proyecto_2_IA_WineQuality.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "791e2f38"
      },
      "source": [
        "# Introducción al Conjunto de Datos y al Proyecto"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H890Nu37s_Y4"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from   pandas import DataFrame"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d141d493"
      },
      "source": [
        "## Carga de Datos y Exploración Inicial"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9WPKB2stGwI",
        "outputId": "9fe2aa9a-5e18-46ca-de6f-db7abad11bb4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cc35Xf5HtK2Z",
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          "height": 224
        },
        "outputId": "c9d6622d-5d45-46dc-9b13-ee2ffd7a2638"
      },
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.4              0.70         0.00             1.9      0.076   \n",
              "1            7.8              0.88         0.00             2.6      0.098   \n",
              "2            7.8              0.76         0.04             2.3      0.092   \n",
              "3           11.2              0.28         0.56             1.9      0.075   \n",
              "4            7.4              0.70         0.00             1.9      0.076   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
              "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
              "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
              "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      9.4        5  \n",
              "1      9.8        5  \n",
              "2      9.8        5  \n",
              "3      9.8        6  \n",
              "4      9.4        5  "
            ],
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              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
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              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
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              "      <td>0.9978</td>\n",
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              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "vino_red = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/winequality-red.csv\",delimiter=';')\n",
        "vino_red.head(5)"
      ]
    },
    {
      "cell_type": "code",
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        "id": "9-H3Gjxs_Ki0",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0            7.0              0.27         0.36            20.7      0.045   \n",
              "1            6.3              0.30         0.34             1.6      0.049   \n",
              "2            8.1              0.28         0.40             6.9      0.050   \n",
              "3            7.2              0.23         0.32             8.5      0.058   \n",
              "4            7.2              0.23         0.32             8.5      0.058   \n",
              "\n",
              "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
              "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
              "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
              "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
              "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
              "\n",
              "   alcohol  quality  \n",
              "0      8.8        6  \n",
              "1      9.5        6  \n",
              "2     10.1        6  \n",
              "3      9.9        6  \n",
              "4      9.9        6  "
            ],
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              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
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              "      <th>density</th>\n",
              "      <th>pH</th>\n",
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              "      <th>quality</th>\n",
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              "      <th>0</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.36</td>\n",
              "      <td>20.7</td>\n",
              "      <td>0.045</td>\n",
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              "      <td>8.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.3</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.049</td>\n",
              "      <td>14.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>0.9940</td>\n",
              "      <td>3.30</td>\n",
              "      <td>0.49</td>\n",
              "      <td>9.5</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.1</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.40</td>\n",
              "      <td>6.9</td>\n",
              "      <td>0.050</td>\n",
              "      <td>30.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>0.9951</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.44</td>\n",
              "      <td>10.1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.32</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.058</td>\n",
              "      <td>47.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>0.9956</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.32</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.058</td>\n",
              "      <td>47.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>0.9956</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23843c2b-4e5a-42d9-a4f2-7c23e6a412b8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-23843c2b-4e5a-42d9-a4f2-7c23e6a412b8 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-23843c2b-4e5a-42d9-a4f2-7c23e6a412b8');\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",
              "\n",
              "\n",
              "<div id=\"df-4a25a236-0e69-4fa9-b5c3-320a59cc2579\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a25a236-0e69-4fa9-b5c3-320a59cc2579')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
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              "\n",
              "<style>\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
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              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --hover-fill-color: #FFFFFF;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4a25a236-0e69-4fa9-b5c3-320a59cc2579 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ],
      "source": [
        "vino_white = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/winequality-white.csv\",delimiter=';')\n",
        "vino_white.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "GN8JvitO_iVq",
        "outputId": "00d5e584-7395-473c-84bf-92e0b3e44da1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
              "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
              "mean        8.319637          0.527821     0.270976        2.538806   \n",
              "std         1.741096          0.179060     0.194801        1.409928   \n",
              "min         4.600000          0.120000     0.000000        0.900000   \n",
              "25%         7.100000          0.390000     0.090000        1.900000   \n",
              "50%         7.900000          0.520000     0.260000        2.200000   \n",
              "75%         9.200000          0.640000     0.420000        2.600000   \n",
              "max        15.900000          1.580000     1.000000       15.500000   \n",
              "\n",
              "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
              "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
              "mean      0.087467            15.874922             46.467792     0.996747   \n",
              "std       0.047065            10.460157             32.895324     0.001887   \n",
              "min       0.012000             1.000000              6.000000     0.990070   \n",
              "25%       0.070000             7.000000             22.000000     0.995600   \n",
              "50%       0.079000            14.000000             38.000000     0.996750   \n",
              "75%       0.090000            21.000000             62.000000     0.997835   \n",
              "max       0.611000            72.000000            289.000000     1.003690   \n",
              "\n",
              "                pH    sulphates      alcohol      quality  \n",
              "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
              "mean      3.311113     0.658149    10.422983     5.636023  \n",
              "std       0.154386     0.169507     1.065668     0.807569  \n",
              "min       2.740000     0.330000     8.400000     3.000000  \n",
              "25%       3.210000     0.550000     9.500000     5.000000  \n",
              "50%       3.310000     0.620000    10.200000     6.000000  \n",
              "75%       3.400000     0.730000    11.100000     6.000000  \n",
              "max       4.010000     2.000000    14.900000     8.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f886a29c-e5d8-482e-bd4a-9fbf1b53ff3e\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "      <td>1599.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.319637</td>\n",
              "      <td>0.527821</td>\n",
              "      <td>0.270976</td>\n",
              "      <td>2.538806</td>\n",
              "      <td>0.087467</td>\n",
              "      <td>15.874922</td>\n",
              "      <td>46.467792</td>\n",
              "      <td>0.996747</td>\n",
              "      <td>3.311113</td>\n",
              "      <td>0.658149</td>\n",
              "      <td>10.422983</td>\n",
              "      <td>5.636023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.741096</td>\n",
              "      <td>0.179060</td>\n",
              "      <td>0.194801</td>\n",
              "      <td>1.409928</td>\n",
              "      <td>0.047065</td>\n",
              "      <td>10.460157</td>\n",
              "      <td>32.895324</td>\n",
              "      <td>0.001887</td>\n",
              "      <td>0.154386</td>\n",
              "      <td>0.169507</td>\n",
              "      <td>1.065668</td>\n",
              "      <td>0.807569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.600000</td>\n",
              "      <td>0.120000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.012000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.990070</td>\n",
              "      <td>2.740000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.100000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>1.900000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.995600</td>\n",
              "      <td>3.210000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.260000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>0.079000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0.996750</td>\n",
              "      <td>3.310000</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>10.200000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.200000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.997835</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.900000</td>\n",
              "      <td>1.580000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>15.500000</td>\n",
              "      <td>0.611000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>1.003690</td>\n",
              "      <td>4.010000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "  <style>\n",
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              "\n",
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              "      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-f886a29c-e5d8-482e-bd4a-9fbf1b53ff3e 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-f886a29c-e5d8-482e-bd4a-9fbf1b53ff3e');\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",
              "\n",
              "\n",
              "<div id=\"df-8a4c7b30-6151-4ea5-8577-ccced7e0e022\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8a4c7b30-6151-4ea5-8577-ccced7e0e022')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8a4c7b30-6151-4ea5-8577-ccced7e0e022 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "vino_red.describe()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x0QmhEmLBZwe",
        "outputId": "8ae32ee4-f041-4331-cb45-08d5c049c75a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Datos nulos por columnas: \n",
            "  fixed acidity           0\n",
            "volatile acidity        0\n",
            "citric acid             0\n",
            "residual sugar          0\n",
            "chlorides               0\n",
            "free sulfur dioxide     0\n",
            "total sulfur dioxide    0\n",
            "density                 0\n",
            "pH                      0\n",
            "sulphates               0\n",
            "alcohol                 0\n",
            "quality                 0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "cantidad_nulos = vino_red.isnull().sum()\n",
        "print(\"Datos nulos por columnas: \\n \",cantidad_nulos)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "112cf613"
      },
      "source": [
        "## Limpieza de duplicados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8GrdH6ITCajk",
        "outputId": "e97cce7f-5949-47ff-e9b3-32b7cc1430fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tamaño del dataset original:  (1599, 12)\n",
            "Tamaño del dataset sin duplicados:  (1359, 12)\n"
          ]
        }
      ],
      "source": [
        "print(\"Tamaño del dataset original: \", vino_red.shape)\n",
        "datos_mod_red = vino_red.drop_duplicates(keep='first', inplace=False)\n",
        "print(\"Tamaño del dataset sin duplicados: \", datos_mod_red.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-NMajrqJLt3C",
        "outputId": "959839bd-8510-40df-98d2-d02ef04da41e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tamaño del dataset original:  (4898, 12)\n",
            "Tamaño del dataset sin duplicados:  (3961, 12)\n"
          ]
        }
      ],
      "source": [
        "print(\"Tamaño del dataset original: \", vino_white.shape)\n",
        "datos_mod_white = vino_white.drop_duplicates(keep='first', inplace=False)\n",
        "print(\"Tamaño del dataset sin duplicados: \", datos_mod_white.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b6792b24"
      },
      "source": [
        "## Visualización y Análisis de Datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C8l_Xo3NASdz"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "TybsnTSYATjb",
        "outputId": "edebad54-6848-4b51-c9fb-f8dd4c958e25"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "correlation_matrix = datos_mod_red.corr()\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "4miOOh1uFOF6",
        "outputId": "5ff8205d-b50d-4fd5-ad34-fb6bf572b7ac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.bar( datos_mod_red['quality'],datos_mod_red['alcohol'], color ='maroon',\n",
        "        width = 0.4)\n",
        "plt.xlabel(\"Calificacion calidad\")\n",
        "plt.ylabel(\"Grados de alcohol\")\n",
        "plt.title(\"Calidad y grados de alcohol\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "1um6B9m6L9Qr",
        "outputId": "12c7dda3-8e08-4359-8fd4-a19647125843"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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H76oYsuZQMvGJ6VvtTga94NbjFCJiVTwMU7HjVDKWOZVY5dRNjU+lUrFj0woafJN6zXF29aJz72FEhj/lzPGMrznbX15zKlZvgIOzO227DUi95uzepE5Tq2EL6n/dBg+f9L8FAPQNDLC0slG/cppZcubkQb6q1kBnN7dUKhUHty2hZpPOFC5RlbwuPrTqPpqoiCdc+DfjHj0F/L6iXvOfKFKqeoZpSlZsSO1m3fAuVFYnsb6PV+Wp3qQLhV6Wp8WPY4iOeMLFt5Qnv+9X1Gnei8IltZfHxNSMroPm4Vu2NnnyuuHiVZQm7QZx/9YlIp4+zKzifDYKPUWWvbKbbF3hmzx5MiNGjMDR0ZFHjx5x6tSpdGny5cvH+PHj+fHHH7l79y7379+na9eu/PHHHxQokNot4ZtvvuHJkyds27aN06dPU6xYMapVq0Z4eOodsZUrVzJs2DBGjx7Nv//+i729PTNmzHhrbCkpKTg6OrJq1SouX77MkCFD+O2331i5cqU6zcyZM+nevTudO3fmwoULbNy4EU9PT635xcbGUr9+fQoUKMDp06cZNmwYv/zyS4afX65cOSZNmoS5uTmPHj3i0aNH/PLLL7Rv354rV65ofFdnz57l/PnztGvX7q1l+n8WGvKAqIgwChQppV5mmiMnHt4FCb56Xus6z5OTuR0cpLGOUqmkYNFSBF+98N6fnZycjL6+Pkpl2uFs+LLl99rlwA8syXt83vPnBN26T+mCaV35lEolpQp6cf7GnXeur1KpOHnxGnceh+KXz0Pn8b0uOTmZ4BvXKOpbTL1MqVRS1LcYV4Mua13natBlivoV11jmV7yERvp8+Qty8sQxwp6GolKpOH/uLA8e3MevWAl1msSEBCaMG0WXH3/CytpaJ+V5npzMrRtXKeyb1hVWqVRS2LcE14Iual3netAlCvuW0FhWtFhprgVdyvBz4uJiUSgUmL7WjSsyIpy5U/+g+8/+GBoZf2JJ0jwNeUB05FPyF03rxmiawww3r0LcfMuxczf4CvmLpK2jVCrJX6R0unVOHNpKnzZVGNbra9YunUJiYrz6b3YOruQws+Tw7vU8T04mKTGBI7vXY+/oRq48eT+oHFm5r73p2bNYTE1N0dN7j2aq95R63N+lVOG0rm9KpZKShfNx4dr73bhJSEzixfMXmOfIofXvYZHRHDl7gYZVyukk5nexNlNgbqrg+oO0inFCMtx7koJznrf/PGpcwYCgeynceJC+de9NBvpQwkefsOgUop7ppktnaMhDoiLCKFhU85rj7l2QGxlcP15dcwoW1Tx/FCxaMsN13sfZkweJjYniq2r1PzqPN4U9uU905FO8C6dVykxMzXDxLMLta+fesuaXKfzJfWIin+JdqIx6mYmpGc4eRbhzXbflSXh5/jYxNddpvuK/LVuP4bOwsMDMzAw9PT3s7OwyTPfjjz+ydetWvv/+ewwNDSlZsiQ9e/YE4PDhw5w8eZInT56ou0+OHz+e9evXs3r1ajp37sykSZPo0KEDHTp0AOD3339n9+7db23lMzAwYPjwtC53bm5uHDt2jJUrV/Ltt9+q8/n555/p1auXOl3JktrHPC1fvpyUlBTmz5+PsbExBQsW5P79+3Trpr2fvaGhIRYWFigUCo3vJmfOnNSqVYuAgAD1ZwUEBFCpUiXc3d0zLM//u6jIMAAsLHNpLDe3yEVURJjWdWJiIklJeYGFpfUb61jz6P7t9/7sAkVKsCLgL7auW0LN+t+RmBjPqsXTUuOKePoBpXg/kTHPeJGSgrWFZrdoawszbj96kuF6sXHx1Ok5jKTnz9FTKunf9mvKFP64cVLvKzo6ipSUFCzfGOtqaWnF/XvaWz8jI8KxtEyfPiIirctT5249mD5lIu1bf4eenh4KhZLuvfpSsHARdZr5c2eQL39BSpctr8PyaN9nLCyteXD/bgblCdOa/tU++6akpESWB8ykXMXqmJqm/jBXqVTMnDSK6nUa4+GVnychH976lZHoyNR91MzijePAMhfRGRw7sTERpKS8wPyNcplZ5uLRg9vq96W+qkOu3PZYWOfmwe3rrFkymZAHd+jWfwIAxiY5+GXEXGb80Zctq+cCkMfemd7+09HT+7DLY1buaxpxREWx8u+l1KxT74Pif5fI6NiXx73mj0hrC3PuPAh5rzymL1+HjZUFJQtrHy+19eBxchgbU/kzdec0M029ix8bp1kJi41XYfaW4c9FPfTIa6Nk2rqMu+IBlCmgR93SBhgZKHgSmcK8LUm8eHf98L28uq6ku35YWmd8zXnL+ePR/XffrMvIwd0bKexXBmsb24/O400x6vOC5jXVzCKX+pzxXxId9ao8mt3hdV2e5KREtvw9Ed9ydTE21f0Y3s9NmQ1b2rJKtq7wfYgFCxbg7e2NUqnk0qVL6m4J586dIzY2lly5NE868fHxBAcHA3DlyhW6du2q8feyZcuyb1/G3ZEApk+fzoIFC7h79y7x8fEkJSWpZ/Z88uQJDx8+pFq1am/N45UrV65QpEgRjI3T7ryXLftx3RU6depE+/btmThxIkqlkuXLl/PXX39lmD4xMZHERM0LX7IqBQNF9m1APnpgG4tmjlG/7zM44+8nszk4e9Dxp2H8HfAXq5dMR6lUUr1+c8wtrVEov5xtYGpsxPJRvxCXmMSpS9f4a9l6HHLnokQB7a3WX7LNG9dzNegKg4aOJE8eWy5dvMDsGVOwts6Fr19xThw/yvlzgfw1dXZWh/pBnj9/zuSx/qhQ0aF7WjfU7ZtWkxAfR+NvfvjkzzhxYCtLZ/+uft9j0JRPzjMjFWs2U//f0cULC2sbJg7twpPH98hj50RSYgKLZgzHM19ROvUZQ0rKC3ZuWMzUUT/x27ilOm3J/Fjv2tdeFxf3jBFDf8PJ2YUWrdpkUcTaLdqwg11H/2XGkD4YGWqfNG3T/qPUqlAqw79/Kl9PPZp+lZZ3wPakD87DIoeCBmUNmLc1kefv6DEbeP0F1++nYG6qoGJRfVpVN2Tmxnevp83R/dtZ+No1p69/1l1zXhf+NIQLZ4/T/dfRn5TPv4c3s3Ju2k3wzv3f3kvqS3f68GZWzxumft+x38xM/8wXz5NZPLkvKpWKr9sPyfTPE/8tUuF76dy5czx79gylUsmjR4+wt7cHUrtK2tvbs3///nTrWFpafvTnrVixgl9++YUJEyZQtmxZzMzM+PPPPzlxInUMlImJyUfn/akaNGiAkZER69atw9DQkOTkZL7++usM048ZM0ajtRKghcKaVnq6ndjhS+JXqiIe3mnjG54np/5wiIoMw/K1CS2io8JwdtM+i6GZmSVKpR5Rb0wyER0VjoVVLq3rZKRspdqUrVSbqMgwjIxMUCgU7Ni4nNy2Dh+Uz/uwNMuBnlKZboKW8KgYcllk3IVEqVTiZJc6LsfHxYFbD0JYuGl3plb4zM0tUCqVREZoTpoRGRmRYTdLSyvrdJNsREZGYGWVmj4xMZGli+YzcPBwSpRK7Z7j6ubBzeAbrF+7Cl+/4lw4d5bHjx7S8puGGvn8MXo4BQoWZtQfEz+yPNr3majIcCytMipPLq3p32yNflXZC30Sgv/oKerWPYBL509zLegi3zeporHOb707UqFyDX7s+/7je4uWqoSbxrGTOjlKTFQ4ltZp47aiI8MynCkzp5kVSqVeuglaYiLD0pXrdW5eqeNgQx+lVvhOHtpG2JOHDBizSN0lumOfMfRuXZHAU/spVaH2e5crq/a1V+Li4hjmPwATU1MG+o9AX1+3l3dL85wvj3vNCVrCo6Kxtnx717Glm3axeMMOpg3qhZeLo9Y0Z69c587DEH7v1VFnMb/p8p0X3HuS1sT2amKWnKYKYuLTWvlymih4GKa966WDjQIzUwU/NU2bME1PqcDNXkXZgnoMmp+A6uWqCcmQkKwiLFrF3SdJDGtjTEFXPc4Ff3iNz6/UV3j4pM2kmay+5oRrXnMiwzO+5rzl/PGh15xXDu3ZTE4zC/xKVfyo9V8pVLwKLp5prdavrqkxUWFYWKWdF2KiwnBwydyeIbpQsHgVXDzTxt2nneeeYv5meXQwQ2hqZe9nIp4+pNvggGzRugegUEoLn65IhQ8IDw+nbdu2DBo0iEePHtGqVSvOnDmDiYkJxYoV4/Hjx+jr66snfXlT/vz5OXHiBK1bt1YvO378+Fs/88iRI5QrV44ff/xRvexViyGAmZkZrq6u7NmzhypVqmjLIl0MS5YsISEhQd3K964YDA0NtQ7q19fXp02bNgQEBGBoaMh333331growIED6du3r8ayvdbFM0idPZiY5MDEJO0HsUqlwsIqF5fPn8LFPfViFB8XS/C1S1Sprb2yrG9ggKtHPi6fP0XxMpWB1LGdl8+folrdbz4qrlc/dg/u3oiBgSEFXxsXpSsG+vrkc3Pk5KVrVC6RekFLSUnh1KXrfFvj/R85kKJSkZT8XOfxvc7AwAAPT2/OnzurfhxCSkoK5wPPUrdBY63r+OQrwPnAMzRsnNY6FHj2ND75Usf0vnjxnOfPn6ebnEBPT4kqJfXHZLNvWlCjVl2Nv//0Y0fad+pGqdIfP1GAvoEBbp4+XDz3LyXLVlSX5+K509Sq30zrOl75CnIx8DR1GzVXLzt/9hTe+dJ+PL6q7D16eI8hY6ZiZm6hkUfbzr1p/n1n9fvw8FDGDOlLr/7D8XztR+j7MDbJgfEbx465pQ1Xzp9QV/Di42K5df0ilWprPw70DQxw9shP0PkT+JWuov4erpw/SZW6zbWuA3Dv1lUALKxSfyAnJSagUCg1tqVCmTqJlSrlw8ZaZdW+Bqkte8MG98fAwJDBQ0ZiaGj4QbG/j9Tj3plTF69SqaQv8PK4v3iVb2pVznC9JRt3ErBuG5N/60n+DB7bArBp31HyuTvjnUGFUBeSkiEsWXO7Rsep8Myrx6Ow1HORkQE45VFy/Ir2WVpvPExh4irN4RrfVDIkNCqF/YHP1ZU9rRS81+yf2piY5sDENKNrTmoFLz4ulpvXLlG1tvZzQcbXnH+p/hHXHJVKxaE9myhfpe4n32DI6Lxw/eJx9SMTEuJiuXPjPOVrfPtJn/U5aCuPmaUN1y+ewME1dSbThLhY7gafp1yNjM9Z7+NVZe/p4zt08w8gh5nlJ+Unsiep8AFdu3bFycmJwYMHk5iYiJ+fH7/88gvTp0+nevXqlC1blsaNGzNu3Di8vb15+PAhW7ZsoUmTJpQoUYJevXrRtm1bSpQoQfny5Vm2bBmXLl1665g3Ly8vFi9ezI4dO3Bzc2PJkiWcOnUKNzc3dZphw4bRtWtX8uTJQ506dYiJieHIkSPq8YWva9myJYMGDaJTp04MHDiQ27dvM37826fldnV1JTY2lj179lC0aFFMTU0xNU0duNCxY0fy5089KR05cuSt+RgZGaV7PERmdufUy2FKDs+05wyZujliXjQfSeFRJNzT3diiD6FQKKjZoAWbVi3ALq8TNnkcWLt8FlbWNhQrXUmd7g//bhQvU4Xq9VIvWLUatWTu5OG4eebH3asgOzf9TWJCPF9VS3ueW2TEU6IiwnjyOHUc0P07NzA2MSVXbjtymqX+MN+9ZSWe+YpgbGzCxXMnWLlwCt+07pHueX260qpOZYbNXk4BNycKeriwfPsB4hOTaFAptYI5ZNYy8lhZ0KN56iD+gI27ye/mhKNtLpKTX3Dk3GW2HvmXgW0/rmL7IRo1+ZrJE//A08sbL+98bNqwhoTEBKrXqAXAX+PHkiuXDa3bpbYsNGjUlEH9+7B+7UpKlCzDoQP7CL5+je49U29qmJrmoFDhoixcMAdDIyPy5LHl4oVz7Nuzi/adUsfMWllba23VyZ07D7Z29p9UnnqNmzPzr1G4e+XD07sAWzesJDEhgUrVU8dsTZ8wEutcNrRomxpLnYbfMmJAdzav/Ru/kuU4enA3N28E0blH6mMXnj9/zl9jBnEr+Br9h4wjJSWFyJdjgHLmNEffwACbPJpjoI1e3gCytXcgl82nPetJoVBQvX5Ltq6eRx57Z2xsHdjw9wwsrXPjVyrtZtfEoV3wLV2FqnW/A6BGg+8JmDoEF88CuHkVYvem5SQlxlO+aiMAnjy+x8mD2yhcvAI5zCy5f/saKwMm4FWgGI6uqT+Q8xctw+rFk1g+ZwxV632HKkXFtnUBKJV6+BTKeFKUjGTFvhYX94yhg/qTmJhAn19/Iy4ujri4OADMLSx0OnFLi3rVGDFzEfndnSng6cqKrXtJSEykfqXUmxjDpi8kt7Ul3Vs0BmDxhh3MWbWZET3bkTd3LsIiowAwMTbC9LXhB7Fx8ew5cYZe32uvqGSmwxeeU7WYPk+jU4iIVlGzpAHRcSou3U67GdqpniEXb7/g2KUXJCVDSIRmrS7puYq4hLTl1mYKinjocf3+C57Fg0VOBZV99Ul+DkF3dTNzqkKhoFaD79i4cgG29k7kts3L2uWzsLS2oViZ1685P1KsTGVqvLzm1H7jmrNj04rUa071tAlXUq854epZnlOvOTnIldtWfc0BuHz+FKEhD6lUo5FOyvRm+SrW+YGd6+aQ284F6zwObF05DQurPBQukTbUZfrIDhQpWY2varcEIDEhjtDHaeOZw5884P7tIHLktMDKJvXc+yw2ioinj4iOSB1z/uRh6mNQzC1tMLfMnJ5Jr8qze/1sbOycyZXHkW2rpmJulYdCr5Vn5u/tKVyyGhVqtXpZnmc8fb08ofd5cPsKpjktsLLJy4vnySya1If7t67Qsd90UlJeEB0ZCoBpTgv09XV/80f8N/3fV/gWL17M1q1bOXv2LPr6+ujr67N06VIqVKhA/fr1qVOnDlu3bmXQoEG0a9eO0NBQ7OzsqFixIra2qQOUmzdvTnBwMP369SMhIYFmzZrRrVs3duzYkeHndunShbNnz9K8eXMUCgUtWrTgxx9/ZNu2beo0bdq0ISEhgb/++otffvkFGxubDLtW5syZk02bNtG1a1f8/PwoUKAAf/zxB82aZXwBLVeuHF27dqV58+aEhYUxdOhQhg0bBqRWSMuVK0d4eDilS+u+lehTWBQvRNk9S9TvC4z/DYB7i9dyvsPArAqLuk1ak5gQT8CM0cQ9i8U7f1F+HjIFQ8O0yvCTxw+IiY5Uvy9doSYxUZGs+3s2URGp3T9/HjpFo1vavu1r2fDPXPX7MYNSW1o69ByirhjevH6JdSvmkBgfh72jK226/Ub5KpotTLpUs4wfEdGxzFqznbCoaLxdHJjarwu5Xk7k8vhpBMrXWiXiE5P4Y+FqnoRHYWRogGvePIzs9j01y2T+5AxfVapCdHQUy5csJCIiAjd3D4aOGKvuAvk09AnK17qN5C9QkJ/7DWLp4gUsWbiAvA4ODPQfgYtr2s2YX/oPZvHCeUz8czSxMTHkzmPL963bU7tu+gev61q5itWJjopk1dJ5REaE4+LuxYARE14rT4hGNxif/IXp+esw/lkyhxWLZ2OX15FfBo3ByTX1hlR4WCinTxwGoP9PbTU+y3/0VAoWKUZmq9WkLYmJ8Syd9Ttxz2LwzO9LL//pGLx27IQ+vkfsa8dOyQq1iImOYOPfM4mODMPRzYef/Kdj/vLY0dc34Mr5E+zZvJzExHisbWwpVrYa9b5O6zJo7+hGj4GT2bRyNmMHtEGhVOLslo9e/tM1upe+r6zY14JvXOfa1SsAdO2gOcZyTsAybG0znrDsQ9UoV4LI6FjmrNpMWGQ03i6OTBrQk1wvu3SGPA3XOO7X7jpI8vPnDPxrrkY+HZvVo9M3aRWMXUf/RaVSUbO89knJMtOBc88x1IdmXxli/PLB6wu2JWmMs7M2V5DD+P27liW/UOFmp6RCIX1MjFIngbn1KIUZGxJ59vYnNn2Quk1bk5iQwMKX1xyv/EX5ZejkdNec14+b0l/VIDo6grXL56ivOb8MnZzumrN+xTz1+9G/dQGg409DNGbiPLh7I575ipDX0VV3hXpNtYbtSUqM55+5w4iPi8HdpxhdBszSOC88DblHbExat+i7wReZPrK9+v36JeMAKFmxEa1+HAXAxX/38festOfaLZ6SOl65VrNu1Pmme6aUBaBKgw4kJcazel5qedx8itF5wGyN8oSF3ONZTKT6/b2bl5g5Mm2G9I0vy1OiYiNadEt9TMWl06nzRUwYoPl7r5t/AJ4FSvFfptD7cuYh+K9TqFRv7YAg/k+pVCq8vLz48ccf03XXfB9bDL78PvYfwvp8+kd6/FcVij2c1SHo1ANr7bMV/lfFp2T9RCG6Ep30lqkO/4NsjcPfneg/xC76WlaHoDNjTn2+Z6p9Dk0qfviEMl+yiPjscy54ocpe48rqF/ty236Olvj8N4FeKfdv9vndB9LCJ7QIDQ1lxYoVPH78WJ69J4QQQgghPjt5LIPuSIVPpJMnTx5sbGyYM2cOVm88U0oIIYQQQgjx3yEVPpGO9PIVQgghhBAie5AKnxBCCCGEEOKLIs/h0x2Z/kYIIYQQQgghsilp4RNCCCGEEEJ8UWTSFt2RFj4hhBBCCCGEyKakhU8IIYQQQgjxRVFIC5/OSAufEEIIIYQQQmRTUuETQgghhBBCiGxKunQKIYQQQgghvigKpbRL6Yp8k0IIIYQQQgiRTUkLnxBCCCGEEOKLIg9e1x1p4RNCCCGEEEKIbEoqfEIIIYQQQgiRTUmXTiGEEEIIIcQXRSnP4dMZaeETQgghhBBCiGxKWviEEEIIIYQQXxSZtEV3pIVPCCGEEEIIIbIpaeETQgghhBBCfFHkweu6IxU+kSmsz5/K6hB0KrxIyawOQWd2HL6S1SHoVBHuZ3UIOmWqjM/qEHQmJNk8q0PQKT3jF1kdgk49MMuf1SHoTL3yKVkdgk6pVNmrK5ujWXhWh6AzF0PyZHUIQnwwqToLIYQQQgghRDYlLXxCCCGEEEKIL4pM2qI70sInhBBCCCGEENmUtPAJIYQQQgghvijy4HXdkRY+IYQQQgghhMimpMInhBBCCCGEENmUdOkUQgghhBBCfFFk0hbdkRY+IYQQQgghhMimpIVPCCGEEEII8UVRKKVdSlfkmxRCCCGEEEKIbEpa+IQQQgghhBBfFBnDpzvSwieEEEIIIYQQ2ZRU+IQQQgghhBAim5IunUIIIYQQQogvinTp1B1p4RNCCCGEEEKIjzR9+nRcXV0xNjamdOnSnDx58q3pJ02ahI+PDyYmJjg5OdGnTx8SEhIyLT5p4RNCCCGEEEJ8Uf4rLXz//PMPffv2ZdasWZQuXZpJkyZRq1Ytrl69Sp48edKlX758OQMGDGDBggWUK1eOa9eu0bZtWxQKBRMnTsyUGKWFTwghhBBCCCE+wsSJE+nUqRPt2rWjQIECzJo1C1NTUxYsWKA1/dGjRylfvjwtW7bE1dWVmjVr0qJFi3e2Cn4KqfAJIYQQQgghxEuJiYlER0drvBITE9OlS0pK4vTp01SvXl29TKlUUr16dY4dO6Y173LlynH69Gl1Be/mzZts3bqVunXrZk5hkAqfEEIIIYQQ4gujUCqz7DVmzBgsLCw0XmPGjEkX49OnT3nx4gW2trYay21tbXn8+LHWcrVs2ZIRI0ZQoUIFDAwM8PDwoHLlyvz222+Z8j3C/8EYPoVCwbp162jcuLHO8rl9+zZubm6cPXsWX19fncT5od4nhv3791OlShUiIiKwtLRk4cKF9O7dm8jIyM8aa0ZUKhXr/p7NgV3riXsWi1e+IrTuOgC7vM5vXW/31pVsW7eUqMgwnF29+L7Tr7h7F1T/ff+OtRw7uIM7N6+SEP+M6Uv3kiOnmUYet4ODWLV4KjevX0app0eJMlVo0b4PxiammVLWt7GuUAL3nztgUawQxnnz8G+zHwnZuOezx/EuKpWKPWuncmr/KhLiYnDx8qNh26HY2LlmuM6toFMc2rqAh7cvERMZSqteUylQvHq6dE8eBLNj5QRuBZ0i5cUL8jh40LLnZCxt8uok9i2bNrB2zSoiIsJxc/OgS7fuePvkyzD94UMHWLpkEU9CHpM3rwNt23ekRMnS6r9HRESwMGAugWdOE/vsGYUKFaZL1+7kdXBMSxMezoL5cwgMPEN8XDwOjo5827wl5St8pZPyrFuzUl2ezt16vLM8y5YsVJenTftOGuWJj49nUcA8Thw7QkxMNLa2dtRv2IQ69Rpo5BN05TJLFi3g2tUglEolbu4eDP99LEZGRp9cprdRqVRsWzWdY3vWEP8sBjcfX77p6E8ee5cM17lx+V/2blrIvVuXiY4IpcMvkyhSslqmxvm+Nm/a+Nr+6E6Xbt3xyWD73blzm2VLFnPjxnWePAmhU+euNGrc9LPFum3zOtavWUFkRDiubp507PoTXj75M0x/9NB+/l46nychj7HP68gP7bpQvGQZjTT3795hccBsLl88x4sXL3B0dqHfbyPInSf1B9PObZs4dGA3N29cJz4+jiX/bEp3Dv9YKpWKjStmcWjXOuLiYvDMV5RWnX/D9h3XnX3b/mHH+sVERYbh5OpNi479cPMqpDX/Kb/35OLZo/zYfwJ+pasAcGTvRhZOG6Y17wkLdmNuaf1RZdH1NTQ2Jop1f8/hUuBxwp6GYGZuSbHSlWnasiumOXKq81g6dzzXr5zjwd1g7B1dGTlp+QfH/6Ztm9ey8eW+5uLmQYeuvfDyKZBh+qOH9rFi6XxCQx5jn9eB79t1pVjJslrTzp42nl3bNtK2Uw/qN/4WgIvnzzJsYC+t6cf+NRtP74z384+hUqnYt34qZw6uIiEuGifPYtRvPZRctq4ZrnP76imObp/Pw9uXiI0KpXmPaeQvpnkNjY16yq7V4wm+eISE+BhcvEtQt9Xgt+Yr3m3gwIH07dtXY5murnX79+9n9OjRzJgxg9KlS3Pjxg169erFyJEj8ff318lnvEla+N4wbNgwrRWoR48eUadOnc8fUAacnJx49OgRhQqlv+BkpHnz5ly7dk39PqOyfi5b1y1m1+Z/aNN1IEPGBWBkbMKE4T1JSkrfZP7KicM7WbFgEo2/68jwiUtwcvVi/PCeREeGq9MkJiZQuFhZ6n/dVmseEeGh/Dm0O3nsnRjyZwA/D5nMg3s3mTdluK6L+F70cpgSff4qF3/Kms9/X4e2zOPYrqU0ajuMbkP/wcDIlIV/diL5LdsrKTEee2cfGrTO+AQWFnKXOb+3Ire9Gx0HLqLnqPVUadQNfUPdnFgPHdjPvLmzadHyeyZNnYmbuztD/AcSGRmhNf2Vy5f484/R1KxZm8lTZ1KmbHlGjRzGndu3gNSL9qiRQwl59JhBQ0YweepMcuexZfBv/UlIiFfnM3HCHzx4cB//ISOYNmMO5cpVYNzY3wkOvvGJ5dnH/Lmz+K7lD/w1dRau7u4M9R/w1vKM/2MUNWrWZtLUWZQuW57RI4eqywMwf+5Mzpw+Rd9fBzB99gIaNG7K7JlTOXH8qDpN0JXLDPMfgF+x4kyYNI0Jk6dTv0EjlJ9hUP2ejQs4uG0533b0p8+oZRgamzBrdJd37nsOLt583X5Qpsf3IQ6+tj9Onjrj5f74W4bbLzExETt7O9q0a4+V1YdXCj7F4YN7CZg7g29btmX8lLm4unkwwv/XDGMNunyRieNGUK1mPSZMmUepshX44/fB3Ll9U53m8aMH/NavJ45OzowYO4m/ps/nm+9aY2BoqE6TmJiAX7FSNPu2lc7LtH3dIvZs+Zvvu/7Gb2MXYWhkwqSR3d+6L506vIOVARNp8G1n/Mcvx9HVi0kjumtcd17ZvXkZKNIfEyXL12T8/J0ar4K+ZfEuWPyjKnuQOdfQyPBQIsNDad62F6Mmr6DjT0O5cPYYC6aNTJfXV9UbUKpCjY+K/U1HDu5h0dzpfNOyLeOmzMPVzZPf/X8hKsN97QKTXu5rf06ZR8myXzHu90HcfW1fU5f56EGuB13GOpeNxnKf/IWYu2SdxqtarfrksbXHwyvjG2gfXcZt8zixewn1Ww+j4+CVGBqZsGRCR5KTM95eyYnx2Drlo973Q7T+XaVSsWJadyJC79Pipxl0HboWy1x5WTy+PUmJcTovw+em1FNk2cvIyAhzc3ONl7YKn42NDXp6eoSEhGgsDwkJwc7OTmu5/P39+eGHH+jYsSOFCxemSZMmjB49mjFjxpCSkpI532Wm5JoN2dnZZfpd7A+hp6eHnZ0d+vrv30hrYmKidbagrKBSqdi56W8aftueYqUr4eTqRadew4kIf8qZEwcyXG/HhuVUqtmYr6o1xMHJnTbdBmJoZMzBPRvVaWo1bEn9Zm3x8C6sNY9zpw6hp6fPD537Ye/girtXQdp0Hci/x/YS8uiezsv6LqE7DnJt6CRCNuz+7J/9vlQqFUd2LKZyw64UKF4NO2cfvukylpjIJ1w5k3HcPkUrUuPr3hQskfGPgl2rJ+FTtCK1v/uVvK4FyGXrTP5iVclpnksnsa9ft4ZatetQvWZtnJ1d+LFHL4yMjNi1c4fW9Bs3rKNY8ZI0/fpbnJxd+L51Wzw8PNm8aQMADx884GrQFbr1+Alvbx8cHZ34sftPJCUlcWD/PnU+QVcuU79BI7x98mFnb0/zFq3IkSMHN65f0/q572vDujXUrF33tfL0xsjIiN07t2tNv2nD2pflaf6yPO1w9/Bky8vyvIq1arWaFC7ii62tHbXr1MfN3YPrV4PUaebNmUH9hk34+tsWOLu44ujoRIWKlTEwMNT2sTqjUqk4sHUpNZt2pnDJqji4+PB999FERYRy4dTeDNcr4PcV9b77iaKlvoxWvVde7Y81atbC2dmF7u/YH729fWjfoTOVKlXBwMDgs8a6ad0qatSuR7UadXBydqVLj74YGRuzd+dWrek3b1yDX/FSNG72HY7OLrT8oQNuHl5s27xOnWbZ4nkUL1Ga1u274u7hhZ29A6XKlMfS0kqdpkHjb2j6bSu882XcuvMxVCoVezYvp97XHfEtVRlHV2/a/zSCyPBQzp7cn+F6uzYt46saTShfrRF5ndz5vssgDI2MObJ3g0a6u7eusnPDUtp2H5ouD0MjYyysbNQvpVKPoIunqFCt0UeXJTOuoY4unvQcMA6/UhXJY+9IgSIladaqG4GnDvHixXN1Pt93+oXqdb8lt63DR8X/pk3rVlK9dn2q1qiLk7MrnXv8/HJf26I1/daNq/EtXopGzVrg6OxKix864ubhzbbNazXShT0NZf6syfT61R89Pc3fSwYGBlhZ51K/zMwtOHX8MFVq1EWhpdL+KVQqFcd3LaZig67k86uGnZMPTTr+QUzkE4Lecg31KlKRak17k7+49mtoWMht7gefo/4PQ3FwK4yNvTv1fhhGclICF05o/+6EbhkaGlK8eHH27EnrmZWSksKePXsoW1Z7i3NcXBxKpWYVTE9PD0jdVzLDF1vhmzNnDnnz5k1X023UqBHt27dXv585cyYeHh4YGhri4+PDkiVL3ppv//798fb2xtTUFHd3d/z9/UlOTgZg4cKFDB8+nHPnzqFQKFAoFCxcuBBI7dK5fv36DPO9ePEiderUIWfOnNja2vLDDz/w9OnTDNOHhYXRokULHBwcMDU1pXDhwvz9998aaVJSUhg3bhyenp4YGRnh7OzMqFGjgNQunQqFgsDAQHX6rVu34u3tjYmJCVWqVOH27dsa+S1cuBBLS8u3lrV9+/bUr19fY73k5GTy5MnD/Pnz3/LNfpjQkAdERYRRoEgp9TLTHDnx8C5I8NXzWtd5npzM7eAgjXWUSiUFi5Yi+OqF9/7s5ORk9PX1NQ42w5eV+WuXAz+wJP8fIkLvExv1FI+CaScvY1MzHN2LcPfGuY/ONyUlhavnDpDLzpWAcR0Z3b08M4c15/Jp3VR+k5OTuXHjGkV9i6mXKZVKfH2LcTXostZ1goIu4+tXTGOZX/ESBAVdUecJqSf51/M0MDDg8uWL6mX58hfg0MEDxMREk5KSwsED+0hKSqZwkaKfXB7fN8pT1LcYQW8pT9E3ylOseEmN9PnyF+DkiaOEPX2KSqXi/LlAHj64j2+xEgBERkZw7WoQlpaW9Pv5J35o+TUD+/Xl8qX3P+4+VtiT+0RHPsW7cFq3QBNTM1w8C3Pr+sfve1khdftdx9fXT70sdX/0U+9fX4rk5GSCb1yliG9x9TKlUkkR3+IZHjvXgi5ppAfwK1ZKnT4lJYXTp45j7+DECP9faduyMf37dOPEsUOZV5DXPA15QFTkU/IXTevObJrDDHevQtx8y3XnTvAV8hdJW0epVJK/SGmNa1ViYjzz/vqNVp0HYGFloy0rDcf2b8bQ0JjiZdN3cX8fn/MaGh8Xi4lpjnQVJl1JTk7m5o1rFPEtoRFXYd/iXA26pHUdbfuab7FSXHstfUpKClMn/E6jZt/h5OL2zjj+PXGY2JhoqtbQfW+u1GtoKO4FyqmXvbqG3g8O/Oh8XzxPAkDfIK1BQqlUoq9vyN3rpz863y+FQqnIsteH6Nu3L3PnzmXRokVcuXKFbt268ezZM9q1awdA69atGThwoDp9gwYNmDlzJitWrODWrVvs2rULf39/GjRooK746doXW+H75ptvCAsLY9++tDvm4eHhbN++nVatUrt5rFu3jl69evHzzz9z8eJFunTpQrt27TTWeZOZmRkLFy7k8uXLTJ48mblz5/LXX38BqV0ef/75ZwoWLMijR4949OgRzZs3f2eskZGRVK1aFT8/P/7991+2b99OSEgI3377bYbrJCQkULx4cbZs2cLFixfp3LkzP/zwg8aUrAMHDmTs2LH4+/tz+fJlli9fnm5Q6Cv37t2jadOmNGjQgMDAQDp27MiAAQMy/PyMytqxY0e2b9/Oo0eP1Gk3b95MXFzce30X7ysqMgwAC0vNVhxzi1xERYRpXScmJpKUlBdYvNH9xdzCOsN1tClQpARRkWFsXbeE58nJPIuNZtXiaalxRWRcSf9/FhOV+r3ktNDcXjktbIiNDP3ofJ9Fh5GUEMfBzfPwLlKBtv3mUaB4dZZP+YlbQZ8+PXF0dBQpKSlYWVlpLLe0tCIiXHtXociXY17fTB8ZkdrlydHJidy587AoYD6xMTEkJyezetUKnj4NJSI8rYtX/4H+vHjxnJbNm9G0UV2mT53Eb/5DyZv34++IvyqPpZbyRL61PG+mtyQiIi3WLt164OTsQrvW39G0YW2G+Q+ky489KVS4CACPH6eeD/5etpiateoybOQYPDw9GTywHw8f3P/o8ryPmJfnCrM39j0zi1zERP63jtfo6OgMt9/r+86XIObVvvbG+fb1Y+FNkRHh6dJbvJY+KjKChPh41q1ajl+xUgwd+Sely1Zg3KghXLoQmCnleN2r6465hWaMZpa5Mjz3x7687rzZ7dLc0proyLTrzsoFE/DwKYpvqcrvFcvhPesp/VUdDI2MP6AEaT7XNTQmOpKNK+dTqWaTj4rzfaTuay+wSHeesv7ofQ1g/erlKPX0qNvw6/eKY8/OLRQtVpJcNrrvCRUbnXqdfLPnSg5zG2KjPv48ZmPnjkWuvOxePZH4Z1E8f57E4a1ziY54/EnXZvFhmjdvzvjx4xkyZAi+vr4EBgayfft29W/2u3fvavyuHjx4MD///DODBw+mQIECdOjQgVq1ajF79uxMi/GLnbTFysqKOnXqsHz5cqpVS+2Ss3r1amxsbKhSJXUQ9Pjx42nbti0//vgjkFrDPn78OOPHj1enedPgwYPV/3d1deWXX35hxYoV9OvXDxMTE3LmzIm+vn6G/W61mTZtGn5+fowePVq9bMGCBTg5OXHt2jW8vb3TrePg4MAvv/yift+zZ0927NjBypUrKVWqFDExMUyePJlp06bRpk0bADw8PKhQoYLWGF61dE6YMAEAHx8fLly4wB9//KE1fUZlLVeunLqltF+/fgAEBATwzTffkDNnTq15JSYmppuqNikpEcPXxmAdPbCNRTPTZjfqM/gvrXl9Dg7OHnT8aRh/B/zF6iXTU6fPrd8cc0trFMov9h7IZxV4dBMbAoap37f+eWamfM6rrgv5i1WlfO22AOR1yc/dG2c5ufcf3PKVesvaWUNfX5/fBg9lyuQJtGjeNLWFxq8YxUuU5PWeGMuWLORZ7DN+H/0H5uYWHD92lHFjfmfsuL9wdXv33ebPafPG9VwLusLgoSPJnceWSxfPM3vGVKytc+HrVxxVSmrBatWpT/WatQHw8PDiXOBZdu3cTpt2HXUWy7+HNvPP3BHq910GTNdZ3iJrvTreS5UpT4Mm3wDg5uFF0JVL7Ni6kYKFfXX6eccPbGXp7FHq9z0HTdFp/q8EnjxA0MVT+I//+92JgeCr53h0/xYdeqUfF5eRrLiGxsfF8tfI3uR1cqPxd50z/fN0Kfj6VbZuWM24KfPeq3tm2NMnnDtzir4Dhunk888f28SmxWlde1v1nqWTfN+kp29A8+5T2BAwmD96lkah1MO9QFk8C1eETOoaKLTr0aMHPXr00Pq3/fv3a7zX19dn6NChDB2avvt3ZvliK3wArVq1olOnTsyYMQMjIyOWLVvGd999p+6Kd+XKFTp31jwJlS9fnsmTJ2eY5z///MOUKVMIDg4mNjaW58+fY25u/klxnjt3jn379mmtEAUHB2ut8L148YLRo0ezcuVKHjx4QFJSEomJiZiamqrLlpiYqK7svsuVK1coXbq0xrKM+g6/S8eOHZkzZw79+vUjJCSEbdu2sXdvxmNlxowZw/DhmhOOtP9xAB17pDVf+5WqiId32gQzz5NTuyFERYZhaZ3W/SU6Kgxnt/TfF4CZmSVKpR5RbwyUj44Kx8Lqw8Z7la1Um7KVahMVGYaRkQkKhYIdG5frbDzCf11+v6o4eRRRv3+1vWKjwjC3TLv7GRv1FHuXj5/JzNTMEqWePnkcPDSW587rzp1rZz4631fMzS1QKpVERGi2fkVGRmBlbaV1HUsrq3Qz2UZGRmD52oQZnl7eTJk2m2fPnvH8eTIWFpb83Lsnnl5eADx69JDNmzYwbeZcXFxcAXBz9+DSpQts2byB7j17f1J5IrWUx/Kt5XkzfaR6ApDExESWLFrAwMHDKFkqtdukm5s7t4KDWbd2Fb5+xbGyTk3r5Kw5K6aTkzNPQ598VFkyUqhEFVy80u97MVFhWFjlVi+PiQrDwVX3EytkJnNz8wy336vv+Eth9mpfe+N8++ax8DpLK+t06aNeS29mboGenh6Ob+xHjk4uXLms++7BvqUq4f7adedVd+zoqHAsrV/blyLDcHLz0ZpHzpfXnTcnaImODMf8Zeta0IWThD6+T68fKmmkmfnnr3jl9+PXkXM1lh/avR4nNx9cPN5/jOLnvobGxz9jwvCfMDYxpeeAPz9ovoAPlbqv6aWboCUyMvyj97Url84RFRVB17bfqP+ekvKCxfNnsGXDamYGrNRYd++ubeQ0M6dEae031T+Uj28VHNzTzmOvul7GRodh9to19Fn0U+ycP2020Lyuheg2fD0JcTG8eJ5MDnNr5o78lryu7z+p35dKbsLrzhf9TTZo0ACVSsWWLVu4d+8ehw4dUnfn/BjHjh2jVatW1K1bl82bN3P27FkGDRpEUlLSJ8UZGxur7kr5+uv69etUrFhR6zp//vknkydPpn///uzbt4/AwEBq1aqljsXExOSTYvoUrVu35ubNmxw7doylS5fi5ubGV19lPJX8wIEDiYqK0ni17qw5la2JSQ5s7Z3Ur7xO7lhY5eLy+VPqNPFxsQRfu4SHT5E3PwIAfQMDXD3yaayTkpLC5fOn8PDRPkHLu1hY5sLYxJQTh3dhYGBIwaKl373S/wEjkxzksnVRv/I4eJLTwoabl4+r0yTEx3L/5nmcPT9+TJq+viGOboV4+uiWxvKnj29jmevTH8lgYGCAp6c358+dVS9LSUnhXOBZfDKYECJfvgKcCzyrsSzw7Bny5Ut/Uc6RIwcWFpY8fHCfGzeuUbps6viMxITUFm/lG3eWlUrlJw3IflWec+fSKsMpKSmcDzxLvreU53y68pxWp3/x4jnPnz9HqdC8HCj1lOqWPVtbO6xz5eLBfc1JjR48uK+eSl9XjE1ykNvOWf2yc/TA3NKGaxdOqNMkxMVy58YF3Lw+ft/LCqnbz4tz5wLVy1L3x0Ct+1dWMjAwwMPTh/OBb+5rpzM8drzzFeTCOc0bNefO/qtOb2BggKdXPh6+sR89fHiPPDrejyB1X8pj76x+5XVyx8LShqDzad3F4+NiuXn9Iu5vue64eOTnymvrpKSkcOX8SfW1qk7Tdgyd+A9DJvytfgE0b/czbXsM08gvIT6Of4/s+uDJWj7nNTQ+Lpbxw3qip29Ar0ETNXrrZAYDAwPcPb25EJg25iwlJYULgWfwyVdQ6zra97VTeL9MX6lqLSZMC2D81Pnql3UuGxo2/Y7BI8drrKdSqdi3ayuVqtbSWcXWyCSnxjU0d15Pclrk5tbltAdxv7qGOnr46uQzjU3NyGFuTVjIbR7evoiPX1Wd5Cuyhy+6hc/Y2JimTZuybNkybty4gY+PD8WKpU0+kD9/fo4cOaLu8ghw5MgRChTQfjE6evQoLi4uDBqUNk33nTt3NNIYGhry4sWLD4qzWLFirFmzBldX1/c+WRw5coRGjRrx/fffA6knt2vXrqlj9/LywsTEhD179tCx47u7S+XPn5+NGzdqLDt+/HgGqVNlVNZcuXLRuHFjAgICOHbsmHrQaUaMjIzSzWBqaBj91nUUCgU1G7Rg06oF2OV1wiaPA2uXz8LK2oZipdPukv7h343iZapQvV7qeMhajVoyd/Jw3Dzz4+5VkJ2b/iYxIZ6vqqU9Lywy4ilREWE8eZz6o+L+nRsYm5iSK7cdOc0sANi9ZSWe+YpgbGzCxXMnWLlwCt+07qGzZz19CL0cpuTwTHtukqmbI+ZF85EUHkXCvUdvWfPzUSgUlK/Vmn0bZpHL1gWr3I7sXjMFM8s8Gs8Emj+2HQWKV6dsjdQbM4kJzwgLuav+e0TofR7euYJpDgv1M/Yq1G3PP9N/xtWnBO4FSnPt/GGunt1Ph4GLdBJ74ybN+GviODy9vPH29mHDhnUkJCZQvUYtACaO/4NcuWxo064DAA0bNWFg/59Zt3YVJUqW5tCB/dy4fo0er7XKHT50AAsLS3LnzsPt27eYO3sGpcuUo9jLSU4cnZywz5uX6VMn075jZ8zMzTl+7AiBZ88wZNj7d+PSplGTZkyaOA5PLx+8vX3YuGEtCYkJVKuR2tXyr/Fjsc5lo+5m2aBRU37r35d1a1dRsmRpDh7Yx43r1+jesw8ApqY5KFS4CAEL5mBoZJjapfPCefbt2UX7Tl2B1O3fpNm3/L10EW7uHri5e7B3904e3L/HgEGZ2yVFoVBQqe737Fw3m9z2zuTK48DWf6ZhYZWbwiXTftBMG9mRIiWrUrF2SwASE+IIfZy274U9ecD920GY5rTA2sY+U2N+m9T98U+8vLzw9s7Hhpfb79X+OGH8OHLlykXbl/tjcnIy9+6mluP582TCwp5yMzgYYxPjTxoP+j4aNPmGqRPH4Onlg5d3fjZtWE1iQoJ6UovJE0aTK5cN37dN7WlTv2Ez/Af0YsPafyhesgyHD+4l+MZVuvb8WZ1no2bfMfGP4RQoVJRCRXw5e/ok/544ysixk9RpIsLDiIwI59GjBwDcuX0LExMTbPLYYmb28T1yFAoF1eq3ZMvqeeSxd8bGNi8b/p6JpXVu/F4bezdhaBf8Slehat3vAKjRoBULpg7F1bMAbl4F2b1pOUmJ8ZSv2hBAPfPmm6xt7NL1Gjl1ZCcpKS8oU6neR5fjVVky4xoaHxfLn8N6kpSYQJcBI4iPiyU+LhYAc3MrlC8nlAh5dI+E+DiiIsNITkrkzs2rADg4uaP/EbPJNmjyLdMmjsHDywdP7/xs2bCKxIR4qtSoC8CUCaPIlcuGVm27AFC34dcMHfATG9euoHjJshw+uIebN67SteevQGqroZm5hcZn6OnpY2lljYOj5nMKL5w7w5OQR1SvpTlhnS4pFArK1GjNwc2zsLZ1xSq3A3vXpV5D8712DV30Z1vyFatO6Wqpvw0TE54R/iTtPBb59D6P7l7BJIeF+qbopVPbMTWzwsI6L08eXGPb8lHkK1YNz0K6aa3MSh86eYrI2Bdd4YPUbp3169fn0qVL6srRK7/++ivffvstfn5+VK9enU2bNrF27Vp279Y+w5+Xlxd3795lxYoVlCxZki1btrBu3TqNNK6urty6dYvAwEAcHR0xMzN75+MYunfvzty5c2nRogX9+vXD2tqaGzdusGLFCubNm6d1xh0vLy9Wr17N0aNHsbKyYuLEiYSEhKgrfMbGxvTv359+/fphaGhI+fLlCQ0N5dKlS3To0CFdfl27dmXChAn8+uuvdOzYkdOnT6tnGM3I28rasWNH6tevz4sXLzQq1LpUt0lrEhPiCZgxmrhnsXjnL8rPQ6Zo3E188vgBMdGR6velK9QkJiqSdX/PJioitevKz0OnaAxc37d9LRv+SetCM2ZQ6o+RDj2HqC9qN69fYt2KOSTGx2Hv6Eqbbr9RvkrdTCnnu1gUL0TZPWmzyxYY/xsA9xav5XyHgRmt9tl9Va8jSYnxrA8YSkJcNC5exWj7yxwMXtte4U/uEheT1i3nwa1LzB+Ttv9sXZ46ptSvQmO+7pw6HqVgiRo0bDuUg5vnsHnpaGzs3WjRczKuPpozsH103JUqExUdybIli4iIiMDd3YPhI0arJ3IJDX2icVHJX6Agv/QbyNLFC1m8MIC8Dg4M8h+Gi2vauLvw8HDmz52d2hXPypqq1WrQvEVa7wN9fX2GDR/FwoD5jBzuT3x8AvZ589K7768aDzz/uPJUISo6iuVLFqrLM2zEmDfKk9Zal79AQX7u9xvLFgewZOEC8jo48Jv/cI3y/Np/MIsXzmfCn2OIjYkhdx5bvm/dnjp1026kNGrcjOSkJObPmUlMTAxu7u6MGPUH9vaf3hL7LtUaticpMZ5/5gwnPi4Gdx8/ug6cpbHvhYXc41lMpPr93eBLTBuRNqPz+sV/AlCqUkNa/Zg2rutzq1ipMlHRUSxdsvjl9nNnxIhRGtvv9WcbhoeH8VPPbur3a9esZu2a1RQqXISxf4xPl78uVahYleioSP5eGkBkRDhu7p74jxin7jb3NDREoxU7X4FC9PnVn+VL5rNs0TzsHRzoP/h3XFzd1WnKlPuKLt37snbVMubPnkJeByf6/TaC/AXTWqV2bNvIyuVpN3wG9/8JgB69+3/yDIq1m7QhKTGeJbN+J+5ZDF75fenlP01jXwp9fJ/Y1647JSvUIiY6gg1/zyT6ZffPXv7T1F06P8SRPevxK10V0xyffnMxM66ht4OvcvNa6mzD/bppTtTy5+wN5LZNPd4XTPudq5fSWtiG9v0+XZoPUb5iNaKjIlmxdAGREeG4unsyaMT4t+xrhen16xBWLJnH8kVzsXdwpN/gUTi/tq+9r707t+CTvxAOTi7vTvwJytdJvYZuWjSEhLhonL2K833fuRgYZHwNfXj7IovGpV1Dd6wYC0DR8o1p0iH1/zGRT9ixYuzL7qK5KVq2ERUbpp0zhABQqDLrgQ86kpKSgqOjI48ePSI4OBh3d82DeebMmYwfP5579+7h5ubG4MGD+eGHH9R/VygUrFu3jsaNGwPQr18/FixYQGJiIvXq1aNMmTIMGzZMPWYnMTGRVq1asWfPHiIjIwkICKBt27Ya+dy+fRs3NzfOnj2rfnD59evX1d0zExMTcXFxoXbt2kycOFHrgOHw8HDat2/Pnj17MDU1pXPnzty9e5eoqCj14x9SUlIYM2YMc+fO5eHDh9jb29O1a1cGDhyoNYbNmzfTp08f7t27R6lSpWjXrh3t27cn4uWsgwsXLqR3797vLCukdnFwc3OjYMGCbNny4c9yOXbl7S18/zXhRUpmdQg6E3/4y5r+/VMVscncWSI/NxXZ547mrRjdd9PLSh5mX0aLu64kqTL3GYqfU1jip43F/9IYKD+sp9GXLqdBfFaHoDMXQ76M5xnrSovyX+4153bHj3tOpS64ztvw7kT/IV98hU9kjdjYWBwcHAgICKBp06YfvL5U+L5cUuH7skmF78slFb4vl1T4vmxS4ftyfckVvjudG2fZZ7vMWZ9ln50ZvvguneLzSklJ4enTp0yYMAFLS0saNmyY1SEJIYQQQgghPpJU+ISGu3fv4ubmhqOjIwsXLszUqZiFEEIIIYTQRh7LoDvya15ocHV1/aRp44UQQgghhBBfDqk6CyGEEEIIIUQ2JS18QgghhBBCiC+KPIdPd6SFTwghhBBCCCGyKWnhE0IIIYQQQnxRZNIW3ZFvUgghhBBCCCGyKWnhE0IIIYQQQnxZFDKGT1ekhU8IIYQQQgghsimp8AkhhBBCCCFENiVdOoUQQgghhBBfFHksg+5IC58QQgghhBBCZFPSwieEEEIIIYT4oshjGXRHvkkhhBBCCCGEyKakwieEEEIIIYQQ2ZR06RRCCCGEEEJ8UWTSFt2RFj4hhBBCCCGEyKakhU8IIYQQQgjxRZFJW3RHvkkhhBBCCCGEyKakhU9kikKxh7M6BJ3acfhKVoegMyYV8md1CDqlDNqe1SHolB4vsjoEnXEzC8nqEHRKj+dZHYJOmaueZXUIOnMhyi6rQ9CpgnmeZHUIOvUk3iKrQ9AZL5vIrA5Bx6yyOoAMyRg+3ZEWPiGEEEIIIYTIpqTCJ4QQQgghhBDZlHTpFEIIIYQQQnxRpEun7kgLnxBCCCGEEEJkU9LCJ4QQQgghhPiyyGMZdEa+SSGEEEIIIYTIpqTCJ4QQQgghhBDZlHTpFEIIIYQQQnxRFAqZtEVXpIVPCCGEEEIIIbIpaeETQgghhBBCfFEUMmmLzsg3KYQQQgghhBDZlLTwCSGEEEIIIb4o8uB13ZEWPiGEEEIIIYTIpqTCJ4QQQgghhBDZlHTpFEIIIYQQQnxZZNIWnZFvUgghhBBCCCGyKWnhE0IIIYQQQnxRZNIW3ZEWPiGEEEIIIYTIpqTCpwO3b99GoVAQGBj4UesrFArWr1+v05g+hKurK5MmTXprmqyOUQghhBBCCPHhpEunDjg5OfHo0SNsbGwA2L9/P1WqVCEiIgJLS8t3rv/o0SOsrKwyOcqMnTp1ihw5cmTZ57/Nyl2HWbJlL2FRMXg55+XX1k0p5OGiNe3eU+cJ2LiLeyFPef4iBWdbG1rVrUy9CiU/c9SpVCoVe9ZO5dT+VSTExeDi5UfDtkOxsXPNcJ1bQac4tHUBD29fIiYylFa9plKgePV06Z48CGbHygncCjpFyosX5HHwoGXPyVja5M3EEr2bdYUSuP/cAYtihTDOm4d/m/1IyMY9WRqTNps3bWTNmtVERETg5uZO124/4uPjozXtnTu3WbpkCTduXOfJkyd06tyFxo2baKS5eOECa9as5saN64SHhzN48BDKliv3OYoCwMZNm1m9Zg0RERG4u7nxY7euGZbn9p07LFmylOs3bvDkyRO6dO5Ek8aNNdJs3rKFzVu28iQkBABnFxdatWhByZIldB77lk0bWLdmJRER4bi5edC5Ww+8ffJlmP7woQMsW7KQJyGPyZvXgTbtO1GiZGn13yMiIlgUMJfAM6eJfRZLwUKF6dK1B3kdHAGIiYlm+dJFBJ45TWjoE8wtLChTtjytfmhLjhw5dV6+TZs2aWybbt26vWVfu8OSJUvU26Zz587pts0///zDkaNHuX//PoaGhhTIn5/27dvj6Oio89i12bB5KyvXric8IhIPN1d6dOlIPh9vrWlv37nLwmV/c/1GMCFPQunWqT3NGjVIl+7p0zDmLlzMydNnSExMIq+9Hb/27omPl2dmFweVSsW+9VM5fWAVCXHROHsVo/4PQ8n1lvP07aunOLJtPo/upJ6nv+s5jfzFNM/TiQnP2L1qAkFn9xAXG4lVbkdKV/+BklW+01ns2zavZeOaFURGhOPi5kGHrr3w8imQYfqjh/axYul8QkMeY5/Xge/bdaVYybJa086eNp5d2zbStlMP6jf+Vr385o2rLA2YzY3rQSiVSsqUq0SbTt0xMTH95PKoVCo2/zODI7vXEh8Xg7uPLy06DyKPvfZr/isHtq1g18ZFREc+xdHFm287DMDVqzAAYU8e4P9jXa3rdez7J8XK1eTYvg0smT5Ea5o/5u/FzCLXO2PfuWU1W9YtJSoiHGc3T9p0/hkP74IZpj9xeA+rls3h6ZNH2OZ1okWb7viWSLtmqFQq1iyfy76dG3j2LBbv/IVp360fdnmdAbh84TSjBnXXmveICQvw8CpAUlIiC2b8wa3gqzy8dxu/kuXpO2jcO8vyJVEopF1KV+Sb1AE9PT3s7OzQ1/+w+nNSUhIAdnZ2GBkZZUZo7yV37tyYmn76yVrXdh4/y1/L1tOpSS2W/v4z3s556fnHbMKjYrSmN89hSvuGNQgY2psVo3+lQcVSjJizgmPngz5z5KkObZnHsV1LadR2GN2G/oOBkSkL/+xEclJihuskJcZj7+xDg9b+GaYJC7nLnN9bkdvejY4DF9Fz1HqqNOqGvmHW7UOv6OUwJfr8VS7+NDyrQ8nQwQMHmDt3Li1bfs+UqdNwc3fH338QkZGRWtMnJiZiZ29H23btM7wxk5CQgJubG91+1H4BzkwHDhxk7ty5fN+yJdOmTsHd3Y1B/v7vLE/7dm0zLI+NjQ3t27Vl6pTJTJk8Gd+iRRg+ciS379zRaeyHDuxj/txZfNfyB/6aOgtXd3eG+g8gMjJCa/orly8x/o9R1KhZm0lTZ1G6bHlGjxzKndu3gNQfSaNHDuHxo0cMGjKcSVNnkSePLf6/9SMhIR6A8LAwwsPCaNexC1NnzqNXn36c+fcUUydN0GnZAA4cOMCcuXNp1bIlU6dOxc3dncFv2TYJiYnY2dvTrl27DLfNhYsXaVC/Pn9NnMjoUaN4/uIFgwYNIiEhQefxv2nfwcPMmhfADy2aM2vyBNzdXBkwZAQRbymPvZ0tHdv8gHUG5YmJjaVXv4Ho6+szZpg/82dMoWuHdpjl/Dw3IQ9vnceJXUto0HoYnfxXYmBowpKJHUlOzvg8nZwYj51TPup9r72SALBjxVhuXDxM087j6DF6C2VqtGbr0pEEnd2rk7iPHNzDornT+aZlW8ZNmYermye/+/9CVAbHTtDlC0waN4JqNevx55R5lCz7FeN+H8Td2zfTpT1x9CDXgy5jnctGY3l42FNGDOqLXV4HxkycxeARf3Lv7i2m/zVGJ2XatT6A/Vv/pkXnwfw6eilGRiZMHdntrdfMf49sZ82i8dT7pgsDx63AwdWHqb93IyYqDACrXHaMmbtH41W/eTeMjE0p4FcBgOLlaqVLU8C3HF4FSrxXZe/YoV0smz+Zpt915Pe/FuHs6sXYob2JigzXmv7alfNMGz+EyjUaMGrSIkqUrsjE0f24dydYnWbz2iXs2LySdt36M+LPeRgZmTB2aG+SXn4X3vmKMH3RFo1X5ZoNyW2bF3fP/ACkpKRgaGhErfrfUKho1tz4Fl8OqfC9p5SUFMaNG4enpydGRkY4OzszatQoQLNL5+3bt6lSpQoAVlZWKBQK2rZtC0DlypXp0aMHvXv3xsbGhlq1agHpu0vev3+fFi1aYG1tTY4cOShRogQnTpzIMLb+/fvj7e2Nqakp7u7u+Pv7k5ycrJFm06ZNlCxZEmNjY2xsbGjSJK2F4s0undevX6dixYoYGxtToEABdu3a9Slf3Udbtm0/jauUpWGl0rg72DGw3TcYGxmy8YD276JEAU+qlCyCm4MtjrY2tKhdCU8newKvpr+gZTaVSsWRHYup3LArBYpXw87Zh2+6jCUm8glXzuzOcD2fohWp8XVvCpaokWGaXasn4VO0IrW/+5W8rgXIZetM/mJVyWn+7gtTZgvdcZBrQycRsiHjMma1devWUrt2bWrUrImzsws9evTE2MiInTt3aE3v7e1Dhw6dqFSpMgYGBlrTlChZktZt2lKuXPnMDF2rtevWUbt2bWrWrIGLszM9e/TAyMiYHTt3ak3v4+1Npw4dqFypUoblKVO6NKVKlsTBwQFHRwfatmmDsbExQUG6vXmyYd0aatauS/WatXF2duHHHr0xMjJi987tWtNv2rCWYsVL0vTr5jg5u/B963a4e3iyZdMGAB4+eMDVoCv82KMXXt75cHR0olv3XiQlJXFw/z4AXFzdGDh4GKVKl8XePi9Fff34vk17Tp44zosXL3RavnXr1lGndm1q1qz52rYxYudbtk3Hd2yb30eOpEaNGri4uODu7k7fvn15EhrK9evXdRq7NmvWb6RurRrUrlENF2cnenfvipGREdt3aW/Fz+ftRZf2balS6SsMDLTfEF2xei25bWz4tXdP8vl4Y29nS4livuS1t8/MogCp5+njuxZTsUFX8hWrhp2TD007/UFMxBOC3nKe9ipSkWrNepO/eMbn6Xs3AilavjFu+UpjZeNIicrNsXXy4cHN8zqJfdO6lVSvXZ+qNeri5OxK5x4/Y2RszN6dW7Sm37pxNb7FS9GoWQscnV1p8UNH3Dy82bZ5rUa6sKehzJ81mV6/+qOnp7nNTp88ip6+Ph279cHB0RlP7/x07vEzx48c4NHD+59UHpVKxd4ty6jdrBNFS1XB0dWbNj1/JyoilHMnM64k7920hPLVm1K2amPsnTxo0XkwhkbGHN27HgClnh4WVjYar8ATeylWribGL1slDY2MNf6uVCq5evEk5ao1fq/Yt234myo1G1Gpen0cnd1o/2N/jIyMObB7s9b02zf9Q5FiZajf9HscnNz45vsuuLr7sHPLavV3sX3jPzT+th0lylTE2c2Lbn2GEhn+lNPHDwKgb2CApVUu9SunmQVnThyiUrX6KBSpE50YG5vQ/sf+VK3VGAsr6/cqyxdHqci6VzYjFb73NHDgQMaOHYu/vz+XL19m+fLl2Nrapkvn5OTEmjVrALh69SqPHj1i8uTJ6r8vWrQIQ0NDjhw5wqxZs9KtHxsbS6VKlXjw4AEbN27k3Llz9OvXj5SUlAxjMzMzY+HChVy+fJnJkyczd+5c/vrrL/Xft2zZQpMmTahbty5nz55lz549lCpVSmteKSkpNG3aFENDQ06cOMGsWbPo37//e39PupL8/DlBt+5TumBaVyGlUkmpgl6cv/HuVgaVSsXJi9e48zgUv3wemRmqVhGh94mNeopHwbTuMsamZji6F+HujXMfnW9KSgpXzx0gl50rAeM6Mrp7eWYOa87l019uBetLkpyczI0b1/H19VMvUyqV+Pr6ERR0JQsj+zjJyclcv3EDP19f9TKlUomfry9XdFQ5e/HiBfsPHCAxIYH8+fPrJE94tS2u4etbTL1MqVRS1LcYQUGXta4TFHSZon7FNJYVK15SnT45ObXXhIGhoUaeBgYGXL58McNY4p49w9TUFD09vY8uz5tebRvfN7aNrw63DaTGDqnXgcyUnJzMtRvBFPMtql6mVCop5luEy0FXPzrfYydO4e3lyYgx4/i6VRu6/NSXLdu1V4h1LfU8HYp7wbSudMamZjh4FOHejcBPytvJ05erZ/cSHRGCSqXi1pXjhIXcxqPQp98USk5O5uaNaxTxTetirVQqKexbnKtBl7Sucy3oEkV8i2ss8y1WimuvpU9JSWHqhN9p1Ow7nFzctH6uvr4+yteejWb4smdJ0KULn1SmsCcPiI58Sr4iad2zTXKY4epVmJvXtFeSnycnc/fmFXyKlFEvUyqV5CtchltXta9zN/gy929fpVzVJlr/DnDiwCYMDU3wK5Nxhf71GG7duEoh37QWNKVSSaGiJbkepP07uRF0MV2LW5FiZbjxMn1oyEMiI8Io+Foa0xw58fAuyPWr2vM8c/IgMTFRVKxe/50xi/9PMobvPcTExDB58mSmTZtGmzZtAPDw8KBChQrp0urp6WFtnXonJU+ePOnG8Hl5eTFuXMZ9qJcvX05oaCinTp1S5+Pp+fZxDIMHD1b/39XVlV9++YUVK1bQr18/AEaNGsV3333H8OFp3eyKFi2aLh+A3bt3ExQUxI4dO8ibN3U82OjRo6lTp06Gn5+YmEhiomaXi6SkZIwMtd+hfh+RMc94kZKCtYXmjxhrCzNuP3qS4XqxcfHU6TmMpOfP0VMq6d/2a8oU1j5eJjPFRD0FIOcb3UFyWtgQGxn60fk+iw4jKSGOg5vnUePrn6jV/Geunz/M8ik/0WHgQtzyaa/Ii1TR0dGkpKRgaWWpsdzS0pJ79+5lTVCfIDPLc+vWbfr8/DNJSUmYmJjg7z8YF2fnT8rzddHRUS9j1+zqZ2lpxYMMYo+MiMDS8s30lkREpHadcnRyJnfuPCwOmEf3nn0wMjZm4/o1PH0aSkR4mPY4oqL45++l1KpTTwelei3fl9vmza6ZVpaW3NfRvpaSksLs2bMpUKAArq6uOskzI1HRManlsbTQWG5lacm9+w8+Ot9Hj0PYtHU7XzduSItvv+bq9RtMnzMfAwN9alar+qlhv1VsVOq5+M3eETnNbYh9eQ7/WHVb+bNxoT8T+lZCqaePQqGgYduRuPp8ete6mOgoUlJeYJHuWLDmwb27WteJjAjH0lKzlcfC0orIiLRuh+tXL0epp0fdhl9rzaNw0WIsmjeNDWv+pm7Dr0lMSGDZwtkARERoP77eV1RE6vdtbqm5LcwtchEdqX1bxMZEkJLyAvM3rrNmlrkIeXBL6zpH9q7DztEdj3y+GcZydO96SnxVB0Mj43fGHRMd+XJbaH635pZWPHxwW+s6kZFh6dKnbovU7/DVv+nTWKv/9qb9uzZRxK80uWzyvDPm/xKFPHhdZ6TC9x6uXLlCYmIi1apV++S8ihcv/ta/BwYG4ufnp67svY9//vmHKVOmEBwcTGxsLM+fP8fc3Fwjz06dOr1XXleuXMHJyUld2QMoW1b7oO5XxowZo1GZBBjQsSW/dW713mXQFVNjI5aP+oW4xCROXbrGX8vW45A7FyUKZO7g/8Cjm9gQMEz9vvXPMzPlc1QqFQD5i1WlfO22AOR1yc/dG2c5ufcfqfAJnXF0dGDGtKk8e/aMQ4ePMGHCRMaN+0OnlT5d09fXZ+DgYUydPIGWzZukthj6FaN4iVLqY+d1cXHPGDF0EE7OLrRo1ToLIv4002fM4PadO4wfPz6rQ/loKpUKb08POrT5HgAvD3du37nLpq07dF7hO39sE5sWDVW/b9U7fS8bXTmxewn3b56jZa8ZWORy4M7VU2xZOgIzyzx4FPx8Ezq9r+DrV9m6YTXjpsxTdwl8k5OLGz36/saiudNZtnAOSqWSug2bYWlpneE6GTl5cAt/zxmpft9t4LRPiv99JCUm8O+hbdT5OuPfQzevnuPx/Zu07Tkq0+PRlbCnTzh/9gQ/9fs9q0MRXzCp8L0HExMTneX1rtkwP/Szjh07RqtWrRg+fDi1atXCwsKCFStWMGFC2gQEuoxfm4EDB9K3b1+NZUkX9n1SnpZmOdBTKtNN0BIeFUMuC/MM1krtSuFklxsAHxcHbj0IYeGm3Zle4cvvVxUnjyLq989fdi2LjQrD3DLtjlts1FPsXT6+W5ypmSVKPX3yOGh2U82d15071858dL7/L8zNzVEqlURGRGosj4yMxMo662bK/ViZWR4DAwP1jR8vLy+uXb/G+g0b6NWz5yfl+4q5ucXL2DUnmYiMjMAyg9gtrazSTegSGRmJ1WvjUzy9vJk8bTbPnqXe/LKwsOSX3j3w9NKcSTIuLo5h/gMxMTXhN//hHzzp1ru82jYRb5QvIjISqw+4oZeRGTNmcPLkSf4cN47cNjbvXuETWZibpZYnMkpjeURkJFZvtDB/CGsrK1ycnTSWOTs5cujIsY/OMyM+vlVwcE87T794/vI8HR2G2evn6ein2Dl9/Hk6OSmBPWsm8V3PqXgXrQyAnZMPj+8GcXT7gk+u8JmZW6BU6qWboCUyMhzLDMZqWVpZE/nGJCJRkRHq9FcunSMqKoKubb9R/z0l5QWL589gy4bVzAxYCcBXlWvwVeUaREaEY2RsjEKhYPP6ldjafdgM0UVKVlbPpAnw/OW2iI4Mw8Iqt3p5dFQYjq7ae+nkNLNCqdQjOkqz1SsmMgxzy/THxNnju0hKiqd0pfQzxb5yZM9aHF19cPbIeLbT15mZW77cFprfbXRkBBaW2sfVW1rmSpc+dVukpn/1b1RkOFbWNq+lCcfF3Stdfgd3b8bMzIJipSq+V8zi/5O0lb4HLy8vTExM2LPn/aaXN3w5fuRjJgAoUqQIgYGBhIdrn93pTUePHsXFxYVBgwZRokQJvLy8uPPGTHpFihR579jz58/PvXv3ePTokXrZ8ePH37qOkZER5ubmGq9P6c4JYKCvTz43R05euqZelpKSwqlL1yni+fYpml+XolKRlPz8k2J5H0YmOchl66J+5XHwJKeFDTcvp313CfGx3L95HmdP7d1p34e+viGOboV4+kizu8rTx7exzJW1j2T4LzAwMMDT04vAc4HqZSkpKQQGBpIvn+7Gp30uBgYGeHl6ai1P/nwZP9rgY6hSVOkmg/oUqdvCm3Pn0m5UpKSkcD7wLPnyaf+xlS9fAc4HntVYFnj2tNb0OXLkxMLCkocP7nPjxjVKl037kR0X94yhg/ujr6/P4CEj1edsXUrbNmljdnWxbVQqFTNmzODosWOMHTMGOzs7XYT7TgYGBnh7enDmXNrYqJSUFM6eu0CBfB/fbb5ggXzpuoTef/AQ2zy5M1jj4xmZ5NQ4T+fO60lOi9zcvJxWuUyIj+VB8HmcPH0/+nNevHjOixfJ6aaUVyiVqFQZj8d/XwYGBrh7enMh8LR6WUpKChcCz+CTT/ujALzzFeTCOc2bgufOnsL7ZfpKVWsxYVoA46fOV7+sc9nQsOl3DB6ZvgXZ0soaExNTjhzci4GBIUX9PuyRLcYmOchj76x+2Tt6YG5pw9ULaZOyxcfFcvv6Bdy9i2jNQ9/AAGf3/BrrpKSkcPXCCdx80q9zdM96ipSojJmF9kpxQnwcZ47upFy1jMf3aYvBzdOHS+dOacRw8fwpvPIV1rqOZ75CXDp/SmPZxcCTeL5Mn9s2L5ZWuTTyjIt7RvC1S3j5aOapUqk4sGczFarU0flNqy+BQqnIsld2IxW+92BsbEz//v3p168fixcvJjg4mOPHjzN//nyt6V1cXFLvem3eTGhoKLGxse/9WS1atMDOzo7GjRtz5MgRbt68yZo1azh2TPvdTi8vL+7evcuKFSsIDg5mypQprFu3TiPN0KFD+fvvvxk6dChXrlzhwoUL/PHHH1rzq169Ot7e3rRp04Zz585x6NAhBg0a9N7x61KrOpVZv/84mw+e5NaDEMYErCY+MYkGlVIHdQ+ZtYxp/6TNghWwcTfHL1zl/pOn3HoQwtKt+9h65F/qltf9s8PeRaFQUL5Wa/ZtmMWVM3t5fO8aq2cPwMwyj8bzmuaPbcexXcvU7xMTnvHwzhUe3kmdQCQi9D4P71wh8ulDdZoKddtz4cR2Tu1bSVjIHY7tWsbVs/spXa3F5ytgBvRymGJeNB/mRVN/0Jq6OWJeNB/GTpk/4977atKkKTu2b2P37l3cvXuX6dOnkpCYQI0aNQGYMP5PFgYsUKdPTk4mODiY4OBgnj9/TljYU4KDg3n4MG2bxMfHq9MAPA55THBwME+eZDzeVFeaNmnCtu072LV7N3fv3mXq9OkkJCZQs0bqhAN/jp/AgoCFGZbnaVhYuvIsCFjIhQsXeRwSwq1bt1kQsJDzFy5QtXIVncbeqEkzdm7fyp7dO7l39w4zp08mITGBajVqA/DX+LEsCpinTt+gUVPOnD7FurWruH/vLsuXLuLG9WvUa9BInebwoQNcOB/I40cPOX7sCEMG9ad0mXL4FUs9D8TFPWPIoP4kJCTQs/cvxMXFEREeTkR4uM5n6WzSpAnbt29Xb5tp06eTmJhIjZfbZvz48QQEBKjTp9/X0m+b6TNmsHffPvr164eJiQnh4eGEh4enG0edGZo1bsjWHbvYuWcvd+7dY/KM2SQkJFC7eupwh7ETJjNv4RKN8ty4eYsbN2+p97UbN2/x4GHaDcVmjRpw5eo1lq9czYOHj9iz/yBbt++kUb2Mx43rikKhoEyN1hzcNIugs3sJuXeVdXP7Y2aVh3yvnacXjmvLid1L1e8TE57x6O4VHt1NO08/unuFyLDU7WRskhNXn5LsXPknt4JOEBF6n7OH13Lu6AbyFXv3RCDvo0GTb9m9YzP7d2/j/t3bzJ0+gcSEeKrUSH3m3JQJo9Tj6wDqNvyawNMn2Lh2BQ/u3eGfZQu4eeMqdeo3BVJbDZ1d3TVeenr6WFpZ4+CY1o1726Y13LxxlYcP7rFt81rmz5pEy7adyZHz0yYNUigUVK3Xim1r5nL+1H4e3LnOoqmDsbDKTdFSaV17Jw/rxP5tf6vfV23wA0d2r+X4/o08un+TFXN/JzExnrJVGmvk/+TRXW5cOU25ak0zjOH00e2kpLygVMUPG89bp1EL9u3cyME9W3hw7xYBM8eRmJBApWqp+cz8azgrFs1Qp6/doDnnzxxny7plPLx/mzXL53LzxhVq1vta/V3Ubtic9SsXcvrEQe7evsGsv4ZjaW1D8TKarXiXzv9LaMhDqtRsqDW2+3dvcfvmNZ7FRhMXF8vtm9e4ffOa1rQie8t+twMyib+/P/r6+gwZMoSHDx9ib29P165dtaZ1cHBg+PDhDBgwgHbt2tG6dWsWLlz4Xp9jaGjIzp07+fnnn6lbty7Pnz+nQIECTJ8+XWv6hg0b0qdPH3r06EFiYiL16tXD39+fYcOGqdNUrlyZVatWMXLkSMaOHYu5uTkVK2pv+lcqlaxbt44OHTpQqlQpXF1dmTJlCrVr136v+HWpZhk/IqJjmbVmO2FR0Xi7ODC1XxdyvZzI5fHTCJSvjRuIT0zij4WreRIehZGhAa558zCy2/fULOOX0Udkqq/qdSQpMZ71AUNJiIvGxasYbX+Zg8Frz8sLf3KXuJi0bjkPbl1i/pg26vdbl6dWzP0qNObrzqnPOipYogYN2w7l4OY5bF46Ght7N1r0nIyrz9vHh34OFsULUXZP2g++AuN/A+De4rWc7zAwq8LSULFSJaKio1i6ZEnqw7Dd3Rkx4nf15BqhoU807u6Fh4fxU8+05+utXbOGtWvWULhwYcb+8ScA169fY+CAtNls582dA0C16tXp2/eXTC1PpUoViYqOYsmSpery/D5ihLo8T0JDNcoTFh5O954/qd+vWbOWNWvWUrhwYf78YywAkVGR/DlhAhHh4ZjmyIGbmyujRo6kWDHdHktfVapCVHQUy5csfBm7B8NGjHljW6Tdl8xfoCA/9/uNZYsDWLJwAXkdHPjNfzgurmkzCkaEh7Ng7iwiIyOwsrKmSrUaNG/xvfrvwTeuc+1q6iyZXTpojtubG7AUW1vdtZhVqlSJqOholi5ZQnhEBB7u7oxMt23SyhceHk6P17rMrlmzhjUv97VxL2/SbdmSOu3+m7Mn9+3TR12RzCxVKlYgKiqahUtXEBERgYe7G2NGDFF36XwSGopSY1+LoOtPad39V63dwKq1GyhSqCATx6aON8rn7cXwQf2Zt2gpS/5eib1tHrp1ak+1KpUytSyvVKjbkeSkeDYtHJL64HXv4nzfdy4GBmnn6Ygnd4mLTTtPP7x9kYV/pJ2nd6xIPW58yzemScfU/3/dbSK7V09kzexfiX8WhWWuvFRr1ltnD14vX7Ea0VGRrFi6gMiIcFzdPRk0Yry6i+bT0BCN62O+AoXp9esQViyZx/JFc7F3cKTf4FE4u7p/0OdevxbEP8sCSIiPx8HJmS49fqFS1Vo6KVONxu1ITIxn+ewRxD2LwSOfHz0Gz9C4ZoaG3Cc2OlL9vkT52sRGR7B5xYzUB6+7+tBj0Ix0k78c27sey1y25C+a8ZwER/esx7dUNUxzZDxsRJuyX9UgJiqS1cvnEhURhou7F/2H/YXFy66ZYaGPNcY4eucvQvefR7Bq2WxWLpmFXV4n+v42DieXtKEa9Zv+QGJCAvOnjyXuWSzeBYrQf9gk9ayor+zftQmvfIXJ6+iqNbY/R/Th6ZPH6veDeqee85ZtfHvPrS+GPHhdZxQqbSPZhfhEMae2ZnUIOrUj5fNXeDOLSYX/XtfFt/EJ0v7Mtv8qPXTbypSVkjB6d6L/EAOSsjoEnTJIyfxWwc/lSGj2Oq8VzJP5vQM+pyfxFu9O9B9hYRif1SHoVAmfL3f8etT4Xln22Ra/TH53ov8QqToLIYQQQgghRDYlXTqFEEIIIYQQX5TsOHlKVpEWPiGEEEIIIYT4SNOnT8fV1RVjY2NKly7NyZMn35o+MjKS7t27Y29vj5GREd7e3mzdmnnDoaSFTwghhBBCCPFlUf432qX++ecf+vbty6xZsyhdujSTJk2iVq1aXL16lTx58qRLn5SURI0aNciTJw+rV6/GwcGBO3fuYGlpmWkxSoVPCCGEEEIIIT7CxIkT6dSpE+3atQNg1qxZbNmyhQULFjBgwIB06RcsWEB4eDhHjx7FwCD1udWurq6ZGuN/o+oshBBCCCGE+L+hUCiy7JWYmEh0dLTGS9vzTpOSkjh9+jTVq6c9u1OpVFK9evUMn6G9ceNGypYtS/fu3bG1taVQoUKMHj1a58+CfZ1U+IQQQgghhBDipTFjxmBhYaHxGjNmTLp0T58+5cWLF9ja2most7W15fHjx+nSA9y8eZPVq1fz4sULtm7dir+/PxMmTOD333/PlLKAdOkUQgghhBBCCLWBAwfSt29fjWVGRrp5tmxKSgp58uRhzpw56OnpUbx4cR48eMCff/7J0KFDdfIZb5IKnxBCCCGEEOLLkoWTthgZGb1XBc/GxgY9PT1CQkI0loeEhGBnZ6d1HXt7ewwMDNDT01Mvy58/P48fPyYpKQlDQ8NPC14L6dIphBBCCCGEEB/I0NCQ4sWLs2fPHvWylJQU9uzZQ9myZbWuU758eW7cuEFKSop62bVr17C3t8+Uyh5IhU8IIYQQQgjxhVEoFVn2+hB9+/Zl7ty5LFq0iCtXrtCtWzeePXumnrWzdevWDBw4UJ2+W7duhIeH06tXL65du8aWLVsYPXo03bt31+n39zrp0imEEEIIIYQQH6F58+aEhoYyZMgQHj9+jK+vL9u3b1dP5HL37l2Ur3VPdXJyYseOHfTp04ciRYrg4OBAr1696N+/f6bFKBU+IYQQQgghhPhIPXr0oEePHlr/tn///nTLypYty/HjxzM5qjRS4RNCCCGEEEJ8WRQy8kxX5JsUQgghhBBCiGxKWviEEEIIIYQQX5YPnDxFZExa+IQQQgghhBAim5IWPiGEEEIIIcQXRSFj+HRGvkkhhBBCCCGEyKakwieEEEIIIYQQ2ZR06RSZ4oF1kawOQaeKcD+rQ9AZZdD2rA5Bp67mq53VIeiUe9DerA5BZxzDArM6BJ0yiHic1SHo1O15/2R1CDqj/0v2Oq/FJptkdQg6deBM9mlfKFM4Z1aH8P9DJm3RmexzBAohhBBCCCGE0CAtfEIIIYQQQogvikIp7VK6It+kEEIIIYQQQmRTUuETQgghhBBCiGxKunQKIYQQQgghviwKmbRFV6SFTwghhBBCCCGyKWnhE0IIIYQQQnxZZNIWnZFvUgghhBBCCCGyKanwCSGEEEIIIUQ2JV06hRBCCCGEEF8WmbRFZ6SFTwghhBBCCCGyKWnhE0IIIYQQQnxRFDJpi87INymEEEIIIYQQ2ZS08AkhhBBCCCG+LAppl9IV+SaFEEIIIYQQIpuSCp8QQgghhBBCZFPSpVMIIYQQQgjxZVHKYxl0RVr4hBBCCCGEECKb+r+v8LVt25bGjRu/NU3lypXp3bu3Tj932LBh+Pr66jRPIYQQQgghsgOFQpllr+zm/75L5+TJk1GpVFkdxv+lLZvWs37NSiIiwnF186Bzt554++TLMP2RQwdYtiSAJyGPyZvXkdbtO1GiZGn13+Pj41kcMJcTx44QExNNHls76jdsSp16DdLlpVKpGDFkIGdOn2Lg4OGUKVdBB+XZwNo1q4iICMfNzYMu3bq/tTyHDx1g6ZJFL8vjQNv2HTXKExERwcKAuQSeOU3ss2cUKlSYLl27k9fBMS1NeDgL5s8hMPAM8XHxODg68m3zlpSv8NUnl+dNmzdtZM2a1URERODm5k7Xbj/i4+OjNe2dO7dZumQJN25c58mTJ3Tq3IXGjZtopLl44QJr1qzmxo3rhIeHM3jwEMqWK6fzuD+FdYUSuP/cAYtihTDOm4d/m/1IyMY9WR0WWzetZ92af4h8eex06tYTb5/8GaY/cmg/y18eO/bqY6eM+u+N61bVul6b9p1p8vV36vf/njzOP8sXc+f2TQwMDSlYqCi/DRmps3K9snLXYZZs3UdYVAxeTnn5tXUTCnm4aE2799R5Ajbt5l7IU54/T8HZzoZWdSpTr0IJjTRr9h4l6PZ9omLjWPb7z/i4OOg8bm1WHDzDor0neRr9DG+HPAz4ujqFXezfud6201cYsGgTVQp7MqlTU/Xy3eeusepwIFfuPSYqLoF/+rUhn6NtZhZBg2WtBuRq+A16ltYk3rlJyILpJNy4mmF6q7pNsKxVHwObPLyIjibm+CFCl89HlZwMgHXj7zArXR5DBydUSUnEX71M6LJ5JD28/1nKo1Kp2LN2Kqf2ryIhLgYXLz8ath2KjZ1rhuvcCjrFoa0LeHj7EjGRobTqNZUCxatrpBnUWvvxWLv5L3xVr4POYl/392wO7FpP3LNYvPIVoXXXAdjldX7reru3rmTbuqVERYbh7OrF951+xd27IACxMVGs+3sOlwKPE/Y0BDNzS4qVrkzTll0xzZFTncfSueO5fuUcD+4GY+/oyshJy3VSpvdR1VeP4l5KjA3h7hMVm44/Jzzm/db9qpCSGsX1OXb5BdtOvcjcQN+gUqnYvno6x/auJuFZDK4+fnzT3p/c9trPbQDBV/5l7+YA7t+8THRkKO37TqZwyWrqv794nszWlVO5EniIsCf3MTbJiXfhMtT/rg8W1nk+R7HEf8R/ugqblJT0yXlYWFhgaWn56cH8n9DFdw5w6MA+FsydRfOWrZk4dRZu7h4M8+9PZGSE1vRXLl9i/B+/U71mHf6aOpvSZcszZuQQ7ty+pU6zYO5Mzpw+RZ9fBzJtdgANGzdjzswpnDh+NF1+G9evQaHQXd/wQwf2M2/ubFq0/J5JU2fi5u7OEP+Bby3Pn3+MpmbN2kyeOpMyZcszauQwdXlUKhWjRg4l5NFjBg0ZweSpM8mdx5bBv/UnISFenc/ECX/w4MF9/IeMYNqMOZQrV4FxY38nOPiGzsoGcPDAAebOnUvLlt8zZeo03Nzd8fcfRGRkpNb0iYmJ2Nnb0bZde6ysrLSmSUhIwM3NjW4/dtdprLqkl8OU6PNXufjT8KwORe3wgX0smDuT71q2ZuLU2bi6ezD8LcdO0OWLTHh57EycOofSZcsz9o1jJ2Dpao1Xz96/olAoKFu+ojrN0cMHmTR+DNVq1OavaXMZO34KFStX0/aRn2Tn8bP8tXwDnZrUYunIvng756XnuDmER2n/RWee05T2DasTMKQXK0b/QoOKpRgxdwXHzgep08QnJuHr7UbP5vV1Hu/bbD9zhfHr9tGldnlW/NoGH4fcdJuxkrCYZ29d70FYFBPX76OYh2O6v8UnJuPn7kDvhpUyK+wMmZWrRJ42XXi6aim3+/9I4p2bOA0ajZ65pdb05hWqkLtVB8JWLeVW7448mjkRs3KVyN2yvTqNacHCRO7YyJ3fenFv5AAU+no4DR6Dwsj4s5Tp0JZ5HNu1lEZth9Ft6D8YGJmy8M9OJCclZrhOUmI89s4+NGjtn2GaAVMOaryadhyFQqGgYMmaOot967rF7Nr8D226DmTIuACMjE2YMLwnSW+J/cThnaxYMInG33Vk+MQlOLl6MX54T6IjwwGIDA8lMjyU5m17MWryCjr+NJQLZ4+xYFr6GztfVW9AqQo1dFae91GhkJLS+ZVsOv6cOVufk/QcWtcwQP89fs3mzaWghLcej8NTMj9QLfZuWsDB7cv4psMQeo9cjpGRCbPGdnnnvubg7EOz9oO0/z0pgfu3LlOjSRd+Hr2Sdn0n8eThbeaN75FZxRD/Uf+pCl/lypXp0aMHvXv3xsbGhlq1agFw8eJF6tSpQ86cObG1teWHH37g6dOn6vVWr15N4cKFMTExIVeuXFSvXp1nz1IvuG926Xz27BmtW7cmZ86c2NvbM2HChHRxKBQK1q9fr7HM0tKShQsXqt/3798fb29vTE1NcXd3x9/fn+SXdzTfR0REBK1atSJ37tyYmJjg5eVFQEAAAPv370ehUGj82A4MDEShUHD79m31srlz5+Lk5ISpqSlNmjRh4sSJGpXb4OBgGjVqhK2tLTlz5qRkyZLs3r1bIw5XV1dGjhxJ69atMTc3p3Pnzu9dhrfZsG41NWvXpXrN2jg7u9KtR2+MjIzYvXO71vSbNqylWPGSNP26OU7OLrRq3Q53Dy+2bFqvThN05RJVq9WkcBFfbG3tqFWnPm7uHly/GqSR183gG2xYu4qevX/VSVkA1q9bQ63adV6Wx4Ufe/TCyMiIXTt3aE2/ccO6l+X5FidnF75v3RYPD082b9oAwMMHD7gadIVuPX7C29sHR0cnfuz+E0lJSRzYv++1Ml+mfoNGePvkw87enuYtWpEjRw5uXL+ms7IBrFu3ltq1a1OjZk2cnV3o0aMnxkZG7MygfN7ePnTo0IlKlSpjYGCgNU2JkiVp3aYt5cqV12msuhS64yDXhk4iZMPudyf+TDasW0XN2nWpVrMOTs6udOvRByMjI/bs3KY1feqxU4omX3/38thpj7uHF1tfO3asrK01XieOH6VQEV/s7PMC8OLFC+bPnkabDl2oXa8hDo5OODm7UqFiZZ2Xb9m2AzSuXIaGFUvh7mDHwHZfY2xkwMaDJ7WmL5HfkyoliuDmYIujrQ0talXE08mewGtpFdp6FUrQqUktShX01nm8b7Nk3780LVeExmUK42Fvw+Bva2FsaMD64xcyXOdFSgq/Ld5Mt7oVcMxlme7vDUoVpGud8pT2cc28wDNgXb8ZUXu2EbV/J0n37/J4zmRSkhKxqFpLa3oTnwLEX71E9OF9JIeGEHf+NDFH9mHsmdYz4P6oQUTt30XS/Tsk3rnJo+njMchti7G7V6aXR6VScWTHYio37EqB4tWwc/bhmy5jiYl8wpUzGR/zPkUrUuPr3hQskXFlx8wyt8brypm9uOUvjXUeJ53FvnPT3zT8tj3FSlfCydWLTr2GExH+lDMnDmS43o4Ny6lUszFfVWuIg5M7bboNxNDImIN7NgLg6OJJzwHj8CtVkTz2jhQoUpJmrboReOoQL148V+fzfadfqF73W3Lbfp6W8lfK5tfj4PkXBN1TERKhYu3h55iZQj7nt/+cNdSHr7/SZ8Ox58Tr5r71B1GpVBzYtoSaTTpTuERV8rr40PLH0URHPOHCvxn3Gsnv+xV1m/9EkZLVtf7dxNSMboPm4Ve2NnnyuuHqVZRm7X7j/q3LRDx9lFnF+XyUiqx7ZTP/qQofwKJFizA0NOTIkSPMmjWLyMhIqlatip+fH//++y/bt28nJCSEb7/9FoBHjx7RokUL2rdvz5UrV9i/fz9NmzbNsBvnr7/+yoEDB9iwYQM7d+5k//79nDlz5oPjNDMzY+HChVy+fJnJkyczd+5c/vrrr/de39/fn8uXL7Nt2zauXLnCzJkzsbGxee/1jxw5QteuXenVqxeBgYHUqFGDUaNGaaSJjY2lbt267Nmzh7Nnz1K7dm0aNGjA3bt3NdKNHz+eokWLcvbsWfz9M76j+b6Sk5MJvnGNor7F1MuUSiVFfYtxNeiy1nWuBl2mqF9xjWV+xUtopM+XvyAnTxwj7GkoKpWK8+fO8uDBffyKpXXtSkxIYMK4UXT58SesrK0/uSyvynNDS3l831KeoKDL+PoV01jmV7wEQUFX1HkCGBoaauRpYGDA5csX1cvy5S/AoYMHiImJJiUlhYMH9pGUlEzhIkV1Ura08l3H19fvjfL5qeMVn8erY6eIb9qxkHrsFH/rsVMk3b5WkqtBl7Smj4wI5/Sp41SvWVe9LPjGNcLCnqJUKOjTozPtWn3NCP8BGq2EupD8/DlBt+9T+rWKmVKppFRBb87fuP3O9VUqFScvXePOo1D8fNx1GtuHSn7+giv3HlPmtYqZUqmgjI8L5289zHC92duPYmVmStOyRT5DlB9AXx9jdy+enT+btkylIu78WUy8tXdfjL96GWN3L3UFzyCPHTn8SvHsjPbKO4DSNAcAL2Lfs4/eJ4gIvU9s1FM8CpZVLzM2NcPRvQh3b5zT2efERj3l6rkDlKjYTGd5hoY8ICoijAJFSqmXmebIiYd3QYKvnte6zvPkZG4HB2mso1QqKVi0FMFXM74JER8Xi4lpDvT0snYUkFVOMDNVEPww7fdbYjI8CFXhlPvtP9Drldbj2oMUbj7KmiE8YU/uExP5FO9CafuaiakZLh5FuH1dd/sapG4vhUKBiamZTvMV/23/uTF8Xl5ejBs3Tv3+999/x8/Pj9GjR6uXLViwACcnJ65du0ZsbCzPnz+nadOmuLik9pMuXLiw1rxjY2OZP38+S5cupVq11K5KixYtwtExfbeadxk8eLD6/66urvzyP/buOrypqw/g+Depe+ruirc4wx224TDGYLjrkCFjOAx3HxvuGy5jyPAxvHgLFLdS6ppK8v4RSAlNGJJSxns+z3Mf6M05N7+Te5N7zz1yBw1i/fr1DB48+I3y37t3j7CwMEqXLq3extuYO3cu9evXZ9CgQQAEBQXx999/s3PnTnWaEiVKUKJEbsVg3LhxbNmyhe3bt9O7d253gBo1ajBw4MC3ev/XSUpKRKFQIHulq59MZsuD+/e15kmIj0Mmy5s+Pj5O/XfXHr2ZP2cGHdt+jYGBARKJlF79BlCkWO6F069LFhBSqAjlKuivVelFeV7tuvj68sTn6Uosk9mS8Lw8Hp6eODo6sWLZr/Tu8x0mpqZs27qJZ89iiI/LLfOQYSOYMmk837RshoGBASYmJvwwYhRubvq765qUlPR8f70ar4z7Oson5I9kHd8dG5ktD+7f05pH23fHRmZLfLz2LqB/7d+LmZk5FSrmjgONfqK6U7x+zQo6dOmJk7ML2zZv5Meh/VmwZCVWVtbvU6zcWJNTyVEosLPRvFCxs7bizqOnOvOlpKVTv+8YMrOzMZBKGdKuGeWLaR9f+qHEp6aRo1Bib2Wusd7eyoLb0XFa85yLesCWExfZOKT9B4jw7RhaWSMxMCA7UfO4yU6Mx9xde6tV0rGDGFjZ4D1uBiBBYmhI/N4dxG5Zr/1NJBKc23cnLeIymffv6LcAWiQnqnoCWdrYa6y3tHEgJSFGb+9z7thWTEwtKPyaFsG3lZgQC4CNTDN2axt7EuNjteZJTk5AocjBRmb3Sh47Hj+4oz1PUgLbN/5K1TpNtL7+IVmaqSp1KRmalbaUDCWWZrrzFfWR4mYvYfHObN2J8pnuY82e5IRn2rK8k6xMOTvXzSTss88xNbf89wwfu09w8pSC8p+r8JUqpdnKc+HCBQ4ePIilZd4DOyoqijp16lCzZk2KFStG3bp1qVOnDs2bN9c6rigqKorMzEzKlcudOMPOzk7nxBSvs2HDBubMmUNUVJS60mlt/eYXRT169KBZs2acO3eOOnXq0LhxYz57iwktIiMjadJE8we6bNmyGhW+lJQURo8eza5du3j8+DHZ2dmkp6fnaeF7UenURS6XI5dr9kHPlMsxNjF543j1Yef2rURGXGP4qHE4OTlz5fIlFi+Yg52dPaFhpTj5z99cvBDOzLmLP2hc78LQ0JAffhzFnNnTadWyqapFLawkpUqX4eXG6TWrlpOaksr4nyZjbW3DPyf+ZsrE8UyaMhMfX9+CK4Dwn3Vg3x9UqV5To3VZoVAddM2/bsNnlVTj+voOGEynb1vy99HD1P0878RIH5K5qQlrJwwkLSOT01duMHPtNtyd7CldKKBA43obqRlyhq/axahW9bC1NP/3DP8B5oWLY9/0a54smUvGzQiMXNxx7tCD7GZxxG5akye9c+femHj6cHfEgHyJJ/zvHWxbNlr9d9uBC/PlfV519shmSlT4EiPjdz8n/n34D1YsnKj+u/+Pb95j6F2lp6Uwc9x3uHn60vhr/QzneBvFfaU0qGCg/nvNgbevsFmbw+dlDVixL5vsDzh07+yxnWz8JXfsd5fBC/L9PXOys1gxeyBKpZIWHd+/N5bwafnPVfgsLCw0/k5JSaFBgwZMnjw5T1pXV1cMDAzYt28ff//9N3v37mXu3LkMHz6ckydP4vuOF8QSiSRPl9CXx+edOHGC1q1bM2bMGOrWrYuNjQ3r16/XOh5Ql/r163P37l12797Nvn37qFmzJr169WLatGlIpao7Hi/H8DbjA18YNGgQ+/btY9q0aQQEBGBmZkbz5s3zTMzy6mf+qokTJzJmjOakFr369Kd3P+0nbWtrG6RSKQmvtDAkJMTr7GYps7XLMylFQkI8traq9HK5nNUrfmXYj2MoXVY1+6CPrz+3om6ydfNvhIaV4tKF8zx5/IhvWjTU2M7kn8ZQuEgxJkye8dpy6vKiPK+2mKjKo33CEpmtbZ4JTxIS4pHZ5pY/IDCIOfMWk5qaSnZ2FjY2MgZ+14eAQNXYlsePH7FzxzbmLVyCt7cPAL5+/ly5coldO7fRq89371SevOWzfr6/Xo03QWf5hPxhpeO7k/iW353EhHitN72uXL7Iwwf3GTR0pMZ6u+fb9vTKnU3OyMgYZxdXYmJ0t7y9LZmVBQZSaZ4JWuKSkrGX6e6eJJVK8XR2BCDY253bj6JZvuNAgVb4bC3MMZBKiE1O01gfm5yKg1Xe39T7zxJ4FJdI3583qdcpnv/Gl/xuKtuGd8bTseC+b9nJSShzcjC00YzB0MaW7ATtLZYOX7cj8cgBEv9Sjc2W37uD1MQUl279iN28lpfvXjl36oVlyfLcGzWQ7Dj9tXi8rFBYDTz9c3t8ZGepznUpibFYy3JnNExJfIart+5Zb9/GncgzPHt8m697vdv55YWwslXwDyqq/vtF7IkJscjscod7JCXG4uWrfayqlZUMqdSAxFf2V1JiHDa2mi1P6empTB/TF1Mzc/oMnYqh4Ye/XIy4r+DBs9xamoGBqoXP0lRCSnrusWNpKuFxnPaumm72EizNJHT/Mjd+A6kEb2clZUOkjF2dRX5M1F6kVHUGBWg/1mxsHdXrUxJjcfN5/94ILyp78c8e0fPHpZ9G6x6AHifX+3/3n28rLVmyJFeuXMHHx4eAgACN5UVFRSKRULFiRcaMGcP58+cxNjZmy5Ytebbl7++PkZERJ0+eVK+Lj4/n+nXNCTAcHR15/Dh3MOyNGzdIS8s9qf/99994e3szfPhwSpcuTWBgIHfv3n3rsjk6OtKuXTtWr17NrFmz+Pnnn9XrAY0YwsPDNfIGBwdz+vRpjXWv/n38+HHat29PkyZNKFasGC4uLhqTvrypYcOGkZiYqLF07a575kUjIyP8A4K4eCF3LIhCoeBi+HmCQwprzRMcUpiL4ZpjKcPPn1Wnz8nJJjs7O8/MmwYGUpQK1QmjWYtWzJ6/hFnzflYvAB279KBv/3efwMXIyIgALeW58JryhIQU5kL4eY114efPERKS9yLDwsICGxsZjx4+4ObN65SroGrplWeoWlWlr5RZKpXq9VEjqvIFEn4hXL1OoVAQHh6uNV4h/+R+d3K/C6rvzrm3/O6cITikSJ60+/f+gX9AEL5+/hrr/QODMDIy4uGD3C682dnZPH0ajaOT/h4JYGRoSIiPB6eu3lCvUygUnL5yg+IBPm+8HYVSSWZWwXXfAjAyNKCQpwsnr+f+9isUSk5G3qW4r1ue9L7O9vw+tAMbBrdXL9WKBlAm0IsNg9vjYqufbrPvLDubjFs3sCgWmrtOIsG8WCjp17WP5ZWamIJCs1lFqchR533BuVMvLMtW5N6Y78l6+kTfkauZmFlg7+ytXpzcA7C0ceDW1X/UaTLSU3hw6yJeAfoZB33m8CbcfIrg6qX7ET1vwszMAmdXT/Xi5umHja09Vy/mntfT01KIun4F/2Dt4z8NjYzw8Q/RyKNQKLh68TT+wblDXdLTUpg2ug8Ghkb0Gz4D4/domXwfmdkQl5y7xCQoSU5T4ueae+yYGIG7o4T7MdrPebceK5m3LYuFO7LVy8NnCi7eUrBwR3a+VPYATM0scHTxUi8uHv5YyRy4fvmlYy0thbtRF/EJfL9j7UVlL+bJPXoM/wULK9l7Ri98iv5zLXyv6tWrF0uWLKFVq1YMHjwYOzs7bt68yfr16/nll184c+YMBw4coE6dOjg5OXHy5EliYmIoVCjvhaqlpSWdOnXi+++/x97eHicnJ4YPH65uUXuhRo0azJs3jwoVKpCTk8OQIUM0ZiIMDAzk3r17rF+/njJlyrBr1y6tFczXGTlyJKVKlaJIkSLI5XJ27typjjkgIABPT09Gjx7NhAkTuH79ep7Wwz59+lClShVmzJhBgwYN+Ouvv/jjjz80KkSBgYFs3ryZBg0aIJFIGDFiBArF2/d5MDExweSV7pvGJkmvzdOoSXNmz5hMQGAQgUEh7Ni2iQx5BrVqq2Z7mzltEvb2DrTt0BmABo2aMnxIf7Zu3kjpMuU5evggUTeu06uPqhXR3NyCosVKsHzpzxibmODk5MzlSxc4eGAfHbv0AHJnInyVo6MTzi7//lys12ncpBkzZ0whIDCIoKBgtm3bolGeGdMmY2/vQLsOqucvNWzUhGFDBrJl82+ULlOOo4cPcfPGdXq/1Cp37OhhbGxkODo6cefObZYsXkC58p9R8vkkNB6enri6uTF/7mw6du6KlbU1/5w4Tvj5c4wcrd9nozVp0pQZM6YRGBioUb7atVVTjE+fNhV7e3vad1BNt56VlaXuGpydnU1s7DOioqIwMzPDzU11sZuens6jR7mTVzyJfkJUVBRWVlY4OX0czw8ysDDHIiD3mVbmvh5YlwghMy6RjPsFMwNaoyYtmD1jEgGBwRrfnZq16wEwa9pE7O0d+LZDF0Dbd+cvom5cp2cfzXG5aWmp/H30MB06d8/znubmFtT9vAHrVy/HwdERJydntvy+EYCKlfT7eIDW9asy+ud1FPb1pIifF2v/PEy6PJMGVVQTTYxctBYnW2t6P3/EwrLt+ynk64mHswNZWdkcv3CN3cfPMKx9c/U2E1NSeRKbQEx8IgB3H6taJe1trHCQ5V9F6tvqpRmxejdFPF0o6u3K6kNnSM/MonE51cX18FW7cLKxpF/DqpgYGRLo5qiR38pM9WiCl9cnpqbzOD6JmMQUAO48VbXWOFhb4GCdv3f143ZuwrXX96RH3SDjZgS2XzRFamJK4kHVbL2uvb8nOy6WmLVLAUg58w+2XzYl43bU8y6dbjh+3Y6Us/+oK4LOnftgXak6D6aMQpGRjsHz8aaKtFSUenoMkC4SiYSKddtycNsi7J29sXX0YP+mOVjJnChUMndWxF8ndaBwqVpUqN0aAHlGKrHRuUMf4mMe8OjuNcwtbJA55FbmM9JTuHzqT+p/82Zj99829joNWrHjt6W4uHni4OTO5rWLsLVzoGS53O/k5BE9KFW+OrW+UE1kV7fRNyyZPQbfgEL4BRZh7451yDPSqVxT1S07PS2FqaP7kCnPoNvQsaSnpZCepjrWrK1tkRqoulhGP75PRnoaiQmxZGXKuXtL9SxGd08/DHXMzKwPJ67lULW4AbHJSuKToWaYAclpEHEv99qlfR1Drt5TcCpCQWY2PE3QrNVlZkO6PO/6/CSRSKha/1v2bf0ZRxdv7Jzc+eO3eVjbOlGsdO7jbRaM70SxMjWpXPcbAOQZaTx7knusxcY85OGdCMwtbbB1cCUnO4vlswbw4PZVOg+ej0KhIOn5mEBzSxsMDfNvXwj/Lf/5Cp+bmxvHjx9nyJAh1KlTB7lcjre3N/Xq1UMqlWJtbc2RI0eYNWsWSUlJeHt7M336dOrXr691e1OnTlV3E7WysmLgwIEkJiZqpJk+fTodOnSgcuXKuLm5MXv2bM6ePat+vWHDhvTv35/evXsjl8v54osvGDFiBKNHj37jchkbGzNs2DDu3LmDmZkZlStXZv161UB3IyMj1q1bR48ePShevDhlypRh/PjxtGjRQp2/YsWKLFq0iDFjxvDjjz9St25d+vfvz7x589RpZsyYQceOHfnss89wcHBgyJAhJCW9vqKmL5WrVicpKZG1q5arHuTt58+osZPUXRqfxTxF+tK0uIUKF2Hg4OGsXrmUVcuX4ubuzrARY/H2ye2WO2jIj6xc/gszpv5ESnIyjk7OtGnbkXofYHxR5arVSExKYM2qFcTHx+Pn58+YsT+pu83FxDxF8kp5Bg0exuqVy1m5fBlu7u4MHzFaozxxcXH8umSxuutqjZq1admqtfp1Q0NDRo+ZwPJlvzJuzAjS0zNwdXPjuwHfazzAXR+qVK1KYlIiq1etel4+P8aOHa+zfHFxsfTtk9vKu3nTJjZv2kSxYsWYNHkqADduXGfY0CHqNL8sUbW41qxViwEDBuk1/ndlU6ooFQ6sUv9deNoPANxfuZmLnYYVSEyVqlYnMSmBdauWvfTdmaz+7qj2Re5NqpDCRRkweDhrVi5l9fJfcXN3Z+gr3x1QPRtTiZLK1bQ/hL19p+4YGBgwa9okMuVygoILMW7iNCyt9DsTXJ3yYcQnp7Bo0x5iE5MI8nJn7vddsX8+kcuT2HiNVu10eSaTV2ziaVwCJsZG+Lg6M657a+qUz51V9si5K4xZkjtRyA/zVfu0S5M6dGtaT6/xv6xeyULEp6SzYPcxniWlEuzhxIIeLbC3VvU+eRKflKeF/t8cunyTkWtyH8ExZPkOALrX+4wen1fSX/BaJP99GANrGxxbtsVAZov8zi3uTxhOTmICAEYOThrdNJ9tWoNSqcSxVTsM7RzISUok5cw/xKxbpk5jW1f1++w9RvOm5eP5U0k8tC9fywNQ+YvOZMrT2bpsFBlpSXgHlqT9oJ81xtvFPb1HWnJut+iHt6/w68R26r93r1UNKQmr1JjmXXPH2V38ZzegpET5L/Il9s+btEWekc6yBT+RlppCUKESDBw5R6NF7umThyQnJaj/LlepDsmJCWxZt5jEeFX3z4Gj5qgnf7kTFcmt66qZoAf30JwHYOribTg6qyq0S+eNJ/JKbs+BUQPa5EmTH45dVmBsKKFhBUPVg9ejlazan6UxPs/WSoKFycfXFbBGg45kytPZ+Mto0tOS8Q0uSbehizSOtWfR90l96Vi7f+sy88flPrdy2yrVpIVlqjTimx4TSIx/yuWzqkc1TRuae5MLoNeIpQQULst/mvQ/3xHxoyFR6rPvl/BR69KlCxERERw9ejTf3ysi6kG+v8eHJKVgHtSaH6TkFHQIehUZkn8X7AXBL+Kvgg5Bbzxiwws6BL0yis+/7oYF4c4vGwo6BL25PEj7M1z/q9ytUwo6BL3645RpQYegN+WLfXyVyffxecmPtxUwY1P+T06ki2mz/gX23vnhP9/CJ+g2bdo0ateujYWFBX/88QcrVqxgwYL8nylKEARBEARBEN6LeCyD3ogK3yfs1KlTTJkyheTkZPz8/JgzZw6dO3cu6LAEQRAEQRAEQfhARIXvE7Zx48aCDkEQBEEQBEEQhAIkKnyCIAiCIAiCIHxcpJ/WeMmCJDrHCoIgCIIgCIIgfKJEC58gCIIgCIIgCB8XMWmL3ohPUhAEQRAEQRAE4RMlWvgEQRAEQRAEQfi4SMQYPn0RLXyCIAiCIAiCIAifKFHhEwRBEARBEARB+ESJLp2CIAiCIAiCIHxcpKJdSl/EJykIgiAIgiAIgvCJEi18giAIgiAIgiB8XMSkLXojWvgEQRAEQRAEQRA+UaLCJwiCIAiCIAiC8IkSXToFQRAEQRAEQfi4SES7lL6IT1IQBEEQBEEQBOETJVr4BEEQBEEQBEH4uIjHMuiN+CQFQRAEQRAEQRA+UaKFTxAEQRAEQRCEj4t4LIPeiAqfkC/SFaYFHYJemUvTCzoEvTEgp6BD0Cu/iL8KOgS9uhVSo6BD0BvDiL0FHYJeWVm7FXQIeuX8Y0BBh6A398kq6BD0ysTg0yqPqalFQYegN8+SDQo6BEF4a6JLpyAIgiAIgiAIwjuaP38+Pj4+mJqaUq5cOU6dOvVG+davX49EIqFx48b5Gp+o8AmCIAiCIAiC8HGRSAtueQsbNmxgwIABjBo1inPnzlGiRAnq1q3L06dPX5vvzp07DBo0iMqVK7/Pp/RGRIVPEARBEARBEAThHcyYMYMuXbrQoUMHChcuzKJFizA3N2fp0qU68+Tk5NC6dWvGjBmDn59fvscoKnyCIAiCIAiCIHxcJJICW+RyOUlJSRqLXC7PE2JmZiZnz56lVq1a6nVSqZRatWpx4sQJnUUbO3YsTk5OdOrUKV8+uleJCp8gCIIgCIIgCMJzEydOxMbGRmOZOHFinnTPnj0jJycHZ2dnjfXOzs48efJE67aPHTvGr7/+ypIlS/Ildm3ELJ2CIAiCIAiCIAjPDRs2jAEDBmisMzExee/tJicn8+2337JkyRIcHBzee3tvSlT4BEEQBEEQBEH4uEgLriOiiYnJG1XwHBwcMDAwIDo6WmN9dHQ0Li4uedJHRUVx584dGjRooF6nUCgAMDQ0JDIyEn9///eMPi/RpVMQBEEQBEEQBOEtGRsbU6pUKQ4cOKBep1AoOHDgABUqVMiTPiQkhEuXLhEeHq5eGjZsSPXq1QkPD8fT0zNf4hQtfIIgCIIgCIIgfFSUEklBh/BGBgwYQLt27ShdujRly5Zl1qxZpKam0qFDBwDatm2Lu7s7EydOxNTUlKJFi2rkl8lkAHnW65Oo8AmCIAiCIAiCILyDli1bEhMTw8iRI3ny5AmhoaHs2bNHPZHLvXv3kBZg91QQFT5BEARBEARBED42b/kA9ILUu3dvevfurfW1Q4cOvTbv8uXL9R/QK/47n6QgCIIgCIIgCILwVkSFTxAEQRAEQRAE4RMlunQKgiAIgiAIgvBx+Q916fzYiU9SEARBEARBEAThEyVa+ARBEARBEARB+Kj8Vx7L8F8gWvgEQRAEQRAEQRA+UaLCJwiCIAiCIAiC8IkSFb58cOfOHSQSCeHh4TrTLF++HJlM9t7vdejQISQSCQkJCfn+XoIgCIIgCILwQUikBbd8YsQYvv+4zz77jMePH2NjY1PQoby1P3duYsfmtSTGx+HlG0CHbv0JCC6sM/0/x/5i4+olxEQ/wcXNg2/a9yCszGcAZGdns2HVz4SfOcHTJ48wt7CgaIkytGrfHTt7xzzbysrK5McBXbh7+yaT5izDxy/ovcuza8c2tmzaSHx8HL6+/nTt0Zug4BCd6Y8dPcyaVct5Gv0ENzd32nXsQuky5dSvp6ens2LZL5w8cZzk5CScnV34smET6n/RQGM7EdeusmrFUq5HRiCVSvH182fM+EmYmJi8d5letn3HTn7ftIn4+Hj8fH3p2aM7wcHBWtPeuXuXVatWc+PmTZ4+fUq3rl1o0rixRpqdu3axc9dunkZHA+Dl7U3rVq0oU6a0XuMG2L1jK1s2bSAhPg4fX3+69OhDUHAhnemPHz3E2lXLeBr9BFc3D9p27ELpMuXVrzf+vIbWfO06dqVJ86/Vf5859Q8b1q7k7p1bGBkbU6RoCX4YOU5v5XpbdpVK4zewEzYli2Lq5sSZZj2J3n6gwOJ5Uzt3bGfzpt+ef7f86NajF8E6vlt3795hzaqV3Lx5g6dPo+nStTuNGjf9wBHn2rLrT9Zv3UFcfCL+Pl7069qBQkEBWtPevnefpWt/43rULZ48fUbvTm1p0fBzjTRpaen8unYjR/85TXxiIoG+PvTp0p5Cgf4fojj8vucgq3fsJS4hkQBvDwZ2bEWRAF+tabfuP8ofR05w6/4jAIL9vOjRqolG+rHzl7H78AmNfOVLFGHW8H75Er9SqWTXxgX8fWAT6anJ+IWE0rLzjzi5er823+E96zmwYzlJCc9w9w6iRcdh+AQUU78e8+Q+W1ZN51bEebKzMylUoiItOg7DWmavTrNn889cOXeUB3ciMTQ0Yury4+9Vlr27NrFj8xr1ObR9twEEBL3+HPrb6p+Jeao6h7Zq35Ow0p+pXz/19yH2/7GF21GRpCQnMXH28jznxoT4WNYsncel8NNkpKfh6u5F46/aUa5i9fcqiy5VikoI9ZNgYgQPnsGeswriU3SnL+kvoWSABBsL1d8xiXDsioJbTzTTudtD1WJS3OxBqYToeFh/REF2Tr4UA1Ade0e2z+H80d+Qpyfh4V+S+q1HY+fsozPP8T8WE3luL7FPbmFobIqHXxg1mg3C3sUPgPTUBI5sn8utq8dIinuMuaUdQWG1qNqwH6bmVvlXGOE/59Orwv4fycrKwtjYGBcXFyT/sYGtfx/Zz6pf5tK8VUcmzl6Kt28AE0cOIDEhXmv6yGuXmDNlNNVrf8mkOcsoXb4y0yYM4/6dWwBkyjO4ExVJ06/bM3H2Ugb88BOPHt5j2rghWre3ZukCbO0c9Faeo4cP8uuSRXz9zbfMnLsIHz8/Ro0YSoKO8ly7eoVpkydQu049Zs1dRLkKFflp3Cju3rmtTvPrkoWcO3uaAd8PZf7ipTRo3JTFC+dy8p+/1Wkirl1l9IihhJUsxfRZ85g+ez5fNmiEVKrf4+Hw4SMsWbKENt98w7y5c/Dz82X4iBE6W5blcjkuri507NAeW1tbrWkcHBzo2KE9c+fMZs7s2YSWKM6YceO4c/euXmM/dvggS5cs5Otv2jJj7mJ8/PwZM2KIzn0TcfUy0yePp1ad+syY+zPlKlRk0riRGvtm2erfNZY+332PRCKhQsUq6jR/HzvCrGkTqVm7HjPnLWHStDlUqVZTr2V7WwYW5iRdjORy3zEFGsfbOHL4EL8sWUyrb9owe+4CfP38GDniB53778Wx165DR2xt7T5wtJr+Ovo385euol3L5iyZMRF/X28GjZ5IfEKi1vQZ8kzcnJ3o+u032NnKtKaZMm8xZ8IvMbx/L5bNmUqZsOIMHDmemNi4fCyJyr6/TzN75W90bv4lKyb/SKC3J99NmE1cYpLW9OeuRlK7YlnmjxrIkvFDcLa3o9/4WTyN09x35UOLsOvnqeplbL/O+VaG/duWcfiPtXzdZQSDflqDsYkZ8yd0JytTrjPP2b/3sGXlVOo3786QyRtw9w5m/oTuJCfGAiDPSGP+hG5IJBL6jFpC/3EryMnOYvHkPigUCvV2crKzCCtfh8p1vnrvcpw4up9Vv8yhWauO/DRrGd6+AUwa2Z/EBO3HwfVrl5g7dRTV6jRg4uzllC5fhekThnL/bpQ6jTwjneDCJWjVrqfO910wYyyPH95j0IgpTJ63ijKfVWX2lBHcjop87zK9qnyIhNKBEv44o2D5fgVZOfB1VSkGr7lyTUpXcvCigqV7FSzbq+DuUyUtKklxsM5N424PLatIuf1EyfJ9CpbtU3D2phKlUu9F0HDizyWc/msV9duMpv2wjRiZmLFudieys3Qfe/eun6JU9da0H7aRb75bRk5ONmtndSJTngZAcsJTkhOeUrP5ELqO2kmDDhO5dfkou1YOz9/CfCgSScEtnxhR4XsPCoWCKVOmEBAQgImJCV5eXkyYMEH9+q1bt6hevTrm5uaUKFGCEydOvGZrsHDhQvz9/TE2NiY4OJhVq1ZpvC6RSFi4cCENGzbEwsKCCRMmaO3SuXz5cry8vDA3N6dJkybExsbmea9t27ZRsmRJTE1N8fPzY8yYMWRnZwOqu1CjR4/Gy8sLExMT3Nzc6Nu373t8Unnt2rqBGnUbUK32F3h4+dK51/cYm5hwaN9Oren/2L6REqXK0aBZa9w9fWj5bVd8/YP4c+fvAJhbWDJ8/GwqVK6Jm4c3gSFF6dh9ALduRvLsqeatvfNnTnDx/CnadOqtt/Js27KJOvU+p1adenh5edOz93eYmJiwf+8erel3bNtMyVJlaNq8JZ5e3rRp2wE//wB27dimThNx7So1atahWPFQnJ1dqFf/S3z9/LkRGaFO88vPC/iyYROaf9UKL28fPDw8qVSlGkZGxnorG8DmLVuoV68ederUxtvLiz69e2NiYsqfe/dqTR8cFESXTp2oVrUqRkZGWtOUL1eOsmXK4O7ujoeHO+3btcPU1JSIiAit6d/Vti2/Uafe59SsUx9PLx969O6PiYkJB/b+oTW9at+UpUnzr/H08qZ12474+Qeye8dWdRpbOzuN5eQ/f1O0eCgurm4A5OTk8OviebTr1I16XzTE3cMTTy8fKlWppteyva2YP49wfdQsorftL9A43sbWLZuoW68+tevUxcvLm169+2FiYsK+vX9qTR8UFEzHTl2pWrW6zmPvQ9m4bRdf1qnB57Wq4ePlwcAenTE1MWb3/kNa0xcK9KdHhzbUrPIZxkZ5O+DI5ZkcOXGK7u2/oUSRQni4utChVQvcXV3Y9se+fC4NrNu5j0Y1K/Fl9Yr4ergxpEtrTI2N2XlQe0vV2L6daV63GkE+nvi4u/JD97YolErOXNL8jhsbGmIvs1Ev1pYW+RK/Uqnk4O7V1G3aheJlquPuHUTb3hNIjI/hwum/dOb7a+dKPqvZjArVG+Pq4c/XXUZgbGzGiYNbAbgVGU7s00e06TkOd68g3L2C+Lb3eO7dusL1y6fU2/niq17U+PJb3LwC37ssu7aup0bdhlSr9SUeXr506jn438+hJcvRoKnqHPpVm674+gfz585N6jSVa9SnWauOFAsto/N9r0dcpu6XzQkIKoyziztNW3bAwsKS2zf1X+ErGyTh+FUlNx6pWup2nFRgZQbB7rovxm8+gqjHEJ8CcSlw+JKSzGxwt8/NUytMypkbSk5EKHmWBHHJcO2+khyFzs2+N6VSyan9K6n0RQ+CQ2vh7BFCww5TSE54SuR53b/Hrfr9SonPmuLoFoizZwgNOkwiKe4RT+5eAcDJPYjmPeYSVKIGtk5e+IRUoFrj77hx8S8UOdn5VyDhP0dU+N7DsGHDmDRpEiNGjODq1ausXbsWZ2dn9evDhw9n0KBBhIeHExQURKtWrdSVqldt2bKFfv36MXDgQC5fvky3bt3o0KEDBw8e1Eg3evRomjRpwqVLl+jYsWOe7Zw8eZJOnTrRu3dvwsPDqV69OuPHj9dIc/ToUdq2bUu/fv24evUqixcvZvny5erK6qZNm5g5cyaLFy/mxo0bbN26lWLFiuV5r3eVnZXF7ZuRGicVqVRKsdDSXI+4rDXPjYgrFAvV7OpXomQ5rkdc0fk+aWkpSCQSzC1zuzUkxMexZO5keg0cgbGJ6XuWRCUrK4ubN68TGlpSvU4qlVIitCQREVe15omIuEqJsJIa60qWKqORPqRQYU6d/JvYZ89QKpVcvBDOo4cPCC2p+hwSEuK5HhmBTCZj8MC+fPtNc4YNHsDVK5f0Uq6Xy3fj5k3CQkM1yhcWGso1PVXOcnJyOHT4MPKMDAoV0t3V8m1lZWURdfM6xUNLqdep9k0pInXsm8iIqxR/Zd+ElSpDpI5jLSE+jrOn/6FWndyud1E3rxMb+wypREL/3l3p0Lo5Y0cM1WglFP6d6rt1g9DQMPU6qVRKaGgYERHXCjCyf5eVlc31qNuUKpH72ymVSilVohhXIq+/0zZzcnLIUSgwfqUia2JszKVr+r1R8qqs7Gwib92jTLHc76dUKqVMsUJcun7rjbaRIc8kJzsnT4Xu3NXr1O88kK/6jWDykjUkJr+mz957iH36kKSEZ4QUz+2ebWZuhU9AMe5cv6A1T3Z2FvdvXSO4WG4eqVRKcLFy3H6eJzsrE4lEguFLN9oMjUyQSKRERZzTezlenEOLlsg9J0qlUoqGluFGpK5z6GWKvlKRKx5Wjhs6zrm6BIUU5cTRA6QkJ6FQKPj7yD6yMjMpXKzkv2d+CzILsDSTcDs6t9lNngWPYsH9DTvnSCRQ2FOCkSE8jFVtx9xEVflLk0PbmlL6NZLSproUD/11+NEq4dkDUpNi8CmU24XW1NwKd98SPLx1/o23I09PVuW10D2MJyM9BRNTS6QGn8CoLam04JZPzCdwNBSM5ORkZs+ezbx582jXrh0A/v7+VKpUiTt37gAwaNAgvvjiCwDGjBlDkSJFuHnzJiEheceeTJs2jfbt29Ozp6orxYABA/jnn3+YNm0a1avn9o3/5ptv6NChg/rvW7c0T7SzZ8+mXr16DB48GICgoCD+/vtv9uzJbWkaM2YMQ4cOVcft5+fHuHHjGDx4MKNGjeLevXu4uLhQq1YtjIyM8PLyomzZsu/7kaklJSWgUORgI9PsbmUjs+Phg3ta8yTEx2pNn5iQt/USIDNTztplC/msSi3MzVUXF0qlkoWzJlCrfmP8AwvxNPqxHkoDSUmJKBQKZK90XZTJbHl4/77WPAnx8chkr6aXER+f2x2nW4/ezJszkw5tv8bAwACJRErvfv0pWqw4AE+eqOJft2YlHTp1w9ffn4MH9vHjsMHMW7gEN3cPPZUv6Xn5ZHniva+jfG/q9u079B84kMzMTMzMzBgx4ke8vbzea5svS9axb2xktjy4r+tYi8uzb2xktsTHa+9C+Nf+vZiZmVOhYmX1uujn+2b9mhV06NITJ2cXtm3eyI9D+7NgyUqsrKy1bkvQlHvs5f1uPXjPYy+/JSYlkaNQYCvTvDCzldlw78HDd9qmubkZRYIDWblxM94e7tjKZBw4epwrkddxd3HRR9g6JSSlkKNQYCfTPHZtZVbcefRmv6Xz12zCwc5Go9JYIbQI1cqF4ebkwMMnMSxct5X+P81hyYShGOj5oisp4RkAVjb2GuutbOxJ0nEuSUmKR6HIwUqmmcdaZk/0I9UNHJ+g4hibmLFtzUwatuqLUqlk29rZKBQ56vfUazlenENt854THz3Q3iU+ISEWGy2/awk6yq1LvyHjmTNlBF2+qYeBgQHGJqYM+GEiLm76Od+8YPH8fmxqhub61Ayl+jVdHG2gXU0phgaQmQ2bjit49rzXscxS9W+lIhL+uqAkOl5JMR8J31STsmTP68cHvo/UpBgALKw0jyMLa3tSkt7sGFEqFOzb8BMe/iVxctc+70BachzHdi0gtHLL9wtY+OSICt87unbtGnK5nJo1dY/JKV68uPr/rq6uADx9+lRrhe/atWt07dpVY13FihWZPXu2xrrSpV8/ocW1a9do0qSJxroKFSpoVPguXLjA8ePHNbqf5uTkkJGRQVpaGi1atGDWrFn4+flRr149Pv/8cxo0aIChofbDRS6XI5dr9kHPzJRjbKzfSUPeVHZ2NrMnjUCJkk69vlev37PjdzLS02jc4tsCiett7dy+lesR1/hx1DgcnZy5cvkiixfMxc7OntCwUigVqjuWdet/Sa069QDw9w/kQvh59u3dQ7sO+TcORl88PNxZMG8uqampHD12nOnTZzBlymS9Vvry24F9f1Clek2MjXPv7iue75vmX7fhs0qqcX19Bwym07ct+fvoYep+3kDrtgTh3wzv34vJcxfTrGNPDKRSAv19qVm5IpFRb9bKVlBWbv2D/cdPM3/0IEyMc1soa1fMvZkY4OVBgLcHzfoM59yVSI2K4bs4fXQX634eq/67x7D577U9Xays7eg0YBobfhnP4T/WIpFIKVWxPp6+hf5z4+v/zcY1S0hNTWH4+DlYWdtw+p8jzJ4yglGTFuLl8+4TBxXxllC/VO5ntfHou/evjE2GX/cqMDGCEA8JDcpKWX1QVel78Q7no5RcvK36nY4OV+LjLKGEr4RDl/QzkO/yye3sXj1K/XfL3ovfe5t71o0h5tEN2g5eq/V1eXoKG+Z2w8HVnyoN9DdkRfg0iArfOzIzM/vXNC+PH3nxo//yAO53YWHx/mMbUlJSGDNmDE2b5p25ztTUFE9PTyIjI9m/fz/79u2jZ8+eTJ06lcOHD2sdEzNx4kTGjNGcBKJr7+/p3new1ve3tpYhlRrkGVyemBCHTMckCzJbe63pbV656/qishfzNJoRP81Rt+4BXLl4lusRl2nTRHM2sR++60ylarXpOWCE1vf+N9bWNkilUhJeaQFKSIhHZqd9whKZrW2eSScSEhLUk0zI5XJWrVjKsB9HU6asqiuRr68ft6Oi2LL5N0LDSmFrp0rr6aU5u5ynpxfPYp6+U1m0sba2fl6+hLzx6ijfmzIyMsLNTTXuLTAwkOs3rrN12zb69enzXtt9wUrHvklMiFd/fq+S2drl2TeJCfFaJ5+5cvkiDx/cZ9DQkRrr7bTsGyMjY5xdXInR47751OUee3m/W7r238fCxtoaA6k0zwQt8QmJOidkeRPuri7M+WkU6RkZpKWlY29ny+gps3B7aThBfpBZW2IglRKXoDlBS3xCMvYy3d3LANZs38vKrXuYO6I/gd6vbwlyd3ZEZmXJgydP37vCV6x0NXwCc7vUZmdlApCcGIuNbe7szcmJsXj4aJ9x2NLaFqnUgORXWsKSEmKxluX2AyxU4jNGz91NSlI8UgMDzC2sGdalOqWc9dvyBS+dQ+Pf4hwqs88zKVpiQjyyV86hrxP9+AF7d/7OlHmr8fRWzRLp7RtI5JUL7N21ic69tJ/z38SNh0oexeZWtl5MzGJhqtnKZ2EqITrh9ZUyhQJ1S92TeCWudhLKBEn444ySlOfbeqZ5GPMsSYm1HoeOBpaoQWffEuq/c7JVx15qcixWMif1+tSkWJw9dc/m/cKetWO5cfEQbb9fjbVt3tZ8eUYK62Z3xtjUghY952NgWLDjl/VF+YndMClIn14n1Q8kMDAQMzMzDhzQz5TmhQoV4vhxzYHvx48fp3Bh3VMs69rOyZMnNdb9888/Gn+XLFmSyMhIAgIC8izS511ozMzMaNCgAXPmzOHQoUOcOHGCS5e0jw0bNmwYiYmJGkvH7rqn1DY0MsI3IJjLF86o1ykUCi5fOEtQSFGteQJDinA5/KzGuovnTxMUUkT994vK3uNH9/lxwiysrDUvQtp3/Y4pc1Ywec5yJs9ZzpDRUwHoN2QMLdt20xnvvzEyMiIgIIgLF3LHaigUCi6GnyckRPv+CwkpzMVwzX774efPqtPn5GSTnZ2N9JVnwUgNpOqWPWdnF+zs7Xn4QLNr28OHD3B00t/Fn5GREYEBAYRfCFevUygUhIeHU0hLa/X7UCqUZGVl6W17RkZG+AcEcTHPvjlHsI59ExxSmIvhmuNuws+fIfilY+2F/Xv/wD8gCF8/zTvb/oFBGBkZaeyb7Oxsnj6N1uu++dSpvluBXHjl2LsQHk5IiP7GeuYHIyNDgvx9OXsxd4yUQqHg3MXLFAl+/8fAmJmaYm9nS3JKCqfDL1KxXKl/z/QejAwNCfbz4vTl3LGCCoWC05evUSzIT2e+Vdv2sHTTTmb90I9C/j7/+j5PY+NJTEnF3vb9HzVkamaBo4uXenHx8Mda5kDkpdxzZHpaCnduXsInqITWbRgaGuHpV4jIy7l5FAoF1y+fxFdLHktrW8wtrIm8fJKUpDiKla723uXIE9OLc+jF3HOiQqHgyoUzBAbrOocW5cpL51yAS+GnCNRxztXmRU8e6StdbaVSKUrl+93MzsxWVdJeLM+SICVd1fL2grEhuNnDw7fsJSuR5FYgE1MhOU2J/StPLLCzkpCY+l5F0GBiaomdk7d6cXANwMLakTvXcifvk6en8PD2Bdz9wnRuR6lUsmftWCLD99FmwApkDp550sjTU1g3qxMGhkZ81WshhkYF07tK+LiJFr53ZGpqypAhQxg8eDDGxsZUrFiRmJgYrly58tpunrp8//33fPXVV4SFhVGrVi127NjB5s2b2b//7WbT69u3LxUrVmTatGk0atSIP//8U6M7J8DIkSP58ssv8fLyonnz5kilUi5cuMDly5cZP348y5cvJycnh3LlymFubs7q1asxMzPD21v7c4pMTEzyPPPN2DjztXF+0bglC2dOwC8whICgwuzethF5RgZVa6nGPM6fPg47ewdate8BQP2GXzF2aC92bl5HWJnP+PvIfm7djKBrb9VjF7Kzs5k5cTi3o64zZOQUFAoFCfGqO7KWltYYGhnh4KR5V8zkeSuts6s79g5OvI9GTZoxa8YUAgKDCQoKZvu2zWTIM6hZW9XVcua0SdjZO6i7WTZo1JQfhgxgy+bfKFOmHEcOH+Tmjev06tMfAHNzC4oWK86ypT9jbGKs6tJ56SIHD+yjY5fugKrVuEmzr1i3egW+fv74+vnz1/69PHxwn6HDR2kP9B01bdKEaTNmEBgYSHBQEFu2bSNDnkGd2rUBmDptOvb29nTs0B5QTbZx755qjFx2djbPYmOJiorCzMxM3aK3dNlyypQujaOTI+lp6Rw8dIiLly4xYZx+n1PXqEkLZs+YREBgMIFBIezYtklj38yaNhF7ewe+7dAFUO2b4UP6s3XzRkqXKc/Rw38RdeM6PfsM1NhuWloqfx89TIfO3fO8p7m5BXU/b8D61ctxcHTEycmZLb9vBKBipap6Ld/bMLAwxyIgt7usua8H1iVCyIxLJOO+fsa06lvjJs2YOWMqgYGBBAWFsO35d6tW7boATJ82BXt7e9p36ASojr376mMvi9jYZ9yKisLUzBQ3N/cPGvtXjb5g4uyFhAT4ERIYwO87dpOeIad+LdUxMGHmfBzt7ejattXz2LO5c//B8//n8Cw2jhu37mBmZoqHq+r369S5CyhR4uXuxoPHT1i0fA1e7m58XrNavpen1Ze1GTd/GYX8vCkc4MuG3fvJkGfyRbWKAIyZtxRHOxk9v1H1Hlm5dQ9LNm5nTN9OuDrZE/u8tdPM1ARzU1PSMjL49bedVC9XEjuZNQ+jY5i3ehMeLo6UL5H3Bsv7kkgkVP+8DXs2/4yjqxf2Tu7sWj8fG1tHSpTJfbbmnLGdKVG2JlXrqfZLjS/bsmr+j3j5FcYnoBgHd69GLk+nfLXG6jwnDm7Fxd0XS2s7bl+/wO/LJ1P9i29xdst95mDcs8ekpSQS/+wxCkUOD+6oKs+OLl6YmJq/VVm+aPw1C2eOxy9AdQ79Y9uG5+fQLwHV4xNs7R1p1e6lc+iwnuzcspaw0p9x4qjqHNqld+6ji1KSk3gW84T4OFWN6vFD1fdIZmuPzNYeNw9vXFw9+GX+ZFp37IOVlTWn/znCpfDTfD9y6lvF/yZOXVdSsbCE+GQlCalQpaiU5HSIfJjbwvdNNSmRD5ScvalaV62YhKgnSpJSwdgIinhJ8HaCdYdz8/wTqaRyEQlPE5REJ0AxHwn2VrD5eP49l0EikVC2VluO716InZM3MgcPDm+bjZXMieCwWup0a2a0Iyi0NmVqtAFgz9oxXDm1kxY9F2BsakFKomosoImZFUbGpsjTU1g7qyPZmek06jgVeUYK8gxV86a5lR1SqUG+lemD+AQfgF5QRIXvPYwYMQJDQ0NGjhzJo0ePcHV1pXv3vBd/b6Jx48bMnj2badOm0a9fP3x9fVm2bBnVqlV7q+2UL1+eJUuWMGrUKEaOHEmtWrX48ccfGffSRXTdunXZuXMnY8eOZfLkyRgZGRESEkLnzqrKiEwmY9KkSQwYMICcnByKFSvGjh07sLd/864f/+azKrVISkzgt9W/kBAfh7dfIEPHTld3R3kWE43kpWfJBRcqRp/vR7Nh1c+sX7kYFzcPBg2fiKeP6s5yXGwMZ08eA2BI3/Ya7zXip7kUKa7fGcReVblqdRKTElm7arnqweR+/oweO1HdDTAm5imSl+6KFipchIGDf2DNymWsWr4UN3d3fhgxBm+f3IuD74f8yMrlvzJ96kRSkpNxdHKmTduO1H9p/Fejxs3Iyszk158XkpycjK+fH2MnTMb1+eMB9KVq1SokJiWyatXq5+XzY/zYseryPY2J0dhfsXFx9OqT+yiPTZs2s2nTZooVK8bUyZMASEhMYOr06cTHxWFuYYGvrw8Txo2jZEnddzvfRaWq1UlMSmDdqmXEx8fj6+fPqLGT1cfaq/smpHBRBgwezpqVS1m9/Ffc3N0ZOmKsxr4B1bMXlSipXE37Q9jbd+qOgYEBs6ZNIlMuJyi4EOMmTsPSquAehmtTqigVDuQ+7qXwtB8AuL9yMxc7DSuosF6rStVqJCYlsnrVSvWxN3bsBI3v1svPnYyLi6Vvnx7qvzdv+p3Nm36naLHiTJo87YPGXqPyZyQkJbF07W/ExScQ4OvN1FFDsZPJAHj67JlG7M/i4ujcf6j67/Vbd7J+605CixZi9gTVTZyUtDSWrFpHzLM4rKwsqVqhLJ3bfK1zjLU+1f6sDAlJySzZuJ3YhCQCfTyY+UNf7J9P5PLkWZzGmLXN+w6TlZ3NDzM0xy91av4lXb5qiFQq5ea9B+w+fILk1DQc7GSUK16Yri0b5ZmJVF9qNeqAXJ7OusVjSU9Lxj8kjJ4/LMTopTHnz6IfkJKU2/2x1Gf1SEmKZ9fGBSQnPMPdJ5hePyzUeKj600d32L52Nmkpidg5uVO3aRdqfKE5XnzXhvmcPLxd/fekwarn8fUd9StBRXQ/CkGbCpVV59Df1yzJPYeOmaF5Dn3pYjmoUDF6DxrDxtU/s+H5OXTg8El4euf2Tjh78iiLZueO7Z8zRdVVvVmrjjT/pjOGhoYMHj2d9csXMnXc98jT03F29aDHdz9qPMBdX/6JUGJsCPVLSzE1hvsxsOGwQuPxCTJL1cybL5ibQoNyUixNVbN6Pk2AdYcV3InOTXP6uhJDqerxDKbGuWkS9NjCp02Ful3Ikqeze/VIMtKS8Awoxdf9ftFokYuPuU96Su6xd+7wOgBWT9c8lr5sP5ESnzXlyb0rPLqtmi12wY+1NdL0+ukAMgf9dykW/pskSmV+P2pS+H90/ob+ZyYrSObS9IIOQW+M0f2Q1/+iDP59PO1/ya0Q7RXI/6KgCO3PafyvssrK/4ebf0gmcu0PTP8vOkP5f0/0H2Jvlk/TRRaQPedlBR2C3ni4/MdbzV7RtuA6nfyr1BNbC+y9LSo0LrD3zg+ihU8QBEEQBEEQhI+KUnTp1BvxSQqCIAiCIAiCIHyiRAufIAiCIAiCIAgfF/FYBr0RLXyCIAiCIAiCIAifKFHhEwRBEARBEARB+ESJLp2CIAiCIAiCIHxUxKQt+iM+SUEQBEEQBEEQhE+UaOETBEEQBEEQBOHjIiZt0RvRwicIgiAIgiAIgvCJEi18giAIgiAIgiB8XMQYPr0Rn6QgCIIgCIIgCMInSlT4BEEQBEEQBEEQPlGiS6cgCIIgCIIgCB8VpZi0RW9EC58gCIIgCIIgCMInSrTwCYIgCIIgCILwcRGTtuiN+CQFQRAEQRAEQRA+UaLCJwiCIAiCIAiC8IkSXToFQRAEQRAEQfioKBGTtuiLaOETBEEQBEEQBEH4RIkWPkEQBEEQBEEQPipKMWmL3ohPUhAEQRAEQRAE4RMlWviEfJGUaV7QIehVdJZ1QYegN75W0QUdgl55xIYXdAh6ZRixt6BD0JvrIXUKOgS9qjSxXkGHoFfSavULOgS9sbHMKOgQ9MpQklPQIeiVo71BQYegN0Vc4wo6BD2zK+gAdBMtfHojPklBEARBEARBEIRPlKjwCYIgCIIgCIIgfKJEl05BEARBEARBED4qSol4LIO+iBY+QRAEQRAEQRCET5Ro4RMEQRAEQRAE4aMiHsugP+KTFARBEARBEARB+ESJCp8gCIIgCIIgCMInSnTpFARBEARBEATh4yImbdEb0cInCIIgCIIgCILwiRItfIIgCIIgCIIgfFTEpC36Iz5JQRAEQRAEQRCET5Ro4RMEQRAEQRAE4aOiRIzh0xfRwicIgiAIgiAIgvCJEhU+QRAEQRAEQRCET5To0ikIgiAIgiAIwkdFTNqiP+KTFARBEARBEARB+ESJFj5BEARBEARBED4u4sHreiNa+ARBEARBEARBED5R71XhUyqVdO3aFTs7OyQSCeHh4XoK68OQSCRs3bpV/XdERATly5fH1NSU0NDQfH//Q4cOIZFISEhIAGD58uXIZDK9bf/OnTv/ul9ejUEQBEEQBEEQhDc3f/58fHx8MDU1pVy5cpw6dUpn2iVLllC5cmVsbW2xtbWlVq1ar02vD+/VpXPPnj0sX76cQ4cO4efnh4ODg77iKhCjRo3CwsKCyMhILC0tP/j7t2zZks8//1xv2/P09OTx48cf7X5RKpVsX7+Qo/u2kJ6WjH9ICVp3/QFnN+/X5jv4xwb2bl1BYkIsHj5BtOo8BN/AourXp43ozPUrZzXyVKnTjDbdf1T/fefGFTavnsPdqKtIJBJ8AovS7Nt+ePoG67eQr1Aqlfzx23xOHNhEemoyvsGhtOg8AidX3WW+efUMf+1Yzv3bV0mKj6HToFkUL1MzX+PctWMbWzZtJD4+Dl9ff7r26E1QcIjO9MeOHmbNquU8jX6Cm5s77Tp2oXSZcurX4+PjWbFsCeHnzpKSmkKRosXo1r03bu4eACQnJ7F29QrCz50lJuYp1jY2lK9QkdbftsfCQv/fxY37jrFq90FiE5MJ9HTj+7ZNKOqvfR/8dfoiy3bs5370M7KzFXi5ONC6fjW+qFRaI82mv/4m4s4DElPSWDN+IMHe7nqP+03t3LGdzZt+e77//OjWoxfBOvbf3bt3WLNqJTdv3uDp02i6dO1Oo8ZNP3DEb8+uUmn8BnbCpmRRTN2cONOsJ9HbDxR0WHkYh1bCpEwNJBbW5MQ8JOPAJnKe3NOdwcQM00pfYBRYHImpBYqkODIObiH79lXVy2VrYRhUAgM7J5TZWeQ8vE3GkR0o4p9+kPJs3HuUVbv+IjYxiUAvd75v10znd2fLX3+z69hpou4/BqCQryc9W36pkT42MYm563bwz6UIktPSKRniz/ftmuHl4vTesSqVSjav/ZmD+7aSlppCUEhx2vcYgoub12vz7dv1G7u3riYxPhZPn0Dadh2Ef1AR9euZmXLWLp3NyWN7ycrKolhYedp3H4yNzB6AIwd2smTOWK3bnrdiDzYyOxbPHsOxv3bled3d05dJ8za8Ufn27NzM9s3rSIiPw9vXn47dviMwuLDO9CeOHWT96l+IiX6Ci5sHbdp3p2SZCurXN65ZyvGjB4iNeYqhoSF+AcG0atuFwODcsj96eI9VSxcSee0S2VlZePn683WbzhQtXvKNYn4bSqWS47vmcOn4b8jTk3DzK0ntr0dj6+SjM8/JPxdzPXwvcdG3MDQyxd0vjCqNB2Hn7KdOs37Wtzy4oXlxXaJSS2q30r7P3sXeXb+zc/MaEuPj8PINoF23AQS8dAy96p9jB/ht9c88e6raN1+370VY6c/Ur5/6+xAH/tjC7agIUpKT+Gn2Cnz8gjS2cWDPVv4+vJc7UZGkp6exZN1eLCyt9FamgqD8j3RE3LBhAwMGDGDRokWUK1eOWbNmUbduXSIjI3FyyvtbdujQIVq1asVnn32GqakpkydPpk6dOly5cgV39/y5dnivTzIqKgpXV1c+++wzXFxcMDTMW3/MzMx8n7f4oKKioqhUqRLe3t7Y29u/0zZycnJQKBTvlNfMzEzrgfGuDAwMdO6Xj8GfW5bz1651tOn+A8MmrcTExIzZ43qRlSnXmef0sT/5bdl0vvyqGz9OW4unTxCzx/YkKSFOI13l2k2Z+us+9dKs7Xfq1zLS05g9rhd2Di4Mm7yKwROWYWpqzuxxvcjOzsqv4gJwYPtSjvyxlq86j6D/hDUYm5qx6Kdury1zpjwdd+8gmnccnq+xvXD08EF+XbKIr7/5lplzF+Hj58eoEUNJSIjXmv7a1StMmzyB2nXqMWvuIspVqMhP40Zx985tQHXS/mncSJ48fszwkWOYNXcRTk7OjPhhMBkZ6QDExcYSFxtLh87dmLvwF/r1H8y5M6eZO2u63su395/zzFy7jS5N6rJ63ACCvNzoM+Vn4hKTtaa3tjSnY8NaLBvZj/U/DaJBlbKMXbKeExcj1GnS5ZmEBvnSp+WXeo/3bR05fIhfliym1TdtmD13Ab5+fowc8YPO/SeXy3FxdaFdh47Y2tp94GjfnYGFOUkXI7ncd0xBh6KTUXAYptWakHHiT1JWTUXx9BEWzXsgMddxE0NqgEWLnkht7EjbvozkpRNI37seRUqCOomBZwCZ54+SsmYmqb8tAAMDLFr0ACPjfC/P3hPnmLlmC12a1mX1+O9V351JC3V+d85eu0ndCiVZNLw3y8b0x9leRu9JC3kapyqPUqlk0Ixfefg0lukDOrNmwve4ONjR86cFpGfo/k18U7s2r2Tvrg106DGU0VOXYmJqxpTRfcl8ze/tP0f3sXbpLJq07My4GSvx8g1kyui+JL50jlnz60zCTx+l9+CJDJ+wiIS4GGZPHKJ+vXylWsxdvltjKRZWnpCiJbGRqb5j33YeqPH67F93YGllTdmKb3Yz7/iRA6z4ZR4tWrVn8uxf8PYNYMLIgSTq+J5HXrvErCljqFH7C6bM+ZWy5SszZcIP3LtzS53G1d2TTt37M33+CsZNWYCjswvjRgwkMTF3m5PGDCEnJ5tRE2YxedYv+PgGMGnMEOLjY98o7rdxat8Szh9aRe2vR9P6+40YGZvx+7xOZGfp3n/3b5wirEprWg/aSIs+y8jJyea3uZ3IlKdppCte8St6/HRMvVRpPFhvcZ84up/Vv8yhaatOTJi1HC/fQCaN7K9xDL3s+rWLzJs6imp1GvDT7BWUKl+FGROGcP9ulDqNPCOd4MLFadWul873zZRnUKJkeRq1aKe3sghvZsaMGXTp0oUOHTpQuHBhFi1ahLm5OUuXLtWafs2aNfTs2ZPQ0FBCQkL45ZdfUCgUHDiQfzct37nC1759e/r06cO9e/dULSQ+PgBUq1aN3r1789133+Hg4EDdunUBuHz5MvXr18fS0hJnZ2e+/fZbnj17pt6eQqFg4sSJ+Pr6YmZmRokSJfj9999fG8OCBQsIDAzE1NQUZ2dnmjdvrn7Nx8eHWbNmaaQPDQ1l9OjRWrclkUg4e/YsY8eORSKRMHr0aK3dHcPDw5FIJNy5cwfI7Ya5fft2ChcujImJCffuab97u3v3boKCgjAzM6N69erqbbygrUvnwoUL8ff3x9jYmODgYFatWqV+rWPHjhQvXhy5XPXjl5mZSVhYGG3btgW0d+n8txgAjh07RuXKlTEzM8PT05O+ffuSmpqqtUzvSqlUsn/nWr5o3oXQstXx8AmiQ99xJMTFcP7UQZ359u1YTaXaTalYsxFunv607jYcYxNTjv+1VSOdsbEpNrYO6sXspQusJw9vk5qSSMNWPXBx98HNy58vW3YjKSGWuJjHei3ny5RKJYd3r6ZO064UK1MDd+9g2vT6icT4GC6d/ktnvsJhlfni676UKJu/rXovbNuyiTr1PqdWnXp4eXnTs/d3mJiYsH/vHq3pd2zbTMlSZWjavCWeXt60adsBP/8Adu3YBsCjhw+JjLhGz979CAwKwcPDkx69+pGZmcmRQ6p97e3jy7AfR1O2XAVcXd0oERpGm3YdOXXyH3JycvRavjV/HKZxtfI0rFIWP3cXhnVojqmJEduPaO9OUbpQANVLF8fX3RkPZwda1a1CgKcr4ddvq9N8Uak0XZrUpWyRIK3b+JC2btlE3Xr1qV2nLl5e3vTq3Q8TExP27f1Ta/qgoGA6dupK1arVMTIy+sDRvruYP49wfdQsorftL+hQdDIuXY3MS3+Tdfkkitho0vdtRJmViXHR8trTFyuPxNSctK2/kPPoNsqkOHIeRKGIeaROk7ZpEVlXTqGIfYIi5hHpf6xBam2HgbNnvpdnzR+HaFz9MxpWLY+fhwvDOn6FqYkx2w//ozX9+F5taVG7MsE+Hvi4OfNjl1YoFQpOXbkOwL0nMVy6eYehHVtQxN8bHzdnhnVogTwriz9PnHuvWJVKJXt2rKdhi46UKlcVL59Aun03moS4Z5z957DOfH9sW0u1Oo2pUqsB7l5+dOgxFBMTU47s3wFAWmoKh/dv55uO31GkeBl8AwrRpe9IbkRc5GbkJQCMTUyR2TqoF6nUgKuXzlC1VkP1+5hbWGqkuXXzGqkpyVSp2eCNyrdz6wZq1m1A9dpf4OnlS9degzA2MeWvfXlbDQF2bf+d0FJladTsGzw8ffj62874+QexZ+dmdZrK1WpTPLQ0zi5ueHr70q5zH9LTUrl3W1XxSEpM4PGjBzRp3gZv3wBc3T1p3a47cnkG9+/e1vq+70qpVHLu4ErK1+tBQIlaOLqH8Hm7KaQkPuXmBd3f+ea9f6VohaY4uAXi5BFC/W8nkRz/iOh7VzTSGRqbYmHjqF5MzPTXk2T31nVUr9uQarW+xMPLl049B2NiYsLhfTu1pt+zfSMlSpajQdM2uHv68FWbbvj6B7N3Z+41cOUa9WnaqhNFQ8vofN/6jb6mYYu2BIQU1Znmv0YpkRTYIpfLSUpK0lheXG+/LDMzk7Nnz1KrVi31OqlUSq1atThx4sQblTMtLY2srCzs7PLvpus7V/hmz57N2LFj8fDw4PHjx5w+fVr92ooVKzA2Nub48eMsWrSIhIQEatSoQVhYGGfOnGHPnj1ER0fz1VdfqfNMnDiRlStXsmjRIq5cuUL//v1p06YNhw9r/2E+c+YMffv2ZezYsURGRrJnzx6qVKnyrsXh8ePHFClShIEDB/L48WMGDRr0xnnT0tKYPHkyv/zyC1euXNHaSnf//n2aNm1KgwYNCA8Pp3PnzgwdOvS1292yZQv9+vVj4MCBXL58mW7dutGhQwcOHlRdJM+ZM4fU1FT1doYPH05CQgLz5s3Tur03iSEqKop69erRrFkzLl68yIYNGzh27Bi9e/d+48/jTTyLfkhSwjMKlcjt9mduYYVvYFFuRV7Umic7K4t7UdcoVDw3j1QqpVDxcnnynDy6m/7tqjO6X3M2r56DXJ6ufs3F3QcLKxnH9m8lOyuLTHkGx/dvxdXDF3snN72W82WxTx+QlPCMoGK5F3tm5lZ4BxTj9o0L+fa+byMrK4ubN68TGprbPUcqlVIitCQREVe15omIuEqJMM3uPCVLlVGnz8pStfIbGee2QEilUoyMjLh69bLOWNJSUzE3N8fAwOCdy/OqrOxsIu48oNxLFTOpVErZIkFcvHnnX/MrlUpOXbnO3ccxhAX7/Wv6D021/24QGhqmXieVSgkNDSMi4loBRvZ/SGqAgbMn2Xevv7RSSfa96xi4+WjNYuhflJxHdzCr2QKrHuOxbD8Uk3K1XztTncTETLXljDSdafQhKzubiNv3KVf0le9O0SAu3rjzRtvIkGeSnaPAxsJctc2sbABMXrrRIJVKMTY0JDzyltZtvKmY6EckxsdStERZ9TpzC0v8goqoK2avys7K4k5UBEVK5F5US6VSipQoo85zO+oaOdnZFHlpu24ePtg7unAjQvt2jx3cjYmJKWU/q6Ez3sP7t1OkRFkcnFz/tWxZWVncunmd4qGlNOIsHlqa6xFXtOa5HnGZ4qGlNdaVKFmW6xHaf4OzsrLYv2c75haWePsGAGBlbYObhxeH/9pDRkY6OTnZ7NuzDRuZLX4B+h0OkRj7gNSkGLyDc7s1mphZ4epTgke3z7/xduTpqtZnUwsbjfXXTu9g/uByLBv/JUe2TScrM11b9reWnZXF7ZuRFH3lGCoaWoYbkdo/6xsRl/NU5IqHleOGjn0jfBgTJ07ExsZGY5k4cWKedM+ePSMnJwdnZ2eN9c7Ozjx58uSN3mvIkCG4ublpVBr17Z37+tnY2GBlZaXuNviywMBApkyZov57/PjxhIWF8dNPP6nXLV26FE9PT65fv463tzc//fQT+/fvp0IFVX9yPz8/jh07xuLFi6latWqe97937x4WFhZ8+eWXWFlZ4e3tTVhYWJ50b+pF10dLS8s85fk3WVlZLFiwgBIlSuhM86Klbvp0VRe14OBgLl26xOTJk3XmmTZtGu3bt6dnz54ADBgwgH/++Ydp06ZRvXp1LC0tWb16NVWrVsXKyopZs2Zx8OBBrK2t3zmGiRMn0rp1a7777jtAtS/nzJlD1apVWbhwIaampnm2K5fL89z1yMzMwdjYRGfZkhJUrbtWNpp3M6xl9iTp6BqSkhyPQpGDtUwzj5XMnscP76j/Llu5PvaOrtjYOfLwzg02rZpN9MO79BiiKrepmQWDxi5hweQB7Pp9CQBOrl58N2I+Bgb51/01OUFVLisbze7CVjb2JCc805blg0tKSkShUCCztdVYL5PZ8vD+fa15EuLjkcleTS8jPl7VfcXD0wtHRydWLvuFXn36Y2Jqyvatm3j2LIb4OO37OikxkQ3rVlO3/hd6KNVLsSankqNQYGejOa7BztqKO490j4FKSUunft8xZGZnYyCVMqRdM8oXy9/xnu8iKSlJ5/57oGP/CflDYmaBRGqAMlWzu6MyNRmpnfau+1Ibe6RegWRdO0vq5kUYyBwxrdUCpAbIT2hrYZdgWr0p2Q9uoXiWf70T4N2/Oy+bu347DrbWlC2q+u74uDnjYm/LvA07+KFTS8xMjFnzxyGi4xJ4lpD0fvE+P4/YvHK+sJHZkajjHJOclIBCkZMnj7XMjkcP7gKQGB+LoaFRnrFRNjI7EhO0b/fwvu1UqFIXY5O850+A+NgYLp49Qc+BbzaGLDkpUWucNjJbHj6P81UJ8XF50stkdiS80s3w7KnjzJwyhkx5BjJbe0aMm4G1jQxQ9YQaOX4mU8b/QNsWdZFIpNjIZAwfMw1LPY8VS02KAcDcWvN8aW5lT2rSm50vlQoFBzf9hLtfSRzdcm9UFCr9JdZ2bljaOBHzMJIj26YRH32bRl213yx/G+pjyDbvcfdI175JiNV6nCboOJ7+nxTkg9eHDRvGgAEDNNaZmOi+rn1XkyZNYv369Rw6dEjrNba+5MvVbalSpTT+vnDhAgcPHtQ6EUpUVBRZWVmkpaVRu3ZtjddedFHUpnbt2nh7e+Pn50e9evWoV68eTZo0wdzcXH8FeUPGxsYUL178tWmuXbtGuXLlNNa9qNy+Lk/Xrl011lWsWJHZs2drbGPQoEGMGzeOIUOGUKlSpfeK4cKFC1y8eJE1a9ao1ymVShQKBbdv36ZQoUJ5tjtx4kTGjNEcR9Ouxw906JU75uzk4d2sXjxe/Xfv4XN0xvm+qtRppv6/h3cgNnYOzBjVjadP7uPk4kmmPIMVC8YQEFKCLv0nolDksHfbSuZO6MsPU1brPCm/rTNHd7JhSe4JvNvQ+XrZ7n+NoaEhw34czdzZ0/mmZRNVi2FYSUqVLotSqcyTPi0tlbGjhuPp5U2r1m0LIOK8zE1NWDthIGkZmZy+coOZa7fh7mRP6UIBBR2a8CmRSFCmpZC+dz0olSiiHyCxtMGkTA2tFT7TWs0xcHAhZd1sLRv7uCzfvo+9J86z+MfemBirWvQMDQ2Y2r8T435eR42uwzB43mL4WYlCkPen4bWOH9rDsoW5d98Hjpipz/Df2Y2Iizx6cJvu/UfrTHP04C7MLSwpVa7aB4tLlyLFSzJ1zlKSkxLZ/+cOZkwexcTpi7GR2aJUKvll4UxsbGwZO3kexsYmHNi7k0ljhzJp5s/Y2r37BHFXT21n37pR6r+b9lz83mXZv2EMzx7doNWAtRrrS1Rqqf6/o3swljaObJzTnoSYe8gcXz+hj/D/w8TE5I0qeA4ODhgYGBAdHa2xPjo6+l8bkKZNm8akSZPYv3//v9Yj3le+VPgsLCw0/k5JSaFBgwZaW7NcXV25fFnVbL1r1648s9Po+rCtrKw4d+4chw4dYu/evYwcOZLRo0dz+vRpZDIZUqk0z8VkVtbbTcghlaruLLy8HW3bMDMzQ1JAD4dUKBQcP34cAwMDbt68+d7bS0lJoVu3bvTt2zfPa15e2n8Itd0FORmlOe6qRNmq+Abl9ivPfv45JifGIbNzVK9PSojVOVOmpZUtUqlBnglakhNi1bOjaeMbWAyAmMeqCt+po38Q+/QRQyeuUO/jzv0n8l3bKoSfPkTZSvV0buttFC1dHe/A3C9w9vOujcmJsdjY5pY5OTEWdx/dM2B+SNbWNkilUhLiNQf+JyTEI7Oz1ZpHZmubZ0KQhIQEjQlAAgKDmD1vMampKWRnZ2NjI2PQd70JCNQc85aWlsboEcMwMzfjhxFj9D7hkMzKAgOpNM8kE3FJydjLdN+hlkqleDqr9lmwtzu3H0WzfMeBj67CZ21trXP/2ebj2AAhL2V6KkpFDhILzeNKYmGVp9VPnSc1CaUiB1465yjiopFa2oDUABS5v6umNZth5FeElA1zUKYk5k8hXvLa747N61t3Vu36i+U7DrBgWE8CvTTP8YV8PVk7cTApaelkZedga21Ju5EzKOz7dmMSS5atTMBLs0m+6EqemBCH7KWKSGJCHN6+2sfaWlnLkEoN8kyukZQQh8xWdY6xsbUnOzuL1JRkjVa+xIQ4reehQ/u24e0bhG9A3puloLq+OLJ/BxWr1cfwDcfQWlnbaI0zMSFeHeerZLZ2edInJMQhe6VlydTUDFc3D1zdPAgKKUKfLq34a+9Omnz1LZcvnOXs6b9Zvn435uaq6zy/gGAunj/DoQN7aNKizRvFr01A8Rq4+uT2ksrJVu2/tKRYLG1yW8TTkmNx8vj38+X+DWO5dfkQLfuvxsr29RfdLs/fNz7m7ntX+NTHUPyr+yZO976R2WvZl3HIXnNdI3w8jI2NKVWqFAcOHKBx48YA6glYXjccasqUKUyYMIE///yT0qVL60ynLx+krbRkyZJcuXIFHx8fAgICNBYLCwuNyU5efd3TU/ePvqGhIbVq1WLKlClcvHiRO3fu8NdfqskvHB0defw4t4tLUlISt2+/3aBiR0fVBd7L23nXZw0WKlQozzM2/vlH+0D3l/McP35cY93x48cpXDh32uWpU6cSERHB4cOH2bNnD8uWLXuvGEqWLMnVq1fz7IeAgACMjbXPAmdiYoK1tbXG8mp3TlMzC5xcvdSLq6cf1jIHrl08qU6TnpbC7RuX8QvWfpfD0MgIL/9CRLyUR6FQcO3iKZ15AO7fjgTAxlZ10s+UZyCRSDUq6RKpBIlEglLxlreVX8PUzAJHFy/14uLhj7XMgeuXcuPPSEvh7s1L+Abq7g78IRkZGREQEMSFC7kTJigUCi6GnyckRPt03yEhhbkYrjmmIvz8Wa3pLSwssbGR8ejhA27evE65CrnjM9LSUhn14xAMDQ35ceQ4ncfb+zAyNCTEx4NTV2+o1ykUCk5fuUHxAJ833o5CqSTz+fijj4lq/wVy4UK4ep1CoeBCeDghIdovOIV8osghJ/o+hl4vVy4kGHoFkfPojtYs2Q9vI5U5ALm/TVJbJxQpiXkrewHFSd04H2Wi9pn/9M3I0JAQX0/1hCvw/Ltz+TrFA3105lux4wC/bPmTuYO7U9hP98W0pbkZttaW3HvylGu37lG1VLG3is/M3AJnV0/14u7ph42tPVcu5s4vkJ6Wwq3rVwgI1r5tQyMjfPxDuPpSHoVCwZWLZ9R5fP0LYWBoqJHm8YO7xMY8ITBEc7sZ6WmcOnaAqrUbokvE5XNEP75P1dqN3risRkZG+AUEcelC7uOHFAoFly6cJShE+9T/QSFFuRSu+biii+fPEPQvE3wolQr1je4XQzdevcEtkUpQKt9tdvIXjE0tsXXyVi/2rgFYWDtyNzJ30gt5egqP71zAzVf38B2lUsn+DWO5eWEfX/Vbgczh328cxDxQjW+2tHH8l5T/ztDICN+AYK5cPKNep1AouHLhDIHB2j/rwJCiXL5wRmPdpfBTBH5Ck6+8KyWSAlvexoABA1iyZAkrVqzg2rVr9OjRg9TUVDp06ABA27ZtGTZsmDr95MmTGTFiBEuXLsXHx4cnT57w5MkTUlJS9Pr5veyDzNffq1cvlixZQqtWrRg8eDB2dnbcvHmT9evX88svv2BlZcWgQYPo378/CoWCSpUqkZiYyPHjx7G2tqZdu7xTzO7cuZNbt25RpUoVbG1t2b17NwqFguBgVetQjRo1WL58OQ0aNEAmkzFy5Mi3nvzhRYVz9OjRTJgwgevXr6vHv72t7t27M336dL7//ns6d+7M2bNnWb58+WvzfP/993z11VeEhYVRq1YtduzYwebNm9m/XzVD1fnz5xk5ciS///47FStWZMaMGfTr14+qVavi55d3Qok3iWHIkCGUL1+e3r1707lzZywsLLh69Sr79u3TORnMu5BIJNT68ht2//4LTq5eODi7s23dAmR2joSVra5ON2NUN0LLVafG518DULtBG5bNHYl3QGF8A4uyf8daMuXpVKyhOlk+fXKfU0f+oFipSlhYyXhw5zobl00nsHBJPHxUF16FSpTn95WzWPvzRGp88TVKhZI/tixDKjUguGj+3WWRSCRU/bwNe7csxtHVC3snd3ZvmIeNrSPFyuQO5p83rjPFy9SgSr1vAJBnpBHz0nO7Yp8+5MGdCMwtbbBz+PcB/m+rUZNmzJoxhYDAYIKCgtm+bTMZ8gxq1la1fM6cNgk7ewfadegMQINGTflhyAC2bP6NMmXKceTwQW7euE6vPv3V2zx29DA2NjY4Ojpx585tflm8gHLlPyOspOrzTktLZeTwIcjlcgZ8P4y0tDTS0lSTUFjb2Oh14pbW9asy+ud1FPb1pIifF2v/PEy6PJMGVVSTMIxctBYnW2t6P3/EwrLt+ynk64mHswNZWdkcv3CN3cfPMKx97qzAiSmpPIlNICZe1dJy97FqTJO9jRUOMu1javNL4ybNmDljKoGBgQQFhbDt+f6rVVs1Y/L0aVOwt7enfYdOgKrXwv3nMwtnZ2cRG/uMW1FRmJqZ4uZWcM8S/DcGFuZYBORWIMx9PbAuEUJmXCIZ9/N3PNubyjxzCLP6rcmJvkfO43sYl6qKxMiYzMuqmz5m9VujSElEflQ1e1/mhWOYhFXGtEZTMs8fQWrriEm52mSey528zLRWC4xDSpK69ReUmRlIzFWtTMrMDMjnx8q0rl+N0YvXUNjXiyL+Xqzd8/y7U1U1VGDkwtU42drQ+2vVTJPLd+xn8e+7Gd+rLa6OdupxeeamJpibqm4K7j95HpmVJS4Otty895jpqzZTtXQxyhd/v14PEomEeg2+ZtvGpbi4euLo7Mbvaxchs3OgVPnceQEmjuhJ6fLVqP2FahK5+o2+4efZY/ANKIRfYBH+3LEeeUY6VWqpfg/MLSypWqsha5bOwsLSGjNzC1b+PI2A4GJ5KpL/HNtHjiKHz6rW1xnn4f3b8Q8qiqe3/1uV78vGLZk/8yf8A0MICCrErm2/Ic9Ip3ot1bN8504fj529A63bdwfgi4bNGTW0Dzs2r6dkmQocP3KAqJsRdOv9PQAZGels3rCS0uUqYWtnT1JSIn/u3Exc7DMqVFKdk4NCimBpacX8mT/R/Ov2GJsYs//PHTyNfkzJl54Zpw8SiYSS1dvyz56F2Dp5Y2PvwfGds7G0cSKgRO7EFhtntyOgRG1KVlO1Lu7fMIaIMztp3G0BxiYWpCaqxgIam1lhZGxKQsw9rp3ZgW+RqphZyIh5GMnBTRPxCCiDo7t+etp83rgVi2aOwy8gBP+gIvyxbT0ZGRlUfX4MLZgxBjt7R75up5qfoV7Drxg3rCe7tqwltPRnnDi6n1s3I+jcO3dSvZTkRJ7FRBMfpxq/+Pih6jdbZmuvbjlMiI8lIT6W6EcPALh/NwpTM3McHJ2xtNKctEbQr5YtWxITE8PIkSN58uQJoaGh7NmzRz2Ry71799Q9ykA1p0ZmZqbG0wVA9TxwXU8TeF8fpMLn5ubG8ePHGTJkCHXq1EEul+Pt7U29evXUH8C4ceNwdHRk4sSJ3Lp1C5lMRsmSJfnhhx+0blMmk7F582ZGjx5NRkYGgYGBrFu3jiJFVHe3hg0bxu3bt/nyyy+xsbFh3Lhxb93CZ2RkxLp16+jRowfFixenTJkyjB8/nhYtWrz1Z+Dl5cWmTZvo378/c+fOpWzZsvz000907NhRZ57GjRsze/Zspk2bRr9+/fD19WXZsmVUq1aNjIwM2rRpQ/v27WnQQHVy7dq1K7t27eLbb7/lyJEj7xRD8eLFOXz4MMOHD6dy5coolUr8/f1p2bJlnu29r7pN2iOXp7N60XjSUpMJKBRKvxHzMXqpdTDmyX1SkhLUf5epVJfkpHi2r1tIUkIsHr7B9B0xH+vnXR8MDY24dvEkB3auRS5Px87BmZIVavJF887qbbh6+NJ72Gx2bFzMpKHtkEilePmG0G/EfI3upfmhZsOOZMrT2fDzGNLTkvELDqP7sEUaZY6Nvk9qcm6Z70VdYd7Y3H20deVUAMpWbUjrnhP0HmPlqtVJTEpk7arlxMfH4+fnz+ixE7F9PhFITMxTJC/9cBUqXISBg39gzcplrFq+FDd3d34YMQZvH191mvi4OJYuWaTqWmhrR/WatWnZKrf7T9TNG1yPVD3XrlsnzXF7S5atxtn57SZSep065cOIT05h0aY9xCYmEeTlztzvu6q7pT2JjUf60h3sdHkmk1ds4mlcAibGRvi4OjOue2vqlM+9y3zk3BXGLFmv/vuH+arHp3RpUoduTfXTRfhNValajcSkRFavWvl8//kxduwEjf0nleaWLy4ulr59eqj/3rzpdzZv+p2ixYozafK0Dxr727ApVZQKB3IfU1N4mupccX/lZi52GqYr2weVFXkeibklphU/R2JuTU7MA1J/X4QyTdUtUmptq9F9U5mcQOrvCzGt3gTLdkNQpCSSee4w8lO509CbhKrGaVt+rdntPu2PNWRd0f5oEX2pU6Gk6rvz+27Vd8fbg7lDumNvo7qp8ep3Z9P+42Rl5zBktmbPky5N69GtmaoS9Cw+iZmrtxKbmIyDzJovKpehc5O6eon3i6ZtkWdksHTBT6oHrxcqwfejZmv0QHn65CHJL51jyleuTXJSPJvW/kxifCxevkF8P2q2RnfN1p36I5FImTN5KFlZmRQPK0+77nmf43Z433ZKl6+m8+HXaakpnP77L9p0GfjWZatYpSZJiQlsWP0rCfFx+PgFMHzsNGTPu9I/i4lG8tL3PLhQMfp9P4p1q5awduXPuLp5MHj4T3j5qG4OS6VSHj64x6EDP5KclIiVtTX+gYUYO3kent6q33JrG9UELetW/syY4f3Iyc7Gw8uXIT9OxMdP/93by9buQlZmOnvXjkSenoS7fyma9foFQ6Pc/Zfw7D7pqbld2C8cXQfAhlnfamyrXpuJFK3QFKmhEXcjTnD24Eqy5GlY2boSFFqH8vV66i3uCpVrkZQYz+9rfiEhPhZvv0CGjpmpnsglNiYa6UuTkQQVKk6vQWP4bfXPbFi5CBc3TwYMn6xxE+DsyWMsnp07D8LcKSMAaNqqE82/UV3f7P9jC5vX/apOM3ao6ne9W78fqVpLvxOgfSgFOWnL2+rdu7fOLpyHDh3S+FvbI9Hym0SpbdYEQXhPh6/k7xThH1p61sf58Pp34WsV/e+J/kPcYrU/xuO/6on9p9ON53pInYIOQa8qTfywlff8Jq2mu+Xpv+aa5esnQfuvMTV4/wfPf0xO3tU+M+1/UUmvD9ON+kMpFfTxju9+pOMxXR+C22uGCv0X/XeqzoIgCIIgCIIgCMJb+XSaLQRBEARBEARB+CQoC2gG/E+RaOETBEEQBEEQBEH4RIkWPkEQBEEQBEEQPipv+3gEQTfRwicIgiAIgiAIgvCJEi18giAIgiAIgiB8VP5Lj2X42IlPUhAEQRAEQRAE4RMlKnyCIAiCIAiCIAifKNGlUxAEQRAEQRCEj4qYtEV/RAufIAiCIAiCIAjCJ0q08AmCIAiCIAiC8FERk7boj/gkBUEQBEEQBEEQPlGiwicIgiAIgiAIgvCJEl06BUEQBEEQBEH4qIhJW/RHtPAJgiAIgiAIgiB8okQLnyAIgiAIgiAIHxUxaYv+iE9SEARBEARBEAThEyVa+ARBEARBEARB+KiIMXz6I1r4BEEQBEEQBEEQPlGiwicIgiAIgiAIgvCJEl06hXzhbBpX0CHolYFpTkGHoDcGZBd0CHplFP+koEPQKytrt4IOQW8qTaxX0CHo1bFhewo6BL2qvsSuoEPQG2mFcgUdgl5ZSxIKOgS9irxhWdAh6I2VuW1Bh6BXpYIKOgLdlBLRpVNfRAufIAiCIAiCIAjCJ0q08AmCIAiCIAiC8FFRKkULn76IFj5BEARBEARBEIRPlKjwCYIgCIIgCIIgfKJEl05BEARBEARBED4qStEupTfikxQEQRAEQRAEQfhEiRY+QRAEQRAEQRA+KkrEpC36Ilr4BEEQBEEQBEEQPlGiwicIgiAIgiAIgvCJEl06BUEQBEEQBEH4qIgunfojWvgEQRAEQRAEQRA+UaKFTxAEQRAEQRCEj4po4dMf0cInCIIgCIIgCILwiRItfIIgCIIgCIIgfFREC5/+iBY+QRAEQRAEQRCET5So8AmCIAiCIAiCIHyiRJdOQRAEQRAEQRA+Kkql6NKpL6KFTxAEQRAEQRAE4RP1UVf42rdvT+PGjfNt+6NHjyY0NDTPOmdnZyQSCVu3bs23936hWrVqfPfdd+q/fXx8mDVrlt62/yaf4asxCIIgCIIgCEJBUiIpsOVT81ZdOqtVq0ZoaOhbV0jeNd+Hdu3aNcaMGcOWLVsoX748tra2HzyG06dPY2FhobftzZ49G6VSqbft6dOuHVvZumkj8fFx+Pj607VHH4KCQ3SmP370MGtWLeNp9BPc3Dxo27ELpcuUU7+enp7OymVLOHniOMnJSTg5u/Blw6bU/6IBAMnJSaxbvYLz587wLOYp1jYyylWoSOtv22NhYZnv5d25YzubN/1GfHwcvr5+dOvRi2Ad5b179w5rVq3k5s0bPH0aTZeu3WnUuGm+x/g6O3bs4PdNm4iPj8fP15cePXoQHBysNe3du3dZtWoVN27e5OnTp3Tt2pUmr9x42LBhA8f//psHDx5gbGxM4UKF6NixIx4eHvlelvVHzrHir1M8S0olyN2Joc1rUczb9V/z/XH2GkNX7KB6sQBmdcndH/svXOe3Y+Fcu/+ExLQMNgxuR4iHc34WQcOWXX+yfusO4uIT8ffxol/XDhQKCtCa9va9+yxd+xvXo27x5OkzendqS4uGn2ukSUtL59e1Gzn6z2niExMJ9PWhT5f2FAr0/xDFwTi0EiZlaiCxsCYn5iEZBzaR8+Se7gwmZphW+gKjwOJITC1QJMWRcXAL2bevql4uWwvDoBIY2DmhzM4i5+FtMo7sQBH/9IOU503YVSqN38BO2JQsiqmbE2ea9SR6+4GCDiuPDWevs+LkNWJT0glysmVInVIUdXPQmnb7xVuM2vWPxjpjAyknB3+tNf34PafYdP4mg2qWpHVZ3eeCN7V31+/s2rKaxPg4vHwDaNd1IP5BRXSmP3nsAL+t+ZlnTx/j7OZJq3a9CC39mfp1pVLJprVLOLh3G6mpKQQVKkbHHoNxcfNSp3n88B5rl83l+rWLZGdn4eUTQPPW3ShSvBQAd2/fYMfvK4m8doHkpEQcnVyoWa8p9Rq2fO/ybt+5i983bSbu+W90z+7dCAkO0pr2zt27rFy9hps3o4h++pRuXTrTtHEjndvesPE3lq5YSeNGDenRtct7x/qm6pYxolwhQ8xM4PYTBZuPZPIs8c2uaaqHGfJFeWOOXMxi+/Es9foeDU3wdzfQSHviShabjmS9ugm9UiqVHNw6l7OHfyMjLQmvwJJ8+e0o7F18dOa5E3ma43/8yuO7V0hOiOHrPvMoVLKWRhp5Rir7f5tOxPkDpKUkYOvoQbla31KmuvbvmfD/6aNu4fvQoqKiAGjUqBEuLi6YmJi803YyMzPfOQZHR0fMzc3fOf+rbGxskMlketuevhw9fJClSxbR8pu2zJi7CF8/f0aPGEJCQrzW9NeuXmHa5PHUqlOfmXMXU65CRSaOG8ndO7fVaZYuWci5s6fp//0w5i1eRsPGzfh54RxO/vM3AHGxscTFxtKhczfmLPyVfv0Hc/7MKebOmpbv5T1y+BC/LFlMq2/aMHvuAnz9/Bg54ged5ZXL5bi4utCuQ0dsbe3yPb5/c/jwYX5esoTW33zD3Llz8fXz48cRI0hISNCaPkMux8XVlQ4dOui8cXLp8mUafPklM2fM4KcJE8jOyWH48OFkZGTkY0lgz7lrTNtykG71KrL++3YEuzvSY8FGYpNTX5vvYWwiM7YepKR/3gppujyLMD93vmtYNb/C1umvo38zf+kq2rVszpIZE/H39WbQ6InEJyRqTZ8hz8TN2Ymu336Dna1Ma5op8xZzJvwSw/v3YtmcqZQJK87AkeOJiY3Lx5KoGAWHYVqtCRkn/iRl1VQUTx9h0bwHEnMdN2WkBli06InUxo607ctIXjqB9L3rUaQkqJMYeAaQef4oKWtmkvrbAjAwwKJFDzAyzvfyvCkDC3OSLkZyue+Ygg5Fpz+v3mX6gXN0q1SUtR3rE+Qso+eGg8Sl6v7OWpoYsa9PE/Wyu5f2SsVfkfe59PAZjpZmeon1xNF9rPl1Nk2/7sz4mSvw8glk0qjvSEzQfgxfv3aRedNGUq12AybMWkHpclWY8dNg7t+NUqfZuXkVf+7cSIceQxg79RdMTMyYNOo7MjPl6jTTxg1Eochh+Ph5TJi5HC/fQKaPG0hCfCwAt29GYC2zpeeA0UyZt5ZGLdqzYeUC9u787b3Ke+jIUX5e8gutv2nF/Dmz8PP1ZfiIkTp/o+VyOa4uLnRs3w67f7m5HXn9Orv27MHX1+e9Ynxb1UMNqVTMkE1HMpmzKYPMLCVdvjTB0ODf83o6SqlQ2JBHzxRaX//najZjlqepl50n8reyB3Bs9y+c3LeKBm1H02XERoyMzVg1ozNZWXKdebLk6bh4hvBFm5E60/y5fhI3Lx+jadcp9P5pF+Vrt2X36nFEnP8rP4oh/Ee9cYWvffv2HD58mNmzZyORSJBIJNy5cwdQXQyWLVsWExMTXF1dGTp0KNnZ2a/Nl5OTQ6dOnfD19cXMzIzg4GBmz579VsHfvXuXBg0aYGtri4WFBUWKFGH37t0ALF++PE9FZ+vWrUgk2ptpR48eTYMGqpYgqVSqTqetu2Pjxo1p3769+m8fHx/GjRtH27Ztsba2pmvXrlrfIzU1lbZt22JpaYmrqyvTp0/Pk+bVLp337t2jUaNGWFpaYm1tzVdffUV0dDQAERERmJubs3btWnX6jRs3YmZmxtWrqjvbr3bpfJMY5HI5gwYNwt3dHQsLC8qVK8ehQ4e0luldbdvyO3XqfU6tOvXw8vKhR+/vMDExYf/ePVrT79i2mZKlytC0eUs8vbxp3bYDfv6B7NqxVZ0m4toVatSsQ7HioTg7u1C3/pf4+vlzIzICAG8fX4b+OJqy5T7D1dWN4qFhtGnXidMn/yEnJ0ev5XvV1i2bqFuvPrXr1MXLy5tevfthYmLCvr1/ak0fFBRMx05dqVq1OkZGRvka25vYsmUL9evVo06dOnh7edGnd29MTEzYu3ev1vTBQUF07tSJalWr6ox//Lhx1K5dG29vb/z8/BgwYABPY2K4ceNGfhaFVQfP0PSz4jQuXwx/Vwd+/KoupsZGbP3nks48OQoFP6zcSY/PK+FhL8vzeoOyRehevyLlgn3yL3AdNm7bxZd1avB5rWr4eHkwsEdnTE2M2b3/kNb0hQL96dGhDTWrfIaxUd5OHnJ5JkdOnKJ7+28oUaQQHq4udGjVAndXF7b9sS+fSwPGpauReelvsi6fRBEbTfq+jSizMjEuWl57+mLlkZiak7b1F3Ie3UaZFEfOgygUMY/UadI2LSLryikUsU9QxDwi/Y81SK3tMHD2zPfyvKmYP49wfdQsorftL+hQdFp9KoKmJfxpVNwffwcbhtcri6mhIVsvRr02n4OlmXqxt8hboXuanMbkfWf4qeFnGBro5z70H9vWUb1OI6rW+hIPL1869hyCiYkph/fv1Jp+z44NFC9Zni+btsHd05cWbbrh4xfM3l2/A6rWmT3bN9D4qw6ULl8FL99AevQfRULcM87+cwSA5KQEnjy6T4NmbfHyDcTFzYuv2/ZELs/gwfOKY7XaDWjbZQCFipbEycWdStXrU6XWl5w+cei9yrt5y1bq1atL3dq18Pbyom/vnpiYmvDnXu3f2eCgILp06ki1qlVee45JT09n8tTpfNenD1aW+d8T5mWVixux/2wWV+7k8DhOyfq/MrE2l1DU9/U1PmND+KaWMb8dyiRdrr01MDNbSXI66kWez/U9pVLJP/tWUqVBd0JK1sTFM5imXSaTHP+UiHO6v/OBxatQs9l3FCpVW2ea+zfDKVGxMb4h5bB18KB0tZY4ewbz8NbF/CjKByW6dOrPG/+yzp49mwoVKtClSxceP37M48eP8fT05OHDh3z++eeUKVOGCxcusHDhQn799VfGjx//2nwKhQIPDw9+++03rl69ysiRI/nhhx/YuHHjGwffq1cv5HI5R44c4dKlS0yePBnLd/xBGjRoEMuWLQNQx/k2pk2bRokSJTh//jwjRozQmub777/n8OHDbNu2jb1793Lo0CHOnTunc5sKhYJGjRoRFxfH4cOH2bdvH7du3aJlS1XXj5CQEKZNm0bPnj25d+8eDx48oHv37kyePJnChQu/cwy9e/fmxIkTrF+/nosXL9KiRQvq1auntwvxrKwsom5ep0RoSfU6qVRKidCSREZc1ZonMuIqJcJKaawLK1VaI31IoSKcOnmC2GcxKJVKLl44z8OHDwgrWVpnLKmpKZibm2Ng8Aa3DN9RVlYWN2/eIDQ0TL1OKpUSGhpGRMS1fHtffcnKyuLGzZsa411V8YdyLSJCb++TlqpqYbOystLbNl+VlZ3DtftPKP9SxUwqlVA+2JuLtx/pzLd4z9/YWpnTtELxfIvtXWRlZXM96jalShRTr5NKpZQqUYwrkdffaZs5OTnkKBQYv3IRaGJszKVr+tvfWkkNMHD2JPvuy7Eryb53HQM3H61ZDP2LkvPoDmY1W2DVYzyW7YdiUq426Li5ByAxUVU6lBlpegz+05aVk8O1J3GU83VRr5NKJJTzceHiw2c686VnZlN//lbqzdvKd78fJiomQeN1hVLJjztO0K5cIfwdZXqJNTsri9s3IykaWiY3VqmUoiXKcCNC+42dmxGXKVqijMa64iXLc/N5+pjoRyTEx1LkpTTmFpb4BxXhRqQqjaWVDa7u3hw9uJuMjHRycrL568+tWNvY4hugu4tqemoKFlbW71zeF7/RJUNLaJQ3LDSUqxGR77xdgHkLF1G2TGlKhoW+13belp2VBGsLCTce5N6MzciEe08VeDu//tK1aRVjrt3N4cZD7a17ACUDDRnT3oxBLU2pX84ILfe+9Co+5gEpiTH4FcntImxqboW7f3Hu3wx/r217BoQSef4vkuKjUSqV3L72D7HRd/AvWvE9oxY+JW98iNvY2GBsbIy5uTkuLrk/+AsWLMDT05N58+YhkUgICQnh0aNHDBkyhJEjR+rMZ2BgwJgxuV1XfH19OXHiBBs3buSrr756o5ju3btHs2bNKFZMdbHj5+f3psXJw9LSUt0i+HKcb6pGjRoMHDhQ5+spKSn8+uuvrF69mpo1awKwYsWK145XOnDgAJcuXeL27dt4eqruRK9cuZIiRYpw+vRpypQpQ8+ePdm9ezdt2rTB2NiYMmXK0KdPn3eO4d69eyxbtox79+7h5uYGqCrDe/bsYdmyZfz0009v98FokZSUiEKhQPZKNxKZzJYH9+9rzZMQH4dMljd9fHxu95yuPXozf84MOrb9GgMDAyQSKb36DaBIMe0X6UmJiWxct5o69b94zxK9XlJS0luX92PyIv5Xu2baymR6i1+hULB48WIKFy6Mj4+PXrapTXxqGjkKJfZWmt2m7a0suB2tvavXuagHbDlxkY1D2udbXO8qMSmJHIUCW5mNxnpbmQ33Hjx8p22am5tRJDiQlRs34+3hjq1MxoGjx7kSeR33d/htfBsSMwskUgOUqcka65WpyUjtnLTmkdrYI/UKJOvaWVI3L8JA5ohprRYgNUB+QluPAQmm1ZuS/eAWimdvd2Pv/1l8mpwcpRI7c1ON9fYWptyJTdKax9vOilFflCPIyZZkeSarTl6j/ap9/N75C5ytVd/BZSeuYiCR0Kq09vHA7yI5KQGFIgcbmWZ3eGuZLY8e3tGaJyEhNk96G5mtuivmi3/zprFTvyaRSBg2bi4zfxpM55Y1kEikWMtsGTJ6FhaW2it0169d5J9j+xk0csZbl/MF9TlGlvc3+v79B++83UOHj3DzZhRzZ717bO/Kylx1wyY5XbOFLiVNqX5Nm9AAA9wdpMzepLub8bkb2cSnKElKVeJqL+WL8kY4ySSs+PPdh+P8m5TEGAAsre011ltaO5CSqPuGyZv4vPUIti8fwfQBVZEaGCKRSGjYfhw+wWX+PfNH7lNsaSso731P49q1a1SoUEGjq2TFihVJSUnhwYMHeHl56cw7f/58li5dyr1790hPTyczMzPPrJmv07dvX3r06MHevXupVasWzZo1o3jxgrkDX7q07lYkUI0PzMzMpFy53ElG7OzsdE56AarP1tPTcA5uPQABAABJREFUU13ZAyhcuDAymYxr165Rpozqy7x06VKCgoKQSqVcuXJFZ7fVN4nh0qVL5OTkEBSkOdBbLpdjb6/5Q/Xya3K5Zh/0TLkc43ccA/mudm7fSmTENYaPGoeTkzNXLl9i8YI52NnZE/pK62BaWipjR/2Ap5c3rVq3+6BxCnnNX7CAO3fvMm1a/o+nfBupGXKGr9rFqFb1sLXU39jaj93w/r2YPHcxzTr2xEAqJdDfl5qVKxIZdaugQ8tLIkGZlkL63vWgVKKIfoDE0gaTMjW0VvhMazXHwMGFlHVvN4RAeHslPBwp4eGY+7e7I81+3snv52/Qq2oJrj6OY92ZSNZ2qKfzvPVfolQqWb5oKtY2toyYuAhjExMO7d3OtPGDGDd9GbZ2mpPb3L8bxYwJg2nydSeKh5XTsdWC8TQmhoU/L2Hi+LEYG+f/WNewQAOaV819n1936R7XpouNhYRGFY35eUcG2a8ZpXHyWu6LT+JySE5T0r2hKfbWWcQm6WeSu4sndrBjxSj1362/W6SX7Wpzcv8qHty6wDf9FmBj787dyNPsWj0WK5kT/i+1KAr/3wrswevr169n0KBBTJ8+nQoVKmBlZcXUqVM5efLkG2+jc+fO1K1bl127drF3714mTpzI9OnT6dOnD1KpNM/slFlZb99J+023o8+ZNd/WhQsXSE1NRSqV8vjxY1xd/322QV1SUlIwMDDg7Nmzebo56uouO3HiRI3WWoBeffrTu98AremtrW2QSqUkxGtOWJKQEI+tnfYJSmS2dnkmOElIiFdPaCKXy1m94leG/TiG0mVVY318fP25FXWTrZt/06jwpaWlMXrEUMzMzRk2YiyGhvn7NbC2tn7r8n5MXsQf/0r88QkJeol/wYIFnDp1iqlTpuDooH22P32xtTDHQCohNlmzK19scioOVnm/w/efJfAoLpG+P29Sr1M8/z0o+d1Utg3vjKfjh5/N9wUba2sMpNI8E7TEJyTqnJDlTbi7ujDnp1GkZ2SQlpaOvZ0to6fMws05f2ceVaanolTkILHQ7NYrsbDK0+qnzpOahFKRAy/9TiviopFa2oDUABS5F3emNZth5FeElA1zUKZon9RG0M7W3AQDiYS4NM2Wk9jUDOwtTXXk0mRkICXYxZb78SkAnL//lLjUDD6fv02dJkepZMZf51lzJpLdPXXPGvk6VtYypFKDPBO0JCXEYyPTfuNSJrPPkz4xIR6ZrSr9i38TE+I0Km6JCXF4+wUCcOXiGc6fOc7Pa/dhbq76PfHtEcKl8FMc/Ws3DZu3Ved7cO82P/3Ymxp1G9GkZcd3KucL6nNMgpbf6HecbfzmzZskJCTQq+936nUKhYJLl6+wfcdOdm7drNehEFfv5DAjOvfYejExi5WZhOS03O+2pblE50QsHo5SrMwlfNci93g0kErwdZNSsaghQ39OR9uk5feiVduzt5HorcIXHFodd7/cBoicbFXrYUpSLFay3N4KKUnPcPEs9M7vk5WZwYFNs/i6z1yCSlQDwMUzmCf3Ivh7z9L/fIVPPHhdf95qdLSxsXGeyS0KFSrEiRMnNCpFx48fx8rKSt1VUFu+48eP89lnn9GzZ0/CwsIICAhQz5L5Njw9PenevTubN29m4MCBLFmyBFDNdpmcnExqau7Me+Hh4W+9fUdHR43xfDk5OVy+fPmtt+Pv74+RkZFGhTY+Pp7r13WPsylUqBD379/n/kvd5q5evUpCQoJ6jF5cXBzt27dn+PDhtG/fntatW5Oenv7OMYSFhZGTk8PTp08JCAjQWHR1dR02bBiJiYkaS9fuvXSWy8jICP+AIC5eOK9ep1AouBh+nuAQ7WMPg0MKczFcc6xh+Pmz6vQ5OdlkZ2fnuUtsYCBFqcg9OaSlpTL6x8EYGRrx48hxH+TOpZGREQEBgVy4EK5ep1AouBAeTkjIu//QfyhGRkYEBgQQfuGCep1CoSA8PJxCIe8+dbpSqWTBggX8feIEkyZOfKeu1G/LyNCAQp4unLx+V71OoVByMvIuxX3d8qT3dbbn96Ed2DC4vXqpVjSAMoFebBjcHhfbdx93ow9GRoYE+fty9mLub5JCoeDcxcsU0TEd+9swMzXF3s6W5JQUTodfpGK5Uv+e6X0ocsiJvo+h18uxSzD0CiLn0R2tWbIf3kYqc4CXuv5IbZ1QpCTmrewFFCd143yUifk/2+inxsjAgEIudpy8E61ep1AqOXX3CcXd3+xGTY5Cwc2niTg8ryB+UdSXjZ0/Z32n+urF0dKMtuUKsaBl9XeO1dDICN+AYK5cOJ0bq0LB5YunCQwppjVPQEhRrlw8rbHucvgpAp6nd3R2Q2Zrr7HNtLRUoq5fITBYlSZTrqqwSF85D0mlmuehB/duMWF4TyrX+Jyvvu3xzuV84cVv9Pnw3Ek6VL/RFygc8m5dZUNLlGDx/HksnDtHvQQFBlCjWlUWzp2j93Hv8iyITVKql+h4VZfLQI/c9zExAi8nKXejtVf4bj7MYdqGdGb+lqFe7j/N4fz1HGb+lqG1sgfg5qC6FE5O1d8jrEzMLLF39lYvjm4BWNo4cuvqCXWajPQUHkZdxDMg9J3fJycnm5ycLCQSzct5iVSKUql7DKPw/+etmjZ8fHw4efIkd+7cwdLSEjs7O3r27MmsWbPo06cPvXv3JjIyklGjRjFgwACkUqnOfIGBgaxcuZI///wTX19fVq1axenTp/H19X3jeL777jvq169PUFAQ8fHxHDx4kEKFVBfQ5cqVw9zcnB9++IG+ffty8uRJli9f/jbFBVRj8wYMGMCuXbvw9/dnxowZOqc5fh1LS0s6derE999/j729PU5OTgwfPlz9GWlTq1YtihUrRuvWrZk1axbZ2dn07NmTqlWrqruQdu/eHU9PT3788UfkcjlhYWEMGjSI+fPnv1MMQUFBtG7dmrZt2zJ9+nTCwsKIiYnhwIEDFC9enC++yDvezcTEJM8jLIxNtI/peKFRk+bMnjGZgMAgAoNC2LFtExnyDGrVrgvAzGmTsLd3oG2HzgA0aNSU4UP6s3XzRkqXKc/RwweJunGdXn1UrYjm5hYULVaC5Ut/xtjEBCcnZy5fusDBA/vo2EV1Qk1LS2XU8CHI5Rn0//4H0tLSSEtTtfRY29jk68QtjZs0Y+aMqQQGBhIUFMK2bZs1yjt92hTs7e1p36EToGpFvn9P9dyx7OwsYmOfcSsqClMzU9zc3PMtTl2aNGnC9BkzCAwMJDgoiK3btiGXy6ldWzVz2LRp07C3t6dDhw7q+O+p488mNjaWqKgozMzM1GND5y9YwKFDhxg5ciRmZmbExakuwi0sLN75kShv4tvqpRmxejdFPF0o6u3K6kNnSM/MonE51UXb8FW7cLKxpF/DqpgYGRLo5qiR38pMdbH68vrE1HQexycRk6hqubjzVFUWB2sLHKzzd2a7rxp9wcTZCwkJ8CMkMIDfd+wmPUNO/VqqR0RMmDkfR3s7urZtBagmernzfFxPVlYOz2LjuHHrDmZmpni4qirdp85dQIkSL/f/sXfX0VFcbQCHf3F3SELcBQ1uxd2tAoXiUpwCLe4UKO7u7g7F3aG4u1uIh3iy3x8LG5Zs0E1D873POXtgZ+/Mvjdje+eaE4+fPWfmwmW4OTtRvULZDM0LQMKZA5hUa0zyi4ckP3uIYcEy6BgYknBZ+aDKpFpjUqIjiD+sHG0x4cIRjPKXwrh8fRLOHULXJjtGRSuRcPagapvGFX/AMKAArzfORZEQh46psgZRkRAHSRk/HPun0DMzxcwntQuEqacLlvkCSAiNIO7Rt9HXsEmRAAZuPU5OR1tyO9mx/PQNYhOTqJNX2X++/5Zj2FuY0qVsEACzjlwir1M2XG0siIpPYNGJazyLfE29IOUckdamRlibqp/r+nq6ZDMzxsPu6x6mVKvTiFkTh+HpE4i3X052bF5FfFwcZSoo72EzJgzBxjY7DZt1AKBqrZ8Y3rc92zYsI3/hkhw/tJu7t6/RqmNvQNk/r2rtn9i4eiGOTq5kd3Bi7bLZWNtmo2Cx0gD4BuTBzMyCmROHUq9hKwwNjdi/axMvXzwlqLByAI1HD+4won8n8uQvSvW6P6v6/+nq6mJp9eWtBerXq8vY8RPw8/XB38+PDZs2ERcXR+VKyjnbRo8bTzY7O1o2V3ZhUF6jlQ+TE1XX6LsYmxjj7OSEqakpHh7uat9hbGyMhaVlmuUZ5fDFRCoUNCA4QkFoZApVixgQGaPg8r3UBzntahlx+V4yRy8nEZ8Iz0PVC20JifA6XqFabmepQ35fPa49SCYmHnLY6VK7hAF3nipHAs0oOjo6FKvUlENbZmLn4IFNNmf2bZiMhY09Ae/Mq7dwdHMCC1SkaMUmgHKOvdCXqXOQhgU/5tnDa5iYWWFt54SxiTke/oXZtXoM+oZGWNs5c//GKS4c20SVhr0zLD/iv+ezCnw9e/akWbNm5MyZk9jYWO7du4eHhwfbt2/n999/J1++fNja2tKqVSv69+//wfXatWvHuXPn+Omnn9DR0aFRo0Z06NCBv//++5PjSU5OpmPHjjx+/BhLS0uqVq3KhAkTAGXftKVLl/L7778zZ84cKlSowODBg9OdMiE9LVu25MKFCzRt2hR9fX1+++03ypX7siePY8aMITo6mlq1amFhYUGPHj2IiEi/WZGOjg6bNm2ic+fOlC5dGl1dXapWrcqUKVMA5QAu27dv59y5c+jr66Ovr8/SpUv57rvvqFmzJtWqVfuiGBYsWMDw4cPp0aMHT548IVu2bBQrVoyaNWt+Ub41KVWmHJGRESxfspCwsDA8vbwZNHQU1m+aaL4KfomubupT0sCcuejxRz+WLp7PkoXzcXJ2ps+Aobh7pD4g6NmrP4sXzmX8mBFER0WR3d6BJk1bUrW6crqNO7dvcfOGclTMX1v9ohbP7AXLcHDIuBqm0mXKEhEZwdIli5UTl3t5MXTon6rmNsHv5Tc0NIQunVOf/K5ft5b169aSO09eRv317/dzK1OmDBGRkSxdsoTQsDC8vbwYNnSoKv6XwcHovPPgIDQ0lE7vDB60bt061q1bR548eRj9118AbNu2DYBevXqpfVf3335TFSQzQtUCgYRFxzJ9+xFeRb7G38We6e1/wM5S2QTreVhkmif0H3Pg8m0GLku9dvVauAWAX6uWoH3177QXvAblS5UgPDKS+cvXEBoWjo+nO2MG9cb2zSBUL1+9Uju2XoWG0vq31B8CKzduZeXGrQTlDmTSn8o+J9ExMcxZsoLgV6FYWJhTpngRWjdpmOHNnwESb5xDx9Qc45LV0TG1JDn4Ma/XzkQRo2zSqWtpo9Z8UxEVzuu1MzAuVw/zZr1IiY4g4exB4k+lDnVuFKTcB+YNu6h9V8zfy0i8cirD8/QprArmpvjeJar3Ocf2BeDR4vVcbNUns8JSUyWnO2Exccw4fJGQ13H429sw7cdyqqkWnkfGqJ07UXEJDP37JCGv47A0NiTQ0ZaFv1TCO5tVel+hNcVLVSIqIpy1y+cQERaCu5cvvQZPwOpN08yQ4OdqLUL8AvPSscdQ1iybxeolM3F0cqV739G4unur0tSs/wvxcXHMmzaKmNfR+OXMS6/BEzE0VBZaLSyt6TV4IquXzmRE/44kJSXh4uZF936jcfdUNvs8dXQfkRFhHD2wg6MHUvuYZrN3ZNLcjV+c37KlSxEREcHipctU95g/hw555x4TrLZvQkJD6dClq+r92vUbWLt+A3nz5GbMqJFfHIc27T+fhKGBDt+XMcTEUDnx+pyt8Wr98+wsdTAz/vTrdVIy+LroUSqvAYb6EB6t4NLdZPb8k/EPfr6r3prEhFi2LByonHjdryBNus/BwCD1oUfYy4fERKc2zX16/zIL/0odZ2DnylEABJWsS73Wyv9/3348e9aOZ92s34l9HYG1nRMVGnTLEhOvp8igLVqjo3i/g5oQWnD9zpePDPYt0iNj5+n7N+mRlNkhaJXT7QOZHYJWhbsX+Hii/wiTrQszOwStOtJH8zyh/1Xl5vyc2SFozdXiXT6e6D8km15wZoegVVN3pz8i+X9NoXxp55L8L2tY4tstVJ2/lXnnQZBv9o8n+g/JtEFbhBBCCCGEEEITmZZBez5r0BYhhBBCCCGEEP8dUuATQgghhBBCiCxKmnQKIYQQQgghvikyD5/2SA2fEEIIIYQQQmRRUsMnhBBCCCGE+KbIoC3aIzV8QgghhBBCCJFFSYFPCCGEEEII8U1RKHQy7fW5pk2bhoeHB8bGxhQtWpRTp059MP2aNWsICAjA2NiYPHnysH379i/9M30SKfAJIYQQQgghxBdYtWoV3bt3Z9CgQZw9e5Z8+fJRpUoVXr58qTH9sWPHaNSoEa1ateLcuXPUrVuXunXrcvny5QyLUQp8QgghhBBCCPEFxo8fT5s2bWjRogU5c+Zk5syZmJqaMn/+fI3pJ02aRNWqVfn9998JDAxk2LBhFChQgKlTp2ZYjFLgE0IIIYQQQnxTFOhk2is+Pp7IyEi1V3x8fJoYExIS+Oeff6hYsaJqma6uLhUrVuT48eMa83X8+HG19ABVqlRJN702SIFPCCGEEEIIId4YOXIkVlZWaq+RI0emSffq1SuSk5NxcHBQW+7g4MDz5881bvv58+eflV4bZFoGIYQQQgghxDclMyde79OnD927d1dbZmRklEnRfD0p8AkhhBBCCCHEG0ZGRp9UwMuWLRt6enq8ePFCbfmLFy9wdHTUuI6jo+NnpdcGadIphBBCCCGEEJ/J0NCQggULsnfvXtWylJQU9u7dS/HixTWuU7x4cbX0ALt37043vTZIDZ8QQgghhBDim5KS2QF8ou7du9OsWTMKFSpEkSJFmDhxIq9fv6ZFixYANG3aFGdnZ1UfwK5du1KmTBnGjRtHjRo1WLlyJWfOnGH27NkZFqMU+IQQQgghhBDiC/z0008EBwczcOBAnj9/TlBQEDt27FANzPLw4UN0dVMbVZYoUYLly5fTv39/+vbti6+vLxs3biR37twZFqMU+IQQQgghhBDflMwctOVzderUiU6dOmn87MCBA2mW/fDDD/zwww8ZHFUq6cMnhBBCCCGEEFmU1PAJIYQQQgghvikK/js1fN86KfCJDOEYeTOzQ9CqJxaBmR2C1lgqXmd2CFp1f+6qzA5Bqxz6+2R2CFqjW7ZaZoegVeXm2GZ2CFq1v83yzA5Ba8zO/ZHZIWiVub5pZoegVSe2n87sELTG3r5UZoegZVKo+n8gTTqFEEIIIYQQIouSGj4hhBBCCCHEN+W/NGjLt05q+IQQQgghhBAii5IaPiGEEEIIIcQ3RQZt0R6p4RNCCCGEEEKILEoKfEIIIYQQQgiRRUmTTiGEEEIIIcQ3JUWR2RFkHVLDJ4QQQgghhBBZlNTwCSGEEEIIIb4pMmiL9kgNnxBCCCGEEEJkUVLDJ4QQQgghhPimyMTr2iM1fEIIIYQQQgiRRUmBTwghhBBCCCGyKGnSKYQQQgghhPimKGRaBq2RGj4hhBBCCCGEyKKkhk8IIYQQQgjxTUmRaRm0Rmr4hBBCCCGEECKLkgKfEEIIIYQQQmRRUuDLZGXLlqVbt27/yncNHjyYoKCgf+W7hBBCCCGE+FIKhU6mvbIa6cP3f6Rnz5507txZ9b558+aEh4ezcePGzAvqHWt2HmDZlt2ERETi6+ZCjxY/kcvHQ2PajXuPsP3QCe4+fgpAgKcb7RvWVUtftGF7jet2alyPX2pV1nb4/L11AxvXrSQ8LBQPTx9a/9oFX//AdNMfO3yAFUvn8fLFc3I4ufBLi3YULFxMLc3jhw9YvGAWVy9fIDk5GRc3d/7oO5Ts9g4A7Pp7C4cP7uHu7VvExsawZNUWzMwttJ43gE1bt7N6/UZCw8Lx9vSgU7vWBPj7aUx7/8FDFi5bwa3bd3jxMpj2bVrSoE6tNOlevQphzsLFnPrnLPHxCTjlcOT3bp3x9/XJkDy8ZV2lFna1f0DP2pb4B3d5MX8acbdvpJvepno9rKvUxCCbPcmRkUSdOEzw8nkoEhMBsK3bEIuiJTF0dkWRkEDsjasEL5tLwtPHGZqPt9bu2M/SLbsIDY/Ax92FHi0bkcvHU2PajXsO8/eh49x9pDx3/L3caN+onlr6odMWsP3gcbX1iuXLxcR+XTMuE+9YveswS7bte3MtcOb3Zg3I7e2uMe2GfcfYduQ0dx49AyDQ05UOP9VUSx8SEcmUFVs4cek6UTGxFAjw5vdmDXBztM/wvKz65yaLTl4jJDoWP3sbelUuSG6nbBrTbr54l0HbTqgtM9TT5eQfDTWmH77jFOvO3aZnhQI0LhKg9di/hu13hfDq0QqrArkxdrLnTIMOvNi8N7PDQqFQsGXlDA7vWU9sTBTe/kH83LYvDk6aj6+39v+9kt2bFhERHoKLhx8NW/XC0zePWpo7Ny6waflU7t26hK6uHi4e/nQdMB1DI2O1dImJCYzq3YTH92/Sf+xKXD2/bN9t27KRjetWExYWioenN23bd8bPP/1tHT18kGVLFvDyxXOcnFxo2rINhQoXVX0eGxvL4gVzOHn8KFFRkdg7OFKzdn2q1VBeu6OiIlmxdBHnzp7hVfBLLK2sKVq8JI1/aY6ZmfkX5eFjWjX2oFZlRyzM9Ll0LZKx02/x+FlsuunXzC1KDgfjNMvXb3vC+Jm3Ve9z+VvS9hcPcvpbkpKi4NbdaLoPukRCQkqG5AOUx97ZPVO4cWYNCbFROLjnp0SdQVhl80h3nWsnVnDt1Eqiw54AYG3vQ/7yHXD1L61x+7sWtePxzcNUaDIFj5wVMyor4j9ICnz/R8zNzTE3z5iL8tfafewMk5aso1dr5Q/Vldv30XXkZFaPH4ytlWWa9Gev3qRyycLk9fPC0MCAxZt30WXEZFaMHYi9rTUA22eOUlvn2Pkr/DlrKeWL5Nd6/EcO7WPBnOm069QdP/9Atm5cy9ABvzNl9hKsrW3SpL9+9TLjRw+lSfO2FCpcnEMH9/DX8P6MmTQbdw8vAJ4/e0LfPzpTsXJ1GjZpgampKQ8f3MfA0FC1nfj4OPIXKEL+AkVYumiO1vP11v5DR5g5dwFdO/5KoL8f6zZtoffAoSyYNRUba+s06ePi48nh6ECZkiWYMXeBxm1GRUfT9Y8+BOXNw8jBA7CysuLJ02dYmJtlWD4ALEqUwb5ZO17Mnkzs7evY1qiPa78R3O3aiuTI8DTpLb8rR/bGrXg+YxyxN65ikMOFHB17AgpeLpoFgGmuPITv3Ezs7Zvo6OmR/ecWuPYfyd3f2qCIj8vQ/Ow+dppJi9fQq01jcvl6snLbXrr9OYlVE4emc+7coFLJIuT198bQQJ8lm3bSdfhElo8fjL1t6rFaLCgXAzo0V7030P93bhe7jp9lwrIN9Gn5I7m9PVix4wCdR81g3dh+2FqlfZjxz7XbVClegLxNPTEyNGDRlj10GjWD1X/1xt7WGoVCQc/x89DX02Nc99aYmRiz7O8DdBgxnTWj+2BibJRhedl59QHj9p6lX9XC5HbKxvLT1+mwaj8b29bC1iztj1IAcyMDNrStqXqvk86D5n03HnHpySuym5tkROhfTc/MlMiLN3i0cB2F1k7L7HBUdm5cyL7ty2neeRjZ7J3ZvHI6k4d1YPCk9RgYaj4WTh/dydqF4/i5XT88ffOwd+syJg/rwJApm7C0sgWUhb3JwztSrV5LGrbqha6ePo/v30BHN21DqvWLJ2Btk53H929+cT4OH9zP/Dkzad+pG34BAWzZuJ7BA3oxffZCjfeca1evMPav4fzSvDWFixTj0IF9jBw2kPGTZ+LuoXzYM3/ODC5eOMdvv/fB3sGR82fPMHPaJGzt7CharAShISGEhoTQonU7XN08CH7xghlTJxAa8ore/QZ/cV7S07iBK9/XdObPidd59iKO1o09GD80D006nCYhUfN4/W26n+XdP7mXuxkTh+dj/5Fg1bJc/paMG5KHpWsfMnH2bZKSFfh6mqNIydg5AC4emsvV40sp/f1ILGxc+GfPZHYuaEP9blvRN9B87JlZOVK4Sncs7dwBBbfObmLP0k7U7bQOGwdftbRXji7K0Pgzg0zLoD3SpPNf9Pr1a5o2bYq5uTk5cuRg3Lhxap/Hx8fTs2dPnJ2dMTMzo2jRohw4cED1+cKFC7G2tmbnzp0EBgZibm5O1apVefbsmSrNgQMHKFKkCGZmZlhbW1OyZEkePHgAqDfpHDx4MIsWLWLTpk3o6Oigo6PDgQMHKF++PJ06dVKLKzg4GENDQ/buzbinsyu27aVO+ZLUKlsCL5cc9G7dCGNDQ7YcOK4x/dDOLfm+chn8PFzxcHakX7smpCgUnLl8XZXGztpK7XXozEUK5vTD2SG71uPfsmENlarWoEKlari6edCuU3eMjI3Zt2u7xvRbN68jf8Ei1G3QEBc3d37+pRWe3r78vXWDKs2yxXMpWKgoTVv+ipe3L445nClSrKTazbxW3R+o/2Nj/AJyaj1P71q3cTPVq1SiaqUKuLu50q3jrxgZGbFjt+ZjIsDPl3Ytm1OuTCkMDDQXFFauXU/2bNn4vVtnAvz9yOHoQKECQTjlyJGRWcG2ZgMi9v5NxIFdJDx+yPPZk0hJiMeqfBWN6U38cxJ74wqRR/aTGPyCmIv/EHV0P8Y+/qo0j//sR8SB3SQ8fkD8g7s8mzYWg+wOGHv5atymNq3Yups6Fb6jZrmSeLo40atNY4wNDdm6/6jG9EO7tOb7KmXfnDs56PtrU+W5c+m6WjpDfX2188cygwviby37+wB1y5WgdplieLk40qfljxgbGbL54AmN6Yd3bMoPlUrh7+GCh5MD/ds0QpGSwqkryh/TD58Hc+n2fXq3/IFc3u54ODnQp8UPxCcmsvP42QzNy9JT16mfz5s6eb3xzmZFv6pFMNbXZ+PFOx9cL5u5ieplZ5a2QPcyKoa/dp9hRO0S6Ot9m7fx4J2HuDloIi827cnsUFQUCgV7ty6j+vdtCCpSDhcPP1p0HkZ4WDDnT+1Pd709W5bwXcX6lCxfFydXbxq364+hkTHH9m5UpVmzYCzlqzeiav2WOLn54OjsQaGSVTAwMFTb1uWzR7h64QQNmnX/qrxs2rCWylWrU7FyVdzcPGjfqRtGRkbs2bVDY/otm9ZToGBh6n//E65u7jRu2gIvb1+2bUnNw/VrVyhfoTJ58gbh4OBIlWo18fTy5tYN5bXB3cOT3v0HU6RoCXLkcCJvUH6aNGvF6ZMnSE5O/qr8aPJDbWcWr37AkZMh3Ln/muETrmNna0SpYppryAHCIxMJDU99lShsx+OnsZy7HKFK06W1N2u3PGHp2kfcexjDoyex7DsSTGJSxpUuFAoFV44tJqjcr7jnrIBtDn/K/DCKmKiXPLia/jniFlgOV/8yWGXzwCqbJ4Uqd0Pf0JSXjy6opQt5eo1LRxZSqsGfGZYH8d/2bd4psqjff/+dgwcPsmnTJnbt2sWBAwc4ezb1B0enTp04fvw4K1eu5OLFi/zwww9UrVqVW7duqdLExMQwduxYlixZwqFDh3j48CE9e/YEICkpibp161KmTBkuXrzI8ePHadu2LToaHhH37NmTH3/8UVVgfPbsGSVKlKB169YsX76c+Ph4VdqlS5fi7OxM+fLlM+TvkpiUxPV7DymSJ7Upiq6uLoXzBHDp5t1P2kZcfALJSclYmmn+URoSHsnRc5eoXa6EVmJ+V2JiIndu3yBvUEHVMl1dXfIGFeTG9asa17l5/YpaeoD8BYqo0qekpPDP6RPkcHZl6IDfaf5zXXr91p6Txw9rPf6PSUxM5ObtOxQIyqdapqurS4GgvFy9nn4zyI85fvI0fr4+DB05mu8bN6Ndl+5s27FLGyGnT18fYy9fXl88l7pMoSDm4jlM/DQ3v429cRVjL19VAc/A3hGz/EV4ffZUul+ja6o8DpOjo7QXuwaJSUncuPuQwnlSY1eeO4Gff+68V6A7e/Um1Vr34MeuA/hrzjIioqK1GrsmymvBI4rmTm0qrKurS5Hcfly8df+TthEXn0BScgpWZqbKbSYmAWBkYKC2TUN9fc7f+LS/0ZdITE7m2vNQino6pn6vjg5FPRy5+ORVuuvFJiRRbdpGqk7dSLe1B7kTHK72eYpCQf8tx2lWNBDv7NYZFH3W9OrFEyLDXxGYN7UZo4mZBZ6+ebh744LGdZISE3l455raOrq6ugTkLcrdmxcBiIwI5d6tS1hY2fJX36b0bFmesQNacfvaObVtRYaHsGTGUFp0GZ6mmefnUN5zbpIvqIBaTPmCCqR7z7lx/Sr58r93zylYSC19QGAuTp08TsirYBQKBRcvnOPJk8fkL1Ao3Vhev47G1NQUPT29L86PJk4OxmSzNeL0+bDU74pJ5urNSHIHpG25oIm+vg6Vyzmwbc9z1TJrKwNyBVgSFpHAjNFBbF5cnCkj85E356dt80tFhT0mNuoVTt7FVcsMjS3I7pKXlw81H3vvS0lJ5s6FbSQlxGDvGqRanpQQy4FVv1Oi9gBMLbT/QDszKdDJtFdWI006/yXR0dHMmzePpUuXUqFCBQAWLVqEi4sLAA8fPmTBggU8fPgQJycnQFko27FjBwsWLGDEiBGA8kI/c+ZMvL29AWUhcejQoQBERkYSERFBzZo1VZ8HBmr+EWtubo6JiQnx8fE4Oqb+IKlfvz6dOnVi06ZN/Pjjj4CyZrF58+YaC47aEB4ZTXJKSprmZ7ZWljx48uKTtjFt+Qay2VhROI/m/gvbD53AzNiYshnQnDMqMoKUlBSsrW3Vlltb2/Dk0UON64SHhaZJb2VtQ3hYKAAR4WHExcayYc1yfv6lFb80b8u5f04x+s+BDB05gVx5grSej/REREaRkpKCjbWV2nIba2sePX7yxdt99vwFW7bv4Pu6tWn04/fcuHWbabPnYWCgT+UKGfNwQd/CEh09PZIiwtSWJ0WEYersqnGdyCP70bOwwn3YeEAHHX19wnZtIWTDSs1foqODQ/Nfibl+mYRH97Wbgfeozh1r9XPHxtqC+0+fpbOWumnL1pHN1kqt0Fg8KBdli+bHyT4bT54HM2PFRn4bMZk5f/ZGT0MTNW0Jj3r95lqg3nTT1tKC+09fftI2pqzcTDYbS4rkVhbQPZwccLSzYeqqLfRt9RMmRoYs+/sAL0LDeRUeqfU8vBUWE0+yQoGtqfoPezszY+6HaP5ed1sLBtUoip+9DVHxCSw5eY3mS3aztnUNHCyVBdgFx6+ip6NDo0L+Grch0hcZrixoW1rbqS23tLIlIjxE4zrRUWGkpCRjkWYdO54/uQ/AqxfKvrpbV82kQbPfcPUI4MTBLUwY3JaBE9bi4OSOQqFg4dSBlK7yAx4+uXj18suvnZFv7zk26k03ra1tePzokcZ1lPectOnD3txzANq278S0yeNp2bQhenp66Ojo0rFrd3Llyas5jogIVq9YSuVqNb44L+mxtVHWjIaFJ6otDwtPUH32MaWLZcPcTJ/te1MLfM6OyvOxZSMPps2/w617r6la3oGJw/PRtOOZD/YP/BqxUcpjz8Rc/TgyMc9GbHSwplVUQp/fZMvMRiQnxWNgaErFJlOwcUjt535i2yjs3YNwz1lB+4GLLEMKfP+SO3fukJCQQNGiqU8JbW1t8fdX3rQvXbpEcnIyfn7qg2DEx8djZ5d6gTA1NVUV5gBy5MjBy5cvVdtr3rw5VapUoVKlSlSsWJEff/yRHJ/RRM7Y2JhffvmF+fPn8+OPP3L27FkuX77M5s2b010nPj5erUYQID4hASPDT7sof61Fm3ay+9gZpg/8DSNDA41pthw4RpXviqT7+bdG8abhepFiJalV7wcAPL19uX7tCju3b/5XC3wZRaFQ4OfjTatmTQDw9fbi/oOHbNm+M8MKfF/CNGde7Oo35PmcKcTdvo6BozMOLdqT1CCUkHXL0qR3aN0JI1cPHgz4uiZb/4bFG/9mz9HTTBvcU+3cqFSyiOr/Pm4u+Li70KBzP85euaFWMPzWLNy8m13HzzGrfydVfvT19RjzWyuGzV5B+bZ90HtTY1giXyB8Y/1D8rlkJ59L6hP6fM7ZaTB7K2vP3aJjmXxcfRbKijM3WN6iaoY9gMtKTh7axrJZw1XvO/WdkiHfo0hRDvRRqnIDSpavC4CbVwDXL57i2L5N1GvShf3bVxAX+5pq9VpmSAzasHXzRm5cv0a/QcOwt3fgyuVLzJo+GVtbO4Leqx2MiXnN0EF9cXVzp1HjZl/93ZXK2PN7x9TfP38MvfTV26xRyZGT/4QSEpqgWvb2vNm04xnb9yofKN+6G03BvNbUqOTIrMX3vvp7AW6f38LRjYNV7ys3nfHF27LK5kG9zutJiIvm3uWdHFrTh+ptFmPj4MODa/t4dvcEdTut10LUIiuTAt83Ijo6Gj09Pf755580TSPeHWjFwEC9wKKjo6MqHAAsWLCALl26sGPHDlatWkX//v3ZvXs3xYqpj/74Ia1btyYoKIjHjx+zYMECypcvj7t7+iOYjRw5kiFDhqgt69W2Kb1//bSbgLWlOXq6uoRGqD/1Do2ITFNz8b6lW3azeNNOpvbriq+7i8Y0567d4sHTFwzv2vqT4vlcFpZW6OrqEh4eqrY8PDwMaxtbjetY29imSR/xTnoLSyv09PRwcVP/u7u4unPt6tffCD+HlaUFurq6hIVHqC0PCw/Hxsb6i7dra2ODu5t6rZqbqwuHj2rut6kNSVGRKJKT0bdSf9Ktb2VD0nv7461sDZsRcWgvEfuUfWPiH95H18gYx3ZdCVm/XK1XuUOrjpgXKMbDQT1ICk2/2Z62qM6d92qqwsKjsHuvRvZ9yzbvYvHGHUwZ8Fu6585bzg7ZsbYw5/Hzlxla4LO2MHtzLVBvChsaGYWdhgFb3rVk2z4WbtnL9D4d8HVzVvss0NOV5SP/IDomlsSkZGwszWk2cDw5PTXX6mqDjakRejo6hMaoD9oT8joOO/NPa85noKeLv6MNj8KUzWnPPXpJ6Os4qk/bpEqTrFAwft85lp25wfYOdbSXgSwgX+GyaiNpJiUqf/hHhodgZZNasI6MCMXVQ/OIw+YWNujq6hH1Xg1gZEQIVtbKvmRvt5XDxVstjaOLJ6GvlDXt1y+d4u7Ni3RsWEQtzYg/GlOkdDVadB7Op7J8e88JU2+pEB4eho3th+45GtK/uefEx8ezdNE8+vQfQqEiyt8LHp7e3L1zm43r16gV+GJiYhg8oDcmpqb0GTAUfS0M6HTkVAhXb55RvTc0ULYksLE2ICQstcBmY23I7bsfb17ukN2IQvls6Dfyitryt9u6/+i12vIHj2NwyK69AZzcAstj75paM5qcpPze2OgQTC1TRweOjX6FbY4PX1P19A3fDNoC2Zxz8erxJa4cW8J39Ybw7M4JIkMfsWRYUbV19i3rioNHQWq0WaytLGWKDB5H5/+K9OH7l3h7e2NgYMDJkydVy8LCwrh5UzmwQP78+UlOTubly5f4+Piovd5tcvkp8ufPT58+fTh27Bi5c+dm+fLlGtMZGhpq7GidJ08eChUqxJw5c1i+fDktW374iWSfPn2IiIhQe/3WstEnx2ugr0+ApxunL6f2B0tJSeH05Rvk8fNKd70lm3cxf/12JvbpRGA6Q7YDbNl/jAAvN/w+8qP2SxkYGODt48/F86n9MVNSUrh4/h/80xlMxS8gF5cuqA8YceHcGVV6AwMDfHwDePpYvXnO06ePsH8zJcO/xcDAAD8fb85euKhalpKSwrkLl8gZ8OXNynLlDEjTJPTxk6c42GdgH4SkJOLu3sLs3RpSHR1M8wQRe/OaxlV0jYwhRX2obkVKsmrdtxxadcS8SEkeDvmdxJfP+TcY6Ovj7+XG6XcGK1KeO9c+fO5s2sH8dVuZ2Lcrgd4eH/2elyFhRES/xs7mw4XIr6W8FriqBlyBt/m5SV5fj3TXW7RlL3M37GTKH7+S08st3XTmpibYWJrz8PlLrt19SJmCedJN+7UM9PQIdLTl5P3UZukpCgWnHjwnr3P6g068KzklhdsvI8j2poBYI7cnq1tXZ2WraqpXdnMTmhYNZPpP5TIkH/9lxiZm2OdwU71yuHpjaZ2N65dS+9/GxkRz79YlvPzzadyGvoEBbt6BXHtnnZSUFK5fPIWXn/IHvZ29E9a22Xnx9L7aui+fPcA2u7KFTcNWvRgwbjX9x62i/7hVdO6nrG1s0/0v6v7cmc+hvOf4cfFCah9B5T3nXLr3HP+AnGr3KIDz51LvUcnJSSQlJaWpOdbT01XVYIKyZm9w/z8w0Deg/8BhGGqpJU9sbDJPnsWpXvcexvAqNJ5C+VIfzpma6JHTz5LL1z/eFLtGRUfCIhI4flq9oP7sRRzBIfG4OZuqLXd1MuH5S/WWSl/D0MgMSzt31cva3gcTi2w8vZM6+FRCXDTBjy9i76b52EuPQqEgJVlZgMxbpg31Om+kbqf1qhdA0Rq9Kd1ghNbyI/77pIbvX2Jubk6rVq34/fffsbOzw97enn79+qH7pj+Mn58fjRs3pmnTpowbN478+fMTHBzM3r17yZs3LzVqfLyN/L1795g9eza1a9fGycmJGzducOvWLZo2baoxvYeHBzt37uTGjRvY2dlhZWWlqkFs3bo1nTp1wszMjHr16n3we42MjDAyUn8ylvKZN4FGNSowdMYiAr3cyOnjwcrt+4iLj6dmGWUH58HTFpLd1pqOjeoCsHjTTmav2crQzi1wym5HyJvaJxNjI0yNU5+eR8fEsvfkWbo2afBZ8XyuWvV+YMr4kfj4+uPrF8iWTWuJj4ujfKVqAEwaNwI7u2w0ad4WgJq1GzCgd1c2rV9FwcLFOHJoH3du3+DXzj1U26zToCHj/xpCztz5yJ03iHP/nOLMyWMMGzVRlSYsNITwsFCePVMWnB7cv4eJiQnZ7B2wsNBeJ/QGdWszesJk/H298ffzZf2mrcTFxVG1orLPwKhxk8hmZ0vr5r8Ayr6mDx4p+7UkJSXxKiSE23fvYWJsjLOT8gdQgzq16Pp7H5avXkuZ70py/eYttu/YxW+dNM+fqC2hW9eRo+PvxN65Rdzt69jUqI+ukTER+3cCkKPT7ySFhhC8fD4A0WdOYFOzPnH37rxp0ulE9obNiP7nhKog6NC6M5bflePx6EGkxMWi96avTErMaxQJCZoD0ZJGNSsxbNoCAr3cyenjyarte4iLT6BG2ZIADJk6n+y21nT4uT4AizfuYM7qzQzp0ooc9mnPnZi4OOat2Uq5ogWwtbbkyYtgpi5dh4tjdorly5WheQFoXK0sg2ctI6enG7m83Vi+4yCx8QnUKqN8gj1wxlLsbazo1FA5N9jCLXuYtXY7wzs2JUd2W1W/PFNjI0zfTLmw5+Q5rC3Mccxmw+2Hzxi3ZD1lCuWhWN6MnbuuSZEABm49Tk5HW3I72bH89A1iE5Ook1dZGO+/5Rj2FqZ0KRsEwKwjl8jrlA1XGwui4hNYdOIazyJfUy9I2V/H2tQIa1P1a62+ni7ZzIzxsMvYQSc+l56ZKWY+qYVvU08XLPMFkBAaQdyjT+tfqm06OjpUqNmY7WvnYJ/DjWz2zmxaMQ1rm+wEFUktMI8f3Jb8RcpTrrpy/sOKtX5h4ZQBeHjnxMM3N3u3LiMhPpYS5euotlupTjO2rJqJi4cfrh7+HD+whedP7tOu51gAVcHvLSNj5eir2R1dsLH7/Id4dep9z6Txf+Hj64evXwBbNq0jLj6OipWUow1PGDsKO7tsNG2hbNlSq059+vX6jY3rV1OocDEOH9zPnVs36dhZ2fTc1NSM3HnysXD+bAyNjLC3d+DypQvs37ublm2U1+SYmNcM6teL+Pg4fvu9LzExMcTExABgaWWl9YFb1mx+QrOf3Hj0NFY5LUMTD0JC4zl8IrX1xMTheTl0/BXrtz1VLdPRgeoVHdmx7wXJGqbVW77+Ea1+9uD2vWhu3YumWnlH3F1M6T9K84A32qCjo0OuEk05v38mltncldMy7J6MqYU97u/Ml7d9bgs8clUkZ/HGAJzeOR4Xv1KYWzuRGP+aOxe28uzeKao2V07DZGqRXeNALWbWObCwzZiH3P+mrDgBemaRAt+/aMyYMURHR1OrVi0sLCzo0aMHERGpzeQWLFjA8OHD6dGjB0+ePCFbtmwUK1aMmjVrfmCrqUxNTbl+/TqLFi0iJCSEHDly0LFjR9q1a6cxfZs2bThw4ACFChUiOjqa/fv3U7ZsWQAaNWpEt27daNSoEcbGXz6a2KeqVKIQ4ZHRzF6zlZDwSPzcXZjYuzN2b5p0vngViu47Tx7X7z5EYlISfSaozz3XukEN2vyQ+vfafewMCoWCyiULZ2j835UuT2REOCuWLiA8LBRPLx8GDB2taqL5KviFWvwBOXPz2+8DWL5kHssWzSWHszO9+g9XzcEHUKxEKdp17M76NcuYN2syTs6u/NF3KIG5UpuJ7Px7M6uXp869079XFwA6deulKmxqQ7nS3xEREcnCpSsJCwvD28uTkUMHqpp0vgwORlc3NX8hoWH82iW1D9ua9ZtYs34TeXPnYvwoZdOlAD9fhvTrxdxFS1myYjU5HOxp36YlFcqV0VrcmkQdO4iepRXZf2qKnrUN8ffv8ujPfiRHhANgkM1erZnmq3XLUCgUZG/UDH3bbCRHRhB95gTBK1LnF7Spoix8uA9Rn2rl2bQxRBzYnaH5qVSiMOGRUcxZvZmQ8Eh8PVyY0LeL6tx5/ipU7an9+t0HSUxKou/4WWrbafV9Tdr8WBtdXV1uP3zM9oPHiXodQzZba4rmzUnbn+pgaJDxfWArFy9AWFQ0M9duJyRCeS2Y0utX7N4M6vQ8JEztXFq35yiJScn0mqQ+32Ob+lVp10B5DrwKi2TC0o2ERESRzdqSGqUK07qe5mk4tKlKTnfCYuKYcfgiIa/j8Le3YdqP5VRTLTyPjFHLS1RcAkP/PknI6zgsjQ0JdLRl4S+V8M6WsTWrGcGqYG6K712iep9zbF8AHi1ez8VWfTIrLKrUbU5CXCxLZw4j5nUUPgH56TJgutocfK+ePyI6KrX5Y+GSVYiOCGPzyhlEhr/CxdOfLv2nqw3+UrFmE5ISElizYCyvoyNw8fCj28CZZHfMmGbDpcqUIzIyguVLFhIWFoanlzeDho56557zUu2aHJgzFz3+6MfSxfNZsnA+Ts7O9BkwVDUHH0DPXv1ZvHAu48eMIDoqiuz2DjRp2pKq1ZXXtzu3b3HzhrIlxK+tflGLZ/aCZTg4fF5rpI9Ztu4RxsZ6/NHJD3MzfS5djaDHoEtqc/A5O5pgbal+XSoUZIOjvTHbdmtuabFm8xOMDHXp3NobSwsDbt+L5reBF3n6PGPnTM1bujVJCbEc3TCIhLhIHNwLUKXFbLU5+KJCHxL3OvXYi4sO4dCa3sREBWNobIGtox9Vm8/B2bdkhsYqsh4dhUKmNRRp3b9/H29vb06fPk2BAgU+vsJ7ws/ty4CoMs8Ti293oIrPZakI+3ii/5DXfbtldgha5dC/X2aHoDX6iRkz4l1m0bt88uOJ/kP2t9Hc3P+/yOzc+cwOQascTTSPGvpf1brbh+ee/C+p3bxUZoegVX80+HZ7d/19LvHjiTJItfz/jUH+PpXU8Ak1iYmJhISE0L9/f4oVK/ZFhT0hhBBCCCG+hlRJac+3W6wXmeLo0aPkyJGD06dPM3PmzMwORwghhBBCCPEVpIZPqClbtizSylcIIYQQQmSmFGTQFm2RGj4hhBBCCCGEyKKkwCeEEEIIIYQQWZQ06RRCCCGEEEJ8U6SHkfZIDZ8QQgghhBBCZFFSwyeEEEIIIYT4pigUMmiLtkgNnxBCCCGEEEJkUVLDJ4QQQgghhPimpEgfPq2RGj4hhBBCCCGEyKKkwCeEEEIIIYQQWZQ06RRCCCGEEEJ8U2RaBu2RGj4hhBBCCCGEyKKkhk8IIYQQQgjxTVEg0zJoi9TwCSGEEEIIIUQWJQU+IYQQQgghhMiipEmnEEIIIYQQ4psi8/Bpj9TwCSGEEEIIIUQWJTV8QgghhBBCiG+KTMugPVLDJ4QQQgghhBBZlI5CIeVnoX29ZsdmdghaVaNkSmaHoDVPI0wzOwSt0tfLWpcwK+PEzA5Ba6yM4jI7BK3S1ck61wGA6ETjzA5Ba17nD8rsELQq97XNmR2CVsXrmGR2CFqjT9a5RgN4eXtndgjpWn088665PxbPWnViWSs3QgghhBBCCCFUpMAnhBBCCCGEEFmUDNoihBBCCCGE+KakKHQyO4QsQ2r4hBBCCCGEECKLkgKfEEIIIYQQ4puiUGTeK6OEhobSuHFjLC0tsba2plWrVkRHR38wfefOnfH398fExAQ3Nze6dOlCRETEZ32vFPiEEEIIIYQQIoM1btyYK1eusHv3brZu3cqhQ4do27ZtuumfPn3K06dPGTt2LJcvX2bhwoXs2LGDVq1afdb3Sh8+IYQQQgghhMhA165dY8eOHZw+fZpChQoBMGXKFKpXr87YsWNxcnJKs07u3LlZt26d6r23tzd//vknTZo0ISkpCX39TyvKSQ2fEEIIIYQQ4puSmU064+PjiYyMVHvFx8d/VX6OHz+OtbW1qrAHULFiRXR1dTl58uQnbyciIgJLS8tPLuyBFPiEEEIIIYQQQmXkyJFYWVmpvUaOHPlV23z+/Dn29vZqy/T19bG1teX58+eftI1Xr14xbNiwDzYD1UQKfEIIIYQQQohvSooi8159+vQhIiJC7dWnTx+Ncfbu3RsdHZ0Pvq5fv/7Vf4/IyEhq1KhBzpw5GTx48GetK334hBBCCCGEEOINIyMjjIyMPiltjx49aN68+QfTeHl54ejoyMuXL9WWJyUlERoaiqOj4wfXj4qKomrVqlhYWLBhwwYMDAw+Kba3pMAnhBBCCCGE+KYo/iMTr2fPnp3s2bN/NF3x4sUJDw/nn3/+oWDBggDs27ePlJQUihYtmu56kZGRVKlSBSMjIzZv3oyxsfFnxyhNOoUQQgghhBAiAwUGBlK1alXatGnDqVOnOHr0KJ06daJhw4aqETqfPHlCQEAAp06dApSFvcqVK/P69WvmzZtHZGQkz58/5/nz5yQnJ3/yd0sNnxBCCCGEEEJksGXLltGpUycqVKiArq4uDRo0YPLkyarPExMTuXHjBjExMQCcPXtWNYKnj4+P2rbu3buHh4fHJ32vFPiEEEIIIYQQ3xSFIrMj0D5bW1uWL1+e7uceHh4o3sl42bJl1d5/KWnSKYQQQgghhBBZlNTwCSGEEEIIIb4pKVmwhi+zSA2fEEIIIYQQQmRRUuATQgghhBBCiCxKmnQKQNkpNCgoiIkTJ6otX7hwId26dSM8PPxfi6VSQX2KBOpjYgj3n6ew4UgiIZGfVq9fNp8+1YoacORSEluOJ6qW1y9lgI+zLpamOsQnwoMXKfx9MpHgCO21F1AoFGxeOZPDuzcQExOFT0A+Grfti4OT2wfX2//3KnZuXExEeAiuHn40av0Hnr65NW5/8vDOXD53jA69xpG/aDkAju7bzMKpgzVue9z8PVha23513t5+//6NU/jn4BriYiJx8y1AzV8GYefoke4692+c5ujf83j24ApR4cE07DyVwAIV1dLEx71mz5pxXD+3l5jocGyyu1C04i8ULtdQK3F/KD9710/h9IE1xMVE4e6bn9rNB5HtA/m5d/00h7fP5+l9ZX4ad51CzoLq+enXNFDjulV/6kmpGq20Fvu21dM5tncdsa+j8AoI4qfW/bHP4f7B9Q7uWMneLQuJDH+Fs7sfP7Tsg4dPHtXnwc8fsWHJOO5eP0dSUgKB+UryQ8s+WFrbqdLsWD+bK2cP8/j+DfT1DRiz8Ohnx75++Wz2795IzOto/ALy0rx9Lxw/cp7s3raG7RuXEhEWgquHL03b9sTbL5fq84SEeJbPn8TJI7tITEwkT/5iNP/1D6zexH5o71bmTB6qcdtTF+3AytqWWZOGcGTftjSfO7t6MmrqKo3r7tq2lm0blhIRFoqbpw/N2vZQi+t9J4/sZc2y2bx6+QwHJ1caNetIUKESan+fdcvnsH/XJl6/jsYvMA8t2/+h9vd59uQhyxdM4ea1iyQlJeLm4cP3jduRK69yXqcH926xZe1ibly7QFRkBNntHalQtT5Va//0gb+wZgqFgi0rZ3B4z3piY6Lw9g/i57Z9cXD68LG2/++V7N60iIjwEFw8/GjYqheevnnU0ty5cYFNy6dy79YldHX1cPHwp+uA6Rgaqc8xlZiYwKjeTXh8/yb9x67E1TPgs/PxNWy/K4RXj1ZYFciNsZM9Zxp04MXmvf9qDJ9i89ZtrFm3gdCwMLw8Pen4a1sC/P00pr3/4CGLly7j1u07vHj5kl/btKJ+3Trpbnvl6rXMX7SYenVq0b5tmwyJf9uWTaxft4awsFA8Pb1p174jfv7p7+sjhw+ydMkiXr54jpOTM81btqZQ4dS5zMLCwli4YA7nz/5D9OvX5M6dh3a/dsTJ2UWVZuqUiVw4d5bQ0BCMjU0IzJmTZi1a4+r64evRl9iyZQtr160j7M3+ad++Pf7+/hrTPnjwgCVLlnDr9m1evnxJ27ZtqVe3rlqaVatWcfTYMR4/foyhoSE5AwNp2bIlLi4uGrf5X5QVB23JLFLDJ74pZfLpUzK3PhsOJzB1YzwJSdCquiH6eh9f1yW7DkUD9XgakpLms8fBKaw5kMi41fHM2x6Pjg60rmGIjhbn9NyxYRF7t62gya996TtqEYZGJkwc1pHEhPh01zl9ZCerF4yn1o9tGTB2OS4evkwc2pHI8NA0afdsXYamgAuXrMzYebvUXrmCiuOXq6DWCnsAR7bP5eTuJdRqOpg2A1ZjYGjCkvGtSUxMP3+J8bE4ugZQo8nAdNPsXDmK25ePUL/taDqN2EaxSk3ZvnQY18/t01rsmhzeNpfju5dSp/lg2g9ahYGRKQvHtPng/kqIjyWHmz+1mg5IN03vyYfUXvVb/4mOjg65ClfWWux7Ni3g4N/LadhmAD1HLMPQyIRpf/76wdj/ObaDDYvHUO37X+n11yqc3f2Z9uevREWEABAfF8O0P9uho6ND50Fz+G3YIpKTEpn1V2dSUlLPqeSkRPIXq0ypyj9+Uezb1i9m17ZVtGjfm8Fj5mNkbMLowV1I+EDsJw7vZvn8idT7qTXDxi/GzdOX0YO7EPHOebJs3gTOnz5Mpz9G0u/PmYSHBjNpZC/V58W+q8iUhdvVXnnyFyMgdwGs3pwnv7Tuofb5pHlbMLewpEjJChrjOn54N8vmTaJ+w9YMn7AINw9fRg3qphbXu25eu8jUsQMpW6kWf05cRKGipRk/4g8ePbijSrN1/RJ2bl1Ni/a9GDpmLkZGJowa1E3t7zN2WA9SUpLpN3wqf05YiJunL+OG9SA8TLkv792+jqW1DR26D2b01OXU+aE5qxZPZ9fWNR/fQe/ZuXEh+7Yvp3G7fvQeuQQjYxMmD+vw4eva0Z2sXTiOGj+2o9+YFbi4+zF5WAciI1L/LnduXGDy8I7kzFecPqOW0uevZZSr9hM6uml/lqxfPAFrm49PapxR9MxMibx4g8tdhmRaDB9z4NBhZs2ZR5OfGzJ98gS8PD3oO2AQYek8rI2Pj8fR0ZGWzZtia2PzwW3fuHmLbTt24OXpof3A3zh88ABz58yi0c9NmDhlBp5eXgwc0Ifw8DCN6a9dvcKYv0ZQuXJVJk2ZQbHiJflz2GAe3L8HKB9U/DlsEC+ePaffwKFMmjKD7PYO9O/bi7i4WNV2fHx86fpbT6bPmseQ4SNRKBQM7N/7s+Y3+xQHDx5k9pw5NP75Z6ZMmYKnlxf9BwxI92F6XHw8jjly0KJFC2zS2T+XLl+mVs2aTBg/nhF//klScjL9+vUjLi5Oq7GLrEEKfP8nypYtS6dOnejUqRNWVlZky5aNAQMGaGWoV236Lo8++84lcfVBCs9DFazen4ClqQ65PD5c4jPUh4blDFl3OJHY+LR5OnU9mXvPUwiLVvA0RMHO04lYm+tiY66dEp9CoWDv1uXU+L41QUXK4uLhR8suQwkPDebcqQPprrd7yzJKVapHyQp1cHL1okm7fhgaGXN03ya1dA/v3WDXpqU07zgozTYMjYyxssmmeunq6nH98mm+q5D+09ovyd+J3YspXetXAgpUwNHVn/pt/iIq7CXXz+5Jdz3fvKWp0KAbgQUrpZvm0e3z5CtZF8+Aothkc6FQ2Z9wcPXnyd2LWov/fQqFgqM7F1O29q/kLFgBRzd/fmg3iqjwl1z7QH7885Wm0vfdyFUo/fxYWGdXe107uw/PwKLY2rtqLfb925dSpX4b8hYuh7O7H007/UlEWDAXTqdfSN63dTElKjSgeLm65HDxpmGbARgamnB8/0YA7t44T8jLpzTpMAxnNz+c3fz4pdNwHt69ws3Lp1TbqfFjR8rX/AUnN98vin3HlpXU/qElBYuWwc3Dl3bdBhMe+op/ThxMd72/Ny2nbOW6lK5YC2c3L1q0742RkTGH9mwBIOZ1NAf3bObnlt3Ilbcwnj6BtOkykFvXL3L7xiVAeZ5Y22RTvXR19bh66QxlKtZWfY+pmblamru3r/E6OorSFWqlE9cKylWuQ5mKNXFx86Rlh14YGRlzcM9Wjel3bFlF3gLFqFm/Cc6unvzQpB0eXv7s2rY29e+zeRV1f2xBoWKlcfP0pf1vg978fQ4BEBUZzvOnj6jVoClunr44OrnRsGkH4uPjePym4Fi2Ui2atulOYO4C2Ds68125apSuWJPTxw989v7au3UZ1b9vQ1CRcrh4+NGi8zDCw4I5f2p/uuvt2bKE7yrWp2T5uji5etO4XX8MjYw5tnejKs2aBWMpX70RVeu3xMnNB0dnDwqVrIKBgaHati6fPcLVCydo0Kz7Z8WuTcE7D3Fz0ERebEr/2pDZ1m3YRLWqlalSqSLubm507dQBI2Mjdu7SHLO/ny9tW7WgXJnSGBgYpLvd2NhYRo0Zx2+dO2Fubp5R4bNxwzqqVK1GxcpVcXNzp0OnrhgZGbF7106N6Tdv2kCBgoWp//2PuLq506Rpc7y9fdi6RXnvfPrkCTeuX6N9py74+fnj4uJKh45dSEhI4OCB1GO3arUa5M6TFwcHR3x8fGnStAWvgoN5+fKFVvO3YcMGqlWtSuXKlXF3c6Nzp04YGRmxa9cujen9/fxo3aoVZcuUSXf/DB82jEqVKuHu7o6Xlxfdu3fnZXAwt27d0mrsmUmhyLxXViMFvv8jixYtQl9fn1OnTjFp0iTGjx/P3LlzMzssFVsLHSxNdbj1JPXJWlwiPHqZgpv9hw/Vut8ZcP1RCrefpK3de5+BPhTy1yckMoWI19o5q1+9eEJE+CsC86U2JzE1s8DLNzd3b2guuCQlJvLgzjUC86auo6urS2Deotx5Z534+FjmTuhL47a9sbLJ9tFYjh/YiqGhMQWLV/xo2k8VFvyY6IhgvHKlNj0zNrXA2Tsvj26f/6ptu/oEcePcPiLDXqBQKLh37QQhL+7jnbvkV0adPmV+XuGdq7hqmbGpBS5eeXl4+4LWvic64hU3LhykUOkGWttmyMsnRIa/IiBvMdUyE1MLPHzycP+m5tiTkhJ5dPca/nlS19HV1cU/T1HuvVknKTEBHR0d9N/5wa1vYISOji53rp/VSuzBL54SERZC7nxFVMtMzczx8sulKpiliT0xkft3rpMrX2G12HPlK6xa596dayQnJZHrne06uXhgl92RW9c1b/fI/u0YGRlTpET5dOM9uGczufIVIZt9Do1x3bt9g9xB6nHlzlc43e+8ff0yud/JB0DeAsW4/SZ98IunhIeFqOXV1Mwcb79c3HqTV3MLK3I4u3N4/3bi4mJJTk5i386NWFrZ4OmTfvO32NfRmFlYpvu5Jq9eKI+1d69RJmYWePrm4e6NdI61xEQeariuBeQtyt2byutaZEQo925dwsLKlr/6NqVny/KMHdCK29fOqW0rMjyEJTOG0qLL8DTNPEWqxMREbt2+Tf6gINUyXV1d8gfl49r161+17SkzZlKkcCEK5A/6aNovlZiYyO3bN8kXVEC1TFdXl6CgAty4flXjOtevXyUofwG1ZfkLFuL69WuqbQIYGqZez3R1dTEwMODq1csatxkXF8ue3TtxcHQkWzbt1Si/3T9B7+2foKCgr94/74p5/RoACwsLrW1TZB3Sh+//iKurKxMmTEBHRwd/f38uXbrEhAkTaNNG2R5/+vTpaQqASUlJGBt/+EYbHx9PfLx6856kxBT0DYw+Kz4LU2VtW3SMeiEsOlaBhWn66+Xz1sMpmy5TN6TfxAigWE49qhc1wMhAh5fhKczdlkDyx8uHnyQiXNmUytJKvQmlhbUdEWGvNK4THRVOSkpymmaXlta2PH9yX/V+9fxxePvnI6hI2U+K5cjejRQtVU2rP5CiI4IBMLe0U1tubpmN6AjN+ftU1RsPYPPCAYzrXgZdPX10dHSo3XwYHv6FP77yF4p6E7O51Xv5scpGdHiw1r7n7JGNGBmbkfMDNYKfKzJcGbvFe7FbWNkR+eY4fF90ZBgpKclYWKuvY2ltx4unyiZQHn55MTQyYdOyCdRu1AWFQsGm5ZNISUlWfefXetvk0Oq9Y97K2paIMM2xR0Uqz5P317G0tuXp4wcARISFoK9vgJm5+g8dK2tb1bn5voO7N1O8dJV0z5OwkGAu/nOcDj009/tLPy4bnr5z/r4rPDxEQ95tVH+XD/193n6mo6NDn2FTmDDiD1r/VB4dHV0srW3oNXgiZuaaC3Q3r13kxJE99Bw4XuPn6Xm73y3fP26s0v+7Rkelc6xZ2amua69ePAZg66qZNGj2G64eAZw4uIUJg9sycMJaHJzcUSgULJw6kNJVfsDDJxevXj75rNj/n0RGRpKSkoKNtbXachtrax49+vK/2/6Dh7h9+y5TJ477ygg/LDIyQhn/e00Xra1tePzokcZ1wsPCsH4vv9bWNoSHKZsNu7i6kj27PYsWzKNT524YGRuzaeM6Xr0KJixUvcn1tq2bWTh/DnFxcTi7uDLsz78+WOv5+fmL1Jg/G2vrdPP3uVJSUpg1axY5c+bEw8NDK9v8Fsi0DNojBb7/I8WKFUPnnT5gxYsXZ9y4caq26o0bN6Zfv35q66xfv54RI0Z8cLsjR45kyBD1vg0lavblu1r9P7hekI8e9UulXlQX7Ej4pHy8y8pMh1rFDZi7PZ6kjzS5P38rmVuPU7A01aF0Pn0aVzRkxuaPr6fJiYPbWTrrT9X7zv0mf/5GPsH5Uwe5fvk0A8au+KT0d25c4Nnje7TqOuyrvvfi8S1sWZTafLRxt5lftb0POblnCY/vXuDnrtOxsnPmwY3TbFs6FAtre7zfqVH8GuePbWHTgsGq9017zNDKdj/mn0PryVe8JgaGn/fw412nD29jxezUQkf7PtO0EVoaFpa2tOo+llVzh3Pw7+Xo6OhSsGQ1XD0D1a4bn+PogR0smDFS9b7HgAnaCver3Lp+kaeP7/Hrb4PTTXN4/zZMzcwpWLTsvxbXp1AoFCycOQZLKxsGjJyJoZERB3ZtZuzwngwbtwAbW/VWAI8e3GH8n39Qr2Er8uYvms5WlU4e2sayWcNV7zv1nZIxeXjTJ7RU5QaULF8XADevAK5fPMWxfZuo16QL+7evIC72NdXqtcyQGMSHvQwOZsbsOYwaPlStluy/Ql9fn779BzF50jga/VRfWaOWvwAFCxVO01yvbLkK5M9fgNDQUDasX8NfI4czeuzE/1S+p02fzv0HDxg7dmxmhyK+UVLgEypWVlb4+PioLbO3t//oen369KF7d/X+FUOWfLzq7OqDZB69TE33dmAWc1MdomJTr8jmJjo8DdH8mMc5mw4Wpjp0qZ/6g1pPVwfPHAqK59Kj37w41cU9LhHiEhWERCp4+DKBwc2MyeWhx4U7n1/iCypSBi+/1JE03zYfiYwIxdo2tSlIVHgIrp6aR+Eyt7BGV1cvzQAtkeGhqifq1y+dIvj5Y7r+UkYtzYwxv+MbmJ/fh81RW354z0ZcPf1x98752Xl6l39QOZy98qreJycpC+PRkSFYWKceE9GRr3B01Twq5adITIhj77qJNOw8Bb98ZQFwdPXn+cPrHNsxX2sFvsD85XH1Ts1PUuKb/ESEYPlufiJekcP9y/Pzrvs3zvDq2T0advy8WpX35SlUFo93Rjd8G3tURAhW7wxkERURgotHOseapQ26unpEvVcrExkegqV1agEhMF8JBk/ZTnRkGLp6epiaWdKnTTkKOnzZqG8FipTCxz91xMrEN7FHhIdi/U7BJCI8FHdPzaMJWlgqz5P3B0KJDA/F2kZ5nljZ2JGUlMjr6Ci1Wr6I8FDVKJ3vOrB7E+6efnj6aN7XCoWCQ3u2ULJsNfTTedKfflxhGr8TwNraLk36iPAwVT7e/hsRHqpWcIsID8XdS9ln8srFM5w7c5TZy3djamoGgGf7AC6dP8Xhfdup/X1T1XqPH95jRP9OlK9Sh3o/fbzglK9wWbWRNN8ea5Hh6sdaZEQorh6a95e5RTrHWkQIVm+OtbfbyuHirZbG0cWT0FfPAOW17+7Ni3RsWEQtzYg/GlOkdDVadB6OULK0tERXVzfNAC1h4eHY2lh/0TZv3b5DeHgEHbr8plqWkpLCpctX2LRlG9s2rkNP7xNGU/sElpZWyvjD1AdoCQ8Pw8ZW84Al1jY2aQY8CQ8Pw9omtXbcx9ePyVNn8fr1a5KSErGysqZHt874+Kr3PzYzM8PMzAwnZxf8AwJp9GN9jh87Qpmy6Tf3/rz8WWrMX1h4ODa2Xz+w2vTp0zl16hRjRo8me7aPd/sQ/5+kwPd/5OTJk2rvT5w4ga+v71dftI2MjDAyUq/B0DeITSd1qoRECElUL8hFxijwcdLjWUiSctsG4Gqvy4lriZo2we2nKYxfoz4i1Q9lDAmOSOHA+aQPd7zV4ZNG/9TE2MQMYxMz1XuFQoGVdTauXzyF25sCXmxMNHdvXaZM1R80bkPfwAB370CuXTylmmIhJSWFaxdPUb66cvj0avVbUKpiPbX1Bv/2Iz+16EHeQqXVlsfFxnDm6G7qN+n0ZZl6h5GJOUYmqR30FQoF5lbZuXv1ODncAt98XzRP7lykcLlGX/w9yclJJCcnoqOj3kdTR1cXhUJL7W0BIxMzjN7bX+ZW2bh79QRO7qn5eXz3IkUraGc6iDMH1+HkkYscbl83hLymY83SOhs3Lp3ExUO57diYaO7fvsR36Yycqa9vgKtXIDcunyRfEeWPmJSUFG5ePknpqmn3n7ml8kfWjcsniY4MJU+hsl8Uu4mpGSam750nNnZcuXgady8/Vex3b16hQlXN/Rz1DQzw8A7g6sXTFCpWVhX7lYtnqFRdeW55egeip6/P1YunKfymT96zxw8ICX6Ob4D6VABxsTGcOrKXH5t2SDfu65fP8uLZI8pUSn/gI30DAzx9/Lly4TSFipVRxXX54mkq19B8zvsE5ObKxdNUq5N6jF0+fwqfNzFmd3DC2saOKxdO4/Hm7xMT85o7N69QsVp9ABLildc73fdqXXV1dVU1ZwCPH97lz34dKVW+Oj/+0j7dfLwrvWPt+qVTqqkQYmOiuXfrEmWqpH9dc/MO5NqlUwQVTT3Wrl88Rblqynzb2TthbZudF0/vq6378tkDcuVX9t1t2KoXdX5OvZZFhL5k0rAOtOn+F55+6vv0/52BgQG+Pj6cP3+BksWV/XRTUlI4f/4itWvW+KJt5s+Xl1nT1Gt4x02chKuLCz9+30BrhT1Qxu/j48fFC+coXkK5/1NSUrhw/hw1amk+BwMCcnLh/Dnq1K2vWnb+3FkCAtI+xDEzUx7TT5885vbtmzRu2uwD0ShQoFA9xNUG1f65cIESJZQPMZX75zy1a2keEOpTKBQKZsyYwbHjx/lr1CgcHR21FfI3IysOnpJZpMD3f+Thw4d0796ddu3acfbsWaZMmcK4cRnbNv9zHbmURPkC+ryKTCEsUkHlwgZExii4cj+1Fq5NDUMu30/m+JVkEhLhRZj6FSEhSUFMXOpyWwsd8nrrcetxMq9jwcpch7JB+iQmwfWH2hl6WUdHhwo1f2bb2rnY53Ajm4MTm1bMwNo2O/nf6Xs3blA78hctR/nqyh8+lWo1Zv6UQXj45MTTNxd7tiwnIT6WkuWVIwe+HXnzfbbZHMnu4Ky27PTRXaSkJFOszJfd4D+Wv2KVmnJoy0zsHDywyebMvg2TsbCxJ+CdefUWjm5OYIGKFK3YBFDOsRf68qHq87Dgxzx7eA0TMyus7ZwwNjHHw78wu1aPQd/QCGs7Z+7fOMWFY5uo0rC31vPxbn5KVmnK/k0zsXNwxya7C3vWTcbC2l5tnsB5o1qQs2BFildqrMpPyAv1/Dx9cA1TMyusszmplsfFRnP51E6q/fxHhsRernoTdqyfTfYcbtjZO7Nt5TSsbLKTr3DqE+nJQ1uTr0gFyrwp0JWv2ZQl0/rj5pUTD5887N++lPj4WIqVrata5/j+jTg6e2Juacu9mxdYu/AvytX4BQcnT1Wa0FfPiImOIOzVM1JSknl8XznoQHZHN4yMP9DZ9k3sVWs1ZNPq+TjmcCW7gxNrl8/E2jYbBYul1mKPHNCBQsXKUqmGsgBbrc7PzJ40BE+fQLx8c7Fzy0ri42IpXbEmoBzYpEzF2iybr+zHZmJqxuLZY/Hxz4OPv3rh4MSR3SSnJFOiTLV04zy4ZzPefrlxdfdON40yrkbMmjgMT59AvP1ysmPzKuLj4ihTQXkOzpgwBBvb7DRspixcVq31E8P7tmfbhmXkL1yS44d2c/f2NVp17J3696n9ExtXL8TR6c3fZ9nsN38f5QMe34A8mJlZMHPiUOo1bIWhoRH7d23i5YunBBVW/lh+9OAOI/p3Ik/+olSv+7Oq/5+uri6WVh8egv9dyutaY7avnaO8rtk7s2nFNKxtshNUpJwq3fjBbclfpDzl3lzXKtb6hYVTBuDhnRMP39zs3bqMhPhYSpSvo9pupTrN2LJqJi4efrh6+HP8wBaeP7lPu57KJmm22dUHyjEyNgEgu6MLNnYOn5wHbdAzM8XMJ3VeNlNPFyzzBZAQGkHco2f/aizpaVCvDmPGT8TX14cAPz/Wb9pMXFwcVSoppxQZPW4Cdna2tGquLOwkJiby8KGy/1hiUhKvQkK5c+cuxibGODs5YWpqiqeH+lyLxsbGWFpapFmuDXXrNWDC+NH4+Prh5+fPpk0biIuPo2KlKgCMH/sXdnbZaNZCOZdp7Tr16NOrBxvWr6FQ4aIcPniA27du0qlzN9U2jxw+iJWVNdmz23P//j3mzJpO0WIlKFCgEADPnz3j8KED5C9QEEsra0JeBbN2zUqMDA0pVLhImhi/Rr169Rg3fjy+vr74+/mxcdMm4uPjqVRJ2b977Nix2NnZ0aJFC+Dt/lHea5KSkggJCeHOnTuYmJjg5KS810ybPp0DBw4wcOBATExMCH3TN9HMzCzNQ3ghpMD3f6Rp06bExsZSpEgR9PT06Nq1K23bts3ssNQcvJCEoT40KGWI8ZuJ1+f/naDWz87WUgcz40/vU5SYrMDTUZfvcutjYqQcBObesxSmb4rntRanq6larxkJ8bEsmTmcmNdR+AYG0XXAVLX+W8HPHxMdGa56X/i7KkRFhrFpxQwi3zT/7DpgappBEj7F0b0byV+0PKZmGTNC13fVW5OYEMuWhQOVE6/7FaRJ9zkYvDM4T9jLh8REpzZbeXr/Mgv/Sn2aunPlKACCStalXmvl/79vP549a8ezbtbvxL6OwNrOiQoNumX4xOularQmIT6WjQsGERcTibtvAZr3nK22v0JfPiQmKjU/T+5dYd7I1PxsX/4XAPm/q8v3bVP7ql08sR1QkK+Y9gvfABXrtCA+PpYVs4YqJ8MOyE+HvjPUYn/14jHRkamxFyxRlejIMLatnk5U+CucPfzp2HeG2rH28ul9Ni+fREx0BLb2zlSp34byNX5R++5tq6Zx8uBm1ftRfygLZV0GzcMv18cH2qlRvynxcXHMnz5COfF6YD5+HzQJw3dif/n8CVHvnCfFSlUiKjKMdctnExEWgpunH78PmqTWdLJxq9/Q0dFl8l+9SUxMIG/+YjT7NW2B++DuzRQqVjbNAC9vxbyO5vSxfTRp0+OjeSleqhJREeGsXT6HiLAQ3L186TV4AlZvmmaGBD9X6//oF5iXjj2GsmbZLFYvmYmjkyvd+45WK1jWrP8L8XFxzJs2Svn3yZmXXoMnqv4+FpbW9Bo8kdVLZzKif0eSkpJwcfOie7/RuHsqm6qdOrqPyIgwjh7YwdEDO1TbzmbvyKS5Gz+ar3dVqduchLhYls4cRszrKHwC8tNlwHT1Y+35I6LfOU8Kl6xCdEQYm1fOIDL8FS6e/nTpP13tWKtYswlJCQmsWTCW19ERuHj40W3gTLI7amf6Em2yKpib4nuXqN7nHNsXgEeL13OxVZ/MCktN2dKliIiIYPHS5cqJvb28+HPoYNVAIS+Dg9WOxZDQUNp36aZ6v3b9Btau30DePLkZO+rD/fYzQqkyZYmIDGfZkkVv4vdmyNARqviDg1+io5saf2DOXPT8ow9LFy9k8cIFODk702/AYNw93nk4FRrKvDmzlE1DbWwpX6ESPzVqrPrcwNCAK1cusXnTeqKjo7G2tiFX7jyMHjcJa+tPfzDyKcqUKUNEZCRLlywhNCwMby8vhg0dqr5/3pmDMjQ0lE6dO6ver1u3jnXr1pEnTx5G/6W872zbtg2AXr168a7uv/2mKkj+16Vor6HP/z0dxbc2EZvIEGXLliUoKIiJEyf+K9/Xa/bHm3T+l9QomXWuOk8jPlwL81+jr5e1LmFWxtprSpTZrIyy1gTAujpZ5zoAEJ2YdaY6eJ2B0wZkhtzXNn880X9IvI5JZoegNfpknWs0gJf3h1syZKZZmqcp/Fe0q5x5350RZB4+IYQQQgghhMiipEmnEEIIIYQQ4psibRC1Rwp8/ycOHDiQ2SEIIYQQQggh/mVS4BNCCCGEEEJ8U6SGT3ukD58QQgghhBBCZFFSwyeEEEIIIYT4pqRIDZ/WSA2fEEIIIYQQQmRRUuATQgghhBBCiCxKmnQKIYQQQgghvimKTB21RScTv1v7pIZPCCGEEEIIIbIoqeETQgghhBBCfFNkWgbtkRo+IYQQQgghhMiipMAnhBBCCCGEEFmUNOkUQgghhBBCfFNSUjI7gqxDaviEEEIIIYQQIouSGj4hhBBCCCHEN0UGbdEeqeETQgghhBBCiCxKaviEEEIIIYQQ35QUqeHTGqnhE0IIIYQQQogsSgp8QgghhBBCCJFFSZNOkSHqlU7I7BC0SqHQyewQtCaX/cvMDkGrohNNMjsErTLSS8zsELRGXyc5s0PQKkud8MwOQavM9U0zOwStMbm2ObND0KrLgbUzOwStsr14OrND0BoLg5jMDuH/hgzaoj1SwyeEEEIIIYQQWZTU8AkhhBBCCCG+KYpMHbUl67TsAqnhE0IIIYQQQogsSwp8QgghhBBCCJFFSZNOIYQQQgghxDdF5uHTHqnhE0IIIYQQQogsSmr4hBBCCCGEEN8UmZZBe6SGTwghhBBCCCGyKCnwCSGEEEIIIUQWJU06hRBCCCGEEN+UFBm1RWukhk8IIYQQQgghsiip4RNCCCGEEEJ8U2TQFu2RGj4hhBBCCCGEyKKkhk8IIYQQQgjxTZEaPu2RGj4hhBBCCCGEyKKkwCeEEEIIIYQQWZQ06RRCCCGEEEJ8U1KkTafWSA2fEEIIIYQQQmRRUuD7l3h4eDBx4sRPTn///n10dHQ4f/58hsUkhBBCCCHEt0iRknmvrEaadGZxZcuWJSgo6LMKm/8WhULBhuWzObB7IzGvo/ENyEuz9r1wdHL74Hp7tq3h741LiQgLwdXDlyZte+Ltl0v1+f6dGzhxaCf379wgLvY105ftxczcQvX5tUv/MKp/e43bHjR2IV6+Ob88PytmcfCd/DT9tffH87N9NX9vWEpEeAhuHr40afM7Xm/yEx0VwYYVs7ly/gQhr15gYWlNgaJlqf/zr5iamau2sXTOWG5du8CTh3fI4eLBsInLvygPb/29dT2b160kPCwUd09vWv3aFV//9P8uxw7vZ+XSeQS/eE4OJ2eatPiVAoWLa0w7a+pYdv+9meZtOlGz7o+q5Xdv32DpglncvnUdXV1dipUoQ7M2HTExMf2qvEDW2jcAu7atY8v6ZUSEheLm6UPzdt3x8Ut//5w4so81S2cT/PI5jk4uNGregfyFSqg+P3XsAHv+3sC9OzeIjopk5KSFeHj5qW0jPCyEZfOncun8aeJiY8jh7EbdH5tRtGS5r8rLjq3r2bx+hepYa9mu2wePteNH9rNy6VyCXyjz0qS5+rG2etl8jh7eS0jwS/T19fHy8adR0zb4+qdeI54+eciS+TO4ce0SSYmJuHl607BJa3LnLfBVedFk89ZtrF23ntCwMLw8PenwazsC/P00pr3/4AGLly7j9u07vHj5knZtWlO/bp10t71q9RrmL1pM3Tq1ad+2jdZjB9i2ZSMb160mLCwUD09v2rbvjJ9/QLrpjx4+yLIlC3j54jlOTi40bdmGQoWLqj6PjY1l8YI5nDx+lKioSOwdHKlZuz7VatQCICoqkhVLF3Hu7BleBb/E0sqaosVL0viX5pi9c15py+at21izboNq/3T8te0H9s9DFi9dxq03++fXNq0+uH9Wrl7L/EWLqVenVobtny9h+10hvHq0wqpAboyd7DnToAMvNu/N7LAy7TodHRnOzAkDeHz/NtFREVha2ZC/aBm+b9IBE9MvO+b+3rqBTW/uoR6qe2hguumPHd7PiqXz1e6hBQsXU30+ZfxIDuzdobZOUIEiDBg2RvV+5JA+3L93m4jwcMzMzckbVJBfWvyKrV22L8qDyFqkhk9kmu3rF7N72yqat+/NwDHzMTI2YezgLiQkxKe7zsnDu1kxfyJ1fmrNkPGLcfX0ZezgLkSGh6rSJMTHkSd/cWp931zjNnwD8jJp4Xa1V5lKdcju4ISnT/oX5I/mZ8Nidm9dRbNf+zBw9AKMjE0YN6Tzh/NzZBcr50+kbsPWDBm/BFcPX8YO6azKT3hoMOGhwfzUvCt/TlpJ6y6DuHTuOPOnDkuzrVIVa1Hku0pfHP9bRw/tZdGcafzwc3NGT56Lh6cPwwf0JCI8TGP661cvMXH0UCpUrsGYyXMpXLwUo4f34+H9u2nze+wQt65fTXMDCg15xdB+3XF0cmbk+Jn0HzqGRw/vMW3CyK/OD2SdfQNw/PAelsydTINGLRkxcQHunj6MGvgbEe+cA++6ee0SU8YMomzlWoyctJBCxUoz7s/ePHpwR5UmPi4W/5z5aNSsQ7rfO338UJ49eUjPAaP5a+oSCpcow6TRA7h358YX5+Xoob0smjuVHxo1569Jc3H39OHPgT3SPdZuXLvExNFDKF+pBqMnz6NIsVKM/rOv2rGWw9mVVr/+xrhpixg2ejrZHRwZNqAHERGp2xw1pBfJyUkM+nMif01UHuOjhvQiLCzki/OiyYFDh5k9Zy6Nf27EtMkT8fL0pN+AgYSHh2tMHx8fTw5HR1o2b4atjc0Ht33j5k227diBp6eHVmN+1+GD+5k/ZyY//dyU8VNm4unlzeABvQhPZ/9cu3qFsX8Np2LlakyYMouixUsycthAHty/p0ozf84Mzv5zmt9+78PUWQuoXbcBs2dM5uSJYwCEhoQQGhJCi9btmDxjHl1/+4NzZ04xZeJYrefvwKHDzJozjyY/N2T65Al4eXrQd8Agwj6wfxwdHWnZvOkn7J9bbNuxA68M3D9fSs/MlMiLN7jcZUhmh6Ims67TOrq6FChShq79xjFq+jpadxnElQunWDRj1Bfl4+ihfSycM40ff27GmMlzcPf0ZtgH76GXmTB6GBUqV2fs5DkUSecemr9gEeYuWa96/fbHQLXPc+fNT4/eg5k8ewm/9x3Gi2dPGTtCPY34/yUFvs+wdu1a8uTJg4mJCXZ2dlSsWJHXr19TtmxZunXrppa2bt26NG/ePN1t6ejoMGPGDKpVq4aJiQleXl6sXbs2Tbq7d+9Srlw5TE1NyZcvH8ePH1d9FhISQqNGjXB2dsbU1JQ8efKwYsUK1efNmzfn4MGDTJo0CR0dHXR0dLh//z4Aly9fplq1apibm+Pg4MAvv/zCq1evPppXbVEoFOzcspJaP7SkQNEyuHn40rbbYMJDX3H2xMF019uxaTllKteldMVaOLt50bx9bwyNjDm0Z4sqTZXajaj5fTO8/XNr3Ia+gQHWNtlUL3MLa86eOkSpCrXQ0dH54vzs2rKC2j8q8+Pq4UubrkMIC33F2ZPp52fnm/yUqlAbZ1cvmrXvo8zP3s0AuLj70Ln3aPIXKY19Dhdy5i1Mg8btOX/6MMnJSartNGnTk4rVfyS7g/MXxf+uLRtWU7FqTcpXqo6rmwdtO/XAyNiYfbu2aUy/ffNaggoWoU6DRri4edDol9Z4evvx99b1aulCXgUzb+Ykuv4+AD099cYF/5w6hp6+Pq3b/4azixs+foG07dSDE0cP8uzp46/KT1baNwDbNq6kfJXalK1YExc3T1p1+ANDIyMO7N6qMf3fm1eTr0BRatVvjLOrBz82aYuntz87t65TpSlVvhoNGrUkT1DhdL/35vXLVKn5PT5+OXFwdKb+Ty0wMzPn3u0vL/Bt3biKClVqUa5SDVzdPGnbsSeGRsbs2635WNumOtZ+xsXVg4a/tMbL248d7xxrpcpWIm9QIRwcnXB196RZ687Exrzm4T1lATcyIpxnTx9T7/smuHv6kMPZlcbNfiU+Po5HD+5p/N4vtX7DRqpWrUKVShVxd3OjS6cOGBkbsXPXbo3p/f38aNOqJWXLlMbAwCDd7cbGxvLXmHF069wZC3Pt13q9tWnDWipXrU7FylVxc/OgfaduGBkZsWfXDo3pt2xaT4GChan//U+4urnTuGkLvLx92bZloyrN9WtXKF+hMnnyBuHg4EiVajXx9PLm1o3rALh7eNK7/2CKFC1BjhxO5A3KT5NmrTh98gTJyclazd+6DZuoVrWyav90Ve2fPRrT+/v50rZVC8p9wv4ZNWYcv3XuhHkG7p8vFbzzEDcHTeTFJs35zAyZeZ02M7ekfLXv8fTJSTb7HOTMV4QK1b7n5tVzX5SX9++h7d7cQ/fu2q4x/bbNa8lfsAh1VffQVm/uoRvU0ukbGGJja6d6mVtYqH1eq96P+AXkwt7ekYCcuan3Q2Nu3rhKUlIS/1UKhSLTXlmNFPg+0bNnz2jUqBEtW7bk2rVrHDhwgPr163/VQTFgwAAaNGjAhQsXaNy4MQ0bNuTatWtqafr160fPnj05f/48fn5+NGrUSHXyxsXFUbBgQbZt28bly5dp27Ytv/zyC6dOnQJg0qRJFC9enDZt2vDs2TOePXuGq6sr4eHhlC9fnvz583PmzBl27NjBixcv+PHHHzMsr+8LfvGUiLAQcuUrolpmamaOl18ubt+4pHGdpMRE7t+5Tq58qT9KdXV1yZWvcLrrfIpzpw4RHRVBqQo1v3gbwS+eEBEWQs686vnx9svFnRsXNa7zNj/vrqPMTxHufCA/sTHRmJiapSk0aUNiYiJ3b98kb1AhtZjyBBXkxvUrGte5ef0KeYMKqi0LKlCEm++kT0lJYcq44dRp0BBXd0+N36uvr4+ubuolydDQCIDrV75830LW2Tdv47p3+wa586nvn9xBhbl147LGdW5dv0zu9wpyefMX5dZ1zenT4xeQm+OH9xIdFUlKSgrHDu0mMSGBnHm+rBlk6rGWeuzo6uqSN6iQ2rHzrpvXL6sdmwD5ChThZjp5SUxMZM+OzZiamePu6QOAhaUVTi5uHNy3g7i4WJKTk9i9YxNW1jZ4+fh/UV7S++5bt29TICifapmuri75g4K4ev3LC8kAU2fMpEjhQhTIH/SVUaYvMTGRO7dvki8odf/q6uqSL6gAN65f1bjOjetXyZdf/VqQv2AhtfQBgbk4dfI4Ia+CUSgUXLxwjidPHpO/QKH3N6fy+nU0pqam6OnpfWWuUr3dP/mDglTLlPsnH9euX/+qbU/5F/ZPVvMtXafDQoM5c3w//rk//9r29rxJe10r+IHrmqZ7aOE099wrl87T4uc6dG7bhFnTxhEVGZFuHFFRkRw6sBv/wNzo60vvLSF9+D7Zs2fPSEpKon79+ri7uwOQJ0+er9rmDz/8QOvWrQEYNmwYu3fvZsqUKUyfPl2VpmfPntSoUQOAIUOGkCtXLm7fvk1AQADOzs707NlTlbZz587s3LmT1atXU6RIEaysrDA0NMTU1BRHR0dVuqlTp5I/f35GjBihWjZ//nxcXV25efMm0dHRWs/r+yLeNJ2ysrZVW25pbav67H1RkeGkpCSnWcfK2pZnjx98cSyH9mwmT/5i2GZz+OJtRIS/zY+d2nJLK7v08xOlOT+WVrY8e3xf8zqR4WxePY8ylet9cawfEhUZ8SYm9eZK1ta2PHn0UOM64WGhWKfZJzaEh6U2Mdy4djm6enpUr/29xm3kyVeARXOnsmndCqrX/p74uDiWLZwF8NXN7LLKvgGIfHsO2KQ9B56mcw6Eh4ek2Z9W1jaEh3/e37Vrr+FMHj2ANj9XRU9PD0MjY7r3HYmjk8vnZeKN1GMt7bHzJL28hIWmSW9tbUv4e81Z/zl1lAmjh5AQH4e1jR0Dho3H0soaULauGDh8AqOH96XpD1XQ0dHFytqafkPGYm6u/sT8a0RGKgvG1u/97W2srXn06MtrrQ8cPMTt23eYMnH814b4QZGREcr4bd6/Ftjw+NEjjesorwVp04e9cy1o274T0yaPp2XThujp6aGjo0vHrt3JlSev5jgiIli9YimVq9X4yhy9t903+8fG2lptuXL/PPni7e4/eIjbt+8ydeK4r4zw/8u3cJ2eMa4f504eJCEhnqDCpWjRsf9n5+Ptde3988DK2uaD99C091z1e2j+gkUoVqI09o6OPH/2lOWL5jB80B+MGDtd7UHIkvkz+XvrBuLj4/ALyEnfQV/WLPVbkZIFB08JDQ2lc+fObNmyBV1dXRo0aMCkSZM+qTWAQqGgevXq7Nixgw0bNlC3bt1P/l4p8H2ifPnyUaFCBfLkyUOVKlWoXLky33//PTYfacf/IcWLF0/z/v1ROfPmTb0J5siRA4CXL18SEBBAcnIyI0aMYPXq1Tx58oSEhATi4+MxNf3wIBcXLlxg//79Gg+uO3fuULly5c/Ka3x8PPHx6m3sExLiVTU0AMcO7GDhjNT+WN0HTPhgjP+W0FcvuHTuBB1/H/HxxO84dvBvFr2Tn9/6Z3x+YmOimTCsG06untRt2DbDv09b7ty6wfZNaxk9eW66TWZd3T3p1L0vi+ZMY9nC2ejq6lK9dgOsrW0/u5mt7JuMsXrZHF6/jqbf8MlYWFpx+sQhJo0ewKBRM3Dz8M7s8NTkyluAMZPnExUZwZ6dWxj/1yBGjpuFlbUNCoWCuTMmYGVlw9C/pmJoaMTeXVsZNbQ3oybMxsb22x3g4GVwMDNmz2Hk8KEYGhpmdjhfZOvmjdy4fo1+g4Zhb+/AlcuXmDV9Mra2dgS9VzsYE/OaoYP64urmTqPGzTIp4k/3dv+M+g/vn3/Lt3idbtTyN+r81IYXTx+wZsk0Vs6fQNNfe2d4XJ/iuzIVVP939/DG3cObjq0bceXSebXawToNGlKhSg2CXz5n9fJFTB43gr6DR31xdxWhfY0bN+bZs2fs3r2bxMREWrRoQdu2bVm+/OMDuk2cOPGL96UU+D6Rnp4eu3fv5tixY+zatYspU6bQr18/Tp48ia6ubprmjomJiVr53nf7CbzdySlvHnmMGTOGSZMmMXHiRPLkyYOZmRndunUjISHhg9uMjo6mVq1a/PXXX2k+y5Ejxwfz6umZtjneyJEjGTJEvfN3q469aN2pj+p9/iKl8H5nlLzERGWMEeGhWL/zAysyPBQ3T80jpFlYWqOrq5dmcIqI8FCsbOw0rvMxh/duxdzCivxFSn/WevmLlMbbL7WPYJIqPyHq+YkIST8/FprzExmRNj+xsa8ZN6QLxiamdO49JsOaaFhYWr2JSb1zeXh4KNbv1Sq9ZW2TtoYlIjxMlf7alQtERITxa/MfVJ+npCSzeN50tm1ay4wFqwFl36tSZSsRHhaKkbExOjo6bN24GgdHp8/KQ1bdNwCWb8+BsLTnQLr7x9ouzf6MCA/D2vrTz5kXzx6za+taRk9diqu7FwDunr7cuHKBXdvW0brjH5+Zk3ePNU3HjubYrG1s06QPD09bw2xsbEIOJxdyOLngF5CLzm0asW/XVur9+AuXL/zDP6ePsXDldkxNzQDw8vHn4rkzHNi7g3o/NPnsvGhiaWmJrq5umgFOwsLDv/hB4e3btwkPD6djl26qZSkpKVy6fIXNW7aydeN6rTV7tLS0UsYf9v61IAwb2w9dCzSkf3NsxsfHs3TRPPr0H0KhIsoRCD08vbl75zYb169RK/DFxMQweEBvTExN6TNgqNbPq7f75/0BWsLCw7G1sf6ibd66fYfw8Ag6dPlNtezt/tm0ZRvbNq7TarPU/7Jv8Tr9tl+/k4sHZuZWjOjbhto/tlaL52PeXtfePw/evSem/V5bDffc9NMDOOZwwtLSiufPnqgV+CytrLG0ssbJ2RUXV3faNvuBm9ev4B+oeUyDb11W60t37do1duzYwenTpylUSNmMfcqUKVSvXp2xY8fi5JT+753z588zbtw4zpw5o6oA+hzSh+8z6OjoULJkSYYMGcK5c+cwNDRkw4YNZM+enWfPnqnSJScnc/nyx/vHnDhxIs37wMBPHyXy6NGj1KlThyZNmpAvXz68vLy4efOmWhpDQ8M0Hd0LFCjAlStX8PDwwMfHR+1lZmb2wbxq0qdPHyIiItReTdt2V0tjYmqGQw5X1cvZ1QsrGzuuXjytShMbE83dm1fw8dfcfFTfwAAP7wC1dVJSUrh68Uy663yIQqHg8N4tlCxX/bN/TJiYqOfHKZ383Ll5BW9/zU2V0s/PabzfyU9sTDRjB3dGT9+Arv3Gq9WcapuBgQFePn5cOv+PWkyXzp/FPyCXxnX8AnJx6cJZtWUXzp3G7036MuWrMG7qAsZOmad62dplo3b9hvQflnbkPWsbW0xMTDl6aB8GBobky59+3x5Nsuq+eRuXp48/ly+q758rF87gm84gRb4Bubly4YzaskvnT+Eb8Ok/AN7W4L/bx/Lte8UXTlikOtYuvHesXfhHdey8zy8gt9qxCXDx3Bn8PpIXhSJF9RDubV7ef0qqo6vzxXnRxMDAAF8fH86dT+1/lJKSwvnzF8gZ8GV9BYPy5WPWtKnMmDJZ9fLz9aF82TLMmDJZq4UJAwMDvH38uHghdeCKlJQULp4/h3+A5mkz/ANycvG8+rXg/Ll/VOmTk5NISkpK87fX09NF8U7brZiY1wzu/wcG+gb0HzgsQ2rL3u6f8+cvqJYp989FAgPSn3biQ/Lny8usaVOYMWWS6pW6fyZJYe8d3/p1+u214O3D6U/19rx5/x568fzZD1zXcnHxQtrrWnr3XICQVy+JiorE5gMPu1NSlIUlbVVA/L+Jj48nMjJS7fV+a7bPdfz4caytrVWFPYCKFSuiq6vLyZMn010vJiaGn3/+mWnTpql10focUsP3iU6ePMnevXupXLky9vb2nDx5kuDgYAIDAzEzM6N79+5s27YNb29vxo8fn+6w2+9as2YNhQoV4rvvvmPZsmWcOnWKefPmfXJMvr6+rF27lmPHjmFjY8P48eN58eIFOXOm3ow9PDw4efIk9+/fx9zcHFtbWzp27MicOXNo1KgRf/zxB7a2tty+fZuVK1cyd+5czpw5k25eNTEyMsLISP0Camj44acyOjo6VKnVkM2r5+OQw5XsDk6sXz4Ta9tsFChWRpXurwEdKFCsLJVqKAeUqVrnZ+ZMGoKnTyBevrnYuWUl8XGxlKqYOuBKeNgrIsJCefFM2c/k8YPbGJuYYZfdAXMLK1W6qxdPE/ziKWUqpT+P0qfS0dGhcq1GbFkzH0cnV7LZO7N++UxsbLNRoOi7+WlPwWLlqPgmP1Xey8+uLSuU+amgnJMqNiaaMYM7kxAfR7veQ4mNiSY2JhoAS0sbdN/8gHjx7BFxsTFEhIeQmBDPg7vKQSGcXb3Q/8BocprUqvcjU8ePxNvXHx+/QLZtWkN8XCzlKlUHYPK4P7Gzy0bj5u0AqF77ewb17sLm9SspWLg4Rw7t5e7tG/za+XdA+cTTwtJK7Tv09PSxtrHF2SV1fqW/t6zDPzA3xiamXDh3miXzZ9C4eTu1ORS/RFbaNwA16jZkxoThePkE4OOXk783rSI+Lo4yb86B6eOHYmOXnUbNlHNNVqv9I0P7dGDrhuXkL1SC44f3cPf2ddp06qXaZnRUJK+CnxMWqhyp99kTZV8Taxs7rG3scHJxxzGHC3On/UXjlp2xsLDk9IlDXDp/mt8HjuFL1az7E9MmjMDbN0D9WKuoPNamjBuOrV02Gjf/VZn32t8zqHdntqxfSYHCxTl6aC93bl+nXSflsRYXF8v6VYspVPQ7bGztiIyMYOfW9YSGvKL4d8r5Av0CcmFubsG0CSP4vmFzDI0M2bNzCy9fPKPAO3MTakP9enUZO34Cfr4++Pv5sWHTJuLi4qhcqSIAo8eNJ5udHS2bK5srJiYm8vCh8rqVmJRESEgId+7cxdjEGGcnJ0xNTfHwcFf7DmNjYywsLdMs14Y69b5n0vi/8PH1w9cvgC2b1hEXH0fFSlUAmDB2FHZ22WjaQtkXvVad+vTr9Rsb16+mUOFiHD64nzu3btKxs/IBoKmpGbnz5GPh/NkYGhlhb+/A5UsX2L93Ny3bKI/XmJjXDOrXi/j4OH77vS8xMTHExMQAYGllpdVCU4N6dRgzfiK+vj4E+PmxftNm4uLiqFJJ2Xxu9LgJ2NnZ0iqd/fMqJDTN/vHUsH8sLS3SLM9MemammPmkXntNPV2wzBdAQmgEcY+efWDNjJOZ1+kLZ44SGRGCp09OjIxNefLoLqsXTsY3MB/ZHT6vhQko76FTxo/E2zcAX78Atm5aS3xcLOUrVQOU91Bbu+w0aa5sVlqj9vcM7N2FzetXUaBwMY4e2sed2zf4tbNyjIbY2BhWL19E8ZKlsbax5fmzpyyZPxPHHM4EFVQOyHXz+lVu37pOYM48mFlY8OLZU1YsmYdjDmf8A9MvOIr0aWq9NmjQIAYPHvzF23z+/Dn29vZqy/T19bG1teX58+fprvfbb79RokQJ6tT58t+rUuD7RJaWlhw6dIiJEycSGRmJu7s748aNo1q1aiQmJnLhwgWaNm2Kvr4+v/32G+XKfXwy4iFDhrBy5Uo6dOhAjhw5WLFihVph7WP69+/P3bt3qVKlCqamprRt25a6desSEZE6clPPnj1p1qwZOXPmJDY2lnv37uHh4cHRo0fp1asXlStXJj4+Hnd3d6pWrYquru4H86pN1es3JT4ujoXTRygnWQ3MR89Bk9Sevr18/oToyHDV+6KlKhEZGcb65bOJCFM29eg5aJJaR+/9O9azceVc1fsRfZUFk9ZdBqqNxHloz2Z8AvLi5OKhnfzUa0p8XCwL3uTHLzAfPQZOTpOfqHfz811loiLC2bBilio/PQZNVuXn/p0b3L2prC3+o716J/Mxszapbkbzpw7nxpXUJ+uDujdJk+ZTlSxdgciIcFYuna+cNNbLh35Dx6qal7wKfoHuO0/oA3LmoevvA1m5ZC7LF80hh7MLf/T/EzcPr8/63ls3r7Nq2QLiYmNxdnWjXaeelClf5bO2kZ6ssm8AipeqSGREOGuXzVFOVu7lS+8h49X2j45Oak2cX2AeOvUcwuqls1m1eBaOTi706DcKV/fUfnf/nDzMzEl/qt5PHq2cu6lBo5Z8/3Nr9PX1+WPwOFYunMGYYb8THxuLQw4X2nfrrzaB++d6e6ytWjov3WNNRzf1WPMPzEPX3wexYskcli+eTQ4nF/7oN0J1rOnq6vLk8UMO7O1PVGQEFpaWePsGMvSvqarRYS2tlAO0rFg8myH9upKclISLmye9+o/Ew8vni/OiSdnSpYiIiGDx0mWEhYXh5eXFn0OHqJp0BgcHq51LIaGhdOjSVfV+7foNrF2/gbx5cjNmlHbmpPwcpcqUIzIyguVLFhIWFoanlzeDho56Z/+8RPed/ROYMxc9/ujH0sXzWbJwPk7OzvQZMBR3j9SuAD179WfxwrmMHzOC6Kgosts70KRpS6pWV/5Av3P7FjdvKEer/rXVL2rxzF6wDAeHL3u6rUnq/ln+zv4ZrNo/L4OD1WojQ0JDaf9Oc9p398/YUZ/XDzwzWRXMTfG9S1Tvc47tC8Cjxeu52KpPeqtluMy6ThsaGXFw10aWz5tAUlIittkcKFisLDXqN/+ifJQsXZ6Id+6hnl4+9B86Ru28efcaHZAzN91+H8CKJfNYpuEeqqurx4P7dziwdwcxr6Oxsc1GvvyFaPRLKwwMlLXfRsZGnDx2iFXLFhAfF4eNrS1BBYvw/U9NVWn+i1IysUVnvz596N5dvbXa+5Ubb/Xu3VtjN6l3vT8K/6favHkz+/bt49y5L5sm5C0dRVZrIPsfoaOj89kj7PyXnLie/nDB/0UKRdbp8GxuEJvZIWhVdKJJZoegVUZ6Waf5jb6OdudNy2yWOuGZHYJWxel8eICv/xIThfbmif0WXA6sndkhaJXtO80u/+ssDGIyOwStyu2jvYco2tZ/4ec1qdWm4c0/vaAcHBxMSMiHR7728vJi6dKl9OjRg7B3+kYnJSVhbGzMmjVrqFcv7eix3bp1Y/LkyWpdKpKTk9HV1aVUqVIcOHDgk2KUGj4hhBBCCCHEN0WRmVV8nyF79uxkz579o+mKFy9OeHg4//zzDwULKgfb2bdvHykpKRQtWlTjOr1791ZN4fZWnjx5mDBhArVq1frkGKXAJ4QQQgghhBAZKDAwkKpVq9KmTRtmzpxJYmIinTp1omHDhqoROp88eUKFChVYvHgxRYoUwdHRUeNALW5ubhpHzk+PFPgyibSkFUIIIYQQ4v/HsmXL6NSpExUqVFBNvD558mTV54mJidy4cUM1WJW2SIFPCCGEEEII8U3JinUjtra2H5xk3cPD46OVQl9SaSTz8AkhhBBCCCFEFiU1fEIIIYQQQohvSsp/ZNCW/wKp4RNCCCGEEEKILEpq+IQQQgghhBDfFBngUHukhk8IIYQQQgghsigp8AkhhBBCCCFEFiVNOoUQQgghhBDfFEVKZkeQdUgNnxBCCCGEEEJkUVLDJ4QQQgghhPimpMigLVojNXxCCCGEEEIIkUVJgU8IIYQQQgghsihp0imEEEIIIYT4psg8fNojNXxCCCGEEEIIkUVJDZ8QQgghhBDim5KSIjV82iI1fEIIIYQQQgiRRUkNnxBCCCGEEOKbIl34tEcKfCJDhMWaZnYIWuViEZrZIWjNy1irzA5Bqw6ezVoNFYyNzTI7BK3JbqeX2SFo1Y1b5pkdglad2H46s0PQmvkTPTM7BK2yvZh19g1AaN7CmR2C1uRf0zGzQ9Auny6ZHYH4F2StX0pCCCGEEEIIIVSkhk8IIYQQQgjxTVHIoC1aIzV8QgghhBBCCJFFSQ2fEEIIIYQQ4puSIqO2aI3U8AkhhBBCCCFEFiUFPiGEEEIIIYTIoqRJpxBCCCGEEOKbIoO2aI/U8AkhhBBCCCFEFiU1fEIIIYQQQohvitTwaY/U8AkhhBBCCCFEFiU1fEIIIYQQQohvilTwaY/U8AkhhBBCCCFEFiUFPiGEEEIIIYTIoqRJpxBCCCGEEOKbIoO2aI/U8AkhhBBCCCFEFiU1fEIIIYQQQohvikIhNXzaIjV8QgghhBBCCJFFSYFPCCGEEEIIIbIoKfB9Y+7fv4+Ojg7nz5//prbn4eHBxIkTtRKTEEIIIYQQH5KSosi0V1YjffjEN0WhUPD3mmmc2LeW2NdRePrn54dWA8iewz3dde5cO8O+LQt4dO8qkWHBtOwxibyFK6iluXBqN8d2r+bRvavEREfQc9RaXDwCtBr731vXs3ndSsLDQnH39KbVr13x9c+Zbvpjh/ezcuk8gl88J4eTM01a/EqBwsU1pp01dSy7/95M8zadqFn3RwAuXzzH4D5dNaYfNWEWPn6BX5UfhULB1lXTObpnPbExUXj5B9GobT/sP7AvAA7+vZLdmxcRGf4KF3c/fmzVGw/fPACEvHzCgA7VNa7XuvsYCpSozPH9m1gybaDGNH/N24eFld1X5etDygfpUdBXF2NDePhSwZYTSYRGfdq6pXLrUqmgPsevJvP36eQMizE9pXPrEOSlg5EBPH4FO/5JISw6/fQFvHUo4KODlZnyfXAEHLmSwt3n6umc7aBMHl2c7EChgBdhsPJQCkkZmEWFQsHRbZO5dHQN8bGROHkVoFLDwdjYe6S7zsmds7h5fhehL+6ib2CMs1d+Stftia2DlyrNyom/8PjWKbX18n33E5UaDc2orKhUKWxA0UB9TIzg3vMU1h9K4FXEp/2oKJdfnxrFDDl0MZHNRxNVy9vXNsLbWU8t7fEriaw7lPj+JrSqVWMPalV2xMJMn0vXIhk7/RaPn8Wmm37N3KLkcDBOs3z9tieMn3lb9T6XvyVtf/Egp78lKSkKbt2NpvugSyQkpGgl7m1bNrF+3RrCwkLx9PSmXfuO+Pmnfx84cvggS5cs4uWL5zg5OdO8ZWsKFS6q+jwsLIyFC+Zw/uw/RL9+Te7ceWj3a0ecnF1UaaZOmciFc2cJDQ3B2NiEwJw5adaiNa6ubl+dH4VCwYYVszi4eyMxr6PxDchL01974+j04W3v2b6avzcsJSI8BDcPX5q0+R0vv1wAREdFsGHFbK6cP0HIqxdYWFpToGhZ6v/8K6Zm5so0keHMnDCAx/dvEx0VgaWVDfmLluH7Jh0wMTX/6nx9LtvvCuHVoxVWBXJj7GTPmQYdeLF5778ex8esPHaJRYfO8SoqBr8cdvSuU5o8rg4fXe/v87fovWIX5XJ6MrGZ+v3z7otQJv59nH/uPiUpJQVvB1vGNalKDhuLjMqG+I+SAp/4puzdPJ9DO5bRuMOf2GV3Zvvqqcwc2Y7eYzdhYGikcZ34uFic3P0pWrYe88d305gmIS4Wz4ACBBWvwqrZg7Ue99FDe1k0ZxptO/XA1z8n2zauYfiAnkyevQwra5s06a9fvcTE0UNp3LwtBQsX5/DBPYwe3o/Rk+bi5uGllvbksUPcun4VW7tsasv9A3MzZ8kGtWUrl87j0vl/8Pb9+sLs7o0LOLB9BU07DcPO3pmtK6cxZVh7Bk7ckO6+OHN0B+sWjaVR2/54+OZh37ZlTBnensGTN2FhZYeNnSMj56jfiI/uWcvuTYvImf87AAqWqELOoJJqaZZMG0BiQkKGFva+y61L0UBdNhxJIixaWfhrWsmAqRsTSfrI700nOx0K+enxPFQ7P0w/V7EAHQr56rDlZArhr5UFtIZldJn9dwrJ6YQUGatg/0UFoVGgA+Tx1OGH73SZtyuFV5HKNM528FNpXY5fU7DrrIIUBThY65DR/ehP7Z7DuQNLqPbLKKyyuXBkyyTWTm1FiwHb0TfQfOw9unWK/KUb4+ieh5SUZA5vHs+aKa1oMWAbhkamqnR5S/5IyRpdVO/1DU0yNjNAuSB9vsujz8p9CYRGplCliAFtahoxZmXcRwvOrtl1KZ5Tn6evNO/IE1eT2HkqQfU+IUmbkafVuIEr39d05s+J13n2Io7WjT0YPzQPTTqcJiFR84HRpvtZdN9pT+TlbsbE4fnYfyRYtSyXvyXjhuRh6dqHTJx9m6RkBb6e5loblv3wwQPMnTOLjp264BcQyOaN6xk4oA8zZ8/HWsM1+trVK4z5awTNmreicJGiHDywnz+HDWbi5Om4e3iiUCj4c9gg9PX06TdwKKampmzcsI7+fXsxfdZcjI2Vx5WPjy9ly5Ynu709UVFRrFi2mIH9ezN3/hL09PTSfO/n2L5hMbu3rqJN18Fkd3Bi/fKZjBvSmT+nrMYwnWv0ySO7WDl/Is3a98bLLze7Nq9g7JDOjJq2FktrW8JDgwkPDean5l1xdvXiVfAzFs0cRXhoMJ16/QWAjq4uBYqUoUHj9lhY2vDy2SMWzx7N66hIfu0x/Kvy9CX0zEyJvHiDRwvXUWjttH/9+z/Fjgu3GLv1CP3rlSWPmwPLjlyg/bwtbOr5M3bmpumu9yQ0kvHbjlLAM0eazx6FRNB85nrqFc5J+0pFMDc25M6LUAwNvu64+pbIoC3aI006M8GOHTv47rvvsLa2xs7Ojpo1a3Lnzp1001+5coWaNWtiaWmJhYUFpUqVUqVPSUlh6NChuLi4YGRkRFBQEDt27Eizjbt371KuXDlMTU3Jly8fx48fV/t83bp15MqVCyMjIzw8PBg3bpx2M/0JFAoFh/5eQuV6bclTqDxO7v407jiCiLCXXDqT/tO6nPlLUeOnLuQtUjHdNIVL16Zqg/b45dZcg/a1tmxYTcWqNSlfqTqubh607dQDI2Nj9u3apjH99s1rCSpYhDoNGuHi5kGjX1rj6e3H31vXq6ULeRXMvJmT6Pr7APT01J/PGBgYYGNrp3pZWFpx+sQRylWqjo6OzlflR6FQsG/bMqo2aEO+IuVw8fCjWefhRIQFc+HUvnTX27dlCSUr1qd4+brkcPWmUdv+GBoZc2zfRgB09fSwssmm9jp/ch8FSlTG2ER50zM0Mlb7XFdXlxuXT1GiQt2vytPHFA/U49DFZK4/UvAiTMH6I0lYmEKA24cvk4b68H0pfTYdTyI24YNJM0wRPx2OXlVw66mypm7LyRQsTMDfOf3j4PZTuPMMwqIhNBoOXlKQkATOdqnrVMyvy5lbiv+1d9dxUaR/HMA/u3SXoHSntK2Hhe1ZeJ6thMXZrWfHqT8VVE49z8bu9lTsQMUEDJAWVFCku3Z+fyCL64JSOuz6fb9evM6dmV0+zzEM88xTuBfO4GMmkJoFhCUwlVYi6wLDMHhyfQ9advOGmUMnaOpaocfI1cjO+ICokCuVvu+3CTtg28oNDXTMoaVnhe7DVyEr7R3ex78QOE5SWhYKKpr8Lxm5798i4WIvhSuPi/AirgSJqQwOXSuEsjwHtsZfvzGTlgSGdJLG0RuFyCuo+MansJhBVh74XwXft3EPA3rrYs+R17gTlILouBwsXxcODXUZuLRsUOl70jOLkJpe/tW6mQbevMvD0+cZ/GMmjTLFsbNvse9YAmLjc5HwNg/X7iSjqLhubvhOnTyOrt26o1OXbjAwMMQfEyZDRkYGlwMuVXj8mdMn4dykGdx++x36BoYYNsIdpqZmOHf2NADg3du3eBUeBu8Jk2BhYQk9PX38MX4SCgsLcfPGdf7ndOveE7Z29mjYsBHMzMwxbIQHPiYn48OH97UqD8MwCDh7EL1/94Rzi3bQNzLH6MlLkJb6EU+Cblb6vkunD6Bdl75wce0NXX0TjPSeC2kZWdy6egYAoGdoholzVsOpeVtoaevBxr4Z+g/1RvDD2ygpKX2aoKCojI7df4OxmQ0aaGnDxqE5XLv/hoiXT2tVpppKvnQLEYvW4/3pyq8PbNt7OxhuzRujbzNrmDZUx/x+7SErJYlTD8MqfU8Jj4c/D12Gd+fm0FNXEdr/98X7+MXSEFN7tIa1rib0NVTQ3sb4qxVI8vOiCh8LcnJyMG3aNDx69AhXr14Fl8tFv379wOMJ30W9ffsWbdu2hYyMDK5du4bHjx/D09MTxcWlF94NGzbAx8cHa9euRWhoKLp27YrevXsjMjJS4HPmzZuHGTNmIDg4GBYWFhg8eDD/Mx4/fozff/8dgwYNwrNnz7B48WIsWLAAu3fv/u7/Lz6X8uENMtM/wsKuvFImJ68EQzN7xEWE/NAs1VFUVISYqAjYOzblb+NyubBzbIJX4S8qfE9E+AvYOzYR2Obo3BwRnx3P4/Hwt89y9Ok/CPqGxt/M8SjoDrKzMtGxc/calqRcyoe3yEz/CCv78u5LcgpKMDK3Q0xEaIXvKS4qQnxMGCztW/K3cblcWNm1ROyrit8TH/0Sb+JeoXXHfpVmCbp5FtLScnBq2bmGpfk2NUVASZ6D6HflN5cFRcDbZAb6ml+vPPdsIYGItzzEJLLzJFJVAVCU4yD2vWD2dymAbuX34AI4HMBGnwMpSeBtSunnyMuUVv5yC4ARrlxM7sPFsA5c6FXxM2sqI+UNcjKTYWjZmr9NRk4J2kYOeBdb9RvKgrzSvriyCoI3SmEPz2LTrBbYtfxX3Drtg6LCyrsi1gV1JQ6UFTiIfFPelJdfCMR/4MGw4df/BLu1lUbY6xJEvq28hu1sLokl7nKYMVAW3VtIQeo79tvRaSiLBuoyeBicxt+Wk1uClxGZsLVSrtJnSEpy0KVDQ5y/Ut53WFVFCo2tlJGWUYh/VjvizJ5W+HulA+xtqvaZ31JUVISoqAg4ODrzt3G5XDg6OuNV+MsK3xMe/hKOTs4C25yaNEV4eBj/MwFAWlpa4DOlpKTw8uXzCj8zPz8PVy5fQsNGjdCggWatypT8/i0y0lJgY9+cv01eQRGmFo0RXcn1trioCHHR4QLv4XK5aOzQHNGvnlX6vfJysyEnryD00LFMWmoyHt27Dktb5wr3/+yKiksQ9jYZLc3Lu/pyuRy0NNNDaHxSpe/798pDqCnKwa258NAQHo/B7fDXMGyginHbz6D90p0YuvEorr2I+S5lYAvDY1j7EjfUpZMF/fv3F3i9c+dOaGpq4uXLl1BUFHzavGnTJqioqODQoUOQkpICAFhYWPD3r127FrNnz8agQYMAAP/73/9w/fp1rF+/Hps2lXdtmDFjBnr27AkAWLJkCRo3boyoqChYWVnB19cXrq6uWLBgAf/zX758iTVr1sDd3b3Oy1+ZrPSPACDUbU9JRQOZn/bVR1mZGeDxSoS6bqqqquNtQnyF70lPS4WqqrrANhVVNaSnpfJfnzp2AFwJCfTo/VuVclwNOA8H52bQaKBVzRIIy0gr/f+trCr4s1D+ys8iOysNPF4JlL/8+alq4P3b2ArfE3jtJBrpmcDUyrHSLHevnUJTl+6QlhEeA1RXFOVKK3XZ+YIX+ex8Bopf6fFna8SFjgYH/577zv3ovkLh0/+WnHzB7Tn5DH9fZTRVgJGuXEhKlHYFPB5Y3p1T9dOl6JfGHFwLKW31tDPiYEh7LrZd/Pr4wNrIySzt5ievLHgeyStpICezatcBhsfD9eMroGviDE2d8uulddNfoayuA0UVLSS/fYVbp9ci7X0s+ozZWHcF+IKSfOm5lZX3xbmVy/D3VcTRTAK6DbjYcDy/0mOeRBYjLZtBZg4DbQ0ueraUgpYqB/6Xvk9Ts7paaeUmLV2wGTEtvZC/71vatmwARQVJ/He1/EZXt1Hpieo52AibdkYjMjYH3To2xPrlDhgx/tFXxwdWRWZmBng8HtTUvrxGq+FNQkKF70lPS4OqqqrQ8WXXaD19fWhqasF/1w5MmDgFMrKyOH3qOD5+TEZaaqrA+86fO4PdO7chPz8funr6WPbX//h/z2sqIz0FAKBSwTU6Iy2lwvdkZaV/+lul/sV71JH4Jq7i92Sm48yRHWjXRfih3D8+8/A06CYKCwvg2MwFHuPn16Ak4i8tNx8lPEao5U1DSR6xyWkVvudJ7DucfBiGI1MGVrg/NScXuYVF2HnjCSZ0bYEpPVoh8FU8pu29gO1j+qKpiW6dl4OINqrwsSAyMhILFy5EUFAQPn78yG/Zi4+Ph42N4JOc4OBguLi4VPjHITMzE+/evUObNoLjndq0aYOQEMEWMXt7e/6/tbVL+4J/+PABVlZWCAsLQ58+fYQ+Y/369SgpKfnmOIOCggIUFBQIbCsq5FY6zqvMozvncGTbEv7rMbM3f/X4n0l05Cv8d/oYVvttr1L3zJSPHxDy5CGmzVlco+/34NZ5HNy6jP/ae+73uwEuU1iQj0e3L6D7b6MrPSbmVQiS3sTAfeJfdfq97Y256NWq/Lzef7X6FTZleaBHcwn4Xy7+5hi/utTYkIPuTcrPiSO3a/7NU7KAHQE8yEgBVnoc9GrOxb7rpZW+su/wNJpBaGxpZeV9MAOjhhw4GHNw41ndPAF9+eAMLh9cxH/t9se/tf7MK4eX4OO7SAyedkBgu8Mv5TdPmrqWUFTRxBE/d6Qnx0NVs/aTaACAk7kEfmtXXvnZcb7gK0dXTEWBgz5tpLH17NfH+AWFle9MSi1BVi6Dcb1loaFchJTM2v98OrfTwszx5RXmWUsrbwWqqp6dGyHocSpSUssrpWXXuNMXE/Hf1dKujpEx2Whir4qenRvh3z0VPzBik6SkJP6cvwh+G3wweKBbaYuhkzOaNG0mNMa1fQdXODk5IzU1FSdPHMX/Vi7H6rXrBVoHv+XuzQvw/2cl//XU+evqqiiVysvNxrplU6Cjb4y+g8YI7R/sORV9Bo7G+3evcXTvJhzauQ4jxs357rnEXU5BIeYdvoJF/TtATaHiJ45lDVAdGhtjuIsjAMBKRxMhr5Nw9P4LqvARIVThY0GvXr1gaGiIbdu2QUdHBzweD7a2tigsFH4qKydXNxMKfF5hLPvjWlEX0ppYuXIllixZIrBtyJj5GDau4pkWy9g26QBDs/KKaHFRafmzMlKgolbe3SUrIwW6hpZ1kvV7UFJWAZcrgYx0wSd16empUFVTr/A9qmrqSE8XfAqckZ7GPz7sRQgyMtIwzn0Afz+PV4I9Ozbj/Olj+GfXEYH3Xrt8AYpKymja4pcalcG+WXv+TJoAUFxc+rPITBf8WWRmpEDPqOKfhaKSGrhcCWRmCD5dzkpPgbKqcD/Ap/cvo7AwDy3a9ao0V+DVE9AzsoSBaeWzndZEeAIPbz6bBENCovR3QlGWg+zPWmIUZTlITK34xllHgwNFOQ7G/Vp+GZXgcmDYkEFzKy6W7iv6LpObRL5l8C6l/IMlPvUKVJAVbOVTkOXgffrXA/B44LfUJaUx0FbnoJkFBxceMcj+9FllLX5lPmYyUFaobSnKmdl3hLaRA/91yadzLzczBYoq5a3VuVkp0NL79mREVw4vRczzGxg4dR+U1Bp99dhGn75vWvLrOqvwvYwrge/78h+E5KfnCkpyHGTlfnZuyXMqnYhFT5MLJXkOpgwob6KV4HJgrMNFG1tJzNmaV+G5Ff++9PM0VDh1UuG78yAFLyMe8V9LS5WebGqqUkhJK/97paYqjaiYbzf5NtSUQVMHNcxbKdjVveyz4hJyBLa/fpOLhppff3BYFcrKKuByuUhL+/IanQY1deEJWwBAVU0N6enpQsd/fk03M7eA38Z/kZOTg+LiIqioqGL6lIkwMzcXeJ+CggIUFBSgo6sHSytrDP7dDffu3kG79h2rXAan5m1hamHLf1329zIjPQWq6uXX18yMFBgYWwi9HwCUlFQ//a0S/NuTmZEKFTXBlsK8vBz4LJkEWTl5TJyzBpKSwreLqmoNoKrWADp6RlBQVMGKP0ej9++jBPIQQE1eFhJcDlKycwW2p2TlooGS8Hi7hJQMvEvLwiT/8jkAeJ9+4Z3nbsbpGUPRSEURklwuTLQE7zGMtdQQHJf4HUrBDnHsWskWqvD9YCkpKXj16hW2bdsGFxcXAMCdO3cqPd7e3h7+/v4oKioSauVTVlaGjo4OAgMD0a5dO/72wMBANG/e/MuPqpS1tTUCAwMFtgUGBsLCwqJKs4jNnTsX06ZNE9h2I+zbw0Nl5RQgK1d+58gwDJRVGyDy+X3+kgn5udl4HRWKNp1/r0pRWCElJQUTMws8C36M5q1Kf6Y8Hg/Pgp+g+68Vj02zsGqMZyFP+EssAEDI04ewsCqdGrtdx64CYwIBYPnCGWjboQs6dBaclplhGFy//B/adexa4R/lqqjsZ/HqWRD0jUt/Fnm52YiLfIa2XQZU+BmSUlIwMLHGq2dBcGxeeiPD4/Hw6lkQ2nUfJHT83aunYN+0PZRUKq4U5+fl4sndAPQZOqnC/bVRWIwvlltgkJXLwESbg6S00j8wMlKAriYHD15VfFMek8hg42nBrm392kggOYPBnee87zaTZWExUPjFvXV2XmnL24dPFTxpSUBHA3gSVcEHfAWHU16BzMgBsnIZaHwxu7e6EgfRdTheUVpWEdKy5V3ZGYaBgrImXr+6By390qVFCvKykRgXAkeXwZV+DsMwuHpkGaJCLmPglL1QbaD/ze+d/KZ0PJaiSu3GU32uoAgo+GK2yswcBuZ6EniXUtqSLCMFGGhxce9FxS3LUW9LsPawYDfGgR2k8SGNwfXgyh8k6DQo/eFl5dTNzycvrwRv8wSbGD+mFqCpgxqiYksrZ/JyErCxUMap/9598/N6dmqEtIxC3Hso+FAo8X0+klMKYKArePOrryOH+48r7vJWHVJSUjAzs0BoyFO0al3aI4bH4yEk+Cl69upT4XusrGwQEvwUffq68bcFP30CKyvh5W4UFEqvne/evkFUVASGjhj5lTQMGDD8MYBVJSenALkvrtEqahp4GfoQhialD+HycrMRHfECHbpVPAxAUkoKRqZWeBn6EE1atgdQ+v/hZehDuPYov67n5WZj7ZJJkJSUwuR5vpXO+ClQKqb0OllUxNLMVfWYlKQErHU1ERT1Bh0bl87CzeMxCIp6g0Gt7YSON9ZUw7Gpgn8zN10KQk5BIWb1dkEjFUVISUqgsZ4W4r7oEvr6YzotyUAqRBW+H0xNTQ0aGhrYunUrtLW1ER8fjzlzKu8CMWHCBPz9998YNGgQ5s6dCxUVFdy/fx/NmzeHpaUlZs6ciUWLFsHU1BSOjo7YtWsXgoODsX///ipnmj59Opo1a4Zly5Zh4MCBuHfvHjZu3IjNm6vWxVJGRgYyMoJ/EKSkqz9VHIfDQdvuwxFwcis0GxlCXat0WQYVNS3YNS1fV2/TMi/YN3OFS7chAICC/FwkJ5WPlUv98BZv4sKhoKgCtQal3VdzsjOQ9jERmWkfAAAf3pV2EVJWbVBh61N19er3Ozb6roSpuSXMLKxx/vRRFOTn8Stnfj5/QUOjAYa6jwUA9Oj9GxbNmYQzJw6hSbNWuHPrKmKiXmHcxJkASlsNlZQFJ5uQkJCEqpo6dPUEWyKehTzBh/eJ6NT111qXowyHw0HHnkNx4fg2aGkbQkNLF2cPbYKKmiYcmpc/ld6weDQcWnRE++6lN+Idew3Hno0LYGjaGIZmtrh+fh8KCvLQqkNfgc//kBiPqLDH+OPPyqfQfnz3Ini8EjRv27POyvU198JK0M5eAilZDNKyAFcnCWTlAuHx5RU+9y6SeBnPw4NwHgqLwa9glSksBvIKhLd/bw8iGLSx4SAti0F6DtDWlousPODV2/IcQ9pz8eoNg8dRpdva23EQncQgMweQlgIaG3BgqAUcvFn+nvuvGLg0Lq1Ivk8H7Iw40FACTgR+v/JxOBw4dxiB+xf/gZqWIVQ09BB4bgMUVbRg5lA+E++RDSNh5tAZzu2HASjtxhn+6Bz6jt0MaRkF5GSUjgWUllOClLQs0pPjEfboLIwbt4OcgiqS377C9eMroWfWDJq6dbsm55duhxbBtYkUkjMYpGby0K25FDJzGTyPLa9Mje0lg+exJQh8XoyCIiDpi5blwiIgp4Dhb9dQ5sDJXAJhr0uQWwBoa3DRu7UUot+VVNoqXReOnnmLkQMNkPAur3RZhmFGSEktwO375eMr1y+3x617H3HifHklkMMBenRqhIvX3lc4y+uBEwnwGmKEqNhsRMZmo3vHRjDUk8f8VRVPqlJdffv1xzrf1TAzt4CFhSVOnz6J/IJ8dOrcFQDgu/Z/0NBogJEeXgCA3n36Ye7s6Th54iiaNmuB2zdvICoyAhMmTuF/5p3bN6GiogpNTS3ExcVi27+b0aJlazg7lz6sS0pMxO1bN+Dk3ATKKqpI+ZiMY0cPQUZaGk2bVf2hbEU4HA669BqMs0d3opGOPhpo6eLEgS1QU28A5xblD4D/t8AbTVp2QKeepQ8Xu/YZgm0blsDYzBom5o0RcPYgCvLz4OJa2tMiLzcbaxZPRGFBPsbOWYq83Gzk5ZY+YVJWVgNXQgIhjwKRmZECYzMbyMjK421CDI7s9oO5tQM0G+rUqlw1IaEgDwWz8r+L8sZ6UHawQmFqBvIT6kdr13AXRyw4chWN9bRgq6eFfXdCkFdUjL5NSx8gzDt8BVrKCpjcvRVkpCRh3uiLsfBypfdYn28f2c4Jsw5cQhNjHTQz1UVgRDxuhcVh+5i+P6xc3xuPlmWoM1Th+8G4XC4OHTqESZMmwdbWFpaWlvDz80P79u0rPF5DQwPXrl3DzJkz0a5dO0hISMDR0ZE/bm/SpEnIyMjA9OnT8eHDB9jY2ODMmTMw/6JLydc4OzvjyJEjWLhwIZYtWwZtbW0sXbr0h07YUsa1tycKC/JweNviT4t9O2PsnC0C4wE/vk9Adlb5U6346OfYtMyT//rU3tUAgGZt+2DoH6Vjv54/uo6DW8oHlO/xK61Yde3vje4Dxtc6d5u2rsjMSMehfTuRnpYKIxMzzFu6lt/952Pye3A/G4tnZWOHyTMX4tDe7Tjgvw3aunqYNf8voTX4quJawHlYWttCV//rC6JXV+e+HigoyMOBf5ciNycLplZOmDB/s8DPIvn9G2RnpvNfN23TDdmZaTh3aHPpwutGlpgwb7PQ5C/3rp2CqkZDWDtUvkzG3aun4NjcFfIKdTNT37fcec6DtCQHvVtJli68/p7B3iuCa/CpKXGgIFO7JS++h/vhDKQlge5NSxeNT0gGDt8UXINPVbF05s0y8rJArxZcKMqWtkp9SAcO3uQh7rPZ4h9GMJDkli7PICtdfky6YM+7Ote882gUFeYh4MBCFORlQte0CfqP3y6wBl/6xwTk5ZRfB0JuHwQAHF4/XOCzug1bCdtWbuBKSuF1+D08vr4HRQW5UFLThoVjF7Ts9sf3LQyA68HFkJbi4Ld20pCTLl14fdu5AoHxeRrKHCjIVv3cKi4BzPUk4GIvBWlJID2bwbOYElx5/H3XZdh/PAGyshKYNcECigqSePYyA9MXPRNYg0+3kRxUlQV7pDR1VEMjLVmcv1zxrIRHz7yFjDQXE0eZQllJClGx2Zi6MBTvkiqftKY6XNq1R0ZmOvbv9UdaWhpMTEyxZOkK/kQuyckfwOGW//+3tmmMGbPmYt+e3dizexd0dHUxb8FiGBqVz5icmpqKHdv+Le0aqqaOjq6dMXDwUP5+KWkpvHjxDGdOn0B2djZUVdXQ2NYOq302VLj2X3X16DcCBfl52LV5BXJzsmFh7YDpC/0EWuQ+JL1F1mfX6Ba/dEFWRjpOHvwXGWml3T+nL/LjT/4SF/0KMRGls4zO8hbsobLm39PQbKgDaRkZ3Aw4hQM71qG4uAjqDRqiScv26OnmXusy1YRKE1u0urqX/9pm7Z8AgIQ9JxDqNZeVTF/q5mCOtJw8bA4IwsesXFjqNMBmz1+h8alLZ1J6lsA9QlW42ppgfr922Hn9Cf535jaMNFXhM6wbnI1/fKWb1H8chlY1JN/BhaffeTGoH0xPKfXbB4mID3nC6/mIsptPxGt1GVlZ8Vk0V1NDfMoCAK8ic799kAi5/99DtiPUmZ3rv710jShJKVRlO0KdSrVvxnaEOuN6tPYPiesT2b51P2yirrgvrt16lbWxe3FD1r7390AtfIQQQgghhJB6hSZtqTvi9WicEEIIIYQQQggftfARQgghhBBC6hUadVZ3qIWPEEIIIYQQQsQUVfgIIYQQQgghRExRl05CCCGEEEJIvcKjSVvqDLXwEUIIIYQQQoiYohY+QgghhBBCSL1CyzLUHWrhI4QQQgghhBAxRS18hBBCCCGEkHqFlmWoO9TCRwghhBBCCCFiiip8hBBCCCGEECKmqMJHCCGEEEIIqVcYHo+1r+8lNTUVQ4cOhbKyMlRVVeHl5YXs7Oxvvu/evXvo2LEjFBQUoKysjLZt2yIvL6/K35cqfIQQQgghhBDynQ0dOhQvXrzA5cuXce7cOdy6dQtjxoz56nvu3buHbt26oUuXLnjw4AEePnyICRMmgMutejWOJm0hhBBCCCGE1CvitvB6WFgYLl68iIcPH6Jp06YAgL///hs9evTA2rVroaOjU+H7pk6dikmTJmHOnDn8bZaWltX63tTCRwghhBBCCCGfFBQUIDMzU+CroKCgVp957949qKqq8it7ANCpUydwuVwEBQVV+J4PHz4gKCgIWlpaaN26NRo2bIh27drhzp071freVOEjhBBCCCGEkE9WrlwJFRUVga+VK1fW6jOTkpKgpaUlsE1SUhLq6upISkqq8D0xMTEAgMWLF2P06NG4ePEinJ2d4erqisjIyCp/b6rwEUIIIYQQQuoVhmFY+5o7dy4yMjIEvubOnVthzjlz5oDD4Xz1Kzw8vEb/D3ifJpAZO3YsPDw84OTkhHXr1sHS0hI7d+6s8ufQGD5CCCGEEEII+URGRgYyMjJVOnb69Olwd3f/6jEmJiZo1KgRPnz4ILC9uLgYqampaNSoUYXv09bWBgDY2NgIbLe2tkZ8fHyV8gFU4SOEEEIIIYTUM4yITNqiqakJTU3Nbx7XqlUrpKen4/Hjx2jSpAkA4Nq1a+DxeGjRokWF7zEyMoKOjg5evXolsD0iIgLdu3evckbq0kkIIYQQQggh35G1tTW6deuG0aNH48GDBwgMDMSECRMwaNAg/gydb9++hZWVFR48eAAA4HA4mDlzJvz8/HDs2DFERUVhwYIFCA8Ph5eXV5W/N7Xwke+ihOGwHaFOPX+v9e2DRIR5g3S2I9SplnaKbEeoUx+zJNiOUGcaa6eyHaFOKcmrsR2hTmlpubAdoc5IIpbtCHVKSSqX7Qh1yunoeLYj1JmrAzaxHaFO9SyaxHaESolKC1917N+/HxMmTICrqyu4XC769+8PPz8//v6ioiK8evUKubnl14ApU6YgPz8fU6dORWpqKhwcHHD58mWYmppW+ftShY8QQgghhBBCvjN1dXUcOHCg0v1GRkZgGOGK7pw5cwTW4asu6tJJCCGEEEIIIWKKWvgIIYQQQggh9QqP4bEdQWxQCx8hhBBCCCGEiClq4SOEEEIIIYTUK+I4aQtbqIWPEEIIIYQQQsQUVfgIIYQQQgghRExRl05CCCGEEEJIvUJdOusOtfARQgghhBBCiJiiFj5CCCGEEEJIvVLRAuSkZqiFjxBCCCGEEELEFLXwEUIIIYQQQuoVHo8WXq8r1MJHCCGEEEIIIWKKKnyEEEIIIYQQIqaoSychhBBCCCGkXqFlGeoOtfARQgghhBBCiJiiFj5CCCGEEEJIvcIwNGlLXaEWPkIIIYQQQggRU1ThE2OLFy+Go6Mj/7W7uzv69u3LWh5CCCGEEELIj0VdOn8iGzZsAMOUD4Bt3749HB0dsX79evZCfYFhGFw6thH3rx1DXk4WjC2d0N9zITS1DSt9T3TYI9w4txNvYl4iMz0Z7tP8YNfMlb+/pLgIF474ISz4NlI/vIGsnCLM7Vqh56CpUFHX+q5luX7qbzy5dRT5uZnQN3PGryMWQaOhUaXviXv1EHcv7sC7uBfIzkjGwAkbYe3cSeCY7IyPuHxsLaKfByI/LwuGFk3RY+j8r37utwScP4bzJ/chIy0VBsZmGDlmOkwtGld6fNCdqzi6fys+fkhEQx19DB45Ho5NWwuU/fiBbbgecBo5OdmwsLaDp/csNNIxAAC8fPYYf80bX+FnL/XZCVNzGxQWFmDn5v8hNvoV3iXEwalZG0ybt7rGZfwSwzC4eGwT7l07hvycLBhZOmGA54JvnmvXzu3in2ue0zYInWv/HfkbYcG3kfLpXLOwa4lff8C5duuMH57ePoqCvEzomTqj+9DFUP/KORF44V+8ehKAlKQYSErLQs/ECR37z4BGIxMAQF5OOm6d+RsxL+8gMzUR8orqsHDqhHa9J0NWXqnOsgecP4ZzJ/aXn3tjp8HsK+fe/TtXcXTfVnz8kIRGOnoY5D4eTp+dew/u3sDVCycRGx2O7KxMrNjgDyMTC4HPuHrxFO7eDEBc9Cvk5eVi28EAKCjWXZm+VHYteHyz9FpgYO6MX4cvgkYjo0rfE/fqIQIv7EDi6xfISk/GoInC14KC/BxcOeqD8KdXkZudDjVNPbToNBzNOgz6rmV5cuVvvHp0FIV5WWho6ITWfRZBpUHlZQm7fxBhDw4hO+0tAEBVywxOHf+AvmXbCj8/wH8s3kTchuuwv2Fk00nomLp09uxZHDt+HGlpaTAxNoa3tzcsLS0rPPb169fYu3cvIqOi8OHDB4wZMwb9vniIevjwYQTevYs3b95AWloaNtbW8PT0hJ6eXp1nv3DuJE4fP4T0tFQYGZvCa9xkmFtaV3r83dvXcXDfTiS/T4K2ji6GeYxDk2Yt+fv/9l2JG1cvCrzH0bk5Fixbw3+9cslcxMVGISM9HQqKirB3bILhHuOgrtGgzst36O4z+N96io9ZubDQ1sCcPm1hp9/wm++7EByJOQcD0MHGGOtH9hDYF/M+Fesv3MPjmHco5vFg2lAdPsO6QVvt+/3+V4f6L01hMt0LKs62kNXRwqP+f+D9matsx2INTdpSd6iF7yeioqICVVVVtmN81fWzO3D74n785rUIk5cdhLSMHLauGoOiwoJK31NYkAcdA0u4ec6veH9hPt7EhqFzv3GYuuIo3KdtQPK7WOxcO+F7FQMAEHhhO4Ku7MWvIxZj1PwjkJaRw16fUSgqqrwsRQV5aKhvhZ7DFla4n2EYHNo4HmnJbzB40maMW3QCqho62LPWE4UFuTXKee/2ZezfsQFug0Zh+Tp/GBiZY9WiKchIT63w+IiwUGxcuxDtO/fCX+v90bRFW/iumIWE19H8Y86d2ItL547Aw3s2lq7ZDhkZOaxaNAWFn36OFlb22OR/XuCrfZfe0GyoAxOz0hsWHo8HaWkZdP11AGwdmtWobF9z7exO3Lq4HwO8FmLKsgOQkZHDllVjv3mu6RpYor/nvIr3F+bjTexLdO43FtNXHIHHtPX48C4O27/zuXbv0jY8vLYX3YcthvvcI5CSkcPBDV4o/sq5Fh/xAE06DIX73CMYMmUXSkqKcWC9F/88ykr/gKz0D3D9bTbGLDqHXh4rEfP8Ns7vqbjsNcp9+wr2bfeD22Av/LV+NwyMzbFq4dSvn3trFqF9l15YscEfTVq2he9fswXOvYL8PFja2GPwyIofKABAYUE+HJxbos+AkXVWlq+58992BF3ei14jFmP0giOQkpbDXt9vXwsafeVaAACXDq1C1PM7cBuzGhNWnEfLziPw375lCH967XsUAwAQems7Xt7bhzZ9FqO392FISsvj0q7RXz3XFFQaoVnXaegz/hj6jD8KHdOWuLJvAtLeRwod+yLQ/7tl/9LNmzexdds2DB0yBH///TeMTUwwf8ECpKenV3h8fkEBGmlrw8PDA2pqahUe8+z5c/T69Ves8/XFir/+QnFJCebNm4f8/Pw6zR546xp2b9uE34eMxBq/bTA0NsWyBTOQkZ5W4fHhL59j3eplcO3SA2v9tqF5KxesXj4P8XExAsc5NWmO7XtP8L+mzhI8/2ztnTB9zmL4bd2LmX8uw/vEd1i7ovJztKYuhkRi7bk7GOvaDIcm/Q5L7Qbw3nEWKdlf/zv3NjUTvucD4WysLbQvISUD7ltOwFhLDdvH9sWxqYMwxrUppKUk6jx/TUkoyCMz9BWeT1rCdhQiZqjCx5KcnByMGDECioqK0NbWho+PD9q3b48pU6YAADgcDk6dOiXwHlVVVezevZv/evbs2bCwsIC8vDxMTEywYMECFBUVVfo9P+/S6e7ujps3b2LDhg3gcDjgcDiIjY2FmZkZ1q5dK/C+4OBgcDgcREVF1UXRK8UwDG5d2ItO/cbCtmlH6BhaYvAfK5GZ9gHPH1X+hMva0QXdB06GXbOKnwTLySth3LztcGzVDVo6xjA0d0A/j3l4E/sCaR/ffbey3L+8B217jYOVkysa6Vui36j/ISv9A8KfXKn0feb2beHqNgXWTTpXuD/lfRzeRIfg1+GLoGtshwbaJug5fDGKCvPxLOh8jbJeOH0QHbr0QbtOv0LPwBief8yGjIwsbl45V+HxF88ehr1zS/zqNgy6+sYYMGwsjEwsEXD+GL/sF88cRt/fPdC0ZVsYGJvDe+oipKd+xOP7twAAklJSUFXT4H8pKqngSdBttHP9FRwOBwAgKysHzz9mo2PXvlBRU69R2SrDMAxuXtiLLv3GwO7TuTbkjxXITPuAZ98413oMnAT7r5xr3vO2w+nTuWZk7oD+Hn/iTexLpH1MrNMyfF6WB1f24Jee3rB07ISGelbo7bEaWekf8Opp5efa4Mk74NDaDZo65miob4VeHquQmfoOSa9fAAC0dC3wm/ffsHDoCDUtAxhZtUL7vlMQGXoNvJLiOsn+36mD6NC1N9p/Ove8/pgFGRkZ3Lxcybl35ggcnFugl9sw6Oob4fdhY2FsaomAc8f4x7h07A63wV6wdaz8IUH3PoPQe8AImFnZ1kk5vkbgWuBcei1wG/0/ZKVV4VrQv/JrAQAkRAXDoU1fGFu1gFoDPTRtPxAN9S3xNib0exQFDMPgxd09cOwwDoY2rlDXtkS7AauQm/UBr19WXhYD6w7Qt2wHlQZGUGlgjKZdpkBSWh4fEkIEjkt5F4Znd3bDpf9f3yX/l06ePInu3bqhS5cuMDQwwMQJEyAjI4OAgIAKj7e0sMAoLy+0b9cOUlJSFR6zfNkydO7cGYaGhjAxMcG0adPwITkZkZHCldvaOHvyCDp1+xUdO/eAvoERxk6YDhlZWVwN+K/C48+fOQanJs3Rt/9g6BkYYfBwLxibWuDCuZMCx0lKSUNNXYP/pagk2PLVq9/vsLBqDC2tRrCysUW/AUMR8eoliovr5ppQZu/tYLg1b4y+zaxh2lAd8/u1h6yUJE49DKv0PSU8Hv48dBnenZtDT11FaP/fF+/jF0tDTO3RGta6mtDXUEF7G2NoKMrXafbaSL50CxGL1uP96cp/n34mDI9h7UvcUIWPJTNnzsTNmzdx+vRpBAQE4MaNG3jy5Em1PkNJSQm7d+/Gy5cvsWHDBmzbtg3r1q2r0ns3bNiAVq1aYfTo0UhMTERiYiIMDAzg6emJXbt2CRy7a9cutG3bFmZmZtXKV12pH94gK/0jLGzLu5jIySvBwNQeryNDvvLO6svPzQaHw4GcvHKdfm6ZtOQ3yM5IholNeVczWXkl6JnY4010cI0/t6S4EAAgKSXD38blciEpKY34yMfV/rzioiLERr0SuDnmcrmwdWiGyPBnFb4nKvy5UIubvXNLRH06Pvn9O6SnpaDxZ8fIKyjC1KIxIl9V/JlPHtxCVlYG2nb6tdplqIkU/rnWir9NTl4Jhqb2iKvjcy2Pf659ny5D6R/fICczGUbWguearrED3sY8rfLnFORllb5XQfhGqUx+XjZkZBXBlaj9aAD+uefwxbnn2AyRr55X+J7I8OdCFTl7pxaIDK/4+PqAfy1o/MXPx9QeCVHBtfpsfTNHvHp6DZlp78EwDGLD7iPlfRxMbdvUMnXFstLeIC/rI3RMy39vpGWVoKlnjw/xVfu94fFKEB1yHsWFudDSd+RvLy7Mw43DM9G69wLIK2nWdXQhRUVFiIyKEhjnzuVy4ejoiLDw8Dr7Prk5OQBK/17XlaKiIkRHRcDesQl/G5fLhb1jE0SEv6jwPRHhLwSOBwBH52Z49cXxL54Fw2NIH0wcMwz/bvJBVmZGpTmysjJx68ZlWFrbQlKy7kYIFRWXIOxtMlqal3eD5XI5aGmmh9D4pErf9++Vh1BTlINbcxuhfTweg9vhr2HYQBXjtp9B+6U7MXTjUVx7EVPBJxEifmgMHwuys7OxY8cO7Nu3D66upeN//P39q93Hf/788i6MRkZGmDFjBg4dOoRZs2Z9870qKiqQlpaGvLw8GjVqxN/u7u6OhQsX4sGDB2jevDmKiopw4MABoVa/zxUUFKCgQLA7T1GhBKSkZSp5R8UyMz4CAJRUBMcCKKloIDP9Y7U+62uKCgtw/qAvHFv3gKy8Yp197ueyM5MBAIrKGgLbFZQbIDuj5mVp0MgEKho6uHLMF71GLoGUjBzuB/gjMy0J2enJ1f68rMx08HglUFEVbEFTVlXDu7dxFb4nPT1F6HgVVTWkp6WU7v/0X+Fj1Pn7vnTj8lnYO7WARoPvN87tc1mffgaKKoI/H0UVDWTV8bl27uA6OH3Hcy3n07mmoPTluaaB7MyqlYXh8XD58AromTpDS9eiwmNys1Jx5/xmOLoMrF3gT/jnnprwefLuzesK31PxuaeO9PSKz6v6IDuj4muBYi2vBQDQY+gCnNm9AD7T2oErIQkOh4Pe7stgZFn3XaABIC+rNK+comBZ5BQbIC/769ef1KQInN0yGCXFBZCSlkenYX9DrWH5Q8T751dBy9ARhjauX/mUupOZmQkejyfUNVNNVRVvEhLq5HvweDz8+++/sLGxgZGRUZ18JgBkZWaAxyuBqqpgdhVVNbxNiK/wPelpqVD54nhVVTWkp5V3n3Zq0hwtW7eFVqNGSEp8hwP+27B80SysWLsZEhLl3R737tyCC+dOoqAgHxZWNvhz0ao6KxsApOXmo4THCLW8aSjJIza54i6rT2Lf4eTDMByZUvH1KTUnF7mFRdh54wkmdG2BKT1aIfBVPKbtvYDtY/qiqYlunZaB1A0eLctQZ6jCx4Lo6GgUFhaiRYsW/G3q6uqVDhSvzOHDh+Hn54fo6GhkZ2ejuLgYysq1a7HS0dFBz549sXPnTjRv3hxnz55FQUEBBgwYUOl7Vq5ciSVLBPubDx6zAEPGfr1f/+M753Bs+2L+61Gz/qlV9qooKS7Cng3TwDAMfvOsu3EHoffO4uyeRfzXQ6dsqbPP/pyEpBQGjvfD6V3z8b+JLcDhSsDEphXM7NoCjGh2QUj5+AGhT4Mwadby7/Y9Ht85hyPby8/R0bM2f7fvVaakuAj+G6aDYRgM8FxQZ5/7POgM/ttXfq4NnPBvrT/z4sElSH4XiRGzDlS4vyAvG4f/HosG2qZo2+v7jkcUdaH3zuKs//e/FgBA0JW9eBMTgiGTN0NFQxevXz3E+X1LoaSqBdPPWhRrKir4LAJPLea/7jKi5tdolQZG6DfxBArzsxH7/BJuHZ2LHqP3QK2hGV6HXUNizH30nXCi1pnrk02bNyPu9euvPjCtT35pV17ZNjQyhaGRKcaPGowXz4IFWgf79B8E1649kfwhCUcO+MPPZwX+XLyK3x3/R8spKMS8w1ewqH8HqCnIVXhMWQ+9Do2NMdzFEQBgpaOJkNdJOHr/BVX4iNijCl89xeFwBGbUBCAwPu/evXsYOnQolixZgq5du0JFRQWHDh2Cj49Prb/3qFGjMHz4cKxbtw67du3CwIEDIS9feR/3uXPnYtq0aQLbrr789iDoxk06wNDMjv+6+FP5sjI+QlmtvEtPVkYKdI2sqlsMIaWVvelI+/gO3vN31WmLi6VjB+ia2H/2vUq7XmZnpkBJtbzVKifzIxoZVD6LWlXoGNnCe8kp5OdmoaS4CArK6ti27HfoGFV/PJKSsiq4XAmhSTIy09OgoqpR4XtUVTWEjs9IT4OqWunxZf/NSE+FmnqDz45JhaGJudDn3bpyDkpKKnBuLjxjX11p3KQDZpiV/3yKiz79fDJSoPLZuZadkQIdo+o9eKlIWWUv7eM7/DF/Z52ea+YOHTHK2OGz71ValpysL8+1FDTU//bvzcUDSxEZegMjZu6Dslojof0F+dk4uGEUpGUVMOCPTZCQrHjsUnXxz720L8+lVP459KWKz71UqFZyrrKhqteC7MyPaKRf82tBUWE+rh5fj0ET/4aFQ3sAQCN9SyTFh+PuxZ11UuEzsO4ILX3hsuRlp0Beubwsedkfoa799bJISEpDWaN0BtwGuo3x8c0zvLi7F7/0W4LE6PvITE3A3mUtBN5zbf9kNDRqgp6j99S6LF9SVlYGl8tFWppgi1FaejrU1Gs/Znjz5s148OAB1qxeDc0GdTuDpZKyCrhcCaR/MUFL6XW44uyqaupCE7qkf+V4AGikrQNlZRUkJb4VqPApq6hCWUUVOrr60NM3xJiRAxAR/gKW1nUzJlZNXhYSXI7QBC0pWblooCR8L5KQkoF3aVmY5F8+jp336f7Jee5mnJ4xFI1UFCHJ5cJES7C8xlpqCI77PuOrCalPqMLHAlNTU0hJSSEoKAgGBqXT1KelpSEiIgLt2rUDAGhqaiIxsfwiFBkZidzc8ovf3bt3YWhoiHnzymfMe/264m5QlZGWlkZJSYnQ9h49ekBBQQH//PMPLl68iFu3bn31c2RkZCAjI9h9U0r62wO4ZeUUICunwH/NMAyUVBsg8nkQdI1Kbx7yc7MRHx2K1p1r142srLL3Mek1vBfsgoKSaq0+70sycoqQkSu/qWcYBooqmoh9eQ/anyp4+XnZeBMTiqYdBtfJ9yybGj/lfRzexT1Hh36Tqv0ZklJSMDazxIuQh2jasvTc4/F4eB76EF16Vtyqa2ZlixehD9G9T/nU78+DH8DMqrTyrtlQB6pqGngR8pA/HX5ubg6iI16gU3c3gc9iGAY3r57DLx261+kYkC9Vdq5FPL/Pf5iQn5uN19GhaN3591p9r7LKXnJSPMYv2Fn355qsImRkBc81BWVNxIXd41cgCvKy8TY2BM7tKj/XGIbBpYPL8Cr4MoZP3wvVBvpCxxTkZePgBi9ISErj9/H/CIwdrS3+uRf6CM1alZ97L0IeoUvP3yp8j7mVLZ6HPBI4954FP4D5D5h8paoquxbEfHEteBsdima1uBaUlBSjpKQIHI7gUHwOlwumjrpBScsoQFpG8PdGTqkB3kXfh4ZOaVkK87OR/CYUVi2qtxQEwzDglZRWIO3bjYZFU8Gf+Um/PmjRcw4MrDrUshQVk5KSgrmZGYJDQtC6dWnlmMfjITg4GL179arx5zIMg3/++Qd3793D/1atEhgyUVekpKRgamaBZ8GP0aKVC4DS7KHBT9D9134VvsfCqjFCQx7j177l1/XQp49gaVX5EigpHz8gKysTapU8gCn9vqUVq69NGFddUpISsNbVRFDUG3RsbML/PkFRbzCotZ3Q8caaajg2VfD823QpCDkFhZjV2wWNVBQhJSmBxnpaiPuiS+jrj+n1ZkkGIkwcJ09hC1X4WKCoqAgvLy/MnDkTGhoa0NLSwrx588Dllv/h7tixIzZu3IhWrVqhpKQEs2fPFpgVzNzcHPHx8Th06BCaNWuG8+fP4+TJkxV9u0oZGRkhKCgIcXFxUFRUhLq6OrhcLiQkJODu7o65c+fC3NwcrVq1+vaH1QEOh4O23Yfjyql/0aCRATS09HDh6N9QVtOCbdPyrib/LPeEXTNX/NJ1KIDStag+JpWPW0hNfoO3cWGQV1SBWgOd0hvw9VPxJjYMo2ZtAo9XgsxP493kFVUgKSn9XcrSsvMI3Dq3BeoNjaCmqYtrJ/2gpKoFq8/W0vJf4w4r505o4TqMX5bUD+VlSf/4BonxYZBTUIGqhg4A4MXDi5BXUoOKug4+vI3AhQN/wcrZFWa2v9Qoa/c+g/Hv+mUwNrOGqYUNLp45jIL8fLRz7QkA+GfdEqipa2LQyD8AAN16DcTyP71x/uR+ODVrg3u3LiMmKgxe4+fwy96t90CcOrIbjXT0odlQB8f2b4WqegM0aSnYivci9BGS379Dhy69K8z2Jj4WxcVFyMnORF5eLuJiIgBAaF216uJwOGjXfTgun9oKzUaGUNfSxYWjG6GspgW7z861zcu9YNfMFS5dhwAACvJzBc61lOS3eBsX/ulc00ZJcRF2r5+GN7EvP51rPP7409JzrW5ax74sS/NOIxD43z9Q1zKEagM93Dy9AUqqWrB0Kj/X9vuOhIVjZzTrWHquXTywBC8enMOAPzZDWlaBP9ZMRk4JUtKyKMjLxoH1niguzEMfzzUoyM9GQX52aVmU1MHl1n4q8x59B2PLumUwMbOCqUVjXDh9CPn5+Wj3afKezb5LoK7x2bnX+3csm/sHzp88AMemrXHv9hXERIVj1IQ5/M/MzsrAx+T3SEst/f+e+Lb051U2IyxQOs40PS0F79+9AQAkvI6GrJw8Gmg2hKJS5ZPW1AT/WnB2CzQaGkGtwadrgZrgtWD3andYO3dCi04VXwvSkgWvBbJyijCybIaAI2sgKS0DVQ1dxL16gJC7p9F10ByhHHVVlsatRyD4+hYoNzCEkpoeHl/2g7ySFgw/Wy/vv+0eMGrcCTatSq/RDy/5Qs/CBYqqOigqyEF0yDkkxj5AN/dtAAB5Jc0KJ2pRUNWGknrdr19Xpl+/fvDx9YW5uTksLSxw6vRpFBQUoHPn0plR165dCw0NDXh4eAAordTEx5f+TIqLi5GSkoLo6GjIyclBR6f0+rxp82bcuHEDCxcuhJycHFJTS1ukFRQUhB6M1kavfr/jb9+VMDW3grmFFc6dPoaC/Dx07NwdAODn8xfUNTQxzH0MAKBn79+wcM4knDlxGM7NWiLw1jVER73CuIkzAAB5ebk4csAfrdq0haqaOpIS32Hvzi1opK0LxyalY0Ijwl8iKjIc1jZ2UFBSwvvEdzi4dwcaaevC0rryimNNDHdxxIIjV9FYTwu2elrYdycEeUXF6Nu09EHDvMNXoKWsgMndW0FGShLmjQQrpUpypf+vP98+sp0TZh24hCbGOmhmqovAiHjcCovD9jF96zR7bUgoyEPBzID/Wt5YD8oOVihMzUB+ArVEkpqjCh9L1qxZg+zsbPTq1QtKSkqYPn06MjLKZ8Py8fGBh4cHXFxcoKOjgw0bNuDx4/JZGHv37o2pU6diwoQJKCgoQM+ePbFgwQIsXry4yhlmzJiBkSNHwsbGBnl5eYiNjeUPLPfy8sKKFSv4f+h+lA69vFBYkIdj2xcjLzcLxpbOGDPnX4EJYFLeJyAnK53/OiHmBf5ZVp7zzN7Sxbmbtu2Dwd4rkJH2AS8eXwcA+MzpL/D9vBfsgplN8+9SljbdR6GwIA9n/Rd+Wmy5CYZN2wapz1pJUj/EIzer/Inju7jn8F9dvjbYpUOlg+Ed2vRFP6/Sf2elf8ClQ6s+dRHThEOrPmjb27vGOVu5dEZWRjqOHdiGjLQUGJqYY/bidVD5dHOckpwkMDbDwtoe46cvxdH9/+LI3i1opKOPaX+uhr6hKf+YX92GoyA/Hzs2rUJuTjYsbOwxe/F6SH8xkc+Ny2dhbmUHHT2jCrOtWToVHz+Uz8o2b8oIAMD+M/drXN4yHXt5orAgD0c+O9fGztkicK59fJ+AnM9+Pgkxz7FpmSf/9elP51qztn0wxPsvZKR9wPNP59raOYItFuMX7Pxu51qrrqNRVJCH//aVnmv6Zk0waPJ2gRa5tOQE5GWXl+XJzYMAgH0+wwU+61f3lXBo7Yak+Bd4F1s68+Lm+YJLA4xfcRWqDWp/I97KpRMyM9JwbP92pH869+YsWcefyCUl+T24n7VgWVjbY/yMJTi6bysO7/l07s37n8C59zjoDv7dUD4e9O/VpeMn3QZ74bchowAAVy6cxImDO/jHLJ1T+vszdvJ8tOvUs9bl+tIvPUahqDAPZ3d/uhZYCF8L0j7EIzdb8Fqw+3/C1wLHNn3Rb1Tpv3/z9sWVY744/u9M5OVkQFVDB679p3zXhdft245CcWEeAk8uQmF+JhoaOqOrx1aBcy0rNR75OeVlyc9Owa2jc5CblQxpWSWoN7JAN/dt0DX/PrOJVlW7du2QkZmJfXv3IjUtDaYmJli2dCl/IpcPycngfPYgNjU1FRMmTuS/Pn78OI4fPw47Ozus/t//AADnz5d2K5w9e7bA95o2dSq/IlkX2rTtiIyMdBzatxPpaakwNjHD/KVr+F00PyZ/EGj9tbKxxZSZC3Bw7w7s998GbV09zJr/FwyMSlvQuFwJvI6Lxo2rF5Gbkw019QZwcGqKwcO9ICVV+lBURlYGQXdv4fD+XSjIz4eaujocmzTHbwNH8I+pK90czJGWk4fNAUH4mJULS50G2Oz5KzQ+delMSs8Ct5pjBl1tTTC/XzvsvP4E/ztzG0aaqvAZ1g3Oxjp1mr02VJrYotXVvfzXNmv/BAAk7DmBUK+5bMViDcOjSVvqCof5cqAYYU379u3h6OiI9evXsx0Ft2/fhqurKxISEtCwYcNqv//ck7pdk4dtWXn1Z2HW2jJvkM52hDr1Ief7zH7Jlo9Zdd8KyJbG2hUvoC6qIj9WvNi2qIpPFJ8//785xrIdoU7lMgrfPkiEmD0/wnaEOnN1wCa2I9SpnkWv2I5Qqc5Dq7/cVF25vL/Jtw8SIdTCRwQUFBQgOTkZixcvxoABA2pU2SOEEEIIIYTUD7TwOhFw8OBBGBoaIj09HatXr2Y7DiGEEEII+QkxPIa1L3FDLXz1yI0bN9iOAHd3d7i7u7MdgxBCCCGEEFIHqMJHCCGEEEIIqVfqaokZQl06CSGEEEIIIURsUQsfIYQQQgghpF7hieFYOrZQCx8hhBBCCCGEiCmq8BFCCCGEEEKImKIunYQQQgghhJB6heHRpC11hVr4CCGEEEIIIURMUQsfIYQQQgghpF4RxwXQ2UItfIQQQgghhBAipqjCRwghhBBCCCFiirp0EkIIIYQQQuoVhqFJW+oKtfARQgghhBBCiJiiFj5CCCGEEEJIvUKTttQdauEjhBBCCCGEEDFFLXyEEEIIIYSQeoUWXq871MJHCCGEEEIIIWKKKnyEEEIIIYQQIq4YQkRUfn4+s2jRIiY/P5/tKHVCnMojTmVhGCpPfSZOZWEYKk99Jk5lYRgqT30mTmUh9QOHYRiaAoeIpMzMTKioqCAjIwPKyspsx6k1cSqPOJUFoPLUZ+JUFoDKU5+JU1kAKk99Jk5lIfUDdekkhBBCCCGEEDFFFT5CCCGEEEIIEVNU4SOEEEIIIYQQMUUVPiKyZGRksGjRIsjIyLAdpU6IU3nEqSwAlac+E6eyAFSe+kycygJQeeozcSoLqR9o0hZCCCGEEEIIEVPUwkcIIYQQQgghYooqfIQQQgghhBAipqjCRwghhBBCCCFiiip8hBBCCCGEECKmqMJHRMaiRYvw+vVrtmPUmV27diE3N5ftGOQLRUVF8PT0RGxsLNtRyFcUFhbi1atXKC4uZjsKIYQQUq/RLJ1EZDg6OuL58+do164dvLy80L9/f5Gesrhhw4bIy8vDgAED4OXlhdatW7MdqVrOnDlT5WN79+79HZPUPRUVFQQHB8PY2JjtKN9FSUkJnj17BkNDQ6ipqbEdp1pyc3MxceJE+Pv7AwAiIiJgYmKCiRMnQldXF3PmzGE5YfVFR0dj165diI6OxoYNG6ClpYULFy7AwMAAjRs3ZjtetVy/fh0dOnRgOwapRF5eHhiGgby8PADg9evXOHnyJGxsbNClSxeW0/3cYmJiYGJiwnYMIqaowkdEytOnT7Fr1y4cPHgQxcXFGDRoEDw9PdGsWTO2o1VbcXExzp49i927d+PChQswMTGBh4cHRo4ciUaNGrEd75u4XMEOAhwOB59fTjgcDv/fJSUlPyxXXRg5ciQcHR0xdepUtqPUiSlTpsDOzg5eXl4oKSlBu3btcPfuXcjLy+PcuXNo37492xGrbPLkyQgMDMT69evRrVs3hIaGwsTEBKdPn8bixYvx9OlTtiNWy82bN9G9e3e0adMGt27dQlhYGExMTLBq1So8evQIx44dYztitcjIyEBPT49/LdPX12c7EvlMly5d4ObmhnHjxiE9PR1WVlaQkpLCx48f4evrC29vb7YjfpWfn1+Vj500adJ3TFL3uFwu/4H2b7/9BllZWbYjETFCFT4ikoqKinD27Fns2rULly5dgpWVFby8vODu7g4VFRW241Xb+/fvsW/fPvj7+yM8PBzdunWDl5cXevXqJVSxqo+uXLmC2bNnY8WKFWjVqhUA4N69e5g/fz5WrFiBzp07s5ywepYvXw4fHx+4urqiSZMmUFBQENgvajcSenp6OHXqFJo2bYpTp05h/PjxuH79Ovbu3Ytr164hMDCQ7YhVZmhoiMOHD6Nly5ZQUlJCSEgITExMEBUVBWdnZ2RmZrIdsVpatWqFAQMGYNq0aQLlefDgAdzc3PDmzRu2I1bLx48fsXfvXvj7++PFixfo2LEjvLy80LdvX0hLS7Mdr1rU1NQEHlxVJjU19QekqRsNGjTAzZs30bhxY2zfvh1///03nj59iuPHj2PhwoUICwtjO+JXVbXXBYfDQUxMzHdOU7eCg4P5D7QLCwsxcOBAeHl5oXnz5mxHI2KAKnxEJBUWFuLkyZPYuXMnrl27htatW+Pdu3d4//49tm3bhoEDB7IdsdqCgoKwc+dO+Pv7Q1tbG2lpaVBTU8OuXbvqfQuMra0ttmzZgl9++UVg++3btzFmzJh6fxPxpa/dVIjijYSsrCyioqKgp6eHMWPGQF5eHuvXr0dsbCwcHBxEqpIkLy+P58+fw8TERKCCFBISgrZt2yIjI4PtiNWiqKiIZ8+ewdjYWKA8cXFxsLKyQn5+PtsRa+zJkyf8G1gAGDJkCLy8vODg4MBysqop6zYMAAzDwNvbG0uXLoWWlpbAcSNHjvzR0WpMXl4e4eHhMDAwwO+//47GjRtj0aJFSEhIgKWlJY0rrweKi4tx5swZ7N69GxcvXoSFhQU8PT0xfPhwaGpqsh2PiCqGEBHy6NEjZvz48Yy6ujqjra3NzJ49m4mMjOTv9/PzY7S0tFhMWD1JSUnMmjVrGBsbG0ZWVpYZNGgQc/nyZYZhGCY7O5uZNWsWY2BgwHLKb5OVlWWePXsmtD0kJISRlZVlIRH5nIGBAXPp0iWmuLiY0dfXZ86dO8cwDMM8f/6cUVVVZTld9bi4uDB+fn4MwzCMoqIiExMTwzAMw0yYMIHp2rUrm9FqRFdXlwkMDGQYprQ80dHRDMMwzIkTJxgTExM2o9WJt2/fMosWLWJkZGQYBQUFRkJCgvnll1+Y58+fsx2t2j7/+YgqOzs7ZsOGDUx8fDyjrKzM3L17l2GY0r+tDRs2ZDldzfF4PIbH47Edo07l5+czvr6+jIyMDMPhcBgZGRlm+PDhzLt379iORkQQVfiIyLC1tWUkJSWZHj16MCdPnmSKi4uFjklOTmY4HA4L6arv119/ZaSkpJjGjRsz69atY1JSUoSOef/+vUiUx8XFhencuTOTlJTE35aUlMR06dKFadu2LYvJCMMwzKJFixgVFRXGysqKMTAwYPLz8xmGYZgdO3YwLVu2ZDld9dy+fZtRVFRkxo0bx8jKyjKTJ09mOnfuzCgoKDCPHj1iO161TZ8+nfnll1+YxMRERklJiYmMjGTu3LnDmJiYMIsXL2Y7Xo0UFhYyR48eZbp3785ISkoyLVu2ZLZt28ZkZ2czsbGxzNChQxlra2u2Y1abOFT4jh49ykhJSTFcLpfp1KkTf/uKFSuYbt26sZisZvz9/RlbW1tGRkaGkZGRYezs7Jg9e/awHatWHj58yHh7ezNqamqMnp4eM2/ePCYmJoa5desW4+rqyjRr1oztiEQEUZdOIjKWLVsGT09P6Orqsh2lTnh5eWHUqFH8MW8VYRgG8fHxMDQ0/IHJqi8qKgr9+vVDREQEf5KGhIQEmJub49SpUzAzM2M5YfW9efMGZ86cQXx8PAoLCwX2+fr6spSq5o4dO4aEhAQMGDAAenp6AEq7rKmqqqJPnz4sp6ue6OhorFq1CiEhIcjOzoazszNmz54NOzs7tqNVW2FhIcaPH4/du3ejpKQEkpKSKCkpwZAhQ7B7925ISEiwHbFaJk6ciIMHD4JhGAwfPhyjRo2Cra2twDFJSUnQ0dEBj8djKWXNfN7lVpQlJSUhMTERDg4O/DHiDx48gLKyMqysrFhOV3W+vr5YsGABJkyYgDZt2gAA7ty5g02bNmH58uUiN+mWr68vdu3ahVevXqFHjx4YNWoUevToITCO/82bNzAyMqLlaEi1UYWPiIylS5dixowZ/Omky+Tl5WHNmjVYuHAhS8lqZs+ePRg4cKDQ0hKFhYU4dOgQRowYwVKymmEYBpcvX0Z4eDgAwNraGp06darSpAf1zdWrV9G7d2+YmJggPDwctra2iIuLA8MwcHZ2xrVr19iOWGP5+fk0+1s9FB8fj+fPnyM7OxtOTk4wNzdnO1KNuLq6YtSoUXBzc6t02Zzi4mIEBgaiXbt2Pzhd7YhLhQ8ofUgXHR2Ntm3bQk5ODgzDiNy12tjYGEuWLBH6W+nv74/FixeL3Fqq5ubm8PT0hLu7O7S1tSs8prCwEAcPHhSpcaOkfqAKHxEZEhISSExMFBown5KSAi0tLZGb+l/cyiNOmjdvju7du2PJkiX8mzwtLS0MHToU3bp1q/dTl3+ppKQEK1aswJYtW/D+/Xv+2nULFiyAkZERvLy82I74VdWZVEZZWfk7JiHfcuvWLbRu3RqSkpIC24uLi3H37l20bduWpWTVN23aNIHXmzZtwrBhw4RmghalFv+UlBT8/vvvuH79OjgcDiIjI2FiYgJPT0+oqanBx8eH7YhVJisri+fPnwv1IImMjISdnZ3ITXgUFxcHAwMDoZm5GYZBQkICDAwMWEpGxIHktw8hpH6o7AlkSEgI1NXVWUhUO5WV582bNyKxtISfnx/GjBkDWVnZb66NJGrLGISFhfFnFpSUlEReXh4UFRWxdOlS9OnTR+QqfH/99Rf8/f2xevVqjB49mr/d1tYW69evr/cVPlVV1Sq3PojCg5IvKxJfI0qVCQDo0KFDhQ+yMjIy0KFDB5H4+ZT5ck3H1q1bC83QK2qtYlOnToWUlBTi4+NhbW3N3z5w4EBMmzZNpCp8ZmZmOHLkCP7880+B7YcPHxbJFnJTU9MKf3dSU1NhbGwsUr87pP6hCh+p98rWQuJwOLCwsBBa0Ds7Oxvjxo1jMWH1ODk58cvj6uoq8CS8pKQEsbGx6NatG4sJq2bdunUYOnQoZGVlsW7dukqP43A4IlfhU1BQ4I/b09bWRnR0NBo3bgygdJ0xUbNnzx5s3boVrq6uAr8rDg4O/C649dn169f5/46Li8OcOXPg7u4usOajv78/Vq5cyVbEavmyIvHkyRMUFxfD0tISABAREQEJCQk0adKEjXi1UtmDrJSUFKH1LOu7z8+7MmWdokStolcmICAAly5d4o/jLWNubo7Xr1+zlKpmlixZgoEDB+LWrVv8MXyBgYG4evUqjhw5wnK66qusw112djZ1wye1RhU+Uu+tX78eDMPA09MTS5YsEWj9kpaWhpGR0VcnPqlv+vbtC6B0kdWuXbtCUVGRv6+sPP3792cpXdV9Pj5C1MZKfEvLli1x584dWFtbo0ePHpg+fTqePXuGEydOoGXLlmzHq7a3b99WOHEOj8dDUVERC4mq5/OxXkuXLoWvry8GDx7M39a7d2/Y2dlh69atIjG25fOKhK+vL5SUlODv7w81NTUAQFpaGjw8PODi4sJWxGpzc3MDUFoRcnd3Fxi/V1JSgtDQULRu3ZqteLW2Y8cOrFu3DpGRkQBKK0hTpkzBqFGjWE5WPTk5OULj4IHSVqTKxlzWV/3790dQUBDWrVuHU6dOASgdO/7gwQM4OTmxG64aylr8ORwOFi5cKPDzKSkpQVBQEBwdHVlKR8QFVfhIvVd2A2dsbIzWrVtDSkqK5US1s2jRIgCAkZERBg4cSE/u6iFfX19kZ2cDKH2KnJ2dze8mJGpd7ADAxsYGt2/fFprt9dixYyJ1YwSUtuZt2bJFaHvTpk1F7uYbAHx8fBAQEMCv7AGlvRqWL1+OLl26YPr06Symq7qyB3EMw0BJSQlycnL8fdLS0mjZsqVAd2JRsnDhQvj6+mLixIkCrcpTp05FfHw8li5dynLCqnNxccGePXuwbNkyAKWVDB6Ph9WrV6NDhw4sp6u+Jk2aYN++fWzHqJWyFn+GYfDs2TNIS0vz90lLS8PBwQEzZsxgKx4RE1ThI/VaZmYmfxIGJycn5OXlIS8vr8JjRW2yBlFoiaiq/v37o3nz5pg9e7bA9tWrV+Phw4c4evQoS8lq5vNZ+BQUFCqsYIiShQsXYuTIkXj79i14PB5OnDiBV69eYc+ePTh37hzb8apFX18f27Ztw+rVqwW2b9++nb8kiCjJzMxEcnKy0Pbk5GRkZWWxkKhmdu3aBaD0QdaMGTNErvvm1/zzzz/Ytm2bUKuyvb09Jk6cKFIVvtWrV8PV1RWPHj1CYWEhZs2ahRcvXiA1NRWBgYFsx6s2Ho+HqKgofPjwQWiZD1GZIKisxd/DwwMbNmwQuXsZIhpolk5Sr30+kyWXy61w3ETZmBFRGNCsrq6OiIgINGjQgD82sTKpqak/MFntaGpq4tq1a0LroD179gydOnXC+/fvWUpGyty+fRtLly4VWLtu4cKF6NKlC9vRquW///5D//79YWZmhhYtWgAoXUMsMjISx48fR48ePVhOWD0jRozA7du34ePjg+bNmwMAgoKCMHPmTLi4uMDf35/lhERVVRUPHz4UmggkIiICzZs3R3p6OjvBaigjIwMbN24UuBaMHz++0qUA6qv79+9jyJAheP36tdD4N1G5JyDkR6EKH6nXbt68iTZt2kBSUhI3b9786rGisKaTv78/Bg0aBBkZGezevfurFT5RagGUk5NDcHAwf9KJMuHh4fyWWVFSWWWcw+FAVlYWZmZmcHd3h4eHBwvpSEJCAv755x+BNR/HjRsnki18ubm5mDFjBnbu3MkfTykpKQkvLy+sWbNGJFrKnJ2dcfXqVaipqfEnparMkydPfmCyujFx4kRISUkJdeeeMWMG8vLysGnTJpaSVV98fDz09fUr/BnFx8eL1NT/jo6OsLCwwJIlS6CtrS1UJlGY7drNzQ27d++GsrIyfxxsZU6cOPGDUhFxRF06Sb32eSVOFCp03/J5Jc7d3Z29IHXMzs4Ohw8fxsKFCwW2Hzp0CDY2NiylqrmFCxfir7/+Qvfu3fmtLg8ePMDFixcxfvx4xMbGwtvbG8XFxSI7LkmU6evrY8WKFWzHqBPy8vLYvHkz1qxZg+joaACl07OLQkWvTJ8+ffgTfpRNSiVuduzYgYCAAP6kTUFBQYiPj8eIESMEltmo72N8jY2NK13/VdSm/o+MjMSxY8cqnJBKVKioqPArqqJQQSWii1r4SL0WGhpa5WPt7e2/Y5K6Ia4LSJ89exZubm4YMmQIOnbsCAC4evUqDh48iKNHj4rcTWD//v3RuXNnoeU+/v33XwQEBOD48eP4+++/sXXrVjx79oyllF/3rS7Dn6vv3YdDQ0Nha2sLLpf7zWuCKFwHiGip6mQmHA4H165d+85paofL5eL9+/fQ1NQU2P769WvY2NggJyeHpWTV17FjR8yaNUskljEihG1U4SP1Wtm4vcrWdvqcKDyZrGwcYkVEoTyfO3/+PFasWIHg4GDIycnB3t4eixYtEsmWWUVFRQQHBws9OY6KioKjoyOys7MRHR0Ne3v7enuD9PnYr5SUFCxfvhxdu3YVmGXw0qVLWLBgAaZOncpWzCrhcrlISkoSGMtb0Z8uURm3I87duBISEsDhcPjrvD148AAHDhyAjY0NxowZw3K6n1dZK+SGDRswevToCqf+l5CQqPcTt3z+wCc6Ohrz58/HzJkzYWdnJzSDNz38IaQcdekk9drn67s9ffoUM2bMwMyZMwVuWn18fIRm7KuvxG0B6c/17NkTPXv2ZDtGnVBXV8fZs2eFKkJnz56Furo6gNL1rJSUlNiIVyWfdx/u378/li5digkTJvC3TZo0CRs3bsSVK1fqfYUvNjaW3yIhDms+inM3riFDhmDMmDEYPnw4kpKS0KlTJ9ja2mL//v1ISkoS6vZNfgxxmfrf0dFR6IGPp6cn/9+fPyAWhYc/3xrz+jlRHP9K6g9q4SMio3nz5li8eLHQLHz//fcfFixYgMePH7OUrGZcXV0xatQogam+AeDAgQPYunUrbty4wU4wgm3btsHb2xs9evTgj+F7+PAh/vvvP2zZsgVeXl7w8fHBgwcPcPjwYZbTfltVWizFQVV6ApDvS01NDffv34elpSX8/Pxw+PBhBAYGIiAgAOPGjUNMTAzbEX9qoj71/+vXr6t87JfrjtZHS5YsqfKxZWv4ElITVOEjIkNOTg5PnjyBtbW1wPawsDA4OzuL3EyQ8vLyCAkJqXCqb0dHR+Tm5rKUrPpKSkqwbt06HDlyBPHx8SgsLBTYX9/HiFUkMDAQGzduxKtXrwAAlpaWmDhxIlq3bs1ysuozNDTEpEmThBbx9vHxgZ+fX7Vuotjm7u6OTZs2CU1qEhcXh+HDh+P27dssJauZ8PBwWFlZVbjv0qVL6Nq16w9OVDuKiop4/vw5jIyM0Lt3b7Rp0wazZ89GfHw8LC0tRe46TQgh4oC6dBKRYW1tjZUrV2L79u387iiFhYVYuXKlUCVQFIjTAtJLlizB9u3bMX36dMyfPx/z5s1DXFwcTp06JbJduNq0aYM2bdqwHaNOLFmyBKNGjcKNGzf4a9cFBQXh4sWL2LZtG8vpqickJAT29vbYt28fvyu0v78/Jk2axJ8wSJQ4OztjzZo1GD9+PH9bQUEBpk+fju3btyM/P5/FdNXXuHFjbNmyBT179sTly5exbNkyAMC7d++goaHBcjoCAI8ePar04ZyojRmNjo7G+vXrERYWBgCwsbHB5MmTYWpqynIyQuoXauEjIuPBgwfo1asXGIbhD8YODQ0Fh8PB2bNn+V3vRIU4LSBtamoKPz8/9OzZE0pKSggODuZvu3//Pg4cOMB2xGqJj4//6n5RWquqTFBQEPz8/Pg3RtbW1pg0aRL/3BMVRUVF+PPPP+Hn54fp06cjKioKFy5cgK+vr0gukXHkyBF4e3ujRYsW2LVrFxITEzFkyBDweDzs3bsXzZo1Yztitdy4cQP9+vVDZmYmRo4ciZ07dwIA/vzzT4SHh4tchULcHDp0CCNGjEDXrl0REBCALl26ICIiAu/fv0e/fv2wa9cutiNW2aVLl9C7d284OjryH84FBgYiJCQEZ8+eRefOnVlOWD3i2FOG1B9U4SMiJScnB/v37xdYcHnIkCEitWbV58RlAWkFBQWEhYXBwMAA2traOH/+PJydnRETEwMnJydkZGSwHbFavjWbqihMBiDuFi1ahGXLlkFSUhI3b97kt/aJojdv3sDDwwNPnz5FTk4O3N3d4ePjIzCToigpKSlBZmYm1NTU+Nvi4uIgLy8vtP4b+bHs7e0xduxYjB8/HkpKSggJCYGxsTHGjh0LbW3tao0pY5uTkxO6du2KVatWCWyfM2cOAgICRG6Sk4ULF361p8ykSZPYjkhEGHXpJCJFQUFBrKb2FpcFpPX09JCYmAgDAwOYmpoiICAAzs7OePjwIX9BZlFSNqNdmaKiIjx9+hS+vr7466+/WEpVPZmZmfyJGb61/qMoTeBQVFSEOXPmYNOmTZg7dy7u3LkDNzc37NixQ6Raxb9UWFiIkpISlJSUQFtbG7KysmxHqjEJCQmByh4AGBkZsROGCIiOjubPpiwtLY2cnBxwOBxMnToVHTt2FKkKX1hYGI4cOSK03dPTE+vXr//xgWpp//792LZtG3r27InFixdj8ODBMDU1hb29Pe7fv08VPlIrVOEj9dqZM2fQvXt3SElJ4cyZM189tnfv3j8oVc2J6wLS/fr1w9WrV9GiRQtMnDgRw4YNw44dOxAfH1/vp/yviIODg9C2pk2bQkdHB2vWrPnm2mn1gZqaGhITE6GlpQVVVdUKWyxFafryMk2bNkVubi5u3LiBli1bgmEYrF69Gm5ubvD09MTmzZvZjlgthw4dgre3N1xcXBAREYHg4GB4eHjg0qVL2Lt3L0xMTNiOWC3v37/HjBkzcPXqVXz48EFovURROtfEkZqaGrKysgAAurq6eP78Oezs7JCeni5SE4UBgKamJoKDg4UmPgsODhbJluSkpCTY2dkBKJ38qKxnzK+//ooFCxawGY2IAarwkXqtb9++/AWX+/btW+lxonLT6ujoyC9PResJlRGV8pT5vEvNwIEDYWhoiLt378Lc3By9evViMVndsrS0xMOHD9mOUSXXrl3jrxn4+fqPoq5p06bw8/Pjd+PmcDiYPXs2unTpguHDh7Ocrvq8vLywdu1aeHt7AwA6d+6M0NBQjBs3Do6Ojt9sna1v3N3dER8fjwULFkBbW5uWyahn2rZti8uXL8POzg4DBgzA5MmTce3aNVy+fBmurq5sx6uW0aNHY8yYMYiJieHPnhwYGIj//e9//IXmRYm49ZQh9QuN4SPkB3r9+jUMDAzA4XC+ORW+KKwhJK6+vMlmGAaJiYlYvHgxwsPDERwczE6wGiguLsaKFSvg6ekJPT09tuN8VwUFBSJ3Y/Tq1StYWlpWuG/v3r0iV4lVUlLC7du34ejoyHYUUoHU1FTk5+dDR0cHPB4Pq1ev5j+cmz9/vlBX3PqMYRisX78ePj4+ePfuHQBAR0cHM2fOxKRJk0TuYcOcOXOgrKyMP//8E4cPH8awYcNgZGTE7ynz5VhFQqqDKnyEEPKFiiZtYRgG+vr6OHTokMhNEKKkpIRnz56JzTiqmzdvYu3atQJTsc+cORMuLi4sJ6u5x48fC5TH2dmZ5UQ1Y2Njg/3798PJyYntKOQnUtZNVUlJieUkdefevXu4d++e2PWUIeygCh8RGZMmTYKZmZnQwOWNGzciKipK5AZpr1y5Eg0bNoSnp6fA9p07dyI5ORmzZ89mKRm5efOmwGsulwtNTU2YmZlBUlL0esL36dMHbm5uGDlyJNtRam3fvn3w8PCAm5ubwFTsJ0+exO7duzFkyBCWE1bPhw8fMGjQINy4cQOqqqoAgPT0dHTo0AGHDh2CpqYmuwGrKSAgAD4+Pvj333/F5gGDuOHxeIiKisKHDx/A4/EE9rVt25alVNUXGxuL4uJioTF8kZGRkJKSovOPkM9QhY+IDF1dXZw5cwZNmjQR2P7kyRP07t0bb968YSlZzRgZGeHAgQP8sQdlgoKCMGjQIMTGxrKUjIibLVu2YMmSJRg6dCiaNGkitIyJKEx4VMba2hpjxowRmgzI19cX27Zt47eSiYqBAwciJiYGe/bsgbW1NQDg5cuXGDlyJMzMzHDw4EGWE1aPmpoacnNzUVxcDHl5eUhJSQnsp7XE2HX//n0MGTIEr1+/Fho/Lmpjx9u1awdPT0+hB1n79u3D9u3bcePGDXaC1dCePXu+un/EiBE/KAkRR1ThIyJDVlYWz58/h5mZmcD2qKgo2NraIj8/n6VkNSMrK4uwsDAYGxsLbI+JiYGNjY3IlUec+Pv7o0GDBvzpy2fNmoWtW7fCxsYGBw8eFLnxlVwut9J9onaTJyMjgxcvXojNdUBFRQVXrlwRWmD9wYMH6NKlC9LT09kJVkP+/v5f3S8OrcyizNHRERYWFliyZEmFk+qoqKiwlKz6lJWV8eTJkwqvBU2bNhW5350vx08WFRUhNzcX0tLSkJeXp4clpFZEr28S+WmZmZnh4sWLmDBhgsD2CxcuiNzU5UDpGnyBgYFCFb7AwEDo6OiwlKpmHj58CB6PhxYtWghsDwoKgoSEBJo2bcpSsppZsWIF/vnnHwCl4yg2btyI9evX49y5c5g6dSpOnDjBcsLq+bLblijT19fH1atXhW7yrly5An19fZZS1RyPxxNqBQMAKSkpkfy5UYWufouMjMSxY8eEfn9EEYfD4Y/d+1xGRoZIPcQqk5aWJrQtMjIS3t7emDlzJguJiDihCh8RGdOmTcOECROQnJyMjh07AgCuXr0KHx8fkRu/B5ROKT1lyhQUFRUJlGfWrFmYPn06y+mqZ/z48Zg1a5ZQhe/t27f43//+h6CgIJaS1UxCQgL/hujUqVP47bffMGbMGLRp0wbt27dnN9xPbvr06Zg0aRKCg4MFpmLfvXs3NmzYwHK66uvYsSMmT56MgwcP8h/0vH37FlOnThW5afLLREdHY9euXYiOjsaGDRugpaWFCxcuwMDAAI0bN2Y73k+tRYsWiIqKEosKX9u2bbFy5UocPHgQEhISAErXeVy5ciV++eUXltPVDXNzc6xatQrDhg1DeHg423GIKGMIESGbN29mdHV1GQ6Hw3A4HMbY2Jjx9/dnO1aN8Hg8ZtasWYysrCzD5XIZLpfLyMvLM0uWLGE7WrUpKCgw0dHRQttjYmIYRUVFFhLVjqamJvPkyROGYRjG0dGR2bNnD8MwDBMVFcUoKCiwGa3Gbty4wfz666+MqakpY2pqyvTq1Yu5desW27Fq5MSJE0ybNm0YdXV1Rl1dnWnTpg1z6tQptmPVSHx8POPo6MhISUkxJiYmjImJCSMlJcU4OTkxCQkJbMerths3bjBycnJMp06dGGlpaf51YeXKlUz//v1ZTvdzCgkJ4X+dOHGCsbGxYXbt2sU8evRIYF9ISAjbUavlxYsXjIaGBmNqasq4u7sz7u7ujKmpKaOpqck8e/aM7Xh15unTp4ySkhLbMYiIozF8RCQlJydDTk4OioqKbEeptezsbISFhUFOTg7m5uYit44YAGhoaODcuXNCyxXcvXsXPXv2rLCrSn02dOhQhIeHw8nJCQcPHkR8fDw0NDRw5swZ/Pnnn3j+/DnbEatF3Ga2FDcMw+DKlSv8J/jW1tbo1KkTy6lqplWrVhgwYACmTZsGJSUlhISEwMTEBA8ePICbm5vITa4lDsqWmansdq9sn6iN5wWAd+/eYePGjQgJCYGcnBzs7e0xYcIEqKursx2t2s6cOSPwmvm0/uvGjRuhr6+PCxcusJSMiAOq8BFCam3w4MFITEzE6dOn+YP+09PT0bdvX2hpaeHIkSMsJ6ye9PR0zJ8/HwkJCfD29ka3bt0AAIsWLYK0tDTmzZvHcsLqEbeZLUn9paioiGfPnsHY2FigwhcXFwcrKyuRm1RHHLx+/brKx4rahFTi5MvJtTgcDjQ1NdGxY0f4+PhAW1ubpWREHFCFj4iUY8eO4ciRI4iPj0dhYaHAvidPnrCUquYePXpUaXlEaWKQt2/fom3btkhJSeEvuBwcHIyGDRvi8uXLIjmZhjgR9Zkt1dXVERERgQYNGkBNTU1oZsHPicJMdn5+fhgzZgxkZWXh5+f31WO/XHe0vtPT08ORI0fQunVrgQrfyZMnMWPGDERHR7MdkYiw0NDQKh9rb2//HZMQIlpo0hYiMvz8/DBv3jy4u7vj9OnT8PDwQHR0NB4+fIjx48ezHa/aDh06hBEjRqBr164ICAhAly5dEBERgffv36Nfv35sx6sWXV1dhIaGYv/+/fyuNR4eHhg8eHCFMxCSH0vUZ7Zct24dlJSUAEAkJ2j60rp16zB06FDIyspi3bp1lR7H4XBErsI3aNAgzJ49G0ePHgWHwwGPx0NgYCBmzJhB64jVE69evcLff//Nb9m3trbGxIkTYWlpyXKyb3N0dPxq99Qyotg9ddq0aVU+1tfX9zsmIeKIWviIyLCyssKiRYswePBggSfHCxcuRGpqKjZu3Mh2xGqxt7fH2LFjMX78eH55jI2NMXbsWGhra2PJkiVsRyRi4p9//sGUKVPg6elZ4cyWY8eOZTkhEReFhYUYP348du/ejZKSEkhKSqK4uBhDhw7F7t27+bMpEnYcP34cgwYNQtOmTfljru/fv4+HDx/i0KFD6N+/P8sJv06cu6d26NABT548QXFxMb/yHRERAQkJCTg7O/OP43A4uHbtGlsxiYiiCh8RGfLy8ggLC4OhoSG0tLRw+fJlODg4IDIyEi1btkRKSgrbEatFQUEBL168gJGRETQ0NHDjxg3Y2dkhLCwMHTt2RGJiItsRv+rMmTPo3r07pKSkhAabf6l3794/KBWpzMmTJ+Hj4yPwVH/mzJno06cPy8m+LTMzs8rHKisrf8ckpKoSEhLw7NkzZGdnw8nJCebm5mxHIgBMTU0xdOhQLF26VGD7okWLsG/fPpHscvvy5UuhYREcDge9evViMVX1+fr64saNG/D39+cvwp6WlgYPDw+4uLiI3HJNpH6hCh8RGSYmJjh+/DicnJzQtGlTjB49GmPHjkVAQAAGDRokEmN3Pqenp4cLFy7Azs4O9vb2mDt3LgYPHox79+6hW7duyMjIYDviV3G5XCQlJUFLS0tosPnnRLFrDalfymYZrApRONfEreuWuJVHnMnLyyM0NFSoe3dkZCQcHByQm5vLUrLqi4mJQb9+/fDs2TOBbp5l1wpRuBZ8TldXFwEBAUJrVT5//hxdunTBu3fvWEpGxAGN4SMio2PHjjhz5gycnJzg4eGBqVOn4tixY3j06BHc3NzYjldtbdu2xeXLl2FnZ4cBAwZg8uTJuHbtGi5fviwSCy7zeLwK/01IXbt+/Tr/33FxcZgzZw7c3d35XdLu3bsHf39/rFy5kq2I1fL06VOB15V142rSpAkb8apN3Mojztq3b4/bt28LVfju3LkDFxcXllLVzOTJk2FsbIyrV6/C2NgYQUFBSE1NxfTp07F27Vq241VbZmYmkpOThbYnJycjKyuLhUREnFALHxEZPB4PPB4PkpKlzykOHTqEu3fvwtzcHGPHjoW0tDTLCasnNTUV+fn50NHRAY/Hw+rVq/nlmT9/Pr9LR31XVFSEbt26YcuWLSLdbcvJyanKrUiiMCPst2az/JwotY67urpi1KhRGDx4sMD2AwcOYOvWrbhx4wY7wWpI3LpxiVt5xM2WLVuwcOFC/P7772jZsiWA0jF8R48exZIlS6Cjo8M/tr53xW/QoAGuXbsGe3t7qKio4MGDB7C0tMS1a9cwffp0oQcR9d2IESNw+/Zt+Pj4oHnz5gCAoKAgzJw5Ey4uLvD392c5IRFlVOEjhNSapqYmv7IqqqozSc6iRYu+Y5K6UZ2bg5EjR37HJHVLXl4eISEhQudaREQEHB0dRapLGiB+3bjErTzi5mvd7z8nCl3x1dTU8OTJExgbG8PU1BTbt29Hhw4dEB0dDTs7O5G7FuTm5mLGjBnYuXMnioqKAACSkpLw8vLCmjVroKCgwHJCIsqoSychpNaGDRuGHTt2YNWqVWxHqTFRqMRVhyhV4qpDX18f27Ztw+rVqwW2b9++XSSWmPiSuHXjErfyiBtx6n5va2vLn926RYsWWL16NaSlpbF161aYmJiwHa/a5OXlsXnzZqxZs4Y/eY6pqSlV9EidoAofIaTWiouLsXPnTly5cgVNmjQR+gNFEzWwj8fjISoqCh8+fBC66Wvbti1Lqapv3bp16N+/Py5cuIAWLVoAAB48eIDIyEgcP36c5XTV169fP3h4eFTYjUsUxyaLW3lI/TV//nzk5OQAAJYuXYpff/0VLi4u0NDQwOHDh1lOV3MKCgq0aDypc9SlkxBSax06dPjq/s8n3RAFJSUlWLduHY4cOSI03TcgWmPegNIxOkOGDMHr16+FFiwWha5bX3rz5g3++ecfgSUmxo0bJ5ItfOLWjUvcyiMO/Pz8qnzspEmTvmOS7y81NbVa45cJ+VlQhY8QQr6wcOFCbN++HdOnT8f8+fMxb948xMXF4dSpU1i4cKHI3RQ5OjrCwsICS5Ysgba2ttDNkIqKCkvJSJmcnByx6sYlbuURZcbGxlU6jsPhICYm5junIYSwgSp8hLAsKioK0dHRaNu2LeTk5MAwjMg9nfT09MSGDRugpKQksD0nJwcTJ07Ezp07WUpWM6ampvDz80PPnj2hpKSE4OBg/rb79+/jwIEDbEesFgUFBYSEhAhNxU4IIYQQ8UcVPlKvidtU+Z9LSUnBwIEDce3aNXA4HERGRsLExASenp5QU1ODj48P2xGrTEJCAomJidDS0hLY/vHjRzRq1AjFxcUsJasZBQUFhIWFwcDAANra2jh//jycnZ0RExMDJycnZGRksB2xWjp27IhZs2ahW7dubEchhBBCyA9Gk7aQeq1v3778f+fn52Pz5s2wsbHhL7h8//59vHjxAn/88QdLCWtu6tSpkJSURHx8PKytrfnbBw4ciGnTpolEhS8zMxMMw4BhGGRlZUFWVpa/r6SkBP/9959QJVAU6OnpITExEQYGBjA1NUVAQACcnZ3x8OFDyMjIsB2vSkJDQ/n/njhxIqZPn46kpCTY2dlBSkpK4FiaIICQn4Onp+dX94tabwxCSNVQhY/Ua59PlT9q1ChMmjQJy5YtEzomISHhR0ertYCAAFy6dAl6enoC283NzfH69WuWUlWPqqoqOBwOOBwOLCwshPZzOJxqrW9XX/Tr1w9Xr15FixYtMHHiRP6yE/Hx8Zg6dSrb8arE0dERHA5HYJKWz2/2yvaJ0qQtDMMgISEBWlpaAg8XCCFVk5aWJvC6qKgIz58/R3p6Ojp27MhSKkLI90ZdOonIUFFRwaNHj4QWXI6MjETTpk1FrpudkpISnjx5AnNzcygpKSEkJAQmJiZ49OgRunbtipSUFLYjftPNmzfBMAw6duyI48ePQ11dnb9PWloahoaG0NHRYTFh3bh37x7u3bsHc3Nz9OrVi+04VVKdhwaGhobfMUnd4fF4kJWVxYsXL4SuA6KoqKgIY8eOxYIFC6o8sQYhdY3H48Hb2xumpqaYNWsW23EIId8BVfiIyGjUqBFWrVoFd3d3ge27d+/G7Nmz8f79e3aC1VCPHj3QpEkTLFu2DEpKSggNDYWhoSEGDRoEHo+HY8eOsR2xyl6/fg0DAwORm2zmZ7Fy5Uo0bNhQqDvXzp07kZycjNmzZ7OUrPoaN26MHTt2oGXLlmxHqRMqKioIDg6mCh9h1atXr9C+fXskJiayHYUQ8h1Ql04iMqZMmQJvb288efJEYEHfnTt3YsGCBSynq77Vq1fD1dUVjx49QmFhIWbNmoUXL14gNTUVgYGBbMf7ptDQUNja2oLL5SIjIwPPnj2r9FhRGyO2Z8+er+4fMWLED0pSN/79998KZxZt3LgxBg0aJFIVvlWrVmHmzJn4559/YGtry3acWuvbty9OnTolMl2FiXiKjo4Wucm1CCFVRy18RKQcOXIEGzZsEFhwefLkyfj9999ZTlYzGRkZ2LhxI0JCQpCdnQ1nZ2eMHz8e2trabEf7Ji6Xi6SkJGhpaYHL5QqNFysjSmPEyqipqQm8LioqQm5uLqSlpSEvLy9yC6/LysoiLCxMqBUpJiYGNjY2yM/PZylZ9ampqSE3NxfFxcWQlpaGnJycwH5R+9ksX74cPj4+cHV1RZMmTYTWqxO1NR9J/TZt2jSB1wzDIDExEefPn8fIkSOxceNGlpIRQr4nqvARQmrk826c3xovJipjxL4mMjIS3t7emDlzJrp27cp2nGoxNzfHokWLMGzYMIHte/fuxaJFi0RqsWV/f/+v7h85cuQPSlI3vtaVkxbCJnWtQ4cOAq+5XC40NTXRsW4gwqQAABafSURBVGNHeHp6QlKSOn4RIo6owkdESnp6Oo4dO4aYmBjMmDED6urqePLkCRo2bAhdXV22433T51Plf4uodYP8GTx69AjDhg1DeHg421GqZfXq1Vi9ejXWrFnDn4nv6tWrmDVrFqZPn465c+eynJAQ8iPk5uaCYRh+S3JcXBxOnToFa2trkXuQRQipOnqUQ0RGaGgoOnXqBBUVFcTFxWHUqFFQV1fHiRMnEB8f/81xV/VBRVPlV0TUukGK06QgXyMpKYl3796xHaPaZs6ciZSUFPzxxx8oLCwEUNrNc/bs2SJX2YuPj//qfgMDgx+UhBDR07dvX7i5uWHcuHFIT09Hy5YtISUlhY8fP8LX1xfe3t5sRySEfAfUwkdERqdOneDs7IzVq1cLLGNw9+5dDBkyBHFxcWxH/CZxnCofAIyMjHDgwAG0bt1aYHtQUBAGDRqE2NhYlpLVzJkzZwRel41z2bhxI/T19XHhwgWWktVOdnY2wsLCICcnB3Nzc5FZRP5zZeNFKyNKD0oAWgib/FgNGjTAzZs30bhxY2zfvh1///03nj59iuPHj2PhwoX88fGEEPFCLXxEZDx8+BD//vuv0HZdXV0kJSWxkKj6RKkSVx1JSUkVTjSjqakpktN89+3bV+A1h8Phj3Px8fFhJ1QdUFRURLNmzdiOUStPnz4VeF1UVISnT5/C19cXf/31F0upao4WwiY/Um5uLpSUlAAAAQEBcHNzA5fLRcuWLav1QJIQIlqowkdEhoyMDDIzM4W2R0REQFNTk4VE1XfmzBl0794dUlJSQq1IX+rdu/cPSlV7+vr6CAwMFJqAIjAwUCQXXufxeGxHIJVwcHAQ2ta0aVPo6OhgzZo1cHNzYyFVzZ08eVJo2+cLYRNSl8zMzHDq1Cn069cPly5d4i8H8uHDBygrK7OcjhDyvVCXTiIyRo0ahZSUFBw5cgTq6uoIDQ2FhIQE+vbti7Zt22L9+vVsR/ymL5cyqIyojeGjSUEI26KiouDg4ICcnBy2o9QJWgibfA/Hjh3DkCFDUFJSAldXVwQEBAAoHYd969Ytke2uTgj5OqrwEZGRkZGB3377DY8ePUJWVhZ0dHSQlJSEVq1a4b///hNav4r8OAzDYM6cOfDz8xOaFGThwoUsp6uaL9en+hpfX9/vmIR8zZet/GXjKxcvXozw8HAEBwezE6yO/ffffxg5ciSSk5PZjkLETFJSEhITE+Hg4MB/8PjgwQMoKyvDysqK5XSEkO+BKnxE5AQGBgosVN6pUye2I9XInj17MHDgQKGJMwoLC3Ho0CGMGDGCpWQ1J8qTgny5PtWTJ09QXFwMS0tLAKVdhyUkJNCkSRNcu3aNjYgEFU/awjAM9PX1cejQIbRq1YqlZDVDC2ETQgj53qjCR0RGeHh4pU8fL126JHJrCElISCAxMRFaWloC21NSUqClpSVSXTrFja+vL27cuAF/f3+oqakBKJ1cw8PDAy4uLpg+fTrLCX9eN2/eFHhdtnC0mZmZSC4a3b59e4EKLC2ETQghpK5RhY+IDHl5eaxZswbjx4/nbysoKMD06dOxfft25Ofns5iu+rhcLt6/fy804UxISAg6dOiA1NRUlpIRXV1dBAQEoHHjxgLbnz9/ji5duojkWnyizNnZGVevXoWamhqWLl2KGTNmQF5enu1YNfb55E2EEELI90aPDonI2L17N7y9vXH+/Hns2rULiYmJGDJkCHg8Hm7fvs12vCpzcnICh8MBh8OBq6urwBP8kpISxMbGolu3biwmJJmZmRWOnUpOTkZWVhYLiX5uYWFhyMnJgZqaGpYsWQJvb2+RrvD169cPSUlJ0NTUrLSlnxBCCKkrVOEjIuP3339H69at4eHhgcaNGyMnJwfu7u7w8fERqZu/sjXegoOD0bVrVygqKvL3SUtLw8jICP3792cpHQFKb8g9PDzg4+OD5s2bAyhdRH7mzJkiN+2/OHB0dISHhwd++eUXMAyDNWvWCPzefE4UJgnS1NTE/fv30atXLzAM89WF5AkhhJDaogofETmFhYUoKSlBSUkJtLW1ISsry3akalm0aBEAwMjICAMHDhS5/D+DLVu2YMaMGRgyZAiKiooAAJKSkvDy8sKaNWtYTvfz2b17NxYtWoRz586Bw+HgwoULFY5t43A4IlHhGzduHPr06cNv6W/UqFGlx9JYXkIIIbVFY/iIyDh06BC8vb3h4uKCHTt2IDg4GB4eHjA0NMTevXthYmLCdkQiZnJychAdHQ0AMDU1paU/6oHP17IUZeHh4YiKikLv3r2xa9cuqKqqVnhcnz59fmwwQgghYocqfERkKCgoYO3atfD29uZvS0tLw9ixY3Hx4kWh9bnqu5KSEqxbtw5HjhxBfHw8f/26MjRpCyHib8mSJZg5c6ZIdUsnhBAiWqjCR0TGq1ev+GuifWnv3r0YPnz4D05UOwsXLsT27dsxffp0zJ8/H/PmzUNcXBxOnTqFhQsXYtKkSWxH/Km4ublh9+7dUFZW/uY4vRMnTvygVAQondWyqnr37v0dkxBCCCGih8bwEZFRWWUPgMhV9gBg//792LZtG3r27InFixdj8ODBMDU1hb29Pe7fv08Vvh9MRUWFP3mGiooKy2nI58omOvoWDocjEmPeymbqrYonT5585zSEEELEHVX4SL02bdo0LFu2DAoKCpg2bdpXj/X19f1BqepGUlIS7OzsAACKiorIyMgAAPz6669YsGABm9F+Srt27arw34R9PB6P7Qh1qqoVWEIIIaQuUIWP1GtPnz7lz5L45MmTSp+Ki+K05np6ekhMTISBgQFMTU0REBAAZ2dnPHz4EDIyMmzH+6nl5eWBYRj+uKrXr1/j5MmTsLGxQZcuXVhOR0Rd2Uy9hBBCyI9AY/hIvRYaGgpbW1twuVy2o9S5OXPmQFlZGX/++ScOHz6MYcOGwcjICPHx8Zg6dSpWrVrFdsSfVpcuXeDm5oZx48YhPT0dlpaWkJaWxsePH+Hr6yswcRD5sZYuXfrV/aKwLAMhhBDyI1GFj9RrEhISSExMhJaWFkxMTPDw4UNoaGiwHeu7uHfvHu7duwdzc3P06tWL7Tg/tQYNGuDmzZto3Lgxtm/fjr///htPnz7F8ePHsXDhQoSFhbEd8afl5OQk8LqoqAixsbGQlJSEqampyI1543K5X+2hIApjEgkhhNRv1KWT1GuqqqqIjY2FlpYW4uLixG4sz+datWqFVq1asR2DAMjNzYWSkhIAICAgAG5ubuByuWjZsiVev37Ncrqf29OnT4W2ZWZmwt3dHf369WMhUe2cPHlS4HVRURGePn0Kf39/LFmyhKVUhBBCxAm18JF6bcyYMdizZw+0tbURHx8PPT09SEhIVHhsTEzMD05XfTS9vGiwt7fHqFGj0K9fP9ja2uLixYto1aoVHj9+jJ49eyIpKYntiOQLz549Q69evRAXF8d2lDpx4MABHD58GKdPn2Y7CiGEEBFHLXykXtu6dSvc3NwQFRWFSZMmYfTo0fyWF1EkbtPLi6uFCxdiyJAhmDp1Kjp27MhveQ0ICBDqUkjqh4yMDP5Mt+KgZcuWGDNmDNsxCCGEiAFq4SMiw8PDA35+fiJd4SOiIykpCYmJiXBwcOBPGvTgwQMoKyvDysqK5XQ/Lz8/P4HXDMMgMTERe/fuRbt27XDgwAGWktWdvLw8zJ07FxcuXMCrV6/YjkMIIUTEUYWPEEIqERUVhejoaLRt2xZycnJgGEYklwARJ8bGxgKvuVwuNDU10bFjR8ydO1fkHgipqakJnFMMwyArKwvy8vLYt28fde0mhBBSa1ThI4RFN2/exNq1a/mzPtrY2GDmzJlwcXFhOdnPLSUlBb///juuX78ODoeDyMhImJiYwNPTE2pqavDx8WE7IhETu3fvFqjwlVVgW7RoATU1NRaTEUIIERdU4SOEJfv27YOHhwfc3NzQpk0bAEBgYCBOnjyJ3bt3Y8iQISwn/HmNGDECHz58wPbt22FtbY2QkBCYmJjg0qVLmDZtGl68eMF2RPJJZmYmrl27BktLS1hbW7MdhxBCCKl3qMJHCEusra0xZswYTJ06VWC7r68vtm3bRmu9sahRo0a4dOkSHBwcoKSkxK/wxcTEwN7eHtnZ2WxH/Gn9/vvvaNu2LSZMmIC8vDw4ODggLi4ODMPg0KFD6N+/P9sRq+XixYtQVFTEL7/8AgDYtGkTtm3bBhsbG2zatIla+QghhNQal+0AhPysYmJiKlxgvXfv3oiNjWUhESmTk5MDeXl5oe2pqamQkZFhIREpc+vWLX6X55MnT4JhGKSnp8PPzw/Lly9nOV31zZw5E5mZmQBKl5aYNm0aevTogdjYWEybNo3ldIQQQsQBVfgIYYm+vj6uXr0qtP3KlSvQ19dnIREp4+Ligj179vBfczgc8Hg8rF69Gh06dGAxGcnIyIC6ujqA0tax/v37Q15eHj179kRkZCTL6aovNjYWNjY2AIDjx4+jV69eWLFiBTZt2oQLFy6wnI4QQog4oHX4CGHJ9OnTMWnSJAQHB6N169YASsfw7d69Gxs2bGA53c9t9erVcHV1xaNHj1BYWIhZs2bhxYsXSE1NRWBgINvxfmr6+vq4d+8e1NXVcfHiRRw6dAgAkJaWBllZWZbTVZ+0tDRyc3MBlD7sGTFiBABAXV2d3/JHCCGE1AZV+Ahhibe3Nxo1agQfHx8cOXIEQOm4vsOHD6NPnz4sp/u52draIiIiAhs3boSSkhKys7Ph5uaG8ePHQ1tbm+14P7UpU6Zg6NChUFRUhKGhIdq3bw+gtKunnZ0du+Fq4JdffsG0adPQpk0bPHjwAIcPHwYAREREQE9Pj+V0hBBCxAFN2kIIIZ8pKipCt27dsGXLFpibm7Mdh1Tg8ePHiI+PR+fOnaGoqAgAOH/+PFRVVfkz3oqK+Ph4/PHHH0hISMCkSZPg5eUFAJg6dSpKSkqEFponhBBCqosqfISwZNSoURg2bBi/hYLUH5qamrh79y5V+AghhBAi8mjSFkJYkpycjG7dukFfXx8zZ85EcHAw25HIJ8OGDcOOHTvYjkEIIYQQUmvUwkcIi9LS0nD06FEcOHAAt2/fhpWVFYYOHYohQ4bAyMiI7Xg/rYkTJ2LPnj0wNzdHkyZNoKCgILDf19eXpWSEEEIIIdVDFT5C6ok3b97g4MGD2LlzJyIjI1FcXMx2pJ/W15Ze4HA4uHbt2g9MQwghhBBSczRLJyH1QFFRER49eoSgoCDExcWhYcOGbEf6qV2/fp3tCIQQQgghdYLG8BHCouvXr2P06NFo2LAh3N3doaysjHPnzuHNmzdsRyOk3rp9+zaGDRuGVq1a4e3btwCAvXv34s6dOywnI4QQQuofauEjhCW6urpITU1Ft27dsHXrVvTq1QsyMjJsxyKkXjt+/DiGDx+OoUOH4unTpygoKAAAZGRkYMWKFfjvv/9YTvhtbm5uVT72xIkT3zEJIYSQnwFV+AhhyeLFizFgwACoqqqyHYUQkbF8+XJs2bIFI0aMwKFDh/jb27Rpg+XLl7OYrOpUVFTYjkAIIeQnQpO2EEIIERny8vJ4+fIljIyMoKSkhJCQEJiYmCAmJgY2NjbIz89nOyIhhBBSr9AYPkIIISKjUaNGiIqKEtp+584dmJiYsJCIEEIIqd+oSychhBCRMXr0aEyePBk7d+4Eh8PBu3fvcO/ePcyYMQMLFixgO16NHDt2DEeOHEF8fDwKCwsF9j158oSlVIQQQsQFtfARQggRGXPmzMGQIUPg6uqK7OxstG3bFqNGjcLYsWMxceJEtuNVm5+fHzw8PNCwYUM8ffoUzZs3h4aGBmJiYtC9e3e24xFCCBEDNIaPEEKIyCksLERUVBSys7NhY2MDRUVFtiPViJWVFRYtWoTBgwcLjElcuHAhUlNTsXHjRrYjEkIIEXHUwkcIIUTkxMfHIyEhAXZ2dlBUVISoPruMj49H69atAQBycnLIysoCAAwfPhwHDx5kMxohhBAxQRU+QgghIiMlJQWurq6wsLBAjx49kJiYCADw8vLC9OnTWU5XfY0aNUJqaioAwMDAAPfv3wcAxMbGimwllhBCSP1CFT5CCCEiY+rUqZCSkkJ8fDzk5eX52wcOHIiLFy+ymKxmOnbsiDNnzgAAPDw8MHXqVHTu3BkDBw5Ev379WE5HCCFEHNAYPkIIISKjUaNGuHTpEhwcHITW4bO3t0d2djbbEauFx+OBx+NBUrJ00uxDhw7h7t27MDc3x9ixYyEtLc1yQkIIIaKOlmUghBAiMnJycgRa9sqkpqZCRkaGhUS18+bNG+jr6/NfDxo0CIMGDQLDMEhISICBgQGL6QghhIgD6tJJCCFEZLi4uGDPnj381xwOBzweD6tXr0aHDh1YTFYzxsbGSE5OFtqempoKY2NjFhIRQggRN9TCRwghRGSsXr0arq6uePToEQoLCzFr1iy8ePECqampCAwMZDtetTEMAw6HI7Q9OzsbsrKyLCQihBAibqjCRwghRGTY2toiIiICGzduhJKSErKzs+Hm5obx48dDW1ub7XhVNm3aNAClLZQLFiwQ6KZaUlKCoKAgODo6spSOEEKIOKEKHyGEEJFQVFSEbt26YcuWLZg3bx7bcWrl6dOnAEpb+J49eyYwOYu0tDQcHBwwY8YMtuIRQggRI1ThI4QQIhKkpKQQGhrKdow6cf36dQClSzFs2LABysrKLCcihBAirmhZBkIIISJj6tSpkJGRwapVq9iOUufevHkDANDT02M5CSGEEHFCLXyEEEJERnFxMXbu3IkrV66gSZMmUFBQENjv6+vLUrKa4fF4WL58OXx8fPhrCCopKWH69OmYN28euFyaTJsQQkjtUIWPEEKIyHj+/DmcnZ0BABEREQL7Kprtsr6bN28eduzYgVWrVqFNmzYAgDt37mDx4sXIz8/HX3/9xXJCQgghoo66dBJCCKnXQkNDYWtrK5atXTo6OtiyZQt69+4tsP306dP4448/8PbtW5aSEUIIERfi99eTEEKIWHFycsLHjx8BACYmJkhJSWE5Ud1JTU2FlZWV0HYrKyukpqaykIgQQoi4oQofIYSQek1VVRWxsbEAgLi4OPB4PJYT1R0HBwds3LhRaPvGjRvh4ODAQiJCCCHihsbwEUIIqdf69++Pdu3aQVtbGxwOB02bNoWEhESFx8bExPzgdLWzevVq9OzZE1euXEGrVq0AAPfu3UNCQgL+++8/ltMRQggRBzSGjxBCSL138eJFREVFYdKkSVi6dCmUlJQqPG7y5Mk/OFntvXv3Dps2bUJ4eDgAwNraGn/88Qd0dHRYTkYIIUQcUIWPEEKIyPDw8ICfn1+lFT5REx8fD319/QpnGI2Pj4eBgQELqQghhIgTqvARQgghLJGQkEBiYiK0tLQEtqekpEBLSwslJSUsJSOEECIuaNIWQgghhCUMw1TYupednQ1ZWVkWEhFCCBE3NGkLIYQQ8oNNmzYNQOli8QsWLIC8vDx/X0lJCYKCguDo6MhSOkIIIeKEKnyEEELID/b06VMApS18z549g7S0NH+ftLQ0HBwcMGPGDLbiEUIIESM0ho8QQghhiYeHBzZs2ABlZWW2oxBCCBFTVOEjhBBCCCGEEDFFk7YQQgghhBBCiJiiCh8hhBBCCCGEiCmq8BFCCCGEEEKImKIKHyGEEEIIIYSIKarwEUIIIYQQQoiYogofIYQQQgghhIgpqvARQgghhBBCiJj6P8OqRE1oyrNPAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "correlation_matrix = datos_mod_white.corr()\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ce1e05a"
      },
      "source": [
        "## Selección de Características para la Construcción del Modelo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "iwj2T1OdXCMY",
        "outputId": "6ac49ba3-43dc-428f-9185-3577e2089711"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.bar( datos_mod_white['quality'],datos_mod_white['alcohol'], color ='linen',\n",
        "        width = 0.4)\n",
        "plt.xlabel(\"Calificacion calidad\")\n",
        "plt.ylabel(\"Grados de alcohol\")\n",
        "plt.title(\"Calidad y grados de alcohol\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "84235494"
      },
      "source": [
        "## Preprocesamiento de Datos para la Red Neuronal - Vino Blanco"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "import statsmodels.api as sm\n",
        "from scipy import stats"
      ],
      "metadata": {
        "id": "aQuBgFjkBBP5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = datos_mod_white[['fixed acidity','residual sugar','chlorides','total sulfur dioxide', 'density','pH','sulphates','alcohol']]\n",
        "y = datos_mod_white['quality']\n",
        "\n",
        "# with sklearn\n",
        "regr = linear_model.LinearRegression()\n",
        "regr.fit(x, y)\n",
        "\n",
        "X2 = sm.add_constant(x)\n",
        "est = sm.OLS(y, X2)\n",
        "est2 = est.fit()\n",
        "print(est2.summary())"
      ],
      "metadata": {
        "id": "Bl59TVYJBBM5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc06df0a-f735-4a3a-aa3a-ac51469dcb1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                quality   R-squared:                       0.253\n",
            "Model:                            OLS   Adj. R-squared:                  0.251\n",
            "Method:                 Least Squares   F-statistic:                     167.2\n",
            "Date:                Wed, 22 Nov 2023   Prob (F-statistic):          1.25e-243\n",
            "Time:                        13:44:45   Log-Likelihood:                -4583.9\n",
            "No. Observations:                3961   AIC:                             9186.\n",
            "Df Residuals:                    3952   BIC:                             9242.\n",
            "Df Model:                           8                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "========================================================================================\n",
            "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------------\n",
            "const                  195.3255     20.059      9.738      0.000     155.999     234.652\n",
            "fixed acidity            0.1035      0.023      4.577      0.000       0.059       0.148\n",
            "residual sugar           0.0906      0.008     11.074      0.000       0.075       0.107\n",
            "chlorides               -0.9404      0.583     -1.614      0.107      -2.082       0.202\n",
            "total sulfur dioxide     0.0003      0.000      0.924      0.355      -0.000       0.001\n",
            "density               -196.9173     20.348     -9.678      0.000    -236.811    -157.024\n",
            "pH                       1.0407      0.117      8.930      0.000       0.812       1.269\n",
            "sulphates                0.7433      0.114      6.506      0.000       0.519       0.967\n",
            "alcohol                  0.1219      0.026      4.696      0.000       0.071       0.173\n",
            "==============================================================================\n",
            "Omnibus:                       91.811   Durbin-Watson:                   1.773\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              197.839\n",
            "Skew:                          -0.085   Prob(JB):                     1.10e-43\n",
            "Kurtosis:                       4.082   Cond. No.                     3.37e+05\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 3.37e+05. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = regr.predict(x)\n",
        "plt.scatter(x['alcohol'], y_pred, color=\"royalblue\")"
      ],
      "metadata": {
        "id": "bGGRJzWUBBJ6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "79ebe093-67d5-4f51-fbee-a4fe0a13d977"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x78a92a8faf80>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fixed acidity\n",
        "Q1 = np.percentile(datos_mod_white['fixed acidity'], 25, method='midpoint')\n",
        "Q3 = np.percentile(datos_mod_white['fixed acidity'], 75, method='midpoint')\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "upper=Q3+1.5*IQR\n",
        "lower=Q1-1.5*IQR\n",
        "print(upper,'fixed acidity')\n",
        "print(lower,'fixed acidity')\n",
        "upper_array = np.where(datos_mod_white['fixed acidity']>=upper)[0]\n",
        "lower_array = np.where(datos_mod_white['fixed acidity']<=lower)[0]\n",
        "print(\"Observaciones antes: \", datos_mod_white['fixed acidity'].count())\n",
        "datos_mod_white.drop(index=upper_array,axis=0,inplace=True, errors='ignore')\n",
        "datos_mod_white.drop(index=lower_array ,inplace=True, errors='ignore')\n",
        "print(\"Observaciones despues: \", datos_mod_white['fixed acidity'].count())\n",
        "\n",
        "#Residual sugar\n",
        "\n",
        "Q1 = np.percentile(datos_mod_white['residual sugar'], 25, method='midpoint')\n",
        "Q3 = np.percentile(datos_mod_white['residual sugar'], 75, method='midpoint')\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "upper=Q3+1.5*IQR\n",
        "lower=Q1-1.5*IQR\n",
        "print(upper,'residual sugar')\n",
        "print(lower,'residual sugar')\n",
        "upper_array1 = np.where(datos_mod_white['residual sugar']>=upper)[0]\n",
        "lower_array1 = np.where(datos_mod_white['residual sugar']<=lower)[0]\n",
        "print(\"Observaciones antes: \", datos_mod_white['residual sugar'].count())\n",
        "datos_mod_white.drop(index=upper_array1,axis=0, inplace=True, errors='ignore')\n",
        "datos_mod_white.drop(index=lower_array1, inplace=True, errors='ignore')\n",
        "print(\"Observaciones despues: \", datos_mod_white['residual sugar'].count())\n",
        "\n",
        "\n",
        "#alcohol\n",
        "\n",
        "Q1 = np.percentile(datos_mod_white['alcohol'], 25, method='midpoint')\n",
        "Q3 = np.percentile(datos_mod_white['alcohol'], 75, method='midpoint')\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "upper=Q3+1.5*IQR\n",
        "lower=Q1-1.5*IQR\n",
        "print(upper,'alcohol')\n",
        "print(lower,'alcohol')\n",
        "upper_array2 = np.where(datos_mod_white['alcohol']>=upper)[0]\n",
        "lower_array2 = np.where(datos_mod_white['alcohol']<=lower)[0]\n",
        "print(\"Observaciones antes: \", datos_mod_white['alcohol'].count())\n",
        "datos_mod_white.drop(index=upper_array2,axis=0, inplace=True, errors='ignore')\n",
        "datos_mod_white.drop(index=lower_array2, inplace=True, errors='ignore')\n",
        "print(\"Observaciones despues: \", datos_mod_white['alcohol'].count())\n",
        "\n",
        "#density\n",
        "\n",
        "Q1 = np.percentile(datos_mod_white['density'], 25, method='midpoint')\n",
        "Q3 = np.percentile(datos_mod_white['density'], 75, method='midpoint')\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "upper=Q3+1.5*IQR\n",
        "lower=Q1-1.5*IQR\n",
        "print(upper,'density')\n",
        "print(lower,'density')\n",
        "upper_array5 = np.where(datos_mod_white['density']>=upper)[0]\n",
        "lower_array5 = np.where(datos_mod_white['density']<=lower)[0]\n",
        "print(\"Observaciones antes: \", datos_mod_white['density'].count())\n",
        "datos_mod_white.drop(index=upper_array5,axis=0, inplace=True, errors='ignore')\n",
        "datos_mod_white.drop(index=lower_array5, inplace=True, errors='ignore')\n",
        "print(\"Observaciones despues: \", datos_mod_white['density'].count())\n",
        "\n",
        "#pH\n",
        "\n",
        "Q1 = np.percentile(datos_mod_white['pH'], 25, method='midpoint')\n",
        "Q3 = np.percentile(datos_mod_white['pH'], 75, method='midpoint')\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "upper=Q3+1.5*IQR\n",
        "lower=Q1-1.5*IQR\n",
        "print(upper,'pH')\n",
        "print(lower,'pH')\n",
        "upper_array6 = np.where(datos_mod_white['pH']>=upper)[0]\n",
        "lower_array6 = np.where(datos_mod_white['pH']<=lower)[0]\n",
        "print(\"Observaciones antes: \", datos_mod_white['pH'].count())\n",
        "datos_mod_white.drop(index=upper_array6,axis=0, inplace=True, errors='ignore')\n",
        "datos_mod_white.drop(index=lower_array6, inplace=True, errors='ignore')\n",
        "print(\"Observaciones despues: \", datos_mod_white['pH'].count())\n",
        "\n",
        "#sulphates\n",
        "\n",
        "Q1 = np.percentile(datos_mod_white['sulphates'], 25, method='midpoint')\n",
        "Q3 = np.percentile(datos_mod_white['sulphates'], 75, method='midpoint')\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "upper=Q3+1.5*IQR\n",
        "lower=Q1-1.5*IQR\n",
        "print(upper,'sulphates')\n",
        "print(lower,'sulphates')\n",
        "upper_array7 = np.where(datos_mod_white['sulphates']>=upper)[0]\n",
        "lower_array7 = np.where(datos_mod_white['sulphates']<=lower)[0]\n",
        "print(\"Observaciones antes: \", datos_mod_white['sulphates'].count())\n",
        "datos_mod_white.drop(index=upper_array7,axis=0, inplace=True, errors='ignore')\n",
        "datos_mod_white.drop(index=lower_array7, inplace=True, errors='ignore')\n",
        "print(\"Observaciones despues: \", datos_mod_white['sulphates'].count())"
      ],
      "metadata": {
        "id": "grXKHM65BBGp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a273a3c-31a4-4ed8-f3e4-b36544c291f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8.8 fixed acidity\n",
            "4.8 fixed acidity\n",
            "Observaciones antes:  3961\n",
            "Observaciones despues:  3863\n",
            "19.6 residual sugar\n",
            "-9.200000000000001 residual sugar\n",
            "Observaciones antes:  3863\n",
            "Observaciones despues:  3849\n",
            "14.25 alcohol\n",
            "6.6499999999999995 alcohol\n",
            "Observaciones antes:  3849\n",
            "Observaciones despues:  3849\n",
            "1.0018500000000001 density\n",
            "0.98545 density\n",
            "Observaciones antes:  3849\n",
            "Observaciones despues:  3846\n",
            "3.5900000000000003 pH\n",
            "2.7899999999999996 pH\n",
            "Observaciones antes:  3846\n",
            "Observaciones despues:  3813\n",
            "0.7600000000000001 sulphates\n",
            "0.19999999999999987 sulphates\n",
            "Observaciones antes:  3813\n",
            "Observaciones despues:  3742\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-104-2284b5a97137>:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  datos_mod_white.drop(index=upper_array,axis=0,inplace=True, errors='ignore')\n",
            "<ipython-input-104-2284b5a97137>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  datos_mod_white.drop(index=lower_array ,inplace=True, errors='ignore')\n",
            "<ipython-input-104-2284b5a97137>:30: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  datos_mod_white.drop(index=upper_array1,axis=0, inplace=True, errors='ignore')\n",
            "<ipython-input-104-2284b5a97137>:31: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  datos_mod_white.drop(index=lower_array1, inplace=True, errors='ignore')\n",
            "<ipython-input-104-2284b5a97137>:48: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  datos_mod_white.drop(index=upper_array2,axis=0, inplace=True, errors='ignore')\n",
            "<ipython-input-104-2284b5a97137>:49: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  datos_mod_white.drop(index=lower_array2, inplace=True, errors='ignore')\n",
            "<ipython-input-104-2284b5a97137>:65: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  datos_mod_white.drop(index=upper_array5,axis=0, inplace=True, errors='ignore')\n",
            "<ipython-input-104-2284b5a97137>:66: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  datos_mod_white.drop(index=lower_array5, inplace=True, errors='ignore')\n",
            "<ipython-input-104-2284b5a97137>:82: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  datos_mod_white.drop(index=upper_array6,axis=0, inplace=True, errors='ignore')\n",
            "<ipython-input-104-2284b5a97137>:83: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  datos_mod_white.drop(index=lower_array6, inplace=True, errors='ignore')\n",
            "<ipython-input-104-2284b5a97137>:99: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  datos_mod_white.drop(index=upper_array7,axis=0, inplace=True, errors='ignore')\n",
            "<ipython-input-104-2284b5a97137>:100: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  datos_mod_white.drop(index=lower_array7, inplace=True, errors='ignore')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_set = ['fixed acidity','residual sugar','alcohol','density','pH','sulphates']\n",
        "features=datos_mod_white[feature_set]\n",
        "target = datos_mod_white['quality']"
      ],
      "metadata": {
        "id": "SBjFa6jFBBAi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3b132fba"
      },
      "source": [
        "## Datos para el modelo con dos vinos"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creacion de dummy --- volver a cargar datasets\n"
      ],
      "metadata": {
        "id": "YYd4cHVwAzgy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "RAkOqP3NVHoj",
        "outputId": "65ad0b32-bd6c-4a4e-e871-44631e4ce10d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0               7.4             0.700         0.00             1.9      0.076   \n",
              "1               7.8             0.880         0.00             2.6      0.098   \n",
              "2               7.8             0.760         0.04             2.3      0.092   \n",
              "3              11.2             0.280         0.56             1.9      0.075   \n",
              "5               7.4             0.660         0.00             1.8      0.075   \n",
              "...             ...               ...          ...             ...        ...   \n",
              "1593            6.8             0.620         0.08             1.9      0.068   \n",
              "1594            6.2             0.600         0.08             2.0      0.090   \n",
              "1595            5.9             0.550         0.10             2.2      0.062   \n",
              "1597            5.9             0.645         0.12             2.0      0.075   \n",
              "1598            6.0             0.310         0.47             3.6      0.067   \n",
              "\n",
              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
              "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
              "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
              "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
              "5                    13.0                  40.0  0.99780  3.51       0.56   \n",
              "...                   ...                   ...      ...   ...        ...   \n",
              "1593                 28.0                  38.0  0.99651  3.42       0.82   \n",
              "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
              "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
              "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
              "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
              "\n",
              "      alcohol  quality  RedWine  \n",
              "0         9.4        5        1  \n",
              "1         9.8        5        1  \n",
              "2         9.8        5        1  \n",
              "3         9.8        6        1  \n",
              "5         9.4        5        1  \n",
              "...       ...      ...      ...  \n",
              "1593      9.5        6        1  \n",
              "1594     10.5        5        1  \n",
              "1595     11.2        6        1  \n",
              "1597     10.2        5        1  \n",
              "1598     11.0        6        1  \n",
              "\n",
              "[1359 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fe81438f-2033-4ceb-a638-55fceff0d525\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "      <th>RedWine</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.700</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.99780</td>\n",
              "      <td>3.51</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.880</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.99680</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.760</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.99700</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.280</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
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              "      <td>60.0</td>\n",
              "      <td>0.99800</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.660</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0.075</td>\n",
              "      <td>13.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.99780</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1593</th>\n",
              "      <td>6.8</td>\n",
              "      <td>0.620</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.068</td>\n",
              "      <td>28.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.99651</td>\n",
              "      <td>3.42</td>\n",
              "      <td>0.82</td>\n",
              "      <td>9.5</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1594</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.08</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.090</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99490</td>\n",
              "      <td>3.45</td>\n",
              "      <td>0.58</td>\n",
              "      <td>10.5</td>\n",
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              "      <td>1</td>\n",
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              "      <th>1595</th>\n",
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              "      <td>0.10</td>\n",
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              "      <td>39.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.99512</td>\n",
              "      <td>3.52</td>\n",
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              "      <td>11.2</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>1597</th>\n",
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              "      <td>3.57</td>\n",
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              "      <td>3.39</td>\n",
              "      <td>0.66</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
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              "<p>1359 rows × 13 columns</p>\n",
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              "\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",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "1               6.3              0.30         0.34             1.6      0.049   \n",
              "2               8.1              0.28         0.40             6.9      0.050   \n",
              "3               7.2              0.23         0.32             8.5      0.058   \n",
              "6               6.2              0.32         0.16             7.0      0.045   \n",
              "9               8.1              0.22         0.43             1.5      0.044   \n",
              "...             ...               ...          ...             ...        ...   \n",
              "4893            6.2              0.21         0.29             1.6      0.039   \n",
              "4894            6.6              0.32         0.36             8.0      0.047   \n",
              "4895            6.5              0.24         0.19             1.2      0.041   \n",
              "4896            5.5              0.29         0.30             1.1      0.022   \n",
              "4897            6.0              0.21         0.38             0.8      0.020   \n",
              "\n",
              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "1                    14.0                 132.0  0.99400  3.30       0.49   \n",
              "2                    30.0                  97.0  0.99510  3.26       0.44   \n",
              "3                    47.0                 186.0  0.99560  3.19       0.40   \n",
              "6                    30.0                 136.0  0.99490  3.18       0.47   \n",
              "9                    28.0                 129.0  0.99380  3.22       0.45   \n",
              "...                   ...                   ...      ...   ...        ...   \n",
              "4893                 24.0                  92.0  0.99114  3.27       0.50   \n",
              "4894                 57.0                 168.0  0.99490  3.15       0.46   \n",
              "4895                 30.0                 111.0  0.99254  2.99       0.46   \n",
              "4896                 20.0                 110.0  0.98869  3.34       0.38   \n",
              "4897                 22.0                  98.0  0.98941  3.26       0.32   \n",
              "\n",
              "      alcohol  quality  RedWine  \n",
              "1         9.5        6        0  \n",
              "2        10.1        6        0  \n",
              "3         9.9        6        0  \n",
              "6         9.6        6        0  \n",
              "9        11.0        6        0  \n",
              "...       ...      ...      ...  \n",
              "4893     11.2        6        0  \n",
              "4894      9.6        5        0  \n",
              "4895      9.4        6        0  \n",
              "4896     12.8        7        0  \n",
              "4897     11.8        6        0  \n",
              "\n",
              "[3742 rows x 13 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "      <th>RedWine</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.3</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.049</td>\n",
              "      <td>14.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>0.99400</td>\n",
              "      <td>3.30</td>\n",
              "      <td>0.49</td>\n",
              "      <td>9.5</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.1</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.40</td>\n",
              "      <td>6.9</td>\n",
              "      <td>0.050</td>\n",
              "      <td>30.0</td>\n",
              "      <td>97.0</td>\n",
              "      <td>0.99510</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.44</td>\n",
              "      <td>10.1</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.2</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.32</td>\n",
              "      <td>8.5</td>\n",
              "      <td>0.058</td>\n",
              "      <td>47.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>0.99560</td>\n",
              "      <td>3.19</td>\n",
              "      <td>0.40</td>\n",
              "      <td>9.9</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.16</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.045</td>\n",
              "      <td>30.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>0.99490</td>\n",
              "      <td>3.18</td>\n",
              "      <td>0.47</td>\n",
              "      <td>9.6</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8.1</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.044</td>\n",
              "      <td>28.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>0.99380</td>\n",
              "      <td>3.22</td>\n",
              "      <td>0.45</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4893</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.039</td>\n",
              "      <td>24.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>0.99114</td>\n",
              "      <td>3.27</td>\n",
              "      <td>0.50</td>\n",
              "      <td>11.2</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4894</th>\n",
              "      <td>6.6</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.36</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.047</td>\n",
              "      <td>57.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>0.99490</td>\n",
              "      <td>3.15</td>\n",
              "      <td>0.46</td>\n",
              "      <td>9.6</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4895</th>\n",
              "      <td>6.5</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.041</td>\n",
              "      <td>30.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>0.99254</td>\n",
              "      <td>2.99</td>\n",
              "      <td>0.46</td>\n",
              "      <td>9.4</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4896</th>\n",
              "      <td>5.5</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.022</td>\n",
              "      <td>20.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>0.98869</td>\n",
              "      <td>3.34</td>\n",
              "      <td>0.38</td>\n",
              "      <td>12.8</td>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>4897</th>\n",
              "      <td>6.0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.020</td>\n",
              "      <td>22.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.98941</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.32</td>\n",
              "      <td>11.8</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3742 rows × 13 columns</p>\n",
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              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3769e684-7900-4e87-b8e3-0cbc2b4c08d3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "datos_mod_red = datos_mod_red.assign(RedWine=1)\n",
        "display(datos_mod_red)\n",
        "datos_mod_white = datos_mod_white.assign(RedWine=0)\n",
        "display(datos_mod_white)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g2u3728gNRC1"
      },
      "outputs": [],
      "source": [
        "red_white= pd.concat([datos_mod_red,datos_mod_white ],ignore_index=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5ZZLSlPlNdpn"
      },
      "outputs": [],
      "source": [
        "from sklearn import linear_model\n",
        "import statsmodels.api as sm\n",
        "from scipy import stats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2jYWuRKYOsHQ",
        "outputId": "040c75a6-3fb0-4b5c-9383-69993360a097"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                quality   R-squared:                       0.263\n",
            "Model:                            OLS   Adj. R-squared:                  0.262\n",
            "Method:                 Least Squares   F-statistic:                     227.0\n",
            "Date:                Wed, 22 Nov 2023   Prob (F-statistic):               0.00\n",
            "Time:                        13:44:46   Log-Likelihood:                -5816.0\n",
            "No. Observations:                5101   AIC:                         1.165e+04\n",
            "Df Residuals:                    5092   BIC:                         1.171e+04\n",
            "Df Model:                           8                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "========================================================================================\n",
            "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------------\n",
            "const                  140.1777     12.403     11.302      0.000     115.863     164.492\n",
            "fixed acidity            0.1353      0.017      8.142      0.000       0.103       0.168\n",
            "residual sugar           0.0694      0.006     12.274      0.000       0.058       0.080\n",
            "chlorides               -1.3299      0.356     -3.739      0.000      -2.027      -0.633\n",
            "total sulfur dioxide    -0.0005      0.000     -1.839      0.066      -0.001    3.12e-05\n",
            "density               -141.2539     12.670    -11.149      0.000    -166.093    -116.415\n",
            "pH                       0.7430      0.104      7.169      0.000       0.540       0.946\n",
            "sulphates                1.0388      0.087     11.960      0.000       0.869       1.209\n",
            "alcohol                  0.1849      0.018     10.202      0.000       0.149       0.220\n",
            "==============================================================================\n",
            "Omnibus:                      139.586   Durbin-Watson:                   1.782\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              286.665\n",
            "Skew:                          -0.168   Prob(JB):                     5.64e-63\n",
            "Kurtosis:                       4.112   Cond. No.                     2.13e+05\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 2.13e+05. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ],
      "source": [
        "x = red_white[['fixed acidity','residual sugar','chlorides','total sulfur dioxide', 'density','pH','sulphates','alcohol']]\n",
        "y = red_white['quality']\n",
        "\n",
        "# with sklearn\n",
        "regr = linear_model.LinearRegression()\n",
        "regr.fit(x, y)\n",
        "\n",
        "X2 = sm.add_constant(x)\n",
        "est = sm.OLS(y, X2)\n",
        "est2 = est.fit()\n",
        "print(est2.summary())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49ba8439"
      },
      "source": [
        "## Limpiar datos atipicos ambos vinos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "AkJbkJaBDQHj",
        "outputId": "01f74838-f219-4020-94d4-421d4b999cb0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x78a91b569570>"
            ]
          },
          "metadata": {},
          "execution_count": 110
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "y_pred = regr.predict(x)\n",
        "plt.scatter(x['alcohol'], y_pred, color=\"royalblue\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7AAokCtlcMiP"
      },
      "source": [
        "## Eliminar datos atipicos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "fJMfdduBWWAp",
        "outputId": "f93be2ba-bdd0-4246-a984-05d41faf618c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "boxplot = red_white.boxplot(column=['fixed acidity','residual sugar','alcohol'], return_type='both', figsize=(10,10))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "H8SG_N5pWn1h",
        "outputId": "4d576ba7-f351-443a-8d63-41a30cafcbd5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "boxplot = red_white.boxplot(column=['total sulfur dioxide'], return_type='both', figsize=(10,10))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "ElAAQ2-zW2AY",
        "outputId": "93ba1e78-6748-451c-cf06-aabde673bc41"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "boxplot = red_white.boxplot(column=['chlorides','density','pH','sulphates'], return_type='both', figsize=(10,10))\n",
        "plt.show()\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l2LawuwnljxK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7516e959-f807-4e60-ebe0-87354a329016"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Observaciones antes:  5101\n",
            "Observaciones despues:  4799\n",
            "Observaciones antes:  4799\n",
            "Observaciones despues:  4687\n",
            "Observaciones antes:  4687\n",
            "Observaciones despues:  4687\n",
            "Observaciones antes:  4687\n",
            "Observaciones despues:  4676\n",
            "Observaciones antes:  4676\n",
            "Observaciones despues:  4494\n",
            "Observaciones antes:  4494\n",
            "Observaciones despues:  4492\n",
            "Observaciones antes:  4492\n",
            "Observaciones despues:  4459\n",
            "Observaciones antes:  4459\n",
            "Observaciones despues:  4367\n"
          ]
        }
      ],
      "source": [
        "#Fixed acidity\n",
        "Q1 = np.percentile(red_white['fixed acidity'], 25, method='midpoint')\n",
        "Q3 = np.percentile(red_white['fixed acidity'], 75, method='midpoint')\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "upper=Q3+1.5*IQR\n",
        "lower=Q1-1.5*IQR\n",
        "\n",
        "upper_array = np.where(red_white['fixed acidity']>=upper)[0]\n",
        "lower_array = np.where(red_white['fixed acidity']<=lower)[0]\n",
        "\n",
        "print(\"Observaciones antes: \", red_white['fixed acidity'].count())\n",
        "red_white.drop(index=upper_array,axis=0,inplace=True, errors='ignore')\n",
        "red_white.drop(index=lower_array ,inplace=True, errors='ignore')\n",
        "print(\"Observaciones despues: \", red_white['fixed acidity'].count())\n",
        "\n",
        "#Residual sugar\n",
        "\n",
        "Q1 = np.percentile(red_white['residual sugar'], 25, method='midpoint')\n",
        "Q3 = np.percentile(red_white['residual sugar'], 75, method='midpoint')\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "upper=Q3+1.5*IQR\n",
        "lower=Q1-1.5*IQR\n",
        "\n",
        "upper_array1 = np.where(red_white['residual sugar']>=upper)[0]\n",
        "lower_array1 = np.where(red_white['residual sugar']<=lower)[0]\n",
        "\n",
        "print(\"Observaciones antes: \", red_white['residual sugar'].count())\n",
        "red_white.drop(index=upper_array1,axis=0, inplace=True, errors='ignore')\n",
        "red_white.drop(index=lower_array1, inplace=True, errors='ignore')\n",
        "print(\"Observaciones despues: \", red_white['residual sugar'].count())\n",
        "\n",
        "\n",
        "#alcohol\n",
        "\n",
        "Q1 = np.percentile(red_white['alcohol'], 25, method='midpoint')\n",
        "Q3 = np.percentile(red_white['alcohol'], 75, method='midpoint')\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "upper=Q3+1.5*IQR\n",
        "lower=Q1-1.5*IQR\n",
        "\n",
        "upper_array2 = np.where(red_white['alcohol']>=upper)[0]\n",
        "lower_array2 = np.where(red_white['alcohol']<=lower)[0]\n",
        "\n",
        "print(\"Observaciones antes: \", red_white['alcohol'].count())\n",
        "red_white.drop(index=upper_array2,axis=0, inplace=True, errors='ignore')\n",
        "red_white.drop(index=lower_array2, inplace=True, errors='ignore')\n",
        "print(\"Observaciones despues: \", red_white['alcohol'].count())\n",
        "\n",
        "\n",
        "# total sulfur dioxide\n",
        "Q1 = np.percentile(red_white['total sulfur dioxide'], 25, method='midpoint')\n",
        "Q3 = np.percentile(red_white['total sulfur dioxide'], 75, method='midpoint')\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "upper=Q3+1.5*IQR\n",
        "upper_array3 = np.where(red_white['total sulfur dioxide']>=upper)[0]\n",
        "print(\"Observaciones antes: \", red_white['total sulfur dioxide'].count())\n",
        "red_white.drop(index=upper_array3,axis=0, inplace=True, errors='ignore')\n",
        "print(\"Observaciones despues: \", red_white['total sulfur dioxide'].count())\n",
        "\n",
        "#chlorides\n",
        "\n",
        "Q1 = np.percentile(red_white['chlorides'], 25, method='midpoint')\n",
        "Q3 = np.percentile(red_white['chlorides'], 75, method='midpoint')\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "upper=Q3+1.5*IQR\n",
        "lower=Q1-1.5*IQR\n",
        "\n",
        "upper_array4 = np.where(red_white['chlorides']>=upper)[0]\n",
        "lower_array4 = np.where(red_white['chlorides']<=lower)[0]\n",
        "\n",
        "print(\"Observaciones antes: \", red_white['chlorides'].count())\n",
        "red_white.drop(index=upper_array4,axis=0, inplace=True, errors='ignore')\n",
        "red_white.drop(index=lower_array4, inplace=True, errors='ignore')\n",
        "print(\"Observaciones despues: \", red_white['chlorides'].count())\n",
        "\n",
        "#density\n",
        "\n",
        "Q1 = np.percentile(red_white['density'], 25, method='midpoint')\n",
        "Q3 = np.percentile(red_white['density'], 75, method='midpoint')\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "upper=Q3+1.5*IQR\n",
        "lower=Q1-1.5*IQR\n",
        "\n",
        "upper_array5 = np.where(red_white['density']>=upper)[0]\n",
        "lower_array5 = np.where(red_white['density']<=lower)[0]\n",
        "\n",
        "print(\"Observaciones antes: \", red_white['density'].count())\n",
        "red_white.drop(index=upper_array5,axis=0, inplace=True, errors='ignore')\n",
        "red_white.drop(index=lower_array5, inplace=True, errors='ignore')\n",
        "print(\"Observaciones despues: \", red_white['density'].count())\n",
        "\n",
        "#pH\n",
        "\n",
        "Q1 = np.percentile(red_white['pH'], 25, method='midpoint')\n",
        "Q3 = np.percentile(red_white['pH'], 75, method='midpoint')\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "upper=Q3+1.5*IQR\n",
        "lower=Q1-1.5*IQR\n",
        "\n",
        "upper_array6 = np.where(red_white['pH']>=upper)[0]\n",
        "lower_array6 = np.where(red_white['pH']<=lower)[0]\n",
        "\n",
        "print(\"Observaciones antes: \", red_white['pH'].count())\n",
        "red_white.drop(index=upper_array6,axis=0, inplace=True, errors='ignore')\n",
        "red_white.drop(index=lower_array6, inplace=True, errors='ignore')\n",
        "print(\"Observaciones despues: \", red_white['pH'].count())\n",
        "\n",
        "#sulphates\n",
        "\n",
        "Q1 = np.percentile(red_white['sulphates'], 25, method='midpoint')\n",
        "Q3 = np.percentile(red_white['sulphates'], 75, method='midpoint')\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "upper=Q3+1.5*IQR\n",
        "lower=Q1-1.5*IQR\n",
        "\n",
        "upper_array7 = np.where(red_white['sulphates']>=upper)[0]\n",
        "lower_array7 = np.where(red_white['sulphates']<=lower)[0]\n",
        "\n",
        "print(\"Observaciones antes: \", red_white['sulphates'].count())\n",
        "red_white.drop(index=upper_array7,axis=0, inplace=True, errors='ignore')\n",
        "red_white.drop(index=lower_array7, inplace=True, errors='ignore')\n",
        "print(\"Observaciones despues: \", red_white['sulphates'].count())\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yUHGSFao26yr"
      },
      "source": [
        "# Construccion del dataset de entrenamiento"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7i7tjq0m2zc5"
      },
      "outputs": [],
      "source": [
        "feature_set = ['fixed acidity','residual sugar','alcohol','density','pH','sulphates']\n",
        "features=datos_mod_white[feature_set]\n",
        "target = datos_mod_white.quality"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3xwyNss43bMK"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "feature_train,feature_test,target_train, target_test = train_test_split(features,target,test_size=0.3,random_state=1)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Perceptron"
      ],
      "metadata": {
        "id": "ttWTAHaUviM7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "X = datos_mod_white.drop('quality', axis=1)\n",
        "y = datos_mod_white['quality']\n",
        "\n",
        "# Dividir los datos en conjuntos de entrenamiento y prueba\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Normalizar datos\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Definir la clase Perceptron para regresión\n",
        "class Perceptron(tf.keras.Model):\n",
        "    def __init__(self, num_inputs):\n",
        "        super(Perceptron, self).__init__()\n",
        "        self.dense = tf.keras.layers.Dense(units=1, input_shape=(num_inputs,), use_bias=True)\n",
        "\n",
        "    def call(self, inputs):\n",
        "        return self.dense(inputs)\n",
        "\n",
        "# Crear una instancia del modelo Perceptron\n",
        "num_inputs = X_train.shape[1]\n",
        "perceptron = Perceptron(num_inputs)\n",
        "\n",
        "# Compilar y entrenar el modelo para regresión\n",
        "perceptron.compile(optimizer='sgd', loss='mse')  # Descenso de gradiente estocástico, error cuadrático medio\n",
        "perceptron.fit(X_train, y_train, epochs=100, verbose=1)\n",
        "\n",
        "# Realizar predicciones con el modelo entrenado\n",
        "predictions = perceptron.predict(X_test)\n",
        "\n",
        "# Calcular y mostrar el error cuadrático medio (MSE)\n",
        "mse = mean_squared_error(y_test, predictions)\n",
        "print(f'Mean Squared Error (MSE): {mse}')\n",
        "\n",
        "# Calcular y mostrar el coeficiente de determinación (R^2)\n",
        "r2 = r2_score(y_test, predictions)\n",
        "print(f'Coefficient of Determination (R^2): {r2}')\n",
        "\n",
        "\n",
        "\n",
        "# Redondear las predicciones al entero más cercano para obtener etiquetas de clase\n",
        "predicted_labels = np.rint(predictions).flatten()\n",
        "actual_labels = y_test.values\n",
        "\n",
        "# Calcular la matriz de confusión\n",
        "cm = confusion_matrix(actual_labels, predicted_labels)\n",
        "\n",
        "# Visualizar la matriz de confusión\n",
        "cmd = ConfusionMatrixDisplay(cm)\n",
        "cmd.plot(values_format='d')\n",
        "plt.title('Matriz de Confusión')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "# Asegúrate de que tus predicciones y etiquetas reales son enteros y están en el mismo formato\n",
        "predicted_labels = np.rint(predictions).astype(int).flatten()\n",
        "actual_labels = y_test.astype(int).values\n",
        "\n",
        "# Calcular las métricas de clasificación\n",
        "accuracy = accuracy_score(actual_labels, predicted_labels)\n",
        "precision = precision_score(actual_labels, predicted_labels, average='weighted')\n",
        "recall = recall_score(actual_labels, predicted_labels, average='weighted')\n",
        "f1 = f1_score(actual_labels, predicted_labels, average='weighted')\n",
        "\n",
        "# Imprimir las métricas de clasificación\n",
        "print(f'Accuracy: {accuracy:.2f}')\n",
        "print(f'Precision: {precision:.2f}')\n",
        "print(f'Recall: {recall:.2f}')\n",
        "print(f'F1 Score: {f1:.2f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3iwXfJGbzaZE",
        "outputId": "cd7f2bdd-dabe-4a56-d8ad-a8de0cb86bff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "94/94 [==============================] - 1s 2ms/step - loss: 10.0822\n",
            "Epoch 2/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.7741\n",
            "Epoch 3/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5652\n",
            "Epoch 4/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5613\n",
            "Epoch 5/100\n",
            "94/94 [==============================] - 0s 3ms/step - loss: 0.5604\n",
            "Epoch 6/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5601\n",
            "Epoch 7/100\n",
            "94/94 [==============================] - 0s 3ms/step - loss: 0.5605\n",
            "Epoch 8/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5604\n",
            "Epoch 9/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5597\n",
            "Epoch 10/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5599\n",
            "Epoch 11/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5596\n",
            "Epoch 12/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5595\n",
            "Epoch 13/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5592\n",
            "Epoch 14/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5585\n",
            "Epoch 15/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5591\n",
            "Epoch 16/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5591\n",
            "Epoch 17/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5585\n",
            "Epoch 18/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5587\n",
            "Epoch 19/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5583\n",
            "Epoch 20/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5586\n",
            "Epoch 21/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5589\n",
            "Epoch 22/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5581\n",
            "Epoch 23/100\n",
            "94/94 [==============================] - 0s 3ms/step - loss: 0.5600\n",
            "Epoch 24/100\n",
            "94/94 [==============================] - 0s 3ms/step - loss: 0.5584\n",
            "Epoch 25/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5582\n",
            "Epoch 26/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5584\n",
            "Epoch 27/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5585\n",
            "Epoch 28/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5584\n",
            "Epoch 29/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5583\n",
            "Epoch 30/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5585\n",
            "Epoch 31/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5586\n",
            "Epoch 32/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5582\n",
            "Epoch 33/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5577\n",
            "Epoch 34/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5586\n",
            "Epoch 35/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5580\n",
            "Epoch 36/100\n",
            "94/94 [==============================] - 0s 3ms/step - loss: 0.5568\n",
            "Epoch 37/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5586\n",
            "Epoch 38/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5583\n",
            "Epoch 39/100\n",
            "94/94 [==============================] - 0s 4ms/step - loss: 0.5583\n",
            "Epoch 40/100\n",
            "94/94 [==============================] - 0s 3ms/step - loss: 0.5589\n",
            "Epoch 41/100\n",
            "94/94 [==============================] - 0s 4ms/step - loss: 0.5577\n",
            "Epoch 42/100\n",
            "94/94 [==============================] - 1s 6ms/step - loss: 0.5588\n",
            "Epoch 43/100\n",
            "94/94 [==============================] - 0s 4ms/step - loss: 0.5581\n",
            "Epoch 44/100\n",
            "94/94 [==============================] - 0s 3ms/step - loss: 0.5585\n",
            "Epoch 45/100\n",
            "94/94 [==============================] - 1s 6ms/step - loss: 0.5577\n",
            "Epoch 46/100\n",
            "94/94 [==============================] - 1s 5ms/step - loss: 0.5584\n",
            "Epoch 47/100\n",
            "94/94 [==============================] - 0s 5ms/step - loss: 0.5590\n",
            "Epoch 48/100\n",
            "94/94 [==============================] - 0s 4ms/step - loss: 0.5590\n",
            "Epoch 49/100\n",
            "94/94 [==============================] - 0s 3ms/step - loss: 0.5586\n",
            "Epoch 50/100\n",
            "94/94 [==============================] - 0s 5ms/step - loss: 0.5581\n",
            "Epoch 51/100\n",
            "94/94 [==============================] - 0s 2ms/step - loss: 0.5587\n",
            "Epoch 52/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5580\n",
            "Epoch 53/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5574\n",
            "Epoch 54/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5588\n",
            "Epoch 55/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5582\n",
            "Epoch 56/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5580\n",
            "Epoch 57/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5582\n",
            "Epoch 58/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5589\n",
            "Epoch 59/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5588\n",
            "Epoch 60/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5580\n",
            "Epoch 61/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5584\n",
            "Epoch 62/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5582\n",
            "Epoch 63/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5583\n",
            "Epoch 64/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5582\n",
            "Epoch 65/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5576\n",
            "Epoch 66/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5588\n",
            "Epoch 67/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5573\n",
            "Epoch 68/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5582\n",
            "Epoch 69/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5586\n",
            "Epoch 70/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5584\n",
            "Epoch 71/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5576\n",
            "Epoch 72/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5587\n",
            "Epoch 73/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5580\n",
            "Epoch 74/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5585\n",
            "Epoch 75/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5577\n",
            "Epoch 76/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5585\n",
            "Epoch 77/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5590\n",
            "Epoch 78/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5583\n",
            "Epoch 79/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5586\n",
            "Epoch 80/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5586\n",
            "Epoch 81/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5577\n",
            "Epoch 82/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5581\n",
            "Epoch 83/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5586\n",
            "Epoch 84/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5584\n",
            "Epoch 85/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5587\n",
            "Epoch 86/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5589\n",
            "Epoch 87/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5585\n",
            "Epoch 88/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5585\n",
            "Epoch 89/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5585\n",
            "Epoch 90/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5584\n",
            "Epoch 91/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5579\n",
            "Epoch 92/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5584\n",
            "Epoch 93/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5584\n",
            "Epoch 94/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5587\n",
            "Epoch 95/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5580\n",
            "Epoch 96/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5580\n",
            "Epoch 97/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5589\n",
            "Epoch 98/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5586\n",
            "Epoch 99/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5584\n",
            "Epoch 100/100\n",
            "94/94 [==============================] - 0s 1ms/step - loss: 0.5578\n",
            "24/24 [==============================] - 0s 1ms/step\n",
            "Mean Squared Error (MSE): 0.5891002052101777\n",
            "Coefficient of Determination (R^2): 0.3041818169464483\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.52\n",
            "Precision: 0.48\n",
            "Recall: 0.52\n",
            "F1 Score: 0.47\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fd2886b4"
      },
      "source": [
        "## Construcción del Modelo de Red Neuronal"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##Red de 1 Capa"
      ],
      "metadata": {
        "id": "bsJ4VFpbz3IL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score,ConfusionMatrixDisplay\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "\n",
        "# 'X' ahora incluirá las nuevas características 'log_alcohol' y 'acid_ratio'\n",
        "X = datos_mod_white.drop('quality', axis=1)\n",
        "y = datos_mod_white['quality']\n",
        "\n",
        "# Continúa con el preprocesamiento de 'X' como antes\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Codifica 'y' si estás tratando un problema de clasificación multiclase\n",
        "y_encoded = to_categorical(y)\n",
        "\n",
        "# Ahora puedes dividir tus datos, entrenar y evaluar tu modelo como lo hiciste anteriormente\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_encoded, test_size=0.2, random_state=42)\n",
        "\n",
        "# Modelo con una capa oculta con función de activación sigmoide\n",
        "model_sigmoid = Sequential()\n",
        "model_sigmoid.add(Dense(X_train.shape[1], activation='sigmoid', input_shape=(X_train.shape[1],), use_bias=True))\n",
        "model_sigmoid.add(Dense(y_encoded.shape[1], activation='softmax', use_bias=True))  # La capa de salida usa softmax para la clasificación multiclase\n",
        "\n",
        "# Compilación del modelo\n",
        "model_sigmoid.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Entrenamiento del modelo\n",
        "model_sigmoid.fit(X_train, y_train, epochs=200, batch_size=10, verbose=1)\n",
        "\n",
        "# Evaluación del modelo\n",
        "loss_sigmoid, accuracy_sigmoid = model_sigmoid.evaluate(X_test, y_test)\n",
        "\n",
        "y_pred = model_sigmoid.predict(X_test)\n",
        "\n",
        "# Si son categóricas, necesitarás convertir las predicciones de probabilidades a etiquetas\n",
        "y_pred_labels = np.argmax(y_pred, axis=1)\n",
        "y_test_labels = np.argmax(y_test, axis=1)\n",
        "\n",
        "# Métricas de evaluación\n",
        "accuracy = accuracy_score(y_test_labels, y_pred_labels)\n",
        "precision = precision_score(y_test_labels, y_pred_labels, average='weighted')\n",
        "recall = recall_score(y_test_labels, y_pred_labels, average='weighted')\n",
        "f1 = f1_score(y_test_labels, y_pred_labels, average='weighted')\n",
        "\n",
        "# Matriz de confusión\n",
        "cm = confusion_matrix(y_test_labels, y_pred_labels)\n",
        "\n",
        "cmd = ConfusionMatrixDisplay(cm,display_labels=['3','4','5','6','7','8','9'])\n",
        "cmd.plot()\n",
        "print(\"---------------------------------------------\")\n",
        "print(f'Accuracy: {accuracy * 100:.2f}%')\n",
        "print(f'Precision: {precision:.2f}')\n",
        "print(f'Recall: {recall:.2f}')\n",
        "print(f'F1 Score: {f1:.2f}')\n",
        "print(\"---------------------------------------------\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "w6vBBe9a9ujq",
        "outputId": "8c26a702-d538-45e4-bbbf-240516738b22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "300/300 [==============================] - 1s 1ms/step - loss: 1.5808 - accuracy: 0.3755\n",
            "Epoch 2/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.2789 - accuracy: 0.4607\n",
            "Epoch 3/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.2087 - accuracy: 0.4825\n",
            "Epoch 4/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.1688 - accuracy: 0.5072\n",
            "Epoch 5/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.1434 - accuracy: 0.5236\n",
            "Epoch 6/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.1257 - accuracy: 0.5319\n",
            "Epoch 7/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.1134 - accuracy: 0.5302\n",
            "Epoch 8/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.1052 - accuracy: 0.5376\n",
            "Epoch 9/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0993 - accuracy: 0.5429\n",
            "Epoch 10/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0942 - accuracy: 0.5363\n",
            "Epoch 11/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0905 - accuracy: 0.5399\n",
            "Epoch 12/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0875 - accuracy: 0.5429\n",
            "Epoch 13/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0854 - accuracy: 0.5416\n",
            "Epoch 14/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0835 - accuracy: 0.5436\n",
            "Epoch 15/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0817 - accuracy: 0.5383\n",
            "Epoch 16/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0806 - accuracy: 0.5406\n",
            "Epoch 17/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0788 - accuracy: 0.5396\n",
            "Epoch 18/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0778 - accuracy: 0.5466\n",
            "Epoch 19/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0766 - accuracy: 0.5486\n",
            "Epoch 20/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0759 - accuracy: 0.5473\n",
            "Epoch 21/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0747 - accuracy: 0.5476\n",
            "Epoch 22/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0740 - accuracy: 0.5469\n",
            "Epoch 23/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0734 - accuracy: 0.5433\n",
            "Epoch 24/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0723 - accuracy: 0.5506\n",
            "Epoch 25/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0720 - accuracy: 0.5426\n",
            "Epoch 26/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0708 - accuracy: 0.5479\n",
            "Epoch 27/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0703 - accuracy: 0.5439\n",
            "Epoch 28/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0699 - accuracy: 0.5496\n",
            "Epoch 29/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0689 - accuracy: 0.5486\n",
            "Epoch 30/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0681 - accuracy: 0.5489\n",
            "Epoch 31/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0674 - accuracy: 0.5459\n",
            "Epoch 32/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0668 - accuracy: 0.5499\n",
            "Epoch 33/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0662 - accuracy: 0.5439\n",
            "Epoch 34/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0653 - accuracy: 0.5499\n",
            "Epoch 35/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0650 - accuracy: 0.5493\n",
            "Epoch 36/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0646 - accuracy: 0.5506\n",
            "Epoch 37/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0636 - accuracy: 0.5510\n",
            "Epoch 38/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0637 - accuracy: 0.5533\n",
            "Epoch 39/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0625 - accuracy: 0.5496\n",
            "Epoch 40/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0617 - accuracy: 0.5483\n",
            "Epoch 41/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0618 - accuracy: 0.5516\n",
            "Epoch 42/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0610 - accuracy: 0.5560\n",
            "Epoch 43/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0607 - accuracy: 0.5540\n",
            "Epoch 44/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0600 - accuracy: 0.5543\n",
            "Epoch 45/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0596 - accuracy: 0.5506\n",
            "Epoch 46/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0589 - accuracy: 0.5526\n",
            "Epoch 47/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0587 - accuracy: 0.5536\n",
            "Epoch 48/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0577 - accuracy: 0.5550\n",
            "Epoch 49/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0576 - accuracy: 0.5513\n",
            "Epoch 50/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0562 - accuracy: 0.5499\n",
            "Epoch 51/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0562 - accuracy: 0.5533\n",
            "Epoch 52/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0553 - accuracy: 0.5526\n",
            "Epoch 53/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0543 - accuracy: 0.5513\n",
            "Epoch 54/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0543 - accuracy: 0.5553\n",
            "Epoch 55/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0535 - accuracy: 0.5593\n",
            "Epoch 56/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0530 - accuracy: 0.5510\n",
            "Epoch 57/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0527 - accuracy: 0.5566\n",
            "Epoch 58/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0519 - accuracy: 0.5533\n",
            "Epoch 59/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0507 - accuracy: 0.5563\n",
            "Epoch 60/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0503 - accuracy: 0.5523\n",
            "Epoch 61/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0496 - accuracy: 0.5543\n",
            "Epoch 62/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0491 - accuracy: 0.5520\n",
            "Epoch 63/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0484 - accuracy: 0.5563\n",
            "Epoch 64/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0480 - accuracy: 0.5543\n",
            "Epoch 65/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0472 - accuracy: 0.5543\n",
            "Epoch 66/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0471 - accuracy: 0.5580\n",
            "Epoch 67/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0463 - accuracy: 0.5576\n",
            "Epoch 68/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0453 - accuracy: 0.5540\n",
            "Epoch 69/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0446 - accuracy: 0.5586\n",
            "Epoch 70/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0442 - accuracy: 0.5563\n",
            "Epoch 71/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0441 - accuracy: 0.5553\n",
            "Epoch 72/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0431 - accuracy: 0.5560\n",
            "Epoch 73/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0426 - accuracy: 0.5556\n",
            "Epoch 74/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0422 - accuracy: 0.5550\n",
            "Epoch 75/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0414 - accuracy: 0.5586\n",
            "Epoch 76/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0405 - accuracy: 0.5536\n",
            "Epoch 77/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0400 - accuracy: 0.5590\n",
            "Epoch 78/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0395 - accuracy: 0.5586\n",
            "Epoch 79/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0390 - accuracy: 0.5540\n",
            "Epoch 80/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0384 - accuracy: 0.5586\n",
            "Epoch 81/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0380 - accuracy: 0.5556\n",
            "Epoch 82/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0378 - accuracy: 0.5553\n",
            "Epoch 83/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0372 - accuracy: 0.5543\n",
            "Epoch 84/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0370 - accuracy: 0.5570\n",
            "Epoch 85/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0360 - accuracy: 0.5583\n",
            "Epoch 86/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0357 - accuracy: 0.5596\n",
            "Epoch 87/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0355 - accuracy: 0.5560\n",
            "Epoch 88/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0347 - accuracy: 0.5586\n",
            "Epoch 89/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0338 - accuracy: 0.5586\n",
            "Epoch 90/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0340 - accuracy: 0.5546\n",
            "Epoch 91/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0331 - accuracy: 0.5566\n",
            "Epoch 92/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0334 - accuracy: 0.5580\n",
            "Epoch 93/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0327 - accuracy: 0.5616\n",
            "Epoch 94/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0321 - accuracy: 0.5590\n",
            "Epoch 95/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0319 - accuracy: 0.5540\n",
            "Epoch 96/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0312 - accuracy: 0.5580\n",
            "Epoch 97/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0307 - accuracy: 0.5563\n",
            "Epoch 98/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0305 - accuracy: 0.5523\n",
            "Epoch 99/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0300 - accuracy: 0.5590\n",
            "Epoch 100/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0290 - accuracy: 0.5590\n",
            "Epoch 101/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0290 - accuracy: 0.5600\n",
            "Epoch 102/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0290 - accuracy: 0.5566\n",
            "Epoch 103/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0284 - accuracy: 0.5583\n",
            "Epoch 104/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0279 - accuracy: 0.5586\n",
            "Epoch 105/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0268 - accuracy: 0.5647\n",
            "Epoch 106/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0277 - accuracy: 0.5590\n",
            "Epoch 107/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0268 - accuracy: 0.5630\n",
            "Epoch 108/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0264 - accuracy: 0.5663\n",
            "Epoch 109/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0261 - accuracy: 0.5613\n",
            "Epoch 110/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0258 - accuracy: 0.5576\n",
            "Epoch 111/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0256 - accuracy: 0.5623\n",
            "Epoch 112/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0240 - accuracy: 0.5643\n",
            "Epoch 113/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0246 - accuracy: 0.5623\n",
            "Epoch 114/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0241 - accuracy: 0.5626\n",
            "Epoch 115/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0236 - accuracy: 0.5643\n",
            "Epoch 116/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0234 - accuracy: 0.5616\n",
            "Epoch 117/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0232 - accuracy: 0.5636\n",
            "Epoch 118/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0226 - accuracy: 0.5596\n",
            "Epoch 119/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0217 - accuracy: 0.5620\n",
            "Epoch 120/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0218 - accuracy: 0.5650\n",
            "Epoch 121/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0218 - accuracy: 0.5673\n",
            "Epoch 122/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0213 - accuracy: 0.5647\n",
            "Epoch 123/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0209 - accuracy: 0.5650\n",
            "Epoch 124/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0208 - accuracy: 0.5580\n",
            "Epoch 125/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0202 - accuracy: 0.5670\n",
            "Epoch 126/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0197 - accuracy: 0.5663\n",
            "Epoch 127/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0195 - accuracy: 0.5673\n",
            "Epoch 128/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0196 - accuracy: 0.5630\n",
            "Epoch 129/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0192 - accuracy: 0.5660\n",
            "Epoch 130/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0190 - accuracy: 0.5640\n",
            "Epoch 131/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0185 - accuracy: 0.5633\n",
            "Epoch 132/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0181 - accuracy: 0.5663\n",
            "Epoch 133/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0177 - accuracy: 0.5663\n",
            "Epoch 134/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0175 - accuracy: 0.5633\n",
            "Epoch 135/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0172 - accuracy: 0.5653\n",
            "Epoch 136/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0160 - accuracy: 0.5657\n",
            "Epoch 137/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0163 - accuracy: 0.5687\n",
            "Epoch 138/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0160 - accuracy: 0.5647\n",
            "Epoch 139/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0156 - accuracy: 0.5673\n",
            "Epoch 140/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0157 - accuracy: 0.5667\n",
            "Epoch 141/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0150 - accuracy: 0.5690\n",
            "Epoch 142/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0151 - accuracy: 0.5670\n",
            "Epoch 143/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0144 - accuracy: 0.5680\n",
            "Epoch 144/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0139 - accuracy: 0.5687\n",
            "Epoch 145/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0136 - accuracy: 0.5657\n",
            "Epoch 146/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0141 - accuracy: 0.5703\n",
            "Epoch 147/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0135 - accuracy: 0.5660\n",
            "Epoch 148/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0126 - accuracy: 0.5683\n",
            "Epoch 149/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0136 - accuracy: 0.5677\n",
            "Epoch 150/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0127 - accuracy: 0.5660\n",
            "Epoch 151/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0127 - accuracy: 0.5690\n",
            "Epoch 152/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0116 - accuracy: 0.5737\n",
            "Epoch 153/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0119 - accuracy: 0.5707\n",
            "Epoch 154/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0118 - accuracy: 0.5700\n",
            "Epoch 155/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0108 - accuracy: 0.5673\n",
            "Epoch 156/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0112 - accuracy: 0.5687\n",
            "Epoch 157/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0105 - accuracy: 0.5720\n",
            "Epoch 158/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0099 - accuracy: 0.5727\n",
            "Epoch 159/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0102 - accuracy: 0.5697\n",
            "Epoch 160/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0090 - accuracy: 0.5720\n",
            "Epoch 161/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0093 - accuracy: 0.5750\n",
            "Epoch 162/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0090 - accuracy: 0.5690\n",
            "Epoch 163/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0091 - accuracy: 0.5680\n",
            "Epoch 164/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0089 - accuracy: 0.5717\n",
            "Epoch 165/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0084 - accuracy: 0.5720\n",
            "Epoch 166/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0083 - accuracy: 0.5700\n",
            "Epoch 167/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0080 - accuracy: 0.5733\n",
            "Epoch 168/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0080 - accuracy: 0.5727\n",
            "Epoch 169/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0079 - accuracy: 0.5690\n",
            "Epoch 170/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0072 - accuracy: 0.5750\n",
            "Epoch 171/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0071 - accuracy: 0.5703\n",
            "Epoch 172/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0074 - accuracy: 0.5693\n",
            "Epoch 173/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0067 - accuracy: 0.5707\n",
            "Epoch 174/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0060 - accuracy: 0.5717\n",
            "Epoch 175/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0059 - accuracy: 0.5700\n",
            "Epoch 176/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0056 - accuracy: 0.5723\n",
            "Epoch 177/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0055 - accuracy: 0.5750\n",
            "Epoch 178/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0056 - accuracy: 0.5730\n",
            "Epoch 179/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0044 - accuracy: 0.5743\n",
            "Epoch 180/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0051 - accuracy: 0.5697\n",
            "Epoch 181/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0048 - accuracy: 0.5717\n",
            "Epoch 182/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0048 - accuracy: 0.5690\n",
            "Epoch 183/200\n",
            "300/300 [==============================] - 0s 2ms/step - loss: 1.0049 - accuracy: 0.5787\n",
            "Epoch 184/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0044 - accuracy: 0.5733\n",
            "Epoch 185/200\n",
            "300/300 [==============================] - 1s 2ms/step - loss: 1.0041 - accuracy: 0.5743\n",
            "Epoch 186/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0038 - accuracy: 0.5730\n",
            "Epoch 187/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0040 - accuracy: 0.5747\n",
            "Epoch 188/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0035 - accuracy: 0.5743\n",
            "Epoch 189/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0033 - accuracy: 0.5740\n",
            "Epoch 190/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0033 - accuracy: 0.5743\n",
            "Epoch 191/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0021 - accuracy: 0.5760\n",
            "Epoch 192/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0026 - accuracy: 0.5733\n",
            "Epoch 193/200\n",
            "300/300 [==============================] - 1s 3ms/step - loss: 1.0032 - accuracy: 0.5757\n",
            "Epoch 194/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0028 - accuracy: 0.5717\n",
            "Epoch 195/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0023 - accuracy: 0.5747\n",
            "Epoch 196/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0023 - accuracy: 0.5753\n",
            "Epoch 197/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0018 - accuracy: 0.5747\n",
            "Epoch 198/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0012 - accuracy: 0.5797\n",
            "Epoch 199/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0013 - accuracy: 0.5747\n",
            "Epoch 200/200\n",
            "300/300 [==============================] - 0s 1ms/step - loss: 1.0018 - accuracy: 0.5750\n",
            "24/24 [==============================] - 0s 1ms/step - loss: 1.0662 - accuracy: 0.5554\n",
            "24/24 [==============================] - 0s 1ms/step\n",
            "---------------------------------------------\n",
            "Accuracy: 55.54%\n",
            "Precision: 0.52\n",
            "Recall: 0.56\n",
            "F1 Score: 0.53\n",
            "---------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Red de 2 Capas"
      ],
      "metadata": {
        "id": "DUZyV9yG0OoQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "X = datos_mod_white.drop('quality', axis=1)\n",
        "y = datos_mod_white['quality']\n",
        "\n",
        "# Continúa con el preprocesamiento de 'X' como antes\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Codifica 'y' si estás tratando un problema de clasificación multiclase\n",
        "y_encoded = to_categorical(y)\n",
        "\n",
        "# Ahora puedes dividir tus datos, entrenar y evaluar tu modelo como lo hiciste anteriormente\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_encoded, test_size=0.3, random_state=42)\n",
        "\n",
        "# Modelo con dos capas ocultas con dos neuronas cada una y función de activación sigmoide\n",
        "model_sigmoid = Sequential()\n",
        "model_sigmoid.add(Dense(2, activation='sigmoid', input_shape=(X_train.shape[1],), use_bias=True))  # Capa oculta 1\n",
        "model_sigmoid.add(Dense(2, activation='sigmoid', use_bias=True))  # Capa oculta 2\n",
        "model_sigmoid.add(Dense(y_encoded.shape[1], activation='softmax', use_bias=True))  # Capa de salida con softmax\n",
        "\n",
        "# Compilación del modelo\n",
        "model_sigmoid.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Entrenamiento del modelo\n",
        "model_sigmoid.fit(X_train, y_train, epochs=200, batch_size=10, verbose=1)\n",
        "\n",
        "# Evaluación del modelo\n",
        "loss, accuracy = model_sigmoid.evaluate(X_test, y_test)\n",
        "print(f'precision of model with two hidden layers and sigmoid activation: {accuracy * 100:.2f}%')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Realizar predicciones en el conjunto de prueba\n",
        "y_pred = model_sigmoid.predict(X_test)\n",
        "\n",
        "# Si son categóricas, necesitarás convertir las predicciones de probabilidades a etiquetas\n",
        "y_pred_labels = np.argmax(y_pred, axis=1)\n",
        "y_test_labels = np.argmax(y_test, axis=1)\n",
        "\n",
        "# Métricas de evaluación\n",
        "accuracy = accuracy_score(y_test_labels, y_pred_labels)\n",
        "precision = precision_score(y_test_labels, y_pred_labels, average='weighted')\n",
        "recall = recall_score(y_test_labels, y_pred_labels, average='weighted')\n",
        "f1 = f1_score(y_test_labels, y_pred_labels, average='weighted')\n",
        "\n",
        "# Matriz de confusión\n",
        "cm = confusion_matrix(y_test_labels, y_pred_labels)\n",
        "\n",
        "cmd = ConfusionMatrixDisplay(cm,display_labels=['3','4','5','6','7','8','9'])\n",
        "cmd.plot()\n",
        "print(\"---------------------------------------------\")\n",
        "print(f'Accuracy: {accuracy * 100:.2f}%')\n",
        "print(f'Precision: {precision:.2f}')\n",
        "print(f'Recall: {recall:.2f}')\n",
        "print(f'F1 Score: {f1:.2f}')\n",
        "print(\"---------------------------------------------\")"
      ],
      "metadata": {
        "id": "HYPO-c4k05No",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b538caba-ccc1-49ae-d510-00dbfeb6f9e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "262/262 [==============================] - 1s 1ms/step - loss: 1.8213 - accuracy: 0.4582\n",
            "Epoch 2/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.5210 - accuracy: 0.4582\n",
            "Epoch 3/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.3996 - accuracy: 0.4582\n",
            "Epoch 4/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.3510 - accuracy: 0.4582\n",
            "Epoch 5/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.3264 - accuracy: 0.4582\n",
            "Epoch 6/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.3116 - accuracy: 0.4582\n",
            "Epoch 7/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.3006 - accuracy: 0.4582\n",
            "Epoch 8/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.2921 - accuracy: 0.4582\n",
            "Epoch 9/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.2843 - accuracy: 0.4582\n",
            "Epoch 10/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.2766 - accuracy: 0.4582\n",
            "Epoch 11/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.2688 - accuracy: 0.4582\n",
            "Epoch 12/200\n",
            "262/262 [==============================] - 1s 2ms/step - loss: 1.2606 - accuracy: 0.4582\n",
            "Epoch 13/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.2518 - accuracy: 0.4582\n",
            "Epoch 14/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.2423 - accuracy: 0.4582\n",
            "Epoch 15/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.2322 - accuracy: 0.4582\n",
            "Epoch 16/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.2219 - accuracy: 0.4582\n",
            "Epoch 17/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.2115 - accuracy: 0.4582\n",
            "Epoch 18/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.2013 - accuracy: 0.4582\n",
            "Epoch 19/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1910 - accuracy: 0.4582\n",
            "Epoch 20/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1815 - accuracy: 0.4582\n",
            "Epoch 21/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1727 - accuracy: 0.4727\n",
            "Epoch 22/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1647 - accuracy: 0.5120\n",
            "Epoch 23/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1574 - accuracy: 0.5216\n",
            "Epoch 24/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1512 - accuracy: 0.5193\n",
            "Epoch 25/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1458 - accuracy: 0.5235\n",
            "Epoch 26/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1411 - accuracy: 0.5212\n",
            "Epoch 27/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1369 - accuracy: 0.5227\n",
            "Epoch 28/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1336 - accuracy: 0.5265\n",
            "Epoch 29/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1306 - accuracy: 0.5269\n",
            "Epoch 30/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1279 - accuracy: 0.5265\n",
            "Epoch 31/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1256 - accuracy: 0.5262\n",
            "Epoch 32/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1237 - accuracy: 0.5273\n",
            "Epoch 33/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1218 - accuracy: 0.5296\n",
            "Epoch 34/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1203 - accuracy: 0.5258\n",
            "Epoch 35/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1190 - accuracy: 0.5281\n",
            "Epoch 36/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1177 - accuracy: 0.5292\n",
            "Epoch 37/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1166 - accuracy: 0.5281\n",
            "Epoch 38/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1155 - accuracy: 0.5254\n",
            "Epoch 39/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1148 - accuracy: 0.5250\n",
            "Epoch 40/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1137 - accuracy: 0.5258\n",
            "Epoch 41/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1131 - accuracy: 0.5242\n",
            "Epoch 42/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1123 - accuracy: 0.5269\n",
            "Epoch 43/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1114 - accuracy: 0.5269\n",
            "Epoch 44/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1110 - accuracy: 0.5284\n",
            "Epoch 45/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.1105 - accuracy: 0.5269\n",
            "Epoch 46/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.1098 - accuracy: 0.5265\n",
            "Epoch 47/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.1093 - accuracy: 0.5262\n",
            "Epoch 48/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.1087 - accuracy: 0.5277\n",
            "Epoch 49/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.1083 - accuracy: 0.5296\n",
            "Epoch 50/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.1080 - accuracy: 0.5254\n",
            "Epoch 51/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.1075 - accuracy: 0.5284\n",
            "Epoch 52/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.1071 - accuracy: 0.5284\n",
            "Epoch 53/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.1069 - accuracy: 0.5273\n",
            "Epoch 54/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.1064 - accuracy: 0.5326\n",
            "Epoch 55/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.1060 - accuracy: 0.5304\n",
            "Epoch 56/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1057 - accuracy: 0.5307\n",
            "Epoch 57/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1051 - accuracy: 0.5292\n",
            "Epoch 58/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1048 - accuracy: 0.5277\n",
            "Epoch 59/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1047 - accuracy: 0.5284\n",
            "Epoch 60/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1043 - accuracy: 0.5284\n",
            "Epoch 61/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1044 - accuracy: 0.5277\n",
            "Epoch 62/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1041 - accuracy: 0.5300\n",
            "Epoch 63/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1036 - accuracy: 0.5284\n",
            "Epoch 64/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1032 - accuracy: 0.5338\n",
            "Epoch 65/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1029 - accuracy: 0.5368\n",
            "Epoch 66/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1027 - accuracy: 0.5365\n",
            "Epoch 67/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1025 - accuracy: 0.5323\n",
            "Epoch 68/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1020 - accuracy: 0.5372\n",
            "Epoch 69/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1019 - accuracy: 0.5315\n",
            "Epoch 70/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1016 - accuracy: 0.5349\n",
            "Epoch 71/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1014 - accuracy: 0.5315\n",
            "Epoch 72/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1011 - accuracy: 0.5399\n",
            "Epoch 73/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1009 - accuracy: 0.5342\n",
            "Epoch 74/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1007 - accuracy: 0.5414\n",
            "Epoch 75/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1004 - accuracy: 0.5380\n",
            "Epoch 76/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1005 - accuracy: 0.5410\n",
            "Epoch 77/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.1001 - accuracy: 0.5395\n",
            "Epoch 78/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0999 - accuracy: 0.5376\n",
            "Epoch 79/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0997 - accuracy: 0.5445\n",
            "Epoch 80/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0998 - accuracy: 0.5407\n",
            "Epoch 81/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0995 - accuracy: 0.5403\n",
            "Epoch 82/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0992 - accuracy: 0.5414\n",
            "Epoch 83/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0994 - accuracy: 0.5380\n",
            "Epoch 84/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0989 - accuracy: 0.5418\n",
            "Epoch 85/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0988 - accuracy: 0.5403\n",
            "Epoch 86/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0986 - accuracy: 0.5437\n",
            "Epoch 87/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0984 - accuracy: 0.5357\n",
            "Epoch 88/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0984 - accuracy: 0.5430\n",
            "Epoch 89/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0981 - accuracy: 0.5403\n",
            "Epoch 90/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0980 - accuracy: 0.5403\n",
            "Epoch 91/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0980 - accuracy: 0.5376\n",
            "Epoch 92/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0977 - accuracy: 0.5464\n",
            "Epoch 93/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0976 - accuracy: 0.5388\n",
            "Epoch 94/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0976 - accuracy: 0.5426\n",
            "Epoch 95/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0973 - accuracy: 0.5361\n",
            "Epoch 96/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0973 - accuracy: 0.5456\n",
            "Epoch 97/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0973 - accuracy: 0.5430\n",
            "Epoch 98/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0972 - accuracy: 0.5472\n",
            "Epoch 99/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0969 - accuracy: 0.5410\n",
            "Epoch 100/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0969 - accuracy: 0.5380\n",
            "Epoch 101/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0969 - accuracy: 0.5407\n",
            "Epoch 102/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0968 - accuracy: 0.5414\n",
            "Epoch 103/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0969 - accuracy: 0.5437\n",
            "Epoch 104/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0966 - accuracy: 0.5445\n",
            "Epoch 105/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0965 - accuracy: 0.5441\n",
            "Epoch 106/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0965 - accuracy: 0.5407\n",
            "Epoch 107/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0961 - accuracy: 0.5384\n",
            "Epoch 108/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0966 - accuracy: 0.5403\n",
            "Epoch 109/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0962 - accuracy: 0.5403\n",
            "Epoch 110/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0963 - accuracy: 0.5410\n",
            "Epoch 111/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0962 - accuracy: 0.5395\n",
            "Epoch 112/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0963 - accuracy: 0.5468\n",
            "Epoch 113/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0959 - accuracy: 0.5422\n",
            "Epoch 114/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0962 - accuracy: 0.5399\n",
            "Epoch 115/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0961 - accuracy: 0.5433\n",
            "Epoch 116/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0959 - accuracy: 0.5395\n",
            "Epoch 117/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0959 - accuracy: 0.5456\n",
            "Epoch 118/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0957 - accuracy: 0.5357\n",
            "Epoch 119/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0957 - accuracy: 0.5414\n",
            "Epoch 120/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0959 - accuracy: 0.5433\n",
            "Epoch 121/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0956 - accuracy: 0.5414\n",
            "Epoch 122/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0957 - accuracy: 0.5399\n",
            "Epoch 123/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0956 - accuracy: 0.5399\n",
            "Epoch 124/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0955 - accuracy: 0.5407\n",
            "Epoch 125/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0957 - accuracy: 0.5437\n",
            "Epoch 126/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0956 - accuracy: 0.5418\n",
            "Epoch 127/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0953 - accuracy: 0.5403\n",
            "Epoch 128/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0953 - accuracy: 0.5437\n",
            "Epoch 129/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0954 - accuracy: 0.5388\n",
            "Epoch 130/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0952 - accuracy: 0.5395\n",
            "Epoch 131/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0951 - accuracy: 0.5426\n",
            "Epoch 132/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0952 - accuracy: 0.5380\n",
            "Epoch 133/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0950 - accuracy: 0.5395\n",
            "Epoch 134/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0951 - accuracy: 0.5414\n",
            "Epoch 135/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0952 - accuracy: 0.5368\n",
            "Epoch 136/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0949 - accuracy: 0.5380\n",
            "Epoch 137/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0949 - accuracy: 0.5384\n",
            "Epoch 138/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0950 - accuracy: 0.5418\n",
            "Epoch 139/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0949 - accuracy: 0.5376\n",
            "Epoch 140/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0947 - accuracy: 0.5418\n",
            "Epoch 141/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0948 - accuracy: 0.5365\n",
            "Epoch 142/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0950 - accuracy: 0.5399\n",
            "Epoch 143/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0947 - accuracy: 0.5414\n",
            "Epoch 144/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0949 - accuracy: 0.5388\n",
            "Epoch 145/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0949 - accuracy: 0.5414\n",
            "Epoch 146/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0946 - accuracy: 0.5368\n",
            "Epoch 147/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0946 - accuracy: 0.5403\n",
            "Epoch 148/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0943 - accuracy: 0.5395\n",
            "Epoch 149/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0944 - accuracy: 0.5407\n",
            "Epoch 150/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0945 - accuracy: 0.5376\n",
            "Epoch 151/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0943 - accuracy: 0.5368\n",
            "Epoch 152/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0944 - accuracy: 0.5376\n",
            "Epoch 153/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0944 - accuracy: 0.5376\n",
            "Epoch 154/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0944 - accuracy: 0.5365\n",
            "Epoch 155/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0942 - accuracy: 0.5384\n",
            "Epoch 156/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0943 - accuracy: 0.5380\n",
            "Epoch 157/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0942 - accuracy: 0.5365\n",
            "Epoch 158/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0941 - accuracy: 0.5426\n",
            "Epoch 159/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0939 - accuracy: 0.5357\n",
            "Epoch 160/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0940 - accuracy: 0.5380\n",
            "Epoch 161/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0941 - accuracy: 0.5403\n",
            "Epoch 162/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0939 - accuracy: 0.5410\n",
            "Epoch 163/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0939 - accuracy: 0.5372\n",
            "Epoch 164/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0938 - accuracy: 0.5384\n",
            "Epoch 165/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0939 - accuracy: 0.5376\n",
            "Epoch 166/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0938 - accuracy: 0.5414\n",
            "Epoch 167/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0937 - accuracy: 0.5384\n",
            "Epoch 168/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0935 - accuracy: 0.5372\n",
            "Epoch 169/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0935 - accuracy: 0.5430\n",
            "Epoch 170/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0934 - accuracy: 0.5376\n",
            "Epoch 171/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0935 - accuracy: 0.5349\n",
            "Epoch 172/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0936 - accuracy: 0.5384\n",
            "Epoch 173/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0934 - accuracy: 0.5376\n",
            "Epoch 174/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0934 - accuracy: 0.5384\n",
            "Epoch 175/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0937 - accuracy: 0.5368\n",
            "Epoch 176/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0934 - accuracy: 0.5357\n",
            "Epoch 177/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0934 - accuracy: 0.5384\n",
            "Epoch 178/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0933 - accuracy: 0.5380\n",
            "Epoch 179/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0931 - accuracy: 0.5418\n",
            "Epoch 180/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0933 - accuracy: 0.5388\n",
            "Epoch 181/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0933 - accuracy: 0.5376\n",
            "Epoch 182/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0929 - accuracy: 0.5395\n",
            "Epoch 183/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0934 - accuracy: 0.5372\n",
            "Epoch 184/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0930 - accuracy: 0.5388\n",
            "Epoch 185/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0931 - accuracy: 0.5384\n",
            "Epoch 186/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0933 - accuracy: 0.5391\n",
            "Epoch 187/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0932 - accuracy: 0.5372\n",
            "Epoch 188/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0929 - accuracy: 0.5380\n",
            "Epoch 189/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0929 - accuracy: 0.5372\n",
            "Epoch 190/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0928 - accuracy: 0.5357\n",
            "Epoch 191/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0928 - accuracy: 0.5353\n",
            "Epoch 192/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0926 - accuracy: 0.5407\n",
            "Epoch 193/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0928 - accuracy: 0.5399\n",
            "Epoch 194/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0927 - accuracy: 0.5376\n",
            "Epoch 195/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0925 - accuracy: 0.5388\n",
            "Epoch 196/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0926 - accuracy: 0.5395\n",
            "Epoch 197/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0925 - accuracy: 0.5384\n",
            "Epoch 198/200\n",
            "262/262 [==============================] - 0s 1ms/step - loss: 1.0925 - accuracy: 0.5372\n",
            "Epoch 199/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0925 - accuracy: 0.5353\n",
            "Epoch 200/200\n",
            "262/262 [==============================] - 0s 2ms/step - loss: 1.0924 - accuracy: 0.5395\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 1.1053 - accuracy: 0.5316\n",
            "precision of model with two hidden layers and sigmoid activation: 53.16%\n",
            "36/36 [==============================] - 0s 1ms/step\n",
            "---------------------------------------------\n",
            "Accuracy: 53.16%\n",
            "Precision: 0.49\n",
            "Recall: 0.53\n",
            "F1 Score: 0.49\n",
            "---------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Bono"
      ],
      "metadata": {
        "id": "kX_P_ZqX_hC9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score,ConfusionMatrixDisplay\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Concatenas los DataFrames modificados\n",
        "\n",
        "# 'X' ahora incluirá las nuevas características 'log_alcohol' y 'acid_ratio'\n",
        "nosignificativo= ['quality','residual sugar','RedWine']\n",
        "X = red_white.drop(nosignificativo, axis=1)\n",
        "y = red_white['quality']\n",
        "\n",
        "# Continúa con el preprocesamiento de 'X' como antes\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Codifica 'y' si estás tratando un problema de clasificación multiclase\n",
        "y_encoded = to_categorical(y)\n",
        "\n",
        "# Ahora puedes dividir tus datos, entrenar y evaluar tu modelo como lo hiciste anteriormente\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_encoded, test_size=0.2, random_state=42)\n",
        "\n",
        "# Modelo con una capa oculta con función de activación sigmoide\n",
        "model_sigmoid = Sequential()\n",
        "model_sigmoid.add(Dense(X_train.shape[1], activation='sigmoid', input_shape=(X_train.shape[1],), use_bias=True))\n",
        "model_sigmoid.add(Dense(X_train.shape[1], activation='sigmoid', input_shape=(X_train.shape[1],), use_bias=True))\n",
        "model_sigmoid.add(Dense(y_encoded.shape[1], activation='softmax', use_bias=True))  # La capa de salida usa softmax para la clasificación multiclase\n",
        "\n",
        "# Compilación del modelo\n",
        "model_sigmoid.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Entrenamiento del modelo\n",
        "model_sigmoid.fit(X_train, y_train, epochs=200, batch_size=10, verbose=1)\n",
        "\n",
        "# Evaluación del modelo\n",
        "# Evaluación del modelo\n",
        "loss_sigmoid, accuracy_sigmoid = model_sigmoid.evaluate(X_test, y_test)\n",
        "\n",
        "y_pred = model_sigmoid.predict(X_test)\n",
        "\n",
        "# Si son categóricas, necesitarás convertir las predicciones de probabilidades a etiquetas\n",
        "y_pred_labels = np.argmax(y_pred, axis=1)\n",
        "y_test_labels = np.argmax(y_test, axis=1)\n",
        "\n",
        "# Métricas de evaluación\n",
        "accuracy = accuracy_score(y_test_labels, y_pred_labels)\n",
        "precision = precision_score(y_test_labels, y_pred_labels, average='weighted')\n",
        "recall = recall_score(y_test_labels, y_pred_labels, average='weighted')\n",
        "f1 = f1_score(y_test_labels, y_pred_labels, average='weighted')\n",
        "\n",
        "# Matriz de confusión\n",
        "cm = confusion_matrix(y_test_labels, y_pred_labels)\n",
        "\n",
        "cmd = ConfusionMatrixDisplay(cm,display_labels=['3','4','5','6','7','8'])\n",
        "cmd.plot()\n",
        "\n",
        "print(f'Accuracy: {accuracy:.2f}')\n",
        "print(f'Precision: {precision:.2f}')\n",
        "print(f'Recall: {recall:.2f}')\n",
        "print(f'F1 Score: {f1:.2f}')\n",
        "\n",
        "print(f'Accuracy of model with sigmoid activation: {accuracy_sigmoid * 100:.2f}%')\n",
        "\n",
        "\n",
        "# Nota: Para realizar un análisis comparativo, necesitarás repetir este proceso con otros modelos y comparar estas métricas."
      ],
      "metadata": {
        "id": "6oiyLfo6-DqR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0bb9b4ee-f576-4bb8-bea7-912ea4f66018"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "350/350 [==============================] - 3s 4ms/step - loss: 1.6005 - accuracy: 0.4392\n",
            "Epoch 2/200\n",
            "350/350 [==============================] - 1s 4ms/step - loss: 1.2896 - accuracy: 0.4423\n",
            "Epoch 3/200\n",
            "350/350 [==============================] - 1s 3ms/step - loss: 1.2398 - accuracy: 0.4827\n",
            "Epoch 4/200\n",
            "350/350 [==============================] - 1s 3ms/step - loss: 1.2002 - accuracy: 0.5079\n",
            "Epoch 5/200\n",
            "350/350 [==============================] - 1s 3ms/step - loss: 1.1671 - accuracy: 0.5182\n",
            "Epoch 6/200\n",
            "350/350 [==============================] - 1s 3ms/step - loss: 1.1433 - accuracy: 0.5167\n",
            "Epoch 7/200\n",
            "350/350 [==============================] - 1s 3ms/step - loss: 1.1250 - accuracy: 0.5265\n",
            "Epoch 8/200\n",
            "350/350 [==============================] - 1s 2ms/step - loss: 1.1124 - accuracy: 0.5291\n",
            "Epoch 9/200\n",
            "350/350 [==============================] - 1s 3ms/step - loss: 1.1017 - accuracy: 0.5342\n",
            "Epoch 10/200\n",
            "350/350 [==============================] - 1s 2ms/step - loss: 1.0956 - accuracy: 0.5308\n",
            "Epoch 11/200\n",
            "350/350 [==============================] - 1s 2ms/step - loss: 1.0899 - accuracy: 0.5334\n",
            "Epoch 12/200\n",
            "350/350 [==============================] - 1s 2ms/step - loss: 1.0857 - accuracy: 0.5417\n",
            "Epoch 13/200\n",
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            "350/350 [==============================] - 1s 2ms/step - loss: 1.0042 - accuracy: 0.5763\n",
            "Epoch 190/200\n",
            "350/350 [==============================] - 1s 1ms/step - loss: 1.0022 - accuracy: 0.5780\n",
            "Epoch 191/200\n",
            "350/350 [==============================] - 1s 1ms/step - loss: 1.0021 - accuracy: 0.5783\n",
            "Epoch 192/200\n",
            "350/350 [==============================] - 1s 1ms/step - loss: 1.0034 - accuracy: 0.5740\n",
            "Epoch 193/200\n",
            "350/350 [==============================] - 1s 2ms/step - loss: 1.0022 - accuracy: 0.5769\n",
            "Epoch 194/200\n",
            "350/350 [==============================] - 1s 1ms/step - loss: 1.0023 - accuracy: 0.5794\n",
            "Epoch 195/200\n",
            "350/350 [==============================] - 1s 2ms/step - loss: 1.0019 - accuracy: 0.5777\n",
            "Epoch 196/200\n",
            "350/350 [==============================] - 1s 2ms/step - loss: 1.0022 - accuracy: 0.5726\n",
            "Epoch 197/200\n",
            "350/350 [==============================] - 0s 1ms/step - loss: 1.0025 - accuracy: 0.5794\n",
            "Epoch 198/200\n",
            "350/350 [==============================] - 0s 1ms/step - loss: 1.0026 - accuracy: 0.5832\n",
            "Epoch 199/200\n",
            "350/350 [==============================] - 1s 2ms/step - loss: 1.0015 - accuracy: 0.5783\n",
            "Epoch 200/200\n",
            "350/350 [==============================] - 1s 2ms/step - loss: 1.0013 - accuracy: 0.5729\n",
            "28/28 [==============================] - 0s 2ms/step - loss: 1.0842 - accuracy: 0.5355\n",
            "28/28 [==============================] - 0s 1ms/step\n",
            "Accuracy: 0.54\n",
            "Precision: 0.50\n",
            "Recall: 0.54\n",
            "F1 Score: 0.50\n",
            "Accuracy of model with sigmoid activation: 53.55%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}