm.tqdm(
+            # check if a user has tqdm installed
+            if showprogress:
+                tqdm = have_optional_dependency("tqdm")
+                cycle_lengths = tqdm.tqdm(
                     self.experiment.cycle_lengths,
-                    disable=(not showprogress),
                     desc="Cycling",
-                ),
+                )
+            else:
+                cycle_lengths = self.experiment.cycle_lengths
+
+            for cycle_num, cycle_length in enumerate(
+                cycle_lengths,
                 start=1,
             ):
                 logs["cycle number"] = (
@@ -760,14 +790,19 @@ def solve(
                     # human-intuitive
                     op_conds = self.experiment.operating_conditions_steps[idx]
 
+                    # Hacky patch to allow correct processing of end_time and next_starting time
+                    # For efficiency purposes, op_conds treats identical steps as the same object
+                    # regardless of the initial time. Should be refactored as part of #3176
+                    op_conds_unproc = self.experiment.operating_conditions_steps_unprocessed[idx]
+
                     start_time = current_solution.t[-1]
 
                     # If step has an end time, dt must take that into account
-                    if op_conds.end_time:
+                    if getattr(op_conds_unproc, "end_time", None):
                         dt = min(
                             op_conds.duration,
                             (
-                                op_conds.end_time
+                                op_conds_unproc.end_time
                                 - (
                                     initial_start_time
                                     + timedelta(seconds=float(start_time))
@@ -820,9 +855,9 @@ def solve(
                     step_termination = step_solution.termination
 
                     # Add a padding rest step if necessary
-                    if op_conds.next_start_time is not None:
+                    if getattr(op_conds_unproc, "next_start_time", None) is not None:
                         rest_time = (
-                            op_conds.next_start_time
+                            op_conds_unproc.next_start_time
                             - (
                                 initial_start_time
                                 + timedelta(seconds=float(step_solution.t[-1]))
diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py
index a92a9309d4..c2b81c1568 100644
--- a/pybamm/solvers/base_solver.py
+++ b/pybamm/solvers/base_solver.py
@@ -38,6 +38,9 @@ class BaseSolver(object):
         The tolerance for the initial-condition solver (default is 1e-6).
     extrap_tol : float, optional
         The tolerance to assert whether extrapolation occurs or not. Default is 0.
+    output_variables : list[str], optional
+        List of variables to calculate and return. If none are specified then
+        the complete state vector is returned (can be very large) (default is [])
     """
 
     def __init__(
@@ -48,6 +51,7 @@ def __init__(
         root_method=None,
         root_tol=1e-6,
         extrap_tol=None,
+        output_variables=[],
     ):
         self.method = method
         self.rtol = rtol
@@ -55,6 +59,7 @@ def __init__(
         self.root_tol = root_tol
         self.root_method = root_method
         self.extrap_tol = extrap_tol or -1e-10
+        self.output_variables = output_variables
         self._model_set_up = {}
 
         # Defaults, can be overwritten by specific solver
@@ -62,6 +67,7 @@ def __init__(
         self.ode_solver = False
         self.algebraic_solver = False
         self._on_extrapolation = "warn"
+        self.computed_var_fcns = {}
 
     @property
     def root_method(self):
@@ -250,8 +256,57 @@ def set_up(self, model, inputs=None, t_eval=None, ics_only=False):
             model.casadi_sensitivities_rhs = jacp_rhs
             model.casadi_sensitivities_algebraic = jacp_algebraic
 
+            # if output_variables specified then convert functions to casadi
+            # expressions for evaluation within the respective solver
+            self.computed_var_fcns = {}
+            self.computed_dvar_dy_fcns = {}
+            self.computed_dvar_dp_fcns = {}
+            for key in self.output_variables:
+                # ExplicitTimeIntegral's are not computed as part of the solver and
+                # do not need to be converted
+                if isinstance(
+                    model.variables_and_events[key], pybamm.ExplicitTimeIntegral
+                ):
+                    continue
+                # Generate Casadi function to calculate variable and derivates
+                # to enable sensitivites to be computed within the solver
+                (
+                    self.computed_var_fcns[key],
+                    self.computed_dvar_dy_fcns[key],
+                    self.computed_dvar_dp_fcns[key],
+                    _,
+                ) = process(
+                    model.variables_and_events[key],
+                    BaseSolver._wrangle_name(key),
+                    vars_for_processing,
+                    use_jacobian=True,
+                    return_jacp_stacked=True,
+                )
+
         pybamm.logger.info("Finish solver set-up")
 
+    @classmethod
+    def _wrangle_name(cls, name: str) -> str:
+        """
+        Wrangle a function name to replace special characters
+        """
+        replacements = [
+            (" ", "_"),
+            ("[", ""),
+            ("]", ""),
+            (".", "_"),
+            ("-", "_"),
+            ("(", ""),
+            (")", ""),
+            ("%", "prc"),
+            (",", ""),
+            (".", ""),
+        ]
+        name = "v_" + name.casefold()
+        for string, replacement in replacements:
+            name = name.replace(string, replacement)
+        return name
+
     def _check_and_prepare_model_inplace(self, model, inputs, ics_only):
         """
         Performs checks on the model and prepares it for solving.
@@ -1366,7 +1421,9 @@ def _set_up_model_inputs(self, model, inputs):
         return ordered_inputs
 
 
-def process(symbol, name, vars_for_processing, use_jacobian=None):
+def process(
+    symbol, name, vars_for_processing, use_jacobian=None, return_jacp_stacked=None
+):
     """
     Parameters
     ----------
@@ -1376,6 +1433,8 @@ def process(symbol, name, vars_for_processing, use_jacobian=None):
         function evaluators created will have this base name
     use_jacobian: bool, optional
         whether to return Jacobian functions
+    return_jacp_stacked: bool, optional
+        returns Jacobian function wrt stacked parameters instead of jacp
 
     Returns
     -------
@@ -1553,17 +1612,28 @@ def jacp(*args, **kwargs):
                     "CasADi"
                 )
             )
-            # WARNING, jacp for convert_to_format=casadi does not return a dict
-            # instead it returns multiple return values, one for each param
-            # TODO: would it be faster to do the jacobian wrt pS_casadi_stacked?
-            jacp = casadi.Function(
-                name + "_jacp",
-                [t_casadi, y_and_S, p_casadi_stacked],
-                [
-                    casadi.densify(casadi.jacobian(casadi_expression, p_casadi[pname]))
-                    for pname in model.calculate_sensitivities
-                ],
-            )
+            # Compute derivate wrt p-stacked (can be passed to solver to
+            # compute sensitivities online)
+            if return_jacp_stacked:
+                jacp = casadi.Function(
+                    f"d{name}_dp",
+                    [t_casadi, y_casadi, p_casadi_stacked],
+                    [casadi.jacobian(casadi_expression, p_casadi_stacked)],
+                )
+            else:
+                # WARNING, jacp for convert_to_format=casadi does not return a dict
+                # instead it returns multiple return values, one for each param
+                # TODO: would it be faster to do the jacobian wrt pS_casadi_stacked?
+                jacp = casadi.Function(
+                    name + "_jacp",
+                    [t_casadi, y_and_S, p_casadi_stacked],
+                    [
+                        casadi.densify(
+                            casadi.jacobian(casadi_expression, p_casadi[pname])
+                        )
+                        for pname in model.calculate_sensitivities
+                    ],
+                )
 
         if use_jacobian:
             report(f"Calculating jacobian for {name} using CasADi")
diff --git a/pybamm/solvers/c_solvers/idaklu.cpp b/pybamm/solvers/c_solvers/idaklu.cpp
index 132e8883f4..be90955b9c 100644
--- a/pybamm/solvers/c_solvers/idaklu.cpp
+++ b/pybamm/solvers/c_solvers/idaklu.cpp
@@ -5,6 +5,7 @@
 #include <pybind11/functional.h>
 #include <pybind11/numpy.h>
 #include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
 #include <pybind11/stl_bind.h>
 
 #include <vector>
@@ -25,39 +26,75 @@ PYBIND11_MODULE(idaklu, m)
   py::bind_vector<std::vector<np_array>>(m, "VectorNdArray");
 
   m.def("solve_python", &solve_python,
-        "The solve function for python evaluators", py::arg("t"), py::arg("y0"),
-        py::arg("yp0"), py::arg("res"), py::arg("jac"), py::arg("sens"),
-        py::arg("get_jac_data"), py::arg("get_jac_row_vals"),
-        py::arg("get_jac_col_ptr"), py::arg("nnz"), py::arg("events"),
-        py::arg("number_of_events"), py::arg("use_jacobian"),
-        py::arg("rhs_alg_id"), py::arg("atol"), py::arg("rtol"),
-        py::arg("inputs"), py::arg("number_of_sensitivity_parameters"),
-        py::return_value_policy::take_ownership);
+    "The solve function for python evaluators",
+    py::arg("t"),
+    py::arg("y0"),
+    py::arg("yp0"),
+    py::arg("res"),
+    py::arg("jac"),
+    py::arg("sens"),
+    py::arg("get_jac_data"),
+    py::arg("get_jac_row_vals"),
+    py::arg("get_jac_col_ptr"),
+    py::arg("nnz"),
+    py::arg("events"),
+    py::arg("number_of_events"),
+    py::arg("use_jacobian"),
+    py::arg("rhs_alg_id"),
+    py::arg("atol"),
+    py::arg("rtol"),
+    py::arg("inputs"),
+    py::arg("number_of_sensitivity_parameters"),
+    py::return_value_policy::take_ownership);
 
   py::class_<CasadiSolver>(m, "CasadiSolver")
-      .def("solve", &CasadiSolver::solve, "perform a solve", py::arg("t"),
-           py::arg("y0"), py::arg("yp0"), py::arg("inputs"),
-           py::return_value_policy::take_ownership);
+  .def("solve", &CasadiSolver::solve,
+    "perform a solve",
+    py::arg("t"),
+    py::arg("y0"),
+    py::arg("yp0"),
+    py::arg("inputs"),
+    py::return_value_policy::take_ownership);
+
+  //py::bind_vector<std::vector<Function>>(m, "VectorFunction");
+  //py::implicitly_convertible<py::iterable, std::vector<Function>>();
 
   m.def("create_casadi_solver", &create_casadi_solver,
-        "Create a casadi idaklu solver object", py::arg("number_of_states"),
-        py::arg("number_of_parameters"), py::arg("rhs_alg"),
-        py::arg("jac_times_cjmass"), py::arg("jac_times_cjmass_colptrs"),
-        py::arg("jac_times_cjmass_rowvals"), py::arg("jac_times_cjmass_nnz"),
-        py::arg("jac_bandwidth_lower"), py::arg("jac_bandwidth_upper"),
-        py::arg("jac_action"), py::arg("mass_action"), py::arg("sens"),
-        py::arg("events"), py::arg("number_of_events"), py::arg("rhs_alg_id"),
-        py::arg("atol"), py::arg("rtol"), py::arg("inputs"), py::arg("options"),
-        py::return_value_policy::take_ownership);
-
-  m.def("generate_function", &generate_function, "Generate a casadi function",
-        py::arg("string"), py::return_value_policy::take_ownership);
+    "Create a casadi idaklu solver object",
+    py::arg("number_of_states"),
+    py::arg("number_of_parameters"),
+    py::arg("rhs_alg"),
+    py::arg("jac_times_cjmass"),
+    py::arg("jac_times_cjmass_colptrs"),
+    py::arg("jac_times_cjmass_rowvals"),
+    py::arg("jac_times_cjmass_nnz"),
+    py::arg("jac_bandwidth_lower"),
+    py::arg("jac_bandwidth_upper"),
+    py::arg("jac_action"),
+    py::arg("mass_action"),
+    py::arg("sens"),
+    py::arg("events"),
+    py::arg("number_of_events"),
+    py::arg("rhs_alg_id"),
+    py::arg("atol"),
+    py::arg("rtol"),
+    py::arg("inputs"),
+    py::arg("var_casadi_fcns"),
+    py::arg("dvar_dy_fcns"),
+    py::arg("dvar_dp_fcns"),
+    py::arg("options"),
+    py::return_value_policy::take_ownership);
+
+  m.def("generate_function", &generate_function,
+    "Generate a casadi function",
+    py::arg("string"),
+    py::return_value_policy::take_ownership);
 
   py::class_<Function>(m, "Function");
 
   py::class_<Solution>(m, "solution")
-      .def_readwrite("t", &Solution::t)
-      .def_readwrite("y", &Solution::y)
-      .def_readwrite("yS", &Solution::yS)
-      .def_readwrite("flag", &Solution::flag);
+  .def_readwrite("t", &Solution::t)
+  .def_readwrite("y", &Solution::y)
+  .def_readwrite("yS", &Solution::yS)
+  .def_readwrite("flag", &Solution::flag);
 }
diff --git a/pybamm/solvers/c_solvers/idaklu/CasadiSolver.cpp b/pybamm/solvers/c_solvers/idaklu/CasadiSolver.cpp
new file mode 100644
index 0000000000..16a04f8eb9
--- /dev/null
+++ b/pybamm/solvers/c_solvers/idaklu/CasadiSolver.cpp
@@ -0,0 +1 @@
+#include "CasadiSolver.hpp"
diff --git a/pybamm/solvers/c_solvers/idaklu/CasadiSolver.hpp b/pybamm/solvers/c_solvers/idaklu/CasadiSolver.hpp
new file mode 100644
index 0000000000..dac94579f3
--- /dev/null
+++ b/pybamm/solvers/c_solvers/idaklu/CasadiSolver.hpp
@@ -0,0 +1,49 @@
+#ifndef PYBAMM_IDAKLU_CASADI_SOLVER_HPP
+#define PYBAMM_IDAKLU_CASADI_SOLVER_HPP
+
+#include <casadi/casadi.hpp>
+using Function = casadi::Function;
+
+#include "casadi_functions.hpp"
+#include "common.hpp"
+#include "options.hpp"
+#include "solution.hpp"
+#include "sundials_legacy_wrapper.hpp"
+
+/**
+ * Abstract base class for solutions that can use different solvers and vector
+ * implementations.
+ * @brief An abstract base class for the Idaklu solver
+ */
+class CasadiSolver
+{
+public:
+
+  /**
+   * @brief Default constructor
+   */
+  CasadiSolver() = default;
+
+  /**
+   * @brief Default destructor
+   */
+  ~CasadiSolver() = default;
+
+  /**
+   * @brief Abstract solver method that returns a Solution class
+   */
+  virtual Solution solve(
+    np_array t_np,
+    np_array y0_np,
+    np_array yp0_np,
+    np_array_dense inputs) = 0;
+
+  /**
+   * Abstract method to initialize the solver, once vectors and solver classes
+   * are set
+   * @brief Abstract initialization method
+   */
+  virtual void Initialize() = 0;
+};
+
+#endif // PYBAMM_IDAKLU_CASADI_SOLVER_HPP
diff --git a/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.cpp b/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.cpp
new file mode 100644
index 0000000000..ad51eda4e1
--- /dev/null
+++ b/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.cpp
@@ -0,0 +1,518 @@
+#include "CasadiSolverOpenMP.hpp"
+#include "casadi_sundials_functions.hpp"
+#include <casadi/casadi.hpp>
+#include <casadi/core/function.hpp>
+#include <casadi/core/sparsity.hpp>
+
+CasadiSolverOpenMP::CasadiSolverOpenMP(
+  np_array atol_np,
+  double rel_tol,
+  np_array rhs_alg_id,
+  int number_of_parameters,
+  int number_of_events,
+  int jac_times_cjmass_nnz,
+  int jac_bandwidth_lower,
+  int jac_bandwidth_upper,
+  std::unique_ptr<CasadiFunctions> functions_arg,
+  const Options &options
+) :
+  atol_np(atol_np),
+  rhs_alg_id(rhs_alg_id),
+  number_of_states(atol_np.request().size),
+  number_of_parameters(number_of_parameters),
+  number_of_events(number_of_events),
+  jac_times_cjmass_nnz(jac_times_cjmass_nnz),
+  jac_bandwidth_lower(jac_bandwidth_lower),
+  jac_bandwidth_upper(jac_bandwidth_upper),
+  functions(std::move(functions_arg)),
+  options(options)
+{
+  // Construction code moved to Initialize() which is called from the
+  // (child) CasadiSolver_XXX class constructors.
+  DEBUG("CasadiSolverOpenMP::CasadiSolverOpenMP");
+  auto atol = atol_np.unchecked<1>();
+
+  // create SUNDIALS context object
+  SUNContext_Create(NULL, &sunctx);  // calls null-wrapper if Sundials Ver<6
+
+  // allocate memory for solver
+  ida_mem = IDACreate(sunctx);
+
+  // create the vector of initial values
+  AllocateVectors();
+  if (number_of_parameters > 0)
+  {
+    yyS = N_VCloneVectorArray(number_of_parameters, yy);
+    ypS = N_VCloneVectorArray(number_of_parameters, yp);
+  }
+  // set initial values
+  realtype *atval = N_VGetArrayPointer(avtol);
+  for (int i = 0; i < number_of_states; i++)
+    atval[i] = atol[i];
+  for (int is = 0; is < number_of_parameters; is++)
+  {
+    N_VConst(RCONST(0.0), yyS[is]);
+    N_VConst(RCONST(0.0), ypS[is]);
+  }
+
+  // create Matrix objects
+  SetMatrix();
+
+  // initialise solver
+  IDAInit(ida_mem, residual_casadi, 0, yy, yp);
+
+  // set tolerances
+  rtol = RCONST(rel_tol);
+  IDASVtolerances(ida_mem, rtol, avtol);
+
+  // set events
+  IDARootInit(ida_mem, number_of_events, events_casadi);
+  void *user_data = functions.get();
+  IDASetUserData(ida_mem, user_data);
+
+  // specify preconditioner type
+  precon_type = SUN_PREC_NONE;
+  if (options.preconditioner != "none") {
+    precon_type = SUN_PREC_LEFT;
+  }
+}
+
+void CasadiSolverOpenMP::AllocateVectors() {
+  // Create vectors
+  yy = N_VNew_OpenMP(number_of_states, options.num_threads, sunctx);
+  yp = N_VNew_OpenMP(number_of_states, options.num_threads, sunctx);
+  avtol = N_VNew_OpenMP(number_of_states, options.num_threads, sunctx);
+  id = N_VNew_OpenMP(number_of_states, options.num_threads, sunctx);
+}
+
+void CasadiSolverOpenMP::SetMatrix() {
+  // Create Matrix object
+  if (options.jacobian == "sparse")
+  {
+    DEBUG("\tsetting sparse matrix");
+    J = SUNSparseMatrix(
+      number_of_states,
+      number_of_states,
+      jac_times_cjmass_nnz,
+      CSC_MAT,  // CSC is used by casadi; CSR requires a conversion step
+      sunctx
+    );
+  }
+  else if (options.jacobian == "banded") {
+    DEBUG("\tsetting banded matrix");
+    J = SUNBandMatrix(
+      number_of_states,
+      jac_bandwidth_upper,
+      jac_bandwidth_lower,
+      sunctx
+    );
+  } else if (options.jacobian == "dense" || options.jacobian == "none")
+  {
+    DEBUG("\tsetting dense matrix");
+    J = SUNDenseMatrix(
+      number_of_states,
+      number_of_states,
+      sunctx
+    );
+  }
+  else if (options.jacobian == "matrix-free")
+  {
+    DEBUG("\tsetting matrix-free");
+    J = NULL;
+  }
+  else
+    throw std::invalid_argument("Unsupported matrix requested");
+}
+
+void CasadiSolverOpenMP::Initialize() {
+  // Call after setting the solver
+
+  // attach the linear solver
+  if (LS == nullptr)
+    throw std::invalid_argument("Linear solver not set");
+  IDASetLinearSolver(ida_mem, LS, J);
+
+  if (options.preconditioner != "none")
+  {
+    DEBUG("\tsetting IDADDB preconditioner");
+    // setup preconditioner
+    IDABBDPrecInit(
+      ida_mem, number_of_states, options.precon_half_bandwidth,
+      options.precon_half_bandwidth, options.precon_half_bandwidth_keep,
+      options.precon_half_bandwidth_keep, 0.0, residual_casadi_approx, NULL);
+  }
+
+  if (options.jacobian == "matrix-free")
+    IDASetJacTimes(ida_mem, NULL, jtimes_casadi);
+  else if (options.jacobian != "none")
+    IDASetJacFn(ida_mem, jacobian_casadi);
+
+  if (number_of_parameters > 0)
+  {
+    IDASensInit(ida_mem, number_of_parameters, IDA_SIMULTANEOUS,
+                sensitivities_casadi, yyS, ypS);
+    IDASensEEtolerances(ida_mem);
+  }
+
+  SUNLinSolInitialize(LS);
+
+  auto id_np_val = rhs_alg_id.unchecked<1>();
+  realtype *id_val;
+  id_val = N_VGetArrayPointer(id);
+
+  int ii;
+  for (ii = 0; ii < number_of_states; ii++)
+    id_val[ii] = id_np_val[ii];
+
+  IDASetId(ida_mem, id);
+}
+
+CasadiSolverOpenMP::~CasadiSolverOpenMP()
+{
+  // Free memory
+  if (number_of_parameters > 0)
+    IDASensFree(ida_mem);
+
+  SUNLinSolFree(LS);
+  SUNMatDestroy(J);
+  N_VDestroy(avtol);
+  N_VDestroy(yy);
+  N_VDestroy(yp);
+  N_VDestroy(id);
+
+  if (number_of_parameters > 0)
+  {
+    N_VDestroyVectorArray(yyS, number_of_parameters);
+    N_VDestroyVectorArray(ypS, number_of_parameters);
+  }
+
+  IDAFree(&ida_mem);
+  SUNContext_Free(&sunctx);
+}
+
+void CasadiSolverOpenMP::CalcVars(
+    realtype *y_return,
+    size_t length_of_return_vector,
+    size_t t_i,
+    realtype *tret,
+    realtype *yval,
+    const std::vector<realtype*>& ySval,
+    realtype *yS_return,
+    size_t *ySk
+) {
+  // Evaluate casadi functions for each requested variable and store
+  size_t j = 0;
+  for (auto& var_fcn : functions->var_casadi_fcns) {
+    var_fcn({tret, yval, functions->inputs.data()}, {res});
+    // store in return vector
+    for (size_t jj=0; jj<var_fcn.nnz_out(); jj++)
+      y_return[t_i*length_of_return_vector + j++] = res[jj];
+  }
+  // calculate sensitivities
+  CalcVarsSensitivities(tret, yval, ySval, yS_return, ySk);
+}
+
+void CasadiSolverOpenMP::CalcVarsSensitivities(
+    realtype *tret,
+    realtype *yval,
+    const std::vector<realtype*>& ySval,
+    realtype *yS_return,
+    size_t *ySk
+) {
+  // Calculate sensitivities
+
+  // Loop over variables
+  realtype* dens_dvar_dp = new realtype[number_of_parameters];
+  for (size_t dvar_k=0; dvar_k<functions->dvar_dy_fcns.size(); dvar_k++) {
+    // Isolate functions
+    CasadiFunction dvar_dy = functions->dvar_dy_fcns[dvar_k];
+    CasadiFunction dvar_dp = functions->dvar_dp_fcns[dvar_k];
+    // Calculate dvar/dy
+    dvar_dy({tret, yval, functions->inputs.data()}, {res_dvar_dy});
+    casadi::Sparsity spdy = dvar_dy.sparsity_out(0);
+    // Calculate dvar/dp and convert to dense array for indexing
+    dvar_dp({tret, yval, functions->inputs.data()}, {res_dvar_dp});
+    casadi::Sparsity spdp = dvar_dp.sparsity_out(0);
+    for(int k=0; k<number_of_parameters; k++)
+      dens_dvar_dp[k]=0;
+    for(int k=0; k<spdp.nnz(); k++)
+      dens_dvar_dp[spdp.get_row()[k]] = res_dvar_dp[k];
+    // Calculate sensitivities
+    for(int paramk=0; paramk<number_of_parameters; paramk++) {
+      yS_return[*ySk] = dens_dvar_dp[paramk];
+      for(int spk=0; spk<dvar_dy.nnz_out(); spk++)
+        yS_return[*ySk] += res_dvar_dy[spk] * ySval[paramk][spdy.get_col()[spk]];
+      (*ySk)++;
+    }
+  }
+}
+
+Solution CasadiSolverOpenMP::solve(
+    np_array t_np,
+    np_array y0_np,
+    np_array yp0_np,
+    np_array_dense inputs
+)
+{
+  DEBUG("CasadiSolver::solve");
+
+  int number_of_timesteps = t_np.request().size;
+  auto t = t_np.unchecked<1>();
+  realtype t0 = RCONST(t(0));
+  auto y0 = y0_np.unchecked<1>();
+  auto yp0 = yp0_np.unchecked<1>();
+  auto n_coeffs = number_of_states + number_of_parameters * number_of_states;
+
+  if (y0.size() != n_coeffs)
+    throw std::domain_error(
+      "y0 has wrong size. Expected " + std::to_string(n_coeffs) +
+      " but got " + std::to_string(y0.size()));
+
+  if (yp0.size() != n_coeffs)
+    throw std::domain_error(
+      "yp0 has wrong size. Expected " + std::to_string(n_coeffs) +
+      " but got " + std::to_string(yp0.size()));
+
+  // set inputs
+  auto p_inputs = inputs.unchecked<2>();
+  for (int i = 0; i < functions->inputs.size(); i++)
+    functions->inputs[i] = p_inputs(i, 0);
+
+  // set initial conditions
+  realtype *yval = N_VGetArrayPointer(yy);
+  realtype *ypval = N_VGetArrayPointer(yp);
+  std::vector<realtype *> ySval(number_of_parameters);
+  std::vector<realtype *> ypSval(number_of_parameters);
+  for (int p = 0 ; p < number_of_parameters; p++) {
+    ySval[p] = N_VGetArrayPointer(yyS[p]);
+    ypSval[p] = N_VGetArrayPointer(ypS[p]);
+    for (int i = 0; i < number_of_states; i++) {
+      ySval[p][i] = y0[i + (p + 1) * number_of_states];
+      ypSval[p][i] = yp0[i + (p + 1) * number_of_states];
+    }
+  }
+
+  for (int i = 0; i < number_of_states; i++)
+  {
+    yval[i] = y0[i];
+    ypval[i] = yp0[i];
+  }
+
+  IDAReInit(ida_mem, t0, yy, yp);
+  if (number_of_parameters > 0)
+    IDASensReInit(ida_mem, IDA_SIMULTANEOUS, yyS, ypS);
+
+  // correct initial values
+  DEBUG("IDACalcIC");
+  IDACalcIC(ida_mem, IDA_YA_YDP_INIT, t(1));
+  if (number_of_parameters > 0)
+    IDAGetSens(ida_mem, &t0, yyS);
+
+  realtype tret;
+  realtype t_final = t(number_of_timesteps - 1);
+
+  // set return vectors
+  int length_of_return_vector = 0;
+  size_t max_res_size = 0;  // maximum result size (for common result buffer)
+  size_t max_res_dvar_dy = 0, max_res_dvar_dp = 0;
+  if (functions->var_casadi_fcns.size() > 0) {
+    // return only the requested variables list after computation
+    for (auto& var_fcn : functions->var_casadi_fcns) {
+      max_res_size = std::max(max_res_size, size_t(var_fcn.nnz_out()));
+      length_of_return_vector += var_fcn.nnz_out();
+      for (auto& dvar_fcn : functions->dvar_dy_fcns)
+        max_res_dvar_dy = std::max(max_res_dvar_dy, size_t(dvar_fcn.nnz_out()));
+      for (auto& dvar_fcn : functions->dvar_dp_fcns)
+        max_res_dvar_dp = std::max(max_res_dvar_dp, size_t(dvar_fcn.nnz_out()));
+    }
+  } else {
+    // Return full y state-vector
+    length_of_return_vector = number_of_states;
+  }
+  realtype *t_return = new realtype[number_of_timesteps];
+  realtype *y_return = new realtype[number_of_timesteps *
+                                    length_of_return_vector];
+  realtype *yS_return = new realtype[number_of_parameters *
+                                     number_of_timesteps *
+                                     length_of_return_vector];
+
+  res = new realtype[max_res_size];
+  res_dvar_dy = new realtype[max_res_dvar_dy];
+  res_dvar_dp = new realtype[max_res_dvar_dp];
+
+  py::capsule free_t_when_done(
+    t_return,
+    [](void *f) {
+      realtype *vect = reinterpret_cast<realtype *>(f);
+      delete[] vect;
+    }
+  );
+  py::capsule free_y_when_done(
+    y_return,
+    [](void *f) {
+      realtype *vect = reinterpret_cast<realtype *>(f);
+      delete[] vect;
+    }
+  );
+  py::capsule free_yS_when_done(
+    yS_return,
+    [](void *f) {
+      realtype *vect = reinterpret_cast<realtype *>(f);
+      delete[] vect;
+    }
+  );
+
+  // Initial state (t_i=0)
+  int t_i = 0;
+  size_t ySk = 0;
+  t_return[t_i] = t(t_i);
+  if (functions->var_casadi_fcns.size() > 0) {
+    // Evaluate casadi functions for each requested variable and store
+    CalcVars(y_return, length_of_return_vector, t_i,
+             &tret, yval, ySval, yS_return, &ySk);
+  } else {
+    // Retain complete copy of the state vector
+    for (int j = 0; j < number_of_states; j++)
+      y_return[j] = yval[j];
+    for (int j = 0; j < number_of_parameters; j++)
+    {
+      const int base_index = j * number_of_timesteps * number_of_states;
+      for (int k = 0; k < number_of_states; k++)
+        yS_return[base_index + k] = ySval[j][k];
+    }
+  }
+
+  // Subsequent states (t_i>0)
+  int retval;
+  t_i = 1;
+  while (true)
+  {
+    realtype t_next = t(t_i);
+    IDASetStopTime(ida_mem, t_next);
+    DEBUG("IDASolve");
+    retval = IDASolve(ida_mem, t_final, &tret, yy, yp, IDA_NORMAL);
+
+    if (retval == IDA_TSTOP_RETURN ||
+        retval == IDA_SUCCESS ||
+        retval == IDA_ROOT_RETURN)
+    {
+      if (number_of_parameters > 0)
+        IDAGetSens(ida_mem, &tret, yyS);
+
+      // Evaluate and store results for the time step
+      t_return[t_i] = tret;
+      if (functions->var_casadi_fcns.size() > 0) {
+        // Evaluate casadi functions for each requested variable and store
+        // NOTE: Indexing of yS_return is (time:var:param)
+        CalcVars(y_return, length_of_return_vector, t_i,
+                 &tret, yval, ySval, yS_return, &ySk);
+      } else {
+        // Retain complete copy of the state vector
+        for (int j = 0; j < number_of_states; j++)
+          y_return[t_i * number_of_states + j] = yval[j];
+        for (int j = 0; j < number_of_parameters; j++)
+        {
+          const int base_index =
+            j * number_of_timesteps * number_of_states +
+            t_i * number_of_states;
+          for (int k = 0; k < number_of_states; k++)
+            // NOTE: Indexing of yS_return is (time:param:yvec)
+            yS_return[base_index + k] = ySval[j][k];
+        }
+      }
+      t_i += 1;
+
+      if (retval == IDA_SUCCESS ||
+          retval == IDA_ROOT_RETURN)
+        break;
+    }
+    else
+    {
+      // failed
+      break;
+    }
+  }
+
+  np_array t_ret = np_array(
+    t_i,
+    &t_return[0],
+    free_t_when_done
+  );
+  np_array y_ret = np_array(
+    t_i * length_of_return_vector,
+    &y_return[0],
+    free_y_when_done
+  );
+  // Note: Ordering of vector is differnet if computing variables vs returning
+  // the complete state vector
+  np_array yS_ret;
+  if (functions->var_casadi_fcns.size() > 0) {
+    yS_ret = np_array(
+      std::vector<ptrdiff_t> {
+        number_of_timesteps,
+        length_of_return_vector,
+        number_of_parameters
+      },
+      &yS_return[0],
+      free_yS_when_done
+    );
+  } else {
+    yS_ret = np_array(
+      std::vector<ptrdiff_t> {
+        number_of_parameters,
+        number_of_timesteps,
+        length_of_return_vector
+      },
+      &yS_return[0],
+      free_yS_when_done
+    );
+  }
+
+  Solution sol(retval, t_ret, y_ret, yS_ret);
+
+  if (options.print_stats)
+  {
+    long nsteps, nrevals, nlinsetups, netfails;
+    int klast, kcur;
+    realtype hinused, hlast, hcur, tcur;
+
+    IDAGetIntegratorStats(
+      ida_mem,
+      &nsteps,
+      &nrevals,
+      &nlinsetups,
+      &netfails,
+      &klast,
+      &kcur,
+      &hinused,
+      &hlast,
+      &hcur,
+      &tcur
+    );
+
+    long nniters, nncfails;
+    IDAGetNonlinSolvStats(ida_mem, &nniters, &nncfails);
+
+    long int ngevalsBBDP = 0;
+    if (options.using_iterative_solver)
+      IDABBDPrecGetNumGfnEvals(ida_mem, &ngevalsBBDP);
+
+    py::print("Solver Stats:");
+    py::print("\tNumber of steps =", nsteps);
+    py::print("\tNumber of calls to residual function =", nrevals);
+    py::print("\tNumber of calls to residual function in preconditioner =",
+              ngevalsBBDP);
+    py::print("\tNumber of linear solver setup calls =", nlinsetups);
+    py::print("\tNumber of error test failures =", netfails);
+    py::print("\tMethod order used on last step =", klast);
+    py::print("\tMethod order used on next step =", kcur);
+    py::print("\tInitial step size =", hinused);
+    py::print("\tStep size on last step =", hlast);
+    py::print("\tStep size on next step =", hcur);
+    py::print("\tCurrent internal time reached =", tcur);
+    py::print("\tNumber of nonlinear iterations performed =", nniters);
+    py::print("\tNumber of nonlinear convergence failures =", nncfails);
+  }
+
+  return sol;
+}
diff --git a/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.hpp b/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.hpp
new file mode 100644
index 0000000000..2312f9cf8f
--- /dev/null
+++ b/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.hpp
@@ -0,0 +1,147 @@
+#ifndef PYBAMM_IDAKLU_CASADISOLVEROPENMP_HPP
+#define PYBAMM_IDAKLU_CASADISOLVEROPENMP_HPP
+
+#include "CasadiSolver.hpp"
+#include <casadi/casadi.hpp>
+using Function = casadi::Function;
+
+#include "casadi_functions.hpp"
+#include "common.hpp"
+#include "options.hpp"
+#include "solution.hpp"
+#include "sundials_legacy_wrapper.hpp"
+
+/**
+ * @brief Abstract solver class based on OpenMP vectors
+ *
+ * An abstract class that implements a solution based on OpenMP
+ * vectors but needs to be provided with a suitable linear solver.
+ *
+ * This class broadly implements the following skeleton workflow:
+ * (https://sundials.readthedocs.io/en/latest/ida/Usage/index.html)
+ *    1. (N/A) Initialize parallel or multi-threaded environment
+ *    2. Create the SUNDIALS context object
+ *    3. Create the vector of initial values
+ *    4. Create matrix object (if appropriate)
+ *    5. Create linear solver object
+ *    6. (N/A) Create nonlinear solver object
+ *    7. Create IDA object
+ *    8. Initialize IDA solver
+ *    9. Specify integration tolerances
+ *   10. Attach the linear solver
+ *   11. Set linear solver optional inputs
+ *   12. (N/A) Attach nonlinear solver module
+ *   13. (N/A) Set nonlinear solver optional inputs
+ *   14. Specify rootfinding problem (optional)
+ *   15. Set optional inputs
+ *   16. Correct initial values (optional)
+ *   17. Advance solution in time
+ *   18. Get optional outputs
+ *   19. Destroy objects
+ *   20. (N/A) Finalize MPI
+ */
+class CasadiSolverOpenMP : public CasadiSolver
+{
+  // NB: cppcheck-suppress unusedStructMember is used because codacy reports
+  //     these members as unused even though they are important in child
+  //     classes, but are passed by variadic arguments (and are therefore unnamed)
+public:
+  void *ida_mem = nullptr;
+  np_array atol_np;
+  np_array rhs_alg_id;
+  int number_of_states;  // cppcheck-suppress unusedStructMember
+  int number_of_parameters;  // cppcheck-suppress unusedStructMember
+  int number_of_events;  // cppcheck-suppress unusedStructMember
+  int precon_type;  // cppcheck-suppress unusedStructMember
+  N_Vector yy, yp, avtol;  // y, y', and absolute tolerance
+  N_Vector *yyS;  // cppcheck-suppress unusedStructMember
+  N_Vector *ypS;  // cppcheck-suppress unusedStructMember
+  N_Vector id;              // rhs_alg_id
+  realtype rtol;
+  const int jac_times_cjmass_nnz;  // cppcheck-suppress unusedStructMember
+  int jac_bandwidth_lower;  // cppcheck-suppress unusedStructMember
+  int jac_bandwidth_upper;  // cppcheck-suppress unusedStructMember
+  SUNMatrix J;
+  SUNLinearSolver LS = nullptr;
+  std::unique_ptr<CasadiFunctions> functions;
+  realtype *res = nullptr;
+  realtype *res_dvar_dy = nullptr;
+  realtype *res_dvar_dp = nullptr;
+  Options options;
+
+#if SUNDIALS_VERSION_MAJOR >= 6
+  SUNContext sunctx;
+#endif
+
+public:
+  /**
+   * @brief Constructor
+   */
+  CasadiSolverOpenMP(
+    np_array atol_np,
+    double rel_tol,
+    np_array rhs_alg_id,
+    int number_of_parameters,
+    int number_of_events,
+    int jac_times_cjmass_nnz,
+    int jac_bandwidth_lower,
+    int jac_bandwidth_upper,
+    std::unique_ptr<CasadiFunctions> functions,
+    const Options& options);
+
+  /**
+   * @brief Destructor
+   */
+  ~CasadiSolverOpenMP();
+
+  /**
+   * Evaluate casadi functions (including sensitivies) for each requested
+   * variable and store
+   * @brief Evaluate casadi functions
+   */
+  void CalcVars(
+    realtype *y_return,
+    size_t length_of_return_vector,
+    size_t t_i,
+    realtype *tret,
+    realtype *yval,
+    const std::vector<realtype*>& ySval,
+    realtype *yS_return,
+    size_t *ySk);
+
+  /**
+   * @brief Evaluate casadi functions for sensitivities
+   */
+  void CalcVarsSensitivities(
+    realtype *tret,
+    realtype *yval,
+    const std::vector<realtype*>& ySval,
+    realtype *yS_return,
+    size_t *ySk);
+
+  /**
+   * @brief The main solve method that solves for each variable and time step
+   */
+  Solution solve(
+    np_array t_np,
+    np_array y0_np,
+    np_array yp0_np,
+    np_array_dense inputs) override;
+
+  /**
+   * @brief Concrete implementation of initialization method
+   */
+  void Initialize() override;
+
+  /**
+   * @brief Allocate memory for OpenMP vectors
+   */
+  void AllocateVectors();
+
+  /**
+   * @brief Allocate memory for matrices (noting appropriate matrix format/types)
+   */
+  void SetMatrix();
+};
+
+#endif // PYBAMM_IDAKLU_CASADISOLVEROPENMP_HPP
diff --git a/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP_solvers.cpp b/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP_solvers.cpp
new file mode 100644
index 0000000000..868d2b2138
--- /dev/null
+++ b/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP_solvers.cpp
@@ -0,0 +1 @@
+#include "CasadiSolverOpenMP_solvers.hpp"
diff --git a/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP_solvers.hpp b/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP_solvers.hpp
new file mode 100644
index 0000000000..3e39e5a303
--- /dev/null
+++ b/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP_solvers.hpp
@@ -0,0 +1,125 @@
+#ifndef PYBAMM_IDAKLU_CASADI_SOLVER_OPENMP_HPP
+#define PYBAMM_IDAKLU_CASADI_SOLVER_OPENMP_HPP
+
+#include "CasadiSolverOpenMP.hpp"
+#include "casadi_solver.hpp"
+
+/**
+ * @brief CasadiSolver Dense implementation with OpenMP class
+ */
+class CasadiSolverOpenMP_Dense : public CasadiSolverOpenMP {
+public:
+  template<typename ... Args>
+  CasadiSolverOpenMP_Dense(Args&& ... args)
+    : CasadiSolverOpenMP(std::forward<Args>(args) ...)
+  {
+    LS = SUNLinSol_Dense(yy, J, sunctx);
+    Initialize();
+  }
+};
+
+/**
+ * @brief CasadiSolver KLU implementation with OpenMP class
+ */
+class CasadiSolverOpenMP_KLU : public CasadiSolverOpenMP {
+public:
+  template<typename ... Args>
+  CasadiSolverOpenMP_KLU(Args&& ... args)
+    : CasadiSolverOpenMP(std::forward<Args>(args) ...)
+  {
+    LS = SUNLinSol_KLU(yy, J, sunctx);
+    Initialize();
+  }
+};
+
+/**
+ * @brief CasadiSolver Banded implementation with OpenMP class
+ */
+class CasadiSolverOpenMP_Band : public CasadiSolverOpenMP {
+public:
+  template<typename ... Args>
+  CasadiSolverOpenMP_Band(Args&& ... args)
+    : CasadiSolverOpenMP(std::forward<Args>(args) ...)
+  {
+    LS = SUNLinSol_Band(yy, J, sunctx);
+    Initialize();
+  }
+};
+
+/**
+ * @brief CasadiSolver SPBCGS implementation with OpenMP class
+ */
+class CasadiSolverOpenMP_SPBCGS : public CasadiSolverOpenMP {
+public:
+  template<typename ... Args>
+  CasadiSolverOpenMP_SPBCGS(Args&& ... args)
+    : CasadiSolverOpenMP(std::forward<Args>(args) ...)
+  {
+    LS = SUNLinSol_SPBCGS(
+      yy,
+      precon_type,
+      options.linsol_max_iterations,
+      sunctx
+    );
+    Initialize();
+  }
+};
+
+/**
+ * @brief CasadiSolver SPFGMR implementation with OpenMP class
+ */
+class CasadiSolverOpenMP_SPFGMR : public CasadiSolverOpenMP {
+public:
+  template<typename ... Args>
+  CasadiSolverOpenMP_SPFGMR(Args&& ... args)
+    : CasadiSolverOpenMP(std::forward<Args>(args) ...)
+  {
+    LS = SUNLinSol_SPFGMR(
+      yy,
+      precon_type,
+      options.linsol_max_iterations,
+      sunctx
+    );
+    Initialize();
+  }
+};
+
+/**
+ * @brief CasadiSolver SPGMR implementation with OpenMP class
+ */
+class CasadiSolverOpenMP_SPGMR : public CasadiSolverOpenMP {
+public:
+  template<typename ... Args>
+  CasadiSolverOpenMP_SPGMR(Args&& ... args)
+    : CasadiSolverOpenMP(std::forward<Args>(args) ...)
+  {
+    LS = SUNLinSol_SPGMR(
+      yy,
+      precon_type,
+      options.linsol_max_iterations,
+      sunctx
+    );
+    Initialize();
+  }
+};
+
+/**
+ * @brief CasadiSolver SPTFQMR implementation with OpenMP class
+ */
+class CasadiSolverOpenMP_SPTFQMR : public CasadiSolverOpenMP {
+public:
+  template<typename ... Args>
+  CasadiSolverOpenMP_SPTFQMR(Args&& ... args)
+    : CasadiSolverOpenMP(std::forward<Args>(args) ...)
+  {
+    LS = SUNLinSol_SPTFQMR(
+      yy,
+      precon_type,
+      options.linsol_max_iterations,
+      sunctx
+    );
+    Initialize();
+  }
+};
+
+#endif // PYBAMM_IDAKLU_CASADI_SOLVER_OPENMP_HPP
diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_functions.cpp b/pybamm/solvers/c_solvers/idaklu/casadi_functions.cpp
index 310575742d..ddad4612c9 100644
--- a/pybamm/solvers/c_solvers/idaklu/casadi_functions.cpp
+++ b/pybamm/solvers/c_solvers/idaklu/casadi_functions.cpp
@@ -7,23 +7,17 @@ CasadiFunction::CasadiFunction(const Function &f) : m_func(f)
   size_t sz_iw;
   size_t sz_w;
   m_func.sz_work(sz_arg, sz_res, sz_iw, sz_w);
-  // std::cout << "name = "<< m_func.name() << " arg = " << sz_arg << " res = "
-  // << sz_res << " iw = " << sz_iw << " w = " << sz_w << std::endl; for (int i
-  // = 0; i < sz_arg; i++) {
-  //   std::cout << "Sparsity for input " << i << std::endl;
-  //   const Sparsity& sparsity = m_func.sparsity_in(i);
-  // }
-  // for (int i = 0; i < sz_res; i++) {
-  //   std::cout << "Sparsity for output " << i << std::endl;
-  //   const Sparsity& sparsity = m_func.sparsity_out(i);
-  // }
-  m_arg.resize(sz_arg);
-  m_res.resize(sz_res);
-  m_iw.resize(sz_iw);
-  m_w.resize(sz_w);
+  //int nnz = (sz_res>0) ? m_func.nnz_out() : 0;
+  //std::cout << "name = "<< m_func.name() << " arg = " << sz_arg << " res = "
+  //  << sz_res << " iw = " << sz_iw << " w = " << sz_w << " nnz = " << nnz <<
+  //  std::endl;
+  m_arg.resize(sz_arg, nullptr);
+  m_res.resize(sz_res, nullptr);
+  m_iw.resize(sz_iw, 0);
+  m_w.resize(sz_w, 0);
 }
 
-// only call this once m_arg and m_res have been set appropriatelly
+// only call this once m_arg and m_res have been set appropriately
 void CasadiFunction::operator()()
 {
   int mem = m_func.checkout();
@@ -31,25 +25,59 @@ void CasadiFunction::operator()()
   m_func.release(mem);
 }
 
+casadi_int CasadiFunction::nnz_out() {
+  return m_func.nnz_out();
+}
+
+casadi::Sparsity CasadiFunction::sparsity_out(casadi_int ind) {
+  return m_func.sparsity_out(ind);
+}
+
+void CasadiFunction::operator()(const std::vector<realtype*>& inputs,
+                                const std::vector<realtype*>& results)
+{
+  // Set-up input arguments, provide result vector, then execute function
+  // Example call: fcn({in1, in2, in3}, {out1})
+  for(size_t k=0; k<inputs.size(); k++)
+    m_arg[k] = inputs[k];
+  for(size_t k=0; k<results.size(); k++)
+    m_res[k] = results[k];
+  operator()();
+}
+
 CasadiFunctions::CasadiFunctions(
-    const Function &rhs_alg, const Function &jac_times_cjmass,
-    const int jac_times_cjmass_nnz,
-    const int jac_bandwidth_lower, const int jac_bandwidth_upper,
-    const np_array_int &jac_times_cjmass_rowvals_arg,
-    const np_array_int &jac_times_cjmass_colptrs_arg,
-    const int inputs_length, const Function &jac_action,
-    const Function &mass_action, const Function &sens, const Function &events,
-    const int n_s, int n_e, const int n_p, const Options& options)
-    : number_of_states(n_s), number_of_events(n_e), number_of_parameters(n_p),
-      number_of_nnz(jac_times_cjmass_nnz),
-      jac_bandwidth_lower(jac_bandwidth_lower), jac_bandwidth_upper(jac_bandwidth_upper),
-      rhs_alg(rhs_alg),
-      jac_times_cjmass(jac_times_cjmass), jac_action(jac_action),
-      mass_action(mass_action), sens(sens), events(events),
-      tmp_state_vector(number_of_states),
-      tmp_sparse_jacobian_data(jac_times_cjmass_nnz),
-      options(options)
+  const Function &rhs_alg, const Function &jac_times_cjmass,
+  const int jac_times_cjmass_nnz,
+  const int jac_bandwidth_lower, const int jac_bandwidth_upper,
+  const np_array_int &jac_times_cjmass_rowvals_arg,
+  const np_array_int &jac_times_cjmass_colptrs_arg,
+  const int inputs_length, const Function &jac_action,
+  const Function &mass_action, const Function &sens, const Function &events,
+  const int n_s, int n_e, const int n_p,
+  const std::vector<Function*>& var_casadi_fcns,
+  const std::vector<Function*>& dvar_dy_fcns,
+  const std::vector<Function*>& dvar_dp_fcns,
+  const Options& options)
+  : number_of_states(n_s), number_of_events(n_e), number_of_parameters(n_p),
+    number_of_nnz(jac_times_cjmass_nnz),
+    jac_bandwidth_lower(jac_bandwidth_lower), jac_bandwidth_upper(jac_bandwidth_upper),
+    rhs_alg(rhs_alg),
+    jac_times_cjmass(jac_times_cjmass), jac_action(jac_action),
+    mass_action(mass_action), sens(sens), events(events),
+    tmp_state_vector(number_of_states),
+    tmp_sparse_jacobian_data(jac_times_cjmass_nnz),
+    options(options)
 {
+  // convert casadi::Function list to CasadiFunction list
+  for (auto& var : var_casadi_fcns) {
+    this->var_casadi_fcns.push_back(CasadiFunction(*var));
+  }
+  for (auto& var : dvar_dy_fcns) {
+    this->dvar_dy_fcns.push_back(CasadiFunction(*var));
+  }
+  for (auto& var : dvar_dp_fcns) {
+    this->dvar_dp_fcns.push_back(CasadiFunction(*var));
+  }
 
   // copy across numpy array values
   const int n_row_vals = jac_times_cjmass_rowvals_arg.request().size;
@@ -67,8 +95,11 @@ CasadiFunctions::CasadiFunctions(
   }
 
   inputs.resize(inputs_length);
-
 }
 
-realtype *CasadiFunctions::get_tmp_state_vector() { return tmp_state_vector.data(); }
-realtype *CasadiFunctions::get_tmp_sparse_jacobian_data() { return tmp_sparse_jacobian_data.data(); }
+realtype *CasadiFunctions::get_tmp_state_vector() {
+  return tmp_state_vector.data();
+}
+realtype *CasadiFunctions::get_tmp_sparse_jacobian_data() {
+  return tmp_sparse_jacobian_data.data();
+}
diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp b/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp
index 03264a8478..1a33b957f8 100644
--- a/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp
+++ b/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp
@@ -3,20 +3,87 @@
 
 #include "common.hpp"
 #include "options.hpp"
-#include "solution.hpp"
 #include <casadi/casadi.hpp>
+#include <casadi/core/sparsity.hpp>
+#include <memory>
+
+/**
+ * Utility function to convert compressed-sparse-column (CSC) to/from
+ * compressed-sparse-row (CSR) matrix representation. Conversion is symmetric /
+ * invertible using this function.
+ * @brief Utility function to convert to/from CSC/CSR matrix representations.
+ * @param f Data vector containing the sparse matrix elements
+ * @param c Index pointer to column starts
+ * @param r Array of row indices
+ * @param nf New data vector that will contain the transformed sparse matrix
+ * @param nc New array of column indices
+ * @param nr New index pointer to row starts
+ */
+template<typename T1, typename T2>
+void csc_csr(const realtype f[], const T1 c[], const T1 r[], realtype nf[], T2 nc[], T2 nr[], int N, int cols) {
+  std::vector<int> nn(cols+1);
+  std::vector<int> rr(N);
+  for (int i=0; i<cols+1; i++)
+    nc[i] = 0;
+
+  for (int k = 0, i = 0; i < cols+1; i++) {
+    for (int j = 0; j < r[i+1] - r[i]; j++) {
+      if (k == N)  // SUNDIALS indexing does not include the count element
+        break;
+      rr[k++] = i;
+    }
+  }
+  for (int i = 0; i < N; i++)
+    nc[c[i]+1]++;
+  for (int i = 1; i <= cols; i++)
+    nc[i] += nc[i-1];
+  for (int i = 0; i < cols+1; i++)
+    nn[i] = nc[i];
+  for (int i = 0; i < N; i++) {
+    int x = nn[c[i]]++;
+    nf[x] = f[i];
+    nr[x] = rr[i];
+  }
+}
+
 
 using Function = casadi::Function;
 
+/**
+ * @brief Class for handling individual casadi functions
+ */
 class CasadiFunction
 {
 public:
+  /**
+   * @brief Constructor
+   */
   explicit CasadiFunction(const Function &f);
 
+  /**
+   * @brief Evaluation operator
+   */
+  void operator()();
+
+  /**
+   * @brief Evaluation operator given data vectors
+   */
+  void operator()(const std::vector<realtype*>& inputs,
+                  const std::vector<realtype*>& results);
+
+  /**
+   * @brief Return the number of non-zero elements for the function output
+   */
+  casadi_int nnz_out();
+
+  /**
+   * @brief Return the number of non-zero elements for the function output
+   */
+  casadi::Sparsity sparsity_out(casadi_int ind);
+
 public:
   std::vector<const double *> m_arg;
   std::vector<double *> m_res;
-  void operator()();
 
 private:
   const Function &m_func;
@@ -24,8 +91,37 @@ class CasadiFunction
   std::vector<double> m_w;
 };
 
+/**
+ * @brief Class for handling casadi functions
+ */
 class CasadiFunctions
 {
+public:
+  /**
+   * @brief Create a new CasadiFunctions object
+   */
+  CasadiFunctions(
+    const Function &rhs_alg,
+    const Function &jac_times_cjmass,
+    const int jac_times_cjmass_nnz,
+    const int jac_bandwidth_lower,
+    const int jac_bandwidth_upper,
+    const np_array_int &jac_times_cjmass_rowvals,
+    const np_array_int &jac_times_cjmass_colptrs,
+    const int inputs_length,
+    const Function &jac_action,
+    const Function &mass_action,
+    const Function &sens,
+    const Function &events,
+    const int n_s,
+    const int n_e,
+    const int n_p,
+    const std::vector<Function*>& var_casadi_fcns,
+    const std::vector<Function*>& dvar_dy_fcns,
+    const std::vector<Function*>& dvar_dp_fcns,
+    const Options& options
+  );
+
 public:
   int number_of_states;
   int number_of_parameters;
@@ -33,26 +129,25 @@ class CasadiFunctions
   int number_of_nnz;
   int jac_bandwidth_lower;
   int jac_bandwidth_upper;
+
   CasadiFunction rhs_alg;
   CasadiFunction sens;
   CasadiFunction jac_times_cjmass;
-  std::vector<int64_t> jac_times_cjmass_rowvals;
-  std::vector<int64_t> jac_times_cjmass_colptrs;
-  std::vector<realtype> inputs;
   CasadiFunction jac_action;
   CasadiFunction mass_action;
   CasadiFunction events;
-  Options options;
 
-  CasadiFunctions(const Function &rhs_alg, const Function &jac_times_cjmass,
-                  const int jac_times_cjmass_nnz,
-                  const int jac_bandwidth_lower, const int jac_bandwidth_upper,
-                  const np_array_int &jac_times_cjmass_rowvals,
-                  const np_array_int &jac_times_cjmass_colptrs,
-                  const int inputs_length, const Function &jac_action,
-                  const Function &mass_action, const Function &sens,
-                  const Function &events, const int n_s, int n_e,
-                  const int n_p, const Options& options);
+  // NB: cppcheck-suppress unusedStructMember is used because codacy reports
+  //     these members as unused even though they are important
+  std::vector<CasadiFunction> var_casadi_fcns;  // cppcheck-suppress unusedStructMember
+  std::vector<CasadiFunction> dvar_dy_fcns;  // cppcheck-suppress unusedStructMember
+  std::vector<CasadiFunction> dvar_dp_fcns;  // cppcheck-suppress unusedStructMember
+
+  std::vector<int64_t> jac_times_cjmass_rowvals;
+  std::vector<int64_t> jac_times_cjmass_colptrs;
+  std::vector<realtype> inputs;
+
+  Options options;
 
   realtype *get_tmp_state_vector();
   realtype *get_tmp_sparse_jacobian_data();
diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_solver.cpp b/pybamm/solvers/c_solvers/idaklu/casadi_solver.cpp
index 67bb2793ae..9fcfa06510 100644
--- a/pybamm/solvers/c_solvers/idaklu/casadi_solver.cpp
+++ b/pybamm/solvers/c_solvers/idaklu/casadi_solver.cpp
@@ -1,494 +1,177 @@
 #include "casadi_solver.hpp"
+#include "CasadiSolver.hpp"
+#include "CasadiSolverOpenMP_solvers.hpp"
 #include "casadi_sundials_functions.hpp"
 #include "common.hpp"
 #include <idas/idas.h>
 #include <memory>
 
-
-CasadiSolver *
-create_casadi_solver(int number_of_states, int number_of_parameters,
-                     const Function &rhs_alg, const Function &jac_times_cjmass,
-                     const np_array_int &jac_times_cjmass_colptrs,
-                     const np_array_int &jac_times_cjmass_rowvals,
-                     const int jac_times_cjmass_nnz,
-                     const int jac_bandwidth_lower, const int jac_bandwidth_upper,
-                     const Function &jac_action,
-                     const Function &mass_action, const Function &sens,
-                     const Function &events, const int number_of_events,
-                     np_array rhs_alg_id, np_array atol_np, double rel_tol,
-                     int inputs_length, py::dict options)
-{
+CasadiSolver *create_casadi_solver(
+  int number_of_states,
+  int number_of_parameters,
+  const Function &rhs_alg,
+  const Function &jac_times_cjmass,
+  const np_array_int &jac_times_cjmass_colptrs,
+  const np_array_int &jac_times_cjmass_rowvals,
+  const int jac_times_cjmass_nnz,
+  const int jac_bandwidth_lower,
+  const int jac_bandwidth_upper,
+  const Function &jac_action,
+  const Function &mass_action,
+  const Function &sens,
+  const Function &events,
+  const int number_of_events,
+  np_array rhs_alg_id,
+  np_array atol_np,
+  double rel_tol,
+  int inputs_length,
+  const std::vector<Function*>& var_casadi_fcns,
+  const std::vector<Function*>& dvar_dy_fcns,
+  const std::vector<Function*>& dvar_dp_fcns,
+  py::dict options
+) {
   auto options_cpp = Options(options);
   auto functions = std::make_unique<CasadiFunctions>(
-      rhs_alg, jac_times_cjmass, jac_times_cjmass_nnz, jac_bandwidth_lower, jac_bandwidth_upper,  jac_times_cjmass_rowvals,
-      jac_times_cjmass_colptrs, inputs_length, jac_action, mass_action, sens,
-      events, number_of_states, number_of_events, number_of_parameters,
-      options_cpp);
-
-  return new CasadiSolver(atol_np, rel_tol, rhs_alg_id, number_of_parameters,
-                          number_of_events, jac_times_cjmass_nnz,
-                          jac_bandwidth_lower, jac_bandwidth_upper,
-                          std::move(functions), options_cpp);
-}
-
-CasadiSolver::CasadiSolver(np_array atol_np, double rel_tol,
-                           np_array rhs_alg_id, int number_of_parameters,
-                           int number_of_events, int jac_times_cjmass_nnz,
-                           int jac_bandwidth_lower, int jac_bandwidth_upper,
-                           std::unique_ptr<CasadiFunctions> functions_arg,
-                           const Options &options)
-    : number_of_states(atol_np.request().size),
-      number_of_parameters(number_of_parameters),
-      number_of_events(number_of_events),
-      jac_times_cjmass_nnz(jac_times_cjmass_nnz),
-      functions(std::move(functions_arg)), options(options)
-{
-  DEBUG("CasadiSolver::CasadiSolver");
-  auto atol = atol_np.unchecked<1>();
-
-  // allocate memory for solver
-#if SUNDIALS_VERSION_MAJOR >= 6
-  SUNContext_Create(NULL, &sunctx);
-  ida_mem = IDACreate(sunctx);
-#else
-  ida_mem = IDACreate();
-#endif
-
-  // allocate vectors
-  int num_threads = options.num_threads;
-#if SUNDIALS_VERSION_MAJOR >= 6
-  yy = N_VNew_OpenMP(number_of_states, num_threads, sunctx);
-  yp = N_VNew_OpenMP(number_of_states, num_threads, sunctx);
-  avtol = N_VNew_OpenMP(number_of_states, num_threads, sunctx);
-  id = N_VNew_OpenMP(number_of_states, num_threads, sunctx);
-#else
-  yy = N_VNew_OpenMP(number_of_states, num_threads);
-  yp = N_VNew_OpenMP(number_of_states, num_threads);
-  avtol = N_VNew_OpenMP(number_of_states, num_threads);
-  id = N_VNew_OpenMP(number_of_states, num_threads);
-#endif
-
-  if (number_of_parameters > 0)
-  {
-    yyS = N_VCloneVectorArray(number_of_parameters, yy);
-    ypS = N_VCloneVectorArray(number_of_parameters, yp);
-  }
-
-  // set initial value
-  realtype *atval = N_VGetArrayPointer(avtol);
-  for (int i = 0; i < number_of_states; i++)
-  {
-    atval[i] = atol[i];
-  }
-
-  for (int is = 0; is < number_of_parameters; is++)
-  {
-    N_VConst(RCONST(0.0), yyS[is]);
-    N_VConst(RCONST(0.0), ypS[is]);
-  }
-
-  // initialise solver
-
-  IDAInit(ida_mem, residual_casadi, 0, yy, yp);
-
-  // set tolerances
-  rtol = RCONST(rel_tol);
-
-  IDASVtolerances(ida_mem, rtol, avtol);
-
-  // set events
-  IDARootInit(ida_mem, number_of_events, events_casadi);
-
-  void *user_data = functions.get();
-  IDASetUserData(ida_mem, user_data);
-
-  // set matrix
-  if (options.jacobian == "sparse")
-  {
-    DEBUG("\tsetting sparse matrix");
-#if SUNDIALS_VERSION_MAJOR >= 6
-    J = SUNSparseMatrix(number_of_states, number_of_states,
-                        jac_times_cjmass_nnz, CSC_MAT, sunctx);
-#else
-    J = SUNSparseMatrix(number_of_states, number_of_states,
-                        jac_times_cjmass_nnz, CSC_MAT);
-#endif
-  }
-  else if (options.jacobian == "banded") {
-    DEBUG("\tsetting banded matrix");
-    #if SUNDIALS_VERSION_MAJOR >= 6
-        J = SUNBandMatrix(number_of_states, jac_bandwidth_upper, jac_bandwidth_lower, sunctx);
-    #else
-        J = SUNBandMatrix(number_of_states, jac_bandwidth_upper, jac_bandwidth_lower);
-    #endif
-  } else if (options.jacobian == "dense" || options.jacobian == "none")
-  {
-    DEBUG("\tsetting dense matrix");
-#if SUNDIALS_VERSION_MAJOR >= 6
-    J = SUNDenseMatrix(number_of_states, number_of_states, sunctx);
-#else
-    J = SUNDenseMatrix(number_of_states, number_of_states);
-#endif
-  }
-  else if (options.jacobian == "matrix-free")
-  {
-    DEBUG("\tsetting matrix-free");
-    J = NULL;
-  }
-
-  #if SUNDIALS_VERSION_MAJOR >= 6
-  int precon_type = SUN_PREC_NONE;
-  if (options.preconditioner != "none") {
-    precon_type = SUN_PREC_LEFT;
-  }
-  #else
-  int precon_type = PREC_NONE;
-  if (options.preconditioner != "none") {
-    precon_type = PREC_LEFT;
-  }
-  #endif
-
-  // set linear solver
-  if (options.linear_solver == "SUNLinSol_Dense")
+    rhs_alg,
+    jac_times_cjmass,
+    jac_times_cjmass_nnz,
+    jac_bandwidth_lower,
+    jac_bandwidth_upper,
+    jac_times_cjmass_rowvals,
+    jac_times_cjmass_colptrs,
+    inputs_length,
+    jac_action,
+    mass_action,
+    sens,
+    events,
+    number_of_states,
+    number_of_events,
+    number_of_parameters,
+    var_casadi_fcns,
+    dvar_dy_fcns,
+    dvar_dp_fcns,
+    options_cpp
+  );
+
+  CasadiSolver *casadiSolver = nullptr;
+
+  // Instantiate solver class
+  if (options_cpp.linear_solver == "SUNLinSol_Dense")
   {
     DEBUG("\tsetting SUNLinSol_Dense linear solver");
-#if SUNDIALS_VERSION_MAJOR >= 6
-    LS = SUNLinSol_Dense(yy, J, sunctx);
-#else
-    LS = SUNLinSol_Dense(yy, J);
-#endif
-  }
-  else if (options.linear_solver == "SUNLinSol_KLU")
+    casadiSolver = new CasadiSolverOpenMP_Dense(
+      atol_np,
+      rel_tol,
+      rhs_alg_id,
+      number_of_parameters,
+      number_of_events,
+      jac_times_cjmass_nnz,
+      jac_bandwidth_lower,
+      jac_bandwidth_upper,
+      std::move(functions),
+      options_cpp
+     );
+  }
+  else if (options_cpp.linear_solver == "SUNLinSol_KLU")
   {
     DEBUG("\tsetting SUNLinSol_KLU linear solver");
-#if SUNDIALS_VERSION_MAJOR >= 6
-    LS = SUNLinSol_KLU(yy, J, sunctx);
-#else
-    LS = SUNLinSol_KLU(yy, J);
-#endif
-  }
-  else if (options.linear_solver == "SUNLinSol_Band")
+    casadiSolver = new CasadiSolverOpenMP_KLU(
+      atol_np,
+      rel_tol,
+      rhs_alg_id,
+      number_of_parameters,
+      number_of_events,
+      jac_times_cjmass_nnz,
+      jac_bandwidth_lower,
+      jac_bandwidth_upper,
+      std::move(functions),
+      options_cpp
+     );
+  }
+  else if (options_cpp.linear_solver == "SUNLinSol_Band")
   {
     DEBUG("\tsetting SUNLinSol_Band linear solver");
-#if SUNDIALS_VERSION_MAJOR >= 6
-    LS = SUNLinSol_Band(yy, J, sunctx);
-#else
-    LS = SUNLinSol_Band(yy, J);
-#endif
-  }
-  else if (options.linear_solver == "SUNLinSol_SPBCGS")
+    casadiSolver = new CasadiSolverOpenMP_Band(
+      atol_np,
+      rel_tol,
+      rhs_alg_id,
+      number_of_parameters,
+      number_of_events,
+      jac_times_cjmass_nnz,
+      jac_bandwidth_lower,
+      jac_bandwidth_upper,
+      std::move(functions),
+      options_cpp
+     );
+  }
+  else if (options_cpp.linear_solver == "SUNLinSol_SPBCGS")
   {
     DEBUG("\tsetting SUNLinSol_SPBCGS_linear solver");
-#if SUNDIALS_VERSION_MAJOR >= 6
-    LS = SUNLinSol_SPBCGS(yy, precon_type, options.linsol_max_iterations,
-                          sunctx);
-#else
-    LS = SUNLinSol_SPBCGS(yy, precon_type, options.linsol_max_iterations);
-#endif
-  }
-  else if (options.linear_solver == "SUNLinSol_SPFGMR")
+    casadiSolver = new CasadiSolverOpenMP_SPBCGS(
+      atol_np,
+      rel_tol,
+      rhs_alg_id,
+      number_of_parameters,
+      number_of_events,
+      jac_times_cjmass_nnz,
+      jac_bandwidth_lower,
+      jac_bandwidth_upper,
+      std::move(functions),
+      options_cpp
+     );
+  }
+  else if (options_cpp.linear_solver == "SUNLinSol_SPFGMR")
   {
     DEBUG("\tsetting SUNLinSol_SPFGMR_linear solver");
-#if SUNDIALS_VERSION_MAJOR >= 6
-    LS = SUNLinSol_SPFGMR(yy, precon_type, options.linsol_max_iterations,
-                          sunctx);
-#else
-    LS = SUNLinSol_SPFGMR(yy, precon_type, options.linsol_max_iterations);
-#endif
-  }
-  else if (options.linear_solver == "SUNLinSol_SPGMR")
+    casadiSolver = new CasadiSolverOpenMP_SPFGMR(
+      atol_np,
+      rel_tol,
+      rhs_alg_id,
+      number_of_parameters,
+      number_of_events,
+      jac_times_cjmass_nnz,
+      jac_bandwidth_lower,
+      jac_bandwidth_upper,
+      std::move(functions),
+      options_cpp
+     );
+  }
+  else if (options_cpp.linear_solver == "SUNLinSol_SPGMR")
   {
     DEBUG("\tsetting SUNLinSol_SPGMR solver");
-#if SUNDIALS_VERSION_MAJOR >= 6
-    LS = SUNLinSol_SPGMR(yy, precon_type, options.linsol_max_iterations,
-                          sunctx);
-#else
-    LS = SUNLinSol_SPGMR(yy, precon_type, options.linsol_max_iterations);
-#endif
-  }
-  else if (options.linear_solver == "SUNLinSol_SPTFQMR")
+    casadiSolver = new CasadiSolverOpenMP_SPGMR(
+      atol_np,
+      rel_tol,
+      rhs_alg_id,
+      number_of_parameters,
+      number_of_events,
+      jac_times_cjmass_nnz,
+      jac_bandwidth_lower,
+      jac_bandwidth_upper,
+      std::move(functions),
+      options_cpp
+     );
+  }
+  else if (options_cpp.linear_solver == "SUNLinSol_SPTFQMR")
   {
     DEBUG("\tsetting SUNLinSol_SPGMR solver");
-#if SUNDIALS_VERSION_MAJOR >= 6
-    LS = SUNLinSol_SPTFQMR(yy, precon_type, options.linsol_max_iterations,
-                          sunctx);
-#else
-    LS = SUNLinSol_SPTFQMR(yy, precon_type, options.linsol_max_iterations);
-#endif
+    casadiSolver = new CasadiSolverOpenMP_SPTFQMR(
+      atol_np,
+      rel_tol,
+      rhs_alg_id,
+      number_of_parameters,
+      number_of_events,
+      jac_times_cjmass_nnz,
+      jac_bandwidth_lower,
+      jac_bandwidth_upper,
+      std::move(functions),
+      options_cpp
+     );
   }
 
-
-
-  IDASetLinearSolver(ida_mem, LS, J);
-
-  if (options.preconditioner != "none")
-  {
-    DEBUG("\tsetting IDADDB preconditioner");
-    // setup preconditioner
-    IDABBDPrecInit(
-        ida_mem, number_of_states, options.precon_half_bandwidth,
-        options.precon_half_bandwidth, options.precon_half_bandwidth_keep,
-        options.precon_half_bandwidth_keep, 0.0, residual_casadi_approx, NULL);
-  }
-
-  if (options.jacobian == "matrix-free")
-  {
-    IDASetJacTimes(ida_mem, NULL, jtimes_casadi);
-  }
-  else if (options.jacobian != "none")
-  {
-    IDASetJacFn(ida_mem, jacobian_casadi);
-  }
-
-  if (number_of_parameters > 0)
-  {
-    IDASensInit(ida_mem, number_of_parameters, IDA_SIMULTANEOUS,
-                sensitivities_casadi, yyS, ypS);
-    IDASensEEtolerances(ida_mem);
-  }
-
-  SUNLinSolInitialize(LS);
-
-  auto id_np_val = rhs_alg_id.unchecked<1>();
-  realtype *id_val;
-  id_val = N_VGetArrayPointer(id);
-
-  int ii;
-  for (ii = 0; ii < number_of_states; ii++)
-  {
-    id_val[ii] = id_np_val[ii];
-  }
-
-  IDASetId(ida_mem, id);
-}
-
-CasadiSolver::~CasadiSolver()
-{
-
-  /* Free memory */
-  if (number_of_parameters > 0)
-  {
-    IDASensFree(ida_mem);
-  }
-  SUNLinSolFree(LS);
-  SUNMatDestroy(J);
-  N_VDestroy(avtol);
-  N_VDestroy(yy);
-  N_VDestroy(yp);
-  N_VDestroy(id);
-  if (number_of_parameters > 0)
-  {
-    N_VDestroyVectorArray(yyS, number_of_parameters);
-    N_VDestroyVectorArray(ypS, number_of_parameters);
-  }
-
-  IDAFree(&ida_mem);
-#if SUNDIALS_VERSION_MAJOR >= 6
-  SUNContext_Free(&sunctx);
-#endif
-}
-
-Solution CasadiSolver::solve(np_array t_np, np_array y0_np, np_array yp0_np,
-                             np_array_dense inputs)
-{
-  DEBUG("CasadiSolver::solve");
-
-  int number_of_timesteps = t_np.request().size;
-  auto t = t_np.unchecked<1>();
-  realtype t0 = RCONST(t(0));
-  auto y0 = y0_np.unchecked<1>();
-  auto yp0 = yp0_np.unchecked<1>();
-
-
-  if (y0.size() != number_of_states + number_of_parameters * number_of_states) {
-    throw std::domain_error(
-      "y0 has wrong size. Expected " +
-      std::to_string(number_of_states + number_of_parameters * number_of_states) +
-      " but got " + std::to_string(y0.size()));
-  }
-
-  if (yp0.size() != number_of_states + number_of_parameters * number_of_states) {
-    throw std::domain_error(
-      "yp0 has wrong size. Expected " +
-      std::to_string(number_of_states + number_of_parameters * number_of_states) +
-      " but got " + std::to_string(yp0.size()));
-  }
-
-  // set inputs
-  auto p_inputs = inputs.unchecked<2>();
-  for (int i = 0; i < functions->inputs.size(); i++)
-  {
-    functions->inputs[i] = p_inputs(i, 0);
-  }
-
-  // set initial conditions
-  realtype *yval = N_VGetArrayPointer(yy);
-  realtype *ypval = N_VGetArrayPointer(yp);
-  std::vector<realtype *> ySval(number_of_parameters);
-  std::vector<realtype *> ypSval(number_of_parameters);
-  for (int p = 0 ; p < number_of_parameters; p++) {
-    ySval[p] = N_VGetArrayPointer(yyS[p]);
-    ypSval[p] = N_VGetArrayPointer(ypS[p]);
-    for (int i = 0; i < number_of_states; i++) {
-      ySval[p][i] = y0[i + (p + 1) * number_of_states];
-      ypSval[p][i] = yp0[i + (p + 1) * number_of_states];
-    }
-  }
-
-  for (int i = 0; i < number_of_states; i++)
-  {
-    yval[i] = y0[i];
-    ypval[i] = yp0[i];
-  }
-
-  IDAReInit(ida_mem, t0, yy, yp);
-  if (number_of_parameters > 0) {
-    IDASensReInit(ida_mem, IDA_SIMULTANEOUS, yyS, ypS);
-  }
-
-  // calculate consistent initial conditions
-  DEBUG("IDACalcIC");
-  IDACalcIC(ida_mem, IDA_YA_YDP_INIT, t(1));
-  if (number_of_parameters > 0)
-  {
-    IDAGetSens(ida_mem, &t0, yyS);
-  }
-
-  int t_i = 1;
-  realtype tret;
-  realtype t_next;
-  realtype t_final = t(number_of_timesteps - 1);
-
-  // set return vectors
-  realtype *t_return = new realtype[number_of_timesteps];
-  realtype *y_return = new realtype[number_of_timesteps * number_of_states];
-  realtype *yS_return = new realtype[number_of_parameters *
-                                     number_of_timesteps * number_of_states];
-
-  py::capsule free_t_when_done(t_return,
-                               [](void *f)
-                               {
-                                 realtype *vect =
-                                     reinterpret_cast<realtype *>(f);
-                                 delete[] vect;
-                               });
-  py::capsule free_y_when_done(y_return,
-                               [](void *f)
-                               {
-                                 realtype *vect =
-                                     reinterpret_cast<realtype *>(f);
-                                 delete[] vect;
-                               });
-  py::capsule free_yS_when_done(yS_return,
-                                [](void *f)
-                                {
-                                  realtype *vect =
-                                      reinterpret_cast<realtype *>(f);
-                                  delete[] vect;
-                                });
-
-  t_return[0] = t(0);
-  for (int j = 0; j < number_of_states; j++)
-  {
-    y_return[j] = yval[j];
-  }
-  for (int j = 0; j < number_of_parameters; j++)
-  {
-    const int base_index = j * number_of_timesteps * number_of_states;
-    for (int k = 0; k < number_of_states; k++)
-    {
-      yS_return[base_index + k] = ySval[j][k];
-    }
-  }
-
-
-
-  int retval;
-  while (true)
-  {
-    t_next = t(t_i);
-    IDASetStopTime(ida_mem, t_next);
-    DEBUG("IDASolve");
-    retval = IDASolve(ida_mem, t_final, &tret, yy, yp, IDA_NORMAL);
-
-    if (retval == IDA_TSTOP_RETURN || retval == IDA_SUCCESS ||
-        retval == IDA_ROOT_RETURN)
-    {
-      if (number_of_parameters > 0)
-      {
-        IDAGetSens(ida_mem, &tret, yyS);
-      }
-
-      t_return[t_i] = tret;
-      for (int j = 0; j < number_of_states; j++)
-      {
-        y_return[t_i * number_of_states + j] = yval[j];
-      }
-      for (int j = 0; j < number_of_parameters; j++)
-      {
-        const int base_index =
-            j * number_of_timesteps * number_of_states + t_i * number_of_states;
-        for (int k = 0; k < number_of_states; k++)
-        {
-          yS_return[base_index + k] = ySval[j][k];
-        }
-      }
-      t_i += 1;
-      if (retval == IDA_SUCCESS || retval == IDA_ROOT_RETURN)
-      {
-        break;
-      }
-    }
-    else
-    {
-      // failed
-      break;
-    }
-  }
-
-  np_array t_ret = np_array(t_i, &t_return[0], free_t_when_done);
-  np_array y_ret =
-      np_array(t_i * number_of_states, &y_return[0], free_y_when_done);
-  np_array yS_ret = np_array(
-      std::vector<ptrdiff_t>{number_of_parameters, number_of_timesteps, number_of_states},
-      &yS_return[0], free_yS_when_done);
-
-  Solution sol(retval, t_ret, y_ret, yS_ret);
-
-  if (options.print_stats)
-  {
-    long nsteps, nrevals, nlinsetups, netfails;
-    int klast, kcur;
-    realtype hinused, hlast, hcur, tcur;
-
-    IDAGetIntegratorStats(ida_mem, &nsteps, &nrevals, &nlinsetups, &netfails,
-                          &klast, &kcur, &hinused, &hlast, &hcur, &tcur);
-
-    long nniters, nncfails;
-    IDAGetNonlinSolvStats(ida_mem, &nniters, &nncfails);
-
-    long int ngevalsBBDP = 0;
-    if (options.using_iterative_solver)
-    {
-      IDABBDPrecGetNumGfnEvals(ida_mem, &ngevalsBBDP);
-    }
-
-    py::print("Solver Stats:");
-    py::print("\tNumber of steps =", nsteps);
-    py::print("\tNumber of calls to residual function =", nrevals);
-    py::print("\tNumber of calls to residual function in preconditioner =",
-              ngevalsBBDP);
-    py::print("\tNumber of linear solver setup calls =", nlinsetups);
-    py::print("\tNumber of error test failures =", netfails);
-    py::print("\tMethod order used on last step =", klast);
-    py::print("\tMethod order used on next step =", kcur);
-    py::print("\tInitial step size =", hinused);
-    py::print("\tStep size on last step =", hlast);
-    py::print("\tStep size on next step =", hcur);
-    py::print("\tCurrent internal time reached =", tcur);
-    py::print("\tNumber of nonlinear iterations performed =", nniters);
-    py::print("\tNumber of nonlinear convergence failures =", nncfails);
+  if (casadiSolver == nullptr) {
+    throw std::invalid_argument("Unsupported solver requested");
   }
 
-  return sol;
+  return casadiSolver;
 }
diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_solver.hpp b/pybamm/solvers/c_solvers/idaklu/casadi_solver.hpp
index 75bc73e9d3..335907a93a 100644
--- a/pybamm/solvers/c_solvers/idaklu/casadi_solver.hpp
+++ b/pybamm/solvers/c_solvers/idaklu/casadi_solver.hpp
@@ -1,59 +1,36 @@
-#ifndef PYBAMM_IDAKLU_CASADI_SOLVER_HPP
-#define PYBAMM_IDAKLU_CASADI_SOLVER_HPP
-
-#include <casadi/casadi.hpp>
-using Function = casadi::Function;
-
-#include "casadi_functions.hpp"
-#include "common.hpp"
-#include "options.hpp"
-#include "solution.hpp"
-
-class CasadiSolver
-{
-public:
-  CasadiSolver(np_array atol_np, double rel_tol, np_array rhs_alg_id,
-               int number_of_parameters, int number_of_events,
-               int jac_times_cjmass_nnz, int jac_bandwidth_lower, int jac_bandwidth_upper,
-               std::unique_ptr<CasadiFunctions> functions, const Options& options);
-  ~CasadiSolver();
-
-  void *ida_mem; // pointer to memory
-
-#if SUNDIALS_VERSION_MAJOR >= 6
-  SUNContext sunctx;
-#endif
-
-  int number_of_states;
-  int number_of_parameters;
-  int number_of_events;
-  N_Vector yy, yp, avtol; // y, y', and absolute tolerance
-  N_Vector *yyS, *ypS;    // y, y' for sensitivities
-  N_Vector id;            // rhs_alg_id
-  realtype rtol;
-  const int jac_times_cjmass_nnz;
-
-  SUNMatrix J;
-  SUNLinearSolver LS;
-
-  std::unique_ptr<CasadiFunctions> functions;
-  Options options;
-
-  Solution solve(np_array t_np, np_array y0_np, np_array yp0_np,
-                 np_array_dense inputs);
-};
-
-CasadiSolver *
-create_casadi_solver(int number_of_states, int number_of_parameters,
-                     const Function &rhs_alg, const Function &jac_times_cjmass,
-                     const np_array_int &jac_times_cjmass_colptrs,
-                     const np_array_int &jac_times_cjmass_rowvals,
-                     const int jac_times_cjmass_nnz,
-                     const int jac_bandwidth_lower, const int jac_bandwidth_upper,
-                     const Function &jac_action,
-                     const Function &mass_action, const Function &sens,
-                     const Function &event, const int number_of_events,
-                     np_array rhs_alg_id, np_array atol_np,
-                     double rel_tol, int inputs_length, py::dict options);
-
-#endif // PYBAMM_IDAKLU_CASADI_SOLVER_HPP
+#ifndef PYBAMM_IDAKLU_CREATE_CASADI_SOLVER_HPP
+#define PYBAMM_IDAKLU_CREATE_CASADI_SOLVER_HPP
+
+#include "CasadiSolver.hpp"
+
+/**
+ * Creates a concrete casadi solver given a linear solver, as specified in
+ * options_cpp.linear_solver.
+ * @brief Create a concrete casadi solver given a linear solver
+ */
+CasadiSolver *create_casadi_solver(
+  int number_of_states,
+  int number_of_parameters,
+  const Function &rhs_alg,
+  const Function &jac_times_cjmass,
+  const np_array_int &jac_times_cjmass_colptrs,
+  const np_array_int &jac_times_cjmass_rowvals,
+  const int jac_times_cjmass_nnz,
+  const int jac_bandwidth_lower,
+  const int jac_bandwidth_upper,
+  const Function &jac_action,
+  const Function &mass_action,
+  const Function &sens,
+  const Function &event,
+  const int number_of_events,
+  np_array rhs_alg_id,
+  np_array atol_np,
+  double rel_tol,
+  int inputs_length,
+  const std::vector<Function*>& var_casadi_fcns,
+  const std::vector<Function*>& dvar_dy_fcns,
+  const std::vector<Function*>& dvar_dp_fcns,
+  py::dict options
+);
+
+#endif // PYBAMM_IDAKLU_CREATE_CASADI_SOLVER_HPP
diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp
index 8a3d96966f..b0ea180641 100644
--- a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp
+++ b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp
@@ -1,6 +1,9 @@
 #include "casadi_sundials_functions.hpp"
 #include "casadi_functions.hpp"
 #include "common.hpp"
+#include <type_traits>
+
+#define NV_DATA NV_DATA_OMP  // Serial: NV_DATA_S
 
 int residual_casadi(realtype tres, N_Vector yy, N_Vector yp, N_Vector rr,
                     void *user_data)
@@ -10,19 +13,19 @@ int residual_casadi(realtype tres, N_Vector yy, N_Vector yp, N_Vector rr,
       static_cast<CasadiFunctions *>(user_data);
 
   p_python_functions->rhs_alg.m_arg[0] = &tres;
-  p_python_functions->rhs_alg.m_arg[1] = NV_DATA_OMP(yy);
+  p_python_functions->rhs_alg.m_arg[1] = NV_DATA(yy);
   p_python_functions->rhs_alg.m_arg[2] = p_python_functions->inputs.data();
-  p_python_functions->rhs_alg.m_res[0] = NV_DATA_OMP(rr);
+  p_python_functions->rhs_alg.m_res[0] = NV_DATA(rr);
   p_python_functions->rhs_alg();
 
   realtype *tmp = p_python_functions->get_tmp_state_vector();
-  p_python_functions->mass_action.m_arg[0] = NV_DATA_OMP(yp);
+  p_python_functions->mass_action.m_arg[0] = NV_DATA(yp);
   p_python_functions->mass_action.m_res[0] = tmp;
   p_python_functions->mass_action();
 
   // AXPY: y <- a*x + y
   const int ns = p_python_functions->number_of_states;
-  casadi::casadi_axpy(ns, -1., tmp, NV_DATA_OMP(rr));
+  casadi::casadi_axpy(ns, -1., tmp, NV_DATA(rr));
 
   //DEBUG_VECTOR(yy);
   //DEBUG_VECTOR(yp);
@@ -101,22 +104,22 @@ int jtimes_casadi(realtype tt, N_Vector yy, N_Vector yp, N_Vector rr,
 
   // Jv has ∂F/∂y v
   p_python_functions->jac_action.m_arg[0] = &tt;
-  p_python_functions->jac_action.m_arg[1] = NV_DATA_OMP(yy);
+  p_python_functions->jac_action.m_arg[1] = NV_DATA(yy);
   p_python_functions->jac_action.m_arg[2] = p_python_functions->inputs.data();
-  p_python_functions->jac_action.m_arg[3] = NV_DATA_OMP(v);
-  p_python_functions->jac_action.m_res[0] = NV_DATA_OMP(Jv);
+  p_python_functions->jac_action.m_arg[3] = NV_DATA(v);
+  p_python_functions->jac_action.m_res[0] = NV_DATA(Jv);
   p_python_functions->jac_action();
 
   // tmp has -∂F/∂y˙ v
   realtype *tmp = p_python_functions->get_tmp_state_vector();
-  p_python_functions->mass_action.m_arg[0] = NV_DATA_OMP(v);
+  p_python_functions->mass_action.m_arg[0] = NV_DATA(v);
   p_python_functions->mass_action.m_res[0] = tmp;
   p_python_functions->mass_action();
 
   // AXPY: y <- a*x + y
   // Jv has ∂F/∂y v + cj ∂F/∂y˙ v
   const int ns = p_python_functions->number_of_states;
-  casadi::casadi_axpy(ns, -cj, tmp, NV_DATA_OMP(Jv));
+  casadi::casadi_axpy(ns, -cj, tmp, NV_DATA(Jv));
 
   return 0;
 }
@@ -163,7 +166,7 @@ int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp,
 
   // args are t, y, cj, put result in jacobian data matrix
   p_python_functions->jac_times_cjmass.m_arg[0] = &tt;
-  p_python_functions->jac_times_cjmass.m_arg[1] = NV_DATA_OMP(yy);
+  p_python_functions->jac_times_cjmass.m_arg[1] = NV_DATA(yy);
   p_python_functions->jac_times_cjmass.m_arg[2] =
       p_python_functions->inputs.data();
   p_python_functions->jac_times_cjmass.m_arg[3] = &cj;
@@ -190,30 +193,61 @@ int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp,
   }
   else if (p_python_functions->options.using_sparse_matrix)
   {
-
-    sunindextype *jac_colptrs = SUNSparseMatrix_IndexPointers(JJ);
-    sunindextype *jac_rowvals = SUNSparseMatrix_IndexValues(JJ);
-    // row vals and col ptrs
-    const int n_row_vals = p_python_functions->jac_times_cjmass_rowvals.size();
-    auto p_jac_times_cjmass_rowvals =
-        p_python_functions->jac_times_cjmass_rowvals.data();
-
-    // just copy across row vals (do I need to do this every time?)
-    // (or just in the setup?)
-    for (int i = 0; i < n_row_vals; i++)
+    if (SUNSparseMatrix_SparseType(JJ) == CSC_MAT)
     {
-      jac_rowvals[i] = p_jac_times_cjmass_rowvals[i];
-    }
+      sunindextype *jac_colptrs = SUNSparseMatrix_IndexPointers(JJ);
+      sunindextype *jac_rowvals = SUNSparseMatrix_IndexValues(JJ);
+      // row vals and col ptrs
+      const int n_row_vals = p_python_functions->jac_times_cjmass_rowvals.size();
+      auto p_jac_times_cjmass_rowvals =
+          p_python_functions->jac_times_cjmass_rowvals.data();
+
+      // just copy across row vals (do I need to do this every time?)
+      // (or just in the setup?)
+      for (int i = 0; i < n_row_vals; i++)
+      {
+        jac_rowvals[i] = p_jac_times_cjmass_rowvals[i];
+      }
 
-    const int n_col_ptrs = p_python_functions->jac_times_cjmass_colptrs.size();
-    auto p_jac_times_cjmass_colptrs =
-        p_python_functions->jac_times_cjmass_colptrs.data();
+      const int n_col_ptrs = p_python_functions->jac_times_cjmass_colptrs.size();
+      auto p_jac_times_cjmass_colptrs =
+          p_python_functions->jac_times_cjmass_colptrs.data();
 
-    // just copy across col ptrs (do I need to do this every time?)
-    for (int i = 0; i < n_col_ptrs; i++)
-    {
-      jac_colptrs[i] = p_jac_times_cjmass_colptrs[i];
-    }
+      // just copy across col ptrs (do I need to do this every time?)
+      for (int i = 0; i < n_col_ptrs; i++)
+      {
+        jac_colptrs[i] = p_jac_times_cjmass_colptrs[i];
+      }
+    } else if (SUNSparseMatrix_SparseType(JJ) == CSR_MAT) {
+      std::vector<realtype> newjac(SUNSparseMatrix_NNZ(JJ));
+      sunindextype *jac_ptrs = SUNSparseMatrix_IndexPointers(JJ);
+      sunindextype *jac_vals = SUNSparseMatrix_IndexValues(JJ);
+
+      // args are t, y, cj, put result in jacobian data matrix
+      p_python_functions->jac_times_cjmass.m_arg[0] = &tt;
+      p_python_functions->jac_times_cjmass.m_arg[1] = NV_DATA(yy);
+      p_python_functions->jac_times_cjmass.m_arg[2] =
+        p_python_functions->inputs.data();
+      p_python_functions->jac_times_cjmass.m_arg[3] = &cj;
+      p_python_functions->jac_times_cjmass.m_res[0] = newjac.data();
+      p_python_functions->jac_times_cjmass();
+
+      // convert (casadi's) CSC format to CSR
+      csc_csr<
+          std::remove_pointer_t<decltype(p_python_functions->jac_times_cjmass_rowvals.data())>,
+          std::remove_pointer_t<decltype(jac_vals)>
+      >(
+        newjac.data(),
+        p_python_functions->jac_times_cjmass_rowvals.data(),
+        p_python_functions->jac_times_cjmass_colptrs.data(),
+        jac_data,
+        jac_ptrs,
+        jac_vals,
+        SUNSparseMatrix_NNZ(JJ),
+        SUNSparseMatrix_NP(JJ)
+      );
+    } else
+      throw std::runtime_error("Unknown matrix format detected (Expected CSC or CSR)");
   }
 
   return (0);
@@ -227,7 +261,7 @@ int events_casadi(realtype t, N_Vector yy, N_Vector yp, realtype *events_ptr,
 
   // args are t, y, put result in events_ptr
   p_python_functions->events.m_arg[0] = &t;
-  p_python_functions->events.m_arg[1] = NV_DATA_OMP(yy);
+  p_python_functions->events.m_arg[1] = NV_DATA(yy);
   p_python_functions->events.m_arg[2] = p_python_functions->inputs.data();
   p_python_functions->events.m_res[0] = events_ptr;
   p_python_functions->events();
@@ -271,11 +305,11 @@ int sensitivities_casadi(int Ns, realtype t, N_Vector yy, N_Vector yp,
 
   // args are t, y put result in rr
   p_python_functions->sens.m_arg[0] = &t;
-  p_python_functions->sens.m_arg[1] = NV_DATA_OMP(yy);
+  p_python_functions->sens.m_arg[1] = NV_DATA(yy);
   p_python_functions->sens.m_arg[2] = p_python_functions->inputs.data();
   for (int i = 0; i < np; i++)
   {
-    p_python_functions->sens.m_res[i] = NV_DATA_OMP(resvalS[i]);
+    p_python_functions->sens.m_res[i] = NV_DATA(resvalS[i]);
   }
   // resvalsS now has (∂F/∂p i )
   p_python_functions->sens();
@@ -285,23 +319,23 @@ int sensitivities_casadi(int Ns, realtype t, N_Vector yy, N_Vector yp,
     // put (∂F/∂y)s i (t) in tmp
     realtype *tmp = p_python_functions->get_tmp_state_vector();
     p_python_functions->jac_action.m_arg[0] = &t;
-    p_python_functions->jac_action.m_arg[1] = NV_DATA_OMP(yy);
+    p_python_functions->jac_action.m_arg[1] = NV_DATA(yy);
     p_python_functions->jac_action.m_arg[2] = p_python_functions->inputs.data();
-    p_python_functions->jac_action.m_arg[3] = NV_DATA_OMP(yS[i]);
+    p_python_functions->jac_action.m_arg[3] = NV_DATA(yS[i]);
     p_python_functions->jac_action.m_res[0] = tmp;
     p_python_functions->jac_action();
 
     const int ns = p_python_functions->number_of_states;
-    casadi::casadi_axpy(ns, 1., tmp, NV_DATA_OMP(resvalS[i]));
+    casadi::casadi_axpy(ns, 1., tmp, NV_DATA(resvalS[i]));
 
     // put -(∂F/∂ ẏ) ṡ i (t) in tmp2
-    p_python_functions->mass_action.m_arg[0] = NV_DATA_OMP(ypS[i]);
+    p_python_functions->mass_action.m_arg[0] = NV_DATA(ypS[i]);
     p_python_functions->mass_action.m_res[0] = tmp;
     p_python_functions->mass_action();
 
     // (∂F/∂y)s i (t)+(∂F/∂ ẏ) ṡ i (t)+(∂F/∂p i )
     // AXPY: y <- a*x + y
-    casadi::casadi_axpy(ns, -1., tmp, NV_DATA_OMP(resvalS[i]));
+    casadi::casadi_axpy(ns, -1., tmp, NV_DATA(resvalS[i]));
   }
 
   return 0;
diff --git a/pybamm/solvers/c_solvers/idaklu/common.hpp b/pybamm/solvers/c_solvers/idaklu/common.hpp
index d6ddb5c16b..55fd4b1c5d 100644
--- a/pybamm/solvers/c_solvers/idaklu/common.hpp
+++ b/pybamm/solvers/c_solvers/idaklu/common.hpp
@@ -44,7 +44,20 @@ using np_array_int = py::array_t<int64_t>;
 
 #ifdef NDEBUG
 #define DEBUG_VECTOR(vector)
+#define DEBUG_VECTORn(vector)
 #else
+
+#define DEBUG_VECTORn(vector, N) {\
+  std::cout << #vector << "[n=" << N << "] = ["; \
+  auto array_ptr = N_VGetArrayPointer(vector); \
+  for (int i = 0; i < N; i++) { \
+    std::cout << array_ptr[i]; \
+    if (i < N-1) { \
+      std::cout << ", "; \
+    } \
+  } \
+  std::cout << "]" << std::endl;  }
+
 #define DEBUG_VECTOR(vector) {\
   std::cout << #vector << " = ["; \
   auto array_ptr = N_VGetArrayPointer(vector); \
@@ -56,6 +69,17 @@ using np_array_int = py::array_t<int64_t>;
     } \
   } \
   std::cout << "]" << std::endl;  }
+
+#define DEBUG_v(v, N) {\
+  std::cout << #v << "[n=" << N << "] = ["; \
+  for (int i = 0; i < N; i++) { \
+    std::cout << v[i]; \
+    if (i < N-1) { \
+      std::cout << ", "; \
+    } \
+  } \
+  std::cout << "]" << std::endl;  }
+
 #endif
 
 #endif // PYBAMM_IDAKLU_COMMON_HPP
diff --git a/pybamm/solvers/c_solvers/idaklu/options.cpp b/pybamm/solvers/c_solvers/idaklu/options.cpp
index 33998470ed..efad4d5de0 100644
--- a/pybamm/solvers/c_solvers/idaklu/options.cpp
+++ b/pybamm/solvers/c_solvers/idaklu/options.cpp
@@ -16,103 +16,106 @@ Options::Options(py::dict options)
       num_threads(options["num_threads"].cast<int>())
 {
 
-  using_sparse_matrix = true;
-  using_banded_matrix = false;
-  if (jacobian == "sparse")
-  {
-  }
-  else if (jacobian == "banded") {
-    using_banded_matrix = true;
-    using_sparse_matrix = false;
-  }
-  else if (jacobian == "dense" || jacobian == "none")
-  {
-    using_sparse_matrix = false;
-  }
-  else if (jacobian == "matrix-free")
-  {
-  }
-  else
-  {
-    throw std::domain_error(
-      "Unknown jacobian type \""s + jacobian +
-      "\". Should be one of \"sparse\", \"banded\", \"dense\", \"matrix-free\" or \"none\"."s
-    );
-  }
+    using_sparse_matrix = true;
+    using_banded_matrix = false;
+    if (jacobian == "sparse")
+    {
+    }
+    else if (jacobian == "banded") {
+        using_banded_matrix = true;
+        using_sparse_matrix = false;
+    }
+    else if (jacobian == "dense" || jacobian == "none")
+    {
+        using_sparse_matrix = false;
+    }
+    else if (jacobian == "matrix-free")
+    {
+    }
+    else
+    {
+        throw std::domain_error(
+            "Unknown jacobian type \""s + jacobian +
+            "\". Should be one of \"sparse\", \"banded\", \"dense\", \"matrix-free\" or \"none\"."s
+        );
+    }
 
-  using_iterative_solver = false;
-  if (linear_solver == "SUNLinSol_Dense" && (jacobian == "dense" || jacobian == "none"))
-  {
-  }
-  else if (linear_solver == "SUNLinSol_KLU" && jacobian == "sparse")
-  {
-  }
-  else if (linear_solver == "SUNLinSol_Band" && jacobian == "banded")
-  {
-  }
-  else if (jacobian == "banded") {
-    throw std::domain_error(
-      "Unknown linear solver or incompatible options: "
-      "jacobian = \"" + jacobian + "\" linear solver = \"" + linear_solver +
-      "\". For a banded jacobian "
-      "please use the SUNLinSol_Band linear solver"
-    );
-  }
-  else if ((linear_solver == "SUNLinSol_SPBCGS" ||
-            linear_solver == "SUNLinSol_SPFGMR" ||
-            linear_solver == "SUNLinSol_SPGMR" ||
-            linear_solver == "SUNLinSol_SPTFQMR") &&
-           (jacobian == "sparse" || jacobian == "matrix-free"))
-  {
-    using_iterative_solver = true;
-  }
-  else if (jacobian == "sparse")
-  {
-    throw std::domain_error(
-      "Unknown linear solver or incompatible options: "
-      "jacobian = \"" + jacobian + "\" linear solver = \"" + linear_solver +
-      "\". For a sparse jacobian "
-      "please use the SUNLinSol_KLU linear solver"
-    );
-  }
-  else if (jacobian == "matrix-free")
-  {
-    throw std::domain_error(
-      "Unknown linear solver or incompatible options. "
-      "jacobian = \"" + jacobian + "\" linear solver = \"" + linear_solver +
-      "\". For a matrix-free jacobian "
-      "please use one of the iterative linear solvers: \"SUNLinSol_SPBCGS\", "
-      "\"SUNLinSol_SPFGMR\", \"SUNLinSol_SPGMR\", or \"SUNLinSol_SPTFQMR\"."
-    );
-  }
-  else if (jacobian == "none")
-  {
-    throw std::domain_error(
-      "Unknown linear solver or incompatible options: "
-      "jacobian = \"" + jacobian + "\" linear solver = \"" + linear_solver +
-      "\". For no jacobian please use the SUNLinSol_Dense solver"
-    );
-  }
-  else
-  {
-    throw std::domain_error(
-      "Unknown linear solver or incompatible options. "
-      "jacobian = \"" + jacobian + "\" linear solver = \"" + linear_solver + "\""
-    );
-  }
+    using_iterative_solver = false;
+    if (linear_solver == "SUNLinSol_Dense" && (jacobian == "dense" || jacobian == "none"))
+    {
+    }
+    else if (linear_solver == "SUNLinSol_KLU" && jacobian == "sparse")
+    {
+    }
+    else if (linear_solver == "SUNLinSol_cuSolverSp_batchQR" && jacobian == "sparse")
+    {
+    }
+    else if (linear_solver == "SUNLinSol_Band" && jacobian == "banded")
+    {
+    }
+    else if (jacobian == "banded") {
+        throw std::domain_error(
+            "Unknown linear solver or incompatible options: "
+            "jacobian = \"" + jacobian + "\" linear solver = \"" + linear_solver +
+            "\". For a banded jacobian "
+            "please use the SUNLinSol_Band linear solver"
+        );
+    }
+    else if ((linear_solver == "SUNLinSol_SPBCGS" ||
+              linear_solver == "SUNLinSol_SPFGMR" ||
+              linear_solver == "SUNLinSol_SPGMR" ||
+              linear_solver == "SUNLinSol_SPTFQMR") &&
+             (jacobian == "sparse" || jacobian == "matrix-free"))
+    {
+        using_iterative_solver = true;
+    }
+    else if (jacobian == "sparse")
+    {
+        throw std::domain_error(
+            "Unknown linear solver or incompatible options: "
+            "jacobian = \"" + jacobian + "\" linear solver = \"" + linear_solver +
+            "\". For a sparse jacobian "
+            "please use the SUNLinSol_KLU linear solver"
+        );
+    }
+    else if (jacobian == "matrix-free")
+    {
+        throw std::domain_error(
+            "Unknown linear solver or incompatible options. "
+            "jacobian = \"" + jacobian + "\" linear solver = \"" + linear_solver +
+            "\". For a matrix-free jacobian "
+            "please use one of the iterative linear solvers: \"SUNLinSol_SPBCGS\", "
+            "\"SUNLinSol_SPFGMR\", \"SUNLinSol_SPGMR\", or \"SUNLinSol_SPTFQMR\"."
+        );
+    }
+    else if (jacobian == "none")
+    {
+        throw std::domain_error(
+            "Unknown linear solver or incompatible options: "
+            "jacobian = \"" + jacobian + "\" linear solver = \"" + linear_solver +
+            "\". For no jacobian please use the SUNLinSol_Dense solver"
+        );
+    }
+    else
+    {
+        throw std::domain_error(
+            "Unknown linear solver or incompatible options. "
+            "jacobian = \"" + jacobian + "\" linear solver = \"" + linear_solver + "\""
+        );
+    }
 
-  if (using_iterative_solver)
-  {
-    if (preconditioner != "none" && preconditioner != "BBDP")
+    if (using_iterative_solver)
     {
-      throw std::domain_error(
-        "Unknown preconditioner \""s + preconditioner +
-        "\", use one of \"BBDP\" or \"none\""s
-      );
-    }
-  }
-  else
-  {
-    preconditioner = "none";
-  }
+        if (preconditioner != "none" && preconditioner != "BBDP")
+        {
+            throw std::domain_error(
+                "Unknown preconditioner \""s + preconditioner +
+                "\", use one of \"BBDP\" or \"none\""s
+            );
+        }
+    }
+    else
+    {
+        preconditioner = "none";
+    }
 }
diff --git a/pybamm/solvers/c_solvers/idaklu/options.hpp b/pybamm/solvers/c_solvers/idaklu/options.hpp
index db5f136a01..b70d0f4a30 100644
--- a/pybamm/solvers/c_solvers/idaklu/options.hpp
+++ b/pybamm/solvers/c_solvers/idaklu/options.hpp
@@ -3,6 +3,9 @@
 
 #include "common.hpp"
 
+/**
+ * @brief Options passed to the idaklu solver by pybamm
+ */
 struct Options {
   bool print_stats;
   bool using_sparse_matrix;
diff --git a/pybamm/solvers/c_solvers/idaklu/python.cpp b/pybamm/solvers/c_solvers/idaklu/python.cpp
index 9ec018109e..03090c9850 100644
--- a/pybamm/solvers/c_solvers/idaklu/python.cpp
+++ b/pybamm/solvers/c_solvers/idaklu/python.cpp
@@ -5,203 +5,211 @@
 class PybammFunctions
 {
 public:
-  int number_of_states;
-  int number_of_parameters;
-  int number_of_events;
-
-  PybammFunctions(const residual_type &res, const jacobian_type &jac,
-                  const sensitivities_type &sens,
-                  const jac_get_type &get_jac_data_in,
-                  const jac_get_type &get_jac_row_vals_in,
-                  const jac_get_type &get_jac_col_ptrs_in,
-                  const event_type &event,
-                  const int n_s, int n_e, const int n_p,
-                  const np_array &inputs)
-      : number_of_states(n_s), number_of_events(n_e),
-        number_of_parameters(n_p),
-        py_res(res), py_jac(jac),
-        py_sens(sens),
-        py_event(event), py_get_jac_data(get_jac_data_in),
-        py_get_jac_row_vals(get_jac_row_vals_in),
-        py_get_jac_col_ptrs(get_jac_col_ptrs_in),
-        inputs(inputs)
-  {
-  }
-
-  np_array operator()(double t, np_array y, np_array yp)
-  {
-    return py_res(t, y, inputs, yp);
-  }
-
-  np_array res(double t, np_array y, np_array yp)
-  {
-    return py_res(t, y, inputs, yp);
-  }
-
-  void jac(double t, np_array y, double cj)
-  {
-    // this function evaluates the jacobian and sets it to be the attribute
-    // of a python class which can then be called by get_jac_data,
-    // get_jac_col_ptr, etc
-    py_jac(t, y, inputs, cj);
-  }
-
-  void sensitivities(
-      std::vector<np_array>& resvalS,
-      const double t, const np_array& y, const np_array& yp,
-      const std::vector<np_array>& yS, const std::vector<np_array>& ypS)
-  {
-    // this function evaluates the sensitivity equations required by IDAS,
-    // returning them in resvalS, which is preallocated as a numpy array
-    // of size (np, n), where n is the number of states and np is the number
-    // of parameters
-    //
-    // yS and ypS are also shape (np, n), y and yp are shape (n)
-    //
-    // dF/dy * s_i + dF/dyd * sd + dFdp_i for i in range(np)
-    py_sens(resvalS, t, y, inputs, yp, yS, ypS);
-  }
-
-  np_array get_jac_data() { return py_get_jac_data(); }
-
-  np_array get_jac_row_vals() { return py_get_jac_row_vals(); }
-
-  np_array get_jac_col_ptrs() { return py_get_jac_col_ptrs(); }
-
-  np_array events(double t, np_array y) { return py_event(t, y, inputs); }
+    int number_of_states;
+    int number_of_parameters;
+    int number_of_events;
+
+    PybammFunctions(const residual_type &res, const jacobian_type &jac,
+                    const sensitivities_type &sens,
+                    const jac_get_type &get_jac_data_in,
+                    const jac_get_type &get_jac_row_vals_in,
+                    const jac_get_type &get_jac_col_ptrs_in,
+                    const event_type &event,
+                    const int n_s, int n_e, const int n_p,
+                    const np_array &inputs)
+        : number_of_states(n_s), number_of_events(n_e),
+          number_of_parameters(n_p),
+          py_res(res), py_jac(jac),
+          py_sens(sens),
+          py_event(event), py_get_jac_data(get_jac_data_in),
+          py_get_jac_row_vals(get_jac_row_vals_in),
+          py_get_jac_col_ptrs(get_jac_col_ptrs_in),
+          inputs(inputs)
+    {
+    }
+
+    np_array operator()(double t, np_array y, np_array yp)
+    {
+        return py_res(t, y, inputs, yp);
+    }
+
+    np_array res(double t, np_array y, np_array yp)
+    {
+        return py_res(t, y, inputs, yp);
+    }
+
+    void jac(double t, np_array y, double cj)
+    {
+        // this function evaluates the jacobian and sets it to be the attribute
+        // of a python class which can then be called by get_jac_data,
+        // get_jac_col_ptr, etc
+        py_jac(t, y, inputs, cj);
+    }
+
+    void sensitivities(
+        std::vector<np_array>& resvalS,
+        const double t, const np_array& y, const np_array& yp,
+        const std::vector<np_array>& yS, const std::vector<np_array>& ypS)
+    {
+        // this function evaluates the sensitivity equations required by IDAS,
+        // returning them in resvalS, which is preallocated as a numpy array
+        // of size (np, n), where n is the number of states and np is the number
+        // of parameters
+        //
+        // yS and ypS are also shape (np, n), y and yp are shape (n)
+        //
+        // dF/dy * s_i + dF/dyd * sd + dFdp_i for i in range(np)
+        py_sens(resvalS, t, y, inputs, yp, yS, ypS);
+    }
+
+    np_array get_jac_data() {
+        return py_get_jac_data();
+    }
+
+    np_array get_jac_row_vals() {
+        return py_get_jac_row_vals();
+    }
+
+    np_array get_jac_col_ptrs() {
+        return py_get_jac_col_ptrs();
+    }
+
+    np_array events(double t, np_array y) {
+        return py_event(t, y, inputs);
+    }
 
 private:
-  residual_type py_res;
-  sensitivities_type py_sens;
-  jacobian_type py_jac;
-  event_type py_event;
-  jac_get_type py_get_jac_data;
-  jac_get_type py_get_jac_row_vals;
-  jac_get_type py_get_jac_col_ptrs;
-  const np_array &inputs;
+    residual_type py_res;
+    sensitivities_type py_sens;
+    jacobian_type py_jac;
+    event_type py_event;
+    jac_get_type py_get_jac_data;
+    jac_get_type py_get_jac_row_vals;
+    jac_get_type py_get_jac_col_ptrs;
+    const np_array &inputs;
 };
 
 int residual(realtype tres, N_Vector yy, N_Vector yp, N_Vector rr,
              void *user_data)
 {
-  PybammFunctions *python_functions_ptr =
-      static_cast<PybammFunctions *>(user_data);
-  PybammFunctions python_functions = *python_functions_ptr;
+    PybammFunctions *python_functions_ptr =
+        static_cast<PybammFunctions *>(user_data);
+    PybammFunctions python_functions = *python_functions_ptr;
 
-  realtype *yval, *ypval, *rval;
-  yval = N_VGetArrayPointer(yy);
-  ypval = N_VGetArrayPointer(yp);
-  rval = N_VGetArrayPointer(rr);
+    realtype *yval, *ypval, *rval;
+    yval = N_VGetArrayPointer(yy);
+    ypval = N_VGetArrayPointer(yp);
+    rval = N_VGetArrayPointer(rr);
 
-  int n = python_functions.number_of_states;
-  py::array_t<double> y_np = py::array_t<double>(n, yval);
-  py::array_t<double> yp_np = py::array_t<double>(n, ypval);
+    int n = python_functions.number_of_states;
+    py::array_t<double> y_np = py::array_t<double>(n, yval);
+    py::array_t<double> yp_np = py::array_t<double>(n, ypval);
 
-  py::array_t<double> r_np;
+    py::array_t<double> r_np;
 
-  r_np = python_functions.res(tres, y_np, yp_np);
+    r_np = python_functions.res(tres, y_np, yp_np);
 
-  auto r_np_ptr = r_np.unchecked<1>();
+    auto r_np_ptr = r_np.unchecked<1>();
 
-  // just copying data
-  int i;
-  for (i = 0; i < n; i++)
-  {
-    rval[i] = r_np_ptr[i];
-  }
-  return 0;
+    // just copying data
+    int i;
+    for (i = 0; i < n; i++)
+    {
+        rval[i] = r_np_ptr[i];
+    }
+    return 0;
 }
 
 int jacobian(realtype tt, realtype cj, N_Vector yy, N_Vector yp,
              N_Vector resvec, SUNMatrix JJ, void *user_data, N_Vector tempv1,
              N_Vector tempv2, N_Vector tempv3)
 {
-  realtype *yval;
-  yval = N_VGetArrayPointer(yy);
+    realtype *yval;
+    yval = N_VGetArrayPointer(yy);
 
-  PybammFunctions *python_functions_ptr =
-      static_cast<PybammFunctions *>(user_data);
-  PybammFunctions python_functions = *python_functions_ptr;
+    PybammFunctions *python_functions_ptr =
+        static_cast<PybammFunctions *>(user_data);
+    PybammFunctions python_functions = *python_functions_ptr;
 
-  int n = python_functions.number_of_states;
-  py::array_t<double> y_np = py::array_t<double>(n, yval);
+    int n = python_functions.number_of_states;
+    py::array_t<double> y_np = py::array_t<double>(n, yval);
 
-  // create pointer to jac data, column pointers, and row values
-  sunindextype *jac_colptrs = SUNSparseMatrix_IndexPointers(JJ);
-  sunindextype *jac_rowvals = SUNSparseMatrix_IndexValues(JJ);
-  realtype *jac_data = SUNSparseMatrix_Data(JJ);
+    // create pointer to jac data, column pointers, and row values
+    sunindextype *jac_colptrs = SUNSparseMatrix_IndexPointers(JJ);
+    sunindextype *jac_rowvals = SUNSparseMatrix_IndexValues(JJ);
+    realtype *jac_data = SUNSparseMatrix_Data(JJ);
 
-  py::array_t<double> jac_np_array;
+    py::array_t<double> jac_np_array;
 
-  python_functions.jac(tt, y_np, cj);
+    python_functions.jac(tt, y_np, cj);
 
-  np_array jac_np_data = python_functions.get_jac_data();
-  int n_data = jac_np_data.request().size;
-  auto jac_np_data_ptr = jac_np_data.unchecked<1>();
+    np_array jac_np_data = python_functions.get_jac_data();
+    int n_data = jac_np_data.request().size;
+    auto jac_np_data_ptr = jac_np_data.unchecked<1>();
 
-  // just copy across data
-  int i;
-  for (i = 0; i < n_data; i++)
-  {
-    jac_data[i] = jac_np_data_ptr[i];
-  }
+    // just copy across data
+    int i;
+    for (i = 0; i < n_data; i++)
+    {
+        jac_data[i] = jac_np_data_ptr[i];
+    }
 
-  np_array jac_np_row_vals = python_functions.get_jac_row_vals();
-  int n_row_vals = jac_np_row_vals.request().size;
+    np_array jac_np_row_vals = python_functions.get_jac_row_vals();
+    int n_row_vals = jac_np_row_vals.request().size;
 
-  auto jac_np_row_vals_ptr = jac_np_row_vals.unchecked<1>();
-  // just copy across row vals (this might be unneeded)
-  for (i = 0; i < n_row_vals; i++)
-  {
-    jac_rowvals[i] = jac_np_row_vals_ptr[i];
-  }
+    auto jac_np_row_vals_ptr = jac_np_row_vals.unchecked<1>();
+    // just copy across row vals (this might be unneeded)
+    for (i = 0; i < n_row_vals; i++)
+    {
+        jac_rowvals[i] = jac_np_row_vals_ptr[i];
+    }
 
-  np_array jac_np_col_ptrs = python_functions.get_jac_col_ptrs();
-  int n_col_ptrs = jac_np_col_ptrs.request().size;
-  auto jac_np_col_ptrs_ptr = jac_np_col_ptrs.unchecked<1>();
+    np_array jac_np_col_ptrs = python_functions.get_jac_col_ptrs();
+    int n_col_ptrs = jac_np_col_ptrs.request().size;
+    auto jac_np_col_ptrs_ptr = jac_np_col_ptrs.unchecked<1>();
 
-  // just copy across col ptrs (this might be unneeded)
-  for (i = 0; i < n_col_ptrs; i++)
-  {
-    jac_colptrs[i] = jac_np_col_ptrs_ptr[i];
-  }
+    // just copy across col ptrs (this might be unneeded)
+    for (i = 0; i < n_col_ptrs; i++)
+    {
+        jac_colptrs[i] = jac_np_col_ptrs_ptr[i];
+    }
 
-  return (0);
+    return (0);
 }
 
 int events(realtype t, N_Vector yy, N_Vector yp, realtype *events_ptr,
            void *user_data)
 {
-  realtype *yval;
-  yval = N_VGetArrayPointer(yy);
+    realtype *yval;
+    yval = N_VGetArrayPointer(yy);
 
-  PybammFunctions *python_functions_ptr =
-      static_cast<PybammFunctions *>(user_data);
-  PybammFunctions python_functions = *python_functions_ptr;
+    PybammFunctions *python_functions_ptr =
+        static_cast<PybammFunctions *>(user_data);
+    PybammFunctions python_functions = *python_functions_ptr;
 
-  int number_of_events = python_functions.number_of_events;
-  int number_of_states = python_functions.number_of_states;
-  py::array_t<double> y_np = py::array_t<double>(number_of_states, yval);
+    int number_of_events = python_functions.number_of_events;
+    int number_of_states = python_functions.number_of_states;
+    py::array_t<double> y_np = py::array_t<double>(number_of_states, yval);
 
-  py::array_t<double> events_np_array;
+    py::array_t<double> events_np_array;
 
-  events_np_array = python_functions.events(t, y_np);
+    events_np_array = python_functions.events(t, y_np);
 
-  auto events_np_data_ptr = events_np_array.unchecked<1>();
+    auto events_np_data_ptr = events_np_array.unchecked<1>();
 
-  // just copying data (figure out how to pass pointers later)
-  int i;
-  for (i = 0; i < number_of_events; i++)
-  {
-    events_ptr[i] = events_np_data_ptr[i];
-  }
+    // just copying data (figure out how to pass pointers later)
+    int i;
+    for (i = 0; i < number_of_events; i++)
+    {
+        events_ptr[i] = events_np_data_ptr[i];
+    }
 
-  return (0);
+    return (0);
 }
 
 int sensitivities(int Ns, realtype t, N_Vector yy, N_Vector yp,
-    N_Vector resval, N_Vector *yS, N_Vector *ypS, N_Vector *resvalS,
-    void *user_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3) {
+                  N_Vector resval, N_Vector *yS, N_Vector *ypS, N_Vector *resvalS,
+                  void *user_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3) {
 // This function computes the sensitivity residual for all sensitivity
 // equations. It must compute the vectors
 // (∂F/∂y)s i (t)+(∂F/∂ ẏ) ṡ i (t)+(∂F/∂p i ) and store them in resvalS[i].
@@ -223,255 +231,255 @@ int sensitivities(int Ns, realtype t, N_Vector yy, N_Vector yp,
 // occurred (in which case idas will attempt to correct),
 // or a negative value if it failed unrecoverably (in which case the integration is halted and IDA SRES FAIL is returned)
 //
-  PybammFunctions *python_functions_ptr =
-      static_cast<PybammFunctions *>(user_data);
-  PybammFunctions python_functions = *python_functions_ptr;
-
-  int n = python_functions.number_of_states;
-  int np = python_functions.number_of_parameters;
-
-  // memory managed by sundials, so pass a destructor that does nothing
-  auto state_vector_shape = std::vector<ptrdiff_t>{n, 1};
-  np_array y_np = np_array(state_vector_shape, N_VGetArrayPointer(yy),
-                           py::capsule(&yy, [](void* p) {}));
-  np_array yp_np = np_array(state_vector_shape, N_VGetArrayPointer(yp),
-                           py::capsule(&yp, [](void* p) {}));
-
-  std::vector<np_array> yS_np(np);
-  for (int i = 0; i < np; i++) {
-    auto capsule = py::capsule(yS + i, [](void* p) {});
-    yS_np[i] = np_array(state_vector_shape, N_VGetArrayPointer(yS[i]), capsule);
-  }
-
-  std::vector<np_array> ypS_np(np);
-  for (int i = 0; i < np; i++) {
-    auto capsule = py::capsule(ypS + i, [](void* p) {});
-    ypS_np[i] = np_array(state_vector_shape, N_VGetArrayPointer(ypS[i]), capsule);
-  }
-
-  std::vector<np_array> resvalS_np(np);
-  for (int i = 0; i < np; i++) {
-    auto capsule = py::capsule(resvalS + i, [](void* p) {});
-    resvalS_np[i] = np_array(state_vector_shape,
-                             N_VGetArrayPointer(resvalS[i]), capsule);
-  }
-
-  realtype *ptr1 = static_cast<realtype *>(resvalS_np[0].request().ptr);
-  const realtype* resvalSval = N_VGetArrayPointer(resvalS[0]);
-
-  python_functions.sensitivities(resvalS_np, t, y_np, yp_np, yS_np, ypS_np);
-
-  return 0;
+    PybammFunctions *python_functions_ptr =
+        static_cast<PybammFunctions *>(user_data);
+    PybammFunctions python_functions = *python_functions_ptr;
+
+    int n = python_functions.number_of_states;
+    int np = python_functions.number_of_parameters;
+
+    // memory managed by sundials, so pass a destructor that does nothing
+    auto state_vector_shape = std::vector<ptrdiff_t> {n, 1};
+    np_array y_np = np_array(state_vector_shape, N_VGetArrayPointer(yy),
+    py::capsule(&yy, [](void* p) {}));
+    np_array yp_np = np_array(state_vector_shape, N_VGetArrayPointer(yp),
+    py::capsule(&yp, [](void* p) {}));
+
+    std::vector<np_array> yS_np(np);
+    for (int i = 0; i < np; i++) {
+        auto capsule = py::capsule(yS + i, [](void* p) {});
+        yS_np[i] = np_array(state_vector_shape, N_VGetArrayPointer(yS[i]), capsule);
+    }
+
+    std::vector<np_array> ypS_np(np);
+    for (int i = 0; i < np; i++) {
+        auto capsule = py::capsule(ypS + i, [](void* p) {});
+        ypS_np[i] = np_array(state_vector_shape, N_VGetArrayPointer(ypS[i]), capsule);
+    }
+
+    std::vector<np_array> resvalS_np(np);
+    for (int i = 0; i < np; i++) {
+        auto capsule = py::capsule(resvalS + i, [](void* p) {});
+        resvalS_np[i] = np_array(state_vector_shape,
+                                 N_VGetArrayPointer(resvalS[i]), capsule);
+    }
+
+    realtype *ptr1 = static_cast<realtype *>(resvalS_np[0].request().ptr);
+    const realtype* resvalSval = N_VGetArrayPointer(resvalS[0]);
+
+    python_functions.sensitivities(resvalS_np, t, y_np, yp_np, yS_np, ypS_np);
+
+    return 0;
 }
 
 /* main program */
 Solution solve_python(np_array t_np, np_array y0_np, np_array yp0_np,
-               residual_type res, jacobian_type jac,
-               sensitivities_type sens,
-               jac_get_type gjd, jac_get_type gjrv, jac_get_type gjcp,
-               int nnz, event_type event,
-               int number_of_events, int use_jacobian, np_array rhs_alg_id,
-               np_array atol_np, double rel_tol, np_array inputs,
-               int number_of_parameters)
+                      residual_type res, jacobian_type jac,
+                      sensitivities_type sens,
+                      jac_get_type gjd, jac_get_type gjrv, jac_get_type gjcp,
+                      int nnz, event_type event,
+                      int number_of_events, int use_jacobian, np_array rhs_alg_id,
+                      np_array atol_np, double rel_tol, np_array inputs,
+                      int number_of_parameters)
 {
-  auto t = t_np.unchecked<1>();
-  auto y0 = y0_np.unchecked<1>();
-  auto yp0 = yp0_np.unchecked<1>();
-  auto atol = atol_np.unchecked<1>();
-
-  int number_of_states = y0_np.request().size;
-  int number_of_timesteps = t_np.request().size;
-  void *ida_mem;          // pointer to memory
-  N_Vector yy, yp, avtol; // y, y', and absolute tolerance
-  N_Vector *yyS, *ypS;      // y, y' for sensitivities
-  N_Vector id;
-  realtype rtol, *yval, *ypval, *atval;
-  std::vector<realtype *> ySval(number_of_parameters);
-  int retval;
-  SUNMatrix J;
-  SUNLinearSolver LS;
+    auto t = t_np.unchecked<1>();
+    auto y0 = y0_np.unchecked<1>();
+    auto yp0 = yp0_np.unchecked<1>();
+    auto atol = atol_np.unchecked<1>();
+
+    int number_of_states = y0_np.request().size;
+    int number_of_timesteps = t_np.request().size;
+    void *ida_mem;          // pointer to memory
+    N_Vector yy, yp, avtol; // y, y', and absolute tolerance
+    N_Vector *yyS, *ypS;      // y, y' for sensitivities
+    N_Vector id;
+    realtype rtol, *yval, *ypval, *atval;
+    std::vector<realtype *> ySval(number_of_parameters);
+    int retval;
+    SUNMatrix J;
+    SUNLinearSolver LS;
 
 #if SUNDIALS_VERSION_MAJOR >= 6
-  SUNContext sunctx;
-  SUNContext_Create(NULL, &sunctx);
+    SUNContext sunctx;
+    SUNContext_Create(NULL, &sunctx);
 
-  // allocate memory for solver
-  ida_mem = IDACreate(sunctx);
+    // allocate memory for solver
+    ida_mem = IDACreate(sunctx);
 
-  // allocate vectors
-  yy = N_VNew_Serial(number_of_states, sunctx);
-  yp = N_VNew_Serial(number_of_states, sunctx);
-  avtol = N_VNew_Serial(number_of_states, sunctx);
-  id = N_VNew_Serial(number_of_states, sunctx);
+    // allocate vectors
+    yy = N_VNew_Serial(number_of_states, sunctx);
+    yp = N_VNew_Serial(number_of_states, sunctx);
+    avtol = N_VNew_Serial(number_of_states, sunctx);
+    id = N_VNew_Serial(number_of_states, sunctx);
 #else
-  // allocate memory for solver
-  ida_mem = IDACreate();
-
-  // allocate vectors
-  yy = N_VNew_Serial(number_of_states);
-  yp = N_VNew_Serial(number_of_states);
-  avtol = N_VNew_Serial(number_of_states);
-  id = N_VNew_Serial(number_of_states);
+    // allocate memory for solver
+    ida_mem = IDACreate();
+
+    // allocate vectors
+    yy = N_VNew_Serial(number_of_states);
+    yp = N_VNew_Serial(number_of_states);
+    avtol = N_VNew_Serial(number_of_states);
+    id = N_VNew_Serial(number_of_states);
 #endif
 
-  if (number_of_parameters > 0) {
-    yyS = N_VCloneVectorArray(number_of_parameters, yy);
-    ypS = N_VCloneVectorArray(number_of_parameters, yp);
-  }
-
-  // set initial value
-  yval = N_VGetArrayPointer(yy);
-  ypval = N_VGetArrayPointer(yp);
-  atval = N_VGetArrayPointer(avtol);
-  int i;
-  for (i = 0; i < number_of_states; i++)
-  {
-    yval[i] = y0[i];
-    ypval[i] = yp0[i];
-    atval[i] = atol[i];
-  }
-
-  for (int is = 0 ; is < number_of_parameters; is++) {
-    ySval[is] = N_VGetArrayPointer(yyS[is]);
-    N_VConst(RCONST(0.0), yyS[is]);
-    N_VConst(RCONST(0.0), ypS[is]);
-  }
-
-  // initialise solver
-  realtype t0 = RCONST(t(0));
-  IDAInit(ida_mem, residual, t0, yy, yp);
-
-  // set tolerances
-  rtol = RCONST(rel_tol);
-
-  IDASVtolerances(ida_mem, rtol, avtol);
-
-  // set events
-  IDARootInit(ida_mem, number_of_events, events);
-
-  // set pybamm functions by passing pointer to it
-  PybammFunctions pybamm_functions(res, jac, sens, gjd, gjrv, gjcp, event,
-                                   number_of_states, number_of_events,
-                                   number_of_parameters, inputs);
-  void *user_data = &pybamm_functions;
-  IDASetUserData(ida_mem, user_data);
-
-  // set linear solver
+    if (number_of_parameters > 0) {
+        yyS = N_VCloneVectorArray(number_of_parameters, yy);
+        ypS = N_VCloneVectorArray(number_of_parameters, yp);
+    }
+
+    // set initial value
+    yval = N_VGetArrayPointer(yy);
+    ypval = N_VGetArrayPointer(yp);
+    atval = N_VGetArrayPointer(avtol);
+    int i;
+    for (i = 0; i < number_of_states; i++)
+    {
+        yval[i] = y0[i];
+        ypval[i] = yp0[i];
+        atval[i] = atol[i];
+    }
+
+    for (int is = 0 ; is < number_of_parameters; is++) {
+        ySval[is] = N_VGetArrayPointer(yyS[is]);
+        N_VConst(RCONST(0.0), yyS[is]);
+        N_VConst(RCONST(0.0), ypS[is]);
+    }
+
+    // initialise solver
+    realtype t0 = RCONST(t(0));
+    IDAInit(ida_mem, residual, t0, yy, yp);
+
+    // set tolerances
+    rtol = RCONST(rel_tol);
+
+    IDASVtolerances(ida_mem, rtol, avtol);
+
+    // set events
+    IDARootInit(ida_mem, number_of_events, events);
+
+    // set pybamm functions by passing pointer to it
+    PybammFunctions pybamm_functions(res, jac, sens, gjd, gjrv, gjcp, event,
+                                     number_of_states, number_of_events,
+                                     number_of_parameters, inputs);
+    void *user_data = &pybamm_functions;
+    IDASetUserData(ida_mem, user_data);
+
+    // set linear solver
 #if SUNDIALS_VERSION_MAJOR >= 6
-  J = SUNSparseMatrix(number_of_states, number_of_states, nnz, CSR_MAT, sunctx);
-  LS = SUNLinSol_KLU(yy, J, sunctx);
+    J = SUNSparseMatrix(number_of_states, number_of_states, nnz, CSR_MAT, sunctx);
+    LS = SUNLinSol_KLU(yy, J, sunctx);
 #else
-  J = SUNSparseMatrix(number_of_states, number_of_states, nnz, CSR_MAT);
-  LS = SUNLinSol_KLU(yy, J);
+    J = SUNSparseMatrix(number_of_states, number_of_states, nnz, CSR_MAT);
+    LS = SUNLinSol_KLU(yy, J);
 #endif
 
-  IDASetLinearSolver(ida_mem, LS, J);
-
-  if (use_jacobian == 1)
-  {
-    IDASetJacFn(ida_mem, jacobian);
-  }
-
-  if (number_of_parameters > 0)
-  {
-    IDASensInit(ida_mem, number_of_parameters,
-                IDA_SIMULTANEOUS, sensitivities, yyS, ypS);
-    IDASensEEtolerances(ida_mem);
-  }
-
-  int t_i = 1;
-  realtype tret;
-  realtype t_next;
-  realtype t_final = t(number_of_timesteps - 1);
-
-  // set return vectors
-  std::vector<double> t_return(number_of_timesteps);
-  std::vector<double> y_return(number_of_timesteps * number_of_states);
-  std::vector<double> yS_return(number_of_parameters * number_of_timesteps * number_of_states);
-
-  t_return[0] = t(0);
-  for (int j = 0; j < number_of_states; j++)
-  {
-    y_return[j] = yval[j];
-  }
-  for (int j = 0; j < number_of_parameters; j++) {
-    const int base_index = j * number_of_timesteps * number_of_states;
-    for (int k = 0; k < number_of_states; k++) {
-      yS_return[base_index + k] = ySval[j][k];
-    }
-  }
+    IDASetLinearSolver(ida_mem, LS, J);
 
-  // calculate consistent initial conditions
-  auto id_np_val = rhs_alg_id.unchecked<1>();
-  realtype *id_val;
-  id_val = N_VGetArrayPointer(id);
+    if (use_jacobian == 1)
+    {
+        IDASetJacFn(ida_mem, jacobian);
+    }
 
-  int ii;
-  for (ii = 0; ii < number_of_states; ii++)
-  {
-    id_val[ii] = id_np_val[ii];
-  }
+    if (number_of_parameters > 0)
+    {
+        IDASensInit(ida_mem, number_of_parameters,
+                    IDA_SIMULTANEOUS, sensitivities, yyS, ypS);
+        IDASensEEtolerances(ida_mem);
+    }
 
-  IDASetId(ida_mem, id);
-  IDACalcIC(ida_mem, IDA_YA_YDP_INIT, t(1));
+    int t_i = 1;
+    realtype tret;
+    realtype t_next;
+    realtype t_final = t(number_of_timesteps - 1);
 
-  while (true)
-  {
-    t_next = t(t_i);
-    IDASetStopTime(ida_mem, t_next);
-    retval = IDASolve(ida_mem, t_final, &tret, yy, yp, IDA_NORMAL);
+    // set return vectors
+    std::vector<double> t_return(number_of_timesteps);
+    std::vector<double> y_return(number_of_timesteps * number_of_states);
+    std::vector<double> yS_return(number_of_parameters * number_of_timesteps * number_of_states);
 
-    if (retval == IDA_TSTOP_RETURN || retval == IDA_SUCCESS || retval == IDA_ROOT_RETURN)
+    t_return[0] = t(0);
+    for (int j = 0; j < number_of_states; j++)
     {
-      if (number_of_parameters > 0) {
-        IDAGetSens(ida_mem, &tret, yyS);
-      }
-
-      t_return[t_i] = tret;
-      for (int j = 0; j < number_of_states; j++)
-      {
-        y_return[t_i * number_of_states + j] = yval[j];
-      }
-      for (int j = 0; j < number_of_parameters; j++) {
-        const int base_index = j * number_of_timesteps * number_of_states
-                               + t_i * number_of_states;
+        y_return[j] = yval[j];
+    }
+    for (int j = 0; j < number_of_parameters; j++) {
+        const int base_index = j * number_of_timesteps * number_of_states;
         for (int k = 0; k < number_of_states; k++) {
-          yS_return[base_index + k] = ySval[j][k];
+            yS_return[base_index + k] = ySval[j][k];
         }
-      }
-      t_i += 1;
-      if (retval == IDA_SUCCESS || retval == IDA_ROOT_RETURN) {
-        break;
-      }
+    }
+
+    // calculate consistent initial conditions
+    auto id_np_val = rhs_alg_id.unchecked<1>();
+    realtype *id_val;
+    id_val = N_VGetArrayPointer(id);
 
+    int ii;
+    for (ii = 0; ii < number_of_states; ii++)
+    {
+        id_val[ii] = id_np_val[ii];
+    }
+
+    IDASetId(ida_mem, id);
+    IDACalcIC(ida_mem, IDA_YA_YDP_INIT, t(1));
+
+    while (true)
+    {
+        t_next = t(t_i);
+        IDASetStopTime(ida_mem, t_next);
+        retval = IDASolve(ida_mem, t_final, &tret, yy, yp, IDA_NORMAL);
+
+        if (retval == IDA_TSTOP_RETURN || retval == IDA_SUCCESS || retval == IDA_ROOT_RETURN)
+        {
+            if (number_of_parameters > 0) {
+                IDAGetSens(ida_mem, &tret, yyS);
+            }
+
+            t_return[t_i] = tret;
+            for (int j = 0; j < number_of_states; j++)
+            {
+                y_return[t_i * number_of_states + j] = yval[j];
+            }
+            for (int j = 0; j < number_of_parameters; j++) {
+                const int base_index = j * number_of_timesteps * number_of_states
+                                       + t_i * number_of_states;
+                for (int k = 0; k < number_of_states; k++) {
+                    yS_return[base_index + k] = ySval[j][k];
+                }
+            }
+            t_i += 1;
+            if (retval == IDA_SUCCESS || retval == IDA_ROOT_RETURN) {
+                break;
+            }
+
+        }
+    }
+
+    /* Free memory */
+    if (number_of_parameters > 0) {
+        IDASensFree(ida_mem);
+    }
+    IDAFree(&ida_mem);
+    SUNLinSolFree(LS);
+    SUNMatDestroy(J);
+    N_VDestroy(avtol);
+    N_VDestroy(yp);
+    if (number_of_parameters > 0) {
+        N_VDestroyVectorArray(yyS, number_of_parameters);
+        N_VDestroyVectorArray(ypS, number_of_parameters);
     }
-  }
-
-  /* Free memory */
-  if (number_of_parameters > 0) {
-    IDASensFree(ida_mem);
-  }
-  IDAFree(&ida_mem);
-  SUNLinSolFree(LS);
-  SUNMatDestroy(J);
-  N_VDestroy(avtol);
-  N_VDestroy(yp);
-  if (number_of_parameters > 0) {
-    N_VDestroyVectorArray(yyS, number_of_parameters);
-    N_VDestroyVectorArray(ypS, number_of_parameters);
-  }
 #if SUNDIALS_VERSION_MAJOR >= 6
-  SUNContext_Free(&sunctx);
+    SUNContext_Free(&sunctx);
 #endif
 
-  np_array t_ret = np_array(t_i, &t_return[0]);
-  np_array y_ret = np_array(t_i * number_of_states, &y_return[0]);
-  np_array yS_ret = np_array(
-      std::vector<ptrdiff_t>{number_of_parameters, number_of_timesteps, number_of_states},
-      &yS_return[0]
-      );
+    np_array t_ret = np_array(t_i, &t_return[0]);
+    np_array y_ret = np_array(t_i * number_of_states, &y_return[0]);
+    np_array yS_ret = np_array(
+                          std::vector<ptrdiff_t> {number_of_parameters, number_of_timesteps, number_of_states},
+                          &yS_return[0]
+                      );
 
-  Solution sol(retval, t_ret, y_ret, yS_ret);
+    Solution sol(retval, t_ret, y_ret, yS_ret);
 
-  return sol;
+    return sol;
 }
diff --git a/pybamm/solvers/c_solvers/idaklu/python.hpp b/pybamm/solvers/c_solvers/idaklu/python.hpp
index 8ae73f2a90..0478d0946f 100644
--- a/pybamm/solvers/c_solvers/idaklu/python.hpp
+++ b/pybamm/solvers/c_solvers/idaklu/python.hpp
@@ -22,7 +22,9 @@ using event_type =
 using jac_get_type = std::function<np_array()>;
 
 
-
+/**
+ * @brief Interface to the python solver
+ */
 Solution solve_python(np_array t_np, np_array y0_np, np_array yp0_np,
                residual_type res, jacobian_type jac,
                sensitivities_type sens,
diff --git a/pybamm/solvers/c_solvers/idaklu/solution.hpp b/pybamm/solvers/c_solvers/idaklu/solution.hpp
index 047ae6ef8e..92e22d02b6 100644
--- a/pybamm/solvers/c_solvers/idaklu/solution.hpp
+++ b/pybamm/solvers/c_solvers/idaklu/solution.hpp
@@ -3,9 +3,15 @@
 
 #include "common.hpp"
 
+/**
+ * @brief Solution class
+ */
 class Solution
 {
 public:
+  /**
+   * @brief Constructor
+   */
   Solution(int retval, np_array t_np, np_array y_np, np_array yS_np)
       : flag(retval), t(t_np), y(y_np), yS(yS_np)
   {
diff --git a/pybamm/solvers/c_solvers/idaklu/sundials_legacy_wrapper.hpp b/pybamm/solvers/c_solvers/idaklu/sundials_legacy_wrapper.hpp
new file mode 100644
index 0000000000..f4855b1bc4
--- /dev/null
+++ b/pybamm/solvers/c_solvers/idaklu/sundials_legacy_wrapper.hpp
@@ -0,0 +1,94 @@
+
+#if SUNDIALS_VERSION_MAJOR < 6
+
+  #define SUN_PREC_NONE PREC_NONE
+  #define SUN_PREC_LEFT PREC_LEFT
+
+  // Compatibility layer - wrap older sundials functions in new-style calls
+  void SUNContext_Create(void *comm, SUNContext *ctx)
+  {
+    // Function not available
+    return;
+  }
+
+  int SUNContext_Free(SUNContext *ctx)
+  {
+    // Function not available
+    return;
+  }
+
+  void* IDACreate(SUNContext sunctx)
+  {
+    return IDACreate();
+  }
+
+  N_Vector N_VNew_Serial(sunindextype vec_length, SUNContext sunctx)
+  {
+    return N_VNew_Serial(vec_length);
+  }
+
+  N_Vector N_VNew_OpenMP(sunindextype vec_length, SUNContext sunctx)
+  {
+    return N_VNew_OpenMP(vec_length);
+  }
+
+  N_Vector N_VNew_Cuda(sunindextype vec_length, SUNContext sunctx)
+  {
+    return N_VNew_Cuda(vec_length);
+  }
+
+  SUNMatrix SUNSparseMatrix(sunindextype M, sunindextype N, sunindextype NNZ, int sparsetype, SUNContext sunctx)
+  {
+    return SUNMatrix SUNSparseMatrix(M, N, NNZ, sparsetype);
+  }
+
+  SUNMatrix SUNMatrix_cuSparse_NewCSR(int M, int N, int NNZ, cusparseHandle_t cusp, SUNContext sunctx)
+  {
+    return SUNMatrix_cuSparse_NewCSR(M, N, NNZ, cusp);
+  }
+
+  SUNMatrix SUNBandMatrix(sunindextype N, sunindextype mu, sunindextype ml, SUNContext sunctx)
+  {
+    return SUNMatrix SUNBandMatrix(N, mu, ml);
+  }
+
+  SUNMatrix SUNDenseMatrix(sunindextype M, sunindextype N, SUNContext sunctx)
+  {
+    return SUNDenseMatrix(M, N, sunctx);
+  }
+
+  SUNLinearSolver SUNLinSol_Dense(N_Vector y, SUNMatrix A, SUNContext sunctx)
+  {
+    return SUNLinSol_Dense(y, A, sunctx);
+  }
+
+  SUNLinearSolver SUNLinSol_KLU(N_Vector y, SUNMatrix A, SUNContext sunctx)
+  {
+    return SUNLinSol_KLU(y, A, sunctx);
+  }
+
+  SUNLinearSolver SUNLinSol_Band(N_Vector y, SUNMatrix A, SUNContext sunctx)
+  {
+    return SUNLinSol_Band(y, A, sunctx);
+  }
+
+  SUNLinearSolver SUNLinSol_SPBCGS(N_Vector y, int pretype, int maxl, SUNContext sunctx)
+  {
+    return SUNLinSol_SPBCGS(y, pretype, maxl);
+  }
+
+  SUNLinearSolver SUNLinSol_SPFGMR(N_Vector y, int pretype, int maxl, SUNContext sunctx)
+  {
+    return SUNLinSol_SPFGMR(y, pretype, maxl);
+  }
+
+  SUNLinearSolver SUNLinSol_SPGMR(N_Vector y, int pretype, int maxl, SUNContext sunctx)
+  {
+    return SUNLinSol_SPGMR(y, pretype, maxl);
+  }
+
+  SUNLinearSolver SUNLinSol_SPTFQMR(N_Vector y, int pretype, int maxl, SUNContext sunctx)
+  {
+    return SUNLinSol_SPTFQMR(y, pretype, maxl);
+  }
+#endif
diff --git a/pybamm/solvers/casadi_solver.py b/pybamm/solvers/casadi_solver.py
index 86246588e9..4cf863ede1 100644
--- a/pybamm/solvers/casadi_solver.py
+++ b/pybamm/solvers/casadi_solver.py
@@ -41,7 +41,7 @@ class CasadiSolver(pybamm.BaseSolver):
         specified by 'root_method' (e.g. "lm", "hybr", ...)
     root_tol : float, optional
         The tolerance for root-finding. Default is 1e-6.
-    max_step_decrease_counts : float, optional
+    max_step_decrease_count : float, optional
         The maximum number of times step size can be decreased before an error is
         raised. Default is 5.
     dt_max : float, optional
diff --git a/pybamm/solvers/idaklu_solver.py b/pybamm/solvers/idaklu_solver.py
index 5ccff7ed14..d9819f1608 100644
--- a/pybamm/solvers/idaklu_solver.py
+++ b/pybamm/solvers/idaklu_solver.py
@@ -43,6 +43,9 @@ class IDAKLUSolver(pybamm.BaseSolver):
         The tolerance for the initial-condition solver (default is 1e-6).
     extrap_tol : float, optional
         The tolerance to assert whether extrapolation occurs or not (default is 0).
+    output_variables : list[str], optional
+        List of variables to calculate and return. If none are specified then
+        the complete state vector is returned (can be very large) (default is [])
     options: dict, optional
         Addititional options to pass to the solver, by default:
 
@@ -84,6 +87,7 @@ def __init__(
         root_method="casadi",
         root_tol=1e-6,
         extrap_tol=None,
+        output_variables=[],
         options=None,
     ):
         # set default options,
@@ -106,6 +110,8 @@ def __init__(
                     options[key] = value
         self._options = options
 
+        self.output_variables = output_variables
+
         if idaklu_spec is None:  # pragma: no cover
             raise ImportError("KLU is not installed")
 
@@ -116,6 +122,7 @@ def __init__(
             root_method,
             root_tol,
             extrap_tol,
+            output_variables,
         )
         self.name = "IDA KLU solver"
 
@@ -174,6 +181,11 @@ def inputs_to_dict(inputs):
         # only casadi solver needs sensitivity ics
         if model.convert_to_format != "casadi":
             y0S = None
+            if self.output_variables:
+                raise pybamm.SolverError(
+                    "output_variables can only be specified "
+                    'with convert_to_format="casadi"'
+                )  # pragma: no cover
         if y0S is not None:
             if isinstance(y0S, casadi.DM):
                 y0S = (y0S,)
@@ -251,6 +263,30 @@ def resfn(t, y, inputs, ydot):
                 "mass_action", [v_casadi], [casadi.densify(mass_matrix @ v_casadi)]
             )
 
+            # if output_variables specified then convert 'variable' casadi
+            # function expressions to idaklu-compatible functions
+            self.var_idaklu_fcns = []
+            self.dvar_dy_idaklu_fcns = []
+            self.dvar_dp_idaklu_fcns = []
+            for key in self.output_variables:
+                # ExplicitTimeIntegral's are not computed as part of the solver and
+                # do not need to be converted
+                if isinstance(
+                    model.variables_and_events[key], pybamm.ExplicitTimeIntegral
+                ):
+                    continue
+                self.var_idaklu_fcns.append(
+                    idaklu.generate_function(self.computed_var_fcns[key].serialize())
+                )
+                # Convert derivative functions for sensitivities
+                if (len(inputs) > 0) and (model.calculate_sensitivities):
+                    self.dvar_dy_idaklu_fcns.append(
+                        idaklu.generate_function(self.computed_dvar_dy_fcns[key].serialize())
+                    )
+                    self.dvar_dp_idaklu_fcns.append(
+                        idaklu.generate_function(self.computed_dvar_dp_fcns[key].serialize())
+                    )
+
         else:
             t0 = 0 if t_eval is None else t_eval[0]
             jac_y0_t0 = model.jac_rhs_algebraic_eval(t0, y0, inputs_dict)
@@ -421,28 +457,36 @@ def sensfn(resvalS, t, y, inputs, yp, yS, ypS):
                 "ids": ids,
                 "sensitivity_names": sensitivity_names,
                 "number_of_sensitivity_parameters": number_of_sensitivity_parameters,
+                "output_variables": self.output_variables,
+                "var_casadi_fcns": self.computed_var_fcns,
+                "var_idaklu_fcns": self.var_idaklu_fcns,
+                "dvar_dy_idaklu_fcns": self.dvar_dy_idaklu_fcns,
+                "dvar_dp_idaklu_fcns": self.dvar_dp_idaklu_fcns,
             }
 
             solver = idaklu.create_casadi_solver(
-                len(y0),
-                self._setup["number_of_sensitivity_parameters"],
-                self._setup["rhs_algebraic"],
-                self._setup["jac_times_cjmass"],
-                self._setup["jac_times_cjmass_colptrs"],
-                self._setup["jac_times_cjmass_rowvals"],
-                self._setup["jac_times_cjmass_nnz"],
-                jac_bw_lower,
-                jac_bw_upper,
-                self._setup["jac_rhs_algebraic_action"],
-                self._setup["mass_action"],
-                self._setup["sensfn"],
-                self._setup["rootfn"],
-                self._setup["num_of_events"],
-                self._setup["ids"],
-                atol,
-                rtol,
-                len(inputs),
-                self._options,
+                number_of_states=len(y0),
+                number_of_parameters=self._setup["number_of_sensitivity_parameters"],
+                rhs_alg=self._setup["rhs_algebraic"],
+                jac_times_cjmass=self._setup["jac_times_cjmass"],
+                jac_times_cjmass_colptrs=self._setup["jac_times_cjmass_colptrs"],
+                jac_times_cjmass_rowvals=self._setup["jac_times_cjmass_rowvals"],
+                jac_times_cjmass_nnz=self._setup["jac_times_cjmass_nnz"],
+                jac_bandwidth_lower=jac_bw_lower,
+                jac_bandwidth_upper=jac_bw_upper,
+                jac_action=self._setup["jac_rhs_algebraic_action"],
+                mass_action=self._setup["mass_action"],
+                sens=self._setup["sensfn"],
+                events=self._setup["rootfn"],
+                number_of_events=self._setup["num_of_events"],
+                rhs_alg_id=self._setup["ids"],
+                atol=atol,
+                rtol=rtol,
+                inputs=len(inputs),
+                var_casadi_fcns=self._setup["var_idaklu_fcns"],
+                dvar_dy_fcns=self._setup["dvar_dy_idaklu_fcns"],
+                dvar_dp_fcns=self._setup["dvar_dp_idaklu_fcns"],
+                options=self._options,
             )
 
             self._setup["solver"] = solver
@@ -555,7 +599,11 @@ def _integrate(self, model, t_eval, inputs_dict=None):
         t = sol.t
         number_of_timesteps = t.size
         number_of_states = y0.size
-        y_out = sol.y.reshape((number_of_timesteps, number_of_states))
+        if self.output_variables:
+            # Substitute empty vectors for state vector 'y'
+            y_out = np.zeros((number_of_timesteps * number_of_states, 0))
+        else:
+            y_out = sol.y.reshape((number_of_timesteps, number_of_states))
 
         # return sensitivity solution, we need to flatten yS to
         # (#timesteps * #states (where t is changing the quickest),)
@@ -579,7 +627,7 @@ def _integrate(self, model, t_eval, inputs_dict=None):
             elif sol.flag == 2:
                 termination = "event"
 
-            sol = pybamm.Solution(
+            newsol = pybamm.Solution(
                 sol.t,
                 np.transpose(y_out),
                 model,
@@ -589,7 +637,37 @@ def _integrate(self, model, t_eval, inputs_dict=None):
                 termination,
                 sensitivities=yS_out,
             )
-            sol.integration_time = integration_time
-            return sol
+            newsol.integration_time = integration_time
+            if self.output_variables:
+                # Populate variables and sensititivies dictionaries directly
+                number_of_samples = sol.y.shape[0] // number_of_timesteps
+                sol.y = sol.y.reshape((number_of_timesteps, number_of_samples))
+                startk = 0
+                for vark, var in enumerate(self.output_variables):
+                    # ExplicitTimeIntegral's are not computed as part of the solver and
+                    # do not need to be converted
+                    if isinstance(
+                        model.variables_and_events[var], pybamm.ExplicitTimeIntegral
+                    ):
+                        continue
+                    len_of_var = (
+                        self._setup["var_casadi_fcns"][var](0, 0, 0).sparsity().nnz()
+                    )
+                    newsol._variables[var] = pybamm.ProcessedVariableComputed(
+                        [model.variables_and_events[var]],
+                        [self._setup["var_casadi_fcns"][var]],
+                        [sol.y[:, startk : (startk + len_of_var)]],
+                        newsol,
+                    )
+                    # Add sensitivities
+                    newsol[var]._sensitivities = {}
+                    if model.calculate_sensitivities:
+                        for paramk, param in enumerate(inputs_dict.keys()):
+                            newsol[var].add_sensitivity(
+                                param,
+                                [sol.yS[:, startk : (startk + len_of_var), paramk]],
+                            )
+                    startk += len_of_var
+            return newsol
         else:
             raise pybamm.SolverError("idaklu solver failed")
diff --git a/pybamm/solvers/jax_bdf_solver.py b/pybamm/solvers/jax_bdf_solver.py
index b69744dd08..2f334ed8ec 100644
--- a/pybamm/solvers/jax_bdf_solver.py
+++ b/pybamm/solvers/jax_bdf_solver.py
@@ -18,7 +18,9 @@
     from jax.tree_util import tree_flatten, tree_map, tree_unflatten
     from jax.util import cache, safe_map, split_list
 
-    config.update("jax_enable_x64", True)
+    platform = jax.lib.xla_bridge.get_backend().platform.casefold()
+    if platform != "metal":
+        config.update("jax_enable_x64", True)
 
     MAX_ORDER = 5
     NEWTON_MAXITER = 4
diff --git a/pybamm/solvers/jax_solver.py b/pybamm/solvers/jax_solver.py
index 8e7b1b5cc5..4c9759008a 100644
--- a/pybamm/solvers/jax_solver.py
+++ b/pybamm/solvers/jax_solver.py
@@ -215,7 +215,7 @@ def _integrate(self, model, t_eval, inputs=None):
 
         y = []
         platform = jax.lib.xla_bridge.get_backend().platform.casefold()
-        if platform.startswith("cpu"):
+        if len(inputs) <= 1 or platform.startswith("cpu"):
             # cpu execution runs faster when multithreaded
             async def solve_model_for_inputs():
                 async def solve_model_async(inputs_v):
@@ -227,7 +227,11 @@ async def solve_model_async(inputs_v):
                 return await asyncio.gather(*coro)
 
             y = asyncio.run(solve_model_for_inputs())
-        elif platform.startswith("gpu") or platform.startswith("tpu"):
+        elif (
+            platform.startswith("gpu")
+            or platform.startswith("tpu")
+            or platform.startswith("metal")
+        ):
             # gpu execution runs faster when parallelised with vmap
             # (see also comment below regarding single-program multiple-data
             #  execution (SPMD) using pmap on multiple XLAs)
diff --git a/pybamm/solvers/processed_variable.py b/pybamm/solvers/processed_variable.py
index 9c404b72a2..f9d967c4b0 100644
--- a/pybamm/solvers/processed_variable.py
+++ b/pybamm/solvers/processed_variable.py
@@ -2,7 +2,6 @@
 # Processed Variable class
 #
 import casadi
-import numbers
 import numpy as np
 import pybamm
 from scipy.integrate import cumulative_trapezoid
@@ -73,9 +72,8 @@ def __init__(
         self.t_pts = solution.t
 
         # Evaluate base variable at initial time
-        self.base_eval = self.base_variables_casadi[0](
-            self.all_ts[0][0], self.all_ys[0][:, 0], self.all_inputs_casadi[0]
-        ).full()
+        self.base_eval_shape = self.base_variables[0].shape
+        self.base_eval_size = self.base_variables[0].size
 
         # handle 2D (in space) finite element variables differently
         if (
@@ -87,15 +85,11 @@ def __init__(
 
         # check variable shape
         else:
-            if (
-                isinstance(self.base_eval, numbers.Number)
-                or len(self.base_eval.shape) == 0
-                or self.base_eval.shape[0] == 1
-            ):
+            if len(self.base_eval_shape) == 0 or self.base_eval_shape[0] == 1:
                 self.initialise_0D()
             else:
                 n = self.mesh.npts
-                base_shape = self.base_eval.shape[0]
+                base_shape = self.base_eval_shape[0]
                 # Try some shapes that could make the variable a 1D variable
                 if base_shape in [n, n + 1]:
                     self.initialise_1D()
@@ -104,7 +98,7 @@ def __init__(
                     first_dim_nodes = self.mesh.nodes
                     first_dim_edges = self.mesh.edges
                     second_dim_pts = self.base_variables[0].secondary_mesh.nodes
-                    if self.base_eval.size // len(second_dim_pts) in [
+                    if self.base_eval_size // len(second_dim_pts) in [
                         len(first_dim_nodes),
                         len(first_dim_edges),
                     ]:
@@ -118,9 +112,6 @@ def __init__(
 
     def initialise_0D(self):
         # initialise empty array of the correct size
-        entries = np.empty(len(self.t_pts))
-        idx = 0
-
         entries = np.empty(len(self.t_pts))
         idx = 0
         # Evaluate the base_variable index-by-index
@@ -146,7 +137,7 @@ def initialise_0D(self):
         self.dimensions = 0
 
     def initialise_1D(self, fixed_t=False):
-        len_space = self.base_eval.shape[0]
+        len_space = self.base_eval_shape[0]
         entries = np.empty((len_space, len(self.t_pts)))
 
         # Evaluate the base_variable index-by-index
@@ -208,9 +199,9 @@ def initialise_2D(self):
         first_dim_edges = self.mesh.edges
         second_dim_nodes = self.base_variables[0].secondary_mesh.nodes
         second_dim_edges = self.base_variables[0].secondary_mesh.edges
-        if self.base_eval.size // len(second_dim_nodes) == len(first_dim_nodes):
+        if self.base_eval_size // len(second_dim_nodes) == len(first_dim_nodes):
             first_dim_pts = first_dim_nodes
-        elif self.base_eval.size // len(second_dim_nodes) == len(first_dim_edges):
+        elif self.base_eval_size // len(second_dim_nodes) == len(first_dim_edges):
             first_dim_pts = first_dim_edges
 
         second_dim_pts = second_dim_nodes
diff --git a/pybamm/solvers/processed_variable_computed.py b/pybamm/solvers/processed_variable_computed.py
new file mode 100644
index 0000000000..78d16c27fb
--- /dev/null
+++ b/pybamm/solvers/processed_variable_computed.py
@@ -0,0 +1,441 @@
+#
+# Processed Variable class
+#
+import casadi
+import numpy as np
+import pybamm
+from scipy.integrate import cumulative_trapezoid
+import xarray as xr
+
+
+class ProcessedVariableComputed(object):
+    """
+    An object that can be evaluated at arbitrary (scalars or vectors) t and x, and
+    returns the (interpolated) value of the base variable at that t and x.
+
+    The 'Computed' variant of ProcessedVariable deals with variables that have
+    been derived at solve time (see the 'output_variables' solver option),
+    where the full state-vector is not itself propogated and returned.
+
+    Parameters
+    ----------
+    base_variables : list of :class:`pybamm.Symbol`
+        A list of base variables with a method `evaluate(t,y)`, each entry of which
+        returns the value of that variable for that particular sub-solution.
+        A Solution can be comprised of sub-solutions which are the solutions of
+        different models.
+        Note that this can be any kind of node in the expression tree, not
+        just a :class:`pybamm.Variable`.
+        When evaluated, returns an array of size (m,n)
+    base_variable_casadis : list of :class:`casadi.Function`
+        A list of casadi functions. When evaluated, returns the same thing as
+        `base_Variable.evaluate` (but more efficiently).
+    base_variable_data : list of :numpy:array
+        A list of numpy arrays, the returned evaluations.
+    solution : :class:`pybamm.Solution`
+        The solution object to be used to create the processed variables
+    warn : bool, optional
+        Whether to raise warnings when trying to evaluate time and length scales.
+        Default is True.
+    """
+
+    def __init__(
+        self,
+        base_variables,
+        base_variables_casadi,
+        base_variables_data,
+        solution,
+        warn=True,
+        cumtrapz_ic=None,
+    ):
+        self.base_variables = base_variables
+        self.base_variables_casadi = base_variables_casadi
+        self.base_variables_data = base_variables_data
+
+        self.all_ts = solution.all_ts
+        self.all_ys = solution.all_ys
+        self.all_inputs = solution.all_inputs
+        self.all_inputs_casadi = solution.all_inputs_casadi
+
+        self.mesh = base_variables[0].mesh
+        self.domain = base_variables[0].domain
+        self.domains = base_variables[0].domains
+        self.warn = warn
+        self.cumtrapz_ic = cumtrapz_ic
+
+        # Sensitivity starts off uninitialized, only set when called
+        self._sensitivities = None
+        self.solution_sensitivities = solution.sensitivities
+
+        # Store time
+        self.t_pts = solution.t
+
+        # Evaluate base variable at initial time
+        self.base_eval_shape = self.base_variables[0].shape
+        self.base_eval_size = self.base_variables[0].size
+        self.unroll_params = {}
+
+        # handle 2D (in space) finite element variables differently
+        if (
+            self.mesh
+            and "current collector" in self.domain
+            and isinstance(self.mesh, pybamm.ScikitSubMesh2D)
+        ):
+            self.initialise_2D_scikit_fem()
+
+        # check variable shape
+        else:
+            if len(self.base_eval_shape) == 0 or self.base_eval_shape[0] == 1:
+                self.initialise_0D()
+            else:
+                n = self.mesh.npts
+                base_shape = self.base_eval_shape[0]
+                # Try some shapes that could make the variable a 1D variable
+                if base_shape in [n, n + 1]:
+                    self.initialise_1D()
+                else:
+                    # Try some shapes that could make the variable a 2D variable
+                    first_dim_nodes = self.mesh.nodes
+                    first_dim_edges = self.mesh.edges
+                    second_dim_pts = self.base_variables[0].secondary_mesh.nodes
+                    if self.base_eval_size // len(second_dim_pts) in [
+                        len(first_dim_nodes),
+                        len(first_dim_edges),
+                    ]:
+                        self.initialise_2D()
+                    else:
+                        # Raise error for 3D variable
+                        raise NotImplementedError(
+                            "Shape not recognized for {} ".format(base_variables[0])
+                            + "(note processing of 3D variables is not yet implemented)"
+                        )
+
+    def add_sensitivity(self, param, data):
+        # unroll from sparse representation into n-d matrix
+        # Note: then flatten and convert to casadi.DM for consistency with
+        #       full state-vector ProcessedVariable sensitivities
+        self._sensitivities[param] = casadi.DM(self.unroll(data).flatten())
+
+    def _unroll_nnz(self, realdata=None):
+        # unroll in nnz != numel, otherwise copy
+        if realdata is None:
+            realdata = self.base_variables_data
+        sp = self.base_variables_casadi[0](0, 0, 0).sparsity()
+        if sp.nnz() != sp.numel():
+            data = [None] * len(realdata)
+            for datak in range(len(realdata)):
+                data[datak] = np.zeros(self.base_eval_shape[0] * len(self.t_pts))
+                var_data = realdata[0].flatten()
+                k = 0
+                for t_i in range(len(self.t_pts)):
+                    base = t_i * sp.numel()
+                    for r in sp.row():
+                        data[datak][base + r] = var_data[k]
+                        k = k + 1
+        else:
+            data = realdata
+        return data
+
+    def unroll_0D(self, realdata=None):
+        if realdata is None:
+            realdata = self.base_variables_data
+        return np.concatenate(realdata, axis=0).flatten()
+
+    def unroll_1D(self, realdata=None):
+        len_space = self.base_eval_shape[0]
+        return (
+            np.concatenate(self._unroll_nnz(realdata), axis=0)
+            .reshape((len(self.t_pts), len_space))
+            .transpose()
+        )
+
+    def unroll_2D(self, realdata=None, n_dim1=None, n_dim2=None, axis_swaps=[]):
+        # initialise settings on first run
+        if not self.unroll_params:
+            self.unroll_params["n_dim1"] = n_dim1
+            self.unroll_params["n_dim2"] = n_dim2
+            self.unroll_params["axis_swaps"] = axis_swaps
+        # use stored settings on subsequent runs
+        if not n_dim1:
+            n_dim1 = self.unroll_params["n_dim1"]
+            n_dim2 = self.unroll_params["n_dim2"]
+            axis_swaps = self.unroll_params["axis_swaps"]
+        entries = np.concatenate(self._unroll_nnz(realdata), axis=0).reshape(
+            (len(self.t_pts), n_dim1, n_dim2)
+        )
+        for a, b in axis_swaps:
+            entries = np.moveaxis(entries, a, b)
+        return entries
+
+    def unroll(self, realdata=None):
+        if self.dimensions == 0:
+            return self.unroll_0D(realdata=realdata)
+        elif self.dimensions == 1:
+            return self.unroll_1D(realdata=realdata)
+        elif self.dimensions == 2:
+            return self.unroll_2D(realdata=realdata)
+        else:
+            # Raise error for 3D variable
+            raise NotImplementedError(f"Unsupported data dimension: {self.dimensions}")
+
+    def initialise_0D(self):
+        entries = self.unroll_0D()
+
+        if self.cumtrapz_ic is not None:
+            entries = cumulative_trapezoid(
+                entries, self.t_pts, initial=float(self.cumtrapz_ic)
+            )
+
+        # set up interpolation
+        self._xr_data_array = xr.DataArray(entries, coords=[("t", self.t_pts)])
+
+        self.entries = entries
+        self.dimensions = 0
+
+    def initialise_1D(self, fixed_t=False):
+        entries = self.unroll_1D()
+
+        # Get node and edge values
+        nodes = self.mesh.nodes
+        edges = self.mesh.edges
+        if entries.shape[0] == len(nodes):
+            space = nodes
+        elif entries.shape[0] == len(edges):
+            space = edges
+
+        # add points outside domain for extrapolation to boundaries
+        extrap_space_left = np.array([2 * space[0] - space[1]])
+        extrap_space_right = np.array([2 * space[-1] - space[-2]])
+        space = np.concatenate([extrap_space_left, space, extrap_space_right])
+        extrap_entries_left = 2 * entries[0] - entries[1]
+        extrap_entries_right = 2 * entries[-1] - entries[-2]
+        entries_for_interp = np.vstack(
+            [extrap_entries_left, entries, extrap_entries_right]
+        )
+
+        # assign attributes for reference (either x_sol or r_sol)
+        self.entries = entries
+        self.dimensions = 1
+        if self.domain[0].endswith("particle"):
+            self.first_dimension = "r"
+            self.r_sol = space
+        elif self.domain[0] in [
+            "negative electrode",
+            "separator",
+            "positive electrode",
+        ]:
+            self.first_dimension = "x"
+            self.x_sol = space
+        elif self.domain == ["current collector"]:
+            self.first_dimension = "z"
+            self.z_sol = space
+        elif self.domain[0].endswith("particle size"):
+            self.first_dimension = "R"
+            self.R_sol = space
+        else:
+            self.first_dimension = "x"
+            self.x_sol = space
+
+        # assign attributes for reference
+        pts_for_interp = space
+        self.internal_boundaries = self.mesh.internal_boundaries
+
+        # Set first_dim_pts to edges for nicer plotting
+        self.first_dim_pts = edges
+
+        # set up interpolation
+        self._xr_data_array = xr.DataArray(
+            entries_for_interp,
+            coords=[(self.first_dimension, pts_for_interp), ("t", self.t_pts)],
+        )
+
+    def initialise_2D(self):
+        """
+        Initialise a 2D object that depends on x and r, x and z, x and R, or R and r.
+        """
+        first_dim_nodes = self.mesh.nodes
+        first_dim_edges = self.mesh.edges
+        second_dim_nodes = self.base_variables[0].secondary_mesh.nodes
+        second_dim_edges = self.base_variables[0].secondary_mesh.edges
+        if self.base_eval_size // len(second_dim_nodes) == len(first_dim_nodes):
+            first_dim_pts = first_dim_nodes
+        elif self.base_eval_size // len(second_dim_nodes) == len(first_dim_edges):
+            first_dim_pts = first_dim_edges
+
+        second_dim_pts = second_dim_nodes
+        first_dim_size = len(first_dim_pts)
+        second_dim_size = len(second_dim_pts)
+
+        entries = self.unroll_2D(
+            realdata=None,
+            n_dim1=second_dim_size,
+            n_dim2=first_dim_size,
+            axis_swaps=[(0, 2), (0, 1)],
+        )
+
+        # add points outside first dimension domain for extrapolation to
+        # boundaries
+        extrap_space_first_dim_left = np.array(
+            [2 * first_dim_pts[0] - first_dim_pts[1]]
+        )
+        extrap_space_first_dim_right = np.array(
+            [2 * first_dim_pts[-1] - first_dim_pts[-2]]
+        )
+        first_dim_pts = np.concatenate(
+            [extrap_space_first_dim_left, first_dim_pts, extrap_space_first_dim_right]
+        )
+        extrap_entries_left = np.expand_dims(2 * entries[0] - entries[1], axis=0)
+        extrap_entries_right = np.expand_dims(2 * entries[-1] - entries[-2], axis=0)
+        entries_for_interp = np.concatenate(
+            [extrap_entries_left, entries, extrap_entries_right], axis=0
+        )
+
+        # add points outside second dimension domain for extrapolation to
+        # boundaries
+        extrap_space_second_dim_left = np.array(
+            [2 * second_dim_pts[0] - second_dim_pts[1]]
+        )
+        extrap_space_second_dim_right = np.array(
+            [2 * second_dim_pts[-1] - second_dim_pts[-2]]
+        )
+        second_dim_pts = np.concatenate(
+            [
+                extrap_space_second_dim_left,
+                second_dim_pts,
+                extrap_space_second_dim_right,
+            ]
+        )
+        extrap_entries_second_dim_left = np.expand_dims(
+            2 * entries_for_interp[:, 0, :] - entries_for_interp[:, 1, :], axis=1
+        )
+        extrap_entries_second_dim_right = np.expand_dims(
+            2 * entries_for_interp[:, -1, :] - entries_for_interp[:, -2, :], axis=1
+        )
+        entries_for_interp = np.concatenate(
+            [
+                extrap_entries_second_dim_left,
+                entries_for_interp,
+                extrap_entries_second_dim_right,
+            ],
+            axis=1,
+        )
+
+        # Process r-x, x-z, r-R, R-x, or R-z
+        if self.domain[0].endswith("particle") and self.domains["secondary"][
+            0
+        ].endswith("electrode"):
+            self.first_dimension = "r"
+            self.second_dimension = "x"
+            self.r_sol = first_dim_pts
+            self.x_sol = second_dim_pts
+        elif self.domain[0] in [
+            "negative electrode",
+            "separator",
+            "positive electrode",
+        ] and self.domains["secondary"] == ["current collector"]:
+            self.first_dimension = "x"
+            self.second_dimension = "z"
+            self.x_sol = first_dim_pts
+            self.z_sol = second_dim_pts
+        elif self.domain[0].endswith("particle") and self.domains["secondary"][
+            0
+        ].endswith("particle size"):
+            self.first_dimension = "r"
+            self.second_dimension = "R"
+            self.r_sol = first_dim_pts
+            self.R_sol = second_dim_pts
+        elif self.domain[0].endswith("particle size") and self.domains["secondary"][
+            0
+        ].endswith("electrode"):
+            self.first_dimension = "R"
+            self.second_dimension = "x"
+            self.R_sol = first_dim_pts
+            self.x_sol = second_dim_pts
+        elif self.domain[0].endswith("particle size") and self.domains["secondary"] == [
+            "current collector"
+        ]:
+            self.first_dimension = "R"
+            self.second_dimension = "z"
+            self.R_sol = first_dim_pts
+            self.z_sol = second_dim_pts
+        else:  # pragma: no cover
+            raise pybamm.DomainError(
+                f"Cannot process 2D object with domains '{self.domains}'."
+            )
+
+        # assign attributes for reference
+        self.entries = entries
+        self.dimensions = 2
+        first_dim_pts_for_interp = first_dim_pts
+        second_dim_pts_for_interp = second_dim_pts
+
+        # Set pts to edges for nicer plotting
+        self.first_dim_pts = first_dim_edges
+        self.second_dim_pts = second_dim_edges
+
+        # set up interpolation
+        self._xr_data_array = xr.DataArray(
+            entries_for_interp,
+            coords={
+                self.first_dimension: first_dim_pts_for_interp,
+                self.second_dimension: second_dim_pts_for_interp,
+                "t": self.t_pts,
+            },
+        )
+
+    def initialise_2D_scikit_fem(self):
+        y_sol = self.mesh.edges["y"]
+        len_y = len(y_sol)
+        z_sol = self.mesh.edges["z"]
+        len_z = len(z_sol)
+        entries = self.unroll_2D(
+            realdata=None,
+            n_dim1=len_y,
+            n_dim2=len_z,
+            axis_swaps=[(0, 2)],
+        )
+
+        # assign attributes for reference
+        self.entries = entries
+        self.dimensions = 2
+        self.y_sol = y_sol
+        self.z_sol = z_sol
+        self.first_dimension = "y"
+        self.second_dimension = "z"
+        self.first_dim_pts = y_sol
+        self.second_dim_pts = z_sol
+
+        # set up interpolation
+        self._xr_data_array = xr.DataArray(
+            entries,
+            coords={"y": y_sol, "z": z_sol, "t": self.t_pts},
+        )
+
+    def __call__(self, t=None, x=None, r=None, y=None, z=None, R=None, warn=True):
+        """
+        Evaluate the variable at arbitrary *dimensional* t (and x, r, y, z and/or R),
+        using interpolation
+        """
+        kwargs = {"t": t, "x": x, "r": r, "y": y, "z": z, "R": R}
+        # Remove any None arguments
+        kwargs = {key: value for key, value in kwargs.items() if value is not None}
+        # Use xarray interpolation, return numpy array
+        return self._xr_data_array.interp(**kwargs).values
+
+    @property
+    def data(self):
+        """Same as entries, but different name"""
+        return self.entries
+
+    @property
+    def sensitivities(self):
+        """
+        Returns a dictionary of sensitivities for each input parameter.
+        The keys are the input parameters, and the value is a matrix of size
+        (n_x * n_t, n_p), where n_x is the number of states, n_t is the number of time
+        points, and n_p is the size of the input parameter
+        """
+        # No sensitivities if there are no inputs
+        if len(self.all_inputs[0]) == 0:
+            return {}
+        return self._sensitivities
diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py
index 4c9ccb993d..d7a27f142c 100644
--- a/pybamm/solvers/solution.py
+++ b/pybamm/solvers/solution.py
@@ -483,13 +483,21 @@ def update(self, variables):
                     cumtrapz_ic = var_pybamm.initial_condition
                     cumtrapz_ic = cumtrapz_ic.evaluate()
                     var_pybamm = var_pybamm.child
-                    var_casadi = self.process_casadi_var(var_pybamm, inputs, ys)
+                    var_casadi = self.process_casadi_var(
+                        var_pybamm,
+                        inputs,
+                        ys.shape,
+                    )
                     model._variables_casadi[key] = var_casadi
                     vars_pybamm[i] = var_pybamm
                 elif key in model._variables_casadi:
                     var_casadi = model._variables_casadi[key]
                 else:
-                    var_casadi = self.process_casadi_var(var_pybamm, inputs, ys)
+                    var_casadi = self.process_casadi_var(
+                        var_pybamm,
+                        inputs,
+                        ys.shape,
+                    )
                     model._variables_casadi[key] = var_casadi
                 vars_casadi.append(var_casadi)
             var = pybamm.ProcessedVariable(
@@ -500,9 +508,9 @@ def update(self, variables):
             self._variables[key] = var
             self.data[key] = var.data
 
-    def process_casadi_var(self, var_pybamm, inputs, ys):
+    def process_casadi_var(self, var_pybamm, inputs, ys_shape):
         t_MX = casadi.MX.sym("t")
-        y_MX = casadi.MX.sym("y", ys.shape[0])
+        y_MX = casadi.MX.sym("y", ys_shape[0])
         inputs_MX_dict = {
             key: casadi.MX.sym("input", value.shape[0]) for key, value in inputs.items()
         }
diff --git a/pybamm/spatial_methods/scikit_finite_element.py b/pybamm/spatial_methods/scikit_finite_element.py
index 0f0a42bbcb..2d51e16c32 100644
--- a/pybamm/spatial_methods/scikit_finite_element.py
+++ b/pybamm/spatial_methods/scikit_finite_element.py
@@ -6,7 +6,8 @@
 from scipy.sparse import csr_matrix, csc_matrix
 from scipy.sparse.linalg import inv
 import numpy as np
-import skfem
+
+from pybamm.util import have_optional_dependency
 
 
 class ScikitFiniteElement(pybamm.SpatialMethod):
@@ -87,6 +88,7 @@ def gradient(self, symbol, discretised_symbol, boundary_conditions):
             to the y-component of the gradient and the second column corresponds
             to the z component of the gradient.
         """
+        skfem = have_optional_dependency("skfem")
         domain = symbol.domain[0]
         mesh = self.mesh[domain]
 
@@ -142,6 +144,7 @@ def gradient_matrix(self, symbol, boundary_conditions):
         :class:`pybamm.Matrix`
             The (sparse) finite element gradient matrix for the domain
         """
+        skfem = have_optional_dependency("skfem")
         # get primary domain mesh
         domain = symbol.domain[0]
         mesh = self.mesh[domain]
@@ -187,6 +190,7 @@ def laplacian(self, symbol, discretised_symbol, boundary_conditions):
             Contains the result of acting the discretised gradient on
             the child discretised_symbol
         """
+        skfem = have_optional_dependency("skfem")
         domain = symbol.domain[0]
         mesh = self.mesh[domain]
 
@@ -258,6 +262,7 @@ def stiffness_matrix(self, symbol, boundary_conditions):
         :class:`pybamm.Matrix`
             The (sparse) finite element stiffness matrix for the domain
         """
+        skfem = have_optional_dependency("skfem")
         # get primary domain mesh
         domain = symbol.domain[0]
         mesh = self.mesh[domain]
@@ -320,6 +325,7 @@ def definite_integral_matrix(self, child, vector_type="row"):
         :class:`pybamm.Matrix`
             The finite element integral vector for the domain
         """
+        skfem = have_optional_dependency("skfem")
         # get primary domain mesh
         domain = child.domain[0]
         mesh = self.mesh[domain]
@@ -381,6 +387,7 @@ def boundary_integral_vector(self, domain, region):
         :class:`pybamm.Matrix`
             The finite element integral vector for the domain
         """
+        skfem = have_optional_dependency("skfem")
         # get primary domain mesh
         mesh = self.mesh[domain[0]]
 
@@ -498,6 +505,7 @@ def assemble_mass_form(self, symbol, boundary_conditions, region="interior"):
         :class:`pybamm.Matrix`
             The (sparse) mass matrix for the spatial method.
         """
+        skfem = have_optional_dependency("skfem")
         # get primary domain mesh
         domain = symbol.domain[0]
         mesh = self.mesh[domain]
diff --git a/pybamm/util.py b/pybamm/util.py
index 562352bfac..af278d752a 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -6,6 +6,7 @@
 #
 import argparse
 import importlib.util
+import importlib.metadata
 import numbers
 import os
 import pathlib
@@ -18,11 +19,10 @@
 from warnings import warn
 
 import numpy as np
-import importlib.metadata
-
 import pybamm
 
-# versions of jax and jaxlib compatible with PyBaMM
+# Versions of jax and jaxlib compatible with PyBaMM. Note: these are also defined in
+# in the extras dependencies in pyproject.toml, and therefore must be kept in sync.
 JAX_VERSION = "0.4"
 JAXLIB_VERSION = "0.4"
 
@@ -345,3 +345,26 @@ def install_jax(arguments=None):  # pragma: no cover
             f"jaxlib>={JAXLIB_VERSION}",
         ]
     )
+
+# https://docs.pybamm.org/en/latest/source/user_guide/contributing.html#managing-optional-dependencies-and-their-imports
+def have_optional_dependency(module_name, attribute=None):
+    err_msg = f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."
+    try:
+        # Attempt to import the specified module
+        module = importlib.import_module(module_name)
+
+        if attribute:
+            # If an attribute is specified, check if it's available
+            if hasattr(module, attribute):
+                imported_attribute = getattr(module, attribute)
+                return imported_attribute  # Return the imported attribute
+            else:
+                # Raise an ModuleNotFoundError if the attribute is not available
+                raise ModuleNotFoundError(err_msg)      # pragma: no cover
+        else:
+            # Return the entire module if no attribute is specified
+            return module
+
+    except ModuleNotFoundError:
+        # Raise an ModuleNotFoundError if the module or attribute is not available
+        raise ModuleNotFoundError(err_msg)
diff --git a/pybamm/version.py b/pybamm/version.py
index 0e8c575aea..970be77f66 100644
--- a/pybamm/version.py
+++ b/pybamm/version.py
@@ -1 +1 @@
-__version__ = "23.5"
+__version__ = "23.9"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000..4569c7c6c3
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,174 @@
+[build-system]
+requires = [
+    "setuptools>=64",
+    "wheel",
+    # On Windows, use the CasADi vcpkg registry and CMake bundled from MSVC
+    "casadi>=3.6.0; platform_system!='Windows'",
+    "cmake; platform_system!='Windows'",
+    ]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "pybamm"
+version = "23.9"
+license = { file = "LICENSE.txt" }
+description = "Python Battery Mathematical Modelling"
+authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
+maintainers = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
+requires-python = ">=3.8, <3.12"
+readme = {file = "README.md", content-type = "text/markdown"}
+classifiers = [
+    "Development Status :: 5 - Production/Stable",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: BSD License",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Topic :: Scientific/Engineering",
+]
+dependencies = [
+    "numpy>=1.23.5",
+    "scipy>=1.9.3",
+    "casadi>=3.6.3",
+    "xarray>=2022.6.0",
+    "anytree>=2.12.0",
+]
+
+[project.urls]
+Homepage = "https://pybamm.org"
+Documentation = "https://docs.pybamm.org"
+Repository = "https://github.com/pybamm-team/PyBaMM"
+Releases = "https://github.com/pybamm-team/PyBaMM/releases"
+Changelog = "https://github.com/pybamm-team/PyBaMM/blob/develop/CHANGELOG.md"
+
+[project.optional-dependencies]
+# For the generation of documentation
+docs = [
+    "sphinx>=6",
+    "sphinx_rtd_theme>=0.5",
+    "pydata-sphinx-theme",
+    "sphinx_design",
+    "sphinx-copybutton",
+    "myst-parser",
+    "sphinx-inline-tabs",
+    "sphinxcontrib-bibtex",
+    "sphinx-autobuild",
+    "sphinx-last-updated-by-git",
+    "nbsphinx",
+    "ipykernel",
+    "ipywidgets",
+    "sphinx-gallery",
+    "sphinx-hoverxref",
+    "sphinx-docsearch",
+]
+# For example notebooks
+examples = [
+    "jupyter",
+]
+# Plotting functionality
+plot = [
+    "imageio>=2.32.0",
+    # Note: matplotlib is loaded for debug plots, but to ensure PyBaMM runs
+    # on systems without an attached display, it should never be imported
+    # outside of plot() methods.
+    "matplotlib>=3.6.0",
+]
+# For the Citations class
+cite = [
+    "pybtex>=0.24.0",
+]
+# To generate LaTeX strings
+latexify = [
+    "sympy>=1.12",
+]
+# Battery Parameter eXchange format
+bpx = [
+    "bpx",
+]
+# Low-overhead progress bars
+tqdm = [
+    "tqdm",
+]
+# Dependencies intended for use by developers
+dev = [
+    # For working with pre-commit hooks
+    "pre-commit",
+    # For code style checks: linting and auto-formatting
+    "ruff",
+    # For running testing sessions
+    "nox",
+    # For testing Jupyter notebooks
+    "pytest>=6",
+    "pytest-xdist",
+    "nbmake",
+]
+# Reading CSV files
+pandas =  [
+    "pandas>=1.5.0",
+]
+# For the Jax solver. Note: these must be kept in sync with the versions defined in pybamm/util.py.
+jax = [
+    "jax>=0.4,<=0.5",
+    "jaxlib>=0.4,<=0.5",
+]
+# For the scikits.odes solver
+odes = [
+    "scikits.odes"
+]
+# Contains all optional dependencies, except for odes, jax, and dev dependencies
+all = [
+    "autograd>=1.6.2",
+    "scikit-fem>=8.1.0",
+    "pybamm[examples,plot,cite,latexify,bpx,tqdm,pandas]",
+]
+
+[project.scripts]
+pybamm_edit_parameter = "pybamm.parameters_cli:edit_parameter"
+pybamm_add_parameter = "pybamm.parameters_cli:add_parameter"
+pybamm_rm_parameter = "pybamm.parameters_cli:remove_parameter"
+pybamm_install_odes = "pybamm.install_odes:main"
+pybamm_install_jax = "pybamm.util:install_jax"
+
+[project.entry-points."pybamm_parameter_sets"]
+Sulzer2019 = "pybamm.input.parameters.lead_acid.Sulzer2019:get_parameter_values"
+Ai2020 = "pybamm.input.parameters.lithium_ion.Ai2020:get_parameter_values"
+Chen2020 = "pybamm.input.parameters.lithium_ion.Chen2020:get_parameter_values"
+Chen2020_composite = "pybamm.input.parameters.lithium_ion.Chen2020_composite:get_parameter_values"
+Ecker2015 = "pybamm.input.parameters.lithium_ion.Ecker2015:get_parameter_values"
+Ecker2015_graphite_halfcell = "pybamm.input.parameters.lithium_ion.Ecker2015_graphite_halfcell:get_parameter_values"
+Marquis2019 = "pybamm.input.parameters.lithium_ion.Marquis2019:get_parameter_values"
+Mohtat2020 = "pybamm.input.parameters.lithium_ion.Mohtat2020:get_parameter_values"
+NCA_Kim2011 = "pybamm.input.parameters.lithium_ion.NCA_Kim2011:get_parameter_values"
+OKane2022 = "pybamm.input.parameters.lithium_ion.OKane2022:get_parameter_values"
+OKane2022_graphite_SiOx_halfcell = "pybamm.input.parameters.lithium_ion.OKane2022_graphite_SiOx_halfcell:get_parameter_values"
+ORegan2022 = "pybamm.input.parameters.lithium_ion.ORegan2022:get_parameter_values"
+Prada2013 = "pybamm.input.parameters.lithium_ion.Prada2013:get_parameter_values"
+Ramadass2004 = "pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_values"
+Xu2019 = "pybamm.input.parameters.lithium_ion.Xu2019:get_parameter_values"
+ECM_Example = "pybamm.input.parameters.ecm.example_set:get_parameter_values"
+MSMR_Example = "pybamm.input.parameters.lithium_ion.MSMR_example_set:get_parameter_values"
+
+[tool.setuptools]
+include-package-data = true
+
+# List of files to include as package data. These are mainly the parameter CSV files in
+# the input/parameters/ subdirectories. Other files such as the CITATIONS file, relevant
+# README.md files, and specific .txt files inside the pybamm/ directory are also included.
+# These are specified to be included in the SDist through MANIFEST.in.
+[tool.setuptools.package-data]
+pybamm = [
+    "*.txt",
+    "*.md",
+    "*.csv",
+    "*.py",
+    "pybamm/CITATIONS.bib",
+    "pybamm/plotting/mplstyle",
+]
+
+[tool.setuptools.packages.find]
+include = ["pybamm", "pybamm.*"]
diff --git a/ruff.toml b/ruff.toml
new file mode 100644
index 0000000000..7304d64570
--- /dev/null
+++ b/ruff.toml
@@ -0,0 +1,8 @@
+extend-include = ["*.ipynb"]
+extend-exclude = ["__init__.py"]
+
+[lint]
+ignore = ["E741"]
+
+[lint.per-file-ignores]
+"**.ipynb" = ["E402", "E703"]
diff --git a/scripts/Dockerfile b/scripts/Dockerfile
index 3cfbeaa11c..8def7ced9e 100644
--- a/scripts/Dockerfile
+++ b/scripts/Dockerfile
@@ -4,7 +4,7 @@ WORKDIR /
 
 # Install the necessary dependencies
 RUN apt-get update && apt-get -y upgrade
-RUN apt-get install -y libopenblas-dev gcc gfortran graphviz git make g++ build-essential cmake
+RUN apt-get install -y libopenblas-dev gcc gfortran graphviz git make g++ build-essential cmake pandoc texlive-latex-extra dvipng
 RUN rm -rf /var/lib/apt/lists/*
 
 RUN useradd -m -s /bin/bash pybamm
@@ -21,45 +21,49 @@ ENV CMAKE_C_COMPILER=/usr/bin/gcc
 ENV CMAKE_CXX_COMPILER=/usr/bin/g++
 ENV CMAKE_MAKE_PROGRAM=/usr/bin/make
 ENV SUNDIALS_INST=/home/pybamm/.local
-ENV LD_LIBRARY_PATH=/home/pybamm/.local/lib:
+ENV LD_LIBRARY_PATH=/home/pybamm/.local/lib
+
+RUN conda create -n pybamm python=3.11
+RUN conda init --all
+SHELL ["conda", "run", "-n", "pybamm", "/bin/bash", "-c"]
+RUN conda install -y pip
 
 ARG IDAKLU
 ARG ODES
 ARG JAX
 ARG ALL
 
-RUN conda create -n pybamm python=3.9
-RUN conda init --all
-SHELL ["conda", "run", "-n", "pybamm", "/bin/bash", "-c"]
-RUN conda install -y pip
+RUN pip install --upgrade --user pip setuptools wheel wget
+RUN pip install cmake
 
 RUN if [ "$IDAKLU" = "true" ]; then \
-    pip install --upgrade --user pip setuptools wheel wget && \
-    pip install cmake==3.22 && \
     python scripts/install_KLU_Sundials.py && \
+    rm -rf pybind11 && \
     git clone https://github.com/pybind/pybind11.git && \
-    pip install --user -e ".[all,dev]"; \
+    pip install --user -e ".[all,dev,docs]"; \
     fi
 
 RUN if [ "$ODES" = "true" ]; then \
-    pip install cmake==3.22 && \
-    pip install --upgrade --user pip wget && \
     python scripts/install_KLU_Sundials.py && \
-    pip install --user -e ".[all,odes,dev]"; \
+    pip install --user -e ".[all,dev,docs,odes]"; \
     fi
 
 RUN if [ "$JAX" = "true" ]; then \
-    pip install --user -e ".[jax,all,dev]";\
+    pip install --user -e ".[all,dev,docs,jax]"; \
     fi
 
 RUN if [ "$ALL" = "true" ]; then \
-    pip install cmake==3.22 && \
-    pip install --upgrade --user pip setuptools wheel wget && \
     python scripts/install_KLU_Sundials.py && \
+    rm -rf pybind11 && \
     git clone https://github.com/pybind/pybind11.git && \
-    pip install --user -e ".[all,dev,jax,odes]"; \
+    pip install --user -e ".[all,dev,docs,jax,odes]"; \
     fi
 
-RUN pip install --user -e ".[all,dev]"
+RUN if [ -z "$IDAKLU" ] \
+    && [ -z "$ODES" ] \
+    && [ -z "$JAX" ] \
+    && [ -z "$ALL" ]; then \
+    pip install --user -e ".[all,dev,docs]"; \
+    fi
 
 ENTRYPOINT ["/bin/bash"]
diff --git a/scripts/fix_casadi_rpath_mac.py b/scripts/fix_casadi_rpath_mac.py
index 9b0a181391..23c8a32d59 100644
--- a/scripts/fix_casadi_rpath_mac.py
+++ b/scripts/fix_casadi_rpath_mac.py
@@ -1,8 +1,8 @@
 """
-Removes the rpath from libcasadi.dylib in the casadi python install
-and uses a fixed path
+Removes the rpath from libcasadi.dylib and libcasadi.3.7.dylib in the casadi python
+install and uses a fixed path
 
-Used when building the wheels for macos
+Used when building the wheels for macOS
 """
 import casadi
 import os
@@ -14,16 +14,32 @@
 libcpp_name = "libc++.1.0.dylib"
 libcppabi_name = "libc++abi.dylib"
 libcasadi_name = "libcasadi.dylib"
-install_name_tool_args = [
+libcasadi_37_name = "libcasadi.3.7.dylib"
+
+install_name_tool_args_for_libcasadi_name = [
     "-change",
     os.path.join("@rpath", libcpp_name),
     os.path.join(casadi_dir, libcpp_name),
     os.path.join(casadi_dir, libcasadi_name),
 ]
+
+install_name_tool_args_for_libcasadi_37_name = [
+    "-change",
+    os.path.join("@rpath", libcpp_name),
+    os.path.join(casadi_dir, libcpp_name),
+    os.path.join(casadi_dir, libcasadi_37_name),
+]
+
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcasadi_name)])
-print(" ".join(["install_name_tool"] + install_name_tool_args))
-subprocess.run(["install_name_tool"] + install_name_tool_args)
+
+print(" ".join(["install_name_tool"] + install_name_tool_args_for_libcasadi_name))
+subprocess.run(["install_name_tool"] + install_name_tool_args_for_libcasadi_name)
+
+print(" ".join(["install_name_tool"] + install_name_tool_args_for_libcasadi_37_name))
+subprocess.run(["install_name_tool"] + install_name_tool_args_for_libcasadi_37_name)
+
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcasadi_name)])
+
 install_name_tool_args = [
     "-change",
     os.path.join("@rpath", libcppabi_name),
@@ -31,6 +47,25 @@
     os.path.join(casadi_dir, libcpp_name),
 ]
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
+
 print(" ".join(["install_name_tool"] + install_name_tool_args))
 subprocess.run(["install_name_tool"] + install_name_tool_args)
+
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
+
+# Copy libcasadi.3.7.dylib and libc++.1.0.dylib to LD_LIBRARY_PATH
+# This is needed for the casadi python bindings to work while repairing the wheel
+
+subprocess.run(
+    ["cp",
+    os.path.join(casadi_dir, libcasadi_37_name),
+    os.path.join(os.getenv("HOME"),".local/lib")
+    ]
+)
+
+subprocess.run(
+    ["cp",
+    os.path.join(casadi_dir, libcpp_name),
+    os.path.join(os.getenv("HOME"),".local/lib")
+    ]
+)
diff --git a/build_manylinux_wheels/install_sundials.sh b/scripts/install_sundials.sh
similarity index 90%
rename from build_manylinux_wheels/install_sundials.sh
rename to scripts/install_sundials.sh
index 709d9c13c7..0fdd4cdc6a 100644
--- a/build_manylinux_wheels/install_sundials.sh
+++ b/scripts/install_sundials.sh
@@ -1,10 +1,10 @@
 #!/bin/bash
 
 # This script installs both SuiteSparse
-# (https://people.engr.tamu.edu/davis/suitesparse.html) and Sundials
+# (https://people.engr.tamu.edu/davis/suitesparse.html) and SUNDIALS
 # (https://computing.llnl.gov/projects/sundials) from source. For each
 # two library:
-# - Archive downloaded and source code extrated in current working
+# - Archive downloaded and source code extracted in current working
 #   directory.
 # - Library is built and installed.
 #
@@ -65,8 +65,8 @@ yum -y install openblas-devel
 
 mkdir -p build_sundials
 cd build_sundials
-KLU_INCLUDE_DIR=/usr/include
-KLU_LIBRARY_DIR=/usr/lib
+KLU_INCLUDE_DIR=/usr/local/include
+KLU_LIBRARY_DIR=/usr/local/lib
 SUNDIALS_DIR=sundials-$SUNDIALS_VERSION
 cmake -DENABLE_LAPACK=ON\
       -DSUNDIALS_INDEX_SIZE=32\
diff --git a/scripts/replace-cmake/README.md b/scripts/replace-cmake/README.md
deleted file mode 100644
index e578a96abb..0000000000
--- a/scripts/replace-cmake/README.md
+++ /dev/null
@@ -1 +0,0 @@
-A modified sundials cmake file which finds the KLU solvers correctly
diff --git a/scripts/replace-cmake/sundials-3.1.1/CMakeLists.txt b/scripts/replace-cmake/sundials-3.1.1/CMakeLists.txt
deleted file mode 100644
index 81f4267c22..0000000000
--- a/scripts/replace-cmake/sundials-3.1.1/CMakeLists.txt
+++ /dev/null
@@ -1,1597 +0,0 @@
-# ---------------------------------------------------------------
-# Programmer:  Radu Serban @ LLNL
-# ---------------------------------------------------------------
-# LLNS Copyright Start
-# Copyright (c) 2014, Lawrence Livermore National Security
-# This work was performed under the auspices of the U.S. Department
-# of Energy by Lawrence Livermore National Laboratory in part under
-# Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344.
-# Produced at the Lawrence Livermore National Laboratory.
-# All rights reserved.
-# For details, see the LICENSE file.
-# LLNS Copyright End
-# ---------------------------------------------------------------
-# Top level CMakeLists.txt for SUNDIALS (for cmake build system)
-# ---------------------------------------------------------------
-
-# ---------------------------------------------------------------
-# Initial commands
-# ---------------------------------------------------------------
-
-# Require a fairly recent cmake version
-CMAKE_MINIMUM_REQUIRED(VERSION 2.8.1)
-
-# Set CMake policy to allow examples to build
-if(COMMAND cmake_policy)
-  cmake_policy(SET CMP0003 NEW)
-endif(COMMAND cmake_policy)
-
-# Project SUNDIALS (initially only C supported)
-# sets PROJECT_SOURCE_DIR and PROJECT_BINARY_DIR variables
-PROJECT(sundials C)
-
-# Set some variables with info on the SUNDIALS project
-SET(PACKAGE_BUGREPORT "woodward6@llnl.gov")
-SET(PACKAGE_NAME "SUNDIALS")
-SET(PACKAGE_STRING "SUNDIALS 3.1.1")
-SET(PACKAGE_TARNAME "sundials")
-
-# set SUNDIALS version numbers
-# (use "" for the version label if none is needed)
-SET(PACKAGE_VERSION_MAJOR "3")
-SET(PACKAGE_VERSION_MINOR "1")
-SET(PACKAGE_VERSION_PATCH "1")
-SET(PACKAGE_VERSION_LABEL "")
-
-IF(PACKAGE_VERSION_LABEL)
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}-${PACKAGE_VERSION_LABEL}")
-ELSE()
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}")
-ENDIF()
-
-#
-SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)
-
-# Prohibit in-source build
-IF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-  MESSAGE(FATAL_ERROR "In-source build prohibited.")
-ENDIF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-
-# Hide some cache variables
-MARK_AS_ADVANCED(EXECUTABLE_OUTPUT_PATH LIBRARY_OUTPUT_PATH)
-
-# Always show the C compiler and flags
-MARK_AS_ADVANCED(CLEAR
-  CMAKE_C_COMPILER
-  CMAKE_C_FLAGS)
-
-# Specify the VERSION and SOVERSION for shared libraries
-
-SET(arkodelib_VERSION "2.1.1")
-SET(arkodelib_SOVERSION "2")
-
-SET(cvodelib_VERSION "3.1.1")
-SET(cvodelib_SOVERSION "3")
-
-SET(cvodeslib_VERSION "3.1.1")
-SET(cvodeslib_SOVERSION "3")
-
-SET(idalib_VERSION "3.1.1")
-SET(idalib_SOVERSION "3")
-
-SET(idaslib_VERSION "2.1.0")
-SET(idaslib_SOVERSION "2")
-
-SET(kinsollib_VERSION "3.1.1")
-SET(kinsollib_SOVERSION "3")
-
-SET(cpodeslib_VERSION "0.0.0")
-SET(cpodeslib_SOVERSION "0")
-
-SET(nveclib_VERSION "3.1.1")
-SET(nveclib_SOVERSION "3")
-
-SET(sunmatrixlib_VERSION "1.1.1")
-SET(sunmatrixlib_SOVERSION "1")
-
-SET(sunlinsollib_VERSION "1.1.1")
-SET(sunlinsollib_SOVERSION "1")
-
-# Specify the location of additional CMAKE modules
-SET(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/config)
-
-# ---------------------------------------------------------------
-# MACRO definitions
-# ---------------------------------------------------------------
-INCLUDE(SundialsCMakeMacros)
-
-# ---------------------------------------------------------------
-# Check for deprecated SUNDIALS CMake options/variables
-# ---------------------------------------------------------------
-INCLUDE(SundialsDeprecated)
-
-# ---------------------------------------------------------------
-# Which modules to build?
-# ---------------------------------------------------------------
-
-# For each SUNDIALS solver available (i.e. for which we have the
-# sources), give the user the option of enabling/disabling it.
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/arkode")
-  OPTION(BUILD_ARKODE "Build the ARKODE library" ON)
-ELSE()
-  SET(BUILD_ARKODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvode")
-  OPTION(BUILD_CVODE "Build the CVODE library" ON)
-ELSE()
-  SET(BUILD_CVODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvodes")
-  OPTION(BUILD_CVODES "Build the CVODES library" ON)
-ELSE()
-  SET(BUILD_CVODES OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/ida")
-  OPTION(BUILD_IDA "Build the IDA library" ON)
-ELSE()
-  SET(BUILD_IDA OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/idas")
-  OPTION(BUILD_IDAS "Build the IDAS library" ON)
-ELSE()
-  SET(BUILD_IDAS OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/kinsol")
-  OPTION(BUILD_KINSOL "Build the KINSOL library" ON)
-ELSE()
-  SET(BUILD_KINSOL OFF)
-ENDIF()
-
-# CPODES is always OFF for now.  (commented out for Release); ToDo: better way to do this?
-#IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cpodes")
-#  OPTION(BUILD_CPODES  "Build the CPODES library"  OFF)
-#ELSE()
-#  SET(BUILD_CPODES OFF)
-#ENDIF()
-
-# ---------------------------------------------------------------
-# xSDK specific options
-# ---------------------------------------------------------------
-INCLUDE(SundialsXSDK)
-
-# ---------------------------------------------------------------
-# Build specific C flags
-# ---------------------------------------------------------------
-
-# Hide all build type specific flags
-MARK_AS_ADVANCED(FORCE
-  CMAKE_C_FLAGS_DEBUG
-  CMAKE_C_FLAGS_MINSIZEREL
-  CMAKE_C_FLAGS_RELEASE
-  CMAKE_C_FLAGS_RELWITHDEBINFO)
-
-# Only show flags for the current build type it is set
-# NOTE: Build specific flags are appended those in CMAKE_C_FLAGS
-IF(CMAKE_BUILD_TYPE)
-  IF(CMAKE_BUILD_TYPE MATCHES "Debug")
-    MESSAGE("Appending C debug flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_DEBUG)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "MinSizeRel")
-    MESSAGE("Appending C min size release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_MINSIZEREL)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "Release")
-    MESSAGE("Appending C release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELEASE)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "RelWithDebInfo")
-    MESSAGE("Appending C release with debug info flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELWITHDEBINFO)
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# Option to specify precision (realtype)
-# ---------------------------------------------------------------
-
-SET(DOCSTR "single, double, or extended")
-SHOW_VARIABLE(SUNDIALS_PRECISION STRING "${DOCSTR}" "double")
-
-# prepare substitution variable PRECISION_LEVEL for sundials_config.h
-STRING(TOUPPER ${SUNDIALS_PRECISION} SUNDIALS_PRECISION)
-SET(PRECISION_LEVEL "#define SUNDIALS_${SUNDIALS_PRECISION}_PRECISION 1")
-
-# prepare substitution variable FPRECISION_LEVEL for sundials_fconfig.h
-IF(SUNDIALS_PRECISION MATCHES "SINGLE")
-  SET(FPRECISION_LEVEL "4")
-ENDIF(SUNDIALS_PRECISION MATCHES "SINGLE")
-IF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-  SET(FPRECISION_LEVEL "8")
-ENDIF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-IF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-  SET(FPRECISION_LEVEL "16")
-ENDIF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-
-# ---------------------------------------------------------------
-# Option to specify index type
-# ---------------------------------------------------------------
-
-SET(DOCSTR "Signed 64-bit (int64_t) or signed 32-bit (int32_t) integer")
-SHOW_VARIABLE(SUNDIALS_INDEX_TYPE STRING "${DOCSTR}" "int64_t")
-
-# prepare substitution variable INDEX_TYPE for sundials_config.h
-STRING(TOUPPER ${SUNDIALS_INDEX_TYPE} SUNDIALS_INDEX_TYPE)
-SET(INDEX_TYPE "#define SUNDIALS_${SUNDIALS_INDEX_TYPE} 1")
-
-# prepare substitution variable FINDEX_TYPE for sundials_fconfig.h
-IF(SUNDIALS_INDEX_TYPE MATCHES "INT32_T")
-  SET(FINDEX_TYPE "4")
-ENDIF(SUNDIALS_INDEX_TYPE MATCHES "INT32_T")
-IF(SUNDIALS_INDEX_TYPE MATCHES "INT64_T")
-  SET(FINDEX_TYPE "8")
-ENDIF(SUNDIALS_INDEX_TYPE MATCHES "INT64_T")
-
-# ---------------------------------------------------------------
-# Enable Fortran interface?
-# ---------------------------------------------------------------
-
-# Fortran interface is disabled by default
-SET(DOCSTR "Enable Fortran-C support")
-SHOW_VARIABLE(FCMIX_ENABLE BOOL "${DOCSTR}" OFF)
-
-# Check that at least one solver with a Fortran interface is built
-IF(NOT BUILD_ARKODE AND NOT BUILD_CVODE AND NOT BUILD_IDA AND NOT BUILD_KINSOL)
-  IF(FCMIX_ENABLE)
-    PRINT_WARNING("Enabled packages do not support Fortran" "Disabling FCMIX")
-    FORCE_VARIABLE(FCMIX_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(FCMIX_ENABLE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Options to build static and/or shared libraries
-# ---------------------------------------------------------------
-
-OPTION(BUILD_STATIC_LIBS "Build static libraries" ON)
-OPTION(BUILD_SHARED_LIBS "Build shared libraries" ON)
-
-# Make sure we build at least one type of libraries
-IF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-  PRINT_WARNING("Both static and shared library generation were disabled"
-                "Building static libraries was re-enabled")
-  FORCE_VARIABLE(BUILD_STATIC_LIBS BOOL "Build static libraries" ON)
-ENDIF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-
-# ---------------------------------------------------------------
-# Option to use the generic math libraries (UNIX only)
-# ---------------------------------------------------------------
-
-IF(UNIX)
-  OPTION(USE_GENERIC_MATH "Use generic (std-c) math libraries" ON)
-  IF(USE_GENERIC_MATH)
-    # executables will be linked against -lm
-    SET(EXTRA_LINK_LIBS -lm)
-    # prepare substitution variable for sundials_config.h
-    SET(SUNDIALS_USE_GENERIC_MATH TRUE)
-  ENDIF(USE_GENERIC_MATH)
-ENDIF(UNIX)
-
-## clock-monotonic, see if we need to link with rt
-include(CheckSymbolExists)
-set(CMAKE_REQUIRED_LIBRARIES_SAVE ${CMAKE_REQUIRED_LIBRARIES})
-set(CMAKE_REQUIRED_LIBRARIES rt)
-CHECK_SYMBOL_EXISTS(_POSIX_TIMERS "unistd.h;time.h" SUNDIALS_POSIX_TIMERS)
-set(CMAKE_REQUIRED_LIBRARIES ${CMAKE_REQUIRED_LIBRARIES_SAVE})
-if(SUNDIALS_POSIX_TIMERS)
-  find_library(SUNDIALS_RT_LIBRARY NAMES rt)
-  mark_as_advanced(SUNDIALS_RT_LIBRARY)
-  if(SUNDIALS_RT_LIBRARY)
-    # sundials_config.h symbol
-    SET(SUNDIALS_HAVE_POSIX_TIMERS TRUE)
-    set(EXTRA_LINK_LIBS ${EXTRA_LINK_LIBS} ${SUNDIALS_RT_LIBRARY})
-  endif()
-endif()
-
-
-# ===============================================================
-# Options for Parallelism
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable MPI support?
-# ---------------------------------------------------------------
-OPTION(MPI_ENABLE "Enable MPI support" OFF)
-
-# ---------------------------------------------------------------
-# Enable OpenMP support?
-# ---------------------------------------------------------------
-OPTION(OPENMP_ENABLE "Enable OpenMP support" OFF)
-
-# ---------------------------------------------------------------
-# Enable Pthread support?
-# ---------------------------------------------------------------
-OPTION(PTHREAD_ENABLE "Enable Pthreads support" OFF)
-
-# -------------------------------------------------------------
-# Enable CUDA support?
-# -------------------------------------------------------------
-OPTION(CUDA_ENABLE "Enable CUDA support" OFF)
-
-# -------------------------------------------------------------
-# Enable RAJA support?
-# -------------------------------------------------------------
-OPTION(RAJA_ENABLE "Enable RAJA support" OFF)
-
-
-# ===============================================================
-# Options for external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable BLAS support?
-# ---------------------------------------------------------------
-OPTION(BLAS_ENABLE "Enable BLAS support" OFF)
-
-# ---------------------------------------------------------------
-# Enable LAPACK/BLAS support?
-# ---------------------------------------------------------------
-OPTION(LAPACK_ENABLE "Enable Lapack support" OFF)
-
-# LAPACK does not support extended precision
-IF(LAPACK_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling LAPACK")
-  FORCE_VARIABLE(LAPACK_ENABLE BOOL "LAPACK is disabled" OFF)
-ENDIF()
-
-# LAPACK does not support 64-bit integer index types
-IF(LAPACK_ENABLE AND SUNDIALS_INDEX_TYPE MATCHES "INT64_T")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_INDEX_TYPE} integers"
-                "Disabling LAPACK")
-  SET(LAPACK_ENABLE OFF CACHE BOOL "LAPACK is disabled" FORCE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable SuperLU_MT support?
-# ---------------------------------------------------------------
-OPTION(SUPERLUMT_ENABLE "Enable SUPERLUMT support" OFF)
-
-# SuperLU_MT does not support extended precision
-IF(SUPERLUMT_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("SuperLU_MT is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling SuperLU_MT")
-  FORCE_VARIABLE(SUPERLUMT_ENABLE BOOL "SuperLU_MT is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable KLU support?
-# ---------------------------------------------------------------
-OPTION(KLU_ENABLE "Enable KLU support" OFF)
-
-# KLU does not support single or extended precision
-IF(KLU_ENABLE AND
-  (SUNDIALS_PRECISION MATCHES "SINGLE" OR SUNDIALS_PRECISION MATCHES "EXTENDED"))
-  PRINT_WARNING("KLU is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling KLU")
-  FORCE_VARIABLE(KLU_ENABLE BOOL "KLU is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable hypre Vector support?
-# ---------------------------------------------------------------
-OPTION(HYPRE_ENABLE "Enable hypre support" OFF)
-
-# Using hypre requres building with MPI enabled
-IF(HYPRE_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling hypre"
-                "Set MPI_ENABLE to ON to use parhyp")
-  FORCE_VARIABLE(HYPRE_ENABLE BOOL "Enable hypre support" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable PETSc support?
-# ---------------------------------------------------------------
-OPTION(PETSC_ENABLE "Enable PETSc support" OFF)
-
-# Using PETSc requires building with MPI enabled
-IF(PETSC_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling PETSc"
-                "Set MPI_ENABLE to ON to use PETSc")
-  FORCE_VARIABLE(PETSC_ENABLE BOOL "Enable PETSc support" OFF)
-ENDIF()
-
-
-# ===============================================================
-# Options for examples
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable examples?
-# ---------------------------------------------------------------
-
-# Enable C examples (on by default)
-OPTION(EXAMPLES_ENABLE_C "Build SUNDIALS C examples" ON)
-
-# F77 examples (on by default) are an option only if the Fortran
-# interface is enabled
-SET(DOCSTR "Build SUNDIALS Fortran examples")
-IF(FCMIX_ENABLE)
-  OPTION(EXAMPLES_ENABLE_F77 "${DOCSTR}" ON)
-  # Fortran examples do not support single or extended precision
-  IF(SUNDIALS_PRECISION MATCHES "EXTENDED" OR SUNDIALS_PRECISION MATCHES "SINGLE")
-    PRINT_WARNING("F77 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "Fortran examples are disabled" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F77)
-    PRINT_WARNING("EXAMPLES_ENABLE_F77 is ON but FCMIX is OFF"
-                  "Disabling EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F77)
-ENDIF()
-
-# C++ examples (off by default) are an option only if ARKode is enabled
-SET(DOCSTR "Build ARKode C++ examples")
-IF(BUILD_ARKODE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_CXX BOOL "${DOCSTR}" OFF)
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_CXX)
-    PRINT_WARNING("EXAMPLES_ENABLE_CXX is ON but BUILD_ARKODE is OFF"
-                  "Disabling EXAMPLES_ENABLE_CXX")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_CXX BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_CXX)
-ENDIF()
-
-# F90 examples (off by default) are an option only if ARKode is
-# built and the Fortran interface is enabled
-SET(DOCSTR "Build ARKode F90 examples")
-IF(FCMIX_ENABLE AND BUILD_ARKODE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  # Fortran90 examples do not support single or extended precision
-  # NOTE: This check can be removed after Fortran configure file is integrated into examples
-  IF(SUNDIALS_PRECISION MATCHES "EXTENDED" OR SUNDIALS_PRECISION MATCHES "SINGLE")
-    PRINT_WARNING("F90 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "Fortran90 examples are disabled" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F90)
-    PRINT_WARNING("EXAMPLES_ENABLE_F90 is ON but FCMIX or BUILD_ARKODE is OFF"
-                  "Disabling EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F90)
-ENDIF()
-
-# CUDA examples (off by default)
-SET(DOCSTR "Build SUNDIALS CUDA examples")
-IF(CUDA_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_CUDA BOOL "${DOCSTR}" OFF)
-ELSE()
-  IF(EXAMPLES_ENABLE_CUDA)
-    PRINT_WARNING("EXAMPLES_ENABLE_CUDA is ON but CUDA_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_CUDA")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_CUDA BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ENDIF()
-
-# RAJA examples (off by default)
-SET(DOCSTR "Build SUNDIALS RAJA examples")
-IF(RAJA_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_RAJA BOOL "${DOCSTR}" OFF)
-ELSE()
-  IF(EXAMPLES_ENABLE_RAJA)
-    PRINT_WARNING("EXAMPLES_ENABLE_RAJA is ON but RAJA_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_RAJA")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_RAJA BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ENDIF()
-
-# If any of the above examples are enabled set EXAMPLES_ENABLED to TRUE
-IF(EXAMPLES_ENABLE_C OR
-    EXAMPLES_ENABLE_F77 OR
-    EXAMPLES_ENABLE_CXX OR
-    EXAMPLES_ENABLE_F90 OR
-    EXAMPLES_ENABLE_CUDA OR
-    EXAMPLES_ENABLE_RAJA)
-  SET(EXAMPLES_ENABLED TRUE)
-ELSE()
-  SET(EXAMPLES_ENABLED FALSE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Install examples?
-# ---------------------------------------------------------------
-
-IF(EXAMPLES_ENABLED)
-
-  # If examples are enabled, set different options
-
-  # The examples will be linked with the library corresponding to the build type.
-  # Whenever building shared libraries, use them to link the examples.
-  IF(BUILD_SHARED_LIBS)
-    SET(LINK_LIBRARY_TYPE "shared")
-  ELSE(BUILD_SHARED_LIBS)
-    SET(LINK_LIBRARY_TYPE "static")
-  ENDIF(BUILD_SHARED_LIBS)
-
-  # Enable installing examples by default
-  SHOW_VARIABLE(EXAMPLES_INSTALL BOOL "Install example files" ON)
-
-  # If examples are to be exported, check where we should install them.
-  IF(EXAMPLES_INSTALL)
-
-    SHOW_VARIABLE(EXAMPLES_INSTALL_PATH PATH
-      "Output directory for installing example files" "${CMAKE_INSTALL_PREFIX}/examples")
-
-    IF(NOT EXAMPLES_INSTALL_PATH)
-      PRINT_WARNING("The example installation path is empty"
-                    "Example installation path was reset to its default value")
-      SET(EXAMPLES_INSTALL_PATH "${CMAKE_INSTALL_PREFIX}/examples" CACHE STRING
-        "Output directory for installing example files" FORCE)
-    ENDIF(NOT EXAMPLES_INSTALL_PATH)
-
-    # create test_install target and directory for running smoke tests after
-    # installation
-    ADD_CUSTOM_TARGET(test_install)
-
-    SET(TEST_INSTALL_DIR ${PROJECT_BINARY_DIR}/Testing_Install)
-
-    IF(NOT EXISTS ${TEST_INSTALL_DIR})
-      FILE(MAKE_DIRECTORY ${TEST_INSTALL_DIR})
-    ENDIF()
-
-
-  ELSE(EXAMPLES_INSTALL)
-
-    HIDE_VARIABLE(EXAMPLES_INSTALL_PATH)
-
-  ENDIF(EXAMPLES_INSTALL)
-
-ELSE(EXAMPLES_ENABLED)
-
-  # If examples are disabled, hide all options related to
-  # building and installing the SUNDIALS examples
-
-  HIDE_VARIABLE(EXAMPLES_INSTALL)
-  HIDE_VARIABLE(EXAMPLES_INSTALL_PATH)
-
-ENDIF(EXAMPLES_ENABLED)
-
-# ---------------------------------------------------------------
-# Include development examples in regression tests?
-# ---------------------------------------------------------------
-OPTION(SUNDIALS_DEVTESTS "Include development tests in make test" OFF)
-MARK_AS_ADVANCED(FORCE SUNDIALS_DEVTESTS)
-
-# ===============================================================
-# Add any other necessary compiler flags & definitions
-# ===============================================================
-
-IF(APPLE)
-  SET(CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS "${CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS} -undefined dynamic_lookup")
-ENDIF(APPLE)
-
-# ---------------------------------------------------------------
-# A Fortran compiler is needed if:
-# (a) FCMIX is enabled
-# (b) BLAS is enabled (for the name-mangling scheme)
-# (c) LAPACK is enabled (for the name-mangling scheme)
-# ---------------------------------------------------------------
-
-IF(FCMIX_ENABLE OR BLAS_ENABLE OR LAPACK_ENABLE)
-  INCLUDE(SundialsFortran)
-  IF(NOT F77_FOUND AND FCMIX_ENABLE)
-    PRINT_WARNING("Fortran compiler not functional"
-                  "FCMIX support will not be provided")
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# A Fortran90 compiler is needed if:
-# (a) F90 ARKODE examples are enabled
-# ---------------------------------------------------------------
-
-IF(EXAMPLES_ENABLE_F90)
-  INCLUDE(SundialsFortran90)
-  IF(NOT F90_FOUND)
-    PRINT_WARNING("Fortran90 compiler not functional"
-                  "F90 support will not be provided")
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# A C++ compiler is needed if:
-# (a) C++ ARKODE examples are enabled
-# (b) CUDA is enabled
-# (c) RAJA is enabled
-# ---------------------------------------------------------------
-
-IF(EXAMPLES_ENABLE_CXX OR CUDA_ENABLE OR RAJA_ENABLE)
-  INCLUDE(SundialsCXX)
-  IF(NOT CXX_FOUND)
-    PRINT_WARNING("C++ compiler not functional"
-                  "C++ support will not be provided")
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# Check if we need an alternate way of specifying the Fortran
-# name-mangling scheme if we were unable to infer it using a
-# compiler.
-# Ask the user to specify the case and number of appended underscores
-# corresponding to the Fortran name-mangling scheme of symbol names
-# that do not themselves contain underscores (recall that this is all
-# we really need for the interfaces to LAPACK).
-# Note: the default scheme is lower case - one underscore
-# ---------------------------------------------------------------
-
-IF(BLAS_ENABLE OR LAPACK_ENABLE AND NOT F77SCHEME_FOUND)
-  # Specify the case for the Fortran name-mangling scheme
-  SHOW_VARIABLE(SUNDIALS_F77_FUNC_CASE STRING
-    "case of Fortran function names (lower/upper)"
-    "lower")
-  # Specify the number of appended underscores for the Fortran name-mangling scheme
-  SHOW_VARIABLE(SUNDIALS_F77_FUNC_UNDERSCORES STRING
-    "number of underscores appended to Fortran function names"
-    "one")
-  # Based on the given case and number of underscores,
-  # set the C preprocessor macro definition
-  IF(${SUNDIALS_F77_FUNC_CASE} MATCHES "lower")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "none")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "mysub")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "none")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "one")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "mysub_")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "one")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "two")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "mysub__")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "two")
-  ELSE(${SUNDIALS_F77_FUNC_CASE} MATCHES "lower")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "none")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "MYSUB")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "none")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "one")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "MYSUB_")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "one")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "two")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "MYSUB__")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "two")
-  ENDIF(${SUNDIALS_F77_FUNC_CASE} MATCHES "lower")
-  # Since the SUNDIALS codes never use symbol names containing
-  # underscores, set a default scheme (probably wrong) for symbols
-  # with underscores.
-  SET(CMAKE_Fortran_SCHEME_WITH_UNDERSCORES "my_sub_")
-  # We now "have" a scheme.
-  SET(F77SCHEME_FOUND TRUE)
-ENDIF(BLAS_ENABLE OR LAPACK_ENABLE AND NOT F77SCHEME_FOUND)
-
-# ---------------------------------------------------------------
-# If we have a name-mangling scheme (either automatically
-# inferred or provided by the user), set the SUNDIALS
-# compiler preprocessor macro definitions.
-# ---------------------------------------------------------------
-
-SET(F77_MANGLE_MACRO1 "")
-SET(F77_MANGLE_MACRO2 "")
-
-IF(F77SCHEME_FOUND)
-  # Symbols WITHOUT underscores
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub_")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## _")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub_")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub__")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## __")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub__")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB_")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## _")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB_")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB__")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## __")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB__")
-  # Symbols with underscores
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub_")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## _")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub_")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub__")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## __")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub__")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB_")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## _")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB_")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB__")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## __")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB__")
-ENDIF(F77SCHEME_FOUND)
-
-# ---------------------------------------------------------------
-# Decide how to compile MPI codes.
-# ---------------------------------------------------------------
-
-IF(MPI_ENABLE)
-  # show command to run MPI codes (defaults to mpirun)
-  SHOW_VARIABLE(MPI_RUN_COMMAND STRING "MPI run command" "mpirun")
-
-  INCLUDE(SundialsMPIC)
-  IF(MPIC_FOUND)
-    IF(CXX_FOUND AND EXAMPLES_ENABLE_CXX)
-      INCLUDE(SundialsMPICXX)
-    ENDIF()
-    IF(F77_FOUND AND EXAMPLES_ENABLE_F77)
-      INCLUDE(SundialsMPIF)
-    ENDIF()
-    IF(F90_FOUND AND EXAMPLES_ENABLE_F90)
-      INCLUDE(SundialsMPIF90)
-    ENDIF()
-  ELSE()
-    PRINT_WARNING("MPI not functional"
-                  "Parallel support will not be provided")
-  ENDIF()
-
-  IF(MPIC_MPI2)
-    SET(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 1")
-  ELSE()
-    SET(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 0")
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(MPI_INCLUDE_PATH)
-  HIDE_VARIABLE(MPI_LIBRARIES)
-  HIDE_VARIABLE(MPI_EXTRA_LIBRARIES)
-  HIDE_VARIABLE(MPI_MPICC)
-  HIDE_VARIABLE(MPI_MPICXX)
-  HIDE_VARIABLE(MPI_MPIF77)
-  HIDE_VARIABLE(MPI_MPIF90)
-
-ENDIF(MPI_ENABLE)
-
-# ---------------------------------------------------------------
-# If using MPI with C++, disable C++ extensions (for known wrappers)
-# ---------------------------------------------------------------
-
-# IF(MPICXX_FOUND)
-#   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DMPICH_SKIP_MPICXX -DOMPI_SKIP_MPICXX -DLAM_BUILDING")
-# ENDIF(MPICXX_FOUND)
-
-# -------------------------------------------------------------
-# Find OpenMP
-# -------------------------------------------------------------
-
-IF(OPENMP_ENABLE)
-  FIND_PACKAGE(OpenMP)
-  IF(NOT OPENMP_FOUND)
-    message(STATUS "Disabling OpenMP support, could not determine compiler flags")
-  ENDIF(NOT OPENMP_FOUND)
-ENDIF(OPENMP_ENABLE)
-
-# -------------------------------------------------------------
-# Find PThreads
-# -------------------------------------------------------------
-
-IF(PTHREAD_ENABLE)
-  FIND_PACKAGE(Threads)
-  IF(CMAKE_USE_PTHREADS_INIT)
-    message(STATUS "Using Pthreads")
-    SET(PTHREADS_FOUND TRUE)
-    # SGS
-  ELSE()
-    message(STATUS "Disabling Pthreads support, could not determine compiler flags")
-  endif()
-ENDIF(PTHREAD_ENABLE)
-
-# -------------------------------------------------------------
-# Find CUDA
-# -------------------------------------------------------------
-
-# disable CUDA if a working C++ compiler is not found
-IF(CUDA_ENABLE AND (NOT CXX_FOUND))
-  PRINT_WARNING("C++ compiler required for CUDA support" "Disabling CUDA")
-  FORCE_VARIABLE(CUDA_ENABLE BOOL "CUDA disabled" OFF)
-ENDIF()
-
-if(CUDA_ENABLE)
-  find_package(CUDA)
-
-  if (CUDA_FOUND)
-    #message("CUDA found!")
-    set(CUDA_NVCC_FLAGS "-lineinfo")
-  else()
-    message(STATUS "Disabling CUDA support, could not find CUDA.")
-  endif()
-endif(CUDA_ENABLE)
-
-# -------------------------------------------------------------
-# Find RAJA
-# -------------------------------------------------------------
-
-# disable RAJA if CUDA is not enabled/working
-IF(RAJA_ENABLE AND (NOT CUDA_FOUND))
-  PRINT_WARNING("CUDA is required for RAJA support" "Please enable CUDA and RAJA")
-  FORCE_VARIABLE(RAJA_ENABLE BOOL "RAJA disabled" OFF)
-ENDIF()
-
-# Check if C++11 compiler is available
-IF(RAJA_ENABLE)
-  include(CheckCXXCompilerFlag)
-  CHECK_CXX_COMPILER_FLAG("-std=c++11" COMPILER_SUPPORTS_CXX11)
-
-  IF(COMPILER_SUPPORTS_CXX11)
-    set(CMAKE_CXX_STANDARD 11)
-  ELSE()
-    PRINT_WARNING("C++11 compliant compiler required for RAJA support" "Disabling RAJA")
-    FORCE_VARIABLE(RAJA_ENABLE BOOL "RAJA disabled" OFF)
-  ENDIF()
-ENDIF()
-
-if(RAJA_ENABLE)
-  # Look for CMake configuration file in RAJA installation
-  find_package(RAJA CONFIGS)
-  if (RAJA_FOUND)
-    #message("RAJA found!")
-    include_directories(${RAJA_INCLUDE_DIR})
-    set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} ${RAJA_NVCC_FLAGS})
-  else()
-    PRINT_WARNING("RAJA configuration not found" "Please set RAJA_DIR to provide path to RAJA CMake configuration file.")
-  endif()
-endif(RAJA_ENABLE)
-
-# ===============================================================
-# Find (and test) external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Find (and test) the BLAS libraries
-# ---------------------------------------------------------------
-
-# If BLAS is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(BLAS_ENABLE)
-
-  # find BLAS
-  INCLUDE(SundialsBlas)
-
-  # show after include so FindBlas can locate BLAS_LIBRARIES if necessary
-  SHOW_VARIABLE(BLAS_LIBRARIES STRING "Blas libraries" "${BLAS_LIBRARIES}")
-
-  IF(BLAS_LIBRARIES AND NOT BLAS_FOUND)
-    PRINT_WARNING("BLAS not functional"
-                  "BLAS support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS TRUE)
-  ENDIF()
-
-ELSE()
-
-  IF(NOT LAPACK_ENABLE)
-    HIDE_VARIABLE(SUNDIALS_F77_FUNC_CASE)
-    HIDE_VARIABLE(SUNDIALS_F77_FUNC_UNDERSCORES)
-  ENDIF()
-  HIDE_VARIABLE(BLAS_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the Lapack libraries
-# ---------------------------------------------------------------
-
-# If LAPACK is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(LAPACK_ENABLE)
-
-  # find LAPACK and BLAS Libraries
-  INCLUDE(SundialsLapack)
-
-  # show after include so FindLapack can locate LAPCK_LIBRARIES if necessary
-  SHOW_VARIABLE(LAPACK_LIBRARIES STRING "Lapack and Blas libraries" "${LAPACK_LIBRARIES}")
-
-  IF(LAPACK_LIBRARIES AND NOT LAPACK_FOUND)
-    PRINT_WARNING("LAPACK not functional"
-                  "Blas/Lapack support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS_LAPACK TRUE)
-  ENDIF()
-
-ELSE()
-
-  IF(NOT BLAS_ENABLE)
-    HIDE_VARIABLE(SUNDIALS_F77_FUNC_CASE)
-    HIDE_VARIABLE(SUNDIALS_F77_FUNC_UNDERSCORES)
-  ENDIF()
-  HIDE_VARIABLE(LAPACK_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the SUPERLUMT libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for SuperLU_MT integer type
-
-# If SUPERLUMT is needed, first try to find the appropriate
-# libraries to link against them.
-
-IF(SUPERLUMT_ENABLE)
-
-  # Show SuperLU_MT options and set default thread type (Pthreads)
-  SHOW_VARIABLE(SUPERLUMT_THREAD_TYPE STRING "SUPERLUMT threading type: OpenMP or Pthread" "Pthread")
-  SHOW_VARIABLE(SUPERLUMT_INCLUDE_DIR PATH "SUPERLUMT include directory" "${SUPERLUMT_INCLUDE_DIR}")
-  SHOW_VARIABLE(SUPERLUMT_LIBRARY_DIR PATH "SUPERLUMT library directory" "${SUPERLUMT_LIBRARY_DIR}")
-
-  INCLUDE(SundialsSuperLUMT)
-
-  IF(SUPERLUMT_FOUND)
-    # sundials_config.h symbols
-    SET(SUNDIALS_SUPERLUMT TRUE)
-    SET(SUNDIALS_SUPERLUMT_THREAD_TYPE ${SUPERLUMT_THREAD_TYPE})
-    INCLUDE_DIRECTORIES(${SUPERLUMT_INCLUDE_DIR})
-  ENDIF()
-
-  IF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-    PRINT_WARNING("SUPERLUMT not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(SUPERLUMT_THREAD_TYPE)
-  HIDE_VARIABLE(SUPERLUMT_LIBRARY_DIR)
-  HIDE_VARIABLE(SUPERLUMT_INCLUDE_DIR)
-  SET (SUPERLUMT_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the KLU libraries
-# ---------------------------------------------------------------
-
-# If KLU is requested, first try to find the appropriate libraries to
-# link against them.
-
-IF(KLU_ENABLE)
-
-  SHOW_VARIABLE(KLU_INCLUDE_DIR PATH "KLU include directory"
-    "${KLU_INCLUDE_DIR}")
-  SHOW_VARIABLE(KLU_LIBRARY_DIR PATH
-    "Klu library directory" "${KLU_LIBRARY_DIR}")
-
-  set(KLU_FOUND TRUE)
-  get_filename_component(PYBAMM_DIR ${PROJECT_SOURCE_DIR} DIRECTORY)
-  set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PYBAMM_DIR}) # use FindSuiteSparse.cmake that is in PyBaMM root
-  set(SuiteSparse_ROOT ${PYBAMM_DIR}/SuiteSparse-5.6.0)
-  find_package(SuiteSparse OPTIONAL_COMPONENTS KLU AMD COLAMD BTF)
-  include_directories(${SuiteSparse_INCLUDE_DIRS})
-  set(KLU_LIBRARIES ${SuiteSparse_LIBRARIES})
-
-  IF(KLU_LIBRARIES AND NOT KLU_FOUND)
-    PRINT_WARNING("KLU not functional - support will not be provided"
-                  "Double check spelling of include path and specified libraries (search is case sensitive)")
-  ENDIF(KLU_LIBRARIES AND NOT KLU_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(KLU_LIBRARY_DIR)
-  HIDE_VARIABLE(KLU_INCLUDE_DIR)
-  SET (KLU_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF(KLU_ENABLE)
-
-# ---------------------------------------------------------------
-# Find (and test) the hypre libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for hypre precision and integer type
-
-IF(HYPRE_ENABLE)
-  SHOW_VARIABLE(HYPRE_INCLUDE_DIR PATH "HYPRE include directory"
-    "${HYPRE_INCLUDE_DIR}")
-  SHOW_VARIABLE(HYPRE_LIBRARY_DIR PATH
-    "HYPRE library directory" "${HYPRE_LIBRARY_DIR}")
-
-  INCLUDE(SundialsHypre)
-
-  IF(HYPRE_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_HYPRE TRUE)
-    INCLUDE_DIRECTORIES(${HYPRE_INCLUDE_DIR})
-  ENDIF(HYPRE_FOUND)
-
-  IF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-    PRINT_WARNING("HYPRE not functional - support will not be provided"
-                  "Found hypre library, test code does not work")
-  ENDIF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(HYPRE_INCLUDE_DIR)
-  HIDE_VARIABLE(HYPRE_LIBRARY_DIR)
-  SET (HYPRE_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the PETSc libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for PETSc precision and integer type
-
-IF(PETSC_ENABLE)
-  SHOW_VARIABLE(PETSC_INCLUDE_DIR PATH "PETSc include directory"
-    "${PETSC_INCLUDE_DIR}")
-  SHOW_VARIABLE(PETSC_LIBRARY_DIR PATH
-    "PETSc library directory" "${PETSC_LIBRARY_DIR}")
-
-  INCLUDE(SundialsPETSc)
-
-  IF(PETSC_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_PETSC TRUE)
-    INCLUDE_DIRECTORIES(${PETSC_INCLUDE_DIR})
-  ENDIF(PETSC_FOUND)
-
-  IF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-    PRINT_WARNING("PETSC not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(PETSC_LIBRARY_DIR)
-  HIDE_VARIABLE(PETSC_INCLUDE_DIR)
-  SET (PETSC_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-
-# ===============================================================
-# Add source and configuration files
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Configure the header file sundials_config.h
-# ---------------------------------------------------------------
-
-# All required substitution variables should be available at this point.
-# Generate the header file and place it in the binary dir.
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_config.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  )
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_fconfig.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  )
-
-# Add the include directory in the source tree and the one in
-# the binary tree (for the header file sundials_config.h)
-INCLUDE_DIRECTORIES(${PROJECT_SOURCE_DIR}/include ${PROJECT_BINARY_DIR}/include)
-
-# ---------------------------------------------------------------
-# Add selected modules to the build system
-# ---------------------------------------------------------------
-
-# Shared components
-
-ADD_SUBDIRECTORY(src/sundials)
-ADD_SUBDIRECTORY(src/nvec_ser)
-ADD_SUBDIRECTORY(src/sunmat_dense)
-ADD_SUBDIRECTORY(src/sunmat_band)
-ADD_SUBDIRECTORY(src/sunmat_sparse)
-ADD_SUBDIRECTORY(src/sunlinsol_band)
-ADD_SUBDIRECTORY(src/sunlinsol_dense)
-IF(KLU_FOUND)
-  ADD_SUBDIRECTORY(src/sunlinsol_klu)
-ENDIF(KLU_FOUND)
-IF(SUPERLUMT_FOUND)
-  ADD_SUBDIRECTORY(src/sunlinsol_superlumt)
-ENDIF(SUPERLUMT_FOUND)
-IF(LAPACK_FOUND)
-  ADD_SUBDIRECTORY(src/sunlinsol_lapackband)
-  ADD_SUBDIRECTORY(src/sunlinsol_lapackdense)
-ENDIF(LAPACK_FOUND)
-ADD_SUBDIRECTORY(src/sunlinsol_spgmr)
-ADD_SUBDIRECTORY(src/sunlinsol_spfgmr)
-ADD_SUBDIRECTORY(src/sunlinsol_spbcgs)
-ADD_SUBDIRECTORY(src/sunlinsol_sptfqmr)
-ADD_SUBDIRECTORY(src/sunlinsol_pcg)
-IF(MPIC_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_par)
-ENDIF(MPIC_FOUND)
-
-IF(HYPRE_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_parhyp)
-ENDIF(HYPRE_FOUND)
-
-IF(OPENMP_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_openmp)
-ENDIF(OPENMP_FOUND)
-
-IF(PTHREADS_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_pthreads)
-ENDIF(PTHREADS_FOUND)
-
-IF(PETSC_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_petsc)
-ENDIF(PETSC_FOUND)
-
-IF(CUDA_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_cuda)
-ENDIF(CUDA_FOUND)
-
-IF(RAJA_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_raja)
-ENDIF(RAJA_FOUND)
-
-# ARKODE library
-
-IF(BUILD_ARKODE)
-  ADD_SUBDIRECTORY(src/arkode)
-  IF(FCMIX_ENABLE AND F77_FOUND)
-    ADD_SUBDIRECTORY(src/arkode/fcmix)
-  ENDIF(FCMIX_ENABLE AND F77_FOUND)
-ENDIF(BUILD_ARKODE)
-
-# CVODE library
-
-IF(BUILD_CVODE)
-  ADD_SUBDIRECTORY(src/cvode)
-  IF(FCMIX_ENABLE AND F77_FOUND)
-    ADD_SUBDIRECTORY(src/cvode/fcmix)
-  ENDIF(FCMIX_ENABLE AND F77_FOUND)
-ENDIF(BUILD_CVODE)
-
-# CVODES library
-
-IF(BUILD_CVODES)
-  ADD_SUBDIRECTORY(src/cvodes)
-ENDIF(BUILD_CVODES)
-
-# IDA library
-
-IF(BUILD_IDA)
-  ADD_SUBDIRECTORY(src/ida)
-  IF(FCMIX_ENABLE AND F77_FOUND)
-    ADD_SUBDIRECTORY(src/ida/fcmix)
-  ENDIF(FCMIX_ENABLE AND F77_FOUND)
-ENDIF(BUILD_IDA)
-
-# IDAS library
-
-IF(BUILD_IDAS)
-  ADD_SUBDIRECTORY(src/idas)
-ENDIF(BUILD_IDAS)
-
-# KINSOL library
-
-IF(BUILD_KINSOL)
-  ADD_SUBDIRECTORY(src/kinsol)
-  IF(FCMIX_ENABLE AND F77_FOUND)
-    ADD_SUBDIRECTORY(src/kinsol/fcmix)
-  ENDIF(FCMIX_ENABLE AND F77_FOUND)
-ENDIF(BUILD_KINSOL)
-
-# CPODES library
-
-IF(BUILD_CPODES)
-  ADD_SUBDIRECTORY(src/cpodes)
-ENDIF(BUILD_CPODES)
-
-# ---------------------------------------------------------------
-# Include the subdirectories corresponding to various examples
-# ---------------------------------------------------------------
-
-# If building and installing the examples is enabled, include
-# the subdirectories for those examples that will be built.
-# Also, if we will generate exported example Makefiles, set
-# variables needed in generating them from templates.
-
-# For now, TestRunner is not being distributed.
-# So:
-#  - Don't show TESTRUNNER variable
-#  - Don't enable testing if TestRunner if not found.
-#  - There will be no 'make test' target
-
-INCLUDE(SundialsAddTest)
-HIDE_VARIABLE(TESTRUNNER)
-
-IF(EXAMPLES_ENABLED)
-
-  # enable regression testing with 'make test'
-  IF(TESTRUNNER)
-    ENABLE_TESTING()
-  ENDIF()
-
-  # set variables used in generating CMake and Makefiles for examples
-  IF(EXAMPLES_INSTALL)
-
-    SET(SHELL "sh")
-    SET(prefix "${CMAKE_INSTALL_PREFIX}")
-    SET(exec_prefix "${CMAKE_INSTALL_PREFIX}")
-    SET(includedir "${prefix}/include")
-    SET(libdir "${exec_prefix}/lib")
-    SET(CPP "${CMAKE_C_COMPILER}")
-    SET(CPPFLAGS "${CMAKE_C_FLAGS_RELEASE}")
-    SET(CC "${CMAKE_C_COMPILER}")
-    SET(CFLAGS "${CMAKE_C_FLAGS_RELEASE}")
-    SET(LDFLAGS "${CMAKE_EXE_LINKER_FLAGS_RELEASE}")
-    LIST2STRING(EXTRA_LINK_LIBS LIBS)
-
-    IF(CXX_FOUND)
-      SET(CXX "${CMAKE_CXX_COMPILER}")
-      SET(CXX_LNKR "${CMAKE_CXX_COMPILER}")
-      SET(CXXFLAGS "${CMAKE_CXX_FLAGS_RELEASE}")
-      SET(CXX_LDFLAGS "${CMAKE_CXX_FLAGS_RELEASE}")
-      LIST2STRING(EXTRA_LINK_LIBS CXX_LIBS)
-    ENDIF(CXX_FOUND)
-
-    IF(F77_FOUND)
-      SET(F77 "${CMAKE_Fortran_COMPILER}")
-      SET(F77_LNKR "${CMAKE_Fortran_COMPILER}")
-      SET(FFLAGS "${CMAKE_Fortran_FLAGS_RELEASE}")
-      SET(F77_LDFLAGS "${CMAKE_Fortran_FLAGS_RELEASE}")
-      LIST2STRING(EXTRA_LINK_LIBS F77_LIBS)
-    ENDIF(F77_FOUND)
-
-    IF(F90_FOUND)
-      SET(F90 "${CMAKE_Fortran_COMPILER}")
-      SET(F90_LNKR "${CMAKE_Fortran_COMPILER}")
-      SET(F90FLAGS "${CMAKE_Fortran_FLAGS_RELEASE}")
-      SET(F90_LDFLAGS "${CMAKE_Fortran_FLAGS_RELEASE}")
-      LIST2STRING(EXTRA_LINK_LIBS F90_LIBS)
-    ENDIF(F90_FOUND)
-
-    IF(SUPERLUMT_FOUND)
-      LIST2STRING(SUPERLUMT_LIBRARIES SUPERLUMT_LIBS)
-      SET(SUPERLUMT_LIBS "${SUPERLUMT_LINKER_FLAGS} ${SUPERLUMT_LIBS}")
-    ENDIF(SUPERLUMT_FOUND)
-
-    IF(KLU_FOUND)
-      LIST2STRING(KLU_LIBRARIES KLU_LIBS)
-      SET(KLU_LIBS "${KLU_LINKER_FLAGS} ${KLU_LIBS}")
-    ENDIF(KLU_FOUND)
-
-    IF(BLAS_FOUND)
-      LIST2STRING(BLAS_LIBRARIES BLAS_LIBS)
-    ENDIF(BLAS_FOUND)
-
-    IF(LAPACK_FOUND)
-      LIST2STRING(LAPACK_LIBRARIES LAPACK_LIBS)
-    ENDIF(LAPACK_FOUND)
-
-    IF(MPIC_FOUND)
-      IF(MPI_MPICC)
-        SET(MPICC "${MPI_MPICC}")
-        SET(MPI_INC_DIR ".")
-        SET(MPI_LIB_DIR ".")
-        SET(MPI_LIBS "")
-        SET(MPI_FLAGS "")
-      ELSE(MPI_MPICC)
-        SET(MPICC "${CMAKE_C_COMPILER}")
-        SET(MPI_INC_DIR "${MPI_INCLUDE_PATH}")
-        SET(MPI_LIB_DIR ".")
-        LIST2STRING(MPI_LIBRARIES MPI_LIBS)
-      ENDIF(MPI_MPICC)
-      SET(HYPRE_INC_DIR "${HYPRE_INCLUDE_DIR}")
-      SET(HYPRE_LIB_DIR "${HYPRE_LIBRARY_DIR}")
-      SET(HYPRE_LIBS "${HYPRE_LIBRARIES}")
-    ENDIF(MPIC_FOUND)
-
-    IF(MPICXX_FOUND)
-      IF(MPI_MPICXX)
-        SET(MPICXX "${MPI_MPICXX}")
-      ELSE(MPI_MPICXX)
-        SET(MPICXX "${CMAKE_CXX_COMPILER}")
-        LIST2STRING(MPI_LIBRARIES MPI_LIBS)
-      ENDIF(MPI_MPICXX)
-    ENDIF(MPICXX_FOUND)
-
-    IF(MPIF_FOUND)
-      IF(MPI_MPIF77)
-        SET(MPIF77 "${MPI_MPIF77}")
-        SET(MPIF77_LNKR "${MPI_MPIF77}")
-      ELSE(MPI_MPIF77)
-        SET(MPIF77 "${CMAKE_Fortran_COMPILER}")
-        SET(MPIF77_LNKR "${CMAKE_Fortran_COMPILER}")
-        SET(MPI_INC_DIR "${MPI_INCLUDE_PATH}")
-        SET(MPI_LIB_DIR ".")
-        LIST2STRING(MPI_LIBRARIES MPI_LIBS)
-      ENDIF(MPI_MPIF77)
-    ENDIF(MPIF_FOUND)
-
-    IF(MPIF90_FOUND)
-      IF(MPI_MPIF90)
-        SET(MPIF90 "${MPI_MPIF90}")
-        SET(MPIF90_LNKR "${MPI_MPIF90}")
-      ELSE(MPI_MPIF90)
-        SET(MPIF90 "${CMAKE_Fortran_COMPILER}")
-        SET(MPIF90_LNKR "${CMAKE_Fortran_COMPILER}")
-        LIST2STRING(MPI_LIBRARIES MPI_LIBS)
-      ENDIF(MPI_MPIF90)
-    ENDIF(MPIF90_FOUND)
-
-  ENDIF(EXAMPLES_INSTALL)
-
-  # add ARKode examples
-  IF(BUILD_ARKODE)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/arkode/C_serial)
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/C_openmp)
-      ENDIF()
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/C_parallel)
-      ENDIF()
-      IF(HYPRE_ENABLE AND HYPRE_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/C_parhyp)
-      ENDIF()
-    ENDIF()
-    # C++ examples
-    IF(EXAMPLES_ENABLE_CXX)
-      IF(CXX_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/CXX_serial)
-      ENDIF()
-      IF(MPICXX_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/CXX_parallel)
-      ENDIF()
-    ENDIF()
-    # F77 examples
-    IF(EXAMPLES_ENABLE_F77)
-      IF(F77_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/F77_serial)
-      ENDIF()
-      IF(MPIF_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/F77_parallel)
-      ENDIF()
-    ENDIF()
-    # F90 examples
-    IF(EXAMPLES_ENABLE_F90)
-      IF(F90_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/F90_serial)
-      ENDIF()
-      IF(MPIF90_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/F90_parallel)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_ARKODE)
-
-  # add CVODE examples
-  IF(BUILD_CVODE)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/cvode/serial)
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/C_openmp)
-      ENDIF()
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/parallel)
-      ENDIF()
-      IF(HYPRE_ENABLE AND HYPRE_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/parhyp)
-      ENDIF()
-    ENDIF()
-    # Fortran examples
-    IF(EXAMPLES_ENABLE_F77)
-      IF(F77_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/fcmix_serial)
-      ENDIF()
-      IF(MPIF_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/fcmix_parallel)
-      ENDIF()
-    ENDIF()
-    # cuda examples
-    IF(EXAMPLES_ENABLE_CUDA)
-      IF(CUDA_ENABLE AND CUDA_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/cuda)
-      ENDIF()
-    ENDIF(EXAMPLES_ENABLE_CUDA)
-    # raja examples
-    IF(EXAMPLES_ENABLE_RAJA)
-      IF(RAJA_ENABLE AND RAJA_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/raja)
-      ENDIF()
-    ENDIF(EXAMPLES_ENABLE_RAJA)
-  ENDIF(BUILD_CVODE)
-
-  # add CVODES Examples
-  IF(BUILD_CVODES)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/cvodes/serial)
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/cvodes/parallel)
-      ENDIF()
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/cvodes/C_openmp)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_CVODES)
-
-  # add IDA examples
-  IF(BUILD_IDA)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/ida/serial)
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/C_openmp)
-      ENDIF()
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/parallel)
-      ENDIF()
-      IF(PETSC_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/petsc)
-      ENDIF()
-    ENDIF()
-    # Fortran examples
-    IF(EXAMPLES_ENABLE_F77)
-      IF(F77_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/fcmix_serial)
-      ENDIF()
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/fcmix_openmp)
-      ENDIF()
-      IF(PTHREADS_FOUND)
-	ADD_SUBDIRECTORY(examples/ida/fcmix_pthreads)
-      ENDIF()
-      IF(MPIF_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/fcmix_parallel)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_IDA)
-
-  # add IDAS examples
-  IF(BUILD_IDAS)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/idas/serial)
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/idas/C_openmp)
-      ENDIF()
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/idas/parallel)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_IDAS)
-
-  # add KINSOL examples
-  IF(BUILD_KINSOL)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/kinsol/serial)
-      IF(OPENMP_FOUND)
-        # the only example here need special handling from testrunner (not yet implemented)
-        ADD_SUBDIRECTORY(examples/kinsol/C_openmp)
-      ENDIF()
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/kinsol/parallel)
-      ENDIF()
-    ENDIF()
-    # Fortran examples
-    IF(EXAMPLES_ENABLE_F77)
-      IF(F77_FOUND)
-        ADD_SUBDIRECTORY(examples/kinsol/fcmix_serial)
-      ENDIF()
-      IF(MPIF_FOUND)
-        ADD_SUBDIRECTORY(examples/kinsol/fcmix_parallel)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_KINSOL)
-
-  # add CPODES examples
-  IF(BUILD_CPODES)
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/cpodes/serial)
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/cpodes/parallel)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_CPODES)
-
-  # Always add the nvector serial examples
-  ADD_SUBDIRECTORY(examples/nvector/serial)
-
-  # # Always add the serial sunmatrix dense/band/sparse examples
-  ADD_SUBDIRECTORY(examples/sunmatrix/dense)
-  ADD_SUBDIRECTORY(examples/sunmatrix/band)
-  ADD_SUBDIRECTORY(examples/sunmatrix/sparse)
-
-  # # Always add the serial sunlinearsolver dense/band/spils examples
-  ADD_SUBDIRECTORY(examples/sunlinsol/band)
-  ADD_SUBDIRECTORY(examples/sunlinsol/dense)
-  IF(KLU_FOUND)
-    ADD_SUBDIRECTORY(examples/sunlinsol/klu)
-  ENDIF(KLU_FOUND)
-  IF(SUPERLUMT_FOUND)
-    ADD_SUBDIRECTORY(examples/sunlinsol/superlumt)
-  ENDIF(SUPERLUMT_FOUND)
-  IF(LAPACK_FOUND)
-    ADD_SUBDIRECTORY(examples/sunlinsol/lapackband)
-    ADD_SUBDIRECTORY(examples/sunlinsol/lapackdense)
-  ENDIF(LAPACK_FOUND)
-  ADD_SUBDIRECTORY(examples/sunlinsol/spgmr/serial)
-  ADD_SUBDIRECTORY(examples/sunlinsol/spfgmr/serial)
-  ADD_SUBDIRECTORY(examples/sunlinsol/spbcgs/serial)
-  ADD_SUBDIRECTORY(examples/sunlinsol/sptfqmr/serial)
-  ADD_SUBDIRECTORY(examples/sunlinsol/pcg/serial)
-
-  IF(MPIC_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/parallel)
-      ADD_SUBDIRECTORY(examples/sunlinsol/spgmr/parallel)
-      ADD_SUBDIRECTORY(examples/sunlinsol/spfgmr/parallel)
-      ADD_SUBDIRECTORY(examples/sunlinsol/spbcgs/parallel)
-      ADD_SUBDIRECTORY(examples/sunlinsol/sptfqmr/parallel)
-      #ADD_SUBDIRECTORY(examples/sunlinsol/pcg/parallel)
-  ENDIF(MPIC_FOUND)
-
-  IF(HYPRE_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/parhyp)
-  ENDIF()
-
-  IF(PTHREADS_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/pthreads)
-  ENDIF()
-
-  IF(OPENMP_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/C_openmp)
-  ENDIF()
-
-  IF(PETSC_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/petsc)
-  ENDIF()
-
-  IF(CUDA_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/cuda)
-  ENDIF(CUDA_FOUND)
-
-  IF(RAJA_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/raja)
-  ENDIF(RAJA_FOUND)
-
-ENDIF(EXAMPLES_ENABLED)
-
-# ---------------------------------------------------------------
-# Install configuration header files and license file
-# ---------------------------------------------------------------
-
-# install configured header file
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  DESTINATION include/sundials
-  )
-
-# install configured header file for Fortran 90
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  DESTINATION include/sundials
-  )
-
-# install license file
-INSTALL(
-  FILES ${PROJECT_SOURCE_DIR}/LICENSE
-  DESTINATION .)
diff --git a/scripts/replace-cmake/sundials-4.1.0/CMakeLists.txt b/scripts/replace-cmake/sundials-4.1.0/CMakeLists.txt
deleted file mode 100644
index fc8acbddc9..0000000000
--- a/scripts/replace-cmake/sundials-4.1.0/CMakeLists.txt
+++ /dev/null
@@ -1,1151 +0,0 @@
-# ---------------------------------------------------------------
-# Programmer: Radu Serban, David J. Gardner, Cody J. Balos,
-#             and Slaven Peles @ LLNL
-# ---------------------------------------------------------------
-# SUNDIALS Copyright Start
-# Copyright (c) 2002-2019, Lawrence Livermore National Security
-# and Southern Methodist University.
-# All rights reserved.
-#
-# See the top-level LICENSE and NOTICE files for details.
-#
-# SPDX-License-Identifier: BSD-3-Clause
-# SUNDIALS Copyright End
-# ---------------------------------------------------------------
-# Top level CMakeLists.txt for SUNDIALS (for cmake build system)
-# ---------------------------------------------------------------
-
-# ---------------------------------------------------------------
-# Initial commands
-# ---------------------------------------------------------------
-
-# Require a fairly recent cmake version
-cmake_minimum_required(VERSION 3.1.3)
-
-# Libraries linked via full path no longer produce linker search paths
-# Allows examples to build
-if(COMMAND cmake_policy)
-  cmake_policy(SET CMP0003 NEW)
-endif(COMMAND cmake_policy)
-
-# MACOSX_RPATH is enabled by default
-# Fixes dynamic loading on OSX
-if(POLICY CMP0042)
-  cmake_policy(SET CMP0042 NEW) # Added in CMake 3.0
-else()
-  if(APPLE)
-    set(CMAKE_MACOSX_RPATH 1)
-  endif()
-endif()
-
-# Project SUNDIALS (initially only C supported)
-# sets PROJECT_SOURCE_DIR and PROJECT_BINARY_DIR variables
-PROJECT(sundials C)
-
-# Set some variables with info on the SUNDIALS project
-SET(PACKAGE_BUGREPORT "woodward6@llnl.gov")
-SET(PACKAGE_NAME "SUNDIALS")
-SET(PACKAGE_STRING "SUNDIALS 4.1.0")
-SET(PACKAGE_TARNAME "sundials")
-
-# set SUNDIALS version numbers
-# (use "" for the version label if none is needed)
-SET(PACKAGE_VERSION_MAJOR "4")
-SET(PACKAGE_VERSION_MINOR "1")
-SET(PACKAGE_VERSION_PATCH "0")
-SET(PACKAGE_VERSION_LABEL "")
-
-IF(PACKAGE_VERSION_LABEL)
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}-${PACKAGE_VERSION_LABEL}")
-ELSE()
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}")
-ENDIF()
-
-SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)
-
-# Prohibit in-source build
-IF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-  MESSAGE(FATAL_ERROR "In-source build prohibited.")
-ENDIF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-
-# Hide some cache variables
-MARK_AS_ADVANCED(EXECUTABLE_OUTPUT_PATH LIBRARY_OUTPUT_PATH)
-
-# Always show the C compiler and flags
-MARK_AS_ADVANCED(CLEAR
-  CMAKE_C_COMPILER
-  CMAKE_C_FLAGS)
-
-# Specify the VERSION and SOVERSION for shared libraries
-
-SET(arkodelib_VERSION "3.1.0")
-SET(arkodelib_SOVERSION "3")
-
-SET(cvodelib_VERSION "4.1.0")
-SET(cvodelib_SOVERSION "4")
-
-SET(cvodeslib_VERSION "4.1.0")
-SET(cvodeslib_SOVERSION "4")
-
-SET(idalib_VERSION "4.1.0")
-SET(idalib_SOVERSION "4")
-
-SET(idaslib_VERSION "3.1.0")
-SET(idaslib_SOVERSION "3")
-
-SET(kinsollib_VERSION "4.1.0")
-SET(kinsollib_SOVERSION "4")
-
-SET(cpodeslib_VERSION "0.0.0")
-SET(cpodeslib_SOVERSION "0")
-
-SET(nveclib_VERSION "4.1.0")
-SET(nveclib_SOVERSION "4")
-
-SET(sunmatrixlib_VERSION "2.1.0")
-SET(sunmatrixlib_SOVERSION "2")
-
-SET(sunlinsollib_VERSION "2.1.0")
-SET(sunlinsollib_SOVERSION "2")
-
-SET(sunnonlinsollib_VERSION "1.1.0")
-SET(sunnonlinsollib_SOVERSION "1")
-
-# Specify the location of additional CMAKE modules
-SET(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/config)
-
-# Get correct build paths automatically, but expose CMAKE_INSTALL_LIBDIR
-# as a regular cache variable so that a user can more easily see what
-# the library dir was set to be by GNUInstallDirs.
-INCLUDE(GNUInstallDirs)
-MARK_AS_ADVANCED(CLEAR CMAKE_INSTALL_LIBDIR)
-
-# ---------------------------------------------------------------
-# Which modules to build?
-# ---------------------------------------------------------------
-
-# For each SUNDIALS solver available (i.e. for which we have the
-# sources), give the user the option of enabling/disabling it.
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/arkode")
-  OPTION(BUILD_ARKODE "Build the ARKODE library" ON)
-ELSE()
-  SET(BUILD_ARKODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvode")
-  OPTION(BUILD_CVODE "Build the CVODE library" ON)
-ELSE()
-  SET(BUILD_CVODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvodes")
-  OPTION(BUILD_CVODES "Build the CVODES library" ON)
-ELSE()
-  SET(BUILD_CVODES OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/ida")
-  OPTION(BUILD_IDA "Build the IDA library" ON)
-ELSE()
-  SET(BUILD_IDA OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/idas")
-  OPTION(BUILD_IDAS "Build the IDAS library" ON)
-ELSE()
-  SET(BUILD_IDAS OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/kinsol")
-  OPTION(BUILD_KINSOL "Build the KINSOL library" ON)
-ELSE()
-  SET(BUILD_KINSOL OFF)
-ENDIF()
-
-# CPODES is always OFF for now.  (commented out for Release); ToDo: better way to do this?
-#IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cpodes")
-#  OPTION(BUILD_CPODES  "Build the CPODES library"  OFF)
-#ELSE()
-#  SET(BUILD_CPODES OFF)
-#ENDIF()
-
-# ---------------------------------------------------------------
-# MACRO definitions
-# ---------------------------------------------------------------
-INCLUDE(CMakeParseArguments) # can be removed when CMake 3.5+ is required
-INCLUDE(SundialsCMakeMacros)
-INCLUDE(SundialsAddF2003InterfaceLibrary)
-INCLUDE(SundialsAddTest)
-INCLUDE(SundialsAddTestInstall)
-
-# ---------------------------------------------------------------
-# Check for deprecated SUNDIALS CMake options/variables
-# ---------------------------------------------------------------
-INCLUDE(SundialsDeprecated)
-
-# ---------------------------------------------------------------
-# xSDK specific options
-# ---------------------------------------------------------------
-INCLUDE(SundialsXSDK)
-
-# ---------------------------------------------------------------
-# Build specific C flags
-# ---------------------------------------------------------------
-
-# Hide all build type specific flags
-MARK_AS_ADVANCED(FORCE
-  CMAKE_C_FLAGS_DEBUG
-  CMAKE_C_FLAGS_MINSIZEREL
-  CMAKE_C_FLAGS_RELEASE
-  CMAKE_C_FLAGS_RELWITHDEBINFO)
-
-# Only show flags for the current build type if it is set
-# NOTE: Build specific flags are appended those in CMAKE_C_FLAGS
-IF(CMAKE_BUILD_TYPE)
-  IF(CMAKE_BUILD_TYPE MATCHES "Debug")
-    MESSAGE("Appending C debug flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_DEBUG)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "MinSizeRel")
-    MESSAGE("Appending C min size release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_MINSIZEREL)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "Release")
-    MESSAGE("Appending C release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELEASE)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "RelWithDebInfo")
-    MESSAGE("Appending C release with debug info flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELWITHDEBINFO)
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# Option to specify precision (realtype)
-# ---------------------------------------------------------------
-
-SET(DOCSTR "single, double, or extended")
-SHOW_VARIABLE(SUNDIALS_PRECISION STRING "${DOCSTR}" "double")
-
-# prepare substitution variable PRECISION_LEVEL for sundials_config.h
-STRING(TOUPPER ${SUNDIALS_PRECISION} SUNDIALS_PRECISION)
-SET(PRECISION_LEVEL "#define SUNDIALS_${SUNDIALS_PRECISION}_PRECISION 1")
-
-# prepare substitution variable FPRECISION_LEVEL for sundials_fconfig.h
-IF(SUNDIALS_PRECISION MATCHES "SINGLE")
-  SET(FPRECISION_LEVEL "4")
-ENDIF(SUNDIALS_PRECISION MATCHES "SINGLE")
-IF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-  SET(FPRECISION_LEVEL "8")
-ENDIF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-IF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-  SET(FPRECISION_LEVEL "16")
-ENDIF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-
-# ---------------------------------------------------------------
-# Option to specify index type
-# ---------------------------------------------------------------
-
-SET(DOCSTR "Signed 64-bit (64) or signed 32-bit (32) integer")
-SHOW_VARIABLE(SUNDIALS_INDEX_SIZE STRING "${DOCSTR}" "64")
-SET(DOCSTR "Integer type to use for indices in SUNDIALS")
-SHOW_VARIABLE(SUNDIALS_INDEX_TYPE STRING "${DOCSTR}" "")
-MARK_AS_ADVANCED(SUNDIALS_INDEX_TYPE)
-include(SundialsIndexSize)
-
-# ---------------------------------------------------------------
-# Enable Fortran interface?
-# ---------------------------------------------------------------
-
-# Fortran interface is disabled by default
-SET(DOCSTR "Enable Fortran 77 interfaces")
-OPTION(F77_INTERFACE_ENABLE "${DOCSTR}" OFF)
-
-# Check that at least one solver with a Fortran 77 interface is built
-IF(NOT BUILD_ARKODE AND NOT BUILD_CVODE AND NOT BUILD_IDA AND NOT BUILD_KINSOL)
-  IF(F77_INTERFACE_ENABLE)
-    PRINT_WARNING("Enabled packages do not support Fortran 77 interface" "Disabling F77 interface")
-    FORCE_VARIABLE(F77_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(F77_INTERFACE_ENABLE)
-ENDIF()
-
-# Fortran 2003 interface is disabled by default
-SET(DOCSTR "Enable Fortran 2003 interfaces")
-OPTION(F2003_INTERFACE_ENABLE "${DOCSTR}" OFF)
-
-# Check that at least one solver with a Fortran 2003 interface is built
-IF(NOT BUILD_CVODE)
-  IF(F2003_INTERFACE_ENABLE)
-    PRINT_WARNING("Enabled packages do not support Fortran 2003 interface" "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(F2003_INTERFACE_ENABLE)
-ENDIF()
-
-IF(F2003_INTERFACE_ENABLE)
-  # F2003 interface only supports double precision
-  IF(NOT (SUNDIALS_PRECISION MATCHES "DOUBLE"))
-    PRINT_WARNING("F2003 interface is not compatible with ${SUNDIALS_PRECISION} precision"
-                  "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-
-  # F2003 interface only supports 64-bit indices
-  IF(NOT (SUNDIALS_INDEX_SIZE MATCHES "64"))
-    PRINT_WARNING("F2003 interface is not compatible with ${SUNDIALS_INDEX_SIZE}-bit indicies"
-                  "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-
-  # Put all F2003 modules into one build directory
-  SET(CMAKE_Fortran_MODULE_DIRECTORY "${CMAKE_BINARY_DIR}/fortran")
-
-  # Allow a user to set where the Fortran modules will be installed
-  SET(DOCSTR "Directory where Fortran module files are installed")
-  SHOW_VARIABLE(Fortran_INSTALL_MODDIR DIRECTORY "${DOCSTR}" "fortran")
-ENDIF()
-
-# ---------------------------------------------------------------
-# Options to build static and/or shared libraries
-# ---------------------------------------------------------------
-
-OPTION(BUILD_STATIC_LIBS "Build static libraries" ON)
-OPTION(BUILD_SHARED_LIBS "Build shared libraries" ON)
-
-# Make sure we build at least one type of libraries
-IF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-  PRINT_WARNING("Both static and shared library generation were disabled"
-                "Building static libraries was re-enabled")
-  FORCE_VARIABLE(BUILD_STATIC_LIBS BOOL "Build static libraries" ON)
-ENDIF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-
-# ---------------------------------------------------------------
-# Option to use the generic math libraries (UNIX only)
-# ---------------------------------------------------------------
-
-IF(UNIX)
-  OPTION(USE_GENERIC_MATH "Use generic (std-c) math libraries" ON)
-  IF(USE_GENERIC_MATH)
-    # executables will be linked against -lm
-    SET(EXTRA_LINK_LIBS -lm)
-    # prepare substitution variable for sundials_config.h
-    SET(SUNDIALS_USE_GENERIC_MATH TRUE)
-  ENDIF(USE_GENERIC_MATH)
-ENDIF(UNIX)
-
-# ---------------------------------------------------------------
-# Check for POSIX timers
-# ---------------------------------------------------------------
-INCLUDE(SundialsPOSIXTimers)
-
-# ===============================================================
-# Options for Parallelism
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable MPI support?
-# ---------------------------------------------------------------
-OPTION(MPI_ENABLE "Enable MPI support" OFF)
-
-# ---------------------------------------------------------------
-# Enable OpenMP support?
-# ---------------------------------------------------------------
-OPTION(OPENMP_ENABLE "Enable OpenMP support" OFF)
-
-# provide OPENMP_DEVICE_ENABLE option
-OPTION(OPENMP_DEVICE_ENABLE "Enable OpenMP device offloading support" OFF)
-
-# Advanced option to skip OpenMP device offloading support check.
-# This is needed for a specific compiler that doesn't correctly
-# report its OpenMP spec date (with CMake >= 3.9).
-OPTION(SKIP_OPENMP_DEVICE_CHECK "Skip the OpenMP device offloading support check" OFF)
-MARK_AS_ADVANCED(FORCE SKIP_OPENMP_DEVICE_CHECK)
-
-# ---------------------------------------------------------------
-# Enable Pthread support?
-# ---------------------------------------------------------------
-OPTION(PTHREAD_ENABLE "Enable Pthreads support" OFF)
-
-# -------------------------------------------------------------
-# Enable CUDA support?
-# -------------------------------------------------------------
-OPTION(CUDA_ENABLE "Enable CUDA support" OFF)
-
-# -------------------------------------------------------------
-# Enable RAJA support?
-# -------------------------------------------------------------
-OPTION(RAJA_ENABLE "Enable RAJA support" OFF)
-
-
-# ===============================================================
-# Options for external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable BLAS support?
-# ---------------------------------------------------------------
-OPTION(BLAS_ENABLE "Enable BLAS support" OFF)
-
-# ---------------------------------------------------------------
-# Enable LAPACK/BLAS support?
-# ---------------------------------------------------------------
-OPTION(LAPACK_ENABLE "Enable Lapack support" OFF)
-
-# LAPACK does not support extended precision
-IF(LAPACK_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling LAPACK")
-  FORCE_VARIABLE(LAPACK_ENABLE BOOL "LAPACK is disabled" OFF)
-ENDIF()
-
-# LAPACK does not support 64-bit integer index types
-IF(LAPACK_ENABLE AND SUNDIALS_INDEX_SIZE MATCHES "64")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_INDEX_SIZE} integers"
-                "Disabling LAPACK")
-  SET(LAPACK_ENABLE OFF CACHE BOOL "LAPACK is disabled" FORCE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable SuperLU_MT support?
-# ---------------------------------------------------------------
-OPTION(SUPERLUMT_ENABLE "Enable SUPERLUMT support" OFF)
-
-# SuperLU_MT does not support extended precision
-IF(SUPERLUMT_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("SuperLU_MT is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling SuperLU_MT")
-  FORCE_VARIABLE(SUPERLUMT_ENABLE BOOL "SuperLU_MT is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable KLU support?
-# ---------------------------------------------------------------
-OPTION(KLU_ENABLE "Enable KLU support" OFF)
-
-# KLU does not support single or extended precision
-IF(KLU_ENABLE AND
-  (SUNDIALS_PRECISION MATCHES "SINGLE" OR SUNDIALS_PRECISION MATCHES "EXTENDED"))
-  PRINT_WARNING("KLU is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling KLU")
-  FORCE_VARIABLE(KLU_ENABLE BOOL "KLU is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable hypre Vector support?
-# ---------------------------------------------------------------
-OPTION(HYPRE_ENABLE "Enable hypre support" OFF)
-
-# Using hypre requres building with MPI enabled
-IF(HYPRE_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling hypre"
-                "Set MPI_ENABLE to ON to use parhyp")
-  FORCE_VARIABLE(HYPRE_ENABLE BOOL "Enable hypre support" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable PETSc support?
-# ---------------------------------------------------------------
-OPTION(PETSC_ENABLE "Enable PETSc support" OFF)
-
-# Using PETSc requires building with MPI enabled
-IF(PETSC_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling PETSc"
-                "Set MPI_ENABLE to ON to use PETSc")
-  FORCE_VARIABLE(PETSC_ENABLE BOOL "Enable PETSc support" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable Trilinos support?
-# ---------------------------------------------------------------
-OPTION(Trilinos_ENABLE "Enable Trilinos support" OFF)
-
-
-# ===============================================================
-# Options for examples
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable examples?
-# ---------------------------------------------------------------
-
-# Enable C examples (on by default)
-OPTION(EXAMPLES_ENABLE_C "Build SUNDIALS C examples" ON)
-
-# C++ examples (off by default, unless Trilinos is enabled)
-SET(DOCSTR "Build C++ examples")
-OPTION(EXAMPLES_ENABLE_CXX "${DOCSTR}" ${Trilinos_ENABLE})
-
-# F77 examples (on by default) are an option only if the Fortran
-# interface is enabled
-SET(DOCSTR "Build SUNDIALS Fortran examples")
-IF(F77_INTERFACE_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" ON)
-  # Fortran 77 examples do not support single or extended precision
-  IF(EXAMPLES_ENABLE_F77 AND (SUNDIALS_PRECISION MATCHES "EXTENDED" OR SUNDIALS_PRECISION MATCHES "SINGLE"))
-    PRINT_WARNING("F77 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F77)
-    PRINT_WARNING("EXAMPLES_ENABLE_F77 is ON but F77_INTERFACE_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F77)
-ENDIF()
-
-# F90 examples (on by default) are an option only if a Fortran interface is enabled.
-SET(DOCSTR "Build SUNDIALS F90 examples")
-IF(F77_INTERFACE_ENABLE OR F2003_INTERFACE_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" ON)
-  # Fortran 90 examples do not support extended precision
-  IF(EXAMPLES_ENABLE_F90 AND (SUNDIALS_PRECISION MATCHES "EXTENDED"))
-    PRINT_WARNING("F90 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "Disabling EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F90)
-    PRINT_WARNING("EXAMPLES_ENABLE_F90 is ON but both F77 and F2003 interfaces are OFF"
-                  "Disabling EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F90)
-ENDIF()
-
-# CUDA examples (off by default)
-SET(DOCSTR "Build SUNDIALS CUDA examples")
-IF(CUDA_ENABLE)
-  OPTION(EXAMPLES_ENABLE_CUDA "${DOCSTR}" OFF)
-ELSE()
-  IF(EXAMPLES_ENABLE_CUDA)
-    PRINT_WARNING("EXAMPLES_ENABLE_CUDA is ON but CUDA_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_CUDA")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_CUDA BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ENDIF()
-
-# If any of the above examples are enabled set EXAMPLES_ENABLED to TRUE
-IF(EXAMPLES_ENABLE_C OR
-    EXAMPLES_ENABLE_F77 OR
-    EXAMPLES_ENABLE_CXX OR
-    EXAMPLES_ENABLE_F90 OR
-    EXAMPLES_ENABLE_CUDA)
-  SET(EXAMPLES_ENABLED TRUE)
-ELSE()
-  SET(EXAMPLES_ENABLED FALSE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Install examples?
-# ---------------------------------------------------------------
-
-# Enable installing examples by default
-SET(DOCSTR "Install SUNDIALS examples")
-IF(EXAMPLES_ENABLED)
-  OPTION(EXAMPLES_INSTALL "${DOCSTR}" ON)
-ELSE()
-  FORCE_VARIABLE(EXAMPLES_INSTALL BOOL "${DOCSTR}" OFF)
-  HIDE_VARIABLE(EXAMPLES_INSTALL)
-ENDIF()
-
-# If examples are to be exported, check where we should install them.
-IF(EXAMPLES_INSTALL)
-
-  SHOW_VARIABLE(EXAMPLES_INSTALL_PATH PATH
-    "Output directory for installing example files"
-    "${CMAKE_INSTALL_PREFIX}/examples")
-
-  IF(NOT EXAMPLES_INSTALL_PATH)
-    PRINT_WARNING("The example installation path is empty"
-      "Example installation path was reset to its default value")
-    SET(EXAMPLES_INSTALL_PATH "${CMAKE_INSTALL_PREFIX}/examples" CACHE STRING
-      "Output directory for installing example files" FORCE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(EXAMPLES_INSTALL_PATH)
-
-ENDIF()
-
-
-# ==============================================================================
-# Advanced (hidden) options
-# ==============================================================================
-
-# ------------------------------------------------------------------------------
-# Manually specify the Fortran name-mangling scheme
-#
-# The build system tries to infer the Fortran name-mangling scheme using a
-# Fortran compiler and defaults to using lower case and one underscore if the
-# scheme can not be determined. If a working Fortran compiler is not available
-# or the user needs to override the inferred or default scheme, the following
-# options specify the case and number of appended underscores corresponding to
-# the Fortran name-mangling scheme of symbol names that do not themselves
-# contain underscores. This is all we really need for the FCMIX and LAPACK
-# interfaces. A working Fortran compiler is only necessary for building Fortran
-# example programs.
-# ------------------------------------------------------------------------------
-
-# The case to use in the name-mangling scheme
-show_variable(SUNDIALS_F77_FUNC_CASE STRING
-  "case of Fortran function names (lower/upper)"
-  "")
-
-# The number of underscores of appended in the name-mangling scheme
-show_variable(SUNDIALS_F77_FUNC_UNDERSCORES STRING
-  "number of underscores appended to Fortran function names (none/one/two)"
-  "")
-
-# Hide the name-mangling varibales as advanced options
-mark_as_advanced(FORCE SUNDIALS_F77_FUNC_CASE)
-mark_as_advanced(FORCE SUNDIALS_F77_FUNC_UNDERSCORES)
-
-# If used, both case and underscores must be set
-if((NOT SUNDIALS_F77_FUNC_CASE) AND SUNDIALS_F77_FUNC_UNDERSCORES)
-  message(FATAL_ERROR
-    "If SUNDIALS_F77_FUNC_UNDERSCORES is set, SUNDIALS_F77_FUNC_CASE must also be set.")
-endif()
-
-if(SUNDIALS_F77_FUNC_CASE AND (NOT SUNDIALS_F77_FUNC_UNDERSCORES))
-  message(FATAL_ERROR
-    "If SUNDIALS_F77_FUNC_CASE is set, SUNDIALS_F77_FUNC_UNDERSCORES must also be set.")
-endif()
-
-# ------------------------------------------------------------------------------
-# Include development examples in regression tests?
-#
-# NOTE: Development examples are currently used for internal testing and may
-# produce erroneous failures when run on different systems as the pass/fail
-# status is determined by comparing the output against a saved output file.
-# ------------------------------------------------------------------------------
-OPTION(SUNDIALS_DEVTESTS "Include development tests in make test" OFF)
-MARK_AS_ADVANCED(FORCE SUNDIALS_DEVTESTS)
-
-# ===============================================================
-# Add any platform specifc settings
-# ===============================================================
-
-IF(APPLE)
-  SET(CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS "${CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS} -undefined dynamic_lookup")
-ENDIF(APPLE)
-
-# ===============================================================
-# Fortran and C++ settings
-# ===============================================================
-
-# ---------------------------------------------------------------
-# A Fortran compiler is needed to:
-# (a) Determine the name-mangling scheme if FCMIX, BLAS, or
-#     LAPACK are enabled
-# (b) Compile example programs if F77 or F90 examples are enabled
-# ---------------------------------------------------------------
-
-# Do we need a Fortran name-mangling scheme?
-if(F77_INTERFACE_ENABLE OR BLAS_ENABLE OR LAPACK_ENABLE)
-  set(NEED_FORTRAN_NAME_MANGLING TRUE)
-endif()
-
-# Did the user provide a name-mangling scheme?
-if(SUNDIALS_F77_FUNC_CASE AND SUNDIALS_F77_FUNC_UNDERSCORES)
-
-  STRING(TOUPPER ${SUNDIALS_F77_FUNC_CASE} SUNDIALS_F77_FUNC_CASE)
-  STRING(TOUPPER ${SUNDIALS_F77_FUNC_UNDERSCORES} SUNDIALS_F77_FUNC_UNDERSCORES)
-
-  # Based on the given case and number of underscores, set the C preprocessor
-  # macro definitions. Since SUNDIALS never uses symbols names containing
-  # underscores we set the name-mangling schemes to be the same. In general,
-  # names of symbols with and without underscore may be mangled differently
-  # (e.g. g77 mangles mysub to mysub_ and my_sub to my_sub__)
-  if(SUNDIALS_F77_FUNC_CASE MATCHES "LOWER")
-    if(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "NONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "ONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## _")
-      SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## _")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "TWO")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## __")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## __")
-    else()
-      message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_UNDERSCORES option.")
-    endif()
-  elseif(SUNDIALS_F77_FUNC_CASE MATCHES "UPPER")
-    if(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "NONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "ONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## _")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## _")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "TWO")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## __")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## __")
-    else()
-      message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_UNDERSCORES option.")
-    endif()
-  else()
-    message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_CASE option.")
-  endif()
-
-  # name-mangling scheme has been manually set
-  set(NEED_FORTRAN_NAME_MANGLING FALSE)
-
-endif()
-
-# Do we need a Fortran compiler?
-if(F2003_INTERFACE_ENABLE OR EXAMPLES_ENABLE_F77 OR EXAMPLES_ENABLE_F90 OR NEED_FORTRAN_NAME_MANGLING)
-  include(SundialsFortran)
-endif()
-
-# Ensure that F90 compiler is found if F90 examples are enabled
-if (EXAMPLES_ENABLE_F90 AND (NOT F90_FOUND))
-  PRINT_WARNING("Compiler with F90 support not found" "Disabling F90 Examples")
-  SET(DOCSTR "Build F90 examples")
-  FORCE_VARIABLE(EXAMPLES_ENABLE_F90 "${DOCSTR}" OFF)
-endif()
-
-# Ensure that F90 compiler found if F2003 interface is enabled
-if (F2003_INTERFACE_ENABLE AND (NOT F90_FOUND))
-  PRINT_WARNING("Compiler with F90 support not found" "Disabling F2003 Interface")
-  SET(DOCSTR "Enable Fortran 2003 interfaces")
-  FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-endif()
-
-# F2003 interface requires ISO_C_BINDING
-IF(F2003_INTERFACE_ENABLE AND (NOT Fortran_COMPILER_SUPPORTS_ISOCBINDING))
-  PRINT_WARNING("Fortran compiler does not provide ISO_C_BINDING support"
-                "Disabling F2003 interface")
-  SET(DOCSTR "Enable Fortran 2003 interfaces")
-  FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-ENDIF()
-
-
-# ---------------------------------------------------------------
-# A C++ compiler is needed if:
-# (a) C++ examples are enabled
-# (b) CUDA is enabled
-# (c) RAJA is enabled
-# (d) Trilinos is enabled
-# ---------------------------------------------------------------
-
-if(EXAMPLES_ENABLE_CXX OR CUDA_ENABLE OR RAJA_ENABLE OR Trilinos_ENABLE)
-  include(SundialsCXX)
-endif()
-
-# ---------------------------------------------------------------
-# Setup CUDA. Since CUDA is its own language we do this
-# separate from the TPLs.
-# ---------------------------------------------------------------
-
-if(CUDA_ENABLE)
-  find_package(CUDA)
-  if (CUDA_FOUND)
-    set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -lineinfo")
-  else()
-    message(STATUS "Disabling CUDA support, could not find CUDA.")
-    set(CUDA_ENABLE OFF)
-  endif()
-endif(CUDA_ENABLE)
-
-# ---------------------------------------------------------------
-# Now that all languages are setup, we can configure them more.
-# ---------------------------------------------------------------
-
-# C++11 is needed if:
-#   (a) CUDA is enabled
-# C++11 should not be enabled if
-#   (a) RAJA is enabled (they provide a std flag)
-if (CXX_FOUND AND CUDA_ENABLE AND CUDA_FOUND AND (NOT RAJA_ENABLE))
-  USE_CXX_STD(11)
-endif()
-
-# ---------------------------------------------------------------
-# Decide how to compile MPI codes. We must check for MPI if
-# MPI is enabled or if Trilinos is enabled because the Trilinos
-# examples may need MPI without us turning on the MPI SUNDIALS
-# components.
-# ---------------------------------------------------------------
-
-if(MPI_ENABLE OR Trilinos_ENABLE)
-  include(SundialsMPI)
-endif()
-
-if(MPI_ENABLE)
-  if(NOT MPI_C_FOUND)
-    print_warning("MPI not functional" "Parallel support will not be provided")
-  else()
-    set(IS_MPI_ENABLED "#ifndef SUNDIALS_MPI_ENABLED\n#define SUNDIALS_MPI_ENABLED 1\n#endif")
-  endif()
-endif()
-
-# always define FMPI_COMM_F2C in sundials_fconfig.h file
-if(MPIC_MPI2)
-  set(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 1")
-  set(FMPI_COMM_F2C ".true.")
-else()
-  set(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 0")
-  set(FMPI_COMM_F2C ".false.")
-endif()
-
-# -------------------------------------------------------------
-# Find OpenMP
-# -------------------------------------------------------------
-
-if(OPENMP_ENABLE OR OPENMP_DEVICE_ENABLE)
-
-  include(SundialsOpenMP)
-
-  # turn off OPENMP_ENABLE and OPENMP_DEVICE_ENABLE if OpenMP is not found
-  if(NOT OPENMP_FOUND)
-    print_warning("Could not determine OpenMP compiler flags" "Disabling OpenMP support")
-    force_variable(OPENMP_ENABLE BOOL "Enable OpenMP support" OFF)
-    force_variable(OPENMP_DEVICE_ENABLE BOOL "Enable OpenMP device offloading support" OFF)
-  endif()
-
-  # turn off OPENMP_DEVICE_ENABLE if offloading is not supported
-  if(OPENMP_DEVICE_ENABLE AND (NOT OPENMP_SUPPORTS_DEVICE_OFFLOADING))
-    print_warning("OpenMP found does not support device offloading"
-                  "Disabling OpenMP device offloading support")
-    force_variable(OPENMP_DEVICE_ENABLE BOOL "Enable OpenMP device offloading support" OFF)
-  endif()
-
-endif()
-
-# -------------------------------------------------------------
-# Find PThreads
-# -------------------------------------------------------------
-
-IF(PTHREAD_ENABLE)
-  FIND_PACKAGE(Threads)
-  IF(CMAKE_USE_PTHREADS_INIT)
-    message(STATUS "Using Pthreads")
-    SET(PTHREADS_FOUND TRUE)
-    # SGS
-  ELSE()
-    message(STATUS "Disabling Pthreads support, could not determine compiler flags")
-  endif()
-ENDIF(PTHREAD_ENABLE)
-
-# -------------------------------------------------------------
-# Find RAJA
-# -------------------------------------------------------------
-
-# disable RAJA if CUDA is not enabled/working
-if(RAJA_ENABLE AND (NOT CUDA_FOUND))
-  PRINT_WARNING("CUDA is required for RAJA support" "Please enable CUDA and RAJA")
-  FORCE_VARIABLE(RAJA_ENABLE BOOL "RAJA disabled" OFF)
-endif()
-
-if(RAJA_ENABLE)
-  # Look for CMake configuration file in RAJA installation
-  find_package(RAJA)
-  if (RAJA_FOUND)
-    include_directories(${RAJA_INCLUDE_DIR})
-    set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} ${RAJA_NVCC_FLAGS})
-  else()
-    PRINT_WARNING("RAJA configuration not found"
-                  "Please set RAJA_DIR to provide path to RAJA CMake configuration file.")
-  endif()
-endif(RAJA_ENABLE)
-
-# ===============================================================
-# Find (and test) external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Find (and test) the BLAS libraries
-# ---------------------------------------------------------------
-
-# If BLAS is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(BLAS_ENABLE)
-
-  # find BLAS
-  INCLUDE(SundialsBlas)
-
-  # show after include so FindBlas can locate BLAS_LIBRARIES if necessary
-  SHOW_VARIABLE(BLAS_LIBRARIES STRING "Blas libraries" "${BLAS_LIBRARIES}")
-
-  IF(BLAS_LIBRARIES AND NOT BLAS_FOUND)
-    PRINT_WARNING("BLAS not functional"
-                  "BLAS support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS TRUE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(BLAS_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the Lapack libraries
-# ---------------------------------------------------------------
-
-# If LAPACK is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(LAPACK_ENABLE)
-
-  # find LAPACK and BLAS Libraries
-  INCLUDE(SundialsLapack)
-
-  # show after include so FindLapack can locate LAPCK_LIBRARIES if necessary
-  SHOW_VARIABLE(LAPACK_LIBRARIES STRING "Lapack and Blas libraries" "${LAPACK_LIBRARIES}")
-
-  IF(LAPACK_LIBRARIES AND NOT LAPACK_FOUND)
-    PRINT_WARNING("LAPACK not functional"
-                  "Blas/Lapack support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS_LAPACK TRUE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(LAPACK_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the SUPERLUMT libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for SuperLU_MT integer type
-
-# If SUPERLUMT is needed, first try to find the appropriate
-# libraries to link against them.
-
-IF(SUPERLUMT_ENABLE)
-
-  # Show SuperLU_MT options and set default thread type (Pthreads)
-  SHOW_VARIABLE(SUPERLUMT_THREAD_TYPE STRING "SUPERLUMT threading type: OpenMP or Pthread" "Pthread")
-  SHOW_VARIABLE(SUPERLUMT_INCLUDE_DIR PATH "SUPERLUMT include directory" "${SUPERLUMT_INCLUDE_DIR}")
-  SHOW_VARIABLE(SUPERLUMT_LIBRARY_DIR PATH "SUPERLUMT library directory" "${SUPERLUMT_LIBRARY_DIR}")
-
-  INCLUDE(SundialsSuperLUMT)
-
-  IF(SUPERLUMT_FOUND)
-    # sundials_config.h symbols
-    SET(SUNDIALS_SUPERLUMT TRUE)
-    SET(SUNDIALS_SUPERLUMT_THREAD_TYPE ${SUPERLUMT_THREAD_TYPE})
-    INCLUDE_DIRECTORIES(${SUPERLUMT_INCLUDE_DIR})
-  ENDIF()
-
-  IF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-    PRINT_WARNING("SUPERLUMT not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(SUPERLUMT_THREAD_TYPE)
-  HIDE_VARIABLE(SUPERLUMT_LIBRARY_DIR)
-  HIDE_VARIABLE(SUPERLUMT_INCLUDE_DIR)
-  SET (SUPERLUMT_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the KLU libraries
-# ---------------------------------------------------------------
-
-# If KLU is requested, first try to find the appropriate libraries to
-# link against them.
-
-IF(KLU_ENABLE)
-
-  SHOW_VARIABLE(KLU_INCLUDE_DIR PATH "KLU include directory"
-    "${KLU_INCLUDE_DIR}")
-  SHOW_VARIABLE(KLU_LIBRARY_DIR PATH
-    "Klu library directory" "${KLU_LIBRARY_DIR}")
-
-  set(KLU_FOUND TRUE)
-  get_filename_component(PYBAMM_DIR ${PROJECT_SOURCE_DIR} DIRECTORY)
-  set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PYBAMM_DIR}) # use FindSuiteSparse.cmake that is in PyBaMM root
-  set(SuiteSparse_ROOT ${PYBAMM_DIR}/SuiteSparse-5.6.0)
-  find_package(SuiteSparse OPTIONAL_COMPONENTS KLU AMD COLAMD BTF)
-  include_directories(${SuiteSparse_INCLUDE_DIRS})
-  set(KLU_LIBRARIES ${SuiteSparse_LIBRARIES})
-
-
-  IF(KLU_LIBRARIES AND NOT KLU_FOUND)
-    PRINT_WARNING("KLU not functional - support will not be provided"
-                  "Double check spelling of include path and specified libraries (search is case sensitive)")
-  ENDIF(KLU_LIBRARIES AND NOT KLU_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(KLU_LIBRARY_DIR)
-  HIDE_VARIABLE(KLU_INCLUDE_DIR)
-  SET (KLU_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF(KLU_ENABLE)
-
-# ---------------------------------------------------------------
-# Find (and test) the hypre libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for hypre precision and integer type
-
-IF(HYPRE_ENABLE)
-  SHOW_VARIABLE(HYPRE_INCLUDE_DIR PATH "HYPRE include directory"
-    "${HYPRE_INCLUDE_DIR}")
-  SHOW_VARIABLE(HYPRE_LIBRARY_DIR PATH
-    "HYPRE library directory" "${HYPRE_LIBRARY_DIR}")
-
-  INCLUDE(SundialsHypre)
-
-  IF(HYPRE_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_HYPRE TRUE)
-    INCLUDE_DIRECTORIES(${HYPRE_INCLUDE_DIR})
-  ENDIF(HYPRE_FOUND)
-
-  IF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-    PRINT_WARNING("HYPRE not functional - support will not be provided"
-                  "Found hypre library, test code does not work")
-  ENDIF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(HYPRE_INCLUDE_DIR)
-  HIDE_VARIABLE(HYPRE_LIBRARY_DIR)
-  SET (HYPRE_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the PETSc libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for PETSc precision and integer type
-
-IF(PETSC_ENABLE)
-  SHOW_VARIABLE(PETSC_INCLUDE_DIR PATH "PETSc include directory"
-    "${PETSC_INCLUDE_DIR}")
-  SHOW_VARIABLE(PETSC_LIBRARY_DIR PATH
-    "PETSc library directory" "${PETSC_LIBRARY_DIR}")
-
-  INCLUDE(SundialsPETSc)
-
-  IF(PETSC_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_PETSC TRUE)
-    INCLUDE_DIRECTORIES(${PETSC_INCLUDE_DIR})
-  ENDIF(PETSC_FOUND)
-
-  IF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-    PRINT_WARNING("PETSC not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(PETSC_LIBRARY_DIR)
-  HIDE_VARIABLE(PETSC_INCLUDE_DIR)
-  SET (PETSC_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# -------------------------------------------------------------
-# Find Trilinos
-# -------------------------------------------------------------
-
-if(Trilinos_ENABLE)
-  include(SundialsTrilinos)
-  if(NOT Trilinos_FUNCTIONAL)
-    PRINT_WARNING("Trilinos not functional" "Verify the path to Trilinos and check the Trilinos installation")
-  endif()
-endif(Trilinos_ENABLE)
-
-
-# ===============================================================
-# At this point all the configuration options are set.
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Configure the header file sundials_config.h
-# ---------------------------------------------------------------
-
-# All required substitution variables should be available at this point.
-# Generate the header file and place it in the binary dir.
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_config.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  )
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_fconfig.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  )
-
-# Add the include directory in the source tree and the one in
-# the binary tree (for the header file sundials_config.h)
-INCLUDE_DIRECTORIES(${PROJECT_SOURCE_DIR}/include ${PROJECT_BINARY_DIR}/include)
-
-# ---------------------------------------------------------------
-# Enable testing and add source and example files to the build.
-# ---------------------------------------------------------------
-
-# Enable testing
-IF(EXAMPLES_ENABLED)
-  INCLUDE(SundialsTesting)
-ENDIF()
-
-# Add selected packages and modules to the build
-ADD_SUBDIRECTORY(src)
-
-# Add selected examples to the build
-IF(EXAMPLES_ENABLED)
-  ADD_SUBDIRECTORY(examples)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Install configuration header files and license file
-# ---------------------------------------------------------------
-
-# install configured header file
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  DESTINATION include/sundials
-  )
-
-# install configured header file for Fortran 90
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  DESTINATION include/sundials
-  )
-
-# install shared Fortran 2003 modules
-IF(F2003_INTERFACE_ENABLE)
-  # While the .mod files get generated for static and shared
-  # libraries, they are identical. So only install one set
-  # of the .mod files.
-  IF(BUILD_STATIC_LIBS)
-    INSTALL(
-      DIRECTORY ${CMAKE_Fortran_MODULE_DIRECTORY}_STATIC/
-      DESTINATION ${Fortran_INSTALL_MODDIR}
-      )
-  ELSE()
-    INSTALL(
-      DIRECTORY ${CMAKE_Fortran_MODULE_DIRECTORY}_SHARED/
-      DESTINATION ${Fortran_INSTALL_MODDIR}
-      )
-  ENDIF()
-ENDIF()
-
-# install license and notice files
-INSTALL(
-  FILES ${PROJECT_SOURCE_DIR}/LICENSE
-  DESTINATION include/sundials
-  )
-INSTALL(
-  FILES ${PROJECT_SOURCE_DIR}/NOTICE
-  DESTINATION include/sundials
-  )
diff --git a/scripts/replace-cmake/sundials-5.0.0/CMakeLists.txt b/scripts/replace-cmake/sundials-5.0.0/CMakeLists.txt
deleted file mode 100644
index fc8acbddc9..0000000000
--- a/scripts/replace-cmake/sundials-5.0.0/CMakeLists.txt
+++ /dev/null
@@ -1,1151 +0,0 @@
-# ---------------------------------------------------------------
-# Programmer: Radu Serban, David J. Gardner, Cody J. Balos,
-#             and Slaven Peles @ LLNL
-# ---------------------------------------------------------------
-# SUNDIALS Copyright Start
-# Copyright (c) 2002-2019, Lawrence Livermore National Security
-# and Southern Methodist University.
-# All rights reserved.
-#
-# See the top-level LICENSE and NOTICE files for details.
-#
-# SPDX-License-Identifier: BSD-3-Clause
-# SUNDIALS Copyright End
-# ---------------------------------------------------------------
-# Top level CMakeLists.txt for SUNDIALS (for cmake build system)
-# ---------------------------------------------------------------
-
-# ---------------------------------------------------------------
-# Initial commands
-# ---------------------------------------------------------------
-
-# Require a fairly recent cmake version
-cmake_minimum_required(VERSION 3.1.3)
-
-# Libraries linked via full path no longer produce linker search paths
-# Allows examples to build
-if(COMMAND cmake_policy)
-  cmake_policy(SET CMP0003 NEW)
-endif(COMMAND cmake_policy)
-
-# MACOSX_RPATH is enabled by default
-# Fixes dynamic loading on OSX
-if(POLICY CMP0042)
-  cmake_policy(SET CMP0042 NEW) # Added in CMake 3.0
-else()
-  if(APPLE)
-    set(CMAKE_MACOSX_RPATH 1)
-  endif()
-endif()
-
-# Project SUNDIALS (initially only C supported)
-# sets PROJECT_SOURCE_DIR and PROJECT_BINARY_DIR variables
-PROJECT(sundials C)
-
-# Set some variables with info on the SUNDIALS project
-SET(PACKAGE_BUGREPORT "woodward6@llnl.gov")
-SET(PACKAGE_NAME "SUNDIALS")
-SET(PACKAGE_STRING "SUNDIALS 4.1.0")
-SET(PACKAGE_TARNAME "sundials")
-
-# set SUNDIALS version numbers
-# (use "" for the version label if none is needed)
-SET(PACKAGE_VERSION_MAJOR "4")
-SET(PACKAGE_VERSION_MINOR "1")
-SET(PACKAGE_VERSION_PATCH "0")
-SET(PACKAGE_VERSION_LABEL "")
-
-IF(PACKAGE_VERSION_LABEL)
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}-${PACKAGE_VERSION_LABEL}")
-ELSE()
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}")
-ENDIF()
-
-SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)
-
-# Prohibit in-source build
-IF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-  MESSAGE(FATAL_ERROR "In-source build prohibited.")
-ENDIF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-
-# Hide some cache variables
-MARK_AS_ADVANCED(EXECUTABLE_OUTPUT_PATH LIBRARY_OUTPUT_PATH)
-
-# Always show the C compiler and flags
-MARK_AS_ADVANCED(CLEAR
-  CMAKE_C_COMPILER
-  CMAKE_C_FLAGS)
-
-# Specify the VERSION and SOVERSION for shared libraries
-
-SET(arkodelib_VERSION "3.1.0")
-SET(arkodelib_SOVERSION "3")
-
-SET(cvodelib_VERSION "4.1.0")
-SET(cvodelib_SOVERSION "4")
-
-SET(cvodeslib_VERSION "4.1.0")
-SET(cvodeslib_SOVERSION "4")
-
-SET(idalib_VERSION "4.1.0")
-SET(idalib_SOVERSION "4")
-
-SET(idaslib_VERSION "3.1.0")
-SET(idaslib_SOVERSION "3")
-
-SET(kinsollib_VERSION "4.1.0")
-SET(kinsollib_SOVERSION "4")
-
-SET(cpodeslib_VERSION "0.0.0")
-SET(cpodeslib_SOVERSION "0")
-
-SET(nveclib_VERSION "4.1.0")
-SET(nveclib_SOVERSION "4")
-
-SET(sunmatrixlib_VERSION "2.1.0")
-SET(sunmatrixlib_SOVERSION "2")
-
-SET(sunlinsollib_VERSION "2.1.0")
-SET(sunlinsollib_SOVERSION "2")
-
-SET(sunnonlinsollib_VERSION "1.1.0")
-SET(sunnonlinsollib_SOVERSION "1")
-
-# Specify the location of additional CMAKE modules
-SET(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/config)
-
-# Get correct build paths automatically, but expose CMAKE_INSTALL_LIBDIR
-# as a regular cache variable so that a user can more easily see what
-# the library dir was set to be by GNUInstallDirs.
-INCLUDE(GNUInstallDirs)
-MARK_AS_ADVANCED(CLEAR CMAKE_INSTALL_LIBDIR)
-
-# ---------------------------------------------------------------
-# Which modules to build?
-# ---------------------------------------------------------------
-
-# For each SUNDIALS solver available (i.e. for which we have the
-# sources), give the user the option of enabling/disabling it.
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/arkode")
-  OPTION(BUILD_ARKODE "Build the ARKODE library" ON)
-ELSE()
-  SET(BUILD_ARKODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvode")
-  OPTION(BUILD_CVODE "Build the CVODE library" ON)
-ELSE()
-  SET(BUILD_CVODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvodes")
-  OPTION(BUILD_CVODES "Build the CVODES library" ON)
-ELSE()
-  SET(BUILD_CVODES OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/ida")
-  OPTION(BUILD_IDA "Build the IDA library" ON)
-ELSE()
-  SET(BUILD_IDA OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/idas")
-  OPTION(BUILD_IDAS "Build the IDAS library" ON)
-ELSE()
-  SET(BUILD_IDAS OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/kinsol")
-  OPTION(BUILD_KINSOL "Build the KINSOL library" ON)
-ELSE()
-  SET(BUILD_KINSOL OFF)
-ENDIF()
-
-# CPODES is always OFF for now.  (commented out for Release); ToDo: better way to do this?
-#IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cpodes")
-#  OPTION(BUILD_CPODES  "Build the CPODES library"  OFF)
-#ELSE()
-#  SET(BUILD_CPODES OFF)
-#ENDIF()
-
-# ---------------------------------------------------------------
-# MACRO definitions
-# ---------------------------------------------------------------
-INCLUDE(CMakeParseArguments) # can be removed when CMake 3.5+ is required
-INCLUDE(SundialsCMakeMacros)
-INCLUDE(SundialsAddF2003InterfaceLibrary)
-INCLUDE(SundialsAddTest)
-INCLUDE(SundialsAddTestInstall)
-
-# ---------------------------------------------------------------
-# Check for deprecated SUNDIALS CMake options/variables
-# ---------------------------------------------------------------
-INCLUDE(SundialsDeprecated)
-
-# ---------------------------------------------------------------
-# xSDK specific options
-# ---------------------------------------------------------------
-INCLUDE(SundialsXSDK)
-
-# ---------------------------------------------------------------
-# Build specific C flags
-# ---------------------------------------------------------------
-
-# Hide all build type specific flags
-MARK_AS_ADVANCED(FORCE
-  CMAKE_C_FLAGS_DEBUG
-  CMAKE_C_FLAGS_MINSIZEREL
-  CMAKE_C_FLAGS_RELEASE
-  CMAKE_C_FLAGS_RELWITHDEBINFO)
-
-# Only show flags for the current build type if it is set
-# NOTE: Build specific flags are appended those in CMAKE_C_FLAGS
-IF(CMAKE_BUILD_TYPE)
-  IF(CMAKE_BUILD_TYPE MATCHES "Debug")
-    MESSAGE("Appending C debug flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_DEBUG)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "MinSizeRel")
-    MESSAGE("Appending C min size release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_MINSIZEREL)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "Release")
-    MESSAGE("Appending C release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELEASE)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "RelWithDebInfo")
-    MESSAGE("Appending C release with debug info flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELWITHDEBINFO)
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# Option to specify precision (realtype)
-# ---------------------------------------------------------------
-
-SET(DOCSTR "single, double, or extended")
-SHOW_VARIABLE(SUNDIALS_PRECISION STRING "${DOCSTR}" "double")
-
-# prepare substitution variable PRECISION_LEVEL for sundials_config.h
-STRING(TOUPPER ${SUNDIALS_PRECISION} SUNDIALS_PRECISION)
-SET(PRECISION_LEVEL "#define SUNDIALS_${SUNDIALS_PRECISION}_PRECISION 1")
-
-# prepare substitution variable FPRECISION_LEVEL for sundials_fconfig.h
-IF(SUNDIALS_PRECISION MATCHES "SINGLE")
-  SET(FPRECISION_LEVEL "4")
-ENDIF(SUNDIALS_PRECISION MATCHES "SINGLE")
-IF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-  SET(FPRECISION_LEVEL "8")
-ENDIF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-IF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-  SET(FPRECISION_LEVEL "16")
-ENDIF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-
-# ---------------------------------------------------------------
-# Option to specify index type
-# ---------------------------------------------------------------
-
-SET(DOCSTR "Signed 64-bit (64) or signed 32-bit (32) integer")
-SHOW_VARIABLE(SUNDIALS_INDEX_SIZE STRING "${DOCSTR}" "64")
-SET(DOCSTR "Integer type to use for indices in SUNDIALS")
-SHOW_VARIABLE(SUNDIALS_INDEX_TYPE STRING "${DOCSTR}" "")
-MARK_AS_ADVANCED(SUNDIALS_INDEX_TYPE)
-include(SundialsIndexSize)
-
-# ---------------------------------------------------------------
-# Enable Fortran interface?
-# ---------------------------------------------------------------
-
-# Fortran interface is disabled by default
-SET(DOCSTR "Enable Fortran 77 interfaces")
-OPTION(F77_INTERFACE_ENABLE "${DOCSTR}" OFF)
-
-# Check that at least one solver with a Fortran 77 interface is built
-IF(NOT BUILD_ARKODE AND NOT BUILD_CVODE AND NOT BUILD_IDA AND NOT BUILD_KINSOL)
-  IF(F77_INTERFACE_ENABLE)
-    PRINT_WARNING("Enabled packages do not support Fortran 77 interface" "Disabling F77 interface")
-    FORCE_VARIABLE(F77_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(F77_INTERFACE_ENABLE)
-ENDIF()
-
-# Fortran 2003 interface is disabled by default
-SET(DOCSTR "Enable Fortran 2003 interfaces")
-OPTION(F2003_INTERFACE_ENABLE "${DOCSTR}" OFF)
-
-# Check that at least one solver with a Fortran 2003 interface is built
-IF(NOT BUILD_CVODE)
-  IF(F2003_INTERFACE_ENABLE)
-    PRINT_WARNING("Enabled packages do not support Fortran 2003 interface" "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(F2003_INTERFACE_ENABLE)
-ENDIF()
-
-IF(F2003_INTERFACE_ENABLE)
-  # F2003 interface only supports double precision
-  IF(NOT (SUNDIALS_PRECISION MATCHES "DOUBLE"))
-    PRINT_WARNING("F2003 interface is not compatible with ${SUNDIALS_PRECISION} precision"
-                  "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-
-  # F2003 interface only supports 64-bit indices
-  IF(NOT (SUNDIALS_INDEX_SIZE MATCHES "64"))
-    PRINT_WARNING("F2003 interface is not compatible with ${SUNDIALS_INDEX_SIZE}-bit indicies"
-                  "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-
-  # Put all F2003 modules into one build directory
-  SET(CMAKE_Fortran_MODULE_DIRECTORY "${CMAKE_BINARY_DIR}/fortran")
-
-  # Allow a user to set where the Fortran modules will be installed
-  SET(DOCSTR "Directory where Fortran module files are installed")
-  SHOW_VARIABLE(Fortran_INSTALL_MODDIR DIRECTORY "${DOCSTR}" "fortran")
-ENDIF()
-
-# ---------------------------------------------------------------
-# Options to build static and/or shared libraries
-# ---------------------------------------------------------------
-
-OPTION(BUILD_STATIC_LIBS "Build static libraries" ON)
-OPTION(BUILD_SHARED_LIBS "Build shared libraries" ON)
-
-# Make sure we build at least one type of libraries
-IF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-  PRINT_WARNING("Both static and shared library generation were disabled"
-                "Building static libraries was re-enabled")
-  FORCE_VARIABLE(BUILD_STATIC_LIBS BOOL "Build static libraries" ON)
-ENDIF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-
-# ---------------------------------------------------------------
-# Option to use the generic math libraries (UNIX only)
-# ---------------------------------------------------------------
-
-IF(UNIX)
-  OPTION(USE_GENERIC_MATH "Use generic (std-c) math libraries" ON)
-  IF(USE_GENERIC_MATH)
-    # executables will be linked against -lm
-    SET(EXTRA_LINK_LIBS -lm)
-    # prepare substitution variable for sundials_config.h
-    SET(SUNDIALS_USE_GENERIC_MATH TRUE)
-  ENDIF(USE_GENERIC_MATH)
-ENDIF(UNIX)
-
-# ---------------------------------------------------------------
-# Check for POSIX timers
-# ---------------------------------------------------------------
-INCLUDE(SundialsPOSIXTimers)
-
-# ===============================================================
-# Options for Parallelism
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable MPI support?
-# ---------------------------------------------------------------
-OPTION(MPI_ENABLE "Enable MPI support" OFF)
-
-# ---------------------------------------------------------------
-# Enable OpenMP support?
-# ---------------------------------------------------------------
-OPTION(OPENMP_ENABLE "Enable OpenMP support" OFF)
-
-# provide OPENMP_DEVICE_ENABLE option
-OPTION(OPENMP_DEVICE_ENABLE "Enable OpenMP device offloading support" OFF)
-
-# Advanced option to skip OpenMP device offloading support check.
-# This is needed for a specific compiler that doesn't correctly
-# report its OpenMP spec date (with CMake >= 3.9).
-OPTION(SKIP_OPENMP_DEVICE_CHECK "Skip the OpenMP device offloading support check" OFF)
-MARK_AS_ADVANCED(FORCE SKIP_OPENMP_DEVICE_CHECK)
-
-# ---------------------------------------------------------------
-# Enable Pthread support?
-# ---------------------------------------------------------------
-OPTION(PTHREAD_ENABLE "Enable Pthreads support" OFF)
-
-# -------------------------------------------------------------
-# Enable CUDA support?
-# -------------------------------------------------------------
-OPTION(CUDA_ENABLE "Enable CUDA support" OFF)
-
-# -------------------------------------------------------------
-# Enable RAJA support?
-# -------------------------------------------------------------
-OPTION(RAJA_ENABLE "Enable RAJA support" OFF)
-
-
-# ===============================================================
-# Options for external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable BLAS support?
-# ---------------------------------------------------------------
-OPTION(BLAS_ENABLE "Enable BLAS support" OFF)
-
-# ---------------------------------------------------------------
-# Enable LAPACK/BLAS support?
-# ---------------------------------------------------------------
-OPTION(LAPACK_ENABLE "Enable Lapack support" OFF)
-
-# LAPACK does not support extended precision
-IF(LAPACK_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling LAPACK")
-  FORCE_VARIABLE(LAPACK_ENABLE BOOL "LAPACK is disabled" OFF)
-ENDIF()
-
-# LAPACK does not support 64-bit integer index types
-IF(LAPACK_ENABLE AND SUNDIALS_INDEX_SIZE MATCHES "64")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_INDEX_SIZE} integers"
-                "Disabling LAPACK")
-  SET(LAPACK_ENABLE OFF CACHE BOOL "LAPACK is disabled" FORCE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable SuperLU_MT support?
-# ---------------------------------------------------------------
-OPTION(SUPERLUMT_ENABLE "Enable SUPERLUMT support" OFF)
-
-# SuperLU_MT does not support extended precision
-IF(SUPERLUMT_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("SuperLU_MT is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling SuperLU_MT")
-  FORCE_VARIABLE(SUPERLUMT_ENABLE BOOL "SuperLU_MT is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable KLU support?
-# ---------------------------------------------------------------
-OPTION(KLU_ENABLE "Enable KLU support" OFF)
-
-# KLU does not support single or extended precision
-IF(KLU_ENABLE AND
-  (SUNDIALS_PRECISION MATCHES "SINGLE" OR SUNDIALS_PRECISION MATCHES "EXTENDED"))
-  PRINT_WARNING("KLU is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling KLU")
-  FORCE_VARIABLE(KLU_ENABLE BOOL "KLU is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable hypre Vector support?
-# ---------------------------------------------------------------
-OPTION(HYPRE_ENABLE "Enable hypre support" OFF)
-
-# Using hypre requres building with MPI enabled
-IF(HYPRE_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling hypre"
-                "Set MPI_ENABLE to ON to use parhyp")
-  FORCE_VARIABLE(HYPRE_ENABLE BOOL "Enable hypre support" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable PETSc support?
-# ---------------------------------------------------------------
-OPTION(PETSC_ENABLE "Enable PETSc support" OFF)
-
-# Using PETSc requires building with MPI enabled
-IF(PETSC_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling PETSc"
-                "Set MPI_ENABLE to ON to use PETSc")
-  FORCE_VARIABLE(PETSC_ENABLE BOOL "Enable PETSc support" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable Trilinos support?
-# ---------------------------------------------------------------
-OPTION(Trilinos_ENABLE "Enable Trilinos support" OFF)
-
-
-# ===============================================================
-# Options for examples
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable examples?
-# ---------------------------------------------------------------
-
-# Enable C examples (on by default)
-OPTION(EXAMPLES_ENABLE_C "Build SUNDIALS C examples" ON)
-
-# C++ examples (off by default, unless Trilinos is enabled)
-SET(DOCSTR "Build C++ examples")
-OPTION(EXAMPLES_ENABLE_CXX "${DOCSTR}" ${Trilinos_ENABLE})
-
-# F77 examples (on by default) are an option only if the Fortran
-# interface is enabled
-SET(DOCSTR "Build SUNDIALS Fortran examples")
-IF(F77_INTERFACE_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" ON)
-  # Fortran 77 examples do not support single or extended precision
-  IF(EXAMPLES_ENABLE_F77 AND (SUNDIALS_PRECISION MATCHES "EXTENDED" OR SUNDIALS_PRECISION MATCHES "SINGLE"))
-    PRINT_WARNING("F77 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F77)
-    PRINT_WARNING("EXAMPLES_ENABLE_F77 is ON but F77_INTERFACE_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F77)
-ENDIF()
-
-# F90 examples (on by default) are an option only if a Fortran interface is enabled.
-SET(DOCSTR "Build SUNDIALS F90 examples")
-IF(F77_INTERFACE_ENABLE OR F2003_INTERFACE_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" ON)
-  # Fortran 90 examples do not support extended precision
-  IF(EXAMPLES_ENABLE_F90 AND (SUNDIALS_PRECISION MATCHES "EXTENDED"))
-    PRINT_WARNING("F90 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "Disabling EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F90)
-    PRINT_WARNING("EXAMPLES_ENABLE_F90 is ON but both F77 and F2003 interfaces are OFF"
-                  "Disabling EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F90)
-ENDIF()
-
-# CUDA examples (off by default)
-SET(DOCSTR "Build SUNDIALS CUDA examples")
-IF(CUDA_ENABLE)
-  OPTION(EXAMPLES_ENABLE_CUDA "${DOCSTR}" OFF)
-ELSE()
-  IF(EXAMPLES_ENABLE_CUDA)
-    PRINT_WARNING("EXAMPLES_ENABLE_CUDA is ON but CUDA_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_CUDA")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_CUDA BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ENDIF()
-
-# If any of the above examples are enabled set EXAMPLES_ENABLED to TRUE
-IF(EXAMPLES_ENABLE_C OR
-    EXAMPLES_ENABLE_F77 OR
-    EXAMPLES_ENABLE_CXX OR
-    EXAMPLES_ENABLE_F90 OR
-    EXAMPLES_ENABLE_CUDA)
-  SET(EXAMPLES_ENABLED TRUE)
-ELSE()
-  SET(EXAMPLES_ENABLED FALSE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Install examples?
-# ---------------------------------------------------------------
-
-# Enable installing examples by default
-SET(DOCSTR "Install SUNDIALS examples")
-IF(EXAMPLES_ENABLED)
-  OPTION(EXAMPLES_INSTALL "${DOCSTR}" ON)
-ELSE()
-  FORCE_VARIABLE(EXAMPLES_INSTALL BOOL "${DOCSTR}" OFF)
-  HIDE_VARIABLE(EXAMPLES_INSTALL)
-ENDIF()
-
-# If examples are to be exported, check where we should install them.
-IF(EXAMPLES_INSTALL)
-
-  SHOW_VARIABLE(EXAMPLES_INSTALL_PATH PATH
-    "Output directory for installing example files"
-    "${CMAKE_INSTALL_PREFIX}/examples")
-
-  IF(NOT EXAMPLES_INSTALL_PATH)
-    PRINT_WARNING("The example installation path is empty"
-      "Example installation path was reset to its default value")
-    SET(EXAMPLES_INSTALL_PATH "${CMAKE_INSTALL_PREFIX}/examples" CACHE STRING
-      "Output directory for installing example files" FORCE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(EXAMPLES_INSTALL_PATH)
-
-ENDIF()
-
-
-# ==============================================================================
-# Advanced (hidden) options
-# ==============================================================================
-
-# ------------------------------------------------------------------------------
-# Manually specify the Fortran name-mangling scheme
-#
-# The build system tries to infer the Fortran name-mangling scheme using a
-# Fortran compiler and defaults to using lower case and one underscore if the
-# scheme can not be determined. If a working Fortran compiler is not available
-# or the user needs to override the inferred or default scheme, the following
-# options specify the case and number of appended underscores corresponding to
-# the Fortran name-mangling scheme of symbol names that do not themselves
-# contain underscores. This is all we really need for the FCMIX and LAPACK
-# interfaces. A working Fortran compiler is only necessary for building Fortran
-# example programs.
-# ------------------------------------------------------------------------------
-
-# The case to use in the name-mangling scheme
-show_variable(SUNDIALS_F77_FUNC_CASE STRING
-  "case of Fortran function names (lower/upper)"
-  "")
-
-# The number of underscores of appended in the name-mangling scheme
-show_variable(SUNDIALS_F77_FUNC_UNDERSCORES STRING
-  "number of underscores appended to Fortran function names (none/one/two)"
-  "")
-
-# Hide the name-mangling varibales as advanced options
-mark_as_advanced(FORCE SUNDIALS_F77_FUNC_CASE)
-mark_as_advanced(FORCE SUNDIALS_F77_FUNC_UNDERSCORES)
-
-# If used, both case and underscores must be set
-if((NOT SUNDIALS_F77_FUNC_CASE) AND SUNDIALS_F77_FUNC_UNDERSCORES)
-  message(FATAL_ERROR
-    "If SUNDIALS_F77_FUNC_UNDERSCORES is set, SUNDIALS_F77_FUNC_CASE must also be set.")
-endif()
-
-if(SUNDIALS_F77_FUNC_CASE AND (NOT SUNDIALS_F77_FUNC_UNDERSCORES))
-  message(FATAL_ERROR
-    "If SUNDIALS_F77_FUNC_CASE is set, SUNDIALS_F77_FUNC_UNDERSCORES must also be set.")
-endif()
-
-# ------------------------------------------------------------------------------
-# Include development examples in regression tests?
-#
-# NOTE: Development examples are currently used for internal testing and may
-# produce erroneous failures when run on different systems as the pass/fail
-# status is determined by comparing the output against a saved output file.
-# ------------------------------------------------------------------------------
-OPTION(SUNDIALS_DEVTESTS "Include development tests in make test" OFF)
-MARK_AS_ADVANCED(FORCE SUNDIALS_DEVTESTS)
-
-# ===============================================================
-# Add any platform specifc settings
-# ===============================================================
-
-IF(APPLE)
-  SET(CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS "${CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS} -undefined dynamic_lookup")
-ENDIF(APPLE)
-
-# ===============================================================
-# Fortran and C++ settings
-# ===============================================================
-
-# ---------------------------------------------------------------
-# A Fortran compiler is needed to:
-# (a) Determine the name-mangling scheme if FCMIX, BLAS, or
-#     LAPACK are enabled
-# (b) Compile example programs if F77 or F90 examples are enabled
-# ---------------------------------------------------------------
-
-# Do we need a Fortran name-mangling scheme?
-if(F77_INTERFACE_ENABLE OR BLAS_ENABLE OR LAPACK_ENABLE)
-  set(NEED_FORTRAN_NAME_MANGLING TRUE)
-endif()
-
-# Did the user provide a name-mangling scheme?
-if(SUNDIALS_F77_FUNC_CASE AND SUNDIALS_F77_FUNC_UNDERSCORES)
-
-  STRING(TOUPPER ${SUNDIALS_F77_FUNC_CASE} SUNDIALS_F77_FUNC_CASE)
-  STRING(TOUPPER ${SUNDIALS_F77_FUNC_UNDERSCORES} SUNDIALS_F77_FUNC_UNDERSCORES)
-
-  # Based on the given case and number of underscores, set the C preprocessor
-  # macro definitions. Since SUNDIALS never uses symbols names containing
-  # underscores we set the name-mangling schemes to be the same. In general,
-  # names of symbols with and without underscore may be mangled differently
-  # (e.g. g77 mangles mysub to mysub_ and my_sub to my_sub__)
-  if(SUNDIALS_F77_FUNC_CASE MATCHES "LOWER")
-    if(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "NONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "ONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## _")
-      SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## _")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "TWO")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## __")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## __")
-    else()
-      message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_UNDERSCORES option.")
-    endif()
-  elseif(SUNDIALS_F77_FUNC_CASE MATCHES "UPPER")
-    if(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "NONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "ONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## _")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## _")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "TWO")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## __")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## __")
-    else()
-      message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_UNDERSCORES option.")
-    endif()
-  else()
-    message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_CASE option.")
-  endif()
-
-  # name-mangling scheme has been manually set
-  set(NEED_FORTRAN_NAME_MANGLING FALSE)
-
-endif()
-
-# Do we need a Fortran compiler?
-if(F2003_INTERFACE_ENABLE OR EXAMPLES_ENABLE_F77 OR EXAMPLES_ENABLE_F90 OR NEED_FORTRAN_NAME_MANGLING)
-  include(SundialsFortran)
-endif()
-
-# Ensure that F90 compiler is found if F90 examples are enabled
-if (EXAMPLES_ENABLE_F90 AND (NOT F90_FOUND))
-  PRINT_WARNING("Compiler with F90 support not found" "Disabling F90 Examples")
-  SET(DOCSTR "Build F90 examples")
-  FORCE_VARIABLE(EXAMPLES_ENABLE_F90 "${DOCSTR}" OFF)
-endif()
-
-# Ensure that F90 compiler found if F2003 interface is enabled
-if (F2003_INTERFACE_ENABLE AND (NOT F90_FOUND))
-  PRINT_WARNING("Compiler with F90 support not found" "Disabling F2003 Interface")
-  SET(DOCSTR "Enable Fortran 2003 interfaces")
-  FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-endif()
-
-# F2003 interface requires ISO_C_BINDING
-IF(F2003_INTERFACE_ENABLE AND (NOT Fortran_COMPILER_SUPPORTS_ISOCBINDING))
-  PRINT_WARNING("Fortran compiler does not provide ISO_C_BINDING support"
-                "Disabling F2003 interface")
-  SET(DOCSTR "Enable Fortran 2003 interfaces")
-  FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-ENDIF()
-
-
-# ---------------------------------------------------------------
-# A C++ compiler is needed if:
-# (a) C++ examples are enabled
-# (b) CUDA is enabled
-# (c) RAJA is enabled
-# (d) Trilinos is enabled
-# ---------------------------------------------------------------
-
-if(EXAMPLES_ENABLE_CXX OR CUDA_ENABLE OR RAJA_ENABLE OR Trilinos_ENABLE)
-  include(SundialsCXX)
-endif()
-
-# ---------------------------------------------------------------
-# Setup CUDA. Since CUDA is its own language we do this
-# separate from the TPLs.
-# ---------------------------------------------------------------
-
-if(CUDA_ENABLE)
-  find_package(CUDA)
-  if (CUDA_FOUND)
-    set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -lineinfo")
-  else()
-    message(STATUS "Disabling CUDA support, could not find CUDA.")
-    set(CUDA_ENABLE OFF)
-  endif()
-endif(CUDA_ENABLE)
-
-# ---------------------------------------------------------------
-# Now that all languages are setup, we can configure them more.
-# ---------------------------------------------------------------
-
-# C++11 is needed if:
-#   (a) CUDA is enabled
-# C++11 should not be enabled if
-#   (a) RAJA is enabled (they provide a std flag)
-if (CXX_FOUND AND CUDA_ENABLE AND CUDA_FOUND AND (NOT RAJA_ENABLE))
-  USE_CXX_STD(11)
-endif()
-
-# ---------------------------------------------------------------
-# Decide how to compile MPI codes. We must check for MPI if
-# MPI is enabled or if Trilinos is enabled because the Trilinos
-# examples may need MPI without us turning on the MPI SUNDIALS
-# components.
-# ---------------------------------------------------------------
-
-if(MPI_ENABLE OR Trilinos_ENABLE)
-  include(SundialsMPI)
-endif()
-
-if(MPI_ENABLE)
-  if(NOT MPI_C_FOUND)
-    print_warning("MPI not functional" "Parallel support will not be provided")
-  else()
-    set(IS_MPI_ENABLED "#ifndef SUNDIALS_MPI_ENABLED\n#define SUNDIALS_MPI_ENABLED 1\n#endif")
-  endif()
-endif()
-
-# always define FMPI_COMM_F2C in sundials_fconfig.h file
-if(MPIC_MPI2)
-  set(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 1")
-  set(FMPI_COMM_F2C ".true.")
-else()
-  set(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 0")
-  set(FMPI_COMM_F2C ".false.")
-endif()
-
-# -------------------------------------------------------------
-# Find OpenMP
-# -------------------------------------------------------------
-
-if(OPENMP_ENABLE OR OPENMP_DEVICE_ENABLE)
-
-  include(SundialsOpenMP)
-
-  # turn off OPENMP_ENABLE and OPENMP_DEVICE_ENABLE if OpenMP is not found
-  if(NOT OPENMP_FOUND)
-    print_warning("Could not determine OpenMP compiler flags" "Disabling OpenMP support")
-    force_variable(OPENMP_ENABLE BOOL "Enable OpenMP support" OFF)
-    force_variable(OPENMP_DEVICE_ENABLE BOOL "Enable OpenMP device offloading support" OFF)
-  endif()
-
-  # turn off OPENMP_DEVICE_ENABLE if offloading is not supported
-  if(OPENMP_DEVICE_ENABLE AND (NOT OPENMP_SUPPORTS_DEVICE_OFFLOADING))
-    print_warning("OpenMP found does not support device offloading"
-                  "Disabling OpenMP device offloading support")
-    force_variable(OPENMP_DEVICE_ENABLE BOOL "Enable OpenMP device offloading support" OFF)
-  endif()
-
-endif()
-
-# -------------------------------------------------------------
-# Find PThreads
-# -------------------------------------------------------------
-
-IF(PTHREAD_ENABLE)
-  FIND_PACKAGE(Threads)
-  IF(CMAKE_USE_PTHREADS_INIT)
-    message(STATUS "Using Pthreads")
-    SET(PTHREADS_FOUND TRUE)
-    # SGS
-  ELSE()
-    message(STATUS "Disabling Pthreads support, could not determine compiler flags")
-  endif()
-ENDIF(PTHREAD_ENABLE)
-
-# -------------------------------------------------------------
-# Find RAJA
-# -------------------------------------------------------------
-
-# disable RAJA if CUDA is not enabled/working
-if(RAJA_ENABLE AND (NOT CUDA_FOUND))
-  PRINT_WARNING("CUDA is required for RAJA support" "Please enable CUDA and RAJA")
-  FORCE_VARIABLE(RAJA_ENABLE BOOL "RAJA disabled" OFF)
-endif()
-
-if(RAJA_ENABLE)
-  # Look for CMake configuration file in RAJA installation
-  find_package(RAJA)
-  if (RAJA_FOUND)
-    include_directories(${RAJA_INCLUDE_DIR})
-    set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} ${RAJA_NVCC_FLAGS})
-  else()
-    PRINT_WARNING("RAJA configuration not found"
-                  "Please set RAJA_DIR to provide path to RAJA CMake configuration file.")
-  endif()
-endif(RAJA_ENABLE)
-
-# ===============================================================
-# Find (and test) external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Find (and test) the BLAS libraries
-# ---------------------------------------------------------------
-
-# If BLAS is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(BLAS_ENABLE)
-
-  # find BLAS
-  INCLUDE(SundialsBlas)
-
-  # show after include so FindBlas can locate BLAS_LIBRARIES if necessary
-  SHOW_VARIABLE(BLAS_LIBRARIES STRING "Blas libraries" "${BLAS_LIBRARIES}")
-
-  IF(BLAS_LIBRARIES AND NOT BLAS_FOUND)
-    PRINT_WARNING("BLAS not functional"
-                  "BLAS support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS TRUE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(BLAS_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the Lapack libraries
-# ---------------------------------------------------------------
-
-# If LAPACK is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(LAPACK_ENABLE)
-
-  # find LAPACK and BLAS Libraries
-  INCLUDE(SundialsLapack)
-
-  # show after include so FindLapack can locate LAPCK_LIBRARIES if necessary
-  SHOW_VARIABLE(LAPACK_LIBRARIES STRING "Lapack and Blas libraries" "${LAPACK_LIBRARIES}")
-
-  IF(LAPACK_LIBRARIES AND NOT LAPACK_FOUND)
-    PRINT_WARNING("LAPACK not functional"
-                  "Blas/Lapack support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS_LAPACK TRUE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(LAPACK_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the SUPERLUMT libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for SuperLU_MT integer type
-
-# If SUPERLUMT is needed, first try to find the appropriate
-# libraries to link against them.
-
-IF(SUPERLUMT_ENABLE)
-
-  # Show SuperLU_MT options and set default thread type (Pthreads)
-  SHOW_VARIABLE(SUPERLUMT_THREAD_TYPE STRING "SUPERLUMT threading type: OpenMP or Pthread" "Pthread")
-  SHOW_VARIABLE(SUPERLUMT_INCLUDE_DIR PATH "SUPERLUMT include directory" "${SUPERLUMT_INCLUDE_DIR}")
-  SHOW_VARIABLE(SUPERLUMT_LIBRARY_DIR PATH "SUPERLUMT library directory" "${SUPERLUMT_LIBRARY_DIR}")
-
-  INCLUDE(SundialsSuperLUMT)
-
-  IF(SUPERLUMT_FOUND)
-    # sundials_config.h symbols
-    SET(SUNDIALS_SUPERLUMT TRUE)
-    SET(SUNDIALS_SUPERLUMT_THREAD_TYPE ${SUPERLUMT_THREAD_TYPE})
-    INCLUDE_DIRECTORIES(${SUPERLUMT_INCLUDE_DIR})
-  ENDIF()
-
-  IF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-    PRINT_WARNING("SUPERLUMT not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(SUPERLUMT_THREAD_TYPE)
-  HIDE_VARIABLE(SUPERLUMT_LIBRARY_DIR)
-  HIDE_VARIABLE(SUPERLUMT_INCLUDE_DIR)
-  SET (SUPERLUMT_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the KLU libraries
-# ---------------------------------------------------------------
-
-# If KLU is requested, first try to find the appropriate libraries to
-# link against them.
-
-IF(KLU_ENABLE)
-
-  SHOW_VARIABLE(KLU_INCLUDE_DIR PATH "KLU include directory"
-    "${KLU_INCLUDE_DIR}")
-  SHOW_VARIABLE(KLU_LIBRARY_DIR PATH
-    "Klu library directory" "${KLU_LIBRARY_DIR}")
-
-  set(KLU_FOUND TRUE)
-  get_filename_component(PYBAMM_DIR ${PROJECT_SOURCE_DIR} DIRECTORY)
-  set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PYBAMM_DIR}) # use FindSuiteSparse.cmake that is in PyBaMM root
-  set(SuiteSparse_ROOT ${PYBAMM_DIR}/SuiteSparse-5.6.0)
-  find_package(SuiteSparse OPTIONAL_COMPONENTS KLU AMD COLAMD BTF)
-  include_directories(${SuiteSparse_INCLUDE_DIRS})
-  set(KLU_LIBRARIES ${SuiteSparse_LIBRARIES})
-
-
-  IF(KLU_LIBRARIES AND NOT KLU_FOUND)
-    PRINT_WARNING("KLU not functional - support will not be provided"
-                  "Double check spelling of include path and specified libraries (search is case sensitive)")
-  ENDIF(KLU_LIBRARIES AND NOT KLU_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(KLU_LIBRARY_DIR)
-  HIDE_VARIABLE(KLU_INCLUDE_DIR)
-  SET (KLU_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF(KLU_ENABLE)
-
-# ---------------------------------------------------------------
-# Find (and test) the hypre libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for hypre precision and integer type
-
-IF(HYPRE_ENABLE)
-  SHOW_VARIABLE(HYPRE_INCLUDE_DIR PATH "HYPRE include directory"
-    "${HYPRE_INCLUDE_DIR}")
-  SHOW_VARIABLE(HYPRE_LIBRARY_DIR PATH
-    "HYPRE library directory" "${HYPRE_LIBRARY_DIR}")
-
-  INCLUDE(SundialsHypre)
-
-  IF(HYPRE_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_HYPRE TRUE)
-    INCLUDE_DIRECTORIES(${HYPRE_INCLUDE_DIR})
-  ENDIF(HYPRE_FOUND)
-
-  IF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-    PRINT_WARNING("HYPRE not functional - support will not be provided"
-                  "Found hypre library, test code does not work")
-  ENDIF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(HYPRE_INCLUDE_DIR)
-  HIDE_VARIABLE(HYPRE_LIBRARY_DIR)
-  SET (HYPRE_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the PETSc libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for PETSc precision and integer type
-
-IF(PETSC_ENABLE)
-  SHOW_VARIABLE(PETSC_INCLUDE_DIR PATH "PETSc include directory"
-    "${PETSC_INCLUDE_DIR}")
-  SHOW_VARIABLE(PETSC_LIBRARY_DIR PATH
-    "PETSc library directory" "${PETSC_LIBRARY_DIR}")
-
-  INCLUDE(SundialsPETSc)
-
-  IF(PETSC_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_PETSC TRUE)
-    INCLUDE_DIRECTORIES(${PETSC_INCLUDE_DIR})
-  ENDIF(PETSC_FOUND)
-
-  IF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-    PRINT_WARNING("PETSC not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(PETSC_LIBRARY_DIR)
-  HIDE_VARIABLE(PETSC_INCLUDE_DIR)
-  SET (PETSC_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# -------------------------------------------------------------
-# Find Trilinos
-# -------------------------------------------------------------
-
-if(Trilinos_ENABLE)
-  include(SundialsTrilinos)
-  if(NOT Trilinos_FUNCTIONAL)
-    PRINT_WARNING("Trilinos not functional" "Verify the path to Trilinos and check the Trilinos installation")
-  endif()
-endif(Trilinos_ENABLE)
-
-
-# ===============================================================
-# At this point all the configuration options are set.
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Configure the header file sundials_config.h
-# ---------------------------------------------------------------
-
-# All required substitution variables should be available at this point.
-# Generate the header file and place it in the binary dir.
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_config.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  )
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_fconfig.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  )
-
-# Add the include directory in the source tree and the one in
-# the binary tree (for the header file sundials_config.h)
-INCLUDE_DIRECTORIES(${PROJECT_SOURCE_DIR}/include ${PROJECT_BINARY_DIR}/include)
-
-# ---------------------------------------------------------------
-# Enable testing and add source and example files to the build.
-# ---------------------------------------------------------------
-
-# Enable testing
-IF(EXAMPLES_ENABLED)
-  INCLUDE(SundialsTesting)
-ENDIF()
-
-# Add selected packages and modules to the build
-ADD_SUBDIRECTORY(src)
-
-# Add selected examples to the build
-IF(EXAMPLES_ENABLED)
-  ADD_SUBDIRECTORY(examples)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Install configuration header files and license file
-# ---------------------------------------------------------------
-
-# install configured header file
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  DESTINATION include/sundials
-  )
-
-# install configured header file for Fortran 90
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  DESTINATION include/sundials
-  )
-
-# install shared Fortran 2003 modules
-IF(F2003_INTERFACE_ENABLE)
-  # While the .mod files get generated for static and shared
-  # libraries, they are identical. So only install one set
-  # of the .mod files.
-  IF(BUILD_STATIC_LIBS)
-    INSTALL(
-      DIRECTORY ${CMAKE_Fortran_MODULE_DIRECTORY}_STATIC/
-      DESTINATION ${Fortran_INSTALL_MODDIR}
-      )
-  ELSE()
-    INSTALL(
-      DIRECTORY ${CMAKE_Fortran_MODULE_DIRECTORY}_SHARED/
-      DESTINATION ${Fortran_INSTALL_MODDIR}
-      )
-  ENDIF()
-ENDIF()
-
-# install license and notice files
-INSTALL(
-  FILES ${PROJECT_SOURCE_DIR}/LICENSE
-  DESTINATION include/sundials
-  )
-INSTALL(
-  FILES ${PROJECT_SOURCE_DIR}/NOTICE
-  DESTINATION include/sundials
-  )
diff --git a/scripts/update_version.py b/scripts/update_version.py
index fb9b15dd31..ab8a9345ba 100644
--- a/scripts/update_version.py
+++ b/scripts/update_version.py
@@ -30,6 +30,16 @@ def update_version():
         file.seek(0)
         file.write(replace_version)
 
+    # pyproject.toml
+    with open(os.path.join(pybamm.root_dir(), "pyproject.toml"), "r+") as file:
+        output = file.read()
+        replace_version = re.sub(
+            '(?<=version = ")(.+)(?=")', release_version, output
+        )
+        file.truncate(0)
+        file.seek(0)
+        file.write(replace_version)
+
     # CITATION.cff
     with open(os.path.join(pybamm.root_dir(), "CITATION.cff"), "r+") as file:
         output = file.read()
@@ -38,26 +48,6 @@ def update_version():
         file.seek(0)
         file.write(replace_version)
 
-    # docs/source/_static/versions.json for readthedocs build
-    if "rc" not in release_version:
-        with open(
-            os.path.join(pybamm.root_dir(), "docs", "_static", "versions.json"),
-            "r+",
-        ) as file:
-            output = file.read()
-            json_data = json.loads(output)
-            json_data.insert(
-                2,
-                {
-                    "name": f"v{release_version}",
-                    "version": f"{release_version}",
-                    "url": f"https://docs.pybamm.org/en/v{release_version}/",
-                },
-            )
-            file.truncate(0)
-            file.seek(0)
-            file.write(json.dumps(json_data, indent=4))
-
     # vcpkg.json
     with open(os.path.join(pybamm.root_dir(), "vcpkg.json"), "r+") as file:
         output = file.read()
diff --git a/setup.py b/setup.py
index 9dd4d41cd3..9cfc4df4ff 100644
--- a/setup.py
+++ b/setup.py
@@ -1,5 +1,5 @@
 import os
-import glob
+import sys
 import logging
 import subprocess
 from pathlib import Path
@@ -7,18 +7,180 @@
 import wheel.bdist_wheel as orig
 
 try:
-    from setuptools import setup, find_packages, Extension
+    from setuptools import setup, Extension
     from setuptools.command.install import install
+    from setuptools.command.build_ext import build_ext
 except ImportError:
-    from distutils.core import setup, find_packages
+    from distutils.core import setup
     from distutils.command.install import install
+    from distutils.command.build_ext import build_ext
 
-import CMakeBuild
 
 default_lib_dir = (
     "" if system() == "Windows" else os.path.join(os.getenv("HOME"), ".local")
 )
 
+# ---------- set environment variables for vcpkg on Windows ----------------------------
+
+def set_vcpkg_environment_variables():
+    if not os.getenv("VCPKG_ROOT_DIR"):
+        raise EnvironmentError("Environment variable 'VCPKG_ROOT_DIR' is undefined.")
+    if not os.getenv("VCPKG_DEFAULT_TRIPLET"):
+        raise EnvironmentError(
+            "Environment variable 'VCPKG_DEFAULT_TRIPLET' is undefined."
+        )
+    if not os.getenv("VCPKG_FEATURE_FLAGS"):
+        raise EnvironmentError(
+            "Environment variable 'VCPKG_FEATURE_FLAGS' is undefined."
+        )
+    return (
+        os.getenv("VCPKG_ROOT_DIR"),
+        os.getenv("VCPKG_DEFAULT_TRIPLET"),
+        os.getenv("VCPKG_FEATURE_FLAGS"),
+    )
+
+# ---------- CMakeBuild class (custom build_ext for IDAKLU target) ---------------------
+
+class CMakeBuild(build_ext):
+    user_options = build_ext.user_options + [
+        ("suitesparse-root=", None, "suitesparse source location"),
+        ("sundials-root=", None, "sundials source location"),
+    ]
+
+    def initialize_options(self):
+        build_ext.initialize_options(self)
+        self.suitesparse_root = None
+        self.sundials_root = None
+
+    def finalize_options(self):
+        build_ext.finalize_options(self)
+        # Determine the calling command to get the
+        # undefined options from.
+        # If build_ext was called directly then this
+        # doesn't matter.
+        try:
+            self.get_finalized_command("install", create=0)
+            calling_cmd = "install"
+        except AttributeError:
+            calling_cmd = "bdist_wheel"
+        self.set_undefined_options(
+            calling_cmd,
+            ("suitesparse_root", "suitesparse_root"),
+            ("sundials_root", "sundials_root"),
+        )
+        if not self.suitesparse_root:
+            self.suitesparse_root = os.path.join(default_lib_dir)
+        if not self.sundials_root:
+            self.sundials_root = os.path.join(default_lib_dir)
+
+    def get_build_directory(self):
+        # distutils outputs object files in directory self.build_temp
+        # (typically build/temp.*). This is our CMake build directory.
+        # On Windows, distutils is too smart and appends "Release" or
+        # "Debug" to self.build_temp. So in this case we want the
+        # build directory to be the parent directory.
+        if system() == "Windows":
+            return Path(self.build_temp).parents[0]
+        return self.build_temp
+
+    def run(self):
+        if not self.extensions:
+            return
+
+        if system() == "Windows":
+            use_python_casadi = False
+        else:
+            use_python_casadi = True
+
+        build_type = os.getenv("PYBAMM_CPP_BUILD_TYPE", "RELEASE")
+        cmake_args = [
+            "-DCMAKE_BUILD_TYPE={}".format(build_type),
+            "-DPYTHON_EXECUTABLE={}".format(sys.executable),
+            "-DUSE_PYTHON_CASADI={}".format("TRUE" if use_python_casadi else "FALSE"),
+        ]
+        if self.suitesparse_root:
+            cmake_args.append(
+                "-DSuiteSparse_ROOT={}".format(os.path.abspath(self.suitesparse_root))
+            )
+        if self.sundials_root:
+            cmake_args.append(
+                "-DSUNDIALS_ROOT={}".format(os.path.abspath(self.sundials_root))
+            )
+
+        build_dir = self.get_build_directory()
+        if not os.path.exists(build_dir):
+            os.makedirs(build_dir)
+
+        # The CMakeError.log file is generated by cmake is the configure step
+        # encounters error. In the following the existence of this file is used
+        # to determine whether or not the cmake configure step went smoothly.
+        # So must make sure this file does not remain from a previous failed build.
+        if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
+            os.remove(os.path.join(build_dir, "CMakeError.log"))
+
+# ---------- configuration for vcpkg on Windows ----------------------------------------
+
+        build_env = os.environ
+        if os.getenv("PYBAMM_USE_VCPKG"):
+            (
+                vcpkg_root_dir,
+                vcpkg_default_triplet,
+                vcpkg_feature_flags,
+            ) = set_vcpkg_environment_variables()
+            build_env["vcpkg_root_dir"] = vcpkg_root_dir
+            build_env["vcpkg_default_triplet"] = vcpkg_default_triplet
+            build_env["vcpkg_feature_flags"] = vcpkg_feature_flags
+
+# ---------- Run CMake and build IDAKLU module -----------------------------------------
+
+        cmake_list_dir = os.path.abspath(os.path.dirname(__file__))
+        print("-" * 10, "Running CMake for IDAKLU solver", "-" * 40)
+        subprocess.run(
+            ["cmake", cmake_list_dir] + cmake_args, cwd=build_dir, env=build_env
+        , check=True)
+
+        if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
+            msg = (
+                "cmake configuration steps encountered errors, and the IDAKLU module"
+                " could not be built. Make sure dependencies are correctly "
+                "installed. See "
+                "https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html" # noqa: E501
+            )
+            raise RuntimeError(msg)
+        else:
+            print("-" * 10, "Building IDAKLU module", "-" * 40)
+            subprocess.run(
+                ["cmake", "--build", ".", "--config", "Release"],
+                cwd=build_dir,
+                env=build_env,
+                check=True,
+            )
+
+            # Move from build temp to final position
+            for ext in self.extensions:
+                self.move_output(ext)
+
+    def move_output(self, ext):
+        # Copy built module to dist/ directory
+        build_temp = Path(self.build_temp).resolve()
+        # Get destination location
+        # self.get_ext_fullpath(ext.name) -->
+        # build/lib.linux-x86_64-3.5/idaklu.cpython-37m-x86_64-linux-gnu.so
+        # using resolve() with python < 3.6 will result in a FileNotFoundError
+        # since the location does not yet exists.
+        dest_path = Path(self.get_ext_fullpath(ext.name)).resolve()
+        source_path = build_temp / os.path.basename(self.get_ext_filename(ext.name))
+        dest_directory = dest_path.parents[0]
+        dest_directory.mkdir(parents=True, exist_ok=True)
+        self.copy_file(source_path, dest_path)
+
+
+# ---------- end of CMake steps --------------------------------------------------------
+
+
+# ---------- configure setup logger ----------------------------------------------------
+
+
 log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 logger = logging.getLogger("PyBaMM setup")
 
@@ -60,6 +222,9 @@ def run(self):
         install.run(self)
 
 
+# ---------- Custom class for building wheels ------------------------------------------
+
+
 class bdist_wheel(orig.bdist_wheel):
     """A custom install command to add 2 build options"""
 
@@ -89,8 +254,7 @@ def compile_KLU():
     # Return True if:
     # - Not running on Windows AND
     # - CMake is found AND
-    # - The pybind11 and casadi-headers directories are found
-    #   in the PyBaMM project directory
+    # - The pybind11/ directory is found in the PyBaMM project directory
     CMakeFound = True
     PyBind11Found = True
     windows = (not system()) or system() == "Windows"
@@ -120,35 +284,9 @@ def compile_KLU():
 
     return CMakeFound and PyBind11Found
 
-
-# Build the list of package data files to be included in the PyBaMM package.
-# These are mainly the parameter files located in the input/parameters/ subdirectories.
-pybamm_data = []
-for file_ext in ["*.csv", "*.py", "*.md", "*.txt"]:
-    # Get all the files ending in file_ext in pybamm/input dir.
-    # list_of_files = [
-    #    'pybamm/input/drive_cycles/car_current.csv',
-    #    'pybamm/input/drive_cycles/US06.csv',
-    # ...
-    list_of_files = glob.glob("pybamm/input/**/" + file_ext, recursive=True)
-
-    # Add these files to pybamm_data.
-    # The path must be relative to the package dir (pybamm/), so
-    # must process the content of list_of_files to take out the top
-    # pybamm/ dir, i.e.:
-    # ['input/drive_cycles/car_current.csv',
-    #  'input/drive_cycles/US06.csv',
-    # ...
-    pybamm_data.extend(
-        [os.path.join(*Path(filename).parts[1:]) for filename in list_of_files]
-    )
-pybamm_data.append("./CITATIONS.bib")
-pybamm_data.append("./plotting/pybamm.mplstyle")
-pybamm_data.append("../CMakeBuild.py")
-
 idaklu_ext = Extension(
-    "pybamm.solvers.idaklu",
-    [
+    name="pybamm.solvers.idaklu",
+    sources=[
         "pybamm/solvers/c_solvers/idaklu.cpp"
         "pybamm/solvers/c_solvers/idaklu.hpp"
         "pybamm/solvers/c_solvers/idaklu_casadi.cpp"
@@ -161,153 +299,15 @@ def compile_KLU():
 )
 ext_modules = [idaklu_ext] if compile_KLU() else []
 
-# Defines __version__
-root = os.path.abspath(os.path.dirname(__file__))
-with open(os.path.join(root, "pybamm", "version.py")) as f:
-    exec(f.read())
-
-# Load text for description and license
-with open("README.md", encoding="utf-8") as f:
-    readme = f.read()
-
+# Project metadata was moved to pyproject.toml (which is read by pip). However, custom
+# build commands and setuptools extension modules are still defined here.
 setup(
-    name="pybamm",
-    version=__version__,  # noqa: F821
-    description="Python Battery Mathematical Modelling.",
-    long_description=readme,
-    long_description_content_type="text/markdown",
-    url="https://github.com/pybamm-team/PyBaMM",
-    packages=find_packages(include=("pybamm", "pybamm.*")),
+    # silence "Package would be ignored" warnings
+    include_package_data=True,
     ext_modules=ext_modules,
     cmdclass={
-        "build_ext": CMakeBuild.CMakeBuild,
+        "build_ext": CMakeBuild,
         "bdist_wheel": bdist_wheel,
         "install": CustomInstall,
     },
-    package_data={"pybamm": pybamm_data},
-    # Python version
-    python_requires=">=3.8,<3.12",
-    classifiers=[
-        "Development Status :: 5 - Production/Stable",
-        "Intended Audience :: Developers",
-        "Intended Audience :: Science/Research",
-        "License :: OSI Approved :: BSD License",
-        "Programming Language :: Python",
-        "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3 :: Only",
-        "Programming Language :: Python :: 3.8",
-        "Programming Language :: Python :: 3.9",
-        "Programming Language :: Python :: 3.10",
-        "Programming Language :: Python :: 3.11",
-        "Topic :: Scientific/Engineering",
-    ],
-    # List of dependencies
-    install_requires=[
-        "numpy>=1.16",
-        "scipy>=1.3",
-        "casadi>=3.6.0",
-        "xarray",
-    ],
-    extras_require={
-        "docs": [
-            "sphinx>=6",
-            "sphinx_rtd_theme>=0.5",
-            "pydata-sphinx-theme",
-            "sphinx_design",
-            "sphinx-copybutton",
-            "myst-parser",
-            "sphinx-inline-tabs",
-            "sphinxcontrib-bibtex",
-            "sphinx-autobuild",
-            "sphinx-last-updated-by-git",
-            "nbsphinx",
-            "ipykernel",
-            "ipywidgets",
-            "sphinx-gallery",
-            "sphinx-hoverxref",
-            "sphinx-docsearch",
-        ],  # For doc generation
-        "examples": [
-            "jupyter",  # For example notebooks
-        ],
-        "plot": [
-            "imageio>=2.9.0",
-            # Note: Matplotlib is loaded for debug plots, but to ensure pybamm runs
-            # on systems without an attached display, it should never be imported
-            # outside of plot() methods.
-            # Should not be imported
-            "matplotlib>=2.0",
-        ],
-        "cite": [
-            "pybtex>=0.24.0",
-        ],
-        "latexify": [
-            "sympy>=1.8",
-        ],
-        "bpx": [
-            "bpx",
-        ],
-        "tqdm": [
-            "tqdm",
-        ],
-        "dev": [
-            # For working with pre-commit hooks
-            "pre-commit",
-             # For code style checks: linting and auto-formatting
-            "ruff",
-            # For running testing sessions
-            "nox",
-            # For testing Jupyter notebooks
-            "pytest>=6",
-            "pytest-xdist",
-            "nbmake",
-        ],
-        "pandas": [
-            "pandas>=0.24",
-        ],
-        "jax": [
-            "jax==0.4.8",
-            "jaxlib==0.4.7",
-        ],
-        "odes": ["scikits.odes"],
-        "all": [
-            "anytree>=2.4.3",
-            "autograd>=1.2",
-            "pandas>=0.24",
-            "scikit-fem>=0.2.0",
-            "imageio>=2.9.0",
-            "pybtex>=0.24.0",
-            "sympy>=1.8",
-            "bpx",
-            "tqdm",
-            "matplotlib>=2.0",
-            "jupyter",
-        ],
-    },
-    entry_points={
-        "console_scripts": [
-            "pybamm_edit_parameter = pybamm.parameters_cli:edit_parameter",
-            "pybamm_add_parameter = pybamm.parameters_cli:add_parameter",
-            "pybamm_rm_parameter = pybamm.parameters_cli:remove_parameter",
-            "pybamm_install_odes = pybamm.install_odes:main",
-            "pybamm_install_jax = pybamm.util:install_jax",
-        ],
-        "pybamm_parameter_sets": [
-            "Sulzer2019 = pybamm.input.parameters.lead_acid.Sulzer2019:get_parameter_values",  # noqa: E501
-            "Ai2020 = pybamm.input.parameters.lithium_ion.Ai2020:get_parameter_values",  # noqa: E501
-            "Chen2020 = pybamm.input.parameters.lithium_ion.Chen2020:get_parameter_values",  # noqa: E501
-            "Chen2020_composite = pybamm.input.parameters.lithium_ion.Chen2020_composite:get_parameter_values",  # noqa: E501
-            "Ecker2015 = pybamm.input.parameters.lithium_ion.Ecker2015:get_parameter_values",  # noqa: E501
-            "Marquis2019 = pybamm.input.parameters.lithium_ion.Marquis2019:get_parameter_values",  # noqa: E501
-            "Mohtat2020 = pybamm.input.parameters.lithium_ion.Mohtat2020:get_parameter_values",  # noqa: E501
-            "NCA_Kim2011 = pybamm.input.parameters.lithium_ion.NCA_Kim2011:get_parameter_values",  # noqa: E501
-            "OKane2022 = pybamm.input.parameters.lithium_ion.OKane2022:get_parameter_values",  # noqa: E501
-            "ORegan2022 = pybamm.input.parameters.lithium_ion.ORegan2022:get_parameter_values",  # noqa: E501
-            "Prada2013 = pybamm.input.parameters.lithium_ion.Prada2013:get_parameter_values",  # noqa: E501
-            "Ramadass2004 = pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_values",  # noqa: E501
-            "Xu2019 = pybamm.input.parameters.lithium_ion.Xu2019:get_parameter_values",  # noqa: E501
-            "ECM_Example = pybamm.input.parameters.ecm.example_set:get_parameter_values",  # noqa: E501
-            "MSMR_Example = pybamm.input.parameters.lithium_ion.MSMR_example_set:get_parameter_values",  # noqa: E501
-        ],
-    },
 )
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py
index 0c203c9fc7..5dc5b2dc94 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_half_cell_tests.py
@@ -45,6 +45,15 @@ def test_kinetics_mhc(self):
         )
         self.run_basic_processing_test(options, parameter_values=parameter_values)
 
+    def test_irreversible_plating_with_porosity(self):
+        options = {
+            "lithium plating": "irreversible",
+            "lithium plating porosity change": "true",
+        }
+        parameter_values = pybamm.ParameterValues("OKane2022_graphite_SiOx_halfcell")
+        parameter_values.update({"Current function [A]": -2.5})  # C/2 charge
+        self.run_basic_processing_test(options, parameter_values=parameter_values)
+
     def test_sei_constant(self):
         options = {"SEI": "constant"}
         self.run_basic_processing_test(options)
@@ -55,9 +64,9 @@ def test_sei_reaction_limited(self):
 
     def test_sei_asymmetric_reaction_limited(self):
         options = {"SEI": "reaction limited (asymmetric)"}
-        parameter_values = pybamm.ParameterValues("Xu2019")
+        parameter_values = pybamm.ParameterValues("Ecker2015_graphite_halfcell")
         parameter_values.update(
-            {"SEI growth transfer coefficient": 0.2},
+            {"SEI growth transfer coefficient": 0.2, "Current function [A]": -0.07826},
             check_already_exists=False,
         )
         self.run_basic_processing_test(options, parameter_values=parameter_values)
@@ -80,13 +89,19 @@ def test_sei_ec_reaction_limited(self):
 
     def test_sei_asymmetric_ec_reaction_limited(self):
         options = {"SEI": "ec reaction limited (asymmetric)"}
-        parameter_values = pybamm.ParameterValues("Xu2019")
+        parameter_values = pybamm.ParameterValues("Ecker2015_graphite_halfcell")
         parameter_values.update(
-            {"SEI growth transfer coefficient": 0.2},
+            {"SEI growth transfer coefficient": 0.2, "Current function [A]": -0.07826},
             check_already_exists=False,
         )
         self.run_basic_processing_test(options, parameter_values=parameter_values)
 
+    def test_swelling_only(self):
+        options = {"particle mechanics": "swelling only"}
+        parameter_values = pybamm.ParameterValues("OKane2022_graphite_SiOx_halfcell")
+        parameter_values.update({"Current function [A]": -2.5})  # C/2 charge
+        self.run_basic_processing_test(options, parameter_values=parameter_values)
+
     def test_constant_utilisation(self):
         options = {"interface utilisation": "constant"}
         parameter_values = pybamm.ParameterValues("Xu2019")
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
index 6c787cea0b..6e3beeb1fc 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
@@ -265,17 +265,47 @@ def current_LAM(i, T):
     def test_negative_cracking(self):
         options = {"particle mechanics": ("swelling and cracking", "none")}
         parameter_values = pybamm.ParameterValues("Ai2020")
-        self.run_basic_processing_test(options, parameter_values=parameter_values)
+        var_pts = {
+            "x_n": 20,  # negative electrode
+            "x_s": 20,  # separator
+            "x_p": 20,  # positive electrode
+            "r_n": 26,  # negative particle
+            "r_p": 26,  # positive particle
+        }
+        self.run_basic_processing_test(options,
+                                       parameter_values=parameter_values,
+                                       var_pts=var_pts
+                                       )
 
     def test_positive_cracking(self):
         options = {"particle mechanics": ("none", "swelling and cracking")}
         parameter_values = pybamm.ParameterValues("Ai2020")
-        self.run_basic_processing_test(options, parameter_values=parameter_values)
+        var_pts = {
+            "x_n": 20,  # negative electrode
+            "x_s": 20,  # separator
+            "x_p": 20,  # positive electrode
+            "r_n": 26,  # negative particle
+            "r_p": 26,  # positive particle
+        }
+        self.run_basic_processing_test(options,
+                                       parameter_values=parameter_values,
+                                       var_pts=var_pts
+                                       )
 
     def test_both_cracking(self):
         options = {"particle mechanics": "swelling and cracking"}
         parameter_values = pybamm.ParameterValues("Ai2020")
-        self.run_basic_processing_test(options, parameter_values=parameter_values)
+        var_pts = {
+            "x_n": 20,  # negative electrode
+            "x_s": 20,  # separator
+            "x_p": 20,  # positive electrode
+            "r_n": 26,  # negative particle
+            "r_p": 26,  # positive particle
+        }
+        self.run_basic_processing_test(options,
+                                       parameter_values=parameter_values,
+                                       var_pts=var_pts
+                                       )
 
     def test_both_swelling_only(self):
         options = {"particle mechanics": "swelling only"}
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_half_cell_models.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_half_cell_models.py
index c13efff621..0d69fbae28 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_half_cell_models.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_half_cell_models.py
@@ -38,7 +38,7 @@ def test_runs_Xu2019(self):
         solver = pybamm.CasadiSolver(mode="safe", atol=1e-6, rtol=1e-3)
         solver.solve(model, t_eval)
 
-    def test_runs_OKane2022(self):
+    def test_runs_OKane2022_negative(self):
         # load model
         options = {"working electrode": "positive"}
         model = pybamm.lithium_ion.BasicDFNHalfCell(options=options)
@@ -47,9 +47,9 @@ def test_runs_OKane2022(self):
         geometry = model.default_geometry
 
         # load parameter values
-        param = pybamm.ParameterValues("OKane2022")
+        param = pybamm.ParameterValues("OKane2022_graphite_SiOx_halfcell")
 
-        param["Current function [A]"] = 2.5
+        param["Current function [A]"] = -2.5  # C/2 charge
 
         # process model and geometry
         param.process_model(model)
diff --git a/tests/testcase.py b/tests/testcase.py
index f2daa7ba9a..ae4019bcb3 100644
--- a/tests/testcase.py
+++ b/tests/testcase.py
@@ -3,9 +3,9 @@
 #
 import unittest
 import hashlib
-import numpy as np
 from functools import wraps
 from types import FunctionType
+import numpy as np
 
 
 def FixRandomSeed(method):
@@ -13,7 +13,7 @@ def FixRandomSeed(method):
     Wraps a method so that the random seed is set to a hash of the method name
 
     As the wrapper fixes the random seed before calling the method, tests can
-    explicitely reinstate the random seed within their method bodies as desired,
+    explicitly reinstate the random seed within their method bodies as desired,
     e.g. by calling np.random.seed(None) to restore normal behaviour.
 
     Generating a random seed from the method name allows particularly awkward
diff --git a/tests/unit/test_batch_study.py b/tests/unit/test_batch_study.py
index 89e6bd62b0..f8762133a6 100644
--- a/tests/unit/test_batch_study.py
+++ b/tests/unit/test_batch_study.py
@@ -5,6 +5,7 @@
 import os
 import pybamm
 import unittest
+from tempfile import TemporaryDirectory
 
 spm = pybamm.lithium_ion.SPM()
 spm_uniform = pybamm.lithium_ion.SPM({"particle": "uniform profile"})
@@ -90,17 +91,19 @@ def test_solve(self):
             self.assertIn(output_experiment, experiments_list)
 
     def test_create_gif(self):
-        bs = pybamm.BatchStudy({"spm": pybamm.lithium_ion.SPM()})
-        bs.solve([0, 10])
+        with TemporaryDirectory() as dir_name:
+            bs = pybamm.BatchStudy({"spm": pybamm.lithium_ion.SPM()})
+            bs.solve([0, 10])
 
-        # create a GIF before calling the plot method
-        bs.create_gif(number_of_images=3, duration=1)
+            # Create a temporary file name
+            test_file = os.path.join(dir_name, "batch_study_test.gif")
 
-        # create a GIF after calling the plot method
-        bs.plot(testing=True)
-        bs.create_gif(number_of_images=3, duration=1)
+            # create a GIF before calling the plot method
+            bs.create_gif(number_of_images=3, duration=1, output_filename=test_file)
 
-        os.remove("plot.gif")
+            # create a GIF after calling the plot method
+            bs.plot(testing=True)
+            bs.create_gif(number_of_images=3, duration=1, output_filename=test_file)
 
 
 if __name__ == "__main__":
diff --git a/tests/unit/test_experiments/test_experiment.py b/tests/unit/test_experiments/test_experiment.py
index 23548be433..ec1a1cbeae 100644
--- a/tests/unit/test_experiments/test_experiment.py
+++ b/tests/unit/test_experiments/test_experiment.py
@@ -183,41 +183,49 @@ def test_no_initial_start_time(self):
             )
 
     def test_set_next_start_time(self):
-        # Defined dummy experiment to access _set_next_start_time
-        experiment = pybamm.Experiment(["Rest for 1 hour"])
         raw_op = [
             pybamm.step._Step(
                 "current", 1, duration=3600, start_time=datetime(2023, 1, 1, 8, 0)
             ),
+            pybamm.step._Step("voltage", 2.5, duration=3600, start_time=None),
             pybamm.step._Step(
                 "current", 1, duration=3600, start_time=datetime(2023, 1, 1, 12, 0)
             ),
             pybamm.step._Step("current", 1, duration=3600, start_time=None),
+            pybamm.step._Step("voltage", 2.5, duration=3600, start_time=None),
             pybamm.step._Step(
                 "current", 1, duration=3600, start_time=datetime(2023, 1, 1, 15, 0)
             ),
         ]
+        experiment = pybamm.Experiment(raw_op)
         processed_op = experiment._set_next_start_time(raw_op)
 
         expected_next = [
+            None,
             datetime(2023, 1, 1, 12, 0),
             None,
+            None,
             datetime(2023, 1, 1, 15, 0),
             None,
         ]
 
         expected_end = [
             datetime(2023, 1, 1, 12, 0),
+            datetime(2023, 1, 1, 12, 0),
+            datetime(2023, 1, 1, 15, 0),
             datetime(2023, 1, 1, 15, 0),
             datetime(2023, 1, 1, 15, 0),
             None,
         ]
 
+        # Test method directly
         for next, end, op in zip(expected_next, expected_end, processed_op):
             # useful form for debugging
             self.assertEqual(op.next_start_time, next)
             self.assertEqual(op.end_time, end)
 
+        # TODO: once #3176 is completed, the test should pass for
+        # operating_conditions_steps (or equivalent) as well
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py
index 6acd7c41b0..225f8e93c9 100644
--- a/tests/unit/test_expression_tree/test_binary_operators.py
+++ b/tests/unit/test_expression_tree/test_binary_operators.py
@@ -5,10 +5,10 @@
 import unittest
 
 import numpy as np
-import sympy
 from scipy.sparse import coo_matrix
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class TestBinaryOperators(TestCase):
@@ -746,6 +746,7 @@ def test_inner_simplifications(self):
         self.assertEqual(pybamm.inner(a3, a3).evaluate(), 9)
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         # Test print_name
         pybamm.Addition.print_name = "test"
         self.assertEqual(pybamm.Addition(1, 2).to_equation(), sympy.Symbol("test"))
diff --git a/tests/unit/test_expression_tree/test_concatenations.py b/tests/unit/test_expression_tree/test_concatenations.py
index df5add0f98..4b07b09fea 100644
--- a/tests/unit/test_expression_tree/test_concatenations.py
+++ b/tests/unit/test_expression_tree/test_concatenations.py
@@ -5,9 +5,9 @@
 from tests import TestCase
 
 import numpy as np
-import sympy
 
 import pybamm
+from pybamm.util import have_optional_dependency
 from tests import get_discretisation_for_testing, get_mesh_for_testing
 
 
@@ -370,6 +370,7 @@ def test_numpy_concatenation(self):
         )
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         a = pybamm.Symbol("a", domain="test a")
         b = pybamm.Symbol("b", domain="test b")
         func_symbol = sympy.Symbol(r"\begin{cases}a\\b\end{cases}")
diff --git a/tests/unit/test_expression_tree/test_functions.py b/tests/unit/test_expression_tree/test_functions.py
index ac5410d9e1..6d22571a01 100644
--- a/tests/unit/test_expression_tree/test_functions.py
+++ b/tests/unit/test_expression_tree/test_functions.py
@@ -5,10 +5,10 @@
 import unittest
 
 import numpy as np
-import sympy
 from scipy import special
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 def test_function(arg):
@@ -120,6 +120,7 @@ def test_function_unnamed(self):
         self.assertEqual(fun.name, "function (cos)")
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         a = pybamm.Symbol("a", domain="test")
 
         # Test print_name
diff --git a/tests/unit/test_expression_tree/test_independent_variable.py b/tests/unit/test_expression_tree/test_independent_variable.py
index 95141f0f03..b748a6fbe9 100644
--- a/tests/unit/test_expression_tree/test_independent_variable.py
+++ b/tests/unit/test_expression_tree/test_independent_variable.py
@@ -4,9 +4,9 @@
 from tests import TestCase
 import unittest
 
-import sympy
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class TestIndependentVariable(TestCase):
@@ -64,6 +64,7 @@ def test_spatial_variable_edge(self):
         self.assertTrue(x.evaluates_on_edges("primary"))
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         # Test print_name
         func = pybamm.IndependentVariable("a")
         func.print_name = "test"
diff --git a/tests/unit/test_expression_tree/test_operations/test_latexify.py b/tests/unit/test_expression_tree/test_operations/test_latexify.py
index be7cc21115..7e0703534e 100644
--- a/tests/unit/test_expression_tree/test_operations/test_latexify.py
+++ b/tests/unit/test_expression_tree/test_operations/test_latexify.py
@@ -8,7 +8,6 @@
 import uuid
 
 import pybamm
-from pybamm.expression_tree.operations.latexify import Latexify
 
 
 class TestLatexify(TestCase):
@@ -19,9 +18,6 @@ def test_latexify(self):
         model_spme = pybamm.lithium_ion.SPMe()
         func_spme = str(model_spme.latexify())
 
-        # Test docstring
-        self.assertEqual(pybamm.BaseModel.latexify.__doc__, Latexify.__doc__)
-
         # Test model name
         self.assertIn("Single Particle Model with electrolyte Equations", func_spme)
 
diff --git a/tests/unit/test_expression_tree/test_parameter.py b/tests/unit/test_expression_tree/test_parameter.py
index f67ee2dd62..d9a756b45d 100644
--- a/tests/unit/test_expression_tree/test_parameter.py
+++ b/tests/unit/test_expression_tree/test_parameter.py
@@ -5,9 +5,8 @@
 import numbers
 import unittest
 
-import sympy
-
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class TestParameter(TestCase):
@@ -21,6 +20,7 @@ def test_evaluate_for_shape(self):
         self.assertIsInstance(a.evaluate_for_shape(), numbers.Number)
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         func = pybamm.Parameter("test_string")
         func1 = pybamm.Parameter("test_name")
 
@@ -98,6 +98,7 @@ def _myfun(x):
         self.assertEqual(_myfun(x).print_name, None)
 
     def test_function_parameter_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         func = pybamm.FunctionParameter("test", {"x": pybamm.Scalar(1)})
         func1 = pybamm.FunctionParameter("func", {"var": pybamm.Variable("var")})
 
diff --git a/tests/unit/test_expression_tree/test_printing/test_sympy_overrides.py b/tests/unit/test_expression_tree/test_printing/test_sympy_overrides.py
index b5ae229ae5..de3ff08c43 100644
--- a/tests/unit/test_expression_tree/test_printing/test_sympy_overrides.py
+++ b/tests/unit/test_expression_tree/test_printing/test_sympy_overrides.py
@@ -4,14 +4,14 @@
 from tests import TestCase
 import unittest
 
-import sympy
-
 import pybamm
 from pybamm.expression_tree.printing.sympy_overrides import custom_print_func
+from pybamm.util import have_optional_dependency
 
 
 class TestCustomPrint(TestCase):
     def test_print_Derivative(self):
+        sympy = have_optional_dependency("sympy")
         # Test force_partial
         der1 = sympy.Derivative("y", "x")
         der1.force_partial = True
diff --git a/tests/unit/test_expression_tree/test_symbol.py b/tests/unit/test_expression_tree/test_symbol.py
index 3a74375ce7..3eb7adae47 100644
--- a/tests/unit/test_expression_tree/test_symbol.py
+++ b/tests/unit/test_expression_tree/test_symbol.py
@@ -4,13 +4,14 @@
 from tests import TestCase
 import os
 import unittest
+from tempfile import TemporaryDirectory
 
 import numpy as np
 from scipy.sparse import csr_matrix, coo_matrix
-import sympy
 
 import pybamm
 from pybamm.expression_tree.binary_operators import _Heaviside
+from pybamm.util import have_optional_dependency
 
 
 class TestSymbol(TestCase):
@@ -386,13 +387,16 @@ def test_symbol_repr(self):
         )
 
     def test_symbol_visualise(self):
-        c = pybamm.Variable("c", "negative electrode")
-        d = pybamm.Variable("d", "negative electrode")
-        sym = pybamm.div(c * pybamm.grad(c)) + (c / d + c - d) ** 5
-        sym.visualise("test_visualize.png")
-        self.assertTrue(os.path.exists("test_visualize.png"))
-        with self.assertRaises(ValueError):
-            sym.visualise("test_visualize")
+        with TemporaryDirectory() as dir_name:
+            test_stub = os.path.join(dir_name, "test_visualize")
+            test_name = f"{test_stub}.png"
+            c = pybamm.Variable("c", "negative electrode")
+            d = pybamm.Variable("d", "negative electrode")
+            sym = pybamm.div(c * pybamm.grad(c)) + (c / d + c - d) ** 5
+            sym.visualise(test_name)
+            self.assertTrue(os.path.exists(test_name))
+            with self.assertRaises(ValueError):
+                sym.visualise(test_stub)
 
     def test_has_spatial_derivatives(self):
         var = pybamm.Variable("var", domain="test")
@@ -480,6 +484,7 @@ def test_test_shape(self):
             (y1 + y2).test_shape()
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         self.assertEqual(pybamm.Symbol("test").to_equation(), sympy.Symbol("test"))
 
     def test_numpy_array_ufunc(self):
diff --git a/tests/unit/test_expression_tree/test_unary_operators.py b/tests/unit/test_expression_tree/test_unary_operators.py
index b0513c974b..fc845cb574 100644
--- a/tests/unit/test_expression_tree/test_unary_operators.py
+++ b/tests/unit/test_expression_tree/test_unary_operators.py
@@ -5,12 +5,10 @@
 from tests import TestCase
 
 import numpy as np
-import sympy
 from scipy.sparse import diags
-from sympy.vector.operators import Divergence as sympy_Divergence
-from sympy.vector.operators import Gradient as sympy_Gradient
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class TestUnaryOperators(TestCase):
@@ -613,6 +611,11 @@ def test_not_constant(self):
         self.assertFalse((2 * a).is_constant())
 
     def test_to_equation(self):
+
+        sympy = have_optional_dependency("sympy")
+        sympy_Divergence = have_optional_dependency("sympy.vector.operators", "Divergence")
+        sympy_Gradient = have_optional_dependency("sympy.vector.operators", "Gradient")
+
         a = pybamm.Symbol("a", domain="negative particle")
         b = pybamm.Symbol("b", domain="current collector")
         c = pybamm.Symbol("c", domain="test")
diff --git a/tests/unit/test_expression_tree/test_variable.py b/tests/unit/test_expression_tree/test_variable.py
index be791903e2..583008f882 100644
--- a/tests/unit/test_expression_tree/test_variable.py
+++ b/tests/unit/test_expression_tree/test_variable.py
@@ -5,9 +5,9 @@
 import unittest
 
 import numpy as np
-import sympy
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class TestVariable(TestCase):
@@ -55,6 +55,7 @@ def test_variable_bounds(self):
             pybamm.Variable("var", bounds=(1, 1))
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         # Test print_name
         func = pybamm.Variable("test_string")
         func.print_name = "test"
diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
index 60eed9d6fb..79c6d8a720 100644
--- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
+++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
@@ -47,7 +47,7 @@
 'surface form': 'differential' (possible: ['false', 'differential', 'algebraic'])
 'thermal': 'x-full' (possible: ['isothermal', 'lumped', 'x-lumped', 'x-full'])
 'total interfacial current density as a state': 'false' (possible: ['false', 'true'])
-'working electrode': 'both' (possible: ['both', 'negative', 'positive'])
+'working electrode': 'both' (possible: ['both', 'positive'])
 'x-average side reactions': 'false' (possible: ['false', 'true'])
 """  # noqa: E501
 
@@ -210,6 +210,10 @@ def test_options(self):
             pybamm.BaseBatteryModel({"particle": "bad particle"})
         with self.assertRaisesRegex(pybamm.OptionError, "The 'fast diffusion'"):
             pybamm.BaseBatteryModel({"particle": "fast diffusion"})
+        with self.assertRaisesRegex(pybamm.OptionError, "working electrode"):
+            pybamm.BaseBatteryModel({"working electrode": "bad working electrode"})
+        with self.assertRaisesRegex(pybamm.OptionError, "The 'negative' working"):
+            pybamm.BaseBatteryModel({"working electrode": "negative"})
         with self.assertRaisesRegex(pybamm.OptionError, "particle shape"):
             pybamm.BaseBatteryModel({"particle shape": "bad particle shape"})
         with self.assertRaisesRegex(pybamm.OptionError, "operating mode"):
@@ -284,6 +288,15 @@ def test_options(self):
             ("swelling and cracking", "swelling only"),
         )
         self.assertEqual(model.options["stress-induced diffusion"], "true")
+        model = pybamm.BaseBatteryModel(
+            {
+                "working electrode": "positive",
+                "loss of active material": "stress-driven",
+                "SEI on cracks": "true",
+            }
+        )
+        self.assertEqual(model.options["particle mechanics"], "swelling and cracking")
+        self.assertEqual(model.options["stress-induced diffusion"], "true")
 
         # crack model
         with self.assertRaisesRegex(pybamm.OptionError, "particle mechanics"):
@@ -294,13 +307,6 @@ def test_options(self):
         # SEI on cracks
         with self.assertRaisesRegex(pybamm.OptionError, "SEI on cracks"):
             pybamm.BaseBatteryModel({"SEI on cracks": "bad SEI on cracks"})
-        with self.assertRaisesRegex(NotImplementedError, "SEI on cracks not yet"):
-            pybamm.BaseBatteryModel(
-                {
-                    "SEI on cracks": "true",
-                    "working electrode": "positive",
-                }
-            )
 
         # plating model
         with self.assertRaisesRegex(pybamm.OptionError, "lithium plating"):
@@ -354,7 +360,10 @@ def test_options(self):
         # thermal half-cell
         with self.assertRaisesRegex(pybamm.OptionError, "X-full"):
             pybamm.BaseBatteryModel(
-                {"thermal": "x-full", "working electrode": "positive"}
+                {
+                    "thermal": "x-full",
+                    "working electrode": "positive"
+                }
             )
         with self.assertRaisesRegex(pybamm.OptionError, "X-lumped"):
             pybamm.BaseBatteryModel(
@@ -494,11 +503,6 @@ def test_whole_cell_domains(self):
             options.whole_cell_domains, ["separator", "positive electrode"]
         )
 
-        options = BatteryModelOptions({"working electrode": "negative"})
-        self.assertEqual(
-            options.whole_cell_domains, ["negative electrode", "separator"]
-        )
-
         options = BatteryModelOptions({})
         self.assertEqual(
             options.whole_cell_domains,
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
index 6815698588..f4e3c3cceb 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
@@ -389,3 +389,22 @@ def test_well_posed_current_sigmoid_diffusivity(self):
     def test_well_posed_psd(self):
         options = {"particle size": "distribution", "surface form": "algebraic"}
         self.check_well_posedness(options)
+
+    def test_well_posed_composite_kinetic_hysteresis(self):
+        options = {
+            "particle phases": ("2", "1"),
+            "exchange-current density": (
+                ("current sigmoid", "single"),
+                "current sigmoid",
+            ),
+            "open-circuit potential": (("current sigmoid", "single"), "single"),
+        }
+        self.check_well_posedness(options)
+
+    def test_well_posed_composite_diffusion_hysteresis(self):
+        options = {
+            "particle phases": ("2", "1"),
+            "diffusivity": (("current sigmoid", "current sigmoid"), "current sigmoid"),
+            "open-circuit potential": (("current sigmoid", "single"), "single"),
+        }
+        self.check_well_posedness(options)
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_basic_models.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_basic_models.py
index 6808709d92..f2f5a5ef40 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_basic_models.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_basic_models.py
@@ -39,9 +39,7 @@ def test_basic_dfn_half_cell_simulation(self):
         model = pybamm.lithium_ion.BasicDFNHalfCell(
             options={"working electrode": "positive"}
         )
-        param = pybamm.ParameterValues("OKane2022")
-        param["Current function [A]"] = 2.5
-        sim = pybamm.Simulation(model=model, parameter_values=param)
+        sim = pybamm.Simulation(model=model)
         sim.solve([0, 100])
         self.assertTrue(isinstance(sim.solution, pybamm.solvers.solution.Solution))
 
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
index c305b21fee..e5e79a6ae4 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
@@ -40,9 +40,7 @@ def test_known_solution(self):
                     k: sol_split[k].data[0]
                     for k in ["x_0", "y_0", "x_100", "y_100", "Q_p"]
                 }
-                energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral(
-                    parameter_values, inputs
-                )
+                energy = esoh_solver.theoretical_energy_integral(inputs)
                 self.assertAlmostEqual(sol[key], energy, places=5)
 
         # should still work with old inputs
@@ -244,7 +242,7 @@ def test_error(self):
 
 class TestElectrodeSOHHalfCell(TestCase):
     def test_known_solution(self):
-        model = pybamm.lithium_ion.ElectrodeSOHHalfCell("positive")
+        model = pybamm.lithium_ion.ElectrodeSOHHalfCell()
 
         param = pybamm.LithiumIonParameters({"working electrode": "positive"})
         parameter_values = pybamm.ParameterValues("Xu2019")
@@ -346,6 +344,9 @@ def test_initial_soc_cell_capacity(self):
 
     def test_error(self):
         parameter_values = pybamm.ParameterValues("Chen2020")
+        parameter_values_half_cell = pybamm.lithium_ion.DFN(
+            {"working electrode": "positive"}
+        ).default_parameter_values
 
         with self.assertRaisesRegex(
             ValueError, "Initial SOC should be between 0 and 1"
@@ -358,6 +359,23 @@ def test_error(self):
         with self.assertRaisesRegex(ValueError, "must be a float"):
             pybamm.lithium_ion.get_initial_stoichiometries("5 A", parameter_values)
 
+        with self.assertRaisesRegex(ValueError, "outside the voltage limits"):
+            pybamm.lithium_ion.get_initial_stoichiometry_half_cell(
+                "1 V", parameter_values_half_cell
+            )
+
+        with self.assertRaisesRegex(ValueError, "must be a float"):
+            pybamm.lithium_ion.get_initial_stoichiometry_half_cell(
+                "5 A", parameter_values_half_cell
+            )
+
+        with self.assertRaisesRegex(
+            ValueError, "Initial SOC should be between 0 and 1"
+        ):
+            pybamm.lithium_ion.get_initial_stoichiometry_half_cell(
+                2, parameter_values_half_cell
+            )
+
 
 class TestGetInitialOCP(TestCase):
     def test_get_initial_ocp(self):
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_newman_tobias.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_newman_tobias.py
index be7d2499c6..4d65804156 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_newman_tobias.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_newman_tobias.py
@@ -22,6 +22,12 @@ def test_well_posed_particle_phases(self):
     def test_well_posed_particle_phases_sei(self):
         pass  # skip this test
 
+    def test_well_posed_composite_kinetic_hysteresis(self):
+        pass  # skip this test
+
+    def test_well_posed_composite_diffusion_hysteresis(self):
+        pass  # skip this test
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_parameters/test_lead_acid_parameters.py b/tests/unit/test_parameters/test_lead_acid_parameters.py
index e0151f0e7b..ddc73f61ee 100644
--- a/tests/unit/test_parameters/test_lead_acid_parameters.py
+++ b/tests/unit/test_parameters/test_lead_acid_parameters.py
@@ -1,10 +1,11 @@
 #
 # Test for the standard lead acid parameters
 #
+import os
 from tests import TestCase
 import pybamm
 from tests import get_discretisation_for_testing
-
+from tempfile import TemporaryDirectory
 import unittest
 
 
@@ -15,10 +16,11 @@ def test_scipy_constants(self):
         self.assertAlmostEqual(constants.F.evaluate(), 96485, places=0)
 
     def test_print_parameters(self):
-        parameters = pybamm.LeadAcidParameters()
-        parameter_values = pybamm.lead_acid.BaseModel().default_parameter_values
-        output_file = "lead_acid_parameters.txt"
-        parameter_values.print_parameters(parameters, output_file)
+        with TemporaryDirectory() as dir_name:
+            parameters = pybamm.LeadAcidParameters()
+            parameter_values = pybamm.lead_acid.BaseModel().default_parameter_values
+            output_file = os.path.join(dir_name, "lead_acid_parameters.txt")
+            parameter_values.print_parameters(parameters, output_file)
 
     def test_parameters_defaults_lead_acid(self):
         # Load parameters to be tested
diff --git a/tests/unit/test_parameters/test_lithium_ion_parameters.py b/tests/unit/test_parameters/test_lithium_ion_parameters.py
index 9d9d892300..0c46eec16e 100644
--- a/tests/unit/test_parameters/test_lithium_ion_parameters.py
+++ b/tests/unit/test_parameters/test_lithium_ion_parameters.py
@@ -1,19 +1,21 @@
 #
-# Tests lithium ion parameters load and give expected values
+# Tests lithium-ion parameters load and give expected values
 #
+import os
 from tests import TestCase
 import pybamm
-
+from tempfile import TemporaryDirectory
 import unittest
 import numpy as np
 
 
 class TestLithiumIonParameterValues(TestCase):
     def test_print_parameters(self):
-        parameters = pybamm.LithiumIonParameters()
-        parameter_values = pybamm.lithium_ion.BaseModel().default_parameter_values
-        output_file = "lithium_ion_parameters.txt"
-        parameter_values.print_parameters(parameters, output_file)
+        with TemporaryDirectory() as dir_name:
+            parameters = pybamm.LithiumIonParameters()
+            parameter_values = pybamm.lithium_ion.BaseModel().default_parameter_values
+            output_file = os.path.join(dir_name, "lithium_ion_parameters.txt")
+            parameter_values.print_parameters(parameters, output_file)
 
     def test_lithium_ion(self):
         """This test checks that all the parameters are being calculated
diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py
new file mode 100644
index 0000000000..a537afc93d
--- /dev/null
+++ b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py
@@ -0,0 +1,48 @@
+#
+# Tests for O'Kane (2022) parameter set
+#
+from tests import TestCase
+import pybamm
+import unittest
+
+
+class TestEcker2015(TestCase):
+    def test_functions(self):
+        param = pybamm.ParameterValues("Ecker2015")
+        sto = pybamm.Scalar(0.5)
+        T = pybamm.Scalar(298.15)
+
+        fun_test = {
+            # Negative electrode
+            "Negative electrode diffusivity [m2.s-1]": ([sto, T], 1.219e-14),
+            "Negative electrode exchange-current density [A.m-2]": (
+                [1000, 15960, 31920, T],
+                6.2517,
+            ),
+            "Negative electrode OCP [V]": ([sto], 0.124),
+            # Positive electrode
+            "Positive electrode diffusivity [m2.s-1]": ([sto, T], 1.0457e-13),
+            "Positive electrode exchange-current density [A.m-2]": (
+                [1000, 24290, 48580, T],
+                2.5121,
+            ),
+            "Positive electrode OCP [V]": ([sto], 3.9478),
+            # Electrolyte
+            "Electrolyte diffusivity [m2.s-1]": ([1000, T], 2.593e-10),
+            "Electrolyte conductivity [S.m-1]": ([1000, T], 0.9738)
+        }
+
+        for name, value in fun_test.items():
+            self.assertAlmostEqual(
+                param.evaluate(param[name](*value[0])), value[1], places=4
+            )
+
+
+if __name__ == "__main__":
+    print("Add -v for more debug output")
+    import sys
+
+    if "-v" in sys.argv:
+        debug = True
+    pybamm.settings.debug_mode = True
+    unittest.main()
diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py
new file mode 100644
index 0000000000..6dde10cd9c
--- /dev/null
+++ b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py
@@ -0,0 +1,41 @@
+#
+# Tests for O'Kane (2022) parameter set
+#
+from tests import TestCase
+import pybamm
+import unittest
+
+
+class TestEcker2015_graphite_halfcell(TestCase):
+    def test_functions(self):
+        param = pybamm.ParameterValues("Ecker2015_graphite_halfcell")
+        sto = pybamm.Scalar(0.5)
+        T = pybamm.Scalar(298.15)
+
+        fun_test = {
+            # Positive electrode
+            "Positive electrode diffusivity [m2.s-1]": ([sto, T], 1.219e-14),
+            "Positive electrode exchange-current density [A.m-2]": (
+                [1000, 15960, 31920, T],
+                6.2517,
+            ),
+            "Positive electrode OCP [V]": ([sto], 0.124),
+            # Electrolyte
+            "Electrolyte diffusivity [m2.s-1]": ([1000, T], 2.593e-10),
+            "Electrolyte conductivity [S.m-1]": ([1000, T], 0.9738)
+        }
+
+        for name, value in fun_test.items():
+            self.assertAlmostEqual(
+                param.evaluate(param[name](*value[0])), value[1], places=4
+            )
+
+
+if __name__ == "__main__":
+    print("Add -v for more debug output")
+    import sys
+
+    if "-v" in sys.argv:
+        debug = True
+    pybamm.settings.debug_mode = True
+    unittest.main()
diff --git a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py
new file mode 100644
index 0000000000..5c6971a7d5
--- /dev/null
+++ b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py
@@ -0,0 +1,46 @@
+#
+# Tests for O'Kane (2022) parameter set
+#
+from tests import TestCase
+import pybamm
+import unittest
+
+
+class TestOKane2022_graphite_SiOx_halfcell(TestCase):
+    def test_functions(self):
+        param = pybamm.ParameterValues("OKane2022_graphite_SiOx_halfcell")
+        sto = pybamm.Scalar(0.9)
+        T = pybamm.Scalar(298.15)
+
+        fun_test = {
+            # Lithium plating
+            "Exchange-current density for plating [A.m-2]": ([1e3, 1e4, T], 9.6485e-2),
+            "Exchange-current density for stripping [A.m-2]": (
+                [1e3, 1e4, T],
+                9.6485e-1,
+            ),
+            "Dead lithium decay rate [s-1]": ([1e-8], 5e-7),
+            # Positive electrode
+            "Positive electrode diffusivity [m2.s-1]": ([sto, T], 3.3e-14),
+            "Positive electrode exchange-current density [A.m-2]": (
+                [1000, 16566.5, 33133, T],
+                0.33947,
+            ),
+            "Positive electrode cracking rate": ([T], 3.9e-20),
+            "Positive electrode volume change": ([sto, 33133], 0.0897),
+        }
+
+        for name, value in fun_test.items():
+            self.assertAlmostEqual(
+                param.evaluate(param[name](*value[0])), value[1], places=4
+            )
+
+
+if __name__ == "__main__":
+    print("Add -v for more debug output")
+    import sys
+
+    if "-v" in sys.argv:
+        debug = True
+    pybamm.settings.debug_mode = True
+    unittest.main()
diff --git a/tests/unit/test_parameters/test_parameter_sets/test_parameters_with_default_models.py b/tests/unit/test_parameters/test_parameter_sets/test_parameters_with_default_models.py
index 062d5caa24..4e18f1ef50 100644
--- a/tests/unit/test_parameters/test_parameter_sets/test_parameters_with_default_models.py
+++ b/tests/unit/test_parameters/test_parameter_sets/test_parameters_with_default_models.py
@@ -20,6 +20,9 @@ def test_parameter_values_with_model(self):
                 }
             ),
             "Ecker2015": pybamm.lithium_ion.DFN(),
+            "Ecker2015_graphite_halfcell": pybamm.lithium_ion.DFN(
+                {"working electrode": "positive"}
+            ),
             "Mohtat2020": pybamm.lithium_ion.DFN(),
             "NCA_Kim2011": pybamm.lithium_ion.DFN(),
             "OKane2022": pybamm.lithium_ion.DFN(
@@ -28,6 +31,13 @@ def test_parameter_values_with_model(self):
                     "lithium plating": "partially reversible",
                 }
             ),
+            "OKane2022_graphite_SiOx_halfcell": pybamm.lithium_ion.DFN(
+                {
+                    "working electrode": "positive",
+                    "SEI": "solvent-diffusion limited",
+                    "lithium plating": "partially reversible",
+                }
+            ),
             "ORegan2022": pybamm.lithium_ion.DFN(),
             "Prada2013": pybamm.lithium_ion.DFN(),
             "Ramadass2004": pybamm.lithium_ion.DFN(),
diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py
index c6a4831e86..fa6e2398ee 100644
--- a/tests/unit/test_parameters/test_parameter_values.py
+++ b/tests/unit/test_parameters/test_parameter_values.py
@@ -15,6 +15,7 @@
     lico2_ocp_Dualfoil1998,
     lico2_diffusivity_Dualfoil1998,
 )
+from pybamm.expression_tree.exceptions import OptionError
 import casadi
 
 
@@ -119,6 +120,56 @@ def test_set_initial_stoichiometries(self):
         y_100 = param_100["Initial concentration in positive electrode [mol.m-3]"]
         self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100))
 
+    def test_set_initial_stoichiometry_half_cell(self):
+        param = pybamm.lithium_ion.DFN(
+            {"working electrode": "positive"}
+        ).default_parameter_values
+        param = param.set_initial_stoichiometry_half_cell(
+            0.4, inplace=False, options={"working electrode": "positive"}
+        )
+        param_0 = param.set_initial_stoichiometry_half_cell(
+            0, inplace=False, options={"working electrode": "positive"}
+        )
+        param_100 = param.set_initial_stoichiometry_half_cell(
+            1, inplace=False, options={"working electrode": "positive"}
+        )
+
+        y = param["Initial concentration in positive electrode [mol.m-3]"]
+        y_0 = param_0["Initial concentration in positive electrode [mol.m-3]"]
+        y_100 = param_100["Initial concentration in positive electrode [mol.m-3]"]
+        self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100))
+
+        # inplace for 100% coverage
+        param_t = pybamm.lithium_ion.DFN(
+            {"working electrode": "positive"}
+        ).default_parameter_values
+        param_t.set_initial_stoichiometry_half_cell(
+            0.4, inplace=True, options={"working electrode": "positive"}
+        )
+        y = param_t["Initial concentration in positive electrode [mol.m-3]"]
+        param_0 = pybamm.lithium_ion.DFN(
+            {"working electrode": "positive"}
+        ).default_parameter_values
+        param_0.set_initial_stoichiometry_half_cell(
+            0, inplace=True, options={"working electrode": "positive"}
+        )
+        y_0 = param_0["Initial concentration in positive electrode [mol.m-3]"]
+        param_100 = pybamm.lithium_ion.DFN(
+            {"working electrode": "positive"}
+        ).default_parameter_values
+        param_100.set_initial_stoichiometry_half_cell(
+            1, inplace=True, options={"working electrode": "positive"}
+        )
+        y_100 = param_100["Initial concentration in positive electrode [mol.m-3]"]
+        self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100))
+
+        # test error
+        param = pybamm.ParameterValues("Chen2020")
+        with self.assertRaisesRegex(OptionError, "working electrode"):
+            param.set_initial_stoichiometry_half_cell(
+                0.1, options={"working electrode": "negative"}
+            )
+
     def test_set_initial_ocps(self):
         options = {
             "open-circuit potential": "MSMR",
@@ -977,6 +1028,19 @@ def test_evaluate(self):
         with self.assertRaises(ValueError):
             parameter_values.evaluate(y)
 
+    def test_exchange_current_density_plating(self):
+        parameter_values = pybamm.ParameterValues(
+            {"Exchange-current density for plating [A.m-2]": 1}
+        )
+        param = pybamm.Parameter(
+            "Exchange-current density for lithium metal electrode [A.m-2]"
+        )
+        with self.assertRaisesRegex(
+            KeyError,
+            "referring to the reaction at the surface of a lithium metal electrode",
+        ):
+            parameter_values.evaluate(param)
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_parameters/test_size_distribution_parameters.py b/tests/unit/test_parameters/test_size_distribution_parameters.py
index 1489d7416c..e633b2764a 100644
--- a/tests/unit/test_parameters/test_size_distribution_parameters.py
+++ b/tests/unit/test_parameters/test_size_distribution_parameters.py
@@ -13,15 +13,21 @@ def test_parameter_values(self):
         values = pybamm.lithium_ion.BaseModel().default_parameter_values
         param = pybamm.LithiumIonParameters()
 
-        # add distribution parameter values for negative electrode
-        values = pybamm.get_size_distribution_parameters(values, electrode="negative")
+        # add distribution parameter values for positive electrode
+        values = pybamm.get_size_distribution_parameters(
+            values,
+            working_electrode="positive",
+        )
 
-        # check positive parameters aren't there yet
+        # check negative parameters aren't there yet
         with self.assertRaises(KeyError):
-            values["Positive maximum particle radius [m]"]
+            values["Negative maximum particle radius [m]"]
 
-        # now add distribution parameter values for positive electrode
-        values = pybamm.get_size_distribution_parameters(values, electrode="positive")
+        # now add distribution parameter values for negative electrode
+        values = pybamm.get_size_distribution_parameters(
+            values,
+            working_electrode="both",
+        )
 
         # check parameters
 
diff --git a/tests/unit/test_plotting/test_plot_summary_variables.py b/tests/unit/test_plotting/test_plot_summary_variables.py
index d845ad80c2..69e32eb023 100644
--- a/tests/unit/test_plotting/test_plot_summary_variables.py
+++ b/tests/unit/test_plotting/test_plot_summary_variables.py
@@ -23,7 +23,7 @@ def test_plot(self):
         output_variables = [
             "Capacity [A.h]",
             "Loss of lithium inventory [%]",
-            "Loss of capacity to SEI [A.h]",
+            "Total capacity lost to side reactions [A.h]",
             "Loss of active material in negative electrode [%]",
             "Loss of active material in positive electrode [%]",
             "x_100",
diff --git a/tests/unit/test_plotting/test_quick_plot.py b/tests/unit/test_plotting/test_quick_plot.py
index 3415777ee8..f569f00152 100644
--- a/tests/unit/test_plotting/test_quick_plot.py
+++ b/tests/unit/test_plotting/test_quick_plot.py
@@ -3,6 +3,7 @@
 import unittest
 from tests import TestCase
 import numpy as np
+from tempfile import TemporaryDirectory
 
 
 class TestQuickPlot(TestCase):
@@ -290,11 +291,13 @@ def test_spm_simulation(self):
         quick_plot.plot(0)
 
         # test creating a GIF
-        quick_plot.create_gif(number_of_images=3, duration=3)
-        assert not os.path.exists("plot*.png")
-        assert os.path.exists("plot.gif")
-        os.remove("plot.gif")
-
+        with TemporaryDirectory() as dir_name:
+            test_stub = os.path.join(dir_name, "spm_sim_test")
+            test_file = f"{test_stub}.gif"
+            quick_plot.create_gif(number_of_images=3, duration=3,
+                                  output_filename=test_file)
+            assert not os.path.exists(f"{test_stub}*.png")
+            assert os.path.exists(test_file)
         pybamm.close_plots()
 
     def test_loqs_spme(self):
@@ -343,7 +346,7 @@ def test_loqs_spme(self):
             # test quick plot of particle for spme
             if (
                 model.name == "Single Particle Model with electrolyte"
-                and model.options["working electrode"] != "positive"
+                and model.options["working electrode"] == "both"
             ):
                 output_variables = [
                     "X-averaged negative particle concentration [mol.m-3]",
diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py
index d0926e5c94..dac94a2538 100644
--- a/tests/unit/test_simulation.py
+++ b/tests/unit/test_simulation.py
@@ -6,6 +6,7 @@
 import sys
 import unittest
 import uuid
+from tempfile import TemporaryDirectory
 
 
 class TestSimulation(TestCase):
@@ -203,6 +204,33 @@ def test_solve_with_initial_soc(self):
         sim.build(initial_soc=0.5)
         self.assertEqual(sim._built_initial_soc, 0.5)
 
+        # Test whether initial_soc works with half cell (solve)
+        options = {"working electrode": "positive"}
+        model = pybamm.lithium_ion.DFN(options)
+        sim = pybamm.Simulation(model)
+        sim.solve([0,1], initial_soc = 0.9)
+        self.assertEqual(sim._built_initial_soc, 0.9)
+
+        # Test whether initial_soc works with half cell (build)
+        options = {"working electrode": "positive"}
+        model = pybamm.lithium_ion.DFN(options)
+        sim = pybamm.Simulation(model)
+        sim.build(initial_soc = 0.9)
+        self.assertEqual(sim._built_initial_soc, 0.9)
+
+        # Test whether initial_soc works with half cell when it is a voltage
+        model = pybamm.lithium_ion.SPM({"working electrode": "positive"})
+        parameter_values = model.default_parameter_values
+        ucv = parameter_values["Open-circuit voltage at 100% SOC [V]"]
+        parameter_values["Open-circuit voltage at 100% SOC [V]"] = ucv + 1e-12
+        parameter_values["Upper voltage cut-off [V]"] = ucv + 1e-12
+        options = {"working electrode": "positive"}
+        parameter_values["Current function [A]"] = 0.0
+        sim = pybamm.Simulation(model, parameter_values=parameter_values)
+        sol = sim.solve([0,1], initial_soc = "{} V".format(ucv))
+        voltage = sol["Terminal voltage [V]"].entries
+        self.assertAlmostEqual(voltage[0], ucv, places=5)
+
         # test with MSMR
         model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")})
         param = pybamm.ParameterValues("MSMR_Example")
@@ -248,32 +276,37 @@ def test_step_with_inputs(self):
         )
 
     def test_save_load(self):
-        model = pybamm.lead_acid.LOQS()
-        model.use_jacobian = True
-        sim = pybamm.Simulation(model)
-
-        sim.save("test.pickle")
-        sim_load = pybamm.load_sim("test.pickle")
-        self.assertEqual(sim.model.name, sim_load.model.name)
+        with TemporaryDirectory() as dir_name:
+            test_name = os.path.join(dir_name, "tests.pickle")
+
+            model = pybamm.lead_acid.LOQS()
+            model.use_jacobian = True
+            sim = pybamm.Simulation(model)
+
+            sim.save(test_name)
+            sim_load = pybamm.load_sim(test_name)
+            self.assertEqual(sim.model.name, sim_load.model.name)
+
+            # save after solving
+            sim.solve([0, 600])
+            sim.save(test_name)
+            sim_load = pybamm.load_sim(test_name)
+            self.assertEqual(sim.model.name, sim_load.model.name)
+
+            # with python formats
+            model.convert_to_format = None
+            sim = pybamm.Simulation(model)
+            sim.solve([0, 600])
+            sim.save(test_name)
+            model.convert_to_format = "python"
+            sim = pybamm.Simulation(model)
+            sim.solve([0, 600])
+            with self.assertRaisesRegex(
+                    NotImplementedError,
+                    "Cannot save simulation if model format is python"
+            ):
+                sim.save(test_name)
 
-        # save after solving
-        sim.solve([0, 600])
-        sim.save("test.pickle")
-        sim_load = pybamm.load_sim("test.pickle")
-        self.assertEqual(sim.model.name, sim_load.model.name)
-
-        # with python formats
-        model.convert_to_format = None
-        sim = pybamm.Simulation(model)
-        sim.solve([0, 600])
-        sim.save("test.pickle")
-        model.convert_to_format = "python"
-        sim = pybamm.Simulation(model)
-        sim.solve([0, 600])
-        with self.assertRaisesRegex(
-            NotImplementedError, "Cannot save simulation if model format is python"
-        ):
-            sim.save("test.pickle")
 
     def test_load_param(self):
         # Test load_sim for parameters imports
@@ -299,33 +332,36 @@ def test_load_param(self):
         os.remove(filename)
 
     def test_save_load_dae(self):
-        model = pybamm.lead_acid.LOQS({"surface form": "algebraic"})
-        model.use_jacobian = True
-        sim = pybamm.Simulation(model)
-
-        # save after solving
-        sim.solve([0, 600])
-        sim.save("test.pickle")
-        sim_load = pybamm.load_sim("test.pickle")
-        self.assertEqual(sim.model.name, sim_load.model.name)
-
-        # with python format
-        model.convert_to_format = None
-        sim = pybamm.Simulation(model)
-        sim.solve([0, 600])
-        sim.save("test.pickle")
-
-        # with Casadi solver & experiment
-        model.convert_to_format = "casadi"
-        sim = pybamm.Simulation(
-            model,
-            experiment="Discharge at 1C for 20 minutes",
-            solver=pybamm.CasadiSolver(),
-        )
-        sim.solve([0, 600])
-        sim.save("test.pickle")
-        sim_load = pybamm.load_sim("test.pickle")
-        self.assertEqual(sim.model.name, sim_load.model.name)
+        with TemporaryDirectory() as dir_name:
+            test_name = os.path.join(dir_name, "test.pickle")
+
+            model = pybamm.lead_acid.LOQS({"surface form": "algebraic"})
+            model.use_jacobian = True
+            sim = pybamm.Simulation(model)
+
+            # save after solving
+            sim.solve([0, 600])
+            sim.save(test_name)
+            sim_load = pybamm.load_sim(test_name)
+            self.assertEqual(sim.model.name, sim_load.model.name)
+
+            # with python format
+            model.convert_to_format = None
+            sim = pybamm.Simulation(model)
+            sim.solve([0, 600])
+            sim.save(test_name)
+
+            # with Casadi solver & experiment
+            model.convert_to_format = "casadi"
+            sim = pybamm.Simulation(
+                model,
+                experiment="Discharge at 1C for 20 minutes",
+                solver=pybamm.CasadiSolver(),
+            )
+            sim.solve([0, 600])
+            sim.save(test_name)
+            sim_load = pybamm.load_sim(test_name)
+            self.assertEqual(sim.model.name, sim_load.model.name)
 
     def test_plot(self):
         sim = pybamm.Simulation(pybamm.lithium_ion.SPM())
@@ -340,17 +376,19 @@ def test_plot(self):
         sim.plot(testing=True)
 
     def test_create_gif(self):
-        sim = pybamm.Simulation(pybamm.lithium_ion.SPM())
-        sim.solve(t_eval=[0, 10])
+        with TemporaryDirectory() as dir_name:
+            sim = pybamm.Simulation(pybamm.lithium_ion.SPM())
+            sim.solve(t_eval=[0, 10])
 
-        # create a GIF without calling the plot method
-        sim.create_gif(number_of_images=3, duration=1)
+            # Create a temporary file name
+            test_file = os.path.join(dir_name, "test_sim.gif")
 
-        # call the plot method before creating the GIF
-        sim.plot(testing=True)
-        sim.create_gif(number_of_images=3, duration=1)
+            # create a GIF without calling the plot method
+            sim.create_gif(number_of_images=3, duration=1, output_filename=test_file)
 
-        os.remove("plot.gif")
+            # call the plot method before creating the GIF
+            sim.plot(testing=True)
+            sim.create_gif(number_of_images=3, duration=1, output_filename=test_file)
 
     def test_drive_cycle_interpolant(self):
         model = pybamm.lithium_ion.SPM()
diff --git a/tests/unit/test_solvers/test_idaklu_solver.py b/tests/unit/test_solvers/test_idaklu_solver.py
index efd0439f32..cc54f3dfd5 100644
--- a/tests/unit/test_solvers/test_idaklu_solver.py
+++ b/tests/unit/test_solvers/test_idaklu_solver.py
@@ -85,6 +85,10 @@ def test_model_events(self):
                 solution.y[0], np.exp(0.1 * solution.t), decimal=5
             )
 
+            # Check invalid atol type raises an error
+            with self.assertRaises(pybamm.SolverError):
+                solver._check_atol_type({'key': 'value'}, [])
+
             # enforce events that won't be triggered
             model.events = [pybamm.Event("an event", var + 1)]
             model_disc = disc.process_model(model, inplace=False)
@@ -182,31 +186,33 @@ def test_input_params(self):
             np.testing.assert_array_almost_equal(sol.y[1:3], true_solution)
 
     def test_sensitivites_initial_condition(self):
-        model = pybamm.BaseModel()
-        model.convert_to_format = "casadi"
-        u = pybamm.Variable("u")
-        v = pybamm.Variable("v")
-        a = pybamm.InputParameter("a")
-        model.rhs = {u: -u}
-        model.algebraic = {v: a * u - v}
-        model.initial_conditions = {u: 1, v: 1}
-        model.variables = {"2v": 2 * v}
-
-        disc = pybamm.Discretisation()
-        disc.process_model(model)
+        for output_variables in [[], ["2v"]]:
+            model = pybamm.BaseModel()
+            model.convert_to_format = "casadi"
+            u = pybamm.Variable("u")
+            v = pybamm.Variable("v")
+            a = pybamm.InputParameter("a")
+            model.rhs = {u: -u}
+            model.algebraic = {v: a * u - v}
+            model.initial_conditions = {u: 1, v: 1}
+            model.variables = {"2v": 2 * v}
 
-        solver = pybamm.IDAKLUSolver()
+            disc = pybamm.Discretisation()
+            disc.process_model(model)
+            solver = pybamm.IDAKLUSolver(output_variables=output_variables)
 
-        t_eval = np.linspace(0, 3, 100)
-        a_value = 0.1
+            t_eval = np.linspace(0, 3, 100)
+            a_value = 0.1
 
-        sol = solver.solve(
-            model, t_eval, inputs={"a": a_value}, calculate_sensitivities=True
-        )
+            sol = solver.solve(
+                model, t_eval, inputs={"a": a_value}, calculate_sensitivities=True
+            )
 
-        np.testing.assert_array_almost_equal(
-            sol["2v"].sensitivities["a"].full().flatten(), np.exp(-sol.t) * 2, decimal=4
-        )
+            np.testing.assert_array_almost_equal(
+                sol["2v"].sensitivities["a"].full().flatten(),
+                np.exp(-sol.t) * 2,
+                decimal=4,
+            )
 
     def test_ida_roberts_klu_sensitivities(self):
         # this test implements a python version of the ida Roberts
@@ -540,6 +546,147 @@ def test_options(self):
                         with self.assertRaises(ValueError):
                             soln = solver.solve(model, t_eval)
 
+    def test_with_output_variables(self):
+        # Construct a model and solve for all vairables, then test
+        # the 'output_variables' option for each variable in turn, confirming
+        # equivalence
+
+        # construct model
+        model = pybamm.lithium_ion.DFN()
+        geometry = model.default_geometry
+        param = model.default_parameter_values
+        input_parameters = {}  # Sensitivities dictionary
+        param.update({key: "[input]" for key in input_parameters})
+        param.process_model(model)
+        param.process_geometry(geometry)
+        var_pts = {"x_n": 50, "x_s": 50, "x_p": 50, "r_n": 5, "r_p": 5}
+        mesh = pybamm.Mesh(geometry, model.default_submesh_types, var_pts)
+        disc = pybamm.Discretisation(mesh, model.default_spatial_methods)
+        disc.process_model(model)
+        t_eval = np.linspace(0, 3600, 100)
+
+        options = {
+            'linear_solver': 'SUNLinSol_KLU',
+            'jacobian': 'sparse',
+            'num_threads': 4,
+        }
+
+        # Use a selection of variables of different types
+        output_variables = [
+            "Voltage [V]",
+            "Time [min]",
+            "Current [A]",
+            "r_n [m]",
+            "x [m]",
+            "x_s [m]",
+            "Gradient of negative electrolyte potential [V.m-1]",
+            "Negative particle flux [mol.m-2.s-1]",
+            "Discharge capacity [A.h]",  # ExplicitTimeIntegral
+            "Throughput capacity [A.h]",  # ExplicitTimeIntegral
+        ]
+
+        # Use the full model as comparison (tested separately)
+        solver_all = pybamm.IDAKLUSolver(
+            atol=1e-8, rtol=1e-8,
+            options=options,
+        )
+        sol_all = solver_all.solve(
+            model,
+            t_eval,
+            inputs=input_parameters,
+            calculate_sensitivities=True,
+        )
+
+        # Solve for a subset of variables and compare results
+        solver = pybamm.IDAKLUSolver(
+            atol=1e-8, rtol=1e-8,
+            options=options,
+            output_variables=output_variables,
+        )
+        sol = solver.solve(
+            model,
+            t_eval,
+            inputs=input_parameters,
+        )
+
+        # Compare output to sol_all
+        for varname in output_variables:
+            self.assertTrue(np.allclose(sol[varname].data, sol_all[varname].data))
+
+        # Mock a 1D current collector and initialise (none in the model)
+        sol["x_s [m]"].domain = ["current collector"]
+        sol["x_s [m]"].initialise_1D()
+
+    def test_with_output_variables_and_sensitivities(self):
+        # Construct a model and solve for all vairables, then test
+        # the 'output_variables' option for each variable in turn, confirming
+        # equivalence
+
+        # construct model
+        model = pybamm.lithium_ion.DFN()
+        geometry = model.default_geometry
+        param = model.default_parameter_values
+        input_parameters = {  # Sensitivities dictionary
+            "Current function [A]": 0.680616,
+            "Separator porosity": 1.0,
+        }
+        param.update({key: "[input]" for key in input_parameters})
+        param.process_model(model)
+        param.process_geometry(geometry)
+        var_pts = {"x_n": 50, "x_s": 50, "x_p": 50, "r_n": 5, "r_p": 5}
+        mesh = pybamm.Mesh(geometry, model.default_submesh_types, var_pts)
+        disc = pybamm.Discretisation(mesh, model.default_spatial_methods)
+        disc.process_model(model)
+        t_eval = np.linspace(0, 3600, 100)
+
+        options = {
+            'linear_solver': 'SUNLinSol_KLU',
+            'jacobian': 'sparse',
+            'num_threads': 4,
+        }
+
+        # Use a selection of variables of different types
+        output_variables = [
+            "Voltage [V]",
+            "Time [min]",
+            "x [m]",
+            "Negative particle flux [mol.m-2.s-1]",
+            "Throughput capacity [A.h]",  # ExplicitTimeIntegral
+        ]
+
+        # Use the full model as comparison (tested separately)
+        solver_all = pybamm.IDAKLUSolver(
+            atol=1e-8, rtol=1e-8,
+            options=options,
+        )
+        sol_all = solver_all.solve(
+            model,
+            t_eval,
+            inputs=input_parameters,
+            calculate_sensitivities=True,
+        )
+
+        # Solve for a subset of variables and compare results
+        solver = pybamm.IDAKLUSolver(
+            atol=1e-8, rtol=1e-8,
+            options=options,
+            output_variables=output_variables,
+        )
+        sol = solver.solve(
+            model,
+            t_eval,
+            inputs=input_parameters,
+            calculate_sensitivities=True,
+        )
+
+        # Compare output to sol_all
+        for varname in output_variables:
+            self.assertTrue(np.allclose(sol[varname].data, sol_all[varname].data))
+
+        # Mock a 1D current collector and initialise (none in the model)
+        sol["x_s [m]"].domain = ["current collector"]
+        sol["x_s [m]"].initialise_1D()
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_solvers/test_processed_variable_computed.py b/tests/unit/test_solvers/test_processed_variable_computed.py
new file mode 100644
index 0000000000..e31f51ab1e
--- /dev/null
+++ b/tests/unit/test_solvers/test_processed_variable_computed.py
@@ -0,0 +1,442 @@
+#
+# Tests for the Processed Variable Computed class
+#
+# This class forms a container for variables (and sensitivities) calculted
+#  by the idaklu solver, and does not possesses any capability to calculate
+#  values itself since it does not have access to the full state vector
+#
+from tests import TestCase
+import casadi
+import pybamm
+import tests
+
+import numpy as np
+import unittest
+
+
+def to_casadi(var_pybamm, y, inputs=None):
+    t_MX = casadi.MX.sym("t")
+    y_MX = casadi.MX.sym("y", y.shape[0])
+
+    inputs_MX_dict = {}
+    inputs = inputs or {}
+    for key, value in inputs.items():
+        inputs_MX_dict[key] = casadi.MX.sym("input", value.shape[0])
+
+    inputs_MX = casadi.vertcat(*[p for p in inputs_MX_dict.values()])
+
+    var_sym = var_pybamm.to_casadi(t_MX, y_MX, inputs=inputs_MX_dict)
+
+    var_casadi = casadi.Function("variable", [t_MX, y_MX, inputs_MX], [var_sym])
+    return var_casadi
+
+
+def process_and_check_2D_variable(
+    var, first_spatial_var, second_spatial_var, disc=None, geometry_options={}
+):
+    # first_spatial_var should be on the "smaller" domain, i.e "r" for an "r-x" variable
+    if disc is None:
+        disc = tests.get_discretisation_for_testing()
+    disc.set_variable_slices([var])
+
+    first_sol = disc.process_symbol(first_spatial_var).entries[:, 0]
+    second_sol = disc.process_symbol(second_spatial_var).entries[:, 0]
+
+    # Keep only the first iteration of entries
+    first_sol = first_sol[: len(first_sol) // len(second_sol)]
+    var_sol = disc.process_symbol(var)
+    t_sol = np.linspace(0, 1)
+    y_sol = np.ones(len(second_sol) * len(first_sol))[:, np.newaxis] * np.linspace(0, 5)
+
+    var_casadi = to_casadi(var_sol, y_sol)
+    model = tests.get_base_model_with_battery_geometry(**geometry_options)
+    pybamm.ProcessedVariableComputed(
+        [var_sol],
+        [var_casadi],
+        [y_sol],
+        pybamm.Solution(t_sol, y_sol, model, {}),
+        warn=False,
+    )
+    # NB: ProcessedVariableComputed does not interpret y in the same way as
+    #  ProcessedVariable; a better test of equivalence is to check that the
+    #  results are the same between IDAKLUSolver with (and without)
+    #  output_variables. This is implemented in the integration test:
+    #    tests/integration/test_solvers/test_idaklu_solver.py
+    #    ::test_output_variables
+    return y_sol, first_sol, second_sol, t_sol
+
+
+class TestProcessedVariableComputed(TestCase):
+    def test_processed_variable_0D(self):
+        # without space
+        y = pybamm.StateVector(slice(0, 1))
+        var = y
+        var.mesh = None
+        t_sol = np.array([0])
+        y_sol = np.array([1])[:, np.newaxis]
+        var_casadi = to_casadi(var, y_sol)
+        processed_var = pybamm.ProcessedVariableComputed(
+            [var],
+            [var_casadi],
+            [y_sol],
+            pybamm.Solution(t_sol, y_sol, pybamm.BaseModel(), {}),
+            warn=False,
+        )
+        # Assert that the processed variable is the same as the solution
+        np.testing.assert_array_equal(processed_var.entries, y_sol[0])
+        # Check that 'data' produces the same output as 'entries'
+        np.testing.assert_array_equal(processed_var.entries, processed_var.data)
+
+        # Check unroll function
+        np.testing.assert_array_equal(processed_var.unroll(), y_sol[0])
+
+        # Check cumtrapz workflow produces no errors
+        processed_var.cumtrapz_ic = 1
+        processed_var.initialise_0D()
+
+    # check empty sensitivity works
+    def test_processed_variable_0D_no_sensitivity(self):
+        # without space
+        t = pybamm.t
+        y = pybamm.StateVector(slice(0, 1))
+        var = t * y
+        var.mesh = None
+        t_sol = np.linspace(0, 1)
+        y_sol = np.array([np.linspace(0, 5)])
+        var_casadi = to_casadi(var, y_sol)
+        processed_var = pybamm.ProcessedVariableComputed(
+            [var],
+            [var_casadi],
+            [y_sol],
+            pybamm.Solution(t_sol, y_sol, pybamm.BaseModel(), {}),
+            warn=False,
+        )
+
+        # test no inputs (i.e. no sensitivity)
+        self.assertDictEqual(processed_var.sensitivities, {})
+
+        # with parameter
+        t = pybamm.t
+        y = pybamm.StateVector(slice(0, 1))
+        a = pybamm.InputParameter("a")
+        var = t * y * a
+        var.mesh = None
+        t_sol = np.linspace(0, 1)
+        y_sol = np.array([np.linspace(0, 5)])
+        inputs = {"a": np.array([1.0])}
+        var_casadi = to_casadi(var, y_sol, inputs=inputs)
+        processed_var = pybamm.ProcessedVariableComputed(
+            [var],
+            [var_casadi],
+            [y_sol],
+            pybamm.Solution(t_sol, y_sol, pybamm.BaseModel(), inputs),
+            warn=False,
+        )
+
+        # test no sensitivity raises error
+        self.assertIsNone(processed_var.sensitivities)
+
+    def test_processed_variable_1D(self):
+        var = pybamm.Variable("var", domain=["negative electrode", "separator"])
+        x = pybamm.SpatialVariable("x", domain=["negative electrode", "separator"])
+
+        # On nodes
+        disc = tests.get_discretisation_for_testing()
+        disc.set_variable_slices([var])
+        x_sol = disc.process_symbol(x).entries[:, 0]
+        var_sol = disc.process_symbol(var)
+        t_sol = np.linspace(0, 1)
+        y_sol = np.ones_like(x_sol)[:, np.newaxis] * np.linspace(0, 5)
+
+        var_casadi = to_casadi(var_sol, y_sol)
+        sol = pybamm.Solution(t_sol, y_sol, pybamm.BaseModel(), {})
+        processed_var = pybamm.ProcessedVariableComputed(
+            [var_sol],
+            [var_casadi],
+            [y_sol],
+            sol,
+            warn=False,
+        )
+
+        # Ordering from idaklu with output_variables set is different to
+        # the full solver
+        y_sol = y_sol.reshape((y_sol.shape[1], y_sol.shape[0])).transpose()
+        np.testing.assert_array_equal(processed_var.entries, y_sol)
+        np.testing.assert_array_equal(processed_var.entries, processed_var.data)
+        np.testing.assert_array_almost_equal(processed_var(t_sol, x_sol), y_sol)
+
+        # Check unroll function
+        np.testing.assert_array_equal(processed_var.unroll(), y_sol)
+
+        # Check no error when data dimension is transposed vs node/edge
+        processed_var.mesh.nodes, processed_var.mesh.edges = \
+            processed_var.mesh.edges, processed_var.mesh.nodes
+        processed_var.initialise_1D()
+        processed_var.mesh.nodes, processed_var.mesh.edges = \
+            processed_var.mesh.edges, processed_var.mesh.nodes
+
+        # Check that there are no errors with domain-specific attributes
+        #  (see ProcessedVariableComputed.initialise_1D() for details)
+        domain_list = [
+            "particle",
+            "separator",
+            "current collector",
+            "particle size",
+            "random-non-specific-domain",
+        ]
+        for domain in domain_list:
+            processed_var.domain[0] = domain
+            processed_var.initialise_1D()
+
+    def test_processed_variable_1D_unknown_domain(self):
+        x = pybamm.SpatialVariable("x", domain="SEI layer", coord_sys="cartesian")
+        geometry = pybamm.Geometry(
+            {"SEI layer": {x: {"min": pybamm.Scalar(0), "max": pybamm.Scalar(1)}}}
+        )
+
+        submesh_types = {"SEI layer": pybamm.Uniform1DSubMesh}
+        var_pts = {x: 100}
+        mesh = pybamm.Mesh(geometry, submesh_types, var_pts)
+
+        nt = 100
+
+        y_sol = np.zeros((var_pts[x], nt))
+        solution = pybamm.Solution(
+            np.linspace(0, 1, nt),
+            y_sol,
+            pybamm.BaseModel(),
+            {},
+            np.linspace(0, 1, 1),
+            np.zeros((var_pts[x])),
+            "test",
+        )
+
+        c = pybamm.StateVector(slice(0, var_pts[x]), domain=["SEI layer"])
+        c.mesh = mesh["SEI layer"]
+        c_casadi = to_casadi(c, y_sol)
+        pybamm.ProcessedVariableComputed([c], [c_casadi], [y_sol], solution, warn=False)
+
+    def test_processed_variable_2D_x_r(self):
+        var = pybamm.Variable(
+            "var",
+            domain=["negative particle"],
+            auxiliary_domains={"secondary": ["negative electrode"]},
+        )
+        x = pybamm.SpatialVariable("x", domain=["negative electrode"])
+        r = pybamm.SpatialVariable(
+            "r",
+            domain=["negative particle"],
+            auxiliary_domains={"secondary": ["negative electrode"]},
+        )
+
+        disc = tests.get_p2d_discretisation_for_testing()
+        process_and_check_2D_variable(var, r, x, disc=disc)
+
+    def test_processed_variable_2D_R_x(self):
+        var = pybamm.Variable(
+            "var",
+            domain=["negative particle size"],
+            auxiliary_domains={"secondary": ["negative electrode"]},
+        )
+        R = pybamm.SpatialVariable(
+            "R",
+            domain=["negative particle size"],
+            auxiliary_domains={"secondary": ["negative electrode"]},
+        )
+        x = pybamm.SpatialVariable("x", domain=["negative electrode"])
+
+        disc = tests.get_size_distribution_disc_for_testing()
+        process_and_check_2D_variable(
+            var,
+            R,
+            x,
+            disc=disc,
+            geometry_options={"options": {"particle size": "distribution"}},
+        )
+
+    def test_processed_variable_2D_R_z(self):
+        var = pybamm.Variable(
+            "var",
+            domain=["negative particle size"],
+            auxiliary_domains={"secondary": ["current collector"]},
+        )
+        R = pybamm.SpatialVariable(
+            "R",
+            domain=["negative particle size"],
+            auxiliary_domains={"secondary": ["current collector"]},
+        )
+        z = pybamm.SpatialVariable("z", domain=["current collector"])
+
+        disc = tests.get_size_distribution_disc_for_testing()
+        process_and_check_2D_variable(
+            var,
+            R,
+            z,
+            disc=disc,
+            geometry_options={"options": {"particle size": "distribution"}},
+        )
+
+    def test_processed_variable_2D_r_R(self):
+        var = pybamm.Variable(
+            "var",
+            domain=["negative particle"],
+            auxiliary_domains={"secondary": ["negative particle size"]},
+        )
+        r = pybamm.SpatialVariable(
+            "r",
+            domain=["negative particle"],
+            auxiliary_domains={"secondary": ["negative particle size"]},
+        )
+        R = pybamm.SpatialVariable("R", domain=["negative particle size"])
+
+        disc = tests.get_size_distribution_disc_for_testing()
+        process_and_check_2D_variable(
+            var,
+            r,
+            R,
+            disc=disc,
+            geometry_options={"options": {"particle size": "distribution"}},
+        )
+
+    def test_processed_variable_2D_x_z(self):
+        var = pybamm.Variable(
+            "var",
+            domain=["negative electrode", "separator"],
+            auxiliary_domains={"secondary": "current collector"},
+        )
+        x = pybamm.SpatialVariable(
+            "x",
+            domain=["negative electrode", "separator"],
+            auxiliary_domains={"secondary": "current collector"},
+        )
+        z = pybamm.SpatialVariable("z", domain=["current collector"])
+
+        disc = tests.get_1p1d_discretisation_for_testing()
+        y_sol, x_sol, z_sol, t_sol = process_and_check_2D_variable(var, x, z, disc=disc)
+        del x_sol
+
+        # On edges
+        x_s_edge = pybamm.Matrix(
+            np.tile(disc.mesh["separator"].edges, len(z_sol)),
+            domain="separator",
+            auxiliary_domains={"secondary": "current collector"},
+        )
+        x_s_edge.mesh = disc.mesh["separator"]
+        x_s_edge.secondary_mesh = disc.mesh["current collector"]
+        x_s_casadi = to_casadi(x_s_edge, y_sol)
+        processed_x_s_edge = pybamm.ProcessedVariable(
+            [x_s_edge],
+            [x_s_casadi],
+            pybamm.Solution(
+                t_sol, y_sol, tests.get_base_model_with_battery_geometry(), {}
+            ),
+            warn=False,
+        )
+        np.testing.assert_array_equal(
+            x_s_edge.entries.flatten(), processed_x_s_edge.entries[:, :, 0].T.flatten()
+        )
+
+    def test_processed_variable_2D_space_only(self):
+        var = pybamm.Variable(
+            "var",
+            domain=["negative particle"],
+            auxiliary_domains={"secondary": ["negative electrode"]},
+        )
+        x = pybamm.SpatialVariable("x", domain=["negative electrode"])
+        r = pybamm.SpatialVariable(
+            "r",
+            domain=["negative particle"],
+            auxiliary_domains={"secondary": ["negative electrode"]},
+        )
+
+        disc = tests.get_p2d_discretisation_for_testing()
+        disc.set_variable_slices([var])
+        x_sol = disc.process_symbol(x).entries[:, 0]
+        r_sol = disc.process_symbol(r).entries[:, 0]
+        # Keep only the first iteration of entries
+        r_sol = r_sol[: len(r_sol) // len(x_sol)]
+        var_sol = disc.process_symbol(var)
+        t_sol = np.array([0])
+        y_sol = np.ones(len(x_sol) * len(r_sol))[:, np.newaxis]
+
+        var_casadi = to_casadi(var_sol, y_sol)
+        processed_var = pybamm.ProcessedVariableComputed(
+            [var_sol],
+            [var_casadi],
+            [y_sol],
+            pybamm.Solution(t_sol, y_sol, pybamm.BaseModel(), {}),
+            warn=False,
+        )
+        np.testing.assert_array_equal(
+            processed_var.entries,
+            np.reshape(y_sol, [len(r_sol), len(x_sol), len(t_sol)]),
+        )
+        np.testing.assert_array_equal(
+            processed_var.entries,
+            processed_var.data,
+        )
+
+        # Check unroll function (2D)
+        np.testing.assert_array_equal(processed_var.unroll(), y_sol.reshape(10, 40, 1))
+
+        # Check unroll function (3D)
+        with self.assertRaises(NotImplementedError):
+            processed_var.dimensions = 3
+            processed_var.unroll()
+
+    def test_processed_variable_2D_fixed_t_scikit(self):
+        var = pybamm.Variable("var", domain=["current collector"])
+
+        disc = tests.get_2p1d_discretisation_for_testing()
+        disc.set_variable_slices([var])
+        y = disc.mesh["current collector"].edges["y"]
+        z = disc.mesh["current collector"].edges["z"]
+        var_sol = disc.process_symbol(var)
+        var_sol.mesh = disc.mesh["current collector"]
+        t_sol = np.array([0])
+        u_sol = np.ones(var_sol.shape[0])[:, np.newaxis]
+
+        var_casadi = to_casadi(var_sol, u_sol)
+        processed_var = pybamm.ProcessedVariableComputed(
+            [var_sol],
+            [var_casadi],
+            [u_sol],
+            pybamm.Solution(t_sol, u_sol, pybamm.BaseModel(), {}),
+            warn=False,
+        )
+        np.testing.assert_array_equal(
+            processed_var.entries, np.reshape(u_sol, [len(y), len(z), len(t_sol)])
+        )
+
+    def test_3D_raises_error(self):
+        var = pybamm.Variable(
+            "var",
+            domain=["negative electrode"],
+            auxiliary_domains={"secondary": ["current collector"]},
+        )
+
+        disc = tests.get_2p1d_discretisation_for_testing()
+        disc.set_variable_slices([var])
+        var_sol = disc.process_symbol(var)
+        t_sol = np.array([0, 1, 2])
+        u_sol = np.ones(var_sol.shape[0] * 3)[:, np.newaxis]
+        var_casadi = to_casadi(var_sol, u_sol)
+
+        with self.assertRaisesRegex(NotImplementedError, "Shape not recognized"):
+            pybamm.ProcessedVariableComputed(
+                [var_sol],
+                [var_casadi],
+                [u_sol],
+                pybamm.Solution(t_sol, u_sol, pybamm.BaseModel(), {}),
+                warn=False,
+            )
+
+
+if __name__ == "__main__":
+    print("Add -v for more debug output")
+    import sys
+
+    if "-v" in sys.argv:
+        debug = True
+    pybamm.settings.debug_mode = True
+    unittest.main()
diff --git a/tests/unit/test_solvers/test_solution.py b/tests/unit/test_solvers/test_solution.py
index 2ef01d7434..9fc93dfb26 100644
--- a/tests/unit/test_solvers/test_solution.py
+++ b/tests/unit/test_solvers/test_solution.py
@@ -1,6 +1,7 @@
 #
 # Tests for the Solution class
 #
+import os
 from tests import TestCase
 import json
 import pybamm
@@ -9,6 +10,7 @@
 import pandas as pd
 from scipy.io import loadmat
 from tests import get_discretisation_for_testing
+from tempfile import TemporaryDirectory
 
 
 class TestSolution(TestCase):
@@ -233,95 +235,103 @@ def test_plot(self):
         solution.plot(["c", "2c"], testing=True)
 
     def test_save(self):
-        model = pybamm.BaseModel()
-        # create both 1D and 2D variables
-        c = pybamm.Variable("c")
-        d = pybamm.Variable("d", domain="negative electrode")
-        model.rhs = {c: -c, d: 1}
-        model.initial_conditions = {c: 1, d: 2}
-        model.variables = {"c": c, "d": d, "2c": 2 * c, "c + d": c + d}
-
-        disc = get_discretisation_for_testing()
-        disc.process_model(model)
-        solution = pybamm.ScipySolver().solve(model, np.linspace(0, 1))
-
-        # test save data
-        with self.assertRaises(ValueError):
-            solution.save_data("test.pickle")
-
-        # set variables first then save
-        solution.update(["c", "d"])
-        with self.assertRaisesRegex(ValueError, "pickle"):
-            solution.save_data(to_format="pickle")
-        solution.save_data("test.pickle")
-
-        data_load = pybamm.load("test.pickle")
-        np.testing.assert_array_equal(solution.data["c"], data_load["c"])
-        np.testing.assert_array_equal(solution.data["d"], data_load["d"])
-
-        # to matlab
-        solution.save_data("test.mat", to_format="matlab")
-        data_load = loadmat("test.mat")
-        np.testing.assert_array_equal(solution.data["c"], data_load["c"].flatten())
-        np.testing.assert_array_equal(solution.data["d"], data_load["d"])
-
-        with self.assertRaisesRegex(ValueError, "matlab"):
-            solution.save_data(to_format="matlab")
-
-        # to matlab with bad variables name fails
-        solution.update(["c + d"])
-        with self.assertRaisesRegex(ValueError, "Invalid character"):
-            solution.save_data("test.mat", to_format="matlab")
-        # Works if providing alternative name
-        solution.save_data(
-            "test.mat", to_format="matlab", short_names={"c + d": "c_plus_d"}
-        )
-        data_load = loadmat("test.mat")
-        np.testing.assert_array_equal(solution.data["c + d"], data_load["c_plus_d"])
-
-        # to csv
-        with self.assertRaisesRegex(
-            ValueError, "only 0D variables can be saved to csv"
-        ):
-            solution.save_data("test.csv", to_format="csv")
-        # only save "c" and "2c"
-        solution.save_data("test.csv", ["c", "2c"], to_format="csv")
-        csv_str = solution.save_data(variables=["c", "2c"], to_format="csv")
-
-        # check string is the same as the file
-        with open("test.csv") as f:
-            # need to strip \r chars for windows
-            self.assertEqual(csv_str.replace("\r", ""), f.read())
-
-        # read csv
-        df = pd.read_csv("test.csv")
-        np.testing.assert_array_almost_equal(df["c"], solution.data["c"])
-        np.testing.assert_array_almost_equal(df["2c"], solution.data["2c"])
-
-        # to json
-        solution.save_data("test.json", to_format="json")
-        json_str = solution.save_data(to_format="json")
-
-        # check string is the same as the file
-        with open("test.json") as f:
-            # need to strip \r chars for windows
-            self.assertEqual(json_str.replace("\r", ""), f.read())
-
-        # check if string has the right values
-        json_data = json.loads(json_str)
-        np.testing.assert_array_almost_equal(json_data["c"], solution.data["c"])
-        np.testing.assert_array_almost_equal(json_data["d"], solution.data["d"])
-
-        # raise error if format is unknown
-        with self.assertRaisesRegex(ValueError, "format 'wrong_format' not recognised"):
-            solution.save_data("test.csv", to_format="wrong_format")
-
-        # test save whole solution
-        solution.save("test.pickle")
-        solution_load = pybamm.load("test.pickle")
-        self.assertEqual(solution.all_models[0].name, solution_load.all_models[0].name)
-        np.testing.assert_array_equal(solution["c"].entries, solution_load["c"].entries)
-        np.testing.assert_array_equal(solution["d"].entries, solution_load["d"].entries)
+        with TemporaryDirectory() as dir_name:
+            test_stub = os.path.join(dir_name, "test")
+
+            model = pybamm.BaseModel()
+            # create both 1D and 2D variables
+            c = pybamm.Variable("c")
+            d = pybamm.Variable("d", domain="negative electrode")
+            model.rhs = {c: -c, d: 1}
+            model.initial_conditions = {c: 1, d: 2}
+            model.variables = {"c": c, "d": d, "2c": 2 * c, "c + d": c + d}
+
+            disc = get_discretisation_for_testing()
+            disc.process_model(model)
+            solution = pybamm.ScipySolver().solve(model, np.linspace(0, 1))
+
+            # test save data
+            with self.assertRaises(ValueError):
+                solution.save_data(f"{test_stub}.pickle")
+
+            # set variables first then save
+            solution.update(["c", "d"])
+            with self.assertRaisesRegex(ValueError, "pickle"):
+                solution.save_data(to_format="pickle")
+            solution.save_data(f"{test_stub}.pickle")
+
+            data_load = pybamm.load(f"{test_stub}.pickle")
+            np.testing.assert_array_equal(solution.data["c"], data_load["c"])
+            np.testing.assert_array_equal(solution.data["d"], data_load["d"])
+
+            # to matlab
+            solution.save_data(f"{test_stub}.mat", to_format="matlab")
+            data_load = loadmat(f"{test_stub}.mat")
+            np.testing.assert_array_equal(solution.data["c"], data_load["c"].flatten())
+            np.testing.assert_array_equal(solution.data["d"], data_load["d"])
+
+            with self.assertRaisesRegex(ValueError, "matlab"):
+                solution.save_data(to_format="matlab")
+
+            # to matlab with bad variables name fails
+            solution.update(["c + d"])
+            with self.assertRaisesRegex(ValueError, "Invalid character"):
+                solution.save_data(f"{test_stub}.mat", to_format="matlab")
+            # Works if providing alternative name
+            solution.save_data(
+                f"{test_stub}.mat", to_format="matlab",
+                short_names={"c + d": "c_plus_d"}
+            )
+            data_load = loadmat(f"{test_stub}.mat")
+            np.testing.assert_array_equal(solution.data["c + d"], data_load["c_plus_d"])
+
+            # to csv
+            with self.assertRaisesRegex(
+                    ValueError, "only 0D variables can be saved to csv"
+            ):
+                solution.save_data(f"{test_stub}.csv", to_format="csv")
+            # only save "c" and "2c"
+            solution.save_data(f"{test_stub}.csv", ["c", "2c"], to_format="csv")
+            csv_str = solution.save_data(variables=["c", "2c"], to_format="csv")
+
+            # check string is the same as the file
+            with open(f"{test_stub}.csv") as f:
+                # need to strip \r chars for windows
+                self.assertEqual(csv_str.replace("\r", ""), f.read())
+
+            # read csv
+            df = pd.read_csv(f"{test_stub}.csv")
+            np.testing.assert_array_almost_equal(df["c"], solution.data["c"])
+            np.testing.assert_array_almost_equal(df["2c"], solution.data["2c"])
+
+            # to json
+            solution.save_data(f"{test_stub}.json", to_format="json")
+            json_str = solution.save_data(to_format="json")
+
+            # check string is the same as the file
+            with open(f"{test_stub}.json") as f:
+                # need to strip \r chars for windows
+                self.assertEqual(json_str.replace("\r", ""), f.read())
+
+            # check if string has the right values
+            json_data = json.loads(json_str)
+            np.testing.assert_array_almost_equal(json_data["c"], solution.data["c"])
+            np.testing.assert_array_almost_equal(json_data["d"], solution.data["d"])
+
+            # raise error if format is unknown
+            with self.assertRaisesRegex(ValueError,
+                                        "format 'wrong_format' not recognised"):
+                solution.save_data(f"{test_stub}.csv", to_format="wrong_format")
+
+            # test save whole solution
+            solution.save(f"{test_stub}.pickle")
+            solution_load = pybamm.load(f"{test_stub}.pickle")
+            self.assertEqual(solution.all_models[0].name,
+                             solution_load.all_models[0].name)
+            np.testing.assert_array_equal(solution["c"].entries,
+                                          solution_load["c"].entries)
+            np.testing.assert_array_equal(solution["d"].entries,
+                                          solution_load["d"].entries)
 
     def test_get_data_cycles_steps(self):
         model = pybamm.BaseModel()
diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index c5060e65a6..730e4cc08d 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -10,7 +10,9 @@
 import unittest
 from unittest.mock import patch
 from io import StringIO
+from tempfile import TemporaryDirectory
 
+anytree = sys.modules['anytree']
 
 class TestUtil(TestCase):
     """
@@ -29,6 +31,7 @@ def test_rmse(self):
             pybamm.rmse(np.ones(5), np.zeros(3))
 
     def test_is_constant_and_can_evaluate(self):
+        sys.modules['anytree'] = anytree
         symbol = pybamm.PrimaryBroadcast(0, "negative electrode")
         self.assertEqual(False, pybamm.is_constant_and_can_evaluate(symbol))
         symbol = pybamm.StateVector(slice(0, 1))
@@ -88,6 +91,25 @@ def test_git_commit_info(self):
         self.assertIsInstance(git_commit_info, str)
         self.assertEqual(git_commit_info[:2], "v2")
 
+    def test_have_optional_dependency(self):
+        with self.assertRaisesRegex(ModuleNotFoundError, "Optional dependency pybtex is not available."):
+            pybtex = sys.modules['pybtex']
+            sys.modules['pybtex'] = None
+            pybamm.print_citations()
+        with self.assertRaisesRegex(ModuleNotFoundError, "Optional dependency anytree is not available."):
+            with TemporaryDirectory() as dir_name:
+                sys.modules['anytree'] = None
+                test_stub = os.path.join(dir_name, "test_visualize")
+                test_name = f"{test_stub}.png"
+                c = pybamm.Variable("c", "negative electrode")
+                d = pybamm.Variable("d", "negative electrode")
+                sym = pybamm.div(c * pybamm.grad(c)) + (c / d + c - d) ** 5
+                sym.visualise(test_name)
+
+        sys.modules['pybtex'] = pybtex
+        pybamm.util.have_optional_dependency("pybtex")
+        pybamm.print_citations()
+
 
 class TestSearch(TestCase):
     def test_url_gets_to_stdout(self):
diff --git a/vcpkg-configuration.json b/vcpkg-configuration.json
index 27c1b0bcb1..8ab4e738fc 100644
--- a/vcpkg-configuration.json
+++ b/vcpkg-configuration.json
@@ -7,7 +7,7 @@
     {
       "kind": "git",
       "repository": "https://github.com/pybamm-team/sundials-vcpkg-registry.git",
-      "baseline": "2aaffb6bba7bc0b50cb74ddad636832d673851a1",
+      "baseline": "af9f5e4bc730bf2361c47f809dcfb733e7951faa",
       "packages": ["sundials"]
     },
     {
diff --git a/vcpkg.json b/vcpkg.json
index 2609370382..f62c18ddd2 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -1,6 +1,6 @@
 {
   "name": "pybamm",
-  "version-string": "23.5",
+  "version-string": "23.9",
   "dependencies": [
     "casadi",
     {