From f27aa2c5e399c65d3073b929489a45e88e6df909 Mon Sep 17 00:00:00 2001
From: Pradyot Ranjan <99216956+pradyotRanjan@users.noreply.github.com>
Date: Thu, 14 Dec 2023 22:49:53 +0530
Subject: [PATCH 1/7] Changing pyproject config

---
 pyproject.toml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 7d25c8e140..31e0b3e9bf 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -196,7 +196,7 @@ extend-select = [
   "RUF",         # Ruff-specific
   # "SIM",         # flake8-simplify
   # "T20",         # flake8-print
-  # "UP",          # pyupgrade
+   "UP",          # pyupgrade
   "YTT",         # flake8-2020
 ]
 ignore = [
@@ -214,6 +214,7 @@ ignore = [
   "RET506",      # Unnecessary `elif`
   "B018",        # Found useless expression
   "RUF002",      # Docstring contains ambiguous
+    "UP007",     # For pyupgrade
 ]
 
 [tool.ruff.lint.per-file-ignores]

From ff6d81c01331c7d269303b4a8321d9881bdf98fa Mon Sep 17 00:00:00 2001
From: Pradyot Ranjan <99216956+pradyotRanjan@users.noreply.github.com>
Date: Thu, 14 Dec 2023 22:56:39 +0530
Subject: [PATCH 2/7] changed string formatting using pyupgrade

Signed-off-by: Pradyot Ranjan <99216956+pradyotRanjan@users.noreply.github.com>
---
 .../work_precision_sets/time_vs_abstols.py    |    2 +-
 .../work_precision_sets/time_vs_dt_max.py     |    2 +-
 .../work_precision_sets/time_vs_mesh_size.py  |    2 +-
 .../time_vs_no_of_states.py                   |    2 +-
 .../work_precision_sets/time_vs_reltols.py    |    2 +-
 docs/conf.py                                  |    3 +-
 .../3-negative-particle-problem.ipynb         |    2 +-
 .../tutorial-8-solver-options.ipynb           |    4 +-
 .../compare-comsol-discharge-curve.ipynb      |    4 +-
 .../models/compare-lithium-ion.ipynb          |    2 +-
 .../compare-particle-diffusion-models.ipynb   |    4 +-
 .../examples/notebooks/models/lead-acid.ipynb |    2 +-
 .../notebooks/models/pouch-cell-model.ipynb   | 1754 ++++++++---------
 .../notebooks/models/rate-capability.ipynb    |    4 +-
 .../models/unsteady-heat-equation.ipynb       |    2 +-
 .../change-input-current.ipynb                |    2 +-
 .../parameterization/parameter-values.ipynb   |   10 +-
 .../parameterization/parameterization.ipynb   |    2 +-
 .../callbacks.ipynb                           |    2 +-
 .../notebooks/solvers/speed-up-solver.ipynb   |    6 +-
 .../spatial_methods/finite-volumes.ipynb      |   30 +-
 .../compare_comsol/compare_comsol_DFN.py      |    2 +-
 .../scripts/compare_comsol/discharge_curve.py |    4 +-
 examples/scripts/compare_particle_models.py   |    4 +-
 .../scripts/experimental_protocols/cccv.py    |    2 +-
 examples/scripts/heat_equation.py             |    2 +-
 examples/scripts/rate_capability.py           |    4 +-
 pybamm/callbacks.py                           |    2 +-
 pybamm/citations.py                           |    4 +-
 pybamm/discretisations/discretisation.py      |   58 +-
 pybamm/experiment/experiment.py               |    2 +-
 pybamm/expression_tree/array.py               |    2 +-
 pybamm/expression_tree/averages.py            |   12 +-
 pybamm/expression_tree/binary_operators.py    |   32 +-
 pybamm/expression_tree/concatenations.py      |    6 +-
 pybamm/expression_tree/functions.py           |    8 +-
 .../expression_tree/independent_variable.py   |    2 +-
 pybamm/expression_tree/input_parameter.py     |    6 +-
 pybamm/expression_tree/interpolant.py         |    6 +-
 pybamm/expression_tree/matrix.py              |    2 +-
 .../operations/convert_to_casadi.py           |    8 +-
 .../operations/evaluate_python.py             |   68 +-
 pybamm/expression_tree/operations/jacobian.py |    6 +-
 .../expression_tree/operations/serialise.py   |    2 +-
 .../operations/unpack_symbols.py              |    2 +-
 pybamm/expression_tree/state_vector.py        |   12 +-
 pybamm/expression_tree/symbol.py              |   20 +-
 pybamm/expression_tree/unary_operators.py     |   42 +-
 pybamm/expression_tree/vector.py              |    2 +-
 pybamm/geometry/battery_geometry.py           |    4 +-
 pybamm/install_odes.py                        |   20 +-
 pybamm/meshes/meshes.py                       |    6 +-
 pybamm/meshes/scikit_fem_submeshes.py         |   10 +-
 pybamm/models/base_model.py                   |   50 +-
 .../full_battery_models/base_battery_model.py |   30 +-
 .../equivalent_circuit/thevenin.py            |    2 +-
 pybamm/models/submodels/base_submodel.py      |    4 +-
 pybamm/parameters/parameter_sets.py           |    2 +-
 pybamm/parameters/parameter_values.py         |   34 +-
 pybamm/parameters/process_parameter_data.py   |    2 +-
 pybamm/plotting/quick_plot.py                 |   22 +-
 pybamm/settings.py                            |    2 +-
 pybamm/solvers/algebraic_solver.py            |    8 +-
 pybamm/solvers/base_solver.py                 |   46 +-
 pybamm/solvers/casadi_algebraic_solver.py     |    4 +-
 pybamm/solvers/casadi_solver.py               |    8 +-
 pybamm/solvers/jax_solver.py                  |    8 +-
 pybamm/solvers/processed_variable.py          |    6 +-
 pybamm/solvers/processed_variable_computed.py |    4 +-
 pybamm/solvers/scikits_dae_solver.py          |    2 +-
 pybamm/solvers/scikits_ode_solver.py          |    2 +-
 pybamm/solvers/scipy_solver.py                |    2 +-
 pybamm/solvers/solution.py                    |   11 +-
 pybamm/spatial_methods/finite_volume.py       |   28 +-
 .../spatial_methods/scikit_finite_element.py  |   12 +-
 pybamm/spatial_methods/spectral_volume.py     |    8 +-
 pybamm/util.py                                |   14 +-
 run-tests.py                                  |    4 +-
 scripts/install_KLU_Sundials.py               |   12 +-
 setup.py                                      |   20 +-
 .../test_models/standard_model_tests.py       |    4 +-
 .../test_models/standard_output_comparison.py |    4 +-
 .../test_models/standard_output_tests.py      |    4 +-
 .../test_asymptotics_convergence.py           |    2 +-
 tests/unit/test_callbacks.py                  |   12 +-
 tests/unit/test_citations.py                  |    4 +-
 .../test_expression_tree/test_functions.py    |    2 +-
 .../test_operations/test_evaluate_python.py   |   24 +-
 .../test_parameters/test_current_functions.py |    2 +-
 .../test_serialisation/test_serialisation.py  |    2 +-
 tests/unit/test_simulation.py                 |    2 +-
 .../test_solvers/test_processed_variable.py   |    2 +-
 .../test_processed_variable_computed.py       |    2 +-
 tests/unit/test_timer.py                      |    2 +-
 94 files changed, 1260 insertions(+), 1360 deletions(-)

diff --git a/benchmarks/work_precision_sets/time_vs_abstols.py b/benchmarks/work_precision_sets/time_vs_abstols.py
index 9a96f07514..d680766c43 100644
--- a/benchmarks/work_precision_sets/time_vs_abstols.py
+++ b/benchmarks/work_precision_sets/time_vs_abstols.py
@@ -98,7 +98,7 @@
 
 content = f"# PyBaMM {pybamm.__version__}\n## Solve Time vs Abstols\n<img src='./benchmark_images/time_vs_abstols_{pybamm.__version__}.png'>\n"
 
-with open("./benchmarks/release_work_precision_sets.md", "r") as original:
+with open("./benchmarks/release_work_precision_sets.md") as original:
     data = original.read()
 with open("./benchmarks/release_work_precision_sets.md", "w") as modified:
     modified.write(f"{content}\n{data}")
diff --git a/benchmarks/work_precision_sets/time_vs_dt_max.py b/benchmarks/work_precision_sets/time_vs_dt_max.py
index 3e428b702c..a1f8ca06bc 100644
--- a/benchmarks/work_precision_sets/time_vs_dt_max.py
+++ b/benchmarks/work_precision_sets/time_vs_dt_max.py
@@ -100,7 +100,7 @@
 
 content = f"## Solve Time vs dt_max\n<img src='./benchmark_images/time_vs_dt_max_{pybamm.__version__}.png'>\n"
 
-with open("./benchmarks/release_work_precision_sets.md", "r") as original:
+with open("./benchmarks/release_work_precision_sets.md") as original:
     data = original.read()
 with open("./benchmarks/release_work_precision_sets.md", "w") as modified:
     modified.write(f"{content}\n{data}")
diff --git a/benchmarks/work_precision_sets/time_vs_mesh_size.py b/benchmarks/work_precision_sets/time_vs_mesh_size.py
index f0f13f706b..cbab18d16c 100644
--- a/benchmarks/work_precision_sets/time_vs_mesh_size.py
+++ b/benchmarks/work_precision_sets/time_vs_mesh_size.py
@@ -80,7 +80,7 @@
 
 content = f"## Solve Time vs Mesh size\n<img src='./benchmark_images/time_vs_mesh_size_{pybamm.__version__}.png'>\n"
 
-with open("./benchmarks/release_work_precision_sets.md", "r") as original:
+with open("./benchmarks/release_work_precision_sets.md") as original:
     data = original.read()
 with open("./benchmarks/release_work_precision_sets.md", "w") as modified:
     modified.write(f"{content}\n{data}")
diff --git a/benchmarks/work_precision_sets/time_vs_no_of_states.py b/benchmarks/work_precision_sets/time_vs_no_of_states.py
index eb27aba322..febc69f0a1 100644
--- a/benchmarks/work_precision_sets/time_vs_no_of_states.py
+++ b/benchmarks/work_precision_sets/time_vs_no_of_states.py
@@ -84,7 +84,7 @@
 
 content = f"## Solve Time vs Number of states\n<img src='./benchmark_images/time_vs_no_of_states_{pybamm.__version__}.png'>\n"
 
-with open("./benchmarks/release_work_precision_sets.md", "r") as original:
+with open("./benchmarks/release_work_precision_sets.md") as original:
     data = original.read()
 with open("./benchmarks/release_work_precision_sets.md", "w") as modified:
     modified.write(f"{content}\n{data}")
diff --git a/benchmarks/work_precision_sets/time_vs_reltols.py b/benchmarks/work_precision_sets/time_vs_reltols.py
index 93964910a8..42e9a1bab1 100644
--- a/benchmarks/work_precision_sets/time_vs_reltols.py
+++ b/benchmarks/work_precision_sets/time_vs_reltols.py
@@ -104,7 +104,7 @@
 
 content = f"## Solve Time vs Reltols\n<img src='./benchmark_images/time_vs_reltols_{pybamm.__version__}.png'>\n"
 
-with open("./benchmarks/release_work_precision_sets.md", "r") as original:
+with open("./benchmarks/release_work_precision_sets.md") as original:
     data = original.read()
 with open("./benchmarks/release_work_precision_sets.md", "w") as modified:
     modified.write(f"{content}\n{data}")
diff --git a/docs/conf.py b/docs/conf.py
index 55692309dc..35edadb249 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 #
 # Configuration file for the Sphinx documentation builder.
 #
@@ -168,7 +167,7 @@
     ],
 }
 
-html_title = "%s v%s Manual" % (project, version)
+html_title = f"{project} v{version} Manual"
 html_last_updated_fmt = "%Y-%m-%d"
 html_css_files = ["pybamm.css"]
 html_context = {"default_mode": "light"}
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 2c338149e7..b04616c5f9 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
@@ -307,7 +307,7 @@
     "\n",
     "r = mesh[\"negative particle\"].nodes # radial position\n",
     "time = 1000  # time in seconds\n",
-    "ax2.plot(r * 1e6, c(t=time, r=r), label=\"t={}[s]\".format(time))\n",
+    "ax2.plot(r * 1e6, c(t=time, r=r), label=f\"t={time}[s]\")\n",
     "ax2.set_xlabel(\"Particle radius [microns]\")\n",
     "ax2.set_ylabel(\"Concentration [mol.m-3]\")\n",
     "ax2.legend()\n",
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 46a7b24346..2e55321659 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
@@ -98,9 +98,9 @@
     "\n",
     "# solve\n",
     "safe_sim.solve([0, 3600])\n",
-    "print(\"Safe mode solve time: {}\".format(safe_sim.solution.solve_time))\n",
+    "print(f\"Safe mode solve time: {safe_sim.solution.solve_time}\")\n",
     "fast_sim.solve([0, 3600])\n",
-    "print(\"Fast mode solve time: {}\".format(fast_sim.solution.solve_time))\n",
+    "print(f\"Fast mode solve time: {fast_sim.solution.solve_time}\")\n",
     "\n",
     "# plot solutions\n",
     "pybamm.dynamic_plot([safe_sim, fast_sim])"
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 90611a91a0..462f03827b 100644
--- a/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb
+++ b/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb
@@ -167,7 +167,7 @@
     "\n",
     "    # load the comsol results\n",
     "    comsol_results_path = pybamm.get_parameters_filepath(\n",
-    "        \"input/comsol_results/comsol_{}C.pickle\".format(key),\n",
+    "        f\"input/comsol_results/comsol_{key}C.pickle\",\n",
     "    )\n",
     "    comsol_variables = pickle.load(open(comsol_results_path, 'rb'))\n",
     "    comsol_time = comsol_variables[\"time\"]\n",
@@ -203,7 +203,7 @@
     "        voltage_sol,\n",
     "        color=color,\n",
     "        linestyle=\"-\",\n",
-    "        label=\"{} C\".format(C_rate),\n",
+    "        label=f\"{C_rate} C\",\n",
     "    )\n",
     "    voltage_difference_plot.plot(\n",
     "        discharge_capacity_sol[0:end_index], voltage_difference, color=color\n",
diff --git a/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb b/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb
index f194a62d02..74157628f8 100644
--- a/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb
+++ b/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb
@@ -272,7 +272,7 @@
     "    solver = pybamm.CasadiSolver()\n",
     "    timer.reset()\n",
     "    solution = solver.solve(model, t_eval, inputs={\"Current function [A]\": 1})\n",
-    "    print(\"Solved the {} in {}\".format(model.name, timer.time()))\n",
+    "    print(f\"Solved the {model.name} in {timer.time()}\")\n",
     "    solutions[model_name] = solution"
    ]
   },
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 6bd9f4cf63..da6f05870e 100644
--- a/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb
+++ b/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb
@@ -124,8 +124,8 @@
     "for sim in simulations:\n",
     "    sim.solve(t_eval, inputs={\"Current function [A]\": 0.68})\n",
     "    solutions_1C.append(sim.solution)\n",
-    "    print(\"Particle model: {}\".format(sim.model.name))\n",
-    "    print(\"Solve time: {}s\".format(sim.solution.solve_time))"
+    "    print(f\"Particle model: {sim.model.name}\")\n",
+    "    print(f\"Solve time: {sim.solution.solve_time}s\")"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/models/lead-acid.ipynb b/docs/source/examples/notebooks/models/lead-acid.ipynb
index f550540182..0dd20126a6 100644
--- a/docs/source/examples/notebooks/models/lead-acid.ipynb
+++ b/docs/source/examples/notebooks/models/lead-acid.ipynb
@@ -228,7 +228,7 @@
     "    solver = pybamm.CasadiSolver()\n",
     "    timer.reset()\n",
     "    solution = solver.solve(model, t_eval, inputs={\"Current function [A]\": 1})\n",
-    "    print(\"Solved the {} in {}\".format(model.name, timer.time()))\n",
+    "    print(f\"Solved the {model.name} in {timer.time()}\")\n",
     "    solutions[model] = solution"
    ]
   },
diff --git a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
index a9431211af..2c58b1861f 100644
--- a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
+++ b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
@@ -1,879 +1,879 @@
 {
-      "cells": [
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "# Pouch cell model"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "In this notebook we compare the solutions of two reduced-order models of a lithium-ion pouch cell with the full solution obtained using COMSOL. This example is based on the results in [[6]](#References). The code used to produce the results in [[6]](#References) can be found [here](https://github.com/rtimms/asymptotic-pouch-cell).\n",
-                        "\n",
-                        "The full model is based on the Doyle-Fuller-Newman model [[2]](#References) and, in the interest of simplicity, considers a one-dimensional current collector (i.e. variation in one of the current collector dimensions is ignored), resulting in a 2D macroscopic model.\n",
-                        "\n",
-                        "The first of the reduced order models, which is applicable in the limit of large conductivity in the current collectors, solves a one-dimensional problem in the current collectors coupled to a one-dimensional DFN model describing the through-cell electrochemistry at each point. We refer to this as a 1+1D model, though since the DFN is already a pseudo-two-dimensional model, perhaps it is more properly a 1+1+1D model.\n",
-                        "\n",
-                        "The second reduced order model, which is applicable in the limit of very large conductivity in the current collectors, solves a single (averaged) one-dimensional DFN model for the through-cell behaviour and an uncoupled problem for the distribution of potential in the current collectors (from which the resistance and heat source can be calculated). We refer to this model as the DFNCC, where the \"CC\" indicates the additional (uncoupled) current collector problem.\n",
-                        "\n",
-                        "All of the model equations, and derivations of the reduced-order models, can be found in [[6]](#References)."
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "## Solving the reduced-order pouch cell models in PyBaMM"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "We begin by importing PyBaMM along with the other packages required in this notebook"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 1,
-                  "metadata": {},
-                  "outputs": [
-                        {
-                              "name": "stdout",
-                              "output_type": "stream",
-                              "text": [
-                                    "\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",
-                        "import pybamm\n",
-                        "import pickle\n",
-                        "import matplotlib.pyplot as plt\n",
-                        "import numpy as np\n",
-                        "import scipy.interpolate as interp"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "We then need to load up the appropriate models. For the DFNCC we require a 1D model of the current collectors and an average 1D DFN model for the through-cell electrochemistry. The 1+1D pouch cell model is built directly into PyBaMM and are accessed by passing the model option \"dimensionality\" which can be 1 or 2, corresponding to 1D or 2D current collectors. This option can be passed to any existing electrochemical model (e.g. [SPM](./SPM.ipynb), [SPMe](./SPMe.ipynb), [DFN](./DFN.ipynb)). Here we choose the DFN model. \n",
-                        "\n",
-                        "For both electrochemical models we choose an \"x-lumped\" thermal model, meaning we assume that the temperature is uniform in the through-cell direction $x$, but account for the variation in temperature in the transverse direction $z$."
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 2,
-                  "metadata": {},
-                  "outputs": [
-                        {
-                              "name": "stderr",
-                              "output_type": "stream",
-                              "text": [
-                                    "/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"
-                              ]
-                        }
-                  ],
-                  "source": [
-                        "cc_model = pybamm.current_collector.EffectiveResistance({\"dimensionality\": 1})\n",
-                        "dfn_av = pybamm.lithium_ion.DFN({\"thermal\": \"lumped\"}, name=\"Average DFN\")\n",
-                        "dfn = pybamm.lithium_ion.DFN(\n",
-                        "    {\"current collector\": \"potential pair\", \"dimensionality\": 1, \"thermal\": \"x-lumped\"},\n",
-                        "    name=\"1+1D DFN\",\n",
-                        ")"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "We then add the models to a dictionary for easy access later"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 3,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "models = {\"Current collector\": cc_model, \"Average DFN\": dfn_av, \"1+1D DFN\": dfn}"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "Next we update the parameters to match those used in the COMSOL simulation. In particular, we set the current to correspond to a 3C discharge and assume uniform Newton cooling on all boundaries."
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 4,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "param = dfn.default_parameter_values\n",
-                        "I_1C = param[\"Nominal cell capacity [A.h]\"]  # 1C current is cell capacity multipled by 1 hour\n",
-                        "param.update(\n",
-                        "    {\n",
-                        "        \"Current function [A]\": I_1C * 3,       \n",
-                        "        \"Negative electrode diffusivity [m2.s-1]\": 3.9 * 10 ** (-14),\n",
-                        "        \"Positive electrode diffusivity [m2.s-1]\": 10 ** (-13),\n",
-                        "        \"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\": 10,\n",
-                        "        \"Positive current collector surface heat transfer coefficient [W.m-2.K-1]\": 10,\n",
-                        "        \"Negative tab heat transfer coefficient [W.m-2.K-1]\": 10,\n",
-                        "        \"Positive tab heat transfer coefficient [W.m-2.K-1]\": 10,\n",
-                        "        \"Edge heat transfer coefficient [W.m-2.K-1]\": 10,\n",
-                        "        \"Total heat transfer coefficient [W.m-2.K-1]\": 10,\n",
-                        "    }\n",
-                        ")"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "In this example we choose to discretise in space using 16 nodes per domain."
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 5,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "npts = 16\n",
-                        "var_pts = {\n",
-                        "    \"x_n\": npts,\n",
-                        "    \"x_s\": npts,\n",
-                        "    \"x_p\": npts,\n",
-                        "    \"r_n\": npts,\n",
-                        "    \"r_p\": npts,\n",
-                        "    \"z\": npts,\n",
-                        "}"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "Before solving the models we load the COMSOL data so that we can request the output at the times in the COMSOL solution"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 6,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "comsol_results_path = pybamm.get_parameters_filepath(\n",
-                        "    \"input/comsol_results/comsol_1plus1D_3C.pickle\"\n",
-                        ")\n",
-                        "comsol_variables = pickle.load(open(comsol_results_path, \"rb\"))"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "Next we loop over the models, creating and solving a simulation for each."
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 7,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "simulations = {}\n",
-                        "solutions = {}  # store solutions in a separate dict for easy access later\n",
-                        "for name, model in models.items():\n",
-                        "    sim = pybamm.Simulation(model, parameter_values=param, var_pts=var_pts)\n",
-                        "    simulations[name] = sim  # store simulation for later\n",
-                        "    if name == \"Current collector\":\n",
-                        "        # model is independent of time, so just solve arbitrarily at t=0 using \n",
-                        "        # the default algebraic solver\n",
-                        "        t_eval = np.array([0])\n",
-                        "        solutions[name] = sim.solve(t_eval=t_eval)        \n",
-                        "    else:\n",
-                        "        # solve at COMSOL times using Casadi solver in \"fast\" mode\n",
-                        "        t_eval = comsol_variables[\"time\"]       \n",
-                        "        solutions[name] = sim.solve(solver=pybamm.CasadiSolver(mode=\"fast\"), t_eval=t_eval)"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "## Creating the COMSOL model"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "In this section we show how to create a PyBaMM \"model\" from the COMSOL solution. If you are just interested in seeing the comparison the skip ahead to the section \"Comparing the full and reduced-order models\".\n"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "To create a PyBaMM model from the COMSOL data we must create a `pybamm.Function` object for each variable. We do this by interpolating in space to match the PyBaMM mesh and then creating a function to interpolate in time. The following cell defines the function that handles the creation of the `pybamm.Function` object."
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 8,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "# set up times\n",
-                        "comsol_t = comsol_variables[\"time\"]\n",
-                        "pybamm_t = comsol_t\n",
-                        "# set up space\n",
-                        "mesh = simulations[\"1+1D DFN\"].mesh\n",
-                        "L_z = param.evaluate(dfn.param.L_z)\n",
-                        "pybamm_z = mesh[\"current collector\"].nodes\n",
-                        "z_interp = pybamm_z\n",
-                        "\n",
-                        "\n",
-                        "def get_interp_fun_curr_coll(variable_name):\n",
-                        "    \"\"\"\n",
-                        "    Create a :class:`pybamm.Function` object using the variable (interpolate in space \n",
-                        "    to match nodes, and then create function to interpolate in time)\n",
-                        "    \"\"\"\n",
-                        "\n",
-                        "    comsol_z = comsol_variables[variable_name + \"_z\"]\n",
-                        "    variable = comsol_variables[variable_name]\n",
-                        "    variable = interp.interp1d(comsol_z, variable, axis=0, kind=\"linear\")(z_interp)\n",
-                        "\n",
-                        "    # Make sure to use dimensional time\n",
-                        "    fun = pybamm.Interpolant(\n",
-                        "        comsol_t,\n",
-                        "        variable.T,\n",
-                        "        pybamm.t,\n",
-                        "        name=variable_name + \"_comsol\"\n",
-                        "    )\n",
-                        "    fun.domains = {\"primary\": \"current collector\"}\n",
-                        "    fun.mesh = mesh.combine_submeshes(\"current collector\")\n",
-                        "    fun.secondary_mesh = None\n",
-                        "\n",
-                        "    return fun"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "We then pass the variables of interest to the interpolating function"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 9,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "comsol_voltage = pybamm.Interpolant(\n",
-                        "    comsol_t, \n",
-                        "    comsol_variables[\"voltage\"],\n",
-                        "    pybamm.t,\n",
-                        "    name=\"voltage_comsol\",\n",
-                        ")\n",
-                        "comsol_voltage.mesh = None\n",
-                        "comsol_voltage.secondary_mesh = None\n",
-                        "comsol_phi_s_cn = get_interp_fun_curr_coll(\"phi_s_cn\")\n",
-                        "comsol_phi_s_cp = get_interp_fun_curr_coll(\"phi_s_cp\")\n",
-                        "comsol_current = get_interp_fun_curr_coll(\"current\")\n",
-                        "comsol_temperature = get_interp_fun_curr_coll(\"temperature\")"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "and add them to a `pybamm.BaseModel` object"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 10,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "comsol_model = pybamm.BaseModel()\n",
-                        "comsol_model._geometry = pybamm.battery_geometry(options={\"dimensionality\": 1})\n",
-                        "comsol_model.variables = {\n",
-                        "    \"Voltage [V]\": comsol_voltage,\n",
-                        "    \"Negative current collector potential [V]\": comsol_phi_s_cn,\n",
-                        "    \"Positive current collector potential [V]\": comsol_phi_s_cp,\n",
-                        "    \"Current collector current density [A.m-2]\": comsol_current,\n",
-                        "    \"X-averaged cell temperature [K]\": comsol_temperature,\n",
-                        "    # Add spatial variables to match pybamm model\n",
-                        "    \"z [m]\": simulations[\"1+1D DFN\"].built_model.variables[\"z [m]\"],   \n",
-                        "}"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "We then add the solution object from the 1+1D model. This is just so that PyBaMM uses the same times behind the scenes when dealing with COMSOL model and the reduced-order models: the variables in `comsol_model.variables` are functions of time only that return the (interpolated in space) COMSOL solution."
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 11,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "comsol_solution = pybamm.Solution(solutions[\"1+1D DFN\"].t, solutions[\"1+1D DFN\"].y, comsol_model, {})"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "## Comparing the full and reduced-order models"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "The DFNCC requires some post-processing to extract the solution variables. In particular, we need to pass the current and voltage from the average DFN model to the current collector model in order to compute the distribution of the potential in the current collectors and to account for the effect of the current collector resistance in the voltage. \n",
-                        "\n",
-                        "This process is automated by the method `post_process` which accepts the current collector solution object, the parameters and the voltage and current from the average DFN model. The results are stored in the dictionary `dfncc_vars`"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 12,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "V_av = solutions[\"Average DFN\"][\"Voltage [V]\"]\n",
-                        "I_av = solutions[\"Average DFN\"][\"Total current density [A.m-2]\"]\n",
-                        "\n",
-                        "dfncc_vars = cc_model.post_process(\n",
-                        "    solutions[\"Current collector\"], param, V_av, I_av\n",
-                        ")"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "Next we create a function to create some custom plots. For a given variable the plots will show: (a) the COMSOL results as a function of position in the current collector $z$ and time $t$; (b) a comparison of the full and reduced-order models and a sequence of times; (c) the time-averaged error between the full and reduced-order models as a function of space; and (d) the space-averaged error between the full and reduced-order models as a function of time."
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 13,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "def plot(\n",
-                        "    t_plot,\n",
-                        "    z_plot,\n",
-                        "    t_slices,\n",
-                        "    var_name,\n",
-                        "    units,\n",
-                        "    comsol_var_fun,\n",
-                        "    dfn_var_fun,\n",
-                        "    dfncc_var_fun,\n",
-                        "    param,\n",
-                        "    cmap=\"viridis\",\n",
-                        "):\n",
-                        "\n",
-                        "    fig, ax = plt.subplots(2, 2, figsize=(13, 7))\n",
-                        "    fig.subplots_adjust(\n",
-                        "        left=0.15, bottom=0.1, right=0.95, top=0.95, wspace=0.4, hspace=0.8\n",
-                        "    )\n",
-                        "    # plot comsol var\n",
-                        "    comsol_var = comsol_var_fun(t=t_plot, z=z_plot)\n",
-                        "    comsol_var_plot = ax[0, 0].pcolormesh(\n",
-                        "        z_plot * 1e3, t_plot, np.transpose(comsol_var), shading=\"gouraud\", cmap=cmap\n",
-                        "    )\n",
-                        "    if \"cn\" in var_name:\n",
-                        "        format = \"%.0e\"\n",
-                        "    elif \"cp\" in var_name:\n",
-                        "        format = \"%.0e\"\n",
-                        "    else:\n",
-                        "        format = None\n",
-                        "    fig.colorbar(\n",
-                        "        comsol_var_plot,\n",
-                        "        ax=ax,\n",
-                        "        format=format,\n",
-                        "        location=\"top\",\n",
-                        "        shrink=0.42,\n",
-                        "        aspect=20,\n",
-                        "        anchor=(0.0, 0.0),\n",
-                        "    )\n",
-                        "\n",
-                        "    # plot slices\n",
-                        "    ccmap = plt.get_cmap(\"inferno\")\n",
-                        "    for ind, t in enumerate(t_slices):\n",
-                        "        color = ccmap(float(ind) / len(t_slices))\n",
-                        "        comsol_var_slice = comsol_var_fun(t=t, z=z_plot)\n",
-                        "        dfn_var_slice = dfn_var_fun(t=t, z=z_plot)\n",
-                        "        dfncc_var_slice = dfncc_var_fun(t=np.array([t]), z=z_plot)\n",
-                        "        ax[0, 1].plot(\n",
-                        "            z_plot * 1e3, comsol_var_slice, \"o\", fillstyle=\"none\", color=color\n",
-                        "        )\n",
-                        "        ax[0, 1].plot(\n",
-                        "            z_plot * 1e3,\n",
-                        "            dfn_var_slice,\n",
-                        "            \"-\",\n",
-                        "            color=color,\n",
-                        "            label=\"{:.0f} s\".format(t_slices[ind]),\n",
-                        "        )\n",
-                        "        ax[0, 1].plot(z_plot * 1e3, dfncc_var_slice, \":\", color=color)\n",
-                        "    # add dummy points for legend of styles\n",
-                        "    comsol_p, = ax[0, 1].plot(np.nan, np.nan, \"ko\", fillstyle=\"none\")\n",
-                        "    pybamm_p, = ax[0, 1].plot(np.nan, np.nan, \"k-\", fillstyle=\"none\")\n",
-                        "    dfncc_p, = ax[0, 1].plot(np.nan, np.nan, \"k:\", fillstyle=\"none\")\n",
-                        "\n",
-                        "    # compute errors\n",
-                        "    dfn_var = dfn_var_fun(t=t_plot, z=z_plot)\n",
-                        "    dfncc_var = dfncc_var_fun(t=t_plot, z=z_plot)\n",
-                        "    error = np.abs(comsol_var - dfn_var)\n",
-                        "    error_bar = np.abs(comsol_var - dfncc_var)\n",
-                        "\n",
-                        "    # plot time averaged error\n",
-                        "    ax[1, 0].plot(z_plot * 1e3, np.nanmean(error, axis=1), \"k-\", label=r\"$1+1$D\")\n",
-                        "    ax[1, 0].plot(z_plot * 1e3, np.nanmean(error_bar, axis=1), \"k:\", label=\"DFNCC\")\n",
-                        "\n",
-                        "    # plot z averaged error\n",
-                        "    ax[1, 1].plot(t_plot, np.nanmean(error, axis=0), \"k-\", label=r\"$1+1$D\")\n",
-                        "    ax[1, 1].plot(t_plot, np.nanmean(error_bar, axis=0), \"k:\", label=\"DFNCC\")\n",
-                        "\n",
-                        "    # set ticks\n",
-                        "    ax[0, 0].tick_params(which=\"both\")\n",
-                        "    ax[0, 1].tick_params(which=\"both\")\n",
-                        "    ax[1, 0].tick_params(which=\"both\")\n",
-                        "    if var_name in [\"$\\mathcal{I}^*$\"]:\n",
-                        "        ax[1, 0].set_yscale(\"log\")\n",
-                        "        ax[1, 0].set_yticks = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e-2, 1e-1, 1]\n",
-                        "    else:\n",
-                        "        ax[1, 0].ticklabel_format(style=\"sci\", scilimits=(-2, 2), axis=\"y\")\n",
-                        "    ax[1, 1].tick_params(which=\"both\")\n",
-                        "    if var_name in [\"$\\phi^*_{\\mathrm{s,cn}}$\", \"$\\phi^*_{\\mathrm{s,cp}} - V^*$\"]:\n",
-                        "        ax[1, 0].ticklabel_format(style=\"sci\", scilimits=(-2, 2), axis=\"y\")\n",
-                        "    else:\n",
-                        "        ax[1, 1].set_yscale(\"log\")\n",
-                        "        ax[1, 1].set_yticks = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e-2, 1e-1, 1]\n",
-                        "\n",
-                        "    # set labels\n",
-                        "    ax[0, 0].set_xlabel(r\"$z^*$ [mm]\")\n",
-                        "    ax[0, 0].set_ylabel(r\"$t^*$ [s]\")\n",
-                        "    ax[0, 0].set_title(r\"{} {}\".format(var_name, units), y=1.5)\n",
-                        "    ax[0, 1].set_xlabel(r\"$z^*$ [mm]\")\n",
-                        "    ax[0, 1].set_ylabel(r\"{}\".format(var_name))\n",
-                        "    ax[1, 0].set_xlabel(r\"$z^*$ [mm]\")\n",
-                        "    ax[1, 0].set_ylabel(\"Time-averaged\" + \"\\n\" + r\"absolute error {}\".format(units))\n",
-                        "    ax[1, 1].set_xlabel(r\"$t^*$ [s]\")\n",
-                        "    ax[1, 1].set_ylabel(\"Space-averaged\" + \"\\n\" + r\"absolute error {}\".format(units))\n",
-                        "\n",
-                        "    ax[0, 0].text(-0.1, 1.6, \"(a)\", transform=ax[0, 0].transAxes)\n",
-                        "    ax[0, 1].text(-0.1, 1.6, \"(b)\", transform=ax[0, 1].transAxes)\n",
-                        "    ax[1, 0].text(-0.1, 1.2, \"(c)\", transform=ax[1, 0].transAxes)\n",
-                        "    ax[1, 1].text(-0.1, 1.2, \"(d)\", transform=ax[1, 1].transAxes)\n",
-                        "\n",
-                        "    leg1 = ax[0, 1].legend(\n",
-                        "        bbox_to_anchor=(0, 1.1, 1.0, 0.102),\n",
-                        "        loc=\"lower left\",\n",
-                        "        borderaxespad=0.0,\n",
-                        "        ncol=3,\n",
-                        "        mode=\"expand\",\n",
-                        "    )\n",
-                        "\n",
-                        "    ax[0, 1].legend(\n",
-                        "        [comsol_p, pybamm_p, dfncc_p],\n",
-                        "        [\"COMSOL\", r\"$1+1$D\", \"DFNCC\"],\n",
-                        "        bbox_to_anchor=(0, 1.5, 1.0, 0.102),\n",
-                        "        loc=\"lower left\",\n",
-                        "        borderaxespad=0.0,\n",
-                        "        ncol=3,\n",
-                        "        mode=\"expand\",\n",
-                        "    )\n",
-                        "    ax[0, 1].add_artist(leg1)\n",
-                        "\n",
-                        "    ax[1, 0].legend(\n",
-                        "        bbox_to_anchor=(0.0, 1.1, 1.0, 0.102),\n",
-                        "        loc=\"lower right\",\n",
-                        "        borderaxespad=0.0,\n",
-                        "        ncol=3,\n",
-                        "    )\n",
-                        "    ax[1, 1].legend(\n",
-                        "        bbox_to_anchor=(0.0, 1.1, 1.0, 0.102),\n",
-                        "        loc=\"lower right\",\n",
-                        "        borderaxespad=0.0,\n",
-                        "        ncol=3,\n",
-                        "    )"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "We then set up the times and points in space to use in the plots "
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 14,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "t_plot = comsol_t\n",
-                        "z_plot = z_interp\n",
-                        "t_slices = np.array([600, 1200, 1800, 2400, 3000]) / 3"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "and plot the negative current collector potential"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 15,
-                  "metadata": {},
-                  "outputs": [
-                        {
-                              "data": {
-                                    "image/png": 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",
-                                    "text/plain": [
-                                          "<Figure size 1300x700 with 5 Axes>"
-                                    ]
-                              },
-                              "metadata": {},
-                              "output_type": "display_data"
-                        }
-                  ],
-                  "source": [
-                        "var = \"Negative current collector potential [V]\"\n",
-                        "comsol_var_fun = comsol_solution[var]\n",
-                        "dfn_var_fun = solutions[\"1+1D DFN\"][var]\n",
-                        "\n",
-                        "dfncc_var_fun = dfncc_vars[var]\n",
-                        "plot(\n",
-                        "    t_plot,\n",
-                        "    z_plot,\n",
-                        "    t_slices,\n",
-                        "    \"$\\phi^*_{\\mathrm{s,cn}}$\",\n",
-                        "    \"[V]\",\n",
-                        "    comsol_var_fun,\n",
-                        "    dfn_var_fun,\n",
-                        "    dfncc_var_fun,\n",
-                        "    param,\n",
-                        "    cmap=\"cividis\",\n",
-                        ")"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "the positive current collector potential with respect to voltage"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 16,
-                  "metadata": {},
-                  "outputs": [
-                        {
-                              "data": {
-                                    "image/png": 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xYkWVy96xYwc452jfvr1rmW3btgGAtNC1SPv27Y0yIjfeeCPeeOMNANraORdddJFtB7Qrr7wS11xzDc4991ykpaXhsssuwwsvvICjR49K8t1k6+lO8oljo7i4GAkJCTX+Ki4urm3Tq5z27dtLP7oRFUcfF3/++aeR9uSTTyIhIcG2HELTpk2RkJCAPXv2GGkvvvgiEhISMHr0aKlsq1atkJCQIK2rWZHH+CqD9bxPnjxZGuszZ8601XnggQewc+dOvPPOO7a8P//8E4cOHYp4nwaA7du3G/JPZCj0hSDqATt27EBRURE6d+4MAFi3bh0AGBE9a9euRcuWLZGUlFRrOrZv3962joAbaWlprnnigsxJSUn4xz/+gb59+2Lw4MFVomdlyM7ORvv27fHuu+/ivvvuw7vvvgvOOe22RRAEcQKyf/9+AMBpp53mmK+n6+WqkspEUVQ24uK6667DPffcg19//RVz5sxxnNx4PB7Mnj0b//znP/HVV19h5cqVeOyxx/DEE0/ghx9+MP7u1rdoD6Jmeeyxx/DYY48ZxyUlJfj+++8lh8GmTZuQmZlZpXI55zW6bTVxYmA973fddRdGjRplHDdu3NhWp0mTJrjzzjsxZcoU248UFb3/1ZX7JDl6CKIeoO+QooeD//zzz2jcuDHS09Px559/YtmyZfj73/9emyoiNTVVuvkeK+IW62vWrMHevXvx/vvvH3e7x8qIESPw4IMPYt26dZg7dy7atGmD7t2715o+BEEQhDO6M2PDhg04++yzbfkbNmyQylUlbdq0AWPMtpmASNu2bQFoP2T06tXLlr9582bjb6BISkoKLr74YowePRqlpaUYNGgQCgoKHGU0b94c119/Pa6//nr84x//QNu2bTFr1iw88sgjaNu2reOulrpsUUfi+ImLi0NhYWGtyK0u/v73v+Oqq64yjkeMGIFhw4bh8ssvN9KcHok5XjZv3lwnHh89EdHHoDgu7rrrLkycONG2HIoeTS9uNjJ27FjcdNNNtkh2PdJGLFsV8wAR63lv3LgxWrduXW69SZMm4aWXXsJLL70kpTdp0gTJyckR79OAeR/csmULunbtegya1wwn/KNbBEGUT/PmzQEAubm5ALSIHj2a5/bbb4eiKJg4cWJtqVeltG7dGj6fD6tWrcK0adNw+eWX1+ojm3r0zpQpU7B27VqK5iEIgjhB6dOnD1q1aoXHHnvMtj6DqqqYNm0asrKy0KdPnyqX3ahRIwwYMAAvvvii8Ti1yOHDh9G/f380atQITz/9tC3/448/xvbt23HNNdc4tn/jjTdi6dKl+Nvf/lbhR4cbNmyItLQ0Q5/hw4dj+/bt+OSTT2xln376aaSkpODCCy+sUNtE+TDGEB8fX+Ov6ox8adSoEVq3bm28YmNj0bRpUymtqtdS/eqrr7B+/XoMGzasSts9WXAaFz6fD/Hx8YiOjnYsKz76GhUVhfj4eMTExFSobFVxPOc9ISEBDz74IKZOnSo5xRVFwfDhw/HOO+9g3759tnqFhYUIBoPo0qULOnbsiKefftpxrZ/Dhw9XWqfqgBw9BFEPyMzMxHnnnYd///vfuPvuu/Hzzz/D7/djyJAhePfdd/Haa68hKysLqqritttuQ+PGjZGcnIzu3btLz+Q6sXPnTgwePBgpKSlIS0uTQsIZY3j++eeRmZmJ1NRUPPnkk9VtKjweD9q2bYs5c+bg0KFDUohwbZCVlYVevXrho48+AgBy9BAEQZygeDwePP3001i4cCGGDh2K3NxcFBQUIDc3F0OHDsXChQvx1FNPVdsaay+++CJCoRDOOussfPDBB9i+fTs2b96MmTNnomfPnoiPj8crr7yCjz76CGPGjMG6deuwa9cuvP766xg1ahSuuOIKKVpCZODAgfjjjz/w6KOPOua/8soruOWWW/DFF1/gl19+wcaNGzF58mRs3LjRWHx5+PDhuOyyyzBy5Ei8/vrr2LVrF9atW4ebb74ZH3/8MV577TVpIelQKIS1a9dKL7eIIKLuUlhYaJxfQPteuHbtWmmdlpqS4ff7kZeXh99//x1r1qzBY489hksvvRQXX3xxjW7IQdQs1XHex4wZg6SkJGOdTZ2pU6ciIyMDPXr0wFtvvYVNmzZh+/bteOONN9C1a1cUFhaCMYbZs2dj27Zt6NOnDz799FP8+uuvWLduHaZOnYpLL720Ksw+fqp9Xy+CIGqE/fv384svvpjHxMRwANzn8/HevXvzJUuWGGU+++wznp2dzY8cOcKDwSBfvXo1LygocG0zEAjwDh068IceeoiXlJTwI0eO8B9//NHIB8D79+/Pjxw5wjdv3sxTU1P5l19+Wa12cs75VVddxQHwMWPGVLsst+3VRV588UUOgJ911lnltkfbqxMEQRw7VbE17QcffMBbtWrFARivrKysat1aXWffvn187NixvGXLltzn8/HmzZvzSy65xNgGnXPOly9fzgcMGMATExO5z+fjnTp14k899RQPBoNSW4iw1fWhQ4ek7dXXrFnDr7vuOp6VlcWjo6N5SkoK79u3r20r9UAgwJ988kneqVMn7vP5eGJiIh8wYAD/5ptvpHKzZ8+W+k9/nXrqqcfdR8SJxddff+14rkeOHFlu3Ypur14RGSNHjjTSvV4vb9KkCc/JyeFvvPGG4xbXRP2goue9IturW/Pnzp3LAUjbq3PO+eHDh/k999zD27Rpw30+H2/WrBnPycnh8+fP56qqGuW2bt3K//a3v/H09HTu8/l4y5Yt+TXXXMPXrFnjqkdNbq/OOK8jqwkRBFEhFi5ciCFDhuCnn35Cly5dpLwlS5bg1ltvxb///W9079693PDdb7/9FsOHD8fu3bsdd7RijOHrr7/GeeedB0Bbyf7gwYN49dVXq8qcWmfUqFH46quvsGbNGni93mPeMrG0tBSFhYWYPn06nnzySfzxxx+Oi8QRBEEQ7pSWlmLnzp3IysqyPSpQGUKhEFasWIH9+/cjLS0Nffr0od0SCYIgiGqlqv6GVQRajJkg6hlbtmwBYwzt2rWz5V1wwQX4+9//jjFjxiAvLw/XXXcdpk2b5vrM7G+//YaWLVtG3LZc3II3IyMDP//88/EbcYKxd+9eNGnSBJ06dTIW66wss2bNwu23317FmhEEQRDHgsfjMX6kIAiCIIj6Bjl6CKKesWXLFmRmZkqr3IvcfvvtuP3227F3715cdNFFOO2001xXwc/IyMDu3bsjblu5d+9enHrqqcbn6titpDa5++67cd111wHQFm87VoYNGyZt6VubW90TBEEQBEEQBFF/IUcPQdQztmzZgvbt2zvm/fjjj+Cco2vXrmjQoAGioqKkUHXd4TNnzhwAwFlnnYUGDRrgH//4B+6++26UlZVh+/btyM7ONuo88cQTOPPMM7F//3688cYbeOutt6rNttqgY8eOjtvZVpaMjAwp+okgCIIgCIIgCKI6oF23CKKe8c0332DRokWOeUeOHMGNN96I5ORktGvXDr1798a1115r5P/222/o3bu3cez1erFw4UJ89913SEtLQ7t27Ywt3HX0qKC+ffvitttuQ05OTvUYRhAEQRAEQRAEQZQLLcZMEAQAIBgM4vTTT8fPP//sumaPFcYY9u7dixYtWlSzdgRBEARRswtZEgRBEERVQosxEwRR43i9XmzatKm21SAIgiCIcqHfKQmCIIi6Rk3+7aJHtwiCIAiCIIg6gR5xWlxcXMuaEARBEETlKCsrAwBpjdTqgiJ6CII4ZugXVYIgCKIm8Xg8SE5OxsGDBwEAcXFxrrtCEgRBEMSJgqqq+OOPPxAXFwevt/rdMOToIQiCIAiCIOoMqampAGA4ewiCIAiiLqAoCjIzM2vkBwpajJkgCIIgCIKoc4RCIQQCgdpWgyAIgiAqhM/ng6LUzOo55OghCIIgCIIgCIIgCIKoJ9BizARBEARBEARBEARBEPUEcvQQBEEQBEEQBEEQBEHUE8jRQxAEQRAEQRAEQRAEUU8gRw9BEARBEARBEARBEEQ9gRw9BEEQBEEQBEEQBEEQ9QRy9BAEQRAEQRAEQRAEQdQTyNFDEARBEARBEARBEARRTyBHD0EQBEEQBEEQBEEQRD2BHD21xPLlyzFkyBCkp6eDMYYFCxZUSbtLly7FmWeeiejoaLRu3Rpz5sxxLfv444+DMYaJEydWiezymDZtGrp3744GDRqgadOmGDp0KLZu3Volbb///vto3749YmJi0LlzZ3z66aeuZf/+97+DMYYZM2ZUiexIvPzyyzj99NORmJiIxMRE9OzZE5999tlxt3ui2mulKsfYiWzzww8/DMaY9Grfvv1xt3si2/z777/juuuuQ0pKCmJjY9G5c2f8+OOPx93uiXoPa9Wqle0cM8YwduzY42r3RD7HBEEQBEEQRN2EHD21RFFREc444wy8+OKLVdbmzp07MXjwYPTr1w9r167FxIkT8X//93/4/PPPbWVXrVqFV155BaeffnqVyS+PZcuWYezYsfj++++xePFiBAIB9O/fH0VFRcfV7nfffYdrrrkGo0ePxk8//YShQ4di6NCh2LBhg63s/Pnz8f333yM9Pf24ZFaUFi1a4PHHH8fq1avx448/4vzzz8ell16KjRs3HnObJ7K9IlU5xuqCzZ06dcL+/fuN1zfffHNc7Z3INh86dAi9e/dGVFQUPvvsM2zatAlPP/00GjZseFztnsj3sFWrVknnd/HixQCAK6+88pjbPJHPMUEQBEEQBFGH4UStA4DPnz9fSistLeV33HEHT09P53Fxcfyss87iX3/9dcR27r77bt6pUycp7eqrr+YDBgyQ0goKCnibNm344sWL+bnnnssnTJhQBVZUnoMHD3IAfNmyZUbaoUOH+OjRo3njxo15gwYNeL9+/fjatWsjtnPVVVfxwYMHS2k9evTgN998s5T222+/8ebNm/MNGzbwli1b8meffbbKbKkMDRs25K+99hrnvP7aG2mM1UebH3roIX7GGWe45tc3mydPnszPOeeciGXq+z1swoQJ/NRTT+WqqnLO6985JgiCIAiCIOouFNFzgjJu3Djk5uZi3rx5WLduHa688koMHDgQ27dvd62Tm5uLnJwcKW3AgAHIzc2V0saOHYvBgwfbytY0R44cAQA0atTISLvyyitx8OBBfPbZZ1i9ejXOPPNMXHDBBcjPz3dtpyJ2q6qK66+/HnfddRc6depUxZZUjFAohHnz5qGoqAg9e/YEUH/tjTTG6qvN27dvR3p6Ok455RSMGDECe/bsMfLqm80ff/wxunXrhiuvvBJNmzZF165d8a9//UsqU5/vYWVlZXj77bdx4403gjEGoP6dY4IgCIIgCKLu4q1tBQg7e/bswezZs7Fnzx4jTP/OO+/EokWLMHv2bDz22GOO9fLy8tCsWTMprVmzZjh69ChKSkoQGxuLefPmYc2aNVi1alW12xEJVVUxceJE9O7dG6eddhoA4JtvvsEPP/yAgwcPIjo6GgDw1FNPYcGCBfjvf/+LMWPGOLblZndeXp5x/MQTT8Dr9eK2226rJovcWb9+PXr27InS0lIkJCRg/vz56NixY721N9IYq6829+jRA3PmzEG7du2wf/9+PPLII+jTpw82bNiAn3/+ud7Z/Ouvv+Lll1/GpEmTcN9992HVqlW47bbb4PP5MHLkyHp/D1uwYAEOHz6MUaNGAai/45ogCIIgCIKom5Cj5wRk/fr1CIVCaNu2rZTu9/uRkpICAEhISDDSr7vuOsyaNavcdvfu3YsJEyZg8eLFiImJqVqlK8nYsWOxYcMGaR2Tn3/+GYWFhYaNOiUlJfjll1+wZ88edOzY0Ui/7777cN9995Ura/Xq1XjuueewZs0a49f3mqRdu3ZYu3Ytjhw5gv/+978YOXIkli1bVi/tLW+M1UebAWDQoEHG59NPPx09evRAy5Yt8d5776G0tLTe2ayqKrp162Y4bLp27YoNGzZg1qxZGDlyZL2/h73++usYNGiQ4cSqr+OaIAiCIAiCqJuQo+cEpLCwEB6PB6tXr4bH45Hy9MnR2rVrjbTExEQAQGpqKg4cOCCVP3DgABITExEbG4vVq1fj4MGDOPPMM438UCiE5cuX44UXXoDf77fJqw7GjRuHhQsXYvny5WjRooWRXlhYiLS0NCxdutRWJzk5GcnJyZLd+iNfbnanpqYCAFasWIGDBw8iMzPTyA+FQrjjjjswY8YM7Nq1q+qMc8Dn86F169YAgOzsbKxatQrPPfccTjnllHpnb3ljbOrUqfXOZieSk5PRtm1b7NixA8nJyfXO5rS0NMlxAQAdOnTABx98AKB+38N2796NL7/8Eh9++KGRVl/vXQRBEARBEETdhBw9JyBdu3ZFKBTCwYMH0adPH8cyuuNApGfPnrateRcvXmysB3PBBRdg/fr1Uv4NN9yA9u3bY/LkydU+QeKcY/z48Zg/fz6WLl2KrKwsKf/MM89EXl4evF4vWrVq5diGm91LliyRtlgW7b7++usd18G4/vrrccMNNxyfUceAqqrw+/310t7yxtj+/fvrnc1OFBYW4pdffsH111+PDh061Dube/fuja1bt0pp27ZtQ8uWLQHU33sYAMyePRtNmzbF4MGDjbT6eC0TBEEQBEEQdZjaXg36ZKWgoID/9NNP/KeffuIA+DPPPMN/+uknvnv3bs455yNGjOCtWrXiH3zwAf/111/5ypUr+WOPPcYXLlzo2uavv/7K4+Li+F133cU3b97MX3zxRe7xePiiRYtc69TkjjW33HILT0pK4kuXLuX79+83XsXFxZxzzlVV5eeccw4/44wz+Oeff8537tzJv/32W37ffffxVatWubb77bffcq/Xy5966im+efNm/tBDD/GoqCi+fv161zo1tXPNPffcw5ctW8Z37tzJ161bx++55x7OGONffPFFvbTXCXGM1Veb77jjDr506VLDnpycHN64cWN+8ODBemnzDz/8wL1eL586dSrfvn07f+edd3hcXBx/++23jTL18R4WCoV4ZmYmnzx5spReH88xQRAEQRAEUXchR08t8fXXX3MAttfIkSM555yXlZXxKVOm8FatWvGoqCielpbGL7vsMr5u3bpy2+3SpQv3+Xz8lFNO4bNnz45YviYnSU72ApB0PHr0KB8/fjxPT0/nUVFRPCMjg48YMYLv2bMnYtvvvfceb9u2Lff5fLxTp078f//7X8TyNTVZuvHGG3nLli25z+fjTZo04RdccAH/4osvjPz6Zq8T1jFWH22++uqreVpaGvf5fLx58+b86quv5jt27DDy66PNn3zyCT/ttNN4dHQ0b9++PX/11Vel/Pp4D/v88885AL5161ZbXn08xwRBEARBEETdhHHOea2EEhEEQRAEQRDEMRIKhRAIBGpbDYIgCIKoED6fD4qi1IgsWqOHIAiCIAiCqDNwzpGXl4fDhw/XtioEQRAEUWEURUFWVhZ8Pl+1y6KIHoIgCIIgCKLOsH//fhw+fBhNmzZFXFwcGGO1rRJBEARBRERVVezbtw9RUVHIzMys9r9dFNFDEARBEARB1AlCoZDh5ElJSaltdQiCIAiiwjRp0gT79u1DMBhEVFRUtcqqmQfECIIgCIIgCOI40dfkiYuLq2VNCIIgCKJy6I9shUKhapdFjh6CIAiCIAiiTkGPaxEEQRB1jZr820WOHoIgCIIgCIIgCIIgiHoCOXrqOH6/Hw8//DD8fn9tq1JjnGw2n2z2AmTzyQLZTBDEycK0adPQvXt3NGjQAE2bNsXQoUOxdetWqUxpaSnGjh2LlJQUJCQkYNiwYThw4IBUZs+ePRg8eDDi4uLQtGlT3HXXXQgGgzVpClGP+f3333HdddchJSUFsbGx6Ny5M3788Ucjn3OOKVOmIC0tDbGxscjJycH27dulNvLz8zFixAgkJiYiOTkZo0ePRmFhYU2bQtQzli9fjiFDhiA9PR2MMSxYsMBWpqrG57p169CnTx/ExMQgIyMD06dPr07Tqg1y9NRx/H4/HnnkkZNq0nCy2Xyy2QuQzScLZDNBECcLy5Ytw9ixY/H9999j8eLFCAQC6N+/P4qKiowyt99+Oz755BO8//77WLZsGfbt24fLL7/cyA+FQhg8eDDKysrw3Xff4c0338ScOXMwZcqU2jCJqGccOnQIvXv3RlRUFD777DNs2rQJTz/9NBo2bGiUmT59OmbOnIlZs2Zh5cqViI+Px4ABA1BaWmqUGTFiBDZu3IjFixdj4cKFWL58OcaMGVMbJhH1iKKiIpxxxhl48cUXXctUxfg8evQo+vfvj5YtW2L16tV48skn8fDDD+PVV1+tVvuqBU7UaY4cOcIB8CNHjtS2KjXGyWbzyWYv52TzyQLZTBBEZSkpKeGbNm3iJSUlta3KcXHw4EEOgC9btoxzzvnhw4d5VFQUf//9940ymzdv5gB4bm4u55zzTz/9lCuKwvPy8owyL7/8Mk9MTOR+v99Rjt/v52PHjuWpqak8OjqaZ2Zm8scee6waLSPqKpMnT+bnnHOOa76qqjw1NZU/+eSTRtrhw4d5dHQ0f/fddznnnG/atIkD4KtWrTLKfPbZZ5wxxn///XfXdh966CGekZHBfT4fT0tL4+PHj68iq4j6CAA+f/58Ka2qxudLL73EGzZsKN1TJ0+ezNu1a+eqT35+Pr/22mt548aNeUxMDG/dujV/4403HMvW5N8w2l6dIAiCIAiCqLNwzlFcXFzjcuPi4o55Yc0jR44AABo1agQAWL16NQKBAHJycowy7du3R2ZmJnJzc3H22WcjNzcXnTt3RrNmzYwyAwYMwC233IKNGzeia9euNjkzZ87Exx9/jPfeew+ZmZnYu3cv9u7de0w6E8cG5xzBkrJake2N9VV4jH788ccYMGAArrzySixbtgzNmzfHrbfeiptuugkAsHPnTuTl5UljNCkpCT169EBubi6GDx+O3NxcJCcno1u3bkaZnJwcKIqClStX4rLLLrPJ/eCDD/Dss89i3rx56NSpE/Ly8vDzzz8fp+VEReGcA6Gav38CADzHfg+1UlXjMzc3F3379jV2xwK0++wTTzyBQ4cOSRFuOg8++CA2bdqEzz77DI0bN8aOHTtQUlJSJXYdD+ToOU5KS0tRVlY7N29ACy8T308GTjabTzZ7AbL5ZIFsPvHw+XyIiYmpbTUIolIUFxcjISG5xuUWFh5GfHx8peupqoqJEyeid+/eOO200wAAeXl58Pl8SE5Olso2a9YMeXl5RhnRyaPn63lO7NmzB23atME555wDxhhatmxZaX2J4yNYUoZXuk6oFdk3//QcouKiK1T2119/xcsvv4xJkybhvvvuw6pVq3DbbbfB5/Nh5MiRxhhzGoPiGG3atKmU7/V60ahRo4hjNDU1FTk5OYiKikJmZibOOuusyppKHCuhYqjvNS2/XDWgXHUQ8Fb+HupEVY3PvLw8ZGVl2drQ85wcPXv27EHXrl0NB1KrVq2O36AqgBw9x0FpaSmSYhuiDKXlF65mMjIyaluFGudks/lksxcgm08WyOYTh9TUVOzcuZOcPQRRjYwdOxYbNmzAN998U+2yRo0ahQsvvBDt2rXDwIEDcfHFF6N///7VLpeoe6iqim7duuGxxx4DAHTt2hUbNmzArFmzMHLkyGqTe+WVV2LGjBk45ZRTMHDgQFx00UUYMmQIvF6aphJ1g1tuuQXDhg3DmjVr0L9/fwwdOhS9evWqbbXI0XM8lJWVoQylOAcXwcs0bzlTGMAU8zMA6CFpCjM+M0Ux88TP4XcWbgPWNhzLW2RIeYo9DcyxPDfSYGnDLC+VEeWLeYI+jmlC+5yZn21tKfZ2reU5TJN0W7hgpq08E2VEyFPc2zBgsiz39i39YMlzLu/QLlzSmKUfymvDRaZjmsUmHdc0S1uR9AHjDmkO7YvlYUEqz+1tCXXtesjlmVsbsJYztWAR2mDGu5Msbqoolgu/K7Y2uGueAm6qK+TZyonlrWlCeUXQzS1PYRwKrGmqUNeUped5mD1Nv/14oOuqGm2a5YU04TMAeJhqyPIYddVwm+JnQaa1jfCxh6mGbkZbUI3boSlbFerIOnrE9oW2PJb+8Ah6Mase4IJuXEiD3H+6XgzwhM+WmcagGGnyu5anWNIUKGA4WqCiZfYulJWVkaOHqFPExcWhsPBwrcitLOPGjTMWAG3RooWRnpqairKyMhw+fFiK6jlw4ABSU1ONMj/88IPUnr4rl17GyplnnomdO3fis88+w5dffomrrroKOTk5+O9//1tp3Yljwxvrw80/PVdrsitKWloaOnbsKKV16NABH3zwAQBzjB04cABpaWlGmQMHDqBLly5GmYMHD0ptBINB5Ofnu47RjIwMbN26FV9++SUWL16MW2+9FU8++SSWLVuGqKioCutPHCOeOC2yppZkVxVVNT5TU1Ntux2Wd58dNGgQdu/ejU8//RSLFy/GBRdcgLFjx+Kpp56qEtuOFXL0VAFeRMHLtBsRY8x09IjOFi3BdPRIaRbHjVJZRw+T6sp5Do4el/LH7uhxz6t2R484Ia8hR0+5DhknR0xVOXrcykMuX22OHqc0HJtNx+ToscisVUcPq7ijx17OydHj7swp19FjXM5V5+gRHTmVdfQ45Tk7emSHhnIMjh6bc4aJDhO7o8fWRjmOHo9RjoXrMeOzqaN+zASnCxfSrA4hCDrq7Tul2R09nko6esw8JuQ5OXpoE06i7sIYO6ZHqGoSzjnGjx+P+fPnY+nSpbZHA7KzsxEVFYUlS5Zg2LBhAICtW7diz5496NmzJwCgZ8+emDp1Kg4ePGg8frB48WIkJibaJugiiYmJuPrqq3H11VfjiiuuwMCBA5Gfn2+sD0RUL4yxCj8+VZv07t0bW7duldK2bdtmPO6XlZWF1NRULFmyxJg4Hz16FCtXrsQtt9wCQBujhw8fxurVq5GdnQ0A+Oqrr6CqKnr06OEqOzY2FkOGDMGQIUMwduxYtG/fHuvXr8eZZ55ZDZYSIoyxKnt8qjapqvHZs2dP3H///QgEAoajcfHixWjXrp3jY1s6TZo0wciRIzFy5Ej06dMHd911Fzl6CIIgCIIgCKI+M3bsWMydOxcfffQRGjRoYKwHkZSUhNjYWCQlJWH06NGYNGkSGjVqhMTERIwfPx49e/bE2WefDQDo378/OnbsiOuvvx7Tp09HXl4eHnjgAYwdOxbR0c6OhGeeeQZpaWno2rUrFEXB+++/j9TUVNtaQARx++23o1evXnjsscdw1VVX4YcffsCrr75qbCvNGMPEiRPxz3/+E23atEFWVhYefPBBpKenY+jQoQC0CKCBAwfipptuwqxZsxAIBDBu3DgMHz4c6enpjnLnzJmDUCiEHj16IC4uDm+//TZiY2NpPSlCorCwEDt27DCOd+7cibVr16JRo0bIzMyssvF57bXX4pFHHsHo0aMxefJkbNiwAc899xyeffZZV92mTJmC7OxsdOrUCX6/HwsXLkSHDh2qtT8qAjl6CIIgCIIgCKIaefnllwEA5513npQ+e/ZsjBo1CgDw7LPPQlEUDBs2DH6/HwMGDMBLL71klPV4PFi4cCFuueUW9OzZE/Hx8Rg5ciQeffRRV7kNGjTA9OnTsX37dng8HnTv3h2ffvopFIWi+AiZ7t27Y/78+bj33nvx6KOPIisrCzNmzMCIESOMMnfffTeKioowZswYHD58GOeccw4WLVokPe77zjvvYNy4cbjggguM8Txz5kxXucnJyXj88ccxadIkhEIhdO7cGZ988glSUlKq1V6ibvHjjz+iX79+xvGkSZMAACNHjsScOXMAVM34TEpKwhdffIGxY8ciOzsbjRs3xpQpUzBmzBhX3Xw+H+69917s2rULsbGx6NOnD+bNm1fFPVB5GOfctuwFUTGOHj2KpKQknIdL4VUirdEjPIZVkTV66NEtenTLqd1I5SGXp0e3ZD3o0S16dMvQ45gf3arYGj2eiGv0qFL78mNaFVujpzof3TpaEELDtr/iyJEjSExMBEGciJSWlmLnzp3IysqitaQIgiCIOkVN/g0jdz5BEARBEARBEARBEEQ9gRw9BEEQBEEQBEEQBEEQ9QRy9BAEQRAEQRAEQRAEQdQTyNFDEARBEARBEARBEARRTyBHD0EQBEEQBEEQBEEQRD2BHD0EQRAEQRAEQRAEQRD1BHL0EARBEARBEARBEARB1BPI0UMQBEEQBEEQBEEQBFFPIEcPQRAEQRAEQRAEQRBEPYEcPQRBEARBEARBEARBEPUEcvQQBEEQBEEQRA3x+OOPgzGGiRMnSumlpaUYO3YsUlJSkJCQgGHDhuHAgQNSmT179mDw4MGIi4tD06ZNcddddyEYDNag9kR9JRQK4cEHH0RWVhZiY2Nx6qmn4h//+Ac450YZzjmmTJmCtLQ0xMbGIicnB9u3b5fayc/Px4gRI5CYmIjk5GSMHj0ahYWFNW0OQZz0kKOHIAiCIAiCIGqAVatW4ZVXXsHpp59uy7v99tvxySef4P3338eyZcuwb98+XH755UZ+KBTC4MGDUVZWhu+++w5vvvkm5syZgylTptSkCUQ95YknnsDLL7+MF154AZs3b8YTTzyB6dOn4/nnnzfKTJ8+HTNnzsSsWbOwcuVKxMfHY8CAASgtLTXKjBgxAhs3bsTixYuxcOFCLF++HGPGjKkNkwjipIYcPQRBEARBEARRzRQWFmLEiBH417/+hYYNG0p5R44cweuvv45nnnkG559/PrKzszF79mx89913+P777wEAX3zxBTZt2oS3334bXbp0waBBg/CPf/wDL774IsrKyhxllpWVYdy4cUhLS0NMTAxatmyJadOmVbutRN3ju+++w6WXXorBgwejVatWuOKKK9C/f3/88MMPALRonhkzZuCBBx7ApZdeitNPPx1vvfUW9u3bhwULFgAANm/ejEWLFuG1115Djx49cM455+D555/HvHnzsG/fPke5nHM8/PDDyMzMRHR0NNLT03HbbbfVlNkEUW8hRw9BEARBEARRZ+Gco6TIX+Mv8ZGWijB27FgMHjwYOTk5trzVq1cjEAhIee3bt0dmZiZyc3MBALm5uejcuTOaNWtmlBkwYACOHj2KjRs3OsqcOXMmPv74Y7z33nvYunUr3nnnHbRq1apSehPHB+ccamlJrbwqM0Z79eqFJUuWYNu2bQCAn3/+Gd988w0GDRoEANi5cyfy8vKkMZqUlIQePXpIYzQ5ORndunUzyuTk5EBRFKxcudJR7gcffIBnn30Wr7zyCrZv344FCxagc+fOle5ngiBkvLWtAEEQBEEQBEEcK6XFZbi46cQal7vw4AzExkdXqOy8efOwZs0arFq1yjE/Ly8PPp8PycnJUnqzZs2Ql5dnlBGdPHq+nufEnj170KZNG5xzzjlgjKFly5YV0peoOri/FLuutTv3aoJWc78Ei4mtUNl77rkHR48eRfv27eHxeBAKhTB16lSMGDECgDnGnMagOEabNm0q5Xu9XjRq1CjiGE1NTUVOTg6ioqKQmZmJs846q1J2EgRhhxw9VUAQAYBrwVGMM+iBUtpnAAi/c2Z8Zlwx88TPAKAyMGZJY+K7YkkTPtvKK/Y0MPMzN/O4kQZLG2Z5qYwoX8xTYS8v6Giay2B2kUNbCmxtWMsbv1MwAIouE+7lzVMQOU9xb8OAybLc27f0gyXPubxDu3BJY5Z+KK8NF5mOaRabdFzTLG1F0geMO6Q5tC+WhwWpPLe3JdS16yGXZ25twFrO1IJFaIMZ706yuKmiWC78zm1tcNc8Dm6oqwp5iqWcfqyA29OE8oqgm1uewjgUWNNUoa4pS8/zMHta+JKFB7quqtGmWV5IEz4DgIephiyPUVcNtyl+FmRa2wgfe5hq6Ga0BdUIezVlq0IdWUeP2L7QlsfSHx5BL2bVA1zQjQtpkPtP14sBnvDZMtMYFCNNftfyYEnT2j1aoIIgiKpn7969mDBhAhYvXoyYmJgalT1q1ChceOGFaNeuHQYOHIiLL74Y/fv3r1EdiLrBe++9h3feeQdz585Fp06dsHbtWkycOBHp6ekYOXJktcm98sorMWPGDJxyyikYOHAgLrroIgwZMgReL01TCeJ4oCvoOPD5fEhNTcU3eZ+aM+1QrapEEARBEMdMamoqfD5fbatBEJUiJs6HhQdn1IrcirB69WocPHgQZ555ppEWCoWwfPlyvPDCC/D7/UhNTUVZWRkOHz4sRfUcOHAAqampALTrU18vRczX85w488wzsXPnTnz22Wf48ssvcdVVVyEnJwf//e9/K2MqcRyw6Bi0mvtlrcmuKHfddRfuueceDB8+HADQuXNn7N69G9OmTcPIkSONMXbgwAGkpaUZ9Q4cOIAuXboA0MbhwYMHpXaDwSDy8/Ndx2hGRga2bt2KL7/8EosXL8att96KJ598EsuWLUNUVFRlzCUIQoAcPcdBTEwMdu7c6boAHkEQBEHUJXw+X41HHBDE8cIYq/AjVLXBBRdcgPXr10tpN9xwA9q3b4/JkyfD4/EgOzsbUVFRWLJkCYYNGwYA2Lp1K/bs2YOePXsCAHr27ImpU6fi4MGDxuMxixcvRmJiIjp27OgqPzExEVdffTWuvvpqXHHFFRg4cCDy8/PRqFGjarKYEGGMVfjxqdqkuLgYiiIv3+rxeKCqWrRnVlYWUlNTsWTJEsOxc/ToUaxcuRK33HILAG2MHj58GKtXr0Z2djYA4KuvvoKqqujRo4er7NjYWAwZMgRDhgzB2LFj0b59e6xfv15yjhIEUTnI0XOcxMTE0JdigiAIgiAIwpEGDRrgtNNOk9Li4+ORkpJipCclJWH06NGYNGkSGjVqhMTERIwfPx49e/bE2WefDQDo378/OnbsiOuvvx7Tp09HXl4eHnjgAYwdOxbR0c6OrmeeeQZpaWno2rUrFEXB+++/j9TUVNtaQAQxZMgQTJ06FZmZmejUqRN++uknPPPMM7jxxhsBaA6riRMn4p///CfatGmDrKwsPPjgg0hPT8fQoUMBAB06dMDAgQNx0003YdasWQgEAhg3bhyGDx+O9PR0R7lz5sxBKBRCjx49EBcXh7fffhuxsbG0nhRBHCfk6CEIgiAIgiCIWubZZ5+FoigYNmwY/H4/BgwYgJdeesnI93g8WLhwIW655Rb07NkT8fHxGDlyJB599FHXNhs0aIDp06dj+/bt8Hg86N69Oz799FNb5AZBPP/883jwwQdx66234uDBg0hPT8fNN9+MKVOmGGXuvvtuFBUVYcyYMTh8+DDOOeccLFq0SPrR+5133sG4ceNwwQUXGON55syZrnKTk5Px+OOPY9KkSQiFQujcuTM++eQTpKSkVKu9BFHfYbyye0MSBEEQBEEQRC1QWlqKnTt3IisriyKqCYIgiDpFTf4NI3c+QRAEQRAEQRAEQRBEPYEcPQRBEARBEARBEARBEPUEcvQQBEEQBEEQBEEQBEHUE8jRQxAEQRAEQRAEQRAEUU8gRw9BEARBEARBEARBEEQ9gRw9BEEQBEEQRJ2CNo0lCIIg6ho1+beLHD0EQRAEQRBEnSAqKgoAUFxcXMuaEARBEETlKCsrAwB4PJ5ql+WtdgkEQRAEQRAEUQV4PB4kJyfj4MGDAIC4uDgwxmpZK4IgCIKIjKqq+OOPPxAXFwevt/rdMOToIQiCIAiCIOoMqampAGA4ewiCIAiiLqAoCjIzM2vkBwrG6SFngiAIgiAIoo4RCoUQCARqWw2CIAiCqBA+nw+KUjOr55CjhyAIgiAIgiAIgiAIop5AizETBEEQBEEQBEEQBEHUE8jRQxAEQRAEQRAEQRAEUU8gRw9BEARBEARBEARBEEQ9gRw9BEEQBEEQBEEQBEEQ9QRy9BAEQRAEQRAEQRAEQdQTyNFDEARBEARBEARBEARRTyBHD0EQBEEQBEEQBEEQRD2BHD0EQRAEQRAEQRAEQRD1BHL0EARBEARBEARBEARB1BNOSEfP8uXLMWTIEKSnp4MxhgULFhh5gUAAkydPRufOnREfH4/09HT87W9/w759+6Q28vPzMWLECCQmJiI5ORmjR49GYWGhVGbdunXo06cPYmJikJGRgenTp9eEeQRBEARBEARBEARBENXCCenoKSoqwhlnnIEXX3zRlldcXIw1a9bgwQcfxJo1a/Dhhx9i69atuOSSS6RyI0aMwMaNG7F48WIsXLgQy5cvx5gxY4z8o0ePon///mjZsiVWr16NJ598Eg8//DBeffXVarePIAiCIAiCIAiCIAiiOmCcc17bSkSCMYb58+dj6NChrmVWrVqFs846C7t370ZmZiY2b96Mjh07YtWqVejWrRsAYNGiRbjooovw22+/IT09HS+//DLuv/9+5OXlwefzAQDuueceLFiwAFu2bKkJ0wiCIAiCIAiCIAiCIKqUEzKip7IcOXIEjDEkJycDAHJzc5GcnGw4eQAgJycHiqJg5cqVRpm+ffsaTh4AGDBgALZu3YpDhw7VqP4EQRAEQRAEQRAEQRBVgbe2FTheSktLMXnyZFxzzTVITEwEAOTl5aFp06ZSOa/Xi0aNGiEvL88ok5WVJZVp1qyZkdewYUObLL/fD7/fbxyrqor8/HykpKSAMValdhEEQRBEdcM5R0FBAdLT06Eo9eK3H6Keo6oq9u3bhwYNGtB3L4IgCKJOUZPfu+q0oycQCOCqq64C5xwvv/xytcubNm0aHnnkkWqXQxAEQRA1yd69e9GiRYvaVoMgymXfvn3IyMiobTUIgiAI4pipie9dddbRozt5du/eja+++sqI5gGA1NRUHDx4UCofDAaRn5+P1NRUo8yBAwekMvqxXsbKvffei0mTJhnHR44cQWZmJvbu3SvJJwiCIIi6wNGjR5GRkYEGDRrUtioEUSH0sUrfvQiCIIi6Rk1+76qTjh7dybN9+3Z8/fXXSElJkfJ79uyJw4cPY/Xq1cjOzgYAfPXVV1BVFT169DDK3H///QgEAoiKigIALF68GO3atXN8bAsAoqOjER0dbUtPTEykLxsEQRBEnYUegSHqCvpYpe9eBEEQRF2lJr53nZAP5BcWFmLt2rVYu3YtAGDnzp1Yu3Yt9uzZg0AggCuuuAI//vgj3nnnHYRCIeTl5SEvLw9lZWUAgA4dOmDgwIG46aab8MMPP+Dbb7/FuHHjMHz4cKSnpwMArr32Wvh8PowePRobN27Ef/7zHzz33HNSxA5BEARBEARRPykpLMDS+0fjm4kDsfT+0SgpLKhtlQiCIAiiSjght1dfunQp+vXrZ0sfOXIkHn74Ydsiyjpff/01zjvvPABAfn4+xo0bh08++QSKomDYsGGYOXMmEhISjPLr1q3D2LFjsWrVKjRu3Bjjx4/H5MmTK6zn0aNHkZSUhCNHjtCvSgRBEESdg/6OEXWNqhqzyyddgu7tV8CXUGaklRX6sGpLH/R95uOqUNWgpLAAK6dNhLfodwTjm6PHvTMQm0CPSxIEQZxs1OT3rhPS0VNXoC/IBEEQRF2G/o4RdY2qGLPLJ12CXtlLkL+3ETaXXYY2w27C9g/+hQ6++WiUkY/vVl9QZc6emnQoEQRBECc2Nfm9q06u0XOisXvLPxEfHwXOtS3TwAGVq+FjCMdaAg+/jGfzmPacHgPAFAYwQGFMS1OgpTMFigJA5QghCMZ8UAFwVQXAoKoqODR5WhqHyllYpgqucugePcY4oDBo/2DKYQBDWA60Y0VRwHkIQTAw5oXKObiq28jBodmkckBVOcAAzlVwNfwOLU8xbFEAcDAFUBTdfgUK0/qDMQWMcYRCAYQUHxi8Whth+6CG+xLQZIBD5VofcFUN68IBxsAZC/ejdg6YwrT+DdusKAwcHB5FAQMQ5CGEEAsW7kdV1ezjnCGkqmCcQwWggoOr4fOKEMAZQioHYx6Aca3PAK1/w3ZzCOeUwej/QDAEVYkDY1p/qqqmPwOg6n3IOTgYOFehqoBmNRBSGRgUMEXLZwoD4xxQwicTHJ5wPzAwMA8DwFCmcjAWG+5DDs5VhLjWt1pfayNF1ceyqgJM6/eAqmh2wOxLMLNPGWNAuN85AKYoYApQGgAUJUbrV64CYTtCuj3ha0IfXzwsX0vzaCJYePSwsGyuXRdcv26gjVcwDjAPSgMcXiVaGDuqJjYUPofhi1PvZxYe0yo4oBoXHsC0C0Ix7BQvEC1d5YCieOAPcEQpPnBVG/tc5eF2tfsBVABM62PjfgBADYXvB9y8H4ABgKJdpYrQvwAUpmjXPecIBYFo5g2fT9XsT6NvEb7vqOHrFoZcpobHpnQeFUO+bisL5zNFgRoKwRtQ4PF6jPMWPrHaPSl8rGqDVZOl93F4zClMAWeadZJMaHlgDEw7sYDCEAwEEc098ChKePzoYwaAGgpfH2EdwvcDcBUq51A0pUxbGAO4fj9iYdu0fubQrl9whlAwhFjmCeusX4faOOLhe542bsL3nPD9Amr4vIvnKnxPYtCHlfFJG2KcQVEUBEr9iGFarxh/QxCWrYbv5Vy/L4Wv1bB9ajBg3l8QvgY5g7RzJ9d00C4fBQqAZpnyGncEUd8pKSxA9/YrkL+3EZLHb0Df2HgwxYv0TjNRVvI48p9rh+7tvkFJYcFxR91IDqU82aHUK3sJlk+6pEqdPVwNAX98C16SBxabCjTpDaZ4qqx9giAIou5Ajp4qoODPuVCLtdm84WwxXuHJgSVP5SpKS7WJpjmp0tpj4IbTRZ8ZKOF3Bo6Y2LADSW9Pb5+bx7qDiYcnepwDQZWjtEzVpjZMa0Cfq+vymf5ZTGOANxqGA4TDSQ6MiZeuj8qBQJCjNMCN1aD0uTMc5ADmwlRM4fBEM8lGs0+ZYb/ucIKQ7w8A/iAzJlYQ5BkydVnhY6YA8DDA67XJRFie6XCx9D/nKCrzIqgyzeHCdMeOMW+XOlafiEEBEOWFqnjAuaaVGu5PcLE/mSBPn2wyHC31QOWmQQza5NnsX2aTzxQgFBUNlXnMcyfZKp9D8/xqxhwpUTRdjUYZxEOu97lwchkDyqJiwaGE7TDHpGSr5iWUrxeVo7BY0fKMNctEI+W+1a4QzfaANwacK+H2uClPOMGqIEvvBFXlKCkWZsfm4ISQCG6Ty6B6YrT+EfrSNEYXInzW7wchgJdoDgvJJvHcSf0KQD/fiDbbNE6kaaORyeU0HlDBSkPh+wEAbuljyWZZF6/qC6dwmy0A128vklzOARYIgZWpQteaFwnnppPCcHgJukWFvNpHONvFhLEkdDKYPwSUhaTbgHGRCjYyvUrYM+tRGaJCHtORZTl3XLhQWPheq/c7K/FD82KG2+bGJ3D9LHNzPGlOLQaPn0MJcbN96Pc26YYTVkO6+SJYVBAeSCycznSDDMnMMCHsSAMPOwcJ4uRh5bSJ6NOpDJvzLkP8Uzega5vPoYYYggEvQgEPYpMBXwM/jrzeBn8GGkJlMVA98WC+RChxyYhu2BTxjdMR0ygNLK4RWGxDsKgEIKoB4I0HvA0ATwxKiwpNh9KErTg3Ng4AqsWhBAB870cIrb4HrHiPdgyAx2XCk/04WMalx90+QRAEUbcgR08V0K61D4kNKreudTi24JgI8pDxmUcoZyUcq1BuHaf8AI+ssVubqkM9t7JiekgF/BHKWNuQ6nIGFR5beqQ6ABBQgVJEhfOYQ1nm2l6Z6oUKxdE2a5+Ln/0hL0p5tCVP/7U/Ul2G0lCUoadTGbMtSOVKQz741SihvDkFdpInHieEfNAn4pLfxEE/0YaioA8B1bzdGHNxUSaHzR4VgC/kg4joqzHOCbfrXRyIQTCkOOinOxDlcyza5Al5AS6fb8k+Yd4tJvlLo8FVS7tOWBrWnD0eu0525eUsFWClHoBb7j8R6oidq4SYkcas+WCWY7OepwSVlskAMBVQQoJTxSbT5TioIqrEni+145QGgIVUeAIubYvlrMdlQUSVhGxlmSRPcP4IdRVPKOywsRjleMFwU7ZSBk9J+PEOyYtukQUgHNKIsJcJ3Bsb9hTDfBccQTY9wo6qIHO60xJE/cVb9DsAoM2wm/D7a38HACgeDp8nAMSYN4ukJgVIgsPizEXh127t0Ol7FVcBT0iBJ0FFPC9Cwb9aIxiKQlkoDgFvCrzJLVHETkVKg1VY/cwodL97FhCdYkaMVhK+9yOEll+Lgt+TUbStDQJHYhCVVIr4tn+gQdG18PSdS84egiCIkwxy9NQS+m+tVdGOE85tM/NX53LLusiKUNgpSwEQKqeMW16k6XKk/jN+9RdaqMi8V6sru3R0x4PeJhOcPaYOzFUfLrThrCuDIpwVsx1Tf7G22J7Cwo/nIVJf6blmKYWrUJhqccrIn8z+Eif7ChSmQuWKGQmll+OiJF2eXaYx12W6Q0frU1NDa7+H8xw7V7Zaj3kx5sSca48pOvaJVl53rIR9RlKkhdWpI0qyOgtMZxO3RP64iLcWcXA6lduGeOAm0jowRdmWPN0Ga7SJk1yOCGZGkmnTXTiOdHFa29CDVkR5zPB5SDrYpk3i8HSSBZjnUg/LEy4jW9dwbgswY+LN0jp4bXbKdyApekpy8FiuTik0Mqwgd3HHixeqJNapIwiifhOMbw4A2P7Bv1CaMRrvvrsEPlaCGFaC6KgytGj2B9r3/gW/rW2OksJY+KKDiPJpL29UEJ6okPFSvCqYNwQlSgXzqlC82jXIFMCjaJ9jGvgR00B0qO4B8BOQoR1lt14E9cNWUEMMZaU+lJbEoCQQjwBrBJbQHA1adkLSqWdAaXgKWGwaENMETBF+OFFDOPr5GCiHE/HzF51wpNuF6Hbn5fhxzodI+mIxsgeuh7roZiSOvpge4yIIgjiJoMWYjwN9MaU/t2bVWkSPG85zpvKlug2GYCUiesQJsmqRWZHBxqFF9AQcyrtNS8S0oBDRI+ZFmtcBWkRPGbzgwtRQrCs6bKx1/aoHKjzO/QBAjAYSHSz+kAcB+Gx53FLHWg8ASkNaFJGbTlxwW4ltlgajEIBX94UYZd31NPOLglGAQ1m9vP6omWw3Q3HQhyA8RqCBOUaY5Exysr84aEb0SPlcPi/S+eEMxQEfQlxxbVeasYtlOFAW9CJStJSTM4iDwe/3godlRsTqVOBAKOiBq8fGZeBzlYH5FdiiawC4XrB6OyEzoscpMgZ6jlVuCPD4UWmZDAALAR4nz285FycLqvD64XgyGHc51usGVHiC7m07tQEASlkQXj8XLxLJFtkJI39WSsWIHm7TW4qeCtdlAHhpGTylQS3ReI5TLyvIEqN9wnm8uBiwypQeY+OCPFNuMOTH0rL/0mLMRJ3heBeyLCksAOZmoiA/AckTtsIXfqQKAMpKinH4uXZo0LAIGLHb9kgV5xyFRwpxcM8B/Lk3D4d/O4jC/QdRkn8EgcNHoBYVwBsohI8XoXWTnTgzZxM2Lm+NwsJ4xPgCiI8vRXyCH7HxpYhJKkFMSjHUMgWKr+LfCTkHykqj4C+JQYk/DvBGoVmT37BvfRpSrr4XMekdweIzgLh0BPxlWH9DP3QZsg6B3gsQ0/LCSveXE2qgDGWr/w1+eBdYciv4sq+HEuUrvyJBEMRJDi3GfBJQVRE9ViK3acajuDskKtAut+e5tcEqUMZJlrW8+NkpYMAqkwGuDqZIc0rts+rQpjyxN+1iUr5VD60197grzRZVakvOd44ggi1NliyncUljhBfJdp5j6+PDGi0T1kYPcBAmtpy79Y2gDVc1B4hNT3MCym12hfuW6Wv6mHJER43sfgtHOImLBNvQH5XR9JG/XsvRFLYACD3Tmh7uFGkdICdc85ixXo1N5YgD1qGCfcA412Ww22l1Qri0y93KuVycUkCPHoDiNABdZNr6Vex/vT3hGDDTmGCnm44cchvg4buAtM6RoI9RUb4iDTsNmZbJm1TfLGvGWnL5pQ8uyX6X822ENHEXWy0dIziJCOJkIjahAZZv6aMtkvxcO2z2D0Xry0Zjx/zX0SF6gbnrlsO6OYwxNEhugAbJDXDq6a0jyikpLEDZ3EyktspH8oRcyaFUfPQoCl/sCMUXxPurb0Pozz8QFzyAZN8RJMUVITGhGAnxfsQmlCI63g9vbACemAA8MUEwBkTHBhAdG0Ci8GhZeuf9wKbbwDdpl3coqKD4aBxST48FAPz27j04ZWQ0WMP2QHQTc0OQSlKyZCqUXc8iKjb8PG0+ENg4GWqr2xF7wf3H1CZBEARR9ZCjp5aorq/WkR1IdidP+XXMMkYrzDnPyRlknQNWBN1JY3d7OOvpNLfUHwlyUCniUySGQyNcytEJ4FDP6ZE4ey2Ls8D4pO/SZbXRHlFi7RV3Z43pBBGjdbQ3bccmcaIuR69Ye94e5SPNm3W/h0sXaAspmzspGbZy85EtY91Ya3SO4OAR1u0N1+M2vblw3sydjUTdhHPIrHmS0rJtsI8zs0VmOj9EH5DbYBXzxML6gtT27pcHhpDPFGY+XmSVWd7FIrQpfd+X5OgXMpP0sM0PnAei2f1cEB3pQnS5OJ2cNcztKSUnP0hFbrjh9pmhBzMHG2CseyO5I/U+stzvTEeWw83S6vySyun9rJ9XVfZcOS60bbWDCc4bF6+WkcVtKhLEyUDfZz42tj0/J+ENYMMbSG0DlBVEV9nW6uU6lDIP4bvVF+Bvrz7i2gbnHPt/+R1bvvsZB37eipK9exBTmockzyEkxRUiKaEEzdLzkdLmDxQfSIDiVeGNCcATG4DHq6JBo0I0aFQIAMjK3AL+9SBwAMEyDwqPxqPQn4JQwqlofEY/JLTuBZbcDsyX5KpPyZKpiMp7DIcONsKPy3vgt9+boEXzP9Ct7zo0jHkMJUtAzh6CIIgTBHp06zg4nke3uEPUSEVxe3SrvPbCm/Iax5V5eKysnF+m3WRzqCjnqQnHPP3RrfLq6PlSXc4QgubMcMq3tqnnawtAex3KWFfusabbF2O2y7Q7Szi0R4S0x8WsTh3rY1EWBwiAUjVKWy8njBg5xG31zHZLgh4EYD6CJZW1Oej0xZK1siXCAtAAM9d8dZAp2l4U8CHAHR6nM+adou7mZxUMJUF5gWxzlzBnp5cecVMY8EHlCqzn2njsStJdeASOA2XhxZgleZIwh/OpAv5AFBwfaSpnEHLOoAb1evKuck7ljSQV2qNb8FTsQhHfVUAJMimfRVoriJsvpVRzUFZEpugvYiFhMWaxzfJkhlREWdcMdrqgIPtmwAElqEIRb0KqpZ6op5geCMBX6jDAAWHgcqOuZKc/BCUk3DNV7n4uRdmlfnhKA/oAt8sU30WnDefgxSWAqprp1ke/4KQDRzBQiqWBD+jRLaLOUJVh7yWFBVg5bSK8Rb8jGN8cPe6dUSU7YInoDiVfQpmRVlYQjVVbz6kSh9J/xz+EIafPxB9lLfFbixnYu3ozCrZtRVLpLjSLP4gzem1DdLwf/kNx8CX44YkLRFxKrqwkCoUFCSgMNAGS26JptwsR26oHeGwmyv6dhaK/4vHc6/3R/m/not91Ofj67S+x5a1lmDD6C8SnFCF6VB49xkUQBOFCTT66RY6e4+BEXKMnsiPFedetigyAgMXR4zQXA2B7CkCFCqu2FZEXFBw9bvXcnCqao8d5wcFItgdVoAxRkuNBfvzLKcJGOw6oHoQsa/RY64nHhoMo5IGf+xzzxM9OOviFNXqsTiVhA2uLDgwlQW/Y0WM6TJwdWXrfmvoXhbyGI8M851YHE4PVQVQU9CHEzZ3Q9HmsrpOTnRyaI6NEdVijh5vlRLsNWzhQFPDJzi/rLlqq3VcAaPPjslCU4eiR5tOGfXbbVQB+vxfS8r9uF4olj6sRdt1yasuQD23XLV2m0wVura+/h0xHD3MqZ6tn9oenFJWWycIyvcEI5dx0DaqIKrPkhwe56GCxtRF29HhCcO178bMippcFEeXnZhl50IbrhRdetfSB4g9BCQqJTg4bvZ6YXxqAx19mpumP5nEjQbgpiY4bDl5Yog0k25pC3Cwr6a+lB4N+cvQQdYqa/JJcVVSnQ6mkqBh5d5yNjD6/AGkD4el8N5DcETi8CaH104H9i7B3xako/dvb2PTFtyjYsB4NQ7vRrMFfaJxSgKSGRYhNKkZUgh/eWKcbtMzRgwnIL8uEN7UzUs8aCF/mOQh6UvDhpUNxxd+WoyTjGST0ublKbCMIgqhv0Bo9JwFuj4EcK5Vp61hku82jrD8SO8mqbLt6uqvMCqDvReXmDHJqSysjrpUhP/qk95v4SBiH+ZCU/qu+PWDA+oiRsD4OF48gSGfC/4DpPND10h7xMB+Fsm+NLsvmpn3caqW+FpC1tixTkyI8pmSLOrJaoZ0BfQWiSD5l0069n8I9y8yzyCGfD3OdIP1Y7gEO67pAHLZFlAWZpo2WJ6zCT9OY0Uvc8ckZ7hQpIh67ORoA41ko8VdWwY/gXk8FHJWpCIKhRlcaWQ6PgwnvnNsfkSpPlP6uPwVmtOnUL07v1nLhQaOfI92ZJN1zGKTNqSI5e/QnmaRE65i1F7KMHvHAUtdhvR+935mRrwq2OlzR3GqEcGjcoIwL1O70cYSe3SKI6iY2oQHOm/p69bQdH4fvj/ZDTG4IiZ2/RkzeIiOvtCAaBetb4fuj/XBNr9PRodfptvqhUAibvl2HjQuXwb9jAxpjL5olHULjlAI0aFiE2MQSeBv44fFpP9slNi1EIjYB2ASs+Q/UNUCwJAp9z9ccV/u/eR2ndhkAltDymNcBEuFqCPjjW/CSPLDYVKBJb9o9jCAIogKQo6eWqEonD1Bx5015zo5I7VvrsPB/0g/HFWhf1NXtK4BtwubSrrujSK6tt8ctaWIbWhl93Ri7Q8Bp1y1n54bofpAdFPKcU1vcmBnOBybUcZKv1eRygiCdSbJ1B5HNjcE0K8U1cuQeMZuXnS+AsY6MLlWIQLHKFN1D4PpCzpby3GITLP3M5TQupMlL7DBpDR+uajba1unRy7j4RnQXmHW+L9uH8MLJljFmPnkl1bMdOwrmsI5ZaRA5HQOaL44x5xtAJAeK/lmwzVbZmqh7aTjsa+aUI1MckzYHUaQbgjig7ReU4eyUrwVZB+m+ZL04dVPF9oxGrP3qfEKt17pZQh93QqreuNO5BNMGkW6rKnivXBcWF4UzuYxkt+gVE20nJw9B1Aeumfsc3r12Ajp9shYpzQ/BExNEqNSLv35viI2JXXDN3Odc63o8HnTu2xWd+3a15QXKAljzxffY8cU3yPjrE/Qe/DN2/ZQBn8KRkFyEmOQSRCWWwhcbQNMW+QCAU1puBP+kE8r8XhzOT0Yhy0JK9iVI6nIxWGJrMFbxCHi+9yOEVt8DVrxHOwbA4zLhyX4cLOPSynUSQRDESQY5emqJijpmKkJl2inP2VERGbayESpHcuQ4/F4tNek8Ea+QWGjxKY7TV9e6mkyzhPXRKdEBZJ8vMkebRHlyFI32SeV6zAuTpMu7fCmCTNMdxJm5lotbdI24o5cunYd33QKsTg3R8SPLNDRiTHLQ6OsAy3NQff0pZkgxo2vknndyNBmWhJ1DokXMLAauMqkvRT30fcWkKB/ro1uCTFkf0eFmFjHm20DYMWOZ+Lus/VIhmB5TJehjHVBObekRPRWVZ3Wq2G4AYWeE05Z1ohjRD1SOTCbI1H1FpjPFQY6TTKcLiwtFdIcPLG2LNrrJFP0uoi/E5lyxO0pssrg5imWjIQ8g0XkkneOQy/l0MMKoo8vRPVaCPABGlJD1BAIAVAfPG0EQdZFr5j6HkqJi/O+eJ1H2ex58aakY/J+7cHp8XPmVXYjyRaHHxX3Q4+I+eP3uRHQv3IyGzQNIHr8V/pIAvnt/MfYuXIxmgc3o2+8n+BLKECiMhi+pFFHRQTRJ+xNN8Cfw+yrw3x9EMKDgcH4SjgZbIrFzfzQ+axhYcnswxT4d4Xs/Qmj5tSj4PRlF29ogcCQGUUmliG/7BxoUXQtP37nk7CEIgogArdFzHNTWGj2B41ijJ2RzNkQqL8pUbWliObdRpBquhsrJVB3W6BHz3RwqgH0xZqf6TulBFfALixRbyzsvdqyhr9HjrqvimKev0SM6OwSXhq3vxMiWspDXaJcbL1FHc5Yrtlkc9CLAo2zppmPGap/pICkJhdeg4YLTA8yin11mYdCHoMNizABzWOxY0EMF/GqUKYM76eYQAcT1NXrkRYqNdi2OAmksccsaPTa5LHws2qm9G2v0uDplHNLDdqohr20+LinuksxKPHBdANrpXf+sVnKNHh0V8JSySstkYZmVWqNHJ+C+GDML62TLC+cba/RY2xV0tPo6GAcQCCKqVChk6wfTS2Tdml3xB831ftycO1YbOIAScY0es33pxghrO9rFyItLw4s+c0ueIEhMC3/W1uj5b51a74Q4uamLa/TUB0pL/Mgd1R99LvkRZcWt4DnzAXjbnI/g9q8QWvNP+OJ2YcXH3eC57D7s/HQxGhVtRMuUA2iWdhgJjQvgSy6B4rH/MQsFFRw91ABH/M0R26YfUvtcDSR1RMGbraAcVrB6UWcc6XYhuo26HD/O+RBJPy5G9sD1UJM4Ekf/To9xEQRRp6A1ek4Cjjei51jqcrhH2Di1KeroNFeU6rk0zLjoRrDPsyLNaZ1cYeXbzaBaJv9iPbsTwyQIhBeONnvKXtbZ2ROCYmtPzHfeY40hJEWGAGbUD4O4qK/TEy2ig0VPtT5UYl3zR3NyyD0vr6jj7LA02hWdH0yUqM8bxb3dzHV/OFdhXcTZlG1GD4WbkJ5e4WItUbxlLmufV6uavtzpXIqxWDJ6RI+kp8UTYl1bCWCyI6cSzhqtuiWSx6meq/OIu+dbVBROtfmS6rHIkUThdJWHz1o5MplFpusjX+XJdGpfMNsaNSS1WV4b3HJvNMaVoJBDlA2zCYTQEBPqSKE+MB6xkhwxershSS+b0uIjWEY9y0VhS9PbU2WZ0iLPBEEQkYmJjcaa6EHwfQKcef46eLf+H7BVm0ioajRWftINa6IH4Y7hA9B3+ACjHuccP3+1Cuve/QSJ+WuR2Wg/0lIPoUGTAkQnl8ATpaJhkyNoiCNAySbwL14EV4GEOODwn8loNepUZFzQC0rTFrj08btQVjoe62/ohy5D1sG/9yvEtLyw9jqFIAjiBIYcPbXE8X61rozDRqwTIaggYhvM8i6Ws87RrPlueWJ7TvNRNxvLm7tFcqKJ80xrOS3Pbqmoi+bEkB0HAKCAh6OInO0QV8oRI4MUxqQtrXUnhNPTJtZ5MLf0ku5W4YLTSdOBSS2K68s4uTvc+lWrywHOBN+Cnis7pVjY2Wb0AVOgP15m9TcY9vBwChNs5cLc1U1HxsIORW23MX0uzKDJ1Ktp41/oCQZ57VuhdevuV/pTN6bOzGjPKKMHkTkNXg55bWtbJ9sdCTacLojyZJbn7HByJlhPjvWEqfoYKl+maJbRh5GcQ042MkDauo/Zs402HHwXut/FdrFLioU/q25lmE1HbqyJEx6ZkmfawVDjPHDhRmRxyDAFQEjQSXAScaZdDKpQXoregeWdCyfAYrx4btVIf00IgiBM7njrHjz9N+Db6a3RteNvaJBYjIKjcfhpUwt4erXBHW/dY6vDGEOXC85ClwvOktK3rtqEVXM+RMy+1WiZ/DvSUg8hqUkBohsVGws/J2ceRjLeAr5+C8EgQ/6fDXFUOQ3+Nq0BrMPa2a/h7IfJ0UMQBOEEOXpqiUjOiIpwLHX1OVh5sq3zt/KcNeXpUlmHTaR2nRxCTsdONnLLZ+d5r2a9NVpDd8BAShVtsEuUZXDBKWCmamv02NwMgMO26WJt63nkUilukcQl/bktikj/3x6VZB8vzDY3tfaXGO1i7pClQuVOj6+ZUUlMzjA+cocM0/GieR64oIPZJ+auW5K1Dmv1mDaGLWJ2Z4/ugDJat3gUuO6tchu4rheKPvHmwmehnqmWPcxN99yWd7E45TneDByuVrfHoiooUzx9uo8x4rIwFb3ZcAdtLd0X8SbjtA6R00VlccJJbl6xfHh4Mw7Yt08LZ+h5HDC2aZNsUmV5Vo+ZbTet8Elk3MzT9RLvAvrCSEZUj95GxIFJEARh44637kFpiR9vPzwHf+08iJQOTXHbf0YhJja6Uu20694R7bp3lNJ2b9mJ7177EK3z5+HMnE34Y3sTxMX7EZNSBE90CI1T89EYy3FKU618x8ZfYfvT5yMt5ybEd7wYLKpqtqwnCIKoD5Cjp5Y43q/WkX6DdWvb+sN4Zes6/Xiv51V0PlUZLBtju+pltcnNiWOt5+Qo0uM9ZJvkB8HCQQ3GZ+2dw+2xJx3FKGk6QBQGYdctvS1mlNP1EHVxssnJySWulyO6fxhjULi5Do5ZgtnasK7M5BQgIPd3eLersDwj2IRp8TVidTMQRZsAG9NO0aFiNC73kYwoEzD7zlzMWepHxk2/Svjd7IfwItwOziCrTrI95mLVNkSnio4l+kPyD0VyoFidQHpEj92H6IzYjlN4n9NiW8xyoMuriEzBLsY0P4PNyeN0cTrh0rW2NiwXhqNTyTzhZtvWGwfTKzOpDgdgeh/DI044gVw/dnIGiU4avV1xjXJFMR1BTGhQb8SILBLa5xY5Ul9YB7m1E5nzmCUIgohATGw0/u+Jm6u83Zbts9DyqTvw3/FHcXrRDoSaNUTpRZ/i81feg2fL12jTZDfSM/9EYsYhKB6O+EbFOAUrgY0rEVoPHPkrEfn+1mhy7igkdR0G5kuusOySwgKsnDYR3qLfEYxvjh73zkBsAjmOCIKou5Cjp5YoL6omEsdTz81Zo+M2d3OTWZH6btMI69y1PJm2OYxQvyLynORb2zMjepxaY7Y6Zl153ytn3TR3kOg8UbkHYiSKcx9ID1456mbW1f+XJctzP6eIHga5hbAjymgrLDFcjDOxrl2mHtUjRvRwi0NHdpMJFlkcOyycqM995XNmzm5Vw2FmrkWkrUfk3meQ2pO9KNb1h6Q5eliOavU0qKJ2clYk3HZId0Qs5OisKUeug9PI6d5gKyB8dnXWON0QHHweFbpGrRe1xWPsFhUkrQtUnrBIdlh33RJssJVnlrNn3c480o1L9PKpwgmVdBOcOvr1YdgmyLKOR4XJY1KSF65A+zEQBHGCMfjxydh3x3xk9NkG7BiHKybeDSTfCBzehLKfp4EdWIy8VRn4oygeLTL+RIPUo/DGlyG5yVEkYw3wyxqEdtyGgvwE/FmchYY9rkXK2deCxTR2lLd80iXo3n4F+nQqM9LK5n6I5Vv6oO8zH9eU2QRBEFVK5baKqiGWL1+OIUOGID09HYwxLFiwQMrnnGPKlClIS0tDbGwscnJysH37dqlMfn4+RowYgcTERCQnJ2P06NEoLCyUyqxbtw59+vRBTEwMMjIyMH369Oo2zbThOOoyl1dlZLs5FdwcGW6yIkX66Pnc5eUm02Ee6qiD875aVg3c+9q5H5nFTllrJrxM15DoZtEXgtYjZOTWNL2Z+WKQJIvynXqXO/aivQWxJSb1FoPCnCw2W7I6OLj4zzJHRHixYxZ+wVj8mEFfI0eyijEw4yU6cbilSQ7OdOnMWFSZW/RmegXo7bGwZEVb/0iQo5nNjRcDB2PaS5Osai/OIZko9IXYVQyAh2nrLClMkypF2DChYDkXrDnXd/NeuHx2ugjKuznox9ySZBMt9AATX4D1qbaK3pBsfpdIN0KxLQ7NVqF/9civ8l4VlmG7uRiDBvrYBRPvABbHrySYR2ifWWxnglxFkGVVVK8rCOIqzMe93O7MTK6vhF9MOCYIgjiBiI2Pw/dH++GP3Fbwb/8a6uLzob6fCnXx+Qj8shx/5LbC8gMD0OXV1Ui+Zwe+9r+M/35wCdYu6Yi/tjdFoCAajAGJKYU4JWM9Gu67F+qHLXHkxSbY8Xg28j57DGrxfgCak6dX9hIU5Cfgmx2jceCM7/HNjtEoyE9Ar+wlWD7pklruDYIgiGPjhIzoKSoqwhlnnIEbb7wRl19+uS1/+vTpmDlzJt58801kZWXhwQcfxIABA7Bp0ybExMQAAEaMGIH9+/dj8eLFCAQCuOGGGzBmzBjMnTsXgLa1Wf/+/ZGTk4NZs2Zh/fr1uPHGG5GcnIwxY8ZUu4361/Vj5XgdRU7tWL/uOzlfypPvVK4y0wi9vJvzJ9IP5CbMduSkr7Ut7bd002Fjd7IwqbwsTf/ftheTUIcZravhY32dFzk/Es4zfr1d0ya55xnM6BqVc7jJdHbymXYzZs4pI8lk+rHxiJgWOeAsU94tzLo4NQOXHQtcGB/WyIkwKjhUKaLHei7FtiwjlElvRlkm5IsbXekOMIADqrDNa6STacljYuNOA9ZtALtdKE51rAPeyVlj8+A417duJ16uTBeR5d4IRV2FiB5nx1Q4r7I3RyeddWeNGG0j+G4cnWLSILaMS+t50huy3vBCXBhcLu/SANXHi9NCSmIdLr1JXjCnKDSCIIha5pq5z+Hdayeg0ydrkdL8EDwxQYRKvfjr94bYmNgF18x9DgDgjYrCwHHDgXHDAQChUAgr3v4f9s//L05J2IqWGX8gKe0IfEmlSGhYjISGW4BDU8EXTEXBkVj0PMOPor/ikDR6Mc5t0h4AkN5pJspKHkf+c+3Qvd03KCksoMe4CIKoczDOT+y4bcYY5s+fj6FDhwIAOOdIT0/HHXfcgTvvvBMAcOTIETRr1gxz5szB8OHDsXnzZnTs2BGrVq1Ct27dAACLFi3CRRddhN9++w3p6el4+eWXcf/99yMvLw8+nw8AcM8992DBggXYsmVLhXQ7evQokpKS8OfWLCQ2qFxwlHqMK9dwcARtX+or5vhRETKkVtRxoxMQZEZymliPOecIurQZSQdVBQIR6rg7gRiCHAjBY8svT4eACpTB5yoH4fad5m5B1YugESBndcDI9cV0f9CDMvhsNrg5lUSZpaEocEGm1XkifuZCWmnAizJEWdpkrraJehQHfaZ9FoeMqDM32tLSigJeBC0y7fNga3vafyWhaNs8FVJ9Ib6Km8clgSiEuEeqZ60v5elOKRUoC0XZHUBG+zpMaoNzoLTMC3BP+ReURSFVBdSQB2bfll9H/8hKFEAY75HKa8LMdyUoOAPddHaor5QyMO5wz4sgkwFACPCGHMqXJzOgIspvb9OpDhOPOaAEVHicFvKyYHNelQXhK1XlNGsZLoxywSmklAWhhCwnCmZ5eeALI80fgKckYJaVHh1TTZniBaRHoJWUmE4bo5pQRtyhy6jPEeQBLPW/jyNHjiAxMdHeMQRxgqF/96Ixe3JQUlSM/93zJMr258GXlorBj9+F2Pi4CtfnnGPlgq/x63/eRSvfRrTMPIiGaYfhSy6xLVFWWhCNA/ktEHvm35B6/s1Y/th9OKfNG1ixcTjOm/p6FVtGEMTJSE3+DTshI3oisXPnTuTl5SEnJ8dIS0pKQo8ePZCbm4vhw4cjNzcXycnJhpMHAHJycqAoClauXInLLrsMubm56Nu3r+HkAYABAwbgiSeewKFDh9CwYUObbL/fD7/fnG0cPXoUgOZ8se6cVD7cdU5UHmqla4hSnQVGiuZxqWLLc5ubusUIWH/Ml34Qd2nTyT1mLcMsEt3acnYWcVh3vDLL2Z0vVp25Qw3rkX6sPSjEw+dTdHI495rVGSLrzi1p+rG50qse1WOOV+Zgo2yb6Kyx2cPEh9ZkmfoCywjLVGFG11jlmTbJjhPJNiaWF9/lyAnjUTDOpIgeUY4k22kbdT2Yy+k6YYZZMCOUBH3cPKlWLPnakzqCfpEiXUSVQ2GFreUregGH6+nBIVrZcmLwuOAUqaRMp7WAI+qn5zn0h6OW3CE9kjluOqsQwtfkPCmih0Fy8MhCuUP/MEtZbnaKyoGQsOuWzTEjtCFGAxkZukzxIhKcQkD43HK5P9VjXS6fIAii+omNj8MVzz90zPUZYzj7svNx9mXnG2lrl/yADW+8id4tFiPzjN9Rmh+L6OQSxDTwo2WDX4C/HkJw3sPokJAMAEgIbjteMwiCIGqcE3KNnkjk5eUBAJo1ayalN2vWzMjLy8tD06ZNpXyv14tGjRpJZZzaEGVYmTZtGpKSkoxXRkbGcVjivrBvdWFO0M2XroP1ZYVZXtY8OKTraW4yKiLbKte6BIrTy8npVtF+1trQXDDmu74ihxp+yWv0aC4E62NXeoSM+W5dv0cBgwf6SjbW/tPblXtGli0vMizK0FeqkdsVV+2R1yISY3HsZ8OUabVaXJ9HW6NHsZxIrWUPtPWI5DVxTJug9294HRgl/NL3z+LcXKeHcQaoukxAXCcI4XIMCK/RA6NNxlSAqWBMk8PAoYSPrXbb4hz1Obcx97auvxSWL27zVZlXuHcr7DA2h4DpdXG6UMTT4HQRCz4B0x8g9AWXTqb2URF8QZWU6egTsTpDnLqAa0NL6i8mvPRjxZJeGSePqLMCgKvhtWzCieF3zi1r9IjeK72oo5MnbJw4/pnQzwzmrluwyJVusoIzR0+U2hd1EduxltcPI3USQRBE/aPLBWfhundexG5vHwDANzsvwX+W3IzlC7ORtzENgYJoKB6OlLRDAICuZ6xBwazG2DatBw798DZ4qCxS8wRBECcEdS6ipza59957MWnSJOP46NGjx+HskWMzKuPwEX+MrQymI8QU6vQV363tSOmR8o51GuEy1y63nLzVtp7mXt7MFx0z9rJO0TSmXm77bkWWzxm3bYrj5Hpx08Gcr3Phs5kmz/rCbgSFSUt6iM4im35W7Zk51wTMtXNku/WJpHAWFAY15DYSwv1udToY71yai3JACLZgQim9tfB5YAxclW2SHg9z8AIYa+iGm7X4q0yNwxNzab6thGVX5OJ0KMMYs83fXS8s6WQzezmnAewkW2iH2WQyuw4M2mNYThdjBWQaaxuL5Zjls5OODGBi4AmXi4uDhlnKuOni2L/CMQOTo10c27ddvNpHbnH96p1rkxdWQtQlUkSP3hgDjEe5rJ3FhTaMQWxVXpdNEARx8tLj3hkom/shujZZjOR/boUvVnskbMXcT5H34Wu46Pxl8CX4wRQgLrEEpyZuAHbcjMDmW3Bgf1OUpQ9E1rD7ocSn17IlBEEQduqcoyc1NRUAcODAAaSlpRnpBw4cQJcuXYwyBw8elOoFg0Hk5+cb9VNTU3HgwAGpjH6sl7ESHR2N6OjoKrFDm9wei7vm2Jw8kkxLAxVtjwGOk1inuZmOAu3pEjcq4yAKzzErRIT5W4Sytn10hPmXOXGzPgKkBQCwCHaazharE4VxBkUaCbpE3XXkju7s4IJOdpvkEAsGACo3ZJqOHP2hLtlJpsA2jRQcU0I/cHeZ0GUy1ZiDWiOgFMFppGsDwIieEJ1AopGG3Y7OBT16yFJJ6A3d2WOcSwZoUUGOVYQ0QX8u9gEvP0LCaXBzJj1mVu5Fae0Idx9aZGeR1U+hN2VzIOiHZn+5mlmeJ/pYnFLWPrM64sJpzH57s28qZV5i7nrp51Jh9nTbueO2vmDijc9607TZKSoteMKMftQHv+VOawgNGw4G+6LMkQRzOHcEQRDEyUFsQgMs39IHvbKXIP+5dtjsH4rWl42Guv0znNttFaKTSrHi+3Oxr7gjsoLfofUp+5DU4hC8sUGkZ+YBmAP+0Rwc+rMBDpR0RMaw+xB36vlgzP2BiZLCAqycNhHeot8RjG+OHvfOoIWeCYKoFuqcoycrKwupqalYsmSJ4dg5evQoVq5ciVtuuQUA0LNnTxw+fBirV69GdnY2AOCrr76Cqqro0aOHUeb+++9HIBBAVJS2SOzixYvRrl07x/V5IuH0I3X58OOKzDFbqQxOv+pWrD1dV+uExuoEkNJ4eKmLSulotiX9yG3RIxLWOaHTsSzLqqH5B1qW726JaWfknbPk4AUGNexYkPOZmR+hJd0xUNGxZ9igMIihOFaZen+JfY5wGe4wXphQyPHRHLDwzFd7rEtf50Z26DjUMdo3nSf28x9ed0ia+OuRWYopzNK20Rf6nNqit+jgcgt8MHTRHQxMezxNBSKfDKsRxmSeyWVgLQN7vv64mFN+eV5RQSQTjyVVmH0gqDAiniotE5Z7iKNM2GQakUAWmUwsq9/eKuBXEds26lgGveJ0ZQnjRTKIC4lc87cwSXlzgBlipQGm/z3gZnm9L/VHt4xIH+biPLKcUOeLUXYqiRcvQRDESUjfZz7G8kmXoHv7FTgn4Q1gwxtIbQOUFUTju9UXoN/zHxtlQ6EQPp/5NkJfvIeOLX5Faqs/EZNSjMTGBUjESuCHS1G6LAp5B5rD23E4mg8aDyU62aivy+nTyXz0q2zuh1i+pQ/6PvMxCIIgqpITctetwsJC7NixAwDQtWtXPPPMM+jXrx8aNWqEzMxMPPHEE3j88cel7dXXrVsnba8+aNAgHDhwALNmzTK2V+/WrZuxvfqRI0fQrl079O/fH5MnT8aGDRtw44034tlnn63w9ur6qtl/bG0VYdct5+5VUYF4HpcCgYgxMpGa48ZCzg5rjEYkIC4B7VDB7XdhDm7+sO1Qx42gCmm3Lrcf/eVjbcIS5AwqFDg86RFR56AKYzcquzzTnSfPZbW0gOpFyMHJIzuI7PXLgh74rbtRCRMvDnsUkO6IKlWjjJ2iVMtkTdZbbrckvOuWmxPLKlNL02QWh3ejMh+bEuafEWQWBaIQ4B4pTbbLSX8FnAPFapQ2T+ayDFEHcPs5Lgz4oHK3hbUBqIrDuNXaLQt5NTvdHnETbObCh9KyKMBpNyp7I1Ia5yy86xZcd8p2OuYqwPyWXbfsA8a5HRVQQky+EERHntXDJZRRSgAmLvFWjkzjCggBivXRL7eLW0wLWnbdsjp8HG5Aut9FCapQgi46Wn04YnogAF8pj9CH3AhvM+zT7fWHtF239JNZzg3MuOJKyuApDULe2l3QwRpqxs00XlyiPWomXShCG8Zb+LOq1Q2GSrE08AHtYETUGWjXLaI6OJZIm22rNmH1jGfRNu4nZJ2ShwbpR6BECbvUqgz5B5Lxl6c7CvMOo2vnH5C/txE2l12GNsNuwvYP/oUOvvlolJGP71ZfQM4egjgJqMm/YSeko2fp0qXo16+fLX3kyJGYM2cOOOd46KGH8Oqrr+Lw4cM455xz8NJLL6Ft27ZG2fz8fIwbNw6ffPIJFEXBsGHDMHPmTCQkJBhl1q1bh7Fjx2LVqlVo3Lgxxo8fj8mTJ1dYT/1EHdza8hi2V4/Q7eWckaDg6KnMyePhTd0d67j8+GvKtGsccd5kzCV4xB/53WSGhO3VxTLl77rFEOQMIcs6407zLGua6ehRXMozW12dgOoRZLrtZmV3gPhDCsq4dUt3ezmnvNKQF1zQ1Vmug6MnGGU6tLiTHKeFpbW8olCU4QCwOogcH+kKR9cUB30IwmPOTfV8bpcntquqDCWq1REGZ92NY61Pist8hgPMdEYJ55A7O4k4B8qCXlgf0ZN0E+fNup0c8Pu94LqjJ9IFZTlRnANq0OtexcGRoaUzwK+YziVeTnmxTCjs6IElysR2oQsOHw5ABTx+wIh8q6BMhrCjJ2SJIXGUaakbUuH1O6Q7XNDWiB4WUOEJymmuzigu6FYWRJRfOEHckm8MYLszSCkNO3rsA8UmT8or9cPjDwgOHOGCEQeL6MwJ5/HiYvMilBb+4pa6kPKCoVIsLSNHD1F3IEcPcSJSWlyKhf98AUm7PkX7VnvROPMv+BL9Uhk1qGDnnjZI7nsLmvS+HswTg7KSYhx+rh0aNCwCRuymx7gIop5z0jt66grH4+hx3F+ngmeiKiJ6dHEVPftlVheLZf7iPr/jkrZOjhYnxIieitQRJ/AhzhC0PH7lNI+0thsqN6LH6uwxPwdUrxRVI++ybUb0iHI5tIieMltEj1nWKkuUWaJGGU4FbtNNbkdy9AS8CFjstOomyzJ1KQ75ID5uJS5obJUj1i0O+BAwtk2yy7Gfn/CuRhwoCQn9w4XyovPGYT5dGPBJ/SPlW9bhMXfQghHRYzi0xLm2WN/SJlcBf5nmKLTh6qjR7WJQg4Kj0OnCcqgvRfSUdy1b21QBJSg7cZhbFI9oAweU0nBETwVkipEyNkdPBZw8+sUZ5XcpZ3k3z2RYVz2iR0e11BP1FNP1iB5HeVx6N2TqOvhDUELCPVO1O4McZZf64SkVHD0Wp4xRQdJByzMiesAFeRZBNh04ggGK6CHqFuToIeoCqxYux+63X0aHlE1o1WEfYhoVS/mhgIJ9vzeHt8v/4dcff0Xv1m9ixcbhOG/q67WkMUEQNUFN/g2rc2v01Bds38ErgdNSFhWTad822mmpjfJkApAWzpV0sTRmbZtZPrvJFndq1stFmCdDs04rHUkmF9LEdpmltL6+htyWlmp/ssZcZIRLJUWZzPF/hZmTax6uZ7bvtN6PUNYybxOlc0sNc/FjBsYYpLV4xIWFy3msSlvMW+itcPiE7vAR+1F0/HDGjEd9hLmprX2rnfI+3tqbMaFmWl1zmREWnsvquijG1te6nWLzTHQaSfZaPjPTLuu5lBbMVoxE58Yi5XEutQ2L2dIgFeoxBZC2dBcvlEgXtuWCMvvQ4jGT5Ivn3WJTBJn6fcG8SgTcLk6nY/H0WW8IVluEdJvTzHYzM9OYpKDtxAM8PLrEhZGZRY7eBBdPOHM8l7a1gfR1pQCYq61z84IHIjuNjPYtJ0hc6Ft3+NBvPARBENVG94v7ovvFfQEAK8ZfiF49v8PKxafhlJYH0TAjH97YIDJa7QUOP4Qmmdrj143xE7gaBFNoekYQxPFTuTAUosowJ1aVhwuvyspkzHluVRmZ4rzJmIe4KORkol5Uhb1dPV11+Oxmk/nZ6myw6+4kH1I+D//THzoztdT/SRED0Fwa3Cabh9dhsrYqtsQR4uYxwiWYYD0zdJCtYdDmkeZ0joU1sTouzJa11lVwzrWFnPV3qIJbRjXkOsfqMG0MMUGmfRsiQyoTrRbkahNms4w1nsiId2NcskzawN4YgFpVrurtqwDXzp8uU7cTgt0cVtv1vnTGGONCm+JLVbm9ktvFZi3KmDGupPElD0HbRcPD66w4XiiODVryBHWMuT8PZ3LLS5Ctd2dFZeqLKVt9XeVenE55+hBiQhKD5SzCcKhIPg8nudZ2Dd+K9ebGw75EDs7Mq9a1Wxm39IWlrfAY5MYjWtq4NS9/QxGzdf3cWG/kkhKis5A722ooy+HeEEEQBFEVhBIzAQCBrB5oOuUXrFBfw6JP+mL/xjQES7zw+rTY9w6dtsI/OwXb/3kG/sj9N3jEnRQJgiAiQ46eWkL87n4sHEs1cVJ0LPLsDg5hAufSppuDxtqm9aVY3p3mqk7SxAAD25zG5QXpMwv/U4xXJOm6DGtgA6BfXKYDSGvP/AcweBiEY1Gm9uLGZ9PVors7xLVILPEggraKIUuzSosYYGFvjSlL721RNoRW9celuDn3hRwh5iQT0GWw8Fhh8ktw4+gtWB+j0vNV3XbdByQ1wcAU7aXv8KU5pRgYU7QXFMFup1EluuDCtstzfNNWxkwZ+j99H2/HC0USY4dzacxycWA5DVTpQmHlXyjc8m61TX8x2BOlArr9qJRMUTwTDdVtgvC5/ItUqqP7K/Rsaz9KgStubQmfdR8K0w/KOaHGOLGYZZQzEi0OGpud4bFrXJLMPMdGJzoMHslwwWDRSWTIlW4a0Lb9IwiCIKqTHvfOQFmhDx188xH0lyLnpisx+N3P0GLqDiwtfRGFB+MRCigI+T2Iig3ilFN2oNHOv6PktRRs/Wc35P/0EWilDYIgKgs5emqJ4/16fSy3+/KnLJHlufy4bv5QXUmZTj80WyN5nH7YL0/PSHa5RRGZLzGiR5zyO2sg2uLmCjJtU23thrgpC0a+HlEjR/NY3SB6FIOWJ0s3z5MpU/ukgnPVjOgRZFojiETXiyFfn/uGUxTh2OwdvT2hL7kqROCYLzOaRpweh6N3LI4HI51pL2PchdvlnIOrHEY0D/RoG9Nm027386n3pm6v/hLt5FLfhdvTI3pcL5QICBE9Uue7XSjSBcPt8lzlWD7bLpSwodZoHuMVFuNkYwSZom+D22RWwE63G5D+0c3xI8py09PSrhEkZo3mgfUY9nECcwwZDUr9zIXmhGvBiN4JmWliWScPl6EzN29AoveTKdqzoRDkQKgDANC3QCPqIy+++CJatWqFmJgY9OjRAz/88EPE8u+//z7at2+PmJgYdO7cGZ9++qmUzznHlClTkJaWhtjYWOTk5GD79u1Smfz8fIwYMQKJiYlITk7G6NGjUVhYaGvnqaeeQtu2bREdHY3mzZtj6tSpVWM0QZyAxCY0wKotfdAoIx+Hn2uHZY+Mx+/r1mLZI+PRteBexDcpwvdrz8OS/BlY+ulZ+HN7E6hlCqLjy9D6lM1I2nwtil9NwZZ/9sSRLV+S04cgiApBD4HWEtItmlkTqlemMTGqhExxriQeo5y2Is0FIzmY9B+13VQsT3W3fKc25R/YnV1E1jpWe1jYPWOFCZ/MdXLMuh7GEOSK0K/mWjpi+7IbJDyvEwroi2yLj5Bx6JErZtsAwJgizYyZ0APWdWoFF4+9Ty3ryugy9Ra5YTXAxLVHbO1Y22fG3FaPWtb7xRqdYcq0nDUelq07LcJijHHMpaIW+c4Lpctj3zyfRllFKOB4ocChE0WF5EWmDSJ5EvXIDzd5Tu+iLm6GOsnS00Lh6KxKyNTHq3XcSvUiyRSdXhaZDJCebuSWD0ys43LxM+5wzKyGMYut3DhfUnQdBxjngldL6AQwuzBJNw9gLF/P7TpbQ+iMfhNlhD8Yg0mXKd7Fw2PGci8g6g//+c9/MGnSJMyaNQs9evTAjBkzMGDAAGzduhVNmza1lf/uu+9wzTXXYNq0abj44osxd+5cDB06FGvWrMFpp50GAJg+fTpmzpyJN998E1lZWXjwwQcxYMAAbNq0CTExMQCAESNGYP/+/Vi8eDECgQBuuOEGjBkzBnPnzjVkTZgwAV988QWeeuopdO7cGfn5+cjPz6+ZjiGIWqLvMx9j+aRL0L39CpyT8Aaw4Q2ktgHKCqK1rdWf1bdWvxGhUAj/e2wWkrbPQ8d2u5GccQgxDfxo02AdsOZSFH4di735HdHy2icQn9XTUd6xbBdPEET9gnbdOg7q2q5bWjSHu6hI4vVdtypbj3N5e3WnuaZTe+L26uXJktPM7dXd9HLZdEfYXl1rRyxjOjSYVEd/t+66Jefbd7TS2ykNKgjA51hHm/hb2zSPS0NeqNLuYmaek8NGzy8JRCEo7LolO1h0uVa7NYpDURB3uRL7yTmgRcsvCvgQ4l5JJ7F9x23Pw3aIu25J9TmgclNfQ2Z4jlsciILKPeFEB8cZt59LQGszEPLa1h7iRr7F4aTbYN11y1omgqNH33XL2t+mUs51y911y0kHoU0lyKQ0KTLM7ULh5ey6ZfFFSMcqtK3Oj+HijCqz5LnIsckMqPCEHNq0yGTWvLIAfOL26tZ2I3gLldIQFNsW50IZcVcsLsgu9cPjD4X7WRgwQlnj3RKhw4tLtAGhVzMcPTx8cXJBf24M5KBaRrtu1UN69OiB7t2744UXXgAAqKqKjIwMjB8/Hvfcc4+t/NVXX42ioiIsXLjQSDv77LPRpUsXzJo1C5xzpKen44477sCdd94JADhy5AiaNWuGOXPmYPjw4di8eTM6duyIVatWoVu3bgCARYsW4aKLLsJvv/2G9PR0bN68Gaeffjo2bNiAdu3aHZNttOsWUZeprAMmGAjgk4efRdN9C9Ch7R40aHEYise8/xfmx+O3o6fj1BueQUza6QBgOJR8CWVGubJCH1Zt6YO+z3xsk0EQRM1Rk3/D6NGtWsJxnlMTP6yGAxzEJzEizD8l1Risq7iYaW6q6227PYYlPdUgpFmX/3AqZ9VbdjC447T+j4Lwj95cCb/kjmJcAQtvya1yJr14OJ07pusvBeAegHugci9U7oHKPWDwgFteKjzgxvo8+vo0SvjFEAq/nJw4MD4zcOlMeaCvx8OEdUfMf+Z6M27nkOtHQoL5OJQZu6OvBcTgCb+H18jR18cR1uhhxrFgR7jb9eVDzDVdzPOh7RRmrvtjrD8EYT0ecQ0dFt7hTJSr2O3TbbQvMG0W0FcAUqQrQl8fSCjvNoCdLhZjVyRmV8jpYpEGrks+IMuK4Hgyq4S9WFpYivZSnD5XTKawVrZppptzSOgC+8UplLXIZJY+tTwd6Ixbe9xaRh+EgrDwi0Mxrn0A8g0OHLbFiIxDLtjFLC/Foh8T3hzGF3jYuWNxRBkOIW72q7UDGAOUSHdvoq5SVlaG1atXIycnx0hTFAU5OTnIzc11rJObmyuVB4ABAwYY5Xfu3Im8vDypTFJSEnr06GGUyc3NRXJysuHkAYCcnBwoioKVK1cCAD755BOccsopWLhwIbKystCqVSv83//9X8SIHr/fj6NHj0ovgqirxCY0wHlTX8c5MxbhvKmvlxtl442KwmVT70bv2d8h/vZf8PHau7BqyWko+C0ZXGVIaFSE9q1yEfV1T+Q/l4qdj7RBr+wlKMhPwDc7RuPAGd/jmx2jUZCfgF7ZS7B80iU1ZClBELUNOXpqCenX44p6W6oKYd5WkTlouIq0bo51Z6xIqlfWYWNNd+siq65MqOc4l7a8nNbrgbDminVh3kjOKbdtus2+EVszezDkKs1p9RqrbOsixvpLd5nYt2tiMHf5kf+pjufBOueWJp7G/JQJA0rY3QuquS4Rt+yAZXw2J/+GbcL4BGCu6SJkMCb2uCnTXB9IlwtjCRQ17JhCeHMjcTML2T4Gx+eLTD8TONN7zDyXXJ9wOw1aq6fT2rD4LJTTABMdEeUN4orcU0SnilUX60uVDIfNQ1yOTEMMB4Qn6dxxskvME162hZ1h8ZuI9awyxHrmZSeXge4s4cZnxjkYD6+jxQQXa9jnZyw6zi39aDjCwsdWjxTnMCNyRMUgyZfTmSmUw3LqxE4QxphVnusgIeoqf/75J0KhEJo1ayalN2vWDHl5eY518vLyIpbX38srY30szOv1olGjRkaZX3/9Fbt378b777+Pt956C3PmzMHq1atxxRVXuNozbdo0JCUlGa+MjIzyuoAg6iXRMdEY9vRDOPv1lYi6eSs+XHkbflraAYX7E8FVIKlJATLb7NP8+NFBpMT9jqYZSTh3ykwkT9iK/L2N0L3dNygpLKhtUwiCqAHI0VNLcOO/Gparzw8ivJzQJ/zWaB67I8CO21y0PJxkOb3c5oOIcGzV23w5/ZPdLqJDxr7tt1Nr2idFal+zTBFKQpJone+bskV3jh3rjlJyL/JwRI+smRnRI/YPYLgwzH/WibEwBzain6AYkTxmRA+TXmY0D6TZOgeHyrQX19OF9rkY1SPpK8bviLtuQYjmCc91FQ6mmIs6i+fWcI5x2dEmja+wg0Hcc0uP8RG2bJIvCuupkNC3e7f3Laxp1natG6ZFulCsjiOnC9G6+LBurO1VQZmimLBvw+ZDc7o43frQ8rL5nXT/hX7MHNp3kmONjAKEc6kPHO2d69E8UMwrX3dO6cE1Tv0oGcyN9qTBqSiCY0a0n5k6GQrq14cqyBNEGR0iyHWKIHJbO4sgqgFVVeH3+/HWW2+hT58+OO+88/D666/j66+/xtatWx3r3HvvvThy5Ijx2rt3bw1rTRAnHvFJCbjq+cfQ7dUfwW7Yig9XjMHe9ekAtFt/w2ZH0SF9EfBxB+x7oiX2zH8MW/yXwNfAj5XTJtau8gRB1Ajk6KkljO/ukSZj1SXY+l3fZW4q4jZtsU5hnOpXZM5ZnkynNN2B5CSvIjjZZI/nMV0AsktGkyS6etxaFd1DqvRJFZxgYinZpSP2EXPpRS58kltQ7cdctsxqrbh+lOycYlKAgD7RBuNGlIs1qsewl4f1cIzmsVjHAcYZWDjdnGdbd+nS9dTsUjmHqu+ype/6FZZjbGpkcRRxLkZFiZ4LbutlJnc0AG6X6RbR4zbn19vWbawIYhsVjHxxzIsoz82jor2cImkcZVoui0h+F1c79XYstlr9TqJPxjguz86I58kYoNpL3ymOC1c9D49a603U6uWy3jT1ItaIHlVw2kj1wpUMncLnQnLWMKFzwx2jCCfA8IiJF4Tw2BdRb2jcuDE8Hg8OHDggpR84cACpqamOdVJTUyOW19/LK3Pw4EEpPxgMIj8/3yiTlpYGr9eLtm3bGmU6dOgAANizZ4+jbtHR0UhMTJReBEGYJDZKxFWvPIvfSk4FAHy0aBg2f9caJX/GgylAs4w/kYVncVaLtwEACYEttakuQRA1BDl6agluPYgwCawW+Zb5RXkqVMZJwy35kea6bvNFvW4kPdxXl+ARjpzbMtuUVnYR7HPqGdPxwIzZqP2l/8/A4BE+66vK2Eu59yt3OVPM+N8ej6SE8/TIE8Wmn+i6ktfp0WWKET3czNCcMeGJv8JhOgOgr9Nj6UlLNI+4fbphVdhppEf06A4ZSE6ZcKt6Bejr8IhrBMlyjOgh/fEvCFu1h51FZkiGy/zcclIUxoyXFtFjOf2APc3h5JrrybiMVutFZyjg3F4kWcaxfThbDty9Kq5bpJcj0+Z3iXS/E9visEUQVfgpsorKsEUnGYMG4ti1xvGJvhWbUMdzL3aG2DbCET16mkMHGp5W3RmkP4cYPjeOhjG5vsLkKCKK6Kl3+Hw+ZGdnY8mSJUaaqqpYsmQJevZ03qGnZ8+eUnkAWLx4sVE+KysLqampUpmjR49i5cqVRpmePXvi8OHDWL16tVHmq6++gqqq6NGjBwCgd+/eCAaD+OWXX4wy27ZtAwC0bNnyeMwmiJOeYHxzAEDjNsk47YWfUTB4DRYsugx7fm6BYKkXUbFBAEDXLmvxx9PNsXnWLVADxbWpMkEQ1QjtunUcHPeuW8fY88e66xaHilAlZIpF9V233PLd0lTLrlvlldePVRUoc0i3vtvbYAhxhqDFhxlJV/29LASUCTtgAeK6O8xSRz4us+y6ZdXJqq8+SfSHPJZdt8R2re3J7ZTqO2DBuT+sbej6lQS8CCLKYgszPtvbMmWUBO0y7fK0fJWbOhcFvAhxq0ynz4C4QxY37BTzRQeMOWnmgLGIsgqguEzfdctp9zJI9aR+4kBZ0AvrOZfqq2Y74uZK/tIoaFtku1S0KRA+5EAo6JHsiVTemOdzmLtuWcu5tSG8KwHTRailOUz6rfVUwFMKOO4uFkEmA8xdt5zKRJIZUhFVYmYzMc9Sh1n0MXbdcpMp+GQkZ00gAJ9ftekiybYPJK1fy0JQQpa6NkeaqHO4fkkZPP7wfoPGc7bWdyeZKnhRiXMdbi2rXyxaiFQwRLtu1Uf+85//YOTIkXjllVdw1llnYcaMGXjvvfewZcsWNGvWDH/729/QvHlzTJs2DYC2vfq5556Lxx9/HIMHD8a8efPw2GOPSdurP/HEE3j88cel7dXXrVsnba8+aNAgHDhwALNmzTK2V+/WrZuxvbqqqujevTsSEhIwY8YMqKqKsWPHIjExEV988UWFbKNdtwjCmZLCAmBuJgryE5A8YSt8sXFG3pJZb+OsstsRk1wC5uGGjz/o92D3niw0uuQxNO46uJY0J4iTh5r8G+at1tYJV8JzYEtCzckUf3SuCNbpH4ez+tZgAQZ3MeXNZSPJd2qXO3yK1K7eht62HjBgczxIn/SJMbekcogBclb9mOA0YEKKB0BQaNds0+lXdm5pV27Neg6YYAsXdNAjeuyBHUyoJ8uxzWuB8KNVIqYsLmhhSGPMZpW2ARGX6zChb3k43XISWbiY3lfcksugRdtwi5VSH0mDmIcf4zILOfofpIEdPod6VIk+BCpzLVvm5a6CnQa8NeJFT3ORYTvhjhdQBOWFgcUZMx0qlZBp2wSqvL4S6wt9G1FVi8xy41XE8mJ/OkXW6GPO6HN9BOo3VssJFesyMV9sUyjrsUTYcC4cW502usNGqG+0y6Q3U1fh5OsLOCvl9hBRB7n66qvxxx9/YMqUKcjLy0OXLl2waNEiYzHlPXv2QFHMv1m9evXC3Llz8cADD+C+++5DmzZtsGDBAsPJAwB33303ioqKMGbMGBw+fBjnnHMOFi1aZDh5AOCdd97BuHHjcMEFF0BRFAwbNgwzZ8408hVFwSeffILx48ejb9++iI+Px6BBg/D000/XQK8QRP0mNqEBlm/pg17ZS5D/XDts9g9F68tGY8f819E5egFiM4rxzQ/98GdJBk5PWI7MDvsQFV+GU9vsADZfhUPLG+C34LnoOHYWPDENa9scgiCOE4roOQ6OJ6JHjTSbLIfjiehRgQpHEonFnCJ6rOWcmq1sRI+eJkb0iGWc2rJO8p0iepxkWefWwRBQCh/sThcYabITw8wvUz1QXaKI3KJJVDAhokds35QHy7HY1/5wRE9ku+yRSCUBLwKIsrTJHO2yOlJK9CgirtvgJFfrB3NOy1AQjuhxipIxdbRE54RfpaEoSYahmzHjNusgrK/KgSIhosepL7Q27JNcPaLHet4kO7ksU59vGxE95V1jlnzOATXolZMjnVg9KQSwUg/AlPIHuPUzd4joAWCL6rG2GxIieiohU4/oUYIOzpfy2qloRA+32gKwoBo5ikj0yZgXJxAKwVccjFwOML1XQrriD0FR7dFAthMstqlyoMQPjz8olFUNuyyD3OJU4uDFJebaO1K+IFgVPoefFwyG/BTRQ9QpKKKHICKzfNIl6N5+BXwJZmx8WUE0Vm09B32f+dhIW/3x1zj036no0n4rkjMPgSna3wc1qGDv7haI7jUZ6eeP1DbRIAiiSqCInpOJGozqMeYLlh+MK4JTRI+Yx4V3a71IIioiXpRdXsBEeMpTaTliVI+4xK8Y46IfaxEoslPEOl22z+uY7ZP+wE1QKMml2B97v0b6U2t1EGlH1mNztyh7+069ZMYF6c4M0SbrDkqyc4QZOnnAwuupmJNPbvSa3rfh/5kZWGE8/sXFVhGOBBJkho9VoZQW0aOV4JKccCscYCwcTVTunt+6zk52WiKETCEVRttVuyJn2dTFKKqfhIpE1oioDsVEp4D0UWiAab4gx4WOrTcGS57j/aY8h1a4T7limsqtss2L09HB5IjbcAe0izPItWgX/cap62F11rBwPJ10MXGH9qVBG5bH5fb1NXq4nsfMwWW079BB4nZ4UkSSKF7Ik84TfYEnCIKoT/R95mOUFBZgxbSJ8Bb9jmB8c/S4dwb6JjSQymVf0g+4pB8CZWX4+O6H0Ta4EKee9ht8iX60PHUPcGAsCl++EzsPdUeHcf9CVFILR3klhQVYaZEVa5FFEETNQxE9x0GVRPToVOIsHHdETzkynZLLi+hxU/9YInoAIKQC/grKkdO0iJ4QlHLLWvMDIaAU0a7tRoqeCagehCoV0aOl+UMK/Igx0u3OGqf1fRCu6wXXo2ci2KXroKeXBqPg5z5LeTOiR6yvWvQpDnm1GbfNRottgiOHAygKRiGgWmWaNqpClJBoI4cc0SPJ0p1A4UTRAcUBFPqjoXK5fyQHnct6NCqAspDXMd967Yh3Tw6gtCQajuvcuDZkthMKOvjdyzmxXAVYiQe2dfUj1dOPVUAJmRFRzFaeWY7Nep5iVFqmEdETcoqKcdFRJyhH9Oj5ks5OaQBYSIUn4NK+IMemU1kQvpKQrY5RTnSKWW5OSlkIStDhzud4wXCzzdIyeErCv8Bad98SZQEIeweNMrxEj+jR65p59rpmuaDqx9Ky/1J0BFFnoIgegqg+tn2/DjteuBdnttmIlKy/oHi1v2U8xLBvbzME2t2MUy6/E4xp3wEco4cKfVi1pY8UPUQQhAZF9JwEiD/81qRU7vBLc0VUMH6Idihs/ZFaRAEiuqXcZOs/XlvlRHIaRWo3ko3Sj9tSTIjVsWNdI0d2yDjFDlgdN9a5rRZbo7tT9F189FLW2CG5LcZgibARnTZOOuqFGBSI27frUTuaNPF8WiOomKWHAMhPiEgyBV05g8JUYw5q7VsmeBrMGCKY259bZRjHLs4IAAoTN4o35Zi9K8owVWZcjiBy9pwxSRfjWnbbz9vahi2ST4/KcLfHdqKtnVFRxNMjOqqsjgwpE5IDywg0ORbcLs5INlu7VY+ugZzGxLRwGVcXfKSbhG6/wmzp+uky07gtKMbWN1aPoFWwpLR0IZgVuHhlAsYOXHp4FWMwVgl3xMlg7pBOEARBnKy0Pft0tD37fwiFQvj0H8+ged48tOu0BzEpxWjeKg/wP4KSfz2OX/efhsJCH3pl5yJ/byNszrsMbYbdhO0f/AsdfPPRK3sJlk+6hJw9BFGL1Mnt1UOhEB588EFkZWUhNjYWp556Kv7xj39ADE7inGPKlClIS0tDbGwscnJysH37dqmd/Px8jBgxAomJiUhOTsbo0aNRWFhYIzbYvtdXYtZ0DFUMqQyQdgyuaHvG3JDJL7GyU5pqab+yL1NzZ+ePs5X2Ntza1fLM7b/1Lcitey1zh3cutWx1CYly5dKmVCWsqyyP2bSVZRvHXJQBoX2n2CNTrvUEMqEVJ9ni/xzm/NJpTOi2c2svMEV7QdF+BTK2WHcYNJLNMMasvjM005tj0LZGZ9CCjBjCW24z8LAsa9vm+VOMl66LaJUi2uiinlFFF6MwbV3byl+YmrMAwoN2kQasmA/mvJiuPiTL83C63QOcjBeGuuPTPhWRGS4nbc/udJFa9GAu/cAAfcd38wk2DnEneEe/ilWeYarQsGJTBrau0MamJRG6v8VpsDDzH9ffFSPd6FxRcWNLdIRvE4JOoiOMW2SK+dayhj3HOmAJgiCI+ozH48GQh+/CmbNW48igNVi46CLs35QGtUxBdIIfHdqsRrcuuQiWenGw0RXo+8DTSO/UGedOmYnkCVuRv7cRurf7RtsJjCCIWqFOOnqeeOIJvPzyy3jhhRewefNmPPHEE5g+fTqef/55o8z06dMxc+ZMzJo1CytXrkR8fDwGDBiA0tJSo8yIESOwceNGLF68GAsXLsTy5csxZsyYGrFB+mpdyR9Vj/13WN0NIDdQkfbEMuE1PI0nC/SXUxpzqh9+qS7pYp6seeQf4cWy1jmntX3ZLj3yQ39XoT1cZ9bQHi5ShWPdmSK6e7iUb8qwb++tyVLDepryRF2sPcSMNN1Op52zTJmy5YLFnIOHX5DkmTLlNPvqMbZW9XMPaZopyFTBjZdFB+40EoSHxlhYB86hGvX0dWeZNs7CTWgTfA7GVa1frQNSPJ9WPay9yOTq5mdmH/cAoHJtTaHKXpgcxuReujbdLgZpYHN5f3cxP9L83ZJvPMkjtcHsdbhZvkIynbqVGafULCOWdbDVdnq4kM1kVfVjacxanUfW9i1qg4cfr7V2jFCeM9jHDg83r/ttw9e2+RiWcJUz8x0s/K43Ij22xc1HssTFlq2dYdgm6CPZbXVIMbN9olpYsWIFAODbb7+tZU0IgiCOnfTWGbj0rffR4p87sKzwefzw1ekoPRQLxoCo2CA6JLwK/+wm2PjQ2Sjer23rvtk/FL4GfqycNrG21SeIk5Y6+ejWd999h0svvRSDBw8GALRq1QrvvvsufvjhBwDal+8ZM2bggQcewKWXXgoAeOutt9CsWTMsWLAAw4cPx+bNm7Fo0SKsWrUK3bp1AwA8//zzuOiii/DUU08hPT29Wm2QvlpbJx7lUMnizqWFRhi35TrINCvokypreenX8/DkQXePVFAr49ic6jvO71zb0etZ15Zxk2n9xZ8JThnZWWRzXQgtAFZnjtwmN/43UYRABj2yx9525CAJ+4NdslZ6KUvoBBMWY+ZyDSdb9RRDD64fm1rrj5GZMi21mQIWttlmD3fqW10/cxbvFEVi6BBu2IzYAcxQG1GgOI7D7bpM9A27LH1gRR8tXJHLV+jiFB02kmfCJsC8IKyTd/HxIv2UlHehWPQTd/A2Zeg6MSFNS5bOhSjT6aZgkcWt9cU2rDrqTiHRewvzs3UxcMCexo3/HHDQj4n/GXK5oSOT+toYeEZlruvFAdvDj4INhrnhPjYeKxQ9U/qCzHpB43lNsUMtnS/pJHaa5UZvuS0QVc9nn30Gr9eL//3vf+jdu3dtq0MQBHHc5IwbBWAUVozvj149v8Xe9elIb3MQUbEBtG+3HvzLbOzalY7Gbf4PAOAt+r1W9SWIk5k6GdHTq1cvLFmyBNu2bQMA/Pzzz/jmm28waNAgAMDOnTuRl5eHnJwco05SUhJ69OiB3NxcAEBubi6Sk5MNJw8A5OTkQFEUrFy5stptcPxuXUGvDQcc51QVlWr8oMth/yXfBTHmQlOC2yIi9EgN7Vdr+9zNqjtH5KieSPNGKzxCGXGaU758eynmmma2bi8T2R4ONfzSP1ujedx0EA21O0h0beRjLv8Tomr0tmX95eglvQWjZSFkR4rusvS3lMI5uKpKY4TpY8jFVm0eLc6atXoqFwIb9OYMdbUDxoVICi7bpqczroYrWkNmIA8iLtupRxRJ8jkHV7l7cER5k+nw5Fy8NkV1Ja+n9CrnImYOLwedjKYA+zm1XkAQutR6Y3CQFUGss95c/iydQmtR5tAllr62db37RWnYpvWFw+A2qojXk9107UN47Eo3W3PQcC5f88a1L54AcWgaulp7VOgMsdMgXhiWDjn2XwuICvLII48gGAzi/PPPRygUwqOPPlrbKhEEQVQZocQMAMDu2EFYEz8X3315JooPJoB5ODJO/R3t8QiA/2fv2wO1Ksq9f7PevTcgV0EBUfBKooigiIjXPiWp6GLaOXqOpRlpX2GJaCWaeiwV9aippVF+mdXJNCs9ZmkRBt4QETSv4K0UL4CK3Pd+b2u+P9aamWeembXed29gv8CeH679rjVr5rnMmrV85/c+6xmg9w6rIeOOLSITEBCwadgmI3ouuOACrF27FiNGjEChUEC1WsUVV1yBU089FQCwfPlyAMCgQYOsdoMGDdLnli9fjoEDB1rnm5qa0L9/f12Ho1gsolg0a0GtXbu2wz5I/af9yCIyaut0J6K1fvzP0hkTkoHLsHIl1ZDj+UEdAia3j6WzDjt9mur9wTqZ/9grSknrLK+vKAkVs2PoHx6AIEF9NRE1yd/I0w8k8oSVaUogzesi4R8TdpmJpkmia0y72JoZG5KIlyXl0ppJizTcgo4r6qdUJULovtV9Ik09bjtNxqwmt2qOG7F6amItgSSRspZnInqEkuUYmb2SmuovmjLF0DGmro52i2BfdE8whVdR2hlWP/iiLTwykkUvhF3PFwrnA79mmTeKNB2RHgpuX45O3R3Sc9rnH78ZPH1hvfrlE0ns0txI3s2Zltu5tIVrn6TxfmkhDaBRpI9jn7D9EqSeuTGhB5FA0slxTNqkAmNfR8J0sLpW+lMXGgNSXwK2HC699FLceuut+P73v49+/frhK1/5SqNNCggICNhsGD/jBpTu+AP2a7kH/T5zFVpOnoz331qJeRd9DYcOX4j+e34AIYADDngOG3+6M159bzxGTv8Vmnru1GjTAwK6DLbJiJ7f/va3+PWvf4077rgDixcvxi9+8Qtce+21+MUvfrFF9c6cORN9+/bV29ChQzssK39ilQ/fj9H16hSeuUtHdGoZPJKHTR58LlI5NJ6i1pbxO3bmRJ0eZ2WBcfXY0TW+aB57hmhH99D6/tpIJScRPTGEpdNnHY+4kUoaiZgwvgpHX5z6Fau8QyxPjkitoFfElFFPElJDJ19Or4g7XyQ20t5kY0V4/WNUk7DTJwtIK+BBh6HoQJ3EP6Q5eqxoMxLhQ8t4tJQv+oTrpFE9SlbMJ+B5Nxc/J8xLg3qc0xsk4yZJkv5Ku5wSI5ZArtM2RyKVp5Pc8M3YLbltVKZHp3reOWZk3STcX1ZXSvijebhsqtMhbVzZPHKLXfiUUHTj75QM7TbvB3VBpe2Eui/0xVRLpscwuZe0gvQ6pMSpC/VAoBeWyKD3i7aRDpiAzY1KpYLzzz8f1Wr4NTsgIGD7Qo9evbFwyVHoP3QVVt+4L+Zd9g0UV72D3nsNAZqS/9Gs+ld/VIsFdO9TxAF7P4z47r2w5NIx+HDJEw22PiCga2CbJHq+9a1v4YILLsApp5yCUaNG4Ytf/CLOPfdczJw5EwAwePBgAMCKFSusditWrNDnBg8ejJUrV1rnK5UKVq1apetwzJgxA2vWrNHbsmXLOuwD/e7eEXSk2abo9M0XE+JIzfr96zzxMmd+286Nm+/TmfxAbk/CfLbb62qpja/AZTa+6pUqsykC2o7rpBoSCxLywv0HTWuYjXohifPcT9eaJJomQpInR68ypaNsVE9E1uauLAZo2kemfSxtfQkij68izQUr7M3pW9j7EqSf7TxIiYh0Jh8JiHTTq3shSlf2Slb6UscmRxEfFf4X6KwJvLqSwvZPQOlG9pYHFgUnabfTjS8Ip1ZhqqUr6+Y0fJpeodtetirdIrVv+t65gbJ0SmvXfpNO+Qmyn3fjex4C2mSoe5/xGtR3nyxSbg9Pe4y6Rvpd5s8D64JSpl2YIuh7I0r6tQBTNwLsHszwxXFYkjrCNVkRSgFbDF/72tcAAF/96lcbbElAQEDA5sfR19+Hxxcdh9791+PI4bdh8PNH4Mjht6H3jhvw+KLjsPOFy/B4+WY88/AIlNZ2Q6GliuH7voLeTx2Ht67cE6/e+0PnB+KAgIDNh22S6Nm4cSOiyDa9UCggTsPc99xzTwwePBhz5szR59euXYsFCxZgwoQJAIAJEyZg9erVWLRoka7z0EMPIY5jjB8/3qu3W7du6NOnj7V1FLXmfbXQkceiT2c9c1ClL+PHdc9sKlunO7Vu3wbU9l39Ru2b19TWa+fMoFN+wSSol20EK5Okvt1fXG6MKrOGnjMRL4YOUV4IWIotG2wfk78mnsdEuSQRBWrFLxPJQ1NZCyYTEGR+KfRy53YtE4Kio5dkTCJwzGZHTCm5abkiHtg1ECLZIJH6kchVeXJ0NI/21az2Zfy2ddr9KOzrSifkyitporL0NVMRPXmDl3en1pFxF1LmwhfdE6s+zdCVBy9xosiJdIvJPlnKysqZU0sn4zZyiaH2PAQk2c0ifqiuvOtA/HIieqhyxlI54wRmDGnBFlckiQ9Sj1uddEpWyfOUXFdFGPk6Tl1/gYQo0gNWvbpIbee+S3gzWgdsVnz44YeNNiEgICBgi+Do6+8D/vNNPPLCKZj/5FF45IVTgFPfSMoBfPSsL2LsrEV4d+zDmPvXCVj3Th+ICNhlj5XYc+MFWH3TYDxz/RmIq6UGexIQsP1hm8zR8+lPfxpXXHEFhg0bhpEjR+Lpp5/G9ddfjy9/+csAkl9ip02bhssvvxzDhw/HnnvuiYsvvhhDhgzBCSecAADYb7/98PGPfxxnnnkmZs2ahXK5jLPPPhunnHLKFl9xC2DzIsELtqxOPW9oh046V6LHACCFyGTkfaU2bZCtr1ZgQpb5eTK5/dSvRKe96pbR5YtY4mXCuZTCU5fqLECgQvhWvRoW2PUiMugcjfpldBtvFCLA+CUEmRkL6y9PtSLJWS1bL7tmKC1jh63XxM6QFbA4yCxd6dNjNDZyFE+k6wmqk5AIaSclr5kRX4l635xXkhLnWpP5uSGD2PXQS2rDXAImI3PQSnPlHMMcMoac9+Xo4e0zdfrOkUGlPiXbl8LwB9wurpNcL92kPTcuHdwZOnk+HOf+4PZzfdJD1kiw8ZoaoduQ68W4EimRJBkXgFkRS30I6Fw6VL6uVgCgXvORrs2+ztNkqLphJOuEVAhfWk2mZBDPXh2w2TF8+HBcfvnl+OpXv5pGAwYEBARsP+jRqzc+esXPcuvsNe4A7HX737Bh9Tr86fypOGTXRzFwn5Xos/N6jMJvUfz5PXhl2Wh85JxfoXv/YV4ZcbmE0qJfQa7+F0S/PdAy9ouImlu2hEsBAdsFhNwGY+bWrVuHiy++GPfccw9WrlyJIUOG4D/+4z9wySWXoKUlueGllLj00kvx05/+FKtXr8aRRx6JW265BR/5yEe0nFWrVuHss8/GH//4R0RRhJNOOgk33XQTevXqVZcda9euRd++fbFy6e7o07t9wVEqpoAU1I0yOva+f6xXefLrzDOhRCgEjvz5q0Ql1yY/KjFQzpKZpw9AVQrE6azbV4/rVHUqVaCEJqsdJVm4PPoaUSkuIE6XSedyEwLF/nKvzrVVC6igWct25QurPrWhWG2CJDppW58+ZW9ruSnVyfvSLEvuI3oAYEO1CUBkJq2kHk0pYtondTaUW1CVTY6dhthhZE86+YwBtFWb/f0qVSJpu+9UgMPGcjNiWUgLXQLNJZgSxFKgXG2ykzeTOnEqxB5bAjIGiqVm6EBJn9EZN1AMAVmOrH6w4Fk8CUjm9aIUAcpPn0O8HbElKhNSjl1T782Z+h61peRd3o0IwkEoHTEQVTKCSOyBY5dXYjSX2DkqN09nOUah6pGpoMlEdr5URktRDWrptBOeMrUfFWNEVdKBnFmMiVxJbG4rolCqELuoDmk+6blUjtzYaidvlqROzAggHbYEVOIi5pZ/jzVr1mxSlGpANmbOnImZM2dir732wg9/+EMcddRRjTZpm4b67hXGbEDAto37L70C+6z9DfY8YBmaeiT/74srEd54fSh6Tr4Wu4z7pK7bOucKRP/6AZp6tOqySmsPxHucix7HXdTptgcEdBSd+f+wbZLo2VqwKURP7J211dd2U4geZ3GcunV6ohyIiKxzVSm91tZS6yN6+Pwua64YS4EKCp6z+XrLVaAI+5cBm+gxLzVxn8sW0eMSCJKsOEUn8+VqZOnkxAtvQ8+XGNGjYGykdpj2bZUmlGUzs1Hoeu5825BLrdUmSGkm+DZJJJxjVWdDpQVVWci4doIsJMT8lEBbpdk65xJ1hnjS41sqoididQmJRwgba64ugVLF/YXImqtrIsomZtqKTQDTaTmdMQBlDFQrTXb9Wkjn6qJNJXXx6PPJ04wVEFWEIRpy9FioAlFRQHA/a+gWadsmH/ObdTMrlGM0F/3nte18CfoUUTlGwUeUMRsFlQEA5Qpa2pgwTurE5O4kVaNiBRFNfGyJccu1D20lFNrSJ1/MDNQkj7TlqFfAWtsM0UOJJF1XMvuTg0qliLnl34VJ8xbGu+++iwsvvBC/+tWv8G//9m+49tprseuuuzbarG0SgegJCNi+sPAPD6DywPdw4JhX0H1HQ+S8t2wAVg3+MvbctQnNK2biwzf746mHD8Rbb++M3XZ9D4cc/Sx2HLYK5cEXBrInYJtBZ/4/bJvM0bM9QH+xl3AmJltcr4TJswp7y4LM2GqtmpX3GhV9kYBvvnJuS56tNs2R74+9eJC96pZ3dR24/WWTHElt2lbJTWoajdXUAp8mn+/8WmX3C89/E+sytcqP/S92dFF95q0kYZ2wEiKngyqRZvLkxLBz5NAVv2h6J+1bOjYjNVdlJ1SOHr0qmDQ6Vb+aHD2J/FiqNCipTjbx5y+ceUNO0rfApDAa6OiRWTl6AHOT+CCTjtRjNuuG5INVwn/zwXPsA309y3p3iWx85a04jaDKuuGJTufZIpPxUvNNIZ9f9FyOTgCwEiorQ3g/ZPWXdVOnhXz1LSkhZJxsQtpdp99QVI6SjfexR65+V5ESNOpYbTRkSaby1DLq+pp6OoHn6/HdCAFbDLvssgt+/vOfY8GCBXjrrbew77774vLLL0exyBnMgICAgK6FcSd+AhNunY/1H38as//6f7D6zR0hJbDz0A+wb/N/o/ndq9D64Q64+Zf/B6sOPxmT//dKrDr8ZNx02/FYvaw/8Pp1iMshx09AAEcgehoEqf90sl4yP8iZK3nhI4UUCeBfwao2IaNk1KOPz0vz2rmpk13ChOtJboZkxSj3n+klnnDZjmWxLbbX8KKrPSW9ViA6werYfhrdZt/vvZ1KmK6klSxS7l0xSvtt9w+lTaxVtsigMbljk8ktXeFL76crfakNeh8JeUP8i4XZEgVKl0giadLN5BwC8SDxNRKpP+n8ViWNTjYJEXHCyFzPRKdNk+nxk+6odb3UCmnQK3o5Q8A3LNglS4kvNYrybkguJ+vGy9LHbwqqTx04S4qnG8ynMG5n6tRDQolXXISPdMnz0XezUj1EB3kjSevPRJ4eQDE2ZuDADKIkki6yr1lKwEnnIrKLqVky3+BUkVl8LAnzqQ1VYzVVynO/6A6hBdSPCCZhc0BnYezYsXjkkUfws5/9DD/72c+w33774Z577mm0WQEBAQENx6C9h+Ljt9+P3ue+hj8/cQbeen4I4oqAKEjs0H8jZnzrfzG69CP0ij7EqRefhkte+DHmzD0ALT2L2PjEzxttfkDAVodA9DQI6kfWmpONLaHYmvSSOUSOCVnEUL0kUT1mbapOOm910yR75nFMVvLbNo/oMRSAIW+ULN+y37aFdA0vlSVJ7ct01S13pS+b0qH2+5cFp33BJcTuseT2uR4reREYJaRm6YZvSYgaAUiyHLeKsDEEUWqHL5pHkzapdxIQUkCkERFm+Wl3BEjtfYxYSsR6ha101a9Uj17USFqGQxLdNnPBF7T3jVGJmK7qpWb5HblZUgIqF8oYd9DWr4+TJpl6yMOBXmxF5vlk17o5ZTsfb1QW99sMNTsykT/Paj2YqK3OPlOoo3nIXS/TUes8RMnIyeoTAVikGl11C55xBFJfXRNN2AimI+0YvSyeBE2ijnR1Ok0SBXQ6Tj75ZCxZsgRTpkzB6aefjo997GONNikgICBgq0BLt274zA9/hN2vfAWLFx0EAChvaEZT9wr22+8F9Hh4PF6+ZCTWvLoY4rAkj8+S2X9ppMkBAVsltslVt7YHSGenc5W3Vy2foElPGT1HP/Nk5tVpr04jTzrn6tGhojRcmohSH4LIlFpn0sLlTQXbK4DmdRHWK1HUPzqvVYhzvDHUBO+jhDyJIXQdGqNk6iv9IlenTlAs7Xa2DckZtdKXBJLoHSl8C2DpFdt0Th9B+lvNaz1XXqjzyhIh9NxZ01MsWsGKg5ImmMFEYUBPiH1cBR+0EZEv1cSbN6rjRqGvsGXCvmDQQ66eG8V3U2ZlJXcPGEGUXEfBq2XpVOd8z516b056e0nro+aDJpfs4cQX7RPFGrF+l55+Et78N9LVoQr0kuiCXC8JRIS0EdxRENJGJsZycoc7xgeWIqRouxDR06kolUpYsmQJnn/+eb316NEDDz30UKNNCwgICNjq8H5xFwDAq/JsfDDnERx84EvYYecN2HvEvyAXTcRR3QcAAFasDFPagACODt0V9913X7vbfOxjH0OPHj06om67hJmQdq7OzLmHB7KO/Zr62gE+/6hXv2uPiQ7hc17fj+pqbmfy9JiZnbRq8pbUVpsBkKwuf9Eq8VMihkpQzMgOps1HwlCbpPM3q58kkldOJLMd+iw9ptSRIrQkksmikefqEyA5YwGTn8er02hJ5re2X4ISP2mhHid64pvaopMkq2gbQ8O5/UWa0uvLyCgfv0KNiamwmPV61sWgEHY/ZzKgdAhm5bCpVyeVZ90onruWy5OMPKnn5szhPHLtpJ1PSBiHZKIi2/tc9dksYZMknLfxPUgseYzZ8l0n2u+K+KlSnVmfaWMRETlZrF3GeIy1k53/P6IuiMsuu0yTOq+99hoqlQr69u2LAw44AAceeCA++clP4sADD2y0mQEBAQFbHZb3OQal9bMxZONvsP9Pl2LNytX424Wn47BRi9Fr8FoMHPY+pAT2H7QYbz3+J+x2+ORGmxwQsNWgQ6tuRVH73vgSQuCVV17BXnvt1V5VWzU266pb7bgKm23VrXbozFte3a8r/cxYdYvL4nKrZNWt2sSOPY+qSiBGAYBwghby5CTLqzdbJIQ7fbKnqup8OW5Cla30lUfKKDklveqWIOdcPb6+SpYdV+POv5S8vVpXst9WbkIJZtUttUoXlcHnpWp/Y8XYyiNfuC5KoGwgS7pbbax5NO+j5E9rpZvTTh0oHXRuro5by82opsuOe6e90i6TKi9PDJSq9jjQTaxOYfZKoK3UBL3Ued4AZ47GEogryZjNrcuOJWBW3aqX8NE3ChBVU4rJR0Rk6Y/bseqWQpyOhCpQqOYTNd725XR5dd6nnj4W7HykllfPkw9ikzpXqqClLbbbOPola5scR6Uyoiqrq/c5AURGWrGMQmvZ1LPIIkk2mHMqIo6uuqX1kTrWK2GGPKrIEuYWw6pbWxIHHHAARo0ahQMPPFB/Dhs2rNFmbbMIq24FBHQdtLUWMf9Lx+OozzyF0sY9UDj4u2gafizW/+N+yMUXoddO60x0dgy888/BiI+4Cnsc+2+NNTwgIAOd+f+wDse5LV++HAMHDqyrbu/evTuqZvtHJ/6Yql6qoYuz5ME3T63X3Hrk0x/zabQE15VH9Pjt4REp/vo8QMJ8Sk9LNwLFlZvVO3ZEj6obQ6Q5eoxO33ybEiYys4zLh6d+Eo1j8gRlk0l29ArpT8VDSABCWkQYzewj9ASV6JRGp20jjeixiY6Et7F7RM9xhWmlJ/nkjRUa0UP1uGSRPV7o62X8Wlj+E491II/FpHKjMyCgc71IpdjXhperwRJnGFvrRhEZ+3x8e8gVIT3n6tApsnTWIn3qjejxPdtqPYx8NseAXsmKnbN002gcCxH0q1W0vu8C00FbJTlzLDKH7JsQML8T3gggafch9Y1HoQVsdjz//PONNiEgICBgm0T3Ht2wuNsn0PJH4OBjn0XT0q8AS4FeAErdu2HBHw/Bu6074NjDn0Xfoaux697LId/9Et664tsoHvRf2PuTpzfahYCAhqFDRM/pp5/ertewvvCFL4RfXbJQ72RnM0DPF4Q5rldnPcQN1ZMnOmN+5uiy3ijJscudp7kaas27lY6IyDC6DVHjs8XIcl8N8p2jugoAKqRVkkPHtZATYj74LBTOMfR6WGo8mD70kUWafknq07kjhPOajE2CmaieAoSbe8fpXUO0KFIyTgeslWYktcNMl01USkxqRcKs5UX1aFpJH5hXvmxf3AJzXe36kUDy9kw9gzZDrtULtW4SEB3809cmf+DSrvAogs1+ifQaZfmZpVPavInPDu+x4vIiQzBJqruGj5mrfOWRXxGAikwvLKwbxcnRk+aG0oPSImV4XW4zJXJg5+hRStWF0TZIWwYUaUOYKAm4+aKoE9Tn9gzWgICAgICAzsV5v7wA150GPHbNPjho/7fQu89GrFu7A55+cTcUDh+O8+68AMW2Ih745hQctuc87Lj7Kuyy50pg9dfxzsxLsHbfCzDixK812o2AgE5Hh17dCkiwrb26JdNXt+ohePjpYiaFki8j79UtLo/uV2OAv6lRq41uK0X66lbtOSQtr1SBNrRk1FWkgT8KphwXUGXJmLNIAUqiFKsFFGFeTTKvUdl6aVt1XCSvF+X1Ca/TVmlGUXI/BYyPXJ+xZ2O1Gc4rSx6/1PLkCusqzajE2X2rE1SnJ2hgTFu12aqtdRKyiM9dJQTWF7shTl8v8l0LH7mDlCwqVZqc62bZTQkHamtrN3jX9M67cVLyoFptqm/AUp0xINoKCQuS1S5Ld5y+uiUNl2BAfOCDIgYKbWifzlSHiIGomkO+ZB1XYjS3uvJ8NgpaBkBUYhQqsOEhe5w+KFXQ0lp12lj1PDl8ACAqVhFVPS+Ocr381a+2EgqtJSZbDW5O8EiSaweQG1vT1bRIW3jaUiJKSlTiIuaWwqtbAdsOwqtbAQFdE22tRfzPf92OD/65EgP2HIgv/NeX0L1HN6tOuVTCg9O+hvFDZqP/nh/o3zLeW9Yfq4aeg/3/8/wGWB4QYLBNvLql0NraCikldthhBwDAG2+8gXvuuQf7778/jj/++E02cHsF/eG3M7VKzy/N9ZhQz2++XE4SPQJkparwlflktBdZbbL0WD9u1+gN+yUs/h5KFjHgRtWoc4ZeUcd2jJKdGhmeGr594dS3jRI6csn21rRW5b43RLj/dNJu6zcEUyQFIiGtOSZto/tdJGSMXmBJkTmeOa7yTsnUUUdplUiYBePdWCvTM5KSUsIEWXiakDLh1pGqMzoYIUHDVOq5UWhncJW0M7LMIRfaugd8DyYVapXKk8gJBOE62XEuyZN1TsKs9JVR1bl2qX9RPQ8E2kafk3ZEj69ZGmHD+0I4oW4ZzxVnsPHniLSuk03iII3+YRUsW1QED39qCGS/ZxgQEBAQELD1oXuPbvjK1V/NrdPc0oJP3/IzVKtVPDD9GzhkwJ+x017vYeehq7AzLsXK//4Blu/0VRx4xiWdZHVAQOPQvjAUDz772c/il7/8JQBg9erVGD9+PK677jp89rOfxY9//ONNNnB7hTUZ8XMEmehAE61VIJ2gEQH1yKNT96z6vjL+m7avPS+zaYJ88Drk923nvI+W8etMSgQ5K9mnu++jTLKPjDxoPVSjz0puA0137FIrUvcF3/QSy4K2Ufuu7/QvhOTNk4k/MZVfg6ROZPpLJMue0w1MH/VZjVkhknm3EICI1DhOJrYySnmItLIUEdTa49SXZIsgyQaPDZGw/fNtwikT2aRCLUhjqVcnhy4X2UxGrfk7IWAsVdkHyYeEn+QRGTpZXcn98t2Y7Fhdfy6TDAFNIOkymWGSx0ZBz6XnI+43u/bmsqtCU11KTwM1vmV6r8skobVQg5d2rtVnZOBHwrze5VwDYW4CRcqpCB9I6Ne7aOZtfY0DAgICAgK2HxQKBXzqxluw80Wv4S/PfBXLlw6CjIEBu67GyG5X44PrhuCZWRfA92JL6/p1mHvRFDw67eOYe9EUtK5f1wAPAgI2HZtM9CxevBhHHXUUAOB3v/sdBg0ahDfeeAO//OUvcdNNN22ygdsrrK/WbmhFLqxJewe06h/sya/5teTZPyhLvanVXLLK6Lwti3jw6a/Xv6x5pT/TjSuf607okWShdUqVCGc9a1UvaZlQCrS+scU/kTILmUutT6VJ5vKVTFVmPKV9S8ul0+tUNblWlj6jk9NEUksWfql0HunzW6Y+ymSzbJBZfavmoakN0iyZniRbTifUEhCpiGSCLyFknPar70rHEIgh2Nh1+knYzXQVNYkm5TLt1w7ltZXQE3fr3qzn5lAdweVJ5M/f2Xnlg6Tn6StozA7ne1GeTt6ttF/BPjN8dS4PfXYJf/eYMcvs8hBMzmWT6eu1vGOUPWqXjx3J+DE1ls3gMf+E+dTMlBLCV8hShE0Mt/Np9JDWKR0fLaZMGankB3Q6Xn75ZVQq/J3CgICAgIDNiUKhgMnXXo9dL3sdf3nxHLz94i6QVYF+u6zBqD4/xOobd8Him87RhM/D0z8D3DEMR428ExMOfQRHjbwTuGNYUh4QsI1hk4mejRs36lW1/vrXv+LEE09EFEU47LDD8MYbb2yygdsrrK/Web/ce9DO6n6t9MfmOmRFaZ0I6S/YbNORGenG527cbkHleXzK8pFPSWrVywgQ8NqQnFclEVQkSEKx2JZKpjnxk56nOn3ZXgTUC0pC6zM6/TZwb6TVv7Tcto71akYkDTx+2rSZISI4jWZHeChiiFwFEUGICFH6aY0fb9+muXVMmA6LAkoDWXQ4R7Il1SMT0aN12P0KRJBq7PpGESUuTLd5B1AiJjGIR8vVDbpyU9Yghe982hH85qlLJxGp/LB0sNFFdDh+WgOEgdkjWb/m3vTCuOncyKmJQrqnaHVNnoEWuD5ZRSId9bR/qT26nrqPzHmLrPNeTGH/kwKQUfIJpVzppsewH1baH9b5tZY284ztgM7Hfvvth9dff73RZgQEBAR0GUy+8koMu/xV/O31b2PZ80MQVwX6DFyH0Tv9P6z94WC8cMGBOHzsHKxb1QuPvjoFK0Y/gUdfnYJ1q3rh8LFzAtkTsM1hk4meffbZB/feey+WLVuGv/zlLzovz8qVK0OSvBw43639M3YvfD98t0er/m7v+cE6Cyo2wiRzlnrzRfPYGl27JZPHfcry0ZnjZNRTJVkyqX7bBreWyCijZIehKAzlk2dn8lt+nJaZSJ78aB7+S77wzP3MzM20kPY/GZtrldEjdgSToW74pJtecjPd9HgvY8g4tsdIhg10rSwhZLKp82k0D29qi4kh0ggiV4cp1xE93r4lH44OaZmciJGQsbRV1QNVLw1zofemNUBpOhVLh3TssQZ8rk67SIvhtzG/gZAxdHLgpR6y2jJ9yiafjnryXgv1J4sIY35q/8E7gthj3U/0GUD0RZKQZfag1c9Mcs/rcSjJuI3h9IffS19npLJ8Y9vD/wR0LsI6GAEBAQGNwccvvQR7XPkK5r3zX/jXP3ZDXInQa6f1GHHga4grEf5VPQZHffcHGDJyFI655Cb0O2cpVi3rj3H7Phpe4wrYprDJRM8ll1yC888/H3vssQfGjx+PCRMmAEiiew466KBNNnB7hZqjtWtSmML3Y3h9OqUziatXBg0siADrV+esaJ5EZ7bdVJ7/d29783VVtv02qZW1ubrNP0Pd5EtRNARgR91wu+gZoSN4ABW5Y0eWREw+p3SEvoj2VM5+bcxsRocQEcuPEzG9ae4az0hTr00phUKYRLSS6QXpFxXRY0WDEf/s/oy0F1KKNFlyWi+N5uHRNSYlT3rAInqMn7Sc95LrBwA61FO9wtat+jKyy2veWNaNImHRefwGybhJhPKB1ldoh37tluo+q49TwoL45ARJ1aNKsvs3q5/qKKdmcViXlerkDyNPQ6sfVAdbncGfE7DGv7VAlr4xycUS9n5yP/KLnZ6PIrsflNNZ7Lyqp1lXdWPwJwLsTqk1TgICAgICArZDTPzO+dj76qV4dPU1WPnqzgCAQnOMsXveg42zBmHh909BXK2gpccOeKl4Alp6F7Fg5rTGGh0Q0A5s8qpbn//853HkkUfi3XffxejRo3X5cccdh8997nObKn67RToH6RA62o7PnToSeEBlqV8ks4gdVS9PVl7QQeY8poZtPpk8KbQv6EH9rk6Ps6+TOUMpAlqaFVxBI0hiNCHJJcN7Suoy1+9Ei1pIx6yglU2yKR3JZDTWfkpdStf+ojojq0+s11SkyMxJw0cGjXywvXD9s3pWUBotnT9LRQL5nDV9KxEDMmJXFDryQaYzcjuttX0ljE7bO5lO4KmfMQ+eaM+NKtzV0aRndW4uU8apFZzE4GyVr73wFMcpwcHbcL0ZJItPnyDnvJxC9qDNPKdf/8oielR9Yeu3bkiPbHtMSVaQyPBFYigOxi5glaR0dCpSSGjlMbFN6nrmUzFtVAA3Rhi7JZfBDBCANywqICAgICCgi+D/fPNreHTa/2Ig3sNrTw/FHiPfQY9+rTi43x+x/se74OXiv2OfE74CvHAbmja83WhzAwLqxiYTPQAwePBgDB482Co79NBDN4fo7Rb6O3sHWJsswqNenR2JGPfSEOmk354DS6de3nHWFMM3T9J6weY+3nqUQvCTCX6dyV93HskXXqf0TrbFLplhVpWSQBq74ralpI1N8BDZ0swJpW4lHB9lGnsglX+CHFvLhHMbXCor6VmRzjFlOqc0BInRTfsw7Vmh9lNJWjEnqOw61jHMPFZFsyTjmuQRUgma0zw82jo1L868gSTrT1PPXo5eaL8tUsiXP0UbzYQ6NwanxmCUUnDbVRSTT2etMuKoMBfO31jbn/atQG2fWLGqTl/zs2XD9o+7xc9JUkxJIKY883nH9UrWlSpyS7f3GenXa90Rup+FMZb4p+1XUWcqQipWRiFhGhN23e8HkBA29LzTry7RZBFKAQEBAQEBXRSVnrsCAN7tfTxWNR+J3k9ejuFj/oWe/TfiINyODQ/fDQww9QICtgV06NWtZ599FnHs+7nZjxdeeGGzry7x9ttv4wtf+AIGDBiAHj16YNSoUXjqqaf0eSklLrnkEuyyyy7o0aMHJk6ciFdeecWSsWrVKpx66qno06cP+vXrhylTpmD9+vWb1c4sCGenfjgT0XbqdBZfaYdO30Zz9WTppMc2bdC+DajPd+8P6nXptXNm2Ctv2VZIXQ6rNa0vmT4jN0m8UrXKqM5Y61O+OHl6cq4jJ4BinQco1itXJa8L0RXGVG6emMm0p7DC2hPOq1tIZVm+6le+pN5oTiPv1ZFqru1eA7VykySJZWScbJBqBbOY5I9K/HYT/NhQE3Rr/AhYA0pFJzm5ldTKSLUGrW8Akxw9DpQcmq+HlmXl6OEyfM4Ksq926BLdMftM5UjuWw2dlhrWn46f9T4EJNk1piWiU17ESuJsD2O/LGuI0HHCHZeWKArnGlr+SqKXPDf1VtXjWdeVqRDplW6uv5CEbUzrRcTReh6cAQEBAQEBXRDjZ9yA0voW7NdyDw4+8VMYecOzeDK+Gf/8x1DIqkDPARsAAMN3+BtefeCOBlsbEFAfOkT0HHTQQfjggw/qrj9hwgS8+eabHVHlxYcffogjjjgCzc3NeOCBB/Diiy/iuuuuw4477qjrXHPNNbjpppswa9YsLFiwAD179sSkSZPQ1tam65x66ql44YUXMHv2bNx///14+OGHcdZZZ202O/NgfefuANmzKTrpvKHe7/58XmbNXQSPdnF1ZpWJDmx5soHs7vTZ76Y/oXtuvh4uxX5diufZEblyAYFCaoWtTeXJsekdhxqRtl+UcOI6qYbImgzSHlC5eSJnKktJIzPnlSnZQvVTOYkvEc074ttIv0p6dQSVnfZFSkCoV3dMzpwkR46I0nw56p9IcxIhQkTyEklHJywfnXuDFKi+jXhuJV+OHj5wM28U6Z62mAvY+XrsZfA6dqN4HwASoMt9R+RTJxeGu7BTlk7CV+jiLELK5wPPTeSpS4kdSzzlaVBbBi1LhpVHsR6zxFXmC02RY7VVD159w5J7IFJyC8QQZm/WE1t1gGWMNJ2gZbm2u8uZBWxPuPnmm7HHHnuge/fuGD9+PJ588snc+nfffTdGjBiB7t27Y9SoUfjzn/9snd/cP6C9+uqr6N27N/r167dJfgYEBARsKnr06o2FS45C/6GrsPrGfTHvsm9gz0NG4e0dPoY1y/vo7xSD93wfe3xwJl67dF+88/QjjTY7ICAXQnZg6YcoinDWWWdhhx12qKv+LbfcghdffBF77bVXuw304YILLsBjjz2GRx7x32BSSgwZMgTnnXcezj//fADAmjVrMGjQINx+++045ZRT8NJLL2H//ffHwoULccghhwAAHnzwQXzyk5/EW2+9hSFDhtS0Y+3atejbty9WLt0dfXq3jzNTcRukoG6U0ziQ9iKJ6yCqJD+fjZKZ5nvk5s1fJfJiubLiwioxUM6SmacPQFUKxOkM0VcvK19PpQqU0GS1s3+3z37FqBQXECNi5009HquhzrVVCyijhemC1sVfW6LcbGu1mfhiy4+ZD9SPjeUmVNDs2OLTmZy3derJOusPnTvH0SuwsdyMqmxy/FTnHT9TWXGqE06bZCeW9nXRE3wAG8vNiGU6cSa2mfa8b40f5WqTfiWN14kZeQHlQwwUS83Q18g/GFzIpM/jcuT0vTHK31bGgChFgPLT5xBvR2yJykafUEQE1emxFRKI2hL6MPdGtOSm+zFQqPh9sS4Ot78So7nk2q/lkmPBz1Viv059QUk7eq5URkuRKGMPHsHz35D9qBgjqpIOpP+bVZFfqpyQVGgtolCqELuoDml/WquBSciNrWmiKDjy9epb9s0DSKASFzG3/HusWbMmrJTZiYiiCEuWLMFHPvKRLSL/rrvuwmmnnYZZs2Zh/PjxuOGGG3D33Xdj6dKlGDhwoFP/8ccfx9FHH42ZM2fiU5/6FO644w5cffXVWLx4MQ444AAAwNVXX42ZM2fiF7/4Bfbcc09cfPHFeO655/Diiy+ie/fuAIBPfOITePfdd/GTn/wE5XIZZ5xxBsaNG4c77rB/AS+Xyzj88MOx88474/HHH8fq1avr9k199wpjNiAgYHPj4emfwbgRj6ClV0mXldZ1w8KlR2KdGIbxO/8vdhy2CgAQVwReXrIPdvv679B3t30aZXLANobO/H9Yh4iej370o3ZS1jpwxx13YJdddmmvKi/2339/TJo0CW+99RbmzZuHXXfdFV//+tdx5plnAgBef/117L333nj66acxZswY3e6YY47BmDFjcOONN+K2227Deeedhw8//FCfr1Qq6N69O+6+++66EklvCtETe2dt9bXtKNETp0SPpapunTKzah7xUpXSsbYelT6ih8+nsuaKsRSooOA5m6+7XAWKKeni1jfEgx3hkrbVRI+fHFBkCSdSytUIRbRkEkhmfi+scxJAsVqAWR2Lz5M5AWJkbqw0oSJtoofrtH2mOpuAdCUsu73RyfVJABsrLajKQgbfIcjcl/kigbZKs3XOJeoiTZbQ+W1C9NjLUFl9RAgbq/8lUKo2gyeptefVdt8otBWbAMmeBS7z5UDGQLXSZNevBZnYJNoigI533sk5BEdUETbRkKHHQhWIigKC+1lDt0jbNvmY36ybWaEco7noP69t50vQp4gqMQpVjw5mo6AyAKBcQUsbE8YfQjG5e0jVQrEKQV9vdgaeXa7bF0sotKVPvpgZqMgduu69Jnwk5MY2Q/TErE4GOQQAlUoRc8u/C5PmTsaMGTNw/vnnY8CAAVtE/vjx4zFu3Dj86Ec/AgDEcYyhQ4fiG9/4Bi644AKn/sknn4wNGzbg/vvv12WHHXYYxowZg1mzZm32H9C+853v4J133sFxxx2HadOmBaInICBgq0Hr+nVYMHMamja8jUrPXTF+xg3o0as3AKBcKmHOOV/E4fvORc+BSbRitVjACy8fiJEX/REtvXZspOkB2wA68/9hHUrGPHfu3M1sRvvw+uuv48c//jGmT5+OCy+8EAsXLsQ3v/lNtLS04PTTT8fy5csBAIMGDbLaDRo0SJ9bvny586tWU1MT+vfvr+twFItFFItmtrF27doO+yBgvn93JgTgJkMlaC+Zk0fyKH15ZXnts8prZYeSUFN6N6JH9btvDhynf90oGtXSlApwosNegcmUG63qDM3dU02P7PmjBH1lxFAfvDcFqa9KsuywJ3h8dTGu05wRTkmiiGpRdkol3tKYzC9jSOmLeCIRJUyJQEp0iYwWitzRrz1xn9JrKe0+UcmkKehrP75RK9NizZHQybbai0XHbhQp0jd60jHL1RNixkFGpE/NGxMgrxBRPUx5Rvhb5qpbnNhh54RI3c2zrd6HQkYd4RuwNUgecnMyQoYSK+acXulMsLapb8kl5a9JqTZqnMSur2qpNT6+6DF/iCk9kpyjibO0QcQXQhB1+v+EAgAAM2fO3GKyS6USFi1ahBkzZuiyKIowceJEzJ8/39tm/vz5mD59ulU2adIk3HvvvQCAf/7zn1i+fDkmTpyoz/ft2xfjx4/H/Pnzccopp2D+/Pno16+fJnkAYOLEiYiiCAsWLNA/oD300EO4++678cwzz+APf/hDTX8253evgICAgFro0as3PnrFz7znmlta8PEf34V1q1Zj3ndOxmEHPYVu/dpw4KinUfrNXnjyzaMw9pLfodDc4m0fENCZ6FCOnkYjjmMcfPDBuPLKK3HQQQfhrLPOwplnnolZs2ZtUb0zZ85E37599TZ06NAOy2oEyQOw7/meLQ+CbarMzXNjtlrzOSWjHn3CU57Vji81rvT5iB+1JTcDz5ej/pleon+VJnutK7PZWXvoUdJrBVIurFp2QmAJYaU3NqSFr0doNE+anyb9FIisHDbI0WkiYIhOxULQhL1SJKt3pblzVJ4fQT9JfhyhcuqINJGzygeT6omFhBTJJ9SEWiY6JdGpCDmRTqojmPxGkSDXUZgUKMkmISIJIcz1o9dS+cnvC33PSmjPBLmWIitXDh+4HOkEXI+irJvSJzfrxvPpB5NnMXGkkCYHpvl6YD6FcTtTp1TDRIkXGQRR3k3J7ffpITq06TD6c5GlBzBkCc0lle7LNA+Vdc3SRNk66XjWhdTnUnnW4CQ5eizbiC1In6yKuJFKKYPuEFpg+wERAe2Mzg3Y+vH++++jWq3m/tjFsXz58po/jqmyvDq1fkD74IMP8KUvfQm333573b9kbs7vXgEBAQGbA73798Oxt/4Fq49biCcfHoPyxma09Cxh7H5zsPGnQ/DkVWd6F6kJCOhMbJNEzy677IL999/fKttvv/10wme11PuKFSusOitWrNDnBg8ejJUrV1rnK5UKVq1a5SwVrzBjxgysWbNGb8uWLeuwD0L9yZvUbAnw7/lqq6E+ixgi85tM4iiPkMlDnk4faUPnrd55m2ef+8FXUqJra5kUx3aCZjsHj20hXcMLRLbqtSqp5a7zZYgcM6e3B4tgOl0CKtETg61EZVnm1wlLC+vRlIxJPmNIkRAnyaZW8iLLREmz2pfZ1MQ8na2rnqXkEYSZ21LSQbAF6IVeUyz1Me1rmbzFEus5sTCbNNcw+TRJpBP5nluTkDZS2NdSrS7mvUGyBq1iKhTpZTrdRfagrf9GoTKy9CliQW2M1NMsTt6Nn+rUq18pngjw+8bBZQm/DmtIULNB9NajA3D7iq/SplaLk3GyIYaQ0vgUUU6IMVH6U51T/UAHp0Sy6lZqiLZPkjIJE/XDyRp6fyqbSLn2k+rLIIkCArYQzjzzTPznf/4njj766LrbbM7vXgEBAQGbE0P23QsTZj2Gfw3/M55/4iOoFgvYYcdWjB12Bz78wRAs/slljTYxoAtjmyR6jjjiCCxdutQqe/nll7H77rsDAPbcc08MHjwYc+bM0efXrl2LBQsWYMKECQCSlcBWr16NRYsW6ToPPfQQ4jjG+PHjvXq7deuGPn36WFtHIelOHnuxJeCbv9RQ7+Oi9FyCbbxePS5lzWuzdPqIKbNvyAul3+cbl2PiT3g0TyLFjuqRVpl67YZvPE4nskoj69jVSb0xC5bbxA7XF2mNhoAi0TxkrSjbRv6iiSR9x3vQEDHJZ6T3DWljjwohhLXqlYrmocun654VZhNQy7JnEA5Q54z3EekHHjCBKI3miSgxpa5hury8Wk0M7qYHjiYXzLUUeqbPBqdv0OrOTkkqtcw2VcxBul+390XUZOnkMnw3CCcXeFSPYk7UJc7yjXAb6vKBiHZ84uCyJLwPGCrfMp24UrcOtjK5JxQMOppH0IieVHkMwskwNsoMGOY7lQ0gIqSNtoUYaa2cpYiaGBY5RH3WHSCNTI9PAdsXdtppJxQKhdwfuzgGDx5c88cxVZZXp9YPaA899BCuvfZaNDU1oampCVOmTMGaNWvQ1NSE2267zWvb5vzuFRAQELAlMOKYwzH6pqfxdPf/h9efGYa4KtB38FqM7n0N3r1yd7x0r//51rp+HeZeNAWPTvs45l40Ba3r13Wy5QHbM7ZJoufcc8/FE088gSuvvBKvvvoq7rjjDvz0pz/F1KlTAQBCCEybNg2XX3457rvvPjz33HM47bTTMGTIEJxwwgkAkgigj3/84zjzzDPx5JNP4rHHHsPZZ5+NU045pa4VtzYVgu7ksRebWaeaN3hXus4xwTsXzNg2xT6li+tsX+CCTVzkdauaQyodKjaDR7lkzaSl9ekSQYYw8UXRxBZ5Y5915/Ui9cyduXOZvFdiq07s2GF0+vrHIoUE9ISfR/RIGm0j7CuWTIB90TzQfah7NiWRhHR1Wq94KZt1WRKtFMu0F7Iih8irX1InlibEVDrx5WNGH0vT84BMI5XSiB6VVbqdN0pyD7p0m+di2CRNvTdG3o0pDOdgb4oEoKyKsLgLjVp6pXHB8SkPhKOgEUROJE+NzZHp0+E8eCQhUMwmINNIHpJ+3Nd3vn6hOZXoowTpuKlSncQOUDvSRiqaJ/JE9PCLrn1TpJTtU0BjEccxfvnLX+Kss87CV7/6VfzqV79CtdqxRRcAoKWlBWPHjrV+7IrjGHPmzNE/dnFMmDDBqg8As2fP1vU31w9o8+fPxzPPPKO3733ve+jduzeeeeaZuhbBCAgICNiaMf4//h3Dr3kJj6y4DO8sGQwpgYF7vI/h67+Bf176ESx7wjxDH57+GeCOYThq5J2YcOgjOGrkncAdw5LygIDNgA4lY240xo0bh3vuuQczZszA9773Pey555644YYbcOqpp+o63/72t7FhwwacddZZWL16NY488kg8+OCDeglQAPj1r3+Ns88+G8cddxyiKMJJJ52Em266qVN8cL5ad8J3bWfe1Q6dfApRa35WK2EyV0/3BfusBUqxGGmJlXku8vmWmur7V86ya/tTHhveVID7xL2h+XDoX3vPnaNLtudrRY9oH5jYkyydvv6ykhxLU1fnlnVsNiV0H8LthSR/rImPUoK1RkXQMCjux2pneQlEgi9rb5NZwhrIUi8PbxM5rr3KALUceUGQ+XhEGtZzo1g8Qo0G/CZR+mrdnL52rFzwek5D0CEFKYT9WlSWTmEf1xXN49PJ+paOj9w2viruzemXQckTIlPfAuQhqke2ZI66tz0ha+DWLShyjShTyZsBmLw85LxuT+WaewnUNuumTeVG9T5pA7YUpkyZglKphFNOOQVCCNx1112YM2cObr/99g7LnD59Ok4//XQccsghOPTQQ3HDDTdgw4YNOOOMMwAAp512GnbddVedFPqcc87BMcccg+uuuw6TJ0/GnXfeiaeeego//elPAcD6AW348OF6efWsH9BmzZqFcrns/IC23377WXY+9dRTiKJIL+EeEBAQsD3g2PPPA3Ae/nLheTi492/Rf/dVGLbv24hf/ixevG8vrGodhMMPeRyrlvXHS8s/h+EnnYlXfn8r9mu5B4ePnYOHp38GR19/X6PdCNjG0aHl1QMSbJbl1TvQ+x1dXl1HkNSpk1YrOsRGffAtr56lQx1LJKsEl3LqZLVPdArE6Qw4ry1FDKBaBdqglh3nxIc59q3KVUqXV/frEh6bk+w5xWoBJb2ku/DqA9FJdbdVm8Ay1bC6Rg6Vv7HchJLXT9dn7m+rWl4dNmSW7Wnd9eVmVGWTRwfvT2Hmr+nWVm0GRZwKsPwjc1yZ6txQakYsC6kf7jUz7eyJbiyBUqUJvvFj/LJ1qtWsi23N0Eudt4PUkDEQV5oc27IbQL8qJIrp8urtvVHU8urgRA+b+NMBluotFAGky9rXpVOm3EkMRJU6o2zofjVGc6sp8hJT0uix9FZiFPiS7h47LZnpA6GlteIQY45uStykn1Gpiqgau6SaO/iITAlsLKJQrBi59GagA47+/4Mvr07JJl4/Ju2QkEaVahFzy78PS1U3EPvvvz9efPHFmmXtxY9+9CP893//N5YvX44xY8bgpptu0pE1H/3oR7HHHntYZNLdd9+N7373u/jXv/6F4cOH45prrsEnP/lJfV5KiUsvvRQ//elP9Q9ot9xyCz7ykY/oOqtWrcLZZ5+NP/7xj9YPaL169fLaePvtt4fl1QMCArZrVCoV/G3a6Ziw9xz0GpS8miUl0LamO6qfewZ9dzEJ5kutG7H6xn3Re8cNwKlv6GXdA7YfdOb/w9pN9DzyyCM46qij8Nhjj+GII47YUnZtE9gsRA9FnVeio0RPnBI9lro6dZbaQfTQOU2cQfTUImCqMVDO0FdzTiiBGGqCX58+AKhUgRKaWZtaxESyleMmVNUEH/DodX81lxAoViOU0GKRM5yg8RFGEkBrtYURLD4bk09z3QVay00oMz/r1llpsf23lPt1SghsLDehQgIIzStbrj56HEugtdLNvX6kQMuRts7WcjOqeUu6W3NlY0tC9DQ7BBAnkzhkDLSVmgBZcM7VIm3iGIgrBce2TBlkLi/aUqLHVz9PbwWIqiSmK68+9TcGoiIgpOeZlyVDpvKrQBN/INRzc5ZjNJVgoohqEEqWreUYhaqnPtPDCSKUKmgpsiXQnbbpiNKES1IUlSqIKtYgtRXS9jHR3VZGoa1sETFJNbbPbZExZGtrSuRIT/+k7S2WNCV64hLmln4XJs0NxMknn4wLLrgABx10EADgmWeewdVXX43f/OY3DbZs60QgegICArZFbFy7HvO/9e84/OD5aOmd/Jxd3tCMZ18/HAdfdg+aWroBAOZd9g0cOfw2PPLCKZnLvAdsu+jM/4e1O0fPAw88gPnz5+NPf/rTlrCna6LWhGwzgbyIoCdeAv4ty8R6tnbbsgk6/QsauV749Pn8TeTR1bQMOZCk643YeQFDhhhL7CTN9jm+RHpM9PgIF9sWuvoXMq01epJMNqZOnJbyfuOki19n8h/xUyUzFiBJlFX2HNVaRZKZnDg2iUKWq07Px+kmkbwjZSVOFhKIkk+p3uGCZCt/qdw5AqB6JdURIZYRpCRZeGXWHWAPGJrEWW+RBGTUvpsl3Uhcja3Lp59WlelB1o0BZOv1vJlkfBSwc/QQSEIKUX21dMIvruZxWsbSPtk5emRy3srdowiULPl5Dx0FlcsG5tO6+2WyWRmgfcK1IWyTgvU1mD4Y2Zaw5F5M6sS2Ot968yp3lLKD2hOCehuOJUuWYNy4cRgxYgRGjBiBsWPH4sUXX8S4ceNw6KGHNtq8gICAgIDNgB369MJxP/kznnr+YAAJydPcs4yxo+Zh4093w8IbLgAA7PO5KQCApg1vN8zWgO0D7crRc9lll6FSqeDYY4/FN7/5TXzve9/DJZdcsqVs266hv6N3slZfAFc9ZmRMfXPl+OZ0Pl28jB7Xo5cjq42sY9+N3+GyVZ4XTiT5CQJ/XXNOwLCtZp4odU1qj4QgxBF0mc9KEqdhSZTpWaoz+bR1qvW8HJ3S1in1H1cnlV9ASgRpGaauIqJUU0ntU/psEwxxoFqmE346105y9Egzv7UsIr1LX0VLeCV3BPCJNq+jyRNb2yYh78ZQxyLd8ZEZlNDxQdVhooXkJapueh1SLstarIkO6lpdkOdX1gNCppxIXlV+7VL/MlPQ+HRZBJqElVTZ1yx9kHNCyYmz8z7spXUN/CSRrw5xUihyUtr1dXPaabxzYlddQENw330hD0NAQEBAV0G1zx4AnsCjSz+DAcXnMfLgV9Gz/0YcjB9i2ffvwj/XH4LBo4FKz10bbWrANo52RfRceumlGD58OL7//e9j+PDhgeTZBKRzY3urEx1oorUKj8565NWjyycni/zhwQm+YIV6dXLYZIm/rue3dVKu/pqzkn3aGrIInuwjWx6ldmBpt0u4N3xxdaNB6l7g8UOAEMJaobxdOgX0Cm3acwFAR/MYnTQAAyIy1jpLrIsMnSIlmuCuEBdRvUgCabRPAjJZS117Za9WppbGTjZlD+8Jy3frtEQSZQS7IyLR8by2Hq6mvptSdDyZLiFkrDHkO5CkIuE/OqSW++W7MWGXOcFF1G5pPq0yxZFkEUtEj6Dn0vORUkztMkPYrFTIly0E0lf7WAM1tmQ6vmSkN6tTmCxLYZQOfhH5B4y1slzqPItIst7R09c4oJG4+eab0bdvX+y+++7Yfffd0adPH/z4xz/WxwEBAQEB2w/Gz7gBpfUtOHCnv2PkNY/i6eYf462XdoGUwJC9V+LwUX9GpVjAgV+/tNGmBmzjaPerW5VKBeeff/4mLf0ZwL5a01l4HZBodxNXKxNQS54+Z62LbTbJNlVG5yuyjg2ezzxkBTmIGhKydSuKwn39KLuVoiRofVVu/nILVCSLf1H1Wr1kS7VbitQiU9O6DvzatVen9Oikk0umM5lsp30qkw3WWOEvr7F+FKZuzJdml0K/pqPmsOp1Gko12cvdxxCI9Ss32g7ikSDRR+5GJ9KkVSx1rtt2QSKdyDOBvpvC7Xg4Sun5rDm8tM9Jplb7mSHXS55k6WR1hYDrD5Xh8VVm9Ie+HLwJKXNeF/P0iXPZZJpHzZsPJ/2QYGMn+bDIMiTjm5It+p8wn3aCJEkMIoNdvx7HOkk43pvzlt+c2SJkUEBDMXv2bPTr108f77jjjvjrX//aOIMCAgICArYYevTqjYVLjkL/oauw+sZ90fbaE2j6t99hwWMHo1osQERAU7cqut8/BvMvO937NkZAQD1oN9Hzta99DQDw1a9+dbMb05Vg3bLWL+i10Y6q2VqZzlrydAAFicagIRa8jM/duEq1RRnl9frp6zpDEbgvVOXZAN0iWWhdtU7oGNtSyTRTnTbJwo9MqUCUzrd51IlqxVu6OumZPH2EwrCvF2jsiokwMvK4TqnlWdaKrP8JpVE5IoIQEaL0E3S8IGJ9R/ZTUoWOOzt9jLRMTKqnEQ9ptI4b95Sck2rsekefGu/slGfTUUmR8OegqQdSwhNiZZtsGUYMjEQ2kZH33YA+DpQfqrnqW98zA3D9VPucqOH2qDp5NzyTa10HJpNG7uhTrEwTVx6/sx4ySV+QgUZstLtf3UfmvEUQKhl0sFh3fBLhA6meN2D6iA3pkHb7k3iaOwAZm+fr74CGII5jrFu3Th+vXbsW5XK5gRYFBAQEBGxJHH39fXh80XHo3X89jhx+GwY/fwQOO3IxqqUI/3p2N1Q2NqNb7yIOHf47vPffQ/HC70NS5oD2o105egI2HwTYfKgdZG3Hed1Eq54WyPplmdWMSOM67FHzCN/8L69tVv162hsaxLesefZ+opPOgmx50ilTGXsMJWQ05yzNbekpwKzBZs/QFQVB21C5kUgTwYL2l9Ce0ytkiCiJSFYhHI5XkBrQPimdai8CkqTHgImo0RJsPVZfyZi8zmL3BQcdpUJI2xdJRjARYP/YEZtPGbn3GhJSxc3FkzHjZUEdqr11mkQatesG1SyoAIRMJv3UHy7Lka0iPjx18m4uwHLXIkOsYBDhlScpuVKHv4If0H7K81ES2zx69Btl0i4TRI4mrlQZHxDcDt2d6oJ67NENkgKHLxFIk3MDTlQQXLHqDkvkxaZdpp1ZTz5J/PPY7yMBO/4/lIDNhHPOOQdHHnkkTj75ZADAXXfdhXPPPbfBVgUEBAQEbEkcff19aF2/Do/MnIamDW+j0nNXjJ9xA/bu1Rsv/m0uCn/5OvY+8E0M2O1D9G/9Jl668AcY/NV7sePu+zTa9IBtBO1eXp3j8MMPx4MPPtgll7jc5OXVO9jzFbZgef1kTZz5akktGWVWI/bU0fMGMqRioK7F4Ln+Srq8ei2djm4AsRTWUuc+Hb59tby6mg3FVj3htFOkioRAOY4Qo5CjQ7D95LhUFSiimz5Hl0HPKlNETLFaSFcB43BJJknqtVWaUSIcbyy5bX4/AWBDpQkqvIT2jT0nVTKEtnVjpQUVsuy47lvp2mstNR8LtMbNHpvoPJjUJ0t+byi1IKb9Llm/SE8ZkjehytUmZwl1M8YM+WHpl0BbsQmgy47nTaYpsSCBaqUG7+4jOySAtgKskJ+Ync/SG6fLq0tGUniJF2G1KxTh15lDLgmZ1PMur55lp745YzSXmDz+QLAIKlKvEqNQ8ejwEFtWYFqpguYiKYil3VeUgWKyorYqoioxMOPB4CwX31ZCoY04qkmftJK1zLpth9y4kawSRs+TjvScq1TbMLf0+7BUdYPx/PPP4+9//zsA4Nhjj8XIkSMbbNHWi7C8ekBAQFfBQ1f+Fw6M/h92HPYhAKDS2oRnXj0SY793D5qaWxpsXUBH0Jn/D9vkiJ4nnngCbW1tjqFr167FFVdcgauvvnpTVWyXcKMM6kdH2/FfnbPmV/XoTCI6pHPOVy9PVs68MFNW1nwt77jGHI/U4xRHlo009sVIMMEKNmFi6zGaYjQhySXjvI+hy1y/09//hXlFJZHlJyaS/URHQoYYCkvqUhqPRHVGVp9Yr6lIUYM4lORYgnPKtG/5fqz2BKWLUj951IulKiZFSUSP5JWs9mZ1Mau/pd3vNpEl0//sPottZrB9N6oQzr2Zy6aqwzi9crScd2ZWex7Rk+rkKbh9N5CO6Kl1w1NiRLinHZvcGzLznLXYlM8ETvyoHU5KERlWJBUlVYgM6Rl4DknkPGwZwaJ8SD+FrsOIHcdOItQJSVMRPcJeFl635fWlR0ZAo3DAAQfggAMOaLQZAQEBAQFbEY698L9QKV+EueeciAkHPo5ufdpwyKi5WHfzULzaMhWHfD0sjBSQjXbn6FH4/Oc/j6uuugpCCKxcudI5v2HDBlx77bWbZNz2DP6dvT1wXhNop872EDxcJ9Xty9Xj1dkOubU2oLbv6jUI6ifvs2wdJmcLz93i5npR5a5U21aafUdpiNJ/Ms0GRP+lOWQgyEZzBKWyyRxOUTW8n6hO/Umum7ASfwiYRCBqVSreW5IQH9LKzWP3r9uHfKxk5uUhlpskwaYv9EJIIiWeBJL8OOmm8vMIsqKWEJHx1VrCifYYTZRr5vpqIk56Hiq3j/VP5crJGrQOk0OPDWWkT/kSWPGySHWERydHDgOq89oIeuDbTN/XpZP5RPuUDFwDX5/5+lCwJuQaWeJ9ZBGXQeTSVd2s8Zp7MW0/jQphTqg2fMk6JV4pR5Rc0wIxJkqFCC3MhuW4NCxcRGz3PZ878j+CgICAgICAgE5FU3Mzjrvlj1h11KN4fsG+iMsReg9cj4P6XY1/XTocbz31SKNNDNhK0WGiZ9iwYbj//vshpcTo0aMxcOBAfOxjH8P555+P//mf/8Ett9yCXXbZZXPaul1BODv1o6Pfz635RDvV87mZmUhJPbnwvQXok8/nu+3ZADjzNp+tvvmnL1jA3exVsOyVt2wraEYaXqbqm+m7+atW9pKIUbU00YXJzUpUlO6xvMi4jm6fyXQNsVQ6XSHNWWFMLe0Dyy+qSVh7hniRpL5a8Ur7Q1ZjUxtfqczxQFIOw74GauUmqZkgCRknG2Ss+9esBBcj5qvFeUaPmqDrsUMGknXlpURs9arUr/ZkDlrfANaKBfHP7Qrr8jjHHr0cXp0gDwVSSJfojtlxKsdLoOTotNRkkUPtfQhIskuuj0X8pGY7Ngp47beGCDzjxVn+y89TWd1h+SuJD/S5qfRU0+tKy1Ih6pND1YmQMFScuQK13YcO/E8ooC488kjy5fuxxx5rV7t3330XxWJxS5gUEBAQELCNYreR+2H0jYuxsHwdlr8yEAAwdN93MPD5T2LRt49EaePaBlsYsLWhw69uXX/99QCAlpYWPPbYY3jnnXfw9NNP45lnnsE999yDOI5xzTXXbDZDtzc4E4Gs7+BbQKeeN7RDJ58r6eN0lp+V6qmWiqwpRj2BCfnTFuHUod3M57dS6/StPOWdggNOmcjuJ8eypKwAgQroyldGrs9Pyw7p+qU8EaQ+kMS3qDqRiFCFSkZMbRIZfUv7UpqZs5RWG0VVKP22JSqSwTdR9VFKaf3YbmO9ASOoTkJgpjN/AbOymCKNlHzfK2e0D6XnhPEJ4By5VEW1Bm0WWF86AwieYz25z1BaU6epY/ySLnNhTpq+5c8sPvDZpxIhAfsVJ4osVtjcnN5+4YuD8WsX+W9G65gT74KTKuoVJ81qktEiYUXXaXkChIEihA2XZaEAiCr0A9rq54yO0wSSAMhrmVqvEqLZUadxwBbCAw88gKamJvzpT3/CEUccUXe7L37xi3jttddw0kknhcjogICAgAALh3/lLABn4a/nno4Jez2AHQZswJgxT6P19r2waPW/YcKFP260iQFbCTY5R8+GDRvQ3JwkS/3sZz+7yQZ1FXQSt5Or0/eDehZkxn49OvNsyZKVd64WNCGRoZ3LpXNSe60q3l8u2WNr4Bl+VKmdy4XuVYkWe09Jt8kjW59LZiWl0tEpYVYNozl6bJuVdN9KVS4Z5OSV0RNLO64pKU3y5fA+4ro5GZDMk0ktQfpBCqLT9J+KxkgietTk1tdXvt5MbLY4ATaMEp2xK6ueHD2ZJAdjVPjQ5bKtcj55r8MOnw5emJHsGDAcRM2bVNj19FtQvsGVlURako2XgXAmSgeT643oYXKse0t1py9siRxyPcYYpVgZxBRYtx+7drJq2jnkORkblKnUg5QwTITfMbIk06uO89LcB3QUl112GSqVCo499lh885vfxPe+9z1cckl9uRT+9re/AQCWLFmyJU0MCAgICNiGcfwPfoE1K9/Hwss+i4PGPo8e/VpxaL9f4t0r/4Q1o6/BiMmn6Lqt69dhAVvdq0ev3g20PqAzsMmrbnVlbPKqWxx1XolyXetY+XTGnmXS69XpJzGUiKxzVSkda+tR6Vt1i7bN0imRJOuteFbdqqW7XAWKsDPYcyJCvdTE9ZdjtQIWp37Up1mpitYpVyNLpy2XRtgI53yp2gS6mpaCsZFGypj91koTyrLZe859Qc22o7WS6KSTbGMrTZts27yBrbrFSRL11hO4nzJZJYyei1lbtQqWRSxJYGO5GTFdAYv7Rua+1lxbAqWKWX2Nlmu/yD7tqyJfdYs6m3OjyBioVgoAkV0T6VxdtKmkLkynfRFde2IgqgjDD+TosVAFoqKA4H7Suh7dIm3bVKlDBz8ux2jmb7JwYoc+3Ej7qBKjUM3RQfgY+6auoKWNFXKyKSajjlSNilVEMVt1yxp4drn2oa2EQlvZ1FM3ga4rSXtyXkrIjUUgTp+2sa8O153sVypFzC3/LqxgtAVw6623Ys2aNejXrx++8pWvZNabPXs2JkyYgF69euGWW27BM888g/POOw/77rtvJ1q77SCsuhUQEBBg49n7/4Rej03HsJFvQURAXBVY+vw+2P3c+/H0NV/HuBGPoKWXWdWztL4FC5cchaOvv6+BVndNdOb/wzqcoydg00B/Pc5lSjY31MRCbbC3Gs2cTWV0yTqf9TYG1eeTmaUTyA5uoLZm+ePTGZMNLO8Kz9SjWrs+0AiaZKOra9FjQTRWUwtcTVSb+Wvr5cuS81bcy9Qeaeyj+YKUdt5fAuStJJ0JOdmshMjCyEvy11TNvjR5elSuHpM/x9alxqZ65UayE0JtxB/TdyrnEPEpnRtbuYnYILLHi3AGj0yLZWqHkU5GT705ejgkdHSUfuXHd1PGbLMva/aWBUE+BdhBuvEcPXFS7iRU9uj0uZH1lpkdGpbjQx1+Wjm/lWjeDzLj07IjFezJ0yNknGxC2l0Xkagl3gN6N93hOaOkRDIwU92KoNEf6uHNbVfyYrujBdHLO9jSlzdIAjYFlUoF559/PqrV/B9nzj//fPTq1QtPPPEEfv3rX2PixIn48pe/3ElWBgQEBARs6zjwU5Ox18ylmPvq2Vj7bh9EBYn9Rr+Clj+PxOFj52Ddqp549NUpWDH6CTz66hSsW9ULh4+dg4enf6bRpgdsQQSip0Gg3+Ebpb89c0LAnbhREsC3QBB9cyDLBiWX68nSx8vzZPvmdj57qB/wrIGV/KMEjvmrKB47U4290T0Bu9cKRCeINmWN7adk2v29K4heuopWQgxFOm8NX+lLkMeB0klpE0WaWJ0pQRIi2yuKCRT0vln9iqz4pfclIYkkYiEhRfJpdCVEgySkA83NozxQK2tFglxHOvFXWyQhInP96LVMnPKniVYFal0vc60iQnh5Nvdi0guWEF9qFGXdlD6ZWTdeFlnESROqTx1Q4oGuuAXzKYzbmTr1ZaOqfEPWd2Nm2e/TQ3TQN5K0/lpkV9YDxV7ezVQSAlKoVenINUsJOKkM8V1Iek6vqmUNTGOYdwyRA0HIGimNvVa/StO3OjkS05fJvgVsKr72ta8BAL761a/WVf/ee+/F//2//xf//u//jo0bN25J0wICAgICtkNM/K+r0fPrr+ORR49EeUMLmrpXIATQ1FxFn6F7YMjIUTjmkpvQ75ylWLWsP8bt+yha169rtNkBWwiB6GkQ9I+seZOyLaXYN/Gtod43baFBBVmBBbVMyUMWGZUl3zcn5vronJvrSHzwRfQYOscmY+h6WD6JtH2igUeeVJ1adM/QD5QqUtbTDDn2/J22tENAhFqJKuMfJbMAm9CwUkZ7x64iBUxETZx+Il31SlqbmpgLQhIJCGk2CTW3lc5mvTQlTCyRTCOIFDEVp5vWFQvIdAPMdZXpkvLJDSId96yBIwAp3Cgi7wpY/Obgo1ImN6Feqj6PEFIXW8mpJ6KH3xhKRhYBJWA/HBS5BvqJ/BtfuSLN0IAkz708CI8sXxkfEtRsEL31PNQAt6+8ETdpNE8cQyCGkNL4FBFOiDNR+hOMWLEGpyFtQMeRJGVpHUg9bjRZQ3gcvUXk4mo/ffoCGokhQ4bgi1/8In7zm9/gU5/6FIrFYs0ooICAgICAAB9aevTAR2/5Cx5/6TgAgKwK9N1lLUY1X4xXLtoPq/75Clp67ICXiiegpXcRC2ZOa6zBAVsMgehpECTdaQ87spmUO/OXGuq9Py5Dz28yf9yvF1nz2iydPh2U9qCvIeXNea05EZDGtfBol0SKHdUjrTKhZ6P2ZqgZ9x+SWBdN28A6Q30mrykRfW4aZZeIsq9QGtFDfLTr015JYBZft/vUdEVCyNBjSNOLkepRIRCxqB4zKTfkkoSJ5pEpmSO1XLbpCaxISQQVl0SuorADJtQ7YSIir4CpKBUkk3d1Q+TemppcMFdM6Jk+60bfoBW2IPoKW+aNyCfyQPZN59MnmQyfPk4u0Kge5bRqX0c0ER0WIKIdnzh8tnseMnroCWY61VuvDrYyuRttk2wSkYnoESZHlB460upIu++UbN3ndHACiMi7X9oWYiStC0XU+Mgh2r+qQMl0fQpoLH73u9/hc5/7HP72t79hxx13xKpVq8JqWwEBAQEBm4SWeD0AYN4b38T7/xwAEQF7jXwTO/ztUDx2yRewz+emAACaNrzdSDMDtiC2C6LnqquughAC06ZN02VtbW2YOnUqBgwYgF69euGkk07CihUrrHZvvvkmJk+ejB122AEDBw7Et771LVQqvkyhmx+C7uSxF5tZp5o3eF9nyTHBN+nd0hxVe3Ta+k28DXJ84npM7IsvW44tRf010SB0Ju1Gx9BIHpobx2TIoHWz8hBxAsd/ldwonZjY5Hpn96P9QphNBQnrDZZkoi3JBtAQi8RD9cqXZLl5CJegtaRiSUQPtE6bcLCIJ12WRCvFUuUF8kcO0Ve/pHodjNKV6c3Ae5gfa61SphFEOXl6atwsKtdR7q3PiQ+g/ogeCXe4ELmS7JtNkQCUVREWd8E6I1sn4Yccn7LA5ZAIIieSh5ut9n0PJV6W2WeKtaGDlcbwScPdOA9Q4ZevVkmzBpPSA6BKdZJ2oHakHaeieSLf61dpfdb/mnGLbZ8COg/PPvssbr75Ztx666144YUXAAA9e/bEiSeeiOHDhwMAdtllFxx//PGNNDMgICAgYBtHpeeuAICmeD12uuCfmDv/4yitb0FzzxIOG3EPuv/1k1a9gO0P2zzRs3DhQvzkJz/BgQceaJWfe+65+OMf/4i7774b8+bNwzvvvIMTTzxRn69Wq5g8eTJKpRIef/xx/OIXv8Dtt99e9/KnmwrJD3ImgZtTp/6+Lz1bjgmcWsjip+g8tJYbtc63R6c9kBV1InJ9UsdURvKjvh0TY2rbEtzcLsYiOzMPLIlKR6T3XZ18yq88kpkeCUcSf+WKltt1lQbh6DVaFWFD1atoHkLMpESAlB4LrNw8dG7MSDVCHgHSIWgs6im1g7/eJkAjiJRqmyEQoFE9huLjET2+MQOhxkyiJxKpj05USI0tFZrcg9LR5yjnBAqPqsnSSex2nUmL84gOxq5IKsenk59TKn3kUBb4A4X4SoaafxM54vNsj+gnOUFIL5vqoTqoUpkvnxugzheILiubtDD1LNInBmKVo8fnFPWD6I6UbJG+3hXQGbjxxhsxZswYXHTRRbjgggswatQojB49Gs8880yjTQsICAgI2M4wfsYNKK1vwX4t96BaKuK4H/4eyw/6G177xzDIGOg3ZA2kBHq0/ROVcqm2wIBtDts00bN+/XqceuqpuPXWW7Hjjjvq8jVr1uBnP/sZrr/+ehx77LEYO3Ysfv7zn+Pxxx/HE088AQD461//ihdffBH/8z//gzFjxuATn/gEvv/97+Pmm29GqdSJg70Tf0zVX/elPffIm4NSM/UP0nVsPhk+e3zHknzW0mmvmGVLquUTn0eagAEezQNLgp2XR03+qMUuGePm4VFZbEBKqb9Gs/R4Yy+JDlCLs3LvUL32GcDQLT5ugJAoFplgXrFKthgQcTovlsl+qiGGiepxyUVKqgmLQJIQNjmjdCmZqR36FTqq08oHZBI5q30T0RNBSpONSDnpG796zi1V3xNdkO2LsJFGqND9nKGYDwF14am+jubr4bKd13rIpok9YkOWDo9OkaUzD+qBQvzLjObxnPOCnvDZriJvQAerGq0yzc0jrVcP9YMVgP2uGtlidY4YQK9nlbz/JWNLrxXNA8DKz2P5o+qT9qpDFNNm2UQHRcDmxm233YbFixejWCziiiuuwFVXXYUPP/wQH3zwAV5//XV84hOfwFFHHYXHH3+80aYGBAQEBGxH6NGrNxYuOQr9h67C6hv3xbzLvoGWbgW8u8NErFvRW6/jcMjYBVj7w93xjzt+0miTAzYztmmiZ+rUqZg8eTImTpxolS9atAjlctkqHzFiBIYNG4b58+cDAObPn49Ro0Zh0KBBus6kSZOwdu1aHU7NUSwWsXbtWmvbZNQ72dkM0PMYOmdD9pZlZr0EUd70getg817nM2vzBTT4NGfpU5uSZeSpSZ2hd/QkD+5v+pxaoq90caImiXVBui6V3cqV4q4KZcrsQcPJJ6XLH0lkonfs/jakEJ2xm6XMaf/ZkTzJfkQ61kiOYEfW6MTDpK+1Hr6EOiVnHH0wr3jp17BSncK8aiZEnJBAIrZkm2ge129nFNE5vwAkIUEikdpFB2K9N4w02nPhIYhq6kLGeeagpDs81I9WIM+OTL05Op3AE2aH9zgtkzSiB3CjeoRn84iq60GnInoo2ZUV0SO440CSsMejmOY7AqBXzlK6rRw97JN3kJbPHNXXRjAf6EmYshDRs0Vx7bXXYvz48ejVqxc++OADLFy4EDfeeCPmzZuHHXfcEVdddRWuuuoqnH/++Y02NSAgICBgO8PR19+Hxxcdh9791+PI4bdh8PNH4Mjht6F7rxIee/JoPLtgBOJKhL6D12Jk9Tw8+62xaF27utFmB2wmNDXagI7izjvvxOLFi7Fw4ULn3PLly9HS0oJ+/fpZ5YMGDcLy5ct1HUryqPPqnA8zZ87EZZddthmsZ+ikH1Str/O+CZd92nu8uUzlc02RoTNjzufdt6ULp4Tr88mMyVlbtk3p8HacGlD+CN1GOm3UPo/O8RE4dn16ZM4Jx4rkSFr+mEgi1x7/iKD6hZDpa1mpbOHrA8UEmJZJRA/3k1ourBMxrSekO83VBIlhShTPok7G0uiklJWW47x/lM67vb0A3T1CC2TUTISEjag1aLNFuwW+CbwzgDyMRu2bxCKaXEOIRZLVY1xFXToVl5EODcF11joWSALEiDz+PNM6uA28c+2bM9sHwI524fIBOBEyWkcEK85QAFaUD5XpPIyk+2nZpQaiNPZZN4gwbRWLqD6VHUKm5okQ0bOF8eKLL6KtrQ3PPfccjjrqKERRhDvvvBMXXXQR2trasPvuu2OvvfbCokWL8Kc//QkjR47EHnvs0WizAwICAgK2Exx9/X1oXb8Oj8ychqYNb6PSc1eMn3EDjunVGwDw2M0/wIgN16Lfbqsx8qAlaP2ffTB/45cx4fxrGmx5wKZimyR6li1bhnPOOQezZ89G9+7dO03vjBkzMH36dH28du1aDB06tEOyBMh38U6D8Cqtxwyh4vtqwDM/q6krb67Vkd+as9rUMQ/1Ujy2bPqCE9fqavaRKHS+rmIEzLFLCpnz5hWnLAs5EaUkUkSkzO4Tn1Sh9QNIX3myPaGTdrtvTG8WINJXrmDmnmmdhIoyE1YJoUMNdbJmrkNXF/pY9YuqGgmzNpmPYtJ0FfVJQC/rbsEz0fbmm8ldz7sWWLtaJIQeQJ7rRjsjyxw2kPQ9QDuYN5AAUi7LeRzUo9Nnbi1SKpVpiLyMqoIRdak9mQErGS6ac+SZ5+kno4Mssa6K+XOWHut+9wxaywhWh9qlLxYn+TgDRuynnUOJrhDQs8XRvXt3jBs3DkcccQRGjx6Nu+66C3EcY8mSJXjmmWfw8MMP46GHHsJpp52GDz/8EL169do8EcMBAQEBAQFIXuP66BU/8547Yuq5KLV9DY+fNxmHjn0KPfq1Ylzfm/Hqd/8Xfb74Bwzcd2QnWxuwubBNvrq1aNEirFy5EgcffDCamprQ1NSEefPm4aabbkJTUxMGDRqEUqmE1atXW+1WrFiBwYMHAwAGDx7srMKljlUdjm7duqFPnz7W1lFIwHADfo4gEx1oYrR6dNYjT9S5BC+Xw+dSPpd9x/XCV1eSv1l1s3QmZeqvqSXZp9Hgp17yyDMzr7Nf1uJranEbTHv72PZLSaeZeWBtfIUg+5rwf3af8PpOZwrPvBUARGTsdpZY5xZQb1LNAtabLEK/xiM9q66L9HUaX2rtpFySTdvj6Xmrk4XRCUj7jZpUXYffgpGwXmezdNJjfh7ouFIyybeuqT6gF5VUrI/zzVab55fvQaSuecZDQufjMVyhlacnn7Rj4sjgjSwlZqPjMdmnB0oMuylIXwqpEolHerMa+24wpSNKB38Uuf2krhN9b02trgXySZd51xc8oDNw3XXX4ZprrsFXvvIVLF68GMOHD8enP/1p9OrVC0OGDMEHH3yAN998E7/97W8bbWpAQEBAQBdCS/fuOOrmOXh54K/w7suDIASw5/5voc8jR+Cxi05utHkBHcQ2SfQcd9xxeO655/DMM8/o7ZBDDsGpp56q95ubmzFnzhzdZunSpXjzzTcxYcIEAMCECRPw3HPPYeXKlbrO7Nmz0adPH+y///5b3Afrq7V3ZpwNa9LeTq2+CU09Juhz3uW6aNJbs4y2+qVbEBm1NktXHcgMcqijnV93kuZXr8CUbsKT6ZauuZNM1eg5CkrSUBtUPXtRd55AmeqjOrN9FI4FtvPs2gFEj5uq2fw1E2c+XmjOWG//y9RHmWywxoovizBg+pGPL2q+i4R+bAAA0iBJREFUsNKeJPtJslx/quvER5Fu1tilfUuij9yNTKQlsTjehLdgBLlLsm4Ke/CYT66U1s27Ecg5vuqe94FAypzAwDydrK7gZdxHfo6MLd9Dgr+5JlmZw2V4yCLnssnkTrAeXumnNkXCjGHdMYwsQzq+Cdmi/wn1GcN5F06SHTXYdbZ41SGuH04nWn4z4kqzsh0dtAHtxZgxY7Bo0SK88cYbOOyww9C9e3f069cPP/zhD3H11VcDAHbbbTd8/OMfb7ClAQEBAQFdEaM+9Rns9l+vY+6Tn0ZpXTc071DGYSPvx7tX7o5X5vyp0eYFtBPb5KtbvXv3xgEHHGCV9ezZEwMGDNDlU6ZMwfTp09G/f3/06dMH3/jGNzBhwgQcdthhAIDjjz8e+++/P774xS/immuuwfLly/Hd734XU6dORbdu3ba4D9ZXa5F1wg8+92iPVt2G6fT+8u3RSScL0nPenvzbKX3b+7txPX76uo5SLVyG8jPLFhXVIUldTqtI1oLr5PqURipTxYwkZb7F4bN01tOLxnvfq1zJf+aVJyPX88qStpl6YvRoTSqSwcx307+pb0KlgDZ9qvuC5cqhvkqVaDnDbf+LWVHaOonWceexJJIqDcTwzXXpXDgPIvVBpnl07X6tA+lkO7FBZF9ifsOpd5kiQzoZg8CXovPoJOK8NwoTSOr468Pvt+/h4iODfDezgHkbTrC6Ev631jy8l5dYInY4wVSC0OKasCH20IqANYCkkkevJ+snm5sxd6vUMpU+Ss6Qm8vrj8wZgAJ6QPBxsinhWQHtxt57743Zs2djxYoVeOKJJ1AqlTBhwgTstttujTYtICAgICAAAHDcDXfireeew/pfnoJ9Rv8LA/d4HwOWnYInzx+Lg2f+FU3NLY02MaAObJNETz34wQ9+gCiKcNJJJ6FYLGLSpEm45ZZb9PlCoYD7778fX/va1zBhwgT07NkTp59+Or73ve91in3O3KcdE8OO//5q8q2ouUC9siT75BObrPpqfpY1B8zSn1W/nvaKsiCZX5y62fuUhjDyLFLDaUfpIdXar9t8Kj0FqNTI3N6khBNElDyxJ51+Qsr8VYmLTfSK8U/VEh45po7Q/AKghoAv4bHtQ/IRa9KG9wkHJZbU6lhS6yOEmppIk3MJVH/GgIzg3GuQ6cpdfJy5E17p9K85sPwlkUbtJnkAQK0QJrkvTJ7voeFJGFyfTlLk0alHu+fCWqtn1aFT8APaTzX81byFR49+o0xmlwn9BxZJxHXQJeMTdZ6LYT0zzXmLM1E6IiWIEDSqJRMrlD5IJBFu8Edq6c+sJ5+0/ctkMGvUCdjiGDRoED772c822oyAgICAgAAvdhs1CvjvF/DQZd/CITv+Aj132oCxBy/E2h8Ow+v9L8TYL32z0SYG1ICQMnzL6yjWrl2Lvn37YuXS3dGnd/vegovbw7IwVFDV++0RESPOfLWklpyyPb3JJW3okIoBYm02uLxKDJR9smu0lwCqUiBGIVeHb79SBYpohh2lo5BdBgCluAkxohwdgu0nx6WqQBEmgswOwhDeMiW3WE105ukyZca2tkozSoTj1QmPQT/9OjdUmgDmZxJ54+srEzmzsdKCijTXRL1Upcgan/0SgIwFWuNmj00gOgU5NvfhhlILYtrvPMmy9JQhmV+XK01OP5prb8gPrr+t2IRkHXDQCjVumKRttVKDd/fdeBJAWwGa/ON18vTGQFQVhNTJaJNeI9quUIRfZ84NKmTStok/EPIeKPrmjNFc9Mjz2O1E55RjFKrI7hdKCNG+KFXQXCQVY2mfpwwU8z9qqyKqkjs3o38EL28rodBWIu1i0g/pQOHEUXpObtxocvJY/pGOtNom9SrVNswt/R5r1qzZpLxzAQGdBfXdK4zZgICAgM7B+vdXYcn3JmH02CWImmPIqsBLz+2Dvb49Gz132lnXa12/DgvY6l490tW9AhJ05v/DtskcPdsDNiVYXpKtvTrpRmW1V6cAkpVlPHl5uM48WTrtRI0tT149tmfp4OX0yM6Jw8sBYVlnzgGSlXK/k/wxyT8BSfLiZOvzzBSFfT2NZVwfoPLVSEidI0ddO1h6kytichKxPkmTzqpXW6R0X43y9WWi1x4rQrq+Klt0Dwpjm+5baeeYhQRJ8xPrTbIcPHq0Wb5zX0l0Btm3onV0e7WbyIpjPsFG/RCetNsx2zzdlQR/SPfmpM44gt16WmQMONmtpbD1g/SF3XVefXpXZNy/vJ/yHgBED0mTZFdlvgp+QfNvTqi0UdYzTQ1yAZNbR/9zXXf7wXfxzH1hLmZMbkPfeCJCnc5UHZ6eM46Qccv6UzAdAQEBAQEBAQEMvXbqj0NuWohFpavw4bIdIQoS+495Bbh7BB678hwAwMPTPwPcMQxHjbwTEw59BEeNvBO4Y1hSHtAQBKKnQdDf2TuArHlbvTqz5k716tR6yepJalUY35ytPXJrbYDrN9ehXoPgc9+8+a/Z+ApNZnNW0iF0D5dq22r/Tf5F6T+pS8xqXxFMeme1sZV5iOPKT3tNMFqbraNFV71KdSUtI7YZisWaJat5KaS1UpStl/qS7pNxkoyVWv2s5qbsCqXzWKH+CEBEgIgEhFqVCElOIONrZPrVWsKJ9hghpYSZG2vOiXoq3PXJEt0el8D2ybUzxzY1BuhLYG+8LDJ94OjkyCFU9CpVAuSAb0a28NmTo9biUzjnwdvyPss6psXEPEs84Wi0Ab4HCfFLrepmjVeq2Fk2LC2mPIrzsE3bqGvFirRyREmfFqgx1EbPE9VynDgc2febk4unI/8jCAgICAgICOiyOOzMqeg3/Z947PHDUWlrQve+bThsj/+H96/ZFYePnYN1q3rh0VenYMXoJ/Doq1OwblUvHD52TiB7GoTtNkfP1g71vdzs1I+OfjdXqqjKetVnEjjSjl7x6cyyI6uNKs87l2WXKvPNqX3teBnNXeP8+K2PhVXT0DqS2WOy9kinTvK3iiarHT2nvHB9Eea0tHf9ftl2xZJGImSNBJMsms+OhUCaoyUrBxL1R61epSIXqAbXVssOqUgd2wrdB5KWQif/VvolYkj9GpWR6cJlGrRtpKvVa48yJbucRNA8uqXWQLVUJp7RRd0NYZAjR6/CVEN+1s0pyGkyeKzF5T03o5WjJ0s+KbO4iizCy70Z6zqmz1HtEh3WvoEmYN6JJOX2EOUskVNBi3L2fWSSai/JPkhfSsC8qiWtOrYGbhM5JaLUbnpC2vVyPQgICAgICAgIyEZTczOO/tFsvPjXv6DXY1/Hrvsux467rUZcFVi6aiyOvvpGCCEwZORNKLVehVU37otx+z6K1vXrwmtcnYwQ0dMgNOKHVDU3ksI+rgeZAQpZvxYTnfXKrXerJZvSI3xux/W6QRN0z460kY4F9ktVYPVdu4VTp8DaqWgfE2XDXyAjfhGSh1pE/YOWGCFS+yKNdNE9EFl6aTQP1aDpFsPyeSbzQstDKlNH9Qi4EQYsssay3mJD0/PqVaK0WBCZKqJHRSoJ2NE8ApGOZLKjhLQ3jCiyT9hjPzI6lK9ZET3OZB8upPviHu0Ka8CaS5Qd0VPP3J0ospoKwInmici+qkORpTPVQSkH4ThZw1efT6RMm8vc0nyJj/TxyFBlJpCGjk913n3WsdXV2WJl6XjVdYgy770QkYvA7M3K9K2vi4D1jqE1iDz+OwoCAgICAgICAurD/sdPwrDLXsMzT+wPAIgKEoePmY0VV+2Ol/78ewBAS48d8FLxBLT0LmLBzGkNtLZrIhA9DYLIPNiyOq0JDS/LgczY9PmMnN5Zsr2Tsjq3emzNq5kvn+7RLBzumljKD0oV0PpcIphsQKLq1UeTstg6rE9B/aU20R5QsmOTCUjl7YBEkhbcbP500Pa+RWzROao+a+TFVLaaBZNNssmrprbI3JX2l0hPJKtxwZKpc+VIafTKxF+aH4jrtHWnfsG+V+iWtOb9Jk1OldwbxVGb6uDxUZ5O9+XsoRE97dbpq6KcFECcbjxfDwiZUUunJk20m/6HgvDIquUT4TNooJZLWrE2FJwTAR2ehDRRldm7YFq8c5MK0gfSNor6E0uzSQmganQ6kVqKlWI3vu4AaTtNSVR9nzAn43qfqgHbIm6++Wbsscce6N69O8aPH48nn3wyt/7dd9+NESNGoHv37hg1ahT+/Oc/W+ellLjkkkuwyy67oEePHpg4cSJeeeUVq86qVatw6qmnok+fPujXrx+mTJmC9evX6/Nz587FZz/7Weyyyy7o2bMnxowZg1//+tebz+mAgICAgE5FazQAAPDqM8MgY2Dn3T/A3u99CfPP+xjiOMY+n5sCAGja8HYjzeySCERPg+B8te4EsodNWewfmmsg44dwPW/x/jrtydnD7eCyam2qTZ7NgvzNOk83GjhAQwj4P75ylm/+6cvvQ6VR2YBAQVtgR/OYqBiqg+lknWATMa6X2hNh22GiU1T0jT1OLBvUXDKdOMp08pjMfamuCAIFnYlIiMjJ0WPleNI60ggmxTNIcs0Y4eAbd0nEEvFLmEgepQ9EJ/XN9LN0rqsqSObsptdoPiWoiB56QazJfw6ktMaXM7joQCUqRYGc990seTcKOef0horioZE81nGGXq6T8hU2R+LaknXTm5vTNpjwGdby6tLeNHfpuwbcdlJPOKwUG7+Qlm/2Ju2m9JzlXzpuVD6dJDmP0aWNgdZJE0PrG5IOFE3q+AYR7bRUb83BGbAt4q677sL06dNx6aWXYvHixRg9ejQmTZqElStXeus//vjj+I//+A9MmTIFTz/9NE444QSccMIJeP7553Wda665BjfddBNmzZqFBQsWoGfPnpg0aRLa2tp0nVNPPRUvvPACZs+ejfvvvx8PP/wwzjrrLEvPgQceiN///vd49tlnccYZZ+C0007D/fffv+U6IyAgICBgi6HSc1cAwPJeH8Ojy6Zhw3u9UGiOcejYx/He1XvglV9dadUL6DyE5dU3AZuyvLpMJwkdQbmuBctd5C2vrpB1uqSnwD65OfKkRKWGPi5TAohjoOSpn9WGnqPLq9cimmidchUow15W29Tjy5jb62Aly6v72hnygtsAAG3VAipohvVKlv6MWH27Dl1e3dUJjx+Jza3lplQnb6eSPnM7jJyN1ebk9SnSxugzk3oqMwawsdyCqmzy9jtdrj2ZNBv5MYDWajMoJNmREIil3S9AQhBtLDcjlgUtz9It/dcjaStQrjZZdXhfxZ5BL2OBYqkZQOQf1DmDVkIgLvNX5rhSn05AFFX2Xk+brBsg/YzKhBbVZB0B16l4oDYgkhnPPEogMf0iBgplfzOXVSTH1XR5dc8AEr5jIktUYhT4Q4j3j3TloFRGSzE1hulNdEivLAFAtFUR0YctfdhLmEga1Z+qvFhEoVg1NxOoDml/Svuc3NgKezl2Wkeac7S9lKjEJcwth+XVtzeMHz8e48aNw49+9CMAQBzHGDp0KL7xjW/gggsucOqffPLJ2LBhg0W4HHbYYRgzZgxmzZoFKSWGDBmC8847D+effz4AYM2aNRg0aBBuv/12nHLKKXjppZew//77Y+HChTjkkEMAAA8++CA++clP4q233sKQIUO8tk6ePBmDBg3CbbfdVpdvYXn1gICAgK0HrevXAXcMw7pVvdDvnKUobSzi1cuPwwFjX4aIJGRVoFIqQJ78VsjRg7C8epeAnlP5fhHfkkj1WMERyJ+Heppb5vpSaagtT6bnd2b9Az4vz7Ijy0aegDhPJw2SsCJfrH80YMGN5rGn526uHhqzI4nGAtEJS19EdAqtgW/+PjF6bQ+TfRWpwPMCCfI4oMEZrq+mQJKoHrUakcoJlMQrqYgeYW32im1JcIEmo9JxGadl+jUuJ6In9VBAe6AibCJBI5hIEIbO5wPNx7g+uiSs6g9lS4GMFB2BRSN6fAM0bzCTHD2ZF9kT0eO98YBsO3xMm6WHKacRPSqqB9JdtIzrFq40APkRPbQ/fA8E3zmuW8KO6MlQ59jqe2AJUok6LASkSO5j3zWTeRdRESxkLOpoHr1iHLHPZ686SMeNXkKdg5A5tkyiL4qInwHbC0qlEhYtWoSJEyfqsiiKMHHiRMyfP9/bZv78+VZ9AJg0aZKu/89//hPLly+36vTt2xfjx4/XdebPn49+/fppkgcAJk6ciCiKsGDBgkx716xZg/79+7ff0YCAgICAhqNHr95YuOQo9B+6Cqtv3BeLfnQJdj7jNsxfMA7VcgRRkGjuUcH71x+IZU/nv0IcsHkRiJ4Gwfpq3R6mZXPpp/M31OaafBNimiYkzqiTa0OG/NhTlmUHt1F9qil4HqjtxgedyYb8s+3y9ZX0eGO3TyRIprVKaiJDr4+cArL7j7YU7AoJlbfG+Rdb7ZROTuJBTZ7NnJcYZgaVk6NHxslqX9ZmT8o9YvS8VqYnJDlJY6psPxJ9KmrOSoGSdodMu8TtS+UYvKBckxopqn917iOH3YBbxoUKYfEKmYPXm6PHIz9LJ2XwcvWRC2s5rco8OjJ0qmsKcj1t/z1+clnCUyaNXHpzWm/z+VzL6h/ngSK9m5AxRBxDIIaQ0vgUwSYUaf/pT3WOGBJT+TEsckbVsfL2pBdd3YhpcnXvgyKiugQhh5TeDJIoYJvG+++/j2q1ikGDBlnlgwYNwvLly71tli9fnltffdaqM3DgQOt8U1MT+vfvn6n3t7/9LRYuXIgzzjgj059isYi1a9daW0BAQEDA1oOjr78Pjy86Dr37r8eRw2/D4OePwOETnkS1rYD3/jkAUgJD9lmJnRcdj4cvOr3R5nYZBKKnQZDOTucqr2N+ZiHvh3seZFCLNMqSz/fzdPp02NMne8lyn29cjsqM48b0JFJoTA1N0GzWxXKto3E9rtSIpB+xa9G0JHTdLarXnb/bMtwrlEQhmOgXWp/HUSVIKQzyj3eqSHPnmGNI04uR6lWR5s6xNjUxpvRUQubEitSBNCttOUmBUzsl94SsJCZMwIReTSqSEFGa0DndzLVUE+2cCKr0gEZpqZGTuQJW3o0haJJorozAN8TyInr4RuUKsu8liAj5YEKqoN/FytLtsUUNC7Vv+VbvA0fC+5DRQ08w04neTB4jry8BOINH5x2LIEWUfqaRZSnZZFLjEAaO9p2SrfucDk6QCBsyjqiRVuZzpZSQNZnklTT73tW+AgI6H3//+99xxhln4NZbb8XIkSMz682cORN9+/bV29ChQzvRyoCAgICAenD09fcB//kmHnnhFMx/8ig88sIpwKnLMPiiN/Hwi19EcW03NO9QxhEjf4fXv7sv3nvt5UabvN0jED0NgqA7tSaCm1Gnmjd4cyfnmJBHDNUiibJQq357dHL9gu1ndSuVo0gNQ2xQubYU9ZdSPVQaj44xEiltEpO1tWgNuuaWDVHHVXLjgmJik+ud3XcugWTRU5owQTrRlmQD7KiepC8Tska94uWL5lFaUrEpeSRUOY3iEXYfJ3NlciwlYklX3JK2Hvbql5Tpku2Urkyz+/Ie5sdKa6xW9kLsrrxVZ6ibev0s99b3kTZZ8n2bQzCZT0nL9OZcbM2oCO5HLZ2EH7Lcz3OYyyE3Rl5EovUs891E9dgtQVkbOljtu15xN/wBqv7XymWqVdKswaT0AKhSnaSdMlq/j6aIn4iQQ/xpSByV7DO2fQrYvrDTTjuhUChgxYoVVvmKFSswePBgb5vBgwfn1lefterwZM+VSgWrVq1y9M6bNw+f/vSn8YMf/ACnnXZarj8zZszAmjVr9LZs2bLc+gEBAQEBjUGPXr3x0St+hiNveBAfveJnOifPsVfMwodHzMW/XtgNALD7/m+h598Ow6NXnNNIc7d7BKKnQZD8oMZEcHPp1N/3pWfLMYFTC1n8lPOD9SagPTrt+aKZYdbqVtqexIHoSbeR65NgJPOIHmHt22SJigSJIEgQgYqBMZEiXFPymeWRrZtG7bixO7wul8G9JP/IPNRE8xBiRpEo0mOBlZuHzo0ZqUbII8CO6JGS2Z7aYV6dI/77IofYq18mqsfELtHkLr4rrroquVaJHrXSlxsVUmNLhSYqrZgpFz4ChUfVZOkkdmvErDiP6GDsCstlXeuGNC5IcqrWQ4ITW8RXX36xrM0rN8tuK/cROUFILx7Hp3X4iJks+dwgdb5AdNHrqQ74hZJxkhnc6lhqNz2GkavySemVtwK2J7S0tGDs2LGYM2eOLovjGHPmzMGECRO8bSZMmGDVB4DZs2fr+nvuuScGDx5s1Vm7di0WLFig60yYMAGrV6/GokWLdJ2HHnoIcRxj/Pjxumzu3LmYPHkyrr76amtFrix069YNffr0sbaAgICAgG0Lu446EHtfsRTzFn8W5Q0t6Na7iMP2+H9YcsFIrFnxTqPN2y7R1GgDujw68cdUPWeSnrlGBvgPwXw/T1c9dThVYZFRGTpr25JI8sVH+Ags+mN7rGsJVp9SPllys3rGftGKzgdpRA+siSOdzQP+HqU22vJ91ugf81MtUtumzic6BasvSB0h0rl/OuGkizJJYrNQx1JlCUpIA64zaUd1JrN3ayyQsAyB9BopLiaNZBDpsRBSr5alcgIl1W1Om07O6fUUgHkrzHM51bxZxtRnsww86xD/vkeokKpvRaZuawhQffXeHL7B79vnY02SYhJR0xGdKpUM5S5qPlBUg5hZ52vXjmeb10arXxWZYp8TtJ56oFLDkoFoKjkPNeY4vd5V+hpWxqdqYC3/Hptypz6gbxDf64G1lmIM2CYxffp0nH766TjkkENw6KGH4oYbbsCGDRt0LpzTTjsNu+66K2bOnAkAOOecc3DMMcfguuuuw+TJk3HnnXfiqaeewk9/+lMAgBAC06ZNw+WXX47hw4djzz33xMUXX4whQ4bghBNOAADst99++PjHP44zzzwTs2bNQrlcxtlnn41TTjlFr7j197//HZ/61Kdwzjnn4KSTTtK5e1paWkJC5oCAgIAugGOvvQOvPvYous0+DUM+sgLDD/wXWn93IB4vT8Xh0y5rtHnbFQLR02j4Jm9bCGbiTNTVqdOaw9Shp5bojDmapUt4yvLsMp+29DxdVI5ZectM4A2ZIslRlizhlPnOGR5BoACQ5ed577re5M2NOQElyF+JxDcJE9Gj+tf+tHXyOvaKzpxK85NjKrYnFnYNRcgI0ruCz9QlwJdHd8nKNJ5KUp4libJRY14QCk9pMt1t7LEJIAY1V9auCV0psggwENl1ILb7NhN55A+9UFltnH61DwV1niuhnS+SSyJoWZ06HZK5nptTtY1sXsWpn3FzOsV5DyhVmUb0cB8cBWlkGWeMvPLpDaSqkgP1Gpa+6RShQ2X6ZJACPRZ8F0YYfYq1DRE92yVOPvlkvPfee7jkkkuwfPlyjBkzBg8++KBOpvzmm28iigwBfvjhh+OOO+7Ad7/7XVx44YUYPnw47r33XhxwwAG6zre//W1s2LABZ511FlavXo0jjzwSDz74ILp3767r/PrXv8bZZ5+N4447DlEU4aSTTsJNN92kz//iF7/Axo0bMXPmTE0yAcAxxxyDuXPnbsEeCQgICAjYWrDPEUcCR7yOeed8GoeNfhg9dmzFofG1+Mf5f8RHLp2LHr1D5ObmgJCy5vQiIANr165F3759sXLp7ujTu31vwcV8ltUOlFFtXwOtJjYta+jkp4teCqW2jFjKXGuduUqKagyUMvRltdFtpUCskw9nt+XllSrQhma4hAW0LE6mqDqluJDqdM9ltQEEWqsFlNGidcZpuV3fJpBU+2K1CckSz64vvD0t21huRtnxU4D2F/2k+lurzZqg4fZYBI8Ulv/ryk2oSn/fSl1mCAW68lpbtdnxTc1rtW3S1h9LYEOpGbFsYu2I7c77R6luCZQqTc51s+xWc23iSCyBYlszIAuu0LwbJyVAqpUCMqNpfGUSkDEg2pLF3jPbZemOgahqIqjsesQOfoGrQKGIhHmpV2eqQ8RAVIWbO6fWcSVGc6srz6pPeA4qQ1RiFCqw4SF7nD4oVdDSWnXaWPUyEmhHpSqiCg3v8utU7bXMjW0oFCtMdhbBI0k0joTc2JY8OGlbSg5xm9O2lWob5pZ/jzVr1oRXYgK2CajvXmHMBgQEBGz7eO7+ezHw2W9ipz0+AACsf68XXu55IcZ95ZwGW7Zl0Jn/Dws5ehoE/cW+U2k24eiUGZvTUprktHkb0+bAp4frpPv16PTbkDNJ99ihzim6I2uLkC6r7JzzW0Nzx6iNZsYpkJqKhuAZQESqE9oGTt1Qv7gN9ipb9j5v6+8Vao+dyyn1x5Ojx/YZKECkCxjZCZXp1RCIk1w5QiJSG6ROmCxpnp442ac6hT6fei5MImeaiyfZJCIRIxIxhMdnp3etKulVIvmCtP56V93yDtwaJI/vRhGANyOxvWxb9ubjKSRgJ+qVdsUoPeqATrWIV6afvgdQWia5LL5F6UaPhf855OhVsB4iEk5Can7nC3InkmrO/clzHUECiM2+Oh95ki5RBzRpk96Ilm2pbDoeQOzXcohcuhJYQEBAQEBAQEAnY9SnTsCAb72G+fMPQ7VUQK+d12NMy0V4avrhqJSKjTZvm0YgehoEZ6LUDviJjTq1euYQ9cgTdS7BW8smny7fcb3IruvO4gTb9/vtn6FK9mlrEOD68vg7TqHkzcSFx0JNsHj98pEo9uJMnIygyZqpXpcKEk4PqQLpmmm3jmxf7SXW+WTaUD8SNJky2fR82IxpZYORZxZAt61WVFmyRLZOEG31AvPeEpFaxwdtlJBLHYJESoB5dHo7lBz7Xr2ph0BWRBFXpf0k10WSiin/0RGdgo4Vqo/rhl1GcxODV5fk07PvBG7Vd3OmrzgyuwTpGlXkWaJcclaKjG/9T0bJZp23m9kdINIkymqlrdRg6x06cr0oKQRps9nWwO3gmA0ICAgICAgI2Axoam7GkT+cg+eab8Tqt/siapI46JB/YO0P98Szv/tlo83bZrFNEj0zZ87EuHHj0Lt3bwwcOBAnnHACli5datVpa2vD1KlTMWDAAPTq1QsnnXSSsyzom2++icmTJ2OHHXbAwIED8a1vfQuVCo/x3zKwvlrXMylj1fOib/K0qrmB84NzDXnm137p3STbzFLExldfvIjPl/b45Pvxv56pC9Vp57ONyWZqCXasaAiFRKf/nE3SUBtUPSrb1i+9Mq0Zm8c3e0UgyWvya6f1mMgh9Y9HJqj0Ic5YIfNJn6/JayIxIGNIGVtjBlJCSNtP1Z+K9lF1Y9/S7M5AUn5lrT2uorJie/xSCk6Q9NmOCDKRlqQv4uRVxQ6B52Hx3RT24DGfPJmuf+h5dJImTK2jX9jHmXlyfDpZXcHLuI/8nPToo6eFp4mwTfcSS3l9I9PXa+nDK/20h5pK+G3qan3KUakSLNtjTQp672XdPGSw68cC7RD2tLOisBishz91pINjNiAgICAgICBgM2LsqWeg99mvYdGC0YgrEfrusgb7bZiK+eceh0q5rOu1rl+HuRdNwaPTPo65F01B6/p1DbR668U2SfTMmzcPU6dOxRNPPIHZs2ejXC7j+OOPx4YNG3Sdc889F3/84x9x9913Y968eXjnnXdw4okn6vPVahWTJ09GqVTC448/jl/84he4/fbbcckll3SKD9ZXa+cX3Hx0/DfYdPLKAxzqkEd/vVYbDbHwlfnmivVu9frpa+fTSetn2ZKUqwXQ1WLn5qUtWpvSEUanfR7OEbUl0ZOQMCbyJOsVMIBH/yitvj6RzALWYyK5jtITksL91OQg8dM3XHlQg7TsS6IQhIjIJxkvzDfaWyoyQo2viCyXrl4FowYlHIyAFOni9cIXNZX4qaJ5IHi/Q8/brUm73mydOtokEnaUT71IJ9tS3Zx5N4XucFVXdQQ7J2DfCF6dpAnxQ/tsMTu2HY6feTqZ7VnPIP/ASot8dQErckc3YWWauLIMsHU63StURA+9wMQetalxzMVr9ljYxgt1T9mRPVCRPUq5itKhz1SVLZ4ba/mTNQAFrGsJ2EusBwQEBAQEBARsBWjp0QOH3vg4Fq6/DOtX9kbUHOPQcU/gg2v3wtLZf8bD0z8D3DEMR428ExMOfQRHjbwTuGNYUh5gYbtIxvzee+9h4MCBmDdvHo4++misWbMGO++8M+644w58/vOfBwAsWbIE++23H+bPn4/DDjsMDzzwAD71qU/hnXfe0atQzJo1C9/5znfw3nvvoaWlpabeTUnGrH7HJQV1o+PJmKVOAq0mP/WqLVnt7FY+GfRH+iqy55xZ+isxUM44lzVnU8exFIhR8OqU7JPuV6tAUS9EJzz1fMmdk4lVOS6gmvKm0jpn73P9pWoBJTRnygfcOB6kBFExNomRqa9+O42stnJTmow5y1b72NghsLHabHzR0R3cN8HapQmgdZJiAX5t3ITVKekj0wTQVB8hYxxbiIwN5RbEkl8TgjhidhhZpWpTmheI2kTqSSYzDaQolprhJCl2FDPEiR9xJUkqkxstw85JCYi2CEm2J4+eGkRPVBH+m1MTJcLtPAlEbUiItFo6FV+RHooqULDeI/TXd44rMZr5a9qUqPL4oLiSqBIjqrA2Phv5uXIZLW1sAFjtpB54Fv8kAVGsIqqmDXQy5Wz9+o5tK6HQlj75qp6LrRrQB3i6Lze2AnGajFlFeUmaEFq6fkuJSiUkYw7YthCSMQcEBAR0DWxcvRovX3YcDjh4KURBIi5HEE0xVi3rj5dKn8Pwk87EK7+/Ffu13IP+Q1fh8UXH4ejr72u02bkIyZjbiTVr1gAA+vfvDwBYtGgRyuUyJk6cqOuMGDECw4YNw/z58wEA8+fPx6hRozTJAwCTJk3C2rVr8cILL2xxm535V9Yv9x60oyrTyWdC5sfiWlA/JCerDpOGGdE8Rqfx1Rek4MaP5Acz8H7L6ge6HHpW0IA/qMBYxdMw09p2mSFH3Kgbo9f+jCB0FhA7goj3ji2XrLIlvS99ePxWUUppBIEgkSxOolkaNkB1MjKHTrLZBaB6dUkayZNsdJxkj4JkHirMldQ2k6Zp6IbJ2ZMeiNQPx8/IbDqawTOK1OSfD2uy6Ry3QkBEyZY5cLNAOzTrRvFdEqIbXG/dOkkZ9dPqFgkn2bN1nunMGIhUpfPWkXDr8xtGAI4eQTZLJStTnEgm6cV16X7gF5+baPJMaV2EgzHKvYNG54Uyzxx2kQuRm+CaCnb80cwr8c0R4PZFPWMmICAgICAgIKCTsUO/fhjzg0V4/J1paF21A6LmGEIApVITdp90CoaMHIVjLrkJ/c5ZilXL+mPcvo+G17gItnmiJ45jTJs2DUcccQQOOOAAAMDy5cvR0tKCfv36WXUHDRqE5cuX6zqU5FHn1TkfisUi1q5da20dhf7O7ttqoB1VM3UK9UOzRF0pGviP8MkcMGnM8/LQIDE+h+CuZmVRyeqS+uexwjrOztbCy8weX/0KnnPUOmt1qgx/zRYjTuOrkjgrnqvD1uWbnUs2+aQvbFHdiVcm904sY5P2Q9I6UtsCyyL3Sqi5r5SCyHD7krblOZxoXh73aiumxRzTlb9iNW7VsNMXMj2QceIz1amuOB2nUuUlckH5BnsBKtVe6U/zB9GOsC+VH9bAZvSYGpx8Y10lVUfwm5M74SOBOLcloVcz05s6Zg8A2p3ODcpJIdhUg6XW9yDzDAkJOHpYqiSzefRbNtW4OfUQ0gfq4kOTVtL657quL6Ig+2wVM52rhypWzsYAvONJmE/G39hMFYgjanwweZDp/wgQEBAQEBAQELDV4ujvXI6Fb04CkHyd2WWflRj83Ccxb8YXAAAtPXbAS8UT0NK7iAUzpzXQ0q0L2zzRM3XqVDz//PO48847t7iumTNnom/fvnobOnRoh2Wp7+odQXt+vPfpVFON9sjwzhc90Ty+ORuXkyc3b8uS4dNhp9bNDxywNztXjpsa2N0kk6oWWze22n9VHqBIZwFR0TzJr/pSb2Yhd/XTfqxXjBIJuQABs+C7uramXazbRPpTiijNY2NWnorJeSNP2RChmm4AoJY6j9O1q1W/2am31b6JSnJzOGVfBfuVLLvcjjoRJIhHRdWoCCmyopbK2ZOZl4fRU8LMjTXnRK+zMLmL9D9fRE8Ww+HcKFIXM5ftrnSDrUAuQv5NnUOo6Jw2NExGSCCix0a2NxgrR63FrXBOz/dQyPqkupTtcMyjfJxNZEu4csm+Hp5RcmWdUC4JT+NUNOXbpDT1NTGjNtsfYdmTRvEUiDGWygxWRg9h4nBEFPhCN30kW0BAQEBAQEDAVojm0vsAgHn/OAnFtd3RvEMZR466B69cOAJrVryDfT43BQDQtOHtRpq5VWGbJnrOPvts3H///fj73/+O3XbbTZcPHjwYpVIJq1evtuqvWLECgwcP1nX4KlzqWNXhmDFjBtasWaO3ZcuWddj2nHlRTfB5Unt1dkR3xo/fTjRPls6ssky5no228e3TMt+cl7fzb/y3ekqf2JZIXU5LbIulVRNEQxJxUoUgupKwDUPT2DoTv0jcjjD0Cu9X218jX4JF1qR2IEOnak+TO6tlwNl8m/Wt8tLELVnjRGZFS9m9lcyLTT2rP/TENimSsUyjIGLtl/HVRPiYVbn8dxGlgOhk3JAVaQQPHy2xhOOOxXA4qohSATWWLN1cFo/qiVPhWTdLHih/QTiJpF+FZzOyHbKmhk5rjGYRUrV8zfBRm+u6ZfMb3EbPtbGGKOhzzbMpcoj5qe5N15hUmdabjkMemiaR5tjxXUzPE1VVUw5bjJzSKV1ReTIDAgICAgICArYiVHruCgBo6tUPa495FP96Ppn773XAMkS/HY3Xfj7DqhewjRI9UkqcffbZuOeee/DQQw9hzz33tM6PHTsWzc3NmDNnji5bunQp3nzzTUyYMAEAMGHCBDz33HNYuXKlrjN79mz06dMH+++/v1dvt27d0KdPH2vrsA9qp4PfsTeJrBH2cb366NzMmtBkRPMoHfXKrXerBz66wjch868xpfbc6B4uxSQ+ttvYklQrXidKfri3rDFhG4ZeMXl5KD2S8C2UZrJh64z0xnPW6HKm08ikfkpACs1BkPmup+ci/Q9p9A9PesOjeqQlSSkhUVWKdCCidIROJNKVr5RfdjSPgMkP5MulZKbWjAKy5/VpKyWTXO+siB4+bLw3ilnMXuuWcGXwqJ5I+Z+jL1Mn8U3a5EhuZI+uQ2Tl6ZTWrkmc7Buweb769CnXyfVRZJX+5AyQR5cgck13CrjEiSoz9tOoL8stkf6xSDKijCeASsetuQi8PzOe2NQvPXCkbUjmuPDoCQgICAgICAjYyjB+xg0orW9JEi/vsTv2vnIpHnnq46gWC9hhwEYcPvZhVNoKGD/jhkabutWgqXaVrQ9Tp07FHXfcgf/93/9F7969dU6dvn37okePHujbty+mTJmC6dOno3///ujTpw++8Y1vYMKECTjssMMAAMcffzz2339/fPGLX8Q111yD5cuX47vf/S6mTp2Kbt26bXEf9ETZOagP7ayu1WhVAm7C0jr1+eepfglZ0whlR5beekiovPMip07+D/vSKudEEV8ditMEfn2EJLF0yjSiJ4Y7CLL0CKtQklkstYXqNJYlOpIEx5LU9esEjM9Uv0l0LfQ8UcDO9wMgjeRJdSJGshpV1lUT5K/UE9Dkg1goDP8hY/sqWxN89U9HSwjYklx/jTRhjx9SzQzz2O03nq/Gt5/pvkh71r6+xrGMtjH80Rq1blgqX/UpddZnL31eKGIl7wZW4snQ1ryJj+RhC0Q5n9wWQq7Q55l1VR0SxGOnNGMKINfY994XkyH4Dn2o67w+rBH1xbl2VVundY7cbNZKXpJVoTcJzM0JT5ss8iggICAgICAgYCtCj1698fCSo3D42DlYdeO+eKl4AoZ/6WI89vMqDj/wITR1q6KpexXvXDcahc/fiyEjD2y0yQ3HNrm8ushYJurnP/85vvSlLwEA2tracN555+E3v/kNisUiJk2ahFtuucV6LeuNN97A1772NcydOxc9e/bE6aefjquuugpNTfXxX5uyvHrs+3Jd55Xo6PLqyWs0TFXdOjlVYZBnTSylc74elVnLq6u2/vS5yflqury6T1fevLRSBYpoyagv2DzUjmAqx02I01mlOzcmOWfYuXK1gCJayHzWHtuxI88cl6oFxGRZHtu+iOwDdPq7odKEimy2fKM6YmYr1VmsNCX1Pcu6WyuCMV82VFpQlYUMvoMmdLblyjhZRj4p9KWlZn2bnowlsLHckowDSgjQz9gcW3ZLoFRthhXtIO3+UOwCt6dYbAIQZZM8GWSDlEC10uS2yYMiKFrJ8uq0fY4+AECcLq+O+kkb1a7QJpC5jHwGOSRk0rapAhe1btRy9vLqIrXJaZt+RpUYhSo756knuK3lCprbOKNEEMduW6WzWEVEl+CyHljS1a32W8soFEvpOdJefciY2EGIKCkhN7aZLOWSKJAg9dy2lUoRc8u/C0tVB2wzCMurBwQEBHRdPDz9Mxg34hG09CrpstK6Zqxa2Q+D9noPQgDFtd2weN3/xZHfurKBlvrRmf8P2yaJnq0Fm0L0JNEGHdO7SURPDZ1Zp0s5RE8W6QIAUkr45nW12lZjoJRxLu836FpEj0+nqlOuAmU0WQQFJTHsY2HN1Uqa6HHl8tWw6Lm2agHllFxySRKX5KGETWuV6zT7sdbLbQHays0oo9lrJ9cJZntrtUmTPKaNkWOWQ7d1biy3oCqbMubzkd1G2n60Vps9bZDOYYXWafEpEthYbkYsC+DRPFyHJRNALAXKjp923dgzcGUsUCw1wyF6tFFuG+pHXC74qxBixtUJiGIG0cMN95yLypzoYSS672aRgGgDIh/Rw3Q6S5/HQMHH4BLZjt0SQDUlejxEjfAdEzmiEqPAH0Ie8o3LQamMlmJagelNdEirjH5GbVVE9GHL66pzKZmjbS4WUShWCTlDCRppf0r7nNzYaoggSdpoPbHbXkpU4hLmln8fJs0B2wwC0RMQEBDQtdG6fh0WzJyGpg1vo9JzV4yfcQN69OqNv3/3LEzY/bdo7lmGrAo8s3gUxlzzMJqam2sL7SR05v/DtslXt7YH+OZU5sSWg3r1xVLTDp1OigeYRE/tIYFU3bwMEfQcl533tpt6Wcgn3VeqjqP0b55NSd+50TnCkmwmz6qUvvZFyZkCBCqktekXSnbwF8S47Ty6RrC/wrIFQkB4SAz75S8WWeN0hO0jJYhUimhHp4dAs95OUjcFHaPSkAV+Ak75o97+oSuepTrp62XqvArKiX3+uSSs9jPVE1n9k/ZFBPuic0OzXiGir9ZowzzgvIq6+Xw3ZZ4dYGV5DwP+tp0AUM2OquQ6edNMcjvvgZDqzDxPZRAuhDb3yrQMS/fpA0uQTrRvzlSHej1LmrxD3BivjZLoF/a1EhGSDlbN0x2R6hPS2Kg7VLrXUhM6pIDK1A+ljMisgICAgICAgICtFD169cZHr/iZU/5/Lv8pXv77yej7xJew0+6rcNC4Z/HBdXth7YTbsc8xxzXA0sYifMNrEKzJR+ZsegshnTzzVZPteBGnSeYWI3uBnCxwXVweL/PV89moPm1aIdunmG2etZQcnb6+kh5v7PamjGqsejRxnVkLkdMZtb0CmHuFYrUKVbryVC2dlDKim9STRAmpN0AKCZFuSqekPSslYrLal9kMx6H1kLEJQC/6pHTC0gOo1cRipldFzSkdcTrJljHUwlwawtoTmTcCtSXWvZbqk2pVL6sj6ZDIGLgCVnbjvJvRHqz+G4/ry7pZKC9JiQe+0ZW3YlXm0ZGhU11TyMTNuvL/clnCUybhJnZOu5KvZO4F7TvA7SdrNSyzL2ScbHqlOij2j+RqZv1HO9kiyaT9upUeQ8yodIU8JzmSJIrVOb2JdIl1ag/RpVaok1l0fEBAQEBAQEDAtoWP/J/jMOD817FowWjEVYEBw1Zht1dOwryLz2y0aZ2OQPQ0CNLZ6Vy9dczPLGSRQek0w7tATj3zOSqH7/tk+QkPV4aagptj1zcuJ/FBeP5RCZSwMWtg0XgWutE1uOySRFtEykwNN6rIzOm5HYresXXa9BBdWSvSPtpt/JFMkuvUu2lUUPqp9qW0e1P3qhCIhFoFi27QpI1ekl0kmxRUp3A2TeVJob1Ua32ZVbfsiT8EICKZbIycSnpMTbTdhd+l3SnWtVN97F0ByzfYKERKeunJO1dI9PoGbb03CiNEnIcBQAgHsmnWTdryfbo9tqjLB2HEOX5x+Gz36LOWhVebPUz98n196fShb/AISERmUyu4ER4m4Wgy2CjBbACVDyCKiC5iC4x+014pjQkxxPylg1Z6fBJMZkBAQEBAQEDANo6m5mYceuPjeOLdc9C2pjuadyjjiBF34PnzR6N13dpGm9dpCERPg8C/v+dOBDejTpHueL/v56jPI4ZqkURZyKpfDxnFdXL9zlzaU8Z1kDgQ8k/JtXtH/aVUD5VIyRFKYygNKvIkdmqYmtx+RSy4dtD2cRrZwj0zNsVk395zoWggM9FMa1kRPbHeh464MeESavUrmc6CVSSPCZRQWlJNEoY8SskaLdfq19RmYeuJZRq95OiyiSKd34eTU0KkuvzcCR07sY5USiOJVFZpu+tr3ij0/ssErVBLvm/z3RTpp6RleiMEh+432NwF7Yw8nYTnqPs5weUwX7MiEq1nWRbJ49Ph+KEHKOiAte56xd04D1Dhkad8UPcQMUKVValOalt6XidoVvdjZJNDui7pcOqz+oxtnwICAgICAgICtjcc9e0rsObouXhr6WAIAex38Ktou3VfPPu7XzfatE5BIHoaBMkPakwEN5dO/X1ferYc9VmT3jyOqqNu+HTU0mnPYc2spla30vb+iB4qk0uwiQcqTVj7sCQqwibS8nkN+7Uz2we+J5z2kSWFkkLmvN0Gjk7bS2KBDs0wkTx8I+83WToUcWAieQxRZV0jAUMcQSY+EnLGkitBdCGRnfoYcV3W+4rpRN2K6klJOLJ0tY/PUBAACmmkUqRyENGoE5D9vEFr3X85dw01RrXlUS5ZOn0g70gKKt9HdLC+c16/qlMn5X5qPuuoLOn66o3m8WxeuVl2R/STnCCkl6Z5WBJwLzGTJZ8bpM4XiC4VaSOI0fxCyThd/YsPDjYgtKxUV6R0CvJ6V0BAQEBAQEDA9oUhI0dh98tew/wFRyAuR+gzeC1GrPsaHj7/xEabtsURiJ5GoxN/TKVzhFrzTz5Xq2dCxSdWtaYPvrmi0tUenTF4N1JiI3/uSeeRJvWJm5eHSjARPDx7TnaP8HgdlVPGPmsm+9n9yAkUu1UMO8uQOkfrxaS+0ZNHMdAJb1qXRPBItvmiemIAvmgeZR2haiwSSfcrJxqUnwKwE7WYTEt2TiBhRfRIK6IngpSG5stL7qLmxnavpbog2xdhQ+blVm6XrBvHN5Dr1QfySeVxud7XegjZoV/XI3LawbQIWpRHQlEodij1NS+/mBoONfgmVz63V0fe0MFq7iUhFUEIc29Z3Irwy44BhyWjh1Xy/pfKnWPZQRoIARPRwwWRNtQxzuhLoOZSjAEBAQEBAQEB2ziOvPGvWFy8Ahve74lCtyqOOPgveO2ifbH6rWWNNm2LIRA9jUbds5FNh54jpFu980NuZnvIoTxbfHNFJb89OnkQBdecMee0ypUck2uI0jmmtmCbTaQYqygRJJkHKqpHZc3xx9/wvnB1Jv9sS01kji1VEkk8W1AW4eYjiZL/1ACyyRNfVI+yJUoFq8gaIahF5urQKBtN4EhXl9EHQwiR17AEVESP0hsnxFO6CSuiJ9aklLT8dAeMzmErSD/AE9FjX/L8jfIIeci6MflnFsvBy4kcqeVLd6O6TVf75dYotxacYjbkQgIpF+ema1LmCfJJzvlkWT5RWA8VPXgAX0SPGrk0kbaW7RtAMAyVZQ85Vq9hWRE96pg5oEmpVJeQ9nnaFgLueEgLQkRPQEBAQEBAQBfA+LO+ifjzz+C1Z3cHAOwx8i0U7j0YT/z42gZbtmUQiJ6tAZ30gyqfYNVD1pDqdW+5ekmZj9DgOmsFLPAVv2xpnGLx20DLE3kqLa9NQQACMSKHXFFUBbVMWJ/2OZoXx+QF8vchoT8cnSJlCVwSCERP7NTh8T48IipXpyDEDHntSbCcPXaUjdQBEiqyRr32AhjChObMiUnUjYkQkmaiHCWfasUvbg+gInqgCSAVuZNs6TUmZZQEyxq0dj4r28cORfRILdoei3kDlsJiOMjmu6h8gBF5ghuRl6iXchVZOrP8zCKJ8qAuCVttzInqoWXknLcffeXcbrWSlS+iRxGFMtlcYocJpxfWtwqXImPi2NFn2EXVjx7mjD7yrI3KASGPYHSHiJ6AgICAgICALoJ+Q4bgI1e9iEcWT0alrQk79N+IsT0vw/xzj0Ucb18rkQaip0EQQDYzsiW1Mp05c0/esq6Nt1E6KLIJDXe/Xr2uDfaRT5/PZzuax35JC5CIECPSVBDd/NbQuBVK0KijAvQLQzl9SOOC7FW+fJbaeox0E3ckOqwzmc/a/ilyxkT0ROS1K2H8FIC9BLuRn0hVUTcSkdogCVGTTJJlLJJlvtUrRCTCJ6mbaDYRPXbkjiARPZGgdB4hbTIGrZkze/pbL5PuHQo1N0oLap0eG6z91D9HJ88Fk3WjSFe0RRDoHD3kXETqZunM8pGSRD4/fQ8g1ee+HD1qQ3JeeiJ++Pj26vX5I2M1aJMCFdFDX/0TIo3osdsKJ2qHOKLHPyFEVT/rxMpgepkwndCZXVDnfTZqP3WSyAqrbgUEBAQEBAR0MXz02t9iyY63Ys07fRE1xTh03AK8c/neePvZpxtt2mZDU6MN6KrQP8xaBfVBtK+6rYR/x/fXympdt968ej63BduvZUdtXWoW7JeZNbWJyRl7/ik8ZVS63Tu2X8J7TlEclDuWzDIeEwTnrAu7vrFEWSGEsH7Ez9Pp6pVWXwrAitBI5rN230sAiCLEVX+va30O6ZCWC2ZheiD1TeR6muxH6atcvE/SMp4rReaMQVZVpGwStRtRqts3IPNuCM2lSPta+Aatz0Ah7EFE29S6YYXSzcroDh3aAgk/4ZOdp5PwEpp8qffmVzyFfaOY6jKnDPb4tGT7HqTkWEAkETY+nfpYOueBZGx5+1SyQiWD2qJflyOEDpWjOlDl8KED1745zDkqR3jqBwQEBAQEBAR0MYz+3L+jdf0n8PwlR2PkwS9jl31Wojj/ODzyp6/gqBnXAABa16/DgpnT0LThbVR67orxM25Aj169G2x5fQhET4Ogv9d3gLHpGMmTaFVRGR2RmUygsmvyuRn9zNKRV1bPFMSmFPLbZZEXtt2xdWT22CQclMTx22Dn5/HrTQIhzGtUeT5TfTw0w0dK+fwAACml1lkfjE4hhEOeJAcsGoWRbDKOIYTQ805DxpAAChIYocgxrYuQQJSAkGSfQyKGEL6gRSWF+aIJCGG/0eLIFk6ZifSRHYyQUJN3vy9eQkJvnKmxxWZeaErQcT7BlzCYcR6ZbnKdbGBnRvTk+Z1xc1CuT2SUOc2y+pnZLCGTiB56c0pGHKUdpwNrlB0RkqXSSR2vHl9Bep9oxkr3AWOLdD1N4RLyx+Mzt4UTSwEBAQEBAQEBXQw9evXG6OufxtxLvo7xQ3+Dbr2LmLDDLVh07lyslzvj0P0fx1EjS7p+6Y4/4OElR+Ho6+9roNX1Iby61SBYP6jac/aaaEdVR6ueBDGdtWTq6nrJagG+Og8v43MprjZvq9dP3s7odAkYn2xXJ02VnJxJX/KxarupmoVzHs6RTX+INN+PqmXHACgKKKtnANq7dkve89Lql2RSKvTs1KZk8nVagQFUr5NkVuq/EgJCRAnpoj/JeIH7GpT2RCVXTuur17HsJdOhX+tJXtcRiR5EMDNwukWQ6abHLtdNSQJHhHTKhAAQmeTP7UJK1ui+zbsprM5PFaulsvn5LNII7jk795DykVRSxIMw9evW6bXd45vPV3KtfbeAlYsH/rJMXoVeP2anEMkTwBhA7KFN1Tgm9kogJcXo4LA361/6vpmwlCiCh3SAzhTPOlSSncwBKGBdS+WkGkcBAQEBAQEBAV0YH/3eLXhnv//Fe/8cAFGQGDPuBRwxbi42rO6BR1+dghWjn8Cjr07BulW9cPjYOXh4+mcabXJNhIieBoF87U7Qjh9VNzWiR8tg3/vr0Zm0k95zvjI17ag3UIGWt8cuijydPHjAPW9+vqc+JGdsSsS0F7Dz5ij9pr6rT6VDFnrP9kBRJLYc01pNGG1bkVFfEKuktDUaYqy2zmROmOpUc1HNHrrQfSdjkoBZlVIL/JNTlUsn5UJs/6TZV/NiIzlNRC39ET2JG8k5EwNhT9gh/QEQZjZPyilZ05EbVOUTYjqZYnac7sReIz0PGa6TNGMEnmlHK9n1hafc0SlNsVPHdxN69k3/es4RwkefYmUWiWNuA0uW5DJk+izgSZatQ3OxGP9ESClyQTMuU0IeqTuSdYw98H2tbUGQGded3GOCyQsRPQEBAQEBAQEB2PvIo1EZ/xoWnf9RHHzoMxAC6NV/I+Tr6zBk5CgMGXkTSq1XYdWN+2Lcvo+idf26rfo1rhDR0yA4X62zfrn3oB1VvVqtOZz6wbkGVDxL8oMy+ZU5K8KHaOSkD92ijHK+2R4YZPWDmjb5AgGy9Cfn7VKThtgX0WOyzyqKRHqt9kwE9eLqic4o3bhe13Ka8VYtr877xFdCZIrIjsxqh061pLviFnRAgPM+ji1DRfREqW47TMM3CqJ0jmvIIaHHG2mqQjd0RI+AFBGkSMMfnHCQSG8mka5nFCn/SHNvcJBI7gcRCRNZ056bk062s24UHcnhnheiA3o9BItz69L+VeQBke/0BZetBScbVemNXIKnTNjF3De6upav3DIpj4BjN6ca00JddHVtHRPVPWTrMuSn+iQDSFitk39SQGeSpsZEUXrtlR3KuVSwl8zJco7UUUYqOfWO1YCAgICAgICA7RxNzc3Y2GsEhABK61vQvEMZR478LV789ihUymW09NgBLxVPQEvvIhbMnNZoc3MRiJ4GQX3H9m410I6q3sZqImQtrFMDfAnzZA4o0yiGZAM5BqmXZbtE/atRZ8nLcNGqmafHLTd77rLl7jlqnb2Eud9fs8WI01gSmS6wThc89+mkEgVkQnowaoacJf7b3koZ29fNo1Nk6YRI9tO5p1mYye5vs4R76ptUUT1mnAjp71t6Ffk63gIwq02n1aVEkppEsQgyBmSMJKKH+pj2gZRWPeFkMlbXk4wiok+RMkZ/oiNWS2DXC2tgG23WsyFmG+sqqTqC35zcCR8JxAkKdR3pFqtPWDoktY/foJwUIqocM3wPMs9QkICjx1pxi24e/ZZNNW5OPTT0gbrY0KSVpP/MKbakuyR2UDmpBHUPUsXK2RhmSXXaL5o1Ew5BZT0F1I0i1XiH+aQCaX8EBAQEBAQEBASgacPbAIDXd7sdby3ZBUIAPVqKaGpuBgDs87kpVr2tFYHoaRDUd/WOoL1BA1ynmmq0R4Z3vuiJ5vHN2bicPLl5G2+Tp4PSJr422Xoi/Us7pW4AHq1jNkkk0sXWjb+C1DDLnkc6hocudq7yx/h0wtYhTUQP99NuYfIOAcLKkSOEOUd18sge45fUs2spYed1sWygcUoi1RexsZLXp4JcXLuvaWoRFVGRpP/RO9ARU9rPNDdQZl4eMgGHNAQC6VzjY2KAEKZ3BdKonqyBW+tmk0q34RScC0oje2gZPHrrASE3TE4bdZBuET02sp1grDrU6H1O5PgeClmf1D9lOxzzDK9BOBrrwQcmK93XwzNdQc2JCNPXhnZGKp7xbRYLZckyIpwoKqRRPAWQpd099nLoIUzInIg65GnsI9kCAgICAgICAro4Kj13BQC8/9Qc7P69V/HIoknodcYD+vyr9/zMqre1IhA9DUK9czEf+DypvTo7opv/8K3LWDRPls565eZtvE2ezIw5oVXHH+VjomskO+NLcgxNSfA6lGiSpAaNdInTOBoezRMTasXVqWiQZJVlX6JnY53ak2lISBKBEJtoAmnO1aMzIYoMteXrW6UR2sfUczZW/NFSds9B++heg0SMmV3LWEKkUTpKt/Ez1pFMOvKMyLKJKkMBUQLA6k8pEUsrriONroG7+TqJd5hQvcvm5VwWH7RxKizrZrEvCtNpNisyRgLeqB4i2yFrasAao76bk9qY5SvfCBcoyfWxOC+HSPHoIcf0UWZF9KgKgu/zsUOOHfKNdViqQ0ecmVCi1HdPB1uHtAO4w5zUybtQm/J/o4CAgICAgICA7QvjZ9yA0voW7NdyD0qtG/HR6/6AnffeGwBQat2I/brdi9K6bhg/44bGGloDgegBcPPNN2OPPfZA9+7dMX78eDz55JNbXKf+2t3B79ibRNYIu6xefXz6kMwrzC/GPlm+Mt/EqD1bPfKzIl1oHV+AhLD2zD9DqZhZI6UqqBZVn+f/4XKBKI2jodaY6BpDr0TaV0unlJZ03zze1mlHuShdqjxLJ7hOKTQHAWlP4jUZpPUmkUtqyXIrwkC4ET2SSdLEArkGakl0O9jC5MkRkYBaNU1H8yg/nZw9lKZTmx0Npuf1Vt+q/qwjoocPPu+NYkdnqb51ZPBBq1Zh6siNwjgLSo44UT00skfXsTokW6e0+RRhOZkhJ+sGBWujXCfXR5FV+lN62rGbk5JBpjv5WCEVKOkFe1+qPZHWtx72gt0wgm0RUSdMPUUE+dhCWp8SU9xv7zXiBQEBAQEBAQEBXRs9evXGwiVHof/QVVh9476Yd9k38Pazz2DeZd/A6hv3Rf+hq7Bw6ZFbdSJmIBA9uOuuuzB9+nRceumlWLx4MUaPHo1JkyZh5cqVW1SvyDyoD/USNLXU+L77+/Vl/+N5eWrppOV5P9hnbSBt/bbm+5Uv357u02gbnvnG0A92mWTWunuqTowqAOkkYvHn6HFeqhI0GsidyymdJn4o3VO5ckAiX3J0gusUKiWzUajmvjHpOSNZRdcAdsiEL89RSucoTkF3LukDkWy2qWnkTpzkyol5biAS3WOieWzYRBVRzXw0V9D0qoQ0OVVqDVwfhIBDlSojVHtfrh4V+dFefUp++im5s040D9nAOIcaN6ciTrRK381J/aQbj+qh8gmfQfkKi++qRbIpGaTckESUXCEVpbQ4GD1OLZ8io8sidrh/qTyda6lq69R+e5ga3f9pPUhY7KcgRkpaD8ZhyZMsBQQEBAQEBAQEHH39fXh80XHo3X89jhx+GwY/fwSOHH4beu+4AY8vOg5HX39fo02siS6/vPr111+PM888E2eccQYAYNasWfjTn/6E2267DRdccMEW0+t8tRa+ws0gl51TE1xZhz7fvM3XhL42weGu/CzZsR/6F3WPTQJ0SWx/2/acU12RnIsy6vKUvULbU0uDIH/p3E8CKECgkq4wxdsndfj039iRJDM2kz9jnwTS/D+mFZErIggpiC2C2cV9E5ZsARNVY+pJCMYbR0xnVu8L2HNQQJg3hWJiF48+8LAG6iiWiWQh3Am3utZ6rgt+HT00kFS9avrHGQP2gmjufh4Bk0Zn6duS1hPsk9iEQnrC17V5Ny0r51fT0skJixhJZBQv9+ki11Vxc/6HCLKGh6uDHYvUJscE5UqtBw1vKBR5IzwnaP3YJGJ2SCjpPtN9/UUZIwkkF7RKGqQn1FiXsd1U6yFsEw9jkrRBWqAyqUdRQuYFBAQEBAQEBARYOPr6+9C6fh0emTkNTRveRqXnrhg/4wYcvZVH8ih0aaKnVCph0aJFmDFjhi6LoggTJ07E/Pnzt6huOtHsCDrSVCJ/0sNP0XlKLtGTo5NOhT3Tpg7JreV7e8+r41j/FV67PDEXZP6uzrlElr0Ol32uChNdYv81P/8LIgewqSjahpI5SaQJ9YPYxSJapEen8k3p0DoVQSTMVTT2xea8JRdpfpwIbt8b6ZTk0IQNIWf44KXEAeVQpN7ihBySdBSqNjY5ZNskrLHJqidWS04HCXsFKt8gy71RGGnECYis9jqXi0efzw5LZ41Cn860LJbJ21xeeAkkWEEmXrX8InrkWcfSfAhywbSKvAcMHyzsXDJGyEkPcaJ5Ri6XP+gssgbJYKJROJaNVWYXcVIJp9FsoCy7tO5L60ELEJJItZe2joCAgICAgICAAAs9evXGR6/4WaPN6BC6NNHz/vvvo1qtYtCgQVb5oEGDsGTJEqd+sVhEsVjUx2vWrAEArFufF1/iR5z15bqO79zl3HiWbDHmhZbalXlR2deOwWdVLM3v01xDnrw4BspsFlUv6RRLgQoA30w2T2elChRZLU7PZM3lKzFQzViCSNEmiqiJSZ1yHKNERoNvPkrrU/KmWAVoRIuZlgrnk8ouVqqoEubCXDcqx943OqVFntj+URn2+VK1kiYv9kGk143bkPwpVygR5c7NJXlNRteRQLUMxNIefTGzi86zdbkEKpUWx0pr3i3dawkAcTGGNwlWHnGR6owrhcT+eufdMmkn2tQyTdnyvfpjABVhcRBZeixUgagkUJWeN3+5Hk76VIH05szXwY8rMaKSv462PePmjCqxuww816P6gD7EKmVUKi7Bo0ESJgvma1SpIopZA63LQxrpa1KGjMvpvofRgiQ2pgMgJWykLANx1dimzuvGnsEugIqspGYFwidg24Aaq2vXrm2wJQEBAQEBAe2D+n9XZ3zv6tJET3sxc+ZMXHbZZU753mOXNcCagICAgICAzYN169ahb9++jTYjIKAm1q1bBwAYOnRogy0JCAgICAjoGDrje1eXJnp22mknFAoFrFixwipfsWIFBg8e7NSfMWMGpk+fro/jOMaqVaswYMCAdBWjbR9r167F0KFDsWzZMvTp06fR5jQcoT8MQl/YCP1hEPrCxrbUH1JKrFu3DkOGDGm0KQEBdWHIkCFYtmwZevfu7Xz32pbuvc2F4HPX8Bnomn4Hn7uGz0DX8bszv3d1aaKnpaUFY8eOxZw5c3DCCScASMibOXPm4Oyzz3bqd+vWDd26dbPK+vXr1wmWdj769OmzXd9k7UXoD4PQFzZCfxiEvrCxrfRHiOQJ2JYQRRF222233Drbyr23ORF87jroin4Hn7sOuoLfnfW9q0sTPQAwffp0nH766TjkkENw6KGH4oYbbsCGDRv0KlwBAQEBAQEBAQEBAQEBAQEB2wq6PNFz8skn47333sMll1yC5cuXY8yYMXjwwQedBM0BAQEBAQEBAQEBAQEBAQEBWzu6PNEDAGeffbb3Va2uiG7duuHSSy91XlHrqgj9YRD6wkboD4PQFzZCfwQENAZd8d4LPncddEW/g89dB13V7y0JIcOaqgEBAQEBAQEBAQEBAQEBAQHbBaJGGxAQEBAQEBAQEBAQEBAQEBAQsHkQiJ6AgICAgICAgICAgICAgICA7QSB6AkICAgICAgICAgICAgICAjYThCInoCAgICAgICAgICAgICAgIDtBIHo6aKYOXMmxo0bh969e2PgwIE44YQTsHTpUqtOW1sbpk6digEDBqBXr1446aSTsGLFigZZ3Hm46qqrIITAtGnTdFlX6ou3334bX/jCFzBgwAD06NEDo0aNwlNPPaXPSylxySWXYJdddkGPHj0wceJEvPLKKw20eMuhWq3i4osvxp577okePXpg7733xve//33QHPbbc388/PDD+PSnP40hQ4ZACIF7773XOl+P76tWrcKpp56KPn36oF+/fpgyZQrWr1/fiV5sHuT1Rblcxne+8x2MGjUKPXv2xJAhQ3DaaafhnXfesWRsL30RELA14uabb8Yee+yB7t27Y/z48XjyyScbbdJmQ/jO1rW+m3W172Fd5btWV/xOFb47NRaB6OmimDdvHqZOnYonnngCs2fPRrlcxvHHH48NGzboOueeey7++Mc/4u6778a8efPwzjvv4MQTT2yg1VseCxcuxE9+8hMceOCBVnlX6YsPP/wQRxxxBJqbm/HAAw/gxRdfxHXXXYcdd9xR17nmmmtw0003YdasWViwYAF69uyJSZMmoa2trYGWbxlcffXV+PGPf4wf/ehHeOmll3D11VfjmmuuwQ9/+ENdZ3vujw0bNmD06NG4+eabvefr8f3UU0/FCy+8gNmzZ+P+++/Hww8/jLPOOquzXNhsyOuLjRs3YvHixbj44ouxePFi/OEPf8DSpUvxmc98xqq3vfRFQMDWhrvuugvTp0/HpZdeisWLF2P06NGYNGkSVq5c2WjTNgu6+ne2rvTdrCt+D+sq37W64neq8N2pwZABAVLKlStXSgBy3rx5UkopV69eLZubm+Xdd9+t67z00ksSgJw/f36jzNyiWLdunRw+fLicPXu2POaYY+Q555wjpexaffGd73xHHnnkkZnn4ziWgwcPlv/93/+ty1avXi27desmf/Ob33SGiZ2KyZMnyy9/+ctW2YknnihPPfVUKWXX6g8A8p577tHH9fj+4osvSgBy4cKFus4DDzwghRDy7bff7jTbNzd4X/jw5JNPSgDyjTfekFJuv30RELA14NBDD5VTp07Vx9VqVQ4ZMkTOnDmzgVZtOXSl72xd7btZV/we1hW/a3XF71Thu1PnI0T0BAAA1qxZAwDo378/AGDRokUol8uYOHGirjNixAgMGzYM8+fPb4iNWxpTp07F5MmTLZ+BrtUX9913Hw455BD827/9GwYOHIiDDjoIt956qz7/z3/+E8uXL7f6om/fvhg/fvx21xcAcPjhh2POnDl4+eWXAQD/+Mc/8Oijj+ITn/gEgK7XHxT1+D5//nz069cPhxxyiK4zceJERFGEBQsWdLrNnYk1a9ZACIF+/foB6Np9ERCwJVEqlbBo0SLrWRRFESZOnLjdPoe70ne2rvbdrCt+DwvftcJ3KoXw3WnzoqnRBgQ0HnEcY9q0aTjiiCNwwAEHAACWL1+OlpYWfaMpDBo0CMuXL2+AlVsWd955JxYvXoyFCxc657pSX7z++uv48Y9/jOnTp+PCCy/EwoUL8c1vfhMtLS04/fTTtb+DBg2y2m2PfQEAF1xwAdauXYsRI0agUCigWq3iiiuuwKmnngoAXa4/KOrxffny5Rg4cKB1vqmpCf3799+u+6etrQ3f+c538B//8R/o06cPgK7bFwEBWxrvv/8+qtWq91m0ZMmSBlm15dCVvrN1xe9mXfF7WPiuFb5TAeG705ZAIHoCMHXqVDz//PN49NFHG21KQ7Bs2TKcc845mD17Nrp3795ocxqKOI5xyCGH4MorrwQAHHTQQXj++ecxa9as/9/efYdFcb1vA7+X3osgTaq9gIhiQWOLxt6N3dhNVKwYY69RscQUY4vmq8ZEY+wmRqNGRdTYBcGGDUURxEKRDrvz/uHL/FxB2i677O79ua693D07Z+aZcWEenj1zBkOHDlVzdKq3a9cubN++HTt27ECdOnUQHh6OyZMnw8XFRSePBxUtJycHffv2hSAIWL9+vbrDISItoys5m67mZrqYhzHXIuZOZYOXbum48ePH49ChQzh16hRcXV3FdicnJ2RnZyMpKUlu+efPn8PJyUnFUZatq1evIiEhAfXr14eBgQEMDAxw+vRprF69GgYGBnB0dNSZY+Hs7IzatWvLtdWqVQsxMTEAIO7v+3e10MZjAQDTpk3DjBkz0L9/f/j4+OCzzz7DlClTEBwcDED3jse7irPvTk5O+SZDzc3NxevXr7Xy+OQlKo8fP8bx48fFb6QA3TsWRKpib28PfX19nfg9rEs5m67mZrqYhzHX0u2cirlT2WGhR0cJgoDx48dj//79OHnyJLy8vOTeb9CgAQwNDXHixAmxLSoqCjExMQgICFB1uGWqTZs2iIyMRHh4uPjw9/fHoEGDxOe6ciyaNWuW75atd+/ehYeHBwDAy8sLTk5OcsciJSUFFy9e1LpjAby9I4CenvyvSX19fchkMgC6dzzeVZx9DwgIQFJSEq5evSouc/LkSchkMjRu3FjlMZelvETl3r17+Pfff2FnZyf3vi4dCyJVMjIyQoMGDeR+F8lkMpw4cUJrfg/rYs6mq7mZLuZhzLV0N6di7lTG1DsXNKnL2LFjBWtrayEkJESIi4sTH+np6eIyY8aMEdzd3YWTJ08KV65cEQICAoSAgAA1Rq06797ZQRB051hcunRJMDAwEJYsWSLcu3dP2L59u2BmZib89ttv4jLLli0TbGxshIMHDwoRERFC9+7dBS8vLyEjI0ONkZeNoUOHCpUqVRIOHTokREdHC/v27RPs7e2Fr776SlxGm4/HmzdvhLCwMCEsLEwAIHz77bdCWFiYeDeE4ux7hw4dBD8/P+HixYvC2bNnhWrVqgkDBgxQ1y6VWmHHIjs7W+jWrZvg6uoqhIeHy/1OzcrKEtehLceCqLzZuXOnYGxsLGzdulW4deuW8Pnnnws2NjZCfHy8ukNTCuZsb+lCbqaLeZiu5Fq6mFMxd1IvFnp0FIACH1u2bBGXycjIEMaNGyfY2toKZmZmQs+ePYW4uDj1Ba1C7ycTunQs/vrrL8Hb21swNjYWatasKWzcuFHufZlMJsydO1dwdHQUjI2NhTZt2ghRUVFqirZspaSkCJMmTRLc3d0FExMToXLlysLs2bPlTkDafDxOnTpV4O+JoUOHCoJQvH1/9eqVMGDAAMHCwkKwsrIShg8fLrx580YNe6OYwo5FdHT0B3+nnjp1SlyHthwLovLoxx9/FNzd3QUjIyOhUaNGwoULF9QdktIwZ3tLV3IzXcvDdCXX0sWcirmTekkEQRCUP06IiIiIiIiIiIhUjXP0EBERERERERFpCRZ6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqISKkEQQAALFiwQO41ERERESkfcy8iep9E4G8CIlKidevWwcDAAPfu3YO+vj46duyIli1bqjssIiIiIq3E3IuI3scRPUSkVOPGjUNycjJWr16Nrl27FivRaNWqFSQSCSQSCcLDw8s+yPcMGzZM3P6BAwdUvn0iIiKi0mLuRUTvY6GHiJRqw4YNsLa2xsSJE/HXX3/hzJkzxeo3evRoxMXFwdvbu4wjzO+HH35AXFycyrdLREREpCjmXkT0PgN1B0BE2uWLL76ARCLBggULsGDBgmJfJ25mZgYnJ6cyjq5g1tbWsLa2Vsu2iYiIiBTB3IuI3scRPURUIkuXLhWH2r77+P777wEAEokEwP9NCJj3uqRatWqFCRMmYPLkybC1tYWjoyM2bdqEtLQ0DB8+HJaWlqhatSqOHDmilH5ERERE5RFzLyIqKRZ6iKhEJkyYgLi4OPExevRoeHh44NNPP1X6tn755RfY29vj0qVLmDBhAsaOHYs+ffqgadOmuHbtGtq1a4fPPvsM6enpSulHREREVN4w9yKikuJdt4io1ObOnYtff/0VISEh8PT0LPV6WrVqhXr16onfTOW1SaVS8TpzqVQKa2tr9OrVC9u2bQMAxMfHw9nZGefPn0eTJk0U6ge8/QZs//796NGjR6n3hYiIiKisMPciouLgiB4iKpV58+YpJdEoTN26dcXn+vr6sLOzg4+Pj9jm6OgIAEhISFBKPyIiIqLyirkXERUXCz1EVGLz58/Htm3byjTRAABDQ0O51xKJRK4t7xp0mUymlH5ERERE5RFzLyIqCRZ6iKhE5s+fj19++aXMEw0iIiIiYu5FRCXH26sTUbEtXrwY69evx59//gkTExPEx8cDAGxtbWFsbKzm6IiIiIi0C3MvIioNFnqIqFgEQcDKlSuRkpKCgIAAufcuXbqEhg0bqikyIiIiIu3D3IuISouFHiIqFolEguTkZJVtLyQkJF/bo0eP8rW9f+PA0vYjIiIiKk+YexFRaXGOHiIqF9atWwcLCwtERkaqfNtjxoyBhYWFyrdLREREpC7MvYi0l0RgaZWI1Cw2NhYZGRkAAHd3dxgZGal0+wkJCUhJSQEAODs7w9zcXKXbJyIiIlIl5l5E2o2FHiIiIiIiIiIiLcFLt4iIiIiIiIiItAQLPUREREREREREWoKFHiIiIiIiIiIiLcFCDxERERERERGRlmChh4iIiIiIiIhIS7DQQ0RERERERESkJVjoISIiIiIiIiLSEiz0EBERERERERFpCRZ6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4iIiIiIiIiIi3BQg8RERERERERkZZgoYeIiIiIiIiISEuw0ENEREREREREpCVY6CEiIiIiIiIi0hIs9BARERERERERaQkWeoiIiIiIiIiItAQLPUREREREREREWoKFHiIiIiIiIiIiLcFCDxERERERERGRlmChh4iIiIiIiIhIS7DQQ0RERERERESkJVjoISIiIiIiIiLSEiz0EBERERERERFpCRZ6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4iIiIiIiIiIi1Rrgs9r169goODAx49elTksjNmzMCECRPKPigiIiIiLVVU7hUSEgKJRIKkpCQAwD///IN69epBJpOpLkgiIiIqVLku9CxZsgTdu3eHp6dnkct++eWX+OWXX/Dw4cOyD4yIiIhIC5Uk9wKADh06wNDQENu3by/bwIiIiKjYDNQdwIekp6fjf//7H44ePVqs5e3t7dG+fXusX78eK1euLOPoiKg8kEqlyMnJUXcYRBrJ0NAQ+vr66g6DypGS5l55hg0bhtWrV+Ozzz4ro8iIqDxg3kWkGCMjI+jpqWasTbkt9Bw+fBjGxsZo0qSJ2Hbz5k1Mnz4doaGhEAQB9erVw9atW1GlShUAQNeuXTF79mwWeoi0nCAIiI+PFy8dIKLSsbGxgZOTEyQSibpDoXKgoNzr8OHDmDx5Mp48eYImTZpg6NCh+fp17doV48ePx4MHD8ScjIi0B/MuIuXQ09ODl5cXjIyMynxb5bbQc+bMGTRo0EB8HRsbixYtWqBVq1Y4efIkrKyscO7cOeTm5orLNGrUCE+fPsWjR4+KPeSYiDRPXrLh4OAAMzMz/pFKVEKCICA9PR0JCQkAAGdnZzVHROXB+7nXkydP0KtXLwQGBuLzzz/HlStXMHXq1Hz93N3d4ejoiDNnzrDQQ6SFmHcRKU4mk+HZs2eIi4uDu7t7mf8cldtCz+PHj+Hi4iK+Xrt2LaytrbFz504YGhoCAKpXry7XJ2/5x48fs9BDpKWkUqmYbNjZ2ak7HCKNZWpqCgBISEiAg4MDL+OifLnX+vXrUaVKFaxatQoAUKNGDURGRmL58uX5+rq4uODx48cqi5WIVIN5F5HyVKxYEc+ePUNubq5Y0ygr5XYy5oyMDJiYmIivw8PD0bx580IPSF7Smp6eXubxEZF65F0bbmZmpuZIiDRf3s8R51wgIH/udfv2bTRu3FhumYCAgAL7mpqaMv8i0kLMu4iUJ++SLalUWubbKreFHnt7eyQmJoqv84o4hXn9+jWAt5UyItJuHDZMpDj+HNG73s+9SuL169fMv4i0GM8XRIpT5c9RuS30+Pn54datW+LrunXr4syZM4V+63jjxg0YGhqiTp06qgiRiIiISGu8n3vVqlULly5dklvmwoUL+fplZmbiwYMH8PPzK/MYiYiIqGjlttDTvn173Lx5U/xmafz48UhJSUH//v1x5coV3Lt3D7/++iuioqLEPmfOnEHz5s2LNfqHiEjVQkND0bVrV7i4uEAikeDAgQNq2cawYcMgkUggkUhgaGgIR0dHfPLJJ9i8eTNkMpnSY9ImxT12np6e4nJ5D1dX13zvv/9H8+TJk9GqVSu5tpSUFMyePRs1a9aEiYkJnJyc0LZtW+zbtw+CIIjL3b9/H8OHD4erqyuMjY3h5eWFAQMG4MqVK2VzMEjrvJ97jRkzBvfu3cO0adMQFRWFHTt2YOvWrfn6XbhwAcbGxh+8rIuISF2Ye2k25l2lV24LPT4+Pqhfvz527doFALCzs8PJkyeRmpqKli1bokGDBti0aZPcnD07d+7E6NGj1RUyEVGh0tLS4Ovri7Vr15a4b6tWrQr8A6u02+jQoQPi4uLw6NEjHDlyBK1bt8akSZPQpUsXubsZUn7FPXaLFi1CXFyc+AgLC5Nbj4mJCaZPn17otpKSktC0aVNs27YNM2fOxLVr1xAaGop+/frhq6++QnJyMgDgypUraNCgAe7evYuffvoJt27dwv79+1GzZs0C75JEVJD3cy93d3fs3bsXBw4cgK+vLzZs2IClS5fm6/f7779j0KBBnMODiMod5l6aj3lXKQnl2KFDh4RatWoJUqm0yGUPHz4s1KpVS8jJyVFBZESkLhkZGcKtW7eEjIwMdYeiEADC/v37i718y5YthS1btihlG0OHDhW6d++er/3EiRMCAGHTpk0l2o4uKe6x8/DwEL777rsPrsfDw0OYOHGiYGRkJPz9999i+6RJk4SWLVuKr8eOHSuYm5sLsbGx+dbx5s0bIScnR5DJZEKdOnWEBg0aFHi+TExM/GAc2vLzRMpTktxLEAThxYsXQoUKFYSHDx+WcWREpA7adJ5g7qV5mHeVXrm9vToAdO7cGffu3UNsbCzc3NwKXTYtLQ1btmyBgUG53iUiUjJBENR2pxczMzOtmpzw448/hq+vL/bt24dRo0apJYa0tDQA8sc2OzsbOTk5MDAwgLGxcb5lTU1Noaf3doBqTk4OsrOzoa+vL3f3oIKWVabSHDsvLy+MGTMGM2fORIcOHfLFJZPJsHPnTgwaNEjultd5LCwsAABhYWG4efMmduzYUeC+2djYlHyHSGeVJPcCgEePHmHdunXw8vJSQXREVB4w91Iededeqsy7cnJylHZLceZdRSu3l27lmTx5crESjU8//TTfLUCJSPulp6fDwsJCLQ9tvJVwzZo18ejRI7VtP+/Yvnz5UmxbuXIlLCwsMH78eLllHRwcYGFhgZiYGLFt7dq1sLCwwMiRI+WW9fT0hIWFBW7fvl1msb9/7KZPny73eVm9enW+PnPmzEF0dDS2b9+e772XL18iMTERNWvWLHS79+7dE7dPpAzFzb0AwN/fH/369SvjiIioPGHupVzqzL1UmXcV5zK4kmDeVbhyX+ghItJFS5culTtZnTlzBmPGjJFre/dEqyyCIGjVN2Wq9P6xmzZtGsLDw8XHkCFD8vWpWLEivvzyS8ybNw/Z2dn51lfc7RIREZFimHtpFuZdheN1TkSk0czMzJCamqq2bZeVMWPGoG/fvuLrQYMGoXfv3ujVq5fYVtCwUkXdvn1brZdg5P1fvntsp02bhsmTJ+e7NDchIQEA5O60GBgYiNGjR0NfX19u2bxvfMryrozvHzt7e3tUrVq1yH5BQUFYt24d1q1bJ9desWJF2NjY4M6dO4X2r169OgDgzp07vL01ERGVOeZeyqXO3EuVedewYcOUGTrzriKw0ENEGk0ikcDc3FzdYShdhQoVUKFCBfG1qakpHBwcinUCK62TJ08iMjISU6ZMKbNtFKWg/0sjIyMYGRkVa1lDQ8MCr/8u68+IIsfOwsICc+fOxYIFC9CtWzexXU9PD/3798evv/6K+fPn50suU1NTYWJignr16qF27dpYtWoV+vXrl+968aSkpHJzvTgREWk+5l7Ko+7cS5V5l7Lm5wGYdxUHL90iIlKR1NRUcTgpAERHRyM8PFypw4CLu42srCzEx8cjNjYW165dw9KlS9G9e3d06dKlwKGu9H/K4th9/vnnsLa2xo4dO+TalyxZAjc3NzRu3Bjbtm3DrVu3cO/ePWzevBl+fn5ITU2FRCLBli1bcPfuXTRv3hyHDx/Gw4cPERERgSVLlqB79+7K2G0iIiKNw9xL8zHvKh2O6CEiUpErV66gdevW4uugoCAAwNChQ5U2QV1xt/HPP//A2dkZBgYGsLW1ha+vL1avXo2hQ4eWyV2ptElZHDtDQ0N8/fXXGDhwoFx7hQoVcOHCBSxbtgyLFy/G48ePYWtrCx8fH6xcuRLW1tYAgEaNGuHKlStYsmQJRo8ejZcvX8LZ2RlNmzbF999/r+guExERaSTmXpqPeVfpSARNmU2IiAhAZmYmoqOj4eXlJXcbRyIqOf48ERFRYXieIFIeVf48sXRIRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4i0ki8YSCR4vhzRERExcHzBZHiVPlzxEIPEWkUQ0NDAEB6erqaIyHSfHk/R3k/V0RERO9i3kWkPNnZ2QAAfX39Mt+WQZlvgYhIifT19WFjY4OEhAQAgJmZGSQSiZqjItIsgiAgPT0dCQkJsLGxUUnCQUREmod5F5FyyGQyvHjxAmZmZjAwKPsyDAs9RKRxnJycAEBMOoiodGxsbMSfJyIiooIw7yJSDj09Pbi7u6ukWCoReMElEWkoqVSKnJwcdYdBpJEMDQ05koeIiIqNeReRYoyMjKCnp5rZc1joISIiIiIiIiLSEpyMWUlCQ0PRtWtXuLi4QCKR4MCBA2W+zdjYWAwePBh2dnYwNTWFj48Prly5UubbJSIiIlI35l5EREQFY6FHSdLS0uDr64u1a9eqZHuJiYlo1qwZDA0NceTIEdy6dQurVq2Cra2tSrZPREREpE7MvYiIiArGS7fKgEQiwf79+9GjRw+xLSsrC7Nnz8bvv/+OpKQkeHt7Y/ny5WjVqlWptjFjxgycO3cOZ86cUU7QRERERBqKuRcREdH/4YgeFRk/fjzOnz+PnTt3IiIiAn369EGHDh1w7969Uq3vzz//hL+/P/r06QMHBwf4+flh06ZNSo6aiIiISDMx9yIiIl3FET1l4P1vlWJiYlC5cmXExMTAxcVFXK5t27Zo1KgRli5dWuJtmJiYAACCgoLQp08fXL58GZMmTcKGDRswdOhQpewHERERkSZg7kVERPR/DNQdgC6IjIyEVCpF9erV5dqzsrJgZ2cHALhz5w5q1apV6HqmT5+OZcuWAQBkMhn8/f3FRMXPzw83btxgskFEREQ6j7kXERHpMhZ6VCA1NRX6+vq4evUq9PX15d6zsLAAAFSuXBm3b98udD15iQkAODs7o3bt2nLv16pVC3v37lVS1ERERESaibkXERHpMhZ6VMDPzw9SqRQJCQlo3rx5gcsYGRmhZs2axV5ns2bNEBUVJdd29+5deHh4KBQrERERkaZj7kVERLqMhR4lSU1Nxf3798XX0dHRCA8PR4UKFVC9enUMGjQIQ4YMwapVq+Dn54cXL17gxIkTqFu3Ljp37lzi7U2ZMgVNmzbF0qVL0bdvX1y6dAkbN27Exo0blblbREREROUScy8iIqKCcTJmJQkJCUHr1q3ztQ8dOhRbt25FTk4OFi9ejG3btiE2Nhb29vZo0qQJFi5cCB8fn1Jt89ChQ5g5cybu3bsHLy8vBAUFYfTo0YruChEREVG5x9yLiIioYCz0EBERERERERFpCT11B0BERERERERERMrBQg8RERERERERkZbgZMwKkMlkePbsGSwtLSGRSNQdDhERUYkIgoA3b97AxcUFenr87ofKP+ZeRESkqVSZd7HQo4Bnz57Bzc1N3WEQEREp5MmTJ3B1dVV3GERFYu5FRESaThV5l9YUeoKDg7Fv3z7cuXMHpqamaNq0KZYvX44aNWp8sM/WrVsxfPhwuTZjY2NkZmYWa5uWlpYA3v5HWVlZlT54IiIiNUhJSYGbm5t4PiMq75h7ERGRplJl3qU1hZ7Tp08jMDAQDRs2RG5uLmbNmoV27drh1q1bMDc3/2A/KysrREVFia9LMgw4b1krKysmG0REpLF4CQxpCuZeRESk6VSRd2lNoeeff/6Re71161Y4ODjg6tWraNGixQf7SSQSODk5lXV4RERERERERERlTmtnXkxOTgYAVKhQodDlUlNT4eHhATc3N3Tv3h03b9784LJZWVlISUmRexARERERERERlRdaWeiRyWSYPHkymjVrBm9v7w8uV6NGDWzevBkHDx7Eb7/9BplMhqZNm+Lp06cFLh8cHAxra2vxwckAiYiIiIiIiKg8kQiCIKg7CGUbO3Ysjhw5grNnz5ZoNuucnBzUqlULAwYMwNdff53v/aysLGRlZYmv8yZTSk5OVtp14ps3b4aDgwM+/vhjmJmZKWWdREREBUlJSYG1tbVSz2NEZUnZn9nY2FicPn0a/fv3L/Nb3RIRkW5TZd6lNXP05Bk/fjwOHTqE0NDQEt+yzNDQEH5+frh//36B7xsbG8PY2FgZYRYoOzsbo0aNgiAIeP78uVjo2b59O/bu3YuePXvis88+E5fP+4BwEk0iIiKikhs8eDBCQkIQExODGTNmqDscIiIipdCary4EQcD48eOxf/9+nDx5El5eXiVeh1QqRWRkJJydncsgwqKlpqaiR48eaNy4MSpWrCi2X7hwAfv378etW7fEtuzsbNja2sLGxgavXr0S269cuYK9e/fiwYMHKo2diIiISJNkZ2cjJCQEADBr1ixo4SB3IiLSUVpT6AkMDMRvv/2GHTt2wNLSEvHx8YiPj0dGRoa4zJAhQzBz5kzx9aJFi3Ds2DE8fPgQ165dw+DBg/H48WOMGjVKHbuAChUqYN++fbhw4YLcKJ0hQ4ZgzZo16Natm9gWGxsLQRCQnZ0tN+H0tm3b8Omnn+Lnn38W23JyctC5c2eMGzdO7ngQERER6apLly6JzwVBwK+//qrGaIiIiJRHay7dWr9+PQCgVatWcu1btmzBsGHDAAAxMTFy118nJiZi9OjRiI+Ph62tLRo0aID//vsPtWvXVlXYxdKwYUM0bNhQrs3LywupqamIj4+XKwq5u7ujSZMmcpNQP3nyBIcPH4aJiQnWrl0rti9fvhynTp1CYGAgunbtWvY7QkRERFROnDp1Su71mDFjYG9vj06dOqkpIiIiIuXQysmYVUVTJrF8/fo19u3bh5SUFAQFBYntLVu2RGhoKDZu3IjRo0cDAF69eoU1a9agZcuW+YpmRESkXTTlPEaUR5mf2aSkJISGhsLa2horVqzA4cOHAQBffPEFZs+ezburEhGRUqky72KhRwGaniBHRkYiJCQE3bt3h7u7OwDgwIED6NmzJ2rXro2bN2+Ky964cQMeHh6wtLRUV7hERKRkmn4eI91TVp/ZrKwsTJs2DT/++CMAQE9PD61bt0bv3r3RokUL1KpVi3flIiIihfCuW6QSPj4+8PHxkWtzcHDAgAEDULVqVbn2Ll264OnTpzhz5gwCAgIAvL2enXf8IiIiIk1nbGyM1atXo3fv3li4cCFOnTqFEydO4MSJEwAACwsLVK9eHdWqVYOnpyccHBxQsWJFODg4oEKFCjA3N4eZmZn4r6mpKQtDVC5kZ2fj5s2buHbtGq5du4br16/j5cuXePPmDYyNjeHo6Ahvb2/Uq1cPzZs3h7e3Nz+7RFqAI3oUoCvfhCYlJcHPzw9Pnz5FYmIiLCwsAACrV6/G5s2bMW7cOHz++edqjpKIiEpKV85jpD1U9Zl9+PAhdu3ahWPHjuHSpUtIS0sr8TqMjY2hp6cHiUSS71+JRAKZTAZBEMR/330ukUhQsWJFODs7w9nZGS4uLqhUqZL4cHFxgZWVFUxMTMSHgYEBv4DTccnJybh+/br4CAsLQ2RkJHJycoq9DltbW3z00Udo0aIFWrRoAT8/PxgaGha4rCAIyMjIQEZGBmQyGYC3n3tLS0t+FokKwEu3NISuJcjPnz+Ho6Oj+LpXr17Yv38/li1bhunTpwMAMjMzERQUhObNm6Nv377Q19dXV7hERFQEXTuPkeZTx2c2NzcX9+7dw71793D37l08ffoUL168QEJCAhISEvD69WtkZGQgPT1drXc31dPTE4s+xsbGMDY2hkwmQ+3atVGzZk1kZmYiLi5OXFZPTw/6+vowMDCAoaEhDAwM8j3PK0ABEJ+/+6dDXvHqQwWtov7NKwbkPTcwMICNjQ0yMzORlJSEnJycfDG8G0tRzxV9v7iPd5c3MDCAlZWV+LC2toaDgwMcHR3h6OgIBwcHGBsbl/j/NzMzU7yrcN7jyZMnePTokfh49uxZgX1tbGxQv3591K9fH35+fnB1dYWFhQWys7Px+PFjRERE4PLly/jvv//yFTWNjY1hZ2cHKysryGQy5OTkICsrC6mpqUhNTRULPO/S19dHhQoVUKFCBVhYWMDMzEx85I16MzMzEwuU7z/e/QzmPYyMjOT65eTkIDMzE1lZWcjMzERKSgqSk5PFf/OeC4IAQ0NDGBkZwdjYWPw/eff/J++5jY0NXFxcYG1tzUIVlQkWejSErifIcXFxOHPmDOrXry9e6hUaGoqWLVvC0dERcXFx4i/JM2fOwNLSEt7e3jAw4BWDRETlga6fx0jzlPfPrEwmkyv6vDtK5/1/Cyp65D3Pzc1FQkIC4uLiEBcXh2fPniE2NhaxsbF49uwZnj17hrS0NGRnZ6t7l6kUbGxs5Io/jo6OsLe3h1QqFT8/iYmJckWdpKSkYq3b3d0dvr6+8PX1Rb169VC/fn14enoWq3CRk5ODsLAwhIaG4syZMzhz5gwSExMV3FvNY2lpCTc3N3h4eKBq1aqoUqWK+PDy8oKJiYm6QyQNxUKPhijvyYY63LlzBxs3boSpqSmWLFkittevXx9hYWHYtWsX+vTpAwB48+YNsrKyYG9vr65wiYh0Gs9jVFrBwcHYt28f7ty5A1NTUzRt2hTLly9HjRo1Pthn06ZN2LZtG27cuAEAaNCgAZYuXYpGjRoVe7v8zMqTyWTiiIb3H1lZWRAEARcuXEBCQgIMDAxQqVIl6OvrQyaTQSqVQiqVIjc3F7m5ucjJycn3b14BqqDHuyNaZDLZBwtaRf377oiY7OxsJCcnw8TEBDY2NuLIl/dH/xTUVpL3S7rsu48Ptec9srOz8ebNG7x58wYpKSlITEzE8+fPkZCQgOfPnyM3N7fU/9/GxsZwcnKCk5OTeEmfl5cXPD094enpiSpVqsDW1rbU63+fTCZDTEwMEhMTkZKSIo4AMzIygqWlJSwsLGBpaSk3J1VGRgYSExPx6tUrvH79Gunp6XKPtLQ08XlmZiakUqn4mSvskZmZKRbBMjMzYWhoKI5eMzExgaWlJaytrcUROnn/6uvrIzs7G9nZ2eLIn3dH/7z7/PXr10UW1CQSCSpVqiRX/KlatSqcnJzkRiyZm5vD3NwcJiYmHB1EIhZ6NASTjeKRyWTo3Lkzzp07h1u3bsHV1RUA8Msvv2DYsGHo168fdu7cKS7PSZ6JiFSD5zEqrQ4dOqB///5o2LAhcnNzMWvWLNy4cQO3bt2Cubl5gX0GDRqEZs2aoWnTpjAxMcHy5cuxf/9+3Lx5E5UqVSrWdvmZJU0mCIJY+Ml75BWAXr58CUNDQ5iamsLMzAzW1tZiQSevuMNLispeWloanj59ipiYGDx69Aj379/HgwcPxMebN29KtD4DA4N8BagPPWxtbcVHhQoVYGtrCysrK06OrUV41y3SKnp6ejhy5AikUqncnD0PHjwAALi5uYltMplMHCb5+++/w8nJSeXxEhERUeH++ecfuddbt26Fg4MDrl69ihYtWhTYZ/v27XKvf/75Z+zduxcnTpzAkCFDyixWovJCIpGIc9fUqlVL3eFQAczNzVGjRo0CRycKgoCXL1/KFX7yCkGvXr0SRyylpaUhKysLwNs5vl69eoVXr16VKh49PT2xCJQ3iipvtFBRz/NGO5mYmIgFRFNTU5iamopthoaGLB5qKRZ6SGXen5h50aJFmDJlitz15VFRUXj69ClevXoFOzs7sX3t2rW4cOEChg0bhjZt2qgsZiIiIipacnIyAKBChQrF7pOeno6cnJxC+2RlZYl/MAFvvw0lIlKHvLvhVaxYEU2aNCl0WalUirS0NLnJoYt6JCYmyj3y7maW97qs9imvQFSc4pGVlRVcXFzEOYxcXV054qicYqGH1Or964irV6+OGzdu4OHDh3K3cjx48CCOHz+Opk2bioWeV69eYdWqVQgICEDXrl1VGjcRERG9JZPJMHnyZDRr1gze3t7F7jd9+nS4uLigbdu2H1wmODgYCxcuVEaYREQqo6+vL97NK2/aipLKysqSK/zk3eksLS1N/Lew53nzd2VkZIiPvDmO8giCIK63NExMTFCtWjXUrVtXnATc19dX7k7NpB6co0cBvE5cdU6dOoWQkBAMHjwY1apVAwD8/fff6NKlC2rUqIE7d+6Iy8bGxsLZ2ZnVZSKiIvA8RsowduxYHDlyBGfPni32HzTLli3DihUrEBISgrp1635wuYJG9Li5ufEzS0RUSnkTn79b/ClO4Sg1NRXJycl4+vSpOI9RTk5OgdtwdHQUiz55RaCaNWvKfZGvizhHD9F7WrdujdatW8u1OTo6YuTIkfkmcPz444+RkpKCv/76C/7+/qoMk4iISKeMHz8ehw4dQmhoaLGLPN988w2WLVuGf//9t9AiD/D2LkN5d14iIiLFSSQS8XerjY1NqdeTm5uLmJgY3Lx5ExEREbh+/TquX7+Oe/fu4fnz5zh27BiOHTsmLm9kZITatWujVq1a8PT0hJeXl/hwd3cvl0Wg2NhYzJ49G2PHjkXjxo3VHU6JcESPAvhNaPnz/PlzVKtWDZmZmXj58qX4/3LixAk8evQIXbp04VBCIqL/j+cxKi1BEDBhwgTs378fISEh4mjboqxYsQJLlizB0aNHi5zjoiD8zBIRlW9paWm4ceMGrl+/LhaAIiIiCp1jTU9PD66urvkKQHkPFxcXlV6tkZWVJY48TU9PR7NmzXDmzBmFJ67W6hE9QUFBJe4zZ86cEk3uR7rL0dERL1++REREhNwPz5o1a3DgwAEsXLgQ8+bNA/A2SQXAmeaJiEirlUXuFRgYiB07duDgwYOwtLREfHw8AMDa2hqmpqYAgCFDhqBSpUoIDg4GACxfvhzz5s3Djh074OnpKfaxsLCAhYVFiWMkIqLyx9zcHI0bN5YbASMIAh49eoTr16/j/v37iI6OFh+PHj1CZmYmYmJiEBMTg9DQ0HzrtLa2RrNmzfDRRx+hffv28PPzK7O/4WQyGUaMGIEdO3YAAJo1a4Zvv/1W4/5mVPmIHj09PQQEBMDIyKhYy589exZRUVGoXLlyGUdWcvxWSXN88803+OOPP7Bp0ybUq1cPAHDhwgUMGjQIgwYNwqJFi9QbIBGRGvA8phvKIvf6UMK7ZcsWDBs2DADQqlUreHp6YuvWrQAAT09PPH78OF+f+fPnY8GCBcWKjZ9ZIiLtIggC4uPjxaLPu0Wg6OhoxMTEQCqVyvWpXLkyPv30U4wePRpVq1ZVWiwxMTEYOXIk/v33X+jr6+OXX37BwIEDlVbkUeU5TC2Fnvj4eDg4OBRreUtLS1y/fp2FHlK62bNnY+nSpejXrx927twptv/7779o2LAhrK2t1RgdEVHZ43lMNzD3IiIiTZWTk4OIiAicO3cOp06dwtGjR5GRkQHg7ZcO3bt3x8KFC4uc860oL168QNOmTXH//n0YGxtj48aNGDJkiDJ2QaTVl25t2bKlRH9A//TTT5xTRYPIZDKkpqYiKSkJSUlJePPmDYyNjcVh2ebm5rCwsCgXk23NmjUL/v7+cHJyEttevXqFDh06QE9PD0+fPi12UkxE6pN394h37w6Rd6vQrKwsWFlZwcbGRnyYmppq3PBbIkUw9yIiIk1laGiIBg0aoEGDBpg4cSLS0tJw+PBhbN68Gf/88w8OHDiAP//8E2PHjsXixYtLNcF0WloaunTpgvv378PT0xNHjx5F9erVlb8zKqSWyZilUin09fVVvVml08ZvlQRBkCvUvPtITEwssP3dR3JyMmQyWZHbMTQ0zFf8yfv3Q8+L02ZoaKjQH3BhYWEYNGgQDA0Ncf36dbF92bJlePPmDYYPH67U4YGaTBAEZGVl4c2bN0hJScGbN2/knhf0b25urkpiMzAwgJWVFSwtLWFpaSk+L6jN0tISJiYm/MNfBfI+M3m36SyoMFPa5yX5bBkZGckVforzsLW1FZ+bmJiU4VFSLW08j1HBmHsREZG2uXXrFubNm4e9e/cCADw8PLBr1y40atSo2OuQSqXo1asX/vzzT1SoUAH//fcfatSoUSbxavWlWwDg5OSEYcOGYcSIERpdKVN1siGTyZCVlYXMzMxiPTIyMj74Xlpa2geLNcUp1BTFyMgItra2sLCwQFZWlvhHWVn/oa+vrw9zc3OYm5vDzMys1M9lMhkcHR1hbm4OU1NTNG7cGHFxcfj777/RqVOnMt0HZZDJZMjOzkZOTg5ycnLE5wW15f3/FFWkKejfnJwcde+qUhgYGBS7KPTucwsLCxgZGcHQ0FD89/3n779W5R0DSiMnJ0cswqSnp8sVZd5/Xdy2d18r4/dLYfJGEOYVgY2MjPDmzRuxUK2M7efdjvT9h5mZGUxMTJT2ULRwXRz8o1l3MPciIiJtderUKYwaNQoPHz6EoaEh1qxZg88//7xYfcePH4+1a9fCxMQEJ06cQNOmTcssTq0v9Hz99df45ZdfEB0djaZNm2LkyJHo27cvzMzMVB2KQpT5H5WUlIQePXoUWqTJzs5WUuRFMzQ0lPsGu7jfdBf1jfeHLq94v6047727jKqOjZ6eHkxMTKCvr4/c3Fzk5ubC3NwcJiYm0NPTg76+PvT19ZXyXCKRFFmk+VBbWf8x/T5zc/N8BZKCCibFnQhUUdnZ2R8cafTu89TUVJXE8y59ff1iFYTef08QBEilUkilUshksgKfK+M9VX12jIyMCh3JV5xRfgU9NzD48BXJhY1YLO7IRVWeMiUSSYEFIAsLC1y6dEkp2+AfzbqDuRcREWmz5ORkjBo1Cnv27AHw9rw3e/bsQr8027hxI7744gtIJBLs3r0bvXv3LtMYtb7QkyckJARbtmzB3r17oa+vj759+2LUqFFyt2Irz5T5H5WcnFyi6wklEglMTU3FxP/d58V5mJqaFlrI0bQ5LHJycuRGDZT0eVHLafrIFYlE8sFCQt4f3EUVaj70r4WFhcZeDpA3p1RxikIfKhS9X3B7/7Wm+tDouA+1FfX63TYzM7NyMU9XSb0/B9n7RaHCRlEW95GVlVVkHObm5korUvKPZs1ga2tb7HPy69evC32fuRcREWkrQRCwYMEC8Y7KEydOxHfffVfgiPoLFy6gRYsWyMnJwdKlSzFz5swyj09nCj15UlNTsXPnTmzduhX//fcfatWqhZEjRyIoKEjdoRVKmf9RUqkU+/btK3ahxsDAQKMKMZou73KW9PR0ZGVlQSqVIioqCr///js+//xz2NjYQCqV4tq1a9i1axd69eqFOnXqfHAERXGeC4LwwcJMSdqMjIw0thCj6QRBQG5uboEFoJI+19PTkxv5VdioMEVem5iYwMzMDEZGRvwdowZ5l10WVgzKzc1F27ZtlbI9/tGsGX755Rfx+atXr7B48WK0b98eAQEBAIDz58/j6NGjmDt3LqZMmVKsdTL3IiIibbV69WpMmjQJANC/f39s3boVxsbG4vvPnj2Dv78/4uLi0Lt3b+zevVsleW+5LvSUJAH49ttvSxzQ33//jSFDhiApKQlSqbTE/VWJyQa9b+DAgfj9998xevRobNy4Ud3hEBEViucxzdO7d2+0bt0a48ePl2tfs2YN/v33Xxw4cKDE62TuRURE2mbHjh0YNmwYcnJy0KZNG+zZsweWlpY4ePAgpkyZgpiYGHh7e+P8+fOwsLBQSUzl+vbqYWFhcq+vXbuG3NxccWbqu3fvQl9fHw0aNCj2OtPT07Fr1y5s2bIFZ8+eRZUqVTBt2rSShkakdl9++SUsLCwwduxYse3p06eYN28exowZU6IZ4ImIiN539OhRLF++PF97hw4dMGPGjGKvh7kXERFps4EDB8Le3h69evXCiRMnULlyZZiamuLZs2cAAC8vLxw4cEBlRR5VK3Gh59SpU+Lzb7/9FpaWlvjll19ga2sLAEhMTMTw4cPRvHnzItf133//YfPmzdi9ezdyc3Px6aef4uuvv0aLFi1KGhZRuVC/fv18I3l+/vlnbNmyBQ8fPkRISIh6AiMiIq1gZ2eHgwcPYurUqXLtBw8ehJ2dXZH9mXsREZGuaNeuHU6fPo2BAwfi7t27SExMhK2tLcaOHYvZs2dr3A0JSqLEhZ53rVq1CseOHROLPMDbCQMXL16Mdu3a5UtC8qxYsQJbtmzB3bt34e/vj5UrV2LAgAGwtLRUJByicqlz5854+PAhunfvLrZlZWVhxowZGDZsGHx9fdUYHRERaZKFCxdi1KhRCAkJESdQvnjxIv755x9s2rTpg/2YexERkS5q0KABbty4gdDQUMhkMjRv3vyDd4jWJgpNxmxpaYm//voLrVq1kms/deoUunXrhjdv3hTYr2LFihg8eDBGjhwJb2/v0m5e7XidOJXWjh07MGjQILi5uSE6OpqTJRORWvA8ppkuXryI1atX4/bt2wCAWrVqYeLEiYXeOYu5FxERkXqV6zl63tWzZ08MHz4cq1atEuceuXjxIqZNm4ZevXp9sN+zZ8808ta6RMpSvXp19O3bF35+fmKRRxAErFy5Et26dUPNmjXVHCEREZVXjRs3xvbt20vUh7kXERGR7sh/Q/kS2LBhAzp27IiBAwfCw8MDHh4eGDhwIDp06IB169YV2Gf16tUluqPDhg0bPjgyiEhT+fv7448//pCbOPPKlSuYPn066tWrh5SUFDVGR0RE5dmDBw8wZ84cDBw4EAkJCQCAI0eO4ObNmwUuz9yLiIhItyhU6DEzM8O6devw6tUrhIWFISwsDK9fv8a6detgbm5eYJ8pU6aUKHn46quv8OLFC0XCJNIIhoaG6NatG/r37y83lO/y5cvIzc1VY2RERFRenD59Gj4+Prh48SL27t2L1NRUAMD169cxf/78Avsw9yIiItItCl26lScuLg5xcXFo0aIFTE1NIQgCJBJJgcsKgoA2bdrAwKB4m87IyFBGiETlXr169XDw4EHIZDKx7dmzZ2jevDnc3Nxw7tw5ODg4qDFCIiJStxkzZmDx4sUICgqSm0j5448/xpo1awrsw9yLiIhItyhU6Hn16hX69u2LU6dOQSKR4N69e6hcuTJGjhwJW1tbrFq1Kl+fD33b9CHdu3dHhQoVFAmTSKPo6f3fQLvbt2/DwsICjo6OqFixohqjIiKi8iAyMhI7duzI1+7g4ICXL18W2Ie5FxERkW5RqNAzZcoUGBoaIiYmBrVq1RLb+/Xrh6CgIKUUeoh0WZs2bfDo0SMkJCSIo+Sys7PRoUMHDBo0CEOGDOHkmkREOsTGxgZxcXHw8vKSaw8LC0OlSpUK7MPci4iISLcoVOg5duwYjh49CldXV7n2atWq4fHjxwoFRkRvWVhYwMLCQny9fft2nDp1Crdv38bAgQNZ6CEi0iH9+/fH9OnTsXv3bkgkEshkMpw7dw5ffvklhgwZou7wiIiIqBxQaDLmtLQ0mJmZ5Wt//fo1jI2NFVl1iQUHB6Nhw4awtLSEg4MDevTogaioqCL77d69GzVr1oSJiQl8fHxw+PBhFURLVHp9+/bFqlWrsGTJEpiamortf/31F7Kzs9UYGRERlbWlS5eiZs2acHNzQ2pqKmrXro0WLVqgadOmmDNnjrrDIyIionJAoUJP8+bNsW3bNvF13jdLK1asQOvWrRUOriROnz6NwMBAXLhwAcePH0dOTg7atWuHtLS0D/b577//MGDAAIwcORJhYWHo0aMHevTogRs3bqgwcqKSMTc3R1BQEEaMGCG2nT9/Ht26dUPt2rWRmZmpxuiIiKgsGRkZYdOmTXjw4AEOHTqE3377DXfu3MGvv/4KfX19dYdHRERE5YBEEAShtJ1v3LiBNm3aoH79+jh58iS6deuGmzdv4vXr1zh37hyqVKmizFhL5MWLF3BwcMDp06fRokWLApfp168f0tLScOjQIbGtSZMmqFevHjZs2FDkNlJSUmBtbY3k5GS522ETqdqff/6JMWPGoGPHjvjf//4nthd2BzwiIp7HSNPwM0tERJpKlecwhebo8fb2xt27d7FmzRpYWloiNTUVvXr1QmBgIJydnQvtm5OTg5o1a+LQoUNyEzkrS3JyMgAUeteI8+fPIygoSK6tffv2OHDggNLjISpL3bp1wyeffIL09HSx7fnz52jRogUmTpyIMWPG8JteIiIt8H7ekkcikcDExARVq1b94F2zyjr3IiIiovJBoUIPAFhbW2P27Nkl7mdoaFhml5jIZDJMnjwZzZo1g7e39weXi4+Ph6Ojo1ybo6Mj4uPjC1w+KysLWVlZ4uuUlBTlBEykBKampnJz9qxbtw53797Ftm3bMG7cODVGRkREyhIWFoZr165BKpWiRo0aAIC7d+9CX18fNWvWxLp16zB16lScPXsWtWvXlutblrkXERERlR8KFXoiIiIKbM/7Vsnd3b3QSZkDAwOxfPly/PzzzzAwULjmJLfeGzdu4OzZs0pbJ/B2wueFCxcqdZ1EZWXWrFlwdHREzZo1xcu3cnNzsWXLFgwePFiuKERERJohb7TOli1bxGHfycnJGDVqFD766COMHj0aAwcOxJQpU3D06NF8/csq9yIiIqLyQ6E5evT09MQ/IPNW8+58IIaGhujXrx9++uknmJiY5Ovfs2dPnDhxAhYWFvDx8YG5ubnc+/v27StxTOPHj8fBgwcRGhoKLy+vQpd1d3dHUFAQJk+eLLbNnz8fBw4cwPXr1/MtX9CIHjc3N14nThpj69atGD58OHx9fREWFsb5e4h0HOc70TyVKlXC8ePH843WuXnzJtq1a4fY2Fhcu3YN7dq1w8uXL/P1L4vcS5X4mSUiIk2lMXP07N+/H9OnT8e0adPQqFEjAMClS5ewatUqzJ8/H7m5uZgxYwbmzJmDb775Jl9/Gxsb9O7dW5EQRIIgYMKECdi/fz9CQkKKLPIAQEBAAE6cOCFX6Dl+/DgCAgIKXN7Y2Fjlt40nUiZzc3O4u7tj0KBBckUeqVTKOXyIiDRAcnIyEhIS8hV6Xrx4IV5SbmNjg+zs7AL7KzP3IiIiovJJoULPkiVL8MMPP6B9+/Zim4+PD1xdXTF37lxcunQJ5ubmmDp1aoGFni1btiiyeTmBgYHYsWMHDh48CEtLS3GeHWtra/ESlSFDhqBSpUoIDg4GAEyaNAktW7bEqlWr0LlzZ+zcuRNXrlzBxo0blRYXUXnSp08fdO/eHTKZTGwLCwtD7969sXz5cvTp00eN0RERUVG6d++OESNGYNWqVWjYsCEA4PLly/jyyy/Ro0cPAG+/dKtevXqB/ZWZexEREVH5pFChJzIyEh4eHvnaPTw8EBkZCQCoV68e4uLiCl3PixcvEBUVBQCoUaMGKlasWOJY1q9fDwBo1aqVXPuWLVswbNgwAEBMTAz09PTE95o2bYodO3Zgzpw5mDVrFqpVq4YDBw4UOoEzkaYzMjKSe718+XJER0dj//79LPQQEZVzP/30E6ZMmYL+/fsjNzcXAGBgYIChQ4fiu+++AwDUrFkTP//8c6HrUUbuRUREROWTQnP0+Pn5wdfXFxs3bhT/eMzJycHo0aNx/fp1hIWF4dy5cxg8eDCio6Pz9U9LS8OECROwbds2cYSBvr4+hgwZgh9//BFmZmalDU0leJ04aYP09HR88803GD58ONzc3AC8/Wynp6fDyclJzdERUVnieUxzpaam4uHDhwCAypUrw8LColj9mHsRERGphyrPYXpFL/Jha9euxaFDh+Dq6oq2bduibdu2cHV1xaFDh8QRNg8fPvzgrZ2DgoJw+vRp/PXXX0hKSkJSUhIOHjyI06dPY+rUqYqERkTFZGZmhnnz5olFHgBYvHgxqlevjq1bt6ovMCIi+iALCwvUrVsXdevWLXaRB2DuRUREpAsUGtEDAG/evMH27dtx9+5dAG+H/w4cOBCWlpZF9rW3t8eePXvyXW516tQp9O3bFy9evFAktDLHb5VIG0mlUrRq1Qpnz57FoUOH0LlzZ3WHRERlhOcxzXTlyhXs2rULMTEx+SZdLuquWcy9iIiI1ENj7roFAJaWlhgzZkyp+qanp8PR0TFfu4ODA9LT0xUNjYhKQV9fH6dPn8axY8fkJloPDQ2Fra0tfHx81BgdEZFu27lzJ4YMGYL27dvj2LFjaNeuHe7evYvnz5+jZ8+eRfZn7kVERKT9FB7RAwC3bt0q8Fulbt26FdqvTZs2sLOzw7Zt22BiYgIAyMjIwNChQ/H69Wv8+++/ioZWpvitEumKzMxM1K5dG48fP8b+/fuL/NkmIs3A85jmqVu3Lr744gsEBgbC0tIS169fh5eXF7744gs4Oztj4cKFhfZn7kVERKQeGjOi5+HDh+jZsyciIyMhkUiQVzOSSCQA3l4CUpjvv/8eHTp0gKurK3x9fQEA169fh4mJCY4ePapIaESkRKmpqWjQoAGys7Px8ccfqzscIiKd9eDBA/GSWiMjI6SlpUEikWDKlCn4+OOPiyz0MPciIiLSfgpNxjxp0iR4eXkhISEBZmZmuHnzJkJDQ+Hv74+QkJAi+/v4+ODevXsIDg5GvXr1UK9ePSxbtgz37t1DnTp1FAmNiJTI3t4eu3fvRkREhNykn9OmTcPhw4fVGBkRkW6xtbXFmzdvAACVKlXCjRs3AABJSUnFuvSKuRcREZH2U2hEz/nz53Hy5EnY29tDT08Penp6+OijjxAcHIyJEyciLCzsg31zcnJQs2ZNHDp0CKNHj1YkDCJSkQoVKojPQ0JC8M033+Dbb7/F/fv34eXlpcbIiIh0Q4sWLXD8+HH4+PigT58+mDRpEk6ePInjx4+jTZs2hfZl7kVERKQbFCr0SKVS8e5a9vb2ePbsGWrUqAEPDw9ERUUV2tfQ0BCZmZmKbJ6I1MjPzw9ffvklcnNz5Yo8UqkU+vr6aoyMiEh7rVmzRsyfZs+eDUNDQ/z333/o3bs35syZU2hf5l5ERES6QaFLt7y9vXH9+nUAQOPGjbFixQqcO3cOixYtQuXKlYvsHxgYiOXLlyM3N1eRMIhIDaytrbFy5Up89913Ytvz589RrVo1rFu3rsg5uoiIqGRyc3Nx6NAhsZiup6eHGTNm4M8//8SqVatga2tb5DqYexEREWk/hUb0zJkzB2lpaQCARYsWoUuXLmjevDns7Ozwxx9/FNn/8uXLOHHiBI4dOwYfHx+Ym5vLvb9v3z5FwiMiFVuzZg2io6OxZcsWjBkzRt3hEBFpFQMDA4wZMwa3b98u9TqYexEREWk/hQo97du3F59XrVoVd+7cwevXr2FrayveeaswNjY26N27tyIhEFE5Mn/+fDg7O8PPzw96em8HDEqlUsTExHAOHyIiJWjUqBHCw8Ph4eFRqv7MvYiIiLRfqQs9OTk5MDU1RXh4OLy9vcX2dydrLUxubi5at26Ndu3awcnJqbRhEFE5YmBggHHjxsm1bd26FWPHjsXcuXMxd+5cNUVGRKQdxo0bh6CgIDx58gQNGjTINyKnbt26H+zL3IuIiEg3lLrQY2hoCHd391LPw6GM4cdEVP6FhoYiJycn3x8jRERUcv379wcATJw4UWyTSCQQBAESiaTQvIy5FxERkW5QaDLm2bNnY9asWXj9+nWp+jdq1KjQW7ATkebbunUrjh49ivHjx4ttERER+PPPPyEIghojIyLSPNHR0fkeDx8+FP8tirJyr+DgYDRs2BCWlpZwcHBAjx49irzjKgDs3r0bNWvWhImJCXx8fHD48GGFYyEiIiJ5Cs3Rs2bNGty/fx8uLi7w8PDI9439tWvXCu0/btw4TJ06FU+fPi3x8GMi0gwSiQTt2rUTXwuCgMmTJ+PUqVNYvHgxZs+ercboiIg0S2nn5smjrNzr9OnTCAwMRMOGDZGbm4tZs2ahXbt2uHXr1gdHcP73338YMGAAgoOD0aVLF+zYsQM9evTAtWvX5KYBICIiIsVIBAW+Ul+4cGGh78+fP7/Q9/Mma5ULqJjDj8uDlJQUWFtbIzk5GVZWVuoOh0gj5OTkYP78+Vi/fr1CE4oSkeJ4HtNMv/76KzZs2IDo6GicP38eHh4e+P777+Hl5YXu3bsX2rescq8XL17AwcEBp0+fRosWLQpcpl+/fkhLS8OhQ4fEtiZNmqBevXrYsGFDsbbDzywREWkqVZ7DFBrRU1QhpyjR0dEK9ScizWNoaIilS5di1qxZsLCwENvnzZuHrKwszJw5EzY2NuoLkIioHFu/fj3mzZuHyZMnY8mSJWJhxsbGBt9//32RhZ6yyr2Sk5MBFH5TjvPnzyMoKEiurX379jhw4ECZxERERKSrFCr0AEBSUhL27NmDBw8eYNq0aahQoQKuXbsGR0dHVKpUqdC+/CafSHe9W+SJi4vDihUrkJWVhRYtWqBz585qjIyIqPz68ccfsWnTJvTo0QPLli0T2/39/fHll18W2b8sci+ZTIbJkyejWbNmhV6CFR8fD0dHR7k2R0dHxMfHf7BPVlYWsrKyxNcpKSmKB0xERKTlFJqMOSIiAtWrV8fy5cvxzTffICkpCQCwb98+zJw5s1jr+PXXX9GsWTO4uLjg8ePHAIDvv/8eBw8eVCQ0ItIgTk5O2Lt3L7744gt06tRJbH/27BknbCYiekd0dDT8/PzytRsbGyMtLa1Y61B27hUYGIgbN25g586dpepfmODgYFhbW4sPNzc3pW+DiIhI2yhU6AkKCsKwYcNw7949mJiYiO2dOnVCaGhokf3Xr1+PoKAgdOrUCUlJSfmGHxORbpBIJOjcuTM2bNgAiUQC4O23uM2bN0fz5s3x6NEj9QZIRFROeHl5ITw8PF/7P//8g1q1ahXZX9m51/jx43Ho0CGcOnUKrq6uhS7r5OSE58+fy7U9f/4cTk5OH+wzc+ZMJCcni48nT56UOEYiIiJdo1Ch5/Lly/jiiy/ytVeqVKnQYbh58oYfz549G/r6+mK7v78/IiMjFQmNiDTc1atXERcXh4cPH6JixYrqDoeIqFwICgpCYGAg/vjjDwiCgEuXLmHJkiWYOXMmvvrqqyL7Kyv3EgQB48ePx/79+3Hy5El4eXkV2ScgIAAnTpyQazt+/DgCAgI+2MfY2BhWVlZyDyIiIiqcQnP0GBsbF3it9N27d4v1h5kyhh8TkXZq2rQp7t69i4cPH8rdqnfz5s3o2bMnbG1t1RgdEZF6jBo1CqamppgzZw7S09MxcOBAuLi44IcffkD//v2L7K+s3CswMBA7duzAwYMHYWlpKX7BZ21tDVNTUwDAkCFDUKlSJQQHBwMAJk2ahJYtW2LVqlXo3Lkzdu7ciStXrmDjxo3F3i4REREVTaERPd26dcOiRYuQk5MD4O3lFzExMZg+fTp69+5dZH9Fhx8TkXZzdXWVu01vaGgoRo4ciRo1auDNmzdqjIyISH0GDRqEe/fuITU1FfHx8Xj69ClGjhxZrL7Kyr3Wr1+P5ORktGrVCs7OzuLjjz/+EJeJiYlBXFyc+Lpp06bYsWMHNm7cCF9fX+zZswcHDhwodAJnIiIiKjmFRvSsWrUKn376KRwcHJCRkYGWLVsiPj4eAQEBWLJkSZH984YfZ2ZmisOPf//9dwQHB+Pnn39WJDQi0kL6+vrw9vZGs2bNYGlpqe5wiIhUbvHixRg0aBC8vLxgZmYGMzOzEvVXVu5VnInyQ0JC8rX16dMHffr0KUnIREREVEISQQm3tDl79iwiIiKQmpqK+vXro23btsXuu337dixYsAAPHjwAALi4uGDhwoXF/mZKnVJSUmBtbY3k5GReM06kIlKpFBkZGeLt2Z8/f47PPvsMCxYsQNOmTdUcHZFm4XlM8/j6+uLGjRto3LgxBg8ejL59+8Le3r5E62DuRUREpHqqPIcpVOh58uSJ0m5zmZ6ejtTUVDg4OChlfarAZINI/SZMmIA1a9agYcOGuHjxonjXLiIqGs9jmunmzZvYvn07du7ciadPn+KTTz7BoEGD0KNHjxKN8GHuRUREpDqqPIcpNEePp6cnWrZsiU2bNiExMVGhQMzMzDQq0SCi8mHmzJkYNWoUvvnmG7HIk5OTk+8WvkRE2qJOnTpYunQpHj58iFOnTsHT0xOTJ08u9DblBWHuRUREpJ0UKvRcuXIFjRo1wqJFi+Ds7IwePXpgz549yMrKUlZ8RESFcnFxwaZNm+Qmbf7ll19QuXJlrFixQo2RERGVPXNzc5iamsLIyEi8OQYRERHpNoUKPX5+fli5ciViYmJw5MgRVKxYEZ9//jkcHR0xYsQIZcVIRFQiR48eRXp6OoyMjNQdChGR0kVHR2PJkiWoU6cO/P39ERYWhoULF4q3OCciIiLdppTJmN917do1jBw5EhEREZBKpcpcdbnD68SJyidBEHD48GF8/PHHMDU1BfB2BOKJEycwfvx4mJubqzlCovKB5zHN06RJE1y+fBl169bFoEGDMGDAAFSqVEndYakMP7NERKSpVHkOU+j26nmePn2KHTt2YMeOHbhx4wYCAgKwdu3aEq0jMzMTJiYmygiHiHScRCJB586d5dpmz56NY8eO4enTp/jxxx/VFBkRkWLatGmDzZs3o3bt2gqvi7kXERGRdlLo0q2ffvoJLVu2hKenJ7Zt24Z+/frhwYMHOHPmDMaMGVNkf5lMhq+//hqVKlWChYUFHj58CACYO3cu/ve//ykSGhGRSBAEDB48GDVr1kRQUJDYnpqayjktiEijLFmyRKEiD3MvIiIi7adQoWfx4sVo3Lgxrl69ihs3bmDmzJnw8PAoUf+tW7dixYoVcnNpeHt74+eff1YkNCIikUQiwWeffYZbt27By8tLbF+4cCFq1qyJw4cPqzE6IqKSefr0KdatW4cZM2YgKChI7lEU5l5ERETaT6FLt2JiYsTbGZfGtm3bsHHjRrRp00ZuBJCvry/u3LmjSGhERPm8+/sqJycHe/fuRXR0tEK/x4iIVOnEiRPo1q0bKleujDt37sDb2xuPHj2CIAioX79+kf2ZexEREWk/hQo9eX8cpaenIyYmBtnZ2XLv161bt9D+sbGxqFq1ar52mUzGyymIqEwZGhoiMjISe/bsQYcOHcT2v//+G7m5uejWrRsLQERU7sycORNffvklFi5cCEtLS+zduxcODg4YNGiQ3O+yD2HuRUREpP0UunTrxYsX6Ny5MywtLVGnTh34+fnJPYpSu3ZtnDlzJl/7nj17itX/XaGhoejatStcXFwgkUhw4MCBQpcPCQmBRCLJ9+CtSYl0h7m5OYYOHSoWdHJycjBx4kT06NEDmzdvVnN0RET53b59G0OGDAEAGBgYICMjAxYWFli0aBGWL19eZH9l5l5ERERUPik0omfy5MlITk7GxYsX0apVK+zfvx/Pnz/H4sWLsWrVqiL7z5s3D0OHDkVsbCxkMhn27duHqKgobNu2DYcOHSpRLGlpafD19cWIESPQq1evYveLioqSu7WZg4NDibZLRNojJycH/fr1wx9//IH+/fuL7RkZGeJt2omI1Mnc3FwcQe3s7IwHDx6gTp06AICXL18W2V+ZuRcRERGVTwoVek6ePImDBw/C398fenp68PDwwCeffAIrKysEBwfnu73x+7p3746//voLixYtgrm5OebNm4f69evjr7/+wieffFKiWDp27IiOHTuWeB8cHBxgY2NT4n5EpH3MzMywdOlSLFq0CAYG//frsU+fPsjOzsYPP/yAWrVqqTFCItJ1TZo0wdmzZ1GrVi106tQJU6dORWRkJPbt24cmTZoU2V+ZuRcRERGVTwoVetLS0sQRMLa2tnjx4gWqV68OHx8fXLt2rVjraN68OY4fP65IGAqpV68esrKy4O3tjQULFqBZs2YfXDYrKwtZWVni65SUFFWESEQq9m6R5/Hjxzh27BhkMhn09fXVGBUREfDtt98iNTUVwNs7B6ampuKPP/5AtWrV8O233xZrHerOvYiIiKhsKTRHT40aNRAVFQXg7d0afvrpJ8TGxmLDhg1wdnYusn/lypXx6tWrfO1JSUmoXLmyIqEVydnZGRs2bMDevXuxd+9euLm5oVWrVoUWqIKDg2FtbS0+3NzcyjRGIlI/Dw8PREVFYdOmTahevbrY/scff+DWrVtqjIyIdFHlypXFm12Ym5tjw4YNiIiIwN69e+Hh4VGs/urKvYiIiEg1JIIgCKXt/NtvvyE3NxfDhg3D1atX0aFDB7x+/RpGRkbYunUr+vXrV2h/PT09xMfH55sX5/nz53B3d5cbPVMSEokE+/fvR48ePUrUr2XLlnB3d8evv/5a4PsFjehxc3NDcnKy3Dw/RKTdEhISULlyZWRkZODChQto2LChukMiKpWUlBRYW1vzPKahxo0bh0WLFsHe3r7Yfcoq91IVfmaJiEhTqfIcptClW4MHDxafN2jQAI8fP8adO3fg7u5eaNLx559/is+PHj0Ka2tr8bVUKsWJEyfg6empSGil0qhRI5w9e/aD7xsbG8PY2FiFERFReZSZmYl27drh2bNn8Pf3F9sFQeAt2YlIZX777Td8+eWXxSr0lNfci4iIiJRPoULPu86dOwd/f3/Ur1+/yGXzRtpIJBIMHTpU7j1DQ0N4enoW665dyhYeHl6sS86ISLe5u7tj3759yMjIEAs7ubm5aNWqFXr27InAwECYmJioOUoi0nYlGZRdXnMvIiIiUj6lFXo6duyI8PDwYl3fLZPJAABeXl64fPlyiYYcf0hqairu378vvo6OjkZ4eDgqVKgAd3d3zJw5E7Gxsdi2bRsA4Pvvv4eXlxfq1KmDzMxM/Pzzzzh58iSOHTumcCxEpBveveX67t27ce7cOdy+fRujRo1ioYeIypWyyL2IiIiofFJaoac0U/1ER0cra/O4cuUKWrduLb4OCgoCAAwdOhRbt25FXFwcYmJixPezs7MxdepUxMbGwszMDHXr1sW///4rtw4iouLq27cvMjMzIZFI5C6JuH79OurWrctLuohI6d68eVPiPsrMvYiIiKh8Umgy5ndZWlri+vXrJbpjw6JFiwp9f968eYqGVaY4ISARFebq1avw9/dH27ZtcfjwYRgaGqo7JCI5PI9ppgcPHmDLli14+PAhvv/+ezg4OODIkSNwd3dHnTp1Cu3L3IuIiEg9NGYy5nf99NNPcHR0LFGf/fv3y73OyclBdHQ0DAwMUKVKlXKfbBARFSYsLAxGRkZwcnJikYeIlOL06dPo2LEjmjVrhtDQUCxevBgODg64fv06/ve//2HPnj2F9mfuRUREpP2UUui5f/8+7OzsoKenB6D4d54JCwvL15aSkoJhw4ahZ8+eygiNiEhtRo0ahU8++QQGBv/3qzYxMRFLlizBV199le/2xkRERZkxYwYWL16MoKAgWFpaiu0ff/wx1qxZU2R/5l5ERETaT0+Rzq9evULbtm1RvXp1dOrUCXFxcQCAkSNHYurUqaVap5WVFRYuXIi5c+cqEhoRUbng4eGBSpUqia+Dg4OxatUq8Q44REQlERkZWWBBxsHBAS9fvizVOpl7ERERaReFCj1TpkyBgYEBYmJiYGZmJrb369cP//zzT6nXm5ycjOTkZEVCIyIqlzp27IgGDRpg9uzZYptMJkNOTo4aoyIiTWFjYyN+sfausLAwuaJySTH3IiIi0h4KXbp17NgxHD16FK6urnLt1apVw+PHj4vsv3r1arnXgiAgLi4Ov/76Kzp27KhIaERE5VLr1q1x6dIluctbd+3ahfnz5+Obb75B165d1RgdEZV3/fv3x/Tp07F7925IJBLIZDKcO3cOX375JYYMGVJkf+ZeRERE2k+hQk9aWprcSJ48r1+/hrGxcZH9v/vuO7nXenp6qFixIoYOHYqZM2cqEhoRUbmVN59Znh9++AF3797F9evXWeghokItXboUgYGBcHNzg1QqRe3atSGVSjFw4EDMmTOnyP7MvYiIiLSfQrdX79SpExo0aICvv/4alpaWiIiIgIeHB/r37w+ZTFbknR80HW/xSUTKkJKSgjVr1mDSpEkwNzcH8Pb2yZmZmUXeKplIETyPaa4nT54gMjISqamp8PPzQ7Vq1dQdkkrwM0tERJpKlecwhQo9N27cQJs2bVC/fn2cPHkS3bp1w82bN/H69WucO3cOVapUUWas5Q6TDSIqK927d8ehQ4ewZs0ajB07Vt3hkJbieYw0DT+zRESkqVR5DlPo0i1vb2/cvXsXa9asgaWlJVJTU9GrVy8EBgbC2dm5wD69evUq9vr37dunSHhERBopOzsbhoaGkEgkaN26tbrDIaJypHfv3mjUqBGmT58u175ixQpcvnwZu3fvzteHuRcREZFuUajQAwDW1tZyd48pzvJERPRhRkZG2LNnDx49egRPT0+x/fvvvwcAjB07tljzoBGR9gkNDcWCBQvytXfs2BGrVq0qsA9zLyIiIt2icKEnMzMTERERSEhIgEwmk3uvW7du+ZbfsmWLopskItIJ7xZ5nj9/jjlz5iAtLQ0eHh7o2bOn+gIjIrVJTU2FkZFRvnZDQ0OkpKQU2Ie5FxERkW5RqNDzzz//YMiQIXj58mW+9yQSCaRSabHW8+LFC0RFRQEAatSogYoVKyoSFhGR1rGzs8N3332Hv//+Gz169BDbk5OT+W09kQ7x8fHBH3/8gXnz5sm179y5E7Vr1y72eph7ERERaS+FCj0TJkxAnz59MG/ePDg6Opa4f1paGiZMmIBt27aJo4H09fUxZMgQ/PjjjwXeup2ISBcZGBhg9OjRGD16tNgmlUrRrFkzeHp6Yv369XBzc1NjhESkCnPnzkWvXr3w4MEDfPzxxwCAEydO4Pfffy9wfp73MfciIiLSfnqKdH7+/DmCgoJKVeQBgKCgIJw+fRp//fUXkpKSkJSUhIMHD+L06dOYOnWqIqEREWm9S5cuISoqCv/99x8sLCzUHQ4RqUDXrl1x4MAB3L9/H+PGjcPUqVPx9OlT/Pvvv3Kj/T6EuRcREZH2U+j26iNGjECzZs0wcuTIUvW3t7fHnj170KpVK7n2U6dOoW/fvnjx4kVpQ1MJ3uKTiNTt7t27uHv3Lrp06SK27dq1C+3bt+clXVQknsd0D3MvIiIi9dCY26uvWbMGffr0wZkzZ+Dj4wNDQ0O59ydOnFho//T09AJHAzk4OCA9PV2R0IiIdEL16tVRvXp18XVYWBj69esHBwcH3LlzB7a2tmqMjojKG+ZeRERE2k+hQs/vv/+OY8eOwcTEBCEhIZBIJOJ7EomkyEJPQEAA5s+fj23btsHExAQAkJGRgYULFyIgIECR0IiIdFJaWhpq1KgBf39/FnmItJBUKsV3332HXbt2ISYmBtnZ2XLvv379utD+zL2IiIi0n0KFntmzZ2PhwoWYMWMG9PRKPt3PDz/8gPbt28PV1RW+vr4AgOvXr8PExARHjx5VJDQiIp300Ucf4caNG0hNTRXbEhMTMWDAAMyaNQstWrRQY3REpKiFCxfi559/xtSpUzFnzhzMnj0bjx49woEDB/LdiasgzL2IiIi0n0Jz9FSoUAGXL19GlSpVSh1Aeno6tm/fjjt37gAAatWqhUGDBsHU1LTU61QVXidORJpg1qxZCA4ORp06dRAREVGqwjxpJ57HNE+VKlWwevVqdO7cGZaWlggPDxfbLly4gB07dhS5DuZeREREqqcxc/QMHToUf/zxB2bNmlXqdZiZmcndLpiIiJRr4sSJSEpKQpcuXcQijyAISEhIKPVdE4lIPeLj4+Hj4wMAsLCwQHJyMgCgS5cumDt3brHWwdyLiIhIuyn0ta5UKsWKFSvQsmVLTJgwAUFBQXKPovzyyy/4+++/xddfffUVbGxs0LRpUzx+/FiR0IiI6P9zcnLCunXr0KlTJ7Ft9+7dqFy5MpYtW6bGyIiopFxdXREXFwfg7eieY8eOAQAuX74MY2PjIvsz9yIiItJ+ChV6IiMj4efnBz09Pdy4cQNhYWHiIzw8vMj+S5cuFYcJnz9/HmvWrMGKFStgb2+PKVOmKBIaEREV4s8//0R6enq+iVyJqHzr2bMnTpw4AQCYMGEC5s6di2rVqmHIkCEYMWJEkf2VmXuFhoaia9eucHFxgUQiwYEDB4rss337dvj6+sLMzAzOzs4YMWIEXr16VaLtEhERUeEUmqNHUWZmZrhz5w7c3d0xffp0xMXFYdu2bbh58yZatWqFFy9eqCu0YuF14kSkqQRBwMGDB9G2bVtYWFgAAKKiohAREYHevXtzHh8dwfOY5jt//jzOnz+PatWqoWvXrkUur8zc68iRIzh37hwaNGiAXr16Yf/+/ejRo8cHlz937hxatGiB7777Dl27dkVsbCzGjBmD6tWrY9++fcXaJj+zRESkqTRmjh5FWVhY4NWrV3B3d8exY8fEy71MTEyQkZGhztCIiLSaRCLJ9wfZjBkzcODAAUybNg0rVqxQT2BEVCIBAQElui26MnOvjh07omPHjsVe/vz58/D09MTEiRMBAF5eXvjiiy+wfPnyEm2XiIiIClfiQk+vXr2wdetWWFlZoVevXoUuW9S3M5988glGjRoFPz8/3L17V5w/4ubNm/D09CxpaEREVEoymQy+vr44deoUhg8fLtfO0T1E5UtUVBR+/PFH3L59G8Dbu2ZNmDABNWrUKLKvOnOvgIAAzJo1C4cPH0bHjh2RkJCAPXv2yM0f9r6srCxkZWWJr1NSUso0RiIiIm1Q4uzd2toaEolEfF7Yoyhr165FQEAAXrx4gb1798LOzg4AcPXqVQwYMKCkoRERUSnp6elhwYIFiI2NRa1atcT2xYsXo1u3brh586YaoyOiPHv37oW3tzeuXr0KX19f+Pr64tq1a/D29sbevXuL7K/O3KtZs2bYvn07+vXrByMjIzg5OcHa2hpr1679YJ/g4GC53NLNza1MYyQiItIGpZqjZ9GiRfjyyy9hZmZWFjFpDF4nTkTaLCMjA5UqVUJiYiJ27dqFPn36qDskUjKexzRPlSpVMGjQICxatEiuff78+fjtt9/w4MEDtcQlkUiKnKPn1q1baNu2LaZMmYL27dsjLi4O06ZNQ8OGDfG///2vwD4Fjehxc3PjZ5aIiDSOKvOuUhV69PX1ERcXBwcHB4UDSExMxP/+9z+54ccjRoxAhQoVFF53WWOCTETaLioqCps3b0ZwcLB4CVdYWBicnJzg7Oys5uhIUTyPaR4zMzNERESgatWqcu337t2Dr68v0tPTi1xHWeRexSn0fPbZZ8jMzMTu3bvFtrNnz6J58+Z49uxZsX6n8DNLRESaSpXnsFJNvKCsG3WFhobC09MTq1evRmJiIhITE/Hjjz/Cy8sLoaGhStkGERGVXo0aNbB8+XKxyCOVSvHZZ5+hSpUq+Oeff9QcHZHuadWqFc6cOZOvPa9gUhR15l7p6en55vzS19cHoLzckoiIiBS461bePD2KCAwMRL9+/bB+/XrxRC+VSjFu3DgEBgYiMjJS4W0QEZHyvHz5EtbW1jA2Nkbjxo3VHQ6RzunWrRumT5+Oq1evokmTJgCACxcuYPfu3Vi4cCH+/PNPuWXfp8zcKzU1Fffv3xdfR0dHIzw8HBUqVIC7uztmzpyJ2NhYbNu2DQDQtWtXjB49GuvXrxcv3Zo8eTIaNWoEFxeXUh0PIiIiyq9Ul27p6enJTcr8Ia9fvy70fVNTU4SHh+e7S0RUVBTq1atX7m+xzuHDRKSLBEHAo0eP4OXlJbZNmjQJVatWxeeffw5jY2M1RkclwfOY5inuXfAkEgmkUmm+dmXmXiEhIWjdunW+9qFDh2Lr1q0YNmwYHj16hJCQEPG9H3/8ERs2bEB0dDRsbGzw8ccfY/ny5ahUqVKxtsnPLBERaSpVnsNKPaJn4cKFxbqzVmHq16+P27dv50s2bt++DV9fX4XWTUREZUMikcgVeSIjI7F69WoAQIsWLfj7m6gMyWQyhforM/dq1apVoZdcbd26NV/bhAkTMGHChBJth4iIiEqm1IWe/v37l2oy5oiICPH5xIkTMWnSJNy/f19u+PHatWuxbNmy0oZGREQqVLNmTWzYsCHfH4r3799HlSpVlHKpL5GuO3/+PF69eoUuXbqIbdu2bcP8+fORlpaGHj164McffyxwRB1zLyIiIt2i8rtu6enpQSKRFDnp3oeGHJcnHD5MRFSwpKQkVK5cGVWrVsWBAwc4/0Y5xfOY5ujYsSNatWqF6dOnA3g7kq5+/foYNmwYatWqhZUrV+KLL77AggUL8vVl7kVERKR+5f7SLUXujBAdHV3qvkREpBmuXLmC7OxspKWlwdHRUd3hEGm88PBwfP311+LrnTt3onHjxti0aRMAwM3NDfPnzy+w0MPci4iISLeUqtCjyPXhHh4epe5bmNDQUKxcuRJXr15FXFwc9u/fjx49ehTaJyQkBEFBQbh58ybc3NwwZ84cDBs2rEziIyLSJW3btsWDBw8QFxcnd/vkr776CsOGDUOdOnXUHCGRZklMTJQrmp4+fRodO3YUXzds2BBPnjwpsG9Z5V5ERERUPpV6jh5lunXrFmJiYpCdnS3XXtBtQT8kLS0Nvr6+GDFiBHr16lXk8tHR0ejcuTPGjBmD7du348SJExg1ahScnZ3Rvn37Eu8DERHJc3R0lPvDdO/evfjmm2+wadMmxMbGwtzcXI3REWkWR0dHREdHw83NDdnZ2bh27RoWLlwovv/mzRsYGhoWe33KyL2IiIiofFJroefhw4fo2bMnIiMj5a4dz5u4syTXiXfs2FHum62ibNiwAV5eXli1ahUAoFatWjh79iy+++47FnqIiMqAj48PevfuDR8fH7kiT3h4OHx9fTlpM1EhOnXqhBkzZmD58uU4cOAAzMzM0Lx5c/H9iIgIVKlSpcj1KDP3IiIiovJJT50bnzRpEry8vJCQkAAzMzPcvHkToaGh8Pf3R0hISJlu+/z582jbtq1cW/v27XH+/PkP9snKykJKSorcg4iIiqdGjRrYs2cP5s2bJ7bdvn0bfn5+8Pf3R2ZmphqjIyrfvv76axgYGKBly5bYtGkTNm3aBCMjI/H9zZs3o127dkWuR525FxEREamGWkf0nD9/HidPnoS9vT309PSgp6eHjz76CMHBwZg4cSLCwsLKbNvx8fH5Jgh1dHRESkoKMjIyYGpqmq9PcHCw3DBpIiIquXdH7oSHh8PMzAweHh4wMTER26VSqTi3DxEB9vb2CA0NRXJyMiwsLPL9fOzevRsWFhZFrkeduRcRERGphlpH9EilUlhaWgJ4m8A8e/YMwNtJA6OiotQZWoFmzpyJ5ORk8fGhSQ+JiKh4BgwYgJiYGHz77bdiW3JyMry8vPDVV18hIyNDjdERlT/W1tYFFkErVKggN8LnQzQt9yIiIqKSU+uIHm9vb1y/fh1eXl5o3LgxVqxYASMjI2zcuBGVK1cu0207OTnh+fPncm3Pnz+HlZVVgaN5AMDY2BjGxsZlGhcRka6xs7ODnZ2d+Hrnzp148uQJ/v77byxbtkyNkRFpH3XmXkRERKQaai30zJkzB2lpaQCARYsWoUuXLmjevDns7Ozwxx9/lOm2AwICcPjwYbm248ePIyAgoEy3S0REhRs9ejQqVaoEfX196Om9HXgqk8nwxRdfoG/fvmjbti0nbiYqJXXmXkRERKQaEiHvdgvlxOvXr2Fra1viJD41NRX3798HAPj5+eHbb79F69atUaFCBbi7u2PmzJmIjY3Ftm3bALy9vbq3tzcCAwMxYsQInDx5EhMnTsTff/9d7LtupaSkwNraGsnJybCysirZjhIRUbEdOnQIXbt2hZWVFZ4+fSpeekKK4XmMgNLnXurAzywREWkqVZ7D1DqipyAVKlQoVb8rV66gdevW4uugoCAAwNChQ7F161bExcUhJiZGfN/Lywt///03pkyZgh9++AGurq74+eefeWt1IqJyqG7dupg0aRJsbW3lijx79+5F27ZtYW1trcboiDRbaXMvIiIiKp/K3YgeTcJvlYiI1CcqKgo1a9aEtbU1Hj58yD9WS4HnMdI0/MwSEZGm0ukRPURERMXx6tUr1KlTB1WqVJEr8iQkJMDBwUGNkRERERERqQ8LPUREpJGaNm2KyMhIJCcni20pKSmoXr06/P398fvvv6NixYpqjJCIiIiISPX01B0AERFRaUkkEtjY2Iivz549i9TUVDx9+lTulu1Xr15FYmKiGiIkIiIiIlItjughIiKt0alTJzx8+BCxsbHirdkFQUCXLl0QHx+PS5cuoWHDhgDe3rI9bxkiIiIiIm3BDJeIiLSKu7s7AgICxNevXr2CtbU1jI2N4ePjI7avXLkSVapUwZo1a9QRJhERERFRmWChh4iItJq9vT3u3LmD+Ph4mJiYiO1nz57Fw4cPkZubK7alpqaib9++WL16NaRSqTrCJSIiIiJSCC/dIiIinfDuXD4AsH37dvz333+oXbu22Hb+/Hns3r0bFy9exMSJE8X2o0ePwsLCAv7+/jA2NlZVyEREREREJcZCDxER6SQrKyt06NBBrq1KlSpYvHgxjIyM5NqnTp2KmzdvYt++fejZsyeAt6N/BEGApaWlymImIiIiIioKL90iIiL6/ypXrozZs2dj2rRpYltubi5q1qwJBwcHfPTRR2L777//DltbW4wZM0ZuHXFxcZDJZCqLmYiIiIjoXRzRQ0REVAgDAwPs2bMHgiBAIpGI7Tdv3oRUKkXFihXFtpycHLi6usLQ0BAxMTFwcHAAAISHh+Pp06eoW7cu3N3dVb4PRERERKQ7OKKHiIioGN4t8gDA999/j5iYGIwbN05se/bsGfT09KCnpydXANq8eTO6du2KtWvXim25ubkYPnw4vv76a2RmZpb9DhARERGRTmChh4iIqJTc3Nzg7Owsvvbw8EBGRgbu3r0rVxhydnZGvXr1UKdOHbEtJiYGW7duxZIlS+TmBJozZw78/Pywbds2sU0qleLBgwdydwgjIiIiIioIL90iIiJSIgMDA7i6usq1zZw5EzNnzpRrMzMzw+LFi5GWlgY9vf/73iUiIgLh4eFIT08X2548eYKqVavC3NwcKSkpcssTEREREb2LhR4iIiI1cHJywuzZs/O1f/fdd/j888/h4+MjtsXFxcHY2Biurq4s8hARERFRoVjoISIiKkeqVKmCKlWqyLUFBAQgPT0diYmJaoqKiIiIiDQFvxYkIiLSAHp6erCzs1N3GERERERUzrHQQ0RERERERESkJVjoISIiIiIiIiLSEiz0EBERERERERFpCRZ6iIiIiIiIiIi0BO+6pQBBEAAAKSkpao6EiIio5PLOX3nnM6LyjrkXERFpKlXmXSz0KODNmzcAADc3NzVHQkREVHpv3ryBtbW1usMgKhJzLyIi0nSqyLskAr/GKzWZTIZnz57B0tISEolE7r2UlBS4ubnhyZMnsLKyUlOEqqWL+wzo5n5zn3VjnwHd3G9d2mdBEPDmzRu4uLhAT49Xc1P5V1juVRq69PNeHDwe+fGY5MdjIo/HIz8eE3l5xyMmJgYSiUQleRdH9ChAT08Prq6uhS5jZWWlcx9uXdxnQDf3m/usO3Rxv3VlnzmShzRJcXKv0tCVn/fi4vHIj8ckPx4TeTwe+fGYyLO2tlbZ8eDXd0REREREREREWoKFHiIiIiIiIiIiLcFCTxkxNjbG/PnzYWxsrO5QVEYX9xnQzf3mPusOXdxvXdxnIl3Fn3d5PB758Zjkx2Mij8cjPx4Teeo4HpyMmYiIiIiIiIhIS3BEDxERERERERGRlmChh4iIiIiIiIhIS7DQQ0RERERERESkJVjoISIiIiIiIiLSEiz0lIG1a9fC09MTJiYmaNy4MS5duqTukJQmODgYDRs2hKWlJRwcHNCjRw9ERUXJLZOZmYnAwEDY2dnBwsICvXv3xvPnz9UUsfItW7YMEokEkydPFtu0dZ9jY2MxePBg2NnZwdTUFD4+Prhy5Yr4viAImDdvHpydnWFqaoq2bdvi3r17aoxYMVKpFHPnzoWXlxdMTU1RpUoVfP3113h3znpt2OfQ0FB07doVLi4ukEgkOHDggNz7xdnH169fY9CgQbCysoKNjQ1GjhyJ1NRUFe5FyRS2zzk5OZg+fTp8fHxgbm4OFxcXDBkyBM+ePZNbh6btMxEVTpvztXcpK3eLiYlB586dYWZmBgcHB0ybNg25ubmq3JUyUdq8TtuOhzJyPm06TyorJ9TkY6KqfDEiIgLNmzeHiYkJ3NzcsGLFirLetVJRVS6ptOMhkFLt3LlTMDIyEjZv3izcvHlTGD16tGBjYyM8f/5c3aEpRfv27YUtW7YIN27cEMLDw4VOnToJ7u7uQmpqqrjMmDFjBDc3N+HEiRPClStXhCZNmghNmzZVY9TKc+nSJcHT01OoW7euMGnSJLFdG/f59evXgoeHhzBs2DDh4sWLwsOHD4WjR48K9+/fF5dZtmyZYG1tLRw4cEC4fv260K1bN8HLy0vIyMhQY+Slt2TJEsHOzk44dOiQEB0dLezevVuwsLAQfvjhB3EZbdjnw4cPC7Nnzxb27dsnABD2798v935x9rFDhw6Cr6+vcOHCBeHMmTNC1apVhQEDBqh4T4qvsH1OSkoS2rZtK/zxxx/CnTt3hPPnzwuNGjUSGjRoILcOTdtnIvowbc/X3qWM3C03N1fw9vYW2rZtK4SFhQmHDx8W7O3thZkzZ6pjl5SmtHmdth0PZeV82nSeVFZOqMnHRBX5YnJysuDo6CgMGjRIuHHjhvD7778Lpqamwk8//aSq3Sw2VeSSyjweLPQoWaNGjYTAwEDxtVQqFVxcXITg4GA1RlV2EhISBADC6dOnBUF4+yE3NDQUdu/eLS5z+/ZtAYBw/vx5dYWpFG/evBGqVasmHD9+XGjZsqWYEGjrPk+fPl346KOPPvi+TCYTnJychJUrV4ptSUlJgrGxsfD777+rIkSl69y5szBixAi5tl69egmDBg0SBEE79/n9E1Vx9vHWrVsCAOHy5cviMkeOHBEkEokQGxursthLq6Bk5X2XLl0SAAiPHz8WBEHz95mI5Olavvau0uRuhw8fFvT09IT4+HhxmfXr1wtWVlZCVlaWandASRTJ67TteCgj59O286QyckJtOiZllS+uW7dOsLW1lfu5mT59ulCjRo0y3iPFlFUuqczjwUu3lCg7OxtXr15F27ZtxTY9PT20bdsW58+fV2NkZSc5ORkAUKFCBQDA1atXkZOTI3cMatasCXd3d40/BoGBgejcubPcvgHau89//vkn/P390adPHzg4OMDPzw+bNm0S34+OjkZ8fLzcfltbW6Nx48Yau99NmzbFiRMncPfuXQDA9evXcfbsWXTs2BGAdu7z+4qzj+fPn4eNjQ38/f3FZdq2bQs9PT1cvHhR5TGXheTkZEgkEtjY2ADQjX0m0hW6mK+9qzS52/nz5+Hj4wNHR0dxmfbt2yMlJQU3b95UYfTKo0hep23HQxk5n7adJ5WRE2rbMXmXsvb//PnzaNGiBYyMjMRl2rdvj6ioKCQmJqpob8pGaXJJZR4PA8V3gfK8fPkSUqlU7pc+ADg6OuLOnTtqiqrsyGQyTJ48Gc2aNYO3tzcAID4+HkZGRuIHOo+joyPi4+PVEKVy7Ny5E9euXcPly5fzvaet+/zw4UOsX78eQUFBmDVrFi5fvoyJEyfCyMgIQ4cOFfetoM+7pu73jBkzkJKSgpo1a0JfXx9SqRRLlizBoEGDAEAr9/l9xdnH+Ph4ODg4yL1vYGCAChUqaMVxyMzMxPTp0zFgwABYWVkB0P59JtIlupavvau0uVt8fHyBxyvvPU2jaF6nbcdDGTmftp0nlZETatsxeZey9j8+Ph5eXl751pH3nq2tbZnEX9ZKm0sq83iw0EOlFhgYiBs3buDs2bPqDqVMPXnyBJMmTcLx48dhYmKi7nBURiaTwd/fH0uXLgUA+Pn54caNG9iwYQOGDh2q5ujKxq5du7B9+3bs2LEDderUQXh4OCZPngwXFxet3WeSl5OTg759+0IQBKxfv17d4RARKZWu5G6F0dW8rjC6mPMVhTkhlVZ5ySV56ZYS2dvbQ19fP9+s/M+fP4eTk5Oaoiob48ePx6FDh3Dq1Cm4urqK7U5OTsjOzkZSUpLc8pp8DK5evYqEhATUr18fBgYGMDAwwOnTp7F69WoYGBjA0dFR6/YZAJydnVG7dm25tlq1aiEmJgYAxH3Tps/7tGnTMGPGDPTv3x8+Pj747LPPMGXKFAQHBwPQzn1+X3H20cnJCQkJCXLv5+bm4vXr1xp9HPJOzI8fP8bx48fFb2AA7d1nIl2kS/nauxTJ3ZycnAo8XnnvaRJl5HXadDwA5eR82naeVEZOqG3H5F3K2n9t+1lSNJdU5vFgoUeJjIyM0KBBA5w4cUJsk8lkOHHiBAICAtQYmfIIgoDx48dj//79OHnyZL6hZQ0aNIChoaHcMYiKikJMTIzGHoM2bdogMjIS4eHh4sPf3x+DBg0Sn2vbPgNAs2bN8t1+9e7du/Dw8AAAeHl5wcnJSW6/U1JScPHiRY3d7/T0dOjpyf9a1NfXh0wmA6Cd+/y+4uxjQEAAkpKScPXqVXGZkydPQiaToXHjxiqPWRnyTsz37t3Dv//+Czs7O7n3tXGfiXSVLuRr71JG7hYQEIDIyEi5P1Ly/oh5v0BQ3ikjr9Om4wEoJ+fTtvOkMnJCbTsm71LW/gcEBCA0NBQ5OTniMsePH0eNGjU07rItZeSSSj0eJZ6+mQq1c+dOwdjYWNi6datw69Yt4fPPPxdsbGzkZuXXZGPHjhWsra2FkJAQIS4uTnykp6eLy4wZM0Zwd3cXTp48KVy5ckUICAgQAgIC1Bi18r17dwZB0M59vnTpkmBgYCAsWbJEuHfvnrB9+3bBzMxM+O2338Rlli1bJtjY2AgHDx4UIiIihO7du2vcrcbfNXToUKFSpUrirTT37dsn2NvbC1999ZW4jDbs85s3b4SwsDAhLCxMACB8++23QlhYmHhXgOLsY4cOHQQ/Pz/h4sWLwtmzZ4Vq1aqV69uFFrbP2dnZQrdu3QRXV1chPDxc7nfbu3c90LR9JqIP0/Z87V3KyN3ybiferl07ITw8XPjnn3+EihUrauztxN9X0rxO246HsnI+bTpPKisn1ORjoop8MSkpSXB0dBQ+++wz4caNG8LOnTsFMzOzcnl7dVXkkso8Hiz0lIEff/xRcHd3F4yMjIRGjRoJFy5cUHdISgOgwMeWLVvEZTIyMoRx48YJtra2gpmZmdCzZ08hLi5OfUGXgfcTAm3d57/++kvw9vYWjI2NhZo1awobN26Ue18mkwlz584VHB0dBWNjY6FNmzZCVFSUmqJVXEpKijBp0iTB3d1dMDExESpXrizMnj1b7he0NuzzqVOnCvw5Hjp0qCAIxdvHV69eCQMGDBAsLCwEKysrYfjw4cKbN2/UsDfFU9g+R0dHf/B326lTp8R1aNo+E1HhtDlfe5eycrdHjx4JHTt2FExNTQV7e3th6tSpQk5Ojor3pmyUJq/TtuOhjJxPm86TysoJNfmYqCpfvH79uvDRRx8JxsbGQqVKlYRly5apahdLRFW5pLKOh0QQBKFkY4CIiIiIiIiIiKg84hw9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4iUipBEAAACxYskHtNREREROrB/IxIt0gE/pQTkRKtW7cOBgYGuHfvHvT19dGxY0e0bNlS3WERERER6SzmZ0S6hSN6iEipxo0bh+TkZKxevRpdu3YtVhLRqlUrSCQSSCQShIeHl32Q7xk2bJi4/QMHDqh8+0RERERlqaT5WWlyM+ZTROUHCz1EpFQbNmyAtbU1Jk6ciL/++gtnzpwpVr/Ro0cjLi4O3t7eZRxhfj/88APi4uJUvl0iIiIiZZoyZQp69eqVr700+VlJczPmU0Tlh4G6AyAi7fLFF19AIpFgwYIFWLBgQbGvATczM4OTk1MZR1cwa2trWFtbq2XbRERERMpy6dIldO7cOV97afKzkuZmzKeIyg+O6CGiElm6dKk4LPfdx/fffw8AkEgkAP5vsr+81yXVqlUrTJgwAZMnT4atrS0cHR2xadMmpKWlYfjw4bC0tETVqlVx5MgRpfQjIiIi0lTZ2dkwNDTEf//9h9mzZ0MikaBJkybi+8rKz/bs2QMfHx+YmprCzs4Obdu2RVpamsLxE5FysdBDRCUyYcIExMXFiY/Ro0fDw8MDn376qdK39csvv8De3h6XLl3ChAkTMHbsWPTp0wdNmzbFtWvX0K5dO3z22WdIT09XSj8iIiIiTWRgYIBz584BAMLDwxEXF4d//vlHqduIi4vDgAEDMGLECNy+fRshISHo1asX7+BFVA6x0ENEJWJpaQknJyc4OTlh7dq1OHbsGEJCQuDq6qr0bfn6+mLOnDmoVq0aZs6cCRMTE9jb22P06NGoVq0a5s2bh1evXiEiIkIp/YiIiIg0kZ6eHp49ewY7Ozv4+vrCyckJNjY2St1GXFwccnNz0atXL3h6esLHxwfjxo2DhYWFUrdDRIpjoYeISmXevHn49ddfERISAk9PzzLZRt26dcXn+vr6sLOzg4+Pj9jm6OgIAEhISFBKPyIiIiJNFRYWBl9f3zJbv6+vL9q0aQMfHx/06dMHmzZtQmJiYpltj4hKj4UeIiqx+fPnY9u2bWVa5AEAQ0NDudcSiUSuLe/6cplMppR+RERERJoqPDy8TAs9+vr6OH78OI4cOYLatWvjxx9/RI0aNRAdHV1m2ySi0mGhh4hKZP78+fjll1/KvMhDRERERMUXGRmJevXqlek2JBIJmjVrhoULFyIsLAxGRkbYv39/mW6TiEqOt1cnomJbvHgx1q9fjz///BMmJiaIj48HANja2sLY2FjN0RERERHpLplMhqioKDx79gzm5uZKv9X5xYsXceLECbRr1w4ODg64ePEiXrx4gVq1ail1O0SkOI7oIaJiEQQBK1euxIsXLxAQEABnZ2fxwUmNiYiIiNRr8eLF2Lp1KypVqoTFixcrff1WVlYIDQ1Fp06dUL16dcyZMwerVq1Cx44dlb4tIlIMR/QQUbFIJBIkJyerbHshISH52h49epSv7f1bepa2HxEREZEmGzx4MAYPHlxm669Vq5bSb9lORGWDI3qIqFxYt24dLCwsEBkZqfJtjxkzhrcGJSIiInpHSXMz5lNE5YdE4NfaRKRmsbGxyMjIAAC4u7vDyMhIpdtPSEhASkoKAMDZ2Rnm5uYq3T4RERFReVKa3Iz5FFH5wUIPEREREREREZGW4KVbRERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4iIiIiIiIiIi3BQg8RERERERERkZb4fy14mWAONTr2AAAAAElFTkSuQmCC",
-                                    "text/plain": [
-                                          "<Figure size 1300x700 with 5 Axes>"
-                                    ]
-                              },
-                              "metadata": {},
-                              "output_type": "display_data"
-                        }
-                  ],
-                  "source": [
-                        "var = \"Positive current collector potential [V]\"\n",
-                        "comsol_var = comsol_solution[var]\n",
-                        "V_comsol = comsol_solution[\"Voltage [V]\"]\n",
-                        "\n",
-                        "\n",
-                        "def comsol_var_fun(t, z):\n",
-                        "    return comsol_var(t=t, z=z) - V_comsol(t=t)\n",
-                        "\n",
-                        "\n",
-                        "dfn_var = solutions[\"1+1D DFN\"][var]\n",
-                        "V = solutions[\"1+1D DFN\"][\"Voltage [V]\"]\n",
-                        "\n",
-                        "\n",
-                        "def dfn_var_fun(t, z):\n",
-                        "    return dfn_var(t=t, z=z) - V(t=t)\n",
-                        "\n",
-                        "\n",
-                        "dfncc_var =  dfncc_vars[var]\n",
-                        "V_dfncc = dfncc_vars[\"Voltage [V]\"]\n",
-                        "\n",
-                        "\n",
-                        "def dfncc_var_fun(t, z):\n",
-                        "    return dfncc_var(t=t, z=z) - V_dfncc(t)\n",
-                        "\n",
-                        "\n",
-                        "plot(\n",
-                        "    t_plot,\n",
-                        "    z_plot,\n",
-                        "    t_slices,\n",
-                        "    \"$\\phi^*_{\\mathrm{s,cp}} - V^*$\",\n",
-                        "    \"[V]\",\n",
-                        "    comsol_var_fun,\n",
-                        "    dfn_var_fun,\n",
-                        "    dfncc_var_fun,\n",
-                        "    param,\n",
-                        "    cmap=\"viridis\",\n",
-                        ")"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "the through-cell current "
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 17,
-                  "metadata": {},
-                  "outputs": [
-                        {
-                              "data": {
-                                    "image/png": 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-                                    "text/plain": [
-                                          "<Figure size 1300x700 with 5 Axes>"
-                                    ]
-                              },
-                              "metadata": {},
-                              "output_type": "display_data"
-                        }
-                  ],
-                  "source": [
-                        "var = \"Current collector current density [A.m-2]\"\n",
-                        "comsol_var_fun = comsol_solution[var]\n",
-                        "dfn_var_fun = solutions[\"1+1D DFN\"][var]\n",
-                        "\n",
-                        "I_av = solutions[\"Average DFN\"][var]\n",
-                        "\n",
-                        "\n",
-                        "def dfncc_var_fun(t, z):\n",
-                        "    \"In the DFNCC the current is just the average current\"\n",
-                        "    return np.transpose(np.repeat(I_av(t)[:, np.newaxis], len(z), axis=1))\n",
-                        "\n",
-                        "\n",
-                        "plot(\n",
-                        "    t_plot,\n",
-                        "    z_plot,\n",
-                        "    t_slices,\n",
-                        "    \"$\\mathcal{I}^*$\",\n",
-                        "    \"[A/m${}^2$]\",\n",
-                        "    comsol_var_fun,\n",
-                        "    dfn_var_fun,\n",
-                        "    dfncc_var_fun,\n",
-                        "    param,\n",
-                        "    cmap=\"plasma\",\n",
-                        ")"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "and the temperature with respect to reference temperature"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 18,
-                  "metadata": {
-                        "scrolled": true
-                  },
-                  "outputs": [
-                        {
-                              "data": {
-                                    "image/png": 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WrVqFDRs2YMqUKXj99dexceNG9e9xoPX2IG1NmTIFU6ZMUX+/fPky1q9fb5Uw2L17t/oUP08RQmj62GqqGSqe9+eeew4DBw5Uf2/QoIHNOg0bNsSzzz6LCRMm2HxJ4ej7n7+8TzLRQ1RL7dixA4DnkygJCQlWb7LuqK6Op0+fxsCBA7F69WpceeWVePfdd9GlSxePxLbUoUMHLFmyBD169MAdd9yBtWvX2nxbSkRENZspmbFz507cdNNNNst37txpVc6TrrnmGkiShL1791Za5tprrwUA7NmzBx07drRZvmfPHrRo0cJmfv369XHXXXdh8ODBKC4uRvfu3XHx4kW7Ma644gr0798f/fv3xyuvvIJrr70Wc+fOxcSJE3HttddaffteMbZlHcl9ERERKCws9Elcb3nsscdw//33q7/369cPvXv3xj333KPOs3dLjLv27NnjF7eP1kSma9Dyunjuuefw9NNP2wyHcurUKQCwerr18OHDMWTIEOh0Oquypp42lmU91T4wqXjeGzRogKuvvrra9UaNGoV3330X7777rtX8hg0bIiYmpsr3acD8Prh37160bdvWhZpro8bfukVE3rF9+3bodDq7HxpriurqOHz4cCQkJOD06dOYPn067r//fpw7d84rdenSpQs+++wzHDx4EN26dbPq7k5ERDVf586d0bhxY0yZMsVmfAZFUTB16lSkpqaic+fOHo8dGxuLzMxMzJkzB0VFRTbL8/Pz0bVrV8TGxuLNN9+0Wf7dd9/hwIEDeOCBB+xu/+GHH8bq1asxYMAAmwZXZerVq4fExES1Pn379sWBAwfw/fff25R98803Ub9+fdxxxx0ObZuqJ0kSIiMjNZ+82fMlNjYWV199tTqFh4cjLi7Oap6nx1JdtWoVduzYgd69e3t0u7WFvesiJCQEkZGRCA0NtVvW8tbX4OBgREZGIiwszKGynuLOea9Tpw7Gjx+PyZMnWyXFZVlG37598emnn+LEiRM26xUWFqK8vBxt2rRBixYt8Oabb9od6yc/P9/pOnkDEz1EtdT27dvVP8I1VVV1LCwsxJIlSzBx4kRERETg3//+N1q1aoVvv/3Wa/W5++678eGHH2Lr1q3497//bTPwHBER1Vw6nQ5vvvkmli5dil69eiEnJwcXL15ETk4OevXqhaVLl+KNN95wOFHirDlz5kCv16NDhw74+uuvceDAAezZswezZ89Geno6IiMj8f777+Pbb7/F0KFDsX37dhw+fBjz5s3DwIEDce+991r1lrDUrVs3nD59GpMmTbK7/P3338ewYcPw888/46+//sKuXbswevRo7Nq1Sx18uW/fvrj77ruRnZ2NefPm4fDhw9i+fTseffRRfPfdd/i///s/q4Gk9Xo9tm3bZjVV1iOI/FdhYaF6fgHg0KFD2LZtm9U4LVrFKCkpQW5uLo4fP46tW7diypQp6NmzJ+666y4MGDDAY/WhmsUb533o0KGIjo7GwoULreZPnjwZycnJSEtLwyeffILdu3fjwIED+Oijj9C2bVsUFhZCkiTMnz8f+/fvR+fOnbFs2TL8/fff2L59OyZPnoyePXt6Yrfdxlu3iGqh8vJy7Nmzp8a8EdlTXR0PHDiAOnXq4Morr1TntWrVCrt27fJqvQYNGoRz587h2WefxX333YfFixfzSX9ERH7innvuwVdffYVnnnnG6vao1NRUfPXVV1a3mHhakyZNsHXrVkyePBnPPPMMTp48iYYNG6Jdu3Z47733ABgeLPLrr79i8uTJ6Ny5M4qLi3HNNdfgxRdfxNNPP11pbwxJkuyOR2HSoUMH/P7773jsscdw4sQJ1KlTBy1btsSSJUtw6623qtv44osvMGvWLMycOROPP/44wsLCkJ6ejtWrV+Pmm2+22mZhYaHNbQtXXXUVDh486M5hohpm8+bNuP3229XfR40aBQDIzs722OC6jsZYvnw5EhMTERQUhHr16qF169aYPXs2srOz7T79iAKDN857cHAwXnnlFTz44INW82NjY7F+/Xq89tprePXVV3HkyBHUq1cPrVq1wvTp0xEdHQ3A8J66efNmTJ48GUOGDMGZM2eQmJiIjh07YtasWe7uskdIwl9GEyIisrB27Vr079/farT9F198EWfPnsXcuXMd3s7AgQOxatUqbN26FUFBQS49ErG4uBiFhYWYNm0apk+fjtOnT1f5gZuIiFxTXFyMQ4cOITU11eZWAWfo9XqsXbsWJ0+eRGJiIjp37uy1njxERESA5/6GOYJfQxORX6pTp47NODkFBQWoU6eO09s6duwYGjZsiJYtW6qDcTpj7ty5GDlypNPrERGRb+h0Otx2222+rgYREZFXMNFDRH7pmmuuQWFhIY4fP44rrrgCgOGJKc7eq/v888/joYceAgCXkkQA0Lt3b6vH9Zq6dRIREREREWmNt24Rkd+67777EB0djbfffhsrV65EdnY2Dhw4gNjYWF9XjYiIvEDLbu9ERESexFu3iIgc8O677yI7Oxv169fHlVdeic8//5xJHiIiIiIiqtWY6CEiv9WwYUMsW7bM19UgIiIiIiKqMfgcOiIiIiLyKxx5gIiI/I2Wf7uY6CEiIiIivxAcHAwAuHTpko9rQkRE5JzS0lIAhic/ehtv3SIiIiIiv6DT6RATE4NTp04BACIiIiBJko9rRUREVDVFUXD69GlEREQgKMj7aRgmeoiIiIjIbyQkJACAmuwhIiLyB7IsIyUlRZMvKPh4dSIiIiLyO3q9HmVlZb6uBhERkUNCQkIgy9qMnsNEDxERERERERFRgOBgzEREREREREREAYKJHiIiIiIiIiKiAMFEDxERERERERFRgGCih4iIiIiIiIgoQDDRQ0REREREREQUIJjoISIiIiIiIiIKEEz0EBEREREREREFCCZ6iIiIiIiIiIgCBBM95LY5c+agcePGCAsLQ1paGjZu3Fhp2Q8//BCdO3dGvXr1UK9ePWRkZFRZPlA4c4wsLVq0CJIkoVevXt6toI85e3zy8/MxfPhwJCYmIjQ0FNdeey2WLVumUW19w9ljNGvWLDRt2hTh4eFITk7GyJEjUVxcrFFttbdmzRpkZWUhKSkJkiRhyZIl1a6zevVq3HDDDQgNDcXVV1+NBQsWeL2evuLs8fnmm29wxx13oGHDhoiKikJ6ejp++uknbSpLRERERG5hoofc8vnnn2PUqFF46aWXsHXrVrRu3RqZmZk4deqU3fKrV6/GAw88gF9//RU5OTlITk5G165dcfz4cY1rrh1nj5HJ4cOH8eyzz6Jz584a1dQ3nD0+paWluOOOO3D48GF89dVX2LdvHz788ENcccUVGtdcO84eo4ULF2LMmDF46aWXsGfPHsybNw+ff/45XnjhBY1rrp2ioiK0bt0ac+bMcaj8oUOH0KNHD9x+++3Ytm0bnn76aTzyyCMBm8xw9visWbMGd9xxB5YtW4YtW7bg9ttvR1ZWFv78808v15SIiIiI3CUJIYSvK0H+Ky0tDTfeeCPeeecdAICiKEhOTsYTTzyBMWPGVLu+Xq9HvXr18M4772DAgAHerq5PuHKM9Ho9brnlFjz88MNYu3Yt8vPzHeqh4I+cPT5z587F9OnTsXfvXgQHB2tdXZ9w9hiNGDECe/bswcqVK9V5zzzzDDZs2IDff/9ds3r7iiRJWLx4cZU94UaPHo0ffvgBO3fuVOf17dsX+fn5WL58uQa19B1Hjo89LVu2RJ8+fTBhwgTvVIyIiIiIPII9eshlpaWl2LJlCzIyMtR5siwjIyMDOTk5Dm3j0qVLKCsrQ2xsrLeq6VOuHqNJkyYhLi4OgwcP1qKaPuPK8fnuu++Qnp6O4cOHIz4+Htdddx2mTJkCvV6vVbU15cox6tixI7Zs2aLe3vX3339j2bJluPPOOzWpsz/IycmxOqYAkJmZ6fB7V22jKAouXrwYsO/VRERERIEkyNcVIP915swZ6PV6xMfHW82Pj4/H3r17HdrG6NGjkZSUZNPgChSuHKPff/8d8+bNw7Zt2zSooW+5cnz+/vtvrFq1Cv369cOyZctw8OBBPP744ygrK8NLL72kRbU15coxevDBB3HmzBl06tQJQgiUl5fjscceC+hbt5yVm5tr95gWFBTg8uXLCA8P91HNaqY33ngDhYWFuP/++31dFSIiIiKqBnv0kM+89tprWLRoERYvXoywsDBfV6dGuHjxIvr3748PP/wQDRo08HV1aiRFURAXF4cPPvgA7dq1Q58+ffDiiy9i7ty5vq5ajbF69WpMmTIF7777LrZu3YpvvvkGP/zwA1555RVfV4380MKFCzFx4kR88cUXiIuL83V1iIiIiKga7NFDLmvQoAF0Oh3y8vKs5ufl5SEhIaHKdd944w289tpr+OWXX3D99dd7s5o+5ewx+uuvv3D48GFkZWWp8xRFAQAEBQVh3759uOqqq7xbaQ25cg0lJiYiODgYOp1Onde8eXPk5uaitLQUISEhXq2z1lw5RuPHj0f//v3xyCOPAABatWqFoqIiDB06FC+++CJkmTn+hIQEu8c0KiqKvXksLFq0CI888gi+/PLLgO15SURERBRo+GmfXBYSEoJ27dpZDfiqKApWrlyJ9PT0StebNm0aXnnlFSxfvhzt27fXoqo+4+wxatasGXbs2IFt27ap07///W/1yUDJyclaVt/rXLmGbr75Zhw8eFBNgAHA/v37kZiYGHBJHsC1Y3Tp0iWbZI4pMcbx9w3S09OtjikArFixosr3rtrms88+w6BBg/DZZ5+hR48evq4OERERETmIPXrILaNGjUJ2djbat2+PDh06YNasWSgqKsKgQYMAAAMGDMAVV1yBqVOnAgBef/11TJgwAQsXLkTjxo2Rm5sLAKhTpw7q1Knjs/3wJmeOUVhYGK677jqr9WNiYgDAZn6gcPYaGjZsGN555x089dRTeOKJJ3DgwAFMmTIFTz75pC93w6ucPUZZWVmYMWMG2rZti7S0NBw8eBDjx49HVlaWVU+oQFJYWIiDBw+qvx86dAjbtm1DbGwsUlJSMHbsWBw/fhyffPIJAOCxxx7DO++8g+effx4PP/wwVq1ahS+++AI//PCDr3bBq5w9PgsXLkR2djbeeustpKWlqe/V4eHhiI6O9sk+EBEREZGDBJGb3n77bZGSkiJCQkJEhw4dxPr169Vlt956q8jOzlZ/b9SokQBgM7300kvaV1xDzhyjirKzs0XPnj29X0kfcvb4rFu3TqSlpYnQ0FDRpEkTMXnyZFFeXq5xrbXlzDEqKysTL7/8srjqqqtEWFiYSE5OFo8//rg4f/689hXXyK+//mr3vcV0XLKzs8Wtt95qs06bNm1ESEiIaNKkiZg/f77m9daKs8fn1ltvrbI8EREREdVckhDsx09ERERE/kWv16OsrMzX1SAiInJISEiIZmNl8tYtIiIiIvIbQgjk5uYiPz/f11UhIiJymCzLSE1N1WRcUfboISIiIiK/cfLkSeTn5yMuLg4RERGQJMnXVSIiIqqSoig4ceIEgoODkZKS4vW/XezRQ0RERER+Qa/Xq0me+vXr+7o6REREDmvYsCFOnDiB8vJyBAcHezUWH69ORERERH7BNCZPRESEj2tCRETkHNMtW3q93uuxmOghIiIiIr/C27WIiMjfaPm3i4keIiIiIiIiIqIAwUQPeVVJSQlefvlllJSU+LoqNRaPUfV4jKrG41M9HqPq8RgRec/UqVNx4403om7duoiLi0OvXr2wb98+qzLFxcUYPnw46tevjzp16qB3797Iy8uzKnP06FH06NEDERERiIuLw3PPPYfy8nItd4UC2PHjx/HQQw+hfv36CA8PR6tWrbB582Z1uRACEyZMQGJiIsLDw5GRkYEDBw5YbePcuXPo168foqKiEBMTg8GDB6OwsFDrXaEAs2bNGmRlZSEpKQmSJGHJkiU2ZTx1fW7fvh2dO3dGWFgYkpOTMW3aNG/umtcw0UNeVVJSgokTJ7LhUAUeo+rxGFWNx6d6PEbV4zEi8p7ffvsNw4cPx/r167FixQqUlZWha9euKCoqUsuMHDkS33//Pb788kv89ttvOHHiBO655x51uV6vR48ePVBaWop169bh448/xoIFCzBhwgRf7BIFmPPnz+Pmm29GcHAwfvzxR+zevRtvvvkm6tWrp5aZNm0aZs+ejblz52LDhg2IjIxEZmYmiouL1TL9+vXDrl27sGLFCixduhRr1qzB0KFDfbFLFECKiorQunVrzJkzp9Iynrg+CwoK0LVrVzRq1AhbtmzB9OnT8fLLL+ODDz7w6v55hSDyogsXLggA4sKFC76uSo3FY1Q9HqOq8fhUj8eoejxG5A8uX74sdu/eLS5fvuzrqrjl1KlTAoD47bffhBBC5Ofni+DgYPHll1+qZfbs2SMAiJycHCGEEMuWLROyLIvc3Fy1zHvvvSeioqJESUmJ3TglJSVi+PDhIiEhQYSGhoqUlBQxZcoUL+4Z+avRo0eLTp06VbpcURSRkJAgpk+frs7Lz88XoaGh4rPPPhNCCLF7924BQGzatEkt8+OPPwpJksTx48cr3e5LL70kkpOTRUhIiEhMTBRPPPGEh/aKAhEAsXjxYqt5nro+3333XVGvXj2r99TRo0eLpk2bVlqfc+fOiQcffFA0aNBAhIWFiauvvlp89NFHdstq+TeMj1cnIiIiIr8lhMClS5c0jxsREeHywJoXLlwAAMTGxgIAtmzZgrKyMmRkZKhlmjVrhpSUFOTk5OCmm25CTk4OWrVqhfj4eLVMZmYmhg0bhl27dqFt27Y2cWbPno3vvvsOX3zxBVJSUnDs2DEcO3bMpTqTa4QQKL9c6pPYQeEhDl+j3333HTIzM3Hffffht99+wxVXXIHHH38cQ4YMAQAcOnQIubm5VtdodHQ00tLSkJOTg759+yInJwcxMTFo3769WiYjIwOyLGPDhg24++67beJ+/fXXmDlzJhYtWoSWLVsiNzcX//vf/9zcc3KUEALQa//+CQDQuf4eWpGnrs+cnBzccsst6tOxAMP77Ouvv47z589b9XAzGT9+PHbv3o0ff/wRDRo0wMGDB3H58mWP7Jc7mOhxU3FxMUpLffPm7Q8KCgqs/iVbPEbV4zGqGo9P9XiMqsdjQ/7q0qVLqFMnRvO4hYX5iIyMdHo9RVHw9NNP4+abb8Z1110HAMjNzUVISAhiYmKsysbHxyM3N1ctY5nkMS03LbPn6NGjuOaaa9CpUydIkoRGjRo5XV9yT/nlUrzf9imfxH70z7cQHBHqUNm///4b7733HkaNGoUXXngBmzZtwpNPPomQkBBkZ2er15i9a9DyGo2Li7NaHhQUhNjY2Cqv0YSEBGRkZCA4OBgpKSno0KGDs7tKrtJfgvJFXPXlvEC+/xQQ5Px7qD2euj5zc3ORmppqsw3TMnuJnqNHj6Jt27ZqAqlx48bu75AHMNHjhuLiYoSHh/u6Gn4hOTnZ11Wo8XiMqsdjVDUen+rxGFWtTp06hm/3iMhrhg8fjp07d+L333/3eqyBAwfijjvuQNOmTdGtWzfcdddd6Nq1q9fjkv9RFAXt27fHlClTAABt27bFzp07MXfuXGRnZ3st7n333YdZs2ahSZMm6NatG+68805kZWUhKIjNVPIPw4YNQ+/evbF161Z07doVvXr1QseOHX1dLSZ63GHuyaMDYOp2JsE0xrVkGutaMo15LavzJHWeBEnSWZWXJNtylmVMXdwk6NQyMmzL2WzL+K8s6cw/w1xe3YZxnmzcJxmyOZZpPSFbbKPCv0KGee9kdVuyMG1fUv81x5CslsmS5TKYy6v7bpwnmctY/mzYhuX2oK5n+lndrmT+17Rd2eJf03LLbZh+r7gNWbKsm51tVFFetlteWAU1xBR2ti8q7Luw2a5kWq/a8qbtm8uo8yzLq/Mq1EcS6jy5inmSJCyOh1DLGWYIi/001UPYlLOsg029Leto8a/5Z9v9tbf9ituQJaXSZZAt62FRzvRWULE+srAqZ7NducK2ZGEVS10mW++TJAug4nZl03qKw/PU9dVlsCkPy22Yfq5YR1lYzLOsI6zmWb54JcsXsulf8w6a58mmn2Xb8hWWCVkGjO+RtuvJhuUVl8mG8kI9iTrzPDWW6Xed9c/GZep2pYrLgizKB5nrIQWZlxv/VZdDZ7NMqlBekoIA4zxJnadTl8mSnXmyjIKCy2ic/JTHulETaSUiIgKFhfk+ieusESNGqAOAXnnller8hIQElJaWIj8/36pXT15eHhISEtQyGzdutNqe6alcpjIV3XDDDTh06BB+/PFH/PLLL7j//vuRkZGBr776yum6k2uCwkPw6J9v+Sy2oxITE9GiRQurec2bN8fXX38NwHyN5eXlITExUS2Tl5eHNm3aqGVOnTpltY3y8nKcO3eu0ms0OTkZ+/btwy+//IIVK1bg8ccfx/Tp0/Hbb78hODjY4fqTi3QRhp41PortKZ66PhMSEmyedljd+2z37t1x5MgRLFu2DCtWrECXLl0wfPhwvPHGGx7ZN1cx0eMhpoSCoSEi2ZlnmmNcJplbOGoyp8pEj7mM+WednfIVEjKSnSSNw4kecwLH/rxKEj3GpVYxjRFMP5uOhquJHnOCwJVET8VtWJaHVXl3Ej32EzcVy9smZOwleiy373Kix6KMJxM9tnWsLtFj3n5liR7JXqKnkkSMoV6OJXoqm2f6vcpEj1x9okeqNNFjm3AylVcTt3LlMS3L2CZ6JNvty8KcULFJ4Dg+T11frrhMgsVbmHme+cVqvQ3ZspzFPGcTPeaL2Xaezb+y/USPvWSO8V9zAqaKRI9VMsdiHqpK9OhsywGVJHp0VSd67CwzJ3qCjYfHXqLH/K/9RI+xTkR+SJIkl26h0pIQAk888QQWL16M1atX29wa0K5dOwQHB2PlypXo3bs3AGDfvn04evQo0tPTAQDp6emYPHkyTp06pd5+sGLFCkRFRdk00C1FRUWhT58+6NOnD+69915069YN586dU8cHIu+SJMnh26d86eabb8a+ffus5u3fv1+93S81NRUJCQlYuXKl2nAuKCjAhg0bMGzYMACGazQ/Px9btmxBu3btAACrVq2CoihIS0urNHZ4eDiysrKQlZWF4cOHo1mzZtixYwduuOEGL+wpWZIkyWO3T/mSp67P9PR0vPjiiygrK1MTjStWrEDTpk3t3rZl0rBhQ2RnZyM7OxudO3fGc889x0QPEREREVEgGz58OBYuXIhvv/0WdevWVceDiI6ORnh4OKKjozF48GCMGjUKsbGxiIqKwhNPPIH09HTcdNNNAICuXbuiRYsW6N+/P6ZNm4bc3FyMGzcOw4cPR2io/UTCjBkzkJiYiLZt20KWZXz55ZdISEiwGQuIaOTIkejYsSOmTJmC+++/Hxs3bsQHH3ygPlZakiQ8/fTTePXVV3HNNdcgNTUV48ePR1JSEnr16gXA0AOoW7duGDJkCObOnYuysjKMGDECffv2RVJSkt24CxYsgF6vR1paGiIiIvDf//4X4eHhHE+KrBQWFuLgwYPq74cOHcK2bdsQGxuLlJQUj12fDz74ICZOnIjBgwdj9OjR2LlzJ9566y3MnDmz0rpNmDAB7dq1Q8uWLVFSUoKlS5eiefPmXj0ejmCih4iIiIjIi9577z0AwG233WY1f/78+Rg4cCAAYObMmZBlGb1790ZJSQkyMzPx7rvvqmV1Oh2WLl2KYcOGIT09HZGRkcjOzsakSZMqjVu3bl1MmzYNBw4cgE6nw4033ohly5ZBNvVIJDK68cYbsXjxYowdOxaTJk1CamoqZs2ahX79+qllnn/+eRQVFWHo0KHIz89Hp06dsHz5coSFhallPv30U4wYMQJdunRRr+fZs2dXGjcmJgavvfYaRo0aBb1ej1atWuH7779H/fr1vbq/5F82b96M22+/Xf191KhRAIDs7GwsWLAAgGeuz+joaPz8888YPnw42rVrhwYNGmDChAkYOnRopXULCQnB2LFjcfjwYYSHh6Nz585YtGiRh4+A8yTBURddVlBQgOjoaADmW6EMtxQ4M0aPbHO7VfVj9NjeulXZGD1eu3Wr2jF67Ny6JXjrFm/d4q1bFctbjr9TWczqxuixf+uWB8fosbl1y4UxeireEubKGD3qxezIGD2V3bpV1Rg9tfvWrYKCS4iNHooLFy4gKioKRDVRcXExDh06hNTUVKsP70RERDWdln/DmM4nIiIiIiIiIgoQTPQQEREREREREQUIJnqIiIiIiIiIiAIEEz1ERERERERERAGCiR4iIiIiIiIiogDBRA8RERERERERUYBgooeIiIiIiIiIKEAw0UNEREREREREFCCY6CEiIiIiIiIiChBM9BARERERERERBQgmeoiIiIiINPLaa69BkiQ8/fTTVvOLi4sxfPhw1K9fH3Xq1EHv3r2Rl5dnVebo0aPo0aMHIiIiEBcXh+eeew7l5eUa1p4ClV6vx/jx45Gamorw8HBcddVVeOWVVyCEUMsIITBhwgQkJiYiPDwcGRkZOHDggNV2zp07h379+iEqKgoxMTEYPHgwCgsLtd4dolqPiR4iIiIiIg1s2rQJ77//Pq6//nqbZSNHjsT333+PL7/8Er/99htOnDiBe+65R12u1+vRo0cPlJaWYt26dfj444+xYMECTJgwQctdoAD1+uuv47333sM777yDPXv24PXXX8e0adPw9ttvq2WmTZuG2bNnY+7cudiwYQMiIyORmZmJ4uJitUy/fv2wa9curFixAkuXLsWaNWswdOhQX+wSUa3GRA8RERERkZcVFhaiX79++PDDD1GvXj2rZRcuXMC8efMwY8YM/Otf/0K7du0wf/58rFu3DuvXrwcA/Pzzz9i9ezf++9//ok2bNujevTteeeUVzJkzB6WlpXZjlpaWYsSIEUhMTERYWBgaNWqEqVOnen1fyf+sW7cOPXv2RI8ePdC4cWPce++96Nq1KzZu3AjA0Jtn1qxZGDduHHr27Inrr78en3zyCU6cOIElS5YAAPbs2YPly5fj//7v/5CWloZOnTrh7bffxqJFi3DixAm7cYUQePnll5GSkoLQ0FAkJSXhySef1Gq3iQIWEz1ERERE5LeEELhcVKL5ZHlLiyOGDx+OHj16ICMjw2bZli1bUFZWZrWsWbNmSElJQU5ODgAgJycHrVq1Qnx8vFomMzMTBQUF2LVrl92Ys2fPxnfffYcvvvgC+/btw6efforGjRs7VW9yjxACSvFln0zOXKMdO3bEypUrsX//fgDA//73P/z+++/o3r07AODQoUPIzc21ukajo6ORlpZmdY3GxMSgffv2apmMjAzIsowNGzbYjfv1119j5syZeP/993HgwAEsWbIErVq1cvo4E5G1IF9XgIiIiIjIVcWXSnFX3NOax116ahbCI0MdKrto0SJs3boVmzZtsrs8NzcXISEhiImJsZofHx+P3NxctYxlkse03LTMnqNHj+Kaa65Bp06dIEkSGjVq5FB9yXNESTEOP2ib3NNC44W/QAoLd6jsmDFjUFBQgGbNmkGn00Gv12Py5Mno168fAPM1Zu8atLxG4+LirJYHBQUhNja2yms0ISEBGRkZCA4ORkpKCjp06ODUfhKRLSZ6PERAmH4w/a+SfyvOUwBIDkSwLFMxOy/M8SuUUSAgGX+W1A5cwupn8zLZ+LNpW6aYMiQoxtKGMkLI6jZExX+FbLEF0zIJQsjGOknGZRJk48+yxTzT7+ZlsCkvWcwzlZEqlLc3T6qw3HpbFnssLLZhnClLFY6KZLENizJSxXkVfq6svGy3vLAKaogp7GxfWO+7JGy2a74Oqitv2r65jDrPsrw6r0J9JPM1J1cxT5KExfEQajnDDGGxn6Z6CJtylnWwqbdlHS3+Nf9su7/2tl9xG7KkVLoMsmU9LMrJ1vup/isLq3I225UrbEsWVrHUZbL1PkmyACpuV72oFYfnqeury6DGrlgPyMK8bsU6ysJinmUdYTXP8l/Jzjzzi8tiW3LFepjKKzbbFfZejBb/Cov9U/81ratem5bbU6zKC1lY/2xcppYznhOr9WSlQnk9hGRabv5XXQd6tZzpX0mynidJQYBk+PMuycZ/JZ26TJbszJNlFBRcBhF53rFjx/DUU09hxYoVCAsL0zT2wIEDcccdd6Bp06bo1q0b7rrrLnTt2lXTOpB/+OKLL/Dpp59i4cKFaNmyJbZt24ann34aSUlJyM7O9lrc++67D7NmzUKTJk3QrVs33HnnncjKykJQEJupRO7gK8gNISEhSEhIqDRDLWx+sD+PiIioJkhISEBISIivq0HklLCIECw9NcsncR2xZcsWnDp1CjfccIM6T6/XY82aNXjnnXdQUlKChIQElJaWIj8/36pXT15eHhISEgAYXp+m8VIsl5uW2XPDDTfg0KFD+PHHH/HLL7/g/vvvR0ZGBr766itndpXcIIWGofHCX3wW21HPPfccxowZg759+wIAWrVqhSNHjmDq1KnIzs5Wr7G8vDwkJiaq6+Xl5aFNmzYADNfhqVOnrLZbXl6Oc+fOVXqNJicnY9++ffjll1+wYsUKPP7445g+fTp+++03BAcHO7O7RGSBiR43hIWF4dChQ5UOgEdERORPQkJCNO9xQOQuSZIcvoXKF7p06YIdO3ZYzRs0aBCaNWuG0aNHQ6fToV27dggODsbKlSvRu3dvAMC+fftw9OhRpKenAwDS09MxefJknDp1Sr09ZsWKFYiKikKLFi0qjR8VFYU+ffqgT58+uPfee9GtWzecO3cOsbGxXtpjsiRJksO3T/nSpUuXIMvWw7fqdDooiqFHaWpqKhISErBy5Uo1sVNQUIANGzZg2LBhAAzXaH5+PrZs2YJ27doBAFatWgVFUZCWllZp7PDwcGRlZSErKwvDhw9Hs2bNsGPHDqvkKBE5h4keN4WFhfFDMRERERHZVbduXVx33XVW8yIjI1G/fn11fnR0NAYPHoxRo0YhNjYWUVFReOKJJ5Ceno6bbroJANC1a1e0aNEC/fv3x7Rp05Cbm4tx48Zh+PDhCA21n+iaMWMGEhMT0bZtW8iyjC+//BIJCQk2YwERZWVlYfLkyUhJSUHLli3x559/YsaMGXj44YcBGBJWTz/9NF599VVcc801SE1Nxfjx45GUlIRevXoBAJo3b45u3bphyJAhmDt3LsrKyjBixAj07dsXSUlJduMuWLAAer0eaWlpiIiIwH//+1+Eh4dzPCkiNzHRQ0RERETkYzNnzoQsy+jduzdKSkqQmZmJd999V12u0+mwdOlSDBs2DOnp6YiMjER2djYmTZpU6Tbr1q2LadOm4cCBA9DpdLjxxhuxbNkym54bRG+//TbGjx+Pxx9/HKdOnUJSUhIeffRRTJgwQS3z/PPPo6ioCEOHDkV+fj46deqE5cuXW33p/emnn2LEiBHo0qWLej3Pnj270rgxMTF47bXXMGrUKOj1erRq1Qrff/896tev79X9JQp0knD22ZBERERERD5QXFyMQ4cOITU1lT2qiYjIr2j5N4zpfCIiIiIiIiKiAMFEDxERERERERFRgGCih4iIiIiIiIgoQDDRQ0REREREREQUIJjoISIiIiIiIiIKEEz0EBEREZFf4UNjiYjI32j5t4uJHiIiIiLyC8HBwQCAS5cu+bgmREREziktLQUA6HQ6r8cK8noEIiIiIiIP0Ol0iImJwalTpwAAERERkCTJx7UiIiKqmqIoOH36NCIiIhAU5P00DBM9REREROQ3EhISAEBN9hAREfkDWZaRkpKiyRcUkuBNzkRERETkZ/R6PcrKynxdDSIiIoeEhIRAlrUZPYeJHiIiIiIiIiKiAMHBmImIiIiIiIiIAgQTPUREREREREREAYKJHiIiIiIiIiKiAMFEDxERERERERFRgGCih4iIiIiIiIgoQDDRQ0REREREREQUIJjoISIiIiIiIiIKEEz0EBEREREREREFCCZ6iIiIiIiIiIgCRI1M9KxZswZZWVlISkqCJElYsmSJuqysrAyjR49Gq1atEBkZiaSkJAwYMAAnTpyw2sa5c+fQr18/REVFISYmBoMHD0ZhYaFVme3bt6Nz584ICwtDcnIypk2bpsXuERERERERERF5RY1M9BQVFaF169aYM2eOzbJLly5h69atGD9+PLZu3YpvvvkG+/btw7///W+rcv369cOuXbuwYsUKLF26FGvWrMHQoUPV5QUFBejatSsaNWqELVu2YPr06Xj55ZfxwQcfeH3/iIiIiIiIiIi8QRJCCF9XoiqSJGHx4sXo1atXpWU2bdqEDh064MiRI0hJScGePXvQokULbNq0Ce3btwcALF++HHfeeSf++ecfJCUl4b333sOLL76I3NxchISEAADGjBmDJUuWYO/evVrsGhERERERERGRR9XIHj3OunDhAiRJQkxMDAAgJycHMTExapIHADIyMiDLMjZs2KCWueWWW9QkDwBkZmZi3759OH/+vKb1JyIiIiIiIiLyhCBfV8BdxcXFGD16NB544AFERUUBAHJzcxEXF2dVLigoCLGxscjNzVXLpKamWpWJj49Xl9WrV88mVklJCUpKStTfFUXBuXPnUL9+fUiS5NH9IiIi8jYhBC5evIikpCTIckB890MBTlEUnDhxAnXr1uVnLyIi8itafu7y60RPWVkZ7r//fggh8N5773k93tSpUzFx4kSvxyEiItLSsWPHcOWVV/q6GkTVOnHiBJKTk31dDSIiIpdp8bnLbxM9piTPkSNHsGrVKrU3DwAkJCTg1KlTVuXLy8tx7tw5JCQkqGXy8vKsyph+N5WpaOzYsRg1apT6+4ULF5CSkoJjx45ZxSciIvIHBQUFSE5ORt26dX1dFSKHmK5VfvYiIiJ/o+XnLr9M9JiSPAcOHMCvv/6K+vXrWy1PT09Hfn4+tmzZgnbt2gEAVq1aBUVRkJaWppZ58cUXUVZWhuDgYADAihUr0LRpU7u3bQFAaGgoQkNDbeZHRUXxwwYREfkt3gJD/sJ0rfKzFxER+SstPnfVyERPYWEhDh48qP5+6NAhbNu2DbGxsUhMTMS9996LrVu3YunSpdDr9eq4O7GxsQgJCUHz5s3RrVs3DBkyBHPnzkVZWRlGjBiBvn37IikpCQDw4IMPYuLEiRg8eDBGjx6NnTt34q233sLMmTN9ss9ERERERBS4hKIHTv8BcTkXUngC0PBmSLLOb+NoGSvQ4mgZi3Fqpxr5ePXVq1fj9ttvt5mfnZ2Nl19+2WYQZZNff/0Vt912GwDg3LlzGDFiBL7//nvIsozevXtj9uzZqFOnjlp++/btGD58ODZt2oQGDRrgiSeewOjRox2uZ0FBAaKjo3HhwgV+q0RERH6Hf8fI3/Ca9T02hF2McexbKFvHAkVHzDMjG0G+YSqk5J5+F0fLWIEWR8tYjONmPA+/N2j5N6xGPmLjtttugxDCZlqwYAEaN25sd5kQQk3yAIbePQsXLsTFixdx4cIFfPTRR1ZJHgC4/vrrsXbtWhQXF+Off/5xKslDREREFMjmzJmDxo0bIywsDGlpadi4cWOV5WfNmoWmTZsiPDwcycnJGDlyJIqLi9XlL7/8MiRJspqaNWvm7d3wOaHoIfLWQDn8BUTeGkPDwU/jiGPfQvm+FZSV3SHWDYKysjuU71tBHPvWb2NpEUcc+xbK2n5ATEvIXX+FfF8e5K6/AjEtoazt57FYWsXRMlagxdEyFuN4IJ5G73feUCN79PgLU0Zu+86vEBkZASEUCKEAAlAUQEAAQoGAgKLA+DMgBKAIBbIkQQIgAMiybPjQAwCSBFm2+CAECZJsmAcI6BUFOlkHRZGgKAokCChCgRAShKgYU0BAGGMCsiSMMWRIEqqJCeNj3xTjunKVMRVhiGOKqSgChh0UkCXJJqZk2mfTPOMky4Ci6CFJQZAkw7FUFKHGVBQBQDIeb+uYhv3UQ5ZkSBCApKsmJiBJMmQZhnMHXeUxBdQYQggIRQEk8/nUSRIEBGRjlleWJUBUjGmIJxmPt16vhywHQZKEGtN0nSiKYtwvmGMKvXHfAb2iR5AsQxhOH2RJB0kGAEmNZzjuFWIq5dDJIWqC1BRTEcZjYHn+IFnEFMb9lI3XrPk6EUIyXsOmmJbn07CfOl2wMZ5iPGemmJbXrLA4l5LxdWK8jCBZxYSQIOtk87GGpD6mUCdL0CsKZDkIiiKsX5vG/TYfZ2H4MCwZjquiCENM47k1bVOWJeN+69RzaYopAZB1kuGagM7w+jfFNMUwvR8Y6wDjtWs4FHrDdQIAEFbvB0IyH1tJki2uWcnw2tQbrjnDa1NvvH7NCXCgspjC8Bo3vv4tXyfVxRSKgCTpjNesYrx+LWOafjfsj+m8q+8Hsmy8Tq1fhxXfC0zvB3pFgSzJgCQZjq0iIEmKGh8wHW/j9QQYr1fDuTUdT+uYlU+G9wMBAVk9nlAEhGSIrRgbT6b3HzW26T1I0UOnk42vf8l4/RiuI+tYgATZcB4k4/mTg9SYQrF4XzO8oRv+M773KRbHV69XEBSkM5xcyfZvivkYC2NMyRgzEtdcfQd7R5CNzz//HAMGDMDcuXORlpaGWbNm4csvv8S+ffsQFxdnU37hwoV4+OGH8dFHH6Fjx47Yv38/Bg4ciL59+2LGjBkADImer776Cr/88ou6XlBQEBo0aOBwvTz5bSh7cbgQY20/4IrukFs+B0S3AC7shrJrOnD8R8idP/W7WFrEEYoeyvetDI3TWz6HJJm/ZxdCgbKmD5C/G3LWdreuP63iBOI+8dgxjk08L703aNmjp0aO0eNvLl7cAUWEmhMO6gd+UyMAgGWCwPihvaS4HKZrVLYYkMnUCICxgWuZmIAERESEWiQdTAkI07ZNDYEKSQlTzJJymEKZYpobAsYGOoR1MkSWEB4WAuNeGBsfBoaEh6lhZRtTX65HaZne3Cg2xq4upqyTEBZaSUzF0IAzBRUW+w8BlJXpUVZeTUyr42r4OShIh5CQIPN+CfsxKx5zCKC0tBx6vWI8Z5J6jCXDgTaeR1NDzxwzNDQIOmPjzCqmsREnbGKaEltAcUkphAKL/TFfP+Y4xrpYNPjCw0MMSQthPleAgDEPYo6pCKvrFpBw6VKJ+fp0MKYkG2JKkjkmTOfUtN+GWRaJAlNDHSguLjM2iG1jGk8xJPUYG8sYY9o7X8Ii4QEBKLA8toZ6FReXGeovUOH4mV+HMiSr3yUAERGmmObjaJVgsdpf83uEogiUlJRZHDPz/kKyeK1K5pim/Y+ICFGTgebEoLA41rB4jVrGNLwfVB3TdC6tE3jhYcGGc6juh52YwvLYm5IRepSW6G2Om3n7xjpYvCdAkhAkSwgJCzLmiSpeNxbXrnq+Ta8lCWVlepQb34OsX4/m14R6PZkSeJKEkCAdgkJ05vpbvR6sX5um9yDT8S8tLYNeL9R9M4S1uH7VJI+hgOn6DQnVGZM1wiKRYz6Oivr+bn0+AQnFxWUQAhavD8vr1vxaBIzJPWNMftdDlZkxYwaGDBmCQYMGAQDmzp2LH374AR999BHGjBljU37dunW4+eab8eCDDwIAGjdujAceeAAbNmywKhcUFFTp0021VDExIgDvJkZuXmDVUFDW9vNOssJLcYSiNxyvK7pbN7IadIB8y+dQ1vSBsvUFyFfc5ZmGsAaxNNun038ARUcg37zAqnEKGL9EafEslBX/MpSLv6Xmx9EyVqDF0TIW47hMy/c7b2KixwNSUsoQFWU8yepnZsn4i2Q5E+a75SRUffgt1pEqzJbKKilaU2JKFhuQAQR7OKY9noipuBEztJrClcXUA5LiYswwx+LZxCy3PdZVxgPM11C4azGVMkC2uFbsxRQW149kGVNn3rblepKdmGpNBCRRal0HB6psPr5hFRZaXtv2z6UQgITSSo5txdeknQ0gpIpllcQEIIkSc+u+spiV7mcI1HNiE1pNc1osFxBQIImqbgewsz2VDPW8qnWyyE7YnmDjPAVANbcg2N1HU12CKqmXsJilWMdU9NVcP3a2p9ZBrjij8gobsi3GWabXZmWBRYVDZDqPAobXSWVlLM+ldfyCC5eqqCPVVqWlpdiyZQvGjh2rzpNlGRkZGcjJybG7TseOHfHf//4XGzduRIcOHfD3339j2bJl6N+/v1W5AwcOICkpCWFhYUhPT8fUqVORkpJSaV1KSkpQUlKi/l5QUODm3gVWYiTgkhVaxtIojrhseGgMolvYLxDTQi1X3ceWmhBHy1iBFkfLWIzjBi3f77yIiR4PkM7vh1ReMbHgyLek1bW2K16m1ZW3bpDZritgvM+mknWrilnZ/ngrpqPfMtuLCUPjxtmYQlTevqo2pmTdYHM4pmLdSHconr1tOVLGSFEAubLhuao67pUlEh2Iaepq4PAxrqx6lgmPShJGar8UPayHIbOTLKnyMrNMklR/fKVKN1jxeFV2PVRTDzvXjwRRybF19DVUIVNV5aqSxf8B+/uEKo+t4eZGR7KM9o5Nde959spIdnanQhlhJ55kem1WVsXKj63pOhBVHQhhXVqlKFC7IDoYz7wVyViqkvdhe/EASAUlNvOIzpw5A71ej/j4eKv58fHx2Lt3r911HnzwQZw5cwadOnWCEALl5eV47LHH8MILL6hl0tLSsGDBAjRt2hQnT57ExIkT0blzZ+zcuRN169a1u92pU6di4sSJHtu3gEuMBFiyQstYWsWRwhMM78IXdgMNOtgWyN+tlnOHVnG0jBVocbSMxTiu0zSp5EVM9HhAUP5xBJW7cCitvsV24DKxbGebVquW5Qf8it/sOhjT3uacjulE49blmKYVXYxped+Dt2Oq57CamMLeL1KFtm0VlbVXDaWyHhSVxTclz5xLfFjNUoRjnRzUAqZrx3Kek8dWgfYxnXp9eui1aUrwVHv9VFUHYadsFefTlMZwNB9rtXIgvB/YO14VN6BUUdZOYgmwTvw6nGByNHFW4boy/hhcUF7JOkTOWb16NaZMmYJ3330XaWlpOHjwIJ566im88sorGD9+PACge/fuavnrr78eaWlpaNSoEb744gsMHjzY7nbHjh2LUaNGqb8XFBQgOTnZ9YoGWGIk0JIVWsbSbJ8a3gxENoKya7r9sUV2vwFENjaU84c4WsYKtDhaxmIcl2n5fudNTPR4gK7oPHSo5FsfJzpamMtbNsQrK1RZz4lKVqk2ZjVlNI9ZTWPXH2I6clyFAkiV9a7xVcxKjoPVLGH7Y1XXbHU9eRxOGAirf6pcWVT3OgJsG9z2NlshprdS91Y9lDwU06FzaW8dOHDsqlnfJpz1wbU+hU5ljBwiAbC8bckmhNvvB7bLpQrbE06/ThR1nKlKq+QQe68T+3SF3nn6D/m3Bg0aQKfTIS8vz2p+Xl5epePrjB8/Hv3798cjjzwCAGjVqhWKioowdOhQvPjii+qA5JZiYmJw7bXX4uDBg5XWJTQ0FKGhoW7sjbVAS4wEXLJCy1gaxZFkHeQbpkJZ2w/Kmj6QWzxruM7ydxtimAZ2dXOsD63iBOQ+STKktpMhfn8Iym/3QWo+CohpBuTvgdgzAzjxE6SbPwZEGYTeOMyD+sHC8o+t5d/9ypdL14+HyHkEyup7IDV9whhrL8S+t4GTKyClzwP0l2F4HorlF5GmL18rzJMqLDfOM+/T/YYBhf34HGl5fWv6fudFfOqWG0yjZp/9+gpERVbScLb7KaGajw5V9tCQqm8UeSNmtWVciFnTOJQYcGGb1anqnHr6XJqWy5XdTueFmJIE6PWATqdtTKWy2+K8kLGxHIHY09eQI7G1vG5N2Qyn3w8cjVlFYqrKXi6OBHXg9WiVbPPGubSsQ4Xt2z2X1dXZvfeDgiI96vc5xadukY20tDR06NABb7/9NgDDAO4pKSkYMWKE3cGY27Vrh4yMDLz++uvqvM8++wyDBw/GxYsXodPZfvguLCxESkoKXn75ZTz55JMO1cvdJ5aIvDVQVnaH3PVXSHYSI+L0Bigr/gW5y4+Q3OnRE4BPorEa26iyRpY3BrL2Yizl6BKI3/sBSZmQmj0JRDUF8ndD7J0N5P4CKe1dSIl3GL4kE3rjZPEzFIt5lZQx/ixOr4M4uAAoOW2uQGhDSI37ALFtDOWgWGzLYtsV41RRTlzYC+T9BpRZjGcVVBdoeBOkOo0r34b6uzBsp5L5VuUvnwIuHgQUi9uA5VAgMhkIiTGWE9b/qj8L22U2ZYz/ll8GyguMv6uBgKAwQAq23p7TP9dSUhAgBwGQDV8AS7LFz8ZEks182Tzfcp4kA2WFwOVcQFiMdyqHAnVSgfA4QNIZYkpBgGz4WVJ/1pnro5bTqeXMvwcBBfshTiwHSvPNcULqQ0rtC6lhOiCHAHKw8V/jpAux/l0Otp4nBdl80eat9yAtn7rFRI8bTCfq3H/rISrC2QZBZfdaeIqT3+Lb3YQDK1SbdLLTkHElTqDGrKqR7miSSOuYrqwTKAkQqnlcumZdXliJ6q4zb7zXeyZmQZFAbPYFJnrIxueff47s7Gy8//776NChA2bNmoUvvvgCe/fuRXx8PAYMGIArrrgCU6dOBWB4dPqMGTPwwQcfqLduDRs2DO3atcPnn38OAHj22WeRlZWFRo0a4cSJE3jppZewbds27N69Gw0bNnSoXm4negIwMeJoHMMT/MoND0gw/auUG3+uMF+UGwalF6Yyhn/Fqd8h/vrEOlkRUh9Syt1AveuM61hsUygWP+sr/Gwqq7eohx7CNP/SP8CFPYC+2BzLlEQIjrbYRsXESoVtC8VYLztTbW7oE5GRVCEpZEwUKaVA8Vnr5FVkY8g3THH5vZuPV/c3JaWAjg1KckFtSYAwZuDS6ruCWvBZXAhR9a1b3lCiVF+GaqU+ffrg9OnTmDBhAnJzc9GmTRssX75cHaD56NGjVrdjjRs3DpIkYdy4cTh+/DgaNmyIrKwsTJ48WS3zzz//4IEHHsDZs2fRsGFDdOrUCevXr3c4yeMJ9rr/izqNgAt7Ifa+A5xYDrnzp4DQQ5QXG77p1ZlvHRPlRYYfdOEAJEAphSgvNHybrZQb/gToSwClBCI4Gmj5LHBggWHcH5PgWKDJAIhLJyH2vm3cxmVDrwhhfLKhUgahLwH0lw3bhYBQSg1JGOM8cwKmDAhLBI7/BOX4Mou9lQEpGMrvA40NFS+9kZaehTj4f97ZdkVKiaEHiWYkYw8DnfXPsuU8qULvCJ3xs4Bs7oWg9oCQLObpYH5giWyYZ9qGKbasgyQHw9R7Qphuv5eDIRnLqmdVMpY1bleYxn2TjGUl2fgnWzGU1YXC1DvDkFyTAF2wGk8AxmSYDCkoTO3NIUQ5TA1jQzzZ8PdLlBu3G67uvxBlhv2TgyHJIYBkfHCAUg5IEiRdhLmsYrxG5VDzdiEAfamhbFCk2tNEmJKIcjAkXYixXgCUYgASpOBIQx0hGbYr9MayYYZ5EMYEogQERRgTvpKhvooekIPUsgAgyi8ZYuvCjX+nJfPrUQo2HEvJVLbIsJ4u3LxdpcyQNFC3azi/lu8npr//Ql9quM7VcwRACEMdoAC6UEiSoZe8UMqNZSXDdo2fyQxlheEcSYan1wqlzPDeJAGSbHxasFAMZUW5sVeLbJgnyoDyEkAShvNm7FlleJ8qNxxL44MrhDBuVyiGc2HqwaYvNpw7SIbDqJQb63DZUFaS1YSsul0JkIQwrK+UGc6nUm7c33LjNozHXeiN74vG+Ppi4/uh3nCslRJDfKXEnLzWlxrrWlrhdS6M5UqAyoYubNgJ8vUvAg1vrtGPVLfEHj1uUHv0zA1BVLh2H86dv6HAA3XzRdvV6ZhV3aZTPddu1KgJMZ17Cbv74CtXeOsmmBoX0+vH1vZcu7efHrrdKQD56pp153C7cjYLLgvUH1bKHj3kNzz1bag49i2UTSOBYotxiORwoF4LIKQeUHAQKDoMhMQC4fFq8gaXjhvKSsHW3/L6MznYsD9KiaHhFRwFBNUxzFfKgMsnDN9uR7cwJiOCgQt7gdLzhnmRKYbkQnkRkLca0IVBSr5bTWaI3N+AokNAXGdIsW0BOQiitAA4+H+GRn2rsertGuLIV8C5LcCVPSEldQVknaHs1ucBSQf55k/UZItycB5wYjnQ+AHIVw0wxCq/DLHa8E271PU3YwNcB2XvbODv/wBXDYJ83WhDWaGH+LaZoey/90AKjQXkICg7pwG7XgeuGQLdjbPUw6T/LBoQ5ZB7HYAUkQQAUHbPhNg2DlJqP8jpH5jLfpkElF2AfNf/IEVdbSi7/32IzaOA5Luh6/xfc9nFVwOXT0Luvg5SvdaGsn//B2L9Y0BSJnS3fWMu+931QOFfkO/4xXCbCgBx9Bsov/cH4jpDl7HcXHbZTUD+Dsi3fwcpsYuh7PHlUH7rDcTeAF23teayP3cBzqyH3HkRpOQsQ1njbY6Ibg5dj83msqvuAnJ/hZQ+D3JqX0PZs1ug/HQLEJkCXc895rJr+gD/LIXU4R3IVw8ylM3fDWXZjUBoA+h6H1HLKn8MgjjyBaQbXofcbIShbOERKN+1AHQR0PUx9yJTNgyH+GsBpOtfgnzd84ayxaehfNMYAKB7sMhcdstzEPvehdTyOcitXzaULS+C8kWc4fK//5QhiQRA+d/LELumQ2r6OOR20837sdCwXL7nMKQwQ2Ja2TkNYvtESFcNhJw2x1z284aA/hLkf++GVKeRoezedyC2jobU6H7IN883l/26EVByBvKdmyAZxwdTDs6H2DgCuPIu6G753Fz22+ZA0VHImWsg1W9nKHtoEUTOYCDhduj+tdRc9of2wIU9VregimPfQ1nbF2hwE3RdV5rLLu8MnNsK+davIV3RzVD25Eoov/4biGkF3Z3rzWV/6QacWgu5038gpdxjKHs6B8qKDKDOVdD9e7u57Op7DGMb3TQXcpP+hrLn/wflx45AeCJ0d5sTtvq1DwHHFkNqPwPytY8ayhYchLK0NRAcDd19J8znM2coxKFPIbV5FXKLkYayl05AWXINIAVB98AFc9lNIyEOfADpuhcMSRoAojQfyldXAACke49DMiYTlR2vAgc/Apr0h9z0cUOyvawIYpXhIQJy9xxI9a6Hu9ijx8+IMgHhxpF0+nO90y0Q+41DZ7nb6HE2picaWb5oomodszbsI2N6NxrfD7zHqZiWHcJcviPNyRVLa2cij0hK7gmp+AzEJouxgZTLwNkt1gVLzxmmiuwmeSRDgkQXakiMXM4FoBjGfQmONtwSUHwaKNgHhMVDirvZ0BNEFwJxdAlQXgik3Acp8kpADoG4sA/4ZwlQ92pI1z6mji0htr8CXD4BqdV4SLFtDPPObjLMj7nOkHCQggE5GMrv/YD8XZDSPzSMNSMHQ5z6A2LN/ZU34tLm2DbiIpKh6/6HuaypEdf8SdtGXEg9yB3NPXz0ax8Cig5BSrnbuhF38P8AXZhhkFjTKcjfCXFuC6QGN0K+eqCh7KUTULY+D0CClNLLfLRPrjB8JK6Tah5PqTTfPHJbbGtj7xRACo01lA2JghRpfGqbUmYuGxIFKbiO4WdJZ/yoXct66BLVMlJQpPk9Iriu4XUfGmtO6Fi+R3ggyaM19uhxgzoY84zgGt6jx8f8pLK1ptcJYzJmxQ2QDZ/1CLMM6ui5cSM5dOGyQINnytijh/yGJ78NFef+hDj2PYQkG5IoQXUMt6jIIRBSkOF2gaBwSEF1AF0YoAuFUPSGsiHRkILCATnUsD4ASb01xLh9q9syDGXUWzgkXYWyplstwtTbAtTbMiAbYrlU9jIAxXg7TJCxrN54i4szZSVIQRHmsvpi4+0wIWpDyamyQjHcwgGovSkMZUuMt5GYbslxtqwA9JeMxz3C+nYYUeZk2cpv21PPpzNlnTr3nrhOTOfT3eukwvl09zqp9Hy6e52Yz6f710ll59PF66TS88n3iErL1rD3CMvtuoODMfsJ04k6M02jRI9k0fjQsgWipjI1jul3LWbGrDExfXXNMqb3YvrkPqoAjmdUcFmgwfNM9JD/0PJDMhERkSfx1i1/o0iAXptWiAQ73/oGqlqwj7Uh58KYgRdTawKApHEipEb06NEipl7beERERETkfUz0eIAolyDKtf10rmWbx+0GT6C3Qt1U48cN8cOYpmuWMb0XU2uaX7Mi8F8nAAAmeoiIiIgCDhM9HiAUGUIJ7GyG9nfeiErieaoWzjy9iDH9LaYp+VEbYlbOX2NWdv1on0wP9LvFAEAovHubiIiIKNAw0eMJelmzW7d80frwRa8BU1ytCR/k62pFrwHGDBg1qkePlw92oJ9LAIC+VuwlERERUa3CRI8HCL0MoVWix0d80hSwGZTDG8e4ut4K3o1pf0wOT8e03sdaEdN4243vY3r5mrWbdfFuTMkHMe3z/mtT+wFztA0HAIKJHiIiIqKAI/u6AvasWbMGWVlZSEpKgiRJWLJkidVyIQQmTJiAxMREhIeHIyMjAwcOHLAqc+7cOfTr1w9RUVGIiYnB4MGDUVhYaFVm+/bt6Ny5M8LCwpCcnIxp06a5VmFF1nQSGseDIgNC60myUw/JC5P1cbWdJKcmxaFJVichtIgp14yYNpPk1ORUTLvxfBHTuXiuxLQ9vgEY00evTXffX5yto7D7vqfBREREREQBpUb26CkqKkLr1q3x8MMP45577rFZPm3aNMyePRsff/wxUlNTMX78eGRmZmL37t0ICwsDAPTr1w8nT57EihUrUFZWhkGDBmHo0KFYuHAhAMOjzbp27YqMjAzMnTsXO3bswMMPP4yYmBgMHTrUqfoKxcUePa58WWx8/IzF9+tOr+50aeM3+OZI3vyW2+LZzV7vGVExpnOjkXg+prd7D1S2nxrEFLwtjjH9JZ75QvXJ+4H6ozbvB0LxchgiIiIi0lyNTPR0794d3bt3t7tMCIFZs2Zh3Lhx6NmzJwDgk08+QXx8PJYsWYK+fftiz549WL58OTZt2oT27dsDAN5++23ceeedeOONN5CUlIRPP/0UpaWl+OijjxASEoKWLVti27ZtmDFjhguJHmg8GLNWsSTDnQuaDsrhi1vgDDG1HXuktsQ08Mljo1E7HnVeGx6v7guB/9o0xmSmh4iIiCjg1MhET1UOHTqE3NxcZGRkqPOio6ORlpaGnJwc9O3bFzk5OYiJiVGTPACQkZEBWZaxYcMG3H333cjJycEtt9yCkJAQtUxmZiZef/11nD9/HvXq1bOJXVJSgpKSEvX3goICww+m2400o2F/DFOoWjKMgz/2jHBpfTdbk8IHB8qlmL7YTz+L6eq5dCdx50pMdxOFfnMu3eRsyEB/YiQRERFRbeR3iZ7c3FwAQHx8vNX8+Ph4dVlubi7i4uKslgcFBSE2NtaqTGpqqs02TMvsJXqmTp2KiRMn2sw3DMbsmURPtR/Sjbdu1YqP5rViJ93hRivSzWPr7OrCA10VanxM42DM/hbT6VU98Bg+l6rrZtKkxl8/xpia36LGDj1EREREAcfvEj2+NHbsWIwaNUr9vaCgAMnJyepAmlowtD0kzzQGHNyI2t7xxT0ptYKrO+r6CfHHnkuMWbNi1ga14bgKDsZMREREFHD8LtGTkJAAAMjLy0NiYqI6Py8vD23atFHLnDp1ymq98vJynDt3Tl0/ISEBeXl5VmVMv5vKVBQaGorQ0FCb+aanwDjPtWaEO40PV25/kNz8ltmVmEK4mVdyNaZPxpHRNqhPxpGpOLZ2gOIYPYHDZ+dS6+ySwi49RERERIHG7xI9qampSEhIwMqVK9XETkFBATZs2IBhw4YBANLT05Gfn48tW7agXbt2AIBVq1ZBURSkpaWpZV588UWUlZUhODgYALBixQo0bdrU7m1bVXE90eMq11sC/tTbQOvkEuDZMTUc2pSHW5O1JabDIfmkL3KDT86lxkGZ5yEiIiIKPDUy0VNYWIiDBw+qvx86dAjbtm1DbGwsUlJS8PTTT+PVV1/FNddcoz5ePSkpCb169QIANG/eHN26dcOQIUMwd+5clJWVYcSIEejbty+SkpIAAA8++CAmTpyIwYMHY/To0di5cyfeeustzJw50/kKK5Jh0ojQvA8IfNBlwL3Wji8GQXWVW3U1nRcnt+FXMd3oEea1R7rb2Rev3+KodcxKzpcvYsJb8RhT079dRERERKSNGpno2bx5M26//Xb1d9O4ONnZ2ViwYAGef/55FBUVYejQocjPz0enTp2wfPlyhIWFqet8+umnGDFiBLp06QJZltG7d2/Mnj1bXR4dHY2ff/4Zw4cPR7t27dCgQQNMmDDB6UerA57v0VNle1iLnhH2tu9HiRPX1Yqd9DMWA487cd2bbjfUskePr2K6OzCyP8QEfBDPBzF9cvuo17KhREREROQrkhD+1PehZikoKEB0dDSOPJqCqBAXEj0VP187cCbczvM4s7KwiOnttoC9fQ/EmA5VoiqVVdCbL2NfxPQMl09nhRWrTb4ypksxHXrKoLvxanpMTx1XF2MWlCho9P4xXLhwAVFRUe5EJ9KE6bMXr1kiIvI3Wv4Nq5E9evyN1mP0eHSEHkdaFb4a7bU2fIMPyUNJNMc34rleA05sxEfXkMun043rINBjut2hx8UV3ep44kpMdx917up++iAmEREREQUWJno8wLHHqzvaStGiZ4bzMQTgcnbA1Uah1u06uJkAcTxmxZLez4B49KpysLqWMSWHW82+eJ0wprPx3B8oveoteLqfqSOva4/HdKCMOaSz7wGeu36q/9tFRERERP6GiR5PEBKE8FSPHkdaJI4V8yzXv972yVO3HJ5ZIaabjT3HVq9wf4Wb51LzL/Fd6gXi6QvWF43TmhrTFwko1wmN4tjG1FbN6FxT/XFmooeIiIgo8DDR4wGO9ejxdFBtw2nfyBWe711TzTHzzUCo8Mljx7Wm9W5qOpaVX3B2h9y9Slw5gLUhpiPxfPEmRERERESBhIkeT1Bkw6QlDdsChkaz1o0BySftD4+Of+Tgar7okaP5g3Y0jul2As2v2r7eOLA1tfeSv8eseRlE9ughIiIiCjxM9HiAEJJHHlHraNvSgSEuPK7qcF5qUfsgGeGxxxdpEtNFvui5BONjuTUiwdhDS7uQ5sCaq+rA+iLbxZg1L2YV8fh4dSIiIqKAw0SPB3jy1i1HHv0rJF80YL0VsYpHd/ugV4XmORcfJCM83VOqRo5/4qMEmm9iav3a9CbG1DqeUPyq+xoREREROYCJHg/wVI8exwP6xwC8bvPDR7o7vbqvBmP24PmskYkehwt5NmaN69CjeUBPHAFndshTR7z2xuQQPURERESBh4keDxCKDKHlGD0B2Jq03SXtd9K9cV1cOz7aPOWrZtCyrqaBtbU+PrWj0ezt16ak+a2F9vbJ+4OzOx7Tc9eVnZjs0UNEREQUcJjo8QAhKn4Q93TrQFT5q3caXhWDeLfx5cpTsrzB9QZVVQen8o0KwCtjZNTIppvmDxOydxS8/Nq0y7sxtUmSVthPL59Lw6nz/VWsdeJOCGi425IxJsfoISIiIgo0Gj8qKjAJIVeYJI9OhtNknoSQK8yTvDBVjOfZfdJiUlyYvFOXiteHeYIXrhchJEPyqJLJZ+dEqQkTPDrZHFvF3jGHhyeLbSsSFKXisYYLk6hmsiir7r/r50FxaJKtJuHmVHF7jkxax6zqvcLRSXF4Mr/nEVVmzpw5aNy4McLCwpCWloaNGzdWWX7WrFlo2rQpwsPDkZycjJEjR6K4uNitbRIREZHz2KPHU7z4Lazlt8qmsVe1/KbZEMvPGgOuPu3cz3bTXwiICj3CvHWgzSdeCO/fAlTxdah1RxT1FjW3YzpxoPzwdsPaENPVeEz0UGU+//xzjBo1CnPnzkVaWhpmzZqFzMxM7Nu3D3FxcTblFy5ciDFjxuCjjz5Cx44dsX//fgwcOBCSJGHGjBkubZOIiIhcIwn79zeQAwoKChAdHY0997VC3WCdJjG9P25EzYjpMwG8n6Zd8+qDmiohRMVEjx9x8B1SHeJJ23HZffOkOH89l2TjYqkezb7YiQsXLiAqKsrX1aEaJC0tDTfeeCPeeecdAICiKEhOTsYTTzyBMWPG2JQfMWIE9uzZg5UrV6rznnnmGWzYsAG///67S9u0x/TZi9csERH5Gy3/hrFHjwcId7q/O5lmU3sM+CDZo7Xa0JbUMgFieQq1P5+S5r3QfJGMEBI078qh/UtTGHpLaRrRR0+urxUxa8M7LTmrtLQUW7ZswdixY9V5siwjIyMDOTk5dtfp2LEj/vvf/2Ljxo3o0KED/v77byxbtgz9+/d3eZsAUFJSgpKSEvX3goICd3ePiIgo4DHR4wFaP3VL68akoZeCL5qT/hTQ1ZW1TYDUJlofV0kyjF0T6D3uTLfE+cutSe4E1PyuJp/EZKKHbJ05cwZ6vR7x8fFW8+Pj47F371676zz44IM4c+YMOnXqBCEEysvL8dhjj+GFF15weZsAMHXqVEycONHNPSIiIqpdmOjxCNMAxhrwwdNoFAFIHmtJOlN3X3TJcHVFN+rqg910NaSrPQ5801NBy9vFhJpY8kXPJU1jqv/z1MYc5IlzWRsG63EyplC8Vw2qXVavXo0pU6bg3XffRVpaGg4ePIinnnoKr7zyCsaPH+/ydseOHYtRo0apvxcUFCA5OdkTVSYiIgpYTPR4gO3j1b0bS9ubJjy9b/z22Jo7N0641rXLvdMpQbi4BffiunCPo6TlQLOSb8bPUv+ncVxfvIx9cfuoOzGlCv86QNvHqxtj1oDH2FPN06BBA+h0OuTl5VnNz8vLQ0JCgt11xo8fj/79++ORRx4BALRq1QpFRUUYOnQoXnzxRZe2CQChoaEIDQ11c4+IiIhqF798vLper8f48eORmpqK8PBwXHXVVXjllVdgOa60EAITJkxAYmIiwsPDkZGRgQMHDlht59y5c+jXrx+ioqIQExODwYMHo7Cw0On6aPmIal885tzdmN55/HvgTK4fW9euPfcesa7t9W6enHzMNEw/B/brRPLVdSu0nuCTybVH1RsnxYXJR/tJVFFISAjatWtnNbCyoihYuXIl0tPT7a5z6dIlyLL1x0qdzvCgCiGES9skIiIi1/hlj57XX38d7733Hj7++GO0bNkSmzdvxqBBgxAdHY0nn3wSADBt2jTMnj0bH3/8MVJTUzF+/HhkZmZi9+7dCAsLAwD069cPJ0+exIoVK1BWVoZBgwZh6NChWLhwoXMVMjVGnOXsKsL8j/YDzbreGuAYNFXR/ET6jC96u2h/dLV9nfhk0GkvHtjKD4H3+jFWddh9EdNbKoupuPK3i2qFUaNGITs7G+3bt0eHDh0wa9YsFBUVYdCgQQCAAQMG4IorrsDUqVMBAFlZWZgxYwbatm2r3ro1fvx4ZGVlqQmf6rZJREREnuGXiZ5169ahZ8+e6NGjBwCgcePG+Oyzz7Bx40YAhm+OZs2ahXHjxqFnz54AgE8++QTx8fFYsmQJ+vbtiz179mD58uXYtGkT2rdvDwB4++23ceedd+KNN95AUlKSw/Vx+dYtNxp2midP/Ox5yi7XVutkhC8a6j4hNL1m1deJdiGNfJBaqklZAwdUdb374qVQk2IKLybRKt9srXgDIhf06dMHp0+fxoQJE5Cbm4s2bdpg+fLl6mDKR48eterBM27cOEiShHHjxuH48eNo2LAhsrKyMHnyZIe3SURERJ4hCeF//S2mTJmCDz74AD///DOuvfZa/O9//0PXrl0xY8YM9OvXD3///Teuuuoq/Pnnn2jTpo263q233oo2bdrgrbfewkcffYRnnnkG58+fV5eXl5cjLCwMX375Je6+++5q61FQUIDo6Gj8L6sd6gZrlTMztAQCPzngd5dljVTZUdT88vHR9apJwk9Y/xj4r03An1+fzvzF89S5rMkxL5bpcf23W3HhwgVERUV5JjiRF5k+e/GaJSIif6Pl3zC/7NEzZswYFBQUoFmzZtDpdNDr9Zg8eTL69esHAMjNzQUAu4/wNC3Lzc1FXFyc1fKgoCDExsaqZSoqKSlBSUmJ+ntBQYHxJ9PYFVqQ4KveEdryv9ayK8fI28e2xhxFrfMCAoDkxjCzftXbTuN4bgb1dW87XyTiHI3pzGDMnrvMasy7BBERERF5iF8mer744gt8+umnWLhwIVq2bIlt27bh6aefRlJSErKzs70Wd+rUqZg4caLNfG8/davipiVILn82d3Q1q5i1ph3g3kl09Rrwvz51/kAAQtueb6Zbxfxo+CzX47nxOCotk2/ururOufSXO+r4/kNEREQUePwy0fPcc89hzJgx6Nu3LwDDIzyPHDmCqVOnIjs7W31MZ15eHhITE9X18vLy1Fu5EhIScOrUKavtlpeX49y5c5U+5nPs2LEYNWqU+ntBQQGSk5Ph7R49drfs0odzF3s4CNfGmq6wCadpn19yM6Irq/ugt5RvBinWmu96vtWKdrMbbwg+6VGjfUjX3g4cfHF69LquFRcsERERUe3il4meyh7hqSgKACA1NRUJCQlYuXKlmtgpKCjAhg0bMGzYMABAeno68vPzsWXLFrRr1w4AsGrVKiiKgrS0NLtxQ0NDERoaajNfEZJ7Ty5x8oO26w2l6ns4VNaAcLeh5Pz6jhwUFx9b5i3C5gfHVnOzWr5qpzkT1zeJJe/2fLPZfx/16KkNTxfzFGdDe+K4Or277r4fOLk+e/QQERERBR6/TPSYnuKQkpKCli1b4s8//8SMGTPw8MMPAwAkScLTTz+NV199Fddcc436ePWkpCT06tULANC8eXN069YNQ4YMwdy5c1FWVoYRI0agb9++Tj1xy8DNHj1OrCos/u8Klx7hDLfu1LDdmEO8cdtN1Rv0XIPZua24u59Op7s8tKPObcJHrUmNe77BQz16an7b2z8HhHdxjG3tYnrgten0efHHE0lEREREVfLLRM/bb7+N8ePH4/HHH8epU6eQlJSERx99FBMmTFDLPP/88ygqKsLQoUORn5+PTp06Yfny5QgLC1PLfPrppxgxYgS6dOkCWZbRu3dvzJ492/kKeXmMHttwkqbf4Evq/zy1Mcdo39h1Y/Bed6J6LIlWk/vY+KhPj0thXe8G5KlkoW+SaNr2RHN2Gx57GpVnNuNcTB8EdTSmULxbD/KctWvXonPnzvjjjz9w8803+7o6REREVIP55ePVawrT49G2dr8JdTR6vLphHBBtB2gwPxlK48a6l8JVtlmfPBq7dgyYA62b1x4/rA5UX43pg5cJO2WQqy6WlaPN0o18VLUfeOGFF5CVlYXvv/8eU6ZMsVvm/Pnz+Pnnn3H8+HEAQFJSEjIzM1GvXj0tq+pVfLw6ERH5Ky3/hsnVF6GaxJxw8dQkVztJkulnT8atboKh5eyFSVQyQRi+3fbcJKqfhKi0Pt6cvHVsKz/mWl47krHPmwcnqfpJkiTNkzymQ6wINybFuUlv+tmdmC7UwRevk9oyUc03ceJElJeX41//+hf0ej0mTZpkU2bevHlIT0/Hhg0boCgKFEXBhg0b0LFjR8ybN88HtSYiIiJfYY8eN5gyclu6ad2jR5NQNthrIFDUope8F69Ze5sW4OskcPjqdaLtBXSxrBw3/LCBvSP8wIcffogLFy4gJiYGjzzyiM3ypk2bYuvWrYiMjLSaX1hYiBtuuAH79+/XqqpexR49RETkr7T8G+aXY/TUPBa9ULwdSQK0boCYkktMCXqH9omBwM9EGK5Z4dWXSsVN++x14qvELwMSaaq8vBzPPvss3n//fbvLJUnCxYsXbRI9Fy9eNPQ4JCIiolqDiR4P8Hb394qfz4TQ9ok3tefzoW8yWbUlgab9Navtheuz14nG148pieZeWBfWFrXkjajibnr7/HIwZr8xbNgwAMCjjz5qd/kbb7yBW2+9Fddddx2uuOIKAMA///yDXbt24c0339SsnkREROR7LiV6vvvuO6fXueOOOxAeHu5KuFrPMhFQm3rXsKeLtzCh5U2Bnhj1TBItwA+SOzR/nfBc+JtffvkFXbp0semlc9ddd6F79+7YuHEjTpw4AcAwGHOHDh2g0+l8UVUiIiLyEZcSPb169XKqvCRJOHDgAJo0aeJKOD+g9a1b2hIAJF887DyAEwOm8+ibMZdqS8Mu8G9x5JhdRLVPZmYmTp48ibi4OJtlOp0O6enpPqgVERER1SQu37qVm5tr90OGPXXr1nU1jF/wxZNLNL0NxuL/gU+bE1mxl1ZtEOgJLV8kPjhmF7mL59E/vPbaa3jssccQExMDPkODiIiIquNSoic7O9up27AeeughPhnBQ2pLrwHf9RaoDQmtWnDrlqEbmqZMr0smtMi/8IT6gylTpuD+++9HTEyMr6tCREREfsClRM/8+fOdKv/ee++5EsaPBPatW77qNeArWh7j2pAY8AkfXLOK4rueLky+EAW2ir143nvvPXTq1Ak33HAD6tWr56NaERERUU3l9lO3Ll++DCEEIiIiAABHjhzB4sWL0aJFC3Tt2tXtCpItJge8xfBBWuuGem1IDPigg43mEX05flag9/DzFc1fJz44trwJyD+98847mDhxIiRJQnJyMm644QarKSEhwddVJCIiIh9yO9HTs2dP3HPPPXjssceQn5+PtLQ0BAcH48yZM5gxY4b6ONBApggJitOP/nX947WEwB4TyHdqxU4CGicGzFG1F+jXrS/Gzwr0Y2rmo9eJh2I6uh3Bx6v7hRdeeAGxsbHq77t27UJ5eTn+/PNPbN26FVu3bsWHH36IY8eOQZIkJCQk4Pjx4z6sMREREfmS24merVu3YubMmQCAr776CvHx8fjzzz/x9ddfY8KECbUi0eM8y1u9nPtU714bwLW1BQDJWw2eKrbrToPS2XVNjaLa0Yj1xU7WgnGBjGrHNVQb+PeJdPQ6rPiIbqqZxo4dq/5sOmdJSUlISkpCjx491GVnz57Fli1bsG3bNq2rSERERDWI24meS5cuqU/V+vnnn3HPPfdAlmXcdNNNOHLkiNsV9A/ujNHj3HrufSR3Z20vtZirqJLrEd34Jt5H9zEEflsr4HfQqPbcRhX41yxRzVTVU7fq16+Prl278tZ5IiKiWs7tRM/VV1+NJUuW4O6778ZPP/2EkSNHAgBOnTpVe560JeBagsCZhlLF7fPx6lVwta7CreSSO7yWHKhku1r1lrLcr9qRGPDFbVS15/HqgT5QOsfoIUcsX74c0dHRvq4GERER1WBuJ3omTJiABx98ECNHjkSXLl2Qnp4OwNC7p23btm5X0B8IuNjocbWlJAFwekwg86raruhP3NlJ15NLXlVJtWpLbykml7wQzSfH1P8GSveX8X04Ro//YW8dIiIiqo7biZ57770XnTp1wsmTJ9G6dWt1fpcuXXD33Xe7u/kAYq91VHFe9Z/ShXD3Bix3Wh8aNyi1D+kDNX28nIr1c/6adSWqu2sBvhm0HKgtySWt+d9B9cV14EpMjtFDREREFHjcTvQAQEJCgs2jPDt06OCJTfsJd8boqbidakq4Hcb5DRiSS+62mJ1fX0iS5r1ANE8u+eS5454M6O3Ku7Z9YfF/V9d2GZNLRERERETkQ7IrK23fvh2K4nh/b9NjQD3p+PHjeOihh1C/fn2Eh4ejVatW2Lx5s7pcCIEJEyYgMTER4eHhyMjIwIEDB6y2ce7cOfTr1w9RUVGIiYnB4MGDUVhY6NF6BgJJzX64M8lOTpJ57CO3JuHUJIThVgb3JuH4JIR7+1cdO+v4opeL1szjSmlxrRomYbxuhUuTa6dfMf0r3JwU56bacA0R+ZP9+/d7/HMWERER+S+XEj1t27bF2bNnHS6fnp6Oo0ePuhLKrvPnz+Pmm29GcHAwfvzxR+zevRtvvvkm6tWrp5aZNm0aZs+ejblz52LDhg2IjIxEZmYmiouL1TL9+vXDrl27sGLFCixduhRr1qzB0KFDPVZPcoe7iSVXGu2e6hbhXB2dzEVZT9Ull4TtZMj2eGhXHVULEgO+SC6Zr1tXpgqVd3ASkoeSS04mmGpDcskn4/poH5K8oHnz5vj77799XQ0iIiKqIVy6dUsIgfHjxyMiIsKh8qWlpa6EqdTrr7+O5ORkzJ8/X52XmppqVb9Zs2Zh3Lhx6NmzJwDgk08+QXx8PJYsWYK+fftiz549WL58OTZt2oT27dsDAN5++23ceeedeOONN5CUlORwfUyNbvc4twGOq+ANvjimnmpmSY5vy5Tn8UhoxzfCa9bzJIv/u7q2azx03UqOb8fUQc9tTmzDF5es4qm3BEffDjgYc0Co6pHrREREVPu4lOi55ZZbsG/fPofLp6enIzw83JVQdn333XfIzMzEfffdh99++w1XXHEFHn/8cQwZMgQAcOjQIeTm5iIjI0NdJzo6GmlpacjJyUHfvn2Rk5ODmJgYNckDABkZGZBlGRs2bPDBQNKONdRry6ONaw9fjJfjyYvH8QSTLxqUkkt9Fqkq7iWX7G9NU06E9Oums6P7yRcJERERUcBxKdGzevVqD1fDOX///Tfee+89jBo1Ci+88AI2bdqEJ598EiEhIcjOzkZubi4AID4+3mq9+Ph4dVlubi7i4uKslgcFBSE2NlYtU1FJSQlKSkrU3wsKCow/2bkVwiVaNdRdW1+4+Eh3VzGx5C2ePrCObE/ba1ZNiCoaX0QSr1siIiIiIvItv/wqT1EU3HDDDZgyZQratm2LoUOHYsiQIZg7d65X406dOhXR0dHqlJyc7NV49vhiYGQh3B2/xrWhZt0au6aayW4t/frr+5pO22vW/TGXXLtmvX7d2pl43RKRt8yZMweNGzdGWFgY0tLSsHHjxkrL3nbbbZAkyWbq0aOHWmbgwIE2y7t166bFrhAREdUqfpnoSUxMRIsWLazmNW/eXB3w2fSo97y8PKsyeXl56rKEhAScOnXKanl5eTnOnTtn86h4k7Fjx+LChQvqdOzYMY/sT01XeQ8FTzbSqxks1mNMjfFKBimmgKDNNWt93QphMVKxRydzpofXLRFp5fPPP8eoUaPw0ksvYevWrWjdujUyMzNtPjuZfPPNNzh58qQ67dy5EzqdDvfdd59VuW7dulmV++yzz7TYHSIiolrFLxM9N998s80YQfv370ejRo0AGAZmTkhIwMqVK9XlBQUF2LBhA9LT0wEYxg3Kz8/Hli1b1DKrVq2CoihIS0uzGzc0NBRRUVFWU+3h7USMN7Ztr9VczRpe7I1hf2Ij3Xu0SB56evvOXbPqQ9QUjSdetkQBb8aMGRgyZAgGDRqEFi1aYO7cuYiIiMBHH31kt3xsbCwSEhLUacWKFYiIiLBJ9ISGhlqVs3xiKhEREXmGXyZ6Ro4cifXr12PKlCk4ePAgFi5ciA8++ADDhw8HYHi6z9NPP41XX30V3333HXbs2IEBAwYgKSkJvXr1AmDoAdStWzcMGTIEGzduxB9//IERI0agb9++Tj1xi7zF3Vt9XLndx5uDq1TWXYMChS9uq/TudVtdVyMiClSlpaXYsmWL1UMtZFlGRkYGcnJyHNrGvHnz0LdvX0RGRlrNX716NeLi4tC0aVMMGzYMZ8+erXI7JSUlKCgosJqIiIioai4NxuxrN954IxYvXoyxY8di0qRJSE1NxaxZs9CvXz+1zPPPP4+ioiIMHToU+fn56NSpE5YvX46wsDC1zKeffooRI0agS5cukGUZvXv3xuzZs32xSxRQnGsE8wlqVDPUvOtWqvQXIrI0evRo1K9f32PbO3PmDPR6vd2HWuzdu7fa9Tdu3IidO3di3rx5VvO7deuGe+65B6mpqfjrr7/wwgsvoHv37sjJyYFOp7O7ralTp2LixImu7wwREVEtJAneO+KygoICREdHI6fLragT5Jc5M/I5T7z8nN+GxExPADGdfy3OqVCfaObudpwlSRKTPV5QWFaOtBVrceHChVp2OzJV5cSJE7jiiiuwbt069ZZ3wPAl2m+//YYNGzZUuf6jjz6KnJwcbN++vcpyf//9N6666ir88ssv6NKli90y9p54mpyczGuWiIj8jil/oMXfMKdv3Vq7di0A4I8//vB4ZYjIFd4cvJpqPi3Pv6dieXvQdSJyR4MGDaDT6ap8qEVlioqKsGjRIgwePLjaOE2aNEGDBg1w8ODBSsvU7vERiYiIXON0oufHH39ETk4OfvjhB2/Uh4hcwo555G841g9RTRUSEoJ27dpZPdRCURSsXLnSqoePPV9++SVKSkrw0EMPVRvnn3/+wdmzZ5GYmOh2nYmIiMjMqUTPxIkTUV5ejn/961/Q6/WYNGmSt+pFRA5hY5n8Da9ZIn8watQofPjhh/j444+xZ88eDBs2DEVFRRg0aBAAYMCAARg7dqzNevPmzUOvXr1sxgwqLCzEc889h/Xr1+Pw4cNYuXIlevbsiauvvhqZmZma7BMREVFt4dTAMi+99BI+/PBDvPLKK4iJicEjjzzirXoR+YA/Nj4tb33xx/pT7VPxdi1et0Q1UZ8+fXD69GlMmDABubm5aNOmDZYvX64O0Hz06FHIsvX3hfv27cPvv/+On3/+2WZ7Op0O27dvx8cff4z8/HwkJSWha9eueOWVVxAaGqrJPhEREdUWTo8gXF5ejmeffRbvv/++N+pDZORu489XY3/4cmBkjndS2xgGRtZ2QG/z5eru9eZATF7SRD41YsQIjBgxwu6y1atX28xr2rQpKnvGR3h4OH766SdPVo+IiIgq4fQYPcOGDQNgeKICUc0l7ExKNZMAJEND1vVJcnqiwGBo29i77rw9eYKTgyNLgCS7O0nVT5avE75UqJbjwzCIiIjIUXwmuF8S0LLV47leA4BzDVNP7aMTvRU8FJF8y/1r1pkrwRu3Hjm2TcmYm5GcqW9lRUV1BYjIl3788UcEBQXhhx9+wM033+zr6hAREVEN5nSPnoo6duyIgoICT9SFHGDuEe3qt//V9WqporeL67WGK70P3OtZ41ovG/IWf+vp4uxrQxgTS0Lza1aCZN0hp7qpMs5sgy8VIk3xYRhEROQqodfj8s6tKFy7Apd3boXQ6xmnFnC7R8/69etRXFyMqKgoq/kFBQWYPHkyXn/9dXdD+AFXG5aujOei/uRCPNeYYrqeB2GrsHLa9s4yx/Q3zicJnerpYlOswgxfdEQjIjLiwzCIiLQh9HoU7/kf9OfPQlevPsKat4ak0/ltrKL1q3F2wTsoP3VSnRcUl4j6A0cg8qbbGKcaWl4PnuZyoufee+9F+/btIUkSTp06hbi4OKvlRUVFeOONN2pFosf0LbwLa3q6KjU0JlXN3cSLK+trMJBuhTDu9Zjy8XXLl42P+WNy0l/w2PoLPgyDiGoirRrCgZQU0SpW0frVyJs+DhHtOiJu5MsISWmC0qN/I//rT5A3fRzin3vVI7ECLY5lPC2TSp4micoej1CNUaNGYePGjVi3bh0kSUL9+vXRunVrtG7dGm3atMG+ffswf/58/PPPP56uc41RUFCA6OhorM+4BXWCONyRZ/lr46M2JF2ochqPnwVA0vi14ouYWnL27DnzUnLtr62D27b5wTGFZeVI++UPXLhwwaZnLtVMv/zyC7p06VJr38dNn714zZI/CqSkCBBYvTgskwgxvQdYJREubVnn0SSCFrGEXo9jw/sgJKUJ4se8Bkk2j9giFAV5r41B6bFDSH5nkVvXRqDFMfHWOdLyb5jLiR6TkJAQ/PHHHzhx4gT+/PNPbNu2DTt27ICiKJg8eTIefPBBT9W1xvFdosdfB2N2dhue2EfnHuGsjndCAcAXSRfFgZiebO1rt48Vo9TSNmbAKSwrx40/M9HjT3Q6HU6ePGnTk7q2YKKHvCGQeotoGUeLxEggJUW0jHV551acnPAEkqa+j7Cm19ksL963EyfGPorESW8j/LobGMeCN8+Rln/D3M5OFBUVITg4GADQs2dPtyvkn1wdo6eqlpL97RmSLpUvd44TT/Yx/ORmPKn6mFKFH91uTLI1WjP4qteJdnHNV5r3Y0oVfmLShaj2cPP7OSK3cQwT12IE0q0tQq/H2QXvIKJdR6uGcFjT6xA/5jXkvTYGZz+eg4gbO7vdi0OLOMV7/ofyUycRN/Jlq0Y9AEiyjJjeA3Bi7KMo3vM/t5MIWsXSnz8LAAhJaWJ3eUhKqlU5xjHT8nrwJrcTPaYkD7mi4oe16j+8ufQ45cq35tXiXtwIuaWmNRI8Xx/14VC83IgoALz22mt47LHHEBMT4+uqkAsC6XYdjmHivEBLigDaNYQDLSmiZSxdvfoAgNKjf9vtAVN69JBVOcYx0/J68Ca3H69O7nLwcdAVH2vs7KOQPTGRF/jbY8ddiylVMckS3Jp4uRJRoJsyZQrOnTun/v7ee+9h5cqVOH/+vA9r5f+0eERv0frVODa8D05OeAKnZr6MkxOewLHhfVC0frXfxTElRUJSmiBp6vto/OkKJE19HyEpTZA3fZzfxaqYGAlreh3k8Ag1MRLRriPOfjzH7evClKyI6T2g0mRFed4JFO/5n1/EAQKvF4dlEsEeTyYRtIoV1rw1guISkf/1JxCKYrVMKAryv/4EQfFJCGvemnEq0PJ68CYmejxAkgBJdnWSHJsk64nIG0mX6ifXc4Smp9NVnNxV2Xb5MiF31LR+b1S7Vbxd65133sEdd9yBBg0aoHHjxrjnnnvw6quvYtmyZcjNzfVRLf1LICVGAikpomUsrRIjgZYUAbRrCAdaUkTLWJJOh/oDR+DSlnXIe20MivfthHK5CMX7diLvtTG4tGUd6mcPr7R3lxACQlEg9OUQZWVQSkugFF+GcrkISlEh9IUF0F+8AKWwADH3DcSlzX/g5KSRKNqwBqXHDqFwwxqcnDgSlzb/gZi7+0F/7jTKT+ei/ExeFdMp+9PZ09Dnn0NM7/7mOBvXovTEURRtXofcV5/Bpc1/oN4Dj0CUG+pb8dg6Q8vrwZv4qCiiWoqJEPIf2qddas/Lgyktf7Rr1y6Ul5fjzz//xNatW7F161Z8+OGHOHbsGCRJQkJCAo4fP+7ratZYvF3HeRzDxHWBeGuLZUPY3mC13ujF4c04pqRI3rQXkTvleUT/uy+CE69A6dFDuPD95yjevhn1h4xC2cljgF5vSCYoeqC83JAI0euBCv+K8nKLeXqLcnqENm2JorW/4NgTDyDs2paQI+tCn38OJQf3oPzUSYRf3x6n33vdsJ6iN/+rKMZt6QHFPE+NYVqulKvz5agYXNqag0ub/7DYYQlSSChOvTMFmP2qYbtCQAgFUAQgDL87q3j7ZhRv32wz/8zc6e6cHofjnJ41CacxyXqmTgdJ1gE6HSDLhp9N/+p0hmvK6l8dYCxzafMfODLwTgQ1TEDEjZ0Q0fYmq0HAvTU2macw0UNERFQJLROinhhj19ltcFxf//DCCy8gNjYWANRevUlJSUhKSkKPHj3UcmfPnsWWLVuwbds2X1TTLwRaYiTQkiJaxtIqMRJoSRHAIjEyfRzyXhuDmN4DEHxlCkoP/YX8xf/B5T83oMHjY6A/d9qQ4CjXQ5SXGRIf5eXmJEh5OUR5GYS+3JCgKDP9bC4X1qwVCtf8bE6KRESakyKn8xDWsg1OvTXJIulSbvi53OJndb4x4aImYazrAwCXt+bg8tYcm30+++EMt49bReUn/0HhyX9s5l+2k8TwKCEgSoo9tz319hbjmArCME8y/a7GrapKlS20M18Y/6c4kJAyJcbKqq2CXUrhRZQWXkTpoQPI/2I+guKTPDaoubcFRKLntddew9ixY/HUU09h1qxZAIDi4mI888wzWLRoEUpKSpCZmYl3330X8fHx6npHjx7FsGHD8Ouvv6JOnTrIzs7G1KlTEaTpo9KJfEPrBh57EJHrHHhin5f4WyKksteZv+0HWRs7dqz6c1VP3apfvz66du2Krl27alEtvxRoiZFAS4poGUvz3iIWSZGQlFSUHj1k1TsAsmydnDAmQES5HlB/LjP8rv5skRTRlyPixk4o+OFLHH9mIMJb3wg5Kgb6s6dw+X+bUXbiKCJuuhXn/vueYT3LGHo9UFZmnQQxbt82KWOsT3k5pKBgXNr8h3VvEaMz777m1nGrqLKkSPGubR6NY0Ong6QLUv+VgoIAWWf41zRPpwMs/w0y9gwJCjL/W6GcpAsCZBn6/HMQZaWQwiMQHJcEKdi4fVPPE53OqleK1XxTLxTjtqpdR9YZB7iUAVmCZErOyLLh+rf83fivIWEjW6wnW/9eA4YVMfdkUgCl3Pp3U+8nvbH3k6IH9IpVryfzv5brGMqK8jKUHv0bkqxDWIvWXn3KoKf5fUZj06ZNeP/993H99ddbzR85ciR++OEHfPnll4iOjsaIESNwzz334I8/DG9Eer0ePXr0QEJCAtatW4eTJ09iwIABCA4OxpQpU5yqgxDe/xBdMRnKRjO5enOJe5eqa2vzciX38SpyR2V/M3z94Yyct3z5ckRHR/u6Gn4r0BIjgZYUcSeWMN4eo/bgKC+334PEmMSAXo86t2Yi/8sFOD56CCI7dIIuJhblp3JxafMfKD3yF+rcficufLfIvI7ltivpQWI3rr4cutiGuPTneptbaBAUhLwZLwHl5W4fO5PSI3+h9MhfNvMvrf/NYzEcEhQMKciQHDEkS4IgBQcbGspBwWrixJA0Mf0cbE6gBAeb58s66C+chygrhRxZB0HxVxi3VWF9Ndli2JakCwKCdNblrNbRGWNWqGeQ4RYe/p2s+STZmIACAIR6PkDHf3l+mxrw60RPYWEh+vXrhw8//BCvvvqqOv/ChQuYN28eFi5ciH/9y3Bi5s+fj+bNm2P9+vW46aab8PPPP2P37t345ZdfEB8fjzZt2uCVV17B6NGj8fLLLyMkJMS5yrjcenZsRatSkvo/8nu+OI/af7Uv1P9pi3+biSjQsLeOe+wlRpTiywAAKTRMTYzIdaOhFF82fPMebP5MqJYNCVUTEKaeDpBlyCGGRkZY89bQNYzH+S/mI37s65CNvcVFeTmU0hKc/3KBVbJCKSkGhIAUHKJ+W2waBBWSDDnU3HixLGtKipz/6mNDLyVdkFpWKArOfzkfurgEhF7bUl1f6PUQZaVW2xVCQLlUBFFaYvgWXwigvBxKWQmUy5cAvYKobnfj3Cfv4sSLjyMy/TYE1auPstMnUZTzG0r/2ou6d96Liyu+MyQ3ZEm9VUYpuWw4PsaxP9TBUstKIPTlkISxTuVlUEpLgfIyICTUMD7GgO6QomIgyRKUkhKIixcgSksgR0Ti8MA71YQLFL1b37qW/rUXpX/ttZlf+Osyl7fpECGAsrLKl0uyIdmhJiCMvTeCgiAHh6jzTPPlkBC1F4m+6CJEeTnkiEgENYiHHBQMYey5IQUHQwoJMydZBAzJjdBQyMHBkExlIRkSIWERalIFQoGQdJDDww3Xuy7I8HgfAUMdIuuqSRJRVgooiiHhYnoNKIrhOgMgh4Wru6qUljhcVpSVQuj15mQQjIMHG29HcqasFBqmJnNMt45JsmTYV1PdLN4jbMq6+B7hdFkX3yOqL1sCCMV8fmH/PaL6shLk0LCqz6czZZ059y5eJ/bPZ/XXlL/w60TP8OHD0aNHD2RkZFglerZs2YKysjJkZGSo85o1a4aUlBTk5OTgpptuQk5ODlq1amV1K1dmZiaGDRuGXbt2oW3btjbxSkpKUFJSov5eUFDggb1w8MZFUwlhvInBF41mPqMtQLiT/XB1XeFinsf1C91nrxMml4iIaix7vUUOP2j4vJgy7zu1t0jJ/l3InTQSdTOy0PDxMer6RwbdBVFSjOS5XyE4LhEAUPDj1zg7fzbqdL4DcSNfNhSUJCiFF3H5z/U4OX44Yu5+CEHxibjww1co/OV7AEC9fkNRvOd/EOXlOPXWJCgXzqPeg0MR1CAeorwMxXu2o/DXZQhKvBJ1bzclNcpQsOJbKBcLEH7jzQiKqoegBvG4vGUdjjzUFVJYBEKvbgqlqAjlp05CKboIADg2vC+kkBCgrAxK8SUohRfVJIIwJUocVLJvB0r27bCZf3HZV7joykmpgnKpELhUaGd+kWMbMDYchb4cKCuDFBYOXVSMYZ6sQ/k/hwEAIVc3N9yOpChQCgugP3cGQQlXIOyaFmqS5eLKpYAQqNu1J3R1ooCgYJTs34XL2zYg5OpmqHv7ncbbc4Jw5sM3IUpLUP+RUQiKS4CkC8KlLetQsOwrhLW6AfUHjDD0IgnS4eS44dBfOI+El99CaJNrIemCUPj7Lzjz3usIb5uGxHFvqLtzdHgflJ/8B0mT30NYc8PdDIXrVuHUG+MR1rItEl9+Sy37z6hslB4+iIQJMxHRpgMA4NLmdcid8hxCr2qGK6bPU8seH/sYSvbtQPzoqYhMuwUAcHnnVpyc8ASCkxsj+a1P1bInX34Kl7dvRsOnJqDurZkAgOIDe3Bi9CMIapiAlPe/VsvmzXgJlzauRYNhoxF1x78BAGX/HMY/T/eHHBWDxgt+UMuemTMVhWtXoP6gJxGd1QcAUH4mD8ceuxdSaBhSP1tpLvvhDFz85XvUe3Ao6t2bbbgmCvJxZNBdAIAm35h7TZ395F0U/PAlYnoPQGy/RwEAoqRYfd03XvgLJGMj/vwXHyH/608Q1eM+NBj8tLoNU9lG85dCF10PAJD/7UKcX/iB6+8RAI4+di+UgnxcOes/ai/Di78uw5n3XkdEh85IGGO+/e2fJ/uh/HQukl7/P4Rd09xw7n9fidNvTUL49e2tzv3x5wej7NhhJE56W70F9dLmdch7fSxCm7bCFVPnms/nuMdR8tdeJLwwHRHtOxrO/Y4tyJ00EiGNr8aVMz5Wy+a++gyKd/2JuGdfQR1jL5eS/btw4sVhCEq8EilzPjef+2kv4vLWHDQc8QLq/sswllzp0b9w/JlB0MU2QKP/+1Yte/qtV1CU8yvqDxmF6O69Dec+9ziOjegLOaIOGv/3J3PZudNQ+OuPiB3wOGJ69QNg6IF5dEgvQKdDky/XmM/9/LdRsPwbxNz/MGL7DgZgeE850r8bACD1i98AY2Lp3ML3ceHbzxDd8wHUzx5h2IBer557y2vKX/htomfRokXYunUrNm3aZLMsNzcXISEhiImJsZofHx+vPnY0NzfXKsljWm5aZs/UqVMxceJEO0ssHyLtDke34YnWq+PbEMLQeBWKti1YyVOHlWoIV06mO7enufo68a/kEhNLRESOsxwv5eRLTyAktam67PjYR6E/dRIR7Tri0uZ1AAyN3bw3xhu+GS4rgygtBQDkTnnesFJ5GfQX8gEAhTm/omjT74ZvrC0e/V1ifJxxRec//cB23kLbeeUn/7E7//Im24aHKL6E4p1/2szXnz1lMw/C/C24XUGGcT9QWgrIMnQx9dVbW8rPnoIoLYEupj6C4g3jiojSUpTs3wUpOAQRN3Yy3p4ThOJd21B+6iTCWrZF6NXNIOmCoL98CRd//BpScAjqPfSo8VaaYFxc/SNK9u5AZOc7UCf9dkCWUXJgN/K//gSQZCROftfQkyUoCOe//g+Kfl+BqLvuR8zdD0EKCoJSXIxjj94DwNCIM/UOOPvxO7jw7WeIyuylNuJEeTkO3X8rACDxpZnQRdYFAJxbNA/5X3yEiDZpaDD0GfVwXPx1GaDXo959gxBUvyEAIH/Jp4ZET3Kq2kAFgLPzZ0OUliCiTQcEJyUDAMpyDePL6OpEI/Qq83UHY48LXd0oQwIJ8JsxQIio5pJEVaP61VDHjh1D+/btsWLFCnVsnttuuw1t2rTBrFmzsHDhQgwaNMiq9w0AdOjQAbfffjtef/11DB06FEeOHMFPP5kzhJcuXUJkZCSWLVuG7t2728S116MnOTkZ6zNuQZ0AHsDZkOhx5zJxdV3tW7BaJ5dMSTTyBm3f2gTcuXTcSy75Aq/bwFBYVo4bf/4DFy5cQFRUlK+rQ1StgoICREdHu33NFq1fjdPvvQ7loid6ZzvAOCCq0Okgh4QBwcHqLTKmwV0RFAzJYr4wriMFBUEODVPHLoEkGRIuIaGGWzuCgtXbrcrP5AFlpZCjYhDS6GpIkCB0EuTQcIuykuG2jqAQyBERavJGKIrhdpGwcMOtJJJUa27LcPmWHE/cvmPvNhuP3L5jus3G3dt3KpxPd2/f8Ydbtyo7n7x1i7duuclTf8Mc4ZfZiS1btuDUqVO44Qbz0xD0ej3WrFmDd955Bz/99BNKS0uRn59v1asnLy8PCQkJAICEhARs3LjRart5eXnqMntCQ0MRanGx1xaS22MCubKuD5+w45HQDm5EAoRw/di6emRrRyNd4x5oPli7NvVcAgL/unUvWUhEzoi86TZIYRHI/+Y/gF4PKSICQQ0SIIeEGp56ExRiaMwYe48Y/jUkYkz/mhIztsss1jGV89M3MMPTgWwbOJYNUJfKyrJ6u4zV/OAQSMFulJWkSsoGq403V8oC9ht6TpU1jYdTsaxFg9elssbbxWzL2jtHzpSt5Hw6U9aJc+/2dVLp+XTvOgG8eO7dvU4qPZ/uXideOvcB8h7hL/wy0dOlSxfs2GF9b/CgQYPQrFkzjB49GsnJyQgODsbKlSvRu7ehG+W+fftw9OhRpKenAwDS09MxefJknDp1CnFxcQCAFStWICoqCi1atNB2h8gOfxqk2N44Sw7W383GsqsNfeGr3lLkUZLF/11d21nuJZest+L0Whp3LPTFNat4Y6NV7LvilYBE/iGiTQd17BIiIqJA4peJnrp16+K666wfIRkZGYn69eur8wcPHoxRo0YhNjYWUVFReOKJJ5Ceno6bbroJgOGpFS1atED//v0xbdo05ObmYty4cRg+fLjTvXY8+3h1RzfkWguEje2qaDnOkic5G1N4uMOUo0+OY3IpELiXXLLeiqZcCOl39zVXpqp95yj7RERERAHHLxM9jpg5cyZkWUbv3r1RUlKCzMxMvPvuu+pynU6HpUuXYtiwYUhPT0dkZCSys7MxadIkL9fM3iduUc1yi5JujulinZBythnjg8GYyQs8fWAd2Z6nkksOJpVMA4i7cVucSyQ3jq4L+TqX1yUiIiIiooDll4Mx1xSmwZRyutzKwZir3oKL67EHCLlD48GY3R5Y28X6ujGGlmZJKaqxCsvKkbZiLQdjJr+h5UCWREREnsTBmKlG8c/BmF0dA8TbLVh79WJCyzt80QPNmeuuYv0q/l79toRwby+FK+M7CU+8JzjPrWvW0XWF9Y+av044GjMREREReQATPR7g6hg9taOx7So/HAPEIc435qvnSmOdySXv8OROVr8t94+pqxvQviOo+wMx+8Gg5R576p8TIdmnl4iIiCjgMNHjQ+YP2I5/0jZ/4cuGemDwxQDQgZxcsq6Xvz7OtibzRW8ez6jNozFXxR/PJRERERFVhYkej5Cg1a1NhpK+GXvEn775Zfu+Kv6aXHKE8RXi0WtWm0HLXb1mebcPERERERFZYqLHL/nR2Bguc6+F7vrTxdhTyjtq4ng5jtbJwdvt3My4uHLNqgktXrdERERERGQk+7oCRPZJHpxkByfftFxNYzxpOvlkT7VW3XXh4e1K2l+zksevW1HtZHhQo/DNdeuFqdIj4YMXSXUxtT4GRHPmzEHjxo0RFhaGtLQ0bNy4sdKyt912GyRJspl69OihlhFCYMKECUhMTER4eDgyMjJw4MABLXaFiIioVmGixwMUoe3ED+be4smGuqOTj3i0oSgcmnywi4FPHS9HuwSTJFWVXKqprwlR6VT59ap9MgtwLyFT+V5WPRHZ8/nnn2PUqFF46aWXsHXrVrRu3RqZmZk4deqU3fLffPMNTp48qU47d+6ETqfDfffdp5aZNm0aZs+ejblz52LDhg2IjIxEZmYmiouLtdotIiKiWoG3bvkZIQBIvkn2eC0tUcmGa8edIYGwl47sg/DwNVv1xoQAb2nyKm/upDe2XStOikt7KdWSY0POmzFjBoYMGYJBgwYBAObOnYsffvgBH330EcaMGWNTPjY21ur3RYsWISIiQk30CCEwa9YsjBs3Dj179gQAfPLJJ4iPj8eSJUvQt29fL+8REVHNotcr2PHHQZzLvYDYhGi0uvlq6HSe74fBOP4Ry9OY6PEI7Xpn+KIRaWg0Cw9881vJFirdsA92VqotTUKtefqoVr09x8bocYTj21CTS3x0PRH5udLSUmzZsgVjx45V58myjIyMDOTk5Di0jXnz5qFv376IjIwEABw6dAi5ubnIyMhQy0RHRyMtLQ05OTmVJnpKSkpQUlKi/l5QUODKLhGRnwu0JMLab//E3LFfI/fIWXVeQqP6eGxqb3Tu2ZZxfBxH61jewEQPVctzj1N2ZhteTCxVuYqk+a0MksbJJWOnsFpA22vWc8klx5mTS5pGBQA+up4ogJ05cwZ6vR7x8fFW8+Pj47F3795q19+4cSN27tyJefPmqfNyc3PVbVTcpmmZPVOnTsXEiROdqT4RaUiLxEigJRHWfvsnJvb7EDd1vw4vLngYqS2ScGj3CSycvhwT+32Ilz4d4pF4jOMfsbzFP/odUS2kzXgjNWUwZu+M5yTsThDC5bE83JlqB2+M41T55Itki6HHkuSlcWqqGudJ2/FyfHF7rC/20Rf7SYFv3rx5aNWqFTp06OD2tsaOHYsLFy6o07FjxzxQQyLf0OsVbFuzH6u+2IRta/ZDr1f8Os7ab//EgFYT8Ez3mZg86CM8030mBrSagLXf/unRGBP7fYjUlkl4+9fnsDRvJt7+9TmktkzCxH4feiyWVnH0egVzx36Nm7pfh0mfP4YWHZogvE4YWnRogkmfP4abul+H91/42u1zZhnn5YVDESXKcGz1DkSJMry8cCjj1JBY3sQePR6g/YdlwW/TvSLQjqn9/REQHsq8OL4RCdBmvBzLEKK23NKk7U5695jWpBPm6XGltONMvf11H8m7GjRoAJ1Oh7y8PKv5eXl5SEhIqHLdoqIiLFq0CJMmTbKab1ovLy8PiYmJVtts06ZNpdsLDQ1FaGiok3tAgSKQbtdhrxTnVUyKyLLhnJiSIhP6zMX7L/w/e/cdH0Xx9wH8czU9Ib0QSpAOgdB7R4EHqYIoKEVFxYA0ARui/gRERECliUpREEUpUkSQ3nuvoYWWQCCkJ1fn+ePIkktCSHKXu+Tyefs6yc3O7szu7d3Nfm9m9i80f76uRa9X9gv72GNXTBf2/p74dPmb+LT/D1YpBwBO772M2OgH+Gjxa4AAbh28iLS4JLj6eyKkYRW8/F5nvNt+Ok7vvYyI1lUtLmdoZEss6zQJybcfnw8eZX3RsXcr/G/jaZZj57KKEgM9JYxpqIbMPpMxF6drMLKAtV7I/G/H8oF4+Vw/SzabBZey4dvEUZTcV7Ign9X80YByo1ar0aBBA2zduhU9e/YEABiNRmzduhXDhw/Pc92VK1dCo9HglVdeMUsPCwtDUFAQtm7dKgV2kpKScPDgQQwbNqwodoOKCOcwKVwZjjS0xVYBGEcLigBAfGwiAEAWG4dfnv0pR1mNRnY3y2dJOSHOAme+W4uKbcPR6ZvX4VMlBPFRd3Bk/iac+X4tQpwFy7FzWUWJgR6rsPVkzLaN8thnHhDbB5YESvKlXXFn6ZEt2Pqmu9NZq+cSkN8N2TS4lFlMqem5RES2NGbMGAwaNAgNGzZE48aNMWvWLKSmpkp34Ro4cCDKli2LqVOnmq33008/oWfPnvD19TVLl8lkGDVqFL744gtUqVIFYWFhmDhxIkJCQqRgkqNir5TCleMogRH2Sil8YMTRgiIA4BPkhRBnga3jF6Fi2zo5yto2YRFCnE35LOEd4IHaZQC/iEroOvdtyB6dd0GPnq/oPwO1D1+Bd4AHy7FjWUWJgZ4SyR5DNWzfhcg6gaUCbsTGV8yCF+lFwnoTiEtbfGoOKbhksYL1XpIhc94c2+J5S+TY+vXrh7i4OHzyySeIjY1FREQENm3aJE2mfOPGDemCNdPFixexZ88ebN68Oddtjh8/HqmpqXjzzTeRkJCAli1bYtOmTXB2di7y/cmNowVGHKVXij0CI+yVUjC2CsA4WlAEAGo1rYS6fnJkuLuhy/dvQqFUSmV1+f5NzGz6AeqKVNRqWsmicnydADclcDEp54/ZAsClJMBXacqXFyEEjAYjDAYjjAbT30bjo38NRqjTUuGmBE7H6dE6+gEgAKPR1Eg1Ggw4fVeHECVgiHuIGxdjpW2a/pUKyTVdSH8AmttxcFMCJ2I0qH/yJmQy02skl8sAOXDqrhZllQAeJCA2+gHkChnkCjnkcjnk8kd/K+RQKB7/bVqec85Lax07e2Ogh/KppF7VFaTels7JUdiVS+qxpazscXc6y4NLhVvXHsElBpaIbG/48OFPHKq1Y8eOHGnVqlV73DDPhUwmw+eff55j/h57cKTAiCP1SgFsHxhhr5SCs1UApjgGRYQQMOiN0OsMpodWD73eAMOj5zrto7/1j5Zl5nv0SLhwE84wYseVZLzd8ks0aF8D3v4eiLudgKPbzyPlajLaBABLRv8K+JaBQW8wlffoX4PetB2j3hR8yXxuWpaZzwDXpCSUBbB31xV0Dx4DD283KBRyaDJ0SElIg1GjRbeywNT+8xGjV0qBG2NmQMdo+jevz3QACHURaOQLHD4YjVdrf5JjuVImEFIW+G7YEtxKL3xjLrOcY0dv4XDLL3Mtp2xZYNZbiwtcTmYgSCaXQaGQI8TZiLqupmP3f74j4eLhhDa96uPZ/k3x29ebcGT3VTwfAmQ8SC70/tgCAz1EEtsOLzKx5oSvBZgcmVfNDsHy4FLB17VezyWgQIEmO/Ragsy2YVhhyfjRQr4knIyZSiNHCozYariOrYIigG0DI7Yoh71S8mYwGKHX6qHTmgIjuszgiVaP2GNXpKDIqOe+QdsXGsIv2At3rt3HztVH8eCKKSiyZvIaKIN8pW1kBlnMnxug0+qh12YJwjxKc05MQDAeB0Xcy7hCBkCTrkNqcjqg06NbWWBSr29xI9myL87MgEWSDnh4+jaunr5ttlz5qB2w+/cDFgVG/JwEyvoDnirgYYoG6Skas+U+atO/8YkZSNYUvpyMRzee8naW436G+bQJMpkMvh5KADrIXJ3g4azMXJD1H+m6RPZ4xcdpjxLVcj2AVAT5OCMmQQuj4XFBcrkM/h4qAFoIJxWcIIPRIB71Qnr6nbGMRgGj0QAA0AFIMgjA9dGx0+qhe6DHuh93Y92PuxFc0RejJnfH5Tlr4ervWbCDZWMlMtAzdepUrFq1ChcuXICLiwuaN2+OadOmoVq1alKejIwMjB07FitWrIBGo0GnTp0wd+5cqcsxYOp2PGzYMGzfvh3u7u4YNGgQpk6dCqWyYIfFHreo5XU65VTAniBWl/dGGVxyDNYdFpff7Vg+nXehWHOapwKUacttMtBDpQ2H6xSOrYIigO0CI47aK+VCEh716tA/CnboodXocOaeHsFKQBtzH5eORz8OrGgN0Ov0ZoEQnU5vHijRZVmm1cMQe18KwPSvMRGhVQLh5KJCcnwqbl+5B0VyCtoEABNafI5EocwSvDFt2/Co54teq5eG+eTGLChy8BrOHbxmtjwzKLJ+zn8WB0WC8xEUSdMakVvbRS6XQaFSQKVWQqFSQKlUQKlSQKl+9K9S8Wi5Au4GDXDvDpo0qwiDpwdSE9Nh0Bvg7OYM32AvqNNSgaOn0aBrXTQO8IVSpYDi0foKpRwKpdy0PaXpuVKlgFwph0KheJTXlEehkOHslOXoFe6HasO6I/pCDFIT0uDl74Eqdcvh9OzVSLkZh6krI6FQKx8NazL1apGGOWXr6fJ4yJNMygsB/PLsRLzariw6f/cmzuy/Kg2Hrd2sEjaN+AHxUXfw8+bPIbfgM9VoMOKXZyfixap5l7M4l3JyHX4mPX+UJrKka/XY/Pos9GsUgIj3+uDS8RvQpusQVrusVJZnqB9CGlYp9P7YQokM9OzcuRORkZFo1KgR9Ho9PvzwQzz33HM4d+4c3NzcAACjR4/Ghg0bsHLlSnh5eWH48OHo3bs39u7dCwAwGAzo2rUrgoKCsG/fPsTExGDgwIFQqVSYMmWKPXcvT5k/+Ba2cc5r7eLGHi9IUV3Z5b0vvFMcFR5fSIvkdfj4JqFShsN1CsdWQRHAdoGR/JZTo3FFaDN0ZsEOs2E5uQQvsvYgSbp0SwqKvNlsCiJaV4Onrxvibifg1O5LSL9p6pUy942F0Ht4PC5Dp5eGAemyB1uylafTGeAvNKjrBuzbdQWdvd/Nsb9KmUC3ssDcEb9aFBQBzAMwhjsJuH8nIVtZpn8fXLtX4LIUSjlUaqUpSKIGgDRUKu+FdLULDDoDhBBwclXDy9cdrnoNcCsalZs8g8q+3lAoTcEUpVoJlVrx6LlpWyq10my5Um0KwJjSZbg4/Xf0rO2HasO64ealu0hNTIN3oCeqNaiIk1+vRNKNOMzePBZqZ7UpeJMlkJN9vrK8ZAYsKj7jadbzCgCE0YgN78xHfKgfXvn5rVwDFsJghFFvgPHRcLHMv02Px8uMOgOcBrXHgZlrcW3B36jcpQHcw4KReOs+Tk/7HXFno1F3UHtor96GMJoCHOLRw2gw5pImIAwGGI1GCL3R9O+jvN5hgbi+/RR+fXYifKuUhdrDGTcS03D84ztIvZuA4AaVsWXcz6Yf0B71kHh8XfDo76zp0nLzZc5erri+/RQWt34fnqF+ULs64UG6Fkffv4/0B8kIrFMR/45eCJlMBplC/vjfrH/LHgWvsqWZ8skgk5n+DWlYGZc3HsHBD35GaNNqqNakGtw8lNg04gdc33EaXb5906LAlS3IxNMG3pUAcXFxCAgIwM6dO9G6dWskJibC398fy5cvR58+fQAAFy5cQI0aNbB//340bdoU//zzD55//nncuXNH6uUzf/58TJgwAXFxcVCr1U8tNykpCV5eXtjVpgPcC9gLqNCsOmyiYHg9QIVnh6E+AOwRIOD7hEqSFL0eLbdvR2JiIjw9i3cXZCLgcdursOfstj8OY/KQn/H9b4NxaNbfuQZgRvRfjPcXDkLLHvWgUMqhdlJJedJTTb/0O7mopIu7zAtwhUIOtbMp74ldlzCj1zdo4g9UbBuOhm93gU+VEMSdv4WjCzbhxu4zOBgHjF09BhGtqyIjTQshBNTOKqknkUFvgFajh1wug5PL43Zp1ryndl/C5tdnoXzDZ9Djx+FQKBVSXmE0YvlLX+PW0avo8MO7aNihpmm7BiO0Gboc29Wka2E0CqidTBfEWfPqdQYsaPIe4OGG0QemwqA3DYtQqZWQyQRmNv0ASE7FmwemQ+2kgrPr4+1qM3TQ6w2mnr1CQK8zQKvRIS1ZA73OAKVS/mj+EiPSUzMQd+o6zs/6CzvuAYHhFdG8a114+rohNvoBDm85i4cXb6FNAOD5fEvA1xsGoxEwCugeBVY06VrodQYIY+ZcKqYeLTqNaR4To9E0VEiVkoKwhLvYcQ9IU6jh5uliOr7pWmSkalBGYUSbAGB3HHDfgqEtmUGRv28DhlyGIWcGYA4/gMW9Ulr5AzvuAQ+1Obfj7yZHS28DjmldkKp0hspJaQqCqB73DFE5q6BWK02BEJWpx4ZKpYSTq1rKC7kMivgE6HcdhWf3NnAK9cPd6w+QlpIBT193VKlbDrq78bg6by2eebsbvKuXh5OrCs6uTlCqlFAo5RBGAaVaAXcvVykIYzQaIZPL4PQokAIAep0evz73CXwqh6DbgnekoIhWo4Neq8fW937Cw8uxeGXz55DJZchI0wIAXNwez5Kr1ehg0BtN+6c2Xa8JIZCeqoFRb4BKKYdRZ8DV/05g+8e/IrhhFVTv3Qze5f0Rf+0uLqzah9jjV1H39WcRUv8ZCJ0BBp0e2nQt9Bo9hNEAmQCMOj0MWgM0aabtyoSA0Jvy6jV66DU6KWiSEhuP++dvwcnTFS5+HlAoldClaZAWnwx9mgZOZdwgVyogsgRxDHoDhP7pw5CoaHmG+qHFhBfwzHOFG95r6XdYQZTIHj3ZJSaafqXw8fEBABw9ehQ6nQ4dO3aU8lSvXh3ly5eXAj379+9HeHi42VCuTp06YdiwYTh79izq1bPenRCsyg4Xkfa6vbqt58egomTboT6mc9bGY2AkPGuJiIqr3HqmDGz5FTxVwIAWgVLPlJO7o/Dl0CX4v8EtMHbOK9L6fSqOR0aaFsvOfYGgCqZbyK9dsANzJ/yJDi82woeLXgNg6i1SuwwQkwZ0ieyOoPByAIATp2Mxe/kZtApRoq6fUeqV8lqDz3D3Rjzm7JqA6g0qAgC2/3kUU19fhPrtqmP6+pEATIGXYS2n4sbFWHz221vwkunhpgT2XUrCj0FjUCm8LEZ/218KpmzcfRPNPIGjK/cjKT4Veq0Bl0/fwl/fbYVvsBf6DO9gCrLoDfhnyV7cu/kQTTrVRlBFX+i1BsTdfohDm8+irJcSjT1MvVL6PvMBdBo90pIz4BPkiYxULZw16WgTAAx9Ziwe6OTw9HGTerxoMnQF+p3GbLjO0WhcOBpttjyzt8jWJXssntw1LLNXilYHTbrObHnSo2tq51x+tFc7q6BUKWAwGKBJ08HZVQ3fYC/T8ByVEtfO3YbRIFC9UUX4Ko3AzWjUqO6PM+fvo4y/B8pXC4KbpwsCynnj4G/7AGhRr3M4nqtbCUqlApdP3cK2Pw4jrFYI+o16DgqVqZfLd2N+x4PYREROfxFV65WHUq3AyV1R+OHjVfCrXRGuskQMaB6ENl8OxrvtpyPm+gMM/aIX+gxvj00jfsCdU9cRfTIJ1eoHYu7u96X9ebfDdJw9cBWfrXgLLbtFADAFK8d2mYkKNYLx85HHk+yOe342jm0/jxdru8Mn+SG6juiHi8dvILL1NASW98GwKb2x4Z358Cjri317r+PQxHWI/Kov2r/QAEadHjfO38HEPnPh6e2KL1dFIl2nh0Grx+9fb8LpvVHo9HIT1G9bDUadHg9jE3E2Kh7lb8fj1y6fIrBOGJw8nHFi8xloYh7AVQkE138Gm0b+AE2qBse2nYdcBtRuEgbjo4BM/O2HSE1Mh5uHE5yclDBq9TDo9DDqDLmeFzFHohBzJCpH+smftuDkT1sKdpI9hSYpDZqktJzpCan53oYQAOQyqF3UkCnkkCsVSEpIg15nhE+wF1w8XCBTypESn4L42CSonFWoWKc8ZEo55AoFLhy9jpTEDFRrVBG+QV6QKeRIuJ+KE7svwb2MK5o/H2Hq6SKXY9/GU4i7k4gmnWujfLVgyJRy3I9JxL/LDsDD2w0vDO+A5Jh46NI0OLH3Cq5ei0fbPg1Ro1FFADLcj0nA77O2wM3TBa9N6m6aiwfAhiX7EHXiBtr1bYiI1tUgkwEP41Lw86droXZR491v+j2atwfYuHgvTu+/glbtq+KZGkFQuztD7uWBHz78CwqFDO9+8xKE0QhhFNi56ijO7r+CBu2ro37b6oDRiIxUDX6b8S9kAF4e8xxkMhmEwYiTuy7i0vEbqBpRDrWaVDIN4dIbsGnpPrgogHavtkDVbo0R0rBKse/Jk6nEB3qMRiNGjRqFFi1aoHbt2gCA2NhYqNVqlClTxixvYGAgYmNjpTxZgzyZyzOX5Uaj0UCjeTxmMykpyVq7UUA2vtsNAHvcXt3WLJkHlYoXy3vVFPx8t1tAFOxFRESUX7kN1zEIGR5qgeaf9cfSntNQV6TC3ccVAJCSmI5rZ29D92i+ksyJPY/tuAB3LxfotQacP3IdAHAz6i7+mP0fdFodMm7chZsSOBwPfNDre1SrXxHObk64fPImABnO3tejTQDwWadpSFY64cGjoVX/G/gjFEoF9Fo9UhPTAZguuDv7jDAN08gyn8mklxdIQZGLUXEAZLh6+jZGtJsu5VHKBOAJ7PvzMP745YjZsXgQk4gFH63KcYwO/nsmR5pMawqCJOkAw/0UKT0+1tQWzuxA4iwHhFEgMUuep3Ev42Ka00StREpiOjL0GQCAmjUCoHVxRWpSBm5F3QUANOlUGy46DXDpEjyCvIFrCahSrzxqNakEpUoBo8GIVXO3AwBem9QdamcVVGol9m04haPbzqNxp1roNKAZlCoFUq7exuV56+CpAsateAd3bzxESmIazh+6hn0bTqHD/9UCTp7Fh8vfRmjTauhV7j0Y9EasiJoK/5AyAIDfZ27GDx+vRute9THhh0HSPnUPGYPUxHR88OMQhIT54ZdnJyLcSYUz5wXqtKyCSb8OBWDqdfVw/R6k6oHeY59HlXoVAACbftmPbX8cRkCoD57t30Ta7sKJqwEAVeuVR62mlWDUGXDnUixUMgFXtRyN3+iCHZOWY8d7P6Gs2gCdWsD94UOsHjADd09eQ2D7CJS9dAJe6Sk4+8duGHQGGLR6eCc9RHUPgZh/DmLPucsw6gy4F/0A9b0FPNIf4p93F8CgNQVOAm9Fo7U/AJkM17efwoL6oyB3UqFLsIDSGI+5tYdDPHqfBN5+gG5lgRuzV2Lx7JXSfnQIAoA0/PXy43PVFUATXyBh80Fs23xQSq9gmpUDidfvIfH6PQCAAoDro6vYmGNXpLyBzqZ/754wn8vHTQkgXQNNuvncO1nJ5DIImQxanRFqtRJObmoo1Eqo3V0QffEujACq1K8AtasaCqUCMTce4kbUXQRW9EPNJs9ArlJArlRgw5J90OkMeH5oG3j6uUOhVODs4evYu+EUqtSvgK6vtTblVSkwY/gyyNMz0H1ICwRVDYJ/zfI4uOUcln/9L+q0roYR3/SDXGna7rsdv8a92wmYtn4kqjUIg1ylwPa/juLLoUvMgsEA8FrDzxF9PgYzFr0uDUHds+4EJr20ALWalsfY38ZJed9p9SUuXo3G80O7omnncADAka3n8ePaKDxT1hcdpg6U8q47dBenzyeh1/PN0KJ3AwDAmf1XcGHeQZT1d0Ojd/5Pyruz9xxcPv0QvZrWQsSrzQAAl0/exNUv/oOvhxPqvNJOyrvqnyhcT70J9zqVUbtfKwDArcv3ED3+b7iplKjZp4WU9+9NUbiZdhXuTWuj7ejnAABxdxIQPWoVFEo5wvu3kfLuOH4Xl7dcRfPaldHwrc4AgJSENEyatBkA0GRUD6n32JnEv3B6x03UiKiONpNeAGDqrTl19n4AwP++ePWJ505xVeKHbg0bNgz//PMP9uzZg9DQUADA8uXLMWTIELOgDAA0btwY7dq1w7Rp0/Dmm28iOjoa//77r7Q8LS0Nbm5u2LhxI7p06ZKjrE8//RSfffZZjnSbDt0qNex0WrIXERWSdXoRFfJ254z0UCFx6BaVNJZ2e7918CLWDJyJnfdk8K5WFreuxJluT6w1/brvoxYlariOv4sMLX2N2JuoRFyKwezHBqVKgQqBrqiDJNz0C4He08M0TEelgFwug9JJCScn1aNhOgrI5YBcoYCTiwoqJxVUagVkcjnkchmM9x7g4ZpdCB3cBa4VAnHzUixSkzLgE+iJKvUqQHM7Dqe+XIHw9/rCv04YXD2cpe0ahRFymRzOrmo4uzlBqVJAJge0GXoAOYfZ6DR6/NnzC/hWLYuuc9+GAKTeNs4uKtMcJlF38OK6iRACOYbkZA7fcXZVS9+PmXPcZM1r0Bvwy7MT4VM5BM/PHwa5QiHVQZumwX/v/YzEq7F4YcV4CKMRaYnpMOr0UChkpjlKdKZhYrp0LWA0QgZIvUgyUjJg1Bkgh4BRb8T9CzcRteEIPEL94F+rPJw9XJD2IAlx524i9W4C/GtXgIuP+6OhPqbgi16jg1FngNFgeDQsyPQwlWFKK0lkCrlpQmG1EjKl6V+lWmWaCFilhFypgEypgNJJCaWTynSeqpSAXA65Wgm1swrp8SkwaPVQuTvDI9QPKhc11C5qKFRKyFQKGAWgUCng7O4MhUoJhVoJIwDI5VC7qqF2dYJCpYRcpYBOb4RcpYCblysUahXkCrl0njxpyGZu51Rhh3cWNG9hh3c+LW9eQzYLklcmk+UYspk5vDMzmFKQvEajUXrfP20oXkHyFuQzImverNu1BIdu5dPw4cOxfv167Nq1SwryAEBQUBC0Wi0SEhLMevXcvXsXQUFBUp5Dhw6Zbe/u3bvSstx88MEHGDNmjPQ8KSkJ5cqVs9buUCnEXkRkDexFRESUP2lxph4okQuGYPbYP6BJ00IhE1DIAIMw9VgBABcF4OKshMJJCaVKCZWTCkq1Ak5qOZQq04Wo0klpms9EpYBKJTcFR5zVpjlHUlKAgyfRq39DqEP8EX83CdoMHTw8nVG2cgBEfAJil/+HPuP+D17VQiGHgFKphNpNLU32KpfLIJcBaiclnDwye70oIDMaoVDK4eTuDLlcjl+enYj+bULQcfpgnDt8HYnxadKdaDa+Mx8PL8dg8qYPoHx0MWo0GGHQ6CCTy6B0fnyxpc/QQhiF6cL70UVcZl6jUWDF4bNQRt/GsxO6mSbFNRhNF+gKGTa8sxMeZX3R9KXmkCsVUGW5ONSla2DQ6AAhA7Q6aFLTYdDooE3VwKjXI1WhkAIX2rQMGLR6VO7SAMd/3Iw/+nyJCm1qw9nLDUl3HuDWvguIj7qDqt0a48TCTTBo9KZeVkYBw6NgiClAYrqbkxQ4ydCZhuvoTfXOzKtL0+DGrjOYFz4Cikc9grIP6Vnc+n1YS/Kt+0i+dT9HetyZ6FxyF5zCyRQ4UagUMBoFZDIZFE4qOHu5Sq+rTGEKsqicHwdZZAoZFMpH57uzKXAiVz6aj0ethNrVCXK1EgqV6dJRrlJA6aKGysUJMrkcD6JuISMhDa5+nghtUg3KR9sWRgG5Qg6lqzNUj4IWwmiEPsP0RlO5Pr54zpzDRq4y1T+vvAatDka9EfJHASPAdFGuT9cWOK/S+fHFvhymoYHybG0cZS5tnqflzZose5Seve1UkLyKIswrl1meN/sFjfzR+hbnzUbxKK+8kHnxhLzS65mPvCVFiQz0CCEwYsQIrF69Gjt27EBYWJjZ8gYNGkClUmHr1q144QVT16uLFy/ixo0baNbM1HWsWbNmmDx5Mu7du4eAgAAAwJYtW+Dp6YmaNWvmWq6TkxOcnKwTzaOnsdPdqOxwO2XBXkQOwTq3HS/Y+tabi6gw2+BZS0Qlj6u/6RfU6lX9sPjEpzi85RwuTPoZANBn/afQ3E/EusEz8eqbzXH+z32o2bcx2mfpsj8/4l3oE7QYuPULeIb6AQBOLN6KPVNXourzjfDcjMEATAGSebUjYdh5BD3+/hh+1Uw/SJ79Yze2T1wGFz9PeIb6oet7z0OukGNJ+w+RfDsefVe+j8A6FQEAF/8+iE3jFqFc8+rosWgUAFMbeHnXz/DwSiz+b+4wBNapiAZvdsKOScvxU+Ox8Krgj3afv4KH12Ox+pU1uHviKgDg8JwN8K1WFkadAffP38SJxVvhFlAGdQe3N/Ua0Rtw7s+9SIl5iAqta8OjrA+MOgOS7ybg5u6zULo6wadSEK5vP4Wfmo0z3fEpNQMuvh7QZ2ihS9XAydMFCxuOBmQyqN2cpLsCZQ7jKYy4szcQd/ZGjvRL6w7lkrvwhMEIfR71lKuUUg8aZx93qJzVkKsU0KZkIP1BMpy93eBdKVjqsXJz33kY9UZUbFcHLt7ukKsUeHgtFncORcE92BsBtSvCycsVZSoG4OiCTdAmp6PR8OdRpoI/FGol7hy5jFO/bEdgnYpo+X4fU/BDrcT6YXORciceXb5/C8H1n4FCpcT1HaexZdwihDSugt6/jJXqvKLHF7h/4Ra6//wuyrcwXdtc33Ea69+ag4DaFdBz0QdS3j9f+gqxx6/i/+a8jUodIwA87v3mUzkY/TdMkvKuHTILN/ddwLPTh6BS+7oAAJWLGiv7fgmPsj4If6m1lHfDO/NwbetJtPvfANR60TQkJ/5KLH57/nM4e7vjjQNfS3m3fbgUl9YfRssP+iJicAcAQPKdeCzt8DGULmq8feJbKe/Oz1fg3Mq9aDqqOxoOMw0XyniYgp+amYYkDb84X8q7b/pqnFy6DQ3e7oxmo3sCAPTpWiyoZxrq9Nbx2VJg6NCc9Tg6fxPqDmyPVh+9KG0jM+/r+6fDxccDAHD8p804MOtv1Ozbwuwz4qfm46BPN/+MOL1sR5bPiNelvEvaf4SMhyl4ef0n8K0SAgC4sHoftk9chrAOddF17jAp7/Kun+b4jIjaeARbsn1GAMDKPlMRfzkGPZeORmiTatJrvzFyPoLqVUKfFeOlvKsGzMC9M9F4fkEkKrY1Dd26deAC/n7tW/hVD8VLaz+W8v499DvcORSFzrOGonIX09Ctuyeu4q/+X8Orgj9e3fw/Ke8/IxYgeucZdJg6EDV6NwcAPLh0G7/3nAy3AC8M2T1Nyrtl3CJc+fcYWn/yEuoMaAsASLwRh187TYLawwVvHpkp5d3+yTJcWH0Azcf1Rv03TEO3UuMSsbj1B5Ar5Xjn7Fwp756pf+L08p1oNLwrmozoBgDQJqdjYSNTx41hZ+ZIQcUDM9fi+M9bUO+1Z9Figil+YNQbpdc+6zlVUpTIQE9kZCSWL1+OtWvXwsPDQ5pTx8vLCy4uLvDy8sLrr7+OMWPGwMfHB56enhgxYgSaNWuGpk2bAgCee+451KxZE6+++iq++uorxMbG4uOPP0ZkZCSDOURUyshQGubhIiIKaVgFHmV9cWT+JnSd+zY6vtRYCvR4lHHG4el/wiPER7qY06Zk4OHVWNOwGZ0BwmgKBtw+FIX7F26Z5jJ51BsjIfoeTi7ZKuWVKRQQRj3+Hvo9gupUhMrVGXFnTXnT7yfBvVZ5rBv6HQw6PVIf9TTaPPYnyJUKGHR6aZLWWwcvYX7Eu6ZeKlnuurPxnXk59i8xOg5rBs3MkX50waYcaan3ErDvq5xz9ETvyjlHjz5NI+1nRpaJYtMfJEt/a5JMcwpBCGhTMnJsQyKTQf7oTkeAKfiW2YMk/WEytMkZcAssA48QH8iVCmQkpiL+0h0AwDOd60OpVkGuUuDuyWuIvxyDwIgwhDapBrlKAWEQODJvIwCg1cf9pIBM1D9HEb3jNCo9G4HaL5nmSJEBWD3QdKx6/zYOSbfuQ5uYhttHo3Dln2Oo+WJLtPnkZciVptsvz631Dox6I15a8xHcA70BAMd+3Ix901ehYttwdPxysLSLPzQcDW1yOlq+/wLKVDTNAXpq2Q7cORSFwDph6PLtm1LeU0u3QZucjkod68K/hmmkgC7t0VAhb3cEN6gs5c28KHX18YCrr+ejtBJ5OUdkkStXruLibysQHByMelVr27s6xVKJnKPnSfNRLFq0CIMHDwYAZGRkYOzYsfjtt9+g0WjQqVMnzJ0712xYVnR0NIYNG4YdO3bAzc0NgwYNwpdffgllPufbscvt1akIlbi3QuGxFxEVknXvaJb/7XAeoqLBOXqopLHG/AZXNh/HP+/+AL9qZZF06z6MBiP0Gh1gLIHtAJns0ZAcuTTcSiaXQaFWQe3uArlSLs11olCb5j2RKeWQyeWmOVKcVNKcJQAgVyqhdFFC6aR+NMzHFOSQqxRQuzqbAiQKOR5eiYEmRQNXPw8E1an4aI4TGcSjbTh5OEvzrgijgFwpg9LZCSpXJ8gV8oINySnk8B2ly+MhOQatadhWwfLKoVA/niMlM/iidFZJt/kuUN5Hw9NkCrk0jA6AaY4fIaBwUkl38ylIXqPeNKfPE4fiFSRvLsP2IJOZDcUrUN4CDMey29Ctgrz2hT1PnvB6ZiSnYd++/Yi9fw8hZUPQqlVLwAiLX/vseQ0GA3bt2IWYW3cQFByENh3aQvFoTqq8Xs+CnidytQK7d+9BTEwMAv0C0LxpU6icnaTX0xrnyV9//IX3J3yIa9evw2CagQkVK1bE9KlT0KN7d6t/RmhS0rFv337cjY9DcHAwWrVqKR27wrDlHD0lMtBTXDDQQ5bjpNNUslge6OGE08UJAz1U0lirkXxl83Fsm/hrnrcxlivl0pCZzIld5SqFFDCRm6Vn5lNke266IEqNS4RBq4eTlyu8wwKlXimZQRZp2yqldLEj/at8lK7Oubyk3OaXyFIGg0EKIljjgtve5axatRpjx47H9evXpbSKFStixoyv0Lt3L5bzhDL69OmH55/vig8/nIDatWvjzJkzmDJlGtav34A///y92O8TAz0lROYLtbN1EQV6nnRdIzgRquMoPW8/nrNUeEXxPnn6Nm0dXLLHt3GKXo9WOxjooZLDmo3ktPgURO84BU1SOtwCvRDcoApULmrp12oGUYiezhaBEUcKVmSWY4uAhSOVYzAYULlydYSH18aaNX9JdykDTHfd6tnzBZw5cxZRUeetcv4V1T4x0FNCZL5QO4oq0PMEVpnztTBlkoMoPW95BpccSek5by1R0G/0FL0erXcy0EMlhy0byUTWxl4phSvDUYIVgO0CFo5Wzo4dO9GuXUfs379bmnM3q/3796N589bYvv0/tG3bptDlAEW7T7b8DuNPFVYhs9lDwDT+WQjbP8hR2O58ffywrczT1dbvCb5PipI9ztuS95DJCv4gIirtDAYDduzYid9+W4EdO3bCYDA8faUCWrVqNSpXro527Tqif/9X0a5dR1SuXB2rVq0useX06dMP4eG1sX//biQnP8T+/bsRHl4bffr0s0p5BoMBY8eOx/PPd8WaNX+hadOmcHd3R9OmTbFmzV94/vmueO+9CRa/XrYqBwB2796D69ev48MPJ5gFEABALpfjgw/G49q1a9i9ew/LySImJgYAULt27hMvZ6Zn5rOErfapqDHQQ08lHt1y3Chs+7BHMMvW1+mlJy5g44tdYZuL16znTuYcngzCEhGRI7NFUMSWZdkiMGKLoIgty7FVYMTRghWA7QIWjlZOcHAwAODMmZx3BcyanpnPErYMKhUlBnpKGJn0f9v+Qmz7X6ZNQRCbPxw1mJVtH23N5kXKMv9nw/cJbN8zIvO1tGVQyZbnLRGRI7BVYMRRgiK2LIu9UgrH0Xpx2PLC3lYBC0crp1WrlqhYsSKmTJkGo9FotsxoNGLq1K8QFhZmunOZhWwZVCpKvFWUFZSWixLb9vAvLcMJhG2CINkKKQ3nKwA7nEa2DffYY9SNTGT+r+hZ6zwt7GZKw6dQqfksIMoFJ5EteBmZc5j89tsvZnOY9OnTz6p3vLFFWdkDI5kBi8zASM+eL+C99yagR4/uFp0XmUGR33775YlBkebNW2P37j0WzS1iq3IA+/TiyG1elqIIVhRlOYB5wCK3+V+sFbB4WjlTpkxDxYoV0bhxI6SlpcFgMJg99Hp9jrTc8iiVSgQFBWHMmPH4/PNJEELAaDRCCAGDwYDPP/8CgYGBSEpKxvr1GyCEQOYUwZl/5zetZ8/umDXrWzRs2BTdu3dFaGg53L59G+vWrcfRo8cwfvx72LBhI+RyORQKBRQKRba/ZTnSc8sbGloWoaGhmDjxUyxe/BOUSiVcXV3h4eFh9aBSUeJkzBbInExpe6uOcFeq7F2dIiMELLydMhUXMlnm62nvmtiCbc9Zex5Xh345ZWb/2Iw9Xk8B2++naTLmrZzYlkoMa01kyUlkC8aWd7xxtMldf/ttBfr3fxXJyQ/h7u6eY3lycjI8PX2wfPkvePnll4p9OYDtjl1xmlD49OkzOH36OIxGI7RaLXQ6XR7/6nKkZ/378OGj+Omnn1GjRnW0bNkCPj4+iImJwd69+3DlylV07twJFSqUh06ng16vh06nf/SvLktazn+z5tPr9UhOTsbDhw+hUqmgUilhNAro9aY8VDAvvdQP774bialTv+Jdt0qDzBdqW0vbBXpKz0W6fdj8wk6IUnExWTqYjqwdOhHZlGkv+bWRL4U5TJa+ngUsM0WvR9vdDPRQyWGNRrIjBUYcLShiy7JsFRix1f6UxLsSZQZNMh8ajcbsuVarxebN/+HDDz9G06ZN8MILvRAcHIwrV65i9eq1OHHiBIYOfQO1atWAVqvLsW7Ox5Pz3L//ALdu3YKLiwtcXV1gNApoNBnIyNDkGCpUGmTt6ZL5UCqVOdIyH2lpaYiLi4NOp5O2oVarERpaFt7e3gCQ680gCpMGAAkJCdBotFCrVVJPG4PBkONf098il7Tc8pmn6fU66PXmQx3DwsLw9dfTCv0dYctAD4duWYEQMhhtOPmrPS7SS0dgwPYXr8KC88aiEK3NA1qlIUD5eG4pm5UoA4TRtsFC0/5ZUGAJGUdllx49dvh8F6Xk050oE4frFI4t5zBxtGFB9hiqs2rVShgMBimIkZGRgYkTP0VoaCh8fHxw7NixJwY+cgZbdLkur1z5Gaxbtx7ly4ehSpUqcHFxQXx8PC5fvoIHDx7gmWeeQf36jczWz77tgswXdODAQRw4cDBH+sKFP1p03LJLT09Henr6U/MpFAqoVCqo1eon/Kt66nKlUon79x9Aq9XC09MTFSpUkNZTKpWPeuKooFQqzNKy/5szTWm2TCaT4/jxE3jw4AGCg4PQrFlTqNXqJwZv5HJ5oe7KaYvhsLaWuU937txBSEhIidonBnqswGiUwWgs+JuhMBcRdplI124X6bbeWbkdDrCslMyOXPgyc5x7+dhOZhZbn7e2DvWYTh05bN0x0y79eez02VdoWc+9Amyn0EUWcsVS+CMllXKOFhhxtKCILcsqSABGCNOQl+xDcHJ/rsuxvEePbpg9+ztERDREx47t4e/vhxs3bmL79h24ePESevTojrFjxz2190n2crIvT09Px/Xr16FSuTxxv+vWrW/Rccvu9u07uH37To70K1euFHhbSqUSarUaarUaTk5O0t+mhwoajal3jZubG/z9/bPlUWXLn/3xtOWmPAqFEmfOnMXDh/EIDg5B8+ZN4eLiIgVpMgM12T8/iruaNWsUeRkKhcLi3mLFTUneJwZ6rMBY2B49hb3wLWkXPIVWDPsuFaZKTzt2FkQjnrRmnkXmY+yW9Y984U8gS869UnPe5nEOWbs2wg7njz3KtDiyVNjVbXzO2qo3KlFxkVtgJDU1FQDg6uoqpd+8eROpqalQKpVwcnLKkdfFxUW60Mu8+FYoFHB2dgbwOAhx+PBhtGnTJkfeo0ePmuVLS0uDEALOzs7Sr8V6vR4ajQZyuRwuLo8v3LPmzVz/xIkTqFevXo68meUEBARIaQaDARkZGTnypqenw2g0wsnJCUql0ixvgwb1zYIimb0xMnsFTJ36FSpWrIj69eshLS0Nrq6u0nYzMjKkvEqlEjqdDhqNBomJidDp9FCplNLcIykpKVAoFAgKCsLYsRPw+eeToNVqkZqaCq1WC5lMjq+++hp+fn44efIUDh8+DKPRNPlr5nbT0tKg0+lgNBql4ExGRgY0Gq00gWxmXqVSgXXr1sPLyxe+vj6QyeRITU1BYmIStFotnJ2d4OLiYTYcxRKnT5/G6dOnc6SvXfu3VbafF7VaDWdn5xyBFCcntdT7w8nJGc7OTlIwQ6FQwMlJDVdXV2k9mUwGlUoFFxcXKQBy7do1JCcnw8/PD/Xr15PKkclMk9+6ublK21AqlTAajVCr1fD29pa2azAYIISQygZMAbfM3jVubm7Svmg0Guj1ein4ApimQ0hLSytwXldXV6n3SmYwLSKibq7v+9zyFvYzoqB5C/sZ8bS8eb3vC5JXJpM98X2f+XoWJG9BXvvCnie5vZ75OadKCs7RY4HMMXb/NP4/uNl0Mma+ZJQ/uV3/22dIih3mIioVw8Usk6/Dk0smHlbHkaLTodPBTZyjh0oMS+c3yG0eE5nM1Ia7d+8OLl++jObNW+P114fgp58W4Y03XsPChQuk9d3cvJCWloZr16JQsWJFAMCsWbMxevR76N//JSxb9gsAPLpocYXRaMSpU8cRHm4KIC1c+CPefHMYAgMD4erqKs1hUrFiZURHR+PQoX1o1KgRAGDZsuV45ZVB6NixA7Zs2STVoVatOjh37jw2bdqARo0aIiKiIby9vXHq1CnUqxeBpUsXQafTISNDg2ef7YzU1FRMmvQJGjSoB51OhyNHjmLq1GkoVy4UH374vjR57Jw583Dt2jX07t0LlSs/A51Oh+joaKxatQYeHh6oV68udu3ag6CgQGRkaJCQkIDy5cshOTkFDx8+RHBwMGJiYh7dtSZUunBKSEiAXq+HXC53qLlO1Go1hBDQ6XRwdnaGv7+/dJF46dIlGI1GRETUhZeXF9RqNe7cuYOzZ8/B398PVatWhaenJ8qXL4dff12O1NRUvP76EJQrVw5qtRonTpzEH3+sRO3atTB69EgpGDJq1BjExMRi5swZqF+/HtRqFXbu3I333/8QjRs3wu+/L5eCb82atcS1a9cxbdoUjB07BgqFAhs2bMTzz/dAw4YNcPjwAWlfWrRojX379mP16j/Rs2cPAI/fKzVr1sTZsyelvM8+2xn//bcVv/66BAMG9AdgCmg2btwcFSpUwPXrl6W8PXu+gLVr/8YPP8zD0KFvAADOnj2L2rUj4Ofnh7i4xz3NBgx4FcuXr8DMmV9j1KiRAIDr168jLKwKXF1dkZqaKOUdOvQt/Pjjz/jii8/x0UcfAADi4uIQEBACABDicWBu1KgxmD37O3z44fuYPPl/AEwBFnf3MgCAlJQE6SL+o48mYsqULzFy5AjMmvWNtI2snxH+/v4AgMmTp+Ljjz8p9GcEAPj7B+P+/fs4c+YEatWqBeDxZ0SPHt2xZs1fUt6CfUbUxblz58zmY1qzZi169eqD5s2bYe/eXVLeRo2a4siRo1i/fi26dv0/AMCWLf/huee6oG7dOjhx4qiUt23bDti5cxf++OM39O3bBwCwd+9etGzZFpUrV0ZU1Hkpb9eu3bFx4z9YtOhHDB48CEBmQLoRQkJCcPt2tJS3b9+X8Oeff+H772cjMvIdAEBUVBSqVq0JLy8vJCTcl/IOHvwaliz5BV999SXGjRsLALh9+zZCQys+CiQ/HnYXGTkCc+fOx6RJE/Hpp58AMM3t4+1teg212jQpsDRu3AR8/fU3eO+9MZg+fRoAU9BNrTYFpLKeU5bgHD0ljN4oh95oy+57DPSUNla/sLb5lbrI12mb32rl+x1gjzLzoVSXWdBzzxoVKmSZhQ2EFUpB9tOKZdr2u4vI/vIarmMwGPDFF1NRoUIF6WIuOTkFly5dkuYXyQxU7Nq1B2fOnIVWq8WxY8cBAJcvX8GcOXMfzUNi+mVYo9Gga9duaNq0CTw9PXHo0GEAwN27d9GkSWP06NEbOp0OsbGxAIBBg16DWq2GVqvDgwcPHpW1G76+gVLgRKPRAAA6d+4q1f3mzZsAgOPHTyA8vF6O/f7ss89zpN28eQvDhg3Pkb5q1eocacnJydi1aw8AIDb2rpR+48ZN6e/M3lIGgwHR0dHI7klBnszhOiqV6lHPGw28vb3h7OyM+/fvm/WmcXZ2RrVq1VCuXCjOnj2Ha9euoU6dcDRs2ABqtRpGoxE//GCas+Wjjz6QepZs2rQJ27fvxHPPdcTLL78k9Tbp3/9VAMCff/6OmzdvITExEYcPH8GGDRvRv/9L+N//PpOCLGXLVoBer8e1a1GoUKECZDIZpk+fgfHj30e/fn2xePHPUj3LlPFDYmIi/vjjN1SpUgUAMGfOXAwfPhJt2rTBypUrpLzr1m1Aamoqhg9/BxEREQCAxYuX4I8/VqJ8+fJ47bUhUt6PPjJdrDZq1AAtWrQAYHodAVOvkMzAAgDpIrJevXolZk4RIrIe9uixQGZEblX9HnBTOO7t1akoPf3tZ/E1XS5z3GTv6WLtD4EcRdprYttSUKYtAr/sGeW4UvQ69Dy6jj16qMSw5l236tatg6tXr0lDGgoyMWxxk1tvGaVSCT8/v0fDY1RwcnKSJmtVKJRQq1VZ5h5RSUNynJ2dpWE9mROzqlQquLu7SetHRV1GcnIyAgICUK9eBJydnaWgmVqtgqenpzQRrBACcrkcbm5u0vAdhUIBg8EApVJpdser7EMtDAYDdu7chejoaAQFBeG5556VghbWHJZR2CE51hi+k9swG2sM38kcZmPp8J3sw2wsHb7zpGE2lg7fsebQrSe9nhy6xaFbluLt1UuIzBdqRZ3ecM1PoCevq6WiehnyukArUJEFyJxlP2XZ1yyi/cx7Zvi8y5RlyVWQOW/yupG2yGUNy6+Vn7aF/B3bgt1eXfaUc6gIyrTSOWuPMgtEZv6n+fskl+zFKthire4nNji4NiuzZEoz6ND3xBoGeqjEsFYjedWq1XjzzWFSr5ncKBSKXCeFNT1X5Vj2+G/zZUqlCnfv3oVGk4EyZcqgatWqcHZ2MpvcNesdebLelefJyx6vp1QqIZfLHfKON0REjoRDt0qYDIMScqjyvHwo1KS5T1nXtnhxlMmS1zLXlfLxAlvlHMgS0coaNHhSvS0Z6lN6e/TYfi4iWynoeV+Uh8EeZRZEjsBdfhUsCmuVMtP1hSuPqKTr3bsXWrduhY0b/0F8/EOULWu6ba6Li4sUQClpQZKSfHcYIiKyLgZ6rCBNp4QwOvahzO3aI78XFVl7zBRk+7mxdZmF6bdQkDIFRG4jq5645tNfh6f3JMrtWvKpZea5Qi5lZh8alu/hYk/uJ1WQ/RSP/mfrMvP/PsmrP9jT1y7sWtYLhOQsQbqlfS4l5l3u0/cx//EP632SlJYy0/SOMzEqUUH5+flh4MBX7V0NIiIiq3Ps6EQ+zZkzB9OnT0dsbCzq1q2L7777Do0bN873+sl6NXRGdZH2ecn8tVYG84tmW5WZVVGXmVlGjgv3Qmwn67p5XRplHle5LPeyCtrzKnv+3MqWATAKUegyn/a6PLnMwu9nYcs0CEBhwzIBy17PwpYJC96bhSpT9rjM/OynJYGekvx5UKgy89nzraSXmWooLv2gqDgqaPsoISEBH330EVatWoX4+HhUqFABs2bNwv/9n+lOLp9++ik+++wzs3WqVauGCxcuFOl+EBERlTalPtDz+++/Y8yYMZg/fz6aNGmCWbNmoVOnTrh48SICAgLytY0HGiWc5E86lE9vROe8gMm7ef/0X32tX+bTlfwy8/dr+pNzZN63I2sJTypTCgY8CkZkW+HpZWa5As693rlN8vK4TJktysx6wWoEZPInZ32irMPN8rNaXl1srNkdLD9lFrQ7WCGnvbFgtI8FChoqy3vJE98n+aiJLJe/sm/tyS9J4T6DnlZm3qddHp9BufRCy3kMnl5mzuVPLjPDwB49lLuCto+0Wi2effZZBAQE4M8//0TZsmURHR2NMmXKmOWrVasW/vvvP+l55oSeREREZD2l/tv1m2++wdChQzFkyBAAwPz587Fhwwb8/PPPeP/99/O1jbvpCqjleY/jzq2ZLc8l/WkXNgK5BAcKUKb0y3+Whda8SMxPmUVdXmHLzO+cLtYs02jBawnZ455IWZKKtMysAaqClGnJsS1smfmNgORVZh7xskLJsTlZLn8WMOBj0edBLulF/nmQLd0mnweFKNPiz4NClGnR50Ehy2Sgh56koO2jn3/+GfHx8di3b59015Sst3rOpFQqERQUVKR1JyIiKu1KdaBHq9Xi6NGj+OCDD6Q0uVyOjh07Yv/+/fnezs10HZS5XBHk/KU1exNcZMv35Ga5WS+RPC4G8lumedrT5b/XQG7bL3iZBeul8LQy87clIwTkhZz7QgYZhE3LzC0SYYsyC3v+iDzP76Io0whAkcvauW/J1u+T0lKm/T8PnvbefFJMTyDrZ3Le0bfsdctvB63sNTVN5i17tH7Bysyv7GVqjLpCbokcWWHaR3///TeaNWuGyMhIrF27Fv7+/ujfvz8mTJhgNqlxVFQUQkJC4OzsjGbNmmHq1KkoX778E+ui0Wig0Wik50lJSVbYQyIiIsdWqgM99+/fh8FgQGBgoFl6YGBgruPFszc2EhMTAQDnxAXIHx3Kx43z7M1wkeWvzMuHnJcDskfLs6dl3Y4RgFxkTXn6gAHx6HInt35E+Skz5yiY/JQph8xU21zKlOV5uZf7JMXZy8y5hcf7+aRLrSfvpxHGHAGQgpT55IvRvMoUkJtnzleZmftXmDIFjGZBl9wvgnM7fzJfs5yvzdMGmOSvTPNyM8vMzF+wMoV05uUss3DvzdwH7GTmFo/+y+3MflIANnPfMtdRZFs3M495XR6/QpnngCzbdrPXWeRIz1znSfXN+/wxr3/+y5Q/Ok6FKTPnJ1BRl2mEEbJcB2TmVablnwcykVdwSQZTCDN7WuE/D3RIN5UmciuPSquCto8A4OrVq9i2bRsGDBiAjRs34vLly3jnnXeg0+kwadIkAECTJk2wePFiVKtWDTExMfjss8/QqlUrnDlzBh4eHrlud+rUqTnm9QEY8CEiopIn87vLFu2uUh3oKagnNTZiM/bZoTZERETWkZycDC8vL3tXg0owo9GIgIAA/PDDD1AoFGjQoAFu376N6dOnS4GeLl26SPnr1KmDJk2aoEKFCvjjjz/w+uuv57rdDz74AGPGjJGe3759GzVr1kS5cuWKdoeIiIiKiC3aXaU60OPn5weFQoG7d++apd+9ezfX8ePZGxtGoxHx8fHw9fWFLD+TOZQASUlJKFeuHG7evAlPT097V8fueDwe47Ewx+PxGI+FuZJ0PIQQSE5ORkhIiL2rQsVIQdtHABAcHAyVSmU2TKtGjRqIjY2FVquFWq3OsU6ZMmVQtWpVXL58+Yl1cXJygpOTk/Tc3d0dN2/ehIeHR462V0l671kL97l07DNQOveb+1w69hkoPftty3ZXqQ70qNVqNGjQAFu3bkXPnj0BmII3W7duxfDhw3Pkz97YAJDjbhKOwtPT06HfZAXF4/EYj4U5Ho/HeCzMlZTjwZ48lF1B20cA0KJFCyxfvhxGoxFyuWnY46VLlxAcHJxrkAcAUlJScOXKFbz66qv5rptcLkdoaGieeUrKe8+auM+lR2ncb+5z6VEa9ttW7a7s04OUOmPGjMHChQuxZMkSnD9/HsOGDUNqaqp0lwkiIiKi0uZp7aOBAweaTdY8bNgwxMfHY+TIkbh06RI2bNiAKVOmIDIyUsrz3nvvYefOnbh+/Tr27duHXr16QaFQ4OWXX7b5/hERETmyUt2jBwD69euHuLg4fPLJJ4iNjUVERAQ2bdqUYwJCIiIiotLiae2jGzduSD13AKBcuXL4999/MXr0aNSpUwdly5bFyJEjMWHCBCnPrVu38PLLL+PBgwfw9/dHy5YtceDAAfj7+9t8/4iIiBxZqQ/0AMDw4cOf2BW5tHFycsKkSZNyDFErrXg8HuOxMMfj8RiPhTkeD3IUebWPduzYkSOtWbNmOHDgwBO3t2LFCmtVLVel8b3HfS49SuN+c59Lj9K630VJJnhPVSIiIiIiIiIih1Dq5+ghIiIiIiIiInIUDPQQERERERERETkIBnqIiIiIiIiIiBwEAz1ERERERERERA6CgZ5SaurUqWjUqBE8PDwQEBCAnj174uLFi2Z5MjIyEBkZCV9fX7i7u+OFF17A3bt37VRj2/nyyy8hk8kwatQoKa00HYvbt2/jlVdega+vL1xcXBAeHo4jR45Iy4UQ+OSTTxAcHAwXFxd07NgRUVFRdqxx0TEYDJg4cSLCwsLg4uKCZ555Bv/73/+QdQ57Rz4eu3btQrdu3RASEgKZTIY1a9aYLc/PvsfHx2PAgAHw9PREmTJl8PrrryMlJcWGe2EdeR0LnU6HCRMmIDw8HG5ubggJCcHAgQNx584ds204yrEgKo7mzJmDihUrwtnZGU2aNMGhQ4fsXSWrYZutdLXNSls7rLS0tUpjm4ptJ/tioKeU2rlzJyIjI3HgwAFs2bIFOp0Ozz33HFJTU6U8o0ePxrp167By5Urs3LkTd+7cQe/eve1Y66J3+PBhLFiwAHXq1DFLLy3H4uHDh2jRogVUKhX++ecfnDt3DjNmzIC3t7eU56uvvsK3336L+fPn4+DBg3Bzc0OnTp2QkZFhx5oXjWnTpmHevHn4/vvvcf78eUybNg1fffUVvvvuOymPIx+P1NRU1K1bF3PmzMl1eX72fcCAATh79iy2bNmC9evXY9euXXjzzTdttQtWk9exSEtLw7FjxzBx4kQcO3YMq1atwsWLF9G9e3ezfI5yLIiKm99//x1jxozBpEmTcOzYMdStWxedOnXCvXv37F01qyjtbbbS1DYrje2w0tLWKo1tKrad7EwQCSHu3bsnAIidO3cKIYRISEgQKpVKrFy5Uspz/vx5AUDs37/fXtUsUsnJyaJKlSpiy5Ytok2bNmLkyJFCiNJ1LCZMmCBatmz5xOVGo1EEBQWJ6dOnS2kJCQnCyclJ/Pbbb7aook117dpVvPbaa2ZpvXv3FgMGDBBClK7jAUCsXr1aep6ffT937pwAIA4fPizl+eeff4RMJhO3b9+2Wd2tLfuxyM2hQ4cEABEdHS2EcNxjQVQcNG7cWERGRkrPDQaDCAkJEVOnTrVjrYpOaWqzlba2WWlsh5XGtlZpbFOx7WR77NFDAIDExEQAgI+PDwDg6NGj0Ol06Nixo5SnevXqKF++PPbv32+XOha1yMhIdO3a1WyfgdJ1LP7++280bNgQffv2RUBAAOrVq4eFCxdKy69du4bY2FizY+Hl5YUmTZo43LEAgObNm2Pr1q24dOkSAODkyZPYs2cPunTpAqD0HY+s8rPv+/fvR5kyZdCwYUMpT8eOHSGXy3Hw4EGb19mWEhMTIZPJUKZMGQCl+1gQFSWtVoujR4+afRbJ5XJ07NjRYT+HS1ObrbS1zUpjO4xtLbapMrHtZF1Ke1eA7M9oNGLUqFFo0aIFateuDQCIjY2FWq2W3miZAgMDERsba4daFq0VK1bg2LFjOHz4cI5lpelYXL16FfPmzcOYMWPw4Ycf4vDhw3j33XehVqsxaNAgaX8DAwPN1nPEYwEA77//PpKSklC9enUoFAoYDAZMnjwZAwYMAIBSdzyyys++x8bGIiAgwGy5UqmEj4+PQx+fjIwMTJgwAS+//DI8PT0BlN5jQVTU7t+/D4PBkOtn0YULF+xUq6JTmtpspbFtVhrbYWxrsU0FsO1UFBjoIURGRuLMmTPYs2ePvatiFzdv3sTIkSOxZcsWODs727s6dmU0GtGwYUNMmTIFAFCvXj2cOXMG8+fPx6BBg+xcO9v7448/sGzZMixfvhy1atXCiRMnMGrUKISEhJTK40FPp9Pp8OKLL0IIgXnz5tm7OkTkYEpLm620ts1KYzuMbS1i26locOhWKTd8+HCsX78e27dvR2hoqJQeFBQErVaLhIQEs/x3795FUFCQjWtZtI4ePYp79+6hfv36UCqVUCqV2LlzJ7799lsolUoEBgaWmmMRHByMmjVrmqXVqFEDN27cAABpf7Pf1cIRjwUAjBs3Du+//z5eeuklhIeH49VXX8Xo0aMxdepUAKXveGSVn30PCgrKMRmqXq9HfHy8Qx6fzIZKdHQ0tmzZIv0iBZS+Y0FkK35+flAoFKXic7g0tdlKa9usNLbD2NYq3W0qtp2KDgM9pZQQAsOHD8fq1auxbds2hIWFmS1v0KABVCoVtm7dKqVdvHgRN27cQLNmzWxd3SLVoUMHnD59GidOnJAeDRs2xIABA6S/S8uxaNGiRY5btl66dAkVKlQAAISFhSEoKMjsWCQlJeHgwYMOdywA0x0B5HLzj0mFQgGj0Qig9B2PrPKz782aNUNCQgKOHj0q5dm2bRuMRiOaNGli8zoXpcyGSlRUFP777z/4+vqaLS9Nx4LIltRqNRo0aGD2WWQ0GrF161aH+RwujW220to2K43tMLa1Sm+bim2nImbfuaDJXoYNGya8vLzEjh07RExMjPRIS0uT8rz99tuifPnyYtu2beLIkSOiWbNmolmzZnaste1kvbODEKXnWBw6dEgolUoxefJkERUVJZYtWyZcXV3Fr7/+KuX58ssvRZkyZcTatWvFqVOnRI8ePURYWJhIT0+3Y82LxqBBg0TZsmXF+vXrxbVr18SqVauEn5+fGD9+vJTHkY9HcnKyOH78uDh+/LgAIL755htx/Phx6W4I+dn3zp07i3r16omDBw+KPXv2iCpVqoiXX37ZXrtUaHkdC61WK7p37y5CQ0PFiRMnzD5TNRqNtA1HORZExc2KFSuEk5OTWLx4sTh37px48803RZkyZURsbKy9q2YVbLOZlIa2WWlsh5WWtlZpbFOx7WRfDPSUUgByfSxatEjKk56eLt555x3h7e0tXF1dRa9evURMTIz9Km1D2RsTpelYrFu3TtSuXVs4OTmJ6tWrix9++MFsudFoFBMnThSBgYHCyclJdOjQQVy8eNFOtS1aSUlJYuTIkaJ8+fLC2dlZVKpUSXz00UdmX0COfDy2b9+e6+fEoEGDhBD52/cHDx6Il19+Wbi7uwtPT08xZMgQkZycbIe9sUxex+LatWtP/Ezdvn27tA1HORZExdF3330nypcvL9RqtWjcuLE4cOCAvatkNWyzmZSWtllpa4eVlrZWaWxTse1kXzIhhLB+PyEiIiIiIiIiIrI1ztFDREREREREROQgGOghIiIiIiIiInIQDPQQERERERERETkIBnqIiIiIiIiIiBwEAz1ERERERERERA6CgR4iIiIiIiIiIgfBQA8RERERERERkYNgoIeIiIiIiIiIyEEw0ENERERERERE5CAY6CEiqxJCAAA+/fRTs+dEREREZH1sexFRdjLBTwIisqK5c+dCqVQiKioKCoUCXbp0QZs2bexdLSIiIiKHxLYXEWXHHj1EZFXvvPMOEhMT8e2336Jbt275ami0bdsWMpkMMpkMJ06cKPpKZjN48GCp/DVr1ti8fCIiIqLCYtuLiLJjoIeIrGr+/Pnw8vLCu+++i3Xr1mH37t35Wm/o0KGIiYlB7dq1i7iGOc2ePRsxMTE2L5eIiIjIUmx7EVF2SntXgIgcy1tvvQWZTIZPP/0Un376ab7Hibu6uiIoKKiIa5c7Ly8veHl52aVsIiIiIkuw7UVE2bFHDxEVyJQpU6Sutlkfs2bNAgDIZDIAjycEzHxeUG3btsWIESMwatQoeHt7IzAwEAsXLkRqaiqGDBkCDw8PVK5cGf/8849V1iMiIiIqjtj2IqKCYqCHiApkxIgRiImJkR5Dhw5FhQoV0KdPH6uXtWTJEvj5+eHQoUMYMWIEhg0bhr59+6J58+Y4duwYnnvuObz66qtIS0uzynpERERExQ3bXkRUULzrFhEV2sSJE/HLL79gx44dqFixYqG307ZtW0REREi/TGWmGQwGaZy5wWCAl5cXevfujaVLlwIAYmNjERwcjP3796Np06YWrQeYfgFbvXo1evbsWeh9ISIiIioqbHsRUX6wRw8RFconn3xilYZGXurUqSP9rVAo4Ovri/DwcCktMDAQAHDv3j2rrEdERERUXLHtRUT5xUAPERXYpEmTsHTp0iJtaACASqUyey6TyczSMsegG41Gq6xHREREVByx7UVEBcFADxEVyKRJk7BkyZIib2gQEREREdteRFRwvL06EeXbF198gXnz5uHvv/+Gs7MzYmNjAQDe3t5wcnKyc+2IiIiIHAvbXkRUGAz0EFG+CCEwffp0JCUloVmzZmbLDh06hEaNGtmpZkRERESOh20vIiosBnqIKF9kMhkSExNtVt6OHTtypF2/fj1HWvYbBxZ2PSIiIqLihG0vIiosztFDRMXC3Llz4e7ujtOnT9u87Lfffhvu7u42L5eIiIjIXtj2InJcMsHQKhHZ2e3bt5Geng4AKF++PNRqtU3Lv3fvHpKSkgAAwcHBcHNzs2n5RERERLbEtheRY2Ogh4iIiIiIiIjIQXDoFhERERERERGRg2Cgh4iIiIiIiIjIQTDQQ0RERERERETkIBjoISIiIiIiIiJyEAz0EBERERERERE5CAZ6iIiIiIiIiIgcBAM9REREREREREQOgoEeIiIiIiIiIiIHwUAPEREREREREZGDYKCHiIiIiIiIiMhBMNBDREREREREROQgGOghIiIiIiIiInIQDPQQERERERERETkIBnqIiIiIiIiIiBwEAz1ERERERERERA6CgR4iIiIiIiIiIgfBQA8RERERERERkYNgoIeIiIiIiIiIyEEw0ENERERERERE5CAY6CEiIiIiIiIichAM9BAREREREREROQgGeoiIiIiIiIiIHAQDPUREREREREREDoKBHiIiIiIiIiIiB8FADxERERERERGRg2Cgh4iIiIiIiIjIQTDQQ0RERERERETkIBjoISIiIiIiIiJyEAz0EBERERERERE5CAZ6iIiIiIiIiIgcBAM9REREREREREQOgoEeIiIiIiIiIiIHwUAPEREREREREZGDYKCHiIiIiIiIiMhBMNBDREREREREROQgGOghIiIiIiIiInIQDPQQERERERERETkIBnqIiIiIiIiIiBwEAz1ERERERERERA6CgR4iIiIiIiIiIgfBQA8RERERERERkYNgoIeIiIiIiIiIyEEw0ENERERERERE5CCKdaDnwYMHCAgIwPXr15+a9/3338eIESOKvlJEREREDuppba8dO3ZAJpMhISEBALBp0yZERETAaDTarpJERESUp2Id6Jk8eTJ69OiBihUrPjXve++9hyVLluDq1atFXzEiIiIiB1SQthcAdO7cGSqVCsuWLSvaihEREVG+Ke1dgSdJS0vDTz/9hH///Tdf+f38/NCpUyfMmzcP06dPL+LaEVFxYDAYoNPp7F0NohJJpVJBoVDYuxpUjBS07ZVp8ODB+Pbbb/Hqq68WUc2IqDhgu4vIMmq1GnK5bfraFNtAz8aNG+Hk5ISmTZtKaWfPnsWECROwa9cuCCEQERGBxYsX45lnngEAdOvWDR999BEDPUQOTgiB2NhYaegAERVOmTJlEBQUBJlMZu+qUDGQW9tr48aNGDVqFG7evImmTZti0KBBOdbr1q0bhg8fjitXrkhtMiJyHGx3EVmHXC5HWFgY1Gp1kZdVbAM9u3fvRoMGDaTnt2/fRuvWrdG2bVts27YNnp6e2Lt3L/R6vZSncePGuHXrFq5fv57vLsdEVPJkNjYCAgLg6urKi1SiAhJCIC0tDffu3QMABAcH27lGVBxkb3vdvHkTvXv3RmRkJN58800cOXIEY8eOzbFe+fLlERgYiN27dzPQQ+SA2O4ispzRaMSdO3cQExOD8uXLF/n7qNgGeqKjoxESEiI9nzNnDry8vLBixQqoVCoAQNWqVc3WycwfHR3NQA+RgzIYDFJjw9fX197VISqxXFxcAAD37t1DQEAAh3FRjrbXvHnz8Mwzz2DGjBkAgGrVquH06dOYNm1ajnVDQkIQHR1ts7oSkW2w3UVkPf7+/rhz5w70er0U0ygqxXYy5vT0dDg7O0vPT5w4gVatWuV5QDIbrWlpaUVePyKyj8yx4a6urnauCVHJl/k+4pwLBORse50/fx5NmjQxy9OsWbNc13VxcWH7i8gBsd1FZD2ZQ7YMBkORl1VsAz1+fn54+PCh9DwziJOX+Ph4AKZIGRE5NnYbJrIc30eUVfa2V0HEx8ez/UXkwPh9QWQ5W76Pim2gp169ejh37pz0vE6dOti9e3eevzqeOXMGKpUKtWrVskUViYiIiBxG9rZXjRo1cOjQIbM8Bw4cyLFeRkYGrly5gnr16hV5HYmIiOjpim2gp1OnTjh79qz0y9Lw4cORlJSEl156CUeOHEFUVBR++eUXXLx4UVpn9+7daNWqVb56/xAR2dquXbvQrVs3hISEQCaTYc2aNXYpY/DgwZDJZJDJZFCpVAgMDMSzzz6Ln3/+GUaj0ep1ciT5PXYVK1aU8mU+QkNDcyzPftE8atQotG3b1iwtKSkJH330EapXrw5nZ2cEBQWhY8eOWLVqFYQQUr7Lly9jyJAhCA0NhZOTE8LCwvDyyy/jyJEjRXMwyOFkb3u9/fbbiIqKwrhx43Dx4kUsX74cixcvzrHegQMH4OTk9MRhXURE9sK2V8nGdlfhFdtAT3h4OOrXr48//vgDAODr64tt27YhJSUFbdq0QYMGDbBw4UKzOXtWrFiBoUOH2qvKRER5Sk1NRd26dTFnzpwCr9u2bdtcL7AKW0bnzp0RExOD69ev459//kG7du0wcuRIPP/882Z3M6Sc8nvsPv/8c8TExEiP48ePm23H2dkZEyZMyLOshIQENG/eHEuXLsUHH3yAY8eOYdeuXejXrx/Gjx+PxMREAMCRI0fQoEEDXLp0CQsWLMC5c+ewevVqVK9ePde7JBHlJnvbq3z58vjrr7+wZs0a1K1bF/Pnz8eUKVNyrPfbb79hwIABnMODiIodtr1KPra7CkkUY+vXrxc1atQQBoPhqXk3btwoatSoIXQ6nQ1qRkT2kp6eLs6dOyfS09PtXRWLABCrV6/Od/42bdqIRYsWWaWMQYMGiR49euRI37p1qwAgFi5cWKBySpP8HrsKFSqImTNnPnE7FSpUEO+++65Qq9Viw4YNUvrIkSNFmzZtpOfDhg0Tbm5u4vbt2zm2kZycLHQ6nTAajaJWrVqiQYMGuX5fPnz48In1cJT3E1lPQdpeQggRFxcnfHx8xNWrV4u4ZkRkD470PcG2V8nDdlfhFdvbqwNA165dERUVhdu3b6NcuXJ55k1NTcWiRYugVBbrXSIiKxNC2O1OL66urg41OWH79u1Rt25drFq1Cm+88YZd6pCamgrA/NhqtVrodDoolUo4OTnlyOvi4gK53NRBVafTQavVQqFQmN09KLe81lSYYxcWFoa3334bH3zwATp37pyjXkajEStWrMCAAQPMbnmdyd3dHQBw/PhxnD17FsuXL89138qUKVPwHaJSqyBtLwC4fv065s6di7CwMBvUjoiKA7a9rMfebS9btrt0Op3VbinOdtfTFduhW5lGjRqVr4ZGnz59ctwClIgcX1paGtzd3e3ycMRbCVevXh3Xr1+3W/mZx/b+/ftS2vTp0+Hu7o7hw4eb5Q0ICIC7uztu3Lghpc2ZMwfu7u54/fXXzfJWrFgR7u7uOH/+fJHVPfuxmzBhgtn58u233+ZY5+OPP8a1a9ewbNmyHMvu37+Phw8fonr16nmWGxUVJZVPZA35bXsBQMOGDdGvX78irhERFSdse1mXPdtetmx35WcYXEGw3ZW3Yh/oISIqjaZMmWL2ZbV79268/fbbZmlZv2itRQjhUL+U2VL2Yzdu3DicOHFCegwcODDHOv7+/njvvffwySefQKvV5thefsslIiIiy7DtVbKw3ZU3jnMiohLN1dUVKSkpdiu7qLz99tt48cUXpecDBgzACy+8gN69e0tpuXUrtdT58+ftOgQj87XMemzHjRuHUaNG5Riae+/ePQAwu9NiZGQkhg4dCoVCYZY38xeforwrY/Zj5+fnh8qVKz91vTFjxmDu3LmYO3euWbq/vz/KlCmDCxcu5Ll+1apVAQAXLlzg7a2JiKjIse1lXfZse9my3TV48GBrVp3trqdgoIeISjSZTAY3Nzd7V8PqfHx84OPjIz13cXFBQEBAvr7ACmvbtm04ffo0Ro8eXWRlPE1ur6VarYZarc5XXpVKlev476I+Ryw5du7u7pg4cSI+/fRTdO/eXUqXy+V46aWX8Msvv2DSpEk5GpcpKSlwdnZGREQEatasiRkzZqBfv345xosnJCQUm/HiRERU8rHtZT32bnvZst1lrfl5ALa78oNDt4iIbCQlJUXqTgoA165dw4kTJ6zaDTi/ZWg0GsTGxuL27ds4duwYpkyZgh49euD555/PtasrPVYUx+7NN9+El5cXli9fbpY+efJklCtXDk2aNMHSpUtx7tw5REVF4eeff0a9evWQkpICmUyGRYsW4dKlS2jVqhU2btyIq1ev4tSpU5g8eTJ69Ohhjd0mIiIqcdj2KvnY7ioc9ughIrKRI0eOoF27dtLzMWPGAAAGDRpktQnq8lvGpk2bEBwcDKVSCW9vb9StWxfffvstBg0aVCR3pXIkRXHsVCoV/ve//6F///5m6T4+Pjhw4AC+/PJLfPHFF4iOjoa3tzfCw8Mxffp0eHl5AQAaN26MI0eOYPLkyRg6dCju37+P4OBgNG/eHLNmzbJ0l4mIiEoktr1KPra7CkcmSspsQkREADIyMnDt2jWEhYWZ3caRiAqO7yciIsoLvyeIrMeW7yeGDomIiIiIiIiIHAQDPUREREREREREDoKBHiIiIiIiIiIiB8FADxERERERERGRg2Cgh4iIiIiIiIjIQTDQQ0QlEm8YSGQ5vo+IiCg/+H1BZDlbvo8Y6CGiEkWlUgEA0tLS7FwTopIv832U+b4iIiLKiu0uIuvRarUAAIVCUeRlKYu8BCIiK1IoFChTpgzu3bsHAHB1dYVMJrNzrYhKFiEE0tLScO/ePZQpU8YmDQ4iIip52O4isg6j0Yi4uDi4urpCqSz6MAwDPURU4gQFBQGA1OggosIpU6aM9H4iIiLKDdtdRNYhl8tRvnx5mwRLZYIDLomohDIYDNDpdPauBlGJpFKp2JOHiIjyje0uIsuo1WrI5baZPYeBHiIiIiIiIiIiB8HJmK1k165d6NatG0JCQiCTybBmzZoiLW/q1Klo1KgRPDw8EBAQgJ49e+LixYtFWiYRERERERERFW8M9FhJamoq6tatizlz5tikvJ07dyIyMhIHDhzAli1boNPp8NxzzyE1NdUm5RMRERERERFR8cOhW0VAJpNh9erV6Nmzp5Sm0Wjw0Ucf4bfffkNCQgJq166NadOmoW3btlYpMy4uDgEBAdi5cydat25tlW0SERERERERUcnCHj02Mnz4cOzfvx8rVqzAqVOn0LdvX3Tu3BlRUVFW2X5iYiIAwMfHxyrbIyIiIiIiIqKShz16ikD2Hj03btxApUqVcOPGDYSEhEj5OnbsiMaNG2PKlCkWlWc0GtG9e3ckJCRgz549Fm2LiIiIiIiIiEou9uixgdOnT8NgMKBq1apwd3eXHjt37sSVK1cAABcuXIBMJsvz8f777+e6/cjISJw5cwYrVqyw5W4RERERERERUTGjtHcFSoOUlBQoFAocPXoUCoXCbJm7uzsAoFKlSjh//nye2/H19c2RNnz4cKxfvx67du1CaGio9SpNRERERERERCUOAz02UK9ePRgMBty7dw+tWrXKNY9arUb16tXzvU0hBEaMGIHVq1djx44dCAsLs1Z1iYiIiIiIiKiEYqDHSlJSUnD58mXp+bVr13DixAn4+PigatWqGDBgAAYOHIgZM2agXr16iIuLw9atW1GnTh107dq1wOVFRkZi+fLlWLt2LTw8PBAbGwsA8PLygouLi9X2i4iIiIiIiIhKDk7GbCU7duxAu3btcqQPGjQIixcvhk6nwxdffIGlS5fi9u3b8PPzQ9OmTfHZZ58hPDy8wOXJZLJc0xctWoTBgwcXeHtEREREREREVPIx0ENERERERERE5CB41y0iIiIiIiIiIgfBQA8RERERERERkYPgZMwWMBqNuHPnDjw8PJ44Zw4REVFxJYRAcnIyQkJCIJfztx8q/tj2IiKiksqW7S4Geixw584dlCtXzt7VICIissjNmzcRGhpq72oQPRXbXkREVNLZot3FQI8FPDw8AJheKE9PTzvXhoiIqGCSkpJQrlw56fuMqLhj24uIiEoqW7a7GOixQGaXYU9PTzY2iIioxOIQGCop2PYiIqKSzhbtLg7IJyIiIiIiIiJyEAz0EBEREZFN3Lx5E23btkXNmjVRp04drFy50t5VIiIicjgcukVERERENqFUKjFr1ixEREQgNjYWDRo0wP/93//Bzc3N3lUjIiJyGOzRQ0REDic9PR3Lli3D119/jcTERCn94cOHiImJgVartWPtiEqv4OBgREREAACCgoLg5+eH+Ph4+1aKiIgoG41Gg5UrV+Lw4cP2rkqhMNBDREQl2vr16zFw4EAsWrRIShNC4JVXXsG4ceOg0Wik9Pnz5yMkJARvvfWW2TZ69+6N/v374969e1La5cuXsXnzZly+fLnod4KomNi1axe6deuGkJAQyGQyrFmzJkeeOXPmoGLFinB2dkaTJk1w6NChQpV19OhRGAwG3i6diIhsRgiB+/fvw2g0SmmbNm3C4MGD8eOPP5rlfemll7BkyRJbV9EqGOghIqJiKTU1FXFxcdJznU6H1q1bIygoCA8fPpTSz5w5g19++QVbt26V0lxdXdGnTx/0798fPj4+Unp6ejrkcjl8fX2lNL1ej9WrV+O3334zuwvCypUr0alTJ0yePNmsXjVq1EB4eDhu3LghpR05cgTbt283azQQlUSpqamoW7cu5syZk+vy33//HWPGjMGkSZNw7Ngx1K1bF506dTILkkZERKB27do5Hnfu3JHyxMfHY+DAgfjhhx+KfJ+IiKhkMhqNEEJIz+Pi4nDy5ElER0eb5fv111+xYMECpKamSmlbt27F66+/joULF0ppQgh4eXnB398fd+/eldLPnTuHJUuWYMuWLVKak5MTevTogQoVKhTFrhU5BnqIiMiuzp49izVr1iAtLU1KmzVrFtzd3fHee+9JaSqVClFRUbh7965ZL5uOHTti8uTJeO2118y2u3LlSixbtgxK5ePp6D7//HPodDqz4I0QAosXL8aMGTPg7e0tpXt7e6NOnTp45plnpDSdTocLFy7gzJkzcHV1ldK3bNmC9u3bo3///hYeDSL76tKlC7744gv06tUr1+XffPMNhg4diiFDhqBmzZqYP38+XF1d8fPPP0t5Tpw4gTNnzuR4hISEADB1h+/Zsyfef/99NG/ePM/6aDQaJCUlmT2IiMg6tFotkpOTzXo/6/V6XLt2LUeP5mvXrmHfvn1mP3RpNBr8+eef+O2338wCMjt27MD06dOxc+dOs7LeffddDB06FDqdTkpfsGABmjRpghkzZkhpBoMBzs7OUCgUZsN7582bh4iICEyZMsWsbkOHDsXbb7+N+/fvS2mnTp3Czz//jO3bt0tpMplMar/FxMRI6W3atMHkyZPxxhtvADC1De/cuYM33ngD6enpWLdu3dMOZbHDQA8REdmEXq/H7t27zb5wAaBdu3bo1asXLly4IKWVLVsWABAbG2uW95dffsGRI0cQHh4upTVs2BAffvgh2rdvn696yOVyODk5Sc9VKhUGDRqEMWPGmAWF3n77bZw8eRIff/yx2bpHjx7F5s2bzYJCZcuWhYeHBzp37iylabVa/PPPPzAYDPmqF1Fxp9VqcfToUXTs2FFKk8vl6NixI/bv35+vbQghMHjwYLRv3x6vvvrqU/NPnToVXl5e0oPDvIjIGoQQ0Ov1Zj1xNRoNHjx4gISEBLO8N2/eRFRUlNkPUvHx8di3bx9OnjxplnfHjh34888/zYIIN2/exLx587BixQqzvIsXL8Ynn3yCs2fPSmmXL1/GsGHDMHHiRLO8kydPRs+ePbFt2zYp7dKlS2jRogW6detmlnf48OGoVKkSfv31VyktKioK7u7uCA4ONsv7xhtvwNPTE99//72UFhMTg0qVKqF27dpmeadNm4YWLVpg8eLFUlpqair69u2L/v37m7V31q9fj/Hjx2Pjxo1SmhAC3333HX788UezY3nnzh0cOnQIV65ckdIUCoW0vfT0dCndx8cHQUFBOSbw79q1K3r27AmVSiWlNWvWDJMnT8aAAQPM8h49ehQZGRmoX78+ANNrmZGRAV9fX6xduxZt2rSBn58fypYti65du2LSpEn4448/UOIIKrTExEQBQCQmJtq7KkRExd4PP/wgAIgWLVqYpXfp0kU0bNhQ7Nu3T0pLTU0VDx48sHUVLZKSkiIyMjKk5ytXrhQARJMmTexYq7zxe4zyAkCsXr1aen779m0BwOy9KoQQ48aNE40bN87XNnfv3i1kMpmoW7eu9Dh16tQT82dkZIjExETpcfPmzVJ/zmo0GpGcnGz2eaPT6UR0dLS4fv26Wd7o6Ghx5MgRcefOHSktPT1dbN68Wfz7779meY8cOSKWLVsmTpw4IaVlZGSI77//Xnz33XdCp9NJ6Xv27BGzZ88Wu3btktIMBoOYOXOmmDlzpkhLS5PS9+/fL2bMmCG2bNliVt7MmTPF9OnTzV7LQ4cOiWnTpol169aZ5Z01a5aYOnWquH//vpR29OhRMXnyZPHnn3+a5Z09e7b43//+Z7bPJ0+eFJ999pn49ddfzfJ+9913YtKkSWbH7cyZM2LixIli4cKFZnnnzJkjPvroI3Hx4kUp7cKFC2L8+PFi9uzZObY7atQocfLkSSktKipKvPPOO+Kzzz7LsW9vvPGG2fvq2rVrYtCgQWLUqFFmeb/88kvRo0cPsWnTJint+vXrokOHDqJnz55meSdPnizatm0rfv/9dyktNjZWtG7dWnTs2NEs71dffSVatGghFi1aJKUlJCSIpk2biqZNmwqDwSClz5w5UzRu3Fh8//33UlpGRoZo0KCBaNCggUhJSTE7ZvXq1RPTpk0zKy8iIkLUqVNHxMXFSWkLFy4UtWrVEh9//LFZ3oYNG4oaNWqIGzduSGlLliwRVatWzXF8GjZsKMqVKycuXLhgltfT01P069fPLG+FChUEAHHw4EEpbdmyZQKA6NChg1nemjVrCgBi+/btUtrq1asFANGsWTOzvI0aNRIAxPr166W0zZs3CwCiTp06Znnbtm0rAJi9Rnv37hUAxDPPPGOWt2vXrgKA+Omnn6S0EydOCAAiKCjILO+LL74oAIhvv/1WSrt8+bIAIDw8PMzyDhkyRAAQX375pZQWExMj3NzchI+Pj1neSZMmicqVK5ud76mpqaJVq1aiffv2Zp9Jv/32mxg4cKD47bffpDSDwSA+/PBD8cUXX5idJ+fPnxd///23OHPmjFl50dHR4u7du0Kv14vCSk1NFVevqUgQqQAAY0VJREFUXhUHDhwQf//9t1iwYIGYMGGC6Nu3r6hfv74oU6aMAJDrQy6Xi+rVq4tXXnklx2dHYdmy3cVAjwXYQCYiyt3cuXNF48aNxdq1a6W06Oho4evrK1599VWzRqOjWrBggfD29hYffvihWfqWLVuEVqu1U63M8XuM8lIUgR5LlaRzNi4uThw6dMgsOHHp0iUxdOjQHBeoY8aMEfXr1xdr1qyR0k6dOiU8PDxEhQoVzPL27ds3x0VcVFRUrhdxgwcPznERd+fOHekiJqvhw4cLAGLixIlSWubxBiDS09Ol9AkTJggAYsyYMVKaXq+X8mbd588//1wAEG+99ZZZeU5OTgKA2QX8119/LQCIV155xSyvj4+PACDOnz8vpc2bN08AEL179zbLGxoaKgCII0eOSGlLly4VAESnTp3M8larVk0AMAtY/fnnnwKAaNmypVne+vXrCwDin3/+kdI2btwoAIh69eqZ5W3ZsqUAYBaE2rlzpwAgqlWrZpb3ueeeEwDEkiVLpLQjR44IACI0NNQsb69evQQAMXfuXCnt/PnzAkCOi/JXXnlFABAzZsyQ0qKjowUA4eTkZJb3rbfeEgDE559/LqXdv39fej2zfmePHj1aABATJkyQ0tLT06W8SUlJUvrHH38sAIgRI0aYlSeTyQQAERMTI6VNnTpVABCvvfaaWV53d3cBQFy5ckVKmz17tgCQI3gTGBgoAJgF2H766ScBQHTt2tUsb6VKlXJ8nq1YsUIAEG3btjXL26RJE+Hp6WkW6Nm6dauoXLmy6NOnj1neoUOHilatWondu3dLaadOnRK9e/c2e78IYQryRUZGmp2rN2/eFJ9++qlZIE0I07m2YMECsyBWQkKCWLVqVY6g7YULF8T+/ftFbGyslKbVasWVK1dEdHS0Wd709HSRlpZmUTDFmoxGo0hNTRWxsbEiKipKHD9+XOzatUts2LBB/P777+LHH38UM2fOFP/73//E+PHjxbBhw8Qrr7wievbsKTp06CAaN24satasKcqVKyfc3NyeGMTJ/ggNDRVdunQR48aNE0uXLhXHjh0zC1hbiy2/wx73USciIiqEhIQE7N6926zr8Pnz53Ho0CGsX78e3bt3BwCUL18e9+7dg1xeOkYNv/nmmxg4cKDZuPeTJ0/i2WefRbly5RAVFWU2hIyouPPz84NCoTCbwBIA7t69i6CgIDvVyv4SExOxc+dOJCcnmw0R6NmzJ/bu3Yvff/8dL774IgDT5+XChQtRoUIFzJw5U8p75coVHDt2zOzYKhQKJCcnw9nZ2ay8zCGmWYdJKJVKODk55fhM8fPzQ2hoqNkwBycnJ9SpUwcKhQJCCGkS+urVq6NDhw4ICwuT8qrVavTp0wcymczss7tu3bro168fIiIipDSZTIaXX35ZWi9TeHg4XnnlFTRu3NisbgMGDIBerzeb76xWrVoYPHgwmjVrliNvamoqvLy8pLQaNWrgjTfeQL169XLkffjwIfz8/KS0KlWq4O2330bNmjXN8vbv3x937941G87yzDPPYPjw4WbzswGmu++0bNkS5cuXl9LCwsIwZswYhIaGmuV95ZVX0LJlS1SrVk1Kq1ChAiZOnAh/f3+zvIMGDUKrVq3MjmXZsmUxbdo0s/0FgLfeegudOnVCq1atpLSQkBD8+uuvOc6TyMhIPP/882bb9fPzw8qVK3N8D2dut0aNGlKah4eHdNe9rDcqeP3119G+fXuz46NSqbBhwwYAgIuLi5Q+cODAHMcMADZv3gwAZkOgX3rpJTRu3DjH0KKNGzfCaDSapb/wwguoV6+e2WucNW/lypXN8rZq1Qru7u5meQ8dOgSZTAZPT08prW/fvujTp0+O43PgwAFk1759e0RFReVIz22C+fDwcPz111850keOHJkjLTQ0FJMmTcqR3qVLlxxpXl5euc6nlvW8y6RSqVCpUqUc6dnPm6yEEDAYDNBqtdBoNLk+MjIynpqW+TwjIwMpKSlISUlBcnKy9Mj6PCUlxeo3tnByckJgYCACAgIQEBCAsLAwPPPMM9IjLCzM7HPIUciEyDJrEhVIUlISvLy8kJiYaPYhQURUWqSnp8PX1xfp6em4dOkSqlSpAgA4duwYTpw4gc6dO0sTsBLw999/480330Tr1q3Nxnvv27cP9evXz7PBVRT4PUZ5kclkWL16NXr27CmlNWnSBI0bN8Z3330HwHRHlPLly2P48OF4//33i7xOxeWcNRqN0sXg9u3b0b59ewQHB5vdWWzgwIHYtm0bvvzyS7zyyisATHeMmT9/PoKDg6VJPwHg8OHDiIuLQ3h4uDQPkVarxY0bN+Di4iLNWwYAGRkZMBqNUKvVZvOKEVHJIoSATqeDRqOBVquVAir2/Dt7mj1DBe7u7vDw8JD+zf7305aVKVMGgYGBcHd3NwtW2pMtv8MY6LFAcWlsEBHZwsWLFzFr1izI5XKzWy+3b98esbGxWLBggdmvjJQ7nU6Hhw8fIiAgAIBpEsCQkBC4ubnh1KlTZhd0RY3fY5RdSkqKdKeVevXq4ZtvvkG7du3g4+OD8uXL4/fff8egQYOwYMECNG7cGLNmzcIff/yBCxcuIDAwsMjrZ+9zdv369Zg0aRJee+01REZGAjAFXpo3b47w8HAsXLhQ6s2StbcMET1ZZs+R7A+j0Zhruj3yZgY/8hsgyc/fWe88VVI4OztLvQczHwVJyy14k1vAxs3NzSF7gNvyO4w/AxARUa4uX75s9ktySkoK5s+fD3d3d3zzzTfSEIH169c7ZJfXoqJSqaQgD2C6E4a/vz98fX3Nej+dPHkSlStXznFnCaKidOTIEbRr1056PmbMGACm4SWLFy9Gv379EBcXh08++QSxsbGIiIjApk2bbBLkKQ7Onz+PY8eOwdnZWQr0ODs749ixYzny2irIk9krICMjA1qtFjqdLt+PzLsOCSGkf7P/ndeyvNbJrFv2R1Gm26PMokg3Go3Q6/Vmj8zXK/vfAKShdTKZLMff1ngOoEiDLOx3YKJQKKBWq6FWq+Hk5FRs/s78V6lUMnhdgrBHjwXs/asSEVFRGTlyJL799lt89NFH+OKLLwCYhipMmDAB7dq1w7PPPmt2C0uyjMFgwJ07d6QhGwaDAWFhYUhMTMR///2HRo0aFUm5/B6jksbW56xer4dGo5ECrqmpqZg9ezbefPPNHPOD5CU1NRX3799HXFwc4uLi8PDhQ2RkZCA9PR3p6em5/p3f5ZlDuYgclUwmg0KhMHvI5fIcaU97WLKOLQIrCoXC3oeaihh79BARkU0IIXDkyBEsWbIE06ZNky5mGjRoAKVSifj4eCmvXC7H9OnT7VVVh6ZQKKQgDwDcvHlTavSFh4dL6VFRUQgODs4xqSQRWV96ejp69+4NpVKJNWvWQKFQwM3NDR9++OET8x8/fhynTp3C5cuXpce1a9eQlpZms3rL5XKoVKp8PZRKJeRyudV7gGTtCVKQdFutU1y3lXVZ1tco85H9eeYcTfnpYZXfnli5LQNQ5IGUvB7sRUJUcAz0EBGVcv3798fly5fRsGFDDB48GADQp08f9OzZk7087KRixYq4dOkSrl69ajZBc2RkJH755RcGeohs4OjRo9i2bRuUSiXOnDmDunXrmi3PyMjAjh07sGHDBuzbtw+nTp2ShtLkxsnJCf7+/vD394e3tzdcXV3h7OwMFxcXuLi4SH8XNs3Z2Rlqtdoh57UgIqKCYaCHiKiU0Ov12LRpEzZt2oTvvvtO+vVw2LBhOHbsGGrXri3l5Zw79ieXy81uEZuamoqIiIhSMxcKkb21bNkSGzduhFqtNgvyHD9+HAsWLMDy5cuRnJxstk5gYCAaNmyIqlWronLlyqhcuTIqVapU7O78QkREjo1z9FiAcxsQUUmSnJyMoKAgpKWlYe/evWjevLm9q0R2xu8xKmnsec6ePn0aH330EdatWyellS1bFs8//zw6dOiAJk2aoFy5cgzmEBFRrjhHDxERWSQlJQV//PEHrly5gsmTJwMAPDw8MGzYMBiNRgQHB9u5hkRExY9Wq8Ubb7yByZMnS/NmaTQaTJo0CdOnT4fRaIRCoUDfvn3x1ltvoU2bNgzsEBFRscNADxGRA7p16xZef/11KBQKDB8+XArsfP3113auGRFR8bVo0SL88ssvOHr0KM6cOYO4uDj06NEDBw4cAAC88MILmDx5MqpVq2bnmhIRET0ZAz1ERCXc3bt3sXjxYiiVSowdOxYAUL16dbz66quoWbMmnJyc7FxDIqKSoWHDhujSpQu6dOmC2NhYtG7dGpcvX4a3tzd+/vln9OzZ095VJCIieiqbz9GTlJRU4HWK67wBnNuAiIqD9evXo1u3bvD398etW7egVqvtXSUqIfg9VjI5UluqoGx1ziYlJaFt27Y4fvw4KlasiE2bNrEXDxERWcSh5+gpU6ZMgcYyy2QyXLp0CZUqVSrCWhERlQwXLlzAwoULUa9ePbzyyisAgM6dO6N79+7o0aMHOL8+keNjW6roRUZG4vjx4wgICMB///2HZ555xt5VIiIiyje7DN36888/4ePj89R8Qgj83//9X762uWvXLkyfPh1Hjx5FTEwMVq9e/dTutTt27MCYMWNw9uxZlCtXDh9//DEGDx6cr/KIiOxh48aN+Oabb9CoUSMp0KNUKrF27Vo714yIbKko2lKlWUJCAtasWYMuXbpg//79+PXXXyGXy7F69WoGeYiIqMSxeaCnQoUKaN26NXx9ffOVv1KlSlCpVE/Nl5qairp16+K1115D7969n5r/2rVr6Nq1K95++20sW7YMW7duxRtvvIHg4GB06tQpX3UjIipKZ8+exbfffovBgwejWbNmAIBXX30Ve/fuxZAhQyCE4N1eiEqhompLlWabNm3CkCFDULNmTSQmJgIAxo8fj+bNm9u5ZkRERAVn80DPtWvXCpT/zJkz+cqXOXFefs2fPx9hYWGYMWMGAKBGjRrYs2cPZs6cyUAPERULM2fOxE8//YSHDx9KgR5/f3/89ddfdq4ZEdlTUbWlSjODwYCGDRvC398f//zzD0JDQzFp0iR7V4uIiKhQ5PYodNeuXU/NM2LEiCKtw/79+9GxY0eztE6dOmH//v1FWi4RUW5SU1Mxb9483LlzR0obOXIkevXqhXfffdeONSOi4qg4tKUcyYABA7Bv3z6cP38egKk3j7Ozs51rRUREVDh2CfR0794dJ06ceOLyESNGYMmSJUVah9jYWAQGBpqlBQYGIikpCenp6bmuo9FokJSUZPYgIrKGF198Ee+88w7mzp0rpYWHh2PVqlVo2bKlHWtGRMVRcWhLOZq1a9fi+vXrCAgIwBtvvGHv6hARERWaXQI9b7zxBjp37ozLly/nWDZy5EgsWrQI69ats0PN8jZ16lR4eXlJj3Llytm7SkRUAgkhsG/fPmg0GinttddeQ+XKlVG5cmU71oyIbMXb2xs+Pj75euSmpLaliqPMuxX++uuvAEyfxy4uLvasEhERkUXsctetr7/+GvHx8ejYsSP27duHkJAQAMCoUaPw448/Yv369WjTpk2R1iEoKAh37941S7t79y48PT2f+OX+wQcfYMyYMdLzpKQkBnuIqMB69+6NNWvWYOnSpXj11VcBAL169UKvXr0gl9sl/k5ENjZr1izp7wcPHuCLL75Ap06dpPm49u/fj3///RcTJ07Mdf3i0JZyFLNnz8a0adNw7949AKZhXERERCVZgQM9WQMdT/PNN988cdmPP/6IPn36oGPHjti9ezcmT56MH374AevWrUO7du0KWq0Ca9asGTZu3GiWtmXLFqmBlRsnJyc4OTkVddWIyME8fPgQ3t7e0vPGjRvjn3/+we3bt6U0BniISpdBgwZJf7/wwgv4/PPPMXz4cCnt3Xffxffff4///vsPo0ePznUb9m5LOYpLly4hNjYWABAWFoZatWrZuUZERESWkYnM/qr5lL3hcOzYMej1elSrVg2A6ctSoVCgQYMG2LZtW57b0mq16Nq1K06ePInU1FSsXbs2xwTJ+ZWSkiJ1X65Xrx6++eYbtGvXDj4+Pihfvjw++OAD3L59G0uXLgVgumNF7dq1ERkZiddeew3btm3Du+++iw0bNuT7rltJSUnw8vJCYmIiPD09C1VvInJcQgiMGDECP/30E7Zv346mTZsCABITE6HVauHv72/nGlJpx++x4sHd3R0nTpzIMXTz8uXLiIiIQEpKyhPXtWZbqiQoinM2MTERr7/+Ov766y+88847mDNnjlW2S0RElJUt210F7tGzfft26e9vvvkGHh4eWLJkifRr9cOHDzFkyBC0atXqidv49ttvpb/btm2L3bt3o1OnTjh37hzOnTsnLSvInWaOHDliFoTK7Hk0aNAgLF68GDExMbhx44a0PCwsDBs2bMDo0aMxe/ZshIaG4scff+St1YnIamQyGVJSUpCRkYG1a9dKgR4vLy8714yIihNfX1+sXbsWY8eONUtfu3YtfH19c12nKNpSpZWXlxeuXLkCAGjfvr2da0NERGS5Avfoyaps2bLYvHlzji6uZ86cwXPPPWd2m+CswsLCnl4xmQxXr14tbNVsgr+EElEmo9GIefPmYcGCBdi8eTOCgoIAmHo53rt3Dy1atIBMJrNzLYnM8XuseFi8eDHeeOMNdOnSBU2aNAEAHDx4EJs2bcLChQsxePDgHOs4SluqoIrinE1LS4OnpycMBgNu3ryJ0NBQq2yXiIgoq2LdoyerpKQkxMXF5UiPi4tDcnLyE9e7du2aJcUSERU7crkcv/76K06fPo0ffvgBn3zyCQCgatWqqFq1qp1rR0TF2eDBg1GjRg18++23WLVqFQCgRo0a2LNnjxT4yY5tKetISEjAJ598AoPBgODgYJQtW9beVSIiIrKYRYGeXr16YciQIZgxYwYaN24MwPQL1Lhx49C7d2+rVJCIqDg6cOAAfvrpJ8ydOxcqlQoAMGnSJFy+fNlsklUiovxo0qQJli1bZu9qlDrXr1/Hd999BwCoX78+e14SEZFDsOg2L/Pnz0eXLl3Qv39/VKhQARUqVED//v3RuXNnzJ07N9d1vv32W2RkZBSojLx6BxER2ZpGo0GPHj3w448/4s8//5TSO3fujOHDh8PDw8OOtSOikujKlSv4+OOP0b9/f+k23//88w/Onj2bIy/bUtbj4uIi3VCEvS+JiMhRWBTocXV1xdy5c/HgwQMcP34cx48fR3x8PObOnQs3N7dc1xk9enSBGhvjx4/PdXgYEZGt6HQ6bN68WXru5OSE9957D0OGDEG9evXsWDMicgQ7d+5EeHg4Dh48iL/++ku6y9bJkycxadKkHPnZlrKeatWqSQGeKlWq2Lk2RERE1mHR0K1MMTExiImJQevWreHi4gIhxBO7vgoh0KFDByiV+Ss6PT3dGlUkIioUjUaD2rVr4/Llyzh8+DAaNmwIABg3bpyda0ZEjuL999/HF198gTFjxpj1CGzfvj2+//77HPnZlrKuqKgoAAz0EBGR47Ao0PPgwQO8+OKL2L59O2QyGaKiolCpUiW8/vrr8Pb2xowZM3Ksk9svU3np0aMHfHx8LKkmEVGBaDQaODk5ATD13mnatCmSkpJw8+ZNKdBDRGQtp0+fxvLly3OkBwQE4P79+znS2ZayHqPRKN2ZrHLlynauDRERkXVYFOgZPXo0VCoVbty4gRo1akjp/fr1w5gxY6wS6CEishWNRoPx48dj+fLlOHfuHPz9/QEAM2bMgIeHB1xcXOxcQyJyRGXKlEFMTEyOW6YfP34817tAsS1lPUOHDoVWqwUA3nGLiIgchkVz9GzevBnTpk1DaGioWXqVKlUQHR1tUcWIiGxNrVZj3759uH//PlasWCGlBwQEMMhDREXmpZdewoQJExAbGwuZTAaj0Yi9e/fivffew8CBA+1dPYd28+ZNAKZJmTPvoEhERFTSWRToSU1Nhaura470+Ph4adgDEVFxJITArl278Prrr0Ov1wMAZDIZZsyYgS1btmD48OF2riERlRZTpkxB9erVUa5cOaSkpKBmzZpo3bo1mjdvjo8//tje1XNob7zxBgAgKCjIzjUhIiKyHouGbrVq1QpLly7F//73PwCQfoX66quv0K5dO6tUkIioKGg0Grzwwgu4f/8+OnbsiJdffhkA0Lp1azvXjIhKG7VajYULF2LixIk4c+YMUlJSUK9ePU4ObAOZvXgY6CEiIkdiUaDnq6++QocOHXDkyBFotVqMHz8eZ8+eRXx8PPbu3WutOhIRWUyr1WLr1q3o0qULAMDZ2Rnjxo3DlStX0LhxYzvXjogIKF++PMqXL2/vapQqmZNd+/n52bkmRERE1mNRoKd27dq4dOkSvv/+e3h4eCAlJQW9e/dGZGQkgoOD81xXp9OhevXqWL9+vdlEzkRE1paeno4aNWogOjoax44dQ7169QAA48ePt3PNiIiAMWPG5Jouk8ng7OyMypUr53rnLLalLLdnzx4A4JQDRETkUCwK9ACAl5cXPvroowKvp1KpkJGRYWnxRES5Sk9PlyZQdnFxQYsWLaDVanH79m0p0ENEVBwcP34cx44dg8FgQLVq1QAAly5dgkKhQPXq1TF37lyMHTsWe/bsQc2aNaX12Jay3Pbt2wGAx5GIiByKRZMxnzp1KtfH6dOnERUVBY1Gk+f6kZGRmDZtmjQRKhGRpdLT0zFs2DCUK1cODx48kNJnzZqFa9eu4fnnn7dj7YiIcurRowc6duyIO3fu4OjRozh69Chu3bqFZ599Fi+//DJu376N1q1bY/To0TnWZVvKMl5eXgCQ4w6yREREJZlFPXoiIiIgk8kAmO5gA0B6Dph+aerXrx8WLFgAZ2fnHOsfPnwYW7duxebNmxEeHg43Nzez5atWrbKkekRUCjk7O+PgwYN48OABVq5cibfffhsA4O/vb+eaERHlbvr06diyZQs8PT2lNC8vL3z66ad47rnnMHLkSHzyySd47rnncqzLtpRlKlSogDNnzqBhw4b2rgoREZHVWBToWb16NSZMmIBx48ZJk5keOnQIM2bMwKRJk6DX6/H+++/j448/xtdff51j/TJlyuCFF16wpApEVModOHAAixcvxpw5c6BQKCCTyTBz5kwAvIMWEZUMiYmJuHfvntmwLACIi4tDUlISAFObSavV5liXbSnLGAwGAI/vvkVEROQILAr0TJ48GbNnz0anTp2ktPDwcISGhmLixIk4dOgQ3NzcMHbs2FwDPYsWLbKkeCIq5dLT09G1a1fEx8ejXbt26NevHwCgTZs2dq4ZEVH+9ejRA6+99hpmzJiBRo0aATD11HnvvffQs2dPAKYf0qpWrZpjXbalLGM0GgGY90gnIiIq6SwK9Jw+fRoVKlTIkV6hQgWcPn0agGl4V0xMTJ7biYuLw8WLFwEA1apV4xALIsqV0WjE/v370aJFCwCmSZbfe+89REVFoX79+nauHRFR4SxYsACjR4/GSy+9JM21o1QqMWjQIKmHYvXq1fHjjz8+cRtsSxXOiRMnAABnzpyxb0WIiIisyKJAT/Xq1fHll1/ihx9+gFqtBmC61eeXX36J6tWrAwBu376NwMDAXNdPTU3FiBEjsHTpUukXFYVCgYEDB+K7776Dq6urJdUjIgei0WjQqFEjnD59GidOnEDdunUBAB988IGda0ZEZBl3d3csXLgQM2fOxNWrVwEAlSpVgru7u5QnIiIi13XZlrJM5o1DdDqdnWtCRERkPRbddWvOnDlYv349QkND0bFjR3Ts2BGhoaFYv3495s2bBwC4evUq3nnnnVzXHzNmDHbu3Il169YhISEBCQkJWLt2LXbu3ImxY8daUjUicgCZk7wDgJOTE2rVqgVPT0/pV2siIkfi7u6OOnXqoE6dOmZBnrywLWWZzNvZZ58fiYiIqCSTiaxXUoWQnJyMZcuW4dKlSwBMX5j9+/eHh4fHU9f18/PDn3/+ibZt25qlb9++HS+++CLi4uIsqVqRS0pKgpeXFxITE83ulEFEltHpdJg1axYWLlyIAwcOwMfHB4Cph6C7u7t0O1wisgy/x4qPI0eO4I8//sCNGzdyTLqc152zSnpbqqCsfc62a9cOO3bswO+//44XX3zRCjUkIiLKnS3bXRYN3QIADw8P6fbFBZWWlpbrsK6AgACkpaVZWjUiKqGUSiV+/fVXREVF4aeffsK4ceMAAGXLlrVzzYiIrG/FihUYOHAgOnXqhM2bN+O5557DpUuXcPfuXfTq1SvPddmWsgwnYyYiIkdkcaAHAM6dO5frL1Ddu3fPc71mzZph0qRJWLp0KZydnQGY7qLz2WefoVmzZtaoGhGVALdv38aiRYvwwQcfSLdInzZtGu7evYv+/fvbu3pEREVqypQpmDlzJiIjI+Hh4YHZs2cjLCwMb731FoKDg/Ncl20pyzx48ACA6Rb3REREjsKiQM/Vq1fRq1cvnD59GjKZTJpPI/NXEYPBkOf6s2bNQufOnREaGipNrHry5Ek4Ozvj33//taRqRFRC6HQ6NGjQAHfv3kX16tXRp08fAEDnzp3tXDMiItu4cuUKunbtCgBQq9VITU2FTCbD6NGj0b59e3z22WdPXLektqXS0tJQo0YN9O3bF19//bXd6nH9+nUAQHR0tN3qQEREZG0WTcY8cuRIhIWF4d69e3B1dcXZs2exa9cuNGzYEDt27Hjq+uHh4YiKisLUqVMRERGBiIgIfPnll4iKikKtWrUsqRoRFWOZDWsAUKlUeOutt9C6deun/nJNROSIvL29kZycDMA0RDXzVt8JCQlPHX5VUttSkydPRtOmTe1dDWnS6/zMLUlERFRSWNSjZ//+/di2bRv8/Pwgl8shl8vRsmVLTJ06Fe+++y6OHz/+xHV1Oh2qV6+O9evXY+jQoZZUg4hKCL1ejxdeeAHr1q3DyZMnER4eDgCYOHFinr9YExE5statW2PLli0IDw9H3759MXLkSGzbtg1btmxBhw4dnrheSW1LRUVF4cKFC+jWrZsU1LKXsLAwqUcpERGRo7CoR4/BYJB+AfHz88OdO3cAABUqVHjq7Y9VKhUyMjIsKZ6IShilUgm1Wg0A2Llzp1k6EVFp9f333+Oll14CAHz00UcYM2YM7t69ixdeeAE//fTTE9crirbUrl270K1bN4SEhEAmk2HNmjU58syZMwcVK1aEs7MzmjRpgkOHDhWojPfeew9Tp061Uo0tw8mYiYjIEVkU6KlduzZOnjwJAGjSpAm++uor7N27F59//jkqVar01PUjIyMxbdo06PV6S6pBRMWQ0WjE6tWr0b59eyQkJEjpU6dOxYULFzB8+HD7VY6IqJjQ6/VYv349FAoFAEAul+P999/H33//jRkzZsDb2zvP9a3dlkpNTUXdunUxZ86cXJf//vvvGDNmDCZNmoRjx46hbt266NSpE+7duyfliYiIQO3atXM87ty5g7Vr16Jq1aqoWrWqVeprqcxAj1xuUZOYiIioWJGJzBmUC+Hff/9FamoqevfujcuXL+P555/HpUuX4Ovri99//x3t27fPc/1evXph69atcHd3R3h4ONzc3MyWr1q1qrBVs4mkpCR4eXkhMTERnp6e9q4OUbFiNBpRt25dnDlzBl988QX+v737Doviet8Gfi8dLCAiRQTBXkBECEiMLRIVE40xxRhUrNFojIpGxYLBhtFfCHYSolETe9dEjQa7YkOxRbGAYgEsSLNRdt4/fJkvK0jZHVgY7s917eXu2TkzzxkX5uHsmXOmTJmi7ZCI6A28jpUPJiYmuHr1KurWrVviuqWZSykUCmzbtg09e/YUyzw9PfHOO+9g8eLFAF7/rrezs8OoUaMwadKkIvcZEBCAP//8E7q6usjIyEBWVhbGjRuHwMDAArd/9eoVXr16Jb5OS0uDnZ2dZJ9ZMzMzpKam4ueff8aYMWM03h8REdHblGXepdH9El26dBGfN2jQANeuXUNycjJq1KhRrCGwZmZm+PTTTzUJgYjKiZcvX2Ljxo3o27evOGdXUFAQzp07h6+//lrb4RERlVseHh6Ijo5Wq6OnLHOpzMxMREVFISAgQCzT0dGBt7c3IiMji7WP4OBg8batlStX4vLly2/t5MndvjTncMu99S0zM7PUjkFERFTW1O7oycrKgrGxMaKjo+Hk5CSWm5ubF6t+dnY2OnbsiM6dO8Pa2lrdMIioHFAqlXB1dcW1a9dQrVo1fPLJJwCAXr16oVevXlqOjoiofBsxYgT8/f1x9+5duLm55RuV06JFiwLrlXUu9fjxY+Tk5MDKykql3MrKCteuXSuVYwYEBMDf3198nTuiRyp2dna4efMm6tevL9k+iYiItE3tjh59fX3Y29sjJydHvQPr6WH48OG4evWquiEQkRZlZGSIy9Lq6Ojg008/xerVq9X+nUBEVFnlTsT83XffiWUKhQKCIEChULz192pFz6UGDBhQ5DaGhoYwNDQstRhMTEwAgLcuEhGRrGg089yUKVMwefJkJCcnq1Xfw8Oj0CXYS6qkq0CEhoaicePGMDY2hp2dHcaOHcuVwIiKIAgCxo8fDxsbG/z3339ieUBAAG7evInPPvtMi9EREVU8cXFx+R6xsbHiv4WROpcqjIWFBXR1dZGUlKRSnpSUVGFHZ3MyZiIikiON5uhZvHgxbt68idq1a6Nu3br5hhqfO3eu0PojRozAuHHjcO/evRINVS5I7ioQYWFh8PT0RGhoKLp06YKYmBhYWlrm237t2rWYNGkSVqxYgXfffRfXr1/HgAEDoFAoEBISUuzjElU2CoUCsbGxyMjIwNq1azFr1iwAyPfzS0RExaPO3Dy5pMylimJgYAA3NzdERESIEzQrlUpERERU2JUU09PTAbxebYyIiEguNOroybsKgzrUHapckJCQEAwdOhQDBw4EAISFheHvv//GihUrClwF4sSJE2jTpg2++uorAICDgwP69OmDU6dOadIkItm5fPkyQkJC8PPPP8PU1BQAMGPGDHz99dcqE7ITEZH6/vjjD4SFhSEuLg6RkZGoW7cuQkND4ejoiI8//vit9aTMpYDXt+XevHlTfB0XF4fo6GiYm5vD3t4e/v7+8PPzg7u7Ozw8PBAaGopnz56J+VdF8+DBAwDAvXv3tBwJERGRdDTq6Jk+fbpGB4+Li9Oofi51VoF499138eeff+L06dPw8PBAbGwsdu/ejX79+r31OAUt8UkkZ4Ig4Msvv8SVK1fQqFEjsdPUyclJZRJ2IiJS37JlyxAYGIgxY8Zg9uzZYueMmZkZQkNDC+3okSqXynX27Fl07NhRfJ07EbKfnx9WrlyJ3r1749GjRwgMDERiYiJatmyJvXv35puguaIwNDREVlYWR6USEZGsaNTRAwApKSnYvHkzbt26he+//x7m5uY4d+4crKysYGtrW2hdTYYq56XOKhBfffUVHj9+jPfeew+CICA7OxvDhw/H5MmT33qc0l7ik0jbBEHAsWPH0KZNG+jo6EChUCAgIAA7duxA165dtR0eEZEsLVq0COHh4ejZsyfmzp0rlru7u2P8+PGF1pUql8rVoUMHCIJQ6Dbffvtthb1V6021a9fG9evXUa9ePW2HQkREJBmNZp67ePEiGjVqhB9//BH/93//h5SUFADA1q1bVUbXFOaPP/5AmzZtULt2bdy5cwfA60mSd+zYoUloRTp06BDmzJmDpUuX4ty5c9i6dSv+/vtvzJw58611AgICkJqaKj7u3r1bqjESlSVBENC1a1e0a9cOu3btEst9fX2xceNGtGzZUnvBERHJWFxcHFxdXfOVGxoaFmvuGG3lUnLAyZiJiEiONLqq+fv7Y8CAAbhx4waMjIzE8m7duuHIkSNF1l+2bBn8/f3RrVs3pKSk5BuqXFzqrAIxbdo09OvXD0OGDIGzszM++eQTzJkzB8HBweJF/02GhoaoXr26yoOoIsv7WVcoFGjVqhWMjIxw+/Zt7QVFRFTJODo6Ijo6Ol/53r170bRp00LrSpVLVVa5o5fY0UNERHKi0VXtzJkzGDZsWL5yW1tbJCYmFlk/d6jylClToKurK5a7u7vj0qVLxY4j7yoQuXJXgfDy8iqwzvPnz/Nd1HNjKGrIMpEc/Prrr2jcuLHK7Y3ff/89bt++jdGjR2sxMiKiysXf3x8jR47Ehg0bIAgCTp8+jdmzZyMgIAATJkwotK5UuVRllTsZM0dpExGRnGg0R4+hoWGBExJfv34dtWrVKrK+pkOV8ypqFYj+/fvD1tYWwcHBAIDu3bsjJCQErq6u8PT0xM2bNzFt2jR0795dJVEikqvdu3fj5s2bWLRoEZYsWQIAMDc313JURESVz5AhQ2BsbIypU6fi+fPn+Oqrr1C7dm0sWLBAXFXrbaTMpSqjzMxMlX+JiIjkQKOOnh49emDGjBnYuHEjgNe3fsTHx2PixIn49NNPi6yfO1T5zYkEizNU+U1FrQIRHx+vMoJn6tSpUCgUmDp1Ku7fv49atWqhe/fumD17domOS1QRPH78GEuXLsWYMWPEWw6nTZuG999/H0OGDNFydERE5OvrC19fXzx//hwZGRmwtLQsVj0pc6nKyNzcHI8ePSpyAREiIqKKRKOOnp9++gmfffYZLC0t8eLFC7Rv3x6JiYnw8vIqVodJ7lDlly9fikOV161bh+DgYPz2228ljqewVSAOHTqk8lpPTw/Tp0/XeIl4ooqga9euiIqKgpGRkXgbgJubG9zc3LQcGRERzZo1C76+vnB0dISJiQlMTEyKXVfqXKqyMTQ0BADOu0hERLKiUUePqakp9u/fj2PHjuHixYvIyMhAq1at4O3tXaz6mgxVJqK3i42NhaOjIxQKBYDXnaCLFi2Ck5OTliMjIqI3bdq0CdOnT4enpyf69u2LL774AhYWFsWqy1xKM5yMmYiI5EghaDDz8N27d2FnZydJICUdqlwepKWlwdTUFKmpqfwmiMqNoUOHYsWKFdixYwc++ugjAK8nJ1coFGLHDxERwOtYeXLlyhWsWbMG69evx7179/DBBx/A19cXPXv2LPYIn4qYS5WU1J/ZmjVrIjk5GSdOnHjrAh5ERERSKMu8S6OvLxwcHNC+fXuEh4fj6dOnGgViYmIi68SEqKzUqFEDSqUSx48fF8t0dHTYyUNEVI41b94cc+bMQWxsLA4ePAgHBweMGTMG1tbWxd4Hc6mSy81fHz58qOVIiIiIpKNRR8/Zs2fh4eGBGTNmwMbGBj179sTmzZvx6tUrqeIjokLs2bMH7du3x/Xr18Wy8ePHIzo6WlxhjoiIKpYqVarA2NgYBgYGyMrK0nY4sqan93oWg9y5eoiIiORAo44eV1dXzJ8/H/Hx8dizZw9q1aqFr7/+GlZWVhg0aJBUMRLRWyxduhRHjhzB/PnzxTJLS0u4uLhoMSoiIiqpuLg4zJ49G82bN4e7uzvOnz+PoKAgJCYmajs0WTM1NQUAyaYiICIiKg8kmXlOoVCgY8eOCA8Px7///gtHR0esWrVKil0T0f+XmZmJFStWICMjQyybMmUKxo0bh6CgIC1GRkREmmjdujUaNGiAzZs3Y+DAgbhz5w4iIiIwePBgsSOCSgcnYyYiIjnSaNWtXPfu3cPatWuxdu1aXL58GV5eXliyZEmJ9vHy5UsYGRlJEQ6RLPn4+ODAgQNISUmBv78/gNd/HLRu3VrLkRERkSY6deqEFStWoFmzZhrth7lUySmVSgDgPHZERCQrGn198csvv6B9+/ZwcHDA6tWr0bt3b9y6dQtHjx7F8OHDi6yvVCoxc+ZM2NraomrVqoiNjQUATJs2DcuXL9ckNKIKLyUlBXkXxfvqq69gY2MDMzMz7QVFRESSmz17ttqdPMylNJOeng4ASE5O1nIkRERE0tGoo2fWrFnw9PREVFQULl++jICAANStW7dE9VeuXIl58+bBwMBALHdycsJvv/2mSWhEFVpQUBDs7Oywd+9esax///6Ii4vj/FdERDJ07949LF26FJMmTYK/v7/KozDMpTSTnZ0NAJz0moiIZEWjW7fi4+M1Guq6evVq/Prrr+jUqZPKCCAXFxdcu3ZNk9CIKrT09HRkZGRg8+bN8PHxAQDo6+trOSoiIioNERER6NGjB+rVq4dr167ByckJt2/fhiAIaNWqVaF1mUtpxsjICC9fvoS5ubm2QyEiIpKMRiN6cjt5nj9/jmvXruHixYsqj6Lcv38fDRo0yFeuVCr5zQpVGleuXEH//v1x8+ZNsWzcuHHYtWsXv40lIqoEAgICMH78eFy6dAlGRkbYsmUL7t69i/bt2+Pzzz8vtC5zKc3kTsJctWpVLUdCREQkHY1G9Dx69AgDBgxQub0kr5ycnELrN2vWDEePHs13u9fmzZvh6uqqSWhEFcb333+PPXv2wNjYGL/88gsAwMbGBh999JGWIyMiorJw9epVrFu3DgCgp6eHFy9eoGrVqpgxYwY+/vhjfPPNN2+ty1xKM7lz4XEyZiIikhONOnrGjBmD1NRUnDp1Ch06dMC2bduQlJSEWbNm4aeffiqyfmBgIPz8/HD//n0olUps3boVMTExWL16Nf766y9NQiMqlwRBwKFDh+Dh4YEqVaoAACZPngwTExN8/fXXWo6OiIi0oUqVKsjMzATwuqP/1q1baN68OQDg8ePHhdZlLqWZ3Dl6cv8lIiKSA41u3Tpw4ABCQkLg7u4OHR0d1K1bF3379sW8efMQHBxcZP2PP/4Yu3btwr///osqVaogMDAQV69exa5du/DBBx9oEhpRueTr64v3338f4eHhYtl7772HzZs3w83NTYuRERGRtrRu3RrHjh0DAHTr1g3jxo3D7NmzMWjQILRu3brQusylNJN7e1taWpqWIyEiIpKORiN6nj17BktLSwBAjRo18OjRIzRq1AjOzs44d+5csfbRtm1b7N+/X5MwiMqt7Oxs6OrqikPCc0e+5S7nSkREFBISgoyMDACvV13MyMjAhg0b0LBhQ4SEhBRZn7mU5vKuWEZERFTRaTSip3HjxoiJiQHwenWHX375Bffv30dYWBhsbGyKrF+vXj08efIkX3lKSgrq1aunSWhEWrdq1So0btxYJfn28/PD7du3MW3aNC1GRkRE5Um9evXQokULAK9v4woLC8PFixexZcuWfHPvFFSXuZT6cidjrlmzppYjISIiko5GHT2jR49GQkICAGD69OnYs2cP7O3tsXDhQsyZM6fI+rdv3y5wwuZXr17h/v37moRGpHXR0dGIjY3F0qVLxTJDQ0NYWVlpMSoiIirPRowYUeS8PHkxl9IMJ2MmIiI50ujWrb59+4rP3dzccOfOHVy7dg329vawsLB4a72dO3eKz//55x+YmpqKr3NychAREQEHBwdNQiMqU8nJyVi0aBH69esnfoM6btw4ODg4YMiQIVqOjoiIKoo///wT48ePLzSPAphLSSW3oyd3ZA8REZEcaNTRk9fx48fh7u6OVq1aFbltz549Abz+9sTPz0/lPX19fTg4OBRr1S6i8mLQoEHYsWMHEhMTsWzZMgBAnTp1MHr0aC1HRkREFUlux0NRmEtpLnelMwB4/vy5FiMhIiKSlmQdPT4+PoiOji7W/eBKpRIA4OjoiDNnzhT5rRVReRMbGwtra2uYmJgAAMaOHYvbt29zhRMiIioTzKU0l7ejh4iISE4kG6da3G+g8oqLi2NiQhXOpEmT0LBhQyxfvlwsa9euHc6fP49evXppMTIiIqro0tPTSzSJMnMp9enq6orPc7+4ISIikgPJRvSoY8aMGYW+HxgYWEaREBWfg4MDlEoloqOjxTJO4khERJq4desWfv/9d8TGxiI0NBSWlpbiIhfNmzd/az3mUurL29HD5dWJiEhOFII6Q3EKsHbtWnz88ceoUqVKseu4urqqvM7KykJcXBz09PRQv359nDt3TorQSk1aWhpMTU2RmpqK6tWrazscKgVnzpzBjBkzMHbsWLz//vsAgJcvX+LKlStwc3PTcnRERJrhdax8OHz4MHx8fNCmTRscOXIEV69eRb169TB37lycPXsWmzdvfmvdip5LlZSUn9mXL1/C2NgYAPgzQEREpa4s8y5JRvTcvHkTNWvWFFcsEAShWCMczp8/n68sLS0NAwYMwCeffCJFaEQaWb16Nf766y88f/5c7OgxMjJiJw8REUlm0qRJmDVrFvz9/VGtWjWx/P3338fixYsLrctcSn3Z2dnic4m+9yQiIioXNJqj58mTJ/D29kajRo3QrVs3JCQkAAAGDx6McePGqbXP6tWrIygoCNOmTdMkNKISEwQBBw4cwL1798SyCRMmYNCgQQgLC9NiZEREJGeXLl0qsFPG0tISjx8/LvH+mEsVT2pqqvg8d3JrIiIiOdCoo2fs2LHQ09NDfHy8yiR2vXv3xt69e9Xeb2pqqsrFl6gsjB07Fp06dcLcuXPFMjs7OyxfvhwNGzbUYmRERCRnZmZm4pdleZ0/fx62trZq7ZO5VNHyjuLJO18PERFRRafRrVv79u3DP//8gzp16qiUN2zYEHfu3Cmy/sKFC1VeC4KAhIQE/PHHH/Dx8dEkNKIiKZVKKJVK6Om9/jH4+OOPERYWJt6vT0REVBa+/PJLTJw4EZs2bYJCoYBSqcTx48cxfvx49O/fv9C6zKXUl3d+BE7GTEREcqJRR8+zZ88KXI4yOTkZhoaGRdb/+eefVV7r6OigVq1a8PPzQ0BAgCahERVq9+7dCAgIwPDhw/HNN98AADp06IC7d++iVq1aWo6OiIgqkzlz5mDkyJGws7NDTk4OmjVrhpycHHz11VeYOnVqoXWZS6kv7+1aufNMEhERyYFGHT1t27bF6tWrMXPmTAAQv4WaN28eOnbsWGT9uLg4TQ5PpLbY2FhcvHgRS5cuxfDhw6FQKKBQKNjJQ0REZc7AwADh4eEIDAzEpUuXkJGRAVdX12LdNsxcSn15b90qziIiREREFYVGHT3z5s1Dp06dcPbsWWRmZmLChAm4cuUKkpOTcfz4caliJNJIVlYW/vjjDzRv3hyenp4AXk8Ynp6ejmHDhjG5IyKicsHOzg52dnbaDqPSePr0qficI3qIiEhONOrocXJywvXr17F48WJUq1YNGRkZ6NWrF0aOHAkbG5sC6/Tq1avY+9+6dasm4REBAAIDAzF37lx4e3tj//79AABjY2MOaScionLh008/hYeHByZOnKhSPm/ePJw5cwabNm1SKWcuJY3nz5+Lz9nRQ0REcqJRRw8AmJqaYsqUKSXavrQsWbIE8+fPR2JiIlxcXLBo0SJ4eHi8dfuUlBRMmTIFW7duRXJyMurWrYvQ0FB069at1GKk0vfixQu8evUKZmZmAIDhw4djzZo16Nq1K5RKJZM5IiIqV44cOYIffvghX7mPjw9++umnfOWlmUtVJlWqVBGfc3QvERHJicYdPS9fvsTFixfx8OFDlUntAKBHjx75tv/99981PWSBNmzYAH9/f4SFhcHT0xOhoaHo0qULYmJiYGlpmW/7zMxMfPDBB7C0tMTmzZtha2uLO3fuiJ0DVDFt2rQJo0aNQu/evbFgwQIAQN26dREXF8elU4mIqFzKyMgocNUnfX19pKWl5SsvrVyqssldZZOdPEREJDcadfTs3bsX/fv3x+PHj/O9p1AokJOTU6z9PHr0CDExMQCAxo0bqzUhbkhICIYOHYqBAwcCAMLCwvD3339jxYoVmDRpUr7tV6xYgeTkZJw4cQL6+voAAAcHhxIfl8qXGjVqICkpCfv370d2dra4dDo7eYiIqLxydnbGhg0bEBgYqFK+fv16NGvWrFj7kCKXqmxyJ2NmRw8REcmNRvewjBo1Cp9//jkSEhKgVCpVHsXp5Hn27BkGDRoEGxsbtGvXDu3atUPt2rUxePBglfumi5KZmYmoqCh4e3uLZTo6OvD29kZkZGSBdXbu3AkvLy+MHDkSVlZWcHJywpw5cwqN+9WrV0hLS1N5kPa8ePECP/30EzZu3CiWderUCdu2bcOFCxfETh4iIqLybNq0aZg5cyb8/PywatUqrFq1Cv3798fs2bMxbdq0QutKlUtVRq9evQLAjh4iIpIfjTp6kpKS4O/vDysrK7Xq+/v74/Dhw9i1axdSUlKQkpKCHTt24PDhwxg3blyx9/P48WPk5OTki8PKygqJiYkF1omNjcXmzZuRk5OD3bt3Y9q0afjpp58wa9astx4nODgYpqam4oMrY2jXb7/9hvHjx2PChAkqyVrPnj3FUVpERETlXffu3bF9+3bcvHkTI0aMwLhx43Dv3j38+++/6NmzZ6F1pcqlKqP79+8DQLFHoBMREVUUCiF33KoaBg0ahDZt2mDw4MFq1bewsMDmzZvRoUMHlfKDBw/iiy++wKNHj4q1nwcPHsDW1hYnTpyAl5eXWD5hwgQcPnwYp06dylenUaNGePnypcrcLSEhIZg/fz4SEhIKPM6rV6/EDgUASEtLg52dHVJTU1G9evVixUrqy87OxtOnT8Xh6C9evECnTp0wZMgQ9O/fnyN4iIhKKC0tDaampryOVWBS5VIVhZSf2ePHj+O9994D8L/buIiIiEpLWeZdGv1lvHjxYnz++ec4evQonJ2d842i+O677wqt//z58wJHA1laWpZouLGFhQV0dXWRlJSkUp6UlARra+sC69jY2EBfX19l7pamTZsiMTERmZmZBU6KaGhoCENDw2LHRdKJjIzEwIED4eDggL179wJ4PYniiRMntBwZERGR9kiVS5WluLg4DBo0CElJSdDV1cXJkydVVsAqK7a2tgD+NykzERGRXGjU0bNu3Trs27cPRkZGOHTokMo9zgqFosiOHi8vL0yfPh2rV6+GkZERgNejNIKCglRG5hTFwMAAbm5uiIiIEIc4K5VKRERE4Ntvvy2wTps2bbB27VqV5bavX78OGxubAjt5SLssLS1x69YtPH78GImJiW/twCMiIqpocnJy8PPPP2Pjxo2Ij49HZmamyvvJyclvrStVLlWWBgwYgFmzZqFt27ZITk7W2pdouavFco4eIiKSG406eqZMmYKgoCBMmjRJ7CwpiQULFqBLly6oU6cOXFxcAAAXLlyAkZER/vnnnxLty9/fH35+fnB3d4eHhwdCQ0Px7NkzcRWu/v37w9bWFsHBwQCAb775BosXL8bo0aMxatQo3LhxA3PmzCmyc4pKnyAIiIiIwK1btzBs2DAAQP369bFt2za0a9dO67cXCIIgPgp7LdW2uc81KZNqP1LvW1vvFfWvunXf9lyd999Wp6jXJdm2qLrFJcUtD29+UVDQc3W309HRgUKhEB+l9VpHRwe6urql8q8611iqOIKCgvDbb79h3LhxmDp1KqZMmYLbt29j+/bt+VbiepOUuVRZuHLlCvT19dG2bVsAgLm5udZiyf3dxZ8vIiKSG406ejIzM9G7d2+1L5BOTk64ceMG1qxZg2vXrgEA+vTpA19f3xIPo+3duzcePXqEwMBAJCYmomXLlti7d684nDk+Pl4lTjs7O/zzzz8YO3YsWrRoAVtbW4wePRoTJ05Uqy2aevHiBRYsWJBv9bKCVjMrapuSbJ/7R21pPy/qAfzvD+wXL14gMTERCoUCM2bMgI6OTon3URplRETalNvxY2JigpSUFG2HQxJas2YNwsPD8eGHH+KHH35Anz59UL9+fbRo0QInT54s9EsoKXMpADhy5Ajmz5+PqKgoJCQkYNu2bfkmhF6yZAnmz5+PxMREuLi4YNGiRfDw8CjW/m/cuIGqVauie/fuuH//Pj777DNMnjy5xHFKIXfBjjdHUBEREVV0Gk3GPHbsWNSqVUtrF2htk3IypZSUFNSoUUOiyEjuckcr5I4oUPe5Ou9LVacs3tPGvyV9T53ti3pdkm2LqlsWpByFVNDrgjqeS+P1mx3sOTk5Ks/f9m9JL8MmJiZ49uxZieq8DSdjLh+qVKmCq1evwt7eHjY2Nvj777/RqlUrxMbGwtXVFampqWUWy549e3D8+HG4ubmhV69e+Tp6NmzYgP79+yMsLAyenp4IDQ3Fpk2bEBMTA0tLSwBAy5YtkZ2dnW/f+/btw4kTJzBs2DBER0fD0tISXbt2xeTJk/HBBx8UKz4pP7N79uxBt27doFAoxNu4iIiISkuFmYw5JycH8+bNwz///IMWLVrkm4w5JCSk0PqrVq2ChYUFPvzwQwCvV8n69ddf0axZM6xbtw5169bVJLwKxdDQEIMGDRKH6Bf1yDucX51t37z9oLSfv+3x5MkT/Pzzz4iNjcWaNWugr68PhUIBQRCgq6tbZP03HwDKvCzva02eF2c7IpKfvB1FBXUEvVnGP0jlp06dOkhISIC9vT3q16+Pffv2oVWrVjhz5kyR89dInUv5+PjAx8fnre+HhIRg6NCh4q3xYWFh+Pvvv7FixQpMmjQJABAdHf3W+ra2tnB3d4ednR0AoFu3boiOjn5rR09BK55KxczMDADEuY2IiIjkQqOOnkuXLsHV1RUAcPnyZZX3ivNH6Zw5c7Bs2TIAr1dVWrx4MUJDQ/HXX39h7Nix2Lp1qybhVSjGxsZYvny5tsMocxkZGRg6dCgeP36M7Oxs8Z59IqLKQqFQQFdXF7q6uvm+MKHK4ZNPPkFERAQ8PT0xatQo9O3bF8uXL0d8fDzGjh1baN2yzKUyMzMRFRWFgIAAsUxHRwfe3t6IjIws1j7eeecdPHz4EE+fPoWpqSmOHDkizsdXkODgYAQFBWkce0FMTU0BcNUtIiKSH406eg4ePKjRwe/evYsGDRoAALZv347PPvsMX3/9Ndq0aYMOHTpotG8qn+7evYudO3di5MiRAICqVavi999/h4ODA5o3b67l6IiIiMre3Llzxee9e/eGvb09IiMj0bBhQ3Tv3r3QumWZSz1+/Bg5OTn5lnO3srIS5wcqip6eHubMmYN27dpBEAR07twZH3300Vu3DwgIgL+/v/g6LS1NHA2kKU7GTEREcqVRR4+mqlatiidPnsDe3h779u0TL+RGRkZ48eKFNkOjUpCcnIwmTZrg+fPneOedd8SJG3OHmxMREdHrJdOLuzR6Rcyliro9LC9DQ8NSW3497y1hREREclLijp5evXph5cqVqF69Onr16lXotkUNF/7ggw8wZMgQuLq64vr16+jWrRuA10tvOjg4lDQ0KoeysrLEWxHMzc3xxRdfIC4uDgYGBlqOjIiIqPyIiYnBokWLcPXqVQBA06ZNMWrUKDRu3LjQemWZS1lYWEBXVxdJSUkq5UlJSbC2tpb0WGXhxo0bAF5/EUVERCQnJR6rampqKs6/Y2pqWuijKEuWLIGXlxcePXqELVu2oGbNmgCAqKgo9OnTp6ShUTmSlZWFefPmoV69enj06JFYvmzZMhw8eBAtW7bUXnBERETlyJYtW+Dk5ISoqCi4uLjAxcUF586dg5OTE7Zs2VJo3bLMpQwMDODm5oaIiAixTKlUIiIiotgjkMoTTmxORERypdby6jNmzMD48eNhYmJSGjFVGFyW9u1ycnLg5uaGCxcu4Mcff8SECRO0HRIREb2B17HyoX79+vD19cWMGTNUyqdPn44///wTt27dKrNYMjIycPPmTQCAq6srQkJC0LFjR5ibm8Pe3h4bNmyAn58ffvnlF3h4eCA0NBQbN27EtWvX8s3dUxqk/MyeO3cObm5uqF27Nu7fvy9RhERERAUry7xLrY4eXV1dJCQkwNLSUuMAnj59iuXLl6sMVR40aBDMzc013ndpY4Ks6vr162jQoIE4qeHx48dx8+ZN9OvXjxMdEhGVQ7yOlQ8mJia4ePGiOKlyrhs3bsDFxQXPnz8vtL6UudShQ4fQsWPHfOV+fn5YuXIlAGDx4sWYP38+EhMT0bJlSyxcuBCenp4lPpY6pPzMRkVFwd3dHXXq1MHdu3clipCIiKhgZZl3qfXXtxp9QwU6cuQIHBwcsHDhQjx9+hRPnz7FokWL4OjoiCNHjkhyDCobAQEBaNasGdasWSOWtWnTBn5+fuzkISIiKkSHDh1w9OjRfOXHjh1D27ZtC60rdS7VoUMHCIKQ75HbyQMA3377Le7cuYNXr17h1KlTZdbJI7XcW7eYpxARkdyovepW7jw9mhg5ciR69+6NZcuWQVdXF8DrW35GjBiBkSNH4tKlSxofg8qGmZkZcnJyEBkZiX79+mk7HCIiogqjR48emDhxIqKiotC6dWsAwMmTJ7Fp0yYEBQVh586dKtvmxVxKfbm3a6Wmpmo5EiIiImmpdeuWjo6OyqTMb1PUKgbGxsaIjo7Ot6JETEwMWrZsWW6XBc1VmYe879+/H3Z2dmjSpAmA10uUnjx5Eu3bt9dyZEREVFyV+TpWnhR3RIlCoUBOTo5KWUXPpUpKys/sr7/+imHDhkFPTw9ZWVkSRUhERFSwssy71B7RExQUVKyVtQrTqlUrXL16NV9ycvXqVbi4uGi0byo9P/74IyZNmoTOnTtj7969UCgUMDQ0ZCcPERGRGjRZ/Ym5lPosLCwAQON8loiIqLxRu6Pnyy+/VGsy5osXL4rPv/vuO4wePRo3b95UGaq8ZMkSzJ07V93QqJR99tlnmDlzJpo2bYrs7Gzo6+trOyQiIqIKJzIyEk+ePMFHH30klq1evRrTp0/Hs2fP0LNnTyxatAiGhoYq9ZhLSSN3lbAaNWpoORIiIiJplfmqWzo6OlAoFEVO6FzQ8OTypjIMeVcqlVi5ciXS0tIwZswYsTw5OblCrIxGRERvVxmuY+WZj48POnTogIkTJwIALl26hFatWmHAgAFo2rQp5s+fj2HDhuGHH35QqSenXKqkpPzMHj16FO3atUOjRo0QExMjUYREREQFK/e3bmmy6lZcXJzadans7du3D4MHD4aRkRF69eoFe3t7AGAnDxERkYaio6Mxc+ZM8fX69evh6emJ8PBwAICdnR2mT5+er6OHuZQ0cuflkWo1WSIiovJCrY4eTe4lr1u3rtp1qWzk5OSIK3d06dIFPXr0QLt27WBtba3lyIiIiOTj6dOn4u1DAHD48GH4+PiIr9955x3cvXs3Xz3mUtKIjo4GAMTHx2s3ECIiIompPUePlP777z/Ex8cjMzNTpfzNJUSpdL148QLz5s3Dzp07cfLkSejr60OhUGD79u1FrrBGREREJWNlZYW4uDjY2dkhMzMT586dQ1BQkPh+enp6sefBYy5Vcpp8cUlERFSeabWjJzY2Fp988gkuXbqkcq95bqeC3O4rL+8yMzOxZMkSPHr0CJs2bcJXX30FAOzkISIiKgXdunXDpEmT8OOPP2L79u0wMTFB27ZtxfcvXryI+vXrF7oP5lLqy12VrKhzTEREVNHoaPPgo0ePhqOjIx4+fAgTExNcuXIFR44cgbu7Ow4dOqTN0CqNO3fuiM9NTU2xePFibNiwAX369NFiVERERPI3c+ZM6OnpoX379ggPD0d4eDgMDAzE91esWIHOnTsXug/mUprT0ysXA9yJiIgko9UrW2RkJA4cOAALCwvo6OhAR0cH7733HoKDg/Hdd9/h/Pnz2gxP1pRKJYYPH47ly5fj2LFj8PLyAgB88cUXWo6MiIiocrCwsMCRI0eQmpqKqlWrivPj5dq0aROqVq1a6D6YS6kv99YtjlwmIiK50eqInpycHFSrVg3A62TnwYMHAF5PMshlLkuXjo4OcnJyoFQq8e+//2o7HCIiokrL1NQ0XycP8HqFy7wjfArCXEp9uZMwP3nyRMuREBERSUurI3qcnJxw4cIFODo6wtPTE/PmzYOBgQF+/fVX1KtXT5uhyVJcXBxq1aolfjs4e/ZsDB48GO+++66WIyMiIiJ1MJdS3/379wEAycnJWo6EiIhIWlod0TN16lRx2OyMGTMQFxeHtm3bYvfu3Vi4cKE2Q5OddevWwdnZGRMmTBDLrK2t2clDRERUgTGXUl/u0vbm5uZajoSIiEhaWh3R06VLF/F5gwYNcO3aNSQnJ6NGjRq8X1pilpaWePbsGf777z9kZmYWORSciIiIyj/mUuqrU6cOAKB27dpajoSIiEhaWh3RUxBzc3MmJhIQBEG8Tx8AOnXqhIiICBw4cICdPERERDLGXKp4OBkzERHJVbnr6CHNPXnyBD179oSHhwdSUlLE8vfffx86OvwvJyIiIsrJyQHAjh4iIpIf/tUvQ0ZGRvjvv//w8OFDHD9+XNvhEBEREZU7kZGRAIArV65oORIiIiJpaXWOHpJORkaGuJpWlSpVsG7dOhgaGsLZ2VnLkRERERGVP4IgAOCIHiIikh+O6JGBnTt3on79+ti+fbtY5u7uzk4eIiIiordwc3MD8HqJeiIiIjlhR48MnDhxAg8fPsTChQvFb6eIiIiI6O1y5y3U19fXciRERETS4q1bFVR2djb09F7/9wUFBcHCwgLffvsthx8TERERFUPul2NcqIKIiORGdle2JUuWwMHBAUZGRvD09MTp06eLVW/9+vVQKBTo2bNn6QaoofT0dAwbNgxffvmlmKAYGhpi/PjxMDIy0nJ0RERERBXD7du3AQBJSUnaDYSIiEhisuro2bBhA/z9/TF9+nScO3cOLi4u6NKlCx4+fFhovdu3b2P8+PFo27ZtGUWqvtjYWKxYsQJbtmzBuXPntB0OERERUYUUHx8PAEhMTNRyJERERNKSVUdPSEgIhg4dioEDB6JZs2YICwuDiYkJVqxY8dY6OTk58PX1RVBQEOrVq1eG0RZf3nl3XFxcsHDhQhw8eFCcRJCIiIiISsbS0hIAUKtWLS1HQkREJC3ZdPRkZmYiKioK3t7eYpmOjg68vb0RGRn51nozZsyApaUlBg8eXOQxXr16hbS0NJVHaYuMjETr1q1x//59seybb75Bhw4dSv3YRERERHLl6OgIAHBwcNBuIERERBKTTUfP48ePkZOTAysrK5VyKyurtw7JPXbsGJYvX47w8PBiHSM4OBimpqbiw87OTuO4CyMIAsaNG4fTp09j8uTJpXosIiIiosqEkzETEZFcVdorW3p6Ovr164fw8HBYWFgUq05AQABSU1PFx927d0s1RoVCgRUrVmDQoEFYsGBBqR6LiIiIqDJRKpUAwBVLiYhIdmSzvLqFhQV0dXXzrZyQlJQEa2vrfNvfunULt2/fRvfu3cWy3Au+np4eYmJiUL9+fZU6hoaGMDQ0LIXoX8vKysLs2bNRp04dDBkyBADQpEkTLF++vNSOSURERFQZHT16FAC4uAUREcmObEb0GBgYwM3NDREREWKZUqlEREQEvLy88m3fpEkTXLp0CdHR0eKjR48e6NixI6Kjo0v9tqyCrFu3DkFBQRgzZgyX+iQiIiIqRTk5OdoOgYiIqFTIZkQPAPj7+8PPzw/u7u7w8PBAaGgonj17hoEDBwIA+vfvD1tbWwQHB8PIyAhOTk4q9c3MzAAgX3lZ6du3L3bs2IEvvvgi31xDRERERCQdNzc3/PHHH2jZsqW2QyEiIpKUrDp6evfujUePHiEwMBCJiYlo2bIl9u7dK3aaxMfHl+sJ93R0dLBlyxZth0FEREQke35+fujatSuqVKmi7VCIiIgkpRBylxygEktLS4OpqSlSU1NRvXp1bYdDRERUIryOUUXDzywREVVUZXkNK7/DW4iIiIiIiIiIqETY0UNEREREREREJBPs6CEiIiIiIiIikgl29BARERERERERyYSsVt0qa7nzWKelpWk5EiIiopLLvX5xXQaqKJh7ERFRRVWWeRc7ejSQnp4OALCzs9NyJEREROpLT0+HqamptsMgKhJzLyIiqujKIu/i8uoaUCqVePDgAapVqwaFQqHyXlpaGuzs7HD37t1Ks/xnZWwzUDnbzTZXjjYDlbPdlanNgiAgPT0dtWvXho4O7+am8q+w3EsdlennvTh4PvLjOcmP50QVz0d+PCeqcs9HfHw8FApFmeRdHNGjAR0dHdSpU6fQbapXr17pPtyVsc1A5Ww321x5VMZ2V5Y2cyQPVSTFyb3UUVl+3ouL5yM/npP8eE5U8Xzkx3OiytTUtMzOB7++IyIiIiIiIiKSCXb0EBERERERERHJBDt6SomhoSGmT58OQ0NDbYdSZipjm4HK2W62ufKojO2ujG0mqqz4866K5yM/npP8eE5U8Xzkx3OiShvng5MxExERERERERHJBEf0EBERERERERHJBDt6iIiIiIiIiIhkgh09REREREREREQywY4eIiIiIiIiIiKZYEdPKViyZAkcHBxgZGQET09PnD59WtshSSY4OBjvvPMOqlWrBktLS/Ts2RMxMTEq27x8+RIjR45EzZo1UbVqVXz66adISkrSUsTSmzt3LhQKBcaMGSOWybXN9+/fR9++fVGzZk0YGxvD2dkZZ8+eFd8XBAGBgYGwsbGBsbExvL29cePGDS1GrJmcnBxMmzYNjo6OMDY2Rv369TFz5kzknbNeDm0+cuQIunfvjtq1a0OhUGD79u0q7xenjcnJyfD19UX16tVhZmaGwYMHIyMjowxbUTKFtTkrKwsTJ06Es7MzqlSpgtq1a6N///548OCByj4qWpuJqHByztfykip3i4+Px4cffggTExNYWlri+++/R3Z2dlk2pVSom9fJ7XxIkfPJ6TopVU5Ykc9JWeWLFy9eRNu2bWFkZAQ7OzvMmzevtJumlrLKJSU7HwJJav369YKBgYGwYsUK4cqVK8LQoUMFMzMzISkpSduhSaJLly7C77//Lly+fFmIjo4WunXrJtjb2wsZGRniNsOHDxfs7OyEiIgI4ezZs0Lr1q2Fd999V4tRS+f06dOCg4OD0KJFC2H06NFiuRzbnJycLNStW1cYMGCAcOrUKSE2Nlb4559/hJs3b4rbzJ07VzA1NRW2b98uXLhwQejRo4fg6OgovHjxQouRq2/27NlCzZo1hb/++kuIi4sTNm3aJFStWlVYsGCBuI0c2rx7925hypQpwtatWwUAwrZt21TeL04bu3btKri4uAgnT54Ujh49KjRo0EDo06dPGbek+Aprc0pKiuDt7S1s2LBBuHbtmhAZGSl4eHgIbm5uKvuoaG0moreTe76WlxS5W3Z2tuDk5CR4e3sL58+fF3bv3i1YWFgIAQEB2miSZNTN6+R2PqTK+eR0nZQqJ6zI56Qs8sXU1FTByspK8PX1FS5fviysW7dOMDY2Fn755ZeyamaxlUUuKeX5YEePxDw8PISRI0eKr3NycoTatWsLwcHBWoyq9Dx8+FAAIBw+fFgQhNcfcn19fWHTpk3iNlevXhUACJGRkdoKUxLp6elCw4YNhf379wvt27cXEwK5tnnixInCe++999b3lUqlYG1tLcyfP18sS0lJEQwNDYV169aVRYiS+/DDD4VBgwaplPXq1Uvw9fUVBEGebX7zQlWcNv73338CAOHMmTPiNnv27BEUCoVw//79MotdXQUlK286ffq0AEC4c+eOIAgVv81EpKqy5Wt5qZO77d69W9DR0RESExPFbZYtWyZUr15dePXqVdk2QCKa5HVyOx9S5Hxyu05KkRPK6ZyUVr64dOlSoUaNGio/NxMnThQaN25cyi3STGnlklKeD966JaHMzExERUXB29tbLNPR0YG3tzciIyO1GFnpSU1NBQCYm5sDAKKiopCVlaVyDpo0aQJ7e/sKfw5GjhyJDz/8UKVtgHzbvHPnTri7u+Pzzz+HpaUlXF1dER4eLr4fFxeHxMRElXabmprC09Ozwrb73XffRUREBK5fvw4AuHDhAo4dOwYfHx8A8mzzm4rTxsjISJiZmcHd3V3cxtvbGzo6Ojh16lSZx1waUlNToVAoYGZmBqBytJmosqiM+Vpe6uRukZGRcHZ2hpWVlbhNly5dkJaWhitXrpRh9NLRJK+T2/mQIueT23VSipxQbuckL6naHxkZiXbt2sHAwEDcpkuXLoiJicHTp0/LqDWlQ51cUsrzoad5EyjX48ePkZOTo/JLHwCsrKxw7do1LUVVepRKJcaMGYM2bdrAyckJAJCYmAgDAwPxA53LysoKiYmJWohSGuvXr8e5c+dw5syZfO/Jtc2xsbFYtmwZ/P39MXnyZJw5cwbfffcdDAwM4OfnJ7atoM97RW33pEmTkJaWhiZNmkBXVxc5OTmYPXs2fH19AUCWbX5TcdqYmJgIS0tLlff19PRgbm4ui/Pw8uVLTJw4EX369EH16tUByL/NRJVJZcvX8lI3d0tMTCzwfOW+V9FomtfJ7XxIkfPJ7TopRU4ot3OSl1TtT0xMhKOjY7595L5Xo0aNUom/tKmbS0p5PtjRQ2obOXIkLl++jGPHjmk7lFJ19+5djB49Gvv374eRkZG2wykzSqUS7u7umDNnDgDA1dUVly9fRlhYGPz8/LQcXenYuHEj1qxZg7Vr16J58+aIjo7GmDFjULt2bdm2mVRlZWXhiy++gCAIWLZsmbbDISKSVGXJ3QpTWfO6wlTGnK8ozAlJXeUll+StWxKysLCArq5uvln5k5KSYG1traWoSse3336Lv/76CwcPHkSdOnXEcmtra2RmZiIlJUVl+4p8DqKiovDw4UO0atUKenp60NPTw+HDh7Fw4ULo6enByspKdm0GABsbGzRr1kylrGnTpoiPjwcAsW1y+rx///33mDRpEr788ks4OzujX79+GDt2LIKDgwHIs81vKk4bra2t8fDhQ5X3s7OzkZycXKHPQ+6F+c6dO9i/f7/4DQwg3zYTVUaVKV/LS5PczdrausDzlfteRSJFXien8wFIk/PJ7TopRU4ot3OSl1Ttl9vPkqa5pJTngx09EjIwMICbmxsiIiLEMqVSiYiICHh5eWkxMukIgoBvv/0W27Ztw4EDB/INLXNzc4O+vr7KOYiJiUF8fHyFPQedOnXCpUuXEB0dLT7c3d3h6+srPpdbmwGgTZs2+ZZfvX79OurWrQsAcHR0hLW1tUq709LScOrUqQrb7ufPn0NHR/XXoq6uLpRKJQB5tvlNxWmjl5cXUlJSEBUVJW5z4MABKJVKeHp6lnnMUsi9MN+4cQP//vsvatasqfK+HNtMVFlVhnwtLylyNy8vL1y6dEnlj5TcP2Le7CAo76TI6+R0PgBpcj65XSelyAnldk7ykqr9Xl5eOHLkCLKyssRt9u/fj8aNG1e427akyCUlPR8lnr6ZCrV+/XrB0NBQWLlypfDff/8JX3/9tWBmZqYyK39F9s033wimpqbCoUOHhISEBPHx/PlzcZvhw4cL9vb2woEDB4SzZ88KXl5egpeXlxajll7e1RkEQZ5tPn36tKCnpyfMnj1buHHjhrBmzRrBxMRE+PPPP8Vt5s6dK5iZmQk7duwQLl68KHz88ccVbqnxvPz8/ARbW1txKc2tW7cKFhYWwoQJE8Rt5NDm9PR04fz588L58+cFAEJISIhw/vx5cVWA4rSxa9eugqurq3Dq1Cnh2LFjQsOGDcv1cqGFtTkzM1Po0aOHUKdOHSE6Olrld1veVQ8qWpuJ6O3knq/lJUXulruceOfOnYXo6Ghh7969Qq1atSrscuJvKmleJ7fzIVXOJ6frpFQ5YUU+J2WRL6akpAhWVlZCv379hMuXLwvr168XTExMyuXy6mWRS0p5PtjRUwoWLVok2NvbCwYGBoKHh4dw8uRJbYckGQAFPn7//XdxmxcvXggjRowQatSoIZiYmAiffPKJkJCQoL2gS8GbCYFc27xr1y7ByclJMDQ0FJo0aSL8+uuvKu8rlUph2rRpgpWVlWBoaCh06tRJiImJ0VK0mktLSxNGjx4t2NvbC0ZGRkK9evWEKVOmqPyClkObDx48WODPsZ+fnyAIxWvjkydPhD59+ghVq1YVqlevLgwcOFBIT0/XQmuKp7A2x8XFvfV328GDB8V9VLQ2E1Hh5Jyv5SVV7nb79m3Bx8dHMDY2FiwsLIRx48YJWVlZZdya0qFOXie38yFFzien66RUOWFFPidllS9euHBBeO+99wRDQ0PB1tZWmDt3blk1sUTKKpeU6nwoBEEQSjYGiIiIiIiIiIiIyiPO0UNEREREREREJBPs6CEiIiIiIiIikgl29BARERERERERyQQ7eoiIiIiIiIiIZIIdPUREREREREREMsGOHiIiIiIiIiIimWBHDxERERERERGRTLCjh4iIiIiIiIhIJtjRQ0REREREREQkE+zoISJJCYIAAPjhhx9UXhMRERGRdjA/I6pcFAJ/yolIQkuXLoWenh5u3LgBXV1d+Pj4oH379toOi4iIiKjSYn5GVLlwRA8RSWrEiBFITU3FwoUL0b1792IlER06dIBCoYBCoUB0dHTpB/mGAQMGiMffvn17mR+fiIiIqDSVND9TJzdjPkVUfrCjh4gkFRYWBlNTU3z33XfYtWsXjh49Wqx6Q4cORUJCApycnEo5wvwWLFiAhISEMj8uERERkZTGjh2LXr165StXJz8raW7GfIqo/NDTdgBEJC/Dhg2DQqHADz/8gB9++KHY94CbmJjA2tq6lKMrmKmpKUxNTbVybCIiIiKpnD59Gh9++GG+cnXys5LmZsyniMoPjughohKZM2eOOCw37yM0NBQAoFAoAPxvsr/c1yXVoUMHjBo1CmPGjEGNGjVgZWWF8PBwPHv2DAMHDkS1atXQoEED7NmzR5J6RERERBVVZmYm9PX1ceLECUyZMgUKhQKtW7cW35cqP9u8eTOcnZ1hbGyMmjVrwtvbG8+ePdM4fiKSFjt6iKhERo0ahYSEBPExdOhQ1K1bF5999pnkx1q1ahUsLCxw+vRpjBo1Ct988w0+//xzvPvuuzh37hw6d+6Mfv364fnz55LUIyIiIqqI9PT0cPz4cQBAdHQ0EhISsHfvXkmPkZCQgD59+mDQoEG4evUqDh06hF69enEFL6JyiB09RFQi1apVg7W1NaytrbFkyRLs27cPhw4dQp06dSQ/louLC6ZOnYqGDRsiICAARkZGsLCwwNChQ9GwYUMEBgbiyZMnuHjxoiT1iIiIiCoiHR0dPHjwADVr1oSLiwusra1hZmYm6TESEhKQnZ2NXr16wcHBAc7OzhgxYgSqVq0q6XGISHPs6CEitQQGBuKPP/7AoUOH4ODgUCrHaNGihfhcV1cXNWvWhLOzs1hmZWUFAHj48KEk9YiIiIgqqvPnz8PFxaXU9u/i4oJOnTrB2dkZn3/+OcLDw/H06dNSOx4RqY8dPURUYtOnT8fq1atLtZMHAPT19VVeKxQKlbLc+8uVSqUk9YiIiIgqqujo6FLt6NHV1cX+/fuxZ88eNGvWDIsWLULjxo0RFxdXasckIvWwo4eISmT69OlYtWpVqXfyEBEREVHxXbp0CS1btizVYygUCrRp0wZBQUE4f/48DAwMsG3btlI9JhGVHJdXJ6JimzVrFpYtW4adO3fCyMgIiYmJAIAaNWrA0NBQy9ERERERVV5KpRIxMTF48OABqlSpIvlS56dOnUJERAQ6d+4MS0tLnDp1Co8ePULTpk0lPQ4RaY4jeoioWARBwPz58/Ho0SN4eXnBxsZGfHBSYyIiIiLtmjVrFlauXAlbW1vMmjVL8v1Xr14dR44cQbdu3dCoUSNMnToVP/30E3x8fCQ/FhFphiN6iKhYFAoFUlNTy+x4hw4dyld2+/btfGVvLumpbj0iIiKiiqxv377o27dvqe2/adOmki/ZTkSlgyN6iKhcWLp0KapWrYpLly6V+bGHDx/OpUGJiIiI8ihpbsZ8iqj8UAj8WpuItOz+/ft48eIFAMDe3h4GBgZlevyHDx8iLS0NAGBjY4MqVaqU6fGJiIiIyhN1cjPmU0TlBzt6iIiIiIiIiIhkgrduERERERERERHJBDt6iIiIiIiIiIhkgh09REREREREREQywY4eIiIiIiIiIiKZYEcPEREREREREZFMsKOHiIiIiIiIiEgm2NFDRERERERERCQT7OghIiIiIiIiIpIJdvQQEREREREREckEO3qIiIiIiIiIiGSCHT1ERERERERERDLx/wAIMM/+6K3WzAAAAABJRU5ErkJggg==",
-                                    "text/plain": [
-                                          "<Figure size 1300x700 with 5 Axes>"
-                                    ]
-                              },
-                              "metadata": {},
-                              "output_type": "display_data"
-                        }
-                  ],
-                  "source": [
-                        "T_ref = param.evaluate(dfn.param.T_ref)\n",
-                        "var = \"X-averaged cell temperature [K]\"\n",
-                        "comsol_var = comsol_solution[var]\n",
-                        "\n",
-                        "\n",
-                        "def comsol_var_fun(t, z):\n",
-                        "    return comsol_var(t=t, z=z) - T_ref\n",
-                        "\n",
-                        "\n",
-                        "dfn_var = solutions[\"1+1D DFN\"][var]\n",
-                        "\n",
-                        "\n",
-                        "def dfn_var_fun(t, z):\n",
-                        "    return dfn_var(t=t, z=z) - T_ref\n",
-                        "\n",
-                        "\n",
-                        "T_av = solutions[\"Average DFN\"][var]\n",
-                        "\n",
-                        "\n",
-                        "def dfncc_var_fun(t, z):\n",
-                        "    \"In the DFNCC the temperature is just the average temperature\"\n",
-                        "    return np.transpose(np.repeat(T_av(t)[:, np.newaxis], len(z), axis=1)) - T_ref\n",
-                        "\n",
-                        "\n",
-                        "plot(\n",
-                        "    t_plot,\n",
-                        "    z_plot,\n",
-                        "    t_slices,\n",
-                        "    \"$\\\\bar{T}^* - \\\\bar{T}_0^*$\",\n",
-                        "    \"[K]\",\n",
-                        "    comsol_var_fun,\n",
-                        "    dfn_var_fun,\n",
-                        "    dfncc_var_fun,\n",
-                        "    param,\n",
-                        "    cmap=\"inferno\",\n",
-                        ")"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "We see that the electrical conductivity of the current collectors is sufficiently\n",
-                        "high that the potentials remain fairly uniform in space, and both the 1+1D DFN and DFNCC models are able to accurately capture the potential distribution in the current collectors.\n",
-                        "\n",
-                        "\n",
-                        "In the plot of the current we see that positioning both tabs at the top of the cell means that for most of the simulation the current preferentially travels through the upper part of the cell. Eventually, as the cell continues to discharge, this part becomes more (de)lithiated until the resultant local increase in through-cell resistance is sufficient for it to become preferential for the current to travel further along the current collectors and through the lower part of the cell. This behaviour is well captured by the 1+1D model. In the DFNCC formulation the through-cell current density is assumed uniform,\n",
-                        "so the greatest error is found at the ends of the current collectors where the current density deviates most from its average.\n",
-                        "\n",
-                        "For the parameters used in this example we find that the temperature exhibits a relatively weak variation along the length of the current collectors.  "
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "## References\n",
-                        "\n",
-                        "The relevant papers for this notebook are:"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 19,
-                  "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] 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",
-                                    "[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] 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",
-                                    "[6] Robert Timms, Scott G Marquis, Valentin Sulzer, Colin P. Please, and S Jonathan Chapman. Asymptotic Reduction of a Lithium-ion Pouch Cell Model. SIAM Journal on Applied Mathematics, 81(3):765–788, 2021. doi:10.1137/20M1336898.\n",
-                                    "\n"
-                              ]
-                        }
-                  ],
-                  "source": [
-                        "pybamm.print_citations()"
-                  ]
-            }
-      ],
-      "metadata": {
-            "kernelspec": {
-                  "display_name": "dev",
-                  "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": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
-                  }
-            }
-      },
-      "nbformat": 4,
-      "nbformat_minor": 2
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pouch cell model"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook we compare the solutions of two reduced-order models of a lithium-ion pouch cell with the full solution obtained using COMSOL. This example is based on the results in [[6]](#References). The code used to produce the results in [[6]](#References) can be found [here](https://github.com/rtimms/asymptotic-pouch-cell).\n",
+    "\n",
+    "The full model is based on the Doyle-Fuller-Newman model [[2]](#References) and, in the interest of simplicity, considers a one-dimensional current collector (i.e. variation in one of the current collector dimensions is ignored), resulting in a 2D macroscopic model.\n",
+    "\n",
+    "The first of the reduced order models, which is applicable in the limit of large conductivity in the current collectors, solves a one-dimensional problem in the current collectors coupled to a one-dimensional DFN model describing the through-cell electrochemistry at each point. We refer to this as a 1+1D model, though since the DFN is already a pseudo-two-dimensional model, perhaps it is more properly a 1+1+1D model.\n",
+    "\n",
+    "The second reduced order model, which is applicable in the limit of very large conductivity in the current collectors, solves a single (averaged) one-dimensional DFN model for the through-cell behaviour and an uncoupled problem for the distribution of potential in the current collectors (from which the resistance and heat source can be calculated). We refer to this model as the DFNCC, where the \"CC\" indicates the additional (uncoupled) current collector problem.\n",
+    "\n",
+    "All of the model equations, and derivations of the reduced-order models, can be found in [[6]](#References)."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solving the reduced-order pouch cell models in PyBaMM"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We begin by importing PyBaMM along with the other packages required in this notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\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",
+    "import pybamm\n",
+    "import pickle\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import scipy.interpolate as interp"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then need to load up the appropriate models. For the DFNCC we require a 1D model of the current collectors and an average 1D DFN model for the through-cell electrochemistry. The 1+1D pouch cell model is built directly into PyBaMM and are accessed by passing the model option \"dimensionality\" which can be 1 or 2, corresponding to 1D or 2D current collectors. This option can be passed to any existing electrochemical model (e.g. [SPM](./SPM.ipynb), [SPMe](./SPMe.ipynb), [DFN](./DFN.ipynb)). Here we choose the DFN model. \n",
+    "\n",
+    "For both electrochemical models we choose an \"x-lumped\" thermal model, meaning we assume that the temperature is uniform in the through-cell direction $x$, but account for the variation in temperature in the transverse direction $z$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/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"
+     ]
+    }
+   ],
+   "source": [
+    "cc_model = pybamm.current_collector.EffectiveResistance({\"dimensionality\": 1})\n",
+    "dfn_av = pybamm.lithium_ion.DFN({\"thermal\": \"lumped\"}, name=\"Average DFN\")\n",
+    "dfn = pybamm.lithium_ion.DFN(\n",
+    "    {\"current collector\": \"potential pair\", \"dimensionality\": 1, \"thermal\": \"x-lumped\"},\n",
+    "    name=\"1+1D DFN\",\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then add the models to a dictionary for easy access later"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = {\"Current collector\": cc_model, \"Average DFN\": dfn_av, \"1+1D DFN\": dfn}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we update the parameters to match those used in the COMSOL simulation. In particular, we set the current to correspond to a 3C discharge and assume uniform Newton cooling on all boundaries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param = dfn.default_parameter_values\n",
+    "I_1C = param[\"Nominal cell capacity [A.h]\"]  # 1C current is cell capacity multipled by 1 hour\n",
+    "param.update(\n",
+    "    {\n",
+    "        \"Current function [A]\": I_1C * 3,       \n",
+    "        \"Negative electrode diffusivity [m2.s-1]\": 3.9 * 10 ** (-14),\n",
+    "        \"Positive electrode diffusivity [m2.s-1]\": 10 ** (-13),\n",
+    "        \"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\": 10,\n",
+    "        \"Positive current collector surface heat transfer coefficient [W.m-2.K-1]\": 10,\n",
+    "        \"Negative tab heat transfer coefficient [W.m-2.K-1]\": 10,\n",
+    "        \"Positive tab heat transfer coefficient [W.m-2.K-1]\": 10,\n",
+    "        \"Edge heat transfer coefficient [W.m-2.K-1]\": 10,\n",
+    "        \"Total heat transfer coefficient [W.m-2.K-1]\": 10,\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example we choose to discretise in space using 16 nodes per domain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "npts = 16\n",
+    "var_pts = {\n",
+    "    \"x_n\": npts,\n",
+    "    \"x_s\": npts,\n",
+    "    \"x_p\": npts,\n",
+    "    \"r_n\": npts,\n",
+    "    \"r_p\": npts,\n",
+    "    \"z\": npts,\n",
+    "}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before solving the models we load the COMSOL data so that we can request the output at the times in the COMSOL solution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comsol_results_path = pybamm.get_parameters_filepath(\n",
+    "    \"input/comsol_results/comsol_1plus1D_3C.pickle\"\n",
+    ")\n",
+    "comsol_variables = pickle.load(open(comsol_results_path, \"rb\"))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we loop over the models, creating and solving a simulation for each."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simulations = {}\n",
+    "solutions = {}  # store solutions in a separate dict for easy access later\n",
+    "for name, model in models.items():\n",
+    "    sim = pybamm.Simulation(model, parameter_values=param, var_pts=var_pts)\n",
+    "    simulations[name] = sim  # store simulation for later\n",
+    "    if name == \"Current collector\":\n",
+    "        # model is independent of time, so just solve arbitrarily at t=0 using \n",
+    "        # the default algebraic solver\n",
+    "        t_eval = np.array([0])\n",
+    "        solutions[name] = sim.solve(t_eval=t_eval)        \n",
+    "    else:\n",
+    "        # solve at COMSOL times using Casadi solver in \"fast\" mode\n",
+    "        t_eval = comsol_variables[\"time\"]       \n",
+    "        solutions[name] = sim.solve(solver=pybamm.CasadiSolver(mode=\"fast\"), t_eval=t_eval)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating the COMSOL model"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section we show how to create a PyBaMM \"model\" from the COMSOL solution. If you are just interested in seeing the comparison the skip ahead to the section \"Comparing the full and reduced-order models\".\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a PyBaMM model from the COMSOL data we must create a `pybamm.Function` object for each variable. We do this by interpolating in space to match the PyBaMM mesh and then creating a function to interpolate in time. The following cell defines the function that handles the creation of the `pybamm.Function` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up times\n",
+    "comsol_t = comsol_variables[\"time\"]\n",
+    "pybamm_t = comsol_t\n",
+    "# set up space\n",
+    "mesh = simulations[\"1+1D DFN\"].mesh\n",
+    "L_z = param.evaluate(dfn.param.L_z)\n",
+    "pybamm_z = mesh[\"current collector\"].nodes\n",
+    "z_interp = pybamm_z\n",
+    "\n",
+    "\n",
+    "def get_interp_fun_curr_coll(variable_name):\n",
+    "    \"\"\"\n",
+    "    Create a :class:`pybamm.Function` object using the variable (interpolate in space \n",
+    "    to match nodes, and then create function to interpolate in time)\n",
+    "    \"\"\"\n",
+    "\n",
+    "    comsol_z = comsol_variables[variable_name + \"_z\"]\n",
+    "    variable = comsol_variables[variable_name]\n",
+    "    variable = interp.interp1d(comsol_z, variable, axis=0, kind=\"linear\")(z_interp)\n",
+    "\n",
+    "    # Make sure to use dimensional time\n",
+    "    fun = pybamm.Interpolant(\n",
+    "        comsol_t,\n",
+    "        variable.T,\n",
+    "        pybamm.t,\n",
+    "        name=variable_name + \"_comsol\"\n",
+    "    )\n",
+    "    fun.domains = {\"primary\": \"current collector\"}\n",
+    "    fun.mesh = mesh.combine_submeshes(\"current collector\")\n",
+    "    fun.secondary_mesh = None\n",
+    "\n",
+    "    return fun"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then pass the variables of interest to the interpolating function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comsol_voltage = pybamm.Interpolant(\n",
+    "    comsol_t, \n",
+    "    comsol_variables[\"voltage\"],\n",
+    "    pybamm.t,\n",
+    "    name=\"voltage_comsol\",\n",
+    ")\n",
+    "comsol_voltage.mesh = None\n",
+    "comsol_voltage.secondary_mesh = None\n",
+    "comsol_phi_s_cn = get_interp_fun_curr_coll(\"phi_s_cn\")\n",
+    "comsol_phi_s_cp = get_interp_fun_curr_coll(\"phi_s_cp\")\n",
+    "comsol_current = get_interp_fun_curr_coll(\"current\")\n",
+    "comsol_temperature = get_interp_fun_curr_coll(\"temperature\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and add them to a `pybamm.BaseModel` object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comsol_model = pybamm.BaseModel()\n",
+    "comsol_model._geometry = pybamm.battery_geometry(options={\"dimensionality\": 1})\n",
+    "comsol_model.variables = {\n",
+    "    \"Voltage [V]\": comsol_voltage,\n",
+    "    \"Negative current collector potential [V]\": comsol_phi_s_cn,\n",
+    "    \"Positive current collector potential [V]\": comsol_phi_s_cp,\n",
+    "    \"Current collector current density [A.m-2]\": comsol_current,\n",
+    "    \"X-averaged cell temperature [K]\": comsol_temperature,\n",
+    "    # Add spatial variables to match pybamm model\n",
+    "    \"z [m]\": simulations[\"1+1D DFN\"].built_model.variables[\"z [m]\"],   \n",
+    "}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then add the solution object from the 1+1D model. This is just so that PyBaMM uses the same times behind the scenes when dealing with COMSOL model and the reduced-order models: the variables in `comsol_model.variables` are functions of time only that return the (interpolated in space) COMSOL solution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comsol_solution = pybamm.Solution(solutions[\"1+1D DFN\"].t, solutions[\"1+1D DFN\"].y, comsol_model, {})"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparing the full and reduced-order models"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The DFNCC requires some post-processing to extract the solution variables. In particular, we need to pass the current and voltage from the average DFN model to the current collector model in order to compute the distribution of the potential in the current collectors and to account for the effect of the current collector resistance in the voltage. \n",
+    "\n",
+    "This process is automated by the method `post_process` which accepts the current collector solution object, the parameters and the voltage and current from the average DFN model. The results are stored in the dictionary `dfncc_vars`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "V_av = solutions[\"Average DFN\"][\"Voltage [V]\"]\n",
+    "I_av = solutions[\"Average DFN\"][\"Total current density [A.m-2]\"]\n",
+    "\n",
+    "dfncc_vars = cc_model.post_process(\n",
+    "    solutions[\"Current collector\"], param, V_av, I_av\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we create a function to create some custom plots. For a given variable the plots will show: (a) the COMSOL results as a function of position in the current collector $z$ and time $t$; (b) a comparison of the full and reduced-order models and a sequence of times; (c) the time-averaged error between the full and reduced-order models as a function of space; and (d) the space-averaged error between the full and reduced-order models as a function of time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot(\n",
+    "    t_plot,\n",
+    "    z_plot,\n",
+    "    t_slices,\n",
+    "    var_name,\n",
+    "    units,\n",
+    "    comsol_var_fun,\n",
+    "    dfn_var_fun,\n",
+    "    dfncc_var_fun,\n",
+    "    param,\n",
+    "    cmap=\"viridis\",\n",
+    "):\n",
+    "\n",
+    "    fig, ax = plt.subplots(2, 2, figsize=(13, 7))\n",
+    "    fig.subplots_adjust(\n",
+    "        left=0.15, bottom=0.1, right=0.95, top=0.95, wspace=0.4, hspace=0.8\n",
+    "    )\n",
+    "    # plot comsol var\n",
+    "    comsol_var = comsol_var_fun(t=t_plot, z=z_plot)\n",
+    "    comsol_var_plot = ax[0, 0].pcolormesh(\n",
+    "        z_plot * 1e3, t_plot, np.transpose(comsol_var), shading=\"gouraud\", cmap=cmap\n",
+    "    )\n",
+    "    if \"cn\" in var_name:\n",
+    "        format = \"%.0e\"\n",
+    "    elif \"cp\" in var_name:\n",
+    "        format = \"%.0e\"\n",
+    "    else:\n",
+    "        format = None\n",
+    "    fig.colorbar(\n",
+    "        comsol_var_plot,\n",
+    "        ax=ax,\n",
+    "        format=format,\n",
+    "        location=\"top\",\n",
+    "        shrink=0.42,\n",
+    "        aspect=20,\n",
+    "        anchor=(0.0, 0.0),\n",
+    "    )\n",
+    "\n",
+    "    # plot slices\n",
+    "    ccmap = plt.get_cmap(\"inferno\")\n",
+    "    for ind, t in enumerate(t_slices):\n",
+    "        color = ccmap(float(ind) / len(t_slices))\n",
+    "        comsol_var_slice = comsol_var_fun(t=t, z=z_plot)\n",
+    "        dfn_var_slice = dfn_var_fun(t=t, z=z_plot)\n",
+    "        dfncc_var_slice = dfncc_var_fun(t=np.array([t]), z=z_plot)\n",
+    "        ax[0, 1].plot(\n",
+    "            z_plot * 1e3, comsol_var_slice, \"o\", fillstyle=\"none\", color=color\n",
+    "        )\n",
+    "        ax[0, 1].plot(\n",
+    "            z_plot * 1e3,\n",
+    "            dfn_var_slice,\n",
+    "            \"-\",\n",
+    "            color=color,\n",
+    "            label=f\"{t_slices[ind]:.0f} s\",\n",
+    "        )\n",
+    "        ax[0, 1].plot(z_plot * 1e3, dfncc_var_slice, \":\", color=color)\n",
+    "    # add dummy points for legend of styles\n",
+    "    comsol_p, = ax[0, 1].plot(np.nan, np.nan, \"ko\", fillstyle=\"none\")\n",
+    "    pybamm_p, = ax[0, 1].plot(np.nan, np.nan, \"k-\", fillstyle=\"none\")\n",
+    "    dfncc_p, = ax[0, 1].plot(np.nan, np.nan, \"k:\", fillstyle=\"none\")\n",
+    "\n",
+    "    # compute errors\n",
+    "    dfn_var = dfn_var_fun(t=t_plot, z=z_plot)\n",
+    "    dfncc_var = dfncc_var_fun(t=t_plot, z=z_plot)\n",
+    "    error = np.abs(comsol_var - dfn_var)\n",
+    "    error_bar = np.abs(comsol_var - dfncc_var)\n",
+    "\n",
+    "    # plot time averaged error\n",
+    "    ax[1, 0].plot(z_plot * 1e3, np.nanmean(error, axis=1), \"k-\", label=r\"$1+1$D\")\n",
+    "    ax[1, 0].plot(z_plot * 1e3, np.nanmean(error_bar, axis=1), \"k:\", label=\"DFNCC\")\n",
+    "\n",
+    "    # plot z averaged error\n",
+    "    ax[1, 1].plot(t_plot, np.nanmean(error, axis=0), \"k-\", label=r\"$1+1$D\")\n",
+    "    ax[1, 1].plot(t_plot, np.nanmean(error_bar, axis=0), \"k:\", label=\"DFNCC\")\n",
+    "\n",
+    "    # set ticks\n",
+    "    ax[0, 0].tick_params(which=\"both\")\n",
+    "    ax[0, 1].tick_params(which=\"both\")\n",
+    "    ax[1, 0].tick_params(which=\"both\")\n",
+    "    if var_name in [\"$\\mathcal{I}^*$\"]:\n",
+    "        ax[1, 0].set_yscale(\"log\")\n",
+    "        ax[1, 0].set_yticks = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e-2, 1e-1, 1]\n",
+    "    else:\n",
+    "        ax[1, 0].ticklabel_format(style=\"sci\", scilimits=(-2, 2), axis=\"y\")\n",
+    "    ax[1, 1].tick_params(which=\"both\")\n",
+    "    if var_name in [\"$\\phi^*_{\\mathrm{s,cn}}$\", \"$\\phi^*_{\\mathrm{s,cp}} - V^*$\"]:\n",
+    "        ax[1, 0].ticklabel_format(style=\"sci\", scilimits=(-2, 2), axis=\"y\")\n",
+    "    else:\n",
+    "        ax[1, 1].set_yscale(\"log\")\n",
+    "        ax[1, 1].set_yticks = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e-2, 1e-1, 1]\n",
+    "\n",
+    "    # set labels\n",
+    "    ax[0, 0].set_xlabel(r\"$z^*$ [mm]\")\n",
+    "    ax[0, 0].set_ylabel(r\"$t^*$ [s]\")\n",
+    "    ax[0, 0].set_title(rf\"{var_name} {units}\", y=1.5)\n",
+    "    ax[0, 1].set_xlabel(r\"$z^*$ [mm]\")\n",
+    "    ax[0, 1].set_ylabel(rf\"{var_name}\")\n",
+    "    ax[1, 0].set_xlabel(r\"$z^*$ [mm]\")\n",
+    "    ax[1, 0].set_ylabel(\"Time-averaged\" + \"\\n\" + rf\"absolute error {units}\")\n",
+    "    ax[1, 1].set_xlabel(r\"$t^*$ [s]\")\n",
+    "    ax[1, 1].set_ylabel(\"Space-averaged\" + \"\\n\" + rf\"absolute error {units}\")\n",
+    "\n",
+    "    ax[0, 0].text(-0.1, 1.6, \"(a)\", transform=ax[0, 0].transAxes)\n",
+    "    ax[0, 1].text(-0.1, 1.6, \"(b)\", transform=ax[0, 1].transAxes)\n",
+    "    ax[1, 0].text(-0.1, 1.2, \"(c)\", transform=ax[1, 0].transAxes)\n",
+    "    ax[1, 1].text(-0.1, 1.2, \"(d)\", transform=ax[1, 1].transAxes)\n",
+    "\n",
+    "    leg1 = ax[0, 1].legend(\n",
+    "        bbox_to_anchor=(0, 1.1, 1.0, 0.102),\n",
+    "        loc=\"lower left\",\n",
+    "        borderaxespad=0.0,\n",
+    "        ncol=3,\n",
+    "        mode=\"expand\",\n",
+    "    )\n",
+    "\n",
+    "    ax[0, 1].legend(\n",
+    "        [comsol_p, pybamm_p, dfncc_p],\n",
+    "        [\"COMSOL\", r\"$1+1$D\", \"DFNCC\"],\n",
+    "        bbox_to_anchor=(0, 1.5, 1.0, 0.102),\n",
+    "        loc=\"lower left\",\n",
+    "        borderaxespad=0.0,\n",
+    "        ncol=3,\n",
+    "        mode=\"expand\",\n",
+    "    )\n",
+    "    ax[0, 1].add_artist(leg1)\n",
+    "\n",
+    "    ax[1, 0].legend(\n",
+    "        bbox_to_anchor=(0.0, 1.1, 1.0, 0.102),\n",
+    "        loc=\"lower right\",\n",
+    "        borderaxespad=0.0,\n",
+    "        ncol=3,\n",
+    "    )\n",
+    "    ax[1, 1].legend(\n",
+    "        bbox_to_anchor=(0.0, 1.1, 1.0, 0.102),\n",
+    "        loc=\"lower right\",\n",
+    "        borderaxespad=0.0,\n",
+    "        ncol=3,\n",
+    "    )"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then set up the times and points in space to use in the plots "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_plot = comsol_t\n",
+    "z_plot = z_interp\n",
+    "t_slices = np.array([600, 1200, 1800, 2400, 3000]) / 3"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and plot the negative current collector potential"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x700 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "var = \"Negative current collector potential [V]\"\n",
+    "comsol_var_fun = comsol_solution[var]\n",
+    "dfn_var_fun = solutions[\"1+1D DFN\"][var]\n",
+    "\n",
+    "dfncc_var_fun = dfncc_vars[var]\n",
+    "plot(\n",
+    "    t_plot,\n",
+    "    z_plot,\n",
+    "    t_slices,\n",
+    "    \"$\\phi^*_{\\mathrm{s,cn}}$\",\n",
+    "    \"[V]\",\n",
+    "    comsol_var_fun,\n",
+    "    dfn_var_fun,\n",
+    "    dfncc_var_fun,\n",
+    "    param,\n",
+    "    cmap=\"cividis\",\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "the positive current collector potential with respect to voltage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x700 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "var = \"Positive current collector potential [V]\"\n",
+    "comsol_var = comsol_solution[var]\n",
+    "V_comsol = comsol_solution[\"Voltage [V]\"]\n",
+    "\n",
+    "\n",
+    "def comsol_var_fun(t, z):\n",
+    "    return comsol_var(t=t, z=z) - V_comsol(t=t)\n",
+    "\n",
+    "\n",
+    "dfn_var = solutions[\"1+1D DFN\"][var]\n",
+    "V = solutions[\"1+1D DFN\"][\"Voltage [V]\"]\n",
+    "\n",
+    "\n",
+    "def dfn_var_fun(t, z):\n",
+    "    return dfn_var(t=t, z=z) - V(t=t)\n",
+    "\n",
+    "\n",
+    "dfncc_var =  dfncc_vars[var]\n",
+    "V_dfncc = dfncc_vars[\"Voltage [V]\"]\n",
+    "\n",
+    "\n",
+    "def dfncc_var_fun(t, z):\n",
+    "    return dfncc_var(t=t, z=z) - V_dfncc(t)\n",
+    "\n",
+    "\n",
+    "plot(\n",
+    "    t_plot,\n",
+    "    z_plot,\n",
+    "    t_slices,\n",
+    "    \"$\\phi^*_{\\mathrm{s,cp}} - V^*$\",\n",
+    "    \"[V]\",\n",
+    "    comsol_var_fun,\n",
+    "    dfn_var_fun,\n",
+    "    dfncc_var_fun,\n",
+    "    param,\n",
+    "    cmap=\"viridis\",\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "the through-cell current "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x700 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "var = \"Current collector current density [A.m-2]\"\n",
+    "comsol_var_fun = comsol_solution[var]\n",
+    "dfn_var_fun = solutions[\"1+1D DFN\"][var]\n",
+    "\n",
+    "I_av = solutions[\"Average DFN\"][var]\n",
+    "\n",
+    "\n",
+    "def dfncc_var_fun(t, z):\n",
+    "    \"In the DFNCC the current is just the average current\"\n",
+    "    return np.transpose(np.repeat(I_av(t)[:, np.newaxis], len(z), axis=1))\n",
+    "\n",
+    "\n",
+    "plot(\n",
+    "    t_plot,\n",
+    "    z_plot,\n",
+    "    t_slices,\n",
+    "    \"$\\mathcal{I}^*$\",\n",
+    "    \"[A/m${}^2$]\",\n",
+    "    comsol_var_fun,\n",
+    "    dfn_var_fun,\n",
+    "    dfncc_var_fun,\n",
+    "    param,\n",
+    "    cmap=\"plasma\",\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and the temperature with respect to reference temperature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x700 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "T_ref = param.evaluate(dfn.param.T_ref)\n",
+    "var = \"X-averaged cell temperature [K]\"\n",
+    "comsol_var = comsol_solution[var]\n",
+    "\n",
+    "\n",
+    "def comsol_var_fun(t, z):\n",
+    "    return comsol_var(t=t, z=z) - T_ref\n",
+    "\n",
+    "\n",
+    "dfn_var = solutions[\"1+1D DFN\"][var]\n",
+    "\n",
+    "\n",
+    "def dfn_var_fun(t, z):\n",
+    "    return dfn_var(t=t, z=z) - T_ref\n",
+    "\n",
+    "\n",
+    "T_av = solutions[\"Average DFN\"][var]\n",
+    "\n",
+    "\n",
+    "def dfncc_var_fun(t, z):\n",
+    "    \"In the DFNCC the temperature is just the average temperature\"\n",
+    "    return np.transpose(np.repeat(T_av(t)[:, np.newaxis], len(z), axis=1)) - T_ref\n",
+    "\n",
+    "\n",
+    "plot(\n",
+    "    t_plot,\n",
+    "    z_plot,\n",
+    "    t_slices,\n",
+    "    \"$\\\\bar{T}^* - \\\\bar{T}_0^*$\",\n",
+    "    \"[K]\",\n",
+    "    comsol_var_fun,\n",
+    "    dfn_var_fun,\n",
+    "    dfncc_var_fun,\n",
+    "    param,\n",
+    "    cmap=\"inferno\",\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that the electrical conductivity of the current collectors is sufficiently\n",
+    "high that the potentials remain fairly uniform in space, and both the 1+1D DFN and DFNCC models are able to accurately capture the potential distribution in the current collectors.\n",
+    "\n",
+    "\n",
+    "In the plot of the current we see that positioning both tabs at the top of the cell means that for most of the simulation the current preferentially travels through the upper part of the cell. Eventually, as the cell continues to discharge, this part becomes more (de)lithiated until the resultant local increase in through-cell resistance is sufficient for it to become preferential for the current to travel further along the current collectors and through the lower part of the cell. This behaviour is well captured by the 1+1D model. In the DFNCC formulation the through-cell current density is assumed uniform,\n",
+    "so the greatest error is found at the ends of the current collectors where the current density deviates most from its average.\n",
+    "\n",
+    "For the parameters used in this example we find that the temperature exhibits a relatively weak variation along the length of the current collectors.  "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "The relevant papers for this notebook are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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] 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",
+      "[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] 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",
+      "[6] Robert Timms, Scott G Marquis, Valentin Sulzer, Colin P. Please, and S Jonathan Chapman. Asymptotic Reduction of a Lithium-ion Pouch Cell Model. SIAM Journal on Applied Mathematics, 81(3):765–788, 2021. doi:10.1137/20M1336898.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dev",
+   "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": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }
diff --git a/docs/source/examples/notebooks/models/rate-capability.ipynb b/docs/source/examples/notebooks/models/rate-capability.ipynb
index fa01342f1d..056362b8f9 100644
--- a/docs/source/examples/notebooks/models/rate-capability.ipynb
+++ b/docs/source/examples/notebooks/models/rate-capability.ipynb
@@ -97,8 +97,8 @@
     "\n",
     "for i, C_rate in enumerate(C_rates):\n",
     "    experiment = pybamm.Experiment(\n",
-    "        [\"Discharge at {:.4f}C until 3.2V\".format(C_rate)],\n",
-    "        period=\"{:.4f} seconds\".format(10 / C_rate)\n",
+    "        [f\"Discharge at {C_rate:.4f}C until 3.2V\"],\n",
+    "        period=f\"{10 / C_rate:.4f} seconds\"\n",
     "    )\n",
     "    sim = pybamm.Simulation(\n",
     "        model,\n",
diff --git a/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb b/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb
index 2de30eedfe..cf7bef3b47 100644
--- a/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb
+++ b/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb
@@ -407,7 +407,7 @@
     "        T_exact(xx, t),\n",
     "        \"-\",\n",
     "        color=color,\n",
-    "        label=\"Exact (t={})\".format(plot_times[i]),\n",
+    "        label=f\"Exact (t={plot_times[i]})\",\n",
     "    )\n",
     "plt.xlabel(\"x\", fontsize=16)\n",
     "plt.ylabel(\"T\", fontsize=16)\n",
diff --git a/docs/source/examples/notebooks/parameterization/change-input-current.ipynb b/docs/source/examples/notebooks/parameterization/change-input-current.ipynb
index 0285ab69dd..4b3ef7846e 100644
--- a/docs/source/examples/notebooks/parameterization/change-input-current.ipynb
+++ b/docs/source/examples/notebooks/parameterization/change-input-current.ipynb
@@ -307,7 +307,7 @@
     "npts = int(50 * simulation_time * omega)  # need enough timesteps to resolve output\n",
     "t_eval = np.linspace(0, simulation_time, npts)\n",
     "solution = simulation.solve(t_eval)\n",
-    "label = [\"Frequency: {} Hz\".format(omega)]\n",
+    "label = [f\"Frequency: {omega} Hz\"]\n",
     "\n",
     "# plot current and voltage\n",
     "output_variables = [\"Current [A]\", \"Voltage [V]\"]\n",
diff --git a/docs/source/examples/notebooks/parameterization/parameter-values.ipynb b/docs/source/examples/notebooks/parameterization/parameter-values.ipynb
index f0a770af08..6d2b6f707f 100644
--- a/docs/source/examples/notebooks/parameterization/parameter-values.ipynb
+++ b/docs/source/examples/notebooks/parameterization/parameter-values.ipynb
@@ -68,7 +68,7 @@
    "source": [
     "param_dict = {\"a\": 1, \"b\": 2, \"c\": 3}\n",
     "parameter_values = pybamm.ParameterValues(param_dict)\n",
-    "print(\"parameter values are {}\".format(parameter_values))"
+    "print(f\"parameter values are {parameter_values}\")"
    ]
   },
   {
@@ -131,7 +131,7 @@
     "\n",
     "\n",
     "parameter_values.update({\"cube function\": cubed}, check_already_exists=False)\n",
-    "print(\"parameter values are {}\".format(parameter_values))"
+    "print(f\"parameter values are {parameter_values}\")"
    ]
   },
   {
@@ -200,7 +200,7 @@
    ],
    "source": [
     "expr_eval = parameter_values.process_symbol(expr)\n",
-    "print(\"{} = {}\".format(expr_eval, expr_eval.evaluate()))"
+    "print(f\"{expr_eval} = {expr_eval.evaluate()}\")"
    ]
   },
   {
@@ -218,7 +218,7 @@
    ],
    "source": [
     "func_eval = parameter_values.process_symbol(func)\n",
-    "print(\"{} = {}\".format(func_eval, func_eval.evaluate()))"
+    "print(f\"{func_eval} = {func_eval.evaluate()}\")"
    ]
   },
   {
@@ -396,7 +396,7 @@
     "parameters = {\"a\": a, \"b\": b, \"a + b\": a + b, \"a * b\": a * b}\n",
     "param_eval = parameter_values.print_parameters(parameters)\n",
     "for name, value in param_eval.items():\n",
-    "    print(\"{}: {}\".format(name, value))"
+    "    print(f\"{name}: {value}\")"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/parameterization/parameterization.ipynb b/docs/source/examples/notebooks/parameterization/parameterization.ipynb
index f3db45aa44..3ec04e9654 100644
--- a/docs/source/examples/notebooks/parameterization/parameterization.ipynb
+++ b/docs/source/examples/notebooks/parameterization/parameterization.ipynb
@@ -400,7 +400,7 @@
     "\n",
     "rsol = mesh[\"negative particle\"].nodes # radial position\n",
     "time = 1000  # time in seconds\n",
-    "ax2.plot(rsol * 1e6, c(t=time, r=rsol), label=\"t={}[s]\".format(time))\n",
+    "ax2.plot(rsol * 1e6, c(t=time, r=rsol), label=f\"t={time}[s]\")\n",
     "ax2.set_xlabel(\"Particle radius [microns]\")\n",
     "ax2.set_ylabel(\"Concentration [mol.m-3]\")\n",
     "ax2.legend()\n",
diff --git a/docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb
index e4c4295ce1..366d99c1f8 100644
--- a/docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb
+++ b/docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb
@@ -154,7 +154,7 @@
     "sim.solve(callbacks=callback)\n",
     "\n",
     "# Read the file that has been written, which was saved to callback.logfile\n",
-    "with open(callback.logfile, \"r\") as f:\n",
+    "with open(callback.logfile) as f:\n",
     "    print(f.read())\n",
     "    \n",
     "# Remove the log file\n",
diff --git a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
index 2bd7f47ae1..0955b68310 100644
--- a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
+++ b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
@@ -944,9 +944,9 @@
    ],
    "source": [
     "pybamm.settings.set_smoothing_parameters(10)\n",
-    "print(\"Smooth minimum (softminus):\\t {!s}\".format(pybamm.minimum(x,y)))\n",
-    "print(\"Smooth heaviside (sigmoid):\\t {!s}\".format(x < y))\n",
-    "print(\"Smooth absolute value: \\t\\t {!s}\".format(abs(x)))\n",
+    "print(f\"Smooth minimum (softminus):\\t {pybamm.minimum(x,y)!s}\")\n",
+    "print(f\"Smooth heaviside (sigmoid):\\t {x < y!s}\")\n",
+    "print(f\"Smooth absolute value: \\t\\t {abs(x)!s}\")\n",
     "pybamm.settings.set_smoothing_parameters(\"exact\")"
    ]
   },
diff --git a/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb b/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb
index 7afd4da6f9..a0725d4dd3 100644
--- a/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb
+++ b/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb
@@ -207,8 +207,8 @@
     "# Discretise\n",
     "x_disc = disc.process_symbol(x_var)\n",
     "r_disc = disc.process_symbol(r_var)\n",
-    "print(\"x_disc is a {}\".format(type(x_disc)))\n",
-    "print(\"r_disc is a {}\".format(type(r_disc)))\n",
+    "print(f\"x_disc is a {type(x_disc)}\")\n",
+    "print(f\"r_disc is a {type(r_disc)}\")\n",
     "\n",
     "# Evaluate\n",
     "x = x_disc.evaluate()\n",
@@ -343,9 +343,9 @@
     "w_disc = disc.process_symbol(w)\n",
     "\n",
     "# Print the outcome \n",
-    "print(\"Discretised u is the StateVector {}\".format(u_disc))\n",
-    "print(\"Discretised v is the StateVector {}\".format(v_disc))\n",
-    "print(\"Discretised w is the StateVector {}\".format(w_disc))"
+    "print(f\"Discretised u is the StateVector {u_disc}\")\n",
+    "print(f\"Discretised v is the StateVector {v_disc}\")\n",
+    "print(f\"Discretised w is the StateVector {w_disc}\")"
    ]
   },
   {
@@ -405,7 +405,7 @@
     }
    ],
    "source": [
-    "print(\"w = {}\".format(w_disc.evaluate(y=y)))"
+    "print(f\"w = {w_disc.evaluate(y=y)}\")"
    ]
   },
   {
@@ -484,7 +484,7 @@
    "source": [
     "macro_mesh = mesh.combine_submeshes(*macroscale)\n",
     "print(\"gradient matrix is:\\n\")\n",
-    "print(\"1/dx *\\n{}\".format(macro_mesh.d_nodes[:,np.newaxis] * grad_u_disc.children[0].entries.toarray()))"
+    "print(f\"1/dx *\\n{macro_mesh.d_nodes[:,np.newaxis] * grad_u_disc.children[0].entries.toarray()}\")"
    ]
   },
   {
@@ -600,7 +600,7 @@
     "\n",
     "micro_mesh = mesh[\"negative particle\"]\n",
     "print(\"\\n gradient matrix is:\\n\")\n",
-    "print(\"1/dr *\\n{}\".format(micro_mesh.d_nodes[:,np.newaxis] * grad_v_disc.children[0].entries.toarray()))\n",
+    "print(f\"1/dr *\\n{micro_mesh.d_nodes[:,np.newaxis] * grad_v_disc.children[0].entries.toarray()}\")\n",
     "\n",
     "r_edge = micro_mesh.edges[1:-1] # note that grad_u_disc is evaluated on the node edges\n",
     "\n",
@@ -661,8 +661,8 @@
     "(grad_u_disc.render())\n",
     "u_eval = grad_u_disc.evaluate(y=y)\n",
     "dx = np.diff(macro_mesh.nodes)[-1]\n",
-    "print(\"The value of u on the left-hand boundary is {}\".format(y[0] - dx*u_eval[0]/2))\n",
-    "print(\"The value of u on the right-hand boundary is {}\".format(y[1] + dx*u_eval[-1]/2))"
+    "print(f\"The value of u on the left-hand boundary is {y[0] - dx*u_eval[0]/2}\")\n",
+    "print(f\"The value of u on the right-hand boundary is {y[1] + dx*u_eval[-1]/2}\")"
    ]
   },
   {
@@ -704,8 +704,8 @@
     "print(\"The gradient object is:\")\n",
     "(grad_u_disc.render())\n",
     "grad_u_eval = grad_u_disc.evaluate(y=y)\n",
-    "print(\"The gradient on the left-hand boundary is {}\".format(grad_u_eval[0]))\n",
-    "print(\"The gradient of u on the right-hand boundary is {}\".format(grad_u_eval[-1]))"
+    "print(f\"The gradient on the left-hand boundary is {grad_u_eval[0]}\")\n",
+    "print(f\"The gradient of u on the right-hand boundary is {grad_u_eval[-1]}\")"
    ]
   },
   {
@@ -745,8 +745,8 @@
     "(grad_u_disc.render())\n",
     "grad_u_eval = grad_u_disc.evaluate(y=y)\n",
     "u_eval = grad_u_disc.children[1].evaluate(y=y)\n",
-    "print(\"The value of u on the left-hand boundary is {}\".format((u_eval[0] + u_eval[1])/2))\n",
-    "print(\"The gradient on the right-hand boundary is {}\".format(grad_u_eval[-1]))"
+    "print(f\"The value of u on the left-hand boundary is {(u_eval[0] + u_eval[1])/2}\")\n",
+    "print(f\"The gradient on the right-hand boundary is {grad_u_eval[-1]}\")"
    ]
   },
   {
@@ -889,7 +889,7 @@
    "source": [
     "int_u = pybamm.Integral(u, x_var)\n",
     "int_u_disc = disc.process_symbol(int_u)\n",
-    "print(\"int(u) = {} is approximately equal to 1/12, {}\".format(int_u_disc.evaluate(y=y), 1/12))\n",
+    "print(f\"int(u) = {int_u_disc.evaluate(y=y)} is approximately equal to 1/12, {1/12}\")\n",
     "\n",
     "# We divide v by r to evaluate the integral more easily\n",
     "int_v_over_r2 = pybamm.Integral(v/r_var**2, r_var)\n",
diff --git a/examples/scripts/compare_comsol/compare_comsol_DFN.py b/examples/scripts/compare_comsol/compare_comsol_DFN.py
index afdb9eacbf..45bc4182ef 100644
--- a/examples/scripts/compare_comsol/compare_comsol_DFN.py
+++ b/examples/scripts/compare_comsol/compare_comsol_DFN.py
@@ -17,7 +17,7 @@
 
 # load the comsol results
 comsol_results_path = pybamm.get_parameters_filepath(
-    "input/comsol_results/comsol_{}C.pickle".format(C_rate)
+    f"input/comsol_results/comsol_{C_rate}C.pickle"
 )
 comsol_variables = pickle.load(open(comsol_results_path, "rb"))
 
diff --git a/examples/scripts/compare_comsol/discharge_curve.py b/examples/scripts/compare_comsol/discharge_curve.py
index b5cc23d946..7544730eea 100644
--- a/examples/scripts/compare_comsol/discharge_curve.py
+++ b/examples/scripts/compare_comsol/discharge_curve.py
@@ -59,7 +59,7 @@
     current = 24 * C_rate
     # load the comsol results
     comsol_results_path = pybamm.get_parameters_filepath(
-        "input/comsol_results/comsol_{}C.pickle".format(key)
+        f"input/comsol_results/comsol_{key}C.pickle"
     )
     comsol_variables = pickle.load(open(comsol_results_path, "rb"))
     comsol_time = comsol_variables["time"]
@@ -95,7 +95,7 @@
         voltage_sol,
         color=color,
         linestyle="-",
-        label="{} C".format(C_rate),
+        label=f"{C_rate} C",
     )
     voltage_difference_plot.plot(
         discharge_capacity_sol[0:end_index], voltage_difference, color=color
diff --git a/examples/scripts/compare_particle_models.py b/examples/scripts/compare_particle_models.py
index d780452004..1be5bbdfd9 100644
--- a/examples/scripts/compare_particle_models.py
+++ b/examples/scripts/compare_particle_models.py
@@ -28,8 +28,8 @@
     sim = pybamm.Simulation(model, parameter_values=parameter_values)
     sim.solve([0, 3600])
     sims.append(sim)
-    print("Particle model: {}".format(model.name))
-    print("Solve time: {}s".format(sim.solution.solve_time))
+    print(f"Particle model: {model.name}")
+    print(f"Solve time: {sim.solution.solve_time}s")
 
 # plot results
 pybamm.dynamic_plot(sims)
diff --git a/examples/scripts/experimental_protocols/cccv.py b/examples/scripts/experimental_protocols/cccv.py
index c99780c4d8..c020588d07 100644
--- a/examples/scripts/experimental_protocols/cccv.py
+++ b/examples/scripts/experimental_protocols/cccv.py
@@ -37,7 +37,7 @@
     t = sol["Time [h]"].entries
     V = sol["Voltage [V]"].entries
     # Plot
-    ax.plot(t - t[0], V, label="Discharge {}".format(i + 1))
+    ax.plot(t - t[0], V, label=f"Discharge {i + 1}")
     ax.set_xlabel("Time [h]")
     ax.set_ylabel("Voltage [V]")
     ax.set_xlim([0, t[-1] - t[0]])
diff --git a/examples/scripts/heat_equation.py b/examples/scripts/heat_equation.py
index 4e80d6adec..20f9601090 100644
--- a/examples/scripts/heat_equation.py
+++ b/examples/scripts/heat_equation.py
@@ -120,7 +120,7 @@ def T_exact(x, t):
         label="Numerical" if i == 0 else "",
     )
     plt.plot(
-        xx, T_exact(xx, t), "-", color=color, label="Exact (t={})".format(plot_times[i])
+        xx, T_exact(xx, t), "-", color=color, label=f"Exact (t={plot_times[i]})"
     )
 plt.xlabel("x", fontsize=16)
 plt.ylabel("T", fontsize=16)
diff --git a/examples/scripts/rate_capability.py b/examples/scripts/rate_capability.py
index 93d93f1cce..0ce5be4263 100644
--- a/examples/scripts/rate_capability.py
+++ b/examples/scripts/rate_capability.py
@@ -15,8 +15,8 @@
 
 for i, C_rate in enumerate(C_rates):
     experiment = pybamm.Experiment(
-        ["Discharge at {:.4f}C until 3.2V".format(C_rate)],
-        period="{:.4f} seconds".format(10 / C_rate),
+        [f"Discharge at {C_rate:.4f}C until 3.2V"],
+        period=f"{10 / C_rate:.4f} seconds",
     )
     sim = pybamm.Simulation(model, experiment=experiment, solver=pybamm.CasadiSolver())
     sim.solve()
diff --git a/pybamm/callbacks.py b/pybamm/callbacks.py
index 09329b201f..32607bb716 100644
--- a/pybamm/callbacks.py
+++ b/pybamm/callbacks.py
@@ -217,7 +217,7 @@ def on_cycle_end(self, logs):
 
     def on_experiment_end(self, logs):
         elapsed_time = logs["elapsed time"]
-        self.logger.notice("Finish experiment simulation, took {}".format(elapsed_time))
+        self.logger.notice(f"Finish experiment simulation, took {elapsed_time}")
 
     def on_experiment_error(self, logs):
         error = logs["error"]
diff --git a/pybamm/citations.py b/pybamm/citations.py
index 70bf4ba9d3..ca260c5cfd 100644
--- a/pybamm/citations.py
+++ b/pybamm/citations.py
@@ -238,8 +238,8 @@ def print(self, filename=None, output_format="text", verbose=False):
             citations = "\n".join(self._cited)
         else:
             raise pybamm.OptionError(
-                "Output format {} not recognised."
-                "It should be 'text' or 'bibtex'.".format(output_format)
+                f"Output format {output_format} not recognised."
+                "It should be 'text' or 'bibtex'."
             )
 
         if filename is None:
diff --git a/pybamm/discretisations/discretisation.py b/pybamm/discretisations/discretisation.py
index 62110b1676..7f20cee348 100644
--- a/pybamm/discretisations/discretisation.py
+++ b/pybamm/discretisations/discretisation.py
@@ -19,7 +19,7 @@ def has_bc_of_form(symbol, side, bcs, form):
         return False
 
 
-class Discretisation(object):
+class Discretisation:
     """The discretisation class, with methods to process a model and replace
     Spatial Operators with Matrices and Variables with StateVectors
 
@@ -54,9 +54,7 @@ def __init__(self, mesh=None, spatial_methods=None):
                     if not isinstance(mesh[domain], pybamm.SubMesh0D):
                         raise pybamm.DiscretisationError(
                             "Zero-dimensional spatial method for the "
-                            "{} domain requires a zero-dimensional submesh".format(
-                                domain
-                            )
+                            f"{domain} domain requires a zero-dimensional submesh"
                         )
 
         self._bcs = {}
@@ -74,7 +72,7 @@ def y_slices(self):
     @y_slices.setter
     def y_slices(self, value):
         if not isinstance(value, dict):
-            raise TypeError("""y_slices should be dict, not {}""".format(type(value)))
+            raise TypeError(f"""y_slices should be dict, not {type(value)}""")
 
         self._y_slices = value
 
@@ -144,7 +142,7 @@ def process_model(
                 "to discretise it more times (e.g. for convergence studies)."
             )
 
-        pybamm.logger.info("Start discretising {}".format(model.name))
+        pybamm.logger.info(f"Start discretising {model.name}")
 
         # Make sure model isn't empty
         if (
@@ -169,20 +167,20 @@ def process_model(
                 if var.domain != []:
                     raise pybamm.DiscretisationError(
                         "Spatial method has not been given "
-                        "for variable {} with domain {}".format(var.name, var.domain)
+                        f"for variable {var.name} with domain {var.domain}"
                     )
 
         # Set the y split for variables
-        pybamm.logger.verbose("Set variable slices for {}".format(model.name))
+        pybamm.logger.verbose(f"Set variable slices for {model.name}")
         self.set_variable_slices(variables)
 
         # set boundary conditions (only need key ids for boundary_conditions)
         pybamm.logger.verbose(
-            "Discretise boundary conditions for {}".format(model.name)
+            f"Discretise boundary conditions for {model.name}"
         )
         self._bcs = self.process_boundary_conditions(model)
         pybamm.logger.verbose(
-            "Set internal boundary conditions for {}".format(model.name)
+            f"Set internal boundary conditions for {model.name}"
         )
         self.set_internal_boundary_conditions(model)
 
@@ -202,7 +200,7 @@ def process_model(
 
         model_disc.bcs = self.bcs
 
-        pybamm.logger.verbose("Discretise initial conditions for {}".format(model.name))
+        pybamm.logger.verbose(f"Discretise initial conditions for {model.name}")
         ics, concat_ics = self.process_initial_conditions(model)
         model_disc.initial_conditions = ics
         model_disc.concatenated_initial_conditions = concat_ics
@@ -210,11 +208,11 @@ def process_model(
         # Discretise variables (applying boundary conditions)
         # Note that we **do not** discretise the keys of model.rhs,
         # model.initial_conditions and model.boundary_conditions
-        pybamm.logger.verbose("Discretise variables for {}".format(model.name))
+        pybamm.logger.verbose(f"Discretise variables for {model.name}")
         model_disc.variables = self.process_dict(model.variables)
 
         # Process parabolic and elliptic equations
-        pybamm.logger.verbose("Discretise model equations for {}".format(model.name))
+        pybamm.logger.verbose(f"Discretise model equations for {model.name}")
         rhs, concat_rhs, alg, concat_alg = self.process_rhs_and_algebraic(model)
         model_disc.rhs, model_disc.concatenated_rhs = rhs, concat_rhs
         model_disc.algebraic, model_disc.concatenated_algebraic = alg, concat_alg
@@ -226,9 +224,9 @@ def process_model(
 
         # Process events
         processed_events = []
-        pybamm.logger.verbose("Discretise events for {}".format(model.name))
+        pybamm.logger.verbose(f"Discretise events for {model.name}")
         for event in model.events:
-            pybamm.logger.debug("Discretise event '{}'".format(event.name))
+            pybamm.logger.debug(f"Discretise event '{event.name}'")
             processed_event = pybamm.Event(
                 event.name, self.process_symbol(event.expression), event.event_type
             )
@@ -236,21 +234,21 @@ def process_model(
         model_disc.events = processed_events
 
         # Create mass matrix
-        pybamm.logger.verbose("Create mass matrix for {}".format(model.name))
+        pybamm.logger.verbose(f"Create mass matrix for {model.name}")
         model_disc.mass_matrix, model_disc.mass_matrix_inv = self.create_mass_matrix(
             model_disc
         )
 
         # Save geometry
-        pybamm.logger.verbose("Save geometry for {}".format(model.name))
+        pybamm.logger.verbose(f"Save geometry for {model.name}")
         model_disc._geometry = getattr(self.mesh, "_geometry", None)
 
         # Check that resulting model makes sense
         if check_model:
-            pybamm.logger.verbose("Performing model checks for {}".format(model.name))
+            pybamm.logger.verbose(f"Performing model checks for {model.name}")
             self.check_model(model_disc)
 
-        pybamm.logger.info("Finish discretising {}".format(model.name))
+        pybamm.logger.info(f"Finish discretising {model.name}")
 
         # Record that the model has been discretised
         model_disc.is_discretised = True
@@ -354,9 +352,7 @@ def set_internal_boundary_conditions(self, model):
 
         def boundary_gradient(left_symbol, right_symbol):
             pybamm.logger.debug(
-                "Calculate boundary gradient ({} and {})".format(
-                    left_symbol, right_symbol
-                )
+                f"Calculate boundary gradient ({left_symbol} and {right_symbol})"
             )
             left_domain = left_symbol.domain[0]
             right_domain = right_symbol.domain[0]
@@ -478,7 +474,7 @@ def process_boundary_conditions(self, model):
             # Process boundary conditions
             for side, bc in bcs.items():
                 eqn, typ = bc
-                pybamm.logger.debug("Discretise {} ({} bc)".format(key, side))
+                pybamm.logger.debug(f"Discretise {key} ({side} bc)")
                 processed_eqn = self.process_symbol(eqn)
                 processed_bcs[key][side] = (processed_eqn, typ)
 
@@ -513,10 +509,8 @@ def check_tab_conditions(self, symbol, bcs):
 
         if domain != "current collector":
             raise pybamm.ModelError(
-                """Boundary conditions can only be applied on the tabs in the domain
-            'current collector', but {} has domain {}""".format(
-                    symbol, domain
-                )
+                f"""Boundary conditions can only be applied on the tabs in the domain
+            'current collector', but {symbol} has domain {domain}"""
             )
         # Replace keys with "left" and "right" as appropriate for 1D meshes
         if isinstance(mesh, pybamm.SubMesh1D):
@@ -694,7 +688,7 @@ def process_dict(self, var_eqn_dict, ics=False):
                 else:
                     eqn = pybamm.FullBroadcast(eqn, broadcast_domains=eqn_key.domains)
 
-            pybamm.logger.debug("Discretise {!r}".format(eqn_key))
+            pybamm.logger.debug(f"Discretise {eqn_key!r}")
             processed_eqn = self.process_symbol(eqn)
             # Calculate scale if the key has a scale
             scale = getattr(eqn_key, "scale", 1)
@@ -1001,7 +995,7 @@ def _concatenate_in_order(self, var_eqn_dict, check_complete=False, sparse=False
                 given_variable_names = [v.name for v in var_eqn_dict.keys()]
                 raise pybamm.ModelError(
                     "Initial conditions are insufficient. Only "
-                    "provided for {} ".format(given_variable_names)
+                    f"provided for {given_variable_names} "
                 )
 
         equations = list(var_eqn_dict.values())
@@ -1024,7 +1018,7 @@ def check_initial_conditions(self, model):
             if not isinstance(ic_eval, np.ndarray):
                 raise pybamm.ModelError(
                     "initial conditions must be numpy array after discretisation but "
-                    "they are {} for variable '{}'.".format(type(ic_eval), var)
+                    f"they are {type(ic_eval)} for variable '{var}'."
                 )
 
             # Check that the initial condition is within the bounds
@@ -1035,7 +1029,7 @@ def check_initial_conditions(self, model):
             ):
                 raise pybamm.ModelError(
                     "initial condition is outside of variable bounds "
-                    "{} for variable '{}'.".format(bounds, var)
+                    f"{bounds} for variable '{var}'."
                 )
 
         # Check initial conditions and model equations have the same shape
@@ -1135,7 +1129,7 @@ def remove_independent_variables_from_rhs(self, model):
             )
             if this_var_is_independent:
                 if len(model.rhs) != 1:
-                    pybamm.logger.info("removing variable {} from rhs".format(var))
+                    pybamm.logger.info(f"removing variable {var} from rhs")
                     my_initial_condition = model.initial_conditions[var]
                     model.variables[var.name] = pybamm.ExplicitTimeIntegral(
                         model.rhs[var], my_initial_condition
diff --git a/pybamm/experiment/experiment.py b/pybamm/experiment/experiment.py
index 898d9b0f79..ca2d266bf0 100644
--- a/pybamm/experiment/experiment.py
+++ b/pybamm/experiment/experiment.py
@@ -135,7 +135,7 @@ def copy(self):
         return Experiment(*self.args)
 
     def __repr__(self):
-        return "pybamm.Experiment({!s})".format(self)
+        return f"pybamm.Experiment({self!s})"
 
     def read_termination(self, termination):
         """
diff --git a/pybamm/expression_tree/array.py b/pybamm/expression_tree/array.py
index 92d86af46c..7694cbc170 100644
--- a/pybamm/expression_tree/array.py
+++ b/pybamm/expression_tree/array.py
@@ -49,7 +49,7 @@ def __init__(
         if entries.ndim == 1:
             entries = entries[:, np.newaxis]
         if name is None:
-            name = "Array of shape {!s}".format(entries.shape)
+            name = f"Array of shape {entries.shape!s}"
         self._entries = entries.astype(float)
         # Use known entries string to avoid re-hashing, where possible
         self.entries_string = entries_string
diff --git a/pybamm/expression_tree/averages.py b/pybamm/expression_tree/averages.py
index e063b16c2a..81834d5871 100644
--- a/pybamm/expression_tree/averages.py
+++ b/pybamm/expression_tree/averages.py
@@ -188,10 +188,8 @@ def z_average(symbol):
     # Symbol must have domain [] or ["current collector"]
     if symbol.domain not in [[], ["current collector"]]:
         raise pybamm.DomainError(
-            """z-average only implemented in the 'current collector' domain,
-            but symbol has domains {}""".format(
-                symbol.domain
-            )
+            f"""z-average only implemented in the 'current collector' domain,
+            but symbol has domains {symbol.domain}"""
         )
     # If symbol doesn't have a domain, its average value is itself
     if symbol.domain == []:
@@ -224,10 +222,8 @@ def yz_average(symbol):
     # Symbol must have domain [] or ["current collector"]
     if symbol.domain not in [[], ["current collector"]]:
         raise pybamm.DomainError(
-            """y-z-average only implemented in the 'current collector' domain,
-            but symbol has domains {}""".format(
-                symbol.domain
-            )
+            f"""y-z-average only implemented in the 'current collector' domain,
+            but symbol has domains {symbol.domain}"""
         )
     # If symbol doesn't have a domain, its average value is itself
     if symbol.domain == []:
diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index be0aa2f517..20c0fc66bd 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -94,16 +94,16 @@ def __str__(self):
             or (self.left.name == "+" and self.name == "-")
             or self.name == "+"
         ):
-            left_str = "({!s})".format(self.left)
+            left_str = f"({self.left!s})"
         else:
-            left_str = "{!s}".format(self.left)
+            left_str = f"{self.left!s}"
         if isinstance(self.right, pybamm.BinaryOperator) and not (
             (self.name == "*" and self.right.name in ["*", "/"]) or self.name == "+"
         ):
-            right_str = "({!s})".format(self.right)
+            right_str = f"({self.right!s})"
         else:
-            right_str = "{!s}".format(self.right)
-        return "{} {} {}".format(left_str, self.name, right_str)
+            right_str = f"{self.right!s}"
+        return f"{left_str} {self.name} {right_str}"
 
     def create_copy(self):
         """See :meth:`pybamm.Symbol.new_copy()`."""
@@ -337,11 +337,9 @@ def _binary_jac(self, left_jac, right_jac):
             return left @ right_jac
         else:
             raise NotImplementedError(
-                """jac of 'MatrixMultiplication' is only
+                f"""jac of 'MatrixMultiplication' is only
              implemented for left of type 'pybamm.Array',
-             not {}""".format(
-                    left.__class__
-                )
+             not {left.__class__}"""
             )
 
     def _binary_evaluate(self, left, right):
@@ -557,7 +555,7 @@ def __init__(self, left, right):
 
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
-        return "{!s} <= {!s}".format(self.left, self.right)
+        return f"{self.left!s} <= {self.right!s}"
 
     def _binary_evaluate(self, left, right):
         """See :meth:`pybamm.BinaryOperator._binary_evaluate()`."""
@@ -574,7 +572,7 @@ def __init__(self, left, right):
 
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
-        return "{!s} < {!s}".format(self.left, self.right)
+        return f"{self.left!s} < {self.right!s}"
 
     def _binary_evaluate(self, left, right):
         """See :meth:`pybamm.BinaryOperator._binary_evaluate()`."""
@@ -614,7 +612,7 @@ def _binary_jac(self, left_jac, right_jac):
 
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
-        return "{!s} mod {!s}".format(self.left, self.right)
+        return f"{self.left!s} mod {self.right!s}"
 
     def _binary_evaluate(self, left, right):
         """See :meth:`pybamm.BinaryOperator._binary_evaluate()`."""
@@ -629,7 +627,7 @@ def __init__(self, left, right):
 
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
-        return "minimum({!s}, {!s})".format(self.left, self.right)
+        return f"minimum({self.left!s}, {self.right!s})"
 
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
@@ -666,7 +664,7 @@ def __init__(self, left, right):
 
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
-        return "maximum({!s}, {!s})".format(self.left, self.right)
+        return f"maximum({self.left!s}, {self.right!s})"
 
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
@@ -1370,10 +1368,8 @@ def source(left, right, boundary=False):
 
     if left.domain != ["current collector"] or right.domain != ["current collector"]:
         raise pybamm.DomainError(
-            """'source' only implemented in the 'current collector' domain,
-            but symbols have domains {} and {}""".format(
-                left.domain, right.domain
-            )
+            f"""'source' only implemented in the 'current collector' domain,
+            but symbols have domains {left.domain} and {right.domain}"""
         )
     if boundary:
         return pybamm.BoundaryMass(right) @ left
diff --git a/pybamm/expression_tree/concatenations.py b/pybamm/expression_tree/concatenations.py
index 71d776f03e..afd9bdc1d5 100644
--- a/pybamm/expression_tree/concatenations.py
+++ b/pybamm/expression_tree/concatenations.py
@@ -58,7 +58,7 @@ def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
         out = self.name + "("
         for child in self.children:
-            out += "{!s}, ".format(child)
+            out += f"{child!s}, "
         out = out[:-2] + ")"
         return out
 
@@ -77,11 +77,11 @@ def get_children_domains(self, children):
         domain = []
         for child in children:
             if not isinstance(child, pybamm.Symbol):
-                raise TypeError("{} is not a pybamm symbol".format(child))
+                raise TypeError(f"{child} is not a pybamm symbol")
             child_domain = child.domain
             if child_domain == []:
                 raise pybamm.DomainError(
-                    "Cannot concatenate child '{}' with empty domain".format(child)
+                    f"Cannot concatenate child '{child}' with empty domain"
                 )
             if set(domain).isdisjoint(child_domain):
                 domain += child_domain
diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py
index d6767f1aa9..d8248eabe8 100644
--- a/pybamm/expression_tree/functions.py
+++ b/pybamm/expression_tree/functions.py
@@ -47,9 +47,9 @@ def __init__(
             self.name = name
         else:
             try:
-                name = "function ({})".format(function.__name__)
+                name = f"function ({function.__name__})"
             except AttributeError:
-                name = "function ({})".format(function.__class__)
+                name = f"function ({function.__class__})"
         domains = self.get_children_domains(children)
 
         self.function = function
@@ -60,9 +60,9 @@ def __init__(
 
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
-        out = "{}(".format(self.name[10:-1])
+        out = f"{self.name[10:-1]}("
         for child in self.children:
-            out += "{!s}, ".format(child)
+            out += f"{child!s}, "
         out = out[:-2] + ")"
         return out
 
diff --git a/pybamm/expression_tree/independent_variable.py b/pybamm/expression_tree/independent_variable.py
index 146751928e..ee8afac38e 100644
--- a/pybamm/expression_tree/independent_variable.py
+++ b/pybamm/expression_tree/independent_variable.py
@@ -146,7 +146,7 @@ def __init__(
             ["particle" in dom for dom in domain]
         ):
             raise pybamm.DomainError(
-                "domain cannot be particle if name is '{}'".format(name)
+                f"domain cannot be particle if name is '{name}'"
             )
 
     def create_copy(self):
diff --git a/pybamm/expression_tree/input_parameter.py b/pybamm/expression_tree/input_parameter.py
index e66a4c8cdc..2680276c60 100644
--- a/pybamm/expression_tree/input_parameter.py
+++ b/pybamm/expression_tree/input_parameter.py
@@ -91,7 +91,7 @@ def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None):
             input_eval = inputs[self.name]
         # raise more informative error if can't find name in dict
         except KeyError:
-            raise KeyError("Input parameter '{}' not found".format(self.name))
+            raise KeyError(f"Input parameter '{self.name}' not found")
 
         if isinstance(input_eval, numbers.Number):
             input_size = 1
@@ -109,9 +109,7 @@ def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None):
                 "Input parameter '{}' was given an object of size '{}'".format(
                     self.name, input_size
                 )
-                + " but was expecting an object of size '{}'.".format(
-                    self._expected_size
-                )
+                + f" but was expecting an object of size '{self._expected_size}'."
             )
 
     def to_json(self):
diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py
index 1cb5e70d05..5de21da089 100644
--- a/pybamm/expression_tree/interpolant.py
+++ b/pybamm/expression_tree/interpolant.py
@@ -59,7 +59,7 @@ def __init__(
 
         # Check interpolator is valid
         if interpolator not in ["linear", "cubic", "pchip"]:
-            raise ValueError("interpolator '{}' not recognised".format(interpolator))
+            raise ValueError(f"interpolator '{interpolator}' not recognised")
 
         # Perform some checks on the data
         if isinstance(x, (tuple, list)) and len(x) == 2:
@@ -186,7 +186,7 @@ def __init__(
                     fill_value=fill_value,
                 )
         else:
-            raise ValueError("Invalid dimension of x: {0}".format(len(x)))
+            raise ValueError(f"Invalid dimension of x: {len(x)}")
 
         # Set name
         if name is None:
@@ -309,7 +309,7 @@ def _function_evaluate(self, evaluated_children):
                 return np.reshape(res, shape)
 
         else:  # pragma: no cover
-            raise ValueError("Invalid dimension: {0}".format(self.dimension))
+            raise ValueError(f"Invalid dimension: {self.dimension}")
 
     def to_json(self):
         """
diff --git a/pybamm/expression_tree/matrix.py b/pybamm/expression_tree/matrix.py
index d491fd129d..8b36bca53e 100644
--- a/pybamm/expression_tree/matrix.py
+++ b/pybamm/expression_tree/matrix.py
@@ -24,7 +24,7 @@ def __init__(
         if isinstance(entries, list):
             entries = np.array(entries)
         if name is None:
-            name = "Matrix {!s}".format(entries.shape)
+            name = f"Matrix {entries.shape!s}"
             if issparse(entries):
                 name = "Sparse " + name
         # Convert all sparse matrices to csr
diff --git a/pybamm/expression_tree/operations/convert_to_casadi.py b/pybamm/expression_tree/operations/convert_to_casadi.py
index b3a048b1f1..6461a9267f 100644
--- a/pybamm/expression_tree/operations/convert_to_casadi.py
+++ b/pybamm/expression_tree/operations/convert_to_casadi.py
@@ -7,7 +7,7 @@
 from scipy import special
 
 
-class CasadiConverter(object):
+class CasadiConverter:
     def __init__(self, casadi_symbols=None):
         self._casadi_symbols = casadi_symbols or {}
 
@@ -144,7 +144,7 @@ def _convert(self, symbol, t, y, y_dot, inputs):
                     )
                 else:  # pragma: no cover
                     raise NotImplementedError(
-                        "Unknown interpolator: {0}".format(symbol.interpolator)
+                        f"Unknown interpolator: {symbol.interpolator}"
                     )
 
                 if len(converted_children) == 1:
@@ -159,9 +159,7 @@ def _convert(self, symbol, t, y, y_dot, inputs):
                     return res
                 else:  # pragma: no cover
                     raise ValueError(
-                        "Invalid converted_children count: {0}".format(
-                            len(converted_children)
-                        )
+                        f"Invalid converted_children count: {len(converted_children)}"
                     )
 
             elif symbol.function.__name__.startswith("elementwise_grad_of_"):
diff --git a/pybamm/expression_tree/operations/evaluate_python.py b/pybamm/expression_tree/operations/evaluate_python.py
index d0cd4c776d..f65ecc7159 100644
--- a/pybamm/expression_tree/operations/evaluate_python.py
+++ b/pybamm/expression_tree/operations/evaluate_python.py
@@ -203,62 +203,44 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False):
             dummy_eval_right = symbol.children[1].evaluate_for_shape()
             if scipy.sparse.issparse(dummy_eval_left):
                 if output_jax and is_scalar(dummy_eval_right):
-                    symbol_str = "{0}.scalar_multiply({1})".format(
-                        children_vars[0], children_vars[1]
-                    )
+                    symbol_str = f"{children_vars[0]}.scalar_multiply({children_vars[1]})"
                 else:
-                    symbol_str = "{0}.multiply({1})".format(
-                        children_vars[0], children_vars[1]
-                    )
+                    symbol_str = f"{children_vars[0]}.multiply({children_vars[1]})"
             elif scipy.sparse.issparse(dummy_eval_right):
-                symbol_str = "{1}.multiply({0})".format(
-                    children_vars[0], children_vars[1]
-                )
+                symbol_str = f"{children_vars[1]}.multiply({children_vars[0]})"
             else:
-                symbol_str = "{0} * {1}".format(children_vars[0], children_vars[1])
+                symbol_str = f"{children_vars[0]} * {children_vars[1]}"
         elif isinstance(symbol, pybamm.Division):
             dummy_eval_left = symbol.children[0].evaluate_for_shape()
             dummy_eval_right = symbol.children[1].evaluate_for_shape()
             if scipy.sparse.issparse(dummy_eval_left):
                 if output_jax and is_scalar(dummy_eval_right):
-                    symbol_str = "{0}.scalar_multiply(1/{1})".format(
-                        children_vars[0], children_vars[1]
-                    )
+                    symbol_str = f"{children_vars[0]}.scalar_multiply(1/{children_vars[1]})"
                 else:
-                    symbol_str = "{0}.multiply(1/{1})".format(
-                        children_vars[0], children_vars[1]
-                    )
+                    symbol_str = f"{children_vars[0]}.multiply(1/{children_vars[1]})"
             else:
-                symbol_str = "{0} / {1}".format(children_vars[0], children_vars[1])
+                symbol_str = f"{children_vars[0]} / {children_vars[1]}"
 
         elif isinstance(symbol, pybamm.Inner):
             dummy_eval_left = symbol.children[0].evaluate_for_shape()
             dummy_eval_right = symbol.children[1].evaluate_for_shape()
             if scipy.sparse.issparse(dummy_eval_left):
                 if output_jax and is_scalar(dummy_eval_right):
-                    symbol_str = "{0}.scalar_multiply({1})".format(
-                        children_vars[0], children_vars[1]
-                    )
+                    symbol_str = f"{children_vars[0]}.scalar_multiply({children_vars[1]})"
                 else:
-                    symbol_str = "{0}.multiply({1})".format(
-                        children_vars[0], children_vars[1]
-                    )
+                    symbol_str = f"{children_vars[0]}.multiply({children_vars[1]})"
             elif scipy.sparse.issparse(dummy_eval_right):
                 if output_jax and is_scalar(dummy_eval_left):
-                    symbol_str = "{1}.scalar_multiply({0})".format(
-                        children_vars[0], children_vars[1]
-                    )
+                    symbol_str = f"{children_vars[1]}.scalar_multiply({children_vars[0]})"
                 else:
-                    symbol_str = "{1}.multiply({0})".format(
-                        children_vars[0], children_vars[1]
-                    )
+                    symbol_str = f"{children_vars[1]}.multiply({children_vars[0]})"
             else:
-                symbol_str = "{0} * {1}".format(children_vars[0], children_vars[1])
+                symbol_str = f"{children_vars[0]} * {children_vars[1]}"
 
         elif isinstance(symbol, pybamm.Minimum):
-            symbol_str = "np.minimum({},{})".format(children_vars[0], children_vars[1])
+            symbol_str = f"np.minimum({children_vars[0]},{children_vars[1]})"
         elif isinstance(symbol, pybamm.Maximum):
-            symbol_str = "np.maximum({},{})".format(children_vars[0], children_vars[1])
+            symbol_str = f"np.maximum({children_vars[0]},{children_vars[1]})"
 
         elif isinstance(symbol, pybamm.MatrixMultiplication):
             dummy_eval_left = symbol.children[0].evaluate_for_shape()
@@ -281,9 +263,7 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False):
     elif isinstance(symbol, pybamm.UnaryOperator):
         # Index has a different syntax than other univariate operations
         if isinstance(symbol, pybamm.Index):
-            symbol_str = "{}[{}:{}]".format(
-                children_vars[0], symbol.slice.start, symbol.slice.stop
-            )
+            symbol_str = f"{children_vars[0]}[{symbol.slice.start}:{symbol.slice.stop}]"
         else:
             symbol_str = symbol.name + children_vars[0]
 
@@ -296,13 +276,13 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False):
                 children_str += ", " + child_var
         if isinstance(symbol.function, np.ufunc):
             # write any numpy functions directly
-            symbol_str = "np.{}({})".format(symbol.function.__name__, children_str)
+            symbol_str = f"np.{symbol.function.__name__}({children_str})"
         else:
             # unknown function, store it as a constant and call this in the
             # generated code
             constant_symbols[symbol.id] = symbol.function
             funct_var = id_to_python_variable(symbol.id, True)
-            symbol_str = "{}({})".format(funct_var, children_str)
+            symbol_str = f"{funct_var}({children_str})"
 
     elif isinstance(symbol, pybamm.Concatenation):
         # no need to concatenate if there is only a single child
@@ -334,9 +314,7 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False):
                     for child_dom, child_slice in slices.items():
                         slice_starts.append(symbol._slices[child_dom][i].start)
                         child_vectors.append(
-                            "{}[{}:{}]".format(
-                                child_var, child_slice[i].start, child_slice[i].stop
-                            )
+                            f"{child_var}[{child_slice[i].start}:{child_slice[i].stop}]"
                         )
                 all_child_vectors.extend(
                     [v for _, v in sorted(zip(slice_starts, child_vectors))]
@@ -353,18 +331,18 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False):
         indices = np.argwhere(symbol.evaluation_array).reshape(-1).astype(np.int32)
         consecutive = np.all(indices[1:] - indices[:-1] == 1)
         if len(indices) == 1 or consecutive:
-            symbol_str = "y[{}:{}]".format(indices[0], indices[-1] + 1)
+            symbol_str = f"y[{indices[0]}:{indices[-1] + 1}]"
         else:
             indices_array = pybamm.Array(indices)
             constant_symbols[indices_array.id] = indices
             index_name = id_to_python_variable(indices_array.id, True)
-            symbol_str = "y[{}]".format(index_name)
+            symbol_str = f"y[{index_name}]"
 
     elif isinstance(symbol, pybamm.Time):
         symbol_str = "t"
 
     elif isinstance(symbol, pybamm.InputParameter):
-        symbol_str = 'inputs["{}"]'.format(symbol.name)
+        symbol_str = f'inputs["{symbol.name}"]'
 
     else:
         raise NotImplementedError(
@@ -448,7 +426,7 @@ def __init__(self, symbol):
         # extract constants in generated function
         for i, symbol_id in enumerate(constants.keys()):
             const_name = id_to_python_variable(symbol_id, True)
-            python_str = "{} = constants[{}]\n".format(const_name, i) + python_str
+            python_str = f"{const_name} = constants[{i}]\n" + python_str
 
         # constants passed in as an ordered dict, convert to list
         self._constants = list(constants.values())
@@ -574,7 +552,7 @@ def __init__(self, symbol):
         args = "t=None, y=None, inputs=None"
         if self._arg_list:
             args = ",".join(self._arg_list) + ", " + args
-        python_str = "def evaluate_jax({}):\n".format(args) + python_str
+        python_str = f"def evaluate_jax({args}):\n" + python_str
 
         # calculate the final variable that will output the result of calling `evaluate`
         # on `symbol`
diff --git a/pybamm/expression_tree/operations/jacobian.py b/pybamm/expression_tree/operations/jacobian.py
index 56511827b0..a191e2c74d 100644
--- a/pybamm/expression_tree/operations/jacobian.py
+++ b/pybamm/expression_tree/operations/jacobian.py
@@ -4,7 +4,7 @@
 import pybamm
 
 
-class Jacobian(object):
+class Jacobian:
     """
     Helper class to calculate the Jacobian of an expression.
 
@@ -87,9 +87,7 @@ def _jac(self, symbol, variable):
                 jac = symbol._jac(variable)
             except NotImplementedError:
                 raise NotImplementedError(
-                    "Cannot calculate Jacobian of symbol of type '{}'".format(
-                        type(symbol)
-                    )
+                    f"Cannot calculate Jacobian of symbol of type '{type(symbol)}'"
                 )
 
         # Jacobian by default removes the domain(s)
diff --git a/pybamm/expression_tree/operations/serialise.py b/pybamm/expression_tree/operations/serialise.py
index c7768217a3..53505dbb1f 100644
--- a/pybamm/expression_tree/operations/serialise.py
+++ b/pybamm/expression_tree/operations/serialise.py
@@ -175,7 +175,7 @@ def load_model(
             `battery_model`.
         """
 
-        with open(filename, "r") as f:
+        with open(filename) as f:
             model_data = json.load(f)
 
         recon_model_dict = {
diff --git a/pybamm/expression_tree/operations/unpack_symbols.py b/pybamm/expression_tree/operations/unpack_symbols.py
index 96cbca39fd..825cb2db40 100644
--- a/pybamm/expression_tree/operations/unpack_symbols.py
+++ b/pybamm/expression_tree/operations/unpack_symbols.py
@@ -3,7 +3,7 @@
 #
 
 
-class SymbolUnpacker(object):
+class SymbolUnpacker:
     """
     Helper class to unpack a (set of) symbol(s) to find all instances of a class.
     Uses caching to speed up the process.
diff --git a/pybamm/expression_tree/state_vector.py b/pybamm/expression_tree/state_vector.py
index 7354f0ae3f..2f51d4bda1 100644
--- a/pybamm/expression_tree/state_vector.py
+++ b/pybamm/expression_tree/state_vector.py
@@ -47,17 +47,13 @@ def __init__(
                 raise TypeError("all y_slices must be slice objects")
         if name is None:
             if y_slices[0].start is None:
-                name = base_name + "[0:{:d}".format(y_slice.stop)
+                name = base_name + f"[0:{y_slice.stop:d}"
             else:
-                name = base_name + "[{:d}:{:d}".format(
-                    y_slices[0].start, y_slices[0].stop
-                )
+                name = base_name + f"[{y_slices[0].start:d}:{y_slices[0].stop:d}"
             if len(y_slices) > 1:
-                name += ",{:d}:{:d}".format(y_slices[1].start, y_slices[1].stop)
+                name += f",{y_slices[1].start:d}:{y_slices[1].stop:d}"
                 if len(y_slices) > 2:
-                    name += ",...,{:d}:{:d}]".format(
-                        y_slices[-1].start, y_slices[-1].stop
-                    )
+                    name += f",...,{y_slices[-1].start:d}:{y_slices[-1].stop:d}]"
                 else:
                     name += "]"
             else:
diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 2c3166582e..9d68b5f439 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -206,7 +206,7 @@ def __init__(
         auxiliary_domains=None,
         domains=None,
     ):
-        super(Symbol, self).__init__()
+        super().__init__()
         self.name = name
 
         if children is None:
@@ -466,9 +466,9 @@ def render(self):  # pragma: no cover
         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))
+                print(f"{pre}{node.name} = {node.value}")
             else:
-                print("{}{}".format(pre, node.name))
+                print(f"{pre}{node.name}")
 
     def visualise(self, filename):
         """
@@ -491,7 +491,7 @@ def visualise(self, filename):
 
         try:
             DotExporter(
-                new_node, nodeattrfunc=lambda node: 'label="{}"'.format(node.label)
+                new_node, nodeattrfunc=lambda node: f'label="{node.label}"'
             ).to_picture(filename)
         except FileNotFoundError:  # pragma: no cover
             # raise error but only through logger so that test passes
@@ -718,7 +718,7 @@ def jac(self, variable, known_jacs=None, clear_domain=True):
         if not isinstance(variable, (pybamm.StateVector, pybamm.StateVectorDot)):
             raise TypeError(
                 "Jacobian can only be taken with respect to a 'StateVector' "
-                "or 'StateVectorDot', but {} is a {}".format(variable, type(variable))
+                f"or 'StateVectorDot', but {variable} is a {type(variable)}"
             )
         return jac.jac(self, variable)
 
@@ -752,7 +752,7 @@ def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None):
         """
         raise NotImplementedError(
             "method self.evaluate() not implemented for symbol "
-            "{!s} of type {}".format(self, type(self))
+            f"{self!s} of type {type(self)}"
         )
 
     def evaluate(self, t=None, y=None, y_dot=None, inputs=None):
@@ -910,10 +910,8 @@ def create_copy(self):
         copy.deepcopy(), which is slow.
         """
         raise NotImplementedError(
-            """method self.new_copy() not implemented
-            for symbol {!s} of type {}""".format(
-                self, type(self)
-            )
+            f"""method self.new_copy() not implemented
+            for symbol {self!s} of type {type(self)}"""
         )
 
     def new_copy(self):
@@ -996,7 +994,7 @@ def test_shape(self):
         try:
             self.shape_for_testing
         except ValueError as e:
-            raise pybamm.ShapeError("Cannot find shape (original error: {})".format(e))
+            raise pybamm.ShapeError(f"Cannot find shape (original error: {e})")
 
     @property
     def print_name(self):
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index 319429183c..435bd5dce2 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -49,7 +49,7 @@ def _from_json(cls, snippet: dict):
 
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
-        return "{}({!s})".format(self.name, self.child)
+        return f"{self.name}({self.child!s})"
 
     def create_copy(self):
         """See :meth:`pybamm.Symbol.new_copy()`."""
@@ -115,7 +115,7 @@ def __init__(self, child):
 
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
-        return "{}{!s}".format(self.name, self.child)
+        return f"{self.name}{self.child!s}"
 
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
@@ -272,9 +272,9 @@ def __init__(self, child, index, name=None, check_size=True):
             self.slice = index
             if name is None:
                 if index.start is None:
-                    name = "Index[:{:d}]".format(index.stop)
+                    name = f"Index[:{index.stop:d}]"
                 else:
-                    name = "Index[{:d}:{:d}]".format(index.start, index.stop)
+                    name = f"Index[{index.start:d}:{index.stop:d}]"
         else:
             raise TypeError("index must be integer or slice")
 
@@ -416,13 +416,13 @@ class Gradient(SpatialOperator):
     def __init__(self, child):
         if child.domain == []:
             raise pybamm.DomainError(
-                "Cannot take gradient of '{}' since its domain is empty. ".format(child)
+                f"Cannot take gradient of '{child}' since its domain is empty. "
                 + "Try broadcasting the object first, e.g.\n\n"
                 "\tpybamm.grad(pybamm.PrimaryBroadcast(symbol, 'domain'))"
             )
         if child.evaluates_on_edges("primary") is True:
             raise TypeError(
-                "Cannot take gradient of '{}' since it evaluates on edges".format(child)
+                f"Cannot take gradient of '{child}' since it evaluates on edges"
             )
         super().__init__("grad", child)
 
@@ -448,15 +448,13 @@ class Divergence(SpatialOperator):
     def __init__(self, child):
         if child.domain == []:
             raise pybamm.DomainError(
-                "Cannot take divergence of '{}' since its domain is empty. ".format(
-                    child
-                )
+                f"Cannot take divergence of '{child}' since its domain is empty. "
                 + "Try broadcasting the object first, e.g.\n\n"
                 "\tpybamm.div(pybamm.PrimaryBroadcast(symbol, 'domain'))"
             )
         if child.evaluates_on_edges("primary") is False:
             raise TypeError(
-                "Cannot take divergence of '{}' since it does not ".format(child)
+                f"Cannot take divergence of '{child}' since it does not "
                 + "evaluate on edges. Usually, a gradient should be taken before the "
                 "divergence."
             )
@@ -577,9 +575,9 @@ def __init__(self, child, integration_variable):
             else:
                 raise TypeError(
                     "integration_variable must be of type pybamm.SpatialVariable, "
-                    "not {}".format(type(var))
+                    f"not {type(var)}"
                 )
-            name += " d{}".format(var.name)
+            name += f" d{var.name}"
 
         if self._integration_dimension == "primary":
             # integral of a child takes the domain from auxiliary domain of the child
@@ -613,7 +611,7 @@ def __init__(self, child, integration_variable):
                 "tertiary": child.domains["tertiary"],
             }
         if any(isinstance(var, pybamm.SpatialVariable) for var in integration_variable):
-            name += " {}".format(child.domain)
+            name += f" {child.domain}"
 
         self._integration_variable = integration_variable
         super().__init__(name, child, domains)
@@ -712,11 +710,9 @@ class IndefiniteIntegral(BaseIndefiniteIntegral):
     def __init__(self, child, integration_variable):
         super().__init__(child, integration_variable)
         # Overwrite the name
-        self.name = "{} integrated w.r.t {}".format(
-            child.name, self.integration_variable[0].name
-        )
+        self.name = f"{child.name} integrated w.r.t {self.integration_variable[0].name}"
         if isinstance(integration_variable, pybamm.SpatialVariable):
-            self.name += " on {}".format(self.integration_variable[0].domain)
+            self.name += f" on {self.integration_variable[0].domain}"
 
 
 class BackwardIndefiniteIntegral(BaseIndefiniteIntegral):
@@ -744,7 +740,7 @@ def __init__(self, child, integration_variable):
             child.name, self.integration_variable[0].name
         )
         if isinstance(integration_variable, pybamm.SpatialVariable):
-            self.name += " on {}".format(self.integration_variable[0].domain)
+            self.name += f" on {self.integration_variable[0].domain}"
 
 
 class DefiniteIntegralVector(SpatialOperator):
@@ -923,10 +919,8 @@ def __init__(self, name, child, side):
         if side in ["negative tab", "positive tab"]:
             if child.domain[0] != "current collector":
                 raise pybamm.ModelError(
-                    """Can only take boundary value on the tabs in the domain
-                'current collector', but {} has domain {}""".format(
-                        child, child.domain[0]
-                    )
+                    f"""Can only take boundary value on the tabs in the domain
+                'current collector', but {child} has domain {child.domain[0]}"""
                 )
         self.side = side
         # boundary value of a child takes the primary domain from secondary domain
@@ -1115,13 +1109,13 @@ class UpwindDownwind(SpatialOperator):
     def __init__(self, name, child):
         if child.domain == []:
             raise pybamm.DomainError(
-                "Cannot upwind '{}' since its domain is empty. ".format(child)
+                f"Cannot upwind '{child}' since its domain is empty. "
                 + "Try broadcasting the object first, e.g.\n\n"
                 "\tpybamm.div(pybamm.PrimaryBroadcast(symbol, 'domain'))"
             )
         if child.evaluates_on_edges("primary") is True:
             raise TypeError(
-                "Cannot upwind '{}' since it does not ".format(child)
+                f"Cannot upwind '{child}' since it does not "
                 + "evaluate on nodes."
             )
         super().__init__(name, child)
diff --git a/pybamm/expression_tree/vector.py b/pybamm/expression_tree/vector.py
index 758b988ca7..66fe7d8c12 100644
--- a/pybamm/expression_tree/vector.py
+++ b/pybamm/expression_tree/vector.py
@@ -34,7 +34,7 @@ def __init__(
                 )
             )
         if name is None:
-            name = "Column vector of length {!s}".format(entries.shape[0])
+            name = f"Column vector of length {entries.shape[0]!s}"
 
         super().__init__(
             entries, name, domain, auxiliary_domains, domains, entries_string
diff --git a/pybamm/geometry/battery_geometry.py b/pybamm/geometry/battery_geometry.py
index 0dfe3fd256..e15c358128 100644
--- a/pybamm/geometry/battery_geometry.py
+++ b/pybamm/geometry/battery_geometry.py
@@ -140,9 +140,7 @@ def battery_geometry(
             )
     else:
         raise pybamm.GeometryError(
-            "Invalid form factor '{}' (should be 'pouch' or 'cylindrical'".format(
-                form_factor
-            )
+            f"Invalid form factor '{form_factor}' (should be 'pouch' or 'cylindrical'"
         )
 
     return pybamm.Geometry(geometry)
diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 0fbbcdc637..a51c9eea76 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -66,7 +66,7 @@ def install_sundials(download_dir, install_dir):
 
     print("-" * 10, "Running CMake prepare", "-" * 40)
     subprocess.run(
-        ["cmake", "../sundials-{}".format(sundials_version), *cmake_args],
+        ["cmake", f"../sundials-{sundials_version}", *cmake_args],
         cwd=build_directory,
         check=True,
     )
@@ -81,9 +81,7 @@ def update_LD_LIBRARY_PATH(install_dir):
     # for LD_LIBRARY_PATH in activate script.  If no virtual env found,
     # then the current user's .bashrc file is modified instead.
 
-    export_statement = "export LD_LIBRARY_PATH={}/lib:$LD_LIBRARY_PATH".format(
-        install_dir
-    )
+    export_statement = f"export LD_LIBRARY_PATH={install_dir}/lib:$LD_LIBRARY_PATH"
 
     venv_path = os.environ.get("VIRTUAL_ENV")
     if venv_path:
@@ -91,10 +89,10 @@ def update_LD_LIBRARY_PATH(install_dir):
     else:
         script_path = os.path.join(os.environ.get("HOME"), ".bashrc")
 
-    if os.getenv("LD_LIBRARY_PATH") and "{}/lib".format(install_dir) in os.getenv(
+    if os.getenv("LD_LIBRARY_PATH") and f"{install_dir}/lib" in os.getenv(
         "LD_LIBRARY_PATH"
     ):
-        print("{}/lib was found in LD_LIBRARY_PATH.".format(install_dir))
+        print(f"{install_dir}/lib was found in LD_LIBRARY_PATH.")
         print("--> Not updating venv activate or .bashrc scripts")
     else:
         with open(script_path, "a+") as fh:
@@ -102,8 +100,8 @@ def update_LD_LIBRARY_PATH(install_dir):
             if export_statement not in fh.read():
                 fh.write(export_statement)
                 print(
-                    "Adding {}/lib to LD_LIBRARY_PATH"
-                    " in {}".format(install_dir, script_path)
+                    f"Adding {install_dir}/lib to LD_LIBRARY_PATH"
+                    f" in {script_path}"
                 )
 
 
@@ -146,18 +144,18 @@ def main(arguments=None):
     if args.sundials_libs:
         SUNDIALS_LIB_DIRS.insert(0, args.sundials_libs)
     for DIR in SUNDIALS_LIB_DIRS:
-        logger.info("Looking for sundials at {}".format(DIR))
+        logger.info(f"Looking for sundials at {DIR}")
         SUNDIALS_FOUND = isfile(join(DIR, "lib", "libsundials_ida.so")) or isfile(
             join(DIR, "lib", "libsundials_ida.dylib")
         )
         if SUNDIALS_FOUND:
             SUNDIALS_LIB_DIR = DIR
-            logger.info("Found sundials at {}".format(SUNDIALS_LIB_DIR))
+            logger.info(f"Found sundials at {SUNDIALS_LIB_DIR}")
             break
 
     if not SUNDIALS_FOUND:
         logger.info("Could not find sundials libraries.")
-        logger.info("Installing sundials in {}".format(install_dir))
+        logger.info(f"Installing sundials in {install_dir}")
         download_dir = os.path.join(pybamm_dir, "sundials")
         if not os.path.exists(download_dir):
             os.makedirs(download_dir)
diff --git a/pybamm/meshes/meshes.py b/pybamm/meshes/meshes.py
index 182282319f..7fdcd0eede 100644
--- a/pybamm/meshes/meshes.py
+++ b/pybamm/meshes/meshes.py
@@ -74,9 +74,7 @@ def __init__(self, geometry, submesh_types, var_pts):
                             and var.domain[0] in geometry.keys()
                         ):
                             raise KeyError(
-                                "Points not given for a variable in domain '{}'".format(
-                                    domain
-                                )
+                                f"Points not given for a variable in domain '{domain}'"
                             )
                         # Otherwise add to the dictionary of submesh points
                         submesh_pts[domain][var.name] = var_name_pts[var.name]
@@ -272,4 +270,4 @@ def __call__(self, lims, npts):
         return self.submesh_type(lims, npts, **self.submesh_params)
 
     def __repr__(self):
-        return "Generator for {}".format(self.submesh_type.__name__)
+        return f"Generator for {self.submesh_type.__name__}"
diff --git a/pybamm/meshes/scikit_fem_submeshes.py b/pybamm/meshes/scikit_fem_submeshes.py
index 8f80d6f5ce..82a7bd72f1 100644
--- a/pybamm/meshes/scikit_fem_submeshes.py
+++ b/pybamm/meshes/scikit_fem_submeshes.py
@@ -79,7 +79,7 @@ def read_lims(self, lims):
         # check that two variables have been passed in
         if len(lims) != 2:
             raise pybamm.GeometryError(
-                "lims should contain exactly two variables, not {}".format(len(lims))
+                f"lims should contain exactly two variables, not {len(lims)}"
             )
 
         # get spatial variables
@@ -181,7 +181,7 @@ def __init__(self, lims, npts):
         for var in spatial_vars:
             if var.name not in ["y", "z"]:
                 raise pybamm.DomainError(
-                    "spatial variable must be y or z not {}".format(var.name)
+                    f"spatial variable must be y or z not {var.name}"
                 )
             else:
                 edges[var.name] = np.linspace(
@@ -240,7 +240,7 @@ def __init__(self, lims, npts, side="top", stretch=2.3):
         # check side is top
         if side != "top":
             raise pybamm.GeometryError(
-                "At present, side can only be 'top', but is set to {}".format(side)
+                f"At present, side can only be 'top', but is set to {side}"
             )
 
         spatial_vars, tabs = self.read_lims(lims)
@@ -251,7 +251,7 @@ def __init__(self, lims, npts, side="top", stretch=2.3):
         for var in spatial_vars:
             if var.name not in ["y", "z"]:
                 raise pybamm.DomainError(
-                    "spatial variable must be y or z not {}".format(var.name)
+                    f"spatial variable must be y or z not {var.name}"
                 )
             elif var.name == "y":
                 edges[var.name] = np.linspace(
@@ -305,7 +305,7 @@ def __init__(self, lims, npts):
         for var in spatial_vars:
             if var.name not in ["y", "z"]:
                 raise pybamm.DomainError(
-                    "spatial variable must be y or z not {}".format(var.name)
+                    f"spatial variable must be y or z not {var.name}"
                 )
             else:
                 # Create N Chebyshev nodes in the interval (a,b)
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 257bc30ef8..8e4c80a625 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -550,9 +550,7 @@ def build_coupled_variables(self):
                         else:
                             # try setting coupled variables on next loop through
                             pybamm.logger.debug(
-                                "Can't find {}, trying other submodels first".format(
-                                    key
-                                )
+                                f"Can't find {key}, trying other submodels first"
                             )
         # Convert variables back into FuzzyDict
         self.variables = pybamm.FuzzyDict(self._variables)
@@ -561,14 +559,12 @@ def build_model_equations(self):
         # Set model equations
         for submodel_name, submodel in self.submodels.items():
             pybamm.logger.verbose(
-                "Setting rhs for {} submodel ({})".format(submodel_name, self.name)
+                f"Setting rhs for {submodel_name} submodel ({self.name})"
             )
 
             submodel.set_rhs(self.variables)
             pybamm.logger.verbose(
-                "Setting algebraic for {} submodel ({})".format(
-                    submodel_name, self.name
-                )
+                f"Setting algebraic for {submodel_name} submodel ({self.name})"
             )
 
             submodel.set_algebraic(self.variables)
@@ -580,14 +576,12 @@ def build_model_equations(self):
 
             submodel.set_boundary_conditions(self.variables)
             pybamm.logger.verbose(
-                "Setting initial conditions for {} submodel ({})".format(
-                    submodel_name, self.name
-                )
+                f"Setting initial conditions for {submodel_name} submodel ({self.name})"
             )
             submodel.set_initial_conditions(self.variables)
             submodel.set_events(self.variables)
             pybamm.logger.verbose(
-                "Updating {} submodel ({})".format(submodel_name, self.name)
+                f"Updating {submodel_name} submodel ({self.name})"
             )
             self.update(submodel)
             self.check_no_repeated_keys()
@@ -595,7 +589,7 @@ def build_model_equations(self):
     def build_model(self):
         self._build_model()
         self._built = True
-        pybamm.logger.info("Finish building {}".format(self.name))
+        pybamm.logger.info(f"Finish building {self.name}")
 
     def _build_model(self):
         # Check if already built
@@ -605,7 +599,7 @@ def _build_model(self):
                 `model.update` instead."""
             )
 
-        pybamm.logger.info("Start building {}".format(self.name))
+        pybamm.logger.info(f"Start building {self.name}")
 
         if self._built_fundamental is False:
             self.build_fundamental()
@@ -740,7 +734,7 @@ def check_and_combine_dict(self, dict1, dict2):
         if len(ids1.intersection(ids2)) != 0:
             variables = ids1.intersection(ids2)
             raise pybamm.ModelError(
-                "Submodel incompatible: duplicate variables '{}'".format(variables)
+                f"Submodel incompatible: duplicate variables '{variables}'"
             )
         dict1.update(dict2)
 
@@ -778,12 +772,12 @@ def check_for_time_derivatives(self):
                 if isinstance(node, pybamm.VariableDot):
                     raise pybamm.ModelError(
                         "time derivative of variable found "
-                        "({}) in rhs equation {}".format(node, key)
+                        f"({node}) in rhs equation {key}"
                     )
                 if isinstance(node, pybamm.StateVectorDot):
                     raise pybamm.ModelError(
                         "time derivative of state vector found "
-                        "({}) in rhs equation {}".format(node, key)
+                        f"({node}) in rhs equation {key}"
                     )
 
         # Check that no variable time derivatives exist in the algebraic equations
@@ -791,13 +785,13 @@ def check_for_time_derivatives(self):
             for node in eq.pre_order():
                 if isinstance(node, pybamm.VariableDot):
                     raise pybamm.ModelError(
-                        "time derivative of variable found ({}) in algebraic"
-                        "equation {}".format(node, key)
+                        f"time derivative of variable found ({node}) in algebraic"
+                        f"equation {key}"
                     )
                 if isinstance(node, pybamm.StateVectorDot):
                     raise pybamm.ModelError(
-                        "time derivative of state vector found ({}) in algebraic"
-                        "equation {}".format(node, key)
+                        f"time derivative of state vector found ({node}) in algebraic"
+                        f"equation {key}"
                     )
 
     def check_well_determined(self, post_discretisation):
@@ -887,7 +881,7 @@ def check_ics_bcs(self):
         for var in self.rhs.keys():
             if var not in self.initial_conditions.keys():
                 raise pybamm.ModelError(
-                    """no initial condition given for variable '{}'""".format(var)
+                    f"""no initial condition given for variable '{var}'"""
                 )
 
     def check_variables(self):
@@ -909,13 +903,11 @@ def check_variables(self):
         for var in all_vars:
             if var not in vars_in_keys:
                 raise pybamm.ModelError(
-                    """
-                    No key set for variable '{}'. Make sure it is included in either
+                    f"""
+                    No key set for variable '{var}'. Make sure it is included in either
                     model.rhs or model.algebraic, in an unmodified form
                     (e.g. not Broadcasted)
-                    """.format(
-                        var
-                    )
+                    """
                 )
 
     def check_no_repeated_keys(self):
@@ -970,7 +962,7 @@ def check_discretised_or_discretise_inplace_if_0D(self):
             except pybamm.DiscretisationError as e:
                 raise pybamm.DiscretisationError(
                     "Cannot automatically discretise model, model should be "
-                    "discretised before exporting casadi functions ({})".format(e)
+                    f"discretised before exporting casadi functions ({e})"
                 )
 
     def export_casadi_objects(self, variable_names, input_parameter_order=None):
@@ -1287,9 +1279,7 @@ def check_and_convert_equations(self, equations):
                 equations[var] = eqn
             if not (var.domain == eqn.domain or var.domain == [] or eqn.domain == []):
                 raise pybamm.DomainError(
-                    "variable and equation in '{}' must have the same domain".format(
-                        self.name
-                    )
+                    f"variable and equation in '{self.name}' must have the same domain"
                 )
 
         # For initial conditions, check that the equation doesn't contain any
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index b174ef581c..94ea006aa4 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -631,7 +631,7 @@ def __init__(self, extra_options):
                 value = (value,)
             else:
                 if not (
-                    (
+
                         option
                         in [
                             "diffusivity",
@@ -652,7 +652,7 @@ def __init__(self, extra_options):
                         ]
                         and isinstance(value, tuple)
                         and len(value) == 2
-                    )
+
                 ):
                     # more possible options that can take 2-tuples to be added
                     # as they come
@@ -1021,10 +1021,8 @@ def options(self, extra_options):
             and options["hydrolysis"] == "true"
         ):
             raise pybamm.OptionError(
-                """must use surface formulation to solve {!s} with hydrolysis
-                    """.format(
-                    self
-                )
+                f"""must use surface formulation to solve {self!s} with hydrolysis
+                    """
             )
 
         self._options = options
@@ -1053,14 +1051,12 @@ def build_model_equations(self):
         # Set model equations
         for submodel_name, submodel in self.submodels.items():
             pybamm.logger.verbose(
-                "Setting rhs for {} submodel ({})".format(submodel_name, self.name)
+                f"Setting rhs for {submodel_name} submodel ({self.name})"
             )
 
             submodel.set_rhs(self.variables)
             pybamm.logger.verbose(
-                "Setting algebraic for {} submodel ({})".format(
-                    submodel_name, self.name
-                )
+                f"Setting algebraic for {submodel_name} submodel ({self.name})"
             )
 
             submodel.set_algebraic(self.variables)
@@ -1072,14 +1068,12 @@ def build_model_equations(self):
 
             submodel.set_boundary_conditions(self.variables)
             pybamm.logger.verbose(
-                "Setting initial conditions for {} submodel ({})".format(
-                    submodel_name, self.name
-                )
+                f"Setting initial conditions for {submodel_name} submodel ({self.name})"
             )
             submodel.set_initial_conditions(self.variables)
             submodel.set_events(self.variables)
             pybamm.logger.verbose(
-                "Updating {} submodel ({})".format(submodel_name, self.name)
+                f"Updating {submodel_name} submodel ({self.name})"
             )
             self.update(submodel)
             self.check_no_repeated_keys()
@@ -1089,18 +1083,18 @@ def build_model(self):
         self._build_model()
 
         # Set battery specific variables
-        pybamm.logger.debug("Setting voltage variables ({})".format(self.name))
+        pybamm.logger.debug(f"Setting voltage variables ({self.name})")
         self.set_voltage_variables()
 
-        pybamm.logger.debug("Setting SoC variables ({})".format(self.name))
+        pybamm.logger.debug(f"Setting SoC variables ({self.name})")
         self.set_soc_variables()
 
-        pybamm.logger.debug("Setting degradation variables ({})".format(self.name))
+        pybamm.logger.debug(f"Setting degradation variables ({self.name})")
         self.set_degradation_variables()
         self.set_summary_variables()
 
         self._built = True
-        pybamm.logger.info("Finish building {}".format(self.name))
+        pybamm.logger.info(f"Finish building {self.name}")
 
     @property
     def summary_variables(self):
diff --git a/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py b/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py
index 407039b6f6..9d01f89ffd 100644
--- a/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py
+++ b/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py
@@ -207,7 +207,7 @@ def build_model(self):
         self._build_model()
 
         self._built = True
-        pybamm.logger.info("Finished building {}".format(self.name))
+        pybamm.logger.info(f"Finished building {self.name}")
 
     @property
     def default_parameter_values(self):
diff --git a/pybamm/models/submodels/base_submodel.py b/pybamm/models/submodels/base_submodel.py
index ab095b9be2..225ae83705 100644
--- a/pybamm/models/submodels/base_submodel.py
+++ b/pybamm/models/submodels/base_submodel.py
@@ -128,9 +128,7 @@ def domain(self, domain):
                 self._Domain = domain.capitalize()
         else:
             raise pybamm.DomainError(
-                "Domain '{}' not recognised (must be one of {})".format(
-                    domain, ok_domain_list
-                )
+                f"Domain '{domain}' not recognised (must be one of {ok_domain_list})"
             )
 
     @property
diff --git a/pybamm/parameters/parameter_sets.py b/pybamm/parameters/parameter_sets.py
index ea45f2df5c..20c20de091 100644
--- a/pybamm/parameters/parameter_sets.py
+++ b/pybamm/parameters/parameter_sets.py
@@ -50,7 +50,7 @@ def get_entries(group_name):
     def __new__(cls):
         """Ensure only one instance of ParameterSets exists"""
         if not hasattr(cls, "instance"):
-            cls.instance = super(ParameterSets, cls).__new__(cls)
+            cls.instance = super().__new__(cls)
         return cls.instance
 
     def __getitem__(self, key) -> dict:
diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index a5ed9b66fb..be842a7bca 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -222,9 +222,7 @@ def update(self, values, check_conflict=False, check_already_exists=True, path="
                 and not (self[name] == float(value) or self[name] == value)
             ):
                 raise ValueError(
-                    "parameter '{}' already defined with value '{}'".format(
-                        name, self[name]
-                    )
+                    f"parameter '{name}' already defined with value '{self[name]}'"
                 )
             # check parameter already exists (for updating parameters)
             if check_already_exists is True:
@@ -232,8 +230,8 @@ def update(self, values, check_conflict=False, check_already_exists=True, path="
                     self._dict_items[name]
                 except KeyError as err:
                     raise KeyError(
-                        "Cannot update parameter '{}' as it does not ".format(name)
-                        + "have a default value. ({}). If you are ".format(err.args[0])
+                        f"Cannot update parameter '{name}' as it does not "
+                        + f"have a default value. ({err.args[0]}). If you are "
                         + "sure you want to update this parameter, use "
                         + "param.update({{name: value}}, check_already_exists=False)"
                     )
@@ -395,7 +393,7 @@ def process_model(self, unprocessed_model, inplace=True):
 
         """
         pybamm.logger.info(
-            "Start setting parameters for {}".format(unprocessed_model.name)
+            f"Start setting parameters for {unprocessed_model.name}"
         )
 
         # set up inplace vs not inplace
@@ -417,7 +415,7 @@ def process_model(self, unprocessed_model, inplace=True):
         new_rhs = {}
         for variable, equation in unprocessed_model.rhs.items():
             pybamm.logger.verbose(
-                "Processing parameters for {!r} (rhs)".format(variable)
+                f"Processing parameters for {variable!r} (rhs)"
             )
             new_variable = self.process_symbol(variable)
             new_rhs[new_variable] = self.process_symbol(equation)
@@ -426,7 +424,7 @@ def process_model(self, unprocessed_model, inplace=True):
         new_algebraic = {}
         for variable, equation in unprocessed_model.algebraic.items():
             pybamm.logger.verbose(
-                "Processing parameters for {!r} (algebraic)".format(variable)
+                f"Processing parameters for {variable!r} (algebraic)"
             )
             new_variable = self.process_symbol(variable)
             new_algebraic[new_variable] = self.process_symbol(equation)
@@ -435,7 +433,7 @@ def process_model(self, unprocessed_model, inplace=True):
         new_initial_conditions = {}
         for variable, equation in unprocessed_model.initial_conditions.items():
             pybamm.logger.verbose(
-                "Processing parameters for {!r} (initial conditions)".format(variable)
+                f"Processing parameters for {variable!r} (initial conditions)"
             )
             new_variable = self.process_symbol(variable)
             new_initial_conditions[new_variable] = self.process_symbol(equation)
@@ -446,7 +444,7 @@ def process_model(self, unprocessed_model, inplace=True):
         new_variables = {}
         for variable, equation in unprocessed_model.variables.items():
             pybamm.logger.verbose(
-                "Processing parameters for {!r} (variables)".format(variable)
+                f"Processing parameters for {variable!r} (variables)"
             )
             new_variables[variable] = self.process_symbol(equation)
         model.variables = new_variables
@@ -454,7 +452,7 @@ def process_model(self, unprocessed_model, inplace=True):
         new_events = []
         for event in unprocessed_model.events:
             pybamm.logger.verbose(
-                "Processing parameters for event '{}''".format(event.name)
+                f"Processing parameters for event '{event.name}''"
             )
             new_events.append(
                 pybamm.Event(
@@ -465,7 +463,7 @@ def process_model(self, unprocessed_model, inplace=True):
         interpolant_events = self._get_interpolant_events(model)
         for event in interpolant_events:
             pybamm.logger.verbose(
-                "Processing parameters for event '{}''".format(event.name)
+                f"Processing parameters for event '{event.name}''"
             )
             new_events.append(
                 pybamm.Event(
@@ -475,7 +473,7 @@ def process_model(self, unprocessed_model, inplace=True):
 
         model.events = new_events
 
-        pybamm.logger.info("Finish setting parameters for {}".format(model.name))
+        pybamm.logger.info(f"Finish setting parameters for {model.name}")
 
         return model
 
@@ -523,7 +521,7 @@ def process_boundary_conditions(self, model):
                 try:
                     bc, typ = bcs[side]
                     pybamm.logger.verbose(
-                        "Processing parameters for {!r} ({} bc)".format(variable, side)
+                        f"Processing parameters for {variable!r} ({side} bc)"
                     )
                     processed_bc = (self.process_symbol(bc), typ)
                     new_boundary_conditions[processed_variable][side] = processed_bc
@@ -608,7 +606,7 @@ def _process_symbol(self, symbol):
                 new_value.copy_domains(symbol)
                 return new_value
             else:
-                raise TypeError("Cannot process parameter '{}'".format(value))
+                raise TypeError(f"Cannot process parameter '{value}'")
 
         elif isinstance(symbol, pybamm.FunctionParameter):
             function_name = self[symbol.name]
@@ -658,7 +656,7 @@ def _process_symbol(self, symbol):
 
                 else:  # pragma: no cover
                     raise ValueError(
-                        "Invalid function name length: {0}".format(len(function_name))
+                        f"Invalid function name length: {len(function_name)}"
                     )
 
             elif isinstance(function_name, numbers.Number):
@@ -682,7 +680,7 @@ def _process_symbol(self, symbol):
                 function = function_name
             else:
                 raise TypeError(
-                    "Parameter provided for '{}' ".format(symbol.name)
+                    f"Parameter provided for '{symbol.name}' "
                     + "is of the wrong type (should either be scalar-like or callable)"
                 )
             # Differentiate if necessary
@@ -897,7 +895,7 @@ def print_evaluated_parameters(self, evaluated_parameters, output_file):
         """
         # Get column width for pretty printing
         column_width = max(len(name) for name in evaluated_parameters.keys())
-        s = "{{:>{}}}".format(column_width)
+        s = f"{{:>{column_width}}}"
         with open(output_file, "w") as file:
             for name, value in sorted(evaluated_parameters.items()):
                 if 0.001 < abs(value) < 1000:
diff --git a/pybamm/parameters/process_parameter_data.py b/pybamm/parameters/process_parameter_data.py
index 8de8f32ba8..8998c6e583 100644
--- a/pybamm/parameters/process_parameter_data.py
+++ b/pybamm/parameters/process_parameter_data.py
@@ -49,7 +49,7 @@ def process_2D_data(name, path=None):
     """
     filename, name = _process_name(name, path, ".json")
 
-    with open(filename, "r") as jsonfile:
+    with open(filename) as jsonfile:
         json_data = json.load(jsonfile)
     data = json_data["data"]
     data[0] = [np.array(el) for el in data[0]]
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index 686c58f3c5..9b082fd6d4 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -52,7 +52,7 @@ def close_plots():
     plt.close("all")
 
 
-class QuickPlot(object):
+class QuickPlot:
     """
     Generates a quick plot of a subset of key outputs of the model so that the model
     outputs can be easily assessed.
@@ -165,7 +165,7 @@ def __init__(
             self.spatial_factor = 1e6
             self.spatial_unit = "$\mu$m"
         else:
-            raise ValueError("spatial unit '{}' not recognized".format(spatial_unit))
+            raise ValueError(f"spatial unit '{spatial_unit}' not recognized")
 
         # Time parameters
         self.ts_seconds = [solution.t for solution in solutions]
@@ -191,7 +191,7 @@ def __init__(
             time_scaling_factor = 3600
             self.time_unit = "h"
         else:
-            raise ValueError("time unit '{}' not recognized".format(time_unit))
+            raise ValueError(f"time unit '{time_unit}' not recognized")
         self.time_scaling_factor = time_scaling_factor
         self.min_t = min_t / time_scaling_factor
         self.max_t = max_t / time_scaling_factor
@@ -283,7 +283,7 @@ def set_output_variables(self, output_variables, solutions):
                     sol = solution[var]
                     # Check variable isn't all-nan
                     if np.all(np.isnan(sol.entries)):
-                        raise ValueError("All-NaN variable '{}' provided".format(var))
+                        raise ValueError(f"All-NaN variable '{var}' provided")
                     # If ok, add to the list of solutions
                     else:
                         variables[i].append(sol)
@@ -324,7 +324,7 @@ def set_output_variables(self, output_variables, solutions):
                 if len(variables) > 1:
                     raise NotImplementedError(
                         "Cannot plot 2D variables when comparing multiple solutions, "
-                        "but '{}' is 2D".format(variable_tuple[0])
+                        f"but '{variable_tuple[0]}' is 2D"
                     )
                 # But do allow if just a single solution
                 else:
@@ -387,7 +387,7 @@ def get_spatial_var(self, key, variable, dimension):
                 domain = variable.domains["secondary"][0]
 
         if domain == "current collector":
-            domain += " {}".format(spatial_var_name)
+            domain += f" {spatial_var_name}"
 
         return spatial_var_name, spatial_var_value
 
@@ -504,7 +504,7 @@ def plot(self, t, dynamic=False):
             # Set labels for the first subplot only (avoid repetition)
             if variable_lists[0][0].dimensions == 0:
                 # 0D plot: plot as a function of time, indicating time t with a line
-                ax.set_xlabel("Time [{}]".format(self.time_unit))
+                ax.set_xlabel(f"Time [{self.time_unit}]")
                 for i, variable_list in enumerate(variable_lists):
                     for j, variable in enumerate(variable_list):
                         if len(variable_list) == 1:
@@ -540,7 +540,7 @@ def plot(self, t, dynamic=False):
                 spatial_vars = self.spatial_variable_dict[key]
                 spatial_var_name = next(iter(spatial_vars.keys()))
                 ax.set_xlabel(
-                    "{} [{}]".format(spatial_var_name, self.spatial_unit),
+                    f"{spatial_var_name} [{self.spatial_unit}]",
                 )
                 for i, variable_list in enumerate(variable_lists):
                     for j, variable in enumerate(variable_list):
@@ -582,8 +582,8 @@ def plot(self, t, dynamic=False):
                     x = self.first_spatial_variable[key]
                     y = self.second_spatial_variable[key]
                     var = variable(t_in_seconds, **spatial_vars, warn=False).T
-                ax.set_xlabel("{} [{}]".format(x_name, self.spatial_unit))
-                ax.set_ylabel("{} [{}]".format(y_name, self.spatial_unit))
+                ax.set_xlabel(f"{x_name} [{self.spatial_unit}]")
+                ax.set_ylabel(f"{y_name} [{self.spatial_unit}]")
                 vmin, vmax = self.variable_limits[key]
                 # store the plot and the var data (for testing) as cant access
                 # z data from QuadMesh or QuadContourSet object
@@ -684,7 +684,7 @@ def dynamic_plot(self, testing=False, step=None):
             ax_slider = plt.axes([0.315, 0.02, 0.37, 0.03], facecolor=axcolor)
             self.slider = Slider(
                 ax_slider,
-                "Time [{}]".format(self.time_unit),
+                f"Time [{self.time_unit}]",
                 self.min_t,
                 self.max_t,
                 valinit=self.min_t,
diff --git a/pybamm/settings.py b/pybamm/settings.py
index bdc9c1a137..591d9fd101 100644
--- a/pybamm/settings.py
+++ b/pybamm/settings.py
@@ -3,7 +3,7 @@
 #
 
 
-class Settings(object):
+class Settings:
     _debug_mode = False
     _simplify = True
     _min_smoothing = "exact"
diff --git a/pybamm/solvers/algebraic_solver.py b/pybamm/solvers/algebraic_solver.py
index 2a364907f7..d241d5b24c 100644
--- a/pybamm/solvers/algebraic_solver.py
+++ b/pybamm/solvers/algebraic_solver.py
@@ -34,7 +34,7 @@ def __init__(self, method="lm", tol=1e-6, extra_options=None):
         super().__init__(method=method)
         self.tol = tol
         self.extra_options = extra_options or {}
-        self.name = "Algebraic solver ({})".format(method)
+        self.name = f"Algebraic solver ({method})"
         self.algebraic_solver = True
         pybamm.citations.register("Virtanen2020")
 
@@ -215,7 +215,7 @@ def jac_norm(y):
                     success = True
                 elif not sol.success:
                     raise pybamm.SolverError(
-                        "Could not find acceptable solution: {}".format(sol.message)
+                        f"Could not find acceptable solution: {sol.message}"
                     )
                 else:
                     y0_alg = sol.x
@@ -223,9 +223,7 @@ def jac_norm(y):
                         raise pybamm.SolverError(
                             "Could not find acceptable solution: solver terminated "
                             "successfully, but maximum solution error "
-                            "({}) above tolerance ({})".format(
-                                np.max(abs(sol.fun)), self.tol
-                            )
+                            f"({np.max(abs(sol.fun))}) above tolerance ({self.tol})"
                         )
                 itr += 1
 
diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py
index dbc2bfe875..36f101b1d0 100644
--- a/pybamm/solvers/base_solver.py
+++ b/pybamm/solvers/base_solver.py
@@ -16,7 +16,7 @@
 from pybamm.expression_tree.binary_operators import _Heaviside
 
 
-class BaseSolver(object):
+class BaseSolver:
     """Solve a discretised model.
 
     Parameters
@@ -314,7 +314,7 @@ def _check_and_prepare_model_inplace(self, model, inputs, ics_only):
         # Check model.algebraic for ode solvers
         if self.ode_solver is True and len(model.algebraic) > 0:
             raise pybamm.SolverError(
-                "Cannot use ODE solver '{}' to solve DAE model".format(self.name)
+                f"Cannot use ODE solver '{self.name}' to solve DAE model"
             )
         # Check model.rhs for algebraic solvers
         if self.algebraic_solver is True and len(model.rhs) > 0:
@@ -338,16 +338,14 @@ def _check_and_prepare_model_inplace(self, model, inputs, ics_only):
             except pybamm.DiscretisationError as e:
                 raise pybamm.DiscretisationError(
                     "Cannot automatically discretise model, "
-                    "model should be discretised before solving ({})".format(e)
+                    f"model should be discretised before solving ({e})"
                 )
 
         if (
             isinstance(self, (pybamm.CasadiSolver, pybamm.CasadiAlgebraicSolver))
         ) and model.convert_to_format != "casadi":
             pybamm.logger.warning(
-                "Converting {} to CasADi for solving with CasADi solver".format(
-                    model.name
-                )
+                f"Converting {model.name} to CasADi for solving with CasADi solver"
             )
             model.convert_to_format = "casadi"
         if (
@@ -355,9 +353,7 @@ def _check_and_prepare_model_inplace(self, model, inputs, ics_only):
             and model.convert_to_format != "casadi"
         ):
             pybamm.logger.warning(
-                "Converting {} to CasADi for calculating ICs with CasADi".format(
-                    model.name
-                )
+                f"Converting {model.name} to CasADi for calculating ICs with CasADi"
             )
             model.convert_to_format = "casadi"
 
@@ -689,7 +685,7 @@ def calculate_consistent_state(self, model, time=0, inputs=None):
             root_sol = self.root_method._integrate(model, np.array([time]), inputs)
         except pybamm.SolverError as e:
             raise pybamm.SolverError(
-                "Could not find consistent states: {}".format(e.args[0])
+                f"Could not find consistent states: {e.args[0]}"
             )
         pybamm.logger.debug("Found consistent states")
 
@@ -748,7 +744,7 @@ def solve(
             If multiple calls to `solve` pass in different models
 
         """
-        pybamm.logger.info("Start solving {} with {}".format(model.name, self.name))
+        pybamm.logger.info(f"Start solving {model.name} with {self.name}")
 
         # get a list-only version of calculate_sensitivities
         if isinstance(calculate_sensitivities, bool):
@@ -788,7 +784,7 @@ def solve(
                     "'t_eval' can be provided as an array of times at which to "
                     "return the solution, or as a list [t0, tf] where t0 is the "
                     "initial time and tf is the final time, but has been provided "
-                    "as a list of length {}.".format(len(t_eval))
+                    f"as a list of length {len(t_eval)}."
                 )
             else:
                 t_eval = np.linspace(t_eval[0], t_eval[-1], 100)
@@ -981,7 +977,7 @@ def solve(
 
         # Report times
         if len(solutions) == 1:
-            pybamm.logger.info("Finish solving {} ({})".format(model.name, termination))
+            pybamm.logger.info(f"Finish solving {model.name} ({termination})")
             pybamm.logger.info(
                 (
                     "Set-up time: {}, Solve time: {} (of which integration time: {}), "
@@ -994,7 +990,7 @@ def solve(
                 )
             )
         else:
-            pybamm.logger.info("Finish solving {} for all inputs".format(model.name))
+            pybamm.logger.info(f"Finish solving {model.name} for all inputs")
             pybamm.logger.info(
                 ("Set-up time: {}, Solve time: {}, Total time: {}").format(
                     solutions[0].set_up_time,
@@ -1054,7 +1050,7 @@ def _get_discontinuity_start_end_indices(self, model, inputs, t_eval):
         discontinuities = [v for v in discontinuities if v < t_eval[-1]]
 
         pybamm.logger.verbose(
-            "Discontinuity events found at t = {}".format(discontinuities)
+            f"Discontinuity events found at t = {discontinuities}"
         )
         if isinstance(inputs, list):
             raise pybamm.SolverError(
@@ -1215,7 +1211,7 @@ def step(
             and old_solution.termination is None
         ):
             pybamm.logger.verbose(
-                "Start stepping {} with {}".format(model.name, self.name)
+                f"Start stepping {model.name} with {self.name}"
             )
 
         if isinstance(old_solution, pybamm.EmptySolution):
@@ -1246,7 +1242,7 @@ def step(
 
         # Step
         pybamm.logger.verbose(
-            "Stepping for {:.0f} < t < {:.0f}".format(t_start_shifted, t_end)
+            f"Stepping for {t_start_shifted:.0f} < t < {t_end:.0f}"
         )
         timer.reset()
         solution = self._integrate(model, t_eval, model_inputs)
@@ -1263,7 +1259,7 @@ def step(
         solution.set_up_time = set_up_time
 
         # Report times
-        pybamm.logger.verbose("Finish stepping {} ({})".format(model.name, termination))
+        pybamm.logger.verbose(f"Finish stepping {model.name} ({termination})")
         pybamm.logger.verbose(
             (
                 "Set-up time: {}, Step time: {} (of which integration time: {}), "
@@ -1332,7 +1328,7 @@ def get_termination_reason(self, solution, events):
                         "(possibly due to NaNs)"
                     )
                 # Add the event to the solution object
-                solution.termination = "event: {}".format(termination_event)
+                solution.termination = f"event: {termination_event}"
             # Update t, y and inputs to include event time and state
             # Note: if the final entry of t is equal to the event time we skip
             # this (having duplicate entries causes an error later in ProcessedVariable)
@@ -1484,10 +1480,10 @@ def report(string):
         jacp = None
         if model.calculate_sensitivities:
             report(
-                (
+
                     f"Calculating sensitivities for {name} with respect "
                     f"to parameters {model.calculate_sensitivities} using jax"
-                )
+
             )
             jacp = func.get_sensitivities()
         if use_jacobian:
@@ -1505,10 +1501,10 @@ def report(string):
         # to python evaluator
         if model.calculate_sensitivities:
             report(
-                (
+
                     f"Calculating sensitivities for {name} with respect "
                     f"to parameters {model.calculate_sensitivities}"
-                )
+
             )
             jacp_dict = {
                 p: symbol.diff(pybamm.InputParameter(p))
@@ -1611,11 +1607,11 @@ def jacp(*args, **kwargs):
                     casadi_expression = casadi.vertcat(x0, Sx_0, z0, Sz_0)
         elif model.calculate_sensitivities:
             report(
-                (
+
                     f"Calculating sensitivities for {name} with respect "
                     f"to parameters {model.calculate_sensitivities} using "
                     "CasADi"
-                )
+
             )
             # Compute derivate wrt p-stacked (can be passed to solver to
             # compute sensitivities online)
diff --git a/pybamm/solvers/casadi_algebraic_solver.py b/pybamm/solvers/casadi_algebraic_solver.py
index e7983b7f87..cdde5bb99c 100644
--- a/pybamm/solvers/casadi_algebraic_solver.py
+++ b/pybamm/solvers/casadi_algebraic_solver.py
@@ -129,7 +129,7 @@ def _integrate(self, model, t_eval, inputs_dict=None):
             # If there are no symbolic inputs, check the function is below the tol
             # Skip this check if there are symbolic inputs
             if success and (
-                (not any(np.isnan(fun)) and np.all(casadi.fabs(fun) < self.tol))
+                not any(np.isnan(fun)) and np.all(casadi.fabs(fun) < self.tol)
             ):
                 # update initial guess for the next iteration
                 y0_alg = y_alg_sol
@@ -141,7 +141,7 @@ def _integrate(self, model, t_eval, inputs_dict=None):
                     y_alg = casadi.horzcat(y_alg, y_alg_sol)
             elif not success:
                 raise pybamm.SolverError(
-                    "Could not find acceptable solution: {}".format(message)
+                    f"Could not find acceptable solution: {message}"
                 )
             elif any(np.isnan(fun)):
                 raise pybamm.SolverError(
diff --git a/pybamm/solvers/casadi_solver.py b/pybamm/solvers/casadi_solver.py
index 4cf863ede1..6ee8758de3 100644
--- a/pybamm/solvers/casadi_solver.py
+++ b/pybamm/solvers/casadi_solver.py
@@ -102,9 +102,9 @@ def __init__(
             self.mode = mode
         else:
             raise ValueError(
-                "invalid mode '{}'. Must be 'safe', for solving with events, "
+                f"invalid mode '{mode}'. Must be 'safe', for solving with events, "
                 "'fast', for solving quickly without events, or 'safe without grid' or "
-                "'fast with events' (both experimental)".format(mode)
+                "'fast with events' (both experimental)"
             )
         self.max_step_decrease_count = max_step_decrease_count
         self.dt_max = dt_max or 600
@@ -126,7 +126,7 @@ def __init__(
             self.perturb_algebraic_initial_conditions = (
                 perturb_algebraic_initial_conditions
             )
-        self.name = "CasADi solver with '{}' mode".format(mode)
+        self.name = f"CasADi solver with '{mode}' mode"
 
         # Initialize
         self.integrators_maxcount = integrators_maxcount
@@ -184,7 +184,7 @@ def _integrate(self, model, t_eval, inputs_dict=None):
             t_f = t_eval[-1]
 
             pybamm.logger.debug(
-                "Start solving {} with {}".format(model.name, self.name)
+                f"Start solving {model.name} with {self.name}"
             )
 
             if self.mode == "safe without grid":
diff --git a/pybamm/solvers/jax_solver.py b/pybamm/solvers/jax_solver.py
index 313fddc208..5e98c5bf07 100644
--- a/pybamm/solvers/jax_solver.py
+++ b/pybamm/solvers/jax_solver.py
@@ -71,12 +71,12 @@ def __init__(
         )
         method_options = ["RK45", "BDF"]
         if method not in method_options:
-            raise ValueError("method must be one of {}".format(method_options))
+            raise ValueError(f"method must be one of {method_options}")
         self.ode_solver = False
         if method == "RK45":
             self.ode_solver = True
         self.extra_options = extra_options or {}
-        self.name = "JAX solver ({})".format(method)
+        self.name = f"JAX solver ({method})"
         self._cached_solves = dict()
         pybamm.citations.register("jax2018")
 
@@ -136,11 +136,11 @@ def create_solve(self, model, t_eval):
             raise RuntimeError(
                 "Terminate events not supported for this solver."
                 " Model has the following events:"
-                " {}.\nYou can remove events using `model.events = []`."
+                f" {model.events}.\nYou can remove events using `model.events = []`."
                 " It might be useful to first solve the model using a"
                 " different solver to obtain the time of the event, then"
                 " re-solve using no events and a fixed"
-                " end-time".format(model.events)
+                " end-time"
             )
 
         # Initial conditions, make sure they are an 0D array
diff --git a/pybamm/solvers/processed_variable.py b/pybamm/solvers/processed_variable.py
index f9d967c4b0..c5d0683d75 100644
--- a/pybamm/solvers/processed_variable.py
+++ b/pybamm/solvers/processed_variable.py
@@ -8,7 +8,7 @@
 import xarray as xr
 
 
-class ProcessedVariable(object):
+class ProcessedVariable:
     """
     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.
@@ -106,7 +106,7 @@ def __init__(
                     else:
                         # Raise error for 3D variable
                         raise NotImplementedError(
-                            "Shape not recognized for {} ".format(base_variables[0])
+                            f"Shape not recognized for {base_variables[0]} "
                             + "(note processing of 3D variables is not yet implemented)"
                         )
 
@@ -363,7 +363,7 @@ def _process_spatial_variable_names(self, spatial_variable):
             return raw_names[0]
         else:
             raise NotImplementedError(
-                "Spatial variable name not recognized for {}".format(spatial_variable)
+                f"Spatial variable name not recognized for {spatial_variable}"
             )
 
     def __call__(self, t=None, x=None, r=None, y=None, z=None, R=None, warn=True):
diff --git a/pybamm/solvers/processed_variable_computed.py b/pybamm/solvers/processed_variable_computed.py
index 78d16c27fb..fd17dfab7b 100644
--- a/pybamm/solvers/processed_variable_computed.py
+++ b/pybamm/solvers/processed_variable_computed.py
@@ -8,7 +8,7 @@
 import xarray as xr
 
 
-class ProcessedVariableComputed(object):
+class ProcessedVariableComputed:
     """
     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.
@@ -106,7 +106,7 @@ def __init__(
                     else:
                         # Raise error for 3D variable
                         raise NotImplementedError(
-                            "Shape not recognized for {} ".format(base_variables[0])
+                            f"Shape not recognized for {base_variables[0]} "
                             + "(note processing of 3D variables is not yet implemented)"
                         )
 
diff --git a/pybamm/solvers/scikits_dae_solver.py b/pybamm/solvers/scikits_dae_solver.py
index 56b3ff42c3..a5bf1e5a4f 100644
--- a/pybamm/solvers/scikits_dae_solver.py
+++ b/pybamm/solvers/scikits_dae_solver.py
@@ -61,7 +61,7 @@ def __init__(
             raise ImportError("scikits.odes is not installed")
 
         super().__init__(method, rtol, atol, root_method, root_tol, extrap_tol)
-        self.name = "Scikits DAE solver ({})".format(method)
+        self.name = f"Scikits DAE solver ({method})"
 
         self.extra_options = extra_options or {}
 
diff --git a/pybamm/solvers/scikits_ode_solver.py b/pybamm/solvers/scikits_ode_solver.py
index 66132f39bb..9f5ee67604 100644
--- a/pybamm/solvers/scikits_ode_solver.py
+++ b/pybamm/solvers/scikits_ode_solver.py
@@ -57,7 +57,7 @@ def __init__(
         super().__init__(method, rtol, atol, extrap_tol=extrap_tol)
         self.extra_options = extra_options or {}
         self.ode_solver = True
-        self.name = "Scikits ODE solver ({})".format(method)
+        self.name = f"Scikits ODE solver ({method})"
 
         pybamm.citations.register("Malengier2018")
         pybamm.citations.register("Hindmarsh2000")
diff --git a/pybamm/solvers/scipy_solver.py b/pybamm/solvers/scipy_solver.py
index be228e054a..e0065cf4ec 100644
--- a/pybamm/solvers/scipy_solver.py
+++ b/pybamm/solvers/scipy_solver.py
@@ -43,7 +43,7 @@ def __init__(
         )
         self.ode_solver = True
         self.extra_options = extra_options or {}
-        self.name = "Scipy solver ({})".format(method)
+        self.name = f"Scipy solver ({method})"
         pybamm.citations.register("Virtanen2020")
 
     def _integrate(self, model, t_eval, inputs_dict=None):
diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py
index d7a27f142c..90712960cc 100644
--- a/pybamm/solvers/solution.py
+++ b/pybamm/solvers/solution.py
@@ -25,7 +25,7 @@ def default(self, obj):
         return json.JSONEncoder.default(self, obj)  # pragma: no cover
 
 
-class Solution(object):
+class Solution:
     """
     Class containing the solution of, and various attributes associated with, a PyBaMM
     model.
@@ -321,8 +321,7 @@ def check_ys_are_not_too_large(self):
                 # there will always be a statevector, but just in case
                 if statevector is None:  # pragma: no cover
                     raise RuntimeError(
-                        "Cannot find statevector corresponding to variable {}"
-                        .format(var.name)
+                        f"Cannot find statevector corresponding to variable {var.name}"
                     )
                 y_var = y[statevector.y_slices[0]]
                 if np.any(y_var > pybamm.settings.max_y_value):
@@ -470,7 +469,7 @@ def update(self, variables):
         # Process
         for key in variables:
             cumtrapz_ic = None
-            pybamm.logger.debug("Post-processing {}".format(key))
+            pybamm.logger.debug(f"Post-processing {key}")
             vars_pybamm = [model.variables_and_events[key] for model in self.all_models]
 
             # Iterate through all models, some may be in the list several times and
@@ -689,7 +688,7 @@ def save_data(
                         or (i > 0 and 48 <= ord(s) <= 57)
                     ):
                         raise ValueError(
-                            "Invalid character '{}' found in '{}'. ".format(s, name)
+                            f"Invalid character '{s}' found in '{name}'. "
                             + "MATLAB variable names must only contain a-z, A-Z, _, "
                             "or 0-9 (except the first position). "
                             "Use the 'short_names' argument to pass an alternative "
@@ -716,7 +715,7 @@ def save_data(
                 with open(filename, "w") as outfile:
                     json.dump(data, outfile, cls=NumpyEncoder)
         else:
-            raise ValueError("format '{}' not recognised".format(to_format))
+            raise ValueError(f"format '{to_format}' not recognised")
 
     @property
     def sub_solutions(self):
diff --git a/pybamm/spatial_methods/finite_volume.py b/pybamm/spatial_methods/finite_volume.py
index 636243f829..84f76a2bbd 100644
--- a/pybamm/spatial_methods/finite_volume.py
+++ b/pybamm/spatial_methods/finite_volume.py
@@ -641,9 +641,7 @@ def add_ghost_nodes(self, symbol, discretised_symbol, bcs):
             lbc_vector = pybamm.Vector(np.zeros((n + n_bcs) * second_dim_repeats))
         else:
             raise ValueError(
-                "boundary condition must be Dirichlet or Neumann, not '{}'".format(
-                    lbc_type
-                )
+                f"boundary condition must be Dirichlet or Neumann, not '{lbc_type}'"
             )
 
         if rbc_type == "Dirichlet":
@@ -662,9 +660,7 @@ def add_ghost_nodes(self, symbol, discretised_symbol, bcs):
             rbc_vector = pybamm.Vector(np.zeros((n + n_bcs) * second_dim_repeats))
         else:
             raise ValueError(
-                "boundary condition must be Dirichlet or Neumann, not '{}'".format(
-                    rbc_type
-                )
+                f"boundary condition must be Dirichlet or Neumann, not '{rbc_type}'"
             )
 
         bcs_vector = lbc_vector + rbc_vector
@@ -756,9 +752,7 @@ def add_neumann_values(self, symbol, discretised_gradient, bcs, domain):
             lbc_vector = pybamm.Vector(np.zeros((n + n_bcs) * second_dim_repeats))
         else:
             raise ValueError(
-                "boundary condition must be Dirichlet or Neumann, not '{}'".format(
-                    rbc_type
-                )
+                f"boundary condition must be Dirichlet or Neumann, not '{rbc_type}'"
             )
         if rbc_type == "Neumann" and rbc_value != 0:
             rbc_sub_matrix = coo_matrix(
@@ -774,9 +768,7 @@ def add_neumann_values(self, symbol, discretised_gradient, bcs, domain):
             rbc_vector = pybamm.Vector(np.zeros((n + n_bcs) * second_dim_repeats))
         else:
             raise ValueError(
-                "boundary condition must be Dirichlet or Neumann, not '{}'".format(
-                    rbc_type
-                )
+                f"boundary condition must be Dirichlet or Neumann, not '{rbc_type}'"
             )
 
         bcs_vector = lbc_vector + rbc_vector
@@ -1222,7 +1214,7 @@ def arithmetic_mean(array):
             elif shift_key == "edge to node":
                 sub_matrix = diags([0.5, 0.5], [0, 1], shape=(n, n + 1))
             else:
-                raise ValueError("shift key '{}' not recognised".format(shift_key))
+                raise ValueError(f"shift key '{shift_key}' not recognised")
             # Second dimension length
             second_dim_repeats = self._get_auxiliary_domain_repeats(
                 discretised_symbol.domains
@@ -1366,7 +1358,7 @@ def harmonic_mean(array):
                 return D_eff
 
             else:
-                raise ValueError("shift key '{}' not recognised".format(shift_key))
+                raise ValueError(f"shift key '{shift_key}' not recognised")
 
         # If discretised_symbol evaluates to number there is no need to average
         if discretised_symbol.size == 1:
@@ -1376,7 +1368,7 @@ def harmonic_mean(array):
         elif method == "harmonic":
             out = harmonic_mean(discretised_symbol)
         else:
-            raise ValueError("method '{}' not recognised".format(method))
+            raise ValueError(f"method '{method}' not recognised")
         return out
 
     def upwind_or_downwind(self, symbol, discretised_symbol, bcs, direction):
@@ -1404,7 +1396,7 @@ def upwind_or_downwind(self, symbol, discretised_symbol, bcs, direction):
         if symbol not in bcs:
             raise pybamm.ModelError(
                 "Boundary conditions must be provided for "
-                "{}ing '{}'".format(direction, symbol)
+                f"{direction}ing '{symbol}'"
             )
 
         if direction == "upwind":
@@ -1412,7 +1404,7 @@ def upwind_or_downwind(self, symbol, discretised_symbol, bcs, direction):
             if typ != "Dirichlet":
                 raise pybamm.ModelError(
                     "Dirichlet boundary conditions must be provided for "
-                    "upwinding '{}'".format(symbol)
+                    f"upwinding '{symbol}'"
                 )
 
             concat_bc = pybamm.NumpyConcatenation(bc, discretised_symbol)
@@ -1429,7 +1421,7 @@ def upwind_or_downwind(self, symbol, discretised_symbol, bcs, direction):
             if typ != "Dirichlet":
                 raise pybamm.ModelError(
                     "Dirichlet boundary conditions must be provided for "
-                    "downwinding '{}'".format(symbol)
+                    f"downwinding '{symbol}'"
                 )
 
             concat_bc = pybamm.NumpyConcatenation(discretised_symbol, bc)
diff --git a/pybamm/spatial_methods/scikit_finite_element.py b/pybamm/spatial_methods/scikit_finite_element.py
index 2d51e16c32..07a3c0e1be 100644
--- a/pybamm/spatial_methods/scikit_finite_element.py
+++ b/pybamm/spatial_methods/scikit_finite_element.py
@@ -59,7 +59,7 @@ def spatial_variable(self, symbol):
             entries = symbol_mesh["current collector"].coordinates[1, :][:, np.newaxis]
         else:
             raise pybamm.GeometryError(
-                "Spatial variable must be 'y' or 'z' not {}".format(symbol.name)
+                f"Spatial variable must be 'y' or 'z' not {symbol.name}"
             )
         return pybamm.Vector(entries, domains=symbol.domains)
 
@@ -221,9 +221,7 @@ def unit_bc_load_form(v, w):
             boundary_load = boundary_load + neg_bc_value * pybamm.Vector(neg_bc_load)
         else:
             raise ValueError(
-                "boundary condition must be Dirichlet or Neumann, not '{}'".format(
-                    neg_bc_type
-                )
+                f"boundary condition must be Dirichlet or Neumann, not '{neg_bc_type}'"
             )
 
         if pos_bc_type == "Neumann":
@@ -238,9 +236,7 @@ def unit_bc_load_form(v, w):
             boundary_load = boundary_load + pos_bc_value * pybamm.Vector(pos_bc_load)
         else:
             raise ValueError(
-                "boundary condition must be Dirichlet or Neumann, not '{}'".format(
-                    pos_bc_type
-                )
+                f"boundary condition must be Dirichlet or Neumann, not '{pos_bc_type}'"
             )
 
         return -stiffness_matrix @ discretised_symbol + boundary_load
@@ -281,7 +277,7 @@ def stiffness_form(u, v, w):
             _, pos_bc_type = boundary_conditions[symbol]["positive tab"]
         except KeyError:
             raise pybamm.ModelError(
-                "No boundary conditions provided for symbol `{}``".format(symbol)
+                f"No boundary conditions provided for symbol `{symbol}``"
             )
 
         # adjust matrix for Dirichlet boundary conditions
diff --git a/pybamm/spatial_methods/spectral_volume.py b/pybamm/spatial_methods/spectral_volume.py
index 7f7cfdb37a..a10422813f 100644
--- a/pybamm/spatial_methods/spectral_volume.py
+++ b/pybamm/spatial_methods/spectral_volume.py
@@ -528,7 +528,7 @@ def replace_dirichlet_values(self, symbol, discretised_symbol, bcs):
         else:
             raise ValueError(
                 "boundary condition must be Dirichlet or Neumann, "
-                "not '{}'".format(lbc_type)
+                f"not '{lbc_type}'"
             )
 
         if rbc_type == "Dirichlet":
@@ -544,7 +544,7 @@ def replace_dirichlet_values(self, symbol, discretised_symbol, bcs):
         else:
             raise ValueError(
                 "boundary condition must be Dirichlet or Neumann, "
-                "not '{}'".format(rbc_type)
+                f"not '{rbc_type}'"
             )
 
         bcs_vector = lbc_vector + rbc_vector
@@ -622,7 +622,7 @@ def replace_neumann_values(self, symbol, discretised_gradient, bcs):
         else:
             raise ValueError(
                 "boundary condition must be Dirichlet or Neumann, "
-                "not '{}'".format(lbc_type)
+                f"not '{lbc_type}'"
             )
 
         if rbc_type == "Neumann":
@@ -638,7 +638,7 @@ def replace_neumann_values(self, symbol, discretised_gradient, bcs):
         else:
             raise ValueError(
                 "boundary condition must be Dirichlet or Neumann, "
-                "not '{}'".format(rbc_type)
+                f"not '{rbc_type}'"
             )
 
         bcs_vector = lbc_vector + rbc_vector
diff --git a/pybamm/util.py b/pybamm/util.py
index af278d752a..71883e3d27 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -122,16 +122,16 @@ def search(self, key, print_values=False):
             )
         elif print_values:
             # Else print results, including dict items
-            print("\n".join("{}\t{}".format(k, v) for k, v in results.items()))
+            print("\n".join(f"{k}\t{v}" for k, v in results.items()))
         else:
             # Just print keys
-            print("\n".join("{}".format(k) for k in results.keys()))
+            print("\n".join(f"{k}" for k in results.keys()))
 
     def copy(self):
         return FuzzyDict(super().copy())
 
 
-class Timer(object):
+class Timer:
     """
     Provides accurate timing.
 
@@ -171,13 +171,13 @@ def __str__(self):
         """
         time = self.value
         if time < 1e-6:
-            return "{:.3f} ns".format(time * 1e9)
+            return f"{time * 1e9:.3f} ns"
         if time < 1e-3:
-            return "{:.3f} us".format(time * 1e6)
+            return f"{time * 1e6:.3f} us"
         if time < 1:
-            return "{:.3f} ms".format(time * 1e3)
+            return f"{time * 1e3:.3f} ms"
         elif time < 60:
-            return "{:.3f} s".format(time)
+            return f"{time:.3f} s"
         output = []
         time = int(round(time))
         units = [(604800, "week"), (86400, "day"), (3600, "hour"), (60, "minute")]
diff --git a/run-tests.py b/run-tests.py
index 25b1731b18..c523554fc9 100755
--- a/run-tests.py
+++ b/run-tests.py
@@ -40,7 +40,7 @@ def run_code_tests(executable=False, folder: str = "unit", interpreter="python")
         result = unittest.TextTestRunner(verbosity=2).run(suite)
         ret = int(not result.wasSuccessful())
     else:
-        print("Running {} tests with executable '{}'".format(folder, interpreter))
+        print(f"Running {folder} tests with executable '{interpreter}'")
         cmd = [interpreter, "-m", "unittest", "discover", "-v", tests]
         p = subprocess.Popen(cmd)
         try:
@@ -178,7 +178,7 @@ def test_script(path, executable="python"):
         sys.exit(1)
 
     # Sucessfully run
-    print("ok ({})".format(b.time()))
+    print(f"ok ({b.time()})")
     return True
 
 
diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 8f41f5969a..e46831eb5e 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -59,7 +59,7 @@ def download_extract_library(url, download_dir):
 suitesparse_version = "6.0.3"
 suitesparse_url = (
     "https://github.com/DrTimothyAldenDavis/"
-    + "SuiteSparse/archive/v{}.tar.gz".format(suitesparse_version)
+    + f"SuiteSparse/archive/v{suitesparse_version}.tar.gz"
 )
 download_extract_library(suitesparse_url, download_dir)
 
@@ -68,13 +68,13 @@ def download_extract_library(url, download_dir):
 # - AMD
 # - COLAMD
 # - BTF
-suitesparse_dir = "SuiteSparse-{}".format(suitesparse_version)
+suitesparse_dir = f"SuiteSparse-{suitesparse_version}"
 suitesparse_src = os.path.join(download_dir, suitesparse_dir)
 print("-" * 10, "Building SuiteSparse_config", "-" * 40)
 make_cmd = [
     "make",
     "library",
-    'CMAKE_OPTIONS="-DCMAKE_INSTALL_PREFIX={}"'.format(install_dir),
+    f'CMAKE_OPTIONS="-DCMAKE_INSTALL_PREFIX={install_dir}"',
 ]
 install_cmd = [
     "make",
@@ -107,8 +107,8 @@ def download_extract_library(url, download_dir):
     "-DEXAMPLES_ENABLE:BOOL=OFF",
     "-DENABLE_KLU=ON",
     "-DENABLE_OPENMP=ON",
-    "-DKLU_INCLUDE_DIR={}".format(KLU_INCLUDE_DIR),
-    "-DKLU_LIBRARY_DIR={}".format(KLU_LIBRARY_DIR),
+    f"-DKLU_INCLUDE_DIR={KLU_INCLUDE_DIR}",
+    f"-DKLU_LIBRARY_DIR={KLU_LIBRARY_DIR}",
     "-DCMAKE_INSTALL_PREFIX=" + install_dir,
     # on mac use fixed paths rather than rpath
     "-DCMAKE_INSTALL_NAME_DIR=" + KLU_LIBRARY_DIR,
@@ -154,7 +154,7 @@ def download_extract_library(url, download_dir):
     print("\n-" * 10, "Creating build dir", "-" * 40)
     os.makedirs(build_dir)
 
-sundials_src = "../sundials-{}".format(sundials_version)
+sundials_src = f"../sundials-{sundials_version}"
 print("-" * 10, "Running CMake prepare", "-" * 40)
 subprocess.run(["cmake", sundials_src, *cmake_args], cwd=build_dir, check=True)
 
diff --git a/setup.py b/setup.py
index ef82e65e70..6b62aacc99 100644
--- a/setup.py
+++ b/setup.py
@@ -24,13 +24,13 @@
 
 def set_vcpkg_environment_variables():
     if not os.getenv("VCPKG_ROOT_DIR"):
-        raise EnvironmentError("Environment variable 'VCPKG_ROOT_DIR' is undefined.")
+        raise OSError("Environment variable 'VCPKG_ROOT_DIR' is undefined.")
     if not os.getenv("VCPKG_DEFAULT_TRIPLET"):
-        raise EnvironmentError(
+        raise OSError(
             "Environment variable 'VCPKG_DEFAULT_TRIPLET' is undefined."
         )
     if not os.getenv("VCPKG_FEATURE_FLAGS"):
-        raise EnvironmentError(
+        raise OSError(
             "Environment variable 'VCPKG_FEATURE_FLAGS' is undefined."
         )
     return (
@@ -91,17 +91,17 @@ def run(self):
 
         build_type = os.getenv("PYBAMM_CPP_BUILD_TYPE", "RELEASE")
         cmake_args = [
-            "-DCMAKE_BUILD_TYPE={}".format(build_type),
-            "-DPYTHON_EXECUTABLE={}".format(sys.executable),
+            f"-DCMAKE_BUILD_TYPE={build_type}",
+            f"-DPYTHON_EXECUTABLE={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))
+                f"-DSuiteSparse_ROOT={os.path.abspath(self.suitesparse_root)}"
             )
         if self.sundials_root:
             cmake_args.append(
-                "-DSUNDIALS_ROOT={}".format(os.path.abspath(self.sundials_root))
+                f"-DSUNDIALS_ROOT={os.path.abspath(self.sundials_root)}"
             )
 
         build_dir = self.get_build_directory()
@@ -264,12 +264,12 @@ def compile_KLU():
     pybind11_dir = os.path.join(pybamm_project_dir, "pybind11")
     try:
         open(os.path.join(pybind11_dir, "tools", "pybind11Tools.cmake"))
-        logger.info("Found pybind11 directory ({})".format(pybind11_dir))
+        logger.info(f"Found pybind11 directory ({pybind11_dir})")
     except FileNotFoundError:
         PyBind11Found = False
         msg = (
-            "Could not find PyBind11 directory ({})."
-            " Skipping compilation of KLU module.".format(pybind11_dir)
+            f"Could not find PyBind11 directory ({pybind11_dir})."
+            " Skipping compilation of KLU module."
         )
         logger.info(msg)
 
diff --git a/tests/integration/test_models/standard_model_tests.py b/tests/integration/test_models/standard_model_tests.py
index d4074e15ef..43eba8894e 100644
--- a/tests/integration/test_models/standard_model_tests.py
+++ b/tests/integration/test_models/standard_model_tests.py
@@ -8,7 +8,7 @@
 import os
 
 
-class StandardModelTest(object):
+class StandardModelTest:
     """Basic processing test for the models."""
 
     def __init__(
@@ -195,7 +195,7 @@ def test_all(
             self.test_outputs()
 
 
-class OptimisationsTest(object):
+class OptimisationsTest:
     """Test that the optimised models give the same result as the original model."""
 
     def __init__(self, model, parameter_values=None, disc=None):
diff --git a/tests/integration/test_models/standard_output_comparison.py b/tests/integration/test_models/standard_output_comparison.py
index 66c1ccc0ef..4d4d16e5ca 100644
--- a/tests/integration/test_models/standard_output_comparison.py
+++ b/tests/integration/test_models/standard_output_comparison.py
@@ -5,7 +5,7 @@
 import numpy as np
 
 
-class StandardOutputComparison(object):
+class StandardOutputComparison:
     """Calls all the tests comparing standard output variables."""
 
     def __init__(self, solutions):
@@ -56,7 +56,7 @@ def test_all(self, skip_first_timestep=False):
             self.run_test_class(PorosityComparison, skip_first_timestep)
 
 
-class BaseOutputComparison(object):
+class BaseOutputComparison:
     def __init__(self, time, solutions):
         self.t = time
         self.solutions = solutions
diff --git a/tests/integration/test_models/standard_output_tests.py b/tests/integration/test_models/standard_output_tests.py
index 05cb86f249..83b88c0ff0 100644
--- a/tests/integration/test_models/standard_output_tests.py
+++ b/tests/integration/test_models/standard_output_tests.py
@@ -5,7 +5,7 @@
 import numpy as np
 
 
-class StandardOutputTests(object):
+class StandardOutputTests:
     """Calls all the tests on the standard output variables."""
 
     def __init__(self, model, parameter_values, disc, solution):
@@ -58,7 +58,7 @@ def test_all(self, skip_first_timestep=False):
             self.run_test_class(VelocityTests)
 
 
-class BaseOutputTest(object):
+class BaseOutputTest:
     def __init__(self, model, param, disc, solution, operating_condition):
         self.model = model
         self.param = param
diff --git a/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_asymptotics_convergence.py b/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_asymptotics_convergence.py
index c264e26543..c78e7f9223 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_asymptotics_convergence.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_asymptotics_convergence.py
@@ -41,7 +41,7 @@ def test_leading_order_convergence(self):
         full_disc.process_model(full_model)
 
         def get_max_error(current):
-            pybamm.logger.info("current = {}".format(current))
+            pybamm.logger.info(f"current = {current}")
             # Solve, make sure times are the same and use tight tolerances
             t_eval = np.linspace(0, 3600 * 17 / current)
             solver = pybamm.CasadiSolver()
diff --git a/tests/unit/test_callbacks.py b/tests/unit/test_callbacks.py
index 94a00b15d9..b36fef9ec6 100644
--- a/tests/unit/test_callbacks.py
+++ b/tests/unit/test_callbacks.py
@@ -63,9 +63,9 @@ def test_callback_list(self):
             ]
         )
         callback.on_experiment_end(None)
-        with open("test_callback.log", "r") as f:
+        with open("test_callback.log") as f:
             self.assertEqual(f.read(), "first\n")
-        with open("test_callback_2.log", "r") as f:
+        with open("test_callback_2.log") as f:
             self.assertEqual(f.read(), "second\n")
 
     def test_logging_callback(self):
@@ -89,19 +89,19 @@ def test_logging_callback(self):
             self.assertEqual(f.read(), "")
 
         callback.on_cycle_start(logs)
-        with open("test_callback.log", "r") as f:
+        with open("test_callback.log") as f:
             self.assertIn("Cycle 5/12", f.read())
 
         callback.on_step_start(logs)
-        with open("test_callback.log", "r") as f:
+        with open("test_callback.log") as f:
             self.assertIn("Cycle 5/12, step 1/4", f.read())
 
         callback.on_experiment_infeasible(logs)
-        with open("test_callback.log", "r") as f:
+        with open("test_callback.log") as f:
             self.assertIn("Experiment is infeasible: 'event'", f.read())
 
         callback.on_experiment_end(logs)
-        with open("test_callback.log", "r") as f:
+        with open("test_callback.log") as f:
             self.assertIn("took 0.45", f.read())
 
         # Calling start again should clear the log
diff --git a/tests/unit/test_citations.py b/tests/unit/test_citations.py
index b3e2c88422..d8c1de3718 100644
--- a/tests/unit/test_citations.py
+++ b/tests/unit/test_citations.py
@@ -50,13 +50,13 @@ def test_print_citations(self):
         # Text Style
         with temporary_filename() as filename:
             pybamm.print_citations(filename, "text")
-            with open(filename, "r") as f:
+            with open(filename) as f:
                 self.assertTrue(len(f.readlines()) > 0)
 
         # Bibtext Style
         with temporary_filename() as filename:
             pybamm.print_citations(filename, "bibtex")
-            with open(filename, "r") as f:
+            with open(filename) as f:
                 self.assertTrue(len(f.readlines()) > 0)
 
         # Write to stdout
diff --git a/tests/unit/test_expression_tree/test_functions.py b/tests/unit/test_expression_tree/test_functions.py
index e9bd8522e6..33e11459ab 100644
--- a/tests/unit/test_expression_tree/test_functions.py
+++ b/tests/unit/test_expression_tree/test_functions.py
@@ -46,7 +46,7 @@ def test_function_of_one_variable(self):
         b = pybamm.Scalar(1)
         sina = pybamm.Function(np.sin, b)
         self.assertEqual(sina.evaluate(), np.sin(1))
-        self.assertEqual(sina.name, "function ({})".format(np.sin.__name__))
+        self.assertEqual(sina.name, f"function ({np.sin.__name__})")
 
         c = pybamm.Vector(np.linspace(0, 1))
         cosb = pybamm.Function(np.cos, c)
diff --git a/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py b/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py
index df33e0fe27..426e7811f6 100644
--- a/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py
+++ b/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py
@@ -46,7 +46,7 @@ def test_find_symbols(self):
         var_a = pybamm.id_to_python_variable(a.id)
         var_b = pybamm.id_to_python_variable(b.id)
         self.assertEqual(
-            list(variable_symbols.values())[2], "{} + {}".format(var_a, var_b)
+            list(variable_symbols.values())[2], f"{var_a} + {var_b}"
         )
 
         # test identical subtree
@@ -66,12 +66,12 @@ def test_find_symbols(self):
         self.assertEqual(next(iter(variable_symbols.values())), "y[0:1]")
         self.assertEqual(list(variable_symbols.values())[1], "y[1:2]")
         self.assertEqual(
-            list(variable_symbols.values())[2], "{} + {}".format(var_a, var_b)
+            list(variable_symbols.values())[2], f"{var_a} + {var_b}"
         )
 
         var_child = pybamm.id_to_python_variable(expr.children[0].id)
         self.assertEqual(
-            list(variable_symbols.values())[3], "{} + {}".format(var_child, var_b)
+            list(variable_symbols.values())[3], f"{var_child} + {var_b}"
         )
 
         # test unary op
@@ -90,7 +90,7 @@ def test_find_symbols(self):
         # test values of variable_symbols
         self.assertEqual(next(iter(variable_symbols.values())), "y[0:1]")
         self.assertEqual(list(variable_symbols.values())[1], "y[1:2]")
-        self.assertEqual(list(variable_symbols.values())[2], "-{}".format(var_b))
+        self.assertEqual(list(variable_symbols.values())[2], f"-{var_b}")
         var_child = pybamm.id_to_python_variable(expr.children[1].id)
         self.assertEqual(
             list(variable_symbols.values())[3], f"np.maximum({var_a},{var_child})"
@@ -108,7 +108,7 @@ def test_find_symbols(self):
         self.assertEqual(next(iter(variable_symbols.values())), "y[0:1]")
         var_funct = pybamm.id_to_python_variable(expr.id, True)
         self.assertEqual(
-            list(variable_symbols.values())[1], "{}({})".format(var_funct, var_a)
+            list(variable_symbols.values())[1], f"{var_funct}({var_a})"
         )
 
         # test matrix
@@ -144,7 +144,7 @@ def test_find_symbols(self):
         self.assertEqual(list(variable_symbols.keys())[2], expr.id)
         self.assertEqual(
             list(variable_symbols.values())[2],
-            "np.concatenate(({},{}))".format(var_a, var_b),
+            f"np.concatenate(({var_a},{var_b}))",
         )
 
         # test domain concatentate
@@ -158,7 +158,7 @@ def test_find_symbols(self):
         self.assertEqual(list(variable_symbols.keys())[2], expr.id)
         self.assertEqual(
             list(variable_symbols.values())[2],
-            "np.concatenate(({},{}))".format(var_a, var_b),
+            f"np.concatenate(({var_a},{var_b}))",
         )
 
         # test that Concatentation throws
@@ -203,7 +203,7 @@ def test_domain_concatenation(self):
         self.assertEqual(len(constant_symbols), 0)
         self.assertEqual(
             list(variable_symbols.values())[2],
-            "np.concatenate(({}[0:{}],{}[0:{}]))".format(var_a, a_pts, var_b, b_pts),
+            f"np.concatenate(({var_a}[0:{a_pts}],{var_b}[0:{b_pts}]))",
         )
 
         evaluator = pybamm.EvaluatorPython(expr)
@@ -237,14 +237,14 @@ def test_domain_concatenation(self):
         variable_symbols = OrderedDict()
         pybamm.find_symbols(expr, constant_symbols, variable_symbols)
 
-        b0_str = "{}[0:{}]".format(var_b, b0_pts)
-        a0_str = "{}[0:{}]".format(var_a, a0_pts)
-        b1_str = "{}[{}:{}]".format(var_b, b0_pts, b0_pts + b1_pts)
+        b0_str = f"{var_b}[0:{b0_pts}]"
+        a0_str = f"{var_a}[0:{a0_pts}]"
+        b1_str = f"{var_b}[{b0_pts}:{b0_pts + b1_pts}]"
 
         self.assertEqual(len(constant_symbols), 0)
         self.assertEqual(
             list(variable_symbols.values())[2],
-            "np.concatenate(({},{},{}))".format(a0_str, b0_str, b1_str),
+            f"np.concatenate(({a0_str},{b0_str},{b1_str}))",
         )
 
         evaluator = pybamm.EvaluatorPython(expr)
diff --git a/tests/unit/test_parameters/test_current_functions.py b/tests/unit/test_parameters/test_current_functions.py
index d8bac9cc58..10a311fc2c 100644
--- a/tests/unit/test_parameters/test_current_functions.py
+++ b/tests/unit/test_parameters/test_current_functions.py
@@ -77,7 +77,7 @@ def user_current(t):
         )
 
 
-class StandardCurrentFunctionTests(object):
+class StandardCurrentFunctionTests:
     def __init__(self, function_list, always_array=False):
         self.function_list = function_list
         self.always_array = always_array
diff --git a/tests/unit/test_serialisation/test_serialisation.py b/tests/unit/test_serialisation/test_serialisation.py
index 6c43eaa9d7..75ea33fe66 100644
--- a/tests/unit/test_serialisation/test_serialisation.py
+++ b/tests/unit/test_serialisation/test_serialisation.py
@@ -512,7 +512,7 @@ def test_save_load_model(self):
         )
 
         # Test for error if no model type is provided
-        with open("test_model.json", "r") as f:
+        with open("test_model.json") as f:
             model_data = json.load(f)
             del model_data["py/object"]
 
diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py
index ac70f0b43b..4375e745ad 100644
--- a/tests/unit/test_simulation.py
+++ b/tests/unit/test_simulation.py
@@ -227,7 +227,7 @@ def test_solve_with_initial_soc(self):
         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))
+        sol = sim.solve([0,1], initial_soc = f"{ucv} V")
         voltage = sol["Terminal voltage [V]"].entries
         self.assertAlmostEqual(voltage[0], ucv, places=5)
 
diff --git a/tests/unit/test_solvers/test_processed_variable.py b/tests/unit/test_solvers/test_processed_variable.py
index 79de9b0368..d8b4ccfd0c 100644
--- a/tests/unit/test_solvers/test_processed_variable.py
+++ b/tests/unit/test_solvers/test_processed_variable.py
@@ -233,7 +233,7 @@ def test_processed_variable_1D_unknown_domain(self):
             model,
             {},
             np.linspace(0, 1, 1),
-            np.zeros((var_pts[x])),
+            np.zeros(var_pts[x]),
             "test",
         )
 
diff --git a/tests/unit/test_solvers/test_processed_variable_computed.py b/tests/unit/test_solvers/test_processed_variable_computed.py
index e31f51ab1e..c8b1f2597d 100644
--- a/tests/unit/test_solvers/test_processed_variable_computed.py
+++ b/tests/unit/test_solvers/test_processed_variable_computed.py
@@ -207,7 +207,7 @@ def test_processed_variable_1D_unknown_domain(self):
             pybamm.BaseModel(),
             {},
             np.linspace(0, 1, 1),
-            np.zeros((var_pts[x])),
+            np.zeros(var_pts[x]),
             "test",
         )
 
diff --git a/tests/unit/test_timer.py b/tests/unit/test_timer.py
index 3a5e37b435..228cdd5dce 100644
--- a/tests/unit/test_timer.py
+++ b/tests/unit/test_timer.py
@@ -15,7 +15,7 @@ class TestTimer(TestCase):
     """
 
     def __init__(self, name):
-        super(TestTimer, self).__init__(name)
+        super().__init__(name)
 
     def test_timing(self):
         t = pybamm.Timer()

From 75b58bc50646e5599027e73f5c8254714a20754e Mon Sep 17 00:00:00 2001
From: Pradyot Ranjan <99216956+pradyotRanjan@users.noreply.github.com>
Date: Thu, 14 Dec 2023 22:59:07 +0530
Subject: [PATCH 3/7] added commit hash

Signed-off-by: Pradyot Ranjan <99216956+pradyotRanjan@users.noreply.github.com>
---
 .git-blame-ignore-revs | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs
index 9e59bd7f07..0583211dda 100644
--- a/.git-blame-ignore-revs
+++ b/.git-blame-ignore-revs
@@ -6,3 +6,5 @@ a63e49ece0f9336d1f5c2562f7459e555c6e6693
 5273214b585c5a4286609aed40e0b092d0e05f42
 # migrate config to pyproject.toml - https://github.com/pybamm-team/PyBaMM/pull/3557
 12c5d77203bd93542785d237bac00bad5ed5469a
+# activated pyupgrade - https://github.com/pybamm-team/PyBaMM/pull/3579
+ff6d81c01331c7d269303b4a8321d9881bdf98fa
\ No newline at end of file

From 58d81a79e4fc96956c72fd0594867af00b36e50b Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 14 Dec 2023 18:46:54 +0000
Subject: [PATCH 4/7] style: pre-commit fixes

---
 .git-blame-ignore-revs | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs
index 0583211dda..ec0f52cbfd 100644
--- a/.git-blame-ignore-revs
+++ b/.git-blame-ignore-revs
@@ -7,4 +7,4 @@ a63e49ece0f9336d1f5c2562f7459e555c6e6693
 # migrate config to pyproject.toml - https://github.com/pybamm-team/PyBaMM/pull/3557
 12c5d77203bd93542785d237bac00bad5ed5469a
 # activated pyupgrade - https://github.com/pybamm-team/PyBaMM/pull/3579
-ff6d81c01331c7d269303b4a8321d9881bdf98fa
\ No newline at end of file
+ff6d81c01331c7d269303b4a8321d9881bdf98fa

From 40a9dcfe292213acd154a6e636d588485ba54b8a Mon Sep 17 00:00:00 2001
From: Pradyot Ranjan <99216956+prady0t@users.noreply.github.com>
Date: Fri, 15 Dec 2023 01:09:04 +0530
Subject: [PATCH 5/7] Apply suggestions from code review

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 pybamm/discretisations/discretisation.py                | 2 +-
 pybamm/models/full_battery_models/base_battery_model.py | 3 +--
 pybamm/solvers/processed_variable.py                    | 2 +-
 pyproject.toml                                          | 4 ++--
 4 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/pybamm/discretisations/discretisation.py b/pybamm/discretisations/discretisation.py
index 7f20cee348..c250d06e9c 100644
--- a/pybamm/discretisations/discretisation.py
+++ b/pybamm/discretisations/discretisation.py
@@ -72,7 +72,7 @@ def y_slices(self):
     @y_slices.setter
     def y_slices(self, value):
         if not isinstance(value, dict):
-            raise TypeError(f"""y_slices should be dict, not {type(value)}""")
+            raise TypeError(f"y_slices should be dict, not {type(value)}")
 
         self._y_slices = value
 
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index 94ea006aa4..dea066db08 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -1021,8 +1021,7 @@ def options(self, extra_options):
             and options["hydrolysis"] == "true"
         ):
             raise pybamm.OptionError(
-                f"""must use surface formulation to solve {self!s} with hydrolysis
-                    """
+                f"must use surface formulation to solve {self!s} with hydrolysis"
             )
 
         self._options = options
diff --git a/pybamm/solvers/processed_variable.py b/pybamm/solvers/processed_variable.py
index c5d0683d75..d33d6894dd 100644
--- a/pybamm/solvers/processed_variable.py
+++ b/pybamm/solvers/processed_variable.py
@@ -106,7 +106,7 @@ def __init__(
                     else:
                         # Raise error for 3D variable
                         raise NotImplementedError(
-                            f"Shape not recognized for {base_variables[0]} "
+                            f"Shape not recognized for {base_variables[0]}"
                             + "(note processing of 3D variables is not yet implemented)"
                         )
 
diff --git a/pyproject.toml b/pyproject.toml
index 31e0b3e9bf..aa22064e47 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -196,7 +196,7 @@ extend-select = [
   "RUF",         # Ruff-specific
   # "SIM",         # flake8-simplify
   # "T20",         # flake8-print
-   "UP",          # pyupgrade
+  "UP",           # pyupgrade
   "YTT",         # flake8-2020
 ]
 ignore = [
@@ -214,7 +214,7 @@ ignore = [
   "RET506",      # Unnecessary `elif`
   "B018",        # Found useless expression
   "RUF002",      # Docstring contains ambiguous
-    "UP007",     # For pyupgrade
+  "UP007",       # For pyupgrade
 ]
 
 [tool.ruff.lint.per-file-ignores]

From 6e9b3734f71feccc84d2b70d34360bd248f64eab Mon Sep 17 00:00:00 2001
From: Pradyot Ranjan <99216956+prady0t@users.noreply.github.com>
Date: Fri, 15 Dec 2023 18:07:33 +0530
Subject: [PATCH 6/7] Update pyproject.toml

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index aa22064e47..69fb9bfc1e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -196,7 +196,7 @@ extend-select = [
   "RUF",         # Ruff-specific
   # "SIM",         # flake8-simplify
   # "T20",         # flake8-print
-  "UP",           # pyupgrade
+  "UP",          # pyupgrade
   "YTT",         # flake8-2020
 ]
 ignore = [

From e2d8792685bc994a4f44cd3bf3e0fa2ba6a16285 Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Fri, 15 Dec 2023 22:24:33 +0530
Subject: [PATCH 7/7] docs: add prady0t as a contributor for infra (#3620)

* docs: update README.md [skip ci]

* docs: update .all-contributorsrc [skip ci]

---------

Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com>
---
 .all-contributorsrc | 9 +++++++++
 README.md           | 5 ++++-
 2 files changed, 13 insertions(+), 1 deletion(-)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index 317cb38667..7cc68678e0 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -764,6 +764,15 @@
       "contributions": [
         "infra"
       ]
+    },
+    {
+      "login": "prady0t",
+      "name": "Pradyot Ranjan",
+      "avatar_url": "https://avatars.githubusercontent.com/u/99216956?v=4",
+      "profile": "https://github.com/prady0t",
+      "contributions": [
+        "infra"
+      ]
     }
   ],
   "contributorsPerLine": 7,
diff --git a/README.md b/README.md
index 31c6257473..8bad257378 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-70-orange.svg)](#-contributors)
+[![All Contributors](https://img.shields.io/badge/all_contributors-71-orange.svg)](#-contributors)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 </div>
@@ -275,6 +275,9 @@ 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/shubhambhar007"><img src="https://avatars.githubusercontent.com/u/32607282?v=4?s=100" width="100px;" alt="Shubham Bhardwaj"/><br /><sub><b>Shubham Bhardwaj</b></sub></a><br /><a href="#infra-shubhambhar007" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/jlauber18"><img src="https://avatars.githubusercontent.com/u/28939653?v=4?s=100" width="100px;" alt="Jonathan Lauber"/><br /><sub><b>Jonathan Lauber</b></sub></a><br /><a href="#infra-jlauber18" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
     </tr>
+    <tr>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/prady0t"><img src="https://avatars.githubusercontent.com/u/99216956?v=4?s=100" width="100px;" alt="Pradyot Ranjan"/><br /><sub><b>Pradyot Ranjan</b></sub></a><br /><a href="#infra-prady0t" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
+    </tr>
   </tbody>
 </table>