Statistics.ipynb

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  "metadata": {
    "colab": {
      "name": "Statistics.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "2wgGgeE4x1lt"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "xaZ4q8PtDjki"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import itertools\n",
        "import matplotlib as mp\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import PassiveAggressiveClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OgneMRXO56JM",
        "outputId": "d636f5a4-6c95-4a72-dde9-c91743e8a82b"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eE3Pjnk-Gw7F"
      },
      "source": [
        "from google.colab import files"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2wgGgeE4x1lt"
      },
      "source": [
        "# Different models accuracy testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1c0SsE8AEFGC"
      },
      "source": [
        "#reading the train file\n",
        "df=pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/Train.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "xh-G4hbfFXqx",
        "outputId": "4ad9793c-c460-4fd7-f554-915e6ce1b8b7"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .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>SNO</th>\n",
              "      <th>News</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35098</td>\n",
              "      <td>(Welcome) to America We hope you enjoy our...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37968</td>\n",
              "      <td>(Welcome) to America We hope you enjoy our...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>66775</td>\n",
              "      <td>Kendall   Kylie Jenner Jenner NOT Upset Up...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>65622</td>\n",
              "      <td>Kim Kardashian Dethroned Dethroned By Khlo...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>68069</td>\n",
              "      <td>Kim Kardashian Did NOT Hot Staffer Hot Sta...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     SNO                                               News  Label\n",
              "0  35098      (Welcome) to America We hope you enjoy our...      0\n",
              "1  37968      (Welcome) to America We hope you enjoy our...      0\n",
              "2  66775      Kendall   Kylie Jenner Jenner NOT Upset Up...      0\n",
              "3  65622      Kim Kardashian Dethroned Dethroned By Khlo...      0\n",
              "4  68069      Kim Kardashian Did NOT Hot Staffer Hot Sta...      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FPd6Fd9GFm6e",
        "outputId": "32372c2c-4466-45c1-9018-8c26e6f8e65c"
      },
      "source": [
        "labels=df.Label\n",
        "labels.head()\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(69031, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ixhZNHISkvh"
      },
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(df['News'],labels, test_size=0.01, random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABhmPqNXS6V4"
      },
      "source": [
        "#Initialize a TfidfVectorizer\n",
        "tfidf_vectorizer=TfidfVectorizer(stop_words='english', max_df=0.8)\n",
        "#Fit and transform train set, transform test set\n",
        "tfidf_train=tfidf_vectorizer.fit_transform(x_train) \n",
        "tfidf_test=tfidf_vectorizer.transform(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "on_h4-v3TIiB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72ad13e6-0beb-4347-8e3b-17d2d3d30885"
      },
      "source": [
        "#Initialize a PassiveAggressiveClassifier\n",
        "model=PassiveAggressiveClassifier(max_iter=50)\n",
        "model.fit(tfidf_train,y_train)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassiveAggressiveClassifier(C=1.0, average=False, class_weight=None,\n",
              "                            early_stopping=False, fit_intercept=True,\n",
              "                            loss='hinge', max_iter=50, n_iter_no_change=5,\n",
              "                            n_jobs=None, random_state=None, shuffle=True,\n",
              "                            tol=0.001, validation_fraction=0.1, verbose=0,\n",
              "                            warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TX8MK_PHVHqt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3c622e5-05fc-46a5-ca57-50c4cbc38664"
      },
      "source": [
        "#Predict on the test set and calculate accuracy \n",
        "y_pred=model.predict(tfidf_test)\n",
        "score=accuracy_score(y_test,y_pred)\n",
        "print('Accuracy of PassiveAggressiveClassifier model:',round(score*100,2),'%')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of PassiveAggressiveClassifier model: 90.88 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RLsk1tuWVKzR"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "#Initialize a LogisticRegression\n",
        "model= LogisticRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zhr__TzenIjA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a1aaa84-c667-4c6c-a398-6b61b95ff638"
      },
      "source": [
        "model.fit(tfidf_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "To1_vW4ujrxX",
        "outputId": "076bef66-5693-438b-dcd7-b91f996c3fd3"
      },
      "source": [
        "y_pred=model.predict(tfidf_test)\n",
        "score=accuracy_score(y_test,y_pred)\n",
        "print('Accuracy of LogisticRegression model:',round(score*100,2),'%')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of LogisticRegression model: 94.5 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1pSimbxjySa"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "model = KNeighborsClassifier(n_neighbors=20)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hm2t98zr1e50",
        "outputId": "54a3d582-7e21-41ac-95ee-cb8287193cc1"
      },
      "source": [
        "model.fit(tfidf_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=20, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mw1quAbz1mV6",
        "outputId": "0025400d-640e-4cd2-f930-0ff47a84a502"
      },
      "source": [
        "y_pred=model.predict(tfidf_test)\n",
        "score=accuracy_score(y_test,y_pred)\n",
        "print('Accuracy of KNeighborsClassifier model:',round(score*100,2),'%')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of KNeighborsClassifier model: 82.78 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qBbYoggextHo"
      },
      "source": [
        "# **Graphs**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2bwG3TT1uUc"
      },
      "source": [
        "df=pd.read_excel('Main source of information about news and current affairs in selected countries worldwide as of June 2020.xlsx')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "YALenkcB8SVe",
        "outputId": "c58ed146-e4d6-44bc-d5d9-0963e0352708"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Characteristic</th>\n",
              "      <th>Total EMEA</th>\n",
              "      <th>Argentina</th>\n",
              "      <th>Australia</th>\n",
              "      <th>Brazil</th>\n",
              "      <th>India</th>\n",
              "      <th>Nigeria</th>\n",
              "      <th>U.S.</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Printed newspapers</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Online news apps,websites</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Social media</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Video sharing online</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Online blogs or forums</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Radio</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Magazines</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Television</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Family &amp; friends personally</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Family and friens via privates messages</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            Characteristic  Total EMEA  ...  Nigeria  U.S.\n",
              "0                       Printed newspapers        0.28  ...     0.34  0.25\n",
              "1                Online news apps,websites        0.63  ...     0.62  0.51\n",
              "2                             Social media        0.55  ...     0.78  0.54\n",
              "3                     Video sharing online        0.15  ...     0.23  0.14\n",
              "4                   Online blogs or forums        0.13  ...     0.39  0.13\n",
              "5                                    Radio        0.52  ...     0.54  0.38\n",
              "6                                Magazines        0.15  ...     0.22  0.15\n",
              "7                               Television        0.79  ...     0.73  0.69\n",
              "8              Family & friends personally        0.37  ...     0.38  0.36\n",
              "9  Family and friens via privates messages        0.29  ...     0.51  0.20\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "oXfyFqwpLVE1",
        "outputId": "a8a7d949-e12f-4f75-fc4e-ec389a60673c"
      },
      "source": [
        "sns.set_theme(style=\"darkgrid\")\n",
        "fig_dims = (20, 10)\n",
        "fig, ax = plt.subplots(figsize=fig_dims)\n",
        "bar_plot3=sns.barplot(x='Characteristic',y='Total EMEA',ax=ax,data=df,palette='inferno')\n",
        "bar_plot3.set_xticklabels(bar_plot3.get_xticklabels(), rotation=40, ha=\"right\")\n",
        "for p in bar_plot3.patches:\n",
        "  bar_plot3.text(p.get_x() + p.get_width()/2., p.get_height(), '{0:.2f}'.format(p.get_height()), \n",
        "        fontsize=12, color='black', ha='center', va='bottom')\n",
        "bar_plot3.set_xlabel=(\"Souce of information\")\n",
        "bar_plot3.set_ylabel =(\"%age of people using the source\")\n",
        "bar_plot3.set_title(\"Main source of information about news and current affairs in 2020\",fontsize=14,fontweight='bold')\n",
        "fig.tight_layout()\n",
        "plt.savefig(\"Main source of information about news and current affairs in 2020.jpg\",dpi=200)\n",
        "files.download(\"Main source of information about news and current affairs in 2020.jpg\") \n",
        "#line_plot3=sns.lineplot(x='Characteristic',y='Total EMEA',color='black',ax=ax,data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
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              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_a54adb3a-b565-4215-8e6d-08467029465f\", \"Main source of information about news and current affairs in 2020.jpg\", 375762)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "mkVz4kVV8b1m",
        "outputId": "7777e4b8-0b1c-4060-9668-45f71e8f27de"
      },
      "source": [
        "sns.set_theme(style=\"darkgrid\")\n",
        "fig, axes = plt.subplots(1, 2, sharex=True, figsize=(28,10))\n",
        "fig.suptitle('Main source of information about news and current affairs in different countries 2020',fontsize=16,fontweight='bold')\n",
        "bar_plot1=sns.barplot(x='Characteristic',y='India',ax=axes[0],data=df,palette='viridis')\n",
        "bar_plot1.set_xticklabels(bar_plot1.get_xticklabels(), rotation=40, ha=\"right\")\n",
        "for p in bar_plot1.patches:\n",
        "  bar_plot1.text(p.get_x() + p.get_width()/2., p.get_height(), '{0:.2f}'.format(p.get_height()), \n",
        "        fontsize=12, color='black', ha='center', va='bottom')\n",
        "bar_plot1.set(xlabel=\"Souce of information\", ylabel = \"%age of people using the source\")\n",
        "bar_plot1.set_title(\"India\",fontsize=12,fontweight='bold')\n",
        "#line_plot1=sns.lineplot(x='Characteristic',y='India',color='black',ax=axes[0],data=df)\n",
        "\n",
        "bar_plot2=sns.barplot(x='Characteristic',y='U.S.',ax=axes[1],data=df,palette='viridis')\n",
        "bar_plot2.set_xticklabels(bar_plot2.get_xticklabels(), rotation=40, ha=\"right\")\n",
        "for p in bar_plot2.patches:\n",
        "  bar_plot2.text(p.get_x() + p.get_width()/2., p.get_height(), '{0:.2f}'.format(p.get_height()), \n",
        "        fontsize=12, color='black', ha='center', va='bottom')\n",
        "bar_plot2.set(xlabel=\"Souce of information\", ylabel = \"%age of people using the source\")\n",
        "bar_plot2.set_title(\"U.S.\",fontsize=12,fontweight='bold')\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"Main source of information about news and current affairs in different countries 2020.jpg\",dpi=200)\n",
        "files.download(\"Main source of information about news and current affairs in different countries 2020.jpg\")\n",
        "#line_plot2=sns.lineplot(x='Characteristic',y='U.S.',color='black',ax=axes[1],data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_b154f919-2c22-41cf-91b1-46ae4c879a56\", \"Main source of information about news and current affairs in different countries 2020.jpg\", 662948)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2016x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82F9jP208yD8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "bf3cd683-8a02-4f8f-d071-21f9853e169f"
      },
      "source": [
        "sources=pd.read_excel('Book1.xlsx')\n",
        "sources.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>Share of respondants</th>\n",
              "      <th>news source</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>India</td>\n",
              "      <td>0.62</td>\n",
              "      <td>printed newspaper</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>India</td>\n",
              "      <td>0.69</td>\n",
              "      <td>Online news apps,websites</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>India</td>\n",
              "      <td>0.82</td>\n",
              "      <td>social media</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>India</td>\n",
              "      <td>0.41</td>\n",
              "      <td>video sharing online</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>India</td>\n",
              "      <td>0.35</td>\n",
              "      <td>online blogs or forum</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  country  Share of respondants                news source\n",
              "0   India                  0.62          printed newspaper\n",
              "1   India                  0.69  Online news apps,websites\n",
              "2   India                  0.82               social media\n",
              "3   India                  0.41       video sharing online\n",
              "4   India                  0.35      online blogs or forum"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "4luA1h450Hd3",
        "outputId": "d9aa40b8-2678-4f75-f5d3-66d8c56155d1"
      },
      "source": [
        "sns.set_theme(style=\"darkgrid\")\n",
        "fig_dims = (30, 10)\n",
        "fig, ax = plt.subplots(figsize=fig_dims)\n",
        "bar_plot2 = sns.barplot( hue=\"news source\",y=\"Share of respondants\",x=\"country\",ax=ax, data=sources,palette='icefire')\n",
        "bar_plot2.set_xticklabels(bar_plot2.get_xticklabels(), rotation=40, ha=\"right\")\n",
        "for p in bar_plot2.patches:\n",
        "  bar_plot2.text(p.get_x() + p.get_width()/2., p.get_height(), '{0:.2f}'.format(p.get_height()), \n",
        "        fontsize=12, color='black', ha='center', va='bottom')\n",
        "bar_plot2.set_title(\"Main source of information about news and current affairs in selected countries worldwide as of June 2020\",fontsize=16,fontweight='bold')\n",
        "fig.tight_layout()\n",
        "plt.savefig(\"Main source of information about news and current affairs in selected countries worldwide as of June 2020.jpg\",dpi=200)\n",
        "files.download(\"Main source of information about news and current affairs in selected countries worldwide as of June 2020.jpg\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_e6cd20da-b58e-4767-b06e-c157044af37b\", \"Main source of information about news and current affairs in selected countries worldwide as of June 2020.jpg\", 826988)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2160x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "UfaDwH3N2Fjm",
        "outputId": "b6f3126c-7666-48e2-bfa9-95b21bc5f894"
      },
      "source": [
        "level=pd.read_excel('/content/Level of difficulty differentiating between true and false information online among consumers in selected countries worldwide as of June 2020.xlsx')\n",
        "level"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Characteristic</th>\n",
              "      <th>Total EMEA</th>\n",
              "      <th>Argentina</th>\n",
              "      <th>Australia</th>\n",
              "      <th>Brazil</th>\n",
              "      <th>India</th>\n",
              "      <th>Nigeria</th>\n",
              "      <th>U.S.</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Very easy</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Somewhat easy</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Neither easy or difficult</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Somewhat difficult</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Very difficult</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Don't know/not sure</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Characteristic  Total EMEA  Argentina  ...  India  Nigeria  U.S.\n",
              "0                  Very easy        0.03       0.08  ...   0.18     0.20  0.07\n",
              "1              Somewhat easy        0.25       0.31  ...   0.37     0.38  0.32\n",
              "2  Neither easy or difficult        0.39       0.36  ...   0.22     0.22  0.28\n",
              "3         Somewhat difficult        0.29       0.19  ...   0.18     0.17  0.26\n",
              "4             Very difficult        0.03       0.03  ...   0.04     0.03  0.05\n",
              "5        Don't know/not sure        0.01       0.03  ...   0.01     0.01  0.02\n",
              "\n",
              "[6 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkMqtyGkpR2e"
      },
      "source": [
        "f={'fontsize':14,\n",
        " 'fontweight' : 'bold',\n",
        " 'color' : 'black',\n",
        " 'verticalalignment': 'baseline',}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "J_Isu-hXVP4w",
        "outputId": "7ec1a403-75a2-4852-b673-8a3d1d10b24d"
      },
      "source": [
        "EMEA=level['Total EMEA'].to_numpy()\n",
        "Characteristic=level['Characteristic'].to_numpy()\n",
        "current_palette = sns.color_palette(palette='inferno_r',n_colors=6)\n",
        "plt.rcParams[\"figure.figsize\"] = (20,5)\n",
        "plt.pie(EMEA,labels=Characteristic,labeldistance=1.15,colors=current_palette,autopct='%1.1f%%',shadow=True)\n",
        "plt.title('Level of difficulty differentiating between true and false information online among consumers in 2020',fontdict=f)\n",
        "\n",
        "plt.tight_layout()\n",
        "fig4=plt.gcf()\n",
        "fig4.savefig(\"Level of difficulty differentiating between true and false information online among consumers in 2020.jpg\",dpi=200)\n",
        "files.download(\"Level of difficulty differentiating between true and false information online among consumers in 2020.jpg\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_5ae0ffb5-3381-46b4-b7d9-d632d0b9a73e\", \"Level of difficulty differentiating between true and false information online among consumers in 2020.jpg\", 183213)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKdZQ_IYCvLr"
      },
      "source": [
        "India=level['India'].to_numpy()\n",
        "US=level['U.S.'].to_numpy()\n",
        "Nigeria=level['Nigeria'].to_numpy()\n",
        "Brazil=level['Brazil'].to_numpy()\n",
        "Australia=level['Australia'].to_numpy()\n",
        "Argentina=level['Argentina'].to_numpy()\n",
        "Characteristic=level['Characteristic'].to_numpy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 971
        },
        "id": "LK8aw6zOWMEI",
        "outputId": "cdb11228-e9ac-4668-e1ec-db68d7f74bc3"
      },
      "source": [
        "fig, axs = plt.subplots(3, 2,figsize=(20,15))\n",
        "fig.suptitle('Level of difficulty differentiating between true and false information online among consumers \\n in 6 different countries 2020',fontsize=16,fontweight='bold')\n",
        "\n",
        "current_palette1 = sns.color_palette(palette='RdYlGn_r',n_colors=6)\n",
        "\n",
        "a1=axs[0,0].pie(India,colors=current_palette1,autopct='%1.1f%%',textprops={'color':\"black\"},shadow=True)\n",
        "axs[0,0].set_title('India',fontsize=12,fontweight='bold')\n",
        "\n",
        "\n",
        "a2=axs[0,1].pie(US,colors=current_palette1,autopct='%1.1f%%',textprops={'color':\"black\"},shadow=True)\n",
        "axs[0,1].set_title('U.S.',fontsize=12,fontweight='bold')\n",
        "\n",
        "a3=axs[1,0].pie(Nigeria,colors=current_palette1,autopct='%1.1f%%',textprops={'color':\"black\"},shadow=True)\n",
        "axs[1,0].set_title('Nigeria',fontsize=12,fontweight='bold')\n",
        "\n",
        "a4=axs[1,1].pie(Brazil,colors=current_palette1,autopct='%1.1f%%',textprops={'color':\"black\"},shadow=True)\n",
        "axs[1,1].set_title('Brazil',fontsize=12,fontweight='bold')\n",
        "\n",
        "a5=axs[2,0].pie(Australia,colors=current_palette1,autopct='%1.1f%%',textprops={'color':\"black\"},shadow=True)\n",
        "axs[2,0].set_title('Australia',fontsize=12,fontweight='bold')\n",
        "\n",
        "a6=axs[2,1].pie(Argentina,colors=current_palette1,autopct='%1.1f%%',textprops={'color':\"black\"},shadow=True)\n",
        "axs[2,1].set_title('Argentina',fontsize=12,fontweight='bold')\n",
        "\n",
        "fig4=plt.gcf()\n",
        "\n",
        "l=fig.legend([a1,a2,a3,a4,a5,a6],\n",
        "           labels=Characteristic,\n",
        "           loc=\"center\",\n",
        "           borderaxespad=0.1,   \n",
        "           title='Legend'\n",
        "           )\n",
        "fontProperties = mp.font_manager.FontProperties()\n",
        "fontProperties.set_weight('bold')\n",
        "fontProperties.set_size('12')\n",
        "l.get_frame().set_edgecolor('k')\n",
        "\n",
        "l.set_title(title='Level of difficulty', prop=fontProperties)\n",
        "\n",
        "fig4.savefig(\"Level of difficulty differentiating between true and false information online among consumers in different countries 2020.jpg\",dpi=200)\n",
        "files.download(\"Level of difficulty differentiating between true and false information online among consumers in different countries 2020.jpg\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:31: UserWarning: You have mixed positional and keyword arguments, some input may be discarded.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_446543e7-567d-4edd-9ddc-e90406c7feb0\", \"Level of difficulty differentiating between true and false information online among consumers in different countries 2020.jpg\", 522202)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4kjv3r4wAsEEpJZ4P6Iz8FeDYTvoJa/rZ0FltrPV/mVHvHuP5AB/13tqKeM16B/p4bk4TioPQGxQAMqydituAVtbZVruN56P8TFdvPDc/iINDmJdnIz/+RX0Assz691LoC1wB/QvQFwHq3mc8H+DjeaSY7eEyH21xipf31XopM+ua4aOui7y8r9F47ms9+13/nkQkDcDp1r+7obs0OaHPqra5fi8CLYO5jTON51ltmKevz7h7Xu7tUQMg3se+62E/MZveRcsyng/dFfRD6F/9q6GTPXdyt1Yp5RkDELh9RbL9+BLs9N7aeyj7ldZ+zgC0qj2bQmmbkdj/FBrPb4T+lVmgu0QFRSn1OPQZirHQ7e931kszoLtIAaG199ZqS1vz+v0sIk607BOaoI87juNxMOdNU5BlJnO7LIK1zqCvFT4hhAB1edPa45HWruM2z8/LwWxbBVqW1u7z3MYByLWer4M+8+KAHrQnkNa0mUhtU6/fj9bIxMHuh8zjwREex6imiLUTpdQHSqmR0F1Vz4S+BKsauuvzfdZkbTkebes2C6WNnbDPUUrVKaUugv4BagZ099kPoBPJe0Qk0/M94RZqcjfUunm2+ci3dqhvWtMME5H7RSRdROJEZKSIfM943c2dxDnQcl+xdUqpD4xp/mE8f1xEholIrIgMEJFLReRd6MYQqleM53eLvkl5NnQ3Fm/TBE0pVQN9MAfoX1l+ICLdROQi6Gsf7PCS8fxuETnFupFutoiYy2z+InMOAIjIQujrC4JlfuGNERFvv7K4bwDfDcAE63mgs3aA7irhdrmInGQdzN0dQnytZbbFu0RkuojEi0hP63PwFHSyGmpdvxWR8dZnpa+InCsi/0BLAhCqYNa/yd0l1v38CHS7vStM9YfKPPN1uYhMsu7reE8b6rxXRHqLyHDoEfzc3rD+urdHMoA/ikiWtZ/JEZFrAKwy3mMu/zARSTZnpJSqhh70A9DXMwHAO9YPWu9D76/GWuXmsobSviLZfnw5ttxB/LLqydyvnCUiDhEZBt2d0VNbP2ehtudQ/Md4fpuIZIrIEBzfptrCPPg4An3M9g207CP9str4A9D76z3QAwC8aEziPigLpb3b4TW0rIvLRWSGddbx/9By5uIt1X7DjZvb5RCAWBH5Cbz/iu93/+BDxI5Hgpjfj63t3hf6Gq9IzC8c2rrPM7dhPXQiMRCRu6dtpLbperScoZsoIpeISDfofVBQyZ1S6iu09OAaAuB3ItLfOk49xTrm84wvrO1ERO4WkXOgTwq9Bd3b4qD1sns/tQstCd506zsgBXr0/EgI2/eqiJwvIjdAJ+CroM/iuferoSTirReo3yb8jwB1rN8v9Bmh/X6mq/RS9zse09zs8XoS9IGSv/m7+63PMcqWBFimoEcysqav9LUMPuo/Bd6vlzBH8AnUH93n8gR4bYnnPBD8aJlD0XLNhoI+wFBouXDcXN/ZRpnZ7zkTLaOhnrCdjOneMl474bo0P+t2aYD1WmlMu9goX+yjDs+4/L1W6qfNeM7jhG1qvOaA3pH4q2tOgPWc7W0egdY/vF8T94aX6b8MY/2+toNZvsQof9xL/eaIhJuCaCfLjekDjZbZF3pIZp/bw6PutQG2/V0er51mld/uUb7Ao96g2hci2H78rE/PC/aPvQ8B9r3Q3Wg2GNNUQ/duMPcri0NdD35iDaU9LzXKs32Uu9elr9Eyzf3P0iDW5XIf7epSL3UfhZdrabytc+gDBn/rbVFr2nuwy+CrHXiUm21wiec6Nl5r1WiZQewLvH1/LfcXE44fHMPc5ge9zRd+9g8I72iZrfo8o/WjIC73qMddXulv/fmqw8+0ZnnI+zwfyxyD4wdqcT/M/YK5PtvaZiK2TeF7tMw9xv+DArSBadD7YW+xufcnkWwn/kbrNa+1/5NRXgd97GF+b5jra6m3tuBj/ouN8pDbmK9tZtTv+V1vPnYgwIjP4XiEq1smlFKfQ99f6BHoL44G6IuPK6AP1q7z8jazC2YjgL941HkUOlO+HTrrPQp9CvRr6BHwitFy7V4osdZY9d4J/WVdj5b7hdwMfdDlqx9yMPW/Cd19Yy30evgK+gJOW34NU7q1XQTdz/k96KTNfd+gZcZ0X0GPcLYGen3shL6Q/O8hzGs79H1aKnD8aXVPvzGeP6VOvC7Nl2sA/BL6y7UWeiSr2cHG1xZKqRLoUV3fhm7bjdC/ov0Hunvwq0HW44K+sPoG6FP11dDrajP0SFQ3IISuWB51B7v+TZdBX5tzEHq5noQe2CFc9YfqOuh7cLm38WsALjBe99Ydyp9Z0J+9GugfEv4IPSKYCwCUUrsBTIa+sPsLtPyy+yX0ReWFHvVdDn2PqyM+5meekXOfsQP0j1lm+dvmm4JtX5FsP348DD3K4k7oL6igKaWaoHsCvAUdaxX0NYheu9SH4XMWdHsOcTlqAZwKPdKauy39CXq/2mZKqb9C31NqE/T+92PoLksbg6ziIPR6/Qi6+1Yz9LpeAT0y3wvWfEJt7+1OKfVz6Db+FvQ2bIJuA2XQI2S35trx1roPuufAduj90dsA5lpxeRNo/3CcSB+PeJlfM3R3xJuhr1E8as3zc+gfpmZZMXUYbd3nWfugBdD7jiroz8fvAHw7QvFGbJsqpf4CfTnLV9DHcaugl83sVnhCl2WPOt6HPl5/HHodNkK35w9g9TyLcDt5CPqzvcNahjro7rJ3QF+D53YT9D0m91rTvQQ9QFPYhfl79U3ofelXVj3N0N+d5QBmq8Ddx9vMPawyUbsSkWuhDxYBPfSt3V2BqIMQkVEAXEqp9db/SdAJvfsHovuUUu1yUTIREVFHYV2mkAtghVLKZV1vdyX0Dx8C4AOl1FQ7YyT7hft6GSK/ROTn0L8MZ1tFzzGxIw+nAHhQRKqgz470hb5PDKDPNNxvV2BEREQ26g/dC6ReRPZAj6ybYr1WjQidjaTOJWzdMomClAGd2B2C7hJabGs01BGthO6KeRR65L9G6O4sSwBMUUr57XJCREQUpfZAdzvfBX27gljo7n+/BzBeKfWhn/dSF8FumURERERERFGAZ+6IiIiIiIiiAJM7IiIiIiKiKMDkjoiilogo67E8QvX3F5EHReRrEakXkYMislJEvteGOpcbcWdbZdlG2VKP6S8TkVUiUm1MkxrotY5ORFJFZIn1WGR3PJEgInnGMoZ6c3iISKW1TSsjEF6geY8Tkd+JyCcisltE6kRko4j8VURG+njPYhH5UERqROSIiLwtImd7TNNLRO60PgdbrXq3i8gbInKaj3rzReRV6/NXKyJrROS7IuKMxLITEXVkHC2TiKgVRGQ89A2z+xjFcQAmQA8G86t2iCEX+j5AJ/xQ5++1TiIV+r5HgF6OF2yMJVLy0LKMldADB3UW8wHc6FE22HqcJyKzzcEdROQe6JuTm2YBmCUi1yqlfm+VDQPwE4/p+luPU0XkO0qp3xn1ngXdNszjmTHQn7+TAFzSmoUjIuqsOuuXPhFRQEopsR5zwlmviMQAeBo6sWuAvsFpPwDdAZwMfXPYsFFKVRrLsth4aQJa9uN3AHBa0xwK8FqbiUhCOOqh0LnXvVIq29qm2TaEoQD8E8AZ0EOx50DfmBgAEgDc7p7Q+iHEfW/Kdda046Bv7AsAvxaRvkbda6Hv3ZUOPdS7+UPJT91n5EQkDsAfoBO7GgBzoEdkdsdR5HlmkIgo6iml+OCDDz6i8gF9AKoALDfKlhjlCwE8DGA3gMMAXgcwLIh6LzDquKsN8RUD2ACgDsCnAE4HsNyoO9uaLtsoW2qVmdOZj0p/rxnzngrgeWvZGwHsALDUPU9jOrOuMdY6qvFYp2dA377iAHSyWwngQQC9PeqqNGI8GcB/oM9yboG+f2Gcl23k+VgaYJ0KgKsAvGtt03prfk95TDcO+nYsO63l3wfgZQAzPaZb6rk9fJV7bicAV0AnKrUAPgdwpY/16vlY7GV9zQSwwqprqefrHjEPAPAIgE3W9jgI4FUAszym6w3d/r+2tsMRAOut9TIiwHru5qVssrEMXxjlvzTKLzPKf2KU32iVJUH/EGHW67Bic0/bzyo/xyj7ozH9TKP8Wbv3Q3zwwQcf7flgt0wi6sqWQnf/c5sH4GURGa2UavbzvlOM5z1FZDV0d7L9AJ4BcLtSqtrfjEVkMY4/w5cH4B/QB+IRJSIXAXgKgHlNUgb02ZJzRCRfKbXey1uXA+jlUdfNAB7wmG4Q9NnMs0RkqlJqj8fr6QDeBhBv/Z8F4BboA/ifhbxALbEI9BnVC7zEMwhWFz0RKYBOduKNaXoBOBvAfBG5Qin119bGYVkIvT7dRgJYKiIblVLvhlhXH+ikOuDZUhEZAZ3Y9jaKU6ET8NNE5BKl1N+s8j9Dd680dQMwHMBfoRM9r5RSVV6Kzfi2Gs9PMp6v8/H8JKveo17qjUVLW62F/pyFVC8RUVfBbplE1JUdhk6q+kOfWQGAEQCmBHjfQON5CYCx0Ae2mQC+A+B1f4M5iIgDwN1G0RXQXTpvxfHX8PmkdFfTbxhF31BWFz1/r4lIEvRZHSf0DeNHQic5BdBneXoC+IWP2W6GPnuXBOA6EckC8HPrtX9BJ1AJAAqtshwY3fMMiQDKoROQBUb55dayLbHe6/Zn5b1bqqcL0ZLYbQdwGnSXwaHQZwPdHkNLYnc99LpfBKAJ+nvxQRFJ9jOfYKRCt40eAO4zyt3LOAfet5EopZZ61JUE4L/Q17Ol4Pi24+m30Ov1MPQ2TYD+4eEL6GV7yOrOCOhr3gDgOSvO7tBnNG8GsC3I5QRwrKvynUbRo8Zzs8vlYR/P0/1U/2PodQAAjyulGsNULxFR1GFyR0Rd2QNKqVVKqZ3Q1w+5DQrwvljj+RboMx39oZMlAJgGfebGlxHW9ADwqVLqL0qpKqXUr3H8GY9ImA6dwAHAROiD/nroLpLug/55Pt57o1JqnVKqVin1BfTZIPe6OAM6+auDTtzcvI1w2AzgO0qp/Uqpl9FyJibQeg/ETBR/rJR6QylVo5TaqJT6KQCIyHDoZAcAViulHrXW/YvQZ04BfZ3XtDbG8olS6hGl1BEATxrlrV3GbyilNlnLs8HbBCKSCOBU698e0Nu0Drrrr3sEy97Q2x3Q3TYBvay3Azgfug38RikV9OAuIhIPfca0wCp6UCn1bDBvDaLuW9EywMoHAH4QjnqJiKIVkzsi6srMbmc1xvNA3d/2Gc+fU0ptsBLEJ4zyifDN7NroeYZke4B5t1UwZzISfJy5+rQVdfXyUrZbKWWeXXGv+3gv04bCPJNT4WMa88zoFo/XNhvPAy1boMsaWtu2vNmjlNoRxHQ9cXxXW1/c2+Rq6DgzoLvF/gnAxwC+DvbWDCLSDfqHkXOtolLos9em3cZzsxt0d+O5Z9ddiMj9aDnr+T6AMzy6bbaqXiKiaMbkjoi6skbjuQrhfZ8EMY23a4fczORwgMdrmSHE0Rrmwe4fja6Axx4AHEqpGs83KqVq/dR1u4+6vCVJjR7/e1v3oWwPN/Ngf5SPacyYB3q8NtDLdPVGmZmYDQ4QSzBtK9hl9FzvvhyAPisKABv8bNtXAEAp9YFSaiR0F9gzoUe0rIY+u3ifl/qPIyK9oUemnGsV/VQp9S2llOdyfWQ8zzWej/Y2jYg4ReSP0AknoJPHU9WJI72GVC8RUVfA5I6IKHTlaDnoP09EhopIBvS1c25v+nn/l9CjUwLABBG5XES6ich3oQcXiaQVaBm05QoRuUREUkQkWUROFpFfAPhNkHW9Bn2dGgDcLCJniEiSiHQXkdki8iiC60bnzX7j+bAgr4F7yXh+t4icYsWTLSL/BwBWl8YvrWnGicg11vKfAz2gCqDXz/vWc/Ns3jkAICIL0fZum8DxyzjGum6t1azk293uhonI/SKSLiJxIjJSRL5nvA4Rudta7mboJO1ptLQNz8T3ONb1lu9Aj5DpAnCdda2kN0+gJZG9zdoeY6GvdwT0DyFPW/XGA/g79IingB70ZaGPgVZeQ8vtFAqtNtcXx19f+Sd/y0FEFG2Y3BERhUgptR0tZxUGQl/TtAMtXTEfV0p94Of9LuhBItyegB4p8gFEeLRM64zct6APyOOgR0Wsgj5j8z8A3ygpJH8AACAASURBVIe+XiuYuragZTnSoEegrIEe0GI5gGvRum6IsEYbdY96mA+gWkSUNcqoL3+HHq0U0GdE/23FswnAT43prkNLcv4Y9PK/BH39oAv62kL3mcun0ZLA3i8iR6Bvmu3vzGywPoUexAbQg5g0WsuY3YY6b4I+gwfoNrobelk/h74lgXnG8WLo5d5iTfM1Wn5ceC3AfK5Cy3V8DgCPWrEfe7gnVEqtAnCv9e9o6O2xGro7KAB8TynlPus6DS1dPAE94mijR91zrHoboLuWNgFIhm5zu9ByJnGZUuofICLqQpjcERG1glLqQQDnAXgP+kC/DnpAleuhDzgDvX8p9AHyRugD/DVWfasjE/Fx814GYAaAZ6EP/psA7IW+3uo+6CQg2Lruhx5O/1XoM1FN0AfYK6Bvnv7nNoR6OfQokUeCjEUBuAh6/b9nva8BOnlZZkz3H+gRUf9mxdoEnRC9AmCOeRsEpdRX0MnGGuhtvBN6FMy/t2G53HVvhz7bW4Hju3+2pc7PoUeAfQQ6WWuATrYroG+9cZ0x+UPQZ+x2WNPVQSfUd6Dlx4uwUEr9CHp00I+hu5lWQ2/bc5RSj7Wh3lcAzIYerdV9X8O1AL4Ha2RSIqKuRE7sGk9ERERERESdDc/cERERERERRQEmd0RERERERFGAyR0REREREVEUYHJHREREREQUBZjcERERERERRQEmd0RERERERFGAyR0REREREVEUYHJHREREREQUBZjcERERERERRQEmd0RERERERFGAyR0REREREVEUYHJHREREREQUBZjcERERERERRQEmd0RERERERFGAyR0REREREVEUYHJHREREREQUBZjcERERERERRQEmd0RERERERFGAyR0REREREVEUYHJHREREREQUBZjcERERERERRQEmd0RERERERFGAyR0REREREVEUYHJHREREREQUBZjcERERERERRQEmd0RERERERFGAyR0REREREVEUYHJHREREREQUBZjcERERERERRQEmd0RERERERFGAyR0REREREVEUYHJHREREREQUBZjcERERERERRQEmd0RERERERFGAyR0REREREVEUYHJHREREREQUBZjcERERERERRQEmd0RERERERFGAyR0REREREVEUYHJHREREREQUBZjcERERERERRQEmd0RERERERFGAyR0REREREVEUYHJHREREREQUBZjcERERERERRQEmd0RERERERFGAyR0REREREVEUYHJHREREREQUBZjcERERERERRQEmd0RERERERFGAyR0REREREVEUYHJHREREREQUBZjcERERERERRQEmd0RERERERFGAyR0REREREVEUYHJHREREREQUBZjcERERERERRQEmd0RERERERFGAyR0REREREVEUYHJHREREREQUBZjcERERERERRQEmdxRRIrJURJSI/Mb6f7H1/3KbQyMiIiIiiipM7sgvEam0krFFYaqyAsBvATwTpvqIiIiIopq34zER+Y1VttTHexwicoeIbBSRehHZLyIfichV7RY4tbsYuwOgrkUp9SGAD+2Og4iIiCjKfQfAEgBbAPwJQHcAkwBMA/C4fWFRJPHMHQXN6GL5qIi8LCJHRWS1iOQZ08wQkTUiUiMiTwBI8KjjuG6ZIpIhIv8VkX0i0igie0XkSRFJbd+lIyIiIooqBdbfm5RS1ymlLlFKjQBwq51BUWQxuaPWuBZAE4BNAMYCeBAArITsZQBjAPwPQG8AFwaoqxuAROt9fwBwEMClAO6NROBEREREXcRO6+8fROQvIlIiItlKqQO2RkURxeSOWuOfSqlzAdxg/T/B+ns2gFQAXwE4VSk1H8Bn/ipSSn0J4BoAnwM4CmCd9dLccAdNRERE1IXcBeBjAL0AXAbgYQAbRWSJnUFRZPGaO2qNT62/h6y/ydbfTOvvBqWUsp5/CWCir4pEpAjAU15e6tPWIImIiIiiRK31N84oi7f+HvX2BqXUNgAnichYALMBFAKYDuD/ROQhpdS+SAVL9uGZO2qNJuuv8ijfbv0dJiJiPR8eoK6Lrb+PQe+k3P+L98mJiIiIupyN1t9pACAiTgAnW2VfWWUjrUe89f9UEUlSSq1RSj0E3cMK0MdYydY0GdZ70ttrQSiyeOaOwukVAIcBDAXwbxGpR0uXTV92W3/nA4i1/hIRERFRiz8AOAvATSIyHUAa9PHWEQDLrGk+t/5OgL4s5vsA5orIewC2QY+UCeheVVus5z8HcCX0bapuivAyUDvgmTsKG6XUQQALoK+bmwa9w3k2wNt+CuA/0N0wJwG4J5IxEhEREXU2SqkXoa+bWwlgJIAeAN4AcIpSaqePt/0NwEfQx1fFAAYCeB7AOcblMxRlhNuWiIiIiIio8+OZOyIiIiIioijA5I6IiIiIiCgKMLkjIiIiIiKKAkzuiIiIiIiIogCTOyIiIiIioijA5I6IiIiIiCgKMLkjIiIiIiKKAkzuiIiIiIiIogCTOyIiIiIioijA5I6IiIiIiCgKMLkjIiIiIiKKAkzuiIiIiIiIogCTOyIiIiIioijA5I6IiIiIiCgKMLkjIiIiIiKKAkzuiIiIiIiIogCTOyIiIiIioijA5I6IiIiIiCgKMLkjIiIiIiKKAkzuiIiIiIiIogCTOyIiIiIioijA5I6IiIiIiCgKMLkjIiIiIiKKAkzuiIiIiIiIogCTOyIiIiIioijA5I6IiIiIiCgKMLkjIiIiIiKKAkzuiIiIiIiIokCM3QFQ51JWUSIA+gLoAyANQE+Pv6nQPxoo4+Gy/jYDOARgn/XYbzzfV5xb2tCey0JERETUWVjHYBkABkAfd6VaD/fxVwoA8fLWJgBHoI/BDlt/3cdj24pzS/dGPHhqN6KUsjsG6oDKKkrSAYwGMBLACADDAAwGkA0gIQKzdAHYAuBLAOuNv+sBbC3OLWVDJSIioqhXVlGSAWACgLFoOfbKBjAIQHwEZlkLfQy20XpsAPAZgM+Kc0urIjA/iiAmd4SyipI4ABMBTAVwsvU3286YPFQB+ADA+wBWAHi/OLf0sL0hEREREbWN9WP6LACTAORBJ3V9bQ2qhYJO9j6FTvY+AvBecW7pUVujIr+Y3HVBZRUlMQDyAZwFYLZSKk9EIvFLUKQoAJ9DJ3pvAXi1OLf0kL0hEREREflXVlGSCmAOgLlKqQIAo0XEW1fKjqoB+gf3/0Afg73Py2o6FiZ3XURZRUkvAGcCOEspdYaIpNodUxg1AXgHwMsAXirOLd1oczxEREREAICyipKhAM5XSp0HYLKIRNOAhrXQid6zAF4ozi09YHM8XR6TuyhWVlHSE8AlSqlCAFNFxGl3TO3kcwAvAniyOLd0nd3BEBERUddSVlGSC+B85VIXikPG2h1PO2kCsBw60Xu+OLd0t73hdE1M7qJMWUWJE8AZLpcqFsHZIhJnd0w2+xBAGYBlxbmlR+wOhoiIiKKT1UvqMpdLXeXoOgmdLy4ArwP4PYCXi3NLm2yOp8tgchclyipKhiulvqlc6kqH05Fudzwd0FHoX5LKALzN0TeJiIgoHMoqSma7ml3fEocsEpFYu+PpgHYBWArgD8W5pV/bHEvUY3LXyZVVlOQ3NTbf7oxxnNHJLsi103oAvwTwRHFuab3dwRAREVHnUlZREquUutzVrG5zxjiG2R1PJ6EAvAHg/uLc0jftDiZaMbnrhMoqSkS51DlNjc3/FxsfM8nueDqx3Wv31d31vx21T64oXMZbKxAREZFfZRUlyU2NzdeLyC3OGPaUaoOPANwLfW0ek5EwYnLXiZRVlDibm11XqGZ1e0ycc7Dd8XR2SqnqJysON9Y3KweAhwD8ekXhsv12x0VEREQdS1lFSVpjQ/PNDofc6IxxdLc7nijyBYD7AfyF1+WFB5O7TuKRj69e6HDKr2LjY5jUhcn6rUfWv3OweYRRVAX9K9KvVhQuq7MpLCIiIuogyipK4uuONt4SG+f8gTPGkWJ3PFFsPYDbinNLX7A7kM6OyV0H98jHV092udSjiSlx7H4ZRs1NLrVszYHaOqczycvLWwD8EMCyFYXL+AEhIiLqYsoqSqTmSH1xbLzznrj4GHa/bD/vArilOLf0f3YH0lkxueugfr/q2oH1tU0PJ3WLO5vjpITfl5sO7/xvlSsjwGQfAvjeisJl77VHTERERGS/hz/85inikEcSkmI5UIp9ngFwa3Fu6Sa7A+lsmNx1MGUVJc7D+47elZKW8H2n08HhdCPA5VJ4evX+g9UOZ1qQb/kbgG+vKFy2J5JxERERkX1+/d/FvR0OKevWM/Ecu2MhAPo2VksA/Ko4t7TZ5lg6DYfdAVCLu14qnF5zpP6rHr2TfsjELnK2bK/aE0JiBwAXA6jILy+6JFIxERERkX3ue+2yGxNT4iqZ2HUoSdCDrXxUVlHCy5OCxDN3HcCdLxbGxyfGlPbKSFnscDqYcEfYs6v27Tsozt6tfPtLAK5fUbhsRzhjIiIiovb381cvHZ7YLa68e8/ECXbHQn41A/gdgJ8U55bW2B1MR8bkzmY/e7nwtNT05D8ndYvvZ3csXcGOndX7/7m3sVcbqzkE4OYVhcvKwhETERERta+8ghw5/7sn39J3UI+7YmKdcXbHQ0H7CsClxbmlH9odSEfF5M4meQU5zgu+d/JD/XLSrnU4OGJKe/nH6n07d8EZaCCVYL0AYDFvgE5ERNR5XPfL0/rmjEl/vk9W92l2x0Kt0gR9Ld7Pi3NLXTbH0uEwubNB8d1zB4+ckvly78xuuXbH0pXs23v08As763uEudqNAC5cUbjs0zDXS0RERGF269KFCweO7P2npO7xoVx7Tx3TmwAuK84t3WV3IB0Jk7t29t3Hzr54SF7f3yd1i+9udyxdzetr9m/fohyZEai6DsB3VhQu+30E6iYiIqI2yivIcS781uSHs0b0uprjG0SV3QCKinNL/2N3IB0Fk7t2kleQE3P2tRNLB43uc5WTO5V2d+hgXc3ftxxNjvA9A/8C4LoVhcuORnImREREFLz5V0/sN33hiJcyBqedZHcsFBFNAG4szi191O5AOgImd+3gzKsm9Jlx7sh/ZgxOm2x3LF3V2+v2b93Q7Mhqh1mtBjB/ReGy7e0wLyIiIvKj6LYZJ08+ffDzaX1TwnW9PXVcpQC+U5xb2mR3IHZichdhi26cMnH6whEv9urfbYDdsXRV1VX1deUba+LgkPY6Y7oNwJkrCpetbaf5ERERkSGvIEcmnz74ygkFOQ8mdotLsTseajdvAriwOLf0oN2B2IXdAyPo4lunLyooHP0mEzt7rdlWs6cdEzsAGADg3fzyooJ2nCcRERFBXwqTv3D4PVPOHPoHJnZdzikAPiirKMmxOxC78MxdBOQV5DhG5w8oyV844r7ElLgku+PpyupqGxuXfXkEzeKItWH2DQC+saJw2VM2zJuIiKjLySvISclfMPzhiafmXMaBU7q07QDmFeeWfm53IO2NjT7M8gpyYvIKsu+cecGoXzKxs1/FlqqdNiV2ABAH4Mn88qJbbJo/ERFRl5FXkJM649wRT02cN/hyJnZdXiaA/5ZVlEyyO5D2xoYfRnkFOQmjp2fdn79w+A/i4mPi7I6nq2tsaG5eU6v62ByGALg/v7zoBzbHQUREFLXyCnL6FBSOfnbCKTnnOByRHRqbOo3eAN4qqyiZZXcg7YnJXZjkFeQkjTip/y9nXTDqhphYZ4zd8RCwfsuRnY0OR6LdcVjuzS8vutnuIIiIiKJNXkFOxqmXj31h7MyBcyN8yyPqfLoD+FdZRck8uwNpL0zuwiCvICdx2MR+980tGv3N2DinXV0AydDc5FKfVbtS7Y7DwwP55UXfsTsIIiKiaJFXkDNo9oWj/pY7dUC+3bFQh5UI4IWyipLpdgfSHpjctVFeQU7C0Ly+95xy6dirY9kVs8PYuLVqV53D0RFHyPpNfnnRt+wOgoiIqLPLK8jJmnr2sKXjZg+aaXcs1OElAXilrKIkz+5AIo3JXRvkFeTE54xNv/PUy8ddF5cQE293PKS5XAorjzR2lO6Y3jyYX150ld1BEBERdVZ5BTn9Jp6a89hJpw+Zza6YFKQeAF4rqygZYXcgkcTkrpXyCnLievVPueXUS8deH5cQk2B3PNRiy/aqPdXi7GhdMk0C4NH88qIu0/+biIgoXPIKcnqNnZn1UP6C4acLB0+h0KQDeKOsomSg3YFECpO7VsgryHHGJ8WWzP/mhG/z5pgdz8oDDZ1hRx8D4O/55UW5dgdCRETUWeQV5PTIHtPnFzPPH7WAtzugVsoC8I+yipKoPIbnhyJEeQU5IoLzz7p6wrfT+qbYPcw+edixo3r/AXF2lu3SA8Ar+eVF6XYHQkRE1NHlFeQk9cxI+cm8K8ZdGBPLAeyoTcYC+GtZRUnU5UIcsj90M+deMubHA4b3yrE7EDrRyn11DYDT7jBCkQ3gxfzyooIVhcvq7A6GiIioI8oryIlJTIy77qYbzjpvf9wRh7I7oA6gsb4J913xIhobmuFqdmHSaYOx6IYp+P2t/0bluj1wxjiQM7YvrrhjFmJiTzw2eu+FL/CPx1YCAM6+diKmLxqJxoZmPHTDqziwuxoFhWMwt2gMAODPdyzHnItHY1BuZ/n9PCgLANwN4Id2BxJOUZetRlJeQc7ISfMG/2x0ftY4u2OhE+3be/TwLjgz7I6jFaYCWGp3EERERB3RvPnjJc4Rc/Wttyy8buK4wTn5/UY2x9Q799kdl91i4pz4ftkC/PT5i3DHsxdi7btbsXHVLkw9exju/kcR7nzhYjTWNeGdZz8/4b3Vh+rw0iMf48fLzsPt5efjpUc+Rs3heqx7dwuGTszAT5+/GO+//CUAYOsX++ByqWhL7NxuK6soudTuIMKJyV2Q8gpyMrJG9Prp1HOGTbM7FvJu5a7aartjaIOLeYsEIiIir6ZefGH+zSedNHQYACQnJ3SblZPbI7k2fpvdgdlJRJCQrHunNje50Nzkgohg3KxBEBGICHLGpuPg7poT3rvuva0YPS0LKakJSO4Rj9HTsrD23S1wxjrQUNeI5iYXoPT50ecf/BCLbpzSrsvWzv5YVlEyye4gwoXJXRDyCnJS4hJjbpl3xbhTnU4Hu7J2QIcO1tVsdkmm3XG00S/zy4ui/v4rREREwZo3f3z2xCmDf3RB0bRsszwmxhmbP3zkgL6NqZVKdd1Omq5mF5ac9zS+O3MpcqcNwOBxfY+91tTYjPdf/hJjZmSd8L6De2qQ1q9lPJG0vsk4uKcGudOysH97Fe4ueg6nXDoWn721CYNy+yAtPbldlscmCQDKo2WAFSZ3AeQV5AiAK89YPP7MlNSEnnbHQ96t2lFzIArucxMP4G/55UVRsXMhIiJqi3nzx/dI65Vy61XXzy1wOh0nXDQmIhg/JDt7REzmNuVSTXbEaDeH04Elz12EB966ApvW7MG2DfuPvfbkXe9g+KQMDJ/UP+j6nDEOXPOLeVjy7IWYfPoQvPGX1Tht8XiU3/ceSm96DZ+9tSkSi9ERDAVQancQ4cDkLrBZ4+cMujB7TPpIuwMh76qr6us2NHb6s3ZuwxElOxciIqLWmjd/fAyAa6+6fu4pScnxfk8bZWemD5jUfeghaURnvjyjTZK6x2PklEysfXcrAODF0o9QdbAWF/9gutfp09KTcXBXy+o6uLvmhLNz/ylfh2kLRuDrVbuR2C0O1/1yHl7786rILYT9Li+rKLnM7iDaismdH3kFOQN6Z3a7fvrCESfbHQv5tmZbzR44JJra8uX55UVX2h0EERGRjU6bUTBq3uhxWcODmbh3z269p/cfJbF1MXsjHVhHUXWgFkeP1AMAGuqaUPH+VmTkpOK/z1Rg3Xtbce0v5sHh8N6rafT0LKxbsRU1h+v1QCortmL09JbumzWH67H67c3IXzgCDXVNcIgAImisa26XZbNRaVlFyRC7g2gL6cr9lP3JK8hJEIf85LLbZ34jrW9yZxyBsUuoq21sXPblETSLI9rud1MNYMyKwmWb7Q6EiIioPc2bPz4zpVvCz+/93WULk5Pju4fy3qam5qaPvv5qR1VC7cBIxddRbF2/H4//6C0olwsul8JJpw/FgpLJuHrco+jVvxsSkvSh0cRTB2NByWRUrt2D5U+vw+I7CwAA7zz3Of75e30rhLOunYQZ57Z0Uiu/9z3kzc3GyCmZaKxvwu9ueBWHdtdgzsWjccqlY9t/YdvXBwDyi3NLXXYH0hpM7rywrrO7bPqiESWT5g2eanc85NvK9Qe2rKyXaN2Bv76icNnpdgdBRETUXqzumD/87g/PvnjchEGjW1vP2q+3bN7u3D9QouCCfLLFTcW5pb+1O4jWiKaubOE0NjU9ecH4OYMm2B0I+dbY0Ny8plZF5U1XLKfllxcttjsIIiKidlRw8vRhM8bmDWx1YgcAYwYPHDQqLmsHmtEQrsCoS/lZWUVJpzx5wOTOQ15BThKAb5x25bjxMbHOeLvjId/Wbzmys9HhSLQ7jgj7ZX55UW+7gyAiIoq0efPH90tMirvk0m/MnBKOE24DM3pnnpQ2tNrRIEfCEB51LSkAHrE7iNZgcneis8ZMzxrdLzt1mN2BkG/NTS71WbUr1e442kFPAA/YHQQREVEkzZs/3glg8VUlp0zq1j0xbN/vaakpPWcMGBUbXxezK1x1Upcxv6yipNDuIELF5M6QV5AzKC4x5pz8hcOn2B0L+bdxa9XOOoejq9wP7sr88qJZdgdBREQUQTMnThk8a+LknDHhrjghIS5xxpDc9B51yVvCXTdFvd+UVZSENKiP3ZjcWfIKcpwArpxbOHp0QnJcD7vjId9cLoWVRxqT7I6jnf06v7yIF4UTEVHUmTd/fB8RXHrpN2ZOEkdkBkBxOh2Ok4cPG5jl6r1ZuTiaIAWtL4Db7A4iFDF2B9CBzEzrmzxm6IR+4+wKoLG+Cfdd8SIaG5rhanZh0mmDseiGKbj38udRV9MIADhyoBY5Y9Nx44NnnvD+9174Av94TA9pe/a1EzF90Ug0NjTjoRtexYHd1SgoHIO5RfoHsT/fsRxzLh6NQbmdbzySLdur9lSLM93uONrZRACFAJbZHQgREVG4zJs/XgBccdpZeYN69kqJ+Hf7qOwBg7rvTtq5rmZLGmKQEOn5UVS4qayipLQ4t3Sb3YEEg8kdgLyCnO4ACudclDvM4XTYtk5i4pz4ftkCJCTHoqmxGfde/gLGzhyI2/5y7rFpHv7OvzBhbs4J760+VIeXHvkYP/nbBRAR3HnR35FXkIMNn+zA0IkZOOuaifj5Zc9jbtEYbP1iH1wu1SkTOwBYeaBBIE67w7DDz/LLi55ZUbis0e5AiIiIwmS0OGTsmQvy8tprhpl9e2akHEk49NHuDbWueJXWXvOlTisRwM8ALLY5jqCwW6Z2Wt/sHr0yh/ey7awdAIgIEpL1DSebm1xobnLBHC2qtroBX3y4HRNOOTG5W/feVoyeloWU1AQk94jH6GlZWPvuFjhjHWioa0RzkwuweiE8/+CHWHRj57yscMeO6v0HxNk5s9K2GwzgOruDICIiCod588c7AFx49qKJfXukJrfrd3uP7kmpMwflJiXUxe5sz/lSp3V5WUWJrXlCsLp8cpdXkNMLwBmzL8gd5XCI7evD1ezCkvOexndnLkXutAEYPK7vsdc+fXMTRp08AIkpcSe87+CeGqT1axlfJK1vMg7uqUHutCzs316Fu4uewymXjsVnb23CoNw+SEtPbpflCbeV++q6+v1qfpJfXtTN7iCIiIjCYIzDKdmnnjnuJDtmHh8XGz9jaG6/nvUpm+2YP3UqDgC/sDuIYNiezHQAZw0c1Tutb3aPNt0sM1wcTgeWPHcRHnjrCmxaswfbNuw/9toH/9yAKfOHhlSfM8aBa34xD0uevRCTTx+CN/6yGqctHo/y+95D6U2v4bO3NoV7ESJm396jh3fBmWF3HDbrA+Bmu4MgIiJqC+us3UULzj+pX/ceSb3sisPhEJk8bOigbKRvVi7lsisO6hROK6somWZ3EIF06eQuryCnH4CCGeeOGBuOm2WGU1L3eIyckom1724FAFQdrMWmNXswfvYgr9OnpSfj4K7qY/8f3F1zwtm5/5Svw7QFI/D1qt1I7BaH6345D6/9eVXkFiLMVu6qrQ48VZfw7fzyoq5yGwgiIopO450xjqy5p4+x5aydp+ED+w8an5yzR5pw1O5YqEO73e4AAunSyR2AhZlDeyb1zuzeIW5YXnWgFkeP1AMAGuqaUPH+VmTk6Pt4fvL61xg/exBi472P9zJ6ehbWrdiKmsP1qDlcj3UrtmL09Kxjr9ccrsfqtzcjf+EINNQ1wSECiKCxrjnyCxYGhw7W1Wx2SabdcXQQaQCK7Q6CiIioNawbll907kVTMrp1S+wwA5r065Pab2r6iKaYesf+wFNTV9TQrAYtfPGyDn3tXZcdLTOvIGcAgKlTzhoaWj/HCDq09yge/9FbUC4XXC6Fk04fivFzsgEAH776Fc68asJx01eu3YPlT6/D4jsLkJKagLOvm4SfXfwMAOCc6ycjJbVlhN+XH/kYZ10zEQ6HYMz0LLy1bC0+XPQ3zLm4Q/RGDWjVjpoDIo7OeaFgZNyUX1708IrCZZ0jOyciImqRFxPr7D/n1NEd4qydqVtKYveZ2bkNH2z6cvvRhAb+qEwAgF07q/evqmret7XONRrALQAutzsmX0R10fs45hXkXJmSmjD3yjtnX+K08fYHFFhNVUPdso3VcegAA950MBetKFz2d7uDICIiCta8+eNjANxzwSVT885aNGme3fH44nIpterrTZv3xh3JtjsWsodSSu3YWbP9w9113fc7nd2NlxoB5KwoXLbdrtj86ZIHy3kFOT0AzMxfMDyLiV3Ht3pb1R4mdl593+4AiIiIQjQJQPrJ04fl2h2IPw6HyIShg7OHBRPKOwAAIABJREFUSL+tqlmxl0wX4mp2NW/acmTL05/uO/rqvsYBHokdAMQCuNGO2ILRVQ+YpztjHM6ccemT7Q6E/KurbWz8ogFdfYRMX6bklxfNsDsIIiKiYFgjZJ43cnSm6t2ne6fo8jgkq1/WxO6D90uj1NgdC0VWc5Or4YuvD215atWBxjcPNQ+sionxdznQtR11cLsul9zlFeTEAThz8umDe8UnxvJ+YR1cxZaqnc3iiLU7jg6MNzUnIqLOIgdA+ulnjx9udyCh6NOrR3p+xggVU+fcZ3csFH4N9c21n204uPXJNQfl3Wo1sC7GmRD4XUgFcGmkY2uNrtglcTyAlBGT+4+1OxDyr7GhuXlNreoDR9tvU/HVox/g4Kc7ENs9AXm/OBMAUFN5EF8//jFcjc0QhyCneDK6DT3xVjt73t6E7S+sAwBkLhqN9Nk5cDU244sH3kHDgVr0mzcU/U7TA65u/MOH6HvqUKTk9GxzzEE6N7+8qNuKwmVV7TVDIiKiVsp3Oh1NI3IzO/Rog94kJyWkzBqcG//Bxg3bahLrBtgdD7Vd3dHGqtVbqo6srVUZLqcjC86Qz3kVAXgsAqG1SZc6c5dXkCMAzunVv1tTjz5J2XbHQ/6t33JkZ6PDkRiOutJn52DUbbOPK9v81GcYcP5ojL/3DGRdOBZbnvrshPc1Vtdj23NrMeaueRh712nY9txaNFU34NCqXeg+og/G33cG9r5bCQCo2XwQyqXaM7EDgCQAF7TnDImIiEI1b/74BAAzTjljbPfExLgO2Z0tkJgYZ2z+8BED+jWmVXbVAQmjQXVV/cH/rtu/88kvj6SsbpBMl9PR2nxoZn55Uf+wBhcGXSq5AzAAwICJp2QP6mg3LafjNTe51GfVrtRw1dd9VDpiUuKOLxRBc22Tnt/RRsSmnZhHHl61Cz3G9kNsSjxiUuLQY2w/HFq1ExIjcDU0QTW7AGv/vvXpNRh4oS0/Rl5px0yJiIhCMAZA3LSZIzp1zykRwbghg7JHxGRuU82q0e54KHiHDtbteWP1vr3Lvq5J+7LZkQFHm7uGOQBcGI7YwqmrJXeTAbiyRvbu1DuWrmDj1qqddQ5HRH/Zy75iAjb/9TN88q0XUfnXzzCocPwJ0zQcrEV8z6Rj/8f3TETDwVqkju2Hur01WPOTN9DvjGE48PF2JOekIa5nWE40hmpWfnlRth0zJiIiCtLcXr27NWYN6tWprrfzJTszfcDk1KGHHQ3CyyI6uD17j+585bO9B57ZWpu+Gc4+YT7BUxjOysKhyyR3eQU5TgBzskb2UimpCf3sjod8c7kUVh5pTAo8ZdvsfuMrZF8+AZMeXojsyydg4+8/DPq94nRg+I35GH/vGeh18kDsfHU9+p81EpV/+RTrf/0uDnzcrrc+EXTgm2kSEVHXNm/++D4ARp197qQsp9PhtDuecOmV1q13fuZIR1ydc4/dsdDxlFJq+47qbc9/uu/ISzvrM3Y6YiJ1zczU/PKiQRGqu1W6THIHYDCAbmNnDhxhdyDk35btVXuqxRm2Lpm+7P1vJXpO0ddE95qaheqN+0+YJi4tEfUHjh77v/5ALeI8um/ufmMD+szKRtWG/XAmxmL4d/Kx45UvIhv8iYrae4ZERERBmgxAjZs4KM/uQMItKTE+eeaQ0b261SVtsTsWCuoedZFwcTvMI2hdKbmbCqApc1jPMXYHQv6tPNDQLhdExqUl4sjn+se2I+t2I6HfiXfG6DG+Hw6v3oWm6gY0VTfg8Opd6DG+5cRvU3UDDq7cgT4zc+BqaIK+1brA1dju9zsdlV9eNLi9Z0pEROSPdW+7eaPGZLp69kqJyp5TTqfDOW348IGZzb02K460YosQ71EXbh2qa2aXuBWCdW+7/IEje6vE5Lg0u+Mh33bsrD5wQJx9wl3vl79bgSOf70FTVT0++daLGHDBGAy++iRUPrESqlnBEevA4G+eBACo3ngAu9/8CkOumYLYlHgMOHc0Vt/+OgBgwHmjEZsSf6zebc+tRea5uRCHIHVcBna9vgH7bn0VfU8dGu5FCMZ8AA/ZMWMiIiIfhgJImzpjeIcbVTDcRudkDeq+M3F7Re3WPhIjcYHfQW3VUN9cW7HlyL5V1a5+jU7HQMTY0ut3Qn550bAVhcs22DFzT10iuQMwDED88MkZmXYHQv6t3FtXD4T/gzn82/ley8fdc/oJZSlDeiJlyJRj/6cXDEZ6gfeTYtlXTDz23BHnRO6PCtoYaZswuSMioo5mMoDGocP7DbE7kPaQldE7M+Vw4oFP9n7ldMWpHnbHE63CcI+6cCsEcJfdQQBdp1vmeABNGYNTh9kdCPm2b+/Rw7vgzLA7jk5sTn55kS3DdRIREXmaN3+8ADgpMTHucN9+qR1q0IlISuuR3HNGVm5cfF3sLrtjiTZhvEdduJ1rdwBuHWWFRIx14/Ip8YkxR3r0Thpodzzk28pdRzmccNskAphrdxBERESWvgB6TJ8zop8zxtFVeosBABLiYxNnDBmVnlqXvNnuWKJBBO5RF27j88uLOsSZ2qhP7gBkAOg+Ykr/dEcUDb8bbQ4drKvZ7HIMsDuOKHCm3QEQERFZhgLA6HEDu+SAX06nwzFl+LBBA119NiuXctkdT2cU4XvUhZMDwAy7gwC6xjV3QwBg4MjeXXLH0lms2lFzQMTRniMbRatZdgdARERkmQygJmtgr2y7A7HTyOzMQd13J+5cW7MlDTFIsDuejk4ppXbsrNn+4e667vudzgx0npO+swC8YncQXeHM3UQANb0zu2fbHQh5V1PVULehUTjYTXiMzi8vSrE7CCIi6trmzR8fAyA3pVtCTVqv5Ki8BUIo+vftmTG1z/A6Z4McsjuWjsqme9SFU4f4gT2qk7u8gpwYALnOGMeRlNT4vnbHQ96t3la1Bw6J6rbYjhzQv5QSERHZKROA86RpQzMcjg4z6IWtundLSp0xMDcxsfb/2bvv8KjKtA3g9ylTM5PJTHqF0IsQwA6iosaCrmVXUVnXLbbPdS3ruuruWlBWxb72riv2uggawCBFutShE0p6T6Zk2pk57fsjAURagJk5k8nzu6691mTKuQeSYZ7zvud59A1aZ0kkGs+oi6YTE6GxXbL/suUB4IuGZjhYrues6fYmQkgUt0VAHTKj61StAxBCCOn1+gJghg7P7zVdMrvDoNcZxg0cmpsetvb6RiuRsBxaX+Gu/XCjm1niV4sEnuvpW1Z1AEZpHSLZi7tcAEz+AAcVDwlqS42vUWZYndY5kgwVd4QQQrQ2AkCwoCi9UOsgiYZlGebEgf37FCO7RpVVWes88SYERd/Kra66D7d4DKsFFIpcUn0O1Hz3VLKvZvUHIGbkW6m4S0BiRJY3htRMJFw32x7vNK0DEEII6b1KJ5awAIYC8Njs5kyt8ySqgUW5RamtpuYNviqrysOsdZ5Y8/vC7rXVfqFCYnLAMlZwSfn5T/PiLtlX7gYB8NsyzVTcJaDtNR2NIstqvjc5CeWO/fTaPK1DEEII6bUyARjNKQbVZNJbtQ6TyLIz07JPzxos8WHWpXWWWOmaUdfyye6AvUJhc8Em7jyDKNC8uEvalbtRE4p1AAoYlmmwpBl7fZemRCNLirrer6SBrrGOlUEA6IJtQgghWkgHoA4cnONI4LlkCcNiMaWO7zsssrJyR33QGE6a7uEtrcHGn+oDhiaWzwI4HOlHIdTQgYoXl+39OtziR+GVI5A7cfDe76mqiqr318K9vhGcnkP/W0+FpdiBUEMHdry8HKqkoN+NJ8M6KAOqrGDrtEUYfM94cIa4lTxDxn56rXnZNZ8E43XAX0ra4g5ANgAmr589jeOTai9vUthd52sUWJZWl2JnAICFWocghBDSKzkAcIV9MhxaB+kpdDpeP27gkHznrqrqFr23xzahUVVVbWgK1P/UdPQz6kx5qSiZdmHn8ygK1vx5JhwnF+x3H8/6RghNfox+/mL4d7aj8p3VGPHv89H8wy70vX4MDJkpqJq+FoMHnYGm8p3IOKNPPAs7oHNXZCGA7fE86C8DJKtcAEx6niVN6yBkf4qiYo1XTPq95RobqHUAQgghvVY+ADEnLy1d6yA9CcMwGDWguM8ANrcWiippnedoKLIiV9Z21H6+ri04u/X4Z9R5NzXDmG2BIXP/qQiuNfXIHN8XDMPAOjADUlBExB0CwzFQIhKUiASGYyEFInCvrUfm+OLjel3HqODId4mdZF65yweg2DLNNq2DkP3V1Pta/AyXpXWOJDdA6wCEEEJ6rUIAwfQMK63cHYN+BdmF1nZT63pPpVnVqQk9802WlMiOmo6m1R4pS+C5QvDRKS3altUgfWzRAd+PuELQp+9bH9A7TIi4Qsg5fyB2vroCiqSg3w0no+7rzci/fBgYbZr2adohNpmLuzwAgtVuopW7BLPWFWHAcFrHSHa0ckcIIUQreQBCafYUKu6OUWZ6auZY0xD/yvqKNskgZ2id55ciYTm0pbqjzRlQckSOLQIfvc91iiTDvaYeRdeUdPsxhowUDH/oXABAqMmHiCsIU54NO17pvA6vcNIImHKPazHxaGi6cpfM2zKzAQhmm4FW7hJIQ6Pf5WI4aosce/3HfnotXcVOCCEkrkonlugB2AGEU20mKu6OQ4rZYDmzeFiaRTDWap1lj3jMqPOsb0RKsR36tANnmusdJkTa9/UqibhC0Dv2b7xe+9kGFE4aiaa5Fcia0B99Jo9C3Vebox3zcKi4i7ZRE4oZdLbhFcxWPa3cJZC1rUJY6wy9hBmd3coIIYSQeLIDUMwpBt5k1sdtqSRZ8TzHjx00pDBXslepqqpZDr8v7P5xU3vjhxU+y0aRKVC42LU7b1tWg4yxB+8p4xiTj9bFVVBVFb4dbeDMOujt+4o775YW6O0mmHKtUMISGBYAC8jhuF7CSNfcxYCx63+SMYWKu0TR1hr0NoGjmYPxkw6gTesQhBBCehUHAAwYRGMQomlEvz59UxvMddvD9dngELcu8B630LKq1o8qlc1iGDbmy0KyIMG7sQn9btw3Lq6pfCcAIKd0ANJG58K9vgHr7voWrIHHgFtO3Xs/VVVR/7/NGHjnWABA1rn9sfPlFVBlBcU3xHX8HF1zFwM2AAoA6A2cReMspMvapqAP4GibbPzQdhhCCCHx5gDApGdYEroRSE/UJy+zwOoxta9p26VT9WpMV0WPdkZdtHBGHie/9ev9vpdTuq9HHMMw6PengxdqDMNg2L8m7P3anG/DyCcuiE3Qw6NtmTGQBgC8jmVZjqXOHQnA4xYC1Qqr6Q97L0TFHSGEkHjLByAZjLpkXUDQlCPNkn5GwVBeL/DN0X5uVVXV+kZ/3f/WtXXMbAznNrE8fY44No6xn15rOvLdYiNZi7sUAIw51WDQOgjp5GwIuGh7RtzRmzIhhJB4swMQdfoo9cQnBzAZ9ebx/YdlpArmmmg8X7Rn1BEAGq7eJesvnh4AY7Lo9VoHIUDAFxF2iEx+0p5KSFzUUIUQQki86QHIeiruYorjWO60QYOKtlTWVtWybX2YYziDHqsZdQRA50kOTSTr36IeAGNM0VFxlwA21vlbwDIHTqIksUYrd4QQQuLNAEDR6bhk/YyZUIYVF/ZNbTI3bAnWpINnurVjLZYz6shecWt680vJ+otnBqAYzFTcaU0IieLWiJobtytxyc9ptt+bEEJIr6UDoNC2zPgpyEnPs3iN7tWtOwVFrx6ycZ0QFH3Oap93s6DmKRxbCI62VMUQFXdRlgJANpp1B04/JHG1pdbfJDOspi1hj8XO11fCva4BulQjRj190QG3q6qKqvfXwr2+EZyeQ/9bT4Wl2IFQQwd2vLwcqqSg340nwzooA6qsYOu0RRh8z3hwhrj+ytGpOEIIIfGmB63cxV2aLcU+3jhMWFFV0RQ2iTk/v83vC7vXVvuFConJActYwdEJ9zjQrLhL1pLdDEBmYzhgkRyZGJHljUElQ+scxyLrrGIMvf+sQ97uWd8IocmP0c9fjH43nYzKd1YDAJp/2IW+14/BkPvOQsN32wB0zmfJOKNPvAs7gIo7Qggh8UfFnUYMBp1x/MChWfZwSjXQOaOufENbyye7A/YKhc0FS9uo4kizn/9k/cUzA5BlSZG1DtIbqbIqMW5WzIK1/h5ZUlDbyrAhkY0IkiKHZVUJS4waVsCKMsOKCstLKqeTFI4FEupNp8EvcA97hIyHPttxQLvhZ9dUpU3MtIRLP98ZAoA/NQWy//zfza1f7nBbRrukcK7ZIL9f60+964Ot7qk/7UqfNm5QG/vZjrjkFlXoc3iuxm/m3bgmLockhBBC9tADkHkq7jTBsix78sCBfXYs3lbTsKracrErwCsBsckUlINGQeZ1iprQJ36bA2HuqTWVdm9Y4sAAF/TJCFw1MMf/8/uoqoqXnDW2tS0dJj3HKneP7use4kgRqztC/JOrKx2SquL2kiLPiAxrRFJU/GNpRcbU0we0G3lOjWV2BWAZQM3m+bqmVJ2i1WewZP3FYwGoVNzFkYyIRTQ256U4mNxse56hWGdyN3ozTxgyyKbIilq/vcYTqKyLZKmSNc3Imw94uKIgIkhiMCCKoaAohUOSFAmJSiQkKVJYUpWwrKoRhYEoM6ykspyscryicnoVvB6MzgDooj1qwSYyMIPBCWHk//I2VZAwTmdMOSHc+XU/ox52n5j7j6Js3LWh0hpRVEwbXoQvtjSaHuyXh5ER5oDniKmInImgvCyuxySEEEI6t6OJPE/FnVYaK5qFs08cUqSMGoTKdRVthvoGY16KPgcAvF5BcLcFBb8rJAresKz4IgwnyLxJUg0WMCYdw2ha/GVKLJ4ZUoQRNjP8koyJS7fafmu32QZZ97URmN/SgZA/glVnnoB1ngD3sLMma9bYofhqdxueGVqEQpMeD2+tzbzWasV7Va34Y146TpK5PMSrKghLeVkBSbM/x2T9xZMAMJIoU3EXSxKEVMncmmd1sHl59hye5/a7ts5o0qUCAMuxTOGwvnYM6wtFVlC7qVLsqKhRsjiZz0w1cgDAsSxMZr3OZNYf0x5lWVYgBEU56I/IoaCohAOiHA6KiihIkAVJVSIy1IgCSArDyirDySqrU8HqANbIMLyOYdho7FbIN+nxxamDAQCVAQGNgogBFiPudFYioqj4+6A89EuJ26WgYrwORAghhHTRAVA4jk3oFaJkpUgKcsDrAYDlWPQ/aUgGThqC3au2R5RdNWq/DJPRZjMa0f/Ax8qKgg63ILqbA6LfFZIFj6CqQYnhwzJrUsBbWVbPx3hocbZRh2xj50dBC89hgMWIprCInxd337d48Jv8dDAMgzF2CzokGc2CCB3DICQrCCkKeJaBV5RQ3uLBhycPjGXkQ9GsBknW4k4EwMoirdxFGyMhmCqltOanOvS5+fZsjjt0sxRTmpkJu0XZYNDtfYNnORZ9SvrrUNL5rtK6q97n3VYVsYlhc7pFf8zdHTmORYrVwKVYDcf0j4ksKWokJIXDwUgkEpQikZAoBRs6IDqZ9I5cU4MsyKoakRmICsvIKmvVc44dQUEekWaGDoy+UYjocwz7N2d9qqIB9w7Kw7vVLbimIAOFZj2e3N6Al0YVH+vLPFpCvA5ECCGEdNEBUIRQhP4N0oCvxt06rCA985ff73fyYD1OHozGrTWCf2OFUpxmMLPs/nUax7Kwp5t19nTzQU+0K4qCgEfw+9tCgWB7KBLxhhXFLzJsWNbrZdVkZBgryzBR63dRGwxjc0cQo20p+32/SRCRZ9z3mSvXqEdTOILf98nCXRs6T6ZPG16EF3Y24vb+ml1qqNkJ9mQu7mjlLkoYkQmkKSltBbZ0Q3aGLZtl2T7dfawQkbwGg+6Q89Yy++dbM/t37ljsaGh1+7ZXe80Bv9Vm5OM6gJvjWcZk1RtNVv3eZTUp0wRzuR6Dzy0+4PX+cUgqXv1+O/72u+FYubMNjopqDLp6SCgSECORoCjO39LEpmaZ9ZnDMlwtze1pHgsXtBg4qU2W092s6mYVlecU6HhAxzOMgWeYWHRVCsXgOQkhhJDDYQGofr8Q1DpIbyOFJaWvzXrYE+W5Q4uMGFoEX1O7x7NmayBHp+RybPcaELIsC6vDbLE6zJaD3S5LihrwCD5/WzAYcgnhiFdQFL/IshFFb5DVFAPDWNhurvwFJBm3rNuNKUMLYdV177x9gu2e8h/5LrGRzMUdK4kKbUs7RkyE6XCoFndBWropq9CWxTBMypEfdaBQRAodcuDKL6TmZdpT8zLtAOBrcXk7tlV5jB0dZruRP+AMVKz99uXFWLS1GW0+AX3+8hUevnIkRKnzOtxbzhuEiaPyMWd9PQbfPQNmPY+3bxkLs9VgMlsNJlVV8d5nq/HJ7WfCYTFYHuxnw/WvLkmVZAUv/99pGDY464A3XklSlEggIoSDYiQSEsVIUJLEkCTLIUmRO685hBqRAUllWUnhmH3FoV7HMAaOYQ72u+yL9Z8TIYQQ8gtBALzPG6LiLs6EOm+zOTc9tzv3teakp1kvPiMt6PL5W9Zsbs9QIrk6jj2u+dAczzKpGWZraobZerDbZUlR/O7QnuIvEvGGFTUgclxY0esV1WxkGAvDMIyoqLh53W5cnufARTn2A54nx6hDgxDZ+3WjEEEC7p7qiNeBfimZizsm2BEOax2kJ2EjrCcdVm+hPT0loyg1A0Dq8T5nWJGlY3mcNcths2Y5bAAQaPf6PFsrXQaP15hmYLO6e9bneHz0l/GHvZ1hGLz0x1MPedvcf5Tu/Xpovg2rHrv4sM/H8yzL24xms814QLOZ7pAikhwOSeFwQIyE/BE1TcdVMSqajuW5CCGEkOPQAUDv9QapuIsjwS+IxRm2Q+6UOhSzw2oxl55mCftDQuOqzVUOIZBt0HHHfJnM4XA8y9oyU1JtmSkH/XwpSYriawt03Pr2Cn1uVop01agClysgsVxENhgUmA2dxR9Ks9Lw3+oWXJZrxzpPAFae23udHgAsb/ch26hDcYoRIVkBywAsGIQUJRYv61CouIsyEQDrbgkEFEVVWDZ6+3+TDRdmXRlsakehIz3VYbc6AKRF8/mlKJRhKek2a8oZo6wAEPT4Ap4tlW28y613GNicWBV6sqLg1AfKkGc3Y+bfz9nvtrAo4w+vLcXaKhccFj0+uf1M9M20YOn2FvzlvZXQ8yw+/Mt4DMxJhScQwTUv/oiy+87FL/e2RxOv5zlez5tT9hWHdnSePSWEEELiqQNArscViPm/QQ11Ltx183toa/GBYYDJfxyPG/58LjZvqMU/7vwI4bAIjmfx2HOTMfqkA1dsvvhoOV58ugwAcMffJ+Kq356OcFjEDde8isZ6D66/6Sz8/qazAQD33f4BrrvhLIwYVRTrl3VMmJZQiz7HfsyduQ0WkzF/wkl9JSEiNq7eUm3r8DrMeu6gK3CxwvMsu9kbsn27tQkjCtPwuyXbrQAw9erRqG0LQJZk5dqSgo4zW9ODM2f4rKf+uMlkZFn1sRFF/rCiGAwsa1ZVFS/tasQro/oBAH5bmIE7nJWQVODx4XH9u2uP58F+LlmLOz8ADioghqWAwaSL6w9notMJXFsGn+ovTM+wp9lSHACO+kxPd6k6Nqo/Y+Y0a4p57MgUABB8gZBrS2Ur1+biHTomm2Oj1773xTnbMCTPho7QgTt73124E/YUPbY/dzk+W16Jf3yyFp/ccSaeL9uCWX8/B1VtAbwxrwLPXHcSHpuxEfdfdkJMC7vD0OyNhRBCSK/lBVDU3uaPeXHH8RwefPwqjBhVBL9PwMTxj2H8OUPx2INf4a//uAQTzj8B8+duxOMPfo0vZv9tv8e6XQH8Z9q3+HbRP8EwwMVnPo7SiSPx07KdOPn0Abj9notwxXlP4fc3nY0tG2shy2rCFnb+dr8wIMvWre2YR8Ib9br8M0b1USRZaV63vcbU0mJNNfAH7o2MkTMGZ0H66HeHupkFkGbPtaZ9PvL8n3/fDgBiWJI62oK+90syQoJLEN0dYWToTNxnY4cYDCosBpaNyYrkQQQKylZrtnswWYs7HwAFACKC5O/txZ2qqjCEda2ZvC1YmJnuSLWaMwBkxOPYjIE3xOq5jdYUU96pJxQBQCQYCjdvqaxnmts4hw7ZPHvsRWVdewBl6+vxj8tOwH/Kth5w+8w1tXjoNyUAgN+c0gd3/HcVVFWFjmMRjMgIhiXoeBa7mn2oaw/g7GE5xxrleLVodWBCCCG9lgeAvqXZ64n1gbJzbMjO6byy32I1YsDgXDQ1eMAwDHy+zp5iHR0hZOceePX/oh82Y/yEobA7OlsKjJ8wFAvnbYbNZkYoGIEoylC7Rl4/PXUmnnjht7F+OcfM4pddnInNi+ZzsjzH5p48rEhRhqB10656vqZeZzfyWdE8RrTpDDyfnp9qT89PPWgxGhEksaM14Au0h4SwWxCljoiKkMTzomI0qLDoWTZa3VZao/Q8xyRZizs/ABUAwiHJb43b+YbEoaqqagzrmrP0aeHCzIwMS4ox7k1JAIBP0VviMelDbzYZck8aVgQAohARW7ZW1qmNLXBwatbRXiB89werMe3aMfAdZNUOABrcQRQ6Onc/8hwLm1mHdn8Y9116Av7w2lKY9Bzev3Uc7v14DR6dNOp4X9qxUgHUaHVwQgghvZYLgC4UjEiSKIu8jotFN+gD1Fa3YfOGGow+qRhTpk3CdVe8gH//6ysoiooZ8+494P5NDR7kFuz7gJiTb0dTgweXXHEivvp0BS4750nccuf5+P47J0aMKkJOblSvWokab4PXNyzTFtXC7udYlkX2yIH5GDkQbdurm5QdVWqGkYvKKmG86Y28LqPQ5sgoPHirPyEYCfvaQoFgWzAkeMKS5AuDCco8LylGo8pYdCzT3QWL2uilPnrJXNwBAISA2Gs6BqqKqpgi+uYcgz1SkJmeZTYbNFsy2oM38MaIVwrrdbFbwfslnVGvyxk9uAB0lwGeAAAgAElEQVSjB0MKi1Lr9qp6ua5ZdrBKtp5nD5vj27V1yLIZcWJxOhZuObp+JKP6OrDs0YsAAD9ubUZOmgmqClz74o/Q8Sye/u2JyLbFa0cAGrnJ0yNHvhshhBASVftOsIfFIK/juts0+5gF/AJuue4NTJk2CdZUE56e+g0enjYJEy8bg1lfr8bfb5uOT2b9tVvPxfMcXn73RgCAKMq47vIX8M6nf8Yj93+Ohjo3fnPtaTj/4pJYvpyjkgVeABCXHWoZg/vkYHAfuKub2sKbdwgZOuTHo8ldvBjNeoOxSG9A0cF/ZEOBiOBrDfqD7SEh7BFkqSMCRpB5nagYjWCsPMvsWUyg4i4GfAAYABD8Ec3mTMSDKquyWTQ05xrtUkFOerbRqE+4synBkOiPZ3H3c7xBx+854ySLktJWUdMg1TZKdlXKPFg3qGUVLZi1pg6z19dDEGV0hERc/+oSTP/zGXvvk2c3o9YVREF6CiRZgTcoIt2y7+WpqorHZ2zEx7ePx53v/4Rp145BVVsAL83dhn9PGh2fFw5UxutAhBBCyM8Esae4E8RgisUY0+JOFGXcfN0buHzSKbjosjEAgC8/Xo5HnroaAHDJFSfi3r98cMDjcvLSsGJxxd6vm+rdOG38oP3uM/2thfjNtadh7ardSLWZ8MBjV+Lqi59LmOLOU+VyDc9yxH1nlr1PTgb65MDX1O7xrd/Wkckq+dHoe9D/zq9hNerAsQx4jsHKf+/faVxVVfx1+irMdjbArOfwzi1jMaY4HdsbvPjdK0sgygpeveE0nD4wE5KsYOKTP2DG3ybAbIhOuWNK0RtNKXoj+h58FTfQEQ6r/kgdK8jro3LAY5SsxZ0fXcVdR3vQrXGWqFMVVbKETU25KXY1P9uRYzDoYrYcHw0hURTSELcVq0PidDybNbxfHob3gyIrqmtHbWO4uj6SpogZJh2XAgCPXzMGj1/T+Y/Dwi1NeO67LfsVdgDwqzGF+ODHXTh9YCa++qkaE4bn4Ocnrj5YvBsXjcqHw2JAMCKDZRmwDBAMH9NUiGNVFc+DEUIIIV32Fnc+n+BxZFhjdtJZVVX8/bbpGDg4Bzffvm8EUXZOGlYsqcDp4wdj6aJtKO5/4KViZ507HE89MgMedwAA8OP8LbhvyhV7b/e4A/hhzkZ8OOMOlJdtAMOyYBhAEBJjhLIsympRijlqjeSOhTUnPc164biozsqb90ApMqwHv/RttrMBO5p82PbsZVi5sw23vbcSyx+diDfn78Bz15+Mvhkp+OsHq/HFXWfh9XkV+O24flEr7LojJdVgQKqhPzq3JmsmKYu79Qsq5VETigMAdM3V3qRoKqFKasQqmZrzUhxMXq4jR6/jC7TO1F1hWYlrVdMdLMcyGUP65GJIn85Cb3d9c7iyTrBJkYO2/n34y/U4qTgdvzqxEH86ewB+/9oSDL57Buwpenx8+76ZeMGwhPd/3IU5958HALjroqH41VPzoedZfHDbGb982liqiufBCCGEkC57u2S2Nne09inOHBqrA61avgtffbICQ4bn44KxUwEA9z18OZ586XeYct9nkCQFBiOPaS9eBwBwrq3Ch+/8iKdfuR52RwruuPdiXHL2EwCAO++7eG9zFQD4z5Pf4fa/XwSWZXHWecPx/lsLUXrqKlx3w5mxejlHJVDjaSnKTc/WOgcQv1l5s9bU4nfj+4FhGJw2MBPeoIhGd7CzoV1YQjAiQ8cx8AQi+HZtHcruOzfaEbprp1YHBgBG3dMKKMmMmlD8TwCZxhRd8KYnz/1nj9wSLEGwSuaWfKuDy8u05/A8p+kZmmMl1Hiq+2ek9dE6R3e5Kxtag7tqA9awYLcYYn+tQIz8kZs8/b9ahyCEENK7lE4ssQB4CUD1ZVedPPzyq065UutMySYcFOW8MCJGg077bVEHIQkRsXn1loajnZU34K6vYU8xgAFw07kDcdM5+2+TvfTp+bj30hNwxuDOldjSx8vxxDVjkJVqxB9eX4qwKOO1G07D9B934ZIxBVp2Ky/gJk+v1+rgSbly12U3gD5CQPQKQdFtStH3iJ6ZjIRgqpTSVpDq0OXk27M5jk3MoSpHIdqz7mLNXpyXaS/OywQAT21zu39Hjc8SCtjiOeclCjZqHYAQQkivFEBn7wP9rormpNg9lWjkRl+zMdeRsJfkHOusvEUPXYh8hxkt3hAunPYDBufacObQIy9OFmWkYP4DnXPvdjZ1oN4VxNB8G37/6hJEJAWPXDUKg3JTj/+FdY9by8IOSO7irhqADgACHqE1kYs7RoQ/TbG0F9jSDdkZtmyW7fkF3c8xRl205obEXVphdnpaYXY6AHQ0trl926s6zH6/xWbk07XOdiiqqsoMw2zWOgchhJDep7zMqZZOLKkGULRtS327LCsyx7E9cudRIgp6QuHiTFtCz5vb42hn5eV3jZnKsplw2UmFWLW7bb/iLt9hRl17YO/X9a4g8u37L14++Pl6PDppFF6auw1/mjAQfTNS8MDn6/DBbeMRJ6vjdaBDSebirhVdF/R620ItGfmpg45w/7hiRKbDoVhcBfYMc1Z6ahbDMBatM8UKb9bFZdZdrKXmZthTczPsAOBrcXk7tlW5jR0d5jQDl5VI234ZhtnOTZ4uaJ2DEEJIr7ULwFBJlN0d3mCr3WHRfDRTstC7w+18tilhV+0Opjuz8gKCCEUFrCYdAoKI8o2NeOCKEfs9zyVjCvDq99tx9el9sXJnG1JNOuTazXtvX7S1Gbl2EwbmpCIYlsAyAMsyCEbi+iF0VTwPdjDJXNy1oKtjZnuDr7l/ifbXnHIR1uOA1VNgT7dkpqdmAIjbGrGWeANvCHskwaDne+wK3i9Zsxw2a5bDBgBBl9fn3lLpMni8xjQDm5UAM1/WaHx8QgghvVsdABYAmhu9DVTcRYev2RcYlGVLuJFXR+NQs/KaOwRc+fwiAIAkK7hmbDEuLMnHG/M6x1Xcct4gTByVjznr6zH47hkw63m8fcvYvc/bOYZqAz65vbPhzU3nDML1ry6BJCt4+Y+nxvMlUnEXQz4AIQC66i1t9adcNECTEFyYc2Ww1o5CR4bNYbfYARx8OEaSCwoRfzIVdz9ndtis5jNGWQEg5PUH3Zt3t/Iut96hZ7NZlmE1iKT5lgBCCCG9WjO6dk/VVLXVDxmeP0bjPEkhLQIfyzApR75n4vvFrDxvnwxLwdonLjlg++4t5+3beMcwDF46RKHGMAzm/mPfOIyh+Taseuzig943xqi4i5X1CyrVUROKqwHkNe52u8MhscNg0sVlpYwXuLZMPtVfmJ5hT7OlOAA44nHcRBaKSELCXvQYRSabxWwaO7IPAAi+QMi1pbKVa3PxDh2THY0Bn920Mk7HIYQQQg6mCZ3FHbt1U119ogz97sm8NW7PsAx70q2AxmJWnoYatW6mAiRxcddlE4DBADyupkB1bnHaiCM94FioqgpDmG/J5NNChZkZjlSrKQNARiyO1VNFVCUJrro7OkZriinv1BOKACASDIWbt1Q1MM2trINHNs/FpoOoqqodDMPQyh0hhBDNlJc5xa6mKvaN62taJEkWeZ7TaZ2rp1JkBXl6Y1J/jtpvVt7qzdX2UCDTqOPMR35kQtF81Q5I/uJu157/aK7yVEWzuFNVVTWG9c1Zelu4MDMjw5Ji7BGdi7QiaX0Vmsb0ZpMh96ShhcBQiEJEbNlaWac2tqgOTs2O5hkqhmEWc5OnJ/U/AIQQQnqELQAulGWlo63FV5+Tl9ZX60A9la/a01qY48jUOkc8GCwmY/7ZJ/WRhIjYuGZrdarXY0/Rcz2lR8UKrQMAyV/cVXf9P7N7Q3P1qAl9j+vJVEVVTBF9c47BHinITM8ymw1JtzweK6qOztjtoTPqdTmjBxdg9GBIYVFq3V5dL9c3KQ5GydLzrOE4n35+VEISQgghx6cSAAcAO7Y3VlBxd2yksKT0tVkSclh5LPFGvS5/XMmeWXm1ppYWSw+YN1yudQAgyYu79Qsqha7r7tLqKlztEUHy6438UY0cUGVVNkeMTbnmNLkgJz3baNT36C5FWmFNydlM5XjxBh2fPXJAPkYOgCxKSltFTYNU0yjZIWUadNyxvJlTcUcIISQR1KKra/nShdu2j58w9HyN8/RIQp232Zyb3ms/e3bNyivsnJW3u56vqTvsrDytqKraxjDMWq1zAEle3HVZB+ByAB2uJn9VTt+0E470AFVWRYtobM41O9T8bEeOwaDLj33M5KYz662QtE6R2Dgdz2YN75eH4f2gyIrq2lHbFK6uD6cpYoZJxx2xO5aqqu0MwzjjkZUQQgg5gjZ0ds20bN/a4PJ6gq22NHOv2FoYLYJfEIszbL2+KR+wZ1Ze58nwtu01zcqOSjnDyCXMvD+GYeZxk6crWucAumaQJLmde/6jdltbxaHupEpqxCKYagdz+XXnFIxgxg0aWtCvILvQYNDRdsIo4PScTgiLIa1z9BQsxzIZQ/rk5F8wto/pgvEp7r59mxsVvioYkX2HegzDMOXc5OlqPHMSQgghB1Ne5lQBLAVgB4BdFU3btU3U8zAtoVa9jj/eyzWSTsbgouysS87K8w4a0NYcUesUVU2Ezz5lWgfYozes3O257o7duKR2x4nn91f2zh6TIKRK5pZ8q4PLzbPn8jxXqF3M5BcURL/RoOt1+8aPF8uySB9QkI0BBQAAd2Vja3BXTdAaDqVZDLztZ3f9nzYJCSGEkIPaBOAKAFi1Yue2Maf0O0PjPD2Gv90vDMiyUW+Hw7AX5WSgKAe+ZpfHt26rN5NVCuI4dmovVVVlhmG+i/dxDyXpi7v1CyqDoyYUbwdQ4HcLro7G4PY+6VkpBbZ0XU5+WjbHsUVaZ+wtBFEKa50hGdiLczPtxZ3b7721ze2urZViPiP5eQazNY5GCCGE/Fw1gAAAw8qlO+qvv/Fsv8msP6reB71Vil92cSY2YbYdJjJrtkPrWXlLucnTXXE83mH1hm2ZALAMgAUAjxrGd+qggUX52Y5cjmN7y+tPCL1x1l2s2Qqz04vPPy1Hd964rdzk6YfcskkIIYTEW3mZUwGwEkC6qgLVla2HvDyG7ONt8PoKMm1U2B0ls8NqyS89rQ/Gnaw08sZqQZSD8TguwzDfxOM43dVbiputRk5XmKoz/WbR95t5WVYSYW9uryMxvXzYXQwxDPOZ1hkIIYSQg1iLrp1izrVV2zTO0iNkgaceBcdhz6w8/YTTdY0ma3UgInfE6liqqsoAPonV8x+LXlHcrV9Q2W7mDRv0HL8w6IvMamr07NY6U2+k6llqThMDiqqGAczUOgchhBByELsAyAC4RT9sqZREWdQ6UCLzVLnaM9JSEq7Vf0+0Z1ZeyvlnWJptjtqOsOSO9jEUFeXc5OmN0X7e49ErijsA4Fh2FsMwBgDYsLZ6k9Z5eiPOxFMzldj4hsm6mbZkEkIISTjlZc4wACcARygYkWpr2mlr5iHIoqwWpZjj3hAk2e2ZlZd60Zn21qycercgtUTruTmWeSdazxUtvaa4A7Ch6/+Z2TPXbo5EqLlHvPFmvVXrDMmIZZjXtc5ACCGEHMYKAGYAWPTD5tUaZ0lYgRpPizXFmKZ1jmS1Z1Ze+iVnZ7kKi5rbBLnheJ5PVlQ3EnDnVK8p7srLnC50bg1I83UI4vYtDeu1ztTbcDqODwliXC5u7S1ESd7NZN28QOschBBCyGFsx56tmfO2VLld/matAyWacFCUix2pdBI8TqIxK49h8BE3eXokFvmOR68p7rp8DyAVAMpmrP0pMWYe9i7BcCSgdYZkwnPsK1pnIIQQQg6nvMzpA7AcQBYA/LRs50ptEyUeubGj2WjQmbXO0dvYi3IycieOLwiNHO5tkphqWVG73dmdZZj3YpntWPW24m4DgBAAw7Yt9a6GOvdOrQP1NoIo03bYKFEUNcIwzH+1zkEIIYR0ww8ADADwzZerNoZpJ89eQU8oXJyZRk1UNGTNdqTlXTiuj3jSqFCjyleLsnLYFTlJVjZxk6evjVe+o9Griruui3rnouvM0eIFW37SNlHvE1EUSesMyUJRlC+ZrJsTZmgmIYQQchjV6Lw8xhEKRqRNG2rXaB0oUejd4Xae53itc5Duz8rjOfa5eGfrrl5V3HVZAoABwH7/nXOHzxeKeltUcmgyR8PuooXnuZe0zkAIIYR0R3mZUwXwLQArAMz8ctUqRVEUbVNpz9fsCxRm2XK1zkH2d7hZeZKstAH4SMN4h9XrirvyMmc7gNUAMlUVWLeqklbv4ilKs+7+dOf7yB52D0ac+cje763fVIvTL5qG0edMxcnnP4af1lYe9LHvf7Ycg057EINOexDvf7YcABAOi7jomhcw4sxH8Op7C/fe9+a/fYC1G2qiETmqwmFxKZN18wqtcxBCCCFHYROADgCmmqo2X9Xu1i1aB9JaWgQ+lqET34nq57PyGlPt9R5B8qrAC4nYSGWPXlfcdZkHwAgAMz7/aZ0oSgn7F5RsWKMuKrPu/nDN6Zj96R37fe++R7/CQ/dcgnXzH8Qj916K+6Z+fcDjXO4AHn3mW6yYfT9Wzrkfjz7zLdyeAOYu2IJxpwyAc+GD+PCLzprJubkWsqxizMiiaESOKl7H/UvrDIQQQsjRKC9zigC+A5AJAPNmb+jVjVU8tR5PTro1R+sc5MhYnmPzTxmen3rBGZx++MAXtc5zOL21uNsJoBFAqtsVCK9bVblM60C9BZ+iT41Gl9IzTx8ER9r+TaUYhkGHLwQA8HaEkJdtO+BxcxdsxnlnDYXDngJ7WgrOO2so5szfDJ2OQzAUgSjK2BPvoWkzMfX+S487a7QFQ5HVfM7/LdI6ByGEEHIMVqBzLAK/fHFFXXur77hmjfVUiqwgT6fvdmdGkhhESXmVHfNwx5HvqZ1eWdyVlzkVdO77dgDAR+8tXi5Q16a44HiWE8JiTMYhPD91Eu599CsUjb4ff3/kKzz+rysOuE99kweFefa9Xxfk2VHf5EHpWUNRXduO0yc+idtvPAcz5zgxemQR8nISb5aoXsf9U+sMhBBCyLEoL3N2AFiMruZ2C8o3LdY2kTY6qt1t9lRzutY5SPeJoiyYTPontc5xJL2yuOuyCkA7AGuHNxRZuaTiR60D9RYBITbF3Wv/XYTnHp2EmnXT8NyjV+HGv07v9mN5nsNHr9+ItT88gKsuPREvvPkD/nZrKe5+6HNcdcMbmDnHGYvIRy0Uijh1ebeWa52DEEIIOQ4LAOgAMN/NWLutqcFTpXGeuJIiktrXZjVonYMcHVGUXu8JXcp7bXHXte/7UwDpAPDpB0tXB/yCV9tUh9ZQ58Kkic/inJOm4NyTp+CdV38AAPz7X1/i7DEPofS0R3Hjta/B6zn4AuSC8k04a/RDOKPkAbzy7Jy937/9hndQetqjmDblf3u/98JT32HOrPUxey1hKTaz7qZ/vhy/vng0AOCqS0/ET+uqDrhPfk4aahv2NUita3Aj/xerc6++txC/m3QaVqzZDVuqCZ++eROeez0x6imdjrtf6wyEEELIcaoD4ETX6t3nHy6boypRuGajhxBqvU0pJr1V6xyk+yRJDpnNhse0ztEdvba467IWnW8waUJIlH+cv3WhxnkOieM5PPj4VZi/egq+mX8/3n9zISq2NWD8OcMw76eHUb7iIfQbkIVXnp19wGNlWcEDf/sE07++HfNXTcE3X65CxbYGbN1UB6NRh/IVD2HD2mp0eENobvJi/apKXPirUTF7LRFVicke87ycNCxaVgEAmL94Gwb2O3Ae6AUThqN84Ra4PQG4PQGUL9yCCyYM33u72xPAd+Ubcf2k0xAMRsCyLBgGCIXEWEQ+Kj6/sESXd+ucI9+TEEIISVxdYxE+R+dQc27d6srm7dsa1mkcKy4Ef1gqzrA5tM5Bjo4/GH6Mybq5Tesc3dGri7vyMqcM4DMAdgD48uPlTq8n2KptqoPLzrFhxKjOro0WqxEDBueiqcGDs84dBp7nAACjT+6HxgbPAY9dv7oSfftloU9xJvR6Hpf+5iR8/60TPM9BEEQoigJRlMFxDJ7990zc/a/YNhGR2ONv+Tv5lrcx9uInsX1XEwpH3Yd3PlqCN5/9He6Z8iVGTZiKfz0+A288cx0AYPX6qr1bNB32FDxw98U45YIncMoFT+DBv10Mhz1l7/M++ux3+OddF4FlWVwwYTiWrNiBkWc/iuuuOvV4Ix8XWVYUhmFu0jQEIYQQEiXlZc4GAN8DyAOA6W8unC9GpJjs7EkoLYFmvY6nLZk9iM8v1KalmhP+Wrs9eK0DJIBNACoA5CqK2lZe5px/5eTTr9Y61OHUVrdh84YajD6peL/vf/7BUvzqNycdcP+mRg/y8vc1EcnNt2Pd6koMHJKL9AwLLjrjMfz6mtNQtbsViqLuLSJjhTFwx/2m9vEbNx70+6vLD5wQcNKovnh7VN+9X/9p8jj8afK4gz7++amT9v630ajD3M/vOr6gUdLuDkzPHva3bVrnIIQQQqLoOwBnAjA2NngCPy3fuXjcWUPO0zpUrATaA0L/rDQaWN7DdPiEm1L73SFpnaO7evXKHbDf1gALui7sratp36FxrEMK+AXcct0bmDJtEqyp+0bGvfh0GTiewxVXH90K05Qnr8bcZQ/iljtK8czUb3DPg5fixafLcOv1b+Lj92LTwIo18lGZdddbCGHRZzbp79Q6ByGEEBJN5WVOH4AvAOQAwIfv/rjC7xPch39Uz2X2Sy6OZXv9Z++epKXNN7eg5N65Wuc4GvQD1mkngPUAsgHg3dfmf5eIg81FUcbN172ByyedgosuG7P3+59/uAw/zN6Al965AcxBdjzm5KahoX7fe2VjvRs5ufs3EZn77XqMGN0HQX8Y1btb8dr0m/HdN2sRCkb/j4E36y1KNIbd9RLejtBD1uLbE3qmCiGEEHKMlgBoBmATQqL8fZkzMTqYRVlHg9dXkGnL0zoH6b5IRBI4lvmj1jmOFhV32Lt69xk62/LqK3e1eBcv2DZP41j7UVUVf79tOgYOzsHNt5fu/f6C8k14/T/f493PboPJrD/oY0tO7IuqXS2oqWpDJCJh5lerUXpxyd7bRVHGO6/+gFvvugCCENlbICqygkgk+qvQHM9yQozGISQbb0eoIjsz9QWtcxBCCCGx0NW9/AN0zh5mZn21emtzo6da41hRl6HyIa2OXVvvwjlXPIvh46fghDOn4IU3f9h720tvz8fQcQ/hhDOn4N5Hvzro4+fM34QhYx/CwFMfwLQX9/V1u+7Wd1By9qP452P7Oq7/+7nvMKMsdh3X46nV5X8yY8jdjVrnOFpU3HUpL3M2onNrQB4AfPjOolUtzd4abVPts2r5Lnz1yQosXbQdF4ydigvGTsX8uRvx4D2fwu8XMPmy/+CCsVPxjzs/AtB5nd31v3kJQOcMt6nPXIPrLn8BE056GJf8+kQMHrrv5NH7by7AlZNPh8msx9ATChAKRXDeqY9gxKgi2NLMMXk9wRgNMk8moiRLbk9gEpN1M61yEkIISWab8bPRCJ9OXzpblmPTWVsLniqXK9OecmAL7zjheQ7PPHIVNi+eguVl9+PV9xZiy/YGLFiyHTPnOLF+/oPY9OMU3HNr6QGPlWUFf7n/E5R9fDs2L56CT/+3Clu2N2DD5s6O686FD2H1+mp4O0JobPbip7WVuHxi7Dqux0uHL1STn5M2Vescx4IaquzvBwBjAWSoKtref2PhzLv/9atbOY7ltA52ytgBqPW9ccD3z7lgxEHvn5Obhulf3b7f/Q513xtv23ftMsMweOW9gzcriSZB7AUdsY5TZXXbS4NPfzAxpqcTQgghMVJe5lRLJ5Z8BuDfALj1a6qaVyypWJAMzVVkSUGh2azpYkputg252TYAgNVixNCBuahv8uDtD5fgvtsvhMGgAwBkZaYe8Nif1lZiQHEW+vXNBABcfflJ+GaOE5dfNGpfx3Wps+P6Q0/OxJR7Y9txPR5UVYU/EL7R1v/OHnmCgVbufqZra8A7AFIA8Fs21bWvWr5zkcaxklIEdM3d4TS3duxY46z+m9Y5CCGEkHgoL3PWA5gLoAAA3n1t/rLmpp6/PdNf7W5JtRjTjnzP+KiqacO6TTU4dUwxKnY1Y/HKHTjtwidw9uXPYNW6qgPuX9/kQUHevo7rBXl21Dd5MHRQLjLSLTjxvMdwyfkjsbOys+P6mJGx7bgeD3UN7o/zR97bY6/9pOLuF8rLnNUAvgWQDwDvvDZ/qdvlb9I2VfJRuOOfdZesIqIkVtW2Xzn5/96mApgQQkhvMgOdzVUciqKqb7447389efZdJCTK/RypFq1z7OEPCLjyhjfw/NRJSLWaIEkKXO4Als++H0899BtcfdObOJp+d//599VYN/9B/O3WUjw07RtMvf9SPPZ8Ga6+6U289UFsOq7HWnNrR41zc12Pa6Lyc1TcHdy3AFoBpEmirLz10rwvE7F7Zo+m5w7e/YVgV2XrU6dd+MQGrXMQQggh8VRe5hQAvA7ACkC3e2ezt3z2htkaxzpmUoOv2WjQxaZ5wVESRRlX/ukNTP7NKfj1xZ0d1wvy0vDri8eAYRicMqYYLMugrd2/3+Pyc9JQ17Cv43pdgxv5OfsvRH4zez3GlPSBPxDGrqpWfPbWzfjq27UIxqDjeiyFQpHwkpU7r/zVdS/3rOC/QMXdQZSXOcMA3gZgB8Bv3VzfPuvrNTM0jpVUOHNivNklmuq69jX3Tf36Qa1zEEIIIVooL3NWAfgSXTuovvhouXPHtsYed/15yBuKFGfaNGui8nOqquLGv07HkIE5uPv/9jVNueyiUViwdDsAoGJXMyKijIz0/RcaTx7dFzt2t6CyurPj+mczVuPSC/bvuP7Cmz/g3tsuQOhnHddlWUFE7DFzv6GqKlaurXzgyj+9vkrrLMeLirtDKC9zVgD4Bl17v2d9tXrr+jVVy7RNlTx4k86iKHTd3UjH2PAAACAASURBVM+1u/yuJSt3XjJrrpP+XAghhPRmcwHsQNf84ReeKvvO6wm0ahvp6PAuoZXnuYRoXLj0p1344IsVWLBkO0afMxWjz5mKsnkb8adrx6GyuhUjznwE197yFv774h/AMAwamjy4ePK+jusvPXENLrzmBQw742FcdemJGD5kX8f1V95dgOuvPh1msx4jh3V2XB951iMYU1KENFvPOY+/eVvDnOden/es1jmigaFZ0odWOrGEB3AXgIEAGjmOZR577trfZ+em9dE4WlJIawv7UswGq9Y5EoEQFsWZc5yXXn3Tm3OOfG9CCCEkuZVOLMkE8CgAP4DA8JGFGXfdd/HNvI7TaRztiDpafMHBKRYTy1B/gZ6gsdlb9+7HS4c+8MQM/5Hvnfho5e4wysucEoC3AIQApMmyor70zOwvQ8FIUvzlay0YFoNaZ0gEqqpi0bKKx6mwI4QQQjqVlzlbAbyGztU7bvOG2ra5367/VuNY3WIPo4MKu54hFIqEl63adUWyFHYAFXdHVF7m9AJ4CYANgL6+1uX/+L+Lv1AURdE4Wo8nSHKP7YAVTWs31Mx+5d2Fj2qdgxBCCEkk5WXODQBmASgEgC8/WbFh9cpdP2qb6vA8tR5PTro1R+sc5MgURVVXrq3815V/en211lmiiYq7bigvc+4CMB2d198xSxZuq1nw/eYe270pUYhQe32BvKuqteLtj5ZMmjXX2ev/LAghhJCDmIHO6+9yAeCVZ+cs2La5fq22kQ5OlVXk6fQ9cvB1b/Tj8ooPn3t93nNa54g2Ku66byGAxeg6e/Thuz+uXrlsx0ItA/V0Csf06p+/mnpX41sfLr7w9f8uSpqtAIQQQkg0lZc5RQCvAvAByACAZ/4989vqytZtmgY7CG+Nq9Weak7XOgc5soVLty967vV5tyRjE7te/eH6aJSXOVUAHwKoBZADAK//5/tFG9fXrNQ0WE9m6L2z7ppbO9yvvbfo6idfnFOpdRZCCCEkkZWXOd0Anun6Mk2WFfXJR2Z81dzkqdYy189JEUnta7UYtc5Bjmzlmsp1z70+b9Ksuc6Q1lligYq7o1Be5gwBeB5AB4BMAHj+iVlzdlU0bdQ0WA/FmXUpWmfQgqcj6H9z+o83TXtx9mKtsxBCCCE9QXmZswmdBV4KAEsoGJGenDLjE7fL36xxNABAqNbbTB3AE9+GzXUVT78y99JZc50tWmeJFSrujlJ5mdODzjcXEYBDVYFpj8yYUVfTvkPjaD0Ob9Rb5F7WmCYYDAvvfLT0nlXrq7/WOgshhBDSk3QNOH8OQDoAk9sVCD/72KwP/T7Bo2UuIRCW+mXY7FpmIEdWsau55rnX5/3qq2/X1mmdJZaouDsG5WXOFnQWeDoANkmUlSce/t/nLc3eGo2j9SgsxzChkOjTOke8hIRI+L1Plz+2aFnFm8m4x5sQQgiJtfIy51YAr6DzEhl9fa3L//Izsz8QhEhAs1BNgWa9jjdodnxyRNV17c3/efOHy9//bFmF1llijYq7Y1Re5qxDZ4FnBZASDISlaQ//7+O21o56jaP1KMGImJT7nX8pEAyHXnxr/tNzF2yeRoUdIYQQcuzKy5yrAbyPzi7m/PatDa63X/nho0hEivuIpYArIPTNTsuN93FJ9zW1eF0vvb3gmtfeW7hO6yzxQMXdcegakfA8Ors3mdyuQHjKfV+8X1/r2qlxtB4jLMkRrTPEWodfCD718twXl/6069+z5jolrfMQQgghSWABgK8BFAFg16zc3fjKs3PeDQbCHfEMYfZJLo5l6fN0gqprcLc+//q8Pzz76vcLtc4SL/TDeJzKy5yb0bk9IBtASsAviI/+44tPqMlK9yT7rDu3N+ib9sLsp9dtrH141lwnDW0nhBBCoqCri/lMAPMA9AXAbVhX3fLkIzPedrsCcWmy0tHY4SvItOXF41jk6O3Y3VL/8FMzb926o+lbrbPEExV3UdC1PeA5AA4AqZGIpDz24Fdfb1hXvULjaAlP5hlO6wyx0ubyex97vuzxTdsaHqPCjhBCCImu8jKnAuAjAGUA+gDQ1VS1+R79xxfvNda7Yz5qKENhe8WlJT3R+k21u//1+Iw7Wtv9X/e2y2EYVe1VrzemSieWDADwNwASgHYAuOkv550x9szB52oaLIGF2vwtA8yWLK1zRFt1XXvz0698/1hNnes12opJCCGExE7pxBIGwHkArgPQBCCk1/Ps3x+89LIBg3NHxuKYniqXa3iWwxGL5ybHZ9Gyik3PvzHvDkVRF/a2wg6g4i7qSieWFAC4B4AeQAsAXHP9uNGlE0t+xbIMo2m4BBQJhP3FjMGidY5oWrexZtdTL899NBCMfDRrrlPWOg8hhBDSG5ROLDkRwG0APOicSYy/3HPROSee0m98NI8jSwrSXBFPqsWYFs3nJcdHlhXl85mrl3/y9aq/zprrXKV1Hq1QcRcDpRNLMtG5gmcH0AgAZ503rO81vxt3pdGk75WDuw9FVVTkBlSZ49gevz1TUVS1bN7GdW9+sHgqgJmz5jqT+npCQgghJNGUTiwZCOBuADKANgD47R/Hn3jOBSMujtZJdu+u9uZhuenZ0XguEh3hsBh+9b1F8xYs3X7vrLnOLVrn0RIVdzFSOrHEBuBOdF7kWwtALShKt/zlnguvzM5J66NpuAST2ip4rSlGm9Y5jkc4LIbfmL540bwft06ZNde5XOs8hBBCSG9VOrEkD50n2VPQuU0TF/5q1KBfX33qr3X645tHFwmJcq6AsNGgM0chKomCNpff/dzr5TM2bW14aNZcZ1IPKO8OKu5iqHRiiQnAHwCcDqAOQITjWOa2uy88d9RJfccxtEsTAKCr8zdmOSw9dkZMc2tH2zOvfj97+87mqbPmOndonYcQQgjp7UonltjReZK9EJ0n2dFvQLbtljtKf52VYys61ucN7mpvGJibTh0yE8SK1bu3vvDWD18EgpH/zJrrdGudJxFQcRdjpRNLWABnA/gdAC8694HjoktHD7r8qlOu0Bt4o4bxEoJc7a0pyjz2N1qtqKqKxSt2bHr5nQUzhLD0wqy5zjatMxFCCCGkU+nEEjOAmwGMQWeBJ7Isw9x427njTh07cALLHd18upA3FOnL6Bme53SxyEu6LyREgu98tHTF9wu3fAHgv7PmOgWtMyUKKu7ipHRiSX8AfwFgAVAPAP0HZqf9+a8XTHJkWHvsqlU0CNWe6v6ZPWuraiAQ9r0+/ccVi5ZVfA7gQ3pTIYQQQhJP6cQSHsD5AK4C4APgAoATT+2Xe/2NZ/061WbO6O5ziZXu+r7Z9vzYJCXdtbu6tWbai3MWN7V0vA5gaW/siHk4VNzFUenEklQANwAYhc5tmqLBwHM33nbemWNOLj7jaM8gJYtgfUftQHtqodY5umv7zqbKaS/NWdzuCrwK4Cd6UyGEEEISW+nEkr4AbgWQic7PYIo5xcD/+e4Lzh8+ovDkIz3e1+ILDkqxmFi6pkYzsqzIM+c61/z302WzVRVvzJrrbNQ6UyKi4i7OSieWcAAuROcZJDc6t2pi9EnF2dfdcOZljvSee+3ZsRLaAi39zSkJP+tOCIuhr75du/azGatnA3hr1lxni9aZCCGEENI9pRNLjACuBFAKoBWAHwDOv7hkwBWTTrn8cB3N2dqOptz01Jz4JCW/1O72tz/32rylG7fWTwcwa9ZcZ0TrTImKijuNlE4sGYzOfeB2dG7TlHkdx/7h5rNPP3XcwLN6035uMRgJ9EXijohQVRXOzXWbX3xr/oY2l/8TALNpMDkhhBDSM5VOLBmBzs9gBnSOrFKzc2zmW/96wSV9ijOH/vL+3lqPd1h6Wo/u6t1TiZIsLliyfcM7Hy1ZERLEV2bNdW7VOlOio+JOQ13dNC8DcBE6G624AaC4f5btD7dMuKiob8ZgLfPFi6qoyAmoEs+xvNZZfqnd5W9584PFq5av3r0FwJuz5jp3ap2JEEIIIcendGJJGjqb3Z0MoAH4f/buPC6qev8f+OvMDPu+IzuyD6uimKgpKrkU5i7kkluhVDczW251u/e2+G391bXUVso0xa6aqV31apaaZF3NBR3AHZFFBGSHYZbz+2PAyBUVODC8no/HPGBmzvIequm8zmdDAwAMHxUZ8MC4PiPs7C1dAEDUibAsqS91vI2xeXT3RFGEKrfo2Aef/5hTWFzxE4CVm7cfqZK6rq6A4a4TaFpwcw4ANxjuIDUCwOgHe4WMeCBm+O0M9u2qbEoaKmytze2lrqNZo0bb+N8fVQfT1+zL0Wr1GwDs2Lz9iFrquoiIiKhtNM1oPhCGkCeiqRVPYSKXPfTwwD79BgTd13ix5nK4u5PRX4d1JkUXK/M/W7X30P8O510AsBpAJuc3aD2Gu04icXS0KYDhAMYD0AK4CECUy2XChJR+UYMSlEOsbTpP+GlrigvVRW6O0s8aqtXqtP87nHfos1V7z14qq/kVwOrN248US10XERERtY/E0dEuMMyF0A+GuRDKZSJC7azNQ56YOaTynt7+Azpj7yJjU1PbULlu8+//2/D9oXwA/wGwbfP2IzVS19XVMNx1Momjo10BpADoBcNA31IAMDVVyCZN6997wL2h91pYmtpIWWN70J2ryPNxlW45BJ1erz96/MLhT1buPVFQVFECYAWAQ7xTREREZPwSR0cLAIIATIMIX4UeveTAAQE4H+DnYjvnoQGDlSEevWQyzpbZ1jRanWZ35okDn3y193SDWvM/AN/wxvqdY7jrhFp8wUxq+lmFpnVZLCxNFckzBvSNiw8aaG5uYilhmW2qIa/iXICLvV9Hn1evF8XsE0VZn6zcm3P2fGkFgM0Adm3efqSuo2shIiIiaSWOjpYLIgab6PG4zNCKVwxADQBRSk+nGZP7Dwn0d1HKZN1z+aq2VFffWPPLgTOHvl73a0Fpec1pAKsA5PLG+t1huOvEmkKeEsBkAL5osXSCrZ2F6cSH+sfG9PHra2Nj4SBhmW2ivqAqP7AD17pTN2objhzLP/z1+t/yzp4vrQGwFYZxdRysS0RE1M0ljYg2BzAYwDgAJjCEPA0A+Ps620x5sE+fXhHesRY3WT6Brq+0vKZ4196cA99sOlDe2KirBpABYD9nIm8bDHddQNOA3ygAUwB4ACiDoTUPggAMHxkVMDAhrK+Xj1NwV+0uUF9WVxJoYdnua91VVNaV7v311IE1G367WFOr1gHYCUOf7svtfW4iIiLqWpJGRFvDMCfC/QDkMAyXqQMAczOFfPKDfcLvvScoztXF1lPCMjs9vSiKefllJzZuPXz0x59zawHUwnBj/afN24/USlyeUWG460KaFkDvBcMCnG4wdBMoAaAHDEso3D8uNjY8yqu3uXnXupOkrdfU+Yrt081Up9frz18oP7lt1/GsbbuO1YgiNAB+gKH75aX2OCcREREZj6QR0bYwTLhyPwA7GOZFKIdhlk0M7BfokXRfVFxwgFuEXC6TS1dp56LR6jTHsgsOfb3+t5MnTl/UwrC28yYY5jXgQuTtgOGuC2pqyQsAMBRAHAABwCUA9QBgZqaQj36wd1hMH/8ITy/HALmia8zw5Fal07TV4u2iKKK4pDLvwJHzx779/lBRaXmNAoYWzy0A/se7RERERHS7kkZEKwCEAxgJIBR/zHCuBYAebnaWUx7s0ysi1CPMxdnGU+iaHaruik6n150vKD/168Gz2Zu2H6moqVUDwBEA2wCc2Lz9iF7aCo0bw10Xlzg62gGGO0mjANjC0MxdhqY7STa25iZDEiMCo3v5hnn7OQeZmirMpav25qwv1l+2u8vxg6XlNcVHjl/I+m7b4dPnzpeZwhB8s2H4QlFt3n5E1xa1EhERUfeWNCLaE4ZxeQkwdNksh6FFDwDg6+1kfd/gsJCocK8Qzx4O/sa8nIJWp9deKLx89lDWedV3244Ull+utYAh8P4EQ0+pImkr7D4Y7oxE4uhoBQyTr4wAEAZDqKmF4YtGDxiWUxg0NMyvd9+eYf4BriGdbUkF+YXqQndHG4/b2aehQVN3vqD8zPGcwtO7fs65kHeh3ByADIY+8T8AOLx5+5GL7VEvERERUdKIaCsAfQGMBtC84HklDPMjiABga2NuMnJoeGBslG+Iv69zsLmZiYU01badBrWm7sy5SycOHj2fu23X8YvVNQ3WMFyDnQOwG8DvnKiu4zHcGaHE0dF2MHQViIeh64AMhvF5ZWjqNgAAoUpPx6jePt5+PV293T0cvOztLV0FCSdk0eVV5vm42N10rTu1WlNfUlpdeOrcpXO/Hjxz+pcDZ2pEEXYwfMZqGO4QHQSQz6l0iYiIqKMkjYgWAPSA4Sb7QBhmOr/mZrtCLhMSBob4RCm9fLw9HDxcXWx7WFuZ2UlVd2vo9Xp9eUVdSUFRxYXT50ouHMrKLziquqATRTQ3FBQB+BHA0c3bj5RIWGq3x3Bn5BJHR1sCCIZhbF4sDNP5ijDcTapG0xcNANjZW5rG9uvpFRzq4eXl4+ht72DlbGFpZtdRM3BevdZdg1pTf6m0urCguKLo9NlLhYeP5xflnrpYD8ABgGnTZhcA/A+Grpdn2e2SiIiIOoOkEdEOMFyD9QcQAcONaA0MS1s1tNzWw93eMjbKp0dQgJuHt4dDDzcXGw9rK3PJAl9NrbqyuKSy4Fx+2YXjOYUFvx48W1RTpzaHYQhQ84QxJwBkAsgBcIk31TsHhrtuJHF0tCkME7EEAogE4A/DF40Aw5dMFa76sjE1Vcj8A1ztvf2cHNx7ODg4udg4ODhaOdraWTiYm5taKUxkpgqF3PR2Bwzr9aKo1eoa6+saqy8WXLYEcKKyoPKirqKh/nxBefmpMyXl+YWX1QCsAdg01SjCcPfrdxgG5p7ZvP1I5V38SYiIiIjaXdKIaAsYrr/6wBD0HGC4wS6HYWmFalx1DebuamvRK9LHvYebnb2Tg5Wtg52lra2thZ21lZmNuZmJpZmZwlJ+B4upa7Q6TW2duqqmRl1ZVV1fVVFVX1VWXlNVUlZdVVRcWXUuv6yypLQaMAQ5czR1LYXhhvpxACcBnNy8/UjN9c9AUmK468YSR0ebwNB9wBuGLgThMPyHLMIQ+kQYvmjqmx7XbRUTBMDaxtzExtbC1Nra3NTaxtzM0srMVKGQy9Rqjba+rlFTX9eoqatTa2pr1Zra6gaNWq29ciwTHcbLgN8EoBF//hLRADgLIBeG/ttFMNwZ4ixLRERE1GU1rZ/n0fQIBRAEQ+AT8cdN93oYhtVo8Me10Z84OVqZubnYWtpam5uJIkS9Xi/q9aKoF0VRrxdFnV784zW9KJZX1KpLy2qaQ6QCgFmLhykM13oyGFoXcwGoYFi+oHDz9iPq9vhbUNtiuKMrEkdHCzCs3eICwxeMCwAvAJ4wrKtnghbdOPFHq58Aw5eBDoYxfc3hUAbDHanm7UT88eXU/OUlyPVwVIg4JhiCXD4Ma/cVAihlkCMiIqLuoCnw9YDhuisQgDsME7Q033hvvoYSYLi20uOPuRSEW/xs3v7K9RcMAbIMhuuu5sdFGMJcFbtZdk0Md9QqTcHPCoBj00/Tqx5WACwBWDQ9V+OPu04N+OPOkxaGFroaNM0kteM/XMSSiIiI6Hqa1tazgSHkWbZ42DQ9RBiCm/4mvzfgjxk8q2AIb2yJM0IMd0REREREREbgtgdhEhERERERUefDcEdERERERGQEGO6IiIiIiIiMAMMdERERERGREWC4IyIiIiIiMgIMd0REREREREaA4Y6IiIiIiMgIMNwREREREREZAYY7IiIiIiIiI8BwR0REREREZAQY7oiIiIiIiIwAwx0REREREZERYLgjIiIiIiIyAgx3RERERERERoDhjoiIiIiIyAgw3BERERERERkBhjsiIiIiIiIjwHBHRERERERkBBjuiIiIiIiIjADDHRERERERkRFguCMiIiIiIjICDHdERERERERGgOGOiIiIiIjICDDcERERERERGQGGOyIiIiIiIiPAcEdERERERGQEGO6IiIiIiIiMAMMdERERERGREWC4IyIiIiIiMgIMd0REREREREaA4Y6IiIiIiMgIMNwREREREREZAYY7IiIiIiIiI8BwR0REREREZAQY7oiIiIiIiIwAwx0REREREZERYLgjIiIiIiIyAgx3RERERERERoDhjoiIiIiIyAgw3BERERERERkBhjsiIiIiIiIjwHBHRERERERkBBjuiIiIiIiIjADDHRERERERkRFguCMiIiIiIjICDHdERERERERGgOGOiIiIiIjICDDcERERERERGQGGOyIiIiIiIiPAcEdERERERGQEGO6IiIiIiIiMAMMdERERERGREWC4IyIiIiIiMgIMd3RLgiCcEwRBFARhSAecS2x6+LX3uYiIiIi6O0EQfmq69prZ9PzLpuf/kLYyuhMKqQsgaQmCcA6AL4BiAD1FUawXBCEGwCEAEEVRAJAOwBHAhQ4o6V9NP6s64FxEREREnUaL6zIA0AO4BOAXAE+LonimnU67DsBhAKp2Oj51IIY7auYOYD6A/3f1G6IovtLeJxcEwUQURY0oigva+1xEREREndwWAGcBjAAwFoAdgKFXb9R8/XQ3JxJF8cO72Z86F3bLpGYigGcFQbC8+o2ru2UKguAhCMJ/BUGoFQRhnyAI/2x6/3CLfSIEQfheEIQSQRAuCYKwXhAEnxbvN3e/XCAIwlkAuVe97tf0/N2m8zcIglAnCML+jugeSkRERCShz0VR/AuARU3PQ4E/XZO9KAjCcQANTa/f8HpJEAS/FtdXLR9fNr3/p26Z1LWx5Y6a/RvAZACPAdhxi21XAxgM4ASAMwCeb/mmIAjuAPYAsIbhzpMOwEQASkEQYkRRVLfYfHHTuetvcC5/AL8CKAXgB2A0gH8LgtBTFMXq1n44IiIioi5kjiAIQwEkNj1ff9X7/4ShO2VzV8obXi/BMNSlediLAsCjAEwAFLZX8SQdhjtqthZABIBnYOjbfV2CIHjBEOwA4D5RFPMEQSgH8JcWm00H4AAgG8D5ptcuwXDXKQHAthbbPi6KYvpN6poLQzD0A3ASQB0AZwCRADJb88GIiIiIupgHWvyuBnDwqvcXi6L4covnN7xeEkUxE8ACwDBZCgzBbheAf7RD3SQxhjtqpofhLtBaAI/fZDvPpp/1oijmNf1+9QBcv6afYU2PlgKver7vRicSBMEJwFEAHtd52+UmNRIRERF1ZeMAfAcgDsBeAJ8LgrC7xftXrp9ae70kCMKrAB5u2nacKIqN7VA3SYxj7qilfwPIgqF75o0UNP20aGrFA5r6gbdwrunnt6IoCs0PAD0AfH7Vtmrc2CAYvqiKYZjwxQxARdN7wk32IyIiIurSRFEUYWixq4Xhmj2gxdstr59ueb0kCMIjAF4CkA9glCiKnJXcSLHljq4QRVEUBKG5D/eNtrnQdOdoMID/CoJwAMCUqzb7GsALAMYJgrAdhrAX0LRPEP4If7dysemnCwyzeAbAMI6PiIiIyJjNEQQhAUBvAPYwdLM8eoNtb3q9JAhCOIDlTU+PwzCBHgD8Jori6rYvnaTElju62gYY1jq5makwTLriC8MXyHtNr6sBQBTFQhiC3BYAMQCmwdCdcykMA31bRRTFXwC8DsNA4PsArMEfLYdERERExuoBGOYziADwM4AkURRLrrdhK66XXADIm34fCeDJpsd97VI5SUowtPgStZ4gCHaiKFa2eP4xDDMvrRJFcbp0lRERERERdV/slkl3YpYgCA8C2A3D1LvTYJiQZamkVRERERERdWMMd3QncmEYsPscDIN89wJ4VRTF/ZJWRURERETUjbFbJhERERERkRHghCpERERERERGgOGOiIiIiIjICDDcERERERERGQGGOyIiIiIiIiPAcEdERERERGQEGO6IiIiIiIiMAMMdERERERGREWC4IyIiIiIiMgIMd0REREREREaA4Y6IiIiIiMgIMNwREREREREZAYY7IiIiIiIiI6CQugAiIrp9giC4urm5fQYgArxRR21PD+DYxYsX54qiWCJ1MURE1DqCKIpS10BERLfJ3d1903PPPReWlpZWZWZmxi9yalNqtVpYunSp3VtvvaUqLi4eI3U9RETUOgx3RERdkLu7+5m8vLzLDHbUXtRqteDr6+tQXFzcU+paiIioddiVh4ioa5Ix2FF7avr3i9cJRERdCL+0iYhIMlu2bLERBCHW09Mz8k6PodFoMGHCBD8bG5sYQRBiH330Ua/W7CcIQqwgCLGZmZkWABAXFxciCELskiVLnJq3SUtL83RwcIgWBCF25MiRPQHgs88+c/D09IyUyWSxDg4O0bm5uabNx7rTz9DSkiVLnARBiI2Liwtpi+MREVH3wQlViIi6OU9Pz8jCwkLTr7766vT06dMrpK7ndq1YscJhw4YNTvb29tqHH364ZPDgwdV3cpyxY8eWR0RE1EVGRtYDwI8//mi5fPlydzMzM3Hq1KmX4uLiarVaLZ588km/uro62ejRoy8HBAQ02Nvb62bNmtVuk47k5uaahoaGRgKAKIoH2+s8RETU9THcERFRl3bixAlzABgyZEjll19+mX+nx3nhhRcutXyenZ1tDgARERG1q1atOg8Ap0+fNqmrq5MBwKZNm87I5XIAQHp6+h2fl4iIqK2wWyYREd3U119/bRcZGRlmbW3dy8PDI/KRRx7xqq6ulun1enh6ekYKghC7d+9ey+btvby8IgVBiN2zZ4/lzfZv7flzc3NNR40a1dPFxSXK1tY2pl+/fsG7du2yAoCFCxd6vP322x4AsHHjRqeru1U20+v1WLBggYejo2O0m5tb1NKlSx2v3qZlt8wlS5Y4PfbYY/4AcPDgQWtBEGInTJjgFxgYGNW8vUKhiI2Liwu5XrfM06dPm4wfP97Pw8Mj0szMrHfPnj3Dd+/ebQn80R00NzfXtPkzNB//ep+9udWuOXRxywAAIABJREFU5b47duywEgQh1s/PL6L5vV27dl3zGhERdS8Md0REdEPr16+3nTZtWuCFCxdM77vvvsvu7u6azz77zG3WrFk+MpkMEydOLAOA1atXOwDA7t27LQsKCkwDAgIa7r333rqb7d+a81dVVcmGDRsWsm3bNgc/Pz91//79q3/77Teb+++/P/j48eNm8fHxNVFRUbUA0LNnz4ZZs2aVNHerbOmDDz5w+te//tWjrq5OPmjQoKq33nrL42bnjYyMrI+Pj68CAFdXV82sWbNKEhMTq5o/LwDMmjWrZOzYseVX71tdXS0bNmxYyLfffutkamoqjhs3rszOzk6Xn59v2prP3JK9vb3u6nPOmjWrJCoqqqFnz54NeXl5Zj/++KMlAGzYsMEeAMaPH39NTURE1D0w3BER0Q198MEHrgCgVCrr7O3tdUqlsg4ANmzY4FRdXS2bO3duGQBs2rTJEQBWr17tCACTJ08ua83+tzr/N998Y1dQUGDq5eWl3r9/f+727dtPDx8+vKKhoUG2fPly54kTJ1YNGzasCgBiYmJq09PT8xMSEuquPs7atWudAOCJJ54oWrdu3bl///vfp2923oSEhLopU6aUA4Cvr686PT09f968eeWvvfZaYfM26enp+Vd35QSAdevW2eXl5Zm5uLhosrKyVBkZGXmHDh3KmTRpUuWtPu/V3NzcdFefMz09Pd/NzU03bdq0UgBYsWKFEwBs377dHgBmzpxZdv2jERGRseOYOyIiuqELFy6YAUBmZqZtZmambfProigiJyfHtG/fvg0xMTG1hw8fttq1a5fVli1bHGQyGebMmVPWmv1vdf5z586ZAoZWuebxbcHBwQ07d+7E+fPnW90SVlxcbAIAISEhDQAQGRnZ0Np9b9eZM2dMASA4OLjexsZG3/z6jZau0Ol0d3Se1NTUssWLF3tu3rzZ8ejRoyWnTp0yDw8Pr4uKilLf0QGJiKjLY8sdERHdkJeXlxoAXn311XxRFA82P44fP57Vt2/fBgBISUkpA4Bnn33Wq7Cw0LRfv35VAQEBmtbufzN+fn6NAHD27Flzvd6Qk06ePGkOAD4+Po2t/Rzu7u4aAMjNzTUHgKysLPNW/xFuU8+ePRsB4MSJExY1NTVC8+sajQYAYG5urgeAiooKOQCoVCqLmx1PLpdfCYUtg6CHh4d22LBhFeXl5Yr58+f7AMCkSZPYakdE1I0x3BEREQDgb3/7m1d0dHRo82Pr1q3W8+fPvwQAr732mldSUpL/pEmT/CIiIsISExOvrME2a9asclNTU/HgwYPWADB16tQrAaM1+9/M5MmTKz08PBrz8/PN7rnnnpCRI0f23LFjh725ubl+3rx5pa39bC26ifaYOHGi38SJEwNau+/tmjhxYqWvr6/60qVLJpGRkcqHHnrIt2/fviFr1661BwxdVAHgscce80lOTvb94Ycf7G92PC8vL62JiYkIAA8++GDP+fPneza/N3fu3FLA0DIqk8kwc+bMy+31uYiIqPNjt0xqtXRVmikAbwA+LX66ALC6zsMSgByAGkBDi5/NjwoAhQAKWj5mK5fxwoRIInl5eWYAzJqfl5aWKqZPn17R2Nh4+p133nH/6aef7ADAz89PnZqaerF5OxcXF93QoUMrtm3b5mBpaalvuVbelClTKm+1/83Y2trqd+zYkbtw4UKvAwcO2GRnZ1v07du3ZvHixRciIiJa3f3wiSeeKDtz5ozZqlWrXHbv3m37/PPPFy5atMi3tfvfDhsbG/3OnTtzn3nmGc/9+/fbrF+/3snT01Pt7e3dCABLliw5P2vWLP+cnBxLS0tL/cSJE0v//e9/O9/oeObm5uJLL7104f333+/x/fffO1hYWOiXL19eAADjxo2r6tGjR2NRUZFpXFxcta+vr6Y9PhMRSStdlWYPIAiAHwAnAA7XedjD0HCjA6C/zs9qACVXPS42/bwwW7mMkzEZAUEUrzsEgLqxdFWaHYAYAL2aHiEAfAG4ARBusmtbqAdwFkAWgKMAjgA4Olu5jGtIEbXg7u5+rri4uNUtV2S8pk6d6rN69WqX995779yCBQvatFumu7u7c3FxsV9bHpOIri9dlSYDEAogCoYgF9j0MwjADW8AtaFLAFRXPY7PVi5r1c046hwY7rq5dFWaGYD+AOLRFOZEUewpCEJ7h7jbdRmGwPcbgB8B7JmtXFYjbUlE0mG4o4MHD5qvW7fO/qOPPnITRVE4f/78UVtbW/2t92w9hjui9pOuSnMAMKDp0Q9ALADbm+4kjUsA9gHYA2A3gMOzlcva9LuG2g7DXTeTrkoTYGiVGwFguCiK8YIg3HQwfyelBfA/ALuaHpmzlcvabfY7os6G4Y6WLFni9OSTT/r16NGjccmSJXkTJ06sautzMNwRtZ10VZoCwL0AkgAME0UxohPeTG+NSgCZMAS9nwD8Nlu5jIGik2C46wbSVWkmMIS5CaIojhQEwV3qmtpBAwwhbz2A72Yrl3HGODJqDHfUERjuiO5OuirNFsBIAA+KojhaEISbTqDURRUB+BbAOhh6Vt3Z+i7UJhjujFRTC91AURQfgogpgkxwkLqmDqTdV1D3ZXaZOhPA+szkNW1+N5tIagx31BEY7ohuX7oqzRrAFFEUpwAYLAhCq9fkNAKXAGyEIejtmq1cppW4nm6H4c7IpKvSwgFM1+vFqTKZ4CV1PVIQRbF2xbEKQSvCEoYWve8BfAZge2byGv4LT0aB4Y46AsMdUeulq9L66fXiowIwRZAJVlLX0wmUAVgJ4JPZymXZUhfTXTDcGYGm2ZWSdFr9IrlCNlDqeqR2Jq/y4q5Kvdt13joB4EMAX2Ymr6nu4LKI2hTDHXUEhjuim2uaFGW6TqufJ1fIwqSupxP7GcAyAOtmK5dxyZZ2xHDXhaWr0mz0Ov1cUcRCuULWLVvprue/WaUF50W55002qQLwJYAPM5PXnOyYqojaFsMddQSGO6LrS1el9dRp9S/KZMJUQSaY3XoPalIE4CMAH81WLiuRuhhjxHDXBaWr0rx0Gv2zggyzZXIZm/1b0DTqNCtVFYJeJlO0YnMRwFYAr2Ymr9nfzqURtSmpw12/fv2Cn3322eIJEyZcGdP6yiuvuObm5pp//fXX56Wqi9oWwx3Rn6Wr0sIa67WvmZjLxwqCIJO6ni6sHsByAG8y5LUthrsuJF2V5qyu07xiYq6YI5N1q8G5rXY2vzr/h8ta7zvYdTOAFzOT12S1dU1E7eHqcBefkRLblsfPTF5z8Gbvv/POO8779++3Xrdu3bnm16Kjo0PfeOONC6NGjbrlGpRarRYKRWvuwZCUGO6IDNJVab3U9ZrXTc0VI7vo8gWdVS2ApQDe4kznbYN3HLqAdFWa9dLf5r6l0+rzzSxN5jPY3VjuZfWd/judBOBwfEbK1/EZKQFtWRORMZo+ffrlXbt22TU0NAgAkJuba1pSUmIyYsSImg0bNtjGxMSEKpXKsFGjRvWsrKyUAYCnp2fk/PnzPZVKZdiLL77orlQqr4xPycrKMmv5vNnx48fNBg0aFBQeHh4WGxsbcujQIXMAWL16tV1UVFRoWFiYMj4+Pjg/P18BAN9//711aGioMjQ0VBkWFqa8fPmybNy4cX4rV668Mv34mDFj/FetWmWM05ETURv79Oi80OUHH/kBwO9mFiajGOzanBWAZwGcS1elLU5XpTlKXVBXx3DXiaWr0syW/jb3r1qNrsDC2vQZuUJmLnVNnVmjWtdYIMruZg0/GYCHAOTEZ6R8HJ+R4tFGpREZHTc3N110dHTtunXr7ABgxYoVjklJSZcvXryoWLx4cY89e/acUKlU2b1796579dVXr0xw5OTkpFWpVNlvvvlmsY2NjS4zM9MCAD7++GPnqVOnXnPXdu7cub7Lli07f/z48ey33377wvz5830AIDExsebw4cM52dnZqokTJ5a/8sor7gDw7rvvui9ZsiQvJydHtX///hxra2v93LlzS1esWOEEAGVlZfKDBw9aT5kypaIj/k5E1DUtP/iI3ZJ9s78QZMIxMwuToVLX0w1YA/grgLPpqrRnm9ZopjvAPjGd1L9+nnW/maXJJxbWpgwYrZR/sbZYFASfNjiUAsCjAB6Kz0j5G4APMpPXcEFOoqtMnjy5fO3atQ7Tpk2r2LBhg+Onn3567qeffrI6ffq0eVxcXCgAaDQaITY29ko3zRkzZlxu/n3mzJmln376qXNcXFz+d9995/C///3vT1NlV1ZWyg4dOmQ9adKkK63pjY2NAgCcPXvWdOzYsV6XLl0yaWxslHl7e6sB4J577qlZtGiR9+TJk8tTUlIuBwQE6O+///6aJ5980rewsFCxatUqh/vvv/+yiQmvG4joWumqNFlVWd1TFjZm/7B2MLGWup5uyBbAmwAeTlelpc1WLtstdUFdDVvuOpl3ds1we2/PzP/aOFpsMTVXMNjdhhMVjW19s8IawHsAfo3PSOndxscm6vIeeuihin379tn+/PPPlg0NDbJBgwbViaKIgQMHVuXk5KhycnJUp0+fPv7NN9/kNe9jY2Ojb/794Ycfvvzjjz/aZWRk2EdGRta5u7v/6SaKTqeDjY2NtvlYOTk5qjNnzhwHgMcff9wnLS2t5MSJE6oPP/wwT602dMlevHhx8WeffZZXX18vGzRoUGhzN84pU6aUffrpp46rVq1ySk1N5SyjRHSNf+2bNay+pvGkrZPlOyamcgY7aSkB/JSuSluZrkq73vJWdAMMd53IWzunP2PjYH7WztkyUepauhp1g1ZdoL+rLpk3Ewvgt/iMlPfiM1L4ZU/UxM7OTt+/f//quXPn+o0bN64cAIYMGVJ74MAB62PHjpkBQFVVlezo0aPXnSbc0tJSHDx4cOXChQt9Zs6ceU3gcnR01Ht5eTWmp6c7AIBer8cvv/xiAQDV1dVyHx8fDQB8+eWXTs37HD9+3CwuLq7+9ddfL46Kiqo9duyYOQDMmzev9OOPP3YDgNjY2Ia2/UsQUVf2f1sfsnlvz8xvbRwsdlpYm/aUuh76k2kActNVaY+nq9LkUhfTFTDcdQJvbJ8asSRz9nFnD5u3TMwUFlLX0xWdv1h3EbJ2nZJYDmABgOPxGSlJ7Xgeoi4lOTm5PDc312LGjBnlAODh4aH9+OOPzyUnJ/cMDg5W9unTJzQrK+uG44VnzJhRLggCxo8fX3W999esWXPmiy++cA4JCVEGBQWFr1+/3h4AXnzxxcKUlJSA8PDwMCcnJ23z9m+99ZZrUFBQeHBwsNLExEScOHFiJQB4e3trAwICGqZNm8bZ2Ijoipf/PWmcrZPlOTtny7FS10I3ZAfgAwA/p6vS/KUuprPjUggSiknwF8YviHvB3c/+7woTOQeA3IXvj5YVFUHWowNP+RGApzKT17AFgCQh9Tp3beXll192q6yslP/rX/8qbM/zVFdXy5RKpfLw4cPZTk5OHEPbSlwKgYzVwk+TLFy8bFe4ettOFGScAbMLqQLw2GzlslVSF9JZseVOInMWD3NLfi4+0yvI6TUGu7vTUK9tKBKF9uqSeSPzAOyPz0gJ6eDzEhmNxMTEgIyMDKfnn3++XRew3bhxo01ISEj4I488UsJgR0R/WzsxwT/C5Yybr90kBrsuxxbAynRV2qp0VZqt1MV0Rmy5k8Ciz8ck+Ya7rLCyNXOQuhZjkHuu8vzeKn1bzJJ5J2oBzM9MXrNSovNTN2UsLXfUubHljoxJTIK/bOwTfd/xCnJ8UiaXsYGj6zsL4KHZymX7pS6kM+G/2B0oJsFf/vd1k5aHxHlsZLBrOycqNVIu6m4F4Kv4jJQv4zNSrCSsg4iIiG4gaV4f14kL79nnE+r8FIOd0fAHsDddlbZI6kI6E7bcdZCkeX1c+48J/t7dz76P1LUYk/o6Tf2qk9XmgtApulWoADyQmbzmrNSFkPFjyx11BLbckTF4+J9D+kXd6/utg5tVR47Np46VDmDebOUyjdSFSI13LjrAxIX94wZPVv7OYNf28i7WlXSSYAcY1mTZH5+REid1IURERN1dTIK/MP+9EXP7jQ7cxWBn9GYD2JGuSnO65ZZGjuGuHcUk+AvTXx48+d5JYTsc3a09pa7HGOVWaW44xbpEXAH8FJ+RwimViYiIJBKT4G8ybGrE0t7D/D8ytzK1lLoe6hCDAfyarkoLlboQKTHctZOYBH9Z35EBLwwYG/KVtZ05Z/NpB3W1mroSyNykruM6LACsj89IWSB1IUTt6bnnnnMPDAwMDw4OVoaGhip37dol6bhTS0vLXrez/ZYtW2x27NjBsbJERiYmwd/uvoejN4bHe8+XK2Rc+Lp7CQDwS7oqLVHqQqSikLoAYxST4G8+cHzoBzFDfGdz0G77OXux9pIgCL5S13EDMgDvxWek+MOwHp5e6oLIuKWr0mLb8nizlcsO3uz9nTt3Wm3fvt0+KytLZWFhIRYVFSnUanVn6SLdKrt27bKxtrbWJSYm1kpdCxG1jZgEf4+Rs6I3BPfx6Cd1LSQZewD/SVelTZutXLZW6mI6GoNHG4tJ8LePHxP8VUyC3xwGu/Z1olpnIXUNrfAXAKvjM1J455CMSkFBgYmjo6PWwsJCBIAePXpo/fz8NADw3Xff2YSFhSmDg4OVkyZN8quvrxcAwNPTM/Kxxx7zDA0NVUZERIT9/PPPlgMHDgzy9vaOeOutt1yaj/23v/3NLSIiIiw4OFj51FNPeTS/9tprr7kCwJw5c7zvueeeYADYtGmTzZgxY/yb933iiSc8Q0JClNHR0aH5+fkKAFi9erVdVFRUaFhYmDI+Pj44Pz9fkZuba/rVV1+5fPTRR26hoaHKbdu2Wbf8fFVVVbJJkyb5RUZGhoWFhSlXrVplDwC5ubmmsbGxIUqlMkypVIY1t/zl5eWZ9OnTJyQ0NFQZFBQUvm3bNuv333/fafbs2d7Nx3z33Xed58yZ4w0iahexiQG+D6T23spgRzA0YH2drkqbKnUhHY3how3FJPg733N/0KreiT0nyrgoZruqrW2sLYXgKnUdrTQFhuUSGPDIaIwdO7aqsLDQ1M/PL2LatGk+33//vTUA1NXVCampqf5r1649feLECZVWq8Xbb799Jbj5+Pg05uTkqPr161cze/Zsv82bN5/+9ddfc958800PANiwYYPtqVOnzI8ePZqdnZ2tOnz4sOXWrVuthwwZUrNv3z5rADh8+LBlbW2tXK1WC7t377YeNGhQNQDU19fL+vfvX5Obm6vq379/zQcffOACAImJiTWHDx/Oyc7OVk2cOLH8lVdecQ8JCWmcMWPGpXnz5l3MyclRjRw5sqbl53vhhRd6JCQkVGVlZWXv3bs396WXXvKqqqqSeXh4aPfu3XtCpVJlr1279sxTTz3lAwDp6emOw4YNq8zJyVFlZ2cf79evX92sWbMu79ixw665RXPVqlXOqampnOGUqB3c80BwSNL82O09o9yipK6FOg05gK/SVWkzpS6kI7FbZhuJSfB37p8UtKLPfQGjBAa7dnemuK5UEISuNFbmIQC6+IyUmeyiScbAzs5Of+zYMdW2bdtsfvjhB5uHH3444OWXX77Qt2/fOi8vL3VUVJQaAGbOnFm2dOlSVwAlADB58uQKAIiMjKyrra2VOTg46B0cHPSmpqb60tJS+bZt22z37Nljq1QqlQBQV1cny8nJMU9LSyt7+OGHrcrLy2VmZmZiVFRUzd69ey1/+eUXmw8++OA8AJiYmIjJycmVABAbG1u7c+dOWwA4e/as6dixY70uXbpk0tjYKPP29lbf6vP99NNPttu3b7dfsmSJOwCo1Wrh1KlTpr6+vpo5c+b4qlQqC5lMhry8PDMAuOeee2pTU1P9NBqNbOLEiZfj4+PrAWDAgAHVa9eutYuMjGzQaDRCXFxcfRv/oyDq9gaMDY1+ILX3Ro8ARz+pa6FORwYgPV2VppitXPaZ1MV0BLbctYGYBH+X/klBKxnsOs7JGm1XnPlqOoDP4zNS+O8IGQWFQoEHHnig+r333it8++23z2/cuNHhVvuYm5uLACCTyWBqanploVWZTAaNRiOIoogFCxYU5eTkqHJyclTnz58/9tRTT5WamZmJ3t7e6mXLljnHxcXV3HvvvTU7d+60ycvLM+vVq1dDUz2irKk3vEKhgFarFQDg8ccf90lLSys5ceKE6sMPP8xTq9W3/H+fKIpYt27dqeY6ioqKsnr37t3w+uuvu7m6umqys7NVWVlZKo1GIwOAUaNG1ezZsyfX09Ozcfbs2f4ffvihEwA8+uijpStWrHD65JNPnKZNm8ZWO6I21ue+gNiRs2IY7OhmBACfpKvS5ktdSEdguLtLMQn+Lvc8ELSiz30BIxjsOkZNdWN1uSB3ufWWndJMAJ8w4FFXd+TIEbOsrCyz5ueHDh2y8PLyaoyOjm4oKCgwPXbsmBkAfPXVV07N3SZbY9SoUVUrV650rqyslAHA2bNnTQoKChQA0L9//5qlS5e6DRkypHr48OHVK1ascFEqlXWyWwxvrq6ulvv4+GgA4Msvv7yyBpKNjY2uurr6ut2lExISqt599103vd7Q0L5v3z4LAKisrJT36NFDI5fLsWzZMiedTgcAOHHihKmXl5fm6aefLp0xY8al33//3RIAhg4dWltUVGT67bffOs2ZM6e8tX8HIrq1mAT/fqPn9lrjGcRgR7ckAFiWrkqbJXUh7Y3h7i7EJPi7Rt3r81GfEQx2HenMxboyqWu4S3MBfCB1EUR3o6qqSj5jxgz/gICA8ODgYGVOTo7Fm2++WWhpaSl+9NFH5yZNmhQQHByslMlkWLRo0aXWHnf8+PFVkyZNKu/bt29ocHCwcty4cQEVFRVyABg8eHD1pUuXTIYOHVrr7e2tNTMzEwcMGFBzq2O++OKLhSkpKQHh4eFhTk5O2ubXJ0yYUPH999/bX29ClTfeeKNQq9UKoaGhysDAwPCXXnrJEwAWLFhQsmbNGqeQkBBlTk6OuYWFhR4Atm/fbhMWFhYeFhamXL9+veOzzz57sflYY8eOvdynT58aFxcXXWv/DkR0czEJ/tEjZkZ/5hfhGiR1LdSlfJKuSrtP6iLakyCK4q23omvEJPg79Ix2WzJyVvQUhYncROp6upN1R0pLKwS5s9R1tIEFmclr/iV1EdQ1ubu7nysuLmY3vy4gISEhcMGCBRcffPDBVrdgdhbu7u7OxcXFflLXQdRSTIJ/yMBxoSt6D/fnrJh0J6oBDJqtXHZE6kLaA1vu7kBMgr+Vu5/93xOnR05gsOtY1VXqKiMJdgDwbnxGymipiyCi9lFaWir38/OLMDc313fFYEfUGcUk+PvGDPX7oNcwvzipa6EuywbA9+mqNC+pC2kPDHe3KSbB38Ta3nzBqLkxM8wsTLrCOmtG5fTFOmMasyIHkBGfkRIhdSFE1PacnZ11586dO7Z169YzUtdCZAxiEvxdAqLd3hrwYMhQQeBwGLornjAsdG4rdSFtjeHuNsQk+AtyhWzaA6m959s4WNxyVjhqeydr9TZS19DGbABsic9I6Spr9hEREXW4mAR/GztnyxeHTY0YLVfIuG4stYVIAOvTVWlGtTScUX2YDjAyISX8KVcfO0+pC+mOKisbKiplMqdbb9nl+ALYGJ+RMjQzeU2D1MUQERF1JjEJ/nJBJqSOfqTXRHMrU+tb79E5adRavDnjO2gaddDr9Ii9ryfGPh6H7P0X8M07v0Cr0cFP6YKZryZArri2/WXfxhxs+fh3AMADqb0xYGwoNI06fPj4VpRfrEFCcgSGNnUGWvH3nzBkSjh8lV11cvEOMxzAqwD+KnUhbYUtd60Uk+AfHdrP46mwfp7sQieR0yX1FVLX0I76A1gqdRFERESdUNLwqRHTXbxsu/TNdYWpHIvSx+Cf307G39dPwrGf83HqUDE+f3EXUt9JxKvfJcPJwwaZ3+Ves29NRQM2LT+AF9eMx0sZE7Bp+QHUVqpx/OfzCOzdA//8dgp+2XwCAJCfUwq9XmSwa73n0lVpI6Uuoq0w3LVCTIK/q72r1dNDJocPYB9v6Zys1dlJXUM7mx2fkfKQ1EUQERF1FjEJ/lERA73/EtrPM0rqWu6WIAgwtzLMw6fT6qHT6iGTC1CYyOHuZw8AUMZ74eCOa4fpHt+Xj/D+3rC2N4eVnRnC+3vj2M/nITeRobFBA51WDzTNgP/tB79h7BOcb+Y2CABWpqvSuvTNg2YMd7cQk+BvKpML8+9/tNcwU3OFpdT1dFcVFQ2Xq2Xy7jDO8aP4jJRAqYsgag1BEGIfeeSRK7ONvfzyy24LFy70uNk+X3/9td0LL7zgDgArV660P3jwoHnze3FxcSF79uzplt+zEyZM8Pviiy8cAGDKlCm+zX+X9PR0h549e4b369cvGACSkpL8g4ODlf/85z9dFyxY4LFx48Y7Gofs6ekZWVRUpCgtLZW/8cYbvL1PnVJMgr+bm6/d84MmhMUby711vU6Pf4z/Bk8N+hLK/l7wj3SFTqvHuWMlAIAD/z2D8uJrl++8XFILB/c/eqQ6uFnhckktlP29UVZQjddTNmDY1Egc3nUWvkoXOLhaddhnMhLOANakq9K6/HhOjrm7tbFDUyLGOfWwuekFC7Wv0yX1lQC6Q7izAbAmPiMlPjN5jUbqYqjruFDzSWxbHs/L+tGDt9rG1NRU/M9//uNQVFRU3KNHD+2ttgeAqVOnVgKoBICNGzfaa7XaytjY2Lsea6rRaGBi0nlXprmd+tauXZvX/PsXX3zhvHz58rwRI0bUnD9/XnHkyBGr8+fPH2urusrKyuSff/656/PPP9/qheaJOkJMgr+5TC48MXJWzDATU7mZ1PW0FZlchn9smIy6KjU+/Ms2FJwqR+o7ich4cx+0jXoo470gk7U+yMoVMjz6diIAQKvR4b1Ht+DxD0ch4819KC+qQfyYYMQM9W+vj2NsBsEw/u4FqQu5G2ymsaa3AAAgAElEQVS5u4mYBP/o4Nges8Pu6fpdAbq6E3V6e6lr6EB9ALwsdRFEtyKXy8UZM2ZcWrx4sdvV7xUWFipGjBgREBERERYRERH23//+1woAlixZ4jRjxgyfHTt2WO3cudP+pZde8goNDVUeP37cDADWrFnjEBkZGebn5xexbds2awDQarVITU31ioiICAsODla+/fbbzgCwZcsWm9jY2JChQ4cGBgUFXTMeesOGDbYxMTGhSqUybNSoUT0rKytlALBo0aIeERERYUFBQeEpKSm+er0eAPDaa6+5BgQEhAcHBysfeOCBnjqdDr6+vhGFhYUKANDpdPDx8bnyvNnFixflw4cPDwgODlZGR0eH/vrrrxYAsHDhQo+xY8f69+7dO3T8+PF/urrS6/WYMWOGj5+fX0R8fHxwaWnplWM2t2AuWrSox8GDB61TU1P9UlNTvYYPHx5cUlJiGhoaqty2bZt1y9a+3bt3W/bq1Ss0JCREGRkZGXb58mVZ89+6+bgJCQmBW7Zs+VNL39NPP+2Vn59vFhoaqkxNTTXKNZ+o64lJ8BcAPHTvxLARdi6WRjmbtKWtGULjPHHs53wExrjj+ZXj8NLaCQju4wE3v2sveRxcrXC5RYve5Yu117TO/ZhxHP3HhODMkYuwsDHFvHcTsX2FUa7T3Z6eT1el3Sd1EXeD4e4GYhL8nU3NFY8NnqTsx3F20iq/XF9WK5N1p3AHAH+Nz0jpL3URRLfyzDPPlGzYsMGxrKzsT11ZUlNTvRcuXHjx2LFj2d9+++3pefPm+bV8PzExsXb48OEVr7322oWcnBxVeHi4GgC0Wq2QlZWV/eabb+a/8sorHgDw/vvvO9vZ2emOHTuWfeTIkewVK1a45OTkmAKASqWyXLZs2flz5879qTWrqKhIsXjx4h579uw5oVKpsnv37l336quvujXXfOzYseyTJ08er6+vl2VkZNgBwJIlS9yPHTumOnHihOrLL7/Mk8vlmDhxYtlnn33mCADfffedbVhYWL2Hh8efWimfffZZj+jo6LoTJ06oXn311YKHH374SpA7efKk+Z49e3I3b958tuU+K1eutD916pTZqVOnjq1evfrs77//fs0MgO+8805RRERE3VdffXXm448/vrB58+ZT3t7e6pycHNXIkSOvXOU1NDQIU6dODXj//ffP5+bmqnbv3p1rbW2tb80/v3ffffdC8zE//vjjC63Zh6gDxPToaf9gxADvXlIX0paqy+tRV6UGADQ2aKH6JR89/O1RVVYHANA06rD180MYMll5zb7hA7xxPDMftZVqw0QqmfkIH+B95f3aSjWO7s5D/IMhaGzQQiYIgCBA06DrmA9nPAQAn6Wr0rrs0lvslnkdMQn+cgBzhz4U0dfCxrS7hYpO53RJQzUAY1wC4WbkAFbEZ6REZiavUUtdDNGNODo66idNmlT2xhtvuFpYWFwJFPv27bM9efKkRfPzmpoaeXPL2c1MmjTpMgDEx8fXPvPMM6YAsHPnTtucnBzLTZs2OQBAdXW1XKVSmZuamopRUVG1oaGhjVcf56effrI6ffq0eVxcXCgAaDQaITY2tgYAtm7davP//t//c29oaJBVVFQolEplPYDKkJCQ+nHjxvmPGTOmYurUqRUAMH/+/NIxY8YEvvzyyyXp6enOM2fOLL36XL/99pvN+vXrTwHAmDFjqh999FFFeXm5DABGjhxZYW1tLV69z+7du20mT55crlAo4Ofnp+nfv3/1rf42N3L06FFzV1dXzeDBg+sAwz+TOz0WkdRiEvxtBRlm3zcjuo9Mblzr2VVcqsPnL+yCqNdDrxfRd0Qgoof44Zt3MnF0dx70ehEJU8IRdo+hEf3csRL89M1xzHwlAdb25nhgXixem7IOAJA0vw+s7a8MWcbm5Qdw/6O9IZMJiBjgjV1rjuG3sWsxZEq4JJ+1i/MG8CaANKkLuRMMd9c3wDvEKT6wlzu7Y3YCp+r1jpB1y0bmIADPwtD/m6jT+utf/3qxd+/eyuTk5CvBRxRF/P7779mWlpbXBJubMTc3FwFAoVBAp9MJTccS3n333fMTJkyoarntli1bbCwtLa8bZERRxMCBA6uubjGrq6sTnn76ad9ff/1VFRgYqFm4cKFHQ0ODDAB+/PHHk1u3brX57rvv7N55550eubm5xwMDAzXOzs7aTZs22Rw+fNhq48aN105jdxNWVlaSBS2FQiE2dzkFALVa3S2/SKnraOqOOeXeCco4OxdLo5vrwDvECf9YP+ma1ycvisfkRfHXvO4X4YqZEX/0Sh00PgyDxodd99jJzw+48ruJmQJPf5rUBhV3a6kv7pu76vUBn2VKXcjt4hf9VWIS/B1lMmHasGmRfWUygX8fiZWV1ZfWymS2Utchob/GZ6RwJDR1am5ubrqkpKTLq1evdm5+beDAgVX/93//d+WqJDMz0+Lq/aytrXVVVVW3/J5NTEysXL58uYtarRYA4OjRo2a32m/IkCG1Bw4csD527JgZAFRVVcmOHj1qVldXJwMAd3d3bWVlpWzz5s0OgGE83enTp02TkpKqly5dWtDU0igHgNmzZ1+aO3euf1JSUrlCce090X79+lV/8cUXToAhcDo4OGhv1Xo2ePDg6nXr1jlqtVrk5eWZ7N+//467AEVFRTWUlJSY7N692xIALl++LNNoNAgICGg8fvy4pU6nw6lTp0yOHj16zfR5dnZ2utraWv6/jjqLKDc/u9GRg7x7S10IdV8aja7gv+dqjv6YX/t5fEaKqdT13C5+obfQdMcoZeCE0EhbRwt3qesh4NSl+mvnA+5eLAAskboIolt58cUXiysqKq4kn08++ST/999/twoODlYGBASEf/jhh9dMtz916tTyJUuWuIeFhV2ZUOV6nnrqqdLQ0NCGyMjIsKCgoPBHHnnEV6PR3HQstIeHh/bjjz8+l5yc3DM4OFjZp0+f0KysLHNnZ2fd1KlTL4WFhYUnJCQER0dH1wKGsX4PPfSQf3BwsDIiIkI5d+7cEmdnZx0ApKSkVNbV1ckfffTRsuud68033yw8dOiQZXBwsPLFF1/0/PLLL89eb7uWpk+fXtGzZ091YGBgREpKil+vXr3u+LvO3Nxc/Prrr0//5S9/8QkJCVEOGTIkuK6uTpaYmFjj7e2tDgwMDJ8/f76PUqmsu3pfd3d3XWxsbE1QUFA4J1QhKcUk+NsAmJM4PSpWJpexZxl1OL1e1B/OLitbebyix/kqTQyAUADPSF3X7RJE8bZ6zBi1mAT/GMce1i+lPD9gjFwh67xzancToihi9ZHy6nqZrMsOam1DD2Ymr9kkdRHUebi7u58rLi6+ZvwXtb09e/ZYPvXUU94HDx7MlbqWjubu7u5cXFzsJ3UdZNyabq7PibzXZ0rClPDhUtdD3U95WX3ZD+eqrSpNTMyveqsBgDIzec0tb9p1Fmy5axKT4G8FYNaQKcpQBrvOobSs/hKD3RX/is9IuaZbGxG1rxdeeME9OTk5YPHixQVS10JkxAJlcuHefqMDjWp2TOr8tBpdY2bWpbL1+fVO1wl2AGAO4PWOrutuMNz94QHPIEdvz0DHa+efJUmcvNRwTReibswPwF+lLoKou1m8eHFxYWFh1ogRI7p7F3GidhGT4C8D8NCAsSE+ljZm3W1mbJJQQUH1pbVHywWVqHASbr5wfHJ8RkqXufHAcAcgJsHfFcCIwZPCIrmmXeeg14viGbXIL/k/WxifkXLNuCUiIqIurLeFjWlIxADvvlIXQt2Dul5bu+NQSeXWMq1LvYmiNb31BBiWRugSGO4MHgyO7eHs7GkbKHUhZFBaVn+pQSa7ZlHfbs4KXXBgLxER0fXEJPibAZiakBweaGKmsJS6HjJuoijixJmKkjU5FRZ5chO729w9MT4jpUuMB+324S4mwd8HwID4B4O7THNrd3CytKFe6ho6qcfYekdEREZiiGMPa4+eka5c+oDaVU2VumLT75dq99SIrlq5/E7zzxvxGSmdvodftw53TbMzTYpJ8HWzdbLkFNCdRFOXTAaY67OEYWFzIiKiLismwd8OwPiB40J7yuQyudT1kHHS6/S6Q9llpWvP1NhdMjG5Zq3P2xQL4NpV6DuZbh3uAIQAiIoZ6h/VkSfVqLV4bcp6/H3cN/jbmAxs/PA3AMAb07/FP8Z/g3+M/wYLh6zAB09sve7+ZYXVePeRzXgpaQ1eSspAaUEVAOCTZ3fi7+PWYv37+69su/mjg/j9hy4zeysAoKS0rkQtk7F7xo2lxWekuEldBNFzzz3nHhgYGB4cHKwMDQ1V7tq1627/x3lXLC0tb6sHxpYtW2x27NjRqpo9PT0ji4qKFADQq1ev0ObXU1NTvQIDA8NTU1O9CgsLFVFRUaFhYWHKbdu2WQ8ePDiwtLT0ti9ac3NzTYOCgsIBw+Lva9euvd3uQ0RdwQhre3NL7xCnaKkLIeNUVlpX+u/DZdqDGpmzKLv5jCm3odPfYO+2i0Q2tdpNDo7tYWXraOHRkedWmMqxKH0MzK1MoNXo8Mb0jYgc5IPnV467ss3SJ7eh11D/6+7/+Qu7cP+jvREe742GWg0EGZCfWwZTczn++e0UvDt3M+qq1Whs0OLs0YtImhfbUR+tTZwsbWjgfYebam69e1rqQqjzEEs+adP/0AXXRw/e7P2dO3dabd++3T4rK0tlYWEhFhUVKdRqdafvrtLSrl27bKytrXWJiYm1t7PfoUOHcpp/X716tfPly5cPKxQKfPLJJw5hYWH1a9euzQOAkSNHnrrbGg8cOGB54MABqylTplTe7bGIOoumBcuHDxgb4sPlp6itaTU69f7s8rpsUe4smLR51ImNz0hJyExe82NbH7itdOcr6CAA/rGJPcM7+sSCIMDcyvBdptPqodPq0XKSzvqaRuT8VoBew64Nd4WnyqHT6hEe7w0AMLcygZmFCeQKGRobdNDrRei0eshkMmz84H948PGuNfmUXieKZxrhKnUdXcD8+IwUziZKkikoKDBxdPz/7N13eFRl2j/w7ynTW6YkmfSEJJNCgEgn1AiIoNhRsGBBxbLuKq6uq69lLWvbXd91xZ/YdtX3VVDX17Ug2EGJjV5CEghJSJuUKZk+Z075/RESQEILyUzK87kuL8nMmXPuE4aZc5/nee7bxKtUKgkAkpKS+MzMzAgA/Oc//9EVFBQU2my2wkWLFmUGg0EK6Bz9uv3221Py8/MLi4qKCr7//nv1tGnTctPS0oqeeeaZ7qnYDz74YGJRUVGBzWYrvOuuu5K7Hnv88ccTAGDZsmVpkydPtgHARx99pLvgggu6PyzvuOOOlLy8vMIxY8bk19fXswDw9ttvG7pG1EpKSmz19fVsZWWl/M0334x/6aWXEvPz8wvXrVt3VAEnu93OTJ06NTcnJ2fkFVdckSFJUvdzXSOEZ599dk4gEGCKiooKH3jgAevDDz+c+vnnn8fl5+cX+nw+6sjRvhdeeMFss9kK8/LyCi+66KIsALj00ksz//nPfxp/vd8uoVCIevLJJ5M//vhjY35+fuErr7xiBEEMDdNkCkYxYnTC+FgHQgwt9Q3e1tW7XEwFWGM/FsAf0DfXh3Nyd25ihkFuSdXlxeLgoiDikUvexV3T/4XCKakYMfrwLLttX9WgYFIqVFr5Ma+z13VArVdg5e/W4ZFL38O7fymDKIhIzjZCZ1Ti0cvew5hZGWg92AFJkpBROLiWrrW0B1oiNE2adZ+cCsB1sQ6CGL4uuugiT1NTkzwzM7Po6quvTv/000+1ABAIBKjly5dnrVmzprqqqqqc53k8++yz3R9E6enpXEVFRfmkSZN8N9xwQ+bHH39c/dNPP1U8/fTTyQDwwQcf6Pfv36/cuXPn3r1795Zv375d/dlnn2lnzZrl27RpkxYAtm/frvb7/Uw4HKY2bNignT59uhcAgsEgPWXKFF9lZWX5lClTfP/4xz/iAWDu3Lm+7du3V+zdu7f8sssucz766KPWvLw8bunSpW233HJLS0VFRfm55557VB+7++67L3nKlCm+/fv377n44ovdzc3Nx3wgf/311/sVCoVYUVFR/sQTT9j/+Mc/Ni1cuNBVUVFRrtVqu7PBzZs3K//yl78kbdiwoaqysrJ81apVB0/ld6xUKqUj93nTTTe5Tv9viiAGluLSLCWA86cstFlJhUyir4QCEd/6ra2e9U4+IcQy/T0zcUHJ6iUF/XyMXhuW0zKLS7OsAM6astCWE6u+djRD45EPLkfAE8YLv12Hhn0OpOZ2DsT8tHYfpl/a83tG5EXs29KMh99fBFOSFi/d/Tk2fViJ6ZcWYMkfp3Vv9/xta3HNIzPxyaotqK90oHBKKmYuGvj92avaQ1xf3nPY/9JPcG1rgkyvRPGz8wEA/joXDry2GUKIhzJeg5zbp4BVHzsrpGltJVq/rgYoCuo0A3JumQRazmDfCz8gcNAN49hkpC/uXCrQ8MEeqNMMME2Ial2eW0pWL/lb2eJ3pJNvShB9y2AwiLt37y5ft26d7quvvtJde+212Q899FDDhAkTAqmpqeHRo0eHAeC6665zrFy5MgFAKwBcfvnlbgAYNWpUwO/300ajUTQajaJcLhfb29uZdevW6Tdu3KgvLCwsBIBAIEBXVFQob7vtNse1116rcTqdtEKhkEaPHu377rvv1D/88IPuH//4x0EAkMlk0uLFizsAYNy4cf4vv/xSDwA1NTXyiy66KLWtrU3GcRydlpYWPtn5/fjjj7oPPvhgPwAsXry4Y/ny5UJvf1fr16/XL1y40JWUlMQDQGJiYq/3RRBDwASKgjpvQvLgmlpEDEiSJEmV1e62H72ihWdl0Rq0otA5endjlI53WobryN3ZGoOCTsk1xbz9gVqvQP7EFOz+vh4A4HUFUbOrFWNmZvS4vdGqQVq+GfFpejAsjbNmZ6GuvO2obbZ9XYOMkfEIByJorffg1r+dgy2fH0A4GOn38zkToiCKtZG+nZKZMDMLBffNPOqx6pd/QfriMSh+Zj5M41PR9MneY14XdgZgX1eFUX8+pzMpFCW0/1AHf50btJzBmGfmw1ftBB/gwLmC8FU7op3YAUAOgEHRc4UYmliWxfnnn+997rnnmp599tmDH3744UmnDSqVSgkAaJqGXC7vvjFB0zQikQglSRLuvPPO5oqKivKKiorygwcP7r7rrrvaFQqFlJaWFn7xxRctEydO9M2YMcP35Zdf6urq6hRnnXVW6FA8Ek3T3bHxPE8BwG9+85v02267rbWqqqr8hRdeqAuHwwPiu49lWUkQOvM8QRAQiUQG1ZpFgjhdxaVZLIALi0sz41RaOZlmTJwRb0fY/eHWtuD3ASTwDB3tz/WrS1YvGZDLiAbEF1w0FZdm6QGcPem83KRYLeL1OoMIeDpvHHMhHuU/1CMpKw4AsOXzAxgzMwMyRc+DqllFCQh4OHidnW3gKn5qRHK2qft5PiLgizd34twbihEJ8egalxRFEUJE7MezOnPNbQF7hKaVfblPfUEC2F9Nbw01e6Ev6JwlZhidCOfPDT2+VhJEiJwASRAhcALkRhUolup8TJQgCSIomkL9e7uQellRX4Z9Om6J1YGJ4W3Hjh2KXbt2Kbp+3rZtmyo1NZUbM2ZMqLGxUb57924FALz55pvmrmmTp2L+/Pmet956y9LR0UEDQE1NjayxsZEFgClTpvhWrlyZOGvWLO+cOXO8b7zxRnxhYWGAPsl3utfrZdLT0yMA8K9//at7rapOpxO8Xm+P1SwnT57s7dr23Xff1Xs8nl6Xap83b57n448/NtrtdgYAWlpaGADIyMjgtmzZogaAt99+O64rGT2SXq8XfD7fsPuuJoasUQAs+RNT8k+6JUEchyCI/Jby9vY1B3wGh0wWq6m9CgBXx+jYJzQcvzCmAKAzC+NHxSoAd1sAz1z/ER6+eA0eu+J9FE5Jw5hZmQCAnz/bj4kLco/avnZ3K/71UGdRHpqhcfk9U/CXZR/hoYvWQJKAGZcdnsL5zTu7UXJhHhQqGVLzzOCCPB66aA0yCuOh1iswkO1zhKMyVUmVqodrcyMAwPFjPcKOwDHbKExqJJ+fj62/+Ribb/0PWLUMcaOToE4xgNUpsPP+9TCOTUHI7oMkSdBmmY7ZR5RcULJ6SVSrvRIEAHg8Hmbp0qVZ2dnZI202W2FFRYXq6aefblKr1dJLL71Uu2jRomybzVZI0zR+//vft518j50uueQSz6JFi5wTJkzIt9lshRdffHG22+1mAGDmzJnetrY22dlnn+1PS0vjFQqFNHXqVN/J9vnAAw80LVmyJHvkyJEFZrOZ73r80ksvdX/66adxPRVUeeqpp5o2bdqkzcnJGfnBBx8Yk5KSuNP5/Rxp/Pjxobvvvrt5+vTp+Xl5eYW33XZbGgDccccdbWVlZbq8vLzCsrIyjUqlOuYO3Pz5871VVVUqUlCFGCLO1ZtVnDlFZ4t1IMTg1Nbqb39ve7u4jWcsYPqsvUFvXRvj4/eIOrIC2FBXXJpFA/hber5Ze9EdE5fFOh7iMIEXxTd3uyMCTfV5Bhpq86Hime+619wFGz2oeWMLeB8H49gU2NdXYcIrlxz1Gt7HofK572H7XQkYtRxVf98E88Q0xE/PPGq7imc3YsSyCWjdcACBOjcMo6xInJ3d16dwMo+ULX7nT9E+KBFbVqu11m63t8c6DmJos1qtFrvdnhnrOIjBr7g0KwHAU6WLRyaNmp5+bqzjIQaXCCeEftjrDFZR7EC7yVVctvidHbEO4kjDbeQuG4Bh1IyMmFTIJI6vqTXQ3B+JXU9UKXoU3l+K0X+eB8vUdCgStcds07HbDkWCBjK9EjRLwzwhFd6qo6+jnZsboMkyQghHEGrxwXbnVDh+rocQ5o/ZXz+7PtoHJAiCIIjTNA6AlFkUT5qWE6el7qCn9Z3dTnYAJnbAABy9G27J3WQAkZRcU8ymZBI92+cKR21BYKQjBACQRAkN/1cO6+ycY7aRWzTw7XNACPOQJAkdu1ugStF3Py/yIpo/q0LywgKInNBZNwkARAkSH/W1jRklq5eQXkEEQRDEgHRo5tSc9AILpTOqkmIdDzE4BAMR72dbWz1fuIUEjmUHaoX/K0tWLxlQsQ2oYPpTcWmWAsDUwpJUlVItM8Q6HuIwnheF2ggS++NWQ9XzZfDsbQXvDWPL7f9B6mVFEEM87J/vBwCYJqYiflZn/2POGUT1Kz+j4A8zocsxwzwpDTvvXw+KpqHJjDtqumXL5/sQPyMTjIKFOj0OYljA9ns/g7E4Cazm2P6EUXAJgM2xODBBEARBnMQIAKYxMzPIWjvipCRRkvZWu9t+8gkWIXrtDXorEcA8AJ/GOpAuwya5A5APQJ4/MWXgN3sbZppaA80iTfdLHwHbb0t6fDxp/rEzc+UmFQr+cLhtQtqiUUhb1PMgb9KCw6+nKOq4x4miSwDcH+sgCIIgCKIHJQC4pGzjyFgHQgxsHe6Q66tqj8IpkyWA6XWR4mi7AgMouRvo2XBfmg4gmJiuJ+V3B5hK50n7CRMnl1eyegn50iQIgiAGlOLSLCWAqRmFFpCZU8TxCILI/7y73fFebcDojF17g95aULJ6yYDJRIdFcldcmqUDUJxTbJXLFOxge8MMaXxE4Bt4JMY6jiHikpNvQhAEQRBRlQtAZhuXlBXrQIiBqaXF3/budoe4U2TMiHl3g14xo3N0ekAYFskdgBwAdHZx4ohYB0IcraElYBfo2DSTH4JIckdEzaRJk2z//ve/9Uc+9uijjyZcddVV6f11zMrKSnlubu5IANi4caP6uuuuSwOAYDBIlZSU2Lp6wa1bt06bk5MzMj8/v7CmpkZ27rnn9uqz//nnnzcvXbo0HQDeeuutuC1btij77mwIYtgYDYBPGmHMPemWxLDChfngN9tbXR+3cPF+GRuTggV9aGGsA+gyXNbcFQPgEjMNx5ZFJGKqysVh+Nxj6HfFJauXpJYtfqch1oEQ0Se8vXRcX+6PufLNLSd6ftGiRc533nnHdOmll3q6Hvv3v/9teuqpp07p/cfzPM6k+NmMGTMCM2bMCABAWVmZGgAqKirKAeDKK69MX7FiRfNtt93mBIB169Yd6PWBDvnwww/jeJ7vGDduXOhM90UQw0VxaRYFYIJaJ/fpLep+u/FDDD41dR2t3zkiZo6VqWIdSx+ZB+DeWAcBDIOr6kPld8eq9Qqf3qzul6IdRO9EOCHSIMAa6ziGmBmxDoAYHq655hrX119/bQiFQhTQOarW2toqmzdvnu+DDz7QFxcX5xcWFhbMnz9/REdHBw0AKSkpo2699daUwsLCggceeMBaWFhY0LW/Xbt2KY78uct3332nzsvLK8zLyyv829/+ltD1+CeffKIrLS3NaWxsZK+//vqsXbt2qfPz8wufffZZy6effmp64oknUi644IKsI0f7eJ7HzTffnJqbmzvSZrMVPvHEEwldcTU3N7NA54jgxIkTj6q49MUXX2i+/PLLuP/6r/9Kzc/PL9yzZ09UenISxBCQDEBfWJKWTNPUkL/mJE4u4Oc8n25p9X7VISZw7OCpmHIKRpesXjIg2nwMh39oyQA0hVNSU8kHy8DS0BKwizQ9XEaPo2VarAMghofExERhzJgx/vfff98AAG+88YZp4cKFrpaWFvbPf/5z0saNG6vKy8v3jh07NvDYY491r6s1m818eXn53qefftqu0+mEsrIyFQCsWrXKctVVVzl+fZxly5Zl/vd///fBysrK8p7iSElJ4V988cW68ePH+yoqKsrvueee9jlz5rgff/zxho8++qjmyG3/+te/xh88eFBeXl6+p6qqqvzGG2885ng9mTt3rr9rnxUVFeUjR44kVaAI4tTkAUB6gYVMyRzmRFESd1c621ZXeXTNMpku1vH0k7mxDgAYHsmdDQDS8szZJ9uQiK5KV3g4vP+ibXqsAyCGj8svv9y5Zs0aIwB88OG9s6oAACAASURBVMEHpmuuucb57bffaqqrq5UTJ07Mz8/PL1y9erX54MGD3Wspli5d6ur683XXXdf+yiuvWHiex3/+8x/jsmXLjkq22tvbGa/Xy8yfP98HADfccMMpJWPH8/XXX+uXL1/eLpN1LvNNTEwUzmR/BEGc1EQAXnOylhRTGcbcrqDz/7a1cT+GqXiRYQZlxZRTNCCKqgyHi+uJALzGRE1arAMhDuPCPNco0WRKZt8bWbJ6iSnWQRDDw5VXXunetGmT/vvvv1eHQiF6+vTpAUmSMG3aNE9FRUV5RUVFeXV19Z533323rus1Op1O7Prztdde6/rmm28Mq1evjhs1alTAarXGJNliGEYSxc6wgsHgcPheJIh+V1yapQaQq7eoOJVGbox1PET0CbwY+XF3e/v7dUGTSyYbDgWpJsU6AGCIJ3eHeqvkypWsT61XxMc6HuKw+pZAi0RRQ2mu9UBBAZga6yCI4cFgMIhTpkzx3njjjZkXX3yxEwBmzZrl37x5s3b37t0KAPB4PPTOnTt7XKOmVqulmTNndqxYsSL9uuuua//18xaLRdDpdML69eu1APCvf/3rjG5czJ4927Nq1SpLJBIBALS0tDAAkJqaym3atEkNAO+++26PF6FarVbweDxD+juTIPpYOgBkFSWQG7nDUHOzr23NToe0W2Qsg7S9QW+MKlm9JOYt14b6F1USAGQWxSeQ9XYDS5WbI4ld/yFTM4moWbx4sbOyslK1dOlSJwAkJyfzq1atql28ePEIm81WOH78+Pxdu3Yd947t0qVLnRRF4ZJLLvH09Pxrr71W+9vf/jY9Pz+/UJKkM7pCuOuuu9pSU1O5/Pz8kXl5eYWvvfaaCQAeeuihpnvvvTe9qKiogGEYqafXXnXVVc7nn3/eWlBQQAqqEMSpSQNAWbPiUmIdCBE9XIgPfLWt1f1pWyQ+wJ5ee4Pmzyqx/Z7PsP33a9G8tvKY5yVJQs2/tmDrnZ9gx72fwVfjBAAEmzzYef967Lj3M3irOu8TSoKI8ie+gRDm++CsThkDoE8rV/cGJUk9fo8NCcWlWVMBLJt1xcjE0TPSF8Q6HqJTOMSH36rwyEAS7v6yqWzxO6SwyhBntVpr7Xb7MaNdg81DDz2U2NHRwfz9739vinUsxLGsVqvFbrdnxjoOYvApLs36LYDcJX+cOjc+VZ930hcQg151jbv1Oxdv5ntRBTNQ70bV8z9g1ONzQbM09j61AVnLxkNlPVx7xbWtCfb1+5D/hxnw7Xeg9o2tGPX4Oah9axtME1KhiNeg9s2tyLtrGprXVYFRsUiYGfUW1/eULX7nL9E+6JGGeqXCPABhc7I2OdaBEIcdbPG3gKZIv5v+MzLWARDEqZg7d252XV2dYsOGDVWxjoUgiL5zqL9dLgCv3qwi12BDnN/Leb6ucjMtMlkC2N5NzAo2eqDNMYFRdKYm+oJ4OH9uQMoFhzvkOLc0In56JiiKgi7XAj4QAecKgmIoiBwPkeNBMTR4PwfX1kYU3DerL07vdE2OxUGPNNSTOxsAr8GiHhB9J4hOVR28bOjPCI6puJLVS6xli9+xxzoQgjiRL774ojrWMRAE0S+MADTmZG1YoRqyZe+HPVGUxN1VLsfmoGQRZbIzmjavSjPg4JpdiHjDoOUMXNuboc06epk15wxCbj68pE1uUoFzBmE9Jxf7X/wRIi9ixLIJaPhgD1IuKgQVm7V+Z8XioEcassldcWmWCkCCXMk2kWIqA0coyIeaJcqKYbO2NmbyAZDkjiAIgoiFZABier6F3FwfopyOoOOrWq+mQyaLRx90N1CnGJB8QT72PvktaAULTYYRp1qIRWHRYORDswEAQbsXnDMAVbIB+1b+AIkXkXb5KKiS9Gcc4ynKKFm9RFa2+J1ItA74a0M2uUNnMRUpaUSckRRTGTjqWvwtoKiMWMcxDBQA+DbWQRAEQRDDUjoAymjVmmMdCNG3+IjA/Vzh9O4RGDN1qGdoX0kszUZiaWdb6oOrd0BuOrrwpNykAucIdP/MOYOQm1RHbVO/ZifSLh8N+/oqJJRmQxmvwcE1O5H7myl9GusJMABGADi2IkyUDOWkJwkAZUnRk55fA0hVR4RUmYuO/FgHQBAEQQxbNgB+vVlFrsGGkMYmb+uanU6qXGLN/THlMdIRAgCE2/1w/NIAy9SjxwJMY1PQ9l0tJEmCd187GLUMcuPh5K6jvBVyowqqJB3EMA+KBkAj2hUzASAn2gc80lAeuUsFwMclqMkHywARDESCdtCJZEZmVJDkjiAIgoiVZABBrUFJrsGGgHCQ92+scPJ1jCwBsv5LHSqf+x68jwPF0Bhx/TiwGjnsX+wHAFjn5iDurCS4tjdh252fgFawyFl+uGe4JElo/L89yP1dCQAgYXY29r/wIyRBRNay8f0W83GQ5K6fJAMI6oyquFgHQnSqbQm0UmRKZrQUnHwTgjgzDMOMy83NDfI8TzEMIy1evNjx0EMPtTCnXwUbAJCSkjKqsbFxV3t7O/Pqq6+a7rvvvrZfb1NZWSk///zzc/ft27fnjE/gFLz88svG6upqxdNPP31aa1hPdA4EMZQVl2axACwA6mekF8oZlm7xhUKcPxIWA3yY4hCRczSvFlhRS5FlMwOaJEnYd6CjtczDW3hG1u9/V0WPzDnmMevcw3kSRVEYcUPPiRpFUSh8oLT7Z3WKAaOfnNf3QZ6a3FgdGBgGyZ1KJzfEOhCiU5U3ouycikxEQXLJ6iVU2eJ3hm4jS+IoDQvG92nj1NS1m7ecbBuFQiFWVFSUA0BjYyO7aNGiER6Ph3nuuefOqGedw+FgXnvttYSBkBitW7fOcNddd7We7uv66xwikQhkfbzOhSD6EkPRZoaikyRIGotZb5XLWVliD9uJoiQFgmGvLxAK+ELBsJ8Li0GBo8Pg5BFa0AgyUUtRFJnsEyM+T9j91b4OWZtMloBe3rAbxmI6cjck75gUl2YxAEwAwkqNLGrlcYjjC/gjgVaJ7unznegfDDpLURNEVKSkpPCvvvpq7T//+c8EURQRCASoyy67LNNmsxUWFBQUfvzxxzoAeP75583nnHNO9vTp03MzMjKKbrnlltSufRiNRh4A7r777tT6+npFfn5+4fLly1OPd8zy8nJ5QUFB4YYNG9Qn2u+qVatMNputMDc3d+Stt96aAgCvv/668cYbb0wFgMceeywhNTV1VNc+x44dmw8Aoihiz5496qlTpwZWrFiRvGjRosyJEyfmpaamjnr88ccTuvb/yCOPJObm5o7Mzc0d+eijjyac7Bw8Hg89a9asnLy8vMLc3NyRr7zyivHQ73BUc3MzCwAbN25UT5w4MQ8AVqxYkXzRRRdljR07Nv+SSy7JampqYufNm5ddVFRUUFRUVPD5559rzuTvjiD6klGh0WllCnmiMS5VLmePeyeCpilKq1HqrPFxiTlpSeljsjMzJ9ts6TNtRdY5OWN0c1LHSFNMeR2jlBn2LCQeTOAMtbqQql4eYluoCHySRO5d9gdREIVtex3taw74DG0yGfls6Z2UWB58qI7caQ/9X1KoSHI3ENS0+NvIlMyoswBwxjoIYvgoLCzkBEFAY2Mj++qrr5opikJVVVX5tm3blAsWLMitrq7eDQDl5eXqHTt2lKtUKjEnJ6fo97//fUtOTk5k9+7dewHgr3/9a8P555+v6hoV7MmOHTsUixcvzn799ddrpkyZEtyxY4eqp/2yLItHHnkkZcuWLXvj4+P56dOn29566624OXPmeP/2t79ZAWDTpk3auLg4vqamRvbVV1/ppkyZ4gWAsrIydWFhYYCmO++D7t+/X1lWVlbpdruZgoKConvuuaft559/Vr399tvmLVu27JUkCePGjSuYPXu290Tn8MEHH+itVmvk22+/3Q90jvKd7He7b98+5U8//VSh1WqlhQsXZq1YsaJl3rx5vn379snnzZuXe+DAgahMUyWIk6EoSsdQTK3NlhQCUNLb/TAMTeu0KoNOq+pxBpYgiII/EPL6AqGgLxSK+CNhMShyDIeIIsIIGkkmkcTkNDnaA+1f1vl1XhlrGZrDP1ET07WmQzW5MwCQAICVM6qTbEtEQZWXV4Eiw/pRZgFQFesgiOGprKxMe8cdd7QCwFlnnRVKTk7mdu3apQSAadOmecxmswAAOTk5oerqakVOTs4p9wRyOp3sRRddlPP+++9Xjxs3LtT1eE/7bWtrYydPnuxNTk7mAeCKK65wbtiwQXvNNde4A4EA7XK56KamJvmiRYscn3/+ue7777/XXnLJJW4A+OSTT/Tnnnuup2v/55xzjlulUkkqlYo3mUyRhoYG9ttvv9UuWLDArdfrRQA477zzXN98841u0aJF7uPFP3bs2OADDzyQduutt6ZceOGFHeeee67vZOd87rnnurVarQQAmzZt0u/bt6/7u83n8zEdHR20wWAQT/V3SBD9SA+ASkwy9OvNdYahGb1OHafXqXusrcDzAu8PhH2+QDDgC4cjgUhICoocw1G8MsLwGomFuqfXDUd8RAj/uNfp3ysxFqofC6YMIyS56wd6AJRMwTCkx13s+X2cz0ExCSffkuhjllgHQAwv5eXlcoZhkJKScsK603K5vHs+FcMwUiQSOa11NTqdTkhOTua++eYb7ZHJ3enud/z48b6VK1dasrOzQ6Wlpb6XX37ZsmXLFu2LL77YAABff/214aOPPtrftb1CoThy/+B5vlfrgUaPHh3eunVr+b///W/Dgw8+mPLll196/vKXvzQzDCOJYmd+FgwGj/ru0mg03YmbJEnYunXrXrVaTealEQNRAgA+Lk4T04J2LMuwBr06zqDvOfmLRISIPxDy+YKdI38BPoygyDFhKqLkGUEDFsNicKC+wdu6oTVsCrGsiaxw7DPKktVLVGWL3wnG4uBDNfFRAoBaryA91QaAAy0BR6xjGKZIckdETVNTE3vTTTdlXH/99a00TWPq1Km+//mf/zEBwM6dOxXNzc3y0aNHh062HwAwGAyC3+8/7veTTCaTPvvss+p33nnH/NJLL53wDun06dP9P/30k665uZnleR7vvfeeadasWT4AmDZtmm/lypWJ06dP95WUlATKysp0crlcNJvNgsPhYARBgNVqFU60/9LSUt/atWvjvF4v7fF46LVr1xpLS0u9JzqH2tpamU6nE2+77TbnihUr7Nu3b1cDQGpqKrdp0yY1ALz77rvHXTM7bdo0z5NPPtl9w6ysrGxYXIQSg0Y8gDArG9hVOGQyRhZn0BhTrebk/MyUjLE5IzKm2vJTz84dZTlnRLGqNKmIm6DPcRTIUhvTRUudOayr0wSVDbIw0w4ep/RZNpCFAhHf+q2tnvVOPiHEMkN1sCeWYjZ6N1T/MuUAaJVWLo91IASwz8eryZTMmCDJHdGvwuEwnZ+fX9jVCuGKK65wPPzwwy0AcO+997YuXbo0w2azFTIMg1WrVtWqVKpTGmmyWq3CuHHjfLm5uSPPPvvsjlWrVjX8ehu9Xi+uX79+/6xZs2w6ne64CVhGRkbk4Ycfbpw5c6ZNkiRqzpw57quvvtoNALNnz/b97ne/k8+ZM8fLsiySkpK43NzcEAB89NFH+pkzZ3pPFuu0adMCV155pWPs2LEFAHDNNde0TZ06NQgAxzuHLVu2qP74xz+m0jQNlmWlF198sQ4AHnrooaZbbrkl89FHHxVKSkqOe+yXX365/sYbb0y32WyFgiBQkyZN8paUlBw8WawEESVxACKDfeaUTMbKjXFaszFO2+PzHMeHfYGQzxcIhvxcmPdHwgiJHMvREVWEEbQUSw3Ia1BJkqTKanfbj17RwrP9395gGDMBaIzFgamhWG2ouDSrFMDSzKL44AW3jr811vEMZz5v2Lu6JqCLdRzD1JNli9+5P9ZBEP3DarXW2u329ljHMVRdccUVGTfffHP77Nmz/bGOJZasVqvFbrdnxjoOYvCYu2DMQwAM1yybkX/2vFELYh1PrITDkVBn8hcK+biQEOTDCEqcjKN4pcCKOjCIek8Tb0fY/eX+DrlDJiPrDfvfrLLF72yIxYGH6sidGoCo1MjItMwYO9AadAAgyV1sDNV/3wTR79asWVMX6xgIYpBiAEg0Qw/rUSGFQqZUKGRKs7HnS6BQiAv4AiG/LxgK+7gQH+Q5KiRxMo7mVQIr6EBTffYdLggiv73S6d4WpsyQycjKuuiI2cjtUL340wAQFEoZWYcQY1U+XjsYp2Tuf+knuLY1QaZXovjZ+cc8L0kSat/YCtf2ZjByBtm3ToI2y4Rgkwf7XvgBEi9ixI0ToLNZIAki9j61AXm/nw5GEdV/ckP13zdBEAQxcHUmd4N8WmZ/UyrlaqVSrrbg2KKikiQhFIoEfIGgzxcMhf1cWAjwYSqEiDxC8yqeEXQUc2oXV22t/vav6v16n0xmweC7HBvMYvbbHqoXf2oAPMi9iZjyesIeN8UMynVfCTOzYJ2Xi/0v/tTj8+7tzQjZfTjrufPg2+9AzWubMerxc9DyVTUyl46FIl6D2je3Is82DfYv9sMyLSPaiR0Qww8WgiAIYtiiAUjMMB+5OxMURUGlkqtVKrk6Hse2+ZMkCcEg5/MFQgFfKBT2cyExyHNUCJyMo3m1wIo6nhe5H/Y6g1UUa4Es6jNACZLc9Tk1AEHgxRNWOSP6hyRJoJyM7yxtWvs8c8DdUd0cohwdtDwUkUdCvMiHeFEIC5IYFihEBFARkWZ4iWEEiZUJEiuXIGckMBRim57XB8LUra6w8vU3qo4pZfvg7jrF9SadcMGb+3gAOLfZr35q1e7gv2pdsiktvJCiUojP1/nkf3q1PHzn9hrla+NzQnRtdFrOCZBoDUX7IiwVxuKoHJKIDTEcDlNHlucniL4UDocpAKR3HnG6Do3ckeSuv1AUBbVaoVWrFdqEHpI/T7uvI1hlZ5K3HxApu7+CcoYkiRfhhxT2A2EfpLAPUqhDksIdkMJuSCEnpHA7xHAbpDB/qFc0cZokSSEB1Ijrxz8BIGaV4odqcicBgBAhyV00SZIETUjZOEKXGJ98lknLRwRNolkUlWNyGADwOj2RpoqDfr7VIZopQZmgkR93QS/HCWIwwPEBf0QIBSNCKMALXDAi8SFe5MMCJE6AxImgeJFmeIliRImWSWAUEhgFRbFy6szngmopCgwFaClK8+vnXGEeOSoFq6UoBQCkquTwcrx6eWYi7txZI+NECU+NTMdr1XZ2RXYS9DR9zD76DwUAKpaXyJrToW33ypUrC2+//fYOkuARfS0cDlMrV640ANgd61iIQYcBwNMMmZYZC456J5chV+kMxTl0fk6y8uBXP8OmYc1NjR5f036nX2wN0OaQpFdTlOJ4t9DDksQHJYkLSlI4IIlcQJI4vyhxfknifJLIeUWJ80gi5xFFrkMSObcohR2iwDlEkXOIIjeM7wixAOxli99pinUQQ1EEAM2TkbuoUQcVjbmmZENihiGl6zFWxlB1B1ukPFsyAEBn0svySoq6m4m67U7BvqeWF9pdsMgkJl6v7H4/yuUMLZer5Ia43i2bjEQEKejn+JAvwocCnBgORIRIkBcjIV4SQgLEw8khRQsSzYgSzUqg5QArpyhGTtO9Sg5TVHK8NykPAFDjD6E5FEGOVonf7agBJ0q4x5aMERplr86pF7hoHYiIvpaWlhufeeaZV5955pkiDN2epUTsiAB2t7S03BjrQIhBhwEQYcjIXdS11zq4XK1WplYrKABQa9Vs/oWzzFVfbfGmJQjq5BS9FgAEUURLXYe3fb/Lj7YgbeARJ6cPt25QUBSroCg2DlCf7uxCSZIkAQjygC8iST4eko+T4OckyReC5AtJki8gST6/KPm9kujrEEVfuyj67YLgqxN4XxXP+wf5xXtMEztgaCd3lBARBvn7Y+BTBeWNNmOyPjE9LqWn5yWdoh2Atafn4qwmJs5q6v7U8LS6Qo7Kg36x3UUbGVEdp5b3euRJJmMoWZxKpo9T9WqiucCLoqveyWFvnZwpSXJyQZ6PBCJ815RStVZm3i1woRw5FaQEkTkYDCdq5YwnKIoyGSg5S1PyZ6qacK8tGa/XtWJxqgVpajmermzCP4qzentap2vQN1kljk+SpFYAF8Q6DoIgiF+hAUgUTZHKB1HkOODwF5gNKrmMPeb3bps9Ttd2oDni2raHsxqUKoamkZxl1CVndZbyFHhRsh9wuZzVrjAcIZlBpAyyXlbrpCiKYgE1C6iVFJXQi12IADwA3Kfwn6uHx7ypazcP69ksQzW54wHQfETkYx3IUKUKypty45J01nRjj0ldF7lFfcoJmj7BqNQnGLuHtXxtbo/nQKMbDhf0EExqOdNzJ9F+wLA0rdYqlTI5jYQRxmOKwtxoluHFzyu19y/Kx0/725FYrsO0m8Yau57/ZnezmBqyiIULbX7fmq1yPtsQCCtlnPNAU1yHVdUqhgVJ4gQKEZGmBImmBYllJEnGSJCxoBQyuk+anwb6YB8EQRAEcToYAFI4FCGzR6KkfV+bf6TVrGYZ+rgJdfyIJBmfamGqP/vBnaWm4o7MvRmWplJsZmOKzQwA4COC0HrA1dJxwB2kHCGVVqIsTB8sdzlFNIC4Q//1htCwYPyRyWFPCeBxk8TUtZt9ZxT9ADBUk7sIACoSJiN3fU0VlDflxCVpk9KNyaeyvcKgMgYcnE+tkp92YqaNj9Nr4+O6awR7W5xub02jh3K6KT0lmlQypl/WsYU4Aem/eR+eYAS8KMG4bDX+es04RPjOG0HL59gwe6QVK978Bapr/xcyhsbbv5kOANhU2YrbX/8Rde1++vMH5tJxiVrDzQsKMe/JL1QZ8VqsvH4S8vMSMk4Wg8CLEhfkw+EAx3EBnuOCET4S4AUhxAtCmJfEsAAxLAC8SFO8RNOCyDISWEaCnAGUMopiKYry9sfvhyAIgiBOgAYgdbgDg/4ieTBorWjxj0lN0NDHz+u6sXIZnXvhjLjWPQeaNHX1RpWc6XHtCytjmOQ8S2JyXue97UiY51v2u+yeGneYcYfVWomy0FTfj8yGBBGX/VQJTpQgSBIWWI24O/foy82wIOLOnbXY5QnAKGPwYvEIpKkV+MXlw/17DkJGUczK4ixjlkZp7IjwuHXbAfzPhFyc6kByw4LxPIAOnPpI4VH/pa7d7O+TX8YZGMrJHe1zh46pckj0jjIoa8oxJGmT002nlNQdyeELOnuT3P2aLtEUp0s0dd/J8TS1uby1TR7a5WHiaNGskPX8IXW6FDIaB56/BFqlDBFexIxH16EwJQ6Tc+O7t/nnhmrMLkrCi8smY80PNXh7Uw0WjkvDc2vL8cm9s1Hb7seaslpMGGHBez/V4f/uLsWswh5np/aIYWlKpZMrVTp5rxboiaIoQohdpSaCIAhi2AoAYF1OH0nu+llbud1fnJGoOd08K2HkiORgakKg/bstzRYlk3Sy7WUKlk0dGW/FyM7roHCA41r2Odt9tZ4I6+G0WlDmvsj1FDSFNRNt0LAMIqKES36sQKlFj7HGw5eQqxvaESdj8P3MIvynyYk/Vzbi/501Ai/XtODNcTmoD3J462AbHipIw/PVdtyRnXTKid0hLADzof9OW8OC8Rw6k8P81LWbnb3Zx5ka0smd1xkMiYLI0ww9VM+z3ylDsuYcfZKmN0ldlxDbP/Pu9cnxRn1yvBEARFGEt7Hd6atr8jIdHjaOgVnO0r1KjCiKglbZuVQvIojgBQm/PoOPttTjoUvHAAAunZiB3/7rF0iSBBlDI8AJCIR5yFga1S1eNDj8p5XY9QWapmnQcEX1oARBEATRObphdrR5SXLXjxx77P7iLGuvZzCpDFq1YsF0dfP322sTQv505jSazivUcnn6GGsyxnRe2wS84WBLpdMZqPfwcm9Er6Vp40l20SOKoqBhO2d/8pIEXpLw66Tx89YOrMjpzEfPsxrxYPlBSJIElqIQFEUEBREymkKtP4ymIIcpZl1vQjkTcgAWdCZ4MTFUkx4PDlWP40K8T6mR93be7rClCMrsOfokVUq66aR3dE5GZlKZJfHYf6B9iaZpGNISTIa0BBMAiIIodTS2tgdqm32s1yszMLDIWfqU1/8JooiJD6zF/hYvbp2bh0k58Uc93+QKIM3U2cmBZWgY1DI4fGH84YIiXPf/NkElZ/DGrVNx79tb8OjlxX15qqejPVYHJgiCIIYtJ4Ake7ObLA3oB5IowVXeEhg9oveJXReappEyY2ymq6a5jSmvlGsV7LFN806BWqdQZY1PSsH4zktGnyvob61yOoMNXknp5w1qmj7l/QqShAWb9qI2EMa16fE4K+7o07SHOCQrO8sSsDQFHcvAFRHwm2wr7txRCyVD4e+js/BYZQPutfV6XOJMuVLXbo7Z0rChmtz5cKjxajhIkrvToQjJmrO1VnVqnrnPhppkKpnabQ86jHpVr4a4e4NmaMqYbrUY060W4FCyd9DeFjho98u8XrlBRllkDH3coiUMTWPLk+fD7edw6XPfYne9C0VpJ78RVZxpQtmj8wEAG/e2wBqngiQBS57fCBlL49mrxiHR0CezR08FSe4IgiCIaHMAkNub3A5RlCSaVM3sM6IgSt6KtuCoEdbj9gnuDWNWUjyXaOJavv2lPlGOtDPdn9ao0mgnpWgwqfNnT5vf01rl7Ag3+SRVUDCpaPq4S3UYisL6aYXoiPC4aWs1KrxB5OtOft00Uq/GRyX5AIAfnV4kKGSQANy67QBkNIUH81MRr+hVAfXeiOn111BN7rw41Mg8HIyQO0enQBGS2Udorco0m/mMR+p64g6HvUZEL7n7NZqhKWNWcrwxKzkeAEReEN119tZgvT0g8/kUcTIqnu1h+m6cRo5ZhVas39l0VHKXbFSj3hlAqlkDXhDREYjArD08MChJEv784S68fcd0/O6Nn/HUkrGobffjH+sr8PjlZ0XjlAGgNVoHIgiCIIhD2gHIRFGSOMTEUQAAIABJREFUwuGIX9UHa+4JQIiIYmi/I1yYldiniV0XuVohT1owLa35l/J6k7M98UQ3wE+XPl6j18drugvkuZq97vYqZwdn99OasGhW0PQx52SQsSgx6fBtW8dRyZ1VKUdTiEOSSg5elODlBRhlhwt5SpKE5/c3Y2XxCDxUXo8H8lNQH+Dwel0r/mA7YYH3vtQWrQP1ZKgmd93zvEP+CJnzfQKKIGsfobMq02yWfl0UJqjYXves6w80y9Cm7JQEZHf+QxcivOCqaWoJNrYEPU0OdbxaZjbrFEyQ4/Hl7mbcc/7Io16/cGwa3tpYjSm58fj3z3UoHWk9atrpW98dwPziFJi0CgQ4ATRNgaaAQDhq3TlamCvfJK0QCIIgiGjz4tDsqVCQ85Hk7szxYV4Q6jo4W0Z8v0/9SZpQmOZtcbr9v+zsiFOy8Sd/xekzJunijEm67ll17fUdTsc+l7flYIc8TpCM8Qq5MiiI2Ojw4rYRiUe9dm6CAe83OjDOqMWndhemmvVHXX+93+jE2fEGGOUsgqIIGp3XX0FB7I9TOZ6aaB7s14ZqcucFQAFA0MuRkbseyENsS7bGKk/L69+krvt4RnU8HxR4lmUG5HuOkbGM2ZaeCFs6du5pwDm/eR1Bf5jnuYh04djU8PziFPXD72+nx2eZsXBcGm6YlYNr/9/3yFvxIYwaOd6+Y3r3vgJhHm9srMa6++YAAO6cX4CFz3wNOUvjrdunReuU9kfrQARBEARxBB8OzZ4K+MM+o4nkdmciEozwdJOPH5FqjtqaDl2iKU6YN01s/nZznRWRjP6smQAAljSDyZJmMIUPunD9S5sQDEYELiSIc+P1oVkWg+IvVU3y0QY1zkmMw+JUC+7cWYNpG3YjTsZgZfGI7v0EBRHvNbbjfyfYAAA3ZSZg6eZ9kNM0/jEmq1/P4Vf2RfNgv0ZJ0tBr4l5cmkUDeAVAw+Tzc0dOnJ9zaaxjGijkIbZlhCZRnp4S36tKRmeCbfA2J5p0/TLts79FQlzEfaCxLdzUyqlCQbVBwcT3R4+XPvQGc+Wb18U6CIIgCGJ4mbtgTBqAhwE0/PFPF19oK0iOWVWxwS7sD3Oq9pCUFG+I2eyntvKaJk3dwTiljOmX6aAnI/Ci2HrA3e4+4ArAEVLqRFgYihqQAwVHuCp17ea3Y3XwUy57Ophs/6ZGRGcJUrm9xk3WHQGQh5jWPDrFOctWlBiLxA4AfIIQjsVx+4JMKZfFF2Ylp86ZlGk+f1aCOHVipN2a1NgssXXuEN8uDry7JGTkjiAIgogFHw7NnupwB6JSDv7uW99AcdbvMXvin455btXzXyBNtxzO9p5X6bz3vz9gevGDmF78IN773x8AAOFwBFdf/HfMnvgnvPHKt93b/uGOt7Br+8F+OYdfC7iDYZ0rglgmdgAQX5iVTJWMR3tIaIrF8RmWppNspoSCc7MzC64aabVekUeJ4+LtLqO8tgNiqyhJUZ1veYpiOnI30DPfM9EEIK2hytEuipJIn0b/jqFEFmZbs1QJbKYtISHWsUAvj3qzkf4iVyvkCUXZKSjKBgCE/cGQo7rRwdvbeHUkrNXLmT5p6HkGKmJ5cIIgCGLY8uHQ4EFNdWvThCk5/X7ARVdNwXXLS3Hnzf886vGmBic2fl2OlDRTj69zOf3476c+wScb7gdFAefN+DPmLhiNn8v2Y8KUHNzx+/m4eM4zuPamWSjfVQ9BkDCqOL3fz8fr8IUSOZoxmrRRK+94IiqD5lBPvB11CSFfKkNTzMlf1T9YOcukFMRbUwo6lwNyIT5ir3K0+Oo8YcYd1uhAWajYz6yqjOXBh3LCcwCAho+IYtAbHnYl4WUhps1GJTtKc4sSMlMTev5UizKFQWUOhiJDssiHQqNSJo7OSUk5Z0qG8bxZ5sjksYE2S0JDs0DXecJ8LJqJ74rBMQmCIIhh7ou1OyLorH2g2LmtLiqjPZOn2RBnPHbW4J/uew8PPHbJcfvsbvhqD6aXFsBo0iDOqMH00gJ8++UeyGQMggEOkYiArnk5zz72EX7/4AX9eRoAAI/dE0wRWNZoUA+IxK5LZ0+8szJ8thyXL8y7Yx1PF7mSlaWPTkwqXJibmXdNUbzxktwwV2hsdGmZOp8kOmMwsepg6trNnmgf9EhDeeSuHoeSV68r1KoxKGM/chUFbJhpz1Im0lm2hH6pcHQmKJqCwxtwpCoNMZm3HU1KnUatLLapgc5FvcEOX6CjusEhtjpEjRAx6BRsf/ZeDIFMyyQIgiBipwpAfmO9sz3gD3vUGoX+pK/oY+s/2Q5rchwKRx2/bZu9yY2k1MMrVawpRtib3Dj/4nH49+ofceHZT2P5787B55/uwKjidFiT+rdtsqvRHRihVMvVKvmAvT43ZiZZIp098Q4mytD/w5inSaWRKzPGJqVgbGeJB39HKNBS5XQG6z2CwscbNDTd372vY35zfcC+efpAKw5Va+poC7RaM4d2H3M2xLRnKROorNxES6xjOZEQg4G2Ni0qVAatWjU2vzupDTi9vo4DDS6p3SlpBd6gVTCGPjxcBXPlm0If7o8gCIIgTkcFgLEA0NbqacrIio9qchcMcHjhr5/hfz+8s1evZ1kGL7x+IwAgEhFw9UV/x2urb8Of7nsXTQ0uXLpkMs45b0xfhgxHrdOfF6dXKeTsgJ9VJ1Mp5Enzp6U3b95bb3K09WlPvL6mMSjVIyYkqzEhGQDgdQV9rRUOV6jRJ6kCfJyKpvv6vUmSu37UikMjd20Nnpa8Q3+pQw0bZtozFQkYYRvYSV0XxqQaEFNEY01t0mnVpoLu+tB+h9vTUd3ohsMFvSQY1XLmTNYnbu+DEAmCIAiit+px6AZ7U4OzMSMrPj+aB6+taUN9rQPzSh4DADQ3ujB/+uP4+Ns/IiHx8L1Ua3Icfvyuqvtne6MLk6fbjtrXm698i0uXTMbWXw5Ab1Dhv564DFec97c+Te7aqtsCI+NNKhnLDPjE7khJ4wvSvK2JHb6fd3YY+6knXl/TGVVa3ZTU7usvd4vf01blcHPNfkodEkxKmtac4SF2nuHrz9iQTe62f1MTLC7NcgFQ1uxqbZx2cVQ/V/odE6IdmcoEKSs70ULTsV43eurkarm2oyXoNOhIknckjTlOrzHHdd898rY6O7wHmjoop5vSQzCq5MzpNAr6vh9CJAiCIIhT1YRDN9j3V9mbpkzPi+rBC0amYHvNX7p/njLyfny64X6YLEd/lc6cPRLP/OlDuF1+AMDGr8vxh0cu7n7e7fLjq3W78D8f/hZfrN0JiqZBUUAoFOmzWNsqW/2jki1qhhlEF3NH0CWYDMK8aWLThi21SRKXebq1TNx+Dje/8gP2NLhBUcArN5dgSu7hPFGSJNz15i/4bEcT1HIGry0vwdgsMyqbOnDNyu8REUS8uGwypuTGgxdELHj6K3x4dynUilNLceISNfq4RE339Zez0etu3+fs4O1+RsOJZjlNn25/wU2nuX2fG7LJ3SE1AGyuFr8j4Am3q/WKQTG6dSJsmHZmyBOErJzE+MGU1B3JFQx7SXJ3YroEk0GXYOq+vehtdri8tU1eyuWmDZRoOkm/GZLcEQRBEDHzxdod3rkLxjgAqLZvqW26+oYZxy1q0hduv/5V/PhdJZwOHybk/QF3378Qi6+d1uO2O7bW4n9e24hnVy6F0aTBb+89D+fPehIA8Ls/nAej6fDAzX8//SnuuGc+aJrGzDkj8cYr32LupF9w9bIZfRJ36167vzg9URP74o5nhpGxdOqcSZlte2ub1bV1epWMOeXRr7ve+gXzxiTj3TtnguMFBMJHryr5bEcT9tm9qPjrhfhpfztu/+dP+OHRBXj5633429IJyLRocNdbm/HenTPx0pdVuGrqiFNO7HpiStHFmVJ0cQAgiiIc9V6Hc7/TJ7QGWW1Essho6ritKSRJOpj22Zbo9Mo4gaGe3JUDOAuAw9HsrR3MyR0Tpp0ZsnhhRLZ10CZ1XXglM6AqQA0GuiSzUZdk7l713dHQ5vTVNXkZdwdjYCSzgmVUACBJUit71VsxLcFLEARBEOgsqlLsbPe1+rwhp07ffzd1V/7zxhM+/8OeP3f/eczYTIwZm9n98+KlU7F46dQeX/fIU5d3/1mplOHt//RuDV9P2vfY/WdlWc90CuCAEl+QmRRKjg+0fbe5KV7JnHQ9VEeAw3cVLXh9eQkAQM4ykLNHd1n4eEs9rpk+AhRFYXJuPDoCETS7ApAxNAJhHgFOgIyh4PZz+GRrA9b+YXafnQ9N04jPMJjjMwxmABB4UWqvc7e5qt0BqS0o14qwsBTVfU1LUdR3fXbwMzDUk7sD6JrzXe2uTcuzjI9xPKeNCdOudFk8n52dGE/Tg2oq9nHJzep4ISQKDEPHrE/KYGdIjTcZUuNNQOedJffBVhfX1OJIYMR1sY6NIAiCIADsBTAFAFpbOhr7M7kbbBy77f4xI4ZWYtdFadCo5Qumq5s37ahNCPrSTtQTr6bVB4tOiWWryrDzoAtjs8x47prx0CgPjwE0OgNINR/+VaWY1Gh0BXHb3Dxc99ImhCMC/t+yyXj8/3bivguL0J8DIAxLU4nZpvjE7M63Mh8RhLYDrpa2fS5J7+W9CuCbfjv4aRjqyV0DAAEAs29Lc+2kBf3fSLOvMBztSmfjI9nZiQlDJanrwsgYmbPVZ483aq2xjmUooGkapkyrEZlWI0jzcoIgCGJgaAIgAkB9naMxO9c6KsbxxJwoSJKnojU4eogmdl1omkbK9LMyXQft7fSuCvZ47Z94UcK2Wif+fu0ETMqJx11v/oKnP96DRxcVn/QY6RYNvv6vcwAA++0eNDoDKEgx4NoXvwfHi/jTomLYkvq3SCsrY5ikPEtiUp4FAKwANvTrAU/R0MoafmX7NzURdE4LMDjtPn9gEDQzZzjKnSkmtJaOKDLmZiYNucSui4fnw7GOYYj6ItYBEARBEAQ6kzsKAPVL2f5h33tV4EXRX9kWHJmVOOR7/XYxplst6tklGnuE6nEdWqpJjVSTGpNyOguoXDIxHdtqnUdtk2JSo8Hh7/650RlAivHoGicPvrsdj15ejH+sr8ANpbl4aslYPPbBjr4+nROSJOkAc+WbA+J9PjQzh6NtA6AFAEeTtza2oRwfw9HuTCGhdVbWqDhbZvKQTeq6SDrZsPlwixZJkg5SCTdXnXxLgiAIguhfX6zdEQTQDEBTvrvB4ekIOmIdU6zwnCBw1c5wfmbCsLv2kSnlsuT5U9NbjJZ6jhePurFvjVMh1axBZVMHAODrPXYUpBzd9vf8sal467sDkCQJP+5rg14lQ5Lx8K9xw94WJBlVyLXqEQjzoCmApikEuOi2+6UoasDcXB/q0zKBI9bd1exqqxxo6+5ojnanMeZwTlZSIjO42pucEaVBbQl7+ZBCzipjHctQQVHUR7GOgSAIgiCO8BOACwD4DuxvqSwel1kS64CiLRKK8FSDl89Js5xuSf0hJWlcfpqv1erx/7y9w6hkE7oe//vSCVh6aCplVoIWry0vwaovO+9TL59jw4LiFKzb3oi8FR9CLWfx6vLDbyFJkvDnD3finTs6q5fedLYNS1/8Hrwg4oXrJ0X5DPF5tA94PJQkSbGOoV8Vl2bJAKwE0MrKaPGmZ+bcI5MzMU8oaI7qSKUtodyM4ZXUHeWgpyHFok+NdRhDSCmVcPO3sQ6CIAiCIABg7oIxIwA8AKB+emlB+g23nn19rGOKpnCA4xQtQSkl0XDc8vnDjcgLYvO3Ww4mSuEMerD3gDhEkqQwRVEJzJVvemIdCzAMpmUeWne3FYCZj4hiW31HTAtO0BzlSefj7aVZo/T5I1KGb2IHIEBJ0R0zH8JEUWoDsDHWcRAEQRDEEeoAhADIyzZW1odDkUCsA4qWoCcY1rSHSWL3KzTL0ClzJma609LtQU7wxTqePvLJQEnsgGGQ3B3yIwAFAFTvaC2PRQCdSZ3FPivz/7N339FRlfkbwJ93WiY9kIQUSKMTyoQmCNKUEAjFigWV1aioUXd1Lav+7GVXXcvagqLGrlhRgoEYBRSQKjBAQi+hpQGpUzLt/v64E0SkBJjJncw8n3PmTCZz751ncuCe+33vW/qE9+zcMV6tbuOL1XmAup2+3em3opZQqcQc0WGGS+kcREREzYoLjU4AqwBEO50uadfOqs1KZ2oNphpzU7tGl4iLCWdhdxIxPVMS1CMGqaqtrgNKZzlXQoiPlc5wrEAp7rbCvSTCpiV7dznszlabqVHYRX2SI6ZcLuo6xWs06oAv6poFhQVFNJistUrn8BNfKx2AiIjoBNYC0ALAqmXbNymcxesaqhss8Tahio4K1Smdxdfpw0ND4iaN7FiuDytzutpmby6XS6oBUKh0jmMFRHG3ftFuC+RZM6PtNqezel/9Vm9/psomGjrZY8rHpPQJ79W5UwKLuhOrMTfVKZ2hrXOfWHxi4UwiIqLjbAPgAKD5dWFpmcVs85eueH9RW15nThI6bUR4sPb0W1OzjhdkpJh6da9taHLUKJ3lTAmBL9TTPrIrneNYAVHcuS0HoAeAHesrvdZyJOyioZM9unx0ap+w9C4s6k7HphWBMGOrVwmBT0SHGQ6lcxARER2vuNDYBHnWzFiXS5J2bKsoUTqTNxzZe8TcRR8SFBoSxOuasxCVFBcdetGwsJOtieerhBCfKJ3heIFU3G0B4AKgXr9ozw6r2e7RO0bCjsaO9uiDo5P7hKV3SWJR10La6JAYl8vFsWLnQAjxttIZiIiITmE5AB0ALPtly0aFs3jcoV2HTT0jI/X6IK1a6Sxtmca9Jl5V+9i/rInni5wuaa962kfLlM5xvIAp7txdM9cCiJFcklRWUv27J44r7MKUaGt/cHRy39DeXZIStVoWdWdCE6QJqqm3HFI6R1tldzhXig4z/LIVlIiI/MZ2ABYAupXLth84XN1wUOlAnlK9rcrUO7ZdiFYbwNOfe1j8gB5JjoH9mmqsjiqls5yKWiU+VDrDiQTaP8RFcHfNXDV/x1qX03XWgzeFXZgS7e0Pjk7uHdynazKLunNQZz/3qZFz/vEh4tLvQ9+RT/7p96+/uxC9hj+GPiOfwANPfXPCfV956yf0GfkE+o58EtNufRdWq9x1+rrb34Nh9FN4+Nk5R7d95uUf8F3h+nON6zFajTpP6QxERESnUlxotANYBiAWAJb+suU3ZRN5RtXmSlO/xNhQDWdA97iw2KiIduNHxJQLXZnLdxfl9rkumUDgFXdbARwCEFZTaTJV7as/4yl5hR3mRFv7g6OSegf36ZKcqNVqAu1v6HGuUG3wuR7jhqvPx/zZf//T7xYt3Yq5C4xYv/BRbPr1Cdx3e+Zf9jtQXoPX312I1UUPY+Ovj8PpcmH2d6uxoWQ/9HotjIsfw5r1Zairt6C8sg6r1u7GJdkZ5xrXI5xOVz2AL5XOQURE1AK/Qp41UxR8s6bU1Ght0xOqVZdUmPqnxIWqWNd5jUqjVnW86LyU2uTkSrOPrYnncLoWqqd9tE3pHCcSUIXJ+kW7XQDmA2gPABuX7F3d4p3tMCfY2h8cldRH36drcqJOx6LOU4LaBcfa7I5z6ls98vzuaB8V8qffvfXhL/jXXeMRFCRPWtUhNuKE+zqcLlisdjgcTpjNNiTGR0GrVcNqtcPlcsHucEKtFnjs+bl44oEp5xLTswQ+EB1mWJWOQUREdDrFhcZ9ADYBiHE6XdKalbtWKJ3pbB0uqTBlpMWHKp0jUMT0SInXjBikrm7ynTXxNGrVC0pnOJlALFBWQ55YRbN5xYG9jbXWylNtLBwwx9vaHRid1Effl0WdV6jUKtXherPHx91t21mJJSu3Y+j4/2D0JS9i9bo9f9mmY0I73Ht7JlIGPITEfg8gMiIY40ano1f3BMREh2Hg2GcxaVw/7NhdDZdLwoB+yZ6OeVZckuRUq1QvK52DiIjoDPwAIBQAvp29Yq3N5mhTDZSSS8KRTRXmfizsWp0+PDQ4buLIjhXB4WUOl0vRGcKbHM5t6mkfFSmZ4VQCrlBZv2h3A4ClADoAwNbVB5efaDvhgDm+qd2BkR176/t1TenIos67TPD8f1SHw4UjNSYsn/8gXnjsclx1yywc3227ptaEuQuM2LX6WRwwvgCTuQmffC03Jv7vmauwbuGjuPf2TDz23Pd4+sEpePaVQlx1yyy88/EST8c9I01N9i9FhxllioYgIiI6M1sBHAAQWV9nsZUY93lkcrvW4HK6pIYt1ea+neNDTr81eUvicEOKpVePOiXXxNOoVM8p9dktEagFy2LI/b6xYt72jRaT7Y9/IHZY4pqiDozs2DuoX7eUjkFB2kD9G7UqdaQ+0tPH7JQYhcsmDoAQAucNSINKJXDo8J+7bP/06xakJscgNiYcWq0al07sj99W7/rTNt/PX48BhhQ0mpqwc081vnhnBr6ZtxZms83TkVtEkiQpWK978vRbEhER+Y7iQqMLwHcA2gHAnC9XrnQ5fX85JIfd6bJuP2ztldqBhZ0PiGxeE8/R+mvi2R2uCrXK99a2O1agFi5lkKfljXY6XK6tqw4uhQOWuKao/aM69tYZuqV2DOJaJa1KF6GPajQ31XvymBdPyMCiZVsByF00bXYnYqLD/rRNcsf2WLl2F8xmGyRJwsIlW9CrW/zR9+12J16d9TMeuCMLFqsNQsgDp51OF2x2ZXoFWKz2H0SHGVsV+XAiIqJzsx5AHYDgfWWHG3buqPTpde8cTQ6na0+trVty7DlP/kaeo9HrtInjhydXRXfY35pr4kmQnlVP+8jeWp93NgKyuFu/aLcEYA6AcABYPndbWb/gFJehW2qnID2LOqUcMVnP+hb7tFvfxbCJz2PrzgokZfwL7326FDnXDMfusmr0Hfkkrrn1HXzw2g0QQuBgRS0mTnsdADBkYBounzQAAzOfQb9RT8HlkjDj+hFHj/tm/iJMv+p8hITo0C+9EywWG/qNehIDDMmIilSmAS8kWPeoIh9MRER0jtzLIsyDe1mEH+b87rPLItjMNrvqQIM9rWO0XuksdGLx/bt3cg7u13TE6jjlHBqeYHO4Dus06ne8/TnnSvju0hHelTEmTQjgSb1ae6FOrWk36eJBjmumX9BP6VyBzHywfn+3qIhOSufwZWaz7afQ1Dv/uqYDERFRG5GZbQgD8AqAKgD2J1+46urk1JgeCsf6E2uj1RZ62CbFx0YEKZ2FTs/ldEnlv/y+N85pTVY1d7PyMKvNeX/oDZ++6I1je1JA3rkD5Lt3oVp9UbAmqEmr0hT8PH/jd/V1np+xkVpO1z442ocXqlScyyW5dDr1nUrnICIiOhfFhcZGAMUA4gDg0/xfi5xOl1PZVH8w11qaIuscYGHXdqjUKtHxwsEptckpXlkTz2p3HtTr1K97+rjeELDFHQAEa3S/qVWqhUKIMKfTJS0s2rRI6UyBTKPXBtfWW1hgn0RtnfkzbeLtHGtHRET+YKH7WbttS3nN+t/3+ET3zIbqRmusVVLFtAvTKZ2FzlxMj+R47YjB6ioPr4nXZHf+XT3to1Yb23cuArq4Ky40SgC+gjz2Tnz/9erSyvJaTi+voDqbzaR0Bl9kszsswcG6u5XOQURE5AnFhcZDAAoAJALAh7MWLTGbPDux2pmqq6i3dIJWExURolUyB52boPCQ4PiJIztWhER4ZE28I41NK9vfPPsbT2RrDQFd3LltB7AR7nXvPn7v13ltYVreYx3cfwRXZr+ECwc9gYsGP4H38n4GAMyb8zsuGvwEkiNug3HtnpPu/84bP+GiwU/govOexB03vgurVZ4E6K6b3kPm0Kfw3BNzjm776gs/YEHBeq99F2eIhoOWT6C+wfp8SPIdh5XOQURE5EFFAGoBhDfUW+3F8zf8qFSQmv015jSdXhcWGqRRKgN5VuKwfinm9J519U2OI2d7DKdLckrAjZ7M5W0BX9y57959AUAPQFOyYd8hX+ka0FJqjRqP/nsqFq55At8vfBAfzlqMbVsOokevRMz69DYMGd7tpPuWH6zB+28txLxfH8bPqx6Hy+nC3K9XY/Om/dDrtShe8Rg2rC1DfZ0FlRV1WL96N8ZPzvDad9G1C4m1O5w+PcVsazNbbBUx7cP+o3QOIiIiTyouNFoBfAz3zJnffbmqpOJg7Z7WznF49xFz97BwfbBexxnT/UxUpw7RYWOHRZQ7VGe1Jt6hBmt+hxlfbPZ0Lm8K+OIOAIoLjfsgT8ubCADvzVz4S2ODtVbZVC0XFx+JvhnJAICwcD269khAxcFadOuZgC7d40+zN+BwuGC12OFwOGEx2xCXEAWNRg2r1Q6XywW73Qm1WuClZ+bin/83xavfRa1RqQ/Xmau9+iFtTKOp6VbRYYYyK6YTERF51zrIPajiAODzD5fOd7larwdV9Y5D5vToyGCdTsNrYj+lCdJpOo4fllwV02G/zeGytnQ/i81Rq9eq7/FmNm/gP+Q/FEJeVDPcbGpyzJuzplDpQGdjX9khlGzYi/6D0lq0fUJiO9z690wMTX8IA7s+gPDIYIy6KB3deiYgOiYMEy54FmMn9MOeXdVwuaSjRaQ3NUq8c9esvLJuflz6vXOVzkFEROQN7h5Un8Hdg2rDurKqkg37V7fGZ1dvqTL1i28frNGovTJ1PvmW+IzunZyDDfYjVmeL1sSrM9sfaH/z7DY3FwSLO7fiQqMFwAeQuwaIonnG7WW7q9vUbVhToxW3Xvc2nnjuSoRHBLdon9oaE378wYjfNj6LNdtfgNnUhG9nrwAAPPH8VSj67VHc+vdMvPj097jv0Sl47b+FuH36LHz2/hKvfQ9VZFC41w7ehpjMTQ1Hak3XK52DiIjIm4oLjQcgN7InAMD7by1cZLV6d4K16tIKkyEpNlSlUrGwCyCh0ZHh7SeM6FCuDio71fJbNSbbxo65X/n8guUnwuLuzzYAWAMgHgDyZy6cb7c72kR3OLvdiRnirGdwAAAgAElEQVTXvY1LrjwPEy4e0OL9li7egqSUGETHhkOrVWPClP5Ys3LXn7YpmrceffunwNzYhLJd1Zj50Qz88P1aWMze+dMERQa3N1tsHl+jpK3Ze+DIPX1GPMFJVIiIKBAUArAACK05Ymr6af7GIm990KGSClNGanyol9a69pp9B47gwktfQu8RT6DPyCfw6ix5Ar31m/bh/AnPof+FT2PwuGexau3uE+7/4RfL0X3oo+g+9FF8+MVyAEBTkx0Trn4VfUc+ibz3Fx/ddsa9H2PthrMapubzVGqV6DhmcEpdSkqV2eZsOP59m8NpMzc5rlQimyewuDvGMV0DVACC9u451PDjDxt+UDjWaUmShPvv+AjdesRjxl2ZZ7Rvx07tsW71LljMNkiShGWLt6Bbjz/G6dntTryX9zNuvzsLVqsNzSdCl9MFm+2cZ5c9qcONlhqvHbwNOFBeuyR9+OPvKZ2DiIioNRQXGk0APoF79vJvPl+xcduWgx6fnvvwpgqzIS0+1NPHbQ0ajRovPjkVJUuewPLCB5H3/mKUbj2Ifz31DR67bxLWLXwUTz4wBf96+tu/7HukxoSnXpyHFfMfxMoFD+KpF+ehptaEokWlGH5eVxgXP4pPvpJ7bhlL9sHplDCgn/eH4igpuntynHbkedqqJtf+Y3+/s7Lh2eQ7v96iVK5zxeLuOO51V76Cu2vA158t37B9S7lR2VSntnr5Tnzz+Qos+2UrsoY9jaxhT2Nh0UbMn7sOg3v8C2tX7cINV7yBay95FQBQUV6L6Ze/DgDoPzgN2ZcMwIQLnsHYIU/BJUmYduOIo8f+cNYiXDHtfASH6NCrTydYLDaMHfIk+mYkIzIqxGvfyRrAExE3mpoaDtc0XqV0DiIiola2CsAWuHtQvfbC/MK6WpNHJllzOSWprqTS3K9zvPcuXrwsIS7yaMEVHqZHr24JOFBRCyEE6hssAIC6egsS4yL/sm/RohKMHdUL7duFol1UKMaO6oUFC0ug1aphtthgtzvR3Enxsefm4ukHvTuBnq8ICgvWx08c2akiNHKv3ely7j1kWvfQ7HVPK53rXIhTdDcNWJnZBi2ABwB0AlAZHqHXPvPSNTMiIkNiFI4WMOxmmzlF0oa0tS4T58rpdEmr1u6+etjE579UOgsREVFry8w2xAB4CoAZQGPvfkkxd/9r4gyNVn3WC4s7HS6XZfvhph4psS2bkKAN2LP3EEZd8iI2/vI4DpTXYvzVr0KSAJdLwrJ5DyAlKfpP27+Y9yOsVjse+edEAMDTL/+AYL0Wd8+4CH+7831s3l6B+3LHISw0CGs37sUT909W4mspav/mvQdLFqwYNv4/P5UpneVc8M7dCRQXGu0A3na/DG2ot9rzZy78yuk491XuqWW0IbqQugZrwI03M5bs/5yFHRERBSp3D6q3IE9wpy7ZsO9Q0Q/GeWd7PIfN6bTvPOJXhV2jyYorbnobrzx9JSLCgzHzg1/w8lNXYu+65/DyU1Nx8z0ftfhYGo0an751M9b+/AimThmIV2f9jHtvz8Q/H/sSU296G3MX+HTnNY9xOl3Szsr6+9t6YQewuDupY04ucQDUxrVlVYuKNy1QOFZAqbE2/WWQqz8r239463ufLb1B6RxERERKKi40bgBQACAJkIfIbN18cN2ZHsdmsTuwt97eJSnGbwo7u92JK3LexrTLz8NlE+UJ9D76cjkum9gfADB1ykCsWrfnL/t1jI/CvoN/TGew/2ANOsZH/WmbvPcX4/orh2LF77sQGRGM2bNuwctvFXvvy/iQ5Su2zR195WufKZ3DE1jcnUJxodEIefamTgDw6ftLft+9o7JE2VSBwxmsCVI6Q2tpaLQ2Ll25Y0pe/mKu8UdERAR8D2A7jo6/KyysrTFVtXRnq6nJFlRpdqYkttN7K2BrkyQJN9/zEXp2i8c/b/tjAr3E+Cj88ts2AMDCJVvQrXOHv+ybNaY3iheXoqbWhJpaE4oXlyJrTO+j79fUmvBD8UZMv3IozGYbVCoVhAAsFv+/LCnbd3jr5wVrpyqdw1M45u40MrMNOgAPQb6DVxXVLjToyReuvJnj77zPaXc5Eq2ARq3y6+lV7Han8+clm6dPuNo/WoyIiIg84fjxd+l9O8Xc8+Ck046/s9RZmiLqHaJDdLiuVYK2kqUrd2DklP+ib6+OaF6e79mHL0FEeDDufuQLOBwu6IM0ePP5aRhoSMGa9Xvw1oe/4t1XpgMA8j9bhv+8Oh8A8PDdE3DjNcOPHvueR7/ExeMNGD28B6xWOy6e/iYOVNTi1ukjcdfNF7b+l20ldfWWhrlFxoHT78jfrnQWT2Fx1wKZ2YY4yCeXOgDmLt3iou57ZMrN+mBdm5xKty3R7m8s79A+LEHpHN4iSRIWLCp5Ofvq1+5VOgsREZGvycw29AVwH4C9AJxXXDO038RLB156su1NR0zWmCahbh8ZctYTsFBgMFtsTd/PXz912m3vFiidxZPYLbMFiguNlQBmQe4aoNm5vbL2vZkLP7XbnW1igfO2rMHpaFI6gzf98tu2BTPf/+VBpXMQERH5ouJC40YcO/7u8xUb1qzY+euJtq2vbLDEO9Qs7Oi0bDaH4/NvVz3mb4UdwOKuxYoLjb8D+BJAMgDVmhU7y7/69LevXC6XS+Fo/i1CF650BG9Zv2mf8eW3frqqoMjo/x3aiYiIzl7z+LsEAHjz5QWLtpQcWHvsBrUH6swpmiBtRJiehR2dktPpcn0+Z/XM7xcYX1Y6izewuDszPwD4CUAKABQXbthRXLjhrKfnpdMLigyOtjbZzUrn8LTdew/tm/nBLxMLioz1SmchIiLyZe4lqvIANACIAYAXn5k7b++eQ1sB4EjZEXPXkNCgkGCdX4/Rp3MnSRK+m79+9jfz1t5fUGT0yyXOWNydgeJCowTgMwC/w909YPZHy9atXr7jF0WD+TGhEjjUYPar9e4OVtRWv//5simzPvr1gNJZiIiI2oLiQmMNgBfdL6OcTpf03GNz5pX8tvNIz6hIfVCQRq1kPmobihaVLvjwi+UzCoqMfjvsh8XdGSouNDoAvANgD9zdA/JeKVq8ueTA70rm8mcWFfxm1p+KqvqaN/IXXfP0Sz+sVzoLERFRW1JcaKwA8BKAUEiIt5tsY99/e9HamjpzudLZyPctXbljRd77i6cVFBlNSmfxJhZ3Z6G40GgB8Brk2TNjAOC/T303r2TjvtWKBvNTmnb69kpn8ISqQ/W1L79VfPt//jf/Z6WzEBERtUXFhcbdAF7RSDCogHJTY9OyB5/59pNDhxtZ4NFJrdu4t+SFN4ouKSgy1px+67aNxd1ZKi401gF4GYAEoL0kAS8+Pbdw/e+7f1M4mt/RhQaF1TVY2vR/xsrq+poX3vjxri3bK75UOgsREVFbVlxoLFVL+IcKqAKgP3S40frwv+d8fKTGVKl0NvI9pVsP7nz5rZ8mFxQZA+LfB4u7c+DuHvC8+2UMALz6fGHxyt+2L1YslJ+qsTS12YlHKqrqjjz/+oJ7t+2s/LSgyOg3XUyJiIiUMq/IuArAm5CHyOgrquotD//7u48O1zQGxAU8tcyyVTtKH/nP95M++WrFbqWztBYuYu4BmdmGeAAPANBDbkXCjbeNOX/khenjFA3mRywVDQe7RoQnKp3jTO0qqy5/7rUFD1RU1bOwIyIi8rDJWYZhAG4DcABAU2REsO6Zhy6+IqVTdDeFo5GCXC5JmltkXJ3/2bLcgiJjQM2LweLOQzKzDbEA7gcQAaACAK69ccTAi8b3nSSEUDSbP3DanPaOTUKlVqvazGxY6zft2/XcawseMltsX7GwIyIi8o7JWYYRAG4GUAnArFGrxKP3Thzfv2/yeQpHIwU4HE7nh18sX/z9AuPdBUXGTUrnaW0s7jwoM9vQHsC9kLtolgPAxVMH95506cCLNRo1F9U8R7oDjRWx7cLilc7REouXbd30v1k/P+BySQtY2BEREXnX5CxDfwB3AGgEUAsAuTeOOm/c6N7jVSq2sgcKq9Xe9MZ7iwp+XbH9/oIi4x6l8yiBxZ2HZWYbIgH8E0BHAPsBoP+gtLibci+8OjRMH6VouDauqax2T+fYqFSlc5yKyyVJ381ft/KD2cvvKSgyrlA6DxERUaCYnGVIhXwNpoF7mMwlEzK6XT916BVarVqnZDbyvrp6S8N/3yz6fEPpgUcLioxVSudRCos7L8jMNoRBbj3qBWAvAFdsXETwPx+aNDU+sV2asunaLuthU3WX4NBYpXOcjMVqs7z90ZJFC5dsua+gyLhZ6TxERESBZnKWIQbAPyBPtLIfAIYMSIu7+9aLpoWGBEUoGo68pryy7vC/X53/dtm+w88VFBkblM6jJBZ3XpKZbdACuBJAFtyDfNVqlfj7A9nj+vVPGapsurZJckpSbIOzKUin0Sud5XjllXVV/3l1/rw9+w4/WVBk3Kt0HiIiokA1OcsQAmAGgP4AygC4UpKiwx67d+K02OjwBGXTkaeVbj1Y9t83f/zf4RrTzIIiY5PSeZTG4s6LMrMNAsBwADkA6uHuA37FNUP7jZ/cf7Jao9Ioma8tEnvr9yfGRHRSOsexVq7dveWlvOLZ1ib7GwVFxsNK5yEiIgp0k7MMGsiN7OMh38GzhYfptY/ckz2xV/cEg7LpyBNsdoftq7m/r/niuzWvA/iqoMjoVDqTL2Bx1woysw2dIXcR0MM90crAIZ0Tpt886rKIyJAYRcO1MZZ9dXu7RkcmK50DAOx2p/3zOatWfV2wdibkk4pN6UxEREQkm5xlEAAuBDAd8hg8EwBMnTKw5xWTB0wK1utClcxHZ+9gRW35f9/88bede6rfBvATJ6/7A4u7VpKZbWgH4HYA3QDsA+AKDtFpbv175oX9MlKGCs7k1CK2xqb6NJXyfeb3H6w5+PJbPy3dsbvqFQAreVIhIiLyTZOzDP0gX4Op4G5kT4yPCrn/jnGTuqTG9lI0HJ0Rl8vl+unXLetnfvDLIqfTNbOgyLhT6Uy+hsVdK8rMNuggdxHIBHAIQAMADBvZI+nq6cMvCY8Ibq9kvrYiotpaFx6qj1Tisx0Op6Pwp02/53++7FeXS3q9oMi4T4kcRERE1HKTswzRAP4GIAPAQQBWALjuiiF9L56Qke2L4/npz2rrzDWvvbtw+Zr1ZV8B+LKgyGhWOpMvYnHXytzj8HoDuAVAGOTJVlwhoUGa2/4xbmwfQ9IQLnp+ao6y2rKU2KiU1v7c8sq68pffKl65dUfldwC+Ligymlo7AxEREZ2dyVkGFYALAFwPwAmgAgBSk6PD7709c0pKp+iuSuajk/vdWLblpZnFSxpNTXkAjOwxdXIs7hTiXi7hKgCjIPcDbwSAkRf2Srni2vMvDg8PbqdkPl9mLq/f3y2y9SZVsdkdTQsWlqx///PfVjmdrlkASnhSISIiapsmZxk6QJ7srhfkRnYbANx83QUDxl/YO0un1XBNPB/R0Git/+SrFWvmLyz5AcD7BUXGGqUz+ToWdwpy38XrC+BmACGQTzBScIhOc13OyKGDh3a5QKvTBCka0gc5mhxNSXa1TuXlcYqSJGHj5gOb3nh30eaK6vpiALMDfe0UIiIifzA5y6AGMAbANZC7aFYDQPcucZG3Th+R2TWtQ2/2pFKOw+G0L/5t27p3Plm62WKxfQjgl4Iio0vpXG0BizsfkJltCAdwNeSuAkcA1AFAfEJUyHU3jRzdq0/HgSqVSqVkRl+jP2iqio4K7eCt41dU1R+Y9fGv69asL9sF4AMA63m3joiIyL9MzjIkQm5k74Jj7uKdP6hz/LWXD7kouVN7dtVsRZIkYfO28pI33ltUur+8djOAWQVFxgNK52pLWNz5CPddvD4ArgMQB6ASgAUAevXuGH313y7ITE6N6aFgRJ/SVFa7p3NsVKqnj9vQaK2dW2Rc9+X3a3ZLEgoAFBUUGS2e/hwiIiLyDe418cYBuBSAgDyjphMAxo1OT5k6ZeDYuFjfWmPXH+0/WLPng9m/bVi1bs9+ALMBLC8oMjqUztXWsLjzMZnZBi2A8yGPxwuGfIKxA8CIMb2SL75icFZ0bHiighF9guVQY2XXkLA4Tx2vodFaW7SoZPVn366qcDhcawB8UVBkrPTU8YmIiMi3Tc4yRAGYAHlWcwfkCVdcAHD5pP49JmcZLmzvxV5Dgaqyuv7AZ9+sWrdo2dZqAPMBzC8oMjYqnautYnHnozKzDaGQTy6TAEiQizyX/F6/rhdm9R0en+D5O1dthcvpcsU1uuw67bmNSaxvtNYULSpZ/fm3q8odDlcFgI8BlLILJhERUWByT7gyGfJwmSbIvakklUqI664Y2jdrTPqY8DB9lKIh/UD1oYaDc+avN877cUMVgN8hz0RernSuto7FnY/LzDbEALgYf5xgquAu8oYM79Zx/OT+w5NTY3p6e3IRX6TaV38gITqi49nsW32o4eDCpVs2fPH9mip3UfcV5HF1vP1PREREmJxlSAJwGYD+AExwT7qiD9Kop10+pN8F53UdHBMdlqBkxrbG4XA6tu2s3DSncP22lWt3WwGUQC7qdimdzV+wuGsjMrMNKQAmAhgMuatApfsZaV06RE6+fNDg9L6dBgQFaYMVjNmqLHtry7rGtHy9O4fT5di2s7Lku/nrS1as2WWH/DdkUUdEREQnNDnLICBPtjIVQA8A9ZAnvwMAjBrWvdPEsX0Hd+vcobdarVIrFNPn1TdYjqxcu/v3z79dfeDQkUYA2APgSwBb2FvKs1jctTGZ2YZ4yN01R0Ee9HsI7olXQkKDNJMuHdjHMDC1b3xCVJq/381rqrfWdtacvltEfYOlZs36srWffbtqf9WhhuYurt+CRR0RERG1gLvI6w150pXOkOdDqIK7oT0xPipk6uQBGQP6JWe0iwqNVS6p73C5JKls/+FtRYtKNsz/eVOjJEECsArAQgA7ubSBd7C4a6Mysw1RkCdeyQYQBrklqRby+DwkJEaFjs02pPcxJPWN7RCR5K9rtUQdaqoPDQmKOP73JnNT/eZtFSU//bp522+rdzrdv24+oexgKxERERGdKXeRlwRgNIARANSQ7+QdnQDkgiFdE8eNTs/o1S2+TyD1qGpWV285UrL1YMkX363etXvvYQnyNep8ACu5CLn3sbhr49yza/YDMB5AV8jFXS3k/0gA5G6bF43v26dXn0592keHxSuT1DucZXV7k2MjkwHAbLE1bttZufmX37ZtXrh0i0WSEAT5ZLsAwG88oRAREZGnTM4yhAEYBPkaLA7yXbwquGc51wdp1Jmj0lP7903u0jk1pou/zrTpdLqcBytq95RuK9++aOnWPaXbyrUANJDH0xVBnqiOPaVaCYs7P+KefMUAuTWpI+RC7wjkQcAA5EJv8PldUrt2T0hLTGqfGhoaFKlIWA9wOl3Ois0VG83lDYeXrdq5a9W63Q5Jgh7yyXU1gOUAthYUGe3KJiUiIiJ/5b6blwpgKICRAPSQh8zUwF3oAUBqcnT46GHdO/fp2bFrcsf2nfV6bYgSeT2h0dRUv6usevu6jXu3/7h4c3lDozUcgAryIvA/A1haUGSsUDZlYGJx54fcC6LHQZ7daQyAGPdb9e7H0T7O3XoktBs4pHNq1+7xaYmd2qcGh+jCWz1wC9maHNaaI42Ve/cc2rdi4ZbknZsPfttksYcA0EL+TusALIM8ONeqaFgiIiIKOJOzDEEA0gGcByADgM79VgOAOriHzwgBDB3YOX7IwLQuPbrEdYnrEJmkUas0ioQ+DUmS0Ghqqj10pLFy995D+5au3LFzzfoyG+RhQYBcxK4EsBHAroIiY5NiYYnFnb9zF3qdIA8CHgB5xqdmzcXe0X8EKWmxEd16xsd2So6JjYuPjG0fExYbFRUaqwvS6Fsrs8slSQ31lsNHDjVUVpTXVe7dU12xpeRA5Z5d1RYAkQBCNS50U0vYIoAlAIwAthUUGc2tlZGIiIjoVCZnGdSQr8G6QS72ukCeDM8JuSA6et2iUgnRs1t8VPfOcTEpndrHxnWIjIlpHxbTLiokNkjXetdgVqvdfLimsbKyuqFq/8Gaqm27KiuNJfur6+otAkA05O6WEoAtkAu6bQAqOZeB72BxF2Aysw16AMmQTzT9Ic/4JEE+2Vggn2jMOKbgA4COSe3DuvdMiI1PbNcuLFwfEhoWFBIcogsODg4KCQ7WhgTp5YdWow6SAECSJAmQIEGS5BcSJElySZLLarGbLBZbo9nU1NjYYG1sqLc01teZG2uOmEzVlfUNWzcfPGwx21SQW4RC3NkA+VZ/CYB1kLBH70I5Z1oiIiKitmByliEEQBrkBvfzALR3vyXhj+svK467BuuUEBWa3iMxJjUpOiYhLjImWK/V63QanU6r0Wm1ap1Oq9ZptWqdRqPWaTVqnUaj0h07kZ7D6XI4HE67w+G02ezOJqvVbjZbbOZGU5PJZG4y1zVYTWX7Dh/aWLq/an95rQXytVcogCDIPaME5CE+qyA3qO9kg7rvYnEX4DKzDcEAUiDP/NTZ/XPcMZsIyCcaC+R+43bILU7nSkDuqnDsQ4s/Cs1GALsBbAewH/IA5criQqMnPpuIiIhIMe5xetGQG9wTII/ZSwYQiz+KOwGgCX8Ufi26BhICCA/TazVqtaq+wWJzOF0nutjXuh8hAI6f0fMAgF3uRxXkdYFreXeubWBxR3+RmW3QQD7hxEIu9FIBJELuEhmBP8a4HXvyOZnj/4EJ90OC3Pe8xv04BGAf5BNIJYDG4kKeRIiIiChwTM4yaCFfg8VAvg5Lhdzwnog/GsGbH8cSxzxw3DbSCbYxQ74O2w9gB4AKyNdfhzmzZdvG4o7OmHv5hZDjHnr8+YRz/MnHBflE0uh+mFm8EREREZ2e+06fBn/t9XSihxryUBYb5B5XzT833wE0FxSxJ5S/YnFHRERERETkB1RKByAiIiIiIqJzx+KOiIiIiIjID7C4IyIiIiIi8gMs7oiIiIiIiPwAizsiIiIiIiI/wOKOiIiIiIjID7C4IyIiIiIi8gMs7oiIiIiIiPwAizsiIiIiIiI/wOKOiIiIiIjID7C4IyIiIiIi8gMs7oiIiIiIiPwAizsiIiIiIiI/wOKOiIiIiIjID7C4IyIiIiIi8gMs7oiIiIiIiPwAizsiIiIiIiI/wOKOiIiIiIjID7C4IyIiIiIi8gMs7oiIiIiIiPwAizsiIiIiIiI/wOKOiIiIiIjID7C4IyIiIiIi8gMs7oiIiIiIiPwAizsiIiIiIiI/wOKOiIiIiIjID7C4IyIiIiIi8gMs7oiIiIiIiPwAizsiIiIiIiI/wOKOiIiIiIjID7C4IyIiIiIi8gMs7oiIiIiIiPwAizsiIiIiIiI/wOKOiIiIiIjID7C4IyIiIiIi8gMs7oiIiIiIiPwAizsiIiIiIiI/wOKOiIiIiIjID7C4IyIiIiIi8gMs7oiIiIiIiPwAizsiIiIiIiI/wOKOiIiIiIjID7C4IyIiIiIi8gMs7oiIiIiIiPwAizsiIiIiIiI/wOKOiIiIiIjID7C4IyIiIiIi8gMs7oiIiIiIiPwAizsiIiIiIiI/wOKOiIiIiIjID7C4IyIiIiIi8gMs7oiIiIiIiPwAizsiIiIiIiI/wOKOfI4Q4gYhhCSEWOx+Pdr9eo+yyYiIiIj8kxBisft66wals9DZY3EXQIRsj/s/riSE6OXBYzcfM9VTxzzGfgCvAsj3wrGJiIiIPM6b113n4hSN5l9Dvt4qbf1U5CkapQNQqxoBIOWY19cDeLg1AwghtJIk2c9kH0mSdgC420uRiIiIiLyhxdddQggVAEiS5GqFXCckSdIbSn02eQ7v3AWW69zP69zP04QQAgCOaVka7X59fNfIdkKIr4QQh4QQViHEbiHE2+73pGM+Y3fzcYQQT7h//loI8aUQwgLgWiFEphBinRCiTghhF0KUCSGePFno41uYhBBaIUSxEKJCCGETQtQKIeYKIZI8+LciIiIiOhenuu5q7gL5vBBiJQAbgGQhRKIQ4kchhEkIsUwI8aR7u/XNBxVC9BFC/CCEqBJCVAshvhFCJB/zfvOdwjuFENuEEA1CiE+EEDr3dd4i96Ypzdsel+kG9+sP3K/fEkIUCCHMQogNQoiMYz7rMyHEASFEk/tzFgoh+nrnz0ktweIuQAghggBc4X55L4AayK1JI1t4iHvd+28H8D6AzQCGud979Zjt3ne/3n/M7y4H0AXAxwAqAHQEcAjAbPfvwgE8JoS4uoVZVAASABQBeAfALgCT3T8TERERKeoMrrvuB1AF4HMATQA+A5AJ+TpqF4AHjztuPIBf3dssBbAYwGUAityfeawnAfwGQA3gWsh3DvcD+Mb9fgPka7ZXcWq3AnAA2A2gL4DXj3kvxZ3hXQBrAYwB8OVpjkdexOIucEwE0A7yCeQXAPPcv7/upHv8mdb9vBJyAXclgAwAkCTp2C6TT0mSdLe7K2WzXQCGSJI0Q5KkBQA+AvAKgH0A6gDsdG93YUuCSJLUBOBSAEYAJgAb3W+Nam4RIyIiIlJQS6+7PpEkabIkSddDLsJGuX8/zv27t47b/nr3cXcA2AvgAIBqAD0hF1bHuk2SpBsAfOV+3d99fdbc/fKI+5rtdENfCiVJuhTAnc3HOea9KwGsgFwobnD/rqcQIvE0xyQv4Zi7wNF8MimQJMklhJgD+QQxVQhx5wm2Vx/3+n8ADAByAfwDgBPAF0KI61vQP3yVJEmOY17PBDDjBNvFnu5LAIAQYgTkLgXHZ9QDiIBcMBIREREppaXXXcuO+bmj+9kiSVKZ++fjJzdJdT/3cj+O1fW4183dQWvdz2EtzH68448TCgBCiG6Q79ad6LixAA6e5efROeCduwAghIgCkO1+eZO7b/W37teRkLs0mtyvI9zPfY47zBFJksZD7kJpAFACYBqA4e73mwu8E/2bajru9VXu51mBPgkAACAASURBVOZWqpnNUVvyfSB381QD+AHyCWbIMe/xzh0REREppoXXXc2OvUY64H4OFkJ0cv/c87jD73E/z5EkSTQ/IA9Xee+4bZsb1qXjfu90P7e0DjjZcSZCLuzWA4gCEHfMe7weUwjv3AWGKwEEAajHH4NoASAdQDfIRdY69+tnhBAXAbjtuGM8KISYArkLpA1/tBw13yXbB7nf9RtCiG0A/u8UeSohn9z+DmAC5C6WZ6LS/TwUcr/vUafYloiIiKg1teS66y8kSdovhPgF8nXNj0KINfijQbzZp5Bn3LxUCFEEudjr4t6nG/4o/k5ln/u5kxDiXQDbJUl6vgX7Ha/5eqw75HF7GafYlloJ79wFhuauAW9LknRJ8wPALe7fT4A8Bm45gDQAA/BHf+xmayG33FwCYDrk/9B/lySpuX/1vyAP0h0Pudtm8Cny3AxgC+RBueEA3j7D7/M6gO/cnzESwLNnuD8RERGRt7Tkuiv6JPteC6AYcoN5F8jXZ4D7Dp8kSQchF3LzIBdT10Huzvkm5MnqTkuSpD0AXoTcQH8TTlJstsCXkO8WOgGMBfCfszwOeZCQpOPvsBIRERERUWsTQkRKklR3zOu3Ic9T8Il7ghWiU2K3TCIiIiIi33CjEOJiyDNspkG+M+eCfGeO6LRY3BERERER+YatAOIhD3cxAVgC4GlJklYomoraDHbLJCIiIiIi8gOcUIWIiIiIiMgPsLgjIiIiIiLyAyzuiIiIiIiI/ACLOyIiIiIiIj/A4o6IiIiIiMgPsLgjIiIiIiLyAyzuiIiIiIiI/ACLOyIiIiIiIj/A4o6IiIiIiMgPsLgjIiIiIiLyAyzuiIiIiIiI/ACLOyIiIiIiIj/A4o6IiIiIiMgPsLgjIiIiIiLyAyzuiIiIiIiI/ACLOyIiIiIiIj/A4o6IiIiIiMgPsLgjIiIiIiLyAyzuiIiIiIiI/ACLOyIiIiIiIj/A4o6IiIiIiMgPsLgjIiIiIiLyAyzuiIiIiIiI/ACLOyIiIiIiIj+gUToA+Z/80twTNRpIOel5UquHISIiIgpQ+aW5WgCanPQ8i9JZqHUISeL1Np2au1jrCCDV/UgDkAwgBkDUcY9wnPyOsBlA7XGPIwD2A9gDYLf7uSwnPa/JC1+FiIiIqE3JL82NAtADQHf3czfI12B6AEGneTRfk9kA1AA4fMyjGvI12F4A+5qfc9LzrK3xvcg7WNzRn+SX5nYA0N/9yHA/0gDoWjGGBOAggI0A1rkf6wHs4N0/IiIi8jf5pbk6AF3xRwF37HNsK8fZB2AlgOUAVgD4nY3ubQeLuwCWX5qrhlzEjXQ/BgNIVDTUqTVALvKWAPgFwLKc9DyTspGIiIiIzkx+aa6AfA2W5X4MA6BVNNTJ2QAYIRd6ywGsyEnP261sJDoZFncBJr80NxVANoCxkiSNEUJEKRzpXNghtywVAygCsIp39oiIiMgX5ZfmxgEYByBLkqRMIUQHpTOdg0rI12DLAHyTk563U+E85MbiLgDkl+Z2B3C5JEmXCyEGKp3Hi/YDmAPgawBLc9LzXArnISIiogDl7mo5HHIxlwXAIIQQCsfyllUAPgMwOyc9r1LpMIGMxZ2fyi/N7QzgesklTRUq0VvpPAqoBPAdgM9y0vN+VToMERERBYb80twRAG6WJOkyIUSY0nlamRPAIsiF3rc56Xl1CucJOCzu/Ii7hegSl9N1u1CJUX7cOnSmtgJ4F8AHOel5h5QOQ0RERP4lvzS3gyRJf3O5pFvValUXpfP4iCYAhZALvXmchbN1sLjzA/mlud1cLulWSNKNKrWqvdJ5fJgN8t28WTnpeT8rHYaIiIjatvzS3OFOp+ufKpWYIoTg+tEnVw952Mx/c9Lztigdxp+xuGvD8ktzBznszsfUGtVEIcTJ1pajEzMCeA7AVznpeU6lwxAREVHbkF+aq5Vc0pVOp+t+jVZtUDpPG+MC8AWAp3PS8zYrHcYfsbhrg97ddPtYh835pE6vGaZ0Fj+wE8B/IXfZ5BouREREdEL5pbmhLqfrH5KEf6g1qrY806UvcEG+k/d0TnreJqXD+BMWd23IOxtvG+9yuP6rDdL0UTqLH6oA8DyAvJz0PJvSYYiIiMg35JfmCkujbYY2SP2sRquOVjqPn5EAfAvgqZz0vA1Kh/EHLO7agLfWzujvckp5+lDtUKWzBIBdAB7OSc/7QukgREREpKw3Vtw0VqUWM/Whuq5KZ/FzEuR5EZ7KSc9br3SYtozFnQ+bZbw1yWZxvB4crpvCmS9b3SoA9+Wk5y1ROggRERG1rrfWzuhqtzlnhUYEjVE6SwCaC+ChnPS8UqWDtEUs7nxQfmmurqHG+lxohO4ulVrFmZeU9S2Av+ek5x1QOggRERF5V35pbmRjjfXFkAjdjSq1Sq10ngBmA/ACgGe5hMKZYXHnY55bcG12RHTwu/pQXYLSWeioegAPA5iZk57nUjoMEREReVZ+aa66scZ6rz5U+6hGpw60hcd92XYAt+Wk5y1UOkhbweLOR7y4cHo7IcRHUR1CJrEHps9aDmAGZ3UiIiLyHy8t/tvwkDDd50Eh2iSls9BJfQTgnpz0vCNKB/F1LO58wL8Lp93YPj7sVZ1eE650FjotO+T18Z7KSc9zKB2GiIiIzk7GmDT15fcMeTkhLepOlVrF9YJ9XzmAm3PS8wqVDuLLWNwp6NYXx0V27tdhdmyniPFKZ6EztgLANTnpeXuUDkJERERn5vaXx3Xu3C+uIDoxPF3pLHTG3gHwz5z0vEalg/giFncKue+9KRcl94r5PCxKH6t0FjprdQBu5bIJREREbcf97198XVqf2Jn6UB3H1rVduwDcwFnN/4rFXSvLGJOmuviOQS916h59l1rDWZj8RD7kGTVNSgchIiKiE7v4jsG6AWM7f9CxW/urVSpOcOAHXJCXrXpF6SC+hMVdK7r8nqGxg8Z1KYxLiRykdBbyuBIAk3PS83YrHYSIiIj+7M7Xxqen9e0wt11cWBels5DHvQ3gTs6FIGNx10qufWTkBQMz076Kig2NVzoLec1hAFfkpOctVjoIERERARlj0kTWjYbbUnvHvhQUrA1WOg95TTGAqTnpeXVKB1EaizsvyxiTJgaP73JLxpjUV4LDdCFK5yGvswO4Kyc9722lgxAREQWyjDFpQZNvH/hBau/Yq7nMVEDYDGBSTnreLqWDKInFnRdljEnTjroy/aXewzrdrtGqNUrnoVb1JoC72UWAiIio9WWMSUuYOGPAV10MccOVzkKt6hCAS3LS85YpHUQpLO68JGNMWkTm9H6f9DwvcTJbiwJWAYArc9LzrEoHISIiChQDM7ukTbgp4/vO/eL6Kp2FFNEE4Kac9LxPlQ6iBBZ3XpAxJi1+wk0Zc7oNSBiqdBZS3BLIE60EfB9wIiIibxs6qXvP8TkZ3yf3jOmudBZS3NMAHs9JzwuoYofFnYcNHtc19aEHL/1eE6WOPqSv76h0HvIJawGMy0nPO6x0ECIiIn814vJeGVk3ZMxJ7NIuVeks5DM+B3B9TnqeU+kgrYXFnQeNntB78F33Zn8+ZGi3LgCwZvuOsiNBjSlK5yKfUAJgbE56XoXSQYiIiPzNmKv7DMu6wfBlh+RINqzT8T4EcGOg3MFTKR3AX0y6dFD6Aw9f8llzYQcAA7t2SYmwhuxVMhf5jN4AFuWX5sYoHYSIiMifjPubYWz2zf2/ZWFHJ/E3AC8pHaK18M6dB0y8dGDfv9+f/W3vfkldj3/P5ZKk5du3HjAFWzspkY18zloAY3LS8+qVDkJERNSWZYxJE/FpUVPGTe/3XlSH0Gil85DPeyQnPe9ZpUN4G+/cnaPMbEO/m++4aPaJCjsAUKmEGNq1e4Leqi1v7WzkkwYAmJdfmsuFVImIiM5Sxpg00T4+7PLxNxreZ2FHLfRMfmnubUqH8DYWd+cgM9vQ97Krh7w1eGjX9FNtp1ar1MM694zRWTWVrZWNfNoIAN/kl+ZqlQ5CRETU1mSMSRNanfqKcTf0ey0iOqSd0nmoTXkzvzT3KqVDeBOLu7OUmW1IHjayx3MTLxkwpCXbazRq7fmpPaI0TepD3s5GbcIEAB/ll+ZyEUQiIqIzM2H8TRnPdkiKTFA6CLU5KgAf55fmZikdxFtY3J2FzGxDu249E56cfvOoi1QqVYv/hkE6bdD5ST1CVE2ixpv5qM24GsCjSocgIiJqKzLGpA0afWX6M2l9OnRTOgu1WVoA3+aX5p6vdBBv4IQqZygz26CP7RDx2P89c1luZFRo5Nkcw2SyNiwv3wqXTgr3dD5qcyQAU3PS875ROggREZEvyxiT1nXQhZ3fOv+y7hcJEbgdX+xNDjw//XvYbU64nC4MHNcZl9x5Hn7+dCN++ngDqvbV439Lb0B4uxMP71/23RbMe3stAGDSrQMw/JKesNuceOPO+ThS2YgxV/fBhdf0AQB8+PhijL6qN1LSY1vt+7WiGgAjc9LzNikdxJN45+4MZGYbVEFB2py77s++4WwLOwAIDdWHD+7QzQk7zJ7MR22SgNw9M0PpIERERL4qY0xa3KCBnZ99KPeyEVFNYQG9zJRGp8Z9+VPw5Jwr8fg3U7Fp6T7sNFag64B43PveZEQnnvzeQWOtFXNnrsH/fX4ZHpl9OebOXANTXRNKlu5F1wEJeHLOVVhesA0AsG/LIbhckr8WdgDQDkBhfmmuX43b1CgdoI2ZdPs94+5OSok+5z7ekREhUYNcXQ+vObJDBQ30ngjna07WsjTrgZ+wp6QKao0KaX3jMP3xkdBo1X/ZP4BalkIAzM0vzR2ck57HSXeIiIiOkZltCEtJiJl5992TsoKCtLoh3bslb9mzv6wM1clCFXi38IQQ0IfKc7I5HS44HS4IIZDS6/TXQCXL9qH3+UkIi5IvPXufn4RNS/ciJEIHm9UOp8MFuHv1zXl9Fa5/fJT3vohvSALwDoArlA7iKbxz10KZ2YbBV/9t+KOGAake6+PdPiosOiMyrR5O2D11TF9yspaloZO64dl51+Cp766C3erAkm82/2XfAGxZSgLwRX5pLv9PEhERuWVmG9Qqtbjl/n9dPCIqKjSk+fc9Uzul9A1OqYADViXzKcXldOGJy77EPSM+QPr5ndC5X1yL9qupMqFdfNjR1+3iQlFTZUL6+Uk4fKABz17zLS66ti/WL9yNlPRYtOsQ6q2v4Esuzy/NvUXpEJ7CC8kWyMw2dB4xptfTmRMMAz197A7RkR36hCQfkpyS09PHVtrJWpb6jUyBEAJCCKT17YCaStNf9j22ZSk0Muhoy5Jaqzphy9Ild53Xqt/NS0YB+D+lQxAREfmQi6fdMOK61C4dYo5/IzGufcKQmO5WtU3UKhFMSSq1Ck98eyVeXDgduzdWYf/2w+d0PLVGhRn/zcQT30zFoKwuKP54A8bdYMDs55ch7+4irF+420PJfdb/8ktzeyodwhNY3J1GZrYhJjIq5IGrpw+/QOWlW/+Jce0TegZ1Kpdc/je7zalalhx2J5YXbEOfC5L+sl8Atyw9nl+aO0zpEERERErLzDb07ds/OWfM2N4nHZceGRESdUFyerDeqjvYmtl8RUhEEHqe1xGblu5r0fbtOoSipqLx6OuaStNfrqEWzS7B+VN6YJexEsHhOtz2UiaKPjR6NLcPCgHweX5prk7pIOeKxd0pZGYbNAByb7lz7KiQ0CCvVg8pibGduqoT9vnb7KWnaln65Okl6D4wAd0HJrb4eAHQsqQG8Fl+aW6U0kGIiIiUkpltaBcaFnTHzbkXDVOpT73sVJBOG3RB114J7ZvCy1orn5Iajlhgrm8CANisDpQu34eEtJZdNvQenoSS3/bBVNckD3f5bR96D/+jkd1U14QNv5Rh2MU9YLM6oBICEAJ2q991MDuRDADPKR3iXLG4O7Wxw0f1GN67X1Kr3KbtkhSfnCzF+uWJ6fiWpe/zVqOhxoKr/jX8hNsHeMtSCuTBvURERAEnM9ugAnDjbf8YNzoiMqRFMxmqVEIM6tYlJRVxeyWX5PJyREXVVpvxwo1z8filX+Dpq75G+vlJMIxOxU+fbMB9F36EmspGPH7pl/jgsUUAgD2bqo7+HBalx6TbBuKZq77GM1d9jcm3Dzo6uQoAFMxcg4kzBkClEugzPAnb1pbj8Uu+wPlTuivyXRVwd35p7nilQ5wLrnN3EpnZhsTQsKD/PPfadReHhenPetmDs7FhV9meCk1Namt+pjc0HLFArVEhJCIINqsDL99SgAk39UfdITOWztmC+96bAp3+xBO2NtZa8fSVX+Oxr6YCAJ6a+hUe/fKKoycgU10T3rr3R9wzaxKMi/dg/9bDmHjrQPz7mm/xyBeXt9p39LJrctLzZisdgoiIqDVlZhsuGjuh3yPX3jhi9NnsX1FdW7mxYU+4pEHI6bcm+otKAIa2OoM5i7sTyMw2qAE8+I9/Tbw6Y2BqHyUyrN2+a8+hoPpUJT7bU/ZtPYz3Hl4IyeWCyyVhcFZXTMkdhFv6vYXoxHDoQ+TJVgaM7YwpuYOwZ1MVFn9ZghueGgMAWPLtZhTOkpdCmHjrQFxw6R83UGc/twwZF6ai53kdYW9y4LU756O20oTRV/XGRdf2bf0v6x1VAHrmpOfVKB2EiIioNWRmG5Ki2oX++z+vTrtMr9eddXHW2GipX1W+3e4IckV7Mh8FjPkAJuak57W5QonF3QlkZhsuGnx+1/tvv3tcllBw+ZSV27aX1elNKYoFIF+Qn5Oed5PSIYiIiLwtM9ugA/D4PQ9Nurhf/5Te53o8u91hW7lr+/+zd9+BbZVn28CvM7StYdmyZcs7Tpzt7GHIIGCGwyqlbZJSeAuFFgqlbxddtLSU7q8DWjoYZRQIpX2BAikQoBBWSMhwdjzivSTb2vuM7w87w8QkXtKxpfv3F7Z19FxyEnFuPeN2hXRRxwTEI+nnK9fPvv8+pUOMFu25+4jqmspcrU59zTU3rF6mZGEHAEvLy4syItqRHX9EUtX1Dx+65TylQxBCCCFJUL1gccn8eQuKxl3YAYBKxaurps/Mt8XMzRPxfCTt/OThQ7fkKB1itKi4O8XgBt7rbrhl3SKTSTeiDbyJxLIMs3zaDIcuou5QOgtR1F8ePnSL9uwPI4QQQqam6prKHI5jr/rcDWuWTOSH6yzLMAvLS0vK2bw2SLIwYU9M0oEJwD1KhxgtKu6GWrVgScmaxUvLJs2mLY5j2ZVlFbmaCN+tdBaimOkAvqZ0CEIIISQRqmsqGQCf/dQ1Kyus2Rn2RIxRVpBbuNA4rZ+JM8FEPD9JWdc/fOiWhUqHGA0q7gZV11RmA/jsputWLWAS1Kx8rHie46tKZ1pVEc6ldBaimDsePnSLTekQhBBCSAIsyLGbV6yrnrskkYPYskw5VXkVMh/lehM5DkkpLIDfKx1iNKi4w4lPjK5dc8HsAluuaeQdtZNIpeLVK4tnZnBRtl/pLEQRJgA/VDoEIYQQMpGqayr1AK675vpVM1RqXpPo8Qx6bcbq0tkWOtOAjJQsy+fetf0LVyidY6SouBswA8D8Sz+xeL7SQc5Eq1HpVjoq1GyM8SmdhSjiiw8fuiVtuogSQghJCxcXFmfnz5lXmLR7MJ7n+KoZMwvtQmYLnRpPzsQbiNW/2Og/8mpz8P9Vbd6oUjrPSKR9cTc4a3fV2uo51mybadIflavXazKW22fItGY8LfEAfq50CEIIIWQiVNdUZgK45LPXr6pgOTbp96Tzy4qLZ/IF7bIox5M9NpncAr5o/8t7nJ5njgWn94TEWQCmAbhJ6VwjkfbFHQZm7SrWX7lokdJBRsqYoTMvyS6PIo6w0llI0n3i4UO3LFM6BCGEEDIBLp5eYbdMr8hT7CC7YoetYKml3MfQqigCIBoRAm/v73VuPha0tnMqy0d+fGfV5o2T/vTytC7uptqs3akyzQbrosyygCzIMaWzkKT7jtIBCCGEkPGorqm0Abhgw3XnzmYVPsjOmmnMOtcxi1dFeKeSOYhyREGK7Tna3/XEYa/+qMzlgB32r2QugOuTHG3U0rq4wxSctTtVttVkq8wo6ae+LWnniocP3TJL6RCEEELIOKyfPa/AUjotZ7bSQQBAp1PrV0+bnWWK6FuVzkKSR5Jkqb7Z2/HEvj5mV5TJk86+PPgbVZs3ckkJN0ZpW9xN5Vm7U9lzMu2zNIU9siRLSmchScMAuEPpEIQQQshYVNdU5gFYc+WnlpVPZMPy8eI4llsxY0aRQ8xqkemklZTX2RXo+ufe3uhbPskR4/mRHpZSCuAzicw1Xmlb3GGKz9qdqjAv2zGDz++gN6K0sunhQ7cUKh2CEEIIGYP1OXYzVzY9d47SQYYzp7SweLamqBOCHFU6C5l4HnfY9cJul2+LK57n43ndGJ7i2xMeagKlZXGXKrN2pyotyC0sRS71bEkfKgBfVzoEIYQQMhrVNZVWACuv2rC8iOPYSbu8rdCe5ViWNSPExhiv0lnIxAgHY97X97r6/tkWsfXwvGkcTzWvavPGmgkLNsHSsrjD4KzdhTWVc5UOMpFmFOcXOURrs9I5SNJ8/uFDt+iVDkEIIYSMwjkqNc/OX1i8WOkgZ2MxGzLPLZyt1kRU3UpnIWMXj4nhDw729TxZ7zc1sXzWBD3trRP0PBMu7Yq747N2xaU2xp5vKVU6z0SbU1pUkhMztyidgySFCZN83TchhBByXHVNpQbAJZd+YnGWTqfOUDrPSGg1Kt2502blWCIZdG81xYiiJBxscHc+edCj3i+yuTI7/BGYY3RR1eaNRRP4fBMm7Yo7AAUAZqy/clHxZNrEO5EWlJcWZ0bpTShN3Kh0AEIIIWSEFgDQnbOmYtLP2p2K41h22Yzy4iLZ1kIH2E1+siyjuc3X+XRtn/h+CPnxxCz/ZTFJ2yKkY3G3kmUZcfb8wgVKB0mkJeXTik0RHe3BS30rHz50S0otLyaEEJJ6BldOXTa3sojPyjbmK51nLGYWO4rn6Ut6ICCidBYyPJcr1PN/e3oDr7nF/BDPaxI83PWTsS1CWhV31TWVagBr11wwR28waMxK50kkhmGwrHy6Qx/WtCudhSQczd4RQgiZ7MoBONZdOHdKb4nJz8nMW2GbEeVirFvpLOQkvy/a/589LvfzXdFcN88na8lvIYCLkzTWiKVVcQdgNgDdOasr0mKmg2VZdmV5RZ42oupSOgtJqM89fOgWtdIhCCGEkDOoYhjEZ8zKm/L3YCaj3ryqaLZeG1F3Kp0l3UUjQmDb/l7X5mNBawfHZyoQYdItzUy34m6dVquKFJXaKpQOkiwcx3IrSyuy1BHOqXQWkjCZAKqVDkEIIYQMZ/AglaqVqys0hgxtSqycUqt5zbnls/KyYiY640ABQlyK7T7S3/XEYa++TuZszISelTIql1Rt3mhQavDhpE1xV11TaQEw94Ka+VaVikurWQ6VilevLJ5p4iJsn9JZSMJ8WukAhBBCyMeoAKA6Z/XM2UoHmUgsyzCLy8uKy5DbKouyqHSedCBJslTf5Ol4cn8fszvG5Ekcq3QtowMwqXreKf0LSaa5ALBoadkcpYMoQaNRaasKZ+rYGOtROguZOKIoif6YuOODzpCjavNGldJ5CCGEkGGcw/NsdNr03JQq7o4rL8orWmgs62UEhJTOkso6ugJdz+ztjb7llx0xnp9M9zyfVDrAqXilAyTRGr1BEyoszpqhdBCl6HRq/Yq8GcL2rqN+SS0blc5DxkYUpHi3K9RT747JTTE5T2TZZYM/Wgtgq4LRCCGEkCGqayoNABavWjdbq9Gq9ErnSZScbHPuSt1M3wcddX2iVpqoRtkEgLs/7Hq72a918qo88JOydFlftXmj9r0NT02KU1Qn5W9oolXXVFoBTLvgkvlGnucmU6WfdBkGrWlpznT3jt66kMwjZd9kU40QF6NdzpCzzhNjWuLIk1i2AGCAoWvMrwAVd4QQQiaX2QDY+QuLp/QpmSORYdCaVpfOiX9wrK49pIsWKJ1nqgsFY9736r1CM8vbMKkm6k6TAeAiAM8rHQRIk+IOA0symZlzHCn/xjISZpM+c5E4rXeXp5EDh0T3ACFjFI+JkQ5nyFnnjvFtEmOXGaYQYM+0mPoKALcmLyEhhBByVlUAQsWltmlKB0kGlYpTnTNjZkFtY3OzU+0tUTrPVBSLiuHdDR7fgRhywPGKnZQySpdhkhR36bLnbhUAb77DUqR0kMkiK9OYvcBY6pFFOa50FnJSLCKEGlt9LS/v7+1+9JBX81q/UNQqs/kyw4zk32pB1eaNMxMekhBCCBmB6ppKFYA5+QWZMUumPlfpPMnCMAwWlJeWlLN5bZBkQek8U4UoSsKBenfnU4c86gMimwtOuSMwx2DSnFqe8sVddU2lFkBZls0YNZn1NqXzTCY52ebcufpiF53wpKxIOB6ob/G2vLi/t+fRoz7dfz1icbvM2cEyY3lTWzXhAQkhhJCxKQbAnbNmZikzpv+lTW1lBbmFi0zT3EwcAaWzTGayLMvNbb7OzbV94vYw8uMcyymdaQyKqjZvnBTneqTDsswCAFiyvKwgHd9YzsaRa80XOsT2I/F2BzO2YoKMQTgY97Y4Q+6j/rjBKbM2hmEyAA4T8CewCsAD409ICCGEjNtMAKiYlZ8WSzKHk2012c7Rzgp80F7nimtFmmT4CKcz2P12e8jo5vn8SXpYymisA1CndIgp/1scgRIAzIyZ+bQk82MUO2wF8VahtVHuLqICOHGC/pi72RXyHfULxn6GswIwT1BBd6rVE/pshBBCyNgtBeBxFFrLlA6iJL1ek7GqbLZ2x7H6toA2Uqh0nsnA7432vX3My3VyKnsKFHXHrQbwZ6VDpMxv8wzmAwg4iqz0j+kMyovyiuLNYksb01usdJZU4vdG+5pcYf/RoGjxMmwmgEwwCV1tUFy1eWPhexueakvkIIQQQsiZVNdUmgAUTa+w+7Q6mDH2bAAAIABJREFUtUHpPErjeY6vmjGzcP+xluZOrr8kXT9Mj4bjgQ/qPeGjEmdjUu8A+zVKBwBSvLirrqnkAMxQqbn+rGyjQ+k8k92skoLiWKPQ3KPylCidZSrzeiKuY65IsC4kWv0MmwUgCyM6D2XCrAbwRDIHJIQQQj6iDIA8e35hvtJBJpN5ZcUl5k59x5FoRw44pFx183GEuBjd1+jt3xuWcyWOz0jubVHS5Fdt3ljw3oan2pUMkdLFHQA7ANXiZWU2nudS/bVOiMppJSW76htb+jR+msEbIVmWZbc74jzWGwnXh8TsIMvZANiSXNCdahWouCOEEKKsMgBScaktT+kgk01Rvs2R4dH17eprVMkq2aR0nkSSJFlqaPZ2bfcIOTGeywOX8jOWCwFQcZdAhQCY2fMKab/dKCwqLyveUV/f6tWG6Pf2MWRJlvv6wz2NfdFoQ1jKCbPswBHPk+OApxVKByCEEJL2ZgLw2/MsNHM3DKslI2uVdlZoe2udM6YVcpTOkwgdnf7Od7oiVr+Kd4A/+/1R55ajcL7RCDAM9IVmlH9pOVj1yeukuIiG+7cj0OSGKkON6bdXQWvLgO+oC00PfQiGZzH9tiro8owQgjHU/f5dzPr2WjDJ7aiwEMALyRzwo1JzUvSkOQCiRSXZVKSMAsMwWFo+vdAQ1ir6ycNkI0my5HQGO9873N/693390ec6o/b9URSHWVandLaPmFm1eeOkqDIJIYSkn8FtMSUAAlnZRpq5+xharVq/atrsLHNE36p0lonk7g+7nt/t9P+nV8j3q3jtSK6J9ofQ/XId5v30Qiz41SWAJKP3/ZYhj3H+9xh4gxqLfncp8moq0PpkLQCg66WjmHnHGpRcuwg9rzUAANqfPQjHlbOTXdgBA8WdolJ25q66ppIBMBeAN9dupsNURollGWZF+Yy8dxuPdEa0sbT91E0UJcHpCvc09EelY3E5N86w+QADTO6+mhoA0wEcUToIIYSQtGQDwE2vyDOpNSO7uU9XHMdyy2fMKDrU1NbSxvQWTeW2VKFAzPNeo1dqZngb+NFvJ5RFCVJMBMuxEGMi1JlDPzvv39WBwk/OBQBkLS9E0992QZZlMBwDKSZAiglgeBaRHj9ifSGYZ+dOyOsaJSruEsgCwGzJNPTQKU1jw3EsV1VWYXvn2OGemFZQ5F+IEkRBinc7Qz317qjcFIddZFkHwGCiexYk2DxQcUcIIUQZeQAwc67DrnSQqWJ2aWGxuVvfeTDclgUOGqXzjEYsKoR2NXj8B2NMDjh+TDdLGqse+ZfOxO5bXwCr5mCZb4dl/tBJ31h/GOosPQCA4VhwehUEfwyOK2aj4f4PwKo5lN+yAi1P7EXhp+eP/4WNTXHV5o2Z7214yq1UgFQu7goByAVFWSm9UTXReJ5TVZXMtLzTfLhX0IrZSudJFCEuRjudIWe9J8a2xGGXWLYAYKfywuUKpQMQQghJWyUAZHueJUvpIFOJw56Vn+HTunc6G8OSWrIonedsRFESDjV6nbuCUq7AcXqMY0OIEIih/8MOLLr3UnB6Nep+/y5cbzfDtqrkrNcaSjIx7+5qAIDvsBNqixaAjLrfvwuGY1F8zcLB7yXNDAAfJHPAU03dW9ezswBg8/ItVNyNk1rNa6qKKvRcjFXsU4hEiMfEcFObv/XVA32djx7yql7tEwqbRNYhsZPjVJRxmq50AEIIIWlrOgb221mVDjLVmE2GzFWFs7TaiKpL6SwfR5ZluanV17m5tk/8IIJ8gRv/fZP3QDc0OQaoTFqwPIuspQXw1/UOeYzaqkOsLzSQQZQghuLgjepTcw3stbtqDtr/dRDFmxYgZ900dL9SN954o1Wa7AFPlcozdzkA4lk2emOZCFqtWr8iv0J6v/OIT1JP3WN7oxEh2O4M9dZ545oOkckFyxQBLPCRRQS7b/s3WJ0KDMuAYRnM/+lFQ34uyzKaH90N994ucGoO025ejoxSK8KdPtT/4X3IgoSyLyyFcUY2ZFHC4Z+/hYpvrAKnSdo/OSruCCGEKCUPQNicqc9UOshUpNGotOeWz8rd1Xisxa0JTKrWVE5nsHtbe9Do4VX54CfunkadbUCgvg9iVACr5uA90AND2dBbeOtiB1zbmmCckY2+D9pgnpOLU5vBu7Y1I3NBPlQZGohRYeD2jgWkqDBhOUeoJNkDniqVi7tcANFMq2HKFiKTjUGvyViWO937gbM+KKvkKbOPMRKO+1t7Qv11PkHXJTM2hmGKR7Lkcs7310FlGn7Zu2dvFyLdASz87XoEGvrQ9NCHmPeTC9HzeiNKrl0Ejc2A5sd2o2LGueje2oDsc4uTWdgBQHkyByOEEEIAoLqmUgPADMBrNGon/dLCyYplWXbp9PLioy0dLc2ys5BhlW377fNG+94+5uO6ON4+lsNSzsZYnoWs5YXY991XwLAsDCUW5J4/Da3P7EdGqRXWJQ7krC1D/f3bsfurL4LPUGPGbVUnrhejAlzbmjDrO2sBAPnrK3D4F9vA8iym37pywvOeRUmyBzxVKhd3NgBRk0VPxd0EMhn15iXitL6d/Q0ceEzaE7BCwZi3xRl21/mFDBfYbABGgJ2wM1H6d3XAtqoEDMPAOD0bQiiOmDs89MQmjoUQjMG9uwOzvr12YgYeuayqzRu59zY8JSZ7YEIIIWktE4DMsgyj06npHmycKoodxSanvnt/oMUMHklvvRQJx/0f1HujdRKbzXCJLRsKPzUPhZ+aN+R7Rad8zao5VHz1nGGv5TQ85ty57sTXppk5WPDLSxIT9OxoWWaCZAHwGU06s9JBUk2mJSNroVjm3OM7xoKD+uxXJEfAH3M3O0PeuoBg6mc4KwDzmLeVMgwO/+xNgAFyz5+G3POHToSdemITMLgOvD8M+4XT0XD/dkiChLIblqL9/xTrs8ICsAJwJXtgQgghac0MQM4vsBpYjk3lsx2SJi8n027Qab07u+vDokZKynYjIS5Gaxs8/bUR2CWOMyo7bzjllCg5eEoWd9U1lSoAGQD6MjI09KlRAtiyTDnzxOLufcFmG8Mxih1A4vNG+5pcIX9dULR4GS4TQCYmIM6cu86HxqpH3BvBoZ++CV2+CaZZOWe9TpNtwJwfnA8ACHf7EesPQZdvRv0fB/bhFX56HnR5SfsraQMVd4QQQpLLDIDNzTMblQ6SSkxGnXmVZnb0g+a6jrA25kjUOJIkS/XN3q4PvEJujOPyxnMCZho7+w1jAqVqHW4CIAGATk/FXaLk5WTaZ2kLu2VJlpI1pizL8Lgjrt317uan9/b5/tESytoZQomX4SZ0Xb/GOjArpzJrYV3qQKCxf8jPTz2xCRicybMOXS3R9vQ+FH56PrpfqUPOedNQvGkB2v91cCJjnk3Ktq4ghBAyaVkAyEajbkr1apsK1Gpec075rPzsqKk5Ec/f3unv/Mfe3tjbAdkR4xK8BjO1mao2b1SsOXKq/sGZAMhmi16tVvP05pJARXnZDqFdbKsXOwuZBDX5lmVZdvdHnI19kUhDSMoKsqwNgA0JWu0hRgRAlsHpVBAjAjz7ulFw1dwhj7EucqD71XpkVRUh0NAHTq+COvNkcec95IQ6UwddnhFSVADDAmAHNvwmERV3hBBCki0bQFyfodGf9ZFk1FiWYRZNLytpaOtubRS6HBOxeqq/L+zc1hLQ9fL8hJ6AmcZYDNQiXiUGT9U/QRMAxlFopVm7JCgryC2MtwgtLXBN2HG9kiRL/f1hZ0NvJNoQkXMiLJsLAIkq6E4V90Zw9DfvABjoo5J9TjEyF+She2sDAMBeXQ7Lwjy493Ziz1dfBKvhUf7F5Seul2UZHc8exPTbB05xyjl/Ghr+sB2yKKH0hiUJz38KKu4IIYQkWwYAUa/X0IfrCVReaC8y9eqctd6mDFmFMRXSwUDM816DV2ph+Rwq6iacBVTcTSgzADbDqJ00h32kuopiR3H8mNjcyfeXjPU5JFESXX3hnoa+qNAYlXNiLGsHGCDJh5FoczNQ+YuLT/u+vfrkoSoMw6Ds+uELNYZhMPt75534Wu8wY/7PLhr2sQlGxR0hhJBk0wMQdHo1FXcJlpNtzlmpm+nf0VnfK2jEEf8/PxYVQh82ePyH4mwu2FQtBRRnAdCixMCp+idqAyDIstIx0svcsqKSeIPQ7FL7SkZ6jShKgtMV7qnvj0pNcTk3zrD5ShR0KYpmrgkhhCSbAYCo06mouEuCDIPWuKpktnZHU117UBstONNjRVESDjV6nbuCUq7AcfqUPXljclCsx2OqFndaAKIkSVQhJNnC8rKSHXUNLR5t4GOXaIqCFOtyhnrqPVGmOQa7yLIOgMGENaEjx9EZV4QQQpJND0DQaKm4SxaVilNVTZ9ZsO9YS0uPynPa/Zcsy3JTq6/z/b64Lcxz+eCoqksCxXpBp2pxJwGALNHcnRKWlE8r+qChrs2vDRce/54QF6OdzpCzzh1jWwXYJZYtBNjUPa91cqDijhBCSLLpAUQ4jqX/ByURwzConFZS3NzhbD8a77AzLMMDQE9PsHtbR8jk5XkHePojSSLF7nBTubhjpCQe0U9OYlmGWV4+3fHuoYPNba09cnN/lGkXkC0BObIMzgxIYBgBDASAmXR/RuH+EFP72C5d1B9lAKCwqiRedn557NTHyLKMg//Yp3Ed6lFxak6ef82isKU4Uwp0+9m9j3yok0QJczcsiFinZYmSKGHHH97TL/nSihCvScI/OVnmOA3nk1kmdvYHE0IIIRNKCyAgiXQPpoQSR05BRr+ud3v9geiHh3o1XWCzWQZiJhMPAAxNeiQWAwC8hvOLDBT7XadqcQcAkGjmTjEsy7JLdfkZK3OMus7Wo7LOHWQEXzQo+WMhLijIfERUa0VZp5dhZBPVQ2GMeiJxOHNyMG+6HgFBRM0rhzVfd0maGcaTrQ7ecHrxt5Y+vLN0JvZ4gswP/7rT8HTVLPzocBtud9hRqFPjh0/u1/9l0TT8rdmJaq0Bn36tK5kNXfUA9LghiSMSQgghA6tGZEmSqLhTSLbVmL1UWxhcadNKoX0tHapunwxPlDdEJGOGDPMku+1KRRkAOKXuwVK1uJMxMHNHxZ1Cuo/2iIsLc7ORl4Xi2SUI+UJi0/aDoingzXKYtCeqpLggyv2uUMzjCsUD7rAY88VkhOKMOibxBhm8kWHVbJLfhHK1KuRqVQCADJ5DeYYW3dE4Ti3uXnV68ElHFhiGwaLMDPgEET2ROFQMg7AoISxJ4FkG3riArU4P/r50elJfwyBRiUEJIYSkNVo9NQlwIrj56xYasG6h0dXuinXvOhLOZQU9J0hiZ4s34u7yC5I7yugjoiqLYbXcJKn4IqKEqz84ipgkQ5Rl1Ngz8fXp+UMeExUlfHVfM/b7QshUcbh/QRkK9RrsdAfw3YOtUDEM/rigFKUGLbxxATfvOYa/L52OJN9PJrWx8alStbiTAECSJCruFCCJMnJYXsQpe770Jj0358KlJgBoP9AU8x9qihdnsDqtmmdz84zq3DzjsG0r4jFR8vaGwv7eUDTUHxbivpgshwRWFRdVOpnR6hhGm8j3o7ZQFAd9ISw0G4Z8vzsSR/4pnTbytGp0R2O4rjgHX93XhJgk4+dzivD7hi7cNi0v2W8ox1FxRwghJNlEAIwo0sydklhZPnEPZiuwqW0FNrUYE+TWXUe9eokRy2fbrOzgzUk8JkjONp/P0+aLxvrCUIUEtUlmM1SD+/aSScMyeHrZDBh4DnFJxlXbj+C8bBMWZWaceMzm9l5YVBzeWTMXz3f246dHO/CnhWX4a1MPHltcjrZwDI+3uvCDWYW4t7FbqfswKu4mmASAScaBKpFIHFdf/GvEogJEQUTNlYvw9e9djtbmXnz58w/A3R/EvAVF+P0D10OtPv3X/Ydf/webH38XHMviR7/6DNZeMAd9Lj9u3PQneL1hfPPOK3DxZQsAANd/5n789HebYM9T7HTVEfG3uftmO7KyPu7nBXNL1Zhbqo4GI2Lj9oM+ndutyrecnM07lUrNsdn5RkN2vtEw3M+FmCD6+sK+YG84HOkPx+MDM388F5c0GgkGDcuOqbEnAAQFEV/ccwx3zSqEUTWyTcgOnRrPLK8AADQFI+iKxFGeocXttQMF3zdn5KPMkLQDlMLJGogQQggZRDN3k4ApM6MXQN6p3+PUPFO6co4FmAN3Z1/M+eHhUC7iWpNepXVMs5od06wnHitKkuztDno87T5/pCckMr6YRifK5vHcV40EwzAwDB78IsgyBFnGRz/Ef9XpxdfKB17aensm7jzUClmWwTMMwpKEsChBxTJoDkbRGY5hZVYyd8WcQMXdBJOB5Oy502h4PP3i/8KQoUU8LuKqC3+J86rn4oE/vIYvfPkCXHH1Unzn9iew+bF3ce0X1gy5tu5IJ/79rw/x+o4foqfLi42X/xbb9tyN5/+5E9fcsBqXXL4I137yPlx82QJs3VKLuZWFk76wE+KiXGQwjOjvlcag5aafv9gEAN62nj7/wcZQFuK5Gp4bcfN5Xs1z1jxjpjXPmDncz+NRQfD1hvzB3nA44o7EBV9URkgYKP5kZGhYdtiiMi7JuGnPMVyZb8Ul9tOf2q5VoTNy8rySrkgMds3Q2L+s68S3ZuTj4RYnNhRko1Cvxi+OduK+BaUjfXnj5U/WQIQQQsig4zN3tHpEQSzHnrG4yMzPUmdefq5aEkW07DwaFFo6uBKLRssNtkngWJax5hst1nzjkBtPf18o0N/m84W6gzHZE+U1McmkZ9kJ7asryjJq3j2M5lAU1xXZsNDy0dVTsROrp3iWgZHn4I6LuHWaHV+tbYaWY/D7+aW4+2g7vjUjf7ghkiGq1MCpWtwd/9Qo4cUdwzAwZAzMxAhxEUJcBMMwePetI7jv4YGdlFdvWoHf/OzF04q7V1+sxeWfXAKNRoWikmyUlOVg74dN4FUcwqEYotE4OI6BIIh46P7X8bd/3JrolzNuoVZPT3Feln2015kLc7PMhblZQjQm9Oyrb1X1uDRWLZ873jwqDc9nOUyZWQ7TsMVfLByP+XpDgWBfOBx1RwTBF4McinPf2dtkK9Fr+JtKc4edsqvOseCRFieuyMvEHk8QRp47sU8PAN7v8yNXq0KpQYuwKIFlABYDnyglERV3hBBCkk0EwISC0YjSQcjZsRyH0hWzDVgxG8F+X7h756FAZiRkshjUw/YpNGbpM4xZ+oxTvxcOxiL9rV5PoDMQFfojjDosGvQMk8kyzJjaAXAMg1fOnQ1vXMCNuxtxxB/GTOOwn8UPMcekx7+rZgIAtvf7kaNRQQZw855jULEM7pxZAJtGdeYnmTh9yRroo1K5uEM4HIsnYzBRlFCz6h40H3PhuhvXoLjUBpNFD35wWjnPkYnuTs9p13V3ebBo6clZnLz8THR3eXDlp5bhtusfxBOPvI3v/vgqPPbAW7hqwwro9COe0FJENBQTy7LM45pa5DVqPm/pnCIA8Ha4+gMHG/xWKZar4bmErGVU61Tq7EKzNbvQfOJ77xx1YsuLuzGv0IJL9jfIQlySbl9e6mnuDjBSVMDnCm3hpdkZ2q1Oj+Xctw5wOo7F/5tfcuJ6WZZxX2MX/rigDADw2cJsfKW2CYIM/HTgpSWLO5mDEUIIIRhYjsZ4PaGg0kHI6BisJt20i1boJElC39HWLuFYq5itYvI59sxFms6g1jpm2eyYZTvxvXhMEPrbfX2+9kA41huS+ZCg08tMJs8wI66uzCoeVVYj3nR5hxR3dq0anZEY8nRqCJIMvyAi85TtM7Is496GgfuwHxxqw/dmOtAWiuHhFifumOEY1e9kHHqTNdBHpWpxJwNgOtv7g5IkSSzLJrSRIMexeOW9O+H1hHDjpj+hoa57XM9nMuvw6L9uAwB43EHc/5tX8MCTX8K3bn0cXk8IN912ARYvnzYR0SeU1BXo0eRZJ2z+2+ywWc0Om1WMC6JzX0Mb19WjytJyo54VHK1zK3IgPPG5418yGDgY5rQ9hI9iHsLBWMTvCgWCveGozzsw88dERP6vS8t1WpnJAKCenqHDf86ZnejYw1HsUyNCCCFpKwDA4u4PUHE3RbEsC9uskjzMKkHEGww5a486jX6fJUPDj/gDfJWa53PLrLbcsqH7+DxdQben3ReI9gRFxh/X6ETZcuoWmb5oHDzLwKziERYlbOvz45ayoQu5qnPM+GdHHxZnZuClbjfOyTIN2Zf3z45+rLOZkanmEZYksGDAMkBYTNrqKQHA6bM6SZKqxV0YACtJshwJxwN6g2ZC1wJ/HLNFj6rVFdi9oxE+TwiCIILnOXR1uGHPP/3fgz3Pgs72k5MrXZ3u0/bU/f4XL+G2b16C55/ZiaUry7H+ykW48bN/xhPP3Z7w1zMaIXcoWppjyUnEc3MqnrMvnlkIzIS/u8/jO1DvzYxHbVoVl9BNvSOhM6i1OoNai5Lh3+9C/kg44AoHg33hSNQTEUR/nGEjgooXZJ2WYTJG8wnWGFBxRwghJNk8AGyuHh8VdylAazboHasXlQBA79HWnnhjayybl/M5lhnZSXOn4FiWyXIYM7McQ89J8PWF/O5Wry/UHYwfaQ7qf7i3OZthGFaSZVxmz8QFORb8uq4T8816XJhrwYaCbHx1XxPOfesALCruxEopYKCAe6ajF08snQEAuLEkB9d+WA81y+K+yqSdedBfsOVDamI+wXwYPFQlFIr6E1nc9bn84FUczBY9wuEYtr1xGLf870WoWl2Bl57bjSuuXop/PrkdF66vPO3a6vWVuO36h3DjbRegp8uL5kYnFiw5+RevqaEH3Z0erFxVgUP726HRqsAwDCLh2GnPpTSNN9bH5+gTvmvVaM+yGO1ZFjEuSM4Dx9rZji4uS8PmTZL2LKfRG7U6vVGrQ9mwW/4Q9EZCflcoGOoPR2KeqCQGYgwbEXmVIOu1DJPBMeM6hrhrHNcSQgghY9EPQN3d5aUPGFNMdkVRLiqKEPWHws7aOqfB6zGZNPzwNzijYMrSG01ZeiMAzAJwNZYiHIhF+gb28UX6+yPcl8rtBj3DZAJgtByLPy8cfgWbjmPxj8FTywFgudWI11bNGW/E0VL0/iuVizsJAIKBqC/bhoQtsHX2ePG/X3wEoihBkmRcdtViXHDJfEyfmYcvf/5B/Oru5zF3fiE2XHsOAODVl2qxb08LvvH9y1ExKx+XXrUY65beBZ7j8JP/txHHTykCgF/++Hl86wdXAACu+NRSfGHDn3D/b17G179/eaJezpj4un2BmTZzUo8j4lQ8a184owALZyDg9Pi8++v6LdFwtk7NZZz96jMTJQnLv78F+Zl6/Pub64b8LBoX8T9/ehe7m/thzVDjqdtWo8SWgXePOnHr3z6Ammfx91tXYbrdBE8whg33bsOWO84Hyw5ffBrMWr3BrB12BlKSJIS80aDfFQ6G+sPRmCciSsE4y0ZElUo8Ufx93CdnMQDjWx9MCCGEjF4/AFUwEIkLcTHOq7iknWBBkkNj1Osc5y4oBoC+xvaeaF1zLJuV8niOnbC6Qpeh1hbMttkx+7R9fG5veyAU7w2BDwo6PRgrP74PwhOhXcnBGTnxB0omXXVNZT6AHwNo/9p3L7t43oKi5UpnSmV8u78n12oc98mW4yWJkuw6eKwTrZ3I0jD57Bin83675RB2HeuDLxw/rbj709aj2N/qxv03rMDT7zfhuZ1teOorq3H1b9/E765diubeIJ7b2YpfX7ME33xiF9YvdGDt7MRsExQlSQ65I8FAbygY6o/EYp6oGPfHNDpBFjIYpqPghZ0rEzIwIYQQ8jGqayqrAHwBQOu9D15/m9Gks57tGjLxpBZvW6HNXJis8WKhcNS1p65b73ZnmLX8x/Y6nmiiIMmebr/b2+4Php0hifXH1XpRzlSzbNKaCg/jzwVbPrxZqcEnW6U7UXwAWADo7wsotqExHXha3e452ZmKF3YAwHIskzu/3IH55Qj2ef3ufXV9lnAoS6/mRty9sr0viC17O/CdK+bid1sOn/bzf+9qww8+ObDE9pPLivGVR3ZClmWoOBahmIhQVICKZ9HY40d7XzBhhR0wsHZ9uCOJB9UnbGBCCCHk4wUxuHrK5w31U3GXHtR6ncZxTmUxALibOl3ho03hLEa0qzh2REe9R2Ii1t79CmKCBEGUcNWyYtx19dAtTR+zeoo5Eoxab31uj/X46qliuwmtLW7fxvvf1fx1dUU344vz2rhk0rFssrqZKzpzl6rF3fE3Fra7001rvhNEEiQ4NNpJOfVryDIbDectNUqiJPceaeqUmjvkbBWTx57lON+vPf4hfr5xEfzh4btodLpDKLQOrKLkORZmvQp9gSjuuHwu/udP70Kn5vDozefgW0/uwo8/vWDiX9jINSo5OCGEkLQVOP4fva6Ay1GYVa5kGJJ8maX5tszSfMTD0Vh3bV2rtrdPZ9HytjNdo1GxeO171cjQqhAXJKz+8cu4uDIfK6afvOzhNxuQaVDj6G+uxNPvN+E7T+3GU19Zjd9uOYQXvrkOzb1B/OW1Ovz6miW4751jpnuuW4I5s+3Fx68P+aPh/lafJ9Dpj4ruKKeOiBl6hrGMdaXXGbRM8PONSkoWd1u31MrVNZVOANrmRhcVdwnib/U4C+3WhJyQOVFYjmVy5kzLx5xpCHn8QffeOpcpFLAa1Nxph+y8uLsdOWYtFpdm4c1Do9uutqDEivd+fAkAYNvhHtgtOsgysPHebVDxLH712cXINZ+9AecEouKOEEKIEvow0EYIPd2eXqD4LA8nqUql06jzV8wrAgBPa3dv8PCxYJYs2NU8e1qDdIZhkKEd2J4ZFyUIooyPllzjXT2lN2p0+jk2HeacLBhjESHe3+7z+Dr8oXhvmOFDgs4AJnOcB9odGMe145aSxd2gDgCzGuq7+5PR6y7dxKOCVGLOULwVwWjoLUaDfu1iw8nmnG1itgonmnO+V+fEC7va8Z/MzfI5AAAgAElEQVS9HYjERfjCcVx7/zt47JZzTzxHfqYebf0hFGQZIIgSvKE4sjJOvkfJsoyfPrcfT962Crc/ugM/37gIzb1B3PfKEfzk0wuT+XIPJnMwQgghZJAXA32+uPaWPsUaOZPJxVJkz7YU2bOFSCzes6++VeXs1Vq13JAJAlGSsOx7W9DQ48fN1RVYXj50si8Rq6fUWl5lL7fa7OWn9OMTJNnd5e/3tvsDEWdIZgNxjV6ULSPcxycCOH1fTxKlcnHXCmCREBelYCDqoTXfEyva7u3R52XlKZ1jLIZrzpnh91l+umGR5acbFgEA3jzUjd+8dGhIYQcAly0qxOPbGrFyug3/2tGC8+bYhzTOfPztY7hkgQPWDA1CMREsO9A4MxQVkvoaAexJ9oCEEELI4OqpTgCmusOdVNyRIXitWpW3bE4RAHjbXf2BQw1+qxTL1fCclmNZ7PrZpfAEY/jkb9/EgTY35haevdPCRK+e4niWyS40W7MLzUNqB68r6He3+byh7qAAb0yljUtmHct+9NyD+oItH0ZHPFgCpHJx143BZQEed7CXiruJE/FH4mXZlqSdhJRIH2nO2R1vbI1n83I+gBMtBn74z71YUpqFyxYX4vq15bjuT++g4mvPIdOgxpO3rTrxXKGogEe3NeLlb18AAPjqJbNw2S/fgJpn8fiXhxaJCebiNj3WkcwBCSGEkFM0A1jR0+3tiUbjYY1GldR9CWRqMBfYrOYCm1WIxoWe/Q1tfLdTlaXl7BaDGmtn2/HKvs4hxZ3Sq6fMNoPRbDMMOZQl5IuE+lt9Xk+HH1p/3GeUmbfHPdA4pXJx14/B05pam3tbC4uzZyicJ2WwrrBTZdcmrHegUrIriuzHm3NW6Oucf5+WbQKQ+aOrT07ta9Ucnr59zbDX6zU8Xv/+hSe+XjUzF3t/cVmiYw+HZu0IIYQoqQXAGgBw9wW67fmZpQrnIZMYr1HxfElBoWp6MQKhsKdn12HfK7UdBd++Yt6QLVWTcfWU3qTV6+dq9QVzcwAgD8AjCRtshFK5uHNhsB3C/r2tzeesmalwnNQQ6A2Gp+eYp+RyzJFKRnPOBKPijhBCiJJcGPyAvb2tv42KO3I2XT1e/M9XHoEoShZJki2fvHy5tOyCirbb7/1P5vkzczIuX1I0VVZPfZjMwYaTkk3Mj6uuqfwFAIZXcZH7H/nCHSoVP6JeG+QMWrxdDltqF3fDiYXCUdfeum59f3Kbc47RBm7TY08rHYIQQkh6qq6pzAbwSwCt1ZfMn7bp86uuSca4kUgcV1/8a8SiAkRBRM2Vi/D1710OWZbxyx8/j5ee3QWOY/G5L6zB9TevO+36Z554H/f+agsA4CvfrMGnPrsS0WgcN2y4H10dHlx74xpcd+NaAMAdtz2Oa25Yg3kLipLx0kYt2U3ME8nv7Pf69jW4LfGwTafiDErn+TiyLMsMw1i5TY8p2mN7qsxEjFUtgLVCXOx0dvtaHYVW6rUyDt4Oj2+2zZJ2hR0w2JyzauzNOZPsPaUDEEIISWt9GOh3p9m5vbF943Xnygw74b3ETqPR8Hj6xf+FIUOLeFzEVRf+EudVz0X90S50dbjx5u4fgWVZ9Lp8p13r7g/idz9/ES++9V0wDLB+9U9RXTMfO95rwNKV5bjtG5fgExf8EtfduBaH9rdBFOVJW9ilGmOO1Wy8YJlZEkTJdfBYO9o62SwNm5eA/nTjwjBMg9KFHTC4bDGFHcZgAdt8zNmsbJSpL5dRKXr6z2SRWZpvy7/4nCJ29XJ0Z5hbPRHBpXSm42RZbuI2PdamdA5CCCHpa+uWWhnAIQAmjzsY9XhCzmSMyzAMDBkDp9ULcRFCXATDMHj8oW24/Y71ON4VK9t2WqtbvPX6Qaw6bxYyrQZYMg1Ydd4svPnaQahUHMKhGOJxEccXu/3q7n/jG3denoyXRE7B8hybWzm9IPfSNfnRRfP8XaymORQTA0rnOsX7SgcAUr+4a8bgiZkH97Up2i1+qvM09/dlWQy2sz8yfRxvzmm9dK3NN6O8t0tkW2KCpGgBzDDMW0qOTwghhAzaD0AHAJ0d/a3JGlQUJVxUdTcWlH0Dq86bhYVLS9FyzIUX/u9D1Ky+B5+76l40NfScdl13pwd5BSdPZrQ7MtHd6cGqdbPQ3tqHK9b9Ap+/eR1efakW8xYUwZ5nSdZLIsMwZFlMjnVLS/QXrTK47HmdrqjUISm/12yrwuMDSP3izgOgF4B+5/bGznhcjCkdaCoS4qJcZDCk+hLecbEU2bMdF1UV82tXsD2mzNb+iJiUTymH8bpC4xJCCCGnagMgA8Cxup6kFXccx+KV9+7EjiM/x95dzThyqAOxmACNRoUt276HTdedi6/f8tiIn4/nOfzh4S/g5Xe/j0s/sRgP3f86brqtGj/69j/wxWv+gldfqk3gqyFnw3Iskzt3Wn7u+tWO2OIFwS5e1xyMiaevu00weaCwfDXZ4w4npYu7wWUBewGYhbgoOXu8tFxtDEKtHqfRoDErnWMqON6cM/vSNTn+mRX9XRLXEhXESBIjvJbEsQghhJCP0wlABMC9+9aRY5KU3FkVs0WPqtUVeHPrQeTlW3DJ5QN9zi6+fCGOHGw/7fH2fAu62t0nvu7ucMOeP3R27rEH3sQnN67A7p3HYDLrcP+jN+Kv902KyRoCQG81ZjjWLi4xXLzK1Jfv6HRG5XZRkqUkDV/LbXpMqQ/2h0jp4m7QYQAqAGhtcjUrG2XqiYViYpnVdPridHJW5gKb1XHhymLVeVV8jyWrrS8ididyPFmW93ObHkvoGIQQQshIbN1SKwCoB2Dq6faGel2+hH/A3ufyw+sJAQDC4Ri2vXEY5TPsuOjSBXhv21EAwPZ36lBannvatWvOn4NtbxyCxx2Exx3EtjcOYc35c0783OMO4vWX9+PqTSsQDsXAsCwYZuCETjK5sCwL2+zSfPv6VQXC8kWRLpW+ORAVEnrQCcMwryTy+UcjHZbaNR//j/17W5tXrqpQMMrUI3QFejR51nylc0xlvEbF5y2ZVQjMgq+rz+M/UO/NFKI2rYrTT+Q4DMM8N5HPRwghhIzThwBmAXDXHe48mpNrTujxks4eL/73i49AFCVIkozLrlqMCy6Zj6Ury/GVGx7Cg398DQaDBr/6w+cAALW7m/H3h7bhV3+8FplWA77yrfW4dO3PAAC337EemdaTp+7/7hcv4bZvXgKWZbHmgjl49IE3Ub18J665YXUiXxIZJ53ZoHesWVQiSRL669q648da49k88jmW4SZ4qBcm+PnGLKX73AFAdU0lA+BXAERexUX/+LcvfFOt5rVK55oKQu5QtJTXclOoefeUIcYF0bW/sZPt7OazNGzeBJ3mu4Db9Bgt/ieEEDIpVNdU5gL4GYDWmbMd1jvuuvI2pTOli1TqczfRIv5guG9vnTPD5zUZNXzm2a84M1mWexmGyeU2PZasJaBnlPLLMk/Zd2cR4qLUWNd9UOlMU4XGG+ujwi4xOBXP2RdVFOZcuiYvVDnH28WoW8JxMTjW55NkuZEKO0IIIZOME4ALgOHIoY5+nzfUq3QgQrRGg86xamGxef3aTHdxsbM7jlZBlISxPh/DMC9MlsIOSIPibtBBDO672/bGoT0KZ5kSfD3+YIHNnJYNy5PNmGM1O85fVqyrPlfnsuW2u6JS52iP82UZ5p+JykcIIYSMxeAH7O8CyASAxvqeo8omImSorOmFOfmXnFskn7tU6NIZW7wRoW8MT/P8hAcbh3Qp7g4DiAFQbX+nvsProU+OzsYaQ4CdoLWCZGRONOdcv3oszTn/ldBwhBBCyNjsx2DP4e3v1B1SOAshw9IYdFrHOZXFmZeuzfKUlbq6BaY1LkpnPS1HkmUvgElzmAqQJsXd1i21EQBvA7ABwIHa1r3KJprcvK1ud26W8fSjpEjSjKY5pyTJLdymx3YmOyMhhBAyAq0AwgDUO95r6PS4g5PiuHhCPo61zGHLv/icImbVcrnbYG7xRISPnRRigCe5TY8ls+XVWaVFcTfofQwuzXz5hb21ye63MlVIooR8jZZ+N5PESJpzsizzsFL5CCGEkDPZuqVWxMDSzGwAqN3dQttjyJSg1mvU+SvnFVsvXZvtnVHe2y2yLTFBip76GIZhHlEo3sdKp+KuCQObejPaW/sCne39DUoHmoz8LW6XxaizKp2DnO6jzTk7QqJLkuUYgL8pnY0QQgg5gxMfsL/03K5aUZREhfMQMiqZRfbs/Iuqirm1K9huo6XVFYp7BFE6ym16bIfS2T4qbYq7wU29rwCwAsDO7Q20NPMj4lFBKjEbdUrnIGd2vDln4eVrbHLVki3cpscS3hiWEEIIGYcWAJ0ATK4eX7i1uZcOViFTkkqrVuUvn1uUe/l5FtWqpX9QOs9w0qa4G3R8KQDzygt7j0aj8bCiaSaZaLu3R69TZyidg4ycyqB7SOkMhBBCyJkMfsD+KgALALy/7ehuZRMRMj6SJEcZve5JpXMMJ62Ku61bavsBHACQFY0KYv2Rrv1KZ5osIoFIvCzbkqV0DjJysZjQCWCL0jkIIYSQEdgDQAbAvf7K/mPBQMSrdCBCxkoUpWeYnJv6lc4xnLQq7ga9AcAAAG++dpCWZg5inGGnSsWplc5BRo7nufuYnJsmTdNMQggh5ONs3VLrA7ADgE2SZHnXjmPblc5EyFipVNy9Smf4OOlY3B0CEAGg3vXBsa6eLk+L0oGUFugNhktyqGH5VCJJcoxlmQeVzkEIIYSMwlsANADwr6e2747FhEl1hDwhIxGNxt9mcm6atC2o0q6427qlNoaBNxcbALzwf7teVzaR8jKCgodl2bT7uzCVCYL4IJNz08f2XSGEEEImoToAXQBMPm84dqC29UOlAxEyWryKu1PpDGeSrjf0bwHgAbDvvnWkraOtL23bIng7vX6HjWbtphJBlKJqNf8jpXMQQggho7F1S60E4DkAmQDwzyfe3y4IoqBsKkJGLhyO7eLtX3pL6RxnkpbF3dYttV0AtgHIA4Bn/7HjDTlNe5rngKclEVNMMBj9M5Nzk1PpHIQQQsgY7AXgA6Dv6vQEDx/o2KV0IEJGSqPhv6t0hrNJy+Ju0IsYeP3crg+OdbU09R5WOlCyeZr7+7ItBpvSOcjIxeNi0GzS3aV0DkIIIWQsBrfHPI/B7TH/fPL9d6mp+eR1/e2PInf2NzBv9ckFQ7UH21BV83PMX/MjXH7NH+DzD99Z7OU3DmBm1Q8wffn38fN7Xz7x/WtufgiVa3+M797z7Inv/eQ3L+G5LZP7nMNwJLafs3/pVaVznE3aFndbt9S6ALyGwdm7fz21/b+ylD7Td2JclIsMel7pHGR0wpHYb5icmzxK5yCEEELG4T0AIQDa1uZe//69rR8oHYgM7382rMR/Nn9lyPdu/Nrj+Nn3r8K+t36IK2sW4ld/PL3eEUUJt377KWx58jYcfPsubH52Jw4d7cS+g+3QalWoffMH+HBvC7y+MLp6vNixuwlX1ixI1ssaE5WK/77SGUYibYu7QS9joOeK6kBtq6uxvjtt+t4FWz1Oo0FrVjoHGbloTPCYjLqfK52DEEIIGY+tW2ojGJi9ywWAxx54c1s0Eg8pm4oMZ/XKGbBa9EO+V9fYg9UrpwMAqtfMwv+9tOe063bsbkJ5aQ7KSmxQq3l85soleP7lWqhUHCKROCRJQlwQwXEMfvCLf+Oub12elNczVsFQdI8q70v/VjrHSKR1cbd1S60bAwWeHQD+8ff335REKeX7hsVCMbHUajIpnYOMTiwm3MPk3ET/8yOEEJIK3sbA3juDuz8YfW/b0Ul9SAU5aU5FPp7/Ty0A4JkXdqGt4/Re3h3dHhTkZ574uiA/Ex3dHsyakYfsrAwsvuAeXHrhfDQ0uSBJMhbNL0pa/tGSJFlmWeZGpXOMVFoXd4O2AhABqOuPdrmPHu48/eOHFCN0BXq0GpVO6Rxk5CLReI8xQztpG2YSQggho7F1S20YwGYM7r178tF3PvR5Q33KpiIj8dDvrsOfHnkTS6rvgT8QgVo9ul0+v/vJZ7DnjTvx9Zur8YOfP4+7v3057vntFnzmxr/igcffTlDqsetzB57VF906ZQ7+SfvibuuWWh+Af2Nw9m7zY+++JQpSyh7LG/KEo6U5lhylc5DREQTpTibnppjSOQghhJAJtANAKwCrEBelV17cu1XpQOTsZk6345V/fBUfbv0eNn5iGaYVn342n8NuQXun+8TX7Z1uOOyWIY95/j97saiyGIFgFI3NLjz9wE3414u7EQpNntudWFyIGDO0X1I6x2ikfXE36L8Aohjc2LtrR+M7SgdKFLU72sdzLB2kMoV4/eF9GQbNg0rnIIQQQibS1i21IoAnAZgBMFue33O0u9PdpHAschZOlw8AIEkS7vntFnzxutWnPWbpwhLUH3OiqaUXsZiAp5/7EJdfVHni5/G4iN//9XV868sXIRyJgWEYAAMHscTik2eOxe0J/VJX+GWX0jlGg4o7AFu31AYx0FQzFwAeuv+Ntz3uYMr1EfP3+IOFOco2LG/r6Me6T/w/zFl1F+auvgu//+vrAIANN/4VC9fdjYXr7kbpku9i4bq7h70+1Y7VPZt4XBQCwegGJuemtDnJlRBCSFo5CmA3Bu/BHv3rWy9SY/PJY9MXH0TV+l/gaGM3ChfcgYeeeAdPPbsTFSvvxKxzfoi8XDM+v7EKANDZ7cH6TfcBAHiew30/24CLN/wes8/9IT51+WLMmZl/4nn/+PB/ce1nVkKvV2P+7AKEwzHMX/MjLKosgsWsHzZLsgWC0c5cm+knSucYLSZdm3d/VHVNpRbATwBwALzLqsrzv/iVC7/AsoMfJaQArs3fbc8y2pXM0NXjRVePF4vmF8EfiGBJ9T149pGbMbvi5D/4r//wGZhNOvzg65cOuVYUJVSsvBOv/uOrKMjPxLKLfoYn/3wDBEHCvQ++gQd/ey0u/NTv8MxDX0QoHMMXv/44/v33W5P9EidU3bGeX1asuPMOpXMQQgghiVJdU5kH4B4AnQCE629et3LVebMuVDjWlCa1eNsKbeZCpXNMVbIso8vpXe+Y960tSmcZLZq5GzR4LO+DAKwA2B3vNXTu29PyvsKxJoy3ze1RurADgLxc84kTkYwZWsyanoeO7pNt22RZxjP/3oWNn1h62rWpdqzu2Th7/fW79rZ8W+kchBBCSCJt3VLbhYHWCAUA8Mhf/ru91+XrUDYVSWdtne5/TsXCDqDiboitW2qPAHgDgAMAHvjDa//1+8Knn+86xUiihHyVVlQ6x0c1t/Ziz4FWLF9UeuJ7b2+vR67NiOlluac9PpWO1T2beFyMt3e6P7XpSw/S1DohhJB08B8MzNxZJUmWH/3rW8+LojTp7l1I6vN4Q71tHe7/UTrHWFFxd7p/AQgAMIaCUeGpR995VpKmdu87f4vbZTHpspTOcapAMIKrb/gLfnv3p2EynuzK8NSzO7HhE8tG/XxT7VjdsznW2vubxRf8pFbpHIQQQkgybN1SG8PACioTAP5Abatrx3sN1PuOJJUoSXJ9k/Pacy/9RVDpLGNFxd1HbN1SGwDwEIBsAOz7b9e179zeuE3hWGMmRAWpxGycVD3t4nERV1//F2z65DJctX7Rie8LgohnX9qDz1yxZNjrUuVY3bPp+f/t3Xl0VPWhB/BvJpnJZN9DgIBA2JcOCAbF5VnoiIwGrVqtFm1fa309XV7fq33P9i2nr/Wpta0o4kLR0koREEHAwZAh7I/FhJBwQ8CEkH0h+zaTZLZ77/vjNyhaCwGS3JnJ93POnMk5CTNfg8y539/9La09ZYVSzS+1zkFERDSccrOlSgC74JtBtW7N/qOcnknDqaKqdUPmXc/v1jrH9WC5+3KnARzExemZq/cebm3uqdM00TVy1nc3R0YYorXOcZGqqnjyX9dj+pQ0/OwH5s99b+/hTzB9Strnpl5eKli21b0cj0f2NDZ3czomERGNVLsAtABI8Hpk5Y2VtvddLk+/1qEo+LV3OBrOVTQ/qXWO68Vy9yVysyUVwHsAOgAkyLKirl2d+4HH4w2c2z8AnA6nZ2JyXKLWOS51NL8Cf33/Yxw4Uvbp0QfZe08DAN7bUYBvfmEjlWDbVvdKyiubn79x8bOntc5BRESkBd8Gd29BTM/UV1W0dH+wOe8D7u5OQ8np8rjPlF14IGvFawF1rf9leBTCZZgtpgwA/w2gAYDn6w9nzl7+0E0PahxrwFyVHQ2T0hLHap2DBuZMaeO+Xz633Wy1SfxHSUREI5rZYloG4FEAVQDUHz999+L5CzNu1zhWwOBRCAOnKKp68GjZvy15cOVLWmcZDLxzdxm52VIFgK3wTc/cviW/JO9o+QFtUw2Mo93hvCE1XtMDy2ngqmraqtesP/wQix0REREAwAagAMAYAHjzlT0Hmho7q7SNRMEor7Dqw1fW7ntZ6xyDheXuynIAlMH34bJm1Z7D5z5pPKVtpCuLcsgdoTod/34DQFuHo2vT9vys197e33XlnyYiIgp+udmSAuDPAHrgWyKz+g+7t/X3ue0aR6MgUl7ZUvrCqt2PWW1SQO+Mfyle/F9BbrbkBfA6gC6IHTTx0nNW64WGzkpNg11Gd2O3PT0lbsyVf5K01tfvdm3/qOjb//n8jhKtsxAREfmT3GzJDuA1ADEAwhvrO3vffn3vxkDbA4H8U1u7o+ODjwotVpvUp3WWwcRyNwC52VIPgJUQv684t9ur/P7ZnVu6OntbNI72pVIR5tQ6A12ZLCvKrj3Fv37q6b9+qHUWIiIif5SbLVUBWA+xREZXeKKqacuGY1sC/Qxi0lZfv9uVc6DkW7949oOgm+rLcjdAudlSE4CXAcQBiOjs6HW9+rvsjf19bofG0T6nq6azIzk+KkXrHHRl+4+Uvrtha96LWucgIiLyc4cg1uDdACBk7+7TFbZdklXjTBSgXG6vZ6v15DOP/3BdjtZZhgLL3VXIzZbKAbwBYDR82/P+6Y1973o8sl9MD5A9sjo+MoJ/pwGgsLj2+Oq3DzwVTHO8iYiIhsIlR1QVABgHAFs2HDuVf6z8oJa5KPB4vbL87ta817daC1drnWWosAhcpdxsqQDAJogPF93J/MqmDzZ/vFVRtD9Tore2qyUmyhivdQ66vE/OXTj3x/WHl1ttEqfPEhERDYBvD4S3AFTisx00D5WebSjUNBgFDFlR1M07Cjbs2H3qF8E8uM5yd21yAORCTA9AjvVU+YE9JdlaBnL1eeSJibGxWmagKyspbax8ftXuZWvXH27TOgsREVEg8R1wvhqXbHL3+9/s3FVedkHSNBj5PVVVsSP71I4tOwt+ZLVJLq3zDCWWu2vgmx6wCUARgHQA2LDucMGB3JLdWh0KL1+wNxvD9RGavDkNSElpQ/X/rvzo4Q3vf+y3O60SERH5s9xsqRtikzsASFAUVf3tr7bvDIRjqkg7OfvP5L7z3vF/tNqkXq2zDDWWu2vkmx6wFkADxBo8rH/rUL51W8E2WVbk4czS19XvmpgSlzqc70lXp6S0ofqFVbsfeW/HiZNaZyEiIgpkvk3ufg/AgIsF73+27yw9wyma9HmKoqo7dp/a++ZfDj1qtUndWucZDix31yE3W+oD8AqAFvju4G3fkl+y6Z0jG4dzkxVDp6stLCw0bLjej65OYXHN+WdXZj+8cVt+vtZZiIiIgkFutlQD4AUAYQASVRX43W92WM+W1BdoHI38hCwryoateXvWbTz6uNUmtWudZ7iw3F2n3GypE8CLAMrh26J3X87pyrdf3/uO0+kZ8kMR7c323nGpPLDcXx3NP3/22ZXZj27ZceKE1lmIiIiCSW62VAvgtxDXs0mqCvzh2Z0fnT5Vm6dxNNKYxyN7164//OFW68nvWW1Sk9Z5hhPL3SDIzZYcEHfwTgKYAECXf+x846svfrSu1+HsGsr3jnfDrgsJCRnK96Brs+fg2aIXV9se35FdxFFEIiKiIZCbLdVBFDzAV/BWPm/NObT3TI7qBzuZ0/BzujzuVW/t27x7/5mnrDapQes8wy1Eqw1AgpHZYgoD8C0ASwDUAJDH3ZAU87P/yFoRnxA16Gviuuq6umYlxfPoAz8jy4q8bVfhsQ1b8/7JapM+0ToPERFRsDNbTGMAPANAD7FcBsuWz5v6wCMLHwrTh+o1DacBpaa7blxK3Ditcww3R6+z7+U/7lt/oqj6l1abNKQ3WPwVy90gM1tMOgD3Afg6gHoA7sTkaOMzv7r/0dRRceMH630UWUF0q7M9ITYyabBek66f3eF0vPrW/j15hVX/brVJFVrnISIiGinMFlMygH+B2OiuDgDmZ05K++4PFz8WGRkeo2m4YTYSy11dY0fL717b83ZNXfvzI2FXzL+H5W4ImC2mEACLATwB4AIAZ2RUeNjP/2v5/RMzUmcNxnt0V3W0zhyVmDIYr0WDo7q2reG5V3Z/2Nza84LVJtVpnYeIiGikMVtMUQCeAjAXYhaVMn5CcsxPn7nnscSk6DRt0w2fkVbuPj5ZWfbSm7lrXC7vGqtNcmqdR0ssd0PIbDHdBOCHANoBOADgm0/cOm/J0jnLrmeKgNftVZPtiiMqwjCiRqH8laqqOHjsXMnqt/e/4/Uqa602qUfrTERERCOVb5nMIwCWQtzB80RFG/U/+fmyZdNmjpmnbbrhMVLKndcrezfvKMjfsrNgFYAPrDbJq3UmrbHcDTGzxTQTwE8ByPDNAZ8xa2zSkz9a8mBicszoa3lNR0X7hWmjk67pz9Lgcru97nWbjh7N3luyGoCVHypERETa882i+hrEXght8A2yP/DIwtl3L5+bpdeHGbTMN9RGQrmzO5yOl/+4d1/BqZrnABRYbRJLDVjuhoXZYkqDmCIwCWIdntdgCNP94Kd3LZ47f8KiEN3Ad7t0OlzecXKobNCHhQ9VXhqYtg5Hx4uv5uwpq2j+rXYM+pYAAA0uSURBVNUmSVrnISIios8zW0yzIWZR6QA0AcC0GWMSn/pn80OJSdFBO1Ae7OWu7HxTzR/eyP2oubXnRatNqtU6jz9huRsmZotJD+BeiM1WOgF0A8CdX5s14RsrbnlgoAt9XZUdDZPSEscOXVIaiKLTteUvvZm7vcfufHmknZ9CREQUSMwWUxKA7wOYDt8ge3h4WOiPnl5mnjN3/EJt0w2NYC13Ho/s3mo9WbBp+4ltAN6y2iS71pn8DcvdMDNbTNMA/ABADIAGAGpyamzEj5++O+uGiSkzLvdne9t7nRnhEYZQnY7nE2qkq7uv/e13j5w8fLx8K4ANVpvUr3UmIiIiujzfOrx7ADwAsRdCDwB8bdmcjPu/kZkVFW2M0zLfYAvGclfX0FG/cs3evIrq1g0QS2FkrTP5I5Y7DZgtphgAKwDcAqARgBMAHv32bTcuvmv23X9vsxW1prsxPSVuzPAlpYtkWZEPHjtXuOadQ2dcLu9fARzg3G4iIqLAYraYpkNM04yAuAZTY+MiDN//iXnJrNnpN13NUhl/FkzlzuOR3R/apIL1W44fV1Wssdqk81pn8mcsdxrxLfRdBOA7ADzwbbYyZdrohBXfu/2u8RNSpl/6892N3faZ8XHcHVMDdY2dtavW7is8V9GcB2C91SY1ap2JiIiIro3ZYoqH2GhlIYBmAL0AcPNtU8Y+vGLRvQmJgX9kQrCUu7LzTeWvrTtYXFPXbgXwvtUm9Wmdyd+x3GnsC5utNEAUPXz1rtkTlz+44O74hKhUADA09Lak+L6m4eF0evq27io8sWVnwVkAGwB8bLVJita5iIiI6Pr4BtnnAfguACPEXTwlNFQX8sT3/2HhzbdNvdNgCNzN6wK93LV1OJrXbTxacCTvfDnE2rqzWmcKFCx3fsC32cpSAPcDUCAOPldDdboQS9bcJ25bkBExb1zqKE1DjiCqqqL4bMPZVWv3nW7rcOQC2Gq1Sd1a5yIiIqLB5Vsq8wCAxRCb3XUAQHJqbMSK795x+2zTuMzQUF2olhmvRaCWu36nu9e6p/jExm351Yqi7gGwk3frrg7LnR8xW0zJAB6EmK7ZE6IiXq9gdnJClO2px2+/5aa5E27W60OD+lwWLSmKqp6rbD77zubj5WfKGs8B+LPVJpVqnYuIiIiGltlimgKxVGYsxFKZPgCYmJEa9+h3bvvq5ClpXwmk9XiBVu5kWZHzi6qKXl93qLzH3l8EYJPVJtVrnSsQsdz5Id8HzLfCFCzXqSjXAZ8AQPro+KgnV9x+u2lW+oJAHEXyV7KiKKXnmor/svlYWVlFcy+A7QD2WG2SW+tsRERENDx8M6kWAngYQDTEejwXAMydP2HUg4/evCR9fNIUDSMOWKCUO49HdhcW1xat23S05kJzdy3EMphiblp37Vju/JTZYgrVy1iiA5aFAPEQo0j9ADBlUmrcYw9k3jx7xti54YYwo7ZJA5csK/LpTxpOrdt0tLy6tr0fwBEA2Ty3joiIaOQyW0wRAL4KcTZxGMRyGS8AzM+clLZs+bxFEyenztL58dFU/l7uXG6vM7+wqmDdxqPV7Z29fQC2ATjIgfXrx3Ln57KWmgwAboWYrhmJS0aRYqKN+kfuXzDn1syMzKSEaK7JGyCvV/acKqkv+tPGIxUNF7r6ARwEYLPapBaNoxEREZGfMFtMcQDuBnAXABVAE3wlb8KklNj7vpG5cNac9Pl6P9x4xV/LXX+/u/foiYoTf9l8rK7H7uwHkANgP/c2GDwsdwEia+mno0j3QJS8HgCdF7+/+Pbp4yxLZmdmTEyZyUPOv1yPvb+zsLi2eMPWvIaWNns/gL0Acq02qV3rbEREROSfzBZTCoAlEJuuhAFohW82VXxCVPjXH8m8cd6CiQtiYiMSNYz5Of5U7hRVVesbOisOHisr2ZkjdXs8ch+AXQAOW22SQ+t8wYblLsBkLTWFA/gKAAuAiQDcEHfzZAAYPzYx+uH7Ftw43zR+QVRk+Ig/F8/jlT3llS1n9x46W7Lv/0rdqgovABuAfVab1KV1PiIiIgoMvp01FwG4F2JNXifEYDsAYNEd08bddud0U8bUtFkGjZfN+EO5c/Q6u0+V1BVt2XmysrquXQfAAWAHgGNWm9SvZbZgxnIXoLKWmkIA3ADgTohpm6EA2uE7iFOvD9Xda54zecHcCdMyJqRMjYwwRGsWdpjJsiLXNXZWnCiqPrsz51Rbj90ZBvF7sQE4ZLVJPVd4CSIiIqIvZbaYDBBn5N0HYDTEGcWtvmcYI/ShS++ZO23+wgzT2HEJk7VYm6dVuXO6PP1VNW3nDh0vL83ZX+JQFDUUQAWAPQAkq01yDXemkYblLghkLTXFAMiEuJuXCDFVoB2+u3khIcCtmZPHLLopY+r0yWlTkxKjRocEzm6+A+L1yt7G5u6awuLaMx/mSPVtHY4IiPnxEoADAEqtNsmjbUoiIiIKFmaLSQdgEoBbIAbaDRBHKLRDXIMgOTU24o7FMybPnJ0+Jf2GpMnh4fqI4cg2nOWux97fea6ipfR4QUXZ/iNlvbKsREJci+4HcBzABe5+OXxY7oJI1lJTKIAZEHfzvgJxN0+GOJDTefHnJoxPijHfMWPqnJnpU9NHx08KCwsN0yLv9XB7vO6mlp66yurW6lNn6muO5p3vcbm9cQBCANRDFLpCTr0kIiKioWa2mIwAZkJcg82GuB6xA+gCoACAThcSsvDWKWNvzJw0ZdLkUVMTEqPShmqwfSjLndvjdbe02uvPV7VUHTx2rrywuFYBcLG0noXY0+Asd77UBstdkPKtzZsEwATgZgCxvm91QXzYqAAQHRUetuimjLFTJqWmjRuTkJaaEpuWEBeZ4m/n6LncXmdjU1dNRXVrTWFxbc3xgsoOWVZiAVyc034BwCGIO3UtHCEiIiIiLZgtpgQAcyCuv6ZBFD0vxB29T6clxidEhc+7aeKYKdNGp6ePT0xPGRU71mg0RA1GhsEsd3aHs6uxqauuorq1ruh0Xe2JU9VdiqIm4bObCBKAPADnuPRFeyx3I4Bvfd5YiBGlWyDW6gHiA8YBMYXg0/8RDIYw3dxZ6Skzpo5OmzAuKS0tNXZUcmJ02lBPJZAVRentdXV3252dnZ29Ha0djs6m5u7OiurW9pPFNT2qiniIKQ8qxBq6075HNYBmFjoiIiLyJ2aLKRJABsRgeybERiyAmFHVg0tmVgFAxpRR8bNN48ekjYlPSkqOSYpPiEqMiTMmXm3pu9pyp6iq2tfn6uns6mtr6+htb2rpbq9t6GgrKW1sqalrVwDEAdBDXIP1QZS5IgDnuY7Ov7DcjUBZS03xAKYCmAXxgTMa4h+rDmIExgFRnryX/rlRKbER6aPjo0elxsYkJ8ZEJ8ZHxsREGyMjjHqj0ag3hofrjeGGsPCwUJ1eVhSvLCuyLCter+9ZlhXZ41W8slfxemVZdrm97tY2e1f9ha6Oqtq2zvOVLd1eWQFEgYuAOPLh4pTRbgDFAEoA1ABoZZkjIiKiQOFbozcWwHiIZTQzACTgswH2iwPuf1OW4hOiwjOmjkocPSYhLjrGGBEVbYyIigqPjIg0RBgjDBFGoz4iNExncPW5E6OijZ265t6O1NioRFVVVUVVFY9Hdvc73f29fe6+3j5Xn93h6rc7nH1d3X19TS3d9jNlFzr6+t0yxPXXxQcgrg1bIK6/ygDUQlyDKUP3m6LrwXJHFw9KTwWQBnFXb6rv+eJdMh1E0XP7Hi6IHaHka3i7EN/rGfHZh0cIxHz0EN+jE2KaZT2Acog7cx0sc0RERBRMzBZTLIB0iOuuGb6vEyCui1SIqY8eiDt8Ht/Diy8MwAMAVOj1Ch4KBTb9nbfTQwya630Pg+8hQ1x/AWLXzzoAVb5HvdUm2a/3v5OGD8sdfamspSYdxM6baQCSfI9k33MixBo+A3yLhHHJtM5LhHzhWef7OTfEKNAFiA+QFohC1wmgi7taEhER0UjlO2ohAeJ6Kwnibt9oiGuvGIipnUaIa6qLD0BFiF7B/FCg4JKXu/QarA9iKqgdYkZUO8RAepvv606rTfrb0kgBheWOrplvd04jgHDfsx5i9Md7mWeFd+CIiIiIrp3Z8uk1WATEYHsIAF2YAkOYCjc+u/PngSh1/ZxKOTKw3BEREREREQUBndYBiIiIiIiI6Pqx3BEREREREQUBljsiIiIiIqIgwHJHREREREQUBFjuiIiIiIiIggDLHRERERERURBguSMiIiIiIgoCLHdERERERERBgOWOiIiIiIgoCLDcERERERERBQGWOyIiIiIioiDAckdERERERBQEWO6IiIiIiIiCAMsdERERERFREGC5IyIiIiIiCgIsd0REREREREGA5Y6IiIiIiCgIsNwREREREREFAZY7IiIiIiKiIMByR0REREREFARY7oiIiIiIiIIAyx0REREREVEQYLkjIiIiIiIKAix3REREREREQYDljoiIiIiIKAiw3BEREREREQUBljsiIiIiIqIg8P/dbcKMMa88gQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x1080 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "jd9B1ej1OcaX",
        "outputId": "8018b9f6-7e71-4284-abbd-9e4d908690b2"
      },
      "source": [
        "media_share = pd.read_excel(\"/content/Share of adults who have witnessed fake news in print media worldwide as of January 2019, by country.xlsx\")\n",
        "media_share.sort_values('Characteristic',inplace=True)\n",
        "media_share"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Characteristic</th>\n",
              "      <th>Yes</th>\n",
              "      <th>No</th>\n",
              "      <th>I don't use this platform</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Australia</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Canada</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>China</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Egypt</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>France</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Germany</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>India</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Italy</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Japan</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Nigeria</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Pakistan</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Poland</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Republic of Korea</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Russia</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Tunisia</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Turkey</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>United States</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>World</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Characteristic   Yes    No  I don't use this platform\n",
              "18          Australia  0.37  0.41                       0.22\n",
              "5              Brazil  0.55  0.34                       0.11\n",
              "19             Canada  0.35  0.43                       0.22\n",
              "17              China  0.39  0.44                       0.17\n",
              "2               Egypt  0.60  0.26                       0.14\n",
              "23             France  0.31  0.45                       0.24\n",
              "22            Germany  0.32  0.50                       0.18\n",
              "20      Great Britain  0.34  0.43                       0.23\n",
              "16          Hong Kong  0.39  0.45                       0.16\n",
              "9               India  0.48  0.41                       0.11\n",
              "11          Indonesia  0.47  0.41                       0.12\n",
              "7               Italy  0.52  0.35                       0.13\n",
              "25              Japan  0.19  0.46                       0.35\n",
              "4               Kenya  0.57  0.37                       0.05\n",
              "8              Mexico  0.49  0.38                       0.13\n",
              "14            Nigeria  0.46  0.39                       0.15\n",
              "24           Pakistan  0.27  0.60                       0.12\n",
              "12             Poland  0.47  0.38                       0.15\n",
              "21  Republic of Korea  0.33  0.47                       0.21\n",
              "3              Russia  0.57  0.21                       0.23\n",
              "13       South Africa  0.47  0.39                       0.15\n",
              "15             Sweden  0.45  0.41                       0.14\n",
              "6             Tunisia  0.53  0.25                       0.21\n",
              "1              Turkey  0.72  0.18                       0.11\n",
              "10      United States  0.47  0.34                       0.20\n",
              "0               World  0.44  0.39                       0.17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "HMOSkUUGOdHq",
        "outputId": "2b180fe8-a9f8-4fbf-a883-61cc448ad218"
      },
      "source": [
        "tv_share=pd.read_excel(\"/content/Share of adults who have witnessed fake news on television worldwide as of January 2019, by country.xlsx\")\n",
        "tv_share.sort_values('Characteristic',inplace=True)\n",
        "tv_share"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Characteristic</th>\n",
              "      <th>Yes</th>\n",
              "      <th>No</th>\n",
              "      <th>I don't use this platform</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Australia</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Canada</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>China</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Egypt</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>France</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Germany</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>India</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Italy</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Japan</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Nigeria</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Pakistan</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Poland</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Republic of Korea</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Russia</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Tunisia</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Turkey</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>United States</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>World</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Characteristic   Yes    No  I don't use this platform\n",
              "19          Australia  0.44  0.49                       0.08\n",
              "9              Brazil  0.57  0.35                       0.07\n",
              "17             Canada  0.45  0.43                       0.12\n",
              "21              China  0.40  0.53                       0.06\n",
              "2               Egypt  0.67  0.27                       0.06\n",
              "14             France  0.49  0.42                       0.08\n",
              "25            Germany  0.34  0.59                       0.07\n",
              "23      Great Britain  0.37  0.56                       0.07\n",
              "18          Hong Kong  0.44  0.50                       0.05\n",
              "11              India  0.53  0.43                       0.05\n",
              "8           Indonesia  0.57  0.38                       0.04\n",
              "7               Italy  0.60  0.35                       0.05\n",
              "24              Japan  0.35  0.50                       0.16\n",
              "15              Kenya  0.47  0.52                       0.01\n",
              "4              Mexico  0.66  0.29                       0.05\n",
              "13            Nigeria  0.49  0.49                       0.01\n",
              "22           Pakistan  0.38  0.59                       0.03\n",
              "10             Poland  0.53  0.37                       0.11\n",
              "20  Republic of Korea  0.40  0.51                       0.09\n",
              "3              Russia  0.67  0.25                       0.08\n",
              "12       South Africa  0.49  0.45                       0.06\n",
              "16             Sweden  0.46  0.46                       0.08\n",
              "5             Tunisia  0.65  0.31                       0.04\n",
              "1              Turkey  0.76  0.19                       0.05\n",
              "6       United States  0.60  0.33                       0.07\n",
              "0               World  0.51  0.42                       0.07"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DdWaV5O3RXp5"
      },
      "source": [
        "Countries=media_share['Characteristic'].to_numpy()\n",
        "Yes_print=media_share['Yes'].to_numpy()\n",
        "No_print=media_share['No'].to_numpy()\n",
        "Plat_print=media_share[\"I don't use this platform\"].to_numpy()\n",
        "Yes_tv=tv_share['Yes'].to_numpy()\n",
        "No_tv=tv_share['No'].to_numpy()\n",
        "Plat_tv=tv_share[\"I don't use this platform\"].to_numpy()\n",
        "Labels=['Have witnessed','Have not witnessed',\"Don't use this platform\"]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "eWGL_LnaWsfE",
        "outputId": "72432f8f-250d-4d7a-e707-42c4b577bb16"
      },
      "source": [
        "width=0.25\n",
        "x=np.arange(len(Labels))\n",
        "tv_world=[Yes_tv[-1],No_tv[-1],Plat_tv[-1]]\n",
        "print_world=[Yes_print[-1],No_print[-1],Plat_print[-1]]\n",
        "fig, ax = plt.subplots(figsize=(12,7))\n",
        "bar1=ax.bar(x-width/2,tv_world,width,label='Television',color='#720049',edgecolor='black')\n",
        "bar2=ax.bar(x+width/2,print_world,width,label='Print media',color='#eea700',edgecolor='black')\n",
        "ax.set_ylabel('Share of adults',fontsize=12,fontweight='bold')\n",
        "ax.set_title('Share of adults who have witnessed fake news on television v/s print media worldwide in 2019',fontdict=f)\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(Labels)\n",
        "\n",
        "ax.legend()\n",
        "plt.tight_layout()\n",
        "fig3=plt.gcf()\n",
        "fig3.savefig(\"Share of adults who have witnessed fake news on television versus print media worldwide in 2019.jpg\",dpi=200)\n",
        "files.download(\"Share of adults who have witnessed fake news on television versus print media worldwide in 2019.jpg\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
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              "            progress.value = downloaded;\n",
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              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
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            "text/plain": [
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          "metadata": {
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        {
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          "data": {
            "application/javascript": [
              "download(\"download_b20506bf-58a6-4bf6-a0b6-16698543d3dd\", \"Share of adults who have witnessed fake news on television versus print media worldwide in 2019.jpg\", 211237)"
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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4OoGxJFkeEFN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "outputId": "a7d855f0-edc4-40f5-91d8-8e8912e81eed"
      },
      "source": [
        "f1={'fontsize':16,\n",
        " 'fontweight' : 'bold',\n",
        " 'color' : 'black',\n",
        " 'verticalalignment': 'baseline',}\n",
        "width=0.3\n",
        "x=np.arange(len(Countries))\n",
        "colors=sns.color_palette(palette='inferno',n_colors=3)\n",
        "fig, ax = plt.subplots(figsize=(20,10))\n",
        "\n",
        "bar1=ax.bar(x-0.3,Yes_print,width,label=Labels[0],color=colors[0],edgecolor='white')\n",
        "bar2=ax.bar(x,No_print,width,label=Labels[1],color=colors[1],edgecolor='white')\n",
        "bar3=ax.bar(x+0.3,Plat_print,width,label=Labels[2],color=colors[2],edgecolor='white')\n",
        "\n",
        "ax.set_ylabel('Share of adults',fontsize=12,fontweight='bold')\n",
        "ax.set_title('Share of adults who have witnessed fake news on print media countruwise in 2019',fontdict=f1)\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(Countries,rotation=40, ha=\"right\")\n",
        "ax.set_xlabel('Countries',fontsize=12,fontweight='bold')\n",
        "ax.legend()\n",
        "fig.tight_layout()\n",
        "fig2=plt.gcf()\n",
        "fig2.savefig(\"Share of adults who have witnessed fake news on print media countruwise in 2019.png\",dpi=200)\n",
        "files.download(\"Share of adults who have witnessed fake news on print media countruwise in 2019.png\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_2e35adaf-d6b0-4dce-a480-43da221900bf\", \"Share of adults who have witnessed fake news on print media countruwise in 2019.png\", 276188)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lz1tSq513N4Z"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwaL0obdeEp8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "outputId": "89abed5e-606a-46c9-b636-315407206129"
      },
      "source": [
        "width=0.3\n",
        "colors=sns.color_palette(palette='gist_ncar',n_colors=3)\n",
        "x=np.arange(len(Countries))\n",
        "fig, ax = plt.subplots(figsize=(20,8))\n",
        "\n",
        "bar1=ax.bar(x-0.3,Yes_tv,width,label=Labels[0],color=colors[2],edgecolor='black')\n",
        "bar2=ax.bar(x,No_tv,width,label=Labels[1],color=colors[1],edgecolor='black')\n",
        "bar3=ax.bar(x+0.3,Plat_tv,width,label=Labels[2],color=colors[0],edgecolor='black')\n",
        "\n",
        "ax.set_ylabel('Share of adults',fontsize=12,fontweight='bold')\n",
        "ax.set_title('Share of adults who have witnessed fake news on television countruwise in 2019',fontdict=f1)\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(Countries,rotation=40, ha=\"right\")\n",
        "ax.set_xlabel('Countries',fontsize=12,fontweight='bold')\n",
        "ax.legend()\n",
        "fig.tight_layout()\n",
        "fig1=plt.gcf()\n",
        "fig1.savefig(\"Share of adults who have witnessed fake news on television countruwise in 2019.jpg\",dpi=200)\n",
        "files.download(\"Share of adults who have witnessed fake news on television countruwise in 2019.jpg\")\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_36a2bc7c-a4d3-4288-961f-01e51fc261de\", \"Share of adults who have witnessed fake news on television countruwise in 2019.jpg\", 640241)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0VzaPTeJuHl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "outputId": "5b0183ab-96ae-485c-f020-c3af4124edbd"
      },
      "source": [
        "sns.color_palette(palette='Spect',n_colors=6)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/palettes.py\u001b[0m in \u001b[0;36mcolor_palette\u001b[0;34m(palette, n_colors, desat, as_cmap)\u001b[0m\n\u001b[1;32m    205\u001b[0m                 \u001b[0;31m# Perhaps a named matplotlib colormap?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m                 \u001b[0mpalette\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpl_palette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_colors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_cmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_cmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/palettes.py\u001b[0m in \u001b[0;36mmpl_palette\u001b[0;34m(name, n_colors, as_cmap)\u001b[0m\n\u001b[1;32m    442\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 443\u001b[0;31m         \u001b[0mcmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/cm.py\u001b[0m in \u001b[0;36mget_cmap\u001b[0;34m(name, lut)\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m     \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_in_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmap_d\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    135\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlut\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_check_in_list\u001b[0;34m(_values, **kwargs)\u001b[0m\n\u001b[1;32m   2144\u001b[0m                 \u001b[0;34m\"{!r} is not a valid value for {}; supported values are {}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2145\u001b[0;31m                 .format(v, k, ', '.join(map(repr, values))))\n\u001b[0m\u001b[1;32m   2146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: 'Spect' is not a valid value for name; supported values are 'Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r', 'CMRmap', 'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges', 'Oranges_r', 'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2', 'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu', 'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r', 'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3', 'Set3_r', 'Spectral', 'Spectral_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd', 'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r', 'cividis', 'cividis_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'crest', 'crest_r', 'cubehelix', 'cubehelix_r', 'flag', 'flag_r', 'flare', 'flare_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r', 'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'icefire', 'icefire_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'mag...",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-c341fbd0b717>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolor_palette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Spect'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn_colors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/palettes.py\u001b[0m in \u001b[0;36mcolor_palette\u001b[0;34m(palette, n_colors, desat, as_cmap)\u001b[0m\n\u001b[1;32m    206\u001b[0m                 \u001b[0mpalette\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpl_palette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_colors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_cmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_cmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s is not a valid palette name\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdesat\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Spect is not a valid palette name"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 76
        },
        "id": "11p-a2YNNNd7",
        "outputId": "36c861d7-aff0-4292-df6c-f3468e147595"
      },
      "source": [
        "sns.color_palette(palette='RdYlGn',n_colors=6)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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            "text/plain": [
              "[(0.8899653979238754, 0.2867358708189158, 0.19815455594002307),\n",
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          },
          "metadata": {
            "tags": []
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          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DO0hDLe7M7i2"
      },
      "source": [
        "Questions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HeXCckJogxju"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}