calinski_harabasz (1).ipynb

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    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RLQItqoaLhEj",
        "outputId": "3b6aea8f-41d4-4cac-e788-b66f349c79f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/content/drive/MyDrive/Similarity matrix folders 100-20230415T025209Z-001/Similarity matrix folders 100/fifth1000.csv',header=None)"
      ],
      "metadata": {
        "id": "xSb28ClJLlEx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)"
      ],
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          "height": 456
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        "id": "3tTQot_4Z5ek",
        "outputId": "934b2549-eb12-4273-cca3-f44cb3cf39f5"
      },
      "execution_count": null,
      "outputs": [
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install networkx\n",
        "!pip install python-louvain\n",
        "!pip install scikit-learn\n",
        "!pip install infomap\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sDt1pWTxTI4i",
        "outputId": "37f5ad85-17c8-4cce-b61b-8d565e68ad12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (3.1)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: python-louvain in /usr/local/lib/python3.10/dist-packages (0.16)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from python-louvain) (3.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from python-louvain) (1.22.4)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.22.4)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.10.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.1.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting infomap\n",
            "  Downloading infomap-2.7.1.tar.gz (263 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m263.1/263.1 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: infomap\n",
            "  Building wheel for infomap (setup.py) ... \u001b[?25l\u001b[?25hcanceled\n",
            "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n",
            "\u001b[0mTraceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 169, in exc_logging_wrapper\n",
            "    status = run_func(*args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/req_command.py\", line 242, in wrapper\n",
            "    return func(self, options, args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/commands/install.py\", line 417, in run\n",
            "    _, build_failures = build(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/wheel_builder.py\", line 320, in build\n",
            "    wheel_file = _build_one(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/wheel_builder.py\", line 194, in _build_one\n",
            "    wheel_path = _build_one_inside_env(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/wheel_builder.py\", line 241, in _build_one_inside_env\n",
            "    wheel_path = build_wheel_legacy(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/operations/build/wheel_legacy.py\", line 83, in build_wheel_legacy\n",
            "    output = call_subprocess(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/utils/subprocess.py\", line 166, in call_subprocess\n",
            "    line: str = proc.stdout.readline()\n",
            "KeyboardInterrupt\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/bin/pip3\", line 8, in <module>\n",
            "    sys.exit(main())\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/main.py\", line 79, in main\n",
            "    return command.main(cmd_args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 101, in main\n",
            "    return self._main(args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 223, in _main\n",
            "    return run(options, args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/cli/base_command.py\", line 207, in exc_logging_wrapper\n",
            "    logger.debug(\"Exception information:\", exc_info=True)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 1465, in debug\n",
            "    self._log(DEBUG, msg, args, **kwargs)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 1624, in _log\n",
            "    self.handle(record)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 1634, in handle\n",
            "    self.callHandlers(record)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 1696, in callHandlers\n",
            "    hdlr.handle(record)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 968, in handle\n",
            "    self.emit(record)\n",
            "  File \"/usr/lib/python3.10/logging/handlers.py\", line 75, in emit\n",
            "    logging.FileHandler.emit(self, record)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 1218, in emit\n",
            "    StreamHandler.emit(self, record)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 1100, in emit\n",
            "    msg = self.format(record)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 943, in format\n",
            "    return fmt.format(record)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/pip/_internal/utils/logging.py\", line 112, in format\n",
            "    formatted = super().format(record)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 686, in format\n",
            "    record.exc_text = self.formatException(record.exc_info)\n",
            "  File \"/usr/lib/python3.10/logging/__init__.py\", line 636, in formatException\n",
            "    traceback.print_exception(ei[0], ei[1], tb, None, sio)\n",
            "  File \"/usr/lib/python3.10/traceback.py\", line 119, in print_exception\n",
            "    te = TracebackException(type(value), value, tb, limit=limit, compact=True)\n",
            "  File \"/usr/lib/python3.10/traceback.py\", line 502, in __init__\n",
            "    self.stack = StackSummary.extract(\n",
            "  File \"/usr/lib/python3.10/traceback.py\", line 383, in extract\n",
            "    f.line\n",
            "  File \"/usr/lib/python3.10/traceback.py\", line 306, in line\n",
            "    self._line = linecache.getline(self.filename, self.lineno)\n",
            "  File \"/usr/lib/python3.10/linecache.py\", line 30, in getline\n",
            "    lines = getlines(filename, module_globals)\n",
            "  File \"/usr/lib/python3.10/linecache.py\", line 46, in getlines\n",
            "    return updatecache(filename, module_globals)\n",
            "  File \"/usr/lib/python3.10/linecache.py\", line 136, in updatecache\n",
            "    with tokenize.open(fullname) as fp:\n",
            "  File \"/usr/lib/python3.10/tokenize.py\", line 396, in open\n",
            "    encoding, lines = detect_encoding(buffer.readline)\n",
            "  File \"/usr/lib/python3.10/tokenize.py\", line 365, in detect_encoding\n",
            "    first = read_or_stop()\n",
            "  File \"/usr/lib/python3.10/tokenize.py\", line 323, in read_or_stop\n",
            "    return readline()\n",
            "KeyboardInterrupt\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import community as louvain\n",
        "from sklearn import cluster\n",
        "from sklearn.metrics import normalized_mutual_info_score"
      ],
      "metadata": {
        "id": "ezxHkLgTS83R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2=df.fillna(0)"
      ],
      "metadata": {
        "id": "4ia1pShraxDw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "dataFrame = df2.drop(0)\n",
        "#dataFrame = dataFrame.drop(1)\n",
        "dataFrame.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "uaDi-1EjDr1b",
        "outputId": "eb31041d-c4b5-4005-9a09-6ad48a0ef34d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.070513</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.076555</td>\n",
              "      <td>0.048583</td>\n",
              "      <td>0.060241</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>...</td>\n",
              "      <td>0.070270</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.042553</td>\n",
              "      <td>0.053738</td>\n",
              "      <td>0.032895</td>\n",
              "      <td>0.063830</td>\n",
              "      <td>0.066869</td>\n",
              "      <td>0.053452</td>\n",
              "      <td>0.047863</td>\n",
              "      <td>0.037433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8.0</td>\n",
              "      <td>0.043860</td>\n",
              "      <td>0.012658</td>\n",
              "      <td>0.180000</td>\n",
              "      <td>0.562963</td>\n",
              "      <td>0.058824</td>\n",
              "      <td>0.067797</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.094891</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.064748</td>\n",
              "      <td>0.057743</td>\n",
              "      <td>0.048077</td>\n",
              "      <td>0.055944</td>\n",
              "      <td>0.052083</td>\n",
              "      <td>0.031553</td>\n",
              "      <td>0.046296</td>\n",
              "      <td>0.050360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>9.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 1001 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dd1dd212-38cc-482f-9faa-bfdc0651d978')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-dd1dd212-38cc-482f-9faa-bfdc0651d978');\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",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "dataFrame = dataFrame.drop([0], axis=1)\n",
        "\n",
        "dataFrame.index = np.arange(1, len(dataFrame) + 1)\n",
        "dataFrame.columns = range(1,dataFrame.columns.size+1)"
      ],
      "metadata": {
        "id": "iAo-XeKlFD_y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3=dataFrame\n",
        "print(df3.head(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dRkNWjyeFGaS",
        "outputId": "07f56080-2a13-4d4c-ade2-1dbab734afde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        1         2         3         4         5         6     7     \\\n",
            "1   1.000000  0.000000  0.037037  0.029586  0.022857  0.047059   0.0   \n",
            "2   0.000000  1.000000  0.000000  0.007463  0.000000  0.000000   0.0   \n",
            "3   0.037037  0.000000  1.000000  0.131707  0.058036  0.020833   0.0   \n",
            "4   0.029586  0.007463  0.131707  1.000000  0.046332  0.058480   0.0   \n",
            "5   0.022857  0.000000  0.058036  0.046332  1.000000  0.039106   0.0   \n",
            "6   0.047059  0.000000  0.020833  0.058480  0.039106  1.000000   0.0   \n",
            "7   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   0.0   \n",
            "8   0.070513  0.000000  0.076555  0.048583  0.060241  0.035714   0.0   \n",
            "9   0.043860  0.012658  0.180000  0.562963  0.058824  0.067797   0.0   \n",
            "10  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   0.0   \n",
            "\n",
            "        8         9     10    ...      991   992       993       994   \\\n",
            "1   0.070513  0.043860   0.0  ...  0.056075   0.0  0.037383  0.025210   \n",
            "2   0.000000  0.012658   0.0  ...  0.013699   0.0  0.000000  0.003067   \n",
            "3   0.076555  0.180000   0.0  ...  0.075472   0.0  0.069620  0.049505   \n",
            "4   0.048583  0.562963   0.0  ...  0.073298   0.0  0.051813  0.055300   \n",
            "5   0.060241  0.058824   0.0  ...  0.071429   0.0  0.061224  0.047511   \n",
            "6   0.035714  0.067797   0.0  ...  0.034483   0.0  0.026087  0.027548   \n",
            "7   0.000000  0.000000   0.0  ...  0.000000   0.0  0.000000  0.000000   \n",
            "8   1.000000  0.062500   0.0  ...  0.070270   0.0  0.042553  0.053738   \n",
            "9   0.062500  1.000000   0.0  ...  0.094891   0.0  0.064748  0.057743   \n",
            "10  0.000000  0.000000   0.0  ...  0.000000   0.0  0.000000  0.000000   \n",
            "\n",
            "        995       996       997       998       999       1000  \n",
            "1   0.043478  0.031128  0.035019  0.023747  0.017341  0.048077  \n",
            "2   0.000000  0.004444  0.000000  0.000000  0.002049  0.000000  \n",
            "3   0.040000  0.045307  0.065789  0.039627  0.042705  0.050314  \n",
            "4   0.031447  0.050000  0.049853  0.032258  0.045531  0.041451  \n",
            "5   0.030488  0.074184  0.064516  0.058952  0.045151  0.040404  \n",
            "6   0.012821  0.034221  0.041985  0.023316  0.022945  0.017544  \n",
            "7   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
            "8   0.032895  0.063830  0.066869  0.053452  0.047863  0.037433  \n",
            "9   0.048077  0.055944  0.052083  0.031553  0.046296  0.050360  \n",
            "10  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
            "\n",
            "[10 rows x 1000 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "G = nx.from_pandas_adjacency(df3)"
      ],
      "metadata": {
        "id": "g8iXe_Ddnssp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#degree centrality\n",
        "deg_centrality = nx.degree_centrality(G)\n",
        "print(sorted(deg_centrality.items(), key=lambda x:x[1], reverse=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-wcVymSBvbdg",
        "outputId": "475f1fb0-64a3-493b-c18b-dc66c3122ec8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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0.0), (12, 0.0), (10, 0.0), (7, 0.0)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Page Rank\n",
        "pageranks = nx.pagerank(G) # A dictionary\n",
        "print(sorted(pageranks.items(), key=lambda x: x[1], reverse=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IHe3adsONJqc",
        "outputId": "38b0e9eb-1cb9-4a3b-bcad-35454f4ccf64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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0.00016944368495145213), (60, 0.00016944368495145213), (54, 0.00016944368495145213), (19, 0.00016944368495145213), (14, 0.00016944368495145213), (12, 0.00016944368495145213), (10, 0.00016944368495145213), (7, 0.00016944368495145213)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "degrees = [len(list(G.neighbors(n))) for n in G.nodes()]\n",
        "\n",
        "# Plot a histogram of the degree centrality distribution of the graph.\n",
        "plt.figure()\n",
        "plt.hist(list(deg_centrality.values()))\n",
        "plt.show()\n",
        "\n",
        "# Plot a histogram of the degree distribution of the graph\n",
        "plt.figure()\n",
        "plt.hist(degrees)\n",
        "plt.show()\n",
        "\n",
        "# Plot a scatter plot of the centrality distribution and the degree distribution\n",
        "plt.figure()\n",
        "plt.scatter(degrees, list(deg_centrality.values()))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nF22KdhzGaJB",
        "outputId": "f843045f-d6c6-49e8-fbf8-c87ec88184b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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t375d27Zt0+rVqxO/CgAAMKwkFCgXLlxQWVmZXnjhBV111VX2/nA4rBdffFF/93d/p7vuukv5+fnaunWrDh48qEOHDkmSXn/9dZ04cUI/+tGPdMstt2ju3Ll66qmntHnzZnV0dPTPVQEAgCEtoUDx+/0qKSlRUVFRzP6GhgZ1dnbG7J82bZomT56sQCAgSQoEApoxY4Y8Ho89pri4WJFIRMePH09kOgAAYJhJj/eEXbt26ec//7mOHj162bFgMCiHw6HMzMyY/R6PR8Fg0B7zm3HSe7z32JVEo1FFo1H750gkEu+0AQDAEBLXHZSWlhb95V/+pXbs2KFRo0YN1JwuU1VVJbfbbW+5ubmD9twAAGDwxRUoDQ0Namtr05e//GWlp6crPT1dBw4c0KZNm5Seni6Px6OOjg61t7fHnBcKheT1eiVJXq/3sk/19P7cO+bTKisrFQ6H7a2lpSWeaQMAgCEmrkC5++671djYqGPHjtnbrFmzVFZWZv/vjIwM1dfX2+c0NTWpublZPp9PkuTz+dTY2Ki2tjZ7TF1dnVwul/Ly8q74vE6nUy6XK2YDAADDV1zvQRk/frxuvvnmmH1jx47VhAkT7P0LFixQRUWFsrKy5HK5tGTJEvl8PhUWFkqS5syZo7y8PM2fP1/V1dUKBoNatWqV/H6/nE5nP10WAAAYyuJ+k+zvsmHDBqWmpqq0tFTRaFTFxcXasmWLfTwtLU21tbVatGiRfD6fxo4dq/Lycq1du7a/pwIAAIaoFMuyrGRPIl6RSERut1vhcJhf9wAAjDZl5Z5kTyEhp9eX9PtjxvP6zd/iAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGCeuQHnuuec0c+ZMuVwuuVwu+Xw+7d271z5+6dIl+f1+TZgwQePGjVNpaalCoVDMYzQ3N6ukpERjxoxRdna2li9frq6urv65GgAAMCzEFSjXXHON1q9fr4aGBr3zzju66667dP/99+v48eOSpGXLlumVV17R7t27deDAAbW2tmrevHn2+d3d3SopKVFHR4cOHjyo7du3a9u2bVq9enX/XhUAABjSUizLsvryAFlZWfrhD3+oBx98UBMnTtTOnTv14IMPSpJOnjyp6dOnKxAIqLCwUHv37tW9996r1tZWeTweSVJNTY1WrFihs2fPyuFwfK7njEQicrvdCofDcrlcfZk+AAADasrKPcmeQkJOry/p98eM5/U74fegdHd3a9euXbp48aJ8Pp8aGhrU2dmpoqIie8y0adM0efJkBQIBSVIgENCMGTPsOJGk4uJiRSIR+y7MlUSjUUUikZgNAAAMX3EHSmNjo8aNGyen06nvfOc7eumll5SXl6dgMCiHw6HMzMyY8R6PR8FgUJIUDAZj4qT3eO+xz1JVVSW3221vubm58U4bAAAMIXEHyk033aRjx47p8OHDWrRokcrLy3XixImBmJutsrJS4XDY3lpaWgb0+QAAQHKlx3uCw+HQ7//+70uS8vPzdfToUf393/+9Hn74YXV0dKi9vT3mLkooFJLX65Ukeb1eHTlyJObxej/l0zvmSpxOp5xOZ7xTBQAAQ1Sfvwelp6dH0WhU+fn5ysjIUH19vX2sqalJzc3N8vl8kiSfz6fGxka1tbXZY+rq6uRyuZSXl9fXqQAAgGEirjsolZWVmjt3riZPnqzz589r586devPNN/Xaa6/J7XZrwYIFqqioUFZWllwul5YsWSKfz6fCwkJJ0pw5c5SXl6f58+erurpawWBQq1atkt/v5w4JAACwxRUobW1t+ta3vqUzZ87I7XZr5syZeu211/RHf/RHkqQNGzYoNTVVpaWlikajKi4u1pYtW+zz09LSVFtbq0WLFsnn82ns2LEqLy/X2rVr+/eqAADAkNbn70FJBr4HBQAwVPA9KJ8YlO9BAQAAGCgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOPEFShVVVW69dZbNX78eGVnZ+uBBx5QU1NTzJhLly7J7/drwoQJGjdunEpLSxUKhWLGNDc3q6SkRGPGjFF2draWL1+urq6uvl8NAAAYFuIKlAMHDsjv9+vQoUOqq6tTZ2en5syZo4sXL9pjli1bpldeeUW7d+/WgQMH1Nraqnnz5tnHu7u7VVJSoo6ODh08eFDbt2/Xtm3btHr16v67KgAAMKSlWJZlJXry2bNnlZ2drQMHDugP/uAPFA6HNXHiRO3cuVMPPvigJOnkyZOaPn26AoGACgsLtXfvXt17771qbW2Vx+ORJNXU1GjFihU6e/asHA7H73zeSCQit9utcDgsl8uV6PQBABhwU1buSfYUEnJ6fUm/P2Y8r999eg9KOByWJGVlZUmSGhoa1NnZqaKiInvMtGnTNHnyZAUCAUlSIBDQjBkz7DiRpOLiYkUiER0/fvyKzxONRhWJRGI2AAAwfCUcKD09PVq6dKlmz56tm2++WZIUDAblcDiUmZkZM9bj8SgYDNpjfjNOeo/3HruSqqoqud1ue8vNzU102gAAYAhIOFD8fr9+8YtfaNeuXf05nyuqrKxUOBy2t5aWlgF/TgAAkDzpiZy0ePFi1dbW6q233tI111xj7/d6vero6FB7e3vMXZRQKCSv12uPOXLkSMzj9X7Kp3fMpzmdTjmdzkSmCgAAhqC47qBYlqXFixfrpZde0htvvKGpU6fGHM/Pz1dGRobq6+vtfU1NTWpubpbP55Mk+Xw+NTY2qq2tzR5TV1cnl8ulvLy8vlwLAAAYJuK6g+L3+7Vz5079x3/8h8aPH2+/Z8Ttdmv06NFyu91asGCBKioqlJWVJZfLpSVLlsjn86mwsFCSNGfOHOXl5Wn+/Pmqrq5WMBjUqlWr5Pf7uUsCAAAkxRkozz33nCTpD//wD2P2b926VY8++qgkacOGDUpNTVVpaami0aiKi4u1ZcsWe2xaWppqa2u1aNEi+Xw+jR07VuXl5Vq7dm3frgQAAAwbffoelGThe1AAAEMF34PyiUH7HhQAAICBQKAAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOOkJ3sCJpqyck+ypxC30+tLkj0FAAD6DXdQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBx4g6Ut956S/fdd59ycnKUkpKil19+Oea4ZVlavXq1Jk2apNGjR6uoqEjvvfdezJhz586prKxMLpdLmZmZWrBggS5cuNCnCwEAAMNH3IFy8eJFffGLX9TmzZuveLy6ulqbNm1STU2NDh8+rLFjx6q4uFiXLl2yx5SVlen48eOqq6tTbW2t3nrrLS1cuDDxqwAAAMNKerwnzJ07V3Pnzr3iMcuytHHjRq1atUr333+/JOlf/uVf5PF49PLLL+uRRx7Ru+++q3379uno0aOaNWuWJOnZZ5/VPffco2eeeUY5OTl9uBwAADAc9Ot7UE6dOqVgMKiioiJ7n9vtVkFBgQKBgCQpEAgoMzPTjhNJKioqUmpqqg4fPnzFx41Go4pEIjEbAAAYvvo1UILBoCTJ4/HE7Pd4PPaxYDCo7OzsmOPp6enKysqyx3xaVVWV3G63veXm5vbntAEAgGGGxKd4KisrFQ6H7a2lpSXZUwIAAAOoXwPF6/VKkkKhUMz+UChkH/N6vWpra4s53tXVpXPnztljPs3pdMrlcsVsAABg+OrXQJk6daq8Xq/q6+vtfZFIRIcPH5bP55Mk+Xw+tbe3q6GhwR7zxhtvqKenRwUFBf05HQAAMETF/SmeCxcu6P3337d/PnXqlI4dO6asrCxNnjxZS5cu1dNPP60bbrhBU6dO1eOPP66cnBw98MADkqTp06fra1/7mh577DHV1NSos7NTixcv1iOPPMIneAAAgKQEAuWdd97RV7/6VfvniooKSVJ5ebm2bdum73//+7p48aIWLlyo9vZ23XHHHdq3b59GjRpln7Njxw4tXrxYd999t1JTU1VaWqpNmzb1w+UAAIDhIMWyLCvZk4hXJBKR2+1WOBwekPejTFm5p98fc6CdXl+S7CkAAK5gKL6mSAPzuhLP6/eQ+BQPAAAYWQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBx0pM9AQAAPq8pK/ckewoYJNxBAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxuGvGQPACMVfBobJuIMCAACMQ6AAAADjECgAAMA4BAoAADBOUt8ku3nzZv3whz9UMBjUF7/4RT377LO67bbbkjklDKKh+Aa90+tLkj0FABgRkhYo//Zv/6aKigrV1NSooKBAGzduVHFxsZqampSdnZ2saQEwAPEKIMWyLCsZT1xQUKBbb71V//AP/yBJ6unpUW5urpYsWaKVK1f+1nMjkYjcbrfC4bBcLle/z20o/scRAID+NBDRHc/rd1LuoHR0dKihoUGVlZX2vtTUVBUVFSkQCFw2PhqNKhqN2j+Hw2FJ/3+hA6En+vGAPC4AAEPFQLzG9j7m57k3kpRA+eijj9Td3S2PxxOz3+Px6OTJk5eNr6qq0pNPPnnZ/tzc3AGbIwAAI5l748A99vnz5+V2u3/rmCHxTbKVlZWqqKiwf+7p6dG5c+c0YcIEpaSk9OtzRSIR5ebmqqWlZUB+fYS+Y43MxxqZjzUaGobbOlmWpfPnzysnJ+d3jk1KoFx99dVKS0tTKBSK2R8KheT1ei8b73Q65XQ6Y/ZlZmYO5BTlcrmGxf8ZhjPWyHyskflYo6FhOK3T77pz0isp34PicDiUn5+v+vp6e19PT4/q6+vl8/mSMSUAAGCQpP2Kp6KiQuXl5Zo1a5Zuu+02bdy4URcvXtSf/umfJmtKAADAEEkLlIcfflhnz57V6tWrFQwGdcstt2jfvn2XvXF2sDmdTq1Zs+ayXynBHKyR+Vgj87FGQ8NIXqekfQ8KAADAZ+Fv8QAAAOMQKAAAwDgECgAAMA6BAgAAjDMiA2Xz5s2aMmWKRo0apYKCAh05cuS3jt+9e7emTZumUaNGacaMGXr11VcHaaYjVzxr9MILL+jOO+/UVVddpauuukpFRUW/c03Rd/H+O+q1a9cupaSk6IEHHhjYCSLuNWpvb5ff79ekSZPkdDp144038t+7QRDvOm3cuFE33XSTRo8erdzcXC1btkyXLl0apNkOImuE2bVrl+VwOKx//ud/to4fP2499thjVmZmphUKha44/u2337bS0tKs6upq68SJE9aqVausjIwMq7GxcZBnPnLEu0bf/OY3rc2bN1v/9V//Zb377rvWo48+arndbuvDDz8c5JmPHPGuUa9Tp05Zv/d7v2fdeeed1v333z84kx2h4l2jaDRqzZo1y7rnnnusn/3sZ9apU6esN9980zp27Nggz3xkiXedduzYYTmdTmvHjh3WqVOnrNdee82aNGmStWzZskGe+cAbcYFy2223WX6/3/65u7vbysnJsaqqqq44/qGHHrJKSkpi9hUUFFh//ud/PqDzHMniXaNP6+rqssaPH29t3759oKY44iWyRl1dXdbtt99u/dM//ZNVXl5OoAyweNfoueees6677jqro6NjsKYIK/518vv91l133RWzr6Kiwpo9e/aAzjMZRtSveDo6OtTQ0KCioiJ7X2pqqoqKihQIBK54TiAQiBkvScXFxZ85Hn2TyBp92scff6zOzk5lZWUN1DRHtETXaO3atcrOztaCBQsGY5ojWiJr9NOf/lQ+n09+v18ej0c333yz1q1bp+7u7sGa9oiTyDrdfvvtamhosH8N9MEHH+jVV1/VPffcMyhzHkxD4q8Z95ePPvpI3d3dl31brcfj0cmTJ694TjAYvOL4YDA4YPMcyRJZo09bsWKFcnJyLgtL9I9E1uhnP/uZXnzxRR07dmwQZohE1uiDDz7QG2+8obKyMr366qt6//339d3vflednZ1as2bNYEx7xElknb75zW/qo48+0h133CHLstTV1aXvfOc7+uu//uvBmPKgGlF3UDD8rV+/Xrt27dJLL72kUaNGJXs6kHT+/HnNnz9fL7zwgq6++upkTwefoaenR9nZ2Xr++eeVn5+vhx9+WD/4wQ9UU1OT7KnhN7z55ptat26dtmzZop///Of6yU9+oj179uipp55K9tT63Yi6g3L11VcrLS1NoVAoZn8oFJLX673iOV6vN67x6JtE1qjXM888o/Xr12v//v2aOXPmQE5zRIt3jX71q1/p9OnTuu++++x9PT09kqT09HQ1NTXp+uuvH9hJjzCJ/DuaNGmSMjIylJaWZu+bPn26gsGgOjo65HA4BnTOI1Ei6/T4449r/vz5+rM/+zNJ0owZM3Tx4kUtXLhQP/jBD5SaOnzuOwyfK/kcHA6H8vPzVV9fb+/r6elRfX29fD7fFc/x+Xwx4yWprq7uM8ejbxJZI0mqrq7WU089pX379mnWrFmDMdURK941mjZtmhobG3Xs2DF7++M//mN99atf1bFjx5SbmzuY0x8REvl3NHv2bL3//vt2PErSL3/5S02aNIk4GSCJrNPHH398WYT0RqU13P60XrLfpTvYdu3aZTmdTmvbtm3WiRMnrIULF1qZmZlWMBi0LMuy5s+fb61cudIe//bbb1vp6enWM888Y7377rvWmjVr+JjxAIt3jdavX285HA7r3//9360zZ87Y2/nz55N1CcNevGv0aXyKZ+DFu0bNzc3W+PHjrcWLF1tNTU1WbW2tlZ2dbT399NPJuoQRId51WrNmjTV+/HjrX//1X60PPvjAev31163rr7/eeuihh5J1CQNmxAWKZVnWs88+a02ePNlyOBzWbbfdZh06dMg+9pWvfMUqLy+PGf/jH//YuvHGGy2Hw2F94QtfsPbs2TPIMx554lmja6+91pJ02bZmzZrBn/gIEu+/o99EoAyOeNfo4MGDVkFBgeV0Oq3rrrvO+pu/+Rurq6trkGc98sSzTp2dndYTTzxhXX/99daoUaOs3Nxc67vf/a7161//evAnPsBSLGu43RMCAABD3Yh6DwoAABgaCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADG+T+JjP0ICYav+QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from networkx.algorithms.community.label_propagation import label_propagation_communities\n",
        "\n",
        "communities = label_propagation_communities(G)\n",
        "print([community for community in communities])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RMgyHkhz1nx6",
        "outputId": "1cffe021-170a-4a3b-e367-daa5b6354a39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import community.community_louvain as community\n",
        "from sklearn.metrics import silhouette_score\n",
        "\n",
        "# Apply the Louvain algorithm for community detection\n",
        "partition = community.best_partition(G)\n",
        "\n",
        "# Convert the partition into communities\n",
        "communities = []\n",
        "for node, comm_id in partition.items():\n",
        "    while comm_id >= len(communities):\n",
        "        communities.append(set())\n",
        "    communities[comm_id].add(node)\n",
        "\n",
        "# Compute Conductance\n",
        "conductance_scores = []\n",
        "for comm in communities:\n",
        "    cut_edges = nx.cut_size(G, comm)\n",
        "    volume = nx.volume(G, comm)\n",
        "    if volume == 0:  # Skip communities with zero volume\n",
        "        continue\n",
        "    conductance = cut_edges / volume\n",
        "    conductance_scores.append(conductance)\n",
        "\n",
        "# Print the Conductance scores\n",
        "print(f\"Conductance scores: {conductance_scores}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gPZKAAkO2501",
        "outputId": "2b538d37-2daf-4384-8e66-9843deb0a9f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conductance scores: [0.5506797532992904, 0.9940652818991098, 0.9105909302408819, 0.9509807164356828, 0.7692307692307693, 0.9224603914259086, 0.9314541542607524, 0.8757820498236834, 0.9412392884119501, 0.9548872180451128, 0.6666666666666666, 0.8, 0.0, 0.9278059258913353, 0.625]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_communities = len(set(partition.values()))\n",
        "\n",
        "# Print the result\n",
        "print(\"Number of distinct communities: \", num_communities)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HVdLKnfSKQ2p",
        "outputId": "ec79ea26-a7a4-4e72-ddba-f7e1bb747569"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of distinct communities:  150\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "\n",
        "# Visualize Conductance Scores\n",
        "plt.bar(range(len(conductance_scores)), conductance_scores)\n",
        "plt.xlabel(\"Community\")\n",
        "plt.ylabel(\"Conductance Score\")\n",
        "plt.title(\"Conductance Scores of Communities\")\n",
        "plt.show()\n",
        "\n",
        "# Create Network Visualization of Communities\n",
        "G = nx.from_pandas_adjacency(df3)  # Replace with your adjacency matrix\n",
        "\n",
        "# Set node colors based on communities\n",
        "node_colors = []\n",
        "for node in G.nodes:\n",
        "    for i, comm in enumerate(communities):\n",
        "        if node in comm:\n",
        "            node_colors.append(i)\n",
        "\n",
        "# Draw the graph with node colors representing communities\n",
        "pos = nx.spring_layout(G)  # Define node positions\n",
        "nx.draw_networkx_nodes(G, pos, node_color=node_colors, cmap=plt.cm.Set1)\n",
        "nx.draw_networkx_edges(G, pos)\n",
        "nx.draw_networkx_labels(G, pos)\n",
        "plt.title(\"Network Visualization of Communities\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 883
        },
        "id": "-r5P2vKeSEaj",
        "outputId": "bb9d8d78-0c58-43a7-a044-cce9d43ec22d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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7e2nsOq/k+q+kjtrHN/UZvNx5/kgcDgdDhw7lmWeeaXB/tWcuICCAQ4cOsWbNGlavXs3q1auZN28eU6ZMYf78+X96OxWuDMUg+JfwwgsvsGDBAmdgTm1iYmIQQhAdHX1JFzk0/nIB2a04Z84c1q9fj5+fHy1btkSSJNq0acO2bdvYtm1bnY46MjISgFOnTtWr6+TJk/j5+eHq6tqk65MkiYULF3Lddddx0003sXr1agYMGNCkY/V6PePGjeObb74hOzubZcuWMWjQIIKCgi57rEqlYvDgwQwePJh3332XWbNmMX36dDZt2sSQIUOcI96LxVouHkEePXqU06dPM3/+/DrBYL/HVfrII49w6NAhtm7dSmBgYJ19hYWFbNiwgZdffrlO4OSZM2fq1XMl6oyRkZGNfp/V+38v/v7+uLi4NHoelUp1VR36mDFjWLx4MQsWLGDatGmXLFv72R00aFCdfadOnfpDrvOPoqnP4O/lSp6TmJgYysrKnB6BS6HT6RgzZgxjxozB4XDwwAMP8NlnnzFjxgyaN2/+e5qs8AejTBn8S4iJieGWW27hs88+Iysrq86+sWPHolarefnll+uNTIQQ5OfnO/92dXVt1J3ct29fKisref/99+nTp4/zBdG3b1++/fZbMjIynHPEIM/5duzYkfnz59d5WR07doy1a9cyatSoK7pGnU7HihUr6Nq1K2PGjGHPnj1NPnby5MlYrVbuvfdecnNzLztdALLL/WI6duwIQGVlJSDfd6BOBobdbufzzz+vc1z1qKz2/RdC8H//939NvobazJs3j88++4yPP/6Ybt261dvf0PmABlUoq42ypijQjRo1ij179pCQkODcVl5ezueff05UVBStW7e+gqtoGLVazbBhw1i5ciUpKSnO7dnZ2SxatIg+ffrg4eFxxfWOGzeOdu3a8frrr9dpfzWlpaVMnz4dgC5duhAQEMCnn37q/K4BVq9ezYkTJ5xZNf8EIiMjUavVdZ5BgLlz5/6h57mS52T8+PEkJCSwZs2aevuKioqcyqC13z0gG+Dt27cHqHPfFf4ZKB6CfxHTp0/n22+/5dSpU7Rp08a5PSYmhtdee41p06aRkpLC9ddfj7u7O8nJyfzwww/cc889PPXUUwDEx8ezZMkSnnjiCbp27YqbmxtjxowBoGfPnmg0Gk6dOsU999zjrL9fv3588sknAHUMAoC33nqLkSNH0rNnT+68805n2qGnp2eTddJrYzQa+fnnnxk0aBAjR45ky5YtTZJp7t+/P2FhYc40xbFjx172mFdeeYWtW7cyevRoIiMjycnJYe7cuYSFhTnjJNq0aUOPHj2YNm0aBQUF+Pj48N1339WTQm7ZsiUxMTE89dRTpKen4+HhwfLly69q3jYvL48HHniA1q1bo9frWbBgQZ39N9xwAx4eHs4UOKvVSmhoKGvXriU5ObleffHx8YD8/Nx8881otVrGjBnToPfmueeeY/HixYwcOZJHHnkEHx8f5s+fT3JyMsuXL0el+mPGEK+99hrr1q2jT58+PPDAA2g0Gj777DMqKyt58803r6pOrVbLihUrGDJkCP369WP8+PH07t0brVZLYmIiixYtwtvbm9dffx2tVsucOXOYOnUq/fv3Z+LEic60w6ioKB5//PE/5Dr/CDw9Pbnpppv48MMPkSSJmJgYfv755ybFBl0JV/KcPP3006xatYprrrmG22+/nfj4eMrLyzl69Cjff/89KSkp+Pn5cdddd1FQUMCgQYMICwsjNTWVDz/8kI4dOzrjUhT+QfwtuQ0Kl6R22uHFVKcVNSRMtHz5ctGnTx/h6uoqXF1dRcuWLcWDDz5YJ/WurKxMTJo0SXh5eTUo9NK1a1cBiN27dzu3paWlCUCEh4c32N7169eL3r17C6PRKDw8PMSYMWMaFSZqKMWvIWGivLw80bp1axEUFNRkoZunn35aAGL8+PEN7r843W/Dhg3iuuuuEyEhIUKn04mQkBAxceLEeqlU586dE0OGDHGK/zz//PNi3bp19VLBjh8/LoYMGSLc3NyEn5+fuPvuu8Xhw4frpYZdLu2wOp2ssU91WlhaWpq44YYbhJeXl/D09BQ33XSTyMjIqJdOJoScJhYaGipUKlWThYm8vLyEwWAQ3bp1a1SYaNmyZXW2N5QK1xgHDhwQw4cPF25ubsLFxUUMHDhQ7Ny5s8H6mpJ2WE1hYaF48cUXRbt27YSLi4swGAyibdu2Ytq0aSIzM7NO2SVLlohOnToJvV4vfHx8LilMdDGNCYRFRkaK0aNHO/9u7Pfc2G+iofPl5uaKG2+8Ubi4uAhvb29x7733imPHjjUqTHQxDT1zv/c5KS0tFdOmTRPNmzcXOp1O+Pn5iV69eom3337bKRT2/fffi2HDhomAgACh0+lERESEuPfee+t9Dwr/DCQh/oboHwUFBQUFBYV/FEoMgYKCgoKCgoJiECgoKCgoKCgoBoGCgoKCgoICikGgoKCgoKCggGIQKCgoKCgoKNBEHQKHw0FGRgbu7u5XpGaloKCgoKCg8PchhKC0tJSQkJDL6og0ySDIyMj4w7XBFRQUFBQUFP4aLly4QFhY2CXLNMkgqF5S9MKFC1clKaqgoKCgoKDw11NSUkJ4eHiTlgZvkkFQPU3g4eGhGAQKCgoKCgr/Mpoy3a8EFSooKCgoKCgoixspKPyvIqxWbKfPYDl6BHt6BthsSK6uaFu2RNu+HeqAgKuqt7KkkIKkREqzU3HYrKh1BjxDm+Ed1Rqti9sffBUKCgp/FIpBoKDwP4YtKZmyb77BtGgxorxc3qipehUIAXY7ANr27XG7YyrGMdcgGQyXrNNqKiNl2ypOr1lISXqSvFGSkCQJ4XBUlZLwbdGe2BG3EN59GGqt7k+4OgUFhaulSYsblZSU4OnpSXFxsRJDoKDwO3A4HJSWlmK321GpVLi5uaHR/DV2uTCbKXn3PcrmfgIqlbPjbxSVChwO1GFheH/wPvru3evXKQSp239m31evYDWVV29ttEpJUiGEA6NPED0efIOgtj1+xxUpKChcjivpvxWDQEHhT6asrIyTJ09y/vx5CgoKcDhHzHKgj5eXF2FhYbRq1QovL68/pQ22tDTyJ92CLSlJ9gJcCWo1OBy4P/oI7k896QxOslVWkPDhM6TtXQ+SdGX1SioQDuJG30anW55Bukx+tIKCwtVxJf23MmWgoPAnUVFRQUJCAufOnQPk0fTFCCEoLCykqKiIo0ePEhIcRLdO7fHy9kHr8scIgdnS08m99joceflXbgyA05NQ+v7/IUwmPF6cgd1ayeZZd5N76mD1hVxZnUI2ik79Mh9LeSnd731VMQoUFP5mFINAQeFPICUlhS1btmCxWBo0BC6mukxGegY/pl1Af3oLrsWp+Ma0I7hjH6L7X4/ezeuK2yEsFvJvnSIbA5ebImgCZZ9/gSY2lsP5B2RjQDguf9BlSN68AvegCNrccK9zm91h50LpBZJLkii3lqOSVPgYfIjxbI6f0U9RTFVQ+BNQpgwUFC6B3WqhKPUURedPYTWVIalUGH0C8YlujWtAWIMd08mTJ9m6devVn1QIkCR0SbvQp+wCQKXW0GzQODpMfBydy6UFRvbv38/06dPZuXMnwmKhMzDdw5M2FwXxWYXgw7JSvjeVk2W3E6RWM8HFlQfd3NHUuq5DFgufl5WysdJMuRAIQKOSiPDUc0MbX+JD5PY4hGBbSjF708tIKTRTbrHj76qlZ4QHo+N80KnregDWnS0kMcfEuYIK8k02Jt54PTM+e51fk39hZ8YOrA4rABISolZcgpfei+FRIxgeNRI/o9/V3mUFhf8JlBgCBYXfgRCC7MTdnPltAen7NyMc8shaUqlkz3jVqFjn7kXzweNpPmQ8rv6hAKSmprJmzZo/rC36UxvRpR12nl/v4UOPB2cT3L53g+V37NnBoH6D8Ar0ovWIWMrzsjixJQ1LmZWnbmxF92JBu2Ml6KyC+wvy+dlcwQQXFzpodRywWFhWYWKSiytvenk767ynIJ8dlWbcVCpaabUUe2k4mldOpU3upu+MD2JwjBdmq4M7fjhNc18DnYPd8NBrOJNfwdbUYlr5uTB9QHgdA+qRn89itjmI8TFyLLuc5r3DiX2yHWpJjV1c2puhQoVAcFPseCbETUSr1v7+m62g8F+IYhAoKFwlpVmp7Pp4GnmnDyKp1E5joDEklYoKi4NdRJBUbCdh1y7Ky8u57bbb6NWrV73ymzZtYvPmzeTl5eHm5kZ8fDzXXXcder2+Trni4mJWrVrFiRMnKCkqwNugIT7Ujetb++Ou19DzwdlE9R3jLJ9rymHZ6WW8eter5J8qYOjcQajd1CCBucDMxge3ENDBjy7PxWOosBP+w3neXXaCR93cedrD01nPq8VFfF5exhr/AFpXeRT2WSppr9WhkyRyfdWcaGUks9TCs78lodeo8DBoeGdkM2x2QVJhBbF+LnWuZUViHt8n5jGtfzjtAl1r2lxuxc9Fw4UIN6Z/dIiQnkF0fLRD078sZO9BmHsYM3rMJMg16IqO/T3YrRaK085iLS9FUkkYvPxxD4pU4iAU/nEoQYUKCg0igGwgGahAfvxDgChAQ/LWlez57EWnEXA5Y0Au46C00sLnv6zF18uD0NBQTp8+3WDZ5cuXs3btWjp37sygQYPIzMxk06ZNZGZm8uijjzrLmc1m5syZQ2VlJf379cPXqCbr8DbWni3keI6J14dGkfDxs+jcPAju2I+1qWv4z9HPsTqs5B3Px7+TH2p3tbM+g48B3zY+ZO/PxVZhw2zUsMZFHgc07xwAZyudZa81uvBZeRk/VVQ4DYIuuhpjJTNYC0IQ7K4jzEtPdpkVk1W+Txq1VM8YAOgS6sb3iXkUmcqI9a7E22DGoLEjISg06LGFCVQIpEukKzZ6/xGkl6XzzNYnebPf2wS5Bl9xHU2lsqSQpM0rSNm2iuK0c/WeD7XOgG/z9jQbdCMR3Yej1ukbqUlB4Z+JYhAo/A9wAlgGbAZKGtiv4+yGAPZ+fuqqavcyaPjoxvZoh95PSup53njjjXpliouLWb9+PT169GDq1KnO7YGBgXz33XccPnyYDh3k0fGRI0fIz8/noYceol27dgC4eJXgpjvGiuP5nC+qJMrHSMJHz5E2dQwb8nY463NYHah1ai5GrVfjsDkoPV+Kd5w3drs87fHjpHA4XMb1K7OQAGOVS/+I1VKvDgHkGCXMFjsmi52cMisVVgedghsfdagkB27aIgD6hhfSOVCuR1U1cxCAibisQh5wQJ+iTK7NdGGVfxSlmqaLFjmEgxJLCS/ueIEPB32MXnNpEaUrxW6p5OiyDzn5y3zZCGjEqWq3mMk5sY+c43s4MG8WnaY8S3T/65UASIV/DYpBoPBfTBrwKrAfUAMNj/izjlVctTEAoFWrcI3qxMVdaEZGBj/99BPnz5+nsLAQh8NBampqnc6/a9eufPfdd+zbt48OHTpgt9tZtmwZAMePH5cNAocDS1gHvAwnq84n5/xvTDzP3L7TURvUjFo8HADXUFcKTxch7AJJLXdEDquDojNFAFQUmPEG3EJkCeGCE4WsHRaKyiG47qds9lhkb0FWAxkJFUaJb4/msiGpyLkt2lvP7Z0DG7wvPoYKeoZkcuMSCx56GNlCliuo3T2qgf0ZYLbBsiOCVY+dpWv4OdpOiSWlfUyd+s58f5asvTmYskzYKmwY/QwExAfQYlwMek892aZsFpz4ljvb3X2pr+uKKEw9xfZ3H6Es+0LTUiur4kss5SXs/uR5ziespufDb15VhoiCwl+NYhAo/JfyEzAbsFX9XdPB7d1bzvz5+WzaVEpKigUXlYoYbyPj2/oT7F53ZLrrQgm/niogo9SCSoIwTz1j4nzpFFKjyf/9sVxWLJ0PzHdumz+/5v+9evVCkiR27NiBJEnMnTuXyZMn069fP3Q6+XypqakAbNy4kYqKCgAOHz5M165d8fb2Jj3bxI8n8ukS6kaohx6z1cHiIznoNRL2Wv1U1IgIjn6WyKGPj9D8hmYIh9yRmgvljt5RKXdYAfH+GP2NHJ9/ArVezYrOnqTvzOSTrBI0gLmBzs+sVzEiwptmPga+PpiNViXha9Rid9QvG+1ZRPfgLGZtg/XJMHcUeDUwcD+QCX3mgUNApyAY3wbm7hPsn3WKOU+Vsjq+A6JqhF10rhjPKHdC+wSjMWooTSvj/LoL5OzPod+7fdAYNKw89yNDI4cR4RFZ/2RXSP7Zo2x89Xbslsqr0G+Qy2cd2cn6F29hyMxv0Xt4X+YYBYW/F8UgUPgvZDHwTqN758zJYseOMm66yRsfs47jCZWsPVvI8+uSeWVwFOGe8tzvmjMFzD+YQ6dgV26O8sfqEGxNKeat7Wk81iuUbmFyul3XMA88u14DKhV5eXn89NNP9OnTh8TERCorK5k8eTIZGRns2LGD+Ph4VCoV69evp1+/fpw5cwaAoqIiSkpK+OWXXxg5ciSrVq2ipKSEOXPmONvdJdwTtbDz0E9nKTLbkCQIcdeTZZLT82yVdoRDYAwwkrYpnbRN6QAY/A3EXBfN2eVJqI3ydEJlUSUVubLhse/NAwBsqDqPuyThKtUNjrPq9RSFh6KN9uPHXetwd3Xhhckj+Oqnzby1I4NXB9VkEER7FtMjJIvvjsGMTXBnJ7i/a8PfxYxNYNTIy662CYCne8Mt7SH2I1i3IoN7AjV8FtYGJImuz8bXO947zov9bx4ke28OoX1DUElqfk3+hfs6PNDo998UyvMy2PT6Hdgt5lprMVw5wmGnNDOFzW/czdBXF6PSKNkQCv9cFINA4d+HwwHF58FSBmoteISBrjp6fQOXMgYAnngikEWLolFLEj/eayKutTs9wj14bk0yq07k82CPEADWnCmkmY+Bp/rU6A30j/bkoZ/OsS2l2GkQhAf54VOVUZCSksJPP/1EUFAQO3bsoHfv3mg0GiIiIoiOjmbt2rX4+flRXl7OsWPHWLRoEWq1GqvVyg8//EBgYCDdu3dn1apVeHt7M2DAAHx9fTl69Cjbt2/HqJEY1MyL1WcKaeXnwrEck3M+3pRl4th/juPX3pfwQWHYKmyUJJeQdySf7L05ALiFuNa5FyF9g/GI9MBeacPorafLkVLm7bpAM40GAeRHRZHSrQvZcXFUVFbyzjtvY7I6eOrJJ3ANCaFDiYGFCxeSHDaAiMoL+JSeoFtwJmvPwm0/wuhY+PSaxr+Lbakwojn8eqZmW7A79I+En0/D/MzznHL1ZrNPaIPHuwTIQYzWctkocgg768+vY2rbO9Grmx7Ul5iYyMyZM9m/fz9ZWVloJQchrmpGx3k7dRYAzuZXsDWlmLMFFVwoqsQuYNH4lg3WWWy2sfhILocyy6iwHaf5khhmzn6bPqP6cL4kFbPdjFpSE+ASQJRn9BW1V0Hhz0AxCBT+EVTazCSXJJNUnESZpUzW+Nd7EeMZQ4RHJJqKIjg0D07+CJkHwSaPbhNzYOYW2J+tIatM4OIm0bq1gaefDmTMGK865/jooxw+/jiXpKRK/Pw0jOzrSW+rDwaNimB3HaGeOs4Xm/l4VwZJhWayyqyoJCszNqQyrLk3fSM9cNGqMWgkeR6/GlX9n1FycjJCCLp3705lZSVWq5WbbrqJL7/8kvR0eeT+8ccfM2TIEE6fPk1mZiYJCQk8/fTTzumDtm3bMnjwYECORwAw2wTnCsy0DXTluX7hPPbLOXLKrdiKLei99Qx4vy/uEXWFiw59eIQLG9PQeelwC627/LBXM09irm8m/yEEqYZMxK4L9PD0ZO+kieS2aI5kt2O12/noo4/Izs7hscceIyQ0DACrVe6Iy3RemOM60CN7P7tTYexS6BIC0/rA2CWw/bwcJ9DMG+6Jh0eq1kmqtEOpBSpssOAI/HZWnjbQqcFihyM5cJ8hkcPuvhRqDQghsJRaEXZBeWY5J749haSS8G3r67wmi91CSnEycT4Nd9QNkZqaSmlpKbfddhu68lxOrFvG3rRS3tme7tRZADiUWcam5CIiPA0EuOnILK0ffAlgstqZuTGVErOd4S288TJo2JWWz4QJE+j0eAfC+tU1cCQkYryaMyp6NH3D+inGgcLfgmIQKPxtCCE4mneEX5J+ZnfWLhzCgYSEqspdbRd2DHYbt2WdZUReKirhqJealloMpZVwW1sbIf18MfkYWb6iiGuvPcdnn0Vwzz3+ADz7bBpvvpnNuHFePPpoAMePVzB3bi57/St4rl84QghKzHZ8XTQUVFjpFubOkawyUgorsdocfLonk1O5FWhUYLI6GNHCp6YRDaQnJiUl4e3tTYsWLVi0aJFTuVCSJFq3bs2gQYOIiIjA09OTZ555BiEEXbp0ISYmhk8++QQAb++aOWez2SzfM+BUXgVzhkcDoKsyTDwq7ZgCjOg96kfnaz1kN3VgvD+Sqn7Eu81sQ6VWIRyCfT8n4WPQ4fXkE+S5ycaDvSruISkpiQceeICYGDnYz263s2vXLrRaLcEhITSr2E9OdjZjFkOUFzzRAwbOl2MDZvQDNx2cK4S0WokeER6w5qz8/26hMLgZvL1TDj4EyCyB7g47N2Yn8Z+w1lQWWVh3xwbn8QZfA52f6Ih7WI2hIyFxrujsFRkEo0aNYtSoUQgh+O2ZG4iK82V4c2+mr0/h19MFToNgaHNvrm3pi06jYt6BrEYNgg3nisguszK9fzhtqrQXBrXw4rntaRz/+iQhPYNRaWumZQSCc0Vn+eDg+3x57D/c1+F++oX2VzIUFP5SFINA4W8huzybDw6+x9G8o6gkNY6q6GyBcKrUtSor4KnUQ/hYzdRPpJMZ1UL+4KqBJyJBLfHQwwHEx5/g3XezuecefzIzrbz7bja33urDN99EO4+1n1Izd20W+zNKqbA6KKiwMa6NHwOaeQEwooU3H+3KIDHHBMCm5CLcdWqe7x9BrJ/RWY9UWSYbBaqaVhYWFjJs2DAkSWLw4MF07tyZoqIi9u/fj1qtJjIyEg8PDzIyMiguLkalUjF27FhAXh3xYmJjY50KiFHeeoxaFQnnS8gstaCWQK9RYQLyEws4vfQM/h390LnrKDxVxPkNFwAIHxRWr94TC05xfL6cvaDSqsAheOShh9G6uyOqRHaWLVvG8ePHcXNzY/v27Rw8eBCTyURSUhLFxcWMGzcOg8FAZNoGBi2AQjM83A3uXAXtA+CBrnKaYYw33F0rDOCnU1BSKRs5ElBUCcVm8HWBjFK5TIUN1AiGFlzg2+BYHG5aeszshsNipzi5hMxd2dgqbHWuSS2pyTblNPLEXJqCc8coOi9nnKhUEj5GDUmFZud+T0PTXpmn8irw0KudxgCABom+ga4sOZhDfmI+/h396xxTLc9sspbzzr632JG+nSfin8LwB6dRKig0hmIQKPzl7MzYwbv738bmkF/kjgZkauNLcpievB9JiEaNgTp08HXms6nVEuHhOvbuLQcgIaEMmw1uvtmnziHdAt2YizyaO5VXQQtfI/2ialT79GoVIe46fF00FJltHMky4WlQ8/7ONF4cGElQVUaCJByoynJxeNRVyuveXfaLBwUFERQk7+vZsyfvv/8+H3/8Mc8884wzxXDAgAH4+Mjt8/Pz4+zZs+Tm5jrrahsXg6+LhnyTjeTCSh7+WV5BMdxTT065BVF17QYfPZJK4tyPyXJqnr8BnYcOtV6Nd1ytKHdJwjXYBVuFDUuZFUklISHhcAisNhvUUtxLS0sDZEPl0KFD9W790KFDcbPloSlJ4ULV6H/mFvnffZlyLAHAlPbQM7zmuMXHoMrWQgAn8+QPgFoCu5C9CgBGh52uJTls9w7Bv4O8fkFg10D82vuxY1oCek8dgV0DnXU19Ew1heR9Wyi1OjBV2tifXsbhrHJ6hF+5OqvVLpzem9q4ODMmSuoZBNVUGwZ7MnczY8d0Xu39umIUKPwlKAaBwl/KtrStvL3vTYA6C9bUprmpiOeT96MSgqYKwZYHulKRZ6O41MGqVUWsXl3MhAlyB1tZKZ/HaKxbm66q0zuaVY6XUcNjvUJQVbnULTYH7+yQO8K+UZ7MO5BFrK+Rp/qE8cTqcyw9lssjPWvmgTevXU2p0Z+iomIADAYDhw8f5vDhwwwaNAij0ciSJUuwWq24ublx4sQJZs2aRVpaGjqdjr59+5KXJ/eGbdq0YdeuXc40xeCgIM4cPUC+yYZWJTG2tS8uOjXHc0zsTitFo4JskxUkcA12pcdL3ZztOjz3KOfXXaDbC11Q1VpcyMXfyKC5A7BV2Dj3YxKFp4soPV2G2WLm228X8NZbNRLCTz75JF9//TUJCQn17ntgoNwJ+1tTifIC8ZKcQtjhUzm+Q6OS3f8WOyw/AS5aeG8EGDTwYFfZKFgyDoY2g8Rc8NRDu0AIf0+eWoitCg2wIdHCVMx275A65/dp6Y3eW0/a1owag0A40KuvrgN97YPPWJkgK01KEnQNdW9UZ+FShLjrOJZTTm65FX/XmsyCk7myBWQuMDd2qBMHDs4UnubtfW8yvfsMZfpA4U9HMQgU/jLOFp3hnf1vNWoIAGgddp5KOcTsLYIXN0EbfzhWK4PMaodZ22D+YUgvhVB3uKMTnD9fxBfzkgB5cNu3rxuLFhWwaFGB89hBg+QXfUJCHD16uHE4W/Yg2AU82zccb2PNi/v7xDznVEFijok2AS7c1y0YN72aOD8XTudV1Gn3mp0HyCuvmU82m82sWrUKkD0FRqOR8PBwNmzYQFZWFgBqtZrWrVtz/PhxXn755Xr3wm63s2XLFlQqFcaq+WarQ7DkWF6dcjYHrH1oK4HdAuk2rcYnf/aHJM6vu0DcpFgC4wMavN+WUgunl57Fzc+ViLAITp8+TUlJCYWFhXViGAA0Gg233nprnW1Gozx14mc9jx01auxM3wDHahkDIW6QUgx9IuDT/fLUwOIbIbNqZiTYDbyN8v5qyi3yNEPLqsUM1QhamIoavAaH1YHNVDNt4MCBJcuCaCWuuBMdEedLa104hRU2dl8oQQjRoM7CxWSWWlh2LJfTeRWUWex46GW/1v/tTGdKpwA8DRp2XShlT7o8F5LyWyqZOzMJ6R1My8lxaIw1r2KnkXamiKIzxaws+5nSd8qY88SbV3QtCgpXimIQKPwB2JHXB8iq+r8RiAF8OXPmDDNmzGD79u1k52Vh8DMQ2jeEZtc3Q1P10qytQCdMVg64CdJK5NHkxdzyAyxLhGhv0KnkoMIZm6Bly3LWrWtBRoaVpUsLKSmRXcaPPBJA164uzJyZQXq6lVtv9UGnU7FyZSHvrpcj9yVkwaHaNPeVO7oB0Z5Y7IJisw1LlQKQzSGwXyRU88HoZpjaXcM3a3ezbft2Zs2ahUajqbOYSK9evejevTuzZ88mMzOTJ554gpycHPr371+nrpKSEhYuXEjPnj3p2L49LcP8+ej9tynXqZjQvq6bedmxXNKKLcROaEFA55p9FzamceLbk0QOjyD2puaNfnN6bz1DvxrMQAZRcqaU2W/MBqC8vLyeQaBWq+nRo0eD9RgcZYAgvQTeSQAPvRwfcF88zB0N/b+WPQD3dIbPD8ArA6DCWtWGi95CS47JcQgGTY3EsckCOqmSi8lIyMRaZsUzxrPO9uKkEk6HniYuLq7Ra2+IUHcdnlXz/v2iPHljy3ne2p7Gq4MjGzUu8k1WZqxPwUWrYlhzb1x1anmVx5RiUovNzNx4HgB3ndqpb+Tb2geflt6cW5lMWYaJHi/WCDVUG2lGfwMeUe7kHytg8/lNVNgqMGqMDTVBQeEPQTEIFK4SG/LaAN8DR6CecC9cuOBCt2578fT0ZdDEQRwuP0ThqQJOfXeGonPFdHu+C1CjQBfRO5D78s/wfzsEQsiu59qDs73psDQR9Gq5s3ihnxyA9uUh2HWykoAADUOGeDBlii9du54AoE8fV266yYeBA92ZMCGJL77I54sv8gF59BrkpqWksv58c0s/I5IEOWVWpg8I5z/7s5i15TzT+4VzKq+COL/6L2bNyY3sP3CKmJgYfHx8+OSTT6ioqKBFixZ4e3tTXFzMnj17yMrKcgbiRUREEBERUaee6qmDkJAQOnbqiMueRZRZHLhqVXQNrZtS+Ntp2QPiHeuFd6wXAFm7szn88VGCewTR7p42l/wW1Vo1bh7u+Ob7UUpNMKO7u3uD5R0OB5WVlU7PQA1yZ7nyFFgdEOkqGwQT28n3+f4uMGmFPB0AkJBWEzjYf15NvICXAc4XQ1Ct7Mg96XLmwYqTJuziVxDQ9u7WFJ8rJm1LBsYAI82uiZILC1Dna/jhmx945elXqKioICgoiMGDB/Pll19e8l6AvEBRbbqFefDlfjmbIMSj4VTAbSnFmKwOZg6KdBqWg2O8EEKwLbWE5/uHo9eo+P5YLkkFZsqtDkJ6BRM1MhJjgAtH5h4l51AuAVUxBdVGmsFbT9HZIrY9vROLw8KWC5sZET3ystegoHC1KAaBwlWwBXgdKEDWmGtYye3bb89RVGRm+3ZPbP4XeO9ACAXmcIQD0janYymzonPTOhXoBhWk0Wabg7MF8OZQeHKtnKNezVY5PZ9YX9h9F1R7+DuGQtdPYcmSQtq3l4Vq+vVzY98+ExkZVkpL7QQGatm+vSVnzph5/vl0vv++iOGDPdi6pQxXnZrtqcXO8/SJ9MTDoGFAlCebkot5fcsFQt115JtsPLM2BZtDkJhTzj0/niHMQ8folj7Eh7hzLDWH8vJyunfvTmZmJnl5eaSnp3PqlBy1bjQaiYqKYuzYsc61DHJycvjhhx84efIkVquViIgIBgwY4GyLLikBdVkuQW5ajmabyCy1OOWVHRJkVUXYe0TJHXh+YgH73z2IT2sfOj3eocE0Q4DK4kr0VZ2Xt1X2BJSWyj20t7c3np6e9Y6xWCw8+uijWCwWXFxc6Nq1K2PHjsVgMGBWyT34wUxw1UK0F5wtgOog+25V4RZZVUZAYYWsOQDgqqvRIyiplL0CQW413oFfz8APJ8DdRaK4EoRNcHzeSYz+BqJGRdJiXHN0VemWFXkV7HluP2qhpl+/fsTFxeHh4cGePXsavA8X4xnegpL0JOdKhpaqRaBM1sbVCits8j4PQ93wVy+jBkmSvU0OITieYyLSy0BSoRm/9nJwRPiAUBK/Ok7mjkynQaDWqlF71w+l/SX55z/FIHBkl+FIKsSRWYrILZfnoDQqJH9XVCHuqJp5owpwu3xFCv96FINA4QqoRDYEfqVmiZrGX5TVbvvAQC3ePqXMHVzGBwc9OOGtBxWoNHU7q/ZFOTy8Gu7qLCvVAdhrVX+sKpPs8R6yMWCyyt4Cl6pYuf37TTUtrQoknDYtncceS0OtluMK3norjNxcuRP9bb0cEm+yOpi7O9N5bJ9IuTO8Iz6ICC8Dm5OL2JwsGwxqCXpGeRDn60Kl3VFHvOZ4TjlqFbTUFDHn7bcxGo2MHz8es9nMunXr8PHx4aGHHkKjkX92BQUFzJkzB5VKxdChQ9Hr9ezcuZN58+bx5JNP0sbFhO7UJgCuaenL4axyXt4oiyS569UcyCyjsMxKxJBwDD4GTDkV7Jm1D4CQXkFk7siqc389otzxiJKnME58c5LyLBN+7f2wGG2kZl1g86bNAHTr1o2L8fT0ZNiwYURERCCEIDExkS1btpCWlsaTTz5JnjYcNQ4yyyDQDeKDYV2SHOcR5yerD4KsQwDg7wrP94UdF+QYkDeHytvP5EPbufJ3PaW9vO3+LvBkb1gXEsWTvzhIWZ3K6GUj6rURAUc/OY5WpeX5ac/j5uaGSqXi1ltvRa+/tNBPTk4OAQEB+DRry/mE3wB5amhbajE6tURYI94BgNb+Lvx0soAv9mZxYxs/3PVqTudVsP5cESOae2PQqDiVZ8Iu4EJJJWHt/ZwCUSqtCs9oD4qTGlqFsy6pJSkUVOTjY/S9bNnLIYTAcTIP287ziPTSmp9zLY+cyCnHkZgDAqQwDzS9wlHF+SnBjf/FKAaBQhMxA48Ah6r+vnyg1YAB7syZk82dd6bw8ssh+PhoiEpKJm3teaJHR6GpldMthGDjmjxSiuC1gfCI/E7GtZbOTkFVHN/pAujyOezPBK0K2le5odPTZXeC1SpYv74EtRrefjuMsDAdx4+befvtLPr2PcXOnS3p0MHItdeeY+OGUuYMjsavKhK8xFwTnKZWSQxv4c3wFt68vT2NgxllvDEs2pluCNQRr3lnpKz499X2HVjMFUx/7hl8fP1BpSI6Opr333+fnTt30q9fPwB+++03TCYTL730kjMtsW/v3rw4cybfL5hH5/4Bzvd0K38XZg6KZHliHuvPFVJqsePhY6Dl5FhibpDPa8oxOYPrjn6eWO/7iJ3Q3GkQ+Hf0p/y386T8msrZ8nO4GF2IiJCDCqvbUpsbbrihzt9du3YlICCAlStXcuDAATw6yWJFFTbZSBvfBmbvgC8PwqBoeYoH4ECWHGw4IApC3GFIM1mhcEY/cNdDC18IcpenDW6qmu0IrBqcnnH1QvZKNUxpehnZB7OZNGkSbm5uWK1WVCoV586do3Xr1o0eB3DvvfdSUlJCtw5tyD9XQJHZxo7UEjJKLUzuEIChKqgzt9zq9CYlV2UKJBWaaRPgwqHMMvZn1Jp20akxalVsSiriUFUEpYtWResH29U5t95bT8GJwku2r5rHNj3Cc92n09o7DkozwV4JGgO4BddJFb0UorQS68+ncZzJb9AQqClY67/pJViXJqKK80U7OhbJTVFS/G9EMQgUmoAAZiIbA01f6GXECE9efTWEWbMyWbWqxiX//PNBqEfFcKgmzZ7yC2W8vlbuzK79DsI8oLVfjWId4FzVb852OSL9k1HwylbZMADIzrby2muZLFxYwKlTlbzzThgPPCBH12/YcIEhQzxYsaKQSZOS8PTUsGdPOfPnR9GizEDKVjvCAT+eyOd0XgXtg1zxc9FSZrGzJ72UpAIzw5t71zEGoGHxmj1ppXQKciH89M+YWw7B7htJq7g4AgMD2b9/v9MgOHv2LBEREXIHXCVsZLAUEx9kYP2JTLJKveqsvtjc18iz/eRE/rSWgezo6IZDqnlr+7X1ZcwPo5r03YT2DSG0r5zC17m4C8GVIaSmpPLGG2806XiAIUOGsGrVKk6cOEHXrl3J0sZg0Jyj0g6dguGOjvDVIdkD3bNKE+l0vixnXL08wOuDoNeXctDhPfFwoVg2BnyN8hoH1ZhUavZ6BNCoQSBAyM4R3N3deffddzl16hQqlYouXbqwZMkSoqKiGr2WCRMm8OWXXzJv0VLycnMwaCSivQ1MbO9PfK24jdxyC8suyvKo/ttVq2Ji+wDc9GoOZZaxObmYNWcKqbQL9FWaBCNubIEpoG78hVqnxt5AHMvF+FjMDMvai+pYb+yVJtT2WvNpWhcI7gzNR0CnO8G9vlEH8vSA5ZtDso501X1rElXlHKfzqbywD92UjqgCXC99jMK/DsUgUAAgL6mA8/vSyTmTT0FqIbZKOyqNCs9gd1oOTaZF//UAlJXZeeutbHbvLmfPnnIKC+3MmxfJ7bf71avzxIkKli4twGIRuLio6NLFhdBQLW+8kcUbvjpcYltjssmjmpSfkglxh49Gyu7iFSflKPTaswrVgnSuOth2Xv5oJPBzgTwT5ObaeffdbOLjXZgzJ5Rrr/VyHtupk5H33y9FCDh5spJBg3Rs2BDLwIHuWEyCvNMVlGUJOgW7kV1mYUtKMSWVNnQqFeFeeu7tGuQULTLbHFjsDiqsjnriNQUmKyWVdpr5GFCZS3A5tAK7qy/WsA5EhYdy7MQpZ5tsNhsuLi5IlnLU+efRpR9GVZyJwSFH0ycXmusYBJJKjRCCtjc+QIeBA9i2/Znf/b0D2CTrJVNBG0On0+Hm5kZ5uZy+ecxtICFu59icIq8W/Ok1EOEJ8w7JMQAAN7SEWYNr6ugcDOunwLPr4fE1sscH4OWBNWXsSKzzDceiqj+vXi1zGG6O5MQFWXFxwYIFREVFcffdd1NQUMAvv/zCkCFDOHLkCC4uLg1ey80338zNN98MwIU969j+ziMNlmsd4FpvMaOd50v4fG8ms4dH41uVGtMtzB2HgN0XSvh4THMSc8r5ICGD3Gh3Lu5G7RY7av2l5beG56Uy6/gRBHLUTj2nvdUE57fDhZ2w6SVofwsMfwdcaqYXHHkmLPMPQaWt6YbAxQigwopl/kF0d3RG5dvw/VT4d6IYBP/jJCec5+D3ieSezZcD0ISos/R7ZWkxw56bj8MheyTz8my88komERE6OnQwsnlzfZldgLQ0C927n6SszMHMmcFoNBJvv51NcbGdW27x4ZUZaXy6OYRl6T6UZZSTvDGND4bJI8tOwdDaH8Ytk3UGUorkNLbqVO2PRsoL5xSa5XJ5Joj7CNpEaTiW1L6uW6GK22/34/bb/XjmmTTeeiubH36IwcNDfgnrXCQGv2hgw8tm2uNKu6BLj3wWHsphQ1IRUF+8pqhq5OVVazpEXZ6P+tRG/EvloEPtrkVodDpC9HZOpSah3vgJRm1Nh3CqSuOgsCovT1KpEQ477sGR9HhwDr4xbQE7T3UZz57M+bjp5O68uFLFuSIt2aYGu4wGkZAo0ZSgarIEVA1ms5mysjJnRkKyIZ6Y0BWYDhZwIk/+bl4aIH8WHYXJK+T4j4vpEwE77pBVCrv/R/Ym3Fclp+AAzCo1ywNi6rddyOqKrUpbE1XRjMOVRwA53uGhhx5CVeVC9/f359NPP2XRokXcddddl72usK5DCOs6mPT9m53BhZdi/dlCorwMTmOgmvgQN7amFJNSZMa76uEtNtvqGQSVhZUYvBt2wbcvzWcbEGsqbto3VCUBzpEFcPonuH4+xF2DsDuwfp/4+4wB5zkAsw3r8uPo7uqM1MSpCoV/PopB8D9KRZGZbZ/uJjnhgrP/FA0IsLTofxKda6WzTHCwlszM9gQFadm3r5yuXU82WP+sWVmUlzvo3NnIiy/Krulu3VwZOvQMPXu6YjI58C3Ix6jxIq/ADA45bqA6dqCa9FKI/j94tHuNmznIDdrU0tmpzj4IUtmgoBK89TUh6heRlFSJwSDh5lb3JWb0VjHsNSP75lWSut0u96eNvDhHxHrTLdy9QfGaaq0CbQOytdXb7IUZGHRqhkYaOHjezgcJGUxo549eo2Ld2UKSCiucdal1BoLa96TF8MkEte2JpDoNvAqsoV+YmX5hNamZ1ZdcapFYn2pkdbKRLFPjP3GVpEItqbmuw/Wc25HUaDmr1YrdbsdgqJuS98svvyCEoE0bebJfSCo8u9+G9tf3+HgvfFw1eyEEfLpPNu56hV9cu0xWGYxeJCsVfj8eqkUVVcAn4W0p0l7UYQoIqAykdXkbXO1ykIFWK3fI8fHxTmMAoEuXLmg0Gnbu3Nkkg0CSJLre/TJ5p6+jsrToskZBcaUdV239TrFaq+KHxDxyymWvz8m5hzFdKCHqhuZo9GocVgfFySX4tvVh+7QEipOK0bpoCOkdzKPDjfTOPMXHVfdhc4q8WFRDJNwJPWovVSHs7D1bwPxbx7ApP4SUjEJ89O50C2vFzEF30sKv5ou4+4fZLDi0pl6dsX7hHH74m4ZPKGDR2h+4496uuLq6Nrj+hsK/D8Ug+MdhB1KBE0A6YAX0QDTQCgimqaO/xijOKGHV9HVUFFWtoHeJEUObUYdrVp8B9HoVQUGXHxEsX16Ii4sKjaam7JAhHsTG6tm+XX55SA5BvzAzqwrc6fJcZ8ZmJxFnKnKOhG5cKi+Du/hGeWGcnRfgC2QjoZoKK7ywUf5/7zBgeTLcEUdurhX/wLrz/YcPm1i1qpiRIz2cEsW10blJ9HrYQGQvG0eWWClKdSCpVQh73biJUA89oVVR5xeL11Tr11vt9W9q9bbqMh2D3bitUyDfHc3h+XUpAAS6aZnQLpBFh7PpPO4+bprxctUIrAB4DtgIqJGfE5mLL8VdJ7g2xsT1zU38kmxkfqI7lXapVnkVDuEg1juOxzo/wfJ5y9myZYtT/+DIkSMUFspBboMGDaK8vJzXX3+drl27OgMOExMTOXbsGG3atHGmUAKoA1pyQ/92zN10FKtdTjf88aQ8vbNwbE1HX5tiM4xcCEVm2Da1xvAD+Mkvmq1ewSBAJ3QY7fL8++D8YRgddefiq1Mla4tBgZzu6evr67ympmDw9GXQi/PZMPNWLOUllzQKGkoJBdicJMfN5JVX8kQPweJjkFTo4MSSc5SfLaLDC91J25KO3Wwn91Ae7hHutJnaCnO+meQfk/jtlKD3wPrne6QbdK27ejLNfeqXm7NDzuIY1zqH1u0eJbu0kE/3/EDPz+5hy11zaRNYs9CXXqPlk2ufrnO8h6FxT1lZZQXT136Oq04RSvpvQjEI/jFkAz8gC/0UVW1TUzNUrX4hhQMTgGuAK88NLssrZ9Xz66goNjfoEaiNztWMb3TeJcs0RHq6hZwcGy1bGjh40MTp02ZiY+XRZbdurixZUohKBW3bGVGZKlDrvQjuHoRHnoUb0oqQgOXH5ZGvuw6ur5qy9XeFB36VlQktdiizwFcH5UA1gGtigQwTLDrLhHkCo1FFr95uBARoOH7czOef5+HiomL27NAG211NaLyGkM5qCs6N5vwuN/LPHqUo5SS2ShNIKoTDUeOapa54TfVUQVGtbIVqisw23HQqtLV6xeEtvOkf7cn5oko0KokoLz2bU+QUtPZde1YZA7uRjYHqtMrLu7GrTzEquoLuQZW8tsuX5BJ5Y1vfdoxudg3dg3ugklS88847pKamOo89ePAgBw8eBGTZZRcXF9q1a8eJEydISEjA4XAQEBDA9ddfz7Bhw+qMxgEGjn8Ad5f/sHb7fuYfhhY+sOAGmFQ3uB6QY9vGLJa/w/W3ytMMDiRUCA67DiVTeyOjq4JPJSTyLUWc4GQ9YwAgMlLOVS0qKnJukyQJLy8v8vLy8PdveDGhxvAMi2HY60vY8X9PUnDuaKPlGkwJzSjjSJU09vopDjoEwcgWcgCltxHO78/H8z9HSFyXgc5di6RR0evV7mhdtPhaKuh46hzLjsoZGQA/nQavKidJ5xC4pf3l2/9ET1h0I6jU12BTjQYkxrUdSJe5d/D29kXMu3G6s6xGpWZih6FNvjezt36Lu95I/+iO/HRmZ5OPU/hnoxgEfzt2YDHwcdX/HRftu5gLwDvIY+UXgAaGEI0ghGDjezuaZAwA+DXLvWyZhsjMlOe+x471Ys4cOdXvoYcC8PVVs2NHGVarYOpUX8LDdJxOKGP93QcI7R1MWogLtiyJQxmCBUfk9LXaMUtBbjA4Ws5vf/BXOZXN30XOPpjYttaoKamU6wO1LDyj4d13sykpsePvr2XsWC9eeimY5s0vtfCNGtAgSU/i23wsvg2o/m5+4x4yD293ulZqi9eEeOjx0KtJamDxmnMFZiK96p/boFHVWU75aFYZBr2e3r17AzuBx5Gfiyuf/FVJ4GsUvNW/mJMFjxPqNgjfi/LYly9fzrRp09i2bRtCCJo1a8aNN95IeHiNW/mWW25hx44dHD58mPT0dPLz89m/fz8uLi707du3jlGgUqnocs1dmDWebNuyhbMFdubskNsysZZRYHfAhO9l1cKVN0OPcNkYqFS5stVrCqkG2fPQVH9YbGws7u7u7N69m5EjRzqnEBISErDb7Qwd2vQOrxq3wHCGvraYU798w7FlH2KrrKCOy4yGU0IDXbV0D1WxO91BaJXDojqA8ubvoRBI35RGZL9QkjemE3NtM7QuWorOFVP01V52VAVh/iwvv8GKEzVtmr0dnl0H5VbZM3BPZzlD42LvS7QXPPQrrE3aRnbZrwS7+3FNy17E+oZzKjeVi7E77JRbzJf0DACczU/jw4TvWXLzKyxP3FxXTlThX41iEPytFCO/7I9c4XECKAGeBsYA07ncV3ngwAGefOAp9hzYg9Vhxd/Fnz6RAxnUbDh5plxeWP9E/YNWAa/DXXf58cUX8uhr8+ZSBg48XafY1KmpTJ2a6lw0qKJC7iA7dDCyc2dLZs7MYO7cHPLz7bi7y2+tN9+Ue+/2zTUE9wwi72g+Fzan85RNEO0BD3WF6f3kDILarLkFPt4LH+6B5EL5XfRCX3ix7nIAPNLSyiNtbTAgBLr5V4Wvq0Bq7OWlqrqvvarua0i9Ek7xmpi2ZB3ZiRD2BsVruoa5sy2lmHyT1Rlodiy7nMxSCyNbeNertzan80zsTS/l7jtux9OzEHiSqzUGnFcmCXRqO+39PwPqdooHDhygT58+hIeHM2bSXdiLMtiyZQtvv/0206ZNc04R5ObmsmTJElq2bMmQIUMwGo0kJiayaNEikpKSmDp1ap16V65cyW+/bSSu2yBuCFVTenoXk1aUIklwc3sNOGw8uRZWnYIxsbLGxFdHDaTrW5Oub4lVqqB62YT8/Hx27doF4PRk/PLLLwD4+vo611fQarXceOONfP3117z99tt0796dgoICNm/eTN++fRk7duwV3bfNmzczcGDDBvfrQ8KI9qnx0DmEoNJup8LqwMcAY1tZGRQFY5fCnavg5QHw+lb4vlbHXmCCgvXpAJz7MQmPaHcOf3iEroGCt4fC3H1QWglTOsjCTfMOwh2rZPEmu5BNkrQS2Wu2Kx3mX19Td5kFen4pGw33dB1BmGcwR7LO8cmuH5AkQf+oUNRCDkiQxHlMlkoCZo3GZK3E2+jO+LaDeG3ovbjp63tinl79Ef2jOzIitodsEAh5sKEIFv37UQyCv40S4G7keIGrobqD+BnZlTwLeXRbn7Vr1zJmzBjCPSIZFXsdeo2BvPIcCivknG53nTtTO91X77g07RbW7TnBsGH114N/5BF5xP/SS5ncfbcv/fq5O0fe1csMV1YKunVz5ddfWziPq47yd3eX2+rvr6HTA21xCPllYrTbmHtiC162ygavRpLgoW7y57LYBKxPh62ZcOcMCCwEjiEbYtXogVigC3A90Ph0QrV4TZc2sRSezW9UvOb6Vr7svlDKa5vPM6KFN2ab4OdT+YR76ukfXSMJnFtu5YOEdOJD3PA0aDiZa2LH+RJUksT8hd/x/Y+Lad1ax9NPBzBmjFedtnz0UQ4ff5xLUlIlfn4aJkzw5tVXQ3B1rXvXXn89k927y9m9u5ycHBsvvTSGmTO3OffPmDEDo9HIlm07mPLVEVrrc3imew9efHEGP/74I/fdJz8Xnp6evPjii4SE1BhK/fr1Y/78+ezcuZPRo0cTECBHehYWFrJu3To69xmCdtCTJCPxykttKbl3DE9vS+GmyWNRm3M5tHQ7UMBPp2WXuCx+daDqg7Ojz8vLc64cWU3137GxsXUWXOrZsycajYbffvuN5cuX4+bmxr333susWbNQqy+d2tcYjzzyCF29cuHoYue24TFpuOo0WOwqjuY4uGOFjVZ+8P4IuZN+e6cs3fzqQHl1zlU12aZcGwc3Vekk7U6Hj/aA0c/A8Xkn6NbayNbrTWgl2XOy7XyNimOgm7xM9M1tZUP5eK58HrUE3xyWxZ2qYwlWnZIX/lo10Y+hcXei4iBqsZvMYjsrT0GvsAtoxLcAhLjZeaY3dAq2YhftWH3Whc/2ruRI9jnW3v4+mlr3bfXpBNaf28ee+/9T6w4JRKEZyUeJJ/i3oxgEfwsCeB5I4UqEfhqvayPwGfBAvb0lJSVMmTKFAT0Hcr3HJFRS/aguVx1c19IPT30mGpUVu9BSbA7ilf3leHioGDOmvq59375uREXpeOmlTHr1cuOWW2rc0MHB8qi4euqgNpmZVnx81Oj1cjvsjroexwq1hvciO/DKuT044CqS4S5GgrZ3QODMWtuKgQrkx9+bxgypi6kWr5m/9IdLitf4umh5cWAE3x7K4bsjuahVEp2C3bilY0Cd+AGjVoWXQcPas4Xy4kU6Fb4uWsZeM4L4wWGYTD+xfHkR1157js8+i+Cee+Q58GefTePNN7MZN86LRx8N4PjxCj78MIfERDNr1rSo0+YXXsggKEhDp04urFlTApwD9iEbQLBt2zZGjBjB4WwrpWYbu83e9PQUxMbGcvToUcxmMwaDATc3N9zc6sesdOzYkZ07d5KZmek0CA4dOozdbsfebhxaJFQSLNubxv2PPs2kSZNI8L+dPn36sHmiXIcQgpUrV5Kbm4toIMI1Li6Ozz77rEnfEcgqil27yqsHXn/99c52XS19e/dmXNpTUG/e3oaLFt7YBt4G2Hy7nB4LEOUFd/8E7QOgXyTc2Eqe/vrltKzLMLSZbNQerwrRMQYYKTheyJzbJLSSvPyzXl2zIiTAqBbyp5pr42Bca2jzsewxOJFbYxCUVC0MGehmQCueRM1BTuSpWHNO3j6+DUhVU5Kzh1TX6EBwnElt7cT5RjFj4zFWHN/C+HaDALDYrDzz21zu6nItrQKi6t4KsxV5lVOFfzOKQfC3sAqQXaBNEfpxOATffJPPihVFHDxooqDATnS0jptv9uGppwIxGFTA18jxBK3qnGnRokVkZ2cz/tbJ2M+qMFsq0Kp1aFR2mnntpY3fOgJdz9akHgoJSRJklsLBMzDlNt+q+utTXt6wMRMaqsPfX8O+faZ6+/bsKadjx5p5gPSy6sDJGo64+/F+RAceO3/4dxoFEsReA6PnXrTds+pzZdQWr0nd8Qs7P3iq0bJhnnqm9W8kx64KN52aJ/vUyhWTJHRunlz7wUK0LncAQTz0UADx8Sd4991s7rnHn8xMK+++m82tt/rwzTc1UeKxsQYefvgCP/1UVMebkJzclqgoPXl5Nvz9DyPf6yVUGwTVKxfuPpuPWiVhd0BCsQ/FuGGz2UhLz6B5TLNGr6GkRA6AdHNzwyFk8/RAUjYqrQG9nzzN5BBwLK2IxyZ0BuSgxT59+tS6bIkBAwbw/fffN2gQXC0dOnT43cYAANlHKc29gFFbE+RXTUmlHNPyeI8aYwBkN//Dq+WloFMek5U3Aca2kocAz66X4172yytwIyx2tC4aLAWVxC2QgyzVkpxlY7bVSD9fTHMf6BAEezPkYMVq+kXKMRuP/5bG28My0Kth9CIHVjsMj6mbtlubaiPhiR6pvLQJNp1b5zQIPkj4nnxTMTMG3l7vuPOlF7iQlo/NYUOv1hPmHk6oeyhq6eq8Mgp/D4pB8JdjBt51/tUUoR+TycHUqan06OHKfff5ExCgISGhnJdeymDDhhI2boyt6tDnIBsGNaxfvx4PDw/OJZ7jq62fkV2ehUGjZXI7iVvbWtCppTo6PlLVHPt3x+QX+aSJDc95T52aQlmZbBDMnp1F27ZGunSpCUa68UYv5s/P58IFC+HhcirWhg0lnD5dyeOPyyI+NgecLtTWrxzY7BNKmVrDE+cPY7TbUV/JHLqkBmGH7g/DsLdB3fA5fg8RvUaRsu0nMg9vb5J4TZMQgu73vorWJRVIBkCtlggP17F3rxyxnpBQhs0GN99cN8/s5pt9ePjhC3z3XWEdgyAq6mLBG4G8WmUB4ENcXBy7du2ivGO+U0vBYbeRdV4eSp7INhPVTPahOJA7qWpsNhsbNmzAz8+PiMgokitcOFjqRU5hGVo37zpzykJAadWKiBkZGfUu3cvLi/79+7Np06YrvGn1kSSJwMBA4uPjf3ddAFOffZOyCvna+0bCW0NlYSyAo9nyc9zlopATnVr+qKUaY6Caa2Ph60NyJ747Td5mLzQj7A6u+w7u7ARvDJYDAjPLYOpKOfW2ISx2SKrKpmxdK4mitT98fg08tQ56fVljuF8XJ+s8XA4XrcDXCMUVu5HEIYoqmzNn67fc0/U6SitNlFbKxn6ZpQIh4Mn1z1LmakbvVfO86VQ6OgfGM7rZNbT366DEGPwLUAyCv5w1QLnzr6YI/eh0Ejt2xNGrV43L9u67/Z0u+w0bShkyxAN5fvwUEOcsd+bMGWw2G++sfYPeEf15NjaA5MIjfLhHDjhafGPDHe3CoxDsBoNjJNkyqEp21+kkOnQwEh2to7JSsHp1CcnJlfTseZJ77vFn1qxQPD3VPP98MMuWFTJw4GkefTTA6Qlp187I1Kny9IJGBQdzGo/43+cZyP0t+3NPeiJ9irJwIF3aMFDJwWp4RcG1/4HoAY2X/Z1IkkS3e1/ht2fHNkm8pgkV0mzAWMK6DqG8/D9UVNgpLraxalURq1cXM2GCbABUr+JYHadRjUvVko/795dzeRzAUaA/DzzwAPfffz+F818nqNdNIATp2xZjLZXjS04U6yjODCfSaMJfV4m/thKD2oGEYPHCJWRmZjJ46jSW5UZidsijQYfVgtSAEZZVtfplRUVFg61q0aIFdrudrVu3IknSVXsLAgMDGTFihHNVyatFp9Nx4403MsrrJH7lic45+77zYOcdsqJm1ZpFBDeQAewQcod9MdUrKe9Og+Kq5Z6tJhu2Sgd3xcMHI+XjbvsR4nxl4/yVAfLCTxfz0K+QXyHHzF6cwBLqIa86mVwE6SWybPT3J+C59fD2sEtfe2mlrADq5yLQiecpMr1JmaWCd3d8x7s7vqtXfsVDvxDYLZBu02qMMIvDwp6s3ezKTCDKI5rH458g2rNxb5PC349iEPzlrKC2DF5ThH50OlUdY6CaG27w5qWXMjlxwlxlEKiBlUCNxn1ZWRkmk4l+kQP57JpSmnsfQZLkF85n+xt+0ZzOlxcMerwHqPblQkzNEKdXLzeKiuz8+GNNYJ6lao2VuXNzefrpQDw91YSH69iyJY4nnkjjuefS0ekkRo/25J13wpzxAzaHO2XWrsARVJIKIUQ9Tf1SrZG3o+JZaDFxh9lO5/xUNMXnqRd1b/CGqP7Q5X5oNqTJK7/9HozeAbJ4zctTsJQV/w6jQCK8+3C63j0TgCefnMtnnx0C5MsYO9aLjz6Spx/i4uS3/o4dZQwcWBO3sG2brNaUnl4/bqM+auAk0J/77ruP1NTzzHnzLXIPy+tVuIa0ILjXODK2f4dKZ8QiVJwxuXHGVPMMZuxcxoWdWwkbMIWy8P51QmFUWh3CXrcdElBukg0Bo7HxueaWLVvi5eXFpk2bKC0tbbTcxVSPPjt16kSnTp2uOoCwNr169aJXr17wf82hsGbOvv0nMG0D/HZLzRy/voE3qace0i3y7ym21m9s8THZCDiUJXfkfSJhS6p8A6vTMr89LGcK3NkJnlkvBxh6GWQtjmre2gFfHJDrGtmirkDVjvMweqFcd2oRxIfgjB94J0H2XswaLE9HmCxy1s78w7LoV6i7/BHAyOYAFkJcv2LJhFfqyYJ/vHs5uy4k0v6pdhi86xv3jiq9jvOlqTy++VFua3071zcfq3gL/qEoBsFfigX5RfzHzJNmZclvIz+/6q/RDhysU6b65XtrewvNvXc5f8+T2skGQUJafYNgYVUW5OR2wKkiWQ7YS+d846Sk1FeYmTgxiRUripzTAwBt2hjrBbnVRqO6jZd73U5GWTrb0rdxpvA0ZwrPYLKVIyHhofcgzrslsd5x9Avrj7ehavqishRyj4OlXJ4O8IwEz/AG1zD4s5HFa5aS8NEz5J06cEXHSio1IGhzw320GfcAqqrFex57LIJx48rIyLCydGkhdjtYLPIz07mzC927uzJnThahoVoGDnTnxAkz999/Hq1WcqZ8Xp4at/2sWa+z3tEZU04qar0rLoHRXNgwDwCDb/2si9xDa7mw/isC4kcT2m9Svf1aNx9KUg7XSUUTQGmRrGtRO1OhIYKCgrjppps4evQoiYmJmEymBj0G1dskSSI6OpqOHTvi51d/ka3fjaNGZKq5D1zXUtYFsDvAWOUIqayvQ0ULX7mD7TtPTqP1dZF1BVafhdvaw7ITMLw5zBgAPb6Qj9mYDGvPyZ32sBgYEycbBIUVsmaDUQu9wuQMhq8Py142Vy3MHlz33J/tl8tuTZV/tvkm2Wgot8DKU3I7Zg2WJaNjP5S9Fj3C5KmPnRdg+wV5quO6lnJcgbv2INe3uhGH1LvOeX46uZ1dGYkEd294dUXnLawyDOYlfkWptYwprW+7oq9A4a9BMQj+Us7RFJW5pvLmm1l4eKgYObJmBG93JGF3VKLTyHN5ISEhJCYm0id8B1tS62uh3/aj/Kmthb7omOyqjA+BIhPERh0jtxSWLWvGuHENxxSEh+uwWATl5Q7nokGNowaaAbfIbXQLZULczU2/cL07hHVvevk/GbeAUIbM/JYzaxdzbPlcKksKkFRVioYNUL1YkW+LDsRPnY5PdOs6+1u2dKVlS/k7nTLFl2HDTjNmzFl2726JJEksX96MCROSuOMOOWVVrYYnnghky5ZSTp2qbEKLaytfyh1reHAAGYYaj0Nx8kF0Hn4Ya2neAxScSiDpp/fxbtWbqFEPNli7S1Azcg/+RkXeeVz8I2uOTTkOyJkJl0Oj0dCpUyc6dOhAWloaOTk55OTkUFZWhsPhQKfT4e/vj5+fH5GRkY2uYviHoKvrnQv3kD1s5daaqYLM+qE/2Bzy76iZt6wpkG+CaG95yedQd5h/RDa6OwdL9Oniyra9ZczaJnsCquMI9lbZbf6usmLnwqMwe4fsPTCo4cbWMHNAfeni7PIaI8Uh4Fyh/KnmaI78b1KhbAx4G+BINhzMlOuK9pJlj4/lQPtAEKhQixV1DAIHAotkxS5dWabU96eXEuASwIiokVd0nMKfj2IQ/KVcnfJfQ8yalcn69aXMnRuBl1etlfVUdp5etImZNw7Gw6glvnNn1q1bR1qphEYlj7Ae6QYuOlnx7P4u8oIz1S+U3Wny6OOVAfLfL24CU33RvXo0tmhQfVSAFnmBnv+ex09SqYgdMZnmQ8aTtm8j5xN+I//sYUx5mTVl1Bq8ImIJaNWFZgNvxCsitpHa6nZA48Z5c++95zl9upK4OAOhoTq2b2/JmTNmsrKstGhhIChIS0jIEWJjG141ry4qLk4RaxvmRXZRBXYB+YlbKM84TcTQu5FqpamWpB7l7PI38IhsR/MbnqmzrzbecT05v+Zzcvb9TNTIKqNBCH5Z9i2hoaGyG76JqFQqIiIiiIiIaPIxfzjBnSDvpByoityJGjTgpoO2AfIofV+GnMpXjcUuTwmMbw1fXle/ypEL5eOvjQMNgi4jg9m29wx9OxlZc40ZTZUX8T8H5PoHRMlrPHQMguELYGAUrJ7c8FQFQKyP7GkAOP4AtPKXvQPTN8D/7YFdd8r7DmXJ/26/o25Q4r4M6PqFrKpYUgkFFQ4iPA8wvt3HPNb7Tlx0BsyWSiraCLwd3qy9YwO2Chuuwa5EDgsncmgE0kULfAmH4NzKJFJ+O88vhb8RFxvHjOkzmDhx4hV+IQp/Fv89b+R/Bb9Xc0BmyZICXnghgzvv9OX+++vrsyfnlPDI/H3MndqV8f1bMXsOzDskuFvO+qJvpOzy1KjghX51F5NZVCXZPqmdPDr4ZB+82A9e3AycLSY31w1//7oBY5dbNKiGamPgA6ABTeD/AlQaLRE9hhPRYzgAVnM5topyJJUanasHKk1TMh5aAIcBeYhXPQ1QXFzXu9SihYEWLeR52+PHK8jMtHL77Q1EntXDTvX937p1K6+88gphbbqTmWyiLO0kuYfW4hnThaDu1zuPqCzK5vR3M5GQ8GnVh4Lj2+rU6BIYjUugHDCm9/AnqPv1ZCZ8j7DbcAuNpTJ5L+nHdrJw4cI/ZH7/ryA3N1de/yA4Ho4uAuBwliz6Uz1n72mAIc1gwRFZGMi9yh6rjgG4qU0D9ZbD+iQ57bB6xWRzq2DCB1ewcUMak83QP1Je3XDZcZjWR/6NphbBtYvleIxxreV9tWkfKH9A1jj4ZJ+sT/DKVtiULHsNAMLc5VRFqPEiGC/qCYqrBgHJRfBSf/AxytOLr27+noOZGXw36VVWWnfy08JN+LX3pdmYaDQuGnIP5nL0s0QKTxXR6dEOdeo8ufAUZ1ckETE0HO8WXlQcMjNp0iQkSXKm8yr8vSgGwV/KpTXCm8K6dSVMmZLC6NGefPppZINlSsx6sktK+HDtKZ61b+aOThJfHRRkVMVovZsg/7if7V3XGLA7YEmiPHUQ4wODv5Ejk/tWn+ZAPhO+K8EYYqRXT1cCgrRXsGiQhLww0yxqZ0H8t6M1uKK9jDZ8NdXSyNCSamPAahV8800BRqNE69YNZ2Q4HIJnnknHxUXFffc1ZQEfQbVeRWhoKGq1ml8WfU5+UTF6ryDCB95GUM+xVTEOMpVFWdgr5R4lZfXH9WoM7TfZaRAAhA+5A7XRjZz9v5J7eD3hUc1YsGABkybVjzn4pzJhwgSMRiO9OsYRcMTB8Vz4/IDcidees399kLxoUf+v5TUF0kpqYgBGNGD3LkmUpxMmt5OHCDk6I+l6V9rf1xajn4Eda87xwwlBpBe8NxweqxJiTC6SsxJAXsvjYl7qX2MQxPnJXr+zBbDkmPzr83eR4wIOZtWkMsZVhVzsuCBPZ1Tz4R7533APeK5KMuLueDU2Ec6Cwzs55khhZct9DHi/L+4RNS+RqOERHPrwCBc2phE7vjmuwfKzX5Fv5tyqZKJGRtLunqrlsocIvN/24emnn+amm2761xiK/80oBsFfSt0Au/37y5k+PYOdO8sQgkZf+GvXlrBkSQGbNpWSnGxBr5dYurQZGk3d0fjJk2Y++qSEXUuewFyYyT6dgc1hFbzUWxDhIY8YAHZV5T6/vVOWTq3Oq16fJI8ipveFZYlycNGJByGlqOYc1wdbWXjcxrsJZZSUOS6zaFB1NoURmATcCehQaJhqaeR+/boSGppPVpaFhQsLOHnSzDvvhOHmJr8wH330Amazg44dXbBaBYsWFbBnTznz50cREVH3/n77bT6pqRZMJtnLsHVrGa+9Vgj8yK23ehITE8OaNWsA+HjdaRbuSG5wrRqPqA50f/G3Jl+LJKkI7XMzYX1vxsuo5Ycn+mPQ/rte+Ndffz0LFy7k3U93UVIs4W8UjG0ld7y15+yrFy16dj08vkZeobM6BqAhFh6FAFfZswDws18UQpJQaSTibo4l6rog3ju1vZ5+5oAoEC81vf1Wh+whuC8ePrmmZvt9P9dkGI1qAZGe8NRa2dCJD5bfCdXTDdY6Tk1BsJsDlSTxdvMfcbhJuHu7czFBPQK5sDGN0rQyp0GQvScbYRNEjayZ+lGr1HS6vhO7n9lNQkJCHbEqhb8HxSD4S/EC/IFcDhww0afPKcLDdbz0UggOh+C997KB+pK/ixYVsHhxPg4HaDSy/v/FeegAn3+Rx/yvC3GNGUhgl2tQVxZyZv+39PlKTpEaGiN7B0a1qKuFXp1XPby5/MKpsELLj+W0wyivugbBI93hke4CEOCnh85+mFvFY/DOpnpUK+MJtAH6AyOBPzHo67+EamnkTz75mvz8XNzdJeLjXZgzJ5Rrr/VyluvUycj77+ewcGEBKpW8pPSGDbF10hCr+fLLPLZsqYl427SplE2bSoGX6NOnn3PJYIA7+jdj3dFMckrMf9gCdkLA89e3/dcZAyCvYfDII4/IfyRvhvmNryzaJwJ23NG0ehOq5u8dQJlayyafuvERKUYPFgbHMiXzdP2Dr4DqaYDaK0xC/QyjXybB+O/hxqXyfr0apnaET/fLi04dygJfI+y44OCTfWlEjY6i0q3x4OjKQtmNoXOvMU6Lk0pQG9S4hdVeEMqBLVQue7F65cUIIbAeO4Zl7z6sR49iS08Hux2VpxfaVi3RdmiPvk8fVH9mcOn/AIpB8JczHFjMjBnpGI0qEhJasnhxAUVFDoYN82T+/Hw++igXe9Xv7eGHA3j++UA2biwhPd1KbKyB3FwrCxbkO2uMidHTs6cbkyd5o+34IFvOyiv/xJDMB52+pdXHMHOzHDjUq1bQeEN51SAHG1rt8Hzfy1xKXiWsTUcfuhS8uwH5yPPTLsgGgZJrfCXUlkaWBaZupaG4k9tv96sjbX0pNm+uPT0jAQbgB6D+8Uadhpk3tueBeXtqKWVcPZIEozuE0jfuD5AP/ruJHgDx98KB/ziDC38vKiBt0EzC7UWcLDzhTM0D+D4gBl+LmdH556+6/hB3SMyFwItmrAKq/i6s0odqEwDH7pcHCIVmObjQqJGNhnILdKq1jMTNw9wov7OuPHptHFYHST+n4BJoxKtFjTy4udCM3lNfT38gRyunO1xIOE6l92FEZilU2uVH1UWHFOKGPS+Zsp+/wnpkv/xQqdVgqxp8SBLm9evBbkdydcHl5om43XMXmrAwFK4cxSD4y7kRWMC2bWWMGOGJr6+Gt9/OJjXV4iyRkWFlxgw53+iWW3zQ6VRcuCB7DU6elKN9br01xVn+ttt86dnTjeat/Nj5S2fndjV2fF3kGIDNNcXrcHFe9YUSeGsnfDxKjoJuCrL+uQYIbOI9ULg8ccDtwDz+KN0KuZ5nacgYqKZjpDev3tSBGcsOy8vaXuWZJKBncz+eHdP6smX/NQx7C1K3QMHZOtoEV4cE8XfTuvfzzAYsdgspJcmkl6VjtVvRqXWE9g/FfngR6q2vyuWv0BCJD5bXWUgvrYkVAJyxRLVFjiSp7voGv56Rv/s4P3ise42GwtfrSmgTmUL0qKgGz3n0i0TKLpTR7YUuqGot5OWwOFBp63o125SFMz6jN6v4BVNKLqJag7ma0kocJyuQhBvu3R/GErAP0+4lOEprZWsJQfXoSZSbKP/6a0wLF+L50ou43HqLIoB0hSgGwV9OODCCysoDGI31hX7Gj09i2bJCEhLi6NGjxr0mhCwJes01Zzl2rKKeOJAQ8NWWgVjtNV9pBfKcflaZPEXQaItq5VW/uEmWPB0QVTNVkFXlcc4tl7dFeNZVRUP7+4MlFRriLmAPcILfr18hAUOB0ZctObhNEK56DS99f4RSs/WKpg9UVUrXN3YL57ERLdGo609t/WvRu8Ntm+DrAbJR8Hs8Be0n11l0S6fWEesdR6z3RQG3A2dC3LXw4+2Qc7RGnrsJjG8jaxZ8eRAG1ayDVSeVsSEqrLIkMsDy8dCyypgY2wr2uvtz4ptThPYJQedRd8Rw9ockzq+7QNykWALj63qFVDoVjqqABL1Dyy0Z/Rle0IlSi7wmglHdcLqsJKmcjkZddBd0kZ0w7VqMOXF9w4232xF2O0XTnse8fTs+H36ApG9KKq4CKAbBFSOsVsybNmHZuw/LoUPY09PBZkfycEfXrh3a9u0xDBuGJvRSamxPERf3Prt2lWO3C9RV+boWi4Pdu+VI7qZJ0MrY7CpOZISybHfPOtszCGZjipqEC3Ze6Nf48bXzqs8Xy5HJzT6oX+6BqpdE4bO1dNMlFfg37kJU+D3ogA+BB5EVLq82bVVCjuV4maZO4/Ro7sfSR/rw7q8nWXM009nRN4a8UqIg2MvI89e1IT66KemP/0Lcg+CuBPjlATj2HVzJ5Iqkln8vg16DXk81XV47pDPcfxhStsDeuXBuLVQW1y9n8JJVPKsMlU7BcEdH+OqQnNXQUCojwPhl8v9b+8uaA18dlA3/1v41xoAAijQ6jD3DsG/LpTi5BP8ONW6HCxvTOPHtSSKHRxB7U/3UCoO3gfxjBbhY9cxIGU90hWww5JbJXoFg98s/L5JKjZBUuPa5DbVvBOVbL+09M6/+jYIHHsTn88+QlAyGJqEYBE3EYTJR9ulnlH89H0d+vhzdZ6tlqaeD7fQZWPY9xTNexDB0KO6PPYKuQ4cGavPigQce4v7753Dnnak880wgDge89lqmM6CwqRK0NrtEmdnAS8vH4xB1XzDm8lIm/SAR7Q3P9JZH+P4XDeYvzqt+bZC8qEltjuXAjE3wTC/oGS5LpTrxjQWtsg76n4c78BnwPrAceea5qYZB9UvwLmAqV/pz93TR8fK49tw7uAU/7rvA5hPZXCgwcfGaQx5GLZ0ivbmhazjdmvleRovivwCjN4xbDG0mwMbpsox2oyN3STYChAOaD5dX37waA1qS5DiG6AGyO7D4POSfBlslaAzg1xI8QuHn++Hgl862fHqN7NGbdwh+OEG9VEaQM4zmHaqSO9bIU4zlVjlbohoHsNo7GvczcnpFi71taX2+HUISHMrez+FfVxPRMYJ2dzcgvAB4RLtzfr2dibt7EuURgKpqUfM9aScAaB/cNF2S6ikAfcsBCJsF084FjRd2ODCvWUvZp5/h/uADTar/fx1JNGFJsZKSEjw9PSkuLsbDw+Nyxf/rqExIoPDRx7FnZkIjcrT1UKvB4cDtgfvxeOJxJEP9lMLp02/lrbcWYrXKX0GXLi4MH+7B669n8cMPMVx/vVe9Y2pPGdjsKkrNBh78+i6ScurO39stZk588wyO/BR2TbXQPhAGza/RQg9wxZlXrVXJkc+tGklh35wiSx4vu0kOQnQiqaHPczD4tabdE4XfyV7gIyARubNvyGUtIRsNdqAH8CgXp7v+HiosNlJyyzFZ7KhVEiFeRvw96geL/c8gBFzYCceWQPpuyD4CtipVH4M3hHaF8F7QYQp4R1+6rj+C/LMwtw3YLZcvewnGLJZTD4/eD819wSwMLD7+Jh9un8+RrAPMGvY+3gYfzuSf5IOEN4n2ac7D3Z/G4mUmpfUpUuPOIdQ178qKvAo23beVu+Kv4f3RjwFy5sDQeY9xOu88HYKaszvtOEIIuoe34fWh99KhASPBYrPy/s4lLDy8ltSiLNwlifYqFbO9vAhRN2zwflBexpvFRbRp04Zjx479rvvyb+RK+m/FQ3AZyhcspOi5abKF3lRjAJyBLmVzP6EyYRd+C75B5elZp8jrr3/LwHGTKEh7mbjIUtq3MzJ9ejpAkyRo9ybFMGvlDeSW1q3XYbdyZukrmLKT6Tp5Gq0DXwfsTi30d3fJrkF/FxrMq24ywgFd7r2KAxWujq7AfOTpgzXIhsFJoNql4wG0Btohxwr88ZHWRp2GVqGely/4v4IkQURv+VON3Sp7BVR/g5vatzkMmQ1rnvhd1TzdC1afqVmYKdvUjc0p/yEx5wi9IwbgbfAh35TH3N3vgSTRObgbBzL2IDIEHAcXFx/OtzpLRno6ecfyMeeYcVHr+WzPSvLLixkU04WfTm5nR+oRtCo1KUVZPN//Nqx2K69umkePT+/mid4TeH3Yfc42We02Rs1/il0XEnHR6XE4BA6dgWSbmTSbvUGDIMNu48OSYlx/51LY/ysoHoJLUL5kKUVPPPn7K1Kr0bZvj9+yJaguWvo1o9DE+A83cXOPndzccyejBu8hM9NKSko7Z2wByLaIQ6i4/rrTHDps4c73XmLN0Q5cPCcshIOzK+ZQcHwbLcY9j2+rPrzp+SV9S5fLHfgfhaSWA6NumH/5sgp/Mg7k5+B/dJSuUBeHHRZfB2dX/67f/J50OV15b4aOwgo7fi7+9Ajvy7Dmo1Gr1JzKO8F7O2c1enyQWwglUhGB/fwZ59YPfY6K93cspdxiRq1SE+sXhkZSc6Ekh6OPfIuviyf/t3Mpr2ych8lqpk1ANPse/MpZ36zN3/Dqpnl4Gtx4sPtYwjwDOJJ1li/3riJWrWa1fwCqizxVDxTkk+9wYAdKo6M4dvLkVd+PfyuKh+APwHr2LEXPPvfHVGa3Yz18mJI5b+I1U5YaczgEmUUVFJks6DV6Fuzox4df2Dm9dzOTH+rDsTR/wn3z0ajtVFq1nMsJ5ER6GEfOm8ktzWbN0Y4Nnipl9VwKErcQPfoRfFr1QZIgqd3z9D2yE8oy/yCjQJLnUYe/9wfUpfD7+S+K5Ff4/ajUcqTgkhvh7G9cTfKoALqFwnsj+rDl/N2IBp6xOL9WfHrtt43WcbbgNJFe0Zzvf5SZpjGoWktMaDeELnPv4IbW/Zl343QCZo1maPOu+Lp4klNWyBtbvuGpPhN5ZdM8TuamUlZZgZveiMPh4MOEZQDMu/F5hsZ0w2K3cnvnkXip1MzetZzjNitttTWBD7sqK/nFXMFv/oHMKC7CUVp6xffhfw3FIKjF/v37mT59Ojt37sRhMhGv0TLdw4M22rrpNR+WlrDWbCbVbqPc4SBYrWGwwcAjbu74VkWzWvV6ikJDSfL24oszZziYnkbha6/i9cknhHfogyWgA6k7V+DZLB6Ni3udRWXOeE3j3q9q3I2m7CQKT+0CcsnLKMReaSJ9q7zYiktgM7zj5AihzF0/kLPvZ9zCWqHS6sk7sgGA4qA4FqincoN1Nq6aKpXBq6YqSOrGReByNfMMCgoKfzpaI0xcCdvegC2vyNuamCYpUGN3qNiVfjOJeUO4WoOzuU8sAsGIU72RqgTRmvuG0do/ilO58tLdlTYrxqql2mes/5wWvuFM7DCUVzbNwy4cJOYk0z28NSdyUykyy/nPX+//hZu/ewmL3UrbwGYMjJa1Vwy1PGR2IZhRXMREF1daabWyjIO5Ccu2/o+jGARVHDhwgD59+hAeHs7zEydR9t13fFNexri8XH72DyCm1ip1R6xW2mi1XGc04iqpOGuzsshUzgazmWVt25HdswcZ7dpSYbPxyssvU2mx0L9ff7x8fEhPu8C2jT/i578TTw8/shKWYausQO/d8KIyAOWZZ0nb/E2dbdV/+3UY4jQITNmyAHlZ2gnKqqJ3AR79Uf43ecNXuO64T3YpXk0OtaSW50zHfQcxQ6/8eAUFhb8OtRYGvAgtr4ONM+D0z/LvtyGRI0nNgQwHMzfD1lSosIGfy0b6RAoGNZNX7nQIB9tTN7E1ZSO55dno1HoivKIYFXsdMT41S3mnFCaRcGEbp/NPkG/KxcfoQc+INrw8+E6a+4aRXV5Ia/8oAGL9wtmTdoLdFxJZcGgtG+78AKu9Jlsjo0qE6GxBmnPb2rN7eaL3BLyN7ry/cykf7vqePl4BNNfWvKO/NZWTbrfxnXt13qRAVCgGweVQDIIqZsyYgdFoJCEhAfHIo1R6ejHW6EK/nCxml5TwhU9Nnmzt/1fT0ejC/Xk5fNY1nm7t2yHUao4cOEB+QQEPPfQQ7drVCAm5urryyy+/8PwddxAaEcHeYm9OlLvT2Bywf8dh+HccdtlriLnuKWKue8r5t1olMf++njQPrKVxH9cWlk+GwnNXNn0gqcAzHMYuqBtApaCg8M8mqANMWgVF5yFxKWTshfQ9UFEoGwgufqzNjWDMvC3EBMUwrEVXdGo9eeU5FFYUOKtZkfgd65NW0z2sN/2jBmOymtiWuol3dszi6T4ziPaOAWDN2Z85V3Ca+JBuhHpEEOxu45tDq+j52T082/dWMkryeHHgVADu6Xodj/z8Hjcums6w5l1x17nw7JoawSazVc6WKLfIOstqSY1OrWX21pp0QwmJQDcfcMgehEKHnbdLSnjU3cPpsQXkgZDCJVEMgiq2bdvGiBEj8NbpyNyyFYQgUK2mh07PBnMF5Q4Hro0IiZg8PckdPBA++giTyYSoeggrKuSH2N297qIznlXZBnq9DjXQ06uQcEMFGwv8sYk/bj74zgExdY0BkNOg7j8C29+AXf8nC5xI6oY9BtXbde7Q7UHoNwN0yuIhCgr/SrwioPdT9TaXlJQwJTaWEcNHM1I9DknUH5jYHXa2pG6gc3BXpnauifyPD+nGCxueZE/aTqdBMCRmBHfGP4BGJXcvN7QJZErnIXT+eCqvbvqK7uFtuKWj7HW4u+u1rD6VwOozu/jtzG5+O7ObNgE1y2i76uQgbEPVtELrgCgC3bwZ3qI7EV5B7Eg9wocJy1iXlQwBct70myUleKlUTHWtUXpVaBqKQVBFZWUlRqMR67Fj1FZeMUoSFuCkzUq8riYVUAhBocNBmbs7Pw3ox9Jff0WlUhEXVyM92qJFCyRJYunSpYwbNw5vb2/S0tJYvXo1HTt2JCgoyFk2RG9mqG8Oa/MCsf/OaHEJ6N8qgNv6Nmu4gNYAA1+GPtPg+PeQtA4uJNR4DSQVeEVDWA9oNlgWYFEMAYW/mJycHPbv38+5c+dYv349Y8aMISYmhs6dO/9PZTv92SxatIjs7GzuHH0vmb8VUGkzo1XrUEk1gxO7sGO1W3DX1005ddd7ICGhU9fEWdWePgBZ4dJN54KEhAAWjZ+JumpatMRczoHMUzze+2ZGx/XC0+CKu86Flu9PBKCFr5w6W61keCw7iU+u+4T4UPk9e22rPqw/u5fjuSmctlrRSLDQVM5MDy+y7TWDnEoBNrWKlJQUPDw88PFR4p8aQjEIqoiLi2PXrl2Ye/eRJUUdDixCcLDKZZVlrzuCznU46JydCdmZcPY03t7eXHvttSxZsoSkpCSEEDRr1oxRo0axadMm5syZ4zy2Z8+e9OrVizfffJPz589jNBqJj4/n2uuup6unll3F8sNvt1SQufN7ytJPUpZ+Cru5jGbXPtHo9EG1iOqwdsHMuKEt6sspxmkN0OEW+QOyIeSwy1HK/6tCMwp/K/n5+XzxxRd88cUXJCUl1dm3cuVK5/87d+7MAw88wKRJkzAaFaXM38P69evx8PDg9OEzvL1hNtllWejVerqH9eamtpPRqnXo1DqivWPYdWEbzXya08InDpPVxK+nf8RF50qfyMaXhs4sLeb25dOwOmx0D2tNiEeN5PH7O5dgsdu4teNwXLSyeFtaiRw34KZzwaDRYbHJwYOSJKFVqZ3GQDXaKv2BfRYLURo1DuDFkiJeLLmoIVaIjo7mrrvu4vPPP//fFdO6BIoOQRWffvop999/P5O6duWutHQcNjsflJaw2lyBFfg/L29udHGl3OHgk7JSDlgt7LXbqbDZ6NixI5mZmeTl5eHr60u/fv1wOBysXbuW8vJyJElCo9Hg6+tLYGAghw8fRpIkQkND6du3L3v37uXs2bOoVCrsdjtxAyfg1XcqlUVZHPrgdnSeAei9gihNPdKoQaCSwKhT8/Q1rRneLlh52BX+VQgh+PLLL3nsscew2WxMmjSJkSNHEh8fT3R0NJIkYbPZOHnyJPv372f58uX8/PPPBAcH88033zB48OC/+xL+tXTo0IGzZ89it9jpFd6PWN9WnM4/wabkdXQJ7cFd8Q8CkFOWzX/2f8T54hTnsX4uATzU4ymC3IIbrNtqt/Dl/nc4nnsGi93Kp9c9zW2dRzn33/3DbBYcWnPJ9u267ws6BDcn4s0byC0v4tBD84nzjwDgZG4q8R9PxSEEL4WGETt0KNt1Wlkp1m6XB3eSxMqVKzGbzUyYMAF/f39atWpF27Ztad26NaqmrinxL0XRIbgK7rvvPi5cuMBbc+awqMob0EGr5X43dz4oK8W1yn1W4HDwflkpIRoNEdHRnDpzhg4dOlBSUkJ2djZjxoyhW7dubNy4kbIyOcglICCAfv36kZCQwKFDh/Dw8KCkpITJkycTHR3NokWLMBqNzpiDMEMFZQi0bj50emIROjcfyjJOk/ifRwC581dJ8mIyAvBx1TGuWwTXdwnDx01Z2Uvh34Xdbuf2229nwYIF3Hnnnbzxxhv4+9fX0dZoNLRt25a2bdty2223cebMGe677z6GDh3K7NmzeeaZZ/6G1v/7KSsrw2QyMTB2KBNaTgGgU0hXbA4721I3MibuRgLdgjBoDAS7hxLt3ZyW/m0oMRex5uzPfLrnfZ7q/QJu+rrxSg7h4Iv9H3Ms+yR6tbZO7EA1vcLbcijzDO2DmuOuM3ImP41NyQcQQjC5w3CubdWHKG95anVQs3iWHN3AwC8f5vHeEwCYu3sFOrUWs81C5aSJlEdH07GBhYw2bJBTsDt27AjIneTOnTs5ceIEgwYNwtf3v3Qxrivkv9s0ukJef/11kj75lBW+/qzzD+QX/0DnMjLNqqQvA9RqDgQG89211zF23DjnsRkZGWi1Wo4cOQLApk2bcHV1Ra1Wk5+fT58+fXjqqafQ6/WUlMi+rPT0dOd53377bXQ6eR7OqHbgr7Wg0ujQudWd65IkGNQmiGvjw3hydCv+c3d3Vj3ZnzsGxCjGgMK/DiEEDz/8MIsWLWLBggX85z//adAYaIgWLVqwdu1apk2bxrPPPssXX3zxJ7f2v5PqKZce0X3qbO8WJq+emlx4FrvDzvsJszFqXZjY/jY6BXehf/QQHu35HLnl2aw990u9er9PXMSRrANoVBoMGh03tRnI0qMbWXx4HYsPrwOgf7NOBLn5sPbMbr4++CvpJbk81kvu7NsERnNtqz54GuTgwOcHTMGo0VNiLuPVjV/x+uavUQFmm4VWrVoR3by5M6C7qRQVFbFixYp601P/qygegosI6NmDbrXWz95WaSZYpaZ5lUGglyQC1GpOdOoEpnJnOZvNhhDCOco3m81IkuR0dWZkZNCsWTM0Gg2VlZWAPDIC8POT59RCQ0NJTk5GCIh1LSO3qH4HLwSM6RRK9+Z+9fYpKPzb+O677/jkk0/44osvmDx58hUfr1aree211yguLub++++nR48edVJ8FS5PSEgIiYmJBPoHQK15d3ed7F4ut5ZzJv8kGaVpjGszqc6xgW5BBLmHcK7gTL16U4uSAbDYLVjsFp767aM6+yd2GEozn1B+mvJW3eMKs3hvx3f16ov1i2DX/Z8zc8NX7E0/QXZZAVatxNAhQ7j2uusueY1PPtmwBH31jPmGDRtQqVRERUVdsp7/dhSD4CK0sbFgMIDZzKoKE4etVmZ4eDo1sk0OB3atljI/PzhfYxB4eHhQUFBARIQ8txUbG8u+ffuc+5OTk+WgxVpqWdVla9cBshcgUFfZYPskCU5klCgGgcK/nvLycp5++mnGjh3LXXfdddX1SJLEu+++y7p163j00UfZsGGDEkNzBcTHx7Nu3TocXlakcglhlzvJInMhAO46d0orZUtBNKByanfYcVyUtmy1W5zZB4/1fI7mvi24tnUgHkYt0mWi1iK9g6h4eVOD+2L9Ilg0YSYA+11zSHQr/EMCoIUQbNy4kfHjx+Pm9r+brqgYBFVs3bqVV155hWHDhmGIbcGubdtYWl72/+ydd3gUVReH39maTe89IYUkJIQSQu9VmnQFC1hQUbGL2LAgiqLYQD8V7CgKAhaQ3iH0DgkQkkBCeu/Zzbb5/thkk5jQNIjAvM/Dw87Mnbv3bnZnzpx7zu/QV63mgXr5rOdMRiYU5NFuyc9WF39cXBzFxZYfT15eHllZWfTp04dTp05RWWkxGn755RfUajWenp5kZ2cTFBREcHDDcqiKehW5nBQGZIiY/5KCKCBwvrASCYnrnaVLl5KVlcXcuXMv3fgSqFQq3n33XcaMGcPRo0eJiYlphhHeHIwfP545c+awOWEjIxzGW/fvOr8dmSAn3D2SUl0JAAcy99Das621zfmSVHIrsulVL8ugNnbgbHEyj3Z+mhDXMMwixKUVM7yV598e58HM0/x4dD07zh0htSQHG3tbQkJCGDVqFF5edeXfz507x549ezh37hwZGRmYzWYWLFjQZJ8PP9ywWusDDzwAwDvvvMOLLzZTLZvrCMkgqMHPzw+5XM7cuXMpLysjwGxmuoMjU+ztsfXSownRovTU095Gz9DdbmxNTKSwsBCwGAH9+/cHYNu2bVbPgL29PV5eXuTm5qJSqaiuriYnJweAUU24uIzGOslOmQBqmRmtueGamIiI3tiMVQslJK4RK1asoGfPnoSEXEAv4woZPnw4Li4urFixQjIIroCYmBgmT57MN998Q65fHi1dW3Gm8BSHs/YzJGwEzjYuONu4EOkRzd70OHQGHVGe0ZTqSth6biMquYr+oUOs/dXGDrT1iqFKX8m+9F0gA4+WblQqfBhvaP23xvlB3M/sPR/PmKjeyIJdyKgqYOv27cyePZsXXngBPz8/AOLj44mLi8PPzw8PDw9yc3Mv2m9kZCRdu3Zt8HnUXs9vNm4Kg0BfWUbRuZNU5mciGo3IbTQ4+bfEKaAlcoXlKT80NJT16+vSX4qnP49597c4xuahdDVaVH4F8BRg9phANro+QmpqKu+88w5jx46le/fugOWilJ2dza+//oqtrS1+fn6sW7eOl156CScnJ15++WV0Oh1VVVWNxlkbbFhHY9+agIBKIcWCSlzflJaWsnHjxmbxDtSiVCoZOXIkK1as4K233mq2fm8GvvjiCwIDA/l8/hcciT+Iq607t7e+mwH1bvSPdn6GjclrOJi1l4SE4ygEBS3dwhnZ6rYGaYfppZbCRcdzj3A890jdmxwClsLdCbkYfj8NJvMV1Vl7stvtfDf2ZbSmElYGldIWiO3UiVmzZrFu3Trr032fPn0YPHgwKpWKn3/++ZIGgZeXl9UgEASBoKAgWrf+e0bL9c4NaxAYqipI3bmSMxt+oiwjpck2glyBT7sehA2+G5+2PRBq81G1xTjHnkOwK7DK/dcT7UJtvrDL3s7ODicnJ1JSUpg4cSJxcXG4uLjg7e2NTCajZcuWxMfHk5aWRseOHa3nGY1Ga9YBWIIHNaIM7V/6FxEJdLO7os9CQuK/xunTpzEYDPTu3fsCLcxAOnAKyACMgBoIBiIBT5qq/dGnTx++//57qqurUaulrJvLRalU8vrrr/Ps1Of45bGVGKsbS5mr5CqGR4xmeMToi/Y1rceMBtuCTCCggy9DXulrje0QfB0wrExETC2x5Lpdwukpima6BUajjd/AaVUGQmB3RJkMLy8vfH19rZ5X4G9p5ej1eovwkVJJamoqRqOxwRLuzcINN2NRFDm3/XcOfTsbo67xU3iDtiYj2UfjyDq8HefACLo+/i4uro7wXV+EIosRITTxMO5qyGi8sx61T/rnzp0jNTWV2267rYH4hUKhYN++fQwfPhwbG4s61969e9Hr9dY2DiYFSwUbfsfE16KJCuv8oJXvjSkOJXHz0fiiWwD8DiwDCmv2yanT4ay9UQUBdwBDgToD+Wa8iDcnDh52dH+wIzv+t695OhRAoZbT+7EuDQI9Zc4aVJPaIaaVYjyYifl0gUXjuOYcoM57oJajaO+P6GlC1SqGiiwvxJq+RFGkrKwMX1/fvz3EPXv2sH37dkRRxMfHh2HDhjFq1Cg8Pf9+vMP1yg316zFoK9n18bNkH91B3QXk4og1FbBKM5JZ/+I42geZaGWTctHywDZiFfbGwgb7zpw5w+rVq4mKikJekwu7a9cuoqKirOtRxcXFJCUlERgYyPnz53n//ffp1asXxcXFbNq0ifDwcM6cOQMieBg0KAWBvH0ridVVsLqmDGhp8n7W/PgZmxQynnjiCWuhJAmJ6xszsBSYj+WmX/+RsanfYhowB/gceBXoe5XHd/PQalBL8lOKOLWucSrhFSFYvAO3vNQHO9fGtVAEQUAIckYV5IxoNCPmVWDOqQCdEQQBwV6F4GOP4GZrNSZUMRGU/Pgj1Cy57tu3j5KSEkaOHPm3hhgaGkpsbCzu7u6UlJSwbds2vv76a3x9fZk1a9bfn/t1yg1jEBi0lWx58z6Kz52s2XMFi1PUGQZHzoLB3YU2HgUXbPvpfogXF3G6yiKgcvz4cVxdXSksLGT9+vXWQklarRaj0ciOHTvQ6XRs374dg8HAmDFjkMlk/Prrryxbtgy5XI6fnx+BgYGcOXOGpOQk1ujXsMuk4suDK8kqqxtL4ck43nwjDoCJEydKBoHEDUA58Cxw5FIN61H7+y4DngNGAzdfVPjVQBAEej3cGZlcRsLqxMt9tmrYh0xAppAxZEZf/Ns1LWvcoL1ChuDriOwyvJ+1ntScnBx+/vlnQkJC6Nat25UNsIa/qlv26NGD2bNn8/HHH/PSSy/ddHUybgiDQBRFds9/juJzJxHNlx+BfzKvkre2pTd5bMUEW8a2qlty2J0Oz2+Ew9lQbQKzeBo4DcCRI3UXstmzZ+Pu7o7JZOLzzz/nxIkTJCUloVKpCAoK4v777yc83FINrPbL+MEHH3DmzBlSU1MBSExMJDExEYD1931E7+D2mEURA1AyvjUtIm8+V5bEjUWtIZuTk0ybNm8DTcf5XJraO9UfQDmZmU7Y2tpKSwf/EEEm0HNKJ/zaerP9073oK6q5dNUby3miWcSntSd9n+yGg+fVyekvLS3lk08+QaPR8PDDDzdbPQKFQkG/fv1YvHgxhw4domfPnpc+6Qbiuv3VJCQkMHPmTA4dOkR2ViYK0YS/o4rhrVyJ9a3T1P5ifxY7Uv8avQ8+DioeiLXkrg4OcyHU1QZRFDmaXUl8XhUTlmmJcIMZvSDSAwYsgkh3+HAwZJTB+7uhfZgPDzz9GmK9QIPU1FQ2btzIyZMnycvLA0CtVvPyyy9bc2XNZjN79+7lyJEjpKenU1lZia+vLx07duSWW26hX2UAIbo6S1nzer1KYjPrXt6subIS1z8RERGEhITg7f0JUMElo8ouiQhswd1dZOjQodZlO4l/RnDXAHxae3JqfRLxqxOpKtJalwIwixZRIAGrmJF3lAfRt7YiuGvAVROHMpvNfPLJJ2i1Wp577jmcnZ2btX8XFxcAioqKmrXf64Hr1iBIS0ujvLyciXffSe7mxWi1Og5klPFBXCYPxHozINTZ2lYpE3iok3eD8zXKupt4K3cNXQIcWXI8jz3p5fQLcSLUxYaTeSXc9Ws17bzAxQa23QeONYHLQc7w0Kps+sbvJzS6E6JguQCtX7+e5ORkbGxs8PLywmg0Ulxc3CBXVq/X8/333xMcHEzv3r1xcHDg7NmzrFq1inMnk7h/4qeNAqgHhHbk7naWKoeyaA/kYe5SrrXEdYsgCMye3Z2VK7fQtm0WrVvbEB/fMNVLrzfz/vu5LFpUSGqqHicnOR072rJgQQv8/VVN9CqSnZ3FihWHiI6OJj4+/t+ZzA2OjYOamNuiaTcmivzkQvKTiyhMLUZfaUAmF7B11eAR6opnhAeOXldX5U+n0zF//nxyc3N5+umn/1Ew4YUoKLAs0V5uTY0bievWIBg2bBjDhg0jeeNSDpz5FdAwuKUzMzalsuZMUQODQCaDni0ar7WfzKtLH8wqq2Z1YhGDWjpzfweL8dAvxAmT+Qz7MmFatzpjAOCedvDMesjYs4yw1h0wIyAKMgYOHEjv3r2ZP38+r7zyCkuWLEEul1NUVGTNlVUoFDz//POEhoZa++vdsxc+Lp6sWP07284eoX9obIOxtnTz5852gywb9irUd3WtS5OUkLju0NG1ayIPPJCDnV3j77HBIDJ8eDK7d1fy0EPutG2robjYxL59lZSWmvD3b9xjRoaeOXNysbO7bi9r/2lkchleER54RVybG6XJZGLChAmcOnWKRx99tMH18+9QXl6Og0PDCo06nY7Nmzfj7u5ObGzsBc68cbnufzlJG362uK1EEZlMwFWj4GyxrlE7s1lEZzJjq2zsSlxwIAddjfrf2UIdZ4u0hLhqEAQBPyc3yCwko9yFbeft8LDVEuxUiq3SSHtvOJVVwfuFH7HW7UmMqAgODmb27Nn07NnTqpylUCga5MoqFIoGX2ZBBHuTkqeDhrKC3zmdn9bIIADQGqoRELCpAPOZQuStbj4LVuJGYSMvvphM1652mEwiBQXGBkc/+iiX7dsriIuLoHPny9PdeO65jHr96S99gsR1xbRp01i5ciXDhg2jsrKSvXv3NjheKy5UWFhoPZaWZhFJWr3aUo3Rzc3N2m7btm0cPXqUtm3b4urqSmlpKbt376aoqIgffvjBKk1/M3FdGwQGbSU5Z0+jN5nQGswcyqzgWE4lXQMaRqrqjSIP/HaGapOInUpG9wBH7mzriUIm0NnfgfbeduxJL+NUfhU5FXre2HqeN/q3IMjFBge1xYAorYbsSjtyKu04nu9OgEM5HrbZ7MkQ8Tac5bb8WWx3vpfFu7MpLCzk6aeftr7/hXJlBRFEAcK0TsSWe7Cj/DAAbnaNvRk/Hl3HwgN/IIoiER4tmFbyGA99OL2ZP1EJiX+HHTs+Y/nyYo4cieKJJ843OGY2i8ybl8eYMc507myH0Sii14vY2l7YI7ZjR3m9/tJpULZP4obg6NGjAKxZs6bJ47U3+oKCAlauXNngWO12eHi4tV1oaCgpKSnExcVRWVmJWq0mKCiIL7/8kjFjxlylWfy3ua4NguLUUyw+msvmsyWAxVHQyc+B+zrUFbpwtlFwaytXgl1sMItwPKeCjSklpJVW82rfQJ7ubsmPPZBZjqtGyYu9A3hxwzmWnMjnxd4BKGvc8pV6EyBYY5ozyh0o0JZTqS9HFMHBVETX9I+YtFLO7YNvsbiiREAErVZrzZUVxLq4aN9qO6IrXfAyWMbwYdwSHNV2DG7ZpcE8uwa0ZlzrvgS5+JBdXsiC/b8z5aPnOeMkMvf1hmkzEhL/dUymap54YgcPPuhOmzaN07pOntSRlWWgbVsNU6ak8f33hej1Im3aaJg3L4B+/Rz+0p/IE0+k1+tPBBp7CSWub7Zt22Z9XVpayrJlyzA3kVUWERFxwWJG9YmKiiIqKsq6LQChoS3pP+DmrGMA17lBUFmQxZBwFzoHOFCsNbIvvQxRFDGZ6/Jj7mjbMEWve6Aj3vYqfokvYF9GOd0DLd4EvUlEKRfwdlAR62vPgcwKzGYRW6VgPV4fEQG9CeQyOUfy3IjxzOPVLeBuY+LzaA3F+f7kqqtBZ6S0tJTw4FBuje2Hu1aDm0GNr94Oe5PS2t97O35ky9lDzBv+NM6ahoE5Wx9sWEf83pihdF3wMB/NeZNhY++gX5uGZZQlJP7LfPHFbNLSqtm0qWWTx5OSLDfzjz7KxdVVwYIFlu/322/nMGRIEgcOtKJtW9t6/eWTlqZn06awer3oAQOgROLGw8nJiS5durBnz55m6U8QQWWW0zFZg9hRh+Bk0yz9Xm9c11FpotGIn6OaNl529A5yYnqvAHRGM3PjMhAvkjQ7LNwVQYD43LqgQpVcwFBz03ezVWKsiTmwr1kyMJob91eiM+KsUZBY5MrqZBcWHoYnO0NWuYGK/CKqzuSSlpWOXJDx5YBn6ZHlSrcyL8K1zg2MgWXxW5i55Rvu6zCMKZ0bV0H8KyqFkimdR2PSVfDMvBVkFf+14oGExH+TwsJCXnttHq++6oOHR9M364oKy1NfebmZzZvDue8+d+67z51Nm8IRRXjvvdx6/Rl57bWsJvoTgeKrOBOJa010dDRhYWGXbngJBBFkCAwo8UWVV031N4cRS29OD9N17SFQ2DSWw+zs78jXh3LILtfj69h0cROVQoaDSl6zDGDB2UbBybwqRFEkr9KAUi5go5Bhr7IYBLq/lBw2mkTSSqrpGmBxX+7LsscsFvPkOmDdKmBVg/Z9vnoMO5UNZlEkwMmTCW0G8HT3CexJj+fBX+cwNKwrHwx9gvd2/MjiYxtIK8nBSW1PB99wPhkxDX+nhgGEWqMlaOrEopdp8cNLRIS1ZMqUKTz55JNX9BlKSPybvPLKK7i6OvDEExcOiNVoLM8pPXrYExBQF9gVGKiiZ097du+usO575ZVMXF0VF+jvwvLjEtc/giDQp08f5HI5p0+fttiAVyh9IIigEGUMLPbD3VCzfFWhR//DMVQPd0RoIgj9Rua6NgicAhq7HPUmy427ytDwBp5SpGVHaikn86rIrzSgN4mcK9aRXa7Hx0FFC2cbtp4r5UBmOQcyy1HLBe5bcQa13PINSy3WoTWY0NR8QXamlVqMBBGeWp1CsdaIh52Cu9sY6eLfkje2GzhXlIWHnTOZZQW42Toyoe1AIj1asC/9JG9u/Y4tKYc4mpNEB99wvh03g9t/foW96QncHzucNl4hFGsrOJB5krLqCqDugrcp+QCvrP8cAI+Ywdh6BtHO20RGxsWLLklIXEuSkpJYuHAhH3/8LFlZ66z7dToRg0EkNbUaR0c5vr6WJ30vr8aXJ09PBUeOVNX0p2PhwgI+/jiArCxDE/0V4OioxtXV9SrPTOLf5sCBA3z//fds3bqV1NRUnNR2+AcHMnLUKDy9vKyGwcMPP3zBPiIjI/ngkRl0LfNCY673XRPhp20rmfxEV+zs7KioqLhgHzca161BkJeXh7tvMHKlGpOhGrC49XemlaKSC/g7qtGbzJjMIhqlnFWnizhTUEUXf0eyy6s5nltFkdbI9HVnGRTqjKtGiQB8vDsLgHA3DbF+Dvx+soBKg8XAeHPrefqHOFOkNbL6TBEetkq2pZbWCBlpOJRVzsd7jcT6nCO5EJbd+RbPrJ6HiMj6+z8iyjMYgAc6jqBYW8afibuJcA/k17vf4cuDK9mZdozNk+fTyT/SOs/8yhI87Jyt22W6Su5f8TZKhQqTUkXgoAdRKpS4Brvy3r2d/rXPX0LiSsnMzMRsNvPkk+/TlCMrODiep57y5M03fVEqBTIzDY3aZGUZ8PBQ1PRnwGyGJ59M58knG0uQBwe35amnnuLjjz9u7qlIXGPeffdddu3axe23305rWz+yD5zh8/2/8fbst3n1+ZdwC/AGESbff3+D80QsqYhbtmxhZFBX+pb4IvzFrVBRrWXGhoXYqWysEeCVhVXkpxRRkVeB2SSiUMtxbeGMW7ArSpvr9jbaiOt2Jg8//DBlZWUEKW2QF+VTotWzK62MrHI9d7fzxEYpI79Sz0sbUuke6IirRsHwcFcS8qs4nltFO287gpxVrDxdzOazJYgiKGWgN4O7rYIuAY4czCynWGeiVwtHdqaVoTeJ/HAsD41CRtcAB3anlTUQMuof4sgTf6ZwKNvEsPD2FFeVUlpTgjku9TjHspO5s90gyqur2J56FIAJbQawJnE3c3f+RHvvliQXZqA3GonxDcNWZcOC/b+z6nQcw8K7E+DkyZ+JuymoKgEEQm99EtFkwiiTc+BsEXllOjwdb85gGIn/PtHR0fz22281W28CpYDF7V9ebmbevABCQ9U4OMgZNsyRP/8s5fRpHa1aWb7Tp05p2b27gocf9qjpT8NvvzUWp3nllSzKyxXMm7foH4vXSPw3efbZZ/npp59QKpRUz9sDfbpwW3Q/On42mWMrd/L+Hc9SqKgmvO1gDIIZGQK2JgVuBjVvHv8MQRB4MHJoI2MAYM6OH3BQa+gT3J6Vp3ex+MHfqMiviTcTLEsVYr1Szb5tvIge3ooWnfyQya/rsDwE8WLRdzWUlZXh5OREaWkpjo6Xrkb1b7BkyRK+/vprjh05TFFRETZKGcEuNgxu6UKsn2Vdv1Jv4vsjuSQVainRGjGL4GWvpEcLJ4ZHuKKQCby88RwAbw8KZl96GfP2ZOGollFlEPG2VzIy0o223nY88kcy3QMdebyrRUtgY3Ix3x7O5b3Bwfg71cUqPLf2LFnlFxZFOfPsUladjmPamk8u2EYmCJhFkWivEO5qN4jNKYdIyD1LobYMRDCLIv4D7iP3yDp0hZnIlDa4tx3AD1/9j1vat2iOj1dC4irzMfAzYKJv30QKCowNpItPntTSpctpHBzkPPmkJVNo/vw8jEaRI0ci8fO7sGiMpT834uNTr+oMJK49puRCDD+dsG53/2IKALsfWdhk+2qjnhZzxxLu5s6Hw/pSojuPwVSFgAwbpRPl1c7cvWwZP0+Yye8n4/gtYTsfDfuSJmLKrdQWdHLydaD/sz3wDHNv1jn+U67k/n3degjuuOMO7rjjDkRRZOMrd1B0NsFawrgWO5WcqV0urHUtiiJlOhN+NcGHtZmFT3Tzo7VnnTpadU1A4bl6CoipxTrUCgE/x4YXpum9/HlmzVnmDbVhStflgB1ztv/AezsXozVUE/7hBABe6D2RmQMeAOCPUzu5Y8lruNk64qJx5PledwPw3s7FzNz8DXFTPqeNt+VJp9NnD3CmKIv07T/iETOYgP73U5Z2nNz9K5nxLNyy5c8r/SglJK4B44AfL3g0KkrD9u0RvPBCBm+9lY1MBv37OzB3rv9FjQELcuDy1A0lrm/MaaVQU2hJFEVyK4uJ8ghqsm2JNp0vDyykVFdJex8HMkoPINYrqqU1FvPezgwiPWwwm1dQrLVc7101KgqqLvyQV+stKMup4Pfp6+g0sT3tx7W+asWdribXrUFQiyAIdH1sDmunj2pkEFyKXefLKNIaua21xaLzcbBcaM4UaBsYBKcLLG7/Ym2dvGqJzoiTWtHoj+5sYwk6zNbJUN4SiGFDIS2cvenZoi2jI3vjauvIujN7eW/nYrzsXXm0yxgq9Za0wfJqLXse+ZIAJ8sTUZ/gGKLnT+TDXUv4dtwMjKJIqV6L3qDDM3Y4QUOmAuAa2RNMRg5uXU1SUlKzpOJISFxdAoDBwEa2bYtoskWHDrZs3Bh+xT1v2/YJMOkfjU7i+sCcVUbt4/uS45vIKivgtX4N4wbMZiOJBWtJzF/Ln4kZKGsUasW/VNg8klXBiZwK3rklGKNZR3l1NiZRj50qiYKqS3teaw2D/T8cRa810GXS9Vd87vpe8KjB0TeY2PtfuaJzMsuq+fZwLmFuGnoHWaSCg11saOlqw6rTRWw7V0J+pZ6j2RV8fTAHuawugwHqhIz+iqpmnzZ8PLIu0SwvPsBjqz7gs5HTmdzxVkZH9eaL0c8zsd1gXtm4kMKqUmwUFg9Ft8BoqzEAEOjsRffANuxNT8AsipiAMoXFaHGL7tvgfd3bWNS1mkuoQ0Li6jMdcOCKc8UuiByIBO5qpv4k/uuIRZaHqcT88zy9eh5dAlozsf1g63GjSceutE84nb+GSoORI9kVtPexw07VMJ3QaBL54WguA0Kd6y0Bi4iIFOt+RCXfeUXjOro8gdObkv/R3K4F172HoJaWA8dj0FZw9Me5WC4wF170KdEambszA1uljKe7+yKT1V2Qnu7ux/y9WSw8YClEJBMsQkan8qvIrhcbUF/IqD4Gs6UvjYs3giCw8OAq2gW1wvcvOgLDW3Xnh6PrOJadjI+DGwCedi6N+vOwc+ZYdhIm4GWzAdHBDfLTUNbLPABQ1dQ/KC6WxFgkrhecgdnAU4CZi/1mL40MyzLBbCyGgcRNgVkkp7yIMYtfwtHGjp/Gz0Qus/z9zWYje85/TkFVEgD7M8oxmER6tLCso3+xP4sdqQ1rXuQkl7AhuQSALv6WWLTfTxZwOGsRORWL0BlNuGhcaePVnqFhI3FQX3hN/oMZH/PVoP9dV6mLN4xBABA5YjJ2Hn7sX/gaRm1lk0sIVXoT7+5Mp8pg4rV+LXDRNFRLc7VVMrN/C7LL9ZTqjHjbq3DWKJi6Mhlvh7q1y/pCRvWXDYq1llSp2kJGuXm5uLi4cMpDQ+t8LSZRRC4IGEyW5Qej2US0VwhKuYKs8oJG480qK8DF1pmnzQYSELHzaUnZ2cPoywvRuAdY22nLCoGbs4a3xPVMV+Bd4AUsBkFjbfpLIwfsgS8AScb7ZqLMqGX0jy9Qqqtg0+R5+DrWBfQlFqyjoOqMdXtXWhm2ShkxPhZp+P4hLkR72VFtNLPoSB7RXrZEedqy7EQ+LrZKi3kqwun8KnwcVHQNsEEm9CKrvIq4tG2cyD3KK33eQq1onNmlM+pYfvwnbFSNa3X8l7khlgzqE9h1MLd+tIbgPqMR5AoQBESxth6BmffjMsgp1/Ncz4AG2QF/xcdBRSsPW5w1CjJKqynRGYmuF1fQwtmGapNIZlnDYJPkQosLq3379oClutaRo0c46qdnmklPbs1T0NITm5EJMqK9QnBQ2zI4rAt70+NJzD+PKIqYRZH4vFT2pidQHdKehJrz3KJ6A5B/ZH2D980/sg6FQkHfvn3//ocnIXFN6At8BwRw6Zyn+tQa4rHAYkCKnbmZ0Ol0jPv2BZIKM1hx99tEegZZj5XqMkjMr6uKWKw1cjK/is7+DijlMr7Yn8XMLWl8ti+brw/lYjCLHMmuZPGxfPRmyK0wsD+jnGqTyPHcKqoMZoaGuzGoZRa3tR7PPe0fIr8yl+O5R5oc29ozf6BW2NDW8/qKI7ihPAS1PPLUNL7//vsLH+/kTbi7psYVVEFupQGdwYyrrYIYH3tGR7rhWCM2YRZFfj6eh0oukF+l56nVKZTojDiq5AjAmjNFTOnkA1iyFrakVeLn50f37t0BmD59OmvXruWlh27DNnoIa9QOmJP2cz75AHd1GGa1aGcNeJBtZw8z5LtnmdBlDGcR2bDvN2QaB7x63mEdu51PSzza30L+0Q2IZhOOLdpQlnacopM7efHFFxuVWJaQuD6IBH5mxowgnnrKAy8vJZbLk7GJtnIsssSBwP3AcJovDkHiesBkMjFhwgT2njnGsjvfpGtA6wbHT+evof7S8Z7zZYgi9KgpZlfrHQAwmETSS6sRgQ3JxYgiPBDrxZ70cpIKtTze1RcXGwWCICIXilHKTuJma7luaw1VjcaWW5HD5rPreLjTUxzO2o/Z+He8XteGG9IgePjhhxk4cCBFaSUcXZEAwN6MOE7lx6OWqxEEJXFpFplijUJOWy87ojxtySrTsy65iO3nSrkt2h0B2H2+jJQiHXYqGfG5VQwKdcbNVklaiY6NySVsO1eKIEBLVw0HMys4lVPB4sWfI5db1rF69+7N7t27mTlzJnF711BRWoLaxQv/fveR3ON2xpqq8UBA7uZL93vnsm/T13yy40cQZDgFtyNw4IOoHBvmtQYNfxKVkyf5RzdQfHo3KmdP7n/mNd55541/+6OWkGg2Dh48zjvvZDN3bjZa7UYUimNAPHAei2GgBkKB1kB3IAbJELg5mTZtGitXruTWwcMorirn52Mbrcf0pkqc1EcR68Wk7DpfhotGQaSnpf5NuLuGcPeG7vyEvErWJxXjZqtgQKgLSYVaUoqgk5+DJUW92ojJLJBdtpGlJyqRCTLC3SL5K8vifyTcLZI2Xu05lLXvooX2/mvckAZBt27d6NatG+tmb8OmhQuiWWRjisV9VG2q5vP9jWVOH6sRHNKbzWxMLmHJ8XwUMoEQVxtGtnLlj9NFTO/pT4xvXWliO6WM304VcSSrgp2pZXg7qPj6i/9x110No5w7d+7MmjVryCqu4o5Pd6GvZzEWAUW1X1yflrSa9M4l5yeTK/DvMxH/PhORCeDpaMNnj/e80o9JQuI/Ra9evQDw8QlAoRgIDLy2A5L4z3L06FEA/ly/hj9Z0+j4T+PrbtRZZdWcK9YxLNwF2UW0Af48XQRAv2DnRsdKdSamrqrLGnCxcWZyh6l4OzT0yJ7IPcrJ/Hhe6Tvbuk+8mKrRf4wb0iAA0FcZSDuQYQ1cDnENI7MsnTcHfoC7rSP26i8QqEQQGv6x+gQ5szG5hEntPRnY0hL1vynZErnvZNMwerk2IHFG30D8HNW0u+tZokY9dMEx+brY8sQt4Xyw5nRzTROzCDPHtcVGJUVWS1y/5OXlodNZhGD27t17jUcj8V9n27Zt1temlCIMi49btw9n/sD5kr1WD4Gvo5qfxre6aH9Gs0hCnsX9P6jmuv9IZ18e6Ww5bq+S81KfAAwmkdRiHfsy5BhNFXjZJuGiyUApq6baCLO2rqZ/cA98HfzqOhfBZDAhvw4qJ96wBkHBuSKrMWAyGzmUtY8Q1zDcbS1R+FrDCGyVPyOKIuV6E2Yz5FToWXI8H5mA1bUE0MrDFkGARUfyuLu9J64aBedLq/n9VCEd/ezxd7bFJagVrW69v6mhNGBcp0COphWzJSH3HyVZ1fLogDDat2icrighcT1RG4RrY2MjxcFIXBHyUFdM7bwxH88BEYq1aY1Ehy7FwYxyjGYRf0cVDurGN26FXKBNTcxBB18H+gdXMfXP7xgXCb0DQETgvV0iWgMsvW0HSlkCCQUDkQuWGBizWbwukmFvWIOg8GyxNaYkIe8ElfoKOvt1sx43mUPQGYdSbVzVwBXkqlHweFdfq5wxgL+TmgdjvVl8LI/XN6dZ9/cOcmRKZ3/sPP3o8+ICZPJLf5wymcDMsW2B42xOyP1bcxMEEEV4qF8o9/QK/lt9SEj8V9Dr9WRnZwPUK34kIXH5KAeHok8vRSzRYjA3DvS7FGuTLMsFg8MuXSpbwEQnPx0+9rD4BNwaDmU6kdk7YWpHKKsGUSzEy3YpXrYCgiAnPT0Ve0dHPD09L9n/teSGNQh05dXIZAJmk8iBzD3IBTmxfl0atDGYOiAXZLzUezl6k5m0Ei0HMsvRGRpbl64aBaGuGtr72OFupyQxv4r1ScV4eJn48cufsXG8/JrrSoWMN29rR7vA83y68Qwms4jpMteZZAI4apTMGB1Nr4j/9pdLQuJyqE2VFQSBIUOGXNvBSFyXCDZKVPe0Q//9UYQrzKbXGcykFOmQCdCzxeUU7xOQCSI6I5TWlLcp1kGFHt7bbflXhwgYCQ0LY9StQ/l9VeN4h/8SN6xBUBs7ojPqOJZziCjPNtirHBq1E2lPkEsLNIpVxPql09rTnje2puJoo6BDTQBhYkEVc+MymDWgBcEulsjUjr7OqFUR/Lp/H2czcoiKunyDACyegvFdW9A93INFO8+y9lgWBpOIQiZg/ItxIJcJmMwiDjYKxnQM4O4eQTjZXqrAi4TEfx9RFK1y2y+++OI1Ho3E9YzgaIPqgQ7YnvakqqDwss/bmVqKWYQIdw1qRUNjQmc0I0Cj/TtTqyjWQcea1S1PO/htQuO+5++DPRnw8zgZPgGnoKoIbK/sXvFvcsMaBBonG8xmkWPZh9Cb9HT2737BtqLoQpVhEnJjBiGuB3G2yWBXWikdfO0RRdicUoKTjYIQVw0mszMGUywGU1s6tarkt/i97N69m6ioqL81Tn9XW14eFc3jt0QQl5jHqawyTmWWUqo1IBME3B3URPk50drfie5hHqgUN5yWlMRNzJQpU6yvZ82adQ1HInEjINiqcOvSmcK1Zy672N2GmqDx4RGNY7FyyvW8vT2dbgEO+DqqEBA4W6xj9/kKgpzhqRqns60SRjcRt/j7adifCaNbmUFIh1/GwT2bQfbfvI7fsAaBe6griLA/czdquQ3tvDpc4gwBkxiA1hCA3rSe8mo/qvSjEYRqirW/YjKbKdc9g4gl2FCQCTj5WdwQRmNT4ilXhqNGybD2fgxr73fpxhISNwhfffUVAH369EGhuGEvRxL/Iu7hMSSuvrAwXX3KdEYyy/UoZQIdfBp7kF1tFXjaKdmYUoKAxVvrbqtkUK8uvNstm2UnzvPFQUguAjsVdPCBV3tD9zpVeUxmEN4Ai5jWNri/Lrxwz549dO3a9Z9Mt1m5YX+BbkEuVBrLOZWfQCe/rqgUDWWKq406BIRG+w9nHaDKUEmgcyuMZov6lYddPAl5GzldkEaEuyW/VRRF9qTuAiAm5vqSp5SQ+C/w6aefWl8vW7bsGo5E4kbCr0NflHaOGCrLLtl2T3o5AM/29GtQ5K4Wg0kks6watULAw1bJu0NDMXqEUR09hGeX/8qmTeeZ2BamdoISHSw4BH2+g12TobMffDca7msP/b6HJztDJz9ApoRhn4DKjpYtWzbr3P8pN6xBoFArOKc+g1k0NblckFeZy8d75tDRtwve9r4IgkBayTn2ZezGzdad/iG3WNv2DR7EnvM7+Gzfh/QLGYSrxp3kotPsz9jDoEGD6NKlS6P+JSQkLs7TTz8NgI+Pj1SUS6LZkKvUtBw4gdOrvkY0Xzz9cNf5UhzVctrUq1NTn8XH8mjppsEsipRXmxBEEYN/O4xm2L59Ox06dODlCbZEVsUBcHsUhMyHxcctBkF9erWA26IAjBClg64PN8Nsm5f/5kJGM7Hn7E4cVI5EekQ3OuZi40oHn04kFpzi91O/sDzhJ1KKkugbPJCXes1qEIDobe/DS33epLVnG/Zl7GbpiUWkVZ7jueee4/fff/8XZyQhcWOwdetWTCbLGu/y5cuv8WgkbjSiRj2I2sEFhIvf4mYNCOKLUWFNegdO5VexP6OcSe1rs7kEDJ5hmFz8MZlMGAwGHB0ciHO6i0KFP2AJLpQJ8JciulbKqy0iSBz4/J9M76ohiJchtFxWVoaTkxOlpaU4Ol5OWsZ/hw1ztpO6L6NZ5SPlKjnjP7kVR+/Ga04SEhKXxt7ensrKSlQqFdXV1dd6OBI3IFlHdrB9juUpXGcw82diIclFOlKKtFTqzTzcyZs+f5Ep/mJ/FjtSGy81qOQCnvZqXnvrHUSVJY5szpw5ZGVlMfGuOzGVZfHdig3IBXC2gf0PQUhNjOK2VMuSgb3KkpooFyzegrk/bKZjz/5X8yMAruz+fUN7CAB6PdIFpUbJRSSsr5hu93eQjAEJib9JSkoKlZWVALz33nvXeDQSNyq+Mb1pf/dzAJTrjfx6spCssmpaONlc9DylTKBXC0dUcoH7Y7x4tIsvnnZKzDYOVmMAYPLkyXh5efH1t9/x3YoNgEVKftfkOmMAQCWHcZEwbwj8cQe81R9O5EKvgUM5cqTp8snXihveINA42zB4Rh8EuaxZjIKIAaFEDQ3/5x1JSNyk1AoRATz22GPXbiASNzyRIx+gw30v46xR8fmocObf2pK72l08XkUmwJHsCm6PdmdgmCs9Qz2xdfcBecN1gFqZ7YCAAJydnWgZ6APA6KVQUE8ssXsALB8Pk2NgZAS82BP2PmgR0n3ppZeae8r/iBveIADwbe3F8Jn9kasVCE2sFV2SmlMih4TR+7EuCM3pbpCQuIkoKSkhIyMDgLvuuktKNZS46kQMncSt7y7Hyy8QsNQduBhGs4itUkbvFk4YPcOo7HYf/CUbzWQy8dFHH2E2m8nOzmbixEl4+AVho4SUIpi76+JjaukKo/q0axBL81/gpvk1+rbxZsKnI9j2yR4yj+UgyITLiisQBFDZqeg9tQshPVr8CyOVkLhxGTx4sPX1vHnzruFIJG4mXENa4zHhFZK2r8R0aDuQCoAoCFif+ESRKoMZkwiFWhMPr0xGo8mkbdtcqqurMZlMFBQUoNFoSE9PJysrC5VKRXh4OG3atOHQoUMIgoxIDzO70i89pgBfT/R6PZWVlf+Z2LybxiAAsPewY/gbA0jbn0H86kQyj+UAIJMLiCKWikG1hoIIti4aWg8LJ3JIGBrHi687SUhIXBy9Xs/+/fsBaNu2Le7u7td4RBI3E4XFJRi8IqiOUsMfh9AHxKAP9kYwVgMCZrUdssRdkJlgrS2j1WrZt2+ftY8ZM2bQv39/goMtReXOnz/Pq6++ajkomhERMJjAeBnFFs/mV2NjY4O9vX1zT/Vvc1MZBGApoBLUJYCgLgGU51WQm1hAQUoRlYVVmE0iKlslbsEuuIe64hnmhkx+U6yqSEhcdSZPnmx9/fXXX1/DkUjcjGi12gbbZgdP9MENVQIn3BNBbHJyg32HDh1i//792NnZcc899+Dh4WHNjPH09GxQrtssQmIhTKknjJtfCR5/kTk4lq9k5YbtDB06FNl/SMb4hjQI8kp1nM4uIzm3nKpqI3KZDC8nG1r5OtLSy8FaD8DB0x4HT3ta9gq6tgOWkLjBMZvNLF68GABnZ2c6dux4jUckcbNxObFf9vb2tG/fvsG+qKgo9u/fjyiK1mPr1q1DJpORk5PD559/TlRUFOkZGVQbTNgp4el6dsaE5RZdgu7+Fp2CkwUCC4+YsbW1Z86cOc04w3/ODWMQ6AwmNsXn8MveNM7kWOQo5TLBGj5iMouIWKpWDY/x47bOgYR4/ndcNRISNzJvv/229fUHH3xwDUcicbOi0WgoLS294vNUKhVyuRxzjeqhVqtlzZo19O9v0RA4duwY8fHxmM1mZAL8cjvUFMUFLEWPFp+AD/dCWTV42IqMHdqf1+d+JkkXXw0Opxbxxq8nyC211LSuxdRE0GC10cwfhzL49UA647sE8ujAMDSqG+JjkJD4zzJz5kwA5HI599xzz7UdjMRNiYeHB7m5uVd8nk6nw2w206lTJwAqKyuprq5m06ZNTbYf/hOMioDf77BsP9nF8g8AmQJ8YuHB9TSrOE4zcV3fCUVR5KutKXy9PcVqCFyOIGGtobB8/3l2JuYx756OBLo1rWUtISHxz/jll1+sqVWPPPKIlGoocU3w9PTkYsK8BoMBk8mEjU3DAPLVq1cjiiKtW1uK3Tk6OvLoo482On/Lli2kpSTy8zjwuYDzWRRkCGMW/SeNAbiODQJRFPl0wxkW704FLs8Q+CtmEfLKqpny1T6+fLALAZJRICHR7NQPJnzjjTeu4Ugkbmb+/PNPtm/fTlFREQDHjx+nuLgYgP79+1NZWcns2bPp1KkT3t7eACQkJBAfH0/r1q1p164dYFlC+GucgSCaSDy0jQzBskRwIU60eYu27v9dYbv/hkGgr4LMfZB1CAoTwaC1CEG4trS4V/y7gI1Tg1NWH82yGgP/BJNZpFxn5OkfDrH4sR7YKOWXPklCQuKyOHDggFWmuFevXri5uV3jEUncrHz00UekpaVZt48cOWKVDu7SpQu2traEhYWxd+9eaxaBXC6nVatWPPTQQ9ZsALPZzM6dO9mxYwf5+fmoVCoCAwPxMGQ0eL/aGgYNeR54nj179tC1a9e/HrzmXFuDoDAZ9n8KR74GfYWlMpUgs+gBCILlf9EEcjW0vZtDNoOY8dF37Nq9G221ETv/SAIHPoCdd2iDbktSDlGUsJ2KzES0BemoHN2JeWpRo7fP2PYDmTsWW7c1zzQ8HhcXR48ePa7K1CUkbgZGjRplfS0JEUlcS1JTU9FqtSxduhS9Xt/oeFFREUlJSdjb2zNs2DBsbW05e/Yse/bs4dtvv2Xq1KkArFixgk2bNtGlSxf69OmDtqqS3Tu2klhczq7Jjbrl4c4q0n2Hk0hLZIJApxDX/1wwYS3XxiAwGWH3+7D1VRAB0WjZL5ot/8Cy39q+msNrv6fn198Q4OVCzzGTOZVdSc6BVZz6fjqtH5iHxj3A2rwwfiuFCTuw826JysH1gsNwjeyBjatvg31PDo7g/bffoKKiwhpEIiEhceWkp6eTnZ0NgJ+fHzExMdd4RBI3OxqNht69ezcZELh3716qqqqYPn26VVugd+/eiKLI3r17qaysxMbGhu3bt9OhQwfLUphoRo6J14O20+4jWHwcOvjKkCGiF2WAiT2Bj6OJuoVaGa4slRw7R+d/bc5Xwr9vEGhL4KdbIX03De/6F+fVLSY0Cth9Vwnltr/xZNDbuEb349j/HiR9y3eEj3/V2jag//0E3/o0MrmCxJ9foyovtck+bb1CsPUKsW7LBCixV5ORkcGDDz6ISqX6m5OUkJAYMWKE9fV/Ld9a4uYlJCSEdu3acezYsQb7dTodQCMZYScnJwRBQKFQYDKZMBgMljY1AYq9ir7DR12MTACTwpbNdOYY0SzBA3idKjSoqquQKdUIMjlavYldZwoYGO39r8z3Svh3DYLqclg0AHKOcSXGAMDONBjSEtxtRZzJZAHTeNDhI861aENJ0n5Mei1ylSX5U+Xw99YpzSL8sPgnRFHk7rvv/lt9SEhIQGlpqfWCq1KpuOOOO67xiCQk6ujcuTOCIHD06FEEQUAURcLDw1m/fj2LFi1ixIgR2NnZkZKSwvbt2+nfvz9qtaXAUXBwMHv27CEoOIR8jy58WjqZzB02yGyOExc7j4NYqh7qsHz/z678ELNeC4IMh8Bogm95iFOZQZJBwJ+PWIwB8cqrO1WbLGpPAApMeJLPbN5moNIZ0WSgKi8VB//IfzzErCOb8PXzp3fv3v+4LwmJm5VJkyZZXz/11FNSqqHEfwpBEOjcuTM+Pj5s27YNrVZLdHQ0I0eOZO3atQ28B0OHDmX06NGA5aFx8uTJfL7wK7779hvgGwDULj5E3f8BNi4+de8hV+IS2RPnlp1Q2jqizT9P9p4VnPh2Gjt8v+aJwRH/5pQvi3/lV3r48GFmPvcIcXsOoDNCiAtMia0Ta3h7J6xMhJRiKK+GACcYHgYzetVpQEe4Qdx5uHsFHMyGrHIzciGeCqMl8lNfVvCPx1mVl0pV7jmG3zdVKnEsIfE3qa6uZtWqVdbtF1544RqORkLiwgQEBDBhwgROnz5NfHw8bm5uhIWF0T6mA/b2dsSfiGfdunU4OTnRr18/8vUqDmt90bu0xMu7LY7B7TFUFJO16xfO/DKLqPveR2lryYhzCIjCISDK+l4uEd1wjerFiS8eJe6XT2DmxGs17Qty1Q2CDRs2MGLECGK84dXeAvYqkZRiyCira3MoG9p7wx3R4KCCUwXw5WFYnQRHHwY7FUztBI+uhkq9RQXKyx5Wn4EjOZYgxILjm3CL6vWPxlp4YisAMX1v/Uf9SEjczNQ3AIYMGSKlGkr8p1GpVLRt25aEhASWLFnCtPe+psogohTM9GrVk2KDnGUrfiO7xQi0SmdOfPUYji3aEjR0qrUPp+AYjn/+MNm7lxM48IELvpeNqy8uEV3JS9yNyWRCLv9vpblfVYOgrKyMe+65h+F9OrK82+4GssL1WTG+8b5u/nDbMlh1xmIoPNIR0kth7m5YeNjSpqMvvNTL4mEoO3cQx+BfATUmrRsGrQeGcv/LHqsoihTEb8XWMwj/0IsoS0hISFwQs9nMJ598Yt2Wggklrhc+//xzYmJi8ImIZfHuVKuirTG4D6YDO8jNTAMhHW1eKi0GTWlwro2bHxqPAMrTEy75PipHD8xGA5WVlY0CGK81V9Ug+Omnn8jNzWX20A7IyuRUVpvQKLmgYVCfIGfL/yW6un2zB8Bz3SEhH5zU0MYLXtpsOWY2mlBq0pCp5OB0FkEQEc1yFDYFCMKli1NXpCegL83Dq//92Kml9U4Jib/DZ599Zi0CExoaalV3k5D4r5Obm4uLiwsRvo4N6uCINbLbotmMUWcpjiSKje8pZpMJwdx4vy3QW5DRGhmRgsCLJXlsVqhQfBuP3t8Zma8D8kgPBGebRuf+21zVO9+mTZtwdHQkM/EAo5ebOFMIdkqY1BY+GgI29d5dFKFQC0YzJBXCi5tBLkDfoIZ9umgg1gcqDZBaAkvjQQCcw52Rq2vdL5Y/piAzIVNVIFNWYuu1n6rcWKBpF03Bia2AgFubfoR5OzTvByEhcRMgiiLTp0+3br/11lvXcDQSEldGeHg4GzZsQK3Na7C/MH4bCDJsvYIxlFtkjwsTtuHcsq6Ed2V2ErrCDDw7DLXus6ss4SF7d4YIMtSACTiZm8KGM7sZ3LIzslI95rI8zCfzMG5MQRbmiqJPMDLfa3f/uaoGQVJSEkajkVHflPFADLwzwCLn+Ml+KKmGn8fVtc2tBJ96VVH9HeGncdDKvVG3zNtX5xkACHOF7o+HUdTkKCzGgZ3PXlSOaZSmjEQ0qxu0MJuMFJ3ciUNgazTOnoR5SQaBhMSVsnbtWmsut52dHbfddts1HpGExOUzffp01q5dy7jhgwjoNpoig5KiM/spTT6AR8wQVA5uqBzccAzpQMGxTZiqq3AKicVQUUTO/j+QKVR4dxkNQH9Bxs4Vc/hWqSYxIBoPO2dO5afxzaE/sVWqebN2yaFe9r05uQh9chHyHoEoegchKGT/+mdwVQ2CiooKqqqqeCQW5tcYTmMjQW+CBYdgVl8Iq4k3ctGoWD/JF63RhyM5Bn4/dY5SfTgmZMhIZGdaAbO2wy2hIAOGhsK6FPC2t2QlaKrq1hbKUsvIOWCx8ipzqjBWGUhangxiMnY+qagcn0I014kOlaYcwqgtw6Ntf7qHeWCj+m8FekhIXA/UL2v8zDPPSKmGEtcVvXv3Zvfu3cycOZN9u36nuKgItYsX/v3uw7fH7dZ2ERNeJ3vPcgoTtlOafAhBrsAhMBr/fvegcQ9giiDnLpmCT1v1ZOmJTczfs4yy6krc7ZwZFdmLGX3vJdTNr/EAaowDU9x5zOmlqO5sg6D6d39DgnixepA1lJWV4eTkRGlp6RUFQURHR5OQkMD2+6B3i7r9O9Kgz3fw/WiY2C4MozAaMwNBUNVIF5vYcz6B/t88w4q73mZYRDfOFsbx9OqPOZJdSHk1BLvAve3g2W7w+BpYmqqi52f9kKvlpG/J4Ognx5sck38/f1rddSfl6QOs+5JXvEPRqV10mPYz/5vSly4tm3BLSEhIXJBjx45ZK8AJgkB+fr6UXSBx3WIyizz45V7O5JQ3iCe4FPcJcu6TNcNNXAChhTOqu9siyP+Zp+BK7t9X1fzw9fUlISEBr79UFfas2c7X9UMve81Sy0CoGYogA2R42Ltjo1Ax8ZeZIAgEOHoyoe0Ilk7ojZPyQ2TE8/ZO6P2tJU2xrFrP5qnb8O3uQ9htoYz4bZj1/RKXnOHM0mQAMrZmkLF1LjDXejzqvg+IuP1lInwc6RQiXcQkJK6U22+ve4IaOXLk3zYGUlNTWbNmDYcOHeL48eMcPHgQgO7du9O+fXtiY2MZOXIk7u6S0S5x9ZDLBF4f24aJn+1G4PJ0ddsjNI8xgOUNxdQSTHHnUfQJap4+L4OrahDExsayceNGMvWORFAnPJBR7gXk4mZbU/5RaDiM9NI8ei18FL3JQKirP091v5196Sd5c+t3HMlKZNmd81GI33Ao+0fae0OUJ3x7BJxCHEnfkkHeoTx6f9gTRU3Uok9Xb+y866wSETAb7Dn94xlMei12fuHIBHh9bBtkl5MCISEhYSU9PZ2kpCTr9qxZs664j6NHj/Lmm2/y+++/I5fLiY6Opn379hiNRtzd3fHy8mLr1q18/vnnqNVqJk6cyCuvvEKLFi0u3bmExN8gyMOe18e14dVlxy9oFFRkJlJwfBPlqcc4XpLHao0jnQMimdn/AcJqCu6ZzWYWH9vAH6d2cjQ7iWJtOUEu3twe3Z+nu0/ARtm4Zs53h1bz8e5fSH0zm4CAQJ585imeeOKJqzthrrJBMH78eObMmcPXCfb096sA0YyZIL48HIhCVkDv4A5U6rUICNiq6lIufj62gRJdBQC3RfflgY4jeKDjCKoMOlYkbKNYV4GL5gGWTFAiF7/lll/kIJiInhxFaWoZh947Qu6BPPx6WSpWOQY54hjU0FWiLdBy/PMCPGKGIJMreWFEa1q4/8WVISEhcUkmTqxTXIuKiqJt27aXfa7ZbObjjz/mxRdfJCQkhP/9739MmjQJO7umf4sFBQV8+eWXzJ8/n2XLlvHVV19JwYsSV41B0T6IIrzx6wmARssHWbuXUZGeQGxUbyZ5tSSvspgv9v9GtwVT2P7gZ7T2CqbKUM2U39+ls38UD3UciYedM/syEnhz63dsPXuYdfd92EAZ96sDK3niz48YHdWbJ3uMZ3dxIk8++SRVVVVXXfXzqhoEMTExTJ48mW+++QZjKfRuoWRLqje/ntzB9F534evozrHsZIYvmsZtrfsR7h6ITBD45cQWAAKcPHmsa10qwrniLAA+2PkTIa5+FGmV/HHSgYNZ5QQPb4Gdjx2GKkspZWOlkQCtO756VxRmOQaZkSx1EZnqIkRBJGNHNogi7tH9mD48kltjmgjykJCQuCjFxcXs2LHDuj1z5szLPlcURZ599lnmzZvHc889x+zZsy9ZYdTd3Z2XXnqJRx99lClTpnD77bfzxRdf8PDDD//dKUhIXJRb2vgQ6mnPzF9PkJRTjiBYCx3i03UsdmNf4BulLS0QkAkCt0X3o+Nnk3k/7ie+HTcDlVzBlgc+oVtgtLXPyR1vpYWzt9Uo6B8aC4DWUM3MLV8zNLwrP094w9JWGA4+drz55ptMmTIFFxeXqzbXqx7C+OCDD3LixHF+O3SQXxIMKGQHaOsdyqT2QwDwc/RgdGRvFhz4o9G56aV5+L87mu6BbXiky2hO55/Hz8GDD3YtadT23Oo0lHYqtIcrkMtkfKN6htZJIY3a6QUjJ+zTGLvtBWzc7PjutfvpHCqtR0pI/B2efvpp62snJyfGjBlz2efOnz+fefPm8b///Y+pU6de+oR6ODs7s3TpUry9vZk6dSoBAQEMGzbs0idKSPwNQr0c+HZKV9Ydz+aXfWmcyS4HwKVFa7xFCBbqAv9auvkT5RFEYn4aACqFsoExUMvIyF68ufU7TuenWQ2C7eeOUFhVxpROo+oaivDw4DtZvHQJq1evbuCRa26uukHwwQcfkJ6ewcOjBhBliiG3oqiRS+XTkdPo0aKhm3HV6ThWnt6FyWxi9/kT7D5/ghd6T+T+DsNp9fGdjG/TnyFhXSnWlTFtzacAnPklCT9HD74ZN4PW7o2NAQCVqECVIpCXXsiUPqOJ+ZfTOiQkbhS0Wi0//PCDdftKUg1TU1N58cUXeeqpp67YGKhFEAQ++ugjkpOTeeSRRzh16tQFlxokJP4pCrmMW2P8uDXGj3N5FSRklnI6qxSfrArIrrK2E0WR3MpiojyCLtpfboVFOcfNzsm671i2Jfi9g2+9SogygQ7OwchkMo4cOXJ9GwTPPvssP/30E8LGc5gOZQDyRi4VgDvbDWp07r6Mk2SXFfDJiGc5kHGK93YuRlkTxdneJ5w72w1CbzQQ7h5AtdHI0ewkVp2KQ6vXNeqrPsuOW1SNJkcNw/jtUejdAkWfIKnCoYTEFTBnzhxqs5blcvkVBT3NmDEDV1fXf6xmKJfL+fTTT4mKiuKjjz7ilVde+Uf9SUhcDsGe9gR72nNrjB+GTSmYcrWW2sjAkuObyCor4LV+91+0jw/jluCotmNwyy7WfdkVhchlMjzt6y0LmEUUBdW4ubmRlZV1VeZTy1U3CLp3745Jb6T6cDayGtngv7pU/sovJ7YwdeX7qORKegW1twYVmkWR9+N+srar1GtRyhQMCO2EgMDwiO70D4ml/9dP4GHnwrCIbo36FkWRpSc209ozmHbeYQCYdqQhVupRDguXjAIJicvAZDLxzjvvWLfHjh2Lq6vrZZ1bUVHBr7/+yhtvvIG9vf0/HktISAh33XUXP/zwAzNmzJB+wxL/LnqT9WVi/nmeXj2PLgGtmdh+8AVPeW/Hj2w5e4h5w5/GWVP3G9AZqlHJlY3aizojNjY2aLXa5h37X7jq2ojaEh1bX9mErF5wZq1Lxc3WqclzFh74g0AnL8qqK7mj7UDr/uGtuqMz6gF4e9v3uM8ehvNbg+m54FE2JR8AoFtgNN4Obiw5vqnJvnefj+d8SS4T6vULYD6UjWl3+j+ZqoTETcOPP/6IwWCwbl/Jk3mtxHFzZgeMGzeOM2fOkJBw6WpzEhLNSo0BmlNexJjFL+FoY8dP42cilzWteLssfgszt3zDfR2GMaXzqAbHbJRq9CZDo3MEQUCn06HRaJp//PW4qh4CbamO319cj5fRjGintlrul3Kp5FUUU1BVglqhZExUH+v+Wo+CjUKF3mTA3dYJX0d3ssoKGPXjiyy/6y2GhnejqKqUZfFbWBa/pUG/4e4B9AmKQRAEJrQZwFtbv2P2tu/rGrzecBxxcXH06NGjGT4JCYnrE1EUOXv2LMeOHePo0aOIokj79u157LHHrG3atWt3RamGq1aton379oSENB3n83cYOHAgDg4OrFy5kujoxgFcEhJXC8FBRam2nNE/vkCproJNk+fh69h0oPrmlIM8+OschoZ15ZNbn2103MfeDZPZTF5FcYNlA72djMLCQnx9fa/aPOAqGgRmk5n1s7dRnltBkJc9omgxpC7HpRLk7E1SYToDQmIbuFO+2P87AOPbDCDGJ4y8yhK+ObSK8motLjb2vLj+c3RGPXqTEbkg48sxLzbo105pw9RV79M9sA2Bzl6MiuxFqGu9dEMBcNHw+vqFVFRU0KlTp+b+WCQkrguOHz/OvHnz+O233yguLr5o26FDLYVKTCYTa9euZdu2bRw6dIikpCQyMzMBi0hZbGwsXbt2JT8//4KCQmVlZZw9e5a8vDzy8/PR6y0eQY1Gg6enJ56enoSGhjZ6UlKr1fj4+FxyrBISzU21q5Jxi2eQVJjB6nvfJ9IzqMl2+zNOMmHJq3TwDefH8a+jkDf2ILT1CQXgcFYiQ8JrhPtkAkfKUzGbzVZ58KvFVTEIDh8+zLOPTOPA0QMYzAYCHL15pMtIbo/uZ3WpfD/uFT6I+5nFxzaQVpKDk9qeDr7hfDJiGm19WrIx5QB70hPQvN6vUf+Ljqyld1A73tr2HYNCO7E8YRuOalvOFKQz6ZdZ2CltEGkcqLgmcQ+FVWXc0cZSx6CNdyhtvEMbtEkvzSMjI4MHH3zwkjnREhI3GqWlpUydOpWffvoJDw8PvL29KS0txWw24+PjQ1hYWAPdAbAEF3766afY2tqSl5dHUFAQsbGx9OrVi40bN9KnTx9yc3PZs2cPX331FTKZjBYtWlBWVmbVVs/Pz+fAgQNkZGRYPYn1y6wYDAbKy8tJTk5mz549hISE0KlTpyuqrSIh0dyYTCbuev5h9qUnsOzOt+ga0LrJdqfz0xi7+CVaOHvz693voFGqm2zXN7gDrhpHvjywss4gMIss3LwMW1tbhg8ffrWmAlwFg2DDhg2MGDECP/sAhoWPQq2wQRSLOVeUaXWprLvvQ6aufJ+96QncHzucNl4hFGsrOJB5krLqCg5nJWKv0hDhHsihrERkgoCHnQs9AtswNLwbcpmMWN9WjI7sTVzaccyimZyaFI7bo/tjFE2sPbMHk9lEpV6Ho40lFWnJ8U0o5QrGtu57wfH/Er8FURS5++67m/ujkZD4T5OUlMTgwYMpLCxk6NChrF27FhcXF+bPn8/YsWPx8fFpUMQI4PHHH+e3334jMzOTiooKunfvzpo1a3ByssQH/VXGOD09nX79+pGWlkZMTAyLFy9GJpNx7Ngxa5sL1Vur3V+7jJGamkrXrl2JioqSAgklrgnTpk1j5eo/GR7bl2JdOT8f29jg+J3tBlFeXcWIH56nWFvB093vYN2ZvQ3aBLv6Wg0JjVLNa/3v5+nV87hr6UwGtezErsx4Fh9ax+zZsy87cPfv0qzVDsvKyggPD6d1i2hu874HWU3MoqutwLw973Ek6wyr732fXWnHeWPLN2yePJ9O/pEN+sguL6TlB+OZ1H4wd7W7hcHfPcPi8TMZ27pPU2+JKIq0/HA8Cpmc8yW5nH1uOa9t+pLFxzagUaipMuhw0TgwPro/bw16GHv1xYMyOn/2AMX6Ss4XZksXGYmbhpycHLp3745KpcLd3Z19+/bx9ttvM23aNGSyutjjmJgYjh49CoBMJiM4OJjKykree+89XnzxRbKzs2nZsiV79uy5YIGjESNGUFVVhU6no3v37oSFhf2jsUdERNCrVy/Cw8MZM2YMc+fOvfRJEhLNQN++fdm+ffsFj2vf2EpacQ6tPr7zgm0mth/caHn7m4N/Mm/PL6QW5xDg5cvj05/mqaee+lv3pGtW7fCnn34iNzeXZ3vOQGaQUW3UIZcpeHvbJ8TnWVwqnf0iuWvpTEa26kkn/0iMJhN6k8Fay2DZiS2YRXOD7AKA8uoqNAo1xbpyPOycrftrAxRtlTa08QrBx8ENb3s3nu1xB+19wjCLIhuT97PgwB8cz01hw30fN7l2A3Ay7xwncs/ybI87LKkkakm0SOL6o7q6mhMnTnDgwAGOHDnC119/Tbdu3SguLubWW2+lT58+dOvWzSqBKooikyZNQqfT0bZtW9auXcuGDRvo16/hcl1qaqrVGABwdXWlrKyMPXv2EBoaSp8+fejUqROpqakMHz6crVu3NhkV3bp1a7799lsWLlxIdnb2P55vYmIiOp2OlJQUKaBQ4l9l27Zt1tf65QmYT+U3qoLUwsUb7Rtbr6jfyR1vZXKnW8FBjXpqJ4R/SUCvWd9l06ZNODo4kpWVyYfH3ya3Mge5IMckmugX3InCqjI+iFtCdnkBZlFk8LdPszf9JHqTgWivEN4f+jhLT2zCx8Gd3kHtiUs7DsDDv79LhV6LXCbD3dYZb3tXRrTqiQC8u3MxCpkcg8nI+0MtwihvDnqowbjGt+lPmJs/r2/+ml9Pbmd8m/5Njr82VfGOtgMRS3UInv88R1pC4t/i3LlzfPLJJ3z55ZdUVFQ0OLZr1y4ATp48yXvvvQdAq1ateOaZZ3BwcGDTpk28+OKLzJkzh0cffZTffvuNN998k61bLReywYMHk5yc3KDPwsJCdu3aRWioJQ4nMDCQb7/9luHDh3Pw4EHmzp3La6+91mic48aN4/jx4+Tm5jbwPvwT0tLSrKWRJSSuBcqhYVSfLYZq4+XVS74UIihHtfrXjAFo5iWDdu3akXQmCZPBRI/APoS7RbI0/gdKdBeO/F042lK96b2di0krzsFgNvJkt9t5d8hU9pyPZ/6eZQwJ64KbrROn8tN4b8ePlrgAtS2l1ZXIBIGBoZ2YOeABYnzDL/g+WkM17rOHcU/MED4fNb3RcVEUafXxnTiobDn42DeoHopF5uNwqY9GQuKaI4oi8+bN4/nnn0epVKLX65HL5dx9992MGDGC2NhY/P390ev1HD16lP/9738sW7YMna5O0VOlUmE0GjGbzYClVoBcLqeoqKjJNX1BEJg4cSKLFi1qdOyWW24hISGBoqIikpOT8fOzZPJUVFQwd+5c4uLi2Lt3L1VVVdx777107969wfnfffcde/bsadSvl5dXk6WV8/Pz+eOPPzh16hRGoxF/f3/Gjx/P7Nmzr+yDlJD4h5gzy9B/fxRM5n9sFCiGhqHo9M+L7l2zJYOKigq0Oi29W/RnQpt7AIjx7cTiY9+yM20LOx76hsSCMzz02xxUciXxT/1IgJMnAH2CY4ieP5E72g7k3SEWbfNugdENikLc2qoHY6P60PGzyZhFEVeNI5smz7tgmkd9NEo1braOFGnLmzxeK1g0a2CNd0HZ9LKChMR/CZPJxN13383SpUsJDw/nzJkz9OvXD29vb7Zu3cr+/fvR6XSUlZVRUFBgveH/ldr0vlpKSkqQy+X06NGDrl27smPHDvbv3289LooiP//8M+3bt+fZZxvmU99zzz1MmjQJtVrNzz//zHPPPQdYShfPmjULT09P/P39OXPmzAXnpVAomDRpUoN9TS0/pKen88EHH+Ds7MygQYOsSwbp6ZLImMS/j8zPEdW97dH/fAK0his3CmSWGAHFsDAUHa6u5kBTNKtBUPuD7eTfUDK4s383dqZtYcmxA3RvYTEAugVGW40BgEBnL7oHtmFv+sWVxvwcPbBXaSioKmXz5PmXZQyAJQahoKoUjwuoIy49vskqWIRMQHCxuax+JSSuFVVVVYwcOZLNmzcjl8utN9haN/8/xWQyERcXR1xcXIP9KpUKLy8vSktLmTZtGrt372bp0qXIa2Jzbr31VpRKJeHh4SxfvtxqEPj4+HDu3Dk2bdrE2bNnG0gf/xW5XE7Xrl0vOj6z2cw333yDt7c3zz77LCqVCjs7O+68885mW4qQkLhSZH6OqB/rjGFtEub4PMtN3nwJy0AARBA87VCOboXsGi1XN6tB4OvrS0JCAg7qhjddB1VNrnFVOSV6i9XjaefCQ7/N4cej6xv1U6s9kDztF/wcPTCYjLy3YzE/HF3H+ZJcxBqzq5VHQ3GTk3nneGPztxzNOUN+ZQm2SjWtPIJ4pscEdqUdRxRFBoV1bvR+BpORX09uswoWCZ52CHLpgiLx30Sv1/Puu+/y7rvvUllZCVhu3lcDJRChVNJGqcRFJkcAymQyOo8cxUNz3qFT9+6sWLGC8ePHs3z5cgRBwNnZmZiYGFQqFbt27SIzMxM/Pz/UajUGg+GCXoq/Yjabqa6uvqBc68mTJ8nKyuKJJ55ApVKh1+sxm80UFhbi4eHRfB+ChMQVImiUqMZGYe7ij/FApsUwMFvuXGKNfSAAtX7oEwLEe9siC3NhkFLGv+8bsNCsBkFsbCwbN26kRFuEt72PdX9tDEGHQRnc+dJpnlktI6u8gLcGPUj/kFhruznbf6C0uoLyai0tnL3wc7T8qO9fMZtfT26nlXsLREQ0ChVao547l7zGfbF1Qg0uGgeKtKXkVRTTyS8SV40Dx3PPcttPloqKt7TszIiIxlLEG5MP1AkWCSCPaFp2UkLiWpOSksLYsWM5fvz4VX2f9kol99rZM0pji0oQEEXR6v0UAOG338n//Q9WhYfzhr8/3/76Kx9++CFjx47l/PnzjB07FicnJyZNmsTq1auxt7fH09MTrVZ7WalTer2ep556Cr1ej62tLZ06dWLs2LHY2NR57k6fPg1Ylhdmz57N+fPnUSgUrFy5kh9//PGq52xLSFwKmZ8jKj9H0rr4sWpdIlVpJbgIAjKgXIRk0UwSIqUmkGVWQ2Yxn29NpmtLNx4ZEE4r339XeKtZDYLx48czZ84cdqfvoJVHnWLTkfLVKBQCE58pwtVDybDhDvz55wlcXGV0DRwAoozT+WmkFGcyPLw7K0/HcUfbgeRXlpBWksOKhG281GcScamWi6C2psDRjrRj7EirEzTRvrGVrgHRPLtmPvszTnIoKxGT2YRaocRBZcvyu2Y36Ur8q2CRvINPozYSEtea8+fP07FjR0pKSq7ae7jJZLzj5MwwjS0mUURec/MWBIG/3sYFUUR++jSzZDLuGD2GRFFk48aNCIJgTWmspaKigsrKyguKDtXHycmJW265hcDAQERRJCEhge3bt5ORkcG0adOsSxN5eXkALFy4kNatWzN06FAyMzNZu3YtI0aMIC4uTtISkbimmM0iP+9J5fNNSYiASRQvGFdQf1Vhf0oR+1P2cG+vECb3CUWp+Hc81s1qEMTExDB58mS++eYbTKKJMNdW5Cr3sD3+DC++6I1/gKWs49tv+7F5czlDFz3DY8N7Ys4M5LNdf+KqccRBralZyx/IpGWzyCkvBEAmyAh1C2Z/xmlElciL4+9l1vdf8nyvu3lj4IPWMThr7Plm3MsNxjVu8cscyjqNUt70dBfd/qrlhQCytt4IDk3LSkpIXCu0Wi2dO3e+qsZAN5WaL13dsK+5icov42Za6ebG0bGjKfX1pfZXcymlwUsxZsyYBtudOnXC09OTP/74g8OHD1trjNRmSQQFBfHAAw8AFi+lt7c3X331FZs3b2bgwIZ6JhIS/xZGk5mZv55gU3zOFZ9rrvmtfLfjLAkZpbx3Zww2qqsf6N7sZscXX3zBs49M41xRCstP/kB6WRoffeTPO+/UpU9ERWnYvj2C1tE2vLNkO3N3/UDnTnZs+vBu1qXsoWtQBC0CbBkZ245KsRSAd3cu4vdTW4gOaEfP97txuJ2l8uHh7MaRypV6LQWVpZwtymT+7mWsT95H3+AOFx+4AGiUKG8JvXg7CYlrwJ133klubu5V67+nSs1iN3ccBAHFZT5VF/v5EjflQcq8vS2Vy67i0/jAgQMRBIFTp05Z99XWGqlfhMxkMlnTGHfv3n3VxiMhcTFEUWT2Hwls/hvGQIN+gIPnCnlx6RFMlwpMbAaaXfFAqVTy/mdzGRQQw8Dn5iNXmpq8TnToYMvGjQ11A/78cx2FFRVMeq8Km0dn8SwQ9Ksb48aV8vXXQQyIacvs9X5k+KaSuv48ANllBYiI1Hdovrj+c746uAqweBZGRfbio+FPXXrsYyIRNMq/P3kJiWbGYDDwxBNP8Mcff1z2OXYqGzr7R9HBN5ww1zCUcgcMJjnFunKyyvI4lHWc0/mnKNJavG8Bcjnfuroh5/K8AgDl7u7smzQRk1IJ/0JEv0qlwt7e3hpECVjrJdTPrRZFkfXrLYHKUuVDiWvFuuPZrD2W1Sx9mUXYm1zI0r1p3NU9qFn6vBDNahAkJCQwc+ZMDh06RE7OeWw/FIiKsmH6dC9GjHBu0NZsFlmwoIAFC/JJTNRhaytDoRBQKGD8eBf0eiXTp5cyf/5ZAO697xxwruEbyiCHYsyiSC5mihEwA6O6jGVkZG/yKgpZkbANk2hGbzI0Peia659ybBTyUCkISeLqYzKZSExMtJYI/u6777j//vtp2bIlsbGxREREIJfLKSkpoWfPniQkWFJxQ0JCOHv27AX7DXMLYGqXMYyLvoWMUiOJeRVUm0QMJsvX3E3jiatNKK09LWnBCblH+en49xTpCmmTk00LhZy7nT2Y2mEoCo9g5B7ByFS2iAIUF2UTs2g6BdVVfO7hjdO4MZgVCpDJLigkVMucOXMaxRRcKTqdjoqKChwc6sTCWrRoQVxcXINlFLlcbi25XL+thMTVJikpiVdffZUdO+PIzS9A5eiBW3Q/fLqPQ660BMOaTUay4pZQcGwT+vJCVA5ueLS/Bd+eExBkdUsCFZmJFBzfRFnqMapLclFoHHlqRSt8v55P3y7tr9ocmtUgSEtLo7y8nHvvHYyv7xqqqsysWFHCyJEpLFgQyJQpdalAkyensXhxIffc48bjj3tSVGTk5Zczad/egZMn+3LmTDhOTjk88MB5iopK2bJlK6WlpdbzlSoVCpWKarkDg6nCaK73ZO/mg8LNhxAEJrQdwLc/vMi4n2aw86HPGgYZCYC9CuXoSOTB/+yCJSFxKTIyMvjss89YuHAhhYWWp3MbGxt0Oh1z585Fq9UC4O7uzuTJk1mxYgUpKSmApZCQwdC0UauQyXmu11281HsSp/KrWJdYzF9jl0RAFAHRjIM2l3MZO/gicTUhSg2C0oYsQzXJRiMflhahKy3mmY5jUKvrcqGfXDWPwmrL+B4vzEN4802MRiP33nsvvXr1olWrVoBFzvjgwYPk5ORY0wtnz55N586dGTVqFGp1w/icsrIyFixYwOnTpzEYDPj7+zN8+HDatGnToN3q1asRRZHWreuCldu1a8fSpUvZvXs33bp1swYMR0REEBcXd1HjSUKiOUlPT6dz5844OTnRddidHMvRU5Z+isztP1CZnUTEHTMBSPntPYpO7sQj5hbsfMKpyDxFxrZFVJflE3JrnRc7a/cyKtITcI3qha1nMIaKYnIPrGJw3+4cOrD/qtXsaFbp4jpeBjYDJkwmkdjYU+h0Zk6ftkzil1+KmDDhHL/+GsKYMZYb8aJFRdx77zkeemgynTp1QhQbuiFFUSQ7O5ucnBwWLFyId7sB5BzbgmtkD8Jum3HhCQL5h9aQsno+Bx7/jmjPFparo1yGPNYHRb9gBKmIkcRloNPp2LhxIwcPHuTIkSOsWmVZlurfvz+tW7cmNjaWQYMG4evbMItYFEU+++wzpk2bhlqtZvLkyYwcOZKYmBicnZ1JSUlh8eLF7Nmzh4MHD1JQUGA9tza/3tfXl6ysxi5IF40Df0x8l2ivMLakFFFQqW/UBkBh1BFQcIDQnB0Y9GX0zsuhtVLJ4epqHOVyJtra4SyTcViv5xdtFbfY2PBdeEfsOk9gVWkBE5e9gbutEwVVpaiUSgxGI6IoNpAePn/+PO+99x5KpZKqqirAsoRoNpst0uCtWtG2bVuqqqooLS1l+/btyOVyFAoFwcHBtGrVir1795KTk0O7du2sRkZCQgLx8fG0bt2axx9/vEGm0OrVq1m5ciWRkZHExMRQXFzMunXraNOmDSdPniQpKYmgoKC//0eXkLgM3n77bWbMmMHRY8d5YW0+JVUW4z3l9/cpOL6J2OnL0BVlkfD1U/j1ugv/fvdYz03b8CU5e3+lzcP/w9YrBIDy9JPY+YYhk9c96OoKMzm+4BHG33YbS37+6bLHds2kiy2YgO01/4NcLhAQoOLAgbq1vw8/zKNzZ1vGjHHBbBYpLxeYP19ArVbTtm1MI2MALGlPHt5+/LbvLIgiSrcAEM04BcdcdDQiYKxJU3xeV8r//B0Ii/JC3s4LwUaKF5C4NCUlJXz44Yd88cUX5Ofn4+HhQWxsLJ07dyY+Ph5XV1fWrVvHJ598gkKh4Pbbb+ell16iTZs2mEwmJk2axM8//8wTTzzB7Nmzra7s06dPc++997Jq1SocHR3p0qULDz74IEVFRSxcuBC1Wk11dTVAk8aAp409cZPn4e7sz/ozBZTpjAAIZhOuFak4VWXiWJWNnS4fJ20OgmhCAJZpq8g3mwmVK9hGNT+5uNFWqUQmCEy0AzOwXFtFUd5ZzGve5fHsLNQyOZ/e+ix3/PI6991/PxkZGaxZs6bBuP744w8UCgU6nQ4vLy/y8vKYNm0aH330ER4eHpw8eZL09HTKy+vkw00mEyaTidOnTzNp0iS6devGq6++SkJCAqdOncJsNuPp6cno0aO55ZZbGqUNDxs2DFtbW7Zu3crSpUvx8vJixowZTJs2DV9fX5YuXcoLL7zQzN8ICYmGlJWVAVBk0liNAQClvSsIMgS5kvLz8QC4RvdpcK5bdB9y9q6gMGGH1SBwCIhq9B42bn5oPFpw+Hj81ZrG1TAI0qisrEKrFSktNbFyZQlr15YyYYJlfb6szMT+/ZVMnerByy9n8skneVRUWFyLYWFh1sjhv6I3C6wt8CL5SBwqR3cKEnagtHfFLbqvtY2hsgRlvdLIYFmzyT++CZlCTaVHIFOzivl0UCitJWNA4jLYvXs3d911FwUFBdx///1MnTqVVq1aNZnfXlxczKJFi5g/fz6xsbHMmTOHlJQUli5dytKlSxk/fry17bfffsvjjz+Or68vX375JXfddZdVka99+/YIgkBCQgJdu3aloKAAmUxmdcHbAE86OPLCmJexcQtgU0oxZTojCkMlQfl7CMrbi9pY2UBISMQaLsPO6mocBIG8mv5uLcjDVhAYp7HldSdnPOVyZIAKWFZVSYlo5i4bW2RbPrX0J0J0dDRr1qzh3Lm6uJ7k5GR8fX05e/YsJSUlhISEEBwcTHh4OCdPngQgMjLSmiI4a9YslEolL730UoPPsXv37mzbto1Zs2bh5eV10b+PIAj069eP/v37ExgYyC233GL92wwdOpQVK1ZIBoHEVadv3768++67TH/yUaqjxqDQOFKRcZK8Q3/i3XkUcpUN5po4Npmi4bKZTGnZrsxOuuh7iKKIsaIEha/31ZkEV8UgSGbatAwWLLC4PWUyGDvWmU8/DQAgJaUaUYQlS4pQKAQee6w727cb2bt3L0lJScTHxzdaH1mwYCElah+qlY5U5Z5DrnHAkH+eiDtnIVfbWtud+3M+puoqHFpEo3Jww1BRTEH8VnQF6QQOeghBqaHaYOLJRQf54dHu+LrYIiFxIdatW8eIESPo3Lkz27dvp0WLFhdt7+LiwlNPPcUjjzzCyy+/zLRp0wD4/PPPGxgDn3/+OVOnTmXy5MnMnz8fOzs76zGtVsvx48fp1KkT5eXlVtd7rTEQrVDQQaniF72RD3+cgdFs8cQ9ET6MqYYMclzacCR4PFUqV8Kyt+BfdASDKDIoL4fkGnljT5kMI7BeZ4kJaCmXk2wy8UNVJT9U1XnywnOy0MgtRopz+HjOmLRQsBTBAK6iZX9pUTF2JgWVMiNGo9H6BF9dXU2XLl0Ay7JHrbRyWlqatX+j0YitbePfYO1Dwfnz5y9pEIDFKFAqlfTq1auBoTZ27FgmTpxIdnY2Pj6S2JjE1WPIkCG8MWsWs96cjSluk3W/b887COh/HwAaN38AKtITsHGpu6nXeg70NZo7F6LwxBb05QX4t+/XzKOv4yoYBFU8/bQXt93mQlaWgV9+KcZkAr3e8rxSUWG5MBQWmoiNdeKTTw5TVVWFWq1GqVSyZs0aoqOj0el0/PLLL5w8eZLi4hLqh0jZegbRYvAj2HmHUpGZSN7hdRSfjsNYU8mwLPUoAIJChb1/FIEDJuMS0Y3cg39Sdu4oFZmJ+M3I55577uX7779r/o9A4rrnyJEj3HbbbdanTKXy8j1KarWa2bNn8/3331NYWNggCn79+vU89thjPPnkk3z88ceNPA2ffvopoihiY2NDTEyMVZUPoI9azVuOzvTKt+gRqOVKjDVLcxVqd7ZH3GHRAhBFAvMPEFB0BIBvKytIq1frQC+KaEWR9kolRw0Gq6FQi6PchjKTDgEBrcliNGjs/SmsMT7M8WpsvSwGQXWljnH5IegFEzPtnElOTgYsN+nY2FiMRmMDL0L9z8LLy4vk5GR0Ol0DSeLaPkpKSkA04199Cv/qBDz1qTgbc5BjxIScUoUX+apgsjWRtL31pUbGRW3sQElJiWQQSFxVzGaRY4UK7AOjcY3siULjQEnSfrLilqK0d8W780icwzqjcvLk/MavkCnV2PmEUZGZSMaW7xFkcsyG6gv2ry1IJ3Xt/7D3jySg0+CrNo+rYBDIadXKhlatLD/we+5x45ZbzjBiRDJ797aivNwLsIgJnTsnx9/fizNnznDHHXeQlJTEvn37MJlMVFRUsGvXLgBkKg1mfc2FyTOIqHvnWt+tJGk/Bcc2IMiVyG3sMekqCBr2BBUZJyk4vgWVvTMuEZY0q6xdv2DWa7HzjcBQUURGcVXzT1/iusdkMvHggw/SsmVLlixZckXGQC1//vknhYWFPPDAA7z66quMHj2aoKAgpk6dyoABA/joo4+aXHZYsmQJAAcPHmThwoW8+eabpKen00GpYpGrO0bgUMwAnPs/yqpTSTy9xuJuN8nVIAjojDq2Jq6gIGMb+6t1NJWXUPuupwwG63JCfcpMFgVAsd6RD3fNto73y4OfIggWT0CFXmstRlYfURT57bffKC0ttRoBf82U6NOnD8ePH+fLL7+0ZiBs27bN6kVwrkzkztwtOJiLMCFDhtk6diWgNpzD3XCe6MotsHgFdHsWuj4Ncmk5UOLf5Zm3PuX3/71B28e/Ql1Tg8c1sieiKJK++WvcovuitHUk4s5ZJK94m6RlbwEgyJUEDnyAzLglyFVNF/HSVxSR+PNryNV2hN3+yt+6Hl0uV8EgaOziGzfOhUceOc9HHw3AZHIHdhIQEMArr7xCamqqtQyqg4MDJpMJvV6Pk5MTr776KpmaMPafLSb+K0tKhnt0w4uPV8db8e0xHplSTera/5F7YBVeHYfj1XE4crUduQdWEnjLFFT2rkTdOxeVkyeCIHDgndGczS1HFEVJ71yiAT/88AOHDx9m9+7dTbq0L4cVK1bQrl07Pv30U7Zs2cLzzz/PwIEDOX/+PKtXr26ypkZpaSnx8Rb34eHDh4mIiODjjz9GBVSIZlpkW/Lryc6EI5uRC3Xeg0VHv2TR0S8va2zFNYlFF34eaYyIaJUetryu8RaIdZULPWydMZiNlOgqAKxlk2t/X+7u7g0CCqOjo7njjjv47bffmD17NgCenp5MGDGAn37fQJT5BPY13ctpXCHRUi2uxrtRkQMbX4Bji2Dsj+Dd7gpmJyFx5dTqDmzfsZOcnGwEuZL8IxusugPVJTkUHNsIwOH3xzc63zmsC6Gjn0OmUJG2YSG2nkGcXjyD8vRTgIi9fyR+ve4ibf3nmHQVRN73Phond3ycmzYcmoOrYBC0arBVVubAwYMewHkKCmQEBzvj6OhIRUVFozNLS0tRKpWo1WpkMhm+fv5sz3FDpMTaxq1NQ4NAaX9h/QC1s8U4Mekqwd7Vul1LRbWR4+kltAuUNAgk6vj+++8ZPHgw3bp1+1vnV1dX8+eff/L8889jY2PDCy+8wNSpU8nKymL48OHWdLq/8tRTT2EwGJDJZERERADgYmeHEThjNDZqbxKvTsnjv4uIaI1p+POe99EZqjHZy1mYvIHNW7dQVVXVKCagX79+dO/enYyMDBQKBR289ORu/JifgAg3GhVUutQIyD8FX3aBO1diMtlc+hQJib9Bfd2ByH63UfDHIhDFBroDCltnvDqNJPfASry73YadVzAAJSkHKTyxFfd2A6xLC4hmytNOoHb2xr/P3YiimdwDqzi16HkEuZzISe9i69ECsyjSyufqVUBsVoMgLy8PT09PIBBIJy/PnT/+GMKGDR+gVCrx8bHkZ3fs2JEtW7Zw8uRJ6xOYTqfj6NGjREREWJ+eio1Kqs1yxHprnGonzwu+v1gTeFVdmk9l9hmy9yxH5eSJjWvT1aUFBA6eLZQMAgkreXl57NixgwULFvztPrKzs6moqKBr166ApVjPo48+yqFDh3jqqacQdTr0J+IxnDiB8exZRL2e3KIiZMuX09/Dg901OgSGlLMUHDnSxLPxf5OCKotwmEImZ0Copay5KJqZvfpLHB0dKSsro2fPno3OU6vVhIaG4mpIZ2TBfO4+a0SjgB4Bf2MQoglMIvw8glThXuzs7CQdAolm54cffqCkpIQ/12/h+bX52Pvuo/TsYVwiulGcuAejthyFxgF9WQEIMny6jkHl4AZA/rGNyNW2uETEIlOfJTPuCwSFHLlKQedXxiFT+mKocKf03GH0pXnYebeypiGKIrQJdL5q82pWg+Dhhx+mrKyM3r2dcXauZPt2L/bsmUNOTg633XabNXBo6NChHDp0iAULFtCxY0cA1q5di8lkYvTo0db+CvUqQKQi81QT79aY6uJsAI7OmwSAnW8YISOebSAJWR8RkZOZpU0ek7g5Wb16NQCjRo36x33VGraenp6EhYWhS0nhltOJZLeLQayosAQA1gQNakwm3nByQYZAuX8wWVOfRFy7msQmPAP/dYxmkzWuoLVnMAl55whxdkenUtGnT58mz5GLBgYWLWTfeQO/noJHO4LT337AN4PZyFDtd4y7dbg1nVNCormo1R2IL7Aspfl0v42S5AOUnD0MCOQf3UjpuSOUJh/AI2aI1RhI/Pl1ys4dxSnEl4r0V0nfcp6q3CpkMgGP9q44tjgLQjIJ35ykLCUVpYMtFVmnyTu8FplChYejDYe25BE1adJVmVezGgQTJkzg66+/5vPP4ygoKECtTqNFixaMHTuWdu3q1vQcHR2ZPn06y5cvZ//+/QDY2try2GOPERBQ91hQalQiA0qTD17W+6sc3AFoedsMys4dpSr3LCaD7qLnnMuvvOhxiZuL/Px8nJyc8PDwaHTMWG0kdX8GuafzyUsqpLKwCtEsorZT4dHSDY+WrgR1bfxYKxqNPKBQMt7TG+MPP0Ktx0sUwWhE5uSNY0RvFN7hKDyCcBVFin54EgxNqw5eTyTkncNJbce5kkJe6dwV13o1DQoLC1m4cCHt2rWjnSqFN9Lz+PwAuGhg0zmwexvcNNDVH97qD+GWaypmERYdg19PwZEcKNJCsDPcEQ3PdQcbBSCacVWZebVreZPjkpD4J9TqDnzw+jQ0sbdbXP19J5Gx/UdkShXpm79B7eKFf7/78O1xu+UkwYjcpgiAsvPZVP2aj2ukCx2ebU/c87uRq2QIMos/sOycxeAwlFsC38/9OQ+AFGDvIph0PRgEt956K6dOnaKiooLy8gqqqqro3LlzA2MALJ6Ev5KTk8M777xDZGQkTz/9NAAmUUBfXoSgrHtUOPrJ/biEd8W3150obRuupchUFoEHt6heuEX1InPnEk7/+DLtHv8alX3ThYsMxuvFIStxraiuqObI8gROrU9CX2VAJhcwm+oi8KuKtJRklnFm21l2f3UQj2hXAhwtmgXmkhIK7rmXu0pKLcF19Za/5C5+2HafiMo/mnJdBXN3L+Pgpq85cO4oJSYD7zs5/9tTbXZUMgXtfcN5sfdEOmXFs/f4cTJrrgc2NjY4OTmxbetW1laW4ecALZyhVAeDQqCtF+RUwKf7ocMC2PsgRHtClQHu/8NiKDwSC552sCcDXt8Gm8/BlntqnC8yCC3dBmVZ4Nj0sqGExN9hyJAhvPnmm8yc9Ramg9us++vrDtRHUFTh3PI3jFW5qF3UDPqqP4KsLkLGzs+O4jMliCYRQS7Q/a2umA1mtjy2DW2+jtjpMQR27MJ3I+fiaGPfqP/molkNgoKCAmbNmoWrqyv+/v6cOXOmyXb3339/g3NWrVpFZGQkp06dIjIy0nrMrNcS/810jLq6p3jnlp3IPbCKsrTjRD/0iTX9qSlco3qSsfU7ihP34BU7vMk2KsXVL90qcf2SdiCDbZ/sobpcj1hTj7y+MVBL7TERyE8o5qU+szi76ixR77yH+UziX4LjBGzaD8e20zhA4GTeOV5av4ANyftqIuctLK+6/tNiA5y9+OH213C3dcTk6kP0b69QEBJCtYMDdnZ2TJ06ldaVW+leugQB2J0OHX1BVW+Vb0JraPM5zImDH8daju2aDN3rOWMeioUg5zqjYKBFAdbyuR/+Evq+/q/NWeLmICgoCMegNjhH9GhSd6AWQVGFS9gytIVZlKaUETIiqIExABA0JJATCxI4+r/jtBwTgmiGpOXJ6IotuUBmvRmzKoM39r7CWz3fQaO4OstgzXo39PHxYdu2bbzzzjuMGzfugu26du1q/VerSmgwGBAEgc6dO1vb5Z/eR3VpHn597rbuCxo6Fd+eE6jKSaEq5+LVzMw1LleT7sIX1hDPq2dtSVyf1KoCHlkez7q3tqErq7be8C8H0SwiE2SYz9oQZ+6JUax3dxME7PpNwbbzeASZAkEm53xJLibRyNPdxvOebyj31ygX7r0BlgxSijLp8+VjVBmqUTj7IFfaEr51W4M2wVWHrGII3QMaGgMAYW7Q2hNO1dR8UskbGgO1jKlJ3jiVX2+naIL4Jc0yFwmJWpYsWcKUKVOIGPUsnh2G4hrZk5CRz+LebiDpm7/GUFVW01LEqcU65OoysnZY0ob9+vg16i9oSAtajgslc2cW257cyfand1KZU0XL0RbLVq6RY8ZMSmkKnx/931WbV7N6CNRqdYM84yvh/PnzhIWFNaibrjJa+lJoGi4NKGvc/7Ua0Iaq0kZtAPKPrAMswYVNISAQ6ev0t8YrcWMSHh5OaWkpm7/aQfKq85adl28LkFuRw6rTy0kuOkOloRJXGzf6eLTizepk7AQRu+6TUIf1YG96AjM2LuBodhKOalvGtu7LS91vx5C2m2MaO76stHjFZICnrS85VY2LG10vnCvOYnXiHsa36Y/KrQV+x09watBAjBoNgmjGXZ/KxaRARBFyKyxGwcXIqclkdv+rdEThGTBoQSkFF0o0D5999hkxMTG4ePlQUF6n6OES0ZWCYxtJW/8FxqpSKrNPYqzS0v6JtmTuzMLOzw7n0Lp7Tnl6BQnfnqToVDEyhQzvLt749/HF1lODYwtHTv2YCEDugTzOLEmiMqeKdZqNrOjwG/PmzLMG5TcXzWoQiKJIfn7+pRsCW7dutZZBBdDr9Wg0GlavXk3//v3RaDQU56QDkLrmU+t5JxY8irYgA41nMBr3AKpLcjm35lMqMxMR5JbpnF48A11RFtXF2TiHd8EpuD0AxYl7qcq1eBVEs4nK3LMcWf0tb8XZMHLkSNq2bdtcH4XEdcrgwYNp5R1F0qo0hCvMgi/SFvLuztexUdjSN3gQdko7zhYn80vqVjKcA/itVTtsogdxLDuZYd9Po5VHC94dPJXMsnw+3r2UM+eO8L0CttTUGABL5cHr2Rio5d7lb3Lv8jdZ2LYfQ00mfONPcr5TLKFV1aiEpvQU61h8AjLLYdYlJNzf2w2Oahj6V/tfNEP+SfCN/WeTkJCoITc3FxcXF6L8nIhLzKPWgVibIl94YgsqJw8cg+0oStBSmVNFZXYVEXfWfTm1BVp2v7IXha2CVndHYNIZSfnjHJWZFfR6rwcABccKkNvIydmbg083b4KGtsBYaeTExhN07dqVdevWMXDgwGabV7MaBJWVlQ2kSS/Gxo0bKSxsWMzh2LFjHDt2jC5duqDRaDhy5DAA5nqZAlW5Fl10mcJSAKW6JIfS5AMN+ilNOQSA2sWH8PGvWfcXnY6j4Fhd4YmqnBQ+fd+ikObv7y8ZBDcx9913H99//711+9GV9zQ4/s6gebhoXPlg12ySCk83Oj/Kow1hbq2oMlTxXI9XAfgz8VeSiyxxNLtL0vHamw57V1vPmT3oYfrX5Ov7O3rwxJ8f0U4QKBSvwCVxnaCUKXii6zg6qRWIRSmYk5PZeDyVt88cJrcCfB1gVATM6AVu9Z7wTxfAY2ugmz/cexHxwbd3wqaz8NkwcG4qXbG6rImdEhJ/j/DwcDZs2MBoc1GD/YXx2wCB6Cmf4hZVjdmwnJ3TCyhOLAbAr1ddcGvSihSMOiO93u+BrUdNEbEwZ/bO3E/61gwUtgpKkksJvjWIVneFo9DU3a4DBviz/9lDzJw5879rEOj1l7/m+fbbbwOW6m7Tp0+ndevWPProow3azJ07l0PHT/HL2h04hXVC5eRF+fl4cvf/gUOgJfbAMagdXV5bd1nvGTrqOUJHPWfdfn1sG4a2k6KPJSyZLwMHDiT7VB6n1lrKkIqI/HT8W9xsPXDR1GWpuNi4MjqyoRSpk40zp/ItssOOaifOlaSgM+qI8mzD3nSLhG+oqx8pRZkIwOCwLrT1bmk9f8tZi/F7IxoDAB52Lszfu5z+HYdgazTyQNxOikQRmSBgNoPWAPP3wdZUODQFZIJlCWD4T+CkhuXjLVkDTbE0Hl7ZAg/EwKOdLjAA2VUQZZW4aZk+fTpr167lg2fuxKb1EBQaR4qT9lt1B+y8Q7FxW0ZltsXLWJJUgku4M3Y+dZVNs/fk4NXRE1sPDYUJRZz5JQmP9u6onVUkLk1CX6LHI8aDqPtaIfvLl9/GyQa/tr6cOnZ5Gj2XS7P+SprSZ78Uhw8fxmAwWEul1ic5OZmvPp/PhCff4Kyj5Zfu2qo7crUtmdsX4xFzC7YeFy9J2xRymUCHIBeGtJUqoElY6NatG926dePX59biFOiJKEJyYSJ6k57Oft0btNUoNXQJ6NGoD5NoYn3ynyw69hUjIsYysf0DxKVtA2BASGd+nzib9p/eR0pRJne3H0xeZRFP/vkRe9PjyS4vRABUQDuFkv1GAzaCgO4GMRAW3fYKU/54l5knd9EDEzlGS2EluUxOgKPFABCBozlwLAdCXGDoYijRwc77LR6EptiYAvf8DsPD4YtbLzIAR//mn5TETUvv3r3ZvXs3M2fOZPvuNWjLS/+iO2BGaZuHIFh+v0atCb/edQ+f2kId+lI9zi0t8QQ2rmoEmUDK7+fQl1serFvdHUHoyOBGxgBYaojk5ebh7u7erPNqVoPg7xSC2b9/PxqNhjZt2jQ6tnPnThwdHenTyhNdoZbsahtEBFzCu5K5/Ucq0k9esUEgE8BWJWfGqGipqJFEAyoLq8hPqlvG2p+5BwGBTv6NaxqYzCYMZgM2ijr/dGvPtoxsNY61Sas4nnO4QfvJHYehNepxUFl+I94ObpwvyaVCX0Vrz2CyywsJtXchraKY/UbLstuNYgwAjPvpZUZF9mLR0XUcqdl3e3R/OvlH8uORBcTnGbFTQrne8hsd8TOcKYRNkyCqsUYUAPsyYMxSS5riL7fBBTOIVQ7gHHQVZiVxM9O5c2fWrFnDycxSHli4t0HssVxdhCCr0xxp/0RbAvrXGaXVxZZlcLWL5fph52NH19ctGXYnvz9Nyu9nCRkZhEzZ9Je68GQReafyue+l+5s8/ndp1rRDlUqFvf3lp/GVlpaSmJhIhw4dmizpWFZWhtlsRhBggGs+HspqBETEmgIqtbULLhe5TECjUvDJvZ3wvooVoySuT/KT64wBk9nIoax9hLiG4W7b8I6UW5HDU2se5Ok1D/H8+sdZeXo5JrNFYthN40GYawR3t5vMwx2fJNLdsrR1/4q38Hx7OEdzLMsR54qyGBLelZWT3qN3UHsAWrkFYAP41Hja1HL1VZ7xv0dpdSWLjlqW9movk+dLc+nkH8WS27viYgOVNSuO0zZYhIaW3Q7dLlDP4FS+ZTkhyBn+vBM0F6oIK8ghsCcXTWOQkPgHRPk5MalncIMQZJny4gq4Jr3l3iVv4oZfawSY9U3f36pLqjn84VFsPW2Z/HjzGgTNvrDm6+tLUlLSZbU9cOAAoig20B6oj6en5//ZO+/wKoq2D997+knvvZFQQgq9N+lKEUGKoPDasXdRP1EEO/beu4Cg9I703hJqEpKQkN57Pf3s98fCCTGAqICoe1+X75uzOzM7MyRnnp15nt9DSkoKaWlptGvXjut8Stld7cWWpG0AOAdEXXS/BCDcx5lXJnaklaw9IHMOKrKqEBQCol0kufQ4DeZ6egQ33x3wdfajnU97gl1DMdlMHCo6yNr0FZTUF9MpsCvzjn3Di4PfdPgc7MvbDYDZZkVAINjNl/zaUh5c9Q7xAVF0CmxDGx9p1Ss2G9ArFIQqVRTZzZhsfyRB8dWNQhBQAFZRRAmE65xJLcth4FcPtCi7OQuubytJEs871vze1A5QZ4Jr50GVEWb0gTW/+bqJ8jzLkBBt0K2lMqqMzKXkrkGtSS6o4XB2JRcjWaLUSIu+zdJy0befvqbQtDQWrEYrB15JwGqw0vfV3uid/1x69vNxyQ2CPXv2sH//fkc44bFjx6iqkjwsz4QTnmH//v14eHjQtm3bc7Y1aNAg9u7dy8cff8ygQYPw9vYm42QaxQcS8IzqjEtINArBjl0890aHgB0Q0KhsTOvXmlv7t0EtKxPKnAdzo8VhEBws2ItSUNI1uLlvy/863d3sc6/Qfsw7+jW7crZRUl9EqFt4MwfEcTGT6B7ahq8SFtItOBqtSk1+bSkgMmvTl6yc9gbtfSMIdvPlUFEGGkHg4HkEiTQKLWb7P9NIsIsioUoVOTYrNqDEYqZ7WBz9wjuyLesQB/OPYTorm/OqdOm/3zK1A1QYIO900MAzm1uWubXjGYNAAa4B0ObcKqUyMpcKjUrBWzd35qmfDnPwVCWi7cK7e2eOCs4cHZyNscqI2kWNUt1coctusZMw9xC1OXX0nNUdt3BXXDSX9uX2khsEX3zxBTk5OY7Phw8f5vBh6dTwTDghSLkLcnNzGTp06DmdEdeuXcuKFSvw8/MjJCSE/fv3U1tbi6+vE3fe2Y6XXnbmeMEXjBu8gcbaBlpPmIl3TH9HfbW9hsajP2ArO0ZaWhF3z7Ki+vZbbrvttks9ZJl/CwIgihitRo4WJxLjF4+L5jzebGcxNGoku3K2UWWoxM/Fv9m9ANcglEpJYu+5Qbfxzq6fAIjwDGR71hFsdhs3zHuaglpJv+NCfgP/VGPgDMW2psyNDTYLRquZdr5hrD+5D5VCg8kmGUKtPCCrGv6vH7w6pGU7ER4gXpQSsR1GfwZKOcJA5vKj16h4d2pXflybyqHVteS2Pf8xld5bh8ZNQ3VGy2y71SdrcG/VXGhPtIsc/uAo5ccq6PpkZ3zivHFVu+Kp9WxR/69wyf9SsrOzKSoqYtWqVRcsFxAQcN6c81VVVaxbtw6tVotKpXIkQxo27Fdatco+XcrAa6/uRLBJIi7P3rCUfkOPYLMrcNEZMVcXExV1nLAwDR076ti2rR6ovjSDlPlX4uzlhGgXOVqUKEUXhPT5/UqAl07aEdCp9eTV5FBSX4S/S1MEy/r0xzZEbwAA5xRJREFU7SgEBXH+kUyMH8z27CPk1ZRitlloMBu5t8c4Ptm3hLzaUkDKZRDqFoYNgbzanHM98h9JtErF0dPpnBWCwNGiDKb98mKLcj2CJVXCD/bDS4POH254QQQFxN8M7a7/i72Wkbk47DY7yatSsf54hDi7SGW1O1Vi1XnLB/YOIG9rPoZyA3of6UW57Fg5DYUNRF4f0axs0pfJFO4qosN9cQT2DkBAoK1Xu0vuGH9Z9s8DAwMdOQr+DIsXL6ZVq1aEh0sRBIJgJzIy4yxjAJKSDHz6aRlPPx0AgKvORJuAYqKDCgnxqiQ4WEVRUQdycuJ5880z3p2Jf7pPMv9+fKK8EEU4ULAHrVJHR/8uF1WvrFFayNv7xmIX7by162XWpC1nW9YmPtz3JrtzD3Br5xEEuflgsEhv+UaLCQGBn45totpYR1ljNUOjumGYs5XeQW0RgKFRIy7XUP8WjlmbdgjsoojBKs1FiJsvLw1tOorxdoIIdymrodnWopnfR1BCSG8Yfe4XDhmZS4250czqWZvY9+0h7FY7WzN/Jff7fHI35wNQfLCE9F8ySP8lA0uDFEXUZkIUSq2SPc/vJ2tNNieXZJD45mFcw10JHdIUkXBqVRbZ63PxbOeBUqMkf1sBedvyMR+wMG/ePBoaLuzA+Ee4bHtpvXr1oqamhry8vD9ULz09nUOHDvHcc8+xcOFCQMTXt4xrrtnRrNwjj+Qxbpwn/fuf+wxFq1UQEPBbeycRKdpZ9jiWaYlPpBf11jpOlCXTPbgXGlXzc0CDxYBKoUKtbHJpF0WRdekrABgQPoS+YQNZnbaUrVm/YrA04qX35oboibw54m4sNisLjv6KVqXGZrfjqnHimQ2f4Kp14tYuIx2LYonJQFZtHmVJ89Gr9KiVGmpNLbcW/wkokaIKBKS/PGeFiobTERnvjnyYHqExdAxozRcHVzjqOKnD+PRgLr1DLxA9cCFaXwcTF4Hm0jpcycicC4vRyupZmynPbFIt3JixlkpDueNz8b4SiveVABByTRBqZzV6Hz19X+5F8rcnOPFjGgqVgF9XP2Jvb9/Mf6AmS3KYqUqrpiqt2nH9MEcByMrKwtm5SfDor3DZDAKFQsHw4cPZtm0bmZmZF1XHbrezaNEi+vXrR3CwJOKgVlsZNWoNanXT28Uvv1SxZ089J07Ekp39RzLCNQJFgKxOKNMSjZOabE06dtF2zuOC3Jpsvk78mO7BvfF19sdiM3OkOJHMynT6hw8i7HSs+0O9ZvDpgfcwWg2UN5ZysGAvI75LJ7cmj5L6StRKFc4aHVvu+pDo0zoat/w8mzlbvqG9bzjq02fe9ZY6FIICjVKLh9aTatP5tx+vVs684KtP/9yvVWc2ZB5ErVDx/KYvua/nOA4VpPH8xq8cdd7ak8ugyFZ8d2MZcObt53dctwUFqPQw4gPofLscZihzxdj12QHKMyubZUR9ddi7AKR2OUpmh5TzvoO6hrk69AfOR+eHO9L54ea63XfH38P1UWPOU+PPc1m9bZRKJYMHDyYiIoKdO3f+rrTx9u3bqaio4LHHHkaptOHuXk1jY10zY8BgsPPkk/k89pg/ERHaP2gQAKQgGwQy52Nv1k5cNW6092155OWt96a1dzuOFCdQa6xBEAQCXIK5ucPt9A9vnnmnW3BPdudsp95UT4WtnMK6fATAU+/KdW16MXPgrbhqm95guwS148fD6/nq4EqsdhsKQcETfWcS5dWWoroCXtn+/OUe+mUlTK3hrfEzWZSyE4AAF09ctc58lbCKakM9IiIahZo3RzzAntzjlDZUURvyBvQugf0fSBkLQToOEBRSwiLxtLnhHg49H4JOt4GT998zQJn/DL/Ne/JbzuQ9SSk9zsFv9pNiO0pNUS16bz1Dvzh3hq6GogZO/JhG+bFybBY77pHuRN/cFp/45r/PCkFBW892jIq8kCznn+eyu98KgkBUVBQhISGcPHmSpKRd1NYqTt+zIwgioihQV9fIqlWrGDt2KIMHp9KuXRo//thIY2Pz9l5/vRiLReTZZwP+TG+Ait8tJfPfJeFIApve2smp3bnNLH4AH2c/pnd76KLa6R7cm+5nNAxEOxprI6NVJ3DpOtbhCDTpp+epMzXQL7wjQW4+TO4wlIXHNpFWnstrw++jttHG27teIb82F6v94pKGXY2oBQV2F2/6R/XgpR0LpGtKNfm1ZdSbDYCIn7Mn++79gkA3H6b3uIEHVr7FyJceIi3vFPru90NdIRQmQnkqWI2g1IB3WymDoXuYvCMgc8U4k/dEtIvs/TYRY60JUWyZ9+RAwR4SC/YT5B2K2fP8L66GcgO7ntmLoICosZEotUrytuSzb84Bes/piXes1J5CUOCucWdGt6dQCJcnfP6KxeNotVri4uqIjf2KhgZnysp8qa72wGZTolDYefvtLfj72/n223y02nOne83ONvHmm8V8/HEYLi7Kc5a5MAJSQlkZmfPTb3oPCo4UY2owtzAK/hSCgo65yzDXpGEL74TSOwxBoWRC3CC+P7SWLxNWUNFYi6vWic6BbXl52HRGR/dlzYkT/JJix2Bt/P1nXMW8ft0DPLHuQ+Yf3UBqWS4C0GgxYrSaCfPwZ3zsQJ4eMLXZjsm4uIF8k7iGHTt2cO2114JbsPQfl36bVEbmj3Am70n+kSIa3QRwO3fek7HtJzGt450oFSreO/QaucYsBLuAqGj+nZKxJBNLg4WB7/fHJVjyiQsbHsbWB7eT/E0KA97uh0JQ4Kn15JV+r+Pr5HfZxnbZDYLk5GRmz55NYmICxcW5ODkpiInRMWOGP9df7wHAyZNGFi48xY03ehAbm0JenhlPTyWCAC4uCrKzTbi5KZk1q5DgYA3R0XrGj89nz55aysslT+U332zExSWEAQMsODmdzxqzA7JKocyF0blpGfHCIFbN3IjNYv/LRkG3ca0J2xKAaXMKtevfxn3McyhcfZkUP5hJ8YPPW+/adu1orHmYRpULCErqTXUsP/Ezu3K3/aX+XG481DqqT6csVymU5NYUoxAUvLT1OxosBjoFtmHvvV9csA2DWap/RuBMRuZqI21LpkPI7Fx5Tzx0TRoBaosGbaMTPoUBlIUUSUvR6Zf8ihNVuLdycxgDACqtkoDu/mSvy6G+sIGRPUYyvcO9uGma6xNcai67bF9OTg51dXXcemt33n8/lOefl7b6x4zJ5IsvJDGWggILdjssXlxNZqYJs1mkpMRKcbGVjAwzrVol8eKLRZw6ZScjw0SfPqksXVpCcbEBq1V64z9woJgRI3bw2WcTWLlyNKdOtcJuP9c2YpvLPWSZfwF+bXy4/pVhaJ01CIo/vh0tKAQQoPcdXeh6Wy+8v/8Or88+RdU2gprlL2ItyQCkKIXzoVIo6CnkAgKIIi5aV6Z2upOn+88m1q8DmnPkOhD+xgia1l5SqNSdPccBoFaouLXzSBYd24xdtNNoloz3O7o2nX+WNVSfs63vD61DEAS6dLm40E8ZmStNcUopol28YN6TsxFEge4br6Hz1r64V5xWM7VLCoQKTdOOt2CXUn8qtdK1ocrhPNntqctuDMAV2CEYOXIkI0eOACYAvoDIgw/60bXrCd55p4Tp033xcdOjVAj07RTEw5M6g8KO4FrLA7P3U1xi4ZlnAomNHYBGE4pOd4jNm7cwcuRIwsPDKSgoYOXKlURGRnLq1CnKy8txdQ2juDgID48qBg3aSpOnshK4+PwHMv9t/Nr4cNMnY9j1xQEyd+YgKAVE24V3CwRp7cYtwIXBj/fFr43P6esC+utHo79+NOakZEw7d2LOPITaOR5RoXKU+S3BMd3puOFHjkZMkBoWBFp5RvFQrxk8u/ExKg3N1QvF3/PGv4wEu/mQUZnP9lOSMqmTWsvWrEQe6T6GZ7d+h8VmJtY7lGmdrnXUmbtjHntzkxjeugeh7n5UGupYfmIHiQWpPPjAg7Ru3frvGo6MzHkxNZipL5OO8s6X9+RcCAgEZYcRlB1GrWcV5YElHPNIpii7CG2hHp1Gh2uVB+4VXhzbdQIAbf2VS3J2hXwI8oAmxTWlUiA0RMvBfSZMXz1M0qZsbPajPNb+Aa4z9wRRAINAhOZBiklm57YgWrUaROvWAgUFkn9Bx44diYiIcEghBwUFcerUKXQ6HeLp3AbLlh1mwQINWq30JblqlYr8/LkAPPTQQ7i7u1+Z4cv8Y9G5aRn6ZH/iRkWTvDaNzN05iDYRQTi9CyDl33QYCj6tvYkb3Y6ovuEttMjPoImLRRMXC4BotGI7VowtoRCxvKWvgNLVhygPDYqsRRyNGI+IAlGQ2h3WeiQrTyw+r4+BcLpvV4rt2UcASChMZWCrLrw98kGeXv8xL2z7ERHopFLyucqKYfGziLHD0EZfw4i2vciqLOT7w+sob6xGp9IQ5x/JVzPmcsfcGVes7zIyfwRjTVMOgjN5T/qHt8NJXYnZ6oxV/P1F3K3KE7cqT653nchHjW9xYs5Jbmg/Ea1Sy/bszeRX5AJgMBgu2zh+yxUyCE7Q0GDDYBCpqbGx/Bs169bVMiF2EGJRCCablK5Mr9bBWYmKxNNvTBlZ+ZwJ5GzTpg2CIPDzzz8zYcIE6urqAClnQqdOnQgIaIo+2LhxExUVTVEFS5ems3SpFL41depU2SCQuWgC2vsS0N6X/vf1oPxUFeWZFTRUGhDtIlpnDd6tPPGJ8sLF548JhAg6FaoeIah6hCCarIjF9djSyrEdKgKLDURw7juNkEVP4ZH0HkdbjafSNRJBtLExY+15jYGXh76Dr5Mv1cZq3t49hxpjDSabBQFQImBHxA6EafSU2Sx46d0oqK88Z1tn016pIk6jZonBwHVhsWQZ6kkty0VERCEIOKl11JsN1Bjr2XbqMHH+rdl+6hA9NRp+9vZFKQjYa8to3LsAw5HV9B80nSG3vOpo3y6KKDRKtA/0vOSyrDIylwrRbifE9TiBzjt5vHQvI1rDfV2fle6JUGvyp7ihDScr+1FQH3PBtuL8O3JT/P9YnrKIV0+HF/s6+3ND+4ksTVmIi8uV83sTxAsdYp6mtrYWd3d3ampqcHP7M+cYH3PvvS/y+eeSxKtCUHBDTD8+vv5JPPWuHC5Mp8/n9/DC4Dt45pppANiw81LpGuZ+/A5arZYPPvjA0dquXbtYsmQJjWfFJPbu3Ztp06ahVJ4/+mDo0CFERspHBjJXP2KjBevOHGyHi8Bsw5x7hLp17yAiUuHaimzf3pR4xpzeLRAdvgNn/pjVSoEgN5ix/gUKa8t4f/SjTPvlRb4Y+zTTOl9HQW0Zrd+ehIfOhWpj/Tn7oFIoifOP5EjR+dOZR3oGUVhXhtFqYf6k2dwYew0/H9/C27sWkFaWi7vWmZGClWdc3XD5bRKz0+cruo6jcOp5k8MAUI1ui6qLrBUic2mpN9eRWZNJhaECu2jHSe1EhFsrAp0D/5jxmbIU+/onUNRm88NRgVuXi/w0Hib/RrrELipQCHZqTH4M/9GZjMo6h2DRuTBZjRTU5qFUqAh1D2d3zjbmH/uWNWvWMHLkyD856j+2fl+hHYJGHnkkkBv8x1J43Islyduw2UXMNim2unNQW7qHtOetnQtIKEilxlhPYnE6BpMRQRCwWJrHYHt4eBAeHo5erycnJ4eqqir27t1LamoqDzzwAKGhoY6ya9euJSsri6ysLOrq6nj22Wd55ZVXrsywZWT+JIKTGvW1rVENboU9tRxlfjCi3kb90vfxqc/Gpy4Lm6CkTh9AQ0R/lG0HIAB6tRJvJzVqhZ3r5z3FqcoC1tz6FmpF8z/1YDdffJ096B/RkXdHPsJtS15m66lDLfrRYDYye/Ad3OfkjOXQMrZ5hHJb6gEO3PcV7f0iuHvZ6yxO2tKsztnRE/U7vsV0YkuLdgHpVQowHl0DNiu63lPYV5HO4M7X/PUJlJEBas21bM7ZyPrs9RQ1nDucXa/S0zeoH6MiRxPlcQGfFUM1rLoHUn5GcdoA/ylJxEUDY9q1LK4QJId3V00Zvk6lFNRqUSsMWOz6czavVemI9Gpyek8tT0av19O3b9+LG+wl4LJHGUgoaV0ymkHKydzS6VqW3vIaDWYD4xfMdHhZ/3TTHNr6hrEmbQ+7co5hMElnNF5eXmi1TecxGRkZfPzxx6hUKo4cOUJ0dDS33HILMTExVFVVkZ2d3ezJK1asIDs722EkFBUVXZkhy8hcAgS1EmW8P+oRbXD/8Am8f5qHwtcHFAqUog2PxgJCC7YR6+9KjL8rrbyccNYo+N/iF9mfl8z8SS/QKzT2nG2PbT+Aden7qDLU0dY7lKmdruX/rvkfAEGuPvg4ubPsltd4+pppqHTOON/+GbNL8rir2xja+0U42lEqlBjmbOXG2KaFXBRFLEVpmE5svahxGpM2cGjXj6S2sclHBTJ/GZtoY3nGMm5f/z++S/72vMYAgMFqYGveFh7b9ghz9r5AheEc4nUN5fBNPzix5PQFkbIG2HQKxkWD0wVybigEaY1TKUyMafMyWuW5d+TOJrMyncNFCdx5551X9Gj7su0QHDx4kO+//56tW7eSnZWJl8adHiG7mD34Ttr4hDIuZgAPrnqHyQtnsTJ1V4v6Hh4eVFdX09jYiL+/lGO+pqaG77//HlEUOX78OK6uruh0Ojp16kRYWBgpKSktvkxeeeUVfHx8qK+v54knnqC8vByz2YxGo7lcQ5eRuWzoBgzAf/s2al5/g8affgKLBXtdCdayLJTe4QgKBU9v+JTVaXsY1a4PVYY6fjq6kawqyRDel5fMtM7XATBjwC0sTdlG78+n08ojkMkdhvLunkXE+UeycuobdPvkDl7Y/BXzJr2AU/y1vLL1B/JrSkktyybo9TFUGeroHdpS4vmbhNX8dGQDqYWp1Nqs+CuV9NZoeczVjVBV01dOoc3KwsZGNhsNZFutCEU/El2dRLuYaIYOHXplJlTmX0eVsYpX979EWlXaRdexnZbBPlx6iPs338NjXZ+gV+DpqAGrCX4cJqlkik3pNxclg9UOt8Sfu81jJbDydBcyKqHGBJ8ezKPB8iz11v8R798NgIrGcr5M+JAOAV1w17pTWFfAjpwttI1ox6uvvnruxi8Tl80gmDt3Lrt372bixIm0rxlFSX4Znx1YQe/Pp7P9rk8caWDNNitalZpPx0gexXZE9ruVUlVfw+LFizEYDERHR2M0Gpk7dy7V1dUAeHt7Ex8fz9atW0lNTWXq1KkA2GzN86X6+Pg0+yyKIpmZmbRv3/5yDV1G5rKicHPD89WXcX96Bo2Ll2DcsQNT9h6cfVsBcKxY0jhYk7aHNWl7mtX9JnE1H495AoBQdz++uXEmN8x7mozKfN7ZvZDr2vbi9Wvvw9/Fiz5hcezNSwKgxljPO7sXYLSaSS/Po413KAfyUzDazIgi5FQVo1dr8XPx5EhhGkF1xQxydsZdoSDPamVBYwObTEZ+9fUn4LSfzwajkU/q67hWp2OikzMWUWR5fj7Dhg3jm2++4fbbb79SUyrzL6HSWMnTO56kzFD2p+rbRTsGq4HX9r/C492e5JqQgbBtNhQf5bcJtuYfBz9nGBp57rYOFcHzv9kgm7VNBKoY1XaZwyDQqfS46zzYlrWRRksDHjpPBkcO54ct3+Dq6vqnxvFnuWwGweOPP86CBQuoTM3DY3keRMGEuCF0++QO3tg5n5PluejVWtx1LqgUSqZ0HAZAtdKEzTeHrKwsQEqQdM0113Ds2DEqKiqIiYkhJSWFkJAQdDodgiBQUFDAe++9B0BYWNjv9q2kpEQ2CGT+8Sjc3XG58w5c7rwD0WbH9PlBqDDw6+3vtSibWJBGvy/u5YuxTze77qaToiLeGvEQd3dvLgtssduw2SUDu9pQT+NpI76wrpzCOim16+FCKelQ9HtTGB3dl4WjHmFOXR52Jx2gc7R1rU7PyPJSFjc28KCr5NjUR6PlgF8AXmc5Av9Po2FU27bMmjVLNghk/hBWu5U5e2ZRZijDLv41iXoRkXcT3ya0voLIXXN5ZYfIc1sh1heS7pfK7L0T7CJ8kQifJUi7AM4a6BIIzw+A2zpJ/53hZIVkIOzKhS2ncjlc9Dhdg/ozLGok9/V4rKmgAB3HxeAXcn6Ro8vFZTMI+vSRNJ3vuedeaksr6RfWgSA3H7z0bqxI2YHJZuH1a+8jqeQUAI+t+YAGcyPhEa04qa1i504pK1rv3r3x8vJyxGJGR0eTkpLC0aNHOXHiBCEhIeTk5GAySV9WDQ0N5+hNc0pKSi7HkGVk/jYEpQLNjbGYv0z4Q/WivIJRCAoWJ23lrm7XO47c8mvK2J1zjD5h0n6or7MHiya/5KiXWVnAs79+RjvvUHJqSvhu/Ey8SjOo/unxcz4nVCUt+rVnBTW1U7c8eNVaLAxrH82H69ZSV1d3xd+QZP5ZnDx5kueff55du3ZRVlGG2ltNSP8gIsdGojqt9Fd6pIzCXUVUp1dTV1B/3qyDdfn15G3Op+xIGQ3Fjaj0KkqDtvBsR4FXd4k4n8NPYMav8M4+mNoB7u8O1Ub4PBGu+Q523wE9gqVyeTXQ4ytw18KDPcBDJ7AhU8ny1KXk1GRz/2mDQFAIuAW40G1Kx5YPuwJc9iiDCdHX8F3pcr5MWEF5Qw12UcRT78q8sS8wOrovdy97nUaLiW8SV0tRB0ekXYHgYGkmo6KkMMEz+gP79u1ztK1UKsnJyUGtVtOvXz8OHjzI2rVriYtrea55No2/TaEoI/MvQBHggur6dlhXNp2dfrp/GTXGeopOv9GvSdtDQa20nXpfz3H4Ontwa+cRfHtoDSO+f4Ib2ven3tTI5wdXYLCYmNH/ZkRRRK/WcH10X4fBkJCfCoCX3pX8mmL675uHaGkuoFJlt2ETocBm4726WgD6aX5HsEWppCg7CycnJ5ycnC5cVuY/TV5eHj169MDd3Z3bpt/G6uKVVKZVkrbwJNWZNfR4VtqSL9hRSOHuItwj3dF56s7bXu6mPPI25RHYO4CIEeEo6oyUrslgTDJ0DACFAOnl4PaadHjQMxh258GEGPhxXFM7S1LgVBX0/KrlM3bdDrF+ACJtvMpYngrHig9x78ppzcpF3LqXXr16/fVJ+oNcVoNAtNiY2KofE1v1A+Cnoxu5Y+mrvDr8HkZHS6EUAS7ePN53Mp0C22AXRX7O3smahG0tfAGCgoKYOnUqP//8s+OawWBopj9gMpnYv38/NpvtgnoEFyG9ICPzj0TVKRBEsK6WjIL39iwit7ppR2zFiZ2sOCHtvk3pMAx3nQsfjH6M+IAovj+0llmbvgSgk5sP73h5EZe4mIa8w6h8IlDoXUFQYDfWY8yUdiKspRmINmsLYwCgW3ERZ4SVPRUKXnTzYIDu/F/IAFlGI6vzc5l4yy0X/BuWkfnxxx+prq5m165dJHCAiIxwwoaHItohf1sB5noLGhc17ae2o+P98ShUCva/fJC63NNe/iKcnfojuH8Q7W5qg0ovLYt9q4uIscKYhdBohpOVoFbCq4Olo4KPDoLRCprf/Jqe+Ty6Ddx0+t10URKsPgn+Z2kMKRVNa9zUDrejFQQ69HTHq3+Xv02y+7IaBKaiJseOtLJcHl3zPj1DY5l6lpb5S8PublYnvmdnVMHurFixokV7Hh4ehIWFcfLkSfz9/YmPj2fLli24uLgwYcIEXF1dsdlsmM1mh6TxuVCfY6tSRubfgqpzIAp/FyzLT5D22MLfL69Ucm+Psdzb/QZEYy31277EkncM0GErz8ZWno3pN3XMZimj6BQnZyY5nVud8QdvH0yiSIbVylJDI42/c65rsNu5t7ICnSDw+uuvX8xQZf7D1NZKu04+vj6sP7jO4Teg89SCAhQqabXXeenQ2FX0qYymxJBDgQUWHHscFUosgpVCTRUZzoUc8ckmQZeBDamdiPpqZm6FVh6QVQ1KASI94YnTGY6ndoCQd6XFfmRr6B8uHRmkVkhl3x8hlQfwcZIMgjtXwpyB4K2HzdJpOXF+nRjnGUKHvOW4V0bjO/WdKzOB5+CyGQSiKHL4+7l05gaK6yoZN///cNM5s2DSbJSK81v+nlYtQ4cObWEQnNEfePrpp/n4448xm81MnDgRnU7HmjVr6NOnDzU1NajV6ma6BefC29v7koxRRuZqRRHkiuaebtgOF2E7UIBY3ijtjNltoFAiCAKi3QYICAoF9oYqjMmbMKVsRjRfmiO1vlppN2AwMFynZ0hpMc4KBbc7t5RitYki91dVctJqYV54K4KCZKVCmQszcOBA5s6dy9TbpmIeZkTjqqYytYrsDbm0GhWBSqdCa1MzobQ3wys6obNr2GjdTjHlqJDWILWoItzkS7DJiyGVHalWNrDK9yBrfRNJ2FJGbjW09YbsamlRV561oxDoCgPCYHsOTF3WdF2nkoyIMHeoN4OLBq5rDS8Ngld3NoUinuEWDcSkfY1aEDDv3Ye9vh7FFZQrPptLahAkJycze/ZsEhMTKSosQI2dGL+NlNRXUmtsYNMd7xPk5iPFKR/bSHp5HtXGegJdvRkQ0YmZA28l0MsXjUaDk5MTjY2NpKWlcfz4cZKSkrDb7Wzbto1u3bqxZcsWUlJS6NixI6tXryYlJYUjR47Qrl07FL+VSP0Nvr5X3ntTRuZKIygVqLoFo+wahFhcjzUlH8OClQioQQS7qQFbRQ7WsiysJScdyoGXgwiViji1hmWNjec0CJ6qrmKTyciHHl70P+0/JCNzIa677jpeeuklXn7lZUzrmvaw2kyIIvqWdrSvD+GhvFF4WVxQnNbgO1968DMGgofNmanF1xBXEMqIDeu4JR4+TZBUts+VBd1VK508jG8v6REU18NjGyCjCpxeAYsd/J3h7i4Q5QUDwqWy3k7w1SFYlwEv5B/h/5By8fbQaHlr8WL63HbbpZ2si+SSGgQ5OTnU1dUx7ZabKd48j/qGRlanZVFrauTxvpMd6mZHi08S4RnIqHZ98dS7kF1VzLeHVrMufS8P9BpPirrc4fh36NChFmf+I0aMIDExkc8//5yuXbsCsH79emw2G2PHjm1Wdt++fVRUVDi2OE+ePMmSJUvYsGED06ZNIzw8/FJOgYzMVYcgCAiBrmgC26Ns70H51KlY0/+EAaBUwm98e/4IRlHEfI7siy/VVLPI0MhsN3fGurqi6dzpTz9D5r9FREQE7bq1RdFBQOWioiSxlJNLMmmvDeOFtjchgsMYuFgEBL5euxxPjS8r0spp5WlFp5SODXzO8nNttMB6SfKDKXEw7nQk++YsWJYKo9rAje1h8Ql4eae0u5D9KIScTifg7wzJRVDUAB94eFJos/F5Qz1Dpk9nT8eOdO7c+a9Ozx/mkkoXjxw5kvXr1/O/wZ0ZEKwjrdxAo8VIuEcAq9N2O8q9P/oxvhz3DPf2GMuNsQN5YcgdLL3lNcoba3h/z8/8smGlo6zZbMZisTjyGZxJ0DBjxgyio6MdUQdeXl48+eSTzfIYAOzevZuVK1eyfv16ANLS0pg7dy7PP/+8Q+tARua/gjIoEL/163B99BFQKqRF/ncrSWVU4eF4L1xwwaJWUaTa3tJX4LDZTKrVQgd1c4XQT+vr+LyhnodcXLnLxRVEEU2HDhc/IJn/LAsXLmT69OncPud2IoaHE9g7gE4PdqBT32g2/byXysZalH9iicuoyOfbxLXYRDu1JjUfjQzk5ngwWCGvFjZmwu5cGDVf2gEA6d4ZFk+COD8oa4RpHWHFZAhwAZsI+bVN5foEw2uxOiyAr1LJg65urPQLQAD+7//+7y/NzZ/lsvgQZG7+hXlHS0ksrKdfeDi1JhXZVUX8dHSjo8yUjsMoqa+k12d3Myl+MMFufgB46t2oNtYTHx1DWGQEWdnZZGdnO3YMUlNTWbZsGd7e3qjVamw2G9HR0XTp0oUff/yRkpISNBoNISEhTJw4kSeekFTZysvLmTlzZrN+DhokxaL+9NNPTJ48+XJMhYzMVYeg0eD25BM43TSJhnnzafhxHmJNjXRTqZT2R+126T9A07ULLnfcge66a/n4888p8PKk8LQxvdFopOj0rsHtzi6IQI+SIq7X62mnUuMkCJywWPjZ0IiroOCRs3QF1hkMvFJbQyulitYqFUsaG0AQcDcaUc6bx7Bhwxyy5TIyv+WTTz6hc+fOBAUHIqZKO0/uFicmhE7g1l0vc6wok8FRXf9wuzlVxdhFO3k1UnbekfObImhqTTB8nvRzK4+mOi6/UcK32CRZ4zPoTq+0m05BrxDpZ0EJxhrJ2Lae3jhrpVIxqlMnVm7d+rvRcpeDS24Q2G1Wik4mkVkpTeKunBzHvTuWNukyT+k4DJtoY1BkV9am7aWoTkooYRNtvDjkLgZ16E3/d+/Ey8uLkJAQ0tPTUSqVuLu7s3//fmpra/Hw8GDYsGHU1NSwcOFCevfuzaBBgzCZTOTl5VFXV9eif927dycuLg6lUknv3r3RaDT07t37Uk+DjMxVjyo0FPf/ewa3p5/CeuoUlmPHsRUWIlosKFxcUEVHo4mPQ+Hh4ajz1ltvkXPW3/Q6o4F1Rulv/Ua9E/5KJVOcnNljMrHWYMAoivgrldyg1/OIS/NcBikW6Rgvy2blkeqqpo498jAAW7dulQ0CmfNSUlKCp6cn9ZkNHP38OOVJFVhLzKxV/gpATnVxs/L6Fwad82eAwZFdWXPrW9jtdj4/uByFIHBb55GkledyvOQUBqsBATtaJTzTD9p5Q4QHdJOidGl7lp/6oSJIq4DpXZqutfeRHBMzK5uuiSL8km9FAbQ/E/lmtxMaFob54EEaGhp+N13xpeaSGwS1Baf4MaGAkxVStkJBgKGR7fhuwly8nJpnbery8e2YrNJRgLeTG89ecyv397oRAJPVzK/Pf01WiJXs7Gxee+01BEGgXbt23HaWw0VCQgJffvkl995770WduYSFhTkEH7p06UKnTp0uwahlZP65CAoF6tatUV9E7HN2djaizUbZmLFYjh8/p0/BHHePi3ruE27uPOF21neCQoHvurVo4s6dnVFG5mzatm3Lr7/+yrx351OUWEzrnhE80Pl63t/zM5WGWp5c9yE9QmKI9ZdyfHxz47O8t2cRBbVlvHndgwAkFqbx8b4lDImSRIweX/sBq1J388yAqXx6YBlBrj7M6H8L3no9r+94n1ojPNu/qQ9uWmnXYOYWuDYKiurgwwOgV8GjZ+kKTYqVHAh/SZFCEb30sPKYgl8bTExxcnbk9wDIMRjQ6XS4/A2RBpfcIDBUlXFdW096hLpSZbCyP6+WamMR1YYSPPQuKISmga+YOhej1UxqWQ4Lj22iwWJ03NOqNLRRB5BF/gWft2nTJiIiIujcuTN2ux2LxfK7YYcmkwmlUklSUhIdO3aU063KyPwBBKUSz/ffo3TosL/kZNi8UQHXRx+RjQGZi2bGjBmsW7eOI4lHiB8dRydzGMsP7SCtPJcbYwayJn03b+1awON9JzuSfBmtZmx2u2P3ILUsG0EQmBQ/hA/3LubzgyvoERLDj0c24K1355E+k9Ao1SDaCXX3olrT9Iq/KEkyBq6NgvQK2JAhiRL1DYVXhkC703n1RLHJ+bBTAHySABWNEKyCp13duM+l6RjthIszqzZuZMSIEb8bLXc5uOQGgSjaCXbTEuwmLcoDItx5bXsu4+bPIPGBn0EhoBAUZFTk81XCKvbkHqfKUIe/ixcvbvkGjULFI30nAVCkbeRURibzFswHwGq1kpWVhdFoRKfTYTAYyM7Oxs/Pj3vuuee8fTpb5GT16tUsWbIEQRAICwvDycmJG2+88VJPg4zMvxp16yg833qTqkce/euNKRRo+/TB9aEH/3pbMv8ZBgwYwJ49e5g9ezb71u/jaOVRIjwCmTPkTh7vO4UBX95HWlkOR4pOMmfLN83qnvmsUaroH96REHdfR5bQA/kpjnL3rnjT8XO34Ei6BcPjGyGrspFVaUaujYLVN4Pq9Nq9LRumLIEfjsKePDBYpIiD3XnSEcLn14NoB0OmnmGL7Rw0mxHr6/BWKDhps7GgvBQnJ6e/TZjrkhsEGqeWyUh6hLjxdWIxPx+fy6QOz5BfU0H/L+7DTefMvT3G4aV3ZX9eCj8eWc/rO350GAQHStJ457138fLyAqTwqZKSEr744gsefvhhysrKEEWR2tpa9Ho9nTp1Qq1Wk5KSQnl5OSqVCl9fXzw9PamsrCQmJoZOnTrh4eFBeXk5mzZtYuLEiaxcuZJRo0Zd6qmQkflX4zRhPKLVQvWTT4FC8ad3C7R9++L17dcIGs3vF5aROYsePXqwdu1ajHX18G5TYi9RFClpqCLGN4Jpna9jWufrWtRdcWInkxfOYnKHoQB8Oe4Zvhz3DM9s+JT39/zMulvfZubGzzlUmI5aqSK3uorUshpMVhFfZzUT43wZE+3O8bIaYnwq0CrthLtD/zDJCCiul7QL2vvAZ6NgelfJGLAbFVTv9eRaXQPLDI18UV9PvWjHW6lk3LhxzH7ttX+PdLF7WBskgeimeGOzTXK3LKg9xfZTc9md6021sZ7Nd35AjJ90vnNnt+tZnbqbKmMdVYY6PPWufLNmIU5OTtxyyy288847KJVKwsLCSE5OJiUlxSFBbDAYeOaZZ2jVSmrLaDTyzDPPYDAY6NGjByCFJT7yyCPN+tq7d29eeuklnnjiCdkgkJH5EzhPnowqqjVVDz+CLT/fEZnwu5yOZnCb8SQu996DoLpsoqky/wE0lVbMZ31eeGwThbXlzBp0/hTaC49tQqtSMy7mmmbXMyukY+qpv8xhWOsePNn/Zo4XZ/LmzgVEeTkxa1Bws2PmtEovsmrc6RlYTCvPen6eeO7niXawmxWUr/ZFNCm408WVO886LnCb+Syu99/3xwd/CbmkhxSlpaWodc64BoY5rlntIjtzatAoBULctFQZCjhcuAkAD530RmAXbRzMP0G1UUo6oVGqqDU2kHziBD179mzmE+Dr64tWqyUhIcFhEPj4+DiMAQCdToe7u+SsdEa46Fy4uDgxevQo0tLSyM+/sK+CjIzMudF274b/lk24PfM0ioAA6aJS2VLa7cyir1ajHzcWv40bcH3wAdkYkPnLiOam3anz5c05m1pjA+vT93Ftm1546Js779WbpaiZrkHRfDt+JuNirmHW4DuYNfgO0srrSSptLu0tImCyKdmRH0Jy+bll8UURrNUqylb4Yq35TS4dpRJ1x464TL/7nHWvJJf0L/Gee+6htraWdt46rFk1VBst7M6ppbDOzC0d/dCpFTSYbWzOrAbgmi/vYGCr1tSZNaxLTwJEpnYcjl6tZnvWQWx26R95925J1Mhut1NYWIizszNJSUl0794doEXOdJvNRkWFFMZ44bANGwEBUgKmyspKQkJCLt1kyMj8hxD0elwfuB+Xe+/BtHs35oREzEePYisoBJsNhbsb6vh41PHx6IYORenl+Xd3WebfxGnj82Lz5iw/sQOj1czkDkNa3NOrpRfQSfGDm12/KX4IszZ9yclyA/H+v03oJT3/WJkvCkGkvXdlkxCoHeqOuFJ3xA3svzGSlUqUISF4f//tVWEYX9Ie3HTTTXz99dcs2X6QirIydGoFrTx1TOngS9dgadHWKhUMifJgb14t+bVm5h1tcuAY3S6U2UOuBRRUGaQNoIKCApKTkwHJIMjLy3OUr6qqws3Njerq6mb9SE5OxmKxoFQqLxhxYLcrKS/PBsDXNxeQFdJkZP4KglKJbsAAdAMG/N1dkfkPIbjrqDHWM3be09QY6x15c87HwmObcNc5M7JtSw2aQFfpLd/PpbnR6uMkvVw2mC/sK3Ok1BdffSPuZiuNac40pjlhN53bMFHHx+P93Tcor5L8OpfUIJg8ebJD8S/x21dI37BAOjg5+4FKgf919ifSS8eunFp6BLviptOQVeXJ8pRjDIrM4v5eXTBapWQVY0Zfz5gxY3jttde49dZb6dOnD99++y1Hjx6lT58+5OXlORIdxcTEAE07Cm3btnWEbtTV1bXYSaiqqmLp0gw6dNATGPgyEAB0u5RTIiMjIyNzmTE5C4z/aSYnK/JZc+tbjrw552J71mG2njqEi0ZP4OtjcFJrifaN4LG+NzGqXR86B7YF1rA4aRufH1jBkaKTpyPhPABw0pz7pH3rqWrWpFVS1mDBR6/iXpULd54jkRcKSTLc7ZmncLn7boQrrEZ4IS7bHkXHKY+Rn7AZQ2UJ4m8cjfbk1vJVQjFvj4gkyM2bfuGP4KoN4J7lb/L8pq+4qcNQdCrpzX7Hrp2oTm+lHDt2jKqqKnJzcx3Xzk50NHToUFQqFUeOHEEQBMaPH+945pIlSygrKyM6OhoPDw8qKsrZuXMnZrON99+PBGzA08ASwONyTYuMjIyMzCXEZrMxefJk9uel8MuUl+gVemEti0XHNgNwQ/sB9AmLo9FiZPmJnUxYMJOPrn+c0dH9eHLdR/xweB3dg9tzd7cx+Di78cEeKfz9UEE9N8b4NHMs3JxZxdeJJfQIcWVkWy9Syxt5IacaoyjygKubI5GYMigI51v/h9Pkm1D6nH8H4+9CEH+bSvAc1NbW4u7u7kgsdLFUZCaxefY07BYz4lk7BS9uycEuwqvDYxjQagbOGh8UgtIRBrLmf2+hU2sY8vXDF2zf2dmZwMBAevbsSXJyMqmpqVgsFmw2G/Hx8ZSVlVFeXo6LiwvBwcEYjUZKS0tpaGjAycmJ+Hh39u4tOGfbcn4DGRkZmaufRx99lPfff5/Rg67lRs+WTuRTOg5r9rnv5/dQVFdJxuOLHDvINruNPp/fg9Fq5uhDP/Dylm95ZfsPDInqxvXRfTlenMk3iasJcdeSV2Pi/64JdfgRmK12HlydSRtvHTP6n06uJwh8kVjJwbxqTrzzLr5t26LuEI/Sz+/yTsY5+CPr92X1YvCOimPQzK/Y9urd2CxmxNNOgjUmG85qBZ2DpjmMAQCLTUoZZbXbiPVrhUqh5Jb+19NnyggQ4Pjx42zevJn09HQiIiLo2rUrhw4dYv78+dxyyy3cd999fPDBB6SmpnL8+HG6dOnC4MGDKSoqYvv27URHR/PWW28BIkqljT59viUmpoApUzwZOfJsWeUH5fwGMjIyMv8Ajhw5AsDqrRtYzYYW9882CNLLczlUmM7DvSc2UwJUKpSEuPmRWJgKwMxBt+Ht7MGn+5cxY93H+Dq7cUN7b7oHuzBzUw6FtSaHQZBc1ki92cbQ1mf5HIgiA8N0bMswsd3NlalDWzovXo1cdrdG3+iuXPfGcvZ9/Az79+1hR3YtVQYLRXUifT9/kT7h8cwefCdtfEL5+fgWBARSy3L44uAKlAolP2xfzubUA3Tt2Y1hw4dTU1PDiRMnGDlyJLW1tWRkSOpS8+fPZ/78+c2e3alTJ3r27AmAv78/Cxcu5OjRo3Ts2JHevfei10tSyV26ODF5oh+5Ca0oSQ+iLMOdvbuPspcj6Fy1+Lb2xreNN+E9QtA6y+IpMjIyMlcL27Ztc/xsSyvHsijpvGXb+oRhmLMVgAazAYPFTK2pntWpe9iQsZ8JsVLSI0EQuK/nOO7rOQ67aGdr5qvUmgo4XtwAgKu2aenMqZLWkUhPXbNnRXrqUCgUHD58mKlTp16SsV5urkicg2tAGEPnzOPtgX1IKCwm3t+FhIJ66kyNbDh5gNWpe+gWHM2e3ONM7XgtT2/4hB4hMdzaeQTfJK6mobaOlatWsXPXLmpra4mJiSEuLo6ysjJuv/12Nm7cSHl5OZ06dWLfvn2A9A8aHR3t6EP37t1ZuHAhCQkHGTXKi/btT3AmaVvOwVZ8NXUKNqMTGrWA3aYApMTVNdRRllGBfY2IUq2gzaBIukyIw9X/yieekJGRkZE5P8p2Ptg6+GM/XnK2Nt45eWbDp3yVsAoAhaDghvb9eXdUc/E6URRRCAra+AzlUMGPrEqrQK9W0DGgKeywymhFIYC7rvlyqtaocHdxorCw8NIM7gpwxQIfBYWCWa+/Q9euXWnYfYTkhYd4Zdv3JBak0mA2cLw4kzlD7uTBXhO4o9toeofFATApfgjPbfycgwWpVFZW0qFDB6ZOnUp9vSRiVF9fT2FhId26daOkpAStVovJZCIsLMwhTgSgOS2LWlCQxbBhGxEEyDsUDiTx5dIETNZdCAiEeURwQ/REYvziHXXtNuk3y2axk7Ypk5Pbsuh9R1dirm2D8FvxFRkZGRmZvw316LaYa02IOdUXNAoe7DWBcTHXUFRXzpLkbdhEO2abpVmZM46Doe49+OLgTpJKUri9iz/OmqbIAItNRHXOdUBAo1JiMBguwaiuDFdUCaFPnz4ACNWudA9tz/KpUgKHPp9NB+CpAdK2yhljAKBveDxb7/qIpJJTdP/kTjq1i2P1qtXs2LlDaksQ6Ny5M5MnT8bZ2Zn169ezbNkyPD3PjiG1c/LkSQBqaipRqcI5OL83u76LIsY3h06B3fDQeVLWWMbmzHV8uO9N7u/5OPH+nVqMQbSL2Mw2dn12gOKUUgY92geF8spnpZKRkZGRaYmgUqKZEo952QnE1PLzlmvnG0Y7X0lV95ZO1zL6hxmMXzCTnXd/0iID7uLkbbyzax3jYuIZ1rq50aBWCljt57A8bDbMZgt6vf6vD+oK8bdII9nzahyW29lJKC5ESb2UdnKg2ApNn+507dKFqppqEhMTsdvt2E4nVjl16hSCIJCSksKePbto2zaaqqoslixZgFotYDDYSVzUk0M/98TLCR7u/XSz5/QK6cucrc+wOHnBOQ2Cs8nYmQ0CDH6sr5xCWUZGRuZv5uDBg3z//fds3bqV7FNZeGld6RHS3uGndgb9C4PO28bgrx9i610fOT5PXzaXeUc2oFaqWJZyHL0qktHRTb5knjoVdhFqjNZmxwYWm42a+kZ8KioR7XaEvyGd8R/lihsEoskKNSbH54tJQgHwzq6FuGmdGdFa0p4e5RxPRkgN2d0G8sLHc/n444956KGHSE5OJj4+HkNjPd9//yMgyao//rg/27dDaoqZxJ/OH0HgrHGhd2h/NmSspspQiafe6wKDgYzt2QTF+dN+eJs/NhEyMjIyMpeUuXPnsnv3bibcOJ6YdqMoqangswPL6P35dLbf9Qmx/lLOm29ufLZF3W8SV7Mr59hpYSKJA/lSFl61QkW/8A5sOZVIgGsckO4oE+4hOROeqjLSObDJt+xUlRE70PrgQSpunorX11+icP6t5PHVxZXfITBaHT9eTBIKgDd2zGPLqUTeH/WoIxGFTlQS1+hFXKMXxa1H8NCqdyhYexir1cqMuPFMfj6DLKWe4mILbdroCAhQExR4DB9dMILCjmhXkFOdxYoTv3Cq6iSiCJFerbkxZjKeekm6ssFc7zAIMivTWZqyiNyabPQqPV2DenBD+0noVDr2fJVASOcgXH2v7n9sGRkZmX8zjz/+OAsWLEBxtAzrOumYeELcILp9cgdv7VrAt+NnUlpf1UKbwGKzMmOdtCtwb49xAKSW5XDj/P+jtXcwO+7+FJvdTugbY9GrPXHTBlNrkjRsYv2ccNEo2JRR1cwg2JRRjV4QGKLTY9q9m4pbpuLz0wKEq/gI4W/LpnCxSSh+SdrC7C3fcFuXkUzvccM5yxgt0o7D5uR9eOnduKF1HwTFSdq00dGmjWS9paQYKCq2cF2bDoh2BbnV2by16yU89d6MajsOEZHt2Zt4e/crdA6U5ItdtZKIQ15NDu/teZ0A1yAmxt5MlaGSjZnrKG0o4aFeM7BZ7BxblkLf6d0v9TTJyMjIyFwkZ/zUTIlNnv2tvUOI8Y0grUwKK3tw1TvUmRroF96RIDcfSuorWXB0IxWGWiI9g2jrG0qdqZHrf3yKKkM9j/aZzPr0fdSZpCyHx4oziPZti1KQDAKNSsHEOF++PVTCe3sK6BjgTGpZI7tya3na1Q1PhQLsdsyJh6ie8yKer792hWfl4rnyBoFefdFJKDZnJnDX0tcZ0aYXH45+nNL6qhYJJyw2KwuO/opWpeFYcSZ3dhuNWqlCcGpwlLHbRZ56qgCtSsOACOnsaGXqYlQKNU/1n4WLRspx0DOkL89vfpID+XsIdgvFXecBwPITP+OkdubxPjPRqyXrztvJl3lHvyal9DgxfvGkbsqkx7ROqPW/SW0pIyMjI3PFEC02xLKm7//f+qlNiBvE94fW8mXCCioaa3HVOhHiJiUXerL/zRwqTOPp9Z+QX1MKwPObvmjW/qrU3ehVAlDOocJ6ShosGC12XLVKkksaOFRYh7eTmqf9PHhQKe0af1BXy2GzmcNvvkH53Nd54YUXmD179mWfiz/KFTcITHYL439+vkUSiruXvc68Iy1VpgDWpu+lpKGSx9Z8QJ2pgXCPAHbmHCW3ugRRFLGJdoZFdWdj5kFu6jCUJ9Z9QO6BraxeU92irWc3Ps5T/V4gozINZ7ULnx14n2jfGNx1nlQ0lmOzW7GJNsa2vwkAg8XAibJkhkRe6zAGAHqF9uOXpPkkFu4nxi8eq8lK3uEiIvuEXfI5k5GRkZG5OMSS+mbhhr/1U5sUP7hFauMpi14gvSKPKK9ghnz9MCHufrw2/D7sop0vDq6gylDHymlzGfjVg8wceCvPDpzKoC9vJNxDR+8wN3QqBYW1ZrZkVePtpOaLrqF0PWl2tP9GXS1+CgWxag3bTUYuImPA38IVNQhsNhs33XQT+7OT+OXm5kko7ux2PYMjJR3qwtpyXt/xI246Z6oa64jwDCDYzZcJcYP4aO9ivj+8DgAntRZfZ08KasvYm5dEqLs/fcNiybKksGGPFPupVgu0baulT1xrrOl9AQE/Z3+sdiueem8EAbZnbaLB0oiT2glXrRvVxiqc1ZJlV1Cbh120Ee4R2WwsKoWKUPcw8mqkbShBKVCWUSEbBDIyMjJ/I2Jd00J8MX5qtcYG1qfv49o2vXh390L0ai3b7voIbydJx2ZKx2F0+GAar2+f56ijEFQ8NaAdZlt9s7baeOt4b28h2ck1dNU0vUDu9QsgVKWi0majQ0kRtqtUrOiKGgRPPPEEK1euZPSQ66hqqOOnoxub3Z/ScRh1pka6fHw7jRYTkzsM5auEVbT3jXCUVQgCAa7eHH3we9x00qL9ytbveHnb91zXpheCIHD7/Xra3BHOoEHpLFjQigkTPNn1+UBOmOOx2yRfBX/nQOpMNbwweC4KQQoHsdqtzNr8JADVxioAakzVAI7jg7Nx03mQUZEGgGgTKc+suLQTJiMjIyPzxzj99n2xfmrLT+zAaDUzucMQ7ln+BsNad3cYAwCBrt70j+jIpoyEZvUE4TdhhKKIr5N0ZNxoscFZKvehquZLrSU5+c+O7rJyRQ0CRxKKzetZzfoW96d0HEZlY63j7OaMrOTSlO0sTdkOgIDAo30nOYwBgAazpCVtsVtAaUPZMQH2N7VbV2ejsVaNKDZpBVzTaggLjn3HD0e+ZHjr0YiinXXpK6gxVktt2czN/l+laDlVaoUay1nKVsazLFMZGRkZmb8Breqi/dRAOlJw1zkzsm1vTFYLepW2RRm9WovFbm12zWqTtv7rzDbsdpGyajPLDpagBHprWrZxNrYCeYegeRKK9AosC4+3KBPuGYBhzlYsNiut3hpPW58wttz5IQB7co8z5OuH6RLUrlmdV6+9l/35KeRWl6AasBFB3yQVefvt2dTX21EojtLaqx3jY6YQ7hHJgIghVBoq2Zixhn15u6Rne7RieOtRrDu5Eu3pXwq1UjLzrL/5ZQDJAFErm5wIZXEiGRkZmb8Xs7uK8QtmtvBTOxdFdRVszzrCtE7XolVpaOsTyoH8E9jsNseOgtlq4WB+arN6DeZybKKZGoOV+1dnOq4HKpR85OlFa/WFncvtVVWIBsNVF4L4t4UdKtt6XzAJxcaMg1Q01jI5fqjjWnGdpFYY4OrdonyAqyd78o6i7LsNAI1GYPx4D0aOdMfHR8X6H/35YeUx3tr1MjP6zyLMPYKx7ScyLGokRXX56NVOBLuFsvzEzwD4uQQC4K71AHDsHJxNrbEaD50U9SAI4OR1df3jysjIyPyXsNlsTL5zGvvzkvllysvN/NTOxS/Ht2AX7UzuIK0z07vfwMOr3+XeFW/yeN/J2EWR13f8SHF903GwKNqpbMwCUSS8TuTLUD/UDTaSLBbWGQw0XIzDoChir6pGKRsETahHtsFc1oBYXN/CKFh0fBNqpYrxcQMd14xWSW9Aq2xufdlFEY1KjYEGRFFEAPr0caFPnyaRiBiPcAIrb+Sl7c+yPOVnHu79FADOGmdaezftOJwoS8ZT50XAaYMg2C0EhaAkp/oU3YJ7OspZ7VbyanLpGtxDuiAI+LVpaajIyMjIyFwZzvipjeo1mCrjuf3UzmbR8U0EuvowIKITAHd3H0N+TSnv7lnkiHoL9wigV0gsO3OOArA7NwljXgJCeRETdU50VGlAB0N1evpptYwtL8NHoWCo7sKL/dUYafC3GgSCRoVmWkfMC44j5tc6rtebDKxO3cOwqObOHbrT2/ims87t7aJIo9lGVkU9WJw4sqQ7MdcdQ+dmRLSD3S4gCODTugQ/F386BnThSFECdtHucCY8Q0LBPnKqTzE+Zorjnl7tRHvfWA7k72FUu7HoVNI/8v68XZhsRroGSUaCaBfxa3v+cyoZGRkZmcvLGT+1Nfu2sIYtLe6fbRCkl+dyqDCdh3tPRHFWnoE5Q+/i0b43kVKajbvOmcfXfsDO7KOO+9uzDrP99M891RpCznIY7KbR4q9QsMzQ+LsGgcLd7U+M8PLytxoEAIJOjea2Ttj25GHdlo3dZmdV6i4aLUbyakrwe3UUoijSMzSWSXFDACiuq8AuiigEgbSyOt7dtZzDRUew2MwMnDafMM9wpg3tR79r9HiFV6DWmXnq1f0s2zDN8dz7V93arB/dg3uTWLifWL8ODI5sHp5yQ/RE3tj1Im/vfoX+4YOoMlSyKXMdMb7xxPp1AKTjgqB4/8s8WzIyMjIy50PyU2sAnse8NBh7cmcQz51UqK1PGIY5W895z1PvSt/weAB+vf09+n1xH8V1FaQ/tpDGXd9hOnHuegBGUaT2XNkPz0Lh5obCxeWCZf4O/naDAEBQKFD1C0fR3pfszw7w+YHlgBQ98H/X/A9RtPPFwZU8ue5DVAoliQVpdA3qyYnSej7d/x2bTq1DISiI9GxNh4Au7MzZyrMLvmBG7vO08uwMQJuGNtzeuZQtWb+SX5PDDdET2Ze/i8K6fECSJx4TPYGhUSNahKeEeUTwaO+nWZqyiF+S5qNT6egTdg3jYiadHgDEjWonp0GWkZGR+VupB+4D0lBfp8OUGQ1G/XmNgovhl6QtJBak8tqwe7AWpWI6sZVGux0B0P8mg+EaQyM1okiH33EqVAYF/un+XE6uCoPgDApvJ/LCNezPT0GlUPHR9a8T5uGFSiHQL7w/I76/GxeNC98d2kCExxDUCg3bczYT5h5Bbk0210d3pEOAB73DBvHUhkUkFKwj0vN2RJyJ9GqDRqEhvyaHOL8ODG8zikiv1ry1+2VuiJ7IiLZjLti31t7teKr/rBbXBYWAs7cTcaOjL9e0yMjIyMj8LiLwDJAG2BGcGlFP+BHL/LtBtAO/bxTsyj7Kq9t/YEhUN7z1bhzIP8EPR9YxrHV37u84lIYVLwKQZbMyuaKcMTo9USo1CgGOmc0sNTQSqlRy12/e/hc3NlBgs2E47Tew32Lh5ZdfBmDatGmEh4dfwnn481xVBgHAoeIDAER4RJJbLZBbXeW4F+UVTUrpcQRB4O3dr9AnrBsWm5n82mzi/Z3pEZqEKCrwdrIDcLzkEN5OmThrvMmrcWVH9jF0CiU3Ro8H0c6Bgr0ICHQPOX865N9DtIsMeqwPat1VN5UyMjIy/2qSk5OZPXs2iYmJFBcX4ORkIyZGz4wZ/lx/vQfKVhlw07dYfr4Nu9XGVwfX8nXiKtLL83BS64gPiOKN6+6nQ0BrAILcfDBZzby45RvMNisCAqHufjzY6ToaVr+GvUFajwIVSkbq9Ow2m/jF0IhVFAlWqrjN2YWHXVzx/M0u88LGBvaZm3Rqth89yvajkl9Cv379ZIPgfPy8ZBEgJQ/6LWqlBpto4+a4/3GgYD1Lk5eiOB363zPUhfIGC40WG0tTKtAoBXQqBWvTKzFYynHVquge7M5TGndCSrexz2UKiYX7ifRqg885nnWx9J3enaBY2XdARkZG5kqTk5NDXV0dt946maCgn2lsNLFkSTVjxmTy+edhTJ/ui7JNKsJd73Pr2BoWHtjDLR2Hc2+PcTSYDRwtzqCsodrRnlqpJr08D0+9K/f3HIeTWs8PR5cxfvFLLPT2pZdWcmz3UiqZ6+F5nl61ZLGPn+Nnj3ffwXnSxEs2B5eSq84g2Lt3L3Gx8WTnnsJut6I4rRBotVvJrpIEIHycDzBniBeC4EVxnZkP9hXwVUIJUAKAn7OaV4dFEOT2G7UoERoQMeSdwnz0AxrM9fQM7fOH+yictkL6Te9OzIi2f36wMjIyMjJ/mpEjRzJy5EhgPuAJiDz4oB9du57gnXdKmD5detlbvD2Zefuy+OW10YzW3QG1nqCwgV0BNAnKvb1rPtXGehIf+IZ2HUDVbzP3evvRLqiEObXVrPP9Cy9/SiXaAQNwmjjhrwz5snLVGQQADz70APfddx8/HP363LLC9loEQQrZ0KsVhLhpaeOtJ87PmWqjlVWpFbyzu4BZg8Nw0541REH6n9wwDWsyMlAjcN2A62jMsSAoBMTf8QwVBEkm2yPYjcGP98Un0uvyTICMjIyMzB/gF8dPSqVAaKiGgwebUiC/804pPXo4MeGZImy2V2hIiURf2g57YShipQ/YVKA2s7s4gU5tfYl7djGKAEle2FlUMDLOha+O1HDKaiFS9SdS3CuVKIOC8Hz37ata0faqNAjuvfdeTv26kXeXL28mKzyqXRArU/PQqaUJtdlFXt2eR3tfPbd1CXDUj/N35qkNp1iTWsmUjn4t2jda7OysaqSnm46bXxhGXkYDSWvSKDhaDEiZCwWatJJEm/STT5Q3caPbEdUvHKX63IkyZGRkZGSuJOU0NORgMIjU1NhYubKadetquOkm6YWtttbGgQMN3H+/L88+W8CHH5ZSX59Iq1YaXn89mEmPNL3YmT+qwMtP7TAGABCVuERo4Agct/wJg0ChQBkais/Pi1D6/vnj6SvBVWkQAMy56y667NjBJ1YdJyxmiuryKK2XnAVVpy2s1LJG8mpMTO3ox4aTVWzMqKK0wYKrVomTSsmJ8sZmbRbUmtieVcPevFpMNpFDBhMjJ03glblvMPrFoTRUNlJ2soLyU5UYakwgimhdtHhHeuLb2hs3/6svblRGRkbmv4TZbKawsJCysjKqqqqwWsv4+GOBtWuPAKBQwI03evDRR6EAZGaaEEVYuLASlUrgjTdCcHdX8v77JUyenIWbm5LrrpME8Nq107FzZz11dTZcXaWXPkFhZ3+xlMum2GaTHmC3/35HlUqw2XCaMhn3559D4ep66SfjEiOIF6GfWFtbi7u7OzU1Nbi5XRl1JUtGJmPj40kwmxji7oNzpIIlKeVUG6xolAIvDo0gr8bER/sK6Rniwv78enqEuBLn50RBrZkNGVXoVQq+vrHpjH/+kVK2ZlWjUymoNdm4sb03u0usFFfVsX79eoYOHXqBHsnIyMjI/F1UVVVx/PhxTp48ic1mQxAERFHku+++Y+/eveett2RJJOPHnwJg375oevZ0prraSps2SZSX22jXTktqahwA69bVMHJkBmFhGpRKKCqyoFIJNDTYEUWYdfPNPFBZjeXYMckwEASw2ZoeplI5PmsHDsT1/vvQ9vnzUWyXgj+yfl+1OwSqyFZM9/bmI7tIUZiGhYKRKoOV66M9WZ9ezcoTFYxoK231HMivp1+4G/f3DAIgq8rIhowqDFY7iYV1dA2SLLPeYW4Ma+3BE+tO0TvMjbGxPgxtKzJzp4rZs2fLBoGMjIzMVYbdbufIkSMkJiYCTTkAzvx///79iY5urgOzYcMGioqKCAnxws9P+v5v1UpDz57OAMyaVYjBINXPyDBhtYqoVAIjRrjTsaOeY8cMnHlV9vJSAArq6+0YQ0Lwmz8fc1IS5r37MB87hjUjE9FsRnByQt0+Gk18PNprBqAKC7vcU3PJuSoNgh07dvDiiy/SNySYk+knWV9cx9bcWjoGODMpzo/jJY0U1JmI9NIR5q4ht8ZMSb2ZTRlVVButbMioQqMEsw325jYZBJFeOjacrMImQt9wyVJy0Sjp1CacxBMn/s4hy8jIyMj8BrPZzLp16ygpKTlvmaioKKKioppdKygooLCwkLZtO5KQMBA4ir+/dPaflGTg00/LmDUriFmzCrHZoKHBjru7dETwySdhtG+vJTXVhEYj0KmTEy+9WMqcF/M5cEDSydHExaGJi7ssY/47uSoNguDgYJRKJZ+kpVFbXY2PVcOkOF9GtvVCIUCt0Ubw6ZDCkW29+OxgMVUGK/OOlqJSCLTz0TO2vS8vbMkmt9qGhy4ck7UWg7WK3bk1uGmVxPtJlqIAFBUV4OMjJyaSkZGRuVqwWCysWbOG8vLyP1w3M1MKUW/Tpi3Ozr64u7uRlycJAz3ySB7jxnnSv7/kE6ZWC7i6NqkYnsmS27t3k/Pg4YOSSFFVVZNQ3r+Rq9IgiIqKYsOGDYiiSMoNgzmmL3bc25VTQ6XByoRYaQEP9dABMCjSg0lxoYR59CLEvTsH84uBZ6k22hkU9QwAZlsDvcIyyanaTXFdEiJ2TpTWk5xdzHPP333FxykjIyMjc2727t1LeXn5BdME19bWtjgXN5vNZGVlIQgC7du3RxQVdOvWlc2bt/Lss0Xs2VPPiROxHD1qACAuTodCcf5QwJ07GlizoQS1UoOP17/7xfGqNAjOIAgCykljYeWnIAgU1Jr49lAJbbz1DIiQvEJbeepo7aVnTVoNgyNvJc6/Kwfyc3hkzfuolSoMVpOjPY3SGX+XWAJdO2CwVLPt1Hd8tG8Nvs5qnnrqqaYH2yxQmgwVaWAxgEoL3m3BL076WUZGRkbmklNfX8+bb77J9u3bOXjwII2Njdx666306dNcQO58joQuLi6o1WrsdjudO3dGp9NRU1NDfX0jgiDw2muF6HQCt92WzcmTRgBuvrkp7DAnx8SkSacYM8aDgAA1yckGPvusDG9Xd0prqomK6osoile1lsBf4ao2CAAEby8QFFQbLLy5Mx8ntYJH+wQ5LDpXbRBLbnmIe5d/yn0r3wZAqVDwcO+J7Mw+ysmKvGbtKQTpnMhmV/PK9jQsdhX/d00YznodpK6EAx9B9jawW1p2RqGC0L7Q40GIvgGUf0KgQkZGRkbmnJSXl/Piiy/i4+NDSEgI6enp5y2rUqno27cvJ0+epLKyEpPJhNlsRqFQoFAomDp1Kkajkblz52IymQgPDyc3NxebTWT79nr0emkNiYhoeslzc1MSGKjmo49Kqay0ERysZtJ4fxb+VE2kZ2tMynge/TGRV2/qhLP2ql8+/zBX/YjUehcazVbm7syj0WJj1qBwPPXSQuyhC6NfxGMoFWq23PUhGRX5FNdX0torhABXL1q9NYHW3qEt2jRbLUxZNJukkkxWTH2dDoHe8GEnqE4GQQmirUUdAOxWyN0FOdvBLRTGfgeRgy/f4GVkZGT+QwQGBnLs2DH27t1LdnY2r7322nnLKpVKbr755mbXjEYjM2bMIDY2FhcXFw4cOEBFRQXTpk1j4cKFTJt2Cw884MTq1St58cWiFm16eqpYvry143NxsYWu8Tk4aZy5q/vDZApKDp6q4KHvD/LRrd1x+pcZBX8+SfQVQucfzlu78imuM/Nkv1BC3CVrTq/2ol/EIygVasdbf2vvEPqFdyDA1YsTpdkU11UwOLJLs/bsdjt3LnuNrVmH+G78c1zTqgse2hDMNXciIpzfGDjDmft1BfDDEFj7ENh/p46MjIyMzO+i1Wqpqam56C15u92OwWBwfD5y5Ahms5mePXsCOO4dPnwYT09P2raNZtcuXzSapvbLyqxkZ5uw/0a6vqrCxrBB2dTUWHmo1ww8dZ5YFAJ2EVILa3l5edJfHe5Vx1Vt3thsNu59+gVOVhiY0tGP3bk1fJlQRHmDBW8nD3qFzmX2kLto49N8F8ButzNz4+foVVqWJm/nzZ0LeHX4vTzW9yYeX/sBi5O2MmfInUxZ9MI5n/vTeJj8exEl4mmlqgMfQ0MZjJ8PClnOWEZGRuavUFJSckFHwjOYzWYeeeQRzGYzTk5OdO/enZKSErRaLR07dgTAy0vyD0hKkhbvmTNntmjn/vtzAcm5MCvLjLOzgg4d9JSW2DiZYeWRXs8Q5BpMYW0+GzeuorLsFJb6Kg6qtax+sz2vvDCT66+//lIN/2/lqjYInnjiCVatWk2fmFbsyS2iuM5MlJeOzgHhqFWB7M49Tu/PpzO6XV9cNHo6BLTGYrey6PhmEgpSmRg3mDVpux3tfbh3MZ8fXEHP0Fj0p50De4bESLmwRZHOwa1xUX5J75D6P9BLEZJ/BvcwGP7GJZ4BGRkZmf8Odrv9okL73N3dGT58OGFhYYiiSHJyMtu3bwegZ8+eaDQaKisr+eabb3B2dsZkMmG1Wh31PT3VPPKID7NnF9GtmxMJCY106KDnoYf8qCi38vIrxTQ22pkQezORXm0AqDCUY7CZ8Ok4FI2rN3aLify03YwZM4bPP/+c6dOnX55JuYJc1QbBkSNHANiTkuW4drykkeMlJ4GTHH/4R7p9cgfZVUUYrCYWHt+EQlDQLTianybN5p4Vb/BE3ym8uPVbTlUW8lXCSgD25yWzPy9Z+jk/hf35KQCk9ltAuGdH9ufu4OalazhcVIabVmRSrMirQ8BF09S35FKYvR0SC6G4XsRJ/SYx8b8y47mX/jXWooyMjMyVxGq1Yr+IPAHjxo1r9rl79+7U19dz/PhxPD09Adi3bx+NjY3ccsstHD58mLi4OLy9vVm+fDlFRUUcOSLlujl2rJEJEzyYPz8SUYR77yinsVHqQ3LpMVw1btiBWq0S7w6D8ekwxPFc/+7XU/7LDN555x3ZILjcbNu2DZAkKjfOupnKjON4asO4JrIpRDDGNwKzzcL++75qVvee5XNp4x3KlI7DeHHrt47r9/e8kW7B0ZQ3VPPUhk+YFDeYl4ZOJ8DVC41KzdHCk1z3wzyifcKYe93NFNSU8t6eRaRX6ll/S52jnZwaqDPBrR0hyBUarQJLTqb/q6xFGRkZmX8KtbW1zf7faJTCCn/66SeefvppIiIiADh16hTFxUWsWFEDgNkM/v5qjHVajizpzrZVGxxtnihL4kRZc1+Bsw0CpVKJTedFQ/HJyzauK8lVbRCcQRAEet//GmufvAFfl/bYRRsKQYkoipQ0VBHjG9Gs/MH8E8w78iub7/wAKZFxE33DO3Bj7DXkVBXz1IZPWJu+l5+TtiAIAp0D2yIAHnpXNtz+Hm46Sc0w3MOP+1e9y5rMmxgZtQaBeka2gZFtzm5Z5MHuBroujvrXWIsyMjIyVxKVSoVCIbRw8LsYnn32WZ588kkaG6U3/7Zt27JhwwYUCgVKpZLKykoyMzPZvn07Y8YEs2JFAR++05avvqrg6y+rMe6PI8ojgklx01ibvpy0ihM8038O3s5+1GkVLIv2QhQEbGYjdqsJm7GBqvR95B7fw5SbJl/qqfhb+EcYBACugRF0nz4HYVWuY5FfeGwThbXlzBp0u6OcKIo8vvYDJsQNpFdoLDlVxS3aqjM1YhftDI3qxpj2/Qly8yGrspD39/xMfm0ZN7Tv7zAGAG7pNIKnNnzG4uRahrT+Fo34OAryWrSrVKkI1TdysOAiUmPKyMjIyJyFFYXiWzw9y6io+OOKgEajkfr6elxPpxmOi4vD19eXsrIyXn75ZUe5ESNG0NHWgRXMZe93fRgfEMdXBR/xdcIXjjI+Tn7M6DcLX2c/RGBHmBvi6ciH3I1fUJq4ViooKPCK7sNTc17/88O+ivjHGAQAkdeMpW7fJgSTgrSyXB5d8z49Q2OZ2ulaR5kfj6wnuTSLBTfNOWcb9yyfS73ZgFKhoG9YB7oEtaNrcDsAWvuEMG7e/3HgtE/BGTQqNR0CWnO0OAPwwCx8iEZ8AAUFNJjBYIUaI6xMs7LuSBE3Tb75HE+WkZGRkTk3hcDjQCb+/n2orPQ6b0mLxYLNZkOn0zW7vmbNGkRRJDY21nHNx8eHsrIyhg4dSlRUFElJSaxfv55Mr2wAQt3D0al0BLoG08qzNdG+sdQaq9mQsZrPDrzHk32fIy3cn3LnJhG6gJ7j8GrfD3NdJZUpOxBFO4WVdXS6hLPxd/GPMggA1BonisuLGTf//3DTObNg0myUp8P9ao0NzNr0JY/1uYlQd79m9ZQKBWNjBnBdm554O7lzoiyH9/csYug3D7P1ro/oFNiGRrMkc1xUV0F+TRkh7r6O+gGuXuzJOQ6CCrvoQrnlDbZkvsLXCQVsypTOohQC3BgNH73aMrRFRkZGRuZc5AN3ADWAyI4dm9i/P5uaGul79dixY47Ig8GDB9PQ0MArr7xC9+7dCQgIACA5OZmkpCRiY2MdIYcHDx7k5MmTaDQadu7ciUqlIjw8gtSkNNIrTtDWO5ow91a8sv052vq0Z3L8/xw9ivaN48WtzzC/aAOaHvc2663eJxT96VB3345DOTHvWZ68eyojjib+4yWN/3EGQY2lkbHznqbGWM+mO94nyK1pa+m9PYsw26xMiBvEqtTdLE/Zzu6cYwAsOPorfcLi6R7cnhj/VoyO7suNMdfQ/dM7eX7jF1wf3Y+3di1wtDV50Sw+GfOEFJII6FQaDFYTpyoLmLPlG7ZmHqLWVI+rFvqEudIxwJl9eXXk14uk/zqfHne9/I//5ZCRkZG5vBiA+5GMAUng7YsvMsjJadqlPXz4MIcPHwakkEInJyfi4+M5ceIEe/fuxW634+fnx9ixYxk+fDgKhaS3t23bNsLDw/nf//7HihUr2L9/P7W1tehVTgAMjRrJyYpUCuvymRDbtKtrB7xcA3D1CuVk1Ulif+d73Lt9P06s+YD09HTatWt3yWbm7+AfZRAYjUYmfPs0JyvyWXPrW7T3i2h2P6+mlCpDHV0+vr1F3TpTIxtO7md71mF2Tf+MWP9WRHkHM7pdX5Ykb2Vb1mH6hncgr6YUgHCPAMoaqpuebTWjEpT0+fweglx9eLjPJLz0Lmw9tZDShgb6R3jQP8KD17bncvMz7/B5QR69H3wdZ5+gyzklMjIyMv9gPuKVVw7x3HMFxMbqSEqKJTs7nvz8YIYMSW2Ry2DmzJnExMTwyCOPOK6lpaXxzjvvsHz5cpYvX96sfGBgIAEBAdxzzz2IIpClImHTYb5K/AilQkmdSYpIsCNyxvMrx13DwWAXjLvtKC8iBNJuldIqn9nR+CfzjzEIbDYbN910E/tOHuOXKS/RKzS2RZn7e97I9dH9AEgryyHKK5gqYx0PrnqHaZ2uo2twO55a/zFv7VrAG9c9gK+zBw0WA3ZR5OtxT6PX6NiedZhQd3/mT2quYlhUW0GDxUh8QBQbbnsXvVqLKNrpGWolvfxXR7keIW58nVjMsYN7qXlmPENmfY9HWNvLOzkyMjIy/zhSyc//kVdfLcLZubmKfkhIAU5ODXh6euLt7U1GRobjXkpKCvfcc4/j8913S6nrBw8eTHh4OCCpGC5YsICioiK2bNnCoIGDwShAmpqDBVKWxGPFh0gsPADAN4c+45phj2Ho0BODWklD0UmMFfn4dRnheI6loRq1s0ezftptVqqTt6DX64mJibl0U/M38Y8xCJ544glWrlzJ6KHXUdVYx09HNza7P6XjMDoHtaVz0OnFt71kGJyJMmjvF8E9Pcby/aG1pJXlMPPXzzlVVUhiQSoAKaXZfJW4CoCeoc3/Yc1WC4eL0jFazcwceCt6tZZGsxGNUkW4R99mBoHZJlmUDSYLloY6Nr94K9e++gsufiGXflJkZGRk/rEs4sknC+jVyxmbTaS83NrsrptbLS4uesaPH0dpaXmL2vPnz8fb29sRVdC6dWu6du0KwMKFC1GpVFgsFlasWEFjgwHnEg+O5xwhufQYPk5+7MzZxpDIazlecoTi+kLWrXmJgIobUWp0FB9YgUKlIaDnWMfzslZ/gM3UiGt4HBpXbyz1VVQkbcVQnsfbb7+Ni4vL5ZuqK8Q/xiA4o1q4etN6VrO+xf0pHYddsH6D2dBMt2BI624c3fUTxtPbPR/tW4LZJqU8Xpa8nXmtNzC1sxS9sODor45yWqWavp/fw6HCdDRKNWPa92NkGxU6tRWrXWRnTg0apUCImxbRbsPSUMfej55m6OwfERRXfS4pGRkZmStAPTt2/MzixZUcPhzDQw/ltighCCKurnX07KkjL68jOp3ecS8jIwOz2UyPHj2a1TEajZSVlbF9+3ZGjx7NypUr8fHxZfvW7TQY6vHR+zIgfAg7cjZzY8xkhrcexfXR41mXvooNmaso3rcUpdYJ17A4Qgb9z+E8COAdO4DSwxsoTViD1VCLQqPHObANn7z/NrfdPPHyTdUV5B9jEJxRLQSwHSvGsjz1ouqFewZgmLOV6757nF6f3k1hbTmDWnUhIT+V9HJJS8BV44TZZsFd70KtsQGr3cbdy19nSco2Oga05v09P+Pj5E55Yw1j5j2NSqFEgYBdtLMkaSu7sp3oF65nT24dhXVmbunoh10UWXC0lIMFdVQaUvD9fAPXjb6BF154gbCwsMsxRTIyMjL/CGy24zz0UBZ33eVDfLz+vOVOnjQyefLLmM0ibm5u9O/fl1GjrufAgQMIgkCPHj2oqKgA4Pvvv8dkkiLFXFxcHG/sI0aMoFs3aedAtMGSH5eiyFXQP1xKXa9Rarih/XgUag1rUn4m/t7P0J4VYXYG77iBeMcNdHxWCDC8QyC33djhkszJ1cA/xiA4G0W8P8KRYsTcGrhIRaueITG8vesnVAolC49twtfFk77hHdicmUCduZEeIe0Z3ronga7eHCk6yVcJK1mfvo9d2Ue5tctIjhVnUJ5bg5NKy2N9J5NTXcz69H0U1VdQXN/IqtRG2vg4MaWDL52DXJi1OYeCWhPDojwJdNVQblHxyy+/sGHDBk6cOOHY5pKRkZH5r/HZZ5+Qk2Nm06Y25y0TFaVl0CBX4uP1NDTY+emnetasWUdxcTGpqelERkbi4+NFTU0NXbp0Ji4unuLiYjZt2oTdbmfRokUAnB0kICghryoX/wB/9D0FSBLBLBVo4x4JQGNx5jkNgrMRBHDTq3l8RPu/OBNXF/9Ig0AQBDRjojF9ngBmK/yOTVBcV8kvSVsJdPNh+10fO0IVEwvS2JyZQIRnINvv/qRZHaPVxLwjGwh09ea9UY8wdt4zALw98iFuOUsI6en1H/PB3sX0CHHjvp5SREF6eSOnKo3c1tmf4W08HWX7jrmJJ196m02bNrVIziEjIyPzX6CiooJZs5bw/PPB+Pqqz1vu668jmn2eNs2b6dPhyy+lEMQJEwIJCipCoYggKuoezGYLL7zwAsOHD6dv377MmXNucbra2hrc3NzA3w7eBjikhQolbjoPAMz1FRfsvwAoBIGXJnbETX/+/v8T+cceagseOjTTOoJGCRcIE60x1jt0C1ZOndtMtyDQ1RsAP2fPFvX8XLwQgWqjlAo5+HQ9P5fmZSfFDwWgvNHiuGawSI6F7jplU38VShQV0hGFXn/+LTIZGRmZfys1jWbueOAJXNy03P/Ahd/Cz8UTT/gDoFDArbdqKSnxRxQFQGD9+vXYbDZGjBiBn58fUVFRAC2yJ5rNZtRqNYICUAM9TOBjQ6WQFne7xXze5ysEUCoEXp/cie6R3n+4/1c7/8gdgjMoglzR3N4Fy+JkxPLGFveNFjPjF8xspluQUZHPnC3fsCf3OFWGOpSCgtSyHBrNRkREDBYztaZ6tp5KBKBvWAfC3hjn0CQorG3ydq03GZi7/QcA0ssN3PxzKvd0D6BzkAtalcAvSeW4ajWEurtRXG/k203r6N69O0OHDr38kyMjIyNzlZBaWMPCvTms2p7IoZ9/5PbH+pJfUINaKS3WRqOIxSKSnW3CzU2Jl9e5lyZPT+klKyxMy/79N2C3KxBFBeXl5fz6669MmTLFIWl85ljWam0evaDRaLBYpBc4QTi9wdzNhKVI8j9QqDWcCwEI9XZizvgORAe5/5XpuGr5RxsEAAo/ZzT3dMO6Iwfb3jywSr9gNruNab/MYX9eMr9MeZleobHk1ZTS/4v7cNM5c2+PcXjpXflo7xJSy3MY+f0TxAdE8VXCKkfb/i5eeDm50miR0miqFUp+OLyeaZ2uQ6FQUNFYw6q0PQAEu2nJrTEBAq294nl/VG9e2Lyal7dlO9obGtWNhbe8ibgxC3vHABRBsh+BjIzMvxej2canm9NZtC8XpULAUFMGop1v39nJt++0LN+qVRKPPOLHe++FtrhXX29jypRTAOTnW7j99oe59dZb6dOnD6tWrcLT05M2bdrw66+/kpyczMmTUkriZcuWkZ+fz5gxY9Bqtbi7u1NdXe1oVxBAFCBVcRSA7DUf4tW+H2ond1QKAatdxNNZw029wrm5TwQa1T92Y/13+ccbBACCUoF6UCtUvUOxHSvGllTK01+/xuq0PYxq14cqg6RbsDp1N9XGep7oN4Un+0tSlaOj+9L+3ZvZn59Ce78I7uo2hlWpuyhrqCbMzY/vD61j5sBbeXHrt9zQfgCLk7cyZt7TXB/dl+8PrQMg0lPHHV39eW5TDnH+N9I3YgJqZTKdA9PpHRZHe78IjhVl8s7uhdz9xQssmDIH28EChEAX1Ne2QRH277Q2ZWRk/ruU1Bh48LsE8quk3VubXUTvF0GbSbMI8arg4Wubwsefe66Aujo7778fSlSUltpaG1qtgFbbtPiWlVnYskU6wg0MDCYvTzqCrauro7KyktLSUp5//vkW/aipqWHTpk1kZ2fz5JNPEhISQlpaGgaDwXF8K2Ln111N/XHWqogKciM2xIOurbzo384XlfLfawic4V9hEJxB0KlQ9QhB1SOEpB/KAFiTtoc1p9/iz/D8pi8dBoG/ixc3dxrO1wmrWJK8DavdRs+QGBbf/ApjfngKN50zfcLiARgbM4A+4fF8un8ZT6z9EJtoJ8LTiVeHR6NVDQTeQaN0JquykBHfP8lXNz7DuJhrALg+uh/hnv7cvWwuG9L2cW2bnojF9Zi/O4yyZwiqIZEI/2LLU0ZG5t+D3W7HZDKhVqtRqVouI+V1JqZ/fYDyOpMkGXwatZM7XtF9MCls9B+ahLeLtMC/914JYGXsWA8Atm2rY8qUU0yZ4kXr1loMBpGFCyVnv6AgL6ZNu4VXX5VSDn/55ZdYrVZ69OiBVqulsLCQnJwcQDouGD58OFVVVRw8eJDU1FS6du3Kxo0b2blzJ8OHDz/9vG3U1tbi4eFBdXU1Sx8dgI/PH0/B/E/nX2UQnE0z3YLUMiw/J/PryQPcMO9pRrfrw9GiDLyc3NiXl8Qvx7fwYK8JvDniAUedJcnbqDM3YrXbyK+VjAtBELiv5zja+oRy4/xn6RsayUO9vBgY+QSpZeWOMj8eWY/RamZk297N+jSqXV8A9uYmcW2bno7oCNuBfOyl9WgmxyOolcjIyMhcTdTV1TFv3jy2bdtGYmIimZmZjnv+/v507dqVXr16cdtttxEcHMLMn49QXmfCdp6wcJtdyZIDPblz4BaUiqYyhw41Mnt2Idu319HQYOejj6TvXrVawNtb+m5s27YDX331DQCLFi3Cw8MDQRBITk7GYDDg6upK165dCQkJYcmSJezevduhT7Bp0ybuueceunbtyrJly6irq8PNzY2lS5cC0L59e/bu3XvpJ/Afwr/WIDgbZbQvthhfhgs9eGHwHbyxcz6rz9o1eHrAVGYPudPx2WAx8X8bPmNAREe2nDpEg9nguHcgP4WbfnqejoGR3NPdmWsiH8dJ440gVDnKlNZXISJi+413q8UmObdY7bbmHRRBzK7GsiQF9U1xcpZEGRmZS4bFaudEYQ2phbVkldVjMNtQKRUEeuiIDnInLsQdd6dzO9IZjUbmzJnDJ598QkNDA7179+b6668nJiaG5ORkYmNjyc3NJTExkblz5/LCCy+gdXLBbDKjcffFO24QgX3Go1TrHG3abRaK9izh8U9/5eHaItzdlXTr5sTddwfQu3cqnTs7MXt2EBUVVhYsqCQ/34xCIVBba0OjUbBt2zZiY2MpLS0lNjaWnJwcKisreeqpp2jVqhUAlZWVzJ49G4DY2FhcXFzYsmULSUlJfPXVV9x99914eXmxb98+6urqUKlU3HPPPWRnZ1/uf46rmv+EQQCgHt0Wc1kDYZ4B9AvvwNj2A/BycmN9+j7m7piHv4sX9/WUtAHe2rkAs81CeWMterWWcA8p53ZBbRkPr3qHMA9/nujjR3zgUFw0vghC863+1t6hiKLIkuRtTOt8neP6z8c3A9AxsHXLDopgT6/AdqgIVdemDIlWkxWr2YZSpUClU8nGgoyMzEVRVmtk6cE8lh7Mo8ZgkeLnFQKiKErfIyLYRBGlQmBQjD+TeobRIawprDotLY2JEyeSnp7Oo48+ygMPPEBoaEtnP4C8vDw6dOiAh6cnzvEjUbl4Up+fSsH2H2koOkm7ybMBKRlQ2oJZ1Oen4NdlBD27KugfeYBdu+p55JE8Ro1yZ/HiSI4cMdC3byqhoRpeey0Eu13k449LqamxEBsbw5gxY0hOTiYuLo4bb7yRmTNncuDAAYdBsG/fPkwmEy+88AJBQUG899576HQ64uLiSEhIwGw2M2HCBHr16sUrr7zC/fffT0xMjGwQ/N0duFIIOjVLndJ5cOXbHH3wB0I9/ADJL2DLqUSeXPcR+TWleOpdeWPnfHyc3DlWnMHr196HXq0F4JWt31FjasAmGpm2OA9X7VEivX7ixphr8HJqcgyc1vlaXt/+A/csf4MHV72DSqHETetEaUMVMX4R3BDdH4C0sly+P7yWzZkJnKosxEWjp9P8ttx93xM4G3wpTS+nsbJpd0LrosG3jTfBHQJoNyQKvbsOGRkZmbMRRZFVhwp4d10qJqvNIeYqQtMW/lkH+za7yNaUEjYlFXN952Aeua4dZUX5DBw4EA8PDw4ePEh8fPwFn/njjz9SXV3Nh4s2MS9Z2gn17zoKRJHyY5uwGupQ6V0p3reMupzjxNz+Ni7B7ShGpOO1ruj1W1m+vJpXXglCoRD4v//LR6cT2Ls3Gm9vaZkaP96TqKgkSkpKmz3b1dUVQRBQq5tEgoxGKTLMzc2NtWvXcuLECW6++WYqKioQBMHh97Bo0SJiY2P/FZkKLwX/GYMA4NOvv6BT1y7s8fBmoijlv1YJAuNiruGDvb/wTeIaaoz1CAK08gpi1qDbGRzVjX15SQDUmBoAqDZKwhWVhloqC2pJKEilX3iTnvWiY5upMTUQ4uZLo9VEjaGecpsFuyjyaJ+b0KikX9xvD63h+0NrGRszgOndb6DG2MDXCauY9PhNPNRrBtG+cc36b6o3k3+kiPwjRRyYd4Q217Si121dZMNARkYGAJPFxgtLjrHtROnvFz6LM4bCmiMF7DlZRvYvs3F2dmbbtm34+/v/bv3a2loADhRYpTC+0/aG2sULBAWCUo0o2ik+sBzP6D64BLdDtNuw2yzMWToR5e4tuLkpKCiwMHZsJunpJpRKKfrg3XdD0ekUREZqcXbWUl5eztGjUohgVVUV3333HU5OTvTv39/Rn7Zt27Jhwwbef/99cnNz6datG05OTixdupTBgwej1Wo5ePAgmZmZvPDCCy3G81/lP2UQlJSU4OnpyVY/JzYV1TFJUDIIBd1DJD3q78bP5O3dP7Ez+yh7c5PYm5t0znbmTezBjbGvIwgCGRX5dPvkDpzOOiP79MAyugZHs/PuTxxb/LXGBqLensjKEzsdxwiT4gfz3MDbcNE2KRfe2mUEnT68jVVpy1oYBIDDEVG0iZzclkXOgXyuebAXrXrLCZNkZP7LWG12nll0hH0ZLVMFXyx2ESrrjeiveZCPJrS7KGMAYODAgcydO5c1X8whaMBUVHo36vNTKE1cTUCPG1BqdDSWZmOpq8DJvxWnVr9P+dFNiDYLer8I7BYLolnBDTdkcscdPpw6VUrr1lo++6yc6mobP/0k5RmIjQ3kwIFs1q5dC3A6m6EPTz31FL6+TcqHcXFx9OrVi3379gGQkJBAQkICI0aMYOzYsQAsWbKErl27olQqKS+X5qyxUQqRzMvLw2w2ExTUdHz7X+A/ZRC0bduWX3/9lWHTa9iiUPCK3crHImQd34QgKLD4R3DLoNu4qfoUdks5DeZSak2FZFSU8UtSOde386K1j54w96bts9beIcT4RpBdXey4VmdqoI13SLPzfjedMy4aPbrTxw8AXYLateijt5M7fcPj2Zx5+HfHI9pFTA1mfn19B33u6kb89dF/dmpkZGT+4XyxJYN9GeXNwvz+DCIKVHoXPt1fQ58uNrQXEfl03XXX8fgzz/PeW29QmdrkpR/UbzKhg28DwFhZAEDxvmWo9K60GvUQAIW7FmGqkr4/B3boxcz7fdi+fTMmk5277/bhyy/LefFFI+HhGrKzpZ2PyMhITp06Ra9evUhPT+fTTz9lxowZjgyHWVlZJCQkoNfrueGGG3B3dycpKYn169fj7u7OoEGDqKqq4sCBAxw4cKDFeLp06ULHjh05cuTIn57HfyL/KYNgxowZrFu3js+e+R+62OtQ6d1IPXmAmoyD+Ha+jrdcPMDFA21QOBNyPiTUXIICEaUgvcFHeunoHuyKr0sEInYElHyybykZlQW4a50BSffAx8mD9Sf38/aun5gQOwij1cyn+5dSY2rggV7jf7efJfWVeOrdLm5Qp//493yVgNZFQ9tBkX9mamRkZP6B1NfX8+abb7J5+2727d+PzVhP5JjH8e00vHm5gjTKjm6kviAVQ0kWot1Gz1nrz9MqlB75laN7l+DyZAkR4WE8/PDDPPTQQxfsi6dfEK7hcXi174dK70r1yQMU7lqE2sWLgB5jsJ2O1rKZDcRN/9iRUdCtVSeOvP8/AOJdh7DimWh6euv4attigv2k78FffqniyJFGSkulN/jY2FhOnTpFu3btGDlyJHPmzGHDhg2MHz+eoqIi3n33XaxWK88++yzBwcGAtMjb7XaWLl1K9+7due+++1qM4eDBgyQkJPDDDz8QEhJyMf8E/yr+UwbBgAED2LNnD7Nnz2b7nrUY6mrQevoTMug2gvpOdJQzKZ1YHP4Qo/O/JsSQ2SKZops2GIUgWc2vbv+eOlMjdSbpF3XFiZ2Ocs9t/ILnNn4BgI+TO2tvfZteobEX7OOunGPsz0vhrm6T/vD4dnyyn8AYP1z9Xf5wXRkZmX8e5eXlvPjii7h4BeAcEElt9rFzlqs+eYCyQ+tx8m+F1jMAY0XBedssSVxD9poP8WzfD89eN9LZpYyHH36YxsZGnn766XPWWbhwIS8/+zjt7/0CrZu00Hu174coiuRt/hrvuIEoVNLuqGtoTLP0wlp3P5Q6F2zGely1knN2N7cbyG9j5tddqwGYObMQf9em44tTpyQJ42PHjlFVVYWzszPp6ekYjUbef/99TCYTXl5e5OXlORQNQdJMMJvN5OXl0alTpxbjyM/PB2DEiBGyMNF/gR49erB27VryKhq4+eM9WGz2c5YzKZ1YEvYAHap3019YwbxJLginTQOVQvrFTivLxWKz0TM0ls13vI9SIRkJ9SYDz238nAaLkRFte1NvbuTDvYsZ8d3jGK3nz6S1/74vuW3xy2hVar5MWMSXLGp2P8Y3nod7P3Xe+quTl3F3wM3ExsaSlHRu/wcZGZl/D4GBgew9dpJHF2dSX5hO8lcPn7Ocf7fRBPWdhEKtJXvdx+c1COwWE/lbvsejTQ/aTnwOhQDDrolCoxR46aWXmD59Op6eHkA1YERKF+jJJ598Qmx8B9RuzTMY/n975x0eRbX+8c/M9k3b9B5CIAm9g3QpIigqihXLVRS714oV27Vg7xX1iqgoKKKAAiq9915DIIH0tptstmXLzO+PSTYJCc2r9/rT+TwPD9ndM2fKzs75nve8JTK7P5U7f8VVmou+vrqsLsTSYr+60EgCHgc2t5WE0EQALu54OemWDD7c/CYXd7yCaFMM/96mlKnfu3cvANu3b2f7dmV5NTw8HIfDgc2m5ISxWq1Mnz692X4yMzMBCASOywWDklQuOvqvV8HwTPjbCYIGUqNDuGd0Nq8u3H/iRoLIrsgh5IT1pFPNJrrbVhPutyHJfkprrVwy81HCjSF8dcXTQTEAcM03T6MVNXx3zdTgexdkD6LjmxPoGt8umO8AQEbmnwveIDUiljvmv4bD66JjbDrFtVbOz7ys2eFE1Nfrbg2b28qinHkYNAYC3pY3u4qKyl8Pg8HA5pIAGvHk+Ul0oS1LvLeGPX8nfred+D4XAIqT4fxthTx25w3MnDmTn366nGuvDQC1TbbSU1a2l8jIKNKibRyratyXXD/wypKEOS4dQdTira1qsV9RqyRGWlm4io6xjVbUrcUbEQUN/VMHU+OpBsBsFnnrrTeRJD0gcOzYMaZOnUpGRgYxMTFMmzaNd999l/379/Pkk082c4z84IMPEASh1eUAQRB44403WoiIvxN/W0EAcGm/VEqq3cxcl3/Sdh5tKNuiR7AtegQhvhrCvXU8NvNhajwOltz4FknhjaalPGsxv+Ru4r0LH2jWR5Q5nKHpPdhWksOE7qOC7689uhuXz4NfkthTdpgF173Ms8s+w6wzc1bqoNM+l+/2fkXbyPbIsoTHUXfa26moqPz/Zvex6hOmCD5TnKVKSuKQpKzgexX2OtKzX0YUYfv2PVx77fGDqZesLIFffjnKjBnPUSb34PWFF1BaE0nVnhUgiJjj26IxmLFk9sWWs5FQ+yHi4hMRgIrSYtxleRhjUtlRsI6PAgGyojuQU7WfbcWbGJN5IRZjJNWRqwBwuSRmz36ZuLih1NTUsHz5cvR6PSNHjgwe0bnnnsvevXt55ZVXGD58OCEhIezevZs9e/YwePBgLBZLi3Pv06dPq+//nfhbCwJBELjr3CzCTTqmLVNKZZ7qd1UrmLj72+corSrkp+tfpWNcerPPy5yKuSogt1yK8EkBAselLZ6961cA8m0lfHnFUwxJ7wGCEhcckAL4JB9G7cnzDByqOsC2ks1MOfs5Zu3+HJ/Lf9L2Kioqfw1kWSan1P679edzWEEQW5j18yrjiY7WUlzc+pLngw/Gs2hRDUOHHuSOO6oZFrmWD37UYzuYQ2zPMYRHWMgy1zDk8vN555VtbJn+OCNGjABg07JlhIaGcN89d7Bk5WYOrF/FjpLNiIiEGyJYc3QFv+T+hCRLWCwazjsvnJ07K1m9+htEUUf79u3JzMzks88+o7S0FFEUSUpKYvz48Rw4cIAVK1bgdDqJjIwkJSWFnJwc7r77bkRRJD4+nmHDhnHppZfSrVu3Vs/t78TfWhCAIgquH5rBWe1j+NfcXeRVONGIQquKW5YCHJozlZrCfcyd8FyrDoLtopIRBZE5e5Yzqc+FwdDDwpoK1h7dFaycCEptg893KJ6+b11wLxd3GqrsR4YiezH3LJyEX/ITbohgcJthjM26GI3Y/CuTZIlZuz9nUNowksNT649Twml1ERJl/n0ukoqKyp+SOp+Ex9e6H9RvQfLVIWp0Ld63uUIwGgXc7tb3NXRoGOvWdeDpp4v58MMyqqoCtG2r5+KbBhObeR29LIUIAOFxTJ48mblz57Jw4UIEQSA7O5tLL72U+LgorrtsNOLloynbVcXHX06j2F6IjExUpJ6zBhioqfHz9dc2pk1L46ab4lmy5BymTz/C7Nmz6dq1K5dccgk+n4/169czZ84cbr311mB0RGFhYbBdVFQUgUCA/fv38dlnnxEXF8cFF1zwu13H/6/87QVBAx2Swpl5xyA2H6ni243H2Hi4qoXD4bFfP6Y6ZwMpaV0othXz1Y6fm9UxmNB9FLEhFq7veR7Tt/3EeTMeYFzHITjqXEzbPA+3v44H68suA9zz45vU+X2kWxIx64x8vfNXJFnG7QvQNb4HvRL7ISGxrWQzC3PmUeYo5eY+dzU7plX5S7G6qrh3wCPN3rcdq1EFgYrKXxy5RQzUf4aoMyAFfC33Iwt4PDIm04lLtPfrF8LChZnB13Z7GD//fC5WWy0CjT4OaWlp3Hvvva3vv75ZXJdopjz7GGw1YJI8XDv9E0SNTCAg07v3fl5/vYxbboll1KhfueWWHNLT23DXXbcDii/XoEGDePjhh9mwYQO9evUCICUlhQceUJZyBUFClgXuvTeSZ58N5b333mPq1KloNH/varOqIGiCKAqc1T6Gs9rH4Fy7jl33PEahOQqvoEUr+XlhzzJKgcJje7izlSyGDb4Bb19wH10T2jFj20KeXPIxAL2TO/Dv8Y8yOL17sP3yI1sByK8u4ca5U1v0d1mXa4gxx9I/dTBf7vw3a46uYKR1DBlRSnEkh7eWBQfmcn7WOMIMzfMW+OvUZQMVlb86Bq0GrSjg/518CHShUSBL+JzVzZYNjKKDqio/SUktrQetYbeHMW/eODweYzMxcLoIIsh6oH8dSYZ8GrrQaARSU/Vs3qykkRdFmbo6F9266UlJKaawMAVBkDGZDBgMhmb1DUBGEGRkWSQhoZR+/TYRH19F27ZeFi504fV6MZlMxx/K3wpVELRC3aZNVF97HWl+P2lSY93vnyLCISKcgAjbu5vp3flBosxtgzkJGtBqNNx+1iXNogmOx1HnptxZzflZA4LRCJIsc7DCwaaCmhbtz2l3PmuOruBA5Z6gIJi/fw5mfQjDM85t0V7UnljJq6io/DUQRYF28aEcLKk9dePTwByvJDbb9tpVmGLb0O32aQC4SnOQJNiyxUWXLnvZv99Daqqe/PyWRY98Pi2TJhk5ePBT8vLyqa2t5YILLuDCCy88o2OpL8jIQWc86UeMCEI18+dXs2hRDVdeGRVsN2xYGHPmVHLhhTMYNiyJnJxUPvvsCB6Pm5EjhyEIEnp9HbGxlcTFlZOcfBCdrhqHI8CMGQ6mTz/GgAED/vZiAFRB0IKA1UrVjTeB3w9S6+tl+W30uMwC24pmMKL944CAKJzZALzgwBpcPg9XdjsHUHwBnF6JbUWtOwhFGZUfgNOrKOMyRymrjy7nii7XUu2xBdv5JR8BKYDNW0Wo1URUVFSr/amoqPw16JJiIbfM8bv0ZYypjyBo8jwLM7r49ss8tFrYuNFB794hLSwFDkeAV14pY+NGJ+vW1VFbW4fRaCQjI4N9+/Y1a7tlyxaWLFnSzAFw9OjRzSoqlpaWsnbtWvbt20dJSUkwb4AgwKWXWnj33cYyzG+/nUplpZ+77y4ACoCNxMRoWbGiHQMGLAeWN9v/iy+W8uijjXkYRo4MY/r0V/6Dq/bXQRUEwNatW5kyZQrr1q1D8njoLWqYEh5OZ50SG7vD62WWy8EvHg+VkoRcrNyYCaFHGN/5QZ4f9TJajQ5R0DBnz3LeXv8tu0oPI9ULCp/kZ8qw63l8+A3Bfc7atQSzzsD8/at5YOE7OLwuIowWeib25+KOl7c4xgqXksM7zBAGQLXHhozM7D1fMHvPFy3a9xjYnXvuuYc333zzd75aKioqfyaqtv3IsRV78TqsANhyNuK1K8V64vuNQ2sMoa66jMpdSwFwFCsRVUWrvgJAb4kjtn5iUrj8c4xRKXishdTVVFC5fSEB1wqWLLXy8MPxPPtsMjqdwAUX5LJnT2Np9spKP888U0JKipHExDbU1uZw5ZVX0q1bt+C6PcCyZctadQB89913ufXWW4Pr/WvWrGHt2rX07NmTnj17YrPZ2LdvMzZbHSUlPrzexiUSs1kkO9tISoqeCy6IoLY2wBtvlDN+/GFWr86mffvmUVoTJkTSp4+Zigo/P/5YQ1mZD7c7Dxj4e34t/y/52wuCbdu2MXjwYFJTU5ly663UfvQxnzsdXFZZwY+xcbTT6njfUcsGbx1WSSLVrMMSpiO3ykOpw8e7G3azv2IS3054l0+3LmXyonc5L6s/dX4vYXozMjI+r58DFUeD+6xwVrP08BZEQWRfeR639jsXq2srRfZwSmsr8QV86Jp4+sqyzKKceQB0ilVCY5LDUrit7z0tzmf+ge/wiV4++mwa7dq1a/Wc3X43Lp8LURAJ04ehFf/2t4GKyv9bZn36PoVHG58vtgNrsR1YC0BMtxH1gqCUwhWfN9uu4XVYm67EdjsH+9HdWPetpust75Hz7XN4a8o4svAD0lK1vPFGCvfcE9esYBsozyZnhYy/UGD9nI5UeEezYbeRqS+82OqxLl++nPT0dO68885gX605APbt25cLLrgAo7FhMJfRaIbz6KMvsHevhwsvzGXjxg4IgsDllx9BqxVYsKB9cD/jxlnIzNzDlCnFzJ7dvL5LmzYG2rRRss1OmBDFLbcc5Zxz7uPgwfF/+2WDv/1I8MQTT2AymVi/fj3i1BdxRVgYbzIztLyUF+12Po6K5pbQUJ4VIyjXgm1QBAgCJbVeHvk5jxizlqWHi/hy+2O8s76c3knZfDvhWQprKmkTmUCetYROb13NrtJGX4TZu5YSkCXaRSfz8SUXUlC9DBkdkgxbi4YwZcl99E0eQGxIPL6Alx2lWzlszWFIm+GkWdIBCDWE0SOxT4vzWXrkZ4QIKVjiEyAgBdhYuoG1RWs4YD1AhbuxVrpG0JAW3obO0Z05J20UGZbWRYSKisqfk/z8fNbmVPDAzG0nbBOe3v2kxYxkKcDRxe8T12sM5vi26MOiEDVaHvrgNp6+dE6L9n6PjNch891NLnzO+j40ehxDs5ApaNG+AY/HQ3x8fDNhYTKZWjgAtmnT5rgtBQKBFPr1i2bFigo2b3aRk1OHTieweLGdjz5qXv49KkrL4MGhrF176qWUyy6L5OOPD7Fq1SpGjx59yvZ/Zf72gmD16tWMGTOGqPBwir//HgIB4jUa+usNLPW4cUoSffSKmpTitTSs1ieG6UmO0OPyKssCR2vsVHuqiTVLHK5aSqrlLADCDEron64+tbEsy0zbPBeA8Z30HKteQp1fQicKiKKbWLNM++hsdpRuwe6pQRAEEkKTubrbRIa0GX7ScxEE0Og0aEzKviRZ4uf8xXx9YCbVddWIgoh0XMKkgBwgr+YIx+xH+fHIArIis7i12+1kRma1tgsVFZU/IYOyYhnTPZFfdpWcMrlaa5Rt/Ym66nI6XPsCoDj1a0SJ+8//sVm7OofM1s/qKN8XwOeWg2IAIBCeAOLJw/aysrLYtm0by5Yto1u3bvj9fpYtW4bb7Q4mKjoRgiBRWChjNos4nRI1NQECAeVkWylNgM8n4/ef+mI05FaoqWnpzP13428vCOrq6jCZTPhzDkFdY8pfkyDgBQ74ffSuFwSOUA2CDLKgDOx2TwCTTnG+CdNr6RhrZlOhjdfXfkqvpG8AMwtzFCfBNpEiq/Jeo8ZTSHa0h9wq0AhepvxaRJ7Ng1YU6JMcyoRuNdzS5+RlRk+ELMOvi34lpUcile5KXt/yKnuqdgc/P14MNCUgK7+oXFsuk1fez/jMy7i243XNajSoqKj8eZl8fkcOldSSX+EkIJ++KvC57BSt+ILkoVejC7EgCBIIMvERNYSbPMF25fsDrHnNg9fZej+B8Hg4yTMG4Morr8ThcDB79mxmz1aKt4WGhnLfffe1WOK02+2EhzeGUx86dIhdu8qJj9diMgl06mTE7ZYQRZg928qtt8Y0JoIr9LJ6tYPBgxsrv1ZU+IiNbRk2+e9/2xAEIbhc8Xfmby8IsrOz2bBhA+5dO4PveWWZ7T4lRWdpE+npCBGR6zNnrD1mx+r2Y5E1xIboaBdl5Pqe8dTWBZixvZwZ2xWzfKheEQwmbQ1VrlylT4fS91vri+meEMK4jlEcra5j/v4qKp3f8cCgQS3W6k6JAB1HZ5LSI5FiRzGPrn6YGm/1GV8PCeUHPffQHApqj/Fwv0fRiacXe6yiovK/I9So490b+nL351vILavldDVB4fIZaExhxPe7CI0YQCtKtI8vo87RmKa4dHeAlS96kAJwolxIsimcU+1Ur9cTHx9PZGQkiYmJbNq0iaKiIl5++WXi4uIYPnx40FIwc+ZMXC4XgiBQWFiI06kokdJSP088kUBoqIbQUA033hjDJ59UkpCwi7o6CadTeYZJEtx3X2Nho+efL2XtWgdjxoSTlqbHag3w3XfVbN7s5J///Cft27dvecB/M/72wep33HEHOTk53P722+RIAQ74fNxrs1JeLwQ8TW7wQP1kuchex/RtZYQbNFR7AtzQKx6NKGDQiCSF6RmaHs49A5K4pW8CEUZFczmaVCD0+JUbtl2UkTv7J9EvJZzLu8RyWZdYcq2VHKjce2YnIUBa72QG3dyXmroapqx5hBpv9UktAqdCRmZz6Sbe3vYm8hnMNlRUVP53RIbo+XjSWUwYkA5wyiqInqoiyrctIrHfOHy1VaTodzD1gpfR4cLnk8nPr+Pofi+rXj65GACQTyP0+qOPPsJqtdKvXz/mz5+PTqfjoosuQq/XI4pisHQxQK9evSgqKuLgwYNBMRAdHcGgQSHN8hA89lgCJpOAzRbA7ZbRaARiYrRIErz3XqO/1NixESQk6Pj00yruvLOA558vQa+H6dOf5K233jrlsf8d+NtbCG677TYKCgp45aWX+KpeBHTX6bg9NIy3HbWENLnJBRmq3X5eWa3k5bbXBbi8Sww9ExWz1FvrixAFeHBIY4xsdoyJBxblsbfcFXxPr1F+pAPSmmcXHJhmYfbuCo5YD9Extsspj10QFEHeYWQ7Bt/WD41W5P2t72Krs/1HYqABGZmVhSvol3AWQ1KG/sf9qaio/PEYdRruHp3NyM7xfLEmj1UHypFkWtRoEQXwOSpBlshf/AEs/oAdwDePNfbVtu0eQgSBISEmJqVGEh1jpCqqcRnx50M2fs21Ue70ERpyjN79+tOzZ0vT+48//siPPzb6I+zerSxl5uXlcdlll5Gfn8/Ro0e59NJLg22qq6txu920bduWgoIC7rnnHgYNsnDJJT806/vrr6243TJ79nSic+fGKIHrr8/j88+t2Gx+IiO1jBoVzqhRTZ+5GqAt8DT8hmyKf0X+9oIA4Pnnn+f2tm3Z/MhjhIkiHXU6XrQrDiYZ2sZLJNX6eGl3CTUeP3UBmZHtLFzSSSl9XObwsrPUyaTeCc36DtErP56qJhUILSalzwhj8/X5cIMiPpy+EyzS1dMgBIwWIwsqv+G2u7+Fu1tve84nIzBFK6E7kk/i8LwjFKwowl3uRmvWYmkfQbfbumCKUX5IlXuqWP/ExmZ9LGAhAOvXr6d///4nPTYVFZU/B51TLLx4VU8q7B425FZyoNhOblktrroAWo1ASpSZ5L6x+IZ8SXKYjrpl0/As2cV7x+zs8HkxCgKXmsyIwE8uN6sOFDMvJo6zdDpCHRIub4AZ28volxLGmMxIjklhLFu+giNH8locS48ePUhOTuann36ibdu25OXlER4ejt/vJzExEb/fH8zbAiBJEkuXLiUiIoKjR49y0003kZ6eRlRUfou+7XZlIhcf33xpMzFRhyiCXn+iwV4G/oUqBhpRBUE9cf37089gCL5eXechUdTQvl4QeGSZf20vocjlJSDJ9EsJY2KvxvWpGo9yU0onMK83NbtnRBpZTg2248oUV3uUoiJh+uaWg6YIokB8h1g6n5dF2wGpZG5JZtxVFwEwc/+XlDlLkYHdH+7BFGdqFAN+iY3Pb8Z2oJq0UamEtwnD5/Rhy6nG30q55LZj07G0jwi+HppytrrGpqLy/5DYcCMX9krhwhP4zHl3iNj+eQ/+vDyQjURFCEytqaZGlplqiQTgRn8oI8vLuNtm5QKTiWMuL66ATHuTjjYRBiLNWoZnJxPToQ+zZs0CYOfOnZSVlQHgdrvx+5XnTFlZGXq9Hrvdjslk4v777wcgMjISn8+HTqejpKQk6PXfvn17pk+fjt/vJzPTwocfxDFiZFjw+IcNC+Oll8q46aZ8/vWvJKKjtaxb5+CDDyq4++44QkJO5Bh9O5D9H17dvxaqIKhHl5UFOh34fMx3u9jp8/FEeASiIBCQZe6wVbHbUwcidIwzc+dZiYj1jn8fbipmVb4STfDptjI+3VbWon9LvS9BTqWL5XnKjf7vraXk2zxc1S0Oo05k+RHl/Y6xXfAFfCw4+B0bC9bi8jnJapvNU48/zfhrLkaja7zBBwwYwIABAzhSfZhvQr8mhWSq9lkJ1AVIGZoUbHdkQR5Ve60Men4AkVmWU16PqE6RJA1MDL6uNdmJilbTIKuo/JVw/fADtrvvVV7UT1r66A3Ks63pJEarI16jYb/fx/7axmqIuW4fuXsrOTstnErXQaqjG4eUHTt2BP8+ePAgBw8eVPbpalw+tVgsiKKIy+XCZrMxY8YMJk2axJIlSwAQRZHy8vKgZXLvzu2cOzqHNauz6T9AWaodMyaCZ59NYurUEubPbwwdnDIlgeeeSz7BmU8AbjiTS/W34G8vCFatWsUzzzzDueeei7F9Bhs2bOQbp4NhBgM3hSg33DP2Gn7xeNCi/EZiTFo+39E46MeYdVzZJYZdZU72V7hJDtcTohM5VKWE7MiAUScyfWspS49UkxSmp1diCNtKnPx6uJq9FS6yY8wsP1JN3+QBpEdm8MnW99hWvJmRGaOJC41nQ/FaJtx8FXPiFzNuzIgWUQhbyrYE8wwUrS4GAZLrBYEsyeT9mE/iWQlEZlmQAhKSX0ZrOHlIod/tR9SLiBqRCnc5V01bAN4owkw6shPD6ZgUzuDsOCJD9L/fF6KiovJfwf3TQmx31a81HmfZnBMT1+y1LMv4ZTjbYGBmdCw/uFzcVW1ldnQMAw1Gao0iQw8UU+la2uq+nn/+eWJiYggEAjz44INBJ0Gr1Up6ejpjx45ly5YtrFq1igsvvJDycsUZUJIk7HY7a9asOa6/CubNC0PUKMednq5n6NAwLr3UQnS0lp9+qmHq1FISEnTcdVfDuWhQlgf+CVyNulTQkr+9IEhOTkaj0fDKK69Qa7eTKkk8GBbOLaFhaOsH3b31IYh+ABlWHW1ZgGh8p2geOzuNJYerWZFXzaEqTzOH3KPVdRytVvIcTOgWS4/EUH7JreaHfZUU2b3U1gU4P2sE52ddR57tMFuKNjC+01Wc234sAP1TBvPImilcd8s/6Xf7O/RsE8n4vqmc1T4GjSiQW30IZGVpoHhtCZHZkZjjlKRItQUOPNY6wtqEsfP93RQuL0LyS4S1CaPLTZ2I6Rrd4nx2vLOLgCeAIApEdYqk0/UdELRHqbMpSxAHiu3M3SyjEfdxTpcErh+SQUZcaIt+VFRU/nz4Cwqw3V2f+vw0oojmul2USgEmm5TlzHb1S6lbvF4GGYyEOSR+6pjKdxEaXl6Wi8Fg4O23327Rj0ajwWw243Q66du3L5MmTQp+JggCq1at4siRI4wYMYLc3Fyys7ODSwryOgPYNLyxbiobV1RwaEUHMocfYNYsK7fccpScnC6kpCiTk/HjI5EkePjhIiZMiCE6WgS6AVOA9N983f7q/O0FQbt27fj555+Dr6tuvAnPkqXNUl81VcsysKObidpQUXHTPY7RmZGMzozk062lLD1czZtjM4gN0ePyBbj1h0OclxVFz6SwYNuRGRZumXeIHgltuLDDRAC2FW9CFESGtGnM3KXR6OmbPowVO2dhLS9hvdfPmpwKEi1GHhvXhXx7HhISFdsr8NX6SDm7cbnAWaKo8SML8tCH6uh2uxLBcGhOLhuf2cyQVwYSnq780EWtSOKABOJ6xaIP11Nb4ODwvCOsnbKB3pO7IGqVNbcGb+WAJLNkTylL9pQyaVg7rhvcFq3mbx/NqqLyp8PhcPDKK6+wceNGNi5bTrXPy+uWSK4whwTbSLLMHLeLRW43e3w+qmWJeFGkOBCgp1bH5SZlktFVr6enTs97tXZW1HnI9flxFEvIQn30gs93osMIOg/26dM89XpDEiKXy0VcXFzwPVkGjmrAplg0w/ThFNQcZcXbo9n05SDe2PAMXTpGkBhvBBodEy+8IILPPqti+/ZenHPOw0Dmf3oJ//L87QXB8VhefIGyocOQnQ5aywEqAB1yPGztaVYcCFtJIOSXZDYU1JIZYyK23pxeUFNHQIaMqOaVtzSiSJrFTH51o2NfQc1R4kISMOkaQ2hEoEtoOisAV+lhDBGxAJTVePjnjC2k9FTW5YpWFyNohWbr//56h8eAO8CA1wcHIwqiu0az7I4V5H5/hF739QAgqkMkUR0ig9sm9IsnaWACK+5dzeEflpB52fgW59sgDqYty2VbvpWXJ/TEpP9tt5Ysy1Qe3E7hlqVYD++m+mgOAa8HQdRgjkkkOrM7cR16kzZgDFqj+TftQ0Xlr4RU7iSwpwypqBa5pBbq/MpzyaRFTApDTIlA0zWeyupKnnnmGVLj4+koCKxvpS+3LHN/tY1eOj3XhYSgR+ANhx0vIApCs8Q1r1siGV1RxmZvYwIjvaxYUnVaUclaKIh4vV7Wrl3Lzp07KSwspLa2FlD8Crp164YoKr1WV1cDihVh1apVgFIqOe9IHt0tvTkvcxyh+jCqPdWE1jteu6yhFBfImPXRfHrlnYTF29EafAT8GnbbyoGp+P2XoYqB00MVBMehiYsj6uOPqLr2OkBS0l0dh8kj03mfhz2djci0FAW7Sp04vAEGNckzUO1WBvwG50IAWRaQMRGmb09pbWOojr2umgijpcV+o/TKe15HVfC9Bs3irpORJT+lm8qJ66HM7oPnVJ8tMbJDZFAMAJhjTUR1iMJ2sDEZSGuEJIaQ0DeBko1HkaUAwknSGW/Ns/LgV9t549reykPhNJFlmWPrFrJn7gfYCw8jaDTIxyUotxcdprY0n7wVc9n62fO0G3k5XS69A33IiaMyVP7MeIFDKDXsvYAeSAPa1/+tcjKkY9X4lh5BLrAr0/KmExhZBqcPKdeKlGvFvzyP6PRQinYfxvDOy2ycN4+xZaUt+tQJAj/ExNJHb8AuSVxeWYEOgRvMZj5zOVnjrWOIQZnUfOl04AViBJGxJhMWUeQ7l5NCSSLg9yOXHUGIb0dFRQWzZ8+mQ4cO9OzZMzjYL1u2DJfLxcSJimV0zZo1iKLI4sWL8fl8xMfHU15eTtuQTFYcWUJOxQFu6HUbR2yHmtV1iQtNYH/FHkpqypEC9RMhAdaVLkAURbp16/bHfAF/QVRB0ArGIYOJnjGdqhsngd/fauWMyJoA3fa42dfBiE9HM1Gw7pgdjQj9UxsHKm99EQ5t/TKDLIMkR+PyXYFWnI0v4GvS1tdqSeKGFMKSz9viM3+dhfJtBwjUBYLOhMHzqbdKGCwtH7IGix573qmLehhjjcj+AAGfB60h5ITtJFkRBZ+tPsLNw08vTNFdXcGmaU9SvG1F8DoeLwYaaHjf73GRs+gLjq75kbPueIGkHkNOa18q/2v8wArgG2An0Nr3rAF6AlcAQ1EfU82RfQH8y/IIbCxs9Is7UUWjJm/rjjqIPGLDunARBFpPXKYXBProDXhkmYnWSo4E/MyKjsEsiHzmcnLI52dIfXT2PI8bAVgeH09k/SRhoMHAlVWVSDLkL5tN9sBziYjuwJNPPE5ScgozZ85EEAR69+7Nli1b2LBhAzExMZSUlLB161a6d+/Ozp07ueuuu4iS4njpo6nklO2nfXQ2Byv38ca6qYToQhiTeVHwmM9tP5a95bt4dc1zDGs7ihB9KHvKd7CnbCeTJk0iKSmplTNVaQ31l3YCjMOGEb/kV6z33Itv2zYQxRbWggi7RJ+tLg5nGCiP1yHLAnX+AFuLaukWH0JYEy/+huyEvgCU2H3M3iORU7kXp28yoiCCLOP116HXGtBrdPgCPn48OJcNBWuo9tiwGCPpGt8DAFHXOLAfnvcqlTuXNDuuba/vYNvrOwAlMVF4mzAErYDH6mnWzlHspHJXFX53gJ+uXIwp1kTKkCQyLs4IRiCse3wDVXutwW22vqRkEoto15sO1zzfrD/J76VwxRdU7lrKxqkO3u3alVdfeoGRgwfjXb8B786d+A8cRKqtBa0GbVISgTbJbF4zk2pJMSOedgJ2QJYkPHYbK1+4hT43PkHm6KtPe1uV/wWbUbLClaEsgp0om2YA2AZsARLrt+n9xx/e/wPkOj/embuQi+odm88kq7gMgcqCVq2eTWkIs97q9fLvqGh66w2s8ijPjiix0epnkySMCEExIMkyHzkcCPWHdajCRbfDa4irOER45jB8gQBbt26lXbt23HjjjQiCwObNm1m4cCExMTFcftnl6PQ6du7cSVhRFEmBtjwwcApz980i16qELCaHp3J1t4lEmhpDoDOjO/Dg4Cf58eBcVuYvwel1kBSbzPPPP89DDz10BhdIRRUEJ0Gb0ZbYH+bi/v4HHJ98gm/3nvoawxrFcgDoJIEOeQHiS3Rs6dKXTUUrqQvIDGoT0ayvhuyEebZU5u7bilEboqhZXQjzD3xHrdfOJ9ve545+9xFusJBffZhDVQcYmDaUNpa2HLHlsjzvVwD0oY1RAXG9zieibU9kCjky/2sisyJpMzqtRWKiuF5xlG8pp7bQQVhKKO5KN6snr8HvDmDJspA2MgXbQRsHZx2i+nAN/R5rdPjRR+jx1fowxWWQ2F/xIdCFtYxMODLvNaz715Bw1sWYolOw7f2F88eM4dukZPpKsnLdZDn4QKrTapH9froC9jCRoiQdFTHaVv0yTkh9iuYtnz6LxmAiY9glp7+tyn+JAPAm8DWN5VNOlVq74fMy4FbgWpR0nH9fh1U5IOH9erciBuqFgFcIYNXVUaX14NL4kQC9LGLx64n2GQkL6BCahNf5q46ecj8NYdajDEaqJYnvXE7ec9RiBGqlRotOAHAj81atnVBB4Ae3mx0+LxGiQLUkU2BXoqo09jJCts5mi1XA6XTSv1s2GmSys7PZvHkzkx+YTEZce6gWKd5XhoDA7BVfc1nnq4k0RTGy3RjKHCWkWdpye797Wz3mtpHt+Gf/B0EAQ4ieKz+4CFO4sdW2KidGFQSnQNBoMF92KebLLsW3bz/eLVvw7t5NoKAQOeBHDA1D16kjkV27sGl6Navzd2DQGOgY8w/c3gAgIBFCpCECUXiADQUVuHxuJg96kqTwFPySnzl7vybWHM+u0m04vU7CDRF4/B5GtRvLpZ2vAmBo+kgqnOUctuYgNEmnHJbaibDUTpRumgcSZF7eHq1R0yIxUcdrs6jcVcn6JzfSdmw6VXsUy4AuVEefh3phijbS5tw0yrdVULa5nP0zD2KONeEsceK1e9EaDbS/5GFMsWmtXidH0UGq9q4k7ZxJJA0Yz7n7V3KVz8V5oshzZWXMi41rufTi9wcfVWG1Eh0P1pFY4iMny4jHeOYP/s0fP01sh16EJbQ5421V/igCwJNAQyTPmdbYaGj/JWADnuLvKgoCa44hH1OW9yp0bg6aq8kz1iILSp2Vhpk5KCXaASL8ejo4LWR4wtHJInKdC05RhKghzPrXOg+/1jW3Kj5qr+G6UCVKKloQqZIlXqu1YwA66fRcbjLzjVtxcHZ6m//e1x8sRCPC0EAOhqW5rFmUR6pWS/91PsqjDCCIJJHGtd1v5Lt9X/Pymn8Ft+2fOpjruk/ilMhw9l39VTHwG1EFwRmg69QRXaeOnGgFXf/zt+yv2Evf5P5oxM74mjz7jDroGNuZ3CrF9BVuUCwIGwvWUBfwkBrRjUpXOVpRQ4heieeXm9gDfQEfVe5KAGqP7sHSrnnITtXu5ejCQontFsOef+9tlpgIICw1jIHP9Wf/Fwc4NCcXqb7iYr8n+gatCABhaWHUVVdxdPEx/G4/CKA1aekyaSpac+wJr41132oQRBJ7jOLBJR9w1tHtyMAEk5kXa+0UB/zUSjKv19rZ7fNSLkmYBIEsrZbbQsMYZVScHcPtEr23uViUJvL+kSoOVrrQigI9E0O5tnsc4cbmt+wP+yrJtXrIrXJjrwtwbcEFfL5y15mXj1b5g/iQRjHwn/ITkIRiMfjrs3nzZmbMmMHy5cvJz8snSh9Kn5QOjBs3Dm9CKIIMAVliw/oNbN++nYKCApxOJzExMfTp04euXbuyaNEijh07hr3GTojOSHtDKDFuGzvqIwOeqalmlsuJHoHDfj/VskSaRsODYeHcGhrGLx43d9qsXGk282xEJI/X2BhZXkpxIEBd/fKeALiBrT4ve3ze4GLQ8dHH/xxQnzVQlvlkcyn5Li8zoqLpnf89m3ThVIVlgCBiMUWRbmlH5/juRJuiya3KYVneL4Tqw7is88mXBQdO6kPbAa1PWlROjSoIfkf2OnciyQH6pQxs9fNxHS7npdVPA/DG+hdIt2SwsXAdyeGp7KvYzfCMczFojUQYFbGw7MhiBCAuJJ71BWuo8VQD4Cw51KLvzje9CQTwub5ukZioAUu7CAY8fRYA5dsq2PjsZnLn5JI9IQt9mA7rARvVuTW0vTCdLjd2AmDt4xuwHahh53uPIAd86EIiie01huSh1yBqGm8fZ+lhTNHJPLJ6Br0LlEpmAtBDr/g77PX5EAGHLHGZOYR4UcQtyyzyuJloreLFCAvXhoQiAsU+P49tKMdg0HBll1g8fomfcqwU1NTx7Mh0tJrGwf6bPZVYjBrSI43sKnXiqiqhYv8W4jr1Pf0vTuUPYg/PPz+Vxx8vpnNnI3v2dG61VXW1n6ysvVRU+Pn22wwuuyyy2edbtzqZMqWYdescyPIdDBjwOS+//B49evT4L5zD/46XXnqJtWvXcvnll9PJH8uR3Fw+2vgDi17byMMPP0xycjLeOi8zZsygbdu2DB06lLCwMI4cOcKCBQvYunUr4eHh9O/fn8gICx5vHT/98AMun4+eWh0lUoDuOj0rvXXogDtCw0jSaNjm9fJarZ0f3W4O+32MNBh5MSKSWlkmx+dnuMFIqlaLCHzmdHDQ3xgynaXV0kav50eXixizrtXzWnDQyrL8Gh4MC2ek0QSyn36HZrC7zcWsEMJ4b+NrPDzkKdpYMgDokdgHo87ITwd/YGDa2SSFNU9HLIgColZkyO39yB7R7o/6Ov4WqILgd2T5jiWEGcJPWLo4zZLOfQMf5d/bPqDIXkCRvQCAInsB52VexLiOlwMQH6KEznSK7crGwrW4fC6Sw1NJ63E+R7f/iLe2qtX+QcOxpfH4an0tIg2OJ65XLNlXZ3FoTi5lmxtrhmde1o4O1yjJh2RZwBQThWnweZjj2hLwebDuW0Px6q/xVBWReVljnVSfw0qyLNGnYFezhKBx9Q5HZYEA14aEKg+AJkwMCeW8inI+djq4tj5V9Lu1tbhkmfmWaErSw/HpRdpFm3hhZQEr82sY2c4S3P6t+sRP9jo/t83LRRBEcn6eqQqC/zkyhYWPMXVqKSEhJzdRP/lkMS5X60sJ27a5GDz4IKmpep56KglJgvff38nZZ5/Npk2byM7+6xanuf/++/nqq6/Q+aH0reUs7pbI5MHt+dezz7B48WJuuukmtFotDz30EO3aNQ6EQ4YMITo6mgULFnD55ZfTsWNHAA4fPozL5yNBFCmv970ZazLRTa/nHUctXXR6zjOZuCYENAJ85XKRpdXyQVQ0WkEgUhBYENs8pfF1IaE8VG3jK5czmMb4fJvyPOmdFMbxrMyrZtauCq41h3BPWGMUlkb20yN/Du87A4TrwxQxUJ/HAKBbQi9+PPg9R6yHgoJA0AjIAZmETnEMu3sA4fFqptT/FFUQ/I5s2LSBtR9vZu9POc2qGzalfXQ2F3e4nI2Fa+mZ1JdQXSi7y3ew+NACwo0WhrcdRZf47kSZYjhWk8+ErteTYmnLTnc+P2ybjiBqkHx1JzyGim1bETQakgYm03xVsSXmWBPRnaNI7J+APkxP2dZyDn13GEOkgfTz2hKoCyd15MvI/kZLQ2y3czjy41tUbFtEbeElmOPSKVk3B6+1mPyAj1Rolv3MUG+6X1tXx/duF4f9fuySRLxGwwC9gfvCwknSaNjZJJTyR7eLGFFkXFk5jnllJEbouahjNIlhejYU2JsJgtjj6ijIskTR5qVIAX8zC4bKf5udTJ68hv79QwgEZCorW1bUBNizx80HH1Tw5JNJPPlkcYvPn3iiCJNJZP36DkTXF8659toosrJyeeyxx/juu+/+0LP4XzJwoGJpdO8pYKmlCL8gEZcQT1JSEqWlSg4BrVbbTAw00LNnTxYsWEBJSUlQELjdbgBitFoK6zMJagWBfnoDUIux/rd6yOfjx/q2l5rMmE6x/NaQxjhe1JDn97HX7UUnCnRPbL64uqXQzsdbShljNDE1wtJqX06PFZ3PR6/DX5Ef2x9baBqyqCUgNVSTVf7Xh+hI759Kl/Oyic1s6eCs8ttQn5i/M/2u7UHe+gJcVlerEXSbi9bz5a5PeWbEK8HQmZ5JfZFlme/3zaJvcn9C9WHcddYDfLz1XaZtUfKBixodqefcRNGaWWj0ppYdAwGvG9vB9URk9MFVfgURkb/glV1IcsvZV9HqYnZ+sJsR750dTFaUOCABWZLZ//lBYnv2wl05vpkYaCCx/3gqti3CfmQ7+tBIilbNRCMImAUB+3En3bDOWCEFSNNoOddowoRAfsDPHLeLHz1uPLLMRSblGA75fFhlGUcgwO2hYcRqNHwt1PH2+mKyYkwcrfa0OJ7jkQJ+7PMfxXLRC6CKgj8MWZaxWq2Ul5dTWVlJXV0dsixjNBrJz/+SOXOq2batI3fffeyEfdxzTwGXXBLJkCGtz+5Wr3YwZkxEUAwAJCYaOfvsZH788UccDgehoX+9mWHTNMPr1qyl1ung+uuvZ8CAAdjt9max9Vu2bGHJkiWUlpYiiiJJSUlBERAaGkpdXR0+n4/Q0FAEQWCP1xt0y3y02oaIQIIo0l2nwyFJXGOtpLb+d1sUCPCdyxncV7pWS1edHock4UFmXEUFNVKASEFgjtvFdKcDCRjXITJYDRZZZn+Fi3fXl9BfZ+DdyKjGz46jrVbLyro68ks3M9C2G0kQcYQksrGbcj5X3D+eEeePIDTGrPoJ/QGoT8vfGZ1Jx8jJg1nw+K8QaKkIVuYtJTW8TbM4WlBMYusLVlNQc5SOsV1ICk/hyWEvUFJbxPpIGWv7TEStnqO/fER4m66t7tt2YB2Sr46YrsPxOVK4IulZjgnzWVGwPFgJsYH8xUeJyIgIioGGcTy+TxKFy4soWdeJiIzWUwPr69Mm+9216EKjOOeWd6n77AHyA37sx0USlNcr+1ubOA4+Um3jyyYPmWytlucilHXjL5wOACaFhPJgeAQScG5sBFcJZRy1eagLyPgCErpT1EuoXvURltodcNUPoD9xIiWVM8fv95OTk8OePXuC6WZFUQzmqJdlmeee+4lBg4awf/9YnM7XgZYFwb791sa6dQ727+9Mfn7LZFsAdXUyJtPxD/4AZrMDr9fLnj17gqVx/0pUVipphhMTkkhMTqI2JweAjRs3Ul1dzUUXKYl5li1bxuzZs+natSuXXHIJPp+P9evXM3/+fHQ6HV26dGHOnDnB7IANNDwJFFujjFuSccrKv+Imv+HPXU4+b/JbvdxkZpgxwJ22xtwkIorw/7fTgUeW6RRrZnzHGARJRhYFPGV1vLmmEBFlieJHd2P5Y4COOh2d6nOrTAwJ5RuXi4nWKiaGhJKi07HZZOW7OZ8xatQoxl1/4e9xeVVOgCoI/gASO8Vx7sND+eWlVSAr5YcbqK2rwdzKANVoEqt/qKJU/yrokI09zowWqD60CWSJiLY9W91v5e7liHoTlmzlAdkhPo7LUidzVfbV/Jy/iJWFK7B6lB9yXbUXXaji9CPLAn53NJ6qLtjzKoHNyCdJXlJnqzdXhkQgavWcU5bDEb2etY6WSxnb672ZO+saHYwmhYQy1mTiiM/HFHsNXlnGV69IdtWbMrvWOyOKQEJlgAHJYcysT6zkDcjoTlq5WcYniZC3HL6+CK5ZBFo1Fe7vQXl5OcuXL6empnl2S6nJ/bJixQqqqqzce+991NSEYreH43JBWVkc8fHK+rLbLTF5ciH33RdPerrhhIIgO9vIhg1OAgEZTb0zqdcrsXGj4kdTVFT0R5zmH4Isy5SUlJCfn095eTlWqxW/348gCJjNZuLi4oiJioVCDXsX5vDSue8Q0cfMMTmXqS+8QE1NDYsXLyYjI4MBAwYAsHz5ctLT07nzzjuDM2an00lhYSFxcXGYzWZGjhxJz549mTFjBm63G71ez8XxCZxttbHP5+UDRy0j6h0FAR4OC+elWjtTIyz8I6Sl9aUiEODr6BhyfX7ed9ipkCQEIFmjoX+mhTEdorA4JMJrJeLL/ey0uaitfwZOqalu0d99oWFBQdBOq2NRbBwv2+3MdbuocARINBqYPHky//rXv1psq/L7ogqCP4j0s1K56PlRLH1tLc5KZ3AG3pB3u8xRQnxoYwGizUXrERBIDk9FAnyiwLq0MPItSp5QyVdHwYrP0YVGEd1lWIv9+ZzV2PO2E91lGBqdEVGA9gmKU09SaBITu9zExC43UVNXQ17NEayd7mLTqs2UrDsbjbkzyMqtULn7GRBEzPFt8dc5ETU6xCaDqSzLFK3+GgBLOyV7XGZ5Hh1MJj5y1DY7pjpZZrbLRU+dnqR6071NCmARRZyyzIo6RUCEiiI3WCv5MSaucebSZOlBlCG0iT7Ra05lKhQQkEEOKKJg1XMw4plTbKNyKvbu3cvatWtPaqp1OBwsWLCAsWPHEhbW4FQmIEki8+aNY+DAdXTpspcXXyzF55N57LGEk+7zjjtiuf32Y9x001EeeigeSYLnniuhpEQRjg3r4r8dGSWdsoCSMvn3N0PLsszhw4fZunUrNTU1CILQzMdIlmWcTid5eXnkHclTnhVxGiKqwyHZg1yotFuyZAkmk4lbb701WBDI4/EQHx8f/E42b97MokWL0Ol0JCYqz5eEhAQqKyuprq5Gq9USHh7OSo+bx2KiGe10k6rRcF+1jWUeNw5Z5uVaO1eZza2KAYBYjYZYjYYhBpgYGso7tXbeddQyKzGJuFoZYaOr2VUcaDBSmJRy2ternVbHtKho0GgwnjuKqI8/UpcH/kuoguAPJKFjHFe8eyFbvt7J3oU5BLyBVvNu7y7bzt7yXQxKG0a4MZK8SAOfbXwbTUkMppg0AnUuynf8Qp2thOwJz6AxtDTlV+1dhSwFiO4yHFGAbmmRGFuZRkcYIugR15OpT7zAiBEj2P/RS0T1ugCNMQzboU3U5G4mtucY9GHR2PN3kjv3RaI7D8MYlYTk92I9sBZHwT7iep1HSKJSQax9RR7ROj2D9QbWeOuY73LhlWXmuFwUBvy8amnMX9CntIQGO0KkKPJMuIXCgJ+PnA5SSxpne/dW27i32sa8mFh66Q0cKFPMjAJwz0+H8fhl4kN1jMiwMDLD0uwcK5xeMt9umMHKwLP1/+Drr7/mqquu+i1f5xlh89iweaxISJi1IcSHxKMRTmrW+FPTIAaAEzrMAsybN4+QkBCGDx9+3CcCILBu3SCKihy88so23nsvjdDQk1+T226LpaDAyyuvlDFjhmIV6NPHzEMPxfP886W/0X9gP7AI2A3kQPCONAEdgW7A+UDGb+i7OS6Xi1WrVnHsWKMfxcmuH4KSqFNODkBiAEELdfXC2ev18sADD2CxWILNs7Ky2LZtG8uWLcNsNvP5558TGRlJbW0tI0Y0lk8/cOAAoCz3uFwubDYbXUSR841mHgkLB2zMdblY6HEHwwxPl/NNJl6qtbN2zBgu/WnhaW93UkQRISQEywtTVTHwX0QVBH8wOqOWARN70/vKrhxakUfa1mRCwkL4btusYN7t6JBYhne/kox+l/JNTChunYixMJuKnb9QtnUholZPWFoX2o9/mJCE1uNsq3YvQxtiISKjJ5IMV5x18uQcQ4cOZd26dTzwyBQ2bl6A31WLITKelOE3kDRICX/UR8QTltYF28F1eB02BEHAFJNG+th/Etfr/GBfoXXKGuMDYeGsqapgq8/Lem8dHXQ6PouKob/BEGz7eXQMdbJMrt/PXLcLlyzhrX9AXmg00VGn5eXaWsyCwC0hoWhleKfWzlqHsg+NCFd1DSHWHGBjYR3Tt5Vhddm4vZ+lxTlO6ALnZwIIkNAdBj4QNLWemgpgGcrgsQ9lDVwEYoDOQFdgGKCIM0mW2F6+jV+P/sLeqr3U1FU3602v0dMuoh1DUs5meOoIQnQn92uQZRmrpwq334NG1BBpiMSo/Q+zr/k8UJUDPheIWrCkQ0jMKTcrKysLioFTtVu9ejVXXHFF0LcAwOfzEQgEqKysxGQy8fTTRSQkmBg2LIz8fGWwKy1VZvwVFX7y8+tIS9Mj1hcCe/75ZCZPjmfvXg8RERq6djXx2GOKeMzKyjqDC7AJeBs4gGINOL6wkhulhsJO4DOUAkv3AK2HEZ8Ku93OggULcLlcp258HIKoZBv0+Xx88803AIwcObJFoZ4rr7wSh8PB7Nmzg+95vV7uu+++ZtEH5eWNocWhoaHYbDbO6t+fhRs2UBBQIkB+9LjpodMHwwxPhYwi80qSQqG8DHe7DEJvuRnHRx+f8fk2QxRBqyX6s0/RxJ44GZrK748gn1SuKtjtdiIiIqipqSE8XC01+3tQ5/Ty+fJcvlx/lDqNcGb5+0+CKEBcuJE59wxBewrHuwYem72DFfvLkc6gsFBTvpp+O4aAj51eL2Mry5uFHTalMhAgRtM4I8z3+xlZXkqoIFAly7xpieQycwhXV1awytvojxAripydYmDOMTfD02HZ9Y3F3W6aD5/tgA03Qf9/w6ODBYa1DWf0FzW8MgomB3NECXBvPlhOlcUsF/gIpSKfjCICjh84tChmZhMwjh1lvXh35xeUu8pbOG8ej4CATtRxSeZ4rsi6Cp2m0bfC5XOxsnAF64rXcsiWg8vvarZdYkgS3WK7MarNaDIjT7O+e00hbP0I9n8PlfuVZZSmhCZC2xHQ51ZIGxy8Dxu83Dds2MDatWtxOp1cf/31wVC4pkiSxOrVq/n111+pqKg46eGEhobidDpPPksGbLbuWCwnnq/063eAkpJIjh49GjSfnxg3Si2F7zh5UaXjEVHugetRsiO2nmin1T263cydOxeXy3XKcz0RkiTx4YcfsmvXLmRZbvX6ezwevvzyS7Zv305YWBijR49m9erV1NbWcvvtt5ORoVg5Xn/9dQ4eVLKkRkVFUVtby6uvvsqq5Sv57oe5ACSKIj/HJxB1gtTG1kCASFEM3iM+LRzKNvNxXShzVm1j6dKlDB8+HPuLL+F4971WC8KdEo0GwWQkesZnGP6CzqL/C85k/FYtBP8jDCF6rh/TgSVHqiiwuk5YvfRMkWR4anzX0xYDAA9d0IkteVYcHt9vOg6HIQSDq/qU7R6useGQZM4yGEgQNVRIAQSgqv6BaRQEHJLEixYL51eUYxQELgozEt+2jjf2KmvFF9fnoamfPBJZb3x4dZ3y/4ZCmTq/slxgc4PTCzoN6LUC7P0GBk0+wdH5UWaFDbObhgdZa+V5G2Lq3UjybNpFzibTEkG5y3BSMQBKOmqv5OWbg7NZW7SGh/s9SlJIMt/kzOKH3O+pC9TVe0DILbYrdhZR5iplcf4i2lvac3v3u04sDFxW+Pl+2PUFILQUAg04SmDvbNg9E+K6wEWfQMpZjV7uiYkkJyeTU+/l3hqff/45GzdupE+fPnTq1Am/309lZSWZmZmkpqYyb948rFYrkiSRmZlJbGwsdrud3NydWK1unnwyEY1G4IkninnooXgGDAglJOTESwmzZ1vZvNnJq6/+6zTEQC1wJ4pVAM6slkJD1MQM3N69wKuYT1T6u6YAijZD6Q5kl5VV1ja4POHIx/kkfPbZZ6xfv/6Ee3zxxReJjFTM9d9++y07d+4kIyODI0eOMGPGDI4dO0Z6ejoA/fv356233iIvLw9ZlqmpqeH7778nPDwcl8vFO++8Q0xMDN26dcPhUKJ3BEHAarXSvn17Nm3axNEmSxnntLMwz1NHWK0UPOo0rZbe9Ra+uW4XX7icjAwxERZjoMwssHOXld1lLi688MLgEkXEo49gGDgA2733IzUIxFOJIo0GAgEMQ4cS+erLaBJO7lui8segWgj+xxworuHmTzbhl6Qzqfx7Qq4ZlM4/zz3z7G27C6q567PN+ALSGYuCh359jz7HdrCnru6kFoJ5bheznE4O+H3YJIkQQcCPIgSq6l87ZRkN0EWrQ6uX2e/zo9dAYijsr4SDd0FWkzwk2e9AjrXFrgAwasGjlGOgdxI8P/Fszn1uRSstPcBkYCNnVk9WQZIVgTL7YAgz94dwuo5poiCiE3VE6COocFe0EAGn2laWZS7PuoIJHa9p7p+Q+wvMvQbcthMLgdYQNEp2uMEPUzfoCazVNaxcuZJdu3bxwgsvtDpD3bJlCx9//DG33XYbPXu2Hv3y2muvYbVaeeKJJzAaG5Y9ZKCUxx9/jqwsA889l8zw4TktUhevWlXLM8+UcO654URHa9mwwcn06ZWMGtWZBQt2oNWebE7jA24B9nLmRZWaE5AE1uZk89bim+nXLpZL+qSQFWeCvd/CpnegcAMOL7yyTmB5SThbC324XK4W1+zw4cNUVFSQl5fH3r17g06GXq8Xi8XCiy++2Oy6nUyIPffcczz++OMnPe6IiAhkWcZuV8I+ExMTiYiICDoZmoxGauvFQmuMjQrh6TaxBDQC2yQvX5bUkFPjocYTQBQhJSqMOx5+irvvvrvFdyE5HLhmf4Pj358SOFpfZVGrVcSBUJ80zR8AQcAwYjihE2/AMGyY6jPwO6NaCP4f0SEpglev7snkr7YRkOT/yFJwYc9k7jznTNZUG+maauGd6/tw/8xtuOsCBM5AnRyKa0ufYztP+LlflnHIMuNMZsaZGh0it3u9XFxZTl+9HrMgMsJoJEoUyfH5+MhVi8srs/YGCDNC74/ggqzmYsAbAK0G2log55+grZ8sHquBmxfAJR0gOQyO2OD1DXDe1JXMH/ATY8eObXp0KGJgE79FDECjteLKbCd+CWYfPD1HN0mWqAvUUe4uP3XjVrYF+CZnNsWOYh7o8yAaUQN758Ccq+rLQp/h+TSIhzUvYbDloxnyRnAgORFLliwhPT2dnj17IkkSPp8PQxOfkQb0en0TMQCKaEqkX79o1q+vPGH/ycl6NBqBV14po7Y2QNu2Bp57LoX77//5pGIg4AuA8AmiZg+C8J8rbY0oM7TDAdYfWsn8bf3Zs2UFL+peI9l/JJhet9IFz6yUiYmElJSUVgfzdu3acfToUVasWEHXrl0ZNWoUJSUlLF++HJvNxrZt2+jVqxcADzzwAJ9//nkz/420tDSOHTvGpEmTgt/N1Vdfzdlnnx1sI0kS9913H16vl5dffhmAVatWMXPmTEaNGsWgQYOUhjL4j5Zy5wtPcUWXGC7u1Lo/SUPlFAsG7mrfPB3xiCc+I77LWa1uJ4aGEnrTjYTcOBH/gQN4d+3Ct3c/sqMWNBo0cXHoundD36MHmri4VvtQ+e+iCoI/AWe1j+H9if14/NudlNs9Z2Qp0NSPRpOGteP6IRlBR6zfQre0SGbfNZgXF+xl9cEKNKJA4BQKRQC+sNdQaa+mvD6hya8eDyX1f08MCUUG+pWVcKHJRLZWh1kQ2O/z8Y3bRZgg8kyEhQxt4/rsC7U1XNVd5uMdcPkcqK6DKBN8OLb5vu9aCPsq4KerG8UAQFoE/Hxt87bXdYdO7ws88MADxwmCL4ANp7w2hw55eOKJYtascWC1+klL03P11VFMnpyA2azsfOrUEubPr2HXAS91rgCmGCNxvePIvKwdhojGQbK20EHB0kIqdlTgLHWhNWmJyAgn+6pMLO0tpzyW41lTvJqQXSHcGdnjt4uBZsiwdzblcjZKdcHWcbvd5Ofnc/bZZ/P999+zfPly6urqiImJ4ZJLLqFPH6Ui5wMPPHDC/RQVScTEaBk2LAxZ7t2iRbt2Bn7+uemyiAiMBZoXuHHbPeQsO0Lx7jLKD1VithRx6RszEQQZhyPAK6+UsXGjk02bnNhsAaZPb8MNN5zYodLnk+nefR/793t45ZVkJk9OQJbhnjELCckp5nb7x8gNK0f14iwxFHY+nMaGjCnk5+fzwgsvtNr38bkDvvrqK0ARTRs2bAgKApvNxtq1a7FYLEEnzdGjR7N8+XK+++47Hn30UQRBYMuWLQwdOjQ4s66pqcHr9TabaXfv3p3Zs2ezbt06BgwYgCiKCMDiFd8D0DXhDJN3CQLtR111QjHQvKmArmNHdPXZE1X+vKiC4E9C11QLs+4axLRluXy36Rj+wMkNyA2DdcekcB6+sDOZCS0LifwWosMMvDyhJ1vyrHy78RirD5Yjy8r+GiojCIC/XiikRptZcWApr9Y2ziQXedws8ihr/uNNZuI1GiaYQ1hXV8dCt5KqOF6jYZzJxD2h4cGEKA10T5JZkKskejxSDeM7gsMLXT9QlgAyIqFdJMzPgWeHK5EE3oDiR/D5Tsivhggj9EmCaRdASrgiKCb21vPiyoMUFhaSkpICHEEpz8tJB42CAi/9+h0gIkLDXXfFsXOni0WL7Dz1VAkvvFDK7bfH8uyzSWzd6qJ7dxNjx0fxQ0EE1QVOjv1aQPnWcoa+PhiPtY6DX+dQtqWcgCeA1qwlpms0lkwLBcsKWfPwerKvzsJZ4qQ6p5raIgemaBPnfHR8CB8cnJVDzuzc4OsFLOSuJp+3tcCRexpf+wIwdTXM2AlFtYrl5Mae8Mjg5mLqhh+UNso33TwRzIwZM+jYsWNwjbuiogJZltm8eTMajYZLL70Uk8nE0qVL+eSTTzAajXTpcmIP/dzcHHburODxxxNP2KY5AmAB7gu+46p2s3HGdg6tzGtMACZD/xu2BjVRZaWfZ54pIS1NT/fuJlasOLGJvIF33inn2LHmyZIEAfQaP3ed9SMsabkEYdCCK6oPwimWaZrmDggEAmzZsoV27dpRUVGBrkkCr2nTpgGQmpoaFATbt28nPDyc3NxcKioqGDRoEGvWrOG1116jS5cuOBwO1qxZgyRJdOjQIdhXREQE559/PvPnz+ftt9+mR48eFBYWsmb9DgamhdMuqvV06K0iiMR37kevfzxy+tuo/L9AFQR/Ikx6LfeO6cDEoRn8tKOYVfvLOFBSi8fX+IARBEiLCqF3RhQX904hK/H39+kQBIG+GdH0zYjG5vSyv6iGA8V2KmrrkGSZEIOWzIQwOiSGkx4bgnB3Ab59+ykfMwYCra/V/usExUyORxfj5dtLlPN96Fd4ZR38mAM9E+CJoRCqhx8OKGLgtt7w+FBlsBv7FawrgJt7Qbd4xaFwYxHUeBRBAJAaocyYrFZrvSCYHtzvyQaNL76ooro6wJo12Xz+eRWzZtm47DILBQVeNm508c475ezd6wnOZGUZonaE8fPRNCKzLWx9eTsFywo5+FUOWrOO1BEphCSGYM+roWCZEj435KWBLP/nKo7Mz8Pv8ROREYEx8sRhhon9EwhpMqsbaismvbyC+xcr46DpOIf4a7+Hb/cqIqBPEmwohCeWK8srHzXJBntrbzinowjpYexyD6HSHktlZSULFiwgIiIiKAagMT7e6XTyyCOP0LZtWwC6devGlClTWLhw4QkFgd1u55NPphMfb+Ghh+JPeJ6NCPX/ngGUL/Tw2qOsem8DPre/WTZQY5ib9kMPImqV9xITdZSUdCMhQceWLU769j3QsvsmlJf7eOaZEh5+OKFFwSVRBHrHwIpi8LeU7BW6dGRO7ujYNHeAXq/H6XQiiiJutzvomOf1esnLywNg9+7dwW23bNkS/PvYsWNcffXVpKSksGDBAg4daiyLnpmZyS233NJsv+effz5ms5nly5fzzTffEBEawriO0YzvfOrQ06Yk9xnOwLtfRaNTs3/+1VAFwZ+QCLOeqwemc/XAdCRJpszuweMNoBEF4sKNGPX/vQQ3kSF6BmbFMjDr5PHAuk4dCbvvPmpfe/3UHsUnIaSzI1j19GD90vL5mfDdFcpa/bwD8PNhuLQjvFdv+X9jA6zMhzU3Qr/kE3bNkQol9XFsbCxgA36hIYrgZIOG3a60kWV4/fUyrrsuis8/b8sjjxSyebOLF19MZvLkIhYsqObCCy3IwIXtXPx81IQ5TvGZqNhRic/pZ9DUAYSlNVpzZAkKVxSBKBDVKZLKXVWcN/NcRK3Ixuc2U3us9dlseHo44enKwGgM+Hlx7x6u2KCIgcTQ5m6Nm4vgm72KoHqm3thwWx+IMcPr6+GufoqIwqxhwD9TGNBNcdRY9HM3CgrasGbNGoBgCFsDDbPZmJiYoBgAMBqNdOvWjY0bNxIIBNBomt+vdXV1vPfee3g8Hp577jpCzNuRyuKQbdEgaUDrQ4gtQ7BY6yPcRJQzmgoooWg75u5l44ztrRb0TO5xFI22UZgaDCIJCcog3TDrv+++Qu6441iLpZ/8/Dratt0DEBQDDz5YxIMPKsJNo4EOHYzc0l3H3ZlKX3cthJm7FfEp8zoajaZFvoAGJEkiLi4OjUbTLHdAcXFxs9wBixcvRqvVEhERwdSpUzl48CCvv/46t9xyC127duWf//wnNTU1aDQahg8fTseOHbHZbFRXV7N161Y0Gg2B4+qKCILA8OHDg0mjQrZ/h6a68KRpyhs3FtEajPSe+Dhtz75Ydfz7i6IKgj85oiiQaDkDc97/kLB/3oVvx048y5adUfxxVSBAtEYDGhlzOxeCCDtL4cf6Cc/UEYoY+DkXrpwDQ9vAzPHKe5IMb22ESzoqYsAvQZEd2lia76PIDp9uh27tk+tTuv5A05DCpoPG8QwbFsZLL5UxcWI+fj+MHBnG7NlWPviggrvvjuP662OYPLmIWbNsXHBBBNaqAHq/D+FIJXs+yUUQBfT1PgR6S3OHO2OkAUQQtQJ11V4MFgOi9uQzzOMZWl3MxrwAP9Vfr7gQ5To0sLo+suyq4ybrV3WB19bD7D3QbUgEXJwORk3QS9JorEMQJPbu3Qu0FAQNGfMaUxQ3EhYWRiAQwOv1YjI13r9+v58PP/yQwsJC7rnnbjL12dS9cBkEWonx13sQM/ej7bsfIfU2BEFZW9+3OEcRA9Cqq0Rsu3ICfrGZKAAoKPBy/fX5AJxzThijRoWzfr2Tp54qYetWF/Pmtefo0ToEAZ54IgGLRcP99xchihAZqaGqKsBNN8UQadFQmFcLePnlMLy/GWLNcFE2lFn6UVpWTkFBQfB8mzJv3jwWL15MYmIiBoOB/HzlePR6PR9++CEPPvggoijyyy+/EBMT0+rA2+BM6fU2LmkkJCSQUB+qN2DAAN58803ee+89HnnkkRZ9CIJA586d6XHpReQu/YZDP3+N11GtfNakOqhcn7DIFBVP1uhryBhxKcbw5kXZVP5aqIJA5XdD0GqJ+uhDrHfehWfR4tPe7nabFaMg0C9SJGOH4ij40TZlXDLrlDXvC75S/AkAfJIyI9NroMAOxbUQZ4ZbFijr396Ast3lHWFQmuJTMG0rOH3w1vUNo+J+GhMMnZwxYyJ49tkknnlGmTHecIMSQjVlSgLPPZeMy6UMPFu3Oikr85OYuCu4rTHaSK/7e6A1aSlYUsDOd3eRPSELfZgO6wEb+T8fo+3YdGqO2LEdtJF5WfvTvm4NdK2u4O5FYDFCiE75v7JJcry6+lM0HfdrN9ePwVudOpjQrj4PU+PgERNTyYEDGezbtw+gRZpgi8VCeHh4s6yEDdTU1KDT6ZpFHEiSxPTp0zlw4AA333wz2ZnZRNVEty4GALxGpP098O7tiZAsoxvnxO7xs/bjLa23ryeqTSWipqUg/eKLKhwO5f2xYyO44YYYbrklFkmS+fxzK1arj4ceKuKqqyL517+S2b3bBRTRqZORuDgtW7a4eOutVIw6YLcV++yj/ON7uLgDzKm3YH2acC1+0cDPP//M3Llzg8IAFCfBX3/9laioKGJiYujTpw/Tp08nISEBt9tNIBDghx9+QKfTERkZSUREBBUVFVRWVgYjCmprHZSWKsXF9PoTm+x79erFzJkzKSsrCwoFUMRATEwM/fr1Q6vV0u2Ku+ly6R3Yi45gPbKX2pJ8JL8PjU5PeHI7ojI6E5aYjnDKfA8qfwVUQaDyuyIYDER9NA3nF19S88wz4PND4OROVqONRr53u5lWWUftQmW2Nb4jbCmCY3YYNwtGt2sUBGuOKf+a8u/tiq/AtAuU9fHPd8KMXfDlbog0KVaFx4dCL0t9PDT7OR0x0EB6up4+fUJYv97JhAmRmEwiU6eWkpCgIzNTGfSKinxERWn49ddMnC6Jr5f6WbTAht/tJ2lQItlXZ3FoTi5lmxvDDDMva0fbsemsmrwWc5yZ9pecef789SuqyLNBrRcm9VR8J5qSXb9EvLYA2jZJUb+6/lIU+epN+sdFqMTGVrB37z48Hs8J992nTx+WLVvGvn376NSpE6BkOdyxYwfZ2dnNEgfNmjWLLVu2cM0119CrVy9kINp7CuuXpByTXFzLxie/4uFfP2Xr0d34JB+x5lgGtxnOiIzRSlNZYs3R5bxzzVLyCxyEhIj06mXmiScSGTgwNLj0AzBx4lEmTjzabFfR0bswGATmzFHM9vPnKwmuhgwJZdq0SiZMiEKvr79GOg1f7YYyJzxfb8FyesHkt1KrV2L9ofksfufOnQQCAaxWK+effz6bNm3CYDAwevRoZsyYQfv27Tl8+DBxcXGUl5cH0w1PmTIl2MfXX38V/LthH63h87Ve/CkmJobzzz+/WcimqNFiScvCkvbbQpZV/jqogkDld0cQRUKv/wfGc0bi/OTfOL/6GtnhUBZgZblxOUGrBb+fm0LDuO3ss4k834S29BulUiHQ7m1w+RTnwQ8uaOz/th+VGX/OXcrg/48flIiEpf+A1Ai4oQc8NgTavw1XdIYvxzc5OEdZ/R/NKzOejFmzrNxyy1Fycrpw2WVHWLCghrffTqWmJsDkyYXExWnR6QTcbgm9XuScc8LxS6DvaqIwMYm1j67HEKHHHGsiunMUif0T0IfpKdtazqHvDlO4ogi/W/Ev0B4/jT8FfruHV5b66ZsEy/Lhmm4tBcH5mdAmAib/olgFeicqbaYsV5zk9uzx0KXrPvbs6RzcZurUEj76aDcFBY2iaeHChZSVlaHX6xkxYgQmk4nzzjuPrVu3Mm3aNM455xzKy8vZtGkTABdffHFw2yVLlrBy5UoyMjKC4XV6SURTm4+IwEUdBxOiP7E4WHJoM5d+NYXuie2Z1PtKqtxQ6SzH5m7MSjV37yyWHFnE+AtSuPs+C9XVAaZNq+Dssw+ydm2H4NJPAxMnRmM2i/z735UMGhTKli1Obr45ltRUZea9Zo1yj2zf7kKSYOZMKz/8UM1110bxxqQQlhyBcINiwRo3Cw5ZwaB7lg6dOlNZqRRiajo7LygoQKfT4fMpSYv2799Pv379yMxUnFGdTieSJDFu3DgcDgd79uxh9erVXHnllbjdbubPn8+5555LwBtg6YqlpKamYrfbWySbCQQCbNiwIVjxsKG6Yvfu3endu/cpkjmp/J1R7wyVPwxtcjIRTz1J2EMP4l2/Ae+uXfj3H0By1IJWizYpCV23buj79kHXvj0sfwrKtSAps5uGsXFC1+b9Xt1VEQTrCxvN3oNSFTHQQFoEDE5TIg+a07DofPom0Pffr6BnTzMpKXq++y6DK688wo03Ns4uBwwIJT+/joMHG+svyCj+DVEdIjFEGjj03WFq8uyMeO9sTDHKwBffJ47y7RW4Kz30ebQX4W3OPHT0wMxDRJkg1wpd4uqdA4/DqFVyNVwxBy5V6uRg0MBjN0fw1Ic1tGYN3rrVRVWVH6mJL0hFRQWLFytLQWeddRYmk4nw8HAefPBB5syZw5IlS/B4PAiCgFarJTU1NbhtYaFSw/fIkSMcOXIk+P60hvO49+sTCgK7x8mk719gTFZ/vr7iaURRZHWelSPWxnWRgBRg5dGl9Ersy9R7O9D+7ANotBKXXx5JRsYeZs608tZbqdx2Wwwffqh4q06frgzaU6YkIIoC27a5mDgxOlhw6dAh5f8NG1wYjQKfftqG9eudvPNOBdW5Tg5ZFV+NcbPgqs6KIKjzBdi5c1fQkbLpLL6mpoawsDBsNlswNLBfv37BNuXl5XTo0IH27ZVlozZt2rBu3TrKysqCuQnSktqwYsFKLKGRZKS046PPpuF2u8nMzCQyMpKamho2bdpEaWkpl112GUajkbS0NHr27EmcmvxH5RSogkDlD0c0mTCOGI5xRMt4+mYYLc1S7SaFwd4KiD8uZ0pc/WubG9rX+zjFt5IcMC4Etpe2sg8AEoCjnA5lZT4iI5WfSnKynjVrOnDokIevvrLy9NMlTJwYzY03HiUrq3G9XCODzaOMtJJPwlHkJCIjIigGZElm+9s7qbMpg47WcOY/RUexk7wlBdzTV3GsfKiL4i/h8St+FvnVygw2ygSd42DP7Yp/hs0DndpquXWnYv6Ojm657+++a8eXX1Zx3XX53HHHdfToMZCtW3cwbdo0Jk2aRExMY6habGwst99+O3PnzmXHjh20adOG7du38/XXX3Pw4EGqqqoICQmhd+/ejBs3jvi4eIyShosr09HLGiRJ4pMtC/j31gXkVBZg1hnpmtCOl8fcQbeE9szevZQyh43UiFiumPUEmwr2UeGq5uKO4xmTeQkAATmAL+AlzBBBRW4cWcMVv4e4OC2iCCaTcq6JiY3+CpMnx1FW5uf550vp18+MzRagc+d9rV5rj0emY0cTEyZE4/XKTJtWSWp4owXrrfMU4Tp7n8ic3DCqa5Tlhl27dmGz2QDFfG8wGIK5AzQaDWVlZRytT+srSRJjxowJ7jMyMpKRI0fyyy+/UFWliJefF/5CQdVRbux1O+LyEHpH9metbRWrVqzC4XJgNBppk9KGu+68i3EXjyM9Pf03lohW+TuiCgKVPw8JPYNZ30Axbf96RDHJZjeOPxTXW/tjQ6BrHOhEJYrgeIprFX+EIIIGkvsB4HC045VXfmDjRscJs9dJEnz2WSU1NQFycuowm7eRkWHgqquimDw5nh073IgiGI0CJSU+Skp8CMLWVk9NY9A0i5Xf8/FeiteUkDYqlWO/FiAHzjxU02P1gKSIAYCX1yn/Gmj7FtxzFrxZP8YIgiIMAFaFhTP3e8XcHhnZehjrzJlWQkNF7r23iuXLRaKjlXDE1sr5lpWVsXTpUm677Ta2bt1KIBBg27Zt9O7dm5SUFGpqalixYgXPP/88Dz/8MP8w9UMvK/u9dd7LzNq1hGu6n8tt/S7B6XWzszSXCmc1AMuObCXcEMJ7G+aiEUQC9ffIoapdjMwYi06jR6/R0zayHRsKVvPN3HjSzvNSXe3n2WdLiIzUcMstscyaZWXq1JLgMX/4YSUOh4QgwObNLp5+OpHu3RtvmDvuOEpJibJk8uqrybRtqwi+qydEMm1aZTDN+ISuioPrORkwab4UFAOgJBLavl2JiMjOzsbn8wVzB6xdu5Yffvgh2LZPnz4tyjlfcsklmM1mVq9ejVarJeDxM7HXbfRLGQgy9A0bRN/ug5ptM+T2fnQao/oDqJw5qiBQ+fOQ2IumgeVXdIYX1yoOgyMaw9z5ZJuSXW9YOoQZlDXyH3PgQCV0qB/P91coywW3NsuGK0OSkk63sjKZZ54pPmn2Oq9X4vbbC+jUyUh5uR+tVkCvF3jyyWLeeKMMqzXATTdF8+qr5RgMAhqNzIABYSQl6RAE2LLYw75yZfC0tI/AdrAaR5GD8m0V5C8+RmS2hZojNSCAs9hJoV1xQEvoH4/WeOqfZlhaGL0f7Mn+t7fTJgymjlTef3yZ4mD41hglo+PxBCS460MHUVEadDqRuDgtNltzx8+KCh+//mpn/HgLYWGFiOICZs/ejyiKZGe3LJ71zTffkJWVRdeuXYNx8C+88EKz9eo+ffrwzDPPsPHH5Tw6TrEWzdmznC93/Mysq55hXMchrZ7n4apC/FIAo1bPxN5j6ZGYya0/vMze8lxm7vyEG3rdAcDEnrfzydZ3eeeXmbzTRtk2I0PP2rUdWLiwhldfLcNi0VJW5icpSUe/fmZCQjT4fBLffFPN1KmlbNjQgZ49FVHw2mullJT4iYoSeeCBel+AgEycS3GyDK138m9qwcq/F3ZWmujxnhuj0UjXrl2ZNGkSAF988QV5eXmIotgsH0BFRQWPP/54i7BOAFEUOe+88xgz5jzI0UHuiUswC6JAQsdYOp57mqWxVVSOQxUEKn8ejOGQeR7k/gxygJ6JcGMP+HSHslZ7dhtYkQ/f7oNHBytLCqAMhEvzYMQMuLs+tfrbGxVT+WNNxxhZhs5XAJCYeAElJYNJSHAHExEtWWKnsNBHcbHiw/Dzz3ZuvjmGtDQ9AweG8OqrZaxcWYssg9Ua4KyzzOze7WbzZhfvvpvKzp1uVq1ysGGDE1+dTKIsImoEIjtEkj0hi/VPbmTtlA0Yo5SZpu1gdfDQ9vy70VSdkZeOLlSPs9SF3+Uj51slRXF4ehgJfRudBAzhejQ6EVcd3H2OEv4G8GZ9aYaG1wBXfKtcr06xsCgX9h7yotXCwoUZPPts46y5gdmzbQQC8O231Xz7bTWwi/j4EG666SYSEpqvRe/evZt9+/bxxBNPBN8TRbG585oM8fHxtEtIpaykcR3n7fXf0ie5A+M6DkGSJNz+uha+BA6vB5fPw819LuL18++m0qnMwHsmZrGhcD3nZY0nPjQBo9ZIYlgybSPbM2JYBLH9NvPii6VcfPFhnM4ABQW+YJ/FxT5++EHp5513Uvjmm2oEAR59tIjFi5UBNS5OOf6MjCYZIzUCxSsUH4SsaDhY1dKCVVWrePZLktTMyz8lJYU1a9ZQUlLSLHFRQ0bCpj4XTZEloFaAwyd+XAuigD5Ez/B7BiL8B/VMVP7eqIJA5c9Fv7vg0MLgyw8vUBwEp++A7/crCYfeGA339m/cpFMsrLwBHl4Cz61SQsBGtIVXRkFygwO2oIH2Y8CiTB0NhhDi469FDnwU7GfxYjszZ9qCrxsGDIC8vC4sXJjJZ59V8uKLpRw8WMeOHW4GDw5l6dIshg9v7hBYeZWNb36p5k6bldQRKUR3jmLQCwPImX2Imjw7glbAHGcmdXgy7S7JQNQo/gYFywrZ8c6uZn0d/EqpmpcyPLmZIAAoXFWMoBW4qIvAyUr89klSruGHW5Q8DR07GZk5sy09ephbFQQzZ1qJjdXwxRdt8flktm93M3eujczMLZhMnfB4jMiygN8f4JtvvmHo0KEnzM4nyGCQNAyoieNf9lpSY9MBxVlwS9EBbuk7jieXfMwHG7/H4XWTHpnIs+fczGVdlBm0qT5F7hVdRzTrt3NcW7aX5JBnyyXGHMub618kK6YjV3X9B9hkLhziY8TwI3Tttpf77otj3z4Pv/xiZ/fuTmRlNQ7yl1xyGFGEMWPCWbTITiAgo9EI6PXKd5KUVD8rl2TYZ+OThW60ItzVFxbkKA6ux1uwREEJOWzTpk3w/R49evDtt9+ycuVKJkyYAIAsy6xcuRKLxRLMUtgUWQI8Amw2gtz6QK+IAR0XPncOYa0506ionCaqIFD5c9FuNKQMgOLNIPnRaeCpYcq/k9ErEX697mQtZBj+TLN3AgVnI+g+DK7fv/pqykkr4AHccEMMycl6zj33ENOnpzNhQvPMbbJfxrO0jrrVXn5wu9CLAokDFHNzZJaFs57oe9L+U0ekkDoi5aRtmtL7gZ4ArC8+wCXlRxCBFTe0bPfQIOXf7T/CkmMC27d3DA54rbF+fYdmry+4wMLIkWEMGrSduXPddOo0hH37OvH996twOByMG9ckLrRJ+kBjQEOWO4KOzkjm7lhGsb2SJ4dPBOCIrRhZlpmzZxlaUcPzo24l3BjCexu+4x9zniXcEMK5mf1IDIthX3k+caHN1z8aLAlOn5NDVQcori3kss5X1x+CwIq3z+Xyt7+gY0cja9c6mDo1mUWLahgy5CB33RVHdLSGH3+sYdEiO5MmxRAZqWHevBqcTomQEJHly2uJjdUyf34NV15xmLMHmFkxvYRv9ygWqnPbw7gsJS301mLlHsy1Ko6sogDpFnist5W99Y6yTZ0EA4EA6enp7Nixg9zcXG666aZmORugPgO4XYAtRqg78aw/PjuG4fcNIlwVAyr/IaogUPlzIYpwyQx4vytnkjjo5Agw5DFI6tXsXd/OXJxf2hEePLMiLS+/XEp4uMh55zWP/5YDMrJLpvrhWmySxEqPh+HhIWecW+C3MCu+Pec7HZhcVc0iNZpyqErJAPnmJWJwWQQUD3qfTyY/v47wcA1RUa0f78CBoSQm6vjmmzK+/nobGRmbuf323Vx+eSeio3PxeAxKZUyNFUEI0CnGTMoGMwmhURysOMa9P73FWamdubaHkkjI4VXM6VUuOytvfo9+KUpiowuyB9HxzQm8uOoLzs3sR8/ELJYe3kKxvZKsmLTg8Ti8in9GpCmc2jrFq7RpjdDasggWPTsOn3cnfh8MHRrGunUdePrpYt5/v5yqqgBt2+p5/vkkHnoogauuOoLRKBAaKvLrr3bKyvy88UYKNpvEjM8q+X5uNW0imluoPrgACr+GnWVwuN64FGuGa7rClKEQI6ynY0U+yy0TqdS3YfzFFwWdBNevX09cXBw33ngj/fopzq7Bw5dRfAaOaFtYBgRRQJZkzJEmel3RhU5jstRlApXfBVUQqPz5iM6Ei6fDd9fQarL6M0EQIX04DH2ixUeB0lLq1vmpfd/XyoatM3VqCUuW1PL++2lYLE3yvvtl8EPVddVIFRI/uV14gcv0Jub/Z2dwSkREuiUNxNjncZgxon4AaXndimoVq/fdcwPcPXdPi8/btt3DPffE8eabra9lA3g8EjU1iuCoqfHjdAaYMWM3M2bsbtF2yKXXcUGHQbxzwf1cMvNRwo0hfHXF02hEJbrApFV8KdIjE4NiACDUYOL87AF8vWsJ/kCAS7sM49U1X/HZtoUMy2gUddtLctCKGjrHdqLUoURMbC5aT+e4bsE2G9cEOHiwjhtviEcKCPTrF8KMGenExjZ3ztu508X8+TWcd144oigwenQEstwbSRIos4dTHTeWl8ofw0AdTUsDJIbBluZFBVsQ6S/hksoXKDJ0YI95OOePOZfzzjsPZBkBGVlotAyYQ8xkts1EKNZTabFREVaFx66EpgqCQERyOPHZMaSflUJan+TgUpOKyu+BKghU/px0nQABH8ybCAgnnPWeHAHajoCrfgBtSyuAHAiAIOBe4Kl/rQzsgrb12dbs2VYef7yYm26K5vbbG6s/yn4Z2SFTdX013i2KuPje7cIiiAw3GFoVBAICEQYLdm8Nsiw3m9me2RmKZEd14MG+DyNojXDZbPj2CkBuFsIJSuKi76+sfzE6BSKVAfnxx4uorZV4661U2rUz4HQGEAQBs7n5YPPddzZstgB9+ihu9XFxOr7/vuW699tvl7N+vZOZr32GcVM5F3/5MDUeB0tufIuk8MYlmcQwJYwxLqRlKERsSCS+gB+nz02PxEyu73keM7Yvwi8F6JmkhNTtK8/nwSFXE2awYNBG0DG2CxsK1uDxeegU14UaTzXL835FJ+ppW34nuasOkDX8AFdekYfJLDBwYChxcVr27fPw0UeVmM0iL76olMsMSAKCIDNnU3/eX3IuHp+et7iFh4V3zvxLAgRkUur2k1K3Hz9arLpkrLoUfP3uQ4jJJjQ0lNjYWMxmc4tiRAG/hBSQ0Oo0qiVA5Q9FFQQqf156/ANismHudWDN5bStBWL9bT38GRj4IGhav83FsLBmdRbsrzvwD4xA11HXQhj8+qudf/wjn7FjI/jwQ8VRTPbJCDoBzy91VD9iR6pSjq/I72eT18s15hBkQ/MYfwEBjaDhovbjmNDhGvZX7ePVLS/j8DmQ5BM7BR6PoMwtGZoylLt6/BODtt5JrtN4uHYxzL0Gjls+iDE3iTzIkmFABIgCb75ZBvi5+GILADt2uDjnnByuvDKKDh2MiCJs2eLiyy+rSE/Xc889SpSB2SwGt2nKDz/UsGmThzHjL+Dcl4dxqKqQn65/lY5x6c3aJYXHkBAaRXFtZYs+SmorMWr1hOmVEMB3Lryf1Ih4Pt+xiHn7VwNwTru+PHb2jczepThF3t7vPn7NXciW4g3s3bsLraClfXQWF3W4jGhNG5a/mc727/rSL3MWy7bv4fXXy7DbA8TG6hg/3sJTTyXSvr0Rm9PMgm19+H5LX0qqG31EfuA8esq7OIdViMJvt1xp8RPnO0rcgH/AsEtP2V6jFdGcYQVMFZXfgioIVP7cpJwFt++CTe/CxrfBXqBEDBw/AxZ1SspjUQddroLBj0BcpxN2C6Dr1LHZa6lYonyMFdN5BkImmjGcpVgVNqx1cMklh+nT28zsr9ui1QrIfhn3Tx6cM9x4NzVfcvjB7UIGLjaZKUhRHN+0gpY24W0YmjKMkW3OIVyv+B/0iOvJB+d8xMe7p7GiYDmiIJ5UGIiISEhYDBZu73En/RMHtGzU7hy46wD8Mhl2fEarFpatlTAooeW2QEqKnksvjWTZslpmzKjC55Np00bPXXfFMWVKYquZDZsjI8saJtx6AxsL9vLthOfon9q51ZaXdhnOexu+Y+nhLYxsV58jwlnDjwfWMaxtz6CjnU6jZcrw65ky/HoqnTWkvnwx/VI6UulsLB6k1+gZm30xY7MvPuGRVRdE05Y7uSlJJqynnZiMcnThHrbGhzJtUygH5yVRbo9QrlmLsxL5Fw8iIDOKVcgytFKd+BTUL+cMfgSG/+tMN1ZR+UMRZFk+pdS12+1ERERQU1PTopCGisp/DUmC/OVQsA6Kt0D1UWWgM1qULIdJvSFzLIScPFKgAdnjoTirAzvdbsZWlvO6JZIrzI1ZZsRYkbw2EhcuKSE+QsPi+9sQ4RLw7vTh2+NHdrT+0xlVXoZdllifmIzvuvFoH7mXpNBktOLJB9JSZyk/5y9iTdFqylxlLT43aAx0iOrIeenn0y/xrFP2B4C9GLZ9DPu/h4q9IDVx1Ly6HbSPaFHl8D9DBNpx771xvPXW24ztM4xL2wxsYdyZ0H0UAGUOK/0/vAWn183dAy4n3BDCJ1sWUGgvZ8Wkd+mW0FgO+qudv3CsugyXz8Mrq7/i7PQetIvqSKXLy1kpg4k2n973fjwJl3fm7WNW3N4A0qkfhwhIXMYC7hI+QSMH0AqnadkRNGAIhws/gs6X/aZjVVE5U85k/FYFgcrflnfffZfiTz+lKOcQnzsdnGc00UWnOJtNDAlFBEZUlFEaCPBwWDgJmubm/3Stlt56Q7P3Dvh8nFNRxp2hYTwaHkHsj/PR9+x5xsfm8rk4VnsUt9+NKGiIMcWQGJKIKPwHpmO/F6yHwOsEjQ4sRjBNAupOuenpIwKfM2zYbaxcufKErdz/Wh78O89azCO/fMiKI9vwSX7OSunEs6NuoU9y89DHc6ffy+r8na32d9/Ax8iO6djqZyej91Vd6TOhO1W1dbz6036W7y9DFEA6yVNRIwoEJJn+0TU8FTOfyMPfQsDbaKVqSsN7+jDoNQmGPAohsa13rKLyB6AKAhWV0yA9PT1YWOZ41scp5vQB5cdXR2rkcpOZNyKb5yF4wV7De45afo2Pp2vX7sT9sriFk9ifi3nAs79jf7cCNwdfybKMd/p25OLak4+yZ4oAx4DlWwqbZrs+vU3rLSL9J/ai20XNRUSh1cX3mwtYeaCcIqurRbeRIXr6ZERxad80uqdZlO/WZYWD8xSrVdEmxXdDECE0QamdkXIWZF0IejMqKv9tVEGgonKayJJExYXj8O3e3czB8Pcg6tNPMI0e/bv2+cfwCfDh79DPpcAjHL/+LlW58H64GX5DAadWEUCIMKK7rQ+H1xew5sNNeF2+UwqDhvj96LaRDL93INHprRR6aIKrzs/RSiceXwCtRiQp0kR0qOGk26io/NlQBYGKyhngyz1M+TmjwHf6+QhOikaD6cILiHrv3d+nv/8Kc4DXUNIfn4kwaljCuBmYRGvOeAD+LUX4Fx76Tw6wEY2A/voeiCkRAPjcPnJX57N3YQ5VebZWNxG1Iqk9k+hyQTbJ3RLU8D2Vvw2qIFBROUOcs2dTff/k/7wjjQZNWhpxC+YhRp58Bvrn4xjwDLAD0HByYdDweXvgaaDDSdoq+Nccxb8s77cfngCIAroruqDJjG61idflozLPiqPMScAfQGfUEplmITI1Qk3io/K35EzGbzXsUEUFCLnySmS3h5opjyvpk6XTzwkQRKNB26YNMd/O/n8oBgDSUJYP9qNYDFYD1lbaRQD9gMuBnpzIKnA82sFtECKM+H7KAV/gzJJQNiwTjO8YtAy0ht6sI6lzPLQe5aiionISVAuBikoTPKtWYbv3PqSKytMXBfUCwnT55Vj+9RRixIkHrP9/WIE8wAvoUERDLKcrAlpDttfh+yUXaX9F/RsnaNjgE6AT0fRNRnt2OoJOc4LGKioqraEuGaio/AdItbXUvvMuzs+/QK6tBa0W/McVWhJFJStNIIC+T2/C7r0H4/Dh/5sD/n+KbK8jsL2EQL4NucSh1GVuwKxDTA5DzIxG0zUewaAaM1VUfguqIFBR+R2Q3W7cP/+Md9NmvNu2ESgqRg4EEMxm9F27oOvWDdOY0eg6nnn8u0pzZFkGtx8CEuhEBKPu1BupqKicElUQqKioqKioqJzR+K263aqoqKioqKiogkBFRUVFRUXlNMMOG1YV7Hb7H3owKioqKioqKr8fDeP2aXgHnJ4gqK2tBSA1NfU/OCwVFRUVFRWV/wW1tbVEnCIk+rScCiVJori4mLCwsD95oRYVFRUVFRWVBmRZpra2lqSkJETx5F4CpyUIVFRUVFRUVP7aqE6FKioqKioqKqogUFFRUVFRUVEFgYqKioqKigqqIFBRUVFRUVFBFQQqKioqKioqqIJARUVFRUVFBVUQqKioqKioqAD/B36Tgfc8R8mnAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cmap = plt.cm.get_cmap('viridis', max(partition.values()) + 1)\n",
        "\n",
        "# Visualize the conflict network with node colors representing communities\n",
        "pos = nx.spring_layout(G)  # Positions of the nodes\n",
        "plt.figure(figsize=(10, 6))\n",
        "nx.draw_networkx_nodes(G, pos, node_color=list(partition.values()), cmap=cmap, node_size=100)\n",
        "nx.draw_networkx_edges(G, pos, alpha=0.5)\n",
        "plt.axis('off')\n",
        "plt.title('Conflict Network with Community Structure')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "1eGsH1aZ3r7_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import DBSCAN, SpectralClustering\n",
        "from sklearn.metrics import silhouette_score\n",
        "import numpy as np\n",
        "\n",
        "# Assuming you have your data stored in a variable called 'data'\n",
        "data=df3\n",
        "# DBSCAN\n",
        "dbscan = DBSCAN(eps=0.5, min_samples=5)\n",
        "dbscan_labels = dbscan.fit_predict(data)\n",
        "\n",
        "# Spectral Clustering\n",
        "spectral = SpectralClustering(n_clusters=3, assign_labels='discretize', random_state=0)\n",
        "spectral_labels = spectral.fit_predict(data)\n",
        "\n",
        "# Evaluation metrics\n",
        "dbscan_silhouette = silhouette_score(data, dbscan_labels)\n",
        "spectral_silhouette = silhouette_score(data, spectral_labels)\n",
        "\n",
        "print(\"DBSCAN Silhouette Score:\", dbscan_silhouette)\n",
        "print(\"Spectral Clustering Silhouette Score:\", spectral_silhouette)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exBkBY_eG-MS",
        "outputId": "3296f611-e145-450e-bc71-13f387e3cc81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_spectral.py:717: UserWarning: The spectral clustering API has changed. ``fit``now constructs an affinity matrix from data. To use a custom affinity matrix, set ``affinity=precomputed``.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DBSCAN Silhouette Score: 0.03558630357554981\n",
            "Spectral Clustering Silhouette Score: 0.3767476946295809\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_clusters = len(set(dbscan_labels)) - (1 if -1 in dbscan_labels else 0)\n",
        "\n",
        "# Print the result\n",
        "print(\"Number of clusters: \", num_clusters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JuJoprJ8KzCl",
        "outputId": "89dabafa-c161-4677-890b-45c22ceab1de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of clusters:  16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_clusters1 = len(set(spectral_labels)) - (1 if -1 in spectral_labels else 0)\n",
        "\n",
        "# Print the result\n",
        "print(\"Number of clusters: \", num_clusters1)"
      ],
      "metadata": {
        "id": "QjbFLjQJK6AA",
        "outputId": "711dc116-5c26-4afe-82fe-145117d0fa7e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of clusters:  3\n"
          ]
        }
      ]
    }
  ]
}