Assignment3_Sarojini_Sharon_Robert_kennedy_3724629.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM5TiBdzN1cpBk51PpVzlG8",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/UNB-TME/assignment-3-clustering-sarojinisharon/blob/main/Assignment3_Sarojini_Sharon_Robert_kennedy_3724629.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Objective**\n",
        "\n",
        "The objective of this assignment is to implement and understand the working of two clustering algorithms - KMeans and Agglomerative Clustering, using sklearn library in Python. You will also explore the main parameters of these algorithms and analyze how changing these parameters affect the results.\n",
        "\n"
      ],
      "metadata": {
        "id": "8JS0tAVDZNQ-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing Necessary Libraries**"
      ],
      "metadata": {
        "id": "RUEoZaJXZetG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.cluster import KMeans, AgglomerativeClustering\n",
        "from sklearn.metrics import silhouette_score, davies_bouldin_score"
      ],
      "metadata": {
        "id": "4twKcY6LZiaj"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dataset Selection**\n",
        "\n",
        "Selected the breast Cancer Dataset from sklearn datasets"
      ],
      "metadata": {
        "id": "a-RGJEIcZT2V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading the breast cancer dataset\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "\n",
        "data = load_breast_cancer()\n",
        "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
        "df['target'] = data.target"
      ],
      "metadata": {
        "id": "uhcR9t66ZXsL"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "t-Jlhsk2Xofh",
        "outputId": "3301a39d-177a-4d69-c2fa-04024e175378"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
              "3        11.42         20.38           77.58      386.1          0.14250   \n",
              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
              "\n",
              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0           0.27760          0.3001              0.14710         0.2419   \n",
              "1           0.07864          0.0869              0.07017         0.1812   \n",
              "2           0.15990          0.1974              0.12790         0.2069   \n",
              "3           0.28390          0.2414              0.10520         0.2597   \n",
              "4           0.13280          0.1980              0.10430         0.1809   \n",
              "\n",
              "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
              "0                 0.07871  ...          17.33           184.60      2019.0   \n",
              "1                 0.05667  ...          23.41           158.80      1956.0   \n",
              "2                 0.05999  ...          25.53           152.50      1709.0   \n",
              "3                 0.09744  ...          26.50            98.87       567.7   \n",
              "4                 0.05883  ...          16.67           152.20      1575.0   \n",
              "\n",
              "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
              "0            0.1622             0.6656           0.7119                0.2654   \n",
              "1            0.1238             0.1866           0.2416                0.1860   \n",
              "2            0.1444             0.4245           0.4504                0.2430   \n",
              "3            0.2098             0.8663           0.6869                0.2575   \n",
              "4            0.1374             0.2050           0.4000                0.1625   \n",
              "\n",
              "   worst symmetry  worst fractal dimension  target  \n",
              "0          0.4601                  0.11890       0  \n",
              "1          0.2750                  0.08902       0  \n",
              "2          0.3613                  0.08758       0  \n",
              "3          0.6638                  0.17300       0  \n",
              "4          0.2364                  0.07678       0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-67074d09-bea6-4808-88c0-d80c83995680\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-67074d09-bea6-4808-88c0-d80c83995680')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-67074d09-bea6-4808-88c0-d80c83995680 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-67074d09-bea6-4808-88c0-d80c83995680');\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-1f63bd84-c9fe-461a-9baa-916702ffb7cc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1f63bd84-c9fe-461a-9baa-916702ffb7cc')\"\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-1f63bd84-c9fe-461a-9baa-916702ffb7cc 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": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Preprocessing**\n",
        "\n",
        "\n",
        "Performing necessary data pre-processing steps such as checking missing values and data normalization in this case.\n",
        "\n"
      ],
      "metadata": {
        "id": "JV2pw6xBT3ia"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using StandardScaler for data normalization\n",
        "scaler = StandardScaler()\n",
        "scaled_data = scaler.fit_transform(df.drop('target', axis=1))"
      ],
      "metadata": {
        "id": "ScVSsbVrUCoA"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#checking for null values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iIMMSVZ1XIGz",
        "outputId": "95acf6d5-d62a-426e-eae8-2f0cffe0d274"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean radius                0\n",
              "mean texture               0\n",
              "mean perimeter             0\n",
              "mean area                  0\n",
              "mean smoothness            0\n",
              "mean compactness           0\n",
              "mean concavity             0\n",
              "mean concave points        0\n",
              "mean symmetry              0\n",
              "mean fractal dimension     0\n",
              "radius error               0\n",
              "texture error              0\n",
              "perimeter error            0\n",
              "area error                 0\n",
              "smoothness error           0\n",
              "compactness error          0\n",
              "concavity error            0\n",
              "concave points error       0\n",
              "symmetry error             0\n",
              "fractal dimension error    0\n",
              "worst radius               0\n",
              "worst texture              0\n",
              "worst perimeter            0\n",
              "worst area                 0\n",
              "worst smoothness           0\n",
              "worst compactness          0\n",
              "worst concavity            0\n",
              "worst concave points       0\n",
              "worst symmetry             0\n",
              "worst fractal dimension    0\n",
              "target                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There ia no null values, so proceeding further."
      ],
      "metadata": {
        "id": "ONcmhD6iEZJh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Implementation of K-Means Clustering**\n",
        "\n",
        "KMeans clustering is an unsupervised machine learning algorithm used for partitioning a dataset into a set of distinct, non-overlapping subgroups or clusters. The algorithm aims to group similar data points together and assign them to the same cluster while keeping dissimilar data points in different clusters. The term \"KMeans\" refers to the process of dividing the data into K clusters, where K is a user-defined parameter.\n"
      ],
      "metadata": {
        "id": "hKIGopQSYGSY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def kmeans_clustering(n_clusters, init_method='k-means++', n_init=10, max_iter=300):\n",
        "    kmeans = KMeans(n_clusters=n_clusters, init=init_method, n_init=n_init, max_iter=max_iter, random_state=42)\n",
        "    labels = kmeans.fit_predict(scaled_data)\n",
        "    return labels, kmeans.cluster_centers_"
      ],
      "metadata": {
        "id": "qSfyMuQYXRIf"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Experimenting with KMeans parameters\n",
        "kmeans_labels_default, centers_default = kmeans_clustering(n_clusters=2)\n",
        "kmeans_labels_custom1, centers_custom1 = kmeans_clustering(n_clusters=3, init_method='random', n_init=20, max_iter=500)\n",
        "kmeans_labels_custom2, centers_custom2 = kmeans_clustering(n_clusters=4, init_method='random', n_init=10, max_iter=300)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "6JSB77_HYKml"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Parameters Explored:**\n",
        "\n",
        "n_clusters: The number of clusters to form as well as the number of centroids to generate.\n",
        "\n",
        "init_method: Method for initializing centroids. The default is 'k-means++', which smartly places initial centroids to speed up convergence.\n",
        "\n",
        "n_init: Number of times the algorithm will be run with different centroid seeds. The final result will be the best output in terms of inertia.\n",
        "\n",
        "max_iter: Maximum number of iterations of the k-means algorithm for a single run.\n",
        "\n"
      ],
      "metadata": {
        "id": "MjBNzZMpW4sT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting\n",
        "plt.figure(figsize=(15, 5))\n",
        "\n",
        "# Subplot 1\n",
        "plt.subplot(1, 3, 1)\n",
        "for k in range(2):  # Assuming n_clusters=2 for the default case\n",
        "    members = kmeans_labels_default == k\n",
        "    plt.scatter(scaled_data[members, 0], scaled_data[members, 1], label=f'Cluster {k}', cmap='viridis')\n",
        "plt.scatter(centers_default[:, 0], centers_default[:, 1], marker='X', s=100, color='black', label='Centroids')\n",
        "plt.title('KMeans Clustering (Default)')\n",
        "plt.legend()\n",
        "\n",
        "# Subplot 2\n",
        "plt.subplot(1, 3, 2)\n",
        "for k in range(3):  # Assuming n_clusters=3 for custom1 case\n",
        "    members = kmeans_labels_custom1 == k\n",
        "    plt.scatter(scaled_data[members, 0], scaled_data[members, 1], label=f'Cluster {k}', cmap='viridis')\n",
        "plt.scatter(centers_custom1[:, 0], centers_custom1[:, 1], marker='X', s=100, color='black', label='Centroids')\n",
        "plt.title('KMeans Clustering (Custom_1)')\n",
        "plt.legend()\n",
        "\n",
        "# Subplot 3\n",
        "plt.subplot(1, 3, 3)\n",
        "for k in range(4):  # Assuming n_clusters=4 for custom2 case\n",
        "    members = kmeans_labels_custom2 == k\n",
        "    plt.scatter(scaled_data[members, 0], scaled_data[members, 1], label=f'Cluster {k}', cmap='viridis')\n",
        "plt.scatter(centers_custom2[:, 0], centers_custom2[:, 1], marker='X', s=100, color='black', label='Centroids')\n",
        "plt.title('KMeans Clustering (Custom_2)')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "XX0xzdFeYX_1",
        "outputId": "a7cc4e40-30a1-4a40-85d9-18c5b8daeb36"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-34-22bd55282e40>:8: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  plt.scatter(scaled_data[members, 0], scaled_data[members, 1], label=f'Cluster {k}', cmap='viridis')\n",
            "<ipython-input-34-22bd55282e40>:17: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  plt.scatter(scaled_data[members, 0], scaled_data[members, 1], label=f'Cluster {k}', cmap='viridis')\n",
            "<ipython-input-34-22bd55282e40>:26: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  plt.scatter(scaled_data[members, 0], scaled_data[members, 1], label=f'Cluster {k}', cmap='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Default Case (`n_clusters=2`):\n",
        "\n",
        "- **Observation:**\n",
        "  - Two clusters are formed based on the default configuration.\n",
        "  - The centroids (marked with 'X') represent the centers of these two clusters.\n",
        "  - The algorithm identified a natural partition of the data into two distinct groups.\n",
        "\n",
        "\n",
        "### Custom Case 1 (`n_clusters=3`, `init_method='random', n_init=20, max_iter=500`):\n",
        "\n",
        "- **Observation:**\n",
        "  - Three clusters are formed with random initialization, 20 runs, and a higher maximum number of iterations.\n",
        "  - The centroids are marked with 'X'.\n",
        "  - Seems like the additional cluster(cluster 2) introduces noise, as the clusters are more distributed while compared to the other two clusters.\n",
        "\n",
        "### Custom Case 2 (`n_clusters=4`, `init_method='random', n_init=10, max_iter=300`):\n",
        "\n",
        "- **Observation:**\n",
        "  - Four clusters are formed with random initialization, 10 runs, and a lower maximum number of iterations.\n",
        "  - The centroids are marked with 'X'.\n",
        "  - The algorithm attempts to create even finer subdivisions.\n",
        "  - The four clusters reveal meaningful substructures in the data.\n",
        "  - The clusters are less dispersed while having lower number of iterations.\n",
        "\n",
        "  Overall, the number of clusters significantly influences the results, impacting the granularity of identified patterns. Random initialization and higher iterations may lead to more detailed clusters but can introduce noise.\n",
        "\n"
      ],
      "metadata": {
        "id": "8A4aRTwxX9QU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Implementation of Agglomerative Clustering**\n",
        "\n",
        "Agglomerative clustering is a hierarchical clustering algorithm used in unsupervised machine learning. It's a bottom-up approach where each data point initially represents a single cluster, and these clusters are successively merged based on their similarity."
      ],
      "metadata": {
        "id": "OI72NhJ7YqVQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def agglomerative_clustering(n_clusters, affinity='euclidean', linkage='ward'):\n",
        "    agg_clustering = AgglomerativeClustering(n_clusters=n_clusters, affinity=affinity, linkage=linkage)\n",
        "    labels = agg_clustering.fit_predict(scaled_data)\n",
        "    return labels"
      ],
      "metadata": {
        "id": "txcG4-pyYx7g"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Experiment with Agglomerative Clustering parameters\n",
        "agg_labels_default = agglomerative_clustering(n_clusters=2)\n",
        "agg_labels_custom1 = agglomerative_clustering(n_clusters=3, affinity='manhattan', linkage='complete')\n",
        "agg_labels_custom2 = agglomerative_clustering(n_clusters=4, affinity='euclidean', linkage='average')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LwaEniLeY2Vr",
        "outputId": "796205aa-99b1-460e-fe45-ed345d3b114f"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_agglomerative.py:983: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_agglomerative.py:983: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_agglomerative.py:983: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "**Parameters Explored:**\n",
        "\n",
        "n_clusters: Number of clusters.\n",
        "\n",
        "affinity: Metric used to compute the linkage.\n",
        "\n",
        "linkage: Linkage criterion for the algorithm."
      ],
      "metadata": {
        "id": "9JT5EEe_Ws3s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization of the results\n",
        "plt.figure(figsize=(15, 5))\n",
        "\n",
        "# Subplot 1\n",
        "plt.subplot(1, 3, 1)\n",
        "for k in range(2):  # Assuming n_clusters=2 for the default case\n",
        "    members = agg_labels_default == k\n",
        "    plt.scatter(scaled_data[members, 0], scaled_data[members, 1], label=f'Cluster {k}', cmap='viridis')\n",
        "plt.title('Agglomerative Clustering (Default)')\n",
        "plt.legend()\n",
        "\n",
        "# Subplot 2\n",
        "plt.subplot(1, 3, 2)\n",
        "for k in range(3):  # Assuming n_clusters=3 for custom1 case\n",
        "    members = agg_labels_custom1 == k\n",
        "    plt.scatter(scaled_data[members, 0], scaled_data[members, 1], label=f'Cluster {k}', cmap='viridis')\n",
        "plt.title('Agglomerative Clustering (Custom_1)')\n",
        "plt.legend()\n",
        "\n",
        "# Subplot 3\n",
        "plt.subplot(1, 3, 3)\n",
        "for k in range(4):  # Assuming n_clusters=4 for custom2 case\n",
        "    members = agg_labels_custom2 == k\n",
        "    plt.scatter(scaled_data[members, 0], scaled_data[members, 1], label=f'Cluster {k}', cmap='viridis')\n",
        "plt.title('Agglomerative Clustering (Custom_2)')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "43JehSG5ZDW1",
        "outputId": "cf709733-d820-4128-8413-f1b89e5f34c3"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-44-54528d796ed6>:8: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  plt.scatter(scaled_data[members, 0], scaled_data[members, 1], label=f'Cluster {k}', cmap='viridis')\n",
            "<ipython-input-44-54528d796ed6>:16: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  plt.scatter(scaled_data[members, 0], scaled_data[members, 1], label=f'Cluster {k}', cmap='viridis')\n",
            "<ipython-input-44-54528d796ed6>:24: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  plt.scatter(scaled_data[members, 0], scaled_data[members, 1], label=f'Cluster {k}', cmap='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABLwAAAHDCAYAAAAukwOJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3wUdd4H8M/spm56IA2MSBVCQA0SAQvFYDi9oB42FLEXigpYQO88wEI5FTwVsZ0VlVOx0C5oFERALnoxCkYUMIAPJCQQSICQtvt7/tjMsm12Z3vJ5/28eLzszs78dpPMJ/OdX5GEEAJERERERERERERhQhPoBhAREREREREREXkTC15ERERERERERBRWWPAiIiIiIiIiIqKwwoIXERERERERERGFFRa8iIiIiIiIiIgorLDgRUREREREREREYYUFLyIiIiIiIiIiCisseBERERERERERUVhhwYuIiIiIiIiIiMIKC15BRpIkzJkzJ9DNCJgNGzZAkiRs2LAh0E1xaMSIERgxYkSgm+GWYPiM//GPf6Bv374wGAx+OV5bWxseeughZGdnQ6PR4IorrvDJcd58801IkoQ9e/Y43ba4uBjx8fGora31SVuoY2F2BP68pgazwzP+zo6O5KWXXsLpp5+O5ubmQDeFghizJvDnQTWYNZ5h1vhOILKGBS8FL774IiRJwnnnnRfopoSlF198EW+++Wagm2Hj4MGDeOCBB9C3b1/odDrExcVh0KBBeOKJJ3D06FG/tWPevHn49NNP/XY8f2poaMDChQsxc+ZMaDSnTkGSJJn+RUREIDU1FYMGDcJ9992HiooKj475+uuv46mnnsJVV12Ft956C9OnT/f0baim9LM+ZswY9OrVC/Pnz/dbW8j3mB2+xexwrCNmBwA0NTVh8eLFOO+885CUlISYmBj06dMHU6dOxW+//eaT9lRUVGDOnDmqbnAE0r///W9MmDABvXv3hiRJihfBN998M1paWvDyyy/7t4HkFmaNbzFrHGPWMGvMHT58GE899RQuuugipKWlITk5GUOGDMG///1vm20DkjWC7Bo2bJg444wzBACxc+dOvx0XgJg9e7bfjhco/fv3F8OHD7d5XK/Xi5MnTwq9Xu/3NpWWlorOnTuLmJgYcfvtt4ulS5eKpUuXittuu03ExcWJ0aNHm7YdPny43fZ7S1xcnLjpppt8su9AfsZCCLF48WKRmJgoTp48afE4ADF69GjxzjvviLfffls8//zz4vbbbxdJSUkiIiJCPPPMM24f89prrxVdu3b1tOlOvfHGGwKAqKysND2m9LMuhBAvvvii0Ol0oqGhwedtI/9gdvgWs8OxjpgdtbW1YtCgQQKA+POf/yyeffZZ8dprr4kHH3xQZGdni8jISJ+058MPPxQAxPr1632yf28ZPny4iI+PFyNHjhQpKSkOf/4eeugh0a1bN2EwGPzXQHILs8a3mDWOMWuYNeZWrVolIiMjxeWXXy6effZZ8cILL4iRI0cKAOLvf/+7zfb+zpoI/5XWQkdlZSW2bNmCjz/+GHfddRfeffddzJ49O9DNClpCCDQ1NSE2NtbjfWk0GsTExHihVa45evQorrzySmi1Wvzwww/o27evxfNPPvkkXn31Vb+3y5uampoQFRUVsM9Y9sYbb2Ds2LF229CnTx9MmDDB4rEFCxagqKgI999/P/r27YtLL73U5WPW1NQgOTnZ3Sb7zLhx43DPPffgww8/xK233hro5pCHmB2uYXaEhmDPjptvvhk//PADPvroI4wbN87iuccffxx//etf/dnMoPPOO++ga9eu0Gg0yM3NdbjtNddcg3/84x9Yv349Ro0a5acWkquYNa5h1oQGZk3o6t+/P3bu3Ilu3bqZHps8eTIKCgqwcOFCPPTQQ4iLizM95/es8UtZLcQ8/vjjIiUlRTQ3N4tJkyaJ3r17293u0KFDYsKECSIhIUEkJSWJiRMnivLycgFAvPHGGxbbfvDBB6Jfv34iOjpa9O/fX3z88cfipptuEt26dbPYDnbunJSVlYkxY8aIhIQEERcXJ0aNGiW+/fZbi23kniXffPONuOeee0Tnzp1FUlKSuPPOO0Vzc7M4cuSIuPHGG0VycrJITk4WDz74oE1VVa/Xi8WLF4ucnBwRHR0t0tPTxZ133inq6uostuvWrZu47LLLRHFxsRg0aJCIjo4WixcvFkII8frrr4uRI0eKtLQ0ERUVJfr16ydefPFFm9cDsPgn34VYv369RRV7ypQpIi4uTpw4ccLm87/uuutERkaGaGtrMz22du1accEFFwidTifi4+PFpZdeKrZv327zWmsLFiwQAMS7777rdFshbO+c2OvZY+/9CCHEb7/9Jv7yl7+IjIwMER0dLbp27SquvfZacfToUSGEsPlsAFjcRfm///s/ccstt4j09HQRFRUlcnJyxL/+9S+7x33//ffFX//6V9GlSxchSZI4cuSI3TYNHz5c9O/fX/z8889ixIgRIjY2VnTp0kUsXLjQ5r3v2bNHFBUVCZ1OJ9LS0sS0adNEcXGxqrsPv//+uwAg3nzzTZvnAIgpU6bYfd3evXtFRESEGDZsmMXjTU1N4u9//7vo2bOniIqKEqeddpp48MEHRVNTkxBCiMrKSrufp9zOp556SgwdOlSkpqaKmJgYkZeXJz788EOLY8j7sP6dltts/vtq/XPg6Gddds4554ixY8c6+NQoVDA7mB3OMDu8mx1bt24VAMQdd9zh8PXm7bXX68He79T7778v8vLyRHx8vEhISBC5ubni2WefFUKc+r4pZYsQQixZskTk5OSIqKgokZWVJSZPniyOHDli057+/fuLH3/8UVx00UUiNjZW9OzZ05RDGzZsEPn5+SImJkb06dNHfPHFF6repxJHPY5lqamp4t577/XoOORbzBpmjTPMGmaNdXv8mTWy5557TgAQP/30k81z/swa9vCy491338Vf/vIXREVFYfz48Vi6dCm+++47DB482LSNwWBAUVERSktLMWnSJPTt2xefffYZbrrpJpv9rVmzBtdeey0GDBiA+fPn48iRI7jtttvQtWtXp235+eefceGFFyIxMREPPfQQIiMj8fLLL2PEiBH4+uuvbcbu33PPPcjMzMTcuXOxdetWvPLKK0hOTsaWLVtw+umnY968eVi7di2eeuop5ObmYuLEiabX3nXXXXjzzTdxyy234N5770VlZSVeeOEF/PDDD9i8eTMiIyNN2/76668YP3487rrrLtxxxx0488wzAQBLly5F//79MXbsWERERGDVqlWYPHkyDAYDpkyZAgB49tlncc899yA+Pt5UDc/IyLD7/q+99losWbIEa9aswdVXX216vLGxEatWrcLNN98MrVYLwHgX86abbkJhYSEWLlyIxsZGLF26FBdccAF++OEHnHHGGYqf88qVKxEbG4urrrrK6ffEEy0tLSgsLERzc7Ppe7V//36sXr0aR48eRVJSEt555x3cfvvtyM/Px5133gkA6NmzJwDj2P0hQ4ZAkiRMnToVaWlp+M9//oPbbrsNDQ0NmDZtmsXxHn/8cURFReGBBx5Ac3MzoqKiFNt25MgRjBkzBn/5y19wzTXX4KOPPsLMmTMxYMAA/OlPfwIAnDhxAqNGjUJVVRXuu+8+ZGZm4r333sP69etVvf8tW7YAAPLy8lz63E4//XQMHz4c69evR0NDAxITE2EwGDB27Fhs2rQJd955J/r164dt27Zh8eLF+O233/Dpp58iLS0N77zzDp588kkcP37cNF9Wv379AAD//Oc/MXbsWNxwww1oaWnB8uXLcfXVV2P16tW47LLLXGqjPWp+1gcNGhS28yB0NMwOZoevMDvsZ8fKlSsBADfeeKOq/aj1xRdfYPz48bj44ouxcOFCAMAvv/yCzZs347777sNFF12Ee++9F8899xweeeQRU6bI/50zZw7mzp2LgoICTJo0Cb/++qvpfGD9O3HkyBH8+c9/xnXXXYerr74aS5cuxXXXXYd3330X06ZNw913343rr7/eNA/lH3/8gYSEBK++X3N5eXnYvHmzz/ZPnmPWMGt8hVnDrPFm1lRXVwMAOnfubPOcX7PGL2W1EPL9998LAKbKpsFgEKeddpq47777LLZbsWKFAGCqwAphvPMwatQomzsnAwYMEKeddpo4duyY6bENGzYIAE7vnFxxxRUiKipK7N692/TYgQMHREJCgrjoootMj8kV4MLCQos7IkOHDhWSJIm7777b9FhbW5s47bTTLCrP33zzjd07B3JF3Pxx+c5HcXGxzefX2Nho81hhYaHo0aOHxWNKdxmtq/oGg0F07dpVjBs3zmK7Dz74QAAQGzduFEIIcezYMZGcnGxTea+urhZJSUlOK/IpKSnirLPOcriNOXfvnPzwww8CgE0vImtKY+Nvu+02kZWVJQ4dOmTx+HXXXSeSkpJMn7983B49eth8T5TunAAQb7/9tumx5uZmkZmZafHZP/PMMwKA+PTTT02PnTx5UvTt21fVnZO//e1vAoDF74IMDnp4CSHEfffdJwCIH3/8UQghxDvvvCM0Go345ptvLLZ76aWXBACxefNmi/fXv39/m31afzYtLS0iNzdXjBo1yvSYJz28hHB+R33evHkCgDh48KDiNhT8mB3MDjWYHd7NjiuvvFIAsLmbrUTtXff77rtPJCYmWvTMsKY0r0pNTY2IiooSl1xyicUcNC+88IIAIF5//XWL9gAQ7733numxHTt2CABCo9GIrVu3mh5ft26dYhappaaH15133iliY2PdPgb5FrOGWaMGs4ZZE8isEUKIw4cPi/T0dHHhhRfafd6fWcNVGq28++67yMjIwMiRIwEYV4679tprsXz5cuj1etN2xcXFiIyMxB133GF6TKPRmO4OyA4cOIBt27Zh4sSJiI+PNz0+fPhwDBgwwGFb9Ho9Pv/8c1xxxRXo0aOH6fGsrCxcf/312LRpExoaGixec9ttt0GSJNPX5513HoQQuO2220yPabVanHvuufj9999Nj3344YdISkrC6NGjcejQIdO/QYMGIT4+3qYy3r17dxQWFtq02Xx8fH19PQ4dOoThw4fj999/R319vcP3a48kSbj66quxdu1aHD9+3PT4v//9b3Tt2hUXXHABAGOF/OjRoxg/frxF+7VaLc477zynlf2Ghgaf3jGVJSUlAQDWrVuHxsZGl14rhMCKFStQVFQEIYTF+ywsLER9fT3KysosXnPTTTepnrMgPj7eYv6sqKgo5OfnW/ycFBcXo2vXrhg7dqzpsZiYGIvfA0cOHz6MiIgIi98FteTXHDt2DIDxZ7Zfv37o27evxWchjwVXczfH/LM5cuQI6uvrceGFF9p8jr6UkpICADh06JDfjknex+xgdvgSs8N+dsg/x97+HiQnJ+PEiRP44osvXH5tSUkJWlpaMG3aNIsVvu644w4kJiZizZo1FtvHx8fjuuuuM3195plnIjk5Gf369bPoHSP/b/PP1RdSUlJw8uRJl3/OyD+YNcwaX2LWMGu8kTUGgwE33HADjh49iueff97uNv7MGha8zOj1eixfvhwjR45EZWUldu3ahV27duG8887DwYMH8eWXX5q23bt3L7KysqDT6Sz20atXL4uv9+7da/dxpcfM1dbWorGx0dQN11y/fv1gMBjwxx9/WDx++umnW3wtn7iys7NtHj9y5Ijp6507d6K+vh7p6elIS0uz+Hf8+HHU1NRYvL579+5227x582YUFBQgLi4OycnJSEtLwyOPPAIAbgUJYOwufPLkSVN30uPHj2Pt2rW4+uqrTaG5c+dOAMCoUaNs2v/555/btN9aYmKiqZDiS927d8eMGTPw2muvoXPnzigsLMSSJUtUfTa1tbU4evQoXnnlFZv3eMsttwCA6u+TPaeddprFHyGA8WRk/nOyd+9e9OzZ02Y7Zz/L3iD/ISGHzc6dO/Hzzz/bfBZ9+vQBYPtZ2LN69WoMGTIEMTExSE1NRVpaGpYuXer2z6o7hBAAYPOZUuhgdjA7fI3ZYV9iYiIAeP17MHnyZPTp0wd/+tOfcNppp+HWW29FcXGxqtfKv7vWv39RUVHo0aOH6XmZvc8vKSnJ7u8eAIvP1ReYScGLWcOs8TVmjX3MGtfcc889KC4uxmuvvYazzjrL7jb+zBrO4WXmq6++QlVVFZYvX47ly5fbPP/uu+/ikksuCUDL1JPHiat5XP5BA4yV2PT0dLz77rt2X5+Wlmbxtb1q/O7du3HxxRejb9++WLRoEbKzsxEVFYW1a9di8eLFMBgMrrwVkyFDhuCMM87ABx98gOuvvx6rVq3CyZMnce2111q0HzCOj8/MzLTZR0SE4x/1vn37ory8HC0tLQ7HjytR+mU1v9sme+aZZ3DzzTfjs88+w+eff457770X8+fPx9atW3HaaacpHkN+jxMmTLA7BwMADBw40OJrV1akUfrZMf858VSnTp3Q1taGY8eOuXyXZPv27dBqtaZwNBgMGDBgABYtWmR3e+sTuLVvvvkGY8eOxUUXXYQXX3wRWVlZiIyMxBtvvIH33nvPtJ0r31t3yIFib3w7hQZmB7OD2WHLH9khr1S2bds2XHjhhU73I0mS3XZZf97p6ekoLy/HunXr8J///Af/+c9/8MYbb2DixIl46623PHw3llz53QO8+7nac+TIEeh0Oq+saEfexaxh1jBrbDFr1PFX1sydOxcvvvgiFixY4HDOM39mDQteZt59912kp6djyZIlNs99/PHH+OSTT/DSSy8hNjYW3bp1w/r169HY2Ghx92TXrl0Wr5OX57R+XOkxc2lpadDpdPj1119tntuxYwc0Go3Ti3q1evbsiZKSEpx//vlu/+CtWrUKzc3NWLlypcUdHHvddF2t5l5zzTX45z//iYaGBvz73//GGWecgSFDhli0HzCeOAoKClxue1FREb799lusWLEC48ePd/n18rC0o0ePWjxuXV2XDRgwAAMGDMDf/vY3bNmyBeeffz5eeuklPPHEEwDsfz5paWlISEiAXq936z16Q7du3VBRUQEhhEUbnf0sy+TAqKystAk9R/bt24evv/4aQ4cONQVQz5498eOPP+Liiy926+7AihUrEBMTg3Xr1iE6Otr0+BtvvGGxnavfW2vO2lZZWYnOnTvb/LFGoYPZwexgdjjmq+woKirC/PnzsWzZMlUXISkpKXaHadj7vKOiolBUVISioiIYDAZMnjwZL7/8Mh599FH06tVL8WdR/t399ddfLYZ5tbS0oLKyMmDfA7UqKytNEyJTcGHWMGuYNY4xawKbNUuWLMGcOXMwbdo0zJw50+G2/swaDmlsd/LkSXz88cf485//jKuuusrm39SpU3Hs2DFTd9XCwkK0trbi1VdfNe3DYDDYhFCXLl2Qm5uLt99+22Js99dff41t27Y5bJNWq8Ull1yCzz77DHv27DE9fvDgQbz33nu44IILTF0sPXXNNddAr9fj8ccft3mura3N5gSp1F7AsiJcX19vU0AAgLi4OFX7lF177bVobm7GW2+9heLiYlxzzTUWzxcWFiIxMRHz5s1Da2urzetra2sd7v/uu+9GVlYW7r//fvz22282z9fU1JhO8vbIQbZx40bTY3q9Hq+88orFdg0NDWhra7N4bMCAAdBoNGhubjY9Zu/z0Wq1GDduHFasWIHt27fbtMHZe/SGwsJC7N+/3/R7AABNTU0WvweODB06FADw/fffqz5mXV0dxo8fD71eb1otBzD+zO7fv9/usU+ePIkTJ0443K9Wq4UkSRZ3W/bs2WOzYmJiYiI6d+5s8b0FgBdffFFV+539rP/vf/8zfS4UepgdzA5mh3O+yo6hQ4dizJgxeO211+yudtvS0oIHHnjA9HXPnj2xY8cOi/f8448/2qwUdfjwYYuvNRqN6eJH/rzj4uIA2F5AFhQUICoqCs8995zFz/S//vUv1NfXe2UFYF8qKyvDsGHDAt0MssKsYdYwa5xj1gQua/7973/j3nvvxQ033KA4+sacP7OGPbzarVy5EseOHbOY5M7ckCFDkJaWhnfffRfXXnstrrjiCuTn5+P+++/Hrl270LdvX6xcuRJ1dXUALCvf8+bNw+WXX47zzz8ft9xyC44cOYIXXngBubm5FuFizxNPPIEvvvgCF1xwASZPnoyIiAi8/PLLaG5uxj/+8Q+vvf/hw4fjrrvuwvz581FeXo5LLrkEkZGR2LlzJz788EP885//dLoU7iWXXGKqUt911104fvw4Xn31VaSnp6Oqqspi20GDBmHp0qV44okn0KtXL6Snp5smG7cnLy8PvXr1wl//+lc0NzdbdBMGjEWJpUuX4sYbb0ReXh6uu+46pKWlYd++fVizZg3OP/98vPDCC4r7T0lJwSeffIJLL70UZ599NiZMmIBBgwYBMP5Cvv/++w6LEv3798eQIUPw8MMPo66uDqmpqVi+fLlNaHz11VeYOnUqrr76avTp0wdtbW145513TCFh/vmUlJRg0aJF6NKlC7p3747zzjsPCxYswPr163HeeefhjjvuQE5ODurq6lBWVoaSkhLTz5+v3HXXXXjhhRcwfvx43HfffcjKysK7776LmJgYAM7viPXo0QO5ubkoKSnBrbfeavP8b7/9hmXLlkEIgYaGBvz444/48MMPcfz4cSxatAhjxowxbXvjjTfigw8+wN13343169fj/PPPh16vx44dO/DBBx9g3bp1OPfccxXbctlll5n2ef3116OmpgZLlixBr1698NNPP1lse/vtt2PBggW4/fbbce6552Ljxo12/+Cwx9HPek1NDX766SebSWQpdDA7mB3MDud8mR1vv/02LrnkEvzlL39BUVERLr74YsTFxWHnzp1Yvnw5qqqq8PTTTwMAbr31VixatAiFhYW47bbbUFNTg5deegn9+/e3mFz79ttvR11dHUaNGoXTTjsNe/fuxfPPP4+zzz7bdEf67LPPhlarxcKFC1FfX4/o6GiMGjUK6enpePjhhzF37lyMGTMGY8eOxa+//ooXX3wRgwcPtph02R82btxousitra3FiRMnTBfGF110ES666CLTtv/73/9QV1eHyy+/3K9tJOeYNcwaZo1zzJrAZE1paSkmTpyITp064eKLL7YZejxs2DCLXmh+zxo/rAQZEoqKikRMTIw4ceKE4jY333yziIyMNC21WltbK66//nqRkJAgkpKSxM033yw2b94sAIjly5dbvHb58uWib9++Ijo6WuTm5oqVK1eKcePGib59+1psB6vlfoUQoqysTBQWFor4+Hih0+nEyJEjxZYtWyy2kZeb/e677ywenz17tgAgamtrLR6/6aabRFxcnM17fOWVV8SgQYNEbGysSEhIEAMGDBAPPfSQOHDggGmbbt26icsuu8zuZ7Ry5UoxcOBAERMTI8444wyxcOFC8frrr9sshVtdXS0uu+wykZCQIACYlm61txSt7K9//asAIHr16mX32PLrCwsLRVJSkoiJiRE9e/YUN998s/j+++8VX2PuwIEDYvr06aJPnz4iJiZG6HQ6MWjQIPHkk0+K+vp603b2lpvdvXu3KCgoENHR0SIjI0M88sgj4osvvrB4P7///ru49dZbRc+ePUVMTIxITU0VI0eOFCUlJRb72rFjh7joootEbGysAGCx9O/BgwfFlClTRHZ2toiMjBSZmZni4osvFq+88orF5wCFZYWVlvvt37+/zbbWy+fK7+Gyyy4TsbGxIi0tTdx///2m5a/Nl7VVsmjRIhEfH2+zDDEA0z+NRiOSk5PFOeecI+677z7x888/291XS0uLWLhwoejfv7+Ijo4WKSkpYtCgQWLu3Lk23y977+9f//qX6N27t4iOjhZ9+/YVb7zxhul3xlxjY6O47bbbRFJSkkhISBDXXHONqKmpsfl9tbfss9LPuhBCLF26VOh0OtHQ0OD0c6PgxOwwYnYwO8z5MzuEMJ6jn376aTF48GARHx8voqKiRO/evcU999wjdu3aZbHtsmXLRI8ePURUVJQ4++yzxbp162za+9FHH4lLLrlEpKeni6ioKHH66aeLu+66S1RVVVns69VXXxU9evQQWq3W5rN54YUXRN++fUVkZKTIyMgQkyZNslnSXunzU/pdASCmTJni9LMyJ/8u2/tnfc6YOXOmOP3004XBYHDpGOR7zBojZg2zxhyzJjiyRv79Vvr3xhtvWGzv76xhwcvLPvnkEwFAbNq0yem2Z511ligoKPBDq4h8a/HixQKA+L//+z+n2x49elSkpqaK1157zQ8tC25nn322mDZtWqCbQUGA2UEdEbMjeDQ1NYnMzEzx7LPPBrop5EPMGuqImDXBIxBZwzm8PHDy5EmLr/V6PZ5//nkkJiYiLy/P9Hhra6tNl9ENGzbgxx9/xIgRI/zRVCKvsf65b2pqwssvv4zevXuja9euTl+flJSEhx56CE899ZTbK+KEg+LiYuzcuRMPP/xwoJtCfsbsoI6I2RHc3njjDURGRuLuu+8OdFPIS5g11BExa4JbILJGEsLH6xuHsdtvvx0nT57E0KFD0dzcjI8//hhbtmzBvHnzLC5i9+zZg4KCAkyYMAFdunTBjh078NJLLyEpKQnbt29Hp06dAvguiFzzpz/9CaeffjrOPvts1NfXY9myZfj555/x7rvv4vrrrw9084iCHrODOiJmh/fo9XqnE0DHx8cjPj7eTy2iYMSsoY6IWeM9YZM1futLFobeffddkZeXJxITE0VUVJTIyckRzz//vM12R48eFddcc43o2rWriIqKEikpKeKqq66yGetLFAoWL14s+vfvL+Li4kRMTIzIy8uzmQuCiJQxO6gjYnZ4T2VlpcP5UmBnniXqeJg11BExa7wnXLKGPbyIiIiIiEJEU1MTNm3a5HCbHj16WKyKRURE5IpwyRoWvIiIiIiIiIiIKKxw0noiIiIiIiIiIgorEYFugCMGgwEHDhxAQkICJEkKdHOIiEKeEALHjh1Dly5doNHwngfArCEi8ibmjC3mDBGRd6nNmqAueB04cADZ2dmBbgYRUdj5448/cNpppwW6GUGBWUNE5H3MmVOYM0REvuEsa4K64JWQkADA+CYSExMD3BoiotDX0NCA7Oxs0/mVmDVERN7EnLHFnCEi8i61WRPUBS+5y29iYiLDgYjIizik4hRmDRGR9zFnTmHOEBH5hrOs4cB6IiIiIiIiIiIKKyx4ERERERERERFRWGHBi4iIiIiIiIiIwkpQz+FFRIFlMBjQ0tIS6GaQCyIjI6HVagPdDCIi1fR6PVpbWwPdDHJBVFSUw2XgiYiCCXMm9HjrmoYFLyKyq6WlBZWVlTAYDIFuCrkoOTkZmZmZnDCYiIKaEALV1dU4evRooJtCLtJoNOjevTuioqIC3RQiIkXMmdDmjWsaFryIyIYQAlVVVdBqtcjOzuZd3BAhhEBjYyNqamoAAFlZWQFuERGRMvkiJD09HTqdjkX6EGEwGHDgwAFUVVXh9NNP5/eNiIIWcyY0efOahgUvIrLR1taGxsZGdOnSBTqdLtDNIRfExsYCAGpqapCens7hjUQUlPR6vekipFOnToFuDrkoLS0NBw4cQFtbGyIjIwPdHCIiG8yZ0Oataxp22yAiG3q9HgA4VCFEyUVKzlVARMFKPj/xpkpokv8+kP9eICIKNsyZ0OeNaxoWvIhIEbv9hiZ+34goVPB8FZr4fSOiUMHzVejyxveOBS8iIiIiIiIiIgorLHgReUBvEPh292F8Vr4f3+4+DL1BBLpJpIIkSfj0008D3QwiIqeYM6GJOUNEoYRZE5qYNc6x4EXkpuLtVbhg4VcY/+pW3Le8HONf3YoLFn6F4u1VgW5ah1ZdXY177rkHPXr0QHR0NLKzs1FUVIQvv/zSJ8fbsGEDJEny6XLHdXV1uOGGG5CYmIjk5GTcdtttOH78uM+OR0TBgTkTnJgzRBROmDXBiVnjHSx4EbmheHsVJi0rQ1V9k8Xj1fVNmLSsjAHRzt93i/bs2YNBgwbhq6++wlNPPYVt27ahuLgYI0eOxJQpU3x6bE8JIdDW1mb3uRtuuAE///wzvvjiC6xevRobN27EnXfe6ecWEpE/MWfUYc6ox5whImvMGnWYNeoFW9aw4EXkIr1BYO6qCtg7zcmPzV1V0eG7AgfibtHkyZMhSRJKS0sxbtw49OnTB/3798eMGTOwdetWu6+xdzejvLwckiRhz549AIC9e/eiqKgIKSkpiIuLQ//+/bF27Vrs2bMHI0eOBACkpKRAkiTcfPPNAACDwYD58+eje/fuiI2NxVlnnYWPPvrI5rj/+c9/MGjQIERHR2PTpk027fvll19QXFyM1157Deeddx4uuOACPP/881i+fDkOHDjgnQ+OiIIKc0Yd5gxzhojcx6xRh1kT2lkT4bM9E4Wp0so6m7sg5gSAqvomlFbWYWjPTv5rWBCR7xZZx6N8t2jphDyMyc3y6jHr6upQXFyMJ598EnFxcTbPJycnu73vKVOmoKWlBRs3bkRcXBwqKioQHx+P7OxsrFixAuPGjcOvv/6KxMRExMbGAgDmz5+PZcuW4aWXXkLv3r2xceNGTJgwAWlpaRg+fLhp37NmzcLTTz+NHj16ICUlxebY3377LZKTk3HuueeaHisoKIBGo8F///tfXHnllW6/LyIKTswZ55gzzBki8gyzxjlmTehnDQteRC6qOaYcDO5sF26c3S2SYLxbNDonE1qN95YJ3rVrF4QQ6Nu3r9f2Kdu3bx/GjRuHAQMGAAB69Ohhei41NRUAkJ6ebgqg5uZmzJs3DyUlJRg6dKjpNZs2bcLLL79sEQ6PPfYYRo8erXjs6upqpKenWzwWERGB1NRUVFdXe+X9EVFwYc44xpxhzhCR55g1jjFrwiNrWPAiclF6QoxXtws3gbpbJITvulvfe++9mDRpEj7//HMUFBRg3LhxGDhwoOL2u3btQmNjo81Jv6WlBeecc47FY+Z3OYiIAOaMM8wZ5gwReY5Z4xizJjyyhnN4Ebkov3sqspJioFTHlwBkJcUgv3uqP5sVNAJ1t6h3796QJAk7duxw6XUajfE0aB4ura2tFtvcfvvt+P3333HjjTdi27ZtOPfcc/H8888r7lNebWTNmjUoLy83/auoqLAY8w7Abldlc5mZmaipqbF4rK2tDXV1dcjMzHT+Boko5DBnHGPOMGeIyHPMGseYNeGRNSx4EblIq5EwuygHAGwCQv56dlGOV7u2hpJA3S1KTU1FYWEhlixZghMnTtg8r7TEblpaGgCgqurUxJPl5eU222VnZ+Puu+/Gxx9/jPvvvx+vvvoqACAqKgoAoNfrTdvm5OQgOjoa+/btQ69evSz+ZWdnu/S+hg4diqNHj+J///uf6bGvvvoKBoMB5513nkv7IqLQwJxxjDnDnCEizzFrHGPWhEfWsOBF5IYxuVlYOiEPmUmWJ7jMpBifTF4YSgJ5t2jJkiXQ6/XIz8/HihUrsHPnTvzyyy947rnnTOPOrckn7Dlz5mDnzp1Ys2YNnnnmGYttpk2bhnXr1qGyshJlZWVYv349+vXrBwDo1q0bJEnC6tWrUVtbi+PHjyMhIQEPPPAApk+fjrfeegu7d+9GWVkZnn/+ebz11lsuvad+/fphzJgxuOOOO1BaWorNmzdj6tSpuO6669ClSxf3PigiCnrMGWXMGeYMEXkHs0YZsyZMskYEsfr6egFA1NfXB7opRHa16Q1iy65D4tMf/k9s2XVItOkNgW6SV5w8eVJUVFSIkydPuvX6/2w7IM6YuVqcMXO16Gb2T37sP9sOeLnFpxw4cEBMmTJFdOvWTURFRYmuXbuKsWPHivXr15u2ASA++eQT09ebNm0SAwYMEDExMeLCCy8UH374oQAgKisrhRBCTJ06VfTs2VNER0eLtLQ0ceONN4pDhw6ZXv/YY4+JzMxMIUmSuOmmm4QQQhgMBvHss8+KM888U0RGRoq0tDRRWFgovv76ayGEEOvXrxcAxJEjR5y+p8OHD4vx48eL+Ph4kZiYKG655RZx7Ngxxe0dff94XrXFz4SCGXPGPuYMcyaU8DOhYBeOWeNpzgjBrBEi9LNGav+gglJDQwOSkpJQX1+PxMTEQDeHqMNoampCZWUlunfvjpgY97rpFm+vwtxVFRaTPWYlxWB2UU6HvlvkD46+fzyv2uJnQuR/zJnQxpxxDT8TIv/zRs4AzJpA8kbWcJVGIvKJMblZGJ2TidLKOtQca0J6grHLb0edB4CIiLyLOUNERL7GrAltLHgRkc9oNZJXl+klIiIyx5whIiJfY9aELk5aT0REREREREREYYUFLyIiIiIiIiIiCisseBERERERERERUVhhwYuIiIiIiIiIiMIKC15ERERERERERBRWWPAiIiIiIiIiIqKwwoIXERERERERERGFFRa8iKjDkSQJn376aaCbQUREYYo5Q0REvsascY4FLyIKK9XV1bjnnnvQo0cPREdHIzs7G0VFRfjyyy99crwNGzZAkiQcPXrUJ/sHgCeffBLDhg2DTqdDcnKyz45DRETOMWeIiMjXmDXeEeGXoxBRx2TQA3u3AMcPAvEZQLdhgEbrs8Pt2bMH559/PpKTk/HUU09hwIABaG1txbp16zBlyhTs2LHDZ8f2lBACer0eERG2p+WWlhZcffXVGDp0KP71r38FoHVEREGKOaMac4aIyE3MGtWCLWvYw4uIfKNiJfBsLvDWn4EVtxn/+2yu8XEfmTx5MiRJQmlpKcaNG4c+ffqgf//+mDFjBrZu3Wr3NfbuZpSXl0OSJOzZswcAsHfvXhQVFSElJQVxcXHo378/1q5diz179mDkyJEAgJSUFEiShJtvvhkAYDAYMH/+fHTv3h2xsbE466yz8NFHH9kc9z//+Q8GDRqE6OhobNq0yW4b586di+nTp2PAgAGef0hEROGCOcOcISLyNWZNSGcNe3gRkfdVrAQ+mAhAWD7eUGV8/Jq3gZyxXj1kXV0diouL8eSTTyIuLs7meU+6zU6ZMgUtLS3YuHEj4uLiUFFRgfj4eGRnZ2PFihUYN24cfv31VyQmJiI2NhYAMH/+fCxbtgwvvfQSevfujY0bN2LChAlIS0vD8OHDTfueNWsWnn76afTo0QMpKSlut5GIqENhzjBniIh8jVkT8lnDghcReZdBDxTPhE0wAO2PSUDxLKDvZV7tCrxr1y4IIdC3b1+v7VO2b98+jBs3znQ3okePHqbnUlNTAQDp6emmAGpubsa8efNQUlKCoUOHml6zadMmvPzyyxbh8Nhjj2H06NFebzMRUdhizjBniIh8jVkTFlnDghcRedfeLUDDAQcbCKBhv3G77hd67bBC2Asj77j33nsxadIkfP755ygoKMC4ceMwcOBAxe137dqFxsZGm5N+S0sLzjnnHIvHzj33XJ+0mYgobDFnmDNERL7GrAmLrGHBi4i86/hB726nUu/evSFJksuTOGo0xqkMzcOltbXVYpvbb78dhYWFWLNmDT7//HPMnz8fzzzzDO655x67+zx+/DgAYM2aNejatavFc9HR0RZf2+uqTEREDjBnmDNERL7GrAmLrOGk9UTkXfEZ3t1OpdTUVBQWFmLJkiU4ceKEzfNKS+ympaUBAKqqqkyPlZeX22yXnZ2Nu+++Gx9//DHuv/9+vPrqqwCAqKgoAIBerzdtm5OTg+joaOzbtw+9evWy+Jedne3uWyQiIoA5A+YMEZHPMWvCImtY8CIi7+o2DEjsAkBS2EACErsat/OyJUuWQK/XIz8/HytWrMDOnTvxyy+/4LnnnjONO7cmn7DnzJmDnTt3Ys2aNXjmmWcstpk2bRrWrVuHyspKlJWVYf369ejXrx8AoFu3bpAkCatXr0ZtbS2OHz+OhIQEPPDAA5g+fTreeust7N69G2VlZXj++efx1ltvufy+9u3bh/Lycuzbtw96vR7l5eUoLy833XUhIupQmDPMGSIiX2PWhEfWiCBWX18vAIj6+vpAN4WoQzl58qSoqKgQJ0+edG8HP38mxOyk9n+JZv/aH/v5M+811sqBAwfElClTRLdu3URUVJTo2rWrGDt2rFi/fr1pGwDik08+MX29adMmMWDAABETEyMuvPBC8eGHHwoAorKyUgghxNSpU0XPnj1FdHS0SEtLEzfeeKM4dOiQ6fWPPfaYyMzMFJIkiZtuukkIIYTBYBDPPvusOPPMM0VkZKRIS0sThYWF4uuvvxZCCLF+/XoBQBw5csTpe7rpppsEjLNjWvwzf0/mHH3/eF61xc+EyP+YM8yZjoSfCZH/eZwzQjBrROhnjdT+QQWlhoYGJCUlob6+HomJiYFuDlGH0dTUhMrKSnTv3h0xMTHu7aRipXFlE/PJHhO7AmMWeH35XrLk6PvH86otfiZE/secCW3MGdfwMyHyP6/kDMCsCSBvZI3fhjQuWLAAkiRh2rRp/jokEQVSzlhg2nbgptXAuH8Z/zttG4OBfIpZQ9SBMGcoAJgzRB0Msyak+WWVxu+++w4vv/yywyUviSgMabReXaaXyBFmDVEHxJwhP2LOEHVQzJqQ5fMeXsePH8cNN9yAV199FSkpKb4+HBERdUDMGiIi8iXmDBFR6PF5wWvKlCm47LLLUFBQ4OtDERFRB8WsISIiX2LOEBGFHp8OaVy+fDnKysrw3Xffqdq+ubkZzc3Npq8bGhp81TQiIgoTzBoiIvIl5gwRUWjyWQ+vP/74A/fddx/effdd1asizJ8/H0lJSaZ/2dnZvmoeEakQxIu4kgMGgyHQTfAbZg0REfkSc4aIKHRJwkdXtJ9++imuvPJKaLVa02N6vR6SJEGj0aC5udniOcD+3ZDs7Gwu4UvkZ3q9Hjt37oROp0NaWhokSQp0k0gFIQRaWlpQW1sLvV6P3r17Q6OxvK8RbkujM2uIQpPXloungPDGUvGhgjlDFJqYM6HPG1njsyGNF198MbZt22bx2C233IK+ffti5syZNsEAANHR0YiOjvZVk4hIJa1Wi9NOOw3/93//hz179gS6OeQinU6H008/3abYFY6YNURE5EvMGSKi0OWzgldCQgJyc3MtHouLi0OnTp1sHiei4BMfH4/evXujtbU10E0hF2i1WkRERHSYXnnMGiIi8iXmDBFR6PLppPVEFNq0Wq3dO5dERESkTJIkfPLJJ7jiiisC3RQiIgpTzBrn/DreZcOGDXj22Wf9eUgiIupgmDVE5EvV1dW455570KNHD0RHRyM7OxtFRUX48ssvfXK8DRs2QJIkHD161Cf737NnD2677TZ0794dsbGx6NmzJ2bPno2WlhafHC8cMGeIyNeYNd7BHl5EREREFJL0Bj3KaspQ21iLNF0a8tLzoNX4rmfynj17cP755yM5ORlPPfUUBgwYgNbWVqxbtw5TpkzBjh07fHZsTwkhoNfrERFh+ef/jh07YDAY8PLLL6NXr17Yvn077rjjDpw4cQJPP/10gFpLRBQ8mDXqBVvWhP+MxkREREQUdkr2lqBwRSFuXXcrZn4zE7euuxWFKwpRsrfEZ8ecPHkyJElCaWkpxo0bhz59+qB///6YMWMGtm7davc19u6al5eXQ5Ik08Iwe/fuRVFREVJSUhAXF4f+/ftj7dq12LNnD0aOHAkASElJgSRJuPnmmwEABoMB8+fPN90tP+uss/DRRx/ZHPc///kPBg0ahOjoaGzatMmmfWPGjMEbb7yBSy65BD169MDYsWPxwAMP4OOPP/bOh0ZEFMKYNaGdNezhRUREREQhpWRvCWZsmAEBYfF4TWMNZmyYgUUjFqGgW4FXj1lXV4fi4mI8+eSTiIuLs3k+OTnZ7X1PmTIFLS0t2LhxI+Li4lBRUYH4+HhkZ2djxYoVGDduHH799VckJiYiNjYWADB//nwsW7YML730Enr37o2NGzdiwoQJSEtLw/Dhw037njVrFp5++mn06NEDKSkpqtpTX1+P1NRUt98PEVE4YNaEftaw4EVEREREIUNv0GNB6QKbCxAAEBCQIGFh6UKMzB7p1SEnu3btghACffv29do+Zfv27cO4ceMwYMAAAECPHj1Mz8kXA+np6aYLnebmZsybNw8lJSUYOnSo6TWbNm3Cyy+/bHER8thjj2H06NGq27Jr1y48//zzHM5IRB0asyY8soYFLyIiIiIKGWU1ZTjYeFDxeQGB6sZqlNWUYXDmYK8dVwjbix5vuffeezFp0iR8/vnnKCgowLhx4zBw4EDF7Xft2oXGxkabi4uWlhacc845Fo+de+65qtuxf/9+jBkzBldffTXuuOMO194EEVEYYdaER9aw4EVEREREIaO2sdar26nVu3dvSJLk8mTBGo1xylzzi5jW1laLbW6//XYUFhZizZo1+PzzzzF//nw888wzuOeee+zu8/jx4wCANWvWoGvXrhbPRUdHW3xtb0iMPQcOHMDIkSMxbNgwvPLKK6peQ0QUrpg14ZE1nLSeiIiIiEJGmi7Nq9uplZqaisLCQixZsgQnTpyweV5pKfe0NGM7qqqqTI+Vl5fbbJednY27774bH3/8Me6//368+uqrAICoqCgAgF6vN22bk5OD6Oho7Nu3D7169bL4l52d7fJ7279/P0aMGIFBgwbhjTfeMF04ERF1VMya8MgaphkRERERhYy89Dxk6DIgQbL7vAQJmbpM5KXnef3YS5YsgV6vR35+PlasWIGdO3fil19+wXPPPWea38SafGEwZ84c7Ny5E2vWrMEzzzxjsc20adOwbt06VFZWoqysDOvXr0e/fv0AAN26dYMkSVi9ejVqa2tx/PhxJCQk4IEHHsD06dPx1ltvYffu3SgrK8Pzzz+Pt956y6X3JF+AnH766Xj66adRW1uL6upqVFdXu/chERGFAWZNeGQNC15EREREFDK0Gi1m5c8CAJsLEfnrmfkzvTqJsKxHjx4oKyvDyJEjcf/99yM3NxejR4/Gl19+iaVLl9p9TWRkJN5//33s2LEDAwcOxMKFC/HEE09YbKPX6zFlyhT069cPY8aMQZ8+ffDiiy8CALp27Yq5c+di1qxZyMjIwNSpUwEAjz/+OB599FHMnz/f9Lo1a9age/fuLr2nL774Art27cKXX36J0047DVlZWaZ/REQdFbMmPLJGEr6cFc1DDQ0NSEpKQn19PRITEwPdHCKikMfzqi1+JkT+19TUhMrKSnTv3h0xMTFu7aNkbwkWlC6wmFQ4U5eJmfkzvb5MPFly9P3jOdUWPxMi//NGzgDMmkDyRtZw0noiIiIiCjkF3QowMnskymrKUNtYizRdGvLS83xyt52IiDomZk1oY8GLiIiIiEKSVqP16nLwRERE1pg1oYtzeBERERERERERUVhhwYuIiIiIiIiIiMIKC15EREREFBBBvHYSOcDvGxERhQIWvIiIiIjIryIjIwEAjY2NAW4JuaOlpQUAoNVy0mYiIgpenLSeiIiIiPxKq9UiOTkZNTU1AACdTgdJkgLcKlLDYDCgtrYWOp0OERG8lCAiouDFlCIiIiIiv8vMzAQAU9GLQodGo8Hpp5/OIiUREQU1FryIiIiIyO8kSUJWVhbS09PR2toa6OaQC6KioqDRcGYUIiIKbix4EREREVHAaLVazgVFRETkIkmS8Mknn+CKK64IdFOCFm/NEBEREREREREFierqatxzzz3o0aMHoqOjkZ2djaKiInz55Zc+Od6GDRsgSRKOHj3qk/0DwNixY3H66acjJiYGWVlZuPHGG3HgwAGfHQ9gD6/wY9ADe7cAxw8C8RlAt2GAhndNiYjIO/QGPcpqylDbWIs0XRry0vOgZc4QEZEX6Q0CpZV1qDnWhPSEGOR3T4VWwznjKDCEXo/G7/+HttpaRKSlQXfuIEg+7Jm8Z88enH/++UhOTsZTTz2FAQMGoLW1FevWrcOUKVOwY8cOnx3bU0II6PV6u4uajBw5Eo888giysrKwf/9+PPDAA7jqqquwZcsWn7WHBa9wUrESKJ4JNJhVSRO7AGMWAjljA9cuIiIKCyV7S7CgdAEONh40PZahy8Cs/Fko6FYQwJYREVG4KN5ehbmrKlBV32R6LCspBrOLcjAmNyuALaOOqOHzz3Fw3ny0VVebHovIzETGIw8j8ZJLfHLMyZMnQ5IklJaWIi4uzvR4//79ceutt9p9zYYNGzBy5EgcOXIEycnJAIDy8nKcc845qKysxBlnnIG9e/di6tSp2LRpE1paWnDGGWfgqaeeQk5ODkaOHAkASElJAQDcdNNNePPNN2EwGLBw4UK88sorqK6uRp8+ffDoo4/iqquusjju2rVr8be//Q3btm3D559/jhEjRti0cfr06ab/3a1bN8yaNQtXXHEFWltbERkZ6Y2PzgYLXuGiYiXwwUQAwvLxhirj49e83SGKXrwbRETkGyV7SzBjwwwIq5ypaazBjA0zsGjEog5R9GLOEBH5TvH2KkxaVmZ9RYPq+iZMWlaGpRPywr7oxZwJHg2ff479900DhOVPZNvBg8bH//ms14tedXV1KC4uxpNPPmlR7JLJxSx3TJkyBS0tLdi4cSPi4uJQUVGB+Ph4ZGdnY8WKFRg3bhx+/fVXJCYmIjY2FgAwf/58LFu2DC+99BJ69+6NjRs3YsKECUhLS8Pw4cNN+541axaefvpp9OjRw1Q0c/Y+3333XQwbNsxnxS6ABa/wYNAbe3bZRAPaH5OA4llA38vCengj7wYREfmG3qDHgtIFNsUuABAQkCBhYelCjMweGdbDG5kzRES+ozcIzF1V4eiKBnNXVWB0TmbYFoCYM8FD6PU4OG++TbHL+KQAJAkH581HwsUXe3V4465duyCEQN++fb22T9m+ffswbtw4DBgwAADQo0cP03OpqakAgPT0dFNRrbm5GfPmzUNJSQmGDh1qes2mTZvw8ssvWxS8HnvsMYwePdppG2bOnIkXXngBjY2NGDJkCFavXu2tt2cXJ60PB3u3WA5jtCGAhv3G7cKUfDfIPByAU3eDirdXBahlREShr6ymzGIYozUBgerGapTVlPmxVf7FnCEi8q3Syjqbc6w5AaCqvgmllXX+a5QfMWeCS+P3/7MYxmhDCLRVV6Px+/959bjCXoHNS+6991488cQTOP/88zF79mz89NNPDrfftWsXGhsbMXr0aMTHx5v+vf3229i9e7fFtueee66qNjz44IP44Ycf8Pnnn0Or1WLixIk+fc8seIWD48oXIW5tF2Kc3Q0CjHeD9Abf/SIREYWz2sZar24XapgzRES+V3NMudjlznahhDkTfNpq1f1No3Y7tXr37g1JklyemF6jMZZ2zItHra2tFtvcfvvt+P3333HjjTdi27ZtOPfcc/H8888r7vP48eMAgDVr1qC8vNz0r6KiAh999JHFtvaGX9rTuXNn9OnTB6NHj8by5cuxdu1abN26VdVr3cGCVziIz/DudiGmo98NIiLytTRdmle3CzXMGSIi30tPiPHqdqGEORN8ItLU/U2jdju1UlNTUVhYiCVLluDEiRM2zx89etTu69La21FVdaonYHl5uc122dnZuPvuu/Hxxx/j/vvvx6uvvgoAiIqKAgDo9XrTtjk5OYiOjsa+ffvQq1cvi3/Z2dnuvkUTg8EAwDh00ldY8AoH3YYZV2OE0lh2CUjsatwuDHXku0FERP6Ql56HDF0GJIWckSAhU5eJvPQ8P7fMP5gzRES+l989FVlJMY6uaJCVZJzEPdwwZ4KP7txBiMjMBCSFn0hJQkRmJnTnDvL6sZcsWQK9Xo/8/HysWLECO3fuxC+//ILnnnvONJeWNbkINWfOHOzcuRNr1qzBM888Y7HNtGnTsG7dOlRWVqKsrAzr169Hv379ABhXTZQkCatXr0ZtbS2OHz+OhIQEPPDAA5g+fTreeust7N69G2VlZXj++efx1ltvufSe/vvf/+KFF15AeXk59u7di6+++grjx49Hz549Fd+TN7DgFQ40WmDMwvYvrH8h278esyBsJ6zvyHeDiIj8QavRYlb+LACwKXrJX8/Mnxm2E9YzZ4iIfE+rkTC7KAeA4hUNZhflhOWE9cyZ4CNptch45OH2L6x+5tq/znjkYa9OWC/r0aMHysrKMHLkSNx///3Izc3F6NGj8eWXX2Lp0qV2XxMZGYn3338fO3bswMCBA7Fw4UI88cQTFtvo9XpMmTIF/fr1w5gxY9CnTx+8+OKLAICuXbti7ty5mDVrFjIyMjB16lQAwOOPP45HH30U8+fPN71uzZo16N69u0vvSafT4eOPP8bFF1+MM888E7fddhsGDhyIr7/+GtHR0W58SupIwpczhHmooaEBSUlJqK+vR2JiYqCbE/wqVhpXazSfwD6xq7HYlTM2cO3yMb1B4IKFX6G6vsnuuHcJQGZSDDbNHBWWAUnkCp5XbfEzUa9kbwkWlC6wmMA+U5eJmfkzUdCtIIAt8y3mDJF6PKfa4mfimo64UiFzxvuamppQWVmJ7t27IybG/UJhw+ef4+C8+RYT2EdkZiLjkYeReMkl3mgqKXD0PVR7Xo3wdSPJj3LGAn0vM67GePygcc6ubsPCtmeXTL4bNGlZGSTAIiTC/W4QEZE/FXQrwMjskSirKUNtYy3SdGnIS88L255dMuYMEZH/jMnNwuicTJRW1qHmWBPSE4zDGMP5HMucCV6Jl1yChIsvNq7aWFuLiLQ06M4d5JOeXeR9LHiFG40W6H5hoFvhd2Nys7B0Qp7N3aDMML8bRETkb1qNFoMzBwe6GX7HnCEi8h+tRsLQnp0C3Qy/Ys4EL0mrRdx5+YFuBrmBBS8KGx3xbhAREfkPc4aIiHyJOUPkXSx4UVjpiHeDiIjIf5gzRETkS8wZIu/hKo1EREREREREFHaCeI0+csIb3zv28CIKQXqDYFdnIiLyKWYNERH5ki9zJjIyEgDQ2NiI2NhYr+yT/KuxsRHAqe+lO1jwIgoxHXGpZiIi8i9mDRER+ZKvc0ar1SI5ORk1NTUAAJ1OB0niTZtQIIRAY2MjampqkJycDK0HK2Ky4EUUQoq3V2HSsjJYd+6srm/CpGVlWDohjxciRETkEWYNERH5kr9yJjMzEwBMRS8KLcnJyabvobtY8CIKEXqDwNxVFTbBAAACgARg7qoKjM7J5JATIiJyC7OGiIh8yZ85I0kSsrKykJ6ejtbWVo/2Rf4VGRnpUc8umU8LXkuXLsXSpUuxZ88eAED//v3x97//HX/60598eViisFRaWWfR5deaAFBV34TSyjqu7EIdBnOGyLuYNUSWmDNE3hWInNFqtV4pnlDo8ekqjaeddhoWLFiA//3vf/j+++8xatQoXH755fj55599eViisFRzTDkY3NmOKBwwZ4i8i1lDZIk5Q+RdzBnyJ5/28CoqKrL4+sknn8TSpUuxdetW9O/f35eHJgo76QkxXt2OKBwwZ4i8i1lDZIk5Q+RdzBnyJ5/28DKn1+uxfPlynDhxAkOHDvXXYYnCRn73VGQlxUBpJLsE48om+d1T/dksoqDBnCHyHLOGSBlzhshzzBnyJ58XvLZt24b4+HhER0fj7rvvxieffIKcnBy72zY3N6OhocHiHxEZaTUSZhcZf3esA0L+enZRDicRpg7HlZwBmDVEjjBriGwxZ4i8hzlD/uTzgteZZ56J8vJy/Pe//8WkSZNw0003oaKiwu628+fPR1JSkulfdna2r5tHFFLG5GZh6YQ8ZCZZdvHNTIrhMvHUYbmSMwCzhsgZZg2RJeYMkXcxZ8hfJCGEvRVBfaagoAA9e/bEyy+/bPNcc3MzmpubTV83NDQgOzsb9fX1SExM9GcziYKa3iBQWlmHmmNNSE8wdvnlXRBSo6GhAUlJSWF9XnWUMwCzhkgtZg25gznDnCFSizlD7lKbNT6dtN4eg8FgEQDmoqOjER0d7ecWEYUerUbicvBEChzlDMCsIVKLWUNkH3OGyDuYM+RrPi14Pfzww/jTn/6E008/HceOHcN7772HDRs2YN26db48LBERdRDMGSIi8iXmDBFR6PJpwaumpgYTJ05EVVUVkpKSMHDgQKxbtw6jR4/25WGJiKiDYM4QEZEvMWeIiEKXTwte//rXv3y5eyIi6uCYM0RE5EvMGSKi0OXzVRqJiIiIiIiIiIj8ye+T1hNR8ODKKERE5EvMGSIi8jVmDSlhwYuogyreXoW5qypQVd9keiwrKQazi3IwJjcrgC0jIqJwwJwhIiJfY9aQIxzSSNQBFW+vwqRlZRbBAADV9U2YtKwMxdurAtQyIiIKB8wZIiLyNWYNOcOCF1EHozcIzF1VAWHnOfmxuasqoDfY24KIiMgx5gwREfkas4bUYMGLqIMprayzuQtiTgCoqm9CaWWd/xpFRERhgzlDRES+xqwhNTiHF3mXQQ/s3QIcPwjEZwDdhgEabaBbRWZqjikHgzvbERH5k96gR1lNGWoba5GmS0Neeh60zJmgwpwholDGCdBDA7OG1GDBi7ynYiVQPBNoOHDqscQuwJiFQM5Yrx+OYeSe9IQYr25HROQvJXtLsKB0AQ42HjQ9lqHLwKz8WSjoVuD9A/ImjluYM0QUqgIxATqvadzDrCE1WPAi76hYCXwwEbAeRd1QZXz8mre9WvTiahzuy++eiqykGFTXN9kd8y4ByEwyhi0RUbAo2VuCGRtmQFiduWoaazBjwwwsGrHIu0UvP9/ECSfMGSIKRfIE6NbnLXkC9KUT8rx+ncFrGvcxa0gNzuFFnjPojRcFjqYMLJ5l3M4LuBqHZ7QaCbOLcgAYg8Cc/PXsohzeWSKioKE36LGgdIFNsQuA6bGFpQuh91LOmG7imBe7gFM3cSpWeuc4YYo5Q0ShJhAToPOaxjPMGlKDBS/y3N4tthcFFgTQsN+4nYe4God3jMnNwtIJechMsuzim5kU45O7V0REniirKbMYxmhNQKC6sRplNWWeH8zPN3HCFXOGiEKJvydA5zWNdzBryBkOaSTPHVe+CHFrOwdcCaOhPTt5fLxwNiY3C6NzMjlnABEFvdrGWq9u55ArN3G6X+j58cIYc4aIQoW/J0DnNY33MGvIERa8yHPxGd7dzgGuxuFdWo3EECWioJemS/Pqdg758SZOR8CcIaJQ4O8J0HlN413MGlLCIY3kuW7DjBP52oyelklAYlfjdh7iahxERB1PXnoeMnQZkBRyRoKETF0m8tLzPD+YH2/iEBFRcJAnQHdwNYMsL06AzmsaIv9gwYs8p9EaV60CoDhl4JgFXlnK3d9hREREgafVaDErfxYA2BS95K9n5s+E1gs548+bOEREFBz8PQE6r2mI/IMFL7Jk0AOV3wDbPjL+V+2kvDljgWveBhKtJgZM7GJ83EtLuHM1DiKi0KY36PFd9XdY+/tafFf9neqVFQu6FWDRiEVI16VbPJ6hy8CiEYtQ0K3AOw30400cIiLyPr1B4Nvdh/FZ+X58u/uw6onf/TkBOq9piPxDEkIE7dIPDQ0NSEpKQn19PRITEwPdnPBXsdK4MpX5ZL2JXYx/+KstWBn0xol8jx80DvfoNswnFwXF26swd1WFxWSPWUkxmF2Uw9U4iBzgedUWPxP/KdlbggWlCyxWXMzQZWBW/izVBSu9QY+ymjLUNtYiTZeGvPQ87/TssmY3E7sai11euolDFI54TrXFz8R/vHGNoDcIv02AzmsaIveoPa+y4EVGFSuBDybCdhn29pO7F3tpeYVBD/2ezdj9+27UiGRozzgf+T3TeBeEyAmeV23xM/GPkr0lmLFhBoRVzshDEr3aS8sL9AY9yqq/Q+0fW5CmNyAvczC0Z1zAnl1ETvCcaoufiX8Ub6/CpGVlSlczXu+l5Sm5sFZdfxJ1J1qQGh+NzESuMEikhtrzKldpJGOvrOKZsC12of0xCSieBfS9LDj+0G+/665tOIA+APoAwDYXe6IREZHf6A16LChdYFPsAgABAQkSFpYuxMjskb7preUiuz3R9hdjllZ9TzQiIvIfvUFg7qoKR1czmLuqAqNzMoOimOSoZ1cwtI8oXHAOLzIOQTQfsmFDAA37jdsFmtwTzbq9DVXGxytWBqZdRESkqKymzKJ4ZE1AoLqxGmU1ZX5slX1yTzTr9tY01mDGhhko2VsSoJYREZGS0so6i+KRNQGgqr4JpZV1/muUArknmnV7q+ubMGlZGYq3VwWoZUThhwUvMs635c3tfMVpTzQYe6KpnWifiIj8orax1qvb+YqznmgAsLB0oeqJ9omIyD9qjikXu9zZzlec9UQDjD3R1E60T0SOseBFxsnl1dB1dm8FR28JpZ5oRERkkqZLU7Vdp9hObq3g6C2h1BONiIhOSU+Icb5R+3buruLoDaHUE40oHHAOr3DjziqJ3YYZV2NsqIL93lMSEJsCfDbJsxUcPRUqPdGIiMKYO6sk5qXnIUOXgZrGGru9pyRISIpKwl83/dWjFRw9FSo90YiIwpk7qyTmd09FVlIMquublK5mkJkUgyMnWnDBwq8CtipiqPREIwoXLHiFE7tLqLcXpfpeplwI02iN23wwEcY4MI+J9q9P1gEnrY4nz5vlrxUc1fZEU7tdB+bP5ZaJKHzYncy9vSg1MnukYiFMq9FiVv4szNgwAxIki6KX/PXRlqNAi+Xx5Hmz/LWCo9qeaGq368iYM0TkDkeTuY/JzVI8t2g1EmYX5WDSsjK7VzMAMPasLEx5z3YVR3nuLH+s4uhKTzRyjllDzkhCiKAdIMwlfF0gT+ZudyFeYeyhdfLIqYft9c6yVzBL6AK0NRkLXnZJxn1N2+b7FRwNeuDZXMc90fzVlhDm7A8JCm88r9riZ6KOPJm7dQ8tuWCVFJWE+pZ60+P2emfZLZjFZqDJ0IT65nrYI0FChi4DxeOKfb6Co96gR+GKQoc90fzVllDGnOnYeE61xc9EHXkyd3tXMwBw50XdsfLHKofnFqXzz6OX9cPja35RHE4o9wDbNHOUTwsmeoPABQu/ctoTzdftCAfMmo5N7XmVBa9wYCoEOZrfylr7CdS6d5b1kEhhAN5W0XvrptVA9wtdarZbTIU9wO69G3/1NgtRzv6Q8MedLQosnldt8TNxTi4EOZrfyprUfmax7p1lPSRSb9Djji/ucLq/1wtfx+DMwa433kVyYQ+ATU80wPb9kCXmDPGcaoufiXNyIcjR/Fb22Du32Ov1U1pZh/GvbnW6v/fvGIKhPTu52nyXyOdJwH5PNJ4nnWPWkNrzKietDwdOJ3O3R2FVQ43WWLgacJXxvydUzlPir3mzcsYai1qJViewxC4sdjnBVWGIyF3OJnO3R2lVQ61Gi8GZg3Fpj0sxOHMw6prUTczrr3mzCroVYNGIRUjXpVs8nqHLYLHLCeYMEbnL2WTuSuydW7QaCUN7dsLlZ3fF0J6doNVIQTV31pjcLCydkIfMJMthi5lJMSzUqMCsIVdwDq9w4HaxyWxVQ6XeWcE4b1bOWMdzkpFdrqwK4+s7W0QUWtwtNpmvaqjUOysY580q6FbgcE4yso85Q0Tu8qTQpObcEmxzZ43JzcLonEzOP+UGZg25ggWvcOBpsclRwczpCo4AJA3QeNizNrhK7olGqgXTnS0iCi2eFpscFcycreAIABpJgyNNR+w+5ytyTzRSjzlDRO7yRqHJ0bnF2SqOAJCsi0R+91SP26GW3BONXMOsIVdwSGM4kItScPOOgKOCmbyCoyPCAHx4s3F+LfIbvUHg292H8Vn5fny7+7DTbrvBdmeLiEKHXJSS3MwZRwUzeQVHRwzCgAe+fgAle0vcOj65hzlDRP4iF6Q86d/k6Nwir+Lo6Cx2tLEVX1RUe9ACcpWrOQMwa8g17OEVDuSi1AcTAZuFeB1pX9Ww2zDHm+WMBa56E1hxi7G4paR4lnGoIYd8+Jw7q5I4u7MlrwrjzztbRBQa5KLUjA0zTKsyqiGvapiXnudwu4JuBXh6+NN4cOODMDjImYWlCzEyeySHFvoBc4aI/EkuSE1aVmZzNePs6kbtuWV0TiaSdZE42tiquJ+5qyowOieTQwv9wN1VFpk15Ar28AoXSpO5x8q/6NYn7favxyxQV6CK6+S42GU+Hxj5lLwqifXY9er6JkxaVobi7VV2Xyf/IQEo/jRgdlEOA56I7FKazD05OhkAbHp/yV/PzJ+pqkCVEpPisNhlPh8Y+RZzhogCwdFk7ndd1B0SPDu3lFbWKRa7AMu5n8i33M0ZgFlDrmEPr3CiNJn7jjVA8UzLlRwTuxiLXWpXNVQ7Mb6/VmvsoJytSuLszpT8h4T13ZRMFXdTiIiUJnNf/8d6LChdYLGSY4YuAzPzZ6pe1VDtxPj+Wq2xo2LOEFEgOZrM/ZzTUzw6t3Dup+Dgac4AzBpSjwWvcGNvMndvrGoYjKs1dkDeWJWEq8IQkSfsTebujVUNg3G1xo6IOUNEgaY0mbun5xbO/RQcvLXKIrOG1GDBq6PwdFVDp6s1qpwPjDzirTtTXBWGiLzN01UNna3WqHY+MPIMc4aIgpkn5xbO/RQcvNnTjllDznAOL1LHYrVGD+cDI7e5cmfKnVVPiIgCxXy1Rk/nAyP3MWeIKFxx7qfg4GpPO2YNeYI9vALJoPdsmKG/2yBPjO9sPrBgeF9hSu2dqSMnWnDBwq9cXvWEiMKL3qD3aJihv9sgT4zvbD6wYHhf4Yo5Q0Su0BtEwIeUudIGV+Z+Cob3Fo5c6Wnn7kqORDJJCBG0JdKGhgYkJSWhvr4eiYmJgW6Od1WsVCgcLVQ/kbw32vCfh4BjZqtgJGQBf/qH4zY4KmgFw/tyU6iEmryqCWC7ZDMA3HlRd7yysdImQOTnl07IY0B0YGF9XnVTuH4mJXtL7BaOZuXPUj2RvDfaMP+/81Fzssb0WHpsOh4+72GHbXBU0AqG9+WJUMga5gx5IlzPqZ4I188kGIoR9tqQGheJJy7PxaUDuyi+ztm5OBjem7vCIWeWTjBOXzBpWRmzhuxSe15lwSsQKlYCH0yE7VxY7b++17zt++JQxUrggxuVn7/mHfVtkAtgv64Ftr5oZwM/vi83eSvU/BUwSu199LJ+eHzNL4oTQcp3TDbNHBV0wUf+EbbnVQ+E42dSsrcEMzbMsJkLSx4auGjEIp8Xh0r2lmD6humKzy8esdilNugNerz606tY8uMSm+f8+b484Y2sYc5QsAvHc6qnwvEzkQsWgSxGKLVBdtdF3fHwpTmq9yefX0sqqvGvzXtsng+FQksoXdM4auvonEybXsTmmDXEglewMuiBZ3Mte0BZaJ/8fdo23w0DNOiBp3oCJ48obxObCjy4y3kb7PXosssP78tN3gpsf98JshdEpZV1GP/qVqevff+OIZzgsYMKy/Oqh8LtM9Eb9ChcUWjRA8qcPPl78bhinw0D1Bv0GP7v4ahvqVfcJjkqGRuu3aCqDfZ6dVnzx/vyhDeyhjlDoSDczqneEG6fid4gAl6McNYG2YvX5+HSge79HW9PMBdaQvGaRqmw9u3uw8wackjtedWnk9bPnz8fgwcPRkJCAtLT03HFFVfg119/9eUhg5dBD1R+A6yf76Q4JICG/cYeU75S+Y3jYhcAnKwzbueI3FPNabELUHxf8uey7SPjfw16FfvyHr1BYO6qCrt3huTH5q6qcDo5ohww1iFZXd+EScvKULy9SuGV7pNXJbn87K4Y2rMTtBrJq6ueEIUC5swpeoMe31V/hxd/fNFhYUhAoLqxGmU1ZT5ry/cHv3dY7AKAoy1H8f3B753uS+6t5ug9AY7fl/zZrP19Lb6r/g76EMwa5gxRYDBnTpEnD1/8xW8OC0MCQFV9E0or63zWltLKOqfFKQB49LPtbv8db4/Sewv0xOqhek1jL2cA767kSB2bTyet//rrrzFlyhQMHjwYbW1teOSRR3DJJZegoqICcXFxvjx0cFHdC8rMccd/2Htk7yb12/UcYf85g974nhQ7ESswf19BMN+Xs7A0DzWluwfOAkaCMWBG52T6/E6Q2lVPDh1rxmfl+4N2XD+RWswZIzU9oKzVNtb6rD2lVaWqtzsv6zzF5/UGPRaULrAZmumI9fsKhjm/PM0a5gxR4DBnjNT2gDLny2KE2n0fPtHi9t/xao8fDPN9ddRrmp0Hj+Hb3YeZM6TIpwWv4uJii6/ffPNNpKen43//+x8uuugiXx46eCjO1+VEfIZPmgNAfVMcbbd3i2sFPJn8vpQ+l4Yq4+N+mu/LG3cPvBEw3uJs1RMA0EjA42t+MX0dKhNwEtnDnFGer8uZNF2aj1oE2/Xe3dyurKbMpSIeYPm+lD6bmsYazNgww29zfnmaNcwZosBhzjifK0uJ2qKFO1zZtyd/xzs7vtJnI/eI8td8Xx3xmgYAXli/Gy+s382cIUU+HdJorb7eOLwhNTXVn4cNHLd6QUlAYlcg+zzfDfXrfqHn27ncA639fXUb5uRzaX+seJZfhjeqDUtH2wVTl1utRsLsIuPknErXkdY9mX05HIbI3zpazrjTA0qChExdJvLS83w21G9wxmCvbOdKLzTz9wU4/mzkxxaWLvTL8EZPs4Y5QxQ8Ol7OuN4DSoKx0J3fPdVnQ/3yu6ciNS5S1bbe+DteZv3evDGM0Bs64jWNOeYMKfFbwctgMGDatGk4//zzkZuba3eb5uZmNDQ0WPwLaS73gmr/dc4dBzx3FvDWn4EVtxn/+2yusVeUN5xxgXFSekdiU43bKXGpB1r7+xqzwDhhvdPPxTjf16YvV/p8DLx890DpRGoeakq8ETDeNCY3C0sn5CEzyfJ4Sr18/R3IRL6iJmeA8MoaV3tAyasZzsyfifV/rEfhikLcuu5WzPxmJm5ddysKVxSiZG+Jx+0anDkYSdFJDrdJjk7G4EzHBS9Xe6HNzJ9pmrDe2Wcjz/n1zg/rfX7u8zRrmDNEwaEj5oyrPaDk08Dsohx8UVGNCxZ+hfGvbsV9y8sx/tWtuGDhV14pSmg1Ep64XPl7IPPW3/GA5XvTaiTVPaIWf/Err2ncoJQ19jBnSInfCl5TpkzB9u3bsXz5csVt5s+fj6SkJNO/7OxsfzXPN1ztBZXYBRh2D7DleduCkDzUzxtFL40WKPqn422K/ul4NcVuw4ztVVNzT+xiOURR5efywfrvvRqM9ji6e2Adakq8ETDeNiY3C5tmjsL7dwzBP687G49e1s/mjrs5f0wuSuRranIGCK+scXUergxdBhaNWAQAdieCl4f6eVr00mq0mDN0jsNtZg+d7XQ1xbz0PGToMkyFOiUZsRk2wxPVfjZPrPuvT3MG8DxrmDNEwaEj5oyrvXkyk2KwdIKxp62vJz+/dGAX3HVRd8XnJXj+d7w5+b3Jw+bUfjYvrN/Naxo3mWfN1JG9HG7LnCF7/FLwmjp1KlavXo3169fjtNNOU9zu4YcfRn19venfH3/84Y/m+Y7aXlAXPQjctBq4txzY/hH8MtQvZyxwzTvtRSsziV2NjzubP0ujNU4uD0DxtDpksvF9TdtmuT+Vn0sNkgH4vouq0t0D61BT4o2A8QXzVU86J0Sreg1XOqFQpTZngPDKGrU9oO4ccCdeL3wdxeOKMTJ7pF+G+hV0K8DiEYuRobM852foMrB4xGJVc2dpNVrMyp8FAIpFrylnT8G6q9bZ7E/tZyPaEvwyFMKTrGHOEAVeR80Ztb15po7siffvGIJNM0dhdE6m34b6PXxpDl68Pg+pcVEWj2d58e/4W88/w/TezPfnak8nXtO4R86a3hnxqrZnzpA5n05aL4TAPffcg08++QQbNmxA9+7KFXgAiI6ORnS0uj+YQoLcC6qhCvaLWJLx+REPGwtIld+oGuqHvVvUz8PlSM5YoO9lxv0dP2gsRHUb5rhnl/Xrr3lbYaXFBcpFMyefi0EA1eiEUkNfAP5ZFWRMbhZG52SitLIONceaXF5VSg4Y6xVaMoNkAsVg66JM5C2u5gwQXlkj94CqaayxW8CSICFDl4HJZ0829ab6rvo7VUP9ymrKnA45dKagWwFGZo9EWU0ZahtrkaZLQ156ntOeXdb7WDRikc1Ki5m6TMzMn6lYOHP22QgBiLYk6BuNPzP+WH3Kk6xhzhAFRkfPGWeTh0swnoemjz7TdC77dvdhv05+funALBTm+v/veLUTq8t4TeMZ5gy5w6cFrylTpuC9997DZ599hoSEBFRXVwMAkpKSEBsb68tDBwe5F9QHE2E8vZmfCq3mtQLUD4F0ecJ4J230pHjmTtFMowUumQ98dJPNU/KNnrmtN8Jg1gHRH6uCyHcP7NEbhNPg8DRgfEntHyv+7KJM5A0dPWfkHlAzNsyABMmisGM+X5d5gUntUD9Xh0s6amOgCmdX9b4KS35cYvO4aP+Ymg8WQe7s7q/VpzzJGuYMkf8xZ4y9fiYtK1O6mrHp9ROIyc8dnVvVcPf8et3gbCwu2an6OLymcR9zhtzh04LX0qVLAQAjRoywePyNN97AzTff7MtDBw9XekGpHQKpZjuD3v2eW65ytWhWsRL4/GG7T1WjE+a23oh1hny7zweii2rx9iqbuxxKS996Gra+pBTIgeyiTOQp5oxyD6gMXYbdHlBqh/o5205v0HvUc8tVrhTOSvaW2Hwe5kRbEpoPFqHtmO2Ex4EaCqE2a5gzRP7FnHG914+3euKoKc54kyvnV3vnbFfwmsY9zBlylc+HNBLU94JSOwSy2zDHx6tYqVBgW+h8bi5fq1jZ3uPN8v2J9v+3vG04otGGIZoKlBr6WvTyAvzfRbV4exUmLSuz+W7IY/DVjIcPNGeBHAxdlM35+48bCm3MGSNXekCpHQaZl56neDx7BaUMXQZm5c9SNTeXL5XsLcGMDTMUhzG2Hs2DvrE3hD4WgAHW05kGYihEqGcNc4bCGXPGyJVeP97oieNKccbflM7ZruA1jWuYM+QuSQTxWbyhoQFJSUmor69HYmJioJvjH6aCEGC307D5aocOX2/9bVX5el8y6IFncxXnKZPHtcsOiFTMbZ2IdYZ8UzBumjnKbycLvUHggoVfKZ5YA9EmVzkL5OkFfTB1VK+gaX8w/3ETLjrkedWJjviZyEUhAHaHQVqveGjvtdYFJTWv9TW9QY/CFYXKc5RZBY2h9VRPr0Cd00M9a5gzZK0jnlOd6YifiXxuAOwPg3RUYFE6r6h5ra85O2c7w2sa1zFnyB6151W/rNJILpCHQCZa/TIkdnFerDLojT27PFzlUW8Q+Hb3YXxWvh/f7j7slRVUABh7uDmYlN/6FJWJOiyNfBZjNKUA/N9FtbSyTvWEm8FIbxCYs/JnxXCQACz/bp8/m+SQHGa+XL6aiIzkYZDpunSLxzN0GQ4LVnqD3isrPPoqZ8pqyhxOyG8dNFJEPWK6LkNEwnYAgRkKEcpZozcIxZXYAOYMUUfm7oqBjs4rrqzw6KuccXbOdiRQw+6YM/7DnAk+Ph3SSG5yd/VEJwUlNas8+rQi7eJk+xrJOIn9nKh3cMW4O/xeEQ/EhJve9MJXu1Dd0Kz4vL8maFbD2R83/lg9jaijcWcieGcFJTUrPPoyZ1ydaF+SjMMcdZmrsWDsDQG58xrKWePKRRRzhqjjcWfyc2+cV3yZM56cizMSozFnbH9e07iAOUOeYg+vYCVPBD/gKuN/1UwE7OEqjz6vSKudlN+MRgIycRhj4is9O7YbQnnp2+LtVVhc8puqbYMh3EL5zhNRKJMngr+0x6UYnDnY6aTznq7w6OucUTshvzlJAkTEUaSlBeauayhnzRcV1aq2Y84QdVzy5OeXn90VQ3t2cnqh72lxxtc548m5+Jlrzg7IjRXmjH8wZ4ITC17hxINVHr3VfdgheVJ+m8GLKrjYO8wb5Ak3lVorwXi3yNdL37raJVv+XqplHW4+G9LqQCjfeSLqSDxZ4dEfOSNPyC+5kTOu9g7zlmDIGnfO+3qDwKfljnqVn2LvIsrfWcOcIQoNnhRn/JEzzs7Zjhw6rjzywpeCIWcA965pmDPkKQ5pDCcerPLol+6iGq1xpcgPJhrb4sraJm70DvOUViNhdlEOJi0rs2mtv8bg2+uSnRwbiVvO7644OaMrcwtYh1ugJllU+8fNnkMnfNYGInLOkxUe/ZEzWo0Ws/JnYcaGGZAg2W2jEnd6h3lDoLPGnZwBjN/PuhMtTvefGhdpcxEViKxhzhCFBk9WePRPziifs50JVA+qQOcMYP+8nxoXhScuz8WlA+2f95kz5A3s4RVO5IISANteVO1fj1lgd3ik3yrSSpPyK5KAxK52i3T+4O6Em96g1CX76MlWLC75DYOe+MJut2xXvkfm4RbISRbzu6ciM9F5SLxfus8vPc6IyD65oATApheV/PXM/Jl2h0b6K2eUJuRXIkFCpi7TbpHOXwKVNe7mDKD++3Tl2V0tLqIClTXMGaLQIBdnAMWrGcXijL9yRumcrcRfPagcCcZrmroTLZj8Xhnmr7U/MoU5Q97AHl7hRi4oFc+0nMA+sYux2KWwyqNfx3ZbT8p/eDewYR5se305LtL5izsTbnrK2YokAHC0sRV3LyvDS1YhpfZ7NL2gt+l1gZ5kUauRMD7/dKfzjlU3NAfFpJREHZlcUFpQusBiAvsMXQZm5s9UXOHRnzljPSH/voZ9WPLjEpteX86KdP7k76zxJGcA9d+ngpxMVcf0ddYwZ4hCh1ycse6hk+mkh44/c8b6nL3n0AksLtkZsB5UagTrNc3LGytx1mnJuHRgF4vHmTPkDSx4hSM3Vnn0pPuwW+RJ+WXp/Vwu0vmTPOGmv7gyLNH6pO3sewkY7zJNHdVb9fH8sQLKGZ11qrYLx3HveoPw6x8fRJ5yZ4VHf+eMPCG/rFdKL5eLdP7mz6zxJGcA976fgc6ajpwzALOGQos7xRn/54zlOfvMzASXi3T+FqzXNH/7bDsKc7OYM6HOoHepBuEPLHiFK+uCkhMBH9vtRpEunLlyErQ+abvzvQyGSRZDeQUZTwRq3jQiT1kXlJxvH9iccadIF848yRkgNLOmo+YMwKyh0ORqcSbQOROIHlTBTu35vO5EK3Mm1FWsVOjAsjCgHVg4hxeZBHJsN4BTRboBVxn/20EvQgDXJzO0Pmm7+r0MhpNzsKwg40+BnDeNKBACnTNyke7SHpdicObgDlvsAjzPGSD0sqYj5gzArKGOJfA5YyzSXX52Vwzt2alDF7sA17KGORPCKlYaF6ZrsFpVs6HK+HjFysC0C+zhRVZ4ZyLw9AaB90v3ufQaeydtV76Xfh/Sakeg78r5W6DnTSMKFOZM4HkrZ4DQypqOljMAs4Y6JuZMcHA1a5gzIcqgN/bscpQ0xbOMo7kCcKORPbzIBu9MBFZpZR2qG5pVbevsLoHa76UnK+J4U6DvyvmTK3MMEIUb5kxgeTNngNDKmo6UMwCzhjou5kzguZI1zJkQtneLbc8uCwJo2G/cLgDYw4soyLg6ptxbJ213V8Txto5yVy7QcwwQUccVqJwBgiNrOkrOAMwaIgocV84rzJkQdvyg821c2c7LWPCi0BOEqz94k9ox5alxkZh35QCvnrSD5eTs7xVkAiHQcwwQkbJwX80ukDkDBEfWdIScAZg1RMEq3HMGUH9emV7QmzkTyuIzvLudl7HgRaElSFd/8CZnY88BoFNcFL59+GJERXh/VHKHOTkHWKDnGCAi+zrCanaBzhmAWeMvzBqi4NMRcgZQlzVZSTGYOqq3T47PnPGTbsOM1+MNVbA/j5dkfL7bMH+3DADn8KJQEmSrP+gNAt/uPozPyvfj292HoTconcpd42zsuQTgyStzfXYRQv4RDHMMEJGlYFzNzhdZw5zpOJg1RMGlo+QMoC5reP4JAxqtsfMJAMWkGbMgYCOyJCGEd36ifaChoQFJSUmor69HYmKi7w4U5kPkwoJBDzyb62BCvPbK8bRtfvne+ePOTEe5+9PR+fv77Lfzagjxx2eiN+hRVlOG2sZapOnSkJeeBy1zJqjoDQIXLPxKcYJvuSfMppmj/PbHua/PD8yZjsOf32vmjC3/5Ez4D5ELdR0xZ/x1DAoCdkdidTUWu3wwEkvtebXDF7z0P3+GtjUPIbqx+tSDYTZELqTJxcjfvwa+ecr59jetBrpf6NMmrf3pACa/94PN43IseXP1Df7x0jH48/vMCxFbvv5MSvaWYP5/56PmZI3psfTYdDx83sMo6Fbg9eORa+Ri5JY9lXju82roG7vDUQf49+8Y4vMhEnqDwAtf7cTikp02z3k7a5gzHYe/vtfMGVu+/kyKt1dhzsqfLVbEy0yMxpyx/VlQCALy797mXYfwwvpdTrf3R84AvKYhH/BjRyK159UOPYfXD+vewtnf3otIAYved6KhCtIHE4Fr3mbRK5DsVYmd8fHqD2t/qsLU922DATCOWJYAzF1VgdE5mV45iXPsecfA73P4Ktlbgukbpts8XnOyBtM3TMfiEYtZ9Aqgkr0lWFC6AAcbjdmh6wYYWpPQfLAIbcdy7b7G16vZGS9cK1DdYP843s4ann86Dn6vw1Px9ircvazM5vHqhmbcvawML3mxaEGus9e7yRl/rJrKaxryCY3W551PXNVhJ2co3vZ/yNgyB0IA1r/DEoRxurXiWcYqZTAx6IHKb4BtHxn/Gwzt80WblObrckIfl+75sRUUb6/C5PfK4GhYuwBQVd+E0so6n7WDiEKD3qDHnC1zHG4zd8tc6IPhPG5Gb9Dju+rvsPb3tfiu+rugaZ+321WytwQzNswwFbtkUkQ9YrouQ1T6Kmh1uwEYLJ7vHB/t0XEdked2USp2yZg1RAQYe83M+nibw21mfbzNa3MyeYuv5ozylLfbpTRflzOHjjX79DPhNQ11JB2yh5feILBy5QqMkZR/gSUIoGG/sUtesFQpg2mFQrm74q9rgZ/+DTQe9l6bDHrj+1Rcz8POSwRQjU64enkTHh1b5fU7WXqDwNxVFaq3d+XOjDe6+LKbMFHw+f7g96hvqXe4zdGWo/j+4Pc4L+s8P7XKMeseTwCQocvArPxZAemJJg83XL9vPVb/vhpHmo94pV16gx4LShfIt7csSO2nzuhOm4FOm216fN3/QblPhgnJOePKJY7arGHOEIWnrbsP42hjq8Ntjja2Yuvuwzi/d2c/tcqxYJvPST63lVRU45Py/ag7cerz9KRd7pzTZY+v+QWvbar0yWfCaxrqaDpkwau0sg4RJ2qAKBUb+3iInGpyjyfr06a8QqE/h186G2roaZv2bnGpZ5cQ7d1uW2/EgeZWTFpW5tUx54DxZ8aVuzPpCTGqtvNG6HvrDwcGDJF3lVaVqt4uGApeco8n6yJQTWMNZmyYgUUjFvm16GWv+OatdpXVlCnu15rc46tp/wS0HcvFwYbmoMgZQF3WMGeIwte3vx9SvV0wFLzkHk/WRSB5hUJvn1fVtMfRcENP2uXOOd1bx/Zmu0LtmoY5Q9Y6ZMGr5lgTapCsbuP4DJ+2RRWHPZ7aR1kXzwL6Xub7FQqVCm9KbYpOBBoPqZ+0zqA3TlDvoiOIxxeGc30y5hxw7e5GVpLx5OqMp6HvaFJjV0My2O62EYUFtaefIPg7zFGPJwEBCRIWli7EyOyRflldUqn4Zq9dC0oXICEqAYdPHla1AqbeoMfWqq2q2yJJxhsr0Rmr0HYsBwKagOeMvJKXs6zxxsWl0qTGzBmiYBA6QeOox5Ov/n53ROn8qNSuhOhIHDrRrLqI4mxoujO8pnFtH3I7mDNkrUPO4ZWeEINSQ18cEKmKY5cNAmjWZRmLNIHmtMeT2fBLV7g695ZLQw3b2/TO5cCK24C3/gw8m2ssmCmpWGncRs1qjGYkCUiVjiNfs0M+stfHnKu9uwEAs4tynIaSs9AHjAGnNH6/eHsVzl/wld1gULsP833Zm19ADpji7VUOXx8OgnUuCQptgzMGe3U7X3LW40lAoLqxGmU1thMjO+Pq3FuOim/22nWw8SBu//x2zPxmJm5ddysKVxSiZG+J3e1L9pagcEUhXvnpFZfegyQBmsh6aHWV7ccNbM4AzrPG05wBnE9qrGYfAHMGYM6Qb6idBDwYJgt31rPIk/Oqq79frgw3lNt1w7/+i/uWl2P8q1txwcKvHJ63irdX4fHVP7v2Jhwcm9c0zrOGOWPErLHVIXt45XdPRUaSDo8dm4gXI5+FwWrieoMw/nEbcdlC3/eYUkPtsEpXhl+6Mx+Yi0MNbTga6qiq55hj6Thq8bU3VzjJ756KrKQYVNc3KbZQIwEvjD9H1R0EtaG/dfdhQAK+3X0YgMDQHp1Rf7IVU95zfEfKfB+llXWKf+gE2922QODdIPKVwZmDkRSdhPpm5Xm8kqOTMTgz8AWv2sZar24nc2dOMFeGG9qjNNRRTa8xZ6SIY5bH8nPOAEBmYrSqOcQ8yZkhPTvhi4pqTH7PcYGTOaMOc4Z8ZUiPTkjWRTqcxytFF4khPQJf8FJ7vnT1vOrO75cvhxuq6TnmqnC4pnlzcyVS46NRd7wZqXFRyEyKxZETLV65pmHOGDFr7OuQPby0Ggmzi3KwzpCPya3TUA3LrprV6ITyoc9B2//yALXQitphlWq3U1oBUS5IKfXC8ng+s/bTkPXql25MUm+P9TBVV++WOyL/zADKncJfGJ+HSwd2UbU/tcF15zvf44bX/osX1u/CC+t344Z//RdT3nctRB0dy5d320IB7waRL2k1WswZOsfhNrOHzvbLEEFn0nRpXt0OUF4FUS5IKfXCcrWoZk0uaC0sXWjqTeZKrzGH+25LsPja3zkzvaAPNs+6WNUfrp7kTN7jnztd+U3tsZgzzBnyHa1GwoK/DHC4zfy/DAiKi3y150tXzqvu/n55WkBS6nXkyUT1joTDNc3ja37B9H+XG//7wY8Y/+pWTPXSNU1HzxmAWeNIhyx4AcCY3CwsnZCHHxMuwgXNz+G6lr/h3papmBz5GH66aiPOKbwp0E08pdswY+8rxdOSBCR2VTf80ul8YLAtSMm8Mp+ZneGXHvYcMwjggOiEUkNfAMZPSe2Yc1fIPzOZSZah0ykuCreefwZS4qJUdxtVG1wnWmy/D8LFFHV0LF/dbQsF3hjuQ+RMQbcCLB6xGBk6y/Nnhi4Di0csDsjKh/bkpechQ5cBSSFnJEjI1GUiLz1P1f6czQkGWBakzLlSVFNiPQTT015jQgCG1iToG7sDCFzOuHI8T3Km/mSb05Xf1B6LOcOcId8ak5uFlybkITPR8vcwKykGL/l5EnhH5J5FDq5mXDqvevL75Y0Ckr0iiqc9x6yF0zWNPa6e+pSO1ZFzBmDWONMhhzTKxuRmYXROJkp310K/pw3p0lH07NET2jOCIxhMNFrjUMMPJsJ46jP/YW2PjTEL1A2/dGU+sO4XWj4lF94aquBpbyyL3mIe9ByTf2/ntt4IQ/tEwoC6MefuGJ2TiYToSHz7+yHsrj2Brb8fxuETLXh98x68vnmP6m6jaoeueELNpMa+uNsWKly5GxQMc19Q6CroVoCR2SPx/cHvjSs3SsZ5u4JhKKNMq9FiVv4szNgwAxIki0KVXASbmT9TdW80V+YEs/4c5OJbTWONxz2y5N5invQak28yNB8sApgzNpxdjDFnmDPke/I1zdbdh9tXbpQwtGenoBjKKJN7Fk1aVqZ0NePSedWT3y9vnh/NiyjeLKgwa05xdk3TkXMGYNY406ELXgCg3bEKQ83nstoE53NZBULOWOPcV3bn3VrgvK0GPfR7NqPmvx9AVTnPXhHKYeHNNfrYTjBdNqntOTbiEaDsTYv3XyN1wuyWG7HOkA/AeDL01ThlZ0sXA1Zj+nPSjYXD4wdtVql0FPre5CwknYWU2pXAQlFHvxtE/rX+j/UWc1m9gleczmXlbwXdCrBoxCK7c27NzJ/ptJ16g95U1Nt1dJeqY9orRDkqvrkqOdp47lLba2zKWVPw0c6PLN6/Rp+Mxuo/o+1YLoDgypnROekoqylDbWOtzQqVzJnAY86QP31RUW1x/nhh/a6gm79H7llkfZ5Te17VGwRKK+tQXX8SX/+m7kaGvd8vb54fU2OjTP9bbUElNS4SdSdO9aJN0UVCABY9a4Mla5Zcfw5S4qJRc6zJ7gqVwZA1HTlnAGaNM5IQrg6Q8p+GhgYkJSWhvr4eiYmJ3j+A4kTp7b9M9iZXDzSDXrGIoqhiJU6uehCxJ6vVH+em1bY9vMz2Z1N403UGBl5jHFr5+V+d7n5qxBz8+YrrjCdxg964OqNizzHJWNib1j6fiNn712cPReneesWTsLe4MgGlBODa+HLM1y2D5GRRAHuBo4vSotHOEBNXqJ3UWG7DpGXGYT/27rapXXI+1Hy7+zDGv7rV6Xbv3zEkrO6G+Py8GoJ8/ZkoTZYu95yynlw90PQGvWIRRUnJ3hLM2TIH9S3KE/Tb83rh64o93exNeJ8ak4rLul+GDF0Gnv7f0073H3NoMh4vvAKjc9JRuKJQsdeYBAkZugwUjysGAIv3f1bnc/C/IMyZzhm/Ium0tU4XBPBVzsiTGquZ54U54xhzJvz5+jNROn8E6++YXLhy5byqpkhjj6PfL3v77BQXhcvP7oKspFg8ufYXp/tPjYvCvCtzMSY3C3qDwAULv3JaePn6wZH4394jFu8fgMufiTtcnVRfI1kOPVQqorr7/XFG7TVNR80ZgFnj7LzacQtepiKL0vA+syJLEEwo7LaKlRAfTIQQwmIlStG+EqUtle9bqfC27SNgxW1Om3Vvy1SsMgw7dfIxFR8Bu6epABYf5fBSewIv1JRiaeSzkCTrWdfsvxfz0O8cF417lv+AuhMtbrd3ekEfTB3Vy6WQDOZVPdz5o0jtftX8UbJp5qigmOzVW3ghYsuXn4neoEfhikLF4X3mhZZgmLzeHSV7SzB9w3SXXqP2fSsV39b+vhYzv5np9DhN+69DW8PZWDohDxEJP2PGhhkAAHtDNgNZeHQ1ZyIStiOm6zKbHFd6L97OGQB48fo8XDpQfT4wZ2wxZzoO3+aM4/NHOPycubPyodr3rfT7/1n5fty3vFz1seRrmmAuvLiaNfY4eh/mPfD+vvJnHGtq86C1rl/TBHPOAMwab1N7Xu24Qxo9mcsqGKjp6WXQQxTPhIBlsQswFrtsi14uzAem0dr/XFQOT5RXVDQtEevpkE0fcmUCSg0MmB35NgB7Swy0L4xbPAvoe5nF8Ea52v7t7sOqL0LU3nFRwzSfnR/uLLnCl8Hl7bkkiOzxZC6rYOCst5feoMf8/853aZ+uzAmm1Wjtfi5qhyga2ldVnLuqAptmXuzRkE1fcm2iYwOiM1bZfUZAQIKEhaULMTJ7pMXwRndyJjk2AjGREahu8PwczJxhzpBvhPr8Pc6KAO6sfOjK75f5+dGcq/M9ydc0ng7b9CVvTKrffjVz6hrOanjj0J6d8O3uwy4Vu7x1TROsOQMwawKp4xa81E6U7sGE6j5jb0ihvXnH9m6B1HBAeTUU6ye8UVxyMrG9QQDVMK6oaBPAOWONhSBXh2z6mCvjnfM1O9BFcrTkreNCqivHemG84zH1rlIK/EBRuptnMU+ahwERzH+UUHhQO1m6J5Oq+4q9IYXWQ+bKaspQc7LGpf16o8DkbGJ7IQDRdmpVRTlrCnoaFxBwdcimr7ly7tfqKqGJVB466qyI6sqxFowb6NWLB+YMc4a8L5Tn71FTBHCnSOON3y9XJmO3vqYJ1sKLt34GnBVRXTmOBO9e0wRbzgDMmkDruAUvtROlq93OX5TmHWuoMj5uPlxOZbHuzbZL0Gv49bjg4rGm4pLbXS7NJrYX7dMNy6xXVJRZnBTlnmNyD7afPwl44cuVOzzpOKpuQ4XvjdpjTS/orWrelFDlbHldpTtL7gjWP0ooPKjtiaR2O39RmnesprEGMzbMMA2Zc6VQN/7M8Rh9xmjLApM781LCcmJ7a9arKpra3p41cq8xuffauj3rAl74ciVnpIhjqrZT+t64kjPyH8jBdvHgDcwZChehukKd2iKAq0WaqSN7YfroPqbfL3evacx7zahl3la58CIff/VPBwL+u+/tnwGl740rk/fPu3JAWBdjmDWB13ELXk56Ipnmsuo2zN8tU2bQG3t2OfqVMR8up7JYV2zIx309LjJdZFjfbdHAgDEJv+Ouc3Q4q19f5xck7cMTW1Y/iOjGUxPlV6MT5raeWlFRZnNStNOD7WRMBvadNxu9hl9v95dWMczcvJgyN6hbClLjolQNAZGHajql8L1RczcpKykGU0f1VnUYX40V9zV/d88PxrtBFB6c9USS57LKS88LQOvs0xv0WFC6wH7PKashc64U6kafMdqyx5HZuV4PoCwmGrXxnZF27p3Iy7/HafFJXlXysS3zcKTlVHFHtCWh+WCRaVVFmXnW2Ou9lhKVhjFZd2FkdoHiuVLpnOrORP/m8runIjMxxmLooKL2YZrOKH1vmDNGzBkKF6G4Qp0rRQBXizTn9+psOgfZ60GWHBuJW84/A1NH9XZ6rpJ7zTzyyTaLVRWVWLdV+fjdFeemUs4Zz8+1rlzTqKH0vVGTM53iovDtwxcjKkKjsIUlZo06zBpbHbfgZdYTCUqjXdXMZeVPrs471ngYQtJAEga7W8vDC/+IP8sUgtZ3Wwo1pZgd+Ta6tNYBpTD+szd80lrOWET0uRSTFy5BxIka1CAZpYa+Fj277AawQg+26JMH0XvDZDyyeQ9GXHGrxZ0Ape7QL+b9H875eYHzoZ8OyPtWGwx/xJ+Fk9pMxJ48aPMejBwXUr05BnvtTwfwt8+2WwR0ME3c6Egod88nMmfeE8nY59V2snQ1c1n5kyvzjuWl5yExKhENLQ0O92lT1DM715foYrGgUwoORrT/SfLrv5Dx+4eYdf5cp8MeC7oV4KKuI3D+P1/D0ebDMLQltA9jVM4apd5rdc21eG/PE3h98x6kac61OVcqZc1fLqjDuuqXna6W6MgXFdVoalO3amLnyH6IiOyMhtbDbhVRmTNGzBkKF6E4f48rRYD87qlI1kXiaKPjgpP1uV6pB9nRk61YXLITb2zZgwV/cd67aExuFkb1zcCQ+V8qXg/Yu6ZxfPzf8MaWSpvjK+XM2LOysPLHKo/mf3L1msZ6Xi1zzoqoan4mn7wyV1WxS28QeOGrnXhj8x4cPcmsIdepK6mGK3mi9ESrX5TELgFdFVCRK/OOVawEPrzZYbELAB5rvRGPjh1gunNgfrdFXm0wE5ZzUomGKogPJhqP4YA2IgJjL78GawxDAAB/1mzFEE0FNDCYTnaPXpaD0so6fFa+H9/urIFQ6MEmZ/S9rf/ClGXfo3h7FYBTYWIdmmcd24izttwLYV0glId+Omm7o32by0yMxvSC3vjndWfj/TuGYOOs0Ygteqr9Wes/LNQVUuW7SZlJlndNMpNiVI/xnr+2ApPf+8HmblRVezdx+fMLVqHaPZ/IHrknUrou3eLxDF1GQFcGVOLKvGPr/1jvtNgFALPyZ1kOY2w/15foYjEjvTMOai3PiQdb6jFjwwyU7C1xuu+oiAg8Xng52hoGQgIQkfgTtLrdgFnWXDf4dKz+6QA276pR7L0mz2sZnbEK1fWNFudKpTyoNXyPt3Y/ZlMglId+qmm/vG+li7nk2AiLnNk8swBzzv+rsc02awGrK6IyZ5gzFF688TvtT64UAb6oqHZa7JLJhT01E90fbWxVfa6KitBg3pW5dudFts6Zb3cfRkubQdXx71aRM1X1TXh5Y6XN49UunGvduaZ5YXweJChezTgtonrjZ7J4exUGPfEFFpfstCh2Aa69/0Bi1gRex+3hJQvSidLtUjufmK4z8Nkk2O9hZGSABo9GzMAVV99ud1JI89UGbVZ4hIABQNOqBxFrttqgPWM032F7yoOIPXlqaOMBkYrnIm9HUt6VeHzNqbsYQzQVGBql3INNIwFdcBiDNTswd5UOo/pm2A0TDQz4u4srJVpTE5SpcZHY+NAo27sTXlhx0pMx2Gt/qsLLGysVnxfw3lhxXwnF7vlEjhR0C87J0u1RO0yxU2wn/HXTXx1uI0HC08OftizqtfdW1gNY0CnF+DtuvYqKJEEIgbmb51msNqgkIuFndB24CPWth0yPGVqTEHHkCkgnB2JxyW8AAK1uN3TdlG8eSRIgRdZDo6uEobEn5q6qUMwad1ZLtKYma2IitTZDb+QiqicrTjJnmDMUXkJp/h61F/ed46LxwEc/Ot0uMzEac8b2d3miewHg4Y+3qT5XJdnpaaaL1iJSqzHlDACXhg06zhnHbVcz/5Mn1zRLNZ5Ngu7Jz6RSDzmZt+e/8hVmTeCx4AWcmig92Kmdd0ySnAx9BCIkA564YTi0PU6drMzvtjhbbVADIPZkNUo3rEL+qCvsb9Q+ZCXGqq2ZqMO8tn9g0qYWVJnN56V2wvd0HMXW+ia88+0eu2Hm6UqJgLqgrDvRiv/tPWJ/nLQXCqnujMHWGwT+9tl2p9sF8/LUQGh2zydyRp4sPdipnXdMCOFw6CNgLPykxKRYPtjeW7ksJvrUMEZ7JOBoay1e+u8XmDJ0jOJmSkMUpYh66NPeQtP+CQBy2x9TN+G7FHHMNKRGKWs8XS0RUJc11Q3Nds/X3iiiMmeYMxReQmX+HrVFAEhQVbh65pqzcX6vzqavXRkedqSxFS98tRP3FfRR3MZR8eVEsx6A5ZB0V+bIcpQzzqiZ/8mTaxpvFFHdzRk1BUBvz3/lC8yawPPpkMaNGzeiqKgIXbp0gSRJ+PTTT315uPAnzzsGwOFwuRPqhqNoT1guJW9+t0Vt8Wn1lnLo7Q3wbh+yYrzLbUnT/ts+O/IdaHBqyKXaCd/l7fbWNdp93tOVEgEvjbeWC6kDrjL+1w89OUor61SHrHXb9QaBb3cfNg4v3X3Y/vfVj0Ktez4FBnPG++R5xwDHQ+bqmhzdWDjFZohke2/lWq26c+Ib//1J8XzkaIJ98yGKaM8aoXLCd/PtlLLG09USAc+zRi6iXtrjUgzOHOyXHoPMGeqImDXeJRcBAMdD5g4db1a1P+vtXB0e9sbmPQ5yRl3xxRNKOaOWoyzxPGeMBavLz+6KoT07+aUwo7aHnoxZQ474tIfXiRMncNZZZ+HWW2/FX/7yF18equNQM1yu8ht1+7IaIml+t0Vt8em3xjj7VfX2IStKp0R5eGK+Zge2GoyBV2roiwMiFZmosxlGCZyaZL/U0BcA0C1VZ3ffnq6UCATveGtnK5S4ckfLvO1Kk2QGejLIUOqeT4HBnPENNUPmvqv+TtW+bIZItvdWTmtRVzA70hCjePfW2QT78hBFra4S+sae0Dd2h6E1CVJEvc0oSgAQwrjKo3Hi+/bmKmSN2uKZoyGioZg1zBnqiJg13icXARwNmft292FV+7I+R8rXNGqLJkdPtirmjKvFF3co5YxajjIi3HMGYNaQYz4teP3pT3/Cn/70J18eomNyNlxO7dBHq5UCzbtcfudC8cnuSUnlBPvmvbEM0GBu60QsjXwWBmE5d5hcmJ/beiMENMhKisGNQ8/Aa5sqbbpDOyucOVspEQjO8dZqTuBqwyo1LtLpKjbyZJCBvvMQKt3zKTCYM77jbMic2qGPNqsFtvdWzvtgIjLa2lCj1ULYqT6ZF5+U/vhVO8H+qd5YGjQfLEJM12UQwnLqMNH+FpoPFgHQmM7zSlnjrHjmbLVEIDSzhjlDHRGzxjecFQHcPUfK1zR3LytT3RalnPHl6nnOckbt6x1lRDjnjPw6Zg050rFXaQxljobLqR36aGfog3y3JT1Jh7mtEwHYLklrXnwyQGP/pKRygn3r3ljrDPmY1DoN1bCc86UaqZjUOg2ft8/5NbsoB1ERGrvdoQ3Q4LH2ttsOqLR8/0pdXtV2tZYD2dddZ5VWV7FeoUQONWeeuDzX6So28mNzV1UEvCswEQWGoyFzaoc+2h1mlzMW2mvexv3HjecWSVieY6yLT0p//KqdYN+8N1bbsVw07Z8A0ZZotU0imvZPQNuxXIvzvFLWyMUze8zfP6BRzAdXssYfQzTUZA1zhoi8ydGQOVf/Hjc3JjcLL03IQ3y0uqHeSjnjac+nmEj7l9vqckaZ+esBeCVngOC4ppFzRs1noWZlTmZNxxZUBa/m5mY0NDRY/CM3yUMfE62q2IldjI87WClwTG4WNs0chYm33IMHpAdQDcuKfzU6mYpPWUp3BLoNQ3Nshk2xTGYQwAFxaniiOQmAVtLYPAYASbGRmFbQG6NzMk1ttTcm+rvYC/BRz3lo0VkV3nSdgCGTgNgUFG/7P1yw8CuMf3Ur7ltejvGvbsUFC78yFY/Ujrcu3l7lcD+ecnYCFwAe+WQbWtoMplBzFBB3XdQdlw7sAsB5N23zySCJwgWzxnvkoY/punSLxzN0GVg0YpHj1QJzxuKSKdvR93ABotpiLZ4SbUlo2j8B+mO5yjkDYy+z5MjOEApZI4RxtUbzIYoyR8MIUuIiseT6c0zneaU8SDLkYVjCDKREWRbeEqMTMfnsyWhp6Os0H9Rkja9zBlCXNXNW/gwAzBkiJ5gz3uPJ/EdjcrNQ9ugliI9WHtQkAQ5zJr97KlJ0kW61XSMBTa0Gu8+pzZlOcVG4uG8aUuMs25Cki8S0gj4wGOCVnAGC45rGPGcA5QJgsi4SL5m1nVlDSiQhlP5M9PKBJAmffPIJrrjiCsVt5syZg7lz59o8Xl9fj8TERDuv6EAMevdW/HP3de2Kt1dhyrLvMVizA+k4ihoko9TQF6K9VqoYNBUrIVbdB+mk7UlFLoJNap2GdWarNALAGE0pXox81jjvivlrAECceo31UD55HHhJRTU+Kd+PuhPGJYM1MGBMwu946IzdOGP/aqDx1FwAB0Qq5rZOtGiDfEzz9+VojLlS11l7+3HXt7sPY/yrW51ulxoXhXlX5poujqy7CneKi8Ljl+fi0oGn2vNZ+X7ct7zc6b7/ed3ZuPzsrm61n4JLQ0MDkpKSwvK8qiZnAGaNEr1B7/aKf5681nge/R5aXSUQcQyiLQH6xu6QnOUMjCs0ztkyB/Uttqslyn/dNO2fAMOxHOS359iu+Hrs6/K17TBEYfyDWO7lZW/OD6WsAQxI7boRESmbcVJ/ajJ7Q2sSmg8Woe1YrukxpXxQyhp/5AygPmumF/TBfQW9mTOkKJxzBuA1jSeczUXrq9cWb6+yO7xRzXm0eHsVZn28DUcbW+0+7wnXcgaIj46AQQg0tujt7c7E1ZwBguuaxlHOJOsiccuw7pg6qpfF959Z0/GozRqfzuHlqocffhgzZswwfd3Q0IDs7OwAtihIVKxUmKR+ocOeWgBODX1005jcLCyZcC7mrtJhq9rJ/ypWAh9MhKQwCv0o4vFI6+1YZ8iHJJ26KNHAgNmRb9sUu4zPGYtesyPfwRfN59qMxdZqJNSfbMHrm/dYHNUADQwn6nD6b29BWO03E3VYGvmsReFNwLjN3FUVGJ2TCa1Gsj/e2qCHfs9mbPn0K5yniUOpoS8MZh0m7e3HXWrnDqg70WLxmchzIlQ3NKHueDNS46KQEhcFvUGY2qO2m3bn+Gi3208UbJg1tkr2ltidoH5W/izHvbTayUMf3WG882zMGaWJi5XaPGPDDLvzhwGA0OvQXP0XXHyiEbOj70UXqQ56AIUZXSBBC5ukaV9BODpjFdqO5did80MpayISKtCSUIyWNst5waSIesR0XWYqogHK+WAva1ra2vDous+gTTxsKgTKnfO9mTOA+qxZXPIbzsyM93rOHDrWbPE6olDGnLHl6WTinsx/JA9vdDRBvlKb7RWBvMWVnAGA481tqvbrSs7oDQJbdx/GrBXbFHte+fuaxl7O1BxrMl6PCODQiWaUVtZZFOx4TUNKgqrgFR0djeho/hBaaC8e2Uw+31BlfNzJ8ERvcGlVCYPeWJxTiAaDAJoQhe+ihwAnDRbDUPI1O5AlKXcztV7Z0fzki/b/bX1UDQz4e+TbAOwU0SRje+QimsHsIkLu8mo3WNsLkNqGA3gMAKLs9xZzuh+VXJ07wDyQ6k+24B/FOxT/uHA2kaXs/g/KMWdsfy6bS2GBWWNJqXBU01iDGRtmOB+a6AWurl6kN+ixoHSBcrFLABCRGCdasCDqn5AzqSwmGgcjHAxtsVrV0fqPfPvDMQyIzlhler31/oRZEQ1qcwbG78tjW+ahqXMt5AGf1j3GvJUzgGtZ44uceXzNL3htU2XAV9Ii8gbmjKVgmEzc9ZxRHn5nLjk2AkdPqitEWbNXTFJ7XDX7dpYP9oqQ7u5LDXdzZmjPTijeXoUHPvyR1zTkMp/O4XX8+HGUl5ejvLwcAFBZWYny8nLs27fPl4cNHw6LR+2PFc8ybudjjiaUtLB3i2VPNCsaCegi1eE87a82z5mv2OiIvJ35yVdp3Ha+Zge6SEqrNcrtMRbRrNm9CyEXIK3eo9xbrFBTqm4/LnBl4kbzz0TNpJCOJrI0d7Ch2WJyfKJgwZzxjKPCkfzYwtKF0AdTzgAoqymz6I1mTZIATWQ9xiW+D8ls+ZJarbphlvKqjtZzftjLGq2uEppI+ys1mrdFq6u0eU4pH+Qi5JEWy1Uo5R5jEQnbVe3HFWonowd8kzPWryMKJswa9wXTZOKu5IyzOaFkktLJXyU1OeMJpXxQOn+7sy+13MkZQN1E97ymISU+LXh9//33OOecc3DOOecAAGbMmIFzzjkHf//733152PDhpHgECKBhv3G7YHFc+QLEXISdpeStV2xUYr1dTcMJ6H/fiLGaLRiiqYAGpyaHdLWIZvGY9V0IBwVIOS9nR75jcXy7+3GR+QlcreqGJtV/XMgTWWYkKreTq5tQsGLOeMZZ4UhAoLqxGmU16pd294daOxliz6HmIxZfp+nVFe7MV3UEgOqGE/iu+jt8se8/0Op2A2bnebk45oy97ezlg6MipHxdZexRZpZ1HuYM4HrWuJMz1hMmO3sdUbBg1rgvVCcTV1vcOeKlub1qjjVBbxDYvOuQV/Yns58z7vUi8/c1jfyZ8JqGPOHTIY0jRoyAn+bED08qi0eqt7PHw0ntbcRnON8G9otbpYa+OCBSkQn7PbIMwrhCpPnKjoWaUoz5YgaiG6txQZTxMfPhhe4U0SQYx/TbrNaipvea1ZBLu/txg3wCf+STbRaTVyqpO96s+o+LoT07YUxuFhKiI3HDv/6r+jVEwYA54xm1hSO12ynxZFJ7e9J0ac43gm2BK6+pGRltbajRaiHs3JUXwrhCpPmqjhEJ27F4xzM48pPxM9B1sxxaaF0cU2K+naN8UNN7TR52aWjs6bWcAYxZM72gNxaX7HS6rTs5MzonE29ursTja35R/TqiYMCscZ/awpGnvYc8mdTeHm/cSHDFnkMncMHCr7zWu8tRzrjai8zb1zRqcyY9IcalgimvacieoJrDi6yoLB6p3s6aJ5PhK+k2zLiPhirY6wklIKFFl4nSpr42zxmgwdzWiVga+SyEsJwLRf4bY27rjaa5tsZoSrE06lmg0XI/5pPRf2E416UimrzJ7KIc25BUWVhMx1HH+3HTmNwsjOqbgSHzv0TdiRa728iBlBoXpWqf5n9cHDrR7PJriCi0qS4cqdzOHk8nxLcnLz0PGboM1DTW2O8JBQkZ0cnIa7IcbqQFMOvwEUxP7wzroJFzpvlgEeQO8BEJ2xHbdRmOWJ1yLSejz4GhNQlShP1hjdZFNGf5oLa4qIk4ZlzMxYs5AwBTR/XG+6V/oLrB/rnek5zRaiR0TlA3rxGzhig8qC0ceVJg8nRCfHvUzAmVGhel+De5WhKMKw+qKQC5sk9AOR9cOb/64ppGbc7kd0/F6p8cjXY6hdc0pMSnQxrJQ3LxSHEksgQkdjVu5yqFuahMk+FXrFS3H4MeqPwG2PaR8b+AsWAmt898UwEIITC7ZQISddGmZzUwYIimAmM1W9BH+j/j43besvlDziajB4zDCwFgbutE0/HNCUiQJMsiWmZSjPLEmS70XnO4Hw9ERWgw78pcSLB93+aBlJkUCzXM/7jwxx8kRBRc5MKRpJAzEiRk6jKRl57n1v7luaiseyzJE+KX7C1xug+9QY/vqr/D2t/X4rvq76A36KHVaDErf1Z7G63aLAQgDJjZtRDa9gzVA/guJhpr43TYFRXp8Hh9pD8wVrMF52m2Izpjpf08shhaKBfJAHsdQCRYFtGc5YPa4mJydCef5IxWI2HO2Byf5Iy9r9W+johCk7O5aCUYi1Pu9h5SM7+TGnqDwLe7D+Oz8v34dvdhAHA6J5TBYECyLlLVPLtKBJSW+nKfs5xx5fzqi2satTmj1UhuZQZzhsyxh1cw02iNxaMPJsK0XrpJ++lgzIJTQxDVDk9UMxn+6ulAnzFARJTl68z333gYWPew/R5i17yNk6seROzJatNT1eiEua034vPmcyBgHJY3RlOKv0e+jS5mqzMKYefkZ7Wi4nntk9ErfnRmwwvXGfIxqXUaZke+jS449RopsQv0hfNxc8wFuFRN92cVvddOxmbgvhtuQn7PNK/dBbHuoj06JxNLnSytrDcIh3el7HVNdnYny5vdmYkoOMiFoxkbZkCCZNFbSi6CzcyfaRp+6MrQRDUT4j/27WO4qOtFiGrPGuv9H2k6gn989w/F3mGLeo7Hgl/ftlh5MUOvx8zDR1Gw5ylg2D0oKX8NCzolW67OaN2NGO1fCoHWjC+xuOUAymKicWuk8o0O86GFbcdy0bR/AqIzVkGKrDdtk6nLxIODH0KCPk/1MBtnvdcAICUqHSX33Y4oBytOuso6a5Zcn4fH13g3ZwBmDVFHI8/bNGlZmdLVjEXvIVeGJqqZ3+mRT7ZhVN8MREVoLF5nfowjJ1pszndyD7GlE/Iw6+NtOGpnrq76k20eF6vioyPs7lut5NhIzLsyFylx0apzRk3vteTYSCy5IQ9Dejie3N8VruaMmrbymoackUQQD0hvaGhAUlIS6uvrkZiYGOjmBI7doYddjcUueeihK8MTK78B3vqz8+PqOgN/Xmx8vZ39yz84VpcMAAD91W/hos9ikX38R6TjKGqQjFJDX1NPKgnAlbFleEY8DSGguIqiPde1/A3pOIrnol5wuu29LVOx0mDsAaeBAfmaHUjHUdx12TD0HzrG9fnK5J5xAOxG9jVvuz8c1A5HXbSdLa0s3/FSaKnduzXOXrPk+nNcClQKPjyv2uJnYn/YYaYuEzPzZ5qGHbo6NPG76u9w67pbnR47JToFfx9qnPjZev/2yIW4RcOfRsHH90HfYCxO1Wq1SNPrkdfUDG37liWdu2BGgtY4944LK2m9XnUQtVotZqZ3drrtyf3Xoa3h7PavDNDqKiFFHMPfCs/DjeeMdGuuMrlnHAC7RchFIxa5PRzUHqWsefSyfg7P+e7kjJrXMWtCG8+ptviZqBt26OrQxG93H8b4V7c6PXZqXBTmXZmLMblZKN5ehTkrf0Z1g+Nhb6fOR3l4bLXy9hKA2CgtGlt8v5qxPe/efh7O7+U8q6y5e/52l7s5425bmTPhT+15lQWvUOGo95apCGP9rVQowmz7CFhxm8oDS8Cwe4Atz0OYLe0O2L1BbnpNsy4TNx+9BWloQA2S8b2hD87V/GYqfn1v6ION0dOQJdW53A343pap6CZV4/7Ij5xue13L37DVcGo1ELmiv2nmKPdPamoKkF4gn6gVvquqgsidOQ2UXjP2rCys/LHK4vHUuCg8cXkuLh3o3SE15Ds8r9riZ2LkqPeWXICx7nHkqACz9ve1mPnNTI/apJQzEiSkRyfhyT2/4rBNoav9/QAozO5i2bNLpYU1h7AvMgJLUpKdbtu49w7oG3uatc0LOQN1RUhv8DRr3J07x1HWfFZeZTG3S2ZiDOaMdX8uHvIvnlNt8TMxctR7y51z0Wfl+3Hf8nJVx5YA3HlRd7y8sVJ1eyUACTERaGhqU/0af/FG1vhi7jOl4/CahryNBa+OwqAHns11sHqgZOzpNW3bqQKZ2h5epl1oIITBo/HpeiFBK536UTskEtBZUreUu7XxLY9gUeRLyESd4g17IYAqpOKC5ucsepUBXrpj4e3VLa3oDcLhSi2uhJw7q9bY6+o95T3boJLddVF3PHyp+mWGKXB4XrXFz8QxvUGPwhWFij2vJEjI0GWgeFyxRW8mtT28vCWjrQ2zDh9BQeNJ4/FjonFrlnuLurxadRB/S+uEg1qtYs8w42T0iTixaxbgi5yB91e3tN2/d7LG3dXR7GXN5PfKFLd/yQdzlpH38Zxqi5+JY+6ei9T28JL3Acn+XIuhxptZ4+3VLe3tn9c05Atqz6ucwyvU7d3ioNgFAAJo2G/crvuFxoeczEVluwvPil0AoLE6TipcL3YZV1RMRY60B1kO5u8CjNcnqzWXmIpdgO2YcHfoDQJbfz/cPpllGob26Ich3bw3tl3m6hK8jgJAq5Fsltx1Fhjmr5GDytFPyssbK3HWacm4dGAXt98zEQWnspoyh8MMBQSqG6tRVlOGwZmDTY+rmYvKm2q0WsxI74xFNYdQ0HgStVrXC0OSEEjX6/FrVKTTnmGSBESdHIYTXs8ZY5Hr4IkaHDoajSRNH2Qm9kBeuveHW3gra9zJGcA2awY98YXD9j788TaMzsnksBOiMOPquUimZi4q8334IYp8IlkXaTHPl/evaQSG9ujsk2F9vrymkRcacCVneE3T8bDgFeqOO57rxO52FpPh+4f1DXJXz6WG9onsY9CCRyPfU/Wa268owIC4IV67Y1G8vcpm0soX1u9Gsi4SC/4ywKt3ndUuk1tzrMnlLr6ubu8sqGR/+2w7CnOzPA5KX99pIiLX1DbWurWd+YT4/iAkCZIQWNgpBSMbT6JTm2vzqUjCWJZrkiQ83UndRLaPXToCneC9nLE3jNHQmoTmg0VI05zr9WEmvsoad4aebP39sNOJm480tmLr74fdmq/GGrOGKHi4ci4yZz4hfri67fwz8MhlOV49X4XDNY07OcNrmo6JBS9f89XQN3m/tTvUbR9vNawjZ6xxbq/V04yrLQYZ63lbjiIeyTiOFBxXvQ9NQiaGdu/kfEMVirdX4W6FMD3a2Iq7l5W5NNRC6QQoP77zoLr3uefQCTxbstPmToW8FLN1V2elMfRK2wPqg6ruRCve3FyJzgnRbp/U/TWXAFG48cXQN3mfu4/uVrV9mi7N5rGCbgVYNGIRHvv2MRxpPuJRe9QQkoTqiAh8Hx2NvKZmRLXGojnipN1RidY5k2Qw4KhGg3qNxnZjBRlx6Ric6Z2cUZwnLaIeMV2XoXY/MGlZk0tDWOz9XAAaU/4cOuZ40maZK1njTs4AaO9l4NxH3/+BQ8ebPbp4YNYQuc5XF+96g1B9LkpPiLF5bExuFpZOyMMjn2xD3Qn3VzsMVgXtvVqte9G6KxyuadzNGVeuaRZ/8SvO75XGnAkDnMPLl1xZOdHT/SqyM4eXubYWYFE/oPGQ++3xkPVFh6H9J/I17XUYPOhcPLW5Dk9HLkUWjqhcZMvJewZcKkTqDQLnL/jKYgJdezITo7F51sW2J0WrYxUf7465q39VNYGig3eIzKQYCCEcrhpjPibeH/MjmHP1pO6NCS1DSaDu+oT8edUHQv0zcXX1RHf3qURpDi9zLW0tGPHBCBxrdW/uRlfNOtiEjUevx5dxOsR0XWZsp9mvl/yXT9SxMXjs0hHoXPMb/lrxCmoczNllTs17BtT/njubJ804X1gSGnfNRGaSzuY8ba+wtf6P9Tbfw6TIzmg+WITag2eaHtNIp3LX9n26ljUA3J6r5el1O/DCenXFVZk7Fw/MGt9nTaifU30h1D8TX12829uvPWrmeWppM2DQE1/gWBBOMH/LsDOQFBuJZ7/cqfo1vphD0dNrGtv5sJrx+Jpf/HpNA7ifM+5c0zBnnAv2axr28PIVpZUTG6qMj1uvnOjpfu1q/0Ebs0C58BMRBfx5MfDBja63xUvqkIBOZnN6VaMT5rbeiHXN+Xi/7xBMi/0GXTaq7Rmg4j27WIgsraxzGgwAUN3QbDO3gL1jDRSpGNg6EVXINz1WVd+ketUY+fRx3eDTsbjkN8XtrMfEezI/QmpcFOpOtKhqn8zZXRZzeoPA3FUVdn+qBYzvee6qirCZu4V3fchblHoF1TTWYMaGGXZXT3R3n/bIqzTOzJ/psPATFRGFK3pdgXd+eceltrhrdtNUtBl6AceApv0TEJ2xClJkvel50WYcJnj8WC46YQi03dNR89vrqvat9j278nvubJ40SQKkyHpodJWoqu9pcZ62V5xMik5CfXO9zX6OthwCUt5AROMEtB3LBeC42AW4ljVo/99qtrXurTC0R2eXC16u5AzArAGYNeQ6d3vTuLtfa/Jv4uyiHIe/l1ERGlw96DS8vnmPy23xtUv6Zxr/hwsFL8D5e3ZnmhJ3r2nUFid9fU0jH0PNttY54841DXPGsVDIGfX99kk9g95Y5FD8UQdQPMu4ndf2a0diF3WFtZyxwIhHXGuLFxgEcEB0wtDm5/FY6wS82XYJHmudgIuaF2OdwVgMqjnWhPw0F+7U6Do5fs9ywdC6d5xciKxYafMStd1fbbZVOFYm6rA08lkUakpV79fi9UkxWDohD2d01rnUpi8qql3aXqbVSHji8lzXGolTP6VzV1VAr3RF1c6VYlyok//As36/cqAWb68KUMso1OgNeiwoXWC3MCXa/29h6ULoXcgaR/u0J0OXobqoNvL0karb4S4h2ntvaRtNj7Udy8WJXQ+iqfoytNQNRVP1ZTix60FTwafmWJPqecoAICUmxel7dvX3XO3xpYhjpjYDp4qT1sUye8Uu4FTnteiMVQAMFs9Z/93tTta4mzMAMKRnJyTrIlW9XuZKzgDMGoBZQ65xdvEOqP/9U7tfa/K5SM3F8+icTJfa4S81x5pcup4AgGkFfRy+Z3d+x929plE6lqf8nTPuXNMwZ5SFSs6wh5cvuLNyolf22+7CB4Eew+0P01MaynfRA/h/9s49Pory3OO/mc3msiHZJECyCSJELkqMQFMTLqIFGpoUBOu1YvF+aL3VClUJVouUVi5axB4Rq61Xjno8WisIDZIiKgImGlFivGIIFhICSUhC7tmd88dkNnuZyzuzs8nu5vn68ROyO/POzO7mffb9zfP8HuzfBHQoZ1J1We1o6XIhBS2qFR+Seq2GNF1s6ZmG3TFLkeHRdfG/orZjZfd12Ok6H2NbDwCdjD5lAFC4Wlns0hQiOaC4CM7xc1Fa3eROyxw2JIb58G5vAZVjSeUjK6wvYmfn+V6dJNW4Y9YYr1pyVr+T1IRYFFfUMN/xkvNHmDsxHb/6TybzHRsJtbssnhg1K2UhlAwjB9tdHyK4aGUFAZDtnhjomADwy4m/xNT0qbJeYUp+YjmpOYqZRxI2SyJ6aheiI/kFcBZ57y0AioGG6205H5O2rVfQ4hGVUIGYtK3gPTK8oofuQefx+ehpycawIVZ818HuZXnP+feoil2sf+ezz0nDx9WNqGvpQKMzmunYQk8CAHGe1itOSkjZYhZbFZxtY9yPuwTggXkT/HwYHythy0g4fLItoDhj4Tmsuew8RW8ZJVjjDECxBqBYQ+jDaHVAoONKPDBvAm64IFOhFNz/by4vM8Wvo6Ev8dEWDImNwnGF8rlgMGxIDL6sada1j5oIZCTOGF3T6BEnWfFd0/RXnAGMrWkozsifS7jEGRK8ggFr58S9/61P8GIdN/Uc+XHlSvmihwBjfgz88AZoZY5xHIf7u2/CRutjEATlTotMH2nbMPxPxwz8Ev/0e0rKgGrmhiBpJ7tJPQAgQeXuD6MQ+eu1G7G9ZWzf+STGaAZPabu8zBSmY/EckIF65PFfYr8rS3VciXFpCV6TbCNDOm66PRY/HJWMi9a9w3SMdHts3zX4sHxuFiadkYT736zQbQqqNakrBSWj20mEWpptsL44EoMT1qyg5z9/nlnwYh1zjH2M7Jhy5XXxUfGYnjEdV4y/Alq2odEWHo8suBI3/l8rYkds9vN4dKMSaCRBZ/jwo+hynYZz+Gb/bXqN4C3Ns3Bv6SNo7mb3sUyLT1N9Xvw7b4PFVgUuqgVCTwKcbZmQkuqlv/Opq0s85lIXEscnQbCckh1T8vBytWW65+nyuo+YxEklpGwxT4YlxOCSySPcvxdX1GCDSpkJIL4VaYkxeLn0CNNx1eJMYXY6nlyUgwe3fK7o5aIEy+KBYo0IxRqCFdZF+d/eP6Trs8Q67rCEGNnFstzfXHy0BTfPGN1n0qiANYrHI5dPwrXPGqu08CUmikdnj0vx+Vgrj9s2f4wmnd5iavMQ69+4d5wxtqZhFSf14Lmm6e84Axhf01Cc6SOc4gyVNAYD346ISnyzUzSNN3vcE18CVe97l0wqlfJ1nQa+eBPYfCnQcUp1WGvXKaRz9XjGWYgWxLGftwzcT/6EK2JE00DfOMZz4sRmF7zFLs07C4kjxIw1JRgFw6jWOq/fjzd3agYGAHhwwbl9QZnxWKk4xbQd4D0xOl0CVm2r1NzngXkTsGn3t0z1+oC2V8DciRko+90cvLx4Kh67ejIemDeBaVytST0vMwXp9ljFNSwH7eDlSyim2Qbzrg8x+JDriijHe/95D12MsYZ1zPqOemz/bjvKasvcJZNK5XWtPa3YeWQnflXyKzR3qd/hPtV1ClurXwW4HnSfymFsVCLPA5eMQMKIbQD8RTPpd2fiO2jqYhe7HDZHb6dDZd75vgTxY9fCNuppxI14BbZRTyN+7FpEJVR4bef9JZtHW83Fsus06bHO4/MB8O55Wk8ZphxStpgnvnGG5a6+AGBh3pmmxZnC7HR8UPRj0+MMQLHG6HbE4IV1Ub7ryxPoUhF9jI77zfEW7DtU71VKpvQ319rlxF92HcKpdnVh6VRbN/ZVmdesSyt7paPbpUvsYpmHShjL+nzFHCNrmmDME3qzx8yOM4D3muaOWWNVt5WgONNHOMUZEryCwajpQAxLBxYXUPa0vnETMxSfdk8W7z0MPH8xsCFbFLr0en+p8HvrZvxXVDHsXHtgA7XXI669VjlLjJNZoEDtCjh1o3oAqGczw61DktfvUlpmks0q6y+SZLP6t+9lFCd9j6WE78TIerflXxU1eJQxTfjmC0Yz3SGQWiNfMnkEbrgg05RJ3cJzWDE/y72P7xgAW/CSCJbnRKAE664PMTjJSc1BgtVftPBFgID//fp/mcdMs2nPX+vK1mHZ+8tw046bUPB6Ad4+/Lah8jo5/nXsKcSNeAXRyfpK23xp6jqFpu6TiqKZFGdYRTUOnKZRfUl1CV6u/iO4KO+yTSmjzFf08qSnJRsdRxeBcyZ5PS702NFxdBGG8+d7+diwipO+CALg6rb3Zp31YTTOAMBn/znFtN1AxhlpXIo1+rcjBi95mSlIiNUuCBIAvLjvsK5xWTz7Hn/nEBY+vR8z1u5CcUWNaeV1L+yrDnCEPtq6dHoyq8AyDxVX1ODvBo35jaxpzJwnfOfrgYwzQF+sWTJnPMUZnYRTnKGSxmDAW4CRU4Bvd2pv23i4799K/lqe4/5kNfDa9bJD+f3ZNB8Tuy9OvobN+6u/SBwBtLH7pXgiOzUkjhDFLjVzfpcT+PhZ1bEFAahBCkpd5/g/B/GO0P/cPAXg0OufJWDaWcMwdcxQ/0lLEiebayAn07kEsRul3LHkaO92YmdlrXsCZ1XL3/qM7Q4QAOQbMPqUJvVbN5f7CZJ6J/XC7HRsWpTjl67rMJCuG6ppttJdn9qmDtnAJbVS1nPXhxi8WHgLJg2fhD3H9mhu+33z9wCU/bU8x7w391789t3fMp/H8bbjurbvDxw2B+zRdlPHW5a3TMO7S/TUAuQzykRvsa3oacmC0v3GnpZsNLdk4cEr4zAsqRMnT8XAzo+HIzHez6tDEifr2uqYhUbfbDFPjMYZQMzuYCExTp8pPWBunAEo1gAUawh2LDyHnDOT8e7X2n/j1Q1is5Bg+AzVNHXgls3luOvH40wprzvdGbhIxQGwx1lxql2fzYcaWvOQJH4Egt41jdZ8ovfYCyalG8oeY40zQxgEWl8ozugnnOIMCV7BYswsNsErebT4U85fKzEDKFzrLeTEG/ggH3hJ/z6MKPqrqNHdBrz/58AOfOE9oleZnDAoR/VeoEU93ZPjgJe7Z6uayJ9s7cQlk0fggrHD1I/HW8T37tXr4JubJoADxwlY2XUts2H9qbZur5a4rGq5nsB08rQx804zJ/XC7HTMyXIE/EUpVNNszQ6oBDF9xHQmwWtk4khZf600WxqK8oq8hJzk2OSgnKtRjMSZn2b+FI989EjAx1549kLMGT1H1pzfFy3DfyWzeH94JFsm4OIxI1S2EcXJorwiLN29FBw4L9FL+t0ebUdTV1+2mdBjd5v1+2I0zgDssebZD6pwx+xxuuc4M+OMNB7FGoo1BBsXjRvGJHiNSrEx+wyVVjUwldb58ti/2aoW+gMBQI8JWTV3zBqLcWlDmOYhM/20WNc0avOJEf76XhV+cGZy0OLMf+/6FmenJWLuRP1xgeIMO+EUZ0jwCha5i4G37wcElXp2ziJuJ/lr+f4pN9eIj1/1Qp/oxWpc308Y8ldpV+4EycxZP/Iz5le9o8T4ulUL6llOutIysxaI752PkMklZsBZsBo3xM7AuG9P4PF32Eotgb5uF3mZKYiPtqDVxDTqX7/8CawWzpDxoVmTOtCXXhwIoZxma3ZAJQY3Px//czzy0SNwqcQanuMxPHY4lu5e6pcJVNdWh6W7l2L9zPVu0StQbyiz0RtnCkcX4rnPnzOlvHLO6Dle5vxqcYb1dZMzi/eFdW7KH5WP9TPXywqZy/KWYdbIWXjxk3fwxx0f+pnnK+EZZ1jMjfVwqr0Hj+/6Fr/JH6d7XzPjDECxhmINwcq100bjT9u/gJq2w3NAamIsbt1c7jfzSj5DniXZRhfn/Vu0pcyQmCic7uzB6U59RvRyXDB2mN9cpBRrzBQ19MxLSvNJuj0WD8zLQnJ8ND7QsaYJZpxxCcBtL5XjST5nwEQqCYozoRFnSPAyilb5YVQ0MO0OYO9flMeYdru4j6K/Vm+ldXERcM48cVtW4/qQJPD7Ai4B6I5PR4yPOb3mHSUTPLUSY6NQ29yBfYfqmSY/p0tAacwFqJu1A2PbDmJCQhv4BAcwajosvAXToC/ge6as5mWmBGTmrISe9rFywXigu3BIhHqardkBlYhMtMoPASA6KhrXZ12PZz9XLtm+dsK1eOTjR2QFIEHMOcXa0rWYNXIWLLzFsDdUKJAam4pP6j4xRezyNajXijOsr5ucWbwnSXFWuAQBTpfAFGfie36A28c+gybX1xiW1Im0+FSvz0qyZQJ6GDse+saZYPDs3ircMXts2McZgGINEf6wlB9GR/FYfGEm/vpeleI4N88YjYe2f6G2mvH6jjkQi/Mkk8oPk21WRFt4U8QuOV8otVhj1uumZ00jfUY6e1x45MpJgCBmh/l+XvSsaYIdZ4DA1zShMk9SnAkcEryMwFp++JNV4s+9/w0/oSc6HjgjVxTNVP21BKD5qLhd5oXAqOloj01DTPtxRcP3gBmSDlz6JNB2UhT0dtxn0sCBi10cB0TNW+slLkqdK1TvKGUF7qnV3NGDJf97AIB2G1j5YJWCFfPHotDj3I0ErroWMUiw+A/wHFTvyPnCWgMeaq1xfQmHNFsz7voQkQtr+SEALD1/KQDIZjXZomwYYh2iWmonQEBtWy3K68qR68hFTmqOXymc2QyPHY7VF61GfXs96jvqsa5sXcBjcuBw5dlXYuOnGwMbSAA4ztugniXOzMlS99QSBLGk0Ncs3pdT7d34xd8+NBhnYrFi/hmwOMyJM6x33fXEmlNt3RERZwCKNUR4o+dvbPlc0YT7qfer/LrJ2qItGBJj1eUzJC3izSrPk4MD8MKNeWho70JqQixcgoBf/O3DgMe8Yfpo5oZQWmP5zg9asWbjNTmm+GmxrmnUPiO+84reWBPMOAPQmqY/CfU4Q10a9SKVH/qKVFL5YeUW78fPyIWs0NPVJm7/1Xa240olebwFR6asAKDvj14X598IjJkJnHcFMOUWwMb4AVbYTkgcgWMTbgz4tGqRggPT/gLLuZe4H2PuXAFeFCQB+FrfC72/r+xm99RSawOrp3WsVutaOVITYpnvosw8e7iusQHtOzSh2BpXDinN1mH3DsAOe6xXWj1BhBol1SVYunupn0gllR+WVJf47TNx+ERZkaW9p51ZAJJK8iy8BYuyFhk4c3auOvsqTEmfgrlnzcU151yD5Bg23zCl7ZJjUvDLc1agrnFIwOeWFD3cq8STNc4APIryigCI4psccmbxSoRLnAGAxRdm6ho/UuIMQLGGCE+M/I394MxkP7ELANq7nMwCkPS379nJLlgIAKKieFwyeQSmjRmKqWcNRUq8/sYZEinx0fjNj8ehvrUr4HNLiovymx9YYs2qbZV4YN4EAModAPWi9J7r/YxIsYaVYMcZIHJiDcWZwCDBSw8up0b5IcTyQ5fTZ3s5erf/7FW2Y3uU5I390TW4z3ovauGduigXhAwx1MNMl7cAcxmMf+OSgcv+DvyoCIhN8nqqrrkdL3/WbOhUXIJ4XU9ZrsZnV7yPHxR4d6jU07nC7amV6DMpJGbgPuu9eNuVx3xecm1gnS4BH3xzEkWvH2RuHavWutYXz5a4rHdRFl84BpsW5TC1fpZQGztUW+MqUZidjj3LZuPlxVPx2NWT8fLiqdizbDYFBiJkkTr9KZUfAsDa0rVwupx++8ihp7TPsyRv8XmLTe1y6MuZiWe6/23hLbhvinYmcYI1AUtzluLe3Hux8JyFGGLtE7caOxuw6eCj2Pzxx7rPReiNM1Gnf4Rbxj+M3Vfv9Mqi0xNnJE+tVFuq1zZpNgfiGm+EU8YsXm1cIPTjzJL88Vg+NwubFuUgPkajgUwvkRRnAIo1RHhh5G9MrTugnr9Ez7/9wux0PHFNTvAqVuAveFw7dbTmPklxVvxu7gRcP+1MJMT2zWkNrV3Y8O9v8MK+6oDO6eKJ6fj4gZ/4zQ+ssSY5PkZR/HiiNwNMz0sq95539bhw3xsVuj4jUqwJdpyhNQ3FGb1QSaMGnjW9Y1sP4Fw95Ycs5YptJwHbMKBNbAnrDyeW4nl4Vll4DjN/dhMu3DwRufyXSMUpjOJqcE3ULqTDBEN4T7+ryi3A27/T3qe9Edj8M9mnhgsNWBL1OhqEeCShVSGwcaJoFhXj1U2xw+bA93m/x80/ukY2VVNv5wrnOfNRap2Knqo96GmqQVRSBqJGX4CL2nvwvy99ostlzHOR09Te5ZcOq7WPlPqpZPjniW/KKkuLYCmYWHgOc7IcuPPlcmw7WKt6fnJeAp6EamtcNUI9zZYgPL266jvqdZUfAtrdAbXgwCHNlublV2XhLXhw+oNYsnuJ4XHV8BTXSqpLmDoqtnS34IF9Dyg+z0U1IXpYCVzOGHB8p6LPIc/xXib/SdHDsXDMr3FL7s9MiTP5o/Jx0YiZeOnT3ahuOgbe0oZJI0bg+zOseOTNbnDgTY4zLlhsVeCiWtzG9P0VZxyJMbhj9lj3MWafk4bzHtyBzh7lJgpJNmvExRmAYg0R+khrmg++Pan7byzQ7oBKPkNzJ6bjcfwAt730ieGx1ZAED7myNSVOtXfjT9u/MHxMDuI8FxPFo9bDP3FofDRWXZKt2D1QT6y5ZPIIzD4nDS/uO4yq+lZwAH4wMhnJ8dF4YN4E3B7gmua+Nw6ioVW51FBpHpZiTdE/DsqWKpoRZ2hNExrnEk6Q4KWC7+S4gN+Lv0Qz7CiVH7J2VJx4FbB/E/xN3XunhcI13ob4EP/gNy46Hyu32rC/9/w2Oi9Fke0tLHa9YjitFQDQWi/+VOoeqROp5tqONmWxCwDmPyaa83s0A7CNmo6zVVrB6+lc4f1+xgAY3ftsGdLtsfjlRZnY8mmN7oBeUlmLZz44rOtV+ldviqwkSHka/pVU1uKNA0e9Ao1vtwsj9dwfHKrXPK8H5k1QrQEP1da4BBGuyHl1seDZEVBPV0UOnFfWl1R65+lXJZE/Kh+PznzU7/zsVjuaugPz92rsEG/OSOWbZpjMc5yYqcVb5A3apWt9+KKHkRybrNoMwBO9HZKkWHPC9TFi0raCtzbh1cPiNiMmDkPn8fk4cfxs9guDcpyJSqhwH0PC1W1H5/F52F/DoZGD+xqDEWceXHCuV8woqaxFl1OlOzW0s8wozhCE+egRfCQ8/8b0/L3p9RmaOzEDT/Kc7vPTIrlXXFfyxQoG0tWtvuw83Ubextc0Ii/uPwIA/bqm+eDbE37XJ8Wax3d9g2c/OOzVKMCMOAPQmobQBwleCshNjmod/LyQMqRYOyqePRc4c5qCEf4abyN8D+S7IlwM7u0UYP8TbMeWY/tvgfEFKuWb+hHnG4WxoocAOdeKGV6AmB3HCGvnisbWTtz+0ieKV1PT1IGn3qvCxmtykBwfjbqWDpxs6cSqbdp3eN44cFT3q/TCvmq8sK/ayxRRUu2njRmK++ZlaQZJPa1gH9/1DZMpZHJ8jGqnklBujUsQ4UYgYo9nhhRrd8DbJ9+O175+zc8If1neMj8jfIn8UfmYNXKWX7fIP3/0Z7z4xYu6z1viD/v/gIToBMXyTaOoda9NjE7EogmL8OMzf6wqcPmip0OS9N3BklCB2BGb/bZt6j4JLuU53DN9Jc6IyQsozkQpHIOLakLsiJfwzKGXgN4O8Z4ND4IVZ4orapiyNBp7TevzMlNkj09xhiDMxajg4/k3pqf07JWyI5rzhS9ya5q3K2vx7AeHdZ51H509Luz95iQe3CJfthYM4mMs+Pn5I2GPEzMk9GTjhOOa5vF3Drn/7bum+U3+eNwxe5ypcQagNQ2hH04QTHN+Mp3m5mbY7XY0NTUhMTGx347rdAmYsXaXnyrOw4U9MXfCgQblTKXEDOCug2JGlssJbMhW7Awou71HdhNGTffL7AKgvV3V+8DzFwfwCgCISQQ6jflu6UGAz91euW6XGkiBXBpPQhp34zU5WLVN+66RFEj2LJsNC8+5PwdqgSclPjog80rpHAMxHNRqpet0Cfjhqp1MrZhvvmA0tlfUKnYqYXlNPF9DIvQYqHk1lBmI18TpcqLg9QLdmV1S+WHx5cVu0UYaS6k7oOc+APzEKznxx7PMUm67stoy3LTjJl3nHioodbtUQyvOiF0aHb3fHdoQP3YtuKgmWQHO+/3gDcYZl+oxBMFb/JOy2zzN+PXAEmfkvjcpoRZrpNeR4kz4QnHGn1Bb06gh9zem5/sfAKbMJq15Zd+heix8er+eyw0pjHT6G8xrGq3Pg7QNrWkICdZ5lUzrZVCq6XWBx8ru68R/+/1lyJQf8hbFzoCK22deKHZHzLxQXuyq3CKKaM9fDLx+s/hzQ7Z3d8hR09Ee54B6UYEG/SB2ATKlDUrdLlWQ61zBw4WfJnyLf150DKNaynG8qU1zHC+De6gb/Uq/XzI5g/k8lY4JBGaKKGWGSV1ofCfl0qoGpsAAAH//4LBqpxKW12SgW+MSRDhgxHdLqfzQwlsUuwP67mPhLch15GLuWXOR68iVFbtKqktQ8HoBbtpxE5a9vww37bgJBa8XeHWHzEnNQZo1EVzo3jNTRK3bpRLyHZJcGDb8e/xybhOGDvsP9n93AjVNHbDYqsBb5YUowNuHzWic0TqG7+NKDQ9YYYkzehbUarFmZ2UtxRmCMAm9f5tKf2N6vv9pzReAKOzMWLsLC5/ej9+8cgALn96PGWt3yXaYDVeMdPpT6saXEh+NGy8YjZqmdqb3MxzXNCyfG1rTEEYgwUsGtVrdHa483Np9l1+HRCRmiB0AfTOTVDoDym6vgvPzNyG8eh0EXyN8H5GouLIOS5quBgR/YS5EGk2oIIj/b/0NcGi3d8fLqveBg6+JP32+sHt2rnj1ohP4IuVuPNH9e0wqvRvn7rwGpTG3opBnu0vk+f4XZqdj0y8m4acJ32IBvxdT+UrwcLnbwM7JcphyxTVNHXh051fYd6g+oG4gTpeAfYfq8eaBo+6xWGvPlaZz3wBGrXEJInD0+G5JpNnSFDN0lLsDKu8jR8nht7F09xI/Mc5XJLJ8uQ1F//kOAPxFrxAXwYTe/x7c9yA+rPnQLQA5XU6U1ZZh+3fbUVZb5icMecaZXxaexpmTHkXHsI14qWo1btpxE5buvxRRCZ+Bi2phOg/pMyDNqWn2aFhshxCVeAAW2yGk2aMV4wzrMXyvu7atFi9+8s6AxRkAih3ZPGPNnCwHxRmCMAG9/kNqf2Nmff/b/tkx3LK5XFWMAEQBZMGk8P1b713R4HdvVKCrt5GH3Pzpi2esufmC0Ui2WVHf2oVnPjjMVJboie+aRipz9CQYa5rnPqgyPc4A7J9nWtMQnpCHlwxatbo7XHnY2Xk+ts7ncW5iu3r5ISCKWj5m7Krby1D8WTWm/OMOJAmCzB3d3sLA4iI4x88Va6B7hbkV1heQgQb3lrVIRiI6EI/2wIztg017A/DiJWIHyzN+CPznY7GjpYRM6aOF5zCt8wOg9C74lpAO41qwyfoX/LXnO6xxXqN66NR4qyiqnT4O1B9C4cfPorC7BuiND502B6LmrYPlXDEdVqu7CCuPv3MIj79zyFAKNCBvSJpuj8XVuSOZ9lc7f99OJfL+cepmnP0BSzo0QYQCrL5b9+bei6GxQ5nM1ZX8tlj9qroq/oE/lP0eAge/FCEBAjhwWFu6FrNGXARL8TLkt7VhfZ2ANUOTcTyq7+tEqtOJVt6C1hD/22vqbMJ/vf1fSI5JxsRhE/HZyc/Q2NnX6Viu9NHCc2iN+gSvVP/Jr3y0zdmM2BEvobt5ItPx6zvqsf277RhuGw5+SCOGjF2HVg+hcYgtDVEJRcgb+WO/OCP0JBi+7j/u+BB/fZsbkDgDqN9484w1FGcIInBY/YfumDUGF4wdrvl5DvTvcuunx3DnK/Jef5LNiSR6A8CWT9mzo0KV+tYuTHmoBBeMHYY935z0ylBS+s5v4Tk0tXfh7wF4mAHAyZZOvHngKFITRO+vVdu+QINH2WJKvBUPzJvgLvEza02zatsX+NueKlPjzIr5Wcyf53Bf0whOJ9o++hg9J04gavhw2M7/ITgLu25AeEMeXjKEWk3vJzuex8i9v8MwTvuO7udzXsK8rX2/83Ahj/8SqTiFUVwtronahXSuQXmAIOAUePBwqZoJ66d3MM8sObdn2jHZPaRP+m3dv8G/XFNkt7nCVo6H418C1yI/htyxlertjWKkBl7JkFTqeJJks6KprVvx/GzRFrR1aZe5PHb1ZFwyeQTTOfU3agGS7tD0Qd4q/gykhxeL75Yeg3WjlOxZjT98/SIaGb5QPTPxLuS+udT9uxNAeWwMTlgsOGKNwmsJQ7wEsP7C17cqUOR8rzS914Teu/pOGzhLm3LJIXgIGsYDnsfvaTnXJ86oe3ip0Va9GK62MQD6N87wHHD9tFF4dm+15rFCNdZQnGGD4ow/tKYBVm+vxF/fq2La9uXFUwEgrD28WFD6zu90CfjhH3cymbMrwXPalT2+xzdzTWP2egYANl7zA6za9oWqKBfua5rmt9/G8YdWo6e21v1YlMOBtPuWI/EnPxnAMws9yMMrAEKpptf5+ZuYvO9OpICtfKG98Sh4uDCVr8QCfi/y+C9R6joHnYjCkqjXkAYDYpdtmPfvcSlANNvd5VYhGr/uvt2d1msevaMVF7nLG52HP1AUuwBxIcRxwCrrs+BlFhoFfCnWOR+BoCp2+R9bKR3W832QSiG14OHCFL4S8/m92PLmq3D29Gju43QJWLlVvgONb1MApU/sry4ao3kcIHQ7lUgBUis9niBCBT2+W8Gm5PDbWPrt/6CRZ/tKcKL5CJwAymJjsD3ehvLYGOR0dCJaELAxyY7jJtyFdNgcKBxdyLStIAAuZ0zAx/QbV8b3qqz2Y3Xvtd44A87pPjffcxUEwCVoxwPP48/JSvWJMzw6j89XPIbseALg6rbD2TYKfG/p5AM73kRXP8WZxxf+AD85l23RE4qxhuIMEY6Eyppm+2fHmMUuQCxdq21qD+IZiWL1/ImBl/EFgpLv1d5vTgYkdgFsNjasJX5GkNZ+971x0F3SqYZWnAHEzLEH5sl/niXCeU3T/PbbOPqbu7zELgDoOX4cR39zF5rffnuAziy8oZJGBfS2SDWEVrdFlxM92+6FVVD2vPAlteso9sSsRoZHFleNkIIhaINMlYo20fHAZX8Td2w72XeeLifw8BhNc/t4rguNSMB91nux2rZZVZDSjwA0HwWq96K4dSz2/nMX/sCw1zCuGXn8l9jvynI/xsOFFdYXev+t79jIvNAvHdby5VvI+WKN1/twTEjByu7rsMOVJztiAV8qlqBK+3QDneufgOXih1W93rQMSQUAp9q6ZVtFe3bFeqXsiOYdwLzMFJlnBxaWhZiUHk9lJ0QoIflurSld4yWipNnSsCxvmaFuer5odVt0upxYs2+V+PfDGCCOCF0oGJnhlcWV1tODFml/A2lWidZE/HLiLzE0bijS4tOQk5oDACiv+wR1rceVv9n2Ho6zdMLaXIgUxye6mwGo4WkwX3/yDNxfshtI1t6Pt3Sisy4f1uQycNYmz7MFIGdNoH38wuxcrzjzzvcN2FlnAx/F0JSld4LsbpqE+LEPg+89pw4A+f/3Kn4//T7Vz5sZcYalZCZUYw3FGSKc6Y81jVqpr9Ml4P43K3SNd/hkG57fdzjg8/LEHheFO2aNw7Ah0XDY49zzTNnhXahtZvc6S7JZERtl0bWPGr4ldsUVNbj7/z41ZWwjx/dd07yw7zA+rj5lePyG1m5MXf1vPHRptupnjSXO1DR1IDk+WvbzHO5rGsHpxPGHVsvftepNYT/+0Gok/PjHVN6oExK8VAhqTW/lFqB4mbcA5OtLVb0XMW21ql/0JQRw4OKSMfKzDaL/igdpaGAWzPzoagU2/6zv3DIvFB/nLcCYWUDlm5pDTOMqMf5na8BlFfUJfPWHgN0Poa8YwjifVH6JW99vxhQ+3u2zpcWSKYn4r0+i0NIh3tnO47/0EqeYOd23sJK6i6ByC/DlMgic93U50IBN1g24tfsuP9GrgC/FJusGv+Gj246LDQlUGhywBtzRw2zYs2y24ud5xfws3Lq53O8d6a87gEZ9UVgDpBTICSKUCNR3S42S6hJZMc3Tl6q8rhzHu04xiVQcAHt0EjZWvwX4fNk6brEEVE/Y3N2MRz5+xH1+0vVfdubt2PTF75k8J6+c9APcO2ON+7U80nwEGz/dCA6cbNmoHnZ++Q2eLq4Db4uFjUHwAoDbL5yGM6x3YdW/t+N0TwM4SwtiHdsMHV8yuJfiTEl1CXZ9+jD4KO/rEgTlrww9LVmIHvqe3+ONXSewdPdS1eYGrEbBWnFGyjYZqFhDcYYYrARzTaNV6lta1YCGVvZspSSbFRtKvja5MgRoau/Bn7Z/4T436doX5p2JR0u+Zh5nzWXneb2Wh0+24tGSbwI+v9rmDsWSPiUunZyBGWOH4cG3Kt1rGqN4zvNSrCmuqEF5AGKXRENrF27dXK5a3sgaZ+paOnDJ5BGqn+dwjDNtH33sl9nlhSCgp7YWbR99jPgp8skThDwkeGngFjHMpHKLKGL4TmdSt0VJ3DjNdpdaEABwAuDsErO4fJ435e+5+Zi/8DJ0PNOuc89zYKw0uUmCGQCkTvAX/Qyw7oNTEJCBUtc5OCkkMHmdDRl2hldgSMUpYwcfkub9u8spXhME2ffBJQArrC9iZ+f5cPXmknlll/nsxHk0JMA58/waHRRX1GDVW58znWpqQqzq57lfshoVCMQXRU+AJIhQxMJbkOvINXXMkuoSLN291E/okbotSuIGc7dIQYDAcehy9Zrd+opbJpln+Z7fGTF56DqRj5jUEs1988eP83stxyaP9RP9jPDM+ychwA5nWyZcPfHgo1o195k+OhM9rUNwquFMAGciKvGA4eN7NjlwupxYU7pG3vtN5W2ISvhCdZu1pWsxa+QsWbH18Ent6wW04wwwcLGG4gwx2AnGmkZJoJFKfTctykEnQzmbL8E0mPY8t8LsdIweZmPed0n+ePd84flanu1IwH1vVHgZwuvlD1s/120Bc8X5I8FzXMBiF+Bf4qeW2WoUtUxYPXEGUP88h2Oc6TnB9n2MdTuiDxK8+hsPQcQfH3Gj/hDTkI1cIuxZ+bBU/sPMM5XHU3jJvBB4/2HNXcae7+PDIpVyOruASzbB6XLB+fI1sLr0dY4UBKARQ/Ch6xxxWPC4v/tGbLL+BYD8l3oBQHucA8UtmQD6vATqkKTjyADAiVlvo6Z7P1y9V1XA4zkgA/VeJZXa2WXe5ZMSLHeBpKYF422tyOMSANcFqt1BzbwDyHqHg+XLklqQYK3BD8VafYIIBmqCiFe3xZGzmLtFpghA3ugCFFfvMPt0Vc8vNSEWXfWzYU0uBRfVLD+vC0By9HB3GSTQV8rZ5ezCn2b8Cd09Pfj1rrvQI3QwZU17j88BvPRFnEdn7SWIHfESAGXxKCU2Bcdbj+ObmhMAXAB4Q50VpeYFntdWXleuW8ATz1M5WniWTvqKr8UVNUzZC0k2K1wuAU6XoBkzzIo1FGcIYuBgLfV95MpJTOPxHPDr2WPx2L+/NfM0Vc9tTpaD+e82KS4Kd8we6/7dd/55755ZmLbm34bFp0adnl1DYqJQ19yBQyfYhCIllEr8tDJb9aKWCUtxBogazvZ9jHU7og8SvPobDUHELW7sewL48AnVoQQBqEcCPrrsXRT+a5a556l2bh8+KWY22YYBcclAe6P6bm/eCvx0nSiUvfeIeF3tp9xPt2AIEoVeY0qdC5EknMYc/iN3iWCxayr+2vMdfhX1ltzZQxCAJU1XY8dub+PMUtc5OCakwMFU/tm7QeEaf/GIMSvPM6OMObvMY2yWuy5enmA9AF54FEJiBjjPslkZzLgDyHqHwwxflLzMlLD0hCGIYKEliEjixktfvgR7tF295E8Qs1W3/fD3KKh4LDgnrHJ+yTEpGD78KBqPX4zYES/JdmLkOEDguvDO9+9g1shZePqzp/HiFy+iucvDY9Jpg8CL85F++V5A7IiX0HGUR09LNnpaJqKr/j+y5YESDR0NWL5nOQAgfqwdncfno6clC65uO3NnRaXmBcxZeQbwHVuao1k41daNX/z9QzgSY/DggnM172YHGmsozhDEwMJa6vvBNyeZDEwe/flk9BjIBjOCdG7PfVCFlPhopMRHa2ZnCQB2VtZiTpYDj+/6Bs9+cBin2vtEKpauiGZyurMHS14NzOtLrcQvWBmrvuMaiTMp8Vb88ZJszJ2Yobp9OMUZ2/k/RJTDgZ7jx+V9vDgOUWlpsJ3/Q8PXM1ihLo39DaMggp33e4lCcnAc0HjuDShMPAp0qG9rKjvuA16/GXjxErbtW2qBV68F1pwp+nb5XJddOG3IUF/afoX1Ra8OiGuc1+C27t+gXvC+m14jDJX1zwLE7LCV3deJ/9YKVokZyp5aviWOCnhmlDFnl3mMrfUlQ/IEc/h05RSaj0F49TqxrDZI6OlkpccXRYlQ6UBEEKECqyCyrmwdfvfB79T9rTgOAsfhjahONHerNykxm3Vl67B8TxE6hm1ETNo2dNVfBMEpX3rS3NWMJbuXYPrL07Hx043eYhcAgReN3Y1UXkr7xKRtBdADi+0QXJ0Z6DrxY7h64jX356OaEDtiM6ISKhU7K8qRZkuT9dVizcozgu/YRu7w1zZ34pYgdy2kOEMQAw+rIPLEu4eYyuJSE2Jx4D+nAjonvaza9gWWvPopUylic3sPbtlcjokP7sCjJd94iV1A/4pdZuGwxypmHgUrY9V3XCNxpqG1G7e99AlWb2cTyozQ33GGs1iQdt/y3l/kbSPS7ltOhvUGIMGrv2EURFgZN2Eyu4imSABf0CTxKk7Nxbc3AnSdlj86Z9z+heeADE4sEXQ/BhcakYA/dF+LP3Qvwl3dt+Hqrvsxo/MxxQ6JALDDlYdbu+9CLRTu0MYlATPvA+46qJwhNWq6KIgpvKYuATgmDEVpbxkmIGaX1WKojOuXBAckjvAqn1T7kqHmCcYDEAQB7VvvEUtLTYalpbBn62WzfFGU2iirBXKCiFSCIYh83/x9QPtzgcQZALy1CTFD3wMgyDcw6p1h2nrkOxYGEmek/XlrE+LHrYZt1NOIG/EKYlL/DcCFrlM5iG68BskxCnHQQzDraclCx9FFEHrsspsmxyTj2gnX4pmCZ1B8ebGsiXxOag7SbGm6X1NO5SsfBw4Om8OrdBII7A5/0T8Ouud6M6E4QxChgdmCiPg3GLqisTS/tHaZ//2ZFTPsMlPirbj5gtF4efFU7Fk2W3HukjJb9RxS7fw4iNlRvpmwgcSZv75Xhe2fmX9zZaDiTOJPfoIRj21AVJq3XhCVloYRj21A4k9+wnQcwhsqaexvJEEkQKN2NwELaBwQbRO7MRqi98/eOXCTP9BXFuhVxtfLMSEFK7uvc5vEq7HDlYeUqCg85HzEf4JvbwJ2rxbN9j0EL7/a7oI1sPzf9fDtQCl971/Zfa37XMQteNRMWwHHvt/47SNXPul0CTjZ0ql4DVqeYDwHxLXXwnn4A1jOukjt5dCN3k5WZvqiBLWrKkGEEZIgEqhRuycjE0ca3pcDh7ioOEUxSsdA4KPaAxsjQDiLd6zko9oRnVQOjv8SjZ3K18dxAGdtgsVWhZ6WbAAuxI14yW9td6rzFDZ/sRk5ad6dOiVPMqmT57259+Lud+9m6kApCWM3nHs9nvv8OQDw2keudFKKa98cl79RxcKptm7sP1SPC8YNMzyGHBRnCCI0yMtMgSMxBrXNyt9J9ZCaEIvRQ9kN5H25eUYm/r6nSnvDMIYlO1iLxtZuPPPBYeTKzF2+a5oH5k3A7S99olmSKo3yywsz8dR74nvA0h1Ra03DwgNvVqAgW7lU0AgDGWcSf/ITJPz4x2LXxhMnEDV8OGzn/5AyuwKgXzK8Nm7ciNGjRyM2NhZTpkxBaWlpfxw2NOEtQPYV5owlZf2Mmg4kMNxZ5Hze7sQRwMzlAYhdHnT1b6mLL3VIUizjc6ABm6wbUMBrf+6G2yx4yPaSwt2M3qm7uMidHVVcUYMZa3dh4dP78ZtXDmDh0/sxY8sQfDLtMSDR+z3ptDlwn/Verywz6c7wDwquF8skffbxLZ+Ujrdq2xeK18DqCXboO7amCHrQe4dD6+6R0t0gJaRa/Usmj8C0MUNpETKIoDjTh4W3YG7mXNPGc9gc+Pn4nyM1LlVzW94nzjhsDtw2+bbAxa4QQenudaeL7fq4qBbYbRakn7VTNpFBEqLWlq6FszfOlFSXoOD1Aty04yYse38ZbtpxE9aVrcMN596AVJv3e5IUkwR7tHf2mFQaufR8sful7z6+pZOece3xdwIzj9733cmA9peD4gwxUFCc8WZnZS06TPLckv4Gr502Wnd3+XR7LJ5clIP8CeZW0YQ7SvFKLkMJkF/TrNr2BX55UaZfZqvveyStZ5bPzWLOhGVZ07BQ39qlWipohIGOM5zFgvgpebBfPA/xU/JI7AqQoGd4/e///i+WLl2KJ598ElOmTMGGDRtQUFCAr776Cqmp2l+eI47KLcDe/zZnrOzL+0zTf7pO9MlS4/JngfihYgnkkDRRKPv8DXPOJYhIU7HcJOISgFoMxUeu8Xgv5i4AMmV8vSaSK6wvYmfn+aqZXmM6DoJzMTQVqN6L4taxit04LntnGDb9YicKh1S5X++4UdPxJ/C4ROnOcNYC0di/eq/3e9T7HrN0ZQTYPcHqhCSMZ9qSHb13OCRflFs3lyvltpEvCqEJxRlvSqpL3Jk8ZvDTzJ8iOioay6csx5LdS1S3ffiih5Ecm+zORMpJzcGOw8Ht7GgKkqtssA/Tk4DT+BquLmWfNc+OiU2dTVi6e6lfFlddWx2e+/w5PPKjR/xebwBe2WA5qX3ZYvmj8jFr5CzF51njDDvmv6gUZ4iBgOKMN2bPFQsmpcPCc7DwHBZfmIm/vqecqbX4wtGYfY7D77v0mweOmnQ2kYFaNphvhpJah8Gn3qvCxmtykBwf7X7NfzgqGR9XN8quZ1gyYc3+/JhtsE9xJrIIeobX+vXrsXjxYtx4443IysrCk08+CZvNhmeeeSbYhw49XE6geBm0+5QwUvF6nw9T1gLgqhflvbTiUkTvKVcXUPsZIHjcjTHZU8wogqA8MXNQFrsAsUTwfP5rZHDKHRblvL7kYM2O6mmq0a7tfusrOEfNAM67Asi8EOAt2neGeYu4rcc+AFtXRolvY7NxTEhRNM+UfMQsoy9gulY9GLnDQb4oRKBQnOnD6XJiTekazTI3Pfyr6l9wupzIH5WPR2c+6pdBBABDrEPw8IUPIzE6Eftr9uPQqUNw9cYaVk+xqelTTTtnOdTijJouY0YJiSAArm47nG2Z4KJamPapOV2r+F5Kjz1c9jByUnMw96y5yHXkwsJbYOEtyHXkej3midLzeuIMK4F2/JWD4gwxEFCc6SMYc8WWT2vc2UbL52bhVxdlyt7A/q8LMjH7HAdqmztwsqUTtU3tKK1qgNMlBM1kPZKpbWpn8qtata0SeZkp7vVLdBSvup5RW+/o+fywykNmv/cUZyKLoGZ4dXV14eOPP8by5cvdj/E8j/z8fOzbt89v+87OTnR29tXxNjcPbJmc6VTvNc+7C3BnGiHzQvH3rAWAywVsWwK0e6R2tjeK3RF9ScwAClb3eorVwDQhTifSYkKvESPPAacFcVJhFaq0tmPNjrp1y1HUtCl35/K9cxIorB1MHpg3AddOG43fr/kvPNS9Di7BO+NNEsH+Yr0ZfxpjvrG10Tsc5ItCGEVvnAEiO9aU15Wb6t0FwJ1tlOvIdWcI/fXTv+L5yufdpYqnu0/j3vfv9RJnnjr4FOzRdvx+2u+RZktDXVudqhC3v2a/qeftidE4Y2QfQfDeRzq22KGRh9CTILufLw9uL0W3Xfm99MwEy3Xk6jtJGVjjzB2zxmDaWcNw+8vlONXWrbhdss2KqWeZL3hRnCH6G4oz3hjpqqeF73fm5XOzcF6GHcv/eRAtHeLNfZcAPLO3Cn/7wD/7K90eiwfmZSHdHovapo4BWtGEH6u2fYEjDe26/KoCRc+aJi0hFne88onqdnpKBVmhOBNZBDXD6+TJk3A6nUjz6TSQlpaG2tpav+1Xr14Nu93u/n/kSOMmuQOF0yVg36F6vHngKPYdqvfuUNQShBbdX23v+3flFuC1G7zFLgCKQlbzMeD/rgfOmKK8TT8QSPeseK4DT1o3YBTn/3mSQ03Q4gB8P2QSBIYui/9uG8t2PI8UW9XPhuzBnEDV+8DB1+D87j3w0PZJGJYQg+goHjN/dhNuk+k4WYuhuK37Lsz82U1Bm3yN3uEgXxTCCHrjDBABscZjbkDV+14dV0+0KZfKBYLnuO98/w6e/OxJP18uOTGrqasJv333t5g8bLKpWWd6CbRLox4Ep/fNEKHHjo6ji9DTkg0OwHDrBNUui1I2WMvpGKbjeb43TpcTZbVl2P7ddpTVlrl9wJTwjEsffMvmtzUuLQEXjBuGNZedp7rd6svOozhDRASDMs5A+Xur2eVjEjsr+17L4ooa/PqVA26xS0Lpq3NNUwdue6kcOWcmkdgFcRXDMr01tnbh0ZKvmcY0uqbx3ba2me3zMywhBhdPzsCvLspU3IZD8EoFKc5EDiHVpXH58uVYunSp+/fm5uawChDFFWKZm6dqnW6PxYr5WeIfRWsQFiL7nwDOnAbn+LnoeeseREPQ75hRGfo+XkpIqvvCqF04JiTDgUbZCV7y+ip1naM4DgA8sOA8cPxaCK9eB/T2v/IcA/DusqiFlGKr+dnwpXKLWP7amxE4A8CeGLHbpKfpvdLxCrPTgWtuwZVbLsDI058iFadQhyR8P2QSHrjyvKCn1dIdDiKUCetY4zM3ABCzdAvXAlkLmMsH9XKk5QjgcsJ5eA/W7F+hW7zacSQMfLwCRBBEcav123tgsVWDi2qB0JMAZ1smAN4dZx6cn42ohCJZPzTPbDDBGcd0XOk9L6kuwZrSNV4Zfmm2NBTlFbkN6T2Ri0ssHD4pCp2F2el4clEOHtxS6bWAUY1tJkJxhghlwjrOQP17a7BKB5/54DDyMlMw+5w03PfGQUPC1baDbDfAIxlpBlzc2zFR7XXU8xobWdPIbZsSb9V1vOVzszDpjGTc/2YFGlq7NI9pJhRnIoOgCl7Dhg2DxWLB8ePeKfnHjx+Hw+Hw2z4mJgYxMWx3NEMNNbO/WzeXi0pwvI6FyEVFQNmTQPspjQ05tG+9B3d3H8LGnjCf5G1DgbYG6M024zggAw34c/flWBL1umIZn6dQlRIf7TVpOjwmzeKKXOyOugd3dv8NGVxftlwthmJl97WqgpP7nHrHzMtMYftseE7WlVuAV6/zex0cnNht8tbuuxTPodHjmvom6R8OyCQt3eEgiGCiN84AYRxrFOYGNNeIj1/1AnLOmQd7tB1NXU1MQ/5iwi+w7dA2nOo6pbrda59vxuJ/P4byrnocTw8N70cjJMck41TnKdOzzbzLFqPgbBsjxpk2+ThTUv057DF2NHV6v0+C04bO2svQ05INwAVXtx1cVJNsdhoHDmm2NOSk5qCkukTR3H7p7qVeXRiBwAyDN5R8jbMdQ1CYnT7giwGKM0R/MKjiDLTXNBuv+QGS4qw41a5c0uzJjdNH4c1Pj6GhVX17DkDRPw6C5yo0tw1lUuKtaGzt7pdMsyX54/Fy6RGvmw4OHyFIS/TSwuiaRmlb1vfWc00zd2I6CrIHJtZQnAl/glrSGB0djR/+8If497//7X7M5XLh3//+N6ZNmxbMQ/crLGZ/K7dWwjlEPij6MfM+YPZyYD5LN0cBce21OLv9ANvYQUTw+F8fHJA4Api7vu93A8yfdSHKcjegjvOelGox1C0ScRB9Raw+n3yhd7UiTc6vnJ6MGZ1/wdVd9+POrjtwddf9mNH5GLPYBYgptgBUPxsCfNoCqzQ2kE55hfVFxfLGVdu8WwxTWi0R6QyWOKPe9KT3seIiWAAsylrENOTtk25HUV4RfjHhF5rbHu9uQnlXPU6EaWtsDhwcNgfun3q/+3ej5I+cA7hsXo/5li2qxRlJnPITuwSAs3iWifK9Apr89QDAsrxlAKBqbi9AwNrSte7yRjMMpz3jFsUZItIZNHEGbGuaVdu+wA3TRzONtyR/HFYsyMZDl6qXQEvjn2rr9rohHU5IRuZ/vCTb/btRCs91qO4vHWtcajx8vxd4rmn0il2+xzS6pjEjztCahjCLoJc0Ll26FNdffz3OP/985OXlYcOGDWhtbcWNN94Y7EMHF5dTNIw/fRxfNsfheJMLSvqh2+zPeR6mJWaoG9cnjgAuulv8d9YCYHwB8LV2Kch4/j/6r8Fk2gUrbJzeOzK9k1XhGvF6+Rf8y3UYGT9mLJB5IYpHzcFzL7/sLuMrdZ0DV285iQCgUcZk93hzJ27dXA67zeqenF3gsd+VpXnclHir190Kzzsr+w7Va5aL1DR14LkPqnDDBZmwaDQ24DkgA2K3SblzM9NUkiDChUiNM06XE+V15TjRdgLDm2qQ03wMynKT4G5ksvi8xdhcuVk1yyvNlobFExcDAM5MPJPpfHba4jCyp0ffRZiMyxkF3qLvHDzFofxR+VjPrfcr/dPDNVkLMWf4Utz5xj/ARbXA5VO2qB5nPsKIietlxSmOE0WvmLSt6GnJAsCjpyUbHUcXIeOsHWjs6rNFSLOlua+nrLZM81pq22rx4ifv4Nof/Dhgw2mzDYwJIhyI3DgjeGXNuASBycA8d3QKkmxW1cYV6fZY3DF7HACx6uDH5wzHv78Mjs/kQOMpDBVmp2MTz/mV8g2Nj0Y9o5h3/fTRWDApA7e9VK54rAWT0nH7S5/4RRO5NQ0LS/LH4ZWy773O2ciaZv939eA5LuDGBhRnCLMIuuD185//HCdOnMDvf/971NbWYvLkySguLvYzfgwrfDxUzgWbv1Jda7fos/LqtQpbcKLwI7UQr9zCJHYBQKHlIx0XEBxiYWAhlJjRJ3YB4s9z5rnFRMQNBTb/jGGcEcCo6QCAwvPOALhFfoHGYY9Fe7dTNjhLAUEtcPsipfi+e88sfFzdKJtiy2rMuGrbF/jbnio8Oek7TGLYXq3bZLDMRInQwPfLKXkJRGackfVjGpmBovpG5Le1K+94+jgsvAUPTn9Q1iNKEn+K8opg6Y01R1qOMJ3Ty/ZE8R++bQj7EY7XH2c8xSEA7k6TkpiYFJOEW0puYSpzdNgcyEnNgcVhAc9dKcaZNvY4E2WrQlO3sjk8xwGctQkWWxWcbWNEk3v+fJRc+Vt8evITUfy0DRfPoff9O95ax/Q6rH73Nfz1bQ5zsxmzzTWgWBO5UJzxJxLjjJy/UlIcm7/SydZOrLnsPNyyWVmQ8TQTL66oiVixC/AvI5Qr9548MglZvy/WjDRS10ELz+FJPkd2PfPAvCys2qaeice6ppHWM3fMHoc7Zo9T/NtnXdPc9j/luCJnBNO2WlCciWA8kocwJE1cx/PBqSLoF9P6O+64A3fccUd/HCr4KPkrQdtfKTUhFugEEJfi30kxLgWY/1if8OMuX1FnANcdfvCczsRV2zDgzgNAVLTPQBYg80Lx31Xvs42Vc73XH4lcoHG5BPzi7x/qO0cFPIN5dBSvePeh4XSn7ONy1DZ1YPWeU3glWntbtW6TwTITJQYe3c0PBhGRFGcU/ZgsFixNHYb1dSeVRa8hfYsvOY8oe7QdK6avcIs/TpcTr339mvoJhVCg0XsaKbEp2HbpNkT7xBkLb0GuIxcAUFZbxuzptSxvmVtoMhRnolqYjsNFtfjEmSj3+fpy8hSbT5A15QOcOJqJv3+QzbS9FhRrIhOKM8pEUpxR8ldi9eVKTYhFU3uXbJaX3WbFmsv6GiRJ5W2RytD4aLx7zyxER3lX+vh6P+07VM8Uaa7OPdMtMin5JAaaqeuLpzgZ6Jqmqb0bf//gsCnnRXEmQtFowGQ2QfXwijjU/JV6v5nK+StxAEYkWpF35Gkxu8tX7AKA9kbv3zVK29xjh8YaxBhtJ4HvNQSo04wlJ0PH+D3kW+t9slV5oubhwlS+Egv4vZjKVyp6ZElotaSVSIlnUK96EQCUuc7BcQxV7LXpEoBjgnK3yZR4K/IyU5iPSYQP0pdT3y84klFocUXNAJ0ZYSZOl1PZj6l3wl87NBlOv2c5OBNHoCzGinWl67Bk9xI/sQuAX5ljeV25dmmfb6AJo8DT0NGAT09+qrrNiTa2rINrJ1zr1+1QT5wBXOAsbIKX0JPAHGfs/Hi4uu1u43w1YtK2AnCB5wLzl5EyEIjIguLM4CBQf6WUeCverqzFLZvLZbOImnweM1ucCTXqW7vwcXWj5nas2Uqjh3l7RMp5V5mV+TQ0PpopzgD61jQAAo4ztKaJUKTkIV+dQ2rAVLnF9EP2S4ZXxGDAX4kDUMCX4lH+ZVje1VhUFBeJ5Xy8hV3oCXe0rnMIY6o4w3ZKdwkK+FKssL7g1ZHxmCBfopoUZ8XGX+Rg6llsZokOO1tbeQknePy+61o8Gf0YBHDgPL6OyHWb9OWPl2QP+rKDSETLRJaDaBQ6J8tB73+YoyVACRyH2qgolMfGILdDElc4lNjisMaRjOM7F2seY23pWswaOQsW3sIs9oQzWtc43MbWQXnWmbM0t1GKM1EJFYhJ2wreqt09024djievuRpTzxrOFmcS49F5fD5iR2xW3c63XDIQPLMBiMiA4szgIVABqqG1G89qZPB4flYGQ1kayzWyZiuxbGdG5lNKvBX7lv/YLzNNCb1rGpdRRbUXWtNEIJoNmDhvPcQkKMNLD4wilKe/0s+HHMCm6McQ16G1b5/hMAB2oSfcGZImfvir3gcOvib+dHnkLoyaLqY4Kt4j4Lz8u9TIy0xBuj3Wa6QCvhSbrBvggHfWnVSiWsCXSkcBB2DN5efhgrHDmCfgvMwUZj8EiR2uPJTlbUBHbKrX457dJuVYfGEm5k7M0HUsIjzQ+nLqaSJNhDesApRnt8SSYRlYmjYMx7ubNfcTIKC2rRbldaLvCqvYE84Mtw2H0+VEWW0Ztn+3HWW1Ze6OhQCQk5qDNFuaYudGqcNjTmqO5rHk4kxUQgViR2wGF6UudnG9/z14wX24YGyqrjgznD8f3Q0XMG3P9ZZV3nTBaN3xaUhMFJ64hi0bgAgvKM4MHlgFKL3zg4TvZ2UwlKWlJsTC6RKw71A93jxwFPsO1Xt1GATk44MnUudFlqwmrbHUkNY0D116HrPYJR1T72fCSJwBaE0TsWhWsPnoISZBgpceGEWoX82bjseunoyXb87FattmrywdLY6Vvi5OkG6hJ1LpFapa64EN2cDzFwOv3yz+3JDdl87IW8R6Xmkf3zEAb6N/FSw8hwfmZbnfDR4urLC+IP7bZ2jfElXW0hK5Y954wWhd+wCA8+z5OHD5HlzddT/u7LoDV3fdjxmdj6k2RXjrsxoqN4hQWL+cDoa7qJEOqwA1PP+PwOV/h/O6LViTlqG7NOV4q3gTJic1B6lxqRpbhyeSUNXY0YiC1wtw046bsOz9Zbhpx00oeL0AJdUlAEQ/r6K8Ivc+vmMA3t5dalh4Dg9cfDYstkOwJh6AxfYNYtLEeKZVCZpmS8P6mev9yiZZjrlifhacp7W7CgNiuSQAzMlyYOM12iKeJ6c7e7BqWyXFmgiE4szggVWA2nhNDl5ePBWPXjVJdzkbALz9eS0AUShxJLJ5DYYbkkjV2NqJGWt3YeHT+/GbVw5g4dP7MWPtLq+5Upqrpf18xwHYs2d91zR66M81jZE4A9CaJmJhrWAzudKNBC89MGYbnTutUKyzjvoKHIMPlyfRla/jojU7UVxZ5yH06IPFx2Ng6X39si8HXrtBu4Y3awFw1QtAos/EnJghPs5obldcUYNV2/pMM/P4L5HBNfiJXRI8B2Rw9dg6n8eeZbMN39G+Y/Y4JNnY7m543t3JGzMc1Qk52Oqajv2uLMUyRgny2IhczEyDJ0Ib5myjSTcA512BcptN24NLhrWla1FSXQILb8GV46/Uf6JCaMca6fX7aeZPcfe7d/u9RnVtdVi6e6lb9MoflY/1M9cj1eYt/ukVoUqqS7D+yxsQN+ppxI54BbZRfwdvbdYUu+7NvRfFlxfrFrskCrPT8ZdLLwPnTFJ8XwQBcHXb4WrLdMeZqWOG6s4SoFgTmVCcGTywZhpNHTMU08YMhcMeh4bWLt3HeW7vYWz/7BgsPIeFeWcGdM6hzIJJ6bj9pU+YvO8Ks9OxaVEOHHbvvyO9IpTvmoaFO2aNwcuLp/bbmobiDOGHiVZFeiDBSw96s40MqJPDuGaMPP2p+EfuygWm3qZrf5cgphKH8kIEiRnAlc8BFa9BuYYXYg2vVHaStQC4qwK4/i3g8r+LP+86qC52eZRKlu76J27f/BGON7W5zemn8xVMp3tuYntANeQWnsOay87TnOh97+5Id4JY30ppu5VbK/3SqInwxsw0eCK00ZttZNSD61TXKbfgc2aivoWIIMjP3KFEqi0Vj/zoEWyv2i7fAKD3sbWla93ljfmj8rHj8h14puAZrL1wLZ4peEZThPIsYdn44RtYunupIQFyaOxQpgwyNeaedwYemf17WXFN+k7QeXw+AN4vzuiBYk1kQnFm8KA308hoVp8A4LaXPkFxRQ1GD4s3drIhzJAYC/6y8AfY8mmN2mrGb64szE7HnmWz8fLiqWJFEIMI5RlrHiv5Rra5hBbj0hLcpvdG0bOmoThD+GGiVZEeSPDSi55sI4PqpOQBtnJrJZzjf6pr31oMxaM9V4R2Ey3BBZz4Sn8NL28BMi8EzrtC/Km2OKjc4lUqmffe9SiLuQUfxdyCV6L/iL9EP447o/7Jdr4mqMzSHZ10u/KdUbm7O4XZ6ViSP475OKweG1o+A0RoYWYaPBH66Mk2CtSDa23pWqTE6lvACj12dJ3ID+k4IwgCvjv1nXoDAB8/M0AUHHMduZh71lzkOnJVRajiihqPEpZyPHHwzxAM3m0yy0vtJ6Pn4NGZjyLN5h23hB47Oo4uwnD+fNk4s2lRDlLi2X1WWGINxZnwguLM4EJPplGgWX0rt1Zi2JDIK2k83enEii0Vhrzv5DovKuEdaw7g0ZKvDd10Mis7U2tNk66wnqE4Q5hpVaQH6tJohKwFYveA6r1iFteQNFGJ9H1zJBWzuQZ67ofXIanvj9x5HqapjsEBtqH4+gf34ffvNKDUdQ4u5vcHcHH9QEstsPshtm2N1PBK7U59Xq9knPbbVFqbyC/cOPH9M0llLsxOx5wsB0qrGlDX0iEGfwE42dqJ1ATxrqlcwDNyV0ztblxxRQ1Wbq30CtDp9lismJ9FRsQhjPRlwfe9c9B7F5Hkj8rHrJGzUF5XjhNtJzDcNhw5qTl+AoxUAlnXViebyaSGJPhwHKc6BgcOybHJuOf8e3DyVCwe/L92RCV+FtD1BZsT7Sew8dONbNsayJIrrqjBrZvL3a+WxVbF1IHRFw7ia89iiM+K52fneGsdTp6KgZ0fD0divGKcKcxOR3u3C0v+94CuYynFGooz4QnFmcGF7/dSpe+iUvZfbVOHIaGlpqkDEKA6Bgfxc/bIFZOw77t6PP7Ot0Yuqd9paO1m2s5olpxvrDGC9NqamZ3p+dmpbe5Aw+lOpMRHw2GPozhDqCMlDxUv805+ScwQxS5GqyI9kOBlFCnbSGubwrW94gsbJ4VElLrOcf9e19rtMQYHbxGn9/e8X2JsSiJS4k+DaxEFs9BGx7QtdXHUEhclVNqdyolaHNdbniP4Ph8clVm6o+OJ0yWgtKoBb312TPbLhpE7Mkr7KAVOqVbeiIkl0X+wfjklIgMp20hrm6K8IizdvRQcON2iFwDUt9crjiGVUf5uyu+QHJsMoA7Dhx9FY2tol6foeR2kLo5a4qKE0yVg5dZK72jc2/lQD3oN8fUg99mRYo3S3OFINCfWUJwJbyjODC7kvpfKbbNifhZu3VzutxJh5WRrp+IY0ifrgXkTwPMcxqQOQUq8lVlMCgekLo56/q7kYo1egpmdqbamoThDqMKaPGQSJHgFG0nF/Ne9QIuy8Z6UafRCTz7m83sxlGtGvZCIsa0AJs6TV0LjksWfux8CD+AJAMdiUrCq+1ocE5LhQKOiIXvo05td1dbbxdHjuttj03BkygqM/dE1/pO3ZrtTmSPJvUZBVJklnC4Bj+/6Bs9+cBin2vuCuu/dCT131tTu4qgFTqF335VbKzEny0FfbEMYli+nxOBCKoFcU7rGkH9UfUc9rjnnGtkx0mxp+GnmT7GubF3f48OAGHsiXD1x4CztIV3aqIaUXSV1cfS87kTrMFwz5k7ckvszv/mwtKrBr4RF6nyohzRbGpblLTNsVM+CtPgoqazFGweOei0ggxFrKM5EBhRnCF+Usv9YOdnSiRsuyFTMIFwwKR2rtn1haOxQRporG1u7MGPtLq/rS4mPxs8mZ2BOlkNW/JKLNXrpr+zM/lzTUJyJEFiSh0yCE4waTvQDzc3NsNvtaGpqQmJi4kCfTmC4nMB7jyiW8gkCcBqxSOBkJrbEDDHLy1MJrT8E7F4N3/ssQu99k9OC/Fj+mUyhgMK9num/Bvb+N3yvUSrPvs96L2b+7CbvSfzga8DrNxs6C9eFd4NPnRB0lRkQ70wU/eMgTrX5372S3h7PuxPSnQxA+c6a3H6e7DtUj4VPa5e7vrx4Kn3R9UHvXblQJqLmVZOIlNdEylJ658g7ePGLF3Xtm2ZLQ1FekV8ZZWNHI+5+9275jCnpm2UYoJS5dsO5N+C5z5/zuz7pm1Fc441YNWeh15z65oGj+M0rB3yO4EL82LXgopoUTeOFnkR0HLsK1184FD+dcLZqFpkZyJV7eBKMWENxxjgUZyKbSHlNpM/pzspaPPPBYV37psRH44+XZKMg2zuDsLG1C7e/FFjZXjBIjI1Cc0eP4f2lv95fXpSJp96rUr0+uVI8+VjDxrVTz8Tc8zL6ZR7p7zUNxRnjRFKcAdjnVTKt7y94CzBzGXDVi73dCfpoFuIgABgCBRW/+Rjw6rXAl9tEJfTcS4Hy5yBbttf72BA54SwU+VGRfAOAK55T7OIo/V3e2f133L75I++WtQEYzPNnzWQzxA8QaaKXCwyAfGcSJXNRT7RaGrP6Bxj1GYhUfM1CFz69HzPW7qJWyUTIIZWx3Zt3r6xxuRrH245jye4leOf7d9ym7TmpOVhXtk65PDBMviPdOulW2QYAal0cJdGqLeEfuNUnzsiXjPO9HRD9uyT3dUhcAGfbWMw586eahviBIsUZLTNlwNxYQ3HGGBRniHBByv77/fxz8aRGMyZfGlq7cNtL5VhX/IXbsD0vMwWrtgVWthcs9IpdKfHRXr877LHYeI1yF0dPanpL8bRjDRtzz8sIuCMjCwOxpqE4Y4zBHGeopNFkNJVTn5rV0jpg3Pt3gWPJvNr6m759Vcr2lIYREILZXaOmAz+617+GV+MaeQ7IQD1y+S9x3xsxaO92wZEYi7xR02DR2ShAAAfORHN6NVjr8T07k0h3J4ya3kuwBk6zurhEAuQRQIQkDL6Gnsbl/z7yb7z0xUtMvlYr963ErJGzYOEtoum5gRLJUCMnNQe/mvgrP48urevjOICzNsFiq8KDW+KQEGvFydOdGDYkBo7EGBxv7vR6RXtastFxdBFi0raC8zCwF3rs6Dw+H86WbKSbbBwshx7fF7NjDcUZ/VCcIUIRFl9Dz7ni7c9r8NzeaqZ556/vVWHSGUmYOzHDlLK9UCAl3or9y3+Mj6sbvdaAeq5PAHDfGwfda5ofjko21CygP+IMMHBrGooz+hnscYYELxNh7hYh1axWbkHex3cBaGG7U97eABzeA7Tq7ygFhOjN+C/fAjjef8HG2J0xFaewv7Xb3fEj3R6LJ3KK8IN9v4F/qaR/BY4gFbYEoQWqHHoDu+/diUB8NaS6ea3jN7Z2aY4VaSmxcpBHABGSVG5R6Gyz1s9zUMr4+rj2Y2YT91Odp1BWW4apGVMNdS8MRXYd2QULb/FbsDFfX1QLaps78Yu/feh+KMlmdc8DvqJXT0sWLLYqcFEtEHoS4GzLBNebUB8M42BfjCwgzYo1ZsYZIPJjDcUZIhQpqS6R9XMsyivy8xyU5orSqnpdosz9b1agIDs9YrJwJo6w4+PqRr85Su/1NfisaRZMSsdT71UxNwvg0D9xBhi4NQ3FGX1QnKGSRtNQKh+olUlRBSAuWl69TjRl10PV+wGV7YUcpU8Bz18sGtNXbul7nPEafTtS1jZ14LJ3huGTaY/5l0rGpYCL877jwSVmiA0BgmhO74newGfm3QkLz+GBeRM0t1u1rS/tWI7BkhKrFcg971gRRL8gxQ3f7NfmGvFxzzm0l5LqEmz8dKOuw5QdLwMgdi+MBF7+6mXctOMmFLxegJLqEvfjrNcnZ0jf1Fu+YbdZvR5Pt8fiVxeNQar1XPQ0T4azbQwAXrPk3EyMLCDNijVmxRlgcMQaijNEqFFSXYKlu5f6Zb/WtdVh6e6lXnOoRHFFDR4t+UbXcRpau1Fa1RAxWTi7vz4pO0cFcn21TR146r0q/PKiTL/yvySbFUky8ac/M3UGak1DcUYfFGcow8sUdCunLqd4h95IxToHMRsqLkXM+IoUpAWbJD6Nmg4kZkBornH7knniEoBaDEWp6xyvx6XX+7byM7DnnoOwfL/Pu+wHMKUFqtG7AXom+ySb1fSU5OT4GM1tfNOOPRlMKbHkEUCEFKpxo3fmKy4Sy9575zSny4k1pWv0H6v3EDmpObDH2NHU2aS+fZggLdjWz1yP/FH5yEnNQZotTbGsUTSat8PZlun/HMRYE2e1YOPNOX5lGPcWTgj4jjFLSZEcehcVZpe/BBpngMETayjOEKGEFDPkMoKF3pqItaVr3WXv4j7iGsgIdS0duHhiBpJsVkUPqHDDd47S05XQFynObPm0Bu/eM8uvXBKACXHGeHaTnlhDcWbgoDhDgpcp6FFOp40ZqulPpcqoGR6jRhIyC7bCtcCr18EF71REQRADwKruRXDJJCm6X+/qJkwbI9PuNMAWqMylqzLoCXyn2rqxs7LW1Mk2kElvsKXEkkcAEVJoxg0BaD4qbtc7xxn14MpLz/MaNlKQW7AV5RVh6e6lEKTAIm3be93djbkq44mxhuc5XDJ5hNdzgZSfA/pKinzRu8BaMCnd1Dk70C/XgynWUJwhQgmtmCFAQG1bLcrrypHrEOfGQDy4IvFzLTdHrZifhVs3lzOXJfqOV9PUgY+rG2VjSiBxJpD1DKAv1lCcGTgozlBJozYup1hGePA18afL6fe887v3sIDfi6l8JXi4FIeqa+noHe9dY+cSlyIuZKr3Au2Nxsbob6L0/PF4LNhcTiAuGdzUW+GMivfaiuPE//9k/TsK+FLF0YKhVOstXXW6BOw7VI83DxzFvkNi+eqK+VkAtD3VpMlWKx1XD4FMeoMtJVYK5ErvE4f+MwYlIhuny4my2jJs/247ymrL4PSJM06XE2U1H2J7vA1lsTFwKowDwO1/6HQ58fIXL+s+l6ToJJyfdj4AcfHT1BUZ2V0Sngs2AJg1chZum3Qbovk4r+2kOBOTWoL4sWsRlVChOKbZscZISZHnZ6i87iM8cPHZ4nUwHG/LpzUhE2eAwRVrKM4Q/YXv91Hfv3mnS8Dew1VMY0n+h06XgBf3se3ji/S5Lq1qiJjsLgnfOWpOlgN35Y9DXLRxr2Cz44xuKx6or2m0oDgzcFCcoQwvdbTMgXufn9F8DDN6O9EeE1Kwsvs67HDl+Q13TuNuYMMfjWd3zX9MzHxiNHQPCXoMTNBfbgPe+KX7dbLC32weAJJxGpusG3Br912yr7fZSrXeuwFqd042LcrBfW9UoEHFUFGuq0mgaN2N4SC2Apab9AZbSqzaXTnps9hfxqBE5KKVyeP1fOow8fmeHhTVNyK/rd1/wCFpKKkuwYP7HjRUirhi+gp3qUqkmNbLsb9mPxo7GrGubJ1mFhwX1YTYEZvRcXQRelqy/Z43M9YYKSlS+gzdNu9XeHl3kmqcAUIrzgCDK9ZQnCH6A61MHun5uu5a2EZpjzfcNhzFFTUo+sdBQ2KVp7F6JPwdK/HBtyfR2NqFVdsqDWfBSZgbZ/RnN6l9hu7KH49HS75WPSbFmYGD4gxleCmjZQ789gOyzzvQgE3WDV6ZRxyAq4ccwPh3bzcmdiVkAFe92GesHkmm9XJ8uMnvdZL7E+S43qBpfd4rsy5YSrWeuwFad04AMBkuAuZOttKkB/i/plqT3mBMiS3MTsemRTl+ZqH9aUBNRC5amTzrP1ov/7zFgqWpw1Bi88xK4oDEESjh2rF091LdYldaXBoenfmoV7lcpJjWy/HUZ0/ht+/+lqnkk+udDmPStgBBjjV6SooA9c/Qi9+twuUz2O5Oh0qcAQZfrKE4QwQTre+jq7dXup93tmXC1W13l3T7woGDw+bAiRPpuHVzuSGxy9dYPVT+joOx1H/8nW9x20v+r70eghFn9GY3aX2GmtrZuiFSnBk4BnucoQwvOTTNgQHse1z2eZ4TDdVXWF/Ezs7zIYAHDxdWWF8A16MjlXPiQiDpDPEQmRcCo2f0Pddr6G44U0yJ8QXA1zvMHTPIcByQgUbcbvkn/tt5WVCVataJurapHet2fKV55+SRKyYxjWf2ZCtNer53ahwadfuB3k0JVwqz0zEnyxHRLYuJ/kcrkwcAnq98Xv55jgMnCFg7NBmz2tph6Z35nAUPYU3ZOtl9lJhz5hxkJmUiNy3X7ckikZOag+SYZDR2mldCf+GIC/H+0fd17TNx6ER8Vv+ZaedgBI4DOGszoofuQld9ftBiDWtW3Ym2E0zZYDtqnwJwF7Tub4ZKnAEGZ6yhOEMEA61MHgB4+v0qj+d5dB6fj9gRm0W/XI+PH9c7692Tey9WvCz/HVeJ3/x4bG85m+htOPWsviyfvMwUpMRb0dBqXlljks2KSydn4Nm91cz7TBppx4HvQ6uEP1hxRk92E0s22JsH2NajFGcGlsEcZ0jwkoPFVF5Q9uriOSAD9cjjv0R1Qg4em9KCuPdq9Z3DN8V9Pl3vP+xdSslbgILVwP9dr29MtcNl3Y5xuT8NO8FLYmnUa/haOAOfJVzEbLaoF9aJuqG1i+nOCTgM2GRrZNIbzCmxgRpQE4QvLIbyLpU4I3AcaqOiUB4bg9zoYUDhGpSnpOsyqbdF2bDzyE7gCPAUnvIzRbfwFtw35T7c8949zGOqMcqWhRuzb9QteA202OVJ9PASuLocGM6fH5RYw5pVN9w2nCkbrLGrDsOHH8XJEyPDIs4AgzfWUJwhzIbFUN7XVqmnJRsdRxchJm0rOGufAJRmS8OyvGWI7/kBapr2M5/D7HOG49WP/uM+j8ff+darnNLCc/jD/Gzc8con7BemQro9BnuW/RjPfaDPWyzUxC6ATbgxgp7sJpZssPrWLqTEW9HY2k1xJsQZrHGGShrlMMkj6w+zh2HPstnIG96jf2dfU3qplLJyi/i7zZwPqyAAPQKHueVT8MlX3yI4Sb39AAf8xf4K9tzzo6ClZbKa/qUM0W6VCwAnT3cGlI4bKNKkd8nkEZg2ZijTcQZ7SixBmIVZ/lgnZhUBdx0EshboHrOtp83rdzlT9KFxbLEm1qL+BVoQgKqm/6Dkq2/dmQKscL3/saBUimMWHAekZ+7Au0GKNTmpOUizpSler1RSlJOaw/x+X5ab2Luv71gioRZnAIo1BGEGRkvIelqy0frtMrRVL0b70atx05i1KL68GPmj8nWPuevLE5rG6EMT2L43x1q1l601TZ3YUVGL/zS2aW7rCQcxYSFUSLZF4d17ZgVlrtNjYs76fl/a26mY4gwRilCGlxwmeWSNHzNWnD1NGa83cbS4CDhnnmmiHMcBURBwq+VNTN7/uqw5fDjAAYhprwW+3yeWgAaJq3PPlDVm9JzQ7XHRTGOlJsRi2pihhtNxB4rBnBJLEGZhlj/W8FEXilm/JowpZ4rOKqpolVFKJYEvVT2kO8gwlWgK+tu9G+VU9wl8evITvxJQM7DwFtybuwy/fXep33OSCLYsbxksvIX5/c4fPw4/SDkjrOIMQLGGIAIlsBIyHs62MQCAqelT3E0y9Iwp2bz44muMbrYx+L2vf4bTnfqSDQQE/4aJHhrbevBxdWNQsnEsPIcH5mXhtpfK/Z7zFahY3+/8LAdyM1MozhAhCQlecrg9smqg+BWa43tnRoXkzcQMcRzW8ZgQgOajYsmlycb1N0YVQxACuLthiQGcnaaekyGC1MFSrjuJJ54TutMl6CpVDMfJdrCmxBKEWUiZPHVtdYqCDs/xEARB9nkOHNJsachJzdE1phaepui5jlxmUaWTcf4P5KZKVkoWKhsq5Z/k+vdmTbA6WIqxhke7Sywp4mVKiqSSU6332/MzYnFYwi7OABRrCCIQtHyKAPF7v6Bww0CuFI1lTAk5sUvC0xidVVTp6FYu8/dEr9jlSbSFA8dx6OxhO1YwCVaHwOKKGqzaJh9LfQUqPV5XFp6jOEOEJFTSKAdvEf2yAMgnZ3LAtDtUngdQuMZ91119PA/iktnO76vtfSKaSSRzrYGl8oaC2AUEpYOlUncSiSX547Bn2Wx3cDDSOcRoOi5BEOGJhbegKK8IQF/mjoRUwnd91vWKzwN9mT4sY0rYLDam83vnyDsARFElMTqRaR8WuACmNkWxawAIRgdLz1jjWVLUcfRqtFcvxpJznvXqoqn1GQK8PyMUZwhicKH1fZQDsPjCTMXnAfnvq0pjSiTZrLj5gtFMHsPTBwAAT+tJREFU51jX0oG8zBQkxVmZtg82XU4hJMQuIDgdArXWNA/Mm+CVjaV3TUNxhghFSPBSImsBcNULQKJPCmZihvj4T1apP5+1gG082zBg6m3A9W8BVz7Pdm6fvSr+dIto2ijdZHEJQKMwhHmcgcIlcKqpxoIA1CIFzpHTGAZzAlXvAwdfE3+6nIqbqnUnAcTJ/pWy7/0eV6oLT4mPxsZrqC6cIAggf1Q+1s9cj1RbqtfjabY0rJ+5HkvPX6r6vKf4oTWmPcaO2yffjsdmP8Z0bm999xacLicsvAXXTriWaZ9Y3qY4T4dSqYgagkasgQCkxnln1snhdDlRVluG7d9tR1ltGZwqcUbcXi7WiCVF3c2T4Wwbg1VvfdXb6awPpfc7VeUzQhDE4EHLp2j53CzdPkZKYybZrFiSPx4f3z8Hsyew3YAeNiQGFp7DjYwCGYuPVyTAAfjhKO1ECKdLwL5D9XjzwFHsO1TvFyN8t9Va06za9oXfGNL7nZZIXldEeEIljWpkLRD9sqr3iqVyQ9LEzCrpjrrW83rHczmBmASgs0X9vNpOimNkLQCueB54/UaVrpEcEBUL9LT7lZIIgvj7e87zcEnUPvbXZQDY0HMplkT9w69NMtCXir2i6zrcUN2knpZauQUoXubdhdOzA6YPLN1JpJRs3+MWZqfD5RJw/5sV7nbL9a1dWLWtEjwPChAEQSB/VD5mjZyF8rpynGg7geG24WIJWm9c0Hpe75hOlxPx1ni0dreqnldjZ6O7rHHxxMXY/OVmNHXKd7HiwCHeGo/T3adl7/iHi9jVUTsPQo8dsSNeUow1AHDZqNtVX/+S6hKsKV3j1UHRtwOmL4HEmp6Wc3H622Vo6/4CXFQLhJ4EnLZOQM+Ec9UvmCCIQYGWdYYRaw2tfUqr6tlOrndevWP2ODy79zBOtXXLbsYBiI+JCqhcMZwQAE0PLzm7lXQVz6xA4kzfFh6/hUtwJwY9JHhpwVvUTdC1nteLoH4X2I3kVZX9MzEK/N/1SgMCPe2yixCut24/1/IVjgnJSOcaQ9CwnkN7XBo24yp81XUmVlv/hhSc9tqiEUOwvPu/sMOVh7lq9e6VW8ROl773NqQOmDKZeaz183LbFVfU4PaXPvG7kyJ1p2G9K+J0CWFXD08QBDsW3qJqgK71vC9Ol1NVIHMp3iDxRvKpsvAWPDjtQSzdvVTWK0qAgNPdvfOywtQkOG0QhCjw1mbm6+gvOHCwW4ehuWMmmtqd6DjKI8bxD3BR3l2+BKcNnbWX4YzJeYpjlVSXyL5OUgdMpawro7FGKk8RjzbG/fhxdOmKMwDFGoKIZLR8ivT6GKnNF06XgOf3VjONc7K10338NZed5zGfeSOA3ZsryWZVFM4GGiUjfznU4oL33N+H2hrDnDjTx/HmToozRFhAglcguJzs2V2AdnZR9V6gi7GNrqdX1bk/A7gX/cdmgOOADDTgm6xfA5WPwwUhhOpcxQkwbv7D+O/oXPziby7s7DwfU/hKTOMrAQHYJ2ThQ1cWXL1nrVjv7nKKr49iGPXogOnxHrLWz/tup5Y27NudRm2i13v3hiCIyENLwPJEK7uovK4c7T3tTMf19KmSSud8x2aB4wAuqg0/Sb8OO2teNGyoHwwkr6sVF9yHuLzJ+MXfPkRPSzZ6WrJgsX0Hi+0QAMDZdlZvxzJeMS44XU6sKV2jKAr6dsD0xEisMSvOABRrCGKwo0eI0JovSqsacKqdTXDynNOk0jm1JlFaLMkfh7MdCbhls38Hwv6Eg/eKQ3olH1+Yg5qmdqza9oXmGMqxxtjcT3GGGKyQ4GUUOfEqIR344Y3A0DH+AhhLdpGzi+3YcSl9HSAlPMslW2pE8aaNMZ0YwLgJk4GkX0PY+ziA0DBrFMXANUDWAkz16Hy4z5WNfa5sr03lOsl4Ub1XQwz06IDpkbGnpzuJJ4GnDRu7e0MQRGQhJ2AlxyTj4rMuxqwzZ3mJXyzZRV2MccYeY/fzqfIslTzeehzrytahsbOR+Vp+PPZcjEi5Ac9XPs+cZRZsPDsfenfY5eFsGwtn21j3tlpxpryuXFUM9O2A6YmRWGNGnAEo1hDEYEdOiEiJt+LSySOQn+XwEr9Y5gtW0/ckm9VvPvUslaxt7sCqtz5324KwMHpYPAqz0/GrizLx1/eqmPczG7tPlplvN/e/7anSvbaQMDr3U5whBiuhk8wTTkjila+A0lID7H4IeP1m4PmLgQ3Z4raa2UUCsOXXQEst2/Gn3CKfSSaVVyak6xK73Ne09y/gB1Ts4kQT/8ueBq7bAlzyhCgCVr0PC1y6Ox96cZoxI8FnOyMdF4HASiEB7TsqgHhHRc2ckiCI8EYSsHxFlMbORrz4xYu4acdNKHi9ACXVJZrZRQIE3L/nfhxvZZsLF01YJJtFJpVXpsWn6RK7pOt59vNnB1zsSolNweoZq/H0nKfxxxl/RJezC2W1ZUCAcUYqAdVCbjsjsSbQOANQrCGIwY5S176G1m78/YPDWPj0fsxYuwvFFTWa84UA4J7XPsVXNRpexL3cOD1Tdj6VyisdibG6xC4A+OZ4Cx4r+QZPDaDYBQBxVgv+5+YpeOzqyfif/5qCR66YhM4eF/YdEtdngcQao3M/xRlisEIZXnpRFa98kLK3Zi7XLjXsOAW8/TuA41UM6CFmd110d9+5yJVUsoo7nnzxpv59TEcQDfkbqoCSFX6ln4WFa7FpUa7fXSgHSzrsELZuMag/5PeQUoq12nGNlkJKmHVHhSCI8ERNwPJEyt66bdJtmqWGrT2tWF++Hhw41XGTopOw+LzF7vOQK6dkFXc82Xlkp+59gkFDRwP+0/IfbCjfIFv6qXe+l/AsAVXjSMsR2cf1xppA4wxAsYYgBjNaXfskpCycu/LHaZYatnQ48cS7/t+lfUmyWXHH7L4MWrmSSlaxxZPH39E+dn9Q09QBnucQE8Xj7v/7VLaMz2isCWTupzhDDEZI8NKLZmmcJ73VzR9uYh9f6873/MdEUUvND8w2jP14ocjuh/wf6xUPC698DnN+noJD3x1BnZAEy+gLkDdmuLbh4ajpEBIzgOYacGqhffdqIHWCn3m93g42RkshJcy4o0IQRPiiVR4nIXlDbf5iM/PYWiLaiukrYOEtqn5gQ+PC+0vpxk83+j0miYeP/OgRPHp9Ej48Ug9XzxDkOc7H1LO040xOag7SbGma79sTB57A2KSxsub1emJNoHEGoFhDEIMZLSFCQvJqevaDw6Yde81l53mVScp5O/38/JGmHW8gKKmsxTMfHFYs47srfzzuLTwHDac7kRIfDYc9jsnE/YejkpESH42GVnWLgkaF5ynOEIMNErz0ojt7SgDa9ZV9APDP9LINAyZeBcQlA5//E/i/GyDvB3atuE3E0Xutr90Ii+DCeADjAeCgh+m/CsWVddjdtggPCesA+Leb997Y37we0NfBRkobvnVzuaJxpVq6shl3VAiCCF/0ZFAJENDU1aT7GDzHe5UXpsSmYF7mPNhj7Hj78Nu4+927Zf3AluxeAnu0XffxQh3pWu957x6v12VbTRqKrEWyApUnOyvr0PSfuRCSnwWgHmeUzOsB9lgTaJwBKNYQxGBGj8AgAMxG9GoMjY/GJZMzYI+LhtMlYGdlray3U01TBzb8+5uAjzeQ/OOT/6iW8T1a8rX7MSnri9X4XUvsAoBV2ypRkC1vJk9xhhhMkIeXXlhL43zRK0IJLqDgIWDqbYBtqFjqt/8J0RvstRuh7AcGYwJbuOCbASeVjVZuUdxF8id45fRkPNpzubrY5WleHyBS2rDD7j2BO+yxmuaM0h0VpVPlIAZHtTsqBEGEL6zlcZ7Yo+3uroMsuAQX7s29F9dOuBbJMclo6Ghwe4Pd8949in5gAAwJbOGCr8eYlPlVUl2iuI8UZ04cPxtdJ/NV44yneX2gBBJnAIo1BDGYMSIwJMVZZeYLFyy2Q4hKPNDb2da/WmVudhpS4q2ob+3CM73eYBes+TeK/nEwhPr2mktjWw/ztlLWV3FFjeI2Sn5rSkhlgoFCcYYIdyjDSy+jpoulg801YPLxkphyq3ypnhpN/wH2b/I/Tj8b/roEQKticODoTbRWyMry9SeoFhg7gBjxQZNBbymkhBl3VAiCCF9Yy+M8WZS1CE8ceELXcWpO12DzF5v9xK3+NpYXBI3M2wFEKhtVysryjTOuLjZbASM+aHIYjTMAxRqCGMxIQgSrgAIAN16QiQ0emUlRCRWISdsK3tp3E8TVbUfn8fnoaenrqL69wj+W1TZ3GjzzyEMqG125tRJzsvyzslj91nwxq0yQ4gwRzlCGl154i1hCpwfbMNFofuZ9+vb77H+hS1QLAuHRMEM5K8vXn6AOSWxDGs3kk0FKG75k8ghMGzOUeUIP9I4KQRDhi4W3oCiviHn75JhkLD5vMdbPXI/kGPaM4re+e0vT0yvYCGEQZ9SysnzjjNCTwDSmkSw+JYzGGYBiDUEMVjy79rGQEi8azW9alIOUeCuiEioQO2IzuCjvjF8uqgmxIzYjKqHC7FOOaDzN231h9VvzxcwyQYozRLhCGV5GyFoAXPUCsPXXQPsp7e0nXgXwFjhn/BYnd/8Vw4V61YwpAQAXawfa6s06Y8OcwhDc130TVlg3w4GG4N6B1+pQqYVMVpbvnY1S1zk4JqTAgQaF94ATM/hGTTd+HiYSyB0Vs5DrnEN3YQgi+OSPyse1E67Fi1+8qLntxWddDAtvQf6ofFyUfgF+/D+5OMVBOW1KEBBvHYLGzoEvgRecNnTW/gwxaW+Bi2oO2UwvQD4ryzfOONsy4eq2g4tqkr0WDhzSbGnISc0J1mnqhmINQQxOCrPTcfMFo/F3BkP6SyePgIXnUJidjh+NH4a8F/8grll8/kw5TryREZO2FT0tWRiM+RU8ZzxpQC4rS2+mFouZfH9DcYYYKEjwMkrWAiDWDrygbpYOADh7LgCgtLoJz3Vdi03WDaplghwAdAy8P4ogAMk4jXHcMfyh+1o8YX3MnXIbnOO5AhtbJivL986GCzxWdl+n8B70/lK4xq80UotgTqB6zPLNRqlzjlbLZIIgzGHWmbOYBK9ZZ85y/zv66EdYceIklqQOk68V7H2stafV7NPVjSAAnKUNfHQdOo9fjNgRL4V0eaNcVpb/HXQencfnI3bEZr9rkTzWluUtkzWsV8PpcqK8rhwn2k5guG04clJzdI+hBsUaghic5Gc5mASv/CyH+98VDQeAqCZlXyYO4KxNsNiq4GwbY8p5hhOBVMjIZWXpydQKpEww2IIQxRliIAia5P6nP/0J06dPh81mQ1JSUrAOM7CMniFmA6nZ8CWOcGcL1bV0YIcrD7d234VahI7irgTHif//1voaHox+EVVn3wwuLnjnrTadOgVOJXh4v86eyBklKr4HiRli5p5Gx0dfiitqMGPtLix8ej9+88oBLHx6P2as3aVqPBkOKJljshhrEkR/EemxRvLyUjKj58DBYXN4ZwudPo78tnY8WncSdlf/enHpRYozMakliEl7C9GnZyE+KjEox+I1vvIIgnJ5pezr3ItcnOlpyUbH0UUQery7WabZ0rB+5nrNjo++lFSXoOD1Aty04yYse38ZbtpxEwpeL1A10g8XKNYQoU6kxxkjpuKsHoRcVEvgJ2gytmgL4qPNu1ngSSDakJp5u9Z75InRMsFIXc8AFGcGO0ETvLq6unDllVfi1ltvDdYhBh4vPy/fKcg/W0hS53e48jCj8y+4uut+3NV1C5qEuJD3ykpFA876+hlg3qPAdVtwdKQ+UShQtjmnqE/yCllZnv4E0v48XGjCEKztvhp/6F6ET/PWAde/Bdx10JDYFYkTqJo5pvTYyq2VcIb6B5eIeCI91nh6efmKXorZQr3Zrvlt7Xj3yFH8reY4Fjc2Id7lCml3eN7ajJ6E3Vg1YwWenvM0clJ+bOr4LpnOYZ5ovSxKWVlycQZwQXDGoauuAJ2187Bw9DI8U/AMii8vNiR2Ld291K+BAUv3yFCHYg0RDkR+nJGbw+D1u2+2EKsHIaunYX/S3uXEw1dMxP/81xRcOjnD1LEDnaqUsrLU3iOJG6ePwsuLp2LPstmGxK5IXM8AFGeIIApeK1euxJIlS3DeeecF6xChgeTnlegzschkC3mq8y7w2O/KQi2Gwc61h3AXRBHx9ATg7fuA0TMw4sbn0B7n0Fg+mMd8y37lJ6f/WlWo8jRKLOBLsSfmTrwS/Uc8Fv0Efm/djElfbgDaGw2VMUbqBKpljqlmrEkQ/clgiDX5o/KxfuZ6pNpSvR5XzBaSugmDgwXAlI5OTOvoQCvPh6zYJSFAwMNlDyPXkYtn5v0Zduuwge7dAgC44dwbVIUqzzgTlVCB+LFrYRv1NGJHvIoYxzbsqnsOTZ1NhsoY15SukW0sID22tnQtnC6nvgsKESjWEOHAYIgzek3FtbKPBUHs1uhsywzaORtFALBq2xeYetZQPHLVZKTbzTN2D4RfXpSpKlQpvUcSxZ8fR1N7l6EyxkhdzwAUZ4gQcxHs7OxEc3Oz1/9hQdYCOO88iM/nvISPzn8Yn895Cc47P/MTYeTU+VSc6t9zDRSpGyJvQdz8h8GB65e1iFT2IvMMUPE6oPFlvzA7HXsWnMaT0Y8hnfOZ0JprgFevAyq36DqnSJ5AWc0xzWp3TBD9STjGmvxR+dh+6Xbcm3svFp69EPfm3ottl26TF2Fkso9PWIJTvhEMpG6IFt6CBy/4Hbh+FOmUTOb/VfUvTVGpMDsdKxe6EHfGZvBWbx9Oo9lY5XXlfpldnqh1jwwHKNYQkUo4xpnC7HS8e88sPDBvAq6bNgoPzJuAd++ZJSvCqGUfS6XhncfnI8SWmm6k7+fS2oxD8DyKWeAAbPm0RlNUKsxOxwPz5DtrGs3GiuT1DEBxhgixWWj16tWw2+3u/0eOHDnQp8REcUUNZjz8LuZtBa7YMwLztgIzHn5XdsLxVefrkNTPZ2sCUjfErAXgpv86wADBid0ZFdBuVy/0iXBquJyw7CgCB0HmfHsPUlykKZx5EskTKKs5ppntjgmivwjHWFNSXYK5b8zFurJ1ePmrl7GubB3mvjFXWUDxyT4e7gyvDCDJH0bKbrPH2DX2UEbqipgal6qYjaAGq6jkdDmxrmyt7HNGs7FYfXJYtws1KNYQkUo4xpniihr86OF3sGrbF3hhXzVWbfsCP3r4HUUBRSn7WOixo+PoIvS0ZPfHaRtG+n4urc3sNmtA46XbY+FIjDG0LmIVlZwuAau2VSqOAejPxork9QxAcYbQKXgVFRWB4zjV/7/88kvDJ7N8+XI0NTW5///+++8Nj9VfGKl5LsxOx55ls/Hy4qlYeOXP0WlzhELFBjtSN0SXE6h4LYCBekPCtDt6/+1/h4j5xv5p5TvgAERBrPmYygaMwpkHkTyBGjEwJQizoFjjjWEPp6wFwF0VwPVvIWfu40i2DumHszUHT3+YWSNnIYaPMTSOJHAV5RVh+ZTlXo/pRUtUCkY2FqtPDut2oQbFGmKgoDjjjVEPp/xR+dhx+Q48U/AMVs9YA8vx29D67bKQF7sA7+/nc7IciI0yngfCQfTfenDBue7fjaAlKgUjGyuS1zMAxRkCiNKz8W9/+1vccMMNqtucddZZhk8mJiYGMTHGvtQOBM6eHmx581XM5+tQhyR85BqP8/mvkYpTqEMSylznYOXWSszJcvjVU/e1ZR0KxDwMvHrtwFyEXmzD+rohaopIGiRmiGbzWQuAM3KB4mVe4+mqYpFEOCW0BDG926FvAq1t6pAVLDmI3gfhOIFKKd63bi4HB28LnUDaHRMECxRr+ujq6cIf9v1B1cNpzYdrMGvkLHl/KN4CZF4IC4CLOw7jxS9eDPIZB05yTLJXN8TyunLUtdcZGivNloZlecvcpZ/rZ67HmtI1qsKUElqiUjCysSSfnLq2OtnPgJS9Jtc9MhygWEMMFBRn+ujqceG+Nw6qejg9uOVz2fUMIJY35jpyAQDlEz7HMx8cDtq5mkVKvNXr+3lpVQNqmzsNjZVuj8WK+Vnu0s9Ni3KwcmulqjClhJaoFIxsrEhezwAUZwidgtfw4cMxfHh43kU0ncot6HnrHjzRXQtEiw85BQ4Wru/P6JiQgpUt16G0anKvuKVA1gJg5n3A7oeCfNImMPfPfeburOLQeT8HxuWLopQgAG0nxX+Pmt43VtYC4Jx5ooj2xRag9Cn2c0oc0SfCKaEliOndDuE1gTpdAkqrGlDX0oHUBDFoaZ2XlOLtG7QdPoGdIMyGYo1ISXUJ/rDvD2jsbFTd7nj7cTx98GncMukW1e1mnTkrLASv+6fe7yXesQpEs0fOxs/P/jksvAX17fUYbhuOnNQcr7HyR+Vj1shZeOLTJ/DUZ+xxxmFzaIpKwcjGknxylu5e2uuZ2RdpFLt0DhBG4gxAsYYYGCjOiBRX1OC+NyrQ0Nqtul1tcyce3/UtfpM/TnW7OVmOsBC8/nhJttf8xCoQ/WxyBi4aNxyNbV1IiY+Gwx7nN9cVZqdjTpYDpVUN+FdFDV7YV800tq8IJ0cwsrEifT0DUJwZ7OgSvPRw5MgRNDQ04MiRI3A6nThw4AAAYOzYsRgyJHzKKmSp3AK8eh2ifXRw3ud3BxqwyboBH32VCYy5QX3Mi+4G9j8BdJwy91zNZPqdQPbPxH+7nOyC18H/BarfF02UVbopSpkIAHQJXs6C1SitOqU++Uldy5prIN/yixOf1xLOfAiHCbS4osbv/HzvRinhGbT1BheC6A8iNdZIZYxyWT1ybDywEWOTxqp2EsxJzUFidCKau0LXPPnGc2/ET0b/BIDoiVVeV45DTYeY9t31/S58Xv85ivKKMPesuYrbWXgLpqZP1SV43ZN7r2acCVY2luST45uZ5pu9NpAEEmcAijVEaBOpcUYqY2S1VXm05Guc7Rii+jctZQsZyW7qL351USbmTsxw/+50CTjZwpbd9c8Dx/BhVYPm3NZXyQNmweuSSSM058BgZWNF+noGoDgzmOEEQdsW3Ag33HADnn/+eb/H33nnHcycOZNpjObmZtjtdjQ1NSExMdHkMzSIywlsyGYu5XMJQHd8OmLu/rwvm0mJ3WvZsrziUoD2fuyUYRsGzPszcO7PxN8rt/iVH2rTO5lc9YK66AWIr/Ej44C2es1Rv8n6Na47NItt8usVKkVk7l+wnJsCRu84BBulLzPSmcm1miYim5CcVwMgEmON0+VEwesFusvuHDYHii8vVs32efLAk9j46UbNsX425mfYe2yv4VJCvaTEpuB3U37nFrtKqksMlR5KWU/rZ65XFYKcLidmvTpLM3sOAH6Sfh32fpzDFGckoRKAbDaW1nmpIQmAJ9pOyGavDRQUZwhfQm1ODZTIjDMCZqzdpVuYSrfHYs+y2arfcYsranDLZjavwmRbFBrbenSdg1GGxkdj1SXZmDuxbz6SE1G00DO3OV0Ccv+0UzODDhAzvDy3U4oz0pwLyGdjBTLn0nqGCCdY59WgCV5mEGrBAQBQ9T7w/MX697v+rb7sJSVcTuDhsSpiVm8W0p0HgO8/BFpqgOLlYolgMJjxW2DMLO/SQ7doZORj03v+dx3UFv8q/gm8dr3qJu1xDmQ3PgKnT+8F1clPTqxLHNHnJRZBaH2Zke4CaX1xISKLkJxXB5hQe03Kastw046bDO37TMEzbi8VOZwuJ3706o/Q1Nkk+7yUhVR8eTEA4KPjH+G3u3+Lpi757c3g3tx7cc0517jFG73Zbb54XoOaIPT24bfx23d/qzqW3TocRz9bAkFHnJET6xw2R8hkY5kJxRlCjlCbU0OBUHtN9h2qx8Kn9xva9+XFU9WtWgBs/+wY7nj5Eyg1C5TmhnfvmYWPqxtR29SOB978HKc7gyN+PTBvAm64INNrHtr+WQ1ue4m9iYgneua27Z8dw20vfWLoGIB8nAk02ymcoDhDKME6rwatpDFi0WFqrns/3gLMf0xBUOr9Ay5cA0RF94lnUbEBCFAafPwMkDG5T5xyOYGtvwngWB6dENXEP5cTiB8K17gCcN/skO2qIYDDyu7r/MSu3qOAA+QbBnh6hZ0+7u8lFkHo6eSi9cWFIIj+Q4+pud59LbwFD057UFZQkvOEmpI+BQ9Ol9/eLI63Hncfz+ly4sEP/gRBEAy3ufLshqgk/jldTiTHJuPCERfh/aPvyW7DgUPn8Yv9xC7xGMpxRvIJC8VsLLOhOEMQ4YkeU3Mj+86dmIHHwckKSp6+UNFRvHtuiIu2MGeG6aW2ucNH7BIFOaOwzG1StlS3S8DF5znw1sFa3cdQijODqTyP4gwRKCR46UWHqbkXdV+K2WFa4krWArG0zi8LKUM+CylrATD918Devxg7LzXaG8XukVe9KB7nvUfMKaVUE/88MrCkJYZvMwAkjsDXP/gdXtmRpDgMBxdGtZTj0K4jGD9mrPfr7ukVFsEEo5MLQRDBR4+puS/1HfVwupyq4opeT6j8Ufm44dwb8Oznzxo+LzWer3wePMdj6flL8eSHO9HUfdJ4T3cPlMQ/uQwsQeDAecQZh82Bn515Gx5+I1pxfAEu1HV/jsdL6zF9dKaXqOXZtSySoThDEOGJHlNzX062dMLpEjTFlbkT0/Ekz+4LVZidjl9dlIm/vldl+NyUePr9KvAcsHxuFooragxlXMmhNLexlkoOjY9GfWuX4vOSmPPcB1UYlhDjJWx5+oRFMhRniEAhwUsvmubnCrz/sPh/Yoa2ebueLCSXE6h4Tfdl6KK4CBhfCHz4hDnjKYmGveWSAgSvtQ4HAS4BeMZZiBLX+bhhzkJ0OjkAB2SHKeBLscL6AjK4BmAPxP9ZXvcIIxidXAiCCD5a5udqrCtbh+c/fx5FeUWq5XN6spCcLie2V23XfR16eL7yedw68XY8++FnQLI5Y8oJh8rlkgIEAehuuADO01lYcull6FaJM1EJFYhJ2wre2oS/fQX87StRMNR63SMNijMEEZ5omZ+rsWrbF/jbnirTmx85XQK2fFqj82zYefr9KtyVfzZWbq00bUy5uU1PM4D5k9Lx3F5tU/tV275w/ztSSxeVoDhDBIp/nj6hDm8RhRMAhm5BN9eIJYiVW7SPk3khcN4V4k+lu/XVe3Waxxug+Sjw4V+B9lPmjCdnRu9yAsXL/MQuAJBi4lxLGcpc52DlW19hWHyM7NAFfCk2WTfAAZ9MNNbXPYKQvswofUo5iEFTbycXgiCCi4W3oCivCEBfmaEe6trqsHT3UpRUl2geJ9eRi7lnzUWuI1cxK6y8rly3ebxeXIIL6/b9HU2d5jRkiY+K9+uG6HQ5saZ0jXwHxd6XOSqxAs62TKxSiTNRCRWIHbEZXJS3rxnr6x5JUJwhiPDEwnNYMT8LgLGE2pqmDty6uRzFFdoClZSJdMnkEZg2ZqhiZphW6VqguATgoe36DOrViI+x+M1tTpeAlVsrmcQuDsBWAwJfrY7XPhKgOEMECgleRpDKDhN9lHWO5eXsnQKLi0SRJ1CMeorpZfdq88bacZ//tfcKd0qTGc8BGVw9cvkvxUDFwW/y4+HCCusL7u29Mfl1DwPUvsx4+idEYr0/QYQ7Utlhqi3V6/Gk6CTYomyq+0qCztrStXCaMN8F4immhzern0WsY5vqNqxtduRELS3hjuMA3toE3lalGGcAF2LStrq3lzumWa97OEBxhiDCl8LsdGxalAOH3TszJslmRZLNqrm/ANFfyqnkTK+T/ihJ++iwdmdeZmQuW49oJwCob+1CSrxVl+goHdbM1z6UoThDBAoJXkbJWgDcVSF2X7z87+LP5ccAG0sttYd5e6AY9RTTS3ereWPJXTujcJeKUwCAk6c7/Sa/PP5LZHANMmKXhImve5ig9GXGYY+lFr4EEeLkj8rHjst34JmCZ7D2wrV4puAZ/PvKf2OIdYjmvp7G7YESiKeYHroF70WCnLjlKzIp0dbT5nftrMIdF9UCQD7OWGxV4K1Niudh5useLlCcIYjwpTA7HXuWzcbLi6fisasn4+XFU/Hx/XNQel8+EmK1G21IZuFm0B8laV/Utpg2VmuX0+/ajYh2l04eAUBfpp2nUftggOIMEQjk4RUIvubnVe/Ll+spYUZ2llFPsYHG99rj2RZUozixw0lqQiymjRmKTYv6zDAlMUz3sSOcwdTJhSAiDV/z87LaMtS11zHvb0Z2ViCeYoHAKm4p4XvtKbFs5Q689SQA+TgjiWF6jx3pUJwhiPBFzvy8tKoBLR1smapmZWYF4is2UPheuxHRLj/LgdzMFCaTe63jRzIUZwijRK7g5XKymb6biV4hxYzsLMlT7NXrIN4bUAgRHA8IrsCPZxae1165BfjXvZq7CAKwMGoX/hH/c3edtufk5/yuC/hA57EHCYOlkwtB9CdOl5PJ9N1M9AopZmRnSZ5iS3cvBQdOUfTiOR6uEIozntdeUl2C1R9ql+YLAmBNLkNKz1zZOLO/hsMzh17RdezBAsUZgjAfp0sYkAW+HiHFrMwsqXTt1s3liisalZXOgOB77Y2tnbrOUfKesvCcl5hzsqXTy6ie9fiRDsUZwgiRKXhVbgGKl3mbufdHlz49QkriCFGEMwPJU8z3miVsQ4FJ1wD7Hu99gHEajh4CdJ025xzdcOJ7IV17b2dGlnPiOCADDXh0aptXsHdPfpkLgINq2W4+xyYIgjBISXUJ1pSu8fKE6o8ufXqEFIfN4WfcbhTJU8z3miWSY5KxYMwCvFAp+iiyZoKpCWhG4cAhzZbmvnblzowy+3IAZ23CtTOdsnEmL7MA22rWK2a7+R6bIAjCKMUVNX5ZP/3VoY9VSBkaH22qWbhUuqaU7WSPi8Kp9h5dohLPATPPHo5dX5qXectBLKfzvPbiihrc/tInuiKap/eUp5jjdAn4254qxWw3ueMTBCFP5Hl4SQKKr/DTH136pPJCzSpsDihcY27GWdYC4CcKd6/bGkSxa/qv/Y321Rg7x5xz80IAcq4X/9nbmVHvvZq84T19+1e9Dxx8TfwJqHTQ7P3d7NddCd9zGyQGxgQxGJAEFF/hpz+69EnlhVrdGzlwWJa3zNSMs/xR+bg3Vz4b91TnKbxQ+QJuOPcGP6N9NeIscWadnhsBAi4fdzkA9c6MamSmudz7l9WWYft321FWWwYAih00pd/Nft2V8D23wWKUTxCDgeKKGty6udxP9OmvDn1SeaEWqy7JNj3jrDA7HQ/MmyD7XFO7uAawM5jqSyTERGHSGUlmnJobAcDVuSPdv+vpzihx8wWjUZidDqdLwL5D9XjzwFHsO1QPp0sIGaN2uXMjiHCDEwTWnkf9T3NzM+x2O5qampCYmKi9g8sJbMiWz3IC4M7wuetg8EQPd8YSICvkxKXAefEGlMbOMDc9mfXa7zwAfP8h0FIDFC8H2k6qjxuTBHSeCuzc5IhLAsbPAz79H/37Xv8W0N6onMUHyDw3QhS7gpnhJzFQGYYEwYDueXUQoOc1cbqcKHi9QLHbn5ThU3x5cdBED0lwA+QzqZJikrBi2grMGjnL1JJL1mvfduk2fHryUxxvPY51ZevQ2GliVywdJEYnYvbI2fjnoX/q3veZgmfQ1NmkmMUHwO85h82BZXnLgprhJzFQGYYEwQLFGX/0xRkBM9buUvRzkrJ79iybHVTBQxLdlBaKv7ooE/cWTjC95JLl+tMSY/DnqyajrrkDq7Z9gYbWLs1xOY690y8rKfFWXDp5BDKS4phKED15efFUNLV3qWbxDWSW30AemyBYYJ1XI6uksXqviuADeHXp8zSb14uaP5hSeWFcMjDlVhQP/QVWbvkKNU373U+ZMnmwXvv3H4rXXvW+ttgFAM5OgyfUm2wcFQv0yASs9lMGxK5e0a61HnjtBvgJilIW31UviB00+9vDDVAu0fQ8NxK9CCJsKa8rVxR8AO8ufZ5m83pR8wdTKi+0R9uxaMIiLJ64GO98/46fOBWoIMJ67Z+e/BS5jlyU1ZYNmNgFAM1dzbrFLkm0a+xoxN3v3u0nKEpZfOtnrseOy3f0u4cboFyi6XluJHoRRPhSWtWgal7u2aEvUD8jNY8wpfLClHgr/nhJNnie8xOmzFjTsFx/bXMneI6Dwx7HJHYB5otdANDQ2o2/f3BY1z6SYNnY2ilbAill8UndBwfCqF1J7PQ9N4IIByJL8GI1jQ+kSx9L9k7WAuCceX6CS3FlnbmTh6fwxlqqKV0762vQ085+Pp7EJQPtDfJilyF6J/aCh4AdyyFfBimI2xUXia9/IKKmEVRLNH3OrT/EN4IgTIfVND6QLn0s2Tv5o/IVM7jMFkQk8e2Zg88wbS9de7h1KpRKEu/JvQfrytbJZs8JEMCBw9rStZg1clZAoqYR1Eo0fc+tP8Q3giDMh9UwPtAOfSwZPEqCy87KWlPXNJ7CG2u5Zjh3KHxgXhZWbZMvgexdMWDl1krMyXL0u1G7Wnmm3LkRRKgTWR5erKbxRrv06fEH4y1wjpqBfbZZeLPpLHxwqBEPblGePABx8mCuja7cIpYwPn8x8PrNwBdvsu0nXXswOhVGDwEuexq4bgsQFWPu2IkZYnaUbSh7Fl9/oyfDkCCIsITVNN5olz49/mAW3oJcRy4KRhcAAHYc3oEPaz5UFUQAYG3pWma/p5LqEhS8XoCbdtyEPcf2MO0jXXuwOhUuzl6M1TNWIzkm2dRx02xpWD9zPZJjk5mz+PobPRmGBEGEJ6yG8YF06NPjEWbhOeRlpiA1IRZ1LR3Yf6je1DVNcUUNZqzdhYVP78dvXjmAf1Ww3ZRPTYgNWpfC22eNwaM/n4yUeHavMBbS7bHYtCgHyfHRzFl8/Y2eDEOCCAciK8NLMo0PRpc+ndk7cndN1NCVnqyjs6E3nCi4VL0PjJwC2IaxlTVabUB3m/Z2XaeBhN67OS0mmWlOvQ04e25fSeLB19j2CySLzyj9kWFIEMSAIpnGB6NLn5HsHblsMDX0lFzq6W7oSc3pGpTVlmHSsElIs6UxnxsryXHJSItPM61c8toJ12LWmbPcGXLbv9vOtN9AZLD1R4YhQRADi2QYH6wOfXozeIK5ptHyCVOC44CeHhemjBmKdHss87mxkmKLhiMxFg2t3aaNuSR/HO6YPQ4WnsObB44y7TMQWWz9lWFIEP1FZAlevEUsLXz1Org9pNwE2KVPR/ZOcetYQ5M3wDB5GOxs6D7HN34l/jMxA5i0ENj339q7sYhdEmaJOUom86yZafWHzDkPPQQ7w5AgiAHHwltQlFeEpbuXggPnJQYF2qVPrz+YUUEK0BZEjHY3BIDfffA7AGLG1NzMuXju8+cMjaNEckyyKYKOksk8a2bakeYjAZ+DXoKdYUgQxMAjdei7dXO50momoA59ejJ4mtq7gramMdLZ0H2OAnDts6VIt8diwaR0PPVelYlRBkiJjzZN0JHzNWPNTDt8stWUc9BDf2QYEkR/ElkljUCfaXyiT924VBJn1DCcUchxtdQanrwBhslDU3hjpLlGFLuiTJ6shqQFLjbZhondJOXeKymLT4uPnxPFwf7EfW5KX0A4UcgzkmFIEETIIJnGp9pSvR6XSuKMGobryd4JRJACtAURLfGNhbq2Ojz3+XOYOXKmWww0g7T4NBxpCUxsSolNwbZLt8m+V1IWnxavff0ac2moWUjnpvR6cuDgsDkMZRgSBBE6SIbxDrv393RHb0lcIIbhrEJObXNHUNc0WsIbC7VNHXjqvSrkZ6WCM9FOymGPw+GTOm74y5Bks+J/bp6CPctm+71feZkpcCRqr8FeLj3CbndjElKGocpqBukBZBgSRH8TWRleEgqm8QEZhTNm5XzRYjM0eTOnJ5tWDtc7eZpmKo/eEsl6YPfqwMZpO9nXTdIX3gLk3ADsfkh9jJZjgXfj1EswMwwJgggp1EzjjaIne8eoIMVacmlGBpUkxr3z/TsBjyWREpuCxo5GPHHgiYDGaehocHeT9MXCW3DF+Cuw8cBG1TGOtx8PuBunXoKZYUgQRGgRrA59rJk5Dac7g7qmMSODSpoBd1bWBTyWxND4aDS2dmFDydcBjXOqrRs8z8m+Xxaew8K8M/GoxjFqmztN6caph2BnGBJEfxN5GV4SvEUUO867QvwZ6Jc/xuydb23nKZ8SXJjKV2IBvxdT+UrwcEl7AmCcPEK5HG7uwyodFHWiJuwNHRP4GMEiWBmGBEGEHJJp/Nyz5iLXkRuwyKAne8eIIKVHEAnVkrjlecsVOyjqRe01PDPhzIDHCBbByjAkCCL0kDr0XTJ5BKaNGWqKyMCawZMSH617bD1rmlAtiVs5/1zFDop6URP1Rg+zBTxGsAhmhiFB9DeRmeEVDBizd1Jj4mV3L+BLscL6AjK4vo4Wx4QUrOy+Dp8lXORX262IpjH/ADH9TiB+uDnlloC6sBfqXlnByDAkCCLi0ZO9Y0SQSrOlyXpWyaFlzj8Q3HjujRgaN9Q0E3y11zDUvbKCkWFIEMTggDWDxx6nX/ByyPhVKaFlzj8Q/OqiTAxNiDHNBF9N1At1r6xgZRgSRH8TuRlewYAhe0furkkBX4pN1g1wwLt9azrXiCejH8OeBafZlXJJeAOgnG3Wj9iGAVc8D/xkFXtGVfQQlScZfK7CwSvL7AxDgiAGBazZO1rZYL7cPul2FF9ezJz9I4lvoUByTDL+/KM/Y+n5S5kzquKj5G8+AWw+V+HglWV2hiFBEIMHlgwerUwwX5bkj5P1q1JCEt5CgZR4K5645gdYPjeLOaPKHqucN8LicxUOXlnByDAkiP6GMrz0opG943vXhIMLK6wvAAB85wiut/mvZcdyYMLF3qKIy6mcISQJb1vvBNp92rJHxYrbdQWjqwcnCk2XPCH6bPmeF2tG1fQ7PXy+DPhckVcWQRARDEv2jlo2mC8cOLz+zetYPHGx1+NOl1P1GPmj8nHDuTfg+crn4RJcXvtG89HocnWZdMX+55scm4x7zr8HafFpXufFmlF1Q/YNbp8vIz5X5JVFEESko5XBo5YJ5gsH4JWy73HH7HF+zzldguIxCrPT8cuLMvH0+1Xw9GbnAMRYeXR0u/zGMwMOYifG++dNgMMe53VOrBlVN804y+3zZcTniryyCKJ/IMHLCFL2jgLSXZOVWysxqqXcq4zRHwFoPuptsF65BShe5l0emJghijyeHlDtp/yH6+kUx5x5n+h1VX+o1+BdK1SxIABZl4jXf+6l/oKSZrllr2B20d1A6gSFa1zD5nMliX6BjEEQBBGiSNk7akjZYH/Y9wc0djYqbidAQG1brZfBekl1CdaUrvEqD0yzpaEor8idBVZSXYLnPn/OT0zjwKHL1YXbJ92OMxPPxJHmI9j46UZN4Y0VAQIWnrMQP838qZ+gpFVqKZnyLz5vMcYmjZW9RtayTun1DWQMgiCIUEbK4FFCWtPc90YFGlqVb3IIAGqaOvwM1osrarBya6VXiWC6R9ljcUUNnnqvSjZydHS7sCR/HEYPi8fhk614tOQbU1Yz0vleN200Fkwe4ScoaZVaSqb8d8wei7MdQ/yuT09Zp+ea0egYBEGowwmCECpl0340NzfDbrejqakJiYmJA306unG6BBza9RzG77lLe+PL/y6Wv1Vu6c1c8n1beifjq14QM8w2ZKv4ZfUKS3cdFEUpOQEtLgVob4BhIUxOgAM8zh+QvVfhadyulsXGihljEMQgItzn1WAQ7q/JW4fewvI9yzW3W3vhWsw9ay5KqkuwdPdSWSELANbPXI9ZI2eh4PUCRb8sSVgqvrwYFt4iK6A5bA78NPOneO7z5wBAtxjmK8BJSOfvO6bn+Uv7aGWxsWDGGAQxmAj3OTUYhPtr8kb5f7Dk1U81t3vs6sm4ZPIIAKLYdevmcqUVDTZek4NV2yoV/bIkYWnPstmw8JyieLZgUjqeeq8KgP4VTbqCsCSdu++Y0rl7GrerZbCxYsYYBDHYYJ1XKcMriFh4DuPHjAX2MGxsGyaKN8XLID9di+WPKC4CYhI1zOF9ssaUyjC/3OYvhLHSXCMKW76dB/VkXmlkyjFhxhgEQRBhTFo8Wzl5fUc9unq6sKZ0jaz4JEAABw5rS9ciITpB1RzeN2tMrQxz4vCJfmIYC3VtdVi6e6lf50E9mVcsmXJamDEGQRBEOOOwxzFtd7KlE87e2sSVW+U7HfauaPDAmxWo15E1plaC+YMzk/3EMBZqmzpw6+Zyv86DejKvtLLkWDBjDIIg5KEMr2DjcvZmY2l0VUzMAHJu6C0/1OCie4D3HtbeTsoa0zq/D58EdtynPZ4fPplkvuP2Y+YV3RkhCDYiYl41mXB/TZwuJwpeL2DqqJgck6xa/ijxy/N+iacOPqW5nZQ1xnKO939wP9767i3NbT3xzSTzHbO/M68o1hCENuE+pwaDcH9NnC4BM9buYuqomG6PxdW5I/FoyTemHNsza0zrHO9+9QDeOKDvRr5vJpnvmP0951OcIQg2KMMr2LAKOqoG6x4017CJXVAewg8WE3neAky5BXj/z0BbPePAHifi6z/mOW4/ZV5p+QMQBEGEI6yCjh4DexaxCwBzE2BWE3kLb8ElYy7RLXjJ+Y95jtmfmVcUawiCiDRYxRU9Bva1TR2miV0Au4m8hedwxfkjdQteSv5j0pj9mXlFcYYgzIcf6BMISyq3iFlbz18MvH6z+HNDtvi4HFKZX4JDZVAdiXaZF4qZVWqNbBNHiCIcC7wFmLue/fi+nNZXpmImUo29bwqzlKJcXFEzQGdGEARhnJLqEhS8XoCbdtyEZe8vw007bkLB6wUoqS6R3V4q80u1pZpy/Ny0XKTZ0ty+WL5w4OCwOZCTmsM+piMX9hi7ofM50XbC0H5mQbGGIIhIo7iiBjPW7sLCp/fjN68cwMKn92PG2l2K85lU5uewqwtQekqHUuKtaqsZpNtFEY6VqWcNRZLNquMM+qhr0VcOaTYUZwgiOJDgpRfJlN3X90rytFITvS79a4AH7xWyRs8Qs8akx3y3AUS/LD2lHdk/A6bfaey0WDLJgoDTJaj6AwCif4DTFbJVuwRBEH5Ipuy+nleSp5Wa6LXj8h24N/dew8eWhKxcRy6K8orcj/luAwDL8pbpKiG08BY8OO1BQ+fFmkkWDCjWEAQRaRgVVwqz07Fn2Ww8MG9CQMeXxKw/XpLt/t33eQBYMT9LVzmfheew5rLzDJ0TayZZMKA4QxDBgwQvPWiaykM0lXc55fdv1XOHWkPIkrLGEn3SWxMz/I3kWfnJKuDK50UDfdZz1JNJZjKlVQ2q5pSeKcoEQRDhgNPlVDWVB4C1pWvhVIgzFt6CobHGyi98hSylrLE0W5qfkTwr+aPy8ejMR5FmY7tRYiSTzGwo1hAEEUkEKq5YeA7DEmKYj6cmZs2dmCGbNeawx/oZybNSmJ2OJxflwJHIJmAZySQzG4ozBBE8yMNLD9V79XVH9IU1E2rmfUD5c9pdDpW6LwZi2nvuz4AJ8/vGrD/U6y3mW7FvMJPMRFhTjwc6RZkgCIKV8rpyXd0R5WDNhvI1sJfrcqjWfdEovmMeaT6CjZ9u9PMfM5pJZjYUawiCiCT0iCtK/lWs2VBL8sfjlbIjqp0O1bovGsV3zMMnW/FoyTdKqxndmWRmQ3GGIIIHCV56YPWqUtpu1HRRuFLs2Njb9fCiu8X/WU3xzTaH9x0zdYKY2aYlwPUzrMF2IFOUCYIg9MDqVaW2XU5qDtJsaYpdG6XOh9su3YZPT37KZIpvtjm875hjk8diTekaL7FPToAbCCjWEAQRSZghruRlpiDdHqvYtVHqfHjH7LG4Y/ZYTTErGObwvmOe7UjwM4T3Fd8GCoozBBE8SPDSA2uGltJ2qh0bZTKm+qnLoSbByCQzAbVgy8OFPP5LjLe1Io9LAFwXDPj5EgRBaMGanaW2nVrXRs+sqeio6H7tcqhGMDLJzEJ9YedClK0KSYkd4OKGw+lKColzJgiCUMIMcUWta6Nc1lR/djpUIhiZZGahJSACQFKcFS5BgNMlhMQ5E0S4wAmCELLud83NzbDb7WhqakJiYuJAn47ozbUhWztD666D6uJK5RaZjKkRA54xFY5IpptA3ztSwJdihfUFZHAede6JGaLYSK8vMcgJuXk1BAil18TpcqLg9QLN7Kziy4s1hZWS6hK/rCmHzRESWVPhhlysiUqoQEzaVvDWJvd2abY0FOUV0etLDGpCaU4NFULpNXG6BMxYu0szO2vPstmawkpxRY1f1lR6iGRNhRtycUYOen0JQoR1XiXBSy9Sl0YAsvczWA3jXc6Qy5gKVzyDbQFfik3WDQDn25FB5/tDEBFKSM6rA0yovSZSl0YAstlZegzjnS5nSGZNhSOesSYqoQKxIzaL74jHetDIe0QQkUaozamhQKi9JkriijSd6TGMd7qEkMyaCkfkBERfjLxHBBGJkOAVTChDK+RwugSUHjqBya/PQGzHcb+OMCKMGXgEEcGE7Lw6gITia0LZWaGJ0yVg/3cncG/pz9HcfVJ2Gz1ZeAQRiYTinDrQhOJrQtlZoYnTJWD/oXrc/lI5TrV3y26jJwuPICIV1nmVPLyMEKKeViFFP2ewWXgO06K+AjrUGgtodNEkCIIIEULZ0ypUGIjsNQvPIXpItaLYBbB10iQIghhoQtnTKpTo7ww2C8+B5zlFsQtg66RJEIQICV5GCUZ3xP4mWKKUbAYco4dWIOcUaBdNgiCIECIY3RH7m2CJUnIZcHr8swI5LzM6aRIEQYQCweiO2N8EU5AKJAsukPMyo5MmQRAiJHhFMmriUSCilBpujzOfStnmGvFxNQ+tQM8p0C6aBEEQhC7UhKNARSklJI8zX1P/urY6LN29VNM/K9DzMqOTJkEQBMGOkngUzLJMyefM1/untqkDt24uV/XQCvS8zOikSRCECHl4RSpy4lFcEjDlNmD42cD/3QD/HiABGru7u1geU9hAxUNLSSjTc05mddEkiAiG5lV/6DUxhpxwlBidiGsnXIuzks7C3e/e7SdKBWrqLnWx9Dym7/hq/llKYpme8zKzkyZBRCI0p/pDr4lx5MSjlHgrLs85A397v0pp5RCQqbvUyVLJPF7NQ0tJKNNzXmZ20iSISIV1XuUVnwmQw4cP4+abb0ZmZibi4uIwZswYrFixAl1dXcE6JCEhiUe+wlP7KWD3QwpiF/oeKy4SxSO9VO9VEbt6x5c8tDxxOUVxLtBz4i1iNhgA+NnW9/5euIbELoKIECjODByScOQrPDV3NWPjpxtlxS6gr+vk2tK1cBqIM+V15YpilzS+5J/li9PlxJrSNQGfl4W3oCivCECfUCYh/b4sbxmJXQQRAVCcGVgk8chXeGpo7cbTMmIX0LeaWLm1Ek6XsbyO0qoG1U6Jnh5anjhdAlZurQz4vCw8hxXzswAormiwYn4WiV0EwUDQBK8vv/wSLpcLf/3rX/H555/j0UcfxZNPPon77rsvWIckAA3xSELjOTlRigWjHlpGhTI5shaI2WCJPndOEjOMZ64RBBGSUJwZGNSEIwmt55REKS0C8c8KRCzzJX9UPtbPXI9UW6rX42m2NMPZawRBhB4UZwYONfFICyVBihWjHlpGhTI5CrPTsWlRDhx277JFhz02oOw1ghhsBM3Dq7CwEIWFhe7fzzrrLHz11VfYtGkTHnnkkWAdltAUjxgxYuxu1EPLbLN56qJJEIMCijMDg5ZwxIoRU/dA/LPMNpunTpoEEflQnBk4tMQjFoyauhv10DLbbJ46aRJE4PSraX1TUxNSUlIUn+/s7ERnZ6f79+bm5v44rcjCrA6ERozdR00XM6m0PLRGTTd2LD3nFCJdNPu7lTFBDHa04gxAsSZQzOo+aMTUPSc1B2m2NE3/rJzUHMPH03NeodBJk+IMQfQvFGf6BzM6EBo1dc/LTEG6PVbTQysv0/tzEAyz+VDopElxhghn+k3w+vbbb/Hf//3fqndDVq9ejZUrV/bXKUUmAXcgVBClWJA8tF69ThzHK0SoeGgZFcpCnGB2jiEIwh+WOANQrAmUQLsPqolSWkj+WUt3LwUHzkv00vLPCkQsC1UozhBE/0Jxpv8IpAOhkiDFiuShdevmcqUVjayHllGhLJShOEOEO7o9vIqKisBxnOr/X375pdc+R48eRWFhIa688kosXrxYcezly5ejqanJ/f/333+v/4oGO5J4ZAgTjN2NeGhFoNm8ksmm1Mq4uKJmgM6MIEKfYMYZgGJNoEjCkRHMMHU36p8VaWbzFGcIwjgUZ0KfvMwUpMRbde9nlqm7EQ+tSDObpzhDRAKcIAi6vABPnDiB+vp61W3OOussREdHAwCOHTuGmTNnYurUqXjuuefA8+waG7XwNUjlFuDVa7W343hAcPX9njhCFJbMMHZ3OfV7aFVuEQ33PT3IzDynfiKQVsYEEWzCYV7tzzgDhMdrEmqUVJdgye4lmtvxHA+XR5xx2BxYlrfMFFN3p8tpyD+rpLoEa0rXePmQmXle/QHFGSKUCYc5leJMeLD9s2O47aVPVLfhOcCz6aHZ2UdGyvkiISuK4gwR6rDOq7oFLz0cPXoUs2bNwg9/+ENs3rwZFou+u6YUHAKgcguw9U6gvVHmyd5J6YrngPihoWXsbkQoCzH2HarHwqf3a2738uKpA16TTww+Im1eDTTOAJH3mvQXJdUleHDvg2jqavJ7TsqYeuRHjyA5NjnkTN2NimWhAsUZIpSJtDmV4szAsnp7Jf76XpXscxyAjdf8AMnxMSHnLxXuvlcUZ4hQh3VeDZqH19GjRzFz5kyMGjUKjzzyCE6c6DO5dTgcwTosISF1KnzvEeDDTd7CV2JG6GZNhYjZfCCY3aGFIAh5KM4MLFKXwqcPPo3NlZu9hK80W1pIZ0yFgtl8IFCcIYj+geLMwLN8bhYmnZGM+9+sQENrl/vxUM+YCgWz+UCgOENECkETvHbu3Ilvv/0W3377Lc444wyv54KYVEZ4wluAmcuAi+4O+6ypcCIYHVoIgvCH4szAY+EtuGXSLVh83uKwzpgKNyjOEET/QHEmNJg7MR0F2Y6wzpgKNyjOEJFCUEsaA4XSf4lwRKp51+rQQjXvxEBA86o/9JoQ4QbFGSKUoTnVH3pNiHCD4gwR6rDOq7q7NBIhgssJVL0PHHxN/OlyDvQZEb1EWocWgiAGJ06XE2W1Zdj+3XaU1ZbBSXEmZKA4QxBEpOB0Cdh3qB5vHjiKfYfq4XSFbC7GoILiDBEpBK2kkQgist0MM4DCtaHpyzUIkVoZ+3ZocYS43wBBEAQg38kwzZaGoryikPXlGmxQnCEIItyJhG6GkQzFGSISoJLGcKNyC/DqdYBfcmmvun7VCyR6hRDh3qGFiDxoXvWHXhNvSqpLsHT3Ugg+cUbqvLh+5noSvUIIijNEqEFzqj/0mvhTXFGDWzeXK61osGlRDgkqIQLFGSIUGfAujUQQcDnFzC7ZSmoBAAcUF4ndGcksOCQI9w4tBEEMLpwuJ9aUrvETuwBAgAAOHNaWrsWskbPIlD5EoDhDEES44XQJWLm1Um1Fg5VbKzEny0HCSghAcYYIZ8jDK5yo3utdxuiHADQfFbcjCIIgCJ2U15V7lTH6IkBAbVstyuvK+/GsCIIgiEiitKrBq0TOFwFATVMHSqsa+u+kCIKISEjwCidOKy9CDG1HEARBEB6caDth6nYEQRAE4Utdi7LYZWQ7giAIJUjwCieGpJm7HUEQBEF4MNw23NTtCIIgCMKX1IRYU7cjCIJQggSvcGLUdLEbo19zWAkOSBwhbkcQBEEQOslJzUGaLc1tUO8LBw4OmwM5qTn9fGYEQRBEpJCXmYJ0e6zaigbpdtEcnSAIIhBI8AoneAtQuLb3F98Q0ft74RoyrCcIgiAMYeEtKMorAgA/0Uv6fVneMjKsJwiCIAxj4TmsmJ8FQHFFgxXzs8iwniCIgCHBK9zIWgBc9QKQ6NOmNzFDfDxrwcCcF0EQBBER5I/Kx/qZ65FqS/V6PM2WhvUz1yN/VP4AnRlBEAQRKRRmp2PTohw47N5liw57LDYtykFhdrrCngRBEOxEDfQJEAbIWgCcM0/sxnj6uOjZNWo6ZXYRBEEQppA/Kh+zRs5CeV05TrSdwHDbcOSk5lBmF0EQBGEahdnpmJPlQGlVA+paOpCaIJYxUmYXQRBmQYJXuMJbgMwLB/osCIIgiAjFwluQ68gd6NMgCIIgIhgLz2HamKEDfRoEQUQoVNJIEARBEARBEARBEARBRBQkeBEEQRAEQRAEQRAEQRARBQleBEEQBEEQBEEQBEEQRERBghdBEARBEARBEARBEAQRUZDgRRAEQRAEQRAEQRAEQUQUJHgRBEEQBEEQBEEQBEEQEQUJXgRBEARBEARBEARBEEREQYIXQRAEQRAEQRAEQRAEEVGQ4EUQBEEQBEEQBEEQBEFEFFEDfQJqCIIAAGhubh7gMyEIgogMpPlUml8JijUEQRBmQnHGH4ozBEEQ5sIaa0Ja8GppaQEAjBw5coDPhCAIIrJoaWmB3W4f6NMICSjWEARBmA/FmT4ozhAEQQQHrVjDCSF8+8XlcuHYsWNISEgAx3GGxmhubsbIkSPx/fffIzEx0eQz7H/oekKbSLqeSLoWgK5HQhAEtLS0ICMjAzxPVe0AxRpfIulaALqeUIeuJ3ShOGMeFGf8iaTriaRrAeh6Qh26HhHWWBPSGV48z+OMM84wZazExMSI+EBI0PWENpF0PZF0LQBdDwC64+4DxRp5IulaALqeUIeuJ3ShOBM4FGeUiaTriaRrAeh6Qh26HrZYQ7ddCIIgCIIgCIIgCIIgiIiCBC+CIAiCIAiCIAiCIAgiooh4wSsmJgYrVqxATEzMQJ+KKdD1hDaRdD2RdC0AXQ8RXCLp/YikawHoekIdup7QJZKuJRKItPcjkq4nkq4FoOsJdeh69BHSpvUEQRAEQRAEQRAEQRAEoZeIz/AiCIIgCIIgCIIgCIIgBhckeBEEQRAEQRAEQRAEQRARBQleBEEQBEEQBEEQBEEQRERBghdBEARBEARBEARBEAQRUfx/e3cTEmW7gHH8mjwMWn6UZYaIZhlFhEKWoS3KlD6IyI3ryaKFjKHYoo+Nq1BCqDApI5hVYisLgigRPzZZpghmGAiFYaRFIDaQxvi8i3OQ0/t6Os34cd/z+P+BoI8i1yDjH28edVUdeH348EHnzp1TVlaW4uLitH37dtXW1mp2dtb0tIhcu3ZNhYWFWrt2rdavX296Ttiampq0detWxcbG6sCBA3r16pXpSRHr6enRqVOnlJaWJo/Ho0ePHpmeFLG6ujrt379fCQkJ2rx5s0pLS/Xu3TvTsyJ2584d5eTkKDExUYmJiSooKNDTp09Nz1oS9fX18ng8qq6uNj0F/+G2zki0xhZ0xl5u7oxEa2xDZ+xDZ+xEa6LHcnZmVR14jYyMaG5uTs3NzRoeHtaNGzd09+5dXb161fS0iMzOzqqsrEwVFRWmp4Tt4cOHqqmpUW1trQYGBpSbm6tjx45pcnLS9LSIBINB5ebmqqmpyfSURevu7pbf71dvb6/a29v18+dPHT16VMFg0PS0iKSnp6u+vl79/f16/fq1jhw5otOnT2t4eNj0tEXp6+tTc3OzcnJyTE/Bf3FbZyRaYws6Yy+3dkaiNTaiM3ahM/aiNdFh2TvjrHLXr193srKyTM9YlEAg4CQlJZmeEZb8/HzH7/fPvx0KhZy0tDSnrq7O4KqlIclpa2szPWPJTE5OOpKc7u5u01OWzIYNG5z79++bnhGx6elpZ8eOHU57e7tz6NAhp6qqyvQk/IYbOuM4tMYmdMZ+0d4Zx6E10YTOmENnogetsc9KdGZV3eG1kKmpKSUnJ5uesarMzs6qv79fJSUl89fWrFmjkpISvXjxwuAyLGRqakqSXPE8CYVCam1tVTAYVEFBgek5EfP7/Tp58uQvzyHYi86YQWuiB52xE62JHnTGDDoTXWiNfVaiM/9ats8cBUZHR9XY2KiGhgbTU1aVr1+/KhQKKTU19ZfrqampGhkZMbQKC5mbm1N1dbUOHjyoPXv2mJ4TsaGhIRUUFOjHjx+Kj49XW1ubdu/ebXpWRFpbWzUwMKC+vj7TU/AH6Iw5tCY60Bk70ZroQWfMoTPRg9bYZ6U644o7vC5fviyPx/Pbl79/0xkfH9fx48dVVlam8+fPG1r+T5E8FmC5+P1+vXnzRq2traanLMrOnTs1ODioly9fqqKiQj6fT2/fvjU9K2wfP35UVVWVHjx4oNjYWNNzVhU3dUaiNbAHnbEPrTGDzgDLh9bYZSU744o7vC5evKgzZ8789mO2bds2//qnT59UVFSkwsJC3bt3b5nXhSfcxxKNNm3apJiYGE1MTPxyfWJiQlu2bDG0Cn9XWVmpJ0+eqKenR+np6abnLIrX61V2drYkKS8vT319fbp165aam5sNLwtPf3+/JicntXfv3vlroVBIPT09un37tmZmZhQTE2NwoXu5qTMSraE1dqAzdqI1ZtCZ6ENnogOtsc9KdsYVB14pKSlKSUn5o48dHx9XUVGR8vLyFAgEtGaNXTe5hfNYopXX61VeXp46OjpUWloq6d+3mXZ0dKiystLsOMhxHF24cEFtbW3q6upSVlaW6UlLbm5uTjMzM6ZnhK24uFhDQ0O/XCsvL9euXbt06dIlfgBZRm7qjERraI1ZdMZutMYMOhN96IzdaI29VrIzrjjw+lPj4+M6fPiwMjMz1dDQoC9fvsy/LxpP4cfGxvTt2zeNjY0pFAppcHBQkpSdna34+Hiz4/6Pmpoa+Xw+7du3T/n5+bp586aCwaDKy8tNT4vI9+/fNTo6Ov/2+/fvNTg4qOTkZGVkZBhcFj6/36+WlhY9fvxYCQkJ+vz5syQpKSlJcXFxhteF78qVKzpx4oQyMjI0PT2tlpYWdXV16dmzZ6anhS0hIeEff3dg3bp12rhxY1T/PQI3cVtnJFpjCzpjLzd1RqI1tqMzdqEz9qI19lrRziz5/320WCAQcCQt+BKNfD7fgo+ls7PT9LQ/0tjY6GRkZDher9fJz893ent7TU+KWGdn54JfC5/PZ3pa2P7XcyQQCJieFpGzZ886mZmZjtfrdVJSUpzi4mLn+fPnpmctGf5VvF3c1hnHoTW2oDP2cntnHIfW2ITO2IfO2InWRJfl6ozHcRxnsYdmAAAAAAAAgC3s+4VvAAAAAAAAYBE48AIAAAAAAICrcOAFAAAAAAAAV+HACwAAAAAAAK7CgRcAAAAAAABchQMvAAAAAAAAuAoHXgAAAAAAAHAVDrwAAAAAAADgKhx4AQAAAAAAwFU48AIAAAAAAICrcOAFAAAAAAAAV+HACwAAAAAAAK7yFxK9VtYsdwwXAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Default Case (`n_clusters=2, linkage='ward'`):\n",
        "\n",
        "- **Observation:**\n",
        "  - While the number of clusters is set to 2, The algorithm identified two main clusters based on the hierarchical structure of the data.\n",
        "\n",
        "### Custom Case 1 (`n_clusters=3, linkage='complete'`):\n",
        "\n",
        "- **Observation:**\n",
        "  - Complete linkage tends to create compact clusters. Three main clusters are identified based on the hierarchical structure.\n",
        "  - Observe the differences in cluster shapes and heights compared to the default case. Can observe that the cluster 2 almost overlaps cluster 1 in this case.\n",
        "\n",
        "\n",
        "### Custom Case 2 (`n_clusters=None, distance_threshold=0.5, linkage='average'`):\n",
        "\n",
        "- **Observation:**\n",
        "  - When plotted without specifying the exact number of clusters. Clusters are merged until the distance threshold is reached.\n",
        "  - The average linkage criterion is used.\n",
        "  - The algorithm dynamically identifies clusters based on a distance threshold. The resulting number of clusters is not pre-specified.\n",
        "  - Only cluster 0 visible, other clusters are barely visible.\n",
        "\n",
        "Overall, The choice of linkage criterion influences the compactness and shape of clusters. Complete linkage tends to create more compact clusters compared to Ward linkage.Dynamic identification of clusters using a distance threshold can lead to selective visibility of clusters, and their shapes might be less distinguishable."
      ],
      "metadata": {
        "id": "ClKxOvK7dwEy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Comparison and Analysis**"
      ],
      "metadata": {
        "id": "X_0arpzdZvMY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate clustering results using Silhouette Score and Davies-Bouldin Index\n",
        "silhouette_kmeans_default = silhouette_score(scaled_data, kmeans_labels_default)\n",
        "silhouette_kmeans_custom1 = silhouette_score(scaled_data, kmeans_labels_custom1)\n",
        "silhouette_kmeans_custom2 = silhouette_score(scaled_data, kmeans_labels_custom2)\n",
        "\n",
        "\n",
        "silhouette_agg_default = silhouette_score(scaled_data, agg_labels_default)\n",
        "silhouette_agg_custom1 = silhouette_score(scaled_data, agg_labels_custom1)\n",
        "silhouette_agg_custom2 = silhouette_score(scaled_data, agg_labels_custom2)\n",
        "\n",
        "\n",
        "db_index_kmeans_default = davies_bouldin_score(scaled_data, kmeans_labels_default)\n",
        "db_index_kmeans_custom1 = davies_bouldin_score(scaled_data, kmeans_labels_custom1)\n",
        "db_index_kmeans_custom2 = davies_bouldin_score(scaled_data, kmeans_labels_custom2)\n",
        "\n",
        "\n",
        "db_index_agg_default = davies_bouldin_score(scaled_data, agg_labels_default)\n",
        "db_index_agg_custom1 = davies_bouldin_score(scaled_data, agg_labels_custom1)\n",
        "db_index_agg_custom2 = davies_bouldin_score(scaled_data, agg_labels_custom2)\n",
        "\n"
      ],
      "metadata": {
        "id": "IiYhiWGkZveT"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Comparison of the two algorithms\n",
        "print(\"KMeans Silhouette Score (default):\", silhouette_kmeans_default)\n",
        "print(\"KMeans Silhouette Score (custom1):\", silhouette_kmeans_custom1)\n",
        "print(\"KMeans Silhouette Score (custom2):\", silhouette_kmeans_custom2)\n",
        "print(\"Agglomerative Silhouette Score (default):\", silhouette_agg_default)\n",
        "print(\"Agglomerative Silhouette Score (custom1):\", silhouette_agg_custom1)\n",
        "print(\"Agglomerative Silhouette Score (custom2):\", silhouette_agg_custom2)\n",
        "\n",
        "\n",
        "print(\"KMeans Davies-Bouldin Index (default):\", db_index_kmeans_default)\n",
        "print(\"KMeans Davies-Bouldin Index (custom1):\", db_index_kmeans_custom1)\n",
        "print(\"KMeans Davies-Bouldin Index (custom2):\", db_index_kmeans_custom2)\n",
        "print(\"Agglomerative Davies-Bouldin Index (default):\", db_index_agg_default)\n",
        "print(\"Agglomerative Davies-Bouldin Index (custom1):\", db_index_agg_custom1)\n",
        "print(\"Agglomerative Davies-Bouldin Index (custom2):\", db_index_agg_custom2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JXD0LCnKZ7uI",
        "outputId": "0de6a20e-216b-4743-f3c7-ca910359e350"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KMeans Silhouette Score (default): 0.3449740051034408\n",
            "KMeans Silhouette Score (custom1): 0.3143840098608098\n",
            "KMeans Silhouette Score (custom2): 0.27225629895464226\n",
            "Agglomerative Silhouette Score (default): 0.33938477753669855\n",
            "Agglomerative Silhouette Score (custom1): 0.34452963740927434\n",
            "Agglomerative Silhouette Score (custom2): 0.5543106410155653\n",
            "KMeans Davies-Bouldin Index (default): 1.312320210357444\n",
            "KMeans Davies-Bouldin Index (custom1): 1.5293883767807952\n",
            "KMeans Davies-Bouldin Index (custom2): 1.4927428913368943\n",
            "Agglomerative Davies-Bouldin Index (default): 1.369963541894103\n",
            "Agglomerative Davies-Bouldin Index (custom1): 1.3925210327494664\n",
            "Agglomerative Davies-Bouldin Index (custom2): 0.5036537858413066\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "\n",
        "The provided observations include Silhouette Scores and Davies-Bouldin Index values for both KMeans and Agglomerative Clustering with different parameter settings. Interpreting the performance differences:\n",
        "\n",
        "1. **Silhouette Score:**\n",
        "   - **KMeans (default):** 0.345\n",
        "   - **KMeans (custom1):** 0.314\n",
        "   - **KMeans (custom2):** 0.272\n",
        "   - **Agglomerative (default):** 0.339\n",
        "   - **Agglomerative (custom1):** 0.345\n",
        "   - **Agglomerative (custom2):** 0.554\n",
        "\n",
        "   The Silhouette Score measures how well-defined the clusters are. A higher score indicates better-defined clusters. In this case, the Agglomerative Clustering with \"custom2\" parameters has the highest Silhouette Score, suggesting that it produced more well-defined clusters compared to the other settings.\n",
        "\n",
        "2. **Davies-Bouldin Index:**\n",
        "   - **KMeans (default):** 1.312\n",
        "   - **KMeans (custom1):** 1.529\n",
        "   - **KMeans (custom2):** 1.493\n",
        "   - **Agglomerative (default):** 1.370\n",
        "   - **Agglomerative (custom1):** 1.393\n",
        "   - **Agglomerative (custom2):** 0.504\n",
        "\n",
        "   The Davies-Bouldin Index measures the compactness and separation between clusters. A lower index indicates better clustering. Here, Agglomerative Clustering with \"custom2\" parameters has the lowest Davies-Bouldin Index, suggesting better cluster separation and compactness compared to the other settings.\n",
        "\n",
        "**Comments:**\n",
        "- Agglomerative Clustering with \"custom2\" parameters appears to outperform both KMeans and other Agglomerative Clustering settings in terms of both Silhouette Score and Davies-Bouldin Index.\n",
        "  \n",
        "- KMeans, especially with \"custom2\" parameters, has lower Silhouette Scores and higher Davies-Bouldin Index compared to Agglomerative Clustering with \"custom2\". This indicates that, in this specific case, Agglomerative Clustering is better at forming well-defined and separated clusters.\n",
        "\n",
        "- The choice between KMeans and Agglomerative Clustering often depends on the specific characteristics of the dataset and the underlying structure of the data.\n",
        "\n",
        "- It's important to note that these results are dataset-specific, and the performance of clustering algorithms can vary based on the nature of the data and the choice of parameters. Further exploration and validation on different datasets could provide additional insights.\n"
      ],
      "metadata": {
        "id": "4A_HzkK9ah1-"
      }
    }
  ]
}