Ex4.ipynb

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        "p6y_AfXIb9pc",
        "C4GHGw2IDMbu",
        "2ZOSWPhGDZwS",
        "TeNAqwe7DetO"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Machine learning - final project**\n",
        "## **Clinical Data**"
      ],
      "metadata": {
        "id": "fuZPymEY_Ml1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Names and IDs:**\n",
        "\n",
        "1.   Natalie Morad\n",
        "2.   Stav Nimrod"
      ],
      "metadata": {
        "id": "aPe1pkTHAyPN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Assignment:"
      ],
      "metadata": {
        "id": "AkD_s20EA2Ui"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this exercise, you are given (a part of) a data set from a clinical trial. You will need to use 3 types of prediction models and compare between.\n",
        "The first model should be a linear classifier.\n",
        "The second one should be an ensemble model.\n",
        "The third should be a DL one."
      ],
      "metadata": {
        "id": "QMqAxyn7_pOg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**imports here**"
      ],
      "metadata": {
        "id": "e7oy7jItJaEa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# libs\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# preprocessing\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn import set_config\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "# metrics\n",
        "from sklearn.metrics import make_scorer, roc_auc_score, recall_score, precision_score, accuracy_score\n",
        "from tensorflow.keras.metrics import Recall, Precision, AUC\n",
        "\n",
        "# models\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout\n",
        "from keras.callbacks import EarlyStopping"
      ],
      "metadata": {
        "id": "iK3UEQ-6JYhg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Loading the data**"
      ],
      "metadata": {
        "id": "eFykWEtnBRac"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 95
        },
        "id": "rW862puuLdBV",
        "outputId": "90b7c7c1-37ca-426d-cbb8-8447136ab3c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-8d91a360-d956-49bf-8445-901a72275350\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-8d91a360-d956-49bf-8445-901a72275350\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving ex4_data.csv to ex4_data.csv\n",
            "User uploaded file \"ex4_data.csv\" with length 1546386 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/ex4_data.csv')"
      ],
      "metadata": {
        "id": "31XGXskAL0v6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "qNhMBpFaI12N",
        "outputId": "bdabaab8-9098-4166-b17c-124b7656642b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-2fa2e357-65b3-4822-88a1-cb1977dbc724\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>INTENSIVE</th>\n",
              "      <th>NEWSITEID</th>\n",
              "      <th>RISK10YRS</th>\n",
              "      <th>INCLUSIONFRS</th>\n",
              "      <th>SBP</th>\n",
              "      <th>DBP</th>\n",
              "      <th>N_AGENTS</th>\n",
              "      <th>NOAGENTS</th>\n",
              "      <th>SMOKE_3CAT</th>\n",
              "      <th>ASPIRIN</th>\n",
              "      <th>EGFR</th>\n",
              "      <th>SCREAT</th>\n",
              "      <th>SUB_CKD</th>\n",
              "      <th>RACE_BLACK</th>\n",
              "      <th>AGE</th>\n",
              "      <th>FEMALE</th>\n",
              "      <th>SUB_CVD</th>\n",
              "      <th>SUB_CLINICALCVD</th>\n",
              "      <th>SUB_SUBCLINICALCVD</th>\n",
              "      <th>SUB_SENIOR</th>\n",
              "      <th>RACE4</th>\n",
              "      <th>CHR</th>\n",
              "      <th>GLUR</th>\n",
              "      <th>HDL</th>\n",
              "      <th>TRR</th>\n",
              "      <th>UMALCR</th>\n",
              "      <th>BMI</th>\n",
              "      <th>STATIN</th>\n",
              "      <th>SBPTERTILE</th>\n",
              "      <th>EVENT_PRIMARY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Regular</td>\n",
              "      <td>74.0</td>\n",
              "      <td>29.732061</td>\n",
              "      <td>True</td>\n",
              "      <td>145</td>\n",
              "      <td>80</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>67.69</td>\n",
              "      <td>1.11</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>60</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>WHITE</td>\n",
              "      <td>155</td>\n",
              "      <td>81</td>\n",
              "      <td>36</td>\n",
              "      <td>92</td>\n",
              "      <td>5.80</td>\n",
              "      <td>33.115201</td>\n",
              "      <td>True</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Regular</td>\n",
              "      <td>8.0</td>\n",
              "      <td>29.677619</td>\n",
              "      <td>True</td>\n",
              "      <td>138</td>\n",
              "      <td>71</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>60.64</td>\n",
              "      <td>1.17</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>75</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>WHITE</td>\n",
              "      <td>243</td>\n",
              "      <td>107</td>\n",
              "      <td>61</td>\n",
              "      <td>188</td>\n",
              "      <td>5.45</td>\n",
              "      <td>28.842380</td>\n",
              "      <td>True</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Intensive</td>\n",
              "      <td>25.0</td>\n",
              "      <td>17.443819</td>\n",
              "      <td>True</td>\n",
              "      <td>143</td>\n",
              "      <td>92</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>68.44</td>\n",
              "      <td>1.09</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>62</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>WHITE</td>\n",
              "      <td>180</td>\n",
              "      <td>116</td>\n",
              "      <td>47</td>\n",
              "      <td>125</td>\n",
              "      <td>13.33</td>\n",
              "      <td>33.643060</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Regular</td>\n",
              "      <td>96.0</td>\n",
              "      <td>8.627849</td>\n",
              "      <td>False</td>\n",
              "      <td>123</td>\n",
              "      <td>68</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>71.94</td>\n",
              "      <td>0.78</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>75</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>WHITE</td>\n",
              "      <td>234</td>\n",
              "      <td>93</td>\n",
              "      <td>89</td>\n",
              "      <td>109</td>\n",
              "      <td>6.12</td>\n",
              "      <td>29.337871</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Intensive</td>\n",
              "      <td>42.0</td>\n",
              "      <td>23.751437</td>\n",
              "      <td>True</td>\n",
              "      <td>126</td>\n",
              "      <td>51</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>50.19</td>\n",
              "      <td>1.36</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>81</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>WHITE</td>\n",
              "      <td>126</td>\n",
              "      <td>108</td>\n",
              "      <td>39</td>\n",
              "      <td>84</td>\n",
              "      <td>28.78</td>\n",
              "      <td>36.660286</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fa2e357-65b3-4822-88a1-cb1977dbc724')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2fa2e357-65b3-4822-88a1-cb1977dbc724 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2fa2e357-65b3-4822-88a1-cb1977dbc724');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   INTENSIVE  NEWSITEID  RISK10YRS  ...  STATIN  SBPTERTILE  EVENT_PRIMARY\n",
              "0    Regular       74.0  29.732061  ...    True           3          False\n",
              "1    Regular        8.0  29.677619  ...    True           2          False\n",
              "2  Intensive       25.0  17.443819  ...   False           2          False\n",
              "3    Regular       96.0   8.627849  ...   False           1          False\n",
              "4  Intensive       42.0  23.751437  ...   False           1          False\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![צילום מסך 2022-01-04 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zn0VQSegBrg9J+PnhPW9bbSLSLxK+oo8UcsMnhLVo/IMn3DKWtgI1PJ3OQMAnOAaera7h8K8jsvEHhnR/Fuky6Vrmk2Os6XKVMllqFsk8L7SCuUcFTggEccEVY0nSbHQdNttO0yyt9O0+2QRQWlpEsUUSDoqooAUD0ArN8E+MLPx54atdc0+KeG0uGlREuVVZAY5GjOQCR1Q456YrdpC00ZVt9Ms7S8uruC0ghursqbieONVeYqu1S7AZbA4Geg4rE8PfDLwf4Rvby80Lwnoei3d4pS5uNP06GCSdSckOyKCwJ5we9dLWF468YWfw/8G614l1GKeaw0m0kvJ47VVaVkRSxChiATgcZI+tBW+hX8N/DPwh4NZG0DwpomhtGzujabp0NuVZlCsRsUYJCqCe4AHanD4b+EhDrUI8LaKItcfzNVj/s+HbftkndONv705JOXz1NW7PxILzxJe6ONM1GIW1rDdf2hLb7bSbzGceXHJn5pF8vLLjgOnrWtI/lxs21m2jO1Rkn2FEtncmPl/X9XOe034b+EtF0yfTdP8LaLY6dcW5s5rS20+GOKSAs7GJkVQCm6SQ7SMZdjjk1pa34b0nxNo82kavpdlqukzBVksb63SaBwpBUNGwKkAgEZHBApnhfXv+En8O6fq39nX+k/bIVm+w6pB5N1BkfckTJ2sO4zWpTlfqNd0chN8HvAVx4et9Bl8EeHJdCt5jcw6Y+k25to5SCDIsRTaGIJ+YDPJrZs/CWh6c2mNaaNp9s2lwNbWDQ2saG0hYANHFgfIpCqCq4B2j0qC98YWVj400rwzJFOb/UrO5vYZFVfKVIGiVwxzkEmZMYBHByRxndo1tfv/wAN/wAALBRRRSAKKKy/EXiXTfCenpfarc/ZbV7iG1WTy2fMs0qxRrhQTy7qM9BnJwOaPIDUorCtfGFleeNtR8LpFONQsbG31CWRlXyjHM8qKFOc7gYWzkAYI5PON2gNnb+u4UVma14j07w8+nJqFx9nbULtLG2Gxm8yZgxVflBxkK3JwOOtadAeQUUUUAFFFFABRRRQAUUVzvjbxxY+A9P0+81CK4mivdStNLjFsqsRLcTLEjHcw+UM4JIycZwD0oWrSXXT7w8zoqKgvr630uxuLy7mS3tbeNppppDhURQSzE9gACawfDfju08VX6Q2GnasLKXToNSh1O6sJLe2mSVnAjUyAMJAE3MjKCA6nvR1t/XX/Jh5/wBdP80dLRRWX4b8Tab4u01tQ0m5+12i3E9qZPLZP3sMrwyrhgD8rxuM9DjIyMGgDUooooAKKzPE+uf8Iz4d1LVv7PvtW+xW7z/YdLh866n2gnZEmRuc9AM8mtCGTzoY5NjJuUNtcYYZHQj1oAfRRRQAUUUUAFFFFABRRRQAUVh+JvF1n4Uk0ZLuOeQ6rqEemweSqnbI6swLZIwuEPIyenFblC11Xp/X3oOtv6/rQKKzPE3iTT/B+g32tarK9vptjEZriaOF5jHGPvMVRS2AOSQOACTwCa0IZkuIUlidZI3UMrqchgeQQfSgB9FFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfNfxI/5GT9o7/sQ7P/ANE6lX0pXNal8OPDur3PiS4u9O82bxFYJpmqN58i/aLZFkVU4b5cCaTlcH5uvAxM1zQlFdUzWlNQkpPuvwaf6HhF1MnwZ8LuHDReDvG2isfMJ/d6frDWvQ8/Kl0M+3mr6y10Pi7/AJN7+F//AGEfCv8A6WWleteIvh/4e8XeCbjwjrGmR3/h24tltJLGVmwY1A2jcDuBG0EMDuBAIORmnXngTQ7/AMPaXoc9jv0vTJLSa0t/OkHltbOjwHcG3Ha0aHknOOc5NdCmvauf96D+UW3b11sulkl5vkUHyxXaMk/WSSv+Gv39dPMPi/b65dfHj4WR+H9Q0/TNR+wa0RcalYPeRbdtrkeWk0JyfXfx6Gqfxc8Iv4u8WfCrw54vltNX+3yarb3slhbPaRSA2TkFEaWRkK/KQd5IZQQRxj0/xp8L/D/j7UNLv9Wj1BL/AExZltLrTdWu9PliWXb5g328sbENsXgk9Kdp/wANdB0+XQ5hHfXlxosk01hc6jql1eTRNKhSQmSaRmcFSQA5IHbFYWThyv8ArW5u3s0eH2/i69ufiP8ADDwV4kmaTxb4Z8QzwTzSjB1G1bSb77PfL6iRQQ+PuyJIOMCuq0iPxF+znb6TpFw9t4k+G7XsGm2d0Mw6npHnzFIUlXlLmIO8Sb12SKDkrJgmvSta+G/hvxD4y0HxXqGlxz+IdCWZNPv97o8KyoUkUhSA4Kk8MDjJIwTmsjQfgh4S8O3dhPbQardCwKtZ22qa7f39tbMowjRwTzPGjKOFYKCvYitOa7i5fPz1/P8AJt203lpWaj8vx/z+fXU8qj8J3fjLwF8arTSxnW7XxdcajpbYyVvLeO2mg/N41H0JrofBPiaz+OHxQ0nxRYvv0Tw74fimhXOQL+/QOyt6PFAiAg8j7Sa9a0HwtpfhmTVJNNtfsz6nePqF2fMZ/MnZVVn+YnGQi8DA46VU8F/D/wAP/DvTryw8O6ZHpdpeXk1/PHGzMHnlbdI/zEkZPYcAAAAAYrFJqmodopfNR5X9/wCFlp2b1bfdt/Jy5l/l82eS/slWHi9fg/4Inudc0SXw4bA7NPi0aZLwDLbQbk3ZQkHknyRkcYHWvX/FXiC/8O2kM1h4Z1TxPJI+xrfSpbRHjGM7mNzPCuO3BJ56d653wj8D/CvgSaxbQzr1nDY5+z2TeJdSltEBz8v2d7gxFeTwVwOtd9W03zO4+rZV0y7lv9Otrmeyn02aWNXezumjaWEkZKMY2ZCR0O1mHoTXnvgf/kt3xP8A+uOk/wDomWvTKzLHw3p2m65qmsW1t5eo6mIVu5t7HzBEpWP5ScDAY9AM55zUPVkW2PlvQdLvvBvwB0fx1b+INb/tqx11fJt4tSmjsTbSauYWt3tVIhcFJXy7IZMkEONqgbevat4w8ceKviG1no3je7vtF1FtO0Kbw5rNpZ2Vk8cEUiNPBJeRfaGd5A7CaKRChUKCM59zm+GPhmbwavhR9N3aAsy3AtPPl++s4uAd+7d/rQGxnHbpxVTxJ8H/AAv4q1qfVry2v4L65jSG6k0zVruwW7RchVnSCVFmABIHmBuCR04qYqyt6/L4dfXR/fcvp5/nvp6a/hb0o/EbxhrvhH4Hat4iaGGx8R2ukCeRGAlitbgoAzNg4ZI2JJ5xhTziuG+M3w5tfB/wB+It4mt+INXvZfDV5FczarrNzdRTuY8mUQu5iiYkHiJEUBiAAMAe6XFjbXljJZz28U1nLGYZLeRA0boRgqVPBBHGOmK4m3+Bvg+30fVNJ+yajcaVqVk+nTWN3rV7cQpbNgNFCkkzCBcADEQTAVQOAMa83vuW39f1/SJh7vLfW3/A/H+up5d8XPFGtaYvxxWy1i+s/wCzvAtjd2PkXLp9lnb+0N0sWD8jnYmWXBOxfQVt32l33w4+JngNbbxFrmp/8JDbX9rqqanqU1xDPKlsJ1uEhZjHbuGRvlhVExIRtwBj0vWvhl4a8Rf8JB/aGm/aP7f0+PStS/fyr59tH5uyP5WG3HnS/MuD83J4GNDVPCOk61qmkaje2nnXmkmVrKTzHXyjJGY34BAbKEj5gcdRzUVPei+Td/8AB/z/AKshJbJ9P8o/qn/TZ4T8PNS1X4jaV8KvDeq69q0FhdeEP7b1Ca1v5ra81KZTBGqm6RxKFUyF22MCSU3EqSrdJ450fdqWjeBtCi8Ra5qNrZy3/wC/8YXulwxQtLhXub2JmuZWyGRFxIMA78fK1dxd/CDwpeeHNE0T+z5rWy0OMRaZLZX9xbXVogXZtjuY5FmUFflbD/MODmq+ofBXwnqa6X51vqSy6dDJbQ3UOtX0VxJFI4d0mmSYPOrMAxErOCcnuaqbUpeV2/W97fddL0W9x935K3ltf79X8zyX4N+INT8TeLPhVeaxdi/1BdA8QWsl0JvO80Q31rErGTavmEqgy+1dx5wM4r0X4x3v9pahoPhaytdV1HW9QE15b2thrs2iweXDsWSS4uoD5yovnLhI1cszLlcAsvR+GvhX4V8HvpDaLo8enDSYLm2sUhkfZBFcSLLMiqWxguikcfLjC4HFTeMvhzoXjyTT5tWhvFutPZ2tbzTtRubC5i3gB1E1vIj7WAGV3bTtUkZAwSfMku1/zb/Vfj6jjp/XkfP+jax4n1/w74J0G81+9spF8dX+h3U2n6vLPI9nFb3Z8g3ZVJJSAoUSMofKK2d43V1v/CES+JvjF4s8MyeKfFFn4e0vw1pZtbOz127ikWeSS9X7QZxJ5zOFjAIZyr8Fw5RNvpGi/CHwh4dt9Ng0zRY7KDTtRl1a1jhlkCxXUiOjyAbsHKyONp+X5s4zzW5beGNMs/EWoa9DbbNV1C3gtLm48xj5kUJkaNdpO0YM0nIAJ3c5wMS9YW62/HlS/O7+d9w66f17zf5WXytsfPngO61mPwd8HfG914l1vUNf8QXtraan9o1CU2dzDLBKCv2QN5CEbEYOiKxZcknc2e3/AGovDtrr/wAP9N+0y30Xla/pKr9i1C4tc77+BG3eU67iATgnO04ZcMAa7uz+G/hzT9C8P6Nb6d5em6BLHPpsPnyHyHjVlQ7i2WwGb7xPXmtHxR4X0zxnoV1o+sWxutPuNu+NZHiYFWDKyuhDIysqsGUgggEEEVTd3d/zXXp7rt96f3j0Urra1vz/AEaXyPGY/h1b6x8ftd0l9X1y20ez8J6YhjtNZuobmZ/tN5sd7pZBcMVAfrJht/zBsDGD4b8WX/jrwH4Q8Mv/AG54g8TtJqqxGPxDPo6S29jeG0+0Xl5b4mJwY/ljV9zvllwNy+96H4H0fw7qT6hZw3DX72UOnvdXd5NcyyQRM7RqzyuxYgyudxJY7uScCsC4+Bvg2axsLSPT7ywWwnuri2n03VbuzuI2uZDLOBNDKshR3bcULbchePlGDSyX9btr8/u07MHu5LfT7uVJ/iv62PCLfT7n4geB/AK+JtQ1KW+03x5daIJNP8Q3mfJjmuUUGeNommdVjVROyiQgE5BZs+6fGWx1Kx+EerP4fvLy21HSII7+2eO6k82X7M6ymJ3LbnEixlG3E7g5znNWIfgr4MtfB/8Awi1po/8AZ+iLenUY7ewup7Z4LgyGXzIpI3V4jvJI2MuASBgcV2NvZRW9jHaDfLAkYi/fyNKzKBj5mYlmOOpYknuTUe8oNJ63v8+WK/NN/MWjqczWm3y5m/yaXyPm/V/jBqmj+M9c8cxalNceC7zTbvS9KsGkzDLeW1slzHLGOgZ2N3ETzu8lOwpPFWm61Lok+h6fc6/qeu+E9AgXVNcvPGV1pFtBceQZDIPJWRrmY8SHz4zFgKN33lr16L4H+B4fB2geFV8PwnQNBvItQ02zaWRhbzxuZEcMW3HDM3DEggkEEcVJ4o+DXhLxlrNzqeradcT3F3AlreRRahcwW97Eu7alxBHIsU4G9h+8VuDjoBRNe6409P6evk227pdLWelgjunPXv57adrJJW03vpqeO+JdY1vVPD3hvxn4tXxLdeCrnwtaXN1eeDtWmsJdIumAa4up4YZY3niKOjDb5vliF/3XzEn1P46a1PpvwN8V6rpd/Nayw6W9xDe28rRyIAAd4cYI45zU198CvB2pRWkE9nqH2G2s4bAafHrV7HZzW8Qwkc1uswimXHB8xW3Dhsjiu01DSrPVNLuNNvLWG50+4ha3mtZEBjkjZSrIV6EEEjHpWs5J35V1bX3/ANdPvIgnaKm9lZ/cv68/LU8/8darqUfxR8B6bp2pNaNfWOqt5JdjFI6RReW7xgjeFZs89M9s15FJ4y1b4V+CfFTX0XinT/ihYeHJrt11rWJNS0zUCsiJJf2oaVoUVHKt5e2AqsoDRhcEe0aT8B/BWjzwTxafe3Vzb2s1lDcajq95eSxW8qBJIUeaV2VCFGFBAB5ABJNaHh/4S+GPDc15NDaXeoz3lsbOabXNSutUkNueWhDXUkhWNjyUUhWwMg4FZbXt1T/Xbtur73+SZSvaN+n3dPv627fNo8l0e58ZeBb6TV7Hw142i0qHR7ybV28X67a38VxPHD5kEsKR3k7RMzhlKxKkeJPu/KuMPx14QaL4dfDHxPc+KNa1XWNS8SeHrm/kudUkmtLxpbuFyI7cnyYVDEFfJRDhACSC2fdfCXwm8N+CbpbjS4tRykBtoYb7V7y9ht4jjKQxTyukS/KowirwAOnFZEf7O/gGOeyk/se4kj0+8jv7C1m1S7ktbCeOQSI1tA0pjgAYfdjVRtypG0kVpGXLUjJ7JxfpaTbXzVl8kieV8rXk/wAUkn8te++4vx+bzfh/FYOqvaaprGl6ZdowJV7ee+gjlQjB4ZGZT7NXEfFzxJq+l6l8Yo7LVb2zSw8BwXdmsFw6C2nLX2ZYwD8j/InzDB+RfQV674/8Jp448IajoxmNrLOivb3K8mCdGEkMoHfZIiNjvtrPHw90/wATabqE/ijSbX+2Nc0mPStZjsryZ4ZIV8w+UjEIdoM0mGCq3zc9BiPsuL8/xVvwab+ZvGSi035fhJN/etPkebXei3fgvx38Mox4j8Qan/wlS3Wl6yL3Vrho7g/YXnE8ce/ZbSB4ePIEYAkb2rgPDsN98P8A4Gq/heTxBLf6740u9GuGh1eSeeO3/ta6Vvs/2ubyYpnRdnmZUl5AxYvg19Qah4P0jVdQ0G+urTzbrQpWm06TzXHkO0TQscA4bMbsvzZ6564NYj/B3wlLY+IbCXTZZ9O152lvrGa+uHtvMLlzJFE0hSBy5L7ogjbsNncAauUk3otL/he9vR6/5GMY2gl1/WzV/wAn52PEvF0vjfwz8P8A4h/2fZ+LvB+hnS7eTTrrxFrMOoXtrfGfZIYpku7iTyyhjO13wCGxgMRXYajpWq+AfjD4e0nQ/EOrXLeJtE1Tz11vUJ72BbyDyHiuUjkYrFzK4McQRMEAKNq47y3+DvheHRdV0qWDUdRtNUWNLttU1i8vZnRGLIglmmeRVDEnarAZJOOTWr4i8B6F4s1C1vtUsjc3dra3VlDKs8kZSG4VVmX5WH3gi89Rjgis3v8AJ/in/n+C6pFR0ld7f8N/l+fdnznrOtXng/4V/EKzvZPGXhz4i2nhO5vJTqWvXF9bXJUYa8spfNZI8SfwoIXUOMxqNuOx8YePtW+FvizxpaTXk93/AG/pltfeGYrqdnUX7Mlk9tGDkqplktHwOMzOfWu/h+B/g9NN1qxnsr7U4tYs2068l1bV7y+ne2OcwrNNK8kaEknajKM89Rmt/WvBOieItV0PUtRsFub3RJmn0+Uu6mF2XaTgEBhjHDZGQpxkAjRuMkk1p16aPt26P1XRbTG8flt17b99mvR9WeCX0fiO68dan4SurTx54n0zwzpGn21rceG9dgsJpLiSNmkvLiSS8gkmZioVVbfGNj5BJOPXNC1zxTo/wXj1bxHYbvF1lpEk91afITJcRxscHyiy5YqCQhIBbAq/4s+Fnh3xnq1vqt/DfW2qwwG1W+0nVbvTZ2hLBvLeS2ljZ0DchWJAJJAGTno9N0220fTbWws4Vgs7WJYYYlyQqKAAOfYVnO8oSV7N/wDB1/r06IqNoyT3S/4H9fe92z5+vrLUfCfwp8KfEaDxVrmq+KppNKuLtpdTuHsdS+1SxRywiyyYI1ImOzy41ZSqHP3s9b8I9MvNc8YeOdZ1TWdWvTp3iW7s9OszqM621vD5UWVMIcJJyxxvDBcDaFO4nptF+Cvg/wAP6tbX9jpk8f2WVp7Sxk1C5ksLOQ5+eC0eQwQsNzYMaKRubGNxz0uieG9O8OtqLadbfZzqF299c/OzeZM4UM/zE4yFXgYHHStOZc7klZWdl2u42S8rJ/ftqzPlfLGLd3dXfeyl+rX9JHmXxA0+88TfHjwl4ffWdWsNCm8P6ndXlnpmoTWf2ho7iyWMl4mV1Klz8ykHBZc7WYHzWfVvH3jD/hMtX0fQfG154k0/Vrux0GbT9atINJtltpDHHHNayXsfnBypaRpoWciU7CAqEfS03hnTbjxNaeIJLbdq9paTWMNx5jDbDK8byLtztOWhjOSMjbwRk553Vvg34U1rxBc6zPaX0F5dsj3kdjqt3a214yAAG4t4pVinO0BSZEbcoCnIAFKL5eXyv97ldP7tPw2NJa39V9yjZr5vX5HAafp158Tb34haxqviDWtHvNC1CTTdNtdJ1ea1h08Q20UnmSRRsI53Z5C589XUrsXG3O6h8B/FGsatcfChdQ1e+vft/wAP2vbgXVy8n2icS2gMr7j87gOfmOT8/XmtL4meC21rxRrrWfww1XVNQ1CGO1GqQ69Ha6PfDywqtqFuLpGlWMu6sGt5iUQAbuFHU6R8CdAh8B+C/D2pm8uLnwxYR2drqenX9zp1yMRLHIRLbyI4V9oJTdtOFyDtGCm+WN3/AHfn7sk397T/AF7TNX92/fX5xaX3K336Hnravc6x/ZL3N7NfeR8Up7aJpZTJ5caeeojXJOFXkBRwKxvHnirV2uH8d+Fxqlvptt4mt9MfUtU8VXMcdwRqCWc8UOlKr27w/fjDOYpMguBkK7e7aV8JvCeh2FpZWGjpa2lrqbazDFHLIFS7bdmX73OdzfKfl56Vk6h+z/4E1TUJru70iefzL5dT+ytqV19kjuxIJPtEdv5vlRyFxkuiAnc4JIdgYpe4op9LfhGCf38r/p6VL3nJrrf85v8A9uX4+RhfCnRrjxJ4q8e6prWrapqMdl4jvLCxsJNQmFpDbmGIMjQBtkmSzY3hgvG0KSxPQ/AeRv8AhVuk2uS0Wny3WnQMxJJht7mWCI57/JGvNbN54an8PaD4g/4Q62s7fW9SmkvA2oTSmA3UgVTK+NxwAoOxQAduPlzkXvBvhe38FeFNJ0K1kknh0+2SATS/flIHzO3+0xyx9yacdIRj2UV6tLX8b/f6k295vu5P5N6fh+XpfZooooKCiiigAooooAKKKKACiiigAooooAKzo/EWlTa7Nokep2b6zDAtzLpy3CG4SInAkaPO4KTwGIxmtGvmTxf4V1LUv2iPG/ifw6pPivwvpOj3llCmF+3Qsb0XFk57rKigDPCyLG3Y5PN7f1/XoOzabW//AAUfRN94m0fTGvlvNVsbRrC2F5drPcIht4DuxLJk/InyP8xwPlbng1zmm/G74da0Lk6f4+8L3wtYvPn+zazbSeVHuVd77XO1dzKuTxlgO4rwrx54u03x5pfxh1/SJjPp9/8ADS3liZl2svzaiCjL1VlIKsp5BBB6V6pp2heJ/iN8MNQ8NeK9C0PTNK1LRPskU+nazNeyMzxBQXR7WEJgHdkM3Ix70O8VK62W3/gX/wAiN25oq+j6/KL/APbvwPSdS1rT9HW2N/fW1iLqdLaA3Myx+bK/3I0yRuY9lHJouda0+z1CCxuL+1gvZ4pJoraSZVkkjTG91UnJVdy5I4G4Z618peK9V1L43+CfCv2dY5tc8KeHpfEtzAkzbk1m2lEMEZYcf6y3vVOfT8a6XV9DX9prXfF15pF9bw2dr4e0+x0y6miaaB5rho7+ZJF4DRPGtojqOqswPpTa3tqlf7tUn85Jr0sRHW3No9NPuv8AcpJntXh34seCPGDXa6D4y8P621nCbi5XTtUguDDEOrvsc7VHqeKg1r4z/D7w3LbRav468NaXJdW6XcCXur28JlhfOyVQzjcjYOGHBxxXFXXj651HRfEnhvxZ4YHhjxfb+H76a3MMi3VleWoGx3tbgKp2giItHIqOu5PlIw1ZGvf8mf8Ahv8A7BWif+jLal3t05fndyX4cv33QrtWT63/AA5f8/yPSW+Nvw7XRV1g+PfDA0hrg2i6gdZtvs5mChjEJN+3eFIO3OcEGtTR/iJ4U8RWdneaV4m0fU7S8uTZ21xZ38Usc84UsYkZWIZwoLbRzgE4rzf49T6rb/ET4OyaJZWeoamut3nlW2oXj2sL/wDEtuc7pUilZcDJGEOSAOM5DfiVcazda18HZNesLHTdSPi077fTr17yED7DebSJHhiJJHUbBj3609LrzaX3tL57lPRP/C39yl/kdvqnxs+Hmh6xNpOpePfDGn6pDJ5UtjdaxbxTxv8A3WRnDA8jgiuzryf4I28V5dfFWCeJJ4JfF14kkUihldTBACCDwQR2qx8AYE0zR/FWjWZ/4kWkeIryx0pFJKRW67GMKZJ+WORpYwBwoTaMYqYPmjHu4qX38un/AJN/wwpaN9uZx+7m/wDkT1GsvxH4o0bwfpcmp69q9jommxsqvealcpbwqWOFBdyACTwOazdP+IWl6p4lm0KG11xL2JnRpbjQL+C0JTOdty8IhYccEOQ3bNcx8WPCfiS48ReGfGXhez0/XdS8OpdgaDqcxt0uhNGBuhm2sIpxs2qzLtKyOpKgk0dvMpbtdUdHbfFTwhf2+i3Fh4isNUtdZvTp1hc6bMLqGe4CO5jDxblBCxuTkgcY6kV0FjqlnqRuRZ3cF2baZrefyJFfypQASjYPysMjIPPIryST4sf25qng1dDhuvD8954pm0jX9MvrWJbmORLCeUxyY3qT8kDiRGIZduGIOK4fw7q2tfCHwL8YvFUWvap4mu7HXLuC20/ULe2MMly3kLHMwt4EkZtzqCqNgqPlXdzT6tPs3/6T+Hvb+nmyZacturS/9L/H3dV6+h9O1Um1ext9SttOlvbePULmN5YLR5VEsqJt3sqZywXcuSBxuGeteO/DXxtr0njzT9JbU/FXirSr6ymlvbzxB4Sn0hLC5j2FBE7WkCmOQNINjl3BRfmOTWr4u/5OU+HX/YD1n/0Ozp9Uu9/wT/yBO6b/AK6HSWfxs+Heoa1Ho9r498MXOrSTfZ0sIdYt3naXOPLEYfcWzxtxnNdpXjX7P+g6b4o/Z/tdJ1iyg1LTLy41KK4tbqMPHIhvZ8hga3/2e7+61D4O+H5bq5kvdiTQwXUrM7z28c0iQSFmJLFolRtxPOc96hP3bv8Ar+v6tbU66f1/X9eXYeFvFOl+NNBtdZ0a6+2abc7vKn8t4921ijfKwBGGUjkdq1q+fNC8beNvGFh8JrW38Sf2bJ4gttSfVr1LOB5mWIDY0QZCiyAkYypXrlW4Fdz8HfEWs3mpeN/DmuapJrtz4b1cWkOqTwxRTXEElvFOnmLEiJvXzSmVVQQoOM5ql7za/rdL9Q2S/r+tj0qsvxF4l03wnp6X2q3P2W1e4htVk8tnzLNKsUa4UE8u6jPQZycDmvFvGnxC8a6evjrwrpWoRp4yk1q2h8Nzz28bqLWeETZKbQGCCC9XLZP7vnPGeU+KfxE1r4leFBeaFPFDpkcPhi6hjnhV4TqF1qEMoD4If93GsWVDLkTeuCHBczj2vFPyvr9/T1Htv5/g7f8AB72TPqDUdQg0nT7q+upPKtbaJppZME7UUEscDk4APSotE1mz8RaNYarp032iwvoI7m3m2su+N1DK2GAIyCOCAa8f1LxJ4m8AeONa8P3Pim88RxXfhW91yym1C0tY5LGe3dEKqYYkV0bz0IV1Yjy/vHdS6H4o8UfEjUPDnh+18RXHh0ReGLHXdV1bT7a2e7uZrgOqRIJopIkTMUjsfLJOUClRnKj723l/7ff/ANIf/Bvope67Pz/9tt/6Uv8AgW19Yg8S6bc+JLzQI7ndq1pbRXk1v5bDZFKzrG27G05MTjAORt5AyM6leBQeF/Et98fNc0y38Y32neT4T0wXWs21nam9uJRc3gQ4kiaFQRvLARcnbtKDIPoHwQ8Xah44+Gen6hqsyz6pHNdafc3UcQiE8lvcSW7TBBkLvMW7aOBux0p293m/rdr9By0lb0/GKf8AmdBq/jjQdD0XU9Xu9Ug/s/TJPJvJoCZvIkBUFGVASGBZcrjIyK3a+RNZ8O6xpPwZ+OVwPFGp61Kut3MEVrqUVosLTB7YiZjDBG+48AgMEx0UHmvTda8X+I/hL49sota8SXfivS9U0LU9Sks57S2g+yT2YikIt2iRW8tlkZdspkYbVO885z5koxk+sVL/AMl5v6+Qo80puC6Nr7nb+vme3UV85fDn4p+LNU1LwXez3vibW7jXXVdY0i48H3VlpmmJJEziS2uWtEO2NwiZlmkDqxIwcV6J8YvEHiHSdU8A6f4e1BNNk1nXfsF1LJAk37j7JcSNgN/EDGrAjuozkZB1lFxdut7fP+nvt5hFqSbXa/y1f6bbnpNFeLTeLtY+G/iLxRoHiDxjcanpaaEusWevX+nwyXllIZTA0TRW0UaTAtsaNVj3lt6nd8uOJ1H4peOvCdp44tZbjxMFj8H3mu6VfeLLTS0uo54SFDIloAuwiRCUniV1K85yVWdHqvP8L3+ej9baFJXly9bpffa3/pSPpa91Oz01rcXd3Bam5lEEAmkVPNkIJCLk/MxAPA54NUrHxTpepeINV0O2uvM1TS0hkvIPLYeUsoYxncRtOQjdCcY5xXh3xK8I+Jb6w+G1zqHj/W0vrzxHayOtpa6esVs8kMhxCGtWOEIYLvZ8h23FvlxseJfih4h8HX/xAtYblNWn0waJY6Ut/EioLm8byTJL5aoWBdldgMcKQu3NGtn3UuX8F+r/ACM+ZNxS2av+L/yPWfE/irS/BuljUdYuvsdkZ4bbzPLd/wB5LIsUa4UE8u6jOMDOTgc1rV4740j8W/DHwimoHx5qniC6uNU0m0Y6jY2KLEJb6GKbyxDAmA6SMuH3kcEMDyY9Pbxp8QPGvxH0238a3nhnTtF1KG205tOsbOSQFrKCRlkM8UgaMPJuwArZYjfjAB0bXS/4cv8A8l/XXSx7NRXgEfxQ1zxx4P8AB5ttQ1+z8VX1lLc3WleCbGykebZJ5TTGa/VoIYCyOVV2V33AKzbSDW8N/ELxz47svhbaJrH/AAj91rC6tb61OlnA85NqfKEkanzI0l3Lux88YLNw4Ao6XXf7/NeWhHMr2PftR1Sy0e3We/u4LKBpEhEtxKsal3YIi5JxlmYKB1JIA61ar5V+IF34k8QeEfE3hzU/F+pS3PhjxtpFnHqkNtZJNeQzz2UsRmX7OU3xGY4MaoGKLuDAsp968bWfiPTfhXrMHh/V7i68UW2myGy1G7igMs1wiEoXRYxHliACAgHPAFK6VN1Ht+nKn+pootyUV/WtjsKK8Ft/jlf6l8SPDuoWt3Cvw9uNMhhvNyLlb64tZL2Ni5GQEhiQY4B+0gnkCqf/AAmfivXfDXhm3j17xY/i2909tYuNJ8L6ZpnnQwXEjtCbiW9TyESNQIlTcsjlWPz4YrUk4tp7rfy3/RX9N+pEWpfdf8v1dvXQ+hqRmCqWYgKBkk9BXzhN8WPE+reB/AniDW9T1jwh4dvdKmn1fXPDelw3jwXiEACdHiuBBblVlYuEYBgAZFA+f22S3m1r4frFb+IpJZp9PXbr2nJAWlJQHz4wyPF83X7rLhuO1TU9yEprW1/wv/l9zTHF80lHv/wP8/0NrTdStNY0+3vrC6hvrK4QSw3NtIJI5UIyGVgSCCO4qzXy38Pp/Ffhr4L/AAQ07RvFt9v8ST21rNNd2tm5tLY6fNIY4QIVHymMMpk3tuA3Flyp3vE3xC1qHx9q3hI+KfFmmWnh6wtVGqaN4UbVrnUbuVWdmuDDZSwxxqvl/IiRMxZiCABWlSKhJrs2vuDt6X/G36H0NVabU7O2vraymu4Iry6DmC3eRRJKEALlFJy2ARnHTIrwe++Inijxhonh6K0n8W6P4xfSY73UPD3hjSrJZLdnLqJbh9TUJGhaNtkO5JSNxwwBK42l3mu/FbxF8DvFE/ibU9AvNV0W7uJ7XTIbMwxyCKIyFPOglYBycEFmwFG0g5Yzb87fn/kJ7f15f5n01Uc8yW8Mksh2xxqWZsZwAMmvmG8+OXibVE8SeIdLufFT3Wl6pdWem+FdM8H3N5p9/DbTtCwlu1tHJlkKPho50SM7Ayna+7u5L3xX8Srjxxd6f4nuPDFhoN5Lpllp9vZ200d28cCPI92ZEaQqzuVCwvEQq53bmyuc21ByXZv5afq0u5cUudRfe3z1/wAn5HqnhnxJp3jHw7puu6PcfbNK1G3S6tbjYyeZG4DK21gGGQRwQDWnXmf7Mv8Aybz8N/8AsAWf/ola9MrorRVOpKC6NmNOTlBSfVBRRRWRoFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVzum+B7HSvHGueKopbhtQ1i1tbSeN2UxKluZShQBcgnzmzknoMY5z0Vcn4m+LngbwVqX9neIfGnh7QdQ2CX7JqeqwW0uw5w2x3BwcHnHai9g6HMN+zj4UW8+IlxbyahZr45s/sepW8EyCKEESB5IFKHY7GV2bO4FjnHJz0fg3wHqXhOeP7R458QeIrOODyI7HVIdPWJMYwwMFrE5IAxyxHJyDxjo5Nc06G4sIJL+1SfUN32OJplDXO1dzeWM5fC/McZ45rD1D4qeCtJ8Sp4dvvGGg2fiB5I4l0m41OCO7Z3x5aiItuJbcuBjnIx1pq6fKvT7tvuuDfN7z9fy/wAkU/Avwj0L4e674s1XS/tLT+Jbz7bdx3EgeONvmJSJQBtQs8jkHPzSMc9hleDvgTpXw9+H954U8N63rmjxXF8b8anDPE13E+9GCqXiMewLGkYRkI2Dac9a7uHxBpdwmovFqVpIumyNFesk6EWrhA5WXn5CFZWIbHDA9DUUfirRZtBh1uPV7B9FmVHi1JblDbyK5AQrJnaQxIAIPJIpenZL5dPy0Drr3b+fX8/xOWsvhFb+dfXWt+IdZ8Uanc2E2mR3+qfZkktbeXHmJElvBFGCxVSWKFjtUZwAKvXfwx0u8+G9j4Je4vBpVnb2ttHMrp55W3ZGQk7duSY1z8vc4xW14k8V6J4N0ttS8Qaxp+hacrKhvNSukt4Qx6De5AyewzSWfi3Q9RbTFtNZ0+5bVIGubBYbqNzdwqAWkiwfnUBlJZcgbh60Lrb+rXf6t/NsVtr/ANbf5L7jD+IPw0h8fXnh+9Guap4e1LQrqS7srzShbl1d4XhYMs8MqEFJGH3e+ahb4Wrff8I7JrXiXWvEF3oWqHVbW6vVtI3Z/JkiEbiCCNSgWRjwobOPmxxW7r3jfw74VFwda1/S9IFvALqb7feRweXCXEYkbcwwpchdx4yQOtReE/iD4W8ex3L+GfEuj+IktionbSb+K6ERbO0N5bHbnBxn0NC7r1+5/wCaG/P0/wCB9zOSX4Ita6tr1zp3jvxVpFlrl89/e6bYtZJE0jqquElNqZ4wVQDKShl6qynmu68NeGtN8H6HZ6Po9qtnp1omyKEMzHrkszMSzMSSSzEsxJJJJJrKt/il4LuvFB8NQ+L9Bm8RrI0R0ePU4Wuw6gll8kNvyACSMcYNdRQtIpLb9Ft9wPd33/z3CuY8XeDr/wASXdnc2HjDXfC0lukkbLpAtHScMVOXS5gmXI28EAEbmGea6euR0n4veBPEGvLoel+NfDupa0zvGum2mqwS3JZAS6iNXLZUKxIxxg+lG78w6FDT/gzounyaPcG71C61Cw1Z9cmv7iVDNf3j2727ST4QLjy3wFjCKoRAoCrtp6/CHSmuvFqXF9qF3ofibe99oMpiFqJXREkljZYxMrMEH/LXAJJABwR3VFH+Vvlpp+C+5B/X5v8AV/e+5yHg3wDe+FLlZLvxp4j8Txx2/wBngg1h7XZEuV+b9xBE0j/KBulLnrg5Zib2o+CLHU/HGi+KZZbhdQ0m0urOCNGURMk5iLlhtySPJXGCOpyD2t+JvFWl+DtPivtYuvslrLdQWaSeW75mmlWKJcKCeXdRnoM5JA5o0HxTpfiaTVI9NuvtL6ZePp92PLZPLnVVZk+YDOA68jI5609ZO/a//B/9KX3ruLSOn9f1o/uZ55pX7PcGl6K+gf8ACceLJ/C0kkzyaGJ7S3idZZGkePz4LaO5ClnP3ZgSPlJKkg+o2NhbaXY29lZ28draW8awwwQoESNFGFVVHAAAAAFN1HVbLR4Y5r+8t7KKSWOBJLiVY1aSRgkaAk8szMFA6kkAcmrVJaRsth9TiPDvwk0fw1/wif2W5vpP+EbhuYLTzpEO9Z8bzJhBkjAxjHvmtbw74IsfDOveJtXtZbiS58QXcd5dLMylEdII4QEAUEDbGp5J5J57V0NZPh/xVpfij+0v7LuvtX9nXsmn3X7t08uePG9PmAzjI5GQc8Ghb6dvwuv1sGy/r+u5kXnwx0e9+Jlh46drldZs7B9PWNXHkOpYlXZcZLoGkVWyMCVxzmsHS/2f/DWj+GdR0O2n1EWt9rkevyyvOrSiaOeOaONSV4iXykQLjIQYznmvQ9R1CDSdPur66k8q1tommlkwTtRQSxwOTgA9Ki0TWbPxFo1hqunTfaLC+gjubebay743UMrYYAjII4IBoi7O66fq7/mr/IH59f0VvwWhz3iL4Z6X4m8Uf27dT3kd3/Y11oeyF0EfkXDxs7YKk7wYlwc45OQe2defB6z8jQW0jXtZ8N6po9gmlxapprwNNNaqABFKk0MkTjKhgTHlTnaVDMD1lj4k07Utc1TR7a48zUdMELXcOxh5YlUtH8xGDkKehOMc4rSpbLT+t/8A5J/ew+J3f9bf5L7jl/Dvw/tfD/iKfXDqOoalqlxptrpk8968ZMqwPK6yEIigOzTOWxhemFUCp/Afgex+Hfh0aNp0txPai6urvfdMrPvnnkncZVQMBpGA46AZyeaf4P8AHGj+PLGa90Sa4urKOUxLdS2U8EU2P44XkRRNGe0kZZD2JrfqrsP6+5W/LQ861T4I6bqq+LbZ9b1iHSfEsguLrS4jbiCKf91umiYwmQM3lLkM7L8zYUE8dDrngHTfEHi7Q/EV20zXWkW93awwAr5MiXAjEnmKVJPEa4wR1Oc10lFTZWS7K3y2/LQNm5LdnBeDfhOfBNxZR2ni/wASXWhWG4WWg3c1u1rboQwRPMEInkRAxCrJK4GFzkqpGN8c/B9z4z1b4cW0C6jHFb+ITcy32mbhJZbbK62TFgCqgSGP74KMSFYMGKn1aiqu20301/Ua0vbqmvvVjzdvgXpN/pPiO31nV9Y17VNegjtrrXL2SFLuOOIloViEMUcUYjclxtj5Y5bdVG6/Z50zWLjWbvW/E3iLXdS1bRJ/D9xfXk1sjC1lYNhI4oEjVlIOGCc7ju3cY7zUPGOkaX4itdCurvytVurOe/ht/Kc7oISiyvuA2jBlTgnJ3cA4NT+GfEmneMfDum67o9x9s0rUbdLq1uNjJ5kbgMrbWAYZBHBANC/mX9br/NfeLZ267/dZ/hp+BleMfAFr4x0Gw05tQvtKm0+4hu7PUbAxfaLeaL7rr5iOhyCVIZCCGPFUZ/hJouof8JJ/act5qv8AwkNta21+Z5VjJ8hCqSIYlQxvk7tykYYArtxXbUUnqmn11/QSSVrdDzx/g+2o6S+n654z8SeI4PtNndQnUDZo0DW06zIF8m2j3bmRQxfcxA4Kkk1yGh/CnUda+InxR1Aa74l8JR6jqsEZfTfKSPULcWFsoK+fDIF2t5q+ZDsbOQWO1dvuVFO+9+t/xt/kV0sjzq7+COlw3Giy+HdX1bwb/Zenf2Qi6K8BWazDKyxP58Uv3SCQ67X+dvm5qXwf8E9B8Dw+FodNn1AxeHBerZLcTiQsLp90gkYrubB4U5z6ljzXoFZmueJNO8NrYtqVx9mF9dxWNv8AIzb5pDhE+UHGT3OAO5ov07v8W/1b+9+ZNlv2/Rf5L7l5HJat8FtF1iHxcr3moQS+JL621KaeGSMPa3FusIheHKEDaYI2w4cE5yCDiu10qym0/Tba1uL+41SaKMI95drGssxHVnEaIgJ/2VUe1W6KXS3T+kM8mk/Zn8Jv8LtR8BJc6tBo97f/ANoGeG6VLmJvNV1jjcL8saqixKMEiMBc963vEXwjtNa8SJrVhrus+GrhrFNMu4tHeBY7y1RmZI38yJ2TbvkAeIxuA5+bhcd3XJ6l8VPC2jzazHd6p5L6Pd2lhfD7PK3kz3Pl+QnC/Nu82PlcgbuSMHB/d/rbl/KyHq7v+t7/AJ6nO2PwNj0PwrpPhzQfGnifw/o+n2X2D7PZvaSefHuY5dpraQq2HI3RlDjGTkAjvdC8P6f4a0Cw0TTbZbbS7C2jtLe3BLBIkUKq5JJOFAHNaNFG6afXfz/q7+8XZ9v6/RHnXh74I6d4es/DVkNb1i/svDd/9t0m3umt9tqvkSQLb5SFWeNUlbG8s+QMuRxV/wATfC9da8THxBpXiXW/CWry2y2l1Po5tnW7iVi0YkjuYJk3IWfa6qrYcgkjAHbUU7thvv8A1rf8zzvVfgva6hqyahbeJvEWkzy6dDpWotZ3MRbU7eIuUE0kkTur/vJf3kLRv+8PzAhSI7L4G6Xo/hvwVpOj61rGjv4Sj8jT9Qt2t3uGhKbHjk82F42VgBnCA5UEEV6RRRf+v69WJ67/ANdP0X3LscA3whS11y9vdI8V+IfD+n3939uvdF0+S2+yzzMQZWBkgeWLzMfN5MiZJLDDMWMOtfBWy1LWtbvrLxFr2gWmvbW1fS9Knhjt75xGIzIWaJpYnaMKpaCSMkIp+8M16LRS6cvTb5dvTRabFXd79f619fMwvAvg+z+H/g3RPDOnSzzWGk2cVlBJdMrSskahQWKgAnA5wB9K3aKKqUnJuT3ZKSirIKKKKkYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXzvrHjqbwF8e/iff8A/CLav4jsF8PaPJdSaYIHW1jVr3LSI8qyMpBJxEkjYVvl6A/RFc7pvgew0vxxrvimKW4bUNYtbWzuInZTEqW5lKFBtyCfObOSegwBzk6Md9H8vzTPFfD/AIdHhXUP2eNMF3aXqx/2jIs2nuXttr2UkgWEkDMShgqHA+VV4HStfw5NqnwKWTQfFWhw6r4NvNXkntvFmngO0UtxceZH/aEDDcriRwonjMinCFhHXYeHvgbonhm+0Ga0v9Ta30G+vL3TLGWWMw2q3EZR4FxGG8pdzFFLErnGdoCg/wCFNveyQw63428TeI9FinS4Gjai1mLd2Rw8Yd4rZJpFVgp2tIQ20B9wyC9Pacy2f5aaevbz30M+W0bdv+CeFaXcDW/jN458H+Jm/s7wFq/i+TfPu+TVb5bGzMenTNkeWjKN+3nztpjyACsnqn7TPgXw/qvgU67e6Ra3eq6TJaxafcTpvNoHvLfcYgeEY7QNwAbHGcEiuquPgt4b1DSPGemX8dxqFj4rvjqN9HO4zFN5UUYMLKAUK+QjKclgwyD0xf1L4dwa54Bj8K6trGqapEohD6lcPELuYxyrIpcrGEJyigkIMj35oWkILrFR/JJ/dt6W8ype9OT6O/53/Hf1+Rz+qIuoftGaFDdKJIdP8N3d5Zq3RJ3uIo3kH+0E+XPUB2H8VecJ4Vu00vxxqWkW4N54H8Z3Gr6HDGoQNG1tDLeWy9gJftF0npuYHtXs/jT4c23jDUdL1WHVdR8O69pgkS11bSTD56xSACSJlmjkjdG2ocMhwUUjBGauaH4H07QPC9xods07xXQma6upnDXFxLLkyzO2MF2LE8AAcAAAACFzRhp8STt68/Mn8rW/4BW8tdm1f05eVr53v/wT54+IU03iH4A+J/iNNBLcXPijUNIubC1UqHTTY7+D7HENxADOrNKckYacgniu80C8l+JHxqvbybTL3wZf6HoMum3Gn6iYhqE4upI3inVoHkiaBPJcKyyMfMMgKpt+fsbr4O6Nd/CnSvh+9zfDRtNgsYIp1kT7Qy2rxPGWbZtJJhXdhRnJxjtoeKPhzY+J/Emja/8AbtQ0rV9Ljnt0utOlVGmgmUCSGTcrbk3KjjGGVo1II5zpPku1HVa29HHlX/B+/cUW+VX33/FP9NDzv4bX+pfCGz8L/D7xl4dtY7CBo7DRfFGkhXsruYb9gmiID2tw6jJOGjZ3bEmSFr1fxR4qsvCGnpe38GpTwtIIgul6Xc6hLkgnJjt43cLwfmIwOBnkVy2l/CN49S0y617xl4i8YRaZItxZ2esfY0hjnUYSY/ZraFpGUFseYzKCd2NwVh6DRJuV23q3/l+O+2mxMVZ+Vv8AP8NvPco6LrEGv6Xb6hax3UUE67kS9tJbSYDJHzRSqroeOjKDXzD8N9N1z4jfBnQ/Clp4SvbW2j1+S8Hii8ubVbaBYNUkmMkCpM1wZfkKKDGgyxJbb1+rK5/wH4Jsfh74Zt9C06W4mtIJJpVe6ZWkJlleVslVAxuc446Y69an7Skun+aaHrax5IfiF4o0fR/iv401LWZ7nS/B99qMdhodvBbpFdRxW6Oomk8sycM2F2Mp4JYvkAUPDPxW8R6Pq2k3F3feLPFVreWtzNrEGpeDrnS7TTHjt3nD20z2sWIyyGLZLJKx3xndkNu9j0v4d6Rpun+JbCRJL+y8Q3dxd31veFWRjMipJGAAPkKrjByeTzWd4V+F8nhmaKObxh4i17SYIGt7fSNWktngiQjaAXSBJZtq5UedJJ1ycthhKv7NRXxcqV/Pl1/H7/IenNd7c0vub0+5f0zxT4jWvi7WvhT4K8Wax4rmuf7X1vQLy60D7HbrZWqy31u6JbusYm3ISg3SSOGG/wCUErs6+4+Jnie4j1LRrO/jj1XUvG0/hywvpYEYafbLb+c0gTAEjqiSbd+RuZd24AqdZv2a9Jl0vSdHl8VeKJvDuj3tvfaVorXUAt7IwTCSKMMIRLLGoXYFmeTC4xhlVl6S8+Dui3mlaxZG51CKTUNXbXEvYZlSeyvCFCyQMFwNuwfKwYEFlYMrFa1vFN2Xu3bt5Xp/jaMlv89SLS011tv52nr6XcXa3TbQ84+O3gfXbPwLokM/xC8QX5PivRtk81rpqyjff26DOy0VTsb51+UfNw29flqPxR8RNaj8f6t4T/4SfxZpVp4esLVRqmi+FG1a51G8lRnZrhorOWGNFUR/IiRMxdiCAAK9JvvhR/bnhK60TWvFeva1NLdwX0Oq3X2RLm0lhdJITEsVukPyvGG+aNskndkYAj1L4Svd6rb6vY+MvEWh679iSxvdSsBZF9RRCSjTxS2zw71LPho40OHYdMAT9nl82/vil+a1+W5e+vlb8W/10/Q43wx408Y/FjUtI0WS9vvh9c2+gW+r6s1nYxC7e4mkliSJFu45BFGPIkchoy53INy4bdf/AGZ4b620vx9FqNzHeXsfjHUlkuI4/LEuDHhtuTgkYJA4znHFdPrXwkg1G/0zVNO8R654f12xshpraxYSQS3F3bg7gk4uIZUk+bLBim4FmwQGYG/8N/hnpfwu0vUrHSrm/uo9Q1GfVJpNRuPPlM0pBf5yASMjPOTyeacbKTfk198k18rLy/Uia5uW3Rp/dFp/i/P1PPLPxD4v1bXPizfv4nuLTS/Ct7NBpun21rbbZP8AiWwTYmZ4mdlV5Ny7SpyWDFlwq5HiL4g+Io9N8IX2ueJ9a8G+Gr3w5bXcnibR9LtrqE38gTeL0yW8q28QDIyttjQ7pN0i7VFeuWfw502yh8ZRpPdFfFVy91e7nXMbNbRW5EXy8DZCp+bdyT24GJq3wZXUtHtdFi8Y+JNO0CPTIdJn0q1e08q6gRSh3u9u0iM6HazRPGcYxggGoV0vlD71GSlf5tfg+hb+K/T3vxcbeWyfyPLNY8carY/HLxV4S8N3kdr4i8Tx6XFba9cwhrW0jS2leWZQfklmKg+XCCcn5iNiMa9417Sbt/At7pw1u+S8WxaI6sqQfaWYJzIVMRi3HHIEe3k4UcY53WfgT4X16DXobqO6EWrR2aDyptjWT2qkW8ts4G6ORM5DZPIHbIPZ2ekvBoUenXWoXWpuIPJkvroRrNNxgu3loqBj/sqB7VFSPNTnFdb/APA9PP8ApIo+44N9P8/x/r5/OXwdvfEcPgX4P+CNO8V6hb/254XXWZ9Vlt7Nrixt4YbVFtbQeR5fLzr80ySsFVuSSGXT8X/Ebxn4KtfFnhi317+1tc0m+0KSx1q+tIBJLb3955JinSNVjLL5co3IkeUdcYYbj6QfgrpVv4V8J6Rp2q6tpF54WtEstL1uzki+2RRCNY2Vt8bRSB1RdyvGVyAwAZVIU/BbSLjRbqzvtR1TUr681C01O91i6ki+13UttLHJCrbYxGsY8tV2RoqgFiAGZmPZUnGdVy6c33rmv/6To+/zZEE4xS8v0/H/AIPkjqdFsbnw3oJj1HWr3xBNCHkkvr6OBJWHJxthjjQAdB8ucDkk81518Nb/AMR+KPCuj/EjXPGlxp2m6haf2s/h+K1tRp1vaPEWjVpGi+0FlUo7P5oBZWwqqdo9crzzSfgvZ6Msmn23iLXB4TZJox4Vd7c2CRyIytGr+T9oCDexVPO2rwAAqqo5ne0rb207f09NelvM0VtL7df6/r8DgPBfxC8Ux/EbwjDJeeKdZ8MeJFuY1vfEWnafZW7skBmSS1ihCXUY/duNlyhyrA7sjLQeHPHXjn/hU+geJbrxE93rvia+t9GtLeWztxZ2Xm3Rj+1FURXeQRqzbS+wnaNo5J7zw98B7LRdc8Mate+KfEfiC68NpJDpa6nNbhIYnhMTIVhgjD/LtO98vlR82CQdOT4N6FN8M7bwRJLfNp1rse1vFmCXdvMkvmxTo6qAJEkAYHbjjBBBINStzK2yt6tX1+bWl+j27mcVKzvv+F7flfW3Vbnm11out+Ff2jPDgv8AxTeeJk/4RPWJbe41K2to7iJxNZ7lJt4oo2ThCAU3A78swICzeB/HXi34mWHw60NPEMmiXOoeDrfxHrGtWdvbNeTSyCNESFJI3iRSxlZyYjxsC7c5rt9K+CdraeMofFWp+J/EHiLXI9OuNKE+pS26p9nlZGIEUMMcasDHwyqCdx3FsLtcnwR02x0LwpY6Pres6Df+GdPXSrHWLGSA3TWwRFaOVZYnhkDeWjfNHwy5XbVRaUUpa2/zqP8A9ui33t1G7ttrT/hof5SS7XOA/wCFleLfCfihdN1PWTq+meHvE9vpGq3zWsKPd2d9bqbWWURqAkkU8sSMUCKy/NtGeNbUviJqV/p+v33/AAkWoafZ6jrx0fw3baDp8N1fXH2eNhMIRLG0e6SaKfMkoMaRx7iUBLDtf+FQaLL4H17w3c3F9ef28JG1LVbiRGvbiZ1C+eW27FdQqbQqBE2KFUKAKiv/AINaTP4T8L6JYajqWiSeGXjl0zVLFojdQyLE0TOfNjeNy6SSBtyEHeTwcET6+X6X+6z335ttCvNef67+t1ttY8w0n4i/EKfwl4x097fxAdQ0HXbW0mu5bOwn1uHT5YYZpJBBbb7aWZBI20IpymD5bOCrbVh46v8AXrPwt4f8LeO7jXJNd1C8S48RX1lbx3+nQW8YaaEwCGONbgOUTEsI2ByWRivPT6R8DYfDsOtPpPjHxNp+q6xqMeqXurLNbS3EsywCFvlkgaLYwUEps2qcbAgVVE4+B+krosduur6wmtx6k+sL4kSaJb/7YyeW0uBH5JBj/dmPyvLKgDZxmjvfy+/S9vLR9nfyeku91y+f6/jqu6S21WtGK88Q+E/ih4L8M3Pie98QWGoWWq3U82oW1qk0hiNp5QYwxRr8pklxtVchhuyQDXFeKPE2p+INcvbW/ufPt9J+Jul2NknlqvlQ/ZbWXbkAFvnlkOWyfmxnAAHo+pfCN9Us9Jefxl4iOv6XcyXFt4iU2YuwJEMbxFPs32cxlcfL5PVVb7w3VX0n4D6JpMc4/tPWL2a48Qw+Jpri7uEeSS8jjjTk7AAjeUCUAAGSF2qFUOOkouXTl/CcZfgk1/w4VPepSit2n+MZL82v6R578Qvih4i0jWL/AF7wzqfibW9I07VYLO7ij0/TotCh/fpBNAXlCXcrjLEyQM6LIQMfKyDtfBmoeJ/FXxW8cx3PiO4tfD/h7VYba0022trfFwHsoJHSZ2jZ9qs5ZdjK2WbLFcKGax+znpGtx6jZTeI/EUWgXmpf2x/YUM8CWsN356zmRW8nzSDIC3lvIyZc4UYXb3nh/wAH2XhvWPEWpW0s8k+u3iXtyszKVR1gjhATABA2xKeSTknnsFDRK/b8fd/yf+d2y5aylbb9Nf8AgX8/JI5T4ga7rWo/EDw34I0XVZvD39o2V3ql5q1rDDLcRw27wII4RMrxhnedcsyPhVIAywZfIrHVvEPgDxB8WJF1xr7VF8VeG7NtSltolkmglWziYOoXy95jcqWRVGSSoXgD3nxt8OrXxle6VqUep6h4f1zSzJ9j1bSmiE8aSKFkjKyxyRujALlXRuVUjBANc1Yfs96HZ2+urNrGuajca3qljrF7d3lzG8r3Fq0TIRiMKqsYVyigKASECKFAdOyab+fn7yf/AKSrf8ON2/r0/wA/60MO58Yaxb/F6fTPEfirV/B8Mmpww6HYrplu2kavblFYxtdvCzfaHKzL5YmiYYTajD5n4uz+OHifXLeXxPYXPiqe6XVpILbwnZ+D7mfTLizS5MJ3XgtCTMUDSeYtwI1baCpAYN7JqPwnXW9eivNV8U69qmkw36anDoFw1qLOOdH8yIhlgE5VJAGVWlK5UDBAAptn8Ik0jWZ7jSPFXiDR9HuL5tQm8P2clt9jeZn8yTDPA08au+WZY5VUlmwBuOVDTlv/AFtv9zvuu27Szkn71v632/C2z7vTXzP4hfFDxFpGsX+veGdT8Ta3pGnarBZ3cUen6dFoUP79IJoC8oS7lcZYmSBnRZCBj5WQb1z4w1i3+L0+meI/FWr+D4ZNThh0OxXTLdtI1e3KKxja7eFm+0OVmXyxNEwwm1GHzPqax+znpGtx6jZTeI/EUWgXmpf2x/YUM8CWsN356zmRW8nzSDIC3lvIyZc4UYXbuaj8J11vXorzVfFOvappMN+mpw6BcNaizjnR/MiIZYBOVSQBlVpSuVAwQAKVP3eW/wA//Jfx0dlqrbu7unU97m5fl+Nv0vbW+2is/G7P44eJ9ct5fE9hc+Kp7pdWkgtvCdn4PuZ9MuLNLkwndeC0JMxQNJ5i3AjVtoKkBg3bL4w1aH4uTab4k8U6v4SMmprDo2ltptudI1a32Aqq3bQMxuHxJmMTI4K8Rsoy3WWfwiTSNZnuNI8VeINH0e4vm1Cbw/ZyW32N5mfzJMM8DTxq75ZljlVSWbAG45fdfCcap4ggv9V8V6/q2m21+NTt9DumtRaQzqxeMhkgWdlRjlVeVlGBkEACiGnLfpv+H9K913sKd/et8vx/z1tqujdjvKKKKCgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKK+a/iR/wAjJ+0d/wBiHZ/+idSqZPli5dlc0pw9pJR7tfi0v1PpSivmvwLfTfDr4e3/AMPNTuWlsLzwzNqfheeZizPbC3BnsyxOWaBnBXPJideuxjWj4Ds7b4m33h/wlrYN14b0Pwdo9/Jo7t/o+oT3KyKDOn/LWONbfiNsoWk3FSVQrq4vnlFdLfjzX+5xt118tTnU/djLvf8A9tt9/Nf089D6DorwT4xfDvwv8P8AwroI8MeH9O8PRXnjHw+08Ol2yW8Tst/EA2xAFzg4yBk8Z6Cus+M//IxfCn/sbY//AEhvKlK6T7y5f/Sdf/JvwNO9+kXL7ub/AC/E9PorzH4iKzfF/wCFgVtjGXUwGxnB+xnmvOvhnofhbwn4i0bSPiN4Jg074kC5DW3jW8tVuE1y5LyFXh1AZdZGVM/Z5SjBSERWQClH3pcv9fL+v1sm/db7f1/X/DX+k6K+XvD2oXzaBc+H7O+uNLj8RfE/VdOu7y0laKZLcS3E8iRyKQyM4g8vcpDAOSpBANdh8T/hf4U+FHgm+8Y+C/D+neE9e0JFu0u9GtktXuo1dTJb3BQDzo5FBBEm7Bw4+ZQQ46pSezt+KT/C/wCenep+7KUe1/wbX6HuNFfKsMl74D+N/wAUfiRZySNpdjq1pZeJLJSWEmnnT7dluVXP37d2L8cmN5hydoqb4xTQ33g/9oWaILe280GmOojKssqm3iIwScEEe+Oahy9xSXa/4X/p+vZh9rl/r+v+A+p9SUV8v/FDw34e0f4A/EaXTvhEvw5L6dGk0n2HTIzep5qkpiymlLAejgfeGM81t+A9K8L33xSRvhdokXg6Cw0q4i16OPSH0dnllCGy8yykjjeQgiZ1lMe0AOoY5YU27NrfT/P/ACJWqX9dv8z6For5x+EOi+EvC2veH9G8beBYPD/xSgyYvE13bLP/AG5cBZBJNBqPLSO6h3MMrLIFbGwqua948TeLtC8Fab/aPiHWtP0HT94j+16ndR20W85wu9yBk4PGe1VK0eoJ3exrUVQ0PX9M8UaVb6no2o2mrabcAmG8sZ1mhkAJBKupIPII4PavjbwNo+k2vhXTdbHgZ/Dt/beM7qa4+JKW9qNkS6tOGRnhkN0yuv8Ao7eZGsYVyWbYvLS95Rf9a2G2lBy/rZv9D7Ns9XsdQur22tb23ubmykEV1DDKrvA5UOFcA5UlWVsHsQe9W68K8TfFDVfCdv8AEm+0LQNFl1aw8T6XpcStG0BvvPSyUtcSKSS4E5VXwdoVPlbGD0Gn+PvE/h34g3/hjxVd6BdrLoc2uafqFnby2EUSwyBJo7jzJZvlHmxHzAV43/JxU9E+6/8AbVJ/hqU4tf152/M9VorwjwP8ctX1HxvFoN7f6f4jtL7SrvULPVdN8O3+l226Ax5CSXEkkd3E4lBEkL4G0cEOCG+EPi547ufBvw68aeIItBTQfE0ljFe2dnaTJNYC5iKxy+c85Dq07Qjb5Y2LLjc+N9Uot7eX4tpfimQ3bX1/BJv8z3mmySLGjO7BEUZLMcAD1Ncj4O8XX3ivxV4uiWKBdB0m6j022lVW82a5VN1yxbdgopeNAAAQySZJ4xzOl+NvFviTx14506NdGtPDnhm7W3fz7OWae+V7KKbYGEyrGVaQ5Yq4ZWA2qQWOcpWi35X+Wn+aLjHmdvO3zPTNM1Sz1rTrbUNOu4L+wuY1lgurWRZIpUIyGVlJDAjoRRFqtlPqVxp8d5byX9vGk01qsqmWJHLBGZM5AYo+CRg7Wx0NeA6V8YdRtfB/wutLS68KfDyDXvD1vexXmqabI2mG4ZI9thbIlxCsbYYsFaQkqAFVsMVg1r4h33g348eJ9Ls7a11Xx7rnh7RLbTdMRmWCSZX1AyzOfvLbxD5mbrjaoyzKDvKHLKUeza+52/T9dtTJSTjfyT++3+f6bn0fRXN6x4jm8C/D6+13xHNDeTaTp8l5fSafbtFHIY0LP5cbO5UHBwCx+teaal4/+JnhX/hBr7WF8MXFl4m1q00+4sbS0uI5tMjmDtjzmmIuGAAXd5cQzzsIOBmtZcq7pfe7IbfLHnfZv7ld/ce30V4bp/xm8VQeDvF/jTU7TTJdG0S+1DTbTR7O2lF3qE0V20EDee0pWMMdiEeW3OWyB8g35PFnjjwV4k8MR+LJtB1PSPEF2dOxo9hPbTadctG0kQZ3mkE8Z8t0LbYiCUO3BICWtvP9dvvKl7ra7X/Df7v+Guep0V5Cnxe1hvgn4d8YG2sf7T1G/sbWWLy38lVmvkt3KjfkEI5Iyx5x1HFJZ+MviH4y8SePtO8Pv4c0yDw3qi2VtLqVjPcm9Bs4JvLbZPH5ZDSn94N3DD938uWFqm+1/wAOX/5JD2Sff/g/5Hr9Fc58OfGK/EDwPoviJbRrA6hbLM9q7hzC/R03DhsMCM98Zro6bVnYlaq4UUUUhhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXmviT4N/8ACQal8R7v+1/s/wDwmGgw6Js+zbvsnlpcr5ud435+052/L9zrzx6VRSa5k0+pUZOLuv6s7nmvxI+Ctt8RPhdaeFW1OTTNS0+GL+ztct4gZbSdI/L80ITyrKWVkzhkdlzzmmTfB+70238NX3hzxAujeKNF0uHR21Cex+0Wt/bIFzHcW/mKxG5SylJFZCx+YqWVvTaKq7u5dW7v1/4Ztea0ehmopJLsrfLT/JPyeqPK/Fnwv8X+OPCy2Wr+MNJOrW+rWOq2dzaaBJHaQm2mWVUaA3bO+5l+Y+cOMYA5Ju6t8O/FPizQ/I8QeJ9KOsWV3DqGjano2iSWv2K4j3Dc8ct1N5qsGKMoKZRnGQSCPR6KOlvO/wA9NfwRX/DfLXT8WcBovw+1648VWviLxb4istZ1DTreW30yDStLaxtrYy4EkzI88zPIQoUHeAFLDbliaz2+Gvi7xM2kWvjPxdpesaRp13b6h5OlaE9hPczwOskRlla6lXaHVXKoiElQMhcqfT6KE7NNdP01FbRrueY2nwMsh4W1rR7vVLhpbzxDc+I7PUbONYZ9PuJJzNG0e7epZCcZYFWGQy4JFSP8N/FPih7K28beK9P1jRbWaO6On6Noz6ebuWNg0YuHe5m3xhgGKIEDFRklcofSqKF7trdLfgrL52S130XYcvebb63/ABd3+Lf3nIeF/h9H4d8SeNtUlu1vovE17FdvavBtEIS2jgKE7jvB8vOcD72McZPnOmfsuw6L4J8f+F7TxLN/ZviSWL7Cs9r5h0q3jChIAd481FC4XO0hdo5xk+60VNrK3lb5f1/WrB67+v8AX9fkcp8T/Av/AAsj4f6x4Y+3f2d/aEIi+1eV5vl4ZWzt3Ln7vqOtU/F3w7uta8VaF4k0TWRoOtaeklpPMbUXCXlnIvMLruXlZAkiNk7WU8EMwPb0UW1uC0PNYvhz4s1/UtFfxn4r0rV9P0m6j1CG20fQ5NPkmuYwRG0sj3U2UBJbaioSwX5toKt6VRRVX6Ctrf8Ar+tQrxPR/gH4htfDc3hC/wDGNhc+CbjULm9uLO10R4b6aOa5kuWgNy106BSz7WIhDFAQCpO4e2UUJ2dxvVWZ5jrHwV/tb/hKP+Jx5P8AbfiDTtd/49c+T9l+y/uvv/Nu+y/e4xv6HHL/AIkfBO2+JWvXV7e6nJa2t14av/DklvDCC+25eFzMHJwCvlY27Tnd1GMH0uii7SSXT/5Hl/LT8dyuZ/163/M8psPhH4lvPG2k+JfE3jK11W403S7zS4rPTtHNnblZ/KzKQ08reZmLk7tpG0BUwxfO8c6Ho3w5/ZxtPBGq6p9ruotGi0XTPJCxXeoXkUSrCLeIscyl0VgoLYxknAJr2eik7uLjtf8A4P8Am/vJj7rTS2/4H+SOV+F3hObwT4D0jSrx45tUWIz6hcRjCzXkjGS4kH+9K7n6EUzQfAP9iap42vPt3nf8JJeLd7PJ2/Z8WsVvtzuO/wD1W7PH3sdsnraKJWnzX66fJ/8ADBC8EknseRt8HvEtr8N9H8Daf4p0Y6DbaDDod7Hqvh43bXASPy2lTFyioWXHyusqgqOvIMV5+zbpV3NeMNWvICulaXp+l3cIC3mmzWP2jyrpJs4ZyJ8FdoBAYHKuQPYaKuUpScm3q3d+v/Avp26CUUrLsrfLT/JX79TmL/w62rfDu+0Txhdw6sLqwltdSurG1e1SZGRldkj3yMh2nsx56elfPsWoSfETXvhromj/ABN0v4iwaPrVtqJHh+zQPBawRSZl1KVZpF3nKKoVYdzsx2NjCfVNFSm1Pn80/mndfj+vyTV4cnSzXyas/wCvQ89s/g3YN8OfEHg/U7yS9stYvb+7eeGMRSQm4uXnXZksN0bOMMcglAcdqj0/4a+INU1rRL3xp4msvEMGhTfatPt7DSDY7rjy2jE9wxnl8x1V3wEESguxKn5Qvo1FHW5T1u31v+O54rcfAPXptFtfDSeNYIPCNhq8Oq2VnHo/+lqsd4tyttLOZyrxDBUbY0YYjJZgrK+R4N8M+K9W8efGJ/D3i2DQra58Rrb3EV1pf2xo8abZ/vbZhLGI5SHOTIJV+SP5Bht/0DRRH3U49LNfe46+ekbDetvX/P8AzMbwb4UsPAvhPSPD2liQafpdrHaQec25yqKFB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OJvMVZGU4XacGrviDwP408Zad8WrqXwy+mS+IYNLTS7Ka9t3mcQj94JSrlEcEngMVxjDHmi37uKT1SV/XlX36/qZyk1Ju2jb/OX6Wsdkfj5CPETaB/whPiv+3ZrYXunWDW9qjalbbyrTRu1wEjCfKWW4aJx5iDbuYLXP+MPi9HqNx8Jtd0v+14LO/1+7srvSY1K3MkqWN4htZY1baWWeMA5YoGQNu2jdXb6n4Z1O4+OXh3xBHbbtItPD+o2M1x5ijbNLcWbxrtzuOVhkOQMDbyRkZ8xk+HvjXQ7fwZe2PhxdUudI8b6xrV1ZteQxsbKf7cEdGLbTIyzptUkfMyhyg3MukbWi+/4e9b8tfx20Kd7St6ffFv89Pw3PRIfjlodrY6/J4hsNT8JX2hxwz3emapHHJcGKY7YXi+zySpKHcFFCMzbxtIBxmOT452Gm6Tr91r3h3XfDV5o+lyay+mails09xaIDukhaGeSNiCNpUuGUldwAZSeC8d/CnxR8ZG8S65e6TJ4clNvpsGkaPeX6x3UptLs3chmntXcQ+a22NWjkZkCh+D8tQ6h8K5vEng/xs1j4E8V6T4iuPDV5pVlL4s8VnUnladQTDCGvrhEUtHGWdjH0XqM4hfDd76/k7ffpddNdL2LilzpdLr9L/drZ/PZM9u8G+LG8Zaa9+ujalpNmzA2smoiJTdxEArMipIzKpB6SBH9VFcrrHxwsdPn1aSw8N+IPEGj6PM9vqms6VDA9vZyJ/rl2PMk0pjHLeTHJjlRllKjv9Kt2tNLs4GQI0cKIVGMDCgY4rxzR9I8Y/D3Q/Efg/TfCM2vR6hf6hd6briXtrFZRrdzSTYu1dxMpjaVgfLil3KFI5JVSejlya2vbzelrv8A4b1Ip+9GLnpe1/Ja3/TuRa94vv8AxV8fvDWjwaX4jvPDMOkjVbe60fVIbW1uHeaHbcy7LpJJYEXK+WyncXY+W4wR6B48+KFh4A1Pw/ptxp2papqWvSzW+n2umwo7SyxxGQoSzqq5AOGYhR1ZlGTXM+AvhffeBPGnheKINd6LovgyPQRqDMimSaOWPGUzuGVQt0wOma3/ABh4b1HVPiV8P9VtbbzbDS5b5rybeo8oSWxROCcnLccA4703ZRSi+svW3O0v/Jbfh0sjNSk221/L/wCkq/43v8+upzK/tKaY2m6nfHwj4ojg0S4kttf329sDorIwy037/Ei7CJc2/nfIQxxkA9L4m+LVpomvSaLpmhax4t1S3tE1C8t9DSBvslu5YRu7TSxqS5R9saFnIQnbjBPGa18OfEV34K+PFhFp++78TXNzJpMfnxj7Sr6ZbQKclsJmSN1+fb0z0INZXib4W32lfELUNebw34p8SWOs2Fokg8KeKZNKntLiBPLKSx/bLaORGUgq4ZmBDgjBBpPZW3sn82rtfJ6d+l7mzVr27v7k1b703+drXR6HefGbSpYtF/4RzTdT8aXWr2Y1K2tdFSFWFocATSPcSRRxgkhQrOHJzhTtbb5r8PfHK+JNPurrVNV8TW4l+I89lZRRTGORNoytrOrniAFXDRr3AxxzW1ovgvVvhL4m0/WvD/gq61XSLrw/baTPo+najDLeadLDLLKD5t3NGs0bee6s2/duRThgx25XhX4ceM/scM+r6LDZXsnxEl8QSwQXkcqR2TIwVw2Ru6gEYDZz8uKI6VE/6/iR/wDbVf73sZVL8ll/X7uX/tzt93U9IX4tRX/iO40zRPDWueIrSzvF0++1jT1thZ2k+4K6EyzJJJ5eRvMSOF5H3lZQXXxZXTfEVpp+p+Ftf0vTby+/s22126jtxZyzksEXasxnQOylVZ4lUkrz8yk4fgW38TfDW6vfDJ8J32uabc6zdX1tr9ld2qwJDdXLzt9oSWVJQ8ZkYYjjkDKqEHJKr5ivwl8R6hfeGpL34drdeLdO8TW99qnjbVLy1nluLWO6LZtJDK0yps2HyWWJUXcqqTgEjrKCeztf52v6W1t+pdTRTa6Xt8r2+/S/6G6fi5q2raB8bI/FGleLND0PQ5rhI9T02awiurCBbG2cxQtHOW88mR5VchlAcAuCNo9P1r4nQaJrQ8PaToeteMNbt7SK7urTSvs4e2hcssbyyXE0MeXKPhQxc7SduOa848aeAvFV1oHxz8O2Xh25vf8AhLEnvtL1GO5tlt5Hawt7cW7B5RIsm+Jjkpsxj5geK6ubT/EPw9+IniDXtP8AC974t0zxDbWhkj0q4tI7m0uIEaMhluJYVaNkKkFXJDBhtwc1TtyQ9Ff1S1/G/r87jl8Tt3l911b8Pu+TRZuf2gPD6af4VntNP1jU7nxJLc2tjp9taBblbqBGaW3lWRlETqUdSWIVSpLMq/NUlp8ddKuND1C6k0XWbTWbLVE0WTw5OkH29rx1V441KymEh0dXD+bs25JYYOOZ8J/CrxBofibwZql3bwNL/bGtazqq20qmKza8RikSkhWkwSqlgvJBYgA1DrXgG+/4SL4m3GreBrvxdomvapp01tbafe28Nzshso1NxE0k8XlvHNGADvRx95ScDMPd/wBdY3Sf36+j6O4tb+V7ee9n08tN91109c8K+ILnxHpr3N3oWpeHbhJWiax1XyTKMAEMDDJJGykHgq57g4IIrh7v4+2Ef9uy2PhbxJrFloN5PZ6teWNvAY7MxH53w8yvKNuW2wrI4A5UFlDanwZ0nxHo3hW5g8RSah819NJp1tq92l3fWtmceXFcTIWEjg7ju3yHBUF2IJrzPwXrXjCzsfiTpeheD21eTUPE2px2OqQ3lvFbW7swQteCSRZAFb5v3KSkqMYzwZlfmajvy3XreO/3u+u/UI6pN6e9b5Wlt9ytpt0PSJPjRplz4utvDujaTqniO9uLC11VZtNWDyBZzs6rOZJZUG1fLyQPmIddqt82K8fx20aXUCyaVq7eHF1E6SfFPlRDThdiXyfL5l84r537rzBF5e7jfjms74W/C29+H3jQqYRJpFn4R0jQ7e+3LmWS2a4Eg25LDh4zyMfN1ODXBeFPgk+g+V4V1fwZ4q1myg1MtFq1v4ynj0aW3M/nJLJaG9VldQfmiW3ZC6cHa24bSUVO0Xda/de35fjrtoTd8t7a6f8ApN3+On4b6nruh/FZPEviA2WmeGdcu9GF5PYHxIgtvsAmh3rKMGfz9okjePf5W0sOCQQai8PfF6HxZfxtpHhnXb7w7LJJHF4nRbZbCTYrEsoacTshZCqyCLYxwQSpDVxFj4M1yX4pRahoHhbXPAtpc311J4hmm1W2k0rVImjKCSG1jnkK3DusMnm+VC2BJvYk7Tu/C2HxR4N8L6J4B1DwhePFpNmNOHiS3u7Q2E8UcRWOUKZhOHcBQymLAYt8xUBjk78ja3t976/8BaX7aa6O3N5f1/T6ffpn3Xxi0r4heBdI1uOw8X6DoF9qWkiy1W1e3t3upJruJFiwJWfyw5CS5UBkL7C4INVvDfxT1HRfF3xUtv7E8SeL103xAp8nTTE4sLY6faOAvnzRggt5reXFubJJ2/MMlv8ADXxDD+z78OPDI00LrOkXmgy3tqJov3S293BJOd27a21Uc/KTnHGSak0ZfHXgHxl8S57XwJNrtpr2sLe6Td2moWkYyLK3hJuRJKrJFvj4KCR+JPkHyb9naPOo/wB63nrTt9+vyT82S9eX5f8At/8AwPm/kdbe/GrR5U0NfDmn6l40vNYsf7UtbPRVhV/sfA892uJIo0XcyqFZg5JICna2Gv8AG7RrvTdDm0TT9U8SalrCzPbaPp8MaXarAwS4MvnyRxxeU5CN5jr85CjJOK8pj+Aup+B7rwzfS6Zr3i/y/D8Gj6inhPxDLo9xHcRSSS+co+02yyxM00gIZ9ykJtUhmx0Oj+BNU+GuveHPE/h3wRf3dk+nXljqGgjVornU7Z57kXKzme5nCSksHEg844ZxtLgE1Flez7v8Oa332X39Ok3fTy8u1/mru3p1Nr4C+ML7xh4i+KE92dTgjtvEKW8Gn6oT5liBY2peELuZVAkLn5CUYkspYMGPr1eWfBPw/wCJNM1r4i6t4j0ZdEfXddW/s7dbqO4PkCzt4lLFDgODGQw6BgdpZcM3qdOW0f8ADH7+VXBby9X+bCiiioKCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiivFda+OmqeGfj9L4U1GwtP+EMFvYQNqkSv59pe3bTCDzjnb5LmExggAh2QHhqa1aS6/1/wPUHonLse1UV5l4n+MH/CGeKvGkOpwK+ieH/D9rrA+yxs11NJLLcoYx82DnyUCjA5Y5PpFeah8WrHR5PEDxeF7gRRm5k8Jw2tx9p2AZMKagZtjSY6E2wUt8vyg7wtld7f1/l8uvQbTvy9f+An+qPUqK8w8dfFW+b4e+H9U8Bw2ep674mMR0W21IN5UimIzuZAjAjESPznAYrnPQ3r74u27eAfDevaTYHVNQ8SGCDS9MaYReZcSIXKSPg+WsYWRpG2sVEbYVjhS7NNp7p2+bJvs+6v8j0GiuH0Wx+JFvq1lNq+t+Fb/AEtmP2qzstHubWaNSjY8udrqRXIbaOYl3DJ+U8VzPwk8UfED4jaeniG51nw1aaL/AGpfWjaZDoVwbgxW93LAMXBvdoZhEDu8rAJ6Gi39f1+gPa/9df8AI9eorxT4N/HTVfGfjbxF4c8S6faWDi/1BdBu7JXEd5bWtwYJY33E/v0IRjggMsqkAbWrYs/FXjPxdoviC60XUdB0abSNZv7E/b9JmvVmhhbCcLdRbX65OSDngCocko83S3N8tF+pfK+bl635fnr/AJHqdFfP+l/F3xzZ/C3wb4p1nUfC8lx4y/s+CxCaVcWltpc11GZA9xI11IZlGNiqoiLuUXcm7K+u+EbXxbZteJ4o1TRdWUhDbTaRps1iV+9vV0kuJs/wYYMOpBHAJ1lFxbT6O3zXT8V5GfNe1uuvy11/BnR0UVwfxw8bap8PfhrqGt6Ktm2px3Fnbw/b4nlhXzrqKEsyK6M2BITgMOQOajyLSu7HeUV5Tb/ETxD4H8bHQvHF3pOqWdxo9xq9vqeh6dPatELeSNJYnt2mnZy3nxbGRsk5XZnBO/Y/GbwnfaTr2oG9vLGLQrb7ZqMOp6Xd2VzBBtZhL5E0SSshCPhlUglGAyQRTtpdf1q1+aYtb2t/X9NHb0V4742/aM0nQ18NzaRb6rqNpqOspYTXCeG9SmjeHYzM8DpDtlJ+TaULhxu2htpx1Wk+MrKHV/GGoX/ilP7H0+G1nks76yNkNIQweYxklcKW3ghzuxsxtOORU9HLov8AJP8AUV9VFbv/AIK/Q7iivMNY+LGk+JPDssmj69qnhWSK809Tfal4avIt6zXKIsaJcQpvEuDHvXITeGJGBWprnxu8H+H7/VrG5vr24vdJkEd/b6dpN5eyWuYllDyLBE5VNjg+YflzkZyCA9t/62/zX9WK32O7ork9a+KPh3Q7HS7xri81S31OH7RZtoWm3OqGaHCnzQtrHIfLw6/ORt+Yc81Rvvjd4KsbbQpjrX2ka9ayXmlxWVrPcy3sabN/lRxIzu4DqTGBvwGOPlbB/X9fcxLXY7qivIPH/wC0NpOifD2w8T+HvtOqRXOsW+mMv9j3srQn7WkNwksSRiSKVVLhVkCkvtADEhT0uqfGvwro7W6XMuredLapevDDoV/LLawuWCvcokBa2B2vjzgn3H/unDSb272/BP8AJibSV3/XQ7qiquqarZaHptzqGpXkGn2FrG0091dSLHFEijLMzMQFAHUmuX8L/Fzwz4w1hdKsbi/t9ReA3MNtquk3enPcRAgM8IuIo/NVdy5KbgNy5xuGV1sPZXZ2VFeeaV+0B4E1u70qCx1ia4j1Sf7Ja3i6bdCze4yw8g3Ji8pJcow8tnD5wMcjPU+MPGmieANCl1rxDqMWlaVFJFFJdz58uNpJFjTcQDtG5lBY8DOSQATR0TDrbqbVFcl4Z+KnhrxZqOpWFjd3MF7p0K3Nxb6np9zYOsLFgsyi4jQvGSjDeuV461U0H41eEPEmtWemWOoXXnX277DcXGm3VvaX+0bj9muZIlhnyoLDynbcoLDIBNOzewHcUVleJvE1h4R0l9R1Frj7OrrGEtLSW6mkZiAFSGJWkc89FUnAJ6Amub/4XZ4Mj8K3viO51g6dpNhdx2N7JqVpPaS2czsios8UqLJFnzIzl1A2sGzt5pLXb+v6uvvDsdzRXAx/GzwzfWmvGyk1N7/R7UXc1jNod9HcmNiypJHA0IkmjLKRujVhwea5f4f/ABef4keFfhdrEmsT+HL/AFiSP7Zp0mg3EcWpzGwkmkghedQViBBkWZWZWEW0Md1VGLle3S343/yE3b8fw1/U9mrM0Pw5p3htL1NOt/s63l3LfTjezb5pDl2+YnGT2HA7CofGeoXGk+D9dvrWTyrq2sJ5opMA7XWNipweDggda4T4Z/Hbw74o0nwpY3Oqz3Gu6ppkc63DadPHbX0ywLJMILjyhBK6/MSkTEjawwNpAhNXl5Jfc7//ACOvyCWnKn1v+Fv/AJL8z1SiuXh+J3hm48G6Z4rj1MPoOptbpaXSwyEytPIsUSiPbvDM7quCuRk5xg4xG/aA8Crlv7XuHto7qSxuL6PTLprS0mSYwslxOIvLgIcY/esoIIYZUgmrO9reXzH0ueh0Vx3i34ueFfBGo/YdW1CZbpYPtU6WdjcXf2SDJAmuDDG4gjOG/eSlVOx+flbBqnxc8LaTr0OiPf3F3qs0EF1HaabYXF67Qys6xyjyY3/d5jbL/dX5dxG5crpcDsaK8p+Gfx407xlYeK7zVBcaNa6JfXatd6hpN5p9vHaRPhWkluEVBIByy5DDuq1uWPxt8H32k6zqP9oXVlBpFmdRvI9S0u7s50tQGPnrDNEskkfysA6KwJBAOeKOifkn8mC1dl3t89juqK4vw58YvCXizXoNH0zUZpby5t2u7RprC4hgvYV27pLaeSNY51G9TmJm4YHpVRfi/wCHPEEOq2elanf2s0NpczR6q+iXf2PEQIeWGZ4hDcBTziN23AcZFKT5E2/N/cOPvOy/q539Fcj4C8aabrtrbaTHrv8AbmtWml2V7dXLWjWxuI50JjuAhAUCQo52rnaQQcYqnffG3wfZafp14NQur5dQErWsGm6ZdXtzLHExSSUQQxPJ5QYY8zbs+Zfm+Zc1JcjaZEZcyTR3VFUtG1qx8RaTaanpl3Df6fdxrNBc27hkkQjIYEdRXM6d8X/C+qeJIdDiur6G+nlkgtpLvSru3tbqSMMWSC5kiWGY4VmAjdshWIyATRZ35epXS/Q7OivN/EX7RHgHwq2rnU9YuILTSJGhvtRj0u7lsYJlA3Qm6SIwmUFgvlhy+47cbuK1vFnxe8K+CtQFlquoTi5W3+1zJZ2Fxd/ZICSBNcGGNxBGcN88u1fkfn5WwuifcOtjsqK43xB8XvCnhnVINNu9RmuNSuLNdQgsdNsbi+nnty23zIo4I3aRQeu0HA5OBzXIeMP2itI0lvh9eaMLzWNG8S3rRyXFnol9dsIFgnYhBFGSswkjUGNgXAD5QbSVP+GDv5K/4XPYaK43UPi54Z0zxBHoss+oS37SQRSfZdIvLiG2kmK+Uk80cTRwMd6HbKykBlJwCDXZU/MAooopAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV46vgMeKvi/8VLTWtMuH8Pa1oGk2QuHiZY5ipvPMEcmMb03oeDlSVPHFexUUdGn1Hdrb+rNP9D5X0nwD4+8Xap8TPDuu28sesWugadpumeJLyFhZ6rLb3FzPBOWAxkhoRKoyVbdxgivVbr4xXeoaLLp9n4L8TL4yljaFdJu9JuIrWOfBB33+w2vlD729ZWyo+UMxCH1OiiXvq0v6/wCH6/hYNpcy/rRL8Lf53Pnfwn8I9fn8W6Po8fiHXPDel+A/D1rpFlqdjZ24+33Uqg3Lp9qt5UZFSOBQyjILON33hVHTfh/4t8Iag+n2ltqPiY+DvEo8RaXLdRxQtqdleQTR3UEcirFB56STXLBPkGPLB2hgw+lqKrmle/X/ADd/z/DQm2ln/Wlvy/HU8H1fXk8bfEzwPqHhi28eWd6NTDatFfWmsadpy2S20+4SRzqlqx8wxfdBYkDGa574BaJ4K8NyRLrPw9u7Xx7/AG7qbrrE3gi8Mg8y+nMT/bvs2wKYnT5/MwFOMgV9M0Uovl29fy2+77wleSs/L8L799/wR8/+Fvhfq2vfDHXIY0m0DxTY+LdW1jQ7y8gZDFN9smaJyCMmGVGKNj70cjY6itz4Ctrmo/DbxRea54evvDmq6hrGpXLaXeRkSIXb+Hj51JztYcMMEda9korPkXK4d48v5a/PlRfN7yn/AHub89PxZ414NmsPCv7OPw+0nxh4Y1PU7SbRbHTr/S00KfUWhYWoLC4tkjd9oZNpyhwxUHHUT/AvSJNF1TxBBoUGt2Xw6EVqNHsfEEVxDLbzgOs6QR3IE8duAIcK4ADb9gC169RW8589SVR7yM1Hlioroc1rHhPVNS8QW+o23jPXNJtIim/SbOGwa2m2nJ3NLbPKNw4O2QcdMHmuS/aZ0C88UfBzVNMsbO8vrie9079zYCQzbBfQM7L5fzDaoZtwwQATkYzXqVFZ9vJ3LTs7nkfjbwK3w18Ea3q3ge31abxHMbaOfUZLmfWNTFmJ084QG6aZmKRmV0iwyluiMTg+Tav4Vn1P/haL+HdC8a3el3vgOWztbzxGdRnnvrovNujijuyZYzyPkCpuJyq4IJ+taKVtbvz/ABTX3a39Soy5bW7p/c0/0t6HlPxSsJ9N8K+BLyDTr25tND1ixurq30+0luZ44FjeMlYY1aR9pdchVJwCccVyPi/wjrXiqb4jXWn6PeXUM+qaBqtvZ3EBtzqMFt5E0sKeaFAciNlw2MNgNt5x9CUVV3dy683N89P/AJFfiZRjy8vkrfn/AJv8Dxf4j+KF+J3gZYdC0XxE0ttrWizSJqGg3lk20ahC7lVniRnCKjMzKCqjkmsfwv49t/BPxS+MAufD+uX7XGr2zW9xpGlz3wuJF062/ct5Kt5JGVIaXYh8zhvlbH0BWF4f8H2XhvWPEWpW0s8k+u3iXtyszKVR1gjhATABA2xKeSTknnsBW1XR3/Hl/wDkS76a/wBb/wBf5HgmmaFrfgnRfBPhjxVB4qs/DqaNJNLZ+DI7qQ/2i05b7NLcWS+bEkayAIQ8cbbW3MwAAf8AA3wjrOnax8M59S8Panpn2LTPESzLqCPK9pJLqELRpJM2cuybsMWJcBiCRzX0rRTi+XX1/G/5Xt6JL1i2t/66f5X9W36fNms+F9bj8N/FSQaPqMiL4+sNXghjtJGkuLWI6dJLJAgGZR+7lOEBLMrAZbir/wARNZuB4hm8R+AI/GGn+Nru2tkj0+bw1dnStZUENEl20sAW3KhpEMhkhkTJ3bgFB+hKKSdlFfy2/CKj+KS/EqXvX87/AIu55/8AHLwvqXjD4Z31hptt9uvUntLz7AGVftaw3EczwAsyr86xso3EDLDJArnrzVpPiz488DXWi6Vrmn2nh68m1LULzWdIutNCq9rNCtugnSMyszS5OwMiiM5YEoG9hopLS/8AWv8AX9b3T2t8j5wuPB+rR/ss+FtJh0S9TVYNV024exjtHE8e3VI5HcoBuGF3OTjgZJr0n4+aNc694Ct7O1sZtRc63pMj28MJlJjS/gaRioB+VVDEnoACTxXo1FEfdafZp/l/kV/lb8/8zwT44eANd8deP9QsdIgniXUvAGsaWt+UZbdbiSa28qN5AMAkb8AnONxA61Y1nVJ/ihpvg7w5pHh3XdD1DTdX0+/v21LSbizt9OjtZVeRFndRHMW2eSvkPICJN2dmWr3Oiqi+VRXZ3+ak5L8X/wANqKXvX80196UX+C/4c84+OF9rVjoejf2c2sRaTNqaQ63P4etXuL+KyaOQExLGjyDMnlKzRKZFVmZSpG5fFofB97f6X49XT/Dfif8Asq98T+HZ7Ia+by7uru2jmtjLMftJeUIArkq5ygU7guCB9YUVMfdfN5p/c0/0/FjetvL/AIP+f4I8p8SaFqV5+0Fpd7BZXDWDeENStJLxYm8gTNc2pjjZ8bQxAcgE5wGI71w3gKx1G68N/s+2H9i6xb3Phi5Fjq63el3FuLaSPR7mJm3SIA0fmMqiRSUYsAGJNfR9FXCXJHlX9ayf/tz/AAFL3m3/AFsl+iPPdf8ABes2uh+Kp38Xa/4hS6027jg0e6t7HyUZ0baI/JtUlJH3QGds55yea4vUfC+pW3gX4BWdnpV4jaTqmmG7hit3zaRrp86OZABlFBYKd2ACQD1r3akYFlIBKkjqOoqI+67rvF/+Atv8bg9fukv/AAJJfhY+e/CPh27X40y+CGg8vw54Vv5/FdsVOUIvFZbeLHYLM+oMB28qP2rmPCPiWa6+B/jjwdYeE9Y1DXtX1XxDZ2Crpsstjdma/uUEsl0FMMKKSwZZXR8REhTuTd9EeC/A9v4NXUpf7QvtZ1PUrgXN7qmpGMz3DBAiAiJEjVVRVUKiKOCcFmYmbwT4NsvAehvpeny3E1u15d3xa5ZWffcXEk7jIAGA0rAcZwBkk807R5OR6q1n57fokr9d99ndqXOt73X4/q27eZ4FdeEdT+HfjrxN/aHiLx3pthrFvZNb3fhXQYtWhnENqkDxS/6DcyxurIWG7YjCX5eQ9dn8Jfh//wAIX8SJYbew1KPSbLwbo+mWd3qSDzD5ct1uiZ0AjMigx7gvAyOxFe0UVXM93v8A8Br8mRypR5Vtp+af6Hzb4i8Kav4i8BfE3wbbWGsQa1/wkL63CI7Fkivbb7VDcBYLiVPszu6qV2MxAbh1xms/xN4bPjnwn4z1W11P4i+KNch8Jalp9tb+I/Diacg+0KhMKItjbvNIzRLgJvA2nOCy5+oqKziuWKj2Vv8AyXl/Ff1qaKTjPnXe/wCN/wAzyLxzoV63jL4MzWmmXMtppt7dC7NvbsUt4jpk6APgYRS+xRnALbR1xXE+GV1O1ttV8PeDo/Fd14Gk0G+36T4m0K4tH0mYqTBb2k00UckysXdfL/fbBGuHUbQ30nRSqr2qkn9q/wCN/wDMVP8Ad8tvs2/B3Pm/xN4X8WeHfDfw+1rwtpl0fEV94ci8J30ZgY/Y/NhVobmZQNyrBKr5zjHmtnFO8TeArj4b/Eu11Cx1TxdoHhhvDlnotpceEdGi1RoWtpJiYpojaXMiqyyqyuihSUcOc7M/R1Faym5Scl1bf33/AFk38+xlCCjBQ7JL7rf/ACKXy0OL+Evhy38K/Duxs9Pl1aeORri8STxBEkV2zTzPMTLGiII8tIcJsUqMAqCCK8F0y21vxNrHw4vtT0zx3qHjKDX4ZvEUuoJe22l6ftjlUiKDCWskYYBVkhVztwzyEtlvq6ip+2p+af3O/wDw5f2HHun+KPkj4g6rdeFf2VPGngO+8Na7J4isrO8jmcadM1nOrztJ9rW8ZfJYMJBJsDmTOV2bhium17QdS8JfE7xjfX2veO9H07xEtrc2s3hTQIdVhlWO3SFoJf8AQbmSJ1KlhuKIRL8vIevTdQ+Daa5fMNb8XeItc0E3hvf+EdvXtfsZbzDIiMyQLO8aNjEbyspCqGDAYr0SlZOKv8/69fX8bKr62Wy2/D/L+t3438OPAQ8H/EbRYLWw1EaTpvgm00y2vNRjUyrtnY+U8iDZ5gUKWVeOBxjFcTpOi6t4Y0Pw7qt7omrfY9J+ImsahcQ2umzz3CWszXyRzJBGjSOhaaM5RTw+7pk19NUU+Z3UvX8Zc3/AF0a7/wDyDh+tzwHxJdXmj/Eya+8Ar4qttd1LU7P+1tFu9BuDomoxbUSW5NzJEEgkWAg7kmXc1uqNG7cH36iii/uqPb/gf1/wA3dwooopAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVzut/Ebwp4Z1yz0XWPE+jaVrF7t+y6ffahFDcT7mKrsjZgzZYFRgckYroq+ePGnjCw8C/FLV5PD/AIm0LxBqesahYQat4BvwH1J5AqRia0KtvULG0chV43TEbMGjyxoWslHv/X9MH8LfY9jvviX4Q0vVLXTbzxVolpqN1cmyt7OfUYUmmnBUGJELZZwXQbQM/OvHIqxoPjrw34q1DUbHRfEGlaxe6a/lXttYXsU8lq+Su2VUYlDlWGGxyp9K+c/F3h3TD8D/ANpO8NjC11capqM0sxXLs8NvE0Jz/sMAy+hyRyTXc+ONJuLT4neGbDw2ken37+CtZtLLyQIwjK1mIF6cKrHjsMmlf93GdtXG/wD5Jzf8D8fJtLmk436tfdK3/BPTbX4j+Er/AMUS+GrbxRotx4jhLCTR4tQha7QqMtmENvGByeOKwfDXxRtm0/xhqPia+0zQ9M0PXZtMW8uJhbwiNRFsMjyNt3FpMdQDwAK8e/trwrrvwW8D+B/CctqfHGmXGlG30GMgajpNzDNGbmaeIrvh2qJ98jqobeRkmUbsvT5F8O/FTW/FHjAC9+Htj4uvUhYJiDR9RZIBHe3YOdyYJRZeFhLZIO7elqKVXkburPXpo4a/O7S31e/bNtuEZW6rTr8MtPwTfo9NNfpi+8e+GdL8RWfh+98RaTaa9eqHtdLnvokup1OcFIi25gdrdB2PpW7Xx3eRtcQ/EbwjrvxG8IeF7/xDrVxc/ZdU0SWfVLuORx9iuLSQXsfnlY1hERiiOx49vLKa+wLeN44I0d/NdVAZ8Y3HHJxSS9xSe7/y/TYp/E0ul/wdr/P+mzzzX/iP4k8I65YtrPhWzi8MX2qxaVDqFpq5mvUaWTy4ZZLUwKoRn252TOyhwSvDBem8TfETwp4Lu7O18Q+J9H0G6vM/ZodTv4rd58EA7FdgW5IHHqK8X+IXiPwprniOwvvAfiiPxB8QotbtcaAuqnUBEqyrFc7rKVnWyCQmXdNGkTqeC3zlW2PDvivwr4C8ffEWDxvqWn6Lrms6ojW39ryrGdSsPs8aQR2+8/vlVvNUxpkh2f5fnBYh7ySfn8/h0Xnq36J6aCn7rbX/AA2+r8tEvVrU9Om+JHhK38VL4Yl8U6LH4lYgLoz6hCLwkrvAEO7fyvzdOnNc94Z+OXhfxR8TvEPgm21jSG1PSzEsUcWpxSTXTlXaZFiHzAxFMMBnGecV4H8VfHkfiHSfEVve+LF0nWrDxPDBF4H0eztfNCRahGI7263xvP8AOuyXz0aJAGjAyeX9o8L61p+k/tC+OtKvr+1s9T1Wz0y40+znmVJruNI51kaJCcuFKnO0HGOaUdYxl3/yTHP3ZOK6f5tf1+p6xWd4i1b+wPD+p6n5Xn/YrWW58rdt37ELbc4OM464rN8WWPi28e2PhnW9F0hFDeeNW0ea/Mh427DHdQbMc5zuzkdMcnj8Ovw68RiVleT+yrncyrtBPktkgZOB7ZNYV5OFGc47pM2pRUqkYvZtHn+nfGTxjD4K07xprXgXT4/CtxZx6hO+ia7JfX1rbvGH8w272kIcIDlwkhYAHarkYPoOufEXwr4Xs9Ou9b8S6RottqOPscupX0VutxkAgRl2G44I4HqK8Y8I/GbwrefADw/4f8O6tp/i/wAWT+Hbewh8P6PcJd3HntbLHidIyxhiVjh5JNqJ3OSAeX/s+5+EPxCu7DW/G3hHwtby+GNM0qxvvGOlPPb3ltBGyTwwzG7t0U+axZ4juYiSNunTrqLlqTilonp573+6y+++py025QhJ7ta+Xw/5v7rabn0tr/jjw54UONb8QaXo52LJjUL2OD5WcIrfOw4LEKD3JA61Q8M+KmuLy8s9W1jQJbybUrqDTbfTLnMjwxAEo6scmZBneF4XjpXkvwv8H2uifFLwLbPf/wBunS/AbJaag9u0G5GuYwriN2ZkPlnaMkkAkZ5NZeoWMlp4L8R+MLUf6b4O8d32t8DlrZZCl2n428k34gVnFq6vtaXytUUb/dq/uKbetujXzvByt9+n4n0FqXi7Q9Hh1KW/1rT7GLTEWW+e5uo41tEYZVpSSNgODgtjOKNL8XaFrnh8a9putafqGh7Hk/tO1uo5bbahIdvNUlcKVbJzxg56V8zeJtP1JtG8F/EOe7sdBstY8SyeINRvtZspLy0ske2aHTZLiNZoiqJGsA3FwqSOGI6kTapZ2t54d1bxRL4t0Txx4XvPFOmX3iBvDGmGPTo4oFUSyODPcCUAi2aVg3yrFyODRZ3tLR3S9L2vf0v5beelN/y67/O19vWyt67aHqdr8d9G1j4lWOm6Rr2gap4RbQL/AFW71i0vEmSCS3mt1IMyuY1UJMzNkZGAcgdfRrzWoIvD82rWrx3tstq11E8MgKTIE3AqwyCCMYIz1r541Px18PvEn7RUmvQXema5oEHgDUxrGqWsQvLSe3S5tmaIugZZtimTdGu4gOAR8wFe43l1p178NZ7jSFjXSZtIaSzWGPy0EJhzHtTA2jbjAwMdMVNVuOH51vZ/+lTt+St6GkEpVlF7Nr8o/wCevqjgvAvxv8Qa9H4Dvtf8JafpGjeNIVbTbnTdae9lhke2NyiTxvbQ7QY0k+ZGfDADGDuHoMPxH8J3Pip/DEPijRZfEkZIfR01CE3i4Xccwht4wvPTpzXzB8Eb3wrH4a+DUngnxK/iDxstpYw6npK61Lqy2lo8CC8LwySSCxVMLgoIvmVI+Q2wx+KvH0fiiPw29x4sW016Lx3Yxv4H0aztkTTQNVCNLd5je4R3BYmbzI0kMygLh/m6pwXtlTWzly/ilfzWu/WzWlrnOpPk5nva/wCf3PTb567L6j/4WN4U/wCEs/4Rf/hJ9G/4Sb/oC/2hF9s+5v8A9Tu3/c+bp056VLa+PPDN94ouPDVv4i0m48R2y759Iivomu4lwDuaENvUYZTkj+IeteG+F/Flr4c+IqeGPCPifQPHGmatrOo3MulxgNq/h25cSSSzyurNuhE3mx/vY0dfPjXe/CnjvhPay6h4d8AeD9U+InhPS/Emj6pb3lx4dXQ5V19b2OQyXQZjeliZQZt84h2tHIz/AHWrGC5uXzt+O9vTyvfTU1n7t7ef679r2+Wr7X9w8J/ETXfE9jpdwZ9AsTJ4n1HSZ4rt5I3ntreW5RFthuO6ciFGIPG0SHAwMdVN8UPBtvq1npcvi3Q4tTvJWgtrN9ShE08iyGNkRN2WYOrIQBkMCOorwrwx/wA0+/7KXr3/ALlKzNa0exsP2T/i/f29pDFeyazrmoNOqDebiK/k8qXP95fKjwe20U4xvb+ukf8AO/8Aw+mk4++0urt/5NP/AORR9I+IPHnhnwnfafZa54i0nRr3UW2WVvqF9FBJctkDbGrsC5yyjC55YetblfKviW8l8P8AxI+JsHibxt4N8JWviJLdbceMdIe4+36eLRI/LglN5AjKspn3QhWKs+4/6xa9tfQdP0v4F/2L4m124m0uDw+LPUdbkVraZohb7JJyDlo2IyxByQeucVDuqbl10/XTyts/P0M4tSkl0f8AwPvvf8u5p6P8WPBHiC21C50vxl4f1K308Kbyaz1SCVLYMSFMhVyEyQQM4yRTX+LvgWO31e4fxp4dWDR5Vg1KVtVgC2UjMUVJjvxGxZSoDYJII6ivnTxR4sPir4P/ABA8Ly6v4f8AHujafo1nBF4s8OxiOOVGlVDaTiNnjEwC7z5bgYkB8uPjPq2peG9KtP2nPBLwadbQtbeEdSjhMcQXy1juLNIwuOgVZpVHoJGA6mqSUpqK63/BSf8A7bb5323jm0l5W/Fpfr+Hdu3pkvi7Q4PDf/CRSa1p8fh/yRc/2q11GLXyjgiTzc7dpyPmzjmuR8YfFzTovhnfeK/B+q6R4jhguILdbi1uFurYs88cbqWif7wWTOM8HGa8a0JrPRbXwVrGt7YPBmh+NPEJuJZeLWwmNzdJaTS8bUjQl1DthUaRDkcU34oXtt4uf4jeJfB13aXXh2807RrWTWLXE1leajHf8OpQgTGOJo0Zlf0TIKECfT/huuvrt6276XL3W12dvXW2n5/f2ue+eOPG0Gk2Gq2Gk694cs/FNtaLeJa65eCOOKIyBBLMqtvWMnKhum7AzVrxF8SvCPg/zBr/AIq0TQ2idI5P7R1GG32O6lkU72GCyqxA7hSR0r5w1a8s/D/wl+JfhfX1+zfE5nW/1e4unBfWYzOgivLc8brcLiMRgfuSpjPI3N6v4U8P6bffHj4rXdxZQz3MunaXZPJIgYmBopi0fP8ACT1HfAz0FRN8quu1/wCvy/G3Qm/vOL/r+lr+F7anoXiPxv4d8H6TDqmva/peiaZM6xx3upXkdvC7MCyqruwBJAJAB5ANWdD8S6R4mt5Z9H1Wy1aGJgkkljcJMqMUVwCVJwSjo2PRlPQivlv4KeJdJ8J6f8JNf8aahb6Xocfg97DS9U1WRIrS2uvOAkj818CORoEjC5I3LG4GcGvR/wBmSbTLq/8Ai3Po9v8AZtOm8Z3Esa+Q0IYtaWhZwpAOHYswOMMGDDg1uopuS7Jv7pKOvre45Xjo+6XrdX09NvU9N1L4jeE9G8R2/h/UPFGjWOv3AUw6Vc6hDHdS7s7dsRYMc4OMDnBrmvhv8efCfxKufEkGn63ozyaLdzRMttqsU5ktowh+1EKfljJYjPIG371efXXi6w8G/Fa7sfDfiPQfFo8QeI4Dq3g2YK2r2NxhInu4mVi3lxCGKQrLHgLGSsqjYtRr46tvBvhv41WkbaZeeIbLWbrUJdHvgJilnIlv/pMtuGDvAEYucYDBSNw6jKLur/3W/neH32u9dmuz2JXTUV/Ml8rS+67S80+/X1G++N/gdfBmu+JdN8W+H9X07SIi9xNa6tA8SSEHy43kViEZzhRnkk8A1a+H/wAVfDnxD8M6Hq2nazpcz6pGoW2tb+OcrceUJJIAVPzOik7hgEAZIFeEaPqEHij4qa8uj+Nrj4iy3fgS9jfULeG0+xtL5ybYIJLaJQ+0u3yO8jJ5gy2WOdaz8d+HY/B/wM8U/wBuae/hzR5EsdU1NLpGg0+Z9MeMLcPnEX7woh3YwzqDjNVFXTb/ALtvvmn99l96t5xKT5ko9L3+6LX3Xf3P5e/X3irRdLg1Oa81iwtIdLUPfyT3KItopXcDKSfkBXnLY45pvhnxdofjTTf7R8Pa1p+vafvMf2vTLqO5i3jGV3oSMjI4z3r501PXvCfjTVPiZqM3jG08NaYutaLc6Z4kutjWJuYbZJoZCZCI5YSyddwDY+Rg21q9c+CPjS88deH9WvryHS5nh1OW2TWtDDCx1lERAt1DuLHGMRn53AaFgHYAUoe8nf8Arb/Pbfrrra5OzXn/AMH/AC3+Xr6LRRRQMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKyW1HVF8Ux2A0jdozWbTNq32lRtnDgCDysbjlSW39Btx3o8Qajqmn/ANm/2ZpH9r+fexwXX+kpB9lgbO+f5h8+3A+QcnPFG9vP/O39eWu2obX8v8r/AD/z03NauHvPg7oN9qc1/Lf+KlnmlMzLD4v1aKIMTnCxJchFXP8ACFCgcYxXcUUbO4dLCUtFeb+IPjSNH8Va5oVj4N8SeIpdEt4bq/udL+xeVEkqsy4WW5jkc4RuERjxgZJFJtLcPQ9IorgtQ+MWl/Z9AOgabqfjG71yzGpWVnoyRLIbMhT9odriSKONPnQAM4YlsBThsTxfFK3jufCdpqeg6xomo+JL25sbeyvkgMlvJDFLKxlMcrrtZYWKlGfO5emTi+V/1/XfT1FdWuv66/lqdtRRXPeCfG9j48029vdPiuIYrTULrTXW5VVYyW8rRORtY/KWUkHrjGQOlT1t/X9aj6X/AK/rQ6GiuO8P/EC48XxaJfaJoc9xo15dXlteXlxPHC9l5DSRhvLyTIHkjKjacgEE+lddNIIYXkbJVFLHHXik2krsFrsPorE8FeLLTx54R0fxFYRzQ2WqWsd5DHcqFkVHUMAwBIBwexNUPiN8Qbf4caPZ30+mahrEl7fwabb2em+T50k0zbUGZZI0Az1JYVUvdfK972+e35h0udVRXCaL8WILvxFZ6Frvh7WvBuqX4Y6fFrQt2jvSoLOkcttNNHvABby2ZXIBIUgEju6QBRXC6p8Q9U0GbT01Tw59j/tDxENFtT9uR/MgZGZLr5VOM7D+7OCO5ruqFqrr+tE/ya/LcOtv63a/NMKKK474b/FTR/ihDq8mkx3cP9m3jWki3kQjMgwGSaPBO6KRTuVu47CjyA7GiiigAorJ8N6lqmp2dzJq2j/2LPHdzwxQ/aUuPNhWQrFNuUYG9QG2nld2DyKLbUtVl8UX9jNo/kaPDbQy2+rfakb7RKzSCSLygNy7AsZ3HhvMwPumgNr/ANf1/T2NaiisPW/F1noOveHtIuIp3udcnlt7Zo1UojRwvKxckggbUIGAecfWgDcorCvfGFlY+NNK8MyRTm/1Kzub2GRVXylSBolcMc5BJmTGARwckcZ3aOl/67AFFFFABRRRQBzPi7S/Ft9c2cvhjxDpejLGki3EGraO9/HMSVKMuy4gZCuGH3iDv6cA0nw/8Dp4F0i5gkvptW1O/u5NQ1HUJlCG5uJMbmCDhEAVVVBnCooJY5Y9PRQtNgetrhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfLX7QFvp/jD/hZeqtp3hPSZPBtkIJvEGvWMl9qPmm2WeH7IyyxGz2mUbJFZy0hJ2ArlvqWuc1j4b+EvEGuR61qnhfRdS1iOE26aheafDLcLEQwKCRlLBSHcYzj5j6molFyWjt+j7lxkovVHg3xC0eCKzi+InijQNF+I3h2HRLFNQtdQdRqOisFDSTWQZSheQTB2UNDISibWf5FXR0Hwj4D8cfEz41xeMLTTNaSx1C1uHs9WVZEsoW06DNwqvxGx2uPMGG/d4zxXslz8LvBl5r1nrdx4R0KfWrMRrbajJpsLXEAj/wBWEkK7l29sHjtXMaf8EtG1HxZ4t1bxXoOheIY9Q1aPUdM+3WaXT2oFpbwt/rE+Ri0GflJ4C854G0mpSl0XvW8ryi/6/Pa2UE4RWuunzsv6/wAu/kPgm+1DUtNsbnU7m5vbmX4Y3jJc3pJnlh+0fuXkJ5LmPYSTySSe9bmk+DdI8K/Dv4LXFjYwR6jqGqaPLf6gIlFxeSCzlw0r9XIyQMnCjgYAAr6CudD028upLq40+1nuZLdrR5pIVZ2gY5aIkjJQnkr0NI2g6Y1tYW7adaG3sGR7OIwLstmRdqGMYwhUEgEYwDgVEfd++L+6c5/+3L5omUbt+jX3wjH/ANtf3ny94k+HmgX3wl+NXii6sI7vxDpur6zfaZqU43z6bNDh42tWPMPzxqx2Y3Hrmvpqxvr668K295bxQ3OoyWSyxxTymGOSUoCAzhWKqT1IVsDselSSeG9Ik06/099KsnsNQMjXlq1uhiuTJ/rDIuMOW75znvV+KJIIkiiRY40AVUUYCgcAAdhSjeMFDsor7lZv56fcaOzlzecn97TX3a/eYnhO88T3kNwfE2j6TpEqsPJXSdVlv1cYOSxktoNp6cANn1FeL6h4Qu/FXxo+LaReNNW8I2i6VpiXDaeLURyK0M+WkeWJpECjPMUkZAJO4HBH0NXJ+IPhL4G8W6wNW1zwZ4f1rVAFUX2oaXBPPhfuje6FsDtzxSlHm0/rb8QV0eaeEfCKfErwH4F8R6Fqq+AvGOn6OsVnJo8SPaNZt8qo9pIT5lq5jV0G4OoxtkByTkjxpqHjjxd8GbzVIrNdQtPFOr6dPPprM1pcvBYXkbSwluQjFT8pJKkMu5sZPs/ir4Y+DvHdxbz+JfCeh+IZ7dDHDJqumw3TRqTkqpkU4GewqTXfhz4T8UaPZaTrPhfRdX0ux2/ZLG+0+GeC3wu0eWjKVXC8DAHHFa8zu2/P8Xe/56bNtsnlSVl2t/5Lb/L5Kx0VfPHwR8D6zrmi+KLuy+IPiPw/bt4q1kCx063014UxfSgkGezlfJ6nLn2x0r3Dwx4P0HwTpp0/w7omnaBYNIZTa6XaR20RcgAttQAZIA5x2FXNN0mx0aGSLT7K3sYpZXuHjtoljV5HYs7kKBlmYkk9SSSai1pc3lb8U/0KvePL53/Br9T5t+F8LWrfB2KW6knePVPEqvdTBA7kPcgu21QoJ6nAA9ABxVjwR4C0Twz4ki8KeKvBnhrXtU1zTL6a38YWMcc0uuQB1eU38bru3sHhbO6WNmyQU+Va+gYPC+i2v2TydIsIfsjyvb+XbIvktLnzWTA+UvubcR97JznNUPCvw38JeBZrmbw14W0Xw9NdACeTStPhtmlAJIDmNRuwSevqaiUebmv1Vvw/r9RRvFad0zyT9n/wL8P9O+E/wkvZdO0fStZa3ivbEwstrJdXzWjJK+1CvnyeUZM7gxwCe2R1P7Qn/IN8Df8AY46T/wCj67PTfhz4T0e6Fzp/hfRbG5F29+JrbT4Y3Fy6lXmyFB8xlJUv1IJGau+JPCuieMtLbTfEGj6frunMyubPUrVLiEsOh2OCMjscVrUk5zU+0lL7mnb8LBZcso90196a/W/3nAfGC+tta8TeBvCtjOs3iP8Atq11g28L/vbaygZmluJADlY2AMIJ4ZpAvPOPF9Qa2uNY8C/ETT7Pwp4Wm1zxfDa2y21lJLrl6rzvHOJr7zFzlPM3wGJ1QKFDkqpX6g8K+B/DfgSzmtfDXh/S/D1rM/mywaVZR2qO+ANzLGoBOABk+lVLb4X+DbPWLrVrfwloUGq3cwubi+j02FZ5pQ/mCR3C7mYOA24nORnrSh7kk+zv/wCk3XzUbfj2sprni+7Vvz1+93/D18Ut/wDmH/8AZVrj/wBrVXis5LfVLj4IxK32STxGb8qzD/kAv/pkg/3PPLWmB0VhX0OPDOjrtxpNiNt2b9f9GTi5OczDj/WHJ+fryea5jwr4O1RvHmseMfEcdhDqc1sml2Frp8zTpb2aO0hJkaONi8jsCw24UIgBPJM0lyKMX0t+EYL84/8AgLY6nvOUu9/xcn+Cl96XkVPj54m1Dw38MtQh0MhfEWsPHouk5YrturlhEj5AJATcZDwcBDXnuhx+Ivhd8TPCK61o2jeG/DmraUPCqNpOtS3wWe3jea0d/MtYQmEW5TPzZLrnGBn3q/0XT9UuLKe9sLa8nsZftFpJcQq7W8m0rvjJGVbazDIwcMR3o1bRdO163S31OwtdRgjlSdIruFZVWRGDI4DAgMrAEHqCMihaX8/ytb79Zfeuw3rp0/W9/u0X49z5dsvDdn8M9L1/w7rHhjQ5PFF54Q1We08caKwafW4EA86S9DKJFmbdbuSWmRmZ8OvQ6Vv4d8K6P4L+EHifwoLRvGOrX+lQx65bDN3qkLBftiTSDLyxiBZTsclU8tMBdi49fvPg74Z0fwb4o0rwf4Z0Hwzd6zp81m0mn2EVosjNG6p5hiTJALnscZOBV7wR8MPD3g8WuoW/h/R7XxK1lHa3urWdlGlxcYVAwaUKHdSUU/Mew9KuMvfcn05X62cnb01Sf5dCai5kvPm+V1FX+9Nr8zwODQrXxNpHgXTL5PNsbj4l64s8J+7NHu1MtGw7qwBVgeCCQcg1J8XtHsdDs/j9pumWdvpun23w90+KC1s4liihQf2kAqKoAUD0Ar6Ui8L6NB9n8vSLGP7PcyXsO22QeVcPu8yZeOHbzHyw5O9snk0X3hfRtUOom80ixuzqVutpe+fbI/2qFd22KXI+dBvfCtkDe3qaaa5eV/1ol+hrKV5839fG5frY8buPAuh/DX4y/C6fw5p8Wm3erx6hYapdRj99qSLbCUPcyfemk8yNW8xyWyW5+Y11XxVuI9N+IHwr1C6kW3sY9ZntnuJDhFkms5o4lJ7F3IUerMB1IFeh3GkWF5eWV3PZW891ZFmtZ5IlZ4Cy7WKMRlcqcHHUcU3WNF0/xFpdzpuq2FtqenXK7J7O8hWWGVf7rIwIYexFS5O9zGMUlZ/1pueY+LpItQ+PWiwwX62r6b4W1R724jdd1is0tsIZHzwufJlK56+U3YGvIb6wvPg/4R8W6DZ+FNI0r4hw+Eri7tvGHhtiX1S3SWNJri5ynnJc8pL83nZYuVdiGB+m/D3gHwx4R0m60vQvDmk6Lpl0Wa4s9PsYoIZiyhWLoigMSoAOR0GKb4T+H3hbwHHcp4Z8NaP4dS5KmddJsIrUSlc7SwjUbsZOM+ppdOXya+/mfytzfPqtrW9Wm+j/AMv8vl0638h+FvgDUPD/AI38N6vpWg+DPCejXWnzC8bw/r017LrkZRWimdWs4RM6sQ3nszNiV+fnOffq5vw38NfCPg3ULq/8P+FdF0K+ugVuLrTdOht5ZgTuIdkUFhnnnvXSVTd7f1/X4ERXLv8A1/XzCiiipLCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiivmTxR4Z1b4h/GP4gWOoeFvC/ir+z4bWPSo/EWuTWc2m2zwBjc2qJaTeWzTeaDOjI+6FRx5YNS3Z/wBf1/TGkfTdct8Q/Gk3gvSrF7KwTVNV1K+h06xtJrg28TzSE8yShHKIFV2JCsflwASRXjvhXwvD45+IOmeHPicdJ8ZyaZ4N067sobpRd2V7cM8qXl5GrqFlb5bceYUyolGNu85898J+DvB3i3wf4f8AM0zS/Euk/wDC0dQt7S4vYkvlktnlnJUO4bcjBEJ5IbapOcCrcffUE+q/9LUf1+XqS3aDl2/+Qcv0Psaxe5ks4GvIooLto1M0UEpljR8fMFcqpYA5wSoz6DpU9eIfCvwDoE3xc+JWqTaTZzXGla1bQaX5kCldPA0y1BMAIxEWUhSVwSFUdABXp/iy+8VWa23/AAjGjaPq7MW88atq8tgI+m3Z5drPvzznO3GB1zwO1k+6T+9XHZ7GxqN8mmafdXkoZoreJpWCDLEKCTj34rO8F+KrTx14P0TxHYRzQ2Or2UN/BHcqFlWOVA6hgCQGwwzgkZ7mm+ImnfwVqbXUccNydPlMscMhkRX8s5CsVUsAc4JUZ9B0rzHwD4ng8F/seeHNeurWa+ttN8F211JbW8hjklVLNSVVxypOPvDkdaPs1G/s8v481/yQ0uZwS63/APbf8z1jxFq39geH9T1PyvP+xWstz5W7bv2IW25wcZx1xUXhPXf+Eo8K6NrPkfZv7RsobzyN+/y/MjV9u7AzjOM4GcdK+WX8Pr8P/iMunWqeD9EOteCtYvLrQ/CGmtaqI1EPkNcSeYRdEFpVSYxRE/vOMMQNLSfD/hXS/hL8HfFPhgWr+NNRutFtrfXLYD7bqAYxrdwyyffkjWBZ90bEqgiGAvlrggryafeC9G5Ti/yX6ESezXaT+5Ra9N3959VUVzXgXSfDOkWOpR+FntpLWbU7qe8NrdG4AvHkJnDEs21t+cpxtPGB0r5nt/Cnirx1b+OtaXRfCA8Y2WvX0Nv4s1nX54NR0QRTt9lRUWzfyoRF5beWswWVJWLf61qI+80vK/5f576fiU9E352/P/I+jPEvxMt9F1ptF0vRdV8Wa5HGs1xp+irDutomzteWWeWKJN2PlQvvbkqpAYjL1n4yjw/4Xg1XUfB3iS0vZ9Vh0eLRZEtBdSTykCMq/wBo8hkO4fMJcA5BwQQMb4cXz6b41+J3huS7trDxfdai2r2n2xN/2i1e2hjiuFjDq0sUboYmwy4KYJUsDXnnjvxF4l8aaG+i63q1nDqei/EfS9Jh1bw/Y/ZvlMdvMJBFO9wodWnYfMWX5RkdaUU3yLvyv75RTS81e3rr5JN+7KXbm/BNq/k7X06HrWtfGKfw14eh1PV/AviXT57jUbfS7TTHk057i5lmO1CpS7aNVzwS7qfatfwf8R4/FOs3+i3mhat4Y1yzhjuW07WFgLyQOSqyo8EssbLuVlID7gRyBlSfNvjd4d1vQ/B3hS1XxZea5qkvjHS2t9Q1+0tnEDeaAAY7WO2DoCM4yGOT8w4xtfDia9g+L/iW18ZXNvN42/s+AWU1jGYLK60tZHIkghZndHEsjLKrSSYPlEEKwAIe836tfdGL087vXyvbYUtNuyf3ya18rLTztfc6fxl8XtF8DeOvCPhbU4bz7Z4meaO1uoo1NvC0eziZiwK72kRFwDlmA4zUnxf+LOi/BTwLeeKteS6nsbd0jFvYorzyszAYRWZQcDLHJGFVj2rzf45+DYfiB8YPB+hSyeRJdeHdZNvc4y1vOslk8My/7SSKjD3WvPPiR4muvjd8KvGeuahZmzj8HeGL61u7NiD5WvSQPHdJx18mLKg9xcn0p0/eUb99fS7/AMrX7uJrb3l20+/+n9yZ73q3xhe18aaj4Y0nwZ4i8T3+n2lteXUmmvYRxRJOZBGM3F1EST5T5wDjHXmotQ+PXh+w8A6r4n+xapM+lXMNjf6GIUj1C1upZI41hdJHVAcyod2/YyncrMpBOX4F/wCTjPiP/wBgHQv/AEK+ryr4wXC+KtD+K3ijQLyKbR7q68OaVa3wBltp7u3v1MsqYYCRFM0cZKkZaJ13ZU4rl95R9PzS/X8te+cdbN+X4pP/AIPome+eGfieuteJDoGqeHNZ8J6w1q17bWusfZnF1CrBZGje2nmTKFkyrMG+dTgjmuW0b9o2DV/Cdt4sbwH4us/CM0X2g600VlOkcOcGVoILqS4KjHO2IkDJIABIq+HX1Sx+Oa2/j29s7vXm0eZPDd3pdu1pZXNsWia8XyXkldbhXWHOZWUx7SqqRJVf4C+LNH8D/sp+EtZ169isdMttLBkkmP3iXYKijqzsSFVRksSAAScVD0hz/wDDbyXr0XzuWlq0/wCtLntVjfW2qWNveWdxHdWlxGssM8Lh0kRhlWVhwQQQQRWN4R8aWnjT+1ZNPt7kWdhfS2C3koTyrqSI7ZTFhiSqyB4yWC/MjYyOTznwX0PU/CfwP8MabeW0lpqlrpSZtJG3PAxUssRPcpkL/wABryWHQ7PxN8Lf2brG9MsltdXlpNcqshQzsdLuZJFfBOQ7bg6kkMGYHIJq3H3pJdGl97f5Wf3krWN/Jv7lf8eh9O1z8njaxj8fQ+ETFcf2lLpr6osu1fJ8pZVjKk7s7tzjjGMZ57V4C/wa8GfbPjRpzaBZvpWlRR3Glac0Q+zaVLJYh5JLSMDFu7OAxaPByOCK6bwDqFzq3xg8DX15K1xd3Xw6WaaZuru09uWY+5JJrOL5pqP3/OM2vxiTN8sb99vlKKf4SPXPGvi608C+GL7W72G4uYbYKFtrRQ008jsEjijDEAu7sqgEgZYcitpGLIrFShIyVbGR7cV5r4skPjL4ueHPDMZD2GgxjxFqi4BDSEvFZRnI7uJpeOQbdfUV4l428AaJefDL48eKri08/wARaTreoX2lalIxM2mTQQwSI9q3WEl0VmKYL4AbIAAcfedvJv5Jxj+bf3LuU90u7S+9N/kl9/kfWs7PHDI0cfmyKpKx5xuOOBntVDwzf6lqnh3TbzWNK/sPVZ7dJLrTftC3H2WQgFo/MX5X2nI3Dg4rxqTwfovw/wDi9o8Hh3TrfSV1jwvqkmptbIFa/liktTHNOessoM0p8xssfMbJOa4n4c+DdH8b3nwDsddso9T02P4bTSvYXI321wf+JaAs0R+WVQW3BWBAZVbGVBGkI839dvaf/If1reW9FLp/+z/8l/XT6tryvX/2kvCfhnwFD4sv7fWBZ3UN1c2drbae9xcXVvA2GnUR7lSJgUYSSsi4kQsVzivONEvpdB8A+MPAHh+U6fLqHjK78M6FFD0sYJUWeYxjoFhia5kVeANqqOwr0b9obTbXRf2Z/Hen2UK21na+HLmCGGMYVEWAqqgegAAqY2klLo7P70n+TXfr2LjrPkfdr7m1+afbbzPUJbhktHnjhknYIXWGMqHfjIUbiBk9OSB71503xmvbPxBoGk6p8OfFWkSa1efYraa4l0uVAwRpGZhDeu4RURmLBTjHqQD3S61p9rd2GmzX1tDqN3E0lvZyTKs0yoF3siE5YLuXJA4yM9a4LwrIPGnxm8U62+JLLwvGvh+w44E8iR3F44/BraPjoYnFJfHbp+i/4Nl8zNO9NPr+r/pv5GlcfFhNN8RWunar4W1/R9PvL3+zrTXLuO3NnPOSQi4SZpkDkEK0kSKSQMjcue8rxL4vw3Oj3vhbxRDrsvie1m8Raetj4dvFia3zK3ll7UwLG7yIjSTAzNOgEbNtUgOnn3i74e6Dr2iftIeINRsVvNX0y+nuNNupiWfTpodItJUmtif9TJvClnTDNsQEkKMSn7kpP7Kb+7k/H3vTY1UXKooLrZffzfhp67n1dXL3XxG0Wz+IFn4NkllGs3Vo12mIiYQATtjL9BIypKyr1KxSH+GvMtB8I6P4L+KPw4vNFsIdPvtc0q+/te7hXEupMI4JBJct1mk3lm3vlss3PzHPZ3vwnN5ourg6sU8R3mqf2xDrQt+badCBbgR7uUSJViZdw3qZORvNW1yys+l7/fbT8+m1vMyjLnjePXb89fy69/I2PGHja98MTJFY+D9f8Ut5RmlbRxaqsK5OATcTxB2OG+WPcwwMgblzV8PfFjRPE2raJZ2SXfk61pranp19LEEhuFRlEkQyd6ypvUlHVeCcZ2ttq+NPGesNrkXg7wpDBP4nntBdXOoXan7HpVuzFFnkUHdI7Mr+XCpG4o250A3Vz+ueELT4f6R8KNGsZpJ59P16GGK4lIEk7Pb3P2h2xgZZWlcgYGe3AFSul9m0vvbX3J2/4Otrl1t0Tf3K/wCKv/Vr+vUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc54s+G/hLx5Jav4m8L6L4ie1DCBtW0+G6MIbG4IZFO3OBnHXAro6KPMDzf4qeDdQ8VSWFtF4F8E+NdKt4yyQeKp2hNrN03Rj7JcKQVwOiEY6nPGh8O/hlb+EfDMVlqhtdZ1N9SuNauLo2qrGt7NK8jvChyUClyq8lgo5J5ruKKFp/X9eX3LsD106f0v8/vZVtNLsrC4u57a0gt57yQTXMsMSq07hQgZyBliFVVyecKB2q1RRQAyaFLiF4pUWSJ1KsjjKsDwQR3FcfofwW+HvhjUFv9H8CeGdJvlRoxc2Oj28MoVlKsu5UBwQSCO4OK6Dw34k07xdo8Gq6Tcfa7CZnWObYyZKOyNwwB4ZWHTtWnQG5y+h/CzwX4ZiMWj+ENB0qMiVSljpkEIxKFWUYVRw4RA3qEXOcCptH+HPhPw9rcms6V4X0XTNYkiFu+oWenwxXDRgKAhkVQxUBFGM4+Uegq5r3irS/DM2kxandfZpNVvV0+zXy3fzZ2R3CfKDt+WNzk4HHXpWtT13+X/A/H8Qt0fr/X3fgU9L0bT9Eimi06xtrCOaZ7mVLWFYxJK53PIwUDLMeSx5J61j6t8NfCGveIrbX9T8K6JqOu2pQwapd6dDLdRFDuQpKyll2nkYPB6VY8TeLrPwpJoyXcc8h1XUI9Ng8lVO2R1ZgWyRhcIeRk9OK3KS7rp/kv0aB9n1/r80YfirwN4b8d2kNr4l8P6X4htoX82KHVbKK6SN8Y3KsikA4JGR603TfAPhjRdItNK0/wAOaTY6XZ3Au7axtrGKOCGYNuEqIqhVcNzuAznmt6igN9ypqGk2OrrAt9ZW96tvMlxCLiJZBHKhyki5HDKeQw5FMutC02/1Sx1K50+1uNRsPMFpdywK01vvAD+W5GU3AAHBGQOavUUAVJtJsbjUrbUZbK3k1C2jeKC7eJTLEj7d6q+MqG2rkA87RnpUFx4b0m606/0+fS7Kaw1Auby1kt0aK5LjDmRSMPu75znvWlRQByXiX4R+BfGmpDUPEHgvw9rt+I1iF1qWlQXEuwZwu50JwMnAz3revdB0zUtLXTLvTrS601fL22c0CvCNjBo8IRj5SqkccFQR0q/RQBR1LQ9N1iaymv8AT7W+msZvtFpJcwLI1vLtK+ZGSDtbDMNwwcEjvXOaF8GvAHhfWIdW0bwN4b0jVYSxjvrHSLeGdCwKttdUDDIJBweQTXY0ULTYArkPCHw7t/C9u1nMbTUdOtNQlvtGikswJNNEgYsiuWbODJKFZQmEcJzjJ6fUdQg0nT7q+upPKtbaJppZME7UUEscDk4APSsdPGlrdS+GjY2WoajZa8jSwaha2xMEEflearzk4MYYYC5GSxA4oW7a/q+35afPzE+if9d/z1+XkaX9h6d5l+/9n2u/UAFvG8lc3IC7QJDj5wF+XnPHFYXiD4T+CPFi2K654N8P6ytjCLe0XUNLgnFvEOiR70O1eBwMDiurrJ0XxB/bOoazaf2bqNj/AGbdC18+9g8uK6zEknmQNk70G/bu4+ZGHai2v9en6/iP+v6+4zPA3w30P4dDVF0O2W0h1C4E5t440jigVUVEiiRFVUjULkKB1ZiSSSa1pvDGjXGn6jYy6TYyWOpM731s9shjumcAOZVxhywAB3ZyBzWnRQLbYpzaPYXN7DeTWNtLdwxPBFcPCpkSN9u9FYjIVti5A4O0Z6CoLHwzo+lvYPZ6TY2j2FqbKzaC2RDbW525hjwPkj/dp8owPkXjgVp0U/6/r72M5mP4e6PD4xh8Rww/Z7uKOcCCBUjheWYx+ZcOqqC8xWJE3sT8uR3Nbmp6XZ61p9zYajaQX9hcxtFPa3UayRSoRgqysCGBHUGrVFLZJLoHW5Sm0XT7jUrTUZbC1l1CzR47a7eFTLCj43qj4yobauQDzgZ6VJY6ZZ6Ws4s7SC0E8rTyiCNU8yRjlnbA5YnqTyapt4m01fE6eHvtDHV3tGv/ALOsTkCEOE3M4G1cscAEgnDYB2nFLVvHFhovjTQPDM8Vw1/rUF1cW8kaqYkW3EZcOS2QT5q4wD0Ocdzz9f8Ag/l+AeX9f1Yi0T4X+DfDOuz63pHhLQtK1mff52o2Omww3Em85fdIqhjuPJyeT1rVfw3pElvqkDaXZNBqpZtQia3QreFkEbGUYxJlFVTuzlVA6Co/CvirS/G3h2w13Rbr7bpV9H5tvceW8e9ckZ2uAw6dwK1qLW0foHW5TbR7BrizuDY2xuLJGjtZTCu6BWADKhxlQQACB1wKuUUUAcr4o+FPgnxxqCX3iPwdoHiC9jjESXOqaXBcyKgJIUM6kgZJOOnJqlpvwr03RvEuj3Wmw2Ok+H9FgmGnaDptgltBDcykiW4JU4J2EqoCrjzJCSxYbe3ooWm39f1/wQeu4UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeFePvtnh/4l3Gr+Lx4nHhWW4sY9J1jw7rE8NnpxyAYr20ilXeHm/5amOVSsoVvLC8+61xmpfCHwxrHiJ9avrfULq4e4juntJdXvGsXmQKEdrPzfs5ZdiEEx/eUN1GaFpJP+v6/ra4P4Wu5yHgXQZfidqWu+Jdc1rXILmy126sLDT9O1a5sre0htZ2jQPBE6pM0m0yMZlfIkCj5QBXCx6v4h8P/B34iePLfWdW1LxLa6nq9hpiz6hI9tZwjUJIkP2d3ELGPlg0gOFULuVBge16h8JfDOo+JX15ra+tNRlljnuBp+qXdpBdyR42vPBFKsU5wqrmRWyqhTkACtbTfBWh6Xod/o8GnRNpd/LczXVpcZmjma4dnm3Bych2d8r05wBjil9m3W1vnpr87O/r1Kvq/wDFf5a6fK6+48V0XUNf+HeoXGs3Oi+OtH8HWujXdxrl14o1m11NzLEivFNbqt5cMjnEoKIqxHK/KNoql4Ttde0v4n+HdHvItV0fw/4o0q/32d941vdUv5FjWJklYNxaSqHOTbTsuXxk7VYetaB8F/Cfhy3uLe3tL68tZ7JtOa21bVrzUIUtmADQxx3ErrGhCqCEABCgHgCm+Gfgn4Q8I6zYavYWF5LqlhC9va3uo6pd300MLgBoleeVyI8KMJ90HJABJJvS9/66/wCa/K2iM9eVJd/8rfr5+erOX/ZV8M2eg/CWwntptQle7mufMF7qVzdIuy6mUbFlkYR8ddgG48nJ5rvW8WaoPFX9kjwXrhsN23+3hNYfY8bc7tv2nz8Z+X/VZz2xzUvhXwLo/gl9ROjxXVtHfzvcy28l9PNAkjOzsYopHZIQWdiVjCgk9K6Cp7XBKyseTfHj/kNfCb/sc7b/ANJbqtP4yakslvoXh6C31a91XWbt0tLPS9WfShJ5cTOxnu4yJYoV4Y+VuckABXG5T0fjf4faJ8RLGxtdchupEsbtb61ks7+4spoZ1VlDrLA6ODtdh171iz/BHwtdabBZXB1y6FvdfbLe7uPEmpSXlvKYzGTFctcGaMFGZSqOFIY5BzSXwuPnf8Ir9DSTu012t+Mn+p4LFqHiDUtHttA1XVPsFzo3xMttLtrhNVfU5LWJ7RZTGLu4jV5nDTuqtIm4Eqp3bcn0eXwnLp/xJvvAGm+J/EsGha74anvZ5G1qe6vtNnSeKJJre4uGkkTzFkcYyVBhyACWz10fwB8AR6NPpQ8OxtYT6musyxPcTN5l6saxi4Zi+S+FBLZ5bLnLktU8fwR8HLoOvaRNp11qFtrsP2bUptS1O6u7q4hAwIzcyytMEGThQ4ClmIALEm4tLf8A4f3FHX/t5c3X7zNrW68vl77lp8nbp9xr+PNN1688A6zY+GL5bTxBJZPFY3dw2Nsu3AYttbB/2trYJztOMHwDWPHE/hfwbq2l+HrPxtoniybUtI07UtI1zWDqN3aRXMpUTWlxc3EkJMo8yNWEwUOoyFYYP0frXhuw1/QZdHvEm+wyKqEW9zLBIu0gqVljZXRgVBDKwII61gWfwf8ACVnoesaS+myaja6woW/k1a9uL+4uFUYQNPPI8mE6qN3yHJXB5qer7afOzu181p1/zdtF0av8rq1/l8jxLxZceM/CHw7+KU9hpnjHwt4fTwvPdWc/iTXor+7ttQRWG6CZLy4mVWTaSGcKrR5UAs2fQY9NufB3xI+Hlgms6tdpqkepTagb7Up50ubgQREEI7FUAIdljQKi/NtUc11EHwa8LR6FrejzwalqVjrVt9jvhqutXt9JJDhh5ayzTO8a/M3CMvJz1rW8X+AdE8dWVnbavbzuLKcXNrPZ3k1pcW8gVk3RzQukiEqzKdrDIYg5Bqm9Fbf+v6+S7Ct+v5L/ACf3s8K8feItfuYfiFaad4l1LS54/Hug6bbXdrPuezilGnCRI1bKhT5jkoQVbc24EE52rP4fmb4yeIPCD+KfFh8MLolnrEdn/wAJFeiaO6kluIWZbrzfPCbYgfK8zy9xzt4Fei6b8FvBuk6fc2Nto5S3udSt9YuN13O7zXsBjaOd3ZyzPuhjZiSd7Al9xZiejj8M6bF4muPEC22NXuLSOwkuPMb5oI3d0TbnaMNI5yBnnrwKWnKo/f8A+AKK/wDJlf8AHc0bvdryt/4Ff/0nT8Njlvgjr1/r/wAH/DWpancSahqDWeJbhwPMmZCV3NjALEKCcYySa5z4L+H5fGXhnQPiBrWt65ceIdTT7fJbxavcxWEAcEC2FmriDbGuFyyFyyli27mvTvDfh3TvCWiWukaTb/ZNOtVKQw72faCSerEk8k9TWBp/wk8M6T4kOt2NtfWVyZ3ujaW+qXcdgZnB3yfYxL9n3sWLFvLyWJb73NF25Nvr/X4/p5kWtHlR4Ja6j4n0n4E+GNeg1bxHrHiTxNqVvpVzPDqhaSK0e5fK26XEq28czIgjErYbdJ9/hRXTW2ra14HsfFJ1TQvHOjeC7q1tbezt9T1u3vtUOoTT+QY7a4+2zOiuHh5klUIwLKVzkewSfDXw1N4GbwdLpUcvhtojD9hkd2AXduBDltwYN8wYHcpAIIIBrOj+DPhUaHq2kXFtqGp2mqLGl02q6xeXs5CMWj2TTTPJHsYll2Mu1vmGDzUpaNP+lZK35tb6u9t7vqn/AFe+/wB2nTRWv28Vt9N1yG8+Ing7Xzd6dpsvhMavHp8HjLUNWnhlDyru+1SrHNGGCANEHaM7OPvMD0XgKzfwnH8CtO07UNUFlqVjPPdw3Wp3Nysrf2crAHzZGwikZVB8qn7oFeneGfhH4V8JanealYafNJqV7aizur7UL64vZ7mEMWCSSTyOzgZIG4kgYUcACl8O/Cfwz4VTRVsLW8I0VpG077Zqd1dG2EkYiZUMsjYTYAAn3V/hApx0Uu7t+Dk/ya+4znFykmtlf8VH9U/vPAbdda0f4G6R8RZPFviK+8SWuuxhfO1Wf7K9q+rGA20ltv8AKkXy5GG91MmcYYBVC6/jXX/ENxpnxDtrHxJqelXK/EbRtNtLy3nLPaQSjTA6Rq2V2EyyEoQUbe2Qdxz7dN8MfDM3g1fCj6bu0BZluBaefL99ZxcA7927/WgNjOO3Tilufhl4avP7Q87Td/2/VbfW7n9/KN95B5PlS8Nxt+zw/KMKdnIOTmqLUGubbT/0qD/KL+b82W9YW6/8CS/N/d6I8Z8aa5qnwN8SeNo/D9/q2q2q+CLrXoLLWtSuNREN5byFQ6NO7uFYSDcgYL+7GAOav/De08Z2Pi3wtcQaF42jsLxJB4i1DxRrtneWs4MLOk8MMd7N5L+cFASBEj2SMCMKu32e48I6Rd+JBr01ksuqixfTfOd2Km3Zw7RlM7SCyg5Iz2ziuQX4E+H9J0nUbbw/LqOkXE1hPY2bTand3ltpwkQrut7WWYwxbQcAIq4GVGFJFTFuMddXr+crfg0vls7IbipS3sr/AKR1+9N/PzZ3esSNHpN66MUdYHIZTgg7TyK+XfDOia3/AMK++Besy+OPFU+q+KvslhrM8mrTMtzbzWEsxVYy2yKRfKRRNGqy9WLl2LH6e03Q7PSdBtdHtoRHp9tbLaRwrwBGqhQvHsKyrf4c+HbTR/C+lxafssfDLxPpMXnyH7M0cLQoclsviN2X5yc5yeeapWi31V4/cm7/AHp/5hduKvo7S+9pW+56/ieJ6zrGr+D38WeDtM1/VIbCXxLo+lW+o39/JdXdhBepF54juJi7kk7ghcsVaXjooHQWOiyeA/2i/DWj6f4h16fQrzw5qV3Npmq6zc38azJPaKJd08jvnaxABYquDtCln3dv488D6RP4f8USr4TbxTNrfkf2hpsd0sUl2EKorRtI6pHJGg3KQyfMincpww8w0j4I6h4m8Sanrdvp2reACdDuNBgute1Eavq83nyRPLK0v2mcBI1jKxKZmw0kjbAMB1TbT131/wDSLJffr13FJK33f+lXb+702DUPFGp/8IJ4h+IGnXLafeeLtc0/SNO1AhS1npZu0tIplDBl5Ek9wpIIzOpIIGKs33ge18E/tFfDSKx1TWb2CbStZJg1jVbjUGVwtsC6vcO7ruGAVDbPlyFBJJ9mvPBuiaj4SbwvdaZb3GgNaiyOnyIDEYQoUJjsAAMY6YGK5/Qfgn4R8O+JLPxBbWV7c67aQy28Gpanq13fXCQyY3ReZPK7FPlyFJwpLFQCzEppapeevly2V/z9W+w9Wk3v/wAH+l6LzZhfsqf8m7+BP+weP/Qmr1euB8I/A/wr4EmsW0M69Zw2Ofs9k3iXUpbRAc/L9ne4MRXk8FcDrXfVcpc0nLuD3CiiioAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoorxPxZ4v1fTfitLY674p1jwPorT2cWiy22m282lanuGZI7i5kgkMUzOGjCGSHIMezezGhatLuHRvse2Vl2vibTbzxJqGgw3O/VrC3gurm38th5cUxkWJtxG05MMnAJI284yM+V6h8QvEEHwd+LOuJqG3VNDudYj0+48mP9wsAbyht27W24H3gc981m2Gla54m+Pni2Ox8RXXhyJ/DGhy3F7p0FvJcs/m321V8+OSNVOXz+7JPy4K4OXH3oqXdJ/erhL3U/Vr7mk/z0PeqK4X4O+KNV8TeFrtNamhvNT0rU7zSJr63TYl2YJWjE20cKzADco4DbgMDArN8Tapr3iz4nP4N0fXbjwtY6dpcWq32o6fBBLdzPNJLHFCnnxyRqg8mRmOwsfkAK/Nldrddfla/5B0d+m/32/P+rHcQeJdNufEl5oEdzu1a0torya38thsilZ1jbdjacmJxgHI28gZGdSvmPxZ4w1jwH8b7vw7YX32/xZr+g6RpNlreqQJHbxy+dfs08+wLGX2glYV2mVxtUAbivfeZ4o1/x5J4Ft/Gep6ZFoOj2l/f67b2dkb7UJ7iSdEXEkDwxoBA7ELFkllwyhTvq11df1a//A+b9bEvdk15J/el+rfyWvn6R4d8VaX4shvpdKuvtUdlezafcN5bpsnhcpInzAZwwIyMg9ia1q8d/Zgiu4PCni6O/njur1PF+sLNPFH5ayOLpssFyduTzjNd7Z/EHS77xRJoEdrri30bMpmm0C+itMqMnF00IhI44IfB6DNLpG3VJ/ek2D0lJdm19zaLPirxz4b8CWkN14l8QaX4etpn8qKbVb2K1SR8Z2q0jAE4BOB6U/wt408P+OdPkvvDeu6Z4gso5DC9zpd5HcxrIACULIxAbDKcdcEetef/ABa/5K18Fv8AsN33/psuqb8RLeDw78Zvh/runQLFqV+b2x1VoRh7mxjtJJlMgBAby5kiCls7fNIGNxytIq8tv6/r+tW07pLt+r/y/Q9brK8UeKNM8F+H73W9ZufsemWab55/LZ9ikgZ2qCx5I6CvnbwH8a/FHia38I+Jo7nxTqVxr15bfavDK+D7mPSrSzuHCh4r02iktEjLI0rTvG+2TaoDJtvW/jbxz/wp3xh44uPFdx9rs9R1KwsLKOztBbpFFqbwpI+Yi7SBFKfeC7QMqWy5twkk29LX/C3+f9WZDkkr/wBdf8j6PB3AEdKWvFde+KGu+DdS+JmkX1wl1qUAtbrwuJo1jEq3YFvDBkKAxW7VgScna65rmda+JniObxZ4g8Pt4m8S6XL4Yt7azjudC8ITaqmp3zW6TSSXTR2kqLHl4wIozC/LncAU2530v/Xn9zsn5vS5Vtbf15ffuj6QrJ8M+KtL8Y6dLfaPdfbLWO5ns3k8t0xLDK0Uq4YA8OjDPQ4yCRzXk3hPxX4x+MWqfY21HUfhyNN0XTry+t7K0ga7a9ukd3iYXUUgSOMIBt2ByWOWG3Bq/s26T4gufBOm3C+IIrSKy8Ta82rWkFipj1LN7cqArMxaELIQ4wSTjaT3rTls2n0/zsTze6muv+Tf6HtOveINL8K6TPqmtalZ6RpluAZr2/nSCGPJCjc7kKMkgcnqRVbwt408P+OdPkvvDeu6Z4gso5DC9zpd5HcxrIACULIxAbDKcdcEetcn8XPBuva9c+Ftd8OJp19qnhy/a+TSdXdo7a8DRNGw8xVYxSqHJSTYwByCMMSOdn+Lk+qN4fWx0678Ka5J4otdL1/Sb+CIzpvgdsF13JIrKsZWWNjlQBkEFREdXbrdL72lf0u9ew5aK/ZN/cm7fh8z1bxF4gsPCmg6jrWqz/ZdN0+3e6uZ9jP5caKWZtqgk4APABNXoZkuIY5YzujkUMpxjIIyK8G+OXibUriy+M2gyXG/SrTwIt5Bb7FG2aT7artuxuORFGME4G3gDJzqzXni/wABXXgXVbvxPLrVnrV7b6Xf6FNaW8dvb+dESslqyRiUGN0GRLJIChfoQCGvzt+LaX4/8Eqa5F97folGX5P/ACPZ6K+ZvGHjD4g3HwH8UfFTSvGlxo9yILm807Q/7Os5LK3tkcogYtG0rzFFL7jLs3t/q9o2ntYfE3iW8+IXjyW5166tPDXhWK0u47Cyt7bddlrXzZInd42YJxn5SrZbhgBgzKShHmlt18v68rhZtpLW+3nt/n1PZKK8AufFHjbQfhdo/wAUr7xXJdedHZ3994XS1tRpotp2RWihk8sTiRFkDK7TMGdeVw2F6eHxxrbeFfjBeG9zc6De3sWmv5SfuFjsopUGNuGw7MfmznODxxRU/d83NvFNv5WX6/mTBqo48v2mkvnd/oesUV4fo+teMfHXj46FH4rudC0pfCml6pLcafaWrXP2qZ5wxQzROgVhGNwKN90bdvJPafB3xJq+v+H9UtdduI7/AFPRdVutIk1GKMRi9ETDZMUHyq5Vl3hcLvD7QBgDRxcZOL3V/wAHZv7/AOrC5lZPvb8Y8y/A7ysnxB4q0vwv/Zv9p3X2b+0b2PT7X927+ZPJnYnyg4zg8nAHc18/+IfjJ4y0PwjFaR3yS+JtB1vUJtbaW3jYz6RZP5rnaqhVMkE1oAyrnMnrkjqL74jeIbjxcZ7LUDFoEnjCz0C2iEMTLLCtqz3DBtpb5pmKZzx5HGMnML3uW3Vr7m4q/wD5Nby7d3L3VJ9r/ek7rztbp956jrXjnQPD+ia7q97qluthoSu2pyQkzNabYxIyuiZYMEZW24zhgccitxWDqGHIIyK+RvEnh3UdO+HP7T9zN4p1bVI45byE2d5FZrFI50q0YSsY4EfeAQgAYLtUZUtlj6xZah4s8FfEbwNY6v4pn1+08TwXUNzp8tlbwwWVxHCJg9s0aCQR/K6bJXlOGU78gk3ZcsX3UX96v/XqrhJ8r+bX3NL/AIf8D2CSRY0Z3YIijJZjgAeprkNF+M3w/wDEjXi6T458N6obO2e8uRZavbzeRAmN8r7XO1FyMseBnrXOfF2G28QeOPh54U1YLL4e1a7u5byzlGYb54LcvFbyg8MhJaQoeG8kZBGRWn8e7O3b4L+PbgwRm4h8N6lHHMUG9Fa3YsoPUAlVyO+0elYylyQlPtc1px9pUjDu1+Z1174n0fTYtOlu9WsbWPUpUgsnmuUQXUjjKJESfnZgCQFyTjiudi+Nvw7uNaXR4vHvhiTV2uPsi6eus2xuDNu2+UI9+7fu424znivM/wBnSRfGOo/2r4sVIPG2hWcVlZ6IxzHplhJGuyeEn/W/aAoLTgD7vlYXy23bf7PGi2HiL4H3OmapZwahp13q+tw3FrcxiSOVDqd0CrKeCK3nHkb62/pa/n/wNcIS5oqT8l+Dv+Wn3ns1U7bWdPvNRvNPt762nv7IIbq1jmVpYN4JTeoOV3AEjPXHFcD+ztc3U/wj0pbqeS6S2uLy0tLiZ2d5bSK7mjtnLMSWzCkZ3E89a82Xx9d+D/hJoOr6abiDWvHviGRrrUrXT5NRuLdZDK7SJBErtI8dvAsSDawXYhZSqsDm7KVv61aS/q/+ZSu1fte/yTv67fM+k65Jvi94ETxJ/wAI83jXw6uv+f8AZv7KOqwfavOzjy/K37t2f4cZrxDXviJ4ik8K+J9Hk1DxLd6E15o1tH4o1zRZtEvFgvLxYLuLJgt1LRp8yyRopAmAPzLuPqvjTxFovwl0DSNM/wCEHu7rwmktraq+lw2Qs7BnuI44Q0UkyPxIyNmNGx16iq5dm9m7fk/1+b/EfZf1/n+n5auq/Gb4f6FrUuj6l468Nafq8TiKTT7rV7eK4RzjCmNnDAnI4x3FPv8A4weAtK8QHQb3xv4cs9cWVYDplxq1ulyJGxtTyi+7ccjAxk5FeZfCu/8AF1v8RPitFo2h6Lf6Q3i//SLq+1ma1uIybKzDbIVtJFfC4IzIuSSOOp4288XPpFv8eNHufDFzfaXqviOWyfWrowHSrV57C0iX7UN7TrGCVLOsLqAwJI5xKu0rK7cVL1b5dPL4tymtWm7JO3/pWv4bH1dRXnHjHxxF8D/h/ocdxbXmuSxxw6eLxsxW0bKgX7ReXJBS3hyAWkbJGeFc8Vg/EfXPFmj/ALP/AIw1+51e0vNWutPaayPhuPEVpHIqqohldiZmAYsJjsDYBCRjiiWnM46pERd1Hm0ueh6V8RvCmu+IrvQNN8T6NqOu2e/7TpdrqEUt1BsYK++JWLLtJAORwTik034keEtY1LVNPsPFGi31/pau1/a22oQyS2gQ4cyqGJjAPB3AYNeNeNfHmufC/wCGOqeHX8JWng+B/DmoHw9caTqQuvJkgtmk8udPJjEUuwFxsMqEo/z8AtveKvD9l4ZX4If2HFHZmx1aLTrZYUzutJLGfzYsj+EiNXOeC0anqBTtrZeS+bbV/JafcO+nN6v/AMBSbXzvuew6bqVprGn21/YXUN9Y3UazQXNtIJIpY2GVdWBwykEEEcEGrNedfBtE0n/hMvDluCtjoevzW9ohJOyKaGG7CDPRVa5ZVHQKqgcCvRaH0a6pP71ceuqfQKKKKQBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFcH4j+E48WatNLqfivX7jQ5rmK6k8ObrUWTNGUZRvEH2gLvjVyomAJyCNpIPeUUdbh0seWeLP2fdM8Wr4otH8SeItM0PxLvfU9F0+eBLeWVoljMoZoWlQnYjFVkCMV+ZWDOGuat8FLa68Y3HinS/FHiDw5rk+nW+lPcabJbOht4WkYKYp4JIyS0mSxUsNoClQXDej0UbJJdP+G/LT002Df+vn+av6nndr4f8RfDfS7HQvAnhvRdS0eJGlluNb8Q3FtcvcPIzyu22zn8wszFy5YEljwABUt98P8AUfFzabrt9fXPgfxhBC9rLc+F76O7RoC5IiY3VrslXIVgWhDIxYK2C27v6bJIsaM7sERRksxwAPU0N9X/AF/X5B5L+v639Tz/AFD4H+H9eXVV12a+186ppNrpN419IgaRYHkkScGNEKTb5WbcmApVdoXFRTfBn/SNN1C18aeJLHxFa2X9nTa9CbNrm/tw5dEuEktmhYqSdriNXGW+b5mz32m6laaxp9tf2F1DfWN1Gs0FzbSCSKWNhlXVgcMpBBBHBBpLHVLPUjcizu4Ls20zW8/kSK/lSgAlGwflYZGQeeRT1vb+v61s/Wwt9f66f5L7rnO/DX4a6Z8LNDutJ0m4v7q3uL+41B5NRuDPL5kzl3BcjcwyerEt6knmusoqrfapZ6WIDeXcFoLiZbeHz5FTzJWOFRcnlj2A5NK+33foh938/wBWcv8AEH4aQ+Przw/ejXNU8PaloV1Jd2V5pQty6u8LwsGWeGVCCkjD7vfNP8K/DWz8O6xNrd5qepeJvEMsP2U6vrEkZlSDIbyo44kjhiUlVLeXGpcqpcsQCOvoo2B67/11OA8N/CJPCN9bLpfivxDbeHbaZpoPDIktvsUWckRq/kfaBGGORH520YC42DbUjfB3Rm+HepeDPtN9/ZeoXVzdyzeYnnh57p7lwp2bcB3IGVPy4zk813dFO+lhP3t/6/q7OQ8TfC/RPFnjTw14nvxMdQ0Eym3jRl8qXftI8xSp3bGVXXBGGGaq+IfhYNU8SXOu6P4p13wjqF5CkN8dHNq8d4E4jZ0uYJlDqCVDoFYggEkKuO5qrFqtlPqVxp8d5byX9vGk01qsqmWJHLBGZM5AYo+CRg7Wx0NLpb+tdx+f9eRxesfCG3vNeh1rSvEmveGdV+wx6dd3WmywSNfwRkmMTi4ilBZSz4kUK/7xsseMR+DfgrpngOy0Cy0jW9fhsdIvr2/W2kvy63jXJkLJckrmZUaUsu47sqpLMck+hUU7v+vvFb+vlb8tDnfF3hO98TfY3sfFOteFprYvmTSPszecGA+V0uIZUOMAg7QRzzya52f4I6Td+Hr6xudW1e41e8vodTl8RvLEL/7VCV8mVcRiJdgRVCLGI8bsqdzZ9EqrcapZ2l5aWk93BDd3ZYW8EkirJMVG5tik5bA5OOgqf6/r+t9R+p51/wAKB0u6h8XnVPEGva1feKtJXRtSv7yaASGFfOCtGkcKxxsBMwwqBTtBKlizNc0H4L2Oj6to91ea/rmv2uh5Oj6bqs0Lwae2wxhlKRJJKyxlkDTvIQGJzk5r0Oiqu9/6/pXduwPXf+tl+iPlb4heBbXxX4d8S+AvDA8fwrq95NAnh650yS00aykafMt0L1rYZhBDzCNbh1fftWM52j6K0fwXY6Lrmv6pG800+tGE3McxUxjyohEoUYzgqOck8+la1vqdnd3l1aQXcE11aFRcQRyKzwll3KHUHK5HIz1FWaWnKo/1/X9dgfxcx5rpvwL03TksdPfXtcvfCunyxzWXhe6lgaxtzGweFQwhE7pGwBVJJWUbVGMKoCeKPgTp3ie98TMPEWvaTpniaIprGkadNAlvduYRCZSzQtKjmNYwfLkUN5a7gctn0LUdTs9Hs3u7+7gsrVCoae4kWNFLMFUFicDJIA9SRVmk/eVn/wAP69+m/Zdhp8rujw1fhTe3Hxq1CfT9X8QeGYLHwvpmn2ms6esJEyrNdeZCwnhkhdsCJj8m5cjBUMQ3q3gvwbpvgPw/DpGlrL5CPJNJNcSGSaeaRi8ksjn7zu7MxPTJ4AGBW5RVuTe/9Xd/zJsr3Xl+CS/Q4q3+Eeg2/jXxP4nxcSXviGzSyvLd5AYAqrsZkXGVZ1EYY5ORFHx8tUvDvwR0Hwz4R8K+HbW61GS18O341OC4uJlee4nzIS0zlfm3GVicAHpzXWx+K9Em8SS+Ho9Y099fhg+1SaUt0hukhJAEhizuC5IG4jHIq1Nq9jb6lbadLe28eoXMbywWjyqJZUTbvZUzlgu5ckDjcM9alaWt5fg219zuN63v/V1b8rI4TX/ghpniD/hNYX1rV7TTPF1u8Wp6ZbNbiHzWhSE3CM0LSLJ5caLjeU4zszzXSa14HsNc8SeGdanluEuvD7zSWqRsoRzLEYm3gqSflORgjn16V0VFPol2B67mB428EaZ4+0YafqYnj8uVbi2vLOZoLm0nUELNDIvKOMnkcEEggqSDzkvwjuNU0nWtN13x14m8RWWqabcaW8V79hhWFJlCvIgt7WIGQAcFwwGTgcnPoVFTZWa6P/hhqTTTW6OH1L4SaVfah4T1K2vdQ0rVvDaLb22oWLxiWe22hXtp9yMskT4UkbQQyhlKkZrB0n9n+LR9OuNHh8c+LP8AhGri5uLmXRI5rSCJvPmeaRBPFbJcqpeRvuzA44zjivVqKpty3/q/9f1cS0SS6fpsVdL0uz0PTbTTtPtorKwtIlgt7aBAkcUagKqqo4AAAAHtXmejfC+W98Nar4O1dL6zsdN1Rr/Qtes7iJJow8jTxtFjJR4Wdo8Omx0AyHDMterUUnq7vf8A4N7+orK1l/XS3p5HI2Hw5ifQdX0nxHrWp+NrXVI/IuBrotwvlbSPLWO3hijA5J3bdxyMsdq4565+Bn9oQ2thqHjvxZqfh+3uoblNFvJbOSJvJlWWKN5zbfaXVWROWmLnb8zHnPpN3eQafazXV1NHbW0KNJLNM4RI0AyWZjwAACSTRaXcGoWsN1azR3NtMiyRTQsGSRCMhlI4IIIIIp31v2t/wA6W/rz/AK9DD8K+B7HwfqHiS8s5biWXXtSOqXInZSElMMUW1MKMLthU4OTknnsKuk/DPRtLj8YQust/a+KryS81G2vCrxkvBHA8agKPkKRLwcnJPPYdZRS6W8rfLTT8F9xV3e/nf59/xMTwX4XTwT4X07Qo9RvtVgsY/JiutSkWScxgnYrMqru2rhQSMkKNxJyTRh+F/hS10PXdFt9Bs7XSNc8w6hYW6eVBOZE2SNsXAVmHVlAJPJOea6miiXvNtiXu7dDgNK+DtjBqC3Wt63rHjDybaWzs4dceB47SGRdsqqIoo/MLIApeYyPgEBhufdD4Z+Dtl4OmsLttW1zxNFocMi6JpupTQOmngoU2wsI42d/L/dh53dgpI3Dc5b0WijXv/X9N/e+4rLt/X9Jfccd8L/D+oaLoV3eazCtvrmtX0uqX0COHEDSYCQ7hwxjiSKMsOCUJHWuxoop/19wf194UUUUhhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeaWfjDxZr3xf8ReG7H+ybHQtCSwuJrq5tJZp7hZlcvCmJUVG+TPmEELwNjZyvpdcrpfgVdP8X+LNbe8Mya/FawtbCPaYRDG6H5sndu356DGO9S7rVBvoeYaX8dtUX4i6BpkmqaT4k0XWtRl01ZNF0K+jgtJBFLIu3Und7a6KmIxuqCNsknA2MtdR4O8YeMPHUK+KbW40TSfBzXMqxafdWE099cWsUjIZ/tCzqkZfazqnlP8ALty2WO3G0H4B69p//CAWV941hutC8E3SS6bY22keQ88SW8kCLcyGd97qjrh0WNeHyh3Ls6Xw18OPEHg+6Gm6X4msT4K+0yTrpN5pBlu4o3Yu1vHcidUEQZiFDQswU7dx4Ip7Wi9Vtf5Wv+Pf8ge/l1+93t8rdvzPLvCn7WEGvT+GtWbxP4KuNN8QahBYxeE7K6D63ZJPII4ZZHE5EjBijPEIU2B2+dvL+f6J1v8A5At//wBe8n/oJrgvB/w98VeAYbDSNP8AFNpc+DdM+W00xtHzqIt1B8u2+1m4EZVflUMYQ2wAFt3z1es/G2u+IJxptz8N/FGiwXStE+oXlxpTwwAqRuYRXzuR/uqTU1oqdOUIbu/5aLt31v17WKg+WopS2/4P39v+HueUfC3xh408EfAH4a+JLhNGm8Kx6XpVrc6SltKL5IJFjhFwtyZdjEFkkMXkjjKhyQCdzQfFEnhHw38S7+HWNK0Ob/hMrmOO81iGS4iBKQ/IkEbo88rAFUiRgzMRjJ+U3PDHwB13TPB/hXwZrPjZNa8IaD9jkW3TSvs97dtbMrxRyz+cy+SJEUhFiD7URTI2GZ9XUvgleMs13pfiGKy1qPxPJ4msri508zwRNJCYHhkiEqGRSjP8yuhyQe2DvUalXnNPR3/GUH5dpP8ApIwjFqEY9VZ/dGa/Npf0zmtB/aA1u50PWrSXTo9T8TW+s2mi2DyaXd6NDcvcorpJJbXW6aFYwZC3L7ljyp+bCnxk1TWfBfhnw7qvj7xBoU2n2fijTLk3+n6fLYRwxq7F/MWSebOMAghhnONvc7lv8Bb64XxNe6p4umuvEGrahZ6tb6la2Kwrp11bxqieVGzuDEdpBRiSUZlLkndWxqXw38ReLrDTofFXiLS9Qey1e11JF03RXtYWjhJJiZJLmUkvuwW3YAA+TqTjr7rW6lF/JOL/ADUm+6LkrxkujjJfNqX/AAEjR+GHiHxP4vsLvXdbsbXR9Iv3WXRtM8l1vorbHyyXTF9okkGG8tUHlg4ZmOcaF18TvB1j4oTw1c+LNDt/Ebusa6PLqUK3jMwBVRCW3kkEEDHIIqh8N/h7c/Ddb/S7TWDeeFPM36VpdxATLpin70Cz7/nhB+4hQFAdu4qFC9rVu11bb8f+HHrrff8AA8q/aE/5Bvgb/scdJ/8AR9YWqfEb4kaldfE+TQU8NWlh4NvGit11C1nnk1ECyhuGiYpMggIMhHmYkB3j5BsJf0f4heBf+E8ttDh+3fYf7M1i01bd5XmeZ5D7/L+8Mbum7nHoao6f8M/sNv8AEKL+0t//AAlt5Jd58jH2XdZw22373z48ndn5fvY7ZOK5o06lt/ea9bU0vyluXo6kW9tE/S87/mv0Od0/4meI/iZqtvZeCTpei28ekWWrXmpa3ZyXoBulZordIY5oTuCqWaQvgZUBW3EqvgprmH49eOG1KS3+1p4Z0I3MkAKw7xJqG8qGJIXOcZOcU/Sfg5rXguPS5/CPiez0/UYtHs9H1A6ppLXlrerbIVimESXETRyjcwz5jAqQCCVUiv42+FuvXU3iG60+/e+vvFOn6ZoF9LGq2xs7eKSf7TdBtxyzR3D7UVchgvJBJXpk1eSp9Xp9+novu7vqYw5uRe03sr/g389H+S6I8i+IWnWfjDSvCXjXUfDY8Vx+LfHFtJBpJhgc3WmxWt3HaR4ndYyGUNP87AZnb2r1pb1vhf4FXWvDHw2t/BGj2uoCbW9D+xWkdw9pgLJdRfYpnj3IMOQ25mWJlwCVNdF8Rvhfc+KtK8J2/h7U7Pw7P4b1OHUbP7Tp7XdviKGSJYjEssR24k7OMbar6z8O/FPjXSLTSfFfifSrvTPtyXF/b6RostmL63QBltmL3cpVS4Bc87lGzABJOeyaj369rRWv3O/XtqaS95xb/l/G8nZferfjoYUfxa8T6xY6S+lWGnxzeLdTkt/Dv22KQC3sI4mdr25USAyblQukaeXkSRKzKSzKavb+IbX4pfC+PxJf6ZqV+LvVPLuNLspLOJo/sXAMbzSkMDu/jIIx06Vdt/gbcaRp1tb6P4i+wSaLqkuoeG2ey81NNilQrJZyL5g8+A75AFBjKqUCkeWprag+H+uahr/hfW/EHiGyv9R0Se7lxp+ltawyLNB5QRVaeRl28sSWbJJ+6MYT2dvL16fj38762sZyTf3/AK/1b5dbnS+D/wDhIf8AhG7H/hKv7M/4SDaftf8AY/mfZd2448vzPmxt29e+a+QtL0nSIdC8S60/gSTSb+18bX083xKjgtQbCFNSYuxeKQ3ZUIDGQYwm1jvYR7mr698H6Xq+i+G7Gy17W/8AhI9XhUi41T7Ilr55LEg+UhKrgEDj0z3ry2D4DeIv7F8QeF5/GVgfBmuaje3l5a2+iPHqDRXMzyyQLcm6ZADvKFhDu2k42thg1pV5un/20X69Gau0qfK+/wCFpL06rcltdeHhzxh8ZNTGpaXpHkPpzDUNZk22cGbRAHkIZcgZ+7uXccDcucjB0f8AaQ1PT18VWuq2g1+9061sLnTpIdCvdAF493O1tFAYbwu4Hmqv75SylXPGUIPZ+J/gj/bzeKJbfWRYXOqX2malZP8AZPMSznsfLaLcu8eahaJSVyhwSAR1rOk+Al/4i1vxHq/ivxYdSvtZ02zsV/svTxZx6fJbTyTQzWyvJMQQ7q2JC/zqTnaQizHTfb9LdPO+n622Ps+en5q/4X/yuZfxiXxdpPwd8RXPjPWvD11aKbCUPpenTWS2xF3EZN7y3EodQMfNhMYORzx23wz8ba58R7y+1+K3tLPwJKvl6KZIn+234B5uy2/akLf8s02FmGHJUELVTVfhz4t8XeE9T0TxP4t0u9W4NsYJtM0N7TyzFMsrNIHupd5bYo+UoF5ODwBQf4Y+JPBGq3S+BdaFj4d1bU7e6uNHktkkGmkzq93JbO7YSOVFcGHy2xJKXUrkiqXxWfX/AIG/+fTd91PRv+tun+XXp2fpeva3Z+G9E1DVtQmW2sLGCS5nmc4CRopZifwBr5l/Z/8AiV4cj+IWlPa+KtI1TVfiJZT6nqen2erw3MljqKMZo4jGjEg/Z5TCTx/x5oOpr3z4o+BH+JXhGbw61+NPsrueD7d+480z2yyq8sA+ZdvmKpQtzgMeDS/EbwGPHmkadBBe/wBlajpmo22p2N75PmiGWJ84KblyrIXjIBHyueaUdJXfp8ur8907d4jlrGy9fn09Oqb7M8O8O+KPGnw38KfFvxPJeaHrk9v4mdPskOkzW7zzlrWLdvN0/wAvlkKqYyCB8xA216HceJNV8O/Ebw7b+KotAvrltG1bUX1Kx054pbOKJ7X9zG7yucEOd7fKHKIdq4xWhf8Awa+3eFfFujf2xs/t/W/7Y8/7NnyP3kL+Xt3/ADf6nG7I+90453de+H8PiDx1pXiC5uFa3s9MvtMksGh3CdbloCSW3cACHGMHO/qMcyublguqil8+S3/pX/A0CNueTls5N/Jy/wAv+Dqc/wCBvEnjjxJo+n+LtTuPDuleG7+0a+XRpLWYXVtC0ZeIy3vneWWxsLgQALuYBm27m5bwH8cNX1Tx1Y6He6jpviSx1Oxurm21TSfDt/ptqskG0/u555JIryJwxxJC4xtBwQ+V37f4O683g+68EX3i6G98Evps2lQw/wBlldTWBomjjD3RnMblARyIFLbF3E/MWXSPhJ4mk8W+Hdf8S+MrbVZdEsbmwhtNO0g2UEizIimRw08reYCnJDbcHAVeSaqby5Nrafjv57f57ijey5t76/ht5b/5HK+GvjF4+k8G/Dvxrr0Ph6LQ/El1ZWV1pVlbT/aYDdHy4p0uDMVIMjRExGLKqxHmMVyfWtP+J3g7V/Es3h2x8WaHe+IIWdJdJt9Shku0ZM7w0QbcCuDkEcY5rlW+Ce74W+CfBv8AbP8AyLd1pdz9t+y/8fH2OWOTbs3/ACb/AC8Z3HbnvXoOvWd5qGiaha6feLp1/NBJHBePF5ogkKkK5Tcu7BIONwzjqKuo0lJxV9Xb0srfjf8A4CsO12tbbfm7/hb/AIe58sXHxi8Gr8YG+IB8ZaKmow66PCp0n+1YhcPpefJLmHdu4uyZgcf6sZ6HNeqeIvHHxD1Lxr460Pwsnh2yg8O2Nrd293q9tNcG6llilYwMkc0ewZRT5uTtBxsfOV6m2+Eejw/BtPhxIWl0f+yP7IeTGHZTHsMnf5ict9ai8GfDO98OXOuXmpa6ur6hq+n2VncTraeSPMghaNpceY33y27bnjpk1nO8YOMHdrZ99O3quZ36y7XCLvOMmtHv5a/jp7vou+pyXhL4teLNUk+HWu6nDo8XhrxwAltpltBL9s09ntHuYme4MmyYFYmDKIo9pdcFtpLcna/tZ295Imtr4m8FrpT6v/ZyeEvtanXWgNx9nFxu88APn975HkZ2cb816dpPwd/svwz8LtI/tfzf+EIMB877Nj7b5dlJa9N/7vPmburYxjvmjw38N/FHg27Gn6N4tsofBy3rXUWm3GjGW9t4mfzHto7kThBHuLBd0LMqNt3EgMN5cntHy/Dd29Lr59/P52Is+Rd7K/rr/wAD+rnOt8QPFmv6b8Trye20FfD3hmbUbEWV1YyzvqIjtg67m84KigthhtbeMgBMZa/ZeNfEXiDUNH8K+Do9E0Oa10Gz1W/vb6xkuba3SYOkNvDbRzRHnypDuMgCKigB93y9BbfC/wCz+GfHekf2nu/4Si6vLnzvs+Ps32iJY9uN3z7duc5XOe1Ubj4V6vpOo6dq/hTxDaaRrUWl2+j3zalpjXlrewQ7jExiSaJ1kVnfDCTGJGBVvlK4U9ElLtD7+WXN/wCTNf8ADFS6td5fdzRt/wCSp/8ADnO/E74oeJ/BCw2sGveHZNdtdLW7utKtfDuo6rPcSANukKW0pezt2ZQqySLKMk8krhrFr8T/ABd458UaJpnhaHSNJsdU8L2/iA3+rW8l29q8kmBF5UcsXmArkZ3rtIJ+bIWrWq/CDxRdX2uy2Pja30+PxHYQWusOujb7gzRxNE09q/nhYNylfldJcFcg81t+BvhV/wAIXquj3v8Aan2z+z/Ddr4e2fZ9nmeSxbzs7jjOfu849TTjv73f9Jfry/5LUUr293t+sf05v83oWPg944vvH3gtb7VYLe21i0vLrTL5LTd5Bnt53hd4wxJCMU3AEkgNgk4zXb1yfw38B/8ACvdH1Kx+3fb/ALZq19qnmeT5ez7RO83l43HO3fjPfGcDpXWUXuk3vZX9ba/iVs2ltd29L6fgFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUV4p8aPjDrHgHUdQOj6vo9w2m2P22XQU0G/1W8kCgsTNNavtskcDajyxsvDNyAVEuSjuNRctj2uivKf+E+8UePPFEukeDZdJ0WCw0y01C9vtcsZb3fJchmigjjjmhxtRCzOXP3lAU8kcFB+09qQ8NeHodavfCng7xDrGq6taSajq87DTLGGyuXiY4eSMzyNiNQoePJZn4C7DbTTs9/6RKkpJSW3/AAL/AJf5bn0nRXnXwV+KUfxO0nWv9P0nV7jRtRbTpdU0GUSWN7+7jlSaH532grIoZC7bWVhuYAE9AvxN8HyeKj4YXxXobeJQxQ6MNRhN5uC7iPJ3b87fmxjpzRZ3S76/LcL9TpaK8a/aS0XTvEc3wv03VrC11TTrnxjbJPZ3kKzQyr9lujhkYEMPYiofEvg3RPg34y8Ear4N0618M22qaqNI1TSdLQW1neQvBNIJDboNhmjaJWEgUPsDKSV4CVrXemtvy/zKkrbfy835/wDyJ7XVXVNUs9E0651DUbuCwsLaNpZ7q6kWOKJAMlmZiAoA6k185eH/ANrKDWJtE1keJvBc2l6xqkNhH4TtrsNrlvDNMIYp3bzyGfJSR4fIUqrMN5KfNpa18TPFmsfCP4meJ9RsPDVxoWiprNlFo95pss4vzbTSojysZgvllU2tHsO4gkMoO0Uotq/9dP8ANff5MdtbeaX33/yf9NH0GjrIiujBkYZDKcgj1p1eVzfFybwxrPxB0/W7e2ih8PaZBrOmraRsrXFm8TDaQScuJ4pU+XAw0fGevG65+0DqmmeIP+EY1LxV4D8D69pek2l3qk3iWUiK6vJkLG3tojcxsqIFBMxaT76gKcE1Lsv69fvtbW36ohO6v/XT87r5a9GfQ1VNN1ex1hJ3sL23vkgme2la2lWQRyodrxtgnDKeCp5B614/4X+MuvfGCTSrbwUNK0N30W21rUL3WLaTUI4vPeWOO3iSKWHed0EpMvmAYVcK24lM/wDZ7fxZJpOpNBHo8MZ8b63/AG5HK8rsqedJgWpAALebs++Mbd3fFUovmcX2f4NL9f6Q21y8y7r8YuX6I97orjPjF8QIPhf8Nde8STS28L2kGLf7VKIo2nchIlZjwAXZck9Bk15D8BfHXgz4fSeK/CWjeLtP8U6RpemJ4jjudP1KK9YDZtvV+RiQfNj84573VRde830X/Bf3LUdnpbr/AMN+LaS+fY+kaK8E1T4nfE3QfDfg/wAUXsXhhtN8R6xpto+kpaXC3Gm293cIoJnM22eQI6qf3UQDEtyF2ti+KP2r4NKuvFepQeJ/BVjp/hu9msz4Z1K7C6zqggbbM8R89fJYneI4zDJv2AllEg23yu/K97telrX/APSlt+jJWqTWqdred72/J72PpWisjxBriaX4Vv8AVku7GySG0e4W61SXybWLC5Dyv/Cg6k+ma8El+LXiXxdp/jHwuNT029mk8Nz6pZa3/wAIpqOm27RqdkqCK4n/AH6sroUmilKjJJUjG7GpLkUnbVJv7k3+S/y10LhaTjro2vxaX6/0j6SorgvgVb6vb/CXwqNZvrK/mbTLVoXsbJ7ZUhMCbEYNLJuYd2BUH+6KwvDXxC8V68/jTVLq48O6N4f8O6hqFgpvopU8/wAkZSaS4Mu2GNcjedj5w2NmOd6kfZ1JU+139zS/Nr/hjGnL2kIzt8VvxV/0PWqK+e9G/aA1tNQ1/Tbi5sfEDw+GrzX9O1S28N3+kWzm3KAqBcSOLmNvNjZZYZMYB/vKTuaR8SvHGnap8O7vxNFoQ0fxgVtTp+nW8wudOuWtWnTM7SlZ0PlSKcRIQWXlsEmbfp+LaX4xaLvb8fwSb/Bo9d1XVrHQtNuNQ1K8t9OsLZDJPdXUqxRRKOrM7EBR7k1aBDAEHINfLvxu8XeM/H37N/jzxPZ/2Jb+Ermyu4rfS5rab7dJbK5j+0G58wIrHaZBF5J4wvmZO4enDxd4x8aeI/EeleDrjRNGtPDckVlPea3YTXv2y6eCOYoiRzw+UiLLHlyXLFzhRtyxbS/9f1qvvtuPt53/AE/4P3X21PVaK8N0/wCNHivxtqngrTfDtjpOmXGtWeqnUpdSSW5XTrmynjgfYqtEZk8wuuMoSCrZG0q1CP4vfES28H6v4m1CDw3DaeGdWk0vVLOG2naTUVjuFjknt5POxb5RsrG6y8jBYA5BbW39aOzfpf8AMUnyq7/rS9vwf3eh7tb6tY3l9eWNve2897Z7PtNtHKrSQbxlN6g5XcORnqKt15nZalqmreMfiNY6GuhaHqljNYKuq3WmPcGdWgVz9oVJomkKgsqneu0Hv0PMeE/il4svvh94k8U614j8IWeii8W20PW5dMuLSCeJXEclw0JupGkDvuWKNXUybVOf3gxKd0/6/r/h+w/M9zqpJq9jDqkGmyXtumozxPPDZtKomkjQqHdUzkqpdASBgbhnqK+XPiF8SvE/j74S/EfRpLqyhvNFNlLJfXPhi/04XdtM3Ci0uZVkidZI2+fdIjKBgAk7fVNU8W3XhH4h6JB4li0bUrm28Latql3rVlpjQTosM9rmOENLIyRssmWXc25o0ORjFPRO0uzf3Rcv0JT5tI+X4ux61RXijfEnx9ovhXQfHetwaHH4a1CSza70C3tJ/t+nwXLoiP8AavNKTPG0kZdfIQEb8N8oLZ2tfFn4hw+F/Hviqzg8OwaP4Q1O9iNlPbTyz6na25VnKyCZVt5Nu9QSkgZgDhRwW1ytxlpa9/K1r/mtr/gxr3knHW9red72/J/00e+UVz/iSa81bwPfT6Nc29ndXFkZIJ722aeNQVz80ayRk8Z6OOfyrxL4Q+LPFnh74M/B/wAO2k2k6hrfiLS7dbC5awlit9NsorJJGknTz2Nw6/IuFaEOZB9wA0+V+9feNvxv/l/wwXVotbO/4W/z9T6Norwvxd8YvF/w50fxxY6vb6TrHiLRdMt9Y0660+0mt7W9gllMRR4XlcpIjqw4lYEMh45FdM3jDxj4T8a+GrLxMdFvdI8SXEtnANLtpYZtNuFhedEkd5XFwhWKRd4SIghTswxCrdq39f0016h38v8Ah7/dr6Hp1Feb65+0B4KtvC+varoHiHR/F95pNv5zaZouqQTzOxYJGh2sdu6RlXJGATVPXvF/jj4a+FL/AF/xM+g687LDBZ6Ro9nPZuLyaZIooWuJJpQ6FpApk8tCMbtnO0L+v69R+R6pVTU9XsdFt0n1G9t7CB5Y4FlupVjUyOwREBYgbmYhQOpJAFeY3vjzxZ8Nde0aPxxeaHq2j6wJ4kutE024tJLO4igkuNrK88wlRo4pRuBjIKr8p3fL518UNe8c+LPhn4Q8R6mNDt9A1jXtBuk0iG2mW8sY5L+3eIvcmUpM2NqsoijALkhiE+eormnGPdpPyu7f52FJ8qb8m152Vz6eor5y8ZftRJpOt+Mja+J/BOj23hW5e2Oha7eBdT1l4o1eXyT56fZwSTGhaOXcyk8Aiu3tPH3ij4h+INUg8Fy6Pp2laOluLibWrOa4kvJpoUnESBJYxCqxyR5kPm5Ln5Pk+aL3jzLtf5PZ/P7+m43o7f1puel6Xq1jrlhFfabeW+oWUufLubWVZY3wSDhlJBwQR9Qat15L+yjJLJ+z/wCE3niEEzJcF4g24I32mXIz3we9YGpfHTVdJ8eWNpHquj6/o11ri6NLb6ToV+62TSOY036mHa1aVG2b4SsbDcyg5A3U17/J12/T+uhCl7nO/X9f66nvNFeLfs8Wfia31X4hNq+r6TfWn/CSXatFY6VLbSGfbDmQO1zIAmONm3IPO89KrfE7R9LuPilJc+P/AAndeL/Bz6RFHpEUOiTazBbXglkNyXt4o5CkjoYNspXGEcBhkgzvGDX2kn96v/w3f10KW8vJtfc7f8H+rnuVFfLHwlZfi5qPhzwz4v05tR0LSLPWJV0fWCt2kzw6m1pb/aAWdZniiUrks43EtkkA16t8CA+lw+NfDUa7dJ8O+IZrDTEBJWK2aCC4WJc/wxmdkUdFVAowAALS5tuzfyUuX77+unXoJuza6p2fra/5HqNFeON8YNeh8E61HJZ2C+ObXxA3hy2tQjm2eaSUG2mZd+7yzbyRzMA2cB8Yxxxnif8Aavh0q68VajD4n8FWNh4bvZrNvDOpXYXWNUEDbZ3iPnr5LE7xHG0Um/YCWUP8sx95pLr/AMD/AOSX3l+Xy/P/ACflofS1FeCfF34+aj8P7rV9R03VNJ1PT9IhjurnQrTQr+/uxFsV2+0XkD+VYsyltgmjIIUMWwTs6yz8beLNf+LHiLw/pyaXaaHocVhdSz3FrJNcXSzI7NAmJkVG+TIkOQvA2NnKj0Sk/wCv6+/5k3R6fRXkXwR+Jmt/E6SS/utc8NPDHCwv/DFnZzQ6rolyXwkF0zztkrslUkwxbimV+WvXabTjuPq0FFFFIAooooAKKKKACiiigAooooAKKKKACiiigAooooAK8j8XfBXXdcuvG8Gj+MItD0XxhH/xMIf7LM95FL9mW3LQT+cqorJHHlWjc8NtZdwK+uUVLipJp9dCoycXdHgXiqbRPhD4ugni+KXh/wAF6nPolpbajZ+JbYSRX8UG9Ip7dTcQlZQPNU7WkGDHlOFLQfCP4Q67H8N/Dmq2urXGg+LbPUdUvra81rTxM81reXckhju7dWhOXTypCqtGUdR0AKn6EorVybu3v/wb/wBdOmxnypJJbL/K39dTH8L2OtWOmldf1a21jUnkZ2msrI2cCrwAiRmSRgABklnYkk8gYUbFFFSUcN8Vvh7qPj6Dw3JpOsWui6joerx6tBNeWDXkMjJFLHsaNZojg+bnIcdKi0f4c6peeJbLxB4z1+DxDqGmrIunWen6ebGwtTImx5vKaWZ3mKl03tKVCsQqKSxbvqKS0TX9bW/JA/etfpp+Lf6s838H/DnxR4Jez0jTvFtl/wAIXZzZttPm0YtfxW+dy2wuvP8AL2KflBMG7YAN2756iu/gv9q+Efi/wP8A2xt/4SCTU5Pt/wBlz5H2yaWTHl7/AJtnm4+8N2M8ZxXptFNNr+v67Dv+d/z/AM2edfEL4N2fxA8TeFNWuL57ZNGk/wBJtljJW/hWSKZImIYbQs9vC/IYEKy4+bIl174f+IIfGN34j8IeI7HQ7jUbeODUrPVtKa/t52jz5UyBJ4GSQKWQncysoT5QVyfQKKXS3q/v3/r5k2/y+4831L4a+IbfxJb+JfD3iq1sdfl0yHTNUk1XSjd21+sTM8cvlRzQmORWklwQ5XEhBU4BEHw++EeufD21tbW38azXsLa7faxqbXGmxeZqKXG9hCxBxGVdlbegGduNoBr0+imnbVf1rf8AMb1Vn/WlvyZyXjbwG3jXWfCdxNf+Rp2ial/aktiId32uVI3WEF9w2qjv5mNpyVXpiqnxA+Gdt431rQNUkuvsraYLqC5j8kyC8sriFo5rc4YFcny3DDODGODmu4oqGrxcf67flp6aDTs7/wBf1qfJct0njq2+H/hLw58UNN8fW2na7pl7b2ei2aG8gsrWVZGfU5RM4G1FVc+XAWk2jBJ217Xb/DfxP4d1jUx4X8WWOleHtSvGv5tPvNGN1PbyyNunNtMJ0VA7EuBJFLtdmPKkIPSaK15vzb++3/yK3Iavp0/yv/mzm/iN4NX4geCtU0A3jafJdovlXaxiTyZUYPG5QkbgHVSVyMgEZGc1yVh8Ita1DxjdeIvFfiqHWJrnQ59Cay03TTZW0cUkiuZIw00rB/lIYlmB+XG3bg+o0Vk4p3v5/imn+DZf9fc0/wA0cx8N/DWreDfB+naJq+q2esy6fClrBdWdg1mDCiKiB0aWXL/LywIBzwornr34Nxah4B8Z+GZdWkQeIr67vlu4oAGtXmcOmFJIfYwHXG7HQV6RRVyblJye70++z/REqKilFLRf8FfqeP3nwT8ReJPEV7rniTxnbX15ceHL7w6lvp+jm1tYVuGibz1Rp5H3gx/MC5DDZjZtO7pdY+F/9rQ/D6P+0/K/4RO9ivM/Z8/atlrLBt+98mfN3Z+bpjHOa7uijt5W/Btr8W38w/4K+9JP8Ejw3xN+zz4g1nwBr3gHT/HUWl+DNQEy28J0fzr61WRi/k/aDOEeEMWG3yg+whfMyN1dXefDXX9J8S6vq/g3xLY6GNbMcup2up6Q18jTpGsSzwlZ4TG5jRVYN5inYpCg7t3o9FJaJJf1/X/BG9Tzvwz8GbDwjrvha906+l+z6Hp99ZtFPGGlu5bqaGaW4d12gMXiZiAuCZDjaABUOpfBr+0PAXi/w1/bHl/8JBqc+o/avs2fI8yVZNm3f82NuM5Gc5xXpVFH+TXybu/xE1dWf9aW/JnlHj34Mat4osvHMOj+Ko9Cl8VyWonlfT2n8qCKIRyRDbNGf3gGCwKlQxA5wwLz4V+LdU8O6dp9z4m8PWtxot3aXmivpfhyaC3tmhyuyWJr1/MjMbFQFaMr1ya9XopJW28n9233f1uxvXfz/Hf7zx6b4D6lrFn4/bXfFg1DUvFtna27TQad5EFjJAH8sxRGViY8srbGcnIb5/m+XoT8MrvWvE2la54l1Oy1S4t9EvtFvLaz09raC6W5lgcsFaaQoAsG0qWbO4nIxivQKKfn/Wqa/JsSVtv61T/Q8ms/gvrM2k6T4Y1vxXBrHgnSpIHt7D+yvKvp1t3V7eO5ufOKOilEJ2Qxlti5ONwbTvfg/wDbPh3488Lf2ts/4SmbUJftf2bP2X7UCMbN/wA+3PqufavRqKJe9fm63v8AO1/yRUXy25dLNNfK/wDmyjZ6UtvocGmu5lSO3W3ZwNpYBdpPtXl+j/BDV9H8I+FdOi8UWa6z4QZY9B1SPSCFS1EPkmC6iM584Mn3mjeEkhSNuOfXqKpybbb67/18397JUUkorZHz38bPh3d2Xwl+IOva/rKap4n1TTrewe+060NlDbWyTgpHBG0krL80juWZ3JY9lCqPQNN+G2uXni3R9a8V+JoNdTQfNbSraz0w2eJJIzE09wxlk82URsygoIkHmSHZyoX0Sip2dwavv/X9WS9NDF8ZeE7Lxz4X1LQtQaWO1vojG0tuwWWJuqyISCA6sAwJBwVHBrkbr4a+IvFnhvUNC8Z+KbPVrKWKMWs+k6P9iuYZo3V47h2eaZHkVkRhtREznKkEAekUUhnmtv8AC3V/EWrWF54917T/ABLFpiSixtNN0lrCISSxNFJNNunmZ38t3VdpRR5jkqxKlOauvgF4n1DwzoHha68fxy+G9Bv7G6sYxo2LyaK1nSSKG6nM5WXCIF3RxxHcqscjcre30Vak01JeT+7VfcJpNNP+r7/eebzfDjxPoviDWrnwn4ssdG0nWbj7ZdWGoaMb14LhlCyyW0gnjCbwFbbIkoD5bGGK0/UPhrr1j4u1jWfCvii30SHXfKbVLW+0w3pMscYiE9uwmjEUhjVVO9ZUJRDs+8G9Foqelvl8u3oPv5nH/CP4ff8ACrPh3o3hU6lJrB05JEN9LH5bzbpGfLLk8/N684zx0rhbb4B69HY+H9Fl8aw/8Iz4f1mLVbCyt9IMdxIsc5lWC5mM7CQANgMkcZyqsd2MH2qind8yn13+d7/mTyrl5Om36HGeD/A+o+EPFHia6j1a1uNB1i8bUV09rFluYLh0jVz5/mlWQ+WSF8oEbvvHFWPGGi+MNVuFXw74o0/QLOSHypxdaMbydWyf3kL+eio2Dx5iSLlQdpGQeroqbaJdtPu0K6t9zzCb4LNoMfhe48F6yuhapoFpLp8c2p2rX8N3bylWlE8ayxMzmRFkDq6kNu4IYiuu8D+EV8HaTNA92+o6heXMl9f3zrs8+4kILMEydigBVVcnaqqMkgk9DRVX/r53/PW3fUVl/X3flp6Hh+h6Lpnjr9ozUPE2jXsepaDo9nGl28DrJbNrC+bCpRlyDJFbvIkg7b4hwQa6e3+G/ifw7rGpjwv4ssdK8PaleNfzafeaMbqe3lkbdObaYToqB2JcCSKXa7MeVIQek0Ul7tvL/O/9eSQPW9+v6K39ebZ4p43+AOu+KNH8deH9P8axaN4b8WTS3dxCNI868gmkjRXVZ/PCmJjGCyGLdhnUOuQV9A0XwO+i+LPFOuR6huk1qG0iWLyMfZzBG6Bs7juzvzjAxjvXV0UfZUOi/wArfkP7XN6v79/vPNvDfwx1tfHtj4u8V6/pms6tp+nS6batpGjNpwaOV0ZzOXuJmkOY12hSiruc4O4Y9Joop36f13FYKKKKQwooooAKKKKACiiigAooooAKKKKACiiigAooooAK8C1Ke/u/2mNWvNe0Hw/d6D4b0K1vbbUL6/d5tNjd7kyXMMRtiBK/lBWAdcLGpDnJUe+1wN58N5NW8feLdTvpIm0XXPD9toxijdhOCj3RkJ4wAVnXBBJyDwOMy7r3o7q9vWzsWuVxal1t/wClK/4GR4b+M2q6nJ4Yv9X8KDRfDXilxHpF5/aHnXas8bSwi6t/KVYfMRGxsklwxVWwTxz9n+0Z4guPDXh3xM/w9kXw/rV6NMi8vVVa9W5eR4om8kxBDA8iovmGUMA+SmBkp8LfgS3gnVNFju/h78N4TpWVHinS7QJqNyFVljkEH2VRBI3ylmE7gHcADuBXasfhHrFr8K/BfhlrmxN/ouq2V9cSLI/lNHDdeawQ7MlivAyAM9x1rR8vOrbXX3XX42v29DH3uV33s/vs/wANu/qa/h34j+INSvfFWh6h4Zs7XxbotvBdRWNjqxuLS7inDiFvtDwRsnzwyqwMZKhcjdkCvL/FvxytfiT8K/i1oDv4fm1HT/DFzd+d4Y15dYtGjeOVNrSiKJkkVl5Rk6MpBPIHXfEr4Lax4z1bxzeWs+mtDrOnaPBb2l6zmKd7O6nnkhuAEOIZVkWMkb+GbKnGGzte+EnjnxpJ42utTPh7STrXhJ/D1hplhczTRWkm6QhnmMCF0O8HiMbcYCtjcZert0t+j1+bsv8AI6KbUZJ32a/NX+7X7up1d58R9ZGrL4Z8IeHrXxDqunaZb3uoPqGpNYW8CyBhFErrDMWlby2O0qqhQCWGQD2PgnxdZ+PPCum69YJNDbX0XmCG4AEsTAlXjcAkblYMpwSMg4JHNeUeNvgKuoeNm8SxeDfBfjp7zTreyurLxaojNvJDuCywTfZpzhlcq0ZQD5EYHqD6z4S8Pw+H/CmnaSNN0vTY4YAj2OkwCKzjY8usSYGEyT2GeuBmqk78z63/AM/66+py000op9v0X/B7G1XjXjLR/wDhMP2g9L0K+1TXLbSV8MT3v2XSdbvNOVphdRoHb7NLGWIViOc9a9B8J/DXwj4CluZfDPhXRfDslyFWd9J06G1MoGSAxjUbgMnGfWuT8YeFfGNv8WtP8YeGdP0PVoI9El0qa11bVZrFgzTpKHUx204YYTGDjrWf/LyD6a/+kyt+NjV35JJb6f8ApSv+FzCuPGp+A/iTXtN1jVNU1rwnDob67YreSG7vrdo5hHJarM7b5t7SxeX5hLAsQXxtC69x8Ytc8NfaovFnhS30e8k0i71fTorLVTdpOLdA8sEzmFPKlAdPuiRSN2GO3mrrHwRv/iJo/iubxlf2La5r1gmn28NjA01npcCP5scYEhBuD5uGkdhGJAqrsQCsjQPgjcxaPrlufh18N/B2pXWi3Onx6l4aQtLNNLHsyT9kiMMXUlQZScjn5fmUub2bt8ST+/3rfNaLz89yoqPtF/Ldfdpf8b/8AvTeNpPF0nw11XX/AAdbwxarq8c2hOuryNNbs1hcyCaaMRKoO0MgTc4xJuOCu2tjTPjadU8A6FrcWiN/bWp6yuhNov2rmC6W4aK4Bk2crEsU0udoysfbNXtU+HmpXsfwxVJrUHwxeR3F5udv3irYzW5Efy8nfKp528A9+KzND+Dt5pPxo1PxM17CfDMjyajZ6YjHdHqM0UcE8pXbgDy4iQQxJa4lyB1PRLkVRpfDd/hb80mr97dDJc3JFv4uVX9W5fk2nbtfqQP8dL1rafxHB4ajl+H1tqLadNrTajtuztm8h7iO18oq0Cy8bjMHKqzBDgBl8XfEzXNYtfGdv4Z8Lw61o2hpNZahfTaoLeeSdYd8kdrCInEpQMoPmSQgtkAkAmuY0P8AZvt/DWtS2qfDz4deINMbU5LyLxFq1sP7UhhkmMpR4vsrCZ03MqyGdMgISMg7uku/AvjvQZPGGleGW0C40XxFcT3sV9qlzNHcabNOmJV8hImW4XcN65liPzFTnaCeWacqTXVxf36afn29TaL5aifRNfdrqvPbv6D/ANm/VNWf4XfD/T30Xy9Dj8IaXPFrP2tD5s5hAaDycbhtUK28nB3Y7Vznxm1ezj+OnhbS9bvfFq6DL4evrg2fhWXVQ73C3FsqSOmnHzCArSDcw2jdjqRXe/CPwr4r8C+HfDXhzVJNHm0XSPDtjYeZaNK1w19EuyU5YBTDtVdvAbOcgcVavvA9/c/GrR/F6y2402z0G80uSJmbzjLLcW8isBtxtAhbJznJHB5x21ZKWI5ltef5St+NrfJmME40nHraP/tt/wBb/Mw4/H02m3Gj+EvAui3OvaidN/tST/hKNSu7M2tqzlUM8s8U1z5rvuCo6Zwj5K7QDRh+Pl9rmoeFtM8P+E3vtV1uDUTJDfX4to9OuLKVIpo53VJPk3syh0V8kJhSG3KnxU+Ccfirx5a+LIPCfhHxpM1iNNutM8XJtRUR2eOWCbyJ9jAu6svl4cMp3Lsw2l4X+FdzofirwdqsGmeH/D9jpOlajaXGlaFGYreKW5mt5FEKiNQyjym3OQhYndtGSBjG0tZef/t1v0/4GqL0TsttPyV/xvby77mFb/tBa+3h671+58DR2WkaNqLaXrrS6v8AvreVJxFI9qggxcRLuDbmaJiMgKSOdP4zfHOb4OSPd39n4eTRY4TMDqniRLHULwIA0q2ds0LLMyqQArSRlmOOAQxbq3wi1i++Gfjrw7Hc2Ivdd1e5v7aRpH8tI5J1kUOdmQ2FOcAjPesLx38H/G2oSfFO08PDw35fji3Mf9u6nPMLyyX7IsH2bylhYSRZVmVvNXYZ3bY2MPnd8qdtbX+dl7vpe/3Wu3YqCXO1La9vld6/db/gE83jzS/BnxU+J/iy+M0ml2nhTRLsrAm6WUGXUCiIpIy7EqoBI5YdK7Pw38RNck8VWGg+LPDVv4evNUtJLzTmstSN8jiIp5sUxMMflyqJEOF3qRuw/wAvPM618BrjxU/jO21K7s0sNd8OaXpELKhmMVxavcuXeNgFePdLGdufmAYHHe18KPhW3hTXE1C7+G/w78I3MNqYjqHhSPfcTyNtzgm1hMMeA3y7pCcgZG3Lby5edpbL/gmK5mk3vpf7o/8AB/zOl8aeP9Q0fxJpXhjw5o9vrviO/t5b0w3t8bK2t7WMqrSySrFKwJd0VVWNiSSeApNcfY/tDXklrrtrfeE2tfE1jr8Xhq00mLUFlW9u3t45t4l2DZCA7MXKlvLTdsDHyxv+OPD2u2/xH0XxP4Wl0i81ddOuNPudF1i8e0W5tjJG4mjlSKVlaOQKCPLZWEuCVIXPj3h34WXvxc0/4gahfReH9cvI/G/9p2EV7bNNpF60NlDbyQMGDFkH72HzgpIkj3hONlZQ1Wva7/8AA0tP+3fXX7jSWm3y/wDAW9f+3l5aHpWu/Ha/8H+H/Fj674WC+JPDsVpcyaVpeoG5ivILmXy4ngmaKMk7lkUq0akMmPusrG63xmvPDPia70zxvodr4btf7Iudbs7211I3iyW9uy+ekqmGPy5VWSNtqmRTlsMdvOCvwTuJ/AniHTLHwX4H8B3+oTWZSLw3kxypDOspaaYW0JJwGCr5Zwcncd3y7/xS+D8/xM8WWE8t1FbaN/YGraNdlWP2gG7ECq8Y2lTtEbE5I7cHnCldPTs//SXb8bf8AcLSfvaK6/NX/U4P4keMPFPiKX4W3Or+EYdF0jUPF2nTWcsWqC5uY1IkZVuovKVYmK4/1ckoDZGehPYal8db23t9b8QWfhuK78BaFeS2Wo6zJqPl3WYXKXEsFt5RWSKJgQWaVGPlybVbC787VfAPxN8XHwNaaw3hezs/Des2eoXN1Z3VxLLqaQhlLCNoVFuTkNs3Sgk43gLlsmP9m+DSfEOqpF8PPh14q03UdTm1Bda8QW4/tG1E8pklRk+yyfaNjM+wmaPK7UONu46x5dV05m/wgl+T7fJmPvXUnvypP753t9677/d6DoHxM1fxR8RPEPh7TvD1v/ZugXkdrfapc6iULLJbRzIYYhC29syEMrMgACkMSdo9DrjvBHg288M+KPHOo3Elu9vrmqRXtqsLMWSNbSCEhwQADuiY8EjBHPYdjUdI+i++yv8Aia9QooopAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRXD6h8Ulh8VXWiaZ4Z1zxCLGWGDUb/TBbeRYyShWCuJZ0kYhHR28tHwrDvxRu7B0udxRXF3HxX0i28G+LPErW96bHw1LeRXkYRPNkNsCZPLG7BBxxkj3xXEj4kahpvxy8V2NrpmueJkfw/pF3aaNpzxARbpb0Sy5nljhQnEQPzhm2jAbacC1V/n9+v5ag9E2/wCrNJ/dc9qorB8E+NNO8feHYNY0zzlhkd4ZYLmPy5reaNiksMi/wujqykcjI4JGCc7xf8SLfwvrFlotno+p+J9fuomuV0rRxD5qW6nDTSPPLFGibiFG5wWJwoOGwdbf1/XX0DzOvorjPDHxT07xFd69Z3NhqHh++0OCC6vrXVliR44pYy6vmOR1KjZIpION0bYyME49v8crTVrXSDofhbxDr+o3+mQ6w+l2cdtHPZWs2fKedpp441ZtrYQOzna3y4BIP6++/wDk/uYf1/X3r713PS6K8q/Zp8SXXi34a3Gp3d5eXrS65qwikvzJ5qwrfziNCJPmUKgVQpA2gAYGMV6rTasK+/k2vudgory34t6przeOPh54e0fxHfeGrfWrq8S8udOgtZJmWK2aRAPtEMqj5gM4XNFnrWv/AA48eaRoOv8AiF/E+g65FcNa6pqNvDBeWlxDGJGjkMEccTxNGHYHYjKUIJfI2zda30/4a4z1KivOfDHxw0rxPfaMF0fWdN0jXSy6Lrl/DElpqZCF1EYEhlQsis6+dHHuVTjPAPO6v8Y9J8ffDrVdRhsvFuj+H1kWA67pz29vL5y3iwNFEfNL53febbs27hu3AqH1s/6/yE3/AF/X9b9j2iiuJb4uaFDo/jfUbpbqyh8HzTRamk6KHxHAs+9AGO5WjdSvQnpgGsjVPjxp2nyXpi8O+INStdKiil1u6s4IWTR98aylZlMoeR0jYOy26ylRjjJAItfwfyezH/wfwPTaK4fXPita2OtJpOjaHq3i+/FrHfXCaIsGy2t5CRHI8k8sSHftYqiMzkKTtxgnif2dfiVda54H8Npd2+v6zJreo646atNBJJFaRw303lpcvIQ0RKFURSP4CuBtquViurJ/1tf+vVHt1Fcp4z+IEXhG+0zTbfR9S8R61qIle30vSjAsxijC+ZKWnlijVFLxjl8kuuAecO0H4gW2t69Bor6ZqOmanJpUerPb30catCjSNH5b7Xb94GU5xlfRjUrX+vV/oxvRXf8AWtjqaK8Q8dfFuK7uPh/qumNqsFkfF93o9zaQg+ZdtDBexGPYjEOrSxqV3HHCs23HHVWvxw0eGHxAPEGmat4S1DQ7eO8udN1SKKS4e3kJWOWH7NJKkoZ1aMKjFt4wVGVydL/1tcqUXF2/rdr80ei0V5tpPxshuvGGgeGNW8JeI/DGra5HcTWUeqR2rI0cKB3YvBPIqn5gNpO7J5UDmodH/aC0PVvC0viZtK1mw8OKFji1K5hiK3Nw0whFtFGkjSvIZCFGE2knAbIIB5f12/PT103Iunr/AF/XX012Ox8WeAfDHjyG3i8TeG9J8RRW7FoU1axiuliJGCVEinBOB09K1dN0200fT7awsLWGxsbWNYYLa2jEcUUajCoqgAKoAAAHAAritK+LkNxqlxpmteGtb8KamljJqNva6sLVzeQx4Epie3nljLIWTKMysN6nGOavr8TtLbw94Q1kW959l8US20NmmxN8ZniMieYN2AAFOdpbnpmmuy6tfe9F+KaC/V+f3LV/mdfRXlE37RGlrpuq6tB4Y8SXuhaPeXNnq2q29vB5NgbeUxyyMrTCSRAFL5hSQheoDfLXa+MfHOneC/BN/wCKrlZb3SrK3+1yNZbGYw8Euu5lUgKd3XoDjPSlurjejs/6tv8AcdFRXIyfFDRI/iVa+Bybg6vc6YdWjlEf+jmIPs278/fOCwXH3VJri7X4n2XiHVLjxBocHii+vG8NNeWujrLbpazwG5dEnVJJVXzTs3BmYfuyB1O2plLlV35/gm/0Y0ubReX42/zPYqK+Y9N+M3iK+8G/AbX9YsNegvNUvI4ruGHyN2tvJpM7oyxwSlBG8xRgJfLCEBnEaruHuHgX4hweOJtYtH0jUvD+raTOkF5perLEJo96B43DRSSRujAnDK55VgcEEVtOm4ScX0/r9V56makmk+//AAf8jrKK8VvfiRqXh34+eMNJg0rX/FQXQNLu7fSNKaMpCPNvVmlBnljhUn9yCA298DCsEYrsWPxI8O+KPEng3W4NS1u0gvtD1DUYrVmENmIo2txN9riJ3edGzBV/u/vQe1Z+fT/K/wDky/tcvX/hv8z1KivJ9Q+M+pal4F1nWdI8DeKIIv7Jk1HTL6ZbBUuk2jZIqtc5T5XWTZMqMVVhtLArSeFfjROvw78IXuteH9ak8T63BElppEa2jXWpOIFkknj2TeTHHgs2ZXjxjBAJVS7PVPpb8br9H/V7TzL3fO/4W/z3PWaK82uPjto2l+F/EGr6xpWr6HcaBJCmpaTexRG6gWVlWOQeXI0ciNuyGSRh8rD7ylRp+GfilBr/AIs/4Ry70HWPD2pyWB1O3j1VYALm3EojZk8qVyCCyEq4VgJFyAcgJauy/rS/5ajurX/rt+Z21FYXg/xhZeN9Lm1HT451sku57SOaZQBP5UjRtImCcoWVsE4yBnFcbJ8ftLWJtTj8P69P4QW5Ns/iyOGD7ApD+Wz7DMLgxLICplEJTgtu2fPQtWl3HtfyPT6K8StfEmreJP2mtT0m70bxIuk6FptnJaSWupRQWKPM11vuLiJLoNOriJERXjcqUYlEzub0Hxl8RLXwnqOn6VBpmoeIdfv0kmttH0kRee0Me0SSs00kcaIpZRud1yWAGScUdE+4faa7HWUV4zqPjC8vPiJcverrmg6cfBV3d3GltMi3FvIlyFLgI7R+aFB2urEcjBwav6f8YdN0ODwn4b07S/E3ivV9Q8PRatZRgQPdXFuvlozTTSyxxrL84ZizKCcgEsQpUXzL+ujmv/bG/QlySbX9bRf/ALcker0V5RH8S9D8dTfDrUYLjxDpUl9rV1Zx6fEyQ4uoLa6E1vfJuOUTypOFLDesZBK81uaX8WIvEGttbaL4b1zWNHjvGsJfEVqtsLFJkYpIAHmWZ1RwVZ44mUENz8rYq2y7jem/9b6fh/Vju6K4OP4sLD4msdK1Pwrr+h2moXb2VhrF/HbfZbqYBmVQqTtNHvVGKmWNAcAHBIBwPhb45Oj/AAZ1zxL4gvri7i03U9dklmnkaWTyodQulSNSxycKioq+ygdqX2XPoV1t1/4f/I9boryTR/ifrfhrSbDR9V0PXPG3jKPTk1bWLfRY7NP7PWZ5DHGfMlhVsFJI1Cb3YQ5blgW7fSfHdhrGqeKLCGC6Sbw7LHDdmRFAdnt0nGzDc/LIoOccg9uaJe7e/S/4Cj71kuv9fqjpKK8Jt/jI+q/EjwxqekWGu6vYa94MXVbLw/amNZXLzxNvdXlWBGVGwWaQDnaGJZQezj+OWgN4Vl1aS21GG+i1D+x20BoFOo/2h1FqEVihcghgwfy9h37wgLB2dtVrrp6ScfzX4pCvq10019YqX5fkz0OivHfEHjjWdV8afDFJNH1zwgJ/EVza3Wn6jNBm6jGl3Uqk/ZppY3TeFIBbho84GAabovxg0Hwf4Y0d0HirxC2veI9T0myS6CXN0btJ7omLO8BYd0DpGScImzeUUMy1ysf9fn/keyUV5D4g+KmleKPAviuLULfxR4PvdHu7W01C0t5IItStzK8TRPHJHJJGUcOPmVzwHHUYro/EXxYi0vxBe6Lo/hrXPGGo6fEsuoR6ItsFsg43RrI9xNEpdl+YRoWYDBIAZd0dUhLXY7uiuC8WfFpfBt5O9/4V8Qf2DavEl14hjjt/sdvvKjcyNMJ2Rd67nSJlHzc/K2GeB9UvLz4ofEi1mu557W1nsBbwySMyQhrVWYIpOFyeTjqeaAuegUV5vrWsa9418d6l4U0DVn8Nafo8EE2p6xbQRTXbyzBmjgtxKrxJhUDO7o+RIqqATvXG8QXXiv4T6z4Tu9Q8ZXvifw5fa1/Zl/8A2pZ2kUsKXESpbsXgijB23CBeFGRc4OdoNNa2XfT8bfmN6XfbX9fy/wAtz2GivJfHnjrWx8QNS0fQ737JZeH/AAzc6xqTLEjl7iQMlnGSynAHlTyHGCdiZyCQcLQYfiDB8I9H8c2fj3UfEOrNo8OqzaJrVlYLZXWYBJJErW9tFLGxyQjb2CnG5XGQY5lZyeiX+cl/7ax2fMoLd/5J/wDtyPd6KzPDOvW/irw3pWtWm4Wuo2sV3FuGDskQMM++DWnWkouLcXuiIyUkpLZhRRRUlBRRRQAUUUUAFFFFABXh/j/wrrmqfEP+0fC3hTW9A8Ri6tEbxZa6pbRaZe2iEF1u7fzy82EeaNQ1uzBtu10HzD3Cijqn2Do13Pnjxr4X8c2ngv4qeDNG8HT6yfEct/c6ZrAv7WG0C3MQLRyh5RKsiv5iqBGUb93l0BZl3VsfF/gr4wa14htvBl14j0O+8P6Zp4bTby0S7W5hkumYBJ5o1MYEo3MXByU2hhuKe1UU4vliorokvuVl+AS97fu397Tf4o8f8E+KtA+DuiS2fxB8X+GfDfibWb671uewu9Whh8oTzuyonmFS4RQE3gAEo1Pl1Cf/AITlfiL4Psk+ImgaxpUelS/8I/qFo0kTQTTOkkTSyxxPGTLIr/vAysi4Vstt9doo7W6belrW+4O677+t7/nqeGfFD4deJvHGtaDquk6c2lL4g0/+wvFFtNNE0lvYtIk2WKvtZ1UXEP7stzc5BKjcNy80/wAReAPilruuaX4UufFOj+ILGzixpdxawz2U9vvTbILiWMGJkkUgoWYFGBXkGvV6KOlul3+P/Bu/n2Fu7v8Arz+6y9Eeafs9+F9f8I/D2Wz8TWEOm6xNq+p3stvb3AnjVZryaVCrjGVKuCMgHB5VTkDpdP8Ah7pel+JZtdhutce9lZ3aK41+/ntAXznbbPMYVHPACAL2xXTUUN63C29+rv8AqeW/FvS9eXxx8PPEOj+HL7xLb6LdXj3ltp09rHMqy2zRoR9omiU/MRnDZqpqXgzXvjBrBvvEelyeEtFtNPvLPT9PnuIp7557mIwyXEwiZ4kCIXVFSSTd5hZipAWvXaKzcVJNS1v/AJW/r/hik2mmv66nz38L/hbNpt54WsNd8D+KIb3QmUnVbvxnPe6OJIY2RJra3e9dzu42o9um0OckbQG04fhr4hj/AGb08LDTQuu/a/ONoJo/u/2l55O/dt/1fzdfbrxXuNFaSbk7szUEtvQ8W8ffC3W9X+LVjdaVEB4W19bU+JZPNUbTYyGW3wpOWM24RNtB+VOffndc+Ed1pPi/xmZ/CnizxXpviG9bUIJvDvi6XTLdPMiRJIbqD7bAvDIT5kaSFkcAjK4b6LoqUkvx/Fp/ovu9b29Xf0/BW/U8e0/w/rnwn8bavcaH4OuPEWhaxZWMUK6ZeW6S2EltF5IjlNzLGWiKbCHXe2Q+V6Zb8C9L8a+BPCfhzQNa8LRI93faxd6reQalG8enl7yWaAKuN0yyiTgjBUD5gCcD2Oir5n11/r+v6uTypJRWiX+VjzH41aCniBdKjfwRr3iWW3E0trqnhrVLfT77TJioTKSyXMDAOrMDtZgduGUg1z2k6P4+8Fa54Z8R6ro11441OXw6NJ1caTcWkc8VwkwlST988EbqQ0isVwcqpC4Jx7fRUR913/rZr8nb7ipe8rP+tU/zR832ngDx/pfhfwZdnwxbXetaX421LXb3TYtQi2C1ma9IMUjFQzFZ0CAhfmZd/ljcV0PGfw68W/Fi71vxHJoi+HLq3tLGDRtI1i4heWeS3vUvJDO1u8saJI0cca7WYjBYgfdr6Aqhr2h2fibR7vS9QSSSyuozHKsUzwvg91dCGRh1DKQQRkEGne1mt1qvJ2t+Xe45Pmd3138/ecvzZ4nqev694o+Pnwse/wDCt14Ygt7fV5Gt9Tu7aW6Y+RGpcLbyyoIgXVdxfcWP3QAC1qx+FfiCL4E+EdJis4IPEvh+/t9WTT55UEc8kNyZWiMi7lUupYBuQGIJ716R4T+Gfh/wXqFzqGnwXk+pXESwSahqupXOo3XlAlhEJrmSR1j3EtsBC5JOM811NDto1utfmndEJbp63/JqzPJbjw/rnxT8ZaPq+qeHr7wdpui2d9CkOpzWst1czXMaxZUW8sqLGibjkuGZiPlAXJ5XRtA8e3nh/wCFnhu78GTaePCepWX9p6nNf2rQXEUEEsXm2qpK0hUna2JFjYBgNrHOPoSihaNNd0//AAFtr8x2umvVferP8j5n8C33jK++Hfjjw1ovhJrttW13Xbey11bu3WythJezoz3SvIs25DvOIo5AyhOQSQvvMPg2xXwFH4TnQXOmjTRpciyDIki8ryyCPdat+HvDWm+FbKa00u2+y281zPeOm9nzLNI0sjZYk8u7HHQZwABxWpScU48ndJP5K36v7y237R1F3bXzd/0X6Hy6nwl+JMPwxj1RbOKX4mW+oJBEr3kew2iWjaeHMmSCpR3u9hOdzbetepaL8Nbjw/46RLC0W38PW3hCDQ7aYMu1XjlfCbc7uEKnOMe+a9Qopy9+/N13/wDAXH9W/Vtkq0bJaW28veUvzSXojwLwV4G8ULoPwS03UPDVzp0ngq6FtqElxc2ro8celz24uIvLlYlGkdAAQHGclQBmu98J+FtU0z4w+PtcubXy9L1S202Ozn8xT5rRJKJBtB3DBdeoGc8Zr0CirnN1Jc0tyeVLRehwWg+F9Rs/jZ4v8QTWoTS7/RtLtLa53oS8kUt40q7QdwwJYjkgA7uM4OPOfhr8NPFXhO48H3V5oQlOl6V4hintzdQ4aW4vYprePO4j94iHnkL/ABY6V9B0VD1afa6+9NfqX1v6fhb/ACPBfA/w71yO+8QWOkaHrXgXwXe6JPZjQNe1GC7iS9kY+XJaJDPOLeJVaQNGropym2MbSadovh/xZp1h8OfE3/CHX/8AavhfTJNDvtBkvLIXFxFJFAGntnExiOHgXCySRkqXPBAVveKKezv6fhf9JNenoiOVaeV/xt/kn6+rPAvFnw38S+PrHxr4hm0F9M1DXIdL0+00G6uLd51t7W7MryTujtEGbzZCEWRwFRedzFV2f2kodV0vTfDniXw48a+JrG/Om2cb9Zxeobfy/UhZGgmI9Lc17JWHfeC9J1TxPYa/dwzXGo2CFbUSXUxghJDAyLBv8oSYdl8zbv2sV3YOKTSlZPb9Oq+f6lr3byW/9Jfd+geEfCNl4N8GaT4asdwsdOso7GJmOWKogXcT3Y4yT6mvGI/BfjBfg7J8HT4VmELWTaIPFf2u0On/AGMgr55j8zz/ADfKP+r8nb5nG/b+8r6Dopttycnrff8Ar7/vFH3Ukuh594X8I6jo/wAYfFWsSW5XR7vRNJsrW5MiEySQSXhkXaDuGBLHyQAd3GcHEHjDTNc8PfEix8ZaRoU/ii2fS30m80+zuIIruL96JY5YvPeONlJ3K4MinhCN2CK9IoobbkpdV+qa/JsVlZx6O34Wt+SPINb8M+I/FXiq+1w6FNp0d54Nu9MW2uLmBpI7l5tyRPscruKjJKkoDkbj1M3w/wDA+t6J4w8J317ZeTa2PgqHSLiTzUbZdLJETHgMSeFb5hleOtes0VMEoO6/q/P/APJv7l53mUVPV/18H/yC/Hyt4B4Z+F3ifT/Efh+6uNM8uC18fa3rcz/aIjts7i3vEhlwGydzSxjaPmG7kDBx0/w3h8UfD/T7bwVN4SvLy0tbmZLfxLa3dqbNrd5GkWSVHlWcSgNhlWJlLjhsHI9Yop7pR7JL7tC5e9Jz6u7+9t/mz5a8P/CXxHLq3gC7vvh1HH4q0jWYrnxD401O8tbi6v1WOVDJbyiV5jETsPlyCPYuxVTAwu9oPhfUng8N/DnU4Pszy6/qnibVYA4fNgmpzT2ysyEqPOkktztJ5VJQRkED6Hoqoy5Vbpe/4K3kkrLS3kKXvXfy/P8A+Sf4djxP43eFdX8Qa01x4b8I67D4uhslh0nxlo+rW1pBA5feI7xGnV5oEdVZo2hmUqzbV3E1NJpXjDwf4t8atY+GJvEqeKIba4i1G3vLaC3trpLUW7pcLJIsix/uo3DRJKcOw25UA+zUVm4qUXB7O/4/1/mWpNNS/rp/l/kfOngfwj44+HF78OdQPg+XWoNK8CQaHqdrZ3tqt3FdBoTsi8yVI3A2NuJcDHKsSApvr8NfFgkXxy+lxv4hHilvEZ8N/ao9xtvsJsVt/Nz5f2gRYk+95fmZXft+ce+UVs5tycuuv4y53+P4GajZW/r4eX8vxPKdXtfEfjzxR8O9Yfwpf6Bb6NrtxPdQalc2jTJA2nXMSykQzSKQZJVUKrFu5AFc74V+GXiXTf8AhXf2nTfL/snxjruq3v7+I+VbXH9o+TJw3zbvtEPyrkjfyBg494oo52thtXVn/W/+Z4R48+GfiXWtc+J9xZ6b50OspoYsG8+JfO+zzFpuCw27R/exntmughtvEvw28beL7uw8J33i/TPEl5HqUM+m3drFJaSrbQwPFMtxLF8n7lWVo95+ZwVGAW9XorOPuu6/rr+aQz5a+Knwn8SeMLXx1aXHw6TxZ4kvbtpNG8TapeWstrZ2h2MsVsJJfNt5VG9cJEis4DM/Oa9s8GeG9R0n4hePNTurfyrHU5bJrSXep8wR2yo/AORhgRyBntXc0ULSKj2/r9Cbe85dzzDWNH8ReA/iNqnirQtFm8V6Tr0FvFqOlWlzDDd208KuqTw+e6RurIyo6tIhXYrLuyQLfiPR7/4yfDPxHo+raBfeEprxWiso764t5bhHUK8VwfIkkjUrKAQA7H5ATjOB6JRStePKXe0lI8c+Hvg7xZN8L/F9/wCK9PitPHXilLmW60+GdJEgxD5FvAsgbaQERGJzjdI3Ss3w9/wsG++E2jeBLXwTf+FtRXSINKu9d1m9sWtrZRCsUssKW880ksg5KIyopx8zrjB91optKTldaOyt0sr2/MSvHlaeqv8Ajb/IoaDotr4b0PTtJskKWdhbx2sCk5IRFCqPyAq/RRTk3JtvcUYqKUVsgooopDCiiigAooooAKKKKACuQ1v4reHPD/iIaHdTX8uoAxLL9h0m7u4bcynEYnmiiaOEng/vGXgg9CDXX14L40mvdC+Jd5eeBV8UW3ie8vrOPUtJl0OeXQ9UjCqrztctF5ULrC3+sjnQkwqrJIQFItZJf1/X3+gP4W+x6vN8QvD8Hh7XtcfUNul6G9xHqFx5Mn7hoM+aNu3c23B+6DntmuGm+MUWg/F7xNpOqXt3c6TDo2l3mn6fp2mTXtyXlkuxNIsVvE8zLiOHJIKrx03c8H44utT0H4d/GPwYnhfxFqmuatNqdzpg0/SZ5re7iuYt4YXCp5SldzqUZw5MeFViyBugtdYm8BfHPX9X1Xw1r82kXnhjSLWPVtN0i5vlWeOS7LQbIEeQnDhiQpVdoDFS6BnHWCk+qX32u/x09dNXoEtE0u7+5SjZ/NNvzXkey+G/EmmeL9Ds9Y0e8jv9Nu03wzx5wRnBBB5VgQQVIBBBBAIIrO8YfETQvAz2UWq3Fy13elhbWOn2M99dTBRl2WCBHkKLkbm27V3LkjIzgfA7Q9S0fwhfXOq6c2j3Wr6vf6sumuV320c87vGjhSQH2FWYAnDM1ZfiSeTwL8ZJfFmo6XqmpaLqGiQ6XFdaRps1/JZTRTyysjwwK8u2USKd4QqDDhiMrkejSel9/LS9vv0/qwLaT3tt562v92v9XM7Vvidc3+teOjb+I5fD+gWng+11a01CfSy8mnySPeB7hoHQSMVESExOP4MYBJrrdU+MHhfwrfR6LqOqXV9rsdhDfPaafpdzdXMsDllE6wwRuxXcjbiAQmV3Y3LnzH4jabqniv8A4WxqOn6JqzQax8P7eCxSWwlSWaYm+PkhCu7zQHTMeNw3gEAnFdx4R0O7t/jdrepTafNFbN4W0q1jvJISELrNdmSNXIxuGUJUHIyue1Vy/L+pf8D+rFTt8Xp+VP8Azb/p314fGth4n1vwNe6J4rjGk6vb3N1DYx2XmDVIxGpB80jMJjLBiDgknaRwas6d8W/DOseJf7DsLm9vbrz5LX7Vb6XdyWPnRqxeP7YIvs+9drAr5mQylT8wxXj3wq8I69ZzfD9LnS9QsPst54qEkk1q6i2SW8cwFsgbQy4KZwGGMcV1Hwl1+88IfDnRPAH/AAj+sWfjDSNNNifM0i4OnSTxRHEwvAn2cxyMAwxJu/ebSAwIGbaXM+3Tq9/y/G/QSXS/W1+n9M7fw/8AF7wx4n1uLSbG6vo724jeW1+3aVd2kV4qYLNbyzRJHOACGzGzZU7unNc94D+LVvZ/CHwx4h8Xak8uoalmJFt7R5ri7m3vhIreBC8jbVJKohwqsx4BNeVeGLPUvEHxA+EOqy6b4+1DWre7nn8Saj4givbezs52024Ty1tnCWyjexUSW8ez5QC5Mg37PgrR9U8C6f8ACzxPq2harcafpOk6jpt7a2uny3F5Yy3E8LRzfZ0UyspELIdikjzFJG3cRco8skr/AOXXb10SfnfYhO9/S/n8/Nbv7tz2bwv8TfD3jDUv7O065ul1D7L9tNnf6fc2UwhErwlik8aMMOjAgjI+UkYZSaV98Z/CFjZQXP8AaU939ovLmxt7bT9Pubu5uJbd2juBFBFG0kixspDOqlRxzyK4T4r32s3UPhf4k+CND1K91m0+1aV/Z93p89vPLb3KlEaSF0EiKtzFbSEsowgdjgc0218PD4JeMvC17Npuq6r4fg8NnRGvdJsJ7+WC6EwmeSSCBHkImOSZApG5PmI3Ckt0npq1+Da/DlXq9NrDfW2vVel0n+r9F53Oj+B/xFuPiPd/EG4Oo/2hpun+I3sdP/ciIwwraWzmJl2qwZZHkyHG9TlTjGB6jXkP7PkOoNffE3Ub7QtQ0CLU/FUl5aQ6lbmGSWE2dqqygejFWPqDkNhgQO5bwnqh8Vf2sPGmuCw3bv7BENh9jxtxt3fZvPxn5v8AW5z3xxVPaC292P38qvfzvuHWT31f5sxfjN4y1vwboeiN4ffT4tR1TW7PShNqVs9xDEs0m0v5aSxliOw3iqD+LvGHgHxBolr4wm0XW9G1q7XT4dU0ezl09rO5YMY0lhlnm8xJCu0OrqVYqChDblg/aHjuR4e8LXcFhqGox2HifTb24j02ymvJkhjl3O/lRKzsAOuAag1nVLn4ya54csNJ0bWLHw9pupQ6tqOrazps2nbmgJeK2hhuFSZ2aTy2L7PLCqw3FjtGdO7v/i1/w2h/m7eemuwT8v5dP8V5f8D5a6bnUL8YvCLeJjoS6pI12Lv7AbhbKc2Quv8An3N2E8gTZ48syb93y4zxVPWfi54duo/EWlafrdza6pp0N1Hc30Gk3FzFpskURYvKRGYxgfMqsw8zaQu4g14n4R8A3FppMfgDxHrfxGikh1ku2nWOg20ml3P+mfaUuVvhYsFRiRI2+4EikMp+YAH0DS/DF/beAfjbENKuo7vVdT1Wa3j8hg92GtI0jZBjLg7doIznGBWc2+ST/ut/NctvzfnoaR/iRXeSXy953/BdLep6J4L8YabrS/2RHrP9sazp1jZz30zWrW5lWaMmOcKQFAk2OcKSFIK8EYrJvPjt4LtdN06+TUrq/h1C3e7t49M0u7vZmt1YqZ2ihiZ0iyMCRlCHjBORXmfj3wv4p0WDwdqvhewvDqut6DH4R1Ly4WJsvMVGhvJABlRAfPyW4/eY68GLXPBNx8Nfide3drqvjDw54avNF0+wsJvB+hw6osYtRIht5Y/sVzJGAHV0ZQqHc4PzAZ6amk3bu1+fotuW1u9uhhT+Bei/T163+67PY9d+Knhfw/p+kXs2pm9j1dPM06PSbaXUJr2PaHMkMVujvIgUqS6qVAYEkZFcD8M/jtp+s3/iqXV9c86wm8Yjw9oX+iMpDNZwSC3YKgZWDmbPm4KkFWIwBWR4d0WP4O+IvCernTfFGp+GG8Oyaalw2nSXt/ZzPcfacT21rEWUODjKRgIYlVtuRVn4Qa/qHh/UvG1/feDfElpF4k8clLSL+zzvjhksrcLdyjI2Q5jbc3JVjtYBgQCmk5O/Z/8ApcUvm1qn2e1im3y/1/K3f0T0a623PX/Ffi7S/BWljUNWmligaVII47e2luZ5pGOFSOGJWkkY8naik4BPQE1Q0X4l+H9el0qG2uLqK41Q3C2tve6fcWkzGAjzQ0csatGVyPvgZzxmsT40Wej3Wh6U2qjxHby2+oJcWOqeFrCe8vLC4VHIk2RRSkqV3oQ8bIRJtYYNee6fq3iy1/4V94w8Vadq2qQabd6lYzXNposv257SVMW13NZRK0iM3lLvRUBUyAlEGVXJPe/9bfjfp2+ZUtLW/rf8PPvp2Ow+LfxXg8P6BPPo+sR2dxpPiXSNL1aWaIBII57m2Mqs0i7cGCbJYfd3dQRx1Phr4peHPFmqXenWV1dW+oWsP2l7XU9OubCRoclfOjW4jQyR5GN6ZUEjnkV4F4os9e8QeHfiBqtr4L126W78d6HqNlps1m0Nxe2sX9n7pFV8bFIjcnft2YIk2FWC9L8RNN1T46avcnwzpep6ZbWPhnV7AanrOn3GmNJeXaRpFAizokhUBGZnC7B8mCSCBWqpba7/APkkX917q3fTcqydRK9lor/9vNfl+GuyO+sf2g/AmpXmmW0OrXKnVLpbLTbibSruK31CViQv2aZ4hHOpwTvjZlxg5wQTrWfxa8LahrF/ptvqMsk+ntOl7N9inFtaNDnzBNOU8qLG043sN2DtzXjfjbxovia1+EWlweFte025sfFOmLeJqely2sdiyRyKUWSRQs/RhugLrgElgCu7Zm+HGr698H/i7olnp722q61rWpzW8N2GhW8DOuwZOPkkVAm7pg5o/mt05vnbkt/6U/W2hipO8Vbfl+V+e/8A6SvvPRvDvxi8KeKNattKsby8S7u0eSza+0u6tIb1VALNbSzRJHONpDZiZsr8w+XmtD/hY3h3/hE38S/2j/xJEna2a68iTiQT+QV27d3+tG3OMd+nNee61rEnxY1TwPY6NoOuaVJo2swarqE2saTcWEdlHFHIrRI8iqszuX8v9yZF2liTtxu4bVbjVtP+EOseAE8LeIbzxFb687/uNJuDayWr6qJ1uEudnlOPKdWKKxcEMCvysQb7d/vWmvpr+F9h3f8AXR66fh+KW57Lrnxu8H+H7/VrG5vr24vdJkEd/b6dpN5eyWuYllDyLBE5VNjg+YflzkZyCB1S+ItNk8P/ANuw3cd3pBtvtiXdpmdJIdu8OmzO8FeRtzntmvEPC/j238E/FL4wC58P65ftcavbNb3GkaXPfC4kXTrb9y3kq3kkZUhpdiHzOG+Vsej/AAX8I3ng/wCE/h7Q9Wto7e9htibmzRg8cDSMztCCOCqb9gxxheOKn3vZ3W7Sflqr/wBf8FGrsp2e39f1/wAMzR/4WZ4Y+1eFbc6vCJ/FMbS6NGyuDeKsQlYrxxhCD82OoHXise/8f6de+MNMhsPE0kUVnLqEF7pMOlSTtfyQRIzqkgXIMRdThM7ySvJBA8Q03wF4k0zw7rFxJoOpPL8N5YbXw1apbl5b63gvDcubfp5nmWot4ARzuSQfXuvBHg3VtH1n4UXN3p10l08Gr6jqz7Gdba6u9k7o7Y+X53dQD/dwOlVo02v6T+F/de66PsZ6qyf9W+L8bW3vr5GM37SlxrPwpHiQTS+Hp7PxXb6fe3d5pNxZWq2J1j7Mf3l0mwsYFPmbWzGxbIjIwPZPCPxO8PeONS1DTtLnvE1GwSOW4stS025sJ1jcsEkEdxGjMhKMAygrlSM14d/YeoXPwwbwlLoWryanp/j+G9uYm0u4EL2smvm4EscpTy5UELb2KMdgzu216lcaXqH/AA0dZaitpc/2WPCs1u12I28nzTdxMEL427toJAznAJq1ZpLvKX4QTXyuvvbCd05NdLf+ltflr6feVfFnxWTwZ8Y7XR9Svbj+ybjQJLuLTrKwkvLme4Fyibo4oY3mfCEkhQQBliABmtKb4gaX4qt/A2qaD4sFlpuram0McY09pDqW2GcvatvUNbsrRsxLAMDCUOCcVFNodxJ+0RaawbGZrSLwtNarfeU3lrI13Gxj34xuIUHbnOBmvNfDHhfXYdW8PtNpGoJHD8TdavmMltIAlrJFe7JjkcRsXXD/AHSXGDzWdL3owT83/wCVGvyf3K1rFS3qNf1+7v8Amvvfc9Yj+M3hafVLiwt5tUvJYWnj8+00O+mtpZIVYyxxTpCYpZF8txsjZm3Iy43Aiue+GPx80vxV8Mm8U6+02jfZ5HjuGn0u7tInYzvHEkAlTM7ttVdkRdi7bcbiFrnvh9Nf6P8AEbT9O8GjxQvhG6mu59Y0TxJoc9rbaSWUuHs7mWJC26fjyleZMSMybFUZwtF0nU4Ph/4ShbQtYnu/A3ieW/1XTTpk6NcQmW7USWxdVS52CVJh5RcnaAAWKqV0i+9vlrZv0/pvtOvNJdr/AD0ul6v+keyaP8XvC2t2uszRXl3aNo9v9svrXU9MurG6hg2sRL5E8aSMh2PhlUglWAJIIpvh/wCMXhPxPrFjpmnX9zJc36SPZPLp1zDBdrGqs5hmeMRybQwztY9D/dOPN/GUV78UNY1vxFomk6xb6Xp/hDVNJ/4mOmXNjPf3NwI2SOK3mVJWCCL7xTaTKApYhwuj8QNHbwv8FPCHiKC0NvfeBo7HVRCylXS3iiEd3FzyCbZ51wf4gtPRJuem3yu5K/3JP5+g7NtRjre/zso2Xzbav5ep6zpviTTdY1TVtOs7nz7vSpUgvEVGAikeNZFXcRgnY6nAJxuGcVzPiT42eD/CepXllqGo3O+x2/briz026urawyAf9JniiaK3wpDHzWXCkMcKQab8FfD93ofgG1uNTUrrOszTazqAbqk9y5lMfuI1ZYh7RiuF8PatP8MbHxj4Z1Lw1r2q6tqOr399YyadpM9zb6ml1IzxBrlAYoSu4RN57x7RHk4TDFS5otxtqlt3emi/H7io2kk09G/w11/L79zR8TfFCW8+OmgeDrPU9c0zT0sf7Qnl0vQpbmO+kaWIRxvO1tJGtvsL7pEK4LAeYhUg+l+KvF2k+CdL/tDWLv7LbtIsMapE80s0rHCxxRIGeRz2RFLHsK8s+FvgPWfBPjTwdYajHLdDS/AkemXGoIjtAbhJ4soJD1OFJAJzgZrofitZ3On+KvA3iv8Asy81jTNDurn7Xb6fbtcXEAmgMS3KRKC8mzJUrGGfEpIBwat2jFJPrLX/ALeaX4Jf1dvKLcpSuui/9JTf4v1/IiuPic2t+OPANvos97b6XqVzfwX1rqGmS2czGK28xA0dxGkqYJDAgDII6in6J8WtA8NeEdBm1/xedcuNWnu47G9j0uSKS+eOSQmGO3jQsXVV2BQN0hX5QS2KqalqUnjf4ifDXW9N0rWI9MtbnUlmmv8AS7izaMG1KqzxzIroCx2guq7iOMjBPIfD3wjqtu3waN5ot5EdNvNcluDPauPsvmCcRs+R8m4NgE4zu461m7p/15CUndne6p8UNG8U+Eo9R0HxVLoCx67Z6XPNc6RKZ0na5iU2cltMiyRNKHVNzKNolD9MGtrX/ix4a8N64+j3Vze3GoRKj3Eem6XdXy2iv9w3DwROsAIBIMhXgE9ATXjnizwnrlx4t8fyQaNqElvc+NvC15A0dq5SWKJrLz5UIGGVNjbmHC7DkjFdl4V1r/hV/ibxhpmr6Frk1xrGuSanZ6hpekXF7BexypGqBpIUZYWj2CM+cUGEVs7TkaQtKKv/AFpDRfe/u2vdmk/denl8tZf5JfP0R1138X/C9h4kTRLm6voLl7pbFbqTSrtbE3DcLF9s8r7PvLEKF8zO87fvcVW+GvibU/EHiT4jWt/c+fb6T4gFjZJ5ar5UP2G0l25ABb55ZDlsn5sZwAB4P8QLXX/FVneQarpfjnV/GUPim32WtrHewaNaabHqcbRSqIwtrcr5ARju82UMzE7VjPl+3fCnS73TvFXxSmu7O4tYrzxKJ7aSaJkWeP8As+yTehI+ZdyOuRxlSOoNOnqm3/K/zh/m1967jno7Lv8A/Jf5L8H1RJq3i7xH4k8Wan4d8G/2ZZHSPLGpa1rED3UMczoHW3it45ImkfYyszGRAgdMBySEz08ZeMfB/jjwnovi2XQ9R07XpLuzj1HSrOa1K3SRiaFTHJLKFDxpOMbj8yLg/NgUoLy6+D/xB8XXWo6Tq+p+GvE95FqdvqGjabNqD2tyLaOCWCaGBXlCkQIyyBCvzMrFSF3T/ExZ/il8J7zVfD2nanBq+kXSato8Oo2EtnPPc2riRVEMqrIqybWi+ZQSHJwQQTCsrS6aX8tr/drbva/mDTk3Fb9P0+T69tUVvih8YdZ8L6n41t9DtbGeDwr4Vl1u7lvI3fddPvNtCNrrgbYZGcdSGTBXrTNW8Z/EPwD4PXxlrtz4d8T+H7e3S71Kx0nSp9Ou7e3IDSTRvJdTrKY1yTGVTcAcMCArc9J4Z1rXP2cvifrl3od5b+KvGlnqGoHSWgZryJWtzDaWzJjd5iwxxAoB99nx1rV1zxRcfEr4ZyeB/Dmg+IP7R1XTRpl3eatol3pltYQyRiOaZnuoo/MKqW2pFvZmxwFywEpRuvtLl+98za9Fom+yvfqO8Xyt/C2/uXKk/nq0vO1uh7bDNHcQxyxOskUihkdTkMCMgin1BY2aafZW9rFnyoI1iXPXCgAfyqenK13bYiN7Lm3CiiikUFFFFABRRRQAUUUUAFFFFABRRRQAUUVyGt/Fbw54f8RDQ7qa/l1AGJZfsOk3d3DbmU4jE80UTRwk8H94y8EHoQaOtg8zr6K5yb4heH4PD2va4+obdL0N7iPULjyZP3DQZ80bdu5tuD90HPbNcNN8YotB+L3ibSdUvbu50mHRtLvNP0/TtMmvbkvLJdiaRYreJ5mXEcOSQVXjpu5a1V/612+8Hor/ANb2f3X1Oz8VfCvwV46vor3xJ4Q0HxDeRR+THcarpkFzIiAkhQzqSFyScdMk1seH/Duk+E9Jg0vQ9Ls9G0yDd5Vlp9ukEMe5izbUQBRkkk4HUk03w34k0zxfodnrGj3kd/pt2m+GePOCM4IIPKsCCCpAIIIIBBFY3jD4oeHvAt5FaapLfyXckLXP2fS9Ku9QkjiU4Mki28UhjTPAZ8AkHBODg+H3Q+LVanWUVkab4s0rWL7U7O0uvOuNNERu08t18vzIxInJGDlSDxnHfmvNNU+N9jD8QvBs9vqss/hDXPDV9qcMNtYSTT3kqzWYhMcSxmdmEcsp2KucZJX5cgSvLl/rZv8AR2D7PMtv+Cl+p7FRXIW/xZ8KXPhG88TDVlh0izkaC5a5glhnhmVgvkvA6iVZSxUCIpvYsoCncM8vrXxZ/tbXvAkegS6jZW194gbTtRg1TR7ixlkj+w3EwXZdRI4G5EO9RztIz1FCTbt/WodG/X8Fdnq9Fea6X8VNC8L6Ct34h8Yrqkd9r17plndNpzwHzklm22aoiksyCJo1bGZCoxlnAKa18VtE8TfD/wATX2ieJ7jwzPpbLb3V9faLOLnTpG2MpeznjWQ7ldcZXBDZHSl0v/X9ahL3XZ/1a/8Akz0uiuP8V/Fjw14L1IabqNzfXGoLB9pltdK0q61GWCLJAklW2ikMSEhsM+0Ha2M7TiPXvjB4W8M6w2najdXsDo8Uc14ulXcllbtJt2LNdLEYYiQ6HDuuA6k4BFC12B6bnaUV59o/jaaH4h/ES21jUobbQNDtrC4je42RR2yvFI8rtIQOPlBJY4GO1P0v45eENWubCBLjVLR7+4itbRtS0O+skuJJFdowjzQorBgjYYHGdozllBFq7IV9OZ7HfUVzesfEbw34fvNXtdS1aGyl0myi1C+MwYJBDK7pGxfG3LNE4Cg7jjpyM8BpnxlXxX8dPD3h7R7u+g0uTQ9QvL3TdS0qawuPMSW1WGXZcRJKExJKAwGxiGHJQ4cVzSUV5/gm/wBPvHL3U2/L8bf5r5HsdFFZXirVJdD8L6xqUCo89nZzXEayAlSyIWAOCDjI9aznJU4ub2RUYuUlFdTVorxLSfGnxPtvhrpnj28m8M+IrCTTYtVu9D0/SriwuRC0QkcRXD3cyM6A8K0ah8Y3JnI7TVvjV4Q0W306WfULi4N/YrqcUOnWFxfSpasARPIlvG5ji7eY+FyCM8GtZR5W4vdb/j/k/ufYzjJSSktnqvw/zR3NFcdrHxd8KaLe6fZy6lJd3eoWq31nBplnPfPcwFgBJGIEcuvOTtzhcsflBNUvC/jzTodWm0y/8Vf2xe6lrd9Z6dG2nNbiFoU3vabgu1jGqud7EFxnGcVK1f3/AINJ/i/6dh8y/rzV/wAv6sd9RXJ618VPC/h+TVY73VNk2mTQWtxDHbyyyGeZd8UMaIpaWVlIIjjDNgjjkU21+LHhS68J6h4kOrC00rT5DDeNfwS2s1tKCB5UsEqrKkh3JtRlDNvTaDuXJvqhnXVFcwi6t5YS7xiRCm+NtrLkYyD2PvXiknx+sJvi9Hapqd5YeGLDwpqGs6pZalpE9ncRGGe3CTGKaFZ9uxpsYG1sNjJXj1+18QadeeH4dcju410mW1W9W7m/doISm8O27G0beTnGO9Eo+5zPZ/5tfo7eQXtKy3/4Cf4XVzkdF+EaWut6Xqmu+Ktf8ZT6SWfTo9aNqsdrIyFDKFt4IQ8m0lQ0m8qGbbgsSe/rhNP+N3hLUNNvL8XGp2dnaJDJJNqOiX1mGSZxHG0fnQr5iliBlNwGQTgV0WqeKNP0/WLfRXufL1e8tLi8toPLY744SgkbdjaMGWPgnJ3cA4NEpOKu/P8AzYRjd2Rs0V418I/j9o2t+BvA41/V55dd1ezto5dQbTZksZLyROYjdLELZZSwKiPeG3EKFyQK6jxJ8cvBfhLUtSs9T1O4jbS036hc2+m3Vxa2OU3hZ7iOJoom24Ox2DYZePmXLmuRtN7X/API6Dw/4PsvDeseItStpZ5J9dvEvblZmUqjrBHCAmACBtiU8knJPPYbtcVefGPwnZ3Gm25v7m5utSs4dQtLWz065uZpreUkJII442bHBJ4+UDLYHNRax8bfB2hapPY3epXAFtOttdX0OnXU1haSlgvlz3aRmCFgSuRI6ldwzjIo10Xy+7S3y2FzJ638/v1/E7qisyz8Sabf67qOjQXPmalp8UM1zDsYeWku/wAs7iMHPlv0JxjnGRXM3Hxs8GW9rpE51aSUaxbzXWnwwWNxLNdpEyrII4kjLswLr8gG4jJAIBIQ99juaKwvB/jjRvHmmzX2i3TzxwTva3ENxby209vMuN0csMqrJG2CDtdQSGU9CCc7Vvix4U0Lw74g13UNXWy0rQLo2WpXE0Mii3mBQbcFctnzEwVBB3DBNHW39f1qvvD/AIb5/wBI66iuD+JPjnT9N0u702DxSPDWrtaxXkd+unteiCFriOIMU2lTvZtgBOeSw4UkYMPxs+1fFzxb4MFnfxx6bpttPaXI0S9A89/tXmmSYx+UI8Qx7HOFclwrORgS3aLl2v8AhuOPvO3p+O3539D1qivG/gr8ftE8XeDPAFvqusTXXiTW9Mt9142mzR2d3efZxJNHHciIW7SArJmNHypRl2gqQOg+O3je4+Hvgux1iDUodIiXXNLt7u7uPLEaWst7DHPuLjaoMbOC3GOoIIzW0qbjU9m+9vK97ERkpR5l2v8Ahc9ErmvGngW38dJY22oajfRaTDKJbnS7cxCC/wBrKyLOWjMm1WUHajoG5DblOK5nVfjB4f8AEHhTxiul+IbzwzqWj6Y93cXWpaHcxT2EbI+y6FrcRI0yAoxG0FWKEZrT1n4teHPB8lvp+qX99e3y2kd1cPp2kXV35UbZCzT+RE4gVirkGQqPlbHCnEJO/wB39fgU7JXfn+n+f9aHcUV5h4V+NEOvfFjxb4SltL6CDShB9nuH0W9iTJSRpTLO8flKuUGxiVDj7petXRfjb4O8QaxZabZalcNJfu0djdzabdQ2V8ygti2unjEM5KqzDy3bcqlhkAmktdhX7ndUVwGofHjwTpl1ewz6pc/6DcmzvJotMupYLSUSCLbNKsRSPLkAF2AIyRkAmusu/Emm2Ov6fok1zt1TUIpp7a3CMxeOLZ5jEgYUAyIMkjJYAULVJrqPq12NOiua8XfETQvBElpBqlxcPe3e429hp1jPf3cqrje6wW6PIUXKhn27QWUEgsM+ffFr46WOnfC+x1vwxf3sh1fVLbSYr6x0me6msi90kM5aHymKTxhnCxSoCZAF2MfkJva3Vpfe7fmHr/XU9mornbHXrLQvAsGr6nq15JYW9mLifUtZtvstwyBcl5YhHHsf1Xy1OeNoPFZun/F/wzqFt5/nalYr9st7ER6po17YyGadtsIEc8KMVZuN4G0YOSMGnbXl6/0vzFdWuztKK4z4heOINE0PxRZ2F75PiOw0G41aJPKLbECuEkyVKH51+6c9ORisvwR8avDmvR+H9Nn1OdtX1C3jWO5l0+eKzvLgRBpI4bkxiCWQYfKRuSNjjHyNhR969ulvxuvzTuOXu2v1v+Fv80ej0Vx//C2PDT+Jm0GG5vbu9juBZyzWel3U9nDOcfupLpIjDG/IBVnBBYAgEiuO+G/jjVPEmj/D651TxR9nvtRudTWWx/s9W/tNYmmVV3qoEPlqqtkY3bcc5pX0uJtJ2Z7DRXnWn/tBeA9UvNPgtdZmmS+u/sEN4um3X2P7VvZBbvc+V5UcpZSBG7hjlcD5lzb0vxpYaPJ471HV/F0N1pekaiIpUntBbLpI+zQP9n34/fEmQSBuSTMEHK4prVN9N/y/zX9NXfl/XX/L+tTuqK4q3+MHhy40a81PbrcENrJHG9vc+HtQhunaQ4QRWzwCWXJB5jRvut6HHNeNvi/DqHg2x1PwnqM0M6+JtJ0m9iu7F7e4hWa9gSWKWC4jV4y0UvG5QdrhlPINVGLlJRW7aX3tL9UK+l/X8Lv9D1qisHxz4wtfAfhe81q7hmulh2JFa2+3zbiZ3WOKJNxA3O7KoyQMnkgVzVra/Fad4LyfU/B9nHId0mjrpl1M8CkH5RefaVEhXg5+zoGwRhetR/X9f16jPQ6K8s0P407vgReeO9ZtI7fUdKtbpdS0+3Jwt7bM8UsKZ5w0qELnkhl9ax9O8YfE3xB45j8LRah4X0O9tfDdjq1+9zolzeBrmeSZHjjC3kW1F8oYyWJyeaf2uX+tm/yT/DuH2eZ/1ql+bX9I9rorg/h/451XU/EWueE/E1pa23iTRo4Lg3Gnsfst/azbxHcRoxLRndFIrRMW2FRh3BDHvKYeQUUUUgCiiigAooooAK8F8aTXuhfEu8vPAq+KLbxPeX1nHqWky6HPLoeqRhVV52uWi8qF1hb/AFkc6EmFVZJCAp96ooWkkwezXc+Z/HF1qeg/Dv4x+DE8L+ItU1zVptTudMGn6TPNb3cVzFvDC4VPKUrudSjOHJjwqsWQN0FrrE3gL456/q+q+Gtfm0i88MaRax6tpukXN8qzxyXZaDZAjyE4cMSFKrtAYqXQN7xRTj7sVFdEl9yt+X4+WgS969+7fzbT/NHnnwO0PUtH8IX1zqunNo91q+r3+rLprld9tHPO7xo4UkB9hVmAJwzNXI/GqR9J8USav4YfxXpvj2PTVhtX0rQp9Q0zVV8xmitbphE0KAOGG8yQugmJ8wKTXpHir4V+CvHV9Fe+JPCGg+IbyKPyY7jVdMguZEQEkKGdSQuSTjpkmtjw/wCHdJ8J6TBpeh6XZ6NpkG7yrLT7dIIY9zFm2ogCjJJJwOpJpOzSS0tovS1t/T1Hezb3vq/W9/zPI4Nc1LwL488YS6v4d1m8ufEdlY3Nl/Y2mzXcD3CwGGS3aVFKQkOq/NMyJiQHdgMRxHgWPVfAl58EdU1fwnr0lpp3ge6sdRks9MnuJNNmdrLaskUaGQsSjLsVS3JbG1WZfqSiqjLlfMl/VpL/ANu/BE293k6f8FP9PxPm668Oa1qusX/xCHhzUBpC+LbXWE0Y2pS+ubOCxNr9pNu2HEgkYyiJgJCsSjZvwtdj4m1w/ELxF8ONQ0fSdcFlp3iVzcyaho11YmNf7PuhvKTxo4TdIi7yoUs2ASa9gopRtHRdLP7rL9P6VkN3d2+qa+Tv+XM/61PnXQfCOrBfAouNFvQLX4h6zfzCS1f91Cx1ExTNkfKhLxlXPB3Lg8iovip4V1u/8QfGdrTR9QuYdQ0TQ0tGhtnZbmVJrjzBHgfOyqU3AZIBXPavo+ii+lv62S/QKn7xyb6//JOX62PHdP1dvhX8QvHk+taLrd7D4ivodR0/UNH0e41BZI1tIYDbyfZ0cxMjxMQZNqkSAhjhwvmHx6t9e8WeHfijpeo6V461fX9s0Hh7S9FjvYNMNm0CESGS3CwztkyFop3kclCiJ8wDfWNFEXytPsrfda3z0tfs33uO+t/O/wDXl1t3t0Vj598beBdc8Rah8UEtNJuLpZ/7Auobd0Eaaits/mzW6O5CEsEKcnALANgGul8cahD8bfhzr2n6Dp+uWGtWEUOpafJrGi3enBL6F/OtwpnjTfiSIBtm4AHryM+u0VOutn5ryff8vmvUmCUEl2PnK88PeIPEHw7fx1d+HLxtZvfEtj4iu9BEbLeDT7WVFigEbAMZEij87yiMmQsANxFb1jrU3jj9o7wxrWn+HtettCs/DmpW0mq6npFxYxtM89qwi2zokgICEgsoDZO0ttfb7fRWkZKLulpr+MeX8kvnuTy3Vm9dPwlzfn+Ghz/izwzqPiNbYaf4t1jwsYS286TFZOZs4xv+0282MYONu3qc54w3xhY3D/D3W7JHn1G7OlzwqxQGWd/JYA7UUDcx7KoGTwB0roqKwqQ9pTlT7m0Zcs1PseA+FvHGo3nwV0Lwf4e8MeIpfFraHb6Y66roV3p1pYyeQsbzTT3McaMkZ5Kxl3bHyqeSMSbwLe/Cfx9eD+3/ABxpOi3mjabZ2eoeFdCh1ZX+yRGIwzp9iuZYmG7zFOFRvMf+JTX0zRW85c8pS/m3/H/NmMYqMYw7Ky/D/Jfpbc8R+Gvw+Xwn8RvCos7DVf7J0/wW1nDearColjZ7qOTyZGjURrJgcooGAvAwKydS8Gaw3gHxTqttpt4uvaF4yuvEelwCBvNuljm3Mka4y4mhMsY25zv4zX0JRWe1mul7fOfPf5Mq17362/CPLb5o+Ztd+H/iD/hHfBHjK4Gt6XqTa5c69ri6DZRXGo2n2qB4oisEsMvmtBGYYWURs4UMV5GDPdeEZZtHm8X6ZL408XSQ+ItN1LUYtf0tbC7uobVSCYLVba3ZigcNgx5cw4XccZ+kqKrZ3XRprytay9NFtYTXN8XW9/ne/wCb7+h4FL4sn1b48QeMLHwl4om0HSvBupRm6k0Se2lu5hcW0gtooplSQyEIdoYKHJO0nDEeseILnT/EHw6vp9W0bULrS77TGe60g2zNdtE8eXhMSHd5mCV2rzngc10tFJ25FDtf8W2/z/Au75ub+tEkvy/E+ZJrHxZr3gPxloOkSeJPFPhmxsbO40qXxPpMmn6m9xDKrvZqZY4XnGyJSsjx53SYMjnO3rl8QT+PvjV4c1TTfD/iC30a08PapBLqGqaRcWKi4kktCsOydEfdtQndt2tyFZirhfbaKT137P8AFNa/f+HXUUfd2/q1v8vxdraW+WPC51LX/wBnPwn8Ml8G+INM8TPBYW9zFf6VPDbWcaSxyvdNdMnk7gq7wgcyByFKhgQNTWdWuvAfwx+KvhXVPDmt3+r3kmt31tNb6fNNZ31vcebKjtd7fJi2o2xkldWHl4CtlN30nXn3iX4Q/wDCXXWoRap4w8ST+HdQk3XXhsS2y2ciYG6LzBB9oEbEcoJgCCy/dJWip+95ltzJ/jb/AC/rS1wlyNPs0/uv/n5dPO/JfCfQLpPGnhXVZdPnWCP4fWFmt20TCMSGTc8e7GN2ApK9cYrE01r3wt8J/Evw1ufC+s6h4nu21S3t/J0ueSw1D7VLM8c73gUwxqyyguJJFdSrDB+Xd9DRRJBEkUSLHGgCqijAUDgADsKfVVbVbp7Pm/8AJpOX6mNGLpJNbrl/CKj+h4doH234RfEC6i1fTta1qHUvD2lWdtqGk6ZcXqTXVsJo5Y5WjVhCT5kbB5SqEM2WG1q4DwZqF34F1f4JXOueG9XS4t/DmsC8tLeykuLqwLT2vzGCNTJJyQpEascPuxtBI+sKwr3wfZX3jTSvE0ks4v8ATbO5soY1ZfKZJ2iZywxkkGFMYIHJyDxiuZyqc8t9fyl/mOEYwjyray/T/I5D4T2d3qXivx14ul0y60jT9curZbGG+ga3uJooIBGZ5IXAeMu24BXAbbGmQOK47xV8Pb7VvjZd6C2k3EvgrxIbfXtUuhGTbtPbRPA0DtjCs5Fi4U/eEMnvn32is7J27f1+tn6orWzX9f1a6/4Op8q6T4P8Van8KfFlzq+iamNdgbSfDltFJA5lurawuEzcpGM/LJJJO4YdVCntXpt1Hc6N8cvFklxp+oyW/iDw9Y21jdW1hPPbmWBr1pUklRCkRxLHjzCu7dgZPFeu0U53qJqT3Wv3b/fqONobLt+DTX5W9D53sfCer2f7P/wO0yHSL6K+0698PyXtots4ltggUzGRcZQKd27djHOcV33x90O58QeDtItLWxm1EjxJos0sMETSERJqEDSOQBwqqGYnoACT0r0qitHO9Rzt9vn/APSdP/JTNRSVv7vL+f8AmfPnx88N61rHiXxY+n6Vf3sFx8OtSso2trd5FkuGlUpCCoOZCM4XqecCj4iPf6Brz6l4KXxTY+PzZ2lubCPQ57nRdZ27TGlzMYjFDtDSIZRNE6ZO7cAoP0HRWcPdUY9n+bk//bi5e9d/1tFf+2nhXirw/qWqeL/iv4eS3v7O78XaBbw6XqUdjO9mJFt7iNw9wimOJgxHyuykhhjOaj1PULj4jeF/CXg/SvDWu6Fq2n6hptze/b9JntbXTI7SaOSULcsBDNny/LQQPJu3g/cDMPeaKcfdlzeafzi21+ev6EtXjy+v/kyV/wAtP1PI/BPw7h1zwb8S9B1rTpba18Qa7qolE8TIZYpSEWVc9RtAKsOOARWX+zp/bPi661Txd4kXGq2dtD4WUdVZ7Qt9smQ+klyzj6QJXr/iDS7jWtHubK11a80OeUALf6esLTxcgkqJo5E5Axyh4Jxg4Ij8LeGdP8G+H7HRdLiaGws4/LjVmLse5ZmPLMxJYseSSSetKHuq3ZJfcrX+66fe/kipe982397v+dn8vM878SSy+A/jRL4u1DTNU1LRNR0OHS47nSdNn1CSyminkkKtDAryBZRIp3KhXMOGIyueM1TwnrmpeG9X1qPSdQij1/x9ous2umvbv9ogtI7ixRppYuTHkQPKysAUU/OFYMB9GUVVN+zcWun/AMkp/mvu+8Uvev5//I8v5fj9xy3xPs9K1HwHq1rrljqOoaVMixzw6TDLNdAF1AkiWEGTchw+UBYbcgEjFeKXEvjHWPB+pTSLr/ivQfD+u6TqWm3WraK9jrF5BDMj3aG38uJpTGoyjCFDJyoDkbj9KUVMfdlzLfR/c7r+tPUJLmVn5/c1Y+ffEF5f+PPFXjvUtM8Pa7Hpc3gSWxtLm+0ue0a6uDJOTEkUqLIGGVwGUFs5AIwTHBcXnjjwf8NvBln4U13R9T0q70m81L+0NNmt7fTIrQxyOFuXQRTMxRYwsTOxEjZAAbH0NRRD3LW6NP5qUpL8ZBNc92+qa++MY/lE8e+D+tf8IFo9n4F1TQtcg1y3vrlDcQ6RcS2V0JJ3l+1fa0QwKHDlyrurg5UrnAPOfDnwxrNj/wAKd+06TfW/2G71xrvzbZ1+ziTzvLMmR8gbIxnGcjFfQlFKwnG7ufNt54P1eL9k+10m30O9TV11iG4+wx2ji4H/ABOllaTYBu+5ly2OmT05q1q3hHWmuvHWpLoV7fwWXj+x10WCw4k1C1hsrIM8G/AkKOpcAH5mhKj5uK+iKKcPc1X9axf/ALainrDkf9XTX6njXxE+IV54l8O6fN4ftfF9hof9qRW2uXVp4fvbbUVtHik3fZ4ZIBOR5nkq0kUZZVclGVlLL5paeE9RuF8VSaX4d8UxaZceMvDN3ZNrjXl1dXFtFPbNLOTcM0yooViVcgxhfmC4IH1hRVRfJJSXRp/c0/0/FhL3lbya+++v4/gjhvjP4Q1Lxp4FltdGMR1myvLPVbKOdtsc01rcRzpEzY+UOY9u7tuz2qPQfjDY67fWWn/8I54t0/VZ5PKltb7w7dxR2zDO7fdGP7MVXB+ZJWDcbC2RnvaKS006Xv8APT/Jf8AHr6/1+R86654D12T4zXPhCHSLifwHr+q2vi+91Exn7NDJAuJbQnGN0k8NpLtPVWm9Od/VtfXwP+0Vrmr6npWvzaZe+GrC2gu9L0C+1GNpUuLpnQtbQyBSA6nBx94V7XRSXu8tul/y5fwVl8hvXm87fg+b8Xe/r5I848AaXqWuePPEHjnUtMuNFhvrK10vTbG8wLg20TSyedKgJ8su85xGTuVUG4KxKr6PRRT6JL+v63Fre73/AKX5BRRRSAKKKKACiiigArndb+I3hTwzrlnouseJ9G0rWL3b9l0++1CKG4n3MVXZGzBmywKjA5IxXRV88eNPGFh4F+KWryeH/E2heINT1jULCDVvAN+A+pPIFSMTWhVt6hY2jkKvG6YjZg0eWNC1ko9/6/pg/hb7HureJNIj0+/v21SyWx09pFvLo3CCK2Mf+sEjZwhXvnGO9cPdfGPTfD/xE8TaX4j1fRtC8O6bpumXVvqN/crbh5bl7tWVpHcIRi3XaAAeW68Y8g8dePNB8I/Cv45eFNV1KC18TzXGrz22jls3dzDPD5kcsUQ+aSPa2WdQVXZIWICMRvWPjLwj4I/aO1+/8U6lZaGzeEdHittT1SRYLZcy3heLznwqu2FITILBGIBCHDjrBSfVJ/erv7tv6sEvdTt3a+6UVf5pn0JY31vqdnBeWdxFd2lxGssM8Dh45EYZVlYcEEEEEdaxvFnxC8LeAxanxN4l0fw6LosLc6tfxWvnFcbtnmMN2Nwzjpketcj+zvZvb+AbqaOyn0zS73WNQvNLs7iNomis5Ll2hIRsFFYHeFIGA4GK5D43+KdO8BeNrnxBpvi/QLbxUNGW2m8H6/hzrNv5rvHHagMJFmZvNQFFlViyhoyQppS91q+nfutL29ejt9w4rmv/AFfW1/1X59T1/wAQeP8Awx4Tm0+HXPEek6NLqLbbKPUL6KBrk5AxGHYbzll+7n7w9ab4i+IXhbwf539veJdH0TyVjaX+0b+K32CQsIy29hgMUcDPXY2Ohr501zU5tC+JXjmTxJ4r8H+BLfxJYWaW1v400hp1uLAWwV7aKU3luhCSvPvhCsQZAx4da634QeDrXRvi8Yrq4/t2/wBL8B6NYxatdW7RTSxtLdByUclkL+WhKsSeBk1Si2v67P8AK1n6vtrN1y38l+Liv1/Lvp6zdeILi61/wuNK1LQ5dG1GKeaUSzlrm6jEatE9ptO11BYFicjaQR1pzfEjwkviseFz4o0UeJScDRjqEP2zO3fjyd2/7vzdOnPSvnv4OLL/AMWvtrZhH9muPFlparj5Ykju3SJAOyqqqAPQVUt9X0O4/ZntfhzaXNr/AMLWjto4v7AZwdUi1oSB2umiI3qBNmczkbdh37tp3URV5Nf8Prf8uvqg2Tv5ry0/z/zPpabx14bt9bh0aXxDpUWsTTNbxae97ELh5VRZGjWMtuLBHRioGdrKehFbleTfCPTYI/ih8X78xqb2TWrSB5sDJVdOtSBn0y7H8a7nxZZeKrxbb/hGNZ0fSGUt551bSJb8SdNuzy7qDZjnOd2cjpjldIvuk/vSY+pq6tf/ANl6Ve3uzzfs0LzbM43bVJxnt0ryLQvjV4zu/AOk+OtS8BaePCt5YRanMNF12S91G3t3iEm/7O9pErlQQWVZS2AdocgKfTfESzp4K1NbqSOa5GnyiWSGMxoz+WclVLMVBOcAscep614X8K/jV4Uj/Zx8H6JourWPivxYPDNpZx+G9HuEurxp/sypskjQkxKG4d5NqJ/ERU68tRx1a5bL15vn0RSs+S+id7/+S/Lqz2rWviV4S8NaTp2qaz4n0fRtO1FVazutSvoraO4BUMNhkYbvlIOB2NWde8ceHPCvGteINL0f5Fk/0+9jg+RnCK3zsOCxCg9yQOtfM9po998FfG1tp2s+NfCnha0i8I6Xo1hqXi7THuLW5jgV0uYYZftduiNv2u0ZyzKyHopA6v4T+D7TQ/ih4It/7Rj19dN8Dy/YdQW1a3jMb3ibDHEzMUAjIRfmJ28ZwTWjUfacsXpeS+SUn/7b+O2muHM1C7Wtl97cV/7d+G+p6/4Z8VNcXl5Z6trGgS3k2pXUGm2+mXOZHhiAJR1Y5MyDO8LwvHSta98UaNpsepvd6vY2qaXEJ79prlEFpGQWDykn5FIBOWwMA1896hYyWngvxH4wtR/pvg7x3fa3wOWtlkKXafjbyTfiBWZ4v0vU7zwL4Y8eSTWukWus+LE8S6vc6pZSXtta2nkSR2D3EKyxkxxKtmWO9QjfvG4Vqzh70IvbSP4qP+b9eV9zV6Skuzl+Dl/kv/AlvY+ktH8Z+H/EHh9td0vXdN1LRFV2bUrO7jltgEzvJkUlcLg554wc1wVr8d9G1j4lWOm6Rr2gap4RbQL/AFW71i0vEmSCS3mt1IMyuY1UJMzNkZGAcgdfJdYt7XVrXV/F03jTQPHfhm413R5fES+FdLKafFDbFy8sxNxcCUjdbGXDfLHChYYrZ1Px18PvEn7RUmvQXema5oEHgDUxrGqWsQvLSe3S5tmaIugZZtimTdGu4gOAR8wFXH4tf734U3K/pf8ALcPL0/GSVl5/5n0jaXcGoWsN1azR3NtMiyRTQsGSRCMhlI4IIIIIrm9O+K3gnWDqYsPGOgXp0tS9/wDZ9Ugk+yKDtJlw3yDII+bHIqW5utAvfhxLcRzCz8LzaUZFmtVaERWZhyGQAApiM5AAyMdK+c7jXrrX/hr418E6Fqmh/ErTNK8KOdP8QeGYlM0SoQq2M6xM8RmZEDL5bJu2H90vBJa03F6W/wCDv2tb5+ujlNuEWtb/APA273v8vTb6j1DWLSxkjt5Lu3jvZ45Ht7eSQB5tgyxVc5YDIzjpkVwnwr+NGi+NPDfhGPVNe0O18Za1pcF+2hw3iJOS8XmExwM5k24DHvwCc8Vx198TfC/xD+LXw9fwxrVp4gt003VnkutOkE0MRMMOI3dchZMHOw/MByQOM+beD/EfhLXv2X/BHgnQpoJfHEp02W20eIBdRt7pZ45DetFw6xqoaXziApTHJDDKindprt+ck/68rabp3XJzf10PqLXPiL4U8M61a6PrHifRtK1e6UPb2F9qEUM8ykkApGzBmBIIyB1Bpt98SfCOl2Nte3ninRbSzuYI7qC4n1CFI5YXYKkisWwyMzKAw4JYAda8UfxL4T8Nw/GLQ/GM1qniXWLy7m/sy8jVrrVrI24W2FvEBvuEEalAEDbWVwcHNVPgDpltdeI/hHdzRiSe0+F9sYWbnYWaBSw9DjIz6Mw7min79vO34wnK3quW36BUfIm/l/5NGN/R810e7a18SPCXhvXLTRdW8U6Lpes3mz7Np17qEMNxPvbamyNmDNlgQMDkjFbMOqWdxf3NjFdwSXtsqPPbJIpkiV87Cy5yoba2Ceu046V87aL4k8JeFvBvxE8KeNrizHi3VtV1WW40W7w17rCTSyC0a3hI33CtD5MaeWrAFNvBQgWPhx4lj+E3i82fxG1i20rXNS8KaGkct9Oo/tC6gWeO4jhP/LaUO6ZRMsfMTAO6jTlcvT53Tbt6W19VogldSsvP8Glf530/U9vufHXhqz02LULjxDpUFhNDLcR3Ul7EsTxRjMkgYtgqg+8QcDvVnw74o0bxhpUep6Dq1jremyMypeadcpcQsVOGAdCQSDwea+WPCuuaDpuj/s86v4iH2TS4oNUuDPex7YrR9oKSTk8Rqpx8zYCtt5FevfCO8sfEvxM+Ififw9JHc+FdRWwhhv7XDWt/eRJKtxPE4+WQBTBEXXIJhIySpwJb36Cb2f8AX9f8H5+iXni7RdP8SWWgXepW9rrF9G0tnZzt5b3QUEuIs4EhUAllXJUYJABBLvE1zrtrpu/w9p2n6pqG8DyNTv5LKLZzk+YkExz042c+ory74+eONHimg8G6tqHgLS7W+gW6nuvHOow+XGok+QxWRdHnYMh+YyRBDtKsxBUbvw517w54T+EM17YePJPiBo+hwTy3GvT6jFfysEUyOrzR8EqDwGJYDGSetRdcjm9Lfp/Xz6GlnzqKW5j/APC6/Eml2/xCm13wppMEXg/SzfSzaVrst3HNceU0otSXtIijbArMQGwJU4OTjuPBer+LtWVpfEeg6Lo9s8SyQNpetTXzsTyQ6vaQhRjuC30rh/DOh6FYfs+3U3xAaO3sPEVvJqXiKS4leJQ14d0is6kMiqJFjyCNqoOQBVP4U6rp8/xQubTwJ4pu/FngQaU0l/NNq82sW9rfiWMQpDdyvIdzRGUvEJCFCxttXfltbWlySWttfVJt/wCS76dzNyTipx2v96dkv836vXRHsaavYyapLpiXtu2pQxLcSWayqZkjYsquUzkKSrAHGCVPpWZq3j7wx4f0q41PVPEek6bptvcG0mvLu+iihimBwYmdmAVweNpOa82+O2rP8L/EGgfEm2s5bsW0FzomoQw5JlimTzLbjuftUUUY/wCvhq891nw1qvwi8RfDW71PxPoPh63tNFvI7nX/ABFp73VgNYnkjluXZhcQCKSUGXY7scgOgwW5zWtvu+er/JL5yW5Uvd/P5aJ/j+CZ9Eav8Q/Cvh/w/Z69qnibR9N0O82fZtTvL+KK2n3qWTZKzBW3KCRg8gZFSy+OvDcGi2esS+IdKj0i8QyW1+17EIJ1EbSFkk3bWARHfIP3VY9Aa+eLPxDb+B9P8O2rfETw/pfhvxBqGq6iPG9np0Vra2sheNjaWLXMk0MfmsZ33uZA4WQKufmXH8D2dprlh4DgllvNSsl+J+pyRyanbRwSy7YLyRXeJI0Rdz/PgIo5zgZq4rmly+n5pP7m/K9tNGmG0eb1/BSafz5fO1++h9H3nxa8Dacukm78Z+H7UasiyacZtUgT7ajEBWhy/wC8BJABXPWurZgqlmICgZJPQV89fFLxhYeAfiFreoaH4l0HV9e1BLO01P4f6qA17qAGBH9jKt5isUlJ2mOWNiD/AKs72ruf2i9Lu9Y+FV5b29rPfWovbGXUrO2RpJJ7BLuJrqMIoJfMIkyoBLDIAOcGd4p7XdvTXf0W7/QF8XL5J/1+n9M6fw98SvCvjSK//wCEY8T6L4ilsk3TppeoRXRhznbvEbHbnaevXBrE8F/FewuPg/4Y8a+L9U0nw7HqVhb3FxcXVwtrapLIgO1WlfjknALE1xmq+KfD/wASviZ4Gv8AwFqum6+NJjvjq+oaPMk8VvYvauq28kiZCl5/IZYiQx8pmAwpI4r4a69pHg23+CeveLr230rw4ngprWx1PUnWKztr9hAzBpGwscjwqwUkjIWRR1INpbr0/Kb/ABsl6hf3brX+kvwvf0Pafhb8SZPiHrHjZI5LG50rSdUitdOu7Bt63Fu9nbziQvuKvkzNhlwCu36nd1r4keEvDeu2miav4o0XS9ZvNn2bTr3UIYbibc21dkbMGbLAgYHJGK8x/ZrvtJ1LxH8X7zQ41TS7jxV50TRwmJJd1jalpVBAyrsWcOOHDBgSGBPnv7Q3jqG+0n4w6JqnimPwtPZWMsVj4b02ztXv9fRrJH+0SedHI8sQYsmYVTy1hkZpOMpM3yqLt9lN/wDgKv8AO77fIqnHnk1f7TX4v9PP5nt1l8c/DF58XtS+H51fSU1S1tYJY1/tOIzTzu0we3EP3t6LErEAk4kGQOp7LXPE2j+GIY5tY1ax0mGTfskvrlIVbZG0j4LEZ2ojufRVYngGvJvCevWWj/HUxarqFvZ3et+EtKj09buZUe+kjku2lWLccyModSwGSAwJ61pfGfTbfVPiF8GobqJZol8SzTBWGRvTTbx0P4MoP4Vbha0fVX9HYz5k7y6WTt/26n/X9I666+LngaxuNLt7nxp4et59Vijm0+KXVYFa8jkO2N4QXzIrHgFcgnpVrxJ8RvCfg4yjX/FGjaGYfL8z+0tQht9nmb/LzvYY3eVJtz18tsfdNfPnx68cQXzfFPQNV8Up4Vez01ksvD+m2VrJqHiKN7MP5jCeOVpog3mRgQopTy5Cz/3Ot+E2l2mo/GnWtTureO51BPA+gQLcyrucRyPeNIuT2YxoT67R6VEVzptdLfje35a+u11YqXu2v/WsV/7d+G/U9g1bxjoHh/w+Ne1TXNN03QyqONTu7uOK2KvjYfNYhcNkYOecjFcj8P8A4rReNPE3jyJbzS5/Duhy2hstSs5Q0c0EtnHO0jS7ijAFjhlwMDv1rw74Y6tp3hOz+EeseJ7mHTvB2mW+vWNvfXzKlnY3guwltvdvli/0eOeNGYjqUBy2DRnn0bxNrnxP1bSrf7f4MTxpoepasLW3Yx3VolvC8s4UKfNi8wJMWUFXRS2SpySNpO62f4e8lr8tfQNUrtbL79G9Put637a/Ufhz4j+EvGNje3ugeKNF1yzsRm6uNN1CG4jtxgn94yMQvAJ5xwDVdPiv4Jkur62TxjoDXFhafb7uFdUgL29ttVvOkG7KR7XQ7zgYZTnkV5H408QaL8QvG0uu+CtRsddsbDwpq9rrWr6TOk9uVdUa2t3lTKu4dZHCbsoCSQBIM07Twjotnpn7MMEOl2qRW0yiFRGPkzpM8x/EyxxyE92QN1GacFzN9LW/FzX/ALbf579SZPlt/wBvP7lF/r+vkewat46g1bwnYaz4Q8Q+Gb21utQtrdNQu70SWcqNcLHLHHJE2GmILIi55kKg0l58afh7puuPo13478M2usRzfZ30+bWLdLhZc48sxl9wbPGMZrxPxYohuvHkSAJEvxN8OOI14UMzaYzHHqSST6k1qX3xk8EePvjZJYav4z8O6Z4e8GXXlQ2N/qsEMupaxgDeI2cFo7cEqvGGldiOYgacVzKL7v8ADlg//brbbtbXHP3b36L9ZL9NfK57Q3xI8JL4rHhc+KNFHiUnA0Y6hD9szt348ndv+783Tpz0rjdD+P3h/wAZan470TRdd8OprPh9pEtfP1aOSOdVt45GndUIZYkkcxuRnHltyDwPD/iV4+TxFpd1HeeK10vXbPxnawp4F0e0tg8CR6tGqXl3uje4Bddsvnq8SHzY1AJb5/QL/WtP0+8/aD0O6vra31q7il1C20+WZVnntv7JgQzIhO5kDqylgMAqR1rCTtSlN/yv7/denyb79/TaMb1OTzS/Fo9du/HmkeEfCmlar4w8RaDoouIolkvZr1ILOWdk3EQvKwypwxXnJUZq7P448OWuk2WqTa/pcWmX0bTWt7JexrDcIsbSs0bltrARo7kgkbVJ6AmvFNB17R/APxH0jxB401Gz0XSLnwbp1louq6pMsFrHKGke7hEr4RJHH2dtpILrHxkIdvM6b4fttW8Y+CJ5bCN/CuofEe+1XQreWLbH5K6VO4nRCBhWuY5ZkOOdyuMhgT1yhepKPr+ErW9ba9NmvM5Yy9xS8l/6Tzf8D+rH0t4Z8XaH4003+0fD2tafr2n7zH9r0y6juYt4xld6EjIyOM961q8o+Hsa2vx6+LEEKiKGWPSbp0UYDStBIjOf9orGgJ/2R6V6vWF09jRX6hRRRQMKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOW8VfDjSfGN9Fd393r1vLHH5QXSvEWoadGRknJjt541Lcn5iCcYGcAVseH9BtvDWkwadaS3k1vDu2vqF9PezHLFjummd5G5PG5jgYA4AFaNcZ46+LGh/D3XvCejakLmbUfEt+LCyhtUVip4zLJlhtjBZASMnLqADmhdIrq/xB7OT6fkjs6KKKACivMn+Nk11rXiPT9G8AeKfESaDeGwvLzT205YvOEUcpVFmu45G+WVOick4Ga7Twj4w0nx1ocWr6Ldfa7KRnjy0bxSRyIxV45I3AZHVgVZWAIIIIp7q/wA/k9gejszZoorJ/tHVP+Eq+wf2R/xJfsXn/wBr/aU/1+/b5Hk43fd+bf07daXWwdLmtRRRQAUVyXxM8ef8K68P2mqfYf7Q8/U7HTfK87ytv2m5jg352tnb5m7GOcYyM5rrae6v52/J/qg62/r+tAorzif4yTzeK9f0LRvAniXxHLoc8dteXdhJp0cIkeFJQF8+7ic/LIvO3Gc16MOQDjHtS6J9w2dhaKKKACiiuY+HvjT/AITzRbzUPsf2H7Pqd7p3l+b5m77PcSQ787Rjd5e7HbOMnrQHS/y/P/I6eiiigDz/AMXeC/Gvia41Wxt/GtjpnhrUR5bQxaIW1G3iZFWRIbn7QIwSQ5DPA5Xf3wK7PRdHtPD+j2Ol2EP2exsYI7a3iBJ2RooVVyeTgAdax/iL4y/4QHwjd659j+3/AGeSCPyPN8vd5kyR53YOMb89OcYrpaFtZf1b/h/xB6u7/r+rBRRRQBzWteDf7Y8beGvEP2zyf7Gju4/s3lbvO89UXO7Py7dnoc57V0tFFABRRRQAUVHOzxwyNHH5siqSsecbjjgZ7VQ8M3+pap4d0281jSv7D1We3SS6037Qtx9lkIBaPzF+V9pyNw4OKP6/r+vzAw/F3ge78Za9o5vdThXwzYTx3z6SlofNubqJi0ReYybfKVtj+WI8l41O/GVPX1zGpeNP7P8AiLofhX7H5n9p6feX32vzceX5DwLs2bed3n5zkY29DnhPiJ42/wCEC0ewv/sX277Vqthpnl+b5e37Tcxwb87Tnb5m7HfGMjOacU3ypfaenq3y/mrf8AH1b6L8FqdRRRTZJFijZ3YIijLMxwAB3pbasB1FYPgfxhbePfDVrrtja3Vtp95ue1a7VA08O4hJlCs3yOuHXODtYZAPFV28abfiVH4S+x/f0htU+2eb0xMsXl7Mf7Wd2e2Md6PtKPV/om/yTFdJOXT/ADdvzZ01FFFAwooooAKKwm8YWa+OE8LeVP8A2g2nNqYk2r5XlCURkZzndlhxjGO9btG6TX9WdvzQdWu3+V/yYUVieNPFUPgfwvqGu3NleX9pYR+fPFYIryrED87hWZdwVcsQMsQp2hjgHWtriK8t4riCRZoJUDxyIcqykZBB7gigCWiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvFNH8J6Pq37QHxE1jVtPh1WTSrfSZ7KO4iWUW0oimbzo1bgS4AAfqBkAjcc+11Vg0uztb66vYbSCG8ugguLiONVkmCAhN7AZbaCQM9MmpafTcPXY+TdB+zw+NPg54202z8KeFpPGWptJHp+k2TnVrq1ks5pJPtd8ZB9p+bymdWh+WQph2Khm9H+EPhHw34k8NHxlrOnabrHxBj1O6FxqmoRo95ZXiXDqlosh+aJYxsjEalVIOcHeSfTtL+F/g3Q9Sl1DTvCWhWF/LcC7kurXTYY5XnAcCUsqgl8SSfNnPzt6mprj4d+FLrxVF4mn8M6PN4kix5esSWETXiYXaMTFd4wpI69OKp2astO3dLTb0s/nr5A9Xff9dXv96+75nzL8JvCfifXPC/grxjDovhGx8SyX9vPq3i19fmbV7xjKFu7OaM2agsRvhFu0xSNkQD/VqQvjnVvFsepeFtd8S/DrxHFr994w091/0rSnhht4zL5FlAVvCSfmZi7hQzs5JUbFH0tH8NfCEPis+KI/CuiJ4lLFjrK6dCLwkrsJ87bvyV+Xr046Vs6hpNjq32b7dZW959mmW5g+0RLJ5Uq52yLkfKwycMORmr5vejK2zT/FPT7tP+C7xKPNGUe6a+9Pf79f8AgK0Ot3Gqw6PNNo1ja3uqYUxWuoXbWsTEkZDypHKVwM9EbJAHfIi8M3Ou3Wm7/EOnafpeobyPI0y/kvYtnGD5jwQnPXjZx6mtaioL7HkPwZvINP8AEHxnurqaO2tofFskks0zhEjQafZkszHgAAEkmvI9a/tLXJbK70+1sdT8FeNPHNzcx2WsXsllY6jCtiscCSSLDLmGeeCSVVMZWXMY6ON30VqXwc8A6xrz63f+B/Dd9rTyLM2pXOkW8lw0i42uZChYsMDBzkYFdFrWh6b4l0u40zV9PtdV024XbNZ3sKzQyjOcMjAgjIHUdqOi7qMY/dy/nyr08wl7zfm2/vv/AJ/PyPmPXvh9qWkaZbaJf2uieHdCu/Gujta+H/DWpy3MWnhv9eikwQeSsvD7EUAF3Yctmus8VfC3R9c8deI/A+l2NpoWm3XgcW9rDYQLBFayG7lZHREAClZMPwOozXr2j/D7wt4e0uDTdK8NaPpmnW9yLyGzs7CKKGOcdJVRVADjA+YDPvWuNMsxqR1AWkAv2iEBuvLXzTGCWCb8Z25JOM4yaVvPq398FD8Nydb38l+E3L8T5jm8beIfid4R13xhplvPF4i8G+Ebm0jhjXd5evSxE3IVcfM8KxIF/wCu7cHNT+DPBevaCfDXibwtofgzQ45dPuZJ9Q0bX57+78SBrSR4jIrWcP2iTzvLl8x3ZgPMwfnbP0jpei6foi3K6dYWtgt1O91OLWFYxLM5y8jbQNzserHk96yNB+GvhDwtrV1rOi+FdE0jV7oMLjULDToYLiYMwZg8iqGbLAE5PJANOXvKS2uunRu9/wAXprotNelK0eXy/Hb/AC89X9/zF4m8MeBo/gh8LvFluLGXxXrWs6A8mvFgt/qtw93C9wk0n35sFXYxsSEMQwq+WuPpvwLpPhfSYdbHhZ7WSO41a5udR+y3ZuAL5mBnDZZtj7sZTjb6Co4/hT4Jh1W81SPwdoCaleSrPc3i6XAJp5BIJQ7vtyzCRVcEnO5QeozW7pei6foi3K6dYWtgt1O91OLWFYxLM5y8jbQNzserHk961c1Z2Vrtv0vy6enu/kRyvTW9rb9bc2vrr+Z4j4H8K6nrnxj+LlzZeMtb8Oww63Zh7PTYbF4pj/Z9scuZ7aVwccfKyjHvzUWk+DvB/wAQNU+Kd78RbTT9V1DStVmhMmr7C2jaetvG0D27Mc2ysmZfMQoS5Zs5UY91s9JsdPur25tbK3trm9kEt1NDEqPO4UIGcgZYhVVcnsAO1ZOvfDvwp4q1ay1TW/DOj6xqdjj7Le39hFPNb4bcPLd1LLhueCOeawS0S/u2/LX8PLc0erv53/PT8fvSPG/gbqmq3fjHwjc+JJJhrGoeA4W33o2TXHl3R3MwJPz7ZImbnOZK4zQ7HSvH2n/DOFpItS0W/wDiJ4hkOxt8N1Ds1Ntpxw8bgYI5DKxByCQfpvxV4E8NeOre3g8S+HtK8Qw27mSGPVbKK5WNiMFlEinBx3FWIfCui2rWph0ewiNrcSXluUtUXyZ5A4klTA+V2Ekm5hyd7ZPJrZSXVd/xlzfrb/h7JPZpdbfhBx/4JjRx+JNB1Wx0nQfC/h2DwhbrHCkq6rJbTQRAAFY7VLNo8KOFXzQDj+GvnW8bU7zwx4X0iGz0/UdE1Px9rsF/YavfPZ2V6wubtoLeeRYZco7jPllCrsiKeuD9cVj3Xg/Qb7RLvRrnRNNuNIvHeS50+W0ja3mZ3LuzxkbWLOSxJHJOTzWe++v/AA6f6D+zy/1s1+v/AAx88ap8P9d8GeFNe1uLTtA0WLwprNp4j0zw54d1CS8isUSMi/iUNbw+UJoHkYRKm3c5bqxNZ/jy8v8Axdp7+L7KG3m0XxR4ptdFkTVLx7C2n0m2E0cUUsyxyMsVxd7/AOAiRZkUgBjX0t4f8GeH/CWjvpOh6FpujaU7MzWOn2ccEDFhhiUQBSSOvHNSnwvox8O/8I+dIsToP2f7J/Zf2ZPsvk42+V5WNuzHG3GMU7/mn+V+/wDKkumr01J6W9V+du2zbfe9tT5v8SeC9X8G+C/HkUul+G/C2h3B0eS38M+G9Se6htZvtgEk4RraAQiVRGNqrhmiZupYn1f9prn9nn4jjOP+JDec/wDbJq6rR/ht4R8O6NcaRpXhbRdM0m4lE81hZ6dDDBJICpDtGqhSw2ryRn5R6Vs6npdnrWn3NhqNpBf2FzG0U9rdRrJFKhGCrKwIYEdQaHZlQ92XMzxDxt4F8PQXXhX4f6X4S8MXV5rK3OrSXviKy+1WplgWJJbiW3BU3dywlAy7qQCzbzja3AeA5rfWvC3hHwxqWqWl/wCB5vGuqaXP9gRrfTrmOJZmtrII0jlbdpRhYi7IwRY+VYKfp3xT4H8OeObGGy8SeH9L8QWcMnmxW+qWUdzGj4I3KrqQDgkZHYmny+DdAn0W80eXQ9Nk0i9LNdWDWkZgnLHLF48bWJwM5HOKV3dt9f8ANNelkrf1Yjl92Me3+TT+9u//AAdTwj4q+DLDRfFvgXwh4Z8LeF28JX1zfTXnhy8uv7N0y8v1hiMMUiRQSq7eX5kohMeGMYfqoz6L8DvDOr+E9N8Q6fqKaPYWS6oz6fo2iag97BpULQxE24d4Yio3l5BGEAVZQB8uAOjX4X+DY/Cr+GV8JaEvht38xtHGmwizZ9wbcYduwncAc46jNavh7w1pHhHSYtL0LSrLRdMhLGOz0+3SCFCSScIgAGSSTgdTVRfKn5/8D8rWXlYb6f13/wAz551i1l0nxF4o+DyxyfZfFmtx6jaGFSRFpl0Hm1AE9F+e3ulHobmL1rmPih9j1C11P4g6fYeE/C8+n+KIdItr6Wwe4166uIb1Ldgt2JY/s6sqFRBtlXygckBiq/WcmjafNq0OqPY2z6nDE1vFetCpmjjYgsivjcFJVSQDglR6Vi3Hww8G3WuXetT+EtDm1m8AW51CTTYWuJwCpAeQruYAonU/wL6ClH3eV9v0tb8F16tvXQcveUkuv63v97f3K3c8v8I+C9H1X4mfF/Xb+wt7/UbDVY1sJrmISGzJ0u23vCTnYzAgMy4JCqCcCuM8K6baePvhz8JvCUuh+F72a18EWmrPqHjCyOoWttF5USHyrPcgkfKjdIZE8tSMbtxFfTtvo9hay3ssNlbwyXz+ZdvHEqm4bYE3SED5jtVVyc8KB0FYuq/DHwdrtnpNpqXhPQ9QtdIUJp0F1psMqWSgKAIVZSIxhFHy4+6PQUlbl5Xt7v8A5LGS/Np+drPccnzarfX8Wv0TXle/Q+YPhkNF8aX/AMErDxFqFrrWm3Wk+IbO2hn3G21KOO6hWKIo7MXj8qPcEcvxGMliM1ta1Y2Ohx+KdF8Oqlv4R0/x94ais7G3GLazuWu7V7qKBQNqJvZGKLwHeTgEmvojUvhv4S1iwFjf+FtFvrILIgtrnT4ZI9skiyyDaVIw0iq5HdlBPIzVu18HaBY6LZ6Nb6HptvpFnIkttp8VpGtvA6OJEZIwNqlXAYEDgjI5rSMrTjN9Hf19/m/4H9axb3eXya/C36/1fT5m1jwrr3j7xJ8VrifRfCl9rmm6jJDY69rniCa0vvDsCwI1rJbotnKIEx+/3pIu8s27oQPafjNNqlh+z34xkacNq0Xh25Ms9r08wW7b3Tp7kdPwrpPEHw18IeLNXtdV1zwroms6pahVt77UNOhnnhCsWUI7qWUBiSMHgnNbt/Y2+qWNxZ3cKXFrcRtDLDIMq6MCGUjuCCRWW1PltfRL7lb8eun3msXaqp+d/vd/w6fkjyDxhoej+J/ip4L8K+ILeC78Itolzc2ej3aq1lf3cbQqqvGfllMUTM6o2RyXAzHlce6+Ffg/VfjtpGiHRtPuPC9j4UugmhCJW08ub9CQ0H+rYI+7CkEK3IAKjHpelfDOwm8B2fhTxZDp3jLTrPEcK6lp6yI0SHEPmJIXDyKm0GTjcQThc4rd0jwnofh/7KNL0bT9NFrb/Y7f7HaxxeTBu3eUm0Dam7naOM84qvtRfZyfrdSWvmua3XRbnPyvkcf8Pys47eTtf1Z81eGUttW8L2Xw4XQtF1W3Pi3XLDSovEiPc6fY21rLIVU2+f8ASCiPiOIsgAXIddgrJ8O6ZDrHhKy8NnUbG90ix+KH9nxjRYHtbNIRCXlggiMshij3tKu0OQNx24GAPp7V/hv4R8QaXJpuqeFtF1LTpLtr97O80+GWFrlslpijKQZDubL4ydx55q3a+DtAsYYorbQ9Nt4op1uo0itI1VJlQIsgAHDhAFDdQAB0pR0ab/u387OLv/5K/v1NKi5ubl0vzfK6krfivu0PAfil4T8M6xqPiTQbPw34H0HSfBmiRTy6jr+lG7S0hlE0gW0to5IRbD5XYzrIGLKBsO0MI/hvb2vxavPh7Y/EFYPEVlJ4E0/VbHTtYVZ4L+9cEXdy0TZWWSNTCMsCU89iMbya981rwD4Y8SaxZavq3hzSdU1WxwLW+vbGKae3wcjy5GUsvPPBHNR6x8OvCniHQLLQ9V8MaNqeiWWwWum3mnxS20GxdqbI2UquFJAwBgHFOLsnfW7v+El/7cvu76g/LTT/AOR/ya9H20PJbb4X+CbH9ojS5dN8J6CDbeFmnspEsITsmhuY0jdG25DIu1QQcquAMDFcB8MfCPifxJ4d8MeLF0nwhp/iltYhl1TxhN4gmOqySC52XNlLH9iGcrvtxbGYomEC8opr6cn+H/he6/sTzvDekS/2Hj+yvMsYm/s/AUDyMr+6wFX7mPuj0FRf8K18IDxX/wAJR/wiuif8JLu3f2z/AGdD9szt2Z87bvzt+Xr046U6fucvW1//AEpy/Wz9F6CavzNdbf8ApNv0v82ed/BPwTo1xrPjjxBeWFte6uPE+qQW95cQq8lrCXAaONjkorHcW243Z5zgV1PwDct8G/CQDF4VsVjgYnO6FSViOe+UC1t+JPCT6h4X1bStBuofDNxqTMZr62tFZ1MhHnSKuVHmsu7DtnDEMQ2MHZ0nS7XQ9Ks9NsYVt7KzhS3ghXokaKFVR9ABURXLFR7KK+5a/wDA9X82/ib7uT+9/wBfci3RRRTGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVzni74g6F4Ha0j1W6l+13hb7NYWNpNeXlwFwXMdvAjyuqggsyqQuRkiujryz4eyJL8avij9uXGsRyaett5gG7+zjbAx7P9gz/AGvP+1n2o1bt/X9fpcOlzotN+LnhjVI9OZLq8tX1DUTpNvBqGl3VpMboRGby2jliV0/dgtuYBSMYPIre1LxJp2kappOnXdx5V7qsrw2cexm810jaRhkDC4RWPJHTHWvLP2iIdSuNa+FMej3drY6k3ipfJuL61a5hQ/YbvJaNZIywxngOv9Kqa9p/jKz+LnwubxLr2hatbG/vhFHpWiTWLq/2Cblmku5wwxngKPr2px95PydvwT/N/kKd429L/jJfkvzOrsf2g/A+oXSRR3+oxwPetpy6jc6HfQWBuFlMRj+1vCIM+YpQfPy2FGSQK9Gr5B8J6hrNz8HYNB1jT7HTfAeq+Kr+zuPEcF489xAG1eZts1uYkWJZHHkiUSybS6MUwSB9Z6tezadptzdW9hcapPEhdLK0aNZZiP4VMjogJ/2mUe9H2b/1sv8AP/gd2/jcf63a/Qt1k6X4q0vWta1rSbK687UNGkjhvofLdfJaSNZUGSAGyjKflJxnB54qPwrr194hsZZ7/wAN6p4YlSTYttqslq8jjAO8G3nlXHOOWB4PGME8B8Lf+Sz/ABn/AOwjpv8A6boaLateV/xX+Y1qrnrNYXh/xhZeJNY8RabbRTpPoV4llctMqhXdoI5gUwSSNsqjkA5B47n5++MniPWGt/F/jDwquqwL4bu1hk1XUPFdxZ2omhaMPDb6dGrw3CZyjeeI2YudrEbWHQeF/A9v44+I3xgjv9c1iwt4dYtjFDourT6e8LnTbbMzNA6M/GMK5ZPkPy5zUxd1zPa1/wAY2/B+nnbUNLtfL87nv1ZPifxXpPg3SW1LWb6OxtAyxqzAs8sjcLHGigtJIx4VEBZjwATXBfBL+0vGfhbwn421fXtQu9QfSJLKS3ilWOwvQZgVvGgUYErLGCCCABIwA5p2vyRt+0Z4Si1Ff9HGg38mlmQfJ9r82ES7f+mghPHfaZMcbqq3vKK63/C7/FL72TdKLk/L8bL8G/uRcm+PHhmOzmnW311XhuLK3e3vdDu9Pl/0q4WCJ1F1HFuXe3JXOADxnAPoteT/ALT1vcXnwrEFndNY3Uut6QkV0iqzQudRtwHAYFSQecEEccg1yXi7WtQ+CvjbxBBoV7rGrwTeCtQ11bHWNTuNQVbu0kjCNH5rOyBxOQyKQvyLhRildcrfnL8IqX5X/D5aKLk1by/F2/N/d+P0LWF4N8YWXjfS7m+sYp4obe+utPZbhVVjJbzPC5GCflLISO+MZA6V5BrWj3nw78NeD/GFj4m1/Wdeu9S0221D7ZqtxPaamt3NHFNi0Zmhh/1hkTyUj27AB8u4Hj4vEmtWPhnQNB0u01e6tdc8b+IIr6PQbqG1vZoYrm7l8mKaWaERlioJZZFfaj7eeRbi438nb5+7+HvdvkZKSlBSXWz+TUvx9308z6srM1rxHp3h59OTULj7O2oXaWNsNjN5kzBiq/KDjIVuTgcda8Ot9H+I83hHxFp1lo/iWy0mHUrW4sdP1jX4Tq9zZEZu7aK+iuZWQ7hlHklDYcpvQBSuH4g0Xw3420XwMtpc+M7Q2fjNdPurDV9f1CO9spTE5kiaQXBLkYVkkEj4Dny32sRUbySXeK+9pfra+tn8rjlaLfk/wTf6X9Pnb6gor5m1m+8S+KPHnjnTLfSPHl/b+G5YNM0WTw3rtvaR2z/ZIZvPuBPewvdSM0i/65JYyqAclpM7/hO08SeM/ixFbeLtQ1TTJbHwpo+oXui6bqktvCmotLciQkwOAygoVKhijgLuDBVxSV0n/Vmm/wAl+KKeid+n53S/N/gz3qsPR/F1nrXiLX9FgjnS60V4UuHkVQjGWMSLsIJJ4POQOfWvCFh1PxB8Dtd+J934p1yw8WW0WoanbfZ9TuIbGy+zyymK1azBEMiKIhG5kjZ2yxznaR3nwiv5dU+IHxCvZ4fs01z/AGVM8Jz+7ZrFCV59CcVP6frt+on07O/4Wv8Amj1eivN/2jL/AFHTPgp4qudI1G40jUkt1EF9asVkhYyIAykEdM/j0rjL7wJNafG7S/DP/CWeK5dC1rQbrUdRtW126DS3NtcQKskcquHtg32pi0du0aHy0G3aCpI+9LlX9WTf5J/O3nYbsm+3+aX6/g/K/vdFfNvh3WtY8Uaf4J8F3mv6pDYXOva1ZXWow38kN/dW2nzSrDAblSJNzBULurK7LG3zfM2dbwtri/C/4mfFyO/8QaxqfhnQdD0vUILTU7+W9a0DC7aREaRmdmcopyxLHKjOAoA7JOT9f6/r9L1Z3a6p2+d7f1/w9vZ9L8T6brOr6vpllcGe80mSOG9QRuFid0EirvI2sdjKSFJI3DOMiud1j4weHfD2qeLbPVZLiwi8L6fbanqN48JeIQzGXaUCbnYjyWyNvcYzzjze10nWbW3+HvhbU7+60a78X3d9rPiKawu3guJJRH5xtI5h86AFkTKMriO3wCBmvOPid4fj8LwftKafBqN9qFtH4Q0sxf2ldvdTQqReHyzNIWkcZy2ZGZvmxnAAFqOtpb/r1/Hb+rkLT220+5tL8t+3mfYqsHUMOQRkUtRW3/HvF/uD+VS1L3Ii+aKYUUUUigooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuX8YfDXw946urK71W1uF1Cy3C21DTr6ewu4lYYZFnt3SQI3GU3bSQCQSBjqK5zxd8QdC8DtaR6rdS/a7wt9msLG0mvLy4C4LmO3gR5XVQQWZVIXIyRS0GU7H4UeGdPh0qNbS6uW0zUDqltPfajc3U4ujG0XmPLLIzyHY5UBywAwAOBjc1Lw3p2r6ppOo3dv5t7pUrzWcm9l8p3jaNjgHDZRmHIPXPWuct/jJ4Vm0O71aS7vrC0tbyDT511LSbuznjmmaNYlMMsSyYYyoA23byeeDjo9c8Sad4bWxbUrj7ML67isbf5GbfNIcInyg4ye5wB3NV5ef46JfPb8CdLX/q2r/wA395l2/wANfDVv4MvvCa6VG/h6+Nybixld5Fk+0SPJNksxb5nkc8HjPGMDG7pemw6PptpYWxlNvaxLDH58zzSbVAA3SOSznA5ZiSepJNVdW8TaboepaNp97c+Rd6xcNa2Mfls3myrE8zLkAhcJE5y2B8uOpArjbH9oPwPqF0kUd/qMcD3racuo3Oh30FgbhZTEY/tbwiDPmKUHz8thRkkCjW9u/wDX6jfd/wBX/wCG/A9Grg9W+CfhbWPEWp66/wDbljqepNG15JpPiPUbBJ2SMRoWjguETIRQM47V3lFIZ51rX7PvgXxFcalJqWk3V3HqTiW7s31W7+yTShVUTG3EvlebhFPm7d+Ru3Z5qbWPgT4L1zWNU1W4068gv9VZW1Caw1W8tPtoEaxqkwhlUSRhEAEbAqMucZds9TpfirS9a1rWtJsrrztQ0aSOG+h8t18lpI1lQZIAbKMp+UnGcHnitajt/X9dBbHP23gHw/ZeI7DXbbTIrbUrDTm0i1eAskcNoXRzEsYIQDMaY+XI24BA4qTxh4J0Tx7paafrtgt9bxzJcwtvaOW3mQ5SWKVCHikXnDowYZODRo/i6z1rxFr+iwRzpdaK8KXDyKoRjLGJF2EEk8HnIHPrW5Ruv69fz19QWm39dPyOHtPgv4Vtba4gkh1TUlnmtp3fVtbvr6QNbyiaHa88zsqrIA21SAT1BroLnwjpF54nt/EM1mJNXgspdOjuGdsC3kdHkQpnactGhyRnjrgmneJ/Fek+DdJbUtZvo7G0DLGrMCzyyNwscaKC0kjHhUQFmPABNc7p/wAZPDuqR6l5MetQT2NlLqD2+paHe6dJJDHjeY/tUUYkwSoO0nG5c4yKTkkm29r/AJa/hv5DSey6/wCen4i+Hfgv4Q8Lara3+nafcK9mWNla3Go3VxZ2JYEE21tJI0NuQpZR5SLtVmUYUkG1efCnwtfeHZdDl0wjT5L6TUwI7maOaK6eVpmmimVxJE+9mIZGUjJAwOK3fDutweJfD+mavapJHbahaxXcSzABwkiBgGAJGcEZwTWhVyTT5ZdCIyUkpR2f9fqcT/wp7w3/AGGdLzrXlG6F6br/AISDUPtvmhPLB+1+f5+NhK7d+MHGKfJ8H/CknhT/AIR37BcLp/2oX5lTULlbw3IcOJzdCTzzLkD94X3Y4zjiuzoqRnEa98G/C/iK9jvbmLVLe+W1Sze707W76ymuIUzsWeSCZGnxlsGUsRub+8c7+n+EdI0rXJ9XtLJbe/ms4dPeRGYL5EJdokCZ2gKZH6AHnnoMbFFO7D+vy/yX3I4a5+Cng671qfU5NMn3XFz9tnsU1C5TT57jIbzpLISC3kk3BWLNGSWUNnIBqXxN8LNK8RTTyAfZXvtSsdR1I/NJ9r+yMrRIQWwgzHHnA5CnI5zXaUUtrW6a/d/wyH38zM8SeG9O8X6HdaPq9t9r066AWaHeybgGDD5lII5A6Gkm8M6bceJrTxBJbbtXtLSaxhuPMYbYZXjeRdudpy0MZyRkbeCMnOpRRs7oDzHx54B0jT/C9tp9h4H1HxLatq0motDo2praX1pPI0kr3UM0txCUYyMVISVSFlIAK5WuO8G/AWfUNV1nWb2yfwrY6vf6bLcaPdT/AG++u7ax3vGLu481w0skrqWO+X93GqliWO32rQ/E2m+JJNTTTrhrg6bdvYXLeU6KsyBSyqzAB8bgCVyAcjOQQOd/4XF4cgk8Si9kuNPh0DVrfRrmaaEsr3E6wNFsCbmKk3Ea5IGDnPAzTjdPTyf/AKTZ+fT1CWu/mvnrf57v113Nrxf4J0bx3p8NnrNrJPHBMtzBNb3EttcW8q5xJFNEyyRtgkbkYEhiOhIrmrf4BeBbfTvEtkNGklj8SWsdnrEs+oXMs99Gm/Z5kzSGRnAkYb924jAJwq49CopbXS6j7HL+FPhxpPgy8ludPutdnkkj8orqniG/1CMLkH5UuJ5FU8feAB7Zwa6iiindk2S2CiiikMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAryvwJJu+MnxUNyqSa5GdPW0WY7f+JebYGIKcEhDcfaskA/Nu9BXqlcx4v8Ahv4f8cXFndapaTrf2e4W+o6dez2N5ErDDItxbukgRuMpu2kgEgkDC63/AK/r9Gx9Dxf4s+OtZ8TeB/Eej69oljo2paH4i8PxuNN1J76GUS3ltICHeCEggHBG38a9D+OjCOy8Eysdscfi3S97ngLmbaMntlmUfUgd6vN8C/BsnhvVNDlsL25s9UuYby8nuNXvJbyaaIoYnN00pmynlptw/G3irVr8IfDVvo+q6TcJqusadqkYiurfXNbvtSUqMkbPtE0nlnJzuTacgHOQMNaNPtJS+7l0+9f8DoTJc0XF9U1991+ph/FQGT4mfB5EG51127lKryQg0u8Bb6Asoz6sPWvEfCeoazc/B2DQdY0+x03wHqviq/s7jxHBePPcQBtXmbbNbmJFiWRx5IlEsm0ujFMEgfSHhb4V+HfB+rPqllFqF3qbQ/Z1vdY1a71KaKInJSN7mWQxqxALBCAxVc52jE9v8NfDVv4MvvCa6VG/h6+Nybixld5Fk+0SPJNksxb5nkc8HjPGMDFX0Vtf+H/r9Uxv3lZ/1o/8/wDhjd1S8l0/Tbm5gsbjU5oY2dLO1aNZZiBkIhkdEBPQbmUepFZvhXxBf+IrOaa/8M6p4YkjfYtvqslo7yDAO5TbTzLjtyQeOnetLS9Nh0fTbSwtjKbe1iWGPz5nmk2qABukclnOByzEk9SSatUtLuwtbK55N8Lf+Sz/ABn/AOwjpv8A6boa86+MniPWGt/F/jDwquqwL4bu1hk1XUPFdxZ2omhaMPDb6dGrw3CZyjeeI2YudrEbWHsWrfBPwtrHiLU9df8Atyx1PUmja8k0nxHqNgk7JGI0LRwXCJkIoGcdqra1+z74F8RXGpSalpN1dx6k4lu7N9Vu/sk0oVVExtxL5Xm4RT5u3fkbt2eaUdJRb6Jferf8Ec/eTSOP0nwbB44+M3xQt7/WNVtLSFtMdbXRtVn0+TzGtMea8kDpIQAMKpbYTuJUkKRu/A9NS8XeHNA8U6r4g1O+u7OPUNJCLOq2moxpeNHHdyxKu0ylIFIYYA8x+Oa3te+CXhDxJrWoavc2V7bapqCpHeXmm6teWMtwiJsWN2glQsgH8B+XPOM81r2fw78OabrWk6rZ6TBZ3mk2D6XY/Zt0UVvauyM0SxKQgGY0/hyNvGOacfd/ryf/AA3p6JEtf193/D+q82cl4geJv2ivCMeo4MH9hX76YHB2/axLAJSO2/yScd9pk7Zqh44+IGux614i8H6toOnWlneeG9V1Kx1Gy1WS4kkjgMUeJYWt4xGzC4U/K7gbSMnrXoni7wXovjrS00/XLCO+t45VuIWLMktvMudk0MikPFIuTiRCrDPBFYej/Bnwtot1e3SQalf3V5Zvp8lxq+tXuoyrbucvHG9xM5jViATsK5Krn7oxlOPNSdPykvv5rfc2vkjWMuWfP5xfpa1/vS+9nF/CLVLz/hKvCmnNdz/YD4A0+4S0Mh8rzA+1nCZxuwVBOM4IFc54d8Qaz4tg8PeG5de1C3sdY8VeIIr2+gu3S5e3tbmcx2sU4bdFnCjKEMEiYKV6j1vWfhD4X1y30SGa1vbRtFtvsdhcaZql3Y3EMBCAxedBKkjIRGmVZiCVBPIzTY/gx4Mg8IxeGbfQ47PRoLt7+2htJpYXtLhnaQywSoweFtzvgxsu0MQMDiuiclJt+b/GTl+Tsc9OLhTUL6+7+EOX8Xr/AMFHF69oEll8QPCXw8tda1+18NahZ6jq13I2s3cl5cNC1siW63jSGdEBmaQ7ZATt25CEqcHUrPxdbXnj3wN4Xv8AVtYs9JOlalClzq8ov3t53lN1YRX0hLq7LDlHeTcvnY3oNrJ6lcfB7wveaDb6VcW+oXEdtcG7gvZdYvH1CGYjaXS9Mv2hSV+TiQfJ8v3eKcPhD4YTw8dGit7+3t2uvtz3Vvq13FfS3G3b5r3ayid32/JuaQnaAvQAVF9Ev6et9fRaeaWu7NP69PT179L+SPE9W8bsdN8N+G/C1r45eHUPEdzY65pN1q2datmhtPONnHd3V0NocBJd8dwTsLGNufl9S+C8HiaxbxFZ6vpOvaVoMdxHJo0fiXUYL++VGT99G00dxOzosgJUySFsORnaqgbTfB7wk3hdtAbTJHsWuhfmZr24N4boEEXH2sv5/nDAAk37wABnHFVj8H9Jt49Ht7O4vRaWerJq9yuoXk+oTXkkcbLErz3EjyAI/luOTjygABk009fW36X+7Wy19dWybP7r/rb79LvT8DK/aLutUtvAOnpo+sXmgX114g0e0GoWLASxrLfwI2AQVYFWIKsCpBIIIJFcpZ/D8zfGTxB4QfxT4sPhhdEs9Yjs/wDhIr0TR3UktxCzLdeb54TbED5XmeXuOdvAr2jxF4Z03xXZ29rqtt9qt4Lu3vo08xk2zQyrLE2VIJ2uinB4OMEEcUR+GdNi8TXHiBbbGr3FpHYSXHmN80Ebu6JtztGGkc5Azz14FSvhs+7fycUl9zV/x3NG9Hbsv/Srv8NPw2PBPAmtat8U5Ph54c1zXdTism8N3Gq3kun38tjd6lPFOlum+aBkkCqGLsEZdzOueBg2/BfjO8+G+k/GeW51XU/Eq6Drq2ekW+o3bXMzM9la+RaIzHJJlkCZJ3MWLMWYsx6nx58PdJ0vS/DOm6d4A1vX7HS/P+yXHhvWlsL/AE4tgFVme6t5DHIGfcBKeUXKngrkfDX4ET6aV1LUIodCt7vxE/iWfw7CBcESC3SG3SafcQ0ilPPkYbt0xyHYLueo6qXM907/ADmn96jdL03VyZe7bl7rT0i1b0b1frsyHTfCd1a+JvCHw0vtZvrXT4dBudb1OTS7+WyuNTvmuIxI5miZZVQSTSyEIy5MiA8DB811TShofhX4tWC311qKW/xD0NEuL2YzTbQdM2q0h+Zyowu5iWO3LFmJJ+nfGXw70Lx41hJq9vcfatPd3tL3T76exuoN67XCTwOkgVhjcobDYGQcDGHH8A/AsPhvWNBi0V7fTNXvY9RvUgvrmOSW6j8srP5qyCRZMxIxZWBZgWOWZiSLs7vf/wC3UvwSsvkDWy/r4Wvxbueg0VznhPwHpngtrltPudauDcBQ/wDa2uXupAYzjaLmaTZ1OduM8ZzgV0dIYUUUUgCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACqkOr2NxqVzp0V7by6hapHLPaJKpliR92xnQHKhtjYJHO046GrdeCxX3iuz/aU+IX/AAjGi6Nq+7RNF+0f2tq8th5fzXu3Z5drPvz82c7cYHXPDQdG+3+aR7JqHizQ9Jt9TnvtZ0+yg0sK1/LcXUca2gYAqZSThAQQRuxkGs3wr8UvBfjq8ls/DXi7QfEN3DH5skGlanDcyImQNxWNiQMkDPvXzv8AEC41m68E/tAya9YWOm6kRpu+302+e8hA8mLBEjwxEkjqNgx6mvWv2jtPtv8AhWE2uLGia9ok0F5o92ARLFdGVFREYEH94W8oqD8wcqcg1Keuvl+P9bCvpp5npdnq9jqF1e21re29zc2UgiuoYZVd4HKhwrgHKkqytg9iD3q3Xk/whz/wtD4yZ6/25aZ/8FttXo3iLxBa+F9Jl1G8ivpreMqGTTrCe9mOSAMRQI8jcnnCnA5OAKL+6n3S/FFdWaVVLHVrHVGulsry3vGtZjb3AglVzDKACY3wflYAg7TzyKr+HfEVr4o0qPULKK+ht5GZQmo6fcWM3BwcxTojgehK89RXnvwJ/wCQl8UP+xxu/wD0Rb01rNxfZv8AGK/Ulu0U11dvwb/Q9VrM0PxHp3iRL19OuPtC2d3LYznYy7Jozh1+YDOD3HB7GvBde+KniHTvE2m65oepeJdf8NXPiS20ieSbT9Ot9EVJroWzJEWC3rshdSJlMkTup/hJCT+D9F8W3+m/EbUNH8Xy+GhYeJNVksraGzt5oLl1YMTdGVHcx7ht2xNEwG47iSpWebRzeyTf3cv6SG73UVu2l96l+sT6GqodWsV1RdMN5bjUmhNwtn5q+cYgwUyBM52hiBuxjJArkfhjqmteMNL03xheaiY9K13RbG6g0A2qr9hmZC8red95929BtIwvl8dTXEeNLvxHZ/tMaS3hrStL1a7PhK4EkWq6nJYxqn2yLkOlvMSc442jr17VcouFVU5d2vuTf6EqSlTc15fi0v1PXb/xNo+lSXiXuq2Nm9la/bbpbi5RDBb5Yec4J+VMo/zHj5W54NY3hn4t+BvG2pHTvDvjPw9r2obDL9k0zVYLmXYMZbYjk4GRzjuK+f8A4n3XiK8vvjS/iTS9M0m8Hw2URw6VqUl9GyeZfYYu9vCQ2c/LtIwAc84HpXxo0iy1D9nDUb24VY7/AEfRhqmmXi8TWl7DDvgliYcq+8AcdQxU5BIMxfuuT6f5yX/tu3y0NZR99Qj17+kH/wC3bnq8Or2NxqVzp0V7by6hapHLPaJKpliR92xnQHKhtjYJHO046GrdeOfDmae4/aC+IEt0nl3L+HtAaVAMbXJvSw/PNd7dfELS7PxQmgSWuuNfM6oJotAv5LPLAEZulhMAGDyS+Acg4INaSjyvlMlK6v00/FJ/qdNWZ4b8Sad4u0eDVdJuPtdhMzrHNsZMlHZG4YA8MrDp2rzz4azeKPiNZ2fjS48V3mlafdXcj2vh2ytbVrQ2iSsiCV3haZpHVdxZZEALABflO7ynwbqni7wJ8DdF8Zx+J5o7Oz1v7O3h2KztzaXFpLqrQNvdkMwmxKWDrIqgqo2H5i2a1aXf/gf5/gPW+n9f1/kfVlZh8SacviVfD5uP+Ju1ob4W+xv9SHCF92Nv3iBjOfavMNPuPF/xU1Dxjc6Z4xuPB9ro2qTaRpttp9naXCzPCib5bvz45GYGRmwkbRHYBzlsjidL+MEl1f23xHurBftEXw2udTms4HyjSR3CllRsn5SynByeCKXN8LezV/lySkvyG07NR3TS+fMov82fTFR3FxFZ28s88qQQRKXklkYKqKBkkk8AAd6+efBfxK8ZS614aEV34k8U3OsRyjVLHVPCF1penabL9neWN7e4a0jIiEqiLEskrMJFYNlTuraTr2reMPAvizTtX8f+JNO8XR+H5p9T8N6jpFlZyWcnO57XdbfvbfckkW/fOCrriQMAxKt6cJS7Jv7r/wCXTuh07TlFfzW/H8vR6n0baXcGoWsN1azR3NtMiyRTQsGSRCMhlI4IIIIIqavmtfF+tfD34afCfw/B4g165fxDbRy3GsW+jLqN7YWkVojmO3gtrYgncY1DyRSbQzFtxxUx+JXjibTbPTLC/wBWQv4rtNKtdf17QGsLi9s5oGeQvBNBEpeNww3RoitsXjls7VI8tRwXe33tL9TGnPmpqo+qv+Df6H0dWXq3ibTdD1LRtPvbnyLvWLhrWxj8tm82VYnmZcgELhInOWwPlx1IFeaWreJ/E3jrV/Btt411TS7Lw3Y2ktxrENtYNqOoXFwZWAcPbtCkaoi/chVix6gAhuTtfFep+JvFXwwttZmju9W0PxtqejXF7DGI1uzDpl5tm2AkKzIyFlHAbcBgYFTFKTt8/kna/wB7X3luVo3/AKvZu34P7j6LrmNf+Jfhvwveava6nqP2afSdK/tq9TyJX8qz3Ovm5VSG5jf5Vy3HTkVj/FjxPq2lzeFNA0S5Gnaj4m1Q6cNS8pZWs4lt5p5JER/lMm2Equ4MoLAlWA2nxrxZP4j+G/xE+J+oDxNc6zqenfDlbyw1O+tbdbiNlnumXzFjjSJ8ODjEYGMAgkEmdW0vX8It/wBfM2jG+n+H8ZKP+f4H0/a3UV9aw3ELb4ZkWRGwRlSMg4PtUteQQ6t4q8T/ABln0OHxJcaRoNjoOm6rIlnbWzSzTyTXCshaWN8RusY3YG75V2snzZ8+0P43+KPEFrb+KLO48UXN3cat5UPhO28H3T6W1h9p8nJvRaEmYRZlMgn8rcNu3FU171l/WtjG/u3fl+KufUFVbHVLPUjcizu4Ls20zW8/kSK/lSgAlGwflYZGQeeRXl3gu48UfFK4vvEieLr3w9pFtrNzY2ejabaWjxTwWty8LtcPNC8heRopP9W0YVSoAJBZqP7PHh3UNK1X4hT3XinVtZiXxJdwG2vorNY2cLCfOJigRt5HGA2zH8IPNTHWVn/K3+MV+v3jm+X/AMCt+Ev8j2miuB+PPiTWfCPwi8S6v4fuY7TWrW3VrSaaMOiyGRVG5SCCOeeK4y7t/Hlr8WrHwg/xDvW07WtGn1WS6j02yW5sZbeeJHjtSYSgif7SgxOszqsR+fc24Efelyr+tG/yQPRX/rdL9T3GivAdF+IPi3xVpHhLwvFrbWOtX+r6rp994jhtIGn+zafNIjSJE6mJZpdsQJKMi7nIT7uNfwZ4u13wr8R/iVpPivxS+teHvDmkafqVvd3drBDNFHILppnlaFEVm/cjkKqhVXC53Fh2Scm9EVZ3slqtPne359j1y31axvL68sbe9t572z2fabaOVWkg3jKb1Byu4cjPUVl3HjzQLPWNX0u51SG0u9Iso9Sv/tGYo7e2kMgWRpGAQD91Jn5uNuTjIryHRb3xDZ6X4UtLaZ9B8SfEbVLnVdS1EwxSz6fAIDKscauChlSJLeEF1ZRhmKseDw/jzSdT0TUv2grTVNcm8QyL8P7Yw313BFFcNGRqBxL5KJGWDbgCqINu0EEgsyneClfdJ/ele33jp2qNJPdr7nJL8tT6yimSeJJYnWSNwGV1OQwPIIPcU+svwr/yLGj/APXnD/6AK1K0nHlk49jKnLngpdwoooqCwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArndN8D2OleONc8VRS3DahrFra2k8bspiVLcylCgC5BPnNnJPQYxznoqKA8jgvFPwb0fxZp/jS0ubzULdfFawLeSW7xhofKRUQxbkIBwoJ3Buabp/wAI421bT9Q8ReKde8ZvpsouLG31hraK3t5gMCXyrWCFJXHG0yh9hGU2nJPf0ULR3QdLHm9x8G54fFXiDXdG8d+JfDkuuTx3N5aWEenSQmRIUiBXz7SVx8sa8bsZzXoyKVRVLFyBgs2Mn34p1FGysG7uFeb2nwbuNI17WdR0bx74m0WHVtQbU7nT7aLTpIDMyqrYMtm8gUhFGN/0xXpFFC0fMt/+G/yQPVWf9f1c8lk/Zx0eb7BbS+JPET6LpusR65p+i/aIFtrS4S5+0YUiESOhYuNkruAG+XaQpWaf9n3Tmk1+O28UeJNP0zxDfTX2saXa3MAgvTLgOhZoTJEpUbP3LxsR1YnmvStT1Sz0XT7m/wBRu4LCwto2lnurqRY4okAyWZmICgDqTVkEMAQcg0LRW/rW2/fZb9kHW/8AXXb7395g6L4Nt9B1y6v7S+1BbWWzt7KLSGnzY2iQ7grQxY+RmDAMc8hE9KSTwTYyePofFxluP7Si019LWLcvk+U0qyFiNud25BznGM8d66Cine7Unv8A53T/ADYrK3L00/C1vyRxHi34R6P4yu/Etxe3N9E/iDQh4fuhbyIAluGmbcmUOHzO/JyOF465oaf8Foi2nx+IPFniDxhp+ntHLa6bqxtIrZJIyDG7Ja28PmlSoIWUuoIDbdwBHd6hq1jpCwNfXlvZLcTJbwm4lWMSSucJGuTyzHgKOTVulHTb+uv63+Y5e9v/AFsvySXyPPda+EL33jjU/FOleM/EPhjUNStbe0u49MSwkikSAyGM4uLWUgjzX6EZz7V3djbyWtnBBLcy3sscao1zOEEkpAwXYIqrk9TtUDngDpTRqdmdSOni7gN+sQnNr5i+aIySofZnO3IIzjGRVmjoHW5wuj/ClPDuttc6R4n17TdGe7e+fw7C9u1iZXYu+GeFp0VnJcokqpkkBQCQY7j4M6Jc/DOPwO11fjSUuUuhMJE8/ct2LoDOzbjeMfd+778131FG1vIOtzgNY+EEN9rWqX2meJ/EHhqHV2V9UsdJmgWG8cIEL5khd4XZAFLQPGxwDncA1X7X4U+HbPU4LmC08u1h0Q+H000bfsv2QsG27cZJ+XHXGM8d66W+1ex0yazivL23tJbyX7PbRzyqjTybS2xAT8zbVY4HOFJ7UuqapZ6Jp1zqGo3cFhYW0bSz3V1IscUSAZLMzEBQB1JpWVv67NW+5teSY9dv63v+a+9HE+HfhLN4dhFonjnxVeaVDbyW1np1zcW+y0VlKKVlSBZpCinCmaSToCdzAMJNF+EltZ6tc6nrmv6x4w1CWwk0tJ9Z+zJ5FtIVaWNFtoYV+cohLMC3yLggV3SOsiK6MGRhkMpyCPWnU5e9fm/re/5tPy0JWiXL/X9WR5vH8E7ZPB+i6G3ijxBJc6HOk2ka3vtUvrAInliNSsAjkTy9yFZY33BjuyQCNOP4Xx3Frpaat4i1rXrrT9VTV472/eBZHkRCioViiSNYwD91EUk8kkkk9rRTcm3d73v89NfwQWVrLbb5dvTyOM8T/DOPXPEH9vaX4g1fwnrb262dxe6ObdjdQqzMiSR3EMsZ2s7lWChhvYbsEio7P4Q6Dp58LG2N3E3h/UJ9UhYzB3urmaKaOWSdmBZy32iRiQQd2OwxXb0UL3dhvXf+un5aHN+OvAlj4+021t7q4urC7sbpL6x1KwdUuLO4TIWSMsrKeGZSrqysrMGUg4rjm/Z50q8n8UXWq+I/EOtaj4k0U6Ff3t7PAG8jLkNGkcKxxsBIR8qBTjJUsWZvVaq6pqlnomnXOoajdwWFhbRtLPdXUixxRIBkszMQFAHUmktNv60t+Wn/AAxSb0t/VndfjqY+l+B7DSfFt54hhluGvbrTrbTHjdlMYigeVkIAXO4mZsnOOBgDnODpnwhTQtYln0rxZ4i0zRZb5tQfw7by2/2LzWcSOFZoDOiM+XMaShcswACkrXfI6yIrowZGGQynII9adTu73/ruRpbT+rKy/A4WH4UppviOfUtH8Ta7oVhdXn2+80Sya2azuZy26Rz5kLyx+YQN6xSICcnG5mJoar8Jr2C+1e48P+JtT0yDWtUtr++sBLGkMe2SEzvC6RCYPIkOwgyFP3jHAr0mikvdaa6flpp6aLQb969+v59/XV6mF438IWfj7wrf6BqEs8NneqqSPbMqyABgwwWBHVR2pLrwbZXfjnTvFTy3A1Gx0+402OJWXyjHNJDI5YYzuBgTBBAwW4PGN6ihaO63/wCBb8mL+v1PHfG3gDw74N8O6bmTxXDPDrl1qdnrPh2wa/vdPuLlppZT5UcEu6Ft7x4aJ1wy7ucMON8MfCXV/GWteLNW8zVF0LxJdaVbX1z4pDwahqFnZ+Y8ri2EaeSJXdYfLKRDYJH2DI3fSlFOL5df66f5elt0OXvfP/h/z19Tl/HXw/svHltpvm3l7pGpaXdLe6dqumui3NpKFKEpvR0YMjOjK6MpVjkZwRyi/s96RMnjN9Q1/X9XvvF2lJpGp317cQmQxqJVV41SJY422zEYVAnyg7dxYt6nRS6Ndx3d0+36O/5nH+DfAepeE54/tHjnxB4is44PIjsdUh09YkxjDAwWsTkgDHLEcnIPGOwooptuWrJSUVZBRRRSGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFeC/Gr4R+BtS+IPw5vLvwX4eurvVPEbpf3E2lQPJeL9gumxKxTMg3Ihw2eVB7Cveq5jxd4L/wCEq1rwlqH2z7L/AGDqbaj5flb/AD8280OzO4bf9duzz93GOcgVuZMf2ZLyf32djwr4maH8N/C/xu0Cy8SeDbK98L2PhG5a30+28MPqVvaEXcbFxBDBIIlA3ZfaByeeawvFmk6ZZ/C34Zrrmlrqfg+78d/atP0hLc6uo0uSK7a1iWKIS+YvllGWNAwVSFHC4H0dL4F8z4pW/jH7bjydHk0n7F5XXdMkvmb93bZjbt75z2rjofgLLZ32mxW3iER+HtK8TL4j03S2sstaAxSrLarJ5gHlmSZpF+X5MlfmGNqp3Sipd7/+VObXu7a37XW7E7Xnbtp/4L5dO2un3PoUvgNZ6NL4q8Yan4K0+PRPAUgt7ODTYbc2aDUYTKt2/wBjIVrdgDCjB0RmMe7aRtZvRPE3xM8H+Cb63svEXivRNBvbld8Fvqeow20kq5xlVdgWGeOO9U7PwBJpPxMvfFOm6l9kstUtFh1XSWt9yXM8fENyrhhskCEo3Db1WMcbAa7GrbvZ/wBf1+gurt/X9de7uzyr9qj/AJNy+If/AGBp/wD0Gu08Xa5/wjfgu91L+0tL0c28AIv9afZaQE4G+T5lyBn7u5dxwNy5yKvxV8Cn4mfDjxF4UF9/Zp1eyktPtnk+b5O4Y3bNy7semR9a5fXvhp418Y+H5NM17xdoT+TNa3unz6Z4dmt2gureeOaJ5BJeyiSPdGAyDYSDwwpbq39f193qV/K+1/0/y/4B5R45+LeqeMPhP8ZfDmrFb6Wx8KPqNvqEXh++0QSxypMhQ294zOcGLIkVirB8YBU59Wbxh408K654VfX10W50LxBcrp62thbSxXWmTPE0kW+V5WW4XKMjERxEEqcYyKyNa/Z/1rxYvjyfX/Gcd3qPivw8ugM1npRgtrFVaYh4ojO7EfvslWkJLBjuAIVeg0/4Wa7ea94eu/FHiyPXbDw5I0+mW9vpv2WWSbymhWa7k81xM6o748tIV3Ox2/dCu90r/P0u/wAWra9/mTLy87erUfw5r3/LY67wvb63eeE7aDxlFpM+syxMl/Fpau1k+SRhRL8xUrjIbvntXiNj8GPh3YfHPxfHF8P/AAsLex8N6deW1v8A2Lb+XFMZ7zMirswrHYmWGCdo54Fe5eD9L1fRfDdjZa9rf/CR6vCpFxqn2RLXzyWJB8pCVXAIHHpnvWX/AMID/wAV3r/iT7d/yFdIttK+y+T/AKrynnbzN275s+fjbgY29TniJ3XM4b6/k7fiaR5WnGW2n/pSv+B5bofxD8U+Jvhz8OfFfiaw8N3MfiTUdHaDTV02VjZNLy0yyvMwL5wyYQeXnBMn3q09a+JfjvUvD/jXxX4YHh+PQfDzX1vbadqVpO9zqMlqHWVzOsyLCvmIyqvlyZCZLLv+TpbP4P8A2T4efD7wt/a+/wD4ROXTZftf2bH2r7IoXGzf8m/Hq2PevIviTf6Z4b0z4h+EfDvxK05H1ZrwnwZBZrca8L25QlorQ+cCsckkgf5oH2q8jB1XBR1v+Xqp95cv3QS0+9f0iKa96Dnt7t//ACe//tv467m7c+MPEdzr9x4p0Sys7vxLJ8OrXU4rGRW8mWUzPI0QAYEZ+ZV+bgkZz36bxd+0BBpMWn6jo1vHqukDw7J4mvCv+sa3cKtpFGdwCvNIxwWBGIn74rU8N+DrX4cppvifXtatdNttM8LWmi3f2srFDF5TbmlaZm2gZOMHj3rkPgz8H9M1n4a+K4r+O6Gj+KJZrfTt4EdxbaSjuLNUbGVA3vLHnoJV4B4q5WTlGOy5vxlO333TXozGnzcsZS3fL/6TG/3Wa9WvI6PxD448ZfCzwJrvjLxpP4fv7Gy09rhdJ0m0mtZIbgsoSFrqSZ1kTLbTL5UeMBtoGVHIab+0hPDcX9j/AMJX4H8a30mhX2q2zeFZdy2M1tGHMNwv2iUyI275ZQY8+Ww2jIx6G3w58R+JPD+qaB4y8U2etaVdWn2aKTTdJNjdo+QVuHkaeVGlUqGBSNF3ZO3GACLwL401bw7q+j+J/GWm6pb3mlzadG+n6E1o++RNnnylrmQOwGflQRrlm4+7tyqXcZcu9nb8f1t+Gu5vTtzR5u+v4f8AB289NjmofF3iO8/4VhqHiSx8P3R8RarE1tbLp0nnaWrafcTZWV5WzNlQu4KuFZ1w2d1cb8ZvF/jL4gfs9/ETxHpx0W18JPYalaxaZc2sxvp4IzJC1x9pEoSMkq0ixeS2VAUuCxKezah8N/tyeAV/tHZ/wit0lz/qM/attpLb7fvfJ/rd2fm+7jvkcL4j/Z98Qap4I8TeBtM8cxaV4N1j7UYrdtH8++tBOxkeFbgzhGh8xn+UxbwjbRICAw2qcvM+Ta7t+CX4f1exNO8VBy3sr+t5X/T9OpR8V/HxdG8WT+FLTxb4L8GSaRplrczXXjCfm9mmQssMMfnwlVVVBaXL4MigIcGmn9oLUvFjeEBpGp+H/BEXiDSEv7S68T20l5b3900nlmzt5I54F3ocHqzOJFKpgMa7rUvhv4g03xNJrnhDxLY6NcXlnDa6la6tpLX9vcNEMRTIqXELRyBSyk72VlCfKCuTD48+G3irxxoU/h+bxXpT6BqGnpY6lFf+HxcXErYIlmicTpHGzAggNFIFZQcHpUSerktdf8/Xy79NNxU1ypJ9l+n/AAfv0ex6TD5nkx+bt83aN+zO3djnGe1eQeIvHHxD1Lxr460Pwsnh2yg8O2Nrd293q9tNcG6llilYwMkc0ewZRT5uTtBxsfOV7nwTputaZca1DqUzf2VDcRW+j27lGZLWOCNS7MoyWeTzD8xyBjpTLHwH9i8VeL9a+3b/APhIILaHyPJx5Hkxumd275s789BjHeoq3s3T9V92n/DfJlU94qXz/X/h/mujOA8JfFrxZqknw613U4dHi8NeOAEttMtoJftmns9o9zEz3Bk2TArEwZRFHtLrgttJbE8WfGL4iW/wl8S/E7Qo/Df9gWaXE2naNf2dwbmW3jcxieS4EyqCdrSCIRfd2r5mTuHe6T8Hf7L8M/C7SP7X83/hCDAfO+zY+2+XZSWvTf8Au8+Zu6tjGO+a8L+I0dnefD3xT8MPCPxL03V1vrifT7DwlY2ay63bzPOd8EjmY7baNvM3M0ClYh/reAx6JcntWobXdvS6t5/13sKGii59lf11v+Fv6ue3/E7Z408QeFPAzKssF9KNY1aPaSv2K2ZWCHnjzJzAuDwVEgrzH4zeL/GXxA/Z7+IniPTjotr4Sew1K1i0y5tZjfTwRmSFrj7SJQkZJVpFi8lsqApcFiU9h8BeGdQh8VeKvEutWv2W+v5o7CyiaRXaPT7cERcqSB5kjzS4zwJFBAIIrivEf7PviDVPBHibwNpnjmLSvBusfajFbto/n31oJ2MjwrcGcI0PmM/ymLeEbaJAQGGPRLvr99v0sn53e9h07xacull92/4316q2ti38Nf8Akv3j7/sXfD//ALe17LXlc/wt8V6T4+1XxN4Y8VaNYf2np1jp9xa6toMt7j7N5u10aO8hxu85sgg9BzXptitzHZwLeSxT3axqJpYIjFG74+YqhZioJzgFjj1PWtJyUm2jOKcUk+y/BJE9FFFZmgUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRXn3hfVLy4+NPjyxlu55LK20/SngtnkYxxM/2neVXOFLbVyR12jPSgTdj0GivmH4ta1r934U+L1nZeItW0qdfFmj6dZ3dleyRS2UcyaeHELA/ICZXJA4JZsjk16DH8XNSj+CR1h7WJ/GyOdD/s4E7H1gSfZxGOM7DL8+f7h3dOaN43W+mne6T/AFS9Wi2uVq/n8v61fyZ67RXzz8N/GGs+B/2X7i5vtXvPEviS11HUdGs77UZmlnvLs6nNaWoZmJPLmMdeAPQVt+EdQ1Twr4J+IPgzUdb1DVta8MWkk9tq17OWuri1ngeSCUvncWV1mj3df3NKq1SU5bqKv62/XZ+jCK5pRj3dvQ9roryGHx9qnhn9m3wxr0T/ANoa/eaXpdtBNfM0ge7ufJhSSU5DMA8oZuckA85Nb2mfB21s7uy1G78T+LNR1qCZLiW8k1+6jhuGVgSrWaOLUIR8pRYgMe/zVpKPLNxeydv6/rqjKMueCmuquegUV8fWPiKz1Tw741l0vx3rFz8XLbxBqcekaJb+J7qeXdHeutvG2m+c0Zg2BQ26Lase5iVxuH1+m7au772OcetQtYRn3s/vSZb0m49m19zt/wAMOoorzTwZq19dfHH4k2E15cTWNpa6S1vaySs0UJeOYuUUnCliBnHXAzSA9LoryD4vfHxvg7fvNq9p4ft9DjVZAb7xGltql3GNvmvaWZhIm2bsbTKjMVIA5UtmWOveIbX44/Em38MaDaa1eNa6TNI2p6i1hbInlTAASLDMxcnouzGASWX5Qx0Uugno7HuVFcH4F+JV/wCPm0m6s/DU9ro9xBdLf3dzdRrJp99BMIWtGiGS5LCX94p24jH94UvxA+Iuo+FPE3hjQNJ8P/27qGvi6ELPd/ZobdoY1fdK2xyIznBZVYjjCtnFU/d3/r+vy12H3/r+v89Nzu6K8Nk/aD8SW3h3xDrV14EhtrLwpdyWviMvrR3RCMqzvZgW/wDpKiFll+fyM7goyc467UviVrl94u1PRfCPhuz16PRliOqXd9qv2IJJIokWCBRDJ5knlkMd5jUb0G/ltqeiT6P/ACuLv/XkeiUVx/wj+IB+Knw70bxSdNk0c6ijv9hlk8x4dsjJhmwOfl9OM4561lfA3VL3VvCOrTX13PezJ4h1iFZLiVpGWNL+dUQEnhVUBQOgAAFNpptPoPeKkurS+9N/oei0V4B8P/i54gtPC3w60K00r/hKPEHiDTb27W81PVHgRTbzRg+dJ5crYKyn5grHKqNuGJW5cfFCx+Htv8Qda0/wdNc6lH4u0/Sb62sr8vJqFxPDYxiWPzAFUqs6KE+VW8oEsu4kEVzOy/r3lH83/S1RL3Vd/wBaN/oe50V5NN8cbnwjqHiOy8eaFb6DLpOj/wBuxSaTqDahHd24do3VS8MLCUPsGzaQfMXDdca3hv4ka7J4o0zRvFfhi38OyazbSXGlyWup/bdxjAaSGcGKPypQjBgEMiHbJ8/yjclrt/W/+TFdLT+un+aPQ6K4j4xa/daJ4HuLfTJpINa1iaPR9Olh+/HPcN5YlH/XMFpD7RmvJ/GWoXtv+zH8XPCurahdapq3hiwv7B729YtNc27QmW2ldiSXYwyIrMero5px96/9dr/mvvKtrFd/6/R/cfR9FeFQ6xr1h+0N4zt/Dui2usX8vhvRZD/aN+1lbRos18DukWKV9x3DAEZBw2SvGfTvh343Tx94cGoGxl0u9guJrK+0+ZgzW1zE5SVNw4ZdwyrDG5SpwM4Dt2/roQpX/D8Vc6eivPb7ULnxN8ZLHSbS9uLfTvDVl/aGox28zxrcXFwGjtonxgOqok8hU55MJx0pPH15caz4+8HeEba/uNPiuPP1q/a0meGWW3tTEqwh0YMA008JbHDIjqeGpLW3n+m/5N+hXfy/r/L5nodFeJeKP2gNe0vwZ4l8baP4Kt9a8GaL9qUXT6z5F5d+QzRvJHB5DIIfMVhuaUMVVmCN8qt6L458cR+Cvhrrfi2W3MqabpkuoG3B+9sjLhc+5GM0vs8w0ryUerOoorzLTfhBJq+n22o+JfFPiW68Syqk09zpmuXdhaxScHZFawyLD5angCRHZgPnZzkmvZ/G64luPGl7d6HBp/hXwhdXdtqmrT37ea3kwrLuhgEJ35DDILrjI2lzkAejcXuk38lb/NfpcmL5kmtnZL1d/wDI9VorzTw/8VNbl1vQbTxN4Vj8P23iJZDpEkOp/api6xtN5NzGYkEMpiVmwjyrlHBcYUtjaN8fbm6+IkPgzULHwzFrt5FcfZrHTPE63tzbzxR+YIb2IQK1vuXPzL5qgqRzldzs0+Xrr+G4cytfp/meyUV5B+zRr/i3xB4Lv5vE0FptTWNTjguo9WmvZnK6hcq0bCSBNqR7QiEM2VUfKn3RPr1948X4wWK2MMbeGIl+zS3xtrjy4/OMb7WgEm2dlFu6/aAVWP7Wo2tsky+X3lF9Q6N9v6/r/hz1iiuA+M2sahZaHo2l6ZeSabc69rFrpLX0BxLBC5LTNGezmNHVW/hLBu1ZHhG3uPAfxhuvCVtfanf+H9S0X+1rddW1Ke/ltriKZYpVWWd3k2OskR2liAysRjcamPvO3m180uZ/h+OgSfLv5P73yr8T1aivF5NHvPi34s+IPna9rmkDw7dppGkxaRqk1mkU32SG4a4dY2CzsWnQbZg6AR42/M+7nPFXijxX40+AHhTx3b+KL3w40tjYT3un6XFEv2qeS4t1bdMys6xgeYNsZQnfyxHBa2V/L7pXa/L8V52b0/H8P6/B/P6Lorz/AMYTXnhn4j+EdYivbptN1WV9DvrBpmNuGZHlgnVCdquHjMZKgFhMM52rj0CktVf+v6tZ/MHo7f1/V7r5BRXht3+0VrdroOp+JP8AhApJfDGla3Not5PHqYN4+y6+zia3g8rbIhYrnfJGVO8AMFDHo4/jJdeHdQ8QWvjnRbXw4NL0dteWew1Fr+OW0RmWTdmGIpIpC/KAwO8bWODSvpf+tr/lqNqz5et7fO9vz0PT6K8gk+MnizR9Q8HLrvgS206w8U6lHYWs1vrfnzWgeKSUG5j8hVV9qAbY3kXO75xgbr3i7xbGvxIdp9TuNP8AD3gvSpNa1loZXRJZJUdYY3A4dVjjnkKHPzGE46U37ur21/BXf4CWtkutrfN2X4nqNFeOTfGvxXpLeEbnWfAcFjpXijVLfTrSWHWvOuLVZgzK91F5AVG2qPljkkG4kbxwT1PxX8Tano9r4f0jRbhbHVvEWqJpcN80ayfZE8uSaWUK3ys4jhfaGBXeVyCAQXZ9utvnp/mhKSf3X+Wuv4M7qiuD034Yad4Pu49ct9Z8UXt5ZxySSrqHiK8uYLomNgd8EkjQrydw2IoUgYAHFJ8HfiFrHxS8I6b4nvPD9voOlapZQXlkn9oG4uG3rlxInkqqKD90h2LKQSqH5aEr3t03+d/8gv36/wDA/wAzvaKKKQwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArznXvC/irQ/iBeeKfCVvo+qrqljDZ6hpusXstkA0LOYpopo4Zu0rqyFOflIYYIb0aigPI8W1j4L69rngfWobm800eJ9e8Q2Gu33ltILSEQTWx8mNipZ9sNsFDlV3tkkIDhdgfBpx8an8W/bYz4fdRqDaSwYn+1xF9mF0P4QPs3yY/vc16jRTWm39aJfhZfNJhumn1/wA7/wBeWh4VZ/s/3+pWvhfQ/Esejar4V0/XNY1y/sJd063klxcXD2kbRvHtZUW5Zm3Hh0UAMOa0NY/Z/stD8SDUfAOkaB4YtL7R77R9WsrS2WzjnEiBreYCKPDPHIpXnHySsc5UKfZaKiUVJNPrf8VZ/hoNNqXN/W9/z1PFNH+HPjLXfhlp/wAPvFekaDp+lWumwWi61o2vT3F1FPAqGGeOGSyjUESRo/MnGP4q6zQ7r4ox3VlZ6vpnhKW0jlC3Os2mpXKyzRA8utkbfCOwAG03DBSc5fGD39Fayk5S5n11+ZnGKjHlXT+v6ueN2fwNvX+HWs6Nc3tra68dfv8AX9G1S13ObGeS5kmt5OQpyA+11HDKzrkg8+l2umza/wCE47HxXp2m3M91aiLUrGPNzZyEriRB5iLvQ88Mo46itmis1pHl6afgrfla/oi3rLm66/i7/ne3qzD8K+B/DfgSzmtfDXh/S/D1rM/mywaVZR2qO+ANzLGoBOABk+lcHceGPHfhv4qeK/Efh/SfDus6drlvYxBNS1uexmhaBZFPypZzBgd/94dOlesUU3q0w0tY+dvGXwY+IGteHviZoulReFbV/G26aXW7m7ne6hDQIhtGQW/7yNSsiJL5g2rJkRZG09fL4R8f+GfiR4p8SeHovD2r6frVrY26afql9NZvDJAjq0vmpBLkHcB5e3kc7lxhvWqKfRR6L/K35f576itrd/1fV/j/AJbaHnnw48E+JPh/DpOkG90zUtHMV5eateNFJHdzajPcecWiTJRYcyTcMSwwgBPNaviDwjeat8RPCOvQyQLZ6RFfJOjswkYzJGqbABg4KHOSO3WuuooeoWPIfEXwh1jV/h78XNBhubFbzxdcXUti8kjiOMS2sUK+aQmQd0ZJ2huCOvSi70rxD4A8V+L9Y0K98OXGi6mIb7VW1q/ktn0eWO3WJpgEicTRmKJH2O0OCjfPhsr69XMa98L/AAb4q1qDWNa8JaFrGr24VYdQv9NhnuIwpLKFkdSwwSSMHgmlrZRW1rfJK36Lqh935t/Nu7OT/Ze0270v4B+Dob4s1y9q05dojGXWSV5FbYeVyrg47ZqKy8K+P/Ak2u6Z4UtvDeoaJqF7cahaXerX9xbz2Elw7SSq0KQSC4VZGZ1/eREhthxjefWKKcrSk33/AK/q1hR92Kj2/Pb9ep5d4V+D9x4P8ReA5ba9iutO8O6FeaZPJNlZp5pntn8wIAVAJhkJG7jcoGe2Zq3wb1q//wCEo8u6sB/anjbS/EkO6R/ltrb7D5iN8nEh+yyYAyOVywyceyUVUZuMlJdP/klL80N6x5f62a/JnkvxQ+CUvxO8ValNdXcFvo1/4WuNDY4LzRzPPHKknlkbWQbMkE89MYJqL4WfCc+FfEEF/d/Db4c+F57e2aP+1fC8WbqeQ4BIBtYvIQjcSu+Q8gZ4yfX6KiPuqy8/xbf5tikuZ3f9WSX6L9Dzj4gfCmP4mePPDc/iGy0rV/Buj29zM2l38QuPtF9JtSN3idChSOPzcEkndJ0GM1xPjr9mtVj8WW3w+0/w/wCGdM8TeGLjRr6xhg+xw/agSbW4CwxkHAkmVzjOCuM4xXvtFOL5WmvP8b7/AH/l2Kvr934Wf5o8mv8Awd470H4sa14t8OxaBqun6jo9hpraZqd7NZv5kElwxl85IJcACYAJsO7cTuTYA/S/DnwncfDnwbcx6nef2tqk9zdarqFxawMBJPNI0rrFHkttXIRRySFHeu0opNuztv8A0/z/AE7IlJK3bT8Fb8jgvg3oV7p/hm51nV4JLfXfEd5JrF7DOmySHzMCGBhngxQLDGfdCepNU/Hksfhf4oeC/FN5MlvpTwXegXM8sm1IZLl4JIGbPGDJb+WCf4pkHevSaKd7STXT8rWt92ga2d+v57/nqfLHxV0LxX4N/Z+8c+FdPvPDt/4ZuPttppmp/apXv5GuJ222H2VY9jTebI8AkE2eAxjJBU/Rer+FbLxR4Lu/Dmrw+fYX9g1jdRqcbkePYwB7HBODVS2+F/g2z8UN4lt/CWhQeI2dpDrEemwreF2BVm84LvyQSCc8gmunpacnK/6/r+u7e01JdNvn/wAN/Ssl5joVr8VfDlna6I1v4X160tdlvH4gutSuLa6khAA8yW0W2dWlAznbOquRkeWDtVbf4QtqXg/4keHNauI/sfiy/vpVktGJeKGeJIxncB842k45HTk16bRQ/eu3u018m0/0QkuW1ujuvxX6ng/hH4Dzaf5lvP4C+HXhW7SwuLVPFPhi3237SvEYhLHF9mi+z5DMxAmkx93JzuE3g34WeNbLVvhqt/ZeFdA0LwesyPYaHPLL9qZ7V4vOXdBEIvmYkx4bO9jvJAB9yoquZ3b7/pf/ADZPKrJdv+B/kv6ucD8JfCWueBLPWNF1CLT5NK/tO8vrC9trqR55VubqW4ZZYWiVYyhl2grI+7Gfl6V31FFIvq33ON+Kngy/8aeHLZNHu4bHXdMvrfVNOmulLQmeF9wjkxyEdd0bFeQHJAOMHnT4X8f3Goar4tEXhux8XyWkGmafpz3M91YQW4nEk7vOIopHdwTgBAqmNOuWNeqUUo+67rvf52s/vWnoKXvb/wBWd19z1PKtQ8E+NPDvirxVdeEn0SfTfFLx3Fw2rTyxSabdCFIGmjRInFwpSONvLZovmQjfhvlv+JPhSz/Biz8C6FNGosoLG2gmvWIBSCWJiWKqfmKxnoOp7V6NRT6W9Pw2+4b1d/6/r+urPOfHl1H4i+IXg3wzauJbmxu/7e1BVBPkW0cciRbiPul5nQKDywjkxnacdL8P9W1jXfCVjqOu2i6fqF35k32QQvC0MTSMYUdHJIkEezeD/Fu4HQdDRSWit8/6+SX3A9Xf5f182zyGT4Q6y3wl13wuLmx/tC+1241OKTzH8oRSakbpQx2Z3bDggAjdxnHNW/iV8J/+E41zXrrUb+Cw0DUPCd1oU828iWFpJA5lwRt2qoJyW6jpjmvU6ZNClxC8UqLJE6lWRxlWB4II7ipa2a3X+Tj+TKvq2+rv/wCTc35nzj48u/G9x4m+Edh4kTQTJZ64t+Y9AmnvLjUI4LOfzLoRGJPIRdyjYDNlp0XeMDf2/hb4ft4h+EPiiPxMZtL1Dxql3e6o0qiOWzSeMpFGwJwGhgEUZzxmMk9TXbeE/hr4R8BS3Mvhnwrovh2S5CrO+k6dDamUDJAYxqNwGTjPrXRSRrIjI6h0YYKsMgj0NVLWMkut/wAbX29F+gotxlF/y2/C9vzfc+cfGf8Awm19qnwi0LXJfDnmReIrW5ifR7ma5m1WG3ikaS48tokFsirtYgNMMyKu8cF/Y/iR4EPjrSbJbW9Gl61pd7HqWmagYvNWC4jyBvTK70ZWdGUMpKu2CDgix4T+Gfg/wHcXE/hnwpofh2a4UJNJpOnQ2rSKDkBjGoyAexrpapu6+fN89P8AJMzUbP5cvy1/zZwWkzfEbVW+ya/ovhfTLJreRZrnTdYubuSVyhChY3tYhGCxySXcgDGDncNP4U+FbvwL8MPCXhy/khmvtI0m1sJ5LZi0TSRRKjFSQCVypxkA47Cuqoovo13t+F/82Vbby/W3+QUUUVIwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAr5k+I2uae3xr8e2WvX/jxraz0DTptOtvCdxq4jglf7X5jsLMiJGbZHhp8KdvXAavpuvJNS8J+PPD/wAWPFHijw1pvhzVrPWtNsLNV1bV7izkt5Lc3BLFY7WUOp88fxKflPrSavp/X39PUpOyfy/NHl3xD8ZeNtNj+CesWniBtV1C10W81nV4tJu82espDHaGcERfJLmJ52jwMbypHHFdb+1N4+1Gb4Wyaf4N1eex1DUdLuNbOq6fK0cttYW8XmmVHUgqZHMMQOQcSsRnaa6Dw58E77w14g+HVwuoWt9aeHbPVYr+R0aJp5rx43zFH8wVAyyfKW+UbRluTWLp/wCzpqGk+A/iNokep295c6xp9xougNcu+yw04rI0Fu5wcbXmcEqDlUjzkqK0k+a/q3+L09Ph07c3cdJpOm35J/15a/O3Y6LwnrWoXHx61jT5b+5lsI/CemXKWrzMYllee6DyBScBmCqC2MnaPSvV64Hw/wCAdQ0r4r6j4nlmtmsLnw/Y6UkaMxlEsMs7uxBXG0iVcHOeDwO+14r+GvhHx5NbS+JvCui+IpbYFYJNW06G6aIEgkKZFO0HA6elJ7L1f/pT/SxjG/XtH/0mN/xuZHx41K70f4J+PL6wuprK9ttDvJYLm3kMckTrCxVlYHKkEZBHIrg/FXg+5+HvwpuPHHhfxD4ii1zSNL/tWSHVtevdTtL5I4xJLBJDczSKocKQHj2upwQcZVvS/in4RufG3wv8U+GtNa3t7vVNKuLG3aclYkaSJkXdtBIUEjoDx2rjrjwd498c+GYPCniO38P+HPDzQx2+oS6TqU+o3N9AoAeBfMtoFgDgEM/7w7SQFBIdYje8rb+7b/ya/wCl118zW6tDmV1rdf8AgNv1s+nkWbz4yatqWoavF4R8Kx+ILTQoYpdWmutS+xuHkhWcQWyiKQTSiJlYh2iQF0G/ltpp3xqu/FnjKy0XwpoVvqtpc6NY6/8A2pe6g1rELS4eVfurDIxkAjUqvRstlk2jdzPiz9n2L/hOtf12x8A+AvG8OuNFO6+LE8qexmSFIcRyC1uPMiZY0Ow7NrB8E7/l73wn8PJ/DPxCv9YjTTrbSZNB0/Sbe0sIzCsTW8lwzBYsbUjxMgUBjjBHYE6Llev9bO/42t5fhnqk11/4Mf0v8/Q5L4Z/EfS9Lm8O6Rp/hr+xdJ8Q6zr9us/29pwt9BeTFgQy5PnhLiUYICbSoBGDWrefG+aS6NhpHh/+0tTvdcuNF0aN7zyobv7PHuubmWTy2MMUbrLGSqyEsi4HzYGZL8F9cj+Ecmi2l3p8Xiux1+78QaTeMzmCKZ9QmuYwzbNwzFKY3wp4dwNw6x+LP2eoL7wT4GsLbT9F8U33hU82fiuLzbTUxIm24MreW+yVmPmiURvhhjbhjhaa/d+Wvy1Vu1upT3dut/1/O6d+97mpL8criztptLu/Dyr46XVU0WLQ4b4PbzXEkBuI5FuWjU+R5IZ2cx7l2OAjMAG4+y8ReL4fj14uvJvCFtN4ms/BVqbbSLHVw9vet9suCojuZIo9uc4JeNcEHqMMdqH4L6hY6PpGpaB4Y8GeCvEeja0NVttK0QMthcr9ne3eOedbeNizRyy4kEPyHZ8r4O6a38FfE3/hO/E3jYjwpaaxeeHYdL0vTftNzcW9vPHPLIPPl8qNpEO/O5UQjO3adu9iNlJPtf8A9Ia/P/hlpd/Za72/9LTf4L/gnR/tASM3wB8fvInlOfD94WTOdp8hsjPes/SvihrmkXXhSPXvC0Wm+HNdaOysdSh1Iz3MU7RbolurfylWIPtZQySy4YoDjdkdR8TvCt/44+Fvibw7ayW8Op6rpVxZRySlhCsskRUEkAkLk+hOO1cnbeCPHHiXUPClp4rfQ7fR/Dlyl6bnS7iWWbVZ4o2SIvC8SrbKGbzCoklOVVQcDJS+J9rr7tb/ADS2/XpMv4cbbrm+9qNvva1/Qr3Xx01iTw5qPjLS/CMN/wCAtOlnE+oSar5V/LBBIyT3MFr5JR4xsdl3TIzqpIUEqDtXXxQ1XWvEV/pngvQLPxHb6XHDJqN7d6r9jQNKgkSKALFIZZPLKsQ/loN6Dfy23m0+GPjbTvBepfDuwl0X/hErz7TbRa7JeTrqFpZzszNELbyikkiK7IsnnqPusU+Uq2nZ+AfFnw/8Ta1L4Nj0W/0XWxbvJHrN5NBLp80UCW/mII4n+0KY4oz5bNEdyH58P8s7xf4evZ/p5+RXn/XS1vx+XS5yX7MPj9rXwT8O/Dlzpcttb6zpuoXltfSSYIniu2L2zJjhtj7wdxzsfjjNev8AgHxoPHml32pw2f2bT0v7i0s5jJu+1RwuYzNjaNoLrIAOcqobPzV4h4k+HevfDn9nnwXpC6laSfEDRdVjXSrq1Rminu7iaWIgKQDtMNxKW7KFJzhc1774P8L2XgnwrpGgacmyx0y1jtIQSSdqKFBJPJJxkk9Sa00fM+za9et/kml6atdTJXSivJP9LfPe/fTyPIm/aO1yPw/N4mbwC7eF7XXZtCuJ49VU3jut41ok0EBiCyRs/lg75Y2BLgKwUM/ZeHviJ4gude1rw/rnhe2sNes9NTVLS30zVftcV5EzOgTzJIofLkDpggrt+YEMecc+fg3rX/Cnbjwn9qsP7Rk8Rvq4l8x/J8k6yb4LnZnf5Xy4xjdxnHzVa+KXwi1bx5qXimazvrSzj1TwwNGhM28nzhO8n7xQOYmBCtg5ILDFRtp6/wDpN1/5N67mz6td/wD27/L8jH0v9p+wh1DxRp+txaHPfaFo9zrUsXhHXhrOI7dgs8UuYYWhlBZMKy4OW+YbTWzffGHXvDPgW98UeI/Dui2tgyWX9mzaX4gN3bXDXMoiTzpmtoxDGpeJmkAkXaxIzjBzrDwJ8Ql8d2Xik2HhLSYtK8N3Wjad4fsryeS3SV5bd0YzfZ48RlYdpVYv3e1cb9xC1fCvwl8ReFV8WXum+FfB2l22tRW9u3gm21Cc6PNgstxcO/2VQkksbKhVbcgiJdxbd8t6WXfr/wCBP81bvbsQnv8A10Xps79r9GtD1XwjqHiLULO5PiXRtP0a8jm2xJpupNfQyx7FIcO0MLA7iy7Sn8OcnNeWX3xQ074dp8RNZ0vwhcXl9H4vsNJvILW+LS6jPPDYxrMgk+VGCToojyFJjyWXcSOr+Cnw5vfh1petQ3MGn6Ra6hqDXlr4f0e4knsNKQooaKBnSP5XcPKQscahpCAvGTg6t8G9av8A/hKPLurAf2p420vxJDukf5ba2+w+YjfJxIfssmAMjlcsMnFQ5faK+1lf/wAChf8AC/lpfogfwPvr+UrfjY0YfjRdeHNX8Raf470Wz8OPpOjHX1uNN1Fr+Ge0RmWXloIWWRCFyu0g71wTzXEfEPxR4q17xB8I5dc8J2+haddeLbe4tJIdV+1ToPstyQlzH5SLG5VgcRvMuQw3cAt2vxG+DM3xF8XapcXN5FbaLqXhO78OymMk3CSTSqwkVcbSoCnq2c4GO9ZOqeB/iZ4y1TwCdeHheys/DOrw6hdzWN3cTS6jsgljLqjQoIDl8+Xukzv/ANYNnz5U9XBve6/Cb/RJ/l1s5bTS2tL8YKy+9tf1reg+O17c2o8SR+Gk/wCFfHUv7MGtNqB+2MfP+z/aVtRFtNuZeN3nb9nz7McVZ/4XdP8A21L4Z/sBR40XVxYJpBvCI3tSvm/bhN5WfJ8kMc7P9YpiyTgnkPC/7OMPhPWktF+Hnw41nTItSNzB4kv7YDVY4Gk8za8X2VhJKmWRZfPXOFYrkFTvL8LPGDeNY/Hz6lYjxYL/AOzGwF1KbAaKWCm1B8vPmcefv2f635c7Kcfs3/rb/g320u1qkKV/et/W/wDXrZO2p1fxj1a+s/CUWm6XePp2qa9fW+j297E2JLbznxJKh/vpEJGX/aUV5w3ja3+F/wATPHtrZ2dzr2uXcej2GjaP9pLXN/MLeXhpZCSFVQXklfO1VZjk4B9B+NUMtv4Z03XoYzKPDuq22rzqpIP2dGK3DAAZJWF5WC99uO9cfrnwZu/FHjbXPiJ4a1jTrHXrmCxm8PaxCTMjxpEwkhuABh7aYMvCsegcYZVqFtr/ADfhZfrzfjvsEvjjbt+Ov/2v/A3OnuviR4nu/Er+HdB8L6Zqmr6dZQXWttc621va2kkuSkEMgtneZyFdstHGu3YSQWwM7Sfjo+ufA278d/2JJpV+ZbqytdHuJxJI10l09rDEzqMBnlVAcZC7urAZMa6b4w8NeMNY1+wHheR9WsrWbXdOvtXljXS5YlZBPFKLcmaNkU/LIsPMWQwydvA/AHw/qviPQPAFlqEU1xo+nXOpeKZtRW1eC2vppr24+xeXu6gpLJPtBbbiEkncpq7cy5f663/BO3fTW9wvypS/rbr87X7drWO20vxl4q8IapH8O9I0NvGOuaToNrqdzrWs6y0EVxJJJKjq0nlSuHLRkoApXBIJQKNz/DPx61PxBZeENdm8If2Z4T8SXMdhDdXGo5v4LhwwXzLYRbfLMiFAwlLchigGcdlp/g69tPixrvih5YDYX+j2WnxRqzeaJIZbl2LDGNpEy4wSeDwOM8np3wj1iz+G/gHw+9zYm90DV7XULqRZH8t445Xdgh2ZLYYYyAM9xRe8ot9Wr+Sctfw/IU1yqSh0Wnm+Vf8AtwnjzXvEuqeKru98MXNx/Z3gsJNfWFsA39rzuoaW1x1Jjt23KB1lli5+Qiuy1XS9G+KHh3TL9dZ1EaHLGL2KbRtVnsBOrLlXaaB0k2gEnbuC8/MCQMdZXmXxm8G+JvGdxoFvpdjo+t+HYZJZdW0TV9SlsYr5sL5Cu0dvN5kSneWiZQrHZnIBBn7PL/Xn/wABdPnctL3r/wBf137/AIHBaPruqQfDJvGFj4g1PUdF8Na/NLp15c3ckg1TRQ6xyiUk/vwqtKY5X3M3kxvuO4s30TXj3xE1LXtX8CnwjrulaTpWueJrlNJsbHR9Tkvla2O03MxL20JQRwiRj8pHCDdlgK9hA2gAdKvpZf1ov+Hfm35mavfX+tX+Wy8reQtFFFSWFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFcPqHxSWHxVdaJpnhnXPEIsZYYNRv9MFt5FjJKFYK4lnSRiEdHby0fCsO/FG7sHS53FFeP/Dz4q+IdZ8Z/Eiy1bwzrkem6LfbbWQpZSCFFtIJPICwzNLI7s7SL8rcOAWU/KNq3+L01xcajpdx4N8Q6P4ih099RtNJvWsTNfQqwR2heO5eHKM6bleRCN68YNJu0ebyv8rX/AC/qw7a287Ho1FeC/Dr4kX3jXwL8I9R8Ux+JtH1fUruFY5oJbSOHVJjYzSM8yQyN/o7BXOwqjb1T5QBW/J+0hoy2t/qKeHPEUmg6bqsukalrIt4FtrGaOfyWZw0wkePJVt8SOAp+baQwGkouMnB7p/5fq7C2V+n/AA/+TPW6KSlqACivF9NXxT48+K3xG05PH2ueHdM0K6srezs9KtdOZAslnFK5Zp7SVyS7E/e71b0P4xXPhnTPEOneNNt/4h0LU4dKjbR7VlOsPPGklqYYmYhZHD7WBfYrI7FlT7otV9z++3+a037Dat/XzPXaK81uvjvo2keHvEGpa1pWraFd6AbdtR0i8jha6ijncLFKDFK8Txsd3zJIfuOD8ylaoar8RoIfFGl6nfaT4w0y9t9H1W+g0MPbeVewQm3LSPEszAynevlBipG6QMFJpN239fwv/X/ABJydl/X9f1ues0VyDfFLRGtvBtxbma8h8V4aweBVIEX2d7gzSEsNqBE5IzyyjHNY/h345aV4i1DSAmjazZ6HrUjRaR4hu4YlstQcKWVUCymVN6q7I0saK4Xgksoa3FqTi91/X9dyeZNcy23PR6K+efjj8cZbn4V69d+H9L8SWWnfaIbW08W2wjitZJPtUcbCPbL9oCk7kEhiEbdmKspPtuj+JP7Y1nW9P/svUrL+y5o4ftV5b+XBd741fdA+fnVd20nAwwI7VK1V1/W3+aC+rX9f1obNFcJ4h+LEWl67f6Po3hrXPGGoaaiSajFoi2wFnvXdGrtcTRKzsvzBIy7AYJA3LuoXnx60JZvC8Gladq/iG68S2dxeaZb6dborSeQUEkbmZ4xC43/8tCoBRlJDbVJvt/X9WH1t/X9ancXHhvSLvXLTWp9KsptZtI2ht9Rkt0a4hRvvIkhG5VPcA4NaVcz4A8e2fxC0e5vbW0vNNuLS7lsLzTtRRFuLWeNsMj7GZTwVYFWYFWBB5rJ8XfFhfBd9cG/8La+2g2skcd14iijt/sdvvKjeVaYTsi713OkTKPm5+VsHZLr+v+d9/MXdneUV5tr/AMcLTSfEHiPRdP8AC/iLxLqPh+OGe/i0mG3wkUkRkV1aaaNX4UjYpLk9FI5rW8H/ABa0DxxrElhpkkzf8Sq01uC4lQLFc2lxv2SRnOeDGwYMBg49aFqrr+t/8n9z7D2/r0/zX3o7Oiud8K+OtO8WeD4/E8CzWekSLLKkt4FXdCjsBMMEjy3Vd6nOSrKSBnFcTqHxn1LUvAus6zpHgbxRBF/ZMmo6ZfTLYKl0m0bJFVrnKfK6ybJlRiqsNpYFamcuRSb6av8AH/J/cyormaiup6xRXmfgv4ma5f8Awr8Oa3e+DfEN/rF7bwK1lB9h864JhDtcBhcCGONuSN7o3QbQSBXM+O/jvqi6N4au/DfhzXTdSeJodI1WwK2Kz2zK4D2snmzhd0ispR42ZCCDvUEE6Si4z9m97pfe0vuu1+W+hjGalT9p0s39yb9L2X67anuVFeT6t8StI8H65478Qal/wkiSaH4astU1DR5JYZLeCIm6YeQiuV88+XIrndtO2PDHGa2PDvxjs9d8VadolxoGuaGdWtZbzSb3VIYUh1COPYW2BZWkjba6v5cyRvjPy5UgHK72/rr/AJMtu2/9bf5o9AoooqRhRRRQBzHib4X+DfGmpQaj4h8JaFruoQII4rrU9NhuJY1BLBVd1JABJOAepNdNS0UdLB1uFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXh/j/wrrmqfEP+0fC3hTW9A8Ri6tEbxZa6pbRaZe2iEF1u7fzy82EeaNQ1uzBtu10HzD3Cijqn2Do13PDfEPhXxtaXHxcsNB0y8juPEe3UdK1u3vYYIg62kELW5bzBNFKxhfDqhUBgd4IwM74ffDC5sfjNaeIdN+GsPgLQf+EcvdPuZ55bR9QuruSe2cPcGCWTzcrGxEjSOxIk37SV3fQdFC0+63ys4/kxvX+vNP8AQ8C8F+CPFUXhf4QaRfeHLnTpvB2pLFfTTXNs8c0KWFxD9oh2SsxRnkQAMFfnlAATT9c+GPiW8/Zv+IvhaHTd+vatda1JZWnnxDzVnu5pITvLbV3I6n5iMZ5wa96oq3Nttvd/8D/IfNqvK34c3/yT/ApX2lx6po8un3LXEcU8Jhka1uJLeUAjB2SxsrofRlYEdjVDwr4PsPBtnNbWE+qXEcr+Yzarq93qLg4Awr3MsjKOPuggd8VuUVPW5CVkl2PFtPbxX4D+KnxI1GLwBrfiPTtdurK4srvTLvTkjIjs44mDie6jdTvUj7p4GaxfF3wP8Qa7osniLUoRqPixvEUevz6TourTWY8mO3Nqlpb3YMTh1hJYOfLDyFg2xG4+g6KXReVvwt/kv6uU23/XdW/r/gK3z3ffCmXWvA/is6X4L8QaTrGpHT7Yr4q8R/2ndXUMNyJSMvd3CRxqGcgeYCxLZUcbvSdc8MajffGbwtrkdr5mkWejalaXM5dflklktDGu0ncciKTkAgbecZGe7op31v8A1qrfqJaX8zwn4bfBnWdN1DxDpetjyPDum2d3ofhpkZHcWd1K0zvwxI2J9nhAYA/6OTjBycn4b/B+TTV8L6Hr/gvxS9zohjDazN4xnuNG3wIRHPBaveu/zEKVja3UJuIzhQT9GUVP/A/C7v63bfq/Syl717+f420ttayS+Xrf5k1zwb8Qk+AafCyx8EvdXunJbWaa39vtY7C6t4Z42EkamXzhK0aglHjVQ2/5zhd/0Fo+qateazrdtf6L/Z2n2k0aWF99rSX7ehjVnfYBmLa5KYbk7c9DWzRV8zer/rb/AC/FhbW55Lb2XiT4Z+N/GN5p3hO+8XaZ4mvI9Til0y6tIpLSZbaKB4phcSxfIfJVlaMufmcFRgF8z4d/CTW/BXivwRPcpDcw2ena2+pXFvIvlQ3d7eQXPlRg7WZAfNUNtHCAkKSBXttFEZONvLT5Wa/L8h9/P8/6/M88+FPhXVfDfiD4kXOpWv2aHV/EjX9k3mI3mwGztY9+FJ2/PG4w2DxnGCK8d+K3wm8R+Lrfx3Zz/DtfFviO9vGk0XxNqt5ayWtnZ/IyxW6ySmW2lUb1wkSKzgMznOa+paKS0cX2SX3W/HT8wl7yafV3/P8AzOB8L+F9T07xx8Q9QuLby7TVmszZyeYp83ZaiN+Acrhhj5sflXiereBPE3gv4U/CTT4EGmeLZrI+DryBZlaSOK6i/eSIVJVzC0Ky8EgKrmvqqsSTwbpM3iuHxJNDNPq0EJggea6leKBTwxjhLGONyOC6qGI4JI4qUlfXbS/or6fNNphdpWXy9e/y3+RNFpkXh3wsun6XYLcQ2Nn5FrY7lQSKibUjyeBnAGTxzXjXgf4d65HfeILHSND1rwL4LvdEnsxoGvajBdxJeyMfLktEhnnFvEqtIGjV0U5TbGNpNe9UUpr2nNzfaTT+d/8AP79Qj7nLy6W2/D/L7tD59vPDnijVvAfw7stX8BaxqGlaPGLHW/Co1CyDX2y2VYpiPtQhngEinMUrjO4ExnaKzdB+FfiXw74L1B7XwVa6U9v45g8Q2XhrRZrZA1kohGyPLpEsgAYlSyruVsEgqT9KUVo5NzdTq2n9zUvzijP2ceRU+i0+VmvyZ4H8SPh54n8Xw/F24stEljfxJ4NstO0+3nuIBI10v20vCxEhVSvnxgsTsO7hjg13PjLwpqurfEf4Z6pa2vm2Gjz3r30vmIPJElo0aHBOWyxA+UH34r0OiiUubQtq+/8AWiX6BRRRUjCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvLLzxr4p8WfEjxH4W8L6loGgN4ejt2uG1rT5r+e7M0YdXjjjuINkS8rvLPucOMLsy3qdeK/F5vBnijVriz1bw14sHirSgU0rXdB0G/wDtMbvECrW1/BGY1XMhVlkkCblbzF2jNS9/6v8ALu/Ipap/195oa74q8ezfEfRfB+l6j4c0q5k8PNqt/d3mlXF6jTrLHEyRKt1CVTLkjcWPArEk+MnizWvBPw7utKj0bTNX8S65Lo811d2s15Z7I0uT9pgRZomaOX7Orpl/uSDlup5fxB4fhuPiB4I1D4teDpPFjR+Djb3skPhebWrePUTNEzjbBBKqMQH5AA644NOu/h9rPiDw/wDDmwvNN1+10ZfGc93bWyXNxDe6ZpZt7ryBLNE/mQAFlAG8FFdIzgjbVrZJ/wA2vb+I1b5x28lcz25n5af+C7/P3vx0PQ9H8SeP9X13xF4RbU/Dtp4g0UWl5/bI0aeWzurW4EoVPs32tXilVoWz++cFdpwN2Fzvh18UPEP/AAhVp4x8d+IPD8ekXby2sOn6RoN0l09wJ2iRYz9qmaZm2MRGkRY546HPpfhHwNovgW0uINHtZIjdS+fc3N1cy3VzcyYA3yzys0khCgKC7HCqAMAAV89yeBvEFv8ADX4YamV8Q6R/wj2sahPfpo9hHPqNvHM1zGk6W00MvmbfMXKrGz7JGZemDMpWat/Wy/r7w1bt6/1/Xoe2f8Lo8HR+FpfEU+rmy0qG9j064a9tZ7eW1uHdUSOaGRFkhJMiH94q/K6t90g1yjftCWc3xY8PeGrbTtaGmanpl1ctJceF9UinWdJ7eOLG6EbYsSSF3K7V+QllDDdxWseCf7c8K3mp2E3jLxRc6p4r8PveS+JNHWymaK3vIC0iW6W0DLGqE7naMcITnC5r0Txj5+i/Hbwbrs1hqFxpT6PqGktcWFhNdiG4muLN4xKIkYxoRFJ+8bCDbyRW0EuZOXdr/wAkuvvk9PO24N+67dk//JrfktfnsZ3/AAnOqQ33guOz8U/27ban4x1DTbub+zkt8QxxXjC12lc/unhRfMGC/l5zhjnr5vjH4Rt/E39hPqcn2v7Utg1wtlcNZR3R+7bvdiPyElJIAjaQMWZVxlgD5X4d8L6zB/wg3maRfR/Z/iFrV7NutnHlW7/2j5czccI3mJhjwd64PIrm9A+H91Z6XeeAvEeu/EW3abWZ3NjpOhW82m3SSXhnjuFvvsLbFO5XfzLgOrK47LmI/Zj/AFtH/g92XW92Ta6X/wDSp6/guy28j6sorN8RaTda1pMtpZ6zfaBcOVK3+nJA80eCCQBPFJHyODlDweMHmk8O6TdaHpUdpea1feILhWYm+1FIEmfJyARBFEmB0GEHvnrUiPL9B8TfEnxxrHjP+xtY8K6ZZaJrM2l21pfaHc3EkwjjjcM8y3qBcmTGRGcYzg9K3dD+NmkyeEJdW8RR/wBgaha6k+jXelxFr2X7cjY8mBYkLzlhh0CpuKHJUYIHHeA/Gi/D/XviNZaj4e8VXF7e+Jrm8so7Hw5ezRXUbRQqhS4EXkAMykbnkVR1YqOa5rxJ8N/E2m/8I340v21zSLxvEF7q2qW/hi3g1C80xbqAQQlYninWYxRokcnlRu2ZJGTKg5mHwQvs4x/8CfLf9b9vLqS+KVt+aX3e9b9Ld/Pp7O3xm8Hw+G49en1c2umtejTmN1azQyw3JPEUsLoJIm74dV4IPQjORefFXRtN1i512/8AEt3p3h+20QXk+i32hXFvLCpuDH9rcvGJVHBTyyowPn6c153J4HTVLLT9Wsn8XeJJdQ8a6Te3tz4k0hbKYrbhV84QJbQMkahVBd4xkpnJGCe/13wd/wAJR8XdcttRsJpdB1LwiNNmnMZ8py9xLvjD4xu2tnHXBBpq+9tbtfdT5vxbt/wdRabN6Wi/vnb8Ev8APTQ7fWPGWi+H9RsbHUdQitLm9hnuIRJnb5UKhpZGbG1EUMuWYgfMBnJFZXhH4seGPHGoPY6Te3DXXkfa4o73T7mz+0wZA86AzRoJ48lcvFuUb0yfmXPhNj8NfGnxG+GHjSz1qzmg8QadoZ8IaW92nkvfeSd09wrOMBLorCu7BU+XnBHXa0nwy/xCuAU8RfEW51qz0e+htYvE+gw6Xa2Uk8PlFWljsYPNYEjHlSOv7vd0AJJ+6pOOtldee9vPa19NL2CPRS0u7Py2v+N152Nj4kftDaV9k8Ow+E9ZnFxfeJdM05b06ZMLO+hkvEjnjt7mWLyZyU38xOxADEEbSR6z4Z8Y6R4yj1J9Hu/ti6bfzaZdHynTy7iI4kT5gM4J6jIPYmvn3xJrF74g+Hnw28K2PgnxNDq2ja7oQ1GCXR50g05La5iEji4ZBHMg2H54WcbTuOFyR9BeGfE3/CTR6k39lanpX2G/msdup2/kmfyzjzouTuibqr8ZHatuVKMktfeevdWhZ+m+nd3vuTdvlb00Wnb4tPXbXsttjzzR/Gnjb4iav4nbwvqfhnR7PQtTm0ltN1bTp7y7lkiPLyPHcRCBZMgoPLk+Qq+Tu2L2y/Evw03hXWvEn9pqujaK91HqFw8Mim3a2ZlnUoV3EqUboDnjbnIz5D8VP7F8Valf3/hjw34q0n4sWbSWemaxZ6De2glkSQBFnuzELaa0bywxErsuw/KA5AqPxN4cv/8AhcqeCVgUaJ4subXxRfPCfkU2e0XSY9JJU08c9fMkPPNZwvJRj/V9Lv0td26bIuVouUvw8u3rdpJ9dWz1Z/i94ZXxANGWbUZ7zzUgeS30e8ltopWUMsUlwsRijkwy/IzhhuUEZIrm/hp8edN8YWHiq81X7Rotpot/dI15qOk3mnW6WsbhVaSW4RUEmPvJkMO6iuda4vtF+Kir4HHiiBtQ1vOvaHqmhzjSGj5We8hu5I1WNyFVwI5mVyf9VuZiMnWPDerat4W8deGYNG1KfVrXxaniJbWSyljttStFvYLny4rlwsDs6Ky7PMyGGGwOaUNWm9mn8tYa+aSbd+qT2d7KV9l0a/KWnley9G1urX9a0H4t+HfFl5cabp1xf2uqLatdx2uraTd6dJLEODJEtzFH5qglclNwG5c43DPLfCnxtqXijSvhfdap4o/4mOreG5L+70r+z1/4mEgFvun81VAi8syY2DAbzeny1X1LUJfix468G6homl63YWHh57y8vrvWNJutNLebayQJbIlwkbSFmkDkqrIBFywJUHlvh/4L146f8JrV7C+0ya38BX+n3E89q6iyuHFkESTI+V8qxCnBOxvQ1XwqTtr/APazf42X4PsXG0tL6f8ADf1+Hc9e0P4teGfEmvDSdLub2+laSSFLyHS7o2EkkYbeiXnlfZ3ZdjghZCcqw6gisj4peLvE2k+LPBHh7wzc6TYT6/cXUU15q1hLeLEsNu0o2xxzwnJK45bv0qp8F/E8Wl+F/DHgm58O63o2taXp8VldQyaTcCyhaGIKzJebPIkVio27HJO4ZUEMFw/2htD0vVPGHw1ufEPhi48UeGrO8vWv7eLQptXjTdassbPDFFIcb8YJXg0qiUdIu6799dzOLcldqz7dtNitqvxl8X2Xgm+MS6I3iC08YWvhUaqLOZ9PuRLNDG06QecGBTzijJ5xxJC43dh2ng/xV4ntfiBfeD/FMml6ncLpqara6po9nJZxmMyGJopIZJpirBhkMHIYEjClfm8c8QeBb7XfB93Z6J4f1vS/BV14y0SXStJSG4s7i1tkuIftU8US7ZbOHcGYLiMptdwFDAn6D8IfD3QvAzXsulW9wbu9Km5vtQvZ767mCAhFeed3kZVydqlsLubAGTmo/Dd97evuR+73m396CXaP9e8/v00/E5PxF8Zk0D43aJ4Jeyv5LS90y4uZZ4NEvZyJxNbJFtljjMflbZZN78qhCbmTODuTfGPwjb+Jv7CfU5Ptf2pbBrhbK4ayjuj923e7EfkJKSQBG0gYsyrjLAHB8Y+fovx28G67NYahcaU+j6hpLXFhYTXYhuJrizeMSiJGMaERSfvGwg28kV5LoHw/urPS7zwF4j134i27TazO5sdJ0K3m026SS8M8dwt99hbYp3K7+ZcB1ZXHZcqNmoJ+d/8AwJ2Xraz22sOfu8zXl/6T/n/w57bqnxs8MWk2s2lpNqGpahpRmjuodP0a9vBBJGudshhiYLnPGSN2G252nHM+A/i7J8SPBfwy1uTV5/DV/q88AvLKTQriOLUZmsnmkt4WnXKxZBdZlZlIi2hjurV+F/hy6sYfiYJbCWzm1LxJezxNNGY/PVoYUSQEj5lO3AYcce1cH4HsdRu/BPwF04aNrFveeGb6Cy1aO70u4g+zPFpVxE7ZkQBo97KolUlCWADE0UrSinLdqm/vu5fJaJ/K/YH9q3T2i+7b77f5Hc+B/ipHZ+A9S1vxbqf+r8QalptuUty8sojvZooIYoolLSvtRQFVWdsZ5OTXb+EfGmkeOdOlvNHuJJUhma3nhubaW2uLeVcExywyqskbYKth1BKsrDhgT8733gXxBD4f8Oazt8Q6TFovjPXL26/sawjnv4oLie6SO5it5oZRKoEinCxsxSRmTOOfWPgzotrFP4j16HVPFesXGqTwpPdeK9MTTpXMUe1THALa3YLhtpZo+SvBIFJap+i/Jf10HU0m0trv83/wPl5HplFFFIAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiivMdU+JXiu6+JWueEvDHhfRtS/sexs7y4vNW12Wy3faDMFVEjs5s48hskkdRxQHS56dRXGeE/iBca3408R+FtU0pNK1XR4bS6UxXXnxXUE6H94hKIQFljmjIIz8itxuwON8QfHzUbe/ltdB8M2uqbvFKeFbS4vtUa1innFu8s8hKwSlVjdDHwGJYN0xy+qXf/ADS/NpB0b/rZv8k2ey0V5R8QPix4s+GPw9fXtX8G6ff6u2o21hbaPoutvP54mkWMESy20WHyxwm0g4A3DPGzq3xg0yP4b6N4z0ZBrOl6rc2EEGJPKO25uI4Nx+UkMhkJKEZypU4PQSctu6Xze35i628rnfUV5R4i+I3xC0Xxtpfh6HwZ4ZuTqxumsriTxRcR5jhCkmVRp7bGKuvyqXAORnufTtNku5dPtnv4Iba+aNTPDbTGaJJMfMquUQsoOcMVUkc4HSktVcfWxZorxm++N3imJfGmp2XgrT7/AMN+E9QmtL2ZdcddQljhRJJZIbc2vlsQjkhGmXcVxkZFX5vi34l1zxxqug+DvC+kazBp+mWOptearrktgZVuvNMaoi2k3IEJyWZfvD3oXvRUls0n8mrr70PZtPpdfc7P8Wer0V5lpvxut9YT4cSWukTLH4wuri0ZbiZVksXht5pXDBQwchoGThgOdwJHBT4Q/G6D4qa34x0mTTP7Hv8Aw/qU1okRuRN9rt0leJbkfKu0NJDKpTnGzqcinbW39f1qTf3VLo7fje35M9OorzWw+NEOqfHi8+HFrpbSxWekvfT6wJ/kW4R4d1qI9vLCO5hctu48wDHerGk/FxNU+LGpeDTpMsNpbxslvrHm5juLmNIpJ4Au35SiXERByd2JBgbDkSbs11Ta9Fp+n5d0N6Xv0t+P9f1ZnoVFeX6p8TPF114/8SeGvC/hPR9WXQ7e1nmuNT16WyeUzq7KqItpKONhGWYdRUng/wCNkHi3xF4dsv7Im0/T/EOjtqWm3lxMpZ5o323Fq6AYV0DKwKuwYbyMBclR97b+t/8AJ/1YTdt/62/z/qzPTKK5jwX4zPjSbXpbe0SPSrDUJNPtb1Zi/wBsaIBZmC7QFCy+ZH1bJjY8V09HZj8gorlPHnj1PB39mWVrYS6z4g1eZrfTNLhdYzM6oXZ3duI4kUbnfBIGAquxVWx7H4ieIdH8RaZpfjXw1ZaLHq8v2bT9R0bVX1G2a42s3kzF7eB4mZVJQhWVsEFlbaGFqD0Vz0Oua0DwLb6L4l1fX59RvtY1XUFWET35i/0a3VmZLeJY0QBAzsckF2yNzNtXGj/wlGjf2RNqv9r2P9lwu8Ut99pTyI3RzG6s+doKuCpBPDAg81xuk/HTwxqvxd1nwAuraSup2FvbyRKNTiaa5mcz+bAsP3t8SwqzAEnEgyF7tb2W4PZ32/r+v+GPRqK563+InhS88Uy+GYPE+jzeJIs+Zo8d/E14mF3HMIbeMLz06c03S/iR4S1zxHdeH9O8U6LqGvWpcXGl2uoQyXUJQ4ffErFl2ng5HB60gOjornLr4keEbHVbTS7jxTotvqV3O1rb2cuoQrNNMrBWjRC2WcMQCoGQSBU/h7x14b8XXeoWuheINK1q605xFew6fexTvbOSwCyKjEocqww2Pun0oWqug2NyisLRfHnhrxLq2oaVpHiLSdV1PTmK3tlZXsU01sQxUiRFYshyCPmA5GKqePvGUvg+x04WWnjVtX1S+i0+xsDN5IlkbLOzPtbaiRrJIx2k4jOASQKN7eYd/I6iiuY1T4oeDtBuobbU/FmhadczXDWkcN1qUMTvOu3dEoZgS43plRyNy8cipvFnxE8KeAzajxN4n0bw6brd5H9rX8Vr523G7Z5jDdjIzjpkUdrdQOhorzrxl8dfCvgfx74c8MarrWj2U2rwzTtLeapFA0CqF8olGPIkJZVORkocZ7bmi+KHt9L8Q6l4h1bQYtP0++uFF3ZXOIba2jxxcu5wkq/Nv5AHFH9fdZfqK/8AX9eh1NFYOm+PfDOsaHBrNh4i0m+0e4nW2h1C2vopLeSZnEaxrIGKli5ChQcliB1rNj+L3gm98O6vren+LtA1HTdJXN5dW2qQPDbk/dWR1YhCTwN1KT5U5PZFL3mkup2FFcZ8IfilpHxg8BaT4k0q6spWuraGS7s7O9S6NjO8au1vIy4w6bsEEKfYVQ+LHxh0P4d+H/EEaa9okXi610i51Gx0W+vIxPcGOGR1/c7xIykxn7vZW54p1L0rqXQKa9o0o9T0KiuN8H+J9V17VoftN1ov2GXRLK++x20j/b455S+9nQkgQkKAh6krJknFavhvx54Z8ZXF/b6B4i0nXJ7Bgl3Fpt9FcNbMSQBIEYlCSrcHH3T6VUouLcXur/g7EKSkrry/FXN2ivK/iP8AHPSPDstlp3h7XNB1XxANe07TL/SxdpNPbRT3ccEjNEjhkYB+CwwDjIPSvUZpkt4XlldY4kUszucKoHJJPYVP2efpe34J/qV15ev/AA/+Q+iub8N/Erwj4xs7660DxVouuWtiN13Ppuow3CW4wTmRkYhRhSecdDV3Q/GGg+J57uHR9b07VprQRm4jsbuOZod67k3hSdu5eRnqORT8hXRr0V5ho/xv0az8ReLtO8Wa7oXh1dP13+ytMF7eJavdKLS1mP8ArH+dw9wR8oHBXjPJ67xZ8RPCngM2o8TeJ9G8Om63eR/a1/Fa+dtxu2eYw3YyM46ZFFtE+9vxV/1H1a7fo7fodDRWJ4m8b+HfBemxaj4h1/S9B0+ZxHHdaneR28TsQSFDuwBJAJwD0BrA+FHj65+IFv4nnnFoYdN166021ks87ZYIwhRySxyxDdRgHjApLVtLor/il+qE2opN9dPwb/RndUVzt98R/CWl+JoPDl54o0W08Q3BUQ6TPqEKXchb7u2ItvOe2BzXRUdLj62CiuW034reCtY/tX7B4w0C+/smNpdR+zanBJ9jRchmmwx8sDByWxjBqXSfiV4R8QaDfa5pfirRdS0Wx3fa9StNRhltrfaoZvMkViq4UgnJGAc0LXVB5HSUVzkvxJ8IwaVqepy+KdFj03S7g2l/eNqMIhtJgQDFK+7CPllG1iDkj1rgbf4xX/ia+8fR+HdU8LtY6QNMbStWvbhjZTLcLucySo5DA8qmzHJAOaBN2PYaK57xZ8RPCngM2o8TeJ9G8Om63eR/a1/Fa+dtxu2eYw3YyM46ZFalvrWn3WozafBf2s1/DDHcSWscytKkTlgjsoOQrFGwTwdpx0NG+o9ty7RXMa18UPBvhqxjvtX8W6FpVlJPJapcXupQwxtNGSskYZmALqQQV6gg5qbxB8RPCnhPSbPVNb8T6Po2mXuPst5qF/FBDPldw2O7ANleeD05o8w62OhorA17x/4Y8K6La6xrXiTSNI0m6Ki3v7++iggmLKWXZIzBWyoJGDyBmrukeJdI8QDOl6rZakPIiuf9DuEl/cygmKT5SflcAlW6EA4zT118hXNKiuFk+IhuviR4W0XTJrG/0TV9Kv783kLeYS0Elui7HVtu398+eDyByMHOxoPxH8J+KtYvNJ0TxRousapZ7vtNjYahDPPBtba29EYsuG4ORweKN0n3v+Dt+g3pudFRWJpPjjw5r2pNp2meINL1HUFh+0m0tL2OWURbynmbFYnbuBXdjGQR1pvijx34a8ELat4i8Q6VoAu3MdudUvYrbzmGMqm9huPI4HrSA3aKqW2r2N5fXdlb3tvPeWez7Tbxyq0kG8ZTeoOV3DkZ6jpWNrHxK8IeHdFttX1XxVommaTcymCC/vNRhiglkG7KLIzBWYbW4Bz8p9KAOkorF1rxt4d8N26z6vr2maXA0DXQlvbyOFTCpUNICzAbAZEBboC6+orjviF8f/CPgXwloniJdc0XUNN1fUbaytrgatDHDLHJcJFNMkmSHWJWZ2xwAhyV6h9Uu7S+92/MOl/601PS6K47wv8AEaDxJ4sv9HSK3Nv9jh1PStSs7oXEGpWcnymVGCgAq4wVBYYeNt3z4HY0Cv0CiiikMKKKKACvmTxPdeArX9pTxx/wm/jD/hFN2iaP9k/4qu40Pz/mvN/+quIvN2/L13bd3GNxz9N0UD6Nd/8ANM8H+NniaH4Zal4T+KulI2s6YtnNolyLNzObqG4USWbK2TvzcRxIGz0uCc9a5X4neENL+Hfws+Duk+KNVGlwx+Kbe41rVRqL2H+ky293JcTfaEdGj3Su5yGHXGa+oqKa0+9P5J3t83dv5dtV/k182rX+SskfOnxA1DwfefDnw4ngzxMninT4vG+hiW5XxBLrLRyG+gOwzSTSsvGDt3YGc45rH+OFrd/C3VLfSoLZ5PBvi3xNpV1aeShK6bqQ1CCSeM+kc6q8oPQSCT++K+o6KqMuVp9pX/8ASdP/ACW/3dtT/K34t3/G339zzbxp/wAlt+Gv/Xvq3/oqGvSaK4bUvg3oGq6lc302oeKkmuJGldbbxfq0EQYnJCxx3Soi+iqAB0AqF5geYeAPhqfHusfFSC/8T63b6BceLLqC80CzNtHa3aeTBuWSTyDcAODtYJKoI47nLdQ8I69r3x4+JcPhbxVd+E7228PaMsEdvb28lrO2b3YsweJpAgxj9y8bAMecgY+iQNoApaSVoRh2SX3K39J3QdZPu2/vlzf8C580aR4g0bWtU+Atto9j/Yx0nW7/AEy90d5DJLp91Fpd0JYZGYlmYH5t7Elwyvk7s1iaDfXHw78NXXxJsbGbUf7H8Wa9YapZ2qlpbiynv5AAigEsyTrAw9FMnrX1lRVSfNLn2dmvvav+TXo9x6cqj0Vvw5rf+lfetj5iVk+CHjDQ9c8RzRyajb+DNe1rWbhTxNdvdWEkgGeo3ERoP7qoBVK68A/EXwL8KdB8V32q6HeXfh6/Pi680210Odb1pJmke+i+0fa2Vj5dxcKP3PO1QAvBH1VRT5mkkum3l7zlt21S+RNr/F1389Evv3/8CPm+TQdd8c/Fr4oXvg3xldaHcPpOjy2qwRW8tne7oZmQSs0ZlCEcboZI2AYkHIFReNNb0nVP2Xodf8Iwnwxq3gxkFpaffm0e9hb7PPbSBuT8jyRsTyyvuB+YGvpWio5VZpdf82167/5W1u1fRvf/AIFn/X336ZHhHw3Y+DvC+l6HpqMljYW6W8W9izEKMbmY8sx6knkkk1Rs7DxgniiSe613Q5vDpZimnw6LNHdhcfKDcG7ZCQcZPkjPoOtdLRVNtvmYkrR5Tyz4pTN4S+IXgzxvdxSSeHtNt7/TtTuI0L/YEuBC6XLgAkRq1vtduiiTc2FViM3xZ410H4xal4X0LwXq9l4o+z6za6pqGoaRcJdWthDbv5v72VCVDyMqokedx3FsbUYj2WiiOjXk7r77/mOXvJrurffp+R8keIPG2h+HfgH468FXupW8fi631zUDJoivuvBHNqrTxzeUPm8oxTRt5mNoB616bdaomk/H7xxY/wBoWmna1rnhjTo9Eiu51ia8mifUC4iBOXKb0LbckBgTXtVFTL3otPqrP7unb8QXuylJddfxv8+x87eFfEWgXnwDTwJ4U1OzsviND4fuIk0KSZE1S11H7O/nPLEcujmRnJlYYYyZ3HeCYLzxF4Q8WeDPh34V8BXNjL4n0fUtMlg0i1wLzRooJEF2biLbvtgIfOifzAuTJs+84B+kKK05rz9o+6fzTbXy12/Ejl9xQ7Jr70lr56Hy14k8M6VN8BvjrO1jCZ7vX9Rnnl24d5IpU8pt3UFdq4x0IyK6X4saLfj4hQ6Z4TRbDVZ/h3rVrYragRYdJbIQKMcAKXO3su44xk19AUVko+7GPZW/8kcL/jf8DRO0nLzv/wCTKX6WPmH4S/2f4m1z4b2tn8Q/CE83heKQQ+HND0GW01O3i+zNDLb3KteytAobYXEkYzJHGOG216144kEPxa+GrzOq28j6jDGGIGbg225Mccny0n9OM16JWH4v8J2/jDTYbWa5ubCe3uYru2vrIoJ7eaNsq6F1ZeRlSCpBVmBGDWs5c7u+/wCuv9fiZRjyxsui0+Wx4B4a8ceAfC83xp0/xLLaxalqfiC8hOnXiDz9Yj+zxKsFsh+a4OWZfLj3ENJ0G8Zq2fiS/wDC1j4b8H+MPFS+A7i18H6czXMdvbT6rrdyVlSWzha5SVZfLKqDEkTyO0ykFc4b3/wT4L/4Q2TxE32z7Z/a+rTap/qtnleYqL5fU7sbPvcZz0rpqyiv3UYP+WKfyi1+bfdaaGl3zSfnJr5yv+S/qx8s/DLxBYeG/Bv7Oesa5qEOlaYNFuLI3upSrDGsr28flRMzEKGYRsAvfbgelVPFlvqH/CPnUYb2z0/RtL+Jd7eapd6jZPeWlvGolEU08KSRlo0nMLFt6hCA54Q19Y0Vbf7x1P6+KMv0/Hy1Vvd5P62kv1PkfxZo+neMtA1zVZ/GPhrx/p2veJvDFnfQ+G9OMOn70v4lfexuZ1lkeN41YbgQscYI5FeraxaiP9pCZLWMJJe+CJllEYwZTHdqIs+u3zHA9N59a9ioqZJSio+b/GHJ+G/4eY+t/T8Jc3/A/E8u/Zl1zTdW+Bfgq1sdQtby50vSLPT7+G3mV3tLmOBBJDKAcpIp6q2CK8a8TeKvCugfA34yeGPFN1ap43vLjXJ59JukDX17veR7OeOIDfLGsH2ciRQVRYjkqI22/W1FVWbrObf2r3+epVJqly2+y0/uv/mfK3iPS9Y1zTfH1hoKzPqtx8NdHjhjtwTLIC15uRACCWZdwABBJI5HWrnh+RvHGoWV14Y+JHg/WdY0bw7fW1hpPhTRXs7mGOSFVjinLXk3kKkiwkRuincmOoOPp2iiq/auXTmv+N/8yIrl5fK34KK/HlPkTxL4u8Aah8LfhD4b0aazm1rSPEmgK2kxxg3mlTLdRpObmNRut3LGRGLhdzPjktXtf7RFhJfeArQvbTXuj2+s6fc6xbQo0hksUuEabKKCXRQAzKByqt16H1CiqlNy1683N8/d09Pd79RKKS5enLy+fXr31Pnjx74k0T4i+LbfWfBGp2OvWum+HNYg1nVdInSe3WF4V8m2klTKlzKN6pnKhXOAG59S+CemW2j/AAe8E2lpGIoE0a0IUdyYVZmPqSSST3JJrtaKiPu81utvwcn/AO3f1cGm2n2v+PL+XKfPuj+HdMuk/aSup7G3nmvdQktbhpYlbzYk0i2KI2RyoMjnB4+Y+tc5Z/EQtpvhfSdd8XJ4It5fBmmXcN9Da202p6/M6SCS2hNzHKsgTCZhSJ5GadcFc4b6loqbaW8or/wGLX43v/Vy+t/OT+9xf4Wt/wAMfJPwI8VaP4Lh+HmueNtSt9F0mXwJZWmi6prUkcFvFMJXNzCJXwqSsgtjsyNyx8ZCHHpX7K8mnTeH/Hcuk2rWemyeMdTeCFoGgwpZDkIwBUHOQCBwRwK9sorXm95y7pr75KX6ESjzW9U/ui4/8E+Qfjt47h1fw/8AEvT77xamhapp9/5Vt4N0iztTd3gQwtHe3PmxyTOpAjkE0XlCNEHzErur6A+OWn6jrHwe8TWmlxXVxdTWe1obLd9olhyPOSMLyXaPeFA5JIHeu+orP7Ch/WyRX23L+tzw7xn8ZvBifDmSbwNqXhnVW037FDIoRbmHRLR7iGNpriBGVolhX5/LYxkeSemw48d8XapZatbfHh9O8Y3njq0k+H0JbWpY7UQzuHvBiGS1hjjlVMld3zFW3KWypA+06KtNXu9d/wAU1+t+9/lZx91WXl+DT/S39a+KfECTR/Afjn4X6hrbW+l+CNJtLyBLu6CJZ2N6Y4Utnkc/LFmMXCK5wMvtzlgD5bqeraHr0/x61Lw95cunXeoeHZRdwJiG6YtFumjbo6t2deGwSCc5P17RSvu3/V2nr935djPl0il0/wCCv6+fc8L03xX4a+HPxU+JUnj3VdP0S71q5tn0y41iZYkvdOS0iQQwF8CTZMbjdGuSDKCR86lvP7NNd+CfhHwZ43TSrhriW21HQE0aRPKk8ie4ln0eJwwBjKERw7WGV+0YIBGK+tKKOiXay+Vrfe03r3bZfV/1r/le2nkl0PnbWLyD4V33hDwXqHjCz8B2cehyTv4kkhtze6ldmVfPhgkuVeIM0hErp5bs+9doXbmuA8EeINL8I/Dnwf4js/Hej+F/FWn2Oq2tppXjOFIk1Gza+lIhESmJoZQ8EY/cg7cBTEw2AfZFFK7t56/jf9HbtbpqTyrbpp+Fv8r979T5/wBM8X6R4b+JWl+LPHUFt4F06+8JWMGjDWZFgt9OnLyveWfmOFSObH2b5flLrFwCIyFvab8QfCvg34wa9d6lrFjpOmeItD0qfQ5ppFjj1BUa4Vo7cf8ALRx5kX7tAWIlTAOa9yoqpPm0Wm/43f5v+rhbq99PwSX5I+WPhLqU0afCXULWzluLhfBuuzxWbqUd2+0WjLGR1BJwPas7wp4ytPFfxF+B95b+NU12+muLj7Voej2VtDp2iBtLnP2XCIZoXBVR5U0zFjE52jYAv1xRTjLl/rzk/wBfw0sVL3lb+tkvw/XW55T+zDpltpvwd0028YRri8vriZu7ubuYZJ78AAegUDoBXAeOtRn8IfHfxXfax4x8JeD7XWNItLbT7jxlpT3EFzapvE0EMxu7dARJIWeL5mIeNunT6VorLl2T/rSwLRW/re/9eZ8rahp+p/A/T/CF/wCH79tem8TeHo/CdvefZntxJfAl9NmMbszKqpLcAhiSFVcmrGvaQ3wa+Kmlovinw34L8OWvhS10fR9R8W6dJdWpaOWQ3MSS/aoFjlYeQzBizSBcj/VtX1DRWl9b+b/G/wD8k/k7W0F0t6fhb/5Ffn1Pmv4e+DdO034gfCGBtRs/FEFlouvX2m6jb2RtrdFkubYx/Z42d9saxSmNDuYFMEHBFY+rvHpvhv4ksxS10nSPilpl7KxwkVrAJtNnnlY9FUM8kjE8DLE19WUVUZ8slNbr/wCTU/0sN6x5X/WjX6nmd5qlj4g+MfgO+0i6h1C3k0HU7j7ZaSCSKS2eSz2EOpIYM20gjrtOK9MrC0vwjbab4m1jXjc3N5qGpLFFuuWUrbQxg7YYgqjCbmdznLFnOTgKBu1HRL+t2/8Ah/O4a/1/X9IKKKKQBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB//2Q==)"
      ],
      "metadata": {
        "id": "kzxhopWmAlND"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Preprocessing**"
      ],
      "metadata": {
        "id": "wp4reJZaI-86"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Cleaning"
      ],
      "metadata": {
        "id": "CUIAw3adBU8q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2mdBriSQBmPt",
        "outputId": "672b83d7-819d-4246-d152-04bf66a4e1d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "INTENSIVE               0\n",
              "NEWSITEID             465\n",
              "RISK10YRS               0\n",
              "INCLUSIONFRS            0\n",
              "SBP                     0\n",
              "DBP                     0\n",
              "N_AGENTS                0\n",
              "NOAGENTS                0\n",
              "SMOKE_3CAT              0\n",
              "ASPIRIN                 0\n",
              "EGFR                    0\n",
              "SCREAT                  0\n",
              "SUB_CKD                 0\n",
              "RACE_BLACK              0\n",
              "AGE                     0\n",
              "FEMALE                  0\n",
              "SUB_CVD                 0\n",
              "SUB_CLINICALCVD         0\n",
              "SUB_SUBCLINICALCVD      0\n",
              "SUB_SENIOR              0\n",
              "RACE4                   0\n",
              "CHR                     0\n",
              "GLUR                    0\n",
              "HDL                     0\n",
              "TRR                     0\n",
              "UMALCR                  0\n",
              "BMI                     0\n",
              "STATIN                  0\n",
              "SBPTERTILE              0\n",
              "EVENT_PRIMARY           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that there are 465 missing values at \"*NEWSITEID - Site ID in which the participant was treated*\", since replacing nulls with mean value of ID's would be meaningless, we decided to set them to 0 and not to drop these rows so that the models can learn it as a category"
      ],
      "metadata": {
        "id": "zOSWxEWuULPP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"NEWSITEID\"].fillna(0, inplace = True)"
      ],
      "metadata": {
        "id": "4YQX6xAJV6PV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Columns type check:**"
      ],
      "metadata": {
        "id": "D3KMA1UeXumJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E4zwk4dbRatD",
        "outputId": "12e485f8-7184-4da9-e9cd-4751d6157cb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "INTENSIVE              object\n",
              "NEWSITEID             float64\n",
              "RISK10YRS             float64\n",
              "INCLUSIONFRS             bool\n",
              "SBP                     int64\n",
              "DBP                     int64\n",
              "N_AGENTS                int64\n",
              "NOAGENTS                 bool\n",
              "SMOKE_3CAT              int64\n",
              "ASPIRIN                  bool\n",
              "EGFR                  float64\n",
              "SCREAT                float64\n",
              "SUB_CKD                  bool\n",
              "RACE_BLACK               bool\n",
              "AGE                     int64\n",
              "FEMALE                   bool\n",
              "SUB_CVD                  bool\n",
              "SUB_CLINICALCVD          bool\n",
              "SUB_SUBCLINICALCVD       bool\n",
              "SUB_SENIOR               bool\n",
              "RACE4                  object\n",
              "CHR                     int64\n",
              "GLUR                    int64\n",
              "HDL                     int64\n",
              "TRR                     int64\n",
              "UMALCR                float64\n",
              "BMI                   float64\n",
              "STATIN                   bool\n",
              "SBPTERTILE              int64\n",
              "EVENT_PRIMARY            bool\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that *'INTENSIVE'* and *'RACE4'* columns type is object, and column 'NEWSITEID' is currently numerical, thus we will change them to categorial"
      ],
      "metadata": {
        "id": "IAQxo1MBWnSY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['RACE4'] = df['RACE4'].astype(\"category\")\n",
        "df['INTENSIVE'] = df['INTENSIVE'].astype(\"category\")\n",
        "df['NEWSITEID'] = df['NEWSITEID'].astype(\"category\")"
      ],
      "metadata": {
        "id": "7idzh1nnR-Cg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Splitting the data"
      ],
      "metadata": {
        "id": "HbYd6V6XCN9k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Target label counts:"
      ],
      "metadata": {
        "id": "h_UPimYOop0T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['EVENT_PRIMARY'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vKxf9ceWM4zo",
        "outputId": "cf5bcf11-1e3a-42b7-ed5d-45119036ec88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    8207\n",
              "True      539\n",
              "Name: EVENT_PRIMARY, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see there are alot less *True* records in the data set, therefore we want to keep this ratio for train/test sets using stratify:"
      ],
      "metadata": {
        "id": "wqnq4vOjoy8a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:, :-1]\n",
        "y = df['EVENT_PRIMARY']"
      ],
      "metadata": {
        "id": "LJhZSTvKRo2f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, shuffle=True, stratify=y)"
      ],
      "metadata": {
        "id": "2V5b5V-PiKi7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Scaling the data"
      ],
      "metadata": {
        "id": "p6y_AfXIb9pc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will fit scalers on train set only, then transforming test set accordingly, this way we will not cause data leakage between train and test sets.\n",
        "\n",
        "Numerical features- preprocessed by fitting a SimpleImputer so production model will be able to deal with missing numeric values, next we fit StandardScaler.\n",
        "Categorial features- 1-C dummie variables using OneHotEncoder."
      ],
      "metadata": {
        "id": "gRR7Tmm-cRBj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# numeric scaler\n",
        "numeric_features = df.select_dtypes(exclude=['category', 'bool']).columns.tolist()\n",
        "numeric_transformer = Pipeline(\n",
        "    steps=[(\"imputer\", SimpleImputer(strategy=\"median\")), (\"scaler\", StandardScaler())])\n",
        "\n",
        "# categorial scaler\n",
        "categorical_features = df.select_dtypes('category').columns.tolist()\n",
        "categorical_transformer = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
        "\n",
        "\n",
        "# append scalers\n",
        "preprocessor = ColumnTransformer(\n",
        "                transformers=[\n",
        "                  (\"numerical\", numeric_transformer, numeric_features),\n",
        "                  (\"category\", categorical_transformer, categorical_features)])\n",
        "\n",
        "# create column scaler pipeline\n",
        "scaler_pipeline = Pipeline(\n",
        "    steps=[(\"preprocessor\", preprocessor)]\n",
        ")\n",
        "\n",
        "\n",
        "scaler_pipeline.fit(X_train)\n",
        "set_config(display=\"diagram\")\n",
        "scaler_pipeline"
      ],
      "metadata": {
        "id": "n_rWxhqHXwrU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "8386a274-be8b-4f33-c9b6-c161e0d7e9f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<style>#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c {color: black;background-color: white;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c pre{padding: 0;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-toggleable {background-color: white;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-estimator:hover {background-color: #d4ebff;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-item {z-index: 1;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-parallel-item:only-child::after {width: 0;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-be3f4481-e0ca-484d-8edb-15befd6b3d1c\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;numerical&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                  (&#x27;scaler&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  [&#x27;RISK10YRS&#x27;, &#x27;SBP&#x27;, &#x27;DBP&#x27;,\n",
              "                                                   &#x27;N_AGENTS&#x27;, &#x27;SMOKE_3CAT&#x27;,\n",
              "                                                   &#x27;EGFR&#x27;, &#x27;SCREAT&#x27;, &#x27;AGE&#x27;,\n",
              "                                                   &#x27;CHR&#x27;, &#x27;GLUR&#x27;, &#x27;HDL&#x27;, &#x27;TRR&#x27;,\n",
              "                                                   &#x27;UMALCR&#x27;, &#x27;BMI&#x27;,\n",
              "                                                   &#x27;SBPTERTILE&#x27;]),\n",
              "                                                 (&#x27;category&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
              "                                                                sparse=False),\n",
              "                                                  [&#x27;INTENSIVE&#x27;, &#x27;NEWSITEID&#x27;,\n",
              "                                                   &#x27;RACE4&#x27;])]))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"03838e53-eeea-45ca-acad-95a6c23935ed\" type=\"checkbox\" ><label for=\"03838e53-eeea-45ca-acad-95a6c23935ed\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;numerical&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                  (&#x27;scaler&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  [&#x27;RISK10YRS&#x27;, &#x27;SBP&#x27;, &#x27;DBP&#x27;,\n",
              "                                                   &#x27;N_AGENTS&#x27;, &#x27;SMOKE_3CAT&#x27;,\n",
              "                                                   &#x27;EGFR&#x27;, &#x27;SCREAT&#x27;, &#x27;AGE&#x27;,\n",
              "                                                   &#x27;CHR&#x27;, &#x27;GLUR&#x27;, &#x27;HDL&#x27;, &#x27;TRR&#x27;,\n",
              "                                                   &#x27;UMALCR&#x27;, &#x27;BMI&#x27;,\n",
              "                                                   &#x27;SBPTERTILE&#x27;]),\n",
              "                                                 (&#x27;category&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
              "                                                                sparse=False),\n",
              "                                                  [&#x27;INTENSIVE&#x27;, &#x27;NEWSITEID&#x27;,\n",
              "                                                   &#x27;RACE4&#x27;])]))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"523eba90-1fa3-4ed1-8b97-f305cf631c21\" type=\"checkbox\" ><label for=\"523eba90-1fa3-4ed1-8b97-f305cf631c21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;numerical&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
              "                                 [&#x27;RISK10YRS&#x27;, &#x27;SBP&#x27;, &#x27;DBP&#x27;, &#x27;N_AGENTS&#x27;,\n",
              "                                  &#x27;SMOKE_3CAT&#x27;, &#x27;EGFR&#x27;, &#x27;SCREAT&#x27;, &#x27;AGE&#x27;, &#x27;CHR&#x27;,\n",
              "                                  &#x27;GLUR&#x27;, &#x27;HDL&#x27;, &#x27;TRR&#x27;, &#x27;UMALCR&#x27;, &#x27;BMI&#x27;,\n",
              "                                  &#x27;SBPTERTILE&#x27;]),\n",
              "                                (&#x27;category&#x27;,\n",
              "                                 OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
              "                                               sparse=False),\n",
              "                                 [&#x27;INTENSIVE&#x27;, &#x27;NEWSITEID&#x27;, &#x27;RACE4&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"4789ffe1-8582-424f-b7c9-140e5a5b44b0\" type=\"checkbox\" ><label for=\"4789ffe1-8582-424f-b7c9-140e5a5b44b0\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numerical</label><div class=\"sk-toggleable__content\"><pre>[&#x27;RISK10YRS&#x27;, &#x27;SBP&#x27;, &#x27;DBP&#x27;, &#x27;N_AGENTS&#x27;, &#x27;SMOKE_3CAT&#x27;, &#x27;EGFR&#x27;, &#x27;SCREAT&#x27;, &#x27;AGE&#x27;, &#x27;CHR&#x27;, &#x27;GLUR&#x27;, &#x27;HDL&#x27;, &#x27;TRR&#x27;, &#x27;UMALCR&#x27;, &#x27;BMI&#x27;, &#x27;SBPTERTILE&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"dba0da3c-4f02-4c6e-88b5-560e064b7434\" type=\"checkbox\" ><label for=\"dba0da3c-4f02-4c6e-88b5-560e064b7434\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"82012e5c-78fd-434f-a42d-8b1e44554270\" type=\"checkbox\" ><label for=\"82012e5c-78fd-434f-a42d-8b1e44554270\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"9070ff6f-722d-481c-a83c-a2218a7b0e76\" type=\"checkbox\" ><label for=\"9070ff6f-722d-481c-a83c-a2218a7b0e76\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">category</label><div class=\"sk-toggleable__content\"><pre>[&#x27;INTENSIVE&#x27;, &#x27;NEWSITEID&#x27;, &#x27;RACE4&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d84e1074-3b6a-40e7-8661-0d352ec16985\" type=\"checkbox\" ><label for=\"d84e1074-3b6a-40e7-8661-0d352ec16985\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "Pipeline(steps=[('preprocessor',\n",
              "                 ColumnTransformer(transformers=[('numerical',\n",
              "                                                  Pipeline(steps=[('imputer',\n",
              "                                                                   SimpleImputer(strategy='median')),\n",
              "                                                                  ('scaler',\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  ['RISK10YRS', 'SBP', 'DBP',\n",
              "                                                   'N_AGENTS', 'SMOKE_3CAT',\n",
              "                                                   'EGFR', 'SCREAT', 'AGE',\n",
              "                                                   'CHR', 'GLUR', 'HDL', 'TRR',\n",
              "                                                   'UMALCR', 'BMI',\n",
              "                                                   'SBPTERTILE']),\n",
              "                                                 ('category',\n",
              "                                                  OneHotEncoder(handle_unknown='ignore',\n",
              "                                                                sparse=False),\n",
              "                                                  ['INTENSIVE', 'NEWSITEID',\n",
              "                                                   'RACE4'])]))])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = scaler_pipeline.transform(X_train)\n",
        "X_test = scaler_pipeline.transform(X_test)"
      ],
      "metadata": {
        "id": "GiJ2QPdctL4h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Training Models**"
      ],
      "metadata": {
        "id": "MMmydK6bDv2B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 1"
      ],
      "metadata": {
        "id": "C4GHGw2IDMbu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Linear Classifier - Logistic Regression**"
      ],
      "metadata": {
        "id": "Ml1xSJt1IFGC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression()\n",
        "\n",
        "# Choose some parameter combinations to try\n",
        "parameters = {'class_weight': ['balanced'],\n",
        "              'max_iter':[20, 50, 100, 500] ,\n",
        "              'penalty': ['l2'],\n",
        "              'solver': ['lbfgs', 'liblinear'],\n",
        "              'C':[0.001,0.01]\n",
        "              }\n",
        "\n",
        "# Define scorer\n",
        "scorer = make_scorer(roc_auc_score)\n",
        "\n",
        "# Run the grid search\n",
        "grid_obj = GridSearchCV(estimator = lr, \n",
        "                        param_grid = parameters,\n",
        "                        scoring = scorer,\n",
        "                        cv = 5,\n",
        "                        n_jobs = -1)\n",
        "\n",
        "grid_obj = grid_obj.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "z4qoH71neF5d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_cv_scores = pd.DataFrame(grid_obj.cv_results_)\n",
        "lr_cv_auc_score = max(lr_cv_scores['mean_test_score'])\n",
        "\n",
        "clf = grid_obj.best_estimator_\n",
        "clf.fit(X_train, y_train)\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "lr_auc_test_score = roc_auc_score(y_test, y_pred)\n",
        "lr_recall_test_score = recall_score(y_test, y_pred)\n",
        "lr_precision_test_score = precision_score(y_test, y_pred)\n",
        "lr_accuracy_test_score = accuracy_score(y_test, y_pred)\n",
        "\n",
        "print('-'*20)\n",
        "print('Model 1 results: \\n')\n",
        "print('CV-5 AUC: %.5f' % lr_cv_auc_score)\n",
        "print('Test AUC: %.5f' % lr_auc_test_score)\n",
        "print('-'*20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JwHhzRGx8Vf2",
        "outputId": "d29fadb6-54fa-4897-e629-d412b26de4be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------\n",
            "Model 1 results: \n",
            "\n",
            "CV-5 AUC: 0.62908\n",
            "Test AUC: 0.61091\n",
            "--------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 2"
      ],
      "metadata": {
        "id": "2ZOSWPhGDZwS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Ensemble Model - Random Forest**"
      ],
      "metadata": {
        "id": "9jidPAbYIOIM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier()\n",
        "\n",
        "# Choose some parameter combinations to try\n",
        "parameters = {'n_estimators': [3, 4, 5],\n",
        "              'max_features': ['log2', 'auto'], \n",
        "              'criterion': ['entropy'],\n",
        "              'max_depth': [2, 3, 5, 10], \n",
        "              'min_samples_split': [2, 3, 5],\n",
        "              'min_samples_leaf': [1,5,8],\n",
        "              'class_weight': ['balanced'],\n",
        "              'n_jobs': [-1]\n",
        "             }\n",
        "# Define scorer\n",
        "scorer = make_scorer(roc_auc_score)\n",
        "\n",
        "# Run the grid search\n",
        "grid_obj = GridSearchCV(estimator = rf, \n",
        "                        param_grid = parameters,\n",
        "                        scoring = scorer,\n",
        "                        cv = 5,\n",
        "                        n_jobs = -1)\n",
        "\n",
        "grid_obj = grid_obj.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "D0BL0lDix7hh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_cv_scores = pd.DataFrame(grid_obj.cv_results_)\n",
        "rf_cv_auc_score = max(rf_cv_scores['mean_test_score'])\n",
        "\n",
        "clf2 = grid_obj.best_estimator_\n",
        "clf2.fit(X_train, y_train)\n",
        "y_pred2 = clf2.predict(X_test)\n",
        "\n",
        "rf_auc_test_score = roc_auc_score(y_test, y_pred2)\n",
        "rf_recall_test_score = recall_score(y_test, y_pred2)\n",
        "rf_precision_test_score = precision_score(y_test, y_pred2)\n",
        "rf_accuracy_test_score = accuracy_score(y_test, y_pred2)\n",
        "\n",
        "print('-'*20)\n",
        "print('Model 2 results: \\n')\n",
        "print('CV-5 AUC: %.5f' % rf_cv_auc_score)\n",
        "print('Test AUC: %.5f' % rf_auc_test_score)\n",
        "print('-'*20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p9KaJqJgBDKb",
        "outputId": "fcc8dd1f-55e6-4d11-d291-71cc2726f940"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------\n",
            "Model 2 results: \n",
            "\n",
            "CV-5 AUC: 0.61244\n",
            "Test AUC: 0.58156\n",
            "--------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 3"
      ],
      "metadata": {
        "id": "TeNAqwe7DetO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Deep Learning Model - Neural Network**"
      ],
      "metadata": {
        "id": "G1q3tw3_IWU5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# preprocessing nn model output(size 2)\n",
        "y_train_dl = to_categorical(y_train)\n",
        "y_test_dl = to_categorical(y_test)"
      ],
      "metadata": {
        "id": "RWcAhwk5BYN3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.backend.clear_session()\n",
        "\n",
        "# define stopping criterion\n",
        "stopping = EarlyStopping(monitor='val_auc', patience=5)\n",
        "\n",
        "# define the keras model\n",
        "model = Sequential()\n",
        "model.add(Dense(12, input_dim=104, activation='relu'))\n",
        "model.add(Dense(8, activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(8, activation='relu'))\n",
        "model.add(Dense(2, activation='softmax'))\n",
        "\n",
        "# compile the keras model\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=[Recall(), Precision(), AUC(), 'accuracy'])\n",
        "\n",
        "# fit the keras model on the dataset\n",
        "history = model.fit(x=X_train, y=y_train_dl, batch_size=512, epochs=30, validation_split=0.2, callbacks=stopping, workers=-1)"
      ],
      "metadata": {
        "id": "caM-nYIzDgm2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1797a2e2-0843-4d0e-d604-a5534ef77fd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "11/11 [==============================] - 2s 61ms/step - loss: 0.6862 - recall: 0.5436 - precision: 0.5437 - auc: 0.5854 - accuracy: 0.5438 - val_loss: 0.6622 - val_recall: 0.7686 - val_precision: 0.7686 - val_auc: 0.8405 - val_accuracy: 0.7686\n",
            "Epoch 2/30\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.6497 - recall: 0.7802 - precision: 0.7802 - auc: 0.8509 - accuracy: 0.7802 - val_loss: 0.6229 - val_recall: 0.9121 - val_precision: 0.9121 - val_auc: 0.9200 - val_accuracy: 0.9121\n",
            "Epoch 3/30\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.6113 - recall: 0.8956 - precision: 0.8956 - auc: 0.9230 - accuracy: 0.8956 - val_loss: 0.5805 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9228 - val_accuracy: 0.9336\n",
            "Epoch 4/30\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.5687 - recall: 0.9274 - precision: 0.9274 - auc: 0.9298 - accuracy: 0.9274 - val_loss: 0.5324 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9238 - val_accuracy: 0.9336\n",
            "Epoch 5/30\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.5195 - recall: 0.9380 - precision: 0.9380 - auc: 0.9318 - accuracy: 0.9380 - val_loss: 0.4800 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9249 - val_accuracy: 0.9336\n",
            "Epoch 6/30\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.4669 - recall: 0.9391 - precision: 0.9391 - auc: 0.9330 - accuracy: 0.9391 - val_loss: 0.4262 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9261 - val_accuracy: 0.9336\n",
            "Epoch 7/30\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.4147 - recall: 0.9392 - precision: 0.9392 - auc: 0.9352 - accuracy: 0.9392 - val_loss: 0.3757 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9270 - val_accuracy: 0.9336\n",
            "Epoch 8/30\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.3633 - recall: 0.9396 - precision: 0.9396 - auc: 0.9362 - accuracy: 0.9396 - val_loss: 0.3316 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9280 - val_accuracy: 0.9336\n",
            "Epoch 9/30\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3280 - recall: 0.9394 - precision: 0.9394 - auc: 0.9353 - accuracy: 0.9394 - val_loss: 0.2976 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9304 - val_accuracy: 0.9336\n",
            "Epoch 10/30\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.2966 - recall: 0.9396 - precision: 0.9396 - auc: 0.9371 - accuracy: 0.9396 - val_loss: 0.2741 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9324 - val_accuracy: 0.9336\n",
            "Epoch 11/30\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2757 - recall: 0.9396 - precision: 0.9396 - auc: 0.9383 - accuracy: 0.9396 - val_loss: 0.2600 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9351 - val_accuracy: 0.9336\n",
            "Epoch 12/30\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.2624 - recall: 0.9396 - precision: 0.9396 - auc: 0.9404 - accuracy: 0.9396 - val_loss: 0.2526 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9388 - val_accuracy: 0.9336\n",
            "Epoch 13/30\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2527 - recall: 0.9396 - precision: 0.9396 - auc: 0.9437 - accuracy: 0.9396 - val_loss: 0.2489 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9427 - val_accuracy: 0.9336\n",
            "Epoch 14/30\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.2480 - recall: 0.9396 - precision: 0.9396 - auc: 0.9450 - accuracy: 0.9396 - val_loss: 0.2470 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9438 - val_accuracy: 0.9336\n",
            "Epoch 15/30\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.2441 - recall: 0.9396 - precision: 0.9396 - auc: 0.9469 - accuracy: 0.9396 - val_loss: 0.2457 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9448 - val_accuracy: 0.9336\n",
            "Epoch 16/30\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.2418 - recall: 0.9396 - precision: 0.9396 - auc: 0.9470 - accuracy: 0.9396 - val_loss: 0.2447 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9461 - val_accuracy: 0.9336\n",
            "Epoch 17/30\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2376 - recall: 0.9396 - precision: 0.9396 - auc: 0.9474 - accuracy: 0.9396 - val_loss: 0.2439 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9457 - val_accuracy: 0.9336\n",
            "Epoch 18/30\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2394 - recall: 0.9396 - precision: 0.9396 - auc: 0.9477 - accuracy: 0.9396 - val_loss: 0.2433 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9461 - val_accuracy: 0.9336\n",
            "Epoch 19/30\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.2378 - recall: 0.9396 - precision: 0.9396 - auc: 0.9491 - accuracy: 0.9396 - val_loss: 0.2426 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9459 - val_accuracy: 0.9336\n",
            "Epoch 20/30\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.2355 - recall: 0.9396 - precision: 0.9396 - auc: 0.9507 - accuracy: 0.9396 - val_loss: 0.2419 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9454 - val_accuracy: 0.9336\n",
            "Epoch 21/30\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.2312 - recall: 0.9396 - precision: 0.9396 - auc: 0.9512 - accuracy: 0.9396 - val_loss: 0.2415 - val_recall: 0.9336 - val_precision: 0.9336 - val_auc: 0.9453 - val_accuracy: 0.9336\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot NN model progression and finetune parameters accordingly:"
      ],
      "metadata": {
        "id": "GJSh-5VY5cBa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize history\n",
        "# Plot history: Loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Loss history')\n",
        "plt.ylabel('Loss value')\n",
        "plt.xlabel('No. epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='right')\n",
        "plt.show()\n",
        "\n",
        "# Plot history: AUC\n",
        "plt.plot(history.history['auc'])\n",
        "plt.plot(history.history['val_auc'])\n",
        "plt.title('AUC history')\n",
        "plt.ylabel('AUC value (%)')\n",
        "plt.xlabel('No. epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_ZEFEkFM1PY",
        "outputId": "d0b03583-01ab-4973-f89b-f96cee9ad14e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nn_cv_auc_score = max(history.history['val_auc'])\n",
        "loss, nn_recall_test_score, nn_precision_test_score, nn_auc_test_score, nn_accuracy_test_score = model.evaluate(X_test, y_test_dl, verbose=False)\n",
        "\n",
        "print('-'*20)\n",
        "print('Model 3 results: \\n')\n",
        "print('CV-5 AUC: %.5f' % nn_cv_auc_score)\n",
        "print('Test AUC: %.5f' % nn_auc_test_score)\n",
        "print('-'*20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sbdmPyyPJsr_",
        "outputId": "5a13863a-4a07-4139-cc6c-4a13d5571f8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------\n",
            "Model 3 results: \n",
            "\n",
            "CV-5 AUC: 0.94613\n",
            "Test AUC: 0.94778\n",
            "--------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Models Comparison**"
      ],
      "metadata": {
        "id": "meSFr3m5E97k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The metric we used to measure the models preformence is AUC, because it composed of Recall(sensitivity) and FNR(1-specificity).\n",
        "The reason we care about these metrics is because for this specific task(clinical trail - heart failure) missing a patient with a heart failure can lead to serious health issuses. Moreover, predicting an healthy patient as sick might make him go through unnecessary tests, prolong the queues in the hospital, etc."
      ],
      "metadata": {
        "id": "FK-Zv1pzhLHt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr_scores = [lr_accuracy_test_score, lr_precision_test_score, lr_recall_test_score, lr_auc_test_score]\n",
        "rf_scores = [rf_accuracy_test_score, rf_precision_test_score, rf_recall_test_score, rf_auc_test_score]\n",
        "nn_scores = [nn_accuracy_test_score, nn_precision_test_score, nn_recall_test_score, nn_auc_test_score]\n",
        "index = ['Accuracy', 'Precision', 'Recall', 'AUC']\n",
        "metrics_comparasion = pd.DataFrame({'LR': lr_scores, 'RF': rf_scores, 'NN': nn_scores}, index=index)\n",
        "ax = metrics_comparasion.plot.barh(figsize=(10, 10), title='Metrics Comparison')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ear8zYpwpGWC",
        "outputId": "584d9a9f-297d-4c5c-8935-97d51e7d73f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model3(NN) presents the best preformence overall and we can see that his AUC is the highest."
      ],
      "metadata": {
        "id": "_8F86nUj6aWv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can check for overfitting with AUC score as we saw in class. AUC of train should be similar to test so we can decide there is no overfit.\n",
        "* Note that the models were overfitting at the first attempts but we fixed them. "
      ],
      "metadata": {
        "id": "lhQodA-GmLmT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train = [lr_cv_auc_score, rf_cv_auc_score, nn_cv_auc_score]\n",
        "test = [lr_auc_test_score, rf_auc_test_score, nn_auc_test_score]\n",
        "index = ['Logistic Regression', 'Random Forest', 'Neural Network']\n",
        "auc_overfit_check = pd.DataFrame({'Train AUC': train, 'Test AUC': test}, index=index)\n",
        "ax = auc_overfit_check.plot.barh(figsize=(10, 7), title='Overfit Check')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vgRymAysOY7",
        "outputId": "bdf83495-692e-4ca6-b721-7563dfe702e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that all 3 models are not overfitted now and model3(NN) has highest AUC for training(cross-validation) as well as for testing."
      ],
      "metadata": {
        "id": "scTZOXZp9Dkg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Answer the next questions**"
      ],
      "metadata": {
        "id": "GyrvHqF5F1TV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Questions:"
      ],
      "metadata": {
        "id": "UOTH4-wKg41-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. For each model, does the model overfit?\n",
        "2. In the DL model, what's the effect of batch size in general, and do you see this effect in the current task?"
      ],
      "metadata": {
        "id": "flzF6PWeE4JJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answers:"
      ],
      "metadata": {
        "id": "jwNG8UfQg1RH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1.** \n",
        "* Logistic regression - no over fit, grid search helped us to find the right parameters.\n",
        "* Random forest - no over fit, this model tends to overfit, it took alot of tuning, we noticed that when n_estimators is high, the model is fitting too strict to the training set, thus it had good results on training set but couldn't generalize well and did poor on testing.\n",
        "* Neural Network - no over fit, at first attemps it did overfit, we added validation split and monitored the AUC score with earlystopping function, we then plotted the model progress to check for overfitting and for fine-tuning."
      ],
      "metadata": {
        "id": "E5DR9AZ3GT0y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2.** \n",
        "* The effect of the batch size has is that small batch results generally in rapid learning but a volatile learning process with higher variance in the classification accuracy. Larger batch sizes slow down the learning process but the final stages result in a convergence to a more stable model exemplified by lower variance in classification accuracy. (https://machinelearningmastery.com/how-to-control-the-speed-and-stability-of-training-neural-networks-with-gradient-descent-batch-size/)\n",
        "* Yes we saw this effect in this task when we were fine-tuning the model parameters."
      ],
      "metadata": {
        "id": "JRC1Tq2xGcne"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Additional details**"
      ],
      "metadata": {
        "id": "U9dsotPUGvKA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Notice: You need to well explain why you choose the specific algorithms, configurations, or other decisions you made. In the end, briefly summarize the method and details.*"
      ],
      "metadata": {
        "id": "97oo3vd4G9bB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* In order to avoid data leakage from the test set, we made a pipeline that scales each column as we wish. For numerical columns we used SimpleImputer to handle missing data and on top of it we used StandardScaler, for categorial columns we used OneHotEncoder(dummie-coding). The pipeline is first fitted to the training set, then the pipeline transforms train and test sets.\n",
        "\n",
        "* Model 1 - We chose logistic regression model,\n",
        "because it can make binary classification, we also tried SVM(for classification) but noticed that logistic regression preforms better, we used grid search to find the best parameters for our scaled data.\n",
        "* Model 2 - We chose Random Forest model,\n",
        "because it can make binary classification, we also tried Adaboost(with classification trees) but noticed that Random forest preforms better, we used grid search to find the best parameters for our scaled data, we also noticed that for high n_estimators this model tend to overfit, thus we kept n_estimators low.\n",
        "* Model 3 - We chose Neural Network model,\n",
        "we adjusted the output layer so the model can make binary classification, we also tried MLP Classifier(from scikit-learn) but noticed that keras is more adjustable thus preforms better, we used earlystopping and plots to find the best parameters for our scaled data, we also noticed the effect batch size has, added dropout layer to help the model generalize and avoid overfitting."
      ],
      "metadata": {
        "id": "cfjD597sHMHz"
      }
    }
  ]
}