augment.ipynb

{
  "cells": [
    {
      "cell_type": "code",
<<<<<<< HEAD
      "execution_count": 1,
=======
      "execution_count": null,
>>>>>>> wooksbaby
      "id": "matched-balloon",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-05-18T02:35:31.259617Z",
          "start_time": "2021-05-18T02:35:30.246082Z"
        },
        "id": "matched-balloon"
      },
<<<<<<< HEAD
      "outputs": [],
=======
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mFailed to start the Kernel. \n",
            "\u001b[1;31mTraceback (most recent call last):\n",
            "\u001b[1;31m  File \"/opt/conda/envs/dkt/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n",
            "\u001b[1;31m    return _run_code(code, main_globals, None,\n",
            "\u001b[1;31m  File \"/opt/conda/envs/dkt/lib/python3.10/runpy.py\", line 86, in _run_code\n",
            "\u001b[1;31m    exec(code, run_globals)\n",
            "\u001b[1;31m  File \"/opt/conda/envs/dkt/lib/python3.10/site-packages/ipykernel_launcher.py\", line 15, in <module>\n",
            "\u001b[1;31m    from ipykernel import kernelapp as app\n",
            "\u001b[1;31m  File \"/opt/conda/envs/dkt/lib/python3.10/site-packages/ipykernel/kernelapp.py\", line 51, in <module>\n",
            "\u001b[1;31m    from .ipkernel import IPythonKernel\n",
            "\u001b[1;31m  File \"/opt/conda/envs/dkt/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 19, in <module>\n",
            "\u001b[1;31m    from .debugger import Debugger, _is_debugpy_available\n",
            "\u001b[1;31m  File \"/opt/conda/envs/dkt/lib/python3.10/site-packages/ipykernel/debugger.py\", line 22, in <module>\n",
            "\u001b[1;31m    from debugpy.server import api  # noqa\n",
            "\u001b[1;31m  File \"/opt/conda/envs/dkt/lib/python3.10/site-packages/debugpy/server/__init__.py\", line 7, in <module>\n",
            "\u001b[1;31m    import debugpy._vendored.force_pydevd  # noqa\n",
            "\u001b[1;31m  File \"/opt/conda/envs/dkt/lib/python3.10/site-packages/debugpy/_vendored/force_pydevd.py\", line 34, in <module>\n",
            "\u001b[1;31m    pydevd_constants = import_module('_pydevd_bundle.pydevd_constants')\n",
            "\u001b[1;31m  File \"/opt/conda/envs/dkt/lib/python3.10/importlib/__init__.py\", line 126, in import_module\n",
            "\u001b[1;31m    return _bootstrap._gcd_import(name[level:], package, level)\n",
            "\u001b[1;31m  File \"/opt/conda/envs/dkt/lib/python3.10/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_constants.py\", line 394, in <module>\n",
            "\u001b[1;31m    from _pydev_bundle._pydev_saved_modules import thread, threading\n",
            "\u001b[1;31m  File \"/opt/conda/envs/dkt/lib/python3.10/site-packages/debugpy/_vendored/pydevd/_pydev_bundle/_pydev_saved_modules.py\", line 91, in <module>\n",
            "\u001b[1;31m    import code as _code;    verify_shadowed.check(_code, ['compile_command', 'InteractiveInterpreter'])\n",
            "\u001b[1;31m  File \"/opt/conda/envs/dkt/lib/python3.10/site-packages/debugpy/_vendored/pydevd/_pydev_bundle/_pydev_saved_modules.py\", line 75, in check\n",
            "\u001b[1;31m    raise DebuggerInitializationError(msg)\n",
            "\u001b[1;31m_pydev_bundle._pydev_saved_modules.DebuggerInitializationError: It was not possible to initialize the debugger due to a module name conflict.\n",
            "\u001b[1;31m\n",
            "\u001b[1;31mi.e.: the module \"code\" could not be imported because it is shadowed by:\n",
            "\u001b[1;31m/data/ephemeral/level2-dkt-recsys-06/code/__init__.py\n",
            "\u001b[1;31mPlease rename this file/folder so that the original module from the standard library can be imported. \n",
            "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
          ]
        }
      ],
>>>>>>> wooksbaby
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns; sns.set_theme(color_codes=True)\n",
        "import missingno as msno\n",
        "import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "c1fa5b21",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: matplotlib in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (3.8.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib) (4.47.0)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib) (1.4.5)\n",
            "Requirement already satisfied: numpy<2,>=1.21 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib) (1.26.3)\n",
            "Requirement already satisfied: packaging>=20.0 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib) (23.2)\n",
            "Requirement already satisfied: pillow>=8 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n",
            "Requirement already satisfied: seaborn in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (0.13.1)\n",
            "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from seaborn) (1.26.3)\n",
            "Requirement already satisfied: pandas>=1.2 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from seaborn) (2.1.4)\n",
            "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from seaborn) (3.8.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.47.0)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.2)\n",
            "Requirement already satisfied: pillow>=8 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2023.3.post1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2023.4)\n",
            "Requirement already satisfied: six>=1.5 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n",
            "Requirement already satisfied: missingno in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (0.5.2)\n",
            "Requirement already satisfied: numpy in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from missingno) (1.26.3)\n",
            "Requirement already satisfied: matplotlib in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from missingno) (3.8.2)\n",
            "Requirement already satisfied: scipy in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from missingno) (1.11.4)\n",
            "Requirement already satisfied: seaborn in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from missingno) (0.13.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib->missingno) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib->missingno) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib->missingno) (4.47.0)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib->missingno) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib->missingno) (23.2)\n",
            "Requirement already satisfied: pillow>=8 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib->missingno) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib->missingno) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from matplotlib->missingno) (2.8.2)\n",
            "Requirement already satisfied: pandas>=1.2 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from seaborn->missingno) (2.1.4)\n",
            "Requirement already satisfied: pytz>=2020.1 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from pandas>=1.2->seaborn->missingno) (2023.3.post1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from pandas>=1.2->seaborn->missingno) (2023.4)\n",
            "Requirement already satisfied: six>=1.5 in /opt/ml/miniconda3/envs/dkt/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->missingno) (1.16.0)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "%pip install matplotlib\n",
        "%pip install seaborn\n",
        "%pip install missingno"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "WfRcrQpdosVd",
      "metadata": {
        "id": "WfRcrQpdosVd"
      },
      "source": [
        "버전 확인\n",
        "\n",
        "- `python` >= 3.10.12\n",
        "- `numpy` >= 1.26.0\n",
        "- `pandas` >= 2.1.1\n",
        "- `matplotlib` >= 3.8.1\n",
        "- `seaborn` >= 0.13.0\n",
        "- `missingno` >= 0.5.2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "efptJOlFosVf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "efptJOlFosVf",
        "outputId": "4e8250b1-4551-4f38-df18-cf5d668b865c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "===== 나의 라이브러리 버전 확인 =====\n",
            "python version  <> 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0]\n",
            "numpy version   <> 1.26.3\n",
            "pandas version  <> 2.1.4\n",
            "matplotlib version <> 3.8.2\n",
            "seaborn version <> 0.13.1\n",
            "missingno version <> 0.5.2\n"
          ]
        }
      ],
      "source": [
        "import sys, matplotlib\n",
        "\n",
        "print(\"=\"*5 ,\"나의 라이브러리 버전 확인\", \"=\"*5)\n",
        "print(f\"{'python version':15s} <> {sys.version}\")\n",
        "print(f\"{'numpy version':15s} <> {np.__version__}\")\n",
        "print(f\"{'pandas version':15s} <> {pd.__version__}\")\n",
        "print(f\"{'matplotlib version':15s} <> {matplotlib.__version__}\")\n",
        "print(f\"{'seaborn version':15s} <> {sns.__version__}\")\n",
        "print(f\"{'missingno version':15s} <> {msno.__version__}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "blind-ocean",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-05-18T02:35:35.846469Z",
          "start_time": "2021-05-18T02:35:31.260617Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "blind-ocean",
        "outputId": "f5d154e4-3d28-4457-844e-8dfb6327fda8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 6.01 s, sys: 867 ms, total: 6.87 s\n",
            "Wall time: 6.9 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "dtype = {\n",
        "    'userID': 'int16',\n",
        "    'answerCode': 'int8',\n",
        "    'KnowledgeTag': 'int16'\n",
        "}\n",
        "train = pd.read_csv('/data/ephemeral/home/level2-dkt-recsys-06/data/train_data.csv', dtype=dtype, parse_dates=['Timestamp'])\n",
        "test = pd.read_csv('/data/ephemeral/home/level2-dkt-recsys-06/data/test_data.csv', dtype=dtype, parse_dates=['Timestamp'])\n",
        "\n",
        "df = pd.concat([train, test])\n",
        "df = df.sort_values(by=['userID', 'Timestamp']).reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "F1kwt-RnEhrg",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "F1kwt-RnEhrg",
        "outputId": "60cf6b9a-e572-407b-a298-ce4d9f25d920"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userID</th>\n",
              "      <th>assessmentItemID</th>\n",
              "      <th>testId</th>\n",
              "      <th>answerCode</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>KnowledgeTag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>A060001001</td>\n",
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              "      <td>1</td>\n",
              "      <td>2020-03-24 00:17:11</td>\n",
              "      <td>7224</td>\n",
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              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>A060001002</td>\n",
              "      <td>A060000001</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-24 00:17:14</td>\n",
              "      <td>7225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>A060001003</td>\n",
              "      <td>A060000001</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-24 00:17:22</td>\n",
              "      <td>7225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>A060001004</td>\n",
              "      <td>A060000001</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-24 00:17:29</td>\n",
              "      <td>7225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>A060001005</td>\n",
              "      <td>A060000001</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-24 00:17:36</td>\n",
              "      <td>7225</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userID assessmentItemID      testId  answerCode           Timestamp  \\\n",
              "0       0       A060001001  A060000001           1 2020-03-24 00:17:11   \n",
              "1       0       A060001002  A060000001           1 2020-03-24 00:17:14   \n",
              "2       0       A060001003  A060000001           1 2020-03-24 00:17:22   \n",
              "3       0       A060001004  A060000001           1 2020-03-24 00:17:29   \n",
              "4       0       A060001005  A060000001           1 2020-03-24 00:17:36   \n",
              "\n",
              "   KnowledgeTag  \n",
              "0          7224  \n",
              "1          7225  \n",
              "2          7225  \n",
              "3          7225  \n",
              "4          7225  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "371OZt02-x1a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "371OZt02-x1a",
        "outputId": "63278637-bc23-4b52-c48a-5b03fedc728e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userID</th>\n",
              "      <th>assessmentItemID</th>\n",
              "      <th>testId</th>\n",
              "      <th>answerCode</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>KnowledgeTag</th>\n",
              "      <th>SolvingTime</th>\n",
              "      <th>CumulativeTime</th>\n",
              "      <th>Month</th>\n",
              "      <th>DayOfWeek</th>\n",
              "      <th>TimeOfDay</th>\n",
              "      <th>problems_cumulative</th>\n",
              "      <th>problems_last7days</th>\n",
              "      <th>problems_last30days</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>A060001001</td>\n",
              "      <td>A060000001</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-24 00:17:11</td>\n",
              "      <td>7224</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Dawn</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>A060001002</td>\n",
              "      <td>A060000001</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-24 00:17:14</td>\n",
              "      <td>7225</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Dawn</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>A060001003</td>\n",
              "      <td>A060000001</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-24 00:17:22</td>\n",
              "      <td>7225</td>\n",
              "      <td>7</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Dawn</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>A060001004</td>\n",
              "      <td>A060000001</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-24 00:17:29</td>\n",
              "      <td>7225</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Dawn</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>A060001005</td>\n",
              "      <td>A060000001</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-24 00:17:36</td>\n",
              "      <td>7225</td>\n",
              "      <td>11</td>\n",
              "      <td>36</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Dawn</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userID assessmentItemID      testId  answerCode           Timestamp  \\\n",
              "0       0       A060001001  A060000001           1 2020-03-24 00:17:11   \n",
              "1       0       A060001002  A060000001           1 2020-03-24 00:17:14   \n",
              "2       0       A060001003  A060000001           1 2020-03-24 00:17:22   \n",
              "3       0       A060001004  A060000001           1 2020-03-24 00:17:29   \n",
              "4       0       A060001005  A060000001           1 2020-03-24 00:17:36   \n",
              "\n",
              "   KnowledgeTag  SolvingTime  CumulativeTime  Month DayOfWeek TimeOfDay  \\\n",
              "0          7224            3               3      3   Tuesday      Dawn   \n",
              "1          7225            8              11      3   Tuesday      Dawn   \n",
              "2          7225            7              18      3   Tuesday      Dawn   \n",
              "3          7225            7              25      3   Tuesday      Dawn   \n",
              "4          7225           11              36      3   Tuesday      Dawn   \n",
              "\n",
              "   problems_cumulative  problems_last7days  problems_last30days  \n",
              "0                    0                   0                    0  \n",
              "1                    1                   1                    1  \n",
              "2                    2                   2                    2  \n",
              "3                    3                   3                    3  \n",
              "4                    4                   4                    4  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "###### FEATURE AUGMENTATION ######\n",
        "\n",
        "# SolvingTime, CumulativeTime : 해당 문제를 푸는데 걸린 시간(초), 해당 시험지 내에서 해당 문제 전까지 누적 풀이 시간(초)\n",
        "df['NextTimestamp'] = pd.to_datetime(df.groupby(['userID', 'testId'])['Timestamp'].shift(-1))\n",
        "df['SolvingTime'] = (df['NextTimestamp'] - df['Timestamp']).dt.total_seconds()\n",
        "df['SolvingTime'] = df['SolvingTime'].astype('Int64')\n",
        "df['CumulativeTime'] = df.groupby(['userID', 'testId'])['SolvingTime'].cumsum()\n",
        "df = df.drop(columns=['NextTimestamp'])\n",
        "## Issue : 시험지 내 마지막 문제에 Solvingtime 결측\n",
        "\n",
        "# Month, DayOfWeek, TimeOfDay : 문제를 푼 달, 요일, 시간대(0~6시는 새벽, 6~12시는 오전, 12~18시는 오후, 18~24시는 밤)\n",
        "df['Timestamp'] = pd.to_datetime(df['Timestamp'])\n",
        "df['Month'] = df['Timestamp'].dt.month\n",
        "df['DayOfWeek'] = df['Timestamp'].dt.dayofweek\n",
        "day_dict = {0: 'Monday', 1: 'Tuesday', 2: 'Wednesday', 3: 'Thursday', 4: 'Friday', 5: 'Saturday', 6: 'Sunday'}\n",
        "df['DayOfWeek'] = df['DayOfWeek'].map(day_dict)\n",
        "def label_hour(hour):\n",
        "    if 0 <= hour < 6:\n",
        "        return 'Dawn'\n",
        "    elif 6 <= hour < 12:\n",
        "        return 'Morning'\n",
        "    elif 12 <= hour < 18:\n",
        "        return 'Afternoon'\n",
        "    else:\n",
        "        return 'Night'\n",
        "df['TimeOfDay'] = df['Timestamp'].dt.hour.apply(label_hour)\n",
        "\n",
        "\n",
        "# problems_last7days, problems_last30days, problems_cumulative : User가 해당 문제 푼 시점으로부터 7일 전까지, 30일 전까지, 처음까지 푼 누적 문제 수\n",
        "df = df.sort_values(['userID', 'Timestamp'])\n",
        "grouped = df.groupby('userID')\n",
        "df['problems_cumulative'] = grouped.cumcount()\n",
        "\n",
        "results = []\n",
        "for name, group in grouped:\n",
        "    group['problems_last7days'] = group.rolling('7D', on='Timestamp')['assessmentItemID'].count() - 1\n",
        "    group['problems_last30days'] = group.rolling('30D', on='Timestamp')['assessmentItemID'].count() - 1\n",
        "    results.append(group)\n",
        "\n",
        "df = pd.concat(results)\n",
        "df['problems_last7days'] = df['problems_last7days'].astype(int)\n",
        "df['problems_last30days'] = df['problems_last30days'].astype(int)\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "OaOXGNqdIQFc",
      "metadata": {
        "id": "OaOXGNqdIQFc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 492354/492354 [34:55<00:00, 234.92it/s]  \n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userID</th>\n",
              "      <th>assessmentItemID</th>\n",
              "      <th>testId</th>\n",
              "      <th>answerCode</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>KnowledgeTag</th>\n",
              "      <th>SolvingTime</th>\n",
              "      <th>CumulativeTime</th>\n",
              "      <th>Month</th>\n",
              "      <th>DayOfWeek</th>\n",
              "      <th>TimeOfDay</th>\n",
              "      <th>problems_cumulative</th>\n",
              "      <th>problems_last7days</th>\n",
              "      <th>problems_last30days</th>\n",
              "      <th>same_tag_last7days</th>\n",
              "      <th>same_tag_last30days</th>\n",
              "      <th>same_tag_cumulative</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>A060001001</td>\n",
              "      <td>A060000001</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-24 00:17:11</td>\n",
              "      <td>7224</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Dawn</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>A060001002</td>\n",
              "      <td>A060000001</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-24 00:17:14</td>\n",
              "      <td>7225</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Dawn</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>A060001003</td>\n",
              "      <td>A060000001</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-24 00:17:22</td>\n",
              "      <td>7225</td>\n",
              "      <td>7</td>\n",
              "      <td>18</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Dawn</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>A060001004</td>\n",
              "      <td>A060000001</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-24 00:17:29</td>\n",
              "      <td>7225</td>\n",
              "      <td>7</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Dawn</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>A060001005</td>\n",
              "      <td>A060000001</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-03-24 00:17:36</td>\n",
              "      <td>7225</td>\n",
              "      <td>11</td>\n",
              "      <td>36</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>Dawn</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userID assessmentItemID      testId  answerCode           Timestamp  \\\n",
              "0       0       A060001001  A060000001           1 2020-03-24 00:17:11   \n",
              "1       0       A060001002  A060000001           1 2020-03-24 00:17:14   \n",
              "2       0       A060001003  A060000001           1 2020-03-24 00:17:22   \n",
              "3       0       A060001004  A060000001           1 2020-03-24 00:17:29   \n",
              "4       0       A060001005  A060000001           1 2020-03-24 00:17:36   \n",
              "\n",
              "   KnowledgeTag  SolvingTime  CumulativeTime  Month DayOfWeek TimeOfDay  \\\n",
              "0          7224            3               3      3   Tuesday      Dawn   \n",
              "1          7225            8              11      3   Tuesday      Dawn   \n",
              "2          7225            7              18      3   Tuesday      Dawn   \n",
              "3          7225            7              25      3   Tuesday      Dawn   \n",
              "4          7225           11              36      3   Tuesday      Dawn   \n",
              "\n",
              "   problems_cumulative  problems_last7days  problems_last30days  \\\n",
              "0                    0                   0                    0   \n",
              "1                    1                   1                    1   \n",
              "2                    2                   2                    2   \n",
              "3                    3                   3                    3   \n",
              "4                    4                   4                    4   \n",
              "\n",
              "   same_tag_last7days  same_tag_last30days  same_tag_cumulative  \n",
              "0                   1                    1                    1  \n",
              "1                   1                    1                    1  \n",
              "2                   2                    2                    2  \n",
              "3                   3                    3                    3  \n",
              "4                   4                    4                    4  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# same_tag_last7days, same_tag_last30days, same_tag_cumulative : 모든 문제가 아니라 같은 KnowledgeTag를 가진 문제에 대해서만 위와 같이 계산한 컬럼\n",
        "df = df.sort_values(['userID', 'KnowledgeTag', 'Timestamp'])\n",
        "grouped = df.groupby(['userID', 'KnowledgeTag'])\n",
        "\n",
        "results = []\n",
        "for (userID, KnowledgeTag), group in tqdm.tqdm(grouped):\n",
        "    group['same_tag_last7days'] = group.rolling('7D', on='Timestamp')['assessmentItemID'].count()\n",
        "    group['same_tag_last30days'] = group.rolling('30D', on='Timestamp')['assessmentItemID'].count()\n",
        "    group['same_tag_cumulative'] = group['assessmentItemID'].expanding().count()\n",
        "    results.append(group)\n",
        "\n",
        "df = pd.concat(results)\n",
        "df['same_tag_last7days'] = df['same_tag_last7days'].astype(int)\n",
        "df['same_tag_last30days'] = df['same_tag_last30days'].astype(int)\n",
        "df['same_tag_cumulative'] = df['same_tag_cumulative'].astype(int)\n",
        "## Issue : userID * KnowledgeTag 개수(over 100000)만큼의 그룹을 생성한 후 연산해야만 하는 컬럼이라 연산 시간이 많이 소요됨. 매번 생성하지 않고 한번 생성 후 csv파일 형태로 사용해야 할듯\n",
        "\n",
        "df = df.sort_values(['userID', 'Timestamp'])\n",
        "df = df[df['answerCode'] != -1]\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "eac53828",
      "metadata": {},
      "outputs": [],
      "source": [
        "df.to_csv('augment.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "LiwaY7oYtbkQ",
      "metadata": {
        "id": "LiwaY7oYtbkQ"
      },
      "outputs": [],
      "source": [
        "def percentile(s):\n",
        "    return np.sum(s) / len(s)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "absolute-preserve",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-05-18T02:35:36.466273Z",
          "start_time": "2021-05-18T02:35:35.862806Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "absolute-preserve",
        "outputId": "600e7f07-3eed-4800-df44-25854e913999"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>assessmentItemID</th>\n",
              "      <th>answerCode</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7442.000000</td>\n",
              "      <td>7442.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>339.418973</td>\n",
              "      <td>0.628749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>321.465413</td>\n",
              "      <td>0.159608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>78.000000</td>\n",
              "      <td>0.526782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>232.500000</td>\n",
              "      <td>0.651923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>519.000000</td>\n",
              "      <td>0.750728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1860.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       assessmentItemID   answerCode\n",
              "count       7442.000000  7442.000000\n",
              "mean         339.418973     0.628749\n",
              "std          321.465413     0.159608\n",
              "min            9.000000     0.000000\n",
              "25%           78.000000     0.526782\n",
              "50%          232.500000     0.651923\n",
              "75%          519.000000     0.750728\n",
              "max         1860.000000     1.000000"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "stu_groupby = df.groupby('userID').agg({\n",
        "    'assessmentItemID': 'count',\n",
        "    'answerCode': percentile\n",
        "})\n",
        "stu_groupby.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "516264ce",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.6212750253713443"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[df['SolvingTime'] > 10000]['answerCode'].mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "favorite-insured",
      "metadata": {
        "id": "favorite-insured"
      },
      "source": [
        "## (a)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "acute-infrastructure",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-05-18T02:35:36.482286Z",
          "start_time": "2021-05-18T02:35:36.467272Z"
        },
        "id": "acute-infrastructure"
      },
      "outputs": [],
      "source": [
        "itemnum_ans = stu_groupby.groupby('assessmentItemID').mean()\n",
        "itemnum_ans['num_items'] = itemnum_ans.index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "greater-investigator",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-05-18T02:35:36.794779Z",
          "start_time": "2021-05-18T02:35:36.483272Z"
        },
        "id": "greater-investigator",
        "outputId": "9ea0521e-4405-41d2-96d4-e61d4c83f578"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Answer Rate')"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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XXXdx2WWX8ba3vW3G+3/zN3/DXXfdNYMErdSxX3zxxXz961/nW9/6Fr7vc+GFF6KqKhdddFFEgi688EI0TQPkOGcyGYIgWNT1XQm89a1v5bbbbuMzn/kM1113HYqi8NOf/pRiscitt97acg8fPnx4xvZz3UMbN25k165dXH755aecH1OMlUGsCYoRYxacffbZXHHFFfi+z+mnnx7pgcbHxyMtULO0eCG8+MUv5uc//zmPPfZY9Fq9XuerX/0q69atW1BTNBvOP/98rrrqKr7+9a9HlWJT8elPf5r9+/dHFVYA69atQ9O0SD/SxFe+8pWW/3d1dXHppZdyxx13MDIyMmPfU1MlExMTLe9lMhk2btzYUp6cSqWAyTTPXNA0jSuvvJLvf//7LRPh2NgY//mf/8lFF100I6W0EEql0gzn7DPPPBNg3hLq0047bdHX+dFHH+XIkSO86U1v4pWvfOWMP69+9at56KGHIh3VSh97U+vz93//9+zYsSNKz1500UX85Cc/4cknn4xSYSDH+RWveAXf+c53ePbZZ2d87/RU2EpA13Xe+c53smfPnshmoRkJm3qOjuPw//7f/5uxfSqVmjU99qpXvYrh4WG++tWvznjPsizq9fpKnUKMkxRxJChGjHnws5/9jAsuuACQJnFPP/0073vf+5a0j/e+971861vf4j3veQ833XQTbW1tfPOb3+Tw4cN87nOfW7aj8yc+8Qluvvlm3v/+9/Pa176Wiy++GMdx+O53v8tPf/pTXve617WUOudyOV75ylfyr//6ryiKwoYNG7jvvvtm1U185CMf4W1vexvXX389b3nLW9iwYQNjY2M8/vjjDA0NRemf17zmNVx66aWcddZZtLe388QTT/Cd73yHd7zjHdG+zjrrLECWhF911VVomsZrXvOaWc/pgx/8IA888ABve9vbeNvb3oamadxxxx04jsPv/u7vLnmMvvGNb/CVr3yFl73sZWzcuJFarcZXv/pVstksV1999ZL3NxvuuusuNE3jJS95yazvX3PNNXz605/m7rvvXpLB5mKPfdOmTaxZs4Z9+/Zx0003Ra9fcsklkfP4VFE+SB+phx56iLe85S28+c1v5rTTTqNUKvHUU0/xk5/8pCUVvFJ405vexGc/+1n+/u//npe97GVccMEFtLW18fu///vcdNNNKIrCf/zHf8za7uWss87i7rvv5mMf+xjnnHMO6XSaa665hte//vV8+9vf5iMf+QgPPfQQF154Ib7vs3fvXu655x7+4R/+gXPOOWfFzyXGyYOYBMWIMQd83+e///u/I93Ek08+ieu6ESlaLLq7u7n99tv5y7/8S/71X/8V27bZsWMHX/jCF+acOBe7369+9av88z//M/fccw/f+c53sG0bkJqm3/zN35yxzR/90R/heR633347pmnyyle+kt/7vd/jta99bcvnTjvtNL72ta9x66238o1vfINisUhnZycveMELIj0USCO7H/zgB+zcuRPHcVi7di0f/OAHede73hV95uUvfzk33XQT3/rWt7jzzjsRQsxJgk4//XS+/OUv81d/9Vf83d/9HUIIzj33XP7yL/9yVnH5Qrj00kt54oknuPvuuxkbGyOXy3HuuefyqU99qqUCbblwXZd77rmHCy64YE7h9Pbt21m/fj133nnnkkjQUo79oosu4p577uHCCy+MXjvrrLNIpVJ4njdj7Lq7u/m3f/s3Pv/5z3Pvvffyla98hfb2dk477bQo1bvSSCaTUar0oYce4rLLLuMLX/gCn/jEJ/jMZz5DPp/nda97HZdffnnL/QPwtre9jV/84hd8/etf57bbbmPdunVcc801qKrK5z//eW677Tb+4z/+g3vvvZdUKsX69eu56aabZhXZx4gxFXHvsBgxTiIMDw9z44034nked9xxx7L0RjFixIhxqiDWBMWIcRKht7eXf/iHf8BxHN7znvdQKpVO9CHFiBEjxqpFHAmKESNGjBgxYpySiCNBMWLEiBEjRoxTEjEJihEjRowYMWKckohJUIwYMWLEiBHjlERMgmLEiBEjRowYpyRiEhQjRowYMWLEOCURmyXOAyEEQbByxXOqqqzo/p4PiM/51EB8zqcG4nM+NfB8P2dVVRbdSy4mQfMgCASFQm1F9qXrKh0dGcrlOp63cHfkkwHxOcfnfLIiPuf4nE9WnAzn3NmZQdMWR4LidFiMGDFixIgR45RETIJixIgRI0aMGKckYhIUI0aMGDFixDglEZOgGDFixIgRI8YpiZgExYgRI0aMGDFOScQkKEaMGDFixIhxSiImQTFixIgRI0aMUxIxCYoRI0aMGDFinJJYdSRoz549vPOd7+T888/nyiuv5JOf/CSO4yy4XaVS4Y//+I+57LLLOO+887jpppv4xS9+cRyOOEaMGDFixIjxfMSqIkGlUombb74Z13X53Oc+x4c+9CG++tWv8vGPf3zBbX/rt36L733ve/zu7/4uf/3Xf42madx8880MDg4ehyOPESNGjBgrhUAIhgp19g2WGSrUCcTzt4VDjNWNVdU24/bbb6dWq3HrrbfS3t4OgO/7fPSjH+V973sfvb29s273+OOPc//99/O3f/u3XHPNNQBcdtllXHvttfzjP/4jf/RHf3S8TiFGjBgxYhwFDgxV2PnkIINjdVw/wNBU+rvTXHl2P5v6cif68GKcZFhVkaD777+fyy+/PCJAAK961asIgoCdO3fOud3TTz+NoihceeWV0WupVIqLL76YH/7wh8fykGPEiBEjxgrhwFCFO3fu48BQhUxKp7cjRSalt7y+mrGYCFYc5VpdWFWRoL1793LDDTe0vJbP51mzZg179+6dczvHcVBVFU3TWl43DIMjR45gWRbJZPKYHHOMGDFiLBaBEIxMNGjYHqmETk9HCnWR3a6PZtvnAwIh2PnkINWGS39XOuoCnkroJE2NwfE6Dzw5yIbe7Ko878VEsOIo1+rDqiJB5XKZfD4/4/W2tjZKpdKc223atAnf93n66ac599xzAQiCgCeffBIhBOVyedkkSNdXJlimaWrL36cC4nM+NRCf8+Kwf6jMj38+yMBYDdcLMHSVtd0Zrjq3n819M597K7XtSuFYX+fB8RpDhTpd+SSaOu07FIWufJLBQp3xskV/V+aYHMN0LPac9w+VueuB/VQbDp25JAlDw3Z9Dg5XKJRt3nD1FoAFP3O8ruV8ONV+z6uKBC0XV155JRs3buQjH/kIn/jEJ+jq6uL//t//y6FDhwCiFcVSoaoKHR0r+2PL51Mrur/nA+JzPjUQn/Pc2HO4yLd+cpDxUoNs2qAtm0BVFA6P1rj7wYPceN0Otq1vn3Pbux88SLnm0NWWJGnqWI63qG2PBY7VdR6tOAgUctkEqjrzma0ZGhXLQzeNFX8uL4T5zjkIBA//aC+W67N5bVs03yRTBvlcgsMjVR7eNQow72ceeWaM83b0zXrus33n4HiNWsMlkzLo78osarul4FT5Pa8qEpTP56lUZuZ8S6USbW1tc25nmiaf/vSn+e3f/m2uv/56ALZv387NN9/Ml770pRaN0VIQBIJyub6sbadD01Ty+RTlcgPfD1Zkn6sd8TnH53yyYinnHAjB7d/dxTMHJgAYLTZQFYVMUqe3M0Wh1ODbO/fyy9dtn5HmCYTg7p17KZQarO3KoCgKnuujKwo9bUkGxmtzbrvSONbX2XNcFASVqk0qMXNqatgeCgLPcZmYqK3498+GxZzz4HiNfUeK5FMGruPPeD+fMti1vwBAVz4552f2Himya+/oglGuYx0VPBl+z/l8atGRrFVFgrZu3TpD+1OpVBgdHWXr1q3zbnv22Wdzzz33cODAAYQQbN68mT/5kz/hrLPOwjCMZR+T563sTeD7wYrvc7UjPudTA/E5z46fPTPKU/sKCAGphIamqvhBQLnu0rB9+jrTHB6pMTBao68z3bLtUKHOkZEa7dkEAhDTRLTt2cSc2x4rHKvr3JVP0teZ5sBQpUUTBPK8x8sWm/tydOWTx/0+m++cq3UXxw0wsuqsImdDV7FD4mPoc3/GcQOqdRevbe5zawrEqw2XjlyCRFam1PYNlBmdaPC6K7esmLboVPk9r6qk39VXX80DDzxAuVyOXrvnnntQVbWl8msuKIrC5s2b2bJlCxMTE9x99928+c1vPpaHHCNGjBhzIhCCh3YN43oBmaSOrqkoCuiaSjqh4/oBExULx/Np2N6M7Ru2h+sHJAxtlr1DwtBw/WDWbZ9vUBWFK8/uJ5syGByv07A9gkDQsD0Gx+tkUwZXnN2/6kTRqYSOoanY7swID4Dt+piGhmlq837G0NRZI2BNTBeOpxI6qqqQSuj0d6WpNlweeHIwrjZbIlYVCbrxxhvJZDLccsst/PjHP+ZrX/san/zkJ7nxxhtbPIJuvvlmrrvuupZt//Zv/5a7776bhx56iNtvv50bbriBs88+mze96U3H+zRixIgRA4CRiQYTZRvT0GZMTooCCUOl2vAQMOsEuJgJdqHJ8/mETX25KJpRa3gMTzSoNTw2T3l9taGnI0V/d5qJij0jUieEYKJis7kvy+be3LyfWdudpqdjbh3OyESDwbE6HbnEDJ2roih05BIMjNUZmWis3MmdAlhVv5y2tja++MUv8qd/+qfccsstZDIZfumXfokPfehDLZ8LggDfb30olMtlPvGJTzA+Pk5PTw+ve93reP/73486vcogRowYMebBSpaiN2wPRYFsUqdquWiq0jKBqYqC4/l05RKzToDNCfbAUIWkqc1IEckJNjfv5Pl8w6a+HBt6s88bO4BmBGu8ZDE4LklKs/JromKTTRlcec5aAMbLc39moSjXYqKCxaqz4lHBk92aYVWRIIBt27Zx2223zfuZL33pSzNe+/CHP8yHP/zhY3RUMWLEOBWw0j4uqYSOqWuYeRXbDajbHglDQ1MV/EDQsGUk55Ize2edWBYzwZ7IFNGxmiBVRTluGqeVQDOC1bx3ilUHQ1PZ3Jfjiin3zmI+MxemRgVni/wdi6jgqeBrtOpIUIwYMWKsJBY7Uc8QnYZk48BQhfGStax0zNRIzqbeLMMTdeqWhyNkOkxXFXZsauf807vn3MdiJ9jjjWM9QT7fIhCLiWAdTZTreEcF9w+VV/z3sBoRk6AYMWKctGhO1AOjNeq2h6oo9HaluO6iDWzunywnPlZuxVMjOZWGy9ruDIGQqY1aOLm88tJNC+5ztaWIjgVhnL7/52MEYjERrOVGuY5nVDAIBD/++fPXvXspiElQjBgxTko0J+qxkoXj+tiuj+fLvk3PHS7xxhdt5YVn9QFLE50udQKbHslpTurbN7QvKZKzWlJEyyWMJzIid7LgeEUFB8drDIzVjsnvYbUhJkExYsQ4adCcaOuWyw9+doSxokXddiNBadJU8HxBpe7yjf/aS19Xms19+WMuOl1tkZyjwXChvmTCuNjIzvO9f9jxwPG4l2oNF9cLSGSPrwj7RCAmQTFixDgpMHWirVkuIxN1BDKCkksb0YRq6ArZlE614fG9Rw/zP1595ryiUyEExaqN5wXUGu6yfVhORCTnWOhqlkoYlxLZOZYRuZMJx/peyqQMafJ4HEXYJwrP/zOIESPGKY/pE62hK4xMNHBcH02V0R9Dn5xUdU1DVwOGxuWEOpfotFJ3GS7UmKg4mIbKPT89yM/3jnP1+WuPe/+qpeJY6WoWW6WUMDXZ1uOhAxTKFht7c1F/q7kiOyeqDDxGK/q7MqztzrBvoHzSWzPEJChGjBjPa8yWQhECNFVWYAVCULc98ppJ81nuBwGaqkSOxLOJTl0vYN9gGcvxSZoam/vymIbKgaEK37x/H/lcis7M8lvyHEscS11Nb2d6wSqlznyC+352hP1DFQbGauiaiueX6e1Mk0vLMZstsnMiysBjzISqKlx1bj+jE41Vac2wkoidBGPEiPG8xmwplFRCI5XUEQJUVfYA9APZB0kIsN2AhKmRTurRhDrVrbhad9k3WMZ2AzpzCbaubaMta05pUeDwg0cOrcoWBce6vcJC7S1URaFYcTgwXME0VHRNxTQUKg2XA0MVKnU32tf0th+LcV9eyFk5xuIRCFkosG+wzFCh3nJPbO7LP+/cu5eDmE7HiBHjeY3ZUiiKorCuO0u56uL4ASqCIBB4BNhugK4qmIbGuu5My4TaFJ0+va/AN3+8j0zSCMkVLfvuzCU5OFxhuFBnTdvqmpCPh65mriqlTb1ZKnWXQsWmvytNw/bDiJxCOqFRtz2GC3WyqTYUZWZkZ7WbQ55MmCtdOjXVezIJ+udCTIJixIjxvMZcKZR8xmRzf469g2U8L6Bh+5i6SsrUMA2N7rbkrBOqqihkUga6ptKeNZnteW8aGhXLW5XalLl0NUJIh2rX86lZLnXLnWMPi8NsE2QgBF+5d3dEwFIJGW2rNFzSCYWEoVIPxy2V0GbVlqxWc8iTCfOlS6eneleLNcOxQkyCYsSI8bzGfE66PR0pag0XLwhImgZCCNJJnXXdmXkn1IW0KY7rY+hqNPGvppXybMfeFHjXLQ/XFwgh+MHPjqCp6lGRiukT5L7BcgsBUxSF3s4M1lCFuu1h6hp+EFC3XIrVuSM7p0IE4kRhIRuCoUKdHzxyiDddveUEH+nxQUyCYsSI8bzGQimU7vYU11+xmWRCX/SEulCLgkLF4ozNXViOz+3f372qnI2nH3u14XFgqILr+5i6hhf4ZJIGIxMN7ty5b0X1HbMRsFzaYFNfjuFCjUrDw/MFthewZYHIzskegThRWChduppTvccCMQmKESPGqsFyoyornUJZiFjl0ybbN7bzH/+1j0rdWVXOxlOPfWCsRqXu4ng+CUPF8QJMXWPdmizZlL7i5oNzkcdsSkfpSuOP1ljTm+Ut15xGf1dm2d+52qJvzycsZEOwmlO9xwIxCYoRI8aqwNH62hxNCmW2SXU+YnXlef08+uw41YazKp2Nm8f+3UcORhVbvg+5lNFSpj5VJN3TkVoRYnHGxg4ODVc5MFShpyON5wcRGVMUSCcNfvT4wLKjZc/XvmKrBUtJ9Z4KODXOMkaMGIvCclfYQSAYHK9RrbvLmkDnE2qOFRtcdc5aOvKJBfe9nBTKQpPqbMRqvGxxaLhCZy45a0qhPWuyb7DCz54ZZX1P9oREKjb15bjmwvUMjtVpz5oYukYqobcIvZvmg3sHyvzgZ4ePilhMHceG41Gtu0xUJ3DdABSFfNqgvysTeS0tJ1oW9xU7eiw21dvbmSbwV58FxEojJkExYsQAlr/C3j9U5uEf7WXfkSKOu/QJdD6hpucF7B0oc3DkWbrySUxdW9FV/2In1enEqmF7uF5AW2qmWWKl7jI0XqNYdfjPB/aTS5snLFKRSRpkkgamoc1pPuh5AQ88OYjrBcsmFtPHsTOfoG45/OJgCV8INnRnWLtmMv21nGjZ87Wv2GpL3S0m1XvNxRtQFYWAmATFiBHjFMByV9gHhirc9cB+LNcnnzIwsuqSJ9C5hJqVusuB4SpeIFAF5NMmmqas2Kr/aCbVVEKPeislzEltRaUuDQFt10fXVNa0p1b0mJeKBVf9ZQvX91FdhbXd84/BXJhtHCt1l8OjNRzXJwgEh0erWI4fpeKW41d0vPqKTSUt2bRBW9vy97VaU3fzpXpfdP5atq1vZ2KidsKO73giJkExYpziWC4ZmNzOYfPaNlzHJxBizgl0rtXwbEJNIQTDhRqu75NJ6liOj0CQShhL2vd8OJpJtbczzYbeHM/sH6evs9mqY/KYFUVWRWVSBoqyvMjHUjBXtGGhVb+hawQCOvMLj8H6ntmJ0PRxbBJBy5E+RIauEAgoVi1qDZferjT5tEnCUClOcYteCMejr9h00mIaKlvWDXPJjm7Wd89NBOfa12pO3c2V6jXnGN+TFTEJihHjFMdyyUBzu7l0Mc3tHt89xq6DE3OuhmcTajZsn7rlkTA0AiFQFQVNVZe87/lwNJOqqihce/EGjgxXInLhB4JKwyMI5La9nelIf3MsO6AvFG2Yb9W/qS/H/f89eFTEYuo4TiWCqYSO67ugKIggwPfBclzqA2XSSZ2EoZFJGosW4B7rvmKzkRbXC9hzuMiR4QrXX7F50aTl+ZK6i20IYhIUI8Ypj+WSgcVsN1Soc+/DssfWXKvhDb3ZGSkbLwgIhHxINxyfXKp1slzsvuebtI52Ut22vp03XL2F+x8fYHCsTqXu4HkB7VmTvq5MVIG10DgeDRYbbZhr1T8y0eAnTw4fFbGYOo5CEJFXTVXQNRXbkcQQQNMURCAAwUTVxnZ9joxWFxW9m57aA0mWvbAZbrFis6U/v6y+YvORlnwuwf6B0pJIy/FK3cU4esQkKEaMUxzLJQNTt0vOIhC2wgohBRl6n2s1/Nbe02ekbFRkp/dyTWpuejtTLVVNi933fJPWQnqZ2Vo6TMfmvjxrr80wMtHg8EiV7z5yiI5sgnRyZSIV84lqFxttWNeTYaxoRfvY1JeL9rESYzB1H9mUTiBAUxUURSFpaDQsSfo0VabnXCGoNuRrddvjK9/fzTMHi1x5zvzRu6mpvX2DFRzXl8JuX/aFy6R0tvTnlxVZWYyB4FJIy/FI3cVYGcQkKMZJjdVWmbEasdyJsLndweEK+Vyi5T0RjjsK9HSkF1wNT03Z7D1SZrzckDogIaMHw4UGoJBLG0ve91yT1ko162ymFHo6Ujx7uMiBoQqpxPIIBUzes3sHyjy9v0CxauP5YkaaazHRhj1Hytx29y5KVWfWVNlKjMHUfRTKFgLwfAEENBwfVZUESAhw/IAgEOi6QjZloKkKjivYfbjIeHnh6N2mvhyXnNHDN/5rL9WGh64q6KpCIqljGhoP7xqhvyuzZK3NYgwEXd9eNGk51qm7GCuH+ArEOGmxWiszVhuWOxE2tyuUbQ6PVGV1WFgxNVGxSZgaAqLUxXRMXw1v6sshEAyM1chnEqxpT1MoWzheQKkmP9fflZHux0vc91xYyGl6Q2+WoUJ9USR6JQhF857de6TM8ESdQDCnv04ghBTv6hp1y8MPAjRVjbyAXC9geELe+/1d6XlTZUfrtt3cx4+fGOCxZ8co1xxAkj8BCAFS0iXQNRlZUUNi5HoenfkE1Ya7YPQuEIK9g2XaMgk29eYIhIjOWYiAg8NVvv3QAV531Rb6OtOLXvAsykBwCaRlJSJsMY4PYhIU46TEaq/MWAqORzRruRPhpr4cb7h6Cw8/M9biE7S5L8eODR388LEji14NB0LwwJNDuF7Aln6Z4spnTIYLNWqWR932GCzUuHD7Gs5Y4r4XOvfZ9DKHhqtz9gXbtr5tRccRJu/ZSt2hZrmoqkLKUGk4PodGqmzqy9HflY7SXC++YB2eH7D7sBz3poA8nZTHPzBWIxDQ25GKxmGudOFi3bbnM8WU+9hOd1uKbz2wH8cPSCd0GrYPCFwvQAhIp3VUVW7jBwGqoqBrGh05bcHoXSTGD40zm2g2iK00PEaLFoWQZCx2wdPTkaK/K83uw0U68wl0LfRVUiYNBDf1Lp60rFSUMcaxR0yCYpx0eL5UZiwGxzOatdy2E5v78py3o49de0dbJkeAXYcmFr0ani29k0sbZFNtNGyfmuXiuAEvuWAdfZ3pJe17IUyvklmIRL/xxVvp6Mi07KNJVgMheMkF6wCwHX9R4zj1nm3PJRgvWSRNDV1T0TWpnRku1Mmua4tSfYNjNSp1h0rdJZvSSWg6fhBQabhUGw6WG9CeMUknW/Vac6ULF6oUWqwp5njZoiOfxPcD6qEeyA/A0FRcP8DzJRkCsN0gEr0LIZZUidZEsyS/2SAWIUjoS3OlPjRcpdxwKJRthicamLpGNqXT1ZbEF5BLmUsmLSvdzy7GsUFMgmKcdDhZKjNORDRruSWzqqrQ35XBawtaXl/KanguXYYSRjeSpsbwRAPb8Y/pSnsxJHrnzwc5b0dftM18ZHWpRoCuH0TiYnn+kDBU6mFTy6SpMVGxeXjXCElTJwgEjhegKAqaqmDqKuWaQyBEePwzv2+udOFcUcfFmmI2z6O/K03S1GnYHpW6w1BBjosvBJ4XYLs+ruejqSrtWaknW2olWpM4NUvy0wkdP5DpsXTSoCuxuAXP1N/Zxr4sE2WbquVRqNhUGi4X7ujh2ovWLdknCI6un12M44OYBMU46XAyVGacLNGspayGlyomXUk9z1QshkQfGasxOF4jrSsrQlZbvXZAVcAPpH4GQFNVHCF1P7YrNTaFik1/VxrPl0Sgbnk44ba5tBHqhGb/vtkIx1xE7oqz+njgqaFFmWI2bA/H80n6GtWGg6aq9HSkSScNhsarjHkBrhtQqTuS5OkwMFZjomKhaSpnbGxfdCVa0tRa/KRAwXYn7RQUhQUXPLP9zrrySVl674dtJDImG3tzy+6jFXvxrG7EJCjGqsdSNTHHqzJjtuNaKZws0SxY/Gp4qWLSQAgSpsYLX9BL3fJIJXUySWNBPc9KmCiWag61hksya6wIWW29ZzXSSZ1Kw41KzZvaGVVRmKjYdOYTUnxuaKQSSpQy9IIAXVUxdYWn9k9QrNp05MwFx3L/UJl/v28PlbpLezZBTy6F7XrsPlTkucNF/ICWthpNTL8XJ8o242WL4UIDRSHSKPV2pjltfTvaQJkj43UURSGT1DF0DdfzKYTnslCJ+/QIoKGr+AFoQlC3PQxNbTGpXGjBM9vvrBl5BJ2EoXNopMpwoc6atueniDmukJ0fMQmKsaqxHE3M8ajMmOu4rj5/7QytyHJwMkSzpmKxq+EzNnZwaLjKgaEKPR1pkubsKa757otDw9WjiswsRKItxyMIBKPFBoMjZQbGakdNVqfes/1daXo7M1hDFeq2h6nL408ndIpVh1za4NIzeluE4ZMTt0TdcsmlTPSwb9l8Y7l/sMw/f/sXjJVsDE2hWncZ0hVAwfVkpCUQQhIxTSOpqy3HPrUT/SO7hgl8QSAEmYROIASVhos1VGFDT4ZK3SWd0MinTRq2h+X4qAp05WW12L7BMpe+oHfeSXpqBHD/YAXPDwCVXNqIepM1sdCCZzGl8ZUwDfl8RFwhuzBiEhRj1WK5aYZjXZkx33F98/595HMpOjMzzQOXglPNZ2Tqw7oRGiFWGiWyKYNcymxJn803/mPFBglDW1RkBmbvOTYfia7U5GSvaQp33r8Hy/YolC029+VJJWael6mrjFgue46UAOZchc92z27szTIwVqNSd1EUyKQMtvRPpvrmEoY3j1HVFDKKQa0x/1j+2317GC/ZpEwV09CxHI+JigMCchmDTFKjXPcoVhw8v8jG3iyZZCvR0BR49JkRxss2a7vTDE9YNByZpkqZGjXLY89AGYBNvXm62hItkatUQsNy/EVHN5vRxaFCnTt/vI/hQp2Nvbmo6gwWt+BZVGm8/vz8nZ1MFbLHEs+/KxvjlMDRamKOVWXGQsc1VKjzg0cO8aartyz/5Dm1fEamP6w78wksx2NkokHC1HjpBes4f3s3qqIsOP4HhipUGi7b1rYdVT+z2Uh0oWKxf7ACwIbePH3dWUbGawyN19k/VGbr2raWKESl7nJktEq14fL9Rw/z4FPDM1bh01MV11+5mQeeHIqOqyufZNu6PC/Y3MnWtW0tJGqhY9y8Jr/IsXTQdTVsnDkpshaIqHpLU2VTWNv1GRqvs3VtG0pYPj44Xsf1fCojVVRFRpJ0XSGh6nieL9ufqAoIQTZlRM1ap7tqLzW6qSoKa7syvOqyTdy5cx9DhaUveBb6nRUqFmds7qK3M71sTdCJwMmiKTweWHUkaM+ePfzZn/0Zjz32GJlMhte//vV88IMfxDTNebebmJjg05/+NPfffz/FYpH169fz9re/nV/+5V8+TkceYyWxEpqYY1GZsRh7/YPDlaPWEJxon5HjpSOY62GdThps6tMZHK/zzKEJzt/eDSw8/pmUwWjRIghmn7CW0nNsKomeqNiMlywMXRLphKFRqTskDJX2rEmhYjM0XiObakdRmmXbZWqWR1vGZP2aLI7X+h3AnELkZEJfcOznO8Yt/XnyGXNRY9meTVCtu/jhmHl+EFamKXhegOvJdJGmKfhCUK671BoumqYwOF6nXHNIhAQinZQpMNsN0FWF/u4MCVNDVRTGig20YxDdPJoFz0K/s3za5JqLN0jSyPOHBJ1MmsJjjVVFgkqlEjfffDObN2/mc5/7HMPDw3z84x/Hsiz+1//6X/Nu+5u/+Zvs3buX3/qt36K/v5/777+f//2//zeapvGWt7zlOJ1BjJXCSmliVroy43hqCE6Uz8jx1BEs9WG90Pg3J1DL8ciml9bPLGGoLY7DG3qz3Nh7ektfMFNXGS02qNt+tE9dUzB0lYmqw0TFpi1jcGS0Ss3ySCc0uttS1G0XTVXp60wzVKhzz08P4LjBvKmKLf35BcdvKtGfr3fZfGPZk0tSCIXYhqaCkGX5RNEgn/Zsgr6uNCPFBuMli9Fig2zKAAT5jElvZ4o9h0sEQqBrKpqqUrc9SlWHbevasByPTNKgPZdgtNhY8ejm0Sx45vudvej8tWxb387ERG3Jx3QicbJpCo8lVhUJuv3226nVatx66620t7cD4Ps+H/3oR3nf+95Hb2/vrNuNjo7y0EMP8bGPfYw3velNAFx++eU88cQTfOtb34pJ0PMQ8+XqpamajecF1Bpu5JQ7F1YyqnG8NQTHIpo133gcbx3BUh/WC42/qshWGhNVB9NQI+ffZupmrp5jCzkON2wPx/WZKFu4fkDS0DAMDdf1sVwp7tVUhZrlUqk7VBsumYSGpqmhc/Okm3NbxmT3oRK5tLHs5q+t56xEpEZVlEW3EmmOpeMFkRDbcj1k1y8BgWxMauiy4iqfNkkldZKGxnUXbyBpatzz04NkUwZJc2ZFW9PbqG65lGoOm/tyXH52H3ft3H9MoptHs+CZ63dmznFfrhbM9Vs+1TSFR4NVNQL3338/l19+eUSAAF71qlfxkY98hJ07d0YEZzo8T/6oc7nWh3M2m6Verx+z441x7DBXrr45WU1U5CR3z08P8vO943NGKVY6qnEiNAQrGc2abzzW9WS495FDjJUsejtSJE1JHo6ljmCpD+uFxn+o0MDQFco1h0LZipx/O3LJOXuOLcZxOGFqVOsujheQTemoipzodU0lpUCl7qGp8KJz+1FVhe88dBDL8bHcgIShoqlai5uz7QYt6b8mjiZVcTRj2d+VZlNfjqHxKuNlG9cTKKEX0Na1+ahxbaFss3Vtngt3rOHAUAXPFyQMeR2mVrQlDJkC83yf4YkG3W3JKHq5Wl2Un29+PvP9ljf0Zk8ZTeHRYlWRoL1793LDDTe0vJbP51mzZg179+6dc7v+/n6uuuoqvvCFL7Blyxb6+vq4//772blzJ5/61KeO6pj0aeWgy4WmqS1/nwo42nO++vy1fPN+KXjszCVx3IB9gyUajvRS2dKfx9Q1Dg5XKJRt3nD1Fjb3TaYQ9g+VueuB/VQbDp25ZLTqnOvzyz0u09BwXJ9CxSKXkRoCQ9fwlTmc6uZBIATDUwz+epfQBHIhzDceh0eqJEyNPUdKKIoiIxlJnb6uNLmUCaGJ3GChznjZor9r0gbgaK7z2jUZ1vVk2D9YJh2WejfRjPht6c+zdk0mGoe5xn9gvEa55tCWNelpT1OoWFQbHhOh8+/ZWzu55MwefvDoEVwvIJXQCYKAI6NV6rZLQlcJAoGmKWTSBmtMnYHxGg8+PcSLL1iHooDMEik0jWhcP6BmediuFA4/8OQQazqSVC0PzwuitJSiyLYRuqZSqtp4vvz+2a5t0tQp1Rwcz1/S82clxnLbunYyqTqHR6qgwOb+PNmUie1IL5/OtiRXn78O09DIpg1MQ43Gsi1jsqU/x1ChTs3y8HwfgWBTX5aXX7ox+q1tW9/GlnX5Y3afryRW63N7Mc+2+Z5T+bTJi85fO2uka7We87HCqiJB5XKZfH7mpNTW1kapVJp328997nN86EMf4jWveQ0AmqbxR3/0R7ziFa9Y9vGoqrIini9Tkc+fesx7uefc0ZEhn0vx/UcOcXCozKHhKo4X0NORYl1PLhJ+5nMJDo9UeeSZMc7b0YeqKgSB4OEf7cVyfTZPqRRKpoxZP7/c4zo0XKFieRi6yhmbu7jm4g1sW9++rPPdc7gY7dP1AgxdZUNvjmuPYp9NzDceqApP7h1HBVRVJZsyCISgankcHK6ybX07+YyJFuqddNOY9Xex2OvcbMJZa7hkUgavumIrd3zvGUZKFl1tSRKmju14jJcsOttSvOrKrXR1TrYsmG38dU1B01Q625KcvqEdRVHo78lStzxcz2esZNHdnualF29m31CNPYeLoCo8d7hIoWQRCLAcqfNJJXQ0VSORNOjtyjBctChbPm25JJWaTcPxSZoarhNQrjl4viROpq6RSRscGKpQrTugKLi+j6qqGJpKOmVgaApKmC5CUTBnidjULZdU0qCvJ7/k58+rr9zK7ffOPpYd+SQXvaCP8aoc9/6uDBfMMpaZtMmlZ/UhgHLNYbxih/d3Z8v93daWZsu6YfYcLpIPNV1dCZ3O9hS1hsvAWI0t/Xn+51sumJXMTb2mqx1LfYZNv8f7uzJLfs7Mt+/FPNve9bqzj+o5darMVauKBC0XQgj+4A/+gP379/NXf/VXrFmzhgceeIC/+Iu/oK2tLSJGS0UQCMrllUmnaZpKPp+iXG7gz+Vlf5JhJc65M2Nww9VbeHLvOF//0V6y6QyduQQKCs4UUV8+ZbD3SJFde0fp78owOF5j35Ei+ZSB6/gz9jv988s9rumrWUOXK6ulnvP+oTLfvH9ftLJrSxnYrs8z+8c5MlxZdtSqibnGQwjB4eEKCBBhpMPzfJnmMTXqlsehoTKnrW/Dsn0UBJ7jtghFl3Kd9w+V+fHPBxkYq0VEb213hgtO62LPQLnl9Q1rMlx5bj+dGWOGMHX6+FcbLt9+8CC5aednqAqGqbMmn2TfQIln949xyY5udh8o8PizoziuLOFWFGgW/9iOzy/2j3Pa+jbSCYOG5VKtWqQTGplEmomKTa3hUrc9fF+QMFSSpo4AXNenWvdkWTgCUBFCYDuynYSpqyR0mSoamaiTTc6M2AyPS/KQ1FiyILczY/DqF26cMcZtaQMB3Hn/npZxv+rcfjb35We9l4GW19auydLelm65zpfs6ObIcIX9A6UZ0YbObIKXXLCWSqWxpHNYTVjOM2yue7w51keLpT7bZru2qqLMeW+dDHNVPp9adCRrVZGgfD5PpVKZ8XqpVKKtrW3O7e677z7uuece7rzzTnbs2AHAZZddxvj4OB//+MeXTYIAPG9lbwLfD1Z8n6sdK3HOKVNH11Ta0iZCgJhWrmroqqy2qbt4bfJvxw0wsiqBmKnNmf755WJqGXzgiygFttA5TxU0JkyN+x8boFJ3WnQiCVOjrzPN4Hid/3p8gL5r0owVrVkFzQuJv+caj7rlyfYPppy8EoaK5fqkw2iFaajUGrKiqilu7conZz23hc55hug6K0P4+wbKjE40uP6KzbOWhs+3z+b47xssRxPOQtd7U1+OfNqIPqcACNA0BU2V29ctj8GxOmu7M+hhVVdfp9RYbOnPU6zZHByuoilgGjp12yOb1ClWbLwgwNAVRCDQVVlWjqLg+wKhQV9XGsv2SSY0joRu09MFwi98QZ90Xl5GWfb67ixveelp0f0wUbHZ+cTgnOPeFLtPv5enjm8gBAOjVUZKNp7jSodnRWF9d5brr9g8RZtiY2gqm3qlxmd9d/akeN4t9hm20D2+EoUFy3m2Tb+2i7mvTpW5alWRoK1bt87Q/lQqFUZHR9m6deuc2z333HNomsb27dtbXj/zzDP5t3/7NxqNBqnUqRHaO1mxVNFnwtQIhKBQtkgnJxsqzvX5Y42pJGWiYrPrwIQ0mfMDgkAKqvs7M3MKZfccKXPb3bsoVZ0ZIkiY3W9mqvh7rvHzgrBjObL79pr2FMOFxrzi1mPVmf0nTw3x1mtPX9b+l3J/jEw0KFQckoaOlhA0nADPD9A1VQqehWxeWq47aAVY3yMbg56xsYPxksVQoS7FwMiu7c2eVe25BINjNVKmhgjADqR2TdPkZBUEgsAX1Bou2ze0c/nZfZEx4koLhJsi30AIfvLU0FGZ5jUFuEOFOgIFBUFf5+T9dbQVjEdTvbmcbY+VB9bxMiiMK79WFqtqlK6++mq+8IUvtGiD7rnnHlRV5corr5xzu3Xr1uH7Ps888wxnnHFG9PpTTz1FV1dXTIBOAizFQfnAUIUfPzFAoWRRtz2ShkYmNdlX6HhXR0yt4qg0HCbKNqomV9G9HSnGy5aMPIzXSJhai+swgOsFDE9IgtPflW4pWz80XAVoMf6zHNn48uBwhZdfvJHzt3fPOX66qqIoYDkBbRmT7jZZFdbsSu76AiEEG3qzXHfRhmVPzsfavG2p94fjyjRCwtTRNEGl7kizQHVym4bjodUVhgoN/u2HezA0lUxap1NPUKjYksAKhVx4bwkEgYCEpmIaAtcLcDxBSpOiaA9Bw3ZJmnpEdDb25o6pKeXRjvvUyEZXPkkum6BStWdYJiy3supoqjeXs+2x9MBa6Xt8LrJ2KrnJHw+sKhJ044038qUvfYlbbrmF973vfQwPD/PJT36SG2+8scUj6Oabb2ZgYIB7770XkORp7dq1fOADH+CWW26hp6eHH//4x3zjG9/gf/7P/3miTifGCmKxDspTm2f2d6cZGq9juwGlmvRH6e/K4Hj+MXdcbmLqJNKeNZmoyEB0EAgZUTA1Mknps+J4AcOFOtlUWxS1EkKEPjPQ25GKVndNc78n9hRAgXO2dqGqSmQhULM8rAmf//f9Z9l1cIIrz+mfdfykGEh+T09HCkWBXNogm2qjbnsMF+ps6Mnyq686A11dfrXI0Zq3LbR6X4rDdiqhy6oYRUZ8DF0llzap2x5eGJkLAoGqys92t01W30yUbTJJnVe+cAOPPzfBoaEyG3qyqKpC3fJQFfB8SYbacyamLr1yHDnMpBM6112yIep71TyfJpE4Gsw2Rkcz7tMjG5qqRmPS35U+6sjG0XhSLWfbY+2BtZIGhQuRtRPpJn+yYVWRoLa2Nr74xS/yp3/6p9xyyy1kMhl+6Zd+iQ996EMtnwuCAN+fFIRls1luu+02Pv3pT/OpT32KSqXC+vXr+f3f/33e8Y53HO/TiHGMsJDHyIbeLLd/f3dLODph6BEpqNseg4UaF25fEz1MjmV7iOmTSMP2w+/RIkfd4UKdrWvzZJI6pZpsR9CwPdJJPfIdKtZsskmD1DQXYMsJCAACWdnkByLyu0kYGrqm4LiC3YeLjJflQ3628duxqZ1iRRr8GboaPVBLVYfuthTXXbxxTgIUCFkFM1pxWrQi07HYEH4i7L829XocGq4uavW+mPtjqFCnbrmsaU8yWmzI5p+qdHzOawaeL6g1HDwF0gmD09a1RVU9U9Mazx4s8aaXnMaX7n56Ss8q2X+rVHNIJ3Q29OTIpnTZKNT3KZRtTl/fRmc+we3f372oaMRi78+5Js0zNnQsO3VyLKN3R5M6Ws62xyNVtVJpqsWStdXqt/R8w6oiQQDbtm3jtttum/czX/rSl2a8tmnTJj7zmc8cm4OKsWown/5gqFCf8dBuRjUatk/NkoLCl1ywjrVdmWPeHmL6JBLpb1QFRSFy1LUcXxrNObLXVN1y8fyAgbEaxaqD6wXYms/eI+UopQdSzyP1jQLP9xmZaOD6fuQRIwS4nkdnPkG14fLAk4O89drTo1YQcxGNxT5QF9KKTMViQvid+QT3/exIpJVqpp9KFWfBPl9z3R+J0BRx/2CFex85JJ3GfYHnBbIhqBMQBF5oniiwnABQMDSV9WuyM8qam5P/kbEaqaTOG67ewv2PD8hx8wMySSm4Tpo6uiavgaJAteHRmU+ydW0bd+3cv6hoxGz3Z19XijM3dtKRT7Rcu7kmzbFig2xKp1Cxl5w6mSuyIULhuOvJ31Tdcuf9HcyGoyFYy9n2ePTSWok01VLI2rFwkz8VsepIUIwYC2Eu/cFcD+1mY8ekqTE80cB2/OPSHmL68eiqihqmYPSwEskRHn4QkEub9HWlGRyrU667FMoVAgHZlI7tqpi6QqXhYg1V2NSXI5c2ZHRGAYSC58uJqeneC+AHAaqioGsaHTmt5SE/ffyW+kBdrFakiYXSVYoiJ6qBsKFnTy6F7Xo8e6CI6wds39DekgpsTgg7nxjAMNZjO37LMTcrue577Ah7j5QZnqgTCMinpWeLmVVx/QBwCAI5dgDJhMbavhzlukNnPjHruSfCaE+t4bK5L8/aazMt42Y53qyC58vP6uOBRYqUZyM2hYrFo7tG+ekvRujIJcilTPq6UlTr7rz7TJga2ZSx5NTJ9OKCTEqnXHM4NFSm1nAjvdgPfnYETVWX9Hs5mtTRcrY9Hr20VqLp8VLJ2vPN5Xo1IiZBMYDj1zX8WGIpKZf7Hjty3Ks4UonW/kp+IPtJaar0knHcgAtO76Ieen80NUB7B0pUGi4pU6fh+JFuKGmqUseryuhSM8oEIATYbkAuJSvjpGPw3NqPqdd+IX3KcrUic4XwO3ImB4aqlOsuhqZQrbsUkjptWRMU+fAfmWiQS5uRVkpRFAxd4eFdo+w6WELXJNFd252JKubu3LmPSt2hZrny+AyVhuNzaKTKpr4cW/pzDIzVWNOe5LzT16CisHaN9Iz6yr2757+PdJVMSkbkZpuIZhM8L3aCGyrUZ0QDKnWXofEGAQIhiNyonztcolCx2diTm3Of1brHSy9cx66DE4uO9M1WXGAaKq4/2U/MC3wySYORiQZ37ty3pIXDbL9VIYRMHQayNFsP76nFbDvj+kxLOx2viqqjTVPFjU+PP2ISFOO4dg0/llhsOBo45qHxuY5nan+lIJCRCYDB8TrZlMGZm7r44WNH6O9KRw/k5jYNx0NXFWoNl4mKjeV4kandRMVGIEW5iiKwXXkdezvTYfXX7A/55Vz7o0ktTI84TVRsvv/oYSp1l5SpYhr6lB5bLn4QkAxNG5taKZAmfvsHyzhegOv6mIZGw/ao1F3Gig0ShibF6LkE4yWLpKmhayq6RqTFyq5rozOfpFzz2NiTi441EGLGdYsm6FDbs2NjO/1dGUql2c1UZyNGi53gBkZrLeMrhGC4UItSnX4YubIcme5zXJ+JSoOutiTTuWtznx25BDdeOzMNOpe+aHpxgeXI1JcAcikDxwswdY11a7JkU/qSFw7TfxvVhhdVJPohyetqS2A5Myf75aSdjmdF1dGkqeLy9+OPeCRPcRzvruHHEosNR9uOf1xWW7Mdj+zHleLISA2hCPSweqi5UgyEmHFszW7jTYG37coJadvaPFeEUY8fPzHAY8+OUW24JA0tKtuezxJgudf+aFerMz1sHHRdiooVBXRNRVNVKg0XzwtImjqBCPADadxWrjnsH6zgeDKS1myKWrNcXM8PS9N9tq1ta9FhAS1arIYttUDTj3X6dTMNlYmyTdWSneQNXaVcd9lzpIhru1Tr7qImulRC6oSKVQdDlxHAqf5VzQkOaBnfhu23pDp9P6BmuTx3pIQIpIXCaNGiPddgTXvrJD510lxM6mQ2TUrC0Dk8WsF2fakHsj2621It+rSlLhymjvG+wUpkUWBo0mTSNFRA4a6d+5ecWp0t7bQSqaqlYLlpqrj8/fgjJkGnMI6XudfxxGLC0UOFestqa2oYXlZBiRVbbc11PBefsYYzpglcm+Lu2VaCTYG3bNng8YartvCCLZ3RddnQu50zN3Vy78OHsByPno40SVOLoi3TH/JHc+1XarXajCi1ZxNU626klQJJVlKmSsWTBMDU1ShteGSsiusHKIpUHdesSQLjuAEpP8D1pQ3BdB0W0KLFsl1mPdbmdfv2Tw/wi30TuL6MfHTmEnTkkgyM1fjklx4hk9TRwt5gC0XQLNujUncYK9kYoSYsnZRtDLIpPZrg1q7JROObNHWqDRfXD9A0WYZfqTsEAtwpFbKBL9h9qIjl+LRnTTRVJWlqMybNhdLes0X5cmmDdd0ZGpaPYah4vqC/O0NmSrXichYOm/pyXH/FZv75278II5cKQijk00Y0JktNrc6Xdno+VFQdb7IWIyZBpzSOR8XEicBC4eipqy0vNCKsW15LD6kdm9pXbLW1lPD4fCtBkGmtbevyLQQI5MPzwu1r6Mono4d8qTb3Q/5orv30Y2SZq9VmRKknl6QwRSsVGTlqGromS/8Jv6JmedQaHkJIbYymyUbHiiJ1UJ4vqDY8NF3Fcjy62pItOixFUSLBuKoo8x7rht4s+ZRJZz5BZz6BrmmkEpKUDBWk5QJCcNq6dhxvYY+aux7YDygkDBU/kGnLct2h2nBIJQzyGYMdGzqi8X3mYBHfD6g2XCzbpxGmiiavkxyWIHzN9QX7BsqkkxqaqqGo0N+RjibNxaQ+p0b5WjQ6viSRhq7hBx5B0NpOYblpGtPUSBga67rTmIZGOmmE1Y3y/aWkVhcTjXs+VFQ9H8jayYSYBJ3COJlFePOFo5urrUPDVZ45VERRFJKmhhaWSAshKFYcDg1XV+yBs9jw+GJWgpef1TfnQ3yxD/nZrn2z9Llme4hA0LC9Wcufpx9jVz6JFmpyxsvWrKvVZgSibrnULY9UUqcRdn93vKBFK5UwNDRViZqb9nel6WlPUW3IKIjnB6CAqsg+SQqT0SNDA8sHVSAr19qSLfs2dTmW6YROseqQS8+9sh6ZaDA4Xqe3c1Kf1dTneH5APmNgu4HcX3JxHjVb+nMt+pcgEDQcH9v1MQ2VHz52hF2HJsinDco1R0bbTBVdV7CcSQakEJpbKwqKmOykJwDHC0iZGgQC2/U5MlZlvGxF/cPasyaBkNdr96EiY8UGr79qK5v6clGUr1CxKFbsycUBAjcQOHUHXVNwvYBy3UFXVZKmOiuZXCjqdGCowt0PHWBgrB5VSzYjY80022JTq0vBSldUHYuikucDWTtZEJOgUxingghvrgfUht4s7VmToQkVRUhdhaootGVMejpSka/OsUwFznVs01eCExUbIaAzn2BLX16WX4/PvZpfzEN++rWv1F0OjVQo1xzZ6BNJKu7auZ83vGhrCxkMhCBhaly0o4en9xco1RwqloeCmHW12oxA7D1SplCxokanHbkEnh9QqUty0NQ91S0POxTHrmlLcNMrdkTVVodHqtz1wD6GCg2ECG2Yp1yeQAgUVSGhq6STk2XhG3uzDIzVqNRdFAUyKYMt/fOvrGcjipP6HBVd00KTShkVWaxHTTO1OVayODJaQxiyeeuathSaprB/sEyp5pA0NbIpXTpR03oPCvmFBIGIhkAAmiLTez0dSeqWx/BEg3/74XNoqoovBH0daQbGai2Rz1LV4Z6fHuA9159FT0eKTFrnqb0FNE2JCKkfCLzQcFRTFQ4MyXYtKKAIQWc+yaY+eY0WY3DZ1KONlxqo6mQ0r1J3sBw/soE4Uc+g5m/T8Xz6PEFy9nXiMS0qicvfjw+ev7NbjKNGd3uStqzJoZEqvR0p0kmjpV3D812EN98DKhFWpGxf3w5IT52pQlVDV49pKnChh2dzJfj47jF++othChWbwfE6v9g/0dJ3bLki9ukpwT0DZWph1EfTpO+QAjx3pMTt39/NjdeeHk1ezeN2PJ8gEGRSOhee2cMZ69vpbkvOWO3fuXMfYyUrEr8mwlLriYod9jrz2TdYob8rzZb+PKWaQ7Fqk02Z/NKLt5I0dQ4MVUgldM7f3s3Dz4wwXGigqApeINBCu6QAGQUxNIV0yuDsLZ1MhOPm+gFd+aRMJW7uZOvatgVX1rM14Z0qtG6m1bQpbtpL8agpVW0EglzawHJ8BIJUwqA9JFK5tMHWtW1Yjk+pbnNwsCrFyeH2QghUVaYBQY6BoioIFEaLFgJB0lRxPYEfBPiBYO9gGVOXEZcmuWnYPk/vm+Dx3WOcf3o3imjuv3mkkmI1iReK/J8C+L4U8g9PNPjuw4fIpYYXNLi8/orNPPDUEGMlC98PcL2Auu2jKsgoky+jbZlk2wl5Bk29x70gIJU06GlPcvkL+maQ+5OlqORURkyCTlFEbr/jdQpli7GSNWkkZ6iLSr2sZiz0gLpoxxpcX5ZfT3cFhmObClzsw/PQcDVKYXRkTRk1YLLvmGmo6JoqHYHLFjufGGBD73ZURZk3RN98b3NfniOjVZ47UqJhezLF0vQvUhXSCR3PDxgq1Nj55CACEbkdm4ZKreFStTyOjHrsHaywuS/HS85bx/nbu6Nj2PnkoBTyBgGBEGSS0s3aFERRhXzaBATVhovnS1H6GRs72NKf5ydPD88gilv6cuw6MIHnCxACPxBRl3YVKXyu1l0ee26MTb1ZXnrBuhkC9MVco9ma8LZlTdkfLJCTfzZMg9Utb05/m9kirlMrvgIhWshUc/xtx8dy/MgWIGnWCZB+UiCr6BRFwfWkQFoIUFVCwqOQTemAgu06hLGjaKy0sHGurilkkgqlmsPDvximrytNteGxuS9PsWqHfc/kvYECKVPD0BU29ORwvYChQh1VVSLvolRCW9Dg8nuPHmJwvB4KvKXnkOt7+L7UdEHAsCdToX2d6eMqBJ7+20yZOoEC+wfLjE40ot/myVhUcqoiJkGnIKb+0JvRoGaqoNoo0tuZZtvaPFv68zzw1NCq8w9aKAe/mAfU0/sL6JpyXFOBgZDk5dsPHaBQttjYm5u1L9UDTw6yriczb9+xSt3h2UNFDF2lmRX62bNjnLGps0UcPf26AS3v1S2XhuvhC4GmSALURMP2Qo0J7DowQd3yqDZccimDA8NVXN+XAmNVoWF5/GL/BAeGKjy8q4NXXraJhKkxOFYnldAZL1ktbtbNUvWG7bO+J4Xn+Vx2Zi+qKlOS6ZTOtx44MIMo7h8sc3hEZU17itFiAwXQhEoQCDw/iErhsymd9qzJweEqhbLN667csuiI3mw+Oc0mvHXLjcwLc2kpZt47UJrX32Y2sXszoqQqCg3Hj0wtQTqL65qC74so1ZZKSBLmeAGe0hRDT16rICRAmqKGbTvC7wnHxA8UPN9v2a8eluMHQmDqGuMVm4HRGq4f0NuRoqstScOWVXSuF3B4tIppSEdsXVMolJ2I2DYjSrbrz2tw2ZFLMDhWZ6xsAWDqKlXHRVUUhCrF7kEocG/YHpec0XPcnjWzPTdURSGZ0FnbleHIWC0iNidrUcmpiJgEnWKY7YeeSsgy2KaJXF9nisvO6p11EjqRod5AiJb0kAKYujaDmC3mAVWsOLTnEowWG8fFj6MZeds/WGFgrIauqXh+ay+wqQ/PXfsnWvuO+T6uL1BUget5uF4QpZZMUwurolzu2rkv9NWZmYo4NFyNxrH53nhZTlYKMvrleLLLvaYqUYpJ+tA0EALWdktNietLklhtuAQCdF0l8AN8P+CZQ0UcL+CiHWtwPNmg1PUDVFXBJZARC0V+hyMCbNdjZKLBj/57QBoaqgqVhoPnC/o6U2H/LSWaGAtlW6Z9wghKd7vJRNnBdiURUlVJLAbH6rPquxaKks3mk9P0aGo4UsCcSxsYhhamTOb3t5lN7K4qSuhtJKNBTVNLkIQnYWiUXTc6rsho0/FxvCCMgIEQQaTtMUOvJUlwlMg1PJsycD0fy/EwNIWAyVTX5Gf0SHU0NWrVjEKV6w6Sj8kxn96mRV5LD8fzEQKSpjrD4JIp95j0f5IO3oEg8kcSgih1l0ro7Bssc+kLeo9LNGUpxOZkLio51RCToFMMc/3QFUUhk5TpsGLF4QePHvu2EkvBgaHKDM+WbErHzGkziNmiHlCBwws2d/LoMyPH3I9jamShmcIyjZm9wKJjqzqUak50DpW6y5GxGpbjYdkiKolWFFBVmRJRFEFCVymUbTTN4ZytXS1RpoSh8sSeAii0vJdJGiRMTXavd7woshPJbUWAgpxwizWb3s5U5NtTt+UEpquKJCWBEpZWQ6Fs8dNfDDNetrCdAMvxqFmePOZwha2F2w2O1/F8QSZp0J41GSxIx2RCoayhaei6guMGeL5Mq/lOgB6WwR8YmpxomvuXRMrFcnz6OtPR5GU7/rxarLl8cqY34X3lCzfwrZ8cpFi2Q8Ixv7/NdLG77XoR6ejtSJENW3A0YRoa2ZRsdaKqclxlaxADNex6X6lLM0l5vSQJ6m5LMVZq4Lg+XkDYdDWD5XgUq7LfV/Pae34QOYt35JIArF2TmdWioem3VLd92tIGuqbiC9CEkMQ5ECER01DD3NlUg8smbNfH1FUMXcXxZCPbqbYIzV54unb8oylLITanQlHJqYL4Cp1iWMwPfcRuYLk+3W3JEx7qbUZ/vvvTgwyO1xFAW8YkEIKq5WG7dTb2ZltW+4t9QG1dm2ddd+aY+nFMjyw0bB+56FUwDZlGOjxSYUNfTqZ0Qk1JW8aMSpWHxhs4nheWJoso/dUUpuqaXM0nEzqW7RMEtGhJACwnIACY9l4qoZFL6TQsFy8AoQj88Aua3wOga/Le2XOkhB8INE2LUi3NjvUooGkqbjix7T1Sjnx5RHN/AlAEikrYsV1Gkda0JWSfq4bL6IRFM9sTBKCZUKw6BH6AGkYMmuk0z/dh0jMQkJGrUs0hnTSwfI/RYp18xmTvQJlHnxmZN7o53bF7upFmZy7BSNECFNqyibCR7Uz35/asyb7BCj97ZpT1PVl6OlIzxO62G1AoW+wbqjBetlr0eN1tSS45o4e9g+WWe/OMje1cflYfyYTeYjdQqjrsOjDBwFgNgIYT0JE16evKhCROZ7TYkFE0JHHRFIVcyoiiZZv7ZOuQ2SwamsJoIQS9nSmZ+nI8GpYMQ4lAVgw2e7iVam40Lk00I6z93WkUVWFkoh76JYFQZGTPD6TgOmFqpEzp73S8oilLITaxs/PiMTk0CjIO6aEELggPCAi0DlnpeYIQk6BTDIv5oath6e3RhHoDIRgcrzFacfAc2Wl8oaac01MUh4ark+0gLJfAl20m/LCBo6aq1G2ZSunvSkeTzto1Gfq70hwYXvgB1SyXXwnx92znPD2ykEpo6LomK4OEjOrULS9spSD1PV1tCTIp2V7j0V2jBEjNhqzymfw+IaBuu9F4dOQSDNp1QMxYgXtBQJNZTH1PURT6urJMVBy8MDWhKvKjUx9Lpq7iuH7kzqyENdnNYfIDGVEIi4eoNjy8QLCxJ83BYSnobu43ENLhODo2T5rxVeoOIxOyIkdT5Y48P8APZAlUIED4srw+ENBwfLxpBKh50EHoE6SrClYoMH74F8PUbY/+Lkncm55IuqYwXmqw84kBXnLh+uj34YVVSs1yclWREZpMUkZtXC+gO5eQATMhU3VeEGA7PsWKTbHq8J8P7CeXNls1WU9MkuLutuSserwmCb/0Bb2LvjfPP72bkYkGewfKPPDkII4r02JB6BeUNHUySZ9MyqAzlyBp6qiqElbiTUY+5zLrO3NTBxXLY7RoUaraBKFhJUxGlw4O1+huS1KsOtHN0fz+ZoT1ZRdt4IGnhqTo2rNkmb+Q92TzHsqnTRnxOo7RlKUQm9jZuRWtRMcH4aEID4QLwkcJHGgSHyFC0iNQNBNF7+AEcqCYBJ1qWMwPva8rRbHqLDvUG1WeFeoIFBREtMKcq2P19BRFs8zWcuRkmjJ1apYb+crk0gaGrpIwVMo1B9eTzrrNSSeTlr2SFvOAOlo/jqlapYmKjWHIlEBfZ5rNfbmWyEK14YUmgCJyPZaNT2XqIJ0wAIX/fOAAp69vwxcCzxdYtmxeqWkwpVsCjhuQScmJS1MVBpU6iNYVOMh0RjPVMP29bErHDDusK8BU/tS8OyzHD7U5IiQYsrKLQBZLa2FFlO3KijvLlj22QEHVFAya4uWZTzsRjsu+gTK+EKRMmVJzQ+1LdDxKUzMiJ91mldT0fU2FL2RkwfNlefjGHmlWONUTCQjJgMOOjR2RW3PdcqNrp6lSB1OqSTFw00bBdqVGp0mWXE+aJ4IkTGvapffPgaFKS2PX+fR4b77mtLB9y9LuzeZn+zrTs0Y4z9jYzpb+fBRdqjTcOSOfs5n1rV2ToVBz+cxXfobjSWLuuEHou6Rj6ho1y2N4os76NdKHq9rwZnUuVxSFsWKorQn1QSDH2NBVejpSFKvHN5oyG7FJmjLiNjxem/HcOJWcnQMhGCtZ2I6cE7rbDFT8iOgowpMkZxaiwxQzz9WImASdYljMCqa5UltOqHeq/qUrnySXTVCp2nMKqmcrF7ccLyqzXbcmgwIYuhIaqsmVbd32yGsmQQANR67ADW1y0pko26iKQmcuQbXhLekBtRQH2BlaJUOjLWvSnjE5MFThyGgt1F74JE2N4UINgZCOwHWXIBT4aJq0w0slNDaH/c32D1Voz5qMl2058StI0zxV5p6apdoI6dWDkCXiqMh2FlOQNNU536vbnuy5ldCi6irLkVU+QUAUsdKa3ElMtqgA0FRBOmnihqm8dEJnomKhqwoD4zVsR3rAKEya+gEYmhKWbMuGoc0y93RCJ51QKHtO2Cai1RFZgZlsZw7I1hoKvR0pBgt1RiZqNByfhi0Jnxb2E/MDEZHol1+ygf9+boya5ZFNyd5gfhDgeAHphE7S1HnmwAQberL89+5RamFvL1NXsV0R2Rj4gYi8f5Km1K5VGi7b1rbNqccrVV3GitZRp5nncxxuRpemptOankjTW7FMPQ5VkZqkXNqkLZPA0GWUrRSW0luOj6pKvdd1l2yIIlNzOZu//qqt0W+nbvuRzq8jl6TacE9INGU6sSnVHFJJgy39eV44zSeo+fmTydlZUSajOio+Ki7DY0V+/twwE6UavmejKz4dOZ1ztnTS15kKSc5qpjnzIyZBpyAWs4JRFGXJod7p+hdNVVHDCEF/V3qGWHSuUnZZYysnu/GSJYWwvmyG6fqBnPy9AM+XXivNaqNc2iCTkoaPTQF3Lm1w/VVbohXMQg+o2cwA00mdMzZ2cN7p3fR1pqPt9w+V+bcfPsehkRpBIGjLmAgBlbpLw/LY0JOlXLNp2B6D4z5tGTOqBgLCsmAZUcimDRTA9QSW49ORS0j9hqLI1gQZNRRBy+hPJfTUEUi9zNP7CqSTUpgLMFSYed3mem849HpZtybLeMnCctzIjTnS8dBqyjf1kRcEhKXz0pdmYKyG6wk8RWCGZeAoAtcT0fZS1C3vAUVAEAQykB5IUpYwNQxdpSkXVsLvl0RRp+F4M45jLuTSBl3tKYYn6oyW7Oj1UL8rK9I0cAJBoWLz5P4CubSJqshok+tJwXg2qdOeSwCwb7DCDdeezoNPDkVkCaTRHxCmaxWGCw2yKVMSnZTBaNGKiO90rHRF0VxRJFWRHkQPzuLBND1aO3VBkE0baIb0juppT6GqCrk0dLclI92UqiiUa05UATcfmdvUl+O91581o+ITOKHRlKnExvF8+nryJLXWFO5ULCeSfCxabSwWSnjjKwoQeCh4EHiAixJ4qL4PFQ2lUWNouMKPHjtMteGQSeokdA3H89l72GForMw1F65nXXfmuBz3sUJMgk5RLLSCWU6odzElpkdGazy9r0AmZVBruAyM1WZ8XjZslH8aVS8kTNLYTSH0SBEC15Orc1WBpGm0lBlHniTjDVRFYUt/fsExmV7FVazYFGsOvh/w1P4JvvPwIc7c2M4rL9uEEIJ//vYvGJmwZFm2ApUGpJM62ZRBpeZweLSKEk4K0jdFErpcypDELnQ7TobpH5DpGz8IyITu3dmUwchEg1RCaiRcT1ZaqYrUA6sho6haHqqq8KrLNtLfNTMVsqk3yxkbO7Fcn6f3FyhW7ei9DT1ZDF2jPWuSTRkcHq1gOVJgPdtjv6kHakZlBFKgXaq5GJqM2DU7tnt+EKbRJj/bLMFXVRUlkCJpEDghSao1XOoNF8PQ2NSbJZ9JUKpa7Bko03x4L0ZD0Iz0rO3KUqnZNOxWAZFMQwqCwI8qsAAOjVTRVZXT17dju7ItRjPiMThWww/kPXrvQwdJGCpqxsRxAxxP9lwzDDW6flPLxJvpY8uRhGI65kszr+SkuVizzulpatNQWdORjiKbzeNUFCUS2jfC/myL1fE0G//OFzVaDo52vJrERtdVOjoyTEzUCFYo2nEsW200EREdhNTk4IZEJxQlR6mrAAhTV2FER+gqiDS+7/LILwYpVS3WtKeiZ2vS1EgY0qfrsWdH6e/KMIvf7PMGMQk6hbGYldpSQr0LVZ65XsDAeI1v/nif9MnxAgoVi819eVKJyc/Zjo/tBAgEqqKSTmo0bOmTo4Yl0H4oKBZAezbBht7JMvMmlrKynhqVyqWMljYShq5KjxrL5cl9BemSqyiMl2wShkIgZGrK9QKqdRdVVVEVhWLFwTTU6CE3XrKYqNiUanYkSiWcKIFIPGw7Abom0wPnn97NoZEqNcsLvVf8yNDQ0NWIQG3qy4btJ6Svyo29p0fXbaJis+vABD987AiuH1af5UzOCttHdLcn+eoPnuPAkGxdsWNDO096E5SqNroGntfSKL6lamzqtNAkJqqqktZlxVoglNAtuvWzk0Z+KgmNFoKiKkpEmkaLFpqqYrsBa9pTUdplMSRI02Q61NRVdh8uzjmFScG1IJuUlW227WOm5Yo3nZR91YYLDVzfl/ogIcBTGS7UKFZtTlvXhqFr1CyXwyM1ec1DkbsjvEiIroYRymZj18WmmVdy0lys0/FUd/AmUXK9gKHxGuWatAnY0p9bsaqoleyTdTxIxtEc20q12mglOs2Ijgv4IdFxJPGZhegsBuNFi9EJi3zGZPojX1EgnzEZmbAYL1usaUsuZRhWFWISFGNeLOXhNF/lWaXusm+wjO0GkR9MsWozVAjYP1Rm69o2cmkj7OBugdKcNQWmoWHosjWB7cqwey5tsLE3R7Hq0NeZbikHb2IpXh3NKFZbxuTAcCUiQLqmhpOyiKpYBgt1DH3S78dygzBtpeD7AbW6EzrfShM9RVFpyyTo7UjxzKESxaot9TA+OL78jKrIVIoABsdrZFIGZ2xs58UXrOW5w0V2HSpiOwGO60tRriFLiF0/IJ826MynsByvxbqgr1MK4KPWG1MeumNFi0efGWVddxZdVbni7D4GxmrsG6zQnk3Q25GWHdu9SeGwH4gZ5ENV5TO2+bLjCRxPmvJpmooMCKkIr3UdHQQBCVNHQVC3JvthNaM36YQeCqZd9gyUyKQMsqGrct12KVddLNePvnh6akxVpJXCujVZDo9WpMHgPBBANUyt+sJF11UGx+ts7ssyXJDmkOmETHnVbY982mBTb55CaZTB8To7NnaQSsiqv0rDlc7MYbm36wWUazaFss1p6/O4nlh0mnml+1MtNlr7vUcOz0qU8rmE9JSy5b3WmV9dVVGruZ/XclpttBKdUIAc/lGEC74TCZERcuG4UkJky/Vwg4A23Zz1fVPXKAcutjO9RHMeBA6qM4pqD6M6wyhKgL327WCsWYEjXh5iEhRjxTC98qy5fBBClhpbjk9nLhE+gKEjl6AjJ4W/Q+M1sqn2sD2ETyZpUG24kQeN9B9pRpgEr71iCy++YG0UwUgljs6ro2F7VBoOo0WPQtmaUiEVRD2aQKBrGrbroatAeDh66I2jh92wHV+axzWFy20ZPfKQWb8mi+14VBoeRqhF8QOBHzrxppO67KmlKVx+dh+6qvLKyzbheAGD47XQSVf2O2sa3TXTgNMjXws9dAfGatz7yEHO3NTJLw5MyDGoOxTKFrqmkDA1WckWiCj8M5VsSKGzMmuawAsEAqkRySR0Goo8JscNQr8ZpKHfNJ2FHkbdqg2Xno4Uni9doNd1y2oj2/UZL0Hd8jGEwNRUtLD9ieeLiKhpmiTvqqpQrU9GAsOCtlkRBAIt9GjSVVl1uPtwmXqY4vHDUm9dVWjPmXhBQDqpUw7baWRShnR1HipTbbi4rjQyPDAkSZihq/R1prnkjDXsHSwzMFpjxJbp2r6uFC+7aMMMPc5K96daqk8YEPVFMzWVNlNjbVcm6hZfqjqrpipqrvFKmrJYYbhQ595HDvKrrz4zqr47nphOQGVKePLvTX0ZPMeiXC7SldOmEB03LDWfEsk5DhVXSUPHUFUcz59RTAHgeD6GqpJovhe4qM6I/GMPhX9LsiP/HkF1x2fsJzH0TSYuu+8Yn83ciElQjBXD9MqzrnwSzdCYqNhMVBySpkZfV6ZFt9PbmaFmeUxUHSYqNpqKbA+hSDdj09DwPB/XkxNYey6Brils6c8xVpSptIGx2lGvSifKNhMVG69pxBOmY4JA4AiZQmoeMwBCRjJs1ydlavh+gBeWbgehDkZTZcuFqVqlXNpgbXeW3YeLaJqCjmwlATK9pSAjGJmkQdKUP8+mPuu7jxzk58+Nh/4vKrmU0dJ2Y2rkKxCCp/cV2DtQDn1tlBbjP9vxKdddDo/WeGy3fDDJ6IZsDlms2rJSTMBI0cIPZN8pxwsmBdJqa6+xqRBC6oSECjXbRQjpASMr/EBBRCLiJswwuiZUSTZGJhokE3roMKxEIvuOfJLB8QbZpEEqoYUO1hqmLk32kqZGreFhewGVegM/vJ90TUFTVGxvjnRaeM+tW5Mlm9LZN1jBdj28sFRfU6UlAygcGa0DdVzPx/UDDo1W5diFerZaGFVSFOmh1JZJ0NORplCxeXjXCBfv6JF6IUdGNwfH69z76CGuu2gDm0P92rHoT7UUnzDXCzgyWp30SVIVcpkG3bkkmqZw3rau6LjWrsm0FA2cCMw2XjKVGdoX+IJCeZzb7t7FdRdvOG5krTkknh+QTav0deiYqi9LzPFQhYcIXALVZcxpYJcUMFLHhejMh672JGs6pI9V0jDIKhOkGSerjJMW46gM09NRYu3+CpozjOIWZMRqiVCckWNw9ItHTIJirBgCIV1jL9rRw9P7C5RqDhXLo2FJofHmvvwM3U4uLctP9w1KDY5MOUmjxjXtYTsBRboOy9WbYLxk84OfHaFUla0lXM+n4UgBqqGrpJP6rKvSplhyamlwJmnQ3Z7kFwcLaIpCEJb4itBfhvDfni9IJnRUJXTORUYMJioOri8nFMf1cb2AIABFlYZvU1tiNKEoMpS8uT9HwtDC6qnJc0wYKiNFq0XLtKkvxztffSa33b2LQyNVejtSpEPxLbRGvizH4/bv72bvkTKD4zUShsZQoQYoIaGUwlaZbRQkTJ1UQqPh+BwerbGpL8eathR7B8oEyIiW7YMQQQt5mKvKqQlBGOUKmucMmaSOrqmRB08TpqFGq3NFUdBCd+ykkPYBU72Nmj5BQSDo78rQbEiqqyqphNRIDRXqXHfxBgDuemAfw4WGrETTIKFruJ7P9GKf9myi5R7t75I6rpQpxe4CQm2Q7CpvGBqNhsBWAhqWx3OHS5TrU7yHmsRL06LfRldbgn2DFb75470kDR3Hk01HSzXBcKHBc4dLvPFFW3nhWX3HpD/VYn3CBsfr7Bssy+MOfZKCQFCu2kyUGiiKwr2PHkZVlFWjuZk+XpW6y4GhSqTlMg2FuuVxaKTKnTv3rWhqrKXiSrSaBTY9dPJKlU51GOoKaPKenWpcajk+nuNj6CxO+b9SEB6qMxZFbTRvBMQ42coR3pwcIOgcJKOUUJRpx9QMsNeW/pWB0UVg9hKkN9DY+ntHfQpHg5gExVgRTBcj6qpCRz7BZWf3oyK4+ycHwuaSM2HoKmu7Mrzyso2Uag53P3iAYtVmYKyGpkpS09uZJpXQ2DdYkdVREwqd+US4WrUoVR2EItsV9HeluPysVk+P5vHtPVKmULFww/REZz5Jb2eK4UKDdT0Z2dfJkat4oYAioi5aYQWYiwjACZuKmoZGQtXxPB9NUwkC6G5P0NORotZolk5PQghBreGSTGikE0aLlqkZqSlUbOnYPS0Erasq1128gTt37qNUm+wpNTXytaU/HwlaM6nQ/yUImKi4ICCbllGippeNjFipGLqGrsny9EMjFYJAprRURWH7hjbKdYehghwbdwkSgMmTA8cPorYaUxU6zYibUEQ0Kath4s31fPIZsyVqMbXLerPabiosR0aGmu0qnjk0wURFkgXH9dE1KVwmCCLjyc6cyVlbOlsiGQlDph1729IUylY4yUptkIIUc5cbsslp3Xbx/bBSKiG1WpG5o+9TtwSHhits7Mthuz7lmoubCCKSkTSV0DXb5Rv/tZe+rnTk3VMoW6STRktbDlhef6rF+IRde+F6brtnF5YjbR2abtC6Jtf55ZqDrqu0Z01SYTR0NWhupka5mp5c0fUKhfi6ptLbkaJUc5acSmwtLfdplpS3VFwJN3xvphC5LQmZBAyMVVuqrUDeJ+Waw7ruDF35FRQZCx/FGYtSVJozjGqPoDpD4d/DKM5Y2M6iFUb4hyVmDgOjUxKcRA+B2UeQ6CUwewgSffJvswfUsGm0niBIbZo7R30cEJOgGEeNucSIo8UGDzwxyCsv28Da7gz7Byu0Z+XE1ey1BJMRjGza4IePHUHXVBKGFvYVEpTrDjVLmqc17f/XdqepNjz2DpSp2y4gJ8VixeHp/UUKZYcbrz09KvW9c+c+xkoWlboTdV+X4XGLasOl1nA5fX07m/vzHBqpMFG2aGppmyXhzSquVEJGM/xAkhZDU+hqS2G7Hvm0ybvecA7VqsU3frR31ommI5egr1OmRppapmbYvhZWP6WTOvf97AhXnjPTybdpXTBdU3Lthev5ydPDkSYCYLzUYLTkNs2iZQ+xQKBpKoIA4Qtc3ycI5ISrIFNhCV0jkzIi08R1a7Lk0ga7D5UIhNfSvmM2NJ/vTQKpaeB5gqonRcNMnQDCv73QMkDaIATh2Cvk0gYN2yNhqJRqDpbrgSC0JmhN82mqQrFis6U/H1UyblvbxqPPjEYO3XYQtGib0gmNzf15LFuWwzfvTdnsU+PSM3v5/qOHGRirkzRVXE9QrTsyNYg0i2yKw1UlbCiKwFBVAiRh9nzpLF23/aiU3vXkuTWJn6ErZFM61YbHN3+8jzX5JIWSRd2WkafMlPTn0VRiLWR/IT2aZFqx4fgkDBmJ8/yAUl3aPZi6ijLFriCb0hkvNfjuwwe45qINZJLGcTcNnBrlasuaLV3uhZDVirmUETWhnS2VON1DRxUuOD6aVwavWXE1e2n5QlAVuHD7GopVm9Fig3zGlK7bnk+55pBO6lywfU1LuXkgCJsQ+zKSmE9Ovi98FLfQqr9p/jvS4YzJNhYrBE9rxzN6UFJ9iERvSHam/r0G1MTCO1pFiElQjKPCfOLNdEJnpGTxkyeG2NKf57+fG+PIWC1srCiFt6ah0d2W5PKz+njgySGqDZct/bK1QTOXryjStM43A3JpMxJsHhqRVVyKEvrOKKFTrx9weLTKPT89wLte+wJ2PjlIpe6EpdqCTBh9UVXZ76m53eB4jR0bOzhzUwdjxQaHmnqIcILTFFlyvaFXkpImaWk4PoWKxYXb13D1+Ws5fUMHExO1eScagDt37mNwvI5pqAyN17HdIEwFqrRnEuw+XGSs1OD1V21tsSpImBqnr29jpNDACxuoFqsO3/vZYYYLjZbGt+25JKNFG4EIWz+EJxO6AysauK58CPuhY7PnCxQlwPD8sNu77Fje7AGmKAqqIuZcvMnUlxq1vpA+QlN4j6JInc0UqIoibRPCZquu3yRDgoGxGp5fxZmi5QnClNjewXJLr68gkO7ZW/rzqIoUJT+8ayQyP6w2nMhkUlNkCkzX5fg3bD9yTU4lNLSw1cT5p3djOR6D4zUcN6DhOPiBnNQMXcrDnagMXkEgib4XukYDkT5IVeX4CgFpVWW61kfXNBR8njtcotQmm402741STUaz+rsyOJ7fonlbqi/OfPYX+wbL6JpMX48WG7LHl/BCywqFTEYnCKBSdxgYcybbhXgBA+N1DgxV6cgloxTZ8XJUnhrlGi7UcX2BaSihr1FA0pDRwaSpkUpI0h/4Dhrq3B46mgJ6EmHXETMa1S0d67ozXHPhen727CijExblwMVQVdZ1Z7hg+5pJ40ERMDx8iL17n8WrDpFWxmjXJjCTRXqSZZLBKKo7iiJWjuAEehtBQpIbI7cOS+nCM5pRnN4wgvP8IjiLQUyCYhwVFhJvdrUlee5IiUMjVemNo0xW8jh1l0xKcMkZPSQTest+ZOfrtmiF74UePM3GrnXLo1xzAJkeURQl7EQdkDA1bDdg96EST+8rMDhWJ5XQGS9ZJAwNz5dtNzxfRkUsx8PQNIpVm7rlEggYDfvkTDUMzKZ0NvRmo5W42pWhZns4YYnoSy5Yx/qeLEdGqwyNlEmYGm+55jTGQn3P9AngdVduiRrE1m0vEl83V3+KAqWqw9d+9Bx9nRkGx+tUGg7Fii37fCmygiObktqmwyNVCmWbfNoIe2xJQpEwVGnCGJIEJdRxZJI6tYYnS829AF1XkZ3AwA97tMlGmxp7B0q4vkwz2I5PJqXLic+ZrA2Lrr5oGhHKvlKqQkvkyJ2lXN31ZLNPU5dtUzRVoS1rhtdH6maajtFNrU0QRhGNMHKoqwqJpI5paDy8a4S+zjQPPDVJrGGyaSphWjKXMTg8Usd2fdIJjaQh9UKFik3C0CIytXVtG/1daUYmLPxARBokRWkVhzeb2fqu/ExTYI/SNIxUohvK9X2EaE1xeb4fjVtnPkE+kyBh6BHhrtseg4UaF25fE2lwVtoXp5lWMg2VbevaaNjS68hyfQbH6rIpreczVKhHpNH1JYH3w/5qSVNn96EiB4crdGRl65rj4dmzqS/HG160lfseP8wzB4oYukrKELSlDda06WSTLioNbNtC0y0ygY7SSMgGrrMJkUMd4qL7tCwC67pSrL0oR2ViAhrDpMQoOXUCrTCEOiQjOoo9QiceZwJkp+3AWvp3BnrbFDIzNXoTpqzMHtDk4lLTVdryaexyHX8Ba4mTATEJinFU7qrTxYhTUxOmppLJmIyXLHJpM5qIpqcu9g2WWdORmiECnepEGwRSLyFLw31qtofvCzRdiciXEJOeNklTi0SQrh+Q1jXZvTzsERWE1VuqJr19UGS6Zu9gGcf1sULzPk1TojRKte6y+1CJrraETGs0J+ZwqB7ZNcL9/z0gRc2Wi65OPvCbjtWBEAwV6tFYv+SCdRwYrtLuB1ILNEWIKvtZeew6UGS8bNOZT1CtO+GkBLomz6FqedhunZ6OFCNFi2cOFkmEJopCCFw/IJXQUU0Fx5MiUdcPIiIxNTUkIz3hZC2vAg3Hj1ILTQKTCLupW45PrSEnSU1T8DwROTHrzWqv8J6YDwKo2z4KsgqtLSs1OuWaw5P7Ci06UUVVyGdM6rZMHWoqbFvfNkUfI314vvfoIYpVp4WgZ1KytQpAPeWy50iZdEL2rIr6XynQmUsQCHj8uTE2himn9lyCA0NVTEPDt8UMAzl5fZtRqrARrQCif8t7r3kqlu2TNHySieZvBxqOHF/T0NA1+frUBUHNkkaFL7lgHWu7Msv2xZmPOG3ozUZppf4uLfoNGrbHsKLQsKVJqSIgZWpUGrJ6LNT3Yzkeh0ermIaK5cj09ZmbOiSBXiH90OTYK6GeJYziCI/NXT6/8pJ2vr1zjJFChXxaNggWgcCuyoXPaLHBuu4M7RkdEaxcNAUhULximJaaqr+R6SlhDaN7o6jCpWuFvjLQcnOTm2YERzs+TWifj4hJ0CmG6YTHcjweeHJo2avIqWJEzxdRCqtZUmvokrRsyE5ORFPFwKoic/MNS/rmWGFUZao2Q1EIV+qyd9NosRG1ZWiKTpqVSLJnk4oXKl4ThoYR6neaxnyBAL3ZjFXIyEjK1AgCEYlnBQqaIk0JE7pKtSEjR5WGS9WSYlhDl9EUIQQ1y+d7jxymM59gU3+etpRBw/FaHvjAjIknndKxHS8S+TZFnNBsWCrwhcB1PYoVsD1pzGgYslrH9nzyaUkIRot1hBBYnuzknkzoeL5Pw/Gp1mWFXkcuSW9HmgPDFaqW/F7DkIaQnifThU0hbMqU1Uu1kDQ2DR1l01E5fqmEbDBabTgYmoIIAnwhaM+arGlPyh5ss3R7nw5NkYSzGalrEsBmlMXUFRRVlat1QdR2xNDkMfl+QFumaeomy8eHxmVF2FxC00DIipy+rjTphPRnapLaZlPQJ/cWKITam96ONIoyHtkoyEMT0b3WhDulOgyY4jmlYBoqpq5QDYlDpe6gqgkURUgCFMjIWi6ltwiemwuCpKkxPNGQUcpl+ggthjjNJp6WZyvHXddl9C0ItVkgia/Uf6kEfoDryXO3bI+6Ndk6ZLEeR/NVXCF8lEifM7NruQqcvzXLD4pFxor2ovQ3CyIiOFO9b4an+eGMoAhnCTudH7ZIU6eLKl3URBc1uih6nRTsdi6+4FzaujaAtjJO26cqVh0J2rNnD3/2Z3/GY489RiaT4fWvfz0f/OAHMc3ZXSsBHnroIX7lV35l1ve2bNnCPffcc6wO93mF6as/zwuoNGTour8rvSx31aYY8ZmDxajs2dRVNORDcaLshnqU2bdvlvnKnls6uw4VI7PopnlgT0eKasNlc1+Oy8/u466d+xkvNSIXY0UJCIM5JHRVlszbPglT5YzNHYwUG+wfLGOGq1Y9jB5FxElT8QO54pYRD52EKaMYmir35wdTLAGFjJJ7vqBcd9FURQp//QDfD0gnDBzHa3ng3/PTAzhuMGPiGS7UKZTtcCzUUI8jws7lUj+iqQoNt+nErEoTPmRExPNkma2hISvGkCX+jheg67IaJhMa+jleQHvWJJPS6etKcWCwgkB2czd0DTUhCZ0TltDXQ71Uw5bRi1rohGwm9DACM2n2lkkZrOtOMzLRwPUCOnNJTF1DIDvEN6NNUwXTU9HspO75Ai00kBwu1MmkdNkgN3TVFoqM3DWjLGqYjprqCC1CgtSwPbSQWE93FJfO5NIXamhcpnVEGMVxfRmlTCV0aSGgqxwYqqBrKh3ZBI7nU6o5uF6AosgEoqZKjVSLhUD4b6kdUsmlDan5UeR7NcvDF4JS1QqJZ6gnCmTrmGrDnWGvMLUibDk+QoslTm+99vQZmjbTUDlnWzf7B0sMFxokDDWsfguvR3ieqiqjLoGQminPF4wW62GrEFmNuKY9xXjZZrxs0dOenhLNCR2Ro9YPs/S4WqRZ4KL1N/KGQPFKM4z+NHcE/FGytQFUZwQlsOf+wiXCDpJU6cLMrsXIrkWYvfiJXkYabXzvaQ8zsxZfnRnBCRQYcyx2KOtpiwnQUWNVkaBSqcTNN9/M5s2b+dznPsfw8DAf//jHsSyL//W//tec25111lnccccdLa9Vq1Xe8573cPXVVx/rw35eYPrqT/ZTKlGpu/i+nGxTCfngTxgqB4erfPuhA7zuqi3zmqCpisIVZ/Xx38+NtfS3ckO9TbN65NBwhXwmMa0sVFYieV7A/sEyxaoTCWZNXSUQQWi0aLGxJ8flZ/WRNHUu2tHDU/vGKVYdqg0XJyQyIEvYm9GDXDqJ5wXRqrZcl60wfF+AKvtZhYk0VIWILJiGNqWqRNBwfNkewp9UB/iBjNwAuEiNimzp4FO3XfTwRBVFoT1rsvtQiVzaYFNfrmXi2dibZaTYwHJ8HFdBhCtfabSoEgQCU5cRkACBqWlR5KvpYu16AZbt43oCTZEnFQgRpnakYLw9m5ACUS9geKKBoamcs62Tw6N1qSFCeuA0XZCbPa5cL2C40EDVpO7DVKS+yHZlC490Ug/FyEaoBdHYtraNSsOlbrmIsAzf1OV20x2io3sBWZ4vCaWKoUlPl0xSj0gDhK1LCKUa4Xk2hdgwaY5XaXi4ro9paFQaDlv78+TDSFHzM6NFC8cLZDWcoUVeT44nLR5MXYQWDQZdCemwraiQzyTo7kgyMFqn1nClRklVw7YnIhR5K1Fz34Qp0zGTaVvZX607n8TxfJnmRRYKZBIaDSegYnnsOVJk27o2cmkz+r1MrQiTHjgBZthSZnr0dDYfoaUQp6ni6bol25T0rcnx38+M8K2f7MdxJ38DikLkrC7EZCrUNFTZ/y+lkzIFuaSCiozeFCt1qA+gJY3wx7U0krMYrOvO0N+ZZqI4iqgPkWaMdn0CrT6Murs1ojMfwZndqWluCDVFkOjFN3s5UMowXM8jEn3U6KRGNzW6cERapuXUDK8+b3MUlXJLFmWxn5SnkZxl7T/Dqfl5hylCOWafV44nVhUJuv3226nVatx66620t7cD0mPjox/9KO973/vo7e2ddbtsNsv555/f8trXv/51giDgta997TE+6tWJqWmvhKlF/aOapdMTFZu67ZFKaHh+wHChTjbVRrUxOYmMFq0oFTBfeiyZ0MmlTfzQ50SKJWVkI5nQqdQcJqouY8UGa8Jy3uZENFFxMHSFux88iC8E3W1JWcrecCMSpSgKNdtl55ODDI03Ih+izf05RosWpZqDbXuR+Z2CTMXVLJ87vv8cN4ar2u8+cpBKzZERm9ADRw9dmn0hGCvZeIEsTbJdlaSp44fOuUK0Ri+mV0Y1Wz2kTKmb0adom5ppl6kr7yZqli+jJIKwQawkDR5EehdD15gaR9FDK/tmdKU5ATafK6auoasqmqLQ25UmnzZJGCrDEw2uu3gDXW1JUgk9apy6f7Dc4oEjwsomkNExU5etQoQmmUcyoaHrUgtUrjsYmkYgXBxXpq8OjdSkaDth0JFL4AUCXVUxFJnimo5mpERWTQVk02Y4cUttjaYqLSk1RZVjoyB7r6VNjfZcosUcLwhkWxap46nw7KEim/vymIbKvsEyDdsNrRrkoLl+gNeYbJgbBIKa5bKmPRWRis58ktFiAyEE1bpPV3tSptAC6X6tqgrtmQRrOlIkTI2xiQYDhTp128N2WlskJE2d9T1Zhgt1LMdnU18OXVMpVW2CwIs0cL/YP8GOje2YofP61IqwVELH8wN2Hy7iuEFocQCmqUULHb0Z0QqxGAPGiYrN4ZFqS8r8waeHGSrUJUkXAW0ZAyEU+jpTDBQa1BsOpiYwdYGOj6r66IqPioepBSj4mHYKBVmVVbc87LqLJnII7yh8cYRA8atTUlPDkf5GlopLP5zuoLH875j+lWoy1Nr0TRMb94R/9yG0LCgKoyWLu/bvJ5XUSE6jUooyexPSrvykU3PCOE5+QkcNJRJqyWdck+iooGjyj6ohFF3+gJF9h4SqRVrOE4VVRYLuv/9+Lr/88ogAAbzqVa/iIx/5CDt37uRNb3rTovf1n//5n2zevJlzzz33GBzp6kST+OwdKPP0/oKMsoQVQYWKRX9nJio9L9cc6rYfVtqolOsOY6VG1C3b1LWWVMBYscFV56ylI5+YIZ5uhCv4pKnheAEJQ7ZH0DUVTVNBQLFqc3Ckgqap2K7H4HgdN9Su9HVmGBivhV3m65i6SjohK7z8sMz40EiNQtlmc1+e3o5U5LmTSxnYjo8Vpm6kDkl2V/cDEZXKv+f6s6TjMrs4NFKhLWPieoLhiXqUcvJ96YniegGVmtT9NHtXLWZZKkKyYzk+qSmTTHMl3myDMfl5qaES0/bRfOjJ9IxsZNiVS6CqCsWqgx8ELc1MFWWSoTXNJZVQwDpRselpT0WeN+t7si2+KFee3c+R0VrkgdOwfaoNJ6rmkikfF01VOW1dnlJNlkQLIVOVricjT5JYNt2eZUm8ZbuMen6UFptrDAWyoWqTaMq0pItl+xwYruG6rRVoWmiZ0CyTz2ZMLNtjaLyGHTaYTRiyRUsubZA0NPYOlDkyVsH3iT4jhGxM6TdDgmH5mamo0di2Zyejl64nI5PppE617mJ7skVG08Onpz0dpXwqdVemE5FpS4VQNO1JAXlbxsByfcp1JxRBq9FvL5mQFW7Vhovt+jx7qERfV5qt/TnO2Ng5RVzvUqk7VOqSfIOMWlYaLoWShaar9LQnsZxJ4rlQ24xCxWK8ZHHvI4cwdBURQMPxyKQM1nan6Mho2FaDsZKN3WiQFDW2tLmMemW8ICCwA2lFgaAWNrhNJXV0RaGSVGnYkgRWag7r12QXnsi9amjwNzxFaNxKdpSgPv8+lgChJqYIjHsQyX6S7eupBZ14ujT6E3oepi1m5oLt+EtuQrocP6GVR7OccaqNQ/O1SVIjiYxsLAwaKCqBoiPJjyRAkxxH/mMG5zmxHGh1kaC9e/dyww03tLyWz+dZs2YNe/fuXfR+xsbGePDBB/n1X//1lT7EVYupjsjDE3UCIXtBNT1FZKVUJdTCyHC15oZVUYHAsVwGx2r4oTjXDyZTAQk/YO9AmYMjz9KZSwChG/QZvZy/vZtUQuo2qpaMLOlhaqr505FhcQ3HlatWN/R7kQQoTSqphb8MKXB15HozNKJTEIHARxKMoUKdhKnJyc3U2B+220iGrR/kalsNJzkZedh9qMRQoc7arkzkuFypOxQqNjXLiyZgRVXIJDQ0VaVhe5SqNqqmzvzRzoNAwEihTntGajmmOkSr055aDTsUHYfNVlVVmrpN/lvgeJLs6LqGocsUix8KUCGy/AFk5K1JfrxAkl/b9aNqsTM2tkfGek3CHAjB2Vs6GRyv0bAlgZN6rEmHYNcT+IE0+pMl0z6u7+N50o+pXHPRNAVVnbQr0FSklsoPcFUpPFcVlVooPp4KIWT3eQUQKgyOS2dqhVB0qyqoItTcIKN2TWH6hp4MW/rb2D9UoVh1ZE+1dGtPtVzGZO2aNAeHa2HT1qlRJQVlqueRkE1eE6YWNpGdTLPJCJKPpqryGilKGMEL6Mon6W5PRtd8uFDDdgPSCZ2EKUXXpjEZYZJ+WSoNR5pAjhYbLe7GaNCuqaHzt0E2pSOAHz52JIqEVhrS+DMIBMWaE1kIKAoEgCpk9PCunfsjfd9UQ8Hmb1UNW8U0LI9q3aavQ2Nrv0lSDxgYLZEI6iRFQMZPkPBNCFx6kz4Hy1XKtk/D8qSTehis1FQFBWl34PpygWIaGkPjNbwARCBIJTU2davojX2T1VOzNd70l9GTYQ4IxWyJ1kyP3kiC09ZCcDRdJZlP4y2zXDxhaktrQhpiSXqmRWEyBdUkNYFQmKjaOJ4gYZq0t6VRFQNF1yGRQXFz4CsIRUMoqvxxKhqtT59ZSA1T3j7BUZ7FYFWRoHK5TD6fn/F6W1sbpVJp0fu5++678X1/RVJhur4y3Ya1kBhocymEjwL7h8rc9cB+KnWbui0npLSuYTk+h0er9HWlSBoqtdB4sDOfQEE6qLqhbqEp1GzLmqE5oU8ubRAIwcGRquwK7geU63LVcmS0yi/2F9j2RBuXvaCHdFLqKdIJrcUUD6BuuYBMbfV2pChU7GiSHytZ9Okp2aHb8cPVsiREmgqKpkaJoGZ5/MhEnXymDVVRSZiyRUE6qc8I8StKWCpvewwV6mzszbFtfRtvfPFWvnbfHvYNViKNiaKAEghqDQ9Vk60PLMfDXURlE+HxNfdTqctoiaooFCoWXfkk/d0ZxssWKFL4qmkKXuDjeDLd1hyzIBDSwD4QaIqCoUlN0YaeDM8cLKKAFHaH2iHXE7I/VdhzrGp5uIGs4NJVBS8QFGs2KVfntPVtmIbG/qEyP/75IANjtdAOwMNxvajVQ0JXUMNoiNRXyQfZUKHGmo4UgRCMFS3KdUd6NwkQvqzUinQvgdR3BAEErnSmbkZEwiKvWZ+RCuA4IUkO3bQNXcXQZNqy0pCkYX1PlpSpUXd8rr1kPUdGa9z54330dIT95oSMwHm+wHY9hgsN2Q4lvI/UMMUWhIRSYcqCVJFiX1PXo3Rps+pRCuJ9EqZOOqUwURZYrs++wTKZlBFV6tUtSeJyGZPeTkk6LNuLGtBKIici4lusOmRTeov2TghZKt+WNdk3WKFcc+nrTIdaH1tGjsKJeQqHAyEX4bLSM0W17vGz3SNsXZ/HUDSuvbifHz3iYDs18kmNhC7w3AYTpSKdqsv6jjTJoE6j7FAcL6OqMFENcByT09e343k+AkFPe5KJqoWmqbRlEzLyHIChQVJzaNcnML0xcto4XWaRrkSJDr1Au16kXS+QHGvA2KJ+XgtCKAZBole6GCf6CBI94b/D9JTZgzA65o3gzPZkbvas01R1WbNlT2eK3q4Uh0erpMxU6/cL6Ya/YU2Wns6ZtiQb+3Ks780yXrSwXI+kodPVngw/16qrUaJ/MxmpafnTJDDy7yNjdR7eNc5AwcdxBZpq0dOpcNkL8mzpb4NECuwGiho6t896didez7MSWFUkaKVw1113cdZZZ7Fly5aj2o+qKnR0LJVxz498fmX8GpoOx5W6w32PD9JwfHo6MxQqE6SThmw9kYBqw6Vcc0klTSoNLxTOSvFpNiXzy64nezoFQgoda5ZMLWXSBkdGa9ie9DIpVR0U2yObMjANlWLV4amwU3kubRIIqWPIZcywuimgVnfxhYwqmaFGKChZMjoUGu+NFS0c15+hs/GDsH2CkBOnosiVVcORzS9TCQ0l7HQeBCKM2jQdeWXpe3O7bDYZXcu2tjT3PnxYrlgVwvL5SZ3P/8/enwdblt31nehnrT2eebjzzczKyqwRTUhCEpIKhJDEJIKhgTbqF0TIfu5ughaOgPAftsMOO9x00x3ya2xst4OINrYI9cO4m9emUYNkBFjIqgLNUpWkmpRTZead77ln3vNa74+1977nDjlnVWWV6hdRlVU37zlnn3322eu7fr/vkKUarVLaDQ9pSVo1j83elPE0PsINAkrFi9YKKc0IbWcQ0qx7PHr/HO972ymubI34+KeeYW17kgNhTZyo0hIAwJohkmsNqTZu0L5n831vOcneOOHUsswXedMJuLQ+NBEYuQdNQRBO8lR7TZ5KX3G4uhuwO0744796geEkxnMstvsBvWFoxlp5JalR3BVKpaLCWPGtCz1jMVCotfKHFd2cTJkMtTTL9s9VPuObJcyaTDXBYBwZZVf+u0Unr+bbRKkiSTJaM/YKzTx7rN308Vybyc4E13N5w0M1PvfUBhXfIUo1V7fHTIKELDPnuJB1F9e6nnljhz/PYgyapIp61WMwjtgbRTl3yaJedcvjadRcsnFEkioub41504MLRFlElCpqvs2pZUPIdhyLb5zfLXk7GkEz33CMpzFJlBEnilplfymO44Rm1SGMjP3E0lyVVsN0m7Jc2aYx6j7Lkgb45rlonqWpurDaBnfOIox2yKY1uk2b9rKi9XafL3xzj7XtMb2cyzWdRsYuoPDmwnS1HNvYIIRxShAn1F1FlR18a4covkJT7rJaH+PPb+Nl2zRkj6p190ZUCAf8RaisgL8MleX8z5XyT+F2sW5yRHU7Va/fPv/mB956ij964gK7o5h2w8V1bOIkpT+KaTd83vPWU3Ras46IswAHOp0OCPsAmDF/2of+vwA81z8P5670+Q+PrzGcxMy1fDoVs+E7d3XIdj/kQz/0CM1m5a6tVfd63VMgqNlsMhqNjvx8MBjQarVu6jleeOEFnnzySf7e3/t7d3w8SmmGw7vzZbYsSbNZYTgMjDnfHdTsTn4SJGz3g3xhNLJ31xKo/DVcy0QedBouQhgCbxRnJhlPG78cbIklBdMoNUAn3xZ/+3IfpQwAmQYJGoHvSLJUMZrGhpRIEVWRlW6/chojLUPMrfkOOpd6B3FmnIfTHGhJ8/rjICmTtw9XscgWoyTT/VDs7E0ZTmJ6wwilMR2qpPBv0SWPWCtN1bdpV2x2e2M2e1Mub425tD4wXjNZwQc5uBimCvqjiJX5Op26SxynTIL42KgIIci7aYJm1WWhW+WH336KE/M1lrpVXtgc8edfvIxnS0TVYRIkuWHkwSc7QOzOu3NZBrv9gKee3zZeOBV/P9BSGLAzmppOW3FZVT2j2Mq0QkpJmirCKOUb57bZ7U/pDQIaFZdzawPGQVJ2ZwpcoK7Txi6cm9Ps6DmDIjX+oG+O1qbraEuBbef+RrnEXmsjo1bGsJeFdpVJDua0Nh4zQT7uBPN5RknGdm9qOn9akcYJft1hse3zzKU9Jrmnk+dIhCysC0y+lxCG+5Vdxx+vUBcGccbnv7FOu+EZ00fLmANqpdGiAGymazjOYgaTiG9f2TMS9nzU69uSOErRmcKRAq/qoDCy/tNLDZTSnLuaEmBcscM4RWI6no4tadYcLm9O8FxjoEk+Og2ihKW2i+cqbJFhiQRHGC8dncUolaEixXRviPAtdvsBm5serjbihqYveN9bV8ouw94o5nNPrtPyMvz4MjV2WGGbM601Os4eLXuPpuzRlX38bLx/smYb9wXf9RYq05KR6hJZC/jNVfzmSTOiyuMblLecd3Cu88QREN094vNsWVJSr/uMx+GBxPcb1z6QadU83vfWUzx5rsfuMGIUgOX4nL1vnjc8uEi702AYG1CjhSEMg9z/7+KGzLW+lir/56jo4Mhvas0fP36e3iBgda6GEII0ybCFYLHls7Y74VN/eYFf+tk3m/d8h2vVy1XNZuWmpy73FAg6e/bsEe7PaDRie3ubs2fP3tRzfOITn0BKyQc/+MG7ckyHM47utLLcn+d267DU3ZaC7X5oMqzy9n+QmFa4Jc28XyUa37Vzx9+s9FCRQtCqm8Tz4STi0oa5uQmESVCH3ByN/Odmd5ykmkznpmi5hDtONPct1ri4OQYhODFfo+rbpJlmtx+Q6YLIq8txiFIQ5wvO7MJ//HnTeK4ZTahMc3lrTJRkJT+mICULjLqn6PCoXAV27uqAP/3SZdZ3pozyRPRCri2OWczBAKHdYcDG7iSXxB9/bFpBrBSOZfxyHj7V4c0PzbOxM+Xbl/v82VeusDsImGt6SCG4vD1BaahIGE/TI689i0E0ECaKJ55az6Xjkvm2nyuhxqAp3aXBXK+DNDZjs5wfY5RyBuBGsWJ1vmo6JXnumi0lQuky/+p6lWYaW0Jy4/vtMedTQwqua8aYQZjkKj1zvdq2ZK7pEYQJaW58aWJWFK5jgPQkMF5UV7bHKAVzLc/wqjLNO79ria8+t52H7dpkypzf4nPOtAFRQtyYi2lL04lMUoXQUK+6DCexGV8BQuvS9ylJjd2C1AaA/uCbT/Ds5T6XNkdGtScEcWZ+x3VMJ7NRcfAcM7Je6FQIY8NVytIMz3dNyO5cFVtmROGUbt3iVCvBEgFhGNJmB0ckpJHhZsVKMc1fS2EAn2VJsixlb5SSJorJeEJW6WHnBn9OtMHJnH+jgnW+p7NOVc4AHBfo3vrnXJTSkn7aZqS6pM4iU+boJR1eGNTYjdtMmGMvqrPcrZuR9tjmfcsnj/JdMuCYlPOXpPIVMlP5hmmGU3OEKJz/o4vOzIwyan5V8t5lyc4wJszDUOdbZrQVFRPnawKcu1cbvSlXtya0615+nzn4ou26x5WtMeu7E6r20Yy/V2PdUyDoPe95D7/1W791gBv0qU99Cikljz322E09xx/90R/xjne8g8XFxRfzUF+WOs7oTGvTDteY0ZdSmiCecTvO3YArrnFOti3JgydbaL2f5K614sLaEM+VZJkmzvkdWsBsALHGdFwKT5CirHxE4Xs2Z1YaxiU4VWTTJN8N6dwbBWxp1AJxpsqfFzWz4Sk7usWYyvjdiFwOrstgTtuS+K5klIMJDXkkhHnftYpR2vzBfz5Pq+bRbXr4rmRzb1q+2OxtQBz6/yA0RGErv98dtxMrfmTGPA7NqsNvf+JbuceRUXhIYfLApBBl7pdKbs4LxfgFGQXVxY0hji1KIm2j6hAnisEkyq8R8xgpjfzWycdjnmMWbRMkW82l6qIk0d6KRON27ovFslFYEDTnqlRdi6wfYtsGlTSqLu26R29oLBLc3JXYsgSjqQEbae4SLRC4juFDFMTfwqqhuE6COC1J54XDeEE0P/w5z5YU5pqW0nSpRkFMJRcLpJnGsYVREOaeVFqbjYDjWEyChCe+ucHbH11kd7jvuGyCVQ1Qr3k2C22frT2jkLSl4vSig1IxrYpmqaWpeRFpMmSnP6WiQhr4JFObSGnGQUx/OCVO09Kg0ZUJTWdA19mj4+zRsfvMeX3mrQENZ5dOvU/9ygiuXOdDuoUujtKCsepg1VZI7EXO9yr04jaJs0RkLbCXtLky8NmbpKx2a3SaLlrDCzsjJmFKxbVKXpTv2bRdM5r96nPbrMzVXmTlExxHFJ5VRBXjJ2E74NYQfgSZKEHOLROF8870fOvgiOml5g3fjEXCYBIzCRKqjWsbFL+a6p4CQR/60If4+Mc/zkc+8hF+8Rd/kc3NTT760Y/yoQ996IBH0Ic//GHW1tb49Kc/feDx3/rWtzh37hx/42/8jZf60F+SOs7orOJZ2LZkb2QWwUJZpLQmilPCyPAWBpOEpVwaPZzEpWtxGKds9qZkWrMyVzfgQBjp73HGdgVfJdHGVdd1rHwcZLgc3YZZlH/obadY7FbQlsU/+90vMw5SZK4cEgJcJElmIhZgv/th51ldBR+3OIK5pk+UZMRpZtRlGDPBmu8ghMlcyvLdNtoAh1bNdLnWdiaMg5TTSw3STOcqleNX8iPvWJixk5H2H/8Ygbl3pkqx2w/4v/7iHFlm5P9p3iXQ2nQVLClKMqtji+suxrPnXEjBSrfCle0J59aGOQgwnYRJmKKVWcOKIxSYsVNxk41TTb1inJ7749ic+0wdkOTfThXUhRvdzA+MGjPNhfWh4XIBxMbscDXvWi11a4QbQ8ZBSqvmstyt8tyVvgkWxSSt+57FYrtKt+mxvjvh0196gYdPtYnilFbdMaG4tqTiWYSRKl2gLZlROB4c13mU0ryZIoLFdSziWKFdXRpI1nyLIDI2BWhdGjw2qy73LTXY7gdc3hrz099/hi89u81OP8ASKfcv2EhSFpswGPewZYTjZ6DNSFOpjCiD7dimZ0kcKVnoeNRdGxVcZT6b4KRb6HSd757foW336Th7tO09Ws5RGsHtltKCYdZikHaZMMfmtEkvaTNSc4xVh17SZaKadFp1fvy7TnNivoazM+FqrmRKlMKRkvuWPeaClEGuXAvjQilm/KbiJKPmG5XntTxzbr5uDtTMdmo0gt4oI4w1rmvTbdaQ8hCosST4NbJgQqZnvv969s+XGMncQd3IIqEYwxb5et8JdU+BoFarxe/8zu/wa7/2a3zkIx+hVqvxcz/3c/zqr/7qgd9TSpEdM9T/xCc+geu6/MiP/MhLdcgvaV0bxYtypyGExLWN9LYYY02mhmD5Y997mpW52gErfMeSLHWraExK+nbfcBxG0/iaC3QxftLacCSiRNGoOKWxWuFHc3KxzvYoppKnlWeZQuSuskWHaLZsSxgOS6awcp5NmhmVVKPm8MblLrYl+czX1qh4Fp5jTOwK40DjSmxGjt2Gz+mVBmFkuBYF92i7H5JkGRXXPmLcJ4vTKMyIS+fHJHLAFSczqeCYBbRecZDCKOvS1EiVs9w5eDjJu1VSYNnSjEJnIiRmHbWPO8eFUWQx6mrWPO63JZc3x8RpRpymhtCsTZBslu2jxkIV5tnWvjnfQo1LmyP645AkJ6LL/DWA29qWFio/cY1R5sE98n5pnStaco+eLNNc3Zngu5YBH7Ykcy3cXAklhaBRc8tE+ihRrO1M2OpPyTLNle0JX3lu5wDRvDi3xjNq/9xbM/ynA8c607UsTsckzy/LJprV+SpokxNnrn2J69jG18q3ObNcpVUVdCuSKO7RtD1+9h0u/UFCGMNgovjKsz0uXxkZk0fPItYJvt7jlNvnTGeCm26zWBlyqjnGUzt46RaCHqKSH5QN3JG3oGCsWoTWAo3uSc73qlwd1kncBaYsMMzmeKHvMYl0eS491+LUYg3bNvYRc0qxYlnsDPY7Nyfma6zMGQVklI975po+67sT/vwrV/K8P/P9lFoTJCm2JZnPfZXgsGfOjUCNVQKbw+On/U7NPtiZvQozpXny27t84elNo9jMX/vFTLe/V2rWIsGAz/0dUOFIfna1ycpcjcHgLpLb7+G6p0AQwAMPPMDHPvax6/7Oxz/+8WN//nf+zt/h7/ydv/MiHNW9Uceh+CDKSNOMRs2oSOI0I4jzqAJH4kiB0mbX9Z+fXOe/fO8DfOj9Dx0IUVVa8+8+/XwZMhpEWQkGjI7p4AKX4y201kwjky6+1K0AB239waR2Nyouo0lSOjVrbV5HCIGc8WhR2ryebRkgpLRpz5490eS/+sDDLHerfOXZbT779fVS2QWUo4Yk72wobUZPmdLUKjZxqhDofIRkvFsA4lQdcCJWuvA4AZUThWQBUnLgJmZeT2hdZohprXNPHZXLqkXJsUmVRuVALbe9Ma+nbtCFKf12FLia0TTGkiaSIUrMeKv4WLL0KAJRygSuurZkoe3juRadXMpc+gNCCX6OA2Q36lTpfLZ0LTrRcY+VAlo1B4UxkzPKQMP3+cbFHlXXSNMbVcObcRxJJTSjrVRpPEeWaqW9UWxyqvJjnT1eQ8jOUErnkSZpzgcSKG0CV6u+yeBKcwBZjG0dSyItie+aRbjmO7RrNqtdGxvFdn+Ma2e4MsG3FZ2axLdHZKFCpppRf8p0IJknYCHbRGabSLHJ6bkX2FUXaVlGKt6wBkhxzFm6TXucYdqkn3YYqi6xtUhiLzAV84z1HFPmmNAhiAVBmPFdrQ5/cfFqyauTQuB7NnMtj3kpGUwitvZCVuZq1CtGyea6NnGcorU+0rmRgiMdnMLv5mvf3mF9J8C27FxS7zLf9o2gI+/QRCm0rRa11hJUqtwsqLnm+Kn8c/8XLm2M+OQXLvH0hb0ycqResXEb1l1Jt7/XSwpxbDhuYT5brzg89qaVI55mr+a650DQa3W0CmO7aZjQqrts7QWszhsUnyoDFnzHjF4QVm6pb6G1Mc5L0oxxAKPpiH/7yaf5Gx/8Lu5fbh54/pX56oGQ0dweo7x95MHhZQfIPA6SxIwZrmyNkdKoWR4+2c6Jx4rFhQbdpmdIokW6tsx3d7njr6QAFrrseoB5neW5Kj/3Aw+yOmfIkqsLNXzPIozVfhil0uVopxhzuLZkdxSxuTchy8zuP4yz0pfFcy0aFZd+FpYdMyiUXgLPsgiTjDTDhKlKud8BE4YwXoCiKE4PALlZh+PyHM+8xiwAme1MzD5GCLMYxzm4GU0Tnn2hf8uNd1ua82EiSiJW5iq5QaBgOIlLhZqR+eeqr5m63usV56Pkcd3g94sqQjanudmfZZkulmtbRKkB8qcW63TzEejG7oTBJMaWIpfLm25wFGfGSDN/UcsSxk06vxaKN5BmiiBKaVQclueqBFHKVj+g2/RY6lbxXYv+OEFKTcVWVFwJpLgyQegMdEynKlnuVNjuT2lWbZQ7pesNaVl7dOw96uxQi3epiV0q7FJr71D/9gBxiNhaBU7cpuvGMG0yzDpMxTyhmGeouqxPGmwHLXaiNntJi/tPdHnd/V2+9Ow2803f0HwOfSiuDTtRyJee2SKMMqq+hS0tMq0YhwlBmDDXqRhFnD4+/Nh4cDlMY5MWL2TRnS66NXmnRlqcODnH6sn72R3GfOZrm2wPIha6dZSQDHTBd9Ns7E45uVij0VkmO+xBcxfGT5c2RvzfnzvPxTw4uFUzth7jMCVKpty3VGccJDdMt3+l1+nlxpFwXMeS3L/c4N1vWDmwNnwn1Gsg6B6vayW/R4nJoiq6EpMwxZYShOEuaE0ef2BGFRXPQimTj/X7nznHf/neB8vdzuzuoAgZLUwE0UUHet+Qr5C3o42HTZppesMIkfNd/t2fPQ/CxB94rs1Wb3qA8CyEJslJwQIzUjqxUGM4iRjn4wfjYgvv/55TB3Zly90qD51o8Y0LPSaBcQgOImNMV/JhBIzCmHRm2lWQeQ2pOjEybMfCsS0cjKoq0yaItdPwaNVczq0Ny1gEK/diSTNVcpZsmTs4H+qC3MwterZbUZKZi86WNNL4dKa7c61x07WqACTGjDHLI1ISxoGJA3ngRJM4NSOlSd5lKZVT+saAxnNkOf4sOmHAkXNxrceaca0qwV/RtQGj4NoehGWcxHzbZ21nSqp16QNVREocINZjuFsupgNm5dEttmXGgQ+dbDHfttkbBLzxtE/Dh95ghNsOaYopaRqiFSRBQk3u0RA9WtYuC/6QE96IVtijXtmlTo96ew8prvFmb2PtDHSTCXOMVJd+0qHSOsFzOxX24jaiusIgbXF+09hXOJZxDq5VbHQTlJ/RzDKYJrz3zSfwPZuvP797TZfiKEmZBAleLpwoeHoCgSUkqVLs9GM816ZS8bAsl2q1hpA2judhJ5pMC8JY4wvwWifQlaoZS12nW9PpwFu/q8IfPn6Bb70wObYL8bZHlhAvgglfISrZG0WQ3w/NBszc16ZRmm8ua2WA7Gy8zKutZsNxi4nAbBTSd1K9BoJehpoNN73exXdYDl/cMJJMEcYpO4PQ3OilQAkz793sGSXSrHLFti1sywIL0ixhND2421Fa47kW3/PwQk6cjQ7wS2ROgK64Jq0aTKelVTMBlVe3J1jS8DrGQZwbB2rSNMN1DRnXdy2klARRQpqqAxs7pTWDSUS77hkzOA12nj91dvXgrkQKwY9+72k2e1PWelOzgB9arbPCNuO6n4HpDNmW4IHVJpkyn0mn4eE65lgfPtXi4vqIcZjg2RJLGIGIUvlYTAqC6DqGMzcomQPFSWT4RAVHRyI4LJy/VbqOPvTfAvLEd5jECeeuDnnoVJs3PTDHNEyZRClBmLK5NyVJTQBp8cDjXtsYZlp4jsUkTM3YMU7pj5Njj6F4v2bBFcRJes2uU5RqdvoBo6nZobqOkc+nuWN5linjgSRA5opHSwrjz+Nb+I7GEhk6S3CsFIsUV2Z0rSlhL2bFHfF9q4IFf4+gdhUZbkBng2Syjq+2adl9rGsBnNsoZbfKmIbMXeRbmx5rozqyusKELlPmyDDqqSvbE7RWBOsmwsSzJZXQoVG1caTx30pSxc4gpOrXyw1OGEPVNZuS2QDOilctO69SGOfQ/iSj2aiyOt+kN0qYxGBZNpNQIWyJjSTVgomWRAie3rW432nSqLrYwiKKzGh7bWfKfYs1ms2O6dzcRLfmRl2IF2sMVYhKahWH/tiMlYsSwgDzab4ZSPLO4au9pBCvaqB3s/UaCHqJ6+LGkM9+ba3s7Bhr+6OEvOPk8GB4QWdWGqztTFjsVPjBt55kMI55/Kl1esMQDUzzDCiVd3HSTNEfR7kaSdCqueVuJ4qzMnOsNwpNtIDWSCmp+TIHGCLvLmniPGbDd00+09beFI2m5tsmhybRSGH8SqJEI8KMZs0hU1D1LFbmKlzcGBHmvCMwhO8gStneC01CeR66+sjp/ayrw+W5NhXXJpZ53tVtdMhF/u/RNKHqO7zjuxZ571tPEsVZ2eX4//3FOb59ZcA42IcltjTp2ZMwJR7Ht/7CMxVEZqRTKJWMfcDRN3N7AwBTlqS0Q5BCkIwyEyWyO+HBk21qFYdaxWGS58epmSDUoiN42CnbnHOTnu7YJqlcZRrfM8aESu0L7+UMudtzrdwT5/rvqBwrasP1Mq9hgJeFwrMVEoUtUwQJkhRURMvao2X1qItd6vYOLatHy+rRcfrMW30abh+LFC6Z1zkAsa38n1uoiDqDtMtQdRjrObS3xPzyGVpzp8qUcayDPBm3NeH8V64w3UsPhGNu90PG0xjfNXJ8E4QLkzAxajdnPyJGa4FCUnVtEIJJFHF6tclCt42QDu/47of53FObDENFvepjOzbTBHrjFOXMMVWKwKqT+Qkbe0MGk4hMmUy0InOuXnFY7prIh9F0l0fua9Oo+4zHMbvDkHrF4Z2vX7nl7sHL0YUoRCWNqpOT63VpmwDm+xHr1JxnSx6rnHqtXp312if9Eta5K33+4LMmvHO2s3McIe84OXxRJv/LZzBOqPkOD6y2mGv6fO6pNb749BbjaVJyYyxr36E3zKXIvWGIbUvOrw358rNb7AxCRlMzOqt4NmGiTKZUYki1RTp8GKVoNPWqy0LbJ80Uk9AQo6eRyaCBQ47DWjOcxNR8l+E0ZjiJDyh4yt/HAL84yUAbflB/FHN5c8yJxRrPXNwzhF6l+ebFHmGc8sazXfrjiGcvD0pn01sZGWkMt2RnGPJA3eWxN66W3KNLGyM+8fhF4kTxwGqTjb2AaZiS5lbJO4PQZG3dRpXqME0ply8mABoDWu6mUavWJgOvCJat+Q7jaUIv9zFq112iJKM/irAsQZQa0ncxqpsNdC2eT2MUWo4t6TZ9Ti83ubQxZLFbYWN3ahyibdOlBFGaCtqWIIr331wxsioiH6QUOBa4lqbmKSw0mUqwREadXdpyi7a1Q8cxPJyG3KVp9XKp+ABH3r0dfKCq7CVt9pIOe0mHftZhL24zyOboJ20yb4mfef/rcS0T/1LJFVFSXN+7tyALf/X5bXb6MdMowbGMgWTF9+g0HNZ3A3zPBWmhsYlSsG2bLBNEmTEijO0GKTZ7wxTPc3j40ftQlTqgWViC72GOv/zmOhe2p0RpjC0lq/NVHjnV4T999Sq9UUjFs2nWXLb7Qa5WFKUq8uRCnfm2j20L1nem9IYR4zBDoO+4c/NSdyEKUYkUpiM6CpJcXGG+eJkyysFxkPDwqWtvvl6rV1+9BoJeolJa82dfusw4iI90dnzXYn13emBEdTOmVv1xXLZtTy83OLH4EDv9kKdf2CPNTI5Vpg6NM4QxPERrvvj0JpMwQSmF0qabI4TAdUwIaMG/CKKUuaaH0/HZ3DVKmvXdabljLMwKr1WZglEQHzFGlBwFLUmm6TRsVudrjIOE3/+L59kdRvQGIcnMiM5zLdJM06yZnZ26jkLpemWiKUzna/9n+124RsXh0ubYcINci0hgspxu8cUc24wIs0znXkjmdWShOhOGA2SA2a2/jxuVZ1ulEs13beOELAST0HBrHEsy1/LpjyOmhTowf2wBagW5ZYAQ/MjbT9Ku+yBgruWzONfgt//wKboNH8+xy8BRk8lquhfkhHTHlni2wrE0jsiQIqZhDeiIDVpym6bcoSV3admmi9OyDfn4bgKcTNbQ3rIJ2HQX8z9N8ObGtMkffSXi4naa5+ntewoVBpzCgnCa8ZdPbfAz73ngoLmfMMDvsLRbI9gbZ0SpwK/P871vmWd3kAASy7F44ps7eK5DqCQ9NUaEJuhV5eagUaxY6vrsjPPw2jii5hsH8He/YYXV+doBB+BTi3VOLDx4pOMC8MzlvVIm7bkWnmvj2qLkZrVqLnO50qvb8IkTxY98732cOdkhjZMc7L1y+COz0vClTpUwHpfKVnO/NaPxbtPn3W+49e7Wa/XKrddA0EtUm70plzdHdBv+sZ2dTsM7QMi7GVMrWwomQcKF9WEpdU8zzYOrLS5uDJlG+xoLqwi8VCbnSwjBxc0RpxZr7A5CPGffM0IIs2hPwwTbMmAjTlXJB9LaJHzHacYkVAynB1ft40i1h8dVh8cr+39hdmrNmstWf8q3r06PHXWFccbOIDBEXymQSpDd4tCoOE7PloynMX/4+AV+8rEzeK7F+s6Udt1lbWdCkhmQMM55Vjctg8rLybscSZo7ReuZEVMusUoPOXPfrSq6ONZM6z9TCktKFjo+P/XYGWoVB8eRfPKvLmFJQyROs6MxIULkEn+pObc2IsuGpEpR8SwWu7WSu7MyX+HkgkuWJARRSDTapCnW6YgdFrwdZLRGM+/gtG1j9ufKhLtVQeazl7Tppx0mukvmLtHPulzu1+ilHZS3hOM2Wej4vPXUwpGYhm4L7j+1xqWdNbJ8MyGlxHcsqr6DY5uF0/MVQWIxCC267Rom+8nkPhU5UAVZ+Mr2lC88u8XGTsAwiBhNEpQ2I6+a5+D7Fhu7MaeXjPotijNGQULVs0tn+DBOTVaZb3P/SoMffOtJar5z3VHStTouszJp37XKMViqwHNslrq1EjRHickFPLVY5+H7OuztTW47TuFm+ZB3u2bFH6MgYXmuwt4wYhymxLlB4KP3dfiRd9z3qpXHv1bH12sg6CUqI1VXtK7hxHm4s3MjU6v1XRP78MnPXyKIsnKHOprGLLQrLHWqvLA1BnQZIZEpw+Eo1FdBnHFpQ5FlZqFMMYtjmimmkSGueo5Z8ePE/My1Zd5BSLEssG1RjsHK47uD86SB7X6AY5kW/PW4PlGikMJ4zPieTRokN80NEvm/hIZa1eG+pQYbPdONe8frlkgyhVIWo6lRwhVxHYezdm6m0kyTqbR8fwWxWsMBS4A7LccWZecwijOSzIw3BPt+RIVzryUFZ1aavO5Ml8ubYz75V5d48tu7uYflQVWaYwlkfg3ZljEynIYRD6/WaLkDKukaTrzGg94VOvE2C3KPGjtU9A4Vawe3c4g3dQdmf5HyzGgqadNLOoz0HMO0w1jM0U87uLUVQl1jbxix1K1Q8W2mYcrVnUnpPVWXNstNm829gM8+uc5733yS1fkaINjoTfnGhT029lLqzTapkghhYbsunuugtCDMJGEMcQZJ7DJQi7Scenl+D5Q+KHBwHcn2XlC6j0dxhj9ns7k7ZW8U0agY/xzjmj0quxU6H0/2hhHdps8Pve3OFutZgvLatjElCmJFp+6y1K1iScEw96XqjyLOrDRLx/nbrcNK12vxIV+sOkzKrlUcqr5Dt+nxjkeXePPD8691gK5RLxd4fSnqNRD0ElXFs3Fs09nxDslWtdb0xxFpqkwYZD4muZap1fquSVD3XYteEhElhmgab5sQxu1+sB/0SD7yUar06VHahJAC5RgrjNOSm1Fmc2nK/KUkVWaMUhoSalRkAkrvdoVRxvn14U3xe8LEtLGV0riWJLrJHWohYDHGfWb3XXTjgtCkkF9YHzIN9xPai+/8QfHvzb3WYSn3tZyib7UEBoimmblmCvPH4qmLaynNDM+rGIOdmKvx2BtWuLw55hOPX2BvnPNDqg5RqgjjhLY7YsndZs7eomNv07G2aVvbdKy8i5P1cELjuIsEChrFofHrzVak3Jx/Y3g4vRzsjFSX3aTDMO3QaHXZ6IXmfQG1yr5pqOvYvPnEPNNewKnlpiEWI+iNU6rVCr7nkWGRphLp12jVLPrjlK9dtVk6eYL13ZA/eSpgFLSwJFydjAhy8ItWCBGX7taFv1G9krDdDzi1WD/2Pc2OVpe7Fc6vDUkyRb1iA4JplNIfR5xZaTAYx1zdGdNtejSqDqeXG2z2jIVBGGdUfZuHTrXvGmiYJSifXxvyxDfWGU0S1nYm5T1F5YajZ1aad7ToXUvp+lIbFL4mDb/1ernB64tdr4Ggl6iWulVOLTV49uIuy919TtBomuRGdjGuI/nUF17gyfO75QV2WE5qiKbGCyjNVMkbsqQuTfvCJKNRsY3bseJIcvZxC7BRJpmOT0HKteT+g5JMkwRJ2UGp+w5e1eRpjaZ3b5RRjItulhejteHS2Ja+baLy5t6UIEpY7FSJ04wXNkf0hqFJWZcAErQ6AMrkTYIYSwqkzA0d88M7DIpupcpoD8i9lASuI0nzCA3bNuaThTN14fEUhCa7zbElnYbP931XldPVi3zpy1/kHd4LdGvbeMkabWuHptimIXt4Mry9gzymEu3QTzr0sy67UYte3KE3QzreSzpMVYVZ1CSlIUrbUmDbklbLw3Es5loWmTbkYNdxENLCb0hSJXh2yybNmoSWR8etoxGsB2OQEhEJosiEzV7YS4GUVGm+daXHXGeOZy/3uboT0Kg4nFs3QZ/HeRFJSSnJV0rz+FPrzDX9YxeEWYFDGJuR8uzouZBmR4nixGKNFzbGvLA5YrFTKfO0tIbFdoUffvt9d71bUYzLlrtVpID/8J/PMw5Sc86lwPNNAPEXn9ni5FKdTufWnR6vp3Q9jg/5Ytdr0vCbr3sFvL6Y9RoIeolKCsH733aKq5ujsrOTpKbbUPjo3L/cxHXkkQtsducyCRI++flL9JLoQATEJExBCBxbk6QwnKZlKrs+NN64VgsjOSJb1qXMvnhM0UEpOlo13yaME5K7xFm9JlfoemW4xTiOiasw4aA3fpaiIxPFGX2lGUxMXlpxrvcDSA9bv928Ck1pjcr2Acmddn+UNouwyG0LLCkI45SqZ+F5prvgiSlL3g4nqju05RbzzjYL3g41vUVTbtOUu/i7AezCjwF4+ZPfojy8qETZDLIu/bTNIO3SWbyfWvskW0GLz18AvGUmqsqVzQmOK5mGJgBXCMO1kY7AcQXt3GHY8GosFBKlBUpbOL5LrVNjfTcgE4JRaMj8RCb+BG3ynzJlolmy3RjPGTDX8tjYi3AdA1iKsWarZuT2YBRBf/yXlxBSMNf0uLgxZjRNjFWEgNlLyQBQ45tV9R1OLlTZHYR88vOX+MnvO5ODif2FfFbgMA4TMg3WjNWEJQWxVmTK5N2Npgn1qsO5q8NSRel7ho8z13rxyMhKa86vD2nVPE4vNVBal7YKYMbvjz+5znc/snzLz30jpethPuTdrlfzKOfFrHsNvL5Y9RoIegnrgZNtfvo9Z/js19ZY256wtjshShTdhsfyXI1G1fCFigvs8afWsOwTbOyYILvVhRpKm2yvKMnKHWWaB3NaUqC1hcDcPI9LyeZQNwN9bV9BDeVOHK3KxUBgxmujIKHu2xyTZfuSVWE4WK+6ZXzGJEwJdJpHUkgTP5GoI+eiDGxVCp1mZJkZcVRcQbPmECeKSZjckVw95z0fUFvdCg4qzr+VGwJW7YhFb4eT1R0e7A5xk3Wqap22tUvbMsqqirx7wYeJsumnbfpJm6GaYzdq08tJx/3UeOPgtnFdlyRRJJnmhFXj/WdOsfpAlWa0xuZeTKPp04jrCGmRuZoohhRBlkkSBUkm8w6gGTUpte8OrbXm9LJDfwKXd2KiOEUIQd13TP5bzioPZYptCaq+Q5RkBHHK5c3EmItGuvRhch0TfFq4l/uORRCbkZNrW/RHETrn0tkCkjQrievSBKBTrzgsdmts7QUMpyaYt5fn5s2OCWYFDlGcEcUpQajLBcXKu1yWlLnYwcRINKoOK3NVfNdGSkFvFJXE/Zsd59zK4l8AlW7TO0aIYYDK1Z0J67sTqvatLXi3qnS9m/VqH+W8mLXZm76s4PWlqtdA0Etc9y83WX1/jW9d6PEHn7tAzXfyi2z/d0Q+4vjC01t88Znt0kvG9yxOzFeZ5hyBLFN5yrhCZRot9kdZUkKj6hrzxCApuzyafESib9yVUAqE1CDMTbrw4inGOXGcsRu/fAhICnAdizMrTTb3AgbjGClNblqWaaI0I0uvrVzLlCZTxlQyz60u+TNRkuVE6FsWgx2oQl6tcxR0+HlmQU7Filj0esx7Oyy6W8y723StHTrWNh17m5a1S1UeSta8ze4NQKos+mn7gBfO7IhqkHWZ6iauYxu3YEsyjRQagWU7KEtiOQ413wfLJppmuJbNbmLxmecsfva9p3nk0UXOf/4S69sxw2neDVGK4Tgx0SPWwbwyKQWFkK1WcUhT446eZSacN06MCKBZ8wxhPeeyCSHy74lmoV3h6vYkHw9rXFseULtpbQxEbcuMcxsVQ449d3XA+u4EpY0BpBTGG0kiERiXc1saB+t23WNjd0IUZ7i2BK3x7KNd3ELg8OwLfSZBXCoDTR6XiV3RQJpljKYJSZYhU8Hp5caBhafimY3Rp75wiUbFZX33+ov6rS7+NwNUBpOYSZBQbbi3dJ3djNL1xTAofLlGObNZj9MwpeLbN1Tx3Yv1coLXl7JeA0EvQxkll4NtSdp190iS+GiacGVrzDhI8FyLqmejlGI8jXnqfFT+3vUYG8UN1LUlmStJgv2V5lZGMpkCnWQHJNZwa6aEL0aJHADdv9zAsSVhlOadMEPiLnx8Do+gZg/7yHsQlKqdu3WMheTcs2IDcNxdFtxtFtxt5pxtOjnIads71OT4rr12pi36SYu9tEMvNmTjQdZlpDrsJl0GqQE4Qlil8gthIaQNtoW2zTiqIh1sxybSkomS4FtMAsVkZAjjSpkFOkhSkjjDsgxx/pkrUwYTxQ+97RQf+J5TPP6NdSZBShgZcOnYsuz4FFWktxsQIvFdm4lKqFUcfuxdpxEafvdPn88fb8aeRbbd7Cdb8QyPRUYpemaWVfgcaWASJCY3zpYsdatUPQvLkkzjNE/QNmT7NDPjteJZDHgSuTt6RtWzSJUZHVV9hzlvf0xwYrHGTj/k9FKDrzy7zTTKqPk2QVQ4ZptwUgFcWB+xMldDaeg2j995u47kWxf2yuDXay3qFzeG/P5nzjGaJrTrHouNCnF6/cX/poCKLanl6tZb6TIdVrqCIbKnSh1Qn91Ng8KXa5RTgM/CgT9J9w1Fz642X1EdqJcLvL7U9co++ldwXesC01qzsTtmGpqWvwmbzIiS9JbGTmmmGU8TENwwoqCogiNzGBwoDSp9mVHPTMlcyXV6qUG96nBpY0SmjbdRkYx+s7e12S7PrfJ1LCmwpMCzMxbcXRa9XebdHeadbdPFsQ3IaVs71K3hrT35dUppyUh1GGaGaNyL2/TiDoO0w4h5UmeJQLQZTzUpRuKNtNFIEBaJJVBSkiFIM0mqIMmE+ZxnxlFKadp1l1P1OgpNxcyPCKIp48AYbipNqRi0pMg9ZwTTMOXy1rgc4Xzo/Q+VKqRvXeyxvjthay/I+VuKwlwwUwYYeLbFNAe23abPfYsNgiilVXeZBEn5dwCZ1mhluiuObQJ1wzjNx10mIFMKQZRmJactyRTNusuJ+TqNqsM0TEwMS5KSacOrK8aYpaCAfZfvIEyp5oAgSjIaFYeKZ5cqw3NXh3zsj59hMI6ZhEkZ8pspA/SKIbRjS8OLEoI3nOny9XO7x+68tdbsDQ0PcHZkdXhRV1rxsU8+w84gwrGEcQb3bZa6VVbmqtdc/GeBiudIwthsJGwp8V3J3iji7GqTlbkaX39246aif4qaVbpeWB8RJ9ldV58drpeDh1R0nmYd+D1HkmSa3tAAolcSmXipW72uTctePv59pbtrvwaCXqa6lg9QEGWMAqPysvKATrMbvfXXSG+xXXMnqqWXsoowzas7E1zHQgBJkqFtScW3meR+QTfzVo77HSEKgCNxrJRFr8eCu8ucu8OCs828Y7o4bWuHtrVN464CHEE/bdEvohrSDiPVJXGW2Ik74K9gVReIMpveKCNRAiFtMm0RKEiVgExiSZthkpEkJsdL54AFrYlSRaZunLdmS7NgBLHJTRuMI6ZhSpwqlFK5b9D++LHiWpCPE21LstSpMJjEPPGNdX5+6aFShfTO1y8ZQHR1wJee3eL82pAgNnEMQggc27h3130by5I8sGq6BFt7AY2KS6Pq0B9FTAKjSswyjevI3JUbhpO4JLZ7jkWz5pJmGenEpNbXfAulYTXn4Y0mMefXhmjM6FfrrPy+GV6ZQEjI8o2AEIIwz5cLYrMbXupWy45ukioTRJspVuaqOLZgZxAi0FhSsDxfo15xIB9lSyEYToy/V7Ex8l2bIEpLY0sNjMMUtwhDnqliUT93dcj59SG7g4iKa3hPBXcv3BhxerlxYPEvzmnRzXn3G5a5vDnmqXM9A9HyEa7ELIiPvWmFC2uDm47+ma3Tyw3e/ujiddVnK3O1uwYOXupRTtF5Gk3jow78GqZRSpapI+HV93Jdz6ZlbxRRrzivCnft10DQy1TXusAmYUIUZ1D4u5QKpZenDvNhbAm3aRZ7V17fc0z7Nc10Hnyq6OQu3J5jodGEcR4EqTVJqsvnKGTlVs7BcWTKvLeXd28MD2fO3qFrm/FU29qhafXv2ntRWjBIW4aDk3YZpG0G6RxD1WWgugzVPBPdRQkP23HJlMw7NBZZJEkzCaFFtmf8gIpoFK0NoXs8TUx4qQZLxriOxWQm+FUA1iEOznXPvRQEUcLzV8LSb8hzZX6Tz40eM42eJDRqLkKYblLBs6n6DlKKIztuKQRRnHFxc0ScKMP9yYyB53K3Qr1iAnenYYLrWJxebrC1FzDf9suNw9nVJmGsGE5jw/9JMuI4QQgIooQ0MyaPtYqTO6BbNKseg0lEEKX4eZjsziDg4voIgNNLDfrjiP44KkN5rTwmRABYposjpSSIE6ZRRqvmsNiplqIGrTVrOxOUhqVOhYpno7U5FssyGXyjqbFjKNaOIDLgZnWhVvKHskwRRFl53i3LeHnNN/1jxw+uLemNQlzHwrYlTq6UUzkRPI4zNntTzqw06WcG9P35V64c6ObUqrYJUBZQoiCdoyBuPfrn4LV/Y/XZ3QQHL/Uop+g8VTz7WAd+z5EEUcZcq/KKIhMfZ9PiWPKOs+PupXoNBL2MddwFppSm4lnEiQFAQorS2PBW6k7IvEVJAVXPJkpSknzhvJvBnjeq426FVd9mZa6Gl8vDt3pTdgYBzZpLkpkQy0xpfEvRsfdoWZssOtvMuWZE1ZZFB2eHuhwgxd1BmEoLhmmTftJmL+3QT7sMsi5D1WWk5hioBUZqDiV9NBYZEqUtlJZkWpIpUFhEqekkqEyR5iOpVGnQ6ZFOXRGJUXgFNase08jMccIkI4jSA9eAIeDe3PsRmPiE1fka59eGqEybMVFmOhezSrc4NXw17TukSh/ojHiOxd4o4srWuOw4hHHKJx6/WBJW51o+vVHI1a0Jm3sBcWrGMElmxlef/fo6f/mNTVbmq5xdabI7CHlhc0y94uTrs+l2idy7J0qycnRlnJZ1HndiulXTyPB+BpOI3jDCsSVnVpo0ay6+Z+f+QAohjEKs8OTyHIv7lhoIAc9dHtCquzyw2mD2Sp1GKaNpQrNqQCAYzlQR2mnctlOCKM3T4PfHCstd8/6++PQWUc438h2LJM0YTlNUpvG9/Qy42RpMYpJUsdiush5NGEziA3yrwgF6MIlJU8UT31gnSVW5+QqihKcv7JFkijMrDWoVF5V3oXzXYqM35U+/cJlxlN109M9s3Yz67G6Cgxs57t/tUU7RearaVtnFn60ipd6SZpPwSiITv9oNJl8DQS9zHb7APNfiP335Cp//1iZRkt02AfluedKEaYqeAT4vRVNKHPoPO1/oHUfiWJIwjDnTCunam7zBuowbX+VkvYeXbtIUWzTlLnW5d9cADsAgaZSy8H46VwKcgZpjqBYYqXky4aMwwEYhSbVFkkmSDFIlS9fugnNjPG0ytDbIxLYMz+V65OxCaVYAIqU05BYAJ+Y9oiQzHaE7fOuuI3noZIsoNkTm8jrUhoNjyTxqJX+hKFFonZjIlu5+Z6Q3CtkdhPzJly6b8ZkUjIIYEJxZ2VdAzbcqdBueATdVB6VApoJOw0Vps8g8f7nPC5sjXFsymiaGU5S3JTt1j8VulVQp1ranuLZgOE0YTGLs3NRQ6f2u6tsfWeThU23+5EuX6dQ9E/CKkaafWKhx7uqATEGaKpRj0ap55fsKopSlboV61WN9d0q7vj8m2OxNEQJW5vazt4QQZQxGlGSGk5RmBBEHxgoA59eHNGsGgBQqUClgvukznCb0hhEn5us5eTv/SHLHeceW+J4BgWlqCNzFKC1VmjhM2NybmNDQRLA6b7o5o2nCle1x6Q5/fm3IfMt8jhXPIogMKfrS5gjPdWjNHQ9SrjdieqnHUy/1KKfoPGUq95ZSRVCynnHgN5YPr0Qy8avZYPKV9Um8SuvwBfZ9b1rlyXM7jMPbvyHcLfVW+iJtWIoOhnFUFuWYypGKpcoei+42HXuLrr1D29qiI3fo2Du07V3qorcPcHz2s6huTblb1jBtlPybftplmAOcoZpjkC0w1IukslJ2bZS2yLQ02VGZIFMil9vrA4oipW7t5BVE0euVyJ0rdamKMqMGMNlhji1vCgBJScn61WC4H/lI0bYlpxbqBFHK+bXBscR6pcBxBK6AJDU+AplStOpuCYBGk5iL6yPjUl338F2L/jhid2DMC0fTBNuSJQG34lksdiqcuzqkUXWYa/qs7Zg0+iL6xRiL2jx6X5sgTrm4PjLdHsB3bSwp2LYCrLyDMw1TYq2wRDEONMqvtd0Jp5cbpKkiSTOmISWxeb7lMxhH9McxaM3JhRrzrUq+sTBdhAdPtPjRd5/lk0+c5+rWpHRzn2/5KEV5LYRxroISgqWuz8ZuQBBn9McxNd85MFbYyH1ZjD+QVSqoinOzOwxLR+lZdZhZ1F1Upnn+yoA0j7xJUk0ms9xCwADZKM6oV9xSgTaaJlzaGBHGSXkNKQ2DSZRnnVmkqeFIxWmG76ZUXKN2OlzXGzG9HEqjl3KUU3SeLq4Py66fUpogNoC0iFm5vDXm0dPtVzyZ+NVUr4Gge7B07ij7SqsS2FgHAY4hGWtcmdFx95h3tulaOcixtmlbu7SsHdr2Dg3ZxxJ3772P0noOcNr0sy7DtMtIdeln8wz0HCO1SCqqKGzSQwAnzsyYKstBTaZ0PhbKUOrF8Uc6HM1hSXHAyVvlESiwz3NSCixbkCnKUVDxd8cSvzF+Nw+sNpmECc2ayxsemKNdN9bRf/alKzi24NylUR7Cyj5YYv+1lTLjIss2I7IkVWzuTug2fKIk5fyaIYyvztVyAIMxILQlcap47nK/JDNLYUadjarLNErxfSvPjzPkZilgOMlQCsIoNVlhrlWOq4Kc83J21WSGDXN1DkLQqNjYthkjxXFGveIwmiT80V9eZDRN2B2E2JakmquoGlWHpW6tHItZUqK1ATRFF+GxN63w4Kk2/68fepi17UmpetsbmbiVnUGIZZlrX6v9SBelNVXPZqFd4Xu/62Bo52y3RAhRdqeKKhyll7pVJkF6YFG/f7nB7//FOaI4w7YFAkmamfMVa4UQUHEtar5TEsa11mz2JmaM7NkkWQJCILQhcBeqtmbNxbI0GnMezq8NsS1Js7a/67jRiOmlHk8V9VKNcmY7T1GiUEozCPLgYJF/l3PRQH8Uc3lz/Krg07wa6jUQdI+V0ppPf/kySaqpVyzGwctnRgj7SikDbuQMqBHYFjiWwhbgyIxW7lrcsXfo2IZ/05IFwOnRtPpY4u69n0lWMyGbaYdB2jUgR3UZZvOMdZepWEI4DVzPpT/JCFNDMo6VIE4FWSZQQJpmxFkhC8/IDgGc6/GrBEatlillctpu433MPn9hZIkAx7GMD07OjYF9cFP8U4x3bEsQJRnTKNk/hpzTeri5JHIukZCCpW6VaZjxprPzLHerbPSmOJbk8tbYEGZtgcrMCOxwKaVJMYu27xrgMY0VV7bHZgQF+I5kfXdaEnwdxyj7ktQs0J4jcXMeV38cs7kXkGWarV6w7+5sWyjMMTh5WOx2f8p9y82SAF9kcIVxZgBMkDJNEmROiha5B5RjG7XYRm9KGGfUfIc4zbAsDqio6hUDyBqYseXmXnCgi3D/chPYJ3l/+dmtkuOkFJxbGxCn2nSgxL79gJSCuZZPfxzx+DfWmWv5x7pLX6tb0qi4/OT3nUEKUS7q822f/+PPv41jW0gpUMqYUDq2RGny0ZgZbw4mCVKKMhOtyDIz32dJnBhRRpwYkFBE7sSpplVzObnY4KlzO1xYH/LQyTa+e3MjppdTafRSjXKKztPnnlrji09H+fdQYwmJ51o0qy6LnQrj4JWjEPtOqNdA0D1USmu+daHHpY0x5EZ7guy6C6vEtPiP5n5du44AG0H+p8gdjBW2pXEkWEJjiYSW3aclt+lYW7TktgE2coem3aNt7dGy97DvIsAZpzXTvcmjGQapATi9tMtIdxlmpoODcKhUPDQ240QRxBBngigTuLZkpVbjhe0pw0ls1DIqJc32/ZAsabyY4uSoZNyWYNsWZ1eajKYxnmtxZXtCko8Hik6NygGQ6YTpkkR+u2VZ5nMp+AWHq3ChlnmXQWFu9Bu7kwNeUteyCSi8bq5sTaj5RtZ+7uoAgPm2T6vhcm5taK4TIZEWZDo78mRKgy2EUespzULLw7Et3vfWE6xtT7i8PSJGUPVsKq6Ra0/DhCgnbJsxqHF71jnpOs106VdVcI5G08SY7BWSbSkIEwVal6OHimuj8gyuRtVlqVthdNWAoDg146hGxcll4VMyZZykF9o+2/2QODWRGVGScXV7TKPqMtf0eOwNK+XbXl2oHckGO2zKB3A1iPEcK+9+GRdr2A+/3ewFPHyqdWQxnG/7tOoul7fGLHUqVH1nv+t3iEA9ewzFGG2pUzHdpFQdGKsWI06lzWfRbnhs9wPqFbv8Dgghcp+kzETxKJ1f26ZD5bs2y3NVWg2PsytNru5M2BtHBtje5IjpO0FpdHq5geOc5NLmGNeWJbi081GfUSrKV5RC7NVer4Gge6RmnUZ3B4GJC8j/Toh9o7bDpaBUjx0cPxX/LUtg41hgS40lFFIopEiRKBqyT0tu0ZZbtKwdmtYuLdmjZe/Rtnq07D6OvHvkoGlWKTk4xsV4jkHWod65j17cYTdp0wsctHBYnm/SrNWYRIq13YjtKCNONFqYjCXPldhqPxxzMI4w1num9kZRyWkpFl0nH72MJmZkYiwJ8nOdn287ByHF+W9UXX7kHae4sj3hK89uc3FzaHbZmS7jMNRtpb8efIhjS2q+jSVlabgm5UFVnsnHMvyXQkZe8x16w5tLfReYDouUsDMIURo++VeX6DSMBH2pYxLF05IEbQjph0e0QhgeTpIqPNdirlVhEqR88Zktnnlhz3CFoAQY9YqD79pMo9QAtLw7opRmEiSkmcaWRpWdKXBtEFKWMSZADl4MuVZpXRKOJ2Gaq9ZEbpZouikr3SqNmlPKsYMoZRqmOJZAa0Gj6lL1HTZ7hnuktQlUXZoznJvPfG3tuoaAh/OVpqF5/qpvkySKKI7RUC6IKh+rXVgfcWK+ztrOlI3elI3dKV94epP13Sm9YcjOIKRZdViZq+E68giBeqM3LTtB48AYMrZqTt5BSvEciUaUQH0apvTHEY/e1+Zdb1jmE49fpDcMc8WgRggDQmu+gxCC8TQmzT/jetUxppIVM/7qNH3CJOOH3naKuZZ/SyOmV7vSCAzvSgrBXNM/QGAv6tUSN/FqqddA0D1QhdNoQRQt1CxJ7p1i54BGHOLZSAmu1DiWxpYaKTIkGVIoBAqhEyoM6FhbNORuCW7ado+WtUfb2aNj791VgBNkPr3c6G8vl4ubLs4cQzXHSM+RyTpS2kjLRVo2GptESZqhT6tRw7Fs7GHIxbURl/qa5a5ivlUhAbYHQxxLstB22RmG2JbpxCS5/4rS+RggX1yjWM1wZ3Tp/WJbkkbNzYHG/vEXXTGlDfjQWnNhY8Rc0+M/fvFyCXpcxypVXAakmAX/dlVZjmVkwgiTCu85gnrFYRqmJNk+z8eSBnioXLnUqLosdapc3Z7k4w91Q4dw0/2yjBmfpszqqvo2lzZG2Jak2/DojSPiRKGV6Q74riTL8lBTDFCU0oSJnlxqsLY9ZjiJWds1YbRF50PlXCVj/2D8ebKc66O0ZhIaLoxrS2q+cW+OU/OZ2pZ5z2mmDLDVBlAUTtBV3+b0Up3za0OkZWTgrm3x0MkWy9OEXg4eCg5KqkwQsFaaZnXf5bleaRFEGUmasbkXGN5RmF7TEPCBky1zvR9SPaW5N48UEOTATUD5/ZVCILV5b3ujEMuS/Ls/fY4XNsb5OTAxOZkyHbBx0GepW+WB1WYJgH7vz55nfWdKnGaEUcYkSpiEKTsDaYxDM9MJKoBynGQkmaZRdcqOSzG2+epzO4yDBN+xaFQclrpVpITnXuiTpIpmzeXhU+0DICVODKg9uVi/rU7GK11pdKPIkO+UuIlXS932pzAej/nd3/1dPv/5z7O7u8t//9//97zpTW+i3+/zH/7Df+B973sfp0+fvpvH+qosjeZLz20RxlnJ6+jWLcCQiS2UATdCI4UClSJ0htYpvhjSkrs05S4tq0fL6uWdmz06zh4dp48rk7t2rGHmzQCcDntpO+/o7HNxUlnFsiRCOCaqQTjGB0cb4nGYCjItQAtsW+A7FnujGI3iPsciyhKiJDTuxJHxUxlOY9Z2J3QaHgttHxB0Wx57o7CMWZiGKanSedaaY5xbtT7KpckVPoNJTCX3GgLTXUhSZTprSuWPE8SZQidmAW5WPZY6PkGUcHlrjM7DOZXSBzpJxWt6tiyf80Z1arHG2dU2z17ul86yhdKsaDBJAUudqsnFEoLluQpveXCB//jFyygMMEsyjStFTuY+/rW01oymMVobIFPzHYLILNgrc1XWdiZISzDfrDAKjP+MWeMFIYo0y7Ck4ORijW7dx7YFg3FEEKakmfk8LEsigUTtS9CSVIFISyL0qaU6zarLcBpxdXtKzbfz4FpD5M0yiJRC5ryaRtUxtg1JRqvmGoPAYUh/HLE8V+X9bz1Jd6YzcXnTxHbMclDSVJGmCtc56PJcEJEngSo7QicXaviuAUmHDQHPnDCcoMMLni0lQlAGHB937gX7gaRK6zy1Hlo1F6U1UaKwpQlRHQcxSx2f97x5lUsbY574xjpxovBcSX8UsTeOyhFiZinqvoPSgiwzHSchBGmqWGh5/Nx7Hyi7WKYj8zDfdbrLp794mTBOWewYVVoYp+VofHW+dmCB11rTG5k8tHtZ4XQr2WbXe3ycZiynGj9X9t9MMO3LRQJ/rW6vbgsEbWxs8Au/8AtsbGxw+vRpzp8/z2Ri0q3b7Ta/93u/x9WrV/kH/+Af3NWDfbWUEAKZDQDFXn9MOLhCQwRondFyUkKdksUjWuwasnHRubF7dJ092k6fjtPHk/FdO6Ywc3Ng0zmQKt5P2vTyTk5CDcuSOUHaAumCdMhso6pKbcvwcRJJlpGn3Js2e6PikGUa2xbM1X0qnnHH3uhNmUYpAsGzL/TNwWizc65VbDzHJYwN8VUIwVsfXuT5K316gxCZt/mtnOhpS0HNd/JumlFLza5DRSet8O1IZsY7QkCj5pKmGUFkeFhC73cztNZs9Ka5SaMhOmahASj1ivGOMR0SEyqqMV0hyxbH5q4JQMh9XlGcaZa7FXaHIWtJRpIZ9+fifJjkcpdW3eOxNy5zdrXFYqfCpY0RUZKicim1lfM5HFtia45Ersg8vbzgG1V9G8+1yogGIWy6TZ/tfmAk86kBDsNpemAc61iCJDaRDK4jWZmrMZ7GRKnCkhZJZrLv3FwRo3JZf5qabodG567S5ha0ZQXG4yhIkEJQr9h51IAxO0Rr5lo+UWJS5W1b8s2LvTKg0rEtnr3S57H2StllOI6DYkvBXMsDzGuQf7ZBlDGcRFzZmhClBsA8dznFcyWL7SrdpkcYG7+cC+sj1ncnzHXrR/KVDOnbmOHNni+tNVqIMjzWtgSDSYrn2FiWyDPEBBLT4ZpGKcNJTKvu8uwLA7b6z5TKo3oOFoPYdHD9PJ8qU5pxkFCvOghhOmsVzxC8/8v3PlCSuctrQQje+vACc02/PEeDieHpPHK6TX8UMw4SHFuabliasdk30SX3clzCzQCVm318qhQV32Gx7XP/UoMvPrPFaBpT8WyqtiH0X1wfHogM+U6Jm3i11G2BoI9+9KNMJhP+4A/+gG63y7vf/e4Df/+BD3yAz3zmM3fj+F6VJbMhVv+LyOll3J1LvD54hpazR9veo2H16Nh7+NbdAziRckuZeJEovpd08q6O+e+IGpZlJMeWJbEtCyEctOOgHAutJULbxMp0c5LY3NDTTOUjEkV2yBfHkoZPkMYwnJqOlJtJkizAtSWTIM3HSPui7mLh12imoZEzC8yCspHzJaq+zWASEydZGRMhc36KEDAOYqSUeA75omQ6QkIY0zh9iFwlyLsUpKSZwrIENdcobcaBcWD28h3y1e0xK/OGmGqM8zIyXXCITMdIWuSxAJCmR18LDKgpCMpKmV3nE9/czBdoI6d1XLMwVlyL+XYFz5H0hhEX14e88/XLSCHYG0Z5VlZGFGc5N4ky0FQfAi61ioPSmmmQlrJvOyfFWwYh5aOaiFrFYTiNyy6RzEmdxQ29P4l523ct8q43LGM5Nv/89/oHwVge6yEtkEiyTOFakkbNQQP9cYzMiemOLdkdhggEjZpT5oeFUUqcmWujN4p468MLnFlu8vg31pFC0K57tGruNVPSj+OgFI7V67tTw7cZRgzyvLGi45ZkCpUqJqHZuTu5tF9g3tcffPY8lYpHt+YcCAcdTeOc9A2FyFBgzCylNHJ7z5HmukIw1/QYTpMDDsNCGNXccGJASJRkdDHXhe8I+pMYlZ/b4rNzhC5HhkGU4tgWYZzxyOkOP/Q9p25IWD6Op3N5c3wAQLqO5MGTbd72yDwn5+vXfL6XswpqQaHUu9lss2s9vuLaKAEX1gZ89bntMi9vN+fSFRuJKFEHSO7fCSTwV0vdFgh6/PHH+fCHP8yDDz7I3t7ekb8/deoU6+vrd3xwr8byn/s1/PP/H0TuEtwATnVu//li5ezzb5LDAMeMrKaZCXHU2gAcS1pYtunmiIqDW3Wwsci0RaItpqkgimTpi1OodpTKUPrmpU+WlEwC43hbrzh5MKSF51iMgoRaxSaKDa/BNCsOAgalNeNpbOTZOSclThR7w6gkyhpOkCZVMJomBLnDrpSCJFG5V4sBP1mmOdyTsSQ4lkWcZmSxcegWpRlhvpALmOShtlESms6G1ni2heeKfGxiFp3+OGRnEKFTMypr1z0sCeu7QZlIbufgpOS5AGhNzXfY6gVMwpSTC3UaNYcwzhiMI9bzPCoNfOW5HR493WWu6fO5p9ZM50wU78fI3ooRXYH3bEvg5lLoMMxM8roGlZu5NesuvmcxmiZcWB8SJYoT8y57o6h8bpmTi33XpuIZIPqVZ7f4ye8/w7fXRgwnUQnGlDYdsX2SVM6ZsmC+XeH73rjC+fWhWSAyZUZ8UmLnnbpgkpQJ8zIfVVU9m/e8eZXPfm2NJFUHHKcr1rXzq47joPzkY2f45Bcu8fSFPeLUfO4mT04wjc1IznXM8cSpIssyHGU8fmxLstkL+L1PP8sH33kfp5cb/MS77+fffvJpI8O3BNKxCdm/nlTejXOdg75Ii50q42BQOgwXJYXpblqWGRk7tmXGl7ZRjBpzSk2RpVrwjapeHg47XyOMM973lhM3teAed44Og6N61eHRswsMBlPSe9DH7LBS71ayza71eCkEvmfTqftc3pyg0OU9rOgqj/Lu5bmrwwOKr+8EEviroW4LBIVhSLfbvebfF6Ox1+pouRf+VQmAblSJsg+Amn7aPkg6TjpMshpFf0FKE9JoyRzsuBLXs3GlgxYmaTzTFrGyiDJBkhjlzfW6OdfzyLleGQ8bw1Vo1Fwc23AlklSz2HHZHUYkac6/OObxmQK0ItEGNLiO4bnMumgbsjPUfJtxmJr8LaVoNX3QMMiMukqjcSyRZ3Dtv4YUhuej0USpkcjP3p7iJCuBi5QCqTVpZtRDxWjHy5VZVd+mXtHU8nDXMMpoNzyWOlWUUmz1w1yFY262We6ybFsi71IJPNeia3ls7gVMwoRG1WWzF5BkWXnTTTMz8viTL7xAp+ExCVMeONHi3NqQSZjkHCmRk8DN69U8m5OLddZ3zbhDz7SHNAYgDscxz1zs5cTsjG7DQ0jjgVN+JpkJLI1TRdWzqXoWu8OI//TlKzx3ZVB2UXQOsIpXmb2GLCF47I0rvPP1y7zjdUvlArE7CPl//vIiQZiyN45MbEbeearkROG9UcRT394t1VhQcMH2XZVvNoPqxGINS5rO2ILvs9OPcGxhRrOC8vMVhTRTG8fqcZjQbXp0mi7b/YDPfX2Nv/a+h/DzsVO77uPYpquWZooL60OSVJXKsKVOjTQ3J4xThZSUMv9Cqg5GUadyYFSrONQqjlHszSgd01SX/kvFZ2rn3DYrHw0X+WW3W7PgyM5HdvdqFflkhVJvtm6UbXajx5t7Yx5IaxvxCpBHkwgmYUpvFDIND3IwX+kk8O+Eui0Q9MADD/DFL36RD33oQ8f+/Z/+6Z/yute97o4O7NVY5670WQ9/gnc5/x6NYJC22Uu67MatIyOqftJhlNWZXZaNv89MN6ciaUkLIY2cXOXdnExbTDNpDAGVGW+kBcjJFErf/C7uNsVO2LbZRdWrLq6V80Jy59w4NZ2UMLo+GCyaCJnS7A3jI8ci8t3wNMqwpMx9gMxi7dpmFx/lKfI133jZmBGQ8W7xXNvEN+QLUDrTKcqUKqXpRuGky+dHkL8X0z0AjCpqZ8JommBZglbNRWvN7jAszeosqREIMq1I05wjk2mENH470zDJVVdG5ZQqTZJlVHMVieEyZVhCsDsM2OwHPHSyRc13eOBEi8tbI+OHlOnSe6fi2izmyexuoQhT+sCYrHju3WEEAmqew/JcjZ1BUKrezAJtHleMIWu+TZYpvvrcNgrBgyfaPH+lz2ialOPH4nUsCRXXeNRcXB/yva9bOrBAVDybuu8wnia5mgrAdMziVOHagjSDZy73jeQ+lVzdHpeRGrOdlSS7fkDlpY0Rf/KlF3jy27tIIZjkIycpDMfDsWTe7eNAAF+WJ6v3hhHTMMW2JP1hyMOnOnQaHmmmmWu6B4DC2dUWm72JUcDFCo3m4VNt3vX6ZZ745gaXNkYsdaqE8ZhplObu2AaIasyId6lbpZqHsPYncX7tm89DKY0SuuQaFZhtHCQ8fOrW4xnulFD8ctad5pNd7/Fptq8yPRztLITAsUzm3/QOoo5eq5enbgsEffjDH+bv/t2/yyOPPMKP/diPAWb3d+nSJf7lv/yXfO1rX+Nf/It/cVsHdO7cOf6H/+F/4Ktf/Sq1Wo2f+qmf4ld+5Vdw3RsHQ21ubvIbv/Eb/MVf/AXT6ZQTJ07wS7/0S/zkT/7kbR3L3SylNX/2pct8fev/zf9lfZiWtU0YhkgBvWFUji1kAXJsSd2XhqchHYSwy25OmnNzZrs5WZ46nmWKNEvLhaQg6b7UVXHtvJ0Mg2lSgiCA7X5Amt38WA2ubfpXdFa01tg2ZKnhXxjXXIHGLAxxokGCFBIw3IyqZ6O0PjiWKl6vGCNJSHMgWXQ6Cq6NzsnFa7sT+iPzGbZqDqdXWgit2RkEOLbF9zw8z1e/vYvKFGu7U9MFYl86rbWR8l/aGBmSdK5gUgpqFdNZmkYpcZKV3aQwNUaJL2yMOLlYx5KSkws1kk6FTEEQJWzvBSDhyvaEjZ1paYB32Mwxn6CZMQ3gu8ar6OJGseAWzRBRdpdSpRgFMZYQjIKE+5ab2EJwcrFu8qty9Fh0geoVA6xcW3J1e3JkN77YqWDZgv4kynkXsgRRSaqIE02rmsvnE8V2PygjNWbHEpNwSLfhX1N+XHA+dgZBqQiL04xppBmHCVqL0kLhWtE1WhtivedYTMKUT3/xMj/0tlPHyqIbVYd6pcXeKGISpPz0953hdWe6xtNIGG7JKEhYnquwN4wYh+ZztqSxSFiZq5VZbEvdGkGUmQ5lRqmai1OjJvMdkzdmW4Ju079l8u2dEopf7rpTafr1Hm/ntgyZOnpNaA1JZkBoxX9N9v5Kq9v6xH7qp36KtbU1fvM3f5N/9s/+GQD/9X/9X6O1RkrJr/7qr/KBD3zglp93MBjw4Q9/mPvvv59/8S/+BZubm/zP//P/TBiG/MN/+A+v+9itrS1+/ud/njNnzvBrv/Zr1Ot1nn/+eeL47hGM76Q2e1Ne2BgaW3osKlUXK19QMxw0Nlq6ZTcn1RbjGaVVEcppAM+NuTmag8qom6nbHX0Vj7VmOidxkmFZkjDOd+q5FL2w5k9vAQMdd0zFsWq97ywscsfm5bkqzZqLLSWjScwL22OCmTGHlPtBpKZzpMuOR1HF/xZ/FrtAmQMgz7UQGO8VpRTdphl91asOnucQRymr8zXWd6fsjSJOL9b58rPb5kbpSsZhloMLQZzuGwHWKw6gGaUqlyqTc7IMKBIYcFcQwnujiP4kxrUl5tnMDTuMM+LEcG2yVBEfcxKLcZPIwY0Zzxg35v44IksVVg6kC9MADSW3KtMgXcNx8V2bNMnwXCtfTPaJ81HOtdrYnZaS9PNrwyNjApVpZrbbHL4ipTFuIk4ywtjI5Iuui53n1Q0mMWmWMd8+GvA5y/lY6lYZT5MSSDlWRphk+TVx/Lcgv2RwbKsMxywk5c+80GNlrsqlzaOyaDCS+QdONEsABEfVa7WKGV91mx5vf2SRZy/3ubQ5yoGnIeLfv9Lk8taI3WGEwLiiO/mZmkYpji159L4OP/KO+24JuNwMobjwRrpX606l6dd7vJ2bzgpheIlFVz5TKs+XM8Czdofjx++Uupc6jrcNW3/pl36Jn/qpn+JP/uRPuHTpEkop7rvvPn74h3+YU6dO3dZz/t7v/R6TyYR/+S//Je12G4Asy/jH//gf84u/+IssLS1d87H/5J/8E5aXl/nX//pfY+VswXe96123dRwvRgVRyiRIieKMWsUhlm0yz2Icw2YUE6e67OaY1OGbb6taMnffPXTvvlVQc0cdI2Hcq4vrOEoULmbcU5CXLSmpVxyyTDHhzrIlZo9VSAGZIT47tqTT8Kn5NqNpwvYgRABV36FZs9kZGC5SphTJMMpHavvPJaWZ4x82HHQd0zkqYkbAKL9qvsvVnTFnV1vUKk4OQ/LjynkI67sBb35onkxv5WaNEsfSubSdkhtkRm4ahKRdcxlOYuOunL9hkYMWNKR5qyrNNGSaOCfkKq3JAvP6RSRImkmyY7oaSlMa9AlhOiCma5YZ4rkwn1dhQjl77otOmZ+7GV/ZGlP1jJy+IKYXDspKG2Wa61jESUYQGyXNiflauVBv7QVEiSr5P6rwFyJXpNnGnDLJNK5jlaGpniMPLEa+a2HbFjv98AjImuV8+K5VcnEcS5IdGhEfGb3mPzTnyBiVxklGs+qw2Kmyvhvwg289we7w1mTRBXl2ozdlbdtwKYt4jvl25cjz2fmotZ7bC0R5fAgIuk2Pdzx6MJT1ZupmCcWFN9K9WncqTT/u8b5rMw0T9sbGwVtKgSWE6chp46dU98194YHV5j3v/XMvgI97reN4WyBobW2NbrfL6uoqf/2v//Ujfx+GIb1ej9XV1Vt63s9+9rO8613vKgEQwI/92I/xj/7RP+Lxxx/nZ37mZ4593Hg85pOf/CS//uu/XgKge60qno3MVUqTICGMJEpnJuphcmfdqmua4t3Rs95aSSHKgMhKvjsueCEFOCmykMLk5lpUltwf0xRf02PHYpkqf96sOlQ9+0BCtpQC37WYhllJ1tbkoHHmCS1pdvlGLq5KR2iBGWsUcQ1am113o+JQ8awc4B3/HjzHYm8UEcZpnuBtOFGz/NLCBTybyWm6b6nOpc0R/VFEtSTRmhHYtcY0aaZzLo2pTGnCyEjhiy7dkXOnTJeicJEuPrNxzutxbYuKp5iEB0GrLQWua7E6V+Xc+ojnL+9Rzd2gk0yVpPJUGY8n1zG3mlRBp+4SH5IUF526Vs1lHCb7na18x10YXDYqDnGqmGs22e4HTMO0XIwaFYeFdqU02Txch1Pal7o1gvUhw9w40rUFcXpUQVhcL4JiFGo6XAC+a1yv4zSj0/CuKYt+1+uX8VyLC+vDI4vPrBT98KJw3POdWTHu0XdLeXSzhOLN3pS57r0pjS/qTqXphx8/mMRUfIezqy1Olz5BCXOtSvmdCqK0dOO+l/lT9wL4uFMLgxejbgsEvf/97+ejH/0oP/ETP3Hs3//5n/85f/tv/22efvrpW3re8+fP87M/+7MHftZsNllYWOD8+fPXfNw3v/lNkiTBtm1+4Rd+ga9+9au0221++qd/ml/5lV/BcW6/RWnb11jdbrFWF+qcWKhzZXOcG9WZ1T1KsjsaQ92NktIklxfND8OaufkSwvj6mABR03r2HItqxaHd8BhPDPE0VQqpBXXfIkmz0un2Wu+96jtEcWYWHAHHjOOBPMgzd5+u+I4ZtynNOEjRynRxskwxCZMyN0uT83z0/nuQOYIoxg8FHwiMqs2xDCE6SjITzjpfLWX4RYhpsYgIIZBAbxTSG4Z86ZkthtMY2zKBo52GUciNpiYzK8u5Pr7rcma1QaPilnlPWU7sUpm6blDu7Ai0OK83CzgL5+1MwcOnWsy3Knz1ue3SLM91dKmKkbl1gO9abA0ibCmJlfk8bdvwJpLUEIttS+TgTxPGxtiyVXdxbYu13Qm7w5CVuZoZI7oWvmOVkRmeU5hbKqaRMSp86yOLfPX5bXzX4qFTrdJQ0bJMOGgYmeusXnWOfHfrVQfXMS7eFc+mVXNZna/y/JXEmDnmLu2FM/UkSPPMPnOdyrJrqHI1lmB3GLE3inPzw5i3PbrImRNNNmeyvcI44/Gn1lnbmZTmjqvzNb7vTSYG4xNPXGQcxHQbfrkovLA5ojeM+On3nOEXfuSRA8+3NBOgenLxzkFJnJpzVnHtYxdx3zV2CMWmwLoW4r9H6oGTrSOfwdKh0NmbfXyUKBbn69RciVaak0t1Pvek+SyD2HyWD5xo8tibVo6YUd5LdXFjeMPrrDj+4vO925+z0pq//NYGkzDhxHytvFfalumyr+1O+KtvbXDmRPMlBZO3BYJmJbbHVZIk5YJyKzUcDmk2j15IrVaLwWBwzcft7OwA8A/+wT/gr/21v8Yv//Iv8+STT/LP//k/R0rJ3/7bf/uWjwVMS7/Tqd3WY4+rn/j+szx9scdgHNOo2qQZJGl2KNLh7gOi6wGNesVBSEBrRlOze76V15cSnHxh1ALiOIPMEHAngTEfbNY8lueNl45jS7SCrzy3RZYdL48H8qgCM15JMgPSrvWehIA3np3n/W8/xbMv9Lm8OWI4jkmVYq7p02n6PHtpDwE4jrXfCVIzIyVNPhYxsmMAx7ERqfFkSVJFKFIsS9JueKwu1GlUHS5vjljoVA1ImuEROK7FYBxxYXOEa0lOLTVQQH8cMQmS0oDPsWSpNiuiChzHxvVs6jUX3zUjieEkPjCiK8jMBz4LkRPFb9PCZRplLHQqfPjH38DZEy0+/80N/vCz5+iPQ4aThHputBjGhvsjLUkcpdQqNsnYLKRx3jHKrZ3K8VaiTbdOARu9oDxPG/2I1z24SKtV5cyJTc5d6fPgqTZrOxMmQUKSGJ8g15G88YF5fuZ9D9OfJpy70qfZ8PC8/Q2O1pqtfsgDJ1o0GhW2RzG1isNSp8rm3hTp2Cx2a6zvTGjmXY96zaPiO7i2zHf0Fd5wdg6N5pvndxlOEsCM4EbTBF1kj2mwLdMJChMDpj//9BZnT3V44GS77Jicu9LnP/znZxlOYuZaPr5rzBqvbE/4o7+8hOfahEnG/aut8pz4FYdmw+PK1pgvPbvDdz+y/KJ2YJZTTcV3UAL8Y0jD0zAxrsm5QWKzeW+PfIq603N23OM7nRrf/cgy67vm+qzl5PV72TpAKc0X/+L8TV1ns+/jbn/OV7fHbPVDluZqeMdwp5bmamz2Q8IMTizcvXX3RnXTIGg8HjMcDsv/7/f7rK2tHfm94XDIH//xH7OwsHB3jvAmSuV3/Xe/+9383b/7dwF45zvfyWQy4d/8m3/DRz7yEXz/KFHyxs+rGQ6nd+UYLUvy0KkOP/sDD/D//ZNn6Q2i3MPGVMGzsK2ciHodJFKovu60pDBycjvnbtxqGT6G8d/JlD6Q1dWqmfiKYZDQH4ZMg4T7VxpUHIsM090RQuDaotxhHigNw0lyIDvr0F9jSZPuXvFs5psu8w2P6oNdHjnZJIgy/uQLL9BteoynCWlqOjbGQFDnZoG6PA9Kg2MLarnTbpwqksSY/lU9G9excB3JYrdKq+aSJIqLawMaFZf3vnmVz39ri4trA+aaFWo1l/E44pkX9tBKc+ZUA8eSLLUrTIOEURSTZiCFKtVgtmUSzeMk4/LGkLOrDXb2JjTz7gXA3igquUHH8b8On5/Zv7sesC3AtxDw3jefYK7uMBhMefRkE/99D/AfP/8CX//2DqNpjG1J6r5Nu+FydXuKgNx7CBoVu7QaCKKMOMvo1D2Uht084d53LWNAmCiCOOM//uUFOjWb+5ebvP2Rea5ujugNAlY6FbJ2hTBKy93r+7/nBKNRUP7exbUB3YZfco12hwFZprmwNuCf/u6XSzVP0X2xc+n7cBIzCWJW52qlg3gQpniuxep8lSSXz63O1QjCAUGk0DpD5RYJs9ztgmtyZrXB3jDgk4+f57/6oYfNeFhr/vjx8/QGAatzZuebJhm2ECy2fC5uDBlNEx482SKJj3LkmhWH81f7PHN+m5W5F29R8C1YbPtcXB+Wx1mU1prN3QlnVprU3NwOYhgcm4v2aizLkjSblSPvuWoLqg2jWB4M7s4a8WLV+u6EC1f7NCvOTV1n13rPd1obW0OCMKFVMcKRwyU0BGHCxtaQqn1noLLZrNx0J+umQdDHPvYx/tf/9X8FTKv/13/91/n1X//1Y39Xa82v/Mqv3OxTl9VsNhmNRkd+PhgMaLWurUwoukfvfOc7D/z8Xe96F7/1W7/FpUuXeOSRR275eIC77oy61K2w1KkSp+Ocr3Ho9W6KL3xrA7RZv5b9LCyzkAZxBocoSTfzzIU8Vwgz3jKhnEnJzTix0DDKpq0xUZwSxhlr2xNOLAi29qbYUuJ4+67J5cKSy7GVpjTqK44ZCr8aw8to1lxef6ZLbxjy5Wd3uLAxNhEClmR5rmKS0IfRvhOvKPx99uX6s4AizN2r42xmTAY06y5aaZJMM54mhJGR2p5e2ucZLLarPP6NdTZ6U4ZBQhynSCm4f7lJvWKCMWt5Svc4SJBSk6YazzWREeZ9GSC2N4r4yrMmVNNzLYbTxER96IOk5MNVKOVs24zvbubzLBRvEphr+Tx0snXgmj85X+fDP/YoH/vjZ7i8NWax7SOkZDiNiHOQmGaG9yOl6Wp5ecdvu59xaXN84PXiJKPmm9iMds4N+s9fW2P1/TVOztf5iXfff4S38NDJNu9+wwon5+ukqTrm9yLSVDEJY0ZBitqd4toWnmNUckk+urt/uYnrGHVPEKVs9QMsKcpzf2qxbkZ3+Qmu+ja1imOcq5UJ2Q3z8bV5nCxDdGu+iyUllzfHPPn8DrWKwyRIuLI1pl338s/t4CdR9R229kJjeuge/ZQc2xzreJqQtl5c0PGu1y2zvRdwdWdyLKH4na9bLuNmskzdk47RL2a9kt/zeGpsJZy6LK/t2brWdXa337NrW9hSEsTpsTYFYZxiS4lrWy/pub5pEPTYY49RrVbRWvNP/sk/4cd//Md5/etff+B3hBBUKhVe//rX88Y3vvGWD+bs2bNHuD+j0Yjt7W3Onj17zcc9+OCD133eKIpu+VhejFJK87kn18mUol1zGWDGYUm2v8Dd7PPc0uvmu3w3l0MXkRN3UkobQ0GtDcBIMoVG0K65nF5p0qg6CGB1vs7a9phplNAfx1QrEfWqk3sjqdJ/pyg5A4KKn7uORZYpar6NbVsH1Fm7g5DNPIR1xZZ0m34+6zaAqDDDE9KkaWtdimlMArzWZcDp4bBRMOdtay8wxHagXrH5icfOUPOdA0TUQuWzOwyxXYcLV/b4j583najZ8lwLzzUS8ijOuG+5ge/YbO5NDMk3VUyjNHfJtkhzs8coH5teq4q/K0ZqSa6+Ow4ul50vS1D1HVKlqeVuy5MgOULetaXkh952it/7s+c5d3WIwpDdi5BQkf//MCf427mC7UA3Kv9c0/z3ahWb5bkatiUOuPjebNTA7O+dXxvy+FNr7A7N6KxRc8iUoj+J0cqA5VRptvsBD5xocWalwdrOhMVOhfe+5QQvbIz4wjNb9IYhtiXx3X0A0Ki6uLkreJymXN2e5PYIxktIKZNNZwJ0FWu7E/7gcxdM1ylV9EYh9y83qRy8DADKhSCMTSTF4bqRr83drBc76+peUCV9p9ad+ifdrbpTC4MXq276Xb/lLW/hLW95CwBBEPDDP/zDPPzww3f1YN7znvfwW7/1Wwe4QZ/61KeQUvLYY49d83EnTpzg4Ycf5oknnuAXfuEXyp8/8cQT+L5/Q5D0UtX67oS1nQkVz2Z3EBoEnrd+7gYP6Hr9oUKBpGZGcLOKISlM6ze5BQSeZRok2K7gvuUWg1HE2dUWliUYTRO29qYEcUaSZLmsWHP/UoOrOxNSZQwLpVRYFIaH5nkPH4JSxmE6iDIallV2dOI0Y7tvpNW+Y1H1jYS1kPau7Uyo+jaNapXdQcgk3X+vTm5+Zgz9zAuXHkLsd1WkNHEdUR40ev7qkME45oHVo51JKQQrczVarSrrW0OyTLO1N6WaJ9tXPDsPFzVfeseeidyotpiGKc9d7ufkbSPhNuMlQZQcbR9LCfqQGabSmkZOREYYnlNxrorPunBYrnimI+Pl52IcJHzqCy+QZvqayhEtAAVS6NI3R2NsCqx8BBTF6YHr6jDI1dpcO7WKDZojLr43GzUghWCxU+HPv3KFaZiWPDIpBUobswItIEwyar7NNDSqsapv0236bOwGfOarawzGMWGcMZ4mjIIB9YpDo+Jy/3KD08sNPvv1ddp1lzC22LKCstsoLUmagtIpo2nMRm9qAmmlpOpZTHPhw8WNIWdXW6Xp4ey58F2LcZAw1/Jf9kXhxcq6uhdUSd/Jda+Ajzu1MHix6rag3y//8i/f7eMA4EMf+hAf//jH+chHPsIv/uIvsrm5yUc/+lE+9KEPHfAI+vCHP8za2hqf/vSny5/96q/+Kv/df/ff8T/+j/8j733ve3nqqaf4N//m3/A3/+bfpFq9N7JbJkFCkqpSAZPlYxdbcsTF90Z1HDH2ekDKkH+NqkUKqPkOvmdiFMZBmkuRBeoaUurjSmPARKPq8iNvP8Wnv3jFKE0izaWNEWmmqPg2rmW6T9NI8ZXnts14SsMkTEr5u5X75My+p2LBLMJAk0zRH4dYUubSeVXmJLVqB12Cx0HKaJqwvjtlvuXTqHqEydSAntxhGAwZuVZx0HrfJE+jyZRRyWWZLl2LdU6a/sxXr/Dmh473Yrm4MeSLf3Geb57b5srOxHCRpAlhbFZdFjsVqr7N7jCi2/DKYxZCoNFMw7R0Cy7cpDNl4jbMb+yPInVx8vJz5ubZTu26S9W3AcFSp1LmS+0OQ65uT0gyVcZg+G7RjchwHYt6xTkiW/2Jd9/PE9/cQGnNm87OEcYZaZZxbm2YE4chSxXSOTiDL8ZGlKNTY+dYjCT7o6jcpd7uLrSQd9cqDv1xXKaxF21/a6YDqLQuHX+TVLG5Zxbllbkq3aZHGKds7QV4rsUPvuUEb354nq29gL/8xma+i973FrKL11GmU2eiNBIsKVnvTYwfVO45NY1SNnYn1Cvt0kdLa01/HPPQqRZxou6ZReFGAFQpzfruhPE0uSmQdC9Kor/T6l4CHy92x/F26o76X1/+8pf51re+xWg0KsnJRQkh+MhHPnJLz9dqtfid3/kdfu3Xfo2PfOQj1Go1fu7nfo5f/dVfPfB7ShnX5Nl63/vex2/8xm/wr/7Vv+Lf/bt/x+LiIn/rb/0t/tv/9r+9vTf3IlSt4pBmiiv9gHBmt3yrVYyLblSGC6NL/o+QgkbFJUkzahUTa+G7Vsm9SWe6BTd87pnFbRwk1HyblfkqF9eHuR9LRi03FzQmkMbDZ28cl2S7ovOj4YDyyXRCZEmKLgitSuUuxbIgNBtPGq2Nm27xHR5NEy5tjIjTDJn7nMSpYncU5sBT5wRjgW0LmlWn9GqS0njFCCApTIpmSiM4vz7ia8/v8NaHD5L/L22M+MQTF9kbR/SHEbYlcxADYZSWMn3PyVOoc0ft4oZ0ddukVDcrxgk5yUdjRVZXOfKSIifeGmBUGDjet9RgGqb8yDtOM9/2+cTjFxlMYlo1hyA2nZZm1SFTmqrv5OGkphPhOtbBVPYZo7w//fJl+uOYTsNDShM1MQ0NSCwk55nSkGkkxmogU2bMaVkCKaQBXjmHTEjIUtMlCePsjnahhfdPo+rkEnZdukfPSi7TPCA3iDKkSLi6M0FpQwjWGsZhgi0l9y3V2egFPHt5jzc/PH9gF70yV2WpWyPcGDGNMipCEMbGpXlvHJl8PqVKD6GCmKmUyZCbG0W06+6BxedH33Ea4J5aFK5VBcC/cLWfm3Nev6Nzp6nu34n1Yo0N7yXw8WJ1HG+3bgsE9ft9fvEXf5Enn3yy9FQpSH/Ff98OCAITzvqxj33sur/z8Y9//Niff/CDH+SDH/zgLb/mS1XTMGE4SRgHCXf0eQtyroZZYA8Dl4Iz49iSVtUxhN5893piocrVrUmZNh6nmnbDMzEAUYZOstKd8FqAqHAEBgNQklQRRBmPvWGFq9sT1nam+K5EY/xkgjDFybkWxbjNtQUgjjX+sy2BlBKhTLjo4cDPNDPdLJMvZYito2nCYgdg3yTRc0zkiGObzlvxJI4lDSBNjUJpay/Yl8XbhsshJfmojDIvoRgdKaX54tObB7pBxQ1/NI3ykFoDONLMjGGiPPdLo2hWHX7y+85wYX144Ia00K7QHxvPmSRVjKZxGTmiZs6B1ppqxTniXu3YFo2q5ORineVulZ987Ay//xff5qnzvfK8SynoNjwee+Myj9zXYZKPwOoV55pGeRu75vzMNfcVlqkyHLBGxWEUGK+jimvIyHGiytfzXRvbkozyQFgTw2ECbMdBylKnctO70OMWiKKTVASoFmG4lhRlRAtaMwmNj9XVnQlaGzVYzbfY6E2PhLC2Gx5Xtyd860KPWsXh0fs6B3bR9y3VWd+dMJrEprslisR5w7OSUs508Mx1qrQug1qPW3zupUXhuCoAfphkNCsOTl3esKNzq6nu3+m8oRd7bHgvgY+bHXm/FHVbIOijH/0ozz77LP/L//K/8KY3vYkPfOAD/PZv/zYnT57kYx/7GF/72tf43/63/+1uH+srupTW/KcvX6HiWcSJxWBy++x3ofMdtpT5yCgryc+OZcBFAXIyLYgTYyiXpIqrW2MsywQtCpHh2hanFs0X7PLmsDS0c3NlzXFAyJKG+2FJAxhsW1DxbU4vN3jsjcZDQykIYpO03agaJ99Lm/sWC+bYDW8jKRK7ZyrNDGeo7hnJepTtE30LJZIAVJqRpJreMKTb9PBcAzrcHPg0Kg6+a3F1e2TAYw4oCtNDMPDGdJyKoZMBgMKcytJl2pIS3zW+N7uj6EAIaHHDr3oOvdEEzzEAzbEFzZpDGAuSRDPX8nFsyenlBt/7uqUDN6RMKf7p//F1A0bz7l0xdslmJH5aGzJjKx9lTaOUum8TRClnVva7Kuu7E65sTdAYPyjHNovzcJrwH79wmXbdY6lbzdVd107eVtoAiFlipZ1fe1IKap7pNAkhSFJtulO527clRTkynUZp7vtk1GTfdbrNY29YuaaT8mxda4F49+uXy07NbBq7a0scSxBEuhxN1Twb2xJMwoxMaSZhRqrikntVhLCOg4RMURKcHUtSq9p0bY9xYDpPcy2f152Z48ETDf7z1zeMCtHOA4/Js+lknj6e+2a957tXOLXUOPZ9Hl4UlNZszJj9vZyAYL+jE3P/qpHzK61v2NG5lVT373Te0Es1NryXwMe9UrcFgj772c/y8z//83zwgx9kb28PMIva6dOn+Uf/6B/xy7/8y/z6r/86v/Ebv3FXD/aVXJu9KZc3R6x0q+wN70ytpgBHUAIdmROEjGLLLNhufkOOkhQ/j+yYhBmTMEMI45njWpLlbpWab5cLXMXLWOpWmGtWSDPFt68OGAf73J3Z0ZVSGZkUWJbNYGzGSWdXW6zO17CkxLEFQkp07tUSxdmMXN9ItKy8mxOnWQmEMg2ebZyGC/l4ko/qioRv3zXRGGmqzAgiznj2cp+KazKmHMsY3C11q4RxRhAZaXYQpXnnKi25MlpILKlzgLOP+kolGWbsU8/l0vWKjYADZN7ihu86Nkrp0tuH/HzFic69bEK0hj/83AV+7HtPH7ixKa156ESLJ8/tEhe+RmI/ud0AMTPWLGTeBoya8zhr3Z8qxSc/f4koMV49syZojm0xmMR86vOX+G9+8vU3VI5UPdMd2doLaNd1ed6KzgvakC+Xu9WSb3R5Y8TeOGYSpiUJ3LMFaQZVz+bH33WaR093eOIbGzdc+A4vEK4tGUxinn1hj6vbE77vjSsH0ti3egH9SWy4QOxPxYwdg+E9mSBiSjAvxH5+W38cIzC8uXJ8NYyo+TY/+JYTdJoe9arDo2cXeOb8Np/+whWsnICupRm1FuGxmn07im9e3OP0cvP6nBut+drzO3zh6U16oyj/LlsvKyAoAH634SMQTKLcd0vK3P38YEenqJtVJe2NIh5/av1l5Q29nF2o18aGL2/dFggaDoel4qpWMyZek8mk/PvHHnuMf/pP/+ldOLxXTxWL71ZvahaOO618dY7zBONG1WYSZKVCxnPtcgFLUs00UrkqyjKEZdfigRMtJkHK5l6AY0kePtVmNE3ojSIqnoUQNg+eaPHc5X4phy7KsoxCBkw34PGn1plr+pxaqrM6X+PZF/oopQgT4zWhlDLpy7AfWip12W0plEaWNCOJYsxQpJuXyial8ygNXSrdLAuyDJLEvAaAdjQnFmo0qg7DfKxU8cyOVOlkn4MkTLK9JSVzLY+N3kHjs30SspEy+65Np2HGQrM39uKGX+SnZcpEORRjrUzp0qYgUwYU/+HjFw7c4KUQ/Oj3nmZ9d2o4K0qj82BPKQS+b2JBgighyLlCnmvRbfo8sLqfJ7XRm/KN87vsDCKquVpq/7IxIMa1Bdv9kN1+cFPKkdPLDZ65tMflrVEJcC0py+tvrl0xJOs4ZW8UsbpQ57E3mayl3jAyXBwpWGr7vOsNKzRrHr//mXPEiTIdvGssfIcXiHGQcnV7yDRMyTTsDiLGQcyPfu9pLqwPOX91yDQy3cxqzSHNdMlbsqSJq/A948CthIkryZQq+VtFF86yjJ3BrNpwfXfKs5f3+Pn3P4TrmL8L8oDbgluXpOoIwb/4vmztHf3MZ+vSxohPfuEST1/YK4Nt6xUbt2G9rETiAuDHacbTl3qMJnHpcF71bRbaFZJMHclruxlV0umlOs9c2ntZAcD1ulAPnDyqAr2TOg5s3erY8LW6u3VbR+cQ2AAAc21JREFUIGhxcbGMqnBdl7m5OZ555hk+8IEPALC5uXnkw/xOr4pnM5pErO0Ed+X5olSXfBClYRqZxahWcXAdKx8JmFHSON+t12tG+RMnGdMoYzSNef9bT9FpeuUX8oXNEb//mXNcWB/Rrnu0ai6nl+ucvzo0u3xLUvNtBMYZ2LENmXQcJDzxjXV+fukhzq40+eLTW0SJIWDbliDMjNlgQXo20vP9lcKxJFJqGhU392QxSebjRBHmpF5jJmiAVzHKsqQo3bMLADUNDUja2guMPD0f3aSZWZxqnsMkDxbV+ehQKcVwYgjCVc9kJYk8wBMhShXQfUs1xkF6hMw73/Zp1V0ub43wHMkkSPCFGVUVC6JjW2TKhLHet9Rgo3f0Bl8QGH/3z54zgE5rpCVp5EaL9YrN3ihiPE1471tOsLpQKz2LLm+O+b0/e571nSk7g4AoTkFbSGkMGZNUMQ0Tksy4JGda8ydfulJ2Uq6lHGlUHf7vz11gEqYm6T7LSDKBlAZMn1qsozVs5iaYs3yXH33naZ65uGeyp+KU9d6Ub5zvsbY7IUoUnbpLs+ZS8cSxC9/sAjEOzNjE8L0sXAEhmq29kD//yhX+q/c9yDQ0C/ZSp0KmNOfXhjkR3WYamSy2RaeSjwbNWKzoakSJueZsy1gUzIo9Di9GRWZXxbOpejaBZ5OkiiTZN9qcLa01nmvRG4Y8/tQap5YePrCoX9oY8X9/7jwX10doTJCs0ppxmBIl0wPfsZe6I1DxbNJMcWF9iMZ0kKVj3LhHQcIkTOg2/SPdnptRJT16X5f/9NWrLxsAuNEY6r/4gbN3LTrpWmDr/uXGTY8NX6u7X7cFgt7+9rfzxBNP8Eu/9EuASXr/7d/+bSzL3Dh+53d+h+///u+/qwf6Sq+5ts/uILzj5ynGSUIYtVklJxtPwpRqxeZnfuAsXz+3yzfP99BlYrkxkHPzL5nr2KRZwjhIy52tFIJLGyOe+MYGYQ6QekPjZdRt+jx8X5uLG2MTO6FMh6RRNQtzo2rGVms7UzZ6U86vD2nWXMI4zQNCFVrpUvJeKL4q7v64J4hS2jWPn/7+s1xYH7K2PWESmowtu+LQqbus7U5N+Gw+adLaAJuiw1K8Nyll7vmScHF9wOnlJo5jdv+uLUlz2b1lGW+bIpR0NE2oV42ireo5WJYoQ0GFNonovWFEt+kfIPMWN7eN3Sl7I6MS0lobgnC238kpQNxSt2py6a5xg3/zw/M888Iez1/p02162JZFxbPLzlgYZzx4ssVjbzp4DLM3c4TPziA0yjJlOD9hkhl1HPtjvvNrQ4Iw5QNvO8WFjeER5cj9yw3+4HMXGE0TGlXbdH/SjDgxUR/1isPKXJWf/sGH2doZ4drWgVGCLSVvODt34Ph811yHFVcyDg2wOb3cyBV+B89LOWa0JVe3hySZ6W6mmWYUGo5RpjRXtif86z96mqpvjqfiGW7YrGLMcyTT0JgaGtBtDCszbbodKh8vVz07J9wflPwftxgtdausLtQYTuPypIr8X0Lsx9tkSnNxfYTv2nzluR0ePd0t1YVFt2tvFIHY9zqSmGOYRka6vzpfe1k6AvNtnyQ1I+W5lo/OO7BFmO5gEpOmGfPto9FEN1IlKa1fNgBwM2Oox59c57sfWb7j17oe2Lq6PSHN1MtuZvidWrd1Vv/6X//rPPHEE8RxjOu6/K2/9bf49re/zW/+5m8CBiT9/b//9+/qgb7S65mLe6S36PQ8WzK/OQZRlnvFCDzHMiRebYivVd/mWxd7vPXhebZ6U2xbsr0X4jqy7KCAcXq2pKRd319sojgrv6TzbZ/V+SqDSUx/HOE5Fm9/dIkgymhWXTS65AMUm7fiZrW2PWF9Z0qz5jCNUhzHyLCLTKnR1HglVT0bpclHZZpG1eW/+P6zvON1S5xebrC2PaE/jvjC05vl+GlvHBMl2YH0dyAHZroMOjXdG0OI7Y9jppf2cB2J70iMSGx/RKLy9+K7NuMgj8SwtVEJ1T32xlEOJIxn0HK3yo/OcHlmb27zbZ923WNjb0JvEBHnsmxLCjzXoll1S9A4e84O3+ClEDz2xhV2h2F+07RK8HOcr8dxN/M4MaPRVBkPn1kV3ux+O04Vl7ZGfOKJC/w3P/E6Kp5Tturn2z7/9o+fZhKkJbEajOLLc0yXK0kVV3emSCk4u9o61u7+8PGNco6Z61q4CCZhwpXtMSfma9iWxHMk/Xy8UowZBxPDL7KlIIgzgtCMoYTITQcdi71RzHCS0Ky6VDwO+PoUMRexTrEtUXo1zTU9VhcaufOz4vLWmDTTtGrOkUXnuMWo6HZc3Z6wPQix8+t8lk9mOFsCrTSWZc7bp794mbmmz+nlxjW9jsjfXwHeCn+l2evlpeCy7PRDHMui4lpMgqT0o8pycYHvGN+urz23w8nF+pFjuJ4qaaM3fdncjG9mDHV1Z8L67uSOsqxuBLbWdiYkaUZvGLE6f+84KX+n1G1dWY888siBLK5Wq8XHPvYxhsMhUkrq9Rcv8fiVWoNxVBJbb9UfSAro5vLkIM5y2a2Rl1vCyJQbVYe9UcTXv73L1e0Jo6mR42pt8qgKtRVAlCjqvo1rS7b7AZc3Rzx/ZXDkS9pt+nQaHuu7U56+1DMkZUtQ8a5t8Q8QpxmTwJAnm1W33Dk6NvnOMSLTmm7dw5Im5+sD33MKIUQ5zkny3JreKKRecWnWHHzXJgjTcqd9wGcoB5hFdAMCar6F1pSjESGgN4pBC4RSpPm4q+pbZX5UmOdhaYyBoMAs1vXcjfonvu8Mq3mY5XE3N+kL5rsV+oOQS5tDplHGycU6dd85ABpnz9lxN/hb8fU4fDMfTRMub02wLUmmsiPjmeJ/hTDqs0yZhe73P3Oev/aDD3Jmxbi1b/SmbO4GJo/MOrxImMU5ijOmYcIkSMpAycN1+PiK8aSxPjDjqF4UMg1THEuUMRXFYrkyX+XJc7s5EFAU9k1SmsXKdaxyZJPmZn7NmosQYsbXxwAogcnnk1LmXk0mhqXiO2bsqs3Cs9ipHPisrrcYzaoi00ygoqw8y0Lk3DkhyHJ7b9+xCOO0HG1dy+uoqAK8hXF64Hp5qRRVQZRi25IzKy12RqHhBCWGY1h0cHrDiP/niYs0qu6xx3AtVdLL6WZ8M+q1wSS+7rV9M3UjsNVt+uz0zWb15TYz/E6suwqvi6iLwWDAxz/+8RfNWfqVWK26Z8YZt/FYc9PTTEJzk274NovdmlF45cDk4vqo7JC06x6WZcZbmYIoTrEsWSrJbEuSZJrnrw5IU8Uf/dUls5B3a9fcEfVHMe2Gx3Y/uO7NanWhlpvPpfi5TFzPvGvLElQ9h07D48ffdX+5c7y8OT7SLu6PIzZ6iosbw3xh1rlHEmWa9+EqF3htSOOeazHXqlDxjAxbCjh7ogWYzspgHJnMrpy0XYwafdfCyk3/gihlEiS88ezcgRv5tW5uQhhu1n1LDc5dHTINEhZa/k0vqkXdrK/H7M1c632fJBP1kDEYx0fOVc5HJ1XaAKFMsTeKDnBOCvWZbYkjCzOYxTnIzGtv9wPSOGGu6V/3+GC/Q9Mfx8YEU+9bH4h8vKK0JoxTpBCcWWnyxFMbueHi/htQCoTQOdlc5V5UkuE0YRom1PLNwenlBpu9CXujGNcxv/vofW3OrDQ5f8ir6ZHTbfqjmHGQ4NjyphejQhUphWBzb8okMGM3KY1BZOFDlWSKVs1jsVMtu7DX8joqrqlMmWtzHCQ8fKrNYqfykjoxF8fnOpJHT3cYDEPiPEpmszclSgyxfKFdKe87N3sML6eb8U2p12zjKXYndTNgy7Yl737DChePGUnfa6aZr7a6ZRCktWZ3d5dms4nrHkTHGxsb/Nt/+2/5P//P/5MgCF4DQTNVzR2a1W10gbSGaZgZwCOM78ruIDQxAJnKuxf7UuDLW+OSBxJEGQqMhD5XLgkBUaJRyiR513zHLOi7JhzycMaR51j0Vczr7u/y5We3jtysDHfIKr+onYbHle0xVe/gl97425gdr+dYzLX80iTtuHZxp+HRabjsDiOubo9JUkW94hin4ej6OSOi6DQoje8Z0Naum1T5JDWKpEYV5ls+QZSRZBkX1oaMpsY5OD/i8rnM/x2EEkGUEqcZfmYxmsZYUpo8rLx816ZeNR2s273B34yvx+zN3FwrxoW6cASfSEgOG2rmI5fC0VoIQb1iH+CclKTfKCXI/aNmwV4YJwRxhj1N+MPPnkMKWO4e7QIcXmyEECx2quwOIpJMk+etmk5O7iHkORZ/+Y0NtNZ86vOXSLMMNKSFqI/9SI44MW7ihR/V81f6bO4FrEgzMrYtQ7pu1oxJ5NnVVgkm33HIq6kA5LfqrLvYqbA6X+PSxohTC3XOrQ2J8pFkYSthOic2S90qvmu6DEGUcnq5cazXkedYORjNsC1RctGAl1RSXXRrXtgc0Wx4VH0HTxmla5KZ8XOj6lCrOOUG4laO4eVyM76ZLtTZ1SYrczUGg+l1nun6dbNWAWdXm7zz9Uevx9c6QC9u3TQI0lrzm7/5m/zv//v/zmQyQQjBD/zAD/A//U//E57n8Ru/8Rv8+3//70mShB/4gR/gb/7Nv/liHvcrqpTW/NU3Nmk3PKMgucmQ0qpn8ePvOs1C2yyCGs3vf+acIfBWbKSWTEMj9xaYhc2WwnAulKZZc3IZb4ZjCXzPYhKkRHGW8zosludqJtvKNeaCm70p9Urr2LHN2dUmJ+ZrB25WxvwuI0oUf/KFy7iu4Q6Y9PaUWlXkvikq9++RRyTm1+uoLHVrTMKU/jg2xny+je9IosSQra99zk0mm5CCMMqo+jatmotjS/rjiE7DjEtELvOdhJok1bTrHhXPMt0hXRDAXdp1l/E0PUBK3RtG7A5DNntBzk0R1Co2p5ab+La56TUqLj/41hM888Lei3aDn72Z1yt2aVII5FEOxz9OYAJGM2W6Kc2axzhISs7JYqfC6kItd4RW5cJsSUEYZwwnCVIKTi7UWVmoMxpHx3YBjltsbEviOCatNkkMz20Sms6PlIbs/62Le5xfH7I7iEpH68EkLu0NrLwtFCUZrmMI57YlWOpUWZ6rMhjH5fk+s9I89nwfBzJvx1l3tqMxChIWOj5BnBr1WW422a57nFys06ga3lUx2jr82OW5/397dx4fVX3uD/xzzpzZMzPJZJskBEJQ44ogO6GgKCoqpdZacaHYumCltaDe6u211u1eUWtbC/6uttal6C3Saq0gYhEXNOACsogLImHNnkwy+3aW3x9n5pDJTJKZySSZyTzv18sXZjJzcr6znPPM9zzf59Gj0xmA2y+XQeBUDMaUmjGpphhajVzleiiXVEf2z+4M4HirG+ZwvSyXj4coyl+SSq0G5ZiRyj4MRzXjRGahaseXRZWYSEUyl/yomOHQSzgI+utf/4qnnnoK5eXlqK2txfHjx/Huu+/iv/7rv2C327F3715897vfxY033ohx48YN5j5nndZOHxrbPRhXYYFFz+Gb445+84IMWhWuvbAGM86QVyaIkoS1Ww5Ap+GUasc8L0GEpKwYkyAHFh4/DxFyZWA2vPpKkOSTjBCufmzQc6gokg/IkiTBqOPg8Mi5HZFO20D8D2nkYFXf6MSWncfg9ISiCiCCkVfZSICyOokNt1goKdDD7QtFXQbqb7pYHek/JsgnTJZlwMRbh9wDGw5yIgUQg7wIq0kHnVYVc9BrsXvBMHL/LbNRDV9AAC+K4Fg5sVteAu5TAoQjzS58+HkjREGuu2PUchAlOfH722OdKCnQw+kJorIkD+NPKsSEk4sG7QDf/WBud/qVmQeGkVfdsSyUFikRUuS1Ct9k1MtVk7vnnHTfbjvk1zIQEuANF75kWQbjys0oztcr9XTKCg0xswDxTjahcNI4y8iBqkYlP89qTq5Q7fGH4A8KMBs14DgWmvDMlsWogcMtt+AIhkSlmKYtXD6gqcOLcRVmXDn3JLR3+VN+vlM5GXWf0Whs88Cg4eAPCcjTcygtMIY7xce/FNpzNsSoV8szLhoWHCv3Xtu6pwnb97XAoOfg8gVhNWvj7sdgrKgaYzPhe7PH4tP97TjU0AVn+AtQfp4GtkJj/NnjJPdhOAKA/mahqmzmAf+NTGpgSmIlHAS98sorGD9+PF588UXlMtijjz6KZ599FjabDa+++mpUsjQ5IVIoUaeRp+RNBo1ctTjII1wLT+kODshLT6+ffyomn1qqrP443urG4WYXbFZDeEmwC/ZAAIwkPzjSwkCCHDBJ4aq1apUcJOnDMz2iKC8nL+924FISSINOePw8vH55GXNvH1KWYVBSoMe/PqhHa6ecONs9h8YfFBASROi1KhSYdNBrVNBw8oG83eFDnl6DGWfYlO31Nl0caYQauV2jVoWfMzkg6k+k/k+kl1OnK4BxFWbMONOGbfua0djmQWvAB5ZhUGjWAhITXgocvfINQFRSauTyncfPo7rcjCMtbvjCjVA5FYMudxCdLnlVnZpTYd073w56td/IwfzDzxux65t2eSm6WgVjuBu9imUg+oLK8yaJkBOnICdHl1mN6HLH5ij1PLF7A3Kui8sXwqgiI4ryo/OZepsF6Hmy8fhDEMNNVrVqlbI8HgBYMFCrGHgjrx9zIidJo1bBkqeB0xuEIMiPZVm5BlNTh1d5r3Ism/YTaveVWHkGNSyW2O13n9Gob3Ri274mBEMijHquzxV+PR/rC/C9VlJusculGEx6Tdxl6YO1oqrKZsbZNTZ8Xd+GI00u/HvHMRTkaZUvTEOxD4NhKGahMqmBKYmW8Dv0yJEjuOOOO6LygK688ko8++yzuOWWWygA6oNey0HNyR3DeUEKJ4PKFVeFE9/JodVwKMiTv/kWWvRRqz9c3iDaHX54fSHYCo2oKDLC6xeg5uTLPbwoKkV4ulesDQnytr0BIZxfJFfIjXS4jjAZ1LAVGtDU7kWAF5Uq0r19SJvtXhxocMhJwLoTHdw5FQujjoXbF4IoShhtM+FosxPH2/wI8fI3dzWnwrYvmsEwDMaET7qR6WKtWi7wyAsCGtq9CPJyXoXZqEVpgQFHWlzwBUNyo9ceGEQnSwvhprBHW5zQqjkUWXRRY3l7x3H4Q0L4hOOHN8CjpQswaDgY9SdqIPX85t798p1eyymJt05PEL5gZFm8PKtkMqiHrNqvfDA/BaeNsWLzp8fgD/Ioztejsd0DpzcEjZoDAyG8uiqcHwa50WuQF6PabsRu98RJosPhx+Ydx5QVi5IkNwf1+eTLY92XuPe2Ha8/hDe2H8HXR7uUPKEISQKCvKS0otBpOaWnXWQ1mNmghdMbQFAQoVepIIjioJ5Qeq7E0qhZjK1owZSaIowqil4NG5nRsFkNMZeP+zvxRR4rShK2f9EcN+9ndGkeHO4gGtrdsJrlBReRmUsVy6DLFcDYMvOgrKhiWQZlhUYUmnX45ngXjjS7wtXls3tZ91DMQmVSA1NyQsJBUCAQQEFBQdRt+fn5AIDRo0endadGmkjS5PE2DxhJQiAoQoLcroFjw5WUGblDeX6efJ2/57dAnVZeLdXlDsDjl09uXLiPllHPwukJQgi3RIi5UhTO+4hcDYkU8+suUtzvnFOKcO45oxAICn1+SBvbPOFcGxV6/lpOjmThDQgYYzOjpcMTzquRK1AH+dhVLLVnluFYixt76zsgiVBmlFhG7uEUCUjG2Eyob3TAi9ggKO5qMUmC3RFAnkHE/GmjMcZmkjti1x2G2xeCXquCwy0n6LIsKxct40Xw4aTVskIjgrwQ9c295+U7k0ENo86M/cccECXAoFcjGBSg16qGvP8PyzA455RiFJp1ysmX41iln1xVqQkSJLTYfUp7CYtJh7FlfQcQ3U8Sei0HDSfPSsiVub3wBQXwvLxsuvsS9762M6mmGN8c61ISiCMziZHcsUi7EqtJi7YuKSonSZLkkg8VxUacN7EiKtk53eKtxArxIg4e70JDiwsLZlb1+rwlc+LrPtPk8YXQ2O6Jm/fDsiwqSow42uzGt8e7IEpQXgtRlGDQqVBg0oYDlME50dIlntRQzk/mSWqusrdWGGyPyqokGsswmDW+TP7me9gOQAovx5ZnbDiV3JwzyAto7PBgUk1xTD8dpycYLk4mhhuChqDmVMqKKTXHQo3wiqhufzuyFBo4ESSIEnC81Y0xNlNsIuBZ5UodnMT0dpCTc5H2HWwHL4gYW2Y68W1WFRsYKI+S5GTd7oPoPp48PQeNmo2qnN1bepBaBRh0GvCiiGBIwJ6D7Zh8WomyssZm1aO+0ak0RQUYuLxynSFOxcAb4NFk9+CcU4qjLmfFu3znD4oIhQQYdZxc7Zc9UXF4OPr/xLu08vWRTjR1eMGHiz7mmzQ4vcqadAARmbnbf7RLSczX6zhoVHL17e5L3PtSXW5BqdUAjy+EYEhEUJJrQOk0KuTnadDhkGsH5YXzY1rsHnj9PALhIpsl+TpcO++UtORtRPQsPliUr+t1JZbZpMXhRke/wW1/J754TVMFQYLd5UeVzQx9nNQfq0muQO8LyosSuPCiCJZj4Q+KePPjo/joyxaY9PHr9qQDXeIhI0FSQdDjjz+Op59+Wvk50lvnnnvugV4fmxvw+uuvp2EXR4YqmxnnTxmNb491QRMOPEK8CI6Ta5uIklw5GRJgsxqx+0B7VPG7oy1uAOFeWRLCHePlJcUhQa7APKbUBIc7AI9fvi/TI5iI1HkRwsm8bl8o5sAVacDZ37fW8mIjdBr50hWnYmKmw/1BAVpObotgNenC3+qlqITj/DyN0mqjbl8TREnCWeOs8AdFuP0hNLR6oFHLCb2RVWu+gIBgUC4XEAp3WpekE0u9I1gWsOTpwj2iJLh9PA4cc+DLQ3blUpY/KEYtJwfkOi2hkIjKkjwI4eTbcydWRAWG8VZ78OEifmx45VSejouaCRmO/j/dT75jy5C25GyWYTDzDBv2fNsOj5+HSa+GimURCgkIhquB6zQctu9rxuhSU69/o6RAj+pyMw43ueTVdz65eW8wJKDZ7kMwKF/Lben0oazQgLFlZqWKeZ5egyvPHRcVAA20enK84oPmPDVa7D4UWXTxC92ZdAMKbntrmmrQqREMyTWyqsstMYnH/qBc28pi1KA4Xy/38fKG0NblByBBEuVA0aDjBvVyLF3iyX49PzflxenplZYtEg6CpkyZEvd2q9Watp0Z6QotehRa9DDp1ehw+tDS6UOIF+H28XL/K7UKRr08SxKv+J3JIHfF9oaXzqpYFpDk3lZWs06uJs0wUHNyc1K1WhU1T8MwcuVoTiVfYrp46mi5/1i3+ijdKzb3VYHWZjXgpEoLvqi3R12miJTSlyQJlaVmCJIcALi8IeWbvChF2oBw4DhWabVRYNKCZVkYdPJKIac7AFe4TL/Xz8MX4OVgAwxU4cslcgNVCcEemdIG7Yk2D5F6OV4/j2OtbuW5dftDUcvJgXB1Xsi5S/k6NVrCLUW6i3cpgGXkpHSPj4dex6HUqlf2V647JA17omg6p+J1Wg4mgwYsI1cu5/0hAFAavXIqpt/goPvz2NLpg8sbDCfzy+8jnVYVzhHj0e7wy8vqVSxOHV0QM9Mw0OrJvRUfPN7qht0ZUFpx9KRRqxASAikFt301TfUH5ebDLm8IzR0e5OnzT9SrCp+0wAAlBfJCiRa7Fx3OgFKPiFOxcPnk1yTeir10oks82Sve56aixIhLaqthNQ6sSGS2SPiIvGbNmsHcj5wgd3hn4Q/xcHl5qDkWeo1KuaYT5EV4fPIBsLfid2qOgUEnr0KqKM4Dp2LA8yLmTx8Do14Njy+Ete98ixa7F4IgV3KNzMLwgqi0gYjsT6RFQrIVaFmGwfypY+BwBZWqsZEMbxYMyoqNOG9SBbbuaYbd6Udjh1fp/h0JlpzeEBgAbV2+mCXy3VseBEJyobwQf6Lrt0Ytr/5xeeWy9vLMWKSliBxERZOnxLRqlfLcdm/foMyShVcjRbqKJ9rWIsgL4FgGIiM31Wyx+5T9iszI1YzJT3uiaLzLNwNZGp4oX4AHp2Jx8qh8BAUBLMtCFEXo1Fy4caiU0MzXGJsJC2ZW4bk3vwrnAjGQJAbmcHNeuYCjByUFepx3zigYdeqYMQ20enJfvZ1KrXJRxxOtOKIfG0xxFVQiTVNVKjl47wyvNszP0yiXrrUalVJg8miLG/6Q/OWC4xgwYMCLEkIBHi5vEAadYcgvx5LM19vn5nCTE2s378cl00fHJP2PRJm/fnEEKSs0oqzQgE+/bIUISelWLQcoEkRRgErFoKnDjbJCeSVUz+J3kZUzJoM8DS5JElo6fUpAI0oSxtjy0OH0g0G4sWg4AJCTTeVZIEO3yzWJdFOO9y1yjM2EReefjA8/b8SRZjeCIQEatQpVtjzUnlWOsRVm1De58eHuBojSifECCK/0EaBiGRxvd4NTMTFL5COJ0A1tbuXSnUHLodCiBSAv0y8p0MMXEODyBVHf4JQ7hHNsVAdwSZJzdnRaFU6tKkBrlw9Hml2wWfVRbQoARq5orVdDFy5Kl0xbi05XAG/vOIZDjU6IklyAkGWAEC9BgoQuVxDHWtxpuyRxuNmJt3ccR7PdCzHcG00ev0qZNRmsfJBIXlSQF2DUqaHRcgiG22wAyS2Rjswq5efpoOaYmOa8VrMODncIRp065gSe6nu3u756Oxm0HEwGdVQrjghJkvN2xpRGv0cSuSyXSNNUnpdQZjWgyS6XFIg8p1U2E2oqC/DOZ8fl5puCAJ1ahVBIBIvwpWlWQogHOt0BlBQYhuVyLMlcfX1uDFoOrQ4/6vY24crzThrxlzYpCBpCLMvgtKoCfPRFi1LHRxQleP0hBMPdz7UMg90HOjBvSiU6nLHF7yIrZyIVWiOzRt0L3M2bVIlvjzvg8oaURF1JkrcRqbVSUWRUDtyJdFPu7VtkZFl2vIM+yzAYf1IR3v/suDJeeRZIXgGk4VSwWQ1w9NGXLE8vn4TGVZiVmQB/kMf6usPKpSidRgVADY1aBSEc9AmiGFW3SJIknDIqH+WFRuUSTLPdh3yTFv6gALdPPjlowiv0mu3epNtajLFJeH93A0K8IK8yCzLhE5oKo4qNECUpbZckPvqiGf/8oB5un9wYVJIkBMNJUQYthyqbGRo1O2j5IN3zogw9Ap1kl0j7AnLpiEKzJqY6r5zrJcHlDeJ4qzsmoBjIe7f73++tWCfDMCgvMsJ9LLoVR4AX0NLlg0mviXqPJHpZLtGmqSzLoLzQiIunRV+6BoCdB1pxuNkFg+5EwChIEphwfpyakxvc+gLy5fbhvhxLMkd/n5tCiw4N7Z6cmDmkZV1DzGqSO7ObDWr4AwIcngCC4fo5FqMGei0Hb4DH7gPtmHJqCU4aZQHHMnD7QgiFZynG2ExR9WvKiwxRJ5uqMjMu/041TAY1/CEx3FtJgl4rF8+L1MuJHLgTafAXilP3JSISCIwtM8NmNUSdpAoteljN8nh5Xq5gzPOSMg6rWQtelHB6lRV5ejWaOuSkbEEQYXf6cajJCTWnwgWTKjGu3AKb1YAqm1k5qXt8PFo6ffD6BZwxtgCVxUao1SoEgiK8/hACQbl2SmVxHi6aOhpsuDaREhRIcrNTDSc3iMwLJ6BWdb9PgnZ/047Dza5wQT8t8vM0MBs0ULEM2h0BaDiVckIeiMNNTvzzg/pwkCs3IxVEeTYRkoQQL4YDSrmCs9sXwrZw4nm6RPJ58vRqNHZ44PWHwnWZ+KiihYkEe91X23Xn8oZwsMGBA8cdaOvyYcP2w3j69S/w2f42ZSwDfe/29fcj1ByL0gIDKkvylPeb2x/CSaPysXD2ifdI5PLCkWYXjHoOpQV6GPVc1O09/2akaWokjy6ie9PUimIjTh9rjfp8sQyDM6qs4T6AAoIhAaIoIRSSv2BEFq3I/diEuMcJkrv6/dxoOLmgbw7MHNLXgiGm13Iw6TXQa1U42uqGKMnBCadSKZczdOFk0ENNTlx1/slRxe9KCuTmi5HLL72dbKafYYOt0BB1ucSg41BRZIxJKk20wV8q3yLlTt4a6DXyYyMzNJHLHZEeSt37ktU3OGF39V5cEeh9VcqxFjfqPm/C4RaXvIpMo0JVuA5R9zH3fLw2nCfVX32k3oiShI+/bgHPi7BadJC6lSrQSCp4wwUZjXr1gA4soiRh885j8Ph4pTQCL4gQJAlqTi4uKIpy24lI+5N4syEDXUkFnMiL2v5lM1q7/PD55eazyS6RjrfaLlItPCQICPESGJZBlyuI1k4fvj7SiU+/LsDF08ak5b2bSG+nnq048gxqnFpdDIfDC54Xk74s1/1vJtI0tbfO9aVWA7pcAbi8ISUwZBi5r1qIF8HzQLNdft2pbk986fgsZJt+PzfBcM5qDswcjvwRZphSqwFlRQYcONaFoNJVGwjy8jfBYFCAMZzv09juRXuXP6b4ncNzYln7jDNs0GpUONTkjPkAV9nM+Mmlp/X7AU+mwV+yygqNKC8y4lCjM3xyOPGWi9eXTIKExnYPTELfxRWBgTe/TOeqltZOHzqdAeWSXPcp1kiOh9vHw6CPX0Qwmb/T0iG3KolcPhHDjeMifbgis0KRnmk980HiXbKxFepx2mgrCszapE4EY2wmjK0wwy8Aza1OaDhVSn26uq+2y8/ToLnDg0BIgBhO6NdwLLQaOeHd4+ex/1gXgryIBTOrBvzeTbTwX/dWHBzHRl26S/ayXH9NU9WcvAruoqmj++xcP7bMhB0OP7RqFlqNWinrwAtyQVaGkUtjLKjtvaBjLhvoqsJs1d8xv8PhR2WxMSdmDpM+GgeDQXzwwQeoqKjAqaeeOhj7NKJFDn5HW1zw2nmwAHhRgiCE22cwgMTI1WI5jlVOXPFO7v4gj237mvv8ACdyoh/M6q8sKxeKbOv09bttOWemGSG+/+KKiebpDJVI3kWeXm5gq6z667ZPQV5AoUk7oAOLL5x8zKlO9NNiGQZgIDfThVwyQG6LIYdi3WdD4q0Isbv82Pl1Gz75qhUFJm3SBfZYhkFFsREGjlFmRRKpNdVd99V2h5tcSrIwAzlPq3tvMb1WTgK2O/3Y/kUzZp5pG/B7d6CF/xK5LNczMbm3pqlWsxZTTy3FhFOK+n2fnzbaik++agUYuUCnnBQvwBdkoGcZFOfroeFY6DT0fbenRFYVjhtlGe7dHBR9HfO73AFYLXrUjs+NmcOkPxlqtRq/+MUv8F//9V8UBKVojM2ECScV4VCTCwH+RIU/lpHPmzwvotMdBMfK7TPGlkV+f+Lk3r31QyrLguPt02BVf43k8PS37XQkuQ6XSCsJnUmFkOCDNyBAo5Y7nPOCCK9fbgFy0qj8Af8dg5aDL8DDF+6npWLlQCgYLqkgAWB5EU3hpeVuXwhVNhOK8nVY9863UZds5Do0PoiQ263EK7CXTDG8gXyzjgT6n+1vw4Zth2EyqNHQ7oGaY6PeD5FaTnl6NRrbvdBpuLS8dwdS+C/Vy3IDLTZYYNaiwKQFz4tyIVGJB8swyDdqUGo1wKjj0BLeNjkh0cuXYyvSV4080/R2zB9bZsb8cJ0gvtv5aaRKOghiGAZVVVXo7OwcjP3JCaIkocPph16jgi/c60ulYpRv7rwggucFaPRqfH20ExNOjv5GmI5lwfEMZvXXRLadyrfpTLmeH5lePtriQnWFBcdbXHJTUV5EUBAhhltL7DrQhpZOb8rT7SUFepQXG+HyheTgKsCHu6yLSgDEMIBBq1KqK1cW52HmmWVo7/JHBZmRQpyBkBywgQW84ec2UmBv0ydHYNJr0NTRf1BzuNk5oHo9gBzojyrJg8mggRQOzFQ9VoxFajnpNHJ5A1+AxxibCeeqK9DY5gEgVzTvmaSfiGRmEUVRQlOHB25vCFqNSilrkexluYHMXEZyDCOd3HvLucuF3I5kJPqFq8XuRaF15NbKiXdcLi82otCah85Oz3Dv3pBI6ZOxdOlSrFy5EhdffDGqq6vTvU8jXmunD80dPpRY9TjS5AYYKfxBlCKN4MGAQYlFj6Y4Mx+DOWMymJeS+tt2st+mM+l6fmR62e4MwOkJoqLICKc3iGOt8oEkz6BOy7L17tPY7ZATGLs8QQhyhwmwrLyyQwIDTgVAYmAxaVBZmhdOND4RZLY7/Gh3+OWK26ET76NIgT2NmsWXhzphNWtRajX0GdSIooQP96YnMI8ElAeOdYHpsXxckqDUcmJZBmoVi05XANu/6PuycLodbnbi0/frcaihC8Fw2QqjgQPLMEPaULR7bkd/OXfkhFS+cI1UPY/LuXAJrLuUgqA9e/YgPz8fCxYswNSpU1FRUQGdThdzv3vuuWfAOzgSRT6AeTo1tBoWQjgnKEKjlpfOGvUcvAEh5oM4GB/gTJhRSSZBe6BVgoH0j3mMzYTvzR6LT/e3o/643KxUECUUmXWwFRqV/k8D7SrffRr7UKMTTm8Ieq3chLe8yABOpYpq1+H28mjt9EUFmbwgoaHNo9SOYsPtR3hBzufRazl0OgMICSKs4WRpIH5QAwBNHZ5eu54nG5hHAr32Lh8c7iB8AQFGnZwzFqmTVVKgl3MXzFrUfd6UtsvCiTjS7ML6bYfhDwkw69VQ58nPaaczAJZhYDVp4fbxQ9JQNJO7uWfCMaU3g7kilmSXlF7hF198Ufn/7du3x70PwzAUBPUi8gGU+yTJDVQZBkpPLUmSV3i4vHKybWT5ds/Hp+sDnCkzKoke0AEM+HLgYI25ymbG2TU2fPDZUbz6fj2MOnU4MDhxn3TkN/XMnynO18OoV8e0dRBFCQ7PiUtGZUUGHG5yKn3N5MrdcrVsUZKgVasgikBDuwf+gAANJ5dv6K7n/o8qyYPHF0KIF6HN6zsw9/pDCSVNj7GZsHBWtdJc1OEJKs1FC0w6uH1yIVBISPtl4b6cuBQdRFW5BaGgvIJNp1HBkqdBi90Lq1mDRRechGBIHJKTfyZ2c8+UY0pvEv3CVZphuYck/VIKgr7++ut070dOiXwADzc5lbYNBi0HjmEQDIlyTy0GaGh3w6Dj8N5nDag968TBI51L2lOZURnMb3iJHNCb7d4BXQ5MxyxSX1iWQZ5eDU7FIj8vtt8UkJ7p9u75MyoVE/fvdA+II0FmQ5sHje1eaNUMJJFVms+qWFapPuz1hRASRBSadXGD6Z77bwzXLOorMOd5Ee981gCHO5jQiXGMzYSbF5yB3Qfa8clXLbC7AkpD4EjriHd3NQxpIn3kUrTVdKKrfPfmwCFBgt0ZAMOwmDe5csgS+DOpm/tgf77SIZNn0MjQyri5voMHD+Khhx7Crl27YDQasXDhQixfvhwajabPx82dOxcNDQ0xt+/duxdabZz2z8Oo+wcwEJKTPD1+HgwkeP0CJMjLgvVaDmVWI460uNDhPHHwSNcHOJUE66H4htffAX0glwMHK6m8p1Rn65INMJMNiMfYTKg9y4amDg8kCWAZeVk9w0BpzMsLcgsOjmVRYNL1G1wB0fWg4u1HU4cX/iAPtpOB1Zz4iZFlGJxzSjEmnFwU87z0zHHqaTDyOnq+91zeoFLUUatWQaNm4PXzONbqxut1h4b0hJ8J3dyH6vOVDpk4g0aG3oCCoN27d+Pjjz9GR0cHrrnmGlRVVcHn86G+vh5VVVUwGo1Jbc/hcGDJkiWoqqrCqlWr0NLSgpUrV8Lv9+Pee+/t9/EXXXQRfvKTn0Td1l/wNFy6fwDrG5zocPrgCQdAeq0KFqOcjBppj9Hz4JGOD3CyCdZD+Q2vrwN6sgFG98DC4wulLXelL5GimMnM1qUSYKYSEFeXW1BeZISKZaHmGPiDAhzuALx+Hr6A/B40ajmMLjXBG+AhSVK/+99XPSi70w9/kIdOw6G8KLUTY7z3w3DkdXT/m1odh2a7FyFBUJoD84IITsWitEAPhyeYMSf8oZJtZS4yaQaNDI+Ujg7BYBC33347tmzZohwgzzvvPFRVVYFlWfzkJz/B9ddfj5/+9KdJbXft2rXweDxYvXo18vPzAQCCIOD+++/H0qVLUVpa2ufji4qKMGHChFSGNCy6fwCPtbqwcfsRGPVqGHXqqC7avR08BvoBTmZGJZO+4SWbQN09sOB5EXaXH1U2M/RxJgjTNXuQbHAykAAz2YC4pECP8iIjjjS7UGAywGTQoMiigy8ggBcE2J0BnDzKgplnlUU1qu0vuOqtHlRJgR6iBBRZdGk9MQ5mpfP+/ubRFhdUHAuPX251IZccOLFyzaCTV69l0gl/KCR7TMmE4CMTZtDI8EkpCHriiSfw3nvv4b777sO0adNw8cUXK7/TarW4+OKLsWXLlqSDoK1bt2LGjBlKAAQA8+fPx29+8xvU1dXh+9//fiq7m9EiH0C5fxWHQrMuppM20PvJeaA1RhL9Jp1J3/ASDTCOtbhjAosudwDNdhGHm52oLrcoK7bijXmgEg1O0hFg9hYQA4ibiBzv+WMYwO3jYTXrUHtWeUqzjfH2w+MP4e/vHkz7ZavhyOvoXgqhsd0TbunBgRdEZeVaqdUQbpWSO8usIxI9pgxHWQNC4knpSP/GG29g0aJFuOqqq+IWTRw3bhw2bdqU9Hbr6+txxRVXRN1mNptRXFyM+vr6fh+/fv16rFu3Dmq1GpMnT8add96JmpqapPejO45j+79TAlQqNurfnvIMamjULEK8GP/gwQtKl/N07VN5sREVJUY5QTs8nR8hSRK63AGMLTOjvFi+Dy+K0Gu4uCcVnYaDwxNEkBeU/etvzAMxbpQFl8+pxod7m9DY7pH7qXFyI9ba8WUYXWrC/23+Bh5/CBVFRmVshWYd7E4/OpwBtNg9MBvzwSBSgyZ6zInkVLV0CzBKrYaYMY8bZcHYCnPM/bpvu6nDg2a7F4VmHVRsj+eKYVBo1qHJ7kWH04+ywr4vMY8qOVHY7XCzU3l+Is1oy4uMmDW+rN/nr8pmTnj/473O3fejqcMzaO/tRMeRTuNGWXDFuePw9s7jaOv0wRvgwalYmA1q2AoNMOk1Ax5XJkrk85zIMaXQrMO2fc1w+4KwmnRK4Hq0xQW7M4DvzR47KK9bKgbzGJapcm3MKQVBHR0dfQYXKpUKfr8/6e06nU6YzbFvfovFAofD0edj586di/Hjx6O8vBzHjh3DU089hWuuuQavvfYaKisrk94XQM5zKChILq+pP2Zz/Kl5i8WAsRUtOHi8C+Yesy2SJKGly4eTRuXj1OriuDNFqbqkthprN+9Hq8OPQosOWg2HQJBHh8MPq0WP+bXVKLTmwS8Aep0aIgPo4pzIvP4Q9Do1bCXmmOestzEPVEGBEWfXyEm+Hl8IRr0aZYVGsCyDhjY3Wrv8KC00QquLnu0ZXWaBP9iJLncQbj+PfJMu7pj7cvB4F7bsOIZjLS4lwKgsNeH8yZUwm/UxY+5re22uICQwMOVp4762KrUKLj8PTqNO+P148HgXNn50FE5PEIUWHXQaudfc8TYPNn50FIvm1WDiaWW9Pn89JVI1d7je2329DyIi1Z37G2cyf/P0ccVY9ffdONToQEWxEUadBpHla4P5mR1u/X2e+zqmFJh1UGs4OLw+VJVblPeCTq+G2aTF8VY3duxvx9k1tox6zgbrGJbJcmXMKQVBZWVlfc7MfPbZZxg9enTKO5WK7jWJJk+ejNraWsyfPx9/+ctfcN9996W0TVGU4HR607J/KhULs1kPp9MHQYjfj2VKTREaWlw43OiA1aSDRq1CMCTA7vLDpNdgck0RHI707E+E1ajGJdNHx8wYVBYbUTu+DFajGp2dHuhUQEm+DoebnCgvNMaeyDo8GFtmhk4Fpdx6ImNOBwPHwGCSv31Hnp/mVid8/hAsejWCPS5H6DgWY0pNqG9ywO7wwxWePeg55t4cbnbita2HlG+yFr0agZCA/Yc70NjqwuJLTkexWZvwmPlgCAwkuNyBuDMlvoC8cpAPhhIqZS9KEjbW1cPu8CmvFR8SwDEMSiw6NHZ48GZdPa6edwpYhon7/CUjU97bvY2jrxmxVGccImM+/5wKvOL0o9XuhdUkDslndrgk+nnu65hSMyYf7+xsgFmvRigoxDzWrFejvqELX9e3KbOe8WZchyp3aKiOYZlkJIzZbNYnPJOVUhB02WWX4bnnnsOFF16IqqoqAFBOiuvWrcObb76JO+64I+ntms1muFyumNsdDgcsluS6+ZaUlGDSpEn44osvkt6P7tLdQE4IJ+fGM6ooDwtmVnVL5A1ArZJP2DPPLMOoorxBaWg3qigPPzzvpLhJit3/3ozTbWjr9KEhvLqqZ/7F9NNtEAUJIqSo7fc15sGi4VTgWBa+IB83sOA4BuWFRlw8bTSMenWvY+5JlCRs3d0IlzcYlb+j1ahgsxrQbPfinR3H8P3ZYyEKUq/b6a7QrFNW33XfJiAHmB1OP6psJhSadQk9j812LxpaPcjP00IKb6O7/Dwtjrd60NjmSWv+Via+t2MSzvPk9+yhRifaOn0DXtFYWTI84xpOiXyeezumHGl2yW1G8liIUuznQ82xCIZEuL0h8BYxY4ouDscxbLjlyphTCoJuueUW7NmzB9dddx2qq6vBMAwefvhhOBwONDc3Y86cObj++uuT3m51dXXMDJPL5UJbW1vO9CgbriWbiSRYZ1NdjURXDp0+1prUc9tfgrjVpMPRFhda7F4UWxKbTk53gm+m9kUa6vf2UK1ozJVl1qIkoaHNjeZWJzScqt8xDrSsQTYUXSTZL6UgSKPR4JlnnsHrr7+Ot956C6IoIhgMoqamBsuXL8fChQtjThCJmD17Np566qmo3KBNmzaBZVnU1tYmta2Wlhbs3LkTCxcuTHo/hlsmL9nMlgP+YK0c6i/A0ITzd5INMNIZYGZyX6ShfG8P5YrGTP7MpsORZhe2f9mM1i4/fP4QODa1GZlEv5wU5euw7p1vM6IkBxnZUj4KMgyDhQsXpjXIWLRoEdasWYNly5Zh6dKlaGlpwaOPPopFixZF1QhasmQJGhsbsXnzZgDAhg0b8O6772LOnDkoKSnBsWPH8Kc//QkqlQo//vGP07Z/RJYNB3xRkqDVqDCppgRfHrajyx1Iy8yVVqOCKEmwO/0w9KjnBADBkAA1l1qAka4Aczjq52SiTJ0RyzaRGRmPP4TSQiMsejV8QT6lGZlEv5y0d/kzpiQHGdlSCoLWrl2LKVOmYNy4cWndGYvFghdeeAEPPvggli1bBqPRiB/84AdYsWJF1P1EUYQgnEiqGzVqFFpbW/E///M/cLlcMJlMmD59Om677baUV4aR7NUzj4BjGVhMGpxRZUV1uSXlmasjzS58+Hkj7A4/vAEeOrUKRr06qrK33eXHqVWFKLUaEs4J6i4dASb1RZLFmxGTJEkuCinK+Q4cy1Cn8D50v6RYUSSvtAyGA/RUZ2QSmfU81OSkAJYMiZQ+/ffddx8YhoHFYsGkSZMwefJkTJo0CWeeeSbYnnVOkjRu3Dg8//zzfd5nzZo1UT9PmDAh5jaSm3rLI2jv8mPn/jZUFKU2fd59u2VFBjR3eBEIiXB45ANxWaERQV6A2aDB3MmVYBkmJkF8KGVT/tZg6Tkj5vbxSqNTQZIXPRRatPAHkz+RJpsfk60G65Jif7OemXxJl4wsKb2D6urq8Omnn2Lnzp3YuXMnHnvsMUiSBL1ejwkTJiiB0bRp09K9v4T0arASYeNtV6vm0GL3wOPn4Q3waLJ7cM4pxZg9oRzjRuUntJR9sGVL/tZg6T4jdqjJBZc3CEGUoFYxkEQJGjULgMH6usNJXdJJV35MNhjMS4p9zXrSJV0yVFIKggoLC3HxxRcr7TLcbjd27dqFHTt24K233sL27dvBMAy+/PLLtO4sIX0ZrG+t8bZrMqiRp7fAFxDg8YcQDIk4d2IFRpdm1kkwG/K3BtMYmwkLZlbhuTe/Cre1YCBJDMwG+TJmnp5LKjhOZ35MNug+I8PFqbuS6IxMsn3C6JIuGSoDnks8evQoduzYofx39OhR6PV6TJw4MR37R0jCBuNbqyhJON7qhssbhE6jgiQhqrGtQSfPMrV0+hCIU/yNDD+dloPJoEF+ng5qjoGKZaMS2hMNjgcjPybTdZ+RMfQIdBKdkUm11g9d0iVDIaUg6MUXX1SCnvb2diU36Oqrr8akSZNwxhlnQKWKfyIiZLCkO48gcvA+1OhEa5cPHU4/jHo1RhUbYTJoUt4uGVq+AA9ekFBo1vTbnLivGYtMaiI8VLrPyDR2eFBaaAQjAf4gn9CMzEBr/eT6JV0y+FI6aj/00ENQqVS48MILccMNN+DMM89M934RkrR05hFEDt7tDj8CQfnkGAyI8Ad4OD1BVNlMKLUaKD8hC6Srs3muLrmPzMj0zIPqb0YmXTl6uX5JlwyulIKga6+9Fjt37sRbb72Ft99+G2eeeSYmT56MyZMn45xzzoHJRNOUuSbeN+ihlq48gsjBu93hh9cfQkgQYdBy8IGHIMq1gA41OaFiGQR5kfIT+pFsPki6JRIcW81a1H3e1OeMRbryY7LRGJsJYyvM8AtIeEVcLs6ckeyT0qf117/+NQA5ITqyQmzHjh14/vnnIQgCTj75ZEyePFm5HxnZervmP3tCecJdz3uT7Ak0HXkErZ0+NLZ5EAwJSgDEMAw4FQtvgEeIFxDiRRxv92DKqSUjclVQumRC76f+gmOjjgMk9DtjceXckwacH5PNWIZBRbERBq7v/noRuTpzRrLLgL6y5OXlYc6cOZgzZw6OHTuGbdu24fnnn8f+/fvxzTffUBCUA/q65v/a1kMwm/SwGtUpbzvVhMqB5BH4AvKy90BIgFZ9YuZAzbEwq9TgBQ5efwgmgxrnTqxAeeHAAr2RKpN6P/UVHNdUFuDdXQ39zli0d/kHlB+Ta6jWD8kGKb/7vv32W+zYsUOpF9TS0gJA7t5+6aWXYtKkSWnbSZKZ+rvm372jerIGegIdSB6BXsvJ3eQFCTpN7EmRYSSlSz2tCItvqJqXJqO34PhIsyvhGYuxZeaU8mNGElGS0Gz39vsFg2r9kGyQUhA0bdo0OJ1OSJKE6upqzJ49G+eccw4mT56MUaNGpXsfSYYajI7qwPCfQEsK9Cgt1KPZ7gUvSFBz3Q/eQCAkQq9RwaDj6FtsLzI1H2Sgnc2B1PJjRoqDx7uwsa4eDa2efmdnqdYPyQYpHcEvv/xyJQnaarWme59IlhisjurDfQJlGQbzJlXi2+MOuLwh5Ok5cCoVBFFEICT3m9KoVagoMtK32F5kUz5IKjMWyebHDJXBTEI/3OzExo+Owu7wIT8vsdlZqvVDMl1KQdDdd9+d7v0gWai/b9CpdlTPhBNoVZkZl3+nGv/8oB5uHw+OFaFiGeg1KmjUKhRZdPQttg/ZlA8yUmYsBjMJXZQkfLi3CU5PEOWFRqUrXrzZWQBRgVhlaR4WlZ5MtX5IRkrpCNTY2IjGxkZMnjxZue3rr7/Gs88+i2AwiMsuuwwXXHBB2naSZKb+vkGn2lE9U06g08+wwVZowNs7jqPZ7oUoSjDoOFQUGelbbD+yLR8k22cs+suhWzCzCjotl3IQ0trpQ2O7B4UWHRiGgSSd+Dx3n53dfaAdXx/tHNbVgIQkI+ViiV6vV+n23t7ejh/96EcIhUIwGo1466238MQTT+DCCy9M576SDNPfN+hUO6pn0gm0ymbGTy49jb7FJikbZ1eytTpxfzl0h5pceO7Nr2AyaOQctxQCE1+AR4gXodNw4EOxiwG0ankhxOZPj0GUpGFfDUhIomIrfiVg7969mDlzpvLza6+9Br/fj3/961/YunUrZsyYgWeffTZtO0kyV+Qb9BibCR4fj5ZOHzw+HlU2ExbOHotxo/KT3mbkBJqnV6OpQ16FIooSfAEeTR3eIT+BRpJpx5aZYbMaMv6kmCn6em9k6gkxG1/rvnLo3D4eLm8Q7Y4AVCyL0gI9jHpOmTk60uxK6G/otRzUHAt/MP4laH+Qh9sbgj/Io6zQIK+wZBnotRzKCg1w+0LYtq8JopT4lyFChkJKM0EOhwOFhYXKz++99x6mTJmC0aNHAwDmzZuH3//+9+nZQ5LxevsGreklpyfRbWbz5Qkiy9bZlWzSWw6dJElosXsgiBLUKgZqjlECk2RXWZYU6FFeZMTxNg9KLDpl+76AAF4Q0GL3AQxQUmDIqNWAhPQnpSDIarWisbERAOB0OrF7927ceeedyu8FQQDPD/+qDzJ0BqO/D51ARwbq/TS4esuh8wUEeP081CoGksRAxZ6Y+E82MGEZBrPGl2HjR0fR2OGBmmPR6QzA7ecRDAlgGUDFsgj1slIuk1YDEtJdSkHQzJkzsWbNGuTl5eHjjz+GJEk4//zzld9/++23KCsrS9tOktxFJ1BC+tZbDh0vihAkQBIlmA3qmIUEyQYmVTYzFs2rwdp/f40v6u0ICSI0nApWkxYGnRqN7R4cbnaiutwCkyG6SnwmrQYkpLuU3pF33HEHDh06hEceeQRqtRq//OUvUVlZCQAIBoN48803sWDBgrTuKCGEkFi9JaHzvAieF6FRsyi1GtBzAjWVwGRsuQUmgwZWsxZWsxacShV+vARfIIQOZwDNHR7k6fOVv5eJqwEJiUgpCCoqKsLatWvhcrmg1Wqh0WiU34miiBdeeAE2my1tO0kIIaR38XLoOJZBoUULgEGePj0NX5s6PGjq8KDUaugRPDEotRrh8fPodAfR6QogP0+T0asBCQEG2EDVZIpNTtXpdDj11FMHsllCCAEwuBWQR5p4OXT+II/1dYcTLlPQ3/Pt8YUQ4kVo82IXPZgMaowtM+NQkxMef0iZaaLFDCSTpRwECYKADz/8EMeOHYPD4YgqngXIiXfLli0b8A4SQnLTYFZAHqni5dAlusoykefbqFdDzfVeyFTNsSgvNOLiaaNh1KspcCUZL6Ug6PPPP8dtt92G5ubmmOAngoIgkqtEUUJThwdub4hOAinqrwJyptYZSkUis10DmRFLZJVlIs/3uFEWlBUaUV5kxKFGZ5+FTE8fa6X3PMkKKQVB999/P/x+P5588klMnjwZZrM53ftFSFY63OzEp+/X41BDF4Ihmr1IRX8VkJOpb5PpEpl9SceMWF+rLBN9vsdWmMGy8lL5tk5f1lQCJ6QvKQVB+/fvx4oVKzB37tx07w8hWetIswvrtx2GPyTArFdDnceO2NmLwdRXBeSRVHgvkdkXAIM+I5bo891i96LQmocqm5kKmZIRI6UgyGaz9XoZjJBcdOLbdBBV5RaEggJESRqRsxeDrbcKyBEjofBeIrMvdZ83AmAGfUYsleebCpmSkSKl3mE33XQT1q1bB7fbne79ISQrRb5NW026fmcvRjJRktBs9+JQkxPNdm9KvaK6V0COZyQU3ktk9uVwsxuHW1wJzYgNRKrPdzb2WSOkp5SOIh6PB0ajEfPmzcOll14Km80GlSr6WwTDMLj++uvTsY+EZLxcmL3oz+FmJ7bubhzwaq7eKiADI6fwXiLvl2A4KBns91Siz3dpFl96JKQ3KQVBjzzyiPL/L774Ytz7UBBEckn3b9M6vTrm9yNh9qIvB4934bWth+DyBgecu9JbBeSRlHzbW7+viEBIkBsQM+jzPul4T+XC801Ib1L69GzZsiXd+0FIVot8mz7a4oLZpI363UiZveiNKEnYsuMY3L5g2nJX4lVAHknJt4nNvuQBYHCkZfBnxEb6801Ib1IKgioqKvq9j8PhSGXTOHjwIB566CHs2rULRqMRCxcuxPLly6Nac/Tn+eefx8MPP4xzzz0XTz/9dEr7QUgyIt+m7c4Ajre65dVh4aJy2fJtOtVaNC12L461uBLKh0pmNddITr5NZPal9qxyAECHc2hmaEby801Ib9I6Nx8MBrFlyxasX78eH3zwAT7//POkHu9wOLBkyRJUVVVh1apVaGlpwcqVK+H3+3HvvfcmtI22tjY8+eSTKCwsTGUIhKRsjM2E780ei0/3t0fVCcqGb9MDqUXjC/AI8SIscS4DAgPLXemrvk22S3T2ZShnaEby801IPAMOgiRJwvbt27F+/Xps3rwZbrcbVqsVl112WdLbWrt2LTweD1avXo38/HwAcnuO+++/H0uXLkVpaWm/23jssccwd+5cNDY2Jv33CRmoKpsZZ9fY8HV9W9ZUjB5odWa9llNmvbSa2CTekZ4PNRCJzL7QDA0hgyflo9K+ffuwfv16vPHGG2hvbwfDMLjkkktw3XXXYcKECTHT4onYunUrZsyYoQRAADB//nz85je/QV1dHb7//e/3+fgdO3bg7bffxqZNm3DHHXck/fcJSQeWZVBWaARvEYd7V/qVjurMpVYDKktN2H+4AzarYUSu5hpMicy+0AwNIYMjqSDo2LFjeP3117F+/XocOXIEpaWlWLBgAcaPH48VK1bgoosuwsSJE1Pemfr6elxxxRVRt5nNZhQXF6O+vr7PxwqCgAcffBC33HILSkpKUt4HQnJJOqozswyD8ydXoqHFRauLCCFZJeEg6KqrrsLevXtRUFCAiy66CA899BAmT54MADh69GhadsbpdMbtQ2axWPpNtP6///s/+Hy+tC/L57iU6knGUKnYqH9zAY058wV5AbwoQq/h4gYpOg0HhyeIIC/0+llQqViMG5WPK84dh/d3N6Cx3QOHJwg1x6K63Iza8WWoso2s/oLZ9jqnA405N+TamBMOgvbs2YNRo0bh7rvvxrnnnguOy5zr+x0dHfjjH/+IRx55JKlVZP1hWQYFBca0bQ8AzObcuyRAY85cNl6CXqeGyAC6ODk7Xn8Iep0athJzv5+F8TWlOPPkEjR1eODxhWDUq1FWaATLjtwZoGx5ndOJxpwbcmXMCUcyv/71r7Fhwwb87Gc/g8ViwUUXXYRLLrkE06ZNS9vOmM1muFyumNsdDgcsFkuvj3viiSdQU1ODyZMnw+l0AgB4ngfP83A6nTAYDCkFbaIowen0Jv24eFQqFmazHk6nD4KQ+bki6UBjzvwx61RASb4Oh5ucKC80xuTztHR4MLbMDJ0K6Oz0xN1GzzEbOAYGk/xlxOFIz+cn02Tb65wONGYac7Ywm/UJz2QlHBlce+21uPbaa3Hs2DGsX78eGzZswLp161BUVIRp06aBYZiUkqG7q66ujsn9cblcaGtrQ3V1da+PO3ToED799FNMmTIl5ndTpkzBn//8Z8yePTulfeL59L4JBEFM+zYzHY05s8043Ya2Th8a2j1x83mmn26DKEgQIfcB61lPqLxYniHKpjGnSypjTrUeU6ag1zk35MqYk54eqaysxK233opbb71VWSG2ceNGSJKE+++/H1u3bsXcuXMxc+ZMaLXa/jfYzezZs/HUU09F5QZt2rQJLMuitra218f96le/UmaAIv7nf/4HOp0Ot99+O2pqapIdJiE5I5lqwfHqCVWUGHFJbTWsxvh1gsgJA6nHRAhJP0aSUmjz3IMoivjoo4/w+uuvY/PmzfB4PNDr9di1a1dS23E4HLj00ksxduxYLF26VCmWuGDBgqhiiUuWLEFjYyM2b97c67YWL14Mg8EwoIrRgiDCbo9/CSBZHMeioMCIzk5PTkTXAI0528bc3wxFb/WEutwBWC16XDJ9NEYV5Q3jCIZOKq9zb89fZMYtmf5qwyGb39upojFn55itVmPCl8PSkv7NsixmzpyJlStXYtu2bfjd736H6dOnJ70di8WCF154ASqVCsuWLcPjjz+OH/zgB7j77ruj7ieKIgRBSMeuE0LCIrVoxpaZYbMaogKgnvWE9FoOLMvIl8MKjXB6gqjb2wRx4N+pRqS+nr+yQgPcvhC27aPnj5ChlpaZoJGKZoIGhsY8csbcbPfipX9/A6Oei6n8zDIMeElCp9OPay44JSOK+g123k2yr3Nfzx8gtx7x+Hhce2FmPH/xjNT3dl9ozNk55mRmgjJnnTshJGP5AjxCggitOrYtBgBoNRxCvJhSf7B4BhLEDDTvJl0BVPftdDj8CPICrOr4eZID6a9GCEkdBUGEkH7ptRzUKrk/WLyZjECQh5pLT3+wgQQxA+2Dlq7E5Z7bEUUJdpcfGk6FonxdzP2pvxohwyM3SkISQgakpECPsiIDOl0B9LyCLkkSOhx+VBQZB9wfLBLEHGl2wajnUFqgh1HPRd3em4Hm3Qzkb/e3nQKTBqIg4XCzE05PMOr+kf5q5UUG6q9GyBCjIIgQ0i+WYVB7Zhny9Go0dXjhC/AQRQm+AI/GDg/MRg1qxw+sP9hAg5hk+qCl+2/3tx2DTo3qcrnsx6EmJ7z+E89fU4eX+qsRMkwoCCKEJCRST2iMzQSPj0dLpw8eH4+xZWYsmlcz4P5gAwligATyltQqhIT4eUsD/duiJKHZ7sVn+9twuNmF/LzY7ZiMGlSVmcAyDDrdAeX5q+r2vBJChhZdgCaEJGyMzYTK0ryYitGF1rxe22okKpEgpq/k4X7zlvrIuxnI3+6e/+PyBtHu8MPrC8FWaITJEF1A0mrSIRgSMW9yJQotuqysGE3ISEJBECEkKZF6Qt1/ToeBBDHAibylI80u6DSqmD5ona4AqmymuHk3qf7tw83OqERsnVaFLk8QTm8IgZALY2ymqEAoEBKg4VQYVZKXsUvhCckldDmMEJIR+ku+7i95uK+8pf7yblL526Io4cO90fk/Rh0Hk54DywJBXkCL3YvI5igBmpDMQ0EQISQjDCSIiegtb6m/vJtU/nZThweN4aazkVknhmFQajVCrVJBkgCXNwSPL0QJ0IRkKLocRgjJGMk0c+1rGz3zlhLJu0n2b3t8IYR4Edq86Dwik0GNMTYTmjs86HIH0dblg8mgSWoMhJChQUEQISSjpBrEdNczb2kw/rZRr4aai59HZDKooWKNMOjUuHBKJUaV5FECNCEZiIIgQkjGSTWIGcq/XVZoRHmREYcanXETsbvcQYwtM+GcmmIKfgjJUJQTRAghKWBZBrPGDyyHiRAyvCgIIoSQFFXZzCklYhNCMgNdDiOEkAFIRw4TIWR4UBBECCEDNJw5TISQ1NHlMEIIIYTkJAqCCCGEEJKTKAgihBBCSE6iIIgQQgghOYmCIEIIIYTkJAqCCCGEEJKTKAgihBBCSE6iIIgQQgghOYmCIEIIIYTkJAqCCCGEEJKTKAgihBBCSE6iIIgQQgghOYmCIEIIIYTkJAqCCCGEEJKTuOHeAUIIIWSkECUJrZ0++AI89FoOJQV6sAwz3LtFepFxQdDBgwfx0EMPYdeuXTAajVi4cCGWL18OjUbT5+PuvPNO7N27F62trVCr1TjllFPw05/+FLNmzRqiPSeEEJLLjjS7ULevCU3tXoQEEWoVi7IiA2rPLMMYm2m4d4/EkVFBkMPhwJIlS1BVVYVVq1ahpaUFK1euhN/vx7333tvnY0OhEK6//npUVVUhEAjgH//4B26++Wb89a9/xeTJk4doBIQQQnLRkWYXXq87BLcvhAKTFlq1CoGQgCPNLnQ4/Phu7VgKhDJQRgVBa9euhcfjwerVq5Gfnw8AEAQB999/P5YuXYrS0tJeH/vEE09E/Tx79mycf/75+Ne//kVBECGEkEEjShLq9jXB7QuhrNAAJnz5S6/loNOo0NThxbZ9TagszaNLYxkmoxKjt27dihkzZigBEADMnz8foiiirq4uqW2pVCqYTCaEQqE07yUhhBByQmunD03tXhSYtEoAFMEwDApMWjS2e9Ha6RumPSS9yaiZoPr6elxxxRVRt5nNZhQXF6O+vr7fx0uSBEEQ4HK58Oqrr+LIkSN44IEHBrRPHJeeOFGlYqP+zQU05txAY84NNObeBXkBvChCr+HizvToNBwcniCCvJC2c8pgybXXOaOCIKfTCbPZHHO7xWKBw+Ho9/H/+Mc/cM899wAADAYDfv/732PixIkp7w/LMigoMKb8+HjMZn1at5cNaMy5gcacG2jMsWy8BL1ODZEBdNrY06rXH4Jep4atxJz2c8pgyZXXOaOCoIE6//zzceqpp6KzsxObNm3C8uXLsXr1asyZMyel7YmiBKfTm5Z9U6lYmM16OJ0+CIKYlm1mOhozjXmkojHTmLvTqYCSfB0ONzlRXmiMuiQmSRJaOjwYW2aGTgV0dnqGYtdTNhJeZ7NZn/BMVkYFQWazGS6XK+Z2h8MBi8XS7+OtViusVisAOTHa4XDgscceSzkIAgCeT++bQBDEtG8z09GYcwONOTfQmOObcboNbZ0+NLR7olaHdboCyNOrMf10G0RBgghpiPZ6YHLldc6oi37V1dUxuT8ulwttbW2orq5OentnnHEGjhw5kq7dI4QQQuIaYzMpy+A9Ph4tnT54fDyqut1OMk9GzQTNnj0bTz31VFRu0KZNm8CyLGpra5Pe3s6dO1FZWZnu3SSEEEJijLGZUFmaRxWjs0hGBUGLFi3CmjVrsGzZMixduhQtLS149NFHsWjRoqgaQUuWLEFjYyM2b94MAHjvvffw2muv4dxzz0VZWRkcDgc2bNiADz/8EL/73e+GaziEEEJyDMswsFkNw70bJEEZFQRZLBa88MILePDBB7Fs2TIYjUb84Ac/wIoVK6LuJ4oiBEFQfq6srEQwGMTjjz+Ozs5OFBQUoKamBmvWrMHUqVOHehiEEEIIyQKMJEnZkaU1DARBhN2enkx+jmNRUGBEZ6cnJ5LNABozjXnkojHTmEeqkTBmq9WY8OqwjEqMJoQQQggZKhQEEUIIISQnURBECCGEkJxEQRAhhBBCchIFQYQQQgjJSRQEEUIIISQnURBECCGEkJxEQRAhhBBCchIFQYQQQgjJSRQEEUIIISQnURBECCGEkJxEQRAhhBBCchIFQYQQQgjJSRQEEUIIISQnURBECCGEkJxEQRAhhBBCchIFQYQQQgjJSRQEEUIIISQnURBECCGEkJxEQRAhhBBCchIFQYQQQgjJSRQEEUIIISQnURBECCGEkJxEQRAhhBBCchIFQYQQQgjJSRQEEUIIISQnURBECCGEkJxEQRAhhBBCchIFQYQQQgjJSRQEEUIIISQnccO9Az0dPHgQDz30EHbt2gWj0YiFCxdi+fLl0Gg0vT6mtbUVzz//POrq6nD06FGYTCZMmTIFt99+OyoqKoZw7wkhhBCSLTIqCHI4HFiyZAmqqqqwatUqtLS0YOXKlfD7/bj33nt7fdwXX3yBzZs344orrsDZZ5+Nzs5O/O///i+uvPJKbNiwAVardQhHQQghhJBskFFB0Nq1a+HxeLB69Wrk5+cDAARBwP3334+lS5eitLQ07uMmTZqEN998Exx3YjjnnHMOzj33XLz22mv4yU9+MhS7TwghhJAsklE5QVu3bsWMGTOUAAgA5s+fD1EUUVdX1+vjzGZzVAAEADabDVarFa2trYO1u4QQQgjJYhkVBNXX16O6ujrqNrPZjOLiYtTX1ye1rUOHDqGjowPjxo1L5y4SQgghZITIqMthTqcTZrM55naLxQKHw5HwdiRJwkMPPYSSkhJceumlA9onjktPnKhSsVH/5gIac26gMecGGnNuyLUxZ1QQlC6rVq3CRx99hGeeeQYGgyHl7bAsg4ICYxr3DDCb9WndXjagMecGGnNuoDHnhlwZc0YFQWazGS6XK+Z2h8MBi8WS0DbWrVuHJ598Ev/93/+NGTNmDGh/RFGC0+kd0DYiVCoWZrMeTqcPgiCmZZuZjsZMYx6paMw05pFqJIzZbNYnPJOVUUFQdXV1TO6Py+VCW1tbTK5QPJs3b8Z9992H2267DT/4wQ/Ssk88n943gSCIad9mpqMx5wYac26gMeeGXBlzRl30mz17NrZt2wan06nctmnTJrAsi9ra2j4f+/HHH+P222/HlVdeiWXLlg32rhJCCCEky2VUELRo0SIYjUYsW7YMH374IV555RU8+uijWLRoUVSNoCVLlmDevHnKzwcPHsSyZctQVVWFhQsXYvfu3cp/R48eHY6hEEIIISTDZdTlMIvFghdeeAEPPvggli1bBqPRiB/84AdYsWJF1P1EUYQgCMrPe/bsgcvlgsvlwtVXXx1138svvxwrV64ckv0nhBBCSPZgJEmShnsnMpUgiLDbPWnZFsexKCgworPTkxPXWQEaM4155KIx05hHqpEwZqvVmHBidEZdDiOEEEIIGSoUBBFCCCEkJ1EQRAghhJCcREEQIYQQQnISBUGEEEIIyUkUBBFCCCEkJ1EQRAghhJCcREEQIYQQQnISBUGEEEIIyUkUBBFCCCEkJ1EQRAghhJCcREEQIYQQQnISBUGEEEIIyUkUBBFCCCEkJ1EQRAghhJCcREEQIYQQQnISBUGEEEIIyUkUBBFCCCEkJ1EQRAghhJCcREEQIYQQQnISN9w7QAghhJCRTZQktHb64Avw0Gs5lBTowTLMcO8WBUGEEEIIGTxHml2o29eEpnYvQoIItYpFWZEBtWeWYYzNNKz7RkEQIYQQQgbFkWYXXq87BLcvhAKTFlq1CoGQgCPNLnQ4/Phu7dhhDYQoJ4gQQgghaSdKEur2NcHtC6Gs0AC9lgPLMtBrOZQVGuD2hbBtXxNESRq2faQgiBBCCCFp19rpQ1O7FwUmLZge+T8Mw6DApEVjuxetnb5h2kMKggghhBAyCHwBHiFBhFativt7rVqFkCDCF+CHeM9OoCCIEEIIIWmn13JQq1gEQkLc3wdCAtQqFnrt8KUnUxBECCGEkLQrKdCjrMiATlcAUo+8H0mS0OkKoLzIgJIC/TDtIQVBhBBCCBkELMOg9swy5OnVaOrwwhfgIYoSfAEeTR1e5OnVmHlm2bDWC6IgiBBCCCGDYozNpCyD9/h4tHT64PHxqOp2+3CiOkGEEEIIGTRjbCZUluZlZMXojJsJOnjwIH784x9jwoQJqK2txaOPPopgMNjv41566SUsXboU06dPR01NDTZt2jQEe0sIIYSQ/rAMA5vVgLFlZtishowIgIAMC4IcDgeWLFmCUCiEVatWYcWKFVi3bh1WrlzZ72P/9a9/obOzE3PmzBmCPSWEEEJItsuoy2Fr166Fx+PB6tWrkZ+fDwAQBAH3338/li5ditLS0j4fy7Isjh8/jtdee21odpgQQgghWSujZoK2bt2KGTNmKAEQAMyfPx+iKKKurq7Px7JsRg2FEEIIIRkuo2aC6uvrccUVV0TdZjabUVxcjPr6+mHZJ45LT3ClUrFR/+YCGnNuoDHnBhpzbsi1MWdUEOR0OmE2m2Nut1gscDgcQ74/LMugoMCY1m2azcNXFGq40JhzA405N9CYc0OujDmjgqBMI4oSnE5vWralUrEwm/VwOn0QBDEt28x0NGYa80hFY6Yxj1QjYcxmsz7hmayMCoLMZjNcLlfM7Q6HAxaLZRj2COD59L4JBEFM+zYzHY05N9CYcwONOTfkypgz6qJfdXV1TO6Py+VCW1sbqqurh2mvCCGEEDISZVQQNHv2bGzbtg1Op1O5bdOmTWBZFrW1tcO4Z4QQQggZaTLqctiiRYuwZs0aLFu2DEuXLkVLSwseffRRLFq0KKpG0JIlS9DY2IjNmzcrt33++edoaGiA3W4HAOzZswcAYLVaMXXq1JT2h2UZWK2UGD1QNObcQGPODTTm3JDNY2bZxKtRM1LP/vbD7ODBg3jwwQexa9cuGI1GLFy4ECtWrIBGo1Hus3jxYjQ0NOCdd95Rbrv77rvxz3/+M2Z7U6dOxZo1a4Zk3wkhhBCSPTIuCCKEEEIIGQoZlRNECCGEEDJUKAgihBBCSE6iIIgQQgghOYmCIEIIIYTkJAqCCCGEEJKTKAgihBBCSE6iIIgQQgghOYmCIEIIIYTkJAqCCCGEEJKTKAgihBBCSE6iIIgQQgghOYmCIEIIIYTkJG64dyAXHDx4EA899BB27doFo9GIhQsXYvny5dBoNMO9a0l588038frrr+OLL76A0+nEmDFjsHjxYlxxxRVgGAYAsHjxYnzyyScxj924cSPGjRun/OxyufDwww/j7bffRigUwne+8x3cc889KCkpGbLxJOLVV1/Ff/7nf8bcftNNN+HOO+9Ufv773/+OZ555Bo2NjRg7dixWrFiB8847L+ox2TLm3l5DAPjd736HSy+9NKtf5yNHjuAvf/kL9uzZgwMHDqC6uhobNmyIuV86X9PPPvsMjzzyCL766isUFhbi6quvxk033aR8bgZbf2N2u9147rnn8P777+Pw4cPQaDQYP348VqxYgZqaGuV+x48fx/nnnx+z/bPPPhvr1q2Lui3Txwyk/3iV6WPu7fUDAI1Gg88//7zP+2Xi6zxQFAQNMofDgSVLlqCqqgqrVq1CS0sLVq5cCb/fj3vvvXe4dy8pzz//PCoqKnD33XejoKAA27Ztw69//Ws0NzfjZz/7mXK/c845B3fddVfUY0eNGhX18/Lly/Htt9/ivvvug1arxR/+8AfcdNNNeOWVV8Bxmfe2fOaZZ2AymZSfS0tLlf9/44038Otf/xq33HILpk+fjo0bN+JnP/sZXnrpJUyYMEG5X7aM+Te/+Q3cbnfUbS+88AL+/e9/Y8aMGcpt2fo6HzhwAO+//z7OPvtsiKIISZJi7pPO1/TIkSO44YYbUFtbi+XLl2P//v347W9/C5VKhRtuuCEjxtzY2IiXX34ZV1xxBZYvX45AIIBnn30WV111FV555ZWogAAAbr/9dkybNk352Wg0Rv0+G8Ycka73cTaMuaSkBC+//HLUbZIk4cYbb8T06dNjtpcNr/OASWRQPfXUU9KECROkzs5O5ba1a9dKp512mtTc3Dx8O5aCjo6OmNvuuece6ZxzzpEEQZAkSZKuu+466eabb+5zO5999pl0yimnSB988IFy28GDB6WamhrpjTfeSO9OD9Arr7winXLKKXHHHnHhhRdKt99+e9RtV111lXTjjTcqP2fTmOOZO3eudNNNNyk/Z/PrHHmvSpIk3XXXXdKll14ac590vqa//vWvpfPOO08KBALKbY8//rg0efLkqNsGU39j9ng8ktfrjbrN7XZLU6dOlR544AHltmPHjkmnnHKK9Oabb/b597JhzJKU3vdxtoy5p48++kg65ZRTpI0bNyq3ZdPrPFCUEzTItm7dihkzZiA/P1+5bf78+RBFEXV1dcO3YymwWq0xt5122mlwu93wer0Jb2fr1q0wm82ora1VbquursZpp52GrVu3pmVfh8qxY8dw+PBhzJ8/P+r2Sy65BNu3b0cwGASQ3WP+7LPPcPz4cSxYsCCpx2XqmFm278Neul/TrVu34vzzz4+6/H3JJZfA6XRi165d6RhSv/obs8FggF6vj7rNaDRi9OjRaG1tTfrvZcOYEzWSXud4NmzYgLy8PMydOzfpx2bCmAeKgqBBVl9fj+rq6qjbzGYziouLUV9fP0x7lT47d+5EaWkp8vLylNs++eQTTJgwAWeddRauu+46fPrpp1GPqa+vx9ixY2OuGVdXV2fsc3LZZZfhtNNOw/nnn4+nn34agiAAgLK/Y8eOjbr/uHHjEAqFcOzYMeV+2TbmiA0bNsBgMMTkCIzE1xlI72vq9XrR1NQUcwyorq4GwzAZ/Tw4nU4lr6Sn++67D6eddhpmzJiBe+65B11dXcrvsm3M6XgfZ9uYI0KhEP79739j3rx50Gq1Mb8fSa9zbzInEWGEcjqdMJvNMbdbLBY4HI5h2KP02bFjBzZu3Bh1PX3KlClYuHAhqqqq0Nrair/85S/48Y9/jDVr1mDixIkA5Oeke35NhMViwb59+4Zs/xNRXFyMn//85zj77LPBMAzeeecd/OEPf0BLSwvuvfde5TXs+RpHfo78PpvG3B3P83jzzTcxd+5cGAwG5faR9jp3l87X1OVyxd2WRqOBXq/P6GPAY489BoZhcPXVVyu3aTQaXH311Zg1axbMZjP27NmDp556Cvv27cPf//53qNXqrBpzut7H2TTm7rZu3Yquri5cdtllUbePtNe5LxQEkZQ0NzdjxYoVmDZtGn70ox8pt992221R9zv33HNx2WWX4f/9v/+HP//5z0O9mwP2ne98B9/5zneUn2fNmgWtVosXXngBt9xyyzDu2dCoq6uD3W6POUiOtNeZRHvllVewbt06rFy5EjabTbm9pKQE9913n/Lz1KlTcfLJJ2Pp0qXYvHkzLrnkkmHY29Tl+vt4/fr1KCoqilrwAIy817kvdDlskJnNZiVi7s7hcMBisQzDHg2c0+nETTfdhPz8fKxatarP69AGgwFz5szBF198odxmNptjVh8B2fOczJ8/H4Ig4KuvvlL2t+dr7HQ6AUD5fbaOecOGDcjPz8esWbP6vN9Iep3T+ZpGZhB6bisYDMLn82Xk8/D+++/j3nvvxa233orLL7+83/vPmTMHBoNBee2zccwRqb6Ps3HMHo8H7777LubPnw+VStXv/UfS69wdBUGDLF7+g8vlQltbW9xr7ZnO7/dj6dKlcLlcMcvGE1VdXY1Dhw7FLN88dOhQ1j0nkf3t+RrX19dDrVajsrJSuV+2jdnv9+Ptt9/GxRdfDLVanfTjs3HMQHpfU4PBgLKysphtRR6Xac/D7t278Ytf/ALf+9738Itf/CKlbWTbmPszEl9nANi8eTP8fn/SCx4isnHM8VAQNMhmz56Nbdu2Kd8iAWDTpk1gWTZqtUE24Hkey5cvR319PZ555pmoWjm98Xq9eO+993DWWWcpt82ePRsOhwPbt29Xbjt06BC+/PJLzJ49e1D2PZ02btwIlUqF008/HZWVlaiqqsKmTZti7jNjxgxl1UQ2jvmdd96B1+tN6CA5kl7ndL+ms2fPxpYtWxAKhaK2ZTablbyTTPDtt99i6dKlmD59Ou6///6EH/fuu+/C6/XGvPbZMOaeBvI+zrYxb9iwAaNHj8bZZ5+d0P1H0uvcHeUEDbJFixZhzZo1WLZsGZYuXYqWlhY8+uijWLRoUUJBRCa5//778e677+Luu++G2+3G7t27ld+dfvrp2Lt3L5555hnMmzcPFRUVaG1txXPPPYe2tjY88cQTyn0nTpyIWbNm4Ve/+hXuuusuaLVa/P73v0dNTQ0uvPDCYRhZ72644QZMmzZNqZq7ZcsWrFu3Dj/60Y9QXFwMAPj5z3+OO++8E6NHj8a0adOwceNG7N27Fy+++KKynWwac8T69etRXl6OSZMmRd2+Y8eOrH6dfT4f3n//fQBAQ0MD3G63EvBMnToVVqs1ra/pDTfcgPXr1+OOO+7A1VdfjW+++QZ/+ctfsGLFiiGrGt/fmCVJwg033ACtVoslS5ZEJa7n5eXhpJNOAgCsXLkSDMNgwoQJMJvN2Lt3L55++mmceeaZuOCCC7JqzJEvc+l6H2fDmCNlTux2O7Zv346bbrop7nay6XUeKEbqOcdH0u7gwYN48MEHo9pmZNObJGLu3LloaGiI+7stW7ZAEAQ88MAD2L9/P7q6uqDX6zFx4kT87Gc/w/jx46PuHylDv3nzZvA8j1mzZuGee+7JuMDwoYcewgcffIDm5maIooiqqipceeWVWLx4cdSS2b///e/485//rLRYuP3223ttsZDpYwbkfIfa2losWbIE//Ef/xH1uyNHjmT169xX64C//vWvSoXcdL6mn332GVauXImvvvoKVqsV11577ZC2FuhvzACiFjh0N3XqVKxZswaA/Jz87W9/w5EjR+D3+1FaWooLLrgAt912W1SZDCDzx2yz2dL+Ps70MUfe2y+99BIeeOCBmPYgEdn0Og8UBUGEEEIIyUmUE0QIIYSQnERBECGEEEJyEgVBhBBCCMlJFAQRQgghJCdREEQIIYSQnERBECGEEEJyEgVBhBBCCMlJFAQRQgghJCdREEQIGTbt7e247bbblNYkzz///HDv0qCqqanBqlWrhns3CCFhFAQRQmJ8/fXXqKmpUTpEP//885g7d27a/87DDz+MDz74ADfffDMeffRRfOc73+nz/l6vF08++SQWLFiAs88+G5MmTcI111yD1157LabL93B5//33KdAhJEtQA1VCSIw9e/YgPz8fY8eOBQDs2rUr4W7Tyfjoo49w/vnn44Ybbuj3vu3t7bj++utx8OBBXHLJJbjuuusQCATw73//G3fddRc++OADPPbYY2DZ4f1u9/777+Oll17Cz3/+85jf7d27FyqVahj2ihASDwVBhJAYe/fuxVlnnaU0Qdy9ezd+/OMfp/3vdHR0wGw2J3Tfu+66CwcPHsTq1aujmkT+6Ec/wiOPPIJnn30Wp512Gm688ca072e6aLXa4d4FQkg3dDmMEAJA7hxvt9tht9uxd+9enHzyybDb7Thw4ACam5sxZswY2O12eDyefrd17Ngx3HbbbZg6dSrOPvts/PCHP8R7772n/P7VV19FTU0NJEnCSy+9hJqaGtTU1PS6vd27d+PDDz/E5ZdfHrdL9h133IGqqir8+c9/ht/vBwB8/PHHqKmpwccffxx13+PHj6Ompgavvvpq1O0HDx5U9vmss87C97//fWzZsiXqPqFQCKtXr8aFF16Is846C9OmTcPVV1+Nuro6AMDdd9+Nl156CQCUMXUfV7ycoC+//BI33ngjzjnnHEycOBFLlizB7t27o+4Teb527tyJhx9+GNOnT8eECROwbNky2O32qPt+/vnnuOGGGzBt2jSMHz8ec+fOxX/+53/2+twSkstoJogQAgC4/PLL0dDQoPz8zTff4Nlnn1V+vuWWW5T7rVy5stfttLe3Y9GiRfD5fFi8eDEKCgrwz3/+Ez/96U/xxz/+EfPmzcOUKVPw6KOP4pe//CVqa2uxcOHCPvft3XffBQB873vfi/t7juNw2WWXYfXq1di1axdmzJiR6LABAAcOHMDVV1+N0tJS3HTTTTAYDHjzzTexbNkyrFq1CvPmzQMArF69Gk8//TSuvPJKjB8/Hm63G/v27cMXX3yB2tpaXHXVVWhtbUVdXR0effTRhP7utddeC6PRiBtvvBEcx+Hll1/G4sWL8eKLL8ZcgnzooYdgNpvxs5/9DA0NDXjhhRfwwAMP4A9/+AMAeWbthhtuQEFBAW6++WaYzWYcP34cmzdvTur5ICRXUBBECAEAPPbYYwgEAvj000/xpz/9Cf/7v/8LjuPw3HPPwW6344477gAAlJSU9LmdP/3pT2hvb8dLL72EyZMnAwCuvPJKfPe738XDDz+M888/H5WVlaisrMQvf/lLVFVV9RsEffvttwCAU089tdf7RH737bffJh0E/fd//zfKysrwyiuvQKPRAACuueYaXH311fjtb3+rBEHvvfce5syZgwcffDDudiZOnIiqqirU1dX1OyYA+MMf/oBQKIS//e1vqKysBCAHehdffDEee+wxvPjii1H3z8/Px7PPPqtcphRFEWvWrIHL5YLJZMKuXbvgcDjwl7/8BWeddZbyuBUrViT1fBCSK+hyGCEEADBp0iTMnDkTXq8XZ511FmbPno2ZM2eiqakJ5513HmbOnImZM2fipJNO6nM777//PsaPH68EQABgNBpx1VVXoaGhQQlokhG5BGc0Gnu9T+R3iVyu666rqwsfffQR5s+fD7fbrVwS7OzsxKxZs3D48GG0tLQAAMxmMw4cOIDDhw8nPYaeBEFAXV0dLrjgAiUAAuQg87LLLsPOnTvhdrujHvPDH/5QCYAAYPLkyRAEQZnBM5lMAORgLRQKDXgfCRnpKAgihMDlcikn/+3bt2P8+PGw2+04dOgQDhw4gFNPPRV2ux0ul6vfbTU2Niqryrqrrq5Wfp+sRAKcyO8KCwuT2vbRo0chSRKeeOIJzJgxI+q/SP5OR0cHAOC2226Dy+XCRRddhAULFuCRRx7B119/nfR4AMBut8Pn88V9rsaNGwdRFNHU1BR1e3l5edTPkaRyp9MJAJg6dSouuugirF69GtOnT8dPf/pTvPLKKwgGgyntIyEjHV0OI4Tg1ltvxSeffKL8vH//frzwwgvKz8uWLQMgn2TXrFkz5Pt30kkn4e2338b+/fsxZcqUuPfZv38/AGDUqFEAEDVj0p0oinF//slPftJrnaLRo0cDAKZMmYLNmzdjy5YtqKurwz/+8Q+88MILuP/++3HllVcmP7Ak9bb8P1IjiWEY/PGPf8Tu3bvx7rvv4oMPPsCvfvUrPPfcc3j55Zf7nEkjJBdREEQIwV133QWn04ldu3bhySefxFNPPQWO47BmzRq0tLTgzjvvBICElrOXl5fj0KFDMbdHCi/2nM1IxHnnnYennnoKr732WtwgSBAErF+/HkVFRcrvI/vac/aqe/I3AOVSlFqtxsyZM/vdl/z8fFxxxRW44oor4PF4cN1112HVqlVKENRb8NWT1WqFXq/v9bliWRZlZWUJbaunCRMmYMKECVixYgXWr1+PO++8Exs3bhySQI2QbEKXwwghOPPMMzFz5kwIgoCTTz5ZyQfq6OhQcoFmzpyJM888s99tzZkzB3v37sWuXbuU27xeL9atW4eKiop+c4rimTBhAmbNmoVXX31VWSnW3e9//3scPnxYWWEFABUVFVCpVPj000+j7vu3v/0t6ufCwkJMnToVL7/8MlpbW2O23X0JemdnZ9TvjEYjRo8eHXW5Sa/XAzhxiao3KpUKtbW12LJlC44fP67c3t7ejg0bNmDSpEnIy8vrcxs9ORyOmMrZp512GgDQJTFC4qCZIEKI4rPPPsPEiRMBAIFAAF9++SWWLl2a1DZuvvlmvPHGG7jpppuwePFiWCwWvPbaazh+/DhWrVqVckXnRx55BEuWLMGtt96Kyy67DJMnT0YwGMS///1vfPLJJ/jud7+L66+/Xrm/yWTCxRdfjBdffBEMw6CyshLvvfeekt/T3W9+8xtcc801WLBgAX74wx+isrIS7e3t2L17N5qbm/H6668DAC699FJMnToVZ5xxBvLz8/H555/jrbfewnXXXads64wzzgAgL2efNWsWVCoVLr300rhjWr58ObZt24ZrrrkG11xzDVQqFV5++WUEg0H8x3/8R9LP0T//+U/87W9/wwUXXIDRo0fD4/Fg3bp1yMvLw+zZs5PeHiEjHQVBhBAA8iWlPXv24PLLLwcA7Nu3D6FQSAmKElVUVIS1a9cqS7wDgQBqamrw1FNP4dxzz015/4qKirBu3To899xz2LRpE9566y0EAgEAck7TL37xi5jH3HPPPeB5HmvXroVGo8HFF1+MX/7yl7jsssui7nfSSSfhlVdewerVq/HPf/4TXV1dsFqtOP3005V8KABYvHgx3nnnHdTV1SEYDKK8vBzLly+Pavtx4YUXYvHixXjjjTfw+uuvQ5KkXoOgk08+GS+99BIef/xxPP3005AkCePHj8djjz2WUpuSqVOn4vPPP8fGjRvR3t4Ok8mE8ePH47e//W3UCjRCiIyRMqXrICGEJKmlpQWLFi0Cz/N4+eWXU8o3IoTkLsoJIoRkrdLSUjzzzDMIBoO46aab4HA4hnuXCCFZhGaCCCGEEJKTaCaIEEIIITmJgiBCCCGE5CQKggghhBCSkygIIoQQQkhOoiCIEEIIITmJgiBCCCGE5CQKggghhBCSkygIIoQQQkhOoiCIEEIIITmJgiBCCCGE5KT/DzWABufft7RpAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots()\n",
        "sns.regplot(data=itemnum_ans, x='num_items', y='answerCode',\n",
        "           line_kws={\"color\": \"orange\"}, scatter_kws={'alpha':0.6}, ax=ax)\n",
        "\n",
        "ax.set_title('# of Questions - Answer Rate')\n",
        "ax.set_xlabel('# of Questions')\n",
        "ax.set_ylabel('Answer Rate')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "pursuant-recipient",
      "metadata": {
        "id": "pursuant-recipient"
      },
      "source": [
        "## (b)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "exposed-transport",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-05-18T02:37:16.047567Z",
          "start_time": "2021-05-18T02:37:16.008187Z"
        },
        "id": "exposed-transport"
      },
      "outputs": [],
      "source": [
        "itemnum_ans = stu_groupby.groupby('assessmentItemID').mean()\n",
        "bins = 300\n",
        "itemnum_ans['bins'] = pd.cut(\n",
        "    itemnum_ans.index,\n",
        "    [i * (itemnum_ans.index.max() - itemnum_ans.index.min()) // bins for i in range(bins)]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "fixed-transcript",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-05-18T02:37:16.189076Z",
          "start_time": "2021-05-18T02:37:16.182074Z"
        },
        "id": "fixed-transcript",
        "outputId": "231163ca-3884-4844-e706-39b2ba707efa"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_373483/894362937.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
            "  itemnum_ans = itemnum_ans.groupby('bins').mean()\n"
          ]
        }
      ],
      "source": [
        "itemnum_ans = itemnum_ans.groupby('bins').mean()\n",
        "itemnum_ans['mid'] = list(map(lambda x: (x.left + x.right)//2, itemnum_ans.index))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "republican-monitoring",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-05-18T02:37:16.633297Z",
          "start_time": "2021-05-18T02:37:16.335299Z"
        },
        "id": "republican-monitoring",
        "outputId": "3d92ecdc-c9ad-4a6a-d834-ffebce46e627"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Answer Rate')"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots()\n",
        "sns.regplot(data=itemnum_ans, x='mid', y='answerCode',\n",
        "           line_kws={\"color\": \"orange\"}, scatter_kws={'alpha': 0.6}, ax=ax)\n",
        "\n",
        "ax.set_title(f'# of Items - Answer Rate | bins={bins}')\n",
        "ax.set_xlabel('# of Items')\n",
        "ax.set_ylabel('Answer Rate')"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "V100",
      "machine_shape": "hm",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.10.13"
    },
    "toc": {
      "base_numbering": 1,
      "nav_menu": {},
      "number_sections": false,
      "sideBar": true,
      "skip_h1_title": false,
      "title_cell": "Table of Contents",
      "title_sidebar": "Contents",
      "toc_cell": false,
      "toc_position": {},
      "toc_section_display": true,
      "toc_window_display": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}