{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# YOLOV5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## data\n",
        "\n",
        "[archive.zip](https://www.kaggle.com/datasets/andrewmvd/face-mask-detection)  \n",
        "├── annotations  \n",
        "└── images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "W27lmG3cer8U"
      },
      "outputs": [],
      "source": [
        "# !unzip -q archive.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Git Clone"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M4sF6wHWe7DG",
        "outputId": "acfcc792-e538-40c0-ac27-0ab58e25525c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'yolov5'...\n",
            "remote: Enumerating objects: 13286, done.\u001b[K\n",
            "remote: Total 13286 (delta 0), reused 0 (delta 0), pack-reused 13286\u001b[K\n",
            "Receiving objects: 100% (13286/13286), 11.98 MiB | 14.43 MiB/s, done.\n",
            "Resolving deltas: 100% (9254/9254), done.\n",
            "\u001b[K     |████████████████████████████████| 596 kB 5.4 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/ultralytics/yolov5  # clone\n",
        "!pip install -qr yolov5/requirements.txt  # install"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Hhao9KWPfBG8"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import glob\n",
        "from datetime import datetime\n",
        "import xml.etree.ElementTree as ET \n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## データの前処理"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "eDG_1VeZfEZf"
      },
      "outputs": [],
      "source": [
        "annotations_path = '/content/annotations'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "NN-h9YMbfQX8"
      },
      "outputs": [],
      "source": [
        "dataset = {\n",
        "            'file':[],\n",
        "            'name':[],    \n",
        "            'width':[],\n",
        "            'height':[],\n",
        "            'xmin':[],\n",
        "            'ymin':[],   \n",
        "            'xmax':[],\n",
        "            'ymax':[],\n",
        "           }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "MyFUSWXyfXPu"
      },
      "outputs": [],
      "source": [
        "for anno in glob.glob(annotations_path+'/*.xml'):\n",
        "    tree = ET.parse(anno)\n",
        "    \n",
        "    for elem in tree.iter():\n",
        "        if 'size' in elem.tag:\n",
        "            for attr in list(elem):\n",
        "                if 'width' in attr.tag: \n",
        "                    width = int(round(float(attr.text)))\n",
        "                if 'height' in attr.tag:\n",
        "                    height = int(round(float(attr.text)))    \n",
        "\n",
        "        if 'object' in elem.tag:\n",
        "            for attr in list(elem):\n",
        "                \n",
        "                if 'name' in attr.tag:\n",
        "                    name = attr.text                 \n",
        "                    dataset['name']+=[name]\n",
        "                    dataset['width']+=[width]\n",
        "                    dataset['height']+=[height] \n",
        "                    dataset['file']+=[anno.split('/')[-1][0:-4]] \n",
        "                            \n",
        "                if 'bndbox' in attr.tag:\n",
        "                    for dim in list(attr):\n",
        "                        if 'xmin' in dim.tag:\n",
        "                            xmin = int(round(float(dim.text)))\n",
        "                            dataset['xmin']+=[xmin]\n",
        "                        if 'ymin' in dim.tag:\n",
        "                            ymin = int(round(float(dim.text)))\n",
        "                            dataset['ymin']+=[ymin]                                \n",
        "                        if 'xmax' in dim.tag:\n",
        "                            xmax = int(round(float(dim.text)))\n",
        "                            dataset['xmax']+=[xmax]                                \n",
        "                        if 'ymax' in dim.tag:\n",
        "                            ymax = int(round(float(dim.text)))\n",
        "                            dataset['ymax']+=[ymax]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sFqSNK7IffEX",
        "outputId": "4b7ca250-6d7f-4db2-e339-04832c7e5e82"
      },
      "outputs": [
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            "text/plain": [
              "              file       name  width  height  xmin  ymin  xmax  ymax\n",
              "0   maksssksksss78  with_mask    301     400   108   231   186   336\n",
              "1  maksssksksss116  with_mask    400     225   116    88   150   122\n",
              "2  maksssksksss116  with_mask    400     225   160    79   193   118\n",
              "3  maksssksksss116  with_mask    400     225   235    43   272    87\n",
              "4  maksssksksss116  with_mask    400     225   304    68   336   102"
            ]
          },
          "execution_count": 9,
          "metadata": {},
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        }
      ],
      "source": [
        "df = pd.DataFrame(dataset)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
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        "outputId": "ac8d2fbf-6db8-41d1-fa2b-ba3180daab4b"
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      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "x = df['name'].value_counts()\n",
        "plt.title('name')\n",
        "plt.pie(x, labels=x.index, autopct='%.1f%%');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "YjM-fEw2fjZw"
      },
      "outputs": [],
      "source": [
        "name_dict = {\n",
        "    'with_mask': 0,\n",
        "    'mask_weared_incorrect': 1,\n",
        "    'without_mask': 2 \n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Mq6EvofZflUH",
        "outputId": "19059be2-9726-4bfc-ec2e-954b80d7c0de"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7eceeffd-f1f7-4be5-9e85-ca900f9869ff\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>file</th>\n",
              "      <th>name</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>xmin</th>\n",
              "      <th>ymin</th>\n",
              "      <th>xmax</th>\n",
              "      <th>ymax</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>maksssksksss78</td>\n",
              "      <td>with_mask</td>\n",
              "      <td>301</td>\n",
              "      <td>400</td>\n",
              "      <td>108</td>\n",
              "      <td>231</td>\n",
              "      <td>186</td>\n",
              "      <td>336</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>maksssksksss116</td>\n",
              "      <td>with_mask</td>\n",
              "      <td>400</td>\n",
              "      <td>225</td>\n",
              "      <td>116</td>\n",
              "      <td>88</td>\n",
              "      <td>150</td>\n",
              "      <td>122</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>maksssksksss116</td>\n",
              "      <td>with_mask</td>\n",
              "      <td>400</td>\n",
              "      <td>225</td>\n",
              "      <td>160</td>\n",
              "      <td>79</td>\n",
              "      <td>193</td>\n",
              "      <td>118</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>maksssksksss116</td>\n",
              "      <td>with_mask</td>\n",
              "      <td>400</td>\n",
              "      <td>225</td>\n",
              "      <td>235</td>\n",
              "      <td>43</td>\n",
              "      <td>272</td>\n",
              "      <td>87</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>maksssksksss116</td>\n",
              "      <td>with_mask</td>\n",
              "      <td>400</td>\n",
              "      <td>225</td>\n",
              "      <td>304</td>\n",
              "      <td>68</td>\n",
              "      <td>336</td>\n",
              "      <td>102</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7eceeffd-f1f7-4be5-9e85-ca900f9869ff')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7eceeffd-f1f7-4be5-9e85-ca900f9869ff button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7eceeffd-f1f7-4be5-9e85-ca900f9869ff');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              file       name  width  height  xmin  ymin  xmax  ymax  class\n",
              "0   maksssksksss78  with_mask    301     400   108   231   186   336      0\n",
              "1  maksssksksss116  with_mask    400     225   116    88   150   122      0\n",
              "2  maksssksksss116  with_mask    400     225   160    79   193   118      0\n",
              "3  maksssksksss116  with_mask    400     225   235    43   272    87      0\n",
              "4  maksssksksss116  with_mask    400     225   304    68   336   102      0"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['class'] = df['name'].map(name_dict)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fNoxqTPofnjM",
        "outputId": "dccc2e0b-5a1e-47e2-98a8-e53d9dd6e21a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "500"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "filenames = [*os.listdir('/content/images')]\n",
        "len(filenames)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WdSLICV4fukf",
        "outputId": "6e49a4f4-4089-4394-9d7c-a74ff1e36b89"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Length of Train = 450\n",
            "==============================\n",
            "Length of Valid = 35\n",
            "==============================\n",
            "Length of test = 15\n"
          ]
        }
      ],
      "source": [
        "# data split\n",
        "from sklearn.model_selection import train_test_split\n",
        "train, test = train_test_split(filenames, test_size=0.1, random_state=22)\n",
        "test, val = train_test_split(test, test_size=0.7, random_state=22)\n",
        "print('Length of Train =',len(train))\n",
        "print('='*30)\n",
        "print('Length of Valid =',len(val))\n",
        "print('='*30)\n",
        "print('Length of test =', len(test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "ESUba0E6f4Kp"
      },
      "outputs": [],
      "source": [
        "os.mkdir('./yolov5/data/train')\n",
        "os.mkdir('./yolov5/data/val')\n",
        "os.mkdir('./yolov5/data/test')\n",
        "os.mkdir('./yolov5/data/train/images')\n",
        "os.mkdir('./yolov5/data/train/labels')\n",
        "os.mkdir('./yolov5/data/test/images')\n",
        "os.mkdir('./yolov5/data/test/labels')\n",
        "os.mkdir('./yolov5/data/val/images')\n",
        "os.mkdir('./yolov5/data/val/labels')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "7KBE4CVSf9er"
      },
      "outputs": [],
      "source": [
        "from PIL import Image\n",
        "\n",
        "def copyImages(imageList, folder_Name):\n",
        "    for image in imageList:\n",
        "        img = Image.open('/content/images/'+image)\n",
        "        img1 = img.resize((640, 640))\n",
        "        _ = img1.save(\"./yolov5/data/\"+folder_Name+'/images/'+image)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "rQ7HukEjgCrm"
      },
      "outputs": [],
      "source": [
        "copyImages(train, 'train')\n",
        "copyImages(val, 'val')\n",
        "copyImages(test, 'test')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "WTDZ5drwgPIP"
      },
      "outputs": [],
      "source": [
        "df['xmax'] = (640/df['width'])*df['xmax']\n",
        "df['ymax'] = (640/df['height'])*df['ymax']\n",
        "df['xmin'] = (640/df['width'])*df['xmin']\n",
        "df['ymin'] = (640/df['height'])*df['ymin']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "0UWrIXvSgmu_"
      },
      "outputs": [],
      "source": [
        "df[['xmax', 'ymax', 'xmin', 'ymin']] = df[['xmax', 'ymax', 'xmin', 'ymin']].astype('int64')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "AEZM4QLqmbTS",
        "outputId": "fc6d383f-ed8d-4008-a472-0728093d6cf8"
      },
      "outputs": [
        {
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "              file       name  width  height  xmin  ymin  xmax  ymax  class\n",
              "0   maksssksksss78  with_mask    301     400   229   369   395   537      0\n",
              "1  maksssksksss116  with_mask    400     225   185   250   240   347      0\n",
              "2  maksssksksss116  with_mask    400     225   256   224   308   335      0\n",
              "3  maksssksksss116  with_mask    400     225   376   122   435   247      0\n",
              "4  maksssksksss116  with_mask    400     225   486   193   537   290      0"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "1WWs7OOLgro5"
      },
      "outputs": [],
      "source": [
        "df['x_center'] = (df['xmax']+df['xmin'])/(2*640)\n",
        "df['y_center'] = (df['ymax']+df['ymin'])/(2*640)\n",
        "df['box_height'] = (df['xmax']-df['xmin'])/(640)\n",
        "df['box_width'] = (df['ymax']-df['ymin'])/(640)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VOzw7cISg6Sp",
        "outputId": "5f72fdbe-c167-4138-cbef-ed12e7588b7e"
      },
      "outputs": [
        {
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              "      <th>file</th>\n",
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              "      <td>0.173437</td>\n",
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              "      <td>with_mask</td>\n",
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              "      <td>225</td>\n",
              "      <td>376</td>\n",
              "      <td>122</td>\n",
              "      <td>435</td>\n",
              "      <td>247</td>\n",
              "      <td>0</td>\n",
              "      <td>0.633594</td>\n",
              "      <td>0.288281</td>\n",
              "      <td>0.092188</td>\n",
              "      <td>0.195312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>maksssksksss116</td>\n",
              "      <td>with_mask</td>\n",
              "      <td>400</td>\n",
              "      <td>225</td>\n",
              "      <td>486</td>\n",
              "      <td>193</td>\n",
              "      <td>537</td>\n",
              "      <td>290</td>\n",
              "      <td>0</td>\n",
              "      <td>0.799219</td>\n",
              "      <td>0.377344</td>\n",
              "      <td>0.079687</td>\n",
              "      <td>0.151562</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7620abc-6f95-4bf8-b5a9-77ecd60cf919')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d7620abc-6f95-4bf8-b5a9-77ecd60cf919 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d7620abc-6f95-4bf8-b5a9-77ecd60cf919');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              file       name  width  height  xmin  ymin  xmax  ymax  class  \\\n",
              "0   maksssksksss78  with_mask    301     400   229   369   395   537      0   \n",
              "1  maksssksksss116  with_mask    400     225   185   250   240   347      0   \n",
              "2  maksssksksss116  with_mask    400     225   256   224   308   335      0   \n",
              "3  maksssksksss116  with_mask    400     225   376   122   435   247      0   \n",
              "4  maksssksksss116  with_mask    400     225   486   193   537   290      0   \n",
              "\n",
              "   x_center  y_center  box_height  box_width  \n",
              "0  0.487500  0.707812    0.259375   0.262500  \n",
              "1  0.332031  0.466406    0.085938   0.151562  \n",
              "2  0.440625  0.436719    0.081250   0.173437  \n",
              "3  0.633594  0.288281    0.092188   0.195312  \n",
              "4  0.799219  0.377344    0.079687   0.151562  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "lgIRfK6whFPM"
      },
      "outputs": [],
      "source": [
        "df = df.astype('string')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "WHN-dj81hPRT"
      },
      "outputs": [],
      "source": [
        "def create_labels(image_list, data_name):\n",
        "    fileNames = [x.split('.')[0] for x in image_list]\n",
        "\n",
        "    for name in fileNames:\n",
        "        data = df[df.file==name]\n",
        "        box_list = []\n",
        "        \n",
        "        for index in range(len(data)):\n",
        "            row = data.iloc[index]\n",
        "            box_list.append(row['class']+' '+row['x_center']+' '+row['y_center']\\\n",
        "                        +' '+row['box_height']+' '+row['box_width'])\n",
        "            \n",
        "        text = '\\n'.join(box_list)\n",
        "        with open('./yolov5/data/'+data_name+'/labels/'+name+'.txt', 'w') as file:\n",
        "            file.write(text)   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "an9ZzWgJheTv"
      },
      "outputs": [],
      "source": [
        "create_labels(train, 'train')\n",
        "create_labels(val, 'val')\n",
        "create_labels(test, 'test')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## モデル構築"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gw-QD8bwhxRF",
        "outputId": "a99428c7-9a02-4f33-a8f6-bb2db46d0d18"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/yolov5\n"
          ]
        }
      ],
      "source": [
        "%cd yolov5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "tzYJDpc9hzJU"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from yolov5 import utils"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "zskvuEfNh25c"
      },
      "outputs": [],
      "source": [
        "yaml_text = \"\"\"train: data/train/images\n",
        "val: data/train/images\n",
        "\n",
        "nc: 3\n",
        "names: ['with_mask', 'mask_weared_incorrect', 'without_mask']\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "BJaZj6CjiBZV"
      },
      "outputs": [],
      "source": [
        "with open('data/data.yaml', 'w') as file:\n",
        "    file.write(yaml_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "osHb4RowiEKd",
        "outputId": "d3b5c5cb-2fcb-460f-cd01-b17cbeace729"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "train: data/train/images\n",
            "val: data/train/images\n",
            "\n",
            "nc: 3\n",
            "names: ['with_mask', 'mask_weared_incorrect', 'without_mask']"
          ]
        }
      ],
      "source": [
        "%cat data/data.yaml"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 学習"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ke7v_37RiVBY",
        "outputId": "26f6e409-c2cb-46b3-8be3-1fbf8bb25cce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=data/data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
            "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
            "YOLOv5 🚀 v6.1-177-gd059d1d torch 1.11.0+cu113 CUDA:0 (Tesla K80, 11441MiB)\n",
            "\n",
            "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
            "\u001b[34m\u001b[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
            "100% 755k/755k [00:00<00:00, 18.3MB/s]\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.pt to yolov5s.pt...\n",
            "100% 14.0M/14.0M [00:00<00:00, 120MB/s] \n",
            "\n",
            "Overriding model.yaml nc=80 with nc=3\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
            "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
            "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
            "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
            "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
            "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
            "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
            "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
            "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
            " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
            " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
            " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
            " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
            " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
            " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
            " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
            " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
            " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
            " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
            " 24      [17, 20, 23]  1     21576  models.yolo.Detect                      [3, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
            "Model summary: 270 layers, 7027720 parameters, 7027720 gradients, 15.9 GFLOPs\n",
            "\n",
            "Transferred 343/349 items from yolov5s.pt\n",
            "Scaled weight_decay = 0.0005\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD with parameter groups 57 weight (no decay), 60 weight, 60 bias\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mversion 1.0.3 required by YOLOv5, but version 0.1.12 is currently installed\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/yolov5/data/train/labels' images and labels...450 found, 0 missing, 0 empty, 0 corrupt: 100% 450/450 [00:01<00:00, 435.22it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/yolov5/data/train/labels.cache\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.6GB ram): 100% 450/450 [00:05<00:00, 78.25it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/yolov5/data/train/labels.cache' images and labels... 450 found, 0 missing, 0 empty, 0 corrupt: 100% 450/450 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.6GB ram): 100% 450/450 [00:06<00:00, 70.42it/s]\n",
            "Plotting labels to runs/train/exp/labels.jpg... \n",
            "\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m5.62 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
            "Starting training for 100 epochs...\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      0/99     3.24G    0.1042   0.05887    0.0326        22       640: 100% 29/29 [00:34<00:00,  1.18s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95:  33% 5/15 [00:07<00:14,  1.46s/it]WARNING: NMS time limit 1.060s exceeded\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:21<00:00,  1.41s/it]\n",
            "                 all        450       2162     0.0713     0.0739     0.0383      0.008\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      1/99      3.7G   0.08447   0.04861   0.02054        14       640: 100% 29/29 [00:32<00:00,  1.10s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:12<00:00,  1.17it/s]\n",
            "                 all        450       2162      0.734       0.11      0.041    0.00881\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      2/99      3.7G   0.09067   0.03714   0.01759        23       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:11<00:00,  1.36it/s]\n",
            "                 all        450       2162      0.719      0.151     0.0532     0.0116\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      3/99      3.7G   0.08831   0.03505   0.01692         3       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.40it/s]\n",
            "                 all        450       2162      0.479        0.4      0.205     0.0486\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      4/99      3.7G   0.08109   0.03467   0.01459        20       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.43it/s]\n",
            "                 all        450       2162      0.515      0.404      0.182     0.0419\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      5/99      3.7G   0.07631   0.03006   0.01221         9       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.44it/s]\n",
            "                 all        450       2162      0.649      0.414       0.36      0.103\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      6/99      3.7G   0.06847   0.02802  0.009747         5       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.45it/s]\n",
            "                 all        450       2162      0.601      0.356      0.275     0.0684\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      7/99      3.7G   0.06779   0.02646  0.008943        21       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.47it/s]\n",
            "                 all        450       2162      0.772      0.475      0.516      0.221\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      8/99      3.7G   0.06508   0.02709  0.008862        17       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.46it/s]\n",
            "                 all        450       2162      0.779      0.523      0.537      0.233\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      9/99      3.7G   0.06303    0.0296  0.007813         7       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.48it/s]\n",
            "                 all        450       2162      0.848       0.48      0.575      0.282\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     10/99      3.7G   0.05923   0.02504  0.007176        14       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.48it/s]\n",
            "                 all        450       2162       0.78      0.524      0.553      0.273\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     11/99      3.7G   0.05703   0.02798  0.007739        19       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.49it/s]\n",
            "                 all        450       2162      0.889      0.524      0.599      0.326\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     12/99      3.7G   0.05694   0.02543  0.007827        29       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.49it/s]\n",
            "                 all        450       2162      0.867      0.521      0.637      0.353\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     13/99      3.7G   0.05205   0.02776  0.006748        16       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.48it/s]\n",
            "                 all        450       2162      0.835      0.506      0.569        0.3\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     14/99      3.7G   0.04981   0.02797  0.006613        10       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.48it/s]\n",
            "                 all        450       2162      0.887      0.517      0.632      0.297\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     15/99      3.7G   0.04939   0.02812  0.006291        10       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.48it/s]\n",
            "                 all        450       2162      0.903       0.54      0.667      0.391\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     16/99      3.7G   0.04711   0.02796  0.006501         9       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.49it/s]\n",
            "                 all        450       2162      0.885      0.476       0.64      0.343\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     17/99      3.7G   0.04629   0.02565   0.00631         5       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.49it/s]\n",
            "                 all        450       2162       0.93      0.547      0.708      0.423\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     18/99      3.7G   0.04456    0.0261  0.006285        18       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.49it/s]\n",
            "                 all        450       2162       0.94      0.562      0.679      0.395\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     19/99      3.7G    0.0428   0.02788  0.005862         2       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162       0.95      0.572      0.733      0.471\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     20/99      3.7G   0.04376    0.0256  0.005387        17       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.48it/s]\n",
            "                 all        450       2162      0.953      0.586      0.726       0.43\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     21/99      3.7G   0.04484   0.02805  0.005863        10       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.49it/s]\n",
            "                 all        450       2162       0.94      0.528      0.671      0.399\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     22/99      3.7G   0.04315    0.0295  0.005665        60       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.49it/s]\n",
            "                 all        450       2162      0.955      0.541      0.715      0.438\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     23/99      3.7G   0.04276     0.028  0.005705        13       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.962      0.595      0.732      0.462\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     24/99      3.7G   0.04149   0.02429  0.005167         8       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.956      0.584      0.737      0.467\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     25/99      3.7G   0.04137   0.02517  0.005626        18       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.961      0.594      0.742      0.462\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     26/99      3.7G   0.04094   0.02373  0.004982        13       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.49it/s]\n",
            "                 all        450       2162      0.961      0.603       0.76      0.487\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     27/99      3.7G   0.04069   0.02395  0.005124        13       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.905      0.573      0.711      0.472\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     28/99      3.7G   0.04161   0.02729  0.005272        40       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.972      0.602      0.757      0.509\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     29/99      3.7G   0.03867   0.02461  0.005043        21       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.967      0.594      0.766      0.494\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     30/99      3.7G   0.03778   0.02442   0.00454        26       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.974      0.609      0.765      0.518\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     31/99      3.7G    0.0383   0.02486  0.004799         9       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.966      0.589      0.703      0.473\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     32/99      3.7G   0.03796   0.02382  0.004534         6       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.46it/s]\n",
            "                 all        450       2162       0.98      0.614      0.754      0.515\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     33/99      3.7G   0.03832   0.02572  0.004728        13       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162       0.96      0.618       0.78      0.528\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     34/99      3.7G   0.03689   0.02408  0.005269        19       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162       0.63      0.776      0.791      0.534\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     35/99      3.7G   0.03743   0.02451   0.00382        10       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.642      0.767      0.786      0.534\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     36/99      3.7G   0.03509   0.02489  0.004311        17       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.665      0.768      0.808      0.554\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     37/99      3.7G   0.03444   0.02209   0.00482         6       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.683      0.778      0.826      0.569\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     38/99      3.7G   0.03428   0.02595  0.004634        51       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.691      0.756      0.785       0.53\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     39/99      3.7G   0.03273    0.0254  0.005028        12       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162       0.76      0.774      0.808      0.555\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     40/99      3.7G   0.02918   0.02427  0.005294         7       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.785      0.773      0.829      0.547\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     41/99      3.7G    0.0308   0.02522   0.00409        19       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.851      0.768      0.831      0.572\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     42/99      3.7G   0.03008   0.02644  0.004342        42       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.897      0.756      0.836      0.579\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     43/99      3.7G   0.03039   0.02624   0.00443        31       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162       0.93      0.768      0.846      0.611\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     44/99      3.7G   0.02717   0.02329  0.004332         4       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.893      0.779      0.863      0.603\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     45/99      3.7G   0.02696   0.02324  0.003749         7       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.903      0.783      0.842      0.606\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     46/99      3.7G    0.0273   0.02343  0.003641         6       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.905      0.783      0.861      0.613\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     47/99      3.7G   0.02868    0.0233  0.003822        14       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.49it/s]\n",
            "                 all        450       2162      0.956      0.781      0.881       0.63\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     48/99      3.7G   0.02519     0.022  0.003935         2       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.912      0.805      0.865       0.62\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     49/99      3.7G   0.02607   0.02375  0.003534        15       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.931      0.811       0.88      0.632\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     50/99      3.7G   0.02689   0.02412  0.003446        10       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.952      0.797      0.876      0.616\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     51/99      3.7G   0.02628   0.02297  0.003258         5       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.937      0.826       0.88       0.64\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     52/99      3.7G   0.02629   0.02476  0.003244         5       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.957      0.827      0.895      0.635\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     53/99      3.7G   0.02545   0.02342  0.003683        21       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.944      0.814       0.89      0.633\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     54/99      3.7G   0.02496   0.02176  0.003682        15       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.937      0.827      0.908       0.64\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     55/99      3.7G   0.02572   0.02273  0.002962        12       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.896      0.867      0.922      0.661\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     56/99      3.7G   0.02393   0.02185   0.00316        20       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.945      0.861      0.924      0.666\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     57/99      3.7G    0.0247   0.02229  0.003002         6       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.939      0.847      0.901      0.651\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     58/99      3.7G   0.02474   0.02108  0.002604         5       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.959      0.852      0.925      0.677\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     59/99      3.7G   0.02402   0.02042  0.003106         9       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.947      0.855      0.919      0.681\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     60/99      3.7G   0.02406   0.02236  0.002479        13       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.969       0.85      0.919       0.68\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     61/99      3.7G   0.02405   0.02109  0.003217         9       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.957      0.861      0.927      0.682\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     62/99     3.71G   0.02365     0.021  0.002881         2       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.967      0.858      0.928      0.692\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     63/99     3.71G   0.02348   0.02031  0.003068         3       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.984       0.84      0.933      0.697\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     64/99     3.71G   0.02283   0.01957  0.002669        16       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.979      0.865       0.94       0.69\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     65/99     3.71G    0.0229   0.02169  0.002468        20       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.953      0.872      0.944      0.699\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     66/99     3.71G   0.02333   0.02256  0.002572         2       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.49it/s]\n",
            "                 all        450       2162      0.968      0.868      0.947      0.696\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     67/99     3.71G   0.02259   0.02128  0.002764         9       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.978       0.87      0.931      0.684\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     68/99     3.71G   0.02221   0.02133    0.0027         7       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.975      0.872      0.938      0.689\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     69/99     3.71G   0.02284     0.022  0.002199        16       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.978      0.862      0.954      0.716\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     70/99     3.71G   0.02229   0.02015   0.00235         9       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.49it/s]\n",
            "                 all        450       2162      0.975      0.872      0.953       0.72\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     71/99     3.71G    0.0218   0.01994  0.002446         7       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.979      0.877      0.957      0.724\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     72/99     3.71G   0.02215   0.02245  0.001956        13       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.977      0.877      0.948      0.721\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     73/99     3.71G   0.02206   0.02123  0.002202         2       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.965      0.875      0.965      0.743\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     74/99     3.71G   0.02141   0.01996  0.002404         2       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.977      0.875      0.965       0.74\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     75/99     3.71G   0.02129   0.01998  0.002406        18       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.964      0.877      0.967      0.747\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     76/99     3.71G   0.02063   0.01864  0.002289        10       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.49it/s]\n",
            "                 all        450       2162      0.987      0.869      0.968      0.741\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     77/99     3.71G   0.02137   0.02036   0.00192        22       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.989      0.874      0.967      0.746\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     78/99     3.71G   0.02116   0.01939  0.002025        14       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.49it/s]\n",
            "                 all        450       2162      0.985      0.879      0.975      0.749\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     79/99     3.71G   0.02103    0.0209  0.001855        32       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.985       0.88      0.974      0.756\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     80/99     3.71G   0.02043   0.02023   0.00189         7       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.49it/s]\n",
            "                 all        450       2162      0.937       0.94       0.98      0.769\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     81/99     3.71G    0.0202   0.02094  0.001672         6       640: 100% 29/29 [00:32<00:00,  1.11s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.932      0.946      0.982      0.761\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     82/99     3.71G   0.02078   0.02003  0.001573        16       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.946      0.929      0.982      0.765\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     83/99     3.71G   0.02065   0.01918  0.001961         4       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.931      0.951      0.981      0.756\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     84/99     3.71G   0.02034   0.02099  0.002168        14       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.937       0.95      0.981      0.767\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     85/99     3.71G   0.02142   0.02066   0.00219        42       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.932       0.95      0.977      0.762\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     86/99     3.71G   0.02019   0.02049  0.001839        22       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.961      0.945      0.982      0.769\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     87/99     3.71G   0.02058   0.02008  0.001801        41       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.968      0.949      0.986      0.772\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     88/99     3.71G   0.02002   0.01874  0.002203        11       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162       0.97      0.951      0.986      0.775\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     89/99     3.71G   0.01983   0.01958  0.001688         3       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.973      0.952      0.985      0.776\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     90/99     3.71G   0.01958   0.01897  0.001435        14       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162       0.96      0.956      0.983      0.772\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     91/99     3.71G   0.01961   0.01907  0.001429        14       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.966      0.951      0.984      0.773\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     92/99     3.71G   0.01974   0.01824  0.001828        18       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:10<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.948      0.961      0.983      0.777\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     93/99     3.71G   0.01963   0.02065  0.001453        27       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.966      0.956      0.986      0.782\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     94/99     3.71G   0.01986   0.02052  0.001589        19       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.973      0.949      0.987      0.782\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     95/99     3.71G   0.01914   0.01828  0.001778         4       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.976      0.953      0.987      0.783\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     96/99     3.71G   0.01955   0.01973  0.001596         3       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.975      0.954      0.988      0.786\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     97/99     3.71G   0.01904   0.01879  0.001374         8       640: 100% 29/29 [00:32<00:00,  1.13s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162      0.979       0.95      0.988      0.786\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     98/99     3.71G   0.01907   0.01971  0.001998        27       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.51it/s]\n",
            "                 all        450       2162      0.979       0.95      0.988      0.792\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     99/99     3.71G   0.01937   0.02141  0.001679        11       640: 100% 29/29 [00:32<00:00,  1.12s/it]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:09<00:00,  1.50it/s]\n",
            "                 all        450       2162       0.98       0.95      0.988      0.792\n",
            "\n",
            "100 epochs completed in 1.202 hours.\n",
            "Optimizer stripped from runs/train/exp/weights/last.pt, 14.5MB\n",
            "Optimizer stripped from runs/train/exp/weights/best.pt, 14.5MB\n",
            "\n",
            "Validating runs/train/exp/weights/best.pt...\n",
            "Fusing layers... \n",
            "Model summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 15/15 [00:14<00:00,  1.06it/s]\n",
            "                 all        450       2162      0.979       0.95      0.988      0.791\n",
            "           with_mask        450       1683      0.983      0.988      0.994       0.82\n",
            "mask_weared_incorrect        450         62          1      0.872      0.977      0.793\n",
            "        without_mask        450        417      0.955       0.99      0.993      0.761\n",
            "Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "start = datetime.now()\n",
        "!python train.py --img 640 --batch 16 --epochs 100 --data data/data.yaml --weights yolov5s.pt --cache\n",
        "end = datetime.now()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Kz6FUM8iWoZ",
        "outputId": "03599649-56ec-41da-bf34-8856328d8819"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Runtime = 0:37:28.814237\n"
          ]
        }
      ],
      "source": [
        "print('Runtime =', end-start)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 推論"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GStjmtPIsNwK",
        "outputId": "615d5279-0407-4322-dd75-88e6af39cd73"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['runs/train/exp3/weights/best.pt'], source=data/test/images/, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.4, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=expTestImage, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False\n",
            "YOLOv5 🚀 v6.1-163-gb53917d torch 1.11.0+cu113 CUDA:0 (Tesla K80, 11441MiB)\n",
            "\n",
            "Fusing layers... \n",
            "Model summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs\n",
            "image 1/25 /content/yolov5/data/test/images/maksssksksss102.png: 480x640 2 with_masks, Done. (0.029s)\n",
            "image 2/25 /content/yolov5/data/test/images/maksssksksss117.png: 480x640 1 with_mask, Done. (0.029s)\n",
            "image 3/25 /content/yolov5/data/test/images/maksssksksss121.png: 480x640 7 without_masks, Done. (0.029s)\n",
            "image 4/25 /content/yolov5/data/test/images/maksssksksss145.png: 480x640 3 without_masks, Done. (0.029s)\n",
            "image 5/25 /content/yolov5/data/test/images/maksssksksss199.png: 480x640 4 with_masks, 1 without_mask, Done. (0.029s)\n",
            "image 6/25 /content/yolov5/data/test/images/maksssksksss202.png: 480x640 7 with_masks, Done. (0.029s)\n",
            "image 7/25 /content/yolov5/data/test/images/maksssksksss213.png: 480x640 1 with_mask, Done. (0.029s)\n",
            "image 8/25 /content/yolov5/data/test/images/maksssksksss217.png: 480x640 1 without_mask, Done. (0.029s)\n",
            "image 9/25 /content/yolov5/data/test/images/maksssksksss314.png: 480x640 6 with_masks, Done. (0.029s)\n",
            "image 10/25 /content/yolov5/data/test/images/maksssksksss354.png: 480x640 27 with_masks, Done. (0.029s)\n",
            "image 11/25 /content/yolov5/data/test/images/maksssksksss473.png: 480x640 3 with_masks, 1 without_mask, Done. (0.029s)\n",
            "image 12/25 /content/yolov5/data/test/images/maksssksksss545.png: 480x640 7 with_masks, 8 without_masks, Done. (0.029s)\n",
            "image 13/25 /content/yolov5/data/test/images/maksssksksss555.png: 480x640 4 with_masks, Done. (0.029s)\n",
            "image 14/25 /content/yolov5/data/test/images/maksssksksss558.png: 480x640 14 with_masks, 4 without_masks, Done. (0.029s)\n",
            "image 15/25 /content/yolov5/data/test/images/maksssksksss589.png: 480x640 1 with_mask, Done. (0.029s)\n",
            "image 16/25 /content/yolov5/data/test/images/maksssksksss6.png: 480x640 1 with_mask, Done. (0.029s)\n",
            "image 17/25 /content/yolov5/data/test/images/maksssksksss602.png: 480x640 1 with_mask, Done. (0.029s)\n",
            "image 18/25 /content/yolov5/data/test/images/maksssksksss619.png: 480x640 2 with_masks, Done. (0.029s)\n",
            "image 19/25 /content/yolov5/data/test/images/maksssksksss71.png: 480x640 5 with_masks, 1 without_mask, Done. (0.029s)\n",
            "image 20/25 /content/yolov5/data/test/images/maksssksksss757.png: 480x640 1 without_mask, Done. (0.029s)\n",
            "image 21/25 /content/yolov5/data/test/images/maksssksksss797.png: 480x640 7 with_masks, Done. (0.029s)\n",
            "image 22/25 /content/yolov5/data/test/images/maksssksksss799.png: 480x640 7 with_masks, Done. (0.029s)\n",
            "image 23/25 /content/yolov5/data/test/images/maksssksksss833.png: 480x640 1 with_mask, Done. (0.029s)\n",
            "image 24/25 /content/yolov5/data/test/images/maksssksksss88.png: 480x640 1 with_mask, Done. (0.029s)\n",
            "image 25/25 /content/yolov5/data/test/images/maksssksksss91.png: 480x640 4 with_masks, Done. (0.029s)\n",
            "Speed: 0.5ms pre-process, 29.0ms inference, 1.6ms NMS per image at shape (1, 3, 640, 640)\n",
            "Results saved to \u001b[1mruns/detect/expTestImage2\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!python detect.py --source data/test/images/ --weight runs/train/exp3/weights/best.pt --name expTestImage --conf 0.4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 可視化"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KA5u_adRsYoN"
      },
      "outputs": [],
      "source": [
        "color_dict = {\n",
        "    'with_mask': (0, 255, 0),\n",
        "    'mask_weared_incorrect':  (0, 0, 255),\n",
        "    'without_mask': (255, 0, 0) \n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OObVe1yGsZWF"
      },
      "outputs": [],
      "source": [
        "def show_image(img_id):\n",
        "    df_image = df[df.file==img_id]\n",
        "    df_image[['xmin', 'ymin', 'xmax', 'ymax']] = df_image[['xmin', 'ymin', 'xmax', 'ymax']].astype('int64')\n",
        "    path = 'data/test/images/'+img_id+'.png'\n",
        "    img = plt.imread(path)\n",
        "\n",
        "    imge = img.copy()\n",
        "\n",
        "    for index in range(len(df_image)):\n",
        "        row = df_image.iloc[index]\n",
        "        cv2.rectangle(imge, \n",
        "                      (row['xmin'], row['ymin']),\n",
        "                      (row['xmax'], row['ymax']),\n",
        "                      color=color_dict[row['name']],\n",
        "                      thickness=2)\n",
        "\n",
        "    img_pred = plt.imread('runs/detect/expTestImage2/'+img_id+\".png\")\n",
        "    # ===================================\n",
        "    plt.figure(figsize=(14,17))\n",
        "\n",
        "    plt.subplot(1,2,1)\n",
        "    plt.imshow(imge)\n",
        "    plt.axis('off')\n",
        "    plt.title('Image with Truth Box')\n",
        "\n",
        "    plt.subplot(1,2,2)\n",
        "    plt.imshow(img_pred)\n",
        "    plt.axis('off')\n",
        "    plt.title('Image with Predicted Box')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "pTkxsVnosb12",
        "outputId": "522fdbb8-f271-4c4e-ff5f-ed62ffe7b21b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "data": {
            "image/png": 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tKO4iIjNtksm1FOC9LR4+EBF0FDEcDunv7BBhEKVQ3lMXj4+EWhRRryU06jW8CJv9Eb00xyuNcjGpy/HOgqiZxSvTBhcdCm2QmaMyHR8pHo6CkCOgUDPEakqYih6GfyI4gczkmHxEM4nZszjPQqdDrV5nnOecvXCBjc3LxHFCvLCPuo7opxmRBhENzuPFYawlz3J8mFiccziEktaJKJzzIA7nPEpRPsuAoJTaRShLIl4SPOc83rvQZud2EVelwwMbRRFKqeJBdcVjJIiCKI7QOnyeLIQZYj67d9LuT58doR0FA7jh8McZRgKTfjvvinGR8oJhLF34XquISEehjWUfymUiUnRLEAnESGvNcDRkfX2NTrtNuz1Hq9Wh1ZpjeXmFbvciG9uXUbnHOsEVT58WT6sZs2/PPAvzbeIkQXTEwkKTlf1NVpZWiLQGPNYYsnRAqhSJ1tRLwcFbJIrQsQ799D4Q7hnBws+MxFXHFsjGGf1+LyxtJSitg2CUJCil0VEYd2MMQlgLzjmstShROHGTtVWOs9a6mCvBWktu8l3zGUVhnAFUQdhNbtBKoSTQmyiOieP4Buf7yw8Vn+Km5FNLecrdvSGjUcbT222yOEGLEGtNFGm8CGmWk1sfGmMCfQ+NEO4djfjgcITzMw2u+NQXjk/52U/X4zdXUzQqPlXxqd24rrLhBGzR6ERZNEItjrG5xTqFzR39/oj2/DKHnnAb83v30jt/GlssCKXCQ5IkCe12m2azFQir92gd4QXyPEdpT5JERIlg8oy0P2Q8HHB5dQ2fWXxuSO66i/mlZWxusCYH54hrNVb27aW7vMiRJxynt7nJxtoqaW+HrcubjAYjRoMdzHiIeB+sIggO8ATCEibQl6M6mWqZrt5Cvy6fyVILZ0IYpgshkGIRQVRBvLzHWYvH46Sc1EDcY62JpFh4SNE+UEhhFTCI10ikiOKIwXDAaDSiXYvweBIcK40YF7dp1uvMtVq0WglOhLWtAWtbfYaZhTim22kz6A/pDdLi4SuX7szfcjw8ILNCcfngWwSNkikzEhGsd4X15wqrUWlhEsF4B96wuNjltluOcPzoUfasLNNst7B4Tp05w/s/8EFOnTzPkqrTnVukl+U4giZdWva8B2Ms1lmcC5YTL4JzFpg+NI4wxtaGNSyAV1NiXxKmK60zzpWEOViVgqAhEz4XRRG1WoL3FmNMQdgCORYRkjgm0nryIHvP5PflaIYHHbwv7TeCTMZ7VgW5ipVphqnsmrPpIpydgXB9CX9lV3+LNhdj6n0gRs45EJkwq7Jv5bmiZKqjEiwgOo6xzrO+folBr8eelT0sLixw8PAR8ApRNc5fPMfF1dOMxw7nHQ6IcXTbCQf3dLnl4BKLnQYCjMcpyVyLI8eOceTgMvXE490Y8QYzdgydgTzDZWPqeYZuNiGpIyYC78BYiBN8vYmoQOIcQQC5FiF3zrO91WNnuxfmizBvSqliLeRBQCjWhVJhjPI8D8Q4jiZrIUkSsixjPB5Prh/GU5NlGQ4/WcflMSXFSijuqaMIleeIEtqtFs1m8yqtrgAVn7oZ+ZT0+3zd5U2evrkT7txZfNTzemKc8fPrl9i2LlArcQUNK9WGQiEqeNlUkBd0wZ98IMDBE1IobuX54QoqeDVK2qgU3lnq3XmWFxdYWVqk2+lQq9cwzrK6b5kTJ07Q7w3oNPu4WoMNr/GRp0GKcgYQolyxtGXYF6W0t2soHRiIMzYIfaoQ7sSjtUZpwQW9iEgrokiB91hrwQcBTwBXCNzWuUDrBKy1hVejGBMJ/CWKg/A7t7HFIeeIjQEc2itWtjY5dOIzNNcuTnjAVJcLgv1uJXZGVii9HbOS8YwHxBe/9d7jXVCmnHUYG7wbeZ5NeGtQlMKlLrSbrDXbhXxS8amr4UuZT11X2ag3G4WRQdBKY3JDlo5wLghZWmLwmpV9hzl0/Fbai/O4i6cw1gStnUDEIx00ylLTTuKYeqNOFEVkJseNDSpqoCOFaHDWYrMck47obaxzRikWF7osr6yQJDX6xjDKMtI8C8wAIW60WKo1mOvOo71le6vHoNent3WZ9XOn2dm8RDZMCV6i0o40+3CUlpKpxQMCES5RPkDOM/l+l8xOQfc96EKTNtbgCA8bgIqCy9hZhziPUw4rYXmJCMp7tApty60jkogoidFRRJaNp25ND3Ut7O220KKZa7WZa7ep1wWHZ67RoNNucXlnQDq27FtZxlnHpz5zgvWtbfClMFxq/0w0bcHj/W6htzR+WB/csGom3cd7h0OhZFYIVqV+UBitcpYXuzz9qXfxlLueyKED+5iba1FvNqjV69x19x3s3bfCO97xl5w/u04jaVBTmlRUIMYqQkSFsfGOPM/J8xwKYdpkWdCuC+sFHqx1heIRHmCrBKtmGJWUyseMYiVqQtACU1BE5Zx5i4hHKcEYR55nuyZeKUUcx5OHMlxvcuXJKpoqIVMGORUeplahknhO58Bf4damaJfDO7fru3ItlxbHCWMo5ld5KZ5tFdbARNHyKGEXES/XtCiFEoVFCiFNEKVwXtja2SE/c5paHDFKU6KkRmd+geFoTH8wprswT6fdwGUDUuNxSjNXi9m31OHo/hVWFuZoxIrcWXqDHrGPSZKYxaU9aDsg7WcIHpwhzy1Da8DlWJdR9x103sQrhcKjjMNFCShB6k3wepYnFuN8hSLnHIPBgHScorWejKl3DmtD6Iqa1aV9sFYGgq7QSiNxqUjKxBJpjZ0IBEopRCnEOUTC+kxTQxQJWsfk2bgQQCisyJDEMd1ul3arRYWro+JTNx+fum0wmioajxZ33w0vfSnH/u7v+E9//ueQ56AULC7Czg5kM3RXKZifh+EQ0vRzudu14R1cuhRes+h0YK5DGNARNAHG4KZCG5awLd3a56ktrRZoHfr/uSKfWqs5dRJGQ1haCuN5/ny49g17IgjtWVyEvXsDo7t4ETY2wNrpOSKwb9/0nFlsXIJz56DgXR86eIi33n4XVknFpybD9/jhU9dVNpJ6HZSgtMKYnM3LlxinAxqNOrVGjUa9xXxnmaizxL4DR4ibCUkSI/gwCCWhKyzPtog/LAUgHcegPcaMGaQj4jhCeU+WWdI0x1uHzVK2L6/x0IMPcODwEY4+4TZqjTqjdESWZeR5znCYMkozwIG11JOIuYV5kkaDY8ePsX3LUS6eO8vF8+fZWFtnNBwgzhbCdTGphTbsvEe83WVbnmj2hVR+pbAX5MKCCcy4PXWkg9XfO1whtC4uL2OtY9Tv43KDFcHpYEErI3RjFaF0HOxPXlBRjNIRYWNoJgwyiSIWuh0ipWk1WzSbDeJIQAn1htCdtyzt9Nnsjbjl0F6W5ufpNut87NMPs3p5gyy3Uxde+VDv6lnoqyoXVcHswkILft+waFXo+4xlSUqCUbjYWo0aT73nbp79rK/k0MEV6kmEjjRRTSOxYqE9z/Nf+Dy63QXe8z/ezcmL21wcZ/iojtYhzlhEoVTof17ECkJwT5txjshU2RAEaw2uVKom1rugLAhlDKybtFlNntLgpo10aKPWCo/DGk9uxlinMNYwHmfBbV54GrTW1JKEKIoKYrlrKK/wOswoEROb5CMxPWvGellaMn3gh1dzWYsSvC1dpVyxngWlJIwNFpDgJSrc7aXSpHUU5rm0DqoQ15w7hzXBguc9jEZjLq6tUuv1WOjOoSIFkabTbrCzuU3mctqdLovzXRouxTogSmjXY5YXOix12tSTWqATxpLnOW48ZjAYkFuAmLFx1BRh7kTwOIbZgJHNkWwAkmCdZ6HdoVurY7NxUAydhaSBUhEofdUxBrDO0dvpkWdZILYEwhvogZt47cqQEl8cn523MqQhzzI8gREKgvOBEZhizEJs9tRd7b0m0jpYz5UK8dvOYJ2l3mjQbrdJkuSabf9yR8Wnbj4+pR+FzLoLKyvwwz8Mt92GPP/5QRi97z548pPhR38U/vAP4S1vmQq0t9wCP/ET8IEPwK/+6m5F5DHDIxjlY4tWK4zJwgK8/vVw+fI/7nrHj8MrXwnPeEZQ1IyBz3wG/uIv4Pd/f7eycC10OvC93wvPeQ7s3x++O38e3vc++I3fgF5veq83vAEOHnzkNVZX4cd+DO6/HwDtQUkZulTxqavhS5lPXVfZoLggCDvb26S9lDRtsrS4RJwk1OKExfklarLC/MIiKlHMNZrU4oSeK2Pr/CR+bJxlZHmO0RHjLEO0olZr4IaGwaCPUop6HDHsZ+SpQRx4m2MyYfXieR789KfZe/AQ3aUlkkYdYwzGGNrjMePhGJNnDIcDtnZ22Ny4zObGJk972tO49a67ueWJT+TCuXM88IlPcO7kw/Q3NzHpKCQfieyiH6VHzpfvmUSvFhYiP3FfTQ3TMrHilxOtvUIpwbqw8KIo5tChw2xvbzPY2ZksEmcdOrJEShcxiDHdzhLzSwuMbUbuQKm4sLYbnNUh1lgrkmYDrMM4S2YsKq5RT2rUlDCnFJ1mg5Ulx575NkvzHTrte+h0u3z4o/dx+sI6qZvG24q/wvggIL5wT1zR/zLMynlPrPQM7Q1n7Y6PdRw/fJRnf+XTOXr0MLWGQmuPVhE6ihEdEiTjesRznvOV3Lp3P3/+ng8z/NgDnE8VSkWFZ0MQcZP4w+DZcHil8c4iKjwYxho8HuuCC1pHEapQQJwLnhlPsNpp5YOLeuJtKJe+QiJVhBi44FUgWFVCeJbF5HlhRQzxo1EUU6vXg7JTrKFdYVrXfNCmIQ+eWW9HcdSX35VaxdTK6WeEpSsxdS0X4RLFtcrYaxFfzC84a7DGFvHDiiSp7Xablp4gPLnJybI8EKzckOUDBrmjPW+Ym2uwtbPJhfU18vEQZxwH9h2ku7jI4uISxo+JBSRKSCKh2axRj2JEIjxgbE5Sq9FdWsIay+ZOn0gsl9c3WeokdJp1tAQv03hs2BxcZjMd0RsalPfcfesTuXXvAcQY/HiIz1J0s4PUmkjSgDjEKc8KaRDCZFZXV0nTMWjQShNpVySiqhDiIEGgtcbiCgtQHMUhFIBpkl15baUUOtHFMU+WZRhrJl6zcvy0MIlDVjNmKecczUaDWq22y3Jd4QpUfOqm41Pe3YDAejVsbwfF4cCBoGScOxe+73bh8OHwmmVSzSYcORKs6rtMuo8jKAVPeEIYg39s7la9Dt///fDc58KFC/De9wZl5u67wzieORO+ux60hm/7Nnj5y6Hfhw99KHx/993wrd8a5uItbwnf7d0bPBsXLsCnPrXbc3LyJJw9u+vSUjzLFZ96/PGp6yobsYY4ihl7xXCUoZs1EEWemxCDZ3KUQK2WkMQ1krqm1myik1rQsJzHO4vFYZzDmMLVYx1ZmuOMQek4VNfIc1KT42sNWs0m+xYXWL28icOBMwx625w68SDHjh1jfnGBVqtdaGCebJyy47fxtZh2u4UnVBk4d+E8XmA4TlnZu4fO8hJPfMqTWVxe4sxDD3PhzBnGgx1k1iI8tQ/hJDwbSilqWuOtRwU7TjinCE1RFAlphQwuTK3Nwe0ciDkONi6tk6YjlIBTQeD11mJcjlUaiRJyLAcOH+XYXXdyqbdFb3OTRhzRd2vghdxCZhzaOsw4Zzwe4Yyj0Zhjzs0hQDPRxFGD1tIKe1rztFRGvaZJ2jWOZ4ad3jYj4zi/sYOxGYLDF9YBQVBqIqZOLO8iUqTZlYpJqM7iyzMncbGlICxk1tJs1rjrrjs4fOTAJI7UutIQ4fDkiAWtLU45Dh45wEte8iKWbjnCX/zDQ5zdMGRGE2qLFHHYNiTYGWOIIpAiWMo5Q26KmSwS5yKtCwVAsM4hrnTHBs1d6cJt6B3Wu0n4gSpcttZZIl1aAsO1nBtjnQ339SFvREUxUZwU1oVS0J/EL8083jOQKf296mN6rWfXQ/DKhJfHT28zo+iUq3kazRWiwcVNraV4jzVF6Jh3aB0jURwUQXRB6YJHyAMmywsLiMPlOdYaZOwQrdnY3MZ5y+qFi1w8f5a5Tod9+w+wsnc/W3sO0zc5sU0Bh9JCpBVeFZ4dH5TBpFFn/6FDdLvzDMY5aTrgMw+dZmWxwYE9C0QR5Maw1Rty/tJlzq5d5vLWkOV2jZVOm8VmQg1Bo8hthh6NiOst4nYXPQkTp28AACAASURBVNeBKJms7XISrLFs93pMbECiSOp1BMEYWySu+gkBD8M2tYiXCK7tQnl0jkiXiq3FaoWxQUFEQvibdQbnFONxhrUOkRAiYK1FgFarSRLHITSwwlVR8ambkU99jpOZZfDGN8If/3GwjpcW8gmRfIQlZvr38ayQl/27Ilz2UUEEvumb4FnPgk98An7yJ2F9HaIIXvQi+MEfhH/5L4On4Xrek3vuCcrG9jb8+I9PPBM897nw2tfCoUNQq8F4HMKsROA974Ff+qXdc2TMtftT8anHHZ+6rrKhnMUbE+LTLdQaLeYXlxDvyfKMwWCHdNjDJyGEZa5ZZ25hgVq7g9JrYaKtJbM5xlvsJNTDkw5HZFlOVKsxzizGWLQoTJazMj/PC579FZy/fImLlzaCtuQ8a+fO8KmPfYS9B/dz9Jbb8NahNGRjx/b2FlpF7Nmzh3anzWg4wIxHrF08R5antOfmaDSbzLXaNNttjtx+O1G9wfrZU/S2NrG5KZLzPMpPY10nNEwUSizRJDzGF8S/EORmJlKJIH6a8V/Y1HHOceH8+ZAAJIV5SYcwI+8sxjpGzrI96kPcYOXwcRa0x+1swWCTOgPW1jx5njHoO9LhGDsckmUD0lHGYGeEyTOUbaGadZJmh8UDx5hbXCHfPlsIxwkiOc1GxIGDB3G1NmtrFzDj8GBNwn/KUCkpVAk/LQ8Yeh8Ympp5FEqFJHAyh3WCcYbOwgoHjuxFxUXQkw8MDfE4LNr6oESI4FWEjzIazZgXPuNJHD96hPfee5IPfuI0l1OLVULmIXc+5I/gwtxoVYj2HmdL12kRIgVEqkxmDwpHiGkO5SV9wYZDQpuABMUmuBHLMLIQwqWjCBFNZgy5MShx4VwUKq6h4yDoeA9ql1miSApHdnsirsJDy6VUPiuz55Qihi8Wpi/uXyqJs+FsU8/UrJBSvqYJoriigoYxIZxCCypJkMKbVOpLwcIUCJm1jiwzjMYWIUJFNsSx9nukoyG9zU12trdxTrAirOzdR37rEzltDLa/hhlt4ZXDK4/zBudzxAeXeVxvsLiywmJ3CSRmu9fn0yfP8pkTKQf3L5PUIobpmNW1TS6ub9EbZngHK086QuQMg9E2ucTUVYRyFp1lmHSAtxn1JIIowqN3jXM6Srm0sYGuxZPEOPEqKJHKI9YiShW5QY4oDhZcYwyRDjH+URwjIuTG4IrzXZH1aQoLUVBeTSEcCnESvG55nuOcJc99YUnKiOOYbneeWr22y5JUYTcqPnXz8am5nW1YLwTW/ftDvkCZ+7CwAMvLwbKd58FSfuRICKsZDoMQGsfTHIN9+4LVHIIg+/znw733hvyAWSwthbCqej0Iwp/8ZLjeo0W9HizyFy7A0aPhngCnToU2Q2jvsWPB63D+PDz44CPDj5QKnphbbpmet7YWrn/+/O77HT0axkmpME4nTkwVrWuh2QzhSadO3Vj4WBSFcDSl4A/+IHgxSrzrXfB1Xwe33hr6dj1l4847gzfk7W8P3ielwpz99V+Htly8GBQNCGFx5Vg861mhDTs78PDDsLl5xYVnxeqKTz3e+NR1lQ1jMgaDPnluQriGEOoO4zB5xtb2Dju9HrVWhrWWer1Gd36eZifUGjd5VmS7pzjXwrsgsGo8eTbGZBnG2GCb9eBzg3dCrgcc27+XO44fZm1js6gd7klHAx789APMLe2h056nPdclM1kRxuLZ2tqm1WoRRxFmnGJHfYbjAeNhj816nVqzRaPRYq7ZotVsUm/WWTq4D5VE7FzexKQp4mUSk+rxaAmTnOc5iQiqEBwnvugJ/ORxmVj4vZ/ua1Fogd4parUacZIQFVK8x5OPU+w4WMv7oyH93IBSdFoxrcY8dZOw3BE2Lu9lc2uTUydPcvrENpkrciacw5oR41FC2oho1ptEzS7tpb0023U2e4JVMdZFpFmGcYaoVqM7v0Cvt0U/Hxfx/8GSMEkQvyIMaFIybUofJgrK1IXmJr8XBQuLXdpzzVCdJYnAB6+D88HD4bCYLMXanCiO0TpBa02z0eSJ+1osNp/AylKND3zqPA9f2AqCvs/wqoEDcsodRArGWrZDpEgun7plS5o0EeQLl6tzM8nU+ElMqNIK/DSpPMQqBiuCLao0hCeemfJwU4Hf84hhnBm4MP+7LXJT1e1aKNteKhz43RWqwvxMiW9RpHJ6hyKPJfisHM7nGJMHS0jRjyRO0FohyhUhHIUiacEaS5bljMYZ48wFZcyBHY/IrSFNhyH0w1iGgyHb29tESczBY0fJ0gHrZw2ZGUI2LNziOS4XPBqdtOksLDI/v8DC/ALOws72NsNhzvkL57l4aRsdJQxHGZtbO+TWo6OYTqvOvr0HWOws0ojr4BXea8RrEqVR3uOzDPIQ9rKr5IeHne0dNjY2wkS5sB4sZpJ4b60NHjKlsJM5KIWxcJky2S6OIvJiPqyxRXxvWX0luKatCxV2olCnsKgiIpOQG2tNURmpGXKWKs/GNVHxqZuPTzVtDifPBGH4DW8IFvTXvz4Ipa99LTz72fAzPwN/9mfw1KeGvIt3vjNYv5/61BDL/4d/GF7/+l/D854X+vHMZ4bXH/0R/NzPTbt1++3hPrfeGgTbPIcPfxh+9menoVg3Aq3hO78TXvayEPbzpCcF6zwEJeBtbwvt+IZvCAoJBMXm7W8PbS+F/no95EW8+MVBWSrPW1sLitS/+3dBMD9+HF7zmqBMdTrhvNEIHngAfvu3Q0jZ1ZAk8OpXw0teAr/+6/B7v/fZcy2SJChOm5shR2MW/X7wUNx5Z5izj3706tdQKihGxgRl6FWvCmO0tQV/9VdBCSyVJK2DUql1aOe3fmsYuzwPytSf/3nIEZlRlGbLsVZ8iscVn7qushHFijRNGacpeIexY7IsJRHHOM/Y3Nrk8tY2850Rw9GIKIqYn5+nPTeHV4rchpi58TDFGRvK0XmPtTnWZDhjwHu63QWyJGJ7bY1smGKzHFqGWIFWUoTchCCa7e0tPvmxf2C+u8jTn/FMnBbyPKfb6eAM2Dwk97l8TDbcASWQKvI0IU9HDPUOvSii0QhVkFRNU+u0aeSGkYBNU7wp64iH2bGEcBql4+IhmB0l2fVu1lUl5YLwHpSm21lgZe9+6s32ZKOUKI6otxrYfMypBz/NpfPnSOpNkvYc1mboLKVV93TqMYvtfRzat8RwOOTAyjyJ9jz8mYexeYJGY/OcOEnQSYuovUjSWYakwSDtMcwMtfY8+dgyHGeM0ozRuHhwdNBavfhpKTqZ1jEvOsPusKCp0K4kVH/w2ELwDXF8SlRQNuY7RFow+Rhv4yKpEKw1eC9Yl9Hb2WE47BFFmnqtThTVSYdDus0mezptXvi0wxzcu8jfffwc9584gzIWZYNnwjiH8QatBNFqGo+sI5QuEpkKq2NgsFPFQqlgsTM+rBukUN5EgRTVGgqB3s54GIwxIf+DMpFKE0dxqFwllE7O6dCV2OXUuLbr/xHRAtc4PtU3dp8hSDGHj1ynlD6QIo7CuRxrs+DpQYjjmKSWoKMQL1vOuaBC1YvcYvKQ66PisDVRbjLSdEQ6TrH5GHEOnGM0GHD29Bkub1zmwJ59HH7CcTw542zI+HJGllvU2KBEEzcadJf3ceDAEZbmF2k3mzjraLdadLtLPHTyDP31AaLGpOMMYy21eoJEisXFOY4cOMjy/ArtRo3cO7wTEhX2nlFa46M6IjHlkz1RjZ1je3uHXr8/Udwo5ibUcw9nOu/RUYQ3JiQeShgrpVSoaV4mzRVV0MocK2vshDg748D6MD4E52GZkFwmKEMg7q1Wi3q9Hgop6MqzcS1UfOrm41O19aIMU1nh6I47YM+eIHDffnuwyt92W7CoP/WpQdDu9YIg2mgEIXVpCQYDePObgyD9ghcEIflDHwq/mynZyZ49wWPyt38blIsnPzkkL997bxDabxQi03s/97lBwfjjPw7ff83XBAXC++AxefvbQxte8AL4Z/8sWPbvuy8I9d/93UFpGY/hHe8If5/znNDn4TD0USn4ju8I3586FYRv58L4PPnJIQH7vvse2cY4DorGy14Wch7uvffGkrrL/InNzat7TS5cCJ6HJz0pKIHGPPKcdjsoSHEc+qhUUBa0Dp6RD34Qfvqng/JRr+9WtD784aBIHT4MT3lK6J/W8KY3QZ5P+PZs2duKTz1++NR1lY12u4ExOWmaFlaTlMGgx9CkjNIhF1YvcnlrG73QY3t7B+89nbk52p0OOokxzpEXv8/GY4qwd6zNMXmKs2FTkGa7g63XGGz3GOz0ESecWzvBidPnwkJRMkngc86xub7G/R+7lz179rF8YB/D0YCIUKpw0O+TjgfBIpWPCyEavDPY3KJVjIki0lGfKE6IG3WSOEFiTdJskAN5GnIgvHdlYEoIoykmv3zNyN0TqNJgTamRQ63RZG5+iQOHj3Lw8FF0HFOr1Wg2muhIk9RrjIZ9Eh2UhkMHD3Po2GEiZaiRU9capSFWikajwfx8m4X5OZRWOAc7mzuMx2Oy8Yi43qQ9v0hjfi+6PkeaGrJRH+NjmvUu6fAyveGAwXBImkZkvjbZdRZdJBG7GVHZ+10PP5MRmfazLMWrkDJ7IFgYvUHFYR2JNziT4UyOxPEk30MX7vksHXF5fQ3xnvZch1ZjDt9u01cxPhqzMFfneXcc4Oh8lw8stNgZDGnVNNZ5BmlKb7RNnuUYrREVamrrot3eFyUdJSSDz7YdD8aGnUwFQUd64rb1BSEQNb1O+crzrNgkJ6wDAeLJRkml54GruDT8lICUYVFXhDrtRuGN8TOVwEoCzG6PxuQXUpS2K/0xM03Y5f2Q4MFx3hQWo2L3VR2RJElhpQjiDB6cE3xusXmoE1+rN4l1jMktucnIxmPS4RCcRUsQdpzLOH/2HKdPnaTbnqO7uMIBkzMeD9nCY9IREmt0s8nc0h4WDx1j777DtFsdkkjhtKfT6XDk6C2cOHOei+uXcd5ilCNJQlUhLZa9i3PsWZynUWvgvcV4h1KhZnicNJFGA5I6FNVEZtUw6xybG5tk2bjI3ZmOkXMuJCD76WZSSincbBKj9xhj8WRorcP7onyyyfMpMbchh8AVRFsX5QVNcc2yEIAQ6uq3Wi1qtVqwKkmlbFwLFZ+6CflUrUhkHg6DlfzlLw9eh7W1IMxDEKqXl4M1fTyGv//7mXgwKKxW4fu5uSDU33cf/PIvT63hCwuBwGUZ/OIvBo9HnodQqx//8eAFeetbg9JyI1Bq6mF46KGQ1/Dww0EI37MneGQuXgxemo9/vLQ8hf49/ekhF+LIEfjGbwyC9U/8BHzkI+G8224L3pduNygb7XZI/DYG/vRP4U/+JPxmfh5+5EfCed3u7nK3pUfj274tKBqve90jvRTXgnOhH4PB1csEX7oEZdjc9a5RhsqMx0ERe9/7gjfkZS8LitMP/zD81E+Fuf+VXwkeone+M4SOeR8Ula/6quDdeclLgkfk9OlwzULZqPjU449PXVfZsDYnHWWkozGCYzgcID4n722x3d/h4ZNn2O73aQ0GbG1tkaYZSZIwv7hAu9Nh+9IGJjekoxHpMGyZ3mgk1LwhS4eYbBzq/kqIm1dJwligt7PDP9z/IKcvXsZQ7l4Kzno8DpOlXF6/yKc/dT9EKmy6NBqTjsYMRyMyM5o4i0vrNAjeWbzzjE0GWShpWMtybJKE+H8R4nYTtOCGw6LakJ/IgUpkkqMgE2I9YyWS8N9EHBeh0Zrj0JHjLO07SGd5D825Nlp55ucX6HTm6Pf7bG1usLO9zd4Dh6gnDfbtP0izVaMRe7qtiCT2gGOc54gJm7HEScTx47eQ557TZy8xTDNGWQoIUVwnas3hJSIdpdhMqDUW8VGLjd4pLm9tsL3TY+zaWJ0E7wLBna61xoubaMqloC2zfS0sDd4TdqT0QcUIJRUVFLkS1lkUCh1JsEqYDGtyvHOhEpVWJJFGESMCg16fPM1QXhNLjUYteFnwinyYI3mfA0t1vu5ZT6LfH4IzjDPL1mDA2laDtY0NdoZjUmOxVqOULbwXIfnJFx4HpaY7sRpjyE140EBIfEzQFwoCpwSFnlgARDzeuSJRyiKu9JAo4iQuNl+SEJImnml22xS7VIPSOnEFCgfM9C/TB7xc26VHo6x5vav07S7L5RX39uUKDX20JiO3ebAYiRQ7gSZoHQeFSEJiXprlmOGQPMuJ4wRVj3AqIhvn5Nk45PNYF56z4sYOR29nm/XVVS4sLLN/734arQ6Hjt1Kp9Vm2O+DeObaLRZW9tJd3k9nfolYx0QaXKRozc1x9JajHDhxgvWNTYwzaK2IdYT2QiNOWJ6rE2MZ9ndI8xSrod3sIEkTanVodcLfIuluF50zhrW1tSIsToqSfsHqUxLXMFWBkOui4EC5JoBJaJ0rdm62Ba1xRflFUxDusMFViIvVSRKsSN4jhRKslCLPM3SkqdVqk8IGV+6jUmGKik/dfHxKZxlzYXKCgvDSlwbBf3U1CMxpGnIZnvzkIIRfvDgVOK+Gcv2XCkiJfj/8/fjHQ4hTKUSfOBEUkHJ/ihuFc1PF5C1vgU9/ulxkQZF49rPDfWbDjD7zmXCPdtiQjjvvDArDX/5lsOaXXodTp4L3oN0Olv+dneChueOOEI70ohcFBeeBB+A3fzN4aDY2gqIFYdz+xb8IStfqalB4blTRgCDkOxe8SvX6I70bZbiY1ruZxrXG6Y/+KCh+eR4UwgcfDKFxd94ZrnXuXFA0PvWp3b+1NlS8+uhHQx7HHXcUcy8Tz0bFpx5/fOq6ysaZU6dI4hr5KAvJOMoy3MkYbl5idWODE6dXGbiEwXDI5sYGW1s7NOYbzC8sMr+4xMXT58J+CMYwznIipVjstGm5MeM0pd/rTwQ17xxOFJuDIQ88+DAnz69jnAoJxSoQ0NIcY61l4/Iap04+SFSPmF9YYNgfUm78FumYuXaX1UgTajVHKFFESqFFkVmDsTnG5pBZXJ7jlcJrFfar6HTRUUza75OOU8LmMhSWaE/psROZkSWltPp7KJLulI45cOQJ3Hbn3bgowXhhbC17FxfpzHe5dGmdhx56mPXVC3RabQ4ePMT8nr30xikPPvgAe+eeSH15kUhlOOsZjkYMBwPq9Tq1Wg1BuPXWJ9Do7uPS1ohRnpOOU4bDFI/COI+1jihuUWs2GY4NF1Yvcn51jUubO6hmE1F+oix4wuYseMKukoU1XU3ozsxGfoTpKJUMVyZj+5JOhTjLEBs5Js8t4/GYWj0Le1iooKhopZEi/EgcIem96xEUeW7JBj2sEvpOYcXjE6EexyzVFEkUg65jpcXIr3Bpa4cT59Y4v77JILOYIqHROYcr5k3rsFGQeFdUlSqqNhTKRZ47vFeIKqtvSdDyixhGHYrXkI3HhVJV7DQcRURxLRA+poL/leFUjwiHesQ7mSgYRTzWzFVKS4a/MkZid3jWjMVjatYsxRqZeSch7MTkwUVabOITJRFREhUETBAXqmr0d4akO1sM0wxUCL8TFVGrKVyzSZa2GKcjxqMcR7EhEw6TZeTjlEvr6+TjnJWVZeaX9jLXWSQdDfHO0Gw2mOt0aHfnabeaREpoxDEj7yHWtOY6zLXnqEURJs8R0SgicNBptunUY0a9Tc6mfYwS5peXaTRb1DsLSLsLjXaRcDeRtSY2oyzLuXR5A2MDDaJw55cbHJk8KMjlKObFZ1UIMVqFkDtsWEvG2iKkdrrZ4iQWHj8R8py15MaEHAMf4l3jKMKanHqtTi1OQnUQY6j8GtdGxaduPj5lez2Wywm6//6Qs3HnnSGEZjgMoT/PeEYITep0QghSqTg8GuT5NKxpdtO6IvSNIkzmhlF6KiCE/syiVGS2th7ZhvKeEMKV4jgkYM+GN816bUq87W1BuXnpS0My9Vd/NXz91wdB/dd+LSgjJbpd+NqvnSZkP9o9RS5eDEpKtxsUjiuxb1/oy/337x7LWbRawSuT5yEBf/a806eDsvT0p4eKVdfLlTEmKF/Pf/4k92VWv6n41OOPT11X2Xj4M6c4tH8Zb3NUnOAxZP1tejvbXOpnbI0sJoLhqM/W+iXW1rfYU4tpNFrMLy7TaLTo5duMnWeYW5paU481dRfjrGPt4kX2HDxArRHhTcpw2Oeh0+d4+NwqzkErTkhdYXVWglZQxs5nJuXs6QcZjXZoNloIjlq9zVxngU53kXarRRRHjMcZYoJ72SiFKzZ983iMNWQ2x44dOorQToN1SFSnNVejnjTZ3LhEng5xobghygUPQPlkTC3mhDAd8WgJyUjd+RWWDx7BxwneQy1W1JKIKI7Z2trizOnTnD1zEoVi4dAiiGd1/SK9fp+t/iVcukmNp3DrgQW8yUjHYwa9PuPhiFqzjvaOzvwelvfuIVUD3CCFOMXQI8szjIex8xilcc5zeWONsydPcvbiFoORZaWdFNVI8iK+X4HSxEkdLxH5aIj3Fo9DJJSsnXAwQsHVaWHcaZiPIEQ6bBTljOXy5g6j4RjlDHES06g1ibwGcRjxRHHw1qgoJssczimsF7YHQ4bDAePBCOd9eDg01BNNs1aj0WigdYTBo+t19nXmWG4fZ+fgkNXNPhe3+/TTMSNjGJsc6xUohRdPLBLySIpN/pBy85ugbCgKRWjiMQjVpFQUdhjPs7ChHxR/RKHiOqJixAdCEGpe+6kbsyQbhVdoV6L39TBz7uRVujnKSlqlGxm7y6sRTnK7L1a4VJX3eOuK2M4c5wwo0LUIahqnLd4Kae7Z6aVsbAwY91N8aVkRh/KBwDTqdUynU4SipFgzxhUWqmG/z+bmFu25eaxzNJoNOt0uc602rXYba3JqScRcu02rVaNZU9QTTatWZzAYMEzHGB8qiaEVUR4EJo9FlGNpocniXBsz2mFjJ2NucT+LCwforhxBt7qQNKDYEflqBjtjDP3RGIcKsagiRFEU6oYXu69SuKpLodN5F3aURyZJdFqF3KUsS4NlyIdqHcH6FGhCqdarovSgMSZYhXyoXqY8aBTNgoiL350jWOGRqPjUzcendoXprK+HKk7PeU4Qvk+cCMrFV3xFsGx7H7wfV8sRKFF6J6Lrbw12U+DSpSCEHz4c2l0qHFcLUXIO3v/+EEq0d28IR3rWs0J40SteEfJTRKb9PnUqCPnf8A3wQz8UwpVudJO/NA0K0LFjIcn71KnpsXo9eBiy7PoepvX14EU6cCB4WmYRRUEZybIwBp1OSAr/xCdCLs0snysrkGVZuCZMY4vLDxWf2oUvdT51fWXj7CpKKRbm6sSFpum9oja3QFu30OsD0twz6PdYXb3AufPnaXTqJEmNleUV5rpder0eeR7iYefmElpzXRr5iLrAzuYaFx7+FAvthEzDybOn+f/Ze7MY27b1ru83mtmtrlZV7dq1+32aezpfc31tX24ucYwdIwgCEYyCBBFSFAkkXhAPfuQhPOUJCRASTp7IU+SXvEDAEgFiSzEYg7n9vb7n+DS737WrXf1sRpeHMedctU9nmzzk2KqxVbtWrZq11lyj+b4x/t/3/f8PHj1lU8cTYaYlKghqDMY7RGtwJAEfHGVZspytsZWnrhZ455FSUeQDpAZvLMEanHR4KcErpPeRJtX7GAJLsihi0uYCNqbBK02WpAwmE4KAxfkpZbkEfEsPGFmPQthizl2yUc+kIBX7BweMdyYxv845ijwneFjMZpydnPDwo4+4ODnjlVdfpRgMEEJgTMPTJ484PtJUs1Oa5Rk/+7U/xt0b12g2S1xT44XEeoepS6QuKPanjIc5y9U65hbnRczaDQHbWJyHyjg++OAj3nv3A85mK7Isqj1a7/qoRAgB5z25VAyGI0rvaep11Jzo98mtAxOX8PZL9QQdI5XwMR/QWMvRi2NmszlilKBlQLlAnQ3QSpMXKcUgi3nJaUrUxRAIb1jPL2jqhnIwwnnfczpnuSbLM7IsJ0k01jl0mrKzt8/OdMr+KGU6OuCVmwdsmoblpuR8vuBsWXFRGowLGJUACUIYpJIoJVBK4L2NEZs2lKhULK6KxewOrSR1Y2jqJtpDGbVJpFToJImF8t05QEStkd5w9NGOyxEPcelAc6mPu8eX5le49DIhtEJkhJfQj3jukX0edzzMbNf0VntDRFrcEPqalU4FPU0zVKsX0piG+WzFycmK5XxNMCWxBl6isIRWd0SpWE80GI1YrxbUto73ICSb1Zr3338fqTW3bt6hqmusc0ilyFINLkUnivFoyHQyoshy8nSA84Hlek1ZllgTKQ+lkGitcfGEh5SC8WTMtevX2R2mbFZLJpMx4+kOejyGrCAI1SNFPcB7qa+NdfGerCXg0El0okpJVEv3Z9qNUBeODkQGjy7s3xEGdMqq4AmOl9g5Ol2Y+NqKpmm2Yx1iFNK1nOeTyZg8z9BtePqqfXa78lNfPD+V2o9FGX77t2OefpLAt78dU2jOziI70osXcQP9ec3auIG/dy8i622R7B+o/V700X/Q1/us1/j+92NU5Otfjweqb30rPn/7doweKLWNLvzSL8Valr/7d+Pm/+HDeCD4xjdi37z+enxOyviZ/8E/iJt3IeKB45d+KdaB/H5qUpyLkYef//lYX/Hd78ZULqXi2PzYj8WozdFRvD5N43tMJvBP/2lMu/I+pm79+T8fNTu+9a2Y6qV1jLq88UY8rHz0Ubz3v/bX4vj+nb8Tnw8hXvsn/gT85E/GOdCmWYUOyufKT/1R9FOfe9g431R89OyE+tqUa7sjEuuROiUfDhgmjrw4YdlsqDYlF+envHj+lOs39xlPx0x3p+zu73H84gjXGJq6xo0K8tGUpFHk0lPPzzh5+ojjvSlmOuT9J4+YLVb4iLGjECipUAmUVmAJIFt1ahcIKK5fu8Wbb3+Z5eacarNiPrtgOZ+xmM8IzkWs19lIiyYTlPd41xrbls5La93TfdV1RR0cxmYkaUI+GuFxuDNDMG0dw8fDQet8jgAAIABJREFUoYGYc0vUofBAkheMp1O01pHisB1EZw1NaTl5cczx8+eYuiZRCVVVo6Tg2v4BVVnx5OEDPvzgAdVyyXpV8vWv/hj39gtcFYuDEiFYr0uG6xWjPccwV+BqbGViiF60J9kQsDYwXy158uQ55+crgpNkaYYQAeui9HyUoe/UuS1KxXzIEExkkaIr/I4I/za9Z6vv4HoEPcRHIjIovDg+5ujoiOLWNaSzUFuqbEOWF0g5oSgy0iRDq7hVkARcvWZ5eoR3Aoxtw6cxx1AmmjTPWqpZiXMepRNWyzWr2QVpnpKnGbnOGARBFmBvnHFvPODxYs2Dszmlj5v1TvtcSIHqojdhK1ijE90vVClVVNp1jsY0L6VEaa3J0jSGdNsNvmyjIbG+4uV0Jj52uNjWZ2yRnZdSrD7W5yHQixbG1qFmMeT8chw2Gutt4Tj9ASWiFpHGVyBROiXLijYPVlKXNRfn55yfLTC1I1MBpG6ZyyLVYFRlj32UtkWlptpE9CWAMZbjk2OG4zGDYsjBwXVME6lAlc5Is4y8SJlMdhgVBQjJuq6ZzRe8ODvF+pjWZxqDkAKtdJu2ElGcxhh0UbB/45DJZo1MC6xtMNUGrROQCQKPkDqmyHVGse060xg2m01Eb7S8ZHjb/GHfzgWt+z7c2t5tQbC1NtbuJAm6QyLD9hohIlIkReQ1jzVAojUfoi0w9uSDgtEoggE6iak1L022q/ZSu/JTXzw/NWw+lobz7rtxQ5ym8WBxfByjHXfvxk1oh27Dp8O6770X03LeeQf+1t+KG+5/9a9+fxMkTWM9xKuvfn4twvn57/81P689ehQLov/yX451Fb/+67GY+md/Nh42NpsYZajreD9vvBEL0X/7t+OB4J134nXvvRfrILyPEYAuMlGW8Mu/HOtC/uSfjIeAv//3f++0Ku9jsfzXvw5f+1qsr/jOd+LB52d+Jh46/sk/iXUlsD3MKBVrMH75l+Oh77d+K/b/T/4k/ON/HKMvd+7E+pu6jnTFs9k21eonfxL+3t+LTFWbTYyq/PRPx9f91V/t36/bZF/5qT+afurzdTa84MV8RWUMtbUc7O5QjFM8EqUleZ4j5xucsSwWFzx//oRbJzdIUoXWmvF0h7zIKZcrXONonCfLh1izobYNXmtm6w3f/N4P2QwzHj47xoWWBxhaZAa0kORKU3nb0o9GIxpcVG++cfM293bfoa421JsVs4szPnz3hzz98D2wFusaKm8IzkX5NSURUmFNg7W+pUiNegzOWeymxumGRGmyfEAxGuG8wS032HXZc5J3rUshok1lCUGSZBmi3agmaYLSKub7eo9vJ4RpmphuZB111ZBmCXt7ewwHA6SHd3/0Qx4/O2ZdN1R1w8/8xJfYHRRIApM0I4RYO+CbDblOUcKwWM6RKm0jtgLvHMtyw/HJKZt1xaAYoggU+YDaVGzKDT3VayvoFPN/G7QQZFmKFB5rWs5lAdJvQ/MvdUW4tMel822S9WbD+w8esjfMoUhRSJynp2jUOtKypWlOlqY4YzhfXXB28oIkKTCNiWJ6CKq6pHEelSTodkE47xFSMri44GyQIWQg15pCJUihSHXKeDhmvDvmVqpYJHBS1UgZ8MRFGtHG7aB2XNS0IcSO6g0hsMZimpgrHNoEJq10LEbrpNe7GXHZXnzaQaJPkfoUYO3S4eKlM8rl/cOlDUVnrAPiJZ96OSoSjUb3YvSFYc5Fyt8kzcjzAYnSBBfYrDbML+ZUZYkSCVK2zB8hCjTK0BapuXgQTJKE4WBIs9lQ1xVdmljTNCyXKzabkvV6zWq9Js0SIFDs7pDnBd57ZrM5q03JfL3hfD5nU1eMhjutDkJNAKRSaGLBrTUNRy+OOVsueKt4hb3BkKoxbBYXBAf5eE1QCiUhG+0ii8nLXRyikJG1rkd7tgV34EOM/HXOL7J42KiiSryuC2dvmTri80qpHmHqHEKiNfhAU1atEZf9oEYWNNlTCQoRN7FdIeBV+/R25ae+eH5qbD42X09OImI+HkcWJefiZvprX4sb6sspVE0TN/7z+daAnp7GTexf+SvwF/5C3LgfHcWN9tlZ/Pr4QeL0NH698UbUsuh0MT6rbTbxaz6P7//xzftqFZ//uFBgVcXnO9aouo71FtbG+ou/+Bfj852YndbxGucire/eXowq/NW/un29H/wgUvYuFrHPutSsrp2fwz/6R/Ew85WvxLSmTnDw89p8Hlm7/sbfgLffju/rfWSK+tVfhX/zb7aO6Ogo0uQOBjG60tmghw9jNOVv/s2oD/KLvxj76oMPtqxazsV7/4f/EP76X4/X/aW/tKXKffgwFtD/yq/0/dylM8GVn/qj6Kc+X0FcCBoXOF6WGOcRSnN7OMR7hxKKQZ6iBQQfq+tfHD3l2bNb5EWO9ZY0zykGA+rlGtMYauMYFjmNcdTrNc45FquG4ycfsbSOTRNpwLr0j7iZi+E1JSBXmsY5mu6UFgxPnz7gvfd+yDt//BvkgzHjnR12b9wgKwqq5Yr1yQlCabRrqJ2NdF8uotpeOLzwCGdJk5QkyRA4QlNjnWe9XGCMY7qzw2A8xascYc4xtsb7S7kr9MSDgCQIiVBJzJ2VijRNEULQ1E3MmxWwu7fHdH8PZy3GuoiEy3hSzfMBr3zpDebrJQ8/eh97FpVwgzf81NtvsD/MGBqDVjHX11ZrdCFQynM+O8ehGQxydJIym814dnTEYhH7ezKZUKm4WOumoq7LdhJHteyImkVkPFEKXaRorbF2W2T9ycPrls41dL9vH3R0kB8+PeL2/h7p4QGDIhZWKxk5U5RU5HnBeDRmUOQsVwueP3vC6fEJg8GAnZ1dxqMxPnjKqqRsYvqCUgrZRnCs96SLhEwrgrdkWpMkChkEKQnDvGAyycivX0OpHSQB2k2B9QFa4xTafMduUXac0nERxcXWGENjmh51CSJS5mqtPqGg+RKwGC7TCG/77qXTwMd+9zI4Gd9/S4EbZ95LaRLbQMYn3vcyJW7otkkhGnHvHCEI0iQjy3OU1rjGUK5KqrJEC0gS2R7ItpEV1xqnzsikacZ4PKFcrzHG4tsoUeT0jsbQGsN6vcYGS1VXpGlCCJ4XqxWL2YzFco1x8bWzPMMHT9NE4TXvPVpEsUYXIvvI86MjPnr6mD/21ptMdqakBOq6ZH3esF5cRBY34bh28x6Tw/vILAeZ0IXoGhNTU5TWdPUtXSqadbHQVCvVPhdaRVYTRaKkbFmIWiQr+J568HLUKUbEaKmXfb+hi3V+qkXfYni74yz3XeqgEJ8yb65a16781BfPT92c7vbjc7E/5dkbt5h++9+ihmOe3jnkIx/w3/wu2XKDev6c5WtvcH5+Qd0YXv3OD/jFv/23ES9e8Dt1w/9eVizrCvu//K/wT/8Z07ffojo7Z/WtbxOcR/33f418tSTMzqOwoweePkf8D/8jlBXMZ+QfPGBy9zYdHfjWXsYx9N5TzWb4H36PN+/d4caPvsNocU76E+9QFAXD0ZBJIdG/8W94Vs44vn8D5y3n52esfuNfUhx9yHC+ID+cRLbCpsb8yv+G/M1fY/SlN5BKsX78mOb4BWowQC2OCTsp+uwJ6h/8z+hXXqO4eYciTZHrOfL0hHXtWXz1qzEP/1/+S5xOUHfvIu7dJfhoF5J/8c+iXb91A394rU0JAqkEi/mcDz96gKkMuk3RmezucTcbsvtr/47k4QvSvV1802CePoPjF/Cn/vwWJJMS/vX/Q8hSBCn+z/63tJ0Xp9BvfQvx9JTk8DpuvcY8eAClI/yp/yZugLtD6//9G/gffYg+OAAlqeYLqg8+IJydEt75Cl1Y//l4TOhYja781B85P/X51VYtzZ4LkotNTXJ8xqDISVONSlKGWYqWgsrEsO58dsHJ8THXrl0jHcSw02A4ZCHPaOqasmqwoxwbJOW6ZrauOJovmW8qCJEi7LJ6ZCAQup+FQCOQKm7+hAAlHVW14v33vsfBjUPuvnIvKjwKuHZ4yCtvvM178xWiAZ0qEm+pZMxrc97Fiaw8IohWhj2QaIkSguA8pq6QQlOuS7JRQT7aIXOSWbDYVR0XRFfhLzoawbj5TNJYh9Dl06kWGRIqMpfksuCV117j+PiYxjrqqkYnOubKaU0+HPHGO19mvV5y9OwRxjb85rdLhJd87cdeZzK2pImk3KxZr+YMtUYLmK+XLCvHxIzROuHxw4d89OH7eB8nPsFH6XkpwEYGpo5jORqqQPAxl9cTaIxFEIuRhGypzcJ2g9yl/0QD0U6bENri6Dh2PgQuFivef/SMaTFgMt1lmGiU7iIqkiwtmEwmeGd5+uwZT54+ZbVec203MJnuYX1kOxAyzhMfYuqTDxJHwCFonMcFcI1nFUqQAR0EiVcMsoZlmZH6lHJvTCMzsB7rbStzEVqEwMdDUFdg1eZcRqQllkAZY/pwbmd7tU6iEegXW9tB7c+fWQR+ObOqc4CfthQv/0Ho4x0tQuHp6mX697mUdvVJI3DJ5baGwrV0eFJpkiRBCYkN9HUyUqn+r7zfcnn7EKKCq4j5sFophM8ZDEexCK+pkcEhQsA0BmPqGKWSkqqsscYgRTSS89mM1WKBdZ7haMJkZ6dXJo0c4K79vN1cdBhr2KyXPHz2hPP5jIPBECkEOlWY2rDZlCzXS5pqjbUNIcBo7wA92EHolIBgvVqzKcuI5CaKJNEtIhbnhfcenSRtCktbfCckiEv94EPP9qF1Eime+8Nl6A+hAqKujRI92tRaOEIIpGlKlmU9mmesaaN+yafPn6t25ae+gH7qK/fu8hfa4SmHBQ9u7XMj9QyHmmdvvsa/nW2in3ryLPopG/jo9BTvBb+gJb/44YcAvPCBf1HXnKzXlFWFefGC8M3/1KaNRD+l53MyrRBYrI3aSMF7xMVsawd/7ddBxIOpv2RvI4MYGGsQQJZofozA14qM+/fusHttj8l4xHRnFw4OGI3GzN98jR+eH0U/dX4S/ZQpuXv3LoOiiBSiKRjj8EdPkKfPCSHWWVrvUesFKtM4ISA4KFfoH36P5Pu/wyDLGQ4z0oMDyuu3WckMQuungPTunXZDaCJ1vIp+3d+/2/qBWNqrteDp0yd889FDjBQkMhYu39qZ8pXXXufGzfuokMDZCiECYvcAsXuwBbFau9X7nbe+HGs7wyf9VJgto12+eZdw8w4E16ffWBfHxDSG5slzjI0HaZ8PCLfuXorab1HzbbvyU3+U/NTnHjZMIPJ6ExfoyXyNeHKEzhT7u1OKJOZ5LoxBuIZVueZiNmO+WLGXJOQ6YzgYopKMpqqxjcEH8EnB47M1F4sVtfUIEVEi0fLzeRHTby7D5YFoKJSQDJTq0fMyGM5fHPH+j77H4a0DsuEEIRRaaV5980ucPnvK6cMHZC2CnqSaWlrKJhb/+EsDE4IFNErGbvHO4V3Neu1xwqJGY/JrU3QikEclYdFEqtcQT99CCiKsEkh0EuXmvQMR0JlGGolvDBoBUnHn9h026w2z86gWq1NNZSy50iRKsL+3zyuvf4njoycs5zM265Tf+u7vUAyGjCZTpgUsZmco7dlHYOqG2sGisdh1jZCGtQVBQl2tWxEbF0Pl3SD3KTwxtzGG1gRKCqRqN/Iu4HyItI4iSvfRb73jEPkAQcjeeATfFpUHgfOOta346NkR08GQ6f4u0/0dfMTnqI1hJCEfDThbzHj/o0esVmUsVJoojI1IUZIkjNIRtm4IykE2YlERcyoF6MEAVAK1xTU1wjR4E9W9fVbQFFPE8A5quMdQRq7tsi5xPqICwTuk7IhPWgo4H6Mzog1XEgRN4wlGIEMbxZAKkeZInSOE/BjNJL2T7wrpuz4TojOokoDvD2/d77ZxDNrfdcWv8T7iASOmXHSsE55AnHjdPdCvq+1zHhF8LEINhuA81rajqhVoHfO6iQiH0grfhD4H1HuPNbavaZEqQ+m4URFSIpOUfDAibw2j9ZFa2NQVm3JNkiZMJtOINJdrZrMlIGiqBlAUgyHjyQ7D0ZjBYICUunUcDggR5Q0e7xqapqZqLGezJWW9RiDbf4EkERRB4G0G1jC/OMU4w/56xs7eDfLhFJ8PmJ/PWW82cf17gWnqGBpWGiUFSsmXnIiUMiq0toWFXYqdaFP6otOPh2/n4vpJUw1BRqYPEUPhMX2w26SCatVYR+NRz1seo2twFdn47Hblp754fuoDJOxPIyrvHbPzM0QK+wFsVWNcYNVY/LpCiIbSBBQJdbWikUDRAjddIOb/o5+Cz/ZTXPmp7ff/bD/FlZ+68lP/+ZGN7VSIha4uwNl8zeOnL0h1QqI1eZpC2eCDo65LZvMZp6dnbc4XZFlBXuRUmxjmKsuKPBvQCE1lYi6YbGetaA2LQPQLQVz6v/suW8kipSQyVSyrNY8efMDN9+9w97U3yPMhaaqZTHf40ttvsjk/o1nM40kSGQvuUmiMpbZNRI+kaOmrXRRyk5IkifmyeIdbOwQBPdXk0yl7/gaNtVTrKt7N5U4OAe8txjSYpkFpzWA4jGwnqw3xBClQUrIzHuNNVNJ23vZsThDRqGsHh+wfXOfxh79LCIEXp6d864fvcuPwEHl7n7PZkmq9oqkdzxY1z188ZVZ6RqNxDDVmObfu3qPerLm4OGGzWrRic3FR+Et5Ol2UYpsKFI2PD1EZ3Pvo0JWINGfbzKIQnwi+f63uz7s8Pucc54slP/zwAXu7Iw4OdikGQ6qqYrVaMRwOGQyHWGC2XuNdRKdW65LNpmI02qUoChKZkqcpk2sT1M41vv3uE4wUqCwjDEboNCMJglwNOJze4GAyYpinpHlOPhqTTsaEYYZQCt8YLhZzzs7OODl9wXI5azcLEsJWKbPthj56E8OkoafGFSrSBSudgohhza4DtilN2/nRs3h1UYqwNZCXptClr/bYEUJUdw/0UYwupSqEbjDY5lGJ7T3E17w0twBBAB9TI/ARuVBJpNyURN7yHqUhtI5GRcNsW9X11gh16If3kZkry3OKwRBrmnaeRZ70qtxgTEOSJKRJgfcxDzVNElIdVd93pjsUxQCZaIqiwLktMhP3H/FzOGtilMkHmiaW6w4GY1KVYlyJc5ZEGVKdkic5q3JOtT7nRVOxXl4wmuySTQ44evqIuqljpMxGFKojBgDIsgxro6G2rfBR7GbZG1vnfD8m1lhCjxLFzWN0ui3neTuwskXYfNjm2RZF0avidul8XcHfVfv0duWnvnh+al1ugCkA4/mKt3/wAP27jxmOPySvHOJ4RmU9qU4RShKsww4zTCK4ZWo6WxZtnr/yU1d+6spP/SH3U78nafU2rBYHynl4fjpDSMnh9evkRYpcRLXopqmZz2YcPX+GklBkGQFBNhggLuY0dc1quUaNB+SjHTifbdEgLs1BeXnCt+CG6FQlQ492CASJUhRJSr1a8N3/9B959vgxo+GE4c6YtEgJVUVa5FS9WmbUk9BSExLwouVvDg5viWrrQoAPWNsW+UiBqWvWHhKdMihGTPYOcUiOnz7FlhXgANUunJij56yjqqIwlJRTlJQx3NSGsOqqYrlakiSKvBiQJJrgXbs4FEpLxpMJr7zyOsfPn1JXGwKBj5485t9/9wc04sdJaolfrcn0KY9PXvDh+z9ksRGMhiNGozGTyZSiGDCaTgjSsSlXGNMgiAgRrQHochml7HImPd5bQpD9wET2o9hH0dlIsiTDmmrLw7wdxt5UdFbQBs+L2Yxvv/set+7cZDga92I0SZqwMxlx7fA6SIFxDi8C67pV221GJDpFJoHJeMzBrZuEyT720SllAJUMsCTs5BNevXuf1+6/xq0bN9nZGZEkLe2dVkitCKpNi3KeumnYbNZczOYcvzjmydMnnJ2d0TSREk/rWPDtg2+ZpjzGVITgQKgeQciyrGd2uJwD2TnG0MJBgdAb1Q4e6qMXfb9dSoe6tA7DJcPd/V3fuvXRwU7bp7cLicuXiza82uCdJYSYCpZlGUmSAoK6NpRlE4vyEG1hWgzbS+UIjl5Z9KX7C9FWZHlO3Qxw1tE0Fc46ys2Gs9MTymrDdOcGWZZFsUQl8dYySDP29/dxIdA4Q5ZlNHWk63Pt/KOdn8YarLd4H6grh7EBmSRk+YgkZHhnKYqIChtjmDZjynJJVdeYesX8rMTN5jx4+LtYWxN96pYiECDPMqLi/HYMOhGlbXpadJqfdKxxrJVW/Xj1oWylkO3coVVzTbQiSVO6UHXn9z82dFftU9qVn/pi+anabYuZd5YbfnrZFVW/AOAnPm8wL5W9OSId+5WfuvJTV37qD7ef+gMp5EgRrcCmNjx5cUqIyiooAc57vLOUmzXnZyekWjEej9sQVoZO02iEqpq6yBlOd9FHz2nqiLgEopBIJB9t1U87FCm0KFI7bUX/f5xcqU7w1nJ+9Jz52Sl5kpMUGSJLyKQiVFF5lRAQQcQcRaK4SZbEEFFjRNSc8I5gA0EpfHBo2tNaiNRj1WaDN579mzfY2TvEG8/F8RHONC+hCs7Hk7a1ltVqxU5ZIaXaLsK2LZdL8qxgupcTBNRN3RbnaIROSNKM23fvc+PBLR4+eB/vHevNhu+/+7uIYshr16YMsiHL0vLsxQtOj49YbwTrNGVeDFhP9xlN99jdmUQ6Rq0wjY8oXezY2Juio3fdOtB4Qm8nIyJGRAAXAmmacfPmHcbDCcuLMy4uztlsVi+F0kKLJnXsBYSA8YFHR6d89OgZ92/eRAtJSAKL2YxhkXH3zh3G4zHrTWQXiRmoEmM9ZVWRpQMme7sMdnawwwl6MMasSwo15NbBTd58/XXeePV1Dm/sMdop0EXWeuaPNQECRVYkjHeGXD884M7tW9y8eZNHj55wfPyCs7MTqnoDxLC1TCUhOIzZ8k4HAlpEJhetOice+3Ib/Q+XjOxntPCJB/0GZ4swdfe9RYF8+KQD6NbHJZDqY5+9Raq8xfomFnWGgFKaNM1IdIK1jsVizWZdETx9MT5EI54mac/rba0lSZL+9o01uOBROiXPhzjjW9rCOIbnszM25ZrhcMBwMIyiZY0hOMvuaMRkshOR0QpSnWBNRBy9c31fOuuiYmo7N8tVzXy+YFNtyJNB3CgpDToBBHnweD9mx16jNjV1vaExDQ9enPHi6IhAQEkRCRCkwLVc4yHLemOstY55sVoTQui/d9zlgWjgCW1YX6mtMxYCnOgN/WXmju4anSSRPlnEVJeo2RJRQv9xxfir9pntyk/9/++nfnO95P+Yz/nvdnZau/EHb0+c41eqmiZc+akrP3Xlp/6w+6nf87DRMQPQYwGRLnTTOF6cXDAaTuKkCQ68xTY15XrFYn4BBFSSgtKkeUpdrjGNpXGBYjJlOB63tGMvt+79RB/27bWXW+RIbE/MIuoj5DoFGcNCri4x1RoTHFJIhsmATCd0x/ft4gIlJEJrhJBgG4yNiI0i9Hlu3gfSJI+sJ3WNCIK6alBacXD9FolUnJ0cI7xHCokI0LRF1xBl49ebkkGekaYJtmnwzpEXBUVesFgume5O22IiSzHIMM4gnAWpGI13eOW1Nzg9PWazXIDzXFzM+PCDBwzEawwPr7EsLc+P1zS1B2I+nzUWZzyV8diqIksVwW3l7C8XFCsVQ2kfD4N1CEBfRNRGPg8Ob/BTf/wbXNs/pN6sOXr+lB9879scHz2LWhid0xQRAVTEk7GQ0DSOR0+esX7nLUZ5ASrgG8t6vuDa7h63btzk5GSGDR6pE5Isx/rAQCfs7V9jb/+ANMsROmMymJDmO7x1/03eevU1Xrl7m+FoiNACYQPBeIS6hOC0iKAInZHr+LwFo/GQLw1f5fDwgOPjE548fcLTp084OTmOdMAi4IKnMZYQfG8MO85q1RWn9fZz25eXH3WkfvHaAO29dEhQd1UvkAjb6zuDfgnB2OptXDL20G6Etu+4HVtBwOODjTzfbdGpThS6LUZbLtecn88oyzqGYVsGmm4eKBVVSJ1rMMaSZdEgdXziIYBUmjwrcDZgaoP1JXVdU5YldV0hRUSVpInh/rzI2d2ZopOETVlGpdOWVSTS+G3pMY2xGGM7K0GzqbmYnbHaLJjoAtFKmkoVi/yElAih0WmOzscMxwGP56PnC9arOpoGJRDeE2g5zLv10XpP2aatKBm1XS6vj7h2tykcHaK0JRnYNiG2M8K5iNYqrUha7nedaBKdtGHt2N899fJV+9R25ae+WH7qdLngf3p+xG9taq7tTLlxcI3d0RAlPU+ePeH49AK/7S2USkjzgiLL0FrSNDXfNw2/7Rzuyk9x5aeu/NQfdj/1exw2Pn5K8dGoIRBB0TSwCiVeWiAQXGQMMlVNXZY0+QAtJEIp0ixDCkFd1tS1ZTAu2D84YDGf4Y2lIznbSsSJbo31iIZsJ3Y3KX3oipQFSmoyqXDBIUVEhjIkSml0jKdGxClEZKr/hC2Co5UEkeJp8Na2VF5tSDK0nOMqwRpDkRWsFguKQUE+HnPj9n2CUKzOTnrEqK4rqqpi0J5qG9MwGhQURcGsqjHWUgwLrh0c8PDRQ9JEs39wQKIyhJAY3yqmokil5Nbt+9y6/YSP3vsR3lqCb7h48YLTa9cQSUaRSFalIoRWmA6BD551uSagkNaQJAJvYwpVpyHRwxB0KE88rPSoT9tnHQgBccLu7u9z885dXnv9bfZ2d5mfn7Iz3eU//Yd/z7OnDzGmeVm3glhAFOvLAkdHJ5ydnnGwt4cTBjEUlOsNA53y2t17fPD+AxbrVc/8Yoxhd2+fm7dvMx6P8QKUl7x68z7Xb93mx998h3GeYVyD8RVFNkHlGciWRi8Aov2+XUP9g86+CQWTnRGj8ZC7925zdPQqP/zBj3j8+DHW1axXBlPZmAcs49xRSpEkMX8xAkRxs/AyYrN1Gluz/PIKi0a9HRcRNxGxiGu76egK0LbG5fJa3Y7jJ37To3ih24sRgsO6pmWkiKkJUirKTcVsNufsbIYxUSk08nFjAAAgAElEQVQ0zonOqMU37lAPa02PlFhrsC1rmJQSnaYUHppyg92UVGXFZrNmtVoyXy56YSIhBINiwGg4jCqowfcIlW9Dwr0qqo8Ug8aY/l6sbViXK0yoCcLince7gHCmN6ZSJEiZIrRCJinojOXKRsp7C0kaWoo/AVKiW4Nt2jzYzoEJKVHQs36o9rMaY16iTA4+fgZPSw3Y2jgp4zxxzkZudGKhblEUcTPQOcxu7UGPLl21T2tXfuqL6KcelRW/XJ0w2jS8s3fAtXRAkSg+ME95/2KG9aG/DyE0RTFmMhq+5Ke48lMvPbjyU1d+6g+rn/p9pFHFQYvhxfaDtJ0ulYp83CHmlxIAb/HOYOoG5xyZjBR6WmtUklDXDXVdkY9ydq8fcnZ6yvzkBNF1UGdMeAknevn03H7wrkAsMh3L1qCryFAhQnuql21HtZNCEBkoLjmN7iyupGyRJbDO9qdx7z0mNG1IKsUYR9nM0FqwWksOr99g//AW3jS4ckMQHusMm82SdKlRqWI8HuFDIMnSSGPnIiPA7t4uwVuePX3M3sE1tEqoqxqpFTiH0J4QJOOdKfdfe5PjoyNW56fgDNV6wXI5Jx0OWErJcPc6g/Nz5vOLHnWTBII11FVJXce830GeRE0IiEhZ38+XrEJrSHq0qDcQ0RDOzs55dvSc26+8TjIoyO2En/oT/yV3Xn2FX/u/fpUf/eB7OGsgiKhk21lPKZFBs97UPHx2xL27d9nROgr3yRTvPLcOr3O4v0NTrVDe4OuG4c4ee/v76CyqcnqfMBrt8TNfv8dwOkE4R9VUZIOcdDJA50lvZAO0EWrRI4bdvPrkRiUaK6UkUmbcuXOLvd1djp6/zunJC95/730K/S6KCzogVScJaZKCbFlOLq2dbb9tX7/VV+/v7dOaD6EvPg3tz7QbDs+lqFRnk9v/upSFqER8aTPUfdz+MxLD08bEQrEQQ791VXN6cs7pyRnz81lEO7IUrTqRw67HWkcfPNbUeBfFJp1zeOuQKmLLSil0EjdxZSmpNyXn56ccnz5jtjinKHLyJCXPcwZ5jtaqD5cDEdnzxAO2820xoKGpS5y1kYElCGpjWK4rXAjoVCGdblMbLhlfEQhE0TQhwJma4/M5xgUSrVsbFDs8eE+QskV+PVoncQyE6MPUvjERIVaRvz3OGdE7txDaMfCRMUiq7nn6AkrZcm92aJFSqqVqjHOke68Oobpqn9Wu/NQX1U+V6wWL5Zyk9VOD3esUrZ+i91Mebw1VVVJd+akrP9V/xis/9UfFT33uYSN8bIZ1RHICeoPogotd1aEypsGbhqZuqKuGfGhRWpBlCUmeURkTmTOsY7izw63bd1nPLhCmaZEg4sK/lDu4nYixh12wl4x9f/xt7w6CiGVwHtEjILRIUddpwUE/Yr6tfRGQCIVKBVUjMG1BknMOJBgcQoqoHp1INuWGurZMd3Yx1iKzHOctiZQ4Y6mrDU2VsplLNpMJxWDQIlRQt+wEo/GEncmIRw8fcHJyzE0dlcGHSYqCGCYnIJTm8PY9bt57lQ9WS1y1wdia05NjJtMptbEMhkMOb9/BOst6uYIQw+9KBlxw1E0cGy1BtoItECdSZKjqpj395I+Akoid2o6PkorzszO+9a3/QDoqGI0H7O5OSYubXL9zAyE88/mcxw8+QoQobhOQGO9BeBIpqZ3nvUdP+GNvvcUg0Sx9oDEZYZAzmk45vHWX04uS2lTUTYmWCSooylUJUnJweJ8b995A6hzT1ICgGA3JRkNk64gxsVjLh4BKNDL5ZPj9Ux3YJRRJa814MqIocm7dPuTevVdIdME//z//OUdHj5BCkKYpaZohpQIh+n4TL716eOn7Z645OqPN1tiHmA9J2DrVSCEYtmPUv8Cl7U9L6yjazc62iDaiUsFbTGOxpkEQOf3n8xVns4r5xQVN0wCS0FICxiK0duPUv7/H2RpnaqTI4rXetfSaEUVSGtIsIVEJZbVhdn7KydkLalejUxWpPpUm0SqG/61pxy06K+9c3BB4h/CBpq5pWtXXLoxvrOd8XrLe1HjiZqvLse8HU9B6c4FQinK55tnJKaWrkCKAlwgl6dhqOpQmSTQB0dOP9kV3UuBMm/sqJVrpWDTb5pVb79FSoZXCxN1nZE3phqlFEYUQpGlCmqb9exZ50RaL6kth+qv2ae3KT135qSs/deWnrvzUF9tPfUpF0ssT4jLbQOgsXWjNqhC4cJmWLuBdzFlz1lKVJZtNGU+AQJqmMYzTxFOqlIqDw0NGOzudHmI7kduJGS4fcrcLsBecaSdR9512WEPwuNYgdfcfOZ7D9n6hP9XRvWubs5ZIRZ7EQiohiGF3b/De0piaxpQAVFWNtZbZbMZsNqOxFtEqRlZNxbrcRGaDqmSxWFBuNhGVyiOzQdM0pFnKweEhjWk4OTpis1pRV7Heolu4AvDOU4yGvPL6l5js7eNVRMIW8wvK1RItBZv1mtFowr17r7Ozs4dAEYLAezDeY5yjsYayqaitxfooYtOF10I7wbsIZvv29OkCoXXWUmGc4+jZE7737f/I48cfYW3FcnnBj370Q16cnzHZ22cy3UWqeJ5VolPj9hhraZzh9GLO8ckp6+WSer2kKjeUxiKzEXs3X2dy/TVEOkamBeic9cYTbMpo9xaHd75Elg0xVWTjyMZD8vEQqTU4D5XFVQZfG4QNSL8Frbqp1hnMzuy9xErSzvNufshEMRgNuXn3Br/wZ/9r/uyf+3Ps7OzhgyDNMrIs6UPcfMJRxLXUfW3/bQ3rFvWhR2Rfvtduvl9aj7Tz/mO7rcu5mN1rf7LFVBNjWrXidrzLsmK5XOKsJ0tzBkWBEIKqrCjLkqZpcM5HxMNHzvUo3NSqu/rI6a1lZHzpjJLWmjRLEUjWyw0nxycslwsa22BcA0QE2FlLWZaUZYlr14B1ltrU2OAxwVHWFU3TRISn/TTWBS5mC5arNdZ1IpQxX1cKiVQSKTVSaKTUoFLWlWW2XMU8W2ujcJR1LU1hvG8lZd/voUOMlIqfJ0lI0mQ7Vq3T7ygAQ2jppX1EixC0oXzb0iy2DqDdCHSFeqJlUumKN4WIbENX7TPalZ+68lNXfgq48lNXfuqL66c+P7JBaFU6L5f8xN9cZnIIwfdV6TFc1URD6hxlVROEJ2nDWUKIdtAN3nqK0YiDm7dYzWd45/ucxa5TWmJstuwYMQR3aRnQ9m00MnR5sr4/vYvWRXg8+DjJ46m7uy4acA+RtzlEbvE0SRA2shZ414ruBANUFHqHwXCElJLVeoWzluF4xHp+wXI5pzaWdDDBATjH+fkFxXBEkRdkacpKCOq6AeDw1m2SrGB2fspmtWBndzfmCIp4YpYh4LxDeDi8cYM79+8zn11Ql2vqquLs7ISbt+9GMaokYbq7h7cOJRQiCJIko7Q1tTFY76iNIdFJ2zcBLQUhRBYP51vH6AMuxL4u8oxhmtEYw8o0rRqnwjnD00cP+Pf/7jeYn5+yWq348MMPWW1KArCzfw2FoFwuCD6G8hwe5w13Dw/56ltvoJOE+WrBhBFCamrhUFlgPCw4PJxyayfj2t4u1++9wp033ube/VfZu37AYLhLXca83uFwiBoULWrpL305lBCRNcFHZLAziBHSiSwivdHsdg7dt844hgAhGhQhJNf3d/mFP/VzHD17zK/9+r8mTTKStBO+6dzCSxndfDwUfjnUu0U1uh9iKLWbp931ni2iF2/rY8ZbxHH8pNnu8KtLKztEI+6sxXZGuV2zIGOedxsureua1WrNerNBKUWe5b1SaCBgTXQGWm/p8nSi46bJ2rjyJIhWwbVpGo6fHfP0yRNu37zNTjFikGQ0dY3Fs1wsmM/mjEYTssxhTUNVlVgfnX/d1HjnXxIUciEwWyyYLRZYYyDZbs7i+F2S9mqRo+WmYrOpIkVgays62sLu585OdEiZbNNy8B6ZJEilYjjaeayzLznPuGkJEDxaK4yxLYtQq29DiAhqkpKmKUmS9O8Tc/EvbVyvIhuf2a781JWfuvJT3SS/8lNXfuqL6ad+j5qNOCFEG0JBhDaOG0NevSFtj5tCxcIgY02k1CMiHdZ4ZJuLKITAO09TR3aAYjTg+u3bHD97yur8rKUtFJ+Ye50Rj3fkL51Gtxf1eY/d4zac1zM7xx8QrfhPCLHSv0PGQgAvIlogECghCCrmogUXP1sQnsEw5/D6DUbjCYvlnNnFOUmasjPdYbOcs6lKVJIxnkzwIobMN6s1s7MzppNJ5NGWkXHDOsf+9Rtcu36dJ++/x3I+49bdV9q8unZAvcd5h6sDWaK5e+c+zx495qSu8dZwfnbGzs4uaVFQliXBe7Is4+DggL3pHjdu3OV0ecH7H77L4wdLGmtprEeryFwwyHO0iqFBY6IAjbUW4yxaSg52Rhzu7mGs4cnpKRerGkI81dabDd/55jd59vgxSZKwXq1BRSpEneUMp1MQUK1WBBMFaVKZ8FM//mX+q69+hWo1p94sWG8qEj1BJArvawoM+4Xj+uE97rzyFne/+hVuv/0mO9NrhNpQL9eE4BkMBqg0xTmPxKHoQq9x4XWh2X4+tSgHiLg/6Nd2iz64AO4Sa4YPBGPAmogqBBBKc+PaLn/mz/xpHj58gBUCrdKXUyrE1lCLfjqLfqPRLS8hRHzPbg53G5gWqesvFZcLUVv0okOfOmPehmCD73I/t6u4Wx/dE3Gj41qqvji3nbMI79E6JU+zSG/XoonWOparJWVZ9ahGRETinGlMQ5bFolEpY8GrEuCs2xaYtSgOjeDi9JzHDz7ijfuvkO56TJJT5iuUjAWY1WbDoBjijKFarajWK7wzWNv03OJdv0ipsB6Wyw0XszmNaYipFnKr6BW65Je4kQvBcTFfUFYVhIBUCtUy03RO0Pvo3JIsA6JBTtOEy+kHSkpEGnO4fe17wx9CFESz1hGVg1WPUFtnYgFxu8HQiY6UjIK+ELGjLuwey5ec8FV7uV35qSs/deWnrvzUlZ/6Ivup35fORseqQWvopBLbsA0hSpv3qE5bGNNUZNaQhkF8BQFaK5I0aWnFKuqyxu4EhtMpewcHLM8v6ACjIDr0x186dV8qOBL0J/o2Wt2yd3QsINvJevlkLsOlyUyHOPnON8WTc5tFF0Q05lolgMR6i0pSbty6x63bdzGmYbla4rwjVwLnXRwIqSiKATu7UzblGi2jA1wulsxmMya704icGUNVVQx3pty9d49nH7zHxfkZ5+fnpHnGIInD0zRR4dUZQ4NglA85PLjJ8uKc0jVU5Yblcs71wYDG1pytl0hgWAy4fniNr3/jv8BJUL8Gz558hK0qrPMopcnTjFvXr7O3twMiRCPeNNR1Q2MMKgR2Bjn3b95kkKfsPxrx/Q+eMKs8hBjSLZdrnlUN0+mUPMvwCKyx2OBBa9LpDkEI/FoQas8kz7h/4wZp8JFf3BesNw3jsSDJUpxv0EIxVAMO77zCW9/4Bve/+mVUUeAqi2kiE0uiFSpNaKyFEFCinRLtYEqlorHyHhUkIviIgLYGrluIvXE0llA3COfxNjpO7xzBWLSQWGcx1qISTZoo3nj7S/z8z/08v/3d70PQhAg5bqfWx9ddi1L1b7hdYFt0io/9rgM8Ls9Z387Tdj1eDn2//L69B+hTHbYt5kJ7F8UdQ2iNRSsKpfr82bhhi+qkluA3WGNp6qZPN3DO9mq1OpGt84yKpDrRcQMSfGTO0EkUCisbnn70gGf3H7KTFITJFF/XiCQFH5lFtFKYsmR5dk61XCKsw5sYRu5Q7K5YNxDYlDWz+Zy6qaOjCT6in4D0gSAciAgbOttwfHpCWde9l1Vao4DKbulAkzZNxdgo6KS1fikU34WNpeqcnd0W3bX8495vc2udjWi5a3NqtVaxeLDty+C3LEZAn3srL29Grtqntis/deWnrvzUlZ+68lNfTD/1+YeNELan3RB6Hl4pIj1cTy8W2vC1DwgR8C5QVRvSqqQYj0m0JtESSaxml0rR1A3rzZphPWY0Ktg/OOTF46fUm2UsFqM78b5sqIUQ0SCELTGgIIaeuzB1/KvwiTndYUH9YkBEQ3R5zQQQIWJT3clPCYFOUozX7F6/xe279/EicpRLFVEJnaZYE5VYkZJsUJDkGWYx6yduVZWcHB+DFG3fClbLFcV4h1fuv8q7u7ss5hcsZxdcu34ACIy1rNYrmrJqWaUq0iRhPBpF+rVmg7GG+eyCnckOWZ6zXq8pNxtM3fDgo4+4c+9NvvLTP8H+/n6kOGsRKO8VRV5weH2f6wd77WSKp2bvIm2dNw0pnldv3+Jgd5db1w8QMuHb7z9hWTcgBEqAN45qXaKFRmqJDQ7rDciI3OjRkIGUSDyZTljMZlzowOHhPk2TsAwNlbPUGLLBEFVabr1yyE/8wp/m7lfeRqUJrjJoYxFKIrMEIQWNszjnEcZi2pBhRDlFb+SssyghSFSCUAKU6A2nCIFgLG5TY8sS2xhcE51rbSxNY1FaM92dMhgPSYJDJRKZKPI052d//ucovaQJ7ebDtzNNtBsE0fHth/6+XjLSl2Zj6B99ch1uNyJdasanXdatlU+iC5edQPCuRV8M1raKp0Tkpc9h7dd7nCvIyDUeQoh0meWmLTwz4APWGLxziFZcKPiA1JKsLSZrrI20i6MxQmnKcsWTB0959we/wys3biEbg6gbEBJvHVmSkmhNPV9Qzhe42hCcj8w34VJKgYhUowKBMZ7FekVlGpy3MbHlEpoWV7wDqVitVzx+/oKqaaIhlrK9JvR96VvkNYRYMJckES3yLb2htQZho1UJxJxd34b4jbUE36JFUqKUZLNZ09Q1dV0RgCLP24LDJLIgKR0VokPcuHYKsBE5uopsfGa78lNXfurKT135qSs/9YX2U38gBXHRfrhYhNXPib5gDRnDxjJ4bFNSNyvwU3SIPOYyTcjasFdZlqzXK+rVhlGWM5leY/fgOieP16gQ2S18e1IOrZJqhyL1xrxDqC5N6Q5numzg+djjLl0wUrYJBBFZCD46hsvaJiFEECCXmr29PW7df51iMASiuIuxBp1kSJGQ6qR3PEJJkFEMpSw37E6nOOdZb1ZMqymD4RAbiPRrIXDz1i3uvfoa3/vWd1jPLqhWa/JBQd3UzC/O2CxXON+wXq0wTcMoHzDe2WFdrTDrhqpcU63XDIYjdveuUVUvKDc1Z+6M73znm4z3Rz27igge6w3BaxIhSEUghIbaCYTXJFKiJQiZ4EVgWqRcv7bPwXSX0WjAqqk43yx499Ep1muktBjjqKpNlLLPB/jgsGZDALTO8EIQtETlA0pr+N6HD/DNIVmuWS8u8F4R8JjGIJLAzu07vPmVr3Lny2+hk2jAhY1FTEIIRJZig0f4QChrytmSdH+HwbBACoGtG1zLox3p2AxpmrzMeOE8TbWhfH7M7MkxMklptOTJk6fY4Bnu7XFyseTo7JyDm9f48S+/w93b10kSFZ2dlOwf7vGzP/cNXhyfcj5fM5uv+X/Ze7Nfy5LsvO8XEXs8853y5nBzruoaemJTpDmJEGTIECQZgiE9ytaL/icD/gME2Hqx32zAsESRcpNikyKrms3u6uoasirHm3c4w553DH6I2Ofe7KnotwJ0onAzb546wz57R6xvx1rf+r7eGC9n6DROxNvslHOgtjca1wJG+P8Mpc1fEqKHgOUI2h8+ir0xucU2GxUw4/qEd4T3toAB13qjIx0ekRYVJyQqweEzHSYci7E+oJtBaURrH8Sdz5wIY0BprNE4Y7AO+l4Sx74JLY5i8sRCkuLGY6K6xi0lRbnmyRdPWL5+QTedehOxyZRqtSRKM6LeUNU1um4w2vNsPUXgKjD772oDYz6m0ZrGdmhrUXjztCFeycGUSsHryxUvL0tknJHJGGctOngcDPzaJI79XLIWbS1Se3UhZyxG929ot7shnjhHH/wPwEshZmkC1lAXa7q68rzhOCKSijiKfDCP4xDslXfBDRKOUlwpiuzG323scGqHUzuc2uHUDqe+Xjj1d3IQFyFDM0yK4f3sUB7bDjfMJqzuaeuKtq6wowmIeFu27bqetm1p6obNes1kPCbLR9y8fYfi/DVdWW21gsGrdlgGffOr3bW/JsNiEEivahh4Zm8e1bZcFE70sKi2Gantb7zxuc45nJBk+Yjj41tkWY41llGeYiPfXDTKxzgHURKHhjZ/3qIoRgBN3eDmjjiJ6XvNcrUkz3OyLKXreorViqOjfd56+x0++fgTVuuVf401bFYXnD79YsuxrauG0WjC7NaM/YNjrIMXzzVd27Bc+ea+yd4hBwcHnD5/TlGVPHnyOer7EW1TXi1mY3BW46xXZemahnVVs17WIARR7CfTNFXMbx8jA+BMxhPefvyI003F+abj9GITForFaOt3w0KgnabtKgQSUi8p6IxvXjMInrw+IxaG2TxDdg1ORNR1zTptuXd7wTvf+01uPX5EnGeYpsO0PZFUoCJEJIgi6Rvq8JzO1IwY7c2I8xS0w/V+166UIM+yELwFGEdXNyhrUcbSPH/Bp3/9I0xnyG8c8zdPnvDp51/wzjffZXFvjmodzekln33xiqa1tL3hrYcnpKnPrKhIcufkmMODBZu64fT0gqfPX7NcLdHG+hsbeU3cTgzzOqx6567PvK8c7o3ffdC311KjVw19ASBC1seGxjpEyDm54G7aGaz1zrxxknm97LAanPUB2Q0l6LahrSu6rsH0Ldb0PkvGlXpLEpyMnfOqPlkWkSSJNxEK+t2e9+k4czWn5y95/rOPeOwE+c27FIsZxWbD/tENYuUzW23XUbftNjg6XDinbHXARVjFvTZBklAjRDBikj5rZ53wwd5YXr16zWpd+Bsw5d9X9zqcYZ8dykKGrNeGKFIYY31TXwjeOkgtGu2zliiBNp7SMNxkDleqLAs26xV935MkGUqqbWZNRVEw2nIoGdx9nT9uX7Y2/7/myH+NY4dTO5za4dQOp3Y49fXFqa+gUTEkaLbvIUMZx2J5IwCGilyQuUY4h24b6qKkm3akWUaaJKhRRNe1lGVM0zRsNgWzWUk+ytk/PGS6t8/rqr6a3OLqUK4jxpUU29VjAytue0xXT756rrv+0FUJe9iBi+2rr06CFYLx4oC9gyMa56jKgjSJyfMMay3j8YiyLBEyCtkkP1FiFZNmmdc57zryLMNaQVlW1HVFnh9graUsShbzOScPHnF86zZPn3zJxeUFe4cLuqrg/NVzTNvQ9QYhIyIV44QiySYcHUf0fcfLF1+yLtaMVyuyyYzFYkFdbCiWl1RlwWcff8z+wYLF/j6vXjZYa9C6o9NdEMUQXF5c8sXTl/RWeDfdSPDo1hHu+JBys0Yay/xwn9l0yjtvPebV+Yam/ohVWW0DSq87XAfGaXTXIYREiQQVtJ611iC8tNqz5ZLpl8+4tZiwmIzQVjA9OObuW99g/+YxKoqwrca0XmfbSEsSx7gIXCxQcYwEcjfF5iNEktD3GjodnHR9oN2WoY0BK5C9xTUN7abk5UefYRrN8cNHXFjLsjM0RLy8WHH21x+yLmuEijg4OGA0nnBxseJsPuXW8cFWUzqSkmicMxpnzCYjZvMpz1+cslxvWBUlfd8jri3ON7UNf9kQ25k9zOZBy/yKXOF8U6C12xsZcW3NCryu95Ax8Xxvy1DW9WV7E6QrPQ8UBH2v6YwP9lIqzyd2YLsO3Xe0XYPWPYNKCIAQCmcNfd95KT4HXd/Rtq0P4HG0PW5jLKSOkR2TNxOa9QWfPvmS947vchTHdL3DacijlFhGoCQujuidpe29G6tzXklE4APcliXjHF3Te4qB6VFyADUvqykRICRt2/P67JK68Y2gSviAGkUK6xxae56vC4ofg+mU6bX/fGPQxmCs3paxh4DsJRWHG1lDHMyOVsslVWiKjZM0GCr56yOFvDK2CvSRgQM7XMFB4nA3fsnY4RQ7nNrh1A6ndjj1dcapr5S+fUNM8PqulDdi6hthbzsNdecDSbkhyzOyPCPNEsbjMUVRUjcNTdNQFAV7e3uMJzOObt1heXZB11SARTp4o0uKNwP39lidxQ5dT9ee7gO02GaJhhL7m0f9xrOv/e2/Y5xmjPb26YCi9OVHvxtOSZKEyWRCVdcYZxlNJsRRgu4MEp+tuHCOuqrIUl+a19pRlhV5PiJNE9qmZbVac3DjiJMHj3j6xZdcnJ1y89aR310rxWgy43i2QKiYdVFRdy2JHZFlI27eukfXtZyfnVJsCqZFSXY04ej4GJxmsynRy45RnnLz9gnaWi7OXtN0PdrBeHHIeDKmefKU8+WKzvgsWR5LHt86IlKK1WpF03RoKcgmI44WM7777ltcXlzy4c8+w1ivvKB1h7UaJxzOaBDKyxtGkQ/oUUzvNMbARdXy8fOX9PqIKNnn5PA2j9//NoujYxhcfzuNrv08EVIhp97QRjgZEi+B9wo0RQU4FMLrpQNd04KAOIqRyjeURULRlDWvPn1CUTfcef9djt9+jNqs+c1Zzjsbb0T18uyUF2dn7O3v8c4797l77y6bzZrTy3OkctzY3wsNav4ORgjBKEs5uXmIcI4kjhlPxiyXa9abwj8vNKxeBeqfm8tCXFtYw43M1c3HVaS+uvkQUuDsNfnBa3NZSgnhXInh/YXPqg5SgCAQQtF3mqZtqTsvA5pEKWmaefnI3psqbZvO3LWbOBEMi3qvA65UTN931HVNlmWkcghadvudIxEximdsaPjsbM2LOOYbt24zMopWC5IoQ4iIOMtJJyOc8i6pfVD4EMq/k7F2GzitcbRN55sAjcEJb15l3dX6lzKmrjsuLtcY67aAJoTwc9ReSRUaYzzPXSk6Myh4eOUifxMU+LBDVt34rLSUgkG3XSlF37VedtRoBF75hDR8ZvBIwF3F1YFPi/CvF05sjc124xfHDqd2OLXDqR1O7XDq641TX1nZ2Ja7thPkapL8Al7tXdMAACAASURBVONUELiyvoRtraWpS9arFePxhDzPyfOM2WxGXdVsCq81XJYlTdMymU65eXKfi1evefH0M6yzwdb+WgMgQznq+q7a/22dvQKYN4/sFzJOwxjWzPXvB1c8RSEgn0xR+Zh1WVG3nefHhR2xlJIs9cG86zVJmpOPxt4Qqut9Q42UXjWj6xjlCVEkqeua8/Nzjm/cwGnjlT2mU04ePGT+4V9Trpeslkv2j4+4ef8tJvmIO7fu0HQNH3zw19RtSd5nSCmYTBfcvHmPYl1QVxVVWTHdN4ynE6w5oihL2rrm4uyCGye3uXP/IVpblpevEWnO8d1HjEZj1E8/oTU2SLZJoiRnbzojkTHLzQbVtPTOMa5zxvMZJ0f7PDy5xY8+f4LWfVAicPyChrZzICVZPibPM8qqYHl5TtVrTjcVWp8xn9/n3lvf5vbJI4SSxGmKUIKuaajWJU3fkY1H/m21AwzW+Sa5rmzQnTf9ieOIOE2xztJWLV3b+salxBEHp0ttNGevTrm8XHL44AFH730DOU64uZdxfO+IIbG4KSt++KOfUtUN01mOMQ1VW/Lxp5/y7MuMv//3fot5moMSiFhC0JxXUpDGinKzRiQxN472UVKyXJa+XOwTCtdmqNv+KUKW9Bem6hsBmjcCtnhjtg+p3auGNC+hp5Dhs73PAFujJJ+lgKpqqeuWRncgDL3s6NueOInBWvq2xegeZzU435gphZcJvPIlMCgV+7XfevBVSuGUvOay6kvisUqJ4pzXVcmXXUM1HbEvUnrdkY4yVCJJpyNkmqKdpbMGbQ1KKr9GnQulc4NSXqq0bXu6rvUNbEE+UAqfeREClFO0Tc9mXaGNw0nf4CuHuCG84RFbeowPAj574x/Sxhsqdbrbcl6VUp5/qyRS+lLzoPzR9z3Wuu1nGN2H18htBn6bAXdBKz3ET6XUdj7uxq8YO5za4dQOp3Y4tcOprzVO/drNRqCEXjXWhWyR+CWT7Eq7OZRcQtrG6I6qLtgUa9IsJc9zZvM5bdexXHlH07IsWa9XLPb3WOzvc+/RIy4uTqnL1XaahlTPNmj7qRsmrQvgIoYQHPba4XG3zXIN/FbeiO7bBSSuHhchEEkVMRpPUGmCjCLSLENJz4211tH1DVEcM51O2VQlaZIwnc54/fqcYr0mG6XESULXeV3wVjaAQmu/Wx/lObFU9NZRVhVHx8fcvX+fv/3wQ87Oz5gfH3P/rXfJ0xGz2ZRxVzPb+5yL81O0aTE2xtqE2eyAw8NbnL58SlWWwQRHMp6MOTw44NWzF5TFhtdnr7lx6zYn9x9hnSOf7rG4cZs0zVHZOJAOfO5tlo/Yn82xumNdbkhHE1RR0rc1uu/JxlPyLAsTzW3Pq1Jel9nYsIikQsQR49mcO7eOWS0vqIuCqu+pO8sKw/z4Pif330aqmDSNwUC5WlMvNzRFjUoT4jhBCIntNH3V+CxF1+G0YSjtISyta2m7jq7zIDvMVRnFKKBYr1kXG27cv8uNt94mSmJMr5EEI65YQiRYzCf89m9+m1enF3zy6Sf8xz/+PuerFeeXl8yTjHdv3Wd8dATCEU9GuEgiJIgIppMxF+en/PSzz/n2d77Lo4cPefL5M86XFyHoqO1ku2okG34P3yUo2Vxfa1dF6+EnfDexzd+EJq1rejbCBwaLCjPdBWUOjR4a2npN13tpzTRNQTqEdZiupe8aCMHSOu2Py9rArQ0OokJitabvO5IkQQhv+NP3HVonWBuCuLXb0q6hR8SWvm548fRzlpfPmewdkM0E2RTirEN2mrZvqNsWG1yVFZ4K4fDa/tYapPSl5abtaJoWYzROBa1x4Y3TpAjZGOfotaXrelRwVh2CNCH7ZKxFhVKx67U/29ZidTBvapuglOPjiT83jr732esoismz3AOI9YZIfdfirPESi8bP2UFrfpBhHG4ePVCFeCsF0u6kb3/V2OHUDqd2OLXDqR1Ofb1x6tdXNkLpZphEjrAgCS6R29VxbXM1TB4nEMIhrcFWGzbLiCxNyPMp45livrfH3uUlTVXTtjXLy3NuHB4yHU+5ce8ue8+e0HxSbBv+JAI1qHEwMAPDzB8m5rCL3B5J0DR3flF4R1fhdbVDWdO+gUYiLCR/7DhJFI+YTPcYj0e+eUhKlPRycs46urZhNBqTJjnVF5+hlGJxcMTZ5Zrl8pKT6R2kVPS6om0aTG+wQN8bppMpy9WKw4N9siimWZfkR0e88877/PSjj9is13R1zeHhMUrFOKtJkoR79x5SFBVdb8kzB9IRZwmHN2+zWp9RVCvWyzOmZsF4NOLmrVtUZc352Rl9WVAvL5jO9njnnXeZLfZJR2OG5sqh4VESMc0zkiSiaAv6tmIynmCtpa46BApNShtKdT5r4OdBGkvSSFB30GmBkxKpJFGsGE8nfpKnCa4Gp2MO9+7xu7/zh8z2FkjpG+PaskSbHtN52cI0zRBCeDCsW7quwxjP71RCEkU+WLdNT280WgeXzEh67WznnWq1adBCcu+998knE1QUg9UoLK63WAsijxEqxmFJpWAexZSv13zwX/6WTdugBNQq4vTlGXcWewijQ1bKQqTACfI44b3Hj/nT7/+A71/+Md/8N4/53rce88GPKr54/gyE18WXSuGb8bw0pRMS7zArt3PYZ5EUCBPCuEUIA9IG42LP9VW4YPblVUj8mvRNq3K7nh04gzUa3frsoDaW1jh6q7w2exR5Qy9r6G1L37aeM+uuPs9ZH/yFAOEsMkhzmq7BZkM52quy9H2HlFcmQDpoeGtd+6OzildPPuPVz/6SxcM75KMc2xc4a9Crns3qlKquUc5TD5zz72Odfy+rDVL6JtiirFluNnR9TaqibelZCBGoAd5B1mdyFVEkQ0ldXTMn0j5eKIUzhr5raZsWay0iuKxGWnlZ03As3qzLxxAJCGGRzoUsmyWOUqKoR/ct4AF0wGAR/pBCbn+GjJF3SDa/Nkz/Vz92OLXDqR1O7XBqh1Nfa5z6isqGePPfgBDXG4KGIwiBXghCEQcAJfwCMn1HXa4pNlOydMN4MmNvb8b+/gHFZkOnOzZFwcXFBfO9Q7LxlHsPH7F6+ZKmWCMYmmb8tlgItsgiENhrADJklOT24NxA/9s+R4oh5Icde/iuW/OZ7TcQJOmINB9vpb68qoL1zTJK0CHJsowbN25wuTynqiqm8wWT6Yyi2GxLTMZamrohijQyisLnCNabDePxmPE4xrUdTVlz++SE/cMbvHz1govzM46ObqFyhe47RBRx8+YtLpcrXr54htYGmfkJN55OWOzt8/z5l3z55RMO9iqOb9xkOp9w89YtmrqhLgvW6oK+t8wXCyaTCX3Xes5e33t4FBJQaGdZliWxMMT5nNF4ge4acBYhFIaI1jis1QwdlwJIIsEoVQEYfJEU59C6p6xKOq1xwj+eRBm/91u/x3vvvosvOeMDRjBAchaSNCaKFLrXmK7HdBqrfXOSw2GlQ2sf4Pr+Ss5NCknfG5ACIRV9kHebz2ZEKvKZlXCXYkyP7gxCRCRO4rQH6GZ5yfLpS/Yjxf2jI37y5HOSOEZoS7HZeL6v6bFtB8IhhUU4hcRw99YdHty+w4cffsDy9Snf/M43+c7771EWBT/+yQ9RSjOeJkRJjopzoihBSP8jXQzOzzfPhQwSm85fG+fi7Vy/Wo0DjcNncMAHJIfPmgxscGcdWmvatvclauOlD9PcS9sNzV84UJFC6WhrAiQtbJVFwjJ0NpiNOdDdIOUYrr8NzqwhM6K1oes7dN/Ttb5RT8iE1eWS55/9Lffyl8wORiTRHkhoVhOqcu2Nu4TXAdfGYkTQ1w+ZLKN9U+dyU/HqYkXRNORxHuKYfx0MQVyQZUm4FRRXHNRrvNpBs9wbHBk/76y5ot+EDNPQgDhkjrzKj/XNeV2HNl4JxJsuxYFHq+m12Rom2ZBxGm48rgyWhkzUm4Zxu/Hm2OHUDqd2OLXDqR1Ofb1x6isqGyHEhbkuhdfT/WXjKk/z5sulkBhraJuWzXpFEo/I85zJKGex2KMoCoqqom68hftRseFgcsTJyV0u7j3g85/8LcJ6MxdzbcJ6ecFrHLJrn+eNlAabdcW2dO3ACrfNavkMmLjSK3fXDjz8MxuNyEa5B4vQ/OKcN0bJggRbWVbMZjNu3brFRx/9lCiK2D/Y5+Wzp7RNw2iUc3EJVVWRJAn5eEyapAGMvCKCrBvydMx6vebWrRt861vf4ezslMuLc6pyQ5am3iTKWuIk4f69+6yWF3R9F869LyxPZjOSs5SyKFAyYjKZohLFZDLj7t37fPrZR6zWa7reej5fr1FScXR0RF03GO25nBbH2XrNjz//gpObt3j/G++wWCxYn35OXxmcijFxSmfxWZZwPXAC6RxJLBEqxiJxMkYgaNuW87Nz+r7HaItwMQ/vP+C//Yf/gL29mTff0dqXG4UA6xBKEacJCOh77/6Ju6IfOIbsgW/CssaA806Zg7RkrCIwlt5qJpMxkfSl1IFq0duetm8RQpIlEQ5BvSoplxe0Z6dsnnzBlIh/9Fvf4xuPH+IcpHHCw3snEMqwuvWqEk44hPTl0zyK+f3v/SbNckmzKpEGbuwd8tu/8T2Ki1f85z//DyAbposF+XhKnk+JkxFRPEJFGUqlKBkjhcIhA8A6Dz5C4JzADTxPKbFOIETIpTqHNoayKuh0s+VoKqWQrqPrNG2nMRaEiBjlI7LJFCG8u6gxhshFWBuhDVs5v6F5DjtcAxcUKfyaMsbQti0qSQGv6mGMQSqBMy7IGHZobXFCIlVMlCis7disV0RtxzExN+SaihSjb9L3njvqXVEV2rgt+PobL4vRHV0nOL9c8uzla9Zlxd5oipJDg+ZATRFIJUiTBAHBbTaAvpSoSHnlD6WC26z/vgM/1Tl39bgbGu3CLaExdJ1XCElTRdu26GuGR0mS+NihBcaCtg6hopDF4yquCfFmLLK+rL4bv2LscGqHUzuc2uHUDqe+1jj169Wo3Ju/vMkndW8+9/rzhjKv8IHfGIHVmqYqKOJLRumYcjbn6NYRi4MDXl9cULcNm7Lk4uKCxcEe89ke9x4+5vWzFxTLS3/NhEVIttzcgX87ZHeGP7cZIcE2gAukf/3gIjvkl4YSPINr5rXvIwXZZEyaZZ6ndu0713VNUzXbx5bLJbPZjDzP6NqWxWzO2atXbDYbpnsLpJDUXQdAkmXIzJfrsjzziybyusybzYbpdMRb33iHn/7sI05fn7JZL5nP5kiZeC6f1szmU24e3+Tpl0+8woZxlOUaKSWz2Zy2aWmbmrZvaLqcODYcHB2xKS55+vQJVVXQ9z2r9YqyLLl77x7aGJwTWANCOZZljXn6isXBPRa3HjGdjen7Cu3OIM0hyemdDNkAuQ2s1nlN5jjO0UCnFUivmrDebDDGK0uM0xl/8Lt/wHe+9T55nm6NZ4Zra3E+YxHH21LdkEXy2SLLQEkYGroQnkc4NINFUUSiIuqqAgXWGtq68fNEeRfaRndo58hHMVEk6Yo169cXdJsVbrmkO3tNko+5f3LC7TsnWAdZljOfjxGuR1sH2kDnvyNxhIwiBJZvPHiA+/0/YG/vANE7pIKTWzf5/d/9+7x6+pQ/+g//Hi0+YzofMZ7OyfMJ+XhCNsrJRxPyfEwSZ6g4wSjp53FQffD/EljpwAXOpJBYYzHW0TQNL1++5GL5ijj23Mw8z4mkY7Na0/UGFaekkSDJx4jwWiUVaZqigK7tiHpDpJPAmW23NzMwXHvPNXXCf/+260hVRBQClDEWIew2W2SsJYojkjTFoD0AComxEXEUsRhJxomj0sIbigWFDSkFkYrohLnGcfaa48ZqdOcoy57NakPbdjhnGLLGCF/W9zcBEVHkecHaaAiNdKORp6A4Z+l7t83iGGNBBnpMAA7rfCZYSknX2W0m3RhPIfHrvcFa62/ApCSKo60LrrHGA6i8KqFHQcf8egZriDs/H29342rscGqHUzuc2uHUDqe+3jj11Q7iw4vd0OZmr5kk+VSSu5YmGvI34npQlcJPgK6hqTYU6yWryxXTgz1G4wmzxYKiLqnbltfnZxzdOGIyGnPj9gn3332PZ58/oWtK2qrAmh4bNlR2Cy4Siee9hr0bMng1unBQV2U7/7e79lwxZJHEtUyEA5XE5OMxhBM7BAuAru1x1jKdTtHacHm5JMtvMJ/PefXqlMVizmg0oigKpnsL4jShLkK2aTB9if0u2DfmRD7wtg1nFzF37t3n/W99h/M//vecn73m+MYxURJhjD9WqSR3bt/m4uyM9XqFUpLL5SXSQpaNiOKYrmtp25qRnbPeFEQq5vadu1TVhrOz10EyLWK9WnJ6mpGmGfP5HqvlEumgt1BUGk1GPJqhxhPi/dukIiUbjYmme0zme6RZTl11YQYIjJUYFyNVCpHBaE8X2O7QnSVSEY8ePOIf/uE/YP9g7s9pb7flv2GoKAIhwvcG4aA3vjQ9cG+tdVtgH3b9CC89GEe+qa6tatJxhu61z54EnqPpNQZI0oTIQX36gs3zZxRFRywker1GFyVZOsG2mjjx2tmJcEQ4r/Ah/HG5TmOcRqQpchyBtcRC8ujefaJR5mXLw/x5+PAx/+S/+xc8/2zD//1H/wea54ynI+I0Jc1TxuOM6XTCeDJhPB6RjTKiPCeOEs8rFRIpMoTIg3ReBDLGofxa04amaXj+7BlPPv+IWEnyPGc0GhPHkrauqMoGqRISFeOQHugcjMdj0iTBOYgcpNYi45i062iLgq5rAuD7c26dQw6/h3Kuc17FwgdLf9373uCsI00S8nyMjBO0bWhkh9IKbSO0SHFJTk/GpkloWq8e4jnxoalQXGVWhrni17llPh5zvJgyiiVX5JOrH68MonHOhB+xnZdd15FlGSDoe98oa4xvkrOhzOxN10KjHGz/raRC4xsApZRB8UQThUA/qHfEceKvl9Ye9OzQPOhL1VLK7WdeV/4Ybm5241eMHU7tcGqHUzuc2uHU1xanvnKz4bYB8M2M0ZtP2q7fayc0BFDhObFWOJwxdHVJoVZcXl4wupiz2Juz2NtjXay5aDvW6zWvT0+ZTKaMJhPe+ta3Gc3mrM5POXv2BcVyuQ0ExtkQrP0HiWvgwbZc6rYXXCJDG5JDMhiR+J3oNvXk/DtZYcnyEdl45F/hrn4GE5osy5jP55RlyWazpixLRqPcc2WlZL6Y8/z5hrZrGY/HlKs1UiiM9SY1NvGNQ1mWIoRgUxbEsaKuKuqq5uGjh/zspzc5O31NVVWko1G4HhKprS+J377FBx/8FX3fovsOJSLyNCdJUuqmoig2TPeO0J3B2UtO7tzgzskJxWZNVTcoJM4a6rpiOj3m7t0H1OWPMboLEoFetsIAWkj6fEGqMhbTKelsxoPHa+7ff0hV/HhbmuutouwEsbI01tJaR+wEMd6QRwhJlo74/f/m9/jme+8RKUkdVBOctVvHVYRARQoTJrH0trsMZjJRpLbl0UHCbWiOdCJoQwN1WdHVNaOJ50b65lGvI20cJGlKEsf0ywue/fAD1l98TjreQ4xnlJeXIBRpPkWpGGEskRBIrXGm966aUiEsuK7HOoOREqkNQjp/XawhkhKnwlx1FmUF7377Pf7Vv/4f+fzLJ/zFX/0561WBiy6R0pHEEXmakI8S8jwlH+fk41HQzI/9TzohTaeoZESUjjynVmVYG3kt77bh/Pyc18+ehYaxmDhSyNgDo3KSSGWo1POge92RJun2vBrrUHHMOPaqHc5Y2nxEXZVsNhu6rhluk3wgB5+ZCSogQkqiwK3tOg1CkOUZ41FOkuZYoZAGjBHQWN802fe0dozoYFMamqYPJWQb+MBXwcarhlzNkyyP+cbDe3z7Gw/ZG+f+mJ0DZ9neaQqo25b1ZkOvewyCOIqIoghjvEsxLjiw4m8KjGB7DODlA5VVnudKUEVxIJzDCB0aC70yiFI+xEZRyKDhUEiQCiEUfeDwaq23JWhjfGZNChmOy5e9d+NXjx1O7XBqh1M7nNrh1NcXp77CZyOUdUKt91r19iqR9Etfx7Y8PDww7K5039E0BcX6kvPzCUmakGYjprMFVVlhu56zs3OkirhxfEya50z29pGRREkH1rK+vPBSXPjF6oYyNDJ8dpA0DBcNhhK1QDiJw5etBFwL5sOX8d8ZochHU7Js5Pl04fgdA5hZn905e81sNiNNE169OuXgYJ/xaEzXdUwnnt9ZbArm8wWX8ZnfCVrQxrs8tm1LlmfUTYNxhnE2ojeay9Ule3tz3nvvm/yn0z/h8vyC6WIPKQTWGDQCEymOb95k9MmIT3/6BWkckaUjOqmIogQlFVVV0dYNSZKzKde8PpPMZwuObt7m6ZdP/bH0mqZu0NpyfPOE5eWSFy++CBQDr+hisHTW0llIkpx0tsd4PuPevUd897u/zXJZ8OUXn4NwGCKqXhBLQ2/wYKsNSgikjHBI9hYHfPe732I0ztHWly6HmwMHaOeIlEKpaNsQZYZ0Hn73PRja2NAsJWNviCQQgc8oMV1P01S4azJ+At98hiWoWiToumV5tuT1y0v0qiGKOor6jL5tyGcL0vHUGzAJL00nhcNojUxCEO/dNoulHQhr/Jyd5czUhCjLEIkC58uiDlACvvlb7/E//et/g3MTfvbpj1iXr9CmoLOGvq0pisaDthCoSBLHkiSNSLOYNB+T55NwszEmy8ck2RSlxsTp2INX1TBI4ui+x/Q9rgGCunkcdyS6xymF3Aaz4KArFVmWI0PTqZKCLM0ZTabIKKEs1r5h0fRB8cIFt1hHrzV9r0lSSOKYGIlSijRJSOIEISVYrw6ihEQ7S93WGBujjabTPuvi3Vh9o2VnNdpZpIpwQmybAYVURHHCZJRz5/gGhwcz0lThMAhr8JwWGQK45ZPnF3z28pSmaX0wDWtaSc9f7btuG1DdABSObTbIX2cZMk4gpJfUjKOYOHDjfVOdz95ZZ8lTfwOhywYhvLqICufahgjpbxANSvrmYML8ds5t+cC78UvGDqd2OLXDqR1O7XDqa41TX+kgPqRShPA7M58tEW/8v58P4G8GcwcCIgna+p1l21WsN+dwlhAnKYdHx0ymC4rNhr4saduGF8+fs9msOT6+w2xvjyiJiJSkrVvKzdovICEw4pqxEcI3Pl2rl/98d/xVg5i7CsruKnvkD9viiBmNF4xGI2Tkbe4FoKREG0PT1H4RJIqi2nBxceE1inuNihSb9YbFYkaSpmzWa/YWe+SjnPVmQxSrbZm673VQWrDsH+yT5zmXy0vMxTlxJLl95y63b5/w+vU5e0fHjEYjBsjqek06GvH2u+/y4ukXVMsLRJA5EyIijlO6tqEq10wmY5pGc7G8ZDKbcvPufdZlw+XZOV2nSXpN1/aMjye8+/43Kes16+UlCAvKYRwYi8/YCIGNYmSUsNg/5O33v82qrNDWcXb6AqcirPBNW9IXTLdZHiXBOsX+3iE3jvYwVuO09O+9pQ345jKVpZ6f2WufxQvZOoREiSuzHAAZR0QhEyIQCOXpCV3rJfGUkkRJHOYwCCeQKkLGMVp3rC4uaJueOw/fI7r7GOhZvXiOUBHpYp8oS5E4BDY0u/mMk2oNwoQSo5SQxcSTEWqcIdMYJYUvlYvBcdOitcVYg3AQpzH/6J/9Ifcfn/CXf/4hf/In3+eDH/4Vl6svca4NChqW3gKN3s5RJAi1REUxSaKIk4hRnpBmU9J8wf6NuwgidN0GxUK5DUqD0avD0vctzhlUmiEjhTGaujYgBHGWoZ1DWd9MqGREPMpI8hEyjphOJ/S6o2xqmqaiq2rviGocpje0bUecGtI8Ik9SL9unlP9s50vVXt3TYJzGktI76LRBSoNxgtZo2qbHhjXiGwV9c60OmuEShUNhjGBT1iyLkqprmIjYn3cRYSX0rebp6ZI/+5vP+OLs0md2HfS9I4oiemeDpnsfjvFK6QNE4Fd7nXEpLUoN2WhJ1zbeHElIksQrfTCUsYVgNM7IshFlc4YSgdur1DaAe+Uff5NLiA0WsNabJe1oVL967HBqh1M7nNrh1A6nvt449dU9G0PAdiJogbMt08KVY+oQLt3VP/HcM7/J97sg5SUGTU/dlLjLS5JkzGg0RUaSJI6wSgWjmx7nHPNpw42bOYvRnE0UY9qe1dkZy/bV9mPEtVA9hGGcD9gecsSVDCBi69ToCOXma8gjwyKPo4jJeEKapFjh0PbqJAohGI1yJpMJZVFQ1Q2j8Yhi42URJ+MJ69UGB8zmMy+j17VMZzM2ReH5mqF06qyl63viNGE8GfudYQhORVkyycc8futt/uIv/wsX5xekSeI5c8KXtlWsuHX7Do/eepsP/vw/U9UV2visThwrus6wWl4ymUwAR1lsuLy8ZO9gnzsnJ1SbDU1dU9eKoigoq4qbN2/y4OFj/vaHH4RMoW9O1NqXj8VW59kio5jDm3f45nd/AxlF/OhvPuTlq5fbazGc8wHzHSCd4ObhEaNRTt/rLc/YzzUPlEPJ2ASptuu0iKHh0gYpO6kUURR7yUcEUgqkBN12NHVF33Vki7l3zAxydCpko3COclNQrDeYrmc+X7A4mNNWnlbQ1w3xOEcosM4gnURYgbOgeoGzLRqBTRXJZIKa5MgswSmB9e48NE2zXYRN3dB1esurVipitkh4/zcecP/RCb/1u7/JD37wY/7jH/0//NVf/RnL9TOkakFanJVY66ut1oDtDA6NlCAlXEqBVJekoyVGZkzymQ/SXOOmiyuO+rBirDGgNULrsG7wZWXHVjVFCH+ehfIc12w8Ic1SjLUkfUtTt5TrNdVm4zX6g/KKtb6hVAWH4uG6Oetl92zggobVR28EnQYpHa1x1L2lqlu00RhrwgIPYB8UOPx/EZ2VfPHijB9/8iU39haMb85ARbRWUFaa15cbfvjREz758hVV0yHD8fWdN3ga5vR1DuqW6xpFGK0HoqEjXwAAIABJREFU5UwfrOPEq8kYi1aeohCpiCjMWyEkcZQgpCNJUpRMSJM1rvexbVAUMUH2ESE8bcUa+q73EpJxDIGCsRu/buxwaodTO5za4dQOp76uOPV32GxsIxeD9NWbXecu7NLZfvHtQnPDUg6yfUIiBRgMWnfUVcV6teby/IIoi73MnJBEMsJgscZRVjVlUTKZjJjN99DacOvByqtsbDYhiF//7GuZn3ABHFdZpOtd89uvEI7RMzB9SI8iX+o01utfbw1RQgCWUtE2LVVVewt4rUMDjX9TqSRN3TAejYnjmM1mw97enm9oshYZie2xdV1HkibUdU3btqRpigTaqmYVHGtP7p6wWq/RxhBZL9OmIoUxhiTLePvd9zl9/pJXz59ijKFpKpQSKCWoyoLLiwsmkyl927LZrJjMphweHHBxcMjzZ1/StDWXyzOyVynjccrJyX1ePX/O5XKFcyLoiht/DpTfBWut6Y0lyjLu3HvAeDwlSjPK//ePKdbLKw6yFFg9cFgdmYp4cHJClmbBSEdug4tzXh4vjiKvs933PsPhhmno39Nafw2V8s1MUWjQE8JfSWsMXd3QlTVFUdJJx/z4CGkFRjuEVCgpacqSzXJF1zQI62j7jsY6RByT5RMylSDTlEhJlAThDNIKpBEI54MQkxHx3pR4nOEiRas1ddX5XX/nKQht401ymsYbDwHbbMTFZcp8sWCxmPPud+9zfHLEW2/f58/+9D3+z//rf+enH/8NUlkEersWpZM44SAoVzgn0Mbhug4r/Gc0sgkZIReyiG9mT7cUE+FvJkzXgbIo5U2bdN97GcIoXD8hMUJiQ/lUxDESQZakJJklSXOkStgsl7iguT1QCLwT6VDu9dKPTnuahbOhKS2JSZIEFSUYB1Xbs9rUFGWD0b75TVjhGw+H+YBAqYg4TZFxwtmy4oOfPOHhnbvcOTzByoizTc2rTcdnT0/50acvWRZNcI72MpR9r8NcUtv1PWQ4r86Zf67AGy352GC3N4henQOiSJGEwAs+wySlQ0mJlII4junDzUgUKaJIYY2l6zratgFrMH3vryXel2DIIO3G32HscGqHUzuc2uHUDqe+djj1FdK3Q9mX7QUYgjjXdvgiPMlde60YdnVhtzn8LWXobDca+h7ddWw2G1LtOXIxkl55nqi1lnW55sXL5ySJYu9wn3w24+jkLpvVimc/+xhruhAMxc8f/vbkX5/AVyVotz2+AQbcMLPD99a9N5NBiW2TjbEG0xvQdpsFaFu/UJIkoe87qqomTVLqumQ6zRmPRmw2GyaTCXmee7kzCGopnlOJg4uLC6IoJptNaYsKoyQbtWEynfLg0UOePX1G33WB03nlCKmN4+j4Nu9/+3tY45DCH1NZrnCA0T3lZk0axzhraJuGru+YTabcunmbi/MziqqgKFacn8eMRzn37z/k4aPHbD78EBHE63ywsEET2i/OvuuwUjGeTJnNZrRdx88++okP4uG8eu1ng7YWZzVH0xH3b5+QJLFf3CLMsNC0ZbSXeBvKz5Hw/NHhxsAH8rCTV4oojrZZJIEH6a7taMsK03ZcLi/5/OMf47KURw8fEknvXGq1ptmUdHUNzmcLdd/TFAW63lBeXKCMpRcgpDe8kcpzGKM4Q6YZ+Y1DRntTotkY7SxlUVGWJeuypKlr2qpGwFYxwgW1iCFI9F2Pk4LlcslyPmU0zkmShG995wEPH91ktjfi3/7bf8dnn3yEdZeE2OPXoBhumOSWL2utRinffNj1Lb3u4GqV/sLavrZMML3GaYuLlKch4PmhSvgsmaYDPMfTKa804kK5Nooi8iyjG41o6wajO+LQBNn3GiU7cPFVydcYrDYY/NqKgCxJmI1zxnlKXfdUVcfl0rFeVx4I+g5lI1wShSx2APw4JotjVJzQ6YanLy95/npFrQV91/PkxZKnFxUfP3nF6UXhmwCdL/vb4PLklUncVtYPCMZJg+mU2yp7xEniM9+BMytCllKgtnMyjuNgguSDvrGGpu2DnKbPSKut7KKgaRpkcBC2IZNGOLdKql1l49eMHU7tcGqHUzuc2uHU1xunvrpB3M+S7YUmlHSHKXG1CXXbuXJ9ulzPBOCcd8kUAuF857sUAqMNcRQxnYwxnaaqKpzzAbPtGs4vT0ljSTJKSEY5o70FN0/usTk/Y3l+6o9pKKP/3LDObQHFYn1p8ecC+VB2334T4R0lh1LVoJ8sEHR9R9M05EnqlTvKis2mYDIZY41hvV57ybkkZrPxygaj0ZjlcsVmU5AkKX3f+wYeEzS5Q+aoqmoW84S2aSnKIjTw9RRlydHREfcf3OfF8xeYvkcAqpOe49gbonzEg0dvoxtN3zb0fcfnTz7m/PwFAG1bU1cFIKjriqaqqbKK0Thnf/+Aqq7RWlNVG9brNVXVcOvmbT777Ekw2Ym2wWLQWPZ83h5DRxpJJuMpd2/f5u7JHb788jOs8frLQvhsi3ZezWBvMefGwYGnCbiQhZKe/2qtQ+vGy7FFilhFGHHVKCnEFc763b2fQ3YAZOFvENqm8c6fXc/F2Rl/9oMfsNQt/zRLeXz/AVI4qtWGuihw2voZ4qcnru/44rOf8eonP0EaR48jEoIsy8jS1P9MF4xObpMtJrhxRt20rDYblqsVVVGGkn9D07Zb5RHwDaEKr6PuHBit6UxF3y0pNylxPCGOMqbzMQeHR/zzf/5PkSLj3/1v/yuf/OwvaNo6LMSBRekbtJRU4UbGyzVKKenazutzf+Xw2VJrfYPl9n2lpld9SAoLlPZSeCqKkFGMFcqrqUR+/uquw5meJIkgViSplyXs+z4ot/hYoLX2Lrt9j3F46ocwCGHJYkiUoLaWpuq5OK9ZFxXaDhkjiTAGKwKpREqUlERYpLU4FH0P67qnctC0htOLkucv15yeLunbDtN13njr+k2p8H/raw1uzvnrZLRGuCsFmSiKENagg1OzVBLbG8+7vVbeHhox08ybJNV1TdO0WCG8Pnxo5EP4c9Q4i9G9VwuxzgfxMO9+MbLtxnbscGqHUzuc2uHUDqe+1jj1lQ7i10u4Wz6dZPv7L0RthngY9Ia55p7qLFjPRU2ynNF0HiTH8Lq+zrc2CRkhlPZNWtbQNjWnZ6/IZiPuv/UW84N9+rLi5l2vxV2VG7+4r+WNtl364LXNnS81bb8DImy4rzJEPgiE0ruQCKm2pSdvZ+/1riOpmE5nFJsNZVkwnU7puo6i2DAajwGfsWm7jqIoiGOvntDUtb8gDtI0oWt6rNZEeYbRfqJHUUSx2eCsI4oj+q4DJamqiulkyt6ed7M1NtjGawVKYnTPZDLj0ePHvHj23MskCkexWWH6AhuaBZVSCGdpm4ayKEjjhMOjG5xfXrIpVtR1xXJ1ycXFBTePDzk4PCRJYyKlvEtqHPnMiZTBuKnHWE0vBDZL2VvMePvtt/jxT37E6ekpSnijJCEFaIE1jv3FPrPJJJxzX3rz4OADie69QY0gDU1KMHByZXDa9GU/v6CuLp7DaEvX9fRti9Oavu04v7hkudnwn/7sTzFY/od/9t9za35Ac7mkLiusNgFwJFmcUTYFH//sp7z+7BPSJCXLcqZJTkSETMbE8YjJ/gGHjx8R781ZNy3NasPpq1csV2t014F1yEiR55nn3Uah9IlvGhTSlzd119PUOXVTUBU1RpcoFdN2C3RvuXHjFv/iX/4T9vdz/pf/ueCHf/PXeFEJP2+dAIlDioHX7fmYSkQYXQbX0TeZ4j8/lAolWRdk85z0oGYMfdv4mwbhpfRMpDwdILY4GeEkOOOzK01V01YVUgjiJEEq5WX7rEUJQRSa/3Sv6bqWrm1DwyWoGOqmoWoaepOjTU9Z1ZydbyirmisFGIO1GocnBQsZeREPZ3DWoGKJjGLqXtNZyMdzkuSCzeYVVVmD8Vlqz9EdZAm9QdSQmRkUNYbsVtd1xCpCyhhnvdygEwMFJzjVhnMrlcL2vW8KNJY4zRhPJqRpSq/BnC8xITsZDVQXAcY4jMRnpl0wHFMKbX0cTdP0VwTp3djh1A6ndji1w6kdTn29cerXbjauGqL8MVoCN8yFgCkEEHimYZ74R9zVwsOFXb71duZIsvGM+f4RZBOcUCR5jrHQ9n4CoSKc8B3wiYroraVuGs5en7F/eMTtu7eRd27T1hXnq3OKLyqkDqfyWtnNFz2vSs/O+dLtL46hzDc8T4ABq+02GGIMtjdels1YXp+eAjBfzKnKir7vOTg4wFpLXVdbw5l1UTCfTsizlGK19lKIKgIHcaRoygo3zjG9ZjKe4IxB94bDwwMQgrYofKm7qBBIRqMRWmvqpva7U2MQfQ+qxQnBZD5hvJlQbDbs7R1ycHCLpvLZm67tUJEiAdpiA+MxxBCnKbP5nKou0Lpns1lxfnHK3sGUo+MjRuMUJSFNMmIFWZqCcGjbo63B9ZbOdVR1Q5bn3Lpzh/v3H7C+XNIb4xe6BCkUkYg43D8ky1PffOUIkm0GbOCyDs1txmHEVbOdUt6N0/MfvUrHcIUVjs4Go56uh14jtUW3HaQpVkWUZcH3f/DnWKH4x7/7BxxlGU57V1vhQvkxVp7LWXccLI4YTSeMx1Nmozmz2R6jvQNGh4dM7x8T7Y2p646qqliuVqzWG6qyJlKKNMvIshSn/LwyXp4ClAIZ+RI3kI4E6WhMVo/p+pqyKlgtl+izC4Tx3/fo+Aa/8zt/j08/+se8ePWCsl7jehOydRrx/7H3ZsG2ZWld72+MMdvV7+acfU52lU1lVgJFFlAUUI1ACYoiGBBwBZT7Jhoqb4YhKjYR+mAYyjU0fPBebFAfRIwADLuyAQHLosqiKiurgOoyszLPyTzdblY3+9Hch2/OtU+2lcC9N7nmHhH77LPXnmvt2Ywxvu7//f8hEJwolCogiVJikxA6D35ojn1liXpwcZIkI4oivHdYHwjB4b2WEq5vEfq8ntEiikiSlDhq0DpCmwhlZDOrm5rOOaIoltI2Cg20XYfVmhCJCmnXNrRtQ93WBKeIjcZrQ1EHjsuEVaspnWO1rTldndFa0RjQJsaFVvabHioBwkDTYUiUsLs4Ik6WBXXTcd/RJQ7na5z9PG3XgLO0rUBfpJFQDIvzDu2kIVdrvaMrBIjjiOA8bdvgg5eG0N5gDhlzoZqU0rRAThzaaBb7c/YPLxHHIkj14s2btGVLcIEojzBKQ89a4tCYJO+dFDGaaZqSjUfMZrPX26rf0uPCTl3YqQs7dWGnLuzU72479WUaxAXjNZzkXS+/ypHDZnn3pDlvblNB8KppNuHS1fvIZwuaTlgi4iTBWk/wnWQ0tMbEEVihC4uimM47tpsNt2/eZDqfMJmM2T845MrVq2yWJxRnm5eIqaj+H8WwRQ//ql2Z3Q+RnmJXnhvYCJ3t6LqaznYor3HW0TQNdVXL58cxSZrSNi1VPTTYRWy32x1+ThtN27WEAHk2YnVyRlPXZGlGUzfMZzM2qzXldstkNiOOY2nCixPy0Yj1akUcx0QmoigK2q7j6MoV8jyXqNZ7uraBMAgMCU5v72B/x8N86fIRJ3duUpYF1jt0f4HeCy5TouLAZDpnvN2w2ZxRVxWr5RllVTEej8nyDG0UeZ6jdEycpYDGO4UQBkLrA0VVE7Rhvjjkia96F9uTFc986Rms87tnkcQp+/v7Mqn7JsXQNzg5J9cBQ2nQY61sHrLJBJQKQqcnaYJdDsR5EZhxnRMWBevoanl+X/Pur+XXb1znN77wWc42FR/75KdpVhUf+JoneOSe+0RCy4qgUawjQgd5OiXL9xgvZsz2D5jOFuSzGfnBPvmlA0IeUzTSKLndbFienFHXDVmWkeeyKSqt6bxjtVpxdnbGeDzm8PASUaSlpGyMMJSYSBQ8615RFkO5Kdhut3TB0vmO++95gPd/yzfzhWtP8/wLz9EUFeVmQ1EVtE2D7TqssygfiJIUbSJhzwjnCrYvHYPjonrGE/OS3+0avfqsSOjB7NoYurYjMjFRHBNFcb+JB6q6lhK/0XivCfS40iDNpVFPmydY7A4UaNPvHCHgPLQ2omkVdQXbtacsO3zQPY+9xjl2OGKhBHQ47+UrOKKgxck6PePW2Slf8Y6M/YM9DA7f0/X5vnFVKU2cmHPaQAXBn2N1hYNcaAIHnLZ3jib0DCZKEQ242QBJr7Jsu44QPLPpnIODA/I8R2vNaDxiPp9huxXOWckgaoPSBuul+W++v8fBwZ4Yv6ahrEqMMSwWi1duuhejHxd26sJOXdipCzt1Yad+N9upL1PZ2P1v98Bf/cDzwpdkaYafgtBiEVAqIklG7F26wmRxiMky8l7507mA7SRa9cFjItOX2EApQxRiIi+qibdv3SCKFVcuH7FZb0TKvi8theBRQcR95M+f82FrVJ/tOi8/Dzcf6PVad1VOnG9pmwrXdXilcG0rD3dotIkitDa03RBRe7bbgkFIpa5rojjGmBhrPWmaEcUpTd1yOJ1xdrYU4KXRbMuSNM9pWzFih5cO6dqWqqo5ONjDIywAAWjqmjzPJWtUFjhn+2hecKpeKdI8Y7G34PT4hPlij739A9q2FtGjPgrWSphJIiv3PYpixuMJdV3StQ11XbFZb5hOp3IudcXBvkTYkYmEtcUpImVwiWzEtm2x2pAmKQ8+9HZWZ0tOl2fcPruD6edOnmfs7e315dDQbxD9M3CimysLR+2QA/LYzvGFWuudiIzWcu+7rqXtOjEMncW3LWVR0DnLpcuXGI1H0sxkIsq641Of+zynJyf8vm/6Rr7q0UclewBEQZGajL3DK0zHY0aLGfl0RjaeEk0y4sMpdqQp6y1t1dDWNauzJW1VE0cReZ6RZZmcF4q29hRFQ9cFlssto9GE8Wi0Y5RQ9JueMXgP601JU9dESYLzjs16g3WO+XTB/uUDvuKJJ8j35rRVTbUphNKyLCjKirIoKKuC0XSKMorOdVLG7bNFL2+2EzzxgD+2PT43yH1ioNDsmW/6BxGC31EC7tpdnYh/2UYYW7wxOCX0k3Ec9Q2pHXXdEHre8TiOibOYYMG1HbsSrxJHrKpbNtuarvMoJQ1q3js6wHsr5xikMdN7R2dblA5EkWTZlpuC3/zSc7zjHSeoRDMaZag+yxMnCaZ3LIZmu2F/U0pYR5QWGIEYDCv7x8AB3+PXh4ZR5yzeeYhjYftxjvF4zP7BAaM83xkyYwz3XL2K0Unv7PVOsta9wnQgTRKuXrlKEkVsiw3PPfecNKFeNIi/5riwUxd26sJOXdipCzv1u9tOfRk2qv4xquHn8yhzN16+r9+Ff/VBmAKUjkiyMZP5PqPZHKcg0obJZIL3gbpucc7LInbygcYolDGEoNBGeCZs2XF2csxmdcr1Z56lXBecHt+mLbdC6Dycz11z9bzJ7vxUh1cG7vKBveT8WgLeWepqK5hKZbCdJQQvmFClhILNu53aZFmWNE0jQilAEiekeYZWEXXVMptMGI3GFMV2V2pt25bRdMKd27cpq5Ks2LK3t8d4POLW7dukaUqappyenkGQBp/NdkOSJMxmsx1jR9d1GGNlY9TyDKbzGU3T4Lzl8NIl1psldV32mZVuV3pz3uGdlOCm0wlFud7hfotCBKHquuHO7VtcvnyVxAhdmg6g8aSxQkUG7RSUDWlhSc2cdG/B448+yvUXnufkqSW+xxdOJhMW87lsxJ1sMoMYTfAOvPA66z6rotVAISjnO2QLhnm4o6yzLS4EdAi4pqHelpTrDfFoxKYsOTs7YzaecPme+yjKhpOTYz76qSfZLk9o22/jnY89jibCOk8+mnLp3nsZ5TnpOEdHCXGWEY9HeK2oyg1lWVGXDeV2S1mWsimlSd+cJZhhpTTrbcl4POXS4WU2263wnveZoiF7oZTGOs/pcs3NW3do6prDgwWTyYiyrNiuN2w3a8bzPQ4uXaa0VtR565a2bmialroRbPNydYqOIlCRiP4E38NJXntIZs7dRVunXrFazl+VFleNYGUdLSGIEqt3DmWM8HwD1liSJCbLMgBhzCGQJin5OCdJE7qmo0FBkAySiRXKeKq2pKy3eIQFRhuDslImD94JhGQ4Q+/wviN4EWJSSlM3nl///LNcPXqSw8Ul5os9RqMRRf+sdCa0hK23xFoaWNu228FRdBA60YFWUP5u2O19Sksp2znZtMXhEGOojWFvb4/JdNLj76Hr1W4vXbqMMSnXnr8GCHZWjKkhBEddN3SdJc9SvPOUhUBdLqhvX3tc2KkLO3Vhpy7sFC959cJOwe8uO/WGdDYGHNzu57sfbI+DHX4rD1oTlOp5qBOiLCWdjDGjjDZYTNuQptkuiopj4S7vQtdPqK7P8QQ652TSaMGYrs5OqMstIWjBTOLxnUU7t8ssqH4n9yGclzv77JW665rON/AwVKzlvUGEcZq6oK1rdJLtMhRxz03snNvxmINEzM5ZmsbvSo47gZymYT6dMs7HrHuu7NFoRFVVTBZzvA8URUGWjphMpqzXGwiBg4N9QLJEaZ7LpGsEe3l4eMhsNmcLvcJrJ+wLDkKnmIxGLPb3aNuG+d6Cg8NDTk7uUFaifLstt8z39kiUCAspLWwCURRLGZFBLVVoze7cucPx8R0uHx3tuLwTE0AHoiSQtwFva6K6I48jzGSC2dvjiUce5dq1a9y4fQOUYr6YM5vPGRhC/FD67MvRWoHywgmtvJO55JFsYL+YBhrCOE52zzwMz9o5urqlLkqasmL/6ArXl0tuH59w/9EVvv1bPsjnnnmWD3/0lKIq+MhTn6RpG5RSvPPxryAPnjjPmKi5MJsYjTIancWYLKXqGpqeJWW13lDVFXEUk+a58FZz3simtfBTX7p0wGI+p2mkOdMYoZjbbrZEcUSWZrSdpSpr8nyKVjFtZzFRzCiH1XJJtS2YTOfk+YgkyWVGJxn5+JyesO1a9tZSJj8+XQo0wksZ9uXOzW79KrXbjIYNa8C4v/xwhTh0mj6P1G9MQxZIHpSRdeZFnCzLMpI0kbWl5D6PRiOyPEMZBV7hE1FzlfuVEVDUTUPXl3B3G2e/mbvO9qlEOasQJCustDT1eg/eR7zwwin//WMf5x1vf4zRdM5svkfXdUSxYJEVGrpGDIRSu5L0AC+hCzglzZ62aXfzbqC6dN7T2Q6UIo4iBscwMhFplvYlf4XznrqucM6RpimHBweURUHT9hk237PLBE1V15yenkBw3Lh5g6qq2Nvfl3L+xXjdcWGnLuzUhZ16dTtV1xXRq9kpPUCkDJcOD1gsXsNORRFZ9sbt1OgN2qmTkyXdXXaqNz2vtD39L367dspZ2yujuz6weWN2Ks8zMEqe9evYKfNbsFP6ZXbqxd5OPf5l7JQx0ljzWnbKaC0sVgNN8wDF+l1ip964qN9rjVdUrJXgW+MUnWRkozEmS2iVo7It1ityNG1dUkURSSqsF1EkHNVN00gGwEu5zDopPwXfsF2esl2JCM/+/gGXD4+wtuH49g2q5RnOdgz0YIRhGqq+7Nw7JKg+q9WXw4Z/h+PVcBWBrmvoupY0yXoVTWGWsP2G3llL8B3ei8JmHPt+I9I0TYPvo+O2quk6S5KlRFFEVVbsXzpktd0wDjAej6nKkqoq2W43eB+Yz6ekacJ6vSFJU6JISuFxHNO0DXVdM5tNIYiya1XXwm2uExG6sY4sz5nP53TNlstXrqK15uzshHK7ZbsR6sDRZEocG6q6Fq5lI7zKiJwMXdcxSkY0bcMLL1xnMpkyyhckkcN4i/MVUdtiGk/crInXFXnwJN4RB8U7j65w44GH+O+rJWeV4H/HPZa3tWK0RVTnvOwnhtzirBN2kN7IKqWEZrJuBFuYe+Iklgxcb0w766Rk33Z450jGObe/9AJ1XTGbTHjgnivgA0996pMoFbBG8eRv/jp7kymPPPQwB5HBOos3Ci1rEJ1GME5otKOoG+q6pVhvaaqSOE7I8gzTM7kMzrHSYvz29xc97WEgyxKSZIFSsFwW3Lhxg/F4wtHREVprDg8PWewdsFotOTm9CUpJA1+a0lQVTVUKprzv4UMpdKQwSpzlxCegAlFUcna2ElXV/t4OQ+b6sEH3NI0hINz0ckTonSv6ZyKbc++pBWnMcwHJHe0a9XqV1OCgL127nrdb0VPo9UwnWZpJST6I42WMxqOJ4hxjZnRtRVU6vFf9XBSDobVslk51eGeFPWa4MO9RweOdpWtbtI7oGnj++i0m0z3edt+cJM8ZTycYbXZ6BHGIRZOgb8QbspGD+FFQ4Fq7w8uGfgNHq56ulF02yd+VUWrqRthDEGXesqx2TB3jyZgrV69wenrWO3pBnF4kE71erfHOcXp6RpoKS8x6vf7t7c8XQ8aFnbqwU/+L2SnWSyaffpIoBLTS6DgiGueEOCLqGkZNiy4roqbudR4SotgQ7R+gHnoI9ewX0cslWmsmzu2SYwAHzqM01HWNPT0lTTOmkynjrmNS1ozHE8qqpChWpC8+j9GKsijJr19juljw8MmS2ekZQqUW7vK5ZY+rm5q2bTktVjwaG6r5ntzDXXnypQGHMYbIxFiXYrOhOblnDxugV/39Fxsc+uelCOHc0Q/en68vbVAmJk4ichVI8ITU4KNcgsQkQuMJVtTcbawJJuKS1Tz2bM3+saW94XhfNeKeLFBqg9ERPtJ0icZOAiGOwIryuTYRJtIkcUysQXtL5BSqhuzZ69xTWw73rnDgGspR2jv4HnDYSDPAxILrsH3gqXtYplUBH2msiglOQ1ULpEkJY5RTgU8FuPEm2qnXh1Ep+jIk6CD0ZZ4+mhoyMWFwfGQyRcaQjeYkkxlRlpLlOZogkbZve2l5S12VBCB3Fq0NCoNAY4W5wraux5lK6Wp1+iLVZoVWhvFon4ceeoz3fON7OF2e8pFf/TBlucZ3DSaAQ+GVZLPOsbh3Uxueb/QvveC7SvLQq3tWxPGIWMUSOXYWi2QnylLYPdIkJc8ziaJDIIlj2rYjjiL/fMtzAAAgAElEQVTSOKHYbCiqkulkTJrlFNuCvcN9tBZ10/39A65v15TVhqwQur7FYkZRSPPVIODSWkvd1CitKKuCxd6c+f4eVVPT2I7GdiStItYRXd1igmI6m1E3BVXTkGc5+3sH3Llzm+OTO6w3KxbVHqPxGKUgzTJG4wnbsuqzRp6yLEnTFJVCUWw5uXPM0f6I3Hhav6UqlzTdBmcNaXBE9RpXFPimIh9lPJhrPvDIg5wd3+J/fOlZubFNR5u0dJ2VrN7goIfhvguvt/dOJrdSREoTFHS2o+taYmPoTESERkcGowxtZ+kacQq7usThWCnHl65dxzct127d4Of/y4dYLdecLE8wPVbaekdVVWRRRKJN32zpUdoQYo0epTgNdVXKs9xsaZqKODZkeSLGXUlmXSgye/EcY8hNitbilDvvMZE0DDaNYKirSpyWPE9JkxilIUsjZtOJZHqAfJSjcAKHiASbKpO3jzqGarKSrEnAo7wlTzOafAq+w+N3PPyDayPZul4lNZw3zCo8aZoQmwhvXV8+d7StPK8eNc7QxCqb4ED9GAArCrgh6YV/BG8aa7V71t46fLAovMBPdEI2WoCaUpQV220nHN4BcB7vGwmuEEfKqV44ywe0FdVa19S0AUIiwVISZ0Q6g5Cw2W5pbUeS5YBC95nKoBS2ETiG1iKEBfSYViuBibK7ht7BeVNaExndV40URikRDus3+KqqRI1XKbZFgbWW8XgspWhjOLy0T5JG3Ll9StO0aA1aS+NeWdR0rUWriMl4QlmIEbgYrz4u7NSFnXor2in1pae59K9+6pVxNDB/rcUym8Ff+kvw9e8C7eCf/l+wXMK998L3fz/8m38Dzz57fvzhIQ/+0A/BL/4i/Pqvn0+8LzMeekNHDSc7ka83ayQJfM/3QFnCf/yPoARGhWvOW0Kg95Yj2HbwkecBeBh4H3PIkC+A6RS+/uvh9/5euHwZigI+8hH48IfhxRfv+sAAtPI3ti184Rl4KMCf/JOwv//a5/v00/CTPwknJxDH8M53wu/7ffDQQ8IiZi189rPwX/+rfO95e/9Pr/inb6Kdev0Gce4qU/WlIIaIMjAIQ/bRjvw60oY8y4nSDOJIspk+YDBEOuozox7nOuq6JHhPkqQYExNQGKXpvETUUg5ylMUZm/USgybNp4ymc9LRhIOjy1x+4F5un95heedF2qLo8xz9+Sspa4KskcHRuvtZv+TnvtQpDhd9M1kj4i866lVZRQDJWSuZC6N2EaiJIsHV0WfAQsB5i9KivDiZTEizjKLYUtcVcRxTFhUHe/uYOKJpqr7cmbLtMZZd15FlGUor0jShqirJLuU522LLYr5gOptRNdIs553Dth0a6JQizRMm8znrYouNWvLRmPF0xng65caNG2y2W7I8J00TRpMJTddiTk9J0wSFCLxMp1PJvsSabXHMthgxTlM6u8bZAtt1KGUwkwnR1uJOt7Rnp4Q2YzSd8dg9R7zn8cf54skJ29WK1WpJkiU9C0WfY1cK5XtYjwvSyGUtXvWiWlEMWuP6UnToy9llWaKAUT7CBfBOsha2ayiaki98/H/y1K9/Bhs8t5bHFE83tGXNyeqYzrcoD5GJuXx0mfFkhMZLJgIFWhGPx+gspbIdTVlTFRV1XWOM3gneGC2Y3T7dgjamx1T2mTDEIfLBYwRFSpalPPzwI6zXa3mblgqFYClhlOdY5/tsakywLYGwy7jLugziFIVzJ8X268q7jr3FnDRSeN/QdV2fjRVueNtn67QW6sDghwAGlBZ+/VGaYpQmz0ZUdcvxySllU9+dZ939b+ABH7ItoHGdpe0aEpugjaxL7z02CPQqIEFIkmakScJ8/xK1TzjZeJZloLYS+HnXr2U8BMeVy/ukScKLN29R1S1eKbT1dEAYnP84Zm+0z4MPPMhitqCtG5QGYyKBkgzY6hB6nK1lYDw5FzxSdJ2la7sdJMUOJWov4mvOeYzRRGn6Eoy2VEJEYbhtGqI4xjlLZ1uiLiJJNFmWYCKN6sVzVX/zbX8uo9GEzoqDcdEg/trjwk5d2Km3op0KbftbXyzjMVy9ClEER0cwGkmw8f73ww/8gDjG/+gfnQcVTzwBP/RDEqR87nPwvyKc8+AA/tgfk2v/H/8DTk9/+58Vx/An/gR87/eK0980cq+/8Rvh9/9++It/Ee7cee33X7kCjzwiAcurfXaew/33w7/9txJsfMu3wI//uAQZVYVgnTR89VfDd3+3BJYf+xgAJkhC1L1Jdup1gw1Nr//Yp4SGgq6+6/dGK7phBw8B5x1dXaGTBBMLvdeQNVUI9MBZRzDSlOd82Xf9Jxgdy+J0TuAQTUNVnVGXK4JXJPmM6fSQfDZn3VQ8e+0a73ziq3jia9/Fjeef5vMnZ6Ks+DLc38vZDfoXd9+HstqAARx4BYP3VJslTbFAT6Z4pWmdEzEc5zG92EnbtaAgy9LdA2zbjq4rGI3HjEcjVqsVXdcRJwmqZ/aYzeZsNiVZljIZjzk+uUVVVWiTstluiXtYTmctaM0kn6K0MIjUTcNyuSKJE8bjMZOqEkeytXhncU7T0nNExzDp6QsJgWw65p70fpwPrFcbFnuO0TjrmTUCcZyQpClJkgjGsm13TUl1XfD8tedI43tItMFkI0wyJU8n5HFG6jNUc4tmtcI2NSOl2T+4xFc//BCffv55XlyWHC9PmcynKBBKNRD85Lm3IPSAzuEQbnGHkkUEDD0I1lqasoIQMFqDOofBeAI3b9/iw196hhsnp2DEya8by2ZTsNqWuLZDBUWaj7jvvvtI8wSlA6HruavjnJDG1E4c9bqpsVZUdZMk6cv4sjZ2FIfBo5SUop2V7JXuaecUohoLkGU5WZYTxQnBW+jxpF3X9TAmjQ/CfAKSWSSww1fePa8HETDRDfBY26IN7O3PmE5SbNfQtq1cQy3BUtuZvtIBtvM7B1upAIjzFccxeZqxt9inrhu224JtVe2EyKTfg7uwzIE4NsxmC/LJnG2x7p0Vvds5TGRQKOI4YjadMx6NiJOEOE6I44x1A9blFBzQBEdrzwTiRcD5htEo5mufeDuPPvwIH//Ep/nYJ56ibjvAEHnBs3adJXGO/b09FrMZTSXXrJVoMHRdS9d1UmHpFV/DzjnocdVa73DXztr+2fYY+B4K4Zw030JPQzg0jfb3pOssZV3v7tV2u6XrLEWxAeV7gbUKrRO0kueqtdrB2PI8789bEfSw616Ml48LO3Vhp96Sdsr9NhIQN27AX//r8N73wv/8n/IzQNtK1PvyAGYILrruDVc1/n83hmvzXgKE38l43/vgO78Tbt+WoO2zn4XDQ/jhH4av+zr4wR+Ef/AP4LWc8o9+FP7Un5Ig8O77rRR83/dJEPOf/hN8/vNyzB/+wzLf/vW/hv/wHyS4yXM59g/+QanYPPUU1LX0b+Bwb5Kd+jI9GxLGiEMhm5oj4LRkb+lLh8PeBwrrHEW5IUSaPIlwPT/vIHfe7xM99tmCY8cZnMTSboe3aCxVuaLYnqIJZPkek8kh+WSP8WyKiSOev36d6XzCOM+4et/bOH7hFmd3buHdoKw4ZI9eVovu92mFZJQHHu3dLwdcSoB6s2J550XScAWVjOh650P5gYqsx7335XrvHG3TUpQlWiuSWFRNl8sVZVmSZTlJmtK1LUkcoxEHaTabcXx8k6oqiZMcZ+2ukct7v2twGo/HPUNIx3q9JktTjo6OmM9nlEWB70S1MnhHbUUZ1utANsqIkpima1Fo0iznytV72W6/wHqzZTKb01krLAhJTJaKIqkxhq7rcM5SVhVpEnHt+m2m4wkPve0qSS4ZlyydYJRCe0NcdLiqplyfoY1mPJtz/8EeX/fQw6w/85ucnJ1w+fCASEekSYoi6oWTxLCHIOqqw+hCR6hbglLEeUbvy2OtlWi858UOwe2wm2jD6XrDjZu3aDpHlGekeU5upjTKMc8t8cQwSWMu7y9427337fCq3jlhLslTWrzQXfaVAe+9cMpHA1xKYD2g+zkdsPau2mtg55AH73HBoxBcadt1RJHBWYe1QZhu7sqqDwwR9Otl6AN56Qrtj+2znMZookgLDEpPcG1K19ZYa2VD0LpXvhWaPGv9jkqQfp2fZ4cVcRyT5RkBvQuaXuoUqbvOV4Kh+WLB0dX7Wa1OuXX7hmQyPWgVE/mYPMuYTmfsLfaI4wQQtpmz1YbjVcYsnVKbIzrd4tUG79u+r8KSJhmPPXo/3/re97A/m3Hz1i0+98zzBH8egA0Nr7ExVEUpTXyd9HnI3Gppm2ZH+9j1xmZophuGH/C9WkE4pxsU5hHZz3TvyA2NuSbovrIlzY6DBsB4NKLucf5F0dJ1NWVV4ixMJ3voXrRJ5pL0gBijmfQKxu1vJ4v5lhkXdurCTr317FRwd2N8fgvjC1+Qr2EYAz0TE3ku1Y/t9pXvUwomk/Ms+v8Te9KQrQ9BKgvD/muMnAfI6691rW/0uCgS5xwELvX/Brtfnks1IU3hX/wL+Pf/Xq7r6adhtYK//bfhgx+En/s5uHbt1T/Dezg+fuXrly/D13wNnJ3J53adXPdsJgHGL/yCBCDD+IVfkErKYiHXDqIG75yofr8Jdur1g43gBVPhA6lKeODyZeLEcHN9xlld9mqCqmfePQdXWNtQlRtUmpBoTZTmaG3QSuHv7lZFPl+ysRatml4qvqNtNtTlKbiOOJ0wGi0YjRcoE5GkCbPphM1mxW889Rne9sADPPjw23FNx2984tc4vXPzpbCQIePVl553W/Zdp7KjFQz9VShRlgxtS3l6TKcM0fQScZ4T56Bc2DkmIvzjaepGoCy9Ezkejc6zocZQ1zVJlpGMcsrtCkIgiWK2my2jyZg4lvJzlkt2O0mS/jFI1rvq6dCyLO8z0h1lWbLdbslHIyaTCc42vVNr6bqWstiwLQpMHJFmGXGaoHWEtZ7RZMr+pcucnp5KMyFuJ2oTJwlJmuCcp2lbrLWs12tpJGw7nrt2wn33PsR4OibWYEyCDRY/zjAHc7K6oGu2dGVBtVySX8159MoRn3rmOY6P77DeOyTNc8LIE9kYhqwd50ZrWAyu7ajKCh8CU63IRpLd6pzF+cAoTtDGYK3fPdfOB7Z1xzifcrA3Ze9gn4OjK8zzmTQQh0CeZoySwCRPuXpwiHbgVUCbiHg6JiSG1nbSr1A3NE0jiyYSusCBg1z30bwPnmA7KS9qEXgaMv4i0GOxThRVoyhGoVHK4FyHic5VQUEaoocm0RD87vW27WQG95n2wUHxvfMRxQlZNiKMHL6LcXFLFxts16GVoukzHLbr8AG6zmGtewlM49wxE3iWNkYcnb4MawYrulvGmqGbWvXQjTSNuffee1Aazs7O6DqLVpBmKfPFnPl8Lkwny1OatsF2liiJmMzGhMUeDQdYTvuEk+uzeJ4o1sznE+6754gsynn66ee4feeU1Vbmh/JeqjMBmqpmtVwK/MlamrbenXLbNf0d0wx2f8d0ovUu+PB94+SAkx8UbFUPnRuw3J3t5P4H2RdGsVApjqxlMpkwXyyYBU9Ttz0cq0VpQ9c6kjSTz9+xrGisE+ae2XSGQozBxXiNcWGnLuzUW9BOBaUka/4jPyL9FP/u34nz/cM/DI89Bn/v70mPwCOPwB//45L9/sQn5Ocf/mH4V/8KnntOYD/f9m1yTt/zPfCBD8BP/ZQ4rMO4dEl6Cb7pm8R5PTmR93/4w781x90Ycbi/4RvgV39VqgD33SdO9ic+AT/7swIh+r7vg0cflU33s5+Vv/Wbv3n+OQNU6Pu/X44DOe7WLQk4/vk/l4BoPodv/ma5vitX5LgbN6Sf4Zd/GV6PeOMP/AF530/+pEDIvtxIUwkKlkupGt2dlPviF+Xr8cdhb++1g43XGh/4ADz4IHzoQxK8gARXX/oSvP3tAnUbj+VvHx7CH/kjcq8//3kYqhaI1oZ7k+zU6zeIM2xuilmS8y1f9W4evnLEx77wG/zK557irNoijs5L9kMUnratCcUWoowkTndKld7aPqDXO67nXs1dOKi9pakLtpsTrC3RGLSKiEyC6rOyTVnjshRb1Sy3BZf393nn176L2WTC9uyUYr2kKgvOmQ3ErQhwni3mnH1nd70h9GAUcUKMgohAqCuaszOCGZPkI6LIEEV9E54V6MvggETGkKQJ+Ug22tVqTZqmjMdjVqsV3juSJGXjPG3TMMoz1tst42lOPh6x3Wxp6oq6Fsc2SVKhhlOCDzZRxHQ6JcsyrDF01nJ8fMzR0RF7ewvatqLYrHseZGlSW5+IQzfb3+Pw8mXiNMb1wkqHR0dstls22y2TcU6WCn2i6jNZtpMMWdNIVrwoCpI048Wbd7h5csb+pT2MknvZOU8cG9TemKieM24KipNTtuszmI7ZG43Yn0y4c3rK6a3bTOZz2roRZpHI7Jz4wanVA6zBinPcWUtcSUOf1gbvaiD0SqA7nV00ito6ssmcr3/XfTxw79u4554rLA7mjOKI2EQkJiJ4R9sU+K4jS2LoAspAOhmhJyOKIIFBXdUUmw1t15FnmVDXaVELHeA2IJnP1kpkP1QBnPfgxXVwzlIUBUUhoknGxFLS1DAe55JR7Z15ZzucFwXQoVHZ+0BZFP08Hdba+RRXBIyOyPMx2osD4myLswlt26KNoSgLlBJD2TlH10k2f+g5CQHMIFZFn8EI0vDog4hqDWsl9GUbrcP5az5wenqH/OaI++67n3vvuZckyVmeCTvP4cEBB/sHWNtx+84tzs7OsFa49yeTCWXV0k4zVLyH8ynWOnwQvvIAxLEhyVKydMTb7pnxre99L5//4rN89FOfEUVUZYj6Php8oKkqERoLgaqpae1A4+f6YAKSJNs5kgqFiczOcbLOoZW6i9owxlq1U7INfabI9X0+Vgt+P4lj8jwnSgQ+MhqNiKIINxaYnYlgenrKcrlFKXHwQt93M/TfVFXNbDojy+T8Lsarjws7dWGn3op2Kk4SaST+4AcluPiVX5Hs/Xd9lzicH/kI/PzPS6Py7/k94nh+8pMC5/mO7xCH9/p1aZAelLm1lmpDHJ/PSxB40PvfL5n1poGv/Er4y38ZfuzH4OMff+OLNY7FiX/f++AP/SEJVE5P5e9/7/dKb8NkIl8nJ/Keofn5z/5ZeU1ref+P/ZgEE8Nx3/7t8jm3b0tPQ9PAH/2j0o9R1+eBxRNPwHveI0HOP/yHrzxHpeBbvxV+9EclsHrggTcWbBweSh/MtWuvDGKslcb7r/s6CRqeeuqN37PFQp5pUch1DcFd10kA+e53y/m+//0SYI1Gct4vvCBVlP54oXF2+DfJTn0ZGJVk7wyKcRSxMAlvv3RE2dV8+vkvsiy3u/mole5xixB6mXTQBO97KsoesmCl23OUpmRZhg8iMBKspasrNutTttslznUoPMGDtS1VtcUkonh55+aWutgwHeeMRyOqoqTYFuzvH3DPAw9w/fkvUTU1od+MpHI+gGPPHSP56S7QrKKvWweCCkQokqBwwWG7GlcXFOuYJlKkcYxWktF01hJFEWkckyQCsamqivV6TQgiEJQkCdttAUCSiJNalhWXL83oVkvqqmE6mXManVJXG4rNkmo2I01z0jTD4XtBGk/TNMIgkoqgSl03vYrrPpPJnNVySbmWjXy7XrNanhInCdPRGKPEaOoeS5pEMYcHl9hsluzNp8xnM86WZzRNLZR+SjEejem6jiTusL5DG03pLc9fe563PXgv6XiMcg4NWKVxWYaZTUjaA5yG7XbDujjBpHscHRxweuM2TVMTbwz1RsSjdtAko4n6+zg0NGqlhLHFe6rtltEoA+exfRNk13UoIIoSgm3xTUMUJ7z/m7+Ze972MJNshFYK5xuMCkRa01Ql2+0a60SYZpRlxFoTJTGkEYUPtNbhraUtK7pa6By11uKAGAM7VVm1W8i257PGCUuJtxJwKC3zcL0puHXzFgcHjiwfEVBMJ7lUMvpjBJ+NiAJ50a+IkgSnNFVTi6PRNy2eo57EkBqlyZKMCE1IpcrifEbiLDpL2ZYb0iyhbiKsCwyaxLpvrAYvTehGIFsm0jjXUTU1nQ9oMwQbfreOhmyr9IwEmrrh+vPPQQgcHd3D3mLBeDwlMobxeIxzntu3j7l5S7DfcRSR5VlfwXCkedpnmDXeIY29JkIFgWxkSQYo8jzjsUce5ve89z28cPOY51+8gevaPnAZMRpllGVFWRYoRAegrIqdwZcqjCeKS6I46imFNZGL+mBL7rMP7FjG6N/nvdwnY0xPNyo9MM7JXpBkGSaKCZHMqSRNiIzBR/I3jIbFfEFdd2yLAu8tA296CCKeZHtISp5ldPZ3zlL+v+64sFMXduqtZ6d0nkrl4gtfkOz+5cvipC8W4nQ//LBku7/yK8VR/cQnXtqX0HWw2cBP/IQEHT/6o1JZ+Jf/Upxl7yVbLxMR/tt/k4rHcimZ9O//fqlQDJ/7hpbqeU8L6zX8438sQdJ8Dn/jbwgrVlnK6x/6kFzHj/+4VGMeeUQCi4cegj//58Wp/qmfkl4FpaR/4Qd+4BwydXgogVbTSFDxy78s1/Hud8Of+TNSLfiZn3np+Wktjvuf+3Py/7/7d+G//Jc3dm37+/J3N5tXb6Q/PpZzu3r1HKf5Ru7XBz4gz/JXfgV+4zde+vv1WoK1g4PzZzB8v31bnns/AoHIGLo3yU69vgVTktVMdcQ8SXjx2rN8rNny7PaUbVUQlDgChn6PVAoTAgmaJJswv3wP3kSUxRan5ORVZEjihCRLcdZRNRVtU2Ormq4q2JZrOlv1MA5p4FS+pq5XBK12zbdRpDk83GM0m1BUJU8//TT3Xr3CeDJldnDI8dkpXVUytNPutuyXQGJf+rDVXUeCJwqQouhQONvSFSucidDTKW3XEZxkhaNBkyAInejZ8pTNekMURcxmM8DjvSaKYlxnydKYLM3YliWXeqxxVddMp3Om0ymr5Snr1TGj0ZQ8nzCejtBaWHN0r+jqndtBeeIopqkb1ustSTpCYaiLLWenp9y+c8xmu+Lw8hFRHEuDj+9FiYIHF9jfW2C7mu2mIBCwAwMPgSzPSeIIFXKapsZ6yeBmWcad4xOuXb/B5LFHSLQiwdB5RxtHZPMplhaTB8xKRJ4i47l69SrLoFkc7JEGzWq5Ekxrr46JFh7qKI56/LSAH9JMDHi5bfusW9IzGaU0TbPD0sYEvLXM9va47/F3kOU5rut6qlXpiwh4mrbDxBHTvQXjPCPRiizPwGiKENi2LV2f1bNNSxIl4nAiNJW7z/LdOV7ce3zne9pHdsbHWgdGo2OD9447x8ecLVc8+NAj7O3vEWmFdxYd9C5bIBCJQPAW5y0mmVB1jrpp0ARc3zQt3Ypq56AoRGXXGI13MZH3+JDjgkOlMdnZHaIYYZe4G59+Fy95FAlGOYpj4jgiBEfTNVgfUEb3Fzv8Td/vm0O1RaGCp+scL1x/gbqq2T84ZDSZoZRhs9myWp1xu1cj3p110HgXcL4jiUVESamAiRIiFSBodBeYZhmTXOAuURJx9Z7LfPP7volN0fLvPvSfee7GDRQp89mEOIlQdcD3lJFd29I1QmUpTeCy4buuw0YClYriGBVSMBGogOrvs2gVCPuUc76vaMkzdkEa5XQPg8nynMl0io402gVMFO2+YqXomhYfhMf80Avffts2aKV3z15pI683LfPFFB/y192q39Ljwk5d2Km3oJ2yWolj/ulPS7b+iScEgpRl4nC+850ShDz4INy8Cc888+rrp+vOndKylP6CYQLm/b7zmc/A3/pb8jsQhqPv+z6BBeX5S5za1x3GSCXFe/gn/0Qy8yFIAPPssxJs/NzPSRAxMCs99ZRApt7xDoEnveMd0qvw4Q8LXKqHN/PTPy2QqSwTJ3y5FJjRPfeIwx5Fcg+efFIgZquVVGoGmlmlJDj5kR+R8/yJn5CA540EBcP74TyYevkYXn8JRPPLjPFYgqi2FVriuyFre3vCNvXII9JY/rM/K9f72GMSCL7rXVL9+at/VXpwgqzN6E2yU68bbHjAANMkYZ6POF2f8eLyFjfagk1T9ckVwY36foNMdcQj+0dceeBtnJqIlbXoOKKuSry3RFqjgXK7pSrXbKsC17Y9P7g4Oj4Ixtz3MgLBtdiuIBQeE+VkkxkueIqqJp+M8cCd27cJ1oJz5KMpaTrCvhw/1keTu817lxZ+6RhUPg0Q9w5j8ALDUF2NcynOn2PX6LOaVnU7KMl4IiUpQmC9XjOZzBiPxxTFBqONSNIXW5pWsj9VUTKdjoU/fLumripWyxNGkwlJFhFniVALwg52Ik2vnqgXUPLrwKWjK4wnE55er3nhxRfZbjegA01TU1UlWT4Rp1KfUyGmacreYsELL1ynLKueMUmeRdd2gs9Nxfm0QfQI4jimLEuee+4alw8PONpfCNQHj4vATxMCGSFpUVGGWzmcCywOD/mG+x/kaH+fal1w584xZVVR94xBBMGiDloG2hhxnBXESUw+GeF7zYrgLLNszmQ8lmg8OFKtWa9WQk2bpYJFHioETlR8vVZMZ1NG45wsSenqmmyck45HlFVF07RYJxt9VQrLQhQZggqgDMH3OFxnBVLhOiKjdrzrbVUKIxRIYBA8yhh5hmnCgw8/iLee6WREHieo0IvGOSWiQARhivKijeF9QJmIum4kcOmnr+qbFPvSBgMjlWIQNlKikIrGqBjvLXEcMR6PMFGMv7Oirtt+acgGpJUICeZ5TmQMUa9E3LUd9m4KSBBRvTBsPGrXPKaCFuYnHzg+PmFbFIwn092crWuh4I37jU0EusQRkrkpcLIkiQUq4APOCVwiTRLSOEI6dh15nvDo2x/i213Au44P/eIvcbatyPORaCmkGX7WN3orJMuIxXmHD73eRtdiWy1MMc4RvCJO2IkQ+ruMjdYK6MWvlIgbmSgS5iIfiJOY/f098jwXqIQR2MXAYKWVkt9XzjsAACAASURBVPnspQo0m01RSsgC2uFZ9F8hBLZFwYGXz7sYrz4u7NSFnXor2qnOB3HIP/UpcS7f8x5xTstSKgD33y/QmsVCHPM3GhDcPZZL+f7MMy99/xCQNM1rN2W/2hgCG+ekx2LYW7tOAiKQ/pMhOy8YP1kTVSUBw1d/tcCxnnzyPNAYPmM4lxDEQf9n/0zuybvfLRCmppG//6EPSVXj7orMvfcKE9R4LP0qv/iLbzzQAKlcFIUEfEkiz+HucXgowcK1a2/8c9/7XunJ+PjHJeC7e3zN10jg9eyz8Df/pvSrADz/vPR1/J2/A1/7tRIQfvzjEiT3RBdvhp163WDDAZk27OUjMqMpq5LjtuBOV9EMrDpw7uSEwMFowrseeIxsf58Xrz9LiSMbjRirnKauqeqaqiioyg1VtcT5QJrm5FnGOB+Rjg45vnOD45M7QMAojSaAbwkWVBShtKdqKu6cHKO0YpTneNdy7Fy/mDVJmlFp/erlvdd6zr1BUigipUiCJkJk4bWC4Cy2Lmkjg4oSaZTV0Had4OCDJteCPY+iCOcs6/UGEMep6wQ3a60lzTKU0pRFQZwYymqLtR2TyZTtZk6xPmWzXZIvx4zGORMzI+31CAYIR1XVQpUKOKckG20Ul44u45RmtV4Jjt8YqrJitVwxme6JMeizTYMi8mg8YjTKuXPnNkmSko9GpEkiDX7WcXxyyng8IsnSvtnZURaigv3Fp+fMxl/NJDdEwYFv8cESVEcwDlKFGsVgE2bTA2aX7+XSbE5RViyuHBEIVGVF09QoK0qzQwk+TpOehUmw0/P5DO88xXZLZzuOjq4wm07pqortdkusBPoSpxl+4CkMShaN9xC8JEyijCTPcU2DjjT5fMb15Oa1T6gnv7A1ljbpKMuKRtfEse6paCWSV+gdlKnrKtqmAi/c1K2tqVxF2zWyGAOgFWmUkkUj0skMc2mCDppbXMMog8eilGAkIyNY2Vk1v3rf+t6v8J1F64iOwMnpKc5adgrCod+zfNg5IwNH/929JBJHyAFxnHD1nqu9eN3zbNZbnLM7X0brnskqjgWHrAaKzBZvBQ6wgxNxDvMwkSEypi96qF0FpGlqtpsNVbHt14hQVo5GI+I07+FRQ3Ckd9/zPGW+WJAmCbZp+6ZdqdoEHHW1IQpeFNzTmEcfuo/wre8jjjWfefoas8UeKMnS5vmY4CFNMsCgjOmzjAFc2wdonuADnQWnDUFBopJd8z999cI5j9JDNWfoaQk4pFoyHo+YTmdCiWvFQUqShEHDQyuNiQArAWacJsznc/L8hKqsiUzcZ0rlPtd1zXZbsLe3eL2t+i09LuzUhZ16K9kprxVFUci+D9KLcXIiTrgxAon65Cel8vDBD8oxTz75xqBOv5Ws++90vJbD/fLzHI4bvg+/HyBerzc+/Wn4C39BqjyPPy6Vnscekz6OLBOYVL/fkqYSoNy5I07+d3yH9Ei8UYjY8bE0nx8dSeVlCNRAgqOHH5Zg5Lnn3tjnTSZS1QDpvXl5UmKxkHP+1V99pXbH9evwa78mfTELsR1CVe/fNDv1ZdioApMoYS/NCa6ldg1lcNTB4RWYwE6cSClNBCzSMSOTcPPWTW7euk4RG+bdnPE4J44Nq1XJerWkrStc6EiznOl0TmYS0iji6qVLhK5idXaG8p7Y6N6rEdl25VuaegNo2sqgvGUxmxNHhrqsSUc549mC2XyP4uyOcI2/RmYIzg3QzjGTiyFCEQMR0nVvkCxCUZe0RkE2Qydmx5KjjSaEc4rFrmupa6HXzEcjurbDOfkbVVUxmYyJooiqqljsL0B5qqpmMp4wnc3p6oK6qVitTpnMZsRZSuZyabzL855NxOK0bKhaRWRZRte2jCcTrtx3P1/8/OfwnUzQrutYr9YsDkrSLEUSXWbHQhJHhsuXLrM8W7LebEg6i0pTsiztOZe3+OBZxNGuOajtOpbLJc8+8xxXj67y6INXUcESmhVtV6K6FuUdSgV0EhGnOcl0AkrTdBYVGUbzKaP+eggBHcA5d473zTKCEkMSemODD6xXa9q2YbFYEJmItleutFaaeqdpJq0Tg2p2nylUSqNUoGkayrIgiQyz/T3qsdv+4Oh//98+evixj77umvj/aBzVR/f/3Y/9H780XU4eUsZQ1jVnZ2fCz96jrF6J6wblA/Qc2D6cz2uUwImMMVy+dEg+HnF8Z7njy76bbjcyZhdQDJ87GHtQPa793FAoJRURCRZEOZ0eouR9jLcW23a7OZcYQxonREmys5e7z9CCi55P5uwtFmgjdH0Es4P7dl1FWaygbYnihChJSSPDow9eJYnfz+zwaZ598ZSyrAleOPKzLAelGU2kXBwnorXhbYTvxYk6K2xU3krG1GgNxojo3+5eBAaK47vvvdaaNE+YzeZEkRG2nCQmzTKSJEEbcZoUoIPG657TRvxGoki491UsuLYhkLG2Y7PeMJ2+iQq7v9vHhZ26sFNvETul84zl6SnbuiEdsvjrtVQDvuM75OcnnxQH9Lu/W/oDbt788g3OA/3spUtvvJ/gzRhdJz0i3/md0i/y8z9/3ow9GklFQTZUcfC/67ukUvHTPy3VnSgSde+/9tcEcvUzPyMOexyLM//3/770wfyVvwJ/+k9LVeWXfumNndsQqDz2mFSU7q5gPP64wJ3KUqBbIOd55Yocc+vWK+/5UJX41Kfk6+XDCNSQoyO5rrtpZ9NUKikhnDf/I+Qv8Ztkp1432IjRzJKUXBvarqL2HVXXYl2vsMy5QjJApBTGB27cusnnl7c4rU9xkYG6pitHRGmKazuCc2RZTp4eEGUZaZbSbLa0ZcFsnDCKE8ZJQuU7UXNFmESCD9iu5f9m702CbcvOu87favbeZ5/u3tdnvpep7FNKSZYtJJdcRm4CMLKNA6qwwxMgghHBiACCMQMIRhUU46oKHEwgmMAAqIIynWUk2ViNJaUy8yn7fP27/en33qurwbf2uffJmc8DIuSIyrtevMx3zz33nnP2Xutr/9//n6InKYVWlnkMxKYRrKQtuFKP+Pznv8C1Szss9u8zc0d8ZIko41fPRBL0tKIa+SwiGKVQKVERab2DtiHampjZiPpAyShh3WjaFpAKrgzEBubrNcPhiMFgwGazYjisGQwGtE1DURiKwm5ZikbDMW4ypXUblssZJ8dHDEdTqqqlLCum0ykxC0zVhZVKSVGgjWY+nxGIvPjJV7j13ju8/9Yb2YhF1ps189mc6WSCMXor9tRXgIbDIddv3GD55g85Pjm9bnVdo/VE+JezhkRVVblSFdg/OOTmm2/xxOUJk8oRujm+mWOiwpgCUBilsFpgJ+v1htB0KKMkMM4VMK01VhmMtQz669pDbWygcw7n/HaYuihL2q5j7dasl1Jxi61U0WxhhXI2SGtaKbmPGIUm0W4akvdcffpJivGQY3fg3tRvvfnJ1ad+8i/d/l//ZrtekIJDW0NCAvLgWppmxXo1Z7mYsZid4JpWgtO++pGduMoD2+L7Rfwt5SqALSuG0112L11lOJ5iMvbX2pLCVPzB9W/+2/2r+/9+Y5aLSazxSvjv3Yeqt6Yz8xI82uGIWRlcA0mUQ51zeKtw3UaYePKbjFEE/ZSS6pxgrC1lWWYq2wptjPDox9Qflm1lI2RlbaPtdo5hqw1iDDpJ9dcWVro3SRGyKJXO+iFK6y10YzKZUBSF0PSGIB8rwXrTsJjPWK+XqDJiXIvuGgbWMixLbly7xJX7R7x1aw/nBNok1cYCWoctKqwtGY5l4LdtGoIXWEnbdXjnhdUqJxTGiIHuA4EYRQvF5gCoy0PFhS24cGGX8Xi07YKUZUld13moVExtL87YG2mlVA7wvHyNnIezced6s6ZtzsAFztcj69xPnfsp+Hj4qU3XcHB4ROMC+qzg3ne/KzStMcq/33xTAt9nnhEcfw9R+qj12msStH/+84L1v337lOXpj1tKyXD6zs7jnzefn858/I+smzfl8/zkTwpD1b/+11KF+o3fkORqNjvVC/n1X5eAviyFnQtktqUsZbB6vRamKuekE/G1r8n///E/lnmHv/f3ZN7h29/+49/XZiNzEz/1U8KCFYJ0Vp54Qr6eTuW93rsnz/+Zn5HXSAn+0T+SeZR+jUbSlTBGuisfpnvyve/JZ/jyl4W++KtfFZjYaCQMXl/4glynDL8SEb6C3T8hP/XYZKMylmlRQfBsfMc6OpropRrZBzXIv0VVWBODp3EOl4IIcbmOtnO06wW2GqCtpR6NGY+nXLt4DZci68WcddPRbhZ84BsuXdqhqiq61lNoLdjIPvOP/rSSqwxN8Limoa7HTC9cxBjNp175NH/qc5/m3ntv8+p3v43rWnoiwW1D7EcqSH0lN+UUzkIe2suMP0phk6JIEe0crm3QRYkpS1SMQoFpNL6VyfzJeMJoNBS2j8WKsihzazmwWEjgOMwMJd5HBuWA42N5fDCo2Nm9wHI1Z7Gcc3JyyGS6y7Cu6cqK1XIlDBxaMxgMaJqWoipQ+YZ3MfLkE0/yhS9+ib37d1ktZihD5iCfsV5fFHxeabaiZD0zy9WrV9k0a27evMnx0SEpRQprqXPmq7TBaBn2KwqZI1iuVrz97rs8eWXM5166gkkdkQ6lC3GU2oJyQnG4XNGqhLOW4XiISokm0wr2QXOvYYBS6KahHg6EIz5FWudJIZAQKsEQQ2ZcknmC0HYowNhCWBJCEO0FyA4j4buOzWrJOniSTbjgOOGEtJt4YnXtuV9+68/9zdXxPjp2MugYIpvNmvnJAfsP7rD/cEl54pg0FpsKKmMpiwpT1GhbSKJBJAaPCpEQPZ0XjY2m2dC6Dj2IPPvyZZ5+7iVGO1Phta9GDOsph/XhvW9e/YN/771HGU3bOY5niwxROm1sbLsCeTP3DEo9S9ZZDn+S0LPO5wuO92ZoAw8f7otRyckUKgqGuCwZj4eMhkMqa3GtYzQeUxwtcmclbXMNea2E99sWBaL2fWqIJDBXGKUzrW+kbTtMEiet8rA1CgZ1ze7OjuDDN2tcJ0OTioSOkcODGffu7/P8U09TKRm4JwZU0NgUIWhWyxVN2+RrlLAIJrWsKnSmrrRZc2DTdFkMTNSUfdfhuwbvOvoujs+dj56FSv4KJbF8dkc9rJns7FDk6lA5qBiORlRVtWWt6ro2641IB0ehsv6HiCgVhZjjmNIW+qa1omsdiw9zNucLOPdT537q4+OnnAvMj48JCYZHZ8Tf7tw5DZofPBC4zr17kmzcvv0oO1JRSFKSxRgBwf2/9ZYkG//kn8jv+/t///S51j4KscqzfBgjwfXf/btSSX/cOjqSIN4Y+dmsz5I3trzGjz5+9v32j9+9K52Jf/APJMH6hV+Qx3OgjHNyLWYz0R75G38D/vpfh7/2106f55wMXB8dnXYXMjyRlCTp+K3fku7G3/7b8Lf+1mlH4nHrm9+UAfff/E1h9wpBEiGtJQn8V/+qj+AF9nbpknz9mc/IXEbf3XjlFencvPvuR1MLv/eeJDd/9a8KC9ev//rptTRSPOFf/sttohlDoKj/5PzUY5ONoS0YGivqwwkWIbJO8QxfuTqFWOTHXEpMJ7s8Xw85vNdw1M2JKmCSxzWOZApUOcAHTyRSFwUDW/Pk9EnmuuJBc8j+3gEuhlPcuVYoLZshEXu8iLDDxEhKGoaGuh4wny/45h98h9/89b/In/nzX+HO3Q/Yu3dX4CW95f7RNmH/z5S2bfeCKDhfLZSKMmVoMClSBo9zG7wrSGWBCREbHG0MYEzm5Be+8bbtGI+GlOUgt0oF1rHFwxqD6zxlNSTGE1zXMqprUj1kNN5huVrSrGfMDh+wU08YFjUbu8bFRFlaGtNirWE4GMqQj7GYpEg+cePp53jy6ed4643vEUkYlXBdy3K5ZDQakpLN9sISoieSsNZw/cZTHB4e8uDOLVYLRWkNpS2ohyN8EoT7ZtOQ4gCCONaj2TGvvfEG16bw1AWDUnY7PKy0JlUF65BolmuU0hSTEQoRi3NdJNqQue0jRWEpikLmE1TEWhn0kwq8VH/QMoyXZAISSCgCyrWZ3hJ010FMuQ2YMDHJAPd6Q7daE5zDNQ2boznrcUd6MeK7huXxPdaLGandsF7OOZkdc3C0x+z4iGY5J0WPVok6D1QVtqSsBthqQllVmEKjlHCuqzxg5Ton7EtGQQut85zs7zEd71DohIkBFwJNSgTfkYDWtTitWDQdIaQtjKL/I6MbBuluCHtJz4OhSBnJlCAmklYkH1kvVuzduUdIiZPZIncXeicSsYWhHlZcvDRhPBzhW0+KUNhCBqad3nZNtjMbKRGDxxNJSShDYx5Ktdb2jRVAKowheHTwFAgrkNKKCGhrme7uMh4O2SyWzI8OcW0DPpHoiClxeLzhvdv7fOblBZNygCESiGgsle9YLhJHR3M616FVQVFIKGa0piwtqYuiP2A0GE1CU5QlrnMYY6GuSW5Is9nQdg0xBunaaL3V2jAmCy8SiAS0Vkx3JhRVSTLSybFFST2oqcpKBL+C6HrkAZWcMCY612WBsjxgGtlC2yCfzRBZzM6TjY9a537q3E99XPyUqUrW6znF0T5Xv/P7p3vj9dclsGwaCVCdg3/xLyTh+Df/5tED87u/K/MAv/3bp/trsYB/+A+lmv65z8nvODyUKv9v/dYfFe975x0Ru3v9ddK9exJkX7/++IP64IEEyP/8n0tV/qzitfcSOB8cnHYR+vf2n/6TBM+/8zunj//wh/B3/o7ApD79aTkcP3hN6F5Tkq5OjPI7b96UpOSpp+Tnb98WUb833jhV7P6n//RRJq4QBKJV13IdMpvT9lz+6Oq/5xz8s38mCcIv/qJck8UCvvEN+NY3TxOWGEWoUDB00vE4e9bffluYtvqO04ct70Wp/OtfFxatF188TaRef12u161b2/sWtWLyJ+inHptsjG1FpQ0udKxTYBMDIVdUzl5uGZgVxpe1bxnpgs+//Aq1NXz1re9yFDakQn5GB49uNjQhcDd5LpZjPjt+kj/7hZ9lMT/hmx+8zpt7H3C/O8Er8ERMFP2ARNxWcwXJkdAaBvWQcjil9RCallf/8A/55PPP8qd/7ud584ev8x//n3/HZrXMwdkpzrofst2CZTMERiOGXAK4/jF5/4VSmBhRviO1Dd5YdFmSNPjg5We1pm07YowURUFRlDjv8C5Q17kd37bb1tWmadjd2UEpxWKxEDGkqubihUss5ses1wvmsxP2y4dYW6CtBm2BiHMdZVVRFAVlWTKdTogxCysZzQsvvsSDe7dYzmekQgZhnWvpOkdRlFtxsv5ipJQYDoe88OJLNKsFs5NjjBEKxLKqKIc1kcRyvSKkSI/e71rH/QeHvP3OPXY/fYNxNULFjohDFxUmDUitlSpiaLFdQZ0Hjr1zaCdCdsYo6nqI0ooQOpJK28pPP5CkMzTHB0+KwtyUE2xc29K5Dh88JmTlaaVRCWEMStLW1+OxVLGDo123NFpUdbt2zf1bb/Pw3h0Wx8es53O6bkPnGlIKGAWltShToG1BVdWMRmOGwxG2Hor6bWHQWjDGqrdBXcdsNmM2P2axtKhVK5CB5YJmPMzsRhptW2ISCIDSBodi7TqS1qSM0Y0pbmcmeugBCFzqNFRJ2/upM4bEO0/bdixXS4KPxM6fHgEl1TpjDIPBgEuXLjKsa2aHczarM4xVKisxa7Ot8CXVV/qzodruKGm19jSxpCCJDwkVDFEpiuyUdWmxhQxWaq3Z39vjwYP7tG1DDJ4U5b2u1h3v3b7Lw6Mjbly5QmUt2lhQmi6sOTiZcTKfE5NAIkgizKbyPutVkr1zooyehN5YkqOMabVDjFZonVhv1vL5fCAoYf+ythDGrCjXZDgaMR6PtrGgNia3pI3YrEi2BSVlqYgp0rUiNBhyUnbp0kXqekTbdCwWqy1kru+oNOcwqo9c537q3E99XPyU8oHNesX0jR+we//e6eZuGqGSPbu+/e0Ph//cvy/B9Y+uvT0JlKvqUWan3/qtP/rc5VICXWB94SI/uPkWe6/exIdIygWaGMOZDjjgOrj+CbGdN9+ET74C9D5M/FT83g+YXbjErZMjjvYfEkPC3buL+71v5MaDzOGMx2NujCue/sZ/pnr1v7OcrVgfnbC3d8TDw2Ncm+cXUiK9ewv+w2+TSiukIF0H4bTrr5RC/Z//l7A1xbSFD0FC/W//e2YasxRlgbKW3d0d/tQX/hQ3nniKB3fu8q1v/nfuPbi3vW8KUG+/xY0//Dp/+stf5MUnn2BwUZG+8iU0YJXjwf6SP7x5j6N//W+x2kA9RY8uiK1vG6Gq/nf/r7y3qzcIUSC7MZPBGKWxWuOco3vvDu3/8U8JRm/tTWoarBFrECL4GLlVjxj9CfqpxyYbI2XRCVwMrLy0pnMB55GVCS9JwMq37J0c8oXyM/zy575EbUt+/4Mfcne1jyeAVkQVCWGDX0XMcsMm1Ay15dMvfJbrk0t879Y1vv7B93ltcYdWRbTOWEplkDoTQvOHYMxH4wnTi5fYrNeoFGjijK9/7es899zT/Oqv/EXee+cdfvD970hLNw/HSd2jbz73K0FSaNVDUsgBVR+4yGCYSYDzoBu0NejSkrQm+gBKcKVNI3j4mORr0AwGgn9dry2r1QrnPMZomk1Dmk4py5L1ek3btgx3hozGE0ajCZtmzabZcHxyyGBUU9YVVT0Uevkkm2C9XmOsYTSZoDA0jQMVefHllzncf8B3vvUHBO9w3YbYtUKt5wJWy+AtGRPcw0QuXbrE8y++xKvf+y7L5ULEoYxhd1BiqhJlFEEJzl1nbM9y0fLm2/e5dnGXTz53CauEnjRpcG3CuyhDdNGzaRqK9ZrC2i3uPaVIic0FvbS9T845uq4Tp9MbcpVDh5hIzkGIBO85OTqm8eEUwpBSZltSGTIUqQYldjwgOCc4/RhprfBXzk+OefXbv8fs6EgGh0MUzQytKApLWRTCpjIYMZzuMJ3uMqrHaGOIKqGNDFGVpRUl7i3esWMwHDIcDykOS0I6prDSHnZdi+8cxvpcAZPdaIqStetoo5fgKRvlFNPWgKc8P/GjxZa0tfDIfUVUwHslcKWgLPLcQYgklUSwUGmqqmI8mVCVJcuTlSj0ep9/Tq596oOcpEhRowhIEyVu96TAsyJBKUiaU88DOoHvhHdeaYVVScT9qgGr1Ypbt26zv3+wTRBSFCiV94H7+4fcOzrh0ykysQVlUaGAppmxf/KAVbMh5Wqlz/CEwtqtirLKhlbHKI+1zbayqTEETiFSkIX9Qt/hkC5JSJJM2R66UWQ2GqUoS6H/1NpsHSQp5aqQsP/EIPMzKSXKomQyHTMeB5pNR9PcyWrmbA35Wfrd8/XoOvdT537q4+KnQgqiXH9yCqF6ncB3CNLUylTBva6JKSuq4Yh6MKQsygwtDZDhXdYatFZopfPsncc5T9u2LFcL5osVRluG9Yh6NKSqa2xZsdN5PvX6G+iU6Oqad67f4Na6EXKBLCDpvZddG3s/FSVZ0D073GmxLEbRPQrA/Rj4QQw8WK8y7Xmi7fzWTxmtuVAU/MSopn3qGlVZsu88B4uCd0Pg/fWGrhXq+VM/FUU0NoqfShkNcJbynQ/zU9qgrcHYQj57VfL0oGbnuRdYX73Ot+/v859mS+7PFvgQclEskVLg+gS08Yyef5Ir0x1M9lNds8/NN27x+7dqDlMlEMwYUVpTWMsCTes6yryXBcob2HgR8VRaRIUNmjYkWmXZhI5us5aiWPZTZVnRi/2iDLtVxZN/gn7qsclGqWTKfBMc69Dhtwejr3Ke4sWVvBqb6Hnz5D5X332DL738Wf7cT3yJ564/w/fuvMWb99/n7vyAZezAaEbB07o1bx/e5T++/k2+/OLneP7Sk/zC6ItMLkxxN7/GDw9ub+2r1sK64ZMET0qJcU0+YLShKmt8u0bZxK3bH/Bf/uvX+ct/6Vf587/8a9y7f4/9B/dyXURlodbTz7PdXSnlw5e7ZR9y7ZICQiB2G0yhiG2BsoMtDWY/yJZI+OBRaOp6iDFW8KDO07lOmCyKgvV6g8+Db6vlis2m4eKupioHjCc7LBYzmmbDan3C8UnFcDwWylAtLa1EYjabobRisjPGFgMSGu87Lu5e4pXPfI7333ufvQd3WUfPoqrY3d1F0CAVRVlAEn7xlHGLZVny5PUbHJ8c88Hb7zA7ORHDajQXL1+hnoxIRJquxWRFiU3ruX3/gFffvMuVyztc2SnRKtEEWCxa2kaqzCF5mnaDXWdmEudEy8JoQow0TUNRFELPGoWCcb3ebPWFtM70qzERWsdmsYIYaTcrVosltq6lBZgrFCFGBN2QcmU7UNUldlCgXUkMirVvgMRqOeferffQSYYLy6LcGm9bltRDoYwbTHcZjCaUZYU1QvPo2hWh6URMTtWossAYK1m/NQyGQ7Q1BAWtS6Qow4jEmI2gOL2YK0qrtsOt14QMheqTDSDTIz66QVU+E9ukpN/S+fnOOYIT41CUBYWybEzHbCMiWEqBNYKvHgwG4hAUhCjUtyKEJ/oDUkBRojGQxAhukeapjwlkyFreZhRbkQOklCLR90PkikhEI4xQ+/t73H/wIA+w5komuUOD5mS+4YO7e9zdP0FjGQ0i1sLJYsH+bEHjAnJ55H4n77dzGTEKZSV5z4kwWCfwBWNwXtPFRNsKfXHnWlzXCVQiJaqyghilWmqtCHZlLHG/L0XIyzySAOrM8NWzfgk2tiPFRFmX0i1RFqMs9aCiaTZnc8UtVv98/dF17qc491MfFz+1mrN37zbu/u3tfb6tIl81kLTYlSL7qfFkymCyy2D8qJ9qmxUhip8aDmXGpci0p845gg+0XcvxyRH37++RomI6mjLZ3WG0s8NgNOb6quWTb4hOho+B+WZDkEzm3E+d+6kPXY9NNhQKFwOb4Ohi2FZPyDe3VxxWZwydI3HfzfjGCALl0QAAIABJREFUu6/Sdg3/08uf4/PPvMzLTz7L63fe5Vvv/4Cb+x9wtJkRug6vFPec53ffe5W92SG/9Kkv8tPPfZo/9/mfgYni+Gv/N3vLhcA8Ul8pOG3NaRSb1YrlyTE7u5dYtg1r5yiU4dvf/T4vv/QyP/Ozv8irr/2A3/2P/4Fus5JDcfaabO23fEMjkLX+syaQgC+BR3jdk0rE6EjtBnQhBqEoiIg4jyhNx9weFOzrarVCKamAkKBpGybj8RbWMahrjNGsV2t8CJRFwXg0ph6NpaXs1sznx4xOdiVL1pq6lqG0tu1YrVa0XQc6Z8QIbee1J2/wwkuf5Gj/IV275vh4n/FkzHothmY8HmPKCp9kmFc2pMbYkudfeJn1fMnDe3dYzrNgXErEcAEKg/eRzktLPjqH95qb797j6euXuPi5KxS2YrN2zJYtpa4Z1gNWqyVN47CmwGQ4TlSgS1FxbpoWpTVlWaCSxjkRs4sxUpYlpa6296vbbFjPl1it2axXKK0yK8nZYV4xINZoUtJ03qE7zWBYo6sS5RW+EfaFGANWiZK0NRaMxdiCelAzHI8Z71xgMt3FDEpa1zFbLNBGhh6j2xCjxwcDREIckLRCG4OPgagSyWiq4YjJdEq7llanUVK7VCRS8BkWBYeLBZN2KHsxRmF4yqf6EQ2NH129sc8wJ7K4lPMe56XtWZQFO9WIwmxYda0IREHWpRjmwddm6whCFhnsQzZ5XSUU5fk9xSAGPaqz5wk+LBAiV5ZSSLguEVPAuZbZbMY8zFmtltuBtb7iZ43BmIKuCbz11h0uTV/j3tWLXJ5OuXKxZtWsmC0iLipi8vl9ClNW23X0jXPvpXKngBQCwXsJ3jIWNQFd19K5hq5r8V1HCgI1MVoRU5BBzyhJ6KCuc5XQYosyD4n2nz8BUkmKQbCwfbcLMtbemmwTZB5kPB4zmy8y5a/8hvNc46PXuZ8691MfFz/ljh0P7t6Cwz0opvkAaDD8D/ip+KF+qjzjp4zRMjBM7vamUyE/5wPrrkWZ6txPnfupj1yP19lQCpSImcX4R+k1T3+x3KXtRUqOB+0x37r9Q/aOjvjUjRf47HOv8MUXPsML12/wxvtv8Or7P+SN4/uctBvBRKuOD07u8NWbibEp+dNXvsiv/PTP89re+/zn7/5BHvgSXKNknEoG8pQi+Jbl8QF1NaQoa5bNDFOCcw1f/d2vcePGb/Dnv/Jr3H73Hd5641VCbv2cOTr588gH0knmeOWTqZwHy7MCEc/psG50DmM7VOEFV696/JpcphATqEjbyoR/WRbSbrcWl7PwalDhfZBMvR6yaTasVmvK3R0Gdc10MsV1DavlMW0jiq22rDGZaUOhKKwlhMBssWCMph6MqIoBSktF/ZOvfJr33/khD+68S9du2Nu7jzEWYzQXLlxkPL1A0pYiizFBQtuK8XjCiy+9zHoxZ7Na0q3WzA+P8N5hBxVlPaJzEasko9dFyWK14a13bvGpZ3fY3a1Zr6RaVI2tGM5iQ7NYUbYFVVViCzGCAuNRWU07EIPJ/xbj1mSjYHIbUfCtHpUSwUlWP6gqEVhSSmhaQ5Chy6yGbayhCx7vhX1IWUtwkTZDaUQ506CUQRmLKUrq4ZjpdJfp7i71aIq2BY1vWK5XpARFLtwYYygLS0rCv660pqiqfI7Utq0bfMxtazKdpBFDnhS6V9MGGucZJTAxoaKwsMi+649dH41IVSYiHRDdV5X6o5oNiGu7LbNSYSyDsiT4sKV2RYEtLYN6QGEL2qaRxzI1bwxRRO3y3lZ5wBQtgmYqCYyqr84p2A6G97CQbLm2diOlQPQCRdisVhwcHDIwFVppGbbPhhwSxsowvm8bbn9wH982TMcDnrl+iS9+9nmiGbNYG1I0wBksab+vvAyXei9VohACZCV4EtIGTwmtNM53gl/3Qodb2QJrDCE4fAhEpTG2yLC6krKoKKuK4XBEVZaZItNs4RRCbSkqytaeagCUpUAuBOst9JmT6YRy/4C191sbe46iesw691Pnfupj4qeaVgLLs4mzVhpd2B+vn9Kn2g19Q0Od+ynO/dRHm+k/RmdDUWqL0QUgirtKKRSnGQ/0reLTn0so1tFzvzlh5Rr22hlv73/As9ee4lPPPs/PffqL/PRzn+V7d97h5q13OF4eU5BYrufcmd3jd9/+Lk9cucbnv/BT/IXP/yw3373JrdkJKmmk2atQyoAWqaaUWrrNISeHQy5cfQZlBzRdw2hYcX/vHv/tG7/Hr/2Fr/CLv/TLPHh4l8P9fXRKqKSJpK1KcMpVoQKoEFxhQEnFW6Ut7ajucfIJoahrWwqzluq0LVDGkHwUhgklf0OQDaFUpBpUWGtoGuHaLsuKtulQyjDd2eXgYJ/Z/IS6LrFFyXhygc4HOYTths38hEU5YDQc4oqKjd1gi4LBsGKzaUAbdnZ2UFpmBQKJJ67f4IUXP8XBw7u4rmG5mCMZv8Y7R9s5BqMppZND4toBRTEi1UMuXrrKMy+8zM3Xvk/TrKmqgvVCU/hICnKiuyjsHCkGtE08eHjEzXeOePH5G3SNptRDQgAfHKbURKIobruWorQ5wCdn+IKlV8oTMoWgyq3/0DiaNmCTyrYgYbWS55Nk6BdFikgLNMRsPOX+FWWBpch0lwFcwAdPIA9B571lTEFVDRlPp0x3LjKaThjWw6zEuyEETz2oGVT1lsUh5XsdvScGl1vjAZ1kFiKpRPQR7zwxBZquo46JwlRYVWDLmnJyAYpBPkgKG6Wl6UiEJNWvnmHqNPzIAchZvb9+PysR+8IHQtvgO4/CUGCygVMyDJ2ks2IHmsGoojAlKgjtXVFVog+gFD6FLBYY0UnUvlWGJygtFSOSQkUF6nS2BNWHTafBUkpZHUDL73CdY7NaU00qimpAUZQURQkp0SYZug0pkqJis2m5deuQFBy19bj2OY6c5WQDhIRNSGs8nf6Vqp4nOIdO0HYd7WaF6xp5Ln2FLW6rvcE50QkxNSEm1psmq6AXlCPLZDwWbLRWWG2oikIofrWmsoWw1URRrRZHrrbwlZ6xy1gLSnRKkooMxzXjyUjoe7OxPzt4f74eXed+6txPfXz8VIsPXba5sowt2NnZYfRj9FOFqraHSQE2aVzi3E+d+6mPtNOPTTYMUNmS2gzQag0qolTfTjtl+uizQmG0kcwXEl0KHPk1y1XDcTvjweKAe4f3eenaM7z49LN86aVX+PQzz/DB/Q+4d/c27z9oOO5WvHl8j2++c5Mbn3iGV65+gs9cf4Y7sxkoJUNvJAwGbSxeTisxdCznB1TDKYPxmPWmpWnEkL/6g9d56aUX+dkv/xyvv/Y9vv7V/4LfNJDidnP1+bVSCZM3XuyNd2R7YORGp/wZ5aj54OlcQ5GzxKSlZW+1RplTqlCtHlWBDSHQbBqmO7s4I/Rkw3qItQXL5YLVdMR4NKaqKibjKb5pODloadsNi/mJCCjZikSirCtG05GwoYSA71qpCOWMtaxrXnjxJd7+4as8uHdLhoyzUVgsFsSomHoIVUVnDN16jbYNm+GanZ0pl69e5fL+Nfbu3KJ1HbaucY1g+YpSaM+8kwE6bx0npeX7P3yPwXDC9SeuMR6VLFcr1ssFFk2hFDEGvBehGGtF3EhpOeTeBzHIUYyeLQpQia5pWaxWrDdrdi7tQr6mMYbMuhSFzzzkQbAYpZKXEiEFTNLZaUsVJSVpGSp12kosipLxeMp09wLT3QuMxlNMVQqXdOcIwWGNZTgaMxwO0VoLc0NwtG0r7VmrBafcGxCtCHl4SysZwmw2HShNUQ3ZvXQVVU9YusR6y6TR78NESNJijiFscbEgVR61NZSPoi76h0kyfNg1ba6WypC5c3KdyNVgrTWDQclgUIqxCTEPfOVqTwJlJHCKQd5CjBGVDY0ELALwSEqwuqdVI6HK0lrO21akLIkD0FpTGEthLdYKhM0YizUa39ubjKHVRJLSxKS4MCx45ZnLKAyH84bGsf18PjoRBcxY2BhlODNlR5iCXM+uFVaYlER4UL6fq6ZodFZabTJrlTFS2anKUs5nWWKNZVBVlEWByddIKXIwojIlJiiliVGYWlKSYcct5ACpLhe2YDyZcHx0gg+e8/X4de6nzv3Ux8VPQcR13ZYYAETM8PLlqwwmPz4/1e7tnckWJJmN6txPnfupj16PTTZCitRFzS6Jsl3QplY2Sr6o+o/smr6SkogovJIsrEue1nkWvuW4XXDnZJ93D27z8tUnGU/HLNcLlpsFjWtp8Rx2c75z7z1efOttfubTz/LZ68/ztffeYt62lEkYCwQbKxRgIW9CHxbMTm5zZfASg2LIYrlkWE8J3vO1r/0ev/GXf41f+7X/hXu3b/HOm28QnVDakStF+VrKJk2JSJLBIoQwUbbH6RnbHrTc8iqCJwUHWtqMQmcq2DvdMxMl+doYUWNtu3ZLl9d13VY2fjE7YblYUmZs3Wg0wrV5CG+9wjRrlosT6kGNKTRJJ1bLFdPdHawxzE5maK25eOkSpjD4tuXaE0/wiWee4+GDe9sMVCHOZLNekXzAWhlqLsuSatjhfIPr1kynU5566ilW8xMWyxXJWCaTKdZKtcQHqYyU2uCjZ75ZcW/viHfv7HH96ReYjKd0zrM4maGTxURpdQo+0zMY5DZrNgghBFzn0Nkoo6S6JcxIgeX+AU23oVICCXC5Fe3aFq0N0TtQp63emI1pjIGYIq5zp2JtBpwT5gxrLRcuXGD3wiXGk13q0YiyqlHW5AMcMGZAPRoxGo0FB5krRl3waL2k3SQ0wk6j+vZkP4wZI2VhGY4GGFMwHl3gk5/6CaZXrnHr6IjjB/t0PgMi0qlidT9I1xtlUmagSmerRgJM3WJQ8wZNiPHquoYYxSi4IMOf3vnMFCLt9aqqqKqKmJJUtkLCh0jXuswnD6YQIxaCUMKSerxxQvfOS+UqWr6fjxyu/hSlHsohmNJ6OBQmnKpiMBhgC0OISXC4fRAVEypFUCW6ULz4iQmffGLK0caxWAY6pwhe7q3PVRnn5Ot+8LIfwgtbg1zgfJDXSkHEuFJmF9JKqkxJfp9w61vKomQ8mTAYnNJ5lqWQCcjZUtvPdpaZKyFq8jFK1WlQVRhriVECl5RAacVoOKQsS/zan6kLnq8PW+d+6txPfVz8VJdtmS2L7Q2uqgGT6RQ7+PH5qfntu4/YpRQjUZ37qXM/9dHrscnGJjiqouRqXTNZHbL07bYNnRPIR5YMm/VtHkVS/dcQVCLgcD4yX3XsNyfc3r/NzmSCLSyL1YKTbsMmeBZE3l3s8/oH7/AT16/w7KXrXBlNmbd7GDSKJMNvUTj8o9KolNDK0W6OWJ0cMrlwmcYnVqsVk8kODx8+5Nvf/j6/9Ge+zC/84p/l8HCPw/29UxnmvHpKtpASkbjl6w9JRNJi6rN1TgtN+TGdEFah6FHW5ErGqWEiibFXKTGopPXWNC1tHsRbbzYUhQx5LeYnrJZLBoMB0+mUoiipxxNGkwu0TYvrGjarBavRiOF4RNKK+XyBLUuGdc1mvdlmrlopYkiUg5rrTz/D+OYPOD48fIQ20XUdofOntHlFwcA72q6iHQxQKTCZTLl89RqzxTvM5/M8tDciaQOdw+Tho6Rh3bQUq4YP7u3xzMNjJrsXUYVlvViiogZd4Z2j60w2qA5dlMQUs1KsXCubhWOcc/J4TJiiIG5WHO3tc2G6Q1VYEtC2DV2zQUWpmNlquD244gAiyWipJnih+DNWb3HEJCjLkgsXLjCZTqkGMoBWlCXKikOoqgprLVXW1Cgzi4cPHhU0MQzwrkNFvzXi26AgCo3uoKoY1AOsKbly6TrPPvcSoRzQHZ7QhPjI4FpKcbu/tifuQ850Sqc4WXVmj6pcnXG+w7lWuM9JtMFjXKZZjMKwURQFVTXAWkvITlYhe8d1Xji+jRgmay1KRUJyJESoLyFUuDK0abaOqzfmP/rmldKCU9UaYyx1XVMPa8bjEf7iBS5cuMDBwwe0TbN17CGJszDaUA80LzzzBJN6xAdzx2IVSEER2g1diNtARZh1nLTq8zsIQSo5tigY6jHGWjrnBAKS0taWkcAooT0emMG28jMajZhMJqJwbLQwuhQF5PZzL3ikc/AhtkAq1CmxDerKqspG3uD9Jt9vqAYVdV2LEGR6vBH/uK9zP3Xupz42fsoHrLUClVpLQG6tpaqySvyPyU91/nRAPOV7c+6nzv3U49Zjk42V7zDacnX3CleWexw0Mzxni0SP5Lbb76SUu76pTxLlTwJaEm3s2CTPKnXUfk1lLW30NMHhgERk5pa8t3+b2w+fY/fihKemu7x/vI80v+SVU5RM0mhDAjSaFAPL+QOKQUFZViwWS0ajBePRiO9//zVeeuE5vvwLv8hb7/6Qb/y3/0a7Wp8qLZOHrVKSjDm7o9S3qfPZ0HkznH54MdAGOawuBmKUn47bcycaARGwSuVBRoXNyrdKKRFOycwd1hg2GxnAs4VlMKgpBjU7l6/RtC2b2QHNZsmmWdE0awbGEJuWxWxOXZaorJfgmha8w4WAQXH1yRs8+dTTzI6Pt206gJCCVMVSQEePi54uOmxTUNd1ZvAITHYvMN3d5fjwgPl8xqAesLN7SWjUjMkHLdF5T9M2HBwd8+Y7b3P12mWKbJyjTtTjmnbe0LUdXdmyaQoZVMoVyb46FKVEKToPUQbWCmsYDGq6WSuiU+MJ1aBmfnJAs14RNis26xXjapi542UJ/tA8kslLm55tNaNnbSjyUGNZisFVhZX7ro0cXCuUjurMn9RveKSVrJXZDhP2SEZjNGUlyrMxaOp6BEmxXG1YbDY/yhKYz1PKFS85XClXS3sWiW1FJp2eydPTKI/0uGPhHA90LmGC4HbJxqYsS9l71sqQWkxobSGRoQMmV23kFFqjs0yakxZ4zGrJwWONzYJBNsMF3CPYVFCZE16uUVFYRqMRo+GQnZ0dCltwcnzI3oP78r6bQNd5gSFoS6UNT1ze5dpTz3Dihhwu5ixXDh0SKjki2SbkvdRfx/6xfo+h9JYq0jknDDtOrNCmaVAoirKgLAXX2l/r3d2dzCZjsBnzKo5N9pcxwliTYgJzxgaeMeCnTC3yO3t4RiJhrVyPk9kJXec+wkKfLzj3U+d+6mPkp7SiHg6p1qttsqG1lsHfH6OfOgvjOs1lz/3UuZ/66PXYZGMeHV0IXJ5e5NnLN7h98pAT1+XMS4yycBf3W054v1N/kRDMqAJ0FjfKWsd0KeGiZxU8ptMEICpFiaEk0qWGB4sD3npwl1cmz/DkeFdUYqPCkrYbPGaMqVAbihEIbs5ivsf08idISbG/t0dx/Um6puW//M7v8Ff+yq/zS1/5Fd57/xa333qLFHKbmtOKUSRTo6X871wFU0qchSJsh2n6x40SISkXA853GFuhbUEibfnMVfZu3jsSUBQ2Z5h6+zmMLSjKirZpaJoNxVq+NkXJeGeXZrOiW53QthtWyzmL4QhbDjBoVqsVx8ZgrGU8HHF8dAxWi0ZENWCys8uzz77ArbffZrVaolQumimVA13BXeqQCLHDO03XNmxWG1brDVeu32D30iUWixmbZs3h0QFlVXP58mXKsqRtWpq2w6qE7xxNs+Tuvdu89e4VXnrqBnZQEZWmnAxQK+GGbptWDr0VBdgeL5qSsHj0Bjh4wcyWtmI4HBJdJ8IzKVIOKqwR+sEueJrNmnoaCCGCEnGevnrRi9mUZYFzHqVPRfGUUlhtT6tmg4p6OERZ21tJjDZEIARPr5S6Wq1Yt420v70jBidY6FwVUX3lsGemCQImbduW+fEJMx/YbDZiIdOZIauUq029MFFKW5VTMeA/cmgV2TH1FSOprLquxWURvRilRZqSz4wc8rmrUhy2zRU0n6+tDwEforT9U9q2jIViz8jZJRGdBC+ui8LLXVbb62iMyvAA2fs9xravLg2qAfVQXjv7bWFNKUustRlDiwRVWlGVFc/cuI4eXuL1+yvu7i3woQTnKHQCo7dBli2KR9rUwCmtnwKtLWVVUlVRnFYM8h5ms/weVUaxSNVnPB6zs7ubq0Umqy8X+X0aYpKKGpz+TC+apRSozDbW36MU5RoL/XGPnU/UdU1ZlHRdhz9TSTxfj65zP8W5n/qY+CmtEMFYcwq90kZT1RWDH6OfGnLGR+V17qfO/dTj/NRjk40mdZw0a4ypeP7G83ywOqQ7uE+XuX+VUltz2o/hJU4zoDN59CNf6/7RJI4g9jcnZ1RKCZvAiV9y++ghl48mjExFrQu6FOQ1t9Uo2bQ6Yw7ldQLtZsF6ecJwfIG2bTk6OmBnMubtt9/i93//W/z8z/8MX/rSzzJ7eMDx0QEqiZKxBlRMhCTOqJ+vS7lShVKZdk5tDXiWyMxsEkpoynREaYOmzGm/VClABJQSIs7is5hLVVVYU+B9oCwM4/EuTSOCLW3b4rvEsCyxNjIcDimqAd3yhPVizrKqqasRxTCiUsFqpfAu4KdOss+y4OLFi0Qb8M5z7doNLl68xmq1zHfnTA2wd1zZaKSUwAt2dL3Z4FNkd3eX4bBmtfBs1muOjvYZDgcUdidXQkBh6FxHcI7Z7ISbN99gMqgwGjbNgu7Ys1iuMEFTKYO3li6rbhslmGFSomsaCqshb3SZ3yoZVCVMJiyXCzrvSU1LmXnGQyfGVAx8ZklIERMUsfPoomIwrFHG4IJj08xJoYUku9loOYTaGGxZUBQlyphH8JQ+ODZtw7ppOZ4vuXf/Pov5gt3piGtXLzIcVCSDKHxpMeQJhY+J5bqlWc9QwLDepes61o1QTvYBw9YA9BWWmIQa8YwBP1sX2lYdthWZ/D0FIfrMf58rRhG6zuMSDJXGpIg1isGgpB4MsNbgnWA/N23LYrWm6bpt9YooytzGWow1kApUjKjgSdnwh85l5WGLLaywYFiDtRqtVa4iyeCZDKsZXNNyuPeA2cEh89mMW++/z8HBHuv1EueEflPlMzi9sMOlq5/gcAE3bx2wWmzYuTihS8KoEzqPzYwv2wpk7Gdh8jnOj2+dKuI4bCUt6GFWwE1Rzo3znrIs2dnZZTCoMVboF8tKBMe0EXVhpeS2913l3mn2lSvbQxb6YdyYCM7LkCxCfehDxBaGorS4k471ev04U/2xXud+6txPfZz8lEohp9B5v2aYzY/TT51FzPRBZ9LnfurcT320n3pssuHwPFjP2d80PPPpF/n5py8zeuc1Xv3D79Au5xijiUq4xLd7L52a7pQz2pQSIRv6/nn6jKFPud1r8qaNSoHRbGLL3vyQe/sT2q6jVBqSJ2b+Y4XKLeGIQrIyn2ToJbqGzfyAshphi5LFcoE1gaIY8LWv/Xc+8fTTfOUrX+HO++/zB7/3dVy76d0IpEBUEMlDfogoU1K9o9r2nMnWJtfCwKAyBYKocUblMFpaVjFGfPSZek1+Z0yBFMlVEqlqqNIwqqfM7CHrzQkbW7BebRgOJwwHFX40Zjad0rQbXNeymskA3oBAOdnBdS1t01GWBdW6ZJgH/lzTstk0jIY7vPDip9g/uE/TLFDK0PMlimPOzjHpnNnna+I9RwcPKbSiHtR0mRJxdnKIUdBuNkynFzCmEE5yJa6vWa45Ojrig7t32BlWHB08ZDE7YT1reOry09RXCpw1tFZaloNiIGq7ytBt1rQxUFYVKUW8R5gPtBjiGCMxszg0yzUuBGGA8JHkw9aBoqTV7WKkMRZblVRlSWhXLE/2adcnQBLO8ay4WRQWbSzKGoEtaI3zjqZpWMxnPHj4kNsP9nn37n0W64bQOJ64OEHHyAsvPEdZDDDaZiaHiEYTouLd9+/wwbvvMhmVXL54nZ0Lu5wcnRAS+NQHRqfVhhhlbkPFjLfmdPv1liKltMVcZkuXt6cwWbRtQ+e6fCsTLkgD1+gcflnDaDhgNKoxxtCmjpA8m67lZDan6Towags5S/T0fjqfVwPaorUnBlHDDTHgfEA7hzUiBGSyQqlU7yB6T1Ca1WLJrfffZ+/hfZarFbOTExaLhaimbiut8rr1oOLGJ56hGl9h//CQk8UcnTSDQUWKkaaVAEQJ7mRrrLXWufJyStMnbeKId72hFXEr592W0STQ044qppNpxqZbqrpiOBpRVSJc5oO06bU2OCcUodoWgAio2aIQSEiI24RSWEcyy4oRXvOepYY8vLlaL1mtVo8z1R/rde6nzv3Ux8ZPrY5R0W0r6CCQJ2PNj9VPhTPJBnlvx8i5nzr3Ux9ppx+fbCTFkWs5JPDS9Sd45tIzhMs7HB0f894bPyAlh1aamIQWq8f8/ShCVpba/jepMy+iTp91turU/3/jGg5nx8zdWn53xrP1k/MKJawiIaCNvAcXomR57YrN4pjx7hWSsixWay5dKFkuFvzO73xD2tS/+iu8d+s97n3wHip4TBIDK9WrjDvMTqbHsoEIqkCuqPSOKgnDiUrSpvYhEH2HOTOQo3IG22MxdaYU62nMtoNiRoZylstA17RsyoblfE6z0fjQYYqKYjCgXa1oNg3zk2Oi1kzGY4IXXOOgGuA6x3K1ohoMGI2GTMcTfOd54sZTXLh0iXt3lvJZVM52UZy9a9v7mI1F9J71eiVqrsYSo7AmHJ8cE0LCucBwNGEwqFEoog8kLYqX9+/vsRxWHDy4z8MH92jXDX4T2B3vSradKSnLsQEMKYqQz3qzIUapfCUfpNValgwKEatpvcdaQ0iRVdPgU8R3LUQvzjdASpGgVFbWTWAUm82a+7ff5vDgNvvuTj5MGZ9pDEVZUhQyWKWUQidFaDwnx0fcvnOP+w8P2HTQrBUpDimKCFSslx2WkslwFzJFolEFJMPGLbhz/4j9wwUqDrG2YufiRerOZbEeTg0zfYCQHnn87Inp98zWgP+RpaQ97d1WgVSG+XreGrBaoYyhHgwEMpYxzSIo1bFar/HBSUW1b/tuqywKMuwsWUMKRoKY/j3m/RxD3La8BGbYAAAgAElEQVRntZbfESPEEMW5oFiuFyQSXdfinMuGzWR6wSKLGiUu7F7kySeexIfEcrUhOI+t6m27uW8pa6VzGz5u28iCZ5eAK4QeCy5nM8WY2UlETEkcSOZBj5FhXXPhwgWm0wmj0Zi6Hgofft+KjikPWwodo9Jxy4VOYtsa3wZJpK0mRFmVWwiFc247HBpT3JIXnK8PX+d+6txPbe/j/8/91PzkPsG73H2S1ePxf6x+6kOue1LnfurcT33Irc3rj6G+hTAoKJ64SLi0Q5wUXPnEs/zET3+JxWLG3q130TEiDefeSH/IVspGMPXJ7I9sMvJHi8hEfb+EncAxW805cQtcjGQiw/wTj27qGBPaCCtHTAkVO9z6mKasKIaTjM1suf7kNR4+3Ofb33qNn/zpn+J//rmf57dPjmlPDrFBLup2qAmp7Ag3dv9Kp0atf/GEqLBaDeQs3yhpVSetUNqilRFSeBUzr7JgA60p6EWC+iq2znLw85mwYTjX0PmWpCzWGqaTXZzriM7jmpblcoGuStrmIsVIMI1FYWkah0uB5XKJMZp6d4AxltF0yvWnn+Vgb09Eg1Tf7H/ktm3txvaaKH06BKc1WluMSXSdY7Fc5HZo2g4LKaWYmh2Ci8znC4JzHBwvODxZomPgwcFDDmYHVFUJywg+UKIZjacIrZvGFpam2VDmrNsBnW1RXjCevnNYa6iGI2JUdG3LwYP7DKc77Ozs5mA9oJOo7tIF2pkYft+s2H9wl7vrW3LYFCitpLORmRusMfnSGIzVWGvY3Z0ynEyw1YTnV45N6yF5ahsZVYbBaMRgvMtg9xLD4Uj4y0Ng5+CQWw83DAcTrl6ouXT5KtpaMZwxRxD96lvRW2GkHhOb4SDq7L1Sp9ah35/5LPkgxliYOPpT1wcmYJXGFgVVVTGoKlG8zThf5xxd2xJ8QGWV1H7ALqIER0zPX67OvJ/88mePScbChiBV3RTJPPXiJNsOqaDmyuVZfHj/t6oqnnjiCapqwGa9yUZQhveckz1nrYGoHqlO9cJE1hq8T9kByX5WiOFVSWX+/7gdSAQJsIzWXL58mWtXrzKdTqlHI6GfzMFe/36N/f/Ye88n2ZLjyvMX4qrUr9TTrUEQGiBBgjNckgMSHGVrO9y1Ebv/6drY2AyGQ5AECIDQ3UALPLR6slRWiitC7AePm5nVABowW7P+gM5o6+6qrKrMe+N6+Ik47n7cpPxgkdn0UQoOTQJGmYeeqRJAtSZDab3pkNuzliHGVAxZ0e2LxH/t2OPUHqc+Ljj19Om7aBWu+Vqd5Ik/Upz6VQf1PU7tcepDcOpDDxsqQj4oGZzM6DKDj4qsGvJ7n/0CTbPmOyHw9P338EnuTSH6yQrJPdy8zwe+0DvWt3XI8t+enQlIaM5Fx6JZcdmscKFnauKOwcbNe4YYiB4yI8VsIQZCt2Q9PyXLK6zJWS7WWGs4Pr7Fd//5B9y6f5O//jf/nnfeeovXvv1N1GqdHu6uAW5zfXcNMm6uOi2wlGtHhJhCXdFLsxubSzEcGlrn8M7TN44x2sopM+XcqUphrWYwGDIcTri8uMD7Dq1hOKw2esmta+naBt86mqZmtbhifnFBVVRopWjblhjBaoPrOi7OLzDaMDs4oBgMuf/8y7z/9js8ffQQpURGcRvuTKC84xT6hemTU4CkeZ2XXF3NaZoGpcGYHGmOl+G9Z7FcErVmOp2QZYZ8UIl8YLNm0Sx578l7nNw4IouK1i9ZoCnzgrwocE50ntumoWsaSAssN5ZopMDOdS25gaKqqIYjvKt59923ebpe8cKLL6bPzclR5MqglMZTQ5Yzmx0yGE5YPF1tnKNJqVSZlRxNlQqvlDJUVcnxyRFHJ4dkeY7JKlzQrJuW1rVE36FiYDw9YHL7HqOT+4zH0+Q8PMPjOX9ZzHjvwRvYeMV4MmHdtiybdUoF2I7AtmZDWJ4PLNAYrzlOyS+Pm/BxXxjqnaNumsSQsHU6ieHUgEmHK5tkgPtGSs51dClcqpDQrdbSUKi3C7H7D+S892xoSK9H3ZM5O/eyvbfeSRtt2Hmja5+hlGI2m3FycpPOOdrWk2UFw+EohZtFJtAYUfjoWaK+A3L/Pttr7sFO5jKEAMYQvXRVzrTd6JyPJhNu3rzJdDaT4sQ8S0iVuLs+r/aan+ifW9wydUBMRaXiJ+Q3vQ+p2FQcW0x/W1Ul4/GExWLBfvzqsccpNle3x6nfbZxa/mLFZFhe8/tG66Q69dHh1HWoiptI3h6n9jj168Zv6CAe8K7h4vKUxeUl1WyCLSzTG0f8wZ/8OWU24Mff/hbvv/Mmy+VyowncO/JdRkft3N4HDrfbZ6bk1NbPjVJS+LbualZdI2FFyX6lzy3s30+lufLRY7XC2oLGtRAdoZnTLC4ZTg5og+PJk6e88srv0TaOb37j2/yH//jv+cu//rdcvPs+Zw/ekO6XaUGlFFFCjBjU5uutY093EtkAh1agghQ8gYSqg88kxBYi3iU959iH+5L6QR+aigFtFIUuGE2mLBYLKZxqG2IcbWa0KCvGkxluLQ68Xq04P3tGVQ4oipLMe6pqSJbnxBi5urrCWsNoNmUym9E1t7h//yXmZ+d4326uQ7KA+2WpNsbXn3K7rqVpehUDGI3GtG3Dul7R1Guu9Jw8LxkOh8QYaJoaFzq0iaBkQZzcusX5k0c41/L09AmLqyvGN0pUDLTrFcv5nOL4mCyzEANVWdKs1vi2IwRPU9fkwyFWG8lxdS1KKYajEW4953Ix5/Wfv8Frb7/F7du3mUzGlFlGYXJyazFKYU3Gcy+8wJ3nXuKnZ69unU9iRrQxkg+pkLxeJcVpRZmjo0RAlNaECIMio3aW1nnyYsD05nMc3LxPNZ6SF4UsWN1Sjcf8/mc+x83jG5w9ep2A42p1xfnlpTTq6Rd4mtyYwsib8HRMayuBar+OtmkbyXknG4kku2rbjY64hFt3mKkINmnPa61x3tF1reRkpn8lfcIRURhr0NamdZvUO2LYrFlhijY7tm2ERqtN4KZXvNn8u7ua+nzkzQ4iELwjywsODw4ZjUa4zqOU3vRF8V4akDnXyYwouRc5LEpRruSZ+k2IvO/a2n+usFmemN5DETfKMsfHxykHNtuCk0qqIIBPIfmQZE4VYIxFRSWFk7AJRe+U66Zc5wQoxmzmOnhh5bTSDJLM43786rHHqT1OfVxw6hfvvAGxuVazQb+B/Ahx6tqIpA7oZo9Te5zi140Pj2wAF5dn/OgH32M4nvHcy69gZ1MocmYHJ/zLv/hrXnr+JX78vW/y2qs/4fHDh6xWS2kW1EdzkVPrrsOOUXMtXSS5iz4LUSOGYFMF/to1LH2LiwGVdInZeT/ZJGqIXnTHncPkA6wOxFCjcNRXZ+RZSV4NePLkGa+//gZf+oM/4KevvcE/fev7/NGX/wUPfvIT/u70Cd3Z6eYkG9H4CFI6taOisHNDmy+jFO5p+Ub0yFHgPbFrQWcEJVrRm8B3yol1XZdySSUnUk7mlsFgSJ4XrNZXLJZzrLWSg2dF09xoQ7da07U1nWtZXV1xdnrKoBoymc3E0aRrk4Iikb87OjlhPRhycvM279445Oz0Mdb0IfJ+c7s7y5sdMC51+ZRc2Cjh8skU5xuc61gt5yglag6z2Q2ssazbNU+ePGa9XnFC5PbtW5SF5emTh7joOL14yq3ZDQpjQSnWiyX1YMj0xkyKzXyA3NM1ojbhXIe1ltlkStc1LNdX1MuafDAQS9JwvrjkrUcPqV7/GUVZkBU5mbXkxpJpQ5Hl/BV/zec//QmOju9smCGRhlRYLYWdOj1v3VMegCalG6SGQQLykJcjju++xOzm89iioioMWSZzURUFtVXU65bDmzfRccn8yVMuz89YLq5wPXuyM+1xo+xxnV3Z2H5ihXqb3OUr+uZeznV0bSssT+/EiTtPVQrwMitFgtL91hG8o21F+55eAzx4YpAQfVR9rzFhPlFanHvw6Oi2fciU+PRtSH03BURtHf81/Ezhc6WSb4hk1jAajyAi94OA23A4QitZO+ukG59Zm+ZLHKQ2mtiRHL3bcagSigdFZg0xBJquQ6ukmR8jhwc3ODw+oixLUTXZmsFGwaOfzN4/WGuwvUZ+RA6NRhQ9QghyPUEK63pGCWLK3U3/T//khdQO7cevHnuc2uPUzlPbfP07iVMnd7h48jZabSNyWolNfqQ49YE1GPrUqj1O7XHq14wPRbCopKjpwYM3mIwmDMqSQ2OpUERtKAYDnv/0Zzi+d5dXPvclXv3hD3nj1Z/w9OE7rFcLYsprA9GHVqjERYgDY6OhIaFlkeSTzNoMRW41ykRWnaOmZ3BcmnyN7x+Q0uI6VR/6jkTXUmhNE1LTlLhivXyKzW6isLz587d5+fc/yZ2X7/Pd7/2YF194nn/5V3/Jz998jQf//D1Yr1Ax4qNKBhjptUtCb/xKdNl7C1RK7tOm3w8xorzQWTo48DUmy1E2x3WO4APWZlsGAMBHurajC5EiaSIPqgHr1Zy2XeLDBK01ZVWg84yudXR1TV0v8Zfn+HbNen7B/GJKVlVkVb5RLijLgqzIuDg/o8wzijxnMplw+97zXM4vCL5NRWYpTBaTgap+tUp4U6ltEVEI0HWO6XRK3SxYLK5wzrNaXnBKQGuYTm9QFgVXiwVXl3OsMoyGI2ZHx7TdGtWsOVufcV7PuT05IteymJaXVwzLAWVV4tMp2vog+ZLes16tGI2GjKdTysmQpm6I3nH+qNyEOWPUrH2kbhr0usZEjTGi+FCVFfP5HGtyDmYnAqgobNRkUWNJDjxGVFBb1QitpRGSgqAlJzR4YZZmRzeZ3brPYDigyC1aS56z0qJ7Pxxqyipnvc7R6mW6OuLqJZMqJ7OG1m88q6yUDVO0w/B8wNVvHXi6PtiutyCsb9e0cg8otEobDZXeSym0jpjUddW7QHCSO0yAqDQ2L8ThOb/Zf/VpJjGmYjwNigyUQ+ETc5TupXfU6c7EfyfmVe04d66zy314V2soraYqS4JzuKbFK0XUFhMtuTFkNicW/XzEVIDp6GLAxUBUUvwf8dI8zBi0VoQgIKGJtK7DOUm96JxjOBwym83I85yIaKFLI6QoXWJTEeEm1Y1e2YhNY6cYxF6VMSlSpbd+KyL2ZOQ1ow1O+9RISv7Nc8t43DPF+/HBscepPU59fHDqJvOn7wPb3PjNQeMjxKl4HYLEnvc4tcepD8GpD6fLUhHZcnXJa6/9AJtZPh09N+/dgzCRwpCqYHhwyO9PZzz/4ss8+fIf8eDN13nt1Z/w4OdvcnF2SnAdUff5d6BUnxoiChfEKItCvDAdkVxppnmBUWrnIcgJM/01EgbqT8/Xg+EheFCRzFjaIDu4tl2wWuaMJies1mu+/4Mf8dWvfZXLGwv+9m//kb/5P/6Kv/jav+X84UOevvOA2G1PnNcIlA0Q9Xl+8rMQexe/sUD6HFkdA65rxREY5PdUJASHj3ICJyqcd1JkVq83i3cwHDC/ymmblvVqRWYLrFWEpiFGRVGUDMdT2ramWa1Yra84O3tKPhySFzn4yHA0Js8L8jxnuVpxcXHBZDzG5hm3b9/h9OkjHj98NzEjorhBZNNAKarej2zzKSUdQbNarxhPxsxmBzRNSwg1wQdWqyWnz56ilGYymzGsBrRdy2JxxaNHj7j/wgvcPLnF2aP3WazXvP7uA9xtx52DE4Z5iWuXXDx9zGQ2oxhUUOa0qRmPAuqm5urqShZjbokhsF5egVtTAM/fvs9kUXN+taDxDXhRYIk+4nDUfk29WqHRjCczyaeM0ERFHSGPgl06SuGlhF+1qH9Ej8ZLmNKD81DOjji6+yKDyYQyz7AEAgGlDKBY1w1tu2Y8HjKdDvFlTmng2cN3mN2ucKbk1Qdvb/LIe9auz4vcrd3YzYHdLFclfVJVYmJ62c6uE8lFQsAiBaFOgUVRELEq1ahkFoUAtPMOow1lUWCzjNZLR2Gl9CZ8Kk7GJsZKpTV9HWA2flzon2uvb/7bO/H0iyoxczGGDdustebw4AbT6Yym6XCuw0XofEOWF/iki26zDKWVSPoFJUWHrpO8cB8wKKyRbWIEQhBN+j5c3bYtddMQQqAoCqbTKaMUlraZ3dh9/zdmk78qt2NSEeBWRx763Fud2C80uE6KIK215HkmkpVGEzNhmoW9sqJepM3+sPFhY49Te5z6uODUeIrRGSFs+xn4CG0E9RHi1PzJk+trcDP3e5za49SvHr8hNp/oAQIXV6f85CffI4QO336e43vPU0478nYgOr5lwfTwmOnBIXdeeIlXPvcFHrz+Oq+/+hN+8eabPHvyPm2zRitpzJJ8xOYkrJEFEhLzMygKZkUFncMRpXFRTM86/U2fVxYIbLwpvWuNhOAxxorsYYiE2LFeX1JUQ6wd8uDnv+DBm2/xqU9+mn/65nf48Y/f5Et/9Kf87Cc/5O/OntLML1FJD33LbantSTvuzFPaFPaZbkrJIgoxppCeWGloFTrXWK3wIRK9I1orJ8sIvvN0XUu7riFEgu9SU5YB88sz1uslNsvJMnEMRVEyGo2AYzHstqF1LfOrc6qLseSjhkhelFTI6bRv0qSNYTyd0DYt9+4/x/zilNV6nU76iUlIXJlSUZiB3ihTDp9SUNdrrq4uGY1GjEZjnPN4pAvqYnGVmuPlzKZTylAyn8+Zz+dcnJ9zcngDY0vp4PrsMfPVFecXZ/ze/ee5MZrgmgXz046qm2AHJSa3wkT6QPSO1eIKaxR5WXBxesrT999jcfoE2pZPvfgJpoe3eXZ2xmK1wHknso9KSSfYzHLv7l2MVlRlmZi7gPct3jdE34JXoKQvrwjPQ0xspfOO2kWiLRmND5jdf5nRwQm2KDA6gpeNRCTgushyUbOuV8kh5RTDgjI/phhkZCZjMJzStM3G1/W51R8s8mTXFndMsF8Tuy9J7nInzYYI5AqKFFrOUBRR+gXkVtjJXhUjxpg6qA+SQ6mJ2kjYNpIORAqjIloLOxq8J3StOMawU4hKn1Uta2d7jYmDVREVe9Z1e/VbwIpUg4Lbd28zGAx4dnpJXdfpM2V9hTxDGZ0+R3JfrdIYRAqSEIneS9fhpBzjncPFxGwpUYdpmoa2bSUHdTBIjcEG5Hm+CU338xOjFAIrozasEWrLcgkrLEy3CI3FjfNH9+5VJya8LwoUH2eMwdqYmNmIzfIPd9Uf67HHqT1OfUxwqqqIaGkcuHmsAeVbMPEjw6k3H767xZ/Yq09tZW33OLXHqQ+O3yoRWCY2cHH5lJ/+5PvU9ZLnLs64ee95ZodHDIYjuqqkLirysqAcT3j54JDnnn+RT33qszx483XeeO1V3v7FWzx5/JDV1SXetUTVJScsagNBWTSKaVlwZ3yDylgu1mu6GOmzS3oT3pav7Iy4NZfeCHzwKG3EWcZA8DXLxRnDaYZr4Qff+x6feOkVPvGJl/nOP3+fO3dv8adf+ze8+ebrvPXj76OSjnLoVS82ENFfx/bruOvXNz9MgOIdGiOna+9Q1gpAxAhRZPqU0iidjMR5HC1RBbKioBwMubq6kIIo3xJCoCoHVGVJllkikdGkZrW8wi3mNN2a8/NnjEZjZvqApmlY1zVZnxOaZ0QFk+mU9XrN0fEJt+/c4xe/+DnONZu72uYspllXGqUiBDYbYeda5vM5WZYzGo5p6iZpjoujWyzm2NOcoiiYjMeibFHXnJ2eUuWFsF1Ny+XiklXXUChNBbjjI6bVmMwWgCfzI3QuYVwfhc1zbctqsaBra6JrKcuCcx9pm5ZbozGf/9SnaNpOihY1oDRa6aQ7bchzka3LsjwtwIDvakK7pmtyDB6sRaVumkoLqHWA8xpTjpge3eHozj2qk3vYspQOm0GK1HqJyBDk8NB1nvW6pW06smGBKXMmxRHKee7lJV9sPslBPk72lA4ZMW4a6lw3vJjq2+LWiciRacPORB+kq6h3KCKlVoxSekeGQvmAS2oYWZZfc+LSLEqA23UOTNwoUcQYcYFN+DpEj/ctwbWS+90fkvoNl04rJaot4bq7jvvv03VLM0NZO0oF8twwnY4ElNqOet3g0z153xFiSZZntEEYlizlaQfn0amrbUg+QAUJS/u0EbHGbNgiuWYoqoLZbMZsNk3dau3mfpRS0gyqlzBU4kZ1THrpYcvumaRfL6zY1h4ybVAotE65uFFkK3c11ePGd4RNOsh+/Pqxx6k9Tm1m/XcUp/KswBbl5jkDBN/hmxUqZB8ZTmUvvXCtJqM3rD1O7XHq143fUCCuN1/1DvNyfs5rr/6Q0/NTnnv8kLv3n+f4+DbD6YxyPCarBuRlSVVVjIYDXvr93+P5l1/kc1/6Ag/e/Dk/e/VV3nr9dZ48esRicYFzIvVVFCVVUTEA7lUDTgYVy8U5te9oo8cR8IjGh5Zj5861cc2Ydx2rTzJjWmuCl6Nb01yh1wVFOePp46d85zv/xNe+9le89/7bfOMf/5F/9+/+iq/82b/i6aP3uHzyWE6b20lhc6wjfZ1OupuHvHH1CEQF0SWRnMhIcJ0olqTfFWUCnxaRNAkKKZSY5QVZmVFUFUVR0rY1rmtpO0dhA21dE0KG95G8HDCaHNG2HXW9ZHF1xunTirKsKAcVy+WSoiqoRkOKssQ5T15ahqMh66sB9+/e5/zslNOzJ+Ko6cOHEGN/mk9ncqVSMZYwc01Ts7i6YjY7YDSa0jmfcgoDXddyeXmBMZJHOBoOCSGwWq04Pbvg7u07VKMh54tLBkXFyfERVxdzVpfn3JhOmQzGzCY3qJqGfDSiLAuM0fgghwPXNeBb8izj9r3n6a6WPLpc4toafMekKlHDEptZrM2lG2lSfnBRiqwyKw7MZDnF5JB8NMEWI1QmDlyWk3Sa1VoTlCEfTpid3Ofw7j1GBzOUTd1oUwFoCArnJHPaGE1ZZixWis4FUd0ojYC50mAiJoMX7t7mHifiyGIQ5qUPTe9uUVJVmzI7xi4GeY259D7QdR3eC7tXGcMwAWASn0BnlrzIRUMe6XzrvUdFafojzY48DjAqbp0U4EJMTIgnSleqZDPJ1n1ak3HrqK/5mN7Bbb4XWxPSJemcqyjqKkUmofMgTbm6pkYR0KnrbJ4kEbPMEk2Gc566rnGuw3dOCu4I9Dn4152yTyyNIQuW6WzGwcEBw+EQa5KSh/MEFbbNkdI1942gerao7/reK8T0cogoldRSIjEqtFEbF+IT+xqCFzbWe1zX4ZwT+cSm+eXJ2w9gj1N7nPr44FSeF9w4OKK8OAfO5L51hrIDlLEfGU4dH852s43EZ6H3OLXHqV+evDR+Q2SjD5OxOeUppWjbmvfffcD84hnPHr3P3dvPcXjrNjdObjKa3aAaTWiHA9r1isGwoixLjm7f4eade3z2c1/knQdv89brb/L2O29zeXkJwOHhAUfjEdnlOdXlnKvTxzxrauroaIPkwl5vlbQNz/lNR9F0Xo5bpxuQsJQ1GUalZizBsV5ekmclWg340Y9+xCd+7yW+8KXP8rf/6x/46c/e5Ct/+he89bPX+Ke/+zr1conqnbKQJfJd3LpqSAsu7pzse6YlIqFm5VFeipCDYxOmCs7LslMKrQJRBZp6TVUVDKoSR6AoSgbDEXW9pF6vyLMVsRP5wsFohMkziqJiduOYplnTdDWuq5lfPKUaTiiqClAslyuyskCl02vnHFlRkhclo9GUmzdvMb86F9ZIqRRC6xm4ndBiv/iidJT13rFer6mqlsFgSNs2LJYyU947XNdxeXmOUnDz5k2qqsKFwMV8zqAaMBlXVNWAMqu4f/d51vlTHr7zNu++/y42Kg6nh9y8eZdqMqUcDhmPRtg8SzmqwmR1nUPpjKM7z3F0cY5zHW27JjM6df7qiKYjGI02RqQhM4M1ZtM91+Ylk5N7TGc3GA6GaCVpBCEmQcvUXdzYnKPbzzM8vEksClplyKNHp+6qXppyiqJGlMKxapBTrnK6ztN2HU1nMUqTGS3NlHSkyDVTWwEIM5ZUH4CNZvjGvBTo8Msh6c3+op/7phFJPqsZFDlVCLTek+Rr0HlGXhZkWS4dQTuPD3GrBphsOXpPNNt3l/1M6mgavfiHpMkfg1zgLhv0wSvc5LHHreIGO6/1zGymFUdHM8qqZL7aMsEiu9eJQs7VFSaTItWyKIhKALSXCRSJxC5tTrZa/XlRikSgkoLMpokURcFsOmU4HALi4HESSiYBoFLS3Eghyh3a9vKmST89RmFX0xw55zHR7LzmCV6lrsjivBVsZA77ZlXOydoJO89+Pz449ji1x6mPB07ZLGc2mVFWg4195dWY0cEdHOojwymjdw7SUfL3Y5KC3ePUHqd+1fgt0qh2j6MSSo5EYnBczi9YLpc8fO99bhwccPP2Le7evcfh7XtMDo9ohxPa1ZhiMGA4qBgMKoazCZ/+w8/z+1/8DOvVkuWqJsbAsMzJzs45+94PefN73+Pty3Pmbc08eFZR3Lft2aEPXJfqEYa4uT76/8beqXus0XReoQPgWprlOdU4Z7Fw/K+//Uf+y//zf/PpT32Kb/7DN7n/f/4Nf/lv/oa3HjzgnTdfw3QuFTTF1AthN06YNM2TKoZRuzl9O9PoAyhRzYhEgtIQkyp6iPi2IaTQYaPATyqcy/ExkucFg9GUq/k5XdPimg5dDMgKg8msbIy1YV0UrNsly2ZJt7ikrZecPXuPwaAitwVd3VIvVgzLirLI0dpSlgWD0Zhm3XBy5y5PTx/z7OnDdNkxYVFMIC7dLmMvGE8gBkcI0DQ188sLDo+PGE8ndK6hXnvQFrSia2suLk7JrUEfH1NVFW3rWK6vmM6GzG6cYBwcHB2jhyOMyrian7G6mnO5XLB+++cMR0MGozGj0ZDpdMpsMsFWA8gGWGvofIc2GYdHt3h6/pR6taLMMqwWtXsVHREDWqONIiiSlJ88sK5ZMz97H0ODCTPyrAClCVG6c1qdQa1vjZ0AACAASURBVHBokzOYjjBlRkeAID4x0ut594yEhGp9MGRZwXg04PT8grqNmAZyHDbPCApCNGjlIBVvPn/7hKvHkdXKY5CcbxMiWhu65PS02jpxlZ5RX46qYkCFltA12OCpMs1gUmA6D01HdI6AwhrRI8cYYYg6J947y8hy0XtXRAERlTYfitQIKWzW29Y/K/pcjV3Q3wUYYXl3uF3ZIaR5k/cMiam1Go6mI7TN6LoGYpDiuTwjBE3dtqyWC5SKuGZNY3Pp1JpySwHRYQ8BZeT5eO+x1mKVkg1WDCxXS9q24+DwgCpterquw5jENiP3EzxobQgh0nQOlKJUBSGEpByyZfi0Vqggz8ko6VcQgsO1PnWeTWBPFGWatNS00lIv0HkI0jthPz5s7HFqj1PpBn6HcSpExXq5Zjm/2Dyy4GpcvSAa85HhlNkxHJsZbK7xXdzj1B6nfo1//o1pVHE748hJLdCrCci8t66jcxcs6yuePnvEe+/8gqNbd7l7/wVu3rrD7PCE4XhCMxpQDwcU1ZBqOKCqSqY3ZkwODD44XL3i6es/5a03XuOtdx/weDnn0nUsU1hMo7fGwk4RntpqIceerWFL2PSB9ZC0gq1OUoQx0tYrlJ2TFWN+8eAB3/7Wd/lX/+rPeP+dd/j7v/8W//prf84f/+n/xvmzx6xOTyH6NBXx2ilYJSNNs8SWrxJHd43NCpLbp7SEqkKMaFIr+mT8ITic61guFqAMRVWhUBR5QVkNuFyf0zWNFBbaAu+leZCxFuc7sjynHAwJ9ZpQ1ywXl1yen3HjxhHeO1bLJbPZlGw0otdXHo0m1Ks1nZ9wdHzC/PKUpu02i0Clk61WogSBAUMKyweP95Jvua4XXM4lvDcajaV9fSpuikoW09n5KYHIzdt3GQxKmqbh4nLOsBpxcnTAweEhuXP4xjMaVdSrCW3dEZzDu475/JyLi1MeP7Qc3Tjg+OQmN45vcXB4xHQ8YVUO6IJcT+gcsfOYwgjTp0EpiVRYZbHG4EPExZRrGAPaNyhfE9oVgYDNc2m6owJ4kZtr11AvL5iOx2grDXR8CDvOWwxDG0XbetqV5FlKiFyaKhU+4tcNOgaKPBcGASnkUii+8MpLXF1G/vm1N6i7jpDmm9jb/dYHbhmX5EwTU+OdMEYET5ZbstyiQ0xFofKrWgv7gVJSPOe9bJpszmAwYFANyDK7KQLbGAWRXgVn58Lk29QYKYS40dDfXSdpNVzbisXNe8ZNTqgymumwYDIaSk5o5zBKkWcZHRCDoSgr6mZFW9e4ZkFtreirp3kx2iSd8UBwCRiMJs8sWik617FcLricXzIYDJlOZ1TVYLM2ZH8W0g1oXOfQuvdDUkjX1HKDeVGgjaZzDu09Kkh6AGyZJK3lXl3XSlGu0inXWm1BQCmRTuw6VIxYY36Vi94P9ji1x6mPD04F7zE7nbEBgncEt8KY8qPDqZ3DxmQw5DPPPc+3H7y3x6k9TvHrxodHNtT2f32BXP99TAal0412ztF2Het6zenFOe8/fJejwxNu3brL7bv3ODo5YXrjgGo4YVkOyYuCrLCoLIMQ8O2adx++x6sPXue988ectw1XMdBuHm1vCDqFRbePP+78szEoOYBuTMUHj3ORzEpxX4iSw9fWC4w1KFXy3W9/m0+8/BJf/vKX+fp//5/8+Cc/5S+++jXefustvv/336Crl1tbjWwNUKnUGXabI7hrnpujtEqmGgIqShJj3+wGpZN9b/Nq67pGmQyUxhZRtMwHE+YXlzTtmuX6iqglVNbm5aY9/Wg0wvtDdNex9BKCvjg/5eL8FJtlxCja3zdu3EDIqoCxom++WmUcH9/k/OwZjx89AtU35+m7lBp63k4bg1Im2YBHNSu6rmFxNUdrzWg4ZDAYsFgs0oKSRdV1LVfzOeVgyO3BbSbjEd51BO84OT5iPB6TO0d7fESWa9ZlDl46fwbv6ZyEJF3b4qPn7PxMiuuahuObt8iqksGw4mA6JdeWZrUm65UdCBLODh5thIcMUcKDvQMRyTyHaxuMVlirEzOjt7mbvuXJ2z9n3TpO7j9PtBmgE3O4tVetDEYbohFpPxMVRme0TYd3nizrWRJJ1/Kdg1xCnRNr+fQrL2CwfOu111jVIuHYq0hcL4qU9+jNXzYEgbYTiTzRfwfXtujObxQnlBbGyGiD0goXfOpeqsjyjGqgGA1HZFkhtSPeE6LfslSq7xIrNiIxori7PGUtpgK0a1Tq5jq3vqYP/xqtyPKcajDk7u0ps8mYOoaEEQmu0toRdmfKFYp6taZpG7pO8p377qw6sbTep54HmfQN6LqOzjnm8zld1zEcjTg+PmIymVAURQJv+SytlHRbTZPsnISPN0V5qU+AxYoKSlQ0TUvXOfJc8ngNkczkApaJYTJap+ZoSQceUQhxrtsWQWrNfvyascepPU59THDKeyf58tcOGx7ftZjMfmQ4FVpHlT6/MIY/eukVKMd7nNrj1C/75zR+Q2Rjx4tfJ0n6lzYPsg/J+BhYrhes1gsuTp/y5OG7PHz3ATfv3OX2nfscHd9mPL5BMRhgCktMk2LxYAx6OmT9VDNfOxrVdzANoPpM2N2rQIqDCBvneX1szqBAKm7xHmN0YnIiwTe4ZslgUHJ59ox/+MY3+M//5T/xwosv8q1vf5f/6z/+7/zZV/81b7/5Fo/fe0D03bX37tm03kn3ZU/bg7vaYZi2V987781ZOjlTrY2ETVN+add1NG2Dj5GyKBgOx5RFRbNesVppTGYo8mqzNnJrMUYRxlNi6witIyzmrFdXPHv6iKqq0PYGy+WK1WpFWVSSj6cVxaCiaoZM3A1OTu5wcXFJ2zbXnLiAjKbP9dOpcZAyloHVrFbQNg1X8zmZtQwGA5qmpm275MhFZ7rrWi7OTqmKnOOTY6yxdG1DYS1FYcmtYXpjCgZsnonzib1MoxeFlBCIXhxaZizBtyxWcwYaykHBwcEMFWC5XLCYe0bDCvASeTWisqK0xmY5ZkfdQ1IFOjpdY4wS9iFKd0+jDcpoLJHl5Rmtypgc3UTZgLZ5bxBJfVByXG1WkGWRzjkUIiHXth1d69DaSm5su8Kgia3DV8m2moaDLOMPPvEyTfT8+GdvsFgsiaZnF5Mlxl4jPV5ziCGKcsW6aVIn8oBrWkwXtk5cSXqDscKe+E4KviLitIo8ZzgcYDNL6zTR+dTYTPWoDH2qQtqIxETXxrSxkWvcpmzssrq716sUZJkly0TCbzAcMJtMeO7WiOGgYLn218BBDoaRLLcMhqP0M0XXNJI/2nUopSQMbSwh5cHapGoSYqRtG9quZV2vsdZyeHDI0dExk8mEsizQ2kjhYSqey5KGudQEdDSNdCg2RmQae3AzvUOOYXPfwUu3VdFXl1xXYww2NxhriSHid7pL95+7Kdzbj1859ji1x6mPDU4pRdOuUzdpGSE4vGvwzn5kOKWdp0rOWIXIQZbvcWqPUx+KU7+V9O3WR6nNQ5AHEDft3bcFNomjUZHG1Tw7q5lfnvPk8UMevvMOxye3OTw6YXrjBtVoiMoLlIIcj+oaiqNj3Dvv0V7NZeFoOVH3DFBEtI7jB0LEH7wuMeJUHLTDdHnv0CpLOWgywb5tcXaNNSU/++mrfOfb3+ULf/glHj55xD995wd88Yuf5fN//BW+8V9PWc3PN+diObX3Hxd3ruA63PVMELAJRBMjOipUetYhpptEFpYPws9po+VhIioWZV6lUPKcrmvwwYnD0OBcS7MGnWlUVBTlkMFwQluvabqG+eUp56cTsnLAcrXk9PSUw4MjhsORdHsdVEzClBg8J7fucnr6jCdPHosJxO3S2y1YDii8BosmKyoG2hDjnK5pmM/n3JjOqMoK59xOAxmZp9VqwaOH7xOC5/j4mFGVpQ6ZYDJNNR6grKEoCur1muAc0Xu6tsFoyG3GsBqQ2Ywsk6KmqDTWKrJiyGiQ4ztPVhqCc9hM4VuHbx1tiOBadAjk4wkRv/UqSS7OaYWzms5oTCY62EFptJfOwTqKrTsvm5csus299QAnBxVRQmnbjiwX+ciuc7SdAx1wTvIriyxDOynSAtDW0FwumZQlX/nkS5QKfvj665wtlsRo+qexQ8Ikh5pszkcBjq7rIAaMyjAIOPTsjrYGcovKpOmR85IzCpI+oXVOnifmpH//nQ2TQhi0frv0QcexZZY+6FR++deVUkzGI46Pj6kGAwaDIeNBycnMklkr4dzUlGkjuxhT8yKtKcoBwUdqbYEVXVuLH/BeJEm1xmYZZSnFds45gve0TYP3nkFVMR6PmEwmjMcTqqpAKS1soo8Ya0T730l36LIMuK5MGy+N768lbtdI8B5rw4Z5kj4KvU+KOO+wQZo4xRjo2m7jwJ1ztG1D13Vk2W/nqj/WY49Te5z6Hcep4B3L1eIDhw2P6xpUaz4ynMr9TqSCSNssmQzyPU7tcepXTKCM3xLBrjsm+fjdBxl7wkT2av1fKAMEWtdwfvaM+eU577z7gMl4IrmSE3EoWiuMbzHGMF/WXDYNnoi0TrLEa9oe2+GjdDtUG7eaHuy1q9v49M1Dd86RZZn8IEhRXlMvqAbQNoH/9Xff4MVXXuJP/uRP+H//23/j+PYt/uyrX+PdN37GGz/+Pq7t6It4+3n5JUzpQW9Xmi/9SKc/EEE+hUtXrZJHjyE1f0og5L3k4HZdh80Nw+GIyzzHh466XkNk0yW1NZbhcIhSirwoGU9u0NYr/MLR1CtOnz2hGk/Iq5yLi0vRQB8MMQqyPGcwHOGdg6i4/9zzLBYL1ut1CkOm61JbkJQ+VQG8KGWU1RgVFXN/Jk2UjJFcyrygaerUECbICT44Li8vNnmo43tDMmsByS8sigKb5eRFwbBp8J2jaxqaeomK0sxnUA2ks6XVZMZKOLjP27U5CpgdjIgh4NqOpq5p64aulu6boW1QTUMdVxsGous6si5D64hpFSKyoSGY9NQVAeiiZZgVBAwBg3NSkLoBKgXGSPpGXYtDsc5TNw1tK+HpVklhXpGXdM6jfdg0bFJFQcjALy84GFb8i89+giJXfPu1NzhfrAkxYnriqLfxtNlRyQl3bSvPM9mdjond1R60RllDzAwml3Bt73xEhUvk97ROahS7a6o3cyXMGCpu5fLSehBSdNfJb5nSzXpQ2/ezxjCdTrhz5zZVWWGzjMJqxkOFNSJv2Lssn+ZICtkERFGQF6k5Y9I2h16mr5d1LNFGS7OlENAmIwTRZS/LiqIoyPKMLLMYYxMAyzWLs7bgkw2nrqq9nrkhEqL4FW2kVM53MvdZliW1kQ60Ikshbeclj9zGJOfoHE3b0LaNfN1Ir4L9YeO3GXuc2uPU7zZOrddLLi7OrzX1C97LhlUrPjKc2lUdMhpvI26PU3uc+v9z2Ng4w7g9/e0+zA++0ueKbpwbsgCilpDrul6yblY8O3uCtRlaW9GTjgEfIl1UhODoZez6DpCkXDbJSdNsqJoIgZB+rzca8TO7bNH2SpV0Z/RyinMhoPG4rqZpDGWVcX76mK//96/zH//zf+ITL7/Mj/75B/y7r32VP/nzr/Lk0fucP36ECpKXF4mQKvKJXP+0NBfXwvwJ6VRUojBA6t6qZFO7Ybe0zENT1+RlhYqwXq/RUVGUFdVwzOLyHFevWXUd0QfyYiAnT6UYDoZEoMoKvG9ofCOqKss5VxfPmIwH6OEIQqRrGohWpNisIcsLqlHg1p37XFzMefDgLbzrtvMZtzcnEQFFDJIbaFSkqoYE77iaX7JartAoMlvgncjPiU60RalA0zYsl1c8ev89SpPjWoeOSdVAS7MebTUUGbF1dHlGk2e4rkEr0YA2RhrNSN2F5LqiIXixh8xYdGYlt3M6QqHQUfIN66bDoTCrvuuqwgeVNKQVvmvwWqGUkXxXJc/Lh0CnS8ZZSds5YgpPeu9TQaVcj9UabTRt5xiORjRNw2KxpGsdzjs672nbllAGUV0JflP8p41G5ZZYB9x6QV4N+fKnPkleDPjOj3/K08tLseUYiSrs4isBkRNsG9HvVgGiE7lAFSIuBIKSAjStJVyrlabbpF+I9nnEEWLS/VZKGo/tru8otrrpMBpThEhthDevD7XhWzfOvLd5awyDqqSqyo3TiipS5Io8ysZC/jAkZi9u11UA5zyZtRht6FyHslpS4pKkoDSEMiyXS9brNdZm5CZDG8NoPObo+JjpbLYpouzZsxBlU6F0X2inACPFcr3cY59SAJLyYIRdCtqitBIn7qSRU2Abbq7rmpDmMkSZY6NN0ogHay1d113biO7HL489Tu1x6uOAU4t6jrEFNisB6WkgB5RG8OIjwim7c9hQSqEyS/Ruj1N7nPq147emy9QOM7N5eGwdlFZ9iFWlr3f+Vqut5nf6fR8CseuItPQhrt45SGqm3igm6P7E2YdF6U+1GpEfSy0i5anvXjW7hiR+Vl5z3kMKIcboIGq6tsHmDcbCj3/4Q+7cvseff/VP+W//9b/yve/+iE9/8cv87Gev8r2/+5+sFlfJSyTvvQEWknPf3uvuCOkerIqYCFnKL22Tc3TpKrVSEBwxODmxIw7C+Y6iHDC9ccJ6scTVNXlZohXkWY62hs57ihDxIdI6jx0OGYxndE4Kyc6ePWJYVRwdnhCcZ3E1pxqUFLkUDGW5pXOW8fQGn/jkp7iczzl9/HAXGbfzusH4gOvkWVaDAePZIdFHlleXuKbj5OYh4zv3GFYVB7MblFXJql7x6PEjnjx+QtPUPH38lNOz02uykEqDVX2BNliEJWkQNRSCx3cNBAhK7NAr0RkXrWiFN5GgFcoa8GnhGYPNcwZlSVRwa3KC1prBYMSNo1t4VxNweBdxykNsid5DWsghBGJZkhUFrvPgWrAS8vSJoQjO47UmK3ICiuVqzXq15vzsQtQl2gnresVitcINO5iOyU3cKo04R4wKjwLnUOsVpTL8wcsvcTCc8PXvfpf3nj3b8qRKwE8juc0hONp2jW8bLIrcSKux1rW0zhNCJE9OyKbws/Oi+a21SkyQOOYQvay3xALv+pSYWKHtGk95sNc2e2rzer/Ud/PaheHMKasy7c9Cem9DmQVUvcI3Fu+BmCI/SlJMhD3SWC13j4ZqMGTdaurVGh8jNoXY1+sVy+VC1qYSpunw+JjDg0Nu3brJweGhdGntWkIUNR7JpTVERB1Gadk4+OA3qkc2s5vc39371sbIvKFAaYzN0Eg+ftt1wlwnFRJhFWUurTGUhSj4SP7svkD8txl7nNrj1O8yTuVVxp/9+Vc5/M634Pw76Z4U3kdc+9Hh1LXDRrLXPU7tcerDcOo3FIhvH4BciDgapUTgT6UbAFEx7uXQrodqd4PGXJNM2zJQfdOQnoWQSv9rRXY9QGi1Kba7xs8o+SSFMFMxvfZLh9Y+Rw0JU+d5lhQ/AjGkMPUw0jnNN7/5TX7vk6/wpS/9If/jf36d4+dO+OM//d947+dv8t6bbxDbsFVvVn2HylQgqHYh7/oIxP6SsUpTaQveUQdP6FmlmLySipu5N1ba1zvnyLOCLC9YNcuUD+gkVI3IPEo3SUXn5KQ8mc4IzrG8uqSu18wvL1lcLaiqoTgmAkVZ0Lby+8YKi3BweMTzL7zAajlnnYxf7GDL5O1SZcF76tYxqCpms0NOZge8dP8eX/zCF7h//x6z8YTxcCiyep3j/PyCh48e8uzpMx4+eURVGJzvcFaMWXuIwQtGywoWiTWUSLHFgG9b0YZHLkdZhU/qGkrp1IhGobwBLSdyb5zMq5U8xszKM5tMZ3z2s3/IcnnJ1dUF66Xk9bqmw3QOoyNKRWJU2FJyjtt1izaBGHQKN0rIOYRIVBrnHShNR2S5WrFerynKgs45Fosly3WNVZZqKGkCId1L29RYY1i6FO2IDr1eY4Pm/uGYr3zmU3zjn7/P2fxig6tRbQHQOUfXNoQYsFZR5Bm51TRNl0K6KZVEKYy1oK6Hfa1NMoxRJBdRbJwXfb5sYm3FZuNmfW0X3nXG9lpO7IZIlZB5WUpjtT6XVGtDkSmKDK7Ol7TtgKhEDcMaQ9dJ6Nx7cfpaJUWOEMnyDI+nbVoUkFmL9x3r1QrnfHK6kdFoyEsvvcxkOmE4HCb2UaUZlJSXEAJRRym8VKkpklYEJ0VxNhXeGSM/C0l+sM+H3rJrUnRntMVYg/eBoAPGWuqmIcZInmVCgOXZRgVE/Owvbwj3Q8Yep/Y49XHBqdxanrv/HJP33tk8p7IaUVYHNF37keFU2DmlR6JE35pmj1N7nPoVnkTGhx42enPYsjlsctu2T65/GteNb/N6/6B6ubMPPsHtWXLz6jXnreRnup+8dKJSUV17fXOGTbl4/afFDxrTjtOJqbmR1n0Rnid0gWYN5bDk7PQRX/8f/4P/8Dd/w0svv8KPfvwTvvJHn+fTX/4jzp48wZ2fooInJbXKg08fo1HJoDY3tZ2PuO1EqYHCZBhtaduVOCxIyiVyXV3Xoo0ls1n6XvSQ87xirTNc52jWawgGjCaqiHMtVSXOMhrIjCF6j3eO9fKK5XrF5eWc4WgCBjApbUepnSIrg7GKu/ef4/z8lLfefIO4KUzbPnMV0tdK2Lu2aRkNhnzpc1/gc6+8zCvPP8f9O8eMBhVWG2lapMQBPHc45VPP3aZtO+aLuShItMJoQIsy0pxJIyFlaWwjm4bNhKdmMpF+9yBg6r2i78QpvyuqHpiIDhq0dBSNXuNCCxGMtkymB4zGUw4OT2ibhtVqwfzyGZcXz2iWS7TyoDIGI0XbdDTNEmsziirD92xSckQuepz3ZHkGRnTCi6KkyLNkHwL63oPrm/sk1mi9XGJLQ57ltOsOrcG7jhgWRAW3p2NevHWT+dUFTdLn7wtgJedbnFgM0hnW5sKqdKi0J1KgDcpmaCtqO8ELM2SMhF9jCrf3UnzGGrRT+A1rKCkkJoVrg/dIt95d571dz3F3Ge4scmMyxuMxZVlubENpRZUbCtPxaLVgvQYzKAmIrnlst6BDlDVjMAJk6XOqshSH37Y09UruIemIj0ZjXn7pZW7fuS3AkGXJGZu0j5KOq73Ps8Ymp5wc8o5z3vRP6Nmz1PBpI7UYt3rz3oeNo9cprE+UBky2D8tnmTy7KDKIebaPbPy6scepPU59XHAKkE2j2vqDyfQGL7zyGVb1+qPFqd5iQyQ3do9Te5z6UJz6DYeN7VchnZ7UxuluXe8vyfnF/uFtD5Kqf3DqujPv3y1sGKIt+/PL17Hlnvq8WJmIrVP/1RzNzjuorSFFwLuAzhRaRWLoUIi0XZ432Czj1Z/8kBdefJE/+PKXefT19/n5L97l01/6Mm+9/jN+9v0FNOt0vdLdc/NJavezt6fluMn/Y+PES6UZDAb4MuPZ/FxO5NJpBtc5WttRGgkd+9aRZTkxRrK8ICtK2maJaluKzKHzXBxp11KWFdZmUsSoDUVRMqiGomriHFdXVyyXS/Iyl+I6rajKcnu92uBcYDyZ8tzzz/P0yRPm56fbiewdeYygIsEHondMpyO+9MlP8q//4s/5zEsvMypzcCtcvWDdOoLzKMSBkliRSVVx6+4JdeN5dn5FFzVVNaQsK0xZYHXSrQ+R0EkPDNe1tLWof4QgLI3VGmP6TYMwWookO+mD5I2SFGCiLCClU8dchBnp6oYsyxgOJozHmsN4wvLwiNPTx1ycPmExv6Be1+Q+0tQtTYgE26EoU5Fe/+glZzaEQAySrysSfUPMbg4qlsxorNEirRkkz3N1dUXIPIPBiK5eJ0k6JcxH27COliK3KC3nE5PWU4wCDs51NG0rjJk0XpXGVgkslFJoYzFFgclzyeUNfsN2yHv0v5+aFyVNcI/bbrzSgtJa4/sUjWtO7YNOezs/8mGRoiiYTqeUZYU2Ah7ESGEiBke9WlLXlmElaWYhSP57CFtmOcaYam0CrWvxwTMaj9ExcrZa0XbtZl7KsuTu3Tvcu3ePrCiIMWKspcjypNOvkva5FXDsHXOQDqrWWvlsrzb3rpRKjaG2bBKazbX17yFa+QGdGKY+bc70G5b0Xn7DPv46v7YfsMepPU59jHAK8M7junZjK3lecnR0i6jUR4dTemur3nuCD3uc2uPUh+LUhx42div7+/nuX+mLZ3Yd+AeZH37pwW0h4JqT73+ituEupXdYqs3v7OTYpROijnqjt9H7k37S4ubqtyPGD34fCT6irfxxiBoVDW29JrM5Xef51j/8PS+/9DJf+uzn+Yd/+gduHH6ez3zlj3n/4TvMHz5MLMoue0aK1PXo1f8o3aPqH6owSzZERoMBN07uMjx/xrvvvkPXSV6cd57ohRnzrm+wkh56npGXA9p2LcVZrsU6UeOo67WccE2G8wHvOnznsDbDZjld51iv1iwWC2b5lK5tWaQF0TRJvrAcgVJoZTg+ucnde/dYzi+lkKt34jES/z/23uxZluw67/vtIYeqOtO9t7vR6EaPQAMgQVEgRIAESQuiLJoKeZBt0TRD/hMcYTscetH/43CEIxh+kWUFZZEARIEERZqiOYMiAQJo9HCnM1ZVZu5h+WHtnVXn9sSQHf3Aruy4fe+pOpWVmXvt9e39rbW+RYas8o937p7x5c9/gZ//O1/hs699is44pu2aOFySwojEqPeUk3aeLCHnYbEkHB1jugW2W+CbJWf3nuLo5Fg7ePsSNpSSYxoDMUxsNzeE7UAOE2kasTFic8Ygu0WDFBnKrI2loGhJp6yskTWI3XUQl2lgihMxKpPStC3Hp3c4Ojnl7tMf5/Hjh1w/eohfnSkDk4Q4jWCEpm3ntAtd4GhoXDvyCs5VZQi9tLbrsF7wxrLqGvL6msmuAdhuNgyLgaPVEd63TNOGmCMxCZspcH99xf3HDwhozqbFzFNLRFW1pmkiZ8F7i3NCCJGY1IlbY/T+uhbXNEgubFcJkeeUbzMyKWMaVbuor6mTttSiuD1yuZr8ex7Fs2CNZbVaslqtZn3wiMrvNntDrgAAIABJREFUOZMgjozbASNnWpyaMynGuYgzl866WWTW/B6nEd84urbl6vKSq+srhmEs88ry3HP3eO655+j6DsHMGuTeq0uszJGmGRjECGlMZDJd05VUEasMVZYSzvfM6TE1bI8p59gDwuoHcvWtyi65vVB2/f2qvy5POq7DMR8HnDrg1EcFp7R2Z7fwA4ghMGw3+H7xoeEUcZpnR06JYRhYHnDqgFPvg1Pvv9mgXMTsmmcL2flnszeY7ArLdm6amQESdoU3IPOEMlJ39sx/78JsOz9YnWI9l52bJ+2H0fdZpTxb1M6py9416d2lElLzTUsqDz+Gic3mhn55xFtvfZ+v/eq/4r/+hV/ghedf4o//+E/44c+9xms//CP8wdUFw/VNuVA7U2O6Y779PKtPmZmlem050zUtn/v8j/Epk/iVf/7PeeON17E2IxIRud00JaWkBmSdGp91pDAyDBsaBLyn9Y2GsWNiHMPcFTulDMaSJRPiRJhGQogM06ASbm1DypnUdhjTaH6fhcViyQsvvsibP3idR48ezrnQguguGuHe6Sl/+8tf5j/7e3+Pl579GA6BPGFR1iHGSB5HQoxkMSAOoaHtO9rlEe7oiGa1YrU8pVud0Hd92a1rsZwp1Xc5C41ocdgZT+t9TRPTZsN4fU3YbEnTgElh5xBFNF+2TKScSkJDzkQD0U5lzDJxGjBWdauN94QwYbaOpmtZHZ1wdHoH88LLRByD7bm8GbWYK0VMLl06jY5xzgnjHClGnPFYr6kCxmrjp0XfwxSwBmyaOH/jddb3zgEI48Awbjk5OaXrem3cNAXGkLhcb3n7/Jw3Hz0kSMZYf8vQJKkTj9OEMdD3Ld5bYspzcZ1z6pBd26p8Xtax1OmpaQB2Dssacoog4LxXtY8UMc5iLDPDuZte5n0deJmUgDYWWq2WpTmRIUblp0WE1kRSmBhCZrFo6fuOm5ur3YRCsAamnFQ+MevPKUacN4zjyIMHD7i6uiKmgIjwsWee4pVXP8nZ2d0SitdmUbroscrmWGXMpmnSRlKFKcs5E51qwqsNFX9XZAmN0YZTzroC4hraDyGULq5O/dLekUvedNvq94SyYva+oW3beb4fjnc/Djh1wKmPCk7V7Kmc9/pspMg0DEwpfWg4tXn0cPbfOWfGccuRcwecOuDUez7Gv0IaVWGATP1CuaUZThkss/8JoeQgvnMUazFLVQ0xQCqiWjL/DkWx3BR1h+JuZ7kyfa2Gow1Ge7hA+aQUZ1nen09+m0MqUwWXIy5lNWSbdSIbIYaBNGnnzj/90/+HP/j9z/H5H/0C/+LXvs/jtx7zY5//Cb7/nW/z9vgdGKa9nWEBG2v2HHkpJNOR1TzDcl+BRNN1PPf8K0wrzwuv/DH3H94nxUlTanKYB9I3Xg3Lavt4bx2N7xhTUMYoNbS+Ua1qpw6waxs1JLSxj5RCPkFVIK4vLsAZlkdLrq6vSuOYJZKy6j13HdZa7pw9xdNPf4zzywtVfZCMt57laslR1/MzX/oS/+Dv/l1eePYZbAmzOmMwpsHGBTIEJI1gW/ziiKOjp1gcHbM4XtEuF7im0RCtb7BlEmAAq1JrmOJ0bHnDWJw1eNPAckF/eoJ8TOsshotz4vk5YRwRNHQrMSAxIln10iWrQ6uTDNRxxhhUq1osKU5Y7zAmI2GAqaVpWmzf0y16lm3Hyarh6sayHYWEYUoKDLVrLHkHGJIT3mSapuGo74kxMEkm5sj6wSM23/0+aaUFlDmOjNsbsiRc02KtJ+aBadxycXPFm4/PuR61gNAKquxRLDtn7cKaxoDF0DUWK1LCpiAYzX12lPszkAUrlhrSt0YdbNc0dL5lIwM5G6xv8U0kmox1BkrRKZKxiOYDS51rT/qA3Wv1X85ZFp3HVQcqIDQ4l1jZiRAy2+TpjhxHZyseX15gh6BNkHIuijjKIqUYkZKTm6NjfXPDMAwI0DQdTz31FJ/73I/wqU+9xnK1xBhdDDWNSgvGqKFjyjyuz0AdvaOhUVZJKKFmddhVGQSBmOJc9JlKgXBVI9FUgjQzRzL7S2WtDIURy7tusNUuD8e7HwecOuDURwWnJCWM2asBQBeQYZiwWT40nMpvvDUvwkUS47A+4NQBp94Xpz6wQHz/YVfnPYeR9waoskq2Mj3IzuHObqz+/CQI7P9cz7eXDbtPr+xdl9kzBuV+iglLPcPuEqsjn7kukRLS231yCgHblsRCazA5MQ5brLVM44bf+Mav88LLL/LFL3yZb/7Gr/OZT7/Mj/7Yj/HrD99mHSJEKcBBmQSlq2wFkvKONeYW15VEsK7l7N4zLJ+7x09/5Wd56803+N53/gLJiThNuCbQLxaa5zcF2lZl4Jz3dIsFKY5M06CNcTpVeJjGEe87RPQZN21H2/V4B4ZcJNYGYsz0y57tWhi3A6ujFeTMzfU1Bmgbj0im73qe/8QLvHn/LS4vHmOM5W/88A/zU1/6Mqu25VMvvcTzTz+DM4ARvPc01qPNtBwSEqZdsLr7FO3JGcvFEW3fYbwnlpHxTsFpD3mxtjzPEjJkb/xr7qGpuYiA71qWi558eoc4DnOzqTiNTMNW1aUqmzJuMeMwh6UlJdI4kJ3HuQTWIWI1pSBGxhiIdoRpxI0DXbfAty2nfcfJUYtYx5Qz2xDZbEeG7aj2aoWYRmxMdK3n9GTJ6dkRIQTC2GKs4K4vuZageaBAnDQ8HmPUUKUIMWUuNxvePD/nYrspiyp7Wz0Hg6TENI2l8ZWQp6RFgJP+bK0yP857fNNo0VxQuTxfFghZNMez7Vq6vsdc3RBjoHWWtm3BqLNRdRvZMcfG1qyAdz3mSy3+oW133VJVHVQjTK039N4S15FU8sXPzk45f3zF9cVV0fUWMprGob7JqvO1dtb+ds5x585dXnrpJT772c/yiU98guViofnQxswOVbuuhpmddc5hbUvTNJhZc3+Xq2qdnbuo1kZIVeNcbJEBzKbYsMV5DaXX60spEWIA4zDWEUoedoxRx7zm/O4588PxzuOAUwec+qjgVAoZK7koXhUTySppKyZ/aDj1iLj3/Zlx2B5w6oBT74tTf4WmfvXB7xz4/usUp4Vhdux1AHffWx3W7hx7LhrzROfV2+/uAKK8eevdyhjxhNFUGXMzF8PVa5PdNSuxM7+fU0JCxDcWySBGGyuN48Bi0fKD17/Lr//rf8N/+Y/+IS+9+Ap/+b3v8vIrr/LtF1/iL/70WySJO/n0fQoMM7ud3f8rNukdZDFMIXJvecRP/uSXuXr8kP/tf/1fuLm6JKWAKaofY3FKIo0yIU3Dwq4I45YYJ2KKDONAzALW472Gyb33xQFqB9ajo1NsaSqDATNqQddyucQaw7gdSCmzXC6VoXIO33meffbjPPfcC9xcXbFqW37ib/04P/mFL+By5mS5xFuFY2ct1jis0yY8i2VH1y3pF0ukWxGNw3cNWEOUXLTHLRirXS2d26UxlOflrOYfUgFxdhq3n6vFYNoO71uafFTCjaZoiiuLEmNgc3PDePWYaX1DOw2zPccQIUZiCUla38wFZzlCZMSMA3hP8GuavqftF9i+xfc9R92CVb8gHS2YYibmTEiRKSYWfctq2dP3HY03ZOcxfQMkri/eJowbqjJJipFpKI5YqQQ248T9yzX3r27YTCPUvNRbhxb8TdNETKpTHobAOKoWu0FonNVwaNPgfIsxlpim4mB0LmfJOKcMSXVgIUw4Z2m7BqFFxrHIIGaMEbq2AfGst4kY9wDplj/YXacxlsViqeoe5dUoqhPeOktjYTMFslj65THHRyvunJ3y9ptvl/OKsiupqGZYg4ih61tAGIaBu3fv8sorr/DZz36W559/jr7vGceRaZpm20nlOVXGsEoa1qI7X0LUVbu+5r1qLu5O1cRgtHhRT1ockfZZwOhCOBVjrSkmWIctDFMI2ok1p905DylUH3wccOqAUx8FnArrG2QY2avPJsVAGNaY3H1oOBXH7c6Gsxxw6oBTH4hTH7jZMO/yQ3WWRl3LE8NSbqaOmDGzMzPz5/e7vAKzBFgNdeuDq91W61fvO0L2fn7SNuo5bjNQPOFY9VVT8v/q2zmq1rT1vuQxGnKcyGlEjOP3fu+3+eRrn+JHf/QLvPmvXuf8as3n/uaP8fDBYx49eFB2sMx/ahj9yR2fKQOqxTuWaRz5zl98m7EzfOLZp/jil36S3/rN3+APf//fQU5IViWLLKKNW0QIKbPoOmzXst2ulSFIgRh0FxpiwDfdrK9cDbDxLctuwXYY2GxvqGob1nQ0RVouxoi1jnEcEcmsVkfEGFksV3zi+U/wg+99lxefeYZXX3gRE6MqFBQNeKt9VdU+xGCsp1m0LJZLmr7nYj0xjhFjRryzGOfU4RfGxxhL4zwleq9WVZmR2aDlHVRkNXopNqb0kcPgME41ryX3NMsMInQnJ4Q7x8T1GtkUdQfn8V1HmEbiNCEIzoXCHtg9hsFirCU6TxgGxuYG23jaRU+zWNIul7T9krbxGNuQbQvG0PUdbdsgKTEMa0LQZxfTyMMffJ/t1eXcHCynxLQdiuNsmFLifL3lratrLoaJIEJj6lzbmxeiah1hCrtOsYNRxZGQNeTcegVR7/FNixFlXVJM2tG0BFaxuTAiu5zsKRia1tO1PZIN0zQWFspyfKwh3zFMt5x4vS4zL/b0sE6ZoK7rC07lOa2kdYKTyLgdiclo51zrODs95eTkhO1mU1RMNH3BGr3HCuwhBu7du8enP/1pXnvtU9y9e6+wSJFpCsSY9D6zzA7dlOdprcGV9I7alTVn7dbqneY7p6Lpbq0lSdJiPGMLQ6mLBkTQjsEy+wL1EZo33DSNdvEVmW1fyr8rOFhnZx91ON79OODUAac+Cjg1XF6xPT/Htt08RrVA26b0oeHU5upydx8iTNvxgFMHnHpfnPordxC/fey5U7PjQeSWhyzOS3avmlLKJ5SLrifY223th67r5yz7rM8u/DszL3uf33tRSQVhfnC3rr44DPYmPgApk4jaMbVQXyJCmEaahePm5gFf/eq/4qUXP8HnP//jfP0bv8ZLzz/DK699luura03BeZLheqe/mZ2csw4PTJsbvv1n3+JxuOb8rXs898ILvPzqJ/n2v/8W43bQEHWb6boORFQeTSx21eKd0PYLxnELQ6ZybCKCK5PEOz9Lqi0WS7x1bLYDIRZ1EGchtcQpkLuoxWFOnXjOmaZp8d5hTMu9p+7x4ide4HOffI1F07G5vubk5JhMJknCZ4PJFrFFqcE7fL/ELpZEhBgDkiIpGrxrS0dNMCkxbge2klkulvRdj2ksOA1D79ufMftjvRs/eeI557JTVwYQLaxE8NbSrla0R0s4i8ShwVjt1ro6ucOwXTMNW6Zx0IKu4hj0e4vtGINxHjONpQmOI6xb8Je4fkG36LHe0/YdTd/TtA1ZIuOkyhDjMKiUo1hC2PDwzTeJ23HW+pecCWFg2A40vXC5ueHB9TXn64GQwVi3tziojKjOlRQ1XJpzVu33LJikxapd27BYtEjrMY3HuaYAd5il+qrc4jRNrG/WbDdbZa4QYpgIoaVtFywWmrMcxi3OGVZHK4yxPD6/eIIlqsBUxqcAj3eexWIxq3uIJE2nsELrwUpk2A6MIZGS3uPx8RF3797l4YMHc/qbKXM1FcBtfMOdu3d57bVP8elPf5rT05OiejKy3Q7l9ywhpF2eqjHaKMxZmqYU2xlVQqnMVAxRHXsBmwpe6nj1Zm3xHdYZbeyW4wwMORfblb3FbGHnJO29VgCpqq4cajb+Q44DTh1w6q8XTq3ubLg+Pcf84R/sLLhEDT5MnDLjTnpXRFTM5IBTB5z6D6/Z0MkwB4/lNvsx73LewdnsBm1H0+zcrt5DDRHv6KRZMnBXRVcc+G0nfOtbyu7L7DiKwmLtZ/Le+oCSDaawNujElj02KaWIjTohIioXGJjINuPbFW+8/m2+9tWv8V/9wn/BKy+9yuvf/0s+/vxLfP8vv8P5/bcwYqHkNO4AZvdU7AxVKndtBfI0cvHobYYmsDm/z4NHj3DWcXp6xoPxbVIKIELbeKYpEKZA23tSjhigaTxtt0CSanDnGEmliVLfLWgXS6xzqijStjoxhoGcMq5EfMM0cX15SeMcq6Oj3W5VhPV6Td/3YAz9YsmnP/NZXn7uecIU6JYdxihDllAnQIaUBIshG4v1HVkMWVROkBRxXY+zXosmRYjTxPriiuvrK7q24+7dO3RHS1zXzI1sdiE7cytsZ8qzNnsvGBFcfeiSikOvS4kMSYjO49oO50/AGpp+wfEzz9JcXRGHDeubK4btWgv4cq5y1FpABYixYC0CeLTJkPgG49dYr906m67FNh7XeJpugfVt6YRqCSFinWdY33Bz/hiEUoaqADiNEzc3N/gUeevxY96+uGIbM843CHkO4SPqNKwpShQpFNDW4rFqca23LLqGtmuYvDKj+lwhFsnKnPQzOSdu1hsePnrEZrNGpe/0WYZpInWxgHvDNG4wUMLTZjcot0aoDsaO4fLe0ZXCTpGsRXsGvDWsWoMlMoXAzXpkM2zJkunajjt3zjDWEmIoOvsBSZq/eufuXV588QVeeuVlPvaxj7FYLEgpM44T0ziwXm+KfOAC56T4ggZQ59221dZq99lSrFjyWKfaKbe6xapuYkqYW7IqEBVW0ZVUC+ddWXDkIkQQC7NpaRs/58DWBUplJ2MMhHCbfTscu+OAUwec+qjglHcNp8sV6e69+Zxt37M4OmEYtx8aTp3s27kI4YBTB5z6AJx6383G7KDrxVKcdm1MVEJBUgxpzw+Wz5v57/nCqs2hIb6ZWxGZX0N2Yep3v64dO1V3X5l0y+HXPzNTU3dpCAbBGimKG7kSURizy5FNIdBYhzdCEsjZEMcAZsK5Nb//e/83n/nMJ/mpL32ZX379B1ytb/jYcy+wubxBpgESM5gYAdljGfTBCxZRZQWrznVz+ZirvOaqaXn04CHX19dYo+HDEEeIIxJVW52sigrTdoM0DucamqZj8lFBIQam7Q0mJ7xb0DV96dJqSSGyubokjAM2C96qakHIAUnCetxgvKHperpGjXmzGTk6OqJfLsjWcnz3DiFlwjRhjrrSJEhUOzxmlSXMYJ1jGif6FGnwSDZFd1qVE+YQXkpsN9r4iJi5uHzIdLPh5O4Z/fERXd/RdB2u8UUr2mHquKFOVfKus6kpUtD7aQFGlMG0ti5LBFdCifO4LBccv/QS9q23CJeX2K7Fbzo2F1eMmw0pTFjJpRhPiGJIxpKrEIkF67zm8xYFCZzHOAPOFd1rj21abNtjmhbfNly//SbT9QXWZmK5Ca3Z2HLx+By6njceXvL2zYaNKSBWZ5yuDOYZlyUT0qSFXCKz8kY0sPQG31hyvV9rcMZoEyqps7jMqZi4eHzB2/cfMU3Tbq4YQ06BadwiJEIYyDFgm4bOO6aQ2G2Z9uatoV5omcdC17YawjcormJJTjj2nrMm4UxQvfbtyPXNFZvNlqNVx8npCSenx9x/8Dbe6yW3i46Pf/zjfPYzn+HlV15huVzOId59tmjYbjU33Go31qZp5utzzs8a5uqbM3lKOpNFsCKEohTjm0aLcgsz17bdrJ9uvS1keELTXUsxY87UpenMNBnBW4eYXNRSNJdci/hKw6q9gtDDcfs44NQBpz4yOGWt4sdyMf9+03Qc33sKP6w/NJwSu2OwdWF7wKkDTr0/Tn3AZsPccpiWPccs6I6IPfaHHT8Edbx3rvj2nnE3xHPuX8l/tcay29cXh8wt3Y/d9Rk15lxsWaXMDJg9By7l9ZmcMkjeDcx8bXs3ISKkOOFcCzmRJWLFkULAW8/1zQP+9de/zksvvcxPfumn+Re/8s9wrmG5POJmHPaexe7/9U9ljGbVlBJmjGHk5noktT3b7cjji8dsB+35YETIMRDGASTTtg05RcYcsbZXpQbnaNoOZy2TrMlxYswZ4TFNYzk+PgYDm82GYbsuTZ7UE07TBE5Rdr1ek1Lm6ESb8HineZw1fzBbfabbcQB7ijMWB7gsmJhIFgIG2pbWOKYUimoBIIbGNxU3IYPJQtwMxM2AiZm+aQgYtlfXkDNhCoTlgm61pOk7mtZD8kjJ2zVC0TbfMw7ZG06zW0xQCql2NpRViaE4T2Mt/fExDrhxhnyleZ/LpuPm8pL19RXDZk2OQZlP4zRHGYMYTQ0wJtB4VW5IRsdWrAKFsxasw/gW10e6o2NIkcsHD5EQldAsOeA5R6Zp4PHjR2ywPLhaM+SMWKeSkXpzqnRSGptpXqWC6zSNc4hzg+CNobWGZMBZValwTYP1llBk7lTPW3u8hilwcXnN5dUVIcU991s2QuNAzknZGsn0XUPftUxhW+bVkyxvGYfZSZiiwW8Lc2LKXSU6Z2itau7XHO7tdsN6vcH7HuccH3/u44zTSNd1LPold+7c5YUXPsFTTz3FYrFQBZWgxYTTNBFCkQsUmdkZVxizXR6snX2SlN8D5lC11HxoisyfYS78C2En/SkiigLlXLP2eTlfBRdBIGUy2omW2rBKCv8thxSqDzoOOHXAqY8KTpl9J1wO2zW0qyVN335oOLXfgqGy6OMBpw449T7H+6dRicw5iPW533KkIqS9gZrZpeqUK2Oz58jL2N0+j9kbYMyt92aWZc8e8i1Lp7AG+7m4VSOdokDA3ueLU615rjCHgZ48Uk5IjNqNkqjlZDkR44hrEt/77l/w1V/7Bv/gP/15fvRH/iZf++qv6CA7j0Fl7DRXby+HsjpuY1HXVx6X0dDWhGqCkzLTNGr/h/J+CCNiDM434CzjOGJKoY+1VnW1jWXYZqzzWrAnmWm85vIik9JWHfyoEzwm7bCakykh/qLDPGk+pvOWGDOLxYqu7QGIIZKs0FhLyJkpJXXiKWNMIGXLKJlkYNkUoBI0n6/ou3vfzNKLiBCHkXC9IY/KShiga1u2MTHerLU4MmijpzZEUufJbUvTNqoT7dwsZTkzjaVT6O0caNEczzL2dUx2g4CCCuBPVhy1z4K1hPNz2pWlbRu6owWXFxcMl5ds12tdHFiozidGbSgk3hVgtXNBFglIFoxHxGCahBchDwN5s6Z1Hkk7piXFwDCNDBePOY9wMam0nsVgRUPIYnYzRrKQEc2DHUemYSDkjGTduCRjyMlCSKx80mvrG8QbomhDrdphVLIyI8M4zp1cK2CU1Q8pRTS3QXNIV6sjlRpkUzZN7yw43Z+2FtUG3y2qdMwc0LcZh+qRG2t56ul73L17l6OjI1WicZZPfvKTvPzyy6o9bhxt29H3HcZYhmFUG49RVU5i0PksUkLG+gcRtAOqK447zxrmqvBBkQHU36nSg8aqxCXFfzjn1cJEFVbq6wC1+VJNr6h/cs4zAMcUmfsf1EJgc9sXHo53Pw44dcCpjwpOzZvOvd9vj47oT88IV9cfGk7ZvV2PiDBMI9cHnDrg1PscfwXpWym7bx0QI5UhkrnA7BYTJDuHb2fnvfstU4wzl/crX1OZpep8d4fZ+//+F+2ub/5vjyXgyd+nGtX+eUsIe+9DFS/U8ejO2BuL82DEYMQSQ8a4yDhc8+9+53d47VOv8oXPf4FvfeuPOH/wANu0IKoTbZAZXizMYfL5nuYwuoaiJAuIFIWT3V2LaGGUdolsiCkWjWWr4TKg6zpMbUtvDDiP5ETKge3mkhgHDb1lyAk1GgGJCWdabDazwaWkOYjajMbTNh0pJzbbLa5xtH1LRLjcrEnpjrJqUySIYTRa0LWw2rQmFZm0VHWZZXfvOSXGzZY4TpAyURT8rHM03pOGkbApDJwtziu1mJTIMeLblqZtywRDneO72H0uhVIWbeC0b0bzzr1aVMokLzTHx6yicHmzJoUJ1y9YdQ22b5kWS64eP+bq+poQJs1pNgZn7KywEVPEewvS4MSXxULpMuvynKs7rW+wKWFcQ5I0j3vImc0UuJiEy2wZjaPyjfYJS6+MS03PiOPENBbGSCCkzEhmGgxRhOQMK2twbYM4kKiOuuYYZ8maliFovmyWkjNdQU8tuDrqtus4Wq1u5SvvX1vBTZX8m+daYa2c5o2m0hW3FcuiSXgTSDFineW5557nuZde4s6dO/T9CgyaLtH35JwJk3ZLHcdpZoO0KdEeO5N3Mn0IpJTJEsAYrK3Fd7ucYOccXdcWhwvGyHwus8ef2TnNRv1Yzlnzo/cWs4LMDZAwlGZKypAbw8zs1QVlLSicv+Mgf/u+xwGnDjj1UcApU5n1PR/r+o7V089wuR0/NJyqRenVtrch8li2B5w64NQ7jboc77/ZKARQlQ00wlzktrdPnX9Z7eidoeonm7QqySPkPWdm6ht77NHut594aW8fJXt/lCmqD61egNwy99mRm9ITqVzLu96+QE5CNhFrDZiE5IBYRwyCd1suL1/n61/7lzz73H/Lz3zlK9x/+w3e/MsNRKfsR94x1fq1Mv+7fvO+ekS9xn2Ca5Y0k0SWREyaH+i8MgxTkVVrvFcN9gIKdTKShRQzOW+JzmOwWvBWvisnMC6XfEjBGUOMiSkEGiDEyDRN2O2W7TRy584dwJCMcLG54fLmmqPmSLXTJ2FqFqz6Y4zTLrLTdksME+Noi7qCnxm7nNS6TOPACkY0N1OSxeeMyUIOERkn8B5jHBhDtlANyFiDWMGhetP7dlJ/nieHyNxdGNFQruSM2LQH9hmmhGRoVse0d864HjaQEtZZlQp0Lb5f4M4fc3V5wbjdIkDTt/gsTDGU8GgkAW2GxqliibVgvaFpHClFLh7eJ40j3jjE7ewgimEQwxrL1jRgHLZYbEbm+bmbJWo3OasTjzGWRZgQszCVgrwQE9FZSMJTzmPFElKcASAXLW8RsK6haRdY1+IoKxspTqfIFYoB33SqamKdfqfZMSa3rH/fFxRGT1M0TFnAZDyORZMxMmmuOIaT42POzs4KI1X8R85sNptSUDcxTUUZTgShAAAgAElEQVR2MydSqtKCzMoatflQjJFUHKcpTYtyzqWxkibhVCcOzMoh852U667KICnpIsoXrfe5uZFh55izzAsv770uiq3B5J19AjjnSDFRw/vGMOfmHo73OA44dcApPho4ZYxGYW4VmWehWR19uDhldz4pCwwCazng1AGn3vt4/5oNY267t/2Hvh8yriySiIb+qhHu+CD9NdmZW/3MO8NXRUlk7zOVd67ckU7IvHui+xe4951POsP6vbsfbzNe8zWWyVF3uZITEm3ZDSawhhwt2UyIOefP//z3+cY3XuDv/8O/z49/6Sf4vx7cZwxbTNIdsaS9Yqr5kmUmJ+aQZAmdzfm8BZRMaaqSku6EEwZjHL6xpJiJIWAwTJOqOhhr9/JnlUbTkC1Ikrl7JGh4EJhbz9cOnyEEzd8EvJ0Y3KhPtUitIVpcN0jm7cePOW7B5cz1mMi9YVEaH4VJpWOncaKG551TQyclnLUsVgumxiHjCGT6tsNbyzQMxJstYb0lh4CMAWMn8A68hplTStigzYRMtebSGdTu2Yu1DivMTor5mapmd3SJ/SWBDZk8jEjX0J2cMFxfsr2+xsaEFV3KtKsj7rQN3XLB+aOHbNbXJKOOvMsNJkyM00SUwnSJdj21jce3LbZteHD+mNff/AGLnFj0S8TaOfliEMNWHKNtCKbBAVYsQiY5gzUyM7h1fDMaNo7ThMSSPCLFsWfdSKUsmDGwigLGY7NBCksiZexzhpj0867kWdsckdJ92NT5nnQ+NN7TlkK0uryri5Mnw9Q1jUUL4uJcuFado7eGziWMhHkOG2v3wCOQZFdQt90OjKPKZWquqSmhZbt3Dbk8hzx3QLVWi9m22+2cx9qUbrX6vp27rvZ9DwjeqwJILufKWYq+vMc2u++bHfkeY7Xv3EMMsx6/Kc9E7dTOKUGaWqPh8PdaaB6OA04dcOqjg1NVutbKzh7ylEhT/FBxanFrU24YxDGaA04dcOq9ceoDazakfkEdgBICS6LSffPQ7HlLdT+yGyw9GZUMqhddi+pqTPhJd16ZpT2swFCNaJ81qlyMslD6vXX3b/cvjuoclXCoWt/v/t16v+rIU2ELSIZsEs42SGmsNIU1v/O7/5ZP/tBn+eIXf4pv/9mf8vu/d0MMCYzBUgqIYO/67Pz0cglHpyxI0erOOSMpY7Pen7GaS5lTxGShaXtMykiKOGsQMlOYtCNqKa7KGTIq5yc5kqU2bZE51886qx1ak8asrff1IskpM2wHJINzhq5vWPUrrJSOoZLIruHR9Q2nvWPVNFxtEyY1bG5uCMsVsRQ8haChdd/4khMr5CnSLBZ0yxacJYqGnNvFgtOTE3JOTJst26trNueXMBapxHHCeIu3FhMT2QRcwW/JQnauTApX1Ef0+TlK18yssm/GqXNPUjp7Vpsv4eoUImOMWBJtv0BSJqxvIGvDIW3MY1kdn+C859FDy+XFBSlN9F3HanFE00RSTJqaYFXhw/kG3y24GEb+8Puvcz0MnFjDarul9Z5QQpNrAW8sEYsVsKX7aTXMOp+qxVZ7inFinAaS7IpLd3NIi1SnbBE81nhtlDRFckw4b0iSmUJmOwTGYSSVAj6JAVNYl5o3WudY6w2+5PzmXMKrZW7b2YFXdqSMlcA4jWyHDcf5COc8STK+zfQ2IDExZSFhsa4F4+ac6hrqHceRcRwIYwDYFb85p7KBzs1SmpriEef8cu+1A6w2lVI2JzuPcV5ZsLLYEKGE0NUPxOiwtWFSTbdwZmaQqiygK/m10zTNnV2ttcoIZQHHLlxuNL0hlPvSP7t/H9So3vs44NQBpz5KOGUEzF7zNBlHxgcPsQ76YqdxO2JSKAW/4HLiuGlpzs54nAJXl1ekYaJrO3rfEkT9kk26KGxixnuhwXBzec2ff+c7XN+sObGGEDLLYZi/PwGbA04dcOoDcOoD4/O2PnqzY2DU5952jHVgqkecWaX5N6qJ7bE9e+a3+y2Z/67/vbP4ZMchVXnDqvJRIUKkMCWG+ecKKvq5hBWZWeRbjlx2tzK/KZlcnLIkA0Qk687RtIlHD9/gV//lr/KP//Ev8bM/+/O8+cabvPXG6yq1ZkS1j2eZNYMUbWNBGaKUhWyUiVD2TZ2KRSfVDI5StNpjQgjqyLxjSpEpRLqm1Qs2VlUniopBDAKxaHgnrQuw1isjJuCdRYwyR4JqYSMwhomUE85b2q7laLVifXNN470+n8Yydp77l9cctR0xNZA2+IcPePrkpBRwqd71crHQSS7a7ClsBpxzLFcLWhqmoLmg1nsS4JqW/m7L8dN3GC6vWT98zHR1Q45RmwrVZjbGkA3ElJXZaDw2W4xoziHe6+86j0mRPGVCnBALjV/Q+AbCjskkqV1EBBMy1gibm4FFt6SzjpvLR5DiHB4XoGl77j71NCJw/vARZFitPMu+SBQWp2udoWlblqtjvv2Dt/j+5ZroV2wMLLPQR8O66H+vrccbh8HSiRBtUjYM1bzfn1+VjTWApMQ4aXOjWUlf0NC2MYgxWONwvlVnlDNxmkgx4LwlIwxj4Ga9Yb2+IY4bZfjKJJt1zCn5syJYk3HWkLIw1Y6wUmQ7K7tcAKhEXhERxjCy2er5u67HmsRqZehtJo2RmCFbj/cdGFv07yujsstxTSmUxVYs4eaEZF8wXJCkiz91sLYUzlkaY/EYhKZ0NFa9/T0o1/kTE8iEbxpyFlyuCyFL37W0XYtg5zzc6rBBQ87Oe6jpEXs5uRouT6iUqszPLeU0F9QaYxR4Dsd7Hgecqvd1wKm/zjhVlZuc7Oza/dZvsPzzPwMDbYwcgUa5Smrc/gwQEV5IkRiiFlqX6FJ9b7ZMoxsf4xxTTLwyDnP/Dge0OWPL7ydjWJc6jQNOHXDqvY4P2GzUXWnd6ZlyIeXdYkPqSHc/58LIFNuZjVd3joa089mz46zv1Q+pkoeZ/22LIHUFi8pW5epx66fLRcj+DKv3YnbskIavqXTW7ft9t6MYiEka9hKju0PmYrXAX/zFH/Nvf/O3+Pn/5Of48S/+BL/2q49YXyVtvuQcpuTk1W+Zgc7sX7uyXdq5tgCfgDEWW/LkaghSEHzXqdsvP+esOa41d897LTDSZ+wQicqWoDt7zRPU7qm6200IpetkzqjqgTAOI2u3xhhH3/fcvXNHd785M4nw6GbD1k/0y1PyOODXjnFSBivGMOfr1vBwGCeGYcBay/HxEV3bYo41J9NZxxQmvKjahG+XnHzsGZZ3ThkvrxnPL0njpBKUs80VTfickTEgxiA+YQT8YhfyM87iGq+TJCYap47fRHN7sMtY5JTwztE2S8CyOD0iesvm8UNyGjEFSAywWCx5+ulnIAsP7z8g5sTZ2Qld3+Gdx1pDzJEpTTx1fMTR8SlWHNE2rK1lsoI3sDGqpx1sSzIeLyUUTbUVM4dyjWEOAet8UUc1TdOt4s06N+eUCGtpmwZfQp+phHZttOQkXF5dc//hQ25urjXUXeaw2ZtrpVwUEUr+bCaKOlGZHX5ZJZRQ8f4CS9AGXcOwLVEiQ+8tx60hh8g0BLIITdPifDOHfnXxw6yc4ZtGwbM4QUo4POfEOKY5PQF0rjaNp+t6Gt+UBZayp1Kud8o7GT8RmedSFinM0Q5E5mcqBox+b9PsrrXafA1D1+JPKeOk1yw4Z2ZZwhpdiyUn9iB9+0HHAafm44BTf61xqmJSXh1hmwZCwFxd4q4uAdhl7P//dyyBs/d4T4CbpiO4Bjjg1AGn3vv4wKZ+ysjuiuSqgdWLt8UJ7W+f91zUbWZJbjNNew1Yd45/Pt8egOxRN2qE++/dlges1lHZLTs79dtevVbe1+tVf76Xg8vO2VfLNWKQmDFWlI1xyt6kEo6axkt+93d/h0+++ml+6qe/wne++y3+5A/+SEPKBpwv0qYV0Ax7SFgmSZ1sJc9Ow/fKfHjXMWTtppqsMus5WchajmWdo3aFrDvNKlPmXUMW9sJcZr7zmsdsbGEzUHUOJKmkuTGkcWK027mZjE6GRlVGcipOX3MkY4q4Yct62LLs+rmoKUlxspKRMtE36zWbzYajo3sslku2YWQaR71CV2QWp4mm62hPjnDLnu5kRR4D2QhaxVYmVUikYUCChmVTjJAi4/oGY6FZLvD9ooTlHSkGUoh432rXzNnG8rwoSTkTjaXteobNSBDH0dMfp10tuXlwn+31NU4yriwy+uWSZ579OFjHg/tv89bbb7JY9qyWR1hnGKeJbnVMt1rwyVdf5Y3H1/zJd79HIJOcJRpDLEaQjAfrS0hWZidYncgTmeraLTYpGzfW7qF7c6zmZhrUFruu00KvtGMwYkqEmLi4uOD88WPGcZw9/wwUdaVTzy2ZmFROsXHV0epiRN9XttSKm8PrUs6XUp6/X0RoySzJDONIipqz3Pc9uSwYvKiUorVupy1uDG3bYoDGe4ZyzSklYopY6zBWcNbRtQ7feI5m+UNdVBirjZMMhhBjKZLbOdCclWXtupacZWaEqpOdpkmXneXeU2lwtMsD3oHsnKpnTAHaHRDX+WmMwXs/M0UHNar3Pg44dcCpjxJOpZSIP/GT2L/8z7H/5/9xex+6bxX7fnq3Y33HUf3RbO/1DKZ0PzdaOJxyvj1PgIfLI/7Zaz9C8B32gFMHnPr/oka1+0dZgO2xKnUg3umuzZ5v2nEw727qu3fnSbPnzJQtMbv333FGma9j7yy3vm2Pi5qPXCeYtk29fXXzjzuDtaixpCxI1EEXSQo22SBJpc/u33+Dr3/9a/zSf/cL/Mx/9B/z9ptvc/+NN+aQpX6lnYvtntzJ60Cj4ep5sHV33viO5DND3OikFjOH1HzbYp1jnCJi3HzdanAOY2FKYcdW1QVtKa6q4OmcB1EjJAlt26jaSUzKHIjQtu1cROQbT4gR4zzJe4akE2s9DFxeX9MUibkomZASLmsn1MY3SCdstxuuLq84OlqwWC3xjcPYHi3Qs/io2uTq9B3GO9zJiqZcs1i9I5OFOEwEYwhpQw4RI0IYR9aX56Q4cnTnDu3pGc1qiWkajFhCiBjr5gZB2tU0ZGMsYpWtnNKE9ZqrO4wTR6dLjj72LKbviT94ne35Y5wIjXUIhnax4OnnPg7O8OYb3+P86orNOOK9xzjH6qmncX3Hx+88zRcxPL56xJuPHqoEpDGYPf0biwGrzJ4CPfP4yZ7dmr3xDlMoxXPFYWTZmx/qZNumUQnKUly2ex+mMLFe3zAOwy22Q+oEnefO3uKpMhvOlK6nDmPCE+u74kFmNkXzy32jqi8pCd4nmjgQQkSMx6GNwYJzJdUhlG6yRZqxAGLbakGcQR16KqAyTuPsQypz1DQaTu66/tYcaNsW5xxtTiWnNhfnqdcqhYELQXX/dR7UgkWZn7f3nphUx74qdNQ8WcnqqLNkvHvC/e6xTM65+T7qguxwvMdxwKkDTn1kcMpjbSQZT/jFX8L+7Z9FQmQosrsOw/Z6i+salneOaRctw+aGq/tvs724wEnGGy1ar30TLi/OefDgTYbtVjdmXr/nzjPP8tJnfoj+5C5vP7rgG9/8JvfPHyNe0+qMsVx3Cy77pY7WAacOOPU+OPX+m426yalfIE86ncqxyOzvzG5fvMfCsMfF7HZHu4uvn9NiPJPzjum5DQ3lvDKHuHLJx3u3w5QLzdwinagWKcheuG2Prdn//O4DGDQHMaWEJM0dBW06gwgSM0m2/Nm//wO++Vsv89M/9RN84W99i69fXjDcXGsI1TpsmXyVbDB2d08aNld2vV6IZBBTdrxdxzQNRQovkkSLlMmOnEQVRayh+NKy83R6Dtm1nhcyImZm4KxN5KLRvOtkW5UJ9oqOQtTwctBuq520kDLiPFEMeRzxzpNi4vzyklXfl51xlVTzNFZzA91SGa5xHHj8+JxTyRydHbM8WmIooUTnNayfBYoaRzZCNrp0MLnmf+oO3LctuVdJOcaJtNkwPnzEeHONWQ+EbaC9e4Y/PsK6hpwjwRhE5zNfbX/9V//WvZ/5G7NCw1N5to+cVIPSOqdjfiSkZwIphB1zuXdkUQWWlFR5orIJvmlpu15Zq3vC9SevmUIoBmfYHK3vAzgjVGVssQ5bmcudJ2d2qtXWcyLEEiKeV0W3J4hzjrbkb9qiMa+62spkxhiVcZJcmBE3f6ekTJZ4iw1rG0/X6/0Ys5PAnOehqSzXbgFhS0fWxbJjtVphjHYu9WR8GrSExjZIVs19aRr9dIZsMpTmh845svdYKE2TrDJBe/cRYySGQEz77AwzEJkCUvvXZ4zFOVXYUCfaUuUIQwglP9ztno0xs2SgquVYrDNFKUQL59w89woLW1hTU5ikej+VOW4azzTtZA0Px3scB5za/8ABp/4a41Tbtnivm7XUdvDyq7rputkQ40TftMjlhmQt450V3D2laxtWjx4yfu97bC/PMSGqjToD1tE9/zxH588yPXrEGALeefrliubFlzCvvYa/9xRPZ8NpjPzB7/w2E+CMVxuw4EzezaMDTh1w6j2ODxZwLz7MYchQwmT1hncqH2bv59IUUx1w+bz6zSdZnfLZGSx2C7aa61pdaHm6t3au7+SG9sied7xXJ4DqIZsCAhpxqxBjbn9q7z53A6zXm0NgjhjVPLposXZiHK/5rW/+Jq+89DJ/5ys/xw++9z2+9Ue/T46JbDTHX3XGM2I1LJxnAcbynIqWc/X0RtRpdV3PMGwZxs2sJC8kQhghOaxtNDyWVNGjaRq884SUgFLsl1OR1QNr/awqkqMWKDnr5wYwtYioabSOYBgHzi8ulGkxFud8MU5VBYkxkGzAxMSj88ecHR2zWq0QURm1FBua1oNA07Yce0cII75p5mt1xhYQSyUEn+e83KrcgDP6Wko6QUNkGkct4PINdtExjQPTsIYpEK/XXA8Tq5TJknEp0ayOME413nvXHf3P2//hn/xy87//ciVDTFlU5MJyGVPAojTNMtZi2gW0GuKM00QKAVM3HgbEd5iG2Zcaa7HeY43DGIfxhv605/Lqmu0wAIaTy7MXPvvHP/KP7p3fedUZyFIdTZrBtZpmMeM5dJxTIkyjpkRUSzbFcMt5nHc0bUPTtOo8JSBoyBeBWBinyi5R/uxSNaoT1M6iq1XPndMT+rYlBAWRujiYL7RstPYXcqvlgnt3zzherTBkTI50LmMlYtCFg2QFhaog0niPFEauPmdXgMZ5BUBb5A2haO+PIyFEYlQWzTmnYBdqoyTtRlt90H74GFQZpClyic45da5FBtE5h2Rbcl6VOUpR9c5VOlOLAPdXmpUBSlVfnl1erTFG7yPp5lb11A/HBx4HnLp1FQec+uuJU3VhaIz6B4P24qh1KTEnvNeUozwGwnbEOcfq3tN0fc/Fmz/g4o03mLZbnBiMUUnYs7v3OD27RyhStK7pOL3zFK5pEANd1/DDP/I5/vR73+P1Bw+xrsGJAZOoNUYHnDrg1Psd77vZcPWRy84OqkBfNR5Tx2nPpZq6y52dko5jnr3mbiDnG9NXivM25TV1JJWXqoxUDYXX65jPxxNOH0qR2e3wua9SgnUCvMu97+6rnplZEcQawWQwIWI7w5QDxmoBmySDcR0XD9/iN7/xb/jF/+YX+cpXfo4333idi0cXjDGCK45coOp3ZKNXaEWKNrOqcRiRHVOAFsi5psGOpfOjFX1OWe+z8fokcy46ziJIjOSYVQYx73LtKpcmJHISTLIYr3m31hgyVR6thNaNIYeRq6tLJEPbdppT2Xd4KSMnwjRuSdPEoyzcPT2j6xdMU2DYTiyaXouoRDDOsVwtaZpTvNcQY46ZmLVoLMaoLJgI1kKDLyAMZO30Oo4jYRxJU9AoQoxE33K06nGrhnglSNdgFwtuLs4Zw8BZinQFHOyi1wJxY5p/Iv/TP/0fp//+n6aYcNbhvCNMgZubNQ0Gl2HcDjR9R3+2pDte4BcLTOMI08j5G6/z+LvfR9Y3uCzK3BVJOyMGK4ZutWR57y7d2SmLszNc07IZA//6N7/JV7/5TbKxeOuKVjlGjFRVRESYi7Z0oWQBBaGZMUqBOG1VCcXobJTZoMuJjE444wxiICYpqjOioDhFYtxFdHQtVllMKbM1IQiL3nP37jFnJ8c0znOz3rLerDU8a/Z8hNk58CxCaw13T494+u4Zi0Y77TqbWXgBkzFZF40xGVWeyUEZ68Km5qK0Mi8wjN5nJuOM0wI36/C+xWDp+lqINyizQyblgCmi97UgFLGF0dJnVXNdq6KHRrB08ZJFdo22CpBQ5QBLjqskreepTcv0u/LMYms4WlU9pDB+lc3SFA03s1aH492PA07tn/mAU3/NcYqua0EMOQGSMd5qnxQxxCnjscQwYQZLMwzQGMxiQXN6zJ3FK0jfcP7d7yM3a0xK4KzKtlpP2/WY1tKtlnTLRcEFjeQ/8+zH+KFPf4a3H1+QjJCtw8/b9wNOHXDq/XHqr9yaNs+O1BKlFgqZ2bnusx230kmMyuLVne5sGOWwxciyyDu88JPMUt3N5Xdxu+aJvynnrOAzZ8FXyilLcVTyjs/V0wtCjVlbbrt0A1oclzLeOmLSMHVOkRQnJFu+9ad/xDd/+7f5qS9/kR//0nf4+td+hbi5IuRE43yRRiv5byWEqzmue7JjOSNWHTiFcveNsjyzxFupYJSU5pQdazQnEdGGMtZqmDoEVfhwzpHyRMoRwWCNxRh1bMkkvL9tNDGWHToZ7xuaxpNS4ur6hmMLfSkq7LsOxLBebxnXE6erY05OzvCdTiZvLScnpyyXS6xvcF5VQIZhZNgMygo0TiUNkTmE7lxlL/TZhxDZ3qyZhkEdVsqqLJGFMEauUqA7XnD83HNs+hXN6Rn5zZaLN9/g6tvf4elp4p58QkOWTVPCndYgkGMuKjPaTMpbRwqJHCLbzZYkgusdvvXaZdZamq7n7gsvsjo5Y/32fS7u32dar1nEjBGLNBbXd3RHSxqnbI2zqhW/8B0//Oqr/NEf/yE/ePgQ1/WILd1n92xTLX8/r1V2bGXWxU7KiWkYNRd2f4ViaiFmsbOcsUW9IoRJ85lzQqKqn8yzYvb9VVJ0x2y0Tcvde3e4e/cuXduSRdhst2y223dVplAmhjKeluVqyepoVUKylsYbvAuFwetm+UbttKpNlUQS1jUz45JSKmym5qKmGMlOi/msUQdYc2YV8HQRU5U7NIc1z91Z9zuwel9zdDWv1vtd11nnnfZAScqoOqubyhqe3jkLW+pCy6Jrh2RQmSmUJdKUgjgz8rv8XXvoIv5XPA44dcCpjwBOgSgznVBfU31QxanNZkMrGds7XOuxTvsn+PfDKZeRxuH6lu5ogXce3zRlcSr0vn0HTmW7S+s74NQBp94Pp94XwfabIVXDMbZsA+f3bmsNvKNARLhlePo7t5kdQ2FOZOc09fs1H9QYu3c6mdVB6jn3Lol9k/9/2XuzX02S9LzvF1vmt5y1tt67ZzgciqZJc7EMGaZJwSQEWjItCYZvbRjWn2XAgO95YRiwfCVD4ow4kiiSIinNDGc4vVZXdVd1LWf5lsyMiNcXb0RmnqpeeKcC/GWj65zzLblGPE/E877xvNPepz/N7E9tmy8WU7px6mWGaL70fYQSQnI4C+QMVitHGixXV0/543/7b/i5b3+X3/67f58P7/81P/nRX5D3kUGExtoRgOdnJVlnr5WESoS6LOzRnD9nLTFP5eKrllcrU9ZFRH2vIO28vdFAc07kgTIbNiNogpBTJFuLsZOjRyq5h8ZCJjHEns1mg3WOZhGI+w5rDKvFgmax4BLLs2fPeBga7t25S7toSUnzXl+7e5fju3fZ7vY8e/KU/X6PiOY/O2dpFw3L5ULt29CiRGBwLpOiglbMCTFSqtbaWYVSgZjou55kLev1irPX36Tb7jCloN7zx4/59MMPcSIcvfttWAhDEqzRqpxSZv1SnEysVQ/5br9nv9uBNbidxwZd8B2MxdLgmxZ/5w7tak17+xbXjx8THz9h6PXetM7hjdHwcAjaifsIw47zRcMbZyf86Ef/nrBcYhsN+xtbCLbKRrM2aZzB2AIsRT6NcVAVbSj5rWhI2qK5rlUBsqZW/NQQbtd1WMmY4joyHmP2f7aiRImuWzk6WnF2ekrbtqRRwduP7hs1533M7caMAFWVHl/89ZUULN4MqhKWIlb1emNKmJQIbqZizwC2Hmsq5DTgSvulKDAYueHQUZ+xUBbTlWMNcSi5ulN/EcmkVPpgzmRB+5TVRa51IWq9p6BpMLVStTGobaHRnNmUEjXdpu5/Xrm1qvBSrvMQ2fjq7cBTHHjqwFMHnjrw1CvNU98ol01QaEbQtpRwanlTVZ9JDRKBirQyaktz0J5rTLXzVQKYAabMP89L3+Hmp2+cAy/i7uyQgpSw9YsXKzeOVo84AoSpO5p2llPCRIMPgST6MMgRKfmhn3/2EX/0R9/n93//v+e//u3f5YtHD/ni88/pc6bB6qK9F0+jqkUFn/W4Mv7vvSeEoCHActoiWnBF3QTsuEgo5wTGaSi2eD0jMAwavtMGX/L1bFHRJDMMPaGxOB+wOZHi3Hu84/r6kkW7YrFccXV5hQNWyyVDjCxWK87Oz7m6uOCzx5/z+uPPOL99m5wzV/s9z549Z9Es+OKLL3j8+DExRhaLJcvlgtB4VnFRXA7C2OayGFLMpRN62uWCdrEgxwQxwpBULYiRNERs1Eqj/XZPWB+xOD7Gh8DR+Rm33nqDn/3lX/LRRx/xVmg5f+NNyEJvLM1qqfe72CDmsXhNHsECNF+03+6V1dCiO5igLhfrFafLJcvjY7ZnZ2yfX5CurnA5Y7PgFg3GO81JzgkZOoLJfOftt/mXIvz4h/8BvGWxXOBdzfMsTiTGggHvSpqCK/aR1mIwXF9esdlsJru9sig0jwnnZiws6L0fw72pfL7qKHO14ma7rF74ntVywWK1AIQ4aKGoYShtrnY788IiWwqgOafVSq+QPqkAACAASURBVGfn33iLQ+0JoQ5aysBmNpjSc3DTItKqvBgzhpJFhGTVsrD2BUG9y0MIM7DXvlzzXaUcF1LpS0oAGpq+2U9jSjP3nvKZ8nlEUwjqeelPizOGVPDSQOmDjOc5u9njAG1eeOmwffl24KkDTx146sBTU7s88FTdXhWe+pvH5kdVJmtum8yA+4UHJdQ8U7nx/QqPNwB/Drz1PXkZ5IHRkYNy/LpgrjzmF+5xfacSkBRXDrXr0xld3ffN481VK1Nn4zfeknIO2njSEHV2WWwGjRgkG5197y/44b//t7z7zpv86q//Kh/92l/xh9/7Z2w2WzpJmtNYDq+VUaUUAJLx7Mpkl7ExO0sIDV3Xkco5aK5eJTBTqj2WcJvRwkjGZIz1hZDMGJ6MMZGyNliKKpBzxgwR54L6OpvEkAYljk47YdO0CBqSXITAMAxsN1saHzg6Pub23bt8dv8TPn1wn3ff+Tanp2dY63jw4AGPPvuM7WbLdrvFucDR+oSchNXRkhAScUhlxi46bLCWiC60ayz4xRK/aotcKBCzAngB8dwPpBKm7YY9y+aI1a1T5PYpp7yOOV7xp3/4PT798H2aJrC8c4dswHo7emFrqsFQSDCNIV3rHJKE2A0IewyazxhSwrSB7DQk2azXuNWK5e3b7B99Qf/kCdFa7KLBeKvpCd5hFi3eO7773e/y27/5W1xttvzwpz/i0+urMQ/SOY931UbPEoKGuI2r76nCcb3reHbxXK3onCM0LWLUFSsnIWclgLZpR9CSOviwNY+62IfaWqFUxjBxBSVrwQU3ulhIzgrkcZiBLyMIT6qr/qw++PV1BSoDaWCIsXQIdYYxefLyV/CNmLJQ0jk3gridnT9CccLRa6sDmhB8yW8uRFXC+7YW06ppHcgI4rXfjfKtUeUrxTSqPOqSwlgkquKlzPqnbjU3TlHJeacDJPIE9lT168vJ9LB9zXbgqdlbB5468NSBpw489Wrw1DcW9cuiKkJd7MML4Vpj9GbN3T8m+Jnva3qQNwF/Ah6QEhKv5GDHWVPF0nroMawsN0Pf83Ov0F5JpUR+qPl8Uolp9llQR43x7F4QseYvYwxOtNHEoRZlASQhWUPXSObq4jP++F9/n7ffepvf+s3f5cOP3+fHP/kxQ5/Yx8SqLWG72kgLLo1301RiLNeasoYudTqrnTBP5yo5k1FbM3WTMMUzGe0UaC6gGA1DulE5qoqAASwpReLQ41yLdQaTgJwQsUjKxV2jxxfQ2+/3hOJu4H3D3Xuvsb265IunT7n/4D737t3DW8PDTz+l23esVkuadsFisaBtW3xoWCyXSg7V3QMLZKzNBSwzfd7jMLQIbtFig4NgsXhsFnwSiJE8lMV4KWuYsHW4tgEDd37xu3x36Pir//ef8/CTD3lrEXDHhq4rFcGNqiuqvGRyKr7TI8AocKVhYL8zWlk0JXzOmMaD0yJWOEe7WhPearhuG/r9Dtu2EAImG1w2EDxkOF8f8/v/8B/xrZ//Dn/wB3/A//NP/ylPnzyZAUzpFyW301inoXLrRieMQeB6c00WaL1lfbTEe+j7gaGPCuIhsCgWgJrPnVQlKW4vQx9nFnaV1NU1ZswUKaFVSVopOEdh3/X0/VAUF76kX9brAFfzU7MgWuIEI5EhdprH6jJGNC2ClMixJ8WBbAMxJ0wSfPDqAV4ATkTGULdkxYia/6tAm0YwHEPJJfWhaQI1V3deBKmSgxZUMmPOsVB9xmuoO5WiTzpwshW7ah/NmYyhOoEY4aVVzKMqVs5jwrkvu5eHrW4Hnjrw1IGnDjx14KlXm6e+drIxhnxkrvDMHkkJJ8tYbdHwZcviKqiPDh7VlqycP2YC3ariZBHNzTO25JSV2VfdRwHwshRp1kSmI1LBUCgzL8HVGRx86bduzmtvhqrrPRmvvxKTCHHQIi5N0xBzJhtIUfDOgh349JOP+aPv/yv+0f/4D/mvfvN3+Oyzz7n64imXMRFCYml9sVWV4iaY9I7ZSqaZhBAETEqYsnAQBGsNWWPjuNIRJWeSMbrQzgYF56yWeN47nfEbSxbwLiBiiaknlUqW1mqDG4Y9xgreO5w1uOwwWchDT78zeGsI3qndZM4MqWFIiZiFxXrF+d3XeHD/Yz765GN+6W/9IgyRq4sLjLXgjlifnHD37h2Ojo5pFy1tG/Al1JpTHgcQ2cTJfSRndrsdGaGNiWa5wCw8OM0PxRloGpwEbOkgCOqBXvJDRYS3v/tdto+/4P0/+TPWnz7g1nsLkvX0xqoPegk9V0tDki5MI2q1b+PACaQhYejpMGCtOnR4MM7gRMHRusDRa/eQrASMaF8Z25fVtI/1yRG//nf+C+7cuUu/7fm//u//k32/UwBJuQxGzFhIq443TFExRnUQdXw5WgWaNhCjqnAhtCwWS07OTrDWjs4omv/sIEKM2n7GIc44UKraa2l3OavrlmgYeNf1dN1Nb/MvQwLQPHetAzAg1iGS8GZHyj3RBBqj/vwZCzmRhx2p3xLNmpgFTFbbxLZVhcfaoqSFsXe60u5FciEXO4ErMobJqwf5lNKh6k0silBd2GdyqV0gWQk0C2IEMblYcEISRsWn73sMqtDh3Zg+YlHjgTr4AyWOmOIE5EaKiiRl0elhsvFV24GnDjx14KkDTx146tXmqa+fbDBNaqZXTJ3gjGJKvvEd8yXfmL8v028VXOttNxXYK9hPezAwNtgx9FVlpPFIN8+9Kjv1Hd1LgmopWBpltf+aH8tw46Vpv+NM7mW1KZYwtXHqrqF5iOopve83/PCH/453//Qdfv03/jbv/+TH/PEPfkC323PZ76B1JGNIImXBoTay2qC08UG1dRvDcWk6uZQzXkT9yFMCUUs2bTQvhtzsqA5piM+QcixhtIwLmneZU2boOxANU3tn1RO6zM6HvqfvepqmQUTGsOi2FNs5u32bXbdnv9nxxZMnHDUtiLBarzg9P+O111/j7t07LJbLQkqZHLWIjIbq9dmoCKHVKlU9U4s2GSLiB4w3IA6xRhutQW3yEIrxvlorDhHrHZJUgXr3l36JZw8+5/PPHrFYn7J2DdlZojWI0RCyJJ3x15/1/mt+JVosSnQRoO104VVKHhfUItJaC6WwDsGNjdIkDYGLqFWklFB7Yz3f+s7P8T/9L/8zDx894Ps/+L72rOLEAkbJ3lRFFCg5ma5aLpaiQYtFQ9s2quKIYb0+oV0uWR8fY62li5rDWhXbmBIpVeKbVMpZLx1HXgajeeJS3GGieslLnuetznpJUZRBiiqnzyWLApa3CZPAhRYQco4kKc8uDQz7LYLX10rINueseb2hIYRwIwzuvdPBSRzUB9zIuHDYWjd6/OeciDGN+0tJ+1xK1YddblyLtZrWURWjXBbR5Rt4pFtOmVz6bj1WxZGcNIXEGMeQog4WjIK+wdKlmo9rvtbl4//v24GnXr6AA08deOrAUwee0lv+avDUN6ZRTbddDyw3Zi5FMZJqM0ap6vkCeNpJCaqbPof6qdIYRfcxrY7Ps8/PXUVe1qXsbB/TcYrCNZ6Mvp9FXtpvbZbW6Mxd25cUNavmwpkSbSqNevbMDCA50fc9zUJBUW+EkPMAGS4uH/KDP/oXvPPO6/ze3/sHPHrwGe//7Cfsho68M4TlQsOaVSa68RTK2WYphZ7Ut9waDY1SVIQkSV+vIbakPtOqvIzxsLKIrIbi9FXvNZ+1+i9749FFfKkUTdJZr2FyIun7HuwWX5wrVM1IXF9fsQWO33uP23fv8Vwec7XdcOfsjLuvv8ade/e49/ob3L59S0OlVj2eq1+5lNzcmgPsCuU658YcTM0RLM8hphLyU8VInNXc1gxmSEiMxG4oVpKaQ2mA1dkpP/8bv8Zffe8HfPHgM5p2hfWWbC1ilYxzzgVsJ0s/I6Iheonj4knrLabrSJIJTWkZIhhfKgEYA1jwpR05C8EhEXB2DBU7pwWAfuVXfpn/9Z/8Eza7DX/+53+OkBjzx03tUxXIleid1YGAc5b1asXx8TFN22iYNxsWi0UB9wUhBLZSPLNnuabTorQRrzHjcC6X/m1U9RPIogWS+jjoYrSxb98E8Qpk3mtlWOeKypK1UnpjDWTNsc5RlZmYBJIgKdF3e8QuSGj/0DxcBeDQKgAGH6juJnWQYouaBJpTO69yKmWB2zAMI/5IGS3W/NsXiyXV1+qCOClERiW0rEp38J5EGrEkpVQIoqh+JSffWDMOAKs9obb/ybnksGbjq7cDTx146sBTB5468NSrzVNfP9koD2LMzypAV0GyZoXNMIYaQi57UJifA6thBtjTnkakLXlrhtnNLMVgFIjme5b5N28AeKWGckhtiMaUBjmebr1QmDe28fXpxw3qGmel0996vup9HVPCOlVbjBM0N1ZD1g8//ZB/9Uc/4H/4x/+Yv/Nf/iYPH97n8vKCTb9n6S1tWGHGRTqlsyPjg685mupSorZlsbY+imWaY1QXUs6IJGJSZwbnPMYwgrLmWerzcDaQLQyxJw4JXCyVUL12tpSwxQJO1aFMpgfjSKvEYrEgZ6Hb75FCJF0csE3D4vgIsYazO7dZti0np+ecnp7Sts3YKWroT9WA4jdR2lOMQvSRlAPBhJEwxgI+Ki2geQw6uDBWIAo5RmSIGATjrYawrTouRJu58+47bP+T5/zsT/6Mi8ePOF8t8NZhgimkn8dQpYiqKq60pZyjki5C6kFyxjM5M5gCsybnomBZrCmVZY0uftRQvLDf7XFOnVUA2tWC3/qt3+Lp06dcXlzy/vs/HaXKsU1TgXxMCQUE6xzr9ZrT01NCE4oDh4L1EKO6XTSNwnLWEK6SVc2NVVW1qjxTCkjp06YCXR6BJsZYBhS1o08OF5qPqmfnnKMNDQYlnJQz3iiIO+PxrqGPO92vsRjjQCI59qrSldztZHQBX8paFcV5vZcV8GKpEDu6ZCQlrLqA0DkHYkip5NxWhDOWqYtPC+BsWfTY933xTW+w1mnxpay1lVO5HxWQqwpe72W9r977omgpiRlTBrvWFn95/czNRXuH7cu2A09NPw48deCpA08deOpV5KmvnWzYWlGwtJZqtX0TyDM1R7b+P206k83lnbLkZfy3qjAy/3tsIFMDqGGfl/YvMwB/4cgGBf00A+cXsHfc2fy7WaTUgzUlXEqpgvnil1/coYz3JcaBxnusBZGoS8eSHifFjh/+h7/gW+9+h1/55f+cv/rJD/mzP/1j+m4oYd4Wb7Tr1/AjRTUZyUMYlSNvHAORmqtYFQ1b3QuMJUkBSAvGuAKWs9Cj0c/p7NrhnSpHknusabFuql6J0U6Yy0xbkvo9b7c72rZlGCJ913N0ckK7OGLbdTRty+JoRZTMkBO3jo6w3tEPw5h7qDN2IQRPGxqCmxY/1UZtjBZNstZog3eOsnJKF7CZqu5ZjKSpoBSAc6PfeCVtvRyLW7a88Yu/wNPPH3Lx6AtW17d1cZwxYLSjjQurClipfdxEpgkYjOBEQczZohIliyQ9pnEOky2SLMaXglfGQBI2l1fsdzvOzs5UKC2L0dbHx/zef/v3efjpA/73/+N/4/mz5zf6kGKlqp567cpp3jnWR0ecnJyUIlmR3a7j6nKDpDQSeEppVhVUATwV7/ypJ80BvDCHMSPo4xT0FPyLqlXbx9inp305pz7xiKjDDFnzaslYF3AuIGjBJeMDzWKJGIcLnmFGGHOrPoxl6AcMpriH2CmHvwx+rLFgq6JTyCVPIKmDE0a1rAJuXfioC+tUJc1ZUAt6Dc3nnEmidoh14WYlRDsWayqvF6A3Rp1NcrEDlayKb30OVaGq6u9h+/LtwFMHnjrw1IGnDjz1avPUN6zZqCg5V4gKoBXkNeXE6kzRmmlB3RRSHrFn3JcxU4MzoAtYyn9G3NRIMTePX2fLaFufdKNZqy7/arh5IgfKQ5Mbn/qyzXzpr3reL75Y7srsulIShiHStG5sMBhLTpBTz8XzJ3zvD7/Pm2+8xW//3d/l/v2P+fyzzxj6gbjf49t2BOwa9hrvgQhq66iNsnGGmB19jGQLluneTTNVU3L8qiJXw3WqAmmpeV2wVUPQquBEhqiLHm35jmTN8fXWj419iJHNdkMI2pxSzoQmsFytub7ecOIDy8WC6+2G9z/4gOurDadnZ5ysj7DG0vcd+31HCJ7j4yNktca0bSEiDYNDZkDoHOrVbc1UaEcPqk0Fh9EETSQVYLdOc2WdnQA8ayVXbx1iLctbZ7z3n/2n/Phf/xu67Y72RD3ojSv54KL3ypTnUBcEkgWxAkkXRhpjSFaLNeUUyd5jQwnrO4sdnI6GDNjGY7wn7nouHj9RArehNOxc3OcM56fn/Hf/4Pf507/4E/7l974HRXlQkJJiYTl3iBBC07Ber1mtVyUcr5aCF/lq5LUYE0NRP6QoNzHFEmKWOnYbxdQJRmQcLGTJuJLrWdUSkBugM1dfQKuyOmuQHBmikI3XvFAB71ttZ6mEZX1guVyTxWKXS7Y5MIgdCzppES8h02NqbmzwWKsOIM5NOeDW6mK8VBUY0QWAulBxynGNMRa1qfQHzHg9Xd+NBFIVzgr2KaUxLzYVZ5CcEs46fPDjPjTnVoBqmzmpaapoTX39ENX45u3AUy9/5MBTB56CA08deOrV4alvrCBeq4OOT3R8hKUtVsWngk6dXRpm1nz1e5PuMmGTqkZmto/aCExVTMr+ZHyXekbMIXlsbTVUO9OCjKmdMRelyY5A58zcyWSC6OkapwPOQ+Q3r63+ontNSYv3eOcVHL1amg1DJmX47PNP+Bff++f8vd/7HX7pV3+Dp8//GSl1dP0OihtJVQMEiopV7rwRPYxVVSvgMUOnC8CwiKVYPBpiylinSpDmOUaMqbPQ6kets+XqZy45431D10eGOIBRJxBAc06NLYWMDEOKkDRXdrfb4bwnS2a337HqO+K+I3jPSXuLvh94/4MP+eTDj3nnnXd44/XXscax2+4Yhsjp2SnBe4ILWOsI3s9UEQWJvo9YNyiAW4v1bvLZFjRH1ZiCk7UDuCl2W4gc0c6EUGztLOfvvMXrj3+Oi48+Z+g6XAhTuzS6t+Aci9AQvKcfBt1lyuWZOFISTExEBnJS0nOii/JMNFiri71iTNjgcN6z32yJ+47z83NMcNMAp4v0ux2Xzy44alf8N7/9O3z4/s/ous0YUo2SkKJYCBBTxvSR5bJlsWgJPuDQ+9G5QX1Fig1e33fs+46hgLbPhj4n3aeIhoW1QYz3Tj3BE0bSqNxR0kxSUocaBaGCGi+oqtaAtxYrJSfVOA0bRwHjscaTh4E89JikbV1Dtlpc6bg5RsKC/dCx224ZhkFVp65TtWsYWC5XeK/OLKq9OnUEKaBu7AwHjOatIsXMtAyQjDElBWKq4GqMxUrBMFFl2Fmnol+xD60uNH3fj1gRqx3hbKBXgTpn/Q6u5rUbjEnqbY8Z1aJDZOOrtwNPHXjqwFMHnjrw1KvNU18/2RiVmtm/Mj0gMdxY2S6ILqIxUC3Oytqz8cbIuCsZO2gFzgLlI/Rm1EIti+g+7NQgZEYJ9ecNt5EZto8oXJwhbD1/5IU9TPsup1YoYprRlYn07I5M5z/fm8EwDBFbqq+qDVnWnFQM2/0zfvjjv+Du63f59nd+kR//+Ed89vGWvt+z7RIL24zqRD1WruEvMpiyCMyom0EbAvuu08YogpBBFKQkq3WaKeFUDR2msWFkMcSU8N5qDi9gS4OPJf/QGqt+16jS4GxS1w8fVOAoKpMtubPb3ZbF5ppFuyINvRYxyrDdbDlfH7HfbPnko0+wzhH7xProiDNUzUo50w8RwRBc9W9WkICEMT3BO4J3iBT3jJo7C9WxcrxxknXRWc2fHp9hGTyICno0qzVvfve7bJ9csNlccbJcqppBzcfW/MTWe0A7Ys5ZAbqEmXNZamWMRTAMMZExODtTW9F8VB892Wb63Z7FYsHiaK2LBkXI+8j1swse3P+UJ48fc+/ea/zSL/wyf/vXf42nTx8Soy5KS1LcNeLAEBP7fqDve87OTjg5WtP6BocqiVs/gDX4pqFpWoa+Z4iRoTi/GKt51VHqud4EjhsDNKOL+5REGVMIZuLsza5XXnDOsWjUNhIUP2yxyNSFdjsFXCLWeWK2JLGYZsmQofWB1ekJaxEuwyVd13F9fc3QD3SxI0cNj7dtQzI9MXratgFMUY8szhkEj0jGC5hGBwg5aUViU84zeD8WWUqDwdgSVkar29bOXzi+jEpzUTjBej+qnrXyqpS8ahGhXbQ6QMm5DD4oStoL6pzM7B0P20vbgacOPHXgqQNPvdQvDjz1SvHUN7hRmfGBCDIB2QRtNwB8/tAmlalc9AuKEhUY674xY8GlqhbpQ87jDm1B/BF8yPVUpguenfcNaWdUhW6GzKsUVfP3ZhdZZpQ378mcPOZ3gxde05C90A8DrW2ohVospnhFD1xfXfCXf/nn/PKv/jrf+fYvcvnkCUP6QkPDaFhL6k0aw9XqwDGvful9oAmRIUZ9PWfEGkQmhwGLht+MmPLd6nRgNL+TogzZgPMWjCfnMIbL4pAwVovYiAjD0GOdKZU/GR0MnFVta7vbsdzuOT5Sn+zL62tMFlzbcHJ+jveBZ8+fY4xh0Sw49WfFeaJBhJJvmJHgCpnV3Gj1/N5tzbS4zU4e1DVsymzQIAikBGVB1Y32UQYZAGIMR7fPOX3nTR59cJ/cdYjzZGMgJn1uyVOrcE45jfr4pTwfUsJlhxSVTmIsXuiz82MKZwqwOj3GNgHKwsWLzx7z4U9/xrOnzzg9OabxgfOjY7719tusWgFxRKngmTWVYND/U4wsli23T045WixVHYqJbd8TSkGqtgn0/Z6aR4swhkNr+HZUKsYRkRnVLFsWiRlrdUBRQ6ky4cDLIkfGOU/TBl10htr2WSDgkBzp846UDdkFzGqFiS05HLE4OqOLnmQsznu8tayP1jRtq4DrHNfXG/UM1yR0vNd0BLVMrIMVIXhP07Ylo6FaE05qjrUWZvcgZ3VzyUNNAQCplanzrDIrmt8qEmc4oEXHII7qU3XYkayVZ4dhUPXTlRQQmdrImON+iGx85XbgqQNPHXjqwFMHnnq1eepvZN4+hkWlAnHpAONJF2iuSlEFh4o9s45j5pJL3b/M8lqlHovxc6bsdwT3Ms0fRQGZqGHc4fjjZo6gLh6s+s5EQuOBeFE/YjqXGWHJ7C3dzVw/Kl8pxJFywhm91aMFXk7EYcejz+7zwdEJd+7c5u7rb9EPHbvtFTElvVtGG0zNx6ydbX7NBq226WNk6PsCJuU9q1eTs4aVJ+VOZvdJlbQUdWdhscBYR4oeyQ0xDaScoO9xIZdwcMYMuphMqM9mehYpJvbdnr4fELZstjuO1ysWztOnSEgabtzv9pydnnN+fs5isZzIO2sxnUEy3uqzmYo4CcOQ2G07DS1aSzBGF3PVQYCgIWljqLmxNx5kBaYyiCgPDNrArffe5uLxU4bdHrGG7J0WScpMxDDub2q3prTLmlcqKRYs0MWCdkwyoDhAKPiFRctyfYQB9lfX3P/4E+7/9AM2l5ecnp/z+muvs1ws2HVbGmsIxuJDUH91a6i5mkpw2tad96xXK5arJQTHru8JG81XDj5oTnjKeOsIPoxOFTFFJZacxwFObenVYcaYqRqstq3JLm/eE+cDoArq1tkC4Bq2zTUv2HpStqTUMIgnLM5pj25jTMPi5Izl8RnsM1EcSQQPtI2G3733rJZLQmjYXF+TJNP1HZiGtoBjjIksAyZnchqA4vhhHC674tyhHWC0GYwDKeeSB67pJdU+1eFIs1C2XqN+TvNbp4XD1Rdd+7/DuTyGnYd+GBexmkII1aVE72vNL34Bkw7bS9uBpxjhbbof01sHnjrw1IGnDjz1H4unvnGyMc3AYfZE9T1hVpVVxpk3ZnL2mIKC9fcafq6oOCKjzuqQEQZv5NJKeW8GpF96vuWCbwg/FdBmHfamfqWft0yr6U25jvnxXzziV51D6dXjw00xqUVd+Za3jpS1tP32+jkP7n+ItXB26w7XVxfk2LHb7Yg5lzDYSFcKSuUYFTSzaOn5poTTbpy0sTirs9aU4liiHmNvADloCJbYMwwO50uVy/J2lMgQe7IktbzzgqaCGowL4z5iTPigC+K6/Z6LiwtW6zWhbclAQrjcXNOGwPmtc7bXW+7evcvt23dwpbO1rQKE907rHtmSq2tdWSToxll+3/ca/reGxhgtUDWxuz6DmvN6Q0GkEDuQS8VTZxExrM9OOb5zzvOPHtJvr3DtgsY1ZWGYpzo4ALMOV3IqS55ljFEBw6hammtnH8UqN3rLt0daKGrz9DmffPAhH/z1z9hfb7l95w7vvvce57fO6fqei6sLnj1/xtXVBSEssM6XyqKaR2mtxXm9P74sPAsuIM7ibBzb8zD0bK6uiP2At5bgNFc05jyFUcdBW+lPs/tmsIVQtb/EGBUE8+QL/vKmJOfrIk/JujDSCtkK0XpkcYSzS0Q8YX2L5vgOrbc0bUNo1kQGZOg153W2Z+cczWqNxRC8Z9/tibEULjOaDVsLWznr6HNWL3QRrKjqWJU26ywU68BKzOqJXsLnQxwxsTpyYAyh2EDWVAVtI5qaEEqhIy2uVBbGmrp4MuO9H9VHFehSOfdpwGZnqTmH7eXtwFPT8Q88deCpA08deOpV46lvmGzoTq0tIc3SBmfiyUvgNqpJ4yyqAveEiTJ2KG0gX3Z+k8JUyIK5IlFz7ipQmhdAWcYbVdUMKBVYRXAyow8zO55RoM/lu/PTelFYyrUilMoEo7tJdVCY1CdGBSFYS06C9VnD1JJIsePy4hGPHjec37rDyekZ3eaKIUZiHEBKnuWcUKiNTEEg54wp4OZKSLSSYu2CWuRIC/5gpkWH9fw1DJlIAl0n+NzQhgUhWLSK655YiMAYBVYDDBhMZqwcmWLEB4d1lq7vuby8JDQtw7095QAAIABJREFUi6M12Rj6lCBmxFrOzs64dXbOarnCGCmFmpIuGAuqBJQII74AuHVOLemcLe1GSIOG+Ky1eONHYJXqkFAWR91UQKbGJXVgkTKUazm9e4dHH93n8vFj1qtj7OkZRmqaxbRwysBYaEhsxog+45SzLmS0Vt1CxjYkpU+olZ8B7LWj32159OlDPv3wI4au5+5rr/HWe+9y+7W7uOB5cnXBg4cPePzFF1xvr3GuwxmPszVM7xTIgwJ58C1DjAqsjWPf92MhrG674elTi3e6sLHmroswVp7VU63qVlHWRBeXGaNOHVUJiUMc3UK+ahMEawUf3Kio1LFhwrKNDo7ucXz6Bn0SjF8gYYG3gvWeLKq4eVfDvfo4K+lYC6HR0Lt1riiO2p76LrPd7kbFeSgLJo2BHBkLcqWkRGeDLuo0VtWfFDTfGGFMcdGBamboh7GtVUVMlTRTqqpq4a2aBwvTfVNXHTMSYgXy6rxTPdVVXfzKW3vYDjx187rquR146sBTB5468NQrwlPfMNkooT34yp3MQ7VmBlx11mQKYEyz9RJqNSVHs8gfdS+25JhNqhLjOcza1nRU0fdvEEv53QjTbF1EF7dQlZabc2EpL0glhtKo67XU65rujMx+n1NIsSdjEimq/Vid1eeUMM5jyrx3GPZcXj7FBc+yXXJ6docuDlxvLvSBViSrYJtFfZeleMKXU7FlsdAQh/FFI6VxGQvWjbPdgj5gtHhPJpNy2XfJ5bMm4J3Hh8CQBnXwyMME5E7JQyQRoxaISVkX4JlynV23Z7/b0K4XNMEzxEzEcL3fEUU4Oz5mc3VN1w+0i5amCSyWC3wIYFQ1clYZdVSNrIa2NeVR72H13a4dCDe5epDzZCc4sRcUKzlcUTVjKs4QcHLrnGa9VPcI35LioBZ15TnkEqYc1ZUxh7FULq2uDrkoMMXKcsz3rlVtJfHsi8+5fP6M5188Y9UueefNt7n11uvcff01FsslF9dXPHj4kE/uf8j19ZW610hkSD2qh8xCpM5gnMG7QPAti+USv2iJIgz7PcE7+q7j+uqKxWJJLRw0DrZmoVAZk0amLWfBGgVNWxfciWgYfkxRmHX32jNEnU70eZaCUZTOaCzbKJrzen4HMwzEotBgtcqx3ueslWdTQkQXnKp3+VRNVpVGT5ZEjD19r2257zv6riPGRBwGtXc0Fok64HHOIzljvUUGbTu+DBq87xXEjS3FwxwiwzjoExFiHxEyNut+VZXVtpJSKoMMUwY7k1WgWnuW+18GhxUvuq5HJOO947B93XbgqQNPHXjqwFPTduCpV4+nvrGC+It+39OzmYPYy5udNSxt5LNv1CfNBIDj73NQf0GNGv8yhpqXq42gQLrUfESBWf6oGFgYPae+7EVMsYe+sfdKWDNgN2ZUyeaKlytAn0bla7oX2hF0dqhf1DuYUtKFaSIj+KinuGO/2bFrL1ncWnJy+w06yexiR7+9GoG4TOkVwGPJn/Meax0iGoYOIY25fnUxo9qhUQhvmslWxcQ4S7AekyzD0GvRlpgZ6Mhey9HrLN+rfVtMZR8Gm7VDiTFYCykb4mBp2xbvDCn2bK4usA5sjgTfIs7z5OqS+48faRtLicurS3zTslquaBdLrPMY68jea2jfgFALFMm4kHEU07IgxVHDgqpEJTcWW5/RjF7VuoPagbTRWiTp/Q1Ny63bd3no3mfIkSZ1WCN4o8pJLWiTUXAyzpBNSaEwBmerP/wU8s312eVE7Dr222u6/Zbt8yfEvuP47Db3Xn+DW3fvcX7nDs2ipet7njx+wuPPP+OLLz4ByTS+LXafERFVnuoAhOLnHY2jtxv6riU0S4xXe8R1aDDLsuhvVqVUC/P4Aq623EUlqJpHDfqcDeDKgCUbEJxWnTWFQIuiMvYqAVDA9S5gbDmG0bC6c2BKhShrPE4fDhgdfOQymLTOkjPkNCA568JU1MHEOH0u1jssQuoTXTcwDAMhqEWlZCEN+t1U/MlNIe1UztcmB6WgVo7a870PqGqqTSiUFAVb+oUODC3OCqREJqlnvrXEHCE7UgmXD0M/YUvBPh0UVQKVkqYRZ/nrLyLVYZtvB5468NSBpw48deCpV5unvsH6dm7SVxQgplnvi0FhxtNinH3WVfH6uXxToXnhu3U6L1Q1ZFrQhUyg+uJWBZD6x4uqkBVYW0M2lm7M7ytHNPPvmvFYk2/7y9sI5mhYOpd7UpWYuTJQPqjXhFqXOedIMWF9cf2QTBo6ri4vaNs17777HZarhu3uimd9R04DUcA1ASNO8zAFdTMYn4ECtHeOaOz07Mo51LCaRuilnLuCoOQM1tI0CwBi1DDmEHVRkPO+FCYyhUQoocgIxmKs2qflnKHYD+YcsNaSUmK335EvVAE4P7sDGK43Gx4++pzbp6ecLFZc9D1X11vkDLbbHU3T6PUYg7PNGP7OIljJxJgxxuk5lTagBZ4SOWlGtXfqAIIRtB+UhyMw2bSV/OrCBqZUnbXGcPv1e6yOj4n7ne7XaYgxzRZgaf6pR4whFmL2xVYxJi2aNMRB815F1Fqx39PttlxfXLDdXBOs4d69Nzi7+xrLoxOa5QqMZV9yiT/99BMefPoxl5cXtIumVEwWsjVgVOlIuTiGZJCcEJPJGfpuDxlck8EHtfPznn6I9EPPkBKUMLkxjKpmHbBVlw8VQBzqfV8q/6L93DhbfLzrcOfFPq0t1HtHaPyYozr262LNmPM0gDJomkBVn4wtlVXRxWvIpK7MB311sWuMunjQgA5uliva0NB3ndoPdl0piKREFpOSrEGwWUltKKpOirGoSjqY6IbJrcX7gA/aP2y2Y98S7zHWkjCQU7kOJU/vHd6HcUA1DMOY451TImYthDWmWdyU3w7bC9uBp758O/DUgacOPHXgqVeFp755gbiZHssN5aQAXn2QLx5mtPsrj0REl+JNq/PK/suOq7rBzIVcxm9TFvKVL9xA3vrZ+aelnIOSQGvg1Dq2tjaQafHfDcVqVIQm4pjv20iVJ2Tahymv80KIvNy4SQUr9yVriM2Z6gBgEDRvb+gMm+tLYuw4Pjrh5PgWu+tLdtvnpNyTosUHp/u1syWNWTAeajqALTm3Mt6P2QUa0RSAAupFV9N8Qgdtu6BdLOh2ew2foo1JrFapbJsFxli6bk9KGWsTzjh1tEDAWqKxY8OcL5Drumm23A89V5trPn/8CE5voc4dU76muqOon7QrAGytgahtwDlVqFR5AEpeocEQh0Q3DLQp0y5bXOOR+uy0mo+SfQ2n1vtjgaKYSMqsz8+4986bPHj/g/LcvBJ0mlQWH7S66hAjpnR+gxJjGgb6bk8celJxLZGho9tcc3nxnP12x/roiDt37nJ6fpvVySnNckWUzGazoY89Dx/e54MPfsr9Tz8ipkhowqguSFa3FckZmyzJWAXwrG1OPyPqB548WPWgXzRBCU4SvRFsabvGGBpnccXaDplyW3WAotcXQiA0oSh2dtZHyv/jTa13Vp9Nu2iUhJy2utFxRoSmaVkuluN5jCpe+buGc8d0AObFhywGGe9xLsW7appJlsxyscT7NTlluq4fc3fV0k89+vvS9nQQIqTUkWuF2ByREia2RpWzbNCKvFl90p31Y1heB2kOarG0ci7OGnIyRNEFfHr9WVVX0aJfqTj8eO/JM9/zw/bV24GnDjx14KkDTx146tXlqb9BnY2X80wNZgLV8qzqza8PdJrkyPhfVZFG+DRjMGsGwkXlMWbch4L/XKGaQ1PJm5Wy+K2Aag3nCtACZ+WmT2RUlYaq7JgbZ6EvTWpE4a3ZH8wamoxgXV+ac8y8QRY5CucducwirYWcIzkZ9ttrnjx+xOn5bZbLFUfrU2K/o+92xBjx1o0ABCXH1UxPC1OcJ3KaOaLMnueojtRzNEyBesH5qvQUIK2L7awuwPMu0JYQu9qwZYyNCuRZyFkhwVhHayxN02hnyZmu6+j7nuVS1ZQEPH76lIX13Dm7BWhYu22bUYFIJWc0l1Dy0A/EoadpG05OjlgsmjH/VbJ2rpQSeQ9dN7DqlyzWS5x3mo8otQptDbcqOKkbiNMcSQtYg18veOtv/TxXlxfE6x1NCOXeq+rnfcAFR8qiBXJESEPP9rof73nf74vdXmKIkX57zdWTx4jA+fk5t+/cZX16xvHZOae3b9GsV7pgLiUury/45P5HfPTxX3O9eabFrEYVwYDVdp4AW9q6mKmdZnIpMCaasmDAeIe1noAleUfrXAEnQcSyaz3BFwcPPDlHqsWeSMYave62adWtA9FiTUOkVgMe+2VRexSQHKvVgkXb6mJBjA5EjMEYx507dzk5OR3JUURG55SqtIxpFTCCuobVzZgLW/s0CD6E4rpB+azD+Ubb5mKhhCwK/ClGYkrs910ZEKhiF6NWoTVZ0zb6oQdT0lIEqt9IFg3VVyeXnDNOBFvGsrnigPcK4DLAjMR0kCI3CMsYXfxHmq73sL28HXjqwFMHnjrw1IGnXm2e+obJRv1XRogzc7CTCbTm0FrVFFMa21y7uKEXVUCRCeQV3GdKy9gQpr3P9/Z1kZsK/qoYWa7H7077e+nrMn/3hZdrI5WbRDKeQwXHer5mOt44E65AKuqekCSjXuyaJ9l3O754/LnmXzYNx0en9PsNOUVSzPTDgJ+R5fiMSsed7pUZ7R7HvGGqSlJn5HMK1dmqzlCFUMB8t90Q46DXYp0u6gqetlmCGPqhUyu2cu0imRwtxgxKOE0BWRF2+z2Xl1c4H5QEcuJ6uyXGyMnJCU3b0i5WhNBggJxLldMoI5hvNluGrmO5bPFOXR98cHgT9Hoy5JgZhoG02THs9sS+Z7Fo2V5vyMPAcr2iWS1xwWOKBeFIZaJhUCkOHafvvMGbT77Fp//+J6VzCjhLCI0uDrTQx0lx2Fxfs91uCcHjm0DMcXTC6LqOfrcHMdy6dZvX3nyLo6MTmvWCW/duc3z7HBMCQ4xsL3dcXl3y8OF9Li+fYp2oK8esk6tqYoA0ErsYTZcYB0qWsjgsQ1abQyOCFfAYGucwTQMIKQnBueJwYXFGe3KSKdcctNhQ27Ya+s8yXvtXhVGttSyWC9brFaHxWGN0UazTvtC2La+/9gZtuyDmycM7pTgSgRKJgro1liRTPnb1Xa8L21JKhBAQNy1604EAWBcKOSixeNdgSoqDMUbdQGKmaZqimIKmZKiXed91pJg0vD0Moy3kGDIvtQDGQloxUUd3dWCaqEW21MHHOjeSV72euijZuXnxpMP2ZduBp154+cBTB5468NSBp14xnvraycYYBnrhtTGvsuaOVnAav1M/W4BOKqBN0Fc/pUVZuPFdq8ErDDJVdJX5GelrN+dQ5gagj8qUQGsdS2txsyOPCs/8PMvPXA5Y8/XGa5hf2OwaYHZVAqPf3my/Qol+6vPUSqlGlZ2UEjhtADH2bK4v8QuPd7dYLFccHZ3rbHa7KU4WhlKO5YbThxQQhZEbb/xeQ4wiUvIKKxFWK7yMpDy6hWRDCXVHUhzACbGQTwiN3nOEYejIKWKNgozkRIwD+07VOOe9FqxJwna7YblYINbSS0J8y2a/I0nm5PiYjGHf7REjtLRY77FOZ8z7/Z5uvyOnTEqBGAdi1M4rTq+rhrXTEBm6gdh3SIqY42NMymyvN3T7PevuiHa5wC9brC8+11mwIiAe8aXwzaLh7nvv8Ozjh/T9QLvMOCNaRXSvbhT9MLDdbrm6umKz2eKDo2kCcRgYUq/EmDLOwNH6iNXtO5ye3+Lo7Iz1yQknt05YnawQY+m6HV3X89lnD3j/Zz/l6dMvcB6s02qkSm4TqAoUUNQFcvMwcaVpUxrwmNcq2u+kOM94q172OQ1Qc4SL+mFxhZwpoVtogqdpAs5asqRxIeSIDTIN24yBpgkcH69YrZaqBFbVtZzt+uiIk7MzXaSYpVQoNeWY06I/VVVqfryMmJBHRYsxZSDnhhyVBGLUXOYYBYyGoNU5Y0HQSlyE4HHOq0KVVBEMTRjzdjHFMSir9eWw7+h2O811ThHJmZzVxz3GBNYoISFqI1qUqyElEjpwq37zHjT/tZBLLBWWvYeUdd+HLKqv3g48deCpA08deOrAU682T339ZMNA/faUeZknsEJAai5cnXWX0HVBr1xyYI1M4FW3MtctID/TL2T6/lgVUXIhj/Lo6/FvbDPIFD3T1lhOMDTju5qHZylplbNv1V2M8FYbRpnhziWmItCUTqSvpdl75RSU4OrP4oJgsDixhGxpxbKXyGBBjEEkIbFjc3HJMqxYrFa06xMWMZGGTJ93RIllIZR6Y2eDqgCmZjXWJ1Y700iZ+o4xGk5GNaeck9ot5jyCggLkgBFwVq0ERTISTCnIpECuM9yobgYJnHcYI5AiWTJdzoQmEJYLGu/wziIp0u8jMjiaY8tnz5/w5vMnfOed97i63nK1veb45JTzY0ey1RqwWL9ZR/ANR0fHWBuISbBJEDMUH3Ht0LmQ09BpxVBnPed3zolkLp9fIM8ukOs9fr2kWS9xISBo2oCJCRs8eA/O0J6dcvr22zx5/0OIPX3csdlt2VxvyDh8s2Sz3XG927Bcrzg5O6EfIt1+T0o9RmDZrjg+PqXxgdAG1ucnHN0+Y3V6TFg1pATbqx3by2uePXvEj3787/jwwx8ismfRNlijFn6a8ykFZ7VP9pIhQTSJmr8qUExmZgtgKUOy4jSSisOHwPjsa/VRWwddxhKC+oHHGHEGGu9onFVyrQobU3jY4Er/E7yDk7Xn/HjJwoeymBYkJ4xkCIHbr91jsV4zxETKGmqfFM0ykMilmmmWUpRJBwhZEpaav6t5pJKLw02ug5fJhjAmLXC0Xq8J3qv6lDLZZpwVXeBZChyY0vZ04FXaocn4EAghsFytxrB2Va36Mce2WEamrKAuJcSecym0VRSklIlFjRr6YVSOclLPdFPJ9OBI9ZXbgacOPHXgqQNPHXjq1eapr51s2GqJxxSWrpGxEqUdt7miUxfr3YQSqU1IwaM8KGuqQwZT+NZMO80YvCnqCpNv9AjkM2Adz6WcoEFwCC0Gbyyueqm/8LW5hjU79Eguc+Ce7o1eZ55dY212FThH1cbo3UtZ3RJES8TisFpACejzUFqLkFKPdI6r60tC2xCawNHREXG/pR/2pKhE6oszQA3v1zCWSF1INbl9GBhzJOu5mfp8y4zfzRQJI4JzXr8ZEyn29LknGKVAfV6W0DS0ecl2c80QI2K0sJE1FsmZoe/JKSLGslytsMZoQ85ZSWnoudxcc//Bp5ws1N3iutvjfOB4tQaakYq880jTKtFYR85CTsU5JkMcYilAZErYzyMpEWNic7XBN4Hl8ZoYE/uLKzZ9xMdIHCKL5ZKEYBuval3OuJzVwcJYbr35Gk8/+ZjHnz/k8vI517st3jeE5ZokGzKG46Mjzs7PSJLZ77Z0XYdzltPTM85OTwmhpVksOLl1wtHZKWG1gOBIQ+T66pqnj57w5NEjPv74r/nw45+w77bqXS/ac9TG3aAJliWPtBZwkkyptTu2TR1kFAVHZMw9Rabqq1VFGhfpldxQdQopFo7VGUby1MJlOgfNVdV2dnx8wvH6hO12x+X1BcHDydGKo9UKV0K4mtc6kA2s12tee/11tdvr0wi6tcfZMV+22PeJKPAVr3pv/Kxj6oBEF5HaklNbFnLmjLWlHXmvBbmaUGweFUCt0bC8KR3GOUccdHCgt6LacRq1Xyz9L5TPD8NAs1iod39Vi6KmJqgDjXqpp2LLKaLXIYCxGpYees1ZriHrWoDKmkNo46u2A08deOrAUweeOvDUq81TXzvZcKLQJJiCZlCLH+VRmyhhpNnf07+MHZDZ31VpKUjwIgaPcD+GqGT6bgVVbaRlR2X2fANl0VNugYVVv/FgLTq3BTfS0k3V6CUhqnQGBWgz27cpRCa8fAEyEoC8+F55O9d8OGtoXWBImW1OmFq1Mie215e0TcP65ERnqOs1Q+zYbbUTWJuLN/mk51VLMwrx2ay5jBiKG8l0rdZNDg05Rp3F2xL4Nk69oF0A6RgK+MrQIQjONZjBYG1L2y5LvuGuuBskrHOF3NTDe+g6gg9EP+iiO8nkmNlst9gsfPLgU5ah5e6du+y7HknC6fqIZdsQ0PxJ5z0Gw3a75fLygvVqjbWwaFtyzPR7DQU7Py0eq7aGXdfz9Isn3Al3Obt9i41zDJs9RjJDr2DrnCP3meioMkG5F5nV8RLbWD768AOWTeD0+ATXLtjFjDijCtfJKTklnj17Tux6jo5WHJ+ecHxywnp9xGK5ZH12THuyxIcGEUu/H7i6eMajhw958OBTHj78iM8f3We336rX/Qut01iLySVsy1ThlKJqWKdKjb6oKlsubbqqsrnY6OmLNbxbQ7y62DIOEVMs9FLKIGlUUHOKo3okos4h6/WKo5PAd77z87zz1rtcXFzy8Scfsbt+xvG6JThHtY63ZfAjoqB/cnIyqsHj4t0CYIJgc7UStGWhnSPGNLpfaBhbRsVKvdml+MYXb/Zi/eiNIfigubwFretiRnVyqeCv9o/j4CjOPMZHjtE24qzms2YpfQ0lgOA9ptX99n2v4G9dCWXr/Y4xYovFYIxDCW9HvA+aK5/U6ebm4t3DNt8OPMWBpw48deCpA0+90jz19QvEY9QmZAxYOzUno4t2RlAy9Zr0l1xaVZ25z509RlCv+FtAp/qAK2VMGs4IlJQDF4CaA+ccgM3scxZYGmhLiNhhcBjiuMcKM7Nzz5oraOzsEC/LS3q8FxC6KkXMb/jEf2MnMuPxlEKCcSxDw77fkcYwXCRFw+b6OaHx+NDQLlcsY09KA32neYM551LISM8953zjXK0xY8PSczHlLms42liLt45sc5mtSwEDW6pXBhDDEHtiGsg50fcdITCdf9uyWK5AhL5XZwRLKrmFevEpRvp9pypLmzTWmrRozX7oebTd0/gGnGPdLtleXXN9eaX5mtZydnrKeqm5lLvdjqvLK3VgcMKiXUASul2HiOBSxtlFUST1+rNkUtSFhcv1mrBckPZ70r5jKB0sNAGswTYNNngl1PLA/MJzdu82i+M1K+cJ7YJkLO2y4ezkhOX6iLTvuXj2nJQSZ2dnnJydcnR2ymK5xLWB1dER6/NjUoBhSMR+4PLiis8++YSf/tWP+PDjD+jzNTnvizuFVr2VMkCxRTmpi1O1DU4tv4ZRrb3pkCGpAvX0HYX6jDGCsTIpUIYRyE1JCdE2pYMFMa4AueZ72hB47bV7vPHGmxjr+fbPfYf33vsW19dbTs5PefTph6TtBeREzV63JhOcqjOnpzpAifGmfWENh9f27Zwbc0Wtnf6u11M9953zRf3KOugQ9U1Xhw+vHuqhUaeX4r1f812r0joObqJgxRLLAsBxwWrpJ1LYUcSUkHMVYKX0Rz33LAJlMSPWFpcTPc/QNKpKeo+LHhcCOSf1RXd6zuq1PnDYvnw78NTNw944GAeeOvDUgacOPPUfn6e+Po0qqp0Y1mjO5Qz8XLXqKotiHBNAJUUpnJlmtKOiNJsAj+oGk4I0zRj11QkPzQjNhkyxtJ6Avq7fM/XLBivCwmiAs4K6MyVn1dT9mekrTPurQFtPt5KJKbH3euYviEsjYr+oJE0zvtIJmRqFM5bWW5o4sM0DY0ZxGtjvt2yuLzg+u4VvGparI1LsyTmS+qEsxCvKQg0zVhVBSli/vFavSyg+0AaMqHuEd4GUJuu4qg4Ya7ChxYeGYdgXq8GsjUrQ/FNjadqGdrEqYKm+0JSQYlUZ4zDQ9wNNzrRNo4vFMPTDQNcNfPLwIev1MXd+7udJfWSz2bDZXNP3Pf1r93j3rbdZLpYsFguur6/ZbTeExtI2LSdHRyyPl/SdzrhjHPClc9YOH0LA2QDW0qxbWLdISgz7jtj1WKM5lTYEjA8lyVkQC8Y77r73Lm//wne5/uIp1jf48P+x915fsiTXee9vh8nMMu2OHwOAmIEZDmHEq/t4//CrJy05ktISRbsogiQAQoOxx7Upl5lh7sOOyKw+AxzoTWetWwn0me7q6qw0Ed+X8e29v91wdnGObxrubu+4vdkg1vDo4WMuri5ZLNf4tkWcwXUNbtFow6whMfTacfVXv/oV//SLf+Rf/uWfePH8K569d8XF5ZIY8nTd6lyoOdd6vy2m2DjqfEkT0NQHr8li72h81oeHnCswo57aoMpMAaSYIgZYrVc8ePAQgM3dHUO/n7rO5pw5W6/5znc/5vLqAYdh5Pz8krPzc3y3VOeNeODVFwd2uztSnEQ4BFVZFovlrA4lxZMaTq5APoXWy9Ni9cUHSlMzLYiLKSFGbRQlieb35kzbthpONmbyXafO1fJg0xTwrE3PdG4ajMBYvcZL0V+KiYiSa0YtQXPOhDR7klPmWMyZoe8Vb3JGQpi823O9lyERkjr4QJ46zoakOGesJYfAafvd24mnTjx14qkTT5146t3mqbcuNpZkhpwIIROMTvhYh0NdvVpbgJWyypXJXg7yBICS6wkX67oprDQDoqaIHoW9mVeydZuAVzTaNalTMpec1UFrEVoRfAFwZ0yNkBWAm51CKvAdaT3luI8/ef5bYPKGngalKavxospUUpuJoML3PPiMscW5QFeXJms+p7dOLdUC2r2z6ehWl7RdRworxrFnSBpCkyhTHuu9z6zHIfFILcikXAp60IGVjebV5ox2+CyTR+9nxhmLbxqG3pLiyJxPWVb5w6B5ic7Rdkv6/Y4Y4jRGnJUy0RLD0DOGQFfGiIJ+wonw6vo1z1+/5BM+5urykiEEDocDm9tbwjBwvlqzfG9J13U0bcN+v2Nzt4GcOTtf8ejRI4Zx4Ob6hjAEJOqYbJyuzmtH3BACjXjEG2gszcJj+5EcoloSxohJERELNX/UCKsHD3n8Rx8Rk2G1XLFcLZEM169fsbm5pulWPH76lPX5OU3XYp0nRC1oc43HNp6UMsORUo6qAAAgAElEQVRu4Le//Yy/+pu/4m/+9m/4/PkX3N7dEQ4HLq7WPDAeTCi5q5YYFXQ1LxSS5KIgmZmc7yka2nwnhDlka8VQGznpPY6TQiLFASeZcj8l03YdVw8e8NFHH/Mnn36KdY7Pf/tbvvjsN+Rhi7ORbOD8/Jz3P/iAqwcP2R+GouY4vEeJvW1VkYyRmPPk0pERfHLqFFLCyFX1OlZiK8Cb0Uz5rcCUFxuOwM2Uwtba3Etvm9A0rfrop4RzvhThZVIaiVldbebOsnr/qye9c648l9WeCJUgEzFT3DgUO2IIxQpwPsaUEqHkZ9dkEH3wmlWqhBYbDsOg89jN+b/DMBRyOkam03a8nXjqxFMnnjrx1ImnCh69ozz11sXGA+fYBNjU8EwB1oyibwL1QxZd4SQjUwiNAui6QhQQMx2Y2uCV1ZrRXNdaTlf1FBF9XUAHGGUlVwhEQcpgStsmgymDt4CYETzCUsCV47AFWtNMGzPg5UnsKcBdfygrvTeuTfUinkCzrub1NEi1Ac+0H6UlkQTi5vca6CWxT9rZ1FpLKkU6giApEvoD27sbvF/QNB4WC8ZhRQoaLo4xYMUXghRUccrTgNNwtxbr5VTVrKTFRMbp6zKDQF0VmxARW2zkmo6+WWh+YM5TXiIixDQyjiCmxVlHsHNHyZQS0ZhKyYQwkMKBFBptsCOCsxbnDWMe+fyrz/jVZ4/5009/Rms8YRho25bNZsPnX3zB5eUly+WSrmvZbjfc3W24u7vDCKxWSy6uLvGLhttXN8TDSM6UBlCa1zmMA9vNBtNZTO1yK4JpPOI9xEQaRlIGSz4enBgxPPnOdyBBf7ch9rqv7W7P1bNnPHj0lG65wDmHsY7dfkcII2eXFyyWSwBur+/45T//kv/4n/8Df/bf/pxXt69Znq9JYhhSYjv0qlrApCxUUJDyYGSMjiuDetRXBxBbc52tKakGmgfsrCCmNIEqJJ1jKg8W5d4YMCbRNIYHD845f/g+P/rxJ3z88cf88Ec/YtEtefXiBb/55S/57Fe/4PnXn7HrdzSNx7UtxjcsnXqaO98QyjkMQ884qvNFkkyONZ9UAA03x5gmgNPjzlOI+l6o181AW8dqBWRj7Jw7nFXpcb4h5Z4QAr5pAErzL31g6vtevc4bzz6ps4kYzUvJpQCxIsF8D1TpAi2gSykQY0npMFYfbkqo2jqnXZiNpXZkFrGMsfqtF/cSjZGXAkrNS6/EGkvecaP5IKftd2wnnuLEUyeeOvHUiafeaZ5662LjkWuwWRgzxKw3ofpkZJnDX1Jiw5IqWAmSCsjW94nVG1dDW3IfPB2qwOjwzBPIGdFl2nFIh9LYR0QD1Ro6yiUunsmiA7zNmUUu5Uk17FRBfvqcGcipxy+zOiR19qRjWzaKl3EqJKWAmcv1yXV/5bhzUWb0j5XMnFisGMYc2aXMQCkyAhrrppusFnCB0O/ZbW6wZ2c01nO+PifFkRQDQ6+TxJUV86x7GYxYnJlzITV0l8lZi5NElNpy9cQWJcSUE4REthFsxDpL0y5IYWToe4zRhjkA/dDr5Ajq/2ycwwmEcVD7t6ShPi0Ei6ShR2JAUIL1xmohmYFXty/4l//1Sy7WZzx7+AxjhLbVxjXPX7zg62++5qPvf5/FcoF9bdn2ozpq7HbknPj4Rz/kyQfP8N6zub5l6Edy1gnmCuFsXr1mHA40bauAawxiLdY7nLeI99TbKimRgloukjPdsuXBB0/5/B9v2N7csFgs+PDpM/x6hbN+ajS0P/Qc+p7zyzVnVxcY5/j6+Uv+7M//nP/33/07/vGff0EfelbnaxBLiIExRjb7LWMYsfUhJklxzRGKAWfZ1JmlpnTofZ0fRFIStdmjpnhUVTUjufwdKCGIkBMkazk/X/Pwgw/5wR//G77zve9zcX7B4yePWS7WrJdrOuNwMXDYbQgp4NqGZCzJGBrX4ptG1bmoYyaEwKE/aMOlo4chgemaaqQ8kZMGfWtYuX4ZY+bQcJp9vtPR+4xoAWCKEaEqyaro9HEojh6ZPOaj3PE0OWuEMUwPPKZKv1JC3FFdPrTwU/OpK67p+Cmh5mwwGCJKlBPBFOD31oEIoRxXdVQx3qhKCVPeL1mLLEv7ZiWI0/Y7txNPnXjqxFMnnjrx1LvNU29dbCzEMFjLPif6mLhX+lHCwnWlV4eZ3qAKlnoA2gmyWOyJho2zCLGoOFWtwBjyFI6eSaKCUoXwmpOr4VUQyVPToJQySMZmoS2DVDsgmqMjrNNg3uTo57lQUP+RXFUsOfq93uQJxAspFe+EOezE7FSiIWRLg2FRVvd9zNo0yBZSSpofZ7OdVtY5JQiB/rDFO8NqtaZpWpbLFX1/YBwVkEM8nuTlvIwg2SBx4hpq/nHKGUmBlE1RRaQoSnG6/jEGUrSIszRNSxw7Ukw4p8WAzmlu6dAfdGXuG5xVV46cMjGOSDIYUyYIJYyXImQdvNm68ree/e6Or7/+mn/pfsWqW/Ho8oqubbDGcnt7y5dffsWTx49ZLEpO7HZDBu7uNvyv33xGt1jQrRZcPXmIX7b02wOHzZ7x0JNihpAJQyD0gd7uSgGYnYqfXOtxC6+5q95r8aBHFZYyHldnax68/wxnLa1vabplKUzVUTMMA/v9nm7RcnZxiTjHZ198yb//D/+J//Rnf8YXz79hcXnOyhnaroWc6LeBEBP7fiCkhPPFKrJ8IUyFZvp6GWhVVcnz2K3/rXnA00NLAUN92Dl6v9RwKJydn/H+Rx/xwx/+kIePn2KLPSTAYrnk8sEVlw8e0nQL/GHJcn1G23VY63BNg/cNMQS8c5ydnbO+uER8C/1+HpPTA6BMYJxiVMWXVFw6ig99Thhm541qazgVypX/hhgxSTEmFs/wzByej1ELQZPEKaytypuluoXEqA9lOescbJpm8kN3Zn4grQWBuk9bQthy7+cxaJ46ORfHG91qSkfN7zXFXaZeh3p96n0W0W6z5GO0Om3H24mnOPHUiadOPHXiqXeap97uRpUzHqEzBh+FQ05VVNHf6ycWoC6rVcXQqUhIymrXkWoPkrIaF5LMoJJFyFEVoJozmrKGzTgCz+nTs/5NHaCTSpMzNsNaDOdisFWZQqbGSzNaq8ozA3SegG5SvurnHaG8MH8PRT2qL9ZDFAXJLBqYVV4yOBEeiOHpcs3LHLkeAymrckLxMI5Hq+2qSsXirrHdCc55uraj7ZasViPjkBiHA0MMuFpUNJ1inlIGUq18mm+C+n3HiCmhahG1DtTz1rB2jElJuBCi9a2qLd7jnGNlHWEcCWHQwrXibGCtIafa6EaLMLWzZWAcAynbKZc5pYiznrbpeH19zRfdV1xdXvHg8pLGedZnjpQTu92O5998w3e/+12WqyX+VoE2O89uu+e3n/0W7yyJzNXTR5w9uKRbLtjd3NLf7nVlno06X8REzKEomUKwhrC3NDtD7jrSssUvOlzbQuOmjqbGWh58+B7NasnhbkceInkI4BxDHNjvd4gY1mfnWN/w61//K//+P/8X/vJv/5ZXuw0P3n+GiDpTZBKh77G2BzGEULqTFus5nUNCLuoQMBeHFZPy2o33WGW5Pwh0cpr6MCNyBKRl3IreW980XJxfaBdVq44YzjsFKi+0yyWriwvOLh8QBC4uH7BYLCegiTFNAHh+ccmzZ+/z6usv6A9bJfT6QCZC0zR47ycwlqIEA1NBqSl2OzqGpKicR3PPaAOlMI4YY6f9qRqkec/WWsZxTquo89yUXG8RGMcwh6OlisQJiTPB1eLGTGl8VEC7Hk8lHxFhHMfp+ynUrtrxVExorJmK7I4JaQyjqlLWUv3bQzwViP++7cRTJ5468dSJp0489W7z1FsXG6mAs0WbDRli8cKegaBilwhTUZtB/wYqEGasiOYWUmFYL2QqJ5Akk7K+R0OZZcBqT5Y3TrQcWwHazOxHLmhjxUtrObcGP91xHcTmGMTLsb8hJE03csJv4agAr0yqe3+vn23rChgh5roqVELzQCuwFviub/j+xSV/v7vl8zBOxV21cLEOYO+8+h4jQCKmgWGA3W6LNRbbtCzXF/TDQIxqNRiLMfRETFlvTC1YqsBwfAF0FSsY0UYwZnJnUdBNUfP7lJQMy9Wapm11omTomgVts+Buc81oelz22sTIaKg6lcYwueQrj2Gk7wec7zQ8OA6TG8hqsWZ7t+XVzWs+++q3PHv0mB987yMQWK/XHA4Hbm7v2O52LBdLVqsV+91eJ2ROXL+85jcpa1QvwYMnj2iXS5x37P2G/e0OevU5z6mMXQNi1K/dWYNDsBlMTBAitFl93WOiFuG5Zsm68bjljrQ5sHnxmhQC/TjSHwYuLy9o2pbPPv+c//hf/oz/8fd/x6vNhvOHD2kXLf1hr44fxZvaOI91nozMIF6eCYwp+dupUqs6QJArcB/dzfLDBB45Q0pqj2g0jD09FNXJmEvYujw81QcWAdq2YbVa0bVLcsr0iz3L9RmPHr+HWyxYra/w3hWATRij/vHGap701cPHPHn2Pjevvma/3aC6r6rIi0VH13Wo14tMQG4tk2JUyUHPc1aNqopWzzmEiLVZnVxqwVsorhnFInAcA845fdAocw0Ea+cc3HrtTE2VkTlkreDLRJxTbn8hjBqOVqU1luJaBX+dK+rgEYoXOkDTNKQUJxUWtOlXiBGfAWumYzhtv3s78dT8/YmnTjx14qkTT72LPPXWxcYoGgomqepgMYRSLJMnQJ6tzvQWgDMalp3CLAX0qtpU81FN+f5Inyi/rxdT/7w6gdQxR93XMZGUTTK0COciLEQKiAuVTuYjvY/lHP1cyeFNbJc3X5j2pptDihlgyYfN0JJoEJZiWABnxvK9dsH32o5f97dkiUWpuX9AIQTarsVZq6qOCBkdDIfDHu8aOtfi2wXL9RnjcKA/JIjFp1qq9KU7ddYxWlVqpvMr10ZtAWOxfJMp7y6ESM6i9nJR1aa2W5RGM4Y4aOGSWzYsViv2hw0xjgoKUhQPqYpHrpVkxBgJYaRpl8UfO+OclP96uq7jdnvLy5vX/Ppff8333v8Oq9UKayyLrmMcRm6ub3jy9AkX5+fsdzv6YcSJsN9seP3yFc44HI7URy4fX9GsFnQXa2zn6Xc9oQ+aN5kjzlms95rDW4Dceoc4q4PZGogRYqqoCibj2oZ12xDant3thv3thv3hgCvNeF69esVf/MV/5e//5z+w63vcoqNZLNQPOwExk0JUFw8RbU5kbFFvlNQzTCkGk4aZMyYnJIlmx5YhbkvxVh1Gs793cZSpahFFpc1Z8zyZUz4qKVjnWCwWrFYr1uszGt8SxsBitWJ9cc7DJ0+xiw7fdUeuFUb9wZ0jpsQoiXa55MHjp1xdPSQMPbNdZcb7Bu8VgmJVx6zDey0iHYdh6lA6zUthsr6s51Yf6KryqE4jqjRVFUlEpgZLOsaLjlzGe9fpses1q/vR1+oMj6WYFGanjpwzfT+qc0pV9NJctBeLxaYevxBzmB7SqopUH5KO0xCqSiW/D3RO27SdeOr+duKpE0+deOrEU+8aT709soHmdiYyFvDIlA+rODHLLRUqjSiQmwJoHL1/2oqntr5/BsEK1KasXEXmv6yr4uoXkaU6fUgZoKJhXjJrY1lbQycGb2bgrog/kc4EcuWGlrfN6pdMSpG+Y1asjjF3OveJjMp1yLCOsLaGM2NpEM6s5aHzXBqhMbOqdu9Iyio0FneC1KvzA1lveCCy3+8xvqPzSxarNeNwIKfI2A/EFAo5lpW4MVjjcUE7lGqO5Azien21GA9UUTNT2E0tCEMMNE1L23aMIWGMBttSzoj1dIsV7WLFbnfLEEa8K/uNWkAkWfMApeRV6kQshUdGiSLHRBJD0y7Ybje8ev2Sr7sVn335W37+k5/RWCW7m+trNnd3nJ1rHubFxSW3t7eYDCwW7LdbXr+8pnFfYTD0+wOLsyXLixWrizXdeqXXMUSoIO4sGItYmdFCSopwzMgQNKFZZxlFEgQxGG9xy5b9Ny8Y+4H1Ur27/8df/zV//Xd/z+7QEzMs12eElEhjKB7vI6koC6kQnmtcyftMmGKTmVIqY09HWCZNiscUwgYttPOudP7MRNF3Z9F8S2s0B91ISanIqYw5ffRIgPGedrliuVpxfn7O2fqMrusQDDEG2q7j/OoBDx5vSAaiJPp+JCXo2m7KBSUEjAi+abl48IgHT55ye/2KOPaA3m/rrKJENqQ4knPGioZlxVlysuSSM14LR+vXNEdimgDVOTeFfmt+bFVb1LJQ719Vt/R7HeO146st1popqUWmdnw9bjQ1u9fU14ZhIJd91M9VdxU7gYQ6iChWOQvOO7xvdPZlLTYcY6R2IK7319h6n9985DxtdTvx1ImnTjzFiadOPPVO89RbFxtWxSJiAaYGYQACSUE461hPYtSlgxmQKygZ0RWupAJYch88jBQAKe/NVAKY6SEXMKkkUfczr3IhFzePVgxL61hZS2uMhtWldict9oNJ/dh9+YwaIjdGSlhO7p1LBea6GSnz+eicZyBWBa0FuixcimFd1CIjWszYeU8jHiOObKwWDJbrlHIuNXCl26PTZi/7/gCpuBPkwJAPYG/JXliulpydXxD3A3HUhjE5J/VwdiCik8U5jxGjua6ScfXuS54mB2Ri0rC6hqgj2kAnEJNlf9gTk3B2do4IjKOGmn3T4NsldhxJw4ExxXI9MyS0cVbSwiojhnEc6PsDMSds1pzElOHQ9xg0zHh7e8M3i+d88fwLPh3+mA8+eEpKgXG35/b2js3NLeeXFzTes1yokuS7Fqwlh8jt7S3dywUWQ3+3Z/vqhvD0IVcfPsGtGoTiHsOx7KjfVy99k1AVTuP+JbxbRmfJRTVGaFYdMYxIsav7188/46/+599zvduzHyOL9QrnHHfXryCMjOOg9opJlaiqHnivqpWRpJ1Ra5w9M4MvtVFZJpmMIWNFi8Oss8RUQsUpk8VC1qJCsZYEOKfe4SKGkDIxlbxPZzl7+IwHT95nuV6z7BYsuk4VoBiRrDaEvutYnl/QbW/YbjccSIyjqiBdbkl4xDq6VrBGCBeXXDx+xuqrL9i/fo5kCMbhXIPBKHkX55k8DtPDkhK/NkGq1yHnjJHi/U0ghkAIEefmnFMFeFWRrHUT0McYaVuHLS465GpbODuGVBWoksQwjvdykiuyxRhISR1DKnEo6NciwkxIRy4kXh1glPcN3lhsfSDM4EpIPQO+gHjtcaCi/Wmx8fu2E09RjuDEUyeeOvHUiafeTZ5662Ijw+ylLKI5sUUlqKHmaqF3rBxN0yHXcPP82/q9SNk/RZkpc4IJTGZNZlKFmEE8U9xCyCXdU49tbSxn1rEwWnRX1qTTcbwhXZUPLOdbgVmXpPdAvL43pTznyVHXoEzh5SlEn9GCRQQv6mbirWXlGrz1WBEao+4XE4Ych6LKdQ8xapGbdQwhYk09jkh/2GMPnqbz+KbTMHUYy8DXIidtda+DXjuPOmLsy6C1ZcVcc3DnC5RhDqFl/RrHkZQjXbdiuViwP+j7d/s9595ydfWAplHwPRy2ZeKhRWMml26+BkS7tO4Pe10dl5B1TJnDYY8VYbVas93ccnN7zaE/sFx06mN+12NEV+bb7Zb1+RnL1RLnLDfXNxhrWCyXtL7hsNtzt9nQdC0rWYHJDPs9w2ZH59dY/6ZNW7n+qbpoZHJM5DGQS7hyuk31XmUdf4vVivXlBbubW+52W/7yb/6GL58/Zx8j0nq6szX70ihq8/o1h8OOtm1w3iqIxYQVYdm2NF7VHRJk0RQBkWKbV+zu9MEjTiFhQb2yjTXTfDXG6JNYNhjrEGvU2k/AmEiImcMYkahA1q7WvP/hhzx79h7rszXOu+laQ8kpzfoA1y0KuA8DoTzo9U4YFx3eaUGd9w2Nb7DW8vTZe9w8f58v9ltS3+NEWCwXiDHEnFQREaZi02r7F2Iu/uAyhXxruDklMwFrVR6P9edjNZSq9qKFbXGMRR2uIWpV6UCmz6o5rlAdPMykPlnrEEkTcaiqdx9eam5sdVqZlSt9Xwgj1fe8fo6z6qqSy3HXfRw3hTpt97cTT83vOvHUiadOPHXiqXeRp/5gGlUt01IzPCmgeP9oa5j3fvZoDadx74v6fWZqMCRyBJRSQHT6AAV+KxWM5w+uSo0RsBhao+HftVOv6qoyCbUo7ugvRf+25p9Nek/Ws4V6848HxfztMWjL0a8FcBhaY2iMwYkWTrmqZDk/hcdbMdicSCX39PjzppzFIuU0bUPMmntqBLIYcgz0+z1N29CszllfXGhjmpRIvXaEbMROg1tEB8kgMg3sSesSg7EaQs6FfBuvXtT9OEy5gU3TcrZe0bYth35Pzlm7xI4Nl6vzafKJaAGRDsSR6uquoe6RkCJJDL6E3H0J163Wa5Zty3rZkVNge3fH7c0tr1+/5unDx5N7grFqRxhDZHW1olssGEOg73vatuXJ48fknHn96hVjDmQD3WqJ9w39/gDW0CxbjLMlZF7GlhQFM+UShk6TYqDOGrlKlNO9QbTz6sP3nrDb7/jbf/wH/vnXv+aQE7nxXDx4gDhDHFSZGYv1oqYgQI4anvTWsFq0tN5O7jTqEJOnsU4Zx1oIprZ5YlQNlOmBRAd0dXexGDC25GVoOoG1DjNGDUkbBcTlYsHV5SXn52csFwu8mxv0KFk4vC+5wgIpjBy2d4wZrHeEwRLGgdS0ZKfKjGutNvh69gGHuzt2d7e8+vpLJCeaplEQQ0O3KTGBooZny0ObNZPio8fq1JWmgG/NG62FotbOr1XgNWbexzgMaMMkBVBjDOM4FvBOBdCZwtIxBrRzsj7UhBDvFePVp78xhOm4VQHLc8i7XMfJKjHNjiI6DQXvLFIenKoaTnn/ON4zdD1tR9uJp048deKpE0/p/Tzx1LvKU38wsnFc8GbLF+kNZYiqHh0DdlVpqtLDtLfq4TFZDMo0haZwjZE5TG1FFMRlGtmTemTREJgzan24LE1xdFU9H3vNQ6s392h0omHr4xffvAqVYua/nV9hAnVTgNcBrQitEbwVrIAzwsp51s5jRE9y5ZxOBpE5/FRWsVTVqgCu957UJna7PblIazlFxuHAbutofMf5cs3F1QPGGPQrJMaYsJZpoFnnsNYSw8jkEFFAoqoRMWu312gc1nnEGEKMtN2Si4tzVsv15NksRsBkQhgZh0DXLbi81MKl7Wanv88NuahcUzg2J/rDnnEYWCyWXF1d0XUdvmlwIoThMAHS9etrfvOvv+G773/AarUixYjzjj4E9vs9u/2e1XrN1cOHbDZ3iBi65YKLyzPOr87Z7rZ0bcfZ+RnWGYb9oPmKY6DpWnw5l1yeJjIFxLM2yiHnyYkFjsC8zpCk42j94ILdP/X8wz//EzeHA6nxXFxe0i4X7Pd7xnFgt92RUmKx6IgpFH94Jc62aVgvO7ypY1abVhmj1nOVOJQ8IJkwjxmjudeakylFmdAxr3m+ZiIoqRnlOdPgEInEmPDFItKWYrV6j6e8zCQs2o4hJK5fJzZ3d7x+9QLItMslXduQg9r4GRvxPmFNQ9O0OPeYMI7c3Nyw3dyxv7spQ1LH5ZxCwjTmpShk1ji8d6VgU7PzQVVQa80Exnp/Ypk6oiTivFpbltdCGKnFczWftRbKzeeqQFzVmpS0OdLkH89MIPXa2ALoMqFyKoV7mgYQY9Cc2TwrR/W9Yxw1daQUTsaUipVnvZZ2Kkw8bd/eTjxVr8KJp048deKpE0+9mzz11sVGLCFSzcfKk2qk4JqPAE2BSkE7T0BZVZjjrU4EwxGIU1d8RSERwYkWyHgp9m4TSNZVsHp+V4C3RmiNxYvmqU5WhYU2an4pMutauayw9RiqDFSP+XiNXg9+Jhu9HvNmqLaLGpp2SAlN6/F5Y1laz9LMisrSeZxofrGAKhMTmN8nFc21s3jvCGMiZx1gRNjvD1i3Y9WtOT+/pB8O2g2zrG6jjcXhoRCftcRS6FROBZkkPLUUTDHSpwM+5akrpogUZwavg9AYrDMkIilG9vse51qapqPrVpAFqY4Loh7M1hi898Qwst1sCCGwXGrjo9oltT8cuH71it1+B6L5sS9fvuLFNy/4+Acf8ejRIw6HA/thYLvb8uLFC2KKnJ2fc3Z+zjAMjCmANVw+vOLsUvN2rRiGvmccR+xoMCEjMSFtOysPRki12jIlHUxH4+7ohijYoMqCCLjWYxcthxQJxrBYr1mfren7nhBG+t2eMKgLyjAO2DJp6vxcLjrWyw5L0hlWlCERowVYaXbi0BCuw5gAxk4PPIms1ocK09rsqIZVlW/nPFFjaJye25ADIqpC1YI16wwWNxV/iUDbZNapo20cY39gc3tNjiOHw5K2WzKeD9hhRIzH+0huNUfUO8+DR0959p0/4qvP/5XxcEfTKElUWVrQfOhQlBf98ninIfictcitYopOZ1PA2hWlxkxj2tryYHKkslXlqyqoNQR8jE0iqjzVEHUdG5qaosdSjy/nTKzNkKolYIgFrIv6mCsZzB7ldb+T1WfWUHhNJYkpEaLeC1cfYE/b79xOPHXiqRNPnXjqxFPvNk/9byw2dFUs5SIb1GEt5SNbQSn/FGCzIrgCccfFTfZIrTEoCE9QWdByUoKq+mIEl4+UKFF1qIbJ6z5NllJIJwS0oyPlb0JOjCUcZOtArwt+pgBzmZyKZW+8Om1aEJSn/FrK8Va1yBQCsiih2dIgaeE8rXW44s0eSXTW0hjLXu/ipAbU8PDRIRXLMl3VpzioS4KOWlIIHPY7ttstzcU5y9WabnNXVpyDdj11fqJTawzVH3s+aUB0ZUz1y86ZEEeMOCBz2G+5u73BGqcpkSGoP0ROhBjohwPcZc3xdJ6LyysdZM4hYgkxslh0LFdLyJnb2xvGMil32005HmHoD+y3G/qhxxuLkcwYer746guePnvCk8dP8G3LlbfcbcynkxkAACAASURBVDZ8+c03vL6+JgPL1ZKmbck5F3UNfOP1+A8Hxv6gaopYxlwm1KgKlDUW49VGUIQjV8Y3DC0rx88XUMeFFd7/zgd87/t/xOtf/Aur83OGQQsTc4iYDGfLFTsy+/0W5w3eORKJ1nuePXnKxdkZOW6p3YTLHC8uD7mM+jzNBf0yWGOw1S0lQyRNhF1B2IjgshBzyQO1RXExOppTihx2Ww77fenyKxMB1HlujNA2DWfrM1brFUZg1+/px4F2saJbrEgl78Q7S2rbUnBpMM6zWJ2xPLugtYnHT9/DOkccS3GaseQcGUNAoqop1oGxWtCm+bX+KE81T+NL3TnSVDinjjLcU34q+FfLwuo4czgcdF7YWZUDJZ/68FK7p5qSv55zbY4UJ2AGLUQdx7GEspnC3VU9Oi4OnHNwKX8bSFY/L4wjqSjFwIRnp+3b24mnvr2deOrEUyeeOvHUu8RTb11sBBKRXLqXVjVI/ygDYRrRTMDjxNAWpaQqQQKTzaAtk0OVnrnJEQLeaHOjKcwtCroV0CrYq0JUc8b0dWfqGlmJY1azdK6F0hmnKWqS2hfLXPiXmXJ6TS7ZsEf7AMp78nxOhbh88UqeULJ0ezQGvBE661i6hs55jFgMhiiRhTEsjOOG4shQjp2slokJMGK08U8snsnW4rxnPBwQjHZVlUDud+zvXuOtkKylXa2JMTDuMn0YSRIoGbeam+vUei6X81GDQC2O0xEtRclI5DRiBUJ/YHP9gjwMeN+QMUhUy7WY1J6PHDj0O7xvWCwW02qbsmKPUe0QRQzGt3gs27tr4jCoJdwYGA57QhiIMeAlI/Rsty/Y758oATtH13iWq471xRntquPFq9eMKRBTZLFY4p0ljoFt2uJ6p+OgFNDlnAkkxmGEg7oqtG1H17X4rlWVxDt1voxJ76fGMSfVsU5u6jjQi8jjqwf8Xz/9OS+3B55v7wj9oBNQhM573HLBOOzZhGKlKDqpr87O+bef/hwnG1682GCdYNB7Xx1AyAmqCoE6piCoYidGXSPQxmO5qF7ZQDZGbR0Bk3WUmaKKIIJ3pSBTMofb1+xuXtPv3yP4hG2qnWEG1MPeOsN6fcajx89Yrs7Y7TekGNneXHPtGuLQE/oLJAZaI5iUCDmz2R3Y7w9cXD3i/R/9kPOrR3z9/CUp9ZCSumA4fX+qQD2qnppz1vQFX/Nz9fzmYrtcQs0awh7HkabxBajTFDq+/wXjeL/BkQK+qnFt68s9Njg3q0Zz19c4FQrW4rgQaofXXJRYzfkFqCFzza2dLQiVaFLBrDztS6xBDGo/earZ+L3biafmfcCJp048deKpE0+9ezz19pqNXC3u8gTCDqbmRsL90K7JYEULyrqaP1cGuc1zrqw1Nbe1uHCIvqcx6tRRw36Ufc/qiX6OkxLuzLnkz6mCUyDnCHjLERYFqNoL5mmf896lqlW5IHQ9s6IOTKFxCgFQ7A8r6KFkoYVSSjKtEVpRoF4YS2sdVoySRMo0JXSYcsJlzcWUcp45Uev/jkJaEFPQlbNzxBAQsRAjKcN+t8W6Br9a0XYtElZsx0gf1XaN4ihijSEZdXxQNUT0yuVcCKica1UryoDDGnKK7Ha3eNdijS8kWDEsMYw9GVPCexpmdN6Va2ULualidTgMDENPDgOLRgvHqqNILXZz1pBz5PX1C+42t5rL610prjJ0C8+z9innl1cc+p7qkS5AHEdSCKSQtOlNTqqw1XGdEhKNNovSfAayCNZk2qqUlAlPztq06iiMrUOjzoQyoZzl008/4Tcvn/PVf/9LcikSzCYhYjR0HkZiSnhELety5oP33uMnn/6Er377j7x6+VucdRixxf6xFHoVwE+SQIyOtVLFmYv9YMqzvFqLv+oAl3KfjTElE6Fa5ZX8TOMY9jtuXr7g7tENnW0hLXBeizJFdH7FbBDnODu7YLU+4+bVN1Mh6O72mjD23N1ds7l5zfbmFU3T0oeR2+2ewzDy9L33efbsCcMQJ1WlqnJNo64g4ziqVV+ac1DVacMyPThSi30z6oQiZYwpiOrzUOK4i6qCskxAOY6ao6oqjs5rawxN44v6lKjAWnNmY0wlp3Ym8hriroV208MLlG6xpvzeAHZuplTIKpdjrfM95Qyl4E+VuzcdaU5b3U48deKpOg5OPHXiqRNPvZs89dbFRh2bWce3KkIFLi0Zp2tWUnmjNlSCzkix9NMwrREFf3cPuGs410xh6tpoSeQo9F3/mULlpTESTCHszAx0eTpsmY5df9SwUrXwo6hDU27r7zrncpOUwO6Hzovvgw6Mojbp2lKKh7ph7RsufMvCepbW04rFZJCU8ElYeIu1iRzidMP1OpdjEpmssitM5FKU0zYNh6QFbFpwpUVF+/0WaRyL1YL2/IKcVB3Z73aEGEp6QWkEk6tiNINQygkpEwJhWk1PHS+NI2cYh4FRRlVPSqFYpBCkGGKKhLHcQxEmRqA2gDGMw0gKEWug8774VMdyHBljtKlMSIndfk8GnC9JCMZM4GOdY+U97aJj6Hv6ftDjG9VWkRqKz+qmMQG46HjQsVMKncaxeIILTduogiZ1DuR5Itwj+6OBg7A+P+NPf/4zfvGrX/HFF19rQVUh++12y2a7hazODTlnzlZLfvrTn/L9jz5m3L7gyy8WWkxmHSZGUtb7ZQUlgAIAIQRCDCTnsEbnFSZr2oA1ZFuIswJKOXwoHuBW6guIdRjfEnPk7voVNy+/pmlaIok2NUqaRa0aIvTDgDjHarVmtVhACS9D5LC7Zdy85vXzr/jCaJfWQ78ju4bv//hPePDkMdK0uDzSeM823qmaWcZi27Y0TaPnNyZCSJOiE0LAWquEVbzi6wOD5mp72rZD3TtmgI3lvdXNQ0QmhSjGUMapAnDbtqVjrNps1vdVZMi5NNqCScHS/PA0jck555ZJnaoEIiKFrAzDME6FkvWzbOkQXBukhRhONRtv2048deKpE0+deOrEU+80T73d+rYuNmfU03Cx1CIfBZuyPsORaYAWYWFkLoxDcALOaGFaBfZqJ2YnAMvT5+UjtWeaI1WaqFv9u+ny1pcroB/tLycQOwNkLsV5tVEQ8xfU0HoJM8vsgV7BvB6UQzvWKglBYwyr4qF+7hrWrqUVS2c8araheYgmw9o4HlnP531Pwh6BRDnZ/IZNYwHVEAJN0+Cco+97UiHLnGEYeux+R7do8U3LcrUmo7mq42E/uUa40gAnpsib2+ROn+f7XlfMVZ3LKOBWRW0aJ+WeJKnhNAU7Mba4Ycw5hzEEvcXGaz5tSOouEkdS0rBfzGqNOAwjIUb6YWS325Fzqx1VrSFLYEz3XRuqbVxtPEOefaZTVAq2fl7ZG6O2bzFGiBBGfVgwjZ+UtllBKje7Xp0ygOr5G4Hvffd9/vRP/pjr568ZcimEy7OamItNnnOOD58945Mf/piz83MePHzCol0xpr6oCxnJ+je2eJPXTreZ4inuPc4U282MupWUG1PzOO29JkD14cQURSVjpIapDeNhy+2r57imZQg9q+UC5zTEGuPIELQ5Un/Y44xVEEfTHnJKjClgxsDhcGB7GNnv9/TDgaff/T7Pnj2hWSwIKeKdo+s6wjiSjcHkTB4UxE0p0HTWFGVnpDY9qkV2NZd0ths0k7ppTFWF4r0iulDcbRQjqhpqcc5OjiBa0Gin/ak7R+3eWp09jmFIpkK6+fPtlHM7XfecCKMShj48mFmJCnFy9yAlxJoJF2OMp6Z+b9lOPHXiqRNPnXjqxFPvNk/9wTQqPcgyRXOZ3lIL3xQmbQFCK5oD642qQ0116aAoRaKKyuzYodBoRBWXEggq4dE8g/N0HAUgmJUMBWspf/mWcyn/Tov8suBX9w/9XvKsNdVz5uj9NbxOntWtBqGR6u0OrTGcW8uFbbiwLZ31tMbSWA9Zu22KcUQUxH+0OOOX+x2b4t2cmYFcz+/oGkzqjQ5G53wJlSk65jwX4bldQ15ajPO03YLl4sA2jsRxVMK1lvA2q7IMJTl4uha6ah/JxoJkUgrlUCugyb07ocVjxaYtH+Ug5kROEUkjGYM16uUdUiIMPf1wKAWDI1vJrJqGxaJh3/fcbTc470kpYAykrB7rIavrSM5zwx3nHL7xiFFf6hRi6ZrJVIw20fYU5p3TIVKMxFEQp+rKvXB1ud653CsBqC4OAm3j+dOf/5Rf/vNv+MWvfsWYE8M4gIiqAUkTKZq24XtPnrF2nhgi68sr2uWa4a6fwrLkOVdyTpKYC8LIGWd0HpHA2VKoVVXR8j4x2u13LOSpNnnVWV5zoRunDjDXL75mezjglwu6psP7FoAx9IxRUyX2NzcM/ZbWaZqFtXXeNoTUMS4ifT+w23VI0/Dxpz/hwdUjchIdD0aVk5wz/X6P9R6Sgm19SFFfe/Vb73vNj86lI6pzmutaw/AVcEWYiKp+1dxVshblTsSdtbitft495VlmF5C6f1WMjsb8ETZNudHGTj7+FUWsNcQxEoN2dB3HobjtzF1jcyH7EDUsb6wt5D0rjqft29uJp048dXz/Tzx14qkTT717PPUH+2zUb+rkzMxFaTKBoZTuqNCUcLNBi84akYIFcgQIv+NzpCpTx/rUMZTPxzPlMhZSqSirAHGU/ze9P2FKcqlUwJZZAaqXevrdvU8sIddcV+r6PouSVoPQoufsRT3U19Zy7hrOfYdYozm4ZQI5KcU3Ymiy8IPFGY/cDZuxPxooKGDzbWqac+8y3uvqOqWDnmPSYx77gbvbDcY0tM5jjaXrFoSh5xCj5meW8NlYWti/ede/TYgKEloIFnQsFLUp5+IUITPZiKBgXXc2KXZFfYpKaDVlIEYtlgpDT98fiHEkxsAuBm6dY+0X9OPIoT/Q9z3eWQ69m4gG57BIUZxUPWjbZgobpqTqWE4JVxvYZAWjOj9qDrACXCm0ylnHTp5X/vUKHU/2es/qNZSUeO+9p/zsTz7ln375S7Z3G3b7Pf3QMwxjyet0PLi45P1Hj8n9yDgGmuUKv1iS7l4ipaALhBQ1+C9UC7x6nc1RfmiewEly6QJagGECfAQT0/SwdH9sqauOAQ7bO17evGbf98SQSEnv0TDuCQms9Sy8pZVMI+ro4b26kuQs5CxIp04X4TJx9uQ9nn33IxIOEjTOqTIiQrdYcNsfGPsem9ykKqrakmiaDmvVyWO/35NyxBl/1MxJw7nDMHA4HAgh4JybQsDHKRa1E2rNWQUtvJz8+FF8ySkylpSFKWUiMRFrdQipCpH6q+vv5jzYPDVTymTCqPd9DIExjDjrpnOoxXvWmmIFGrDOQVGdmrb51ow8bbqdeOrEU0effOKpE0+deOod5Km3p1FJDVHPg3Vy0NAXJpeORC5+3abY6OlXVYRgBsfjVW/dCgzf8wTXIOgRjOV5J1mKA8bk9MFENlUJygWAtVhIQcOKTEySCyDrpH0TvOt3WnaYi0qgxXmqfnmgESUqXxSylRGWxrDyjoV3pAJodSUstnTdLLZ9D13LE9/yWb8jlDPJQrEtnMPh6Wj1W8NqNW+uaTz9MJZOtxp+DIcDw25LszrDOE/TLugWAzEEhr4npoyzHmOGe3l+07WXo4t9dPEVyDOSNadUQTkquTtXmiPpV64lmllIMYNEVZWkPhTohEI0j3fo94RwIMRRCVZUWdnu9twu9hzGsag+qRRr6ZhxjQfrqKF7AN80eN+U5jMBYiy5zUZD5eUaagRYc2RzCXUygLcJY909ZWBSk3JWDK2XJXNvbJKBmHHG8uknP+TpXzzi82++Zn/Y0fc9Y9AGSRerNX/y0Q/43off4/zyovheC8YZtADSTPcbUbtAdYFJGOWaYumoYzrmqNfD6aysuc7WllBo0utljdGxX4oHiQkxBmO0m6r3LS4JYbPldrPl+vo1m9sNu/2O7WHDYRhpm5ZnT57y3pMn+EWrD2FS84Y1TG0lY5ynO1/z6NmHOLdgiNqBtvGew0GtC88uLogx8vr1a3KOR4paVrcSUdvA2sipHw9KvEaJyBuv5ybzw8AcJjYl/OyOFEzR5kRGw8rOuhJan9W/FHVMhxB0LNp5zNy3B8xTzm31Xa+WgtUuMBeVqu+H6fs6CcaSh1tVcKmknTPERNOURlD35uJpO95OPHXiKY6uxImnTjx14ql3j6feutgYUy5KzrwYtkdzXaYvUxSUuYuqE8HkAtZGcBTP7zIp7iszZX8yhwbru/I0U+rrBYTLhai2flXbMQW4I0VNynUFqwPeMmlAOhmymVaKdZt1ovkVVY2y5vVCye8VnDF4MXiERqC1wsIaFs7TlKZAmu9ryqRP2JywWY+xwXBhLLb4k+tnmYks52vMfWVHdJVpUsJ5r4VuIZRmKxZDot9taa3DL5f4pmW1PgMgpRtCjLqiNpYQ4qzk3cPuSq31p7IiFqZjpBxnzAmS5ikiJe81VwCsjwF15xWYDFIaM4VxJISekMZCepqfmcWwHwOvNhs2+x3jMJCj5rQi2rxpsVwQgWEYi9IgeOdxoqCcQgFwSog5M+UW5pInSUZzZEWgF3LMGGvxRpUXU8OP9e/uDZj7KC6ZIq1FHjy65Oc//wl/+Xd/x3a/YxgPjCGwaFp+/Ecf8f/8m3/LB+99QDTCy5cvsDaTxoEUA1n8TCLMSRjW6tgjZYacJ0vDXFTNqlg49cwhS57yf0Uo5FS7niaiCUhxY3FNS7tYYqyj6xZ03nO+aLluX3J3Z9nsDdvdjpTAlzmhgqC6jAjCmBIpBgRDdobF2Rnt6gzftDiYisg0rAzWeVYXl+wPB3a316rANB6YC/68c3ivXV6bsSk53Pp5MUTESCnCK11W61XL4L0j505ncVFmaq5sTlkdWLKmHtRQ9PRgU8YZcC+vtX6WKUSiBJwnRamqTFXBS2kmAIOZZkPKSZ1/jJkcc4y1uKydWrX4035LBT9t83biqfmVE0+deOrEUyeeehd56g+kUdVlsY7TN6JZE8gZqZezrEiRqYFQneoigpmcHo6lnzxP9qLeTNE+jvCkbBPIi9z7/b2cRFTZiSWErcc4u3UIb1t/3T+3+XPlKKRduq2KwRcbxOMiQ1/CTt457aaZ53CglGPQZkqUFfGsbDENv5k457xfCoFVFq0rXkvTNHPYkkSOgZC0KY/xHvEWMVaPq2kY+p4wBi0wEg0DVqjVf+Te/f5WmByoTWOmSZxz2Y85gpyju5ZVDVOLN1UTM+rQEMNYchQtxmZt6JOrCpDY7ra8en3N3WbLwwdRXTyGkWEYWK5X+Bp6NSVDO2VyjIQYNN+37CtntaGc8qrLCj5KLN7tUpwHI7axmMm3GkyeLVeObgE1N/b4EmnUPNFYx6c//oRnjx7yxef/i1Ca+Dx9+JCf/vgTnl49xIllv92yH0esJA6HvYbYRYvAUqxNeUDdNGdgzwVkasOjmfiFmodcB5T6zBcyE6Pz0UJGiw7FGJzzNE2L9w1tk2lbx9mq4XzdcXO9ZLvdcSgh665d0nqnd7GOz8nfOzHkhOsWLFZrfNPim4ZcQq+q7ARi1mLJpmm4vLxk7Pf0fY87KIGlGGmagF0bmqal6zq6RUeMgWGyHRw05aLkjTrnIOfS4KiO7foAWIsN9RVblKAQwlT4VsdxzhTQLzB1ZE14nItfFaPp3h+puqoezUV5CHpP86jpKs5NnY4rARijTbScdzSNFiHGooSetm9vJ546/twTT715H048deKpE0/9n+ept1vfVgyXgq5HE/g+gL4xhguAywTi+Ug1YNrXvFcdWLVZ0TwMeXNu6AUuf1NXXceLqUoE0xpejjNsmUAU6spVyv/rxKiB7fru8l5mAqjdV51oB1pfvrfG4IyhMQ3eOJyx4BJhTJALzclsS2hEGFJkJKsdnwgiRYspoMOkVNQbkt84XlU6RFQlGcMAqVryaZdTu9+wsmusMfi2ox1HckoMwwBZB00snT/JbxJnJr/xyreuWQHFnBIxiq6iKeFRXQMfEex8T6pypF03G7rlgpQS/WEPqdciOUCMMISR569e8s2rFzx7+hQRuL290yKttqNdLfQaeFfcQSKhWNCpIjQXok3Dpah2FYhzAXGyIBH6fqDpWpyo77rm2M75sFKcGsrlmO+PnqRel5R479EjfvbJJ/zlf/9vhBR4fPWIn33yx3z04XfIIXB3c8027BhTwhgtPhRqsZ06tKSUNOSZNBVE3UpqLvL8ufcfZmr+p/5UnS6Qkm5RQGoKBRf1qYZfRQTnha51dK3HeUPXtoxDKKCjwE95QCKrapTL5yQRVss1bbdCoBS/6f2IIajjRnnIqTmx55dXvHr5ksPuoJ+Tb/Hes9/tOFsfWC7XLJcLnHXEkMiSSMYSivInMis7WnynzjHfyo8vylE9rmNmrr/TnNhyTrZe31QK/vQBqgL6VGAq9eFmBnpgCpXXXJ1aBKnE49D6Sd1PDcWr979XR57TYuP3byeemvZ74qnj7cRTJ5468dS7wlNvX2xMW/69P+U3v5lA8yiPs7yhgj3ArPcc703mN9yjiTePJk/E8PustvKE7jI5OOgnyD0QP1Y0pu+rolDUGaEU3knxTJdZ9XGgTiaiIWhnHY11k9e5mBKKOjr5XMAr5kwfA30MpZvmUa6xyAQOqrYdkajc17yq57gxgjWlSEssRjI5Bg7bLV3b4roO17Q0XSCXArUwjkVl0eIqOfqcY1Xkd13gSkr1GFMNUYoqQrlODJXGJvacR0i1AUwsV0suL68YxsjrV89JSVWSnLLevwSvbq754puv+cEffR/vLPv9DhFDe3vHSqpCoO4bcbKhK4VTsdJ6vWa53Oc83aeYEtbYslJvEEGvjzPTJKuTsg4vyfPknsj/+IKlyLJp+Mknn3C+XrLZH/j0o4/50z/+CZfLNYdDz2a35eZwq44sXkphpJJgBaBJJUrqhkIF5AI2Yz4CYVDezPoANYevS3dfA1mEUArwUiyNtLI2hIolD9QYgzMNxnmsCDEELMLh0GuTocNY8qG1CRmp2DWWvG/bdizOzjGuYQyRGNWLmwzjODIM45SvW23zVutzUspsbu8Y9gP7w46cE3fXN6xX11xePqBbLoujS0kbmVQcNEyckub5Kghw7F9e1ayJ0LL+bMRMPu8xRapzyESCR/ycUpp80GthH1DyY9ME6LUA1BjFBVF7o3v3s+az6xhUJa+6juScJ0CP7n8Tqv9/vZ146sRT377AJ5468dSJp/7P89RbGayuhurYn8GIaQLPbz5SZ6piNP2q/E+O1+tHwHo06KWKIke+4hXqpagUqhrJ0aQ52l/O94C9gn09XFMUn3peMh/km1x1/+TKZ2nYnclC0BVgr11frbHYuios1yDnqmSpnWASNEyYI7swsBt6kswqF5QVprxxjY+u1zGM12KenDVUHWIEkgJMToxjz36/Vcs22+CbFsIwqyllYh1d0SMcOjqH6Ur87gsl5XhzzsQUVE0xtoBlHTPHAB6nfQlC23a0nWPoD4TQk+JIikEVHGO43dzx5ddfsd1vOD9bTWrTZrMlSqJp/OTckKMCeSoFd5OiwhxKNdN4tZNq5JxjsVjQdA25AECKqYheMn1BneRpApB630SOVJyccMbx3e98yI9/9CNevnzN//2Tn/Hh4yfEYeRut+Vuc8dh2LFYrwlBlSANSabJStCYEswv+8xkQpyb9kBRCUUwOWNrsyAREHVhSdZqvnSZnwrYOm5sLVyMkRDG8j71PRcyzrasVmfknEhkxhhJonPNo8pwjFXF0vvdrVe0ixUxMTUFygUcxzEUBwwdVzUfu207mocebxyvX7xkFwP9OGg6xaDHfnt3y+6wV8cW5/GNZ7las1wup1xWW/zH9f5od9/ZwzxMhJwL4YQci4WfTNekbkYMWpMncw5tznPov8yX2hMAuGdPWO0cVTHSOVbzsp3TYkcx1SlHpoZNqhKebG//0HbiKe798sRTJ5468dSJp941nvoDi415RSxFPTmGx2MgUYC8/1Wna90XgLnn45HfeFcFiAmejrB1RmINZdfCLCnArcpUDV/ngkqzOmSKTdzRRx8dQT3GXIC95gHXcDSliNAArgD4lPOLFh1KTpiJHPRmhFrFn0sYzloNL+ZIynATRm7CoE4NaS62g6zHC2XypvsHaWYyyqDXICasM1hnGccA1PCpoT8c8P5Au7R465BmQQg6sLe78d6gLbu7J3y8qetRlJkKXjO567tTTkU1s0pgZWdVo0s5og2RdPXc9wd22x2r8yvOLy4ZhwNxDETL1JmyH/Z8+c2X3N5tePb4yTTOhn4g5MCi62jbprgiqLpSySmV+/n/sfdmT7YkyXnfzyMi85za7u19mxnMDAYLxSHBzYwAKAnUox70oH9XNGoxmSQjRNEEEABBDGa6e7be+y51q845mRkRrgf3iMy63dOAntRmOjlWPbdO5cklFv8iPnf/vAFliMEnOs60WRzluNuxu9gTY2TOE7VU5iZ1p1bVNqRoCiTdqK7t0ptM/T/+3m+8+Qb/9b/6L/nFBz/nB+99F3Lm9vY5T26fMx0nxjGwHyP397cseSaEZlAyiBc4UtcwF0seVC8o1RZOLQmuMUmqrfDPOhtDiP5IKxhVVYK7iVHTwM/zTJSIxGQKOQhp2DMMJ0I4PphjphaitAJXIsK433Pz+DUkDsbciRmk5GAhIqQY0TRQiqKDte2QBlLcEVQ43h+4Ow6MzsCM487cuhTuj3ccX9wjIgzjwPXNK7z51pvEOCAxmCqHA8YqH7m6k83NnMhLdVe7LXzQxjrSY2tRV/2otSuqNINbqxIjD9jKEGJnfFZpRB/31SLeQwqm3z6MpDQYsxdsTFo8b6QUt7mqPRThfHz1OOPUGae2PdXHhT/HGafOOHXGqf/vcerv9M2vbE5jA9aJ2Kat4tVWdTVwYElUgkWXrgofm6HeBmWfAm7C5aH5ds0L1IEj+OlFTXZPEfeubU0ynkvvu7lg7qdgW7YOF+qCuQAAIABJREFUOmXDJrXPe6SmCFX9WXzzbSyRvWPAQC4ELwQlyuBsjolciCe1CYvvZK9Ypd5QeKLwDAOZ5BM/iJpBRwnqHM4GQLfMjYC7Mk02sWQlDZFQKrVmQkyoRvJSmA73phsdB0LYMe4q85JJw8JcrZDMFjQtfHR1pXVjsQGPHgeLdvWGdp2qpY8VvA97NUotDhwmo7jMR17c3SLjFRf7C65vXmGeFnKpvb0qyqdffMZnT57w29/7PqN47GMUypwhVcIQOrAKm0Q77xPEqmvaTj0SQotHDKYisRsJKXStc0phzoXoi4bseusSg4c3iI+btZ1WxJM+dq4vL/iX/+SfcxNHlvnIZ8+f8uL2BcdlQjAFi1oPHI6fg1RCHAhVyUU74xVocZ4mPhFCJEWl5MbSSE/eqtKMtjqrpGtymTqsaQOhdSZHgFrJy0yMgVrNxS8hoLVQFnf154rUSpQBi61VSwos1sa7qxt2F4+pFWqeLGaWgWFIpJDc6LkySxqswFWprhZkhZBGHwdNwaMloU1lYr/fs9wdKLlQQ2CeT0ynI7sLDz3IUBZjgywxzqQWVS3xzvTjm6EHkSYxqBs7hYEOUFrMsJr6C93e4EySWb1WxKkBxxq2YSyUYLHTTY+9MV6NBW5Ag0LNprmfl8zxeOR8/ObjjFNnnDrj1Bmnzjj17cWp/xebDetr2yk2Q70eW+5HNl9QaRxCMwSblDF5+N31Wi3O8qGLu/2tuRjXZ1yfjc0zaPuWQqmlF/TprnVtht4abXPJDbOkIP5EjjDNcEaf0MHPS+5i0nYx/8m1MJeFIYb1uzGSgdu8sKAWwxoCtWjf8QeXOetu0dZWjb1TYw+2YQSqFS1WROl0OlksXxxQVabpRLyPXN08Ig0jIiN5t3cXbCbnee0QxA2tu0bdYPVmaf/RFWjbeFGlJxblXKyNwlpAxmJ3tS8KwPrndDwS727ZDa9wfX3DdDxymk+Ump05gWfPnvLRRx9x/7u/TwqBfS3sxwvAlCKA1ZC5YoO4qz+FgLgKxJASIUZEmh519AqggVKySeJJgxh7z1orJRdKzN4vYX3nzVKkvVQHOSCGxLvvfYdPf/1rPvibn5BPk7VhiqQhIFF5cfvU+sjjK0MMJE3dnSousl9cvi4EAQ0UKf3eCl3xoo2LJS9re5RC0Zakthr9ZkQsLjWjEojLjLrxFwnkZWY+HlnmxRLBFCTaHAqCsaFVkDRyefOIECPTNCMKYTDmSquSBmN0JKwJpk2lptYCKohWHr32Khc3V+Cxqykljqcj82EmzzMC7Pd7Lh/dcHV9Y0XIirmZq7vZ23ua+z30f8dg1WTbXGyhCfNsLGGI0dz3npTXjHID1DYvrLsbKISelLdlU7cFltKYMHC1+NjBQyrazCqlWH2BEDmdTkzTiePhyPF04nz85uOMU2ecOuMUZ5w649S3Fqe+WfpWX/5daW5bGzGd7nl4vLxxfvAHZXUaaz9LAa+L0jt2exVdpxMtrnb7gD4Emz1+eHOB2mJD+zO0y20mX3uO9lqb09YqtPZJc/iFdr5fwNRNtnGWilARf/5aCylZ5v49yl2ZvUiS3bDxQg/cvRuj8KA1RVaWpl1BLZ6xF1GaZrSavnMulcPhnmFnutAhBna7nSUalYVDzT32Up1lMbej9ne0t9iAqFiiGJ4cJ3FtjGYkjOGwhLyW0NSMRtMTN2MzczrccdoNPLq54ebmEffHA8syA2ZUj9ORX3/6EU9vn3Ox2zFPExeXl+y6AS94Bxhj52oJza0cUyRGq+TZ4kVbApXpTLsOvFpcZtgLKsbApBRcn92SG62gkb9DW6x8ZdI0Vg0ury55/Pgxh8OBOmcuLi+Iw8AwBqbpjhf3txbGUCqqCzEZ4FhBMI+91HURo3jSJQ1oQ3dRiy8mcsm9nYMI1dnFnBsQ2ThtrEbOizG/IbJki0W2+FIoeXGd+aXHnLY2F+9vJDBeWgzsUpQlF4sdz/b3eZ6svXVdmG3dt5LMWO6GyLDfIbUgVTkej9zf33N3d8ezJ8+5ffIFlMy73/kur772OvuLCw7HI3d3d7z++usMaUQ4Mc+zJ95lag39fkGCxbyWFfC6tn2txJRMkjIXd++rxRPjY7guzoLBMs8gwjgOvhAwltZYuuKygpHB/94WXjHaGEShqMVsT9PE6XQiAMejVSFeloXz8ZuPM06tp51x6oxTZ5w649S3Eae+uYL41pBsjq+z25sx626vlwytPDyrGcPudHUDu3WFft3dRTefv3T9pmrw8tENsj50v65JeH5eM+p+XXPN2q9RNnG1tAQ812rHFD4CQiCQJHrMr5KCMLq6gF22WvxljMy1cqcLRTBX8Jaxam2xRQhvp/a5JfmFviO1t1IvAJTNTZYqi7snESvqcri/I7rGehoGdnVvEzTPTHWixyO7m1xZf4Wtsoo9V2VtG1jd5eKApo3JcmNmbd1kFYMXiQKthWU6cnefGPcD+6tLHj96hdPxQKmLu2szH336EV8++5I3Xn3MdDpwuB8o5YIYDJRCsOqraRgs5tUnS0iBEIUgkRCt/bYJoqVkm/TLQnJGYxhHJFghmxADyaumWmxzYw9WpYm++OgLGZf/UyGmgddef42rqyvulueEYIoOyMzxdEvOEzGJGwlPkPMKn83lXmruaF6bqgbGggBEZ2lb0pmqmsKEBGPHXOd9qbUzS2CxnsWN1hCSGe15ppRlVbxww93d2hiIUCu6WGVbGa+4evQKIe05zpbYt844QWQCCQQxBtCSNEtnqySsMesKhGoVled55v7+jqdfPuV4f+D+7p4ohf3lBVfX19SqfPHF59wdDrz19ttcX18xDiP39/dM04mcXdWjAWBQ1z4vZkg3C89mw2xMVte793HsLKSBben9YOOnMM+zza3B3N1NdQRg0OTx6uucLqX4Isrc16fTidPxSC2twiwPx9X5+MpxxqkzTp1x6oxTZ5z6duPU30tPcTtlO1uzjtP13y8xLQ+/rw++sf7W2IX1ShtOp5/ZPlO+cvmN0bE/bp/XGC5/ZgFzKdpfGuPTuChxi122TJT0t+7nB1nVPsCVQxwQGmMEaioEIbAbBqwfzf3WDNxcM3fFjPiDd5X+QH1Hv76P/atrTEM3IlsUaJ/tdjtyOXX3vAKn6cRwOhAub1y+bE9eFkqeTIqvFNYeEkwxxNrmQdtvUUcxPe/N0xrB0dgA7dfsE8TbSkK7sFLKzPF4z4u7kVdf3XN985jnt89Y8omC6Yd//uQLPv3iM377e99lmgK8EKZ58d24kIYBiUJMsVefDTEQUkNi8fvb89RqsbnLYglnQSsahCVnq5LpjMJ2gbF98dJURMRVPrx/pMn0FasTLBK4uXnEa6+9xv3zW+uPWpmmO3I5IUFtNIlF7prb0wZ0r0C8oUNbnHIIYu5eb9M2mbpUnkgH0CABiaAaWXK2RUEz5NUkDGMdrUCTZmpeXMfbwgoaA7iCREVrQZcFDYn9ox37m0cUArksruRhCyKTyBdK8YWRu4Wb9F6tlrhaamWaTNmjVuuT2xcv+OzzzzgcD6SYSEHIeaZoQdLA3ZMv+fDDnzPud6DKOI6Mw9hd8rWqvxNYHLAxnc2VXEt7RzPyW5am5Jb8WPt8bCxXTyJ0ZtKA8ETOS1caCU15xtmomkGkUkIhlmgLh2qs3Ol08mJU2eLjXS1ku1A7H19/nHHqjFNnnDrj1Bmnvp049c1qVA+Ganecrn+VNRmuT9xGsfA1hnpzLfu9DX4feD7Zm1Fe419rv35vDR4SKdCYCrtuEKFgbtHgO/xIM8A4o7U+VzfiWxTof9NmUwkdqFZwacxIFHvWppagtSIpWOXUASC4TJ5J6U1l4S7PLFqJ6obXr1elvaM86IjV/Q2pMVHrvO0g23bgKSV2u5HpNNmEFaBWTscj47gjeVzgbtxTirnCynRaZdUEM+Avma6V6POJ4PfW1h4bA1OrfvX7/l2toevLmxJHIS8zx8M9+/0lF7sLrq+uOR5foIuiAe4Pd/zq419z/Ic/ZowDejxSvTjNNFs8b62WWDWkBO4C7BPB27oiIBZ/XEq2mNGml65CyYVZFgPTVgCnGjg0wNJ2LW+vKubyrlK9mJJ4wR0h68IwDtw8fkwcRrIqn/z6lwy7mavLljxaQaIlcgXpDAUSCI2twRkNN6Yhhg7i2z4wpsjathlKCYKoGYeqFtvb42bBXdcLBItHba7dhtdaG1g1FtQSRCsgKbG/vkGGgbwYu1RyptZMZEBqZVkyQWZny0ZLZixWIRhVdMkcD0eePH3CixcvOM0nTqcj8zxTa2U3jgxxZLq44HAyDfR5nvnk00/49JOP+a0f/MCS+GplN+7Y73fM80ze6L23cRlTInnbLUv2mPDSXe8t/KMVTWpa5IopkmzBbxxHQgjkksnLquyTklVDDmEtgmSVgi1mfSmF0+nkLv/KMs/My4IAaRxW13k8y9/+puOMU2ecOuPUGafOOPXtxqm/w7Ox7kzXT3zXL/7vZrhwDeg1LNC+JeLSb8HZDfubvHTd9fprBU0ruLNlJbQb6ApE1CUFxSpsNjwgdO1uBbzuIihENSnA+gAoXJbNz293bUyQ+AM3ZQ9QAwhVrNiOXT+FCNrccV4IqIq7OU2EcJnMuM+18LzMnKorNPjz2mGuRAn2Y+5JiM46NDWGGMRd8oDahLL4TBBR/6yyTwmNtnu3eD+Tybt78YIoVmo+jSNjvWLJlblkc4NKUxxYQboDL6vreYum4mf1Bt8wRD1BEOnsR6ZY5dMO2CZNt5xOHO5u2cXA5eUF47DrhYNKXvj5Lz/k2YtbHl3coLUVFlqY5xMSoJ68EBTCOFgssIqi0qQa3YUrxtoU17EWgVKNRRA1AMsihCGaEVd3fYfYq3aK4rGVJk1XfcyGYPKOQcWZo4WQEq+9/Rbjh7/g/Z//jD/9s/+Tf/ZPf8T11RvQ1GASQLRxKm0hAuoFqEQCSzUmJkozrK0okClypGjSdIgZmSb71xYJZpAihaaE0dRvLOEyZ5MULFnJufb+aS5v9f6TqkQRdIiEq0uG60fkKpRlgVpQtaTJisBizEtyhQtxNm2aJhtDOTMfT7x4cceXT59yfzwwLxOlLux3ex5dXbHbjeQCV6+8ykV5xNXlDS9un/H+z39GXiZ2MbLMM6d5JsREGkcur69cxrNCbqPUki2jhy9IiLaAcePcAD9FK55VqrvOXYN9BYOWfOlxyTGyT6mHcIzjaEmewaqsxrBq5aMYwCyLJdt5/6UYGXejF+wKznSdPRu/+Tjj1Bmnzjh1xqkzTn2bcervEUalW+rnpWM1xY03suZZ4zhXDe52geokkfTvf909bfe6uoO0M0l2X286GsfUWR1DFZpLTP3Xqk1twQxic0HL5v7q79oZJFajtF6/fdiYmZWhMpBqE9gMUy42iM1NGDigRIWpFj5dTiy1MobgxsUnmitPbJUx8PsHd6cJ0XTl8AlVTZ6vtVbbFWsfTIMzNBXUBunxcGQcRnOThsg47iklM81Hcp79/P7CNgweYqqxW9qSl1q/tu/Qn+VlVZZmyHG2pSkdqL9Pzgunw4HDMLAbd+x2e6b5aKxMED79/FM++ewTvvfOd4gpARbbaNVaje3JS2ZIA3MyI1ExBiwll7ZLiapN4rF6vwVPtDNQz1jMZkJh8GfMFQ1mwBs70Nih6nGb7VohB3b7HeN+RxWIaeC3fvRD/uzP/yP/25/+Oz78+EN+7/ff9fjVSESZa2Hyydzar4/7EE1DG2NFVNRkt90gLstCCJGwD6vMpmLxnhhzlEsrmGRjUtUY1doqqoa1mrAxJzZeSs7kZela/E3hRsQSHC9vHpOGHcvirEt1ze1g8c55aYsI698sC7VUTtNEzpn5dGI5mat2t9+xu9yj3q/GFEWm04SgvP7Gmz0c4YMP3+eXv/olr17dOENZmaaJUpxhGgZ2+x0o5Hlh8mS8NmYbG6OqrrEeVyM+pC4r2Fz8Te0FNSWW5rJv8d5xGBh87g4p+fi0o401rVYg6ng8Mp1OfQExDoMnY9qPetLrmEbOxzcdZ5w649QZp844dcapbytOfbMaFcrLU3JlWX7jl1Zj16/RDJGbRFn/5q23+f+vMari95T11HaRJk3X76cbpkrNLLQCSg/jGRs0SH/B1c6s79fiDeXBcwoS1rhYQTu75E1gEmQYeJScUSDFAYBBA1NWfrGcOAkEhab03SZXeyfV2g0NtNjY0JUlWvKPtfsqcbc9SimgmEFbZlpBF8USfcZxRxp2XfVjt9uz5IzWiaJmxHqftf5tYCLGQDVQ7r3c2tkBaWvE2znNBdhiDIPLtrX7LPPC4XBAJDKMXlRGKyEq96cDP/3wff6L3/8xadgx+DhbcuZwOJjL/WJHzpnF3X0qZmzL4DGdVVGPR20TuI9tNWWYmp0hcXCpQWGwxEnFK7c646I4o+IgH/emhz7sRsKYGmfK9WuPuH71MV8+fcbd4cRpns2VHgWpkfl0NLWNjnMW0xpwuUNdE/2qu3abEe+KHKVg2vkGNi1Ws9Zqbng1DW3ZjPtabawShFBsbmlt/WHzaKvu0RZqS82EtGd/9YiqkOeZUqrH4hrDlesK5tYf5sJflsx0OjEvC9PxSJ4X4jhwcXXJbr9nGHfM82KKG/NCKbPx08kYsWdPn/KTn/wth/sD33nrbVNhcSZmnq3f9/s9+92eFBJll+HunsPhQM7Z5RutAu6yLCY9mZIvAq0dWwGjUkuXc8TtSvWKsn2Z6guTliyJ0pnFnJfOMC3zwjxPvT3HcXS9+HXFbJKc+Wvn9PlYjzNOnXHqjFNnnIIzTn2bcervlbPRHlL9wUPb8fed/2ZqSiNsVhdWY1jQVd3jZaZIWNUjrOQ9bnw3TEM35BuXp/9NdfuSqxtO/NnaMwVZQUiN8qDpEFf7cCUzHETUnylKkxLsSOHJdqFXYBTxpDzfMdZSqbXYNWJiCJEokSe58lnNnKIwFm+vql3VYVUZcNZL13dqxt7cVkpjxxB6eEB7J6AnHcUUiSEx5xlJpqaQ88zxdOA6DeBFai4uriyWsRjD1O7H5r23I6RV/lwNfWM5WiVcBS/Os+3vZgTUlQ6kM2QCYuoP0zST0snfKXibW/v87IP3+eUnv+byR1ckRlfhaBrWgd1u6MYtiDirY+orquaSFkd9CbK5v/YCTX2cZUFL9ee16q9ptFhTU2ow2T8kMO52DKMZ8DgkSBFxwX+t5h7/0Y9+xLvvvccnzz43xszVLRoDI278GotjMbJCLi55KGvbG+Ox+d1ZmeLfs4Q2V6IJq2yehOrzwvvEXdhlyeauJ1lssPjiIgSvNlxcUtFmUlEY9lfE/SW5WPGm5u4HMQ1wNSBp/VFrpVRj19SNnkiwGFwRQooMu5H9xSXDUJhOEzkrhICWyjzNlJz54ssv+fjjj4lj5ObmxlReoktESuzJfRYXPZCX7AY+cTodPeShdgBb/PlsHNsYKGP2+bbaI8EKKBUH0R5LXE15ZSw9KMaLMtUe89uAbFlmf7ZEjE3WcF1obrXlz8dvPs44dcapM06dceqMU99unPo7PBtsjO7a2/LSTzt3naPrN9VZGbeX8IBxkm6czFBs7tXPWH+3XfvDc7T/2D1btUwzwOvzdlcbLzfKw7dsxZ1Etvcy4Aoh9NhYUWywSCC5YW6JOt0lHwKYzLVl66fIYcl8Vk58UAq3udqzSmu8dSe+vpO/+Ra0fOfdDfjmJ6ytiTR8xYoRSbHd8FIzRTMp2G55Op0Yhj0XMRFCZL+7JF/YRLXBt/RWXl3NTR7QDNe2PQ03tRsi1bWEvTFeHkfZ2jo0Y1UeKCKA6T9Pp4kYW/ymjaWqyidffMp/+PP/m/3ugh+99z1Gr7Y6jiO7YUdKA9ndkClGQnJZwWC9WGuloF5Eyw24N19Vl+sTC7eoWJGhqkpdCirK9XBtBZrUEs/U4yh3o8cxugGxB25jTKFUvv+D7/PHf/hH/OSDv6XmtbiTiJCGZPf1RUEu6zgX6NJ+IpDiOkNiahVPvT+6jGObc9J1tLurGKhafIxb2+RayUtlCFicsqyhJ+19GiMlKSBpYH/zmCKJZZktea1m72OXjNT1fRBjTJZsSWoiMAwD6caKHBGEOAw9wTHtBlSEeZ6RYExwXhaWRfnyyy+5v7/nzUdv8tprr/Po5sarnY69Aix4xdSYCCJcXl7amEjRZfss/lpCoC4LU84PGFKtihYz9PiCyhZNwrYokjhghRCYHLBExAso9Qng9/M4alFC8AVbs1FiOvp+u/4O5+PrjzNO9U/POHXGqTNOtblzxqlvFU79nTkbZlxt+Kjrgze9akt+c+Prk7LteNtuMrQ/0s7ZuLxFNlrY6wCxRpLN4FtZqHahdg1LqHl4jYBQ/DpVax/8vfFZB2QzlqtbshnStQ3MVq3vk4KssoIhmCEXk3SrWBpircqYzOVKGigx8rxk/ub0gg8ofFSVqVR20LL5ekM2sIHGWom33WqUdR05bjTp7fYwUW49arWY02EYKIvJtQWxyp+Hwx0xRvYXl8Ya7a8s9jFnlzsr/X7tqitcrCDan1q1jxsz/Gz+bi21trOzI87IhLAOWtXKvMwkbcWNohkIlDkv/MV/+itqqcg//0N++7d/m30pXF1dm4sRS/IKg1ffTBtFEXdJIyvbYpPIJqZpW7dYR4sTba7EZV6s0u6YuLy65uLqylzfXkipaeyLx0iKCuh6b1S5ur7kX//Jn/Bv/+d/awoWWqGCpMg4ugZ7tTjUECP4M7QErtmLYGlM3T0eXVYQMPdwMyIbRgKhu3CjF2JafL2zNfhaba400EMrS20slK6AhzBeXLG7uiFXWJbSmUxjglx+MG/DK6wwUMkG3KqQhoGL6ytiiFTsPiDkmklxYNwNDGP0SrBmCOd54fb2lmVZ2O1GHr/yCtePHq169Y15FSwBUgJhGLi6umLc7bi8vGJZZqbJiiotS+Z4PLqUYe1jIteKFntm06KvZPW42SFZW5fSGaMQDHBqKf2cIMHBWbq1aVKaNpd90VWb/VvneU+MPB+/8Tjj1Bmnzjh1xqkzTn17ceobNxtBrBKhYLGaB1GyKgm8sqJdIAlEUSyX3+IJJfR/tWm97jo9QUzZZMnjA0lYk7tUujWVDX9ipnI1GHizqK5Gvblr21bbxlvw4iuWopYQojNLIrapV9QlrsXByia6+rtYbJ+4W1N7B4wxUTWz1EyuSsGKyAQJHET49enI+4db/ub0gqfXFywxskMZgNJBr7WSWQGLr7V3FNn8jeb+rf1vTd2Dqkhs2sqeiCdrVduq1RgDLA5RkgKVeT5xOCTSkBiHHbtxZBkvOCWTO8ue2LetxrsCa+unLWQ0Q05njh4wX+132lddo7kxEaI0Q18r5NK+lxAWhiGy2+05nO7587/6j4SqaBLee/NtlmlGauXq8orr6xuGIRlh4wuLWtxgAjKIaUGKK7/U6gls2Q2o9HjmFkOqqoQ8c3Gx5+LyinSxgxS9HbyZWoxsNa1s66foLxuIIfLjH/+YP/jxP+LZ019gGi2FRGSICUqlYlVVhxipeIXPFNkPA4LpdzcGbwXTdbEirC7l0MZEVWc+xGNhKzkYOOVcTVkGsYJfYoamubR9NWNufEzdpIqwv3pMiANlziYh6KyP9d0qB9r0wfNiRZTMpW/HvGRiyoQLA99aDUijKuIgnAYbn0sphBDJuXI8WjLm5cWFaZd7smkQl370RUH0+IAQI1EEcWYRLpnmqbut9xd7Tkcz6tM0Mc0WO07JDizWhQHTvG+gX4otLNpCAPDQjkrVYvKOwRJykdjDIZo9aSEHMSZzUUdTiEE9lv0By30+tscZp844dcapM06dcerbjVN/h2cjEDCjPSM8qYVDLV1azzobRoEhRCa15J4xBi4x161iihoCqAjFh6slrAVnaGxn3N09NFZiNdsrz7OyRm3OmPkLNtmq7eaMXal0hkvMbRcQooopJQhIq7IJHstodyioG3UHEN9JBwStUETJap0UgxCDUHMlqOuYl8o9mc+We96/u+U/vnjOr5aZabfjWkfGas9X2tvKGscoYmxU7LGR7nQWdYNs7tmAGuHhfqzGvrSGUa0eW9j3qDS98d1uTy4HqlemtKI9B06nZBJoMXJxeclSFhuss0n2faUfOnPA+v8P2KH2p+Af1f7dHu+L9BjB9Q20TwoEalEM1iIx7bi+vmYYRu4P91xeXfLRl5/xv//7f8ef/OG/4uLdPafTRAiBq4sLwm5w44bHJrtKg6hNpmjPVrWSS6UsmVqUGJWSbQzlZfHiQi4VqMbiSIge6yr9vTXa51KBbGyCiA8MQu/PR6895o//+I/4v/70BESKLoRaiESLD865q05ICOQYCCkRJbDTHXGxcdMrpnpymw3j9X/tMH3ulswVKNXCBBTpoQ61qrOgsSeAKmqFv2JAh2jGd1E0F0LaM1w+svjXeUZrNi33+pDFjWFwg1QpuRUncoDOmXKaOB5P7K8uEFblmHHcURzMQkzs9peUpSAKJRfu7+8IAW5ubkjjQK6VWMz1vht3xhbF5OsiA6AhRi9qZNr0w+ixzcPAxeUFOVvbH45Hnt8+py4LukxWZdXjZqVWlpJ7Yhxol7y0xYur3hRbTKlamMcwjsQ0WH8vS3c9tyTAcRwtlEUt0S+5QkhToTkfX3ecceqMU2ecOuPUGae+zTj1jZsNcz+bu22mcodyaC5HNeYnKCQF0cpTKTzVwvOSeWsYeS0NPIqJMURz8antEFUMBEbvZ90Y8h5rKfQB2KZ22zR112zfxRoHVaiWOON/t3Ltq9kobsBiCIRanJlaLZCZJiuiUxub4UWVqmtEtyOjXRc9iiXlFRGSCGNI3JXMLw7P+Q8vvuSj44EvcmGKiVHMnR2DUnxghU64SC/AYixLu9t6X3t/aG7+Na53dS2K9pNs8LagutaiWolpYBwG5nmiqn2vlMzDaIMDAAAgAElEQVTx/sBu3JvyR4pcXFyQy0zV3BOSvu54WcljfQfvQ/HmEwNuRTfPvp5Pras7zu1iq6DZ5B+vrx/xyiuvcHd/4PLimt3+khf3B/72px/w5uM3ee+t7zLsdlaIZppIwwAxuAEpPTkKQJtUS0poNfm6vCzuYrbJqtWSAJtaiiX4DdSqLPNMzAOqsa1svEGAENEAms2wxVyQFNpwI8XEH/zBP+Hzjz9ACLYQKRXR0t2SgiCjkIYRGQZjIbJV2ZWUaDKRPexiw841Fq7FZ27HUq21x4G2721doBLkQSJj8QRMnAmuQcghMl5cElNinhfyPPucqz2u174SqLImAZbsmubu6m5KLKUUTocDYAlo47gj7xbybsf1zTW7ceTxo0eU6cTxcOJ0OnE4HAhBuLq6smfNav1c1iJQqJJLoUkjhhCcCYu9LzRaC2rRHi+cBitWVEvGJ5LprZ9OLkuYu8s6u5xjXnJPtO2Jm6U6Q1Y8NvvIMJhE4JIbY2TKJ6WOrvgxgkSWeWFMw8trovOxOc44dcapM06dceqMU99unPrGzUZW77wQOIkyo9QuW2czs4qxJxU4qXKfF57owoc68VYe+W7c886453GIJOiMhQ2nlXUoWh+Y03Wn20349hv9aC7p4klUxY1rdx2ra35jRj36br2Kgv87amOd3NUndHnAAbFYRmkvbZ0e/W8Jd1UhZAksFJ5P93w4H/ir43N+Pp2YiyJhsLhMml62+Kub0bW51Habzvroes+wMe7VDV2pldiT3qQzaA0ATebOK3saIvXBJbWwGxLqsa6IFXo6TUfu7l7w+HEipsB+v6eUhVoLuVTyPH3tWNka8N6/m3ex51LfubfnXM9b4yulK0SkIdGc9s3whxC42F9SshIIhBS5vzugpSJV+PO//Etef/0N/pt//SfcXFwwTRO7YURSZF6s+qXg4BIM4EqpgE3IvGRKLjZGau3kWDeosQFe5f7uQBgGECHuBhiSxVxaZSMz5NF/zxUp5n4nmJs6hMD3fuu3+OEPf8RHv/6ZGbSuGb8qeLTnHVJyDfG8VvfUJl8XjBHS5pb2tnQjEmPtRi16Uae8LHafVsEWXHe8dgPW+qfk3N3JASGlSBhGxosrq2Dr1U9pzG0u4CyoCoRg79X0003lYo2Z9WFLdVauCpQlczod2e33xBDYv/Yqj25umA4HptPM/f09p2kiJnM1C/SY3wYQEoJL+lVElGXRNW44rElwqpaAiWoH6xQT19fXFqccDIy0VqZ5ZpktBKJ4OEP2uPG8ZKZ5sgJcpZBz6axSrKUzR7ksfZGzDSeomsllIYaFFEcQQdO5qN83HWecOuPUGac449QZp77VOPWNm43dfs/l4yteHI/cPntGVTV2qE1c2oRcYzazwAtVbkvmaal8mjPv1MwP047XYuRSAlcEoiizthhNd0Y2pkiagWvGbXv4jthfvB0V9YqJLVLWNcUFRKK7l62TaxBmtV2xYDvg4I1ZXNlBMLYsYYxT88K2n0hgICASmTRwXzKfTSeeTkeelIUP84FP60LFdqZJAtEFqUspDDGtYNVYsnafDnK4FJ5n+qt2g2eTy6bUVv1Cq79XaAOk9Vf7DnZSsUG7HxPHUzEXNAFVONzfMY4jV9ePSWng8vKquxVPtbDkpfeLPHj27bZ2NdC/+ZD+XHhfaQjubjVWJw0BS/j0QSzBCusspgU9TUeWee4Fbn792Uf8L//H/8rj1x7zr/7FvyQgHI9HhnFgWTJV8eS1xJCGTWKZS8R5pdyq63gCOoMxju7qrpb49eL2lpCCuVVTJI3GxIkzXkSMCY0eB1tpKw8EuL655vvf/yG//tUHBjBqCiJNrURoBqniJYxQmqFpKhHOrnlSGN3UtsPGTHBGNsRosbbuko4xWhKeu8Qr9BhYEfFCXDY4RQIRNZfv7gaGPcsyk/Ni1UtZQbkxRyFEp+akxxlbQmfTQ8fjUiGoxXJvgUFPJ+T2lt1u5JXHr3B1fcXxeOT+cGCeZy4uB2OLaiXFkegs17zMjDJ2Nkx11Xi3Pl0BErU5FmNkmqZui4Zx6OMzJktsHHf77louufRrNmO+5MWM97L0BVf7yXmm1qUncYoIixdvqm5/al1IUVAntPI8b2KKz8fLxxmnzjh1xim77RmnOOPUtxSnvnGz8dbrr/P2j3+HP/vZT7h78oUZPB/UtVkzm36rTrgISY19OQl8opmn0x1P8om30sjbceQdHXkcA4nVCBTkwb07s/HgkxVARMSntRLUQCT6Zz0pMGCMikQWZ6RijMRxoObC7AlJBWE3JGquHI5HWiJTQBmrGfPBn6BKMz3CUS3+ddaZL04HfjHd82yZuC+Zk92M0V2/wV2XbYCHEKir/eoDrUvbqcUlxhhALUGn8lDTeOv+7ayY6IOWXM/bNq49Q1G7/m5IHOcZgoFZqZn7+zuGcW8xhbv9Jn7RWJeiD13VX2/I1/utPRcQqS8Zd1nBy92aCl2HHGlxsvbcx9O9Jy0F5nkixkAuC/M0UxXe/9XP+Df/47/h8eUV//BHv4tme1YJBqQhRlJKpnWdghvFtTprG2/Vd/2CsNuZAb+4uCDEaIZotsqi0+Fk7uIo1Gkm7HcM486MXlCIAQ2uzR3EYpf9/WMIvPPud7i+vuH58yMiaztGLx5VS6Fi8dnGZLn8XwxUZ/MkmCs5Fy/og8fsNgOvSqUaY+fG44FOfmeenMFwPe/eryGQEKooUTMpRWR/ySSRZTEjVHJFWJU1Aga4NpltgdC0y1EDxlKsarDpjseu378sC2k6sUxWKGk6nXjy5ROLhk4Brcrt7XOWZeYmXpg2eoUw2Dsvy8J0mlzhxGURg8lbtjnY2qEZcXtPa5fiVWVXRleRVnQKYfFFICKkwXTys4chVKztcjGr1hLr1D8XX4Cqh+ycjkevJGvVhEspfXx3d/55s/EbjzNOnXHqjFNnnGptd8apbydOfeNm49VXX+WN773H/MufkX0aVh9k6ptfY1cqq5WoqJpRT0FcelD5VZn5tCx8LBPvph1vjyPvxcR1FEZncyq2Y7SEOVPiiGKu4MZOtf82bRCP0LPMfY8BDLV9Zka8gjeYxcftLy4J2Togj7ZTTpeXaC7cTSeWpcW+wSCBPSanKFo4eVu2eFtd4Lbe8VmZeU6hWCo/gwpDwZPl6IVoVFbGZ2tZ26TqRY78FNX2y/bc9TvSz/FEH4/81VrdLRhASpezE3fL27UVqrIbB2otLFWpYkCyTBN3L26JMZGGgd3FJaUqtc6U6uoKjfZQHgBKe7YHD6zWX/b5pkf7eatRN+Ni8YM5Z1Kynb3S3KoQorC4wS8lk/Ns13CZtr/+27/mf/ifrnl8dc333/0uqnC53yPJktiGYWDYjYjAsizUkqm5WCYYRqqZ67RxNG7ogd1uRxpG8jCbLjxCmTMSoYZsUoBqkn0ERaoZcEJAg+uL16bQItw8fpW33nqHw/0zQnDDqdqNXa1KcBWRZV4sjny/IwSYTtXicYNJDSqYURcrGASxM6si7Z3WSrS0exVzSQ/DgElPJlMGqbUXkWohI1UtjjuGSPYkRlP3cMUZZ0JtEbbq3edsiX8hBEi+8IqJ/X7Hfn9h7RXNQA+7wrAbOd3dk13ib5pOfP7kS/a7HZ9//jlffPE5tRaGITEOIy0UolZLgmvxtduFThos5rkZ9sbktPmgVV0jPXS3da2VZbbEOHMh6xpP7WxXc3WLCKEtjliv31zitVYruoXF/uecSXFkvzcjvyyZeTr1WOK2kGihBOfjq8cZp844dcapM06dcerbjVN/hxqVsOQjp+MBVNDQ4hI3k1UfGldwtQxa9KqbIBUywmdUvliO/DRPfD9Fvj/u+U7asQ82SFXUprgKSCCIqVl0o2lnIZgDLmtTEXH3M+IsUWNnTL2hqCVPpVpJ446aYFhctUMWT5KyeNrZmQkFJq2cFO6oBArihWuKtvdUaoXiv4XGcgm0Zm878bZ7tPdw1sCZtxCEWh9wPd2gi7Rd/so40Z/RrtMCBIJUbxtcEKTt+K1nLN7UPm3PFFW4GkfuphNTtXaXmplOR46nA2FIDLs9w8WeMV9yhbDkwrJM/nwvs0Qtqak9q7/zA0AK7QE3Y0c3WGXvX6oirgMtAFrRKiiRFm9aSgWfdE1dIdfCX7//N/z1hz/lrbfe4TomU1DYJXu/wYpX4TrdtVQkF2rFXOQEYgyk6PGJS+bEiRgju3FH2o+E0Ywcrl+N2HsXrSylWEiET2qxlYRJUcZoRt3duePFnjffeodf/uInXqxLLLYXi5uuWgm+MDJ3dCQOA4HaY1gb8yoCw5i8EmlCNbnruJjSTYDs/Gx0FqU6oNRW4MhjcFO0BYmqAYO5uIVSBQ0m07jkbEap+AJMCxISVcVd6IKE2JkiM+yRYfBCSGkgJWO7oqvLlFqIarKL90+f8fSLz3j99Ve4evSYp4cT813hk08+4fnzJyjKuBsZdjtALI45JWejzMXdx6hIn5vbAkQxRaKueue1VJNEDSaKWjBmB4XFEwarrjHVqoqoJSo2w7uG2Ei/Xzs3i7FCpqmuDGIVfEstJgfqcyDGaExxrSZneT5+w3HGqTNOnXHqjFNnnPo249TfWdRvmiam0+RTzCegtAmJ7/jXqFTZfryZnkFaJJ8pZNxq4T+XhY+OC98LM98Z9ryaBi6DcBkCO4EByAqzCNvbiXolTBF329kA6jarGTv/Vm1PWJWgcDXuuSQSKRS1gYsDAf1e7b2UjFBpagr+Xrq+9yb1bWuD7FFC6DtuwJUuDOLaJG+DQTaNZi6+9pnfq4GVrAa+3Ut8dHYTuVVMoQHB+hWTbvT3VWUYE3sdmE8+8USotXD34gXDODLuTPUjxYQOI9dX19y+KJSy+PUfGvIH7md9eG9c4vBBO3cSbXOdEEySrVZrq+DqFZvrN4lAVSHFHeM4Ms8TWSvH6cQnn33Krz/9mMvxgmE3cDkE0pgQLLFLq42LKIJG0+UmBgZ39wpmwGrOFm+MsuSFyMA4jpQlsyzGaAQ112rTzeZhk/BSE/VOSSnx9jvvsN9dcH8/E4ZVLqT14TJb7G9zt6ob95hSZx6maUIEdrvRDJOrWOQyextlYwBTcne8uaQHZ5rMhWrGQ2ulCj3GuBnxmAbCMJLGPUsx3fu6LN142UMba2xTTk0esRSfM14ddhiRgMv5CV3hB2N3s4cFvHhxy8e//iV5uue9H+y42u24fXbLZ599xmk6IcCQBlKwNjE1j0wIxnwtHrMaYkD8+VJMPRbVXPixt3UDGvV+Cc6uppQM7Fu7N014l3Gs1WoVEOksVWPfQlmNeztPg1o89jCYW9vjhqfT1OeLhLVeQpMWPB9ff5xx6oxTZ5w649QZp769OPXNRf2CJznNS59sDwfhy7/6wAurK6tzR40xaVdRqFX4Qir3deJXc+H1OfBGjLye9rw5DNwEMzYtFjQCOAtkE8IKGwU3iDg70aqjtsfrrJXYRj0pDEEYdjumRZkWr6Ap0lmU9ozNhhb/oBsbP397j6/OUbte3Bjxqqu2s7gxfzmuVbGdv4RtW0pnmLohbwDgf9dNn/Q4P/+f9p6Qta+aVRFzNV5c7JmWA0suxqqgLPPE3e0tMSXb3Q8mY3cZLpmXifv7zFa5pb/DhuHaxhpun601ssjDsbWyHxZDrG4MUntYoZ/f2vPm5jX+yR/8Aa+//jrvf/ABP3v/bxmGiAbhr3/yE8Y4MO5+SEiBnQpLzdzf3VOrFdoRHyBxiKYR7saOxpak2BmTFp8YY3C5vdKL22zjmS3+ctO3ErpkZHeDAyKBR6+8wZuvv8t8PBKDoGKJdabjHc09KTCEAREhLxmtmaHFy1ZT58BjOZvxaa5uEHLJltwXgrtTW3vb/MoUZ+QqVcRj1XED05T2lTTukbSzuNt5oTjAVa8qapl7DmS6kWkMNobGcbTqwD2eemVZmju2VmPDQjC5vWfPnvH4xQtef+eGz09HvvzyC5cNFJfgi6hCeHkStvHm4CFg8e+eJBdjfNBezdCKGGOE2BxsrmXUQEaDgppxb3ayxxNvftpYVl+Exbhhz6r2FWAM1l7jOFqcfDUWMngy4DfJef7//Tjj1Bmnzjh1xqkzTn27ceobNxu2mzFZrK+Y8PZBj33jgRWz+anrv9tXmt1QT8wJcC9wKAvPauXjItwsC793+Qb/9PvfY//inuXZHct8MmOhlkAUgyBqsbIKzJ2VUNdQdgaIjZ45ruSwLDAK+4sLChk9ejLcxq6192tv0Jki6Dvbft6Dl6ZfRHioCd2u1KXfHlh+sUQbT/4K0orqrCxS+2lGIfgAhNKf1wz86sKG5kJbAQnMuEQHhiABCZF9irxyfcHz2xOzhzeLVg6He2JKrhFtSWtI5ebmhmWZmaZTN7orQ6W9Pxoaqt9La212pT9jbx83/r1V2iTQVsinjTahxXSO48jv/e7v88/+2b8g54WLi0veevtNnt8+Y391xe39Lb/46Je8/tqrxBDIy8L94cjzZ8+Yl5lXX32VR48eWyzoOBDj0Ce3xTtWNEZEm0Sdkj0mlSDmKo6RYUykFLusXY8fFTH2KwRvh9qNOO4Svby45jvvfp+nn31KYUZSoKLOeARq9Xhg9YTAWqEqVbz4kVgBrB6fjOv3u9t8HEdmnTmdZnNvx7gutHxxgFjcrfp31JPoau1q/qhAHPdoiCzHE1pavCgGOsXGosQVRFqIgrhB7P2rFses6kC0YUmHZCD6xltvc/vsCXWZubi8IobE0ydPePb8qRvtwH5/6cyTsT99zvkiojNZqoSYjPX05EUDaXu/ENe5uk3OA3oSZB/nVSmU7sLeHuZ2XmNpmy1oYJBSWhnP3IpXmW0IITCMgzGhbsBtmDy0I+djPc44dcapM06dceqMU99unPrGzUZMibkWav3qbkX8prox71ZYCNdfNisgbZA0+kXXb1qinZhB10oR5RnKbZ545WLg8se/w6PjxPHTL7m/fU6+P5CPJ+o0U3IhtIGsisnOuZH0HVroz2ZD0CS7MtOszGmgRncdB0Baxn9o2+T1ZbXzMZ2w0G5cVpbkgWtVMcZH5CXjtt3Fb03YhjHxm5hrin4G0O/34Cds7tFAcrXY/szyYDLR2aT1riEIb772GiIv+OzpHagSRVnKwvH+niElRPaEGIhELvZ7rq+vyHlZJ4q/S2eFaIY5dPe+YDGejeRr79EYh8ZktO8ZmlrCWFXpqWRNEu/y8oof/vD73L54zl/95V/w5ptv8ru/93t8/sXnXF5eoePCF8+e8Itf/4rRK50e7+453N0zLwsxBB6/8gq7C0uoE19ptJjHxj5KZwAqNWdKEPaXF0QvxjQkjx/tM+RBt64d2I3BauhSHHjjjXe4urjh7vilKZE4XSawGgXv25QigWhx4sXczsOQel800Fa8wukwIArLYsHczSXbHglsvGX1kAtnoYpV3jJXrCoxJeK447DAMmcDo43B60wh2sE6hEAY3eXuMoJI6ExKbxZP8htcO11EeOWNN/lhEOp04tVX3+Dpi3t+/fFHHI73baBzcWGFkrbjrc0jA7zSgS+4TFEbwyj97y05bhgGsse84m2vQbtB7cl6avcqtXQ3taIGJNEWANt50FzaidTH/OIJdS3Wtkp7jtBDDM6yt998nHHKjzNOnXGKM06dcerbiVPfHEblE6q6cVjdnD42t0bLP20xor5de0DB2K8rB6MoqdpVi5+aCFQRSgxwvWf33TfZff8dru8OnJ7dcvflE+Zn9+QXB8rpSHWNYK3eoOrxaqW6BJ+Sa2VSZRHlqIXbnDmdFp5LYAB2454hJlAvKNPes1kZbZN4dekBvNy0BmnOZ8iq/wwbA6+yniuCqaKsrbqet163X8sniJnn1QBL/3EDuDHw7VrNVdiv3e27SxGqxS++8forPHr0iLvD+9yfjuBus7zMnI4HUozE3Y4UE1mVq6trTqeJ4+GwAbQV2NuNglgIQa25Jzh9nXO/uSjXZ1+ZqHaWMUWtFU11YxgTf/EXf8af/um/43vf+y1eef11QkgMw8iT2+d89tHHaC68dnPNo8sbTscjaGW/3xPFVEWGZMYjL6aqsEq7+fvE9Ym7vjliccJDIkgzYJsRIRsI7rHUbsBbYqUCIXD16BUeP3qV0+Gp9W8M7noNVC9wVKv2Z4rBr6nagdx0tDMixjwEiVRMIzy4JnpzdTZ3akoRxSUc3a0aY/QHU0IQAoGi1QArRKbpZPG0btAUM27RFwfVFVFiDLQKqDmbIZXmnt3Mkd7vrAZNKqT9jrfffY+kMM+Zj3/yAR999LGFBPj3Li4ubEzVagmx4SHD0qqnNuY054z6YkqrdgOM0BmbWFaXcIgWHmAFloxxLLV2xlIraz/7ILWkudBd0Zb4GHp8bV/IBI+Zdaa4qaDY84iPPU8uPR9fe5xx6oxTZ5w649QZp77dOPWNmw05HSl6RRZTbBAv17JOvDZdGwvUbPY6ktsOV525WScrzdxRMXUPUDNUKEnUdq0hEq5HLm6u2L/5Cvv33mC6O3H//AXH+ztmr5AY5kzOBa3FdqWnheNxZpoz99PCackcVDnVzH3N1GnhuCzE61e5vrhijJZQZbt4N5NO7PQ33bxTs4cPWKJ2uEa2hE0xlt4OlUqhaCWImpEMYgN7Y2TFQa8ZK8EkEptknqgiWk1dAAhUVK3Qj4hJEzZjGTwjsbW+YPMz+C+9sE/JXO8H3vn+W9w9f8Jf/vzIUsGmsDIf78kpMaaExEgIiRSFm+vH5MWSnBrDo6zvbMxL8SZcXXZtN912618Zf84i9TZ2pqstAOwwd2xeCtN0QsTeX6qwC4nj8cC///d/yicff8TxH7zgH/7O7/Dmq6+SJ5NKvL65IYTA/e0d+2HHuNsxzQuTy9gNaWAYEiEKlpDoc6DCUgrhNJFiJMkOBtOkbyyeahvjNmB08w4N7JQ1TGF/c81rr73Ji88+JsvCrJkwCCEOlLlAeAnAfMES0qog0YxpiPasIUbqomTrSI9Zxd3CSgiDJxTac8cYSUMihUgILWa5ucStzkA+LhwPs/e3AZfp21diGlHWBNA2DlRNik9R5mXyMbq6X00FA2JcraEgBKrHfCdunz/hpz/5a548+byPiTRGrm8ubZFQfZGj+mCMKdoTYK2Csz1crdUXRrKqfgikkKiyxubWaipBMZjWfcUUfixR0fTLk7v7e2Ep3TBXDlbJXeMtpKABSIvFbe51K5hkLWfJg2t11/Px1eOMU2ecOuPUGafOOPXtxqlv3GxMx3vm0yO2ahDNCsiW0WA15G3AQosFZLNl3ho8d11v5mf7ugApBrQoeZpJjJb4FGB/fUkcRhaT6yB4FcXoA8d2cJUXdwcOXz7neJg4HDL3x5njsjAtxkYFXShq1TXHoMQUKM2dzDrB1if1wYg+MKxfOTYfqqqB06atWjGaWgsqoU/m7Xdkc8ctAFpC1+oys20vPZHPznmYfNQDCLzrenTthtBRtJ+wGwfefP1Vfv9HP+Dj23s++/LOvidCLZnj8cAwjgy7vbntRNnvL7m+ztzePv+Ky7EdFuIQnFF5GDdrhml96PY3cabmIRPF5rv2btPpyDTP/OAHP+Tu7pbv/9aPGAcz4KUsPH3ylNN04sXxwEeff8Z7b77Nm2+9jQJxSMQQKKUyTzNalaUV9MFcl7EKMQ0PZOHUgUPrCa2FvEyki9Hcnh7DKILFwHrBLPHYy+042a55ht2OV954g092F14gJxO8x2rJvdCTsTEWGwzGaNhAgHEYuzKEGba6jt2qm0WFfdcUKew3Yy2sTc2Qu468L4wQq5o7z0fm45GimTisiYh47Kw0pljEYmNVqYizkl6Z2PXGt/GmQQIxmuu2JbdpKeSqnOaJX/zi53zwwU9Z5hkJ9rzDkNhf7IwtKrW3e6vs2vsCvE/XJLo+b2RNBG1hHE0FhP49W+yUUsglG7voINkANcVIFWvn5na278mqAOMxtS2Zrrmgc7GiUo0tmqeFXNYEQElfa23OB2ecOuPUGafOOHXGqW87Tn3jZuN0OHK8P1gHNjpItqPQjHX/RNZd8gP3qGxNoh/qBrGpkAnrbhx68ZPTNBFrRscd0XdxEmC/35n7LQaOpxNzKS63ZozPRYrstXAHoJFcrNgNoWAq54KoSZctS0HHnRnAvmOlD3QzpG33yWoRv+bYuqW/lk1qH+nLxmr9/hY0zD3rU8vddkBnhB40qW/T1/4wo1393+vNNyyWf0FVVvdZCLz7nbf5By/uOB7/hsMhg8cDznniNB0hBIbdnuhSa9fX1yzLwuFwj1U49Zhbf6dSKiKmL95UQdYddejt8PL/t7/x0js4kQEC9/d3fPD+3/KP/+Af80d/9MdcXd2gpRJC5erqmnfefYc0RN79znv84pOPefHsBf/9f/vf8crjx6Zpn2KPlTSdaDEWyGMyg5ikW3NPtnhdVau0edJCqZmhZlNDibEnKMYhggZ3xdJlEZud88kBzmrcvP46FzePuP/yBVSomhGXOhRx+Tu1BNJuXGjFe4yVawlkPWbTpSSUZjxyDxOwdvT4V888jSmQhkiUQMk29yUISRK5VJbDPcvpiEYBiV7UanEWyH6qalerKKUg0cZVcWnCUjLCOt5A+5wp2UA0xsg8Lyy58PzpLf/pP/0VX375+QPk2+127Pc7Z1R8gaQKWmBhBQlZ/6boKrlZ17G4ZS5LLc7eOBgMgy+GbA4mTww0hq70ayzL0v/eFT028o2tT9rvUVyysVQI9Gfoz6TG9G311s/Hw+OMU2ecOuPUGafOOPXtxqlv3GxoruiSfQDJWiPJJ8+2aNLmW210rqe9bL23R7fzD78To8m6BZ/EeVmQJOadVRjGgZ2Iu7OU27s7TOHAlDJ2Q+L1RzfkKSN1spjZRSm5dBeSAFULOStVB/BiQl9nfIXta2+3+5tzXvrug3+3t98MKptBdcOw+WSr1cmgZi3WuGkAACAASURBVMzCBip9sm3YrK0bzPSVTWrRTlmv297gZakzaf8TK0CUc2YYEj/83rt8/tnnvP/hx2R1A1Qrx+PBmJAUSXHsihhXV1fm1l0yL0mY+JBogBU27yHdkDw00lt2qLVdazb7XjP0uWTe/+CnlJp5773vcHFxSS4Ld3e3SFD+0Y9/zDzPXF/f8J//+m/4s8+f8NZbb/Nf/eEfc3WxR5We9Gc3tZTNFBPDOCBY4tlWh76DjT9vrYU8L850FLQmWjxr9DgH7a8Xuh1qBhxfKFxcX3Nxc4N8FhglMpUCmKxgW0yEGD3Z1PovT7lrZjeNeQTyklfD1ME/rEYzCMMurbUFnLFIgxkgNmyOKbsILBXNE3k5IJogjFZxtRZseBREjR1UlycstRKbsogIWozdEXJXvCgeDN9iR7tBrxZD+8knn/Lhzz9kWeYH82q/25PSwLJknyelL3BCcOnExoj63Mqu8NFAzHpgM4c8qa7FF7drNbY4RLMxwcdJ79i2ABO7XmPI+tRTY7mWZSHn3AFj+769j2JAZ+1FmXi4Xjsfm+OMUw8f84xTZ5w649QZp75tOPXNORu1orWsA25Ld2xs8Tr5dP37xiivx1fZpodnaW+QGCMxWOKRikIttmNOtuM1F5gy7hKP4nXfmcZkLqSkQkgD+fqSBIxJGYfC0xcLx6kw58K8VJZq/FGL1YxeoATB1B42hrprT+s2FpMHjf8bDfkWGKQZJxu4jSUSYRMPq/08G1GrAVNtBk2+Olhac267qHebbF+nX8tYuIhgVTGn+UTRyitXO37/h/8Pe2/2JEeSpPn91Mz8iCNPFOqu7p7uGe7skEtyhdf+ERS+8E/lA1/4RqEIZZcU9s7VU93VXVUoFK4EMpFXRLi7HXxQM3ePBArkI0Q2rCQLmZGRHu526Gf2qeqn33B9fc/LqztlPQS879luNxjnMK3NiVWRxWLJcrnj9nYgRD/78OneSmzrPlM2Z5fY+93clbrXhXtAm9hs73j9+jVffP4lIQy8ePGUH3/4gaqu+eLzL1kt1vz89Cd+evo9b99e87/8b/8rIXj+3b/9bzg9OZ2Np0wAlxeqMcpSjp82A82iyGCKGElWnYkSCSZiXZZ7FIGcjIZFmbC9+a+a+1XbcnR6RmNrJHkGhjGxLSStplpVFaSEtzYzRyop6CqHs7kKsJkK+YAZE7o0sawmJk2yq5xVUIhpdO8aI+PGrfS/AkHCCSybilsHuzBAtKQUIMfETkaRUW7QZRfx6LIXSN2QgTMXVgKVL83JbnVdKwCJhqn8/PQpN9dvx78vrV202ZA7hmHIhjYnFFYV1kyygZIBJcVINInKCM6UYlBpXE/jhmIEMGUKjTFUlRCjXrNsyGIsikZpBKqRKeOBYR8lGONsnqkySFnHZcqHEDIAMSrPHNq77YBTB5w64NQBpw449XHj1AcPG2EY8F0/LpS93istf6IuvrwY8wfqb9KDZZwN4+ymUvl/Nigk0eqWJl9f9JSsBXtK7FnUku8iNHXN6fGxKjeIkFwk9IEggdWyJppAs7Ys1pb12rHZddxvttzed9x3idqp8Y6oooGI8CCEVU/q40M9cLXDOOHnRvyh63n27vE6BRi0/5hO6ZL7cmbEUu7P+YdP8aQTg/SLLYPtGHtoRCMtyxgaQ0rgh4FIoHaGb778jNdX91xv/sx93+upPCW6bodxFXVVj6dr5yyr1Yq+79hs7xnjVWd9MDFD77u5uSHdN5YP/6bEBZd4UGctZ2fnnJ+f8/rNBX/4wz/z7NnPxCQ8efKUZbtke3/DZnePB/7hT/+A9z2fnJzyd3/7tzRNO4J36cK0Nxay91oZW2Mku1ghhoRENccBwZhA8Hr/1hhlAcOkBW5KqEdOOgUQ5zh7/Cnr1RE3tzucGPoYGEKf14EChjGlCBdQVTinVT6JIcs1TtU8vdeqqBp+YHFYIIx9GrNhU3doQCIjC1gSCGOM6vJPifWqpQ/HXN7e4WOYGb5p3OYsjc2u2sKQVFWFJGEY/OhWVrYz1x0QRqNvjOX6+oYnPz2h77Xy7DhLRMNUFosFbbvk7m4zsmMiU1VTIBe1imM15MLSzl3GWpE2jeMTQszyh3otZy1YSwhm/5q5DyMTu1Rc0FLmVFIFjwKspbouaHxzSpHk9bOHYWDww6RWFAP2UEH8F9sBp9i7xwNOHXDqgFMHnPrYcOrDh40YtMQ7ZfHlySYzky4w+U7yqyKj2Ulp3ulldWTzXgae2fXy4FijiglD8EQSi7oaT62Vq7BiGFDZN2Ji0dZYKypbFgIpDUgKWHHUySEBnFtytF4SYmK73XG32XK38UiKxNBzu9lldQ7REzBQXMTKzDCeFMsKeGigePisD74nCSl/GTvF503uxtm1slEvhnyMoZPJraVDUBaPur+MsWAexCjH2SLb6+0EKZCSoW4cbVNjBSRCDHpa/vXXn/Pq8i3fP3023k9KQt/t6LYVTbvCiSYU1bVjuVrRDwN+6EiU+Nc5ozAf8b2RH+8pTR1BcUeXZ9VFbxBRY1T68PWbV/yH//vfc3X1mpubt3plgdu7G+7ubjCUxEQhGnj68hn/8sN3fP75Z3xyek7dtnmRT9M0SsRlSbw9hiBpKAQmL/xiEEPIYAzRWoI3IJFkVV6PPE4p5oJEZnpqk+fc0dkpq9NTrm/eYLLkZpKEIZFCj/daZIikjESShHWWRVNrEmBQ96dzylaoxGBCwzeyRKDVflQGIyCY0d1qxYxhIUNQ+T1ExurHzginx2usFd7c3jMMadSmV6bI69gIJNF1bGZrJ+XNizEW7wdCkdEz8wJg2rqu58cff+Ti1UsiqnmvGyx1r7ftAusq6rplsUgEXwofyTilRMD7gQRUdaPyoajV2nchp/GfstZ8DFkeUMYiUkbQzVfSSr0Uxiivufl6LoBa1qvet0o4IoLJ36coJKd/v+06fO8RgRA9Ifi8eT2097UDTh1w6oBTB5w64NTHjVMfrrMh0LpqjIMrRg1B3cNlUc1iLeeGLOVOVLs/HfUevKP09Uik2Hzi8zGSfL4RUSk054yevkPURDsxDH6gDx5rLElgGDQBykbP4D0mCSaCtZXKrwGrtuH0aE2IepJ79fqCu22X3Y2S4/rKhMmgM2NudF7PjPOsPXRRv8McJZmFwCZSUJZh6pOEYMqHjMyLGo+UQab0V+GIsoqCMBrwcVnlv0+5X5GcjBfVgCOClcTR0YL1qsWJkIwliYOYOD9a8Hd//Wvub+94+eaaaCowDh8Gtps7nKsQqbKmtmHRLNg1HffeE1MpOjP7fNSQFGWPMa50xjro6/r84/zRWanzTQxiBUkRoiFFuLp8w9XV6zERqsQAF9JN9w4pGyrL4Ae+/f47/vVf/w21WE6sw7StGpEYc76pXseKLk7vc9wpCWuVJdrm6qnOOUiqviFZaiWEREyeVE2ynJCl6SRikmgCG3lNCTTLFfXxEbtuwAhUtcNUFok9MQwET5ak1D4chkFdwGaJdYKL0JHZDhLWqrva+0BMHkNJdsuIgsWaSuNRASMWIvik7mJjDKZy2ArSEEgpUlvDo+M1IQS21/cEyiYCovcY8RhXYYwlxkBMDpPUbTv0qv8NymYZExGpEDTeeCxKlCL3d3f8+MP33G/ugGmTQ1JlntXRkW4oek38q2r926qucliBWv0Yg8bXG6PscIxqVsrqEXVN+6wRr5NOCDFhnYwbWVUzSVTZ9Z1SImbQKWxUyVPW6svqso4p4gdPAlxVAEbXYUq6Uo0xeO/p+4HgPS6HCnivBZkO7f3tgFMHnDrg1AGnDjj1cePUBw8blRhs01IbNXyS3ZklAWVaIQDzxZemjskTU3/x7mdMC1sfRr/T05gPOhFcPpVHiSRxhBTZdTti0lN4SFkP2DIm+iTR64boMdZQSa1GL7uvrNNqlTEZgh9Ukxs9AVprRmOjj7J/47/kfn7onp73xfj7bH1jjpnMzvb9PmE6WY8uUu3hfMKNmFxVsmBjSmk05syvmdL4pQf+fA2yezO/1xphvVjSNA0iuQKnc6Toqa3h1198ytXVLW/vvuO2j8ScAbkbOtx2y1FVja52ay3L5ZKu29H1Kq2mdjHN+k7vcpwjs/4a3eUis3lWNgxjZ2IAcbUurKRJlEW1QaSARBr/Pn+i3qPR5LUnPz3h55+fcb48pq5bmsUS4yqy5LtqSQfVCE8ps5ExqqRdDHS9ZxgGTQRtGoxk1iNvekKMqhVv8/3MlsPIvkUQU2IgBaqK5ekZfUg4IotlS7CeJJFBgkog+uL+TFTW5ETLiYV11qnL1VjEWAavahMq56fu4tGoWJs3aiHPS3WjluJJ1thRijKIaH+L0DY1pyfH3Haem/utAmreZEjegCCM1yIVLfukzBr59SxFGKO6v0sLPvDy5UtevnyR400ZPfkJjbFdLpeUYkxzF/g0B9KY5GZhLO5U3N+FGdL1I1r7ICVlaBJ5HDNrnF3XKcbR0JcwgJL4GGNiyFrjpY6AtZbkS+iLmQpwSVFiydcyUz+o7VG7Vu7x0N7fDjh1wKkDTh1w6oBTHzdOffCwEbP7pTIm24vpKC/lsFnok/dZ6NyZvMcQ6h9NWfbltZKMVHS+rUBlc9VFEXofMQRCHpyUNEZWGYgEzlBXkcF7RESlCY3Gte12O8LgNRlHims4IiTqyukCFJ3UqmiR4/zmzNbMgM9fmzMe8/bOa2PuXiRGXdxCSbibDFxiYk/mChSJSRVAY+50sFOalEumWE3NBjMiY0JfiVtU41/uLeKsZdE21K5CUhhZMkMCSazait/95mteXt7xpyfP6bN7NgbPdrejbho1YlklYsGS3W6LDx1FN7rMgTnIz/+l3OGMapyZ4Xdb+XtjIGrykhru8H7wzQyUlUka8O7+jj/88Vu+/uwLqqpmsVpT12qMgw/44Z7ObGgaZU4LaxdCwvuB3W43c/3rmDSiDI1K22kcdwEkTWgzo4LEO8+Tp8Enn33Oyfkj7l4/p5IKk2Cgo7JWjYNkt7QxrJomK08EQp/lAq2lsm6Mbxat4kQMKSuGGIIPkOcSkrtTiiqHzp3aOaxxo/pGiS8tX21Vcbpe4PuenR+IweTnU8MaQtRZ5EOu9qVJcVJVYwGgGHUtqw2NIxsbQuD58595+/ZSx3KMn9c14yrHcrEcJRQhhwDM5xjKSmFBKpmtsYT3Yay+Wq5aZkyMaQSOkkznM/tpZPq8lNKYIGcyEygiGaT0HotRttYSYoIYSWaSyowxZMlNydcq63wCiV9ipw/tgFMHnDrg1AGnDjj1sePUBw8bm+hpJOWOURWC+aWkGO80/pBffzg5x8Ox/jjZq/c2EaGuayrnqKylshpPhzH4ELESqeqKmLSwTd3U2NqpQcvu5n7oEWuom0ZP0rHXio1Bjc94WiRSVxVHqyW1s0A/xuOZHD8oUmIY9w33+5iOMT71wWSf3jd1wNR9c7d+ZnjEjMasXHc81Y59LAoG8/uYPmT6efbZgvZRiRXU2FpYNA3rZQskQoz4FAnJUEnC5nE/O1nzr377Ndc317y4uiGJIYll6Afu7+/VgOfkyLquWK9XdP2WrttNIJRK4p3Mfpbx9f2DsRoXk5+zTKD9zQPZKOXYQ9LIFJXPKfZck6J082CMsFyuIQl/fPIDv/3pCYvlGnf1ButUTabb7tjutvTDQNPUnJ6dsVgsFHSjp+86vA/Z4OVFl4MCSIVxcaP6S3mmInm35z6fPTIJVmdnfPbV19xfvMJ4iEZdlOLU2k5rKFJlgxt8Txg8KQSahc2VRBmT6kAT94rr3jk3uoklG/FilETUeBujWuyxC/jBUzmtaKuGRSX8Vo1jWC/wN1uGGMYqy6REEjdKIJILM5XPno+7aptH6roZf7fd7Xj56kWuuFs6p7iKhaZtWS4Xe0ZVNziTIS8FilxVxmHSAg/BE0LUzysVxpJu4qaNRgkT0espe5iNNdN6L7G8kvKGbpqe+MFnMMhhCUErrcakIDwMGg+cxomtNqys6zkTdmjvtgNOHXDqgFMHnDrgFB81Tn3wsHH65efI+QnmuctPMf3D2J37s3DOnMz/LYZe52jaW4SU8UlAUtBo25aqqqkri7NCwBCS6EQ2CWcN1jl88JnT0Qu4HH+nLiCDcQY/+NHV5tBBquta49OCp2lqjtZrlosWe7fTRSYGVULIs7uM8b6VeW8r7q/3tpHNyf0hKYOPDmDKln10TcdpcEdWSPKpvIBEPu4nEsSEiKW4eIGcBEQ2dGNHo0mACbGW9dGatmnohwFSIorBJ1U3qJyqGzgiv/3mE27uvmLzjzvut56IJaIGravrMZ7PuoqmaVkul4Tg8YMWIZq7n8nsydSns3sbvy9a67L3nkTKFU+VMTK530lCDGW88qIXxU0jkmMLdVyPjo4JYri6uub33/6Bzz//Ml/dU1UV2+2WzeaeEKFdLHMhJF3wYRjwg58Z5Mx0Grun8kB2a6coiDWTwSnIMmNUU4zqxwfEVTz6/EueNn9AAhCjyjQmQUIkJH2vMSkzm+Cz67wyWuhommvaB8WoF6NC2WBIViGxERtFP0OgsjYzbIbKWnxe5845KpdtApHKwNFywa6P3Gw9Q9YnNxbEmTxv7Tie5b5KwSEBNpsNu77j5OyMpq4ZhoGb62su37zR+F1j8yZkaou2pW2acUNjCiuXja4mH+pnOOuUiZ31SUyzwkViRmazAMaY7JbKzCvKKpqwqGtRchGtwnbvA2ZKxTpJXufZPR/V9TwwY51SGv9mjHvPfV7A4tDebQecOuDUAacOOHXAqTLzPk6c+uBh4z/7b/8tt62h/u5PlAIyadTAlmkSluX1gEWRvd/liVpO8CP7lK8zEiYah9a2uVKqmR5i13tSStjaalJeAudqYtSCOdEHrFXD0DRNvl+IvhSScVkFw+ipkMwahURdNZydnPDm7R3bPkxFVvJ9F+m6eXvIBs2f+X2u6qmV02h+ftkHh8loF7WAKV5vPo3nbt7y5/N7SgmN38vubVOM5/i5QkzaL+vVCmf11F5lINQBAFNVIIKLkdXxgn/117/h2csrfvzpgiEWebTEdrPR07nm62GtYbVcKaPkteiP+uenZMKRdSSfzme/m7NeE2CR38Po4i3/GYMWKVL5hvE5x9N2Unekz0xBjJHF2TH32x1/fvqE75//zPHRmqu3lwgw9AMxJRbLNW3dKhPZ9ep6Dp4UkxpLdIGrSkUurJPHK4ZACJkxShpTnnK8qRjJcZzzTY2yhQicPn7M8vgENjcK8iL4EIgl1tJZxKpxi6ghsDk8oHx2EnWRgkOSyuB5PwDqzndGcCI4KwgWK0KMWihplDxMAWcNbePwcRoz1XdPWAOVsxyvl/T+nn7Tk4xVwxhUZcQ6QXIYiSpbaMzscrGEGPn5p6e8ubrk+HjNevWYYRi4ePWK6+trxlCOPD/yNjBv9KrZ3M6ALTIayhAjNpGBW5+n7/txjMrmrqoqELUzwecY4KwGQoqZHRWsU5f/MEx9WGKOx5CSsqVMqgwkovNiGEJ2w0+bwhjCVIQrfxU99MJEagz2//fm8T/VdsCpA04dcOqAUwec+rhx6oO+efP4mLY1tJUjih3P8mlkKGRcaPsG+93XyFRTyichnbcyXm/8Emgqx3qxIKbIkE/BfvBaaTJEjDiMqRCxONcQk5CSJeLYdQNDiLiqpq7bHAso1FWTNapnrqs88MPgsSny+GTNyaKmSuBMdkkTR9iZt/cZ8PK6MdNnvOcNukjzSVK7chrE6U+mk345EZe+nmJjpzhYZZcmUC0GPyUIJKIIESHoLzHC+Fpd1yzbhspa6rrF1Y2yUKmUtwdipMrqJ6vVgvPzU+pFTTLFvSgMQ8/m/o4YQ2bABOca2naZT9/TcxTWKKUwzSpJvK/Lph6Z+ssURjCzZ8oGqCESKkQcRmyWxkNVVaImwsWU8GHg7fUlIonjsxNCSjx58YIOsFWDHwLOOE5Ozzl/9Jjjo2MqVxF8IAyBFCKSRONgo7olrdgHbldBUkRSQlIienXxDt1A9EExLSSYkwH6IJAii/Wa9fkjehGG7DIekrDzyhhJUiUaL0ISgxh1BQcSffR4SWDAGaidoXEa6lGJwVlDZfW12gq1CK2zNFZorWBTIvqOGPRLCDS1pbJoLLrvCUOnLJkYnIGT1ZLjRY1hUMNkalXWETNqfQMjeCVrcLWyT5vrGy5fPOfm+hJrYOg7Xr54yW63y/Mjkgj5S/t20WqRpCJn6IMnlMTbPP+U/Zo2B0WrPATVeS8JeGUjUbTcSYkUAikEraRaPiPFrNkOxT0dY9YZD/tJfUBW3TFqRUTn3xhXbLSgU9E812q4Og2MgLUyxvbyHht0aNoOOHXAqQNOHXDqgFMfN0590LPR972ezLLxk5Szz/dcivutTOD3G7Dxf+/9m+xoZbloadomMx3KaoDqJptceMVaQxrUWAUfNNGoUkk3H1RKMBihD54hBqyzVMYQizSX5GQ30VObT4nFYsFyuUC4UVMj5Y7UmD58pj33++w55s/1bkvZgJP7KU+cGUMCZH1n/ezRVZ3S6A4dHyI9HN6JLTJmAgYjBYpkVBnwUQFq0dYsF40yIVErWIqIxkJmF2UBnX5QubNF42iaim3vsyycjlPfd3RdR7soygeGum6o60bZjlms4rwPoSQaTXasANn0ZPk/2f9S8z4B2wR2M1ZufE8ce/nu7pbrq7d8+unnpK3n1atXPHv5gsd/8zecro9ZNg3L1YqAEMLE4BlJGGRkM+fjD4wVOY2kzHZEUjT45CEXvkmlMI9JJEmofs50p4LBtQ2PPv+Mlz/9hRAiOKPAZOOoGOGDspvWumw4snt0xgqGEBR8ghY80n2EAjK5cGwxhGTwN0YYfNblFhnnUknY88GrIo7TOeKcwZiKs+M1b+/vueujxr46i1hNvEsxYa2jcspA2loLPLnKaHjEYglA13W8fPGCZz//TPDDHgM33yBWdT2ylcXNrGCheuw6LPpzUzdjAt3oPpZSvdXmglJFYaPMmylJMWW2LXg/XgdhDIOxzkEM+FwQSRmrhDH6OcYYAhrjikDWZdFnMqr8oQo1UFWO6DUhz2f26hBG9cvtgFMHnDrg1AGnDjj1cePUBw8bKUaMU0ZBk0oMo8l4YLDKCWjeSeOpuVwPxp8zX7K3CPQXwvHxEU1Tj2zPkIupNI3LnxvxPjIMPSRUozzGmZqF0A8Dg1cQiqLxr4hMJ7mUGIaewQ8M3Q5nHGINi8WCqnakTT4TismnzrR/r+wb7b3JsZdo9bDJaHAklZhJdbWLqBGbxquwKpPMmCaNmcmA5P/Nu1ANfxyNWJlERR2kuO9EBGcMZ8dHLBct5BNtHAZ1Mxs9wcYYs1693oslcbJeYojUdUWRiTPGEENgt9vRtAsEq/GIVcVisWbwgb7fqSH9JYDLyi+TQWb2cGZ6QlGXH2mK9d2/zjRmExswGXrJxu3t5SXnp+ecnZ1xd7vh+x+f8MUnn/Kv/+q3rOoaY8hKEDIa2aLTDmn8XcgVUcsitWKIBoiG4H2WGjQQP3D2Hw2Urg9jLcePHjHERIoGEwQYdD6g7tKU4zexWbIuz1UxJgOJ3psPISekerwfECsE53BZMSSEMErh2axyY50d73VSw9BnjkFjcVU9RD8rBU/bOI5WSzbdPSl6oKYwhGXMTb62tQ4S2Mrxm9/9jpNHZ6xPjri7u+e7P/+JN5evQfYBcj6+9dyIhzCuwZSyZntmYr3X2gZztQwFZGWGRtYnsznTGgnYpGuihHbEDHZVBi+b1UwU6CzWRvowjHeq/VQ+LzOeWZGobKZi3rUUt7SGWphc9XWyKYf2/nbAqQNOHXDqgFNwwKmPGac+XGejqoji1YiLjB8+2fGpQ+bfP+z0lCemFPbjPaxRacYIJycnFNMfRXWM27rCuRxvhnasz7rVReO3uHkKUOzFveYOnJd9LwVIjJicEBNp25bVeom5udNYvj024v0s0IfiX3/p7+LMSKkBz0BQYm6VUiIxJQfpV0nsmbvH8+/IBorCumhcsDHl1G/22AQBjtcrPn10TlNX+f3Tvfa9xvtROVyODbbW0Tjh7HiNMVo5t65ruq4DUe1uH3o2mzuOj05HEGzbZS40NBDGSb4/ZyYDPWd6yr8ZqQx7bFEikUIa732093lDMPUL2nd5ERb2abfZ8PriFb/++rcsVytevHnDH588Yb1c8eWjR1QCxhmMqUeDpnPKZBdnIgafWRRlb4wYxAkSVUWGDHAjg0fe9Iiyd2K1kq4gM6ZHv12enuIWK7bbDW1yQIcPgcGrbGLldHOi8poxF03Sa02JZMr4xCiofF3AYAiS40PzrmHIbAioIa9cpU5hrzGcaZxXuqGIMRD6hHEOI2lMblzVlspo4SG9lssGrqhVpHGtxqTKLGePH7E6Pabbbfn222/57rvv2HXbd+YFaAKbc5bFYjEaO0TDA1S2L8yYLjPGqw7DMH7mmEAXE2L2q6eabC8UHPJ8KXbFyLihKjZGkKxpb1Q33sbRICcYC2yl/BAps0k231/Mih8pj0MCDWsQUZAQRrA4tHfbAacOOHXAqQNOHXDq48apDx826pqQoGlbdVNqCdF32vw0OBkt7fCHC5TCPMxeYvYOZy1HR0eT7FdUs50oLmw9qVprscbRD30GlslFpfcDbdVk6cDA0PfENBnwmFSH2FpBqhpCIJJYLBpOjtfUry/ZdoEkUV3a78Gdh8xY+f59xnz+fjIjkgBnbCEKKAoZ+308MSST0sWMIioGCgXXPfBMJXEx7Y2LEZPZM8Mn5+c8Oj+ldpYhKxe4HALQdzsFzQx+6qIVKms4XrWsli1PL645Wh/jqmosogOJzfaeqmpYLY8JQYsxrVZr+n7LdufRGOOHEzOh7tUHL1E4An0e1Ze3uRvmVJla8dID03k7G/AUKZrQoh1BjJG3V5ecHn/Co0ePud10/OXlS9q2wVGTqwAAIABJREFUJQbP45Mj1qt1jjudWFFRKmkEhRBiZoYA5zQ+FoGYSDZhQtlgACHgs3G1qIHAjsiVLaU+Trta8eizz3ny5g2SLMaodGbvA0aMGvGkcaAxCFInSIZAGOMyqzq7U5NqaCOCGFX9kMRYHGk+T9X9apBsWGJUTftJYSblSq8BKwImYVEne+MMTWUZdrkybFbycM5RVWrQde0ZVbUQQAw+Rl68eMU//tM/cXFxAUTeXUbKDlZZstJam+X49I3WWlViyfdp7CRfqMoZEGLAVBUmA53BjFKLavTNxPAkZe5C1FALtWnqyg5RDfjIMMVZXYGyMwNlpQef17DOwXFjZq2yVPlvxjCUcVOiYTtzjfZD228HnDrg1AGnDjgFB5z6mHHqg4cNTQRxrJcLamfpfTEmEwsyd9c+ZEfSg9U4/ylzANPP+fSvihNrlosFxmhBmsJYxRByfGHAWFUMiCEgKOCUU20MAZLqkjtr8SQCgh8G1UIup3ojGqOXNLbQisGJoVksVRN90xN8OSXyngk1f9bZk77HkE8/a/GY0bCKYEQTf0ZN9VLYRtBlYfSEO/qts+KAsZaUDedoXKwbFQ5Mjq9L41hI1lAGIbFeLHj8yTmrtsEaITHFCotkRQMB5ypckToLEVJg1TY8Pj/j2++fsdvuWCwXeu/eY43gY2Jzf0dTL3CuxsdAVTcsFit86HKcdSmiNDEkD+cSaIIbgNgSn+tAGNmPcRxi4cweosCUdKWgZEZpBBFV87i4eMXx8SlV23C1uef55SXHixZnEnVd01Y5wSvFB9cnM3t5IxEiInlxRzVuhQmMMSIBEgrUzlpUcD+CsaRMlSmLo5sXV1d8+atf8eLPfyGFITM5LrOMU6VTDSVRA8+oCqNxxykV17yyN4huSlxxr+b3h6jAYLJ7uiSSFeNly/yKcVx3xTBJEqwRDJGmclTOEsIOlyIuGyDnLE1TjwzSaGARdl3H8xfP+f3vf893f/qThp7w0KbIyIo2dcV6tR6TIYtEHylR1w1aXVkZnGLER0Me5xJ9ae/3xdZ4PyCJWdx4Qgh5k6DzKWT1IOvsmFhYNqcpxpGFTnEqWBZyzG1KxU4VGyCEkTUqqkJZuWWmp35o77YDTh1w6oBTB5w64NTHjVMfriAeIjHBuq5YVMJup3c4N2YPmZHS2dPiKT/lmzAyWjx5sNhSVHbqeL2irSti6EASTuJYdVMPcZHoewbfEwmj6yaMAwMpBlRqUCeztYbdbqDvO6zT5B8fAsPgIXqwjsEHNgN0wWR1AkFMViUR3jHM+2JeaeybAm5zJm3vOZMnYfGDDrTqpetpPEXwIeKJRNFFF2GsJqtJXdpvlTEklQPAGJuNycx1Ty4klCAlXYCIENJA4wznJ2tOjpYkUXd5VVUMnlHVIIZIVWnCUKnsaURI/UDdtPzqy8cs/9Fwt72nqitcVasaBWBFCL7n7vaSk9MzsBVDSjSLJX3fEX3Cx4GJSSyqMQ8na8ym3uRkN43PHCXjshvPYUkSgSxdqPIKiESQRCrKDLM4ZXWz6ny9v7vmzcUrPvvqVwTveXt7y/Vmy+s3lzTO8umjGmcVhAOekMze+EqeAkWHWqe6YFNmiqJAUZOThJiExEj0el8mJsRaxDmSMSCqDCJiePzZFxyfnLO7fIFLltY2KgFohBATPscug1GcNwrwkoknH4SEzZsRTeYCBWfjqqzFHbN2toqOpBgJg0fEqJs6Gx8jCR89MSaMKNuZAEPCiRbhkhjZdVv60FHjqYzBVhXGWaq6wlqX/06NVgiR68tL/vnv/yP/+E//kdu762xL3lk5+fXEcrnkaH2siYiodGIMET8MVM4iqL66yUltY3hAAJumePjyb0pzl3nMrvRJllDyGgyZRUohUqKDS6GlwkiJNaQYRnARtMLtMAwjK1Siuk1hmEKukovKHwbvx+cfE/0O7b3tgFMHnDrg1AGnDjj1cePUBw8b211HVVdUVZ0roU5m630u2H2GZMYmieRTVhx/3qNYKEs5sV6vWa1WGCNUrqbBZWUF1D2WO1njy/xe/Cto3JnokQ3veySoeoIqTASdfFGLJA39gPcD1gghCZf3Ha9udry93xGTQ6zTOLl84ntoXvQjy3NOBnzOpP1SP6UU8Tk20eQiOpr8NOjpHrJ7MSg7lE+73nsGY6gqBRcf/OjqGvJCG4YwxvmC13w1DDaCjwnDwNnRMZ+cn+KcJseJMVSuJPyEsbQ9Ygkx0vU9RqCuGmVtxPDp40c8Ol1zt7lku9uxWKxw1ulpG53cfb9js7mlPXpEymori+VKq1H2Gs85dWyiJFq902Q+S7IRRyiFoRQAEyrXCISUx8eQ8Hk80nStMgeVpMH7gddvXrM+OeXo7Ixdt+XFxSserb5h13Vsug0nyzWJRN/3RIlY2Tfkk1Sc/lxl3XdGt2OEbPyNaMGdPvRQWSRYbFXhRNRtX+5RoFmtOP3kEc+vXiHJUNtKDQwRH6aNS5nfVeUwoqyarpM+u6UzGGatbVA5zZFFtWqRS8x0jMr2RJKyGSlCLIWC1IhHAUxhqQQxjl2348nTn9kloVmfEkJP3dbUrbIxIXhKldK+H+i7nhfPnvHtt3/g+vrqvQYrzeaICJwcH3F0dIw1jpQ6XUc5FKOwQSVZLeYwAnFq7kpxI2X3zF5SW2FM92LLRRnmAjql+FNhuUuyZYlldsYSUUPcD4Nu0PLcdc7lNR6pTN4oZmYIQZnC7JouITQiU8Lwob3bDjh1wKkDTh1w6oBTHzdOffCwset6kjHUbctysUbkdmQrfjnmc/p5/1cPkvPeGSfBCJwcHaurOQbqSl2RIbuQxwWTlBXRDvH0fY+1qjZRTuwx9ESnSXcgdF1H3w+jO9PE3HHBg1TcbjueXd5z10PTHtEuPHJzj5FAED1Rv7+VUyx7z/dhgMv9l419DBFb5UkyEmt60i1MVSgxvHFKxFNlA43D9N7Te3WRabKQTlD93ARYYjL4GFhV8Oj0hJPjo7GiqeTJ5UNQ5sLo5I8hEmQCrN3OU5mERM9q2fDrX33J05fXDH6A3ZZl1mMOQccrpsj9/T22XlI3LX7oaZqWxXKJD0MOKZgZ7r1whymWdz610qwfJM/IkLXPE1DczylLur2nzlUZlLyA1dBuNje8fPEzi1VLVS148vQn1pXh7OSI65sbKqPyiEPv6YJKbdb5edWlqRsLBeA0qoHEzPQZEUxS12PwIWu/qzvaiIBVlqiEIUhOUquqik8++4wXf/kjMXo10AxjH9e5MNjE1DID9kGxMT+rtVoMKTHJYcao9QEEZRjruialNMrs+eBVrcXaCUZTUuUfJdx0nIwwJLi4uuHP3/9EdDWnj75g2awyY+Ty3PDYHG+dYmK33fHs+XMuLy9/wabsN2ssp6enWmgJRhbGOTeLdQ8Yqlwcyo9JfyM7NALwxBzlaaBjltSNrQxjVtzIhr0k7ZZwkhIKoqy2gvwwDKqn7j0xx1yn0me5C+cJtUVdJKYcy20MJpUCUe8y84c2tQNOHXDqgFMHnDrg1MeNUx88bJRY08o51qulJqTEyf32sMPL91MV0/K7968iYVqzkYQzhpP1EcZZ+uBpsZjMNJXCIjFGuqHXU1RVMfiB3W5H5bLRiCETAaKFaXpVlOi6bqymGGNWf4hRM+0RdruOzc5z8ugLvvz8U66vrui7La8udoxhqGn/7meEWP7+3ef8kLzgjLTQv5yxGpJ1yo0xJO8JYwEifUvwGkenLsWs/BHTqChAdrdZZ5Vxw5IIVMbyydkpX372Katli2EyGDHH81mj8bIpqZdXUENBEu7vdpAiLqnb8tfffM3ff/sTb97eM/Q9AxrzaHJ/qGscut12MnjG0LRL+q4npS0hDNNCyvOn9I0a5vxNZucCWqlS4xybvCnIRW6wxKQuapNZPNJs81BmZCrJiHkhAyl5rq9f8+ZizZdf/RrjLD88fcqvvv4Si+HmxnJ6rAtccmEk57LLP2qCps+bCBHBeaf63rNELh8CQiQa3WDYbBitUXnHTIeBkRHcjIGTT86plyvubnZUySBiiVGZOZOZCpUttFn+bnrGEtNamBExoh78qCyQsRYJyjaVGFI1zpMyRVEj0YJWkzxe5qCwTt/XDZFnL97w7NUl0Rq+fPOa1WJBFENMZM1x1UBHhBTg9vaWZ8+e0XW9Mn/vGPK0Z1vquubs7JyqcnR9h4ga9jmzUuQjVTZQ+6L8TgqDTcrvU9ZWQmacxvUp0zpP2l+C5MQ+GQ1xcUuXmFrvPf3QMybizQxxKeJU4ltTdn+XzeU0L1NOTlTwmWJ3D+1hO+DUAacOOHXAqQNO8VHj1AcPG4s6x7q1FefHa5wVhljcq4UpebhAHhrw/Ta9TztszhY0Vc3pyQniLEOJQcsnWpfLvQ/DwDAMtG0L5FhPMeqWqzWJp0iGDfnkJiIahxb35fgG71nUTmPchoFF7fjVl1/w1RdfsDk7o9vecH97xd29J4olSnjn/h88HcXQlL6ZT8gyKR6ySwXNZjZGX5+dKjWhatbHaT95SE/A6oaLo6sLVZvIqgyWxPnRkt988wWffnLGsq015i4bGWMMTjIDkxP6nLVUVZUTqQLW5SIvIlRYztannBwf8+bqjpgi3dBhbYu1Fu8VQKvKYYDddkO7XBEx2EqT8GKM7GIgRp/7qCxijQeFfQHKyc2csLairltlNiQgThmiXbclhoATw77Wh7aRNZy5JYVEkoT3PZevX3F0cs7p6TlvXrzgj3/5C8d/91+wMzv6pmWxWrJolzRNm6+nCh8h+pnhDCOgmnHchSR5Ece8sK3FmVJhlixNJ0DW6hetRbpYrTg5e8Tt1Rsk5uJIRsdECxcZjLPZmE0bLCMmf+Zs7WWjpuxqjiV29RgPLaLGzmT9cmudspsw6t4XG5tSGg0iCPf3O54+e8XdpsNL5MXLFzw6PSWJI4qhCYHFYoF1un58CFxcXPDi5YuZRjp762e/CVVVsVotCSGw3Wxo24XO2VnSqK6PUHYho/vXWktdFTe5as+DGuhIUTTRuegyAA/DMMYPW6dgEb2yRqWfBz9kIFWlHlX10A1QcWMLMo6VzeoeiWks9LNjZr/syPIVYDi097cDTh1w6oBTB5w64NTHjVMf1FOsndBYw8lqyWePzmgqja/bN0r7HZ4S2UjNvpgGvLxXZD+2VBAWTcvp0TFupnYxnoKNodt1bO43kPTUaIyhco7laqHJa0ZVLKwp8YmTTvF4uh87zRODHzuZ4DlqKs7XS1prOF0t+KuvvuTsaIkVdVkZmZuTB1PrQb/MYyTfnYjziZbG021xGUqJ6ctMUNyz7oVFYWQDUtC/Dbnz1ehWFIUHoiARlrXj608f8dWnn9BUqnNd5NdSUqNWV9XYTyrVppJqiUQIA9ZpcpVPCe8Tb69u6Lu+DCIhRLphyPPEjGNLitzf36m6h9IQLJZqCK2dVBbIi6JM+jQajWlCSZZE1KqxOVksgYhVBZCxz2UEw3KdsmCKBnWZs9r3asy3m3suXr1k8D3VouGP333H6zeXDMNAv9uxbBccrdfTfNoLGyjShXF0jfZ9T993DENPGLy6/EOYkq0ySxR8IAyF8cvG3OimxFQVp48eI1jSkLDidGPDxBLqHNL54XOogohkVjGN96fTaNYPmU2xxuZNUWZTUnZNB6/JnJlVqkbXcoSkXzEGQkq8ub7l6YsLBq8JdS9fvuLt1RXdrqPvOrrdju1mw9D39F3P9c01P/74IzfX1/trg2nMHrKzrtKKrjEzpDGlMaFyYoxyH6cplGPoB4Z+2GNf0jgHUpnC74aBxMQwaBiFKudo8qEy0OCsG2PVp5oIE5AMwzBWs9X1ZPL6CuN8VP35HCrg/Z4BL8l/h/b+dsCpA04dcOqAUwec+rhx6oOeDY9gU6J1FWdnJ6yWLXebHTArvPPeVtiUmdErsys/nACSpsEBYdk0HC8bWiukCM4Y6roipIgRRhmzOico+RCwRli2LckHrCSaSrWgIxHvdVKmGKmMJaRAEdTrh0hlrWbjoxPdhkTst4RugbWRVVuxahuMgDOQomotxywttvfEs1N6GQB9/f2GX0vEZ+BD5cRiNjhF9g+j/RNJ6mEtxX9SZtvyPRRehARH6zXLpZ6m7+43miSWhMYKj46WfPlYmSKfE4JUmtGMi9oZdTuHGBg2HV3nqZ3PsaWxOPXo+8jF7T2//4dvefHqTY4f1GfrhgGMoXY1oLKQg9dT9+bulqNj1R+3rma5XNH3Ha5uMCJs7m6QXFXWGKFEBZhswGNJIMuJdc4ZYtSKp4VtShGSWKIRtDyqIGmeNFWMQ4nnLoyKGvEYPNdv3/DqYs3jzz7j+uqSf/nLd5yt17RVneMcgypseJ9DItSQFX10RI3I0PcEM6tYnFDNcmtUlSQliJEUwliYS5JDjMXk9eJDJBpheX6Cq2ricI9EkxPJpjniY0CChjGEEHLNAUGKXGGe+9k7vb85GN3OKg+oRjDOAE//zlit2CtOCD4bFqMoGhI8f33NxdXNOF+vrq75+dkLVuszVdaJkTAM7Db3BODp02d8//139FlCcLIe+X7T9Jqirca81nVLu1jS9Qowtq6obI0xogxi3gRZI1klw42Fj0ZjK2S5yjQa0Gm9SmanNbG1mDLvtTBWLPOIsnEze++zzmSVj+m6pXCbAEPfZ2Ot+ubTBpfRcNtZbO+h/XI74NQBpw44dcCpA0593Dj1wcPG9a6jwSDOs1i0HJ+sefnmbbntPPH3XV8yThbYG4FUmAO9USgxfEJJOFstFxwtW47bmlSBG6X2hL4fsGI02UYi3W7LbrdDgKZpOVovsUbotokw9CRj6QbL0KumsaSIRUjWYkrCUIoE32t8XdNyv9lxfXNFu1zSIvR+R5+l6KwxeAGRbFZS5H2OoV8Gtn3WQnK3iAiREiOX52mOSSzJPiFqXOdo0QrLkZ8BUVahaRq++fILzs7PCD7w8uUFP794RR8Ci8bx6HTJallrgZ28+K1Rveqh7/W6eVJYY3GuJoTIdrulrS3OCg4hiOPq7R3//vf/zD/95QkxKSMXhiGDGfRdrydpYyaNcaDbbqldxWK9Vk3zdsHRyTnHJ2cYAz/98B3393eQXX9JcmysFTCGaF2Or/UMvqM1La4yuFShRFLRJ08k41SWr4xLKgxH7rw0MQYjS5HHoN9teXPxkvXxEWeffsLTixe8vLqkqRuePPuZx11PUzcjaMcYiSERkkpcCrrpGPLiLZKRIFRJkAqSBOLgCXYCGnEGSZEUIgQ1OP1uBwL18Zpmueb21TVpK8RFQoyjqg0JlVe0hYHEYF1NDD5Xs9X+twZMfiElPxmIlICEH/wI6hq3XCmYxoQhYUWLCVXOEWxkiH7cQNzddTx5dsHtrhsNbt95fnr2ksePP6etakLfj2Bxv9vxp2//wNXVa6Ts7BIzG1FM5MyWkFit1hyfnNIuWupmkRktVbYRFCAVYIr90A1BQudpSolu16kWfFWY1aIOouxZQnLYwSyUIWkMujKphXHUuOxCaKasQFTmlbKVEWaKMDGGvKnxI8vnnMU6myUoixThxHz90mbw0A44dcCpA04dcOqAU/Bx49QHDxu3257oKtoQOF4teHx6zPfGZE3wyWg/jIt9fwxbfv3Bz/rkaqyaqtYy6kHluPR0aBGTssG2tFWF9x1d1+ln5EGqqgojiVTXDCniY6Sq8iKOQgyawBeHgIilrl3uoKxs4bQo0fOXz0jGsGwdPz//mcubWzxZdSPHMQpCEjMZB+bM1xTP9n7WSI2MniBFT/JJE5BgmrbqLs3JgakQVHmG53cYIImoil5I1HXF6cma0/USEQshcHV1ReoSq0XLctFmABPEOk2QM1NFylhi82KRZMsYgczcf47trucPf/qef/7zj3Qx0TYNxUVZxiQBu27HarkCVJVF42Mjm80GW1XYuiEax2/++m/59Te/4eLlc7Z3G8S84u7uhhCmRKyiXS7GQdLY6L7v2G42LJYrqsoBic3mjhA6rNPwBYPF+3cXQMr/7cWppqlvYwxs7m95/eoFX3/zK1gs+PH5Uz7/7DNuNxtSfMnx0TFNU1NVdWby4oPPmMUrixai8j5AGoCIGDDDgNgCqBVO3OQijpHr12+4ub5mdbzGOsdyvebNzwEXhBTKJklGV/kYblGMiGi4hmp3+/G+yAZtVEsRgzFprCCq1XsBwxguErwnpERljBZmYsiAANEb3rx9zbPnL1XBhDQqVtzc3PDkp59wxuKqClKOgb18w7PnzzQ5ViTrre/bjbLiyCou1jk+/fRTzs/PMk5ELb5kBRN0DC2WFAdCUGDw2QhaY5WxKRtHtEBUqX+gRlgBS4yu+UDYB+pYagOom3lU5xhd2XGMcRUYC7ONkoQpEcIkeTiPlxfIIReMRZVK3PHBu/HL7YBTB5w64NQBpw449XHj1AcPG02zAPRU2LQ156fHOOfwYXjnvQ/d1fN4tsKQILMzYD4pFfYDAVdV1K4ihkA39IRUYYZOmZ0QsFZPuhEQY1i0Lcw6zohOONM0MAzUMVE1Ro1djPR+wMdhPCnGGFTbux8gJE5WLc9fX/GXP/8BYuD15Rvu726RpDWexAkhomoAIyDN439/+VT3EOgKM1EGp7h2BZ3MkiBJPsUmBaAyv9M41YWSO1ZUJkLwQMQYS9toRdFh8LSVo64rnLNEIi5foe977XuTDfp0w9hMMVR1BUYIwNB7/vmP3/P3//Rn7nceW2c5uwwEg/eQVTp2ux3WWNq2HWP5rLUM3nN/f8/SOL76+iv+x//pf+b8+Jz/8//437m9ucVWFXVTc3l1QQge1Sg3WOPAWIhQtS11XbHdbPCDp1lUWnm377UyZ9UQfMwLM+V5Vhg7xnljJBXykxKLGzM154eBm8srrtdHPDp7xJvbW168ec2vP/+SXbdjuWhpmmoCL3IstjXjtcrnJtJkBPCI1/AEAXX9A1hR2b3swgzbjuc/PGEIHussToT1yYkmwfkEIamLW8Aahxhh6DWZLMZI33cjI2KUNiGlkFmMaSOWkhota9zokla5Q3CiiYExoX2fGc7oA4MPqiRjLf3gefX6isvLq0I+jc17z8/Pn7Hb7cZkNu89V9fX3N3dT2vkoe2Yv57Unb5eLPnqqy9pmiYzLmBdNoTGYEXDVrQSbsAP00arrmtdOUkN9DxOuvRDKZZkkpn6Zmak5+s5hkggjGxSTHHc8KQQEGNGhkrrKkz9XlQ+ihu6zEcks1Nusg0haizvob2/HXDqgFMHnDrg1AGnPm6c+uBh46ht8b6n9x4XK9brY5q6YdcNezJXD43XHps0mbrMtczG98EP1hgkT3ZrnUoLDgNGElVTI0kHPwaVQaurihQTm809XdfR1I7atRirA9EPg+okGzOqMLicNKQMgybjNXXDECKLwZNi5M3bK7rtlpPWkU6P8AHatqEbPK/e3LAJaWSICqOhzz0Z84df8zjZwvwkyC7omfoI5FhEsnvqwRm6FBNK+d35IBmJ7HY73r59S9s0tO0UJ1iJqBGvXK7OGQkSMMmy2+2IKbJerUaALXrQJYFRRAghMYTADz895//6h3/h4uoeVy/UcPieyjnqpiYmjaEs99x1nYKHtQw+l7QHul3H51+d8V/91/8dX3z1V3zxySP87o7rm0sw0C5bbFVxefmaoespMZ2S2RRnhcotMMbS7TZ0uy0poeob7YKYIMUOYmb2Zr04TteUstxiGmNv99iWGOi3G65ev2bZLOg2G77/4Qe+fvwZToTdbkvT1OP8N0YlAm3WzFftdckLXCsFxyz36A0Yr/GlYnXhYw0Mg7JzxrK7vePtywtMW7HZHHG0WrE+PsHYihQ6lQq0icrquKa8mbFGl/UwDAiCNUIyNqvZZFfuaDEng1Q1Ndba0dg6q2xuDPn5RIgJeu8zICXq2iLJsO0Dz15dcrvd5XW9r85yv9nSdc8hA0qMkWEYZqOyzxS9rwlwcnLM408e5w2QApaESCCODPTQ97o5yX2vQ51I1CqjyL4RfcjsKnuj2vtJF9qeAVfd8ryOE7nokswMOaOdsdbm50yjtGBpen1yFd2yYWPvvcoc///pnf902wGnDjh1wKkDTh1w6uPGqQ9L3zoh2JpN17EbPE27ZLFccnN39/5OnrFE83/HIUhMRoh9G25EqJ0bDUld14g1RJ9w1lBVDX6IWRpQE+78MIyxmjFG6srlYjV6WhRS1tJOGidoNYZPK7sONHVDlauipiTs+o71ouGLR8f4oSPFxMX1PdutsgNv77dsth3bLsdT5g7OvZ2fTcanm4Pb1DeMf2CMgklKQWXGzFTMRYtfZkWA7LoKKWaXXwHHLDUoICnRDz2vXl8RomG5XHF3d8+2G1jXFcfrlbqRpVTrVFZJtZM9JU6wsA2krLCSB2kInic/P+c//P2/8POra1zV5pN/ryxVSlR1TVPV7FKfx1Rdl30/0DYtxsQ8KYXVasVvfvU7Pvv8G569uGBRWf7Nf/mf8/rqgiH0LO4WtO2axWLN9dVrhl3AiDIWzloqa3DOcnx8Qtc0xDCAcVRVQ4oogFsDyYwJjJmyZKQo87OKGHXxR6CwOABEoh+4v77mhXHUzvHq1SsuX13wzRef0w892+1W1WaqirquRwM+zXBlK2OOWdZnMEiIeKMVSvEBYwJiA8Z5vLeIMWxv7xi2OwyJfteRlkvqtqVqGrrdBkICk0a2dsgSjiazVjFpBWIbBKSES+RkQcA51ZNPJo6bjLnevoiMxsWIxmv7eSKe0aJYIXruNjteXlwyDBqPGsv6FzJqThrvc5M0bmkye/PQYCKTlXDO8ujRI46Pj/F+IE9TVTMx+ncDyoIOfsDaSsNTZL4ZiXntybjWyrNORj3LMjLFrqeSUFnslZnVDZit75Jk56os+RgDQ954CpNueWG7jBGNqB/vL4zXGDd9GSgO7f3tgFMHnDoWlSAkAAAgAElEQVTg1AGnDjj1cePUBw8bkhJ1VdGHSB8CVdOwPlpx8Vr1wjWRKD0w1rkl/V9ZEuX5R8dqOQXlMbIirNqaZVthKyEQCCHQ1hWNq4gRiBErqqscozJCwXst6FRVGOvUZRa1I4w1OQlnwBhYVA3GCpvtlq7vWbQL1evOiW61tSzqmvX5Kckk+uBZrG/Z3G9YL1tO7nb85adXXLzdjctUJ78mPunjTAvg4aScJoEQUcbr6GhBZRRsXOVGqTk/BLyPpHzCNqIn9d57iJCSGoaY1JD3g+BjYtf1vLh4TV3d0Gdpv9PjJednJywWWW/bB1IKOFfphDNOCwsZiw9a8MeJYGIEsSRJvLm64f/5x3/h+59eYGxDba1OxKxs0YWexaLh/PSc29tbNtuOKBoaEHxgsD3WOcIQWDYtf/273/HZp59xeXFB1w18Hzoen/8b/vt/9z+w2e347k9/oqlXHB0dcXP+iMs3V2w7z5ASGkeqQBbCQFVVuMUSlZhTJqMsNLKrV8qkTPvjAIkUQ35OnfOlQJTGZkZ2uw3xLXz9zW/wMfLjk+/57PE5y+VqjItslwvqqs2szaxybP6oGCNp8BixVCIEBJHEYDwihiABMV7ZIzEYY7nb3NOHHjsIYei5v72DGGkWC+5vLrCtQJ3nYQFeYzBG4z4xQt/16kZNCbFGY1eR0UWqQBfxQePHg1qWHKNdFuoUAjIManyqqhQGGohiudvsuLq5U7aRmSHO+uFQ5DJnG5mZEX1IiZSPLr9IJKqq5tNPP6VtFyqZGCJFVcVVFTEF/DAQY8Iam2NcZ7KkkpWCzLSRqSoN1CixvgDOQUzKwInIKAFZCpiNLBS6BufAZ60d419DCHR9l1nuKYGvzI0iO1jiaX3warCj6uirq1sN+yjbeWjvtANOHXDqgFMHnDrg1MeNUx88bPQR0m4ABCuGRVNxfnbE058cfe+ng/F80MiGbEYHvc+1Mg6NAClSG8eisVRVltLyiTgM2HpBZR2bricGT9M6xDbsuo7k1d28XC6prMU1S2WTQgdU7EJgN3RIDFQiOBGkaui9JwRo6gXBD3TDFrI0XOUqmqol1rDd3NIPHQK0dcPR2rBetnp6fvjMIpCyAz6R3WGz382bOCKOunb87otPODlulR0LmtTjnOX/Ze89nyQ5ri3Pn6uISFWqq7taAQ2AkCQoZsgn9o2N2e7MXz1qd3bHdofvkRySDyAhCDSB1qp0iohwtR+uR1YVwAd+hdnLMKCtRGZWZrj7Pe7nnnuuioFVH+iiFN9VhfVYdp6cwCANhkKGVRdZrDwpK/rE2p6wdprZuOHOtQm7O2PqpkZszRw5iabRVhW6mF+sOs+q79AKZlUFKLJWHJ8v+P0nX3D/4Su0qqgM5LgiFWfFGBNNbXjztVu8de81jo6O+erxM54dnglA5kgIHuM0o1HFu6+/zk8/eJdWec5ePiT7FhUjH3/6NT/5yTv8b//uH9DG8OXn91ksDa6qGE93OD075ezsnMVyTtd1V07VPkhHWVCIk56CbEk5XhmHvGaMZP6pojNOSnSsQnKU8S2MYEwdztTs376FagOPXzzl8YunvHnvLYiZmIQtM9oQ0lBcFddBXA1ayST3QVgCTQgZ0OgsUgFFaRiVNVpbThfnnLbnzGwmLJecLBaMbIVyji4Emize81qBQQJVSEPToFyYLEMOiRTE8g+jMbZGE0pslQLPmDLtql13AHbakLXcL+kmm8XyMOcrczsl6TJ7tFhy2rYX93d90y+t+CxdkAc2Ka0LFQsDmoVlHMQNFxshAai6rtnd2RPQiwNbIw3UhqLHIVVcVRVK6XXjNGNMATlJHcs9GgrpPGTWDYkGTerlAtqUoqw7Y2R+5EtdV7Okmk0pmpOxTvR9T/BynwdWb/AsX3ddzhc2juugnVKxT1Ukn+i6bu2Us7m+fW1waoNTG5za4NQGp77fOPWdh42ckVOM1riqwlYVtw+u86fmPr0PqME5O+dvBKo8/Ef+1miqi8esn5LQWklRWE6Q5ASljWhhFVYmkjHYuiInSWdbK01KutIIJeQWRaaqLJCInXRmNYBKidB16LpCayPpQAXNaETbd4S+x1UVShu6lDAYtDKMqhqMpPJsyozqCi0ZxXXa+Oo9KxMwX3Y0uZoiG6r9tZZ02+0bO+ScWS2XtF3HZDSispp52zFve8iJxhqssax6cVAYOQsp4FOm9ZFVG+hDxIeErWpAJuXWbMK1Wc10Ml7//cloQoiZRddRVzXT0YjkPau+pdKJyjoqZ8FYln3P7//4J/7w2deEaLHG4mNPSEkCRow4m3n73l1+8eP3uXNjn8Rr3H1ywH/5v3/J4clCNJ45EUPi9mu3efeD97l2/RrzLrB6dczxq2e4qubVy+d89oXlgw/e4n//j/8Hzajhj5/8kdPzE8ZuhKk11chhjzSr5RKldVmsqkgX+mIrJ370Cshela6b39xKXKT+BJATOV9ICtSl4KPItIsFJ69e8c6bP+DZ+TF/un+fa3vX2WrGnJ+eia3lbKs0ThK2M0bpemuV6LxTuV8hBJyzpKTxvhcrwgQOR60U1mVSiBwevuTo8CXJe6wybG9to02k8wu61FLpCUZpQhDvc73eNSViylgtQSEPwULJnDBl7cjakm6jkOjaFpBOrJKrv6QXVYMt4pCuTVAKy/qkhNFbtWsW6LIOXl1Z96xBdABLdfWGX8SSgd0r34/HY2ZbWyUlHq889nJKd9Cg5izuMEOx31oigmh/U4zCese0ZsUur+NBvx9LwytjbXEJkXc/zLXh9YcUe0wXdoBKXTiwDKCVL92jYZ1KLM1l3lx0aR0aJw1NoDbXt68NTm1waoNTG5za4NT3G6e+W0ZVJnnve0xOVHXFrf1rzKZjTs8XFyyJ+oaFoPr2gH3rtSVvu75R1jqqqsGHTI4e4xzO1ZAjISXpvKk1XecJwQsTYe3aBs9YS+d7fN9TqZGcgqMwSqRIigmrRAvrAW0krWqMYTwa02nNdDrDOYfPQxfHwKQeY7WhT+I1XNfSkEWlYfrlCx3fpU9HRjprljl4oWvLoORz55Slpf1oLF7tMZG9p7aGphZZgAsZZw0jq3BGUzkp1JmOxLGg8wFQpKRYtR29D1R1jTGG1aqlco6d2Yit2bSk2DRNM6YPkT71KCJN7bC1Y5YrVl0rwbGesIyZTz/9go8++ZJll3B2DERCUvRRdJFOZd64fcAvPnyPewfX2BoZSUvfvc7nt/Y4OjnDx0xtDDEmll3Hsu1ou47ZbMyya3j24pT52StmWzscnax4/OiIe/du8Td/8/co4/jss884n59jqwnOTXCmYXE+X+tdZRGp9Sl9sVjS+e4iiBUmYmAzhmKqYYsxsHxDwWdJTjM8QmVIPvDgq/vc2N9n+8Y+L5+95PHjx4zuvUEKkePDQ6yxWOtK6lTsA43JZCMa3rVvvrpYJsIiSDfbREZphesDKQROD19xenSI8oFJ3WBj5Hi14ujoBc32BDtpRO/qI4YsDM8w58pM9D4QfBRQyxkfApXV1FZ8v32IUDqyOmsKEyLAJalY0baKc4kWSCuMqDHyvPm85dWrI4IvLHK+2Litt2zf2uhxsR6Grwvbcmmbd+W1JpMxdVWvi+WUUleC9xCDJIAnQkhrl43h78cYUUmt35P3AUIQLXrpAK20pKTJwvSEmKEAsXVWNgU5YZRZF8iFMDDoSthMLoL1ujagPG9ITw9AA4VNjFG0wrKrKJ9pKMz9i2F0c7HBqQ1ObXBqg1MbnPq+49RfPWxYawl9FNu2yrE9HTOdjECVxTEMAJfYkkuD8E1OZT2OQ8FYeYKxhtF4jHGOruvoY0czGuGspe89vk9YJ/rLEHzxnWadinLGklYd7WpF3/fUtaOua5KG6HtJURUgqKsKjVTfpxiwzjEajcXZwEnqfbFsST4xthZnHO0yoFBUlUOXIh9KeiyXibcO1AydRK/i2YVPtBRnif4ukmLAuRrnDHXtGDU1RmUMmcmoluZClYaUyQTqZoSzSrSuZCorTE7jhJXQxqCNoTGKGAO11UxGNUM3WDm5RibjRvSs0TOeTHGmYdRYIprWa774+mt+9/FnnM17rBuTSYTo8aWYyJB5884B//Bvf8zbr91ia+SoDLS+Z1pbfvj2G/z566ccLfpyIla8eHnIl199jTOG19wBe1sTlsuOdnFOu5oz9jdYnPccvjhna7rDz3/2d9y4docnT59ydnbGcjHn9PSQ46NDzs/ndH2P0Ypm5NZ+3Du7gfn8nJPjo6JBlIATU5YFMQSHfHXMyLLQlF6bp5TxlVl8dnLEF3/6jJ98+CFuMuaz+1+yt7vL/s4ei+WS+uyMyWRSurWWgj4ljBXroKfXDhADG6KyMGq+XdHOzzj0PV27ZHV0xNQYRlqh+x4fIidHr+jajnd+/hOS6njx9CG5jCuo4mKR1gErxUhMCeusAAnFB77YFxKF/VRKMWpqUgzCKmlxB4mBMmdLqrcA0lB8p1DMzxe8enV0qdPrmgfiSkD4xo+uxKXL3/yFXyqtGI1GGFuYoEuOGVf0pWv3IXkR0euWAkfsWncqzZ40MWZyYm0NGFNEc9XBSLBG9LDBh/UGbNikXWZ5cr6w/xvS0OuflwJMpRXOuDXLpJPGJ79OXacY1/XJ8tqb4vDvujY4tcGpDU5tcGqDU99vnPorMqpBT+dIxX935AzT8QhtNDkMhXUXp74rzNFfPOZcHaHBaFCXU6s2Fm0Cy9WKqGA2nhAjhLWnsLg8DJXyrhIGJ0ZpzS4FQcNry5VSonEOnTJ9jKI4VJSioZJeMoa+78g54qxFY9YIMxQcRTJ1U2OtIa98+cwXH2tgHobJmlNeN28d7uVgFzc0FxoGKqSIqxx1anCVw6TIqKqpjcNoRW0UIWeST2SliFk0rzELc2ONuFBoI0OqtaaxE7qupXJW9JLFGaP3AaugmozpfY9SSTqg5kzlLG1U3H/wkH/81e948fIEZ4uPfeyEZSidKW9f3+Pf/exHfPjOG9SVw1hDzImYNcZY3nrtLm/cvsny/iNhG5RitWp5+Pgxs9GIsdNcP7jJtZ0dXh3POT89ohlPOHewOD5kMqnZ2d1mf3eHxlrO53PadsViecDRyRGvXr7i8NUrun6FMRnrZGHUTcP2jji1LM9OpJNoXbNcSXC94Ppk7l0OOTknUirBtSxgipY1p8STx484uH6DGzdu8OSrB9x/+IDtnR2crVi1LaloYa011HVT5rUAt6RNhwBecCQl+nbJ8vyc5fkZq8Wc1WJJjj1GRfanE6bjEXVMWG2Y1BV7+9e5+4Mfcnj4CP/gz4XV0WhlpBCz+Gjnwlo6Y6iMQ2mFSaxlIDJWmegDRmcmjQPjyp1hXWw3rGuxKDTiPJMzKUfIiuOjU05PzooHf1l1ShxChoW4Xht/IcZ8Syt+KVIMl9aG0WhUGjdFkh+KGjVKXaRyjTFopTBWdMLDZ5AgTgn0F9rbdUY/F50qIhsZtLXSaC2tvcrFqrCsZ6NJKl1x5hgK8YbAro1Z+5kPnwMy1tiyEU6YbOj7nly0tzHGNXM2aH43Mqp/+drglDxwg1MbnNrg1Aanvq849Z2HjfWJRksTEpK4aTRNLWm/kuIrX6zviJBBl/RwlwdvGFiFNAVSap32imUBGGNxVVUWTsQ56VaJKhZcykCGGLyciHOi73p8SFR1gzGZPgTOl0uU0fi+w47H1MaSc5BCliC+z7aqCCmichJlr1ZMxiPCaMT5fMnK96isaLsenwPT8ZjZdMTZfLX+3Krg0qBxRSGFX5QztRqa6Jj1Pc05E6IEz6ygK17rypiiL9WMmhqfFSqLm0FE0YeEX7ZMmwqMFReNmNeneXEgFO1cVVUYa6icuWBKlKFuKkztiqe1IgWxnovFPePB01f8z9/8nq8fPcHpCq2gix0hyj3OObG/M+UXH77HB2+/gSvMFCh8DKSoqIxmezrj/bff4smLY86WgZwFrM/OFjx9/pyd6QjrRky39pmORrT9OSevvuT81UOIUkDnKstkPGU8mVDXFY3R1NsTtrdn4hJyfMrh0SvOzg7p+1ZcX0JAJWiaBusczXiC1dD1HV0/eEnLaT+XgHUxRYfoKq4YUkQqxWBaiTf2n+/fZ3t3l+neLn9++oRbd+7w5muvoZTY2Vkr87eqK4y2YvE3+FlrTQrS1KnvelarJWdHhzx5+IDzs1MqY3BasTOd0EwaamNptBX5gobdrRl7W3doxjNefXnKsu3Y292l0pq+WwqIKNk8pJQwJS2uc0YVDXlOmd4H+t4TQqbvI0YlmkqaiBkj1p1Dyh4QViUmMqITJyNzJydeHh6xaLtvLO8LFfz6qwxX93j5UtS4gFK1/oeyxhS1s8wmk5KSlo6qWiu0zuvXGgKg1hqVxDpwcNMYmiPllMgYSduHIJ/rG0AybEyH4PyXLP4G1igirFwaaGMolqWBlDM2XzBPA3skzc9yseIcWKaLtHUqWlpVxi7nTc3Gd10bnNrg1AanNjgFG5z6PuPUdx42qqYmBE9sPSpGrHIyGd3wtAum6PIADsOmCosypK3UldUiqbIh1ZXRaOMATU6RaTMq3SijTDzrJECQ8TFjlMbYiuA7SAnrHEpH6roi+J5l7/E5EXtxAFiEWBgCTeNq+twLexIi3veMa0czaqQbafCo5OnaJa2PVLUjqkzXeaaTEXcPtnl5dELblpPdMOvWs7akPkuHzrV1G7pMokhWhj7A8XzJ7biNypkQC8uE2ANCxqqMtRalLe0qiM4y9UDCWAkO6EjvW2o3QSshOJzVGGvQ0aKtEoeGlOl9xFUa5zQoTQyKPiUqJYvi2dE5/+PXH/HpVw9RRgKQj+Iy4GPGp8zutOFvP/gBP3v/TaxVnC9XpJhoRiMyxXEiJZRJvPnGHd58+oyPP39A9BmtxNP56YtD9nZ2RV+bRUoQU6A9O6VfdRgFlTWgDKt6xGQ8LY9JtDFTj3e4dvAa7773Q5Ztx7NnX/H4yUMOX75gdbbg7PyUxWJOPR6J3VwM2GoJq56cI0NyVWaeWgdzc+EYVxamsEYG6UyrVObw6CVfffVn3nrnPZ63PZ/cv8/BtT2qyYzKOumIW1W4yqHQBRMEtH274uzoFacnJ8zPz5mfnxPaBY8eP2TRrji4cYNpVZNiTQwRbHFkyZmcAk1dceP6Piu/4snzJ6iq4c6113G950H3AK8iRmmcMSXQJHLK+JDKvAKdDUTIkeKAkciR8jtNQuNDImlVGndFlDNYW9GnQEQsQK0xrELk0ckJbXHKkLtYUvKXYsMQFa6QRmoICkPsuNQ5d3iaVhgU12YzbuzsEn0CpclpaOoV1+zOcKWU8H0PuWxEtSYVG9JU2BjCUGgnaxKFdBXOGVwlbFBMUHzHnRFNrcSsEr3Kute22P55f+U9XHQ6vrgXMUZxOonyHlOU8ZGNRyZHCeAiv7lU6PgX2ffNBRuc2uDUBqc2OLXBqe87Tn23jIpcmhNlRk1D7RwpJ2EitKRmSjXTlQG7KBi5YIdIoikc8i66eJ/LAEo6L5FZrlpJ3amMcQbj3PpElovOzfteLNVqt36nSmmsc+Sc1m3gp6MxXd/KiT9EzlZLGlcxnc0YNQ3zxQLf91gjTgw5BNrgJd0V4jr1nFJmPBlLlX/dcPfWAZ9/9Zy2vWhhf7H0y83XGqPFy3g45YIwU1klMML+PHl+xBs397m2M6NtW3QGsUwLklbTGlsbsjbk1GJVxlhQOdJ3Hh0i47omhbhOy4WUsFoJW5QMxipiiKjSqGfVdaQuU1eVeEAXt4NF6/lfv/+UP3x2n5BgVFl88a73UTyuR3XFT957h7/9Nz9hb2u0TgnPFwsUGWdt0fFlYhBw/PlPf8iyT3z51SN8CGQS8+U5j188ZTYeY7Rmd38PUk87n5NCpGoq0fhWDaOJpGiNhtWqxa8WvHjxjK8fPuDma2/zg3c+4P13P2B7e5evmy94fP9L9FwCjVICViGBcTXGWJIfbAZlcSSV1wsnFVbgMrOnS7BRegDhzONHD7l244DZ9jYPnzzm/v1rfPjee7jakILn/PQEu6qoqqqwnZnFfMmLJ485fPqI5WJJCoG6qri2s8WtGzd48PgRyXvcaFKKsiDnwVUDYkgkl+naFbG2vPnD99GHZ9iPHtGdHjN6fcSihlppplhCTqyMQiF+9SmJrIESIIw2WCdrKFtDytD1fWE9xTpPaWHQlDJFa1xmehKAOztf8OLoiLDWn5ZF8w0G5hvh+9JauShOu+zCUcKESE6s5o3X7rC/u0MMXjoCK2EpNepincYL+8hU1k4MFEvHvF6Xa4ansILDGh/0rEoFRMMicWUo7owxFG2xdIQVFqd87sKuDcFbG116Kpg1k5XKzcuKwlZJjEwprqUrec1y6dJcanPI+GvXBqfk1Tc4tcGpDU5tcOr7ilPfedgIfiggyoxGY4zKLPuWrcmUcV1x2oUhfJdn5PXJ71vDNaQD86XK//WAZyqrMVrRx0BdV/icyL4nY1BZsVisRA+roTYaH3OZGjKZcgZyxid5T3VVUTtLDsIi5ZQ4P4/rm2qdo64qrAZnxFVD7ODUunitrmoynpgSTVVTGYvP4kCmLk2eb33WoalKSc8PLFDOCZWDaGGVxvvM4+fHfPHgKaNmRG0tKQZ8jBhkneSYpEES8tzpqJJAqTOLLtBHeS/GOPo+EtEEMjkkXEpU1pA1+BzQSOGZD4k+CQNktMZZx3zp+dU/f8qvfv8pbQ/OjUgp0Ecvjh4pUzvHj95+g7/96Y842NtFZS/3buIgBbQGozMxKXxMaJXQGW5e2+Xf/8PfcW1/n08+/5yXRyf4ruf5i+fcvLaHtYAOoq9OibqW1O5kMqWebmOqihgD7WJJ3y7x/YqunXO6eMnhySHHRy/48Y//jru37mJIxOWCnDyusaxWK46Ojum6VvTClSPEEqgYHFpkDiUgFT3l2jlimGO5pAuVsDdtu+LRo4d88MGP6ccTPvnyS67tXePO9QMqramsYbk4Z3kudn+L8zkvX75keXaGiZFR7aDYRBolqfxrWzvUVcW4KVrzIZKVTUAfIt4luuUSM3L82w//Dc//+294+euP0d2ckbrN5PYYmprOivbYoEFJKy9JU1Os8EqQURprJEBJQx7Rgg6+5NZorK7ICnyIpSAxkWPCK8OTF0ccnZyv15+QKEOb22GdX1obl39WGOW/GCsojHNK7O1s8d7bb7IzG/PseE5WCmUcMQZMKo4s6sLxQ+JWRJfPF2MkaNFnU9L3Uqx4wegOae1BYx+jyGKsLet4KJo1ithHBp10jGm9Eco5y+atWBoafWHJOIBYyrLxXWtnUyxNklS5OTLWuUhFNpmNv35tcGqDUxuc2uDUBqe+3zj1nYcN5ywhhqLt8ihryCmzuzVjZ2vG2dmSoSupGpgS9e3W6DJquYxcCbrDiJb3XleW6WSEqxzWOWL0yLzIaAwhRHyOVNZQ14ZxNUZpTe89fd+jtRSwtW0rbh1GToa1c1hlwGimRUs3nODqukbXBlV0pF2I5KwIUSwMcwg4JV7Yzogd4Wnbc3q2oPcXBUmXZ6pSpSumAnEGGLRw8ntd2DRiJGbFfBV48PSYWzfOubk3FaYtsW5Mo5UuhW7CYFWVY9zUWKMxLrLsApU1VEpq/LQ1KOuIKBZ9YGQylamoq7pMNjmFirMBKCWOIJ98dp9f/vaPHJ21uGoEGXyKklKNCWc0b712k7/58Xvc2t+BJAVIvu/RVUVVmAejDZFM13dIBtxBhoODm1STLfZu3OTLB4/46uuHnBy+5Nmrl0yaBqMVs9mE6WiEdZamGTGabGGbhhAji8WCdjknRQHBvb1dptvQ+sz5ySs++fgj3n7vHbZnMw4ODljOT1EqMa4bfO85Xy7QWmGMRStDVlebz1x2qhkW3mD5JppSQ0bAPYdAypH52SnBe/Zv3ODpw4fcf/KYyXhM9h21Fdav6zsODw958uQJXdsyqmpmoxHW1BhnUVnhfY91jr3dXbquk0LKmLDGELyna1tsVRNjSf4qRb9YUR0uOf7tpyyfPmbLKrYfVUzcPi/2MieTRMqKadQok8kaQJOLbvwyuyvOGOLHPRQFppzog9jcOVeVn2Vy0dVqFG0fefziiMWydA29LD9Rl8J1vvjyUkD41v2/HKgGzakzmnt3b3LvtdvUVcPL4xPOz45x9RitFFEBSq+LcQc3kpQSQWm0vuRLXtLnqry/lGSzo4254upx4c4hj9FKAE2Cvi6FeZkQpPlVDFL0m6AU/AmwhFKsnI1oXS9CoRQNpjQUN17EzItCQQH4yw2bNtdfvjY4tcGpDU5tcGqDU99vnPqrblTWSNrKB7GvyymzPZ3y2u1bPHl2KBPr0gAoNYwmVwYlf/MLxbpwT6vMuKmYjEbUroIyScSdQHyDR7VDR0XtNE1laKqarvcEpUsK58Kf2LkKYwzB9zRNQ9/3dH1PXYkmMsuMJsZYGitB74M0JkJjk/gZOyO+2DFnrBKHk5NF5NXxGV3Rva1Tb6oQD1A6LZZJT+YibXcZ7gpLYS3zPnMy77i2NUIhrJYup0o0xJRlwgklA0phjWU8coS0YFQ7Rs6Kc8eootcVh/OOxXJFqyPXnaWua1arjpgjRoF2Bm0qfFR88fUT/udvP+bp8RlVPZJ0buyJMRCipL3v3brG3//0Xe7duY41MmmNFZYqdT1ZKZx1MgGyFB3FpNG2IpqGmDWznev8+OAeB2/NeevFCw6fPuDk2ROMdSgUk/GYpnYY6xiPZ7imIYZAt1zSrjpMNWFna5vJ1oxmNEFbS0ywXPacHa948ewhy+WMuq7Zmkxp5+egoa5rAZfyHqUz58WiAdaWfOsAcul3aTi9F7cPpTWkQLdacnJ0zPYbbzLa2+XPL56yNR1zd3eHWimMgvPTM148f875yYn8HUDliA/CthltcJWlyTXWGM66ji4EdhzFzxAAACAASURBVNWMqlgkhhDQ5WtjZF4uzpa8+N2nnPzhM2oidVWz10bciyUqJPxe5ryytGgcSLFqKcYbGh1ddqdQSuGc3Jtcmgf5GCW1rQS8QGGUbJBMVpwsVjx9fkTfXyiLpahsfSspO7H1KshDGC0buouYf5U5EjCF7emYt+69xmRUYY1md3uL9viMdjmXgmAtRbnZivuHvNglR491UDYkJw2xUBdFxaAFTCkFstZitUhncma9CcsotE7rAjhx/vGSZi5BNqbBlrAU0GWRa6QQrziQXC4SvPz/EBwHZ5jL/ub/khPK5trg1AanNjgFG5za4NT3G6e+87DRttJsp2pqYgjEJBZ2k6bhrddf4+NP73N8OqdkYssfHI6N6tLPLsaU0rxGlRtDSTlPpyOaypKCFx2qVti6QuVIjsKK5JyonBH7Oq3p+yAt4MurO1dR17WcxgBdUkshycmusqZ4M0uqyoeAPETT+cjKCyNlkK6ws5E0XVq2nbxPJH25WCzpe1+ymldPcmmgYobJqS8DmtwXZytZFMagnaVH0fahDKAUzKkIKYuLSQJpvFM5lHGEBCHKQkwp4GzDZNxgVWI0bpgnQ3/WsugD2ULXBzn1I9pQZzLGaKI2PHz2kv/3V7/n/sOn6GokgdEHQvSiKc6Zm9f3+PuffcCP37kLRoHKhZkykMCHclJHiXY6l8msLKqaoNyMEBVW1Yymu7x+7S5vvv8hxq94+PknvHr4gLOTQ5rRmMYZbN1QN40U2S2WrBZLmvGUG3de58atu8y2tnHOFeZR0HM1P+frR4948fKQyjgm0yn1UUW7nOOMpaoq+naJrRymt8QuFLYEhr3G2omhsBXDhiQncUGxyl08Tinadsmzp0/YuXad2e4uTx59zR/uf4G/eYut2kq327MlwXsm48HXXOZZHwOG4sYSanwINNbRBY/3HU3tmDYjqvGYpq5lDscOV0lQiCvP+dExVb9ip5Hxr7WmPl9x20dqD0/3ZxxNNUknGbZhBhZrQwnMeR1AhrR8zJeUq1oRUwKt0FosK50BYubo5IwXhyeEUiyWCyu8jt95ndzncsAerm9+P9g7liejNdy9fcCd2wfE0GOAa7tb9MDzl4e0bYtzNZWrSX0iJpHTaInSUrRXLDaVCsUykCLnKAyTKWNaGCGnVLEoTeQ0uHwUe0AtTd0up7DXnVOtWafac0pr/3thHC9tZnMmlqCekjRHGljJQRowNP9SBWzk/bO5/oVrg1MbnNrg1AanNjj1/cap7zxspJwlJeoMfc6E5EFJo5Fru1O2Zg2HJ2eofFUXqpSkwQaZody5K0fIS4FcAsBsMqF2llW3kq6rRpNiRZd8SRfLKYoEfch4HVn2nhilm6bRCucMVeVYrFpCCEzHtRTgAFuTMc4arMp0vTQmsdbRd52copVGI7rYuqqE8chy8utihBSpjbz+te0pD5++YtUFlM6lDdBwOL6aYhOXk1Q+k8Vay2g8QRtDKIO4XC05WyyIeY+mslIclEJJrYmrSRcCW02NNRYF+AR96MlZihgVGZUzKchngYRxlrqu6CN0fZQutTqTg4eUeHZ8xC9/9wc+//ox1jq00cTkCSmIXWOGndmMv/nJ+/ziw/cYOcWi8wKwWhw4lAJlxP4vRE/bdRit0bZGNdvo8R4+WiqtmMy2GY+nYCtCSsy2drn+i7/jU+v4w0enuKpmMpuijSOEwKpdsFgscXXDa2/+gNfeegdbTeg7z+lZS9dLmrWpK0ZNw7vvv8vW7ku++vPXNNMpO/v7zB+3TCZjZtMx8/MTjKuYzLZlAQV5fh6YICVpQ6U1xYNGWD1dLBdTxJVFb5Qm5sirV8958ugBb77zNjvbOzx+8IDjV0dMLOSuZX+2x7XZjLpyVJUUrorsQMlrI+4SOSXMSOOqmtRLZ09jDJNaJBuvTo7IGvFJny+lWc/IMt2ZUndzKiPFpSEnJiljs1j+uWrCmbPr+SyMl4GhyCsnUvKEEFFaLA/J4v5xZS4jzC1kUoIQI89eHXJyfg5ZXDIu88ND8B4C+OVrHc4HJkldPDmjyCqjcmJvNuYn773FjZ0ttAKlMk5l9iYVoW146XtC6OmUWFUOmpdEJsUASVhAbXRhyuI6BCUj/uaxNLLSRklfAA1ZizZ2rZdO4sCjjLBCZCnUG1gfpRQ2GawxSEdbiXUhBLxPaFWYosIi9X0vXvBKNlXWqjUjpIuOXr6XrwUovitS/+u+Nji1wakNTm1wCjY49X3Gqe8+bERha4iavm9ZtDK5amMxKuIcoIZiuyG1xze+V0jZz6XikSu6w4timRAD2iiUkWYuq7alrp1oK0NgNmqIoePoZIV1Du978Yg2huR7fBvIKYCxZCCFjLUaqzVN0ZDmUc18saINcgIMOUkXVGtItcNZKf5arlb0wYuFYRLXD50zTV3x0w/e5uis5cuHTwXo0OgMWckElMVRpm/KJfVnca6Ckgq01tAve0LoCApOz8/ouo6Rq3HGYowWS8cYiRisq2mcG+4qGE1d2dJN1tH1gYgi5B5vM7UzmFzRWEm9pZQJ0QtYacfh8Sn/41f/zO8/v0/WlsZaYop08eI0PB2N+PkH7/KLH77LbNzQdX1ZGECxuSMixVlWulzWlSMkRT3bZ7J/l9M2ofrEznjKzv4eezf26fue05NzwqojRM+z58/x3jObbbO7u8f5/Jy+m9MtlySjufODd3nj3fdx1ZijkzM+/+wLvvjiC46Ojqic497rr/P+B+9ycPOAg5u3SFpzcnxMMxpxNj/n7OyU2WTCU60Zjca8c/cun/3xIw6fP0EZtRYMyMRNxbgho3QBKWVESnGpm7AC0IoQWp48+oqd7Rn7168z297l5bPnPDp5hV+cce9Gi1K32Z2MqSorcwWNypKGtM7itEZrhasq6rpBnUa2pzN2d6QQrw+eznv2r18n9onF+RIqRz0bsf3GXeLxIanviMaQELah6TquPfc07ZInd3Y43RpxqmVjNtKOWIJMyEPToSwVpVk2Ms44+qLD1lrjjBOHnX5F0oZVF3n24pC27VBK1rHcnIvgfSn5zJVAPoSBNccy/PDCDcQYeOeNO/zonTfYm01JJQXcrloqem7ujjEq8uzwlNViibU1zlbC/BhTKGNKMWhhvXImFw36YCeIMqQoGxylMiF4jBVGWthDmQvCquni3Z6JMV/9SBlI0mwpB2HQSBCKltZaI2swhPX6EmZIwHIo1oMLWcRFYNdrB5LN9e1rg1MbnNrg1AanNjj1/cap7zxsxBhROIxR64IxXbod6uIOcXHauxgwVXqYXxw4rw7ihXZ2KJRJHB6dcHY+Z3tnhtZaOqGGAE6JDlZFSJ5RZXFabLhCDphsaIwlqYp2taDzgagcCVgFT2McSUEXIxURrRKu1rimwjpHq6QYzVhHQhqTkC9s5YZUVUyiX8w5c3Nvl9du3eDhsxeSVlZq7eyRBmDKF42dbLEXU0rRh8BqtaLrWgZXAGus2A+WAG+Upq4cKkVySkRlyNoyqipiiiyXKyyWrcmYjGgB21UPKTFWipwjJkTx7LYWoxIqZnzwhKzpk+HXn37Fx59/jY8wqioptIpDMVFgXNf85O03+bcfvMPUOdqlB21wTpF9h/cenzwZLZ7vKHRVY3WNMhPq7Zv00UDKbE0n7Gxtsbe3y97uLseHRzgyYbXi44/+mY//+SNm45rQ9mgSoVvRt0tCCOzffoM3336fZrzF02fP+c//9b/xq1/+E69evqDve5RWbE1nfPD+B/yH//gfeP9HP+Tm9Zv0bc/TV0eMxmM631G3DZRiurff/YDjw0NevXgmKw0gS1o6pSw+KVmhbAZlJc2phG27mM1y2lcqs5if8ejh10ymE3b3dlitVixOj1n2nieHL6m0Ru3vM52OcdbhtCxescBTjKc1u9vb7G3voIB503Btb5e6qjlfLTlfLZhsbzGZTDg6OpKCUmuICsz1Xahq0mrFKisanQgmYxRMusTsqKNK5zw7ULS7NQsFIQYG7WfUmmTKxiLkddBIQZgRbSSYZaS5TyahlOVs0XF4MheZxHr5X4Dc5ZT/4JLyzeuKheD6Z0BO7M4mvPv2m+xub1NVFRmKFCUXK0TNwd42tTOcnc3xfcT7Fcs2kpSRolKtya4iWyvxunQH1oo1fCQVCZmyOclE76lyZjRqoDSDGtLLRsmG0Biz9k83RotTirP40MtjU/FUz+IYJNarmZhSKRLWBTRUYZxkVuWiI04pSYAvUgG4COyb69vXBqc2OLXBqQ1ObXDq+41T33nYGDcN46bCKIUzmtpabDlNVcrR1LXcDCVs0DeLQy60sHldbAIXKdxBRRozHM8XzFcd29sz+q6TJytxxaisnLRD31K7mqYydH1HZccYbQgpoJSlrhuyDQSf6b0nRk8iSbpZaTofSKGjbTum0wnOVERn6VO/1rVpbaShTwxUlRNN5nxOiOBqR/IBExWTRgqBtNJlorI+NSslVmzGGLH/Kxo/X06KkMhZY11F1TQYpTBaUTnHuGnks8dU0ryaPinxEA9S7CcdXhVGlxRYSvQp0fcBYyQV7MrCGFcVKgdyjlhXs1h5fvvpF/zjx58z7xJ1LVaRXejpQiIECRAfvvsW//5vfsr1nSmEnmgU2jics6QcyRQrtVLIFz1o7Uh2hLdb6FRjVcW4qmicpPxHowbf9dIQJibuf/IJH/3+I6pmxs7uDq8Oj6lrQ7c4p29XmKrh9R+8y2xnn+OTY/7r//nf+C//+T9x9OI5Mfr1XJqfHXN6csx8foYyhg9//GNuXL/B08ePCClinS0NPjOLxZKcDfsHd2nu/wkVF+IVnxMpSZfOfhjHmDFGgoYr9zwjntYoJdaSStwxXr18znQ25bU332J3d5fYLsk5sJif8+TokMpYEjAeNSRT7Om0wqTIYpGFNcqZyklH4rOzc5arlhcvXxBT4r0P3mc+n7M6O2c8GpWFDXl7ht2e0R4dkoE6aHoVSRkaYxgF2DvtIZyiVxNOdkYcV3BaaSrnsNqItjYmegZLwIy2muTT+h6nYX04y/mq409/fsSLw1OuxJbLBNHwo2Hd57W/xqX09DeCBRlyojKKt16/ze2bB4ivuKS/xTVDdOrGKKwyVFtj9sYVISTmy46T+Yo2yKaNDCm2BF/S8KgSsEFsDxVJg3UVdTXClsLRnKJsIK1oVFPMWGPXXW210VRNJd2pc8Y5SXW3fbcuxuu6rkh05DPHJH0XYoqgwBm3jpeiic2EIJaO1rryHNHw971ntWq/K1T/q742OLXBqQ1ObXBqg1Pfb5z6zsPGdDzCWanerypHnRLkwe9YXAqGlkcZ1o4B37wunx7Fszetx1kyfopVH1j1orPs+16YDmvJKPoQSWiMqUCL7tNojasbck4szzsiMB3XaCr6+ZLQZ1BaGgqhMHVF10f5gyh87/G6ZXAcyFlOcwpFVTUMOjSlNUYpaiOpRJ8SyUdGtaGymiUyIXKO60lpjLk4aWcZjJii2IpphbZWJoUx5BgJOZNTLK4mltb3tCnQWNH/JmVKA6iA0YaqqnDOUTmHygGdDb0S3Wzvg6TCrVk3UMqAUpaQFPcfP+LXH3/Cy+MzmmaG0ZYQWvoQCD5idOaN2zf5mx+/z+u3b5B8SwwCqCEGrBGwVCSyCgKMShaHVhV2co0cHTEqRtZhjRQrxpTY3toixsRR1/Hwq6/4+A9/pEua199+h/29GSfPH/Lq8ASdxaHk4MYdbty8S87w2Sef8P/89/+LF08fYpT4pJMRbS+Z+fkRv/1fv+La9X3u3r3D3vXr7F+/zvPHj6UJkO/JSoqefIBbd+9x5+497u5VVE6aMC0WLYtVx6LrWa5avA+SrY6RFFp8kLFOxkhHXEQrrrWi71c8e/aY2e4us609tnevYazh5PgQf3bO0fnZmiEIrsIai3UGE63cnxiYLxaMmganDYvVEjR4Hzi4vk8OkaPzEyauXrOPIURhOvd2WX71NVUCHzOaiM9SJButRaVEc95xt4/cWEWOZzXHk4o0ccxt4ij0RDKuMBSt70uqNAtzhDBHMSa6EPjz45f84fMvOJ3PS+DN6+ZDcBHDv2kT+JeuYfO3ZuFyZn9nxo/efYud2RTve3xhWIzWJC0aZa3EdtNZg7OalCTVbKxl2cu8tNYRfcAHL5p238s8z8ISh5AJOZK8g74j6AqUpnOG0DTUdT18EpQVNimGSGeMdN1V8hld5bDGgpG1OWyyUsqkWArtSnq5cpVsTkv6W5xWrrJCg7NLCIG+78SlqOu/8z7+a742OLXBqQ1ObXAKNjj1fcap7zxsKCUpIWfAGoMzVny7i6ZsGIDB/msYlMvjltep6283F1rX/2foQ6TvvUyIIf3tJLUcopz4bFa0nUcj1ffaik7RJ0WXEiM0tVVMakffdhhbE4Mn9oGoNZFMZR1bswkxePrOM+TTFJqqqkhRTrh1U5MRpxOrNONmJEQOCWU0uztbTKcjTheBggOlS6Re3xNpgsIV0BoKunLOBO/pU8RQGryUbrBaaZSFbBQhJfqMODOoi8r/wV/Z2gqLIYVM1IHeB2wIGPGuY9m1GCN6u/uPX/DL3/2Bpy+PqV1NrQUceu8JPqBV5u7BDf72Zz/i7ddvia5YQzOu6VIi+J5oNFUltms2ZrqQ8Ci6qMjRsjPaYZQ1jdK4pAhtTxtb3KhZOyI8fvCQ3/3mt5wvVxzce5uDN99he9pwdnbC+fKISgeWfWTnxh1Gkynzs3N+86t/4smD+xgV1vePXAK5Ulir6Lo5v/3Nr/jFz3/O3+5f48bBDb4cjzg8PmTZSkFnZR1aW7a3d7nz+j3evTHmzXs3CEGCeLvqabuW+XxB30mB5qJteXF0xKvDE3xSrNpASFGs95JsKFKG+fyMJ0+ecK+ZUo3HTFSmmY6ZP39JOp8zXywxQKzEjaZKFc5BthrlPRHxB6+d43hxTkiRmzcOOLh+QL9cYZRmOptROScMRCsg7Xe36Zsat+xZxEhFpiITUmKVxA7Tao1edkz7yO5iRBrXzKeBZyMDNpEqi7OWLkXalESu4AN9LwHdYskZzs5bvrj/iMfPXtEHT+aiEdAghL9CIq2DwYXO9VvM0foRUNcV77x5jx+88RqjUU0qQRglylmJOUNDJ9HMhyg++wnpuVDFADEzrh3UDu/Fe773lpQTPkR870kJ2uiJKZOTJ/YeHxNJWVLXEeta7D2NYZVbcuneKqzhBXAZa3C2wjYNzWh0RWqSYsYHcSOS4K7XsUFnTQyDg4heS2Ig0/cdMSa8D4QQ124fm+vb1wanNji1wakNTm1w6vuNU99tfdv1ED3ZGUnTqaGIJRJiJq8TzBcBegjaQ1rqwn/3m8FdOquilDQmKr/sup6cwFiHLoVPzlUsly3ed4Q+0VgZPIyXU50SO7sQE7ZoSbWCpnJQWbp2xcU807hqREbRtR3R92gUIQeqZiTNifoelaXYL8aI05qmrulij/cRV1muX9vh1v4ezw8XhEtNbLQWzXCMJQWvLoqLhiLEGDwhix+8nB4tKSRJiZGFkbEGpQI5ZZarnpg12jqMgi5EfAyQIlaDNZnKGCpnmHeePkZqJI3W+Z5GVTx7dcr/95uP+OLrp1hb0VhHTpHge3ovYHNrf5d/+NmH/OyH7zCb1CwXCzmdW4OLYs2YFSxWK2pnBfyUoo+KNltipxj7xNakodGWuPKslks63zKajHn04DHL5Zx/+uU/8vXDR9x68wfceeMNrt+8hV/N6fpA7jqigWxqtndvoI3l5PiILz79hOBbjCm2c/lSWVdhjoyBly+e8Mc/fsyPf/ZTtre2GE8mtH3PatVJ+rauMUqjlGXr2gEvj5/x1uuaOzf2i65bo7Pcd6PFJm7eex6/OuLx0+es2sCLwxMOj484Ojzi9PScUIrTfIi8fPGM8XSLGzcOQCvqeoy7fp1FTOTeE3qPB3LKpXFVIiYpeow5CyNqDbZyWBw7W1tQHnvjxg2aqhLwL0EoVxVpa0aeTPDnc9qsSdliUkmnx0yVIVQOr8C0LTYkqq5na9FSNY7rtaMfK9pG0ZGIdc1qb49DPIdHh0Tfk30ANItlz/NXR6w6X/Se4mCzVrurq2OzDtCX179ieDSXLTkViZv7e/zog3fY2p5J+jcZYkyE0MpmKSWcFY25MooYe0KSGKK0uGlYrUlalXQvdF1b+hoYmsbhvSIUu8GIBNngM8u2x/aQtPjThzZijMYrRR9lm7bePCiKpCGvnTnyuRU2uPjAW+OkcdoQ75Sw4SmnK4GbMh8UQ6fXyKpdXXyfIn+NefvXfG1waoNTG5za4NQGp77fOPWdhw2fQCuLj0AWl4taa0ICnxTGSXEKMa+LRwZ2SAL5xWtdMEdDAC+PzxL0jDKlkK9U6aPpfcRZqGoQU/CIs+I9rlOA4h5ijcGlRIqBs1XHYtWTFTS1xfe92PPVTjpqpsSy72m7QAgZmzUxZ5ZdD32UlG8KhJQIUdLyOst76WMCZQkpM64MB7vbNPVTVl1ElbGVLoulAY/SyEFPlRtwkcpTktuSU6WKZK0xxqEVYvemwKjMeFLTo3lyvGDeJUaVwZKoDFht0NbiY4+2FqyFKO3rTU7gLKjE/HzObz76jE+/eobWNY1xxZqxo/eiTd2eTfn5hx/w8x+9zaTW9DlQj2pC3xFTFvYOxTwEfNuScsV4NKZpHCFUmFijzRhCRwyWOQIQq64ltB06wenRIb/+9T/xq1//iulsl2s3bnF9fx/lVzz96gtePX/MxEVoGqpmyng8ISvF2fkZZ6dHckpfCyIuzS2EOdJKEXzHk2dPWC7mHNw8YDqbobXBdx7jaiZbU3a2prjxmK2927w8X/LV40Nu7Y2prKHtM8s+oHJmVFvRJ2N47foB927exqdI23ecnZ3w4MFD/vDZV3z6xQN8BK0z3WrBy2cPmIwrxpMpy1UnEgPrMF7sL7W1JQBFutUK7y3GGFaqZbGwrEYtO5MJo7pGhcx8seTOzQPGrqL3PW3X0cVI4yqC1ZhmjBnPWMRHmHzBxlRZUwPZC8smHUWhUyWI9FCFwKj1mEWgc5qsQW/VxL//Eef3rvPnP33GV3/6nPnpCeTE+arlbD4nBM9QtKhUUX2UNT+MzrcDTy7v7DJnVPziU2Q2dnz4zl3uvXaLmMG3HTorcRwJPTFErDPoJFaAWjqJoXLGakNfGMlRM0Zpy6oT4I4o6S5ciyY6pkhICWUyEmI1EKk02JE06xLtbxTZScpYxOs8p0z0kaREtmOLfaDKCp0UWStCyOSU0E6hjMhujdYoJBW9br+k8rpeICXRxC4WS54/f0bft1caM20KxP/la4NTG5za4NQGpzY49f3Gqe/uII4W9wqjIAdi6FFGkVLEaE3jrBRdDXq2K+lnacF+MZDpSlCnpLETxVNZgbOGna0pi7Yr+lfou56+9/iUmdQN05FDRelwapz4MDf/f3tn+hzHdV3x31u7ezaABAhapEhKIiVSpBbbieNv/r9T5UolSsqJ40SKFEukBVILJYob1sHM9PK2fHg9ICTbzCdVqcp9qlAFVGEwmO7X975377nnjCrCsslctRiRWqEFCO/Be3ABM5JgLIHEalVTd44UY25hCYHvWpbLJZPRiFJJvM+SX6a/obkLmrXb67ZlESLeR5RUyLWqCfH0cwmRnTxFP1C0NvU5u7ClWKuDCEAglKEsS1qXcM6htUALTSESJmTuqRESq0QerlOasigIAVZdx6r1ECOFElhd0Hmou5YP793no3u7dBHKwhJTwIVA4x0+OMajits3X+f9d97i/MaU5eIIB0zGI7SxmF76rPGe4LpsKCUlddPRJUOwJQGL1JbRZIOytMwPj1kczgmrGmJk/2CP754+4p//5Z94/OQJr43HaGPxbcvDL+/z+OEDgltRh4jWhklZZOMrsgGTC5lKcKoq82cVyHyVE4mT+Zym6VBSM6oqlIQUPFVhKawl+JY3Lt+kDoHQvcrDbz7lzSvbXN7ZJLhVTghKZ/5iSngX6ZyjKEtEihRGcWVnJwf2Kzeom39k98uHWXM/BU6Ojnj+9AmXrlzDKJW53aWlXSxZdQFpBCNbYY1Fy6x5HUNWhhmVJeNqxPnz5ymMpTCGc5ubtG3Ho+NHaGtIIstuCpE1sYUUpMKy9J5Siz7RQeyvkSfrgRulkSmrU5RKoEXvooogxRblgRRpTcFkNmPn1nu88sprbMy2+e///B3zoyPmJ3NW9eq0AgJn+K4pN6H7b1+O001ffo1W8Ma1y7xz+22qMlMZvHOZi6zz5icPugmatsM7T1n1Q6r9m8U+2GmdJTl1lp/BjkqckZAirm3xLuRWd0iEnsMuZOa0SmXIBaOIl9lJK4lME8mtZYEjEUVODgLRSwRGnO8IXuERWFMiokcoiVASozUqShKyfw9xqvSxniGIUXJyMuejjz4khOzkWhQFzjnquv7/rujfLIY8NeSpIU8NeWrIUz/tPPXSw4YPAUmWLhNkLV7vsrmQMobCatYUrReurGtJwdOG1Z+1qtfc0NOvfiApeI9WOUCE4BFa946iKXMvpUYKhbUC18+51U1D5/Jpq1CSshjlyk4IiJhILp+sK1vgYqSuV9RNS989ZkUO+gFBUgpp7SnXkF7bPImEkBotBSplabVF45gvVsT1HxJZxzx/dg9JnFmi/fVJcKaR9wIJlsuavYNjLl+ckp0jNVpJ2sYRW8dmVWHKMhvgqFxN8s7TtoKyMri6ZtXWzKoRpTUoZZmvVnxyd5c/fHqfeefRtiQSidHTxYCPHUWhuXnjGu/fucHmJJtLpZj11UlZPtKHCD633JWUzEYlhbHszxuOa4dSElGURKmIQmFMSQqHnBwcgPdMZxOe7T/l3373r3z7+BuMMazqJXt7zwjecfDsESLUSOFpm46yKPu2ZUKJfrFr3fMhTxubP7iO/c8pr4VEL9coBYKAjB6lBKFrePbko9HQgwAADglJREFUEW/dus2lnR3qumFezPjj5w+ZjUcIAVpnJ1eR8rCV69ucgrwmJFD0lc1Xtrf41c/f4/HTZ5zUNUopvA/sP3/OeDzl3PntrE1dVaRxw9HhAT55XAyMy4pCGgyCqiypqorZbMbm5jkm44oYs4b/d48fI1JiVFimsxllWfXKEiJz0oXAlwU1CXzCKJ+rl0KRtMSE7FYaiCTvsUGCBEM2EkoikZLDhgQx4qsCvbGBMRWTDctbd35O6z0ff/hftN3nWammbzGn3vxpXf1Mf6E13S/xvMlL4jT5ZoUgASmwfX7G++/cYmvrPKHnflpjM0cbgSlsbuX3gTr0VWelNU2bq8K5ZZ9buUpJCp0TnVIKp7IHg/MJrRIpZu2drNiSDYejyOteiUjrAsS+0tZLRyoZAUWSeYNpVOa1uwTSaGIpeXZ4wpePnqKMZjaZMJ1OKUcVhc2bISENSpMrsPGFigpw6ha7Wq24du0q29vbXLp0ia7r+O1vf/uyUP03jSFPDXlqyFNDnhry1E87T730sNE5T9e2FEowqSy2KAl1jfceoxXj0mKUpJP9jTwN2nD2Acs3V5y2YtaOpZIsG5w5X4GTxYJVkx03vevQJEaTLJ/mQuY6CiJKaHyKOJ9Y1S1N6xkXWfpQknLHPEakMdii6KW5AjGbTuK8R2lLEuSBNqWRMhFT5oW2kGXLiIiUbeDbzqG1wSjFdDzhYLnP0ckyy52lRBYsk/10//eHZPJifdHCW9+4lFJua2lJ3Tme7h+xrLepjM1GPAE6H0EIRlWJLcueW5vbWi50NMExthWj0Yi6CxitCRGazvHZ7lf8/n/usne8wtgKISTed4SQB4G0hBvXXuFX79/kysUtUgwsmgapNLawCKVwXYs7HfwRWKVRqXcWVZagBEU5wYynzJc1zgdAkHwgdh0pOo4Xng//+DFfff0lCChHI1zo+OrrXTSvMVKgNASZByhXq5q2aXCdRwrBbDZj49w5njz+9vRanpWwTD03cb3kJpMJZVXldRUDyXtkCiihia7l+dNvebB7j5vv/ZLz2xdoTl5l/7tdvtubc/XSFjFknXqtVeZVphzchACrs0ykEpK2aYg+cev6NR7eeYt///ATfEp98q15/vQppa2wpSWhKTdnnCzn7J/MaV2HAEYbJdPRmHE16s15BG3b0NRLUt+CHo9GbGxuUhYWYyy2LHJASHmdJ6WRGzO8yRr+gVy5chKCl5RIZBJEPNF7JsaiXCAJj4gSERJJZW66lxIuv4K9cAHvE85Hyukm199+j6d7hwj1B3pmRV8hEv0G5kWw/kvI94vMn15XmQSQIqPK8t6dm1x//QqKrPQhY2/aJLOMqTGGtmvz86Q1MWSqiZISJSXedae0EOgl/Lw/vaZSilz9NJlC4l2WUYwxZNfakKULpcy8/5RirkClXClSWuW4EjwxeBKgZDamEioHf6E1KXoeP36CC56qLJhOJ0wmUzY2NphOJtiioihj1mLvg/c60RiTHYmttfzmN7/h3XffpSxL7t+/zwcffPCyUP03jSFPDXlqyFNDnhry1E87T73c1C8JurZDFjq3qpXKNvUxoKRgYzpmVFiWjT/TJnoRyM8qfqwfuPWpUkBvKJRf4p3n6d4Be/MFhTYoCVZElAj5l6TAGonVAkVuZbu+mtF2nkllkUrgvcsGk0JklQyXZcO6tiZJiVQaqTRJSHwIlFZhtSUYUDS0dUtoO6ZVQVEWaBGpu8CirplUI4iRcTVi2XQcL1d5aLA/Oa/bp2s5sReVjX6xk9udiBdKHUrlwN85z/FiSdN5KmPxbUcnslmmUBqjEm3b8ypj5uVFo3NADp7SGjbGExCCug08+PoRf/jkLt/tL9C6zNKBvRqCc5nX/OorF/nlnZtcv7SDJL8uxAZrNalQaJ9o2g6lNVZrggtoIYje5WqEsFTTDbYv7JCMxvXrwneO6Dwja5mfLPn8iy/47N6fCBGq0YiiKHHe8/CbXc6VhjdfvUxDgUgjmtazODnheH7McrVEItg+f57Xr7/B/d17KJ9pAEGI7z0EeQuRsNbw6uUrzGYzYky0TY13LYURGKsxRQHR88Xun7h87To7F7ZYHF1gefCcPz74ho1zEzYqQwg5gRR9AHFdrpQWxmRzKQHRCaL3TMcVv/7F+zx6ts+X3zxibQB2Mj9if3+PrYs7KCVRheHcxQvM9xIpZE6klApTlEhrSCnhYsAvl0TXoYVgtj1ma2uLoixY1avM70yR6WiMUHlXkqTEbm5gxlPqxTFBKkICmRIhCRISpeiVSTxWaqwPJBxJCoRUJJWvq59NGV+/SpjO6FxHiJ6YwI5njDe3+8AXT9f1em3/MHh/796kFy1rcfpd/2wIweXLP+PO7RtMxyXJC7RSvYlQdqtFyKzTvw58/d90zudqZT/4djbG+OBpOpcHbfvhOC0FRimCiMgkcMnTBd9zUfOAG85nHXskUSq8TxACVsue9h9BSqTozdSEhN5sSqSQVW1WLY1z1Kua+fECWxwy6atHVTXCFr1iUC8zuFY0stZycHDQt61zfNjb2+Pjjz+maQafjb+GIU8NeWrIU0OeGvLUTztPvfSwYaQgKoW1WV6vbRxSCoqiQGvFuY0Zo3EJx6vvtQ1fTPSf/f77wfz0ZpMgJTrv2Ds65njRcnGrZGQlhZZZli8KbP+eSYBPuRpkUuxPbgojJYXRdCnQeY8uCqKA49WiP/1rXBspqwpjLE3n8M7hpcD3jovjakTXddnBtA/C2axSUtoCqzXRB1Z1zfODI1at68tjZ4eJvv9Z1wv8xbhN5sC+sH3P+sZNShwvVzQut+idc8QkaEMgho5CCKILKKlw3qONRZW5ZXd0sqTsFQWakPj2yXP+46PP+PrJAcqUaAHRB2L0hJD/01cubPHr929z6/UrTKyi6/JQUNvlU7ItBCFxaiClpcTF3NZ3IuCio0uW0caUnZ0dlm2T1VFCoD6Zn8oRfvv4Ef/72Wc4H6hGE6rKAJK6XrCsj7l//y7nSsvmxphxNWE19qwWC07mJxw8PyC+FZltbPKLX/49H3/0IfvfPek3AOJMHOkrFkmwc/FnvH37DpPJlK5tWMznuLZhVFaMxmNmG5sIqVjUC77Y3eXarTsUxlJ3js/vP+D8Rsnf3bmRpf+8x5Ol8KTIhlZKSmIMBCnBKEwSiCS49uol/uEX73JwcMjxMrepg/ccHu5jRyXT2ZQETDY3OT+ZkFYN0jmWywWubbGFxdr+yxhKYyirimI8om07Dg8OWKwWKClxG5toISnKAiVzS5OqxGxscPT4m6wiIzJPO6oEaMrerbRLEdMPqOXgJhEqknwOUOnqJtOrV2mSIDQNSURcFMwXK05qx8HRHO8D64HZU9qJ4Mwz39+VMw++OHu7eJF4R4Xh1vXX2Nk6hyBLAiqjEf2jlXXHW9q2JfiANZkmIoTEeYfz5GpwjLny4z1C5sG8lPpqn8/VvigAHQk+P3MxZNWaTAeRqJjw3uFDliyUWkNvlpRSXyYTEm2zfryW+fkVqd+4BU/btKRIViEh4kPCLxtWq5aD/SO0MQj5otK5jodKSYyxWa/dOT744F/47LNPqeuae/fuDTMbL8GQp4Y8NeSpIU8NeeqnnadeetjQeJSRGCkzNzTG3FaWglIbJlWFKQtIa5Ok9W16wYdd/5Pr09H6Bva/hkjZ9CRI8EKQUm+GIiXSmCwzFzxVpQhJ0LiIJGCyyxICqErLrLRoEUFJlkRSDEil6YLvqzcS7zxO51MmKaCIhBhp2hZjDONRidIKv4j4GDlZ1UQEpS3ZqiRWSzqfJeWeHxyfBnoSyNSrFZx+fk6H7+h5wkJKZMpBHMSpKUrs3R7nXZMVSgBtS7Q2xNWKQOb2ORdy4A+B6D2igy54OiFwVmEEPFvW/P7ufe4+fELCUEhDSB0+5qHElCJlobj5xhXee/MaW2OLUQpVKtoQCUlRWs1IKnyMtLIftEyRQiSWraNJgaAkjRYUyjAejXAhgk8sF8dYEUk4Dts5H+3e5Wi5ZDyeUZRl5sEulri2BiKPnz/m0y8q3rl5i8IYNkZj2umItus4eL6Pd45iXPL27fd44/qb7D19jIj9SvthmUIqrr72OjfefAtbFCzmh6xO5oSuw9qK2XjKhXMzlm1WLnn23beMZltoGTk+fM7ewSF3d7/ixtVLbI6za2mSAJmXqXv96dYHhIzIKPJD2e9gbr92mU+2z3F4UmezLBGp6wUnx4eMxhVCG2qXuLC5zcbUs9rfZ+/ogL3DA3wIGGupJmPGkwnb588x3dqkS4n58+fEukVribKK5LJLahEsUYJ0jiAhTMa0CXxwBJkw/TMmhaQEYgqsgiMBUSYqCYXQiJCTopeQLmxjzu2Q2kAMniAFtQvsHy44PF6yf5QlFFkPpfXc1x9uYNZ4mRSeIDIbaa5dPM+0rE5lHK3VSJFoW0/nWpZ1S9s5FIJxUWCspCNm+dHUD2Y6f+qKmqDnQUtCzH4LMSZcSqca5K5zJCFA5hhlVB7cy1KdINYJW2TahJIKIxRSJnxKhL67Lki5oG0lrs3l70Sf1L7H3U6E6HGN62OgPA0XOVZI6romxqxXvru7y4MHD0gp4b3/q9dwwJCnhjw15KkhTw156qeep8Sg3z5gwIABAwYMGDBgwIAfA4Mt7YABAwYMGDBgwIABA34UDIeNAQMGDBgwYMCAAQMG/CgYDhsDBgwYMGDAgAEDBgz4UTAcNgYMGDBgwIABAwYMGPCjYDhsDBgwYMCAAQMGDBgw4EfBcNgYMGDAgAEDBgwYMGDAj4L/A9J//YdpcQAkAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1008x1224 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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V4x7IbTndzcRDhriFf8j+Up5pfIio+YB1185/RUGq2lUf125kwLSTHDMysxinJl9XqgHNvi37mybFFTNMEHVNYE3N2vxH+fbke0/Qa2D+skrwlG/HUGvwlKhv8JSvI3gqeOq6PDV92ChPRKimHZHSG+OBrpM3jy5PyDa6lI3qrCyfFYN4kHRoR6OrY6R+tE1Ezm5a20vjOeSocoUa5PCzV0XZACDBT5GYLbdFKUjo6CyTT7oZuBuAQ1tX6k99LK6ZY50516v4dFnLXO4xeMuf2qat0ViSbSvsoi3LOmWBHTDl1A24LAmrBunPgQS5zGijb+M0JlrypWEFSmtND2F/AZO1dKvX67ge3GwoH81/mAltzaoF8PTZ7By1yBj4tTS7xgSUtVTA3TqdNW8Fcg+o/fpY303+C/B2qANfKgme2jX6QFqTM3gqeCp4SlwDgqcu46mdmQ3X+jpAPbV1TBUjZ346aig7dguYq6Hodaj+2hWZ7ahA4IlzyVsL1waYjgIckfYkbAzPehmsgKbLP9OtrmY3Inkdjm5BvCp5IVM1dLR+C/pCQR8VLNeTo/vi1J0yKBk9dQpw84nlBOR34bIdJ+eFqxyBYmYkzGB1/8G5zspkIqQN3YQ+ec+ysARjbz2mTdusWZcW7dvxUzOo7U2EO+Yn+mT6jvSp3TYVRaWs0bStd82P6JCThsW/VC875T+SUf9UQIZDePVei9vNx44+lVGkN/wWYiR4SpcvtQZPBU8FT81NCZ56Njw1fdg45RiELNybMrbFe0f2sLkj1b/yyjq3zh26GyQi6tHn3Bsctj1KGftWjvVZxxu3aC3sAGM0OBb17nR4L4INdHkmK7zdB66WMr+gN7iaVPd6PHDwSUn0/1n0V7fjc2L3MtjL1HgFbBG9TEmS1RV0wFV5An6Gt4OEvRmgHPFqL+KJdhFpZTRl2P3VTueaTaat2Iko2dRWm0zG+lCUNvYHabOxvxKi9s9937KAWdpZlyXTyAiObG93Kln0QQVwh3esD/Cy/baPZ/0TEjzVaYNuj1LGvpVjfcFTwVPBU/13BE9JhZMsC7tRJQcH+qe+QbzBpN0BWVfT6ByA6vCk/EZs/qY+LfGoweGU6YJGnUruAbxzvIGM1tOOzrdViFp5bS+zrnPUzJYcAYAqIMm24C6T9aN9EWUMALWqK1EpkgM96SADKoozZD90QQhyB105PyZbEh6V16O28drsEzk0Hie0p1xmmkr0gUT6Y5l+5NIT3PqEmGtdJfjY/LIM2ZbtAlQv4/BUdcvbRZBke5uiCcgv3M4jYr13s7ogl6msmW1TNTzwIlISvO05Wba6XrYzhL5Orn6NN3b9+Lj8ln/b0r+QmQRPBU+N9Q9LC54ydgRPeRI8NSq/5d/jqfWtbx2Zr4d8GMku3qqtTCjoUwCwj77YLeo6NxqCbUort+Fr547VYVU8S9ipuB4/vjFKVwaLMtDMMK9aFC0OAVDa1UcURzKelh23p8SL/tqIfLn77toD5P3fc0HWr8xgK3jZljpwcQjQmdJLfZIMOelVNxXMdarbRi2Uf7L82m5OWr4yta7JmBl55lyvFyXR59Kd1Hrhaov0db1Jo71h0mNLvMhoImLO/UIjatFPNY8s+9BYs21prrrnWx4XwMFgYve6pZoRmM/stcmI2r+QyyR4qpw7VodVCZ7yygiekvUNnprJy8FTyw8btZHlOWZ3cMix0CDGeZq9VEhoU/sVigTGFjv0LwHgB+anQaH6sKtqPpue5o8baANPbprc2grHZHeSnba8jnSmKb9ak0NrTTFoQwGe+vRYr1ziq5tKXmgtSxVcSzSsxo6qMhI/KqQAAkDZVaVmyRGnRg6lfun7hozwlhi41LflURXxzlmWL/XIpJJGfwZ402ZsoliKVLhQgRZvit7vY4L+4SqZXpWm8pQIn7Yzt+fI0UV7z+xi5cO2/NKYMro1KC/EleCp4KngKXs6eCp4Sih8Tjw1342qdL4wXOocOj3swCGZY9+qRWk+v6dTUc8VwFi6uH1pRwyu3YLG0CcH3H7OgxVa6IaOLNjJJhM5Or210yq7jA5w+tcnd14hdFRasB6B92wLS123AWMQ8i+9+nVT5bLKpscO6Z1snBrpMouNdUxqJCM6ZdzMoCnsOJnoFTFwIob9Qa1mN6np0GZnafsMxVuJVDVHKFvpFZ9I2YSduTIaxMRNQqoMatRXTaeXqXo7Za9brt08AsAZRJt7o9k3/SASJCJYozxe+i6l6IPummgPGS2sJBxPHUMJnhpJ8FSXKHiqLzd4KnhKnn8gnrrXMqp1aU9C9xHVN7YE6RRdmlG5x+yR03ozAB6XczSuMkvvAZSfp3Mqj18HTVEvS6+3RQiOJu7Xo65PWftqpdmzCNBqOUPJersmIUslK7r6U/P1wXYAm1uPPdKRaUx/eMGctvOHvNamp8/nBvCtbNZ/TwBjU+Sb1gKX7ymdBdukbwMzNZAifVNIlE9sQI2A1Rrou4a0VKAd9y11EqDPNbrar532c+da+OnYpPJ8065Rll87wpC6yrkNWzxsPAMJnjqeZy998FSfP3hKZQyeyvL+5qkDDxtNCYm/xcbuwVo4yDXWxyYAL4OMVHdRvjbCGCujYMB1pTmLPqetG9vQv+hUdNxbqLShaEUb0mhJxYEYOPlvP0XbRnSLcvgvi8lSWrPsRPR4aOpQjr0EuCMMp85eoai2umq6SuY2KxGZC7tZ8/Mle5fPhPTfHHUiKifzZPJJFnlqoFdN4QzgTfO2mWnkDO582moUp0StWnRRgjCj/OrwiQhyWrtEkNr0Ptex1V6qtSTRVRtmKDdbIOruZB8RgA/gjQjqOmd+aKx6P0nw1DEJngqeCp4Knnp4ntp52GgDslQm9Zc3KEw+99psIPfSD7QEPQ3CJZQfl9HuHws5sUAVuMboKTs3aPPuqZe6L9mjTTo7/kukQTiuMoeKb7NqIvVzSYOITyVgaZJS7th8gVzjhmJegDmmybWBAj/o0PuBN8UtI1BlxKR+OQLma+OigGM9ECgmrfDXaetlHafTSehMuJPOnMDMNWIi66tAL6MpM+dp4NQ228bmhrLcdOr147oNpe5UB8FD6YtoT3krozDF0L0mi3lnsOquRkAhngRPDXIieErYETwlCjDHwVPBU6q9rs9T84eNM6fprVzxa71edck0Ypmt0k5iemdRfKc/ZI3K36/75cl1ZYkhOD3iS7a6DhGU9ZUyLgOkC8byOJMHWmSu7ejRU49j9Vbtnhx56c4t10XTfCsx0KuGLZVeyweH+0v+ims7lrZ1EchBKUtgTqKNBamuNSGJPMXITV834yKVw6b/FUu1eop8TG2LyLIuttnd8jMD24kFIHP+bOTXiPAkruvp7HLzWG8dS1+cCnHk8kQ7NoDvZWOTyLakaPP0u686AhZiJHhqZI3KHzxl8gRP1bTBU+J68BSA6/PUzi+IU1U6By55/diwW50a7PcnVxY5lZyBnNZlQf1+UQWdd40wxPO1KbpEytrxPUyrStqXiwC9DHb293t383D3pSWrQQfxUlW5ZrdVGPm+Rw5g5V9ev3rnhwC+ICN9ZM7pKEqrVF+s9IlZNEmMpfxXTYvOwNsaC+lzBG/Pc7ctQW3UDabFeeIH9vzK+t8CeMXitH1htTzd/NWbodbOMtpWIksAw5JA9iKRNp0p6Vs/mj3GlZ+K/h3UXUao/FaJh42RBE9dIsFTbp7gKZMueGpiwLCM4Kle5g8bYq9uXeDiKIImgH7t1xqAO1qn5TT9FkVSV5en8Oru5ql4Pm19bdKfA3S/33pxCIJ0jgaEwKgvrAa3F/OTeNsCbtl8X1gnY3jtO0ZmTWErBRrz7k3Kx6VETPYocj8aowf1SjWW4lIrzUHljbfc4s4Y6dpV+p7opj2723rUpoiFGq+cWs+C4MVGk4hEP1gskmOlXFPT+4mNGvByvaKmy0vafg2rjjopW/O5hoHpsoyClfLb3xBPgqeCp4KnjkvwFIKnniFP7byzsRPFMU+qvYyfgGT+Y+LnaVO4Tbd+Km+f/eCx2wKObWS2x/4e7qP8LV8qd1VaGT6pNGdZA6xRydXeMojI+J0bBhgo5P702Gd0H6TBq0yZ2j0qX5bpkbOKEKQTGbSogcQlIojQ19FIuPgCESC3yutySPuzXt9G7rqqRlLGG3Bj3NJqYlno1EWzGGN62LUbj1n9ZNklnfUTeTMAAYD1LGM6Auw6WmuTeyOV/7a1rYTTqYBuG5d6rBf7q7VZ/1a2jYeMaKa0W4tK8YYTEU5E6UXGZ3sf8h6T4KngqeCpiyR4ShgaPNXKvz5PXbz17RH8tSB3+Qtv61LANR89SBmrdhTRnXyMwNbbab3OdbcU6WjWLDZP505pLNLNij/S12nsr6++1v19XO4F2LuSd9soR6aYe5g9iBCp31g94P0yzNNy8qAUn5syGWxGn7RN3FxJkGs3NgOLK6kX8Ba1HEYei2NaoG6kpSNhx9ygRracSJPXr4QzcG50UEgnrXm9AW8Z+fkEBuN0usHdHSfwDzkswVPrdhQJngqeAoKngqeuy1OLDxv6Sai7uhM5ekigNmcgbdVrDbWsPLn2+WfRKk8kotkpuGuK1w57OfJAKBGn9aydqFZpvmmmFluERObyIgMz7vWmfL3oz1Gxw/+qoC6VK5WX6Z/Vc7cNujWq2rd1/mw4y3TDknUSGfIzzN6DqdHTNZa1qeR36kq2irIOjtXc63GBfJB/D/TrlHiJ7lEC6eISsrYpPTWztzRdfjoxcHcPpn9pJHhqpMe3o5ai7AqeCp4KngqeujZPXTCzsTaVqwalMVQ+3e9PcffSpy1PUzNHS1a0NaMeMHdNKo5Xyd5DIMJ6/oHWbprcc/AxwLnCPigeFs2dDVTrGT26y3rbNpi1DfUp3CnGkyPrXVfSeQDOnECqvoyKnZcOjUKqio/KJIJSUgzqb/mi4olEEJvaFLc+LLVCGT3kHX3Wl+U0t1/OQX7ldsOy5alkb/z4pDLAMjY8VSwfkUX9yy1k2JnQZ6bscfGC+FEJnppL8FTwVK8weGqsL3gK9osob5+nFh42NNge9cRiBIFxjYUA7hMtToPUvTU+yMrr4ogk2F8qz+ImwSOanYFfI0VXAHFTRW+a2AJ2eykpW+OQwLOUaYkr5hDSr9JCD9AavaAulDFUtLZedMmkKoVn3eFTppTL14zAKzZYuu505+tqB4w9g4dJ93X0lCdAGj2Az6NXkzJW+dsjSi+duEouGTz7MfHekuCp+0nwFBA8FTwVPPVQPLXwsFGiO/fzqr3ca5Eju9+3biTvyVm/fNc3vr//eEm7XueUVu4gcvCp9oD0T7tdTG5NUYmA8CqY7/vBkTbr10QaXcua9uXBiaG0ofRHfdGhWhu68Qj/WHtbf+69wg4Aca7YmP8Mox8mgpJgB6kTF+5ZOsAcoqLwN4aKmthxrfIQ1y0cmbnvl9pGl+Ga9zLgAaRoxAHGVqmltGRNlv/kl/HiR/12JHhqRYKnrG0L2oKnEDyF4Cncj6fmDxv3CHZ0OxLUGEVuWMf5bL6StmnxwdYvt6XvoxF+We8tKfUj8W/TA2ynfjXlNJLRxx36QbFutV07qLdM9MHHk1LP+yxzUHZl44brK42/jSCnG9LicHWw35dvytR569+i11NcEIXUoWfpeK27KIhVyQl8uxsnQP/g106FHffsjr3p+dru+cXHgc8y2+jwZR2w329af8FFsa26MwJK+8QL4kMJnnqBJXhK5g+ekkUGT0m73u88tfOjfntX7+ltQuygrKWoaJUd8GTS8+C71Fe/7dp0ZI3lsyQEPQiKyDbat7m6SEmeP/X+ELaVTOKiow6CUbkrg2PH5oF+ua4a8PvsWBRrYStBe0lE3vaa33pJ27u7rQ/e87tdGyvpqE3+xha5l9anyfsqV9ZIELTLyFmDxP6CB0K3UrOjtEVlWkyL0d9E9NaPiGpc3qwv2tp7zz7rDZnxlA/kKHQ8awwleCp4KnhqwdTgqeCp58hT04eN6pMuwC547AUyAnMp4zWq8inUuzbSd2z/8Yeb6mSkH0rZS1fqeYJ9mkx5d0BNquktcOigDPQ2sIqRcm3rWEbXVkCi34s7Ff+8o32svso1sJ1lPXI4okr5dlIAACAASURBVNee+ln69pL9WwmthUpcc/tqrEXb9qNydi0wibW1IxKeFjk3WxetdQlyTB8eue+UfYVxvtumAM7chle5ObNRt5CxBE8FTwVPjSR4yq1D8NQhHdfgqfkyqvLEAmCMLPcD8pUfXDqocWKTrsPRTnro9ZQyEnbMjlZfO80rf/yIK8KQX4QJ7HDp+0P1llGlavExFXviVf95iWhSVwjT2f+r+BQReJuEFBj9FO2FL5S6NwR11wpuZymXkcuU2ayP6vNFVohvxd5WlozOHVXt7mC0GLXKRzlfK1sNty6k1JQwX6Up3r8SPHWv9EcleGpRgqdMGcFTY3tbWe9Xntp5QbzkHHX4ZY3sPe3PIwCXlCN/Yv2ow7byliIaNU0Dx0uGyIqd/Ut3omx3YLR9k0WtyugTefNndrSSjlk4m2jPVuWSR9pu6zEm1fEUaJ5OBFk68BIP5TBIFvxZuTlh/ztLRdUhelCa23bg5mgQEVzLPbqJmgNdD8RbT1gkLNkBoh7ki221NcX1vuE7DJzeFHKXZtVPhuuBu3Rj2zS+9OQgP2vK1Y2MXkoJngqeCp6apfW+B08V1cFTnm3X5qndZVTSquczJWjCGA8q1v1XQNU7m0AuNdeVoyVK+n3by/R0dYR8mQ8+gQ83FSA5SLHULfvTnr1pZS2nukBUcXGk8l4RGFnUAboYwoUYyMc2E/J9xj/X36iotbIlgHpl6fvsBGAbt5nas/uIQXksOWB+SAogOtGei11mYdnBPlFnsFbpnIH1LODvPSrBU8FT/YXgqUk2Y0u7GjwVPOUoqGnuw1M7MxvPc95vJmt27ZGOv53gfo/yYIC34kZPiveXZnNy7c62BacZC+enfr8dVKQIbpKD9pQ0/vnOCubdgWP79MjLk6mM+kcbMjJQ5dMH9vajj7I8nLQoHzLxcn+PsiyjGxHTQ1sCcpSXZUuRTPmfBE6psBnWR44ub68uuGpvCLh/Gc8rc7y7id+gM7vLy3TWnJW8ISMJnvIkeKol35PgqeCp4Cmb57o8tbMblX4Kfb5SHObh7eCdp1q1g4JI1kG78PHrW90D7TCKUv9YKybOIndX0R+19AUtB4VUlM2tT4mQrJR7kWH3/fmoPr+YgLyXXmCNFEs6Lva4WSSg9tcuuQGpJW6Amlre0eVjoV4g4PeKIAp7Hg2cGQzqUskdSHgCnnMAH5Vd0qT7DtmeJ3Ft3Kf21L1/s+19LMFTI0uCp4Kn1vMHT+3Z2pmE4ClzPAGQ+cPGcwfuImQ+Aa/5/B9LEkNoyTMXo1Fi4d9Ua3t4Tl9NhOryJi56jsDpIgnWcIOFSq/MeVRmZWeIi9vAyeeVU/bzHutJo335hi5H1ZROKuXPitEFPNSLnLxHdN2Kjxmg63N7ETh9aTz2dOTT5MjjZewx1mpZviSO/ncSShrl1UMAnwuXCGanS+rrCbHZRBjvFdjqwReS6ssiwVOzVMFTNXnwVPCUmz946lnw1MIviO/LsX2+25NUyTsSO824aovMO3e4Xv+RgVUGchss4xfJKg2R7Wh/YI8GfN8OBkiEfyTftCAvy1yMIDkzwiy/3QuL9IuLsv3uBXqc9FXNZVC3oqTi3FaL+llpOiR+HWwb9H60bBrLnpFWkugnS8b7+6Vb/SvAd6y/rB87FMTmQLiefomuHHOXTmcvYwrofw8gJfJJTZCDIP92ysMVeY3EcdkWtIF+aQQN9vG0cV8JngqeulyCp4KnoCsYPIUjPHWVh41kbDGK1HE6Zw24LEqw5xTj6w280kPe/X5QR+ktgFP+8OSJtjiLbBuRb1lEYVaf/bEjUBkTjHlIpB8wFQJyZ6l3pw7KGiBb4NJpjq1rLQ5Z/viAwPJgTybgreySPjFKMylkFGFgNGyb6eauVmT63rA9eHoT04+Vag26FqzbfJCT3gF/edOhcKzHjNb2A2BWNji25SPrDT4hMWagbJLaL/oyy0+937w8r9uonBuqDblAgqcQPDUzOXgqeMp8VxmDp6DbqJwbqq1yr4cNXSE93VTA0sm1b5Wr/1KRXSYbj8SZflCsiXyyLyuHm7NVMJfrSkcFlSTU7FKt67SFHis5nwL05ihU0BzZjV3H7WpnjUgfalCTW6c9wOp+edIBr05ntWGcsO4OogawgjN1Zki4h0WHDajaKs4NSSj5kUgqpNSAZXLTffIqy9NGjwVyxxInOjgWgtpDkJCjqKs6BSw7uD4TC8yJBMRYr1EnnfqiezVZUgtnNr2KUOZAPj7fsIkt4RIBHHvfXirBU8FTwVNGc/BU8JSbb6RP57uEpw49bFwKqvJp9D7r/45PV8qhyuYY/cztZVZ10S+CnloiEeHZUXWBPXZwwlazU1j9W5VhTtiuzpdnpHdJ3x75VdzUg/My7PU29QhIP9BNPVktW8fpQt2YUhm05YAF981IDVjaNKpKoQrm0uZkOiAnbaAicrFI0Ok1JrvRXE2oU2CvxZebjz6hLiP3IvfX5Y8ZzXVIX02T8ArA5VdGp9Mel/FZdMyilrK99a3hzF55vjtjrlkQWLvhCmkSPOVaFTxVSgieQvBU8JSXT5etzphrx3nq8MzGbKrmxXlRD9CgXSQBKtfr6RwA7+FyqLc9NBZywnANLMDpKXq1aS4iEzmChaoJqCYgl6GH1YKvc9PTr1UeR44sUO3ptNKDkFCU63+V3X7kJuU7nD0CK60AAFFSy/2ks5+LBgS9Ipe8iDxge6R2n/1gbFc6t3zaDmtUaYm82lckHkCnKKNPZdfRLtk6ODMnAP+7n6aQPLDd8+b3ZZTgqeCpa0jwFOYJgqeUXSZl/hs8BVzxnY1LXpJ75iIxi7K7OW1j4wejdYL6xZixrA2/ztSBrvLZoiBUQkBiqjBXUdZiR+OaUSTX3grnSi+LtTKsP+iBcjkRqO0cd2yVUYNWdrtODphPMGlenLoJWLQRErBsNLQqg/rmso1Jpzh57Js2krMPLJ74fT4lYxOVKv4xj7KYSE4G7+5HlDbfXh+82zm9llhWjwSBokXuTJ3mn9I55BpbwO+ftl6XgbSOPeQqEjw1luCp4KlZvuCp4Clj7SGeOvyw8cICtCOy+coxoE+2QcTmpMinvEJPRytQJXs2fT0ajZgO0zyVRqp21IENFZumUDK75ljB2a120GkG0nMAH08NKoKYF5/SQ5OavKJPcksv7VgoQ4l3M1CCPqBucGtbRD9WZeZ7BwYyR77pcOvqmHqARL207q4YHXgtF3G4/HRBE3I5t65XZhgBOHoQccpPBNRy9y/UTS3p0rCoUG9hyIoETwVPBU85Ejyl0gdPPTueuuoyKpvGTjMde6lnX2Z7k8sojcFka63TzGtG9qmcwX8B5zEM/rg6zEneB1cwo2egUSbjXMx+51GJjqTyJRiug8X+wFcv6bGI6U3bt7/o+4I5w22vlIumrWsoZCWpBfD0va2fTgRQxk4j52I31XZnsM9b+1YcGpf9dC7LAwNiPMw7O9enGdlKpniPLHs75PkKxm6Okf2afPstIC1Zj/1b68ykIK4xM3i7InC+BBI8NUsVPBU8heCp4KlnylOHHja86dn5/uP9uRUgZ5P5sihV/9SlHYC6MUYidXsJaEe9FTdPfq4frpd11IjoUwGrywCytSflT5CcfpdP/k40pQ4QUx6Z89znXt8CsI9sTdfxyoiFvEmYx9qq0Sod+V3J5osblGDUqUPy2KE2zeV3Lm7ERnxTN1XoJmuX9M2ECklXDmfxjzBomQ7Ax8X6F6ydBTdalMZL648vH89ZpE87a2jClr5VwFVgyuAGaLb+NeVjBwNFRFFQ9CZCYpd70MsnwVOOeivBUy1J8FT+CJ4Knir5rs9TV/ydDdkCi4050oWZG67qkE/i23AglbJqapIOaIY36cYkC2Tdk6PdAcRuHefU1AaCXKsHMmg4ksZC+HBXwiCz0xR+IeKgRg1XCHhGbk48r+yRn/M2LTXBQpkTIfQ/hCV5rpbH0+7rsGNo17iNmo9Z33l2t581AFa6deBndmxYAPNBT+aZ7fLRn+/PlfE3aZsajZE3KOT28TjqJMFc9omut4xyNcKTW6+SIqZcg/xbA6k9SJQVcn8JnsqlBk8heMrYU80KnhrpTnmCpy7hqXs/bPhRo/EvlK7oG52bvdjlSnE2s4VH22kc6lODlQTgAhfWtj2A4jz4yvf++p54UZCpk1rewWrUZmpEBbXuEsmn3fa3+V6aMh2HCn0/GSXvz5Fz0UbBhF0zKYPPTWsiVIOun5Vy7MeeZD/Lz2YA9xzyACJIUoCefxNwz5Ic0Dzmui2yxca+iiE1bdkNpeCX+TEm6TcduJc0EoP0jRmptNZvyTnXStb/5FHIpRI8NZPgqeApoSF4al5S8NRFPDV92KhTtbtglVM/sEcdnpJVkjqqvKSvnuxl4tpB17XnEumn6Pb7YcUmAsS2c1bnA9WJRzG7kZQfh9HApcXGZ2bts3NNAeKoTfIeI2mPP90fsnJmBI/q3Qb4SsvY3SFKfe1Cg5Wx6utdSSsMUOesDnksI6gVmrr03H0f6dQvuo3s9ytVesS5VRTf802ejP5g2xka44s2smXt1VPvwmd4Vr+QIsFT17PnEgmeAoKnWgHBU+168FST+cMGcf2lxRYAEK4utpG7Bp55kSF7bZbPSnsJKetzxhypI5MIdlCYHT7ccvsfT7qmXIs45DTY9e1IjlfjcnSJ3f1Tt7yGols95dtv5PSvFUsEXgpnT/BiVNfXIqpJGA6MFl3Ys292X8EKjrqdN2Y6OY3qma9q0x3Ym0R3tQ7ZRx6Au6W7pSawE/ou8F82X+RtQm8fCSDh2m8yIs4GbEuaRjYj3S1/iv7l+JAkqM6+ECvBU8FTl9kRPAUETzUdwVO+7pb/Pjy1P7NBVH8D5tI39F3dF0RDLhHl1oWNxNSpb/7YFrcNpjkul0vaZJ4nD4zmk8luagAgh447JV0PS8uKaduiLCdMp+SrRL09e+6j29uSrZe5/M5qn0PmqTTA6Os4t0jvpGEHPMvTxdmkNb3de7vVaP8qP/iVhnbVNqmCvTla21Wj+couyZg0PXDnmlTiOoYZtX86UkwA63GlWjZhr8FZJkAE8DbxR3NzJ0C95Wl17Os6fj+gwHUB8nRe2D7EqRAgeMqT4KlaEoKnbAHBU/1x8JS+Zup2BZ6aPmyccWpKbJXuAboPPa1by8mfa8/le8q4BD+a3uwoay1x+frgS2W+XliA2cJAbYlt2E0A1UzF8bFroirWRtu7+tPrc/Ui3TFLujM00OTr7uuwEmlllhbLlz4zrNcXENdoaLbWdGltuacr+/+WfUhHVBeMGun3vskonQBR165OBFHWTxZuOxib9XpuaxH9rACcWb2SF/e3dR4ZdhG+qkfoZ84Af+Anbl8yCZ6SyoKngqeaBE8FT70oPDV92NiwgeqaRPsUxmZ6+v7odI2okepo05AEqE5b9HzxPeup86b3MPSBxY/u1W9D4NYQOBZSoCIvJN1UGM8UU7tnqaslUNvoi7Z42hk5pECdvlUbmk+Vgb+X26UeQUr74MOTo6xD1mcw/jw/GE/792JfgOwjvck46sabBaz+vB+Nyu3NQImipLPt3KAIX29No9llQy1GfpngWLJL4p+UrWwtWZbM8jhvs0dEqFhHjGo+Zmwbi+0FQ6wETyF4aiDBU3MJngqeGuVt9lyHp5Z2o5Lgeg3Qvny3g+tIHtNraV1b2xToel0Wo0WMdXxZEHddcXagC7RpLYt1P9Levowyr5BNr6Hm6gbNvh0Vq5xs/SCtFxas8sWLBumX1Nb07JYzYNVRv1FtA1JrfrU99nPuB+1aa2DV1OXAqXNZ21sSyUhb7WVTuArYTH1hBOCGWCRJq7yexkYcG3OODJnrxcbVG86XXIKngqeyNq0leEpfDZ4KnnoOPHXx1rcSzI9EemYv19k0e+mmdi3onooA0wpWpC9PC7pU7g3gPgv0a0oHSGRsIaaaVL+op0cAcxnY4jwbZeDFSJHMI2225/al1bvvvGMADjWWVOuNojW2XCHDHRZbirbmVTjbwwW45UtlS8nTB3skIxKMsps0Ehhb0JcAuaTDunB3SkaYZFRTV+jI2FWAjRLVycfFztoWUOWD66t0feyay0t27eU6bRJhw4YT0sAqe9eHHJPgKfvlShI8ZfJIm+25fQmeWpXgKd/OF5+npg8ba1sJrgPjXrprRob2IGo32mGqrl6O6pK2Thk32bNaC5sMmEX2GtZaYx3jbJLasHWkoS7Tq/xBWp0M0SwKd7bJHu3tXmvX+4fiVNDC1MdvaXSI3e+E0efkwflOe+7HdULaayvGVuZuF/L7lisLUV/qZKjPdE3mkYobuCVwFiBvyirjrkZ0ZNTGhCsLrB4ZiLIf6s+u5T/EGryrhSwIjvLQKDdFtSosayAVNB8ruh9y66D3uARP6fRFgqdE2uApYcWgvOApkS946to8Nd+NqjR6smMZ1EdyafTHO7c6LXpfG1b1Hsfm2Y8oTXIZ8jk61V/KdgfOThbK3sikPDa1aYkEwuufUp6l1jGx+RGV67R8xdZSn0t0eMV3dw6V1cbZXQDf9wkyn/e9N1h3oXGftXOjRvF8wKbN+guS1STah1RgUoRpvGpUNaNg5pJk/zZlztqtArSodiMRaUOuk8hnbw94e/C7v/esBE+t6w2eCp4KnqrfVPrgqWb+Q/DUwsNGeTJ99pE1/+Wx50e6/UtHPqHsRavGshpVWvdGCfLyhau2f7kcHE2hepKnDM0O4FVLFpcbPGu5jOSG2gC0qcYynZuPTCqI8+O28ewrkSB52vbSZTICX+vX66Be0kp/SXXydS9oNJ8sQpy2rPVIGZfMwiY99Y/2nXq7WY4TFj4AVtHNptuxgQDvN5f6aJjQV8dgPGyMJHhqXnbwVPBU8FTw1PPmqZ13NmyDXluaWz7PgX6puIMvV2kE5G2AeANqLXpE+W//ZClSKAYu9tpUBFB+SYltXpHYAW91bolTxAhZGHh7U6ir7nIfv5I8psdCap9E4tl/Yfc98eu7MjVd427mx6zclvP65p7CrIG4lWrrtEcK/dhm41uaxKjq5DzlrpYpqDflSj4Fp0pHVylRh2k/SF9WtstPMuc0bWu/YcgdAft16YUMSl8WpRuAE0AbLntR9mWR4KmZBE8FTwVPefmDp54lT+28s1HdaSqXPpWzru2SXDcC4MtQOw2hWZ0fW5cjDgeeyC+TI8qpPZBX4CfhiSugKwah06d6Czur64j+kt5G1jRoHBKC/6NQJlGJmrYqlLI2JAgvytIgbbU6Rl7IeZb9I6tdasVBAj/y2UfC7M3HLCrEyn90vrF9G2QfFnpsNwuU+kr6as3bQJrZq4+szspNhPAIBdAeCayPN+mzIxWtXQlgSoTWrQ0PKRI81RU+uBo8VXQFTwVPybTBU1av0HQlnpo+bJzky2aiUnY96TVAdQbOD/arrY7zKgvI656e1rpdSYbtcaUX72ihzStwjm2xyVmM20w36IBZBE76da8aTfpuY7Q2WNlNQnt5BcXSxsLeBBcSNIoN1yF9BkCct1EUIKIxwbYpqfzLUS5Zpnf9QH00uEttIxC04832cd59hFdvRuYJ9C4aNj2bdrNUa9jE1SvyyiK41yZLqS/PmdK0las/lNbbzEq3jBMJEiNG+eXZi25QXhIJngqeCp5qlgRPpePgqReLpw5vfVs6+Fr7mNvjtm7z+ZCrAjO8mBR/LVAymGuviq8komUrZTdHBXxQaFGkdX0tuwHw+jbUsd66JGLJ4s9qP9ynu2TW0a/Btut9nv6qp9kpVwInb9DkKwG9Uc28PY7tcqPWqQpc0OtXS3v0USm1tagtdNBQilBYEC+Z9a7KTue78Cu/TTR86ySll8sLeqnu78XlO89TgqeevwRPBU95Ejz1cvLU8sPGQ0SJZjJ+gr1eFMDTz+Lvqk0q/zWiRW2MPJwUIKondPilGydyrJSU3OeuqgfpAC+SpK6a706jyfYpkatB2476g7v696W7xRYwcdTaGxu9VtM3f1qWd47yHy9KUr8srI7lE0rHdnEYYbedipZ+7IGL3y7p/Ig09dQyMmCSsUzeQGiL822fKlu2RV9giYeKMlQ6QVTM2BQo6zLLgbWIu3OyCEJd58w+rrLwM+ZTPGwsSvBU8FTwVM0YPIXgqReFp+YPG/MH1HnWpakrLe81QrWgfhX7d1Rct41WOpixuxOOuXzk12pH58iwQoGVudrjrHfcxWepx5RwtN9GkTxmfWHcgnuiCbjCYwVwCeR6yrSvypgEmmziusynQWvPVoOiilDqaVfX0YhVBngFZB6BG/hm7yxMmkYBiqZqMRuI22uEhdJCBhI8NZXgqZZEaQ2eEtrzleApcV3mC56yeY/y1OGZjecBtM8yOpVe7pGNXr7dJ2K24zzV58cTjJe92DjJU0euUyahzgJ31uhkLfKSz1+2rKANDi//es1lyoEd3H1xU47BoFz1+mhc/yOT+zYPJEBJ+2zhU/1juGe0fmzn7e4bhVg1oJXr/kt7xTR29FspcJWuqyhSJoAu96QxZ77Pst4psY33dOSwL2UAAB3TKrv8793JsqMJ3+te+qWT4KngKSdZ8JQ8Gzyl0jfTgqeaXf737uQBntp/2BAajuDIJbjrrYV9pgAO+6ws6lGmQdNZAe1kErZz80lC0hGQB62mrFG2i/1rnqhfnlX9k595je2XEY61eM56BRMaaO7XA0A/IpwRsmc/V/Jj6Nei9BRpZ06aa8zXxrZKk8r+IxJwXJvmFk/BY6+7pKnMJwDbJM8IwNM15m3SvmVatx5lgjFttTXvOORr3Hx/gwVu55vR7ReVp5olSJIkYK2zHUkqEfWrO+yYMnCaVCwkeKpdCJ5C8FS1NXhqkNroDp56aJ7a3fq2KjZnj704tS57615n4D6aFnWjEAKMZnXwIxh6p48Cxino4w3OwY4W04FgyOWC6W+dxwO5YwwyenKdRYnW7PbbR4x9fX6ortRxB8wVIZecbUiVIideATZlMeZ1TWDERn+JjFBuwz6vJvl9Pz3mI/KWRd26ALC7eLQozniMFRLz0rUyWl+Xta+t1JKMc9ldSdLcSVX1NpayVo0c5Pm52LFYiEXRa6en9agEZlF6nS4Xyw7yOuAUti4n43c2ZhI8FTw1t6qVs2bDWHPwVPBU8BQu4qnDu1FVUx44kiNFOuh02mn3Sd+/LgffSr1cmFYv72kXWX26NgrROU8FD3FpIUgygd9UX8q0JBxNSSmyi+btlyzb0w7qOtD21SzpX9lrvxS492qad7UOSJcQ575DQF1XSW7FG1onYOuBAzv7rPfl+77n34BJAO+jPrZFPL3eGljd/60cVr5WomIE2TPSa3WUk0zKpqlD7XyuYqRHDKWEQdP6ETZh5WDcsErfX9+7VSg5WDJdyCEJnvJtBIKnVNbgqZYreArBU1Kuw1PTh41WdnI8PU01LfulkuqoAnjXmscOIuOA5CTl/Y5XNnmliS9UyxZ6i6OL8sl8WpNnkRL7rRzPfGheQyfyxc1CPeCdpHtlmHBDl271rVILxMg3DLDttdMWNRw5uKbPHBibPlknvRh39ii/sGfPH1J3US2n3KAtma5A1AFvCe9caKK3uYFxixfWHmftRz4QyzWzqT5q/LPOJ0tKedjkbfoLcZbpfY6JjaEET61J8FTwlF9W8NSoxOCp6/HUzsxGG80sn3QH8tDbDq5t3Xc/3ffW201RFvIbDC5vbCxMv43EgsOuVN/pvLXL3i0dgHVp/7x5k3Equ8NYo4BzSQ/HloqyO6+T7NHm18NQ6HHAvAPyPb+7l1t6dwOTYmob25uMy8dHAeiuf3MkDEDaxk/cFLD1RQPydaK3s8kn2fmQstTeEPhYlfNWjQ7R7LU8q3yp/idlU4gvwVMXKAqeslmDp7p0wVNCbfAU7sNT84eNSV73V03F9+f1Y0dHxY2s5HMX18GLKOw6wgjh15XYvpBTokkbJ/8eoa84Oau5bTMb0eitnEW5WN4rLJXXKtBH2dLRJk/VdO2FyH0ZkeHKgLZgbtfI6uleTe6Z79OApjQRq5YluPZ5pTdb9drTsZ7aVjW9tVvW38Cp6MD5NH3pA1subFdidLRXhmtiyjTIL36GyrrarBzWaabroB2Til+UIUogQGx7qOHnXiz+/pbgqcuUBk8ZCZ4Knioag6dqqVfiqYXf2ShPc/4Iu+YPF11KAns23Ocpd8kOKk1c1pT2AM6Dp9huzeuwuNKzFakGT8uj7i6OptFWu/JcZoS328KjcMpFUgBcKjbXVJQqnbvQC/Rn7S4Vi+pzOYDht5+TOU/dgrn96iyONF1HI9BtVuwxuagHN2t3GmtSx9q4o4xUenNAlbq1tLgBSetAh0V0NyrTpQmOXZJEbZ+t4Maob+v08qG8fkScCKDTe+Om+LlI8FTwlNQQPBU8FTzl6nqePLW09e3eC03XANzZk+yu/tLpJT1MP06QyrPneKQol1j9ZxQxcvSW8+4PEg1coI5Fr4yCAaT1855LlZzrUOEDuIyAiCfxEYpzAVc9+EyPLNtS09tiFtcQVw3dDinCkgpk7WrrkmNU0UWT9B/7sSjSolpS1eJHfRwtLpDxJF8rt2s5Hi0MKPEw3T/1BoVbAxxpgxmeaHv1dUlQl77wK0zW7TCLLHG+Lv2KW9nXulF+30rw1IIET0kjgqfWJXgqeKolvoynLt6NyhMPDNca8zpCaFgosbG4x4OVLMpohXiRjImCpWJW0hXwtohIJk0acMpKXYFec+7PeR9asJRk4l0fnHciF0bRjhwj4pXp3utK+6VQddPASFGiSTXn7d9gY34z4t1Izey9YOxWVyzw3dF9qq8EawAgSr9MWqKpnV+OwZCcmxUZEZI0sUpmI/GbQxJ+sn8XwN3cqPciBd9DriPBU7KQ4Cltz+j64Hzw1FCCp4KnpFz1YaMz6ECnz16qm0WP6tGgqBXwxUkGtwAAIABJREFUvs4UuwChDo8WBl2XRDjAsgkyUqKnEZtOdABZ1l0m0htHVko91BaMphrJddv+3gWk2LNDZxTnGWPw9M/7NwxW/6ViylQEadpBnFewo6JCZUD31zo1Ax3D9GX/awN6x9tiL8LWbgQGlmjs9dQJAGepizXwWcvT6U3obH4zqqbEAT0NLT89YttvszTscj2cF0JFwX5+yPEhSSD59QnkvWcaciUJnso2BE8FTwVPBU89EE8tvSBed0wYPIE+yyl+9ymxXMPY7RS+LeicG6E+BIDY6IjUPQKxySAYAHs7KlOB0hM98J6JyEvcvZTXImFaX9kaTzcG5fL14PbWhdapv0UrpzV4Bv7XlTEoc4+wmWWd/Z1f1uujlwJ4n9L/el/0+sj3n/WIGmO85aLwVxajtYZ0PZT3ypT1cKbdhQ8z0q+w7ssYwP3lNDKnUxeRptDq5jBZrzulaS/joUXOQnwJnhpkUB/BU/UjeCp4KnjqWfPUwsyGHpDPQma/pOqfJwXk1lyqF/prz1SUkddTIp84+YL+yjtq+4DSJTbRHtZRqmctM8BLpkqgAkaNfyhqeJg0GhhoED+men/bS93+LIBSft/PY21qxDvWI29iWLkE5b3AfWxnoZMaHNqbBpibhvrV3ByJ9h3WmPoyxv3P+ruNBtsSuPuSiyGA2N1ds4B1tc3WgRG/s7ErwVNXk+Cpq0vwlEphdAVPvQw8dWgZ1XN5Mt9Lj9SQqRHGIDLTenTf9cta4Uh00gfUuT5yz/pZ9Wji7GCjp3M2x3661oZ2QEjaketpn+W2kzMAn/b3IFKk8kzqUQCqDsYla2e6dlOJT+tHJXYx1jNbGjIGubUbhzn5rHhuacBRVKyp2SY2iR6RvGOiPUanpYTq1FtTKsvw9Ahy6z8bQZTJ+lrOya1GyECCp/Z1TUo5kDN46toSPAUET0mL3z88tfOw8TzDK2uSur9Me/kJZq5hHesosLRp2jWpYGIw4DJ+JPXt4MpZVCNKZMXsNNI7csnVk8cwH8wQF9GHh5YRcM8AvaUxflHO24QEpP3SHR/Uc5gA9fvl6CnQMZD5AJhe3ivXdRoLEy2P5R9pg1rnPAVdvb5Wr/XVZujX3ZDbJUfPuKUpPkEV7GR7WJ8iABuYS9uLHqrDyyMgFv+Q7chtr5DTVMIFcJYfQh+GUqNvOoe6xtY2Pj6yXy4JntotP3iq1xo8JQtqGYOngqcegKemDxsqKzewfAhs7xxG7kk8G/h5rkkNsqJqOPIwrMPK1F9nxqCR+/WTvj3XCMTNogkuURHQFr4eM6D2A6/bXgZvbYJnEH28VNwpWnPcyLu8AJaBoHx3qqcgaTcSM7dnkFLkacepv/ypZQ+w24/4eFO2rPR4ANbWZvflKLcDD9uktalvIxRpNQJk7te+FoJjlmM3kUD97gK4AeQZ7rljuoFy14oGo1QbqGbIL69Oin7ZJXjKzxA8FTwVPBU8pQsxh8+Qp6YPGxvatO+1cXvXMbl3hD3Rza8vyGCI6pfd9YXWLD/d+FlfpyrBGWnNffGsOTmWrGhppTEXGiEqfg1gvm8/NHCZ9chxO0dTs7rJM7iZJu2wSekd27XfFtJ3LIC30uSxS1DLhLJHNqVF7F2Uzl9XblM+5zdthcDOxqKdcj8zQ/4MU9/1+u6pa38zFtw2UuAi9Xigr23VPCB21ZFlcbveaDClZWJsL+49z3OX4CnPrOApJcFTwVOi3OApXcKz4Knpw8apGklLUSL71GnPrUgpxoIuC13ySTZVmeozYzsHNZ7SYWt8S0uX2juTa/5qrZUjevf3sX5GdzJlX2rPiqsQwH3yXgLs3lmaXXQA/DqEMjZ/PnBH9Z6RpFxOQRaMJvXh2jZ5LG76ZTqpw4Jb04Fc+FbLt0TWXtIrOjNKkGTXOZn1ZRpjqn2yfTVpepPs5bi0gfsiHp0g2+AFDq6+EBI8dT8JnuoMCZ7q8gdPBU+Z8g7y1PRhw4sTlWmrhxLpCqMmt0+PZMZNudo9nTs69teh3qOu3P/epHx6Lz+WM1ehifFapKB0yQZbED1N6JGfmPaTZaJ3WN0ma2X2L0rC0XwUnPbLNQXWYmrzKUe0Pd/yPrt7xwIC+/HM9XbRv1hbXWjQB+rmC8DG3A84OQi79rFAqNGAMwCom4Ma0Wl+YnfPkP3mi++XLDOWveK5TED3jCOGe7kTzSbq6FIinpyohLG4rIW97s3l+02Cp4qWCyV4SpcJONgQPPVwEjxli3g/8tRFL4iP1qZeFdxJtNXA/q48c2izUTc9NwLFy+rhT8bp4Xz5S3YL5T+g7rFYurXXiozaVL48dl3LlCXXUC6AgvKxm6z8tWP6agQ8bqs1Qhv799xGQ0wqKXdfR63DArj8Mi36bdDQLv0lg5xTmrT2SNOPliK45K1uKNfuhkb65brXS6OJL6cETx2R4KngKSB4Knjq2fLU/AXxAVg/lMyiAqP0w3Rsp6az3nK2DgSa63FsW7FrfK1ZdE3QGnW614cMm7Y8qeq8asjy4HzxODKJlBHSxmOyt3YTKKC2HxW5REZala+2k/W8R+Cd7ondaz60n9bPf3k76YgngcXG2j3MOuWKKKWMvtSIC3EOOFb4Ncq46YLIW4FcxrnzC3eyfK3FHK/7kBxuS+0pomyjhfkyCUoEiZdNemkleMq3bcWu8bVmUfDUvgRPeWWvp/XzB0+9n3hq6Xc2JMiNpkhnUaSVgbgqVl8/oLOdNp8sW1sy2376kKzU6SjUrLbnKIqQ0isrxce+hxRsNq0sYkTeHgaO9BwxT77oH5IUj8ru1H/xaVHCKDZW2t/2gwsgA8K16favrS9v8P1mFLHZk3G+8lJZW+PZ/rq7fxT/Ea44auPiRKwOlWYBrrrv+qKduxKzMNVGl1W2zu8Y5u08pbrWVpTRZqy1UnkDkDdosbt9hjgSPLUmwVMTCZ4yaoOnSvrgqfvx1PKP+lkgX0lX5L4vtV2irzyIl5yeY7Qn9verlAGsz6amGQO4dHy25zrtYwJRaccjcyrXWv8785lLIqJentYOdg30/aVGonbaQvv0LG1v/2XtXPbs8GrcUz875yVZui1nA5pcwHzNXqvRs6Ej3071aARUBlD935Xf3TTt+ZxDTiG7Ejz1XpTgKakHCJ4SKYdlHJPgqefNUzu/s5EBoDxt7T1hQ6d7yF0uiv55gr4zemucp8JBWfepy4UYtq9X2iU7y4K0LXiCytV1BkikXj7K5U5fIlQRBEfbpFHGbT4fDHLqtwLrwF/uN107A64CNteQDHHDCKwte0/Xmuy1TSGMBrAwxwWgE9inLiAHhOX2jHqa3wVrAogbgTT/K2TXWaoiN9K597vfS8cmhTkWDSLzFaJi5x5K9aEdYgf67GWT4CldVvCUuB48NcgbPKWPg6cemqfmDxtUGigXP5kWvraM1qdSq6Pyxb11tN0kqmv+GIWuWd/7RBOsHS2SAKGV+/ksJ2oENk/w5PTrgH3Y8kUdpr2dl0Zk+puGVJi2cXQjIa+VNhr4ryJBc87VPbvW0lzSx7Np5Kz5sD4prV28iNe6n8spVeVDBabkOOUy/iQyFXgWaJZDKxYedXnSYAnkOr2tHyFtkdrZU+sjs3ttNjpm9a9eSgMspa2NIde3at8c1nVj8MZpd5QQV4KnqsbgqeApP69bbvBU8FS69ix4ancZlXyZ5WjU5KGBflVWojW+rSnnKgbtT5uTw0IP0UbW4EEZJMGc8yk7KLRKNzolks8iQ75tY7nGC58ecBOR8uUCaZdDpR/1GvXufV8UvM5e+z6QH9JQo0ENHCE+ubQqcQatkpLzadbexo0QNDFAN2aZB2b4vgrA7hyzdM+2sFRlGJlj4UG1viJtZY9yM6L7X0WVKP8eA3OOxsa8xp4ETwVPSZXBU24pwVNFpfgMnhJpH5Cnlt/Z6J/kkArAZc60t562fHZPvPBdT+1i4UabLhk0rbRLp6f7HD0xNLVre5p7UqN6nls7+DmsCxXK7kVFcITa5LROe1eSkLnWxI84NRAeDqaJjF5irKC4aKXW0/rLXWc7sLIjDJW35Vr1hRkxcNf+A5r17F/1ex4dFtIvBM9oO4N4elkEbXJ9WK+Q9WuZCuhuIrjdnmT6qMdK06SKep2sTMgVkOuLlyjkIuzq+nJcmPQFBnLkkad5QoQETwVPBU85eoKnPJXBU8KuB+ap9YcNWzwlg68dFFpxmFkKd51g/qSMKnsvX7XrVMH2OgDezvrq9ttzb1CxPZJPsvX0wbqI5MP1pGKwig8hdPH9kv8y5xi0jogXfbn8BVG/aY9EotrwNSCjNBUy0+XttwkNbUz5e9862hal9GLppq5ahDWZvJaqBNnb5EWL9M90UW4UoUi0sClcm0SSnHsbdqVORW/Tm6dyM2L7ru18kpZjvCjR9/eKBE+tS/CUlOApIHgqeOr6PHX4YcMaMHq6vsZLdy5gDAqcRUD0ukdtmx0EfbkHohyD79eWfn2jdEmq3zo3v4SIJnmW+5jQnEXeBcyyGN0PdbPV6R2Us2ZLI/1Rnq5ekzLT4LbtoHKa8166BvwyElts0TrW2nhUl1oiUQWhutxB4TehrhXuxqH1X//Gxq6HLocnpPsWLjpzu7T/5A1HohsW+6UriK/hJ7stpyxf29e+b2jRKuHtagzYussboZSQiHAiwnYAh0K0BE8ZGwbfry3BU9eT4Cnf3pkET71YPLWzGxVXUNjvXv3kdflT93VIlXn8BL9u22W28Ozoili0Xw8dYejPe9fa6cteHXNUKaYcF/nc5aLO6fPMgLkDcFdDOeuNKR7mSCJfUGx6iPqGHxHTpctNmLXHcNbl8443MJvPlTZU+mrops/X0sgWKn8bfLf4KYsyvPEwawNpY2lhb9Q3ezPt6TSKuNh869OH+BI8FTx1DQmekkUETzkH9VTw1HGe2pnZYMxeRjEmLRW4J6On2XVZzTcCuHZt+Qnafqs9YY+vJyPbysBta2Ol2OOxUccA3FZwZ5o9R5DqjgejZFeIOlpZ1aejijy81gEtnCpdXIW21rJpHgOxFmlXIYRBKfdo4+FLgIa3y7cGq6goVv8S6npZMmkGlkPhej0QsEztWA7J+p3JGZ6S0PrzKkKUM9t0BIDFXWRPZJbutKR3917Uu50XTYKnViR4Kniq6odTpeCp+i14qi/jvjw1fdg44+QWQeXkDjitDMJy+b6BouZM6mxe02YBm7p8DyrVJv/Futn6xE7VFLxbYXa60srwh2sWy/bXpq4oWS5u6jt79btI5KAbAPggo7JllbPX0rm0sJM+9Y19QXAp91VIcxDpyUAtPkqCRRP1OHajwZMTZbY5/Us04RepiamPpuUxXJu32c9aReoDN0pYGIsFoDZykU3CBwD9ZZTgqStJ8JRQslxc8FSyYFGbTB885Z14v/LUaXglG1xsbdM6+25VtmyrlViQBuZ0L2At+ZuOcpy+H9V1kQ3AoDwvkpKmE6/DJUl5V3btPIbqPSasbKNW1ThGynaWBXZJa7HNm1o7XVb2JVL9A+j+HdIDIPVn2lt6ij9U7PdLmdVNXiO6dHyYfp+Zes/x1xTlkoXvJUBybgRm5XXrdauX+//Yu9bKz19FOqG76K3NZfJXUO/NbK0r0ZeqDrlzTrJkS4ko/1NGFKJIaB67Uc0leCp4SlkaPNX0AAieminKJQdP4aF5av8XxEfZnQt1PdwkstAyl+u2xEvFdnYBtDzc7KPcgtzv6bk0kCzvoW8YUpldNIWhHeUe9vi7oSxldIrTTrS3NEFGIAtR2UjZrjhP7941r8xel9ZRBvGhlt4jAMcGbY+8tdIlumBz0KfnUTv/mlt/ArDNy24tIWyX07sdiFLLZ/pCYmnLw5oEVAaqU+PJr/bbqesFxyeZG0iz7CtnjLL8HBUU0knwVPBUV0LwlEgfPOWe984FTz0YT80fNioGmS3dWBbWnvzXneYhwOxFYORCGLmBvBSDdrp3ybV7yp7c0A7BJPiEAdabr1l7h2scXdlZ+1rLmEfQZm1zrWUkMzDdK7M73kkPiNZVIzQP6p3IjKfv2EujDRRTW/fl3feFuz6PQc9ywCZiI5K0WVoCYxNTswW0LVWaKFBt25JxM+Qq02kbErwx7OVx/crYYNOnA/+QgVv5A055rlz1SbU/6V8JqIUETx2X4KmBdebTzxc8ta8veCp4ysr+L4hz29aM3Kd+Xb4wGcPEz0DkFO3MOa/6ctfu03/rHBY97O3A4Ktfs1Pry4TiOELqzjHhNF0rztxdVc63ZrozrZ1y1+ta377Svf6tLXWQXb11lHPiy4NWRQnG5a33dUk/86Eyzepf3xsfh2xiWVJacZrCNJLA8ncRASpbD9b1qlObbFTUA3l5Tt5EsEpa9viYjwJJdO1Y/n6DJRpvXLGylxyLni9mvpcleOqABE/Jq8FTJkfwVE4QPFVtugZPTR82SNau00/ib3/+2cnlZOEN8EsAXVvAjjnSrdMhEXCp3bWcY4YV1Na27OjxXj46Snz7SY/qWkt/dIpxmuY+RF8G+sBVqU/aqxiU304/3A3TfW5yEijLqOQIsuTOHgt6zQusKvKlR2MbdAV8eTAChjdhflRVZPPOzqxPMMEGF5wbg3LtIaLM7xcJnrrEguCpXsduioO6gqfa+b2c95fgqRebp+YzGwLA9TSbMJK8LbmKqQ8TNdIV4opNbTAUkFkDqGsa5dfYuAzDvO/mReLsCdoHT9L904JmJaxg9V5W/4vbjdB5Y4t4GDLtypwPpD279mymbIxdyz22Z62cSz1sfYech70LHZPHSlvb5QdpirzeGKjR0m4w0nhuzlyiSSUKs+5+ZcIbAG86nwJ9uADu38A08PWjViU6d0L6wSRNW6xOKBaSZmeQX63nSy7BU4eNCp6aSPDUsgRPBU+t1nP3BfES3aj67AwPcxuJaBX2ghVKt6mIndrzXr4aD5ym6b7rm4+tAS3lq5rqNW/qSk7faxCGs5egz+UpYy+BX+JRmU1T2nqO246NY5D2K6uUgLpLwrHZ44vkOmtBnTTywJmz1/ck+y/iSVU9oBT/9W0cLTPYW3e7uy7YPS6gOyDGDsPENLqNALGTJ5/Q2GztoKJBC8nIkbVrfKNjgb39tYMgFWJvOEtVvNHZIDHbm3eGScTnGBoCIHhqVYKnYI6Dp1QaeRA8JdLY78FTkHVa5KnddzZaSSIeQ7YDXFjShnLLq1P5esaD454o/Uwk2ejtVd6l9EjDq+IQ2EtH6wRlLTCB87SoGAjl76SJZ4PUtdl2+Ez68OODywioRibXgXUPG+2NimNU0p8HLZH9fc8V/R0EmDT3b+NLbmgKRng4odU5cCa4vlSRoYmgAHhVyAWqy5gjo7bdPXT16dmkZvSr7uMdZPms69X0kBh6re/qTLW4I2UwNmzIMa+QFQmeOiDBU1MJnipGBU8FT+G+PLXwsNFPna72aUnmgXmX9p7O1qY5ZcmeNdIi79wzkgx6+1OQ5noJ33XZdIyOGajbCHpgMnNOiQs29OfZVIq/SFaI+whD2KyCfKbJnIE5safEIZzmOWCauLMZRFXGtqGWbCMc7XitzY7VfS3d3vIQSXCa7NpEdAE1Nwq9a5++KaIaWZJEwIowbPRppLOtYdXk0aedeYcpi+W5rJG3FC3KcB4yk+CpB5Hgqc6Q4KngqeCp9rnKUzsPG2Q+L5MxVF4yBMYDen3qbQ7y/ZN+N9m+ZGmZVtWOKn42pTjAQJ1fn+Z8o4zJ2hLWK862D2Z90Iqqc1XSWbBxJF3Uyev+2r0EjQNldxTfZ64RGbEy9J2CvdJcx6q1HTiOi46s2mue/16m+/I0HuC0yKY/HgtAogErS5Adk32KtDGIc8St7FHogjKLfssAuQPeOlJVCKaM2w2ynbWevYixWU8rPuwuMtf37veTBE9lLZjVYizBU6O0wVOXS/BU8JSUtWVUAsu8acrL5VLH7oH8uoPYdkr1pNVncA3aRIOp6mM27/3wlNLa/TiSAE0FAnNykpfZINbI9UfrZiUo9lOEQJ3SKwQlHbzacl2wvtxvdH9eosX+uNjKrjOzKBbndkt6AB9MHb8YaVxom9bXoxfR/DxyrawEcWtbGTcjGixlKj9Dguq6LGOhLj0hsyFEKVthGU0uxaDJDZZICXCmkYlpyT82bFvMbOxK8FTwVPkePFVyBk8heOpF4KmdF8S5PlUVDNC17cHUNdY5k39iyU2zL2Mw6yM0l8tlKnyw9uxhET3yALF72az4x4Jh7VcmjyBN6U/ZLxMdDv5fh0yP3xxcsg+9TX2QUsvIyEeOvnqCxfG4lNla8PnUvfavMk4bCK3VbF7GKE+zwfpKA1hhW70RtMBtf32WdmwStvn437Cy6DBJu8gTPFUSpEuD6vGwokVlyARQxnfnu2xsI2rhx5BOgqcuyxU8dV8JngqeCp5qKuc8tTOzwc0uObBWplNrGhanRuA7G07HBrTul/28u1EctWNHnbTN51YGx5hQptbV8lifEnYMFeaE1OX1iMKC3+i7KZVNCu5pSEZC5PFY9m4KnBxd5OkYYHmpJ5BV1achoVlW9ZYLnpeD6TyaRK2vq32yfrJN18bWeH1sI/rRso8U5ZBlMbxfhR2XJ28k9BiQLu4B7rz7V/yQG84651O1aui1GdNFsI1We4L6cwzZxpJ8GLQdu8V4uSR4KnhqYG/wVPBU8FQz5jny1PJuVGQ+PQMNhqjMPoBL7QJwiiNeHMwTGTswJHGJRaJWw9HwljlnZZeoFbCIKb1pTqmclfkDctZWGvzvB+TSLCphCSfVcvSGRX/jlB15Uu6eshLZLP04scMFcB6s1ZTgPRHCfey/hxASUBLqS2YMb3tL4cXLEbZLIrv9zU8BWRUc6YhJ51c25HptKkmL2+mgizjIL6t63aI9ndUJ+aNK2u5MYmUdbNHvNJEus01nE/c2g1MdN4WFSfHFUPiSSfBU8JQ1K3iql+Cpql2UGzzVSrg+T+3/qB/lJ37VGXOhumsBAKIUdKlZRzokBcycps8/S80qlXOdRO9WRZIEdJRI65zJEcef1NV7cq/ptwSkOaJXnZBWohf5kgqBtGRHpnqb83NlE7tud+mFruUSm+holB6SIo4ytGNlGnjUk0enwy+ZQrf55+Vabyew7dhpfl1WN31LhPWXCO1424S+vq+k9OadTB5q/qbGaxkDPVKL4Jrw8VZ2Nz0sb/EmzV1ecJ2hVat1q0P9Vm74GAC2un43YfczvhF4r0rwVPDUkvXBU6u2Bk8VW4KnrsVTuzMbbYpEd577Elg9xeLJvZ2qaUaVLjpNhOmSCJIsoegaNnWnd9J4fvBpAH6jF+52Shvo0mmN40sglVcnAD614wAHlVWhR/ff1mWtde44GtM/VY+mCPuXtOD65Mwid4DxxMfcPM03pI8rAJ1G5HQkpl7L7cnofW+JTHeJzWuZMfD0a3xbP8iIUsvP9bjLU1KXCJk+W/uTq4J0zV25PPBxljqq3e2KihyVfhi0qyau7qLyPS5t4uiJ18PnEjzlpA2eUhI8VU0IngqeGtT94XjqwO9saEBh5g6l69NkbmV3EEydyEKUfdodD6tUstMrZAaWvWyfjC0wex29DHArAG5H2b7O9m8wBdtjRcbJXDl3oEEHygDX10nYnMaIGFyMur36spi21E/x7bvcTWIkpgrw2t8HX31uj04uiX754FY+yaTr06rIgwC+NBbHhDW0TxLZYPeVkQ4vTfsEPHKyAK/r4L+s2mQT1wvwSU8U1xRmUv1eUyr7LCiLemQb242jIYCuzv35lkv3qSQBPRndMpVlEswM1rsWhrgSPBU8FTy1q8OxZS9P8FTw1DV4amkZlX9hcr68oT/KPsytiaLGH2i8kpYsoO4V6AW6BlvgiXHSLHSqTpNfrb1MBoClfvxorWULeJc1dVKkw86K9qoj/LByvPrswFeW61nqecs+oPjihPJmqXP7HPHVUbHEhI6Yh3LK5aejEYAL9foq6zFzBLj988ccV+eTRKDbP4HRGIn6yJDUm3R746r5m/Q424aSKGw6vz4yasOmwWdkI286gM31A01ujBJZH7Y8p2F/ujemvI8leMrkCZ7qzgVPqWKDp4Knkjw7ntrd+rY8NlUYlVEFZXSJEdlndojrRvdU6isqkHv89mssTWlHmcPIeL2i04HXvgFg86Wrh55kk+e7w8UB7dbUIzIvYY4MQkQPd6c2GZXkV+xcF0nE/dXL16CyARlfZ1kv7g/cnZueWZqyrtxmKZxMx8B75Nvp2lTNZFx4ESILxJ4tXqTMj+aoorlvZZbn5Q2IY3MBzbako+VRFGKVKRN0m3U4NEhf0tbRl7+X6svIWkFdJt7t45dZgqeMPcFTTrHBU0DwVPBUZ42bvqS9Jk9NHza+7du+BQTC6XTCo0ePcL65yREawolOON/c4Hw6p6ceKj8KdMLpdALRKR0D9ZG4LXXNP+gCrtfHoM4ZI3IUijfUxywh6pBJR4E82KtRFOViQkf5bgoakIkL8gtiY2FqDe9ggBIhLY4TbzTKpcqk7OYaNVoF9oGh7iBZylsTmaxcBrIZSZeIIaUZcO2/ALfQl5ab2PEiB2w8Je7Udf6UQUvO3sF1YeSxPtXlaHDVpUmQGhEXw+rwAdzql+An18/O9Hn2tPWjkOVlROxA3uRX9eLWtlvXRu1zWj9xsyN/+9W2AYOhlvCWkCs3oK4/olXrWL6FeBI81WkOngLs0NvPWxMFT3lKgqeCp+7DU9OHje/8tm9NDbill6rOj84gOoHvksKbR49xPp/AW55+IsLNo8d4/OgVnM6n6m2nUwL8m5sbnM9nnOgEOp1BpxJtIJzOJ5xPJ5xO5/qd6IQTkSCF1lqn0wklQEV1jlSGszJwgYe7I9T4S0Emb56Ider+/GXgPZLdlwStj+ktVNJp1lOt2tF1O9Qq53acgZt1x8ult3lVunp52id1uP+vCa/LSgu5tnqEkNNHkHXiAAAgAElEQVSy9fNOz7zuMhrTFznz5QIyPsj2+uSxHn+pHl4/2PAkKXuazi3b49xgGQBXkdFBG+VsdVWqIorFcd62cTTlVBYWhOtgzIhsGWXjwiuCzPtMgqfkcfBU8NQxCZ6qJatzwVMm8z15avqwcXt7lwE6/TvfnXG+OadzG+PJkycoMEAEnE5nPLq9w9N338Xd3S1unz4FwDidziAinM5nAMCWf/iDiHC33eLu9jaB9CkB9ul0ypEqwvmcwJ/OGdRP50QE5zPO51MmhhucTyecT+es54zT6YSbmzNuHt3gdD7jdMqID+AkI045atQ2K84XxGOd13nPDgtsaCwDIBcgZOM4TWbg7Yk3lTxznuqqCyym9ew33kgn60S7egAoEnejMyv2L5Yl29xC0hE9pvBBL0hQ6gHKgpwu+1TT9pEQe5xv0hw9Dfz9m4m+KtLOvoUa0MvxV+rX6pmGKutx2pUmtNtIkdOm3j0cytQwtxTdMOl0ClsVCclM5YXJHA+ajdOdcfiyS/AUEDwVPLWaBgieGpfX6wyeug5PTR82Nt666dPz6QZMjDvcYbtLzzLnDL4gYLu7xbYRtm3L09b5PG/YbnVDnk4pMoTzGbe3t/l6+rdtbb9jSvus5W4vERXgRFTBlJmBjbFtiTReeeUVvPHGG/jghz+MV199FTePbrBx2tOaTikf8alug/fuO+/gybtPQKdEDClKRTif8zR8jXRR/jxlPXnFGgnwJ/2ACGb0M+prLNA/Q5O6wDLlCPhmUaA8+JiNrpog/5HtfIlUP5c9aMpSNnlDrCaYFrX+S7DPVpbtMUsJOvrLNxx1tOy1171E69ZRH31dRpB8UEppWQCiV04jBkm8DPCW6yvvt1iNs6pxFMGRdTB5DIVVO8XPMak6KkLJYIxsqwJdLrvxiFO5Xnbi2d5oMHOaMt+eXaTzvSbBU8FTwVPXk+Cp4CnZJtfiqf3dqLKUqa27uy0fn3A65URUEuc9kzc2nV1AGTnic8oRphPO5zMePXqMJ0+fgDfg9Tdex+uvv4btbsPt7S1ub29xd3cHzgAMSiDcbGLcbZuIUAGvvfY6XnvtdTx+5RXcPDpj41u8887TFKnK63PbtHQaMO8+eRef+p7vxZuffhPb3YbT6QRmxvnmhJvzDW7OZ5xPN6ATAKRrj195jA99+CN49bVXQadTwmrKkatTAvfz+Yybm3NtpxOdcKITsqI65d4+8+uLdaDmuqK1cZsoL22CDti085puZfudczkOoJb+Zx7iQsUcU5xw8dzOlOtkB+9Ab67vyKbuvKhYWZt9LSC/jy43EjGQ3aie+SbBRJdIIs1I59pWgj5wl89VAC9pqObzbZd6yvV2Q6fcjAffh1oJZSdwCeAWRK0dtgc1gLNuDpm0MQ3kYvVaxMwxClsRJ86OH9oYS/BU8FTp/+Cp4KngKXHuxeGp6cPGa6++hm3bUvQnA8zdXWrQNkiA7W7DhrsEkGYqsDh/0nFCAazz+VTTEXGK0pzPeO311/D6G69j2xi8bQC3tawA8prYE0CEE1H5IXYQGHd3G27v7tK09fmcgBWMu9unuNvuwEw457W1zFsqA8nOm5tH+PBHP4KbRyd8+s1P4+2330nT5k8AOp1wOhEIJ3B2/JvzDT568xHc3j7Fm2/e4smTd3F3u6U1vJlkbh7d4PHjV0AEPL19kknrBudsf6kX1e+pbU5EOOeoVUlXwD9FqU7SS0R+dJ/aL3T8jyt4+6IG4MDhtNOPdK2B9n5ka10soM/y9xGmQqrj9Y9yIlIUumvLynkvjQuM9RSJa83CS0SX54Hxlm9WSpkatRqYj4lDlObob3pY1K+8hFpuaVTrmxuDBvjcrilgzVar9al7SwBa7FZma5RULttQrh4Ttlm6Mouy0v7l3ulkE4YUCZ4Knqrfg6e0/uApUXbw1PPkqenDxs2jxzX6QyhvwN9mQG5AfXt3lwAvT1Ofz+c6tWt/iIUoGVd0tL2jUzTh029+Gp9+89O1zLQeNr2MR6e2g8jdtoGQjh89vsErH3gFj1+5wSPcgDfRNExIq19PuLu7S42SATP939b6AsCrr72Ox48/gM+89Rm8+X3fh6e3T2vjbvnJ9ebmET704Q/itQ++AZwIt0/fxd3dUzCA220DMeEmR8Vub5/ibrvF6ZTWAN/e3gLgvOb33JyT0trE7S5F3EApqHQ6UR00RKc6WcYb4/buFtvdHTi3E+VIFjOntj8l0uTiFPXlRdRdWm7ON7W/0nrlm/q9/juV8lPeOh3P6aictSL3n09N2CiD1RO0xw6YEoxJOr8u+neWRmr0ktfdF4YDfi4NBtYIy8bwJBjp7JqkjtV1D3A3NX65Y3M9ZdvGu42y9H2Q0lrbS90Kmm3iuih/RK5dOTatsKu0MzeMU0kMgbZTmrTEVbBtG5lcUc/OtLSuwfj+KCR4KngqeEpI8FTw1IvIU9OHjbvtru7wYR2vOnN+sqEcwSlTz+kf1Wne8oR5OuVI0daAnDdOgCMiUwlQUp473lLnbRtO24bzzQ0eP3oFr7zyCh6/8hg3NwkM7+7ukuPfnNJ63Lstrc3NUa7yUt+2Me7u7rBtmyAcAtENtu2MD7xyxofe+CA++MYH8T3f+z14663vx9OnT8C84ebmMV5//TU8fuUxbm+fgG+BbdtAJ8L5RLi7Y5xOAJ3zry7ylo4pTV9v2122hcTLgFttw/PNCdsdsN3dZbtTO24bA3yb1hTfbRlEN9zepen7VIcbMG94+vQpbu/uwHlJwN3tHZ48eYq7TLZ3d7e4u231f/z4UXXG0+lcI2Q3+QXHU14f/OhRXhN8vsGjm0d4/PiVtM0kKK8hPuc8p9r3ac1w9sG8JpCqV+bRIue32Q4UIyUiBjmUAORBWH2UEtl0A3ogLQqgAbwDOE+FA4L6xkVEdIYWSFuojisXX1X79NEwGa3tdcLRW8ZxAW2tp1336mdxYZ9EPJs1xVm7ii2m3hIUuYPFao+pafZ1Cbom0SZxVwKv1GvP5yqwtin9MVTM9ZvSVTLKvt+YwbyZrQ5DpARPBU8FTwVPBU+92Dw1343qrkRLcnRgS0B5d3eLEuVIEaLHIGKUNZoJIFOGurazvsh2roZvvAFbisScWAzq3CKl02gD6Aw8unmEV175AB49eoxtu8NnPvMWbm+f4o03Xsf55tymZpEIhcW047bl6Mu24UkGshbdOuF8cwLRDe7yziYEwquvvYqP0kdxOhHe/PSbwLbh1Vc/gEePbgAwnt7eAihRsjJFzHnqnTK4IkeDGngTnXE+3+B0OufOytEsJELY7oTLbBs2FvW423C33WWQTOtzmVPdkl2JKG9u71Ik7+kdNuIErnJQg3D79AmePHkHT568A8KpAiSQI0TqCZgakN/c4NGjx3j06DGKg55Opzwd/wiPHj/GiU64efQIN48f46ZsD5nJAYzaV3cbg+82PHn6FLe3T5IuyttNlpuD0yn5UI6a4UTVn055n/xE/m36kk75TkINoKKzti7kThhy2rFcr9+YKzHoyxbc2jkJQMoGqUKCbUNPfZjJrWhuN1R9GEFGHrwoUropkNcVFeYyRWRMpRnXUdo7kr1lArJMD+SJJPk0sKxeOiDcDiTb5oGokSJy8ms1og8cYO+y2r6RdDGLOMqOzrUjLkt4QxwJngqeCp5CbfvgqXEdpb0jCZ56GJ7aedi4y4NEmHFinHGqUR1w2sFDdmyJCpWdMmpDnQhbGQgnAm0pqrJtG568825+Ce9RzQcAjx49qufutg1vft+n8JnPfAbvvP02bm/vcPPoBh/92Mfw8R/4cbz66qtgZtzdpnWuW3aGFina8PY7b+PJkyc4n1PUqYBEAZjT6YTbp0/x5MlTbHcbHj1+hI9/9sfxoQ99EG99//dj2xiPHj3GzaPHdScT5PaWZZUn3G1jEG2VMM7nmwSmlKJHek1hjrKd9eBmpkyMd2BKa2wLgNxt6fGWmXB3254uiXLEB4QznXB3PuNuu8OJCE8YuOWtTvcz56lvKiSaBwsBZ8r7yZ/TVo5ySvuUYT6R4DkT6Sn5xLbhFrfJ/nNp3/TDWndP7/Duu+/g7bc+g8+8/TaePnkXt7dPwRvqzilAQlSi7CvF/5jTMojzDc43j3A+pRcbT+e8VphSdCu1Y45gnco64lNOl/Sf8rri5G+o/VXIow4rJgH8gBzVI6DEVrpUgGDu9xIVsECVkm8wmFMxon7nsh69+U0pX+rVS0OKvZuIVBpyUenk+Za2jXOd39+P3LbLmFz0cUGszVyrXuCAptzyUGYytjQa1JcEgdpeOeXL9UrFbFvQ/8/em8bqlh1nwc9aaw/vdMY7uW93p+22ux3bmRxIUBCQkA8SIBAQRrKQmEEiAn4hCJl+MAhQIBI/ECj5hxgEIVIUhgSMFCBMTgLESbDitnuwu/vevuO5Z3iHPayhvh9VtfZ+7719HRNaNMm7rOO+57zD3nutWlWrnqp6ih7558O38ZiXhiuR6u3RGsLAkAXMztt4u7GzUzs7tbNTOzu1s1Pvbjv15JoNQVNyuMsaODjAQpCMbeq/rAzNEGJmBS4CkogVUWLkIwgzRwwBPgTM54uMCLGgJfi+x3K5hO89giJVojysYXTq5OQETdPi0uVLODo6RFkWiIJCxBhEkTLNYVGUcE4fm4U6BEZtOHWUkQbrDAjM2OEKi6qs4JxD13WYTCeAdRzmFQaS4fuIed9lbowxKArhXLf8fSTXBQbkxEqxnioq3SjGWDjHBiYYnpPBOALkKTMAEBK/31qACClGwBBs4VAYx6FzM1AlGmuRwPm3pTSzAgZUkFGokpW3hJrV0Ob8WcvIUFlVKAQFy0oYzNTStwExJfRdh7Zt0fc9gvcSZh/JjRsppMShfQIABtOykWULSKAY0fU9NjEKn72Ds0548o2kLQyIluZV69xZY2ALZZ2xSJFRyBgCjJPGXWIYCEDXNPDe83cV8ppw6BsYDiESRGZcbviFDF7J2hgMqQcsBHl+de8ArNBiHApa2ciovLHVCSFIigKnJfR9z3LuCtSTCTPdyD4GNOd6WylvA1aUDyR5LR5RtMCj6ojv6e0QocejQKPXRntiW0Hq/ei7B0WuSJrB0At1ZF/106NnoNH+Gr4Nb3tfo3+b0fw8dH8PG8MtbOhhgzx65uH6j37PNl77y80K/7U5dnZqZ6d2dmpnp3Z26t1tp57obFjx/vVGOGTJT5Pye4YwcM5hFSWTUso5pxxW5d9VyekjpxSh0UQvIdMQPBepDRfP351nRMKWBIvNpkH31ltYrVe4evkyprMZqqqEMQbBc7GgohaqMJMoCsg9xMghc83RtaLAi8Kh7z3IGFZWVZU/b0x6yFOPGAuec4Bzg/JQRf/40CVYOSXtxWjzXDKik5CSQQwpC6UBBA3jebXWcRi5qkBE8F2PEDwMDKwjOCpQGnDuseEwbvQhK8AkmsU5bjRVFiXPl+G1LsoCdV2jLCtGbBwrLGsdDBjZarsObdOi7zt4VdgjY0+i7IwbwstaeKlpCyTaRZWbsdK5V+RKv2+8adV2JGF0MWZAflLSvGwpUrQGkPSARMT8LZY3zqZrkKLkScNmY3B+eob79+7h4uICXdei6zv0XZ8NdpSCT50XNZaaksHFoiwXVnKQS1cKykaMBjqHyWSC6XwOC2C1XKHpGgnXa5ieDe16tcLF+QXarkNMASkSnHVY7C1wfOkSjo6PcHh0CUdHl7C3t49JXeWwPsugyqNMIenP2yvcPN1PfvlLGpz98qi243sZ8oJHcFtW4PoBlYWxunv4FlVXknz5Fgj4WEMlan90KTW8NLz60IXexiDk7xmMVb7OyDgQMICA40ZQ8bFfuxvY2amdndrZqZ2devzY2an86v91O/VF+mzwxFlAFLV8qRlC0I8bY97xbUTlIU9Jdp4iD973EqbkwjINiY6H0TiiMdmLI4ogEGJPOLl3D5v1GpcvX8bBwYEUozmYOKAzGjp2BRfhpcjIxBBm59BlWRQoyxKMKgUQCK4sZNPylKfEnrqiQ+PnZPTEARhYNAavnOdhMBhsMEE08L+bODJcMm+yDJT4GqxkLFLifFpFpcqihDFA4QpeC+F4z6aT9POAl7CwdRaGIGiHEwVUoqxKRmQKi6oqUVVVRt1STOi6Hm3Totk0aJoGfehBcSgutDlnesiDpsTKOs+LrK0zFpY4FQGG832NMKTEGBENMouLExkkADEyy0xRlDk8nY0rhrVPMQKwsMbJ91tYVwinvEFVT2DWa/R9DyLAdz36rsXZ6QPcuvkWbt++jbMHZ2jaBokihw+F4pFTImT9IRSaIq/jwwEbGJ4bvsfEKQBWOfCBQhA8IqAXtNTKPokxIvie50RynmEMqrLCdDrDYm+B/Xt3MJ/PMZ3NcXBwiKuXr+DK1as4PDxCPZlAGWayATWqmraxiSG/HI9GtLOs5389+uITx1hx5808woTyXWAbjSJkUkF62ACoeh3/SdTjoDG3bmH8+fGzkN6X/p4PtGNN/jbPrHm7W3OWRr/QMN16CWH3GT436l5g/g9azV9tY2endnZqZ6d2dkr/urNT70o79WRnQ7/fGPlyVl6KUgBaKJaQSNEgDpXBjEMvKgy6EIwecRfXxIiRtagnE5RlCeOcbA6egUH5yXdZy+HxmJhSTw0DSyU2qw1udrewvFjj4HAf08lE8jk5zJgCF8RZVyAlgz569F0nSEmRw4rWcWjee2XS4Osz4sXP632Ptu1gjMFkMkVZFvmZIM/A/7ODZzhCTjinUdAAnWcQYvSAIVjnoOFIRrpYERYF53dq8F9DfKowNR/XWouqLJEowQebFaCmFiQpQFRBLssC9WSKqqqAxPmy9WSCouQc3pgiuqZF13Vo2wZd28ELIkfZnTY5lGyladT4eVWJ6/+scSjKkhEbAihGllmZC0rIRtKC0Z7hb4AhyXuGNuAaqBRFJLL8qYJPlBB9QN916LsO6+UKq+USFxdnOLl/H+dnD7BarbG8WGK93qBrGrQdK87c2VdkJCaWka7r4D0jhiElxMic+sOxhQ0bpYQYVUaYy78SBpWUhPZSwtgWRnKIZe8J2qdFlMr6EmNC27Q4Oz9Hug1mXCkcqrLEdDLF4eEBrl27iqtXr+Hg6BCz2UyMscvNuwZElb9/QCsHqlDnrDC/KJuLFeaXES2lGeUzQxWfwUBHOPzQAFNlJEcV5niMFbj+Rw9jDytSooEBhF9ND33/+PvwiCIfvQ2KCOVXh4RVPDy2Dqv66Xy94a/57kRR0+iz2d6MP/+obd2Nh8bOTu3s1M5O7ezUzk7JeBfaqS/qbADqVbK/E1NE8AFd2yFKyNcVEurkN8MWI/RD7mhQ8MwAosVDutOiIC9ZAYGAAAQMG1A3IxEJ3WHK7zcYEAIYIIWA5fkZ2rbBweE+jo+PYEwp19Pn4qkKMWB5cYEQPeazBfb2D1BWgogkvm9GiIbQaIyMinUtIwt1XaGqhlxQNXJKy6eLIlcW9GC06MR5rJyvaZHI5muNQ7sDKsUohHaBhZoK6/Ic5Tk2lNEh5xz/HQ6gApxIy/NvnEEgQkFccFgWBfo+oG1bdOcduq6F7/vMA8+GmiRHVgruZAPyhlaRFSFNrNBYEQ3KPReIJRqUgrWSW8rPrYqlKIqMPBljMmoUJTdbNzaHoIeNnoskPaFtGty5dxe333oLd+/cwenJCS4uztFsNmi7HjHETKdJpIiP3IfkBRMBXd+jaTp0fY8QJLfZ2q3rKZqkjxITo1a6F/i7HHwIYoCHORmUYUBRFXyoSITk4/YJiTi0bZyFE/GOoti7tsdyuca9+/fxhS+8gelshr39BQ4ODnD58mVcu3YVBwf7ef10vXSMm6UpqqUFm1ZSBpT1pa5rlFWJwhW5qddgBAaWGRJjlNFilY+RvAy6g2V7XCyY5/dxihTDgUaNOH+PKuPRThw0tRwnRn8bfyfx7sqfNI+G7x9R4A8bJxkGslZbCFjW5/mgl+0cRve2G08cOzu1s1M7O7WzUzs7JW9+l9mpJ9dsWIvggwgEC573nKMaPIee67pCUUzhbMEoBATJCTEXCakS0tzTYZNvP4jmURbOyXra4UFksTU8rQ5pZgQhIBJfz1kHA6bmazZrZpRoGly5cgWLxQLGFDknN4Qg6EeLvuvQtT26vsf+/j6qqgKBEHyQ+1aUiJW6cxaz2VTyR40Yp7iF2LBQPdzNklcoIx6Gc1kjBWionpGnmOcFEIHcosAzCDHBJC10VJSIC8/YKBA0c5lRMgsHh0QJtnAoTQ1XVPlaKSU0zQbr1RKFdbz+KWC4geGfitDl8CyJZ07iWBsRWF0jY2CIeOOLooaE3tkmDDnDOWcaQydgJyFb54bQcvamY0TyHr33IHAhoTEWPnj0bYe2abBZr3H64AHeunkTN9+6ibOzM3Rdh+A9fIhcvBY8vI/MHlIWjO6kiLKoYKzhNAVP6L1Hs2kkJcDBCHpijWx6t50XTmJDyBgpPI3wksbRdp755CllBpiiZPpGAw7vB6KMjqbEHYi1cLEsC6F7LBkVTQnBB4QU5TOqmzaw50vcvXcfZVlgbzHH9aev4/nnn8d73nNt4LEnsKLKTDmJnzEBkQBEllsuBrUoYgSIcj770AhtnMttsmykwDz81lpR+HZUxJry8wFAUZaYTCao6xoA0Hcd5713PQCCMS7PGYDM5gIRVU0DsFI8yTcGmVe54Yf+Pugcylz7gwJHVvR6QFXDomPLGAjqnT9DKrCPFtPlryCj5goZMdp5G287dnZqZ6d2dmpnp3Z26t1tp57obFR1zYqwabnwTFwa55Siz0lILSEln3NfeTPrt2yzgGghDYdzJ5jNZgghYLPZcE5pjPBJip6ylzh4ZJY0XE2yd2NWmBDPDmAEQecw+oSz01M0TZNzZIuiFP1BzN4QIshwnuHF+Tma9RqzxQJ1Xec8Rs7HZKYRzVU0AIdTzZDrqfebhV8945HhUoUeo+bCmtFnh03Av+sSm/w59YpZPjh0qWH+wmnhl4YxmfzPWoKGt1NMSDYBxNzrjCDpdwUsN2tsQkThhLLPunw/+T5HgpjvQcOxzgoP+UBHCFLkSp5BVtcYzsHd8ubBYVs1WgRCYYdQaL6mIE1qMPu2xdnJBm2zwXJ5gfPTMzw4fYAHDx7g4vwCfd8hal5qYn4IgkUIPdYNI2JVVcNYKbZMCa4sAMM53v0IMavKAm46hSKAUYyyNYIaGcrIYUiDDAVtWKVoIAjWGRgaFI33zHxjjEFhHYwflAvEgFZVBRg2WE466irC44oCBVxGulSZGcPrkFLC+cUS683LuLi4wIsvvoCnn34a08lErU1GKljhDauuyGZGjXJTLf5x1uX10ILXkXSz/BEhhSh72madFxN/rqoq1BMu8CQibNYrrFdrrDdr9F2fDYXKBhEhkqbOuDwXIEb5rJHnoQgoKimHEoKkNVgHWxTDXhSlbozkiUsjMdVKSn+pejbvz2xTaLxkw9NnRQ6Axu8a2EqAIV89I+G78dixs1M7O7WzUzs7tbNT72479URnwzmHuTQiWq83o8nTUCozZhAJqkHC3w2bPVvO8WSfS3m1nXM4PDrCM88+g0uXLqHvPR6cnOD+vfs4Oz9D17W8uMYAgoKwozZwkhtpxPRwwZsRwdHJMvIcFha+D7h75z6apsHx8TFmsxliSmi7TtCewbvzvcfF2bk0AKpgiyHszF6u5gFHQS+MhJY1rJ1gDCs+vb8xQqahZMjfmMLP5U2fvWxAPGlZZhLkxw4oDStPLvpiKr4eiSRMKArPWoNEDtAcXctFdUXJnnOKESGy0C/me6jrCk3bom06ViYQvvNx7qMInNJLaoh42NA2U/1lZE/WcYym6HyB9FmHvM8xp70TpcFzlpAS4AynEKxWKzy4fx9379zBg3snOD17gLMHD7Bcr9H1vTCnsOfvXIGiKAUd67FcrbBarwEYTKoa1rrcuZYAdG3Dm1l2PBvwEpGUtpLXx1p+1hBZkWrOdghRUD8jhqDjeRLES4sorXMonQMZIHqmCiQkBLmPKB2GjWVueldwzngIEUCf82KJCGVZoigdCmW1yVuEjQbvQy7KvH37Tj6APfPMM5jNZhiBMBlU4fWwkhIhSJBlTvuy4CZadiTrBkPOtaI3jF8mUOLDXuoDr2cCAEJVVZjOZ3DWoe87nJ2eoWk2PGdROx3brT2fUoKPAZTYMBIxraPut+D7fODJaK4NSAREQa90n5LoqpzWEKXDMWttmROTjbUreA8bCCKq6TJWEW7JB5f0CuMsSuHez8aC1BCpkjei5+TgK4Z+Nx4/dnZqZ6d2dmpnp3Z26t1tp57obGghVFXXsMaiL3v0vkffd6JMhgY7YwYHVa46EfowRWkxnUwwm88xnc0QQ0KzaTGZTfHU9eu4fOUylssl7p+c4PTBA3SNXkc2Oo19KwJiApmhKIjDgOOcSH53TAQbI1zJ4beLiwt477GYz5FSQu979iRlk1ujSpQXuaOEIjJHtys5R9NKAx3r2MPkQidmmMjIWUqMSoEV/BDmj9kwDYtssrJ71EvkOSbJWx0bBn0NBjCCxqVIiLEDFQnWTuCcRYwAiXKhlECJuLhKC66MARGnIhhrUdczTKZzxP2Avm3RtT363qP3HdquE697CD8651DVdVbsVl43blD8Kh8kfPL6My5U1DCiEbRF50SVd8x5wQQYi77rcffObbzx+uu4c+stnD44xdnZGdqmBYG4KE7yTq2wn4CApumwbhqsVms0bQvrHKaTCZx1UmSp4f6E0pVZKWtx3GqzQe8DDDiU7FwBGIIPXu0kjDCT6J4gJMTIyKs1Q1ifm2k5FKUT+ktW1HVVwRjI5iZYE0c9RZHlSH+MMcLPXyIRwfsIKgBHDgjMhFOUnMubUoKBQeEKJEp4cHKGl156GSf3T3F0fITjS0c4PDhAVZdQCES5zxMlmMg50IWxavtseQcAACAASURBVN5kfRlBlUXMqAqjVDEX6MYk4XV5mJSYSWe1XqJarwAgI8h6eFOjoTJENOTqkqDUDHYpWstIH0WROZE/pjcd0NaclU3E+kNQKyN6ANby3pHuylA9JEWsj+sTxYcbRTL5fWVZop7UmE1nqOuKUXdSdiHKypxzmLm4FYoW73yNtx07O7WzUzs7tbNTOzv17rZTT3Q26roWNCJmtoGyKlHXFfp+yF0jnQxRfKp7NK+zcA7T2RST6cAWkUJkbuamRVVXmM1nmM/nuHTlCg6Pj7FaLnH64BQPTh5gvVpJPip/sbMWZVmhLJirW8OVEKOhoeEcvpVpTRg8xhACTs/O8ndaZ4dQFBStkmcRz5LZGQrJySyFck8717IC9T4gBKYYZE7wEpnt4iHFpfe8jQ5pvjDlOcxzaYbmPzqYacEAFBFNyuwMGh4MIW6xsKjQjxEbVlDMTW5tyikGhizKukZVVqgnvN5936HZNBKm5WJJEHv6k8UCZVkKHR9l5absEEmFngbllhRlktDmuMFRNnBGjRjlz/R9j9MHD/DqKy/jzde/wDzeTYPNpkHbdiBjEBLTHaaYUDiHsjJIqccmRGzaBn0IfEip6tzcKAamXXTCBuKEatDZAj56rDYbxBDBjaQkRcEI5aHk0wJG8rHlIJCSKBJWEFmuRJFyDwCSe2VjY7GdmqD5v5ADghoaNXQqI1Zyi1OCsJ1YxBQFGWFUryrLzO0PIhSWn3W5XGK1WuH1N97AwcEerl+/jmeeuY7Do0MO2at6IpMNmi1E/kW58bVTNrZGwtjDW4Zi0qG4kRVr3/domjVCTKjrGtPZFK7ktAmbSAxBGv2wHCXVQ2A41RIAI5SV2QAp7MXPYCjr4GwUMg0gOKHDiBGCFUMADptXNXd0VrYfRjvFgqhBwIBwR+nAXJYlaqHjJDATDCUaDHM+nFAOn3cdM+mMw/G7sT12dmpnp3Z2amendnbq3W2nvmifDfYRLZLlIi5rK85TqyO89/C+F8XFF0nKq20MqrrmopnJBIVzgJGszJhAFEHGAdagbTu0bYeL8yUm0wnmotAXiwUuX7mC09MHeHD/BM1mk5EJZgJhVAs0ztHUhRg109HNL6Fc9gQhT6aGZ/C6Hxf+UsG0QO5MqiHi8fsANhRFWSCGhPVqibZtQIkFczKdoigLFIV2yJSF1/CYeJCGhvt8OBduC1ESA1BUBQoq831nb5USfORCJkaoRkpefmKMsCSbsnSwhcthTg6jW9Q1e+plWaBwBbx0veW1j7nwqyiKHFLLoTqj1HNiZBMhGQJMgjP6usshfn5GCbtnLnfeHilGnJ+d4fOvvYZXX/4cTu7dRwgeve/RbNp8P03PCJf3AdYxUuk0lUGQssI4kBtSBtq2AyApBolzWUmQDO89Eog7nlYTGMu0fSkldF2HGBJLk+U5TsJmw6HpkPPD1Qjxsw70htqpVY2esWZAEsCHiIycYaywsbXB2UAP3XwViYnRQ3PRe98LIlVLmgU3lwL4EGEM4f59j/W6wemDB7j2nvfgqafeg8PDQ9R1zUWGGQkaoTbgz+bDHFRxU0ZQ1GgTcTgfkuahKBIREHzgJlvBYzafo3BuZACUCjNmVNo4g9JUMpc0mg9BWSnJPjIjuRakR9ChMQLEYKSBSaofnHTiHXjlraCkilwClI22zkseZkRxqFs+8fV0jfXwqNnlrnCYTKfcL6BuEUPAbrzN2NkpeaadndrZqZ2d2tmpd6edeqKzoQ+cJKwaYxTlNYQaebBCYhSA31Or8tbGNYkpzshgixsZ6pmB8842yzU26zXKqsR8Psd8PsPTTz+N4+NjXJyfY7VcYbPZoG3brbC0ogskksOh7GET6MKz8Ixy02TR1KvUUC0AYWQYOq/y9ZRSzWUFq5tK56Tve7SbDfNetx2U7cSNckM1f7AoOS+Ouc8rVobCZKG5fGx8ooTeebOnpFBYEkYJtzWfZMdKHzAUpZBRH5jyGuh1AEbjHCzIAomUG96IgHLBoZlaFGWJEAKKUkK/zmU+bO89d801w/WCCKHdWisrc+7yxldUK3jPhsdoyDqh7zrcvX0bn3vpJXz+tdewXq0AMLuKDwFBQv9d3yOMWCMKy/NJGHIkC8eNiMgAIUYOBUq4lu9DDVSA5jjXwqIRIzcoImIl7T0jqsYJyqgwJQ35vFYoIokS50GO5t2QFH6ZAWXlubKjNRwOGBlpSykfRlQ+FbFh45xGv6vx5/96H1CXlXD48/oYGElJ4c90bYtbd+7i5MEpbt54C1evXcb1p6/jypWrmM9nWfnlfFciVmUFYKPNBZGJGBnRBln63kHPsAIHUU5lCD5is17D9x6z2Uyalo0Yg2isf3guOefdIEleLSghJo+MNiY2JCpj4wObkyLflEg6xfI6EhFMZMSKrEFKLR/KRjohUyxqriyJLnEORVXmXG7VQ4POQEYP+dBmIfQ4ueO1cQ51VSMV1ZNU9a/psbNTOzu1s1M7O7WzU+9uO/VEZyPEwAo8RIQw0AIqxR2RkZAi35F65FVdoyiK7B3pA2XFybLOC25G4VfwpkpEaBtuQrRcXmA6m2I2neHS5cs4ODzEcrnE+dk51qsVurYVT3tE40fpoR6IxDOltG40hEpZgQtXtPBdJ0EotCDKYCi2Kl2Vw3lDO3dC33domw7NZoOma5F8YKQKg7Apq0PwXiEr2ThDMVvhHMqywmQykXB+JSiMzCFZUPJ50zHv9MgTNpwvyGFeNsTGGC7SMgbKBpISoSj4uVRJZKGE5A9joGYjIhFOi6oqUNY1YmTWi+Alv9NZ3mwY8l11A6UENuQgWFkOyv6xetkSevY+pwOACF3wOD15gNe/8Bpee/VV3Lt3T3J2ecPGwPR8vQ9C98jKfzplKjrnHIfpRbHFxPnPWqTlY2CURQohfQhDAVtVCkMJ32+QMK+BQd95eO/hnBN5NzkkapxFMkYKA7mwEtCiRMvKRtECnXM1dBAmDcfzH8MQyte1Gqc66NqFEGFMEmW0/RqnUozykWNC8BHGMue+MwQfPSBhbUqEPnk4oS7s2rs4OTnBrVt38dx7n8X73vscji9dFnRLafzMsKcw7HHIc5JRVMVsIZq6DiSazcohKUXKlI+TyQRlVamw8CHAcF6yyqfmioOGcLPeC2Wlrw2ndM8bgOTwJ0pVkR99lIxMCRo4ElpWH2LNiBJguRivLLmjMRcj2my8rLEgI+kZaiTku3LInjis7woHa0ukshj01W48MnZ2amendnZqZ6d2durdbaee6Gy0voPRZiXOYBwGJmIKL51EVxQo1XPKuZ/DBjXGwskfhqr/4VpKfTb2poh4Ebuuw8X5Bep6gtlsiv39Pezv72Oz2eD8/BzLiyW6tmWkQpTBw4/MjBPD9QcPl1EEMlbyfSl/x1jRm5GCJAKi5/zCtm3RtBv0XcsKE2oX1EAJkiR3REnpAgcDBjBCEWOAB9A0G1xcABzqs7AFdy4tywplWck1CLPpDGU5gXUFskkQwWQWBkaAyAAUaMg7TEkU+XD9MerGt03QKB8B4u1z19EkRU3OsvLSsKErHFxZwBEbfghCYaxBWVikpJ1hJRQntHXGqn8u9HrWAAXP82azwe0bN/DZz3wGb7zxBjbNBsZwQV/fB8n1JYTIiBFB2DZkI1ljBdUJGSXgkG1EFPQNZFAWJaxzCIFZOwzMCO0kQUwDOHzNqRaKSDlbshIjgjMcIg1CB6i5kGwgB2YOEkVDYKUNMeYEPkQUwuSRiBAR8xopwlEUpSgty6Hz9HBR5pDSMA5rq0wnYg72mBJTRroCNiX0fYfgId8DRBPhYkJRlAjB4/at27i4OMf52QU+9OEP4dlnnsG0koOGYVQMxmgPLjCI5WCMIMYjDU9JFTiH6wtJi1AFwPdAosw5vF5PJmJYi7z/QghIo73E+5pE/iRnNvBac+rIdhGoHjr0dGmMsOiIktZiXowQKgAwlADLyA4byiF1xoce67MVYCxm0xkmU0mpEeRMdiucXCMlkl4REdZZFLJmRVGA4IBdycbbjp2d2tkpefydndrZqZ2depfaqSdT35KEwpSpQHL9+FlIwrwue9sqKCSIizHiYWH0d7n5MY0XoApTFJ1MKiUpnAEL08avsVmt4YqCUaT5FFevXsXR0THWqw1WqyXWmxX6rgelcffQJyl2LQoiQVPG9IAue9zZO08JbbNB13KYyvuQKcfG6EcOnen15RoaetyaD76BbFTUsyUCjEmwlGCsQ107TOoa0+mMQ8GFg++9cL8HoZErmXHEMjtDEnYDaywSOERt4URZc5MmTTcY7muYH/VoyTJCFiN73LC8xkO4mfJ6OusAmxAjJGcSglrxhFijIdYgc6soBgFIiIk35/L8DG/dvIkvvPIq7ty9g873cBJO7T0r5hhJGnh5kUmHsixQ1TUAw6kCbcdc2YYVJR8iRME5B+dYAfWSf2kwFItpaDXEwEZeNgCnGwBlWfPzEpsgbaa1Hfq0jJwISqHhYr1Glg8gF2kZIzmiKQ1yqygGaZhZPyXzaodrAqwYmL1GDy7D55PlQtMYI4JnlFc570MMgKYmqOKrEiuURDg7W6LvX0XXd2iaDZ77si/D0fERCiOsGjRYf2OUN13p/bTTqxQ3pgRtlKRKg42UFtUO7DdOKBud5fSIIoesI6PbSTeTXBMGkBxrlEzfaGVNc2GgyK/Oo5zfBBUcZhfERYucZkGMHksRMVMLFiAirJYrtF0H7/ucPrCuVpJqM2e5tIBJo/xY6P5nuVQkaehejccyiewGj52d2tmpnZ3a2amdncK72k49uUDcmEERGlV4JjMhWCc5oSNPlRUQP76iGLqipDcrgjTk1g5Cyx1KKRfBGAx3r0YiBOarbtsGVV2jribY22MUqWkbLC/OsVqt0HedNE3iDWZGIR4SAZN6qOw5AoaRDBo4tdmFZB7r1WoF7/vMUjF8IQYFrIofcg0zKHGoYIxe1+6X6s1ba1BWFaazOWazOaazKabTqTCLOMnX5OKn6ZQV0+1bt3Hr9i1GP8oCk0mNqq7y+/WzujrYYtAYPQS2pWWcPsAIE4dBrbOwrgAcUMpa6hoVruDnMylvyodDqTwXUqgGQF1izsHs8OD+fdy6cQM33ngTd+/cRQzcLdV7j77ziJFZNbquRx+Y2q90DmXhUBYFkBI6H9C1HXphiOHmOMyEoggRDBfJdX3L7B1GFImsf0gRxoCLv0RGeOMaGCPojxSMReLwtxNlCmPkwCLImBmUVNJDhjVSt8WSXriB5SSllEP0uhc0vUBZPgDtxmsBSKOknB8u9Hii7MeHKFCCNyGvSSTCdDrhorpkhhQNcKMnBDW4/P6m6XDjzZvYbDa4f+8+PvDCC3j22Wew2FtI7vJor41kSfN1eT4Grm82glwcaiXlQvOCjR60wGin9xGEHlXF7ECucGg2G/jQ533HfRPkX8bBgDLLShwdmAwAY0WZY5v5Q5Fl7uDMqFyKSWRfDq0G6H2Hfr2ClzQNI8ZIIt8I3mN5cY62aTCbzzGbz1EWJYwBAg2dj8uyyAqZSBqpUXwIrdqNR8bOTu3s1M5O7ezUzk69q+3UF23qRxZw0HAOcnh1WB3KD63DGsBYlzeE0n8lUkGwWygF33AUNoK4JSy68NbazEvOdFvMsLBZb7BZbVC4kpXdbIrptadwcNhis15jvV6j2ay52UoaBGfcCVU9TBUuRkYAVS9a0MeKPW4V7+hGY5TMqP3Knl6Spk5OlK5+pz6XzmfhHOops6LM5jMs9haYzmZwRQFniywwBiZzpis6cKm+zPnHN0vcu3sXq9USy9UF9AmYt7sQJo4KZVUxrVw9/JtZNvgT9FiB4TxOlwqAOM/XirAqfZ41zPPMRqiU6+vcEShFplEThWpsKfPFoe+AAN97rFZr3L97HzffuIF7d++h6zoYocbznUff9fA9F+YBBrO6EiSM5yQELpjzQbulmmwwNQSvbCQ+BPRdhxQCtLtpjJwrm1LKoVBjeC1ZVsBpljQuJOPfLZDD2oZVB6NDcuBRFEgXJyNGBnB2uD/dU1G+M9dZYrShCQC0gMwA0O63SXKggcLZwZDC5OJEEKNYRFyEFxOxwYIiUoMMaJ62007E8lrfe5yeniJ6bvS1WCwwX8wlz9Ru7YXhAKNoqB5a9DVW2NYZ2GgQiZlHrDEwNJgEEkQqeg8PoJrUmExnKMoKbdPwWqYoz7YlvnplVu6K1hkFmTQUHRED56TGzPevaA5y8WrwHivp0pzUoIpxh9Gj53AETYlTbZiNZoPZYiHpJVyUpykMenBmgyVoUUqgJ4Snf62PnZ3a2anxYv+ftFPOGFhh5AGkjsT3iJsG7dk5zm/fxb3X38TF3XugtkMp9iP1HrbrEfsAEyMmMJjXlSDZvLZR7BRCQpm4MB+kaXAmO0TWWXgf0PU9qhByOlpM3AgupYRZWWT9nMAHcGtN7niuh1IQ8gG9kAJwiJ0KIE7TcyyziYgdUKN+rDggzmI4kIudgtpDyOGfEA04bUhgeGMVnbfIUbHIBUq2dLwKcj9BiB4sDErj+P5ChCGPIMqweOgwbp1DJfIz3uvWBzSnZ7jlAybG4nA6xf6kloiNkdtjO50oATECIcJIzZCeURwREKJQ2SVYLRgH1ynZZJFEF+3s1KPjyc6GLZj7OrGCJZKDjSymPjhgMnOHBl+ziiQutNJCE94E4imSQUgRvuseQWDG4T35GoxDNkQJthDhJ6DrO7Rdi+VymRGWw6Mj7B8eMNKzXOLi/DwvMjCETRW1Ga03BvREr8/a2bkCmTIwxhHfuGzONDARAIAhDgH2yfOG1Y6lklNbFAWm0wn2Dg4wmUxQlDXqukJZFiJdIjhifMYNinSejDHY399HVZSY1DVu3bqF9WaZc46jDwi+y42IVBgLV6KqJpjUTBNZVjUmkxql8I4PxktXGZmLXGnrIBRyeccIulgor7oIY4opKwnd3DH6LePd+x5npw/w+uc/j9df+zwe3D9hur6YQCmwY6AhZEOo64qpKknYGUjo8+Qga62BNQ5khctaN5cBUgwIntlBiEg48EXhpABjgEldo6xKydmMmf1lUAhcTAk9LIvMMm84sjG3hmSPEKwlyQln5MSKogIRyArNnhmdjwor3PPS+TYBUNpG4rzZlPeEPCdBkDizhVIZ2UjMOjPkBccg3UcpbRXo9b5HCjFzuW//WG5E1QdsNhvcuXMH9+/dw5WrlzGdTRlVtCajzMO+QkZWc/idUvamjBZ7EitrvndetASeA2ZEsVkhV5MaRVmgpglgDPq+5dzXvHcVDVa9ovua1zumhBgC/0h+swGYZSMreHZ6mF0kwvuU9+J4Pz5uDGsDUCQEH9G2HTbTdU41saN9rT0hoFHzyGjabjx+7OzUzk69U3aqvnsXz/74J2BiEFCMMs1s27ZYr9Zomw0f+LhSn5/lg1+O9C3fCvrkJ2F+5qdhEnEdzWIBrNdAjJxDXzigBGixBzQN4P0g2OK0EQAqSiRxMrIeTXw/1mCrqHprjAGfh/5srSL76rhpSpyR51DZkv/P9k+jH4MgqkMzto2jj/OYsH6mzSavQ5bekZzocygox7YOopfV+TEwVQUcH4MODkBtC3v/PlzXDZc0ww1aY+A6j8kX3sDh6Tnm8xnfy2QKc+UKUJXA3bug5XKL2U3vhfWxAfb2QJevsDNx9y7o7AwUY5bvzz33LD77Zc8i2p2deng80dlomoYFOiYoRQbTdo1vTkRGGTLkUEPQ8K54l0UBZ3Qz8I1yQQxPXkqcq+gch8GtYeYAIpLQkC4CxHtizmtIMYvm14UQcH52gYvzc9R1jflijtlsitlshsXeHtarFdbrNbqmZWRcJ38Uqh1yFTknzxrHoa0YB5o9klxGOzBhIAsEs3Aol3Rl3bYQGYOi5KZT09kMe/v7mExmsI75pSkBvucUgKJwKFwpRiLBQBlGZF7twGc9nU3x9LNPo6xr3LzxJs7PzoSWDzznvGq8YRMhJo+ND9isZfNalw+bZVmirmtUlRqVKiP8ykeehwGHEVVQDW8f7700L2rQ9x0f7GPKz0JKNUcsY03b4MYbb+D1V1/D6YMT+N4jRc41zkVJMJiU1YC4ycGC0RCAAgGRJVA3GBdOSdRA8nAjJUQkwBnAMapijbBPyCY3lhksopFQI2hEcc0y4JyBgRvpVI6wOMc5sAaEGDktoHBc3MmnajNoZQsYQQto0I9ZCScMyi9EyiwjWtTnAyNxWrCn3XYHZcn/xxz2UZy9BO/7kXHappdMkoduHcv+RnoHaERIw8wh8HXu3b+PV155BQcH+3j+A++Hc6IoiefEyBwOQKygXeADEqkTlAaZIDDKyLW/A2e6ui9EXDwIK+kG1vLhBxP01EmhJLLBHRSIMt0ob7pGp4YCR0V7eM2ZpSVKcySVd0BSc9QY6roZvazZjnzlW2HEudkwwlWVnIpSzyYoRrnyzghVa0pc5Lcbjx07O7WzU++Unbr82udx8PO/sHVm/qLj2jXgu78XeP/7gW/+/4A//+eBV14BPvpR/vc/+2fAv/k3yMUJH/gA8L3fC/zkT/Jrj+tV4B790/9TY7EA/sJfAIoC+Ft/C7i4+JV93/veB3z848A3fANwcMBO2i/9EvCJT/DcPux06Wha/nEO+OZvBr7lW4Cv+iq+r1deAf7DfwB+7MeAtt3+XF3z9b7pm4D3vpf3461bwH/6T8A//afA+TkA4GI2wytf9kwmctjZqWE80dnYrNfQHFeuOOfCHErsjSnKojftjIU1EspjVzSH45S7G8ShqkSMQqWQYCAodDEcZlShBi+0hlKQxAce4SmObCSscZwnGRQVMCJX3Ep9taqwmM9QT6aYXJ7g4OgQzabBer3OLAG6uJQSCutgpNmMs5ZhYmtAFoiJc3FJvGsnOY/KK27lczBsUIhdzHyQc86iKgu+l8kMs/kM09kchSuQGMjlH3AotSqqUbMYguYDDsLGQw+jxlgcHhzAGYNbRYn7Jyfo+w6svEdUdDQgYkaQP5MiUjTCNAKeW6t0hxyerKoKk7pGXU9Q1RWM4QY/RVFw6JeQ3187Docv9uboe22q1TMFYN8zg0vfSerUCrdv3sCbr38BZ6enmVIweI8kBrWyBaP2BPChgiMXSvkXUwKVJXdcBVPjlZJrrc5HSoQ+JPgUECM7MZQSpnWFvekEVcG50D4Q2j5g03u03iNFznXVCImV/E1dl7JwKLSojwhVWaBwUrhIzNjhnJU82UEJRclVdRaAsRl1VINLGAr6gjRgIjKIQncZkoM1AUYODc6w4Xei7EiuEyIhCNNOYSKCIEc5pUuvSJRzOpVlglPLho6wTjj4Oe+WFViIEW+8eQOHh0c4PDrEpSuXBdXRQsMRZ/lIoVrjYA0hghAl0kKC0iBxIVqKGiERXaN+mjgmuheY2UZysStC7xmpVkpDjaIxk0nghlnCBOOMk+JSzYEd2HCioOZbqGFiBJQSDdGoR0BFKa4TR1KjTFkvpoQUDYKkSEz7CeaLBeq64gJRocEc5GE3Hjd2dmpnp94pOzWbTL90gTw/Bz79aeCZZ4CXXwZOTvjvV6+yY/H889vvPzwEXngBuHFDaJF+FY6yBD70IT7kl+Wv7LumU+DP/BngN/5G4O5d4Bd+gZ2Zj36U5/ett4Cf//m3/7y1wO/8ncCf+3P8uc9/np2LF18EPvhB4OgI+Af/QIQLLOi/63cBf+JPcIrVZz/L/33hBeAP/SFgtQL+8T8eXcDs7NRjxhOdjZiS5JErkkLQzotZmZhhfxDJDdJAwZYbk1ibi4oov84eWC6+geTMpe1wWl6k8UQAALGiBEVYM7j+WlDGupcPmpvNBr7vUVQV6rrG4eEhDo+OmE3i4gKb9QZd340EhXJePkNUPJHWOLiClVWeF0FotW7fGAtX8IZKIW0pcTZiBgDzHFd1LU2GiuyjGjmEGUGsMgouIUQaKWMVzE4MVvAB2nXz2WefxWRS4/bt22iaVqcMysIg3BPQrpxKA5eF0AgveoqgvkcHYGMGdMm6AoXlHN75fIHFYoHpdCrNoARVMoC1E+zJQTyGmGXAe49N0+D+vXu4c/s23nrzBk5PTuCF+z2mCBjOE7aGGTsoESZlibJwqCvuBMvzy2H/GJm6rq5LLKYc5qdEIMM51E3TYblpsGoaNJ2Hq0rsz6Y42pvh0sECs7pEiAnLdYuT5QZ3z5ZYNgJCZVmAhCzZ6FtjMa1LlM5KyldCXVWwlu+9Kgtm+iCCsxrxUNRUc6G5eZMiDNq9NBKhjwk+eOZVT4REBlFC6JGAru/RGkYWysKhLguJoPAeDinCWUJhLYrouG4j8U9MiQsGEzKDhToaKgzqQOnhSqMmzGgirCjG4uJihVde+zwW+wt8zWyGxWKRiwRjiqDIObraVRVA5j3ngxghFYXQXyYknWP9H0EOOpw6oM5YCAHoOAdZdZO1FlVVoe97NE2Hru9zutiYB55SAgp2mGLgucq57YrymKFg0Ix0T0oJPgQYk4aDp9muMRkPQwNXuQ5Nb0jeI1z0aJoGs/kM89lc0Hl2EI35fx3afOfGzk7t7NQ7ZadmJ2dfukC2LfB3/y7wIz/Cjoag3vmUN64netLffzUNfTZx7v+3h7XA7/29wG/4DcD/+l/A938/cOcORx6+/duBP/knge/4DuA7v/PtoyfPPAP88T/OTs/f+TvAf/yPHE36iq/gz/2BPwD8z/8J/OzP8vuff57f7z3wN/8m8N//Oz/P1389R2ve/352gJomg3skOmhnp4bxRGdjO49OKeASnGPqMGuk0YsVDl5KCMHzhESujNeOnKq8NfVFaQqVvo8gCtnzd2rxDsuXzWioTnzcujfkXDH13rhwVhrOCMIbU0JoGrRty+HXSY1JPUF95Sqa+Qbr1ZIXRa5rYEaHLsmnIwDGCuezQ9s0WK3W6Ps+f0ZZHpx1QJGks62gLpC/4QAAIABJREFUR4aVfpCwGIgEveEQv3KCMy2fFto+7GWyAez7Hl3bCaWepBlZg7Is0RHn2V66dBnWOrx14wY2zZqfwfATJmOQj5TGDLYCyHOgRpm9Xlb4mspCJsIUBYewK0a2lKbRKQOMpL9Ya5CSrgUBMaFwJVJY4faNN/HyS5/Bg9MH6EMChDsbsjmsMdyQKXjUZYH9eY2jvQX25/O8aWfTKaaTGj54BB8wm1SYTyfs2ccEWAcfIs4vViisReUs/MRjbz7H05cvYzEpMZ8xN3zT9LAw6H1AN6sxr0s4q7UTKUcZIjG6WBfctbWQaIc1wGw6YTYRJ7LCMCCnagmLTEwhF+0Zw2wwKbKTpWwUISb4kBBSlR2RkPjvMbKjEGqO5kQpCneFIrOKklhEx1EUHxOKaBGihY9WHI8EIEqUhWUxpzXR0JVZIx1jBQQCfO9RuALRBzy4f4KXX34FR8dH+NCHPozJZIoQPPrew0ePIaUlf4nsY2nSRA6aW87BDU2nSiBVZJSkcFEOH1CWmUHZah2LKxyqqgYloEs9IoQGE3zYMkB2fmIa8vU1N1h+BQlyPp3NsL+3h7Kq0TYtLpYXaJtmcJ4e0qGkz0h6wDM5ZW7rdRkp9dxduO0xm86wmM9RleWuzcYTxs5O7ezUO2anVM9cvgx03XCAPTgA9vcZRY+REftr19i56Dp2ODYb/gE4qvH+9/O/n34a+PW/HvjMZ4DlcizIHOV45hk+PJ+fA6++yt/3pY66ZoT+5ISv95738Ny8+San/xgDPPUUX8sYfo4bNx51eIwBrl8f3nfrFnB2xmlHGrHR612/zs9pDL9248YQHXi7MZ3y3N669fj0sYdHWQJf+7V8jR/+YY5KADzP/+JfAL/1twLPPcf3+0u/9Pjv+MAHgONj4H/8D+Bf/StA9gQ++Ung3/974GMf4+9QZ+PDH+b3/+RPAv/1v/IcpcQpVHfvArdvD8+Z7Sd2duqh8URnQ1uyu6Lg1IxR19C6qlCUzJRAUOSCGwj1fY++a0AUkYgVsPJ2D/RYBGXTGFBTC0LKmiQGZifSAl5rHZwTVMlwOLx0BeexAoDhYjLr+LDKh6NyVKym06zaiotgisJgMpmiKitBwUaKLg358ZwLS3mhjGU6uqJsEZOV1wiag05y0I3SgEefs3QOVV2hKEuhRBHUzRihK1OaNakhIEaGvXBxd12L3vfwPR8wtRCOc2+dhNZYsZdlgYPDAxgD3Lx5A+vVKjeG0Q62BpSb/WgYLRuyhxS7rhNvAH7OvuuwBtPpAYC1U5DlDpS6EVRurYEUKxKaZoVXPvdZ/OxP/zTu3bnLDadS4oJka2AMIQXmM0dK2J9OcO3SEa5eOcbBYoHFbMbogAEWsyn25lNYaxiVAofqY4jwkdehbTsACZEiZpMKVWFxvL+HSwf7KArLEQcCJtUExhh0vkfhDOazOctK8Gg7j64LwpZEmApDSlVw2oH4FKiKMjsXzknONGReSRSPKI4xm9VQCJ4QiUOgMWr32ZhTn+Io9SNBufeTpH5wvXUkNfjCvR0jeh8RomN5kr/5EGGks60e7lm3DAeH8e/j4jmV1ySOUdO2uHnjLXx69mkcHR3jhRdfxHw+R9u02DQNVjGg3TC/twqF7k0GZ7VeRLo4O3V6thGx/C9FYSMjPUaQG5MVJKRpFuuxvpci35SynBPx/jOCxrIjmIZiXMOHqbqusZjvYW//ANPpDDgCjrtjLFdLLC8u0DRN7qKr11f0KBdoKrqsRkiuoc9jReekmOB77vxblRXcrzT14Ffx2NmpnZ16J+0UnnsO+Kt/lQ+23//9PAl/6S9x2s5f/+vAf/kvwK/7dcD3fR/woz/KKTVf93WMkv+Tf8KH1O/+bkbjAc77/y2/Bfjn/xz4wR8cBPnLvxz4G3+DU3mcY4flp34K+Ht/D7h//5e/IcoS+MN/mFOFPv1pPpxfusQPd/Mm3+NsBnzbt7ETYgxw795w70IPjLoG/uAfBH7372bHRN937x6//n3fxwft55/niMJHP8pOmDHsRP3iLwL/8B9yBOLt7vNP/Sm+j7//9/ng/8WiHpMJ38vJCddYjMf5Oac4PfccP9fjnA1jOF2qrvk5xgX5KfE8O8e1ID/xE+zEPPssv3b7NvD7fh9HQJZL4D//Z3621Sp/BfsYBMn13tmp0Xiis/H+Fz/ATUGE+9pmhgfd5CbrRA296oEk+B7ea/5jzA1tovcZTeq6XrpKcma6ycwQyEUxve8QfMyFd4owbcvPoHiUW7goCjnkcc5+WRSYzec4ODxCVVb5cMfX4dQXZuBwgEn5+imlfE/GMgc6fzbBJCvFcKLcxECF6NG2Lfq+zxSketA0hun25os55osFJnWNqp7k4ldWxmwwYoho25aVdujh+z6z/ADcWCcRAdLAaFzgq5SDMbFhWSzmeO6978XtW7dwcX4x4r8e3GIVfkXYeEGN2DxthuQy2lbXk5y7nFLCZrPGarWEsxZlVaCsStSTCabTGSaTKcqS8/+ddUAKuH/vHl59+bM4PTnhoj1NV5CC6xi5iLl0FtcuHeK5p67i2uVj7O1NMZnUmGSUyqAqS0xKDU/yxjOkrFN8wOimE0yrCvvzOQrnMCkLFM5wsVMWKQNjA+q6xNHBHi4dO+wvFiy7IQqtboSBzSFKZCXABxOmgjPDprV8iLHgVAM9zBdESIm7r3IhJCEW6lDw4d0SIbmEVBQoAjcFiupUiNMxPogrQ0kiLsZOmm6ljoUPfLBI7JSEENH0PYquQ2sM+jjKAyVtjMWOkpVcUS0qzQwXOn+yT9quw1s3b+Mzv/QSjo8v4emnr2Nvfw+LvQWOLx2jlT4A5+fnWC1XWK3X3FwoDriIUjKqXI8LTPW/jEgaQOYdAExKcNJpVpeU09eYVrcomJ0n9J4RIs6PwxjrUWTHKrpqLOd/TyaAIVbYmwaVsONcunSM46MjNE2D5cUSq9VKWNRGhb2Gu7Bqkb0BMkXqwAJCGU0yhg+OTdNgs9ls3d9ubI+dndrZqXfKTjlr+DBZ11xIfOkSI/Af/CBHNl58Efhv/42djcNDRslj5EP3e97DSP9qxQf5ouCIxquv8kH83/27AVUHGI2PEfjUp/gg/BVfAfz2384H2h/90S9tU1y/zofyp54CTk/5WmXJdQ7f8R2sl7qOC6Jj5MP1xz7GyP0rrwBVBfzRP8rORgjD+77+6xnpv7jgg7+1XLfwTd/EkYxPfpJt0Ysv8rWKAviu73r0/sqSHZSPfYwdoFdf/eKOBsBzeu0aR2LW6+3XiPjvVcWO20/91OA4jd/zqU9xqtT73sdOl35PUQDvfe8wNyFw5OWFF/i1j3+cHRH1SL/t2zjd6q/9NZ5jALnbuFxuZ6eG8URn4/jKZVU9Obwyesz8k+9TkBtruUFPVddAGjwmUEAQpd62HS6WFzi/uID3kk4iTUNCDPB9P/RIsGagKlQDYrA1AXkw4TOSLLw+elGVuJQIe/v7giCP2sIXBiHwYQ+SwhFj4K6rBGk4ZJFgsrBY47aQlZQS+q5jysIY5MDJC5JAXKVPQDmpcXhwiMODQ5QlU6tyd1t+Bt97tE2L9XqDtm0QY4DSEAIpK1ljOJRtmEhbUk/4ObhZztBVVjnS63qC608/g7K6i9PTBwi9h9YgmMxGpHI3cLYb8HfO5nPsHxxhb7HInTyzEYABgQ/BMUQEH9C2DU4fnOJ+vA8jhrSuWakTEe7duYvzB6ewIPgUQBTl8MqpCr7vMKsrPH31GB947hlcv3KMxWyKqigFwbQoS24646TgStOVAAEXygJUE+c5hoDFZIJjL6wyI3nW1APdWHvzOabTCay1qKsKlCg7HAYsY94HeD2cxzjUW+jaj9ZqmNiheRZASMkgRSAZAkzkAwEBLiVYK7mbZDlaUViE6DKiEEdh2EERMNd1EqObiPnYY5LoB8XsIJEBvA9YNx3qpsKm67DpPfoQ0IeAIBGGwgyRh5AiLJSOcDsCwqlPzLfftj1u3riJ1159FfPZFJcvXYZT1LkqsZjv4fLlK/Deo2tbrFYrXFyc4eJiiWa9YYIAiQwZYRBT7nlupESiD1LWSKKmmA7S2ty5FcKRrwebEJx0+GWEO0ROvVOIyZDykDtYZ1EJzaexzCgUKEi9So9GrlNXFcv21SmODg+xWq+xWq0YRYpRegeU8h1sjNw4UpOdRkWt9PmAEIY9vBuPjp2d2tmpd8pOHazWuHpxwc7Bt3wLH0a7jp0OY9jpOD7m4ufNhg+yI/CH4efI0Y/5nJ2Nn/1Z4Id+aHA0Dg/5u/qe//4v/yX/+1u/FfiLf5EjIj/xE48yJL3dKApgb4///eqrHC159VV2mH7gB9hpunWLozKf/jS/7zu/k5/va76G3/v881wb0TR8mP65n+Nn+aqvAv7yX+YD/WzG9/7ii3y/P/zDgwN17RrwPd/Dz3x4uH3vVcWOxsc/zo7GX/krwOc+98t7Nn2+1erx6WX37rEDJLWcjx2vv87v+/CHgT/9p4F//a/5OX/zb2anCYCEMvlnlDqMT3yCnZgrV4Df83s4gvVd38WRr/WabWEUBsWdndpeti+6sKRoQc6OxBadnL4tkVBfab53yiHZfBiJQfK3e3S+B0mvBBAhRQ7rxhhAgRfaEOewj86PrICJdNnkfkbeFCmKjFwRSICEtl0OR2leqnIFq5CMedSN4Vx7Lu4FYgBAQ5g7xoimaXBxcYG267hBTeboNnrDjFpLzvt8scDh0TEWe3v5YNuJ8m+bBl3XsVGLEdqt1MAgijNtjUUm/zRMNUYwufhRrxvlEDyemxgjyrLAtWvXUFUVHty/j7ZpoXn0Y8ExBnBFifl8hoP9feztH2CxWKAsy2yUx0WULCPK0CKpCEm47z2nLLRdi77vsLp3H+fn53jl5Zdx7+5dtC2/FoJHVVW8sXzArK7x3PX34CMvvg9PX72EvdkUlTgUpJvScRTEgiMUWnxtDM99XoZICJGdDO85Z9tLugHPseqWhEldgyCc6iDORybK9xWl6LkoClQhIUjUgCQdKhEyVSbsGNngK5GRWgRK0CKyGDRcSjApMn2tAYgGujqNOEShvwXYqUikzchohEJAFJ6IYMJANyyHrwiOUtRViaIs4NaGC2Q988/3mkYgvS9yOpV1MPL7IG+MbmqH3qIosFqt8ODkFMvzJfYWeyjKoV8Hs4uVqMoS08kEe/t7uHLtChe7bzZYLZc4Oz3DxcUFYvCSHiJHSgsoy9Vo2wvqCYQgBcHSoIosQbvqypES1gjNqbOwQYpYA7P4yCrB2QJlXaOU0LA+m9YJ8QEpwvfMmtY0DcqyRFVVODg4wN7+Am3bYb3ZoGu7LBOqS42uj2w6/lUPZZTD7TDIhcm78TZjZ6d2duodsFPN7dt43vvB2fi6r+NDblnyfz/4QeArv5JR8rfe4pqIx8onDfUQKW3XJ2jfiU99iusOtM7j5Zf5fQcH2wfeLzZi5MM9EadxfeYzw98/8xl2GH7sxxiV1/HZzzLjkqZBfeQj7LB84hPAz/zMcO8vvcQF2U89xWlLp6dcYP3H/hinRP2238bOyksvcWrU7dt8sD844M/X9fC+O3f4kP6lOBoV70PMZvzvh6MbR0cQz3tA6x8ed+6w0/U938ORlW/+Zp7nS5cGFomyFEp8GUScDvcDPzA4Tr/wC/z7hz/MztVrr4HA+sQIaLqzU8N4ogRTGlPhudxeXlvRa1v0oEVlsijxIWUIMJd19D2HmYibDpVVhel0lulHGelNkvfpsVqusVwOTX+G5VImDJsBi2xoxCuk0UMTEWC0dbvJi5Aif69eW+9bD6t6yFSFPygtXuned1hvVmjbZkRthmwkEiWQMPGkxEVA0+kUxpqB17vlvFZFsFiANMzMwkQpQvNzkxnoV/OzQYtuFb1Led2G9/AcFY7ZQ/b392GNxcn9E6zXaxEqDt/NZjMcHB3i0qXL2D/Yx2QykXVXLmZFDHkuXFK0XGhdrckhNiLATPneEiX4vsd6tcJmtUYnz+5DQNM0sFYOxMIm9ey1q/jIi8/j/V/2FA4XC9SugNRVsZDLRrLGZKrXgUNJ0T2Z1IJZVYgSypLQ9wVKSUnS58kNr2iUv0gcDeC5LeGsQ4iMeloXEV2E9TH37yBZu5i0WWHa2gd6GmGHSZpXWWIedUUZLa8WN11Cli0+DBGSG9SR1msoUpJAIKHFHRqAKarJhxbuMGsA4ghSIeulysJ1Bs5bOKl30UJ0TXsYK3E14qojiJhlzBjCel3i/PwcSy1MNZoPirxPjURNOP1Fmg8REHrPdH/WctsUMLrrnMvIDY2aaKm8GdHkKXLkqZaUFzKW2WrS0N/FSFqYMTbzn9sYkRIfuMqilIaGFprHDtknEMcwR6tGeq9tW4ngsI47rg/Re49mvcFms0Hf91LMb3MkjkR3AhJdE7k08ox4ex3+a37s7NTOTr1TdsqXn+OJ+vSn+WD9NV/Def7LJR/Qv/qruSh5bw/4t//2ixdEP25ohOPiYtsJ6aWuLcYvjamKaPhOSe/Jf1dH5uEaEH2/93zgvn6dD9xf+ML2tYmynsq//8iPcKTh27+dIz8f+Qjw+38/Ox0/+INDjQfAB/rf8Tv4GsvlwNT1yx03b/K97+1xGtfD4+pVfoaXXno0hWp8zz/3c1xz8o3fyClkZclO1XzOf/vsZ9mpODpietwYOZ1tHKF54w123r7xG1kuXnsNwJAiubNT2+PJHcSdY4TCcGFL23JPBM0hs3mjqgIbUE59KEU/jTGoJlOUZclIbByKTn3hEaOXwxHBB4/ee0QahMUYjApJh4NmNjTYVtoUaatBmnUmH1Q0jMWUh4NCzMWwcrAKMQIglGXF1H4jRQ8QYoiMaAkirgugxo2VN8+FcxaTeoKYEh7cOxEaUy1kNqARq0lKESRzyEaJABJDZh9C60Sf6hzw4VNRraEBnCrUlJ/XYDabw15zcCensBY4PjrG5atXsb+/h7KsspDTeKNYmxW+MQauGBBBDQPqe4kG9AkADBxATAMbQ48QOpYFRE6xKZiHvioLXD0+xAvvfQbPP3sdlw8PMCm4b4XV5xADmb1ukoLILO8a1TCjH/2dUJYJ1gIuUTbACUNHa8obyjAFJAF8+wQXCjjL6Gc0kuModRAxcZG3ATfwKQppIJULrWU+DX83z68oIPm8ISMhTE6zMFYXWlJGxWirK5Wb4JHWZwzIg8oAby2THRSSe7LgudTC88HR4sOEswneSgGpMbkgbSt1Kw2Uek4OSilFrNZr3HzrJq68fhnzvRmuXrmMoiqFFUgOI6KgYojYrNc4Oz3D8uJCerBwt/i8arLOFoBxnE6VEUuRc8hBzoAdPu+9OEHITpkqFEVK+fDDSFRhDABtXOi2FfcjQ7Uvz3NOj0sJPgb0fcuRIuH9X+wtsFjsoeu63DvBS48DfT7d5+L+whrpE7HLonrbsbNTOzv1TtmpXg+sytb0lV/Ji/Lyy8xG9NVfzYfVlNghebsD7nhOnoS6vxsGETspMbLToZ4psD1ROtoW+PEf51Svp5/mAu1v+AaOGPyRP8I9L6pqqG5+/XU+qP+m3wT82T8L/O2//ctv8rfZ8Dp87dfyvd26Nbw2mXBKV9O8fYRJR1Xxe37oh4B/9I94TbqOHZAY2XEgYuatX/xFZhIbRzoA/n06ZQfxTCmSKdvjnZ3aHk90NrqWw6Xr/5+9dwu1ddvOAr/We/8v4zLnXGvtvddJco7RGENSGnIMVUGLAvMQCD74YiG+SL0IUiAo3qh6EDSIVgliKH1MQDhQ4FMhSKEUUQKikpRwEi3PSc5l5+xz2Wff1m3OOcb4L/3S6qG11v9/rLOzdh7O3lUbR9/MveZljH/8f7+01vvXvva1wz0OhyPGcYIjjwcPHmC324GaRlAYNT9mBGsinD6Uc6RVPaWTOQPgXJ/feTGQDvp+kIaFvSaloholqEEylKiWTi9LESHZY5GNlexUGnE6FRVnVoWfJNeAVhIdTgCKJJU1y2CSJgZRkRC0g4Nzeel8rJBwrT7qnJPks1aSdrquQykFQx7qwDlzgsxgt3JGJNzXypEj/Ytuztg8ETSMzwDzOrS+9P+ZkS9OOYHyu912h03fYxxO8N5hngacTg5tV2qiXKW+EKEAC8/XMcDKf9ZFYDv+zCuHaM5e+/jZ8+f45jffwnff/jZOpwPf/uj9b77zM0//T+cIwQd0TcCT/Qs8ee0O//nmG+jbpm4mqI4vn80L24gCUIrE+XOvN8eVLlEkEoDV+GF9PXD9nLr2scy1ipquaEzQzf7q5uTahVGwbBDqnIGheaibknV0ZZnQ541f/pftvleGanVde40dVOx5S7Eq5AkxJcwpY9Z1kfJysMh6b9mQ0NWHGFWhfav9cfd/uT8T2LkQGhCA0+mI9z94H6+/9xr6rsX1gxtR3XBycJxjxP3dHZ4/fY7j4VALlxldpG46yK0+Uw54sLwRtaVkNUxsIhKvkFhZLzaXiyExuobECbAUSaPVNV7RViOnl9EDqh1YwUDJqmI2wpNHExo0XYfrhzfYpT3GccQ4jDWp1vobq/W7RpMu7XvbxU9d/NTH5acO778v9zbPQjv66Z+Wn7/4Rfm6vRX+/ttvCxr+qmb0qR/+YUHSX3Uw+d3aRx1Svh92ohShCB0OkuT94z8uVCdmoU89eiQb7asrQf3/yl+RxO2/83ckovCVr0iS+U/9lOR+/OiPinqU93Ko+Af/QDbzf/NvSlTo7g74R//oPFn+d2spyUHlj/9xUYb6rd+Sw4Vzcq8/9mPAs2dLNKVp5NDTdZJPMo5CwfqLf1EOJr/wC4sU8Oc/Lzk1wyCHS+uLr31Nrv/zPy8KVE+eyM9/7I9JEv/776/GnlZDcPFT6/bKw8ZXv/JV4W/lIobaEdgxxnFE3/dnMlemn71+vKWAyML3JhCML1oVbYhA3oNVQk8KCQmPnDxJoq8aAUOw1wl4emFNRFM2ioXL1cqynhZjyvAFYJaiQl6Ns4Wfuq4DEYTD7kVKkZwgZxaatTB9UsSpsCYNq4NpO1HCaJsWPviqx240E0O8zLABwMtTRtAaS35jkGxVxWgSKR9/CWPVR7UBV+cHEulVofmIMkh1fGBIUrYojzx7+hTf/c63EZoG290e290em02PThU6miaoRKP0iQToZCdeNZbYnmdZZPaMOWXc3R/w5te/hq995bfw7MlTpJDn3/yfvvo/3P3I6UuvmouX9v//5ka3af5M86Put/Bfg4CuI0zDiOdPn+GD95/g+voKoW2w2WyQU8bxcI/b21sMx0EQZKeJ55oDk5JUZSbnARLSmVQtZwv3wDijTAQHX/X+9awJZkZKgvDaArGwNgDdHDHIE2B6+/o854dMa7xEjWwd6yvsXgqUr68LghQZZEhNkzFGhEYM+ma7wWa7RUoJ4zCIQY9RqTN8bssu7UPbxU9d/NTH5afit7+pU4VFStU2tl/+smxIv/Utoe689dZHy9N++cuSL/D5zwN/4S8AX/qSJI7/Xlrbygb7c5979euePPm9X/NV7c03JXLzJ/8k8Pf/PvArvyKHo5/7OclPuLuTw8E0yYHjj/5ROWz8h/8gffX5z0v9jN/+bZEM9l7oTYeD9NntLfCLvygHlj/1p+T3v/RLH30ASwn4p/9UIhs/+7OiCPaf/pPQs372Z2Vs/sk/kX4G5Np/+S/L7197DfjCF5Z7/iN/RO7Z6FN/4k/Iv7/8y/L81n7zNyVq9ZM/CfzjfyyKW2+8IcpcIQh9Tg+lMo/KxU99SHt1BfEkXPWg8lyGCA/DgM12C+e9JJLoA5iRE06lX048ioZKn9FyrSprJ6hSypo0w0Dbtmi7VgZEQ05gVKNln2PvJ1oqGxvnzcrC14x/SMl1DkJA8cGrHJ46nSJa4Jkl8U+cTIBzYuizyvwJulCQYloUBbwYbnnuqklSeaFn0BIs3LUYcWbWEBQvP1eDv55CWH42hEzvQe5L0fECDaWXqgDkvHBxN5sNJK8gVhlTHwIevfYannuHw/0B6e5OnzdiGMZ6CvZeQm1N06Dve7RtW+Ub14faRSJRuNMpJ5yOR3zw3jt4+1tv4XB3awornHbl7vWv3fw3/90vf/6XrrYbPH70AJ/7gcf4wddfw27To1F52qXPtG9w3l+2uIxOtJzkbc0pOq/PzCwbiZSTzvUF9bMrm2MkRd10AFT1yZSoZOEyNARa6UhcK9ta9EPAC422ABVdLGU11rxK+rYxrx4aNXF9nZ+xfKY9r/7NEI2CJSqhyeJZFVlyzoglI+aMcUo4jROGacYwR8SclHJhCfNQ6dxcAZdSGKf//vS/l/+x/OIPtT84A0r9SFJRfZ6jIiIJx8MBt8+fYzgOUhFe174PWv1do0VRN4WyDqQvUpLxdF7yb2w8lkTPjMhG6XLLnKh2R9cjLFHVIlSKvhbW3ZQhrasJzTY3SkWbKu2jIjvST6lknTsE1grPRuNhFDjHkuQXs2w+vUPTtLi6usJut5M6BcOIcZokEXnlTC7te9vFT1381Mfhp777zbfw489XFcTfe0828yHIQSNG2ZT+5E/Kv+t8ixhlQ205EoAkS/+LfwH86T8tkrLPnslXKULDWRf4k86X3794IRvjv/pXZQP/qnY4yIb9cJD3vZzrcTrJfa3rSwBycLi9XXISTifZWKckm/g/9+fk9/f3EsWJUd4To2z+Hz6UqMJP/ZS8bhwlGvKFL8i9PHwo1Kw1hezddyWi8Qu/IFGJf/7P5dof1d5/Xw4mf/7PC63tZ35GnvO99yR68au/urz2+XN5lhBk7MRhiXLW48dyz3/4D8vvnzyRQ9U/+2fnh57vfEeUu/7SX5LX/tk/K/3y9tvAv/pXkoSv/ckscvJE6eKnXmqvriDOyyaLgMq5a5oGJSVM4wRSBSCAasKK1wqdH84fA0zvurCEe8UQoiaaNm2DrNWGgdVGDwA5V6ue8+dhAAAgAElEQVS6niFGQOXemoGv/a+bIiv45J3TzdiC3NSBJq06CnEY0zShawBmcQA5JUVaJLwtBrJFcCLDWvtiBVhBJ1tRnts6jdDZyddeZzcM23Ca7V9tntdjpIZbtJLFYeZcMMdZqm4zIwSPrmyw2+/Q9x3atpMq0+hhJLtSRBq232xw2B9wOp3giWSjnOXehGtfMM8TpmnC6XRcnGR1YkGKazWNSqgtlJjj4R4fvPNd3D57ihiVFuDlJN+MzdUb33rw04+udvgD+Az+4O6z+IHudVznLbrGw1uSJdTB2cFi9S/DDhBc50h9KUNpTkuyltGIjBKVc0FJuV7HKE3ib20Ros5LMFQZZqFFFKUe2XGiZFFsyUXQgqoitbp+0UMB83JYEo6nhfm5ogeFFyRBlKgUpdBnwUq1pv6LhQKVWdWsysLVTrlItfSUMEwR/TDhOE5oxhFDnJDScg8M0hogckiRzwfm/9b/qoxNqc8h8zDiNAx4/uI53n33HRwP93BQpNZJUj/U8Eml4mWDZ2FaAR892q7TTUiPEEx5Q5L1kxVpm5PQJbyzCLUaMpmjUgGdzg54ukhhIWyzCdWG1PVn39r3XN9TCp/ZHwAm/X82b4sm/a1tVE4J8zjDeYfQBDHoN9fYZuHyDsOAOP8equv+F9oufuripz4OP/X43Xfx83erw8JbbwF/+2/L/Pr61+WBv/AFodZ87WvnD/xv/61spr/5zWXDn5K8/t/9O5HKfe89iW4wA3/jb8jr11Sit94SWdXbW/n6e39PaEy/WyOSw8uv/zrwa78G/Mt/eV74rhSp1/HFL35vQbx//a/l877xjeWweTgA//AfSj7Gj/2YXP8b35BNedsuB4P/+B+Bv/7X5Zk++1npn+9+V6Ialsvw/Dnwd/+uXGMdAfrt3wb+1t+S5zbq0u+l/ft/L9GGn/gJyd04HuVab799flD4N/9G+jkEiSypb336pS/h9Nf+Gugnfhz44d8Pmme4r38N/jtvI4Dht5vV2tR+/V//F+BH/iDwh35UZG6//GXw++9LBcjNBkTAYbdF2/fottuLn3qpvVqNirQ6qy5MC7M474XmUEYxesod88EMONWbtn8N0TGDzAlIURAL5xyKJ6QUpRMyME8Top4WiTycym8yyYlrbaTXyX/ys2lxu9WGVJAvkQdTGVQNUTvyinbHuhmUzV6RjZbyxs0ROCdh5jTPaIIDcbMK+dpQWx8AogeoeWl6OGWdYBlFjSMBq9oPjFWiELPKoC6n3/Vz16rOkA11zlLFteQiYbCmQRsC2tDAOUWKxghJnAvwWkTGkUfTdNht97Lx1g1l1mRlW6jFfoZIQVrF65jSKjJAsMJWkrgYECdBn2KMS8K0AS8EeCL0XYdtv5HKvzqXHPklCbAUMFTaTbaqem/F/Jxcs5AkX5ZF1pJXvWubcWKRmkVhOF3QXIwMIAcNo00UzivaA9V/fVC0hhnw4sQFKSsoiVCcQygBbVkOD4ULMmmUgRRtYF4qghbAe4ZjB3ZS4A8sdS8KAJGTFICDVfWiONaEduWFK0ppXWOtYGWoZCuk3wtVRJRwxIZ6csjEMlnVmBG/hGESKrqyHHTkGZPmZHzwwQfYbnsEF7DZ9NBQC5aNCdfr2XwOIaDb9PWA0fUbdK3QR4QWUupSYwbmacbpeMQwnpaQ9Go9FlUGc86p5KZVS6aKOjFLvzK5Wo/BrkKw/vZY9+j6/usaNdqNzhPJETJuOEs+gNqp5XAp6kYDRrjg0DYt+k2PTd9f8sNf0S5+6uKnPg4/9eA04oECSRHARAC++lWxjSpW4k5HuC/9Z3nWzUvqSN9QKs6mO//9t96SLwDsNbr01jfk3379Wga+8y2dLAD/xheB3/ie6f8hjYExyed7ArumuivkCLz5NRn2rqkvZy7A1/XA1C6/Bxj46lfAVZ525UmCX34aR5Hu/c3fONv0nr3m3Xfq787am2/KOCmaX5/i7MC63kTL//j2Fvzrv1Z/x+dvkNeXIgc6MLbk0GpH/G93t/iVJx/Av/1t+ODRhBabTY/Hj1/H5z73Wbzx+hvoN51EIXF+XR7vAAfk/+rHEP/QHwAD6PoO/WYDur7CD253Fz/1Ie2Vh43ddls3D4sRpooI2e8FhRHpSmDRzraHso4SXqkZfTE48zjCaYE2MQ7LwFiGvW7F5Jql1M3RueFeTqF1c2khcSd80KAVpoWeYZVYrY6BKvKogocgISRdRATngzwjCaf8cLjHOIg+ttVRcKvFsgzO6mS6mlKCEcuDUqGziWCPokqGNTmv1JOmq9FuqR2xIOupiPF1RMpdDZVX7FVNwZKjiRwoixRgUeTJkHIAcEGq8bYk9SwcyaY7ZVV6YaHI5HHCPEcQZIxFYpb17xmn04CUEw73BwzDiJwM8UPVZSZAiylJ6Dt4jzZ4ND4ov9FO4VzRQotqyPtJN9CQKpwkm3SGBwpWcnLLptiSu5kJyAsiD6Dyrx2Ed8wFIHiwXzbaOnoVtasLGFQNeMxycCiQxETHXlSvuACcUKgAeFmfHmC/ikwwwCxISGFWBSlRzyr63OwEfbX7ISfzoMBBCgrZYYarFSdFAanI9945NM6j8Q6Nd1LZOBKg/Va0fyQatGwa1raYdHxSlhBt8DJP0jxjHCdM0yxzkW1DZ6Nfly+atsFmt0PXSfXftm0QmqZuEJkB720jsTiltpGDzBT3GE4DhtNJNjPaj7lklDmvHJOsAWap2iprSg+ZkLDyugp0fV49qKkR02uXug4tSdU74+WySofpe7HYNRMVsGbXKHNGjhkpRpB38O4lB31ptV381MVPfRx+6mqFkP9G6/F/XPWgELDrO7zx6AE++/h1/NAbr+G1B9fYtD2Cc2AyGykR8sW7GfazAr4Y8kxmA8QprSix+qwafV+kmlfR69VWmAGNgqH6V/t9RcD5ZT/FKCkv1GIdbfCKaptzPVAWm+W88k+ygBQ/MtTcVA/PBU8s8l3UbRRwZQNIkUr5Smxrk+tcSEmi8DEXxJgwzJMUoh0njHPEFCNyWe4r61zMXOCZ8T9f3+DnNlsAwB0XvJcTqBB8KeiJsOMWlDPaIiINO6XdfaifghYFbdqLn/o9+qlXRzaYK9/TOnEdUvEhCE+WNGu+LCcmM/hm3JyGbcFixEOQUOYwDCiRAXaVstJ1HbabLYhIinqtEvpssa5RE/2LDM4q9GM8VO88vBbFkQWb1Agtm661QzKeKhEk5BwagAkxzjgc73HQMvBWhZr0tUtIazHKxkM36UPoJpeJV5KMqFxhkRBDnaAidebqxljawpfPingzuEqveg0XOw3HBS/P37aCbBUW73AewlfXwpJIyCzqIUQELgTKWdEcpwuJqqRriklDaDPapsHVzTVC4xWBapW3P2EIR1UCkYoY3vl6KrfxFaclBwYfxHGg9rJREsSJFbVYJiFnGER18qSoPrEWovP176UUFCfvzz4jeK2JUWURuTp1yQVdEj/NDS9jv/AuqV5feNOUEogZnkjiMdpnImeZkdmuJvQkfQmMt2mHIu9VyrEULRamkQhoDkfRZ1UjBDa0Ua+FRSVmiRChOgYH1HnTNY3kZeSCFDNmInBeUak0kkF67bUNZn0+EIHJ1YMJyKlqj45mfc85uuy9V5Rki6Zp4ZRbbv0gmytJ/LRVwroYinNwgdG0osE/T1c43B9wOh5EM7yc11GRKI7O+WzBKRsNu7MF1cklL/Qusz0WwSo2d6wfZC2K3RO7Ro7q/LLNJlb3b+82O2KbkxITMl7iWF9abRc/dfFTH4efWnumwRHeDR4uEHaNh990uLneYXrtAfLrj1CaDomcQsqlHki/x0+tNujVfpo9tjnB9Ry70O0YoJJBOWke4OKHytpP2W9f5ad45adyRp4i0jxrfy9+KheRRk2v9FMQW8+m8iYKgkX9lNVnkrmzoi0bVU8PNAtqLuIg0SJV1U8BmYDsCMl7RCKMxDgy45gL7mPCoRTMKcuBQ2embK4ZgeW1yzy6+Cl5nk/OT73ysJFyRhX+e8ngFRZFuawFhwioYeNSuCbeGTfWjKNTxQtgKQImSKhcv20aNKFFCA12O8ajR49wPEpRMOHT08qoL82Q7fOzfgFBKC6CuogBYDtxFwtNEZwTVY86OHUwGNMw4Hg84f7+gGEcJKmvXp/0+prsI+9SFDlXmVA7YFYjwMsEPHcmy+cCQn9cIxjLpJUh0UOrfA9Co+iY6LU7OO9AXkNtxg+EOdlFM50EwEbJJs0oplLu2wkvl6keEAA1LllqNbTBoSTCPM84Hg/Y7nZoQgsi1gRgr/SAVkPiDuAlD0McpoeviVFO8jQghwUl+8DByXm+FFTrajQhNmNe9DkJ5DwMbRS0UhdHzioPKRU4PRGKk7nstI9zzkg+1818KVIt3Mw3SNVMFEkzo4rC4JTBKYMY8DD0qmjehRrfAsmfUIuxsgEqybugNIUtMVuN+Mo5yXxTAwo9KrE43yUp3YEcCwWi3gNXRyiblqLIbpDIP0uEYooRMZfaj0U3DGdha1rmLRcGS7gESSX92rbBfr/DdrfRCsn2XPWm62e3batynobEqUMqQhfzHvA+KFKrpomF3gWWz3bM8H2Ptmmw3W1xPBxwPBwwjqMioqTFEyWyZWsUuqbgFjtgOUBWOd4QVWgEDZBnBvPZ2pD1aXQ2AHUTZnOezow3AC2etCwKcRTA2Ysu7axd/NTFT30cfsq9VFeBtH8ufmrpk0+Ln1oTf23eXvzUJ+unXl3UT084pRSkKOHcbiu63oaM1Ix3RVuKnpLsa42gGD/V/ta27RLmhquLQEJ2BU3T4PHjx5jnGc+fP8eL588xazKZcXABOzeiOomzSVWkaJypVACL5rmd9Nbl3WVsxLjFGHF/f4/b2zsM4ygTTQtISbxppUDiUJFwQaNIFm3JiwOELlQu6yNzDUmt7+Es9A5b1PZ6RUJeNvjVSRqVhuCch/cB3jcLZUnRIqezllcOQTTKXT31k3NrgEQRAaiikTwzE4O8FIXhIglQXdehCXqwCB4NtwAzHjx8hKvrG7x49lQKttXnNJSO0AaPPgh9qkCQK7da7CYPaUpMwDkFyk7uNhdylpM+OYe2aaqUKuvYmGLEupqw8RYdJMzIxCjkENyCzNQAqZKciQiUC0oq4GxbG+gccaBCKLEgJnEGmcWAFywGjWHoxBJZ4VyQiiSY5aQbA5j1oor+yUZG1kNh+yp14MRg09nBwLqeHEDFgTzBA2gAgDvkUjCnhKha/1Zs0DYfRQ3o98rekRpVXXd1buq4GoVFHRgRQD4ghBZt20vS5sp2SAJ/QgaQvYPzQoeAF2dfI2CM1cZJNg9bv0XXd9jtd7i/v5fKwFG54M4JPUbR76ZpqpwoqXMuuSDGWRP7plrgSLp+tbbVmJ9vAnXO8nnSe0XUdUHKpkXniSFGJEnOl3PGq9vFT1381PfdTz14hH67PR8jkmPExU99+vwU43yuLu3ip2y9fNx+6tWRDb1ZBlZGwdXFwSwhVrcKWzea6FW5qrwMOFZIjfeuokjMXI25hNEihPqlcnQxy8IoWJ2UxYg60uu4FTfWLJJOaJAkdJGjGpasiXJ8Hu5mFn3203HA8XjE6TQKmhGa1WvklEl6WjS6CevJUsK7ZkzMoFA9/RNk0djPqNdT6g+WzdvZCVXvN2uYkpkk9OxdHXx7XV2phtgo11juRe+bM8DLpLH3Oue0wJFDCPK+GMU5BSJ48sjMiHEWlQV1grvdDtvHO2y2W7RdA+c8ci44nY64vz/g/u4OOWV0fSd0AV7xSplFlSRltMGjbYNJVIMLS04DWygxL5uLLKFW57wql8gcSSnVZCUbB0eimhICV0dAhIoghKYBgTQUrKhkVm16FiUaC0XHKLKwMp+VOpAyOBVwFuObkhheBsCOEHPBNCdM84TICZkZiQmxMGJOyMXoExkxi4pMKkmROVGOynqvbpXgKmtAeZfKuZQpYE6blrkOczgOjpQzq2tJXlfgiBE8QA3Ql4xd7DFNEbPWMsDKMBlqZQmri8dfaBc5Z0zjhNMwIM4RHMLZxsPuldSIBe+qikfhopximX8A4NlL4jwDlAnsg1AnsBhysR6LDSAQOpXAvL65QZojyEuVd++DFkk6R79ebiUXne9RxjemWrchJZlzcZZkYrExCyIkRrxU7np18Su7szb+NmaGquPVt/ZfdLv4qYuf+r77qZxVEWhpTqNSFz/1KfRTChCct4uf+iT91CsPG1c3D2DynDIxlsx8Q2dsMAR5KZp4FeBXURbrSFHZyWqAZcGF0IC5IISgMngJNBGCV0WKJFxLLgXeSXi0QBCsGCMYJhUoiTwVpVoZcnIAeZnEKSXYhPXOgZpGw08J0zjieBpwPJwwTxNAkkhmE0Mc2sIvrCfB+rMiWfBiiN1ymrTiRlCnQiQZCMK/BEDGv1uMvfybzxJ6SlkSm70Lle9rxsRQKRdcnSTkqOqJ21ity9xX4IAW/p2F6yQJSgxqjBHMBSkzio6jDwHXmw122y12uz2atq31FO7unuKk1IKsBdpCIxKMQkMIoKATmQUF69sWV7sd+q6XnrOCUYXBOaFkDVcWSfobxxHjHMEQ9RnnHaCJaswy7nLyF0cL7VdPTpxFkBoeXtVIWq1uLQoMqoUNcdwy532dz1nzMnKSOh0lJjHgJWOaZ4zThBSjjBkYY044ThMO44jTNGNMCXOSJPKkoX7jyKZiEnUZ3nm0arQJLNJzqoBFkAE3aU9fET5dqWotnNfxDkGLiilloVhY1gy/zWECe0LXNNj3LeLcYU5RUKsiRpHgAMrVaMqtiKNwcFVBJ3hFYMxBFkFkTc2YgSoh7B3h4AitSiNLomdSJyqOypA942bb3LaxgaKyvJrLthKd8+h6j02/kQ0LCYJWkUJby2Rr0ZyNAwUSRK0out2r7GdmlTSMGE4jxkFqiBgSLXUAEqxOSR23VXs5VF2r0Vty5ofY50uTdvFTFz/1cfip9WHDkdjWlC9+6lPpp1DONsJWOfvipz45P/XKw0bbtnDkkFLCpIU75AMd2rY547ISEdpWs+WJNApGSwIwLeEz6CnVTlXznHUg9AE0bGwhZABo2hZ93lRDTLRIfLI+LLMhSYLQkBMOrpxGvSBOzJinQasfZsQUMetiS2okiJyqeri6ES5FE7MsFAq1yWLJl8lDyj9dnV4lnKcKJqQ8QCzPtg4XmjOwonGm7iAvUIOGlzl3Cz3AieVVRMFXtYEmiOJHpb+UAkIB1PmZKsuCSmWdTB4oS6jMEA3nHK6ubrDb7dA2LUphDMOAZ8+e4f5wJwmVKYnPoiXxsmmEE7m/ucY8Dog01a4LzuHRzTVurq5ALiAVgFBAhZByREmiHz+MI27v73F/OGKY5iWhzOaAPk/OSRDHlDFOI+YpopQsCYE+YN93uNltcb3fYr/pses36Psefd8heA+UjBKjzIcmgJqg1WY1sYxFFnOaJswxIsakP0dMccY4zhjGEakUxMK4jzNuTwOOU8QwRgwxYowJ0xwrrcE264AgZI5ZkraDRxMCmuAFlQ3CFfWrjZRToyQGYVnHgvjq3CSCC6Itv2j5Q+R3uYjj9gCzrB+jMOQsYeoxJcx52cgsfFxdu1lUQ0IAPFTVJzhsNj32uz2CVnOW92rSIAqIWOktwDxFpFwWI12NLVX+a91UObU/+vkLl9TQYFle3ryF2GyYHOHLofUPo7hUGovWJSmaSMu6TgUxTogxAWA0TYAjySVY1rhSFwxdPBsgw/FQ7Y1uP4UK4y5HjVe1i5+6+KmPw0+1XbuaZcvhIbj+4qc+bX5KD93WrL8vfuqT81MfQaPK4JJgOr8LksFnD7s2IkwLovRyuMWkBAHhlDaNZPWP44imNMoxlRCVnKxQq39u805ed7jH8XAPTCNSMo6t3FtZyZuKu4kgR8glw5PD6XDANE0YhxNiTGqYgiDsq8EnByBHMFt8lGs4tNJFCGd8VGtuLSUBNeDFpE5FVaKoGV8b1GVQDYFjmFyrukNBnWCn9WVWVv1xRcssVNs0jVS4bVs4v0hA6jSuSidU+bNy/cK5VmwVo7c8nw8eV1fX6PsNHDkMw4D3n38gyZFRHH2x+RACHCw0KggPAGz3e9w8fITj3R2m0yhoABH2my0ePbhB3/dViSLGhGkaMZxOGMYB0xRxPA24Px4xjBMKS3LYnBKmKAjSMM2IKpOX5owpJqkSrouj8QFtE9AGj13X4NF+j9cf3ODR1TUeXO/x8OYa200HKhkcIwBCcIQQGlVcERRjmicM04jj6YTjMOJ0GjHpZ08pYpwT7k+DVOROGacYMc4RcyqYc8IUM+KcEXMEazVg5xwaL4a6bxr0XYe+Cdi0GgmiBTGs+wdaNv5ZCxIuE5MUoTT5YAJHGYumbRQNtER9mwOLI3AkiY6bLbCPGWPMyBmY4lw3YaW8hH+wKXsAwTs0Xiv3Eleu6JqD7pSq4rwgralk+KwG1jOcCwDp+mZUaUFyruqEg3StwVDjFdKrNoqXH0BgFCxqNmIsSVFUWpTIckJOpvZS6ppgW8tsco9c+f3UtnXd101mUW3yYcQ4jsilLBs9LMjZmUEhs5vnjvLSztvFT1381Mfhp9p2OWwwMzhnOFz81KfRT5H6iLN28VOfqJ96dQVxLeblvRUIEiSmacJLiXU2V5YBXXNNF33txcBJCGjheuWUQcHOSVJzYbvd6ilSOrHRap9d3+F0OMDTAcMwIOkiIEVL6OwZCCVl3N3eLqdDtdeFC8ZJkPW266o+N5eMQgSyhaKToKwWB+uJtZ7ybKKUgryALxopU23jFbAkr7dJwcBL5VBqJWY9LS8Hz2Xo9XGlr9WhFi4glokdvDgoCd0vvN91cRj5rIycCaU4lJKQsyArQiEoVZt5Gie0XYcQWgynEafjEeM0IaYsSWwkIU7vpPAcsWqvAyhJnFhoGux2e1xd36DfbnEYbyEnYxIu7W4HIodhHHEajri7vcfpeMQ0TIhZEJU5CmoUU0IujJgjpjlKuHqeEWNCKQBlEXJoHAFwQJHQ6pwiChfERIhxwjxPOM4DTvOETBndrkfrN/CtQzE/7oDEGT7JGM85Y0oJp3nGcZpxP064P55wOJ4wxiROJSUcxwljVMOtCWwpFTBJguGmadC1O2y6Brttj/22xb7v0XcturZDGxp4Z0mSgrCkJMob0yzo1DzHmpSXS1KkgutMW4M6YuYYJTPiLPPF6QbAabxYNmCoRkaoFIyUt5hSQsyiIT9FCRvD4ZxHSiuaA4Cma7DZ9GhCAEM4oSXnanC9KuRghSird9LN2cLVdb6AyKvRD/DBQajdLPNY71WMsZfnYa6ObrlFB8fn6EwpGcM4YhwnpJQ1aVU3nvZ8DIBJteHFEHunCLqidyb9WFQVpJQCV8S2CG/fVYUZW7d1oM6MwNKHeEkh5NKWdvFTFz/1cfippl0K7BEIxJJTcvFTnz4/5fTvZ+3ipz5RP/URhw0CrIaBhhdNIhBYkKLlRKpYhC76dZinFClBZjfqSoF3gmo4cmdh5tPpiBcvXmCz2WC/36Pf9Kp97ND3ojKy7bfY765wf3+P0/GAcZLK1B8mN7h+HkGi9J4hiXKnaULmIgbKeZCqR9hC4DqnXuKwqWGkswGQiWH2Xvi/ZRkMWEjdlCnOubS1L+36OhlkEvszA75OFsxJMLIQJORYZdtW/V85hHYyNpUUlolfSkTJETkLpzPGpIZZVBmOpxPe/+ADvP/+B9hfXdXQfxNcRRPNTEA/O6eMlKIY3Jxrkl7Xdeg2G/hG0ADnHPquQ6Oc6KfPnuPp0yc4nk5gTbRbZBAZwTuU4uAJ6ILHzWYrIXjvIMp5WUOKkmA4pRnDNGIcI2KKcj3T1PaCPsxxxmEccHu4AzzQtY0kZoUACg3gCDEnxHnGMIlxnlLGmEv9GlLCcR4xThFTElRoTknQq8xgSIg+aKi57wI2XYdNG9BvWnRt0GKGHsSMlGYkwQvFGRZByGLMda4H5+GDA/uAwq1WKZc5lpVjC3Cdv1bcr0A2T6Wo0kkIsMRCQ3wJAHkvqCx6zDliTrNWNmbMKZ8ZcpufNaGP5f6apoHzDiXLHKnGTOcxFxYn6RhEoSaHsj0762ewqJ94luRd4VUvyGst3ugYxZXqkOTe1ECpoklmRk5REMnhhGEYhFZRshyKtzv0m80qQXihpkii37r6qn6+/UcAO6GpOCqAYzRM6DtJvpu5KN0F32u8a2MwRIbyZVDu0pZ28VMXP/X99lNWNHHdhyBCaMLFT30K/RTl8qE29OKnPjk/9dGHDZZwkIQ/V5U9eeGkWshTOKCGEJFUW64ydnRmdAA5rXrv0XYtYpz1KyJGCWMejhGH0z0cJOu/73tst1u0rZRHb7uApg3oNj3GccBwOmIcxJjbPUpXSHdUbpzdE5bQ6TBOmOeEvu/RNUE1q+3EJqdXR3SmpmFqIy8rhxReyYetJ7YurIW/ijr5gHPtYlZJOQDa7+vJiDPpMTt5hhBq4pgjCWeLwoevJ2/hiDKIihr6NXIkE6skWcQhmKJFxjSNmKcJ8zzjdBoxzzNef/11hLYFq7wilH9qSXbVkFbn4dCqg9lfXeHho9dwf3ohp27vsd/vAAKePH2G73znbZyORzmNh4CubdEEKTjXtiJV2DYNmqbVatfC20wp4nQSvfnT6YTTOGKeZdE659G1BdTKZiQ4mT8+tFrFtkW76UAOmOcZIQR0ISC0rTgs3VxPKWGcoxjxnBCNJ4mCQoQMQiwFqYjiS86MOWbElDVJjpRKEcAlI8aEk3Noj/J5TSsGPmiCXFXj0KTDog52VpWJokldhQE44z57tNpnvgmq2y5GTNAfxpwzYhEjYfPN103Egv4SAQ4yDru+x6mXiq2pADELnaG67tVbuToSCc2mGNG2TZ3EzqEqBFXai1Mdc9XeN/6uLQ1DSUHShymVhS/rCMxOUV5B9qozUhhFfWQAACAASURBVEObU0aMM4ZhxDCOmIYB8zQjl7xCvgQRGoYBAND3G/ggIfVUlN9ttgOG7paKtomtKeY3Ae/gCqOojn/XteCcMZe42Cj9tya/AqK0U9f95bjxu7WLn7r4qe+3n3LOIfhle2Q1WPq+u/ipT6GfSuW82Jyagouf+gT91CsPG3YTFp7+MH7r+sViuCyUBOW9SYefhbKBleETB3E4HpCSagoHD7DKialqQowJ4zjixe0LNCGg6zr0fQfnPba7LfpNj/1+h2EYcTwccBpOUmBJP+eMw1qN+ypcBSn8NJwG5LZB37Yg5zW0hWVm6kzilUE2I/xyEqB2Yb2+9JmE085On4rGWRLTwreD8EkVeTOjL7fE9brkqPIJLemONcRnlV0BQZNS0qRGOcsCMBk7SbpC5eXKGIp8oSa1waQfMw6HAxiMhw8ewjcBJWWUskjs2aI0ZMYFV/m6OWcJmxNhKqM4f3Ua4zhgOB7RNQ0ePH6Mbdej6xq0jUfXeEGVVLKSQIgaqj4dTzhNM05jxGkYMZwGQQLGEZMu2GE8IcZZkQxR9mjbgN12g6vtDrvtBiUmUGZ4JqQQkNpG+lOjvdIfMg6pmCY4205Bxly6r4bSxylhnqUasKED80xSFdZJIaHtZqMGm1GGGcl7hLYFOcY0z5iniGE4qayebJZApO8xLWwGZ5E7PA0jiI7ouw5Xmw22fVuVQQDAe4YvHj6LDrtZXyYGkXBFlTorIAsRgvNoQ4O+7dA2M8Jc0PgkuA7ZZk1pKyqRaVEr59UxMLAU6TK1D5ER9CqPCXVGzls1V1/5r+vvDZ1dSyLaRkiMOKsTLZjmCeMwYDgNKoWZq41jrOk0astKqQnHzjkELpXLWxVfSBJzqY4729KpP8v1VOpRD9V91wmNJRdBQxWhq2tmZVX1Eet6v7QPaRc/dfFTH4Of6lYJ4k3bwTctfGgufupT6KfI53pNswkXP7X8/En4qVcfNiAdzowlSUYNwpLktSTfyc34OnjrTlmHvOrpCCQVHCETL6ckhUqcSMPlLEoUBYuxZAgqcTyd5AFCQN/3wrVrGuy8VHbshh6n41E4k1EUIlZ2vHa0KYWgmGQiEKO8tu+7qvRR12kpy3us0/WPNTTFy4quoNEKLasnzNVIlcIv/Y1VhYFW17IZoiHzUsDkUdPw1GhmFDgCnFPECQQmAjuHEGRSc+GqsyyfCQ1VZjmVa0g6Zil+ZDrcpiwRU8b93R1Kzri+vlYHEVcczEUvm+r3yyJvuxZXV1fYX13BQohNI6jPw+trPH7wCNu+k6JJzsF7CatzyZiGCYfDCcfTiGGcMcSEOQNjZtyfRnEwzEjzhOF4xDyONTkqZuGSpjiLLn4pCM6j37S42m1xfbPH1fUeV9dXeJAzivO4cg1CUC39lLV6LexYr2jcwvEsqm09TRHTlDSUvFJ3YNT10PgGRNqfpxEHjMLVJYJvGlEHORxxvD8hzbOgcySL2pEkA276Dfq2ReMIrSd0bYPgPZyTpLBhGgDO6HtzgOLAxLgyso6N3B3X+VRnJysi7FRxpGnQNw2GMMMnD68Sg4ApjYjMn/eCDsqGS2gli7EVw+6DV4nEcCY1aVV0nXOVmiI1DyzBVNEVDadDnyEzkFPCrEZ7HidM84yU4tmaW2yRLS+u64tWz8DMmKMiShVpNv45qbPSDS1bcqyuH5ZwOZjq3JdNlYd3QSoU6+apruyXUGZpjA/99aXVdvFTFz/1/fZTaysYQhClJ3fxU59GP4W0qm+h9uLipz5ZP/XKw0ZhrIKXqGiAdcCa61oNtyV1EWrCWcml8snWRlxOgAFt24l0YRGUqTitiLl6j2mlV7lByElxnmbM04zD/T28Vlbs+w7b7Q5932McJwzDCeM4qlRZBhvtg5cBAZYkOmbGFCWZqe1E07oaSV54d9Yn68Oc6TevJwSwoElLOMr6DVUjXpCMrM8bNNy2LqKycoA6yZzql5uTsG+dd5KoGAJAWiRKD8mOHAqVM3lA4zfX+4QqjVQjVbTaqKvqCpwZ9/cHpJyx225FyYGtONPCl66yauCasFSKJHztrvd1QWw3Pa72O/TBo/NiKEJwgJ7S4zTg7vYWd7cHHKeMUyo4ThEvjifcHk8YpglzzjgNEfd3LzAcDojjJM/oVYoOjL5psGkbdNsAznJPp2HGaZhxP014BEJsOpR2Qugi+g1AziGnhHGKGIYJ4xARZykiNWvy25QiJg0bT3NCnDUsTxADJCsZphLhyIFzwfFwEl6p92j7Dfr9FXZXVyjMODx/jlgIod9Ist8Y9XoETwwPkbDbbDbgXHAYTrg7ntC1HXZ9i23XAOBaQEroAdBx1MqkkArLGaza5MualpA21bUhhln+9c4hOIfilkJDHiQVRklqDWz6Hg8e3OD6+hptI5WYvfK1zYgLAnRuS7wLYuj0dWI3NDEWLBx9orqxijFinERBY1JefMkWctZ1Agtdr9Ec+1AnDDB9FueWAmzGJaasOuZeNe01u3TBnwV5c5pI6EiKeVW5TgBZJQgNpXKOvofrekarYbEPZy+4tLN28VMXP/Xx+Kmlw5q2wf56D6J88VOfQj/Fq4KeetWLn/qE/dRH52zoRW2hL3QJ0kI99lqn0dvl7wWmmsHIkIqcZ5xYCBLa9mLEc07qBAoKcjWoL3NoqwMpEP1i66CcRat6HOA8VY3m/W6P7XaLcZgxjAPmeRI97LycIHntrQg1eWscBuSUquzgYsiXwTGpRIbpsquiR1XSEF6tSRLqvNI/0eo96iCd15DygkQRUUWrLBJQOYv6f++FH+q9FKxq27ZKKopBRuXJWp86Z5NFEuKM5hRTlBlEVK+XSwFZ4tzKEcdpxug89rsd+r7DZruB8x7TOGoik2rRp4yMvMwpkkRKWbQON7sdHt5co1GkrCFS9GXG6XTE3YvnuL29x7O7I7775AXeefICUwYevP4afuj3fRaPHj/GDIe3vvldvPnmV0Ep4Xh/wIu7IzIInQe6QGibgKv9HrvtBuRkfra9qGyEJqCUgmGe0cwJt6cRhW7F0B8H0bpXjq/whEeMw0nC4HNaKsUS0AQt4EW6UFd64/MsfMxpliTAEBrsNjtcPXiI7fU12r7HPEfc0x3Guzs03mPfNQiQJPu2EfWWNgR0TRBkLRBa70T1RrmtORcwe4CkwJUUvGJBaFwD7zxYnbxjhnNcx902BiZAQ2DRUFfn6j0tyMliCGD/taHB9X6PR48eYb/foWkksc+r8oxb0SqcJoqScqWbEOBDIzambnjkM3KRSs05Z4zThGkcEOepPhtJp5/x1sGGYq458mu75uq6ykWS4xwvVacJimD5he/OJauxXlBdCScLp9cHD1cy4ijhcNI+dd6h63uQl+Jzs0pjrsxPRZAu7aPbxU9d/NTH4adKWZL4QxOw3+9R4nDxU59CP5XdImdt8/Dipz5ZP/URkQ2W5BXnYPUoSznnbcmvCaybSNLkKu11ea/39RQlXyvEhUhCaZ5EqYJZT2PlTG6rIkeaALWuClsl+/R1haUSYtIEPiLSsJbHdrNB22gV2HkWmbqSYUoXYFRum50Y4zwjp1j5wMBikIV/yktoiQGREdCusdAZA1Z9tYazdSIYguC9g/NN5cbW2BkbGufhIYa4VdWEpX+8GttGF4lMTAmjisNx5mgd4EgQlJgyOAvXuOQCHzxC8Mg6mbmoOgtkEYMsEdPXcGEIjeilK2IXmhZd18E5j5gS5jiD1fBZ8hfp3KgT0Qc8uL7CbruBc+r0koR6c8mY44y744B3njzF+09eYBgTXtvt8ODR6/jBH/kR/MAPfxa76ys8uTvgvfef4bXX38DjBzs83XX42ptv4emLe/RNi7YL8E4GmZwDMjBMI04YkQi46h7g7jjhg3e/hfvxd5CSJLnFJOog5riJRN2kcQ5dkGJGbQhog4N3Wvm1bdHoPMmFwYocWSVgkXtU7XwGwBnpeMQMgOMM5wMePbgGlc/gxbPnmOYRjoBNJ0hgGxps+w77TY/GO1kXjUfXCN+68U4MfCuhX6dzquQCBKU8BQcmEoPFjAamhANFChnsFNUsDKcJm3LfspaNhvByc46w3W6x63vQPGF6/hw0RXgnyaTNbof20QO4q71Wb5VQdRNEBcX7hSOec8E8z5imUYpTzRE5y8bHKB0VkQaWw4+hnyzO2jYqjkyz3+gnpqazbJyYV5x8VZlxRTnjupF0LgAWtnYWTlfuOght2yIrnz+XLJ8JqXYcmiC2hiXR06qxWvveHr20D2sXP3XxUx+Hn8qrQx5AkoPT7i9+6tPopyxUsAxnbRc/9cn4qVcX9TP+q552vHLizlAjNZoSQiGgFJiag4V2luS5CEA4mClF1VsWTWlbHPM8LyfapjlDic7CYGQZ9hYSsr+b0bOqscLzLJyRBMMCgdH5gNB7NCVXBKCY7rO84Sw8lEtGzvEM7WrbVgvoaNi4FJDDuRII1BByQcmo4TQLX7PzoEzVaS0Vaqsn0Wc/DwMSLJlRQsV+lRhJ5EEugLyvkpASkhS0qj6BziBm0Rz3JBVpJZHPAynWhLHECVkXSts2CMHXReCc8HELZ8yxoJyKFMNqW1xdXwNEOB0PsuCIAF44jFmrtzbBY7/dwAen45CR44wcI8Zxwhwzmq7H48c/gJub13AaZ9GZniI+ePNNHJ5+gOvHb6DdX+ONqyu8+823cDwe0HQtPvPGA3iK8F4qmnZNh+1mC2ZgmGbcR8YxMb55f4fp7VuMuSCqBjWXUkOihjacKcCsFp4hCmI8PXZdi+tNi33fYtM2CJ5AIuqO7W6D7XaLYtxcrXZa8ozTXQTuJYHSh4B926F//IZo/BNXycjgCGQJoDmKRKIMOYIjNeBCrwjBV2STIGHh0DQgb1z2ulcQTqw+FjulThDDqbpG32Zs2lZQVFjSn7TCjKSIGZiBOOH0zjugdwk0R7TO1c0JNQHdswe4/n2fw/b3fQ7dZisVhXWTmJLwsIdxxDSKsoxw8qk+hyNxQssm7Nz0sdorB6Covr4YcbvfXNFcWR+6dgsjayKpIGSNOtvlYGVrubCiU+ItNHlROjNoknDOshFhcxAA2hAQrq/RbzYYhwGnk9Bo1sZcnBKZa720D2kXP3XxUx+Ln1qtOVZE/bXrq4uf+hT6qbQOa+Dip/6/8FOvPGzY6cfVrPxlwFgRhVoQJFtBIJFSE91qKdwCFoQor8J6Ysilk8QYcf2SMFqpknqLcVqQqrOfNfy5cGc1LBggJzo9dZYi1+YCdRyivdz4oIWaVNc5i9HGir8aiocVqiklo21bbPpekoXqqbJUlQFacfVK0SI2UTWfvYTtdUQlNG8hW+aK/KMuELk3/ZNMQpV4FJUUX/mn3jcqx0ZVDWFBnTU8VxgOklApKJwUSqocXn2eswVhyFeREHzxOmkhvyPv6uIoXDCOJ/SbDbquAzlBV07HI0xu0jlCyjJHGIK8tU2jCwHgzKr5ncHOoek3uAot9nvBr3IB5phwOo149uQFDnf3uL29BYOQGbhWdYzD4YDOtfjcZz4HF0SacYoZz8eI58cT7ocJU8rCw2aufcBsy9SMtSI/L0lFLjxf4Y6jZEwzcD8UPFGr2HiHvgnYti12mwbbrsVGK8NaUqQgSfIZzBmcE8ps2tVqJLyDV+NQiESizjtNsnQgKvBE6ELApmuk4FLTwIdG54qqUoBAXhyOyYTWYdahto2VbmVATn4WiUePrvVovSTDmaEBVAVljmi8R+cCrgrgTwOKos5zTiKnFyPmOMPf3qIEj+vHb8Dt90gpiRrHcBIqgFYItntbqBlkq0KnthpYMqTVai0IIrdWzmEIJ3VBvXWcCwPIq/Wmcz74+pzrVkPa0PXu5C6NjmE8XquQnFMSFJbkkB0hCGJoHDoWQx9jXBJidenbhu/SPrxd/NTFTy0D/v3zU+uCgkU3ple77cVPfQr91KyF86zlUjBd/NQn6qdeediYpwnGxi6K/kiyVhIZsgLNZC8rwwqsk62MRwmWG0/ZjH6u5y7jiXE1XDJ5ck4ywR1B02LqfYBWMoUaDsraeYCFijRhDyxJYyRoDojhyfSOeZEJcxIqLx7wHAThKDLqDEaOCRKkDbi62qPvpcJoKXy2mNeDzcRgeOTkQQwNzbIql+gEcoCHXxnbpdkkIw0nE5GGhtdSa6qAoCFrp3rzoluuY6MbfHJBdbDlmbMiFayUgRIjquIIGMQMUTnXRcOSNEQIOsPUgaEA5AHnwblgmkY0TUDTBHRdC++u4LzH6XhQecIiKJ4V/PFiHKALSwyIzIkiXg0uiJNy5JFLQVcymkDougalADFnHA4nvLi9RUwOr19fYdd1OI0Tbo8Dnt6f8OQw4m6cMc4R2YwxM2hFKZApJX0FZhQv8x7qaKXPSu1DWesLBQG8GBswNEQvvFp3KyszeIdt22Lbtdj1DfZdi00btHgYiZQlbAywOHQBSCvyRyRIVROkKOKul6++ayUkbevEag9gbQxXmyNg2VjAPuPcyFVEFgxPbqlEWo29rEFfgN12i8/cXOO63wAFmHKCY0bOM06HI07HI9Icsd3vsXn4CM8+eIKjUkpySljMliSy8eoezrnxy1xdO9faXbxIXJpxXlCf82uyl3luKiLmx50TxQ6vyKj93saenIMnoYPUNWyOv8j9N02D1LbAHBdEKuaa3Sz202QVvTgvtZOFy2pDd2kvt4ufuvipj8NPrVUHDBC7ubm6+KlPo5+CHcyga0HswcVPfXJ+6pWHjWdPn+rNrjWp7QMXI+rc8gEWmgYM0QHiNMkJcZrEMHMBF5Wna0Kd8KVIGBv6MKbLHZRLCwZSKbK4V2jOOoQtp2INXxPVUFuBdLZT9IipQAqrMIpnsIbQS4mKoiwOwinlZywFFEVqcLfbITRBQterYkA2cep9OQDEKORAhZFIE88YAElImplRsJxm15NR+tQrCuS1fwUZC96L1jsAVkSPYQ5MJxWzVsMsAFxNfiysWvNcajyycorte3WuhhUyNLGwiGzbmcJDUcMToONeMI+TOJMQwBBFj75sMJ1OwDSB7w/wty+U0uA1+dKKAglSAiaklFdJVTJPpAqvUB+IHLo2oGEClwbeXSEEhzkVvP3sDr/zzhO8++IOpzkisRo/LAbMOaizdzDDCZLXGD/S1Gqs4JUZcDEyJM6aF9Ow3shY7xGA4oQ/OseMaT7h+eEE7wjBEzZtg92mx82mx1XfY9s36FqPxmlFXr2+oYbBO7RNQN+16PsOXdugDUESFxXBI6IzukRh5YXC7LWunbVxUkNXWOUGDUUsKyeuSKMgm4vxd45ws9/is689wMN+gzjPiLoZyCliHgfE0wBOOreYEccRx/t7lK7VJDZajZGhQitkh5f7s3VvqA0rzOLc8t7FJhmHXVBj6PqsFIuwKH0YYstZ0e+YAJbaCk49qEVzLAHZ5o3cpN5LLhraF8530esRANH7lzVMTopatW0LsPDWEwklpGRGPpMgvbR1u/ipi5/6OPxUWOfixIiOC66224uf+hT6KawDcHpfFz+FT9RPvfKwUU/Betqqp2A7pXEBM+ngmuHRji4FUU89RIIcdV2H3dVDtE2D4/0Bt7rRhJ1YcwbnAtZNTFghIgTAsAoLuC6hasBgCENH1ousoiVYDE6deUTw0AlblucuMQOsRpYIc4wYxxEhNLi5ucF2uwWRGBhHbklOI5FSY2at1sliKF1BaMQxlbxI+Nm9ilEVDfGUNYxti6WGnj3IB3jyaBr5cqERNEA/14y0LQQbFptogljZTJOEq7xC/bBCqRlAyaJ4kLPynbWVInxX1Hs0bWkCyIFLwTTPCHODvr7OoW9a7GlE8+KI4zvv4/ZwB8cLyrac7hdD64NHZjHkeRbEMc4i7XcaRsR5Ei4pgFQYd8OIN995ht/+1jt459kthjmezU+GExUGZyoOrprZyutmgCEUiaL0i4qWrvrBDGGdTqD6PesmBswo+mw23+sIESEzI8ciYfPjiO8SEJzDpmtxvdngetvjatvhatPjatNJeLtr0TcNuq5Bo5VXZfMi2uTkXDW2y3jb86/72Qwj6QYN1XhXfnjdyIkxMedkm5sYE1ISNG3Xdfihhw/xxtUezgmn2IqKoSwFtyxZVea/zD+CE+Nn5rEK3XB1HraBBFCdlIE0oGUTKRxxHR9eDD0Tg9hVpwsWSoyMibxnjUjZpQsX5JJABXAa7pfP1Bojq/EHnSNbgHDN27ZBShFxnmTTRw7EhMKyfkShx6HtOszThGkS5RJiwJeES/vwdvFTFz/1cfipLS3UnTYXPGga7LuuXvfipz49fkqm99Ifm7bFD+13Fz/1Cfqpj5C+lQ6wQQQ0FMsLb5PPJrcYiBA8gg/Y9j12V3tsdjtsNr2WpZcQ3PF4hH/723j69EntZEFeUE+5IIcCLSSkIVPQci8MVnTV6em+LIk5TkvJK0fNDLid6vh8XqujUT3hUnQiSQg2zhPG4YSmafH64zfw6LXXtFqncnpjFI4bsyavycVz8ZAKj8IB9kzwroAD19fDqlOS3FdKUoF2nudqHA0ZsnB01/XY7XfY7jYg8hiGEfMcRdPaOQStckoO8ARApQe9c3ryr6sV6/AiEZQfKQ4xRVHoyEolMPQIkPt3OcMFX/u2sNIAoCHxoqiRE8k4LgzkjG6csEsRTdvg0c21LDoDtaolKQABwTeyAHyD0khfzvOMESOsWvWBGS8O9/jm+0/x5jtP8O2nz/HiMGJOy8S3xWWVPoWPa5uAZXNg88H+kYWsqEtZsDPY3xTCWSKI+ndmgJwyJwSJgCvVEPIK8TAbYBxuZiDmgngccHsaQE/FQTeNR9cG7PsOD3cb3Ox2eLDbYtc3aNuAoMhiE/wyZwxNUcNSzW01UIKYiQOTWzcDXqvylqIFleSeY0r6FZFixKzzHwAebrd4fb+FJyDlomH2AqtVwIVBLmmRtNrLAKtRxmLEhWrHyrm3TZJJYspahb6H1ldjSzA1BLBU21GRbiZbFvo7DxcW4w17PXRNQPXvSbeQtErSszmmjnTZRHL9GXqd0ARglM0anCUBOjRNi9BIBeCUEuLUoVVDPg1DPcxd2ve2i5+6+KmPw0+FFGvX922L1x7coO3ai5/6FPqpGCOyb2o/32w2eH3TXvzUJ+inXnnYGE73kGObIiDGYy12wkNFMvq+Q9f12G632Gy26PoO/WaDpmlk8WiFTUeCfjzqe7RtABHw7OmzWvmVWQbfpYziABRXs/6xnoxcwCUjM5SCY8VwZCi5MCIzPBUNVWm4XCeMGU1bfKTIlBUtYj0JT9OIYRzQtB3eeOMx3vjMY7RdJ6FKFDRNg9J11fC7enTUE7Ym00wEFC+h35IzKCXlewKw6o5OFDQsNFxY9ZVDQNv12O+vcPPgBvv9Hl3XCZKSCo7HE47HI1KMFdXLRdA3QTMcHAnPNKWsSYIZJaca4s1V1lCoAIVZETo1Lva9I5CF3nTVCN93hdBZX7PodDvn0HWdhr+B2DSYCZjHA7ybdcEtmvSOAHaKqqg6CTcLmhFjwtiPOJxOuDsM+Mp33sNvvPkdfOPdp7g9nXAeyaOqimGqJLzalBjisBg3rOYRaVVSCdcvL9JvaWX46ewv+v9Sdws2b50nTXq0RNCyMqjFACX9cvVauWSkuWCYZry4H/D2kxcgSGEibxQORwgkm6hGJQ6bEBAciWHXL++sIJCow8j7RZ2kUdSvFlLyisjQgjTNMeHudMLtQRIXY1q4mvtNh8YLWuVIkBJZnwRiWQOsSYQAqyzlUpgItDy/9N3i8AiKhpFXp+CrjJ8Z3uqY1KDWDV8x/rlcz0GoMUb5IKNCK7pX1Wh0Hjp7gY7z2vnrTKm2pX6mGnJDBslJJd2u34DLCVwY8xzB4wwQoWkbtF2rxt6DSDa+fdsjlbNJfWmrdvFTFz8FfP/9VPJLZCN4h5urPTot+HbxU58uP3UYJpS2r/2yacPFT33CfuqVh43TaZCbLKpgoAa4bVt0fY++k7LyXddhtxPj3batqBFoB1uTjHevJ3bp+IfN6zC+7LMnT5ByBqkZZAtbawLWktSnfLaiBkpfW9SQlLIk5cGWGNvJX8OIaqCWhWx6wzZ4KmmWI6ZpwmazxXa/A3mP02kE4NB1LQI1im5ZeNJQq1ST1xbnJGgHM2TieKdKKJYKhXovIvcWQOTx6NED3Dx4gO1uh36zQdu09kJZhK1H8NfYbHpFmqLqk1NdEGCSMHPKshizhh61bwovJ2pB55IuQguLl6pC4siL0c5LGBnq8Ngt0JMcrqUfY4wwXfsCxhgCHBiH21uMfobBLt6p6gUcGKqUYCtaD/Fzzrg7Tfj62+/j//5/fgtf/Orv4FvvP8NpimsbK/3oSXWqlzDmMuY64hVqWILyS7hVaRjrREQyw89nn2cbjDMe87LG64/rv4ux9JqrZ1SQUr8XtYh6ezrXDJESG5eKICpzTIq9rD3SS41Q+7OGZlcbI+8kma7xDk0T0LcNuqZF14pqSAhSSGqcZtweT3hxOGGMEUTCsQaAxkmBq7ZthV6iqKUjL7J+/P+y92ZdcizJeeBn7h5LLrUAF7fRC5ce8lDib54/oUc964US55wZzZmRSIkjkmp2N3mxFqoqt4hwd5sHM3P3LOCCLxc4jaOM7sIFsjIjPXyxz/0zs88YCAtMCaNbDejWK7i+wzydZF6TxqLr+HTBo6cOrJsdZ0yNo/MNmjE9wPm4KjBLnK1WDy5SnToHdDyzrgMd0WY+WRKvjGRx7+tBud0gymbOnf3O8jqdE03zuERMWjXYEpGPp6OApybOhq5D1/Xoxh7hRwf1cl1w6oJTXwKnYquAlDPW6416MOiCU98YTnGMyNfFsCNccOqr49Tn62ywyO31fS+nmX7AaiWs0GazKUoSoevQhQ6W6AUHBF+NuCWKmduzxGo6j5vb5/j1/yaT99Wr10gpYQheJ7IkyDCaPyqX6QAAIABJREFUTtfx8bYIoUbcXndOT5+VZYKeqNEsoJwl5lQWZyoT396xLAvmKWJzdY2+77EsEfv9e3x4/16AaxwwDgP6YcQwyk/XBR00D0fCPBFJ1U2A4H1147N38EGZMWUN7PR8dXOF9WqNzdUVNpsNvDFZuTI74o6vbrph7DGOfQUtm8k60ThnxLhgmhccDkfsHxeZRLEuKFM3qEyFjRMh+AALARCjHwVowfX9ZiAzixa2WiCTSPPeC/ORM3w/grc36MNRXb8QbWuSOMlS3ZblOw/zgt++eoP/++/+Af/Hf/07/M0//hZ3u0MJ3zE2QxISg8osunJqPzOudWXqIjd3qN7HGCJdmGpWy4KsTIFaUjIDbVOMy1yV17O+Tg0bBRuhAlJCSvjarmwbE2lFBoAsbXX2uabdBpzWHv3G+p3ahrM+sX4mYGn6A1zvT0BhZgpzojdkzuh7kS8EgL7vMM8Tpv0RwzBg6AN6rRrrvIfrOvDQY+GEfhiw+e473P7yl7j5+UvElDGdTlVuFMKY9n2Pvu8A0oJqEAlLr8mobZcaE1dYQTXeTpmxwvCQ9UvjSoaxnpA13NzXQeJrHVDi3q1PGXWs7MqUS19ZHHpmWRcuePTDgGVZsCwTmDOct5j4jGVOmOcJgFRbBrEyl5frU9cFpy449WVwqoaE+K7DOA7oPKGjC059azjVo7XPhK4LmA+7C059RZz67GHjZ9+/xDiMGAY1WP2Avu+0SFDQxVtvnmJEzBldF5BdBkfWgW2qtT65nA+4vX2OX//6z5ET49Xr15jnRQqGOYJJtEmWe6suYoZD4gvt1Cstqu3KLDGZWQ0ZZ3O6qlFsfJl2Ko9RCgRttHR9YWLAyCyxsXGZsd/ttD0Bvg/ouw6rccRms8UwjKW6ZHQy4ZYlgoiRI5AhE6sfVui6oH3bo+sCvEoGemW9Miu7oHGzssZI3H8Wk7uIoQwqNShPyIWdgw9aRbXHOIxYr9bY7R5x/+EOHz7c43g4ICWRTCQn7mxyCgKQhW4GXpKWMpicLhitymtA7VBYlzYBiZSBAhHizTX89y+wyQ91QWkCnICOJDu+v9/hv//md/hPf/N3+M9/9w/4/Zt3OJ7mkpgo48YgyLN3XVfn2pndpvJCeTnnYnRz8zYiqFdZ3umIwM5iRam0k1BZMmOUbKMg/VNjTysrdM4y2GcLCyQPr/0l/S023sEYJ9vIFEPVPmhjeO1p6/v0RWdBFPqwTRJawboCENquT8hmkiN03mE1dFic/G61WWN77XH36i0O84zU94hDj9U4gIYewTuEMYD8CqvtFs/+6Jd49ke/xLjdIi4RwUs1X2NSjO5xXsI0QnCVFbZxaJBRmJsMTiyMZ9cV21CS9AgNiHHZAIIIvkkKrWMDid1lKKOMMoblfQryLZiW8WZILK3cEAzAd8IKTzOBU02y9UTIlEsys9WHaJMbL9f5dcGpC059CZyiNowqBAwhwOkm+4JT3xZOjcHmqVzjeoWrQBec+oo49dnDxp/88R/r6VvLyJubD3YKZlgyD2cpbCNxalI+nVkK4XDWCd0s6Pb07LuA2+fP8afp1wAR3r55i5SSaAKr0aruIFb1ouo6JBJGyXSAOWungwBk5CXhNOdyGpRTZC10UhcgsMwSl7ZdrUHO47g/YFlmZHCRKWSuC0Vy2Bak44LpcMTj/T2ce4uu6+XEPAzohx5DLxn8CB3gAryTBLrVZoN+6IXRIgOlVDSy5UQr/eZDQCCvBtWkHCvo5CSFqhgSRyuf8QqiBK+GnIgwjitcX1/j5cufYzpN2D0+4P3dHe7v77Hb7TBNx8L0AKbqQKoewZosNMB5ByugJK7CWk2XbdIWVkYYBXgPXgP998+xSmJ0OYmiCDPjNC/47au3+D//9u/xn/7Lf8ff/uaf8LA7FJe/3T+nBFYgC6GvxqTM9wokNvCs2WV1STxZHGxGT35nBrB+ypggYxi4zAUQgR3Uuj5ZTAQQ2/dSWTdPvlq+n7iA2VP77NC4VcGFSSwSmylVQ06GSGjaDQGvph12CRsINUVP3O8NY2Frz8GhCwGrvkd2S2nkar3CabXC4XGHeZp1MwFNDO1EX7/vcf2zn+H2V7/C6tlzAEDQ9ng14lY8ysHUOwjZkhjJ+qwyfva8Ygt0zrqmLyChGFItOjWShHJv5x1IZTKlLytoZc5WyBhAjbWFjoPZEnGps6j5sNi+HLMk2Xmn7QNAHsNqwLzMiMuCmHJRNHLkQXhiX+ly2Pix64JTF5z6Ejjl2sNG16HzWs9C639ccOrbwin/hEi44NTXxanPHjaurrbSIbDKhrl0FJErGt/MDHgHp+oewQc9LUlyjZm8ljkgPbWJqsUR+8dHMDNe/vzncN7j3Zt3Gj8prAV7X1yjGRnI6vbM4sLqvJy2UsqSMFZWAeCcGT1fTnBgcUPKc4g7PC6zsA59D2bGcb8TRQxjosiVCWuLtp4OUQxAjBHLEnHY72QgNPHs6vYaL158j5vrG2GUvNOB1ZO6MgYyYVtlg8osmNuWqBp3iWMMYkCg7jjookwZEbGwV8ZAlAnjJN5+s93g+5cvsSwzjscTHh8fcff+He7u3uOw3xWVAWOrbInrgV7BkJBiQiJLUjMVFpnQOWf4ILHUuQtYrq9AUe6Xcsa7hx1+++oN/vr//W/4q//nv+Ef//kNjsusg1WB1kDXB48Q9LCi97er9pm0o4xP+W89zVNZjLWfLdYaCvJVu5zLBqYlYYokY/mUzQdbiNUI2utlF4NiG9D8pxpUM8QMYUMsUVNfF0bLGFScGf8KRtz0ReHa6lttbtWPVpapYfLsdUeE4By64KTiqMZ8T/OMmFag4Is2v7FNWWUBfQjo12tsf/4S62fP4Z0vWvkuiP2wmHKrDeCdQ3AemRwytX1dgYwAUQcqv7H5WV3QpgRUEvCcuJDtPtRsOsqzsyqyZK51DbQ/ZOq4+m+dR8L+QPXis4IANb+HhvtELPOM6XSSorCwcI/zQbTmXK6PrwtOXXDqS+BUe7H3WGICTxNO03LBqfqfbwOn/PlBY14WRAoXnPqKOPXZw8Y0TcqsyI9U/xTmyNyiOXN5EFMbsE4EKdvDqlus/56mCfvdDg/393h4eMDxeJAY2GHAs+e3+P7lzzCuRiyLGg49OeoSQ8xRYlXnWdyyKRfjAmjyjC4AAYvmtAuUOE1SY5SzVEgMXhLpLBZPDL8YqNbNWu51Zlzbn4xWBnEcRIrw5cuf4/r6RhOYGMwJKfHZ4q1kg8Wl1kkoCTqLuqs1uZGeAGxpWxl/UWZQiUJR8aCzgjAGRPIzIIQO2+0W3//seyzLgsP+gPsPd3j9+hU+3N0JU+NIma1Y3OFqYkQFpPR9DRVIMcmc0jjpyXc45A4ZwOv3H/C//7t/j//y97/B716/w3Fa4KAKL2oEWCd4CCJR6Kg1IFyMWptUiXZBQm2hMYeNES7uY9QQiJSTSuLZ6NSFxQYmjbE0Y2gDwAyZA5847ZdNgC5qczNTmeVc3mgAXz9bDbEAWwUPFKA7nwf1nnTWH7lhfsXYn3+mvA+56LoTEXzn0fcBYyfJdVD36XyaMe2OiPOi81NCELrQib6993A+oFttsLp5hm61rv0BZXw8wcPDZ69sSip95Z0HkGBVjYlY5CfVHtVn0fVfAIsqOqLOd3N7OwOjimA6l+UlAQnd0Gnyo4xC4yLXOaGkEcyF71Ar2JKhYRa70vcjhmHGPAlzxOyb9WTr3OEcKi9Xe11w6oJTXwKnjEABgBMD//juHr/52/8Pf/P3v7ng1DeGU0Pv0ehAYJklL+iCU18Ppz5fZ8NVhQSbR6xH0pyUqdEEJ5Bl0dc+A1AKzcQYcTjscf/hAx53O5xOJyzzjJxZ3YsyYPd3H3B9c43rm2vs94fyINbpYszEiItbzoxbFuPMLHFkOcGKtCxLlIqwOZcTZVK5PWMhvJc8+nmekVOUwdcCS0QfS5RB+6L0iWmBq96zLXBjx06nI37453/Gw/09NtsrrNcbLY5SF3FuDIaMtdNel387IiRApAAjEFVarTXi4n6We7Txx2aYACCmBI5R31MBqrBPCsTOOYnNWxYcuq7EHpZEJi3OJICeYUWEZB7XmGWy9eMgWtgpITgJp3q/mxFzxg/v7vDv/+o/l76TEAcWfXzOqi3vK5hSY9daALW5x40hRP2dnf5tbMx9aQZcIwL0dTEMDgxqk63a0zzqd1qELGAbBYuP5Pq6WHYkXUdPNwafumzNFZcoqpH+sasF6Go9CMxZp5V9r0ObSEhUDVELkrL5yeCUQTCZxiDVYL3HwR8BANPphOkUQCyVeIP3kkC+GjAOvcoHOvTjgGE1ls1g6UkWptmRqX14ODZUqvKBokTDJQyjbKhKn+m/9N6VqclwRMi6Ln3wRe7UhrWo3pQCTXarjByjSCLqWMiS0o1K2QgAslLV2JviT07gnJESI5qUZ0xYllk3BVp8zVXWiLNIgOJfGe//la8LTl1w6kvgVG7CU3bTgv/wN/8D/+Gv/y+8ff/hglOfuP6gcaprwqiYMU8zJg05uuDU18Gpzx421ptNqUhJUGPrA+rRS41AOWzZImYs84LT8Yjdbofd4yP2+x1OpwkpRe10S8YT961OMUynCXfpA65vbrBarwq7IIZF5MrAAALJw1lrdGEQINJ9jR46wNWAqesYLBM2MUvCYEqIywKwxKQZaplSyVPjXS8bPBQGTdggmQhBY+tyyjikA+4fH0D0CuOwwma7wfX1NbbbLbquqyddW6ysYMO5AIhzDq7vIN5TAbDT8YQligta4mpD6WMDEaKP3bcCiFwYAjP0RITpdMKHu/d49+6txsZOOB6PUiTJDL5zzd89nINMUgUVTgzvGNDNgPNeit8siyRFqWyabRJIT8ZJUBis8bXB99pe6CIvywPGcoC5uAGLwVRexdgdQBMYyen4GzCjYflys3lBYSDIO1BR63g6/tyAcDXOYjc0BpVZGQd5zXFj8J9aZDKE4jOWg6FsnD1Ua6hRv6sAGLM6HLi5NRltoc8GwPgIoo/QofazbiV0HRmD652Db4z9khIyAf04qG56h2HosRp7hKCgGDqsr64wrlfwnpAjIzsHKDMMA1yqGuttqIZzDln1zmWDVZVHyICUIfeTv2gH8hNjaO+tTBM5gmcpnJSSrFsz5tZvxkTJV9hcharwaG0A+6/VBjD3vK4Lq1fWusLJOfl8zAqs0CrPP65dfrkuOHXBqZ8ep2qIkn6nd3B9L0m6F5yygfimcKo9MMWckB1dcOor4tRnDxsp14JITk+IZnhlDjEYGWCHnBLmecF+v8PjwwN2+70YFy3g45zFo1XjwiyLKjinLi6C99IZDw+PAIBBpbcIhGEYgdCh62qcqBiFVIxDzhnkUomtszg1R3qaO83gxtnDKWKeF0zTjGSJZuVgToBm4DvVFCbSeFtzmTkAWoTFftp4SXI9QhAjRHrqTzFiNz/i8fERP/zwA7ou4Pr6Bs+fP8d2uxUNeII+lzEx6mojW1AAk8gQDqsBXRZGx5Grrlyo7JrGitoiMEMkY1sVWOZlxuP9B7x//x53d+9xOp2U7XGY51nHQSazGbjWnWvAgOLaV8k3/Z1zBKexxTlncIzK3MniI6q65d75kgQl42wqCwCrq5Tt32o8S2JeWbT1mY1Fq8wB6WdyBd6cANQwCrF3BGaH4BmcXaOnzwXk8yfWGOvClzGTZ8gmGO9wdn8LMQBQxqKOMnBWlKfsnxzMTU32Vvtl43IWPDBDpW8pkwFAkd7UO5mhUzSomiJNh2qbypx2jeqOc+i3azzbXqkePRDIKh6LceuHHqvbW4Rh0EchOAayk/WWcwaczl1yyC6fgYn3DsweyLJQzxijbBr83PSXK3MeBC341XSXjZNtEqBzW0E9pYQYY0kENCY6Zyl8VgBGx5oNiJq+NvUbQABQipbVPAFjpMCVxbZnrqN/uT51XXDqglM/NU4h18ONDAC0Foa/4BS+QZwypcvyC4d+u7rg1FfEqc8eNryexCRBq+pBQyexJM1NOOz3eHx4wOPjDlH1hStTUxc4mOGteE0ZFMmIb0+9ACGlhIf7e1zf3CClhMeHe/T9gNtnz6TaK1fnYzWe0hFdzuX0K0k24irKKaHrpOgLASAvk2C9WSPOcylrv0wTpkmMlhhjgi+nSlcYEqBRV+BqROQSt5/zwqoYqASVChS2JmOJEcfDAYf9Du/evMZqvcbNtbjn15uNtrd1M6N+T5Z4Pe8lPrSc3NX1lTmCE8Gx/o7PJzypS+40nfBwf4/379/h4cMHTE3cLBFhmmfMs4BcGTs2pxwVgDIXXDVIkhiHwroJA8SAMIEpqwa6LixHoFyNUvusxsiYmjiMockyj8w1rl+kFrJRc7FnVuNtoJ9KX0k7BNRIiRF5L7i5vz48o8Zc17GhYgCkHboIc2WxAGgCljWV62dZntcAp9yk+e8T+9s8Mtdfluds3osKGgA0rtVMRL1H23brq7qlobLBsbAD+wL7e991WihNXotLxJKSJOoFj34csX52i/XtDVzwkpCmGwFPBAQPlFhpY3BceQbOGdCCQlUtphpe4YiEYZUhVK8ZA6yZqaL7z5DqyWKkkVnYrmQGOiHF3GwOU2WzzPa0bI9tDBTzLPzZXkspIi25JNO3Ubr6Rn2FUWMkqIzZWejP5Tq7Ljh1wakvgVNnWycC+iFgtRpB9w8XnPrmcKrtACD4cMGpr4xTnz1shK6rN4YwGHGJOOz32D3usNs94nA4YFliiUnrOtM1l+ssjtV51YzWk59zZRKxPghY3Hgi7pGxe3zAdrtFCAFv377B23dvilJG3/dFptAVVqIrJ9mUohif5CoA6SmNvMbVgdVlPWKajuD9AfNJlCWINPFLT9spZTgnLNbN7S3G1Yh5mnDY73A8HjHPC1JqXF3k4MmDc8aysCa81QVMDug7j+Bd0Sg+HvY4Hg54/eoV+qHHZrvF7bNn2Kj0oLB32iaS+MuWVUlJqnnKxl1A1E7eFWjkfcfTEff37/Hu3Tuc9ieAGN6JdnphwJhxPBwwz3MdJ7b5ICAZOtGaL4lZ9nD2nDqugjtcTsbkPLo+FMDo+05P+rKwUlQFB9VPz+qyPltIhUk5N9JmANp4TmPy7LTPDQMAmDRhaNgkVmWKCtQCVFwOSS3DcMaogMrMruNT22sGXht9RviUN3ziKqwPAcIJQo0GFXA7u1H7PYo5xVgXVY+6xp9+mRh/NeTOndkD1k3SDMYSIwAJ/5jnGceTU116Qug69F1A6HuMN9e4+cUvMF5fwXlX45KZdd46+NKD5y7otoXn8qZqvDPAjgA4AXd1B6dUXcYpaThKTsV9bGo4mdMZqNaR1DFq5hi3465r0Ta4sHmjzJXNMecIIQSMqxXGcUQIHabphP3+oMWRqhqJjIpXprtMnsv1ieuCUxec+hI41YZRheBxc3uNZ9/d4O37uwtO1dZ+vCDxh4lTJdeIhey84NTXxanPJ4iTuFJPpxMO+wMOuz32uz2Ox4MYQznOlBg3xrnRfpr4lTlXo0vmOvvU6ctO34ycIna7R4zrDW6fPcObN6/w+9/+Fg9X97h99gzXNzcYx7F0rC0aZtvA9mfxtM6bqxxYlgnHwwm73R673R4nNVZSlbOelI0lcM5hs93guxcvcPv8tkifZVWv2O/3OOz3OBwOOB6PooZh/8sZGdACUlzYDO8cvCMQWR/KpEkpYb/f4+HhEa9++AHjaoXrmxvc3t6KC3voUUJYvE14B+YF4t1zTViBt+WOeV7w8PCAt2/f4u7uPabpCALQhR5SWVeSgcTNL+MvfcJa8ZZLchwxoeuFIXDO1ZABmY6Iy4IlRtFQ1/jikpQIYWdCL6577xz6LmCeF7BkrZWKqGdMhlEWtsxJmLnKZLpi5Krt4pL8xLq4xIWvC49kQxFC1yQRtou7MfyQkAomJ8DSuLVbA92sojJ/8IQnKJubhnkqd3lCENjrpH+I7cjIpDoTZIyOAGYGmoVPKKbc4ioJEE6lukj5zFBU2UiASpEgYwcNmJe4gBDKd0Wd513osVoN6DvR5ncEUN+jv3kGHlY4HE6Yl6Sa+hor7UwRpq47mdcAyNa1Gu2cy5yz0IkYo85dLoY7ZQHhSm7ZZqIxwM2Q1T7Q14uaiPVtM8QG7LoZKHMq5zJe3nsMY4/VasQ4jOj7Hk6TVb33SGmLcZSE5MPhoCEpOtAWG/x0Sl2us+uCUxec+hI41R42vA+4ur7Ger2+4FR7l28Ep+IS68ELOr644NTXxKnPHjZ+90//hMPhiOP+IHJXWYywGYf2xMbg8nqNB+XGuOrlZGNJ4DJ5bb2VRdV0KAHgmHDc7dEPPV48/w7v3r7DbrfDaZ7wuN/j+bNnuLm5Qdd1OnBJ4ytdUazIqsIR44L9/oTDYYf9bofj8YRlXoob1DkPVmMmA54BchjGAdfX13j2XNib4D1SihLTlkWX+/r2Bte3t0jLgmmacDjsq4SeymHbxCICEjJmY7GM8VJ2xzmHruvR99IPyzTj9Q+v8Ob1a4zDiJvbG9w8u8V2u0HX9aWfhTDQqpX2/Cwyjh/u7vD69Ws8PjwgxQgQSpKeI5HQkwJJWQx6ZjHgKYu0HgsL0/c91us1ttstVus1+r7TPpaCWcssco/HwwFxmZGiQyIvC5C4xD2Dc0lsJFdBn20JqRoFsVP3ohmLyrrZv0HmykM1lNTYMWWbzAhUxkkWmg8BwVv4BRdDIeObzoBE2Av5H1W7WVtTAMRWfDV8XCCOy4eeGs/zS/7trE9A5yuaWSQLWQ312e/s6/m8gWIa1b1NRU7RQhiq2WhYE5ZWm4saCogpE6YlImruTTZQ9B5ONyYpJ6Dv0W2v4DdbHKcZx2kumzXnHUIX0HUduk6qPvvO6z0s5le+1sLwWLoVLhA6BI2RtnYlEGUAGTmJJGHL+FXGDAUZrQ+yrvunQHxm62zucI2FtQ2s9x5+6BFCh37oMa5GDP2AruvE7inrRqqu03XyvmEccff+Dg8PD8XWZmZE1wLt5frUdcGpC059CZxqpW8BRhcCxnG44JQ1+uz6w8ap2GyIAZQaNxec+no49dnDxu9/93swW+McvDfXL8oJqrI8VE59rXvzzCiDbZWBWVjQ5nxYpg7ajrbX44yUEsbViO9efI/Xr19jmWYceIf5eMT93R2ePX+O29tb9P0IghjunJNocB8OarQPWOKimfdSBMcWYtsSO5F7H7C9usLNzQ1WmzX6fihAZa5gQCTCWBdujBHkHdbbDbphwHQ84ng4lsIv0mGSXMaZsSwLkCVZLSUZYKmIK5KDEodJIDUm03TCm1cnvH37FuNqxPX1NW5ubrFer+BDV4oi5ZTxsHvA/d0d7j7cYb/bFV12Y39ykrjBmGN5ZmMD5nnG6XiCdx7jZoPVZoPVeo31eo3NdoOu78Bq6IQ1YKxU9i+lhMNxj4eHHY6Hg7JE+p0ZYE1WBAkv5JyH70I5lduhGQxtK4DcOkdtnMRoGGtS5pmORbvohCnIpf+JJN47dAE+dMLApKiAXr+jncemKGGJgG1r7Ltq7DfqDoUYzJbQd/6Zjy766C96q3N5Qpt75akVwMXc1t9JE+jsvfV1Awdov8saPWO2mqawygVKcQ1GihmZBLwBYTpTkn8njUnnrkPYXsFfb8GOwFkkO7N9bwKm+VSArhTx6kT3PHSdrIe+R+h8VZYhKbwUQodhpaxpkwzHLGtxXmYsszBKc1zk78tSqrMym7qIPaOF+5mx15+zTau8kUhU1bquwzAO6PsBnbri+75HF0xx59M/IIJHwNVVQBd69F2Pu/fvcTwdRJQwZwXgT8yTywXgglMXnPoyONWOq7yesV4PF5yy61vCqXR+2EgpX3DqK+PUvxpG5YwhalxIzmkMWzu5G3doOzFtUNrJrQdOAFqEhMoMkVNYAwAlztU5pMyIS8JqHHF7e4u793eY5xOYOyzLjP1+h7v37/Hs+QsMY4/T6YT9bo+4zBLvGlXz3Ka43Z8cmOpgZt04rdZr3N7eYrNdI3S9MjgdAMaypDM2oQx+SgCzlqIPcL1UlPQ+YLfbYZ4mAAzvAkIIZUnF0jYou2SuTJFSDMED8CVRyEBq9/CIhw/3+CH8CzbrNa5vb3Bzc4uUI+7u7nD//oMUStK+994LiMWoSV7VpWtJcawShkPfY73ZYrValfi9YdDTb5BqtsaySXuhE50QvCQ5rjdbHA8HPD7scDqedOFUg74soqxCoJIkB5vgajzODJ2e7suktkS+ZhGasbO1J3GJgKmlcPM+52VsoGPXskr2OERU+qqyJVaUqRpvgAojUJYd13AJ5lyMY2Vn6mft+6oxtqs1ye3fLM62Nern2HHOwLafrc9iv3xqJowdUgyStirbaeogiRiu+aBTt/0yz5hDwLheY/juOcbra/i+Kxu0AqbnXyj3tGrk0ySJd6qV3w8jVps1VutRtf+9JkmKQgw5L/r0yVhZ6cyYpKhXVLWOpP/O5sJOSRLici71B7Ju8Ozw1LLgDNHw70JAZ8a679EPvRSFCr4wvzibH7VPZSOlc4UlSXW9XiMEDx883r17i8PhUKRRzxnSy9VeF5y64NSXwKl2E23SvRec+jZxKoPPvsNfcOqr49RnDxvOu6LKYT+i7Ryks2Aa4Y2BVrbAjLO93kruIdv5VQynetZkAMviqXGC5T66QZ2mGdvNFWJMeHi4xzTN6LoOzAkPDw+Y5wnDMIIh1UBN3s8Zy0W6yFW+jLVdKcpGuOs6XF9f4ebZLcZh1IXuELSoSi0jn8uEdJqkJ+FAnQIXISPDhyBsCICHnLHMwn5RW4zJuRq/yHISZ57gc1AFD3fW17KyEqyKY4wR9w8P2B32ePP6jZyWF1FcEZe7nOZjnKVPmj52DqWvs7J4w7jCdnuFvu/1pC7t8N4XdZS27dI4SY7inMAk8BP6HuNqhe3VFR4fdrh7914mp56K6UWFAAAgAElEQVSClzzDWB+9iSQ7ghA5N5NfWQ+TFmRWncjqqra5Yn9nM6Wskn36PbaRoGbjUeJzycE5Y7IqoFQpzfa+GoIBJ4wQKrtiRu/pxTBwrr+l1vp+4irGlBsmy76IlR8qTBGr7OGnb8iofFJDA9V3UuWKWPvO2spscaXyPYkZwRi3ThIz/dAjrNfonj/D5vlzrG9v0A0aZ50/Ybibq42RLmowCmhd32McBwymz++cArgDwQq2qSvXS6heNulPiBhBIFHZyYBuQE2akmFVhoXVRZkztmmKy4IYxeCDIAzRMKDvezXaGrLjSJIgmZE41blq4+wAUilElwGGq31OQD+M+O77F+iHAe/evsVht8Mc5wq2l+uj64JTF5z6EjjVbk5TynIAK69dcOrp9YeMU9BwPbtcFy449ZVx6vNqVFqgiKEFbByhU5k5spkKLqep1v3aGnDT/42qWCMsTT1JMdeJZ8XenHOl+qpdzKysUcSyzNhs1kgpYrfbIaWELgQA4u51WjXUeVdOt8xisGKSbP+cGCnq4GUBpPV6jZtnN7i+ukHfD5IU5OVUWt3SESZh6H1AV5gWFvdymdgMV76bEUKHse+RYyouM18q2zasGWThyol1RoznQCrvg/alxXL6MuHNmIUQkDQ2OOdU4nZtDMx4iIETI+kcYRxGbLdXWK/XMjm9TE4GFMAk7rnrwpOYaGkXO6ncy6jyeG7w6J4PGIYRH+7ucH9/j+NhjykdAQUPqYrLZo8qQ6OPKwQAoeqTc6VGYKCfa2XNYnzMpLH2EYs+dvCqhV2NsdOwLnPhu0yN2dJ7cWWjyiDY62ag24Vbrgo47SDaejJ4sLn+ZEroHfRT9odtTACUoj4/jgaq2103BNol5d5nzwPtK+AsAU36WePLieA6Xwz5zfcv8Ivuz3FzfSNrz5GGcZy3qZ1/pTeYm9frT85SMTnlhNN0ktjRYRD2su/BgeCQK+NYnoXLZgPNeATvAHZlDRNIYmk10dIYPNug5ZwQzaXNIhMagoQ0WHxrYd+MIWrGwNRmzp7ZjDtl3QRU6PTOYb1eg58/R9d1eHx4wHQ8fXpML9cFpy449UVwavs//r6MaUoRx+MJx+PpglPfKE5VLxRh+/w5fvHrP77g1FfEqc8eNkoHNJfFLtqp105VLWNkhsSMurh4zpUQdEqI4XYecPVeSfWzjXUyJgNAOeGlyIAjrNZrxCRMUQwBQ98BVIvPEBoFJFVriOpalhOihPGsxhHXNzfYXm0xrlfoul6f6Vy+TD4vbIL30vmO1BgA8E4LNpGDd+IaTVpNlhwh9D2GLO7fWXXB2yq1NtFsgbbPX9zXxqIZUFrfuwoYKaUS6wftYx8I1CQhVYAU+TznPLzvSrxrCL4aLk0EkvG1z3NhkRydx2mateFqceCcw9WVuLuvrq7w9u1bxPsFACEtEdPxKO5Pr8lJhI+NEsmJHFRfbb61MdrVlWgAIAtTFo2oq3hIIZ3K/FnjiWRDweSau8t98tlz1Y2ItYC5Gu+PXc+V1WovBldDYK/ZfajtC3sPl/s/dX1+dG+7pxkRIqvcVNZ4bVOde9DNFUxvxR6FhKwhApYUkY8Z87IAAA7HEx4f9wBVNi7YRgV1c2fx9RYaQYSzfpH3eZV57NF1ssYAYYEfTg9gSOXjEDp0fYdeVTS60KFU4KU2ltkYXQ+CFZnlszXGXuoVmPSmgwPnGtpBNpB6yQZI7CGpfGcBybJpUNaxkdksw6jTiJkRU8TpdMTj4w6nwx4gwrha4fvvX0jM+uX69HXBqQtO4afHqavrm2aKEZZ5wYf3dxec+gZxKqZaQBiQmi0XnPq6OPXZw0bfBRD5GguWGYkr61Pdm+q6JmNUUvmvnXztlFkNVdXhllNhUpdsNdw2AUX7N0jWe4q1A+dZ4y3XyCnh8XEHRw4BhBgSgibGJZ1oKSVEbVvWtnVdj+12i+uba4zjSk6CpeCLTuTSFnV5FUUQnfy+DogxNeUZzKCHDn2fwKVP5LOnk1SvNVCwyZ6b9wiQ0JkrOqWEBJHCM4bNFsGyLBrrx3CeEIIUiOKUJNYviSseBC2IJC5Z7x1W4wqb9QbDOApIeAGJzNKPwuItyhj1mKaoC8IYLWEUGed95xyB4MAEhBBwc3uDcTWivxZN9oUjYhQ2DmTTksyW6GKT1+BEgYSM7iDbUlgCHqHWTqopZ7LQzqsMtwCZbcHBTvnC2jmvOvsaJyvzWRhIA9Ri/AA1eHX+1m//katFIXuJ7b/cuGpbsGi+j1kZp4yiB9GstfZ75OXctI3O7LYYaFUx0U7Masxtb1FyDolEH5y5GPIffniF5e8i1usNhmFECAFdJxJ6wQf0fUDoOokjVVWPYewxDFVmz3uHcRyx2WywWq3hu1C6x566hruIqsx0mrB73El/kbCBwzCi63sJD9EiZUQqr0msQF3ZM1mzFrfPaszl+Zxz8J2CSJJk3szC7sga8WXsiYx9lPcXtkiV1zJn0Vcn6ddpOkli8H6H6XQszLrzBKkp0DDFl+uj64JTF5z6yXEKFiIj12q9xvb6FonpglPtS98QTnFtLN6+fYf/+uH+glNfEac+e9hgdpAiI+0ptp547VTFDDCnYrzP3dUoJ72nUoR2vxi1U9RFXEHBjLkuttRW/RTAmKZJNIJXK2RmnI5HefAYMDsHR0CMtYy7MQree4yrVdHONkNXDSmVZK2ck54CGSnVE2ZKCc5VN2J7mYEgEuVwcg7sA6IP8C4heScVZiGbb6l8mpAoqVGVE69TJkH6WqX4GHVQCzvWGCNlqJyvp/aYEpIl9zEXSbO2nwlA6AKGUZLrQDZpRYfcmL+qghH1tJsRuoDeOzAywOcJdNU9jMLaAUA/9Hj+3XdSYZMSMjESGA4WL1oNFSm7aMyJuMvFmlC5t1A5ZbYSNTGpsiBQntnJvNWYyLPKrmcDSrXtIGHnrGgTQeNxubBBsJcAMFPzuhryT02WZi3Y9xvOccNOVQP+8b/ZmJ+m2Qylt8h68BPfBYZW3SpfyGbYiaV/Epc+PAOszKV6qF0P9484/m4S5Y1hRNcJe2OgP/TiWl6tVhiGEX2vbE/fYxg6DINofTM8QkggmtElhu9cUfcQFipo4hqAtRbhShHLIhuNeVlwPIlhZDXCfT9gGEcMY6/r3VcWTXuIzR2v69smk9P1n3V+OS9FzkrybMPateNg64ysqxUcY5SwjMN+X4qRnYXiQOLEMS2YC2perk9dF5y64BTwE+MU8lly8Gq9wouXL9ENwwWnvkWc8uc4tXvc43f7/QWnbBi+Ak599rAxzafyYRtzU/MoLtAmttIehpVdEAOuutUsRsi5fGY4LE5WFpG4rIL3IJDGcWYACZbMVh6Szzu76zpsr67AzJimCZgmEMSIPo0BHUeN9dxupJiLurNaN5qdRoWxJRCZ8eLSD3y27iuDdjaoWswnZ5FXc15YGM+d3ocUPDyOxyOWZSmsmbjDWFykBHkeZURsIRFcYWCSJpeFEGTREZWKrHFZEFMsLIBzvri3mTNijAghYFki9vs9Yoxl7E7LpIClLj8AKckze2cMm6vsIbkqA6jzQdzk8YlBs0JOdf6klFTVQZZCDTJAYxBJ/191xMUVbQbcqA0UMGGN9zVWS1jKGsdt95cxtNhGKXZlVVzruGoblEkxVtTWSWWVbN1IXzA0+fPJxeUP1JuYIcXTX5YZJ01s3/bR2/nsHlzmjGwq7H0fQxZkA8Nc9MjNhU0atimsizCAMdVQhyUmpNMEzic80L7MBat6673XWOsNVquVGPFOjHff96Ig40UBJ4SAvuvQ9Q3rFAKGYcR6s8Y4jhjHTu4xyH+HcaVDIxvDeZ6xzBFxiUgxYvfwgMcH2cT15tIeBoQuCCvbhIrkbKo/7RwhdH3XrENNVmUz2F43HFznh65by0va7w847HeYpqnOvwbAqWEFhX11n54ClwvABacuOPWFcKrpN0cO4zBgHFd63wtOfVs41XqR5GB/uuDUV8Wpz+ds6M2MIWjjU4tiByqDIsa7PrC5msy9Le+jIhlmrIp1jpyszt3Y4IwYn7r76vvN6BoLdH1zg/sPHxDnBQuhSN85B4Sux2azwXqzRdf18D5gGHpd2KTxnBLfJwWUssbjWZyvsWdUDKe0QWIsU+JympV+M3URjfvT9y0xgVISt5nz8D4XZud0kgQ0e86UGURZFyzpqdlpG7waVtT+zBmpgA0jUy7G6mxyISNDciVK7K53yJlxOs1IKZfnLjrTqEonOYmRrAZMGDZy1QUMM6wWL0jubOyICIk7BRtx3+WUkH2Nm6ZizLgaC2Kz0WWyN5MWxhE1U1jvX135rLKNZqDsNpXRJC1AKOObE4rzV+Z6Ox+ldkhrn5kLZwAiY5TauV0X6tOrPnNzTwUYA2FjztrFz2azqd6nACmAkkT35HPn36trOaseP9szKGeXAZYczzMG1hg32VSZu99qAxgAOATvcDodsd8/qkRnKIoZppohbuuuGHIz3s5ZmEcn8a5B3NAhdFitVri+3mK73WKzUVbIEbpuQAh9YWssTEVcxBnTPON4PImhJUk27vsBfd8habGwzFkq9/oAcrXrrIMcCBR8YYfkd4LgmRnzvOB0PGK/3+N0OiGlCGKWarfKKINEfQis9lS/wjkHZAZfoqh+/Lrg1AWnvgBOiYyv3tFLwcTVarzgVP3EN4NTiavnyvrjglP4qjj12cOGFT1yRIBrpOOAwmg0Z0wANoDVXVfd2VQG1DrS5lDX9dhs1mDOOE1TKS3P3C5GLqyOAcqnXH0hBFxfX+Px4REpJ5CyBF3f6wBfIYQORE4niS/3iSlhmiaNTYXGSxJ8kJFLyuCKMVLD7yxpp7IPBijWRuYaV+h8EGWP6FTRQtR8ahyg/MzzrOoIeio1DW4iOJaKlYnFeFisZDaJNLN2ajjA1c0OKLjkXDSeLf5XXLa5uPLldxoT7IxdkqH2ISgDI6dk54LOD1FVIRZmzGiTosyBOi/EJS6G0YeAcRjxQLuiuGJGv0yv+p9P/OP8Op8bDJg0Zkmky42pJ6AtYAQAVkBHjTg5J4Vrzi6Z5594uS52s6psawIQWb7zhyDTuj5jipT5IrK/FeN9ZjiBYlkM+uwmwjiae1QBrgGu8s7SXwpKbBsvZYe0xwBG4MqUFKOv3x9ThsvnbGpmdXEzY1Fj7pxD8CeNiQ1nBrvve42hrbJ9IXQgB4zjCuvNCnkY0HEnbGRmLDHhcXeAD++00JgXFnmzxrPba2y2KwWG2n+V9QE4y9o/HQ8SipEljjd4D/K+sJP2zAa0RJUptUGSPICM6TRhf9jjuBf3MxtrrFmL9NE8bWymTUtl6cDn77tc9brg1AWnvgROOV8PG0SE1WrEM5UZvuDUN4ZT/nwXnDI3YXZyXXDqy+LU5z0b+iX2eTPIT1kbM6oA0PeEGGMxujY/vC83KfcZxwFXV9e4utqi73sx4qcT9vs99rudxLXaxhQ4U1xo2fG2XTFGOO+x2qxxOp3AAIZxjfVmjc16ozKBVX2gfG6JmOapVC4NWvDEwnzsFFy/W3u5aY8Ybqixj2rkG3dXkjhRSwxyOpAS18sAZoQuYOQRzjltTzLlRuF4WBgjVtYErKoTaGIjbYwYACxBSRZX0qJRAKTyq8YAB29uboZUa5J4THHxi4HPmZBihu8kiQoEZcMCrHCNgY65kW2hiCEwB7JJAtZ5NvQ9rq+2ePPmrcwfzwhaCdjeaq3j8kedl61zuphmMoNoBsv6hWG5I+IydppshvL79mpZHgHs/OT5rK9JjWwzFmftFPAwtqv0FQBTH6lrus4rZk3ENGkN29yUDVLbRusr7TFjeup0eWLAa/+yWMTilrVfMDWxybYWdd5BNxdl6jWG0ZV71/ZC51ROCXOKmOZZ2Jhg+viivnJuxAd0vRRHOp0mzLPULxhXQOgA8gTvgyjzZCByRowZp9OC/X7C3YdHjKsBq/WIceixHnusVnJfB2CJC6bTCdM0IaYkhpssgVZXRMq6OZIxr7UHoH8HUsyY5wnHwxGHw0HYoSghDmKMm43nU4lB7R95b9m66twq6ZSX60euC05dcOqnx6nza70a8as/+uUFp75BnGo9meW64NRXxanPHjZStsIhdnpvGJCzH3m/LWJrWM4MkwTILOoWIXTYbjbYXG1xtd2W5DN5P7SMeo/NZosPH+5wd3eHZRFViaDNzcwlJvf8++rR3XuP1WoF7wNW6xVWw6AygeefSSlinhepAnnGgpFKzqEUSbHnrAmCLFO1ART5qQu9TRQEQRmoAJ8ikmrT5ZSL+ghpP3of0PcAYcYyz/LdTSyqyR22NqeN463u06yqHhExLUgxyXNqBchW5QCZwWQSkV7nQAYhwRNAZH0nwNR1HZzJrKGedkEQt/giC7KcpgHpW1YZOc/wJGAwjgN+9Ue/wO9+/y/Y7/cCGpRgSZDlMkukfzeDyZbMVygVFJApz9e0kU3r24JJCXCw9+azLyruauaijS79bnGOMHtZn7F8npvvN7Np4GLzB8rM1OesTKveG/pcqGuKIWu8fvfTpd4AiN2KXHmXtYMZlaHlxoDXbgRgTCTLnEgZjg0sav8+XQsWg0wAoBsa77x+bxade5aQkBgjrGvKPCMplLQaR4yrEeMwYjpNOB4mrNZrjKsVQi8bka6fa2y7l2quoZOY3eNpwfE0A6whEcOAzXrE0AeAI3Je9HsZKTmVHnToulBj4HUcRLWjYZoXAYHDfo/94YB5WcDKtAs7dM6qkQUUPx0v0roONm/KnEmf3PxcLrkuOHXBqZ8ep4D2H0QOq9Uav/rlLy849Q3iVCCcHagIF5z62jj12cOGJJxAb5SBxsUoE68NoZBGnSlBqJHy3mHsR6w2K6w3W6xWa41BpSI7KGNcFUDsxNh1Hd6+eYNlmpB1tmc1jjlJDKkPYkgKGwBRDpHS6iO2myv0fQULORgLq7MsUiVVku58E+MrLuxqiEnd9VwWubmkbWAEBGpegi3m1m0tsbEqVwcUpQIxRho/6rkOTCcDasmJma28fQY53yx+uV9OqVSatT6SCpHC4Pm+qxrO6kONMSKXBWb6yyQxrVpsymiClAVwQs7qYqyTuYQdKLvBQHGZA9QwSijMQ9Q55oPH999/j+12g8PhqN/VuqmpGDsbw/OJTQ1DZHOUPvl3Ua8oH4O5Kss4FINlIKlGnxvXf/1WbUf7XfKbunbkO0sw71lbpG2O7P3nb2lIQGUVzw2mvEn/IMBicOnsDwMa+rgvuPwhT1++r7rE23YwA5wYkeOZQS634yyvOZJNQXkeTaptVDFEBSVXFkuBTdY3gzkCzJiXiOPhCKfKOH3fox9GrMYVtlfX2F5LYa9hHLD0CX0vrKZ3AQzRO7eiZMxiqE+nGYfDCZwTCISh9xjHgNA5eJeR0hEAYbNewfmg66y6opmBeVpwPEoSncW4svad996MZO0eRo1nLzH8zebH+orP4Bd4Em98uc6vC05dcOqnxym0ixA5S+gcES449Q3iVPYBLe/OUI/mBae+Gk599rBxc3MjCzFZcaGqZkBEReO7sJpqkKRQT0bKCd57jUHdKBskxvRTUoV2X7u6rsPzZ88x9D3evnuL/e6xuGehp1xR3ySwc2WxCdPFWG+ucPPsBl3XIy1SGKkuMItrBcZR3MESf5rgvciNVYNt7mjRA+fsinGqLvKsMZFl2urzAJaUx5mFOVNDV95HoswBIqTyOTGq7AB2BDiHZZL2ERjJXP+mmAJxQQuIxsZtyDWxB1akpitsm8XEOufgfIfQDcICEYF8UF1mLqd4SUwSd7QBi/cWFywnaWM72qRIu1LKmKcZh6OEINyf7hF/JRro33/3HD/7/nvcf3jEEhfpL+Ri/FsDI4sRZyd5u566pa0NNrcIDs5zqQZ8xtaQLSJN9NOXM58zcWb8WiCxNrVXO8xsH2SUedO8pJ+3G3D5HBpWtsyr9kuUJbPxrgYR5VDXPF7Th1ogis3lzOftAmCa7ZVxk7ZllSr08GUTAaiMZZBMHIslJhYGJjSa8dDvYpL5JUDqNB2UyxqH9amO+bwsmOcF/Lgr9xzGEdurKzx79hzXt8+wvbrCuFrBh4RpWTColKGtlQ4dvLfRkM3GaUmYkyXYeXRdAtjpRtArgyqx/sfjCcfDAYfDEXGZC8Bb/1oftsWRGGj2wFSYO+bcbI1RxoqaIl6M3Iz/5Xp6XXDqglM/PU5xqSMAAI8PD/jNP/wDjjFecOobxCk8eV7n6YJTXxmnPnvY+LM//7MSR5myuZLq3aQiqa9FQHI18jmnYhQtzs0qIhpLVLS0mwXfvpazMA/jao0XL16g6wJ2j4+ISyosROaMxLkYHnMdX11d4dnzZxiGEfM8YZ6mZiLKd3jvMQyDxJ2qvJckoJEamrYQlE0mgD3gUkZCZbtKXGPpHy7Gxn6fOCHGpZxSLYlQjK/I/TnnQK5HzqnE5RblkGxuSS5JeY5ZqjgnUVUQ8Mzl5CoLOsMskiTJeUl+IoLvOjmJ+1DckrUAU3XROY1PtB/p66qbLqd/Lv1lyUcg2QRM06QFYUSveVkmcM6IPsLY8Jc/e4E//7M/xbv3d3j1+k2Z1JlzcR0XQ1z6VowWq7E7J5HUeVzGvT2HN4tOVxqfGVjTjlemSm+f+dNxiWZAocyV3d0SDumjRcjFaBu7SFTvAeNrigXm2ohPXQ29w6j9YEbF/m4LtDCY5e9WCMqeoX7VGZuhSZ5OWRFJrqzg5dUtnEqoiPSXfMzmCiq4NDAoevQGQm1c8JO2E2CUX84Jp+Me03TA/Yf36IcV1psrXN/c4Ob6Ftc3N+DtlU5/GdeBgV7nPXmrWeALMMeckU4Jy7LDaV5wfR2xXg2Y5wmnwxHzNJWNYmkf0dnI2UbCAJUgm7HySKzMmAKaUwNT2PCcNEzEpC8/PeyX64JTF5z6Mjj14tUr/EJ7yaq4X3Dq28WpFk+cu+DU18apzx42dvtjLT+uE805J4uGAabcDJaxJixZ+12H9bqHuXK5zjA474r71FzD5oJsL1GcSBoLG/D8+QusVxvc3d3heDwCTCWZhVmSykDA9uoK19c36HyvusWpuad0tRRv6eCc19+T6piLBGJMUdeMMDmhmawyINVYnSVinVEGWlxFgZD1JCw8iMRphi6AWaqtLstUJzl5eE/oND42MuC7HiBCilphds5ShMkkA9Xoeld1z5q1DZDKEGpykw8e/TCg0yTAGJO4z7RvjFUKwZeExRbYTMe+3B4VhJdlwel4wGF/wPF0wjRPmJdFxt85ePJA8MheJjoRYb0a8Bd/8Wu8efcOj487HE+aeAnSvqtSa/ZUZfEwF0k2a4zM04+N3qfYSdk41Ccx0CZySFmNVSSQuX/JGB77stbgooAt6b1/9MBvv1BWpBI9bdiHtpcr6WAbJxh4tN9dLLgZb2qbBVi4RGmbtNSRfYaroX3Sn3bbUNaPFQUTkDUd+/q9DgyJI2dlmWSOKBMFA8V27TDACdB7WZEkewjOucrxEUoVXXDGPB2R4oLj4RF3795hc3WFq5sb3NzeYrO9Qt/3SDliiT36rkPoOpDz6LxsVDILy02cEecT7u52uLu7F+lD7xC81CAgNGodnxhfY8XgNdyDCJS5sNwEqu3WycTMWhAuFeOOZr5drk9fF5y64NRPj1OMG01QlyZRUSe64NS3h1NtGB1wwakyrF8Rpz572Hj1Lz/oDSwRxRdDV0+7dVFYMlbXhVK6PGcqJ0GbOG3RIXlgFAaguntl0XofIOXYJZ5tvd5gvb3CmzdvsHvcCdOiDw5y2Ky32G6v4LsOy7KoC7XqpdspzCT0zEVeEs2iFmhiAMaWBA8iSZDLWfSKQdywXihtzpnFUDp1xWqRImSR4ZMB9QBlwHv4MEgfOwfyUPdyFlaMxWXnnUd2sgjIB3h13S0xIcelxBCb0RLXskyMbIlPTrWefdDCMAK0oQsgMBIyXHDFwEvhKqukK6yNhz8bb4lrFPZqWRZJiDodROHgcGzc0MLoBF+nm5mnsjAV5r/77jv85b/9C3z48IC//4f/KcDcsArE9ZPFuJW/1UsMCBUj+uSXxe7ZnQQMKndR7kKELgRkdftzrElv8ief3fgpy8Goc7u83ryH8WMLVNcWUAyqGIx67/Ye5W/GPuEcpGCAxlyItdbVTk8axs2zlVhhB12TrlQxtsTF+lF5rbH9BWiNQWxjP5nN9c6NCgajqFuQJM5V1ERlrVxl8+y5vRPFFgdCThH73SOOxz3evXuL1WaD6+trXF/fYLu9wjiu0SdhO50LCF7nDHmxUyzFoFIC4mFR++bRdw5D5xBCU4H4iSGvfU8Ng1crAVQKsYbKWKiIvL/O7adx0pfr/Lrg1AWnvgROWdJ2+R2oVGa+4NT5vf7Qcao9BJSWXXDqq+LUZw8brV55HRG9OQEWJxpCwHotlRJNg9iYFDnxaSXXnLQSayr65UGLnqTEIgHIwjiRMhfeOzifhMEhwIWAdfB46V/Ce4e79+8xxwWOSCot9uJujsuCrDGnkrDTg8ipy9cWlsh2QpkBARcz+KSGtSbTSYfLaY7YEu+aJZRZhomBGBkpx5K8WNUquCbQZYeUxNCllKTYkC4SAiHqZMg5wLmISIRlZkRO2uZGNxkosb05M0iL9xA8yLvignbBiy67r3HMTGL4g1bE7Eq1Wk1401N9dd8LS3U6nbDMEx4fH0UC8jSVhSp95+EKa1MNpPVZZkai6koPfYfRe/ybf/vnOE2i/fzDD2+F0VNmA08M05kRtffYb82Q0CeYoyeG88eYpRrjXBMvRRryfBE2TdAm1vjVNk6yOLcVdOvjcAMOHzeT7eZte5lL35nE3afZKXrymSf3wbnBLyRUMa7iXraYZzFcaohJYdjVQ6OJ+7TrowWMrLKK5QUCmAlKpYjNRnXzy8br6SOZO7gyKsXND/1viZlnpGXB4eEBy/GE3U/lWjYAACAASURBVMMj1psNNpsrXF9f4+rqGmDGarVC13cFWFwI6DEipyh2Ky04nCbsjxlD57EeOgxjJ2xyaVaNgc0GVgzAZBk1BIGhrDtnsIaWVHCTHyJZQ8TniveX6/y64NQFp74ETrWX2S8fPED+glNP2vCHj1PnBy7ZpF9w6mvi1Oelb1MCERC8xI0BEPeKAwgiy7XdbuFDwDwvWJaIvu8BcIl3tROV6UOrtoWyQCynNCbMyyLxnU7Kylt8mnOE4Dx89EhJ2B9LKru9vYF3hA8f7pFixDCKzrHTpJbW9SnPktUgN0uDoAxRKkaRnBMACQI+y5LqYiOnE4rhKBc7wswg59C5mlyWcyrG0elKYHPnc11YNmmIJB7PlDz6rgd3jJgSsKCsLgYjxgxAKph6Z/KKpkqSpDorgNB5NcqhjIELQWMBfVkA5oIWt7QonJCCm8isCfDO84K4zDgeDpp7sSBxreJJBn5qVMQw1YXMygZYAqXX+GjnHPq+AzNhu9niL//y32B/nPAf/+qv8eHuHlYRV/a3bDRBGcaz9a3fq5OgjnYxpq3B+ti4t4ZBjIGycNCNNdWqwsysMnC1IfWeTxKmSN2R+j1V39vmot2osijtZ8mmQANmThmJp2ECBVzK5zRMRIGJ2/dSXQ91PpohUVbGqa422UYkgXTOm6EFVIkkNQbJcJY17tPVvq9joN+tv3IkkpclHME2Pe18ytJuJmGOHFtBpwykWBhI8gHEDl77nsGI84zHuGD/+Ij7u3dYr7e4urnFs++e49ntM6zXKx0KRmKJjY1xQVwWYQ05Y1kI07SgPwWsVz3GXgorQT/H2ZRKcunjrA9bww50TTThAu2Yk/58ji26XBecuuDUl8GpswMBifeIhk7fc8GpbwmnMvxZO1kPYxec+no49dnDBjtZZFksS4n9GlTOyxFh9/iIu7sP2O/36IcRP3/5ErfPruG8L4OZllQaz6oo0YUgrpicsd/tcTwe0fc9VsNK3aISf2gn1BDkged5Rk6SuNb1PV58/zNstld4eHhAzoy+7zAMPXJmxCbm0iYptEOFLcolkaromqsb16uBY9TTaHWpyW3qiVDPrgSIW1c+k7JMrKDAADCW2Bi0YrzF1SWDTgBiic8l5xDIlmVZQ/I+ksVpiXJQ9zsSxB3uxYB3IZQYZjitxNpUwnRO+rLreil1T+Z2lJjW4/4gFSaPR8RlOXtm5xyIVc6Qm6S0hrkw9xsgMZQuuNKn5G08GHHRGEAIy/j89hl+/vLnOByOOJ1OOqHtvsJkyRhVI9YaaDHIaN7dGBsbxDNK5snnzRw6LiEA3HzWyABQdU+27tZ28RkjY+uXmGR9ASWM1nAp5xYIDECeNPTHFjZR0TQvhiKn2ramFypIqfvTDHzbNyzsRgE225gVY5rP+iTnJEbX4sMNZ3WuUoZuklxZc75d6+Zybhgsu5cVLGPOYJL+zczgKB4yoiyuaeeAlBFzAnmRFvTJFwWc6DSONzBOE2NaZjzsH/Hh/g6nn/8Cf/Knf4LVagWTEuXMJabekuLK5g2yaTuFBWMf0A8WXsYVuHT8zYa0rF6ZN08GlCGMM2tIzKWC+I9fF5y64NSXwCnrW0DUi5x3UiAuXnDqW8MpzucHqpRTSW6+4NTXwal/NYyKdbC6QQwsAEzThB/UcE/TSQaHHGJc8E+/nbA/fIcXL15gtVrBZltKqWhfG5PknMOSZIL1w4BgiVwsJ1KOuVRPJSJ0XQCYEfWewuqY6oTD8XjEMI7wXZAkF5JkvLLoAFAp3lQNcFY1JG/ucGXb7cTHJHGANnGdWVJYlr4xCVbdtErp5ZwRk27KFDjM3c0sQGMxp+aWj4stvrrkCATvTY6R0Su6cIoy8Y0Jo+oxsL4JIcgyJGG1gg8YhlH63Aw81djLaZLCL6fjEcfjQYo1yUxUtsSXpLycRUHEFDHKJGRJl3P6bFkLbwmDZxVsJezAAD7ljGVOeNwd8MMPr/HPv/9nOAK2mw1ySlhiFOOnfQ/8+MS2Aa/n8CdWrw4hxEzJizY364LLylDmMg4MFObPDCHbBHvahAbgS5IgUBgoa0tlQ548AKj8WZkcGcvz56L6kaZvGAATFVbCQKF+b7uhaJ5R3amkOwcHlAqq9bnq5qaCpQCzbSgk2Y4RvEMIPfq+R9d36LseXejgXCgxtlbhtN5fqwmnJLHIWdlQq5GgCbK1ZkJEAokiDDkgyXzz3sNnD4pi4IMPWBZJuvM+yKYtLkBKuB86zC+/xzAOSDmphOGEeZkBVeOJSTeHnQBQJsY0R8xLxLAEjEOHvvMNSJ5PO1lon0LhOt72h4Hqxyoxl8uuC05dcOqL4FS7kUwZcY7gACS+4NS3hlNyiG26lS449bVx6rOHjb4f0HcdwMDpNOHu/R1Ox2M5/YvR8gB86fS4zHj39i2m04SfvXyJm9sbBOcBiqX4TdKCPqRqFOM4NidqjflmMQ7LMgNqIMVosBoTAnlxTcVFJuE4DBjGobijc05glQpLrctKe8Tiz4IPoODhu1AMTGaJa5XHyrAiNA5i0EVNTyYM6/sYGn8HkwXTQWEgRZHOc7rZDsGpC7uqpFTJPg83B3WFqQvUOVHhAMN3GXAOEeI6A8SwOvKgDLhAJSnKe4++FybIeUmelNOyJN2BRWFkmiYc9nscD0ecTsczts05B2qSrBhAYjaRPzDZmJXZVxKzzI0bI5XY6Jy0/zOQzRgxcDotePPuHq9evca7169x/+EOy3zC0AWshkHaiiTGTvv7x6kTFMaC9H/nbnF161qsJoASr4hzrXJyBMek4JU1ljmVdusNUV/gAuBk7lOLK1a2qLSvtrasofaeZmQz18rHbT9b+IcxFMKGUjnAtcydzbMCIrZJyxE5x6YlFWjE6NFZFWTpB1faknPC4mW+9H2HftULeEj3I4SAvu+UleyaEJEKbqWCaTnEoNw/adViVjd1Sgkp5sIAWYJtUkPPKUnc/Zwlf90AUysbO+cQuk7ARCtBh07CWphzUd8w5ZC4LFjmpTCUoi3OmowpISK24T1NC2JMGMcOQ9/Be2p6vY6p9SWAIgVaQFY3YxbA8K9sVf6Xvy44dcEpufdPi1OteWbt65QTpilecOobw6keVbYaALoQsFqNF5z6ijj12cPGdDrh8f5Bqg7GpMNb4y7PLxkIm7wP9/eY5wWn0wnPv/tOXKTDCoubQVQrsnotntQW1hEjrydS8jIgOWlJeYmTs8nAnDHPE1KKGIZRFiubK4tLZ+cscaspKmPlqExSS/BTG6UGQk/StgTs1KYKA3JfU9EwGUBC5qcnaJXxc64wRaYoYpVgTXXEdMG9DxjHmry4LAuYAeeqrB5D1ETI6biwLMph6DCOQ/EcSC5Ej6FhhzJnpCXicNjjcDjgsN9jnmdlvswWtMlENuk0kckYBiKQJhua90zaCZhEpKimyEkfpf/MrFTzEmPCv/xwh9/97rd4++YV9rtHnI4nHE+iF51T1OQvkkOKsXn/P3tvsitZkqTpfap6Jhvu5GNMmVmVQ2VVk82hAaLJFTcE+ABcckPwObjrh+CW3JJLcsMHINAE2CS6G02yu4bOyiEGj3C/k5mdSQcuRFTPuR5RXiCQEahk2UG4+417r5mdoyoqv+ovIr/ovazDmU8uGZqyAMu30+LA178rD52e/J4iPxiV7XVLg5+FmUnLezz5whQ2TuzLqoPNCzvl/7L/F+B573nsKof0W9CVvb5Zp1CIA8/hUfmxKa+H3LAshz/JfhWL5KNaZ6jUbq2mMbgcWjaOELWvwWocrVN1GOeoq2phZZSVtM4Wp71mNq0yiTk/uwC1FrrKfLFijKQpWvBeHbgvBb0hRqIPzF6ce/5+8mqrJMxoca6ibRratqPtNmzbjt1uh3OOaRzp+55pGvFecsCDn4EkufbWKmuY5BkLkMmGxYfANAdljyqcXaI/ZUoUdEky3tG4Ar5iD1GZa/F75+u7rzNOnXFKnuH3i1PrTXb+vH7wfPXVGaf+0HBK5mF1n2ec+sFx6oOHjbfffIOERYXFyNOf2Zt1KsWTPC9d6Kfjgc/nkdPpyKtXr+k6kc8reYWrxbcOd63fR7wqoDl0zjjqpqZtWmKKTJMwQWIIjhQSQU+VMUaZyGkW/2FFbsw4dcIq8SGhU3k+ua9VPuD6yovAULS7Q8iDDFYdQwaIPE5yaqwwpl69TZlSlrC25NWVkJSCjHOVnCpjDqNX8jmEcpKX1CR5H3HaTXmGrDve9z1933M6niSnOIXMH7CEB40sFu3YKY5nOd0KO7Y0vClMSHY+NjtmnU+XwasuDWbyazFZvhCm2fPm6695fLhnGgb6U8/hcGDoB2bvOZ5OhJCo26yjLp2C8/Xt+VqFBTNrksc9SYF6VqpYfGC2RQSYoSymlJvjGKONv1QiD57YfjRZ0x6c5tBap122M9BlG1DfGYv3Xmzs/eda4IInXryswXyvUOoonozG6m2LD4kZyFJR1sgAUlnpwrt0Uo20daMbBMM4eqYsv7l6T1dVbDZbka0sUqS2aJAbKyFdV9fUVS1sZlVLPmzxC/n+F/Yuat56Qgr7vPcE74k+koiaBuKZveSsSt6qZ548s5+Z57kwSz54fAjMfpL0lZi4ubnmZz//KT/7xZ/g6loUbLzk3eeRFdYqEHS9BCub0TpEkeusbNmMCnBE5tnTtg2brqauHDnNoIxZlhlazY1shLWotta8dbvkj5+vp9cZp967zjj1e8GpJwdVY5g9vH13OuPUysbef66/qzhl1gdyxNbOOPXD4tQHDxtyCsqNcTKjI4NbTp9mGWjvl3BqPgzFMfD2m685HQ88f/GC7Xb7ZMKzpvjaaeVFMU1TYVQyy4LJEoVyevSzhwQuyxaqsyNC9JF5GJnniappJU/WVXrq9HoPVbkPeY6nJ2BhBpbDXS4+zJ0pQ4hI1P3bTiQ72DxOC3OQ5cTWz5z/X5br0pExltCcQT7fmQpTSfAqRNE8Xo/Z6XSiaxuSMRyPR4ZTzzAO2hU2l1HJgsqRhgIESdmPuOJY0uKwSmDRZvbLlmfLhVSSGy1gn8cghCiBfM2NjCERIkxBFuw4jnz55Rcc7m+ZJ2kaFUJgGHr6YSCmRF1VNE6KDJN1jMwC1qzDyQs78940Qj6gp2UhqUmRNfoziyNEYJIcUi3AMqtnl+ec1S5yiFYYlHkGiAt7kn+WKPcoH6xzbSBkZ/ot5msBI7nllKknSmZtfjv9cc59zXb37SuzEmntPzAWnM6l5L3Kc3qVUKwrx65rqazjZCamWcamssv6aRvJd81Sl+XeURbKCpvkjNPNoeSkSsqGFOBJunrUsZE3yfnIIUgDNesCofJFV97PM5ZEpayTsTNhhlSpXGZV0bQNcw5pTzOzF/Wem2c3/OKXv+Tnv/wzUjKcDpLjP40T8yzdpY3aulemCl27dR1L2kA0OidWwTlIo7dp9oxTTdtUdI2jqavVRq7wsottkgBh0rb7LU3bFpWl8/Xt64xTcMYpfu84tWbpfYjc3R/48qsvzzj1h4hTIbB+k6ZuaFw84xQ/HE598LCxdtDiwOx7xi8/9xoi+q5Te3796XTCf/UVV1dXXF1d0TTNyrEtRpCd6TRNzNPM4XBgu92yv9hrYx+Z2BgjfpbTYFYOSWlxeN4HxnFknCacFW3zdT5fVrdwrtawsDADa4B6f6FjVMlCw76iokF5RiG33tM01/eIeo/ZkefwnCxcnozDwmKklZNwJCeLz1UJgsNYh0up5MwaY5nniYf7O46HB0DCZDHlYsEFMJaHQtgua4vWeFid0LOxGWQRGlVgERCKZJBfHLnFYEVmMH+MNTgMxjiSzQ7EELzn4TQiiiyeh/s7Hu7vmKeJ4APDOEg3V0TysKor6ZBZi0QuBoYRUghPxk5lsDMXtsxFPq2v0SnbqUGlKA0hhVXBl9cCSAX4qAFOBZmEYclRTcr0x5Ir+pSFooBkZtwy2yL2p0DOol8ir107YlOIL8jpIEvhoMgYKiCsvpufNCr7s7brPLfW2OK8c8MzgLqq6RrHbtOxqUU6zyKsiBkNTV0Ta5knq47MmoQxUsCKNUV9IyU01cVio3QpzahrNEeVJG2FrNVGYSlhvRTfpeSxNiEZArLJCSkx6yYyRinIy03Pwjqnuaz73NSt4fXrj/j5L3/Jpz/6Mc41zNMoOd5RNkRj3xOUPc2hcfF1aKFszvWeiTHIZlPBa8mr94Qg7Pbc1lxeVFxd7dlsWqQfQJ7fREqGFAM+St57VcvB7f2M5vO1XGecOuPU7x2nzNPDxjR53ry75d3tuzNO/QHi1Dz5QkSAnF3POPXD4tSH+2yocSQN965DrsZoAZYWj62dI1ByXTMr4pxjmibevn3LOE48f/6ci4udGv9i7DmsHEJgHHuOxwcOh0eGceDm5oau6yTMSSq/G9Xei75wXIqettudGJaGDSGs7tOU382TvNQQiQPTdDddCfI8HjDZKVtwlcUEWQ1SHJRP/yuHydKLYFlAK+exWvCZ8civtcaQVjJ8MQlIgbBuzorTlKtmtIbT8YRzjqqpC/P8fhpBdnT5a3GAWtwUs/O3VLXmQhqrLJB94ohjghRlYeYHMKhXUg8icyq5fc5VGFvx2HsejwMxiprL1dUl8zQwDgN93zOOo4CWdgJt6rowjMYYmqYCA+OIpAkkKeAjg+h3siXffa3D78EH3ZhkveoMdqnkZcaYVAIx6muNvlpZPVXcWt+BpDHImBT2h/z1yoG/d9vZwcvnJP1vYZ6ewFRaWN38k8SSbpA/d31vxghr5aysC1dZmqqiqSsqZ+mahq6tJUVFbWXTVji3pT5N9LMnK4Tkn8vtLQWza5Y0ASbLAxqwlaNpaqq8odKUh8y0zdMs76FOXro1x5KyMc8T0zzLv+MkdsMEKRGMwUdfmEVMwljHxf6Cn/785/z8T/6My4trhqHn7nBLUGc8z3PxYZJXO4kEac5NB4xNNKkiZe30FfOdkthBVkXJOcfCeCV8hGc4LvYtdeXKOpHpqWlAxsbmdIenvuN8LdcZp8449X3g1PoAMM9S4LvddGec+gPEqaGfcStbyFN+xqkfDqc+eNiQwcssUVoNbDYZihEVtiB/aJKCt/z9EAIYg/ee29u3jOPAy5evePbsujQlkup9OfXN08DxeJDwb4qi+DBPXF1d0qlU4ey9nuITc5BFJ51XJR90s6l1QqYShs55dgm0kC+VfL3M3OgZmpQWveKsaJI7JWdWooSTbMKgYeaQF11CHNtTViA7Azl5RskFXIXI1ydb5xSQQpa6k5N3ZtxAG9dEua/KWay5oqoaxmFgnmcN/VUFYMsclo3hwohk1YQYE1XlqCunYecl13Ydak9JWALrap1rddNBGs2EEHQRCRrKYkxMAfrR41eqLU3TsOk2nKojh3iQubSWykmhZQZiayksTmUtqamYZ2EwknWkzJqosyvgmN1W8Tmaj21NmfeYcm5l7iAcS/6mvIOG6R3EYBSgJK0gh3WfFuct66k44TWLtFijqm6k1e0ZjRQYInGxn7Lyco5v+QEGnZfVOk5J09fyzRTB9GXTkhCWxhlR5ei6hv2mY9c2NLVd1rp+4ZyhbSqcM6T7wL0CZ1TnLCAeSdplOI93lpTMjg6kuNbZmqpuJKddNwzZTq1rCmvtnHtSNJufL4ZQmGsplBuJXmy5P0kO+DzPGAOb7Y6PP/mUT370YzbdjnGaGMeJaRyYxn4p3gtBgUjGNcTcBE0Ox1XZsHqyb1zuaWEAsz3IpjDSD8KEPj72XO637HYbutzhVW291uZm1iwbo/P13dcZp8449X3g1BOO1li6tqOfpjNO/QHi1MZZKlY+QYvOzzj1w+HUh5v6qfMuYVqTQ9VPF2J2iDElbAj5xU9Yivy9fNp+fBT1kNOp5/XrV6JNTmYuJLQ8DCO5GVAicXh8pB96Li72bLc7YtSmQkly5aZZTpZZ0SKHWY2TYjsZC1VZyCoiyn6snVMOw+fbjtEKc0RmojTUFqOuBWVQkjgXeR/9V59qGYLFwQi4ScGQGMaSl5tzYcvQ64nZpIStHI5K8uNiIoRZjUvYq7qu6bqOeZ5Vg7xnmmamNJTPzYZVGhclAWnnHLV+Zl03cj95Do2qNLynKJVB3FlXmLwcLrdWHJCE7J2E9JzFzyM++uJQY0wMw6DAI/mHMSUqDW3mQkln1DmWT5dcxLpRpmHyBBNXzm5l/CuWZNmA6BzFJN1HQcE4O2JT7AMMJmbGDVIFcZYcTWstlQIZxqzWjq6jMoZWACit83GXHNxlE0Fh8JbNBcvaW2im8v38jAkjXVNT0iI8vReT2SFT7BUk512WhRRVOmDT1uy3LW1V4db+Q7/OMomVayFGbiv5gfczfppIleiCmyjjYZw2DLNWx8KSsMLGGYurG9rtVjsIq3So2mPTLpslY7L+vYyp04LTEEMJT5fnchWJrMQDzlZ0XUe32Uoeuw9M48gwDoxjj5+l0E7ynyU1IXivKQeJGCX3tnKOtmmWfgsZOFNmnyWXOoSEMR5phlapMpGstxgi4zjyznsOxxPb3YbrqwsurzYii2qMsJPGiBTiWY3qb7zOOCW3fcap3y9OWbfaHhmR752n6YxTf4A4tXWGZl6iblI/4c849QPi1AcPG3nG1kt27ZCXXzOZIvnWO+SwijUGrOrxqhREjJHb23fEGHj27Bnb7Yaqdkx+YhhHRKJwxW4YQwyBh7sHxmFUJ1MBov9bOSdFKtaqCoAWRjmLKxrMGnJG7QlT2JIcZithb9VLzkaQF51z+WSuJ2C7nIlFyjAUB5wdeGYLUgIfvIxDYRbkvedpIqq82vuXKE1kdoGyMI01WBwmirShUghUqqCw2WzYTRKym6aJaZ4YenGW2fFaLTiz1tB2Hd2mI4TAPHtCTLiqEbCrXJE9jKlQZirTmMOFSTWdRZlIFAoMVRDwd5XDh0RMDlc1tE1TgqjGVUViUhRNJBcyMwSZjSGPQFoWTja/urEwO4xRucEng/iUSVnGFiDifc5FYFGNUcdGkrxNY21pxCW5kKmwSlnxJG981lJ9JQd35XSfOPLV2lm+XEkE6o3m8GdmXFZcTPmc97sOo5sJA9rF12ENJdyaUsAkS+Us27Zlv2lpnBUm0K6LARegyfrerqq43G3YdQ0APszMYRLGLTis9Tgr0oJV02KNqgUZcLVje3HBsxcv2e72ZfOUYgAF4qSglUP3wnLldAvV1DcUYFj7m4SwiFVVsdlsqKtG2KRhxBqYw8w0juK854noZ1IIoujig4aqo8x38EQfyjgmtXObKCxy3jgUr2ko4xVUmaRSBtZaSWkJ1uLnmXGa6E8jp9PI8xfP2O838r7GAJbcc+F8fdd1xqkzTv3+cWpd7CqbzoRP8YxTvP/l332cqqqKKho04CMqT2ecKs/4Q+DUBw8bMcYSLlob3HIyovw/qIHDU+OEFXNCCWFbY3G1BSIPDw/M88Sz58+5uNjjfWAYRrL++PI+2W9Ehn5gmmbqWpyVdHnU0Pi6k6bRoh8yQyKGUvLc9PRvreb2hqXzYzkN6krMTWKchtysyTrmqrduROEiA4AQBOZJ+NNVEHHl9/Jn6AoHNB81OwDEScw+kIgFPKxZGqphkNCejVqcJA5JVBMcTdfiKgG4EDxdt2HQEFnSMFyMHjxUTcO2aWiNYRhHUeeonXSvzKpD1uIymMREcpHgBViss1hXCwvgnITcrBW1BTXsafZ4L7J7Uqgkm9ar6xucMYxDz/F0pO/7xX50/J6Eb8vYZ+ZOCvTatmGePNM040sTmuwcv+vkXTiVoo5hxePpx6gSTVAJvUxYKEtkjTbl0XcLYcldFNtfMYb5lyKLg9VNQKnXXDndpBskKZhb1l9+/lxeF5UZShpeTyn/PDNg8nvOGU1hAF+kM+VdNo048N2mZdO11C7LiC453flZijynkaLWtpHDhjPIuqwSJiYMkeA0XQVD01i2ux3Xz59z/fw5+8tLulaapU3TXDYAxor8HtqJNzu+/LlW+w2kbBPWKYMjG4PKOvFHhXGMjIN0HPbTpBurVIAsenXeOWXFUBRGohF/4YzRXFyxWZkw1RbPPi6lUqgnSieWZBLgCQHmWf2HWdJhcvrIPHuGceB4PPLixTOubq7YbjqSFRnF8/Xd1xmnzjj1feDU03OqdJbe7vbiR8849QeFUxL1WkbScsapHxqnPhzZWMnNPTnhrldhNqAUIejJfpXXmY0ud0WNXvLGMOBD7kbacDwe8bPn8PhITLlBUFoOXmUhaGjNSIZg8BOTUgFZFs4pUGTjzR1Pc9hrnifNmZWwUVU76VyqObu526LJShxpzfpk2cFc+S8ORBQi1BHAqpOpKoNoaM1Y+cwQAskEvUe3Cg/CPE/M3mAS5f2FWRI5xJSCslyyCLM6g3GV5BLrM8Qk3VMrV+sCCKRUaV5wRatFRtLl0hNDpGmbchpv2lbymTNTY9QJpViMUOzXYl0qBplP0ItMYyyAE0KkH2amaSaGhDGV/q6jqTuquqHrtrRtx9CPmXCRuS+gG5+wRQvgJkyC6/2Ojz57zeHY88W7ey1Cf+oAM5dkFusqs5dN3LCwoOs83yc1UOoQc/g8h56Lg14XXxp1SilhrNhMWr5NtiC9Qf3cQjo9uf/8DOu1JZRYHptY5sCaLOeXZQ9D2TLkVIiubth1LZf7LduuEcZIHSC6/vMzWbO8Lo9b7kC6bzumFOj7AesqrBGpQAx0mw0vXr3k05/8MZfPnmHVXkMMyj45nMvygYmkGvMoWJQNYJYf1FB30DHP9+WcdEWtqhofPFM/MM+e6D1hHjkdH0kx4Sq9/4yeKUpIOkRiyooeYls5tJyZqLVPXOxvNXWZYTS6uSNPYiTGJRVG/mhhYBTd9Xn29P3Aw8OBV69fsd9vcdXfEoT++3ydceqMU98DTmWFKXTc2nZD5yNh8mec+gPDKYukDOZr0zRUB844uN9qsQAAIABJREFU9QPi1AcR7Cc/+ZTD4cTp2JeH0aOoGoP865ylrqWdei4Iy5Xp2ZjyyW7BAJUpzPmYCUIMPDw8kFKSQidlvfPzi1NFWYcEqtAxzzMhRpq6pba1hn0rnmqui1Z2VnAwxlDVDZUW/c3eCyNkHZvNjoQoRqSwMDuLIxdmKMuVGYwKe8tqzE5M55L8AIlUVmNVVRgd/jJeeuLHZN1oT4y+dMXMi98lOYnmAiejU2KM5P9Vda2FuqLPLPdoqeucuydhVFe5wrB5L0bUtRuqqiEm0RvPWt2Kkws7k0JZVHK616nS8cnh01y4KBKQgRgS3ieMs1RtQxMXlYyAJWLpNluurq+ZNZQeYpLGTtlZxrxwZEy9LoCUEo0PTEPDrm350atnfPb6Gb97c8uXb+957Gd88E/0yJdLJ0oZmuJONeQvC9ciuJSU8UvFAawD4TnnNb+dvuWTK5X3L+ax/kt+nnJubv7eylHIwyugZSlMCnOVQ7nSSVUkHUsTLsQJGsS5t3VdwtK7rlWWD1XbEBBAn78wRsZgyeFh2aAAbC8u2Fwb4sOj5DLXNdvtllcff8JnP/4pNy9e0Ww28gwhkoKAcDBLXqtz4phR8LfKyKQkjdvqRgr0crfXeZ4kvSQ72Rjpjz0hHAjeM2sIOkWPiZHaOSJZDSYX7QW1U3GiUsQrG5iua+narjDSsGxQ8mYJLSzOc5MKNapg9MTSZB5LcaExeB/wfi7PNQ49h8ORh8dHLi8v2e92nK/vvs44dcap3ztO+UT0y27dOktV1STnzjj1B4hTVg/9qG1vdzuuYjrj1A+IUx88bPzxT/+YsR959+4d33z9jsPxoM5cNoibTUvbdtQq9bZmlYqqhMkayzk3UYw/n6zzgKxD3jnMDUuh38JarArikg6cNuepm4q2aQtTU+Q4WQZfTn9SUBZj5OH+nocHya21zrLpNnSbLXVTU6mkWnbeIqVn1elaqjqvKMrvLA4mP59MYtLp02MkzuVTuLJRlSsyhaT83LnAyRV2gKTjYxxVJWGrpMbubFWALhjpGpvZr3yltBh7nSpVRYhY64nRYV1FVVeQIt7nk66yfSEKKKVEQJybSat8zZVXyk68sDo+lCTklMDaCpeSNpoSG9hu90xDz1A90nUtF5eXyvCJ6kjOtU1RdMKDNq7x3jOMI957ulqk724fDzy/ueCnP/6IH3/yki+/vuXXX7zly3cPPJwkNB+L8sXKAStFmUgQl5/oskRyhtFTPxJ6TBoCJZU+IusrZUZH49olHmBMAfXsvw2QO5AuMJNza1e/m0QmMKc5ZDa1PEcJf8ocGWdx1mBSoq5VJrARZqWtpWtoXTkqZ1SCc0GEpHetSAbJMMX85hISHqIqcjQb9s8vuXj+Qhz45SU3z1/x7MVrtrs9uRtsSFmPXA4q2U6ssaXZU4wS4m3qBleJL8mdfm0mk4yhcjWRQJg90zwT5pkYPNMohXSGpJKKkoph1Z/whJEWLf1pmphn2eS1bSdSh+rLclFuvoIXpodyYKYUO+aVkBJPPkO+p6wekZBD6FaKe9M845UjtNYy+4m7u/tymDtf377OOHXGqd87TpmyrV9eYwybbkPY+jNO8YeFU7NxzGUMDabuuHq+PeMUPxxOffCwUdUb6mrDZrvj+uaGu3e33N7ecjr1tG3HZrPDYEoeXmFyoDi7bGzygJGkxm/s4tyjOi9Xqublyt//dsg7h4WVPjK2nGZTSitQSCvna8p9hSDM1MP9PafTkVyZD4lx6DH3d2UhOOekaKhytG1L13WqIuKomqqEteV+UwG4nA+bQQudUDFMGaeQEqSINU4nMkuULUog0nBGFT/UCRqyMklNcomgDFjJD0w5hJsbEy0shM3GK9WD1HUjBXmTwftZc3c1tB+jeFwjIUhrKnGmMQg5lnjCmMiiWnL5jXgqQv65M5goRhxDgmioK2mAZayl6Rp2l3ua1vLs5pqHuzu+bL7gzddv6E89s/fMPkv9yQl7nr0683wKD4zjRIiR7W5L17UE7/nsoxe8vLni7d0df/3FW765P/B4kmZas+YyZ1YQYzTFMRXHK5esRpOMzmFUJs+UMQctVMvABsv/rzx15u2E6UkFADPwZQ+wZomsXQM5VMaRkiF4BU67yPeVIraUiERsMqJOUYtcbVtLncWmbagrJw7eGEJK+GRknaotJGORrFYjwKOfYcxSRBmKdr2jalo22w1XL15y8+o1m92FKMConwjqTIMyiWmh5Ap45fVurBQKVlWtWvFWmd21LGYC74njiB9H5mlUxmfVaNDIuvLzrPaXNzempMOM4wRA17WlcHXRH1fGGHHUWXaw+CujAG+UP1v9TKZdbKD4R2RppRAgQlA7SfraHIb3wbOK/p+v77jOOHXGqe8Dp9aXs45Nt2HGnHHqDxCngjXENXNvLVV9xqkfEqc+eNh4+80t292O7abj+qZjt7/k6uYZd3e3HA89p37Ae480BVkVxmjIZc0ULeFqWf1FHzgbuZ7izOp1+WdrneLCRJTCNAlrSb6nFLDI6SsgTZisLrTINM4cDg8cj0emaXxaXKcLTJpC5RNgwPuJaRJFjANaxOecGlZFt9mw2W5pm3aRBDS5uDDhkyeGSfL9rCOHt3NRoTA4iYqlqIgUiCaiO/MnjEwKEVMZzCo3LufJZViLSQvBsppGXiN5HlJc1EFsXjChMEm5a6wNVlialBYJRishwpQyG6U2nBwmUfSWSwdSxLHnxWAT1DEweAmp1pU0PLLWstntqNuGHKge+xPXL55z+dvf8uu//jVv3rwp8ohew+9PyJlEybU9DRPb3ZZXL18wal6hI3H56Uc8v7rg8zdvefP2jrcPB+5PI1OQA5DkQYrvzsoqxZWuFixJwqgpGUJYNi7Z/8q8gSEVu9UX6no2C9DpnOXPL+xL/qG+puTgmqTzBGBJUewp54Iu/IT8jjWJqpJGQ3W7FI5NMZGmgIu20FDGivymdQ6bHAan+a62rMV8/0+17GXe2+2GZ69f8/zFK/Y3z6nbFpIhRi+AO3uM1U0GslELyrjYmEjWak6syjk6ycmuq1oYJmtwtsJiiGHGzxNhnoizJwaPiZ7KAFaAZ/YzQTczMQbt5KzdW4NKDdaWtm3ZbLYs7NyysRQ/pAV6K5+Wc8bza/IVjSEo65XtwhhVxlGbLak+urYSqWyQRN5SPjsGWR/nyMbffJ1x6oxT3wdOPek47Sx109DopviMU39YOGWNwQRfwlVN1/HsxaszTv2AOPXBw8b9wy2H44FNt2O/37PpWq6ubthudxyOR27f3nJ/d8c4DgQ/kxuhuOJI10oWFKcFqLKEDFRREckh7CeOS09dZjHq/L7ZqFxuMKLFMTHnOhr5nP505Hg8cDqdmKaxMClrcFhOd3LmNhomlpxddbxoh9owwyQO8/D4WAr0rHVUdUXTNLStnDa9FwbDGMPl5SV13aiT1GfSCc6KCeLULdY1WG8IWqiY844TkZhMef4MQovGdl6+kIwlh+6NEabM60nZ2UrALaZMYoihKMtn1bmvm+zIHAk4WGOwleq4s2hEExeWI2WWK8+fsg5+VkCzliEtp/rdbsvzZ5e8en7NtmuZppE3b77k//6//g0vXr3mi89/y29+/Ru++vIND4cD+JyPTfZ7pBQZxpH7x0fm2XN1eUn7/Ibbuwe+efsWQqTrWvbbDc8ut7y9feTucOLt/YHjMHIaZ2afFTMkZBjT2kZSHt3CABpUFYakY57ZU2WGCpupAEt2wBRGNTuI7LfFBrXwDQmv5kLAoi6jNlBV1ZOvsxSiHOKMqFNUucdJRTKGYCAagcsqqbSksZKLWS+pJSgYW0xxRhl8DHkDRlmbL159wmfNL+i2u8KSRE0lkPQGVdDAEpNXu0llDRtrqKuGrmtpm0607o3UAIcQtWh2xodACh4/Dox9j/ez+J6sr6/NxSr1B9IETF4bg8xlXVdUVS12bFxZl4XSMwuD51xOS1nsOf/7fjpC9k9k8FabEMASvzhrWLuwetn/hahkYSR6yfOW8f2Qp/77fZ1xCs449fvHqbJpBggBN0+0KdI2NTevn/Hq+TU7xamv3nzFv7664uPnL/ji89/x29/8hjdvvuFwPBJ8Trtfsf+AmSbGx0cYJ242W9rLC267lrdv30KE/fUVV23Dq23H27tH7o49tw8HTuNEP3pV/qLglKTBfbeUbowRTz74RBKuMPWFxVbfl32ekvjLe+iBLhpIzrAWJMg9JWSTq9KrGr3KNtDV9YJTdY01rsyBtRZnwClOOVep3clo5YigM9pIr6q0F4pE1MS6RGgg10johGKAxuQIjVw3z1/x2R+fceqHxKkPHjbypuV0OjIMPW3TsNvv2W23XF+3bDdbrq4vebi/5/Hxgf7US5FUjNjSFCSsg3tl8tfh5hQjk/fLydosG5vCQLE42+UEvlooVjzR7EUdJIbA6XTieDgyjgNJQ1s5BJ7Z2LxxSrogcwhYj/0rZ56dWVq2mwlSCiU/NKVEPAQxxLqh22zZbLdcXFyw1UYw0yQKI5LT5zSkK/OMyUAWi+Em64R+wUAeMyNjhjJz77luGRurL8sGWS55BgnZObBRwn2VI/rM0Gk3VqQjrVmdjuVel46RScfFGk1DMOjiV8dTCsNQS5R7jursc5GiqxyvX73kRx+/5M0XX/FP/9d/hnEVP//FH/GLX/4p3e6SZ68+4Y9+/md89cXv+PWv/i2/++1vub97ZJon0gp4jLWchp67xyMYx+X+krqqudzt6PsT4zTy6uUzfvzpR5xOPce+5+t37/jVb7/k69t7jv3EMM3MPjCHhA8JH82ipqHAmTXvK2dJyRUpwZg0xzPPSmY5kxSR5WcXh56AiLMip0jeRKhDss6qjjbgqvLzbIA+Bm3KsziVSqUcsWtljJzSIPnO1jlsVeEqzfW0ou1vy5pb1pdRX7A4rKURGkgBa9NJMd3Ny9dsw+WTTRE2kqKuY6sDYSQM3bYtTdvSdp2yQl43dw7jZIMwDZN+XzTF/Tzip0lyXlXVZp4mYsoNx6qyjkz2DckSrSO5BBYqV1E1NUt+fZarNIsTjWvGO5Si3XyQy6ze4p/kr5zbuqiCLAdTmxy5S3UwFpMU3EIAYzGOkuudU13K0jlf33mdceqMU98LTq2uj3771/yn/8v/REQiHvvdhspVzNNMJPFnzvKP7h8YpwkfAuPNM05Nw+nULz4jP9Zqg946x0f/8t/w7DdfUleu9A3x3pP9rIgLeHwy9E3DkAyTrcgKYDki9HQruTrbpPX31riTf8mUg1z+Vnr/hcWG0/K+eTbNajObf7h2WPnz0kLVPek9AcW+zfpGdPeaD/A2p1+Z1WE7LlEreUWE979nREHrZ6vvX948Z3txxqkfEqc+3EE86qEvBiY/MY09p+OR26pmt9+x2225urpmt9tx1V9zfDzw8PDA4XBinn0JX5HW3ToXx5zz9bKxrUxs+V56GqpeM0f566qusM5IV0jvGfpe2KFxVNuXMGKMQYt21BGuTvZlXqxuDFG2ozjJhS3Ii3EdRrdWO6Lud+z2F+z2ewmr1Q1VlTsyygby62/ecHd7BwmatqNpRB2l6zqcNaQUyKs/EyJZKQOTped0LSjbnY0kL2djDLIEXBacIDdbmmdPitC2orKCMZLymcEiyeKzrsIlVoAqCyvnP0snTF8MzNh80pZ5s04+nyRgI47fUtdwGkeRMXRePs9IOO9f/av/h7/487/gd59/zjTN/Oa3v+If/+P/iIqKttmy//QZL159zM9+9id88/UX/NVf/jl/9Vf/lrvbe2KUhll1XWGs493Dicdh5lVdsWHDttvgXjxnGES1JviZaTcy+5mf/Ogj/p0/+Slv3r7lm7d3PB57DqeBu4cDtw8nDseR0zgxBUl7iCSSXZRMgo9SDGYNJlm1d3EimRFxLudVxlKApiZX2IqkK7atK5pGmKBhHMg5opil8U4y4ELFNEoOs6sWuUg5VMhGzK6+L2NjlxQKKwcal/W+Wa9Hg+5QdAXIvRlnpUDTVbTdhsub5xyeDwDUbYdzLSHMhDAp21JRN7LB8MgGrqlrmlqkPo21xJiY/aRNzjymBtPUgGEeRw73d0xDL3aV9wFx6TXgS25tEu10tSdjkOLZqqJtW2H+YpZzTPggYfNpmgg+FmcdoxQG+piVUSR1wOum1DiJBuWxKoo3yagvkEZhme2x1j2JSJGQotUoDKrkGUOIRlRrUvZ5CvLnDuJ/43XGqTNOfR84ZTYbojGYlNgdHtkdHj9oh9ff9c2uBdoPvo7be/nz/+XKKmh/r69yKvpbvvfkp1LP0W3POPUD49QHDxshhOKxsgOOMTCOM8Nw4uG+FQZpt2e/u6Zrd+x2lxyPORx8ZJ6mFcMjE7lmfdZ5siBOxWoHVWIOzywnsPyaJY9UwmjzFBiGI0M/4MMMKVFVDcawOvUpFZSpYT1xZyf0RBtbG86YlMrpGpbQUz6hV1XFpuvY7fbsdsIQdd2G3KzOFmUQmZBue0HdOCDx1Zdf8vj4UMbGqNZzXYlsWtM1tF1D5ZrCGFhrQHvYWLsQCevQ9LrJ1KKfLexXU0tuonR9FKcK0tpemAMx7lxU5CqHMfUT8MxjkMehKJHoZy7zs+R3ppSYI4Qo0oIGzcfVAsBxGPlX//xf8s03X3N7+477+zv644Hh/mv+Ylthmz1mc0FtHFXTsbuq2V/d8PL1J/zkj/6YX//1r/jyiy84Ho7UVcXlxSWH08jn39zx8sUzLtuG2lnapmLbiS57TNINt+9PhOC5ubzko1evGLWT7TiN9P3A8Xji7uHA4XjkeBo4HE8c+xEfI8MUOfQjh9NAP0yE4GX8gsPHgLWGWmUzXbZjY2ibil1X45xh8trYycs9VU1L224wxsp9zC2uqnHOEmJiGGf6caIfJUzrWmHZstSeckdIN+JKCjJZmD6rDsjkXNbCngLkqEZem5A0XC5Rj0pC3U3D/uKKZ68+Ynd5zeebz3XuZbuT85SdNjEiJZKTNdk0FZ1uXoKfGfuBeRqZx5HcFyCkkdFAXdXUBlxKzGOPn2cZy8qppKg6VWuobLXKQ9dNUJLGYfOcOxPLnM7zLM2TTif6YZA8WiPruWsaVS5SiUMrPimZrJ+fMUTsOqw2mbkIE930uMouc7Bm//RkWXJrWTaFmWtbr6MP4Off++uMU2ec+j5w6v5HP2L37/0H/OKf/5//P15/7z/Z75nUyJGTv+kjvuPH3+eVgH/6s1/yl68/PuPUD4xTHz5seGGd13KBJX/VWkLyHA6PjMOozEdL223ZbHZcX19zPB54eHzkdDyKLFwQWa8cxHvSHZWFNYqa/y6/L85n7fjXLI0B+nlWKbBJw4+SEx+ChJKksEVMLYEY1IqpEhkznlTmlxC2OidpbiTssq0qula6iV5c7Nlut6oIUMuEKYODlQ1fzruVXH3D/uKKzz5zuKrizZdvOJ1OBD+T0sw0pcVhGsBIoZGw0vXCLm1atpuNGDMLgya3v4Tv0dCYpBgBOOqmVhBwGo7UTaQ1LJFkI1r0GRwST+bAWoOrlrBcDt3Ns1fd9aihRGEAfFgKjiIWqKmqtrAz3s/c3z/w+PjI4fCII/LZqxt2TcWXX3zO9uZjLpsdfvY4awtAt7trXn1Sc/38Y355OnL37htu337NPI4Mw8CvfvUbdl3DZx+9ZLdp2ceatq6onKMxlrZp2Ww2zPNQ7N5itM4iEqIvoU4/TZx6SbuSw4FnngLH44mvb++4ezwyeU8/DNzdPXAaRzDSYXvTNTRtQ9s0tE1F19RYJ2vKh8TsZ8Y5cRonSZkyDu8jvfckYwlRJO/6yRMrD12iSQKKOTLknGWhByUEX1WuyEwW5qIUjInNkyTP1dn8+ryxyhstSe2om5qqaqibhovra168+pjt/rJsvECYTQu0bYdBckirnFe7SoI2MTIej/h5Yp4mYYlC0MZTQIjMp54xHlWxY1RGeJZN2khhl7w2N0oxa9t7Zj9zOg0cjkfGoWeap5ILu74PNByfN4ltK9GkNiXJkzUObwMmap54XldBE+BiROsuy5pLK39mvcPaeSF8jG6gZNVhUK0iYxbfxLI5k/dc1FvO17evM06dcer7wql/8R//J/zu57/EhUQ/9Nzf3vL4+Mjj4wPHwyPOJC53HbU19D6wuXjO7upGZIut1SJzw+wnlTj1xDDTHx55UBbckHh+c8Vnn37Ey2fXbLqGTV3RVJXUMWS7Jun4520eOCMpP+LHVbI3BKZxYponVcSSFJhpnLg/Hjn2g6R6TROPjwfGaSJiqDWaXtcVTV1LsXaV/fqyFuYI4yjF7yAE2Bi8YFZITHNg8p7ZR0Jmw/VwXOo7jJJYCOSU1F2z1CLl+pn1OrCFGNO0n5hrCbTOwpoSoavqmu3+gqvrZ9RNB8YQq4rf3jyjNwbHGad+SJz64GEjRmnGU1qzF9ZA2VA1iBA9x9OB4/FA09Ts9xe0Xct1I47udDxyOh449ifGoZcuifqezjlscQxP8yTjSn8uGxQrNiTnuxvAVZZEpaxykIkNq2ZNK5ZJx1Kcj7JiCQo4rUPlzulrDHRtx3a3o9EN6n6/o+s6aT8PlEImdXK2WjU5yotMT4Kb7ZZPPv6EylV8/vnnHB8PpWBQNnvLvfo0F+eMvk9d1zRdS9s00qSqla/lNC0pLvJW8prl/bQ9Pcuil4K7qCyX6MInIuizBB/wsygkzLP+8RMpRUKQRRO0kDIVWUWKUcaggKygZADHDF5C3KTEV6+/+Of/43/13//n0ywLuq4cbVMLMIRI226o264wYAv7ntUadJ6jFGfN00gMgf/dOf7nTcd200mhlctqKpTFk9Mgcl7vkyvlkL+GYOOSIxlj1DmFWbWsMwMwzbOOSVrynnXDIGNuygamvLceHoRhQ9mIRDKGmHJjnkwWqcOImQF9ehk1cpliU34np2JlcGe1Hr792Mu9GZPZRwHQummpm6YcWu6v3v11fh/rDDaB9wFCxFRJx1zG2avG+DxNTOPAPE1YoOs6TAI/ixygnz3TPDKOI4fDI3d39wxDX7q5ZiYy18pIoVximifuHx54uLvDR0+lkSWjIWLxX6sxQmUCYyQkkaecQ6Spg6j51BVOXWXKY5kjOClpSwJ1KHm8s8+JEjYvVmUXsLRqD8ZIqsaTwTegbY8pk3C+vvM649QZp74vnPJdxVeffcwcDG/vH/n1aeTrh0fu5hm33fL86oraJW5v76g2F3zy8WdcXD+XOrZaotE+RLyf5VCpKk7ezwz9gdPjHYd3b/nXw4nnD0d+fH3Nx9c3PLu+ZLfp2NSWxlnq3H/CSKqMsWCSwQOWBEm7q0cRBxAfGoqMqomQgsccj9CfiH4GH6jHCUJk8gFbN7i2JRpDr/NbOYnQj/NESCJ9Os+Rfpg59QOPx4HH00AfEt7UBByzj/iwHC6yuphB8C3GJQpojcE6oyYQi08mUQ6o5XCd/W4IBfKWNLmEq8XPV5Wj22y4ef6Sm1cf8W5/gTUVufdNjBGb0hmnfmCc+nDNhjIl2cFmuT5hBQKNaTBVdlLSWfV0mjj1R+qqYbfdsd3tuL65ZrPdsB96+r7ncHjkeDgyzcIO541YdjjrQrsnebCI2gIWPe2K4oUYrKW2DVVdKXsRtRlLJDO163bt+d+qqgghlIK4nFufWR6rEoGbzYbLqwt2u706SSmsFUm5rDAkg58VF0yUxi/vy7zl2OFms+X1648wxvDFF1/wcH8vTYXKZcoc5lnMIOq9Z36cOZbvLafepu3oug11rSxT20q4TVkBq8YLylgsYuTiuMmLX/7yYaYfB4a+ZxhHAWGdl+CXnFiRQ1vyeCn79IUVtCVcDuA4DVP9y3/27/6X/8c//N/+uzevv/gXThfcbC1HxAGlmDhwYEn3WUA2pafW/f7/Q+IuHd4rxFpysfOBIw/J00Kn/BBPpuDp56T0ZH3lIqvveIley8Y/v09KMg9yUElPXrfwft/9bn/jB71/AEnvv1n61o/zy9J3/DznQme5UMPhicMC+Nmv/vQ/+y/u/+uftVUlBaUpydvERAweHyNk0JlnaYx1OjFNohs+9OKg+75nnkamcWCchqJFLrKJooZijChvYQxVko3EMI70fc/pdOTw+CiMXQrM84yzixJQjsLkTYYxyzzH4Jlno7edqGkIJIzPAJl7iyzqHvlv5eSejFvJTzcLaycHY5GfjCpfWuyuzIMhWfHj6bvm83yV64xTZ5z6vnHq8eR589XXvP3ma+7fvWXTNfziF3/Cp598ysPDPc3nv2OePZV1WANNU9N1HTEmpnkgaJdn11alxmbnrul2ey6un9M/3jMc7vjzX/2ar7/5mtcvX/Pq1QuuLvds26ocONraUleOphEREOmMnaTfRhKRAOOgspbUICInrhJGPlr2zlF1HZOfCSRRDEswezlwJCyursr3JT1NBA2G4Bkmz3iaJSpCzR01J9MSq4TRzvZUHqsHgvXmvcgoq/6sFGXnOZQDU+5xQUIi8gA2z4vMf0RINkOSFCAHxkjndmMN3XbHi48+5vmLj9nsdhLtMMLOxyQKS9YYzji1XD8ETv0talSVnKLNkmuZdNJTlBOzSzktIzMQhhAjQ39iGgYOhwOb7Zbdfsv+4opuu2W/33N4fOTd7S193y/ygspC5cHJzjB/nQvPRN9Y2JkYEyEuDBRqOM4arBH9ZWFMYnHkmQHIjsg5x2azeeJossRZ00iDpIv9BdvtBldrnq7mvAUfmPU+m6ZRVYsk3TA1lC8s9RJSB3W8xtBtNrx+/VpYm5i4v7/XELv8TpF2JT/eInH4fig9Fx6O48Dh8R5Q5spVVE7SeCS0vSkNYTKjL5KGqqygsmfGgKsrtlXFbr80xvJe2CPp6hoI3jPNk7CB06QM01zCh1k9JSVEzz1JQd4UAm9vH90/+B/+/X/y7L998d+YFPmjP/oRP/7XjuCmAAAgAElEQVT0E7bbjqw//fgwMI0R6oZuf8Fmu1MZSoOfRkKc1W4i8zSrDK3KzMaAn0fG4wPj/S30R7Ztzc3NDTc311xe7Nh2NW1T0dY1XVNTN9KltKkdlTEYMhOmRVkhFUbFey9MQ0xMITAHzxwlfBxSIiSV9s71KdaVtKhx8jycJt499ry777k/jgyjJ+tYybyYwoKWBj8GlcJFJfMkDSv4oJEFWZOVderkJd0j+6qoBWsp5g7C+mlJ90crHe2UoKorbl485/nL12z311KQaQx1LQV01sprkk9VHZu2rsWJkxLz0DOOoxbBid+IXp5l6gf604nT8cDhkHPnR1L05X5LkZ6yocKOJWbvmWbPOE4M48jpdOJ0OokazDwRY5Qi27Sk1QzDwDROuLqi1l4Dzjqp0DdGm0M5jJM/yToiFpO0YZvNefqLMplZ/cm+9ruPhOLkTdJ/NdXlCYutf6dsAbmmJjNS5+s7rzNOnXHqB8Apvv76K+7u3mJN4tOPX/PyZk9tZ55db9ntfsLjfc80JeI0EZuWFKXoeLfb4+uaEL0U9sfIrJEYQ4Xd1TTdjvn6OePxnsf7Wx7/8ld8+fkX3Nzc8OzZNZf7HdtNQ9dIxF/y9Z344MpRWSM2ZC1Eryx60oC/SvlmP1ZL+lWMSSWAZY9Z1wn04LrgVGKYAw9z5N3jzLv7kfvDSD967fPR0WxajDFSKxBEGayqZPOae6pUVqRdZ+9VTQzM+ziVFKf0sFFwqhB76kWt1D+KElPQSIAw+zfPn/Pi9UfsLm5wribMgabRmgRriCEWpv6MU991fX849eEO4s6B0zBMjNpoZLlFr2kjTlNTYkx47zFQpPuGXorwbt9VXFzuubi80OZtLe1my+nwyOPjI8PQ64lwceTrQrvs1CQnr5I8RivyXMmrYa+0tkuYzubTmCw8cTyWeZ6/9bxWgcFZUR8QJ16z3W7ZbDc0baPP7XVSFsCRPgaqn56bwSRREbBJHPda1UT+FccTk+Hy6gqDpap+x7t3b5lnaeiy5s3LolNGObNoginpaRNR/UKUiwITE8PwtNFNVdXUTVM6znZdS9N1Mr7ZyMvpOmlYvKFtJW0oJlkoUvg4M80z0c/MfmYaJ0bNcczFSZJL6plC4jjM9Lcy9+PhVNWjrT775GN+9von7LstcQ5gobU1u+s9s4fDaaa/nzg8SlrVZrOhsRtstcXWFSkmxjQSjF/CtQ6SjXh7yVg/43h3y/H+G463X/JN9Yari0uePXvGxX7PZtvRtjXbrmLbNYS2pakrauv0pG+wQSQWXUrYVIv0XlDnHmail0XnMFgDteyAwDp8gv4UuD963t0PvH3oOQ4zPiZMctjY0ESVxEuylmKSmgqbXGFusx43JmEC2JTAG9IkEsB129C0HZXKN85hIkZZlzFGkk/YkHRzseSHkw8eSfpqGH2vly9e8+mPf8xufwWIwyZGbDBUtgafxDklYY3H40neLwTmvud4eKTvT7LRSpJSMJ56aVqmucXTNJFipKodddthnYybsQISblVnYh2q7DVyOBx5PDwKy+R92aBZ62gao4C17pYciZPk3zpn1Z5bmqaV/iJVVXqVoM5abkVYorzxy+swu96Unha/srihVYhZQ9l5k2dSeS3GlMMgedUnI8opaXmL8/Xt64xTZ5z6IXBqOB2xKfLZpx/xkx9/yn6/FZ9moWtr2ldtwanT8UDfDwWnKudommrBqXHUNBvFKaDtJNIz7i453t3y7v4b3v361/zu899+EKe6glMLI51W3d2DcQQDs/FMRIYEc9JaCmu1saL42IJTY+D+OH4HTiVCNBjnaL6FUxKRqmv3LZwCivqhxxMjVLUVnLISkZnnFU6lfFha0grXOBXy5teIjdRtw8tXfwNOKcGQovbhMO6MU0+X3w+CUx+ObDh5g7wgQox6etEUFE18j2oAxgh7G7wnzuLoUkqaDxp4uL/ndDyy20vzpYuLCzbbDRdXFwzDwOko4WvvvYZy5KnWebLrgjRjLZXNRXaSsxu9NhzSTrG5iEgGJpWCJwm9P+0umt/fWmF/NpstbSusUVXVGOPARBqnueopgTY5yg4vOzvjcu7cal51MoKPjJMUMM+jtKWv6or9fk/92Y+pKsebN18xTaqNzlPnjS6e3OwoNzRKqzDX2riShp5zK/sYEz6ghXLC9PR9y3a34wLY7bY4V2sYzuKqrFKyNEeqDMWZpRRxrqLBEl2FqxradsM2itOLcVXLME08HgbuD3ccjxNDP+GnmZvrK375p7/go49eCrM0e1IKWkglYHGaYDhM9MMjKT7QtA373ZbLy5027bHUVYU1UCVK0aCfZ2IIVFXL7uYlzW6P74+c7m/53TfvePP1G3bbDbvdBfvdnouLHbv9hk3XsmkbNm1FXTlhIdXmgwJTiOBjZIoRnwzJVcrKgI8whcRjP3N7PPJ4mujHwDQnJi9REkyU8itdO0Uu0izzbTAlxUpYlHX3YQnJ142jqhthNpyTPFlU6rJpibEieg8p4FyOmKhT84FIjsCANY6u7bi4vOTFy1e8fv2a/W6vDG3UrqZBNiAhlPWeNEzsp4ngJ8LsmaaR4/HA48Mdx8OBcRiYZ1HtysysdZa2a8idlV2l4KAbFaeKZTm0ToJT33P/cM/b23eyjpShFCebmxs5oCqMkX9SVyPOPUR5bd2MRUfdAKkyWJaNl4TwY1lXpXjxva/Lol856JTWTJIc6EyEpVC2uJKyOcs+qmxi32OOz9dynXHqjFNnnDrj1Bmn/m7j1AcPG865UlDlS98AUa2p6lockoa6SIm6qnFVzeCFXTIGPX0tD2utFVUEf0/TtnSbDdvtJbv9JZdXE6fjkcPhwHDqmYMnaJfMHLpZ/o6k6AmJkgPprMNUhoicosEIuNhYUkVAVXuoFYikQDnBk8GT/DyjzJFIjFWVxZhc0CaLORf95jzhyBJuymFZo3nu3geGYWDoT4zDIOyaMSpfZpjnmbbb8NHHn5CIfPXl14zjTAayYiZ6vykX5qykBZdfWZipnI9nrTy/4KA45Kau2GxFc32739O2jcyjc6Xpi1GVAav/JiLWCCM4hyUvVnIsrTangwopuCunbANN2/JwnBnGiePxkcfDPW1b8x/+o3/In/6DX9J1LeMwMo0Ts5fWqzFFDscJ/67ndBo49j3zPBBCoKoq2qZhv9tzfXnJfr+jaRtlHlXtyQeca6h3rc7dpahqvPyY08MdD19/ztvbr3n77o6ua9jvRJt/t9ux3W3pNh3dRm1Ax6UwftZIaFytK0RDP0YeTp67w8hx8PSzZ/RBxlAZFmOgbioMMM0z4zSSQ5MpSbOxuq6JPmg+uurWK6Mjzk10+7OtaQXYsk6yfToLQVLSQsqpWAaisD9e85c32x0vXr7g8uqGbbehaTrazYbdVqR1xXEvEZF5ngmzNC0a+555HEWy73jkeHhk6HvGYWAcR+Z5JKZQ1mnbbWiaVV5q0hQJbWRodcMgeeeOFKPIXMZEP47cPz7w9vZW15F2XI2St583G4XRSWjhYFVSVHxUVlFT4oZ+YOgHrHssiltt22lKR120yK21VK6STY6CetLPKD5G+inK93LDJcOyfqEUAK68OylqTVI0y71b2X6av++S+h+4zjh1xqkzTp1x6oxTf7dx6oOHjWEa5fSrRkOyZYCixFkAyf8LIdDPvQ4i6vC0Lbsu6tyNNZ+CpnFknmZcJbmom82G7vmGi4tLlSO853Q4FieQksgLljBwStjgSSm3sRenVtVSkNP3A8PQiyNVqxbiyVGamUWVDPQic7pwPPnkpuHA6LFRG6JhS/jWOosztpxkpanKkmcYU8IPA8Mw0p9OJc91fRkjYeQQ5PTaNA0fvf6M4BNffPEl8+wXpowMNgazYrvyggXRUGYFfflZpMD36WlU+jb0hODpTyc2my27/Z7NbivSg2kBhEhcGZh2YuW9LrlGwpTWWsnhtKEofBgM8xw5HAdtqnVPDDOvX73g57/4BTfXz8QoXUNdT8yTABjG0I/3qh0dSMnj55Ghl9xbUqKqG7a7Cy4vL7m4uODq4oLtbqP5joaLix2bboP30hk8P9d2s6NuO941O463b3g43HI4HWlvKzbdhs1+x3a7pd101G0rfxoBdKcduBOWyUM/BR77mcdTYPC+sDwhepyx1E1Diokpzpp6AJB0g5TXk+Q/x+S1LsOAk6Y6BlNYsLImEfC0KTe8UhsocyyMXgizMkZRX5uL0qHrNtw8f8Enn/yIy4sr2ZAYIx1P/czj/Yyzlto5uf9JGqfNY48fR6Zp5HTqeXx44O72HYfHB/w8af6rMD6Vq2jrBqtrt6oqGtUJx2QtcCBF7Wxc09SNOLGYCCQqVzNNM4fjgbfvbjkeDhqS1o2OKmg4Y0tTr1xsmG3YWVUsSVVR9sgMWO5zMM0j4zRweHwQp13LvbrK4Vy92tSpLrk1mCQAy0rGUdRztBxP/z+n+mh5qjjtsoaN2EARQUiUHVp6qoB0vpbrjFNnnDrj1Bmnzjj1dxunPnjYmLzHouHXqiqa/It+eL6T5TWyyCgOQ4p4DFWlWvwaGjJISC6lICHSvudY1Wy2W7a7LReXV1L8VjWFdTHGaqFPheSJSdg1h2eNXZxV1KYr8zQS9GtTjBxs0vxBL10ZZz+X4r+cB9t2rSxWZ4okWa7Iz07TsHSEliVpVDVkFlUMDbeHII3bgKK9nQ1LgC2PqXyvqhwfffQJicSbr75iGmeZUKMwE5cGKzl30bCwcmoWZIaoOHKdg/y5dd2o9npFjJ77+3fc3n6Dq6ShzXa7k9N929J2ohbi7CLfmsN3MeoYq6MRbXPJCaVSFZiQGIYDx8NA3w/0x57a1fzspz/l1cuXdG0rIcYQpUlOVWGsIQQw7ii62ZsOksdET/QzfhrwMZACHA4iOXfqB8bZc0Ngt91KEbMqt1R1Qwwjc5iLvn272fPi44Zuf8X92zf0t1/xcLzj4Xikvr+jbSRHuNts6fZbuu2WZrPFtVvm5Bm9ZQ6GOQRm70mJooQi66BS5+AV8C0RVNM/aAqHFOaF/7e9N1uWJDmSxdR8icw8S1V1N9AASMqdIeU+8D7N//8JhRTyilDuDGZ6qTpbZkaEL8YHNfPI02jUvBAi9RAuqC7UWSIjPMzN3NXU1Brl8mJMmKYjQjB7WsqQCPR5d3tWK0hFNKUJEXKQe2cRX29opdBBCjCliRzTU8TxdMQPf/gD/vTjX3A6nkaPASiQY8RBE67nM8p8xdqpSb/MM5b5gnm+4nq54O31Fa8vb7hc3rCuC9DV5HGngQoRDWJ39xCjadDTR5DvGs2wXSs9bmiYSRY2VVzmGU/PLzi/nVGqSQuHZJuzDSkjRKMjoxzlRtkDitAZ6FJU9GTFxEo0rLU2Ut3aOsqyoiwLJBItD5GcWXZddl6+SzIHuEKQb3iTbfiqkJvr0qFRZGu0GOi3RLhRQbdNpNFTXMFlH3879ji1x6k9Tu1xao9T33ac+upho9YGEeWNCwYiI+bYb5Ur8oE346oOPF0ZH1R1OEZ/MO+T4CMY4gIBllJYyDYd8OFjhH/D0Sb2OWBCkNy8uqV4VAc/VJWKHRoiAE9RA1CiOsuyYl2Xd6oaRJUEhynjdDzidDpSvzhF5DxxQnsfjsubCzFdV3FdiGR4epXybK4fzhfihUCDt2gI2vZ8HetKB/DnP/9PCCHgl59/wbKs2zMGnlDDcKh28tQtRenIkTfNQWAAOUxHpl7v7oxmYGlTMd3qwqY0pax4O7/gy5df0VpHTgnT8YjD4YDDkQhfnjKc07epvQC0vi1wCIg0vLy84cvLG84XomP3x4zH+wdM02E8O0BaAxGtjt4rYorGS05YrgJoxzRlCO5RGlEgnvwr5uWKl9dIfffDCSmS97u63YkVtbWGGCKRoeMd7u4ecLx7wNP9B7z8+ldcvvyE9fKK8/WC9JZw//CIx5DQpw9YSgY0osF6aCQZ6Wfhaiaftytas0JMAA1cxLfKDU4DUGVPjrv7e6Q0jWAfYqciC2Ba+ex87giHa8aTtsB/+zokQkRk73g64uPHj/j46QccjkeoKqac8fDwgNPhSCpBqxBPP1fqwktrKPMV58sbO65fLnh+esLL8wuulzNqK3DO++FwsI0Wu6em5Cn9YDQNWMGeocdWNHjjYY3mQFtwFBQIuF6veHp6xuvrK9ay3qwfjGf363STUSROCTY6Ey+gFNuQ0T1uxa03aJun8LVTQnVZ2OG9V5Q2Y10Fi8xEjK37bEgbEk1JxgCXC/baAU+7iwRKI2oDgoLtlht6xyb9GW8R39/wT/Yxxh6n9ji1x6k9Tu1x6tuOU189bOScsC4rLucZ2huiBDudmaSfzT1vjGlABGFXS7WirZjGDYn4g208NzoBM2jdmqUxtRaGMk+wLI1L2nHCdbyo29fBgjR79RGABHh79lorliv5edUUKG7TqyxuCjicjri7Z8fVw+FoHasNgYmbsPBaCq7XCy6XK9Z1GYHCn4OomQeyOFCigfQAg9O5KQk0uJRgjBF//PHPSCnj559+xuVyGeYGQ66CeNdIQITKSaq+JsTSfQmPj4/44fs/4P7u3jpLh3FvOi6KEWBUlU1tasG6kCu4LAten694+vwrRCJCTMjT0egFRxyOBxxPkyGCdlGlSy+14vUyk3rw8iskdPzz//pP+K//9Z8RDRnxYABL87fasKwzHcTxgCmzC3c73WFeZsSYkDv16kut6K1Ce8G6XHF+O+N4OCGGhN46Sik4Hg7kLucJPXYLgkq1ibVAITg+fITEjHz3iPnlV/TlgofHD/jwhz/h+Pg9YjoAQnpEN33/UYTaO7yxkEDoyFujuoZS3aFpQ6ssSOQ64LuotULtnYfxfqjy5B1SS7FixN74cyJYWxldlHtv6LVC0TlXhyOLWx8/4PHhA07HOzrV202YAvP5wg63tbJoTxuRkpU81tfXZzw/P+N6PrP52eWMUqg0klJCMp6wp+2DISGuEiMi3NhZQy4iW8EaaVVbuVTzCCpowdO+7Ij7erngl18/4z9++gkvr6/cCAHDgY8IcPP/FOSkjv4B8u7HuGET+rEg1JVnNlutTxEd8OGg6HcnW7N8b9UKJ+mrwCZLhZtesSAliX6R3Nk0nDe7NceBMkkLiLEbghatgNbWrsg7ZaV9/O3Y49Qep/Y4tcepPU5923Hq6x3EW0PvDeidmtRa0fsynIs7pV5NZs3dqLJISGK4mTjj8tFX43i6w5/+9Cf86S8/AgLKwN10gHUHNx7E0trFOmNG68ockmmYmwOQQL3u0JVpsJjROxfoMs9Y12UU8EBcc92fl073eHfCw+MHHO/oAIKlzjywqCrm6wVn01xe1gWqihiIinkQCmFziC6x6N+7fU4PIgwgNObNybM75KfvfoAg4Oef6ci7GRPU03fv0+YAEGLE3d0dPnz4gI8fP+Hx4QOmnOGqBURPBNXSceqLzU+4CmhMOOAEvX+kokPf9Mrn64zrPKOsM9blgqcvQEyJnObjEae7A5s2pYymHS+XK37+8gW/fvkVL8/P+P7TI/7pn/4XfPr+E+pajNMI1NZxvs64XGZc5wWXZUWpLK48HQ9YZ+rEx5TQLbAvy4J5WchntnlY1yte3l4BCO7uTggA5vnCAs0o6J2pcG4+TA5QiXDdPbDorv/xLxDjscaUx/sf+vcKrKWiVKJsNHB7r2D6Ub0zZ1em0nVLhSqUTlfVnHIczlAB1LUOPW61dUhkKTLt3Kikob1jyhkfP9zjw8ePuDvdG9eU6hlBwJSwkM+aIvuUtNbQloK6LFTgqAVaK0qhJvj57RVvb294eXnC9V36OVHKMbAJVLICuRiZhna98WDzBYGt2WRZaLU1ZR3TeweEHdM7AG2cw7KueH17w69fnvD05QvOl8s7P+F/RuKZRjzQIABGMfYdiv8Q6ESxrZsoAJy/KhtCzaI52yAaokx5w5smjOqbDnYuZgd5FjX2poi2YZAQEBt16KMjadLZp0zECoYNOrd7ZAza0PV9vB97nNrj1B6n9ji1x6lvO0599bDx+vw8nG/yQreg4zOcgxlSGIYp9qAwI93Sxn00ErJqGkNTFNA+XowX5/XOtBCv24YTZ+v3DqZeNxTpHfJjyiO1VFwvV1yvl6GP7CjKlpKy5+gdpTZMhwMeHz/gw4ePyDnR1rpaaqpZkdEZl+sVrRaefJWzxBdZR0AD7AVsnzTu0w1kFCGuVEW5TcHTUAStUlLv06fvEWLEf/z7v+PtbB2chXQBBQwlU+Qp4sOnj/jxxz/i++//gNPpDhLiUNxQKDzpp9ChynF7b96eXm/vOyd4Krq2iuNpxmlZ7L2zcdS6srvo09NnPD0BMWXkNAEh4eUy43wmh1K14btPH/H9D99hWVa8vrzi5XzF23nB+TpjXlas1lSn0wdw4zAl3D08IKSErg1raZivM1LOOMUw0tu1NpR1xdvLE7RViHyPh4cH9Mr0dUoJACkF2qk+UXuDRkEMmZrVskn59a5YLxe03oBuMpo2O2tjcygJDNa9co5dUWJ7+zZu0qJituObhBgpZVdn42ebkwsi1uGU68ibg0kAjocDHu7u8d133+Hjp+9wf3+PKU+IQhSKjasKemno2iCI6HVFrYYILYuluws7ppYVl/Mbnp+f8fb6guvlQr10KA4T5TSTdf2NOVvzMvtjDYUGX9rSvcFsVYdv4Ny71nyzJoKlUOZynhdcrlcGksuFTbgsaIqQLiPBtOGHlW5IKuCoFP2QQMZ6Ga/BbFkBSHT+vlMswihyVEeyjLcr9rtwVOcGivLrsymbyxY21FZvFG9MKhAKdDUpQQvqvTOI2cY3dFuTf+Od9+Fjj1N7nNrj1B6n9jj1bceprxeIL+somAliRT+QUXA+uHh6i1i4e+CNyY0XU7XToxkri7Osjb0hTa6Q4Q1hxskcjsKIpcjDmCznzI2Uau+4XM748uULLue3wQUMwU6rjSd4VcrVtd7I+RPB/cMHfPz0ieoDAAuNrPPjPF9QykpELAZyFfke4F2Xb+8lRp+jbQlv/L7NkLZ7fz+n/v0QAlrj6fZwuMMf/vhHNO1Y5itspSAEKqV89913+OMf/4hP333C4XCCd6/lQuI9bp/N+4ra0Uy2DeMt8wf4t3Mt7TsBiDBuMI/m8JBwN5wX5xxK/fCX8xnPz084v50xX2c6gumILy8zfn7677hcrrguZcgsDsRKeZpnMWMFBEjThI/HAyAB1+uMKR+wrGy6Q6cpqK2jViqALJczXiCIUZCnjLYUzK/n0WWz9W6Bi51oRYBeSYPwok9PLQo6mjbKHq4rg2CmVJ2WBnVpSUMxKbeJYW/8TGsAFLeATefEuZyv8+hMDmBwr0V4/8fTEYcpI4WI6XTE6XjA3ekOx8MBKbJgMUJQ1oVFjIGp4eu6ohZyXVfrYEqHecF8vWCeZ1yvZ1wuF6zLlWurVah0ZGsk5AV/KSVk4y/TOdqmJMgmC2iuR23jwODfUK2Z1uvrGefzGfMyY1nWoUHeWiH/1DjuYkW85AEbiiKycbxtgr3wlrwI2jK5wg2QMBzzQGqNTiPm46I5cPhGxnzMQFLj1rAJwEBWAb8HuXHuW1CRsK2pgZa5/7FAz8DsvOU+0LTeuMHYMxt/f+xxao9Te5za49Qep77tOPXVw8bDwwNqqVQjgECGVvKWZt0cN7YbU/LUXFrOndEtD1RigpgGNidax2l/S0d346u6Y8TffN47lQkbrTVroLJgCLcp7MVifGa3z0VXdAimlHGYDggScbXOkdfL2Tiu9qxWWIObFOX7VLpff0Ng+PUt5cyvw/i9W9EVF3Qfjp9z1y0F1gYidzqd8Je//Bk///wzWu/4+OEj/vjjj/j++x9wPBwGx87vQc0g1B2FG5Tfq825GIri53xPiXqzKTdwXiEi2SnbP8NP6K01pMiF60VXGiaU//EZb28XKp+UhqeXM/7HX39CiGk47mTBc6CSFliY1mSwDRKQpgmqgmkSpHzAsRIdnJcZqkDSht4yUcZasa5XvL2+4P7hgfKXywyo4u7uDvf3d5SGU0DRUatZTWCwzjENJJDvhh1EYzrSbpUdTWvvKMuMlPLQBXfUTZVdudHZPdU1uhXC4KrsEdBXD4A3izQlnO5O+PjhER8/fsDxcHDPYsWs1ASfMjX5W2mY1+toWFYa082l0FGuy4rr5YplmbEuM67zBW9vr7iczyhlATc8xm9PCTls+vwxJcRA6kmQaBxgR1RMtUPELMjWnghqabhcr3h6fsbnp884vxF1LbXe2Bw2+1IYj9aLJW2zIjI2lV7kyO63xiP1TaF2dqyCQjDBC99o/gIhRLTZtHhhHBFYCVZQGV3yz7rDBg8SVtAq1PcPIVgs4freECc+j6emeSki7X4/PleulAN0azS1WgGlBaJ9/O7Y49Qep/Y4tcepPU5xbX+rceqrh427uxPeXt9ovFBIk5GyCWHT3R2ogQ5X9i7FOU5o5nzpTOwUboohIh0im2PcHLWhRCJorZtkmxc3pXGyqzdc2moNZhgc/BQ2LnfDnTVkRnmyOxwOkCB4evpC9Y9SxrOOArqulmZ/n3rkz90+nzvO906c07UhRIBi8AHHfXLeeH2+1BDCQBhEBHene/z5TwlrLXh4eMTj40eIRJTaEaPaCf63b9QDGlO+GA4G40Qq2FA/vtYNZRKTdvNrB+MSvke4OFeUaqx4ennFy9sFz69nPL29kauqipQzjic25IEI074+H7c2ZJrZXRuCBiqRgEopd3cnxCBYloIUKPcWE9U1Sq2oKGMOaq24vJ0hAPJhwuPjI2IMJqcYsZYCqBoaEZEzC8S6cVclRhyOR+RuXGeYIw4BzeQDWy9MU4MoUK1lBEhVRasFrTYIKoIAOQZo76i14Hg44v7xB8oSruuwrdPpiE8fPuLhwyMO02Qcc+rm927a8CB6gd5RVnIx4Vr82jVPbeYAACAASURBVDHPZ8yXC1qtKLXgcqbTvl6vmBc681oKi2strez2FmNCimlodadMeTxHMWgpaqiLI2u07bJWzPMFzy8v+PXzZzw/P2NZVr5rc8oxRbO1ADbhYgAQyFA/CYGOFY5GOXrnPxtAzrp9TyEsduyOFDPAIDiaY6hQTENlR27W5UCXbtZsTsk+0zelMNerN2vGkdkwrsv1drPugweN977Absm4tt4cqtlGZltf+/jbscepPU7tcWqPU3uc+rbj1FcPG9f5ito2RQ0YYjMQDvMLt4iJOwvtHU10pKh8uDOfpkz5scjur9rZzbXXPjiF7hyWeYEaN7QrC3R+W8jmaAsAk7SzgGD8W3sC6OAxcsSUMOWMaZpwujtBoZjn69CVvr2uTzrTUTIc+83TDZQGZt7+wn/7Em7Ro9633+XoUA1b+k03jqw7JVXgdLqDXi/4t3/9N/w///d/x+l0h9PphLu7O9zd3eF4OuF4OAwViXCDWkGMX3fj6YexDRQJAxmAN+7xd+y/Zqdzv2arHW+XBT//+oRfPj/j9XyG2kLS3iGGnEzHI453d5AUt0Y5cVOF6LYIU0qIU2ZKs68AAnrruFyuVuiVME100kkSTnJCSRVhWTFNLDJs5ux6pzOWEDDlCa4NXkqh0wqRxVKljnexrDOCyE0RGdC6Ut/9dGIq+qiGrDV0KB2Gkhft2ta9ky+7Xq9A7zgcJkRRXN6eMF9nPHy8ww9/+gvRldqgjXJ+2RqJTYeDpWuVzrrV0SdXASuGJBqkrSAGoKzsC8DuqCw6XVYWTJ4vTEmXsqJrZ7HfdCRlROlUYyRSlFOGNwGa8oSY6TZac06uDsc9z1vjpBf7s/kRhUg0NIUa7iLOf4+W5dWxttzBuYrQbZAIxr0dTtUQHlcV8s2Zm2yMATFPQ+a0aQe6by7ZRMl5rgiGXhmiLIbMvVsj8t4BAxvu5WluXyJ+j3T4pChs3arVvuOSh5tfGRuA3t+t0328H3uc2uPUHqf2OLXHqW87Tn31sAEwHSuxj0r54cS7mna3vkMbNsfkqeatIt+dP5uC+OnOeLB2Mmq9Q/rGcV3mGdfLFTFF3N9RuQCOJow0LBEgETY64gukhjO6DufV7eQVApsE5emA6TghJ/IKPX302xPahu74c4g5d3e8lGcj0uWO/r1zvE1Pt5sA4dfczMALgDbkhvrq5AB7cIIIunYcTkd8+u4jfv31F7y8PmGeL7ic33A6nnA4HpEynWIwpYrj4WhfT2ZMREa2oNK3d2fpM9pb2IKMMp3PR4+oVfF6vuLL0wueXt7w8nbB9bqwWEsEky2AdV1xPp+xlIL7D1SioByiGgoSDTTivHa/H3UuJY18mjIkKD5//ozpMOF4PDJQxIC76YR1XZFSHDbpC02VyIoHqN4ViAnwewAbMyWpdIBlAbSjFkWTOoIo32XHcp0BkKOZcgZiAKxZWEwZ+XDAOs+4vJ5NCeWK9XolopASYuj48YeP+G//7X9Hi/dowverraPXilZWtMpCvwiiq6UVLMWQKSG/VwIDW5lnLBd2TG2VvNJlWVDqMpRc1mXB+XrB29sballJCbBi1xiZ4h4OTYjQ5DyZFKihGdia/pBzXDHPVzw/P+PLly94fX3FssykqViaNkaXvZTBLU5jY2Jc1HijugFbQuIBhfaxNVpKiNblNcYE1waHvLdTW543qXVr4gZYoapYAGdQcUnIHBPRUu2kFQSxzUZHCOaY1REiPpN7Xm58aCMK3wDIcMpEum1baTx99mFodv1w4xM2fvw+/t7Y45Tf+x6n9ji1x6k9Tn2Lceqrh43R4lyCnUu50GutIP/rJgXTqc8M8UYobFB0ewO3N+KOnioL3RzilpaCLbSUM+7urZX8lG9kuGQsqtYbQow4nk6IMQzZwK5b6ohdXSPby4/280yL9t6pPuBUNGwcVn9mGVbl966/+fet4xZ7MX+LpDE4WZOj34zWPBDc6pnzRH06nZBzpqRfq0P9AU1xOt7hh+9/gOAzrvOCUBMmS80BPHEudcF8nfEqL+jogAQcMgu2DicidzHHofghIggaNkRQed6lhB+f4zyveH654PPTC55fXjEv1G+3JcJFH5k2rLVgnhespSCkgE/ff8TjhwcGicQUdCvViuDIwWShIKBNoWCnzmo/M00HxMeE6/WC8/lMSbuUUE3n2+dumujkUyKKw2I58rRdvk3BIqwUE0JMRF+WGaWT/1qWgnm+oqyWbjZkwdHBXgtKb+i2cIMI0Bou84zz2yu7A5cF8+UVpRTcne7wcPcJ//TP/wU//vkvqJpxvrLILPjGaUroloIOynRmiAGhB2hrWGciT6QadPRKzm9dV9SydZ0Vk7Vk4eKKeV2IKvVOpQ5DhILRPZiKZqOjYLJ53Ch11Ab0Uk2zf8b5fMbL6xuL5+aZ6VSznXw8DtMfdAwYopIszSuwzVzCbXfSzeHb9033XCz4JbvfGDaufYi0maF4A96zBxHfpHjQuokSI53s6kIbihXe3Ts14nVbzdrHGr9NR3vwc78pMIqL7YgsNg1EikWtlULyfGjbyNzwz/fExt8de5za49Qep/Y4tcepbztOffWwcThm8up+43w5OigNKCPdy3mxbomAoTjjfYwUzJgw7VjXMk5LfMFsMsPX3YGEIf+mvaOLIKcM76zYWmNXTlXEEFFax7JQ4zyEAFgai5zHNBCe1pkq4gRH+B3+1llvf9sJ792/t4XMr5nT1JvOrSMAwDTHO8TS28DmuFWVp3tDAryT7el4xMPDA+4e7inNJ7yOn069ODFPGSlnPD09Y5lX8oZLMXTJTrV26ldEM/KGy/WM8+UVij5O3zEGqkXkjJzp2JyT2HrH8+sZ//7TF/zy+QnXeRlSb24ELGg0PXnpUCRop3pFKwXH0xEPjw+YDgdySGuns4bxfR25U4GKIVnggo458Z2vDSEKpumAulaUuaCGgpQnTPmAO6M+QMiN9s++Xs9o1hCIb0wQI9Gd090dUmLzrpgm3B0ekXJEWVZcLqbw4giAbTSq8UvJhe0oK3/2+Qv1vuf5glpWaG2IQTFNEx4f7vAv//Iv+J//y/+Gzy9X/PRvf0Wv7LpLNEQG+gooVARlLliuVxbPrSvWZUavBbWu6K2iN0WQjbfNTYWpSJikXanU1A62KQiRaFdO28Zm42UKtCt16pcZ8zzjfKHjvl5nrMuC1VR4AAtshozAUVXIQOJGIdstj9W5rcKgGCKVY2La0s6w1LTzb7k2aOshECFyNFos7T02lu60RHxpGhom1uQquDdFDIFpeGxykSMtLoLaKjSZgzYn27UjqnNfuW7csYuhSAwI6SZw2KbW+PTshqtQHZoocHR5o8XsWY2vjT1O7XFqj1N7nNrj1Lcdp7562EgpDgfkzs1AIahScxuWFgw+6arolI+wNGy3lxoGR8xPT611qJAC5xxIXsLTtAHS2dRm66IaTPN346m2qmhNUbSg1BVlnYk2mdPLKZn8naWQDEWyrDgXim6f6ygRn3s7qg3jUJ/Y98odSngFUFhDJtcq9lMzi/NYbLU5+m766NfrFb135JzJAQ3BNKITr1fXLW1oRptFkPOEox5xd/+AT9/9gLoW1NbQeiWntPWbgOo8Xv6+I0/O3KPcW0dv5/FMEhIkJKhkvJwX/PT5M86X61BzATDSg44yIESIspBNe8d8veD19QVrWUYzH5FIOUdD4IKdtCFM/XcrcoQIItgYK8cJgKKUFet1RlkXaCM32gPZlNnBVSRgKSsulytqWemw8hHQhXZpG4PSGsrlite3KwAqdnRDFw5TxuF4HBuTYP+JtrhTyjiFOwDA9XLFl8uM6/mCZb7gen7B9XrholZv9lXx4dP3eH4rqP/vTzifz1iX8ygU6xYQW6OOt3N45+sVl8uZBXPzGUBHTgmHPOEwJUwpw/W3g6lwSGfQFQSkmHCcLLCpF49uG495WfB6vqC2iqUUzPNMhG9dbKNlRX6e8rX1mSI7ibqDHQW1goHWQjAQKd/RuaKL23NKGTHlgVoFv0/jwEcrNlRlwGA32InFeJbu5mawGx+ZTtm3l+6MNwpKhwSikKp8PzmxiypAVNQVZiQAoYdho5D3PsB9Ce1JoaIIhk7HuEkVOnpEsIhyjV06NATk5P6gjU2do2FbE7V9/N7Y49Qep/Y4tcepPU5923Hqq4cNCRkiCYClZcRfigDaIWIPiU02Dejk8pmB+M/rcIIuHcd0Ih9M7MEUv6fvjZvfbb1jWWYknSAhUIatb5rhOSWgUa7QFSdG0ZSn29x1D11upsY53LDtXzdoGVGCMF4WRLY0sUcEPii8EE0cK1O/HoZzUndUvWFZ2PF2OHBLtw0KgPDZ1U66MYbhOEUxNKKd8zoF5+rR0JyL19uGcABArw2r0wjsPQYrgHMd8sta8PT6itczGxjR8dKBj/djxuxyb73HgUIFQhB2PcU0HRHTRGTpxp5qawjdJBsttdeqPW+ONynENvTiW7X3H4hcllpQW8dluQ6bWkuh+kVU3J1OiNMBXYnuoDbT82aK2eA1qAS0zg6xr5eryf/pCM6OHMQQ8eHhAR8eH3C9zHh+fsLl/Iq311dcrzMeHj7iux/+ACjw8vQrU8ul4P/6P/8PSGDDJyIITCHXWlFaRS0LNcUv1BFf5hlrWdDqaqo1nLMUk3VBNcWcGKhfnmxp902jv5YGVZs7C2C18eulcaO0Fau6AwuIOSPLAUPxxNVsTGrSN2m2UAf3c9hFJNJHxDMM+8lTRpomxMCUM+U6I9PPwSX6MOT5um3iotEXUs5MA8Mdq44izpQzDqZxr3YhXyO8zWCNlmA0FiDFNOgZIRq9xHjYOW7Fq/0WDR5+AgA6KR2ABRFvLqbDH7iP4ddufJxEoHdI6DCtTwCWJnewaR+/O/Y4tcepPU7tcWqPU992nPrqYaMWoDdzAqMYLFDuzBwk78ITfTqcuQDwNuvANlmqHTlkxMQum5z4hl4batsUPmB8y1oKFPw6uqLVjtqVPxsj9Ca48KSa0WtlqmvKI9192wZIfRLNmNzYNi+8Te5t8yKmtnRLHRni0u1k6Sflri7DJ9upGUCv1p3RnKuqohaTgIOMbpfJlEdyzkhTAgSmprBdyxdQM2faAehNQZ+44YCndaYO+YhJQCesQBXBWlbyJK9X1NpwPJ1w/3CP3oGn84Kn1xnL2lAbA5XEADHe8uD4DeNkMKBtkBO9LiteXl5xfjujlmKFf9nmwJyG0HarNkjlfLbWUFrHZD9ba4FqResNpaxYCjuXSqCMXCkNtZsdmU3ElLj4rLFPsQBca8Va6TR7o0Sh877FUqsdQG/Gp4wBrRSUdUVpbDiUc8bdMaJpxcvbM15fnvDy/ISX52fMy4zD6QF3H39Az0dSCB6+Q+8Nl1rw+vSCUlbjlTvyQLpFbRW1rKi1cLMEQJIgCREIqpZUaBcgBNTeUQ2d1JXvnRKVDVTVEGDIddKhOcoCABoEKRBt645q2KbJCzG7Fdky7WrrRbkRcnscXNOURoEcbO686I0LK4x3k3JGMA4rxNCgnGnTJpeYbE1A/P0EkxB0RPt2o2erLlF33qxyOGr/t99/144YApIhSu67QoiYpgiBDnnH4ZB7GwV8G6f9ZpcmGze2ubwpFNF8Zq/sCaG9WarbUuKqiCEjiPH4DWlurUF/I1+6j23scWqPU3uc2uPUHqe+7Tj11cNGWZd3aMyQ6rqZMHdGfMHuDDloMOZIRNDNkNQdgZ3yvANrjAlp4gtcl3VUvY8Tn2IYUe82+WYw7kiW+YrL9QrtHa73DZuMcWcq4JmZf8yy6EWAd+lmIkP8vrrG9vDzjvjw2TlXTJP573kakM9p6IZ9RuuWNg5xFA7FlHiSTlywydKOnVCTKXPQ4NUWvprx+GcMZA+OUHX03igXJ0QBtBIN2ri4/Ps6L1hLx7wC19Lxel6YShehqgJcqpGNkAi4uc66mP6yIMDkAYPg0vhOaq3IOeP+/h7TYYJrVasqbA3Di/p6U3IExeTV1GgPYs2JOtARUJTdc0NpY558DiCCQ4jIKYKFYx11LVCwc2sHgJwQEiCWFg6GzLGjqqOCoFMthddWQQyC4/GAjx8fcXc64On5Cy6XM0orqK3g7v4ej9//iJAnlNrQmqJLQvcNRVLkNCF5N19OH2LvmJRz2jvvofeKZsV03eQ3eyt08gOVNRs2JCibdKYXs2G8IYyNhHBhjrQ2IEMSUWABxdRC+Dlh2L5vmmAbAqjTLwKvKQHBO7mGTQpQAjdXvGejX6RkCLMgWVrXN0guJZqMpwqQL5/i5viDFfGNh4RyU4BuRcABwdZzDNGKMH1TudFr1DaTw7+pWrM1InExBNRWEbA5+hCM0ysb6k2+v8ALibnWK3qvRAbtvUabZ34C/WI1f+uIsASBCtBuUPR9vB97nNrj1B6n9ji1x6lvO079J9K324le9LZrqG6Tp54CdUcIW3TDf0Ns4YktUKggpkyHV4uhH0RyqB3NRURFC562xsksCFLMw3nTWSnKWvD2+oqX5y9YlhUAq/XJm7W3exN43PhVN1QojAlvWzGhCL/uDt1fMLYCO9y8PJHfSUV1FmU1S6uK8WF7Y1AIicgKT67OmwMgphYQAkQVhgthqBfgBtWy1BuDKuznDanqLM6LzeXXAhRME3pKMghP6tOkuBbg/LagdqA06soz1ewqKxGJ/hStb02qPDWaU0JQGWgOQHWNWoupsdzh7nQyWUo1KcmG2jtarUQ7GlHBbkGo9U3dgQGnjikWACExbQkLSjEnxJwQUkT3gGfpfu0dEbRHVdA+TDKulTY68aZpQpSAWlZIV0QR0xevqF2gnc1+5nXB2+WCp+dnPH/5gpgSPnz/I6a7Ry5EQ1O6ddldlmUo0DTTNe9QpJBMhk/4WfZ+Sl0gYUEw5Q7tHa0WSCtbitU7dxKyHdxyBllLNwudH9E3SiSGSCTGkdOtOA7kCa8r1nUxhClyXZsTg3BdmBCgbZYAWOo35gQVgReiOX/VNcRjjMiHCTHl8R5Y/MZ5SXAJQx3XD0NnfENjBzoYIwA1PmwfG8B4oxxyi+AGgX2PCHVFQTOflWwDol1vrhGRkE3tY+sWbQ4ALvUZRSCgE3aJ0147tf7dXkMwBNcL+YIpE3mnWvOthgz7e97H7409Tu1xao9Te5za49S3HKf+0w7iTFkWS5/Y+cb+6njv1HDjuG9glfEzAkEK1nDFGtMwW8YF32vl5AaTBQuCNE1MTas7xzAKWpoqSim4vL3h9fkZ1+tloAXuVD2l/o5fawbYO/WqOfc8yRMpwo1xbKiLGPLkPno4fwmmTMDnds6nfQzREFXEIAOfUkNuoqVDoykewArinC/s0oeiCtjJ+1aOUP2JfnNiht87X5StbdOHN7QgxogqsiFzGiAhA6KoraF4qtQQPfJyHb1ialwVN2lCIj6lUEUjmCO6vp1xeX2F9o5P33+P7z59h5wnLMuCGAOiRpSZaEjrlL4rKzXbqQvdUOuCEG0+FCjLjLWsQFfElK24qUO1orXCYG9KEEQYSYOQyHSpWj5cLZgLqCgiUZCnhF4b+lpQlUFzmhJ6o0M6TFSimHJEUOD15RWvX75gvpwhAtw/3CMdTFLPEAlxdMXkDosVkkalfB+U74NFktzIUOYwAJoguaHFYIhRY5MvHK0wryHqzXozR3LrnLc1wBeVHBUdjsK51e6JabdpmhDTNDin24aNv7uhQXy+3ug4U0yIKUKN48vCyANSnoj+2GfE5E2P7B2AcxQDfYSiD2WSNE18p+LNp+oNkgyz6QSNW68Cb6y2OWVy3wO2Yrxeic4JMJyuFwRqNHQomcRgJy0G2FCw1trQ6nfOOTef9BcxJRyEBcTauzWJMieqOnymc4gdUTIMlu52T2z83bHHqT1O7XFqj1N7nPq249RXDxtMHUXogoFOuAd31OWdK3FERv/2WmIPKDFgmia4pGDXLU0LYDgoMX7e1hDpJo0MYFlXXM5nvLw843KmTNztPbxz2gPhccP1dGy7ua5xWHkktufQEQyAMJyXO0kvnlE4X25L1ftolqp2VAz2Gc4b9DRfiO/vN8b3MmrBg4rzF3Ur/qGCCjm/o9GTLUYBzNlSTSAGUgxqrSPlyhN6wFoVSxMstWGtBVU7IIqcsykkGGLgxYZ9Qwv9iWOkwcXAgNTqiuvljOuZqiH39w+2UJimDxIRg39GAMpCB65W4NYFxdLrKkx7qrIRVteOnA8DPYCwKMoLFwEMdZjeG4vQumnDC0zhgTfflSgShLYXc4RGqq+A2AVCEEwTn3TKGfenA0JQzNczlssZvRaklHE4PdgmgS2lQghDrs9lCVOMN3YTTeEkQhLQWkRZF9TSRhEcm1rZZsbsg9ev5jgGPMu5MBvIMZtixo39Q4FGBJNrUkYqe3QRVYU2Xi8kV9IQ378Nu6TDi6NA1DmdIQYiraKGEpFrK6a4I37PatcaKBDQTYZwoJ8wBGZsFNU4+lQiCjEgysZLJQJqvsQ3RDEixQCFK6noKOiLIaA1PpkIO+PyvW9rFFZoKyHA9Yi2zaJtPNWdsvuBbfj66f6sdr9U/CnbPaeE2L1xEgOIby738ftjj1N7nNrj1B6n9jj1bcepr2c2Hj7gjyHj9PKK6+XCIjgzAk//2ONwcqFwtQy/ydvCtQ5Aax2n5c3uZCw6n5jxO4bUuGH13rEsC56+kHtI3XOMyfYU+jtHaIt0nOzM2fLfuk0o3vN3PU3sKSJ3YHxxYUxyE0A50+NrEKYM+00KdgQEwXbSDoIo1vglbkiQz0MpxZwOF6YHu9uf85/tqpa+3VAvAAgJlF3sHaFvMoAMoA2tA5e54loUc2HhWggRB4lQuFQk0JQIEtVGdIRz8oE7qKjCNHfMEaLAcl7owHvDNGV89933SDkzCAgRgw5F7gpyGwOgDFplmVGtKLA18gidWwlQYYMBpRjyY6nzzG6rw8ZUEREQMzmNKdlJv1Lir9VGlCVQh7o3V60BBCvNQWDvfCJCkwOOpwNqWXE+v2EtK3XyDyec7j8gxEzVBqHKSVtZJLisqxVVOSIZIOgDMRExvX7tQO+o6ECMLLLrClgTIxGgNYFoRzN7dQSE64ROx7XnveDOw26IkU2qoNDWye+0jQBVUwIO0wEhCEqhcgpRTnOpwe0OcB1w2r4AFgzYKIuBN4aEfDgg5skm05yiWhfbnKlAU1a0wsLGKLxWNPoGFMMHQd1xUqkGZnvZEJ9a6iji9ZQ0N0IRVRVrqRChfKenftXTy5E/33ofa5bvns8dUxw+hFxdRbDniZG9AZw37en+4ZQ1mF3cKiPdptoDYjKlGw9YAHn4+/jdscepPU7tcWqPU3uc+rbj1NczG+mA/OGIx/uPOL++4peff8Lb+ZXOW5gKIj/Otcr15qG3FLEPytZxcQytZht+4vLr3DoogE6iFKJEz09PmJcFAnLWnBN7WyQ4UkcmgceUcbGg4gVe1RQDot2L369YgFFzdn1MKICbhfJezhCQwUnk/VASr1vKPFu6dOBsqkADWvBFzBOjXQCSEg2iNlOgMEMMt84+jJMn1EoJtSFAEBN5h61VrDbfrZTtszvQm2BeOy5LR+kCVTbrSR4QwSBWGguwVNTWnxViQcDbieYgE4vMWiH/0D5LOnB6uMeHj5+QcrYTO1GQnPi5DY1peJGBGPWuo3EU05OUpVPYiR9h44+mzOft7PjLdCuAIMgxIeUE15TX3tkJtlgxo93rUGGxQH04HSECrKVYMWhHShFTnpCmjFrY5KhaY6rj6YS7u3sGvRFwDb3rFuCt2DLliDjRqVXTCG+1QgEGmMpiu2ypVTSFdF4rGHpWFYQUjHdJxKMar9R5zwpoQ7PiLqZr6exKrejY7I88a1ghqBd5CqRUCwR832IFbb5R8g0QA4Q1GTLfAIABIjgyxuI8cri5zlRp49k2LB7QMNazF6ltaXTf1MUQMGXTPRc2QgMseERDOXvH2ioYYwKLMd0vWYpfudihoG+L0VGpDImC0hjsEX3TKogRCEjGKScvvPYGbbyvEF2acysi7jfBYaz1geS2sQmjPwoAbhSV9vE3Y49Te5za49Qep/Y49W3Hqa8eNlprEAQrHGvjBtw4m8utGQrye+P30jTZ0oeuenHLKeMzCW4DQa0F57c3vL6+YJlnAMBhmsY1bwMFsAUELyrz+/DJcVSHmskyTslEg+iMeaKzU7qqSQfydIiRptVxj73X7fQMGgbl6vhMoxBKPT1N2bzonSnfqQXw85MjXrfPOQIGxj2Lnay1A600dG2QkMa7CYFyhb32wWsdcyERc1GsVcd9aqcDjSkhaMfaK6XmTL1DYfrrYCAIUYyfnOw038di6a1jXld0ETx+/IjHDx+R84HdUQFDtgKiChIU69xQ5xllnlGWBQrB4XTE4XhCPhyJOCg7hpJraws2ynjvXZkmD0EoPRf4Sa3WG9tgEWTOVFWhTGEZBXFi851SQm8NQQKOh+PgPd/dHREEuM4zrpcZyzyb48s4ThO71YLOWGHOu7PQK+Qtxc45T/BULBTmDOqW0lSFupMBxr354pfIbqMQGWosPrddub5iSoPK/h6Z9K2KpXed3gDOQwwRKdBBVtMGD+5EHUEKgRr60s3WqEXeqr2bYNd2VNMcrAQq/xisR2Q0R9swvUeeo1EaaqkDfVGz15ySKXcoOO1cOynH4RxVBNrJoW2d8z449Sb/mQxdrqaJn1JCnNKgRKTkiLQiBFAiUMQ0021TKq45nt6t+RACg0vvG9/Y/aZtatV8oW8Gh05+YFp9H78/9ji1x6k9Tu1xao9T33ac+uphwx3VusxsZ1/LMB7tHWWhlrQbe4wJDrgEcQmxW/SlQySNFGophV+OoIMIW1EZ0ZZKvuvrC66XM9OCMW78NCXXlJxAcuc2iTz9zR9OQhABYhgOjidd8t4cffG0l6rylI3N0G6Nn3Pk+s7+mVb4obIClwAAEXVJREFUZylbHac+bEWB9rWY2R7e5yrY5/sp3Ds6uiG0zpRlsJOo3YGl1fDuM9WcVqvWIEfNCSt/dzocsZaOz29fcJ4rWicXEl1NMYQG4I1toPquMVUQOnRJYSgoDKUJM2QuugJ0xeF4xP3DB+RpopMMrnvOtLt0b0TUgN4g2pFCQMwTpsMJIWVA4ggijjZxajtc3q0Wqn+EwIKlIAyUrhojFlgkBEzHNN4HlHxcAdOxrbWbZjqc/yiUeMxTxjRNELlBBCGYpgmnuxMOxwPWwsLBKWWUWjEvCwOjoXtrrejcD9ChpAQkRWsBrXFOWyX6U8rKDY4Yp1LIC2XTJIWkm66jvp8ylGRsiIJgCtz4bF2DMeydhYkdKUZIDAPZbK1bt9uM2BtpBdiCSAxiqh0MwpAwgkTIRGyhG180BBmbgHhTECtqzlAEAtoGlVvE0ubBAqMy/Q0xDfmEnBMLPW3d9NZtzjrCDTrNoB6hQjURvkMZzlcEpldfUW2+fb7EEEryimk/MdK5l3Ud6fApZ8QULCAbbaMLkCI75TYWo/4WFQ9BoBqdDMKCYDHajip0F6P6u2OPU3uc2uPUHqf2OPVtx6n/VPq2a0M1rmHKGdkQkApAgo6mPDxljcTrSFUOnpmhKTmn91X9qmilQdHRlDzX63XGOi9sTFNWUIc4IMaMUfzDdWepUHceOl6I/saRu8FuQYC/z58PkOgnO3Ms2Li4zlfz59iek/+urQ8ERMbCdwfOVaUCOy3DmsTczIP/saYyh8OEw+EwPmCko+202npFUAauFCPpo53FOkyDNYihR1AMVEsB3N/f4+HxEdfS8NdffsIvn19RvJOu3R89Y0dt61BigQe9zhRnSMkUGojIeNOoHMlFFSiulyueX17w8vqKrg3T8WgLXP1/8EIoFkOZUQN0DocJYTohpAkSkgUvQ9ciHY+jfF0VvQEhZ0wxkvtciOJ5ujJZEVrMCQLrhFoqf7crsnEcuaD53nqnpCYkIKRIjm9gUOgg9ztPE6ZDRj4ckNJEdCEGiGS02oHWRpdOMeQkxIgOKxwLDICOpkAiRIgyCYDeKuUQRUbhZCkFLMjbgr9vrpSLbbx3iHFib79v64CoS0JvXGMsEDTFDpuX1t1pJcSwXTsEYaMjMa6t3bH2DgkYm5DW2VxtKxrtQA9AApKnvwGIRNvUiPFpnfoAAJ2SlTKNNS0gaqON6Kan50UBmExhzmms5Y0j7KhyRwhEDAEQiRJFSkSbVMTQOhagipAOI4jmgEzVxHjdIfC+W+/orRoSpKZ8AggCcgpQTaitYl1Xu4fNV9K3bRKqcXz970Dy+8Aep/Y4tcepPU7tcerbjlNfz2z0wqIcoaEyDWMOTGCOQXGr6+0TVVvBMi8gQMMJZndFGs68sBPo5fXMJjNlHQuGxSwsJBKhzq8GL5SzFyvCFvaGYvgQbGiHGvdQxn/cL6oFFbm579uiPZiT183B+PVlS53xfs35Gfpxmyq/TbFzMSmidVy9lQUELM2WM517Skiji6W9PpGtURIw5plOlu/FUREoKGNoc9RMtaDUijyd8OXlip9+ecZPv3zhyRh0OjFmhEiHvK4da+nj+3y2iHwIY063YLjpU+ecrBsuAMxotVpTHwDCYB8nV4QgP7HWysLEVlHXhU5LIiRnUDOdDXViTgOdokoMC6xq7XRCQRCE1IEgEfk0oTc+d2tEvQA1Lf420sOBBmacZ6ZlnV+tgBXkmYOJ3IhMU0atK8QQqRAicj5gOh4N7SSNo9hn5GkCzAmocu58brW7DQWouPqMO3ijwdZNr50Ntoj+MeZs6XSYmoj2jqbsFeBOgilSSt+pWnMyCw69E4VzXquabTfd1vzgy/oawqakEQ0lVlVUQ1YlBEzuWB1phmmrW6D0e3jHJzcEjhsuIsP+eV026gMU5LOrAEpNdyJa1ILPU8Z0mIhQ1Wqa7SxsdJWVoVgDR7Y3JxpCQFAMZ+4bTzHEq1ln39GXIJr/q50KLb0NRBtgOjyZXTlVQcR9nGwbwsYmWGPDuR82vjr2OLXHqT1O7XFqj1Pfdpz6emZDYcZphuDyZALICkC3xkJ8CToWislemHNZUesCkTCa63QA8+WKZblShxjAlCccjgdy2LClrroq2MWE6bbeLRXtTtRv14IKmzthPLbaf8ZJUzZnOzihFgz4Amlg/SbVvX0GT7zvHbtAYnxn7PE3/xZxbuaNEoM5/1HcZIoAABiKPFWsLhsYNm4fYEiRolcYsmedJVWxtE1vXQSoteDp+Rmfv7xA44S1CdZCZ+F8xhAtFWg27YInIygqg6t4wxpLh/IZTIc6AL2xuUutFfN1Ri0VD4/3+PjpE47HIyCC1qje0U1asPaGVla0um6caHtnKSWkw4ToKFFr/OwOS+WTbuAye6WQRsGOvzo2Ah5Mmm6pRw9GKQZ0ZQGdGAoDWHB2fnSgsytlRQwKbUzzzssVa13x8ZBx//CAlCbMfaH9w6gOhlCpBQmIIEoARNmRs5AG0NVS3rUO1MiRGdWOauvNm/d4qpnNibLp6Ac0a6xEKUIugNYpxacWMIIItexDxPFg/FWzeW4MbgtbGTS7FbKGIMhpwpAghDn1GDAZdSIZ1/R2cwdzokS5ZNAIRJwvLqi+XntHU+eHG3qmTp/gM6QUiTrZ96MV9IUgOJ0OQAiYrzNKIbc454iUJ+N7E00UMY15EUAUKfHeOmz+Y0Izv0H0hmod7JycEFfq0QsX7niWlBOgxsm/QYF6J/qVchpfYxdc5+47irwVNwa83/Tt42bscWqPU3uc2uPUHqe+6Tj11cPG/YePEFhRmqVgj8cjDscDzm+vULzRcVjhSa06TjauqeynQJjjWeYr1mWhB4I5tYnSg7VVYFGEcETOEZsbxnCa7hgV+q65kTsZIlgAuWtmHR1Up7CT9G+dePsN6nSrWOJO/7fo0S1ysyFUdo0bNOi9I4/oHUwtwmTdYmSKL4YbLiuGk3cEzS5ii4/O0+AaaDe0yHiZOooEYc5McbkuaCpYWkdZF6iYTBsiVNu419540iVqJ0iJBZKlsINuTBEatnlJyVQMuvGAbXFd1xmvL6+4Xq+AAMfTHU6HI27TyRJYcDcCsQUzXkswTdaEKjnn1ZCQ2gGQDkEECUAwnrUE9HZT1JYMcQTvnYorznfuhiBkdG1opVmBlyFL8NSpjL9VeZ08TWhrMc5yxJQSDpM3AqKFhhCQQ0JrnJ8Umf5u3egWEwvkWqtoIlboSl5shxpnksWvLKx09IfvPYQAtIoudDRMncpwPAOxBNBUoWgmV2i/KwB5p6bSYZsrERhf3bnEkeu1K8IUkUXMeeuwszicUoTTPFKehi0TXdw0vr3eTi11C8HgKEO4sTCj5NzbZmSSNGQtA4AIK+KTbcORja/MjQaLTsm131AolYCQgKjR5osIVOh8R0R1bD3BUUFrGNW6vRtOVs5Epcpa0EbxJlPjrqVPNJdr2bXeNz+io0ByvBdxJZdI/6Wbf9rH+7HHqT1O7XFqj1N7nPq249RXDxtpmngq144urDpPKeF4uqfRTBMOxzMu5wuW67qlIVWh2F40xn9/OzYHF/y0WBsulxmnEzBNh+E81a+jfn1DeuztsfiOBgjBOF8Np6+6oVCCcYL10+ctZ/YWIQIwnPwtp9bVIgBs/ELghu9367w3iUMucFPwmDJPz2ZYTRsS4jhFq8IclsDRty6KGGFG4CuBCN2mKJKQEgNfa5ZGa0DThKIdTTzI8h2MIKVqaVs+bwyGaCh50EQvtvRlLVRHoLQfNkQpMoDXXtG1YjpMeHj8iJiPaMZbPBwm1FKxrCtqqyil4Hq54PJ2QVkr0jQhxAPidLC0J1BaQa/cMMDm15GNIGE0RiJCZgWhtmBS2BQ7nMvcLGCt60rVENP69o2HKjcvh8MBkDgcUEpEQkqfUSv1tENMkJBQSkEpFb23seh9oQos5ZyIfC3zalr+m94+CwfZzbM30g9omwzMLm8J8eAnI5DwbTLNnAzZVe2oRmEQuJwgOcyu+ILA1GgnfGgObaM7aL1Ff8B17VxN3RAO2PoKMVhn1jQQJS4GACpMtZvyirjtGpdW1XTHjcLBTQzRna6wjRjRVDG+vdMenILQw4YoU8ueiGZ4p+ijlh4mwtkbg3OKMnjZ9Ke29oZ98/mCFd2hdzSpyKA0ZF9m1KUMPwHhRkEtMMYcBrXCefsAexgQlevDP/hGsa4FOuQp9/Hbsccpjj1O7XFqj1N7nPpW49RXDxv/8dd/xSGfcDzcIVl3xilPVoDDCTweDjhOR5wPZ1zneVS8q3HS/naYs3SUwk/jdiIE2E3zfDmjtcZK/BABKwBqnTzA4VDtZEWlAVAlwhxsb80M03m1fB3a3HnbHd048P7OIYud3uxOzYBvEaR2g664A7gNOmIG7vrjQ80kJoRAA/OCwSARIWaEmKBqqICnBbuOVJV/xpAhs3kjAsQ0s2pHb8BaO5YKVCR0USjqKJILoVu6PIwTumtQx5igjY2TJCRMKRga11kcpkSKelOEjJG2CzZXigbnJefpgIcPj0iHjB5M3UMtXaqAKtU5lusF83wFIJiMO51DYoxqbSg/0FmY2scNT9A7/DpyFmOC1cwNpKA1tTQzoLZomipiOhANs4VJviPYTKp1piwNqRMV9Gp0gKEDw4KrWqo5gE1SrrUG3oggJHZ9RVPEnMiz7I1Fgp33RsTTeL2BTq512mK0lKVvIsbGQG/s2BAXETZUuu3uGs1hQ6j3DkM5BYIANmlStSI866AcgssWJqCzGBdj3YZ3y5wJeZNM7G2s82CNmFqjf3BKAPXVBYBtCIJYqhgmExiwzDNqJxJsj0eEydZgzhkhR7TAd6Kd+vUsuFQWh8Y4fo+0D0WHd4a2++PuxN4D3zdDXzekGbaB5IX8GctizbzgdJ5sc0EULtwUqIZI9Z7eO8pqaiIw9HgCghriJ4amDd+0oef7eD/2OLXHqT1O7XFqj1Pfdpz66mHjp8//ioAJUzzidLzHh4cHHI8HxBgwYeIJKwXEJIiTIJwDljlgvS5YV+siqupUQhssXIHaAgaNSrzwzaaoto7z9QqFIueDFZmxCl8gbPBiKacglu5RQM3BO99WRSCWwiytoLe/TS/XVoezBQUuBorEn2mAmBqILRrnrpVamIZMGSpmzeHGeUPG1ztM+lAADRh/s7qHBpCyqS8oU34xCGr3RUPT6FA0ZZdZ75BKZ9i5SEEkpfaOpXVcW0fVgKVWLCsbHsUkCMml2ioSoqFGpmrRYZKEBWIc6K5Kpw5BNMQiBHPZnanpLgw8pa54fXvF9XrB6e4ep/sT0SdYqlQ7ugBdgLUWLOuCeVnQeqVuemJquku3hU7HlISNg9QdRgiWnufPHQ9HrMI5nqaMnOMInAKqJgCwE7hAUkJUHQtcm5/kiUk27eioKH0rvApQIND+82FCPmRyumux+58Gr7MbEqcCTMcJ0ykPu+noiDkigc+0zkbvUOdrR6ghhmJOqBrKBd3SnESODEGJ7ILqG5YgATlkhG5pVuO7YgQpriNyXhNEyN0cayTws3PK1uQKo6DQCwC9+6kHkPEHG7JLZBGAKA6HCFUGGUlCSUAkk78kOsU1TDStWYrYOcrR0s5RgOM03ejPi8n5bU3X+M4DQmKw93DBB6Rz7L2h1g4VuuwQSIPoo3DXVGxqH5utPCUia0Lt+b50lIXcbm6iLLalTTqUvoRqRq2zqLUOZJEbxDgFvoNlQasVKSbkw/tAuY/3Y49Te5za49Qep/Y49W3HKfltKnYf+9jHPvaxj33sYx/72Mc+/v8Yu8TJPvaxj33sYx/72Mc+9rGPf8jYDxv72Mc+9rGPfexjH/vYxz7+IWM/bOxjH/vYxz72sY997GMf+/iHjP2wsY997GMf+9jHPvaxj33s4x8y9sPGPvaxj33sYx/72Mc+9rGPf8jYDxv72Mc+9rGPfexjH/vYxz7+IeP/A4EwPkyisxBGAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1008x1224 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "show_image('maksssksksss102') \n",
        "show_image('maksssksksss117') \n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xKIY3A6mnTNw",
        "outputId": "54405a0f-a944-485b-9aa2-3c422226c921"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Using cache found in /root/.cache/torch/hub/ultralytics_yolov5_master\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m PyYAML>=5.3.1 not found and is required by YOLOv5, attempting auto-update...\n",
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.7/dist-packages (6.0)\n",
            "\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m 1 package updated per /content/yolov5/requirements.txt\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n",
            "\n",
            "YOLOv5 🚀 v6.1-163-gb53917d torch 1.11.0+cu113 CPU\n",
            "\n",
            "Fusing layers... \n",
            "Model summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs\n",
            "Adding AutoShape... \n"
          ]
        }
      ],
      "source": [
        "# 学習済みモデル\n",
        "model = torch.hub.load('ultralytics/yolov5', 'custom', path='runs/train/exp3/weights/best.pt', device='cpu')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7_AQvRFiy7ts"
      },
      "outputs": [],
      "source": [
        "from PIL import Image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M4adU5WAbwjV"
      },
      "outputs": [],
      "source": [
        "from glob import glob\n",
        "paths = sorted(glob('data/test/images/*.png'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kRhneNlRHb4P"
      },
      "outputs": [],
      "source": [
        "imgs = []\n",
        "for p in paths:\n",
        "    img = Image.open(p)\n",
        "    imgs.append(img)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_6bxlYucw54",
        "outputId": "d9c9f642-5a9b-4dbb-bf9b-3ebeb7c6d4e1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "25"
            ]
          },
          "execution_count": 119,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(imgs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dok2HYpyHjZK"
      },
      "outputs": [],
      "source": [
        "results = model(imgs, size=640)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SrzJ1osOdAwq",
        "outputId": "394552ca-4093-4c62-fca2-c6d2a8ae61b4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "25"
            ]
          },
          "execution_count": 121,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(results)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EeCbvWMMHkm0",
        "outputId": "8f483706-bdca-40d6-fa83-d98157c26dd3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "image 1/25: 480x640 2 with_masks\n",
            "image 2/25: 480x640 1 with_mask\n",
            "image 3/25: 480x640 7 without_masks\n",
            "image 4/25: 480x640 3 without_masks\n",
            "image 5/25: 480x640 4 with_masks, 1 without_mask\n",
            "image 6/25: 480x640 7 with_masks\n",
            "image 7/25: 480x640 1 with_mask\n",
            "image 8/25: 480x640 1 without_mask\n",
            "image 9/25: 480x640 7 with_masks\n",
            "image 10/25: 480x640 32 with_masks\n",
            "image 11/25: 480x640 3 with_masks, 1 without_mask\n",
            "image 12/25: 480x640 8 with_masks, 9 without_masks\n",
            "image 13/25: 480x640 4 with_masks\n",
            "image 14/25: 480x640 16 with_masks, 5 without_masks\n",
            "image 15/25: 480x640 1 with_mask\n",
            "image 16/25: 480x640 1 with_mask\n",
            "image 17/25: 480x640 1 with_mask\n",
            "image 18/25: 480x640 2 with_masks\n",
            "image 19/25: 480x640 5 with_masks, 2 without_masks\n",
            "image 20/25: 480x640 1 without_mask\n",
            "image 21/25: 480x640 9 with_masks\n",
            "image 22/25: 480x640 7 with_masks\n",
            "image 23/25: 480x640 1 with_mask\n",
            "image 24/25: 480x640 1 with_mask\n",
            "image 25/25: 480x640 5 with_masks\n",
            "Speed: 25.9ms pre-process, 313.5ms inference, 1.1ms NMS per image at shape (25, 3, 480, 640)\n"
          ]
        }
      ],
      "source": [
        "results.print()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nDhc4QcgHraG"
      },
      "outputs": [],
      "source": [
        "# results.xyxy[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GJVixfHtdQgB"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "FS1sJXQkHt5W",
        "outputId": "484c2e98-0146-42d1-8d30-e23bbec9f325"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>xmin</th>\n",
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              "      <th>xmax</th>\n",
              "      <th>ymax</th>\n",
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              "      <th>0</th>\n",
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              "      <td>400.551483</td>\n",
              "      <td>219.864914</td>\n",
              "      <td>0.782481</td>\n",
              "      <td>0</td>\n",
              "      <td>with_mask</td>\n",
              "      <td>maksssksksss102</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>184.777985</td>\n",
              "      <td>4.586975</td>\n",
              "      <td>211.562622</td>\n",
              "      <td>38.822174</td>\n",
              "      <td>0.653822</td>\n",
              "      <td>0</td>\n",
              "      <td>with_mask</td>\n",
              "      <td>maksssksksss102</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>273.143738</td>\n",
              "      <td>272.095123</td>\n",
              "      <td>480.840820</td>\n",
              "      <td>409.250580</td>\n",
              "      <td>0.964706</td>\n",
              "      <td>0</td>\n",
              "      <td>with_mask</td>\n",
              "      <td>maksssksksss117</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144.604660</td>\n",
              "      <td>65.604950</td>\n",
              "      <td>193.061172</td>\n",
              "      <td>116.956207</td>\n",
              "      <td>0.899693</td>\n",
              "      <td>2</td>\n",
              "      <td>without_mask</td>\n",
              "      <td>maksssksksss121</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>382.483917</td>\n",
              "      <td>88.885384</td>\n",
              "      <td>423.858185</td>\n",
              "      <td>129.479401</td>\n",
              "      <td>0.896138</td>\n",
              "      <td>2</td>\n",
              "      <td>without_mask</td>\n",
              "      <td>maksssksksss121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>173.831757</td>\n",
              "      <td>95.323898</td>\n",
              "      <td>280.287628</td>\n",
              "      <td>245.321121</td>\n",
              "      <td>0.937874</td>\n",
              "      <td>0</td>\n",
              "      <td>with_mask</td>\n",
              "      <td>maksssksksss91</td>\n",
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              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>390.472656</td>\n",
              "      <td>139.161362</td>\n",
              "      <td>499.295105</td>\n",
              "      <td>300.622681</td>\n",
              "      <td>0.905453</td>\n",
              "      <td>0</td>\n",
              "      <td>with_mask</td>\n",
              "      <td>maksssksksss91</td>\n",
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              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>469.360474</td>\n",
              "      <td>210.884583</td>\n",
              "      <td>552.166687</td>\n",
              "      <td>380.362244</td>\n",
              "      <td>0.833239</td>\n",
              "      <td>0</td>\n",
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              "      <td>maksssksksss91</td>\n",
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              "      <td>319.052887</td>\n",
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              "      <td>404.339813</td>\n",
              "      <td>81.675613</td>\n",
              "      <td>0.429323</td>\n",
              "      <td>0</td>\n",
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              "      <td>maksssksksss91</td>\n",
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              "      <td>10.954498</td>\n",
              "      <td>147.840485</td>\n",
              "      <td>89.735504</td>\n",
              "      <td>0.280298</td>\n",
              "      <td>0</td>\n",
              "      <td>with_mask</td>\n",
              "      <td>maksssksksss91</td>\n",
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              "<p>148 rows × 8 columns</p>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ],
            "text/plain": [
              "           xmin        ymin        xmax        ymax  confidence  class  \\\n",
              "0    342.870758  126.946487  400.551483  219.864914    0.782481      0   \n",
              "1    184.777985    4.586975  211.562622   38.822174    0.653822      0   \n",
              "2    273.143738  272.095123  480.840820  409.250580    0.964706      0   \n",
              "3    144.604660   65.604950  193.061172  116.956207    0.899693      2   \n",
              "4    382.483917   88.885384  423.858185  129.479401    0.896138      2   \n",
              "..          ...         ...         ...         ...         ...    ...   \n",
              "143  173.831757   95.323898  280.287628  245.321121    0.937874      0   \n",
              "144  390.472656  139.161362  499.295105  300.622681    0.905453      0   \n",
              "145  469.360474  210.884583  552.166687  380.362244    0.833239      0   \n",
              "146  319.052887    0.665493  404.339813   81.675613    0.429323      0   \n",
              "147  104.638702   10.954498  147.840485   89.735504    0.280298      0   \n",
              "\n",
              "             name             path  \n",
              "0       with_mask  maksssksksss102  \n",
              "1       with_mask  maksssksksss102  \n",
              "2       with_mask  maksssksksss117  \n",
              "3    without_mask  maksssksksss121  \n",
              "4    without_mask  maksssksksss121  \n",
              "..            ...              ...  \n",
              "143     with_mask   maksssksksss91  \n",
              "144     with_mask   maksssksksss91  \n",
              "145     with_mask   maksssksksss91  \n",
              "146     with_mask   maksssksksss91  \n",
              "147     with_mask   maksssksksss91  \n",
              "\n",
              "[148 rows x 8 columns]"
            ]
          },
          "execution_count": 125,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "results_df = pd.DataFrame()\n",
        "for p, n in zip(paths, range(len(results))):\n",
        "    df_ = results.pandas().xyxy[n]\n",
        "    p = p.replace('data/test/images/', '')\n",
        "    p = p.replace('.png', '')\n",
        "    df_['path'] = p\n",
        "    results_df = pd.concat([results_df, df_])\n",
        "results_df.reset_index(drop=True)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "YOLOV5.ipynb",
      "provenance": []
    },
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      "version": "3.8.13"
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