{
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      "source": [
        "```{index} single: application; airline seating allocation\n",
        "```\n",
        "```{index} single: solver; cbc\n",
        "```\n",
        "```{index} pandas dataframe\n",
        "```\n",
        "```{index} single: AMPL; sets\n",
        "```\n",
        "```{index} stochastic optimization\n",
        "```\n",
        "```{index} chance constraints\n",
        "```\n",
        "```{index} sample average approximation\n",
        "```\n",
        "```{index} two-stage problem\n",
        "```\n",
        "\n",
        "# Airline seat allocation problem\n",
        "\n",
        "## Attribution\n",
        "\n",
        "The following problem statement is adapted from an exercise and examples presented by Birge and Louveaux (2011).\n",
        "\n",
        "* Birge, J. R., & Louveaux, F. (2011). Introduction to stochastic programming. Springer Science & Business Media.\n",
        "\n",
        "The adaptations include a change to parameters for consistency among reformulations of the problem, and additional treatments for sample average approximation (SAA) with chance constraints."
      ]
    },
    {
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      "source": [
        "## Problem description\n",
        "\n",
        "An airline is deciding how to partition a new plane for the Amsterdam-Buenos Aires route. This plane can seat 200 economy-class passengers. A section can be created for first-class seats, but each of these seats takes the space of 2 economy-class seats. A business-class section can also be created, but each of these takes the space of 1.5 economy-class seats. The profit for a first-class ticket is three times the profit of an economy ticket, while a business-class ticket has a profit of two times an economy ticket's profit. Once the plane is partitioned into these seating classes, it cannot be changed.\n",
        "\n",
        "The airlines knows that the plane will not always be full in every section. The airline has initially identified three scenarios to consider with about equal frequency:\n",
        "\n",
        "1. Weekday morning and evening traffic;\n",
        "2. Weekend traffic; and\n",
        "3. Weekday midday traffic.\n",
        "\n",
        "Under Scenario 1 the airline thinks they can sell as many as 20 first-class tickets, 50 business-class tickets, and 200 economy tickets. Under Scenario 2 these figures are 10 , 24, and 175, while under Scenario 3, they are 6, 10, and 150, respectively. The following table summarizes the forecast demand for these three scenarios.\n",
        "\n",
        "<center>\n",
        "\n",
        "| Scenario | First-class seats | Business-class seats | Economy-class seats |\n",
        "| :-- | :-: | :-: | :-: |\n",
        "| (1) weekday morning and evening | 20 | 50 | 200 |\n",
        "| (2) weekend | 10 | 24 | 175 |\n",
        "| (3) weekday midday | 6 | 10 | 150 |\n",
        "| **Average Scenario** | **12** | **28** | **175** |\n",
        "\n",
        "</center>\n",
        "\n",
        "The goal of the airline is to maximize ticket revenue. For marketing purposes, the airline will not sell more tickets than seats in each of the sections (hence no overbooking strategy). We further assume customers seeking a first-class or business-class seat will not downgrade if those seats are unavailable."
      ]
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      "metadata": {
        "id": "347cbd20-15e4-4634-8a05-e782dc0e0929"
      },
      "source": [
        "## Installation and imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "ea71de65",
      "metadata": {
        "ExecuteTime": {
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        "outputId": "2338046a-dbcd-45be-aa03-227edb45e891",
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          "base_uri": "https://localhost:8080/"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using default Community Edition License for Colab. Get yours at: https://ampl.com/ce\n",
            "Licensed to AMPL Community Edition License for the AMPL Model Colaboratory (https://colab.ampl.com).\n"
          ]
        }
      ],
      "source": [
        "# install dependencies and select solver\n",
        "%pip install -q amplpy pandas matplotlib numpy scipy\n",
        "\n",
        "SOLVER = \"highs\"\n",
        "\n",
        "from amplpy import AMPL, ampl_notebook\n",
        "\n",
        "ampl = ampl_notebook(\n",
        "    modules=[\"highs\"],  # modules to install\n",
        "    license_uuid=\"default\",  # license to use\n",
        ")  # instantiate AMPL object and register magics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "e91fbe82",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-09-30T21:49:07.404490Z",
          "start_time": "2022-09-30T21:49:05.663157Z"
        },
        "id": "e91fbe82"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2f7df9f7-9a96-4358-9e47-50cd63d7611a",
      "metadata": {
        "id": "2f7df9f7-9a96-4358-9e47-50cd63d7611a"
      },
      "source": [
        "## Problem data\n",
        "\n",
        "Pandas DataFrames and Series are used to encode problem data in the following cell, and to encode problem solutions in subsequent cells."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "51e9fdd8-d586-48e6-be86-0d97fbad8a3f",
      "metadata": {
        "id": "51e9fdd8-d586-48e6-be86-0d97fbad8a3f"
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      "outputs": [],
      "source": [
        "# scenario data\n",
        "demand = pd.DataFrame(\n",
        "    {\n",
        "        \"morning and evening\": {\"F\": 20, \"B\": 50, \"E\": 200},\n",
        "        \"weekend\": {\"F\": 10, \"B\": 24, \"E\": 175},\n",
        "        \"midday\": {\"F\": 6, \"B\": 10, \"E\": 150},\n",
        "    }\n",
        ").T\n",
        "\n",
        "# global revenue and seat factor data\n",
        "capacity = 200\n",
        "revenue_factor = pd.Series({\"F\": 3.0, \"B\": 2.0, \"E\": 1.0})\n",
        "seat_factor = pd.Series({\"F\": 2.0, \"B\": 1.5, \"E\": 1.0})"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cfbd3423-f10d-49a4-b7f7-f6545729c9cc",
      "metadata": {
        "id": "cfbd3423-f10d-49a4-b7f7-f6545729c9cc"
      },
      "source": [
        "## Analytics\n",
        "\n",
        "Prior to optimization, a useful first step is to prepare an analytics function to display performance for any given allocation of seats. The first-stage decision variables are the number of seats allocated for each class $c\\in C$. We would like to provide a analysis showing the operational consequences for any proposed allocation of seats. For this purpose, we create a function ``seat_report()`` that show the tickets that can be sold in each scenario, the resulting revenue, and the unsatisfied demand ('spillage').\n",
        "\n",
        "To establish a basis for analyzing possible solutions to the airline's problem, this function first is demonstrated for the case where the airplane is configured as entirely economy-class."
      ]
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            "\n",
            "Seat Allocation\n"
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              "                                      F    B      E  TOTAL\n",
              "seat allocation                     0.0  0.0  200.0  200.0\n",
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              "                     F  B    E\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d138cf98-077c-4587-9c8e-b9b5fe0e4e87 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d138cf98-077c-4587-9c8e-b9b5fe0e4e87');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b92bc2da-8751-405d-a52c-36691e5be53d')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b92bc2da-8751-405d-a52c-36691e5be53d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
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          "text": [
            "\n",
            "Seats not Sold\n"
          ]
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          "data": {
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              "                     F  B   E\n",
              "morning and evening  0  0   0\n",
              "weekend              0  0  25\n",
              "midday               0  0  50"
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              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3536f7a6-2b1a-4aa1-bb0b-f7e22ad59adc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3536f7a6-2b1a-4aa1-bb0b-f7e22ad59adc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-20e84e55-4caf-49e1-a03b-c7058a641f70 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
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        {
          "output_type": "stream",
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          "text": [
            "\n",
            "Spillage (Unfulfilled Demand)\n"
          ]
        },
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          "data": {
            "text/plain": [
              "                      F   B  E\n",
              "morning and evening  20  50  0\n",
              "weekend              10  24  0\n",
              "midday                6  10  0"
            ],
            "text/html": [
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              "      <th>midday</th>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5077c628-ae93-43e3-97bc-75f02b4d63c5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5077c628-ae93-43e3-97bc-75f02b4d63c5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-da0f923b-9f54-4029-9ec2-34b49ebcb018\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-da0f923b-9f54-4029-9ec2-34b49ebcb018')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-da0f923b-9f54-4029-9ec2-34b49ebcb018 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Expected Revenue (in units of economy ticket price): 175.00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# function to report analytics for any given seat allocations\n",
        "def seat_report(seats, demand):\n",
        "    classes = seats.index\n",
        "\n",
        "    # report seat allocation\n",
        "    equivalent_seats = pd.DataFrame(\n",
        "        {\n",
        "            \"seat allocation\": {c: seats[c] for c in classes},\n",
        "            \"economy equivalent seat allocation\": {\n",
        "                c: seats[c] * seat_factor[c] for c in classes\n",
        "            },\n",
        "        }\n",
        "    ).T\n",
        "    equivalent_seats[\"TOTAL\"] = equivalent_seats.sum(axis=1)\n",
        "    print(\"\\nSeat Allocation\")\n",
        "    display(equivalent_seats)\n",
        "\n",
        "    # tickets sold is the minimum of available seats and demand\n",
        "    tickets = pd.DataFrame()\n",
        "    for c in classes:\n",
        "        tickets[c] = np.minimum(seats[c], demand[c])\n",
        "    print(\"\\nTickets Sold\")\n",
        "    display(tickets)\n",
        "\n",
        "    # seats unsold\n",
        "    unsold = pd.DataFrame()\n",
        "    for c in classes:\n",
        "        unsold[c] = seats[c] - tickets[c]\n",
        "    print(\"\\nSeats not Sold\")\n",
        "    display(unsold)\n",
        "\n",
        "    # spillage (unmet demand)\n",
        "    spillage = demand - tickets\n",
        "    print(\"\\nSpillage (Unfulfilled Demand)\")\n",
        "    display(spillage)\n",
        "\n",
        "    # compute revenue\n",
        "    revenue = tickets.dot(revenue_factor)\n",
        "    print(\n",
        "        f\"\\nExpected Revenue (in units of economy ticket price): {revenue.mean():.2f}\"\n",
        "    )\n",
        "\n",
        "    # charts\n",
        "    fig, ax = plt.subplots(2, 1, figsize=(8, 6))\n",
        "    revenue.plot(ax=ax[0], kind=\"barh\", title=\"Revenue by Scenario\")\n",
        "    ax[0].plot([revenue.mean()] * 2, ax[0].get_ylim(), \"--\", lw=1.4)\n",
        "    ax[0].set_xlabel(\"Revenue\")\n",
        "\n",
        "    tickets[classes].plot(\n",
        "        ax=ax[1], kind=\"barh\", rot=0, stacked=False, title=\"Demand Scenarios\"\n",
        "    )\n",
        "    demand[classes].plot(\n",
        "        ax=ax[1],\n",
        "        kind=\"barh\",\n",
        "        rot=0,\n",
        "        stacked=False,\n",
        "        title=\"Demand Scenarios\",\n",
        "        alpha=0.2,\n",
        "    )\n",
        "    for c in classes:\n",
        "        ax[1].plot([seats[c]] * 2, ax[1].get_ylim(), \"--\", lw=1.4)\n",
        "    ax[1].set_xlabel(\"Seats\")\n",
        "    fig.tight_layout()\n",
        "\n",
        "    return\n",
        "\n",
        "\n",
        "# a trial solution\n",
        "seats_all_economy = pd.Series({\"F\": 0, \"B\": 0, \"E\": 200})\n",
        "seat_report(seats_all_economy, demand)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "60309d8c-a2e4-48ee-9510-d2cccb2fd1af",
      "metadata": {
        "id": "60309d8c-a2e4-48ee-9510-d2cccb2fd1af"
      },
      "source": [
        "## Model 1. Deterministic solution for the average demand scenario\n",
        "\n",
        "A common starting point in stochastic optimization is to solve the deterministic problem where future demands are fixed at their mean values and compute the corresponding optimal solution. The resulting value of the objective has been called the *expectation of the expected value problem (EEV)* by Birge, or the *expected value of the mean (EVM)* solution by others.\n",
        "\n",
        "Let us introduce the set $C$ of the three possible classes, i.e., $C=\\{F,B,E\\}$. The objective function is to maximize ticket revenue.\n",
        "\n",
        "$$\n",
        "\\max_{s_c, t_c} \\quad \\sum_{c\\in C} r_c t_c\n",
        "$$\n",
        "\n",
        "where $r_c$ is the revenue from selling a ticket for a seat in class $c\\in C$.\n",
        "\n",
        "Let $s_c$ denote the number of seats of class $c \\in C$ installed in the new plane. Let $f_c$ be the scale factor denoting the number of economy seats displaced by one seat in class $c \\in C$. Then, since there is a total of 200 economy-class seats that could fit on the plane, the capacity constraint reads as\n",
        "\n",
        "$$\n",
        "    \\sum_{c\\in C} f_c s_c \\leq 200,\n",
        "$$\n",
        "\n",
        "Let $\\mu_c$ be the mean demand for seats of class $c\\in C$, and let $t_c$ be the number of tickets of class $c\\in C$ that are sold. To ensure we do not sell more tickets than available seats nor more than demand, we need to add two more constraints:\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "    t_c & \\leq s_c \\qquad \\forall \\, c\\in C \\\\\n",
        "    t_c & \\leq \\mu_c \\qquad \\forall \\, c\\in C\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "Lastly, both ticket and seat variables need to be nonnegative integers, so we add the constraints $\\bm{t}, \\bm{s} \\in \\mathbb{Z}_+$.\n",
        "\n",
        "The following cell presents an AMPL model implementing this model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "c88edf7a",
      "metadata": {
        "id": "c88edf7a",
        "outputId": "5132a0c1-60fd-45bd-f7f7-03d5fa9e99d7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting airline_deterministic.mod\n"
          ]
        }
      ],
      "source": [
        "%%writefile airline_deterministic.mod\n",
        "\n",
        "param capacity;\n",
        "\n",
        "set CLASSES;\n",
        "\n",
        "param seat_factor{CLASSES};\n",
        "param demand{CLASSES};\n",
        "param revenue_factor{CLASSES};\n",
        "\n",
        "# first stage variables and constraints\n",
        "var seats{CLASSES} integer >= 0;\n",
        "\n",
        "s.t. plane_seats: sum{c in CLASSES}(seats[c] * seat_factor[c]) <= capacity;\n",
        "\n",
        "# second stage variable and constraints\n",
        "var tickets{CLASSES} integer >= 0;\n",
        "\n",
        "s.t. demand_limits {c in CLASSES}: tickets[c] <= demand[c];\n",
        "s.t. seat_limits {c in CLASSES}: tickets[c] <= seats[c];\n",
        "\n",
        "# objective\n",
        "maximize revenue: sum{c in CLASSES}(tickets[c] * revenue_factor[c]);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "434a69fc-3bd2-4eb8-95f2-2613e82432b0",
      "metadata": {
        "id": "434a69fc-3bd2-4eb8-95f2-2613e82432b0",
        "outputId": "b95f81d9-527b-4dab-b552-9de5452a4d0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 226\n",
            "2 simplex iterations\n",
            "1 branching nodes\n",
            " \n",
            "\n",
            "Seat Allocation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                       F     B      E  TOTAL\n",
              "seat allocation                     12.0  28.0  134.0  174.0\n",
              "economy equivalent seat allocation  24.0  42.0  134.0  200.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-562ab8cc-d88a-4749-b486-e38969bd21fb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>F</th>\n",
              "      <th>B</th>\n",
              "      <th>E</th>\n",
              "      <th>TOTAL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>seat allocation</th>\n",
              "      <td>12.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>174.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>economy equivalent seat allocation</th>\n",
              "      <td>24.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-562ab8cc-d88a-4749-b486-e38969bd21fb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-562ab8cc-d88a-4749-b486-e38969bd21fb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-562ab8cc-d88a-4749-b486-e38969bd21fb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cf62f135-f2ef-43b0-9853-76f0111b6cb0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Tickets Sold\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                      F   B    E\n",
              "morning and evening  12  28  134\n",
              "weekend              10  24  134\n",
              "midday                6  10  134"
            ],
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              "  <tbody>\n",
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              "      <th>morning and evening</th>\n",
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              "    <tr>\n",
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              "      <td>10</td>\n",
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              "    <tr>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-388fce86-83c2-498d-8ec0-0cd3aca86b9a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-388fce86-83c2-498d-8ec0-0cd3aca86b9a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8f6588cf-4a25-4610-bb31-3cfafafadf82 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
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        {
          "output_type": "stream",
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          "text": [
            "\n",
            "Seats not Sold\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                     F   B  E\n",
              "morning and evening  0   0  0\n",
              "weekend              2   4  0\n",
              "midday               6  18  0"
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-b42bc121-5b77-48a6-9dfe-dbe164e13088 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b42bc121-5b77-48a6-9dfe-dbe164e13088');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e9f50691-9042-4e79-a537-930f059e24a5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Spillage (Unfulfilled Demand)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                     F   B   E\n",
              "morning and evening  8  22  66\n",
              "weekend              0   0  41\n",
              "midday               0   0  16"
            ],
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              "    <div class=\"colab-df-buttons\">\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a43faad2-2697-4122-8521-cede76cd87c3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a43faad2-2697-4122-8521-cede76cd87c3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9afd6a74-7b9f-4c65-a0a4-044e053bf041 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Expected Revenue (in units of economy ticket price): 203.33\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def airline_deterministic(demand):\n",
        "    # Create AMPL instance and load the model\n",
        "    ampl = AMPL()\n",
        "    ampl.read(\"airline_deterministic.mod\")\n",
        "\n",
        "    # load the data\n",
        "    ampl.set[\"CLASSES\"] = demand.columns.tolist()\n",
        "    ampl.param[\"demand\"] = demand.mean()\n",
        "    ampl.param[\"revenue_factor\"] = revenue_factor\n",
        "    ampl.param[\"seat_factor\"] = seat_factor\n",
        "    ampl.param[\"capacity\"] = capacity\n",
        "\n",
        "    # set solver\n",
        "    ampl.option[\"solver\"] = SOLVER\n",
        "\n",
        "    return ampl\n",
        "\n",
        "\n",
        "# Solve a given model and return a Pandas series of the seats for each class\n",
        "def airline_solve(model):\n",
        "    model.solve()\n",
        "    return pd.Series(model.var[\"seats\"].to_dict()).reindex(index=[\"F\", \"B\", \"E\"])\n",
        "\n",
        "\n",
        "# Solve deterministic model to obtain the expectation of the expected value problem (EEV)\n",
        "model_eev = airline_deterministic(demand)\n",
        "seats_eev = airline_solve(model_eev)\n",
        "seat_report(seats_eev, demand)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d1e11c9c-1ab7-4f33-938b-ca6b927640cb",
      "metadata": {
        "tags": [],
        "id": "d1e11c9c-1ab7-4f33-938b-ca6b927640cb"
      },
      "source": [
        "## Model 2. Two-stage stochastic optimization and its extensive form\n",
        "\n",
        "If we assume demand is not certain, we can formulate a two-stage stochastic optimization problem. The first-stage or here-and-now variables are the $s_c$'s, those related to the seat allocations. Due to their dependence on the realized demand $\\boldsymbol{z}$, the variables $t_c$'s describing the number of tickets sold, are second-stage or recourse decision variables. The full problem formulation is as follows: the first stage problem is\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "    \\max \\quad & \\mathbb E_{z} Q(\\boldsymbol{s},\\boldsymbol{z}) \\\\\n",
        "    \\text{s.t.} \\quad & \\sum_{c\\in C} f_c s_c \\leq 200,\\\\\n",
        "    & \\boldsymbol{s} \\in \\mathbb{Z}_+,\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "where $Q(\\boldsymbol{s},\\boldsymbol{z})$ is the value of the second-stage problem, defined as\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "    Q(\\boldsymbol{s},\\boldsymbol{z}) := \\max \\quad & \\sum_{c\\in C} r_c t_{c}\\\\\n",
        "    \\text{s.t.} \\quad\n",
        "    & t_c \\leq s_c & \\forall \\, c\\in C \\\\\n",
        "    & t_c \\leq z_c & \\forall \\, c\\in C \\\\\n",
        "    & \\boldsymbol{t} \\in \\mathbb{Z}_+.\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "In view of the assumption that there is only a finite number $N=|S|$ of scenarios for ticket demand, we can write the extensive form of the two-stage stochastic optimization problem above and solve it exactly. To do so, we modify the second-stage variables $t_{c,s}$ so that they are indexed by both class $c$ and scenario $s$. The expectation can thus be replaced with the average revenue over the $N$ scenarios, that is\n",
        "\n",
        "$$\n",
        "\\max \\quad \\sum_{s \\in S} \\frac{1}{N} \\sum_{c\\in C}  r_c t_{c, s},\n",
        "$$\n",
        "\n",
        "where the fraction $\\frac{1}{N}$ appears since we assume that all $N$ scenarios are equally likely. The first stage constraint remains unchanged, while the second stage constraints are tuplicated for each scenario $s \\in S$, namely\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "t_{c,s} & \\leq s_c & \\forall \\, c\\in C \\\\\n",
        "t_{c,s} & \\leq z_{c, s} & \\forall \\, (c, s) \\in C \\times S\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "The following cell presents an AMPL model implementing this model and solving it for the $N=3$ equiprobable scenarios introduced above."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "0a1b8dd3",
      "metadata": {
        "id": "0a1b8dd3",
        "outputId": "db5bcd9f-4d26-4d94-d5db-6d5e6cffe7c4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting airline_stochastic.mod\n"
          ]
        }
      ],
      "source": [
        "%%writefile airline_stochastic.mod\n",
        "\n",
        "param capacity;\n",
        "\n",
        "set CLASSES;\n",
        "set SCENARIOS;\n",
        "\n",
        "param demand{CLASSES, SCENARIOS};\n",
        "param seat_factor{CLASSES};\n",
        "param revenue_factor{CLASSES};\n",
        "\n",
        "# first stage variables and constraints\n",
        "var seats{CLASSES} integer >= 0;\n",
        "\n",
        "s.t. plane_seats: sum{c in CLASSES}(seats[c] * seat_factor[c]) <= capacity;\n",
        "\n",
        "# second stage variable and constraints\n",
        "var tickets{CLASSES, SCENARIOS} integer >= 0;\n",
        "\n",
        "s.t. demand_limits {c in CLASSES, s in SCENARIOS}: tickets[c, s] <= demand[c, s];\n",
        "s.t. seat_limits {c in CLASSES, s in SCENARIOS}: tickets[c, s] <= seats[c];\n",
        "\n",
        "# objective\n",
        "maximize revenue: sum{c in CLASSES, s in SCENARIOS}(tickets[c, s] * revenue_factor[c]);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "be868fbe-efcf-4970-beaa-fd85718c8914",
      "metadata": {
        "id": "be868fbe-efcf-4970-beaa-fd85718c8914",
        "outputId": "6a4746b5-9d49-476c-cfb9-5136eed75e34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 628\n",
            "7 simplex iterations\n",
            "1 branching nodes\n",
            " \n",
            "\n",
            "Seat Allocation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                       F     B      E  TOTAL\n",
              "seat allocation                     10.0  20.0  150.0  180.0\n",
              "economy equivalent seat allocation  20.0  30.0  150.0  200.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b22e2502-314f-4497-bea3-077ff6b190c6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>F</th>\n",
              "      <th>B</th>\n",
              "      <th>E</th>\n",
              "      <th>TOTAL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>seat allocation</th>\n",
              "      <td>10.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>180.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>economy equivalent seat allocation</th>\n",
              "      <td>20.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b22e2502-314f-4497-bea3-077ff6b190c6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b22e2502-314f-4497-bea3-077ff6b190c6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b22e2502-314f-4497-bea3-077ff6b190c6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-241ea035-e68e-4837-b6d4-90ab8a3a814e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-241ea035-e68e-4837-b6d4-90ab8a3a814e')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-241ea035-e68e-4837-b6d4-90ab8a3a814e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Tickets Sold\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                      F   B    E\n",
              "morning and evening  10  20  150\n",
              "weekend              10  20  150\n",
              "midday                6  10  150"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>F</th>\n",
              "      <th>B</th>\n",
              "      <th>E</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>morning and evening</th>\n",
              "      <td>10</td>\n",
              "      <td>20</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weekend</th>\n",
              "      <td>10</td>\n",
              "      <td>20</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>midday</th>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
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              "    .colab-df-container {\n",
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              "      display: none;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-86fd9e97-ed72-4ea9-b0b8-1eb957832ba0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-86fd9e97-ed72-4ea9-b0b8-1eb957832ba0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c5c09b0e-55cf-4613-8564-d6e901d11099\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c5c09b0e-55cf-4613-8564-d6e901d11099')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c5c09b0e-55cf-4613-8564-d6e901d11099 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Seats not Sold\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                     F   B  E\n",
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              "weekend              0   0  0\n",
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              "        document.querySelector('#df-529fe79f-8c68-40fe-b443-15babebdee99 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-529fe79f-8c68-40fe-b443-15babebdee99');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
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              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "    (() => {\n",
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            ]
          },
          "metadata": {}
        },
        {
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          "text": [
            "\n",
            "Spillage (Unfulfilled Demand)\n"
          ]
        },
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          "data": {
            "text/plain": [
              "                      F   B   E\n",
              "morning and evening  10  30  50\n",
              "weekend               0   4  25\n",
              "midday                0   0   0"
            ],
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              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-60f9ee44-f8c8-4762-892c-87bdb57e1ed9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-60f9ee44-f8c8-4762-892c-87bdb57e1ed9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-79f8c558-0373-4465-afce-a19303ca9054 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Expected Revenue (in units of economy ticket price): 209.33\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def airline_stochastic(demand):\n",
        "    # Create AMPL instance and load the model\n",
        "    ampl = AMPL()\n",
        "    ampl.read(\"airline_stochastic.mod\")\n",
        "\n",
        "    # load the data\n",
        "    ampl.set[\"CLASSES\"] = demand.columns.tolist()\n",
        "    ampl.set[\"SCENARIOS\"] = demand.index.values.tolist()\n",
        "    ampl.param[\"demand\"] = demand.T\n",
        "    ampl.param[\"revenue_factor\"] = revenue_factor\n",
        "    ampl.param[\"seat_factor\"] = seat_factor\n",
        "    ampl.param[\"capacity\"] = capacity\n",
        "\n",
        "    # set solver\n",
        "    ampl.option[\"solver\"] = SOLVER\n",
        "\n",
        "    return ampl\n",
        "\n",
        "\n",
        "# create and solve model for the three scenarios defined above\n",
        "model_stochastic = airline_stochastic(demand)\n",
        "seats_stochastic = airline_solve(model_stochastic)\n",
        "seat_report(seats_stochastic, demand)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "875bd08b",
      "metadata": {
        "id": "875bd08b"
      },
      "source": [
        "## Model 3. Adding chance constraints\n",
        "\n",
        "The airline wishes a special guarantee for its clients enrolled in its loyalty program. In particular, it wants $98\\%$ probability to cover the demand of first-class seats and $95\\%$ probability to cover the demand of business-class seats (by clients of the loyalty program). First-class passengers are covered if they can purchase a first-class seat. Business-class passengers are covered if they purchase a business-class seat or upgrade to a first-class seat.\n",
        "\n",
        "Assume the demand of loyalty-program passengers is normally distributed as $z_F \\sim \\mathcal N(\\mu_F, \\sigma_F^2)$ and $z_B \\sim \\mathcal N(\\mu_B, \\sigma_B^2)$ for first-class and business, respectively, where the parameters are given in the table below. For completeness, we also include the parameters for economy-class passengers.\n",
        "\n",
        "<center>\n",
        "\n",
        "| | $\\mu_{\\cdot}$ | $\\sigma_{\\cdot}$ |\n",
        "| :--: | :--: | :--: |\n",
        "| F | 12 | 4 |\n",
        "| B | 28 | 8 |\n",
        "| E | 175 | 20 |\n",
        "\n",
        "</center>\n",
        "\n",
        "We further assume that the demands for first-class and business-class seats are independent of each other and of the scenario (time of the week).\n",
        "\n",
        "Let $s_F$ be the number of first-class seats and $s_B$ the number of business seats. The probabilistic constraints are\n",
        "\n",
        "$$\n",
        "\\mathbb P(s_F \\geq z_F ) \\geq 0.98, \\qquad \\text{ and } \\qquad \\mathbb P(s_F + s_B \\geq z_F + z_B ) \\geq 0.95.\n",
        "$$\n",
        "\n",
        "These are can be written equivalently as linear constraints, specifically\n",
        "\n",
        "$$\n",
        "\\frac{s_F - \\mu_F}{\\sigma_F} \\geq \\Phi^{-1}(0.98) \\approx 2.054 \\qquad \\text{ and } \\qquad \\frac{(s_F + s_B) - (\\mu_F + \\mu_B)}{\\sqrt{\\sigma_F^2 + \\sigma_B^2}} \\geq \\Phi^{-1}(0.95) \\approx 1.645.\n",
        "$$\n",
        "\n",
        "For the second constraint, we use the fact that the sum of the two independent random variables $z_F$ and $z_B$ is normally distributed with mean $\\mu_F + \\mu_B$ and variance $\\sigma_F^2 + \\sigma_B^2$.\n",
        "\n",
        "We add these equivalent linear counterparts of the two chance constraints to the stochastic optimization model. Rather than writing a function to create a whole new model, we can use the prior function to create and add the two chance constraints using decorators."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "2a3aeb53-b57f-4e00-91ed-1fb1ac4b75b8",
      "metadata": {
        "id": "2a3aeb53-b57f-4e00-91ed-1fb1ac4b75b8",
        "outputId": "48bcd623-f8b7-4c4e-f006-2940acece56a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      mu  sigma\n",
              "F   12.0      4\n",
              "B   28.0     16\n",
              "E  175.0     20"
            ],
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              "                                                    [key], {});\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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          "text": [
            "subject to first_class: seats['F'] - 12 >= 8.21499564252729;\n",
            "subject to business_class: seats['F'] + seats['B'] - 40 >=\n",
            "  27.127620970404912;\n",
            "HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 531\n",
            "4 simplex iterations\n",
            "1 branching nodes\n",
            " \n",
            "\n",
            "Seat Allocation\n"
          ]
        },
        {
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          "data": {
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              "                                       F     B     E  TOTAL\n",
              "seat allocation                     21.0  47.0  87.0  155.0\n",
              "economy equivalent seat allocation  42.0  70.5  87.0  199.5"
            ],
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              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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            "\n",
            "Tickets Sold\n"
          ]
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          "data": {
            "text/plain": [
              "                      F   B   E\n",
              "morning and evening  20  47  87\n",
              "weekend              10  24  87\n",
              "midday                6  10  87"
            ],
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d6119dc0-6077-45a2-9f88-95f62e849d51 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d6119dc0-6077-45a2-9f88-95f62e849d51');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b6835a80-6af6-4ce6-80b3-4b50a87a03f3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b6835a80-6af6-4ce6-80b3-4b50a87a03f3')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b6835a80-6af6-4ce6-80b3-4b50a87a03f3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Seats not Sold\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                      F   B  E\n",
              "morning and evening   1   0  0\n",
              "weekend              11  23  0\n",
              "midday               15  37  0"
            ],
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>morning and evening</th>\n",
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              "    <tr>\n",
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              "      <td>11</td>\n",
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              "    <tr>\n",
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              "    </tr>\n",
              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-49b82b15-25bf-4adf-856c-6e26376b9181')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-49b82b15-25bf-4adf-856c-6e26376b9181 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-49b82b15-25bf-4adf-856c-6e26376b9181');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-df99bd93-32df-47fc-b74b-78062e6f7180\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-df99bd93-32df-47fc-b74b-78062e6f7180')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-df99bd93-32df-47fc-b74b-78062e6f7180 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Spillage (Unfulfilled Demand)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                     F  B    E\n",
              "morning and evening  0  3  113\n",
              "weekend              0  0   88\n",
              "midday               0  0   63"
            ],
            "text/html": [
              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>weekend</th>\n",
              "      <td>0</td>\n",
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              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>midday</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c6dd66ff-477f-4139-b291-1e043f32f040')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c6dd66ff-477f-4139-b291-1e043f32f040 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c6dd66ff-477f-4139-b291-1e043f32f040');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-15e14a8b-7dd6-4e81-9d1c-0d1f588684dd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-15e14a8b-7dd6-4e81-9d1c-0d1f588684dd')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-15e14a8b-7dd6-4e81-9d1c-0d1f588684dd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Expected Revenue (in units of economy ticket price): 177.00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# import the scipy.stats.norm object needed to use the quantile function of the standard normal distribution\n",
        "from scipy.stats import norm\n",
        "\n",
        "# define the mean and standard deviation of the demand for each class\n",
        "mu = demand.mean()\n",
        "sigma = {\"F\": 4, \"B\": 16, \"E\": 20}\n",
        "display(pd.DataFrame({\"mu\": mu, \"sigma\": sigma}))\n",
        "\n",
        "\n",
        "# create a new model with chance constraints that takes as input also\n",
        "# the target quality of service (QoS) levels for classes F and B\n",
        "def airline_cc(demand, QoSF=0.98, QoSFB=0.95):\n",
        "    # create two-stage stochastic model as before\n",
        "    m = airline_stochastic(demand)\n",
        "\n",
        "    # add equivalent counterparts of the two chance constraints to the first stage problem\n",
        "    # the two coefficients related the inverse CDF of the standard normal are computed using the norm.ppf function\n",
        "\n",
        "    first_class = \"s.t. first_class: seats['F'] - {} >= {};\".format(\n",
        "        mu[\"F\"], norm.ppf(QoSF) * sigma[\"F\"]\n",
        "    )\n",
        "    m.eval(first_class)\n",
        "    business_class = \"s.t. business_class: seats['F'] + seats['B'] - {} >= {};\".format(\n",
        "        mu[\"F\"] + mu[\"B\"], norm.ppf(QoSFB) * np.sqrt(sigma[\"F\"] ** 2 + sigma[\"B\"] ** 2)\n",
        "    )\n",
        "    m.eval(business_class)\n",
        "    print(m.con[\"first_class\"])\n",
        "    print(m.con[\"business_class\"])\n",
        "\n",
        "    return m\n",
        "\n",
        "\n",
        "# create and solve model\n",
        "model_cc = airline_cc(demand)\n",
        "seats_cc = airline_solve(model_cc)\n",
        "seat_report(seats_cc, demand)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "801f475e",
      "metadata": {
        "tags": [],
        "id": "801f475e"
      },
      "source": [
        "## Model 4. Solving the case of continuous demand distributions using the SAA method\n",
        "\n",
        "Let us now move past the simplifying assumption that there are only three equally likely scenarios and consider the case where the demand is described by a random vector $(z_F, z_B, z_E)$, where $z_c$ is the demand for seats of class $c\\in C$. The demand for class $c$ is assumed to be independent and normally distributed with mean $\\mu_c$ and variance $\\sigma_c^2$ as reported in the following table\n",
        "\n",
        "<center>\n",
        "\n",
        "| | $\\mu$ | $\\sigma$ |\n",
        "| :--: | :--: | :--: |\n",
        "| F | 12 | 4 |\n",
        "| B | 28 | 8 |\n",
        "| E | 175 | 20 |\n",
        "\n",
        "</center>\n",
        "\n",
        "Note that we model the demand for each class using a continuous random variable, which is a simplification of the real world, where the ticket demand is always a discrete nonnegative number. Therefore, we round down all the obtained random numbers.\n",
        "\n",
        "However, now that the number of scenarios is not finite anymore, we cannot solve the problem exactly. Instead, we can use the SAA method to approximate the expected value appearing in the objective function. The first step of the SAA is to generate a collection of $N$ scenarios $(z_{F,s}, z_{B,s}, z_{E,s})$ for $s=1,\\ldots,N$. We can do this by sampling from the normal distributions with the given means and variances.\n",
        "\n",
        "For sake of generality, we create a script to generate scenarios in which the three demands have a general correlation structure captured by a correlation matrix $\\bm{\\rho}$."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "10a9b698-59b8-42a9-ae7e-9c37a32d8a0c",
      "metadata": {
        "id": "10a9b698-59b8-42a9-ae7e-9c37a32d8a0c"
      },
      "source": [
        "### Scenario generation (uncorrelated case)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "d537f6ac-407c-4d73-8f52-5f4ac31b8ec8",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2022-09-30T21:49:08.708126Z",
          "start_time": "2022-09-30T21:49:07.626836Z"
        },
        "id": "d537f6ac-407c-4d73-8f52-5f4ac31b8ec8",
        "outputId": "ca419e5f-2b0c-49fa-d0db-fec3c7f6c5ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model Covariance\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    F    B    E\n",
              "F  16    0    0\n",
              "B   0  256    0\n",
              "E   0    0  400"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0f00a0f9-4501-40e5-9fe9-100ae1679c90\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>F</th>\n",
              "      <th>B</th>\n",
              "      <th>E</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0</td>\n",
              "      <td>256</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f00a0f9-4501-40e5-9fe9-100ae1679c90')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0f00a0f9-4501-40e5-9fe9-100ae1679c90 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0f00a0f9-4501-40e5-9fe9-100ae1679c90');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e056486c-f632-4148-a4b3-ff3f0955e6d0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e056486c-f632-4148-a4b3-ff3f0955e6d0')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e056486c-f632-4148-a4b3-ff3f0955e6d0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   mu (mean)  sample mean  sigma (std)  sample std\n",
              "F       12.0       11.889            4    4.088936\n",
              "B       28.0       28.626           16   15.051534\n",
              "E      175.0      172.966           20   19.839669"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7eca1c3a-12eb-4e48-8f81-94f8ddb901b9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mu (mean)</th>\n",
              "      <th>sample mean</th>\n",
              "      <th>sigma (std)</th>\n",
              "      <th>sample std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>12.0</td>\n",
              "      <td>11.889</td>\n",
              "      <td>4</td>\n",
              "      <td>4.088936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>28.0</td>\n",
              "      <td>28.626</td>\n",
              "      <td>16</td>\n",
              "      <td>15.051534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>175.0</td>\n",
              "      <td>172.966</td>\n",
              "      <td>20</td>\n",
              "      <td>19.839669</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7eca1c3a-12eb-4e48-8f81-94f8ddb901b9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7eca1c3a-12eb-4e48-8f81-94f8ddb901b9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7eca1c3a-12eb-4e48-8f81-94f8ddb901b9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8e02492f-0215-4bd4-b6a4-3cc95d93adea\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8e02492f-0215-4bd4-b6a4-3cc95d93adea')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8e02492f-0215-4bd4-b6a4-3cc95d93adea button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# sample size\n",
        "N = 1000\n",
        "\n",
        "# define the mean mu and standard deviation sigma of the demand for each class\n",
        "mu = demand.mean()\n",
        "sigma = {\"F\": 4, \"B\": 16, \"E\": 20}\n",
        "classes = demand.columns\n",
        "\n",
        "# build covariance matrix from covariances and correlations\n",
        "s = np.array(list(sigma.values()))\n",
        "S = np.diag(s) @ np.diag(s)\n",
        "print(\"\\nModel Covariance\")\n",
        "df = pd.DataFrame(S, index=classes, columns=classes)\n",
        "display(df)\n",
        "\n",
        "# generate N samples, round each demand entry to nearest integer, and correct non-negative values\n",
        "seed = 0\n",
        "rng = np.random.default_rng(seed)\n",
        "samples = rng.multivariate_normal(list(mu), S, N).round()\n",
        "demand_saa = pd.DataFrame(samples, columns=classes)\n",
        "demand_saa[demand_saa < 0] = 0\n",
        "\n",
        "# report sample means and standard deviations for each class\n",
        "demand_saa_stats = pd.DataFrame(\n",
        "    {\n",
        "        \"mu (mean)\": mu,\n",
        "        \"sample mean\": demand_saa.mean(),\n",
        "        \"sigma (std)\": sigma,\n",
        "        \"sample std\": demand_saa.std(),\n",
        "    }\n",
        ")\n",
        "display(demand_saa_stats)\n",
        "\n",
        "fig, ax = plt.subplots(1, 3, figsize=(10, 3))\n",
        "for i, ci in enumerate(classes):\n",
        "    demand_saa[ci].hist(ax=ax[i], bins=20)\n",
        "    ax[i].set_title(f\"Histogram demand class {ci}\")\n",
        "fig.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5050323e",
      "metadata": {
        "id": "5050323e"
      },
      "source": [
        "We also introduce a new function to report the solution and its features in this more general continuous demand case."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "6e70be11",
      "metadata": {
        "id": "6e70be11"
      },
      "outputs": [],
      "source": [
        "# function to report analytics for SAA case\n",
        "def seat_report_saa(seats, demand):\n",
        "    classes = seats.index\n",
        "\n",
        "    # report seat allocation\n",
        "    equivalent_seats = pd.DataFrame(\n",
        "        {\n",
        "            \"seat allocation\": {c: seats[c] for c in classes},\n",
        "            \"economy equivalent seat allocation\": {\n",
        "                c: seats[c] * seat_factor[c] for c in classes\n",
        "            },\n",
        "        }\n",
        "    ).T\n",
        "    equivalent_seats[\"TOTAL\"] = equivalent_seats.sum(axis=1)\n",
        "    print(\"\\nSeat Allocation\")\n",
        "    display(equivalent_seats)\n",
        "\n",
        "    # tickets sold\n",
        "    tickets = pd.DataFrame()\n",
        "    for c in classes:\n",
        "        tickets[c] = np.minimum(seats[c], demand[c])\n",
        "\n",
        "    print(\"\\nMean Tickets Sold\")\n",
        "    display(tickets.mean())\n",
        "\n",
        "    # seats unsold\n",
        "    unsold = pd.DataFrame()\n",
        "    for c in classes:\n",
        "        unsold[c] = seats[c] - tickets[c]\n",
        "    print(\"\\nMean Seats not Sold\")\n",
        "    display(unsold.mean())\n",
        "\n",
        "    # spillage (unmet demand)\n",
        "    spillage = demand - tickets\n",
        "    print(\"\\nMean Spillage (Unfulfilled Demand)\")\n",
        "    display(spillage.mean())\n",
        "\n",
        "    # compute revenue\n",
        "    revenue = tickets.dot(revenue_factor)\n",
        "    print(\n",
        "        f\"\\nExpected Revenue (in units of economy ticket price): {revenue.mean():.2f}\"\n",
        "    )\n",
        "\n",
        "    # charts\n",
        "    fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n",
        "    revenue.hist(ax=ax, bins=20)\n",
        "    ax.set_title(\"Revenue Histogram\")\n",
        "\n",
        "    fig, ax = plt.subplots(1, 3, figsize=(12, 3))\n",
        "    for i, c in enumerate(classes):\n",
        "        tickets[c].hist(ax=ax[i], bins=20, alpha=0.4)\n",
        "        demand[c].hist(ax=ax[i], alpha=0.4)\n",
        "        ax[i].legend([\"Tickets Sold\", \"Demand\"])\n",
        "        ax[i].set_xlim(0, 300)\n",
        "\n",
        "    fig.tight_layout()\n",
        "    return"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a4b47726",
      "metadata": {
        "id": "a4b47726"
      },
      "source": [
        "We can now solve the stochastic optimization problem using the generated $N=1000$ scenarios. Note that we can use the previously defined function ``airline_stochastic`` to solve the model by simply calling it with a different dataframe as argument."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "7f100de8",
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        "id": "7f100de8",
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          "height": 1000
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 211180\n",
            "2082 simplex iterations\n",
            "1 branching nodes\n",
            " \n",
            "\n",
            "Seat Allocation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                       F     B      E  TOTAL\n",
              "seat allocation                     11.0  20.0  148.0  179.0\n",
              "economy equivalent seat allocation  22.0  30.0  148.0  200.0"
            ],
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          "metadata": {}
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            "\n",
            "Mean Tickets Sold\n"
          ]
        },
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          "data": {
            "text/plain": [
              "F      9.764\n",
              "B     17.416\n",
              "E    147.056\n",
              "dtype: float64"
            ]
          },
          "metadata": {}
        },
        {
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          "text": [
            "\n",
            "Mean Seats not Sold\n"
          ]
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          "data": {
            "text/plain": [
              "F    1.236\n",
              "B    2.584\n",
              "E    0.944\n",
              "dtype: float64"
            ]
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          "metadata": {}
        },
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          "text": [
            "\n",
            "Mean Spillage (Unfulfilled Demand)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "F     2.125\n",
              "B    11.210\n",
              "E    25.910\n",
              "dtype: float64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
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          "text": [
            "\n",
            "Expected Revenue (in units of economy ticket price): 211.18\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x300 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "model_ssa = airline_stochastic(demand_saa)\n",
        "seats_saa = airline_solve(model_ssa)\n",
        "seat_report_saa(seats_saa, demand_saa)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e3e8e43c",
      "metadata": {
        "id": "e3e8e43c"
      },
      "source": [
        "## Model 5. Adding correlations between different demand types\n",
        "\n",
        "Now assume the ticket demand for the three categories is captured by a $3$-dimensional multivariate normal distribution mean $\\mu=(\\mu_F, \\mu_B, \\mu_E)$, variances $(\\sigma_F^2, \\sigma_B^2, \\sigma_E^2)$ and a symmetric correlation matrix\n",
        "\n",
        "$$\n",
        "P = \\left(\n",
        "\\begin{array}{ccc}\n",
        "1 & \\rho_{FB} & \\rho_{FE} \\\\\n",
        "\\rho_{BF} & 1 & \\rho_{BE}\\\\\n",
        "\\rho_{EF} & \\rho_{EB} & 1 \\\\\n",
        "\\end{array}\n",
        "\\right)\n",
        "$$\n",
        "\n",
        "The covariance matrix is given by $\\Sigma = \\text{diag}(\\sigma)\\ P\\ \\text{diag}(\\sigma)$ or\n",
        "\n",
        "$$\n",
        "\\Sigma= \\left(\n",
        "\\begin{array}{ccc}\n",
        " \\sigma_F^2 & \\rho_{FB}\\sigma_F\\sigma_B & \\rho_{FE}\\sigma_F\\sigma_E \\\\\n",
        "\\rho_{BF}\\sigma_B\\sigma_F & \\sigma_B^2 & \\rho_{BE}\\sigma_B\\sigma_E\\\\\n",
        "\\rho_{EF}\\sigma_E\\sigma_F & \\rho_{EB}\\sigma_E\\sigma_B & \\sigma_E^2 \\\\\n",
        "\\end{array}\n",
        "\\right)\n",
        "$$\n",
        "\n",
        "We assume $\\rho_{FB} = 0.6$, $\\rho_{BE} = 0.4$ and $\\rho_{FE} = 0.2$.\n",
        "\n",
        "We now sample $N=1000$ scenarios from this multivariate correlated normal distribution and use the SAA method to approximate the solution of the two-stage stochastic optimization problem."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "79e75010",
      "metadata": {
        "id": "79e75010",
        "outputId": "49458281-24a9-4388-cae3-2f2fa297a0a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      mu  sigma\n",
              "F   12.0      4\n",
              "B   28.0     16\n",
              "E  175.0     20"
            ],
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              "      <th>F</th>\n",
              "      <td>12.0</td>\n",
              "      <td>4</td>\n",
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              "      <td>175.0</td>\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-14a99230-8b21-4bd6-bff2-d4f4c7859e0b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-14a99230-8b21-4bd6-bff2-d4f4c7859e0b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-da5cedcc-41ff-44d6-86c2-e2022204c807\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-da5cedcc-41ff-44d6-86c2-e2022204c807')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-da5cedcc-41ff-44d6-86c2-e2022204c807 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      mu  sample means\n",
              "F   12.0        12.058\n",
              "B   28.0        28.355\n",
              "E  175.0       175.436"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-86fd21a6-79fc-4905-8101-b978cbe5b477\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mu</th>\n",
              "      <th>sample means</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>12.0</td>\n",
              "      <td>12.058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>28.0</td>\n",
              "      <td>28.355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>175.0</td>\n",
              "      <td>175.436</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-86fd21a6-79fc-4905-8101-b978cbe5b477')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-86fd21a6-79fc-4905-8101-b978cbe5b477 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-86fd21a6-79fc-4905-8101-b978cbe5b477');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-eca40b9a-63f7-4b69-8651-dbe9e8dc6afe\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-eca40b9a-63f7-4b69-8651-dbe9e8dc6afe')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eca40b9a-63f7-4b69-8651-dbe9e8dc6afe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   sigma  sample std dev\n",
              "F      4        4.046852\n",
              "B     16       15.879708\n",
              "E     20       19.503597"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ea5d8aa5-2695-4599-908c-ef0817b22136\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sigma</th>\n",
              "      <th>sample std dev</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>4</td>\n",
              "      <td>4.046852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>16</td>\n",
              "      <td>15.879708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>20</td>\n",
              "      <td>19.503597</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea5d8aa5-2695-4599-908c-ef0817b22136')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ea5d8aa5-2695-4599-908c-ef0817b22136 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ea5d8aa5-2695-4599-908c-ef0817b22136');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f44c60f6-077f-4c25-bab2-2ac3ce7945a5\">\n",
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              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f44c60f6-077f-4c25-bab2-2ac3ce7945a5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model Covariance\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      F      B      E\n",
              "F  16.0   38.4   16.0\n",
              "B  38.4  256.0  128.0\n",
              "E  16.0  128.0  400.0"
            ],
            "text/html": [
              "\n",
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              "      <th>E</th>\n",
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              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>16.0</td>\n",
              "      <td>38.4</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>38.4</td>\n",
              "      <td>256.0</td>\n",
              "      <td>128.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>16.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>400.0</td>\n",
              "    </tr>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-007561cf-54e2-45f7-9a9e-249a1fac33bb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-007561cf-54e2-45f7-9a9e-249a1fac33bb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0dc30906-95cd-4822-b719-7cd45b917d12\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0dc30906-95cd-4822-b719-7cd45b917d12')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0dc30906-95cd-4822-b719-7cd45b917d12 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Sample Covariance\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "           F           B           E\n",
              "F  16.377013   40.352763   15.402114\n",
              "B  40.352763  252.165140  121.395616\n",
              "E  15.402114  121.395616  380.390294"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d203be41-1752-4232-ac52-4a0b106e416e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "      <td>16.377013</td>\n",
              "      <td>40.352763</td>\n",
              "      <td>15.402114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>40.352763</td>\n",
              "      <td>252.165140</td>\n",
              "      <td>121.395616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>15.402114</td>\n",
              "      <td>121.395616</td>\n",
              "      <td>380.390294</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d203be41-1752-4232-ac52-4a0b106e416e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d203be41-1752-4232-ac52-4a0b106e416e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d203be41-1752-4232-ac52-4a0b106e416e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3b6a1e6a-901a-4865-9bb0-9b65b8fe74d4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3b6a1e6a-901a-4865-9bb0-9b65b8fe74d4')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3b6a1e6a-901a-4865-9bb0-9b65b8fe74d4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 9 Axes>"
            ],
            "image/png": 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evNi63tTUdMB9Tps2Da+++ipkWYbH4+m3zfPPP4/jjjvO1WJ3JFi8eDGqq6vx+OOP4/jjj8f69evxzW9+09EuGAzi/PPPx/nnnw9JknDuuefiO9/5Dm655Rb4fL4h39cUBRpIvIggxgo0X9J8CYzefHmwQTndxIjRN6w6FAph+vTptlITZo3svhPpGWecAVVVcf/999uu33PPPWAYBqeffjoAWEqXP/3pT23t7rvvPtd2mqeZfU8v7733Xtd9EAQxOtx8880IBoP48pe/jH379jle//DDD/HjH/8YgDGvAM7f7R/96EcAMGDVg4GYPXs25s6di8cffxyPP/44qqurbYs6wJhn+s4x9913n6NUzVAoNHdJkuSYD4fCeeedh/b2dsfc2/s+n/vc56CqKu644w5HG0VRbPP5cJfAMWFZFp/5zGfw17/+Fb/5zW+gKIotVBJw/k0SBAFz5syBruuuKnIU4q9//SsA2DxEBDFWofmS5ktg9ObLgw3ydBMjxpw5c7BkyRIsXLgQ8Xgcb7zxBv7v//4P11xzjdVm4cKFAIDrrrsOy5cvB8dxuOCCC/DJT34SS5cuxTe/+U3s2LED8+fPx9q1a/HnP/8Z119/PaZNm2b9/HnnnYd7770XHR0dOOaYY/CPf/wD77//PgB3YVGRSASLFy/G3XffDVmWMWnSJKxdu/ZjnX4SBDEyTJs2DY8++ijOP/98zJ49G1/60pdw2GGHQZIkbNiwAX/84x8tUZr58+djxYoVePDBB61Qw9deew2PPPIIzjnnHCxduvSA7Tj//PNx6623wufz4bLLLgPL2s+4zzrrLPzmN79BNBrFnDlzsHHjRjz//PMoLS094Hsee+yxKCkpwYoVK3DdddeBYRj85je/+VgpLV/60pfw61//GjfeeCNee+01nHDCCchkMnj++edx9dVX4+yzz8aJJ56IK6+8EqtXr8bbb7+NZcuWwePx4IMPPsAf//hH/PjHP8ZnPvMZAEYJnEsuuQQPP/ywa3GgA+X888/Hfffdh//5n//B3Llzbd45AFi2bBmqqqpw3HHHobKyEv/5z39w//3348wzz3QlLvXWW2/ht7/9LQAjL/aFF17AE088gWOPPRbLli0blmciiGJC8yXNlyYjOV/2Ztq0aYNGWUwYRlIqnRi/mGULXn/99YKvn3jiiYOWDLvzzjv1o446So/FYrrf79dnzZqlf+c739ElSbLaKIqiX3vttXp5ebnOMIytfFgqldJvuOEGvaamRvd4PPqMGTP073//+47yLJlMRl+5cqUej8f1UCikn3POOfq2bdt0ALYSXma5r7a2Nsfz7N69W//0pz+tx2IxPRqN6p/97Gf1vXv39lt2rG8fK1as0IPBoKtxKsSUKVMGLa9GEET/vP/++/rll1+u19fX64Ig6OFwWD/uuOP0++67z1YKRpZlfdWqVfrUqVN1j8ej19XV6bfccoutja73/zt54okn6ieeeKLj+gcffGCVRPnXv/7leL2rq0u/5JJL9LKyMj0UCunLly/Xt27d6pg3B5p7C5XAeeWVV/RjjjlG9/v9ek1NjX7zzTdbJWFefPFFm92F5qIVK1boU6ZMsV3LZrP6N7/5TWuMqqqq9M985jP6hx9+aGv34IMP6gsXLtT9fr8eDof1uXPn6jfffLO+d+9eh81uS+AcSIkaE03T9Lq6uoLlJnVd13/xi1/oixcv1ktLS3Wv16tPmzZNv+mmm/REIjFgv4VK4PA8rzc0NOg33XSTnkqlDthmghgNaL6k+XIk58veX27K6E4UGF0nVSdi4vP222/j8MMPx29/+1tceOGFo20OQRAEQRAEQRAHCZTTTUw4crmc49q9994LlmUdeUIEQRAEQRAEQRDDCeV0ExOOu+++G2+++SaWLl0Knufx97//HX//+99xxRVXoK6ubrTNIwiCIAiCIAjiIILCy4kJx7p167Bq1Sps2bIF6XQakydPxhe/+EV885vftNV9JAiCIAiCIAiCGG5o000QBEEQBEEQBEEQwwTldBMEQRAEQRAEQRDEMDHhY201TcPevXsRDodd1WgmCGLious6UqkUampqHHVAD3ZoriQIojc0XxaG5kqCIHrjdq6c8JvuvXv3kngWQRA2du3ahdra2tE2Y0xBcyVBEIWg+dIOzZUEQRRisLlywm+6w+EwAGMgIpHIoO1lWcbatWuxbNkyeDye4TavqJDto8d4tn882w4Mzf5kMom6ujprXiD2M9S5kiCIiQ3Nl4WhuZIgiN64nSsn/KbbDP2JRCKuN92BQACRSGTcbUDI9tFjPNs/nm0HDsx+Cgl0MtS5kiCIgwOaL+3QXEkQRCEGmyspSYcgCIIgCIIgCIIghgnadBMEQRAEQRAEQRDEMEGbboIgCIIgCIIgCIIYJmjTTRAEQRAEQRAEQRDDBG26CYIgCIIgCIIgCGKYoE03QRAEQRAEQRAEQQwTw7bpfvnll/HJT34SNTU1YBgGTz31lO11hmEKfn3/+9+32tTX1ztev+uuu4bLZIIgCIIgDpCspGBfMo+spIy2KQRBEEQPNDePDYatTncmk8H8+fNx6aWX4txzz3W83tzcbPv+73//Oy677DKcd955tuu33347Lr/8cuv7wQqPEwRBEAQxsmxvTWPt5hYk8zIiPg+WHVqF6RWh0TaLIAjioIbm5rHDsG26Tz/9dJx++un9vl5VVWX7/s9//jOWLl2KhoYG2/VwOOxoOxCiKEIURev7ZDIJAJBlGbIsD/rzZhs3bccaZPvoMZ7tH8+2A0Ozf7w+I0GMZbKSgrWbW9CVlVEV8aElmce6LS2oidUjIAzbMoMgCIIYAJqbxxZjYsT37duHv/3tb3jkkUccr91111244447MHnyZHzhC1/ADTfcAJ7v3+zVq1dj1apVjutr165FIBBwbdO6detctx1rkO2jx3i2fzzbDrizP5vNjoAlBHFwkcorSOaNRZ1f4FAV8aEtnUcqrwzrwi4rKUjlFYR9PC0gCYIg+jBaczNRmDEx4o888gjC4bAjDP26667DEUccgXg8jg0bNuCWW25Bc3MzfvSjH/Xb1y233IIbb7zR+j6ZTKKurg7Lli1DJBIZ1BZZlrFu3Tqceuqp8Hg8B/5Qo8BEsP3bb7AQNcbVz7x32/Jhtso9E2Hsx6PtwNDsNyNfCIIoHmEfj4jPg5Zk3vKmxIMehH3Dt8SgkEmCIIiBGY25meifMTHq//u//4sLL7wQPp/Pdr335nnevHkQBAFXXnklVq9eDa/XW7Avr9db8DWPxzOkDcVQ248lxrPtosZAVN1tusfiM47nsR/PtgPu7B/Pz0cQY5WAwGPZoVVYt6UFbWljUXfqnKph86RQyCRBEMTgjPTcTAzMqI/6P//5T2zbtg2PP/74oG2PPvpoKIqCHTt2YObMmSNgHUEQBEEQgzG9IoSaWP2IhHtTyCRBEIQ7RnJuJgZm1Ef+V7/6FRYuXIj58+cP2vbtt98Gy7KoqKgYAcsIgiAIgnBLQBiZBR2FTBIEQbhnpOZmYmCG7R1Ip9PYvn279X1TUxPefvttxONxTJ48GYCRX/nHP/4RP/zhDx0/v3HjRrz66qtYunQpwuEwNm7ciBtuuAEXXXQRSkpKhstsgiAIgiDGMBQySRAEQYw3hu0v1BtvvIGlS5da35v52StWrMCaNWsAAI899hh0XcfnP/95x897vV489thjuO222yCKIqZOnYobbrjBludNEARBEMTBx/SKEGKBWrQkRFRFvSgL+Qb/IYIgiHECVWeYeAzbu7hkyRLouj5gmyuuuAJXXHFFwdeOOOIIbNq0aThMIwiCIAhiHEPq5QRBTFRofpuYsKNtAEEQBEEQhFt6q5eXh3zoyspYt6UFWUkZbdMIgiA+FjS/TVxo000QBEEQxLihkHp5IicjlZ94i9KspGBfMk8LboI4SDiY5reDDUoSIAiCIAhi3HCwqJdTiClxMDJWc5lHyq6hzm9jdbwIJ/TuEARBEAQxbjgY1Mt7h5iaC+91W1pQE6ufUM9JEL0ZqwdNI2nXUOa3sTpeRGFo5iYIgiAIYlwxvSKEmlj9hPXwFAoxbUvnkcorE+5ZCQIYuwdNo2GXm/ltOOwir/nwQiNKEARBEMS4IyBM3IXhwRJCTxAmY/WgabTsGmx+K7Zd5DUffkhIjSAIgiAIYgxhhpjGg54JG0JPEL3pfdCUk1S0JPOI+kf/oOlgsIsU00cGmr0JgiAIYhxBIYAGE30cJnoIPUH0ZqxqNRwMdo3VKIOJBo0kQRAEQYwTKATQ4GAZh4kcQk8QfRmrB00T3S5KZxkZKLycIAiCIMYBFAJoQONAEBOXgMCjMuIbMxtbk4lsF6WzjAw0mgRBEAQxDjiYQgAHCh0f6jhM9DB0giCIj8tY9eZPJGhECYIgCGIccLCEAA4WOj6UcThYwtAJgiA+LpTOMrxQeDlBEARBjAMOhhBAN6HjbseBwtAJgiCIsQJtugmCIAhinDC9IoQVx9bjkuOmYsWx9RPOa1sodDyRk5HK2zfKbsbBbV/E+GH16tX4xCc+gXA4jIqKCpxzzjnYtm2b9XpnZyeuvfZazJw5E36/H5MnT8Z1112HRCJh6+ejjz7CmWeeiUAggIqKCtx0001QFPpcEAQxfNCmmyAIgiDGEWNV0KcYDKX27GDjMFbr6xIHzj/+8Q+sXLkSmzZtwrp16yDLMpYtW4ZMJgMA2Lt3L/bu3Ysf/OAHeO+997BmzRo8++yzuOyyy6w+VFXFmWeeCUmSsGHDBjzyyCNYs2YNbr311tF6LIIgDgLoLw9BEARBEGOCYtaeHav1dYkD59lnn7V9v2bNGlRUVODNN9/E4sWLcdhhh+GJJ56wXp82bRq+853v4KKLLoKiKOB5HmvXrsWWLVvw/PPPo7KyEgsWLMAdd9yBr3/967jtttsgCMJIPxZBEAcB9JeHIAiCIIh+GWn172Kq6E6vCCEWqEVLQkRV1IuykK+IlhKjjRk2Ho/HB2wTiUTA88bnaOPGjZg7dy4qKyutNsuXL8dVV12FzZs34/DDD7f9vCiKEEXR+j6ZTBbzEYgxwEjPcVRR4eCE3mmCIAiCIAoyWurfxVLRJfXyiYumabj++utx3HHH4bDDDivYpr29HXfccQeuuOIK61pLS4ttww3A+r6lpcXRx+rVq7Fq1aoiWk6MJUZ6jnB7v7G6MR+rdo0HKKebIAiCIAgH4139e7zbTwzMypUr8d577+Gxxx4r+HoymcSZZ56JOXPm4Lbbbjvg+9xyyy1IJBLW165duw64L2JsMdJzhNv7bW9NY80rO/DwK01Y88oObG9ND4s9Q2Ws2jVeoE03QRAEQRAOTPXvkMCjMyMiJPDjSv17KOrlWUnBvmR+0MW223bE8HLNNdfg6aefxosvvoja2lrH66lUCqeddhrC4TCefPJJeDwe67Wqqirs27fP1t78vqqqytGX1+tFJBKxfRETg5GucODmfkM9CGhP5/HengTa0/lhsflA7SKcUFwAQRAEQRAOwj4ebck8NjV1AjoABljUEB836t+91curIj60JA0xtb72uw33pFD10UfXdVx77bV48skn8dJLL2Hq1KmONslkEsuXL4fX68Vf/vIX+Hz2PP5FixbhO9/5DlpbW1FRUQEAWLduHSKRCObMmTMiz0GMDdzOESN5v0Ib87Z0Hqm84gjnXr+1FWteabLmpIuPm4qTZlUMi+1DsYsoDHm6CYIgCIJwkJUU7OjMQtF08BwDRdOxoyM3bjwbpnp5POjpV73crfeGvDxjg5UrV+K3v/0tHn30UYTDYbS0tKClpQW5XA6AseE2S4j96le/QjKZtNqoqgoAWLZsGebMmYMvfvGL+Pe//43nnnsO3/rWt7By5Up4vd7RfDxihHEzRwzH/UJeDjs60gh5Ocf93JY6bE/nseaVJnRlZVSEjTnpkQ1Nw+bxHmoJRooKcjJsm+6XX34Zn/zkJ1FTUwOGYfDUU0/ZXr/44ovBMIzt67TTTrO16ezsxIUXXohIJIJYLIbLLrsM6TTlDxAEQRDEcNOSEKFqOg6tiWB2dRSH1kSgaBpaEuLgP/wxKdaCbXpFCCuOrcclx03FimPrHZ5pt+GlZrt4QICsaogHhHEVaj9R+NnPfoZEIoElS5agurra+nr88ccBAG+99RZeffVVvPvuu5g+fbqtjZmLzXEcnn76aXAch0WLFuGiiy7Cl770Jdx+++2j+WjEKDHYHDFsMEzBy24PAloSIpJ5GdVRHwICj+qoMXcN1/w8lAMKyv0uzLDFA2QyGcyfPx+XXnopzj333IJtTjvtNDz88MPW931PGC+88EI0Nzdj3bp1kGUZl1xyCa644go8+uijw2U2QRAEQRAAqqJeRHwetKUkVEd9aEtJiAc9qIoOrzew2GHcAymhuw0vDft4yIqG9dv2wcOykDUN82uj4ybUfqKg6/qAry9ZsmTQNgAwZcoUPPPMM8UyixjnFKtawmCYETNpUUV9PIiWZB7rtrSgJlZvu7+bsonm/NycyKM66kNzIj/s87Mbu3pHBZlzaqFnLDbjQVV92Kw6/fTTcfrppw/Yxuv1FhStAID//Oc/ePbZZ/H666/jyCOPBADcd999OOOMM/CDH/wANTU1RbeZIAiCIAiDspAPFx83FY9saEJryljQrTh26rDWuh7pBZvpvVm3pWXw8FLLM2Vu6gp7qgiCIAoxlLzowQ4CRmN+dmPXaOR+jxe9jVE9CnjppZdQUVGBkpISnHTSSbjzzjtRWloKANi4cSNisZi14QaAU045BSzL4tVXX8WnP/3pgn2KoghR3B9akUwmAQCyLEOW5UFtMtu4aTvWmAi2e9nBT6j7/sxYYCKM/Xi0HRia/eP1GQlitDhpVgXm1UbQkhBRFfUO+4KucBi3NKwLNjfem1RegYdjcMKMMkiKDoFnkJVUEhEiJjzjwYM4XjAja3Z1ZRHze9Cdk1EZ8R5wxMxIz8/A4J+HkRanGy3P+oEwatacdtppOPfcczF16lR8+OGH+MY3voHTTz8dGzduBMdxaGlpsVQlTXieRzweR0tLS7/9rl69GqtWrXJcX7t2LQKBgGv71q1b5/5hxhjj2fY7jtRctx2LoWHjeezHs+2AO/uz2ewIWEIQE4uykG9EFnPA6IVxD+a9Cft4yKqOl99vh8AxkFQdC+piFF5OTGjGiwdxvBAQeMyqjmBTUxOSORkRvweLDyn/WJvDkZyf3XwehhQ9VATGk6r6qFlzwQUXWP8/d+5czJs3D9OmTcNLL72Ek08++YD7veWWW3DjjTda3yeTSdTV1WHZsmWuaivKsox169bh1FNPtdV1HA9MBNu//QYLUXMXsvfebcuH2Sr3TISxH4+2A0Oz34x8IQhiDDNWw7h1vcc0BgyjY799BDHxGOsexPHogc9KCrY2J9FQHkTML6A7J2FbSxLHNMTHxDMMNKZD+Ty4iR4qFiPtWf84jBmLGhoaUFZWhu3bt+Pkk09GVVUVWltbbW0URUFnZ2e/eeCAkSdeqOSDx+MZ0oZiqO3HEuPZdlFjIKruFlhj8RnH89iPZ9sBd/aP5+cjiInAYAtlM4x74eQYOrMy4gEPwDCj7rVI5RV4eBbHTyuHpKoQOA45RRl1uwhiuBjLHsTx6oE3x7QuFoRf4BD2esbNmA718zBS4nQj7Vn/OIwZi3bv3o2Ojg5UV1cDABYtWoTu7m68+eabWLhwIQBg/fr10DQNRx999GiaShAEQRDEEHGzUA77eDS2pbGpsROapoFlWRzTUDrqXgsz7P3NnW2kXk4cFIxVD+JQPK7t6fxBne/sFjdjOlZtB0bWs/5xGDar0uk0tm/fbn3f1NSEt99+G/F4HPF4HKtWrcJ5552HqqoqfPjhh7j55psxffp0LF9uhAzPnj0bp512Gi6//HL8/Oc/hyzLuOaaa3DBBReQcjlBEARBjCPcLpQ70iLe25uEounw8RzyiobNexPoSIsIxEd5ITVWw94JYhgYqx5Etx7X9VtbseaVJuuQ7+LjpuKkWRUD9PzxGIv5zm5xM6Zj1XaTkfKsfxyGzbo33ngDS5cutb4386xXrFiBn/3sZ3jnnXfwyCOPoLu7GzU1NVi2bBnuuOMOW2j47373O1xzzTU4+eSTwbIszjvvPPzkJz8ZLpMJgiAIghgG3C6UG9syUFQNk2I+CBwHSVWRyMlobMugLh48oHu7yf3c1ZlBY1sGDeXBgvcZy+rl4zG3lRgfjEUPohuPa3s6jzWvNKErK1s1rB/Z0IR5tZED9niPdL6z299rN+2K5YGfXhFCLFA7otEDE4lh++1ZsmQJdL1/kZHnnntu0D7i8TgeffTRYppFEARBEMQI43ZR11AeREDg0ZWVURpk0JWVEfbxaCg/sA23G+/T7zbtxEP/bERWMjbQl5/QgAuPmeKwfyyql4/X3FZi/DDWPIhuPK4tCRHJvLHhDgg8qqM+tKaMUPMD2SiOdL6z299rN+2K6YGn+ebjwY62AQRBEMTg7NmzBxdddBFKS0vh9/sxd+5cvPHGG9bruq7j1ltvRXV1Nfx+P0455RR88MEHo2gxQezHXNTFg54BF3V18SAuP6EBYR+PrqyEsI/Hl49vOCAvd2/vU3nIh66sjHVbWpCVFKvNrs4MHvpnI9KigpKAgLSo4Jf/asSuzoyzQ5t6OTDa6uVuno8gxhJZScG+ZH7Az6ibNtMrQlhxbD0uOW4qVhxb79j4VUW9iPg8aE4Y/TQn8oj6PaiKOoWW3dg82O+ZeajY1JFGU1saTR1pRP0Hlu9s3q81nYfPw6E1nS/4e221S4nw8RxaU6Kj3VDmiMHGlOabj8/YOboiCIIgCtLV1YXjjjsOS5cuxd///neUl5fjgw8+QElJidXm7rvvxk9+8hM88sgjmDp1Kr797W9j+fLl2LJlC3w+CgEjRh+3YZUXHjMFn6gvwZbmJOZUR3BIVeFyn26U0AfzPjW2ZZCVFMQCAjiWRSwgIJmTHOHspnr5STMrwTCArgOJvDSq4eVjWV2aIPpSLK+syUDe4rKQDxcfNxWPbGhCa8o45Ftx7NQD8nK7zXeO+j34wxu7bBEzB/J7mMor2NmRRWsqj0YtA45lkBM1x+91Kq9gZ2cGrUkRjRrAsUBOtldUKKYHnuabjw+NEkEQxBjne9/7Hurq6vDwww9b16ZOnWr9v67ruPfee/Gtb30LZ599NgDg17/+NSorK/HUU0/hggsuGHGbCaIQbkJVt7em8fx/WpHMy9jbnQfLsge0OHcT0t5QHoSHY7GnKwefh0Ve1hAPeRzh7GZfnVlpzCj3jmU1YYLojZuc52LXBT9pVgXm1UZc5R8PpHJu/p7t6s5YtbUrwz5HDvlzm1vg5zlURrxIZhWs3dKC5YdVFrzvQAeGHAs0J3LoSEsoDQloTYrQdB1cn9hkjgWau/O2vPW+7YY6RwxkF803Hx8KLycIghjj/OUvf8GRRx6Jz372s6ioqMDhhx+Ohx56yHq9qakJLS0tOOWUU6xr0WgURx99NDZu3FiwT1EUkUwmbV8EMdq4CWF0G+boJqS9NOTFnJoIPBwDWdHg4RjMqYmgNOQdcl8jzVi0iSAKUchLmsjJSOWVIbUZKmUhHw6bFB1ww71+aytufPzf+NZT7+LGx/+N9Vtbba8HBB6zqiNobM3gpW2taGzNYGZVxJFD3prKQ9Y0dGZkyJqGfUljI9+X7a1prHllBx5+pQlrXtmB7a1p2+uqBgS9HHKygsa2DHKygoDAQdXgaFcd9aM8LCAvqygPC6iK+G3thjJHDGYXzTcfHxopgiCIMU5jYyN+9rOf4cYbb8Q3vvENvP7667juuusgCAJWrFiBlpYWAEBlZaXt5yorK63X+rJ69WqsWrVq2G0nRodiKlqPZF/mwjse8kDWDK9zIisfcMjk9IoQNK3CClXv6w1P5RVMKw9hVlUYqbyKsI+DrOr99lUsJediKROPRXVp4uCjGOrYlke5K4uY34PunIzKiPdjeVIHq9PtRuU8KynY2pxEQ3nIsmtbSxLHNMStZ436eeRlDWlRQWlQQEePAGTUP3SVc44FMqIKD8ci5ueQkVRkJdXh6Q77eEwpDcDvZfv1wAPu5gi3UQY033w8aLQIgiDGOJqm4cgjj8R3v/tdAMDhhx+O9957Dz//+c+xYsWKA+rzlltusUo5AkAymURdXV1R7CVGl2IqzI50X6ZK+AtbWvtVCR9KmONgyuRmX11ZGXUlgUFDJouh5FxMZeJi2UQQB0qx1LFNj/KmpiYkczIifg8WH1J+wJ9tN3W63aicm4d8dSUB+AUOYZ/Hccgn8BxmV0WwrTWJjKgi4ucxsyICgeds93NzYKhqhkRja0qEomrgORYVEZ/D0917TBM5GZVhX7+e58HmiKEcZNJ8c+DQqBEEQYxxqqurMWfOHNu12bNn44knngAAVFVVAQD27duH6upqq82+ffuwYMGCgn16vV54vUNXciXGNsXMixy1vmwq4Tr6qoS7LW/TW5m8NCigIyPhl/9qxOJDyiyRNLd9FQu341Ds/FaCGA6KWZ96v0c5aHlu+3qU3eK2TndvlXOzXTxoVzl3k9Md9vGYWxtFPOyB38MjJyuojQUch3duDgwlRcWuziwYhkF52IfunIzdXVlIiup4zmLV/HbzjKNJMaOtRpPxazlBEMRBwnHHHYdt27bZrr3//vuYMsXw2E2dOhVVVVV44YUXrE12MpnEq6++iquuumqkzSVGkWIqzA5HX/GAAFnVEA8ISOSc6t9uVcLdLDZNZfLSoAC/h0dpEOjKOpXJp1eEEAvUuhJc+ri4HVNSCibGA8Ohjl0XCxoeZa/To+wWt3W63aicWx74VzqQzHcj4vNg8YwKh5e+t9e5NhYoeHjn5pAvkVPg87DwsALAAKUBARxnXC8Ui1aMmt9unnG0mEi1wUd/NAmCIIgBueGGG3Dsscfiu9/9Lj73uc/htddew4MPPogHH3wQAMAwDK6//nrceeedmDFjhlUyrKamBuecc87oGk+MKMVUmC12X7KiYf22ffCwLGRNw/zaaL+eIDcq4YMtNhvKgwgIPDoyEkqDQEfGqPvdV5l8JBd1bseUlIKJ8cBozjcDeT9ND/aerhxKQwI60hLKwkLBOt2DqZybHviKsIB4QADPoaAH3q3XebB2VVEvKsI+S718INsHG4ehRNYMlrc+Gky0iB9SLycIghjjfOITn8CTTz6J3//+9zjssMNwxx134N5778WFF15otbn55ptx7bXX4oorrsAnPvEJpNNpPPvss1Sj+yCjmAqzRVerNWLGsT9cnHE0KeY96+JBXH5CA8I+Hl1ZY8P95eMbbF5ut0roxcLt85FSMDEeGK35ZntrGg/+oxH3r/8AD/6j0aG0XRbyYfmhVUjmZby7J4FkXsayOVX9RrEMpHKeyivY2NiBF7a24fmtrXhhaxs2fNjxsVTVAwKPyoiv4LOZ3veysIBkXkZZWOi3xvhgiuMFhSkLKMKb7SrDPvg8HCrD/SvHZyUF+5L5YZsjC9lVTEX70YRmb4IgiHHAWWedhbPOOqvf1xmGwe23347bb799BK0ixiLFVJgtVl+pvAIPx+CEGWWQFB0CzyArqR9LJdxNnt+Fx0zB4kPK0NiWQUN50LbhNu0aSnhsMXILi+URI4ixwEjPN1lJwe9f3Yl39iTgYVls1VJIizJuXDbTVvP7rY+6IKsaOJaBrGp4a1cXPn3EpCHbl8hKeHd3N0RFQ8jLIy0qeG9PNxJZCZWR/RvhYkbMuKkx7sYL7EaY0mrnIhJppEO9J1rEz/i0miAIgiCIfimmwmwx+jIXfy+/3z7g4s/tPYey+KuLOzfbve1yu6gr5oLT7ZiSUjAxHhjJ+aY1KeK9vQlwLINYQEBnVsS7exJoTYqoLzN+bmdHBq82doIBg4qwgK6MjNcaO7GzI4PZ1VFHnwOVFmtO5MEyDAIeDpoOBDwc9J7rh1RFAAw9DNrN4V1A4FEexqA58IPpZEDXoeuApGrQdQZ9hSktGAaqpkPXVBhC6fZIpOF4xsEYaaHL4WZ8Wk0QBEEQxPhiEFVytxQzz8/tom6i5RYSxPhFB8AAek/Gig7j+17zSUZUoeoaBJ4FyzAQeAaSqiEjOhXAByst1lAehE/g0JWRjX4UHfGgx6YNMZSIGTeHd67LKw7inU7lFaQlBUEvB1HR4OVZpESloHBjWpQR8vEQZQ0BD4uUKNvaFfsZ3TKRIn4op5sgCIIgiI/NQLl+vVXJFx9SjpNmVoLn2H5z89rTeby3J4H2dL5gX8m8jJCPQ2dWRMjHfaw8v+kVIZw8qwKzqsI4eVZFwcXhRMstJIixymA5wxURI/9a1XV0ZUSouo65k6Ko6BXqXV8WwKSYH1lRQUdGRFZUUFviR31ZwNaXWVqsLZWHl2fRljJKi/Wed0pDXhxWEwHPArKqgWeBQ2uiKA05y4o1dqTQ2J5GY0cKUb8zYsY8vNubyEFVdexN5BwaEkPSmWAYqLoOUVWh6j2HEb3gWKC5O4+93VlkRBl7u7NoSeTB9dn9me26MjKifg+6MrKjXe+ooJykoiWZH/AZi6mTMVAO/HhifFtPEARBEMSoM5hnYyiq5IN5nsI+Hm0pEZsaOyyn16KG0gPO8/vdpp146J+NyEqGx+byExpw4TFTbG0mWm4hQYxF3Ja3+vxRkxHx82hPSygLCThzbo1tQ1YW8uH46eXY3ppGKivDx7M4blq5I3S8JSFiV1cWiayM5qQIFkBe0WylxVJ5BSUBLw6bFEFaVBHycigJCDYPb0DgEfV78Ic3OnrNI1HHJjGVV/DungS2taQgqRoEjsXMqvABeZRTeQXpvIKQwO/3YuftXmxVAzoyIna0Z6HB8LR6PRxUzT7uqgZUR/3gOQZ5WUV5WEB5yGdr5zYqiMod9g95ugmCIAiCOGDceDbcKhObnqeurIyKsNFXX89TVlKwoz0DRdPBcywUTceOjuwBeVJ2dWbw0D8bkRYVlAQEpEUFv/xXI3Z1ZmztSE2cIIaXoXhIp1eEcPqhVVg4OYbTC2zM29N5vPVRF6qjfiyojaI66sf/29XliJwROAZdGQk5WYWHZZCTVXRnJQjcfo8xxwLNiRy6sjKCAoeurIyWZM7mBW5P5/Hc5hZ4ORaTYn54ORZrt7Q47icpKv7TnEQyb/SVzMvY2pKEpOwPezcP+HZ1ZdGazGNXV7agR9m0qyMtwcuz6EhLDrtaEjl82JqG3vOsOoAPW9NoSeRsfYV9PKaUBjC5NIAjppRgcmkA9WUBxz2nV4Sw4th6XHLcVKw4tr5gVJBbj/jBCI0AQRAEQRAHjFvPxvSKELx8db9K4oDheTLFgSRlvzhQb89TS8IIKZ1VGQHDALoOJPL2Nm5pbMsgKymIBQRwLItYQEAyJ6GxLeOwz439bhlIuIkgDkaG4iEdLDrFnEfq4gEEBB4Rv4LWVN4xR0iqjpKggEROhqzq8Ascon4PJHV/friqGXPMvmQeu7vy8HBweIH3e8wVaGkdLBhImua4XyKnwOdh4WEFaDpQ4hfAccb1up42AYHHrOoINjU1IZmTEfF7sPiQcscYqBoQ9HLY3aWgsc3INw8IfptdTe0ZaDBy39WeHHit5/r8uhKrXW8vdiJnlA7r71BxMKG7iSZ+VkxoBAiCIAiCOGDchl4PFjYOAFVRL2RFw5sfdYJlGGi6jimlQVRFvbY2PMtga0sSfoFDTlIxudRva+OWhvIgPByLPV05+Dws8rJRy7a3SNJQ7HdDsfohiPHGQIrWbucRMzolmZcR8wvozkn45b8asfiQMusgrCrqRcTnwZ7uLEqDXnRkRJSFvI45oirqRV1JAAKXR0DgkJVUVEV9tnaSomJXVxYsw6Ay7EF3Tsbu7qzNO73fY64h6OWQERXo0Gwec/N+FWEf9iVyCHh5ZEUFlRH73JWVFGxtTqKhPGg937aWJI5piNvGjGMNwTi/h0dtiYCOtISspNo83VPLgmBhbLgFjoGo6GB7rvdlekUIsUDtoIeBblTJJ5L4WTGh8HKCIAiCIA4YN6HXbsLGnRjeJqaPynlA4DGl1NgsS4oGD8dicmnwgBZ2pSEv5tRE4OEYyIoGD8dgTk3EJpJ04PY7KVY/BDHe2N6axppXduDhV5qw5pUd2N6atr3uNoWjsS2DZE4BAyAtGv8msgoa2/anhJSFfFh+aBUyooqtLUlkRBXL5lQ5NpJmO1HVsKc7B1HVHO1M73RJQADLGv96eRaJ3P6wd0nVEfJ6wDJARlTAMkBQsHvMzfsdMbkE+1Iitu1LYV9KxOF1Jbb7mR7/ulgQFWEf6mLBgqKNZh52Zc+GvTLqRVXE7umuivpx6KSoMb9pOjwcg0MnRVEV9Rd8f/7w+m48/c5e/OH13Y73x8172JuJIn5WTGgkCIIgCILoF7eejYG8JGa4Z3XUWIRVR30Fwz1bEiI8PIt5kyIQVR1ejkFWdgoblYe9+NwnaiEpOgSeQVZSD0ioJ5VXMK08hFlVYaTyKsI+DrKqO/pya/9gFKufg5XVq1fjT3/6E7Zu3Qq/349jjz0W3/ve9zBz5kyrTT6fx9e+9jU89thjEEURy5cvx09/+lNUVlZabT766CNcddVVePHFFxEKhbBixQqsXr0aPD8xlsXFqJFczPu5LbnnxkNaHfVB0zXkJA1Br+HtDQgsqqM+2/0SORnHTS+D38MhJ6tI5mUrHL1vu6OnloBlWGi65mhneqdbU3nE/IanuzJi94ZH/Tx4joGX5xASOKQlFR6eQdTv1Kx466MuVEX9iPl5dOcUK9fc/P136/E387D9XtbyiFeGfbZ2YR+Pk2dVojbqQ07R4ecZzKqJDqg43t/7Y7ZpTYmI+T1oTYlUNnGI0CgRBEEQBFEQt/VWB2tnhns2J/KojvrQnDAWkoXCPWVFwzv70mBZQNOM8j+925mL0t4LxANVEu/dV11JoN++3No/GMXq52DlH//4B1auXIlPfOITUBQF3/jGN7Bs2TJs2bIFwaARMnvDDTfgb3/7G/74xz8iGo3immuuwbnnnotXXnkFAKCqKs4880xUVVVhw4YNaG5uxpe+9CV4PB5897vfHc3HKwrFrJFcrPsNJV97sJzhaEDAtPIQNu9NIJVXwDFAQ1kI0YDguF9DWchKQelPAXxnZwatSQmqhp7Q7IytnekNf+ifjWjMZAyPfB9vuMBzqIv7kchJ6M4r8HBAbYkfAs/ZbDcP3SYPkGvuNifaTR72/vzwDis/fGZV5IAUx/ePlYjGnrHKyc6a30T/0CgRBEEQBOHArXfKTbuykA8XHzcVj2xoQmvKWEiuOHZq/95dpuc/jA69T+3ZYgr1uO1ryPb3Q7H6OVh59tlnbd+vWbMGFRUVePPNN7F48WIkEgn86le/wqOPPoqTTjoJAPDwww9j9uzZ2LRpE4455hisXbsWW7ZswfPPP4/KykosWLAAd9xxB77+9a/jtttugyAItnuIoghRFK3vk8nk8D/oAeL2d3ak71fMknscCwS9PKbEgwh4OWRFFSEf329N6YHuZ9WnzsrWIZim67a+TG/4gtoIcrIOv4dxeMM5FmDAoDzkQ8jLIS2qYBnGUQ/byjXvyqE0ZORhl4UFx6Gb25zowdqZ+eHVUS8mxfzQdK1gfrib8XIzVn3vPZaiLcYCY9MqgiAIgiAOmGIsQIZSLzaZl8GxOrbtSyIe9Fg5iL3bnTSrArUxH7Y0JzGnOoJDqiKOe5rh5fPrYtB0HSzDICMqjvBrt6I/bnC7wD1pVgXm1UY+9j2L1Q8BJBIJAEA8HgcAvPnmm5BlGaeccorVZtasWZg8eTI2btyIY445Bhs3bsTcuXNt4ebLly/HVVddhc2bN+Pwww+33WP16tVYtWrVCDzNx2ekayS7vV8xD8pUDaiO+cCzDBQNKAkA5RHvAdWUdlOfOpVXsLGxA+/tSUBWjbzojqyCc4+otdXDDnp5KJqO1rQEgWMREHhHPWzTa/7LfzairSWPoMBj2ZzJBeeAwTz+btoZdcG7jbrgPak4yZzTO+1mvNyMlclYjLYYCwzbpvvll1/G97//fbz55ptobm7Gk08+iXPOOQcAIMsyvvWtb+GZZ55BY2MjotEoTjnlFNx1112oqamx+qivr8fOnTtt/a5evRr//d//PVxmEwRBEMS4plgLkKHkFr6zuxtv7uyCpgEsCyysLzlg9XKOZbB9X9pSE6+LBxyeoGIvstwucMtCvqJskovVz8GMpmm4/vrrcdxxx+Gwww4DALS0tEAQBMRiMVvbyspKtLS0WG16b7jN183X+nLLLbfgxhtvtL5PJpOoq6tztBsLFNOjXOz7FeugLOwzvNw8y1r52rUl/oI1pQc7TDPzohnoYFgGuqZjSp/61ImshHd3dyMrKhA8LDJ5De/t6UYiK6EyYjyDoSSuQOAZVIR96M4ZnvC+XmDTa75wSgyKCvAcCuaaD4WBDliNuuAppEUFpUEBHRkJW1tSNuV1t+PlZqxMe9ZubkFrOo+YX0BremxEW4wFhs2aTCaD+fPn49JLL8W5555rey2bzeKtt97Ct7/9bcyfPx9dXV346le/ik996lN44403bG1vv/12XH755db34XB4uEwmCIIgiHFNMRcgbr1FuzuzeHdPEoqmQ+BYSKqGd3cnsbsza3mze6t2m6GJj2xowrzaiG3xHRB41MeN3GpF1cHzDOpL/UMOZycmPitXrsR7772Hf/3rX8N6H6/XC693fOTcj3SN5KHcr1gHZYXylE+eXXnANaWjfg82NXVaG9+5tTHbzzQn8sgrKiQNkETDrcvKKpoTeWt+M2tmS10adnfle2pmcw4vcCHPc+YARSCBwcfUUF7nwHMMNB2IBTzgWdZWF9zteLkZK/MZd3Zk0ZrKo1HLgGMZ5ERt1KMtxgLDZs3pp5+O008/veBr0WgU69ats127//77cdRRR+Gjjz7C5MmTrevhcBhVVVXDZSZBEARBTBiKvQBx4y3a0pyEqmmIBQRr052TFGxpTlqLUreq3am8gvKID587YjIkVYXAccgp9nDI0VxkjYe8wYOBa665Bk8//TRefvll1NbWWterqqogSRK6u7tt3u59+/ZZa8mqqiq89tprtv727dtnvTbeGekaydMrQvDy1Whsy6ChPGjVyu5NMQ/KzDzlyfEA/DyHnKIWzFMGjMO+gTzr7ek8ntvcgqDAY3I8gI60hLVbWrD8sMpe7XWIsr30l/H9/mtuamYD+z3PybxiKaH353kuhiK8obxu1CkfqF65G9yNVU/udyLnOvf74zIc0R3DNc+Pmb8YiUQCDMM4QoLuuusu3HHHHZg8eTK+8IUv4IYbbhiwpEN/gheyLEOW5UHtMNu4aTvWmAi2e1l9kJbOnxkLTISxH4+2A0Ozf7w+I0G4ZTgWIIN5i+ZURyBwLLKiAtbLIysadW3nVO/P2Xar2u1GmXykQ2hNxkve4ERG13Vce+21ePLJJ/HSSy9h6tSpttcXLlwIj8eDF154Aeeddx4AYNu2bfjoo4+waNEiAMCiRYvwne98B62traioMNIb1q1bh0gkgjlz5ozsAw0TbtMlioGbtJFiHpT19qSqmg6OZSArzjJ/buwyDwMnlRjRND4P5zgM7MrK4BhA6bU85RjjuknvfGdF1VEZ9RbMd07kFDAMAOjozEpgmf3Xe3uei6UI31u4MZGTURbyHrBwo5uxssaiJ+c+L2soDwmOnPtiUuzojuGc58fEpjufz+PrX/86Pv/5zyMS2f9H+rrrrsMRRxyBeDyODRs24JZbbkFzczN+9KMf9dtXf4IXa9euRSAQcG1TX0/8eGI8237Hke5/K5955plhtOTAGM9jP55tB9zZn81mR8ASghg9Rjq8FAAOqYrg4mOn4pGNTchIxoZ7xaKpNqE0t6rdbuwfjWekkPaxwcqVK/Hoo4/iz3/+M8LhsJWDHY1G4ff7EY1Gcdlll+HGG29EPB5HJBLBtddei0WLFuGYY44BACxbtgxz5szBF7/4Rdx9991oaWnBt771LaxcuXLchJGPFdymjQzloGwwL6MbT6ppV0daslTCC9llHgbuaM9YquPVMXsN7qllQSM8W9HhYQFZA3iOwdSy/R59t/nOUT8PvcdJHvHySIuKdb3387uph22O6a7uTL91ugFDuHFGRXDASAQ3Yz+Ug9Mp8SCgAyzDQNN11JcGh/VQtFjRHcM9z4/6XwpZlvG5z30Ouq7jZz/7me213sIV8+bNgyAIuPLKK7F69ep+J8b+BC+WLVtm29APZM+6detw6qmnwuPxHOBTjQ4TwfZvv8FC1JjBfwDAe7ctH2ar3DOe7R/PnxtgaPaP5VIvBFEshrIAcRNGN1iIJgBcf+ohWDQtjtd2dOKo+jiObihztHG7+HNj/0iH0I5WSDuFs9sx14lLliyxXX/44Ydx8cUXAwDuuecesCyL8847D6IoYvny5fjpT39qteU4Dk8//TSuuuoqLFq0CMFgECtWrMDtt98+Uo8xYXCbNuL2oMyNl9GNJ7UlIaI1lYeiGjnZug7sSzrtKgv5UF8axGtNHVA0HTzL4JiG0j4bcz+mVYSwrTkFWQdYBphWEUJV1G97Pjf5zgLPoa4kgPdy3ejMykY971jAVs/bbT1sK7f9lQ4k892I+DxYPKNiwDHdvDfZr+d2sLEfysFp1O/Bq73GYl6BsSg2xYjuGO55flRncHPDvXPnTqxfv37QTfHRRx8NRVGwY8cOzJw5s2Cb/gQvPB7PkDYUQ20/lhjPtosaA1F1t2kdi884nu0fz58bwJ394/n5CGIouFmAuFngugnR7Nvu9aYuZCTN0c7t4s+t/SMZQjsaIe0Uzu5E1wdPQfP5fHjggQfwwAMP9NtmypQpYzJabrzh1vsJuKsp7dbDO5h6edTPIy9rSORkhL08UqKCWMBj8ygDwK7ODF7+oA1+D4eAwCMrKfjn9jbs6sxYh4IcC5QGvZhcqoKBkcldFvI6POvPbW6Bl2NQEvMjKyr95jszDBAPCFYoOsvC1pfbethmbntDecjKD++b2+5WSdzt2Lspd2jlfns5I/c7IxYci6EwUoePwz3Pj9qm29xwf/DBB3jxxRdRWlo66M+8/fbbYFnWysEhCIIgCGLouAmjcxs66qbdeA/PHumQ9vE+XsTBgVvvp8lgNaWH5OEdQL1c4DmUhgS0JPLIiApYhkFDedDmUQaAxrYMkjkZLMtC1nSwLItEVkZj2/5Nt6oZG+2urGzV6Z5UAodnfVdXFomsDC0tgQUgaXrBfOeMpGBPdw6qbuSGx4KCrS+39bBNr2xdSQB+gUPY53F4Zd0qibsde/M9H2jzbOV+xwID5n67ZSQPH4d7nh+2mTudTmP79u3W901NTXj77bcRj8dRXV2Nz3zmM3jrrbfw9NNPQ1VVKy8nHo9DEARs3LgRr776KpYuXYpwOIyNGzfihhtuwEUXXYSSkpLhMpsgCIIgxj2DeQbchNGZi6eon0eq599ETnYsnsx25WEvGIZBediLzoxoazccYXvF9H646cttSHsx7BqO8XKTJkAQQ8WN99MNQ/XwDqReLikqOtISAgKHUE/udGdGcqiEV0d90HQdOUmF38MiJ2sICCyqo/ufQVJU7OrMgmWAyrCA7pyM3V1ZW18Cx6ArIyEnawh6OWREFTp0CJw98jGRlfBhaxq6zsDvYSDKOhrb0raa32Z+uN/LDpir7San28x/N3PbW5NiwTF1O/ZuGEr0w2AM5fCxWH8PhjN1adg23W+88QaWLl1qfW/mWa9YsQK33XYb/vKXvwAAFixYYPu5F198EUuWLIHX68Vjjz2G2267DaIoYurUqbjhhhts+doEQRAEQdhx4xlwE0ZXFfUinZexZe9+LYTJ8YBj8VQV9YJnGWzek4Bf4JCTVEwu9dvaFTtsr5jej6H0NVhIe7HsKvZ4uU0TIIgDYTDvpxuG4uEdTL3cqE/NgmcFMAwDgWfBc4xDJTwaEDCpxI9tLSkk8xpYBqiJBRENCFYbsy8P6wUYIB7wguPsiuOSqiPk5SEqIjKiCpbRERR4SKo9HaI5kYeiAaquISsZ12SVtdX87u1tTeRkVIZ9Bb2tbnK6zfrhu7sUNLbJPfXD/Y4xdTv2bhhq9MNAuD18LLY3fLhSl4Zt071kyZIBc28Gy8s54ogjsGnTpmKbRRAEQRATFreeATdhdDlJRTKnQIcOFoAGIJWXkZPs3qKAwGNKaRAtCRGSosHDsZhcGhw2xfFi1/wdi30Vc7zcpgkQBxduPYMjmU87pTQAD89YHuxJMb/joMny3vbUnm5NOb2yRn1qn029vCwsOA4MJUVFRlQR9HDw8hxERUVWUm1ebLOvfYk8Al4OWVFFZcSucB718+A5Fn6eR8jHI51X4OFZRw55LOCBoqrQNEDgGUiKDkVVEQvY9Wbc1j7f2pxEXTxg5bb39fi7rR/u1rsOuIuYKVb0g5vDx/GUijO2rCEIgiAI4oAxPQPxkAeypiEe8iCRlQuGJU+vCCEWqO13YdTYlgEYYFIsAJYBNN1Y4PTOdzTvWR724tOH1yAlKgh7ecias25usVTVhxp6Xcy+BsIa+4AAWdUQDwhI5KQDDgkvVpijW4Vp4uDBrWdwpPNpZ1VHsKmxw7rfSbMqHZ97VQOCAo/dXVk0ZjOG99bD27yyjvrUYaGgtzWRUyCrGiRNR16UwTIMJEWzebHLQj4cMbkEazY0QerWIHAsTp9bbetL4DnMropgW2sSkqwjFvJgZkXEkUPOsSwifg8600Z+OMswiPg94Fj7Ltht7XMzD1vtycNWNHu+ttv64W6960OJmClG9IObw8fRqixxIIwtawiCIAiCOGDCPh6yquOFLa0QOAaSqmNBXaygx2KwBXVDuaEQvC8lwsuxEFUN8YAHDeV2r4t5zzd2dA16z2Koqg8l9LqYfQ1G2MdDVjSs37YPHpaFrGmYXxv9WMq3xQhzLGaOJTH+cesZHGkPoum5LQ97UBIUwLO6w3ML9HhvJQVejkN5mEcyqyArKw7v7UmzKlAb82FLcxJzqiNW+HZvBI5BRlSgaYDXw0KUdWQlxZaL3Z7O462PuhAPCpZ6+f/b1YX2dN7aVIZ9PObWRhHwMlA0BjyrY3pFpGCd7oDAQ/Jp8HlY5GUNQS9v84i7jUwx87D352tLDo//UDzYgx3yjVbEzGB2jUZliQPlAFLkCYIgCIIYs+g6GAYAmJ5/nelcvRfU5SEfurIy1m1pQVZSrDalIS8OnRQBzzJQVA08y+DQmghKQwU2ay7u6QY3dpnej3jQM2DodTH7cg1jLtbN53dXQnI4Mb1+8aDnY+dYEuOfQp7BRM6IhjmQdkMhKynYl8zbfgd732/jhx1Yv7UNL/ynFeu3tmHD9g7H/cw85aysoKk9i6ysICBwDu/t+q2tWPXXLXjon01Y9dctWL+11XFPSdXh9XBQNA0ZUYWiaRB4zpaL3ZIQsa0lhZ0dWTS1Z7GzI4utzSm0JESrjVmb+rXGbrz8fiteazRyrAvW6Y4HwDAMMqIKhmFQW2Kv010oMsUUsHSMg8AjIyn4oDWNjKQ4PP7m/FYZ9iEvq/16sHu3r4z4Cr6+XzBTAMswKA8LBe0aDgayq+hz+DAy9iwiCIIgiAnGSOVFpnpyCU+aWQmGAXQdSOSdIc7mgjrs49CVlRD2cdaCune5mWnlIUyO+9GalFAREeDhuIJ9eXgWCyfH0JmVEA8IAMN8vBDtQcLjp1eEkBFL8faubiyoixUMeTX7Cvk4dGZFhAo8o9nXQGH2Q7HdwzE4aXYFGDDQofcb2j/SFCvHkhj/uPUMjrT4YSIr4d093RAVzVIcf29vt03ZGzA8vB91ZNGelsAyhqf6o86co272Ay9+gN2dOfg8LNpTIn764naHV1bgGIiyauRj96iXS4pq83RnRBntKREqjCM0TQfaUyIyomy736Ov7kRnVgLDAJ1ZCb9/bWfhOt0AqiK+nmoQClgGjnx0N5EpHAu0pvM9nnkWWUlBW1p0ePyLlaZSFfWCYxls3pO0vPR1BYQ1R4PhVBwvJmPTKoIgCIKYIIxkXqS5UO7MSoMuqNtSIl5t7ISmG7mFR0+L29qZbTY1dhiOWwZY1FBasK/GtjQ2NXZC0zSwLItjCrRza7+b8Ph7171v5FiqRo7lxcdOxfWnHlLwGQezv9iK451peUyGORYjx5IY/7gV6RuKmN9gh4puQtWbE3mwDIOAwIHp+VfXYVP2BoC2pIiOtAQGxsZTV4GOlIi25H6Ngh3tGeztzsPLs4j4Bei6hD3dOexoz9h+ByRVR0lQAJOVoGlAQGARCwg2T/eODkPbAnqv+B3GuH50QxkAYOveJD7qzIFhdAQED7KSjJ0dOWzdm8Txh9jrdFfHfEb0kAZUR3mUR7wF89EfevlD7OhIozToLRiZks4rkHuEK1mGgYdjISkq0nkFZQf452Wg9zEg8KiPB7CnK4esqILjGNSX+sfMBne4FMeLydi2jiAIgiDGMSOdF+l2oZyVFOzsyEBWNavM10cdGWSl/V7ZrKRgR3sGiqbDx3PIKyp2dGRtbQCgIy3ivb3JXu00bN6bQEdaRCB+AM9oC1XX0TdU/f2WJNZsaLJ5xB7Z2IQz5lbZFudu7B+riuMEMZy49Qy6aefm0MqN2FVDeRARvwfJvIKQl0d3TkY0wDs0JDKSClXXwbIMWIYBywKqriPTq6pCUPCAZwBJ06CoOiRNg4dlEBTsKuFVUS9KgwLSeRk8z0HRVJSF7Crnk0sC0Ppky2i6cd2CYaDqOhRVh64bImk8p/dKNzEI+3hMiQfh9/CI+T3ozsmojHgLHswZ54TMAIk6jPUvwzD2E4FeFEs0L5VXkJVVCDyHvG78m5HUMRHJM16gUSIIgiCIYWIoyqpuSrG4wc1CuSUhQtF0HDopCg/HQlY1dGZEm6J1S0KEquuYWRUGyzDQdB3Jnhy+3vY1tmWgqJpRr5thoega0nmnyjkwuEfMDFU/fkYZJEWDwLPI9VnYbWlOQlI1BLwceI5FwGscGmxpTto23ab9h1ZHIfAsJEVDZ9b+jENVvh3sPRovYY4E4dYzOFA7t4dWZhRIY3vaKm9VW2IvB1YXD+LyExrw4MsfIpGXEfZy+PLxDY45pDLihcCzSIuqUaNb1RH2s6iM7N8oTykL4KipcWza0YlkTgLHMjh6ahxTygK2vsxyh7u6shAVFR6ecZQ7lFTNsfHVe65b9ysNIOTl0JlRISk6dAAhH4cppc77LTu0Cn97Zy/a0yLKQl7HwZwpWJYRVUwpDfYrWBbycT3ebQUqy0LVNAi8DyHf/vzwYormSYqK/zSnkJUUlAYFdGQkbG1J2cqrEQNDfw2IcUv9f/9tSO133HXmMFlCEARRGLd5kUMpxeKGwRbUZt5gW0pEddSHtpToyBs0c/i2NacGzOFrKA+C51g0d4vw8gxERUc86FQ5d+NxsZTQ32/vN7x8TnUEHMuiOyvDwyqQNR0Bgcecars6sfWM6Z5nTDufcSh5q27fo/EQ5kgQxcDtoZUpNPaHN3ZZkSaXn9Dg+D05uqEUOzsy2N2VRW1JAEc3lDruKfAc5tVGsbU5hbyiIeb3YFZ12CZGFhB4XLV0Bqr/3240J3KojvpxzuG1BUsGtqZE5CUNsqZB1Vi0JkWb/e/sSViq5WbpRKbn+okzKy2bysNetKckaDBUqstDXkfJMBtMYZFFtyX+VM14Tlk1FNc9HAO/xy4o5/b9cdMukVPg83DgOQaabtQc51nWVl6NGBhSLycIgiCIYcKNsmrvUiwVYUNl+5ENTWhP54fNLjeK1mYOH88bNV55vnAOX2nIi0Orw1YtWJ5jcGhN2KZy7kZJ3GIQJfTaeABza8LgWKanPi2DeZPCqI3bvUpun9GN8u1ovEcEMZwMpCTult6HVjlJRUsyj6jfeWjVns7juc0tCHo5zKqKIOjlsHZLi+33x5wjJFXHIZURSKpecI4I+3jMnRTDUVPjWHJIOY6aGse8Wqfuw/SKEK5eOh03nzYbVy+dXjCkOpGV8O7ubuRkBTzLICcreG+PId5mclR9HKauGtMzFXGMcb13P3u6cvBwQMzHw8MBe7tztn56P2NbWoSPY9GWFh3PaB4W7unOojMjYk93FlG/U0hNUlTs6sqCZRlURnxgWQa7u7M2z3PvCIPGtjQa29MF3x+zXVNHGk1taTR1ONtVRb2oCHvh5VlUR33w8kZ0QX9CasX4fE006CiWIAiCOKgYKSVxk8FCjt16NoaCm2ccTNE6lVdQHvHhc0dMhqSqEDgOOUUp6CWZVhFGNMDjo44cJpf6URbyO5TQk3kZ8YAAWdUQDwhI5Aqrqnt4FkfUlaArK6Ik4AXDwtHXvLoSzJ3Uf5vezzijIojGtgwayoOOUFXAfTh+Mi+jPOQ1yuWEvI5QdYIYLxRLPNCtjoH5+zMpFkBA4OHzcI45LpVXsLMzg9akiEbNEEnLyc75JiDwmFUdwaamDiRzMiJ+D5YfVt1vOamB5vjmRB6SqkFWAVk1NquiotnE245uKMPiQ8rx4rY2S8F88SHlloia2Q/LsAj5PeAYFhzPQNcYhwhcKq/g3T0JbGtJWSKQM6vCtmcsC/mw/NAqPPTPRrSlkggIPL5wVJVjnunteWbAIB4UHJ5ntxEGbtqZh5iPbGhCIiejLFRY4A0YWfHQ8QRtugmCIIiDhtFaDAy0+HNbIsYtQ3nGgRStTe9H7zy//pTQNzV24N3dCWg68ObObsyrjeK6k2fY2siKhvXb9sHDspA1DfNrowX7+rA1hU1NXdA1HQzLYFFDiUNV3bRrZlXUsKuA96bvWGzem+x3LNyE43MMg83NCUuUra5kbJTLIYihMFTxwMEO8NyU3DPnuF2dGUMsLSejIuKz/f5wLNDcnce+ZB5BwRDp0nTdUQIrKynY2pxEQ3kQMb+A7pyEbS1JHNMQLygYOZDtPg8LSTZytjkGUHVAkjX4PKytjyPr4xB4BomcjKjfg/l1JTZBRkMEjkciK8ErMJAlHbECKTZGXnQSyZxsiMVlJWxtSdq801lJQSIn47jpZVb+ezIvOwQsTc9zR0ZEadD4tyxk9zz3jjCYHA+gIyNi7ZYWRykzs52f51AZ8SKZVQq2c3OIaX6+WtN5xPwCWtPDKx46nqDwcoIgCOKgYEghziOImzBotxTzGd2GXn+wL4Ute5NQdSPnUdWBzc1JfLAvZe/QymE0w8WdOY0daRGbm1NQNCNMXdF0bN6bQkdaHLJdxR6L+rJgTwi9Bp5jUF8aOOgXkcT4o1D+rlm/vi/bW9NY88oOPPxKE9a8sgPbW9MF2/zh9d14+p29+MPruwu2KQv5cMTkEjQn8nh3TwLNiTwOrytx5CibVRXe25vEzo4MMqJiy1HubX9dLIiKsA91sWBB+93YnpcNsUaz/jYDQOAN/Yre99vZkYWo6PBwHERFx472rO1+dfEgFs8oR05W0ZrKIyerOGF6uWNTmsgpkFUNOVnFvp52kqIhkVNs90vmZTSUhdBQHkJDWajg85l/N8pCXiTzhT3PvSMM4kEvJsUCSPSIYfamJSGiNZWHrOnozMiQNR37knlHu+2tafz1383Y2NiBv/67ueCYmuO1sz2Lt3Z2YWd71jFeByv014IgCII4KBiqUnUxw9AH62uwUG+3fbkN4zZxo8Y9mBfr7V3d0HQdfg8LnmWhaBpkVcPbu7oxv67EssvDMVg4OYbOrIx4wAMwjMMuUwl9UswHL8dBVFUkc7JDCX16RQhevnpAj8tQxsKNqnp52IvPHVnXr6q6274IYjRxKx7oxiNueTVTImJ+D1pTYkGvZns6j7c+6kJV1I+Yn0d3TsH/29WF9nTemlMSWQnbW9PQwSDk45ARVXzYlkYiK6Ey4huS/W69rdVRHwTeqN5gaETo8HqMnGUTjgWaEzl0pCWUhgS0JkWHB749nceOjgyqY16wYKFBw87OjO35AEDgGGREBZqmwe/hkZMVZCUFArf/ANKN0rvJSbMqUBr04O1d3VhQF7PmWxMrP7wrh9KQgI60hLKw4IjQifp55GUNabFHmTwrIezjEfUPTeG893h1ZWUrcqtQxMLBCP01IAiCIA4KhqJUXcwwdLd9DRTq7bYvt2HcgDs1bje2L6iLgWUY5GUNHKtB1QCBZ7CgLmazq7EtjU2NndA0DSzL4piGUoddDeVGuZ7urIzSIIPurIywz1mr143tbsfCraq6m1B7ymUkxjpu87DdHFK6zcM2Pa6T40Z0SMSvOHK6jbxoBmEfDxYMwj4GmqY78qLd2G96W1tTeTRqGXAsg5yoOeyKBgREfMYhgNxTjDvs5RENCFYbVQOCXg67uxQ0tskQeAYBwW/zwLckRHzYlkZnRoKm62AZBpKSdmg+SKqOoJeHKOvIKSpYhkNA4CGp+8Ui3eZhA8DvNu3EQ/9stLW78Jgp1utmfvgv/9mItpY8ggKPZXMmO/7OCDyH2dURbGtJISOpiPg8mFllV4R3e2itakB1zAeeNf4mlIcElEe8joiFgxE6dyAIgiAOCkYjLHlU+nIRxu1Gjdvt/WZUhnFoTdQILdeMEPM51VHMqAxbbTrSIt7bm4Si6RB4ridsPGELGwf21+oN+3h09Xhb+tbqHZKS+CBj4fYZ3Xx2xmr6AkH0ZXpFCCuOrcclx03FimPrCx4MmQdNu7qyaE3msasr61C0NvOw29MSfB4W7WkJLYm8w6vZW7ciKyloTuQditxGXrQHqqbD52GhajqiAWdetGn/Z4+sxVnzavDZI2sd9pve1uZEDpKiojmRQ0sy57CrJZFDa0o0wspZY3ZoTYloSeRsfWVEFQLPorbE8IxnJdXWl6pp6EiLEGUN0HWIsvG9qtl3mlE/Dw/HwsczKA164eMZCDxr8yi7UXoHgF2dGTz0z0akRQUlAQFpUcEv/9WIXZ0Zq42ZH75wSgmOnVaGhVNKrPzwvu/13ElRHFVfghNmlOGo+hLM63NA6VapPuzjMSUexOTSII6YHMPk0iDqS4MFD34PNmgECIIgiIMGN0rVQw1DH4jh6Csk8OjMiAgJvJXr19vz5OEYnDS7AgwY6NCRyMr9ep6ifh4p0fjXzPXrrSaczMvwehjs6sog5HPez2x3dEMcs6qC2N2VR22JD9GA19bODBuviAhgdQYaoyMrqo6wcQC48JgpmF4RxGs7OnFUfdymEtzb9vKwFwzDoDzsRWfGqSRujsUxU+NIiQrCXh6yphdWVQ95IGsa4iFPwfECjM+OplVgS3MSc6ojjoV+Md9rghhuBhMP3K8S3mSphC8+pNzp1Yz6wXMM8rKK8rCA8pDP4dXsrXzdn25FXTyIcxZMMg7QMiK8HIuz508qmDoyWESJaux9sS+Zx+6uPDwcCtrV1J6BphsbazAMOFaHphvXzVBtw9PNQ+rSsbs7D4FjERB4W1/dWRkAoAEQVed1E4HnUBc38qqTPfNTbUnA5lF2o/QOGHNqVjLCwf0eHqVBoCsr2ebUQmrpmQJpMdZ73dixf0z7KMK7jZDo3S6Rk1EZ8RZsdzBCI0AQBEEcVAy22BxKGPpgFLuvtmQem5o6DcctAyxqiBf0RnSmBw6Drop6kc7L2LI3aV2bHPfbPE9hH48Pe0LC9yuJxwt6NtpSIjY1dgA60NiewaI+oeMN5UHoAHZ3Gl4wVQPiBdR9AXvI5BNv7nGETFZFveBZBpv3JOAXOOQkFZNL/Y48RdOuVxs7rZDPo6c5x0tWdbywpRUCx0BSdSyoc9b87WtXoVDOYr7XBDHcDKbnYKqE18X98HvCyMmKQyU87OMxpTQAv5e1lMQrw75+848H0q3ISgoEnsXJsyugqADPAV4P61DtdpNbbNawZgCUBnikRMVRwxoAppYFwTKArAIcp0PVAJ43rpsYnm5jgxz1C8hKKrKSYvN0+zwslJ4QcQ6ACkBRdZsKutkXA6Ai7LUU2lkGtr7cVrMwU3Ha0iJifh3dOQlRv31O3a+WLiHs5dGdFbG1RXeMg/lel4cElAQE8CwKKsK70fgYSruDDQovJwiCIIheuA1DH+m+spKCHZ1Zm7L3jo6cLVTQ7f1ykopkXoGu62AA6LrhAc5J+xdjHWkRm/cke+7H9oSEJx0h4VlJwY72jK3djo6szS6/wCHq48EwMDbwDBD18fALnK0vNyGTAYHHlNIgPBwLSdHg4VhMLg0WTBPY2ZGBrGqWWNJHHRlnuLeu90ShMz3/6uiLW7uK9V4TxHCyfmsrbnz83/jWU+/ixsf/jfVbWx1tzHztvV15vN+Sxt4uQyystwq1+ZmvDPuQl1VUhn0DfubLQj4cNilacBNm3q8jI6MzK6MjIzvuZ7YbTH09kVOs3+mEaFbXhk0lHACqon7UlvgB7PdQ15b4URX1W21Mr3lrKo8PWjNoTeWhabB5um0q6Cisgm72FfQaB32taQmyqju85m6rWZiRAbqmoyWZg67pjsgASy1d0tGSEpGTdIdaujmmGxvbsX5bK17Y2or121qx4cP2gorwgynVD6XdwQb9JSAIgiCIPrgJQx/pvloSIlRNR0NZEJKiQ+AZ5GTVEXbo5n6NbcZmsbYkCDA6oDPISHaV8Ma2DBRNw6SoH16ehahoSOYlR0h4S0KEquuYVRkxNtU6kMhLNrtaEiJCPg/mTRIsz5Wq6/2GTIa9HkiqhrDXg4woO0Imy8NenDWvakAl9JaECEXTMas6vD/UvkAIvYdncXRDHOm8gpCPh6Lqjr5Mu2IBD3iWRSzg6VdVnTw8xFimtyaC6Ul9ZEMT5tVGbJ9XM197MBXqocxvA3nXzfuZdadbU06VcGB/RMkH+5JgWAa6pmNKmT1nOOrnoesAGAZxvwfdOdm63htJUaFoQNjHgWeNg0xVh80TbHrNAQbxfrzmpgq6omoQemp/91VBN58xIyoQeAYVYR+6c7LDaw4YUQG1MZ+VytJbSM7EjAw4amoJuvMKYj7eERlgqaXrhiq7KOsOtXTAUI5/d3cCoqIh5OWRFhW8tydhU453q14+HHW6J0pFiPFrOUEQBEEMI4OFoY90X4VCwqfEnWHVbu7XUG54iluSefg8hkcmHrKHJlpK4jkJpUEjdLSQkrgZ7r21JdlvuLdp+87OHBhdh84wqC9ge0N5EIqqYWfHfg9yScB+Tzdh4+Y9OZbB1uaU9Yx18YAjhL53aLwRsl9YVZ3nWOzpysPLMxAVvWB4PKmXE2MdM2e4OupDQOBRHfUVzBl2m68NuJvfBqs4YHqBd3fl0JjLFMydNu8lKRqe29wCUdXg5VisOHaq7f691bglVUPM71TjBvZ7gmVVh6hoParjhie4rlcbhjE0GZN5BSy7/7rZJhoQMK0ihM17kpAUHSzLoqE8ZFNB3/+MHKQuDbu78j1K6JzjGd1UZ0jlFbzwn33YvDcBVQc4BmhPyzj3iNr9YfaqDp+HQzqvICMaHngv77GppQOmcjyLgJcBwzAIeDnoGmNTjnerWeFWOd4tE2lOpfBygiAIghgH5CQVyZwCHT0h4dCRyKu2kHC3lIa8mFMTgYdjICsaPByDOTURlIb2b0jdKIkD7sK9c5KKRF4x9rUsAx1Aok84+34ML4wpPK4XUBx3EzYeEHjUxwPgeQaKqoPnGdSX+h1emcFC483xOrQ6bIT1q0Z4/6E1Ydt4kXo5MR5woyQO7M/Xron5MbMyjJqYH/VlgQPSKDC96/uSeTAwBM76VhwwvcBejsWkmA9eji3oBd7VmcFTb+8BGKAs6AUY4M//3mNL9XCjxg3s9wSrmg4vbyim9/UEm15zTdMREjhoPaXFenvNORYICjzq4n4cUhlCXdyPkJd32G4qoevQ4eWNObyvErrb6gwtiRw2701AUnXwrLHB3rw3YVNeF3oOTDiWRdDLgWNZiIrq8HQbyvE8NE2Hj2ehaTqiBQ473aiXm8rxrSnD3tZUvqByvBsm2pxKnm6CIAiCOEBGMuytsS0DMEBNJACd0cHoDHKKUlABfDCRpFRewbTyECbH/WhNSqiICPBwnMMbceExU/CJ+pIBwxzNcO9PH1GDVF5F2MdB7hOi3diWAQOgJuY1kh5ZIC9pDtsb2zLgOQaTSgyPGscComJvZ4aNN5SFIKkqhAiHnKIUVC8vj/iwfHYV2lJ5lId9EDyszS4zNP7Q6igE3jg06MwWVkKfVhFGQ1kYXVkRJQEvGBYFldBJvZwYy7hREgcKK1qfNKvygD7LLQkRjW1pdGREaDoDltGREe2/s1Y9bFXFni6lXy9wY1sGyZwClmGQ7/FOJ7L2edCNGjdgbFS9vDHvZXqSugWOs3mCBZ5DaUhASyKPjCSCYxjEg4LNa65qxqZb1XR0Z3ts9zi99KoG7O3OYkd7zgysAccwjprfbiIRDOV13VaqUdN1m/L6/rrgco8nn3XUBQf254ev2Wgoxwu8UznerXq5ORa7u7JozGb6HQs3TLQ5dfxZTBAEQRBjgJEOezO9DnsTWbAMA03XEQ8KjhBnN6GJbpTQzWd8/j+tSOZl7O3Og2VZxzOGfTxkRcObOxPwsCxkTcP8Pl4lU718b7cIngEUHYgXqMHbUB6ErgPNSREcA6gF2llh9p29ldcDBT11H7amsKmpq5f6eonNLtPr15YWUR31oS0tFlQKNp/xnT3GMzZ1ZB3PSOrlxHhhMCVxYL+idUN5CLGevOhCitZm24EOH1VNQ3tahKwaWhSSoqO9Tx3r/fWwOVSEeXTnFIcXGDDypzVdQ07U4PVoEGUdAa89f9q0vcRvlNzy80xB2wWOgagY9zA226pxkNfLEywpKjrSEnw8A6/HA1FW0JmRbDndHAtkJAUelkE07EFWVJCVnV76He1p7GjPQocRaqwB2NGexY72tJU7bc5JH3VmEfMb41AZ8TrmJEN5nYGq6WB7KkKwLGNTXjfrgvsFBmGvFylRcdQFN8dL4FmcNqfKypMvpBzvJn/fHAu/h0dtiYCOtFRwLMz7DtTXRJtThy28/OWXX8YnP/lJ1NTUgGEYPPXUU7bXdV3Hrbfeiurqavj9fpxyyin44IMPbG06Oztx4YUXIhKJIBaL4bLLLkM6TQp4BEEQxOgyGmFvfoFDxMeDYYzwbIZhEO6jAO42NNGNEvqQntGMBbeUv+3hi4Z6ucdYaGo6WABRn+BQL/cLHKJ+jxE+rxmB5VGfx9bOHmavQ4eOVF52hKp3pEVsbk7ZnnHz3pRNfd2tUrCbZyT18uFnsLVlOp3GNddcg9raWvj9fsyZMwc///nPbW3y+TxWrlyJ0tJShEIhnHfeedi3b98IPsXYYCAlcWC/l7GuJICKiA91JQGHSjhgHMyteWUHHn6lCWte2VFQqbo7K4NnWTAMICtGtQCeZW11rK162IqG3d15SIpWMKc7GhBQFhIgqRqSeRWSqqE0KNjyp1N5BS9s3YfntuzDi9ta8dyWfXj+P/sctkuqDoFnoelATlah6TDSZHp5gs28b1E1ynKJqlMB3FI4T4uGwnladCicA8DWlhTMrs2XVN243vt9OWJyCfYlcnhnTwL7EjkcXlfieJ+qon5MKw9bzwEA08rDNuV1M7c9FvBCBYNYwItZVRFHbrv5Xs+ojGDupBhmVEYKvteAMc9VRnz9zmuqBlTHfKiMeAEwPQcGTi0AN5+biTanDpvVmUwG8+fPx6WXXopzzz3X8frdd9+Nn/zkJ3jkkUcwdepUfPvb38by5cuxZcsW+HzGB+vCCy9Ec3Mz1q1bB1mWcckll+CKK67Ao48+OlxmEwRBEMSgjEbYm6kAftRUP1RdB8cwSPcJ0TRDE6N+Hqmef/sqdpvtVE3HjIqQFcbdty/zGVkA21oSKAl4rYVYX+EcD8dg3qQIWlMSKsICeI5xhHGHfDyOmhqHqungWKftfdtpmg6WZRxhqGaY/aRYACwLaJpxQFAoVF1RtZ78UA6iqhZUHHejFGw+4wkzyizl+KykOsaimKr3hJPB1pY33ngj1q9fj9/+9reor6/H2rVrcfXVV6Ompgaf+tSnAAA33HAD/va3v+GPf/wjotEorrnmGpx77rl45ZVXRvpxhsxIprOYXsZdXVnL010Z8dq8jJZSdUpEzO9Ba0osqFRdHfWB5xgwMuDxALIMeHjG5p12q+ydyEpoT0vgGcDjYSDLOjoykk1puyWRw+Y9CUiKme8MbN5j5DubbQDD0y0pGqADfoFFXtIMrYhenu7eed8BgbPqdPf1hu/qykLXgJDAIS+rBeuCV0e8joKEes91k/Z0Hm991IWykLdHcVzD/9vVhfZ03qEuXxoSUF8aMPQoVA1lYcE2XmZue2lIgJ/nkFNUTIr5HZ7iYnqUwz4eU+JB+D38oJ+bwZTQgYk1pw6b5aeffjpOP/30gq/puo57770X3/rWt3D22WcDAH7961+jsrISTz31FC644AL85z//wbPPPovXX38dRx55JADgvvvuwxlnnIEf/OAHqKmpKdi3KIoQxf2n2MmkEX4myzJkWS74M70x27hpO9aYCLZ7WWd91GLfYzj7Hor9Y+V9Gs+fG2Bo9o/XZyTGHqMR9maGHfYu4dM3FLqQwnmh0OuqqBeyouHtfd3WxrW+zKns/c6uLrzxUTd0DWBY4BNTYgj7Ztj6Cvt4vLs7gdd3dBp1vxkGn6iPFwzjHsh2t+1M5fV9SbFf5XWzXUDg0Z2VURpk0J2VC6qvuw3Hl1UdL7/fbizUVR0L6mIF3+9iqt4TdgZaWwLAhg0bsGLFCixZsgQAcMUVV+AXv/gFXnvtNXzqU59CIpHAr371Kzz66KM46aSTAAAPP/wwZs+ejU2bNuGYY44Zicc4IEY6ncXKi25qQjInI+L3YPEh5U6l6s4MWpMiGnsO73Ky4jiMMr3TiZwCWTIiWPp6p90qezcn8lA0HToDSIoOMICs6jal7ab2DGRVhwZA6vl5WbXnOwP7Pd1ggJykgWGcnm4rL1rRkJNVcCzjyItO5BTkJAVpSUFKNNTOvSJrUzgHAK+Hh4cD5F57cQ9nXDdpSYjY1ZVFIiub8hdQCpRXNMdLhxmW7xyv3nnYiZyMSSF/QU+x23xtN/S9Z2XE6+hrqIfWE2VOHZUnaGpqQktLC0455RTrWjQaxdFHH42NGzfiggsuwMaNGxGLxawNNwCccsopYFkWr776Kj796U8X7Hv16tVYtWqV4/ratWsRCARc27hu3bohPNHYYjzbfseRB6C04JJnnnlm2Po2GYr9I2HPUBjPnxvAnf3ZbHYELBl+7rrrLtxyyy346le/invvvReAETL5ta99DY899hhEUcTy5cvx05/+FJWVlaNr7ASlmIsUt7gRQOodem3mDBYKvbZgev7D6A6V8N2dWby7NwVV0+FhGciajnf2pLC7M2vzBu/uzOKd3d3QdB0ezlATf3dPt62dW/EmN+1M5fVXGzv7VV4H9quv//Jfjf2qr7utWQwA0PWeCHMGDKMDDp8VMdoce+yx+Mtf/oJLL70UNTU1eOmll/D+++/jnnvuAQC8+eabkGXZtv6cNWsWJk+ejI0bNxbcdPfnzBlJhuIZHEqfA3kPzbzourgffk8YOVlx5EVbtbXTEkpDAlqTUsHa2qZ3WuAYy3vb1ztt5nR7OQ7lYR7JbOGc7ljAA0XVoGoAzwKKBiiqhljAY7UJeFj00QqDqhvXe2N6ulmmxy7F6ek286K9PAufx/Bi982LVjUNiZwMXQN4DlBUIJGTbTnrpu0cw0CGDg6ACkNIrbftAsegKyMhJ6vwe1hkZA06dIfi+P4ceBYVYU9PZIBzvNx6iqdXhBAL1A6Y5++Wwe7pJopiIjIqT9fS0gIAjsVgZWWl9VpLSwsqKuwnzTzPIx6PW20Kccstt+DGG2+0vk8mk6irq8OyZcsQiThDxvoiyzLWrVuHU089FR6PZ9D2Y4mJYPu332AhaszgP3AAvHfb8mHpFzgw+4fTnqEwnj83wNDsH43FUrF5/fXX8Ytf/ALz5s2zXR/PIZPjlaGEvRUrLHQwASRb6DUDaHrh0OuWhAgPz+KwSVFj48qzyMuqzZuypTkJVdMQ9vKWcJukatjSnLRturc0J6HqOkJeI9/cy7MF27kRbzLbDRTubSqvTysLojMrIR4QAIYp6CUZTH3drVJwKq/Aw7M4aWYlGMbI30zkpWFX0R3JcOKJwH333YcrrrgCtbW14HkeLMvioYcewuLFiwEYa0tBEBCLxWw/13v92Zf+nDluKcZ7WOx0Fjdec9OL3dwtQlRUeHkOSp+qBGYe9o6ODNrTIrweFpNLAwW90yzDIOTjwbEsPJxmCCb28k6bqte7lCy6OyX4eK6g6jXHsvDwHPKKArnnNQ9vlMMyycqaNf+ZsIxxvTe961hnJbWnjrVTvbwsJKClO4e0qIBnGJRW2NXLu7MyWJaFChVqz/mmt0/Ouml7UOCRV2SYx6BBgbfZLqk6vB4OyZwCSTFsEgKcQ3HcHPtsh4rOjAyfp3Bdc8Cdp9htJIXbz/NA93QTRXEg9xzrjF/L+8Hr9cLr9TquezyeIW0ohtp+LDGebRc1BqI6PJvukRiTodg/1t6j8fy5AdzZP56fDzBEgi688EI89NBDuPPOO63r4zlkcrxTzMWMW8pCvn43rA3lQfAsi30pEV6OhahqBVXCzfDy7fvS/YaXz6mOQNeA7l7CaX6ew5xq+8a1YDsP62g3mO0mg4V7m6He/97VDYFj0NiW7TfUezD19d41iwcKezc9M51ZacTSCUY6nHgicN9992HTpk34y1/+gilTpuDll1/GypUrUVNTY/NuD4X+nDluKNZ7WMx0Frdec44Ftu9LY3d3Dj6eQ15RkZUCNk8qxwIfdWTQkZbAMDrSIoOPOjMOb6tRB9qDZF5ByMujOyc76kBzrFHTOSMZtbozkoK2tOjoKyPKyIp2ga+sqCAj7t/gVkd9VqRP739755ADvepYcyz8HhY5WXPUsZYUFR91ZKHoRiyQouvY1WnP144FPBBlezSRKKs2D7Z5P0nTwTGwqjNImu7IIe/OSpbQmg4jUqCQp9scexZARjRqmH/cetgDfSaK9Xner4wfRMwvoDsn9auMP5HmwWFTLx+IqqoqAHCoRe7bt896raqqCq2trbbXFUVBZ2en1YYgCOJgYuXKlTjzzDMdi8fBQiYLIYoiksmk7YsoPiOtcl4a8uLQSRHwLANF1cCzDA4tEHptYYWXwxFe7hc4eHh2fzMAAs8UVBwXHO1YRzs3uFVft4d6A4VCvd2MvVv18pFW0R0NdfzxTi6Xwze+8Q386Ec/wic/+UnMmzcP11xzDc4//3z84Ac/AGCsLSVJQnd3t+1ne68/++L1ehGJRGxfbijme1jMz18hr3khpep0XoGsGqklOox/JUVDule7tpSI9rTUE+FiRLp0pCW0pURbX2aqR9TPIy0aAo99Uz3M+/GMoSbOM6zjfgCwsz1j/T/Tz/Wg14PSkBc8azTiWWNuDHrtm2BJ1VESFOD3sFA1HX4Pi1hAsHmVd7ZnkRaNuuAcy4LtiarZ2b4/XW13R7ZgOPvuDntKWzKvgGcZwwsPw/vOswySvZ5x+76UkaveC1HRsX1fynatLSkZlRgYgOOMvPT2tIi2pIS+tKfzeG9PwjmP9uDmM2GJ5qXz8Hk4tKbzA36es5KCfcl8wdctZfxYEBVhH+piwYKfwYk2D46Kp3vq1KmoqqrCCy+8gAULFgAwTg5fffVVXHXVVQCARYsWobu7G2+++SYWLlwIAFi/fj00TcPRRx89GmYTBEGMGo899hjeeustvP76647XRiNkknDHSKucm6HXZUEPdnflUFviRzTgddzPDC+fXR1BXlLhEzjIquZQCfd5WIT9AhQF4HlAVlBQJdzrYVHt9Vqh6qqmO9q5wQz3Lgl4ICkaSgIeh/q6Gep9dEMc6byCkI93hL2a7ZJ5GSGBR2dGREjgC6qvu1EvB0ZWRXc01PHHO6ZgLsva/Ukcx0Hrya1duHAhPB4PXnjhBZx33nkAgG3btuGjjz7CokWLimpPsd/DYn3+3HvNmZ6jLKbnTIuxrptkRBUsC5QEDHuykgJF05ERnRoSFx4zBYsPKUNjWwYN5cECc4PhdU5LCowbMggpPPqW5quL79dn0vu5XhX1orbED1nVrHSQun4EJQMCh30JDQzDQFU0BAXO3o7Roek6FFUHw+jQdWOzDGb/3btyzo1uoetBgYOkqOgd5S4pKoK9Dig7s1JBhfPOrL2vjCRD1QEOhj0cjJDzjGQPaXcrFDnYZyKVV7CzI4vWVB6NWgYcyyAnagU/z4N5p91+BifaPDhsFqfTaWzfvt36vqmpCW+//Tbi8TgmT56M66+/HnfeeSdmzJhhlQyrqanBOeecAwCYPXs2TjvtNFx++eX4+c9/DlmWcc011+CCCy7oV7mcIAhiIrJr1y589atfxbp166ySih+XjxMySbhnpFXOwz4e7+zuxps7u6BpAMt2YWF9ieN+psr5R50569qUuN+hEq4D2JeUwTOAoqNgqHpDeRCKqqM9JVkL3JIC7dxghr2/tbNrQFX1tpSITY0d5tocixpKC5bBaUvmsamps1e7uKOdm0WpyUip6I6GOv54YLC15YknnoibbroJfr8fU6ZMwT/+8Q/8+te/xo9+9CMAhmjvZZddhhtvvBHxeByRSATXXnstFi1aVPQ0nOF4D4vx+XMrAhnycfDyDPKyApZloGk6vB4vQr79G8T6sgAmxfzY1ZkDx2qQFB2TS/2oLyssXFwXL7TZNuBYIN2zaffxLPKKhozoLBkW8PLOwBa953qvZ6wvC2J3IgtNAVgPUF8aKDx2utGdrun7zxd6URH2gWVZ6DDyq3UALMuiIuzr1aZwJFHf68m8jJxkT7rOSRqSebnfn+nvemXEBw/PIi0qADTImo4wz9pKopmRQ21JEZEAj7akWFAo0vxM/O2dvdjRkUZZyKk4zrFAcyJnE50sJJrX2yMe8wuWR7x3qLrbz+BEmweHzeo33ngDS5cutb43F3crVqzAmjVrcPPNNyOTyeCKK65Ad3c3jj/+eDz77LO2BeXvfvc7XHPNNTj55JPBsizOO+88/OQnPxkukwmCIMYkb775JlpbW3HEEUdY11RVxcsvv4z7778fzz33nBUy2dvbPVjIZCH9C6K4jLTK+e7OLN7dk4Si6RA4Q9Ds3d1Jh+K4XeWcgQYdibxqUzn3CxyiPo9RukbTwTIMoj6hn7BxY6mq6+Z3H0PZm8GAqupZScGO9kzP4tzINd3RkUVWsns/spKCHZ1Z2yJ+R0fO1m5I6uUjyGio448HBltbPvbYY7jllltw4YUXorOzE1OmTMF3vvMdfOUrX7F+5p577rHWlL0rPRSbsfweuvGaq0b5aqRFBUqPUrimwybUVRby4eqlM/C/PRUCakv8uPT4hgP63UnkFER8Hvg8LHQNiLGAwHGOsltN7RlwPADFOksDx8NWDiyVV5CVFHg5DnlNs3LEC0X8JHIydF2Hphs5t4msPbJGUnWUBDxQVQ0adPBgjEicXvHkWVmzcrRNuALCbVtbUmCY/d5pU+hta0sKRzeUDakvgedQE/WhMy0iKxv1yKujPpvAW+/yY80p4/nyquoQirTBFNYlUjWgOuYDzzLIyxrKQwLKI16HcJtbj7ibz+BY/h06EIbN6iVLlkDX+/+jyzAMbr/9dtx+++39tonH43j00UeHwzyCIIhxw8knn4x3333Xdu2SSy7BrFmz8PWvfx11dXUjFjJ5MNGezg+qtO1GVdVtWGgxFFr3K45zYMDCyzMFlcRNlfOaSAA6o4PRGeQUu8p5S0JEyMfj8LoosrKGgIeFqDrrxTa2ZcBzLCoiAvKKBh9v5GQWCi8f7BlbEiI8HIu5k6KQFA0CzyLXR1W9JSFC1XXMrAxbqurJvOywqyUhQtV0HFoTgcBxkFQVnRnJ0VcyL6M87AXDMCgPe9GZEftdlI6kiu5IhrOPFwZbW1ZVVeHhhx8esA+fz4cHHngADzzwQLHNczCW38PBvOaJrIQPW9PQwSDkNUTGGtvStjJfgPuqBMDAc2pV1IuKsBf7EnkEAxwyoorKiNcREj61LAgWDBRGh5dnICrGweHUsv1zjaSo+E9zCllJRWlQQEdGwtaWlE38DDBKfbWnRUiKDoFnICk62tOirdSXWTLM52Eh8Kw1L/UuGTa1LAgPx0BVdCsqyMPZbQKABXUxeDjGyF3njL48HIMFdTFHX7qiW9E+hfqSFBXtaQkBgUfIyyMtKujISLZnNMuP5WXVCv8vVH7M9E63pUX4eQ5tadHhnQ77eEyJB+H38AOW+XLrEQfcRW6M5d+hoTJ+LScIgjhICIfDOOyww2zXgsEgSktLresjFTJ5sOAm5HgoqqqDLS6KpdA6pzoCTQe6cyoYqNAB+AooiZuh33sTWWvjGg8KtpDwqqgXsqph+76sFV46pSzoWAQ3lAeRFhVke4VNBgTWEV7u5hn33zNt3bNveHlV1AuOYbBtX8rydNeVFM7XjPg8aEtJqI760JaSHMrkVVEveJbB5j0J+AUOOUnF5FK/oy+39hebkQpnJ4aP8foeNifyRig1A4iqbnlme5f5MilGVYKykA9HTC7Bmg1NkLo1CByL0+dWF9ic+3HYpCg2701A1QAvz+DQmiiqon6rTSKnwOfhwHMMNN1QF+dZ1uE1787KYBlDKE7sES9jGcZW6kvgOQQFDrs7VSRFFRyA2hLO5lGuivpRWxLAh20ZKEaUOmpLAjabAGB+XQk+d+Rk/OGNjyCrxob7c0dOtjz0jr60/vsynpGFh/UCjFHqjONge0ZJ1RH08hAVDRnJEIMLCLyj/Fgqr+DdPQlsa0lBUo2xn1kVtnmne3udEz0b7kJeZ7ce8aEwXn+H+jL+n4AgCIIYsZDJgwE3IcduS6y4oZh9+QUOXp5FXtasAG9fASVxv8Ah4uPRnZWNEE2GQdjHO0PHzfhN6D0i504v497unG3DDRh5inu7c5ane0jPqNv/p+8dzXzNllTeUGjnmIL5mqYy+SMbmvpVJg8IPKaUBtGSEHu8TiwmlwYdfRXzPSKI8UB11AcGxkY7KLCGaBrjLLvlBjdzans6j7c+6kJV1I+Yn0d3TsH/29WF9nTe9jsb9vE4aVYlysICEjkZUb8H82vtuhWW1zyZR0DgkJUKe81jAQ+0nlJgHg6QVUDTdVupr0RWwp7uHFiWQcBjzK17Ezmbx19SVCiajpCXhYdjIasaVF13eNYB4PZzDsOZ86rw2o5OHFUft8LKTcy+Ij6jTnlWVgr2ZTyjz8rXTmYVVPR5RstL33NwkJFUh5fevOd/mpNIi0qvyICk457TK0KIBWoHjGpw6xE/GKERIA4a6v/7b0Nqv+OuM4fJEuJgoqOjA6WlpQAMQbSHHnoIuVwOn/rUp3DCCScccL8vvfSS7fuRDJmc6Jghx9VRHwICj+qoD62pvENBu1iqqmZf8YAAWdUQDwhI5KR++xooxLmxLQMvz6Iu5oOsGWV+RFVzhHoboeMeHF4n9Bs6biqcz6oKISNqCHpZKBocodev7ei00rBNoSG957q5oLSeMeSBrGmIh4xc8f5U1RdMjkHVesSVRMUx9uVhL86aV42ujISSoAAGTMHxOmlWBYIC2+8C1+zrc5+otUJMs5LarxL6RFHRJYjBiAYEzK2N4r09CUiKjoDA4rBJUUQDwpD7cjOnmm0mx40DtIhfcbQBjIMySdGw6cNOyys7qzJq+z00veaPbGiCqBo53acf5vSacyyLaEBAe0o0vMqM8dxcLwX85kQeug5wLANZNf7VNLvHP5FTIKsaFBWQVBUsjPJqfT3rgBEx8+bObmREFW/u7EZpyGeLmDE92BVhP8AAIZ/H4cE2n3H5oVV46F+N6GgXEfDyWDanyvaMAs+hLh5AIpdAIi+D51jUlgRsXvre9/SwAjQdKPELBe/pJtrHrUf8YIRGgCAIYhh499138clPfhK7du3CjBkz8Nhjj+G0005DJpMBy7K455578H//939WxQZi7GCGJTcn8pZXpm9YcjFVVcM+HrKiYf22ffCwLGRNw/zaaMG+Blv0NJQHAR1oSeatcNCSPmHj5jPKihk6buQNTikQxt2ZkbCnKwddNxakk0qcoddH1ccBGHVnTZhe161nVHW8sKUVAsdAUnUsqIsVVFXnGAYftKb7DR13q0oOAPeue98IV1U1/IprwsXHTsX1px5i6yvi89g82IXex4mmojtSnHHGGfj973+PaDQKALjrrrvwla98xRJ87OjowAknnIAtW7aMopVEIcI+HosayhD3e5BTdPh5BrNqCs9Lg+FmTjXb7OzIWN7pqqjPMd/s6szgqbf3gGVZVAW96M7J+PO/9+C8hZOsg0XTa14WEuD1cBBltaDXPOrn4fdwCHo5a74MCJzNE1wd9YFhAFUzDh6ykgbWY/f4CxyDjGh4pH093vCspPSbO703kYOf57A3kXNEzFge7FQeEb8HyZyMiohzHLKSgkROxpF1Mcg64GEMdfTeQpE95btRGfFZ0QMsA0d+tXnPjrSE0pCAjrSEsrBgu+dQon2mV4Tg5asHKA+3v8+JkKvtlgJp7QRBEMTH5eabb8bcuXPx8ssvY8mSJTjrrLNw5plnIpFIoKurC1deeSXuuuuu0TaTKIAZlhwPegYMS152aBXiQU9xVFUtxVgzmNqpINt70VMe8qErK2PdlhZkJcVq4xc4RPw8GIaBDgYMwyBaKGzcdgum4B070xLa05Kh6tuzIG1PS+hM2+vF1sT88Hvsywm/h0VNzJ6DCF3veUym519nqLoZOs5zTL+h471VyXmOgaLplip5b95vSWLNhiaIioagYOQ1PrKxCe+3JG33c/M+Fv39Pkh47rnnIIqi9f13v/tddHZ2Wt8rioJt27aNhmnEIAQEHlG/B//ek8Tbu7rw7z1JRHyeA/rMu5lTy0I+1JcGsbMjg3f3JLCzI4Mp8aDDO93YlkFWUlAeEhDxeVAeEpARDRFIE1O1uyMjY3d3Hh0ZGR91ZtGSEG19CTwHngVSeRXdORWpvAqOgc0THA0ImFYRAssAGckIsW8oD9k8/pKqw8tz0HSjRrmm6z3ijYVzpzds78DfN7dgw/YOvLM7gVR+/9xlerBzsoam9gxysubwYPfu661dCby5swtv7Uo4+jLzq2uiPgg8j5qoD1VRnyO/2nx/ysICknkZZWHB8f4UivZJ5GTb/UzWb23FN598D/c8/z6++eR7WL+11dFme2saa17ZgYdfacKaV3Zge2va0WaiQX8tCIIghoHXX38d69evx7x58zB//nw8+OCDuPrqq8H2hK1de+21JHI2Srg5XXejyOsmv80NqbwCD8fghBllrkKcBwpDN8PGZ1Z6kMyJiPi94DhnSLipEr6wlzc6kbMre29pTgIwcgvNwHFZ0wsqoYd8PEKChryiw8czAMvaQtpTeQUensXCyTF0ZiXEAwLAOEPCrXDvI+v2q5f3GQtTlbyhLGiNV1+Fc9N+SdUQEHjwHIuAwCMnKw773arjTiQV3ZGir9L4QMrjxMgy2DzYns7juc0t8Hs4VEW8SOQUrN3SguWHVTrmOrdz6oyKYL/ez12dGbz8QRv8Ao+wl0dKVPDP7W3Y1WlPjWkoNzQXOjISSoNAR0ZC2MfbonlM1e6crMHvYZGRtYKq3S2JHHZ35QAAPh4QFWB3Vw4tiZyVr82xQFDgManEB57loGgqQl7e5i0WOAaiooJjGHgFFqKsQVJVx/3M3OlkXrHynfvmTpse7KMbSsAyLDRdc3iwe/fVnTUUzDvzIra22HO/h5JfPdj74zbaZ6Q1UcYTE/fJCIIgRpHOzk6rRnYoFEIwGERJyX6F0pKSEqRSqdEy76BlKArUgynyFkvN2gy9fvn99gFDr92EoVdFvdiXyKElKRmR1115VEcER2iiGcrZmZH6Dfc0ldATedXK1fb3o4QuyhoSeQUMgKQIRPssgsM+Ho1taWxq7ISmaWBZFsc0lPYbxj1QuHdV1It0XsaWvfs91lPizrD3OdURcAyL7pxkjVdA4B32A+7VcSeKii5xcONm7mpJiGhNiVBUDR0Zo5b1vqSznJ7bebB3u817k452pge7IuyF32NE53RlJYceRV08iMtPaMAve2qDh308vnx8g62NpOrwejik8gpSPW5dgfc4PM9N7UbpRL/AgWUYsKwOTddsNb/NeuWdGQWKKoHnWNTE7PXKJVVHyMtDVFSjhFnPRr3v/RI5xYjy0YHOrAS2J76od+70/jrXElRNB8cygJ51HFAmckYt8nTeOJRkwEDgFVtfQ8mvHuz9cVsze6Q1UcYTE/fJCIIgRhmGYQb8nhhZxqriOIA+odemFFkBBglD39OVQ3tGtl7VAbRlZOzpyjlCOQdT9vYLHLycXQndyzmV0HOSanlXzHaSoiIn7fe4dKRFvLc3CUXTe3K1NWzem0BHWkQgvn+83CzscpKKZM6oN8uCgQYdibz9fgBQGw9gbm0Eb+zsshav8yZFUBsPFB5bougwDEPz4CgwkOfZ7dwV9fPIy+p+ReusjLCPt+U7m321pvOI+QW0pgv35eaepge7LSUi5DXuGw14HHoUAHDhMVPwifoSbGlOYk51xFHCTOAYiLIKnoWV0y0pTs/znOoIfDyHjKjALxjtQj77wZykqNjVmQXDMCgP+9Cdk7G7K2vzKEf9RjSNl+fg83DIyyo8BVTCo34eug6omtpT81sFYB9Ts861mV/dmhQL1rlWNQ3JnHEYYtYYT+ZkW41xwF2Ejtv30U1f5oHunq5cv/nhB6tGxsR+OoIgiFHk4osvhtdr/KHJ5/P4yle+gmDQWED0znMkRobhUBwPCTw6MyJCAm/ltw1VcdwMvT5pZiUYBtB1IJF3qpe7CUN/e1c3dF2HlwMYloWuGaVr3t7VbasFCwweQt/YloHXw6LKI0BSjdBMred6b6/SluYkGJZBzM9ZNb8lVbOFcTe2ZaCoGsrDHugaEPZzyElOVXXAWNhpWoW1oO7rNWtsM7xTdSVB8KyR050WZUdfqbyCebUxzKoOoy0pojzihcBxE96bMpbQdZ3mwRFmMM+z23lQ4DnMrg5jW0sKGVFFxMdjZlXYlu+83yubR6OWAccyyInaAan/18WDWDyjHH944yMk8zJ4lsFZ82oKinBtb03j+f+0IpmXsbc7D5Zlbc/Yuz61qBiRNYXqUx9SFcGJh1Tgb+/stfK5F8+osG3ieyt7gwFKA05lb4HnUBoS0JLIIyupYBkG8aDgUAk3cshZJPMqdBgRROVh1tZO1YCgl8PuLgWNbTIEnkFA8DvysLuzhhq5oqmQNR0MC/Aca6sxbjJYhI7b99FNX71V1dta8gh4eXxhzuSCmiiDec0nGhP76QiCIEaJFStW2L6/6KKLHG2+9KUvjZQ5BIqvOO5WQXuwRbBpV2dWGlRBe7Aw9AV1Meg6IGqw4h851rheiIFC6I2wcRWJfC+PTp+wcaAnjJtlkRIVeFgGsqY7wrgbyoOQVR1tnXmwMJTOS/yFvVi/27QTD/2z0cphvPyEBlx4zBRbXwGBRzIvozRoCP/0zem0xkvR8O6eJDwsi5ak2K8qPDE80Dw4srjxKJvzza6u7IB5vmEfj7mTYoj4PNZhWn1Z0NbO9Mr2Vtou5JW17tmdQcwvoDsnoTLss/XVns5jR0cGk2L+HiFFHTs7Mw7FcTfPaNan9nIMvB4PRFkpWJ+6PZ1HV1bC5LgfYFhA19Cdk2z3NJW99yXyCHg5ZEUVlX3UxCVFRUdagpdnIPAcJEVDZ0Zy1Lk2csgzYADwrDFF7+7KOHLIM6IKnmURC3NIiyqykuoY0+qoDzzLgGNZ+D0scrIGD8ccUB11833snYdd6H00x38wr3kiJ+O46aXwewwdjUI56cXSRBlP0F8egiCIYeDhhx8ebROIPhTzdL23graPZ5FXNEtBe6hhlUOya5Aw9HhQgF8wFmrWc3s4xINDr61rhI3b+5cUrXAYd00Yb3zU3VNbm8G8SWFHGDcDgNGtMwowBRTad3Vm8NA/G/eHtGYk/PJfjVh8SJnl8XKT07n/poOrwhPDB82DI4sbj3JA4DGrOoJNTU1I5mRE/B4sPqS8oEfTaNdhtVt+WLWtnaoZkTktyTx2deXh4YyDvL5eWauvVzqQzHcj4vNg8YwKW1+m4ngiK0MDeg7nso4ccjfPaPM8y1K/nmfrnnkFmgawLKB32u9p1vxes7EJUrch7nj6XHvNbzO/OiOpSIsqGAbwioqjznVTewaKpkODUaYRABRNL5hD3pYWsTehw8MxqCygOG7UUY/hvT0JyKqOgMAdcB11U+WcZxnkZQ3lIQHlEa/jnm7y9833p6E0DL/AISepBSMpiqWJMp6gTTdB9EP9f//NdVsvp+Puo4bRGIIgikKxFKhNBe1DayI9pWFUdGakfheIYR/Xs0HkCoahuzn1N8PQj64vRVqU/z97bx7fVnXm/3/upt2SLO+OHScOJCEJSb4NEMISICxh6UKhBVqmTSgFOiVlaGiHMp2ytJ1JS/tr6UJL14SZQgudlnYKHUogrCGBJjSlJCSQ2NnteJes9W7n94d8FV1f2boKsiXbz/uFX8RXx+c+OpKO7rnP53we+JwSVGaWALZ1p2vcBpwiZJ3BwXNQGMsp4wbSmZ6RzpmWjQNBt5gpGTZcNm7EtbC5ErPqfOgcSKI+6ILXIVniEgUOjUEnVJ2DyDPImjUuw0ypyuuAWxJR5UVOM6Xrz2zB8tnVo9aBNeT4K06pBQcODAzh+MhbAAii3MmXZbSj5onLKnZ3RNBa481knfd0RnBma8hywzBfO1nVcKg/Dg4cQh4R0ZSKwwNxS4b3eF++THZ9eF+G43hK1eFzpvticavjuJ2suZF5lgQOAs9B05Ez82ycM6no8DoFxFJaeo901jmNmt91FS54HQJisrXmt7G/WtPT9bIVHTn3V1d6JMtCVtPTx7NjT+8hB2ornBhIyJY95MY4LGutwrSgK+Nt0VLlzanksfO+yedybtcLwO57kNzLCYIgCGKSUwwHasMspnsw7f7dPShb3L+BIRn6YAqvtfVBZww8x2HpLKsM3c5dfzuS9tYaL8CArlgqs1Cu9Dpyyrg37e7Chs3tmXOuPnsmVsytzTxuuH+nZeMju38bz3FrWy/AgF0dg1g2zJm8tcYLVWPoGZQz+9aDOUyS7JQDMmgO5V5sZ8fld0noi47shE4QEwU7c4Qd1YxxI7A56IXbIaDCKeXMRNppF06kKxaAAyJJFTwPgMGS4c30VelJ9+Wy9iVrDJVeB8IJBYrG4HYICLitjuN2subhhIqBuIzBLMWPwMuWuI7v/ZYRTangOc6y99twcdd0hsGhRflwF/eBuAJw6WlZzhLVWPdXcxA4IFtAJBgDmBW7S+Ih8g5wHIeQxwFR4CyxD3cmD7hzK6QKfd+M5HJu1wugkPcguZcTBEEQBDEqdty/gfQd/QO9MSianpHaHeyNmWTodu/625G0ux0C/G4RA4m0gznHpfdhD3cct1NL1a77d1xWsX9INpl2JtewvzdukdobEm+jVDOXw6G9IOl4HqaqWQ8x+SgkM5hPzVPInu58GcuAWxz6FHMIedN9gYNl77SdvuoDTjRXeuAUUwi6RQwkVNT5nZYbmUbWvCHgwrSgGzpjlqx5bzSJcEI1/V04oaI3mgQQMMUvCTwkjkEQRWiade+3HRd3l8RDHVpJCwA0AKrK4JKGb4pmGHYPYej34wcze8gjSXgcAuKydQ+5QT6F1Fi8b+x4ohSzr8nE5H52BEEQBDFG5HP/BtJZElVnmFvvNzmT56pZms8J3ZC0n1zryyyCoynV1FdnOAWfS8IZMz1QdR0izyOaUiyyd6OWqkMADvbF4HWkZe/D41rYFMSsWu+IsvFMXIxhZpUXiqZDEngkVc3UV1pezqM55AEDAwcOCUXNKXu//swWLJjmx45DA1jcHLS4rhuMJo03sGvWk09+WWzG+3zExKbQzOBoap5C9nTnu2mV7XAuqzqCbqvDud2+sm9kGtnWXDcyB5Mq/nFkYOic6SoOkaR5vtzfG7fc0mNDx8/LOpZ2EwcGZQYGBRyAWg6m+O24uCcVHQ5JgCZr0JHOWzskAUnFrCXvjysZkzjj+0AUOPRnZcSNPeQPv9qOlKbDKfC4bEFDzrmrWE712a/TaO+bQm5iFrOvycLkfnYEQRAEMYaM5v4NpLMWAsdh97FIJgvcXOmx1Cy144ReH3BC0XTsODgAnueg6wwzqs19GbL37Ax2Ltl7fcCJI/1xdEePX+zV+KRR40rLxnPHFU0q2NUXyRxrCblNfbXWeCEJPLoGU3BJ6drfIV9u9/Js2ftzb3dZZO/D2+SSxhvYkVaOt6HPVDQQIt4bxcwMGpni6SEP3KKAhKrl3NMN2MtYnjotCL9bBM/x0JmOGVW+EbOf+W6A2bmRKasa3u4YxGBSyWTqd3cOmvY8hzy5x2X48bSbeAI8B0gCB0VjONyfMLmJG8/RIwlQ9bTr+El1Fabn2BBwwSFw0AQOTolHStHhFK1O4vMa/PA6BMRlFZLAQ9HSe8mzt+wYe8irK5xwijxSqm7ZQw4U16k+u8/RbgYWyxOl2H1NFHKYwRMEQRAEUQw8DhEzqr1D2Q0dosBhRpVnRNm4KKTrThuycQvM/I/h2RwjWxTySqPK3vd1RdETNe837Ikp2NcVLTiuhKwhklSHMthIG5YlNZPLeZXPiXmN/vSFrZoubTOv0Y8qn/lmQLbsvbbChf64godfbUdPNFlQGyN+46K0xpdut3FXpyl+O22KyXifj5gcGJnBkFd6z5lBoybzkf4E9hwbxJH+BPb3xDGYzP0e9DhE1PldI2ZG5zb40TGQwtsdEXQMpDCn3p+z7d6uKB7/62E8+eZRPP7Xw9ibNddkU+1zYcG0wIg3M8MJNZ0lBtAbVzJzYLacXGOcZYHDDx3Ppr0nBgBwijwkQYBT5E3HjecYcEvYfnAAW9p6sP1geh959nNMO4kH4HEK0BkHjzO3k7hRF1zXgbisQ9etdcE7wykc6oujNybjcH8SvTEZB3vTjurZ5MpiGwqp7NjnNvjR1hPFC+90oa0nOurrs2Hzfqzf3I4Nm/eP+PqM9n4olGL2NRGYGs+SIAiCIErAYFJFTYUTH1jYgL6YgpBXAsCZ5H12ZONGO0nksXh6cNR2K+bWoinowq6OCOY1+E0XdAav7+8DkDbxSbv7MugsfXxpa7UpruZKN2KyhpoKAaquW87X1p2+QG0KejKSyZhslo4PJlXMqvFheqUbXYMp1FY4IYlCTql6JKnA7xYxmEr/f7js3WhT43OC5zjU+Jzoi6cscdmRVo63oc9UNRAi3jvFygwaNZl7YylUeZ3oGhy5JjMwevbTjiu50c7u3uJ820YCbhGMAUwHfENu4sZxg5nVXjhEDknVuBEIOEQOM6vNypp5DX64JAEJRYdL5CCnAI/Dmnn+y85OeJ0Cpoc86I2l8MyuTqxcUJeJL+0kXo1an3PEbLjRV39cxsxqbyYjPrwuuEPg0B+XkRjFUd04Zz4X90Kc6u2+PrQ15sSh0SIIgiCIMWK4s3daOm529rYjGzfaCTyHd49FMxLt5pC1nR3p9RkzQgCXNvHRhpx9OG7oeNb5jkVS6AgfzyA3BqyGPoZ0vDMysnTczjgY54wmFew6OrJU3ZDs7+wIjyjZN86ZT5I73oY+U9VAiCgOxai8oOmA1yHicH8cbfEYHCIHjyRaSlkB9vcMj+ZKnt0u5JOg6On5IVf5vk27u/CLl9swkJARdDtw47mtlrnLIQqo9jnQORDHYEqByAGzar2mPdb1ATcqXCKS0eOZ8AqXiPqA29TX7Ho/Vp81E+s370MspcAl8li1bKYl8xxJKqj2OcFxHKp9TsuNwFw1za/IkVE2+mqu8oxYajLbUT02gqO66ZyjuLgb417nd4EDhzrJlXPc7b4+hWyNocW5FZKXEwRBEMQYke3sLQr8kEQ7bpUT55GNA0NS9ZAHopg24hFFDjOq3KYLGrvS61m1PlR7JdOxaq+EWVkXUEf6E+gZTP+dkWPpHkziSH/C9Hd2pON2xyEha4gkRpeq25HsG+3ySXKLKdu1w3ifjyCGI/BpJYpbSpfic0si4opqyXTb2QqRfRMpIWvojCQRcFtvIlW4RCgaw3O7uvDSni48t6sLqsZM7XqiSfzo+XextyuKWErD3q4ofvzCu5a5S1Y1HOyNQ2XpeUllwMG+hGlP9/6eKHqHbZ/pjSrY32OVTC9sDuKkWj/q/W6cVOvHwuag6fH6gBOKqmP7/n68caAP2/f3Q9F0002+7Izy+XNq0VrjxZ7OiGV+M26c7jwSwdsdYew8EoHI86a+DEd1lySgrsIFlyRYHNXN5/Th/Nm1aK3xWc5pZ9zttitka4xdqfpUg2Z5giAIghgjDGfv1iovZE2HQ+CRGObsbZaNpy+Kc8nGB5MqavwuvH9hA/pjMiq9DnA5pOqRpIKQ14GUqiPkdSCckHNK1adVelDjVdGfUFHpFuFwiKZ2Ow4NgAFwCgDP89B1HRpLH892FDek47OqveiLywh5HACXQ0LPGE6q8WXqlcdk63Ns644BHNAU9ILjGBjjEJMVi1Q9n2TfwI4kd7wNfaaigRBRPmg60BB0QeQ5qDpQ53eixu+0ZLoz2U+PA4qmI+RJzyXZnzPjJtJTbx7F/t4oqn3W+s4ZGIPOGGQV0MEw/Nbi/p44jgwk4JJ4+JwiGEubmu3viZvmiAO98UxNbVHgoWo6okkFB3rjmTli2/4+DE/c60PHje0zwPGblEY5sGhKtZROBJCpwa0zLnNDMNdY5at9btw4PTwQRzylgRdhuXHqEAWcUu/H28fCSMkMAY+IuXV+iyO8cc5KtwOypqHS7chZ9QKMgYFB1tgot3TT7bihmuEcZ3197G6NKUSqPtWY2s+eGHNmfOkpW+2cAsP9Z4xxMARBEOOMIZfe2RcBxxgYx2HGCHLpd7uieeXSdqXq2/f35ZWq90VlHBlIgDGgazCFaZXmuBY3B8FxHFIag3FFLgkcFg/LBBlZkr8fGoBD4NDWHcfi5qA1LlXH348NgOcBXUfOuI5L1ZPvWapuYEeSWwzZbiGM9/kIwqDCJaIllM5w56vTrag6Nu05Bonnoeg6FjUFRt4KwQ1fih5nMKkiKqvwOSWkVA0eUcRgSjUt2LxOAQLHQ1aNxXlaFeN1mhebYAwCz4PndHicIuIpBRzHp80khhjuGj7S8c5wCm3dMfTGUtAZwHNALGW9KarpDDUVTmi6DoFPK3Wy29jdNjKYVBFXNDgFAUk9XQ4sJmumcahwiWgMunA0kgBzpYd1WqUr5+uTr+rFYFJFNKXB6xCRUjU4RQGDSS2nvFwSeayYU2cqbTk8LrvPkXwrclNSefmMGTPAcZzl59ZbbwUAnH/++ZbHPvOZz5QyZIIgCIKwTULWEE6q6WsingMDEE6qJySXLqZUvS8qozcmQ2fpizqdAb0xGX1ROdNmWqUb1V4pk9XhANR4JUyrdFs7NGVJRjgrN/QDbihzZL1IL6ZUnSCmKnFZxbFIMudnwshO1/mdSKoa6vyjZKczC2nj82z+zBpZzWhKw4yQF9GUllNyLPBAx0DaRCzgltAfl9EZTpok7S1VXixtDYHngN5YCjwHnNEaQkuV2fxsbqMf00NugOOQVFSA49BS5cbcxuP7sGfVVoAfNr0IXPp4NpquoyeahKzq4DlAVtO/a/rxPHnALSKp6AgnZHDgEE7ISKm6Se5tjKnPJWB/Xww+l5BzTI1yZ3FZQ6VHQlzWLOXOjHFnjEFR0yZqw8fdGPv9fXEomg5wgKLpluoShmlef1xBwO1Af1xBZyRh2UpgLKgPD8TRE03h8EDcsk3A7tYYu1sOis1o7/lyoaS3HP76179C046/0d566y1cfPHF+OhHP5o5dtNNN+GrX/1q5nePxzOuMRIEQRBTk2IYwbR1x8ABmB5yQ+R4qExHNGl19q6pcOLD/28aokkVPpcIVWM5nb3zSbQNqfrCJj9SGoNT4BBXrI7juzoiYGDwSFxG0s4Yw66OSMZEqDOcQl3AjYagB/GUCo9ThM5YTtm7JPJY0hIc1aFdEngsaakEh7REc7gZkdHXcZdzGbUVDovLeWYcarPGIYccvxCKafpDBkJEKbFjdmVni8NgUoUkcFhxSu3xz+wwcy27WU1NBxoCbogCh6SioabCgRqfyyRp9zhEXLGwEUcHEuiNyajyOnDFqY2W2Kp9Ltyxci5+9tK+TLubls8yffYH4mlTtISiG0lgOEUeA3HzPu+BuDJ0406DoqVvHIqCuZ1DFFDlc6AznEQslQTPcWitcVjk3kDaUR2Mpf+fg3BChUsSIAocdAYEPRJEnkc4oaI5a9zfOTaIg71xKDqDxHMIuB05vw9iqfT3BdMBp8QjmjLPqdlbCZKKjhqfI+dWAqMs2uPbDiEup89z07mtJ7Rdx1icb9zVOW6+FYUYvJWSkn4b1NTUmH7/xje+gVmzZuG8887LHPN4PKivrx/v0AiCIIgpTLG+xFtrvPA4REQS6T2DkXj6YmW4XFpRdWw/EB5VxpmRaHcNgOc46Iyhpcprkaorqo43j0VHlXHPa/AjJevI3PbWAAHMVCrHkL0fHkjAJQroTyRGlL3v647itbY+6DoDz3NYOitkkZf7XRL6YgoaAi50hNMXYyNJ6F9r68ssqHP1pWg6/p7l9t5S7bX0ZZdiXrBNlIs/YnJSyH7afFscjIxlX1R5z+7/FS4RLVUeuJ183vJWdT4HKtwSPAKXs7wVkL8sYtAjQWfpPcwODpAZoDOGoMdsHtkQSC9IeQ5wSRySCoMkcCYZuqxq6I3KcIo8PJKAuJJ2HM/OThvjHpM1zKjyjTju9QEnaiuc6B5MIeAWEU6oqPU7TXNXOC7jH4cHTCXD3joygHBcRp3/eFxGBj6SVNPbBOIKAm7RlIE3thKIPA+3JCChaGiqdOcsZZavLJqBna0xdn0rinGDciLtIS+baGRZxq9+9SusXbsWXNa+kEceeQS/+tWvUF9fjw984AP4yle+Mmq2O5VKIZU6XkA+EkmXHVEUBYqijPRnGYw2dtqWG+UYu1MYwbBheDuemf4/0TiR+MvldSrH900hFBL/RH2OxPhSzC/x5pAXN53bip+/0ob+uIwKl4hPn9OayXJnyCPjtDJU5iuvjDuX7U/a3XeYoBHa0HHjAtaQvXcOJkeVvfdGU9h1NAJFY5l92LuORtAbTcETSret9rmw+uyZePjVdnQNpi/MV50103JBF5dVHOiNQdF0uB0CErKGg72xTPbF9PQNGTs3wjjYoJiv9US6+CMmJ8XcT2snY2k3q5ndLpxQUFfhsrQbTKp4bvcx7DwSydxwOxRO4qr3NVn6y1cWUeB5uCQRSVWBPDQ1uCQRAm/WVQc8Dsyq9WHnkQgSCoPA8Wit8SHgcWTahBMqFE2HrOlIqhp4joOs6pbstJ1xr/a5sHJ+PX72cht6Y6n0uMyrN82DHeEkGEvfCEgqadm7rnPoCCdNNxccooDmkAdvHQ2jezAJUeTRVOkxZeBzlTK78JS6EUuZTQum53eXJKBrMPme1EP5FufFukE5kfaQl000f/jDHzAwMIDVq1dnjn384x9HS0sLGhsb8eabb+LOO+/Enj178Pvf/37EftatW4f77rvPcvyZZ54pSJq+cePGguIvJ8op9kLN0b522gianAlCIfH/+c9/HsNICqec3jcngp344/H4OERClBI7d87ztSn2l/j1Z7agqdKFV/f14qxZVThvTp3lfPlknMBx6fj8xgokZAa3g0NKZVZ5ucBjXoMfCVmD2yFA1qzy8pfe7ckZ60vv9uCSBY2ZuGoqnLjmtGbIqg6HyCMhW0142rrTi+R6vwvgGJDDcRxIZ6dOrvWirTuG1hqv9cbDUPyqPkw6PoKEflFTcFQndIPRXu9ivtZ2a90SRC6KkfUzMs+H+uOjmqTZ5aRaH4KeJnSGU6gPOHN+vuy0sdOuM5zAziNhyCqDQ0wvbHceCaMznDBleA3H8e7BJPxuCd2DSYvjuKbrSCoqeACSACgakFRU015tIL2txusQ0RLyZLLKPqdo2vPsENLbV3SGoWyxjriswiEcv5mZGfeB2IiZfCD9GocTCpbODGXUSpGkYrqp2BBwgeMYNJ3B4+ARl3XwErOYwAl8+r5jtdcBpyggpWrgOZhiN9QD00MeuEUBCVXLqR4wlEgd4eSoSqRiUcwblHbVFuVA2UT0i1/8ApdddhkaGxszx26++ebMv0899VQ0NDTgwgsvxL59+zBr1qyc/dx1111Yu3Zt5vdIJILm5mZccskl8Pv9Of8mG0VRsHHjRlx88cWQJClv+3KiHGNfcO9fbLVz8gxfO03HV7bxSOn5Mjzlx4nE/9a9K8c4KnuU4/umEAqJ31C+EJMTO3fO7bQp9pf4AxvfwYZX2yFrOh776yGsPmsmbr94tuV8o8k4AcMJXcXOvniWE7rHIi+PJhUc7DteS3v6MLd0AFh+cjV+9dohS6zLTz5eTseIK/vCKFdchlT+cH8scyEZ8jpMEnrAPPY7j0Zyjr0dl3ND9r63e3S39+HnzPV6F/O1zq516xA4yBqzuLgTRC6KlfU7ntlsz2Q2l8+uOeFFfLHmVDvt2nti0PS0iMUob6Xp6ePZJQo7wykc6o8jHFfQEUmBB5BUzTcWB+IKnJIAjtMBjoOTZ3Dk2NOdvddc1RiCXutec1ljQ67rSSQUHTzH4HVIkLXj6prMuG/uRSQ5AL9LwvKTa3Pe5DvQF0PXYCrjpQEOphtzx7PvYcRkDQIHS/bdiJ2BoSeagqKlby7UB8yxDyZVHOiNo2swCU1nEHgOimr1C7GrRCoW463IKBfKIqIDBw7g2WefHTWDDQBLly4FAOzdu3fERbfT6YTTaf3ilSSpoAVFoe3LiXKKPaUVtoBO6VzBf1NOFBJ/ubxGBuX0vjkR7MQ/kZ8fMTp27pzbvbtezC/xdzoj2PBqO1KqDp9TTNeB3dKOy0+tN8m47ZwvIWsIJxQwIL2XmQHhpGJyQk/IGiJJFWwoA6yz9AVWdhsAeN+MEPwuAZHk8eN+l4D3zQgVPA5uhwC/S8RAPB0bx3GocIlwO47LHAvKbOSRx9uVvds5Z9Ev2PLUuiXeGy+99BK+9a1vYfv27ejo6MATTzyBK6+80tTm7bffxp133okXX3wRqqpi3rx5+N3vfofp06cDAJLJJO644w785je/QSqVwsqVK/GjH/0IdXV1Oc449hR7i8Pujghaa7yZjOtI+6LtxtU1mELQLaFrMDXinNoVTSLodqArmjt2O301BFxgQ/uweZauq82YNcPrEDj0x2TEZRUCz0HT03WoszPPrTVeVHociCSUzLwb8EiWG4HGXnMOLF1dQmdoqfaYbpQF3CJEgYNDFOAQeMiaDknkTHunjXFvrnLDLVUgoag5x91wcU+Pg4iBhAqdMVN22si+NwZdkAQeiqZbsu9Aeq/5ob4EeI5DXUVa1XC4P2Haa264l/dGZVT5HOiKpCznM7CjRCoWxb6xbXcPeakpi6jWr1+P2tpaXHHFFaO227FjBwCgoaFhHKIiCIIgJgp27pwXcne9WF/iuzoikIcumpxDe+1ismpyCTfO5xQbRr3gaeuOgeOAxoArXUiVSzsBZ8u427pjAICmSu+oUu/OcAqtNT4kkwr6EipCbhEul2SRaJ9U60MsVYUdhwawuDmYM4PVGU7B55KwsElCLKXD6+Sh6jD1ZYx9hUsY2tsuIJwYQUIv8FjcHITGGASOQ3SYM7ld2bvdcxbrtbZT65Z4b8RiMSxatAif+tSncNVVV1ke37dvH8455xzceOONuO++++D3+7Fz5064XMff05///Ofx1FNP4be//S0CgQDWrFmDq666Cps3bx7Pp5JhLLY4NAe9cDsEVDil99TXgb4YuiIptA1lZROKaplTjUxqmx6DwHNIpPScn8V8fXmdEgKetNmijvR9t4BHgtdpvlkua2xojzWAoWyzpOqmzHO2l0ZsaMGdy0vDcO3e2t6XkXif2hQ0xe4QBXidAg73qRhEutZyU6XbtHc6V0ZZVWEZByOTfyySxJF+HaLAD9X/hqVNf1yFoqWN3aZVwuI4nu2EzoFDpddhcULX9HT988P9Ktq6FThEDh6H29IXYE+JVCzGIjttx+Ct1JQ8Ol3XsX79eqxatQqieDycffv24dFHH8Xll1+OqqoqvPnmm/j85z+P5cuXY+HChSWMmCAIgig37Nw5L/TuejG+xOc1+OEQeERT6dqh0ZQKl8SbXMKB/MZAQDp7wxhwNJyEwKWvN0PDsjetNV5IAo/OSDJjahbyWTM8w2XoAwkF00OwSLSzpfEOgbdI47P7OtCXyJK9myXtdlzJjb4EnsO7x6KZ+JuHSejtyt7tnhMozmud2SYQl8t+b+FE5bLLLsNll1024uNf/vKXcfnll+P+++/PHMtWRobDYfziF7/Ao48+ihUrVgBIJ35OOeUUbN26FWeeeaalz5EMeotFsbc4FKsvIyvbHUnB7xERiefOytrJpBp99cZSqPI60TVobRdwi6hwSVC19I00WdXhd0umjDIAHO2PISabV40xWcfR/hgWTAtkjl1/ZgtOqvXi9f19OGNGCEtbqzEcw7XbLfGo83sRSSgW1+5wXMaR/nRG2ePgkZR1HB1ImNzEs+thG3uic41DOjsdBxgQcEmIyRoO98dN2WmjDcdxqK1wDGWw45Za3oYTujGmvbEUqn1mJ3SBB2IpDU5BQE1F+jWMy5olrlKYQJYqO13Kko45BAbjy7PPPouDBw/iU5/6lOm4w+HAs88+i0suuQRz587FHXfcgauvvhp/+tOfShQpQRAEUa4Yd85DXimv0+5obYrN7Ho/Vp81Ey6JR0xOL7hXLZtpynIbxkD9cQW1FS70xxU8/Go7eqJJU19uh4CAWwIHgOlp0XXAJZlk3FU+J+Y1+iEJHBRVhyRwmNfoR5XPvJjOlqFzSMs4h8vQs6XxXoeIlKrj4S3teKczYukrnFTT0nI+7SMeHtZXtiu5Q0xLJg1X8mw8DhEzQh6IYnqPpShymFHlPqHX0e45i0Up3l/EcXRdx1NPPYXZs2dj5cqVqK2txdKlS/GHP/wh02b79u1QFAUXXXRR5tjcuXMxffp0bNmyJWe/69atQyAQyPw0NzfnbHeiFPN9U8y+ND2t1ugcTGDn0UF0Diag67BkZb1OAQlFRVt3DAlFhcchWDKp6XYiErKOtp4YErIOj0M0tXOIAporPeB5DgklXXqwKeix1MPe0xXNGe/w449sPYA7f/cP/PeW9P8f2XrA8jed4RS6BlNQNIa+mAJFYzgWSaEzfPwmS0c4CQZAEDgoevr/Oksfz35+DUEXanyOTD3s4furgeNO6ElVQ3cshaSqZZzQs9u4JB4hjwSB5xDySHCKvKkNcHwfdrXPiUhSQbXPadmHbYx7StNxZCCJlGYddyC32sJQBY0lHoeIOr9r3ObIvV1RbNi8H+s3t2PD5v3YO8J7aawo+TfBJZdcAsase56am5vx4osvliAigiAIYiJi5855se+u90STeV17b794Ni6YW5ORaGebAgHHy7XUVDjAcxxqKhzoi8kWqXdaxi1ifmMFokkVPpcIgLNIr2fV+NDgd6IjnEJDwAmPU8rpOA6MLkM3pPFepwBR4OF1CojLmkUa39YdAwdgWsBlqN6RGCZ7N1zJW6o8SCgaaiucOV3VB5MqavwuXPO+6ZA1DQ5BQEJVT0gSbpxz/rRAZm9kXyz1nsrg5GOi7C2cjHR1dSEajeIb3/gGvv71r+Ob3/wmnn76aVx11VV4/vnncd5556GzsxMOhwPBYND0t3V1dejs7MzZ70gGvcWkmO+bYvUlqxoO9WdnXGUcHohb9gzHUhoknkewIu3+nSuTmm6nwi0JaKp0ozdq7Mk2t+E4oMbnzOzD5nlY+pozguQ5+/ihvhh+9nLb8RrWCQU/f6UNy2dXmyTm6VrXGgaTSqbd8Ox6Q8AFDoCq6xDAQQODi+NNe83t1sM2nNDTzuTp+XS4E3o6g+0y7fuuG1bL22DF3FosbPKP+B2UGXcHjyavG72xlGXcjfgnigP4iVIOJR0nz2gSBEEQUx47MuFi7f2yIwkH0nfXN+/tRSSpIJbqhdcpmfbKGZLqnUciI0qqjXZ90RQODyQzdaqbgy6L9LqtO4qtbX3QdR08z+PM1qqcjuP5ZOjzGvwQ+LTjryRoQ7WzRYs0vrXGCzDg6EAiXVOWweJebsR+ZCCZKbE9bVjsRvx2pONA/tfRKIPTPZhCQ8CF7sHUmJbBsRsXMTboQ+WgPvShD+Hzn/88AGDx4sV49dVX8dBDD+G88847oX5HMugtNsV83xSjr+F7hkMj7BlmALqiKajh9B7l2hwZXiMTLPIcVB2o8ztR47fuZfY6Rag60BWV4RD4nFnZxkovKpwiBlPHs7AVThGNlcfnm7buGCIJBdyQJwQHIBy3eluka1278dYRGccGZUiCdb92wOOAxPNQNEAZMkYUOd7kJm63HrassXTmWdWRUDQIPAePQzTtR8/U8n6lDW09KXic1lre2VT7XCM+Zhn3Cpdl3I34J4oD+IlSDvW8J89oEgRBEMQ4kS0JN/bwDa8VC9h30J4R8qAzkhxRUg0AfVEZPTEFjCGzuO2JKeiLyplz9kZTeOtoBKrOhspp6dh5NIzeaAqe0PH+DBn6a219I8rQm0IeLJzmx7YD/VA1HQLPYeG0AJpCHlNcbocAv1vEQMJwLwcqhsne+6Iyeodi55C+UO8bFrsxFsW6+BvvMjhEaamuroYoipg3b57p+CmnnIJXXnkFAFBfXw9ZljEwMGDKdh87dgz19fXjGW7ZY2fPcPb+45oK14j7j41MMMDAczx0pmNGldd0M83IyjrFdF+RhJIzKxtwi6jyOcAhLUHXdYaqCoclO60zhoSswS3xSCg6PA5+hFrXHOr87kxWmec40zl3d4TRE5NNf9cTk7G7I5zZ0223HnbALUISeLhEHm6HiISswiHyFif0cELB2SdVwS2JSCiqpZZ3Nu90RrCrI4J5DX6TAil73N2SmLdu+2RX6ZRDNn9yjShBEARBjAPHJeHOoQtOZ07psnF33SVyONQfQ4VTtDhoG5LqFXNr0DmQRH3QBa/DKgnf1REBwFDhEjLZYlVnJrl3W3cMqqaj2ucAGIPPJVoczo1zzqrxwecQ0N4bx8wqD+oCbktcpzYFMb3Kg8P9CTRVuhFwO3I6jvtcEpZMd5sk4dljsasjAgaGiqyLS0XTLVJ1wJ6Tu13yyS+JyYPD4cDpp5+OPXv2mI6/8847aGlpAQAsWbIEkiThueeew9VXXw0A2LNnDw4ePIhly5aNe8zlTPZNq3Ai955hY/+xxDsADqjyOCAIMGXDgeMu4a+19WcWjwunVVqcvdNO2zqO9CeHnLat+8ON7HQ4IQ/Vp+ZyZqenVbqxp3MQkaQOngMag96cta6z63Q3BkVLne43j4Qthf/Y0PHz5qTLzNmth23sWw8nZESSajqzPmzfuvGd0VpVAbdDQELWRszI5jO6zL6JGR5acI92E3Myq3TKIZs/OUeWIAiCIMaQ+oATIs9h55Fw5sJoepU7p1w6n9y7wiXizcMD2HagH0wHOB44vaXScgd+XoMfms4Qz3LudYqcSe7dWuOFqjEcjiYyGeWgO3d92hf3dGN35yAYgL8fCmNufQVuu/DkEWP/+6FwTqm6MRZtPdERx2Jegx9MB8JZJmZu0eriDtiX7dtlNPklMbGIRqPYu3dv5vf29nbs2LEDoVAI06dPxxe/+EVce+21WL58OS644AI8/fTT+NOf/oQXXngBABAIBHDjjTdi7dq1CIVC8Pv9+NznPodly5bldC6f6uS7aWXsPzbcy3ujMqorHJZ50HAJd4o8Kj1uxGXN4hJu7A93SyKaKh1D+75z7w/nwKHa64BTEpBSNEt2WlY1xFIaKpxiJqMcl7XcGfgqDySRy2SopwXNe7FnhNw5xyb7uOFe3hFOwCkKSKnaiC7uHAfUV7gzjvDD960bGdm23sFMprsp6LHMu4bRZUJJ+27EUhoe3tKOy0+tL7gcpUEpnb3Hg1Jn8yffiBIEQRDEGONxiGip8qIznIKs6pAEHtOrvJYvcTty78N9cfzjcASaztJ7B3Udbx6J4HBf3JIFxnDj0RxGpAAz/gMAcJY8DfCPwwN459hgJmPOALxzbBD/ODyQKa1jV6puZyzcDgEOkUO2Aa9D5EwSdMC+bJ+Ymmzbtg0XXHBB5nfD4GzVqlXYsGEDPvzhD+Ohhx7CunXrcNttt2HOnDn43e9+h3POOSfzN9/97nfB8zyuvvpqpFIprFy5Ej/60Y/G/blMFEa7aWXJhlc4cm7h6AyncKg/jnBCga4DPA+LkeLwrHNdwGnJOhvtGNJbaxRNhiRwqA+aXdWNDLzoSyuRPI703vRcGfi5DX5sbevN3ORbMde8F9vvdkLkADVrGhW59PHsmHqjKbR3x6ABEAA4BD7v3vaGoGjZY22oAh7f1ptRBdx0bsDy3bKrI4KkokHg0ll1gUtXkhiuHirEe8So0+13SWNap7uUlDKbT4tugiAIYkphx3E8H4NJFTUVTrx/YT364gpCHgnguJwu4aqmY1rQBYcgQNY0hBNWl3CN6fC70iViNJ0hpVovnnZ1RCAIPCoEBg0cBKT/P1xeLgo8GoMuqDqDyHOQNd0iL399fx90lr44FAQOmsags/RxY9FtxO53C1BVBr9bgJKjL2MsVs6vRdegjNoKByRRMI1FW3cMTklAs1uCxgCBA1KqtS+7Tu7E1OT888/PWfEmm0996lOWMrTZuFwuPPjgg3jwwQeLHd6UxM4WDofAoT8mI6XqGWdyxpjJtdvIOrudPILutFt6XYXLkuHN7CNnQMgjIZpULfvIjzuAJ7P2MluNG4292M0hT8Z1fPhe7KBHSt8IUBl4ADoAUeAQ9EiZfjrDCezrjkIH4BI5pFSGfd1RdIYTmX3fmeeYZ4+1oQpwSQLq/U4MJFSLKgAAZlan582kosMhMsgqg0PkMseNvorlPUK8d2gkCYIgiClDsaTLFS4R3YMpvNbWB50x8ByHpbNCOV3CPQ4R/XEFVV4O/XEFFS7R4hLOgUM4rmSyzm6HYJFez2vwQ9N0xDXAyGM7BZZDXq6jZ/C48U+lR7TIy8+YEQIHQAOgDTnn8kPHs/uKJlXEleNpGI/E55Sq55PQG27pPVFlRLd0wL6TO0EQ5UO+LRyyxlDpdSCcUKDoDG6HgIBbMrl2W/YfV7hy7rkNJ1RwHAAeiCTT8mzjuJHFzjiAv9yGtlgs3XcOB/DBpIoDfTF0RVLQ9LTMW9V10w1DgefhkgQkVRXGTOiSBAj8cU14e0+6BKNL5CHwPDhRh84Y2ntiphKRdvZYp2uHp0010+aTDMciScuNx/qAG02VbuzrjiOpMnBIO6/XB9ymviLJ9ILb4xDREEjfiBjJe6SUzt5TARpJgiAIYkpQTOlyXFZxoDc2VEYrvY/5YG/M4jDbHPLipnNb8fNX2tAfl1HhEvHpc1pN2d2Qz4Eqr4QjipaRe1d7JYR8DuuJOWNZbvzKWduAyzRlDGCwtplV60OVz4Hu6PHFeZXPgVlZcsKjAwkkFLM+MqHoODqQMMVvR4Zuxy0dsO/kThDEyBRzb+6hvth7NjWsDzjRXOmBg0/A4xQRT6moD1o9MOzsuQ24xaFdNRxC3nS22DhucNwBvDqTwc7lAC7wQMdA0vSdMHwvtqbrSCoaOCAjM08qGjT9+Nw4r8EPlyQgoehwiRzkFODJcePUeI66XptxHB8u4U7XDtcRTamo8jrQO3SjNvv5AemMv6oDAZeYvimgaNAYLBl/v0tCRziZeX65yiaWg7P3VIBGkyAIgpgS2L3rb7cvVWeYPy0ASeChaHpO93IAuP7MFiyY5seOQwNY3Bw0ZT6MvkI+J4JuCZGkCr9LBC/wlr52dUQg8Bz8Lh76UNmwXO7losBhepUnU1YsLqs5ZdzTKt2o8zkwkFQRdIkQJcF0ztf39wFIS8GN0jzDJejGOfNJ6A239GqvhMP9STRVuhDwOC2ZFMPJ/QMLG9AXUxDySgCssn2CIHJTzL25j2w9gJ+93Ja1t7gV15/ZUnA/Rub55y+3oX8gAe8ImWcg/55bhyjglHo/9nRFICs6gh4Rc2r9uR3Aq32jOoBn7yNPKhpqKhyWfeQDcQX80M1OY183z3EYiCuZNrPr/Vh91kw8vKUdSUWDx8Fj1bKZVk8O5B/T7OcXS2nwu63PDzi+b716qIKGzyVa9q3bLZtYDs7eUwEaTYIgCGJKYPeufyF9dQ+m0BBwoXswNWJf2ZL2597uskja6wNO9MVkHOlPpOtYc4ZM0NyX4QAekY9nMoY7gBty9kyWJCZb5OzGOaNJBQf7EgCArsEUWkLmc54xIwSOAzSWJUHnzBL07HOOJqE3HNq3H+jPGCktmWF1aLcr2ycIwkox9+Ye6ovhZy+3IZJUM/uPf/5KG5bPri44453JPJ9cnXEJH6n2dL4sfYVLxKlNAQg8QzSlwecUMH9awFIRwu+ScKg/Pmp9ajvu5UGPBJ2l9UKiAKgaoDNm2tMNALdfPBuXn1o/Ys1su2NqPL9QhTSqe7mxb/1YJAmPQ0Bc1nLuW7dbNtGus/dkdzgfS2i0JiEL7v0LUlouyaGV/d+4YoyjIQiCKA/s3vUvZl92JO19URk9UTltbDa0yO2OyuiLyqb+7DiA25GzA2mX20gibWZkZMTDSRWJrAX9rFofqjwSumPHMzpVHskkQbd7zsN9cfzjSFqC7hB4yJqOfxy2OrTble0TBGGlmHtz27pjiCQUcByHaEoFByAcVyyqmULimlk1eubZTpbe4xDRGU7i2be7MmaRIa/L1E/Glby9HZGEAr9bwvLZNZYxsONeLvA8vC4JqagMWUtv3vG6JNOeboPZ9bkX24WM6fB9301BT86sc7XPhfdNr8TDr7YjpelwCjwuW9CQ8/vMbtnEfCqDqeJwPlbQNxhBEAQxZbB7199uXyfXekfd72hI2kNeCbKmI+SVEE4oJhn3ro4IAAaPA9DBwQkGBmZxLzccwOslEbKmwyHw0MEsF8HXn9mC02dUjppxaeuOARzQGHRn9pEnFM3UV2c4hWkhD6q8GgYSMoJuB1xO4YQk9Ls6ItB0HUGPI7PoTsiq5TkWItu3m3GhzAwxVSjm3tyGgAs6Y0gpGnxOEXE5LWduCFjnTDvZab9LwrvHIuB4DkxnaKn2muKym6V/pzOC//37EQCA3yUhLqv405tH8E9nTs/MJYYreWuNN+OEPtyV3NzOl8k8D2/nEDhoWtqHQhI4KBqDrusm53W742B3TO1knXuiSbxxsB81Fc6MI/zfDvWjJ5ock0oPhaooaN61QqNAEARBTCns3vXPR/Zd/51HIznv+tcHnFBUHdv394PnAV0HWqrNbtzzGvyQVR2qDhgmaSKvW0x4Wmu8SCk6wsnjqe5ADun43q4onn27C5GkgqMDSfA8b4nL+JujAwnwHAedMVR6naa+6gNO9EVTODKQBEM6+z4th/kRkF9CP6/BD4fAI55SwQ8ZKbkk3vIc7cr27WZcKDNDTCUK2Zubb1EU8DhwalMQbx0Jpw3CJAELpgUQ8JgNHu1mp2VVx192dmaysqvOmmnxc4gkFYQ8DiiajpDHgXBCtmTDd3VEIGs6KlwSnKIAgecQG3YDL7svWWVDfSk5PSQiSQXNlR64HQIqXJIlAy9rDM6hEoiKllYGpb0rzKXr9nZF8cQbh9EZSaDe78aH39dkNUmzOabGmI22WDVcznUGDKY0MIacLudAcRbAmTH1SVD0dPWJcNw6psZY0LxrhRbdBEEQBFEgBd3157L/YfUS74/LJuMeIG3w0x+XTccSsgZ5qKHhYa5oukkSbjcut0OA3yViIK6kM90ch4BLMEnV+6LyUMma4+fri1ll73Yk9NlGQ7Gh7E4uoyE7sn27z5FqzxJTETtZUjuLogqXiGWtVZgWdIEHBx0MLVUnlp0+1BfDH3YcAc9zaPC6MJBQ8Me/H8HVS6aZ9jIrqo5Ne45B4nkouo5FTQFLlt64gRdNpW8+RnPcwLPrDWFHGeAQOEQSCow1tsaASEIxZbrjsoofP/8utrb3wZAOHYsk8NUrTzWNg50xtYtdl/NiLYArXCIUjeG5XV1wCBxkjWFxc9ASO827I2PdkEAQBEEQk5ieaBJvHQmjJ5o84T5y3vUfyqRk0xlOQRJ4zKmvQGPAhTn1FRCHnMkNDJdwHkNO4cOOG+zqiIDjAL+Lh88lwO/iAc6Qp5vjyt7TOVJcPpeE2XU+1PqdmF3ng9clmeLa1REBA4PfLSLgkeB3i9CHZO/D+4okFbglHh0DSbglPiOhz+b2i2fjsZuX4T8/fCoeu3kZbr94ds6xXTG3Fl++/BSsPmsGvnz5KZY66sZz9LkE9MVT8LmEnM/R7mtEEJMNj0NEnd81YobbWBTV+FzojyvYuKsTcVm19HHJ/Ho0BN0QRB4NQbcla547O239jLV1p30Zqn1OVLgcqPY5EUulqyqY4Dgomo5BWYGi6UCOcofGDTyXxI94A8/whpBVHTzHQVb1jDdErucY8kojKgMO9sVNNzsBQNZ0HOyLZ9oc6Inj9fY+KJqe2RbzWnsfDvTEs0+XOV9LlRd+j4SWKu8Ju4QbLuc+l4BIQoXPJWBundnl3O5rbRvGwFj6+afLtjFLE7vfQVORqX3LgSAIgphSZMug/S7JIoO2i927/mmJtowjA1nO5EGPxSWcYejyJesaZrhL+HH38uNp8eHu5Xb3dNYHnDjSn8jU6T7cn0SNz2GRvXPgEEmomUx3rtqz9QEnjoWT6IykMvvD6wPOnDL0fEZDQP7XyMhibW3rzWSVlrVW5cxi2XmNCGIqUajZGtORXmzp1r7sZqeNCge9MRlVXuSsqjCYVHF0IIGBuIKUqsMp8jgykMgZVz6n8M5wCtGUCp9LBGMMDlHEYFLNKb3OpwxIyCp4ngOvMzgkHrKig+M5JLIWrjFZQVTWMjJunTF4HCJisoLh2HUJz0eFS0Rj0IWjkQSYK/3dMq3SZRr7YhrrDSZVRGUVXqeQeX0GU6qlL6r5PTI0AkRBzPjSU6UOgSAI4oSwI4MuCMbAcQDAgeMyy2YTfVEZPTE5U1dbZ0BPLGWSaLskIbOoNeCGjmeT272cN0nC7e7p3NcVRW/ULF/vjcrY1xXNxBXyOVDllXBE0TKL6ZDXgZDPvP/wSH8CPTE5Ez8D0BNNl0ArdFztvEZxWcX+nhhUncElCkiqGvb3xnM7nNt4jQhiKmF3UWRkSWOyhhlVvpFlwpyR/zU+W9bstFHh4Kcv7UNPTIbPIVgqHMiqhrc7IqZyh7s7I5BVzdIfMPoNPEN6HUkqGSO1gFuySK8NRts/vXh6JSrdEvriMmRVB+OASo+ExdOPm0X6XRJUnaXd1Ll0PW9VZ/C7pJx95tuvbRuOg0PgIfEcFN2YpY9TzAWwwAMdA0nT3Mxx6ePZUM3vkaERIAiCICYNPdHkiM7khgza7xYxmEr/f7iTeDaH+mIjOpMPJlVIIo9zZtVA1jQ4BAEJ1XrX33AmD3nTJWY0XUdC0UymPzsODUDgh2TlQ6tvfeh4tgu44V4+zSlB0xkEnoOi6xb3cjuZlNf392UW0hwHMJa+ZH59fx+WtlZnxivkc2JapQeyqsMh8kgommW8dhwaAGMMbonPmLIpmm6J3w7Ga1Q55PZemcPtvTOcgsYY5jcE4BB5yKqOvrjV4dx4jVbMqcs8x3DSasxEEFMJu4siO1nSwaQKSeCw4pRacODAwEY011raWoWDfTEc7k+gqdKNpa1VpsfDibRUXOId0BlQ6XZAENLHmwt8jg5RQHPIg7eOhNE9mIQo8Giq9Jik13ZpDnlx6YIGPLbtIFSNQRQ4XDq/wXzDQGMIuCSoqgaNAQ4eCLgki9laMbEz9sVcAGs60BBwQxQ4JBUNNRUO1PhcFj8SoHjZ/MkGjQJBEAQxKcgnS64POBFNKth19Pie5JZQbjfuR7YewM9ebstkT286txXXn9mSedyQVW4/0G3L9Ccua/A5OcRlzWL6s7g5CJ7nIKsMIg+oerr+9uLmoKmv1hovwICuwWQmax7yOizu5UD+TIohaQcwtDfv+PHs8fK7JFNmI5eTuBF/QtFHjd8Ohtv7G1lu7zOGub1nHM6jQw7n0dwO50aWpy8uk8yRILKwsyiykyXNfMaiiq2suc44vG96KGfWvD7gRG2FC71RGVU+B3qjMqorHDnn53wIabsLVHsdcEoCUooGPkdW1g490ST298YwPeSBU+SRUnUc6IuZSnMF3CJEgYNTEuFzCIjKGiSRGzGzXgyMsT8WSWay+XUVLsvYn1Trg1NsGLW0pd3ztVR54Hbyo57PwE42f6qVFSMjNYIgiDJn3bp1OP3001FRUYHa2lpceeWV2LNnj6lNMpnErbfeiqqqKvh8Plx99dU4duxYiSIef7JlybUVacOYh19tN5mlJWQNkYQKNuQgzsAQTmom928gneH+2cttiKZUVHociKZU/PyVNhzqs5r+pBlZVmnH9OfkugrMa/BD4NLZBIED5jX6cXJdhakvt0OA3y2C4zgwcOA4DhUuySQvt4tLEiwXADzMknbDSTzklUZ0Ei8k/oIYkoSDs/q9243LjkkSQUxVRjNbMx7P9/mx+xmzY65lfK6rKxyIJBVUVzhyfq7toOnp+b0nlsL+3jh6YinojOXMyuYjXZorBcaAlMqGSnOlTEaR6cy6GxzHMJBUwXEMTZXuE8qs28XjEDG3wY+2rhhe2NOFtq4Y5tT7LWO/aXcXvvzEW/jus+/gy0+8hU27u074fJfMr0ddhQtJRUNdhes9zad7u6LYsHk/1m9ux4bN+7G3K3pC/Uwk6JuHIAiizHnxxRdx66234vTTT4eqqvi3f/s3XHLJJdi1axe83vRd689//vN46qmn8Nvf/haBQABr1qzBVVddhc2bN5c4+vHBkCU3BNIXkQ0BF7oGzTVL27pjAAc0V3oh8hxUnSGaUizybMNpt8rrgFsSUeVNl+/KbmdI+849uRqyyuAQ01nskUx/Lphbgx2HBrC4OWiRXA8mVZzZWoWTaz040JtES5ULVT63pS/DcXxxsxMJWYPbIUDW9Jzy+L8f6h/xfEBaEs7zAM8yXmQAZ5W0r5hbC4FjeHVfL86aVYXz5lhN54z4l7QE0RWRUet3QBKEnGORL7PRGU5BEnksmR4yScKHP8cVc2uxsMk/4lYCA5I5EpOJ8c4M2vn8FJI1P9QfR9AtYSChoM7vtGRJV8ytxcm13veclZVVDYf6EoAO+F0iEikVh/sTI+4PH430/nBt1NJc6cw6h1qfEx6niHhKBc9xI2bWi/E6xmUVuzsiaK3xZcZ0T2cEZ7aGMn0W28ekWPNpuZcVG6vPWemfGUEQBDEqTz/9tOn3DRs2oLa2Ftu3b8fy5csRDofxi1/8Ao8++ihWrFgBAFi/fj1OOeUUbN26FWeeeWYpwh5XDMlxRzg5ohTacNCNJBVUedPZlOEOutntRnPaNZyxX3qnJ68z9t6uKDbv7UUkqSCW6oXXKZnqpFa4ROzrGsTW9n4wneHtzkEsa63M6TiuqDr2HotmpNctw6TXAHD3H97C49sOpk19eA7XnDYdX71yganN4uYgdD29d9xAYLBIwrP7evjV/Tn7Mi6o++PA4uZKdEaSCLitElM79WKN17EvLo8qaQfSmTE7F45FMy0iiBJSrHrLhWLn85OvjZGV3drejkhCgd8tYfnsGsvfZD/HnUcjJ/wcwwkVqsaQ1DTEYzp4jkFR2QnvDz+loQJ7OgcRS2nwu0TMqa8wZbE1HfA6RSg60B2V4RB4eBxizsx6sV5HQz3QXOmB2yGgwiVZ9tzbuRldKMWYT4vpql5sxvJzRvJygiCICUY4HAYAhELp/bfbt2+Hoii46KKLMm3mzp2L6dOnY8uWLTn7SKVSiEQipp+JjB3JseGgW+ES0R9PL6SHO+gW0s7sjA3kcsa2Uye1N5rCzo7B9CJZSGfgdx4dRG80ZenvuNKaM/86xN8P9ePxbQeh6AxOkYeiMzy+7SD+fqg/7xgOj95uX3YkpnbrxdqVjhPEVKLo9ZYLPPexSPI9ncvIyjYGXJhX70djwIU9nRFTn8V8jg6BQzSlQGccvE4BOuMQkxU4BOsWoHxUuEScOi2I/zc9gCUzKvH/pgewsMl8g1XggVhKhVsS0FrthVsSEJdVS6a7mM8xe899QtZy3uzMvhkdl1V0hNNtRtonX4zXulixl4Kx/pyV9Nnde++9uO+++0zH5syZg927dwNI71G844478Jvf/AapVAorV67Ej370I9TV1ZUiXIIgiJKj6zpuv/12nH322ViwIJ1x7OzshMPhQDAYNLWtq6tDZ2dnzn7WrVtnmX8nOnYkx9ef2YIF0/yjSq+NdifVevH6/j6cMSOUcfQ2sOuMbdzRD3kcUDQdIY8D4YS5XVt3DKqmo8bngFHMO6loFtl7ZzgFSeDREvJgICkj6HJAFHhT1mLHoQGoOhu6uOTgEDgoGrPIxrPl5Zl6ZcPk5UZfEp/eQy4NSfJzuZKfVOtD0NM04thnxsEnQdF1hHzSiC7HdqXjU82Eh5i6lCozWMys7D8Oh7HnWCSzHSccN1d7KOZzlDWGSq8DfEKBzgCvU0DAfWJu4h6HiIBbwvYD4Yyx5vumV5li0nSgIega2rYE1PmdqPE7LZnuYj5HO87kxk3Mh19tz3sTczyVFOVaVmysP2cl/5aaP38+nn322czvong8pKm+R5EgCGI4t956K9566y288sor76mfu+66C2vXrs38HolE0NxcqPCu/MgnOc52OH/u7S6Lw7lBtnv577YfyelebscZ23A537Tn2Igu5601Xqgaw+HBRGYBX+mRLLL3+oATB3pi6B8q1H0EKVS6RVPWwpCHJxQGIL1/UeStsvHFzUHoLO2AbqS4eZjbLW4OgjEgqWb1xVn7AvJfsBly/Od2deWV4wP5X8dSSW0JohQUs96yXYq571ZWNbzdOawG9zFzDe5iPsf6gBPNlR64xCT8bgmRhIJav+uEnNB7okn8ZWcnXJKAer8TAwkVz+zqxMoFdZk5qsIloiXkhVsSR92zXuzX0c4eazs3MUuxx7oc/TbG+nNWcnm5KIqor6/P/FRXp7MJxh7F73znO1ixYgWWLFmC9evX49VXX8XWrVtLHDVBEMT4s2bNGjz55JN4/vnn0dTUlDleX18PWZYxMDBgan/s2DHU19fn7MvpdMLv95t+Jjt2HM4Be+7lBTlj23A5Nx4zynflysfsPBLOLLgNBhIqdh4JZ34PeR1wDXPMdYsCQl7HSKcc8Xe3JIAfdpXA8enj2diW5NmQ49uhlFJbgigFpXDit+M4bhejBnfQLUFnQNAtwSnyCGfNZ8V8jkaGt9bvQkrVUet3nfA2lbR7eRKqpqM3pkDVdByLJE3u5Rlnb78TSVVDnd+ZM/axeB3zudAD6fFYMC0w4vMv5mtdCHZiH0/G+nNW8mf57rvvorGxES6XC8uWLcO6deswffr0vHsURzIGSqVSSKWOfxCMfYqKokBRlLzxGG3stC03jJidvP0LmUKfp1M4sYukvP0OxVxI7OXEicRfLu+xifyeBwqLf6I+R8YYPve5z+GJJ57ACy+8gJkzZ5oeX7JkCSRJwnPPPYerr74aALBnzx4cPHgQy5YtK0XIZYldUxnDvTzodkDk0zVJI0nZIve2U//UcDlfOM2PrsEUaiucEAXOIi8XBR7Tgi4oQ3LulKZbzvfqvl4A6SW7wBtlcdLHz5tTl+nL4xTgcwkZGafOYOnLkJeLRocsbaqWLR3f1RGBJPBwSwyM8eA4HarOYVdHxFTyzI4kz5Djn3NyNWRVh0PkkRjB7R0YXTpunM/nEtAXT8HnEjIXiOVy8UYQxSbfFo5iU+zMs50a3MXMftrdppKPtHu5Pqp7eSGxl3OGN5+7/FRgLF+fko7m0qVLsWHDBsyZMwcdHR247777cO655+Ktt946oT2KwMj7FJ955hl4PB7bsW3cuNF223Lja6fZL0T45z//uaC+7z+j0GgKo5DYy5GxHPuxZiK/5wF78cfj8XGIpPjceuutePTRR/HHP/4RFRUVmTkwEAjA7XYjEAjgxhtvxNq1axEKheD3+/G5z30Oy5YtmxLO5Xax43AOpOXeIs/jSDgBp8AjpekI5ZB7Z0vV/S4pp1S9wiXizcMD2H6gH7oO8DywZEZlTnl5z+DxjHsueflZs6rw05fawQCoQ1MNN3R8eF8DcWVUqfri5iDAADnrPuFw6fi8Bj8EnkdcViEJDIrG4HYImNdgVkXYuTg35OXbbLq955Oqdw+msLWtN7MXfVlr1ZS8QCSmDuO9paKY+26z9xaHE6PX4C5mtQG7FQ5GwyEKOKXejz1dkbR7uVvEnFp/zhrcdmMvt4oKdt3lpwpj9fqUdDQvu+yyzL8XLlyIpUuXoqWlBY8//jjcbvcJ9TnSPsVLLrnElnxSURRs3LgRF198MSRJOqEYSoUR+1e28Ujp9hwa37p3ZUHnWHDvX04ktLw4eYavnaYXFHs5MR7xF/pa2WUiv+eBwuKfqA7dP/7xjwEA559/vun4+vXrsXr1agDAd7/7XfA8j6uvvtpkPEkcx66pTJXPifnT/Nja1gdV0yHyHOY3+lHlO744t1v/9HBfHP84EhkyN+Mhazr+cTiCw31xU7aYAWBces+XjtzC69NnVmFGtQftPcdvHs2s9uD0mVXDWuaXqoe8DrgkHlH5+I1Ct8SbZOhNIQ8WTvNj24F+qJoOgeewcFoATSHzDWzbF+cmeXnWZvIs7OwtjMsq9vfEoOoMLlFAUtWwvzeeMTkiiMnGWOy5tWNEWMzserEyzwZ24i+G2WKFS8SpTQF4nBxUnYPIM5xU639PN/nKzQTyeM1vL4JuBwYSsqXm94n0WU7PsRwoq1EIBoOYPXs29u7di4svvjizRzE72z3aHkUgvU/R6bQaJUiSVNCCotD25URK55DS7C38Cn2Odvs9UQqJvRwZy/jH+v04kd/zgL34J+rzYyz/tgWXy4UHH3wQDz744DhENHFZMbcWTUEXdnVEMK/Bb1r4GgwmVcyq8aG1xov+mIxKrwMczJJwu1L1XR0RaLoOv0uCwHNw6QwpVTNJtNu6Y5AEDg0BJzSNQRA4qBqzSMIHkypWzq/H3q4I2rqiaK314aRa/whSdTd0xsBzHFKa1Qk9LUMXEfTy0HUGnucgq2ZJ+2BSxalNQZw6LYC+uIyQxwFwXE4Zdz5JXqFu76NJ1TvDKWiMYX5DAA6Rh6zq6Iun3lPtWYIoZ4rtqmw3a17s7HoxMs924ypW7Hbcy4sd+3iTqfkd9KZrfjutNb8LoRyfYzlQciO1bKLRKPbt24eGhgbTHkUD2qNIEARBvBc27e7C1596Gxte3Y+vP/U2Nu3usrQxHMffODCAw31JvHFgAKqmn1D903kNfnDgEI4r6I/KCMcV8OBMEu3WGi8UjaFjIIWeqIyOgRRUjVkk4RUuEVv39eD53T1o703i+d09eK2txyJVBwM6BxPoiabQOZgAwFn6aq3xwuNI71WscElIKjq8TtHUzpCNa4zDnLoANMaNWkt1NFOcbLd3SeDRF5dz9lVI7dnuaAo6Y+iOpkatPUsQE51i1jW2a0RYKsPCfLWijbi6BlNwiQK6BlOWuAqNfbRzGu7lXqeAufV+eJ0CntnVaTHgtPvcytEEshTvr6lISRfdX/jCF/Diiy9i//79ePXVV/HhD38YgiDgYx/7mGmP4vPPP4/t27fjhhtuoD2KBEEQxAlh170cQF7HcUOqHvJKo0rVQz4HqrxSOrvLpbsNeR0I+cxu4ulK2ENluod+H867xwaxq3MQGgN4DtAYsLNjEO8eG8y0cTsE+N0iuKEeOHAIuAS4Heb9h80hL246txUVLhH9cRkVLhGfPqfVlA0vppOr3b7stLM79gQxWSjmZ9GuU3UpHK33dkWxYfN+rN/cjg2b92NvVzRn/Af6YjjQG8MbBwdwoDeG/b0xU1yFxJ7vnIaqaVrQg5DXiWlBD8IJxeRebpdSuYTnoxTvr6lISeXlhw8fxsc+9jH09vaipqYG55xzDrZu3YqamhoAtEeRIAiCKB52JeGG4/iZrSEMJjVUuAQoGrNI7VbMrcXJtd5R3cs7wymEfE7UB9xIKBrckgBZ003nNCThjUEXVJ1B5DnIOdzLdxwagK4zuEQOPM9B1xlUnZkcxzvDKfhcEhZOkxCVNfgcAlSGnNLr689sgd/F4/k9PbhgTjU+sNhap70QJ9d8e/iK6e5rZ+wJYjJRLFdlu67kY1GzeLQ5wu6+dYEHOgaSJj8NnTEIWWlEu7HbOaehrDnYF0fQLWIgoaLO78yprDnUFxt1TipFvXUgfcM531768X5/TUVKOgK/+c1vRn2c9igSBEEQxcKue7ldd+zsfWs7j0Zy7lurDzgh8BwO9Mbhknh0RVJoDnlM50w7juvoGZQzxyo9Ym7HcQBJ9bgJmcibHcfrA05EkwoO9iUyx6aH3DkvEP/l13/DU28ehcaA/91xFM++3YPvfez/WdrZcXK1u4evWO6+dsaemHy89NJL+Na3voXt27ejo6MDTzzxBK688sqcbT/zmc/gJz/5Cb773e/i9ttvzxzv6+vD5z73OfzpT3/KJHa+973vwecr//dPMVyV7RofFtO9HMg/R9jdt67pQEPADVHgkFQ01FQ4UONzQcsqHmM3djvnrPa58L7plXj41XYc7NPhFHhcOr/esnh9ZOsB/Ozltsy+75vObcX1Z7aM6ZjawU6Vjez4xuv9NRWhESAIgiCmBHbdy+24Y9vNyngcImaEPOiMJKFqDKLIYUaV23IBwsANcy+3GjKmHccFRFNa5phbEkyO4wlZQySpgg2ZqOksnaFPyJqpr9faevDUm0ehMkDiAUUHnnrzKD6+tBlLW6sLGtexcFYup/MR5UMsFsOiRYvwqU99ClddddWI7Z544gls3boVjY2Nlseuv/56dHR0YOPGjVAUBTfccANuvvlmPProo2MZellR7JrS+VQu2fuwg24psw87+zNbSAa+pcoDUQTckoiEoqIp6LG0sxO7nXP2RJN442A/6gMu+N0SIgkFfzvUj55oMvPdcagvhp+93Ha8lndMxs9facPy2dWWjHexxtROO7tVNopNOdYiLwdoFKY4M770VKlDIAiCKBr5LlTslK2x445tNyszmFRR43fhw4uaEE0p8DklqEy3OI7bcS9v647B4xDgdQhIqTqcIg82dNxo19YdAwA0VXoBjgGMQ0xWLH29vr8PGkvX5uZ5DiJj0Fj6+PBFd74xNcYi5HFA0XSEPA6EE1Zn8kIY7ZxjcT5iYnDZZZeZys3m4siRI/jc5z6Hv/zlL7jiiitMj7399tt4+umn8de//hWnnXYaAOAHP/gBLr/8cnz729/OuUifrIyF6mQklYuxD7srkkKbnpaIJxTV9JktJAOfrindm6kpfeHc3JnUfLHbOaexLak55B1yMlct25LaumOIy+kFt1sSUeUF+uOyZd4t5pjaaWd3S9VYUG61yMsBGg2CIAhiUmD3QiVf2Zpsd+yGgAvd0ZRFhl5IVkZRdWw/0g+J56HoOhY1BSyO44rG0B2VM5nuSreU03E8pegIZxnSBN1mGXprjReSwKMzkoRL4pFUdIR81r7OmBECB0BlgKqmpeo8lz5e6Jgaz3HTnmMjPsdCyHfOYp+PmDzouo5PfOIT+OIXv4j58+dbHt+yZQuCwWBmwQ0AF110EXiex2uvvYYPf/jDlr9JpVJIpY6bZkUikbEJfgJSzH3YgL0MabFrSuerRW5nW5JRDaI3JqPKC/TG0uaUw+ddO9gd00L2o+fbUkWMD2VVMowgCIIgToRilimx445dkNtrHid04wjHhtzLWa4Wael4amjjovF4UtVN0vEqnxPzGv2QBA6KqkMSOMxr9KPKZ77ImlXrQ5XPXLe+yithVtbitqAxtfEc7WD7nEU6HzG5+OY3vwlRFHHbbbflfLyzsxO1teb9rKIoIhQKobOzM+ffrFu3DoFAIPPT3Gw1HJyq2HWqNvZh11Q4Mvuw6/1u0z7sQs/ZHPSitsKF5qD3Pblj7+2K4vG/HsaTbx7F4389bHEvt/N9YKcaRDajlSgrprs8VXooL+i2MEEQBFH22JU455N728WOO7adrIzhhH7uydWQVQaHyCEuaxZ5uShwaKp0QWOAwKUX08Olibs6IuA5IOjmwUEAgwZZSx+fXe/PnG9WjQ+zqr3oi8sIeRwAx1nGoTOcwrRKD6ZXAfGUBo9TgKYjp4Q+5JOg6OmMeTiu5JTQ53uOdrHzOhbzfMTkYfv27fje976HN954AxxXvJswd911F9auXZv5PRKJ0MJ7iEL3YbudfCY7XVfhGtWccjRlTbHcse1mle1sS7r+zBYsn12dt6KCHSVPMd3l7cROjA/07UQQBEGUNeN9ITb8nKO5Y+fbt1bhEqFoDC+90wOHwEHWGBY3By3ycgDoCCcz5meVXqdFmjivwQ+B4xBNaZAEQNF0eBwC5jX4Lef7+6EBOAQObd1xy/mA47LD/nh6r2Iu2aHR13O7ukaM3e5ztIud17GY5yMmDy+//DK6urowffr0zDFN03DHHXfggQcewP79+1FfX4+uri7T36mqir6+PtTX1+fs1+l0wukkOW4uTsQJPZxQUFfhsrQrxJyyWO7YhdyszbctCUhnvEcrX2jnOY6Fu7yd2Imxh76hCIIgiLKlFBdiRXfHZmxIDc2B446X+zJwOwT4XSIG4kq6QhnHIeAS4HYIpnZNIQ8WNgWx7UAfVE2HwHM4dVoQTSFPQecD7Du52+mroHZ5sP06Ful8xOThE5/4BC666CLTsZUrV+ITn/gEbrjhBgDAsmXLMDAwgO3bt2PJkiUAgE2bNkHXdSxdunTcY54MFMsJvZAF8EStKW33ORbbXZ4oD+jVIQiCIMqWUlyIFXLOnmhyVNneYFKFJPI45+RqyKoOh8gjMUwK3RlOweeS8L7pLiiaDkngkVQ1i8PsYFLFqU0BzKrxoCOcQkPACY9TskqvRR7nzKqBrGlwCAISqpoz9hVza9EUdGFXRwTzGvwZibol9jx9Ge1WzKkDx6X3pYeTJ+4mns/YqNjnIyYO0WgUe/fuzfze3t6OHTt2IBQKYfr06aiqqjK1lyQJ9fX1mDNnDgDglFNOwaWXXoqbbroJDz30EBRFwZo1a3DddddNKedyoDglqQyK4YQ+3gtgI57xrCldyHMslrs8UT7Qq0QQBEGULYVeiBXjAsTuOTft7sKGze0Z2fvqs2dixdxaS1+KxrBtFCl0fcAJkefQ3hOD2yEgIWuYXuW2OMxWuER0D6awta0XYMCuDmBZa5VVeq3q2H6gO6+zd7747fZljFdfXC66tH+0PY/FOh8xcdi2bRsuuOCCzO/GXutVq1Zhw4YNtvp45JFHsGbNGlx44YXgeR5XX301vv/9749FuGVLsUpSFZNCFsDFjGs8s8Xjvcgnygt6lQmCIIiypRQXKXbO2RNNYsPmdlMJnIdfbcfCJn/BEm2PQ0RLlRed4RRkNZ3pnl7ltTzHuKxif08Mqs7gEgUkVQ37e+OIy8MyvDacvW3Hb6Ov8Zb204Xr1OX8888HY/a3Euzfv99yLBQK4dFHHy1iVBOLYpakKjb5VC6FxmU3m2/nZq3dvvJh5zkWQrHiIsYeenUIgiCIsuakWh+cYkNeV1i75JOEG+ccLfvRGU4hklRQU+EAz3GoqXCgLybnlITnk2gPJlXUVDhxzelNo7pxd4ZT0BjDSTU+6IyB5zjEZNXiOG7H2duIP+SVIGs6Ql4J4YSSs68zZ4YwmFJR4RSh6Ow9SfuL5UJPexkJ4sSw+xkrdkUIO9jJYNuNq5jZ8KnQFzH20LcUQRAEUdbYkXGPRV+jZT/qA04IPIedRyJwSTySio7mkCenJDyfRNuQS2dnbnLJpesDTiiqjr8fGwDPA7oOzKj25HQcz+fsbfS1fX9/pq+WHH11D6bwWltfZpG/dFbohKX9xXahp72MBFE4xS5JVQij3fC0m8G2E1cxs/SToS/KhpcHfKkDIAiCIIiRyJZB11a40B9X8PCr7eiJJkval8chYkbIA1HkoGoMoshhRpU79wVNHom2IZcOeaX8cmnO+HMO4ACWQ+5tlrNnnzdHX1n/GN5TXFZxoDcGRUsbwCmajoO9McRlNXd/o5B9gVjjS4/9xl2dlr4KGguCIArG7mes2J/FTbu7sPaxv+Pf//APrH3s79i021y6LVcGO5xQMJgsfI6w25cdJnpfe7ui2LB5P9ZvbseGzfuxtyta8LmI4kDfYgRBEETZYsigGwIueBwiGgIudA0mLTLusehrtOzAYFJFjd+F9y9sQH9MRqXXAQ5cbmdvGxJtOxL6znAKksBjcXMQms4g8ByiqRzycpHHma0hDCY1VLgEKJr1fJm+pldC1XWIPI9oyiwv7wynoOoM86cFIAnpRXdfLDXieI2WxTIuEEMeBxRNR8jjQDiR23GcpOMEMbaMd0kqOx4SRgb7UH8cQbeEgYSCOr8zZ2Y9X1yZvgZiCLodGEjIqKtwnVCWvpCMf76tS8VUD4x3xp9479CIEwRBEGVLfcAJv0tCRziZuVgLeSWLjLvYfdlx0M52EgdndRLPbpdPom1H9m64nL97LDqiy7nd8x3va3DEvozx6h5MoSHgQvdgasTxsuuEvmnPsbyu6gBJxwlirBnPklR2bnh6HCLmNvixtb0dkYQCv1vC8tk1J1SmLNPX5l5EkgPwuyQsP7n2hJ6HXeNGO3N4MU0g7fRVin35xMjQiBMEQRBlS7XPhdVnz8TDr7ajazB9YbHqrJkn5Phqty872QG7TuLZEm1jcWtItI12dp3E7bic2zmf3b7sjlcxndAJgph45NszbOeGZ1xWsbsjgtYabyY7vaczgjNbQzn7zLc/fHdHBM1VbrilCiQUddS+8pEvs15INYtiKnnsZvzHs/Y5MTI06gRBEERZs2JuLRY2+YtSYmXF3Fp4HTxe39+HM2aEsLS12tImI4X2SVB0HSGfhHBcMWUHDCfxOfUV4DkOOmOIDHP/NtqpOsPJtRXQmA6B4xGVrTJuO7J3w+X8w/+vcUSpunG+mTVeKFp6MZ1UtBH7yueYvmJuLU6u9eaVveeL366rOkEQEws7Bol2buAZ825z0Au3Q0CFUxoxK5svqzyYVHGgN46uwWRmK46q4j3NN6Nl1gvdulRMJU++jD+VVywfaNQJYoIw40tP2W67/xtXjGEkBDH+VPtc77meKQA8sPEdbHi1HbKm4xdCO1afNRO3Xzzb1MZwAH9uV9eIDuCGe/mejsFR3csNl/AdXf2ZxXlLlTenjDuf7N2Ia9v+/lHjiiYV7DoayRxrCblz9mXHMT37gnrn0UjOC2o78dt1VScIYuJQyJ7hfDdP7WZl7WSVBR7oCCdMbXTGILwH++jRsvnF3AZVbMgjo3wg93KCIAhiSvBOZwQbXm1HStXhdYhIqToe3tKOdzoj1sZ5HMALci8/3inSPZr7MrJAIa+UX0KfJ66ErCGSUMGGfM0ZGMJJDQlZs8SfzwHYruO47fjtuqoTBDEhKNSNu9rnwoJpgRGzv3bc0nNllcNDKiMDTQcagi7U+BxIKjpqfA7UB1zQ9BN7nvkcwAuaw0uAxyGizu+iBXeJodEnCIIgSkoxa4iO1teujghkTYfPKcIpCgCAmKxiV0cEs+v9mXaGA/g5s2ogaxocgoCEqpqkiRn38gWN6I+nUOlxguOt8sXOcAqSyGPhND9SGoNT4BBXdIvs0I6E3ohraWsI0aQKn0uEOsyZvK07BnDAtKAHPAfoLD0mbd0xizT8pFofeqNBvL6/D++bHrRksI0Lap9LQF88BZ9LyFxQ58pijSZDtzOmBEGUH6PNqcXeM2wnK2tklY/0J1Dlc6A3KqO6wmFR1rSEvHBLYl4n9HzYzeYXsg2K6mZPTeiVJgiCIEqGnf2AxeprXoMfDoFHNJXOwkRTKlwSj3kNflM/htP29gPdIzptV7hEdEeS2Nrel+VentslXFF1vHksCp4HdB1oqbbK0IH8Eno7jumtNV6IPI9jgyk4BR4pTUfII6G1xroX++4/vIXHtx2EqjOIPIdrTpuOr165oKDz5Rr7XDJ0O2NKEER5kW9OHYs9w/n2O1f7XFg5vx4/f7kN3Z1JeB0iLpk33WI6acQVHlpwn2hchTiA29kGVczvPGJiQd92BEEQREkoZg1RO33Nrvdj9Vkz8fCWdsTk9IJ71bKZpix3hjxO23FZxf6++JB7OY+kqmN/b8LiEm7+07Tg+0Q9u+04plf5nJg/zY+tbX1QNR0iz2F+ox9VPvMi/++H+vH4toNQsuJ/fNtBXL1kGhY1V9o+n9HO1utI7uUEMWGw+7ke7z3DcVlFOKHg7JOr4RYFJFQNkaRimZeKFVcxs/mlqptNmfXygEaeIAiCKJhifIkXkkHIdz47juMAcPvFs7FsViive7kkcFg4zY+uwRRqK5wQBc7qXq4zzKr2jCob7wynIAk8Fk+vhKrrEHke0ZTV5RwADvXF8rqE53NMH0yqmFXjw6waL/piCkJeCQBnGYcdhwYyNwwkIS21T6k6dhwayCy6jfPNbwjAIfKQVR198VROJ/R8r6MxpitOqQUHLr3XPMfrQxBEeVDI/FxMN267cc2s8mXKIo5lXMXM5peibjZl1suHkn7TrVu3Dr///e+xe/duuN1unHXWWfjmN7+JOXPmZNqcf/75ePHFF01/d8stt+Chhx4a73AJgiAIFO9L3G4Gwc757DiOA+ZSM39t70dM1k2lZoy+3jw8gO0H+qHrAM8DS2ZU5nAJV7HzaBwcY2AchxkjuJeLPId3jw1mLhCnV1ndxB/ZegA/e7ktk6256dxWXH9mi6WvfI7pxjj8/dAAHAKHtu7c47C4OQiR55BU085CSVWHxHNY3Bw0nc/vktAdTaEh4EJ3NDWiq3q+19Fo0xcd3S2dIIjyoFxrPJciLrtZ83w3h8c79lJl1onclNS9/MUXX8Stt96KrVu3YuPGjVAUBZdccglisZip3U033YSOjo7Mz/3331+iiAmCIKY2dh2t7VBMB20Aed2xs0vN1Fak+3r41Xb0RJOmdof74vjHkUh6r7PAQdUZ/nE4gsN98UybhKwhnFDSW535tB95OKnkdAlvqfJCEtKZYkngMb3Ka3qOh/pi+NnLbYimVFR6HIimVPz8lTYc6otZ+rLlmG7DJXxRcyWuOW06JJ5DamjBfc1p0zNZbsC+I6+d19GuMzFBEOVBuX5mSxVXPgfwfA7npYi9UHd5Ymwp6Sfn6aefNv2+YcMG1NbWYvv27Vi+fHnmuMfjQX19va0+U6kUUqnjZQMikXQpGEVRoChK3r832thpW24YMTv5iVeGxYh5IsYOlF/8hbx/J/J7Higs/on6HMuJQuVx+e7858sg2D1fxh375GrIqg6HyCMhaxZJeCSpoKbCAZ7jUFPhQF9Mtsild3VEoOk6Am4JIs9D1XUkFc3kct7WHQPHAU1B96gu4YNJFTUVTlxzehNklcEhcogPi6utO4a4rCLocUDgeQQ9DkQScu6+/C5c877po7qqSyKPFXPqwHEAY0A4Ked8fb565QJcvWQadhwawOLmoGnBbWDXkddOJojqxRLExKJcP7PlFlchGeXxjL1c1QpTlbIa9XA4DAAIhUKm44888gh+9atfob6+Hh/4wAfwla98BR6PJ2cf69atw3333Wc5/swzz4z4N7nYuHFjAZGXF1877QQLEZYBEzl2oHzi//Of/1zw30zk9zxgL/54PJ63DTE6hXyJ25Whj7bvzu75DFn1tnd6RpSXG/LsnUciI8qzgbTLucDxCCeUjNO2xyGaXM5ba7wQBR7HIik4RQ4plSHktbqEG/FnX4wNj7+1Jp0JP9KfyMQV8p1YXxkZd1y2dZG1qLky52I7GzuOvIC9/ZPjufeTIIj3Trl+ZssprkJvRo9X7GPhLk+cOGUz6rqu4/bbb8fZZ5+NBQuOlyz5+Mc/jpaWFjQ2NuLNN9/EnXfeiT179uD3v/99zn7uuusurF27NvN7JBJBc3MzLrnkEvj9ORxqh6EoCjZu3IiLL74YkiS99yc2jhixf2Ubj5Q+sVxhnTzD107TJ2TsQPnF/9a9K223PZH3TSH9jzWFfGYN5Qtx4tj9Ei/WXrKCLhpMsmqG4bJqQ57dGUmOKs9uCnlwapMf2w70Q9MZBD5tqtYUOn7jtsrnxPyGCmxt70/3JXCY31hhcQm3E3+Vz4l5jX681tYHRdUhCRzm5XAct9MXXWQRBEGML+WcUS43VcBUpmxG/tZbb8Vbb72FV155xXT85ptvzvz71FNPRUNDAy688ELs27cPs2bNsvTjdDrhdFrrn0qSVNAiutD25URK55DSSr/wOxEmcuxA+cR/Iu/dQmIvx8+Gnc9sOcY9EbHzJV5Ml1a755NEHktnVCGaUuBzSlCZbpFe1/hdeP+CRvTHU6j0OMHxyClVX9gUxKlNAfTHZFR6HeCGOYAPJlXMqq1Aa3XFqH0Z8Qc9TSNKtO06jht9OcWGUV3O850vm3yO6QRBEMVispauKvRm53iPQzmpAqYyZfEKrFmzBk8++SReeuklNDU1jdp26dKlAIC9e/fmXHQTBAHM+NJTtts6BYb7zxjDYIhJSb4v8WLf+bdzvu5IElvb+9IJbg5Y1hqySK/ztcmOvT+uYE5dYEQZt6LqePNIGBLPo703jkVNgROS2Vv66omN2Fe2+7rfJWH12TMt7ut2Zf12HNMJgiCKwWQvXWU3ozzZx4EYmZK6lzPGsGbNGjzxxBPYtGkTZs6cmfdvduzYAQBoaGgY4+gIgiCIE2W8XVrjsor9fXGT4/j+3oTJ5dxOm4Ji5wxliCFjtypFbLuv2+jLjvu63fPZdUwnCIJ4rxSz6kU5k8/hfKqMA5Gbkma6b731Vjz66KP44x//iIqKCnR2dgIAAoEA3G439u3bh0cffRSXX345qqqq8Oabb+Lzn/88li9fjoULF5YydIIgCCIP47mXrDOcgqYzzKmvAM9x0BlDJKGYnMmNNifX+jJ7taMp1eJebsSeTxIuCRxWnFILDhwYGMJxJadUPZJUEPJJUPS0QdrwdkZf72sJjihnN+KPJBVUeiXImo5Kr4TwsOdoV9ZvOKYH3CIYAwJuEYNJq/s6QRDEe6WY240mMjQOU5uSvsI//vGPAQDnn3++6fj69euxevVqOBwOPPvss3jggQcQi8XQ3NyMq6++Gv/+7/9egmgJgiCIQhmvvWT1AScEjsOezkG4RAFJVUNzpdmZvD7ghKLq2HFsADwP6DrQUm11LwfsScL9Lgl90ZGdxI12isbw3K6uEV3VK1wi9nUNYmt7P5jOwPEclrVWWvoy4n9jf38m/hnD4rcr62+t8YIBONCXgMgBKgNCHqtjOkEQU5ti7D8uZ6Ox8YTGYWpT0leZsdFrGjc3N+PFF18cp2gIgiCIiYrHIWJGtRedg0momg5R4DCjypNDEp79D5ZDxG3Peb2Yruq90RR2dgxC1RmcAoeUxrDz6CB6oyl4Qjn6G+oLHLM8A7txuR0CAq501l3XGXiOQ8DlgNsh5BxfgiCmHsXaf0xVFdLQOExt6FUmCIIgJjyDSRU1FU5cc1ozZFWHQ+SRkDWTbK8znIIk8Fg8PTiqvNyuBLAQV/UVc+rAcQBjQDgpm/pq645B1XTU+ByZv0sqmkXq3RlOQRJ5LJkeMvU1PH477uWd4RR8LhFnzAzlldoXE7tZs8nqckwQE4VilX00oNJVaWgcpi70ShMEQRATnmzH8ZFke/UBJwSew7vHonBJPJKKjuaQVV5eiATQrot7X1wesa/WGi9UTcfhQTlzLJhD6l0fcGb6agi40BFO9zU8fjvZKaOv/rgyal/FxG7WjNx9CaL0jMX+YypdlYbGYWpSUvdygiAIgigGdhzHPQ4RM0IeiCIHVWMQRQ4zqtyWi59iOq/b74tL/8dl/mmh2ufC6rNnIuSV0DWY7mvVWTNNmWm77rh2+iomduMid1+CKA+ybz4mZA2dkSQCbtp/TBAnCn1yCIIgiElBPtneYFJFjd+FDy9qQjSlwOeUoDI9Z+ammBLAk2p9cIoNaOuOobXGa3EHb+uOQRQ4NFe6wVh64Z3IIS8HgBVza9EUdGFXRwTzGvyYXe+3PEe72akVc2uxsMk/qgy9WNiNi9x9CaI8oP3HBFFcKNNNEARBTBpGq5Na4RKhqDpe29+Ld45F8dr+XqiaPmLmJl/NVbts2t2FLz/xFr777Dv48hNvYdPuLtPjrTVeiAKPrkEZkaSCrkEZksDndBLftLsLX3/qbWx4dT++/tTblr4KzU5V+1xYMC0wpgvuQuKi7BoxGi+99BI+8IEPoLGxERzH4Q9/+EPmMUVRcOedd+LUU0+F1+tFY2MjPvnJT+Lo0aOmPvr6+nD99dfD7/cjGAzixhtvRDQaHednMjE4qdaHVWfNwA1nz8Sqs2bQNg+CeA/QopsgCIKYOnCGcNtwEM8l5C4ePdEkNmxuR39cQW1FWi798Kvt6IkmM22qfE7Mb6iAKAzJ3gUO8xsrUOVzFtxXMaXxxcRuXOUaP1EexGIxLFq0CA8++KDlsXg8jjfeeANf+cpX8MYbb+D3v/899uzZgw9+8IOmdtdffz127tyJjRs34sknn8RLL72Em2++ebyewoSjWDcfCWKqQ58ggiAIYtIwmuv1YFKFJHBYcUotOHBgYAjHlRGly3YctHuiybwu4ZGkgpoKB3iOQ02FA30xs+P4YFLFrNoKNFd60T2YRE2FCw6Jt8Rl9NUQSF8ANwRc6BpM5nQvL0d3XLtxlWv8ROm57LLLcNlll+V8LBAIYOPGjaZjP/zhD3HGGWfg4MGDmD59Ot5++208/fTT+Otf/4rTTjsNAPCDH/wAl19+Ob797W+jsbHR0m8qlUIqlcr8HolEiviMCIKYKtA3GUEQBDEpyOd6nXESj47scG63LyAt9d6wuT3TZvXZM7Fibq2pjeGYvvNIZETH9AqXiO5IElvb+9IJeA5Y1hqyxGU4jneEk3kdx8vVHdduXOUaPzGxCIfD4DgOwWAQALBlyxYEg8HMghsALrroIvA8j9deew0f/vCHLX2sW7cO991333iFTBDEJIXk5QRBEMSEx47rtV3psp2+7Ei9jXPmc0yPyyr298Wh6mlpuaoz7O9NlNxxnCAmMslkEnfeeSc+9rGPwe9PGw52dnaittZ8Y0wURYRCIXR2dubs56677kI4HM78HDp0aMxjn8rEZRXHIkmqWEBMOug2cpkz40tP2W7rFBjuP2MMgyEIgihT7Lpe25EuG32FPA4omo6Qx4FwQjb1ZVfqbccxvTOcgqYznFzrg6YzCDyHaEq19AXYdxy3I40niMmKoii45pprwBjDj3/84/fUl9PphNM5dvXriePYURgRxESFvokJgiCICU+263U+6Xg+6bLhcr5pzzFIPA9F17GoKWDqy67U2+hr+5H+UftSVB07jg2A5wFdB1qqPTll40A64z1adpsuXImpjLHgPnDgADZt2pTJcgNAfX09urrMjv+qqqKvrw/19fXjHSqRRbbCyJjDN+7qRGNwxgl7bhBEOUHycoIgiEnEgw8+iBkzZsDlcmHp0qV4/fXXSx3SuFB01+s8LucFSb3tOKZz5n+cqKe6HWk8QUxWjAX3u+++i2effRZVVVWmx5ctW4aBgQFs3749c2zTpk3QdR1Lly4d73CJLHKplcKJtNHlcPZ2RbFh836s39yODZv3Y28XlXwjyh+6NUQQBDFJeOyxx7B27Vo89NBDWLp0KR544AGsXLkSe/bssexjLCcO9cXQ1h1Da40XzSFrbWrAXlbjpFofnGJD3r7+fqgfOw4NYHFzEIuaKy2PGy7nS1tDiCZV+FwiVI1ZpOor5tZi5+F+bHz7GE6bHrSYqGX3tXCaH12DMmorHBAFziIvlwQe8xr9SMg63A4esqbnlJcDwDudEezqiGBegx+z6/2W80WSCipcAvrjMipcQubCNde4FWvsC2lnB8piEbmIRqPYu3dv5vf29nbs2LEDoVAIDQ0N+MhHPoI33ngDTz75JDRNy+zTDoVCcDgcOOWUU3DppZfipptuwkMPPQRFUbBmzRpcd911OZ3LifHDrlqpkIw4zSNEOUHvQIIgiEnCd77zHdx000244YYbAAAPPfQQnnrqKfzyl7/El770pRJHl5tHth7Az15uQ1xOLwpvOrcV15/ZYmpjVy5tx0387j+8hce3HUyblvEcrjltOr565QJTG7tu4mf957M4GkmXEnrzyCB+/fohvPpvF1n6+sfhMP66vw+MMXAch9NnhCzy8mhSxcG+OMAYwHGYXpVbXv7Axnew4dV2yJoOh8Bj9VkzcfvFs82xD6bwWlsfdMbAcxyWzrLGXuyxL6akneTxxEhs27YNF1xwQeb3tWvXAgBWrVqFe++9F//7v/8LAFi8eLHp755//nmcf/75AIBHHnkEa9aswYUXXgie53H11Vfj+9//WmhzKwABAABJREFU/rjET4yMoVbauKtzVLWSXf8OmkeIcoPk5QRBEJMAWZaxfft2XHTR8UUfz/O46KKLsGXLFkv7VCqFSCRi+hlvDvXF8LOX2xBNqaj0OBBNqfj5K2041BfLtLErl7bjJv73Q/14fNtBKDqDU+Sh6AyPbzuIvx/qN/Vlx018/Sv7Mgtug6ORFNa/ss907HBfHG8eHoDOGESBh84Y/nFkAIf74pk2CVlDJKlAZwDHc9AZEEkqSMiaqa93OiPY8Go7UqoOr0NEStXx8JZ2vNN5/LWLyyoO9MagaDocIg9F03GwN2YZr2KOfTEl7SSPJ0bj/PPPB2PM8rNhwwbMmDEj52OMscyCG0hnvR999FEMDg4iHA7jl7/8JXw+WoyVAyfV+rDqrBm44eyZWHXWjJyL5OyMeELW0BlJIuA2Z8RpHiHKEVp0EwRBTAJ6enqgaRrq6upMx+vq6nKWwlm3bh0CgUDmp7m5ebxCzdDWnV4MVnkdcEsiqrwOxFIq2rqPL/zs7vPL5SYeTijoDB9fGO84NABVZ3CJPCRBgEvkoeoMOw4NWPrSdIb5jX6c0hDA/EY/VF039bXx7WM5n9Pw47s6ItAYQ9AtIeh2IOiWoOoMuzqOL5SN5zu9yoPpIS+mV3kABtM4GH3Jmg6fU4RTFOBzphfe2X11hlNQdYb50wKYU+/H/GkBKBozxV7ssS9kL2Y+itkXQRATD49DRJ3fNaIc3I5/B80jRDlCi26CIIgpSDnUnm2t8cLjENEbk5FQVPTGZHidIlprju8ttpPVAMxu4nFZRUc43S5bor24OQiR55BUdSiahqSqQ+Q5LG4O5uyre1CGzhi6B2VLXxefYr65MdLxeQ1+OAQeMVmDquuIyRqcIo95Dcf3YhvjEEko4DkgklAs45DdVzSlIqVqiKZUS1/HY0+BMYbuwZQl9mKPvd12dihmXwRBTE7yZcRpHiHKEVp0EwRBTAKqq6shCAKOHTNnWo8dO5azFI7T6YTf7zf9jDfNIS9uOrcVFS5xyPRLxKfPaTUZetl1JbfjJr6ouRLXnDYdEs8hpeqQhvZ0DzdTs9PXDefMQqPfvJBt9DtxwzmzTMdm1/ux+qyZcEk8YrIKl8Rj1bKZJgM0O+Ngty+7rurFHPtiOscX3YWeIIhJyWgZcZpHiHKE3n0EQRCTAIfDgSVLluC5557DlVdeCQDQdR3PPfcc1qxZU9rgRuH6M1uwfHb1qA7aJ9X60BickdeFdsXcWixs8qMznEJ9wJnT+furVy7A1UumjepebrevV//tIqx/ZR82vn0MF59SZ1lwG9x+8Wxcfmr9iI7jdsfBbl92Yrd7Trtjb7edHYrZF0EQUxOaR4hyg96B48yMLz1V6hAIgpikrF27FqtWrcJpp52GM844Aw888ABisVjGzbxcaQ6NvMg08DjsXTRV+1wjLjINFjVXjrjYLrSvG86ZNeJiO5vZ9bkXyNnYGQe7fdmJ3e457Y693XZ2KGZfBEFMTWgeIcoJeicSBEFMEq699lp0d3fj7rvvRmdnJxYvXoynn37aYq5GEARBEARBjB+06C4ClL0mphpj/Z7f/40rxrT/ycyaNWvKWk5OEARBEAQx1aBFN0EQZUchi3qnwHD/GWMYDEEQBEEQBEG8BybEovvBBx/Et771LXR2dmLRokX4wQ9+gDPOGNur7AX3/gUpjRvTcxAEQRAEQRAEQRCTm7IvGfbYY49h7dq1uOeee/DGG29g0aJFWLlyJbq6ukodGkEQBEEQBEEQBEGMStlnur/zne/gpptuyrjvPvTQQ3jqqafwy1/+El/60pcs7VOpFFKpVOb3cDgMAOjr64OiKHnPpygK4vE4RIWHpk+sTLeoM8TjOsVeAiZy/BM5duB4/L29vZAkadS2g4ODAADG2HiENqEwxiQSiZQ4EoIgygFjLqD50gzNlQRBZGN3rizrRbcsy9i+fTvuuuuuzDGe53HRRRdhy5YtOf9m3bp1uO+++yzHZ86cOWZxlhMfL3UA74GJHDswseOfyLEDhcc/ODiIQCAwJrFMVIwbEs3NzSWOhCCIcoLmSzM0VxIEkYt8c2VZL7p7enqgaZql3E1dXR12796d82/uuusurF27NvO7ruvo6+tDVVUVOC5/Fi8SiaC5uRmHDh2C3z96HdRyg2IvHRM5/okcO1BY/IwxDA4OorGxcZyimzg0Njbi0KFDqKiooLmyzJnI8U/k2IGJHX+hsdN8mZupNFcCEzt+ir10TOT4x2quLOtF94ngdDrhdDpNx4LBYMH9+P3+CfcmMaDYS8dEjn8ixw7Yj58yNrnheR5NTU0F/91Eft9M5NiBiR3/RI4dmNjxFxI7zZdWpuJcCUzs+Cn20jGR4y/2XFnWRmrV1dUQBAHHjh0zHT927Bjq6+tLFBVBEARBEARBEARB2KOsF90OhwNLlizBc889lzmm6zqee+45LFu2rISREQRBEARBEARBEER+yl5evnbtWqxatQqnnXYazjjjDDzwwAOIxWIZN/Ni43Q6cc8991gk6hMBir10TOT4J3LswMSPf6Iykcd9IscOTOz4J3LswMSOfyLHPpGZ6OM+keOn2EvHRI5/rGLn2ASoBfHDH/4Q3/rWt9DZ2YnFixfj+9//PpYuXVrqsAiCIAiCIAiCIAhiVCbEopsgCIIgCIIgCIIgJiJlvaebIAiCIAiCIAiCICYytOgmCIIgCIIgCIIgiDGCFt0EQRAEQRAEQRAEMUbQopsgCIIgCIIgCIIgxghadGfx4IMPYsaMGXC5XFi6dClef/31Uodki3vvvRccx5l+5s6dW+qwcvLSSy/hAx/4ABobG8FxHP7whz+YHmeM4e6770ZDQwPcbjcuuugivPvuu6UJNgf54l+9erXltbj00ktLE2wW69atw+mnn46KigrU1tbiyiuvxJ49e0xtkskkbr31VlRVVcHn8+Hqq6/GsWPHShSxGTvxn3/++Zax/8xnPlOiiCc/E3G+nEhzJTCx58uJOlcCE3u+pLmy/KC5cuyhubI00FxZGLToHuKxxx7D2rVrcc899+CNN97AokWLsHLlSnR1dZU6NFvMnz8fHR0dmZ9XXnml1CHlJBaLYdGiRXjwwQdzPn7//ffj+9//Ph566CG89tpr8Hq9WLlyJZLJ5DhHmpt88QPApZdeanotfv3rX49jhLl58cUXceutt2Lr1q3YuHEjFEXBJZdcglgslmnz+c9/Hn/605/w29/+Fi+++CKOHj2Kq666qoRRH8dO/ABw0003mcb+/vvvL1HEk5uJPF9OlLkSmNjz5USdK4GJPV/SXFle0Fw5PtBcWRporiwQRjDGGDvjjDPYrbfemvld0zTW2NjI1q1bV8Ko7HHPPfewRYsWlTqMggHAnnjiiczvuq6z+vp69q1vfStzbGBggDmdTvbrX/+6BBGOzvD4GWNs1apV7EMf+lBJ4imErq4uBoC9+OKLjLH0OEuSxH77299m2rz99tsMANuyZUupwhyR4fEzxth5553H/uVf/qV0QU0hJup8OVHnSsYm9nw5kedKxib2fElzZWmhuXL8obmydNBcOTqU6QYgyzK2b9+Oiy66KHOM53lcdNFF2LJlSwkjs8+7776LxsZGtLa24vrrr8fBgwdLHVLBtLe3o7Oz0/Q6BAIBLF26dMK8DgDwwgsvoLa2FnPmzME///M/o7e3t9QhWQiHwwCAUCgEANi+fTsURTGN/dy5czF9+vSyHPvh8Rs88sgjqK6uxoIFC3DXXXchHo+XIrxJzUSfLyfDXAlMjvlyIsyVwMSeL2muLB00V5YHNFeOHzRXjo74niKcJPT09EDTNNTV1ZmO19XVYffu3SWKyj5Lly7Fhg0bMGfOHHR0dOC+++7Dueeei7feegsVFRWlDs82nZ2dAJDzdTAeK3cuvfRSXHXVVZg5cyb27duHf/u3f8Nll12GLVu2QBCEUocHANB1HbfffjvOPvtsLFiwAEB67B0OB4LBoKltOY59rvgB4OMf/zhaWlrQ2NiIN998E3feeSf27NmD3//+9yWMdvIxkefLyTJXAhN/vpwIcyUwsedLmitLC82V5QHNleMDzZX5oUX3JOCyyy7L/HvhwoVYunQpWlpa8Pjjj+PGG28sYWRTj+uuuy7z71NPPRULFy7ErFmz8MILL+DCCy8sYWTHufXWW/HWW2+V9f6s0Rgp/ptvvjnz71NPPRUNDQ248MILsW/fPsyaNWu8wyTKEJory4eJMFcCE3u+pLmSOFForiwfaK4ce8ZrriR5OYDq6moIgmBx0zt27Bjq6+tLFNWJEwwGMXv2bOzdu7fUoRSEMdaT5XUAgNbWVlRXV5fNa7FmzRo8+eSTeP7559HU1JQ5Xl9fD1mWMTAwYGpfbmM/Uvy5WLp0KQCUzdhPFibTfDlR50pg8s2X5TZXAhN7vqS5svTQXFke0Fw59tBcaQ9adANwOBxYsmQJnnvuucwxXdfx3HPPYdmyZSWM7MSIRqPYt28fGhoaSh1KQcycORP19fWm1yESieC1116bkK8DABw+fBi9vb0lfy0YY1izZg2eeOIJbNq0CTNnzjQ9vmTJEkiSZBr7PXv24ODBg2Ux9vniz8WOHTsAoORjP9mYTPPlRJ0rgck3X5bLXAlM7PmS5srygebK8oDmyrGD5srCT0owxn7zm98wp9PJNmzYwHbt2sVuvvlmFgwGWWdnZ6lDy8sdd9zBXnjhBdbe3s42b97MLrroIlZdXc26urpKHZqFwcFB9re//Y397W9/YwDYd77zHfa3v/2NHThwgDHG2De+8Q0WDAbZH//4R/bmm2+yD33oQ2zmzJkskUiUOPI0o8U/ODjIvvCFL7AtW7aw9vZ29uyzz7L3ve997OSTT2bJZLKkcf/zP/8zCwQC7IUXXmAdHR2Zn3g8nmnzmc98hk2fPp1t2rSJbdu2jS1btowtW7ashFEfJ1/8e/fuZV/96lfZtm3bWHt7O/vjH//IWltb2fLly0sc+eRkos6XE2muZGxiz5cTda5kbGLPlzRXlhc0V44PNFeWBporC4MW3Vn84Ac/YNOnT2cOh4OdccYZbOvWraUOyRbXXnsta2hoYA6Hg02bNo1de+21bO/evaUOKyfPP/88A2D5WbVqFWMsXdrhK1/5Cqurq2NOp5NdeOGFbM+ePaUNOovR4o/H4+ySSy5hNTU1TJIk1tLSwm666aay+HLNFTMAtn79+kybRCLBPvvZz7LKykrm8XjYhz/8YdbR0VG6oLPIF//BgwfZ8uXLWSgUYk6nk5100knsi1/8IguHw6UNfBIzEefLiTRXMjax58uJOlcyNrHnS5oryw+aK8cemitLA82VhcENnZggCIIgCIIgCIIgiCJDe7oJgiAIgiAIgiAIYoygRTdBEARBEARBEARBjBG06CYIgiAIgiAIgiCIMYIW3QRBEARBEARBEAQxRtCimyAIgiAIgiAIgiDGCFp0EwRBEARBEARBEMQYQYtugiAIgiAIgiAIghgjaNFNEARBEARBEARBEGMELboJgiAIgsDq1avBcZzlZ+/evaUOjSAIomyguZI4EcRSB0AQBEEQRHlw6aWXYv369aZjNTU1JYqGIAiiPKG5kigUWnQTBEEQBAEAcDqdqK+vL3UYBEEQZQ3NlUShkLycIAiCIAiCIAiCIMYIWnQTBEEQBAEAePLJJ+Hz+TI/H/3oR0sdEkEQRNlBcyVRKCQvJwiCIAgCAHDBBRfgxz/+ceZ3r9dbwmgIgiDKE5oriUKhRTdBEARBEADSF44nnXRSqcMgCIIoa2iuJAqF5OUEQRAEQRAEQRAEMUbQopsgCIIgCIIgCIIgxghadBMEQRAEQRAEQRDEGMExxlipgyAIgiAIgiAIgiCIyQhlugmCIAiCIAiCIAhijKBFN0EQBEEQBEEQBEGMEbToJgiCIAiCIAiCIIgxghbdBEEQBEEQBEEQBDFG0KKbIAiCIAiCIAiCIMYIWnQTBEEQBEEQBEEQxBhBi26CIAiCIAiCIAiCGCNo0U0QBEEQBEEQBEEQYwQtugmCIAiCIAiCIAhijKBFN0EQBEEQBEEQBEGMEbToJgiCIAiCIAiCIIgxghbdBEEQBEEQBEEQBDFG0KKbIAiCIAiCIAiCIMYIWnQTBEEQBEEQBEEQxBhBi26CIAiCIAiCIAiCGCNo0U0QBEEQBEEQBEEQYwQtugmCIAiCIAiCIAhijKBFN1F2bNiwARzHYf/+/aUOhSAIYkyZMWMGVq9eXeowCIIgJi10XUmUA7TonmDs27cPt9xyC1pbW+FyueD3+3H22Wfje9/7HhKJRKnDI2xw/vnng+O4zI/D4cDMmTNx880349ChQ6UODy+88IIpvuyf6667rtThEUTZYlzYbdu2Lefj559/PhYsWPCez/PnP/8Z995773vuZ6IzY8YM0/zkcrlw8skn44tf/CL6+vpKHR5BTAjounLiM5GvKzmOw29+85tShzguiKUOgLDPU089hY9+9KNwOp345Cc/iQULFkCWZbzyyiv44he/iJ07d+KnP/1pqcN8z3ziE5/AddddB6fTWepQxoympiasW7cOACDLMnbt2oWHHnoIf/nLX/D222/D4/GUOELgtttuw+mnn246NmPGjNIEQxCTlD179oDnC7v//ec//xkPPvggLbwBLF68GHfccQcAIJlMYvv27XjggQfw4osv4vXXXy9xdARR3tB15eRhol5XAsCyZctKEM34Q4vuCUJ7ezuuu+46tLS0YNOmTWhoaMg8duutt2Lv3r146qmnShjheycWi8Hr9UIQBAiCUOpwxpRAIIB/+qd/Mh2bOXMm1qxZg82bN+Piiy8uUWTHOffcc/GRj3yk1GEQxKRmIl4EGnN1OTBt2jTTXPrpT38aPp8P3/72t/Huu+/i5JNPLmF0BFG+0HXl5IKuK8sfkpdPEO6//35Eo1H84he/ME2MBieddBL+5V/+JfO7qqr42te+hlmzZsHpdGLGjBn4t3/7N6RSKdPfzZgxA+9///vxyiuv4IwzzoDL5UJrayv+67/+K9Nm27Zt4DgODz/8sOW8f/nLX8BxHJ588kkAwIEDB/DZz34Wc+bMgdvtRlVVFT760Y9a9tEYMswXX3wRn/3sZ1FbW4umpibTY9l/88c//hFXXHEFGhsb4XQ6MWvWLHzta1+Dpmmmfg355q5du3DBBRfA4/Fg2rRpuP/++y2xJ5NJ3HvvvZg9ezZcLhcaGhpw1VVXYd++fZk2uq7jgQcewPz58+FyuVBXV4dbbrkF/f39pr7C4TB2796NcDhsOY9d6uvrAQCiOPK9sGPHjkEURdx3332Wx/bs2QOO4/DDH/4QAKAoCu677z6cfPLJcLlcqKqqwjnnnIONGzeecIwEQRSX4Xu6831uV69ejQcffBAATPI8g1gshjvuuAPNzc1wOp2YM2cOvv3tb4MxZjpvIpHAbbfdhurqalRUVOCDH/wgjhw5Ao7jTBn0e++9FxzHYdeuXfj4xz+OyspKnHPOOQCAN998E6tXr87IUuvr6/GpT30Kvb29pnMZfbzzzjv4p3/6JwQCAdTU1OArX/kKGGM4dOgQPvShD8Hv96O+vh7/3//3/72nMbUzlxLEVIeuK+m6EqDryvGEvpEmCH/605/Q2tqKs846y1b7T3/603j44YfxkY98BHfccQdee+01rFu3Dm+//TaeeOIJU9u9e/fiIx/5CG688UasWrUKv/zlL7F69WosWbIE8+fPx2mnnYbW1lY8/vjjWLVqlelvH3vsMVRWVmLlypUAgL/+9a949dVXcd1116GpqQn79+/Hj3/8Y5x//vnYtWuXRd7y2c9+FjU1Nbj77rsRi8VGfD4bNmyAz+fD2rVr4fP5sGnTJtx9992IRCL41re+ZWrb39+PSy+9FFdddRWuueYa/M///A/uvPNOnHrqqbjssssAAJqm4f3vfz+ee+45XHfddfiXf/kXDA4OYuPGjXjrrbcwa9YsAMAtt9yCDRs24IYbbsBtt92G9vZ2/PCHP8Tf/vY3bN68GZIkAQCeeOIJ3HDDDVi/fr0tUyRN09DT0wMgPYm9/fbbuOeee3DSSSfh7LPPHvHv6urqcN555+Hxxx/HPffcY3ktBEHARz/6UQDpC91169bh05/+NM444wxEIhFs27YNb7zxhq07noODg5kYDUKhUMFSWIKYaoTDYctnB0h/1vOR73N7yy234OjRo9i4cSP++7//2/S3jDF88IMfxPPPP48bb7wRixcvxl/+8hd88YtfxJEjR/Dd734303b16tV4/PHH8YlPfAJnnnkmXnzxRVxxxRUjxvXRj34UJ598Mv7zP/8zs4DfuHEj2tracMMNN6C+vj4jRd25cye2bt1quhkAANdeey1OOeUUfOMb38BTTz2Fr3/96wiFQvjJT36CFStW4Jvf/CYeeeQRfOELX8Dpp5+O5cuX5x0vRVEyY51MJvG3v/0N3/nOd7B8+XLMnDkz798TxFSFrivpuhIo/XUlAFRVVVm+LyYljCh7wuEwA8A+9KEP2Wq/Y8cOBoB9+tOfNh3/whe+wACwTZs2ZY61tLQwAOyll17KHOvq6mJOp5PdcccdmWN33XUXkySJ9fX1ZY6lUikWDAbZpz71qcyxeDxuiWfLli0MAPuv//qvzLH169czAOycc85hqqqa2huPtbe3j9rvLbfcwjweD0smk5lj5513nuVcqVSK1dfXs6uvvjpz7Je//CUDwL7zne9Y+tV1nTHG2Msvv8wAsEceecT0+NNPP205bsS8fv16S3/DMWIc/nPKKaewtra2vH//k5/8hAFg//jHP0zH582bx1asWJH5fdGiReyKK67I299wnn/++ZzxDX9NCIIwY8wDo/3Mnz/f9DctLS1s1apVmd/tfG5vvfVWluvr+w9/+AMDwL7+9a+bjn/kIx9hHMexvXv3MsYY2759OwPAbr/9dlO71atXMwDsnnvuyRy75557GAD2sY99zHK+XPPyr3/9a8t3itHHzTffnDmmqiprampiHMexb3zjG5nj/f39zO12m8ZkJIzvr+E/Z599Nuvp6cn79wQxVaHrSrquzKaU15UAWEdHR8F9TkQoZTUBiEQiAICKigpb7f/85z8DANauXWs6bpjNDN+jM2/ePJx77rmZ32tqajBnzhy0tbVljl177bVQFAW///3vM8eeeeYZDAwM4Nprr80cc7vdmX8rioLe3l6cdNJJCAaDeOONNyyx3nTTTbb22WT3a9wpO/fccxGPx7F7925TW5/PZ9rX4nA4cMYZZ5iez+9+9ztUV1fjc5/7nOVcxt223/72twgEArj44ovR09OT+VmyZAl8Ph+ef/75zN+sXr0ajDHbpX9mzJiBjRs3YuPGjfi///s/PPDAAwiHw7jsssvQ3d096t9eddVVEEURjz32WObYW2+9hV27dplei2AwiJ07d+Ldd9+1FdNw7r777kyMxo8hVSIIYmQefPBBy2dn48aNWLhwYd6/fS+f2z//+c8QBAG33Xab6fgdd9wBxhj+7//+DwDw9NNPA0hnhLLJNR8afOYzn7Ecy56Xk8kkenp6cOaZZwJAzvn+05/+dObfgiDgtNNOA2MMN954Y+Z4MBi0fP+MxtKlSzPj++STT+I//uM/sHPnTnzwgx8k52WCGAG6rqTrymxKeV25ceNGhEKhE+pvokHy8gmA3+8HkJ4U7HDgwAHwPI+TTjrJdLy+vh7BYBAHDhwwHZ8+fbqlj8rKStP+kkWLFmHu3Ll47LHHMhdIjz32GKqrq7FixYpMu0QigXXr1mH9+vU4cuSIaR9hrn0pduV/O3fuxL//+79j06ZNmS+LkfptamqyyFQqKyvx5ptvZn7ft28f5syZM+o+l3fffRfhcBi1tbU5H+/q6rIVey68Xi8uuuiizO+XXnopzjnnHJx22mn4xje+Meqexurqalx44YV4/PHH8bWvfQ1A+rUQRRFXXXVVpt1Xv/pVfOhDH8Ls2bOxYMECXHrppfjEJz5h68IfAE499VRTjARB2OOMM87AaaedZjleWVmZU1qXzXv53B44cACNjY2WC+lTTjkl87jxf57nLfPv8O+MbHLN1X19fbjvvvvwm9/8xjIf5prvh3/XBAIBuFwuVFdXW44P3xc+EtXV1aZ56oorrsCcOXPwkY98BD//+c9HvZFAEFMVuq6k68ps6LpyfKBF9wTA7/ejsbERb731VkF/Z3d/xEh3BNkw451rr70W//Ef/4Genh5UVFTgf//3f/Gxj33MNMF87nOfw/r163H77bdj2bJlCAQCmfrOuq5bzpF9p3EkBgYGcN5558Hv9+OrX/0qZs2aBZfLhTfeeAN33nmnpV+7zycfuq6jtrYWjzzySM7Ha2pqCuovH0uWLEEgEMBLL72Ut+11112HG264ATt27MDixYvx+OOP48ILLzRdvC5fvhz79u3DH//4RzzzzDP4+c9/ju9+97t46KGHTBkngiDKh3L93Oaaq6+55hq8+uqr+OIXv4jFixfD5/NB13VceumlOef7XHNzsebrbC688EIAwEsvvUSLboLIAV1X0nXlcOi6cuyhRfcE4f3vfz9++tOfYsuWLXnr2bW0tEDXdbz77ruZDAeQdigcGBhAS0vLCcVw7bXX4r777sPvfvc71NXVIRKJ4LrrrjO1+Z//+R+sWrXKdEctmUxiYGDghM4JAC+88AJ6e3vx+9//3mSs097efsJ9zpo1C6+99hoURcmYVuRq8+yzz+Lss8+2NYkXA03TEI1G87a78sorccstt2SkQO+88w7uuusuS7tQKIQbbrgBN9xwA6LRKJYvX457772XJkeCKGPyfW5HuvBtaWnBs88+i8HBQVO225BKGnO/8R3R3t5uKqm1d+9e2zH29/fjueeew3333Ye77747c/xEZYfFRFVVALA1lxLEVIWuK+m6Mhu6rhx7aE/3BOFf//Vf4fV68elPfxrHjh2zPL5v3z5873vfAwBcfvnlAIAHHnjA1OY73/kOAIzqUDsap5xyCk499VQ89thjeOyxx9DQ0GBxlxUEwXLn7wc/+IGlBEMhGHcYs/uVZRk/+tGPTrjPq6++Gj09PZkyCNkY57nmmmugaVpGapONqqqmCb8YpR2ef/55RKNRLFq0KG/bYDCIlStX4vHHH8dvfvMbOBwOXHnllaY2w+WZPp8PJ510kqW8B0EQ5YOdz61RI3v4Refll18OTdMs89p3v/tdcByXcdk1XIGHz6E/+MEPbMeZa14GrN87peBPf/oTANiaSwliqkLXlXRdmQ1dV449lOmeIMyaNQuPPvpopuTKJz/5SSxYsACyLOPVV1/Fb3/724zZwqJFi7Bq1Sr89Kc/zUhoXn/9dTz88MO48sorccEFF5xwHNdeey3uvvtuuFwu3HjjjZbyUe9///vx3//93wgEApg3bx62bNmCZ599FlVVVSd8zrPOOguVlZVYtWoVbrvtNnAch//+7/9+T/LDT37yk/iv//ovrF27Fq+//jrOPfdcxGIxPPvss/jsZz+LD33oQzjvvPNwyy23YN26ddixYwcuueQSSJKEd999F7/97W/xve99Dx/5yEcAFF7aIRwO41e/+hWA9ES7Z88e/PjHP4bb7caXvvQlW8/h2muvxT/90z/hRz/6EVauXIlgMGh6fN68eTj//POxZMkShEIhbNu2Df/zP/+DNWvWFDRWBEGMH3Y+t0uWLAEA3HbbbVi5ciUEQcB1112HD3zgA7jgggvw5S9/Gfv378eiRYvwzDPP4I9//CNuv/32TMmaJUuW4Oqrr8YDDzyA3t7eTMmwd955B4A9Canf78fy5ctx//33Q1EUTJs2Dc8888x7yhSdCEeOHMnMpbIs4+9//zt+8pOfjGhoRBBEGrqupOvK4Yz1deXLL7+MZDJpOb5w4ULb+8InNONrlk68V9555x120003sRkzZjCHw8EqKirY2WefzX7wgx+YShwoisLuu+8+NnPmTCZJEmtubmZ33XWXqQ1j6dIOuez/zzvvPHbeeedZjr/77rsZi/9XXnnF8nh/fz+74YYbWHV1NfP5fGzlypVs9+7dlrI4RimEv/71r5Y+cpV22Lx5MzvzzDOZ2+1mjY2N7F//9V/ZX/7yF/b/s/fnUXJW94E3/nnW2qu6q/eWWkuDQIjdgEHCJhjbwo6dgM04ryeeREw89sQRzsHkTGI7dmK8hMSTE/skP4eZOD6Aj83rE79jYw94AW9gEAKDLQTICES31t67uvZ66ll/f1RXqaq6qrt6kbol3c85Oqp+6j73fu99nnqee+93A7yf//znNXLXp+TxPM/btWuXt3Hjxppj+Xze++u//uvKGPX29nr/6T/9J+/111+vKfdv//Zv3lVXXeUFAgEvEol4l156qfeXf/mX3sjIyByZl5LaQZIkLx6Pe7//+7/vPf/88wueXyadTnuBQMADvG984xtzvv/85z/vvfGNb/Ta2tq8QCDgbd261fvCF77gmaY5b73l1A7f/va3W5ZFIBDM/1zzvMbPp/pnYyu/W9u2vY9+9KNeV1eXJ0lSTfqwTCbjfexjH/P6+/s9TdO8LVu2eP/zf/7PSsqaMrlcztu9e7cXj8e9cDjs3Xrrrd7Bgwc9oCaFVznd1+Tk5Jz+HD9+3HvPe97jtbW1ebFYzHvf+97njYyMNE07Vl/Hrl27vFAo1NI4NaI+ZZgsy153d7f3n//zf66kRxMIBPMj5pViXlnmVM8rm/2rfl+czUiet4xtHYFAIBAIBGcF+/bt48orr+Qb3/gGH/jAB1ZbHIFAIBAIzhqET7dAIBAIBOcYjXJYf/nLX0aW5Tk+lQKBQCAQCJaH8OkWCAQCgeAc44tf/CLPP/88b3nLW1BVlR/+8If88Ic/5MMf/jADAwOrLZ5AIBAIBGcVwrxcIBAIBIJzjMcee4y7776bAwcOkM1m2bBhA3/0R3/EX//1X9fkyBUIBAKBQLB8xKJbIBAIBAKBQCAQCASCU4Tw6RYIBAKBQCAQCAQCgeAUIRbdAoFAIBAIBAKBQCAQnCLOesct13UZGRkhEokgSdJqiyMQCFYRz/PIZDL09/cjy2LPsRrxrBQIBNWI52VjxLNSIBBU0+qz8qxfdI+MjIhIrAKBoIZjx46xfv361RZjTSGelQKBoBHieVmLeFYKBIJGLPSsPOsX3ZFIBCgNRDQaXWVpBALBapJOpxkYGKg8FwQnWeyz0rIsHn30UXbu3ImmaadavDWF6Pu52Xc4t/ovnpeNEfNKgUBQTavPyrN+0V02/YlGo+LhKBAIAIRJYAMW+6y0LItgMEg0Gj3rFx/1iL6fm32Hc7P/4nlZi5hXCgSCRiz0rBROOgKBQCAQCAQCgUAgEJwixKJbIBAIBAKBQCAQCASCU4RYdAsEAoFAIBAIBAKBQHCKEItugUAgEAgEAoFAIBAIThGruui+5557uOaaa4hEInR3d3Prrbdy8ODBmjI33ngjkiTV/PvTP/3TVZJYIBAIBAKBQCAQCASC1lnVRffjjz/O7t272bt3L4899hiWZbFz505yuVxNuQ996EOMjo5W/n3xi19cJYkFglPDVNbgpRMpprLGaosiEAgEAoFAIBCcdeRNm/G0Qd60T3vbq5oy7Ec/+lHN3/fffz/d3d08//zz3HDDDZXjwWCQ3t7e0y2eQHBa+NkrE9z/1DBpwyLq17j9+s3ctLV7tcUSCAQCgUAgEAjOCg5NZHn05bHKfHvnxb2c3x0+be2vKZ/uVCoFQDwerzn+zW9+k87OTi655BI+8YlPkM/nm9ZRLBZJp9M1/wSCtcpU1uD+p4aZyVt0R/zM5C0e2DMsNN4CgUAgEAgEAsEKkDdtHn15jJm8RVe4NN9+7MDYadV4r6qmuxrXdbnzzju5/vrrueSSSyrH//AP/5CNGzfS39/P/v37+au/+isOHjzId77znYb13HPPPdx9992nS2yBYFmMpYqkDYu+mJ+grtIX8zORMRhLFekM+1dbPIFAMMumjz9S+exTPL74RrjkMz+m6EgNyx/++3edLtEEAoFAIBDMQ8awSRsWvVE/AV2hN+pnMmuQMWyC+ulZDq+ZRffu3bt56aWXePLJJ2uOf/jDH658vvTSS+nr6+Otb30rr7/+Ouedd96cej7xiU9w1113Vf5Op9MMDAycOsEFgmXQG/MR9WuMpgz6Yn5GUwbxkEZvzLfaogkEAoFAIBAIBGc8Eb9K1K8xljbojfoZS5fm2xH/6VsKrwnz8jvuuIOHH36Yn//856xfv37estdeey0Ahw4davi9z+cjGo3W/BMI1iqdYT+3X7+ZeEhjIlN6AOzasVlouQUCgUAgEAgEghUgqKvsvLiXeEhjMluab799W+9p03LDKmu6Pc/jox/9KN/97nf5xS9+webNmxc8Z9++fQD09fWdYukEgtPDTVu7uWx9lLFUkd6YTyy4BQKBQCAQCASCFeT87jD9bZvIGDYRv3paF9ywyovu3bt38+CDD/K9732PSCTC2NgYALFYjEAgwOuvv86DDz7I7/7u79LR0cH+/fv52Mc+xg033MBll122mqILBCtKZ9gvFtsCgUAgEAgEAsEpIqif/sV2mVVddN97770A3HjjjTXH77vvPm6//XZ0XecnP/kJX/7yl8nlcgwMDHDbbbfxqU99ahWkFQgEAoFAIBAIBAKBYHGsunn5fAwMDPD444+fJmkEAkHetFfN7EYgEAgEAoFAIDgbEbNqgUAAwKGJLI++PEbasIj6NXZe3Mv53eHVFksgEAgEAoHgrEQoO84dxNUVCATkTZtHXx5jJm9VUik8dmCM/rZN4iUgEAgEAoFAsMIIZce5xZpIGSYQCFaXjGGTNkoL7oCu0Bv1kypYZAx7tUUTCAQCgUAgOKuoVnZ0hf3M5C0eOzBG3hTzrrMVsegWCARE/CpRv8ZY2qBgOoylDWIBjYhfaLkFAoFAIBCc2eRNm/G0seCittVyy2Wxyo7TJZfg1CFm1AKBgKCusvPiXh47MMZk1iAe0nj7tl5hWi4QCAQCgeCMplUz7tNp7l2t7Ci79cVDjZUdwgz97EBougUCAQDnd4fZtWMT//X6zezasUk80E8jTzzxBL/3e79Hf38/kiTx0EMP1XzveR5/8zd/Q19fH4FAgLe97W289tprNWUSiQQf+MAHiEajtLW18cEPfpBsNnsaeyEQCAQCwdqiVTPu023uXVZ2xEPavMqOM8kMXWjj50csugUCQYWgrtIT9QsN92kml8tx+eWX85WvfKXh91/84hf553/+Z/7X//pfPPPMM4RCIW6++WYMw6iU+cAHPsDLL7/MY489xsMPP8wTTzzBhz/84dPVBYFAIBAI1hytmnGvRmybVpQdZ0rMnUMTWe5/6jD3PTXM/U8d5tCE2PSvR8ysBQKBYJV55zvfyTvf+c6G33mex5e//GU+9alPccsttwDw9a9/nZ6eHh566CHe//7389vf/pYf/ehH/OpXv+Lqq68G4F/+5V/43d/9Xf7xH/+R/v7+09YXgUAgEAjWCq2acS/G3HslCerzpwpbLbkWg8iA0xpiJAQC1laexNMhy1rqbzVrVa7VZHh4mLGxMd72trdVjsViMa699lqefvpp3v/+9/P000/T1tZWWXADvO1tb0OWZZ555hne8573zKm3WCxSLBYrf6fTaQAsy8KyrAXlKpdppezZgE/xTn6WvZr/G3G2jsu5dt3rOZf6fy70UXD202rMmrUa22atylVNI238ZNYgY9hrSs7VRoyE4JxnLQWoOB2yrKX+nglyrTZjY2MA9PT01Bzv6empfDc2NkZ3d3fN96qqEo/HK2Xqueeee7j77rvnHH/00UcJBoMty/fYY4+1XPZM5otvnHvsc1e7Tcv/4Ac/OIXSrD7nynVvxrnQ/3w+v9oiCAQrwvndYfrbNi24qd9qudPNWpWrzJmgjV8LiNEQnNOsJZOY0yHLWurvmSDX2cwnPvEJ7rrrrsrf6XSagYEBdu7cSTQaXfB8y7J47LHHePvb346maadS1DXBJZ/5ceWzT/b43NUun35OpuhKK1L/S5+5eUXqOdWca9e9nnOp/2XrF4HgbGAhM+7FljvdrFW54MzQxq8FxGgIzmnWkknM6ZBlLfX3TJBrLdDb2wvA+Pg4fX19lePj4+NcccUVlTITExM159m2TSKRqJxfj8/nw+fzzTmuadqiFhOLLX+mUnTmLq6LrtTw+FI408bwXLnuzTgX+n+2908gEKwca10bvxYQ0csF5zTVJjEF02EsbRALrI5JzOmQZS3190yQay2wefNment7+elPf1o5lk6neeaZZ9i+fTsA27dvJ5lM8vzzz1fK/OxnP8N1Xa699trTLrNAIBAIBIJzC5EBZ37EoltwTtNqnsSzRZa11N8zQa7TRTabZd++fezbtw8oBU/bt28fR48eRZIk7rzzTj7/+c/z/e9/nxdffJE//uM/pr+/n1tvvRWAiy66iHe84x186EMf4tlnn+Wpp57ijjvu4P3vf7+IXC4QCAQCgUCwypwbM1qBYB7WkknM6ZBlLfW3mrUq1+ngueee4y1veUvl77Kv9a5du7j//vv5y7/8S3K5HB/+8IdJJpO86U1v4kc/+hF+v79yzje/+U3uuOMO3vrWtyLLMrfddhv//M//fNr7IhAIBKeKe+65h+985zu88sorBAIBduzYwT/8wz9w4YUXApBIJPjbv/1bHn30UY4ePUpXVxe33norn/vc54jFYpV6jh49ykc+8hF+/vOfEw6H2bVrF/fccw+qeu68dwQCwelFPF0EAtZWgIrTIcta6m81a1WuU82NN96I5zVPPyVJEp/97Gf57Gc/27RMPB7nwQcfPBXiCQQCwZrg8ccfZ/fu3VxzzTXYts0nP/lJdu7cyYEDBwiFQoyMjDAyMsI//uM/sm3bNo4cOcKf/umfMjIywv/3//1/ADiOw7ve9S56e3vZs2cPo6Oj/PEf/zGapvF3f/d3q9xDgUBwtnLuzW4FAoFAIBAIBGccP/rRj2r+vv/+++nu7ub555/nhhtu4JJLLuH//J//U/n+vPPO4wtf+AL/5b/8F2zbRlVVHn30UQ4cOMBPfvITenp6uOKKK/jc5z7HX/3VX/GZz3wGXddPd7cEAsE5gPDpFpxR5E2b8bRB3rRXW5Q1w1TW4KUTKaayRsvniHEUCAQCwZlOKpUCSpY+85WJRqMV0/Gnn36aSy+9lJ6enkqZm2++mXQ6zcsvvzzn/GKxSDqdrvknOLdZ7TnUarcvWBpC0y04Yzg0keXRl8dIGxZRv8bOi3s5vzu82mKtKj97ZYL7nxqujMnt12/mpq3d854jxlEgEAgEZzqu63LnnXdy/fXXc8kllzQsMzU1xec+9zk+/OEPV46NjY3VLLiByt9jY2Nz6rjnnnu4++67V1BywZnMas+hVrt9wdIRmm7BGUHetHn05TFm8hZdYT8zeYvHDoyd07t8U1mD+58aZiZv0R0pjckDe4bn1XiLcRQIBALB2cDu3bt56aWX+Na3vtXw+3Q6zbve9S62bdvGZz7zmSW384lPfIJUKlX5d+zYsSXXJThzyZs2h6dyPLJ/ZElzqJXQTos53JmN0HQLzggyhk3asOiN+gnoCr1RP5NZg4xhn5OBtwDGUkXShkVfrJQTsS/mZyJjMJYq0hn2NzxHjKNAIBAIznTuuOMOHn74YZ544gnWr18/5/tMJsM73vEOIpEI3/3ud9E0rfJdb28vzz77bE358fHxynf1+Hw+fD7fCvdAcCZR1i6Ppgq8Op7lyg1ti5pDrZR2WszhzmyEpltwRhDxq0T9GmNpg4LpMJY2iAU0Iv5z9yHTG/MR9WuMpko7p6Op0pj0xppPDsQ4CgQCgeBMxfM87rjjDr773e/ys5/9jM2bN88pk06n2blzJ7qu8/3vf78mtSLA9u3befHFF5mYmKgce+yxx4hGo2zbtu2U90FwZlGtXe6PBXE9j+ePzJAyzJbmUCupnW40hwtoCgXTPmO03eeyP7pYdAvOCIK6ys6Le4mHNCazBvGQxtu39Z7TO3udYT+3X7+ZeEhjIlMak107NjfVcoMYR4FAIBCcuezevZtvfOMbPPjgg0QiEcbGxhgbG6NQKAAnF9y5XI6vfe1rpNPpShnHcQDYuXMn27Zt44/+6I944YUX+PGPf8ynPvUpdu/eLTTagjlUa5djQY2rN8aRJRhJtjaHaqSdThUsMsbiF531czjPc0kbNt/61THuf+owhyayy+nqKefQRJb7nzrMfU8NnxHyrjRipi04Yzi/O0x/2yYyhk3Ef27mc67npq3dXLY+yliqSG/MN++Cu4wYR4FAIBCcidx7770A3HjjjTXH77vvPm6//XZ+/etf88wzzwBw/vnn15QZHh5m06ZNKIrCww8/zEc+8hG2b99OKBRi165dfPaznz0tfRCcWVRrl3ujfmzXY8d5ndxyRT/dUf+Cc6j688fSpcX6Ui0My3O4iXSR7+07Qbbo0BUu1fvI/pGW5TrdlDX+E5kibQGNiUyRxw6M0d+2ac3Jeqo4N3opOGsI6mKRWE9n2N/SYrsaMY4CgUAgONPwPG/e72+88cYFywBs3LiRH/zgByslluAspqxdfuzAWI2F4KbO1nyym52/nDlYUFcJ6DYFy6lo0FVZYs/rU0zlTPqi/jUX1Txj2BxJ5JhIFxlyQZGhYNnnlD/6udFLgUAgEAgEAoFAIFgky7UQPBUWhtUa9HhQ57kjCQD62/wkslaNFjlv2qtu3ajIMJo0mMmXAgCPpgxcz0M5hxydxaJbsGpUPwSAVX8gtEKzB9diH2j15dfCA1EgEAgEAoHgVHEmz3WWayHYyvmLGZ9qDfpIMo8sSVy5oY2YX0eXlUpU85GksezI6Stx3RwX+mIBVEXCsBy6IjpdYT+Ou6TqzkjOrDtecNZQnT7Bsl2QJDRFWlYqhVNNs5QPi00FUV9+a1+UV0bTy04lIRAIBAKBQLAWWam0WWcrSxmfRv7d5ajm8ZCGIlOJnF72J1+sH/VKXbeIX2VjR5CAT6YtoJMsmPRE/OdU9pxzSKkvWCtUp0+I+XX2n0ix/3iSWFBbViqFU0mzlA9TWWNRqSDq65nIGtz/1DATmeKyU0kIBAKBQCAQrDVWMm3WqWC101gtZ3yCusqmzhDvuqx/TmYax2VZkdMXK9d841jWzPdE/BiWQ0/Ef85lzzl3eipYM1SnT7AcF02WAQ8Jid6ov2ISs5Z+iI1SPkxmDcZSxYbHm8lfX09bQCdtJGkLaC2dLxAIBAKBQHAm0WwOtRbmOmtBA78S49PIbzxv2suKnL4YuVoZx3M9e47QdAtOO9XBHzwPLNfFcj08PMbSBrHA0lMpnCqqZS6b7sQCGr0xX8PjzeSvrydZMIn6NZIFq6XzBQKBQCAQCM4kms2hynOdpWiaV0I7vVQN80prxhcan1YJ6io9VenC6vN6LzZyeqtyLWYc62U8lzj3eixYdaqDP6QKJpevjwESqby1IqkUTgXNUj50hv2LSgVRX09PxM8NW7o5OJZesVQSAoFAIBAIBGuF+dJmLUXTvFLa6aVomE+FZvxUpBUrsxztcqtyrWVLhrWEGAnBqlD/EIC1H7282YNrsQ+0RuWvG4yv+f4LBAKBQCAQLIVm5s+LDfS1lHOaUa3JbcX8ejltT826JPbGfHSG/QuOD8B42mg4L1xsNPH5IqcvVFcrc9zFjuO5ihgNwapR/xA4ExabzR5ci00l0ajvZ0L/BQLB2mDTxx9ZVPnDf/+uUySJQCAQtEb9XGcpGtKV1KouVsO81LZ/9soE9z81XNGO3379Zm7a2t10fObTpq+kpr3Vuhaao55KTf3ZxKr6dN9zzz1cc801RCIRuru7ufXWWzl48GBNGcMw2L17Nx0dHYTDYW677TbGx8dXSWKBQCAQCAQCgWDtUu1zvNqRuedjKb7M5XOOJXNMZAyOJXPLioVzfneYXTs28V+v38yuHZtqFp31Y7cUeadms9TM5C26IyV/5wf2DDOVNRqWn88/ejG+0wtd95WOKD/fOC5GrrOZVd2CePzxx9m9ezfXXHMNtm3zyU9+kp07d3LgwAFCoRAAH/vYx3jkkUf49re/TSwW44477uC9730vTz311GqKLjhDacUkp7oMMMcUqvz3dLbI0GSOwa4QA/FQw/Pzpj2vOdFKyDufydJiTZDqyzcbi2afxa6mQCAQCASrR7X20rJdkCQ0RVqTubGXoiEN6ipb+6LsfWqatJEk6te4YUv3suYfjTS5zbTAi5W3nOWmL1YKHtYX8zORKc3bGs0L59Omw9wUYI007a1osE+FH/ZCGvG1ECl+NVnVGfKPfvSjmr/vv/9+uru7ef7557nhhhtIpVJ87Wtf48EHH+Smm24C4L777uOiiy5i7969XHfddashtuAMpZUf+3wvq619UV4ZTZM2LF6fyPDyaAbbcQnqKh968yAfuG5jzfmTmSKHp3I4njevOdFy5J3PZGmxD7f68tX9tRwPPA9NlWvGpfr4ufgAFQgEAoFgrVCtvYwHdX52sGQZetNF3SSy1pJ9n1dSvuXGxcmbNq+MphmIBwloCgXL4eBYmusG4yvWr0a+24+8OMItl6+jv83Prh2ty1vOcnMimacj5GM6V6Qz7KM35mvYbsG08atKU//ohXynW/U7r1gMzORpC5Sy6PREfYuyGFiMYmclffHPVNZUyrBUKgVAPB4H4Pnnn8eyLN72trdVymzdupUNGzbw9NNPN6yjWCySTqdr/gkErZjRVJeJ+XX2n0ix/3iSWFBjYtY8aCJTRJVk9g7PMJO3iAY0skWbf39yiFfH0pXzA7rC3qFpjicLxIO+Bc2JliLvfCZLizUbqi8/kSly/55hJrIGsaDGC8eS7D+RIqCqlXEJ6krleMyvL9s0SSAQCAQCwdKp1l5KEmiyjCZLSEj0Rv2kClZFY3q6OTSR5f6nDnPfU8Pc/9RhDk1kK98tJo1UxrA5ksgxkizw6niWkWSBw9O5Fe1XvRZYkWHPoWm+9uQQ9z91mJGk0bK8nWE/N1/cS67o8MpYmlzRYeds9ptqyuPzrV8dI1O08Tx3jja9lRRgjTTYja572WJgaCrLL16dYGgqy4W90ZYXwPNdz1bGdLXvx9VgzWwtuK7LnXfeyfXXX88ll1wCwNjYGLqu09bWVlO2p6eHsbGxhvXcc8893H333adaXMEZRitmNNVlLMdFk2XAQ0KiLaCTNpKl3cC8ied6+BQJn6LQEZKYyZscmNUK90b9JPJF8MCvKuiqvKA50VLknc9kqSvSmglSs/baAhrpgkVbQEdCQlckQMJ0nMq4mLZXOS5JiBQRAoFAIBCsItU+x/GgjuW6AHh4qxpReiEt50KRvatRZBhNGszkS/Of0ZSB63koK6hGrBnHsMZzh2eQJOiPBUnkzYrWuzvqa0nDmypYXH9+Z0UznzYs8qZdceObSBd5ZP8IMwWTtoCOYZuEdZUbL+xmU2ewZkwWsgxoNZJ42WJgsCtEW0AnWTBbthhYitZaRDhfQ4vu3bt389JLL/Hkk08uq55PfOIT3HXXXZW/0+k0AwMDyxVPcIbTyo99vpdVsmAS9ZfMb8I+DUmWKDoeRcchmbeI+FW29UUZSRqMpQ3CfgUkMGwH03aZzBaJh7SG5kRLlbdssjSaMiovnnIbQX1xD7f69pKFkhY/WTDp0fyYjockeeiKUhkXXZUqxz2Pc/IBKhAIBALBWqHaRzpVMLl8fQyQSOWtVY0oPZ8iYe9QoqXI3mUcF/piAVRFwrAcuiI6XWE/jrty8laP40jSQJHhDRvixIIaKcNkz6FppjJF+mKBBd3qyn0f7AwT0BUKplPp+0jS4NGXxxhNFXjhWJKAruBTFYqWQ8FySRWshm3M5zu92NzaA20hArpCxKe1rDhZij+4iHC+Rhbdd9xxBw8//DBPPPEE69evrxzv7e3FNE2SyWSNtnt8fJze3t6Gdfl8Pny+1hY2gnOHVn7s872seiJ+btjSzcGxNKmCxfbBdl4eyZAulBbc/+1Ng1zQG0WW5dnzLbYPdnB4Ok8iX1pw79qxueVgaq3I2xn2c/v1m3lgzzATGWNOG4t5uNW31xP1ccMFXaX+5i2uGGgDPAq2XRmXvOlUjqcM85x8gAoEAoFAsJZolO95tYOdNvIfbg9pjMwU+Novh0gbdkV58MCeYS5bH206X4r4VTZ2BAn45IqGtifiX/EN//I4TqQNHvrNCDnTIWWYc7TeS9XwKjIVbXFnyM90zsRKuwx2hhiezqGpMp0RX8V1bzG+zyuRW3s+f+2laq0X679/trGqvfU8j49+9KN897vf5Re/+AWbN2+u+f6qq65C0zR++tOfcttttwFw8OBBjh49yvbt21dDZMEZTCs/9oVeVtcNxueNXl5//nKil7ci701bu7lsfbRhG4t9uDUqX93f6rFo9vlce4AKBAKBQLDWqNeErva7uRJxfHiYdMFClSU2xoN8YzrH0FSOzR3BliJ7l+s6qSApKUVO1YZ/UFfZ1Bnm3Zf3N9R666q8ZA2v41Lj0hjxaYymCgxN5SlaLvGgjk9VCEdb10DXt7vU3NoLBeJdjtZ6IbnOZla117t37+bBBx/ke9/7HpFIpOKnHYvFCAQCxGIxPvjBD3LXXXcRj8eJRqN89KMfZfv27SJyuWBJtPJjn+9lVf1dMK7WpAprWEZXl5QqbDHydob9876clvOQXmgsGn0WCAQCgUBwdtBM47nYyNVl/+GQpvD0UIKjMwWu3dSBLCV4fSqHX1eYzJjEAiqKJFV8nhuxXI3pYtOpNtJ6l/N0+1SJY4kcikzTuVgjefOmXdEWh3WVTNHCp8ls7goyPJkjY9oUbYfpWUvCU+G610yuVvy1z3Wt9VJY1RG69957Abjxxhtrjt93333cfvvtAHzpS19ClmVuu+02isUiN998M//6r/96miUVCAQCgUAgEAjOHZppPBebkrTaf9hyXfyqDEiE/Spv3tLFU4cmGU8XCWgKPREf33vhxIL1LlVjutRc0fVa78mswUzOYHg6z5OHphb0R2+kxDjpN16gI6QT1BV0RWGwM0TedJnKFOlvC5xS1716uRbjr30ua62Xwqqbly+E3+/nK1/5Cl/5yldOg0SC1aLVXcfF7k7Od34z0+/5omhWfxfU1UWZWM8n+3L6VX/usURujtn7fG1UH29kMr/Y9gUCgUAgEJzZNNN4tgXX8+jLY0xkDdoCOhPZxUWujoc1TMfD9VzSBQu/pnDLFeu4bjDO068nMB2PrvCpyeO8ErmiyxreI1N5Pv/IAUzba+qPvlBU9pMa9CLf23eiEr08WWgevfxU08hfO+xTKJj2vNYHp5KzZZ555kouOGtodddxqbuTjc6fzBQ5Mp3Ddr2a3cmfvTLRNIpm9XeKJLGpM0RXxIdluyBJaIrUVK75ZF9Ov+rPNW2Xh/adqDwYP/TmQT5w3caWdqtfn8xyYCSN5bg15y6m/cVeE4FAIBAIBGuPZhrPsVSRI9N5JjIGQ24ORZYoFN2W/ZpTeYtNHUEOT+fYMzRFNKBx+47NbOmJ8uSh6UVFxF6pPi3FX9rxPHKm3TBta2fYP+98sr6uTZ0q77qsv+KnrkoyBdPhJ78dP+1zq3p/bc9zSRse3/rVsVWZ551N88wVzGonECye6l3HrrC/EqUxb9pLKtdKO0Fd4ZmhBMcSBeKhUmTIB/YM8+pYmvufGmYmb9Ed8VeOT2UNprJG5bt40MfxZIG9w9OoisT+Eyn2H08SC2oN5ZpP9uX0q/7co4kc9+8ZJm3YtAd1skWbf39yiFfH0g3bmMoaleOaIvHMUIJEziIaOHnusURu2ddOIBAIBALBmUW1xrPsvxwLaMQCKqOpAhPpIgAT6SJj6cKCebLP7w6za8cm3n/NBgbiQS5ZH+PGC7sZ7Arx4vEkiVwRv6rMaa86mvZ42ljWHKNZn6r9pVttpzpta960GU2V6uqN+WrmjPXzyfnG531Xr+dtF3XjU2VkWV7W3Go543V+d5h3X9bHGza0o8gSjuvhVxUmMsXTOs872+aZQtMtWFVa3XVc7u5k9fkzeRPX8wjoCpoiV3YnD4ymSRtWw11LoPKdLEn4VQXbcckaNposAx4SUkO55pO9XO9S+lVfr4yE6bj0hnwENJWOEMzkzUq/Gu1Wl48fm8njuh5+TUZXZDpCOjN5k6HJXFMz85XaMRYIBAKBQLC2aBahWlcVQj6F4zM2Q5MWuioR1AMt5ckO6ioB3aZgOZX80EXLZc/rU0zlTHRFRsJbdDTt5fapPGdZTDvzpW196USq6Xyymal4ue3RtMGrY2nesCG+5LnVcserrKWfzhaZzpp0R320BX0oMhQs+7TN8862eeaZJ7HgrKLVXH9LzQnY6PyIX0GWJAqmg+W4TGZKebS39UUru5Zl/5x4qLRrCVS+6wr7MGwHVSkFAbHc0pvGw2so10KyL7Vf9fW6eOiKTLJQ0lxP50wifpVtfVFGksacNsq7tJUxkSUMy8V0XJL50rmDXc39upd7TQQCgUAgEKxdGqVRPTKVJ12wCWgq69t1prMmedOpaLoX8r+t8e8O6jx3JIHjekR9CjnLoT2oc8vl6+iO+mqiaZ9I5QloKidS+Zb9sBvJ0izq9lL8vZulba3WgjeaTzaSs9x2f5uf346kee5IgqhfI5GfP3p5fayhibTBwy+MkCxYtAU0JjJFHtk/wi1X9NMd9S84ZmUt/XTWJOLXODydJztlc9VGnamshet5C1o1rAR506Zg2hXrh8XmEl9Ke6fab1zMjgWrSqu5/paTE7D+/FTB4trz4hydzpHIFSu7kxf0RpvuWgKV7xL5IgPtQTZ1BLEdj8vXxwCJVN5qKNdCsi8n12H1uRs7Qty+YzPfe+EEM7OL5v/2pkEu6I0iy/KcNjrD/jljcmAkTbpw8tz5gqkt95oIWsdxHD7zmc/wjW98g7GxMfr7+7n99tv51Kc+hSRJQCkw5d/+7d/y1a9+lWQyyfXXX8+9997Lli1bVll6gUAgEJyplCNUVzSxqQIZwyLiVwDoifnoCvtx3NY0rLVRu/MULYeArvDiiXTJPzziEtCVGmvBF4+nODiexrQ9dFXiwh5rQW3nfLI0irq9VK1qo7St82nBG1Hf9tWb2vnN0SQjqfy80cur/cZVWWJjR4igT2Xf0RkCmoJfUzGskmXBVM6kL+pfUOs9lioykTGwHY982kaRmc0pbtMV0SvX+lRSfe0sxzul1g/17Z1Kv3ExOxasOq3m+ltuTsD68xtFL2+2a9nou8VEL59P9uX0q9G5t121bk4E8mZt1B9fbPRykafx9PAP//AP3HvvvTzwwANcfPHFPPfcc/zX//pficVi/Pmf/zkAX/ziF/nnf/5nHnjgATZv3synP/1pbr75Zg4cOIDff/oinwoEAoHgzKZe61ejiY0FeWUsg+l4XLUxSs5y6In4UWRKUc0zxQU1rNURwIcmcyQLJj5VYSJjzNGkmrbDb8fSZIs2HSGd6ZzJK+NpTNtpSd5WtdYrYb1XLcd888n6c+o1uo4LO87vmKPxr7E4mM7xtV8OzS6Gfbx8IsVYqsi7L+tlJmcx4RTZ2hflyHQeTZHpCusVn+j5xiEWUDEsl2zRpi2gY7mgSHBBdwhNVVjXFjilFo2Nrl3Yrza0flhOFPr52lvpqPllxAxZsCZoNdffcnMCVp8f1NWGD8FGu5bNvqvXaLfa9mK+W4j6cwfijRfMzdqoGZO42nKqsIXqFawce/bs4ZZbbuFd73oXAJs2beL//X//X5599lmgpOX+8pe/zKc+9SluueUWAL7+9a/T09PDQw89xPvf//5Vk10gEAgEZw6NtH4Rv1qrid0Y59dHE6SLDv0xP2/f1ovjwpFEjol0kSEXirZDwbSbaliDuko8rM+aYRcYyudK/uGaWqNJTRVs/JqMKmu4HrQFNFRFIlWwGWhR3la01su13mumLZ0v3ddCGt1NnaG55WYz5qQKJkNTWQY7w2iKTEBXMG0Xw3bpjOgkcibpgolPk4mHdHRVoTeqLTgOuqpwUW+UgxNpDMsh5lexHZeXRtJ0hHzctLXnlM75mlkc1Fs/rJSv9+n0GxczZYFAIFjj7Nixg3/7t3/j1Vdf5YILLuCFF17gySef5J/+6Z8AGB4eZmxsjLe97W2Vc2KxGNdeey1PP/10w0V3sVikWCxW/k6n0wBYloVlWQvKVC7TStmzAZ/infwsezX/rwSLHcdqeU5F/QvVc65c93rOpf6fC30U1NJM6/e+q9fXaIFt12PHeZ0VLTbAkakcxxMFkgWTtqDO8FQWTZXpbKJhzZs2iaxJ2rBq/cMtu0bT3Rvz0R3xz2rQVZIFm56oj96YryJvfc7wenlb9QVu1XpvJTTrrWh068vFgzo/OzgOwHWb4yiSzKGJLFv7ohRMB02RifhUVFmmK+Ljqo3tPHd4BkWWmsYdqu9LxK9y6foY8YiGJkn85lgKVZa5bjBOznQ4OJbmusH4KVt4ly0OjiVzlZzlPRH/omIlLaW90xGfSCy6BQtSb9KyHFPi+oAPi62r2YNyPhlfHUtzYDTNtr4oF/RGFyXLsUSuxty6up1q8/RWTM2X2/eVpL5fZZYbSKKV6yO04ovn4x//OOl0mq1bt6IoCo7j8IUvfIEPfOADAIyNjQHQ09NTc15PT0/lu3ruuece7r777jnHH330UYLBYMuyPfbYYy2XPZP54hvnHvvc1Svn2PaDH/xgUeUbybOS9S/EuXLdm3Eu9D+fz6+2CHO45557+M53vsMrr7xCIBBgx44d/MM//AMXXnhhpYxhGPzFX/wF3/rWtygWi9x8883867/+a83z8ejRo3zkIx/h5z//OeFwmF27dnHPPfegqiv/flrt999i2m+k9RtNFRhLFXnzBV08+dpknSY2XOPrPZktUijaJHImhuUSD/nwKQrhOg1rrX+4TdivABI9UR9dUV+Nprsz7Ofmi3v56pNDDE0VCfpUds7GpRlPGw1zhjtu83g5C/nvVlvvNRq7+TTr8aCO5bjEgzqpgtlUW5o3bYYmc0znivTHgg01uuW2C+bJa2I5biVjTsiv8aYtnTx1aIqprMGGjgAbOkJYrscVA22AhyxJvGFDG83iDjUbi4rPfcrAr8m8YUOcde1BCqZTcx1Pxb0d1FW29kXZ+9Q0aSNJ1K9xw5buRcVKWmx7pys+kZj9CualkUmLpkjLSkFQHfChK+Jrua5mD4d68xw8D02VSztXKYPvv3AC03HRFZnbd2zmzrdfUCOLIktsigfpivpr6v3m3iN89ZdD5M3Sw+XWK9ahqzJpw2IybXA4kcdxvZq+1LdfrqumPUliU+fi+r6S1PfrQ28e5APXbVx2IIlWrs9q9Pds4D/+4z/45je/yYMPPsjFF1/Mvn37uPPOO+nv72fXrl1LqvMTn/gEd911V+XvdDrNwMAAO3fuJBqNznNmCcuyeOyxx3j729+OpmlLkuFM4pLP/Ljy2Sd7fO5ql08/J1N0pRWp/6XP3LxkeU5F/c041657PedS/8vWL2uJxx9/nN27d3PNNddg2zaf/OQn2blzJwcOHCAUKm0gf+xjH+ORRx7h29/+NrFYjDvuuIP3vve9PPXUU0ApMOW73vUuent72bNnD6Ojo/zxH/8xmqbxd3/3dysq72q//xbbfr3W77djaSbSBt/9zXE6Qj5uuKCTrlmtY72GtzPiI1e0sRyP8zqDvD6VI1O0KDoO07mTEbgb+YdbjsdVm6Lkiw49Ud8cTWyqYHH9+R0ENJWCVVqE5s2SRnw0VWAmb1WihJd9whtprRejkW40dv1t/qaWAJbt8rOD42iyjOW6XL4+1lBbWq43kTN5bTxLumCztTdao2GtbtuvKliOV4n2Xp0xJ+LXuOWKfm68sJtNncGmCqDqz61Eay+P3UTa4KHfjJAznUpe80YyruS9nTdtXhlNM9gVpi2gkSxYDbXrKxlT6HTFJxKLbkFTmpm03HRRN4nswsEYqimnIJjJWzUBH/7gmvUtBXZo9nBoC64/KWNY46cHJpAkuOnCHg5Npnhk/xgeElG/SrZo88DTw2w/L14li87LJ9KMpQ3+4A0bKrK4bjdf/eVQJXDHZLbIA3uGSw/dWJC9wwls12Nrb4RXRjOMpYq858p+njs8U2k/kTd57MAYPrXvZHthHy+PphjLGPzB1QMt9X0lOZbI1fRrOmfy708Occ2mdn7y24klB5Jo5fqc6gAVZzP/43/8Dz7+8Y9XzMQvvfRSjhw5wj333MOuXbvo7e0FYHx8nL6+vsp54+PjXHHFFQ3r9Pl8+Hxz05domraoxcRiy5+pFJ25i+uiKzU8vhQWO4aLbXelr9G5ct2bcS70fy3270c/+lHN3/fffz/d3d08//zz3HDDDaRSKb72ta/x4IMPctNNNwFw3333cdFFF7F3716uu+46Hn30UQ4cOMBPfvITenp6uOKKK/jc5z7HX/3VX/GZz3wGXddr2mjmirMQpzNA00q1X631G00VmEgb9ET99MeCjKUNfvnaFLt2nDy/WjNuuS5dYR+JnImLxObOcMmnO2tW/L6BORresn94xrAr5arlK7cx2BEhoCsVbetE2iBbdOiK6KizKU+7wnqNprw+5kwz/92JtEFg1rR6vsX5Oy/pLWm0wxqW6xIPa6TyFlnDAUnC8Txcx6Hk/DP3GV0xh58NNhcP6UxkDKIBlc6wrzJG9W1LeIT9KinDbJgxp15Tv9Dn+cairMUO6iqbOsO8+/J+Htk/wuHp7LwyLvfePqnZd0gbFgPtpfsj4m/uh95qTKFWNPKnIz6RmPUKmlLzMK0yaZGQFh1oYCxVJG2UdiIlSaoEfDBtr6W6mj0cyvWWH/i6IgESkgS2K2G7HtGAik8tpbbImTbPHk5UZJElCb8mYzsepuNU6j0wmiZvlhamAU2lLeAxli4gSxKm44AHflVG4mRfMkW7pv1yXUOTudr2VAXbcTFt95QGbGjE0GSupl8dIZjJmxwYTS8rkEQr1+dUB6g4m8nn88hybWJMRVFwZ3e8N2/eTG9vLz/96U8ri+x0Os0zzzzDRz7ykdMtrkAgEJwWUqkUAPF4HIDnn38ey7Jq4lts3bqVDRs28PTTT3Pdddfx9NNPc+mll9aYm99888185CMf4eWXX+bKK6+saaOZK85CnM4ATSvZflnrNzSZ47u/OT7H/Ln6/Jqc22ENVZHpjPi4akM7ecuhPahV/L5Hkgb3P3V4joa33j+8XrZGPree5/HQb0bIFm0mMyYdIZ2NHSGSBWuOprzVugzbWTAQG0hYjsdPD0ygKxKmc9KUO2vYhHWVou3iU2Uyhj1nrDOGXRNsTpGhK+zjPVeuZ7ArRFBXGU8bDdu+5fJ1s4vQ5bt6NhuLeX2ZpZObCCt9b1drzQOagmW7K+ZjvdrWJtWIWa+gKTUP0zqTlsX+CHpjvtkIlQZdEV8l4IOuSi3V1ezhUK63/MA3HQ9J8vA8UOWS6Xeu6KBIEtliKQLmGzfF+dXwzKwsOobloqoSuqJU6t3WFyWoq0znTDpClNJZKDKu56ErCkhg2C4eXk3wiur2y3UNdoVO9j3sw7AdVEVCV+VTGrChEeWHerlf07lSTu5tfVFGksaSH3KtXJ9THaDibOb3fu/3+MIXvsCGDRu4+OKL+c1vfsM//dM/8Sd/8icASJLEnXfeyec//3m2bNlSSRnW39/PrbfeurrCC9YEmz7+SMtlD//9u06hJALByuC6LnfeeSfXX389l1xyCVCKb6HrOm1tbTVlq+NbjI2NNYx/Uf6unmauOAtxOgM0rXT7QV1lsCtER8g37/llzfgjL44wkjS4uD+Kpki4ePREfRW/72rNcV8sQLJgzdHwbuosLYTqtZL1Prdhv0I6b5EznUpdkxmDeFivtNlqhPJyXcmCVZPmbOe2HgKaUtP3sE+hYNlYtjO7/pSQJA/LdZnJmRyfyZPImkSDKpPpIpJETUA4KP09mjTmmMOXY/1MZQ2OJfKosjRn3KuDq5X7shwq165Oi11tfj6RLo3HTN6iI3gyIN5CgerqryM03yRoZFUgSRJhn7JsH+vVtjapR8x8BU2pfjilCo1NWlq9aTvDfm6/fjMP7BkmkStWAj7kTaelupoFOugM+0/KmLcqO44pw+T87ih/cLXO/91/gpxZWnDv2r6Zawc7yZnurCwmA/EgmzoCFGy7xlTnQ28e5N+fHGImbxILaNxy+Tp8mkyqYLF9MM7h6QKpgtUweEXKMCt1DcRDJ/ueLzLQHmRTRykgxakM2NCIgXiopl8Rv8p/e9MgF/RGkWV5yYEkWrk+pzpAxdnMv/zLv/DpT3+aP/uzP2NiYoL+/n7++3//7/zN3/xNpcxf/uVfksvl+PCHP0wymeRNb3oTP/rRj0SOboFAcFaye/duXnrpJZ588slT2k4zV5yFOJ0Bmk5F+4s533MBz6M95ONt27pr/L5hrmb0ot4obQGNW69cV1EGQHOtZLXPbcF0+NavjtIVnr+uZtTXde/jhxqmOdNlCUmSmMwaeJ5L2vB48NljvDqe4fJ1bXRGfExmDV46keYbxaOcmMkzk7c4lgRNgc6oXhMQDsBxoS8WQFUkDKtkGt8VLuXlro/9s7kzhCJzeu4bqdYUvjrQ3QvHUgR0GZ+qoshQsOx5A9VVn99KPKhmWvNbruivMflfCqttbVKPmPkK5qU+uAAs3aTlpq3dXLY+uuQI3s0CHSwk43+5bsOc6OWtyPKB6zZywwWdKxK9fLl9X0ka9Wu+8W2VVq+PWHAvnkgkwpe//GW+/OUvNy0jSRKf/exn+exnP3v6BBMIBIJV4I477uDhhx/miSeeYP369ZXjvb29mKZJMpms0XaPj49XYl/09vby7LPP1tQ3Pj5e+W4laTQ/GU8bp+1duNLvdaiVv6xJTBYsOkI+kgWLn/52gluu6G+Y4ml4OktAVSjYDuvaAjWL5EZayUdeHKlJoVVus17L2hHWW1pwlynXNZU1KprnrrCPg9M5VEWiM6yTNRzCPoWd23p49MA42aJDf5uf346keeFEkjdt6eSFYykkCTrDOtN5C9tx6Yn6SRs2x2cKmLZT027Er7KxI0jAJ9ekwzJtpxL7p6wBH0sZfPBNm9nYGVpSpPCFypfHO1t02BQPNYzF0xnyM52bwM54XNwfZTJjzhuorv46thIPqplFRiNXg8Wy2tYm9YjZr2BB6oMLLOdH0Bn20xk+qXlbbF3NAh3MJ+MFvbWpwhYjy0C8NqVWdTtBXW16fqO6ltv3laS+X2WWG0ii1esjEAgEAsFi8TyPj370o3z3u9/lF7/4BZs3b675/qqrrkLTNH76059y2223AXDw4EGOHj3K9u3bAdi+fTtf+MIXmJiYoLu7GyilgItGo2zbtm3FZS6//1bLt3Sl3uvNUmVV+ygblk3BcpjKmfRF/ZU+BnWVWEDjP547VpM5Zb4gZ4oMew5NM5Up0hcL1NS1UhYE1ZrnXNHGp8jEw3pNmjPHg4LlVOS6elM7vzma5GgijyLDGzbEkSUIaDKmVAqfFg9pqLJMqmBT7YhQa0Fq0RMpBY5LFexK7J+grtIX8zORKbW9lHunlfItxUpyXLoifhLZIjmzVjNffW80q7eVeFCn0iJkta1N6hGzYIFAIBAIBALBmmf37t08+OCDfO973yMSiVR8sGOxGIFAgFgsxgc/+EHuuusu4vE40WiUj370o2zfvp3rrrsOgJ07d7Jt2zb+6I/+iC9+8YuMjY3xqU99it27dy/JjLwVVsq3dLVyfleibmcN2gI6E9mSFvqGLZ0cTxRIGzZdER9Hxkr+yAFVZiJ7so950+bHL48R0lU2xINMZ00ePTDGzZf0VJQR9UHZnjs8g+26yJLEiWShZrxWyoKuWvMc0lSedlwUWaqJy1OOTXNsJk9bQMOwXHac38HOi3r48cvj5EyHsK6izTpw97cFSBZMOsM+YgF1jmXD+d1h2oLrK5aPAK+M5vGrCqMpo6LpjvlVFKmU/We+e6f+nmj1XiuP97FkrkbrXhMrKaijKRKdER9Xb2gjZzn0zLoOzDem9fGgHM8jU7DIWXbD80+lReRasrYUi+4znGaBCqo/199gU7M7Wa2aOS+2/FJkrzbV7gz7m7ZZX64RxxK5GtPpVupqZh4+nS0uuq5quar7eDyRn2Pm3qhcs3FtVqb++GKv12q8xFeyvwKBQCA4N7j33nsBuPHGG2uO33fffdx+++0AfOlLX0KWZW677TaKxSI333wz//qv/1opqygKDz/8MB/5yEfYvn07oVCIXbt2nVLXnJXwLV3NKMwZw+bIdJ6JjMGQm6NoORQsl6OJPGnDJqjL5IoWsgyuBy+cSBHUFApFl4xhM5kpaU/XtQcI6ip+TWEiU5qrlOdM1VrJkWQpgne+aPPT306gqxIX9kbmaEiXOweo1zy/YUM79XF5OsN+tvZF2Ts8TLpgEQ1o3H7BZi7qj6GpSuXccqyfnGnTGfaxc1sv//eF0TnXq/o6TmaKHJnOYbseluMiARMZA58q0xPz870XRpCQSORMLuiJzLl3RpJGQ+uDVu61oK6W+vXUNGkjSdSvccOW7tpYSVXxnByPima+1RhMqYLJxniQw9N5nnp9qtLG6baIXCvWlqsvgWDJNAtUYDkeeB6aKs95MNcHatjUGaIr4mv6AF9s+aXIPpkpcngqh+N5RP0ab9jQzq+Pzsxps77c7ddv5qat3TX1fnPvEb76y6GK+dINW7o4PF1K2aXIEpviQbqi/pq6qtuoHsfXJ7McGEljOe6culRZYmPHSbnKD81quar7uP/YDC+OZHBcF12RuX3HZu58+wVzxqLZuDYrU388FtD48ezfrVyv1XiJr2R/VzP1g0AgEAhOL57nLVjG7/fzla98ha985StNy2zcuJEf/OAHKynavCzXt/R0R2Gu39xWZBhNFU76PicyqIrE78Q6OTKVp2i7XNgT5vB0HvAI6UqN/291BpuyJresRa6mrAV+4WiSn78yTt50CPtUknmLV8Yyc3ykF6JeCdOIes1zvbIib9q8MppmQzxY8Ud/8USSwc4Q/W1+3nd1rdZ6LFUkFlD5vy+MzrleZX/piUwRXZF4+vVpHNfj4nUxJjNFAprMH1y9ntcmsrieRFfYz7GZPKPpAgGfzEBbqHLvKPLcXNmP7B9h58U9+FVlwXut3K/BrjBtAY1kweLgWJrrBuPLjudUPn8ibfDQb0YI+7U5bayFRfDp5tzr8VlCs0AFb97SyROvTiFJcNOFPSTyZo15TzlQQ1fYx8ujKcYyBn9w9UAlDUD1A3wqayyq/FJkD/sV9g5NY7seF/fFGEkVeGDPML0xP32xYKXN37usr6bcZLbIA3uGuWx9tLJLeiyR46u/HCJbLOWhnkwX+Y/njrIhHmR9PMjLJ9KMpQ3eXVXXhT0RDo5nGMsYvOfKdTx/pJTz86oNbTwzlMByPNa1B0hkS3VtjIdYFw/y8okUY6ki776sd7acW3loPrBnmC3doUofZeC5o0kc16MtqJEvOjzw9DC/e2kv6+PBBV+kzV621cEueqN+hqez/MdzxwjpKj1R/4LXazVSKbTSZqv9Xe3UDwLB2cp86cV8iscX3wiXfObHFJ1SxFuRYkwgmJ/l+paezijMzXy3+9r8pTSs5qzvc0gn5NM4rzvEntem+Y2ZQvKgO+rDdqArrNMV9eG40BM9mcFmIlPq/64dm+dYLJbbHpoq5fEu2g4500aWJIK2OsdHej7qlTAfevMgH7huY0v9rY+uXdbyO65H0XbYfyzJVKaIrshzInNfsi7WNNf2WKpY8YFPFazSQjZQMk2P+lSGprP87OAk01mTKze0EdAVBtqDFCybkK7W3DuOS00bqiyx5/WpUuR1RUbCm/deK99TA+3B2fzfWs09tdx4TkFdJaCrGLbTtI1zjXOvx2cJzQIVmLaHrkiU8gdS82Aum/f0xfzIkoRfVbAdF9N2Gz7Ay8EUWi2/FNkT+SJ44FcVdFWmLaByNOESDWjoqlxpM5GzasqVg0xUmyYNTebIm6UFd0BTCfsd0kWLsE9FVxT8mozteMzkzEpd1f3KGnZlHBN5E9f18GsyuiIT9pXMdYI+BU2RCegKpu2SyFu4nkdALx0vyzU0mav08eBYCs8FTS7lApd9pZfWgdE0saC+4Iu0lWAXAV0hoCrkTZsN8WDN2DW7XquRSqGVNlvt72qnfhAIBAKBoFWW41t6uqIwN4wgPqs97YsGCGgqQU1h76zvc9F2GJrM0RHRubg/yssjaRRJ5tJ1UXKmQ0/UV5GxPotL/YK7uu3OkI5hOTiuRzSgkjddcqY1O79dmHolzHTO5N+eeJ1tfREu7IvOu8lfHTEdIJEt5eAu+a3rHBzLo6oSMb/OM4enS32ri8zd6HqFfQqW7VR84OMhDc/zSjnHDZtDU1lURWKwK8RkpsjzR2aIBTUSWYtNHSHed/V6HJcaDXy17/RzRxIA9Lf5SWQtwn61ph/1vuWn4p6qd3ktmHZLWvdzhXOz12cBjQIVAOiqhOl4SJJXEwgi4i+ZB5XNe7rCPgzbQVUkdFVu+EOoNgdqpfxSZA/7FZDAsB1M2yVZKO2gpgsWIV2rtBkPaTXlJrPFOaZJ5XQR0zmTjhBkDRtVlsgWbdocB8NyUVWJ9pBeqcv1vEobYb9aGcd4UEeWJQzLxXRcssVSXfmig+W4FEwHTZGJBzVkSaJglo5PZkpyDXaFeHmkpFlvD/qQZLBcD9NxyBcd/JrMtr5oSw+9ZmWqg130Rv0UbKfU/6yJGpUXvF6rkUphJft7rj+8BctjPm2uQCAQnAqW6lt6uqIw129612tPVRlcPN6woQ2QmMoUKVoOAV3hWKKAT5EpWC5pw6K/LTBHxvosLs3ansmbhPwqhaKD40JYV4kGVUxnYfcCaKCE0Uvzxq8+Ocwl/bGKNnu+iOm6qoDnYTru7Oa+TK7o4FNl4iEfHl7TyNw9UX/N9Srn+f7OvpGKD7znwebuEJPpIhOZArois+P8DvpjQa7eCL8+mmAkadAf81d8y6up9YHPI0sSV25oI+bX0WWFyaxBQFca+n2vdBR4qHN5rXJztRxvQa37ucK52euzgPpABeVAB3nT4YqBNuoDQZQf9GXznkS+yEB7kE0dQQqm0/CH0Bn2L6r80mS32D7YweHpPIl8kZ6oj3dc3Mtvjs3UtAlSTblGpkkD8RAfevMg//7kEDN5k1hI493n93MkkSORMxmIB9nUEUCqqqtsWrOpI4jteJVxRJK49rw4B0bSpAsmsaDGuy8r11VkQ0eADR2hSrmj06XjZbkG4qGaPl6zsY39JzIUzNKCe9f2zZVgags99Jo9GKuDXUxmDda1BfjQmwd59MBYS9drNVIptNJmq/091x/eAoFAIDh3WOkozI2CktYrdBbSnh6ZyjM0mSOZt/CFFbJFh2hA5eaLe9jaF22ozW4mf3U0bV2WCagKqiSxsSNIznDojvlqFC3z1VWthGkLwHi2iCpJdEd9TGSKDTXS5YjpkgSdER+/fHUKz4OrNrahzsZLunpjG6btlhQaijJvZO6Tfs1FvrfvBDMFk6hPRVMkirbLRb1RbC9IW0DjusEO9g5NU7Q9CqaD7XrsOK+TW67or+SrbtTfsj/64ak8vzg4UTm/3u97IlOkLaDV9H0lo8DXuLyGNX56YKLGzbX6vjmX52znbs/PAuYLdFD9ufoGrzfvWSi69WLLL1X2lYpe/oHrNnLDBZ1rInp5bR+3NI1e3spDr1mZRsdvvqSn5eu1GqkUVrq/AoFAIBCcC6xUFOZmfsytak/LMsTDOtGAymiqwNCkhQRYjsOPD0yw/3i6xj96Id/p2mjaFu0hDZCwXY/umK9G0bJQXfVKGAXoi/kYmijl1i5Y9hyN9EjSqOTd9qkKtuuQyFm8cDyFKksYlkPBdrlqYynCecG2F4zMXfJrLkUZL/uEl60if+Uk6Aj5uOn6zbxpSxe9scAcpcKmzvnHrj6gsiRJc/y+q/OoV/d9JaPA17i8um5DN9fq++Zc5dzu/VnAfIEOmt3c9eY9C/0IFlu+VaplD+pqTRvN2qwv14iBeG2UylbrajR2wbi6pLoa9fGC3rmpwhqVa0azMvXHF3u9VuolvhhWsr8CgUAgEKxVlpvmcqXTZDbKud1I+1nW0GaLTo32tNqlS5EhV3QIaCp9UY3XJjO4lkdnWGcmb/HI/hFuuaKfsF9tKYhqfTRtWfLY2hvhioF24mGdl06kiAXmr6s8Xu95wzpuuKCTfUeTfP3pIxi2i1+TGU0ZlajqUNrM96l9/HY0Q1CTsV0P03aYylhYjkvIpzKZKRILaPzhGwfY2BEib9oV7bJPlwloKgXLnhOZO2/aJLLFkz7h4ZKm3fM8tvRE8Cly04jh8/mdNwsuG/YpNdrxqazBaNJgJm9VosZX932lqLcYaObmeq4jRkAgEAgEAoFAIFhhlpvm8lSkyazPua3IUiWfdrUiYVOnyrsu65/XpctxoS8WQFUkckUHv6oQD+v4FAVDdiv+4BGfynSuyAXd0QWDqJYjXY+lDZ56bZJnhmd44Okj4IGmyvhUhXhQ5+pN8ZbyVr9xsIMnD00zmTUwLIeuiE5X2I9TCuFTkxpXlSU2doYwbJV4SCegy9iOWzknHvJV2hhNFXjhWJKAruBTFRRZwrap9Kl87UbTBhnDIqir5Ewb1/Eo2A7PDCUIaDJpw2oaMbx6XFoNLhuoqqP6+jTq+0pR4zaat5q6uZ7riBGoY7E7iiu9A9kKzdpsVZZWylWbUQNNTboXawZe3XZ1mfo2qstVm3gHdKVhvfPlYmylzWZ9rO5fZ9hf0w7QsM3qMh1h36LM/+ej1bGrl3mhuuaTpZW+rNXfiUAgEAgEq8Vy03KeqrSe1Tm3m2k/y+/s/jY/u3Y0d+mK+FU2dgQJ+GRCmsrTjouExHS2yPNHZ0r1SpDIFhlNGQQ0lYH24IJBVMO6yi9fm8R2PdqDGvuOJkGCN2xoZzprMp4xiEc0Bjsi8+atfuzAGO+7en1FxraATrJgVnyvq1PjlsdiPGXwF2+/gI6gTtIwa86pbqMz5Gc6Z2JnPC7uj9bkJa+2Joj6VFRFxrAczusM8fJICsf18KkSyYLNgZE0IzOFpvOj5QSXLV8fVaWijV/fFjwlWufl5vY+FxCjUMVidxRPxQ7kUmVsVZZWylXv+ll2aTtMU2Wifo3br9/MTVu755RTZYmNHSG6Ij4m0waHE/lSqoeqc6rbri5jOW5lBzPq17j54l5SBYu0YfH6ZJYDI+lKmWhAJezXauqdLxdjTZuZIoencjieV+qXBJoiz2m/XG91/6J+jU0dIZ54bZK8aWM7HuChKnJNm9WyqIrMxX0RzuuOzLZXyuVY/bnV+6bp2NVdnzdsaOfXR2cqMldfr0Z1zSdLfV8u6Y8y2BWuiUq5Vn8nAoFAIBCsJstNy3mq0no6LpWc24bl1uTThsW9s+sD427uCnFkOsee16cYyxTRJPhJthT5fF17gKCutBRE9dBEBteD8zpDqLKMLEuAhyxJrGsPcDSRQ5PlefNWl8fLcamRsSfir7RdTu/aFyuZY5fTvmqqwrsv759zTnUbluPSFfaTyBXJFZ2acazP7W3bLhOZIhnDwnFcVFkmmbdno5q7fOOZw5zXFWk41ssJLlvxkx+eJl2wiAY03rr11Gmd53N5FYhFd4XF7iieqh3IpcjYyK+jkSytyFy96xcP6jx/tBS58g0b25nJWTywZ5jL1pf8ksvluiI+Xj6RYixV5N2X97J3OIHtntz5e2DPMFu6Q5W2w7paKbOlOzxnB/PffznE9Vs6aQ9qPDOUwHI8eqN+jidzJAsW12wOMJMvydIR0ubkYvz3J4e44YJOOsK+k236FfYOTZ9sczwJwKXrYxwaz1baL9e7vs1fs/t5ZDrHs8PTBHSVWEDj6HQegI0dITJFi39/cojzu0MVWdqCGidmDPYOzzDYGWH/iRQAb9rSyfNHZoC5eR2b3TfV16167M7vDvPCRLJyfSbSBg/sGaY3GmCgI8hoyqhcr7LGuybCZFDnZwfHG8oynS1W+tIe1DiRNNg7nGCwM8QLx5I1USnX2u9EIBAIBILVZrlpOaujeddraBdioSjhG+MhAppa8Z0u59Neyju72if6+SMJIn4VGfj2c8cpeLAuoDGTt/BmPD79rouIBfUFg6gemcrPmk/bxFUZ1/VAAtfzGE0Z9ET9/OEbN2A6Xo2FZLPx6on68al9c6wTq1PjljXdZS1yZ9g/R3M7kS4S0JRKdHdNlYgGNTbGA0gybOoIVcZxNFVgOlekLaAzljHQZJk3bm7n4f0mtusS96tMZIuoSPRFgzU+8GWf7PpxaRRctlG/qu+Dkp98qDIm9X7nzWjFalKwOMSsd5bF7iieqh3IpcjYzK+jXpZWZC7X1RfzY9ousnRyd7G8AziWKgJUykmSREBXMG2XRM4CD/yqjK4olXPKu4m90dKuYLmM43k1O5gdYZ3JMYOAqpAxHFzXw6/JSJKHjIRH6aFbrnffsWRNLsaOEMzkTYYmc+iqcrLNfLlNBccFWQaQSn2U5/bxwGi6ZvczqCvYrkfEpyKXMorheYDk0RHSmcmbPHs4UZFFlWV8qoTteMzki5VcjqbtNs3r2Oy+qb5u1WPnel7N9WkLaBxN5IkG1Zod27FUsfLArIkw6TSXpTrHpSrL+FUZ03ZI5K2GUSnX0u9EIBAIBILVppmGstV3X20072TD6NiNaCVKeI3mN+qryDWeNhb9zi5bBU7niqTyNm/a0kln2Dc7L/SwHY/2gI6igOl49ETnX8AFdZWL+qN88M2DPLBnmFTBYmNnCAmPtGERD2ns3NbLT347MaePzcar3nKxbAVYnRp3ImPMSUdb1tw2ihKeMkzagxojIwV+8eokQV3lsnXtFVfLkK5yfCZPIpujaLvEoz46wn7Wt/sZTRoUbBddkelrCxD2qzgFq+ID3zcbVb3+utVfg2b9KlPxk28ruWdGfFpLc7CF6hUsDTHrnWWxO5LL3cFcSRlb8etoVebqXb94UMf1PODk7mK5PaBSrivio2A6aIpMPKSBBIbtYjoOk5lSEIXBrhAvj6QrvjrlMook1exgTmdNQrpKwXZoD2rIs+ZPnifhzi4OZUmqyHLFQBtBvZSLsSME0zmTiF9lsCtU6x/kV2bbdFBkcF0AD73BDmo8pLGtL1qz+5k3HVRZIlO0iQU0ZocFPKnS5hs3xfk/z58o5YUMahRtD1WRaA/6GJ7VjOuqjFVqHA+vpfum3s+pPHayJNVcn2TBwqfIpPM2sYA953rV1xUP6k1lqc5x2R7UMGwXVZGJBzWGJluPSrkavxOBQCAQCNYC5TzKS9EYNormvZCWciFN9UK+2ot9Z5etI6ezJm1Bncm0yZOvTfHuy3rRFQU8l/62AMm8RWdEb5pnu1EcoPqUtVBSDMUCKv/3hdGGVp+NxmtLd6iyKdAR8jGdK9ZYAda30yi3eP2Y+lSJKwZi7DuaJOrTiAZV0nmbRw+McfMlPaWo7qaNT1GIt+scmc6TLdrIEkT8Gr4uhUv6orw0kkZXZYqWOycner3Wu956oZE/er1141LmYK3UK1gaYuY7y2J3JJe7g7mSMrbi19GqzNW7fqmCycaO2d3FgjVnB7BcLpErsqEjwIaOECCxfTDO4ekCiZxZOWcgHqrZWS2XyRbtmh3MzojOzm0bSBsWqYLFtefFOTCSJmdatAd9xPwKuaJdqffygfaaXIwRv8p/e9NgxcSmts0ODs8++DZ2BpGAguXM2UHdtWMzF/RGa3Y/e2N+rt3cwS8PTZKbNR+X8MiZVqXNawc7K7KUx+vi/giSDJevjwESBdOpfE7lrZbum/pd6fLY5Yp2zfXpifp5x8V9/ObYTMMd27l1mU1lCcbVSl9SBYv2oMbF/TGQpEVFpVyN34lAIBAIBGuB5cQ0qY/mHfEvrKWcz7qsUWTvVv2Hm7U3lioykTGwHY+JTCklV8FymMqZXHtenKPTOXKmTWdEb5pnuzreTr1WtT4FamfY31QbX7bUrB+vockcE5kituMymjLwPI/xdLHGCrC+nfnGVJUlfnU4wfBUjtcnMwQ0FdsrKSLG0yU5uiI+Qj6F445DJmXjU2WCPoVkwa7Mu1RF4ppNccBjKmfU5ERP5+0arffWviivzFpglq+dYTkN/dGr+7WUOVi1xWuzegVLQ8x8q2jmM7FS5U+ljK3K0kq5ZruL9TuA9eUWil5e3/ZKRS//wHUbueGCzoY+La222aiPjXY/F4peXi/LSkUvX8zYLeSH02qEyVb7shZ/JwKBQCAQrCaNNKSPvDjCLZevozvqW/Bd2EhLGfYpFEy7Eji2lXPmi+zdyFd7Me/sWEDFsNxKXJ285RDyKXzgjRu4sC/acC5YE6emKt7OxX0xJrPFBbWqFV/3mXxFo90eLPXRrypz+t4X82NYzsnYP3mLkK5g2W5lHOebN9VbCJY10gPxAE+85pE1TdbFAiQLZqVe03bIFR10VaE7opIs2LQHNG6+qJut/aW4SNVzuHLO76LtkcpbNVrv8VSRvcPDpblmW6hy7d59WV9Tf/T667kYa4v5/NzXGq1m4lkrrE2pVpFGPhMrWX4laNZmq7K0Uq7R7mIr5cr1BnW14TnVbdeXqa+nUi6u1iykG9U7EJ8bQGKxbTaqt75/9e00arO+TLNIjou9bxbTj4Ueqq1GmGy1L4uRXSAQCASCs52yhjQe1kquXJ7LnkNJpjJF+mKBBbXe9VrKUqRrj2/96lhDTXV5AfLmC7r42W/HOTydpTPs401buhhLFZnOFemPBVvy1W71na2rChf1Rjk4kSZXdIgGVC7sjtLfHqzUUZ82dmgyx3SuSGfEx3iqiOt5+FUFXZXnaFWbLapKEbmHSRdOZs/53r4RLMfDdtxK39++rZeIX+WivgivjGVIGyWts6ZIfOOZwwy0l7Lu/LjKAqDef7l8HR7ZP8KhyTSeB5cPxFCkkrxjqQJpw8I/m0P8O785Ttin4QGm7XI8aSB5Hq7n8eMD4/z84OTJbDaz8rqATCmjzkgqX6P1Lpou6YJFSFOwXJd4WCOVt9BVZV5/9DKLtbZo5Of+/1yzAcel6WbPatBqJp61xNoYOYFAIBAIBAKB4Cwh4lexHI+fHphAkeHEjEFbUKM/Flww80eZstZ5Il3ke/tOkC06dIXnaqrrFyC2W1qEJHJFHvr1cSRJ4rXxLOmCzdbe6IrFV4n4VS5dHyMe0RbMA12WMZEz2XcsiWG5BDSZXNHBVEqBZiezxYpWtaZPValK/apCpmgz2BUq5QYfmuZoIs/5XRFen8oyliywviNYI2N/W5ATMwYh3WU8VcB2FI4nDA5N5JjKFmkLaqxra5z1pRpdVXBcj2eGEoR0leTs4rczrJPImfhUmf5YkOPJPMcSeQJaKaDwaxMZXNMm5td55vA0AG/e0smjB8aZyZmsbwvgeHBxf5Q/fOMGHj0wTrboUDAdkrMbC08PJUpBbR2PKwbaiPjVJfmjt3LfVddrWDbPDs+w/3hyzSxoW83Es1Y2CMrIq9n4E088we/93u/R39+PJEk89NBDNd/ffvvtSJJU8+8d73jH6gjbhLxpM542yJv2ss9v9nkl2llKm63w6liah35znFfH0jXnH0vkePzgBMcSuZoyU1mDl06kmMoa87ZXXa7ZOdVtNPs8X39bobptoKbu6u+qjy93TJudUy/LQsfn63urcq3kfScQCAQCwTmF5yFJlHI1uy4hn0LAJ9Mb9ZMqWGSMhd+tQV0loCsULKfGj7l8fvUCJObX2X8ixYHRDF1hnZdHMuw/kaIj5KM76mc0WeC1iTRhnzKvb2+zd3/98bIWeH1bEFWWWN8WbFhvWcaJTBFdkykUHZIFE0WS6Yn48Kkyo6kCMb/Krh2bCerqyT4FNV44lmT/iRQxv85EpshLJ1LEgzqSJKErEposY3kO09kiScMi7FOZKZg8sn+EI9M5LNvBp8loqkLB9vA8D0n2MGyHZN4i4teQJImuiI9Uwapk6amWPVt02NAewnJcknkLx3ORZq+x43mz2XYULMcl5tfwazLxkI7tljT5XSE/Hh6aLKPJErmiTTpv4XoQ9muossTLI2lyps1bL+om7FM4PJ0l7FfYGA+iyCczx4BXkS+oq3RFGrsrNPLxb/W+6wz7GewK8ezwTCk9cNjPTL60oF3tOWF1vySJypiWM/G02sfTzapuAeRyOS6//HL+5E/+hPe+970Ny7zjHe/gvvvuq/zt860dn4LlBMioP796F6/eTKJRAIWl7jI1M8dYimnGlx97lfv3DGM6Loosc+m6KJetb+P1ySwHRtJYjkvRcjBtD0kGCYmOkEY87KuYA3VFfHPaq05VYDkueKCpMooksamzdE51G7bjUg5KYTseHqApEkFd5dYr1qGr8pLMT+pTJmzqCPHEa5PkTRsPiPlVwn6NrGGRMmwkQFVkLumPMtgVrrmmrY5ps3uqWfqG+dI6NL3WLcq13PtbIBAIBIJzlYxho6kyN13Yg+W6PHc4QbJgkcxb5IrOknJ2N4pC3SwVqGl7NSk+O8M6h6eyWK43b1vN3v3NjrfiA54xbI4kckyki+TNWf/qoI+rN7VTtEuRu9e1BdjcGWJDPFjbJ9et6cdAe5AXTyT56W8n8GtyxYKgaLmMJAtYjstvZ+eHBcvl2ExJ63z5ujZkBY5O50gWLA6MZHBdD8d1eW0iS8SnUjAdNnQEavyX68c36tcI+xS29kVJG6UFeiJrYlgOr01k8Dxw8Qj7VDZ1BYkFdJ4+5KIqErqinMwa44HpuGiKjCJLFCyHRLbI//n1CYKaUrpOkkTRclFn76NyutqUYbYUHG+5GWTWatrXVjPxrDVWVdP9zne+k89//vO85z3vaVrG5/PR29tb+dfe3j5vncVikXQ6XfPvVFC9s7iU3Z+ancmqXbyAqrL/RIr9x5PEghoTs6H7JzLFZe8yNdoN3X88SUBXatpspY1Xx9Lcv2eYou0SmE1l8PyRGVL5Is8MJUjkLHyqTMpwKNilh0rBdDiRNNAVmWOJAs8MJQjqSk171akKYgGNI1N5jiTypXyHyQJ7h6exbKfSRkBTSebtkomPIpHMWyQLFiGfQsawuP/pYY5O52r620ofq+XojpSiZf7Hc0dJF0zCfpWZnMWRRAFFkjiSKDCTt0qRKfMWe4cT4Hk1O7OtjGmze+pYIlcjy0ze4oE9w6Vr0OB42SKg0bUO6kpLci33/hYIBAKB4FymvDBI5EupUHuiftqDGlnDXlLO7p0X9xIPaXOiUFcvQDwPLNfFcj10VcJ0PIq2y/Ts3EySJM7vjJAtOot6909ljYqm2q8qTGSKNecHZ/vXrD+KDKNJg6msScinULRdpnJF8pbNr4/OIEsSGztDFbkUmZN9wsN0PPKWzWiqwOHpLD5NRlUkFEmmPagR0GWSeZOiXVIC+TSFI9N5ZmajfzsuvHAiiSSBYbmz6VZL1gcSEposYc7OVTd0hCr9KFky5lFlqWZ8zdnF+miyiOV4RPwaluuRtxwc10WRJTZ1hGgP6GSMUsTyy9fHKNjlz224HqxvDxILlHy0p7JFogGNsE/lN8eSvDSSIupTKVgOo0mD8YyBpsgk8iaxwMngeBNZA7+mMJE15lzT+e6bxdzDY2mDgukwljaIBVZ/QVvdr5RhVsa01axAq8Xak6iOX/ziF3R3d9Pe3s5NN93E5z//eTo6OpqWv+eee7j77rtPuVzL3f1ptotnOk5ll1JCoi2gkzaStAW0Ze8yNd8NdWvabKWNA6NpTMcl7FNRFRldkbEdj+MzxqyJjYxTt5kqUTKIKVgOAV3BtF1M26tpbzJzMlWBabvIcumscqAN23GZyBQrbXizJjaSBLYrAaWdJMmTaAtojKYNJFmqmJ+02sf6lAlBXcF2PSI+FVWSUWTwXI+8aSN5HrIsIXtSyd/GdkjMbgKUd2ZbGdNm99TQZK5h+oYDs9YPjdI6dEVoaee7mVxrdXdTIBAIBIIzgfpAaINdIf54xya6I74lRVhuplFulgo0bzps7AxyZDrHnkPTpPMm12/pIhbU0FV5Ue/+sVSxoqkeckuL6IJltzwncFzoiwUqFondMR+T6SJPHZoilbfpifp56Xi6Uq/jVqV8zVvEQzovjaT44Uuj+BSFTZ0hdpzXWdH8TuUMbtjShWE5zOQsckUbnyYTD2mE/BpXb2rnN0eTHEsU0FUJRVaQJAmfqoAEb72om/agD10tjVvGsNk7lKhYEiqSxObOEIoMG+NBDk/neWYogeN5tAdUPDz8qoymyFy+oY2emJ/Dk3kMywXPoy3k463beirXvjzWE5kiP/vtOEcTObJmySx+//EUo6kCePCcPENQVwj5SvPQ6oWz48KR6TwTGYMhN1fSlhfdOddkORlk1nLa11Yz8awl1qZUs7zjHe/gve99L5s3b+b111/nk5/8JO985zt5+umnURSl4Tmf+MQnuOuuuyp/p9NpBgYGVly25Zps1JhGhDVMx0OSvFrTEzySBZOov5QSIVIuv0SziWbmGLoqL9o0Y1tfFF2RyRZtgj4V03FL/jztfl44LmFYLmFf7TXyKC28A5rCRLqIpsjoqlTTXnl3s5yiwHU9kECWJAzbQVUkuiM+ZLnURtRfWmh7HqhyaQHuAp7kkSrY6KqM53qV3clW+1ifMiFvOqiyRKZoo6gyjguSVDJh9yQJ1wNX8jBsF1WRiQc1hiZL19TzaGlMm91Tg12hhukbtvVFm6Z1COrNrrVUudfmk2u597dAIBAIBOc6K5EyszqCd7Oo4o0WIBNpg4d+M0LErxLSFJ4eSvDaeJaOkE7OdOiJ+lp694d9CpbtcDxRIG3YlfmG63koC9jLllNxxQIqGzuCBHxyRZZ1bUEuWx/lRy+PM54x6IzoTGbMSr3lPh2ZzvH5h39Le1CjI+RjMlPk2Eye48k8g51hxtIGUb9Gd9THQHuIiL+UEuzpoZLG2cPDcWHH+R28cWM7L51IkS06tAU0ZvJm6XvHoz2oV+Y6pu1ULAnL/T0xU+Cdl/by6yNJwn4NTZZ4+MVRbNdlY8RHrmgjeTCeLJDMFkkUbAJ6mE0dJRmffG2SXTs2VVKUTWaK9MV8fOiGQY5M5fn8IwdIGzbtIY1MwcbDQwIm0kU6Izr/+Y0D6KpSuQ+msgajqUKNjJbtksgW59xr80Wjr7+/Gt1b1SnHgrrKeNo45YvbheSC1jPxrBXWtHTvf//7K58vvfRSLrvsMs477zx+8Ytf8Na3vrXhOT6f77T4fS9396dmZzJvccVAG+BVTE9AIpW36In4uWFLNwfH0sveZWq2G1ownZo2W2njgt4ot+/YzANPD1OYTSFw6foosaCPa8+Lc2AkTdF2ifkVTNvDclyCulJ6mDkuGzoCbOgIkTedmvaCulpJVZAqWGzsDCHhkTNtBtqDbOoIoqlKpY2CZdMeVPEoLSbbgzoe3mzqCo1bLl+HT5Nr+ttKH+tTJvTG/Fy7uYNfHpqsmIbF/CqO57EpHiBl2OSLDu1BjYv7YyBJlWuaMsyWxrTZPTUQDzVMC3FBb3TedBHNdr5bkWst724KBAKBQHCmsJyUmYuJrVLfTkBXMWyHgbYQAV3hgh6LJw9N8YtXJ+gI+7jhgq4F3/3lNGXf2TdC2rAJ6jKG5dAV0ekKl8y2m1Efc+bmi3uRJBhJGWiKxBs2xIkFNHqifhLZIjlzbr1BXcVxIWfarGsrpSHzawpHZ02+q2UspwxTZXA8jzdsaKN+zhfxq1zcF+PgRBrTcmkPaayLBemI+GrmOqmCXWNJGPWrDE1l+c6vTzCVLfKGDXEG4kF+54Iunjo0SbrooMoykxmDx16ZQJElzu8O8zsXdM+xFqzWoJdj8VzcH2V9e5DJrEHOdAhoMgXb40giT0CTCeoBdFWhJ3oyOrnjQl+bH3VWCRXUFdKGxYO/OkZf1L+sOELNypyu1Fxna0yhM2oGPTg4SGdnJ4cOHWq66D6dLHcHcz7TiOrPQV3lusH4iphNLKbNhbjz7Rfwu5f2cmA0zba+KOtng19E/CrT2SJDkzkGu0IUTKdSJh7Wa3bLGrVXnwIBaHhOdRtAw88D8VDTPI8L9bFRKoZjiVyl7oCuVL4rmE7leEfYt+QxbXZPNUsLMV+6iOVe65XYoRcIlsOmjz+yqPKH//5dp0gSgUAgWD6taO+qyy4l3VOZstb6WDJHSFN5dTxDT9THdYMd5Eybg2NprhuMz6mrUZqy/jY/vx2RsV2Pq9fHyJk2PRF/ZT5RP886Mp3ja78cqtGMP3pgjL/+3YtIGxY/96lMZYtMZQwc16Uz4uPqDW3kLIewrnIskUeRSwqQsuXhiZkCHWGd6axJT9THf3vzZhwXvrfvBDMFk4imY9gm7UGdWy5fR3fUR960a+ZHedPm0vUxYkEVGQkXj40dId539XoclxotctmSsCtSSl+mKTKDnSHG00WeGZ5GAvyawi1XrGOwM8hn/u8BZFkm5FPIFR1en8hyYCTJtv62isXAyEx+zrg8sGeYz996CRs7gmiqhOe5HJ3KEZRhsDNEMm+RN50aq4K8aVMwbfqiAQKaSlBT2Ds8jU9T6G/zt5Q2q9H99ciLI5WxC87Ga6pPzeW4HtsH4xUf8uo2FnN/N2O59/1a5oyS/vjx40xPT9PX17faolRYzg5mo/Pn+7xSN1urbbbCBb1RLuiNzjk/GFcZiIdqypWpXhjOp2muLtfonPo2mn1ejvlJvRwD8VBN3fXfNWpjsWPa7FrXy7LQ8UZ1LVaulbzvBAKBQCA4V1ms9m65sVWCusrWvih7n5pmOlskbdjsOL+DdW1BCqYzb11BXSWg2zVpysp+0emCRX9boGL91kgTmiqYDE1lGewMV2LOHE3k+OYzRwnoCi8eT7L/RLoUdEySuGx9G45X6vNLx1M8eWiqJiPLzRf38u+/HGJyzCCkq+zctoGBeIjxtMFI0qj1a464BHSlaWTvrX1R9g5NVx3vmzOHqrZ2HE8X0FWZ68/vpK8tyHndRfa8Ns3jr07QEfFx+47NpAsmtusR9av4VAVFksgYNkXbrdHGf/PZo3PGZSJjkCrYFbmmc0U8Sm6OiizRE/PVaP/rMx+pMmSKFpoic+WGNmJ+HV1WFh1HSJFhz6FppjJF+mIBdl5csgyojg9kOR6JbJHnjiYJakqND/lKaafP5phCqyp9Npvl0KFDlb+Hh4fZt28f8XiceDzO3XffzW233UZvby+vv/46f/mXf8n555/PzTffvIpSCwQCgUAgEAgErbEU7d1CsVXqtYqN/n5lNM1APMhgZ4jfHEsyNJljU2eIRNaaN05LSZPqENAUjiVztAV0DNthx/kdC2pCAa7bHEeRZA5NZPFpCuMpg6Ll4ngemiLx4ok0tuPSOetffjSR47ar+tl3NEnBdCsa7a8+8Tqu6zKaKnD9lk4CqkLBdkgbFnnTLkVFTxWYzhXpCPmYyJR8zU3baTjebcH1vDKaZrArTFugFC+pmcb/pq3dbOkO8duRNM8fncH1SqbqQ5M5umI+tm/uIGfZ/PpIgvM6g6hyKc4RQLZoE9BlPnTDILGAzvf2nWAyW8SnyHgelXGZzBRL7ooBlSdenWSwK8yl62I8PTQNEly6LkbOOmlV0Og+8mkyN17QTSwwTbboMJE2SBashj77ze6veFjjucMzeHhEA1pFi/2+q9dXyoR1lcmMgeW4qFLJ17zsf79U7XQjzfjZHFNoVXvw3HPP8Za3vKXydzkA2q5du7j33nvZv38/DzzwAMlkkv7+fnbu3MnnPve5U+qzvRKmESvRXjn4RL3J8Hznw+JNxZu1X338tfEM+44luWKgjXXtgaZyVZteV5tYV5v3VJthNzPPns8kvNqkvPr8arPz44l8xZz9gt5ojVwjyQLPHk7wxk1x+tsCDU3Sq83hq03m619q1f2qbr96vLb0RBrKXj0+89VbPcavjqWbytXKNT1bOdf6KxAIBIIzi6Vo7+aLrVKvVdzaF+WV2YwmZS1jxK9WIo47LvjUkj/2SNKgP+ZvGqeluu7JtMGRRAHbdWc1z91s6jxp1dcsK07Ir/GmLZ08dWiK8XSBSEBlQ0eQzR1hjs3ksV0Xz/PIz6bvmsqafOf5E7w+mSWgKZiOS9awGZ7O8Xc/OIBpw5u2dLK5L1yjpQdKKWVn8gzlc+iqRFBTKz7ZjaKwpw2LgfYgAV0h4teaXofqcbAdb9YfPY8sSVy1sZ117UH2H0/yk5dHiAVV2gIa0zmTnGnj12R2bd/M5QPtjKcNXhnLcHAsg+m4eHjYHoynC3SGfezasRldVWrkunYz/PpognTRrrlW42mjpl+qLPGr4QSpgk2+WMqFbjulhXMjn/1m99dI0sC0XfyazIvHU5VI6NVR5EeSBUK6SsG0OTJdigIf1AM47tLu72aa8bM5ptCq9uDGG2/E87ym3//4xz8+jdKcfsf9Zu3VB58om9fMd351cAPL8cDz0FR53n40a7/6+N6haQ6MpnFdD0mS6Azp9MT8c+T65t4jfPWXQ+RNG1WRuaQ/ymBXmMlMkSPTOWzXI2tYpAs2SKVI5jG/StivlY4bJ3M93nrFOnRVntOv1yezHBhJY83a2ERnz1ckiU2dIboiPvYfm+HFkQyO66IrMm/Y0M7QVI68aZM1bApWOYI5BHWZsE/FdlxAQlUkipaLabtIMhWTp0vXx+a81Kr7Vd3+M0MJXh5J4XoesiSxrT/KdYMdvD6R4eXRDLbj1ozPfPVWj/GXH3uV+/cMYzqlHJSXroty2fq2Odf3bA0+0Yxzrb8CgUAgOLOo1hovVnvXKLZKvVbx2EyevcPDDMQDdIX8lRza776sj9GkUYluPZN3iQU0/vCNA2ysykVdljFj2JXcz2XN9fNHZnA9jx3nd5IvOnO0ws2y4niUclf/7qW9XLUxzqaOID/57QRD0xkM08Gy3dmErx7Zoo0sS3SGNfYdd8kWbdoDGpPZIpIk0RbwcTyZ58nXpuhv85MxnMrY5U2bnGkT0FTWt5e043nLJhaY9WefyVc02j1RX8U/vKy9TxZMeiJ+FJlKRG4oaXEf2T/CTMGsaPnb/Rq3bVvHowfGyRYdprIGT742heW49EQDyFKR7oiPW6/s542bOypulabt8NvRNNmiTUdIZzpXCpT2X3ds4trBDjrCPibSxZr7w3Y9dpzXyS1X9NNdlf+8fryfO5LAcT0CqszLJ3JIElx/fic5s6SBj/pVNnUGm8ZQqo4QPzSRJW3YtAX1muj0lTJTeYYmcyTzcsUSoexrXgo2N3e857OkmE8zvpSYQmeCAmZtSrUKnG7H/Wbt+dS+OWkKHtgzzGXrozVaz2YmPW/e0skTr04hSXDThT0k8mbDfjRrvy24vnLc9RxePJ7C8ShFUrRcxtJFBuJBZvJWRa6C6fDVXw6VHpRBjRNJg73DCQbaAzwzlMByXAY7wxxIpPHw6I8FOJE0SOYtLl+vcTRRwPM8BuIhkoVSvTdf0svGeLjSr6s2tM3W5dEb9XF8Jk8yb/GGDX6Gp3OMZQxu2trFc0eTOK5HW1AjU3D4+cFJYn6FsF8lP7vgLu3DQt50ifohmS8t+HtiPlKzi/+2gEq2aPPckQSXrosykTXY+9Q0g11h4iGt0q+tvVFeGU8zljG44fwO9h9P4nrgnx2vF4+nOL87yN7hGWzXoy/mYyxVZO9wggt7IkxkiuwdHmawK0Q8qFfqvXhdjMlMkQf2DBPSZe7fM0zRdgn6VJJ5k+ePzHDZ+hgz+ZPBMoCzNvhEI87mYBsCgUAgmMsTTzzB//yf/5Pnn3+e0dFRvvvd73LrrbdWvs9ms3z84x/noYceYnp6ms2bN/Pnf/7n/Omf/mmljGEY/MVf/AXf+ta3KBaL3Hzzzfzrv/4rPT09Ky5vvXJEkqRFa+/qY6vUaxXbAhoTaQPH8Tg2bVRyXacKdiU3dnXE8XjIV1NftYyyJDGdK3JBd7SiudbkkhKgI+ibo71slhUnlbfwPBfbk/j10RkOTWQxbZenXpsupV6VZWQJDMtFnk2/emSmiCxBwXaZyBSxPfCpMJM38asKBcvhaCLPeV3hythlDLsSwdt2oSfqoyvqQ1eVko/08DDpglXR/HaG/RU/97SRJOrX2NIV4T9+dbziI43nYTouLxxLEtCVko/2rK94POzjXZf189iBMV6fzOJ4Lud3lxQoqiwxkTG4YkO8Jo5RqlDSfGuyjutBSFPIWzZPDyf47Vi2oiRrdH9s6qxVItRqp/MULYeArvDC8RRTmSLxsE7EpzGdNfnJyyP8/OAEflWpKIYaKSeCuko85GN9PMBkuohhuXSFdbqivpoo8vGwzvr2ID7NwHa8Gl/zSvyAuvFu1Z+8kWZ8MTGFzhQFjJiZznK6HfebtTc0matJU1AOsjCWKtYsupuZ9Ji2h65IgIQk0bQfzdovm970Rv3sOzaD64Eql/JkQ2mxWjBd+tpOyjWdLZI3Szt4qizjV2VM22FiNt9iQFcwHQeYXfB6oMjguR654uxCWJJQZIm2gMZYuoA8K3+5X4m8iet6+DUZaTZvtwdYjotfVbAdl7GkgeeCJkvoioJfczEd8GkqdlVaC6nqOhizX0gSpQfebB8lSUJTZGzHJZEvBQ1JG0naAhqm7VX6JUlU2j8+Y+DO9k2VZRTJxfXg6HQBz/XwKRKqdHJ8MkWbtoBOumDRFtBr6tUUuXLtnz2cwHRcwj4VVZHRFRnb8UjkLC7sidaYWZ2twScacTYH2xAIBALBXHK5HJdffjl/8id/wnvf+945399111387Gc/4xvf+AabNm3i0Ucf5c/+7M/o7+/n93//9wH42Mc+xiOPPMK3v/1tYrEYd9xxB+9973t56qmnVlTW8sbwRKZIW0DDsFzaA+oc7WWzc5tp7ep9XiezpYVSsmCyri3IaMrAsBzGkgW6IjoBn1yj1a3WPs7RmidzjCYNAppKT8Rfo7lupp1vnCP8ZOTzrrCfoekMT702jU+V6QoHGZ7OI+GxpSvEaxNZJFlCBgqmjedBLKiRyFsUbQ/DdihaDiG/yjsv7uXyDW2V+XDEr7IxHiKgqRUNa3tII5E1efF4ksGuUKXvL55I0hf1sf9YkoF4kICmkDEsfnxgjMGuED1RPz89MIGHx2XrY0xmTRzXZUtPhOm0ieW4JHJFNnaE2LWjpB0eTRqkDZuQz2Y0VRqfctadMr0xH90RP9NZk7agxmsTGVRVJupX+fWRGVRFqijJfKrEjRd2sakzVDEnr3dD7G/zl9ova54LJm3BkjZ+MlskmS9WNPCdIX9FMfQHVw/UKGqq76tG41ivqY74T+Zar7+fyvEDqse7ma98o3t4OX7bZ5ICZm1Js4qcbsf9Zu0NdoUqaQrKmu5GP+JmJj26WspXLUkenkfTfjRrv2x6M5Y26I7qyBLYLmizbgASENDlGrligdIDYTpn0h7UMOySCXV3REeWSnnA9agCgEtpgeu4pYVtyFfKgeB6Ho7rkSxY+BQZl5L85X7FgzrybC5CzyuVLy+MDdtBVSR62/xIMliuh+k4GJaHBBQtm3BV/6sdGvyqTJrSRoCmypXvPK+UW1yRJeJBjWTBJOovPYjiIa3SL8+j0v76dj/ybN9s18XxQJFgQ0eA345lKToetudWxifiU0nO7ggmC2apj7P1Wo5bCbDxxk1xvqYMky3aBH0qpuOiyhLxkDbn+p6twScacTYH2ziTuOQzP6boSAsXRKT0EggEy+Od73wn73znO5t+v2fPHnbt2sWNN94IwIc//GH+9//+3zz77LP8/u//PqlUiq997Ws8+OCD3HTTTQDcd999XHTRRezdu5frrrtuxWTNGHbFp3rIZVYD7SOwgAZvIa1dvc9rLKBxUV+Uou1gWA6S5DE0meNLP30Vv6qwuTOEX1Xoicz1467fvB5oC1EolnI+p4xazfV82vl6rWR95POAppIuWEQCKm6hVCaZt3l5NIPleAR0mWMzBdzZSVg5IBnAZMZEkcGvqfzitSkOjmcb+v+mChaqLJHOWzz47BFeHc9y5YY2uiN+DMthz6FpjibyvDaWIaCXoow7rst0rsgbNrQjIWG7Lomcyb6jSWzHoWC5DE3mUKSSkufBZ0/mwL6oL8YH3zzIA3uGmciU5h+7dmyeNxL6VKaILEl0hnwcGMkwnS0SD/mQJEr+2YcTpAwbXZFr3EQb+ezHw/rseqFAKp9HV2WCmsqxmUJFAx/QlYpiyLTdpsqJ+nHsifrmXOs5ZSJzfc3LOeEjvua+8o3u4eX4bZ9JCpi1Jc0qcrod95u1NxAPVX6c8/2Im5n05E2HKwbaAI+UYTbtR7P2O8P+qnrh0oFYxY9aUyQ6wzqm486R60NvHuTfnxwiVbBoD2pc3B9DUxWuPS/O0ekcBdtmQzxIxrAoWCV/nJi/pIHeEA+QMWyyRYtYQOWWy9fh0+SafiFJXHtenAMjaXKmTXvIR8yvYNgOA+1BNnUECeka12xsY/+JDAXTIeSTuXKgg+HpHLmiTXDW5Nvj5OaB60FbUEVCwnE9YgEV03YxndJL59J1bSBJ9ET83LClm4NjaVIFq9KvlGFW2o8FfVy2vo0Doyksx0VXJS7ui9IZDrB9sJ2XRzJkDbsyPpbr0RP1ccMFXXPqTeSKlTG+drCT23ds5oGnhymYpYfIpeujgDTn+p6twScacTYH22jEiRMn+Ku/+it++MMfks/nOf/887nvvvu4+uqrgdJG0d/+7d/y1a9+lWQyyfXXX8+9997Lli1bVllygUAgOD3s2LGD73//+/zJn/wJ/f39/OIXv+DVV1/lS1/6EgDPP/88lmXxtre9rXLO1q1b2bBhA08//XTDRXexWKRYLFb+TqfTLcmiyDCaNJjOmnSEdSbSZsVPthmtau2qtcuKDP/xq+NMZA10Webh/aMA9EQDTGaKjKUMPvimzWzsnOvHXTBt/Gqtr/mmzmBN3urq4K4AL51IzRvkdyprcCyRR5UlhqezBFSFqdmo4hnDoS0gk8qbeMD6WIDhmTwFy2FTPMBUppQuS1fALBlI0hHWSBdsMsWSr3a9tvZkXnGDh34zQs506A/7eWUsw96haTIFi5dH0xULwr2vT2OlDbZ0RZjOmxizab16In6mMiZFy8YN6uQMG0kqKVSOTBUAh66wXtP+TVu7uWx9tGmA4bJ2+rrBOJetj/LKaIb/388PkcybuK5LwXKYyBiMpwu8MBvArCus17iJjmcLFffGgfZg5Z5492V95Ewbn6LQFVFJ5206Ijr/5doNJPOlWEk+VakohnRVnlc5cX53mLbg+nmDODfztV5spP356losZ5ICZu1JtIqs1A2w3PYW+hE3Ox8WF728WfvVx//8rVtail7+ges2csMFnWsgevmWUxq9/LrB+LzRy+vHq9Xo5c3qLY/xnW+/gN+9tHfB6OWn+x5ebc6V/s7MzHD99dfzlre8hR/+8Id0dXXx2muv0d7eXinzxS9+kX/+53/mgQceYPPmzXz605/m5ptv5sCBA/j9zTMgCNYGmz7+yGqLIBCc8fzLv/wLH/7wh1m/fj2qqiLLMl/96le54YYbABgbG0PXddra2mrO6+npYWxsrGGd99xzD3ffffeiZXFcCPlUjs8UGJrKoSsyQV2t+Mk2YjFau2rtcnkD+vXJLB7eHD9jx6OpH7fleEh4cxQw9eUmM0UOT+VwvNpAr9VUBwLOGjapgoUklSKnr2sPULAcsoaNh4SuyKRNGwkJ03YYSxuzGl/wkJAlD1kqBbX1qTK6KuO5jd0mg7pKQFcx7JPa9a6wj6cOTXFkOo/reuw4v4OwXlqMjSYNhqazBDSVgfYgMb/OZNYs5bx2PA5NZLE9CGkytgt+XSYe0tFVhd5orRa3M+xvOE9vZLGwpSeCpsiMp4uccErxjCxF5qnXp8ibLm86vxNdVWrcRNsCesW9sfqeSBVsQj6F447DiRkbXZWI+FUGuyMVDXwiX6wohgqmM69yolW/6Ea+1ouJtF9d72L8tptxJilg1p5Eq8xK3AAr0V6zH/FC5zf7vNj2q49fPtDO5QMnJ/fN5BqIlxbM9e2XH0rV5RrVVX9uo74E42rT88tlLuiN1gSxqJZrIB7i2sHOhm1Wf64+v16OZv1qNl7NZG+13mqZmslVzem+h1ebc6G///AP/8DAwAD33Xdf5djmzZsrnz3P48tf/jKf+tSnuOWWWwD4+te/Tk9PDw899BDvf//759TZTHtjWRaWZS0oU7mMT26egaLZOa3iU1qve7H1L7bumnNn+7yYvp8tNOr7Yq/rmUy5r+dCn8/EPv7Lv/wLe/fu5fvf/z4bN27kiSeeYPfu3fT399dotxfDJz7xiUpKWyg9KwcGBhY8T5EhN5uveX0owHSuWMkt3Yylau3KWspXRtIcTxRI5i0UWWI6W3L7U6SSthEaReY2CesqN17YzabOYGX+Ua11D/sV9g5NY7seF/fFmMwWa4L85k2bI1N5vvbLoZI7nk/ltZSBh8eFvVHyhkW+6HDVhjYUBR7eP44LKLJMcTaA2IaOIK+N5/CAzZ1BhqbySMD6tgCHE3lURcZyXY4lc3N80+vHLqyrvHA8iU+T2dwVYngyx/4TKTZ0lBQWPk1msDNMsmAiS3DrFf0cS+T5+SvjKLJE1K8ylTOxXI/BuB/X89AUueLbHvYpFEy7Mqb1G//1/vzliPJv3drNsUQeSZLoCPsYTxtIHrxxc5wDIxleHc8Q9asYdsm90fOocW+M+E+6FcYCKrmiUxu5fTaaeDnHeDNFTz2NLCwe2T/SUvyB6ntwoUj78/lbzxfHYKHI5GeKAmZtSiUQCASCCt///ve5+eabed/73sfjjz/OunXr+LM/+zM+9KEPATA8PMzY2FjNpDIWi3Httdfy9NNPN1x0N9PePProowSDwZZl+9zV86ht6vjBD37QclmAL75xUcUXVf9i627EYvp+tlHd98Ve17OBxx57bLVFOOXk8/nVFmFRFAoFPvnJT/Ld736Xd72rFD/isssuY9++ffzjP/4jb3vb2+jt7cU0TZLJZI22e3x8nN7e3ob1+nw+fD5fw+/mw3Gpjawd8ddEhG7EUrV21drESEDlWDLPZNZAU2T62/1874WRSgpW03Z44ViKgCbj0xSKVsl3OVWw6IsFKprIaq17Il8ErxQ4VlflmiC/yXxpYfX6ZGnRGNAU0gUb03aQJImpjIFPVUCCsF/D8TzWtwfIFx2KtoNfkwloCj5V5byuEBOZIp4HmztDSHiYrkdXpDT+zw4niPo1btjSPa+29dBEBteD87vCdEcDqMgMTWc5lijQEfIR0GUUWaYn4senyTy0b4SJjEG2WArkljEdVKkUI+jFkQzRoMaGjmAlKnva8PjWr47VpLWt1uI29ucvBVsrRzK3XbekwVdkYn6dC3oiPHVokqdenyagKWyMB0gZZo17Y/U9oatKJTp9fTTx6vvh5ZH0gtG86y0sVFliz+tTTOXMig97K9HA65UgrVpuzKcNX44Gfq2xtqVbBU53nrep2YjhtSbSc9tuVg4WZ1IOtX2sNsUuWE7FLLpaU1vddiJr1phuV3/3+kS2oel29Q5bvel3I5mCulpT74mZQkPz9upxqK43oCs1puqvjqUr38XDeo1vUiPz9vnMy6upNltvtotY3a9G5ujl8tV1VZvNN7MqWO59upL3eav3sGDpDA0Nce+993LXXXfxyU9+kl/96lf8+Z//Obqus2vXropZZH3Km/lMJptpb3bu3Ek0Gm14TjWWZfHYY4/x6edkim5rgdRe+szNLZUrc8lnfryo8oupf7F1V+OTPT53tbuovp8trETfF3sfrCXK9/3b3/52NE1bbXFOKa36Lq8VylY6slyrSlYUBXc2KOtVV12Fpmn89Kc/5bbbbgPg4MGDHD16lO3bt6+oPK1EhG7EYrV2NSlkwxozOYuusI+L+6K8NJImmbcIqCrPH5kE4LrBONO5IrbrcWFPZFaLLNEZ8dX4K5c1x0NTWTyvFAjWtj1M22UyW6xoW//vC6OlNLdtAQy7lGe7I6iXNhckD0WWZ7W0Kn947QZ0VaEz7GN4Kku26HBipoBPk7moL4LluoR1las2xrmoL0LBLM1LXxlLk8pb2G7J/LxZZOzzu8P41D72HQ1wLJFnJmfheB7pvM1Ae5CbLuzktYkcqYKFjITpOExkzdkFnYrjeFiORzyskTAdNEXm+vM78SQI+1Su29zB3qFpirZHzF9K1+t4Hts3dzCRPanFLfvzV6f/dT2Pvpi/Esm8O6iTK9pomoyExOuTWboifq47r4N80SHsU2qsD+rdEA9P5RtGpy/nWq/Xss8XzbtR/m+A/jY/iWzjiOetUK63Pi/6fBH0q7XhcHalwj3zJD6FnO48b9W+L4okNc2jV11OlSU2dpTKlfMJlvP7Ndptm6+PLx5Psv9EGsd1sRwXt1QFqizxB1dv4LO3XlLTdiJbZDpn4eGhKzK/c0E3M/nSw+pEIs903mI2yDkBTSbsL6W4uqQ/ymBXuKY9XZG5fcdm7nz7BXPGPRbQ+PHs3+OpAlM5C88rRSLvjPjpifpQZIlN8SBdUT/7jyd58Xgax3Px3FIEd5+mENRVBjtD/ProDGbp6U9nWCce0mfHCzRFLvkdGRYSpZyRlu0iySWzp0v7I1w20D5nTL+59whf/eUQedNGlWUuXhflvK5wTbkaX6i0weFEHsf15lxr03Z5aN+JkpmSB9GAStivNfWZWu59upL3ec09XHVN1nKexDMR13W5+uqr+bu/+zsArrzySl566SX+1//6X+zatWtJdTbT3miatqjFRNGVWo5evthFSqv1LqX+xdbdsI5F9P1sYzl9PxsWq4v9nZyJrMX+ZbNZDh06VPl7eHiYffv2EY/H2bBhA7/zO7/D//gf/4NAIMDGjRt5/PHH+frXv84//dM/ASULoA9+8IPcddddxONxotEoH/3oR9m+ffuKRi6H1iJCz3duq4uKmhSyrouuSOiKSltIJ6QrMLu4LKdg9TyJrrCPRM4smVorJX9ln6oQrvJX7on6iQU0/uO5YzXzk0T+ZKBXXVVq2u4M6YykCiQKFrIsocpQtF2ifpULeyPEgjo9UT+eB48fnKTouEhIdIR09h1LosoyGzuC/ProDD9+eYzDUzkMy+FE0sB2yvNcmMgWee8b1s8Zo/o5ayJn4c6Uwud2WjrfeOYYWcMiZdhIgK7K9Eb9nJgpkLccVFmiaLvMzM51OyMaPbEAybzJr4ZnGE0WOD5jcOWGtlKqWcclkTV57sgMQV2hUHQrKVwb5UiPBfVKsORUwWJDR4hNHSWNtixJXLW5nfVtQY4mcvzq8Mwc64Ogrs5ZO9RHp3dcGmrZ54vmXX2vjiTzyJLElRvaiPl1dFlZcjTwoK7OyYteb6UwnzYczq5UuPN4lpxbVO+0dIX9ld2+sr/GSjOVNbj/qeHSzmTQx/Fkgb3D0wR0pabtmnIhH8cSBZ4ZSqDJEi8cS7L/RIqAqrL/RIr9x5PEglpT2av7iOfx3JEZCqaNKntYDjheacFtuR7/8dxRHj84Xmk7oCmcSBoUTAe/plCwXB7ZP8JoMo+MxGTOwvVAlkopufKWiyxDMm+xdzhBqmBW2gvqCkXb5YGnh3nh2EzNuJ9I5fnqL4eYzppoisxY2sRyvFK6BhdGUwaqLHE8UWDv4QQ50+K5IzPkrVKKhYLtkjJKPi7JvMnPD06SL8tslnZUVRmOTOc5Mp3Hp0gcTeSZyVtoKqQNm4Jd2hTImzbPHU3iudSM6bFEjq/+cohs0Sbq15kpWOwdSqAqUqXcVNao9CugquwdTnB8pkA0oNVc6yOJLPfvGSZt2IR9GjN5k6OJAmFfKULnA3uGmcoaK3afruR9Xntv6pVrElDVU/77Odfo6+tj27ZtNccuuugijh49ClAxixwfH68pM5/JpEAgEJxpPPfcc1x55ZVceeWVQCkv95VXXsnf/M3fAPCtb32La665hg984ANs27aNv//7v+cLX/gCf/qnf1qp40tf+hLvfve7ue2227jhhhvo7e3lO9/5zimR9/zuMO++rI/tgx28+7K+ljei86bNeNpoOI+rP16tpfTwMB2vtPhW5ZOfFQXLdbFcD12VUBWZzoiPqzfG6Yz4ZlOmlvyVY4GSD/lU1uDHL48R8ils7Y3SFfURD+l88PrNfP7WS7hpa3dN20XbwXRcwrrMQHuAmF+mLajzlq1dbD+vk8vWt6HI8JujCb7zm+PIskR3xI/jeszkTS5dF8X1PI4m8miKxN6haY7N5NFkmbRhkTMddLWUOvalEylSebNmbKrnJLGAXlpwex4b4gE8zyORMwmoJUvCXNFmXVuAgKpwaDLLSLKA7JXGTlVkLpgNRGc6HrmixXOHZ7Acl3jQh+W4PH9khpxZCi5XdFxCuspU1mQsXUCRT+a03tAR5A0b29nQEWRTZ5CIX+Wmrd18/tZL+NjbLuBL/8/lfPbWS/ngmwbZcV4Hjgspw+S5wzPYTilgXVlTfSyR48nXJvi3x19nJm/RHfFjOh5jKYNbLu9n145Ns5rpIscTBaayJn5NLsmVMuaNJVC+V3ft2FQjS8F0au6JxXIyf3eYGy/oZrArzMGxdNP7t769+b47EzkzpT4FnO48b2OpImmjZHYiS1LTPHqTmZPlJEkioCuYtkumaFeiG1bvYEpITWWv7uPB8RSuOxsYwpOAUm4GWZbxyx5F22XP69OVtqezxUqqLVWWCWgeKdvFpyk1Px5ZlnCckrrbtj38qoxpOxyfKVTa0xSFsE8iZ9rsO5asGfeAVjKb2RAPMjObUqIsl+SU0n3lzJIPkO14jCUNPBc0WUauUrp4notfk0kWwK/JqLKMIoHrQa5Y2hAAibzlgueVfK7sUv88QJYkNFnCcWEmX+TC3lhlTIcmc+RN+//P3p9HyXGe99nwVWtX7z09+2BmAAwW7gTFRdwpmZLIaMkRLX2R7SgOJSvyZ7+SbFlJ7Ch589pSdCLbSY5lJ05sS35FJrGiw3yO5GPTRxK1cgFBkRB3kADBGQADzD49vXft9f1R3YXunu5ZAAwAAnWdI7HRVfWsNTPP89z3/bvpjavIokhEErEdl7JuM9YTZ7Hubh3EQlUasVDiqrkWBRHTcRmKR5BFITi0cFyvJWaq4WZ+tu/puXzPV73D9TkxHectfxp5sXHnnXdy+PDhlu+OHDnC9u3bAV9UbWhoiB/84AfccMMNgO8a+swzz/Drv/7r57u5ISEhIVvCO9/5Tjyvu4Dh0NBQi+BkJzRN40//9E/50z/903PdvFU0WyW7ea+1080brdv3LRb1qhWkja01pZCt2XbH1LKSBDeOZ+iUi3tysUJRt9iWifmq647H5HKZH7+xyPRKLaj/tIW0hiKKlF2BU3kdSRBIyhKSIDCSiXLFUIpHnj3JqzMFlssm/clIXXVcwrJdDNtDkyXATy1m1dO3zhZ1PK++PvM8YhEJzxWYLegtYYrNaxLD9teLjuexUrFxPQ/Ro+716OG4HktlA0kUsW0o19dGggAxWURT/fjyqukyU9AxbYeoKnN4vows+tbr2YJOb1wlqsrYrkd/Qg1i9ld5OTTltO4Wb/3+60fq4+jXpykSL58qIonw5mKJnxxeJFc1mc3X2FWf98Y60fFgJu8be2YLNUq6TVwVW6zsa2kJNIipMjv65KAt5yqH9lhPzM/fra3O372ejsFbRZl8I7w1W70FnO88b0PpSD2pvU5/ItI1j54kcvq+pB97rEgiyYiM6XgIghecYALBSWWntrfHbIiiEOTfbuDWT0IVUeCOXb28NltituArNTY2pLbrUrNcJAEMyyGpqU3Pn/5jKMsCxZqDLImM9kSD+izHoWL4G+cbxjJUjOVg3GuW/4O4XDaJNdXpum6wAY+rErmyiSwLDGU0BBEs18X1Th/jCYKIbtmBy7gsuTh1S3w8IuLWY41iigiCgO2BLBPU4XoelushiQI9sUjLmE70+7kulyu+8qfhuMiiQEKTg/sa89tQ0UQA3XZxPa9lrl3Pt6rnaxaZqILr+S7+kuj/QWmU1WkOz+Q9PZfvecs7nFT9cZYFVEm6qPMkvhX5rd/6Le644w7+/b//93zkIx/hpz/9KX/xF3/BX/zFXwAgCAKf/exn+dKXvsSePXuClGEjIyM88MADF7bxISEhIZchDcvrYskgHZVZLLUqfneiW3xrJja6Zmxrexx4txSl0FkHqPlzY0PTvk49ulRGloS6UeR0nG+j7sOzJX5yZBFJFEhHZF+UDI8PvW2Usd4ojzx7kpWqxUR/nCfeWGKuoDNQX9eqskg2rjC1VAFAUwRqtovteqSiIo1VakKVqVgO6ZgcpHttjJskQlyVObVSIxaRMG2nXpaIW/EwPTAdB8Px8ABFFslXLGzXIaZGGEhpvDFfQpFE9o2mMV2XnpjKPbv7+I/fO0JRt0lHFVaqLumowkffPs5PjiwF67f2mP211LwXyjqZqNoSB96sQP/GXImK6ZCJiUwvV5kt1uhP+GU7nn8wk9KUulVfxrIdvn9o3s9Rno7x+lwJ0/W4aVuaimV3VHtfi43k7O72zjc/s9E151o6Bm8VZfKN8NZt+TnmfOd560toQVzHWnn0Yqp8+r6KwXhvlPHeOJbrdTzBbD+p7NbHQs3i5u1ZXjpVwHZdFMnDc8Gub7g/cvM477hiEMcTgtiTbZkouYqJbjnEVJF79gyRr5kUahb9cYXlqu9iLuDHdLsu9MQUrhlJk46qQX3VuqX6wdt3sm+sh3hECcZ9NBPjk3en+V69jcMplcW6EIYiQn9Sw3Y9xrIxdvRGiasKt2zv4aVTRUzHJVrP5VizbHriKjeO9/D89Aq65RBVJfoSKrYLO/pieAgYjsf2bJyCbmLZkNZkTNvDdFxiqsz125IIImSjTXOSlfnk3RN87clJirpJNqZwzUgK2/Faclw2j/XtE1mOLdco1qyWud7Rm+Bjd8T4mxdPUTZ8N+2kplA27CBmqj0t2dm8p+fyPW95hytmMCc1237Ln0ZebNxyyy1861vf4vOf/zxf/OIX2blzJ1/5ylf46Ec/Gtzz27/921QqFX71V3+VfD7PXXfdxXe+850wR3dISEjIBWCuYDC9UqVQs5gt+of+uu20eK+107AMZhMKluuSTSgUqlZgxW3/vt1i2B7z225db/ZMrJq+N6Uk+n/P2/9eN/+Nny/WUCWRO3b30p/QSKgOs4VasLGPqTKKLCKJAghQNGxEwfd+VGQRx22Nzb1hLMMzU8sslA0UWeTa0RQgBGvZiuEwXDfEuC5k4wpV06Fq+xvef3bXRJCC9ehCmW/97CRzxRqyKJCrGswUHN8rU5XQLZdEVMGwHE6u6MiSSEwRMW2PhCYTUyUGUhEkUWRnX5ya5VCzXUbSvnU6qcmM9sRYLOstluORnhgf2DeyZsx+JzXv48tVFko6k24FSRQoVX0PSg944sgis4UaVdMJLNVxTcLOeyyWDRbLJiIetkPd1VpmMKXxf16Y4chckRvHs6RjCjdvz/KzEzmKhh30YzPrsaMLZf7uxRmWKwa98Qgf2DeybmhEt/duo2vOtXQM3grK5Bvhrd+Dc8j5Pk2598oBrh9Nrav8vNZ9sDn18vY+rqde3l73VqiXdxr3+68dvKjVyz9623bu2du3pnp5e7/WUi//8E3bNqxefrbv6bl8zzf6DoecPR/4wAf4wAc+0PW6IAh88Ytf5Itf/OJ5bFVISEhISCdUSWClYmLYLomG5dfz6qGBnUlqMpbj8YNDC6iSgOn4BpahdKTj9+0Ww+a45oZqdsO63kjtVdT9WORjSxUcz1vT7b3xN/7YUpUfH17AsH239dfmiiwUdb71/El64xHuu2aIdFSui+kKZGO+5RcgHW2Nzc3GVGqWwxVDSa4cTCEKMNwT5YP7tjGQ8tdmC0WDvkSEhbJOVJGpWXaLqnljw101bf7bj97gwFQOt76x91OaRZnN6+i2705uOR5DKY3bJ3p5ZaaIIMANY2n0uvv4WDZGfyJCvmbRE1Na8lNXTZvtvbFVKuFJzd/wbmY9JYkwW6gF8zO1VOHYUgXtpyLTuRqDKY2JvkSLpfrUSoVXThWwHD9/uGFDRBb4tXt2MrlUxbB9tfXXZoo8d9xPqWa7Hnfs6ttUnu0GVdPmfz1znJdOFVBEkdfdEmXD4nP3XdG1nLXeu0vJUn22bKrnk5OT7Ny5E0G4dNVaz/dpSl9CW2XJ3Ox93T53o7mPe4daU3c1pwrrVHdfQmu5v/3arRN9wbXGL8XmdrXX16lNncptblencWgvt7nu9mvNzzd/bn+mvY52xrLxjn3s1q+YKnedw/ay1nPlOdv39Fy+5xt9h0NCQkJCLi/e97738b/+1/8inU4D8Pu///v82q/9WpAje3l5mbvvvptDhw5dwFZuDabj0RNXyVdNTNvXmcnEVEyne0w6AJ6Hv8wWEASPIPCt2/dNNCzi/YkIouCrlC+WdX52PM+hmSIV0yGhSRyYXMZ2Pa4ZTrNYXtvtPabKjGVj3H/tME++4VtiF4o6qaiCgMCpfI1HX5rhzt197BmMc2SuQtmwSUZkdg/EKdRsehORVerYd+zqYzzrGzpmCzXKhs0AkVVxxYWaxWgmxnuuXp0R5fhSlZ9O5XA8j5gqs1QxsByXZETmhONiWA6arOA4Drrtkk2o3LI9y8szeRxPYHtfnPuuHebwXLHFWr2jz6+nkV71nr19/OD1BZbKJn0J9Yw9+Rq52wUBchUT2/EF72KKTL5mISAw1hNj37YML8/kKRo2mXiE3oRvHLJcj6gikY4p9CY0jixUyCb8eXjbWJqXThWZKVQZyUS5a08/0TNo40LR4JWZApIokImp5KoGL58qsFA02NHXubzmePrmWPOGV8dbzVK9VemjN1XSnj17mJ2dZWDAPw37hV/4Bf7kT/5kVW7YkJCQkJCQkJCQy5vvfve7GIYR/LuhS9HYdNu2vUok8lJhKO2rfZdrFpIq4jguvfFIi05LOyXdRpFF7r1iEEEAz/PVrOcKRsfv28VKh9IRJEHg1dkCmixRNiw0ReKxQ3OcyFW5cTyLabt1cVepbhVeLdraoF287Z69fYDAH//gCIdmCrxwIu+HFKq+HtHMik7F8DePFcNiJq/z7edPkY2r3HfNEA/esYOFosHfvHCKsuF0tZrvHkhsyEJaMS1sD1RRRBaFIPa7ajqB1pFhu7geLJUNfnJ4iYgicvVwko/fuSOwAjfnwG7U09x3y/Gw6jHiXpMg2WZTsCY1mUREoVgr4zguFdNhMBWhPxUhKoscni9SMWwEkaCNkigwuVhhsWiQiskUqzYD6QgT/XEef2OpxfvhmpEUH79zJ2XD4vEji2eYGrYum1zXGKLuvdDpkKdBc/x/w9Ldrkn0VmEr00dvatPdrhj593//93z5y18+Jw25WJjOVVpckzdLs7t1s8tyt7JenF5Z5dINq09ZmssFOrofd3P1brY+t/cxVzGD+oGOn3XLCcraNZBoqbu5rGa36Ob6rxvNdBT3aL7/Z8dyPP7GEvfs6eO+a0e6uoQ3l9velo3MSfPYNc9Pc1ua72kf6+Z56daXbm1pbsdabthbdcIWEhISEhJyPmlfN66lPH6pEVNldmRjnFqp4TgekiSyoze65t/1hht2rmq2CE81NjXt3ze7l1dNm7LuMJaNMlfyFbBNxyMb9y3VU0tVnjue49aJbF3c1cG0XRbLvtCbJPplNNrXSfTriTeWeNeVA7wxX6Zs2GSiKqcKNWRdIKaKLJZNdNulJ+qLfC2VTJLR02lEH7xjBzv64oEVu2E1H0xpjKRjqwTi1mNHX5yRjMZ0ropdF78VBQHTdrDrHgWKJKBb/meBxndSi9t1uyW2WdAum1D4waEFXM/jtp295GvWhsTtGuWsWs95ni+mK8kokknFdNAth4rlIIjguH7ucupHCL2JSEtu74F0hF+8ZQzHBdtxW7wfFEkgoUn8/cuza7ZrLQZSGtduS/PSyTwrFQPH89g3mmEg1X2d3Rz/v1DSO2oSdeJiW+92EzLc6Nitx4Xv4UXEXx04zlefmAx+6Xzy7gk+etv2DT/fLCJQ1m0KuoUAXcv6f779Co88dwLb9VNWfeTmcb74wLWrTlnSUYXvBqdt/gmlIostcTjNdZ9aqbJctvAASYD3Xz/CH//S21b1sWrY6JYLAnU1b1/oo344iCj6n+uXEIDehMq2nigpTWFHb5zH31j0hTk8SEVlEprCqZUay2WznnoL9g4meccV/by5WObQTNHvA5DS/PsnF8sUdf8E8X8+M81I6hBVy8/3KCDQG1fJJlROrVRZqljBYVtfQmFbT2zNeKTmcWkeu7JuUdTrgiJNbbdsv22KLLZ8TmkK918zRKFmUdStrn3p1pbmdsiiwPbeOP3JyKpTtK08YQsJCQkJCQk5P5R0m6rloMoSuuf/t2I6a6bS7CZ22izO2kmQqrF2mC3UmC0YvPtK3yL+yqkCqiSiyhI37+jh+RN5ClWL2yd6ObZcJVc10BSBwZTG37ww07Lu6CT6VTNcZgs6miIhSwKO4xKpZ2Mp6TaKKJCN+etEQajhuN6qVLjNauuTixW+9fxJRtKxVWlMGymw1loP9SU0PvS2Uf7i8TcpGw6JiFyPi/ezyrieR9V0wIOEJvO27T0MprVVInSd5q4h/Ga5LrbjkquYHDyxQkyVqFl2S2rYTilYO63nkprc4rGwVDJ4eSbPiVwVEdiWiSIJIo7n8rMTK7jAcMqf+//0kX3MFQxqlsOzUzn2v7nMkfky14+m6U9oLV4RZ5MaNqbK/NLbx0lF5cCd/v3Xjaz7bLvGz3ob7otxvbvV6aPXSZXeiiAIq+K5L5X47ulcha8+MUnZsOmJqZQNm689Ocl0rrKh55tFBBIRmRO5KitVi6QmdyzrxekVHnnuBJbrEZFFLNfjkedO8MzkUnDK0p/QmCnU+OoTkyyXTdJRleNLVY4vV+tpCywe3j/FkbliULckwmLZwsXfcNsePPrSDM9MLrX0UZVFyqaL7fkvgYufwxqv/pnTnxu4wGLZRBZgrqDzyHMnKNYsEhGFlarJiVwN23JYLJu4+OU6Hrw+V2K+UOOZyRy5ikVcVVip+PfruhVsuBvMFA3Kho2mSFRNh1P5GqZps1i28Oppvzz8fkoCwTgslfWuc9I8dhFJ4ESuxkrFJCrLrNT8tiiSyPFcleO5KpoiBZ9TUYXlisFXn5jkVL6GIgkd+5KIyB3b0tyObDzCdM4fi5gqBae/VdNuOWHrT2gt10JCQkJCQt5qXMrrxvUwbYfXZktUTZuemELVtH2BLNtZ87ndAwkevGMHH79zJw/esSPYiHT7vnntMJKO4XoeL58qoMgirgeW6+Hh4bhwx+5ePnHXBF984Fr+6Bf28X+9Yxc3jveQjUda1h1LZZ1c2eTkir/pBnwX9GKN4bTGQDKCIor0JVQUSUSRRXriKghgOC6KJGI4Lo7nka8YTC6XSEdbLfMx1U/71RuPML1SZaGoM73ir28lEd/KXjLQZImFksG3nz/Jc8eWW9ZXVdOmULO4baKXd13Zz2BKIxNTuWNPH4osIksi2biKqog4dYvNfMFY1ZaqaTNf1IP1VrPwm2E7LJYNDMclrvob0bmCrxye0hSm8xUWSjrT+UpQbrf1XCMNcK5qokgigujHtn/01nGSmkLNdJBFgRPLVVYqFv0JNXi2MV7PTuVYKBkkIwqW4/LSyQKe4JGrmqSjrSlra6ZTVznfXArX3QMJPnn3BJ/+ud188u6JDW+G+xK+lXwjFu6Lcb3bPO9nOnZrsWn38o997GNEIr7bra7r/Nqv/RrxeKvr9P/5P//nnDTufDK5WKFq2n6ie0WmNw4rVZPJxcqG3MybRQRKuuW7j4gCkiDSG1dXlfXCdB7b9dBkEUWSAD/u5KfHclQMJzhlicoSVdNmPBsD/PQL4CEIQhCHc2i2GNTdvLGXJAHB9bBd+OmxHNdvywR9LOnW6cY3//1rCttodgJrxBABvhBHRMJ2PRIRGVkSgo1wXj/9AyNJAm49H+LUchXX9dAUEQQPUfCdfHK10/eLQn3jD0iALIp+uR6sNN0niQJO3W2oYjqMZ+Md45Ga58Sw3WDsqpZbr0/AEzzEusORbjqI9QAWy3aDz6Ig0BuPsFgqElUkSrrTsS+O53WMjWpuhyAIRFUJ03Yxba/lFA3Y0hO2kJCQkIuBHf/q0U3df+z3379FLQnZatZbNzbHe19qFGp2YBF2PcjEFGRRpFCzGVvn2W7CU52+b7fO7epP8NQbi+w/ukxUFRnvjbWkk93RF2+xjB+ZL/O28Uyw7jgyX+IvnzhG2fBVzmuWTaFqo8oCMUUmHVO5/5oh3yBUMVBlkWuGkwgIgQW9qFukIhIF3eGx1xbqHp/pjqlsrxxOcWBqimLNIhVVuGdvP44Lx3MVFooGky7kqyYLRZ0fHV6kN64GHoUl3fbvKxlUTcdPqxZXyEQj9CcjzORrlHQbSRSQBZFnj63Qm1C5Z29/x9jtZovraeG3Gr3xCDFVxHbdIGWYKkt+259apqjn/Zj3PQPEVJn5ot5xPee4dPRYSGoyqagfE52rVDBsl2w8gipLDKWUlnViY1wcFxRJpGb6XgGN1GDreUVslK0UP9tqi/KZstXpozdVyoMPPtjy73/yT/7JOWnExUAjz+ByxaQ3DssVk6TmnypthGYRgXRUBkHA9sDxXApVe1VZN4xlkEWhns4AdNtFEQXeviPLweP5IJF8zXb8dpVN+pIR3Pqu1PO8QKjg6uFUUHcsIgV1OI6H7YEsEKTxavRRU5qcHJp3102fm/fizWFYcVWiqPuncWXDRpb801RBgIwmc7Kp/rocAzt7Y0wuVnx3ds93+REEgWxUZqFsAqc33AAOYLu++IUoQE9UZrbo/3F2mm6Mq1JXwYbmOcnG1WDsYvW+u56H4Am4eAgIaKqEW++of0LsBfctVwxiqkzN8vOoi6Kwqi+SIHRsS3M7+pN+LLkiiaiysCouq3HC1i1mKyQkJCQk5K3CRtaN//Sf/tPz1ZzzylA6wkAywnxBJx6RqBi+aNZmxaWa9WA6WRAb1rnplSoxReLIfIlsIsLbxtJYnsdAQgtSRwEcW/JzMFdMh5F0jNfnShw8vkI6pjBf1Jkt1IiqEn3JCBXTxnJc9gwkyVctqpaNaTsUahZ37u4jqkjULIeBVCRI+bVcNnjhRJ7/ceAYquzSm1BZLpt879Ac9+ztQ5WlIIa3atq8Pltkoj8epON6+WSenqjMyVyNfM0kqSm8uVhGEGBvIslKxfco3DMQp2zY/n1Vi0xMQTddFh2TQs3AsFxSEZmxbJzpXBVVFrlzVy+m6/LyqTwTfXESmhRY1DNRhYWSEcTw7h5IkImNcmzJt77naxZRWaJmO74buAivzxYZ640SVZLULJvDc0Vum8i2WEwb67lERKJm2oxkNB68Y8cq3aaKYRNVJIbTmh8zr/tjvaybZOO+9X+xaHAyV6Oo2wynNVaqJmlN4R/fMsb2Pn+PMV/UO9ZxsVA1bWqmjSZLF+V6dytTnG2qpK9//evnrOKLjbFsnE/ePcHXnpxkpepvuP/ZXRMbFlNrFhEo1Cy2Z+OBsmSnsvaN9fCRm8d55LkTGPUN90duHufWiT56E1pwyrItE+WTd0/wvUNzFGomO/pieEChZgVCBXuHUi119ycUlisWTn3D/f7rRwIxtUYfK4ZNUhWpWW7gCt4I3Ba9+oa7/rnhYi7ix3Tbnp/y4LaJXp44ukjZsOiJq6Q1GVmR6K//gm24uO8dTDKYjnLrrmw9ZYVFNq6S1GQ0TSGlSS0u5iOpCDXbF5aIRSR64yqqKtOfUFiqWMEGvy+u4Hh0FWxonZPTY2c4HuPZKCXdpmbb9MQipDUJy3HZ3htHwEO3nOBzsWbRl4jwj98+RFG3KNSsjn0pG3bHtjS3I1cxGO+NMt4bp2o6q07RtvKELSQkJCQk5HxyKa8b16MvoXHjeA8P7Z/CzLuoksh7rxte1/W2mWY9mG6aMc3W4uWSwXLZZCAVYXKphiSC7XhEVTmIkZ4t6hyZK3LjeJZ0TOHm7Vl+diLHTF4npckMZzTGemJYrkt/IuKntnK9QGW7UPOtlBN9CaKqRM10WCzrRFUpqOPNpTLTKzUmehNk4xE0ReJErsLXnpgiqkotMc5F3WIs4wvSGpbL/jeXmF6psli3Xi+W/DRgmZhCTJHR0qfLcsG/z/LdzAXRw7A9fjq1guN6DKejKLJINCKRjStkExHyVZP9R5dZKhkkIgrHlirotsOk6+fRrln2qpjyxZLB8eUqtuuS0hTuvXLQt8bXY96duoibbftW3MFUq7XZ81yKusc3n53uGL/cSCUmiwK2Czv7YtTq4ZojaY0rhlI88uxJZgu+5T6uiuiWE1jds4nIhmLgLzTtivAC3kW53t0qK//F0buLhI/etp179vadsXp5u4jAeurlX3zgWj5807ZV6uWdTlnuv3ZwTfXy9rq7qZe39/FSVy9vH5fmsTuf6uXt7eimXr6VJ2whISEhISEh54elss7PTqwwkNRIRCTKhsPz0ysslfUNbbyb9WAaaZj+3ycmGU5F2N4Xb1HIfn22yHg2xlhPlB+8vsB80aAvqbFYMnA9D9N2Tsd9ZzRemyny3PEcKU3Bdj1u2ZHlnVf4MdF/++Ksb3lMKMiSH6t95WAS2/MY7Ym2xAw3WymDOOyyTm9MBQTeWCwBsFz1Vc1tz6U/EQ9Uof/RzaOBlT6uSjwztQyCL05bNmxsx2N7b5yKUaRqOlQtm+WSX1ZFt4goEkXdwvU8JvoSTC6VUWWRO3b18sJ0AVEQ2DeaxnJcJNFXNX/u2AqCACPpGCfzVV6bKxKPSGzLxJgt6C3jtVAyiEUkJhcr2K7LVcNJBIHT1viVamB1bjwr1R1JG+u5Roq0lZpJUvFV4NsVsZOazPZsHFkUT3sPJCN88IYREprMI8+eDGL2X58rYboeN21LU7FsBpNaMPZnorp9vhTEOymDJzQ58JC4HNa7l34PN8lY9sxShTXoS2gtv0zXK2vfWE9LqrAG7acs7eV2+oXdfE9fQluVKqy5TY12jWXjLfV3+9xcVnv/mvvYrf4gLUNW7nj/fdeOcN+1I8H3e4dS7B1KrVvuRv5wrTV2ndqy1ufmeenWl422Yy310svhl09ISEhISMilylzBYKGk43pQMhw8z3f97ZQPu9vzDT2YmCqTisi8uVTma09Nsasv0VFlvGo6CB4IIlQMK7CENqzTjRjahpL5TKFKRBapWQLff22BlKZw5XCKw3NFClWLHb0xji1XePFUnlRU4V1XDXaNGW63/CZViZmCxevzJWKqyI6+BBO9yVUxzoGVvuxb6fuTEV6dKeF5IEm+ltGu/gQLRZ3lsp+CTFNFfjZdoGbZVHQbTZUwbAdNkcjGVd+7UpF4/kSemuVw43gGEFgsm0gigZXfsCNoikgmprZYjgs1O4idrpoOJ1eq1CyHpbKBJAhE1QLTK37sekyR0C2X/oRKfyqC06RAHFNloqpvNW9XgW+OXz7trbAcxLa/66pBdvQlVsWHNzwTioYdxHE77plpAp1PBfFucdxRVbps1ryXRy9DQkJCQkJCQkJCzhPpqIxuuZQNX8B2uR66mI5ubOndogeTiHB0qYwsCYxnY4Ha80hmB5IIs4VaXQk64qd59WB3fxxEgdFMbJV1uqFkft9VQ3zv0Bxlw6E/4VsfD88V+Uc3j1LWHf7mhVMkonJLvPVEX2xVzDDA8eUKJ1eq5Momcc3P5hKPSNyxqw9JhIWyyXS+wlgm3mIdf322yFg2ylhG4/uvL7JQMrhiUPX1djzYNRBHQKAn1sc7r+hHkUR+85svUKj55VuuC7bANSMpDs2WkEShRa29YUmtmjbHlqr8WJMxbI+a6ZCvWQykNMazMfoSEfI1k8GkRjoqM5vXWa4YxBWJfM3PntOXkJgvGii6TV+8n2OyhO3CzdtSVEw/Zr89Lrl5fjpZxKumzUJR56XpPGPZ9ePDbdfjjl19QZx+IzZ+s5pAnfKwn8uc1O10inO/WOK4zxeXT083SLNgRTcX4GbWcsvodq35e2BD7srdnun2ea2yml3CgY6u5p2s7+v1t1u5zZ97E5ENtbF5HmC1O337XDW7irf3o9t9NdMJXNhHs7Ezchtfb043+t5sxL3nXLoAnS93opCQkJCQkMsRVZa4ajjF4bkSFdMhpSlcMZRElaX1H6ZVD2a+WEOVRO7Y3Ut/QiOhOi2K1qfjgT0GkxoLxRovnCzSm1B515XdFa2Tmi8Q20llO6r6bs7t8dZLFTPIHb17IHFaCb2oc2qlRq5q4qx4WI5LLCIzU9SJKRJxRSauyqut400W5YbEr2G79X7ovDhd8Ptx1U5u3tHLK6cKWI6LbjpBiikBqNluYNHuptZe1C0s20UQBBbLOoOpCPfs7fct+zWLwaRvOVZliXhE5uRKjcWSCZ5HRJZwXJeI7OcllyQx8Bgo6hYjmWjHuOTmeO12i3jz2L14YoWoKhORpSAWv1N8+Ol+nbZIn4nqdrc87FulIL7VyuBvBS6fnm6AZsEKWRTY3hunPxnp6nKxlltGt2vtP/gIAook8OZimVdPFbFdFzxIRWUSmkJKU7j/miEKNWvVM5bjgeehyOKqsg7NFLHqPi4p7XRZO3rjPP7GIlXTxnZcQECWBKqGg17PHSnXRd2++MC1G+7vXx04zlefmKyX6yf8liWxpQ5ZErl2JMVEf2LNNt443sPPTqw09ddPi9AsItI8V2XdolizQYCq6VAzHQTB78cdu/qwXW/VfYbt+mnEBJBEkeu2pbh+NLPmPHQSMGkfl+Y52eh7c+Vwitfrad/O5F3bLOfTnSgkJCQkJORyJKnJXLctTW9CbVG93oxlr6EHc2ypyo8PLwQW2nYr4fZsnKgiE1MkDkwtM9IT4/aJLBXLCSymnTRj1rOQNq5lYyrPHc/5sdECzBRqPPrSDPddM8h3X5mnYjpkojK5qolpu2RjKvMlnapho4gCS2VfgfuD+0YwHa/FwDKb130rfVLF9fy0sFcNJXnpVIGRnuiqfqiSQMWw8YBERKZiOIgC/H9u3Ma+sR6qpt1iPGtWa29WEm+2FDdr7jTaVTH8NGmj0Qjlen3ZmIpuuUiSgGm7WI7XYk3vtIFsxGtHFZlMXQV9MBVpicPuS6gsV0zsksE1w2kWy754XK7se0e0zx34oQrNhpPNagKtZ4HfCi533aItHNq3Fs2CFdl4hOlcjWcmc8RUqWPS9rUSu3e7tlTWg+/TmspLpwq8dDIPeByYzLFSs4ipMis1kxO5GvGInyrsa09MMlOotTwTUyVenM7z0qkCUVk+XZbn8cxkjlzFIq4qrFT8shIRmblCjUeeO0Gx6l/LV23yNQtRgLLpYLv4m3nX45HnTvDi9MqG+judq/DVJyYpG/7pWL5qka/aqJIY1KEpEvmqxYGpHFXD6trGxZLOQ/unWCgZpKMKx5erHF+qko6qrFT9NBFH5orBXCUiCidyNVaqJoogUDYcX7Vd9A8lfnR4kVMr1Zb7IpKfJ1O3XFRJoGraHDy+QqFqtM5D9XS7GnUvlfWW96Z5XNIxJZiTtKZu6L1ZKBl+f8v6qnHdyNhvlnNZVkhISEhISEhnGpa9bZkokiSwrYsldD36Eho378jywNtGycaVVVbCRj2DqQglw0KRRN6+M8u2njhjmTiFmhVYxGOqzGB9o9ncxrXKzcYVZgpVCjWL5ZLB919b4MevL/LoS7P85ZNTPD25hCz6uchlUQA8SoaNLAlIokDFcOhPqMRUiW+/MMPfvTTDI8+e5OhC2bcCp6P0J1Vsx2NnX4y+hLpmP0zHoyemEotIOC7EIhI9MZWkpjKT13nk2ZP83Usz/Lcfvcl/+t4R/vKpY0EbG9b8muUQrffx6EI5eKa5XXFVxrRdFkqWn2rW8ziZ1xFE/9qzx3JMLpW5bluGHU3Cdt3eg8FUBN32XdDb47AjkkR/MkJEEqmYvjp5sWbxjZ8e56GnjnF0oRzM3Uxe56GnjvH1p6aCa811Nc/vWjQs8P0JNbDAD6W1lpj0rWAzbbzUuPx63IVmwQpB8H8wTdvFtL2OggRrJXaHzoIGjTqGUhqW46KIIuCRq1h4rkdEEhEFEBHw8PND9yZUFud0orKEIBA8Y9oequTn+DId53RZVRPX9fw83IKHKPhlOZ5HIiJjux4JTUYQ/BzUAmDap/Nei4KIJnsYtssL0/nAzXyt/k4uVqiafsyS5bgIgp/X23ZP14Hnockipu0wWzC6tjEVVTieq5KJygiCgBjkMoPhtMZCSedQ3So8nNYo6la93WA25e+WRBFw0W0PWRJ8y3X9PqupXQIiquRhOx4nV2qt8yD4acv9tBN+3e0CKM3jYrluMCeCwIbem0xUoVizyETVruIXa439Zn9pncuyQkJCQkJCQrrTyPW8kTC1ZjqFrK2Ve7k5p3S7RTyh+Wm9qmbnv/NrWR8b1w7PFfnJkUUqlu8mP1/UKRoi98b7ObZU5bnjOW7cnsFyPQRBIBtXWCi5yKLIvvE0lu0xvVKlatlBTHfDUj6c1ohGxCBuvCemtsSat1v2JRHGsjEiJT2wHPclIli2yw9em/djyVWJn07nkUSBu/f08ZpLoNaeq5pBWUFcc1Oe7kdfmuGePX0UdYuoIjOcVnhjvkQ6qnDPnj5eXygjADeMpbFdAgt8p7FtzGOnuWt4GUwtl5EFf53bl4qwbzTNc8dXiCgSI+kYuaoZxFrDmauUt9PNAn85xVifb8KRrdMiWJH0Y38VSUSVhY7B/usJAnS61ixkkY2pvvgDfp5pQRQwHBfXwxePQEAUBJbLJnFVpmb7ypeNZ1RZwHQ8BMFDlaTTZcVUxHrcCJ6A6/m/ACVBoGzYyKJAWbdJx1TA31SqshD0y/VcTMdDEYUgddh6/Z3o90/4lismiYiMV9/7+ieefh0IArrtIksiw+lI1zbma6ZvIa/ZDCoSfrf8AmcLfp1XD6eCuUpHlXq7QRVP98Nx3XoOQD8uRvZ377geKE3t8nAxHRdZFBjtifLCyeLpeajnA5dEIai7EWPeaVyyCSWYE89jQ+9Nvq5Sma+ZJCPKGb1rmyEUsggJCQkJCTk/nEk4V7cwxLWe7xaz7HkexarFN589sebza2VNiakyiiQh1S0RuYqJ5/l5qWXptBL6UslgJK1RrXsc9sUjvjXa8UhHFWwnGsSHy6IQxIerkogsCOiWE8RU7x5IoMhix/jfmCoHse6Fmu/xOJiM8H+eP8mL0wWiqggILJUNsnEFVW5Va2+OvZ4v6kFM+aQLhu1QM+1AmTwRkbBdD03183z3pTSEhQq5ssFrc2ViioRlex0NF+vNfUyVSUcVHnlumqpp10Mw09RsF0USAoV1VRbXNeqdieGkOca6UN9wX24x1uebcGTrNAtW5CoG471RxnvjVE2nY7D/eoIAna41C1kUaib7RtM07K23T2R5daZI1bTpiUVIaxIVw6YvqXLf1eMUdavlmarp1DfFHjXbPl2WIHDrriyHZopUTItsXCVZz3c4nIly20QfTxxdpGJYZGIKAv7mMqn68UZWfcP9kZvHW8TU1upvLCvzybsn+NqTk1QMu16uh+m49MRkPPxfpj0xhWtG0sQiStc2DqQ07r9mmOenVyjULLb3xRCAQs0/mXzwjp3sHUq1/MLdno1S0G0szyMZkahaDrbroUh+TLfjeS33GY5LJiqj2/4BQ0yVuW40RToWaZkHv10KZcMO6m4/pW75pVW1gjkp6OaG3ptmEY9uwhLnUnwiFLIICQkJCQnZejrlJV7PKtn8TDam8sPD8wDce9UAubLV8fmO+Y8jUku8dUOZ/NGXZ84oL3I66htUBAF64wpzRRfH9RDqKuG37OjhtolehtPLLJUNRARcPIYzUT64bxsJTeKRZ0+2xIcDjGQ0cmWrJV8z+PHKa1n2T8e6V/jR64tUTAdFFFgs+3nJrxxKYlgui0WTXNnAdN2W2OtGHabtnI4pT0Q4vFxBlgSG0xrHlqpUTZfdA3Eqho0i+R6niyUdy3GRBaGeEm51HPRG5n6prPPdV+eIyhKDqQjFqh+e+qEbRvjJEZV8zWKhqLfEgJd1h6ginTPDycUcY30pCv5eGr04RzR+iDeqXr4Rl5z2a53EEN4q6uVr9fejt23nnr195029vH2uLqR6+VpzutH35raJ7Bm/a5vlYv4lGxISEhIScilwJuFcLSFrTWGIAkLX57vV47ig26eVySUR9h9dZqlkMJyObkpE1VdiT3J4roRpe/TGVWIRmbxuE5FEaqbLk0eXWCzqHM9VsV0/XPC+a4bZ0eevAxsH/jP5KqIg8LbxDGlNRRWlIF/zTF7fsGdAX8KPP16qnAzUz8EXq9Jtl6FUhPmiwVNHl+hNRPjYnQOrlMxFQSCuysh1cbaIJJJNqMRVhf6kyv6jy8wUamiKyEgmRaFq+d6nlq/8rcoCMUVeFQe9kblv5HG3XfwwUw+WygaKLHH9WIaH9k8FObv3DCR45NmTqzwZzoXhZC0vhwvFpSr4e3GN8kVAX0Jr2Vyt9yKu55LT6Vr794GgRVZmLBtvact6z3T7vFZZY9l4y7XG57FsvGuqsPX6tFa57Z830sb2eei04W2/p1s/ut0HsHcotal2dWOt+dno/Wfzrm2Wi/GXbEhISEhIyKVCI5xrcqlMVPHTb432rK1e3nhmeqVKTJHQbSfIO92wYNfqgr2Nv+HdwsZaQhoTCs8dW8H1PJIRP3a52fK6nlXRV2LPkI1Hgr4MJCOBNT2vm2iiwNRyFYA7d/W2KI7H1NMK3AtFg7954dSqeO2Gmne3vNHNBpTGukwSaVI/j+C6HqIAO7MxXp8vMZTRuGEsg+W6HJ4rcv1oqqWOpbJBxbQZ642S0VSedlykehjmSycLRBSRPQNJ8jWTfNXiHXv6eH22hCKJ9CZUlssmVcteZenuNCftsfXd8rirksDrs0Um+uNBG7/76hwT/QnGemId1dcvJc7EQ+Stwlu79SEhISEhISEhISEXGe0xuzHVD8Vba+MQU2WuHE5xYMq3csqiwPZsjELVwvNcirrHN5+dbrH+dQsbaw5pnMn7rtRRVeaVmSKSCDXL32hvxLrcHv87mogGeb5nCzoLJZ2qadfjqFWSUZVsXFxl3Y2pMjv6ZN5//ciq9jouXfNGH5jMBWlim1O4NtTPZUnAdlwG69bt56fz5KsWA8kIk4t+WbbtW5fb64hHZNKaiuN53DjeA3jMFqu4HuzuT9Cf1KgYNodmSyxVDKq6w0BKAwQGU5Eg53a38eoWW5/U5I553E3HT3PbiH8HeOVUgUxUabGaRy9R48mlLPh7QVv/+OOP8x/+w3/g4MGDzM7O8q1vfYsHHngguO55Hr/7u7/LV7/6VfL5PHfeeSf/7b/9N/bs2bNlbToyVwxcjputoM10Om3rdA1Wu0VDa5xCez7Bbi7K3cptfuZkrhq0PapKXd2tm+t/dmqZ/W8uc8euXq7Zlu5YRzeX7L1DqRZX9eZrQMfP2YTasdy13Mub+9g8Xt3GoX3sziYupH2um/vb3Ob1QhFCWrkUY3VCQkJCQkIaNGJ24xGJ8WyM5YrB9w7Ncf+1gx096KqmzULR4OWT+cDK2UnNuxGf3Wz9Wy+k8fhShcmFMkXdJh1VgpzMpu10tCpmYqM4LuvmgV4q60Gu5/5kBMOqslQyMW2HZd3sGm/cKSzv+HKFkytVirrdkje6UDV56KkpFksG6ajMYsng4f1T7BmIB9llZNnP7vP8CYttPVH2jab57qvzLJQM+lMaiyUD3XI4sVTm+HKFiukEdWTjCr/09jFUWTrdlqVqPeOQjVI1/LRcgr/BP6qXWSob3Lozi+V69MSUVd4HjT76ivJ+3LnpeMHcffv5k9y2M8uu/viqPO7NHgq+4K5JSvPVxZNaZ8Hds2Gr1mNnWu6lLPh7QXtQqVTYt28fv/Irv8KHPvShVdf/8A//kD/5kz/h4YcfZufOnfzbf/tvuf/++zl06BCatrG0C5vhK48d4aH9U5iOiyqJfOyOnXz2PXtb7vnh6wsdT9var1m2f+ylyGLLfc1xCotFnWO5Ko7rIQkCO/ri9Ccjq9Qq01GF7zZUKR0XPL/c5mdePunn6XY8D9eDiCQSUUSqpoNu+XEusihw/zXDXDGUpKhbfPfVOY4tVfGAP398ip6ozPa+eEsdZd2iWLNBAMNyMG0PQQRVErlxvIfJJT9dWPM1x/XA85AkEcfx+yKJvmBcX1whm4hQ1m0KNStIL5aOKiTqP2g3jvfwsxMrFHWrpY/N4+WPESiSiCQK7MjG6E9pq8buyuEUr9dTjG02LqR9rnf0xnn8jUU/p7UHqahMQlNa6r+UYk+2iks1VickJCQkJKRBI03stkyMmCqjKVLH1KNw+u/ibKHGkfkybxvPMJDUSEb8/NmO51GznDWtf2uFNGYTEUZ7YiyWdXTLoT+p0p/QKNRWWxWPzJf4yyeO4eGt+hvdXkcj17MsCtiOx47eGDXLYbFsMpLW1ow3bpQV9L2oU9Qt362+nje6PxVhtqAzveLnCZ8t+ulci7rF156YIqpKLJYMjucq1AyHYs3mjj29DKajDKRUchWLimEjeB6TixX++IdHKVRNBlLRljpUWWIwdXpOrhpJ8Ym7J3h4/xSzBR1BgF0DCfoTGp7rMbVcJVc16ImpFHV7lfdBy5wWdY7MFblxPEtUlSjpFt95Y5EfHV5Ek0V29sVJRGS21b0Hmj0UFss6g0mNe/YMrCm4e6Zs1XrsbMq9lAV/L2gP3vve9/Le97634zXP8/jKV77C//1//9988IMfBOC///f/zuDgIN/+9rf5xV/8xY7PGYaBYRjBv4vF4obacmSuyEP7pzBsl0TEV9J++Okp3nfdUGDxXirrPPTUFCtVKzghe3j/FNeP+tcb17IxlYMnfGXGG7f3sFKxglO5xoliQpU5MJXDdj2uGPLFKeZKOj//tm0cPF4AfLXKUys1Hnlumrgq05dUOXhsBQTYN5rh6GKZuZLOu67o59ljOVzPIxERyddcdMtlUFEpG/6GW5MFLMfj0ZdmwBsiqklMLfmxNwJ++qyVms2I5XI8539/9UiKE7kaHh4DCY0F3QSgJ6ZQMWx+dHiRdNR3n1oo+ddSmkTV9A8ckpJH1QHwSGkiJd3hpOWQTagcz/mb/ZG0xkzBV2d8+84sCyV/TIdSUYYzUV6dLTBX0vnA9cPBeO0ZSPDCfB6AG8YzvDFfZq6o8/P7Rjl4aiUYu/mizoGnllviYDYaF9I+18eXKvx0apmYIpOJq0yvVMjXLG7arjG5WGGuqPORG8dZqXZWFw3xuZRjdUJCQkJCQho0p6JtrBnTUT/PdLNVtPnv4kg6xutzJQ4eXyEdU8iVrVXx2Wdi/UtqMtt7Yy35sAeTWlDu9EqVTFRhseJbrqOq1HHd1O6FWDNthlNRZFEMYr0TEYmbtvdw1XCK3kSE+aLe1drZ0veMxmszErYLN29LUTEdBlMRMjGFlYpJzXKJKiJlw6Fq2FQMm6GMxnPHVvCAO3f38dzxFSYXK4xkosiiRH9C4urhJN95dR4EGEpr5KsWC2Wd60ZTIMBoJtZxHBuCva/PFvkvP3yTSj0eu2Q47OqP809u3cHjbyyyUrVIRuSWOHmgrV9FnjueQxIEnnhjEdv1GExFWCyZnFqp8cANI1w5kgoOYzrld19PcHezdFqPnam6/XrlbkY/AC5dwd+LthdTU1PMzc3x7ne/O/gunU5z66238vTTT3fddH/5y1/mC1/4wqbrOzRbxHT8DXdE9mMoKqbNodlisOlunFoOp33hguG0FpxaAsE103YRBX8rKwpCcN/kYiU4UcxVDPBAk0VEQUCTJWzHpazbLWqVUVmiatqMZ2N+7m7RL9f1vOCZ2YKB53kokoiAVE/UAIZ9OshEEkUEwUO3XGq2Ry5Xa7oGjVvzuhnUUWtSgnRcr+l+AU0RMR0HTRHxOH2t6TacYDvv/79Yt2qXdf/UUax/IQnguR6u65GJKpzIVUnFZFRZDPq4UjGD8XJcDz/ttoDjemiKiO14lA2rZewyUZWinl8VB7ORuJD2uY7XczUmNBlZFBAR8ADTPl2/6TiXVOzJVnApx+qEhISEhIQ0aE5Fu1DS0RSBwZTG37ww02L9a/+7ePP2LD87kWMmrwfW4nbr52atf+0x2Y182H0JrSWGXFMkemIKYz2xVX+jm+OqZVFge2/dC7FkcHy5gu162LaLJ8AzU7mWe7pZO1f1vZ5Pu6hbQT5t3XJQZZGy7lByfE9DWRXpiakICKiSAIhk4xHevjMb5AxvpHBdqfpGoV19cXpiEYYzFkcXKjx7LMdQKsq7ruw+jn0Jjbv2aJgOwTw2UsiO9ESZKdSCHN/NcfJAx35NLpVwPb8tKU3FsFwml8p8+8UZJk4VgzHqZik+l+uk9rE/G3X7tcptfoc2o05/KQr+XrS9mZubA2BwcLDl+8HBweBaJz7/+c/zuc99Lvh3sVhkbGxs3fquHk6hSiJlw/9hKRs2miIGMcnQ+dSycQIJBNeyMRXX8zebrucF9030x3l1pugrD6oyCH5aA9fz0G0HWRJIaDKW6++APTxqtkNMlVkum/QlVVzXAwFEQQieGU5HEAQBy3GJyARb4Ih8Wk7RcV0sx9/gRmWBbCLadO30OGQ0lULVt3Q3BBxcqLuHN+73N+8CoFtucEgB/sa6gdS0GW/kAxcESGgyniD4G3RBwPFAEPwDheWySUQSKVZt4qoS9LEnrgbjJYkC/hB5SKKAbrnIskAiorSM3dnEwbTPdcVwkOuKlrIk4tY39qp8un5Vki6p2JOt4FKO1QkJCQkJCWnm3isHGM1oPD+9wuG5MpIororJbv+7aLsed+zqC9SpoXPe6sb3nfRrYHU62U7PH1uqtMSQL5UNTixXmVwq05+IBDmiTdsJvP/6kxFePVVgJq9z/7WDTC1VQIAbxzN855V5HNcLQvvmCgYfuWW0qxdge98bOb/fecUAO/pi9CU0jswVfWOWCJriK4C7rr+ZziZVTMdDEDw8z1/PtufjbsRn52smggAz+RqJiMQt27MgCC0K62vN456BeKDpM5aN+/HsdeX0xp7AclxyZZP+lLqqX3fs7uWe3f38x+8dpqjbaLrF0YUyiiQyno0FY5SJjZ4Xj8DmsW+o2wsCjKRj5KrmGdfZbZ3XUKe/nD0dL7leRiIRIpHIpp/bO5TiY3fs5OGnp6iY/ob7wdt3toiptZ9aNk67Gu4gjWuFmsn23jgCHsWaFdw3lo23nDTePpHl2HKNYs1irCfGjt4YtuOxbzQNCBSqFtsyUT559wTfqz+zvc8vt2LawTOxiMItO7K8fCqP6fiWV00W8YBkxHf3sV0PRfJjuq8cTlKoWUz0xZiqx3QLQCYqoygiO/pivhXXcRnPxijpFpbrktZkTNvFsB3iEYm3jfUwtVyhYtikNQnT9rBdD00W8DwPB4GI5O+0bdcjpkr0xRXAj4Eu6Ba65ZCNKaQ132V9MKXxD64Z5vnpFXJVI+ijgBCMV9mw2d4XQ8A/HBnLxtjRG8X23JaxO5s4mPa5Hspo3DrRyxNHFykbFj2xCGnNH9tG/TXbvqRiT7aCSzlWJyQkJCRka1lPgBfgtdde43d+53f4yU9+gm3bXH311fz1X/814+PjAOi6zj//5/+cb37zmxiGwf33389//a//dZWR51zQ0IZZrhgUqjZ37eljZ188sP4tFH0V6nv29vHEG0stfxd39HW3eB5dKPOtn51krlhjKBXlph1ZfnY8x1xRJ1L3ElRkcZXOzfWjKRwXRFHg5ZOFIIb82m0pNFliWzrGctnk6GKJV2cKpKIKt+7MMrlYYaVq0p/U6t6GAiXd5tljKxSrFtm4Gng6RhQR03aINLwAbW9Vf5sPA5r77nkeNdPl+6/Nk9IU7t7bT65sEI/IGLaLYbvIkq+VlIjIFKpWkBJsplijL6Hy/utGgtzg4Mdnv++6Yb76xCRzRQPD9AXLplf0uqeny0LRIKp2d2U+ulDm0ZdmWKqY9E2rvOuqAUBgIBVBlgR0yyGuiuQqJv/5R28w0Rfnxu3ZVevP3QOJIFZ8vlhDlUXu3N1HX0Ijrjos1t33z4dHYPN6bCbvj8WN41nSMQVVXq08vxEaBz+d3mfH5bL3dLxoezk0NATA/Pw8w8PDwffz8/PccMMNW1LnZ9+zl/ddN7SmenkjxqOTKnn7NVitXt4ep7BR9fL7rx085+rlv/GuPZeFevmZxsF0mutQvfzsuVRjdUJCQkJCtpb1BHjffPNN7rrrLj7xiU/whS98gVQqxauvvtoivvtbv/VbPProo/zv//2/SafTfPrTn+ZDH/oQTz311Dlta7M2zGAqylKpwJNvLDGS0SjpDp7n8e3nZ9BtJ9hgDiQjLZbrbsri/+1Hb3BgKgceuHj87UunsB1fD8mPhdb4wHUjHDy+CPg6N0++scRfPP4msihiuy7XbEtz165+nj+R5zuvzDHaE8NyXEQBrhhO0p/QOLZc4X89e4Kxnii5isnMSo1oRGK5bCBJgh8HXjJYrBhosl9usebguC6Fqk08IqPKAnNFvaW/luML7jbEhu/e208yIvHt52eomL5C++tzRQ5MLTOcilAxbBRZJKMplA2bWETiH986TjqmslAy+MGheZYrBp67eh6qpi/ce+fuPjw8fnhogbxusa0nxmLZoKTb/P8OTmO7q4XjGs//r5+e4MXpPKok8Lxh88QbS1w9kmShaNCbiDCSjvCdulr6SsXkxek8cwWdf/W+K1epwDfWlseWqvz48AKG7bXkKz/b+P3NcDp3uh6MfXNbNlNn+wFRp/f5cvd0vGh7unPnToaGhvjBD34QbLKLxSLPPPMMv/7rv75l9e4d6p4qrEFfQuuY7qHTtU73NccpxFS55Z7mDUjz57XKbdzX3vaxbDz4776xno71v+OKQd5xxenT3W51NMpq1NP8fbdr3T53K7e97k59bB+vjYzd2cSFtLejvb/d6g9Zm0sxVickJCQkZGtZS4AX4N/8m3/D+973Pv7wD/8w+G7Xrl3B50KhwF/+5V/yjW98g3vvvReAr3/961x11VUcOHCA2267bVWZZyrQ264Ns3sgweRSmRO5KmPZGMWqRcV0gg3Ik28s8uAdp11tG7Gx2ZiK5bhkYyqFmsnrMyV+OpXD8Tx6ohEWijUWihY9MYWeeISibrFQ0pkpVBAFAcd1eXOuxCunCpi2SyqhsFi2OTRT4MqhJJoq4pQ9bNcPd7Qdj4F4FAHIlS3yNYvrRtKBcUG0fGtzIiJR1m22pTVsFwo124/xFgzwBGKaRH9CpVizSUbloL/ZhMIPDi1gOx43jKUp6TZPvrHIe68dQrf98ZBEgaWyv4HdlomSjqoUdRPHg0zMz2edjqkkNZm/PniSiumwozfR0WW5MY6jmRhlw6I/FaFQtaiYNj0xhXzVIlex2NXf+fmFos4rpwrIokAyqjCdq7HoGFw7kiIbV32NJjyWSgaKKJDQFAo1k2emciwWDa4aSa96Nxpry0xM5dGXZziWq9CXUM9J/P5m8XOnJ/jAvtW50zdaZ6cDoub3uWHsu3tvP0++sXjZejpe0J6Wy2WOHj0a/HtqaooXXniBbDbL+Pg4n/3sZ/nSl77Enj17gpRhIyMjq1yJQkJCQkJCQkJCLl9c1+XRRx/lt3/7t7n//vt5/vnn2blzJ5///OeDdePBgwexLKtFpPfKK69kfHycp59+uuOm+0wFetu1YYq6za7+BJ+4aydRReabz56gP9Hd1TapyVi2yw8Pz6OIIpZbD6ETPGwPVFFElgRk0dfIkUTBF9QVBCqmw8snC8yXTHTL4c16ju5kRPI3wLpFSXf4yesLlE2HsZ4o9+ztxwO+f2g+qPNkvsqO3hiaItKfUElHZa4YSvH8iRzHl2uczFdRJYm378zy4ZtGiUUkUhEV03FQJYmiYfLA27aRiJzur+W6lA2T2YLBfEknqohcMZTkvdcOB5bQuCoxk69hOR5TSxUkUfCF0nZkEUWB0Z4oSU3ekDhr8ziKgkCuYpKOKdy8vYelsoFhOx2F405vBuuiwIKfFk23HUzH5ZWZIklNpi+hcuvOXp44soBVjzW3HDfwOlgPzwU8r8VKfyE8As+mzs2Ip92zt4/+pHZZejpe0N4+99xz/NzP/Vzw74YA2oMPPshDDz3Eb//2b1OpVPjVX/1V8vk8d911F9/5zne2JEd3g27uw9DZlXm5bLQIKzQ/35uIdHR5bn+mE83u1e3W8maxjGZ362Z3baDj5/Y2druvue1vzJcC93TdcvjpsRxv35Hl1ok+jswVO7qO58pm8P1oNtaxvc39ak8h0K2P3eah+Qd3rbKa5+F8/LCvNY8hISEhISEh54aFhQXK5TK///u/z5e+9CX+4A/+gO985zt86EMf4kc/+hHveMc7mJubQ1VVMplMy7NrifSeqUBvNx2gq4bTgattI1VXQ7Bslaut0FCnbQjT+orgIxmN6VwVt2LgeB5RVcL1fJ0bx/U35CBQM208BDRVRtRtKqZDsWpRs3w3clWUEAQX3XaRZZH5oo4qS7iehyT6G/mS7q+hGkK22bjCctlCAGKqgmE5nMpXGUxF6I1HWqydUUVCt2zS0dPiWpoiMJM3sByPnphIvmbz+lwJSYT7rhni28+f5Oh8iYppE1P853JlE8N2sF2X0UyM91zth6DWTIeoIq3vslwfR0mAbFwlIkvotsNIT5RERCZXNVFlsePzA6kI146keelUgULNxLBdFFEgVXetdz0/9a8qy1RqFgIepuORiMgMpk5rTLWvBxvW4W5W+gvhEXimdW5GPO2JN5ZaPDouJy5oj9/5znfieV7X64Ig8MUvfpEvfvGL56U9f3XgOF99YpKq6SuYpzTFVxN3XPBAkUUk0RcB609pvLlQ4tXZErbjElNl9o1mePFknqrpK1xfM5xk10CyJXblzcUyh2aKWPVnPnn3BB+9bXtLOxrCG41ToY/duZN7rxwAWmMmmtM0lHWLYj1Nge34SbwUyc/NLeAhS+KqNvrXQJYEbMcFBP/UtKntByaXOTRbxHU96sOAgP+La6IvzkLZwKw/25dQfVebssFyxcLDQxJErhtNcf1ohsWizrFcFaceN9PoV3scSEP1stHHY0sVHM+rC4KAIrXOQ7u4SLey2mOIzjQdwkZZax5DQkJCQkJCzh1uPXvJBz/4QX7rt34LgBtuuIH9+/fzZ3/2Z7zjHe84o3LPVKAXuusAxVS5JVVXKqpwz97+lo1ISbdRJIF7rxpAQMDDo1C1UGWJD71tlL94/E3KpkMiIvO+6waYXCqzUNRJRiT6UxFEQUASBRIRmb2DSSISTK/ozJf9lLVDqQjJuEIsKmPaDjN5nZQms2sgzo6+OAICb84XOTC1wlNHl4iqMtuzMWYLOqII125Lk667vucqBoWa3eIWvVLRmVqu8uTRJVKawv3XDCEIcHSxHGTEydesIOtNoWazUDL8lF9lA9N2ycYkdMulJ65SNixcfMvwiVw1WOtZtosgCF1dljuN43LJt8BP9MeZyetrulXHVJlfunU7qZdmOLFSRTdd4pqfzrY/qdKf0JBEkX1jGV6fLaLbDj2yv/5U69l9Oq0HrxlJXTLCYt1EckPxtFYuvx53YTpX4atPTFI2bFJRhRPLVfJ1VcSj82UAbhjP8MZ8mbmizv1XDXFgagXb9diW0VguGTz60gxJTaE3qXJqRefA1AoTAwlenM4jCHDT9gzPTOawHI9tPVHyVZOvPTnJPXv7Aot3s/BGIwXBw/unuH40RUyVgxOjpCbVy3LZ2RfnUK6Ih8dwSuNkSccTYDgdYbFkIgDjvTEKVYtHX5ohoUlkYhGO56oIHmzLaCyVTBBgtCfKYsnkwNQKmZjMyycLOB6oItj1sRIB24MjixVkAdIxmVzF5tRKjYwmcyqv43mQjikUdYvnjq+wayDOgakctutxzUiKxZLJw/un2DMQbzkFm85XOPDUMhP9CXriCgcml7Fdjz0DCV6Yz6+ah4/cON41zcL0SpUDU1NM9McZTGn84NACggD3XjF4VukQNsJa8xhavENCQkJCQs4tfX19yLLM1Vdf3fL9VVddxZNPPgn4Ir2maZLP51us3fPz84GA77kmpsr0J1nlkff6bDFI1ZWvmatSVzWsh7nyaSthQpPIlQ0WSwZ37eknqvhZVIYzGp+8ZyfHl6r8lx8dpWI69MQVphYrlAwb23WpWR69CZUbx3v42YkchuMRj0gslkyiisTbt2e4YijF919bIFe2yCYUppZr9Ccj3Lazl6rlkIhIvG0sw8lcjYrpkIp6LJaMQACsL6ERkYd54cQK/+N4Dt1y6I1HWK4YfO/QHP/mfVexpz/BT9/MUTYsYooUGLoc161vTO36GtliqWxw43gPL53KE5ElBlMai2WDh/ZPMZ6N0ZeI+JtcTQlSrLWv6TqNY29CZaI/HsTaN7tVQ2sqNvBdrz95z0QgOJbXzWDeBpMaQ+kI121L05tQicoSNdtXSJdEeG2mwF8+MUlRt1vWg1964NpLSlisk3t6KJ7WyuXZ6w5MLlaomja99bQHkgie66cuEEU/nsNx/XRctuOxWNLxXI+IJBCRJKKqTL5mE1MlIpJERPZjP1YqJqokAAK5ioVbL0OVRHrjKitVk8nFSrDpbhfeGE5rLJR8l5T+5OkTo5WqiVt3KbLqibZF/JzX/mdwHA9RAM/z82fHVIl8zSKmKoj1ezzAqud5EAA8L2j78WUd1wNZbFz0ESQQHT9/tywJSKKEJNi4HhR1+7Q1XBRQRBHH9ZjL6+CBJouokhT0a3Kx0nIKlomqFPU8maiCabv1Z6R6igu/lc3zYDpOcHLWnmYhE1Uo1iwyUdXPqV2fB0Fgy0/b1prHcNMdEhISEhJyblFVlVtuuYXDhw+3fH/kyBG2b/c9Cm+66SYUReEHP/gBH/7whwE4fPgwJ06c4Pbbbz/nbeqW8qsRAzuW8UMZkxFl1Zqk3XroeR7FqsU3np3myFyRG8ezjGVj1Ew/1ZQqS+wZSrK9L8Zi0cB2YaI/zkLJ36SrssCduwfY0RtnrmiQKxtUTAcBj+PLVf7s8Sl642pgkW6kkbplvJfRbIzpXJVnj+Uo1DfFJ3KVVelzGxbduWKNkyt6PdWsh+d5nMhV+Z8HTuDh4XgeVcuhbDq+52RcJV+1WtZNu/rjTC1XWSjVsB2PqCryyskijusyU6jhOB4nlqtIokAt6RLt4hq9kVSlDbfqbvPVuKdZcKxQ81PTtoufFWoW2xJRrhhK8cizJ3lzqczkUpmJ3kTLerDdM+BSEBZrd08P08S2cnn2ugONE6/likkqqvibPEEgqoq49U2pJArolossC/QnNQRRwHA8DMehZtpIAlRNh2jEwbA9ZEmgJ64yuVhFEDyycQWxXobpuOSrJklNDuKpYbXwxmzhdAqBmHo6ZiKpSYiCQM10UFIi4KeNkOqbYxeQJF9cQwBcz2+b30YLVY7gO4WDUvft8QAEIWj79l6Ng8fBdn1LdwOvvuEG35XdcR2c+sY+pckNyQkc18Ny/RigoYzGodkSuu1iOg6LJZNsXGGiP86rM8XgFCxfM0lpfnxTT1wBAXTbQRLB9xzzWuZBlaSuaRbydZetfM1kUNEwHQ9B8PA8tvy0ba15DAk5W37/93+fz3/+8/zmb/4mX/nKV4Dzm3s2JCQk5EKwngDvv/yX/5Jf+IVf4J577uHnfu7n+M53vsPf/u3f8uMf/xiAdDrNJz7xCT73uc+RzWZJpVJ85jOf4fbbb+8oonY2dEv5NZLZEVhf14vpbk/plNdNUhEJy/F47njOt+BWzSB+tqw7jKSjRBU5KDehSdy2I8vTkzk/prvu0p1NqOzIRnn8jSUQYDDle0d+79AcX3rgWhzXC9JIFaoWzx3P+eGBEQlJhFt39vLOKwbY0RcLUr02PPwGElGOLVVZLBtIokDFcAAPw/bTgZUMGwG/zorhsFQycF2XuCoH66aS4bCjN8b7rxvmfzxzgorhkI6KnMjp5CsmqigwmI6yUDSCGPTGuLdr92xEJKzTfD360swqC3q3snYPJMjERpkrGKSjMn/74iwrVYvxbIwDgsAbiyUQIF+16EuqgWfAelb2jbxnF3MK1jBN7Gku3563MZaN88m7J/jak5OUdd+tJqUpmI7Ljr4YHr5AxVg2xo7eKKoicvtED6/OlHxralzlnr0DvHQqT7FmkY0rXDOSREDghrEMjWjoW3dlOTRTpFjzN9z/7K6JVSmoOglvNKyjzSdpt+7KcmK5gm47bM9GKegOuu3SE1Pw8DfEPTEFAY+qaZOJ+zFDL53KUzFseqIKAmA4LpmY/1m3nKDtvYko142lOTTjx3Q3NtMuINdjuhcrBjXLJar6qSFESWQ0o7FUsTBsh5gqc/22FHFV4faJLMeWa+QqZtCvsWy85RRsMKlxz54BDs8VKdQsbp/o5dhylbJhs70vhtA2DzXbDk7O2tMsDKYi3LO33y+rHioAHgXd3PLTtvXmMSTkTHn22Wf58z//c66//vqW789X7tmQkJCQC8V6Arw///M/z5/92Z/x5S9/md/4jd/giiuu4K//+q+56667gmf+6I/+CFEU+fCHP9xyQHmuWUvReTClrRvT3SCmykTrm9GFko7jeiiSb3SZKVQZyZy2qjZinGVRQLcdBlMR3nO1b601XXhov1+fbbt4gsDz03lKusPugTgpTUUWxcAKe+22dGDVnclXMSyHqCrx8qkikigwkNQYy8aCdU2zh58oCEQViaJuM1fUkUSBdFRhOB3FclyiioRU1+hJRgSqpsPfvTzLUCqCYbsslHQisshIWuPAVI6q4RBXxbq7ukpZt6lZvniwKgvE1CiO292zoDGOa6352udLFgX2v7nEUsVkOKWtW1Zz3aIgsFwx2DuQIqpKXD+aZv/RZV6fK5HSZO67erwlvn89K3s3zuSZC0GYJtYnHIEmPnrbdu7Z23fB1cu7CW/A6hOjrVYv/4137dly9fJOp2C3TWTPSL18vbKa52GrfwGsNY8hIWdCuVzmox/9KF/96lf50pe+FHx/LnPPWpaFZVnrtqVxT0TsLobZ7ZmNEpE2XvZmy99s2S3P1vu8mb5fKlyIvm/2vdlKGm25mNq0VVyMfVxPgBfgV37lV/iVX/mVrtc1TeNP//RP+dM//dNz3bwWuik6N9Y1r88WGc/Gghjg9pjuZiQRZgu1QCdmpWqRjir847dvpz+l8sizJ1sstImI1GKhbY4hjysST0/mEBB4+44evvPqfN0bTw28EBvrrYb19vXZEm8slOvWZpXZgt5iXYZWD79UVKZqOQgCDCS1univy/RKmd64n4dblUWGkhHeXKqgKiLjPQnKps1wJsptE1mefjOH6XhkYyqvz/mektiRatEAAQAASURBVFcOpyjrNqfyNaKyTH8qwnLZpFSzmVmp8vgbS5SN07nPH315hg/u28ZAKrLumq95vrIx1bfsex6piMxCee2yGlbyhbJOJqqyVDaYzetEFZnBpMZS2WRnf5wbxjLYrkdRt6iap0MJ1vKK6NbuM3km5MISzkobY9n4Kstzp89BzE1Wbrm//fn22IZOz3SiL6F13aQ1nxjFVLnlvva2dPu8kfsadewb62HfWE/w/a0TfcHnvUMp9g6lWtrd+G/z993a26lP6/Wx0zxstKxuz2wVa81jSMhm+dSnPsX73/9+3v3ud7dsus9l7tnvfe97xGKxDbfp393srn9Tnb//+7/f8L0Af/j2Td2+qfI3W3YnNtP3S43z2ffNvjfng8cee+xCN2HLqVarF7oJb2nWimedL+ocX64GlmtJFLBsr6vOjOPCcEbzLdiWS39CpT8VIZtQuypEN8c4N8eQW66LVhfrGUxFecfefp46ush80aAvobZ45TUsqbOFGlXDIaaKLfU7Tb8Gmj38Zgs6iiTQH1eJR2TiqkzVsplaqvHmYpVsXAGgaDhEZJE7d/fTn4qQMGUWyzqZWATH84I+7epP8NTRRZ49tkJSkxnriaHU9YeSmm9R/5/PHOfkis7bxjNEVd8Ffv/RZZZKBsPp6LpW4Ob58i37LlFF5OVTBQzLoWa5Xcsq6XYwn5Oun1c8HpGJqRIzhSqiIHDbRC/j2XgQg9881xvJN97OmTwTcmEJZyUkJCTkLcA3v/lNfvazn/Hss8+uunYuc8/ed999pFKpjs80Y1kWjz32GP/2ORHDFda9H+CV37t/Q/c1uPb3vrup+zdT/mbLbiYievy7m91N9f1S4UL0fbPvzVbSeO/f8573oCjKhW7OltLwfgk5c7rFs7ZbrjtZjhtUTZuaaTOcao3Vbo4BXy8+vMWKm1BaNG6SmsL7rhvmpu09XDWcCowvzZbUkXTMtzZbLlcNRbE8j4GkRs10AvXxkm5z20SW60dTvD5T5L/86E1Kuk1vQmWxaKBKIhN9cXpiKjXbIR6RuHY4zSszBVzPd5fvpNGTjam8uVgmm4jwttEMej292XBGI6OpPD25TKRuKT+Rq3Hw+AoxVeK5YysIAoykYxvOWNOYr+NLFSYXX6Oo28QjCodzJWRJoC8ZCTLmNMqqmja5isHJlWqLQnk2rvBLbx/DceFvXjhF2XBa+thtfjaq8n0mz4RcWMKZaaPZXbrZLRpY00W6QbOgQfMz7aki1nOdbv7cl9C6lruRz+3t3Ugfm9vbzYW8IZzRaSy6udl3K7fZKt7+fLObf7NLeXO/Ormab4azFaLYyPMXu9hFyMXL9PQ0v/mbv8ljjz2Gpp0bz4luuWcVRdnUZsJwBQxnY5uvzW5SNlrumZS/2bI7lrGJvl9qnM++X4yb283+nLwVudT7d77oFM/azXLttDmQNMfsWo6HLNISq90od7348GYrbrvGjee52J7Az07kObpQWaWwvsrafDxPVJHQDYdvPnsCy3ZBEFAkIYgrvmvvAKYLD++folCzSMdl+qQIyxWThZKBYfsb0KrhoEoigkCLN0CzRk+z1Xly6bQVOa2pFA0/B/eN41n6UxFu3p7lZydynMhVkUS4cTxLOqagyuKGrcAxVSabiDCajbJYNKiYNhFJJBtXicgSidRppfmZvO57AhR1irqfAq15PtV6mrP3Xz+yrnr6ZlW+Q2Xwtx7hzDTxlceO8ND+KUzHRRIErh/NcN1oGsvxwPNQZJHFos6xXNVXcKwnuL/3ygGg7Zdjh19CuwcSLfcslgyOL1ewXc9P++WBIov+s/ifU5rC/dcMUahZwS/dRlua62j5vEZ7e2IqPzmy4PdRFLluW4rrRzMtzzS3t3lMQKAvrpJNqKQ0pZ7rcSUQcGiMxV8dOM5Xn5ikatrIksi1Iykm+hNdy1UlkY/dsZPPvmcvQMvzHpDWZBKagiQI7OiL05+MsFgyOLZUwfE8ZFFge6///ZkISZytEMVGnn+riF2EXJwcPHiQhYUFbrzxxuA7x3F4/PHH+S//5b/w3e9+97znng0JCQkJWZ9OB+5JTWZ7Nt5iue6JK4HluGFB/d6rcyyUDDJRBd3y72mPK14v53ej/pGMxoN3tCplLxQN/uaFU6zUTJKKykJ5tcJ6i7U5HuHakRQvnypyYqXGeDbBweOLOJ7H7Tt7W56/98oB9gzEmVyskIkp/KfvHWGpbNKfVDk8V0WWBfriGmXTJiILvPOKfnb0xVv0fjKxUV6fKfLGfJmK6ZCOqW1WZC9QdV8o6dRsmzt29XHfNYN895V58jWLhaLeVR2+G83zE1MkDjh+Jh4PL7AoSyKnPQEyGq/NSBiOy5XDMWzXY7QnGtS3EQXvM1H5vtiUwUPj0tqEI1LnyFyRh/ZPYdgu8Xo+6+eO57hyKMGLJ4sIAty6o5cDUzls1+OakRSLJZOH909x/WiKmCoHP3zZmMoPD88DcO9VA+TKvitKJjYa3JPUJJ6ZzGE5LnsGk7xwYgWA60dTvDRfBgFuGs+yVDb42hOT3Lmnj209UX5waAFBgLt29XPw+CIAd+3p4+Bx//m79/Tx+JGlju2dXq6w/+gSkiSS0GTyVZODx1e4aigZ9PHeKwYDN5yK0RuMSUyVyFUsTlk1tvX4p38P7Z9iOB1lLBtjtqDz8P4peuMKX31ikrJh0xNTOJXXOTCV44rBZOCS01xuIiJTNmwefnqK9103RFSVgudTUZkTyzXyVYubxzUmlyvMlXT+4fXDHJhcxnY9rhxM8fpckbmCwUduGV3l9rMeZytEsZHnQ7GLkLPlXe96Fy+//HLLdx//+Me58sor+Z3f+R3GxsbOa+7ZkJCQkJD16Xbg3mJ5rlnIokCxavHNZ08E9yU1meO5CgtFg0nXd0mvWRGiqtSydlgr53fDEtvtwD+q+vc0xyLXDDdQWF9lbVZFji5WyFdNsvEIpuNgOS65sslzdbfuxvPNdYuCQDziK4JX6nHc2XiEiCKi20KQ/7tZJTyIJy/qVEy7qxX5+rEMDz01RVHP1w1AA1w1nGa2YARq7Wupw3eifX5uHM8AAoWqFViU22Ppdw8kePLoEs8dz9GbiPCuqwbX1BfqVu9m14UXizJ4aFxanws/SxcJh2aLmI6/CZRFEUVysB2XhZKJKgmAQNmwwANNFlElKUhwP1cw6E+e/uGzHBdFFAEPASEQN2ikUxhKaaxUTVzPI6pK2K6LKPoJuQzHQxQBBAQBPxZmTicqSwgIQVtMxwnqMG236bPXtb0RRcLxIKGIqJL/P9vxWCgZwTOCQNDeF6bzwZgIgoAogOeBabukYjLHV1zSUf+HvTEWL0znqZo2vXE/9YQmi5i2Q8mwGeuJryo3IksAVEybQ7NFemJq8Lzn+X9kPNfDdFw0WcJ2XHKVRr8kBAGiqoRpu5i2t2khibMVotjI86HYRcjZkkwmufbaa1u+i8fj9Pb2Bt+fr9yzZ8OOf/XohW5CSEhIyHmh3VK9UDJaDtwbltxjS1V+fHgBw/boT5xW3b5ndz8nc7WWOOFOcd9Bzu98JbB098RUcmWD7746T8V0uh74rxdbfjrGucrk4iGKuk1/UsWwXRYrOrbnslgysBwXWRCCnNmm7bQYG6bzFSqGw1g2Rjqm8PRRF1kSMCyX547nABjJaB2NVGtZkU9b+ROBx8DhuaIfV95m/X/5VJ6JvviGlMyb+94tBLNq2i1x8kcXygymItw20UvFtNdUo7/QnGuLdGhc2hjhSNS5ejiFKomUDZu4KmHVXUkGkr4riyB4JCIKCKDbLqbjtKRWiKmtbjiW67uIN7uiNAtDJDUJUfCFI2RRxHXraVgkAdcF6gIXy2WTuCpTsx08vED8QpWkoA5VFps+C8E97e01LAdJgJrlIkkupuPnchxIRpgtGIGoRqO9N4xlgjGJqRKuB6Lg15evWKiSSKFmk4ragbvPDWMZYqrMcsWkJ6ag2y6yJJKMyB3LBT/vtqaIXD2cCk5wlysmqaiM44IgCKiSiG47yJLgq14KflyT50HNdFAkEVUWNi0kcbZCFBt5PhS7CDkfnK/csyEhISEh61PS7Q6Wajs4cG9WBj8yX16lun1iuUJJt4irErrl0J9U6U9oq+K+Y6rsx3Q/tUxRzyOLItt7Y3zj2WmOzBW5cTzb9cB/I7HlfoyzymhPjMWyju147OyLUTUdlsomcVWiZgkcz1WDnNmFWquxYSwTp2a4pKMKrutx0/YewGOpoiMKAm8bz5DWVFRRWmWkWsuKPF+PpR7riflWfk1peb5h/dctZ1NK5s1975b9plXtXK/HkPeyLRPrqFB+sbAVFunQuLQxwpGos3coxcfu2MnDT09RtRxiqsR12zIoshQITtiey+0TWY4t18hV/A13c2qF064oJvtG07S7ojQLQxRqFrfuynJiuULZsNjRF8MDqpZb/yxQ0E36kir3XT1OUbdaxC9qth3UUTOd4HPVdLq2d6QnxtUjGR5/Y4FaPWboutFUSx8Luhm0d/dAIhiTWn1M+uIRapbDQDrC/dcO8fz0CgslPRiLfWM9fPLuCb725CSFmkVPTOGakTSW63Ust2L6G+4Hb98ZiKk1ni/rNtm4QlrzDx3GemLs6I0BArdP9HJsuUpBNxnvjTLeG6dqOpsWkjhbIYqNPB+KXYRsBT/+8Y9b/n2+cs+GhISEhKyPJMJsXme5bNKbUFkomoEVuZMyeLvqdkON23Q8btqeomI5DCa14MC+Ya2URAJrb0yRODC1zIlclbv39PGaC88dz5HSFHJVs+XAf6msM52r0p+IdFVFb5DUZLb3+mm6GnnF+5MR7tndx3/M6+SrFr0JleWySdV0SEdXGxt29MX4RzeP4risiilvV/ZuUS/vYkW+fjRFzbTRZGmVUaPx/HS+QlyR+elUDkkUAiXzR1+aaclj3k0UeD0a1vCFot4SW56vmS1zdbGwVRbp0Li0McLRaOKz79nL+64bOmP18vVcUTrdc7Grl7ePyUbUyz9623bu2du3pnp5e7nN6uXtz2+1evnZClFslUBGSEhISEhIyFsTx4W4KnPSrjK5aKEqAjElhuOutgx2Ut1uVuMuGg4jaS04sG+2VoqCwHLFYO9ACqseigceqixx844enj+RZ6ZQZSQTDZ7/4esL9ThoP558e18cTRFXqaI3CKzpk8uBhfTeKwcZ6Ykx2hMjovgW8MF0hP6EhipLHY0N7RvaHX1yR2XvVvXy1VbkIwtF/t8nj+F6HpbjIeCter5h/V8uGxR1mzt295KOKRR1i/1vLrFUMRlOaaSjCt9tsvw2CyRvhJgqs6MvsSq2/J49AxfdWm+rLNKhcWljhKPRxt6h1g1gJ7eSmCp3PQlbyxWl0z3tZXX7vJ6Ly2bau5E+NtN+f3sbO43FWDYe5HrcaLlrPd8+Fp36dba/MLb6+YtF7CIkJCQkJCRka5FEX69GlUQGEr4VuWr5lunmkMShlIbtei2q2xXTt/zarsctO7Ityt7t1srpfIXZvE5UkRlMai3hjY4Ld+zubVE8XyrrPPTUVEsc93xB55/dtZPtvfGO65S1Yqe398aIRsTT8eRRlZq5Wi29vdxuqurNRqqIPMxrs0WisojtetRMh+mVKrMFHVkS6I9rq1TdAY4tlXlpOs9Ef4JrR9IcmFpmcrHCSCbaEkN+cqXKI88tE1dltvVEA1Hg60dTm7J4dxufiy2meyst0msZl0JVc5/Lt+chISEhISEhISEhW4DjQjwiY654nMzrqJJITPW1arpZBncPJFAkKfje81xqlsD3X1toUTXvFC8dUyUKeufwxh19p40YjXjn4bTWIoTbaFcnAoX0tthpx6VVhV0QKOo233x2es144Y3EFTdb4yVRYGdvrH5gIRFXZGZWdKaX9RZV9+a82Y149tFsjFsh8CRojiFfVkyqps14NtYyFnMFY1Ob7m7jc7HFNG+1RbqTcSlUNT/NxfMmXCR8/ck3eey1ed5z1SAfv2tX8P2ZxHt0cwlfLhubcp1udy/v5t7ere72H4BufdmsC/ta5a7Vl62gW93dTlY3etoWns6FhLx1CNXRQ0JCLhYkESqGTUQWGUiqFGo2VdNepQzevL6aL+qB5bc53jlQNX9phvuuGVwVx9wpXrrb2qUR7zxb0ANLdyMOuhtrWUgHU1o9rrl7zu92q+d6ccWdrPFzRYNP3LWLhCbxb771yirF9WbFdF/xvBjEsweeBFcP8r1D80EMec3yN8ULJQPwxYv7kuqaY7HZ8bnYOJ/hjqGqeSuXX4/X4I5//31miv4P3v43V/jq41Ps/9fvbjlt22i8R/PJjuV44HkossibCyVenS1hOy6eB+moQkKTkUSBHdkY/SmNxaLOsVwVx/VIaQr3XzNEoWZR1K1V1zq1Za1TpW59aWmv7YIgoEhCS9ubv1+rXFkU2N4bpz8ZYbFkcHy5gr1Ge8+W9tPQxji2t3Gzp23h6VxISEhISEjImdAc031yxfaVvRV5lTJ4e4x2s0W7ZjmBRVsWhSAWWZXEjnHMzXTb1PQlND52504e3j/VIoS7llFkPQtpTJXXzPnd3JaNxBV3tcZ7fqz6cDqKLAktqu7tiumd4tl3DyRQ5NOeBKOZGA/coPHtF0/x+lyRWETmH189vmkD0Vstpvl8hTuGquatXH497sLXn3wz2HA3mCka/OfvH+bZ4/mWE7X14j2aT3ayCYUfHFpAEODGsR4OTK1gux6DyQgn8zXyNYtbtmd4c7HKXFHnA9eOcGAqh+16XDOSYr5g8NUnJ7lzdy8D8WjLtcWSuaota50qVU171cnhw/un2DMQP93emMoPD88DcPeePh4/soQgwF27+jl4fBGAe68aCHIptpfbn4zw6qkCcwWDD1w/xDOTOSzH5ZptaRZLxhnFyqxF82lof1Ll1VNF5oo6H7lxnJXq6TYCmzptC0/nQkJCQkJCQs6URkx3VJEZ7akre1v2qjzb3dYb/+jm0ZZUtO35rBOa3BKrvREa3nu3TWS5fjS1KS/E9WJ2c2WTkyvVjecVX6l2VEyvmjaSIBBX5Y7W+Jgqr4ojH0xqgQV/crlEVJGpWXZLPDu0ehI0lN8fefYkd+7uDZ5ZqhgcW6psalzXG5/LlbeSB8D54PLsdQcee22+6/eiKK46bVsr3qP5ZMdyXVRJAARWqgae6xGRBEQBJAE818NwPDRFxHY8VqoGeKDJIqok0ZtQWZzzBTJMx2m51qkta50qLZY6nxxOLlZOt9dxUUQR8DBtL2i76TjB9wJC13IFQSCqSpi2S65q4XoeUVVCkcQzjpVZi+bTUFEQgnE0HaeljcCmTtvC07mQkJCQkJCQM6WRA1u3HOZLOmlNYSi9Os92t/VGc7z0TL7aMZ91VJW6Gg7aN3+drOnXbktvqk9rxezOFmr1NZLYNed3o4wrh1McmJqiWLNIRRXu2du/yuI/lIpg2G5Ha3xzHPlgUgss/emowjefXaJi2MQjMv/fe3axoy/e1XOxked7ojdJVJU4kaucUT7vtcZnPbYqjPFiCI98q3kAbDWXZ6878J6rBtn/5krH7589nj/j2JdsQsF0PATBoycWQRAFDMfD9cDxQBAEIpKAbrnIskBPLAIC6LaL6Tgsl01iEf/0LalGW64tlsxVbVnrVEkS6RjHM9Ef59WZYnCa2lC+VGUhaLsqSS2KmN3K7U9GqJkOiiSSjSmIgp9H3HJcFkvGumO3WZpjk/qTajCOqiStOlHbzGlbeDoXEhISEhIScqYkNZnpXJUXpvO4roco+uF5nXJgbzReuj2fdac1SacN5khG2xLvvU75xi3H46YdKaqG0zHn92ml73hgqW4ooTe30T+0iLYotzfoZFVeKut86/mT6KZLVJHRTZdvv3CSe/b2de17+3q9kSO9kc97qz0ctyqM8WIKjww9AE4jrn/L5cHH79rFSKp1MziSivCZd1/Bx+7cSTaubDr2JRtXKFQtbhjLsG80jSDC7RM9ZGMKVdMmG1PYkY1RtVzGsjFu35mt35NlrCdGruILOnzyrglGMzFqtt1yrVNbmutuP1VqxPG092UsGz/d3rry5b7RDFXTCdpes+3g+2ZFzPZycxWD8d4ot+7KgiBw664s471RchVjQ2O3WVrrNoNxrNl2SxvXGpf15jA8nQsJCQkJCQnZDCdzVV4+WcTxPGQRHM/jpVNFTuaqLfett97w80DHef/1I2uuSaqmzbGlMn/34owfcpfQWKlaPPrSDIdmCuQqZos1vVCzAk/AjVI1beaLOlXTf67ZSp+OKdy8PYsiCZR0u2vO70DpOxNnIKkxlolTqFmB52JzG23XYywb77hujKn+oUSj/GNLVU7la0RVkUxMIaqKnFyp8dpsaVW5jb43xj4RkTi6UAY8bt6eJR1T1hyj9nE4E5oPLBpz9dihubMqcyvLPRva5+py5fLufRv7//W7O6qX33vlwFnHvsD5Uy9f61SpW1/Wam+3z2uVez7Vy9equ7mNmz1tC0/nQkJCQkJCQs6EQ7NFLMdBlUREQUDwPCzb4dBskb1DqZZ7N7LeWOuewMW7KVVWs/jabFFnZsXXEbpqKHVG3nvdLOid8o1/8IYRBrpssrpZ9huei2fqYRiPSEiCiGl7dTVzD1kS6U+qGypXlUQissRS2aAvEel637myIm9VGGMYHnnxEo5+Gx+/a1dLqrAGfQntjNQMm1/w4NQyKzOWPZ0zsd1S3fhv+/fdrm2k7o30pVt71/q8Vrmbae/Z0q3udjYbb3O+FB5DQkJCQkJCLh121nNjm7aLKouYtosiCcH37WxkvdHpnhYX76ZUWZosBeJr23tjOI7HQkknE1XoTaib8t7rJvb24B07Osbs7ujrvgntFufbl9DOKv53e2+cWyeyPDOZI1/1s9m8fSLLFUMpkprasdxGv8qGw+7+JFZ9jFJRmb7Eakv9uRTZ3aowxjA88uIlnIGQkJCQkJCQkJCQc8hQOsqu/gSH50rotosE7OpPMJSOntN62i2bjVRZJ1bKLeJrVw5JpKIyD7xtGxP98U1tEteynp6JV2C3Z87GwzCmyvz6O3cznD4ZbDgfeNsoMVXuWm57v64aSpGJKl3H6FxakbdKZCwUL7t4CWegjaWy3tFNGTq7VbfTTS2w+fuTuSqHZotcPZxiNBvblBv3Ruvr1o/GyV4nV/dm6/t0rtLx+63ifKksNo/LubS+XwwqkSEhISEhsONfPbqp+4/9/vu3qCUhlzOSCL2JCNt7bWRJxHZc+pKRVSm0zpZ2y6bjwh27e7nvqiG+d2iuRXytLxHZ9Ia7Ux3t1tMz8Qrs9szZeBjuHkjwf/3c7o7rsU7lBunL8pWW9GPdxuhcW5G3KowxDI+8OAlnoYkfvr7AQ09NUdR9t5Qd2Rj9KQ3L8cDzUGRxzfiNbnEezd+/dDJfF9ZwkUSR67aluH4001KHZbsg+CqXZ1JfSz8EgR19cfqTfqzMlcMpXp8tUtQt3lws8+qpIrbrElNlPnn3BB+9bTt/deA4X31ikqppt3y/VZwvlcXmcUlpCh+7cyf3Xjlw1uVeTCqRISEhISEhIRcex4W4KuMJAhXDRVUEYoq8KoXW2dLNsrl7IIEii+fE4vlWsp5uZtMepC97apminielKdyzZ2Bdkd1zOQ5bFcYYhkdefISzUWeprPPQU1O+2l9S5dVTfgqtn3/btiCFwL1XDHZNIdAtziMTGw2+F0WP546v4Lge6ahCoWZx8PgKVw0lefFkEUGAu3b1c/D4IgD3XjVArmxtqr6IPHy6H4kIr84WmCvpfOTmMRbKOgeeWmaiP0FCkzgwmcN2Pbalo+RrJl97cpLdA3G++sQkZcOmN66yXPG/v2dv35ZYvM9lfMxaNM9vI13aw/unuH40dVYW7/PV/pCQkJCQkJC3DpIIFdMmIkkMJGQKNZuSYZErm5uyPm5ESHcr3LXbOdfW063yPNwMp9OXJchEFfI1i8NzRW6byHbt31ZakUOvyUubcEbrNFIVDKc1REFAU0Rsx6Os26iSAAgIAl3jN7rFeTSnQDg8X8BzQRFFZFFElfw6FkpGUIfpOCiiCHgICJuub3Kx0toPWcJ2XEzbJRNVKer5+i8WE8/1iEgiEVmkN66yUjX56bEcVdPfcEcVmd44rFRNJhcrW7LpPl8qi83zG1NlhtMaCyV/fs7ml32oEhkSEhISEhLSjuNCPCJzcqXGybyO4Plpw77x0+MMp6Mb8opr9qRbLOocy1VxXK+jt95WuGtvVVlb5Xm4WYL0ZT0xoqpEUlM2tIbbCity6DV56RPm6a7TSFUwW9AxHQfdckGEhCZjOh6W6+J5MFfUSUdXx280x3k0YmfS0dYUCD1xFUEEy3WxXRfTcRFFGEhGgjpUScJyXSzXw8PbdH0T/fHT/bBddNsBEVRZJF8zSWn+SV5CkxFEAcNxMWyX5YpJPCLz9h3+6d5yxaRm2cH3E/1bE9fdrR/nWmWxeX6rps1s4fT8nA3nq/0hISEhISEhbx0kESqGTVSR2N4Tw3AcKqZNXzKyodzJzZ50UVnmwFSO6VyVuCqxXDF4eP8US2W95f6zzR19LlivHc2ehwNJP490e1/OFxfLGu5izK0dcu4JN911+hIaH7tzJ9m4Qq5iMpaNcfvOLLbjccNYhn2jaQq62TV+oxHnkY0rHVMgZOMKAgK3bO8hpsrolkNMlblpRw+KLAV11GybfaNp9o1mKFStTdc3lo2f7kfVYKwnxu07e6mZDoNJv4+DqQi243H7RJZsTKGo+65O/+yuCW6d6OOTd0+Q1GRWqqe/3yoxtW79ONcniM3zu1Dy63nwjp1n7dJ0vtofEhISEhIS8tbBcWE4o/lrLs9DkyX64xEisu8VV6hZlPTum6pmTzrTcbAcD9N2mSnoGJbLfNFgrmAAvpX0oaeO8fWnpnjoqWMcXSifr262sJF2dPI8LNSsoC/nk4tlDdfJa3K99yPkrUe4M2ji3isHuH40dcbq5RuNqTlX6uXd6lurHzFV5raJ7Jrq5R+9bTv37O07b+rl50tlsX1czlUMUagSGRISEhISEtJMUpPZno2DB72uR8WwUWQx8GJcT/W62QobkURqloPtekiiQL5qkdRk0lF5y7VlNhJnXDVtFooGj740Q9lw1mxHs+dhQ2MnGz87z8OziYXePZAgExtdd2240XE4k3aEubUvD8LZbKMvobX8wLWnG1iPjcTU7B1KsXcotW4dZ1Pfev1o/DuWlTtuqsey5ydVWKc2bSXt43KuCFUiQ0JCQkJCtpbHH3+c//Af/gMHDx5kdnaWb33rWzzwwAMd7/21X/s1/vzP/5w/+qM/4rOf/WzwfS6X4zOf+Qx/+7d/iyiKfPjDH+aP//iPSSTObfxsTJVJRxWemfK1chRJZCSTWtOLsf35hlL2TL7GcEqjZtnopktKk7liKIkqS1uqLbOROOPGPbOFGkfmy7xtPLNmOxqehw/vnzonnodnGwu9mT6e7T3deCupw4ecOeFshoSEhISEhISEXPRUKhX27dvHr/zKr/ChD32o633f+ta3OHDgACMjI6uuffSjH2V2dpbHHnsMy7L4+Mc/zq/+6q/yjW9845y2dams891X54hHJMazMZYrBlXT4YP7trG9L0ZMlbtaRhvfj2Q0HrxjBwtFg75EhIWSQVSRqFkOoz3RwBK6FVbSjVjQm+8ZScd4fa7EweMrpGMKubJFIiJRM+0gBW2Dc+V52KmNj740wwdvGGEgpa27ad1sHzvdsxkr/1qEXpOXPhf1jP7e7/0eX/jCF1q+u+KKK3j99de3rM4Xp1d4YTrvx1iP9XS8p/mXJLS6gTenQAA6unh3S/lwZK4YuJ03W8Lh7FIrrPVst2vTuUrgXh5VpQ3Vvdk2blVqhAudcuFC1x8SEhISEnIp8t73vpf3vve9a95z6tQpPvOZz/Dd736X97///S3XXnvtNb7zne/w7LPPcvPNNwPwn//zf+Z973sf//E//seOm/QzpRG7vC3jb7A1RWKhpON4HjFV7moZ7fb9+68f4bFDcxRqFqOJaIsldCuspBuxoLffc/P2LD87kWMmrxORBIq6xzefne5o+T0Xnoft9cuiwP43l1iqmAyntHWtzWfSx+Z7ZvL6pqz86xF6TV7aXPQze8011/D9738/+Lcsb12T/59vv8Ijz53Adj1kUeAjN4/zxQeubbmn+Zeh5XjgeSiySEpTSEcVvhtcc8EDRRaRRIEd2Rj9Ka1ryoevPHaEh/ZPYTouqiTysTt28tn37AXOLrXCWs92u/ZXB47z1ScmfdVED1JRmYSmrFn3Ztu4VakRLnTKhQtdf0hISEhIyOWK67r88i//Mv/yX/5LrrnmmlXXn376aTKZTLDhBnj3u9+NKIo888wz/PzP//yqZwzDwDBOi3wVi8UNtWWt2OVu1tNMbLSrVXUtS+hWWEk3Emfcfo/tetyxq4/7rh7ke4fmKRsO/YlzH2feqf5sTOW54zkczyMVkVkor1/nmfSxcY8ksqaVP4zJDmnnolcvl2WZoaGh4H99fX1r3m8YBsViseV/G+HF6RUeee4ElusRkUUs1+OR507w4vRKcE/zL8l0TOHF6TwvnSqQ1lRO5Wt89YlJlisG6ajK8aUqx5erJCIyJ3M1DhzL4blwYCrHyZUa2bgapEl4ZnKJh/ZPYdgucVXGsF0efnqKI3PFs0qtsNaz3a69OL3CV5+YpGzYJCIKKzWTE7ka8Yjcte7NtnGrUiNc6JQLF7r+kJCQkJCQy5k/+IM/QJZlfuM3fqPj9bm5OQYGWg0CsiyTzWaZm5vr+MyXv/xl0ul08L+xsbENtWWtrCnd1Kob1vFuKtYxVWawi9v0WtfOhI0oe3e65/3Xj5BNRKhZzparcTfXP1OoYlguiiDw8qkCx5eqHFuqrlnnmfbxPVcP4bgEc5WOKdy8PYsowEw+jMkO6cxF/za88cYbjIyMoGkat99+O1/+8pcZHx/vev+Xv/zlVS7pG+GF6Ty266HJIookAWDYLi9M5wM38+Zfkn5ObQEQEASIKhJV02Y8GwNAFAXAw3E9NEXEdjxWqgZ4oMkiqiQxnNZYKOn89FgO03FJRGQisl93xbQ5NFtk90ByVWqFhZLvxr2eW06ntAyNZ4GO116YzlM1bXrjKp4HIgIe4Hpe17rXqqdTG7dK9GMrxUTeCvWHhISEhIRcrhw8eJA//uM/5mc/+xmCIJyzcj//+c/zuc99Lvh3sVjc8Ma7W+xyN+tpwzp+sahYb8SC3umeqmmft3406j++VGFy8TWKuk06pjJb0HE9D2kd82J7+wHmi/qaWYgAFoo6miytsvJvNJ485PLjon4jbr31Vh566CGuuOIKZmdn+cIXvsDdd9/NK6+8QjKZ7PjMmf5yvGEsgywK6LYLgG67KKLADWOZ4J4WN5aEgul4CIKH50Gtnnd7ueJvNF3XA0ASBXTLRZYFemIREPyyTcdhseTn/X77jix/KU1RNvzTuLJhoykiVw+nyCbUM06tsF5ahk7XbhjL1PthktIUXDwEBERB6Fr3ZtM/bFVqhAudcuFC1x8SEhJyKbLjXz16oZsQ8hbgiSeeYGFhocUw4zgO//yf/3O+8pWvcOzYMYaGhlhYWGh5zrZtcrkcQ0NDHcuNRCJEImeezqpT7HI3teq+hHbRqVhvJM64/Z7zrcYdU2WyiQij2SiLRQPdculPqPSnIjjuxp5fK86+2z2W4yHgtfRxR18YUhjSmYt6N9AslnH99ddz6623sn37dh555BE+8YlPdHzmTH857hvr4SM3j/PIcycw6hvuj9w83iKm1vxLpFC16htyj4JuMtoT5ZN3T/C9Q3MUaiY7+mJ4+BvosWyMHb1RBBFun8hybLlGrmIGrka3TvTxsTt28vDTU1RMf8P94O07AzG1M02tsF5ahk7X9o318Mm7J/jak5OUDYueWIS0JlEx7K51bzb9w1b9Mr7QKRcudP0hISEhISGXK7/8y7/Mu9/97pbv7r//fn75l3+Zj3/84wDcfvvt5PN5Dh48yE033QTAD3/4Q1zX5dZbbz2v7e1mRb5UVKzPdz8aedGjikwmqpCvWQymIhs2fJypknlCk/ngvm0MpCJv2bkKOT+8pd6OTCbD3r17OXr06JaU/8UHruXDN21bU728k4tJ8y+U+68dPCP18s++Zy/vu26oo3r52aRWWOvZbtc+ett27tnbtyn18s22cat+GV/oP1YXuv6QkJCQkJBLlXK53LIGnJqa4oUXXiCbzTI+Pk5vb2/L/YqiMDQ0xBVXXAHAVVddxT/4B/+AT37yk/zZn/0ZlmXx6U9/ml/8xV88p8rlG6WbFflSUbE+n/1oMYzVN9ybMXycjZJ5VJUuifkK2VreUm9IuVzmzTff5Jd/+Ze3rI59Yz1dU4U16ORG06Ddjaj5c+O+mCp33JTuHVqdKqxbuZthrWe7XRvLxhnLxlvuO5t6OrFVv4wv9B+rC11/SEhISEjIpchzzz3Hz/3czwX/boQTPvjggzz00EMbKuOv/uqv+PSnP8273vUuRFHkwx/+MH/yJ3+yFc0F1k4j+lZOMXoxtv1sDB9no2QehhGGbISL+i35F//iX/AP/+E/ZPv27czMzPC7v/u7SJLEL/3SL13opoWEhISEhISEhJxH3vnOd+J53obvP3bs2Krvstks3/jGN85hq7qzVozwWznF6MXc9jM1fGwkRDAMIww5Gy7qt+TkyZP80i/9EsvLy/T393PXXXdx4MAB+vv7t6zOpbLe0UV6oyd6zfc1u5E3u5cDXd3Tz4ZzeerYbRzOJRfjKWlISEhISEhIyNmyVowwsOraoy/NnDPl661cX20k9nkr2ng+PAbOVK09JGQjXNRvyje/+c3zWt8PX1/goaemgpO7j925k3uvHNjwiV7zfYslg2NLFRzPQxIFdmRj9Kc0LNsFQUCRhJbPZ3tSeC5PHbuNw7nkYj4lDQkJCQkJCQk5GxrxvwlVJlcxSKhyS67q5thgWRTY/+YSSxWT4ZR20awH1+rX2aRH3Wwbz6fHQBgiGLJVrJO97vJhqazz0FNTrFQtBpIaK1WLh/dPMZ2rBCd6/Qn/+8cOzVE17Zbnm0/+oqrEgcllTuZrpKIKJ3M1DhzLIQsiL50q8NLJPFFVCj6nY0rXcjdCc91rtfFsxmGprG+6rPPR3pCQkJCQkJCQi42kJrNY1Hnk4Am+/fwpHjl4gqWSn/+5OTa4ULV47ngO14ORzNmtic7H+qq57TXTYa6ok45uPK55s21c6/4LsZ48ulDmoaeO8fWnpnjoqWMcXShvWV0hlxbhprvOXMGgqFsMp323nuG0RqFmMblYWXWi13xS2aD55M+0XfBAkyVEQUBTRHChbFgooogiCpi2G3wWELqWuxE6nTqeaVndxmGuYGy6rPPR3pCQy4Evf/nL3HLLLSSTSQYGBnjggQc4fPhwyz26rvOpT32K3t5eEokEH/7wh5mfn79ALQ4JCQm5vKmaNsdyVWzXQ5YEbNfj2HKNqmkHscHZuMJMoYooCNy0vYe0pp6T9WA2pmI5LtmYes7XV81tb8Q1372nj5Jub2iz22kNuFQ2mFysdHx+rTXjVqwnq6bNfFHv2JbQaBRyNoT+E3WG0hFSmsJsQWc4rTFb8H+RTPTHeXWmuK5SYfPJX0KTQADddnA9D91ykWWBRETBcl0AVFkMPnt4Z6WAeC7VFLuNQyMF2rkgVH8MCdkcP/nJT/jUpz7FLbfcgm3b/Ot//a+57777OHToEPG4n2Xgt37rt3j00Uf53//7f5NOp/n0pz/Nhz70IZ566qkL3PqQkJCQy4+5goHjelwzkkKVJEzHIVcxmSsY9CW0IDZ4oWjwNy+comw4geX4bNaDlu3yw8PzKKK/ztw3mj7n66vmuOaFksHjRxY37N7dvgZ8fa7IfEnn28+fIhtXVz2/3prxXK4n13NVPxeu9SGXL+EbUqcvofGxO3fy8P4pFkr+D+2Dd+xkLBvfkFJhe37A2yd6ObZcpVizGMvG2NEbxfb8X34gUDOd4HOhap2VAuK5VFPsNg7nUkwtVH8MCdkc3/nOd1r+/dBDDzEwMMDBgwe55557KBQK/OVf/iXf+MY3uPfeewH4+te/zlVXXcWBAwe47bbbVpVpGAaGcdqDpVgsAmBZFpZlrdumxj0RceNKwpcKjT6Hfb802Mj73n7vZp55q3I59HEraRgxFksmw2mNxZK5yogRU2V29Mm8//oRvv38SV6bKzCU0s5oTVQ1bRaKOpbb+Nls/FfYdNs3IkzW+P6vD57clKha8xpwplBlvqQzkNQYTkc7Pr/emvFcrSc3IhAXGo1CzobwLWni3isHuH40tUq1e6NKhe33nU/18nOppthtHM4lofpjSMiZUygUAD/1DcDBgwexLIt3v/vdwT1XXnkl4+PjPP300x033V/+8pf5whe+sOr7733ve8RisQ235d/d7G62+ZcMYd8vDf7+7/9+08889thjW9CSi4tqtXqhm/CWZjNGjBO5Ks+fyJOvmczmdW7cnt2UGFjDQjtb1DkyV2TfWJq+hIaHR6FqbZnI2ZlafhtrwMnFCt9+/hTD6eiaz6+1ZjxX68mN9CU0GoWcDeFb0kZfQuv4C3GjaobN98VUuaWs9lx/nT6fDedScbHbOJxLQoXIkJDN47oun/3sZ7nzzju59tprAZibm0NVVTKZTMu9g4ODzM3NdSzn85//PJ/73OeCfxeLRcbGxrjvvvtIpVLrtsOyLB577DH+7XMihrt5S8pbmYjo8e9udsO+XyJ9f+X37t/wvY33/j3veQ+Komxhqy48De+XkDNnI0aMhoBtUbfZlokxW9B5eP8U14+mNrQOa7bQjmQ0Xpsp8uJ0gXuv0MjVPSnPRORsI5brs7X8JiISiYi8oefXWjOei/XkRvsSGo1CzpTwTQkJCQl5C/GpT32KV155hSeffPKsyolEIkQiq7UaFEXZ1GbCcAUM59LYfG2WsO+XRt/PZPO82Z+TtyKXev/OF+sZMToJ2C6U9CD2ez3aLbQ37+jh+RN5ZgpVRjLRTVliN2u5PlPLb7M13XI8BLwLbjneTF9Co1HImRC+MW1sJI7lQtSxVNY7npSej/aGhIRcHHz605/m7/7u73j88ccZHR0Nvh8aGsI0TfL5fIu1e35+nqGhoQvQ0pCQkJAQ6L5+a3C2ArYNC+30SpVMVEG3XO7Y3csH921jIBXZ1NrwTCzXm7X8drKmJzT5jNp7rgmt2CFbSfg2NbGZOJbzWccPX1+oux75z3zszp3ce+XAeWlvSEjIhcfzPD7zmc/wrW99ix//+Mfs3Lmz5fpNN92Eoij84Ac/4MMf/jAAhw8f5sSJE9x+++0XoskhISEhlz3d1m/NnK2AbUyVuXI4xYGpKYo1i1RU4WN7d7KjL77p9p6p5Xozlt9u1vSoKl0Um9zQih2yVYRvVZ3NxrGcrzoasT4rVSs4AX14/xR7BuJb3t6QkJCLg0996lN84xvf4G/+5m9IJpNBnHY6nSYajZJOp/nEJz7B5z73ObLZLKn/P3t3HiZHVS5+/Fu9793TM5ktmSQTAgQIJJCwBjAsEjYFwSVe1AQRuBpUjL8rRmUVjaJXuSKCoAJe4YLcq+gFBSL7EpYEcyEEAmRCEpJMZu19q+6u3x893Uz3dM/0TKanZ3k/zzNPMtVVp86pqj7dNfWe97hcfPWrX+X4448vmkRNCCFEZZX6/lZsrPb+JLCNJJK8vTfAnGl2PFYTvmiCre0BjpvjHdH3wUo/7S18Mu+LqjS4zJIBXEx6coX3GYu590ayj1Jjfdo6wzJXoBBTxG233QbA0qVL85bfddddrFy5EoCf//zn6HQ6LrzwQuLxOMuWLeNXv/rVGNdUCCEEDH+s9kgT2Ga/W7Z47FhNepxm435/H6zk095iT+ZPPmiafHcVk55c4X3GYu69keyj1FifOdPsvLknIHMFCjEFaNrQcyJbLBZuvfVWbr311jGokRBCiMHs71jtck20uaOLPZl/Y7ePOXX2qo/pFqKSdNWuwHiRHcfitRsrlkFxJPvIjvXx2o15Y31avPaK11cIIYQQQgxfqe9voz0d61h8fx1N/Z/M1zstWAx6Xnyvm98+38bdL7zPex2haldRiIoYn+/IKhmLrIUj2UepsT6SZVEIIYQYe/Ove6ysKdPe/9E5Y1AbMV7tz1jt4ZhI3wf7P5n3OoxseL8XRYFmt42eSELyE4lJS67oAmORtXAk+yg11keyLAohhBBCjE8jHas9XBPl+2D/DOl7fDH0Ojhqphe3zYjJoJP8RGLSkitaCCGEEEIIMSayT+Y7AjEe+ucewokU0URq3I9HF2J/yJjuArt6wjyztYNdPeEx2V8kkWRfIEYkkRyT/QkhhBBCCFFNNpOB2XUOzl3QPGHGowuxP+Sq7ufel3Zw53NtRBKZsJZLT5rDRcfNqtj+3usI8fib7QRiKi6LkTMOa2RuvaNi+xNCCCGEEBNTJJGcEOO2h6Ma49En43EU459caX129YS587k2QvEktXYT3eEEv3m+jZMPqqPFax/1/UUSSR5/s53eiJqb4kGSRwghhBBja/a3Hyl7XbNe46ZjKlgZIUqYzA9qxnI8+mQ+jmJ8k/DyPm2dYSKJzA231Wig1m4iHE/S1lmZMPPslAmNLgtWk55GlwV/VCUYkzBzIYQQQgiR0f9BzTSHhd6Iyrot7TI0cZjkOIpqkpvuPnOm2bGZDHSHE0TVJN3hBHazgTnTRv8pN+RPmZBNHuG2SvIIIYQQQgjxIXlQMzrkOIpqkpvuPi1eO5eeNAenxUBvJIHTYuBLJ86pSGg5fDhlgiSPEEIIIYQQpciDmvINlqBYjqOoJrnK+rnouFmcfFAdbZ1h5kyzV+yGO6saySOEEEIIIcTE0X9ua3lQU9pQ47XlOIpqkqusQIu38jfb/Y1l8gghhBBCCDHxyIOawZWboFiOo6gWudKEEEIIIYQY5+RBTWnFxmt3hmIEY8kBx0yOo6gGGdM9RgYbYyKEEEIIIYQYGRmvLcY7uekeA+91hLj7hfe564Xt3P3C+7zXEap2lYQQQgghJpRnn32Wj33sYzQ3N6MoCg899FDuNVVVueqqqzj88MOx2+00NzfzhS98gT179uSV0dPTw0UXXYTL5cLj8XDJJZcQCsn3solOEhSL8U5uuitM5gQUQgghhNh/4XCYBQsWcOuttw54LRKJ8Nprr3H11Vfz2muv8ac//YmtW7fy8Y9/PG+9iy66iDfffJN169bx8MMP8+yzz3LZZZeNVRNEBc2td7DihNlcvKSVFSfMzkuiJkS1yZ9/Kmw4Y0yEEEIIIURxZ511FmeddVbR19xuN+vWrctb9stf/pJjjjmGnTt3MnPmTN566y0effRRXn31VRYvXgzALbfcwtlnn81Pf/pTmpubB5Qbj8eJx+O53wOBwCi2SIw2Ga8txit50l1hMsZECCGEEGLs+f1+FEXB4/EAsH79ejweT+6GG+D0009Hp9Px8ssvFy1j7dq1uN3u3E9LS8tYVF0IMcnITXeFyRgTIYQQQoixFYvFuOqqq/jsZz+Ly+UCoL29nfr6+rz1DAYDXq+X9vb2ouWsWbMGv9+f+9m1a9ew6iGJdIUQIOHlY0LmBBRCCCGmntnffmRY67//o3MqVJOpRVVVPv3pT6NpGrfddtt+lWU2mzGbzSPa9r2OEI+/2U4gpuKyGDnjsEYZZyzEFCVPuseIzWSgwWWRG24hhBBCiArJ3nDv2LGDdevW5Z5yAzQ2NtLR0ZG3fjKZpKenh8bGxlGthyTSHZxEAIipRu4AhRBCCCHEhJe94X733Xd56qmnqK2tzXv9+OOPx+fzsXHjRhYtWgTAk08+STqd5thjjx3Vukgi3dIkAkBMRRPiSfett97K7NmzsVgsHHvssbzyyivVrtJ+kb/uCSGEEEIMTygUYtOmTWzatAmA7du3s2nTJnbu3Imqqnzyk59kw4YN3HvvvaRSKdrb22lvbyeRSABwyCGHcOaZZ3LppZfyyiuv8MILL3DFFVewfPnyopnL98d4T6Rbre+iEgEgpqrx8c4fxAMPPMDq1au5/fbbOfbYY7n55ptZtmwZW7duHZAMYyKQv+4JIYQQQgzfhg0bOOWUU3K/r169GoAVK1Zw3XXX8de//hWAhQsX5m331FNPsXTpUgDuvfderrjiCk477TR0Oh0XXnghv/jFL0a9rtlEuuu2tI+7RLrV/C4qEQBiqhr3V/fPfvYzLr30Ui6++GIAbr/9dh555BF+97vf8e1vf3vA+uN5PsX+f91rdFloD8RYt6WdZs9s6WiEEEIIIQaxdOlSNE0r+fpgr2V5vV7uu+++0axWSeMxkW61v4v2jwDI7t9rHz8RAEJUyri+whOJBBs3bmTNmjW5ZTqdjtNPP53169cX3Wbt2rVcf/31Y1XFYZG/7gkhhBCiFMl2PvnYTOPjZjur2t9Fx3MEgBCVNK6v8K6uLlKpFA0NDXnLGxoaePvtt4tus2bNmly4EWSedLe0tFS0nuWSv+4JIYQQQohqGQ/fRcdjBIAQlTYhEqkNh9lsxuVy5f2MF9m/7nntRvnrnhBCCCGEGFPj5buoTKUrpppxfaXX1dWh1+vZt29f3vJ9+/aN+nyKY0X+uieEEEIIIapFvosKMfbG9ZNuk8nEokWLeOKJJ3LL0uk0TzzxBMcff3wVa7Z/5K97QgghhBCiWuS7qBBja9y/01avXs2KFStYvHgxxxxzDDfffDPhcDiXzVwIIYQQYiqSxGtCCDExjPub7s985jN0dnZyzTXX0N7ezsKFC3n00UcHJFcTQgghhBBCCCHGm3F/0w1wxRVXcMUVV1S7GkIIIYQQQgghxLCM6zHdQgghhufWW29l9uzZWCwWjj32WF555ZVqV0kIIYQQYkqTm24hhJgkHnjgAVavXs21117La6+9xoIFC1i2bBkdHR3VrpoQQgghxJQ1IcLL94emaQAEAoEq10QIUW3ZfiDbL0w2P/vZz7j00ktziSZvv/12HnnkEX73u9/x7W9/O2/deDxOPB7P/e73+wHo6elBVdUh96WqKpFIBIOqI5VWRrEV458hrRGJpKXtU6ztMPHb393dXfa6wWAQmLz95UjJ90ohRH/lfrec9Dfd2Q+NlpaWKtdECDFeBINB3G53tasxqhKJBBs3bmTNmjW5ZTqdjtNPP53169cPWH/t2rVcf/31A5a3trZWtJ6Txb9UuwJVNJXbDhO7/XX/PvxtJmN/uT/ke6UQopih+spJf9Pd3NzMrl27cDqdKMrQf5UOBAK0tLSwa9cuXC7XGNRw/JC2S9sne9s1TSMYDNLc3Fztqoy6rq4uUqnUgJkdGhoaePvttwesv2bNGlavXp37PZ1O09PTQ21trfSVQ5C2T822w9Rq/2TuL/eHfK8s31RuO0zt9k+ltpfbV076m26dTseMGTOGvZ3L5Zr0F0kp0nZp+2QmT2wyzGYzZrM5b5nH4xl2OVPluilG2j412w5Tp/3SXw4k3yuHbyq3HaZ2+6dK28vpKyWRmhBCTAJ1dXXo9Xr27duXt3zfvn00NjZWqVZCCCGEEEJuuoUQYhIwmUwsWrSIJ554IrcsnU7zxBNPcPzxx1exZkIIIYQQU9ukDy8fLrPZzLXXXjsg7HIqkLZL28XEtnr1alasWMHixYs55phjuPnmmwmHw7ls5qNpKl830vap2XaQ9ovhm8rXzFRuO0zt9k/ltpeiaDIXhBBCTBq//OUv+clPfkJ7ezsLFy7kF7/4Bccee2y1qyWEEEIIMWXJTbcQQgghhBBCCFEhMqZbCCGEEEIIIYSoELnpFkIIIYQQQgghKkRuuoUQQgghhBBCiAqRm24hhBBCCCGEEKJC5Ka7n1tvvZXZs2djsVg49thjeeWVV6pdpVG3du1ajj76aJxOJ/X19Zx//vls3bo1b51YLMaqVauora3F4XBw4YUXsm/fvirVuHJ+9KMfoSgKV155ZW7ZZG777t27+dznPkdtbS1Wq5XDDz+cDRs25F7XNI1rrrmGpqYmrFYrp59+Ou+++24VayzGs8neX0pf+aGp1leC9Jdi9Ez2vhKkv+xvqvWX0leWT266+zzwwAOsXr2aa6+9ltdee40FCxawbNkyOjo6ql21UfXMM8+watUqXnrpJdatW4eqqpxxxhmEw+HcOt/4xjf43//9Xx588EGeeeYZ9uzZwwUXXFDFWo++V199lV//+tccccQRecsna9t7e3tZsmQJRqORv//972zZsoV///d/p6amJrfOTTfdxC9+8Qtuv/12Xn75Zex2O8uWLSMWi1Wx5mI8mgr9pfSVGVOtrwTpL8XomQp9JUh/mTXV+kvpK4dJE5qmadoxxxyjrVq1Kvd7KpXSmpubtbVr11axVpXX0dGhAdozzzyjaZqm+Xw+zWg0ag8++GBunbfeeksDtPXr11ermqMqGAxqBx54oLZu3TrtIx/5iPb1r39d07TJ3farrrpKO/HEE0u+nk6ntcbGRu0nP/lJbpnP59PMZrP2X//1X2NRRTGBTMX+UvrKqdFXapr0l2L0TMW+UtOkv5wq/aX0lcMjT7qBRCLBxo0bOf3003PLdDodp59+OuvXr69izSrP7/cD4PV6Adi4cSOqquYdi3nz5jFz5sxJcyxWrVrFOeeck9dGmNxt/+tf/8rixYv51Kc+RX19PUceeSR33nln7vXt27fT3t6e13a3282xxx474dsuRtdU7S+lr/zQZG+79JdiNEzVvhKkv+xvMrdd+srhkZtuoKuri1QqRUNDQ97yhoYG2tvbq1Srykun01x55ZUsWbKE+fPnA9De3o7JZMLj8eStO1mOxf33389rr73G2rVrB7w2mdve1tbGbbfdxoEHHshjjz3Gl7/8Zb72ta9xzz33AOTaN9XeA2L4pmJ/KX1lvsnedukvxWiYin0lSH9ZaDK3XfrK4TFUuwKielatWsXmzZt5/vnnq12VMbFr1y6+/vWvs27dOiwWS7WrM6bS6TSLFy/mhz/8IQBHHnkkmzdv5vbbb2fFihVVrp0Q45v0lVOL9JdCjJz0l1OH9JXDI0+6gbq6OvR6/YBMgvv27aOxsbFKtaqsK664gocffpinnnqKGTNm5JY3NjaSSCTw+Xx560+GY7Fx40Y6Ojo46qijMBgMGAwGnnnmGX7xi19gMBhoaGiYtG1vamri0EMPzVt2yCGHsHPnToBc+6bSe0CMzFTrL6WvnFp9JUh/KUbHVOsrQfrLqdZfSl85PHLTDZhMJhYtWsQTTzyRW5ZOp3niiSc4/vjjq1iz0adpGldccQV//vOfefLJJ2ltbc17fdGiRRiNxrxjsXXrVnbu3Dnhj8Vpp53GG2+8waZNm3I/ixcv5qKLLsr9f7K2fcmSJQOm73jnnXeYNWsWAK2trTQ2Nua1PRAI8PLLL0/4tovRNVX6S+krp2ZfCdJfitExVfpKkP5yqvaX0lcOU5UTuY0b999/v2Y2m7W7775b27Jli3bZZZdpHo9Ha29vr3bVRtWXv/xlze12a08//bS2d+/e3E8kEsmt86//+q/azJkztSeffFLbsGGDdvzxx2vHH398FWtdOf0zTGra5G37K6+8ohkMBu0HP/iB9u6772r33nuvZrPZtD/84Q+5dX70ox9pHo9H+8tf/qK9/vrr2nnnnae1trZq0Wi0ijUX49FU6C+lr8w3VfpKTZP+UoyeqdBXapr0l4WmSn8pfeXwyE13P7fccos2c+ZMzWQyacccc4z20ksvVbtKow4o+nPXXXfl1olGo9pXvvIVraamRrPZbNonPvEJbe/evdWrdAUVdoyTue3/+7//q82fP18zm83avHnztDvuuCPv9XQ6rV199dVaQ0ODZjabtdNOO03bunVrlWorxrvJ3l9KX5lvKvWVmib9pRg9k72v1DTpLwtNpf5S+sryKZqmaWP9dF0IIYQQQgghhJgKZEy3EEIIIYQQQghRIXLTLYQQQgghhBBCVIjcdAshhBBCCCGEEBUiN91CCCGEEEIIIUSFyE23EEIIIYQQQghRIXLTLYQQQgghhBBCVIjcdAshhBBCCCGEEBUiN91CCCGEEEIIIUSFyE23EEIIIYQQQghRIXLTLSa1lStXoihK7qe2tpYzzzyT119/vdpVE0IIIYQQE4h8rxQjJTfdYtI788wz2bt3L3v37uWJJ57AYDBw7rnnVrtaQgghhBBigpHvlWIk5KZbTHpms5nGxkYaGxtZuHAh3/72t9m1axednZ3VrpoQQgghhJhA5HulGAm56RZTSigU4g9/+ANz586ltra22tURQgghhBATlHyvFOUyVLsCQlTaww8/jMPhACAcDtPU1MTDDz+MTid/cxJCCCGEEOWT75ViJOTqEJPeKaecwqZNm9i0aROvvPIKy5Yt46yzzmLHjh3VrpoQQgghhJhA5HulGAm56RaTnt1uZ+7cucydO5ejjz6a3/zmN4TDYe68885qV00IIYQQQkwg8r1SjITcdIspR1EUdDod0Wi02lURQgghhBATmHyvFOWQMd1i0ovH47S3twPQ29vLL3/5S0KhEB/72MeqXDMhhBBCCDGRyPdKMRJy0y0mvUcffZSmpiYAnE4n8+bN48EHH2Tp0qXVrZgQQgghhJhQ5HulGAlF0zSt2pUQQgghhBBCCCEmIxnTLYQQQgghhBBCVIjcdAshhBBCCCGEEBUiN91CCCGEEEIIIUSFyE23EEIIIYQQQghRIXLTLYQQQgghhBBCVIjcdAshhBBCCCGEEBUiN91CCCGEEEIIIUSFyE23EEIIIYQQQghRIXLTLYQQQgghhBBCVIjcdAshhBBCCCGEEBUiN91CCCGEEEIIIUSFyE23EEIIIYQQQghRIXLTLYQQQgghhBBCVIjcdAshhBBCCCGEEBUiN91CCCGEEEIIIUSFyE23EEIIIYQQQghRIXLTLYQQQgghhBBCVIjcdItx5+6770ZRFN5///1qV0UIIcYt6SuFEFPJ7NmzWblyZbWrIcSIyE33BLNt2zYuv/xy5syZg8ViweVysWTJEv7jP/6DaDRa7eqJMixduhRFUYr+zJs3r6p1e/rpp0vWbfny5VWtmxDDIX3lxDfR+kqv18txxx3HvffeW9W6CTERZP9ouGHDhqKvL126lPnz5+/3fv72t79x3XXX7Xc5E93s2bNL9qdnnnlmtas3JRiqXQFRvkceeYRPfepTmM1mvvCFLzB//nwSiQTPP/88//Zv/8abb77JHXfcUe1q7rfPf/7zLF++HLPZXO2qVMyMGTNYu3btgOVut7sKtRnoa1/7GkcffXTestmzZ1enMkIMk/SVk8dE6iu7u7t54IEH+NznPofP52PVqlVVrp0Qk8vWrVvR6Yb3vPBvf/sbt956q9x4AwsXLuSb3/zmgOXNzc1VqM3UIzfdE8T27dtZvnw5s2bN4sknn6SpqSn32qpVq3jvvfd45JFHqljD/RcOh7Hb7ej1evR6fbWrU1Fut5vPfe5z1a5GSSeddBKf/OQnq10NIYZN+srJZaL1lV/+8peZM2cO9913n9x0CzHKJuIfGLP99Xgwffr0cd2fTnYSXj5B3HTTTYRCIX7729/mfYnMmjt3Ll//+tdzvyeTSb7//e9zwAEHYDabmT17Nt/5zneIx+N5282ePZtzzz2X559/nmOOOQaLxcKcOXP4/e9/n1tnw4YNKIrCPffcM2C/jz32GIqi8PDDDwOwY8cOvvKVr3DwwQdjtVqpra3lU5/61IAxh9mwomeeeYavfOUr1NfXM2PGjLzX+m/zl7/8hXPOOYfm5mbMZjMHHHAA3//+90mlUnnlZsORtmzZwimnnILNZmP69OncdNNNA+oei8W47rrrOOigg7BYLDQ1NXHBBRewbdu23DrpdJqbb76Zww47DIvFQkNDA5dffjm9vb15Zfn9ft5++238fv+A/YyWffv2YTAYuP766we8tnXrVhRF4Ze//CUAqqpy/fXXc+CBB2KxWKitreXEE09k3bp1FaufEOOB9JXSV1azrzSZTNTU1GAwyDMNIUZb4Zjuod6/K1eu5NZbbwXIC6fOCofDfPOb36SlpQWz2czBBx/MT3/6UzRNy9tvNBrla1/7GnV1dTidTj7+8Y+ze/duFEXJe4J+3XXXoSgKW7Zs4V/+5V+oqanhxBNPBOD1119n5cqVuSFPjY2NfPGLX6S7uztvX9ky3nnnHT73uc/hdruZNm0aV199NZqmsWvXLs477zxcLheNjY38+7//+2geYlFB8qkwQfzv//4vc+bM4YQTTihr/S996Uvcc889fPKTn+Sb3/wmL7/8MmvXruWtt97iz3/+c9667733Hp/85Ce55JJLWLFiBb/73e9YuXIlixYt4rDDDmPx4sXMmTOHP/7xj6xYsSJv2wceeICamhqWLVsGwKuvvsqLL77I8uXLmTFjBu+//z633XYbS5cuZcuWLdhstrztv/KVrzBt2jSuueYawuFwyfbcfffdOBwOVq9ejcPh4Mknn+Saa64hEAjwk5/8JG/d3t5ezjzzTC644AI+/elP89///d9cddVVHH744Zx11lkApFIpzj33XJ544gmWL1/O17/+dYLBIOvWrWPz5s0ccMABAFx++eXcfffdXHzxxXzta19j+/bt/PKXv+Sf//wnL7zwAkajEYA///nPXHzxxdx1111lJflIpVJ0dXUNWG61Wkv+RbShoYGPfOQj/PGPf+Taa6/Ne+2BBx5Ar9fzqU99Csh02mvXruVLX/oSxxxzDIFAgA0bNvDaa6/x0Y9+dMj6BYPBAfXzer3DDusSYqxJXyl9ZbX6yp6eHu677z42b97Mb3/72yG3FUJk/hBX7D2uquqQ2w71/r388svZs2cP69at4z//8z/zttU0jY9//OM89dRTXHLJJSxcuJDHHnuMf/u3f2P37t38/Oc/z627cuVK/vjHP/L5z3+e4447jmeeeYZzzjmnZL0+9alPceCBB/LDH/4wdwO/bt062trauPjii2lsbMwNc3rzzTd56aWX8v4YAPCZz3yGQw45hB/96Ec88sgj3HjjjXi9Xn79619z6qmn8uMf/5h7772X//f//h9HH300J5988pDHS1XVosfabrdjtVqH3F7sJ02Me36/XwO08847r6z1N23apAHal770pbzl/+///T8N0J588sncslmzZmmA9uyzz+aWdXR0aGazWfvmN7+ZW7ZmzRrNaDRqPT09uWXxeFzzeDzaF7/4xdyySCQyoD7r16/XAO33v/99btldd92lAdqJJ56oJZPJvPWzr23fvn3Qci+//HLNZrNpsVgst+wjH/nIgH3F43GtsbFRu/DCC3PLfve732mA9rOf/WxAuel0WtM0TXvuuec0QLv33nvzXn/00UcHLM/W+a677hpQXqFsHYv9XH755YNu++tf/1oDtDfeeCNv+aGHHqqdeuqpud8XLFignXPOOUPWpdBTTz1Vsm79z4cQ45H0ldJXZlWrr9TpdNoPfvCDYZcnxFST7QsG+znssMPytpk1a5a2YsWK3O/lvH9XrVqlFbvdeeihhzRAu/HGG/OWf/KTn9QURdHee+89TdM0bePGjRqgXXnllXnrrVy5UgO0a6+9Nrfs2muv1QDts5/97ID9Feub/+u//mvA50q2jMsuuyy3LJlMajNmzNAURdF+9KMf5Zb39vZqVqs175iUkv0MK/azdu3aIbcX+08eW00AgUAAAKfTWdb6f/vb3wBYvXp13vJs8oTC8YyHHnooJ510Uu73adOmcfDBB9PW1pZb9pnPfAZVVfnTn/6UW/b444/j8/n4zGc+k1vW/y9lqqrS3d3N3Llz8Xg8vPbaawPqeumll5Y1JrF/udknCyeddBKRSIS33347b12Hw5E3ZsVkMnHMMcfkted//ud/qKur46tf/eqAfWX/2vjggw/idrv56Ec/SldXV+5n0aJFOBwOnnrqqdw2K1euRNO0sqeymD17NuvWrRvwc+WVVw663QUXXIDBYOCBBx7ILdu8eTNbtmzJOw8ej4c333yTd999t6z6FLrmmmsG1K2xsXFEZQkxVqSvlL4yqxp95QMPPMBnP/tZvvvd7/If//EfIypPiKnm1ltvLfoeP+KII4bcdn/ev3/729/Q6/V87Wtfy1v+zW9+E03T+Pvf/w7Ao48+CmSijfor1idm/eu//uuAZf375lgsRldXF8cddxxA0T7/S1/6Uu7/er2exYsXo2kal1xySW65x+MZ8Bk0mGOPPbbosf7sZz9b1vZi/0h4+QTgcrmAzBeocuzYsQOdTsfcuXPzljc2NuLxeNixY0fe8pkzZw4oo6amJm8s3oIFC5g3bx4PPPBA7g3/wAMPUFdXx6mnnppbLxqNsnbtWu666y52796dNy6m2Bi+1tbWstr05ptv8r3vfY8nn3wy98W6VLkzZswYEKZTU1PD66+/nvt927ZtHHzwwYOOu3v33Xfx+/3U19cXfb2jo6Osuhdjt9s5/fTTh71dXV0dp512Gn/84x/5/ve/D2TOg8Fg4IILLsitd8MNN3Deeedx0EEHMX/+fM4880w+//nPl/UhBnD44YePqH5CVJP0ldJXZlWrr/z0pz+N3+/n29/+Nv/yL//CtGnThl13IaaSY445hsWLFw9YXlNTUzQUur/9ef/u2LGD5ubmAX+kPeSQQ3KvZ//V6XQD+uDCz43+ivXXPT09XH/99dx///0D+sRifX7h543b7cZisVBXVzdgeeG48FLq6urku10VyU33BOByuWhubmbz5s3D2q7wy1QppZ6eaAWJJD7zmc/wgx/8gK6uLpxOJ3/961/57Gc/m/dl7Ktf/Sp33XUXV155Jccffzxutzs3x3M6nR6wj3LGkPh8Pj7ykY/gcrm44YYbOOCAA7BYLLz22mtcddVVA8ottz1DSafT1NfXl5xztVpfppYvX87FF1/Mpk2bWLhwIX/84x857bTT8jrik08+mW3btvGXv/yFxx9/nN/85jf8/Oc/5/bbb8/766kQk4n0ldJX9letvvK0007j4Ycf5pVXXhl03KcQYv+M1+86xfrrT3/607z44ov827/9GwsXLsThcJBOpznzzDOL9vnF+ufR6rNFdchN9wRx7rnncscdd7B+/XqOP/74QdedNWsW6XSad999N/cXO8hkdPX5fMyaNWtEdfjMZz7D9ddfz//8z//Q0NBAIBBg+fLleev893//NytWrMjLphiLxfD5fCPaJ8DTTz9Nd3c3f/rTn/ISRWzfvn3EZR5wwAG8/PLLqKqaS/BTbJ1//OMfLFmyZFwlmDj//PO5/PLLc2GT77zzDmvWrBmwntfr5eKLL+biiy8mFApx8sknc91118lNt5jUpK+UvjKrWn1lMpkEIBQKjbzyQoiyDPX+LfVH1VmzZvGPf/yDYDCY97Q7Owwn2/9nPye2b9/OgQcemFvvvffeK7uOvb29PPHEE1x//fVcc801ueUjHdYiJiYZ0z1BfOtb38Jut/OlL32Jffv2DXh927ZtuTFkZ599NgA333xz3jo/+9nPAEb8l/dDDjmEww8/nAceeIAHHniApqamAdkS9Xr9gL+43XLLLQOmqxmO7F/2+pebSCT41a9+NeIyL7zwQrq6unLTxvSX3c+nP/1pUqlULjSxv2QymffleCymwcnyeDwsW7aMP/7xj9x///2YTCbOP//8vHUKQ40cDgdz584dMA2SEJON9JXSV2ZVq6/MTgu3YMGCEZchhBhaOe/f7CwHhX/QPPvss0mlUgP6tp///OcoipKbwSE740RhP3rLLbeUXc9ifTMM/OwRk5s86Z4gDjjgAO67777cFAJf+MIXmD9/PolEghdffJEHH3wwl5hmwYIFrFixgjvuuCMXbvjKK69wzz33cP7553PKKaeMuB6f+cxnuOaaa7BYLFxyySUDppA699xz+c///E/cbjeHHnoo69ev5x//+Ae1tbUj3ucJJ5xATU0NK1as4Gtf+xqKovCf//mf+xVO84UvfIHf//73rF69mldeeYWTTjqJcDjMP/7xD77yla9w3nnn8ZGPfITLL7+ctWvXsmnTJs444wyMRiPvvvsuDz74IP/xH//BJz/5SWD40+D4/X7+8Ic/FH2tf2KjUj7zmc/wuc99jl/96lcsW7YMj8eT9/qhhx7K0qVLWbRoEV6vlw0bNvDf//3fXHHFFUOWLcREJn2l9JX9VbqvfO6554jFYkBmzOZf//pXnnnmGZYvX868efPKKkMIMTLlvH8XLVoEwNe+9jWWLVuGXq9n+fLlfOxjH+OUU07hu9/9Lu+//z4LFizg8ccf5y9/+QtXXnllbjrERYsWceGFF3LzzTfT3d2dmzLsnXfeAcobnuRyuTj55JO56aabUFWV6dOn8/jjj+9XFNJI7N69u2h/6nA4BvxBUlTA2CZLF/vrnXfe0S699FJt9uzZmslk0pxOp7ZkyRLtlltuyZsORlVV7frrr9daW1s1o9GotbS0aGvWrMlbR9MyUwgUm27hIx/5iPaRj3xkwPJ33303N8XA888/P+D13t5e7eKLL9bq6uo0h8OhLVu2THv77bcHTPOQnSri1VdfHVBGsWlwXnjhBe24447TrFar1tzcrH3rW9/SHnvsMQ3Qnnrqqbx6F04xoWmatmLFCm3WrFl5yyKRiPbd7343d4waGxu1T37yk9q2bdvy1rvjjju0RYsWaVarVXM6ndrhhx+ufetb39L27NkzoM77Ow1OuW/JQCCgWa1WDdD+8Ic/DHj9xhtv1I455hjN4/FoVqtVmzdvnvaDH/xASyQSg5abnQbnwQcfLKseQoxX0ldKX6lple8r+/+YTKaytxdiqhusb9O04n1UYf9Yzvs3mUxqX/3qV7Vp06ZpiqLk9R3BYFD7xje+oTU3N2tGo1E78MADtZ/85Ce56RCzwuGwtmrVKs3r9WoOh0M7//zzta1bt2pA3hRe2em+Ojs7B7Tngw8+0D7xiU9oHo9Hc7vd2qc+9Sltz549JacdKyxjxYoVmt1uL+s4FTPYlGGFfb6oDEXTZPS9EEIIIYQQQpRj06ZNHHnkkfzhD3/goosuqnZ1xAQgY7qFEEIIIYQQoohoNDpg2c0334xOpxuQr0OIUmRMtxBCCCGEEEIUcdNNN7Fx40ZOOeUUDAYDf//73/n73//OZZddRktLS7WrJyYICS8XQgghhBBCiCLWrVvH9ddfz5YtWwiFQsycOZPPf/7zfPe738VgkOeXojxy0y2EEEIIIYQQQlSIjOkWQgghhBBCCCEqZNLHRKTTafbs2YPT6SxrLj0hxOSlaRrBYJDm5uYB8yZPddJXCiH6k/6yOOkrhRD9ldtXTvqb7j179kiSAyFEnl27djFjxoxqV2Nckb5SCFGM9Jf5pK8UQhQzVF856W+6nU4nkDkQLperyrURQlRTIBCgpaUl1y+ID0lfKYToT/rL4qrZV6qqyuOPP84ZZ5yB0Wgc032PpsnSDpg8bZks7YCxb0u5feWkv+nOhv64XC75IimEAJCQwCKkrxRCFCP9Zb5q9pWqqmKz2XC5XBP6xmiytAMmT1smSzugem0Zqq+UQTpCCCGEEEIIIUSFyE23EEIIIYQQQghRIXLTLYQQQgghhBBCVIjcdAshhBBCCCGEEBUiN91CCCGEEEIIIUSFVPWme+3atRx99NE4nU7q6+s5//zz2bp1a946S5cuRVGUvJ9//dd/rVKNJ69IIsm+QIxIIlntqoh+5LwIISpN+hkhhBCisqo6ZdgzzzzDqlWrOProo0kmk3znO9/hjDPOYMuWLdjt9tx6l156KTfccEPud5vNVo3qTlrvdYR4/M12AjEVl8XIGYc1MrfeUe1qTXlyXoQQlSb9jBBCCFF5Vb3pfvTRR/N+v/vuu6mvr2fjxo2cfPLJueU2m43GxsayyozH48Tj8dzvgUBgdCo7SUUSSR5/s53eiEqjy0J7IMa6Le00e2ZjM036adzHLTkvQohKk35GCCGEGBvjaky33+8HwOv15i2/9957qaurY/78+axZs4ZIJFKyjLVr1+J2u3M/LS0tFa3zRBeMJQnEMl+4rCY9jS4L/qhKMCZhhtUk50UIUWlTuZ+RkHohhBBjadz8KTudTnPllVeyZMkS5s+fn1v+L//yL8yaNYvm5mZef/11rrrqKrZu3cqf/vSnouWsWbOG1atX534PBAJy4z0Ip8WAy2KkPRDLPenw2o04LePm0piS5LwIISptqvYzElIvhBgt8697jJuOyfwbTylDrv/+j84Zg1qJ8WjcfLKuWrWKzZs38/zzz+ctv+yyy3L/P/zww2lqauK0005j27ZtHHDAAQPKMZvNmM3mitd3srCZDJxxWCPrtrTTGcp84frooY0SWlhlcl6EEJU2FfsZCakXQghRDePiE+aKK67g4Ycf5tlnn2XGjBmDrnvssccC8N577xW96RbDN7feQbNnNsFYEqfFIF88xgk5L0KISptq/UyxkPrOUIxgLDnp2y6EEKJ6qvoJo2kaX/3qV/nzn//M008/TWtr65DbbNq0CYCmpqYK125qsZkm/5etiUjOixCi0qZSPzNVQ+qFEEJUV1U/ZVatWsV9993HX/7yF5xOJ+3t7QC43W6sVivbtm3jvvvu4+yzz6a2tpbXX3+db3zjG5x88skcccQR1ay6EEIIISaYqRhSL4QQovqq+ilz2223AbB06dK85XfddRcrV67EZDLxj3/8g5tvvplwOExLSwsXXngh3/ve96pQW7G/IonklAlhFEIIMT5NtZD6LPkMFkKI6ql6ePlgWlpaeOaZZ8aoNqKSJFusEEKI8WIqhdSDfAYLIUS1jat5usXk1D9b7DSHhd6Iyrot7TI/qhBCCFFh8hkshBDVJzfdouKKZYv1R1WCMfnAF0IIMfoiiST7AjG5sUQ+g4UQYjyYOrFVomokW6wQQoixIqHU+eQzWAghqk+edIuKy2aL9dqNki1WCCFExUgo9UDyGSyEENUnPa4YE1M1W6wQQoixUyyUujMUIxhLTunPHfkMFkKI6pJeV4yZqZYtVgghxNiSUOrS5DNYCCGqR8LLhRBCCDEpSCi1EEKI8Ug+hcS4EUkkp2zoW7ltr/Yxqvb+hRDj00j7hkr0KRJKLYQQYryRTyIxLkzlbLPltr3ax6ja+xdCjE8j7Rsq2adIKLUQQojxRMLLRdVN5Wyz5ba92seo2vsXQoxPI+0bpE8RQggxlchNt6i6Ytlm/VGVYGzyf/kqt+3VPkbV3r8QYnwaad+Q3c7rMKKm03gdxjHtUyKJJPsCMbnJF0IIMSYk9kpU3VTONltu26t9jKq9fyHE+DTSvsFpMaCmNJ7Y0oFJr5BIaSxs8YxJnyJDZYQQQow1edItqm4qZ5stt+3VPkbV3r8QYnzar75B01AUAKXvX62ylUXC2ie6Z599lo997GM0NzejKAoPPfRQ7jVVVbnqqqs4/PDDsdvtNDc384UvfIE9e/bkldHT08NFF12Ey+XC4/FwySWXEAqFxrglQoipRr4xi3FhKmebLbft1T5G1d6/EGJ8GknfEIwlMRp0nHpwA4oCmgb+WIJgLFnRvqVYOHxnKFbx/YrREQ6HWbBgAV/84he54IIL8l6LRCK89tprXH311SxYsIDe3l6+/vWv8/GPf5wNGzbk1rvooovYu3cv69atQ1VVLr74Yi677DLuu+++sW6OEGIKkU8YMW5M5Wyz5ba92seo2vsXQoxPw+0bsmHpPZHEmA5ZkaEyE9tZZ53FWWedVfQ1t9vNunXr8pb98pe/5JhjjmHnzp3MnDmTt956i0cffZRXX32VxYsXA3DLLbdw9tln89Of/pTm5uaKt0EIMTXJp4wQQgghxlQ2LH3dlvYxHbJSrf2K6vD7/SiKgsfjAWD9+vV4PJ7cDTfA6aefjk6n4+WXX+YTn/jEgDLi8TjxeDz3eyAQADLh7KqqVrYBBbL7G+v9jrbJ0g4As07L+3co47XNk+mcjHVbyt2PfMqIKS+SSJYVFlnuemNRl/FavhBichvNPqRaQ1aK7Vf6xsknFotx1VVX8dnPfhaXywVAe3s79fX1eesZDAa8Xi/t7e1Fy1m7di3XX3/9gOWPP/44Nptt9CtehsIn+hPVZGjH9xdn/02Xtf7f/va3CtZm/02Gc5I1Vm2JRCJlrSefLGJKKzeL7Vhku630PiRjrxBif1SiD6nWkJX++5W+cfJRVZVPf/rTaJrGbbfdtl9lrVmzhtWrV+d+DwQCtLS0cMYZZ+Ru5seKqqqsW7eOj370oxiNxjHd92iaLO0AWHTDo3x/cZqrN+iIp5Uh19983bIxqNXwTaZzMtZtyUa/DEVuusWU1T+LbXZs37ot7TR7Zud9CSx3vbGoy3gtXwgxuU3WPmSytmsqy95w79ixgyeffDLvxrixsZGOjo689ZPJJD09PTQ2NhYtz2w2YzabByw3Go1Vuzmp5r5H02RoR/ZGO55WiKeGvuke7+2dDOcka6zaUu4+ZMowMWUVy2Lrj6oEY8kRrTcWdRmv5QshqieSSLIvEKvotFcj7UPGom7lKKxH9veOQFz6xkkke8P97rvv8o9//IPa2tq8148//nh8Ph8bN27MLXvyySdJp9Mce+yxY11dIcQUIn/GFVNWuVlsxyLbbaX3IRl7hZicxio0eiR9yHgJ2y6sx7wmF2/vDRCIqViNetRkWvrGCSIUCvHee+/lft++fTubNm3C6/XS1NTEJz/5SV577TUefvhhUqlUbpy21+vFZDJxyCGHcOaZZ3LppZdy++23o6oqV1xxBcuXL5fM5UKIipIn3WLKymax9dqNg2axLXe9sajLeC1fCDH2+odGT3NY6I2orNvSXpGnysPtQ8ayboMprEdHMM7dL26nIxRjmsNCKJ5CURQcZr30jRPAhg0bOPLIIznyyCMBWL16NUceeSTXXHMNu3fv5q9//SsffPABCxcupKmpKffz4osv5sq49957mTdvHqeddhpnn302J554InfccUe1miSEmCLkU0VMaeVmzx2LLLuV3ke1MgULISqjWMh3ZyhGMJasWB/lsc2g3R+n0W2mzmEZN3UrlM1GHk2k8urhsRoJRFU8VlNevc5b2IzVZJC+cZxbunQpmlZ6aqbBXsvyer3cd999o1ktIYQYknyyiCmv3Oy5Y5Flt9L7qFamYCHE6BvrYSPDCRev5pCW/vUsDB/3RVVcViO+aAKn2ZirV73LIn2jEEKIipHwciGEEGICGsthI8MNF6/WkJbCehaGjze4zKw8oZUGp0XCyYUQQowZ+ZQRA2TD8sYqzG5/91dq++GWW7j+WB8HIYQYrv0ZNjKcPq7ccPH+ZVZjSEupehaGjx83xyv9uxBizM3+9iNlr/v+j86pYE3EWJNPGpFnrLPN7u/+Sm0/3HIHy25bzay7QggxlJEMGxluH1lOuHipMsfyprZUPQvDx2WojRBCiLEk4eUiZ6yzze7v/kpt3xWKDavcAdltQzHufmE7HcF4VbPuCiFEJYyk7x0qXHy8ZCuXmRqEEEKMR/IpJHLGOtvs/u6v1Pbt/viwyi0sx2M1EYj58FiNVcm6K4QQxYzWUJzCjN7l9nGDhYtXO1t5ufXcXyMdtqTXQSqNhLMLIcQUJT2/yBnrbLP7u79S2ze6zcMqt7AcXzSBy2LEF1VxWoxjmnVXCCGKGc2hOIUZvYfTx5UKy65mtvJiKhE+PtJhSzu6I+z1R2nyWJjltctwJSGEmIIkvFzkjHVY3v7ur9T2dQ7LsMotLKfBaWHlklYaXGYJTxRCVN1oD8UpzOg9Gn3cZA/rHu45yK7fEYrREYzRG1HpDMTpCMZluJIQQkxBk+PTUIyasc42u7/7K7X9cMsttr5ktxVCDMdozXhQWE6lhuIUZvQeTp2KtbUa2crHataJ4Z6D7Poeq4m2dJgmt4WYmsZjNeKPqjJcSQghphjp8cUAY53VdX/3V2r74ZZbuL5ktxVClGu0Zn4oVk6zx1KRoTiFGb3LrdNgszuMZb85lrNODDd8Prt+RyiGXqew1x9jmsOEL6rS4DLLcCUhhJhiJLxcCCGE2A+jlbm7VDlARYbilJsILG92h2Ccu1/cTkcoVtUs5WNdr+Eew+z6DU4L9U4LXruRaS4zDS7zpAq7F0IIUZ6q9vpr167lT3/6E2+//TZWq5UTTjiBH//4xxx88MG5dWKxGN/85je5//77icfjLFu2jF/96lc0NDRUseYTz2iG3FUqfG9/91O4fldfJvNGt5k6h2XAeuVmky1cv9R2o5mltrDuI61zpc7RWF0D4kO7d+/mqquu4u9//zuRSIS5c+dy1113sXjxYgA0TePaa6/lzjvvxOfzsWTJEm677TYOPPDAKtd88hutzN2DldM/dDvbD0QS5Zc/0tDvgbM7GAlEM2HT1chSXioDe7ZedqMBNZ3G6zDij4xuGPf+DFuS7OVCCDG1VbXnf+aZZ1i1ahVHH300yWSS73znO5xxxhls2bIFu90OwDe+8Q0eeeQRHnzwQdxuN1dccQUXXHABL7zwQjWrPqGMVtjjaJc1mvspXN9tNfJYv99XLmnl1Hn1H2aT7Qmz1xejyW1lVq2tZPmF69tNBsKJ5IDthlvuYJ58u4O7X9ieq/uywxrxR9Vh17lS52isrgHxod7eXpYsWcIpp5zC3//+d6ZNm8a7775LTU1Nbp2bbrqJX/ziF9xzzz20trZy9dVXs2zZMrZs2YLFYhmkdLG/Ritz91Dl2EwG9vhiI37/jST0e+DsDiouqxFfNIHTPLazOwyWgd0XVTHoFNa3dWMx6EikNBa2eEa9Xvs7bEkIMb7N/vYjw1rfrK9QRcSkU9VPgkcffTTv97vvvpv6+no2btzIySefjN/v57e//S333Xcfp556KgB33XUXhxxyCC+99BLHHXfcgDLj8TjxeDz3eyAQqGwjxrn+IXjZL3HrtrTT7Jk97C8Co1nWaO6ncP227iB/3NCN3WRgeo2Vvf4Y97y4nQPr7ZlsssE4HYE4vREVg17BatYVLT+XfbZv/e5Qgg+SEaxGfd52HtuMYZU7mK5QjLtf2E5vRKXJbWG3L8Kdz7VxbKuXjmD5da7UORqra0Dk+/GPf0xLSwt33XVXbllra2vu/5qmcfPNN/O9732P8847D4Df//73NDQ08NBDD7F8+fIBZUpfOXqyocTrtrTvV+buocqpxvuvsE4NLjMnHzSNre2BMc1SXqzt/TOw19iMzKq1s7MnAigoigZoFa2TEEIIUa5x9S3Z7/cD4PV6Adi4cSOqqnL66afn1pk3bx4zZ85k/fr1RW+6165dy/XXXz82FZ4ARivscbTLGs39FK5vNWYy2M702rCZDDS5LXQEY7R1hvuyyRppS9OXTTaFx2oqmk32w+yzmfVrHSbaOlVmeMwkU1puu3Z/fFjlDiZbVpM7k+Co1m6mMxhApyikhlHnSp2jsboGRL6//vWvLFu2jE996lM888wzTJ8+na985StceumlAGzfvp329va8vtLtdnPssceyfv36ojfd0leOrsFCj4czHGOwMPKxev8VDm8pZ3aHSmcRz7bdazOhptJ4bSb8sUQuA3s0keT+V3cxd5oTRQFNA38sMeZh75UMH5dhPUIIMXGNm147nU5z5ZVXsmTJEubPnw9Ae3s7JpMJj8eTt25DQwPt7e1Fy1mzZg2rV6/O/R4IBGhpaalYvce70Qp7HO2yRnM/hetH1cyXrO5QAotRz15/Zvs50+y8uSdARzCOXkcmm6zThC+aoMFpGVB+Lvts3/odgQQmo0J3OE6Dy5LbrtFtzltvqHIHky1rrz9Gk9tCdziOzWQgrWnDqnOlztFYXQMiX1tbG7fddhurV6/mO9/5Dq+++ipf+9rXMJlMrFixItcfFua6kL5ybBULJR7JcIxSYeT7m8W8HIXDW7JDcwab3WEssog7LQbUZJont+7DqNOhptMsmOHOZWCPJJK4LEZ6Iokx75vGYsiNDOsRQoiJbdxkL1+1ahWbN2/m/vvv369yzGYzLpcr72cq25+stZUsazT3U7j+DI+NS0+aQ53TREcws/2KE1pp8doz2WRdZupd5kw2WYeFBqelaPm57LN969c5TRxc76LOYc7brs5hGVa5g6lzWFi5pBWv3UhHMEadw8ylJ81hdp19WHWu1Dkaq2tA5Eun0xx11FH88Ic/5Mgjj+Syyy7j0ksv5fbbbx9xmdJXVt5Is5pXKov5UPoPb6l3ZvZ7z4vb6QrFyq5rR18ZHcH46GcRV5S+/2TDxpXcS9Xqm0Yrc3219yGEEKKyxsU35SuuuIKHH36YZ599lhkzZuSWNzY2kkgk8Pl8eU+79+3bR2NjYxVqOjGNNGttpcsazf0UW3/Z/IYB2cuHm0222PrFthvNLLWnzqvniBmuEWcvr/Q5GqtrQHyoqamJQw89NG/ZIYccwv/8z/8A5PrDffv20dTUlFtn3759LFy4cMzqKfKNNBy83Czmo/3+Kxzekh2a0+6P580AMVhdPVYTgZgPj9U4qiHwwVgSo17h1EPqUVDQ0AZkJy92bEYrJLtUOWMR8i/DeoQQYuKram+taRpf/epX+fOf/8zTTz+dlxgIYNGiRRiNRp544gkuvPBCALZu3crOnTs5/vjjq1HlCWs0M6iOVTbW/c0SW+ewFP2iWKnss6N5XArrPt4y5kpG3rG1ZMkStm7dmrfsnXfeYdasWUAmqVpjYyNPPPFE7iY7EAjw8ssv8+Uvf3msqyv6jHQ4RjlZzCvx/isc3pIdmtPoNpddV180gctixBdVcVpGL7t5dj89IXXQYzlY2PtIQ7IHK2cshtzIsB4hhJj4qhpevmrVKv7whz9w33334XQ6aW9vp729nWg0CmQSAV1yySWsXr2ap556io0bN3LxxRdz/PHHF02iJoQQk9E3vvENXnrpJX74wx/y3nvvcd9993HHHXewatUqABRF4corr+TGG2/kr3/9K2+88QZf+MIXaG5u5vzzz69u5aewkYY8VytUunB4S3ZoTqmn3MXq2uDMlNHgMo9q3Yd7TEYrJHuocsbiXMmwHiGEmPiq2mPfdtttACxdujRv+V133cXKlSsB+PnPf45Op+PCCy8kHo+zbNkyfvWrX41xTae2UqHNY5VJdaj9jPT14YRsD6e8YuVkswG7rQZMBn1uncIswaUUrldu3Yd7jkbStpHsRwzP0UcfzZ///GfWrFnDDTfcQGtrKzfffDMXXXRRbp1vfetbhMNhLrvsMnw+HyeeeCKPPvqozNFdZSMNB6/WMI5iw1uGUmx4zYH1dvzRZK7Py2Zg3x/DOSajFZJdTjljca5kWI8QQkxsVQ8vH4rFYuHWW2/l1ltvHYMaiULZsLod3RH2+qM0eSzM8torkp12sP2X2s9IX8+1qyfMXl+MJreVWbW2IdsxVHnF6pHNBtwRjBNTUxzS5OTw6R7cViOP9dsmmyW4UGE24WWHNeKPqgPOyXCPzWi0bST7ESNz7rnncu6555Z8XVEUbrjhBm644YYxrJUox0jDwas1jKPU0JzB9M+4nu2b7GY94Xiq7P613P2Uc0xGKyS73HLG4lzJsB4hhJi4xk32cjH+ZMPqOkIxOoIxeiMqnYE4u31R7n5xOx2hWEUzqQ4V1jfS17tCmS+GHcE4HYF4pl2hGB2h2KDtGKq8YvXIZgPuDsdJptKE4km2tgfZ1hHkzufa6A7HB80SXJhNuDuU4DfPtbGzO5x3TjqC8WEdm9Fo20j2I4SYnAo/L7pDCba2B+kOJcrqX0fbaIVkS2i3EEKI0SCfGqKkbFidx2qiLR2myW0hpqaxGvUEopnllcykOlRY30hfz2bo9ViNtKXpa1cKj9WEP6qWbMdQ5RWrR2cw81qt3cxef4xau4lwPEUynfmSOtNrGzRLcGE24VqHic72GIpOIZXWcufEYzXm1X24oZUjadtI9iOEmJwKPy9qHSbaOlWa3AbCiRR2oz6vj6rUkJT+Q2+cFgOfWjxj0GFR5QzVGU5otwy1EUIIUYx8IoiSsmF1HaEYep3CXn+MaQ4TUTWFy2rEF03gNI9edtpS+y8V1jfS17MZejuCcfQ6Mu1ymvBFEzQ4LSXbMVR5xeqh14HLYqQ7HEfTNLojKi6LAYMu8wSlOxzHYtSXzBJcmE24O5TAbjKgpbW8c+KLqjS4zGUfm9Fo20j2I4SYnAo/LzoCcRRF4919ISxGPeuTaY6aWYPTYqjYkJRSw6HOOKyRBpdlwH6zw6SGGqoD5YV2y1AbIYQQpUh4uSgpG1bX4LRQ77TgtRuZ5jIzo8bKyhNaaXBaKhpuN1RY30hfr3NY+r6Emal3mTPtclhocFoGbcdQ5RWrRzYbcJ3DjEGvw2kxcHCjk7kNTi49aQ51DvOgWYILswnXOU186aQ5zKqz552TBpd5WMdmNNo2kv0IISanws+LGrsRq9GA0ajD6zCj1ymAVrEhKaWGQ2WH3hQOlekIxrn7xe3s8UcHHaoz3P3LUJvKevbZZ/nYxz5Gc3MziqLw0EMP5b2uaRrXXHMNTU1NWK1WTj/9dN599928dXp6erjoootwuVx4PB4uueQSQqHQGLZCCDEVyTdjMahiWWmzYXPHzfFWPIxuqLC+kb4+WLtGUp/B6tE/G3Bh9vJl8xuGzBJcLJvwaIdEjrRtI9mPEGJ8KqdfGSx8un9f0BNKcN8rO6izWzAbdWga+GOJIYeslDujQ6FseLvdqCeippjmNJFMkRt6U7hfu0lPdyhOk8uML6ritRtJpj9cvyMQw2oylN2nyVCbsREOh1mwYAFf/OIXueCCCwa8ftNNN/GLX/yCe+65h9bWVq6++mqWLVvGli1bcjM5XHTRRezdu5d169ahqioXX3wxl112Gffdd99YN0cIMYXIJ4EYUqmwurHKpDrUfkb6+mhnFB6svFLZgMvNEly4Xrl1H24bR3qsJKuuEBPbYKHZ2RDpcsKns32B02KgyW2lN6LisJQ3ZKVwpoZSMzoU47QYUFMar+zspSsYJ55MM7vOlht603+/Bp3Cy9u72eePsaM7gqaBAsyZZscXVTHoFB765x5iyVTZYeIy1GZsnHXWWZx11llFX9M0jZtvvpnvfe97nHfeeQD8/ve/p6GhgYceeojly5fz1ltv8eijj/Lqq6+yePFiAG655RbOPvtsfvrTn9Lc3Dyg3Hg8Tjwez/0eCAQAUFUVVVVHu4mDyu5vrPc72vanHfOve2xY62++btmw1jfrh55ZKW99nZb372gay/M8Wa4tGPu2lLsf+TQQQgghprBiodkGnYLVaGDdlnaaPbMBcuHT2ZvK7Gul/lB3xmGNrNvSXnTISuHySCKZm6mhyW1hrz/GPS9u54gZrvKfeGuZXBdeh5nOUIxoIk1NwX4feX0PL27rIpXW0Ot1pNQ0xr4n+x2BOGaDQjKlEU6kymrnUO2VP0aOne3bt9Pe3s7pp5+eW+Z2uzn22GNZv349y5cvZ/369Xg8ntwNN8Dpp5+OTqfj5Zdf5hOf+MSActeuXcv1118/YPnjjz+OzWarTGOGsG7duqrsd7SNpB03HTO89f/2t79VtPys7y9Oj2zDQQy37qNhslxbMHZtiUQiZa0nnwZTTKnMraMZFjxUmaVeLwwrHG5dhwqNzJZfGOI9VL0Kl+/qCdPWmcnm7raZBtQvkUzhjybzwiPLPc7lhHeWcyxLlTPSY1qpsHHJ9CtE+Sr1fik1U0X/WRGAvPBpr8PIHl+MjkCM2XUfPgUeKns4FB+Ssnm3P2+mhlIzOgzWBqNBx6kHN6AoEFfTdIVjnLdgOrPr7Ln9nrewma5wAj0a/3grwXS3lVQ6TYPLQjCmsmiWl9d29jLNMfwwcRlqU13t7e0ANDQ05C1vaGjIvdbe3k59fX70hMFgwOv15tYptGbNGlavXp37PRAI0NLSwhlnnIHL5RrNJgxJVVXWrVvHRz/6UYxG45juezTtTzsq/aR7uOWbdRrfX5zm6g064mllWNsOZbh13x+T5dqCsW9LNvplKPKJMIWUytw6mplWhwo/LPV6YVjhssMa8UfVsuuaC43sCbPXF6PJbWVWrW1A+R3BGDE1zSFNLg6f7s69XqpehcsTyTQPbdpNIJokraU5fIab4+fU5er3xm4/b+0NYDHqqHdmkqDN9NrKymhbTnhnOce6VDmFx7DcY1qpTLyS6VeI8lXy/VJqporCWRGy4dN6HWx4vxe9Dh765x7OXdCc3/cU6YcbXPk3zoVDUgpnaig1o8NQbeiJJGh0WegOJ2j2WKl35W9f77LQ5LKw2x/BZFDwRRN4rEaCsSR1TjOHNDl5ryM04jBxGWoz+ZjNZszmgdeh0Wis2s1JNfc9mkbSjnhqeDe2lS4/t11aGfG2pVTjHE+WawvGri3l7kOyl08RhZlVO0KxvpvQ+KhlWh0qe2up13f1hHNhhfVOC93hOHc+18ZuXzQvy2xHKDZouR3BOB2BeCYLbShGRyiWV353KEEyBaF4kq3tQfb4o0Wz2mbLL1y+szvMPS9uxx9NoCga8WSazbv9bOsIcveL29nZHWZre5BQPEkqBd2hBL97vo2H/vnBkBlth8q8W2r9UnUuLGe3P5J3vss9ppXKxCuZfoUoX6XfL6Vmqug/K0J2HYdFzz93+lAUOGqml3Aild/3lOiHh6pr4UwNpWZ0GKoNQ82kkF1vhsfGwQ0unBYDBr1CndPEihNaaen7Q6fMyDDxNDY2ArBv37685fv27cu91tjYSEdHR97ryWSSnp6e3DpCCFEJ8ikyRRRmVvVYTQRiPjxW46hlWh0qe2up19s6w3lhhbV2M53BAFajvq+uRgLRTOjjYOV6rEba0vSFRqbwWE34o2qu/FqHib3+GLV2E+FECqtBXzSrbbb8wuWKTiGeSlNnNxNLpnGYFaJqmmQaAlGV6R4riVSaWruJtAa1DhPd4TjtgRiHNLoHPc7lhHcWW79UnQvLsRoNuWM0nGNaqUy8kulXiPIVvl+8NhN7fBE6AnFm143OcJVyZnSYW+/gvAXT6QrGaXbbcNuMRBOpgr4nvx+2mwzs8UWHrCsUn6lhKP1D7ufWO/DYZhTdvnC9bFuLDQcqFiY+FkOzxP5pbW2lsbGRJ554goULFwKZsM+XX36ZL3/5ywAcf/zx+Hw+Nm7cyKJFiwB48sknSafTHHvssdWquhBiCpBPiimiMLOqL5rAZTHii6o4+5bvb6bVobK3lnp9zjR7XlhhdziOzWQgqqaIJlL4oiouqxFfNIHTPLCuudDIYBy9jkxopNOEL5qgwWnJld8dSqBp0B3JtD2aTDHdYS2ZTbdwuZbWMOt1BOMqOkUhkkhhMeow6MBlNaKlNUx6Hd3hBDVWE92hBDV2U67MwUIVyw3vHOpYZ+tcWE5UTead73KPaaUy8UqmXyHK1//9YtApbNjRg05R+Mum3ZxzRPPgw1VKDLkpppzQ6HqXmSa3lZ5IApNBN7Dv6dcP20w61m/rxqjXDVrX/sqd0aF/G4caMlMqND/b1pYhjsVYDM0S5QmFQrz33nu537dv386mTZvwer3MnDmTK6+8khtvvJEDDzwwN2VYc3Mz559/PgCHHHIIZ555Jpdeeim33347qqpyxRVXsHz58qKZy4UQYrRIePkUURh619A33rjBZR61ELqhwvtKvd7iteeFFdY5zFx60hxm1FgzdXWZWXlCKw1Oy6DlNrjM1LvMmdBIh4UGpyWv/DqnCYM+8wX24EYn0z3WvKy2hfUqXD6rzs6KE1rx2ExomoLFqGP+dDdzG5ysPKGVWXV2Dm504rQY0OuhzmniiyfO4fwjZ5Qd8jhYeGc5xzpb58JyZnhseee73GNaqRDLSpcvxGSSC+0263ltZw8AR870EIqnBh+uMsJQ73LqUrLv6euH3VYDkUQavU4ZtK4jNWDIVIkhM6WGD5Vbj7EYmiXKt2HDBo488kiOPPJIAFavXs2RRx7JNddcA8C3vvUtvvrVr3LZZZdx9NFHEwqFePTRR3NzdAPce++9zJs3j9NOO42zzz6bE088kTvuuKMq7RFCTB3yDXcKKRYyd9wc76iGyA2VvbXU68XCCgvD9war61Chkf3LL5a9vFS9ii2/cNH0otnLs/UrFq5YTkbbcsI7yzmWg5VTeAzLPaaVCKGUTL9ishvNEOT+mbebPRbcFlMutLv0cJXiQ272dxhHOX1PTzjOfa/sGrKuIzVwyFTxITOlhg+VW4+xGJolyrd06VI0rfR8yIqicMMNN3DDDTeUXMfr9XLfffdVonpCCFGSfEJMMYXhg5XItDpUmaVeLwwrHG5dh3p9qLDFUtsXLm/x2mnx2gddrzBcsdzjPNzzUW6dSy3f32O6vyTTr5isKpFtPJt5uyekYtLphx6uUmLIzWgM4xiqj3FaDGXVdaQGDpkqPmSm1PChcusxFkOzhBBCTH4SXi6EEEKMokplGx9uhu5SQ27G4g9dYz1EpdSQmVLDh8qtx1gMzRJCCDH5yadElZQbdjjS8MSuvrC6cjPAlrNtuXUZavtsyHPhv4VZYUstH2722MLysuHfJr1CIqUNeYyy2xdul/23MFy9WPuHqmupY1O4frbswn1mt2/3R9neFaa1zk6j2zrkMSxVj4kU7j0R6ywmt0pm5y93WMZwh6vsr2Lvw3LqWur9W6q/79+WcodMlZONfDAjGZol/ZIQQoj+5JOgCsoNOxxpeOKTb3dw9wvbc9utXNLKqfPqy6pbqW3LrctQ22cz6drNBsLxJHaznnA8lcusm80KW5hxtzBbbLnZYwv3qwG7eiKoqTTheJIau4mWGlvJY5Td/o3dft7aG8htZzHqM9PhmA0Y9ToOaXJx+HQ3bquRx/odp5VLWpnptQ167HJ17I6w1x8dcEyy62ePbUcwTkxNcUiTk8One3LH4om39/Hmbj9pMiEsB9Q7qLWbafJYmOW1D3nMKhEOW2kTsc5i8qt0dv5KDVcZqcHeh4PVodR2pbKFZ/vIbJ9WmIV8sP31Xz6SfmM4Q3OkXxJCCFFIwsvHWLlhhyMNT+zqy6zaG1Gpd2a2u+fF7XSFYkPWrdS2u3rCZdVlqO2zmXS7w3G2tgfpDGT+7Q4l6AzF2OOPcvcL29ndN6drNuPubn8kLyttudljCzP4dgTjbN7txx9R6QkniKkp/BGVzkC86DHKbr/HH2Vre5BANEFPSCWaSNMZjBNNpOkJqQSiCba2B2nrDHHnc210hxK59v/u+Tb+/NoHJY9dro6hGB3BGN2hRN4xyWYb3tUT5u4XttMdjpNMpQnFk2xtD7KjJ8TdL2xn614/b+72o6Y00mkNNaWxtT3IHl+EzkCc3b5o0cy+hfUY7XDYSpqIdRZTw1TKzj/S92Gp7QqzjWf7+93+CB3BWOZzoa8/H8n7vdL9hvRLQgghipGb7jFWLOwwm012JOsVymZqbXJbsJkMNLkz27X740PWrdS2bZ3hsuoy1PYeq5FUGmrtZhKpNC6bgURSo9ZhIpnSsBr0BGIqVqOeVF/G3WRKw2o05GWlzWSPVfOyxw52DLP79VgNJFNp7CY9aU3DZjKQBlw2Q9FjlN3eatCTSKVxmg2kSWM2KgCYjQppMssTqTTJtEYkkaTWYcq1vzei0h6Iljx2H9bRRCqdORb9j0k223D2GNbazSiKQq09s55O0RGIqUSTGikNzAYdiqJg1CukNbAY9STTYDXqB2T2LVaP4V5v1TQR6ywmh0giyb5AbNAbqbn1DlacMJuLl7Sy4oTZ4/pJZzntya7TFYrlrZt9HzosenoicRwWfVnvw1Lv38Js49n+3mo0kEprmc+FNHisxrz9lNOG/vv1Ooyo6TROi549/hgdgaH/MF3OfqRfEkIIUczk+7P7OFdu2OFIwxOzmVr3+mM0uS3s9X+YwXUopbadM83Om3sCQ9ZlqO2zmXQ7gnFMeh2BSBKTQaE7lKDBbSaaTOGyGImqqbyMu1E1mZeVttzssYUZfH3RJAa9jnAihU5RiCQy4ysDkST1bvOAY5Tdfo8/ikmvwxeJo0NHXM1MVxJXMze9wXgSj82MQadgMxnoDiWwGPXs9ceosRlpdFlLHrtcHUMx9DqFjkA875hksw3PmWbHZTHSHY6jaRrdERWXxUBaS+OyGLEaFPQKxJNpFAXUNOgUiKkpDDqIqqmimX0L61GpcNhKmIh1FhPfcEKHJ0J2/nLaUzgEpn94d7PHQmcwzktt3aABChw/p3bI92Gp929htvFsfx9Vk+h1SuZzwWHCF1VpcJlxWgzDOidOiwE1pfHElg6S6TRdwQReu5GHbCbOXdA86B9HytmP9EtCCCGKkSfdY2y42WeHG55Y58hkVvXajXQEM9utOKG1rGRqpbZt6ftyNVRdhto+m0m3zmHm4EYn9e7Mv3VOE9McFqZ7rKxc0sqMGmtext0ZHlteVtpys8cWZvBtcJmZP92Nx27Ea8888XXbjNS7zUWPUXb76R4rBzc6cdtMeB1GbGYd05xmbGYdXocRt83EwY1ODqh3cOlJc6hzmnLt/+KJc/jEUTNKHrtcHZ0W6p0W6pymvGOSzTbc4rWzckkrdQ4zBr0Op8XAwY1OZtc6WLmklXnNbg6b7saoV9DpMk+6D250Mr3GxjSXmRk11qKZfQvrMZHCYSdincXENtlCh8tpT+EQmMLw7u5QnPe7wiTTGga9jmRa4/3uyJDHpNT7tzDbeLa/n+GxUe+0ZD4X+vrzjx7aCDD8c6JpaGj0hBOoqTRWox5fdPDtyj330i8JIYQoRj4FqmAk2WeHkwH11Hn1HDHDNaLs5aW2Lbcu5Ww/VPbybFbYUsvLzR5bar/DyV7ef/tys5cvm98woP2DHbvBjk3/9fsf28J9HjfHywVHzRgye/lgx2yk11s1TcQ6i4mrklnJx0JhRu2h2hNJJGnrDNMTTuCxmmhLh2lyW4ip6Vx4d1tnmJSmcViTG5NBRzSRoisc4/2uCHUOy6BZvEu9fwfLFl7YR3YE4sM6J8FYEqNBx6JZNby6vQeX1YSm5Yerl9qu3P0U69OzkVVCCCGmJvkEqJJKZ5+tc1iGPVXYUNuWW5f93b6c7LP7W17LkFsV376c7Yq1f6i6jtaxbXBZWNBSM+L9TIRw2EITsc5iYprIocPFQqObPZaS7cmu3x2O8+6+ELUOU9Hw7uzQl85QHFffdiaDjqe3dhCIJYecZWIk/X3/tliNetRkuuxz4rQYUJNp/rnLR3c4wR5fjNm1trxw9VLbDefc20wG9vhiksVcCCEEIOHlQgghRFkmauhwqdBooGh74MOQ7Wa3jQaXhZ5wAq/DNCC8Ozv0xW010NYVwqjXsWRuHaFEsqxZJva3LaF4CkVRcJj15Z8TRUGvU/DaTRj1OqJqihrb4NsN99xPtqEIQggh9s/4/qYgcgYL0RvJesPZV6nfs+HW2TDqrlAsL/Q5G+p8aJOLgxpdA/ZTuH5h+He23KHqU1hvfyTBXn8Mj82IXqcbEIpduN/s/grrUfhvYeh2trxdPWHaOsPMmWanxWsvq41DhdiX2+bCtpdb3lDbl3v9ZNs33GEMpfYvIeJivBvNIQ1jdd0PFho9t96BxzYj7338fleYvf4ozW4bVpOeeY0uXFYDnzhyBo1uc14f0xWKcVizi1VLD+APL++kpcaKx2YmGFfzZpnwOozs8cXY0RXG6zCXbHOpPiV7rKKJ/LZky73wsAa89tLl9j8WRr3Cca1egvEkFoOOsJrmvIXNzK4b/Cn0cM594THP1rMjEBtyP0IIISYf+XY7AZSbmXU4GVzL3de8JldeeGD29zc+8PNWewCLUUe908JRM2t4bWcvHcEYMTWNQQcf9EaBzLRVK09o5cqPHpTbz5Nvd+SegsTUFC1eKwoKGrCrJ5Ird9lhjfijasn6ZNuYrff6tm7e+MBHMq2RTKVxWY3YTAYOaXJx+HQ3bquRx95sz9WzzmGiK5RAUUDTyNXDbjYQjiexm/WE4ym6Qwm2dQZJaxo6ReGwZjenHdJAIpnmoU27c+P1Lj1pDhcdN6t4G2tsKAq5MnP7MBkIJ5K5jMCljnmp81qYWThbfpPbyqxa27C375+ZeLDrJ9u+bLkrl7Ry6rz6YV1vxa45CcEU491oDGkYy+t+sNDoYn3+Gx/4eGdfiLfbgyye5SWZ1qhzZELJbSZD0T4nEE3SEYyzaZePaU4zOkXBoFPwRVViyRQb3u8lkUzR1hFiRo2NWbW2AW0u1af0r6PFoEdNabQHYuh1sOH9XvQ6eGzzPs5d0EyDa/A//jktBjqDcV5u68n158ce4KV+iO2yyj33/Y95/3o+9M89Q2ZJF0IIMflIePk4V26I2miEshWW0RGMc/eL2+kIxTK/h2Lc/cJ2dvSE2LovQCieJJnSaPdHufvF7ewLxEimNHzhBNs6I33zSCtE1TT3rN/OO+0BIPMk4+4XttMdjpNMpQnGVDbv9rPXH2Xzbj+BWJJUCjqDMe58ro3dvmjR+mTb2BXKjJvb2RNm824/UTVNTE2hJjV6Qiq+SIKt7UHaOoLc+VwbnYE4yRQEoipv7vbjj6j4IyqBaILNu/3sC8TY2h6kI5j5d3dvhLfbA6gpjbQGiZTGm3v8/N+uHu5+MfMFscZmIhRP8pvn29jVEx7QxkBMZfMeH/v8mTI7A/HMv8E4W/cF6A4l6AzE2e2L5h/zEm3OntfCzMLdoUxbu0MJOkMx9vijg4Z4DpWZuNT1k21fb0Sl3pkp954Xt9MVKm+u29G8boWYaMb6ui8VGg0U7fN7owmOnOkB4LWdPTjM+lwodak+pyeUIJZIoqbS9IQSoMCsOjsOi55/7vSR1jSsJgOBWJLOUIyOUCyvzaX6lF094bw6hhMpFDTMBoV/7vShKHDUTC/hRKqsYxhJJNnRHUZNpTEZdKipNDu7w6N+7LPHPNv+4dZTCCHE5CJPuse5cjOmjkZW3cIyPFYjgaiKx2rq+91EIOZjumIlkdSotZtIa+Aw6/nAF8Vm0hOKp7AY9fhiScwGBb1Oh8WgEFNTbNkb4KDGTPbtQEyl1m7OhIFbjewLJjAb9CRTaaY5LaCAy2qkOxzGatQXrU+2jdnydCioKQ2rUUconsZkyGQXt5kMJFJpVC3zhavBZaYnrOIwGwjFk9hNekJqCrfZQHckic2kpzOUoN5p4YPeGHazHg0w6RXSGhj1kEyDL5YkkUrTaLdiNRqotUNvJEFbZ5hah7mgjSY6gnFsZj2dwRTTnAZ2+2LUO0180JtkRo2JZBqsRn3BMS/e5ux5zZ6zbGbhWoeJtk41U15Kw2rQ54V4DrV9YWbiUtdP9pg3uS3YTAaa3BY6gplzMZww84meDVqIkaj0dV8sbL1YRu0dXWH2BmI0ewb2+fVOC25bJiT6vIXNOCwGNu/2o1co2udMcxoIxpMc2OAkmdJYMMNNNJnmuNm1+CMqTrORzXsCuK1GYmoKj9WU18eU6lPaOsNFj9XSg+vxRzNjzt02I9FEqqxj+H5XhKiaYl6TC4tRn/kjQTg+7L6rnKEBc+sdnLdgOl3BeNF6AjKsRogKm/3tR6pdBSEAueke98rNmDoaWXULy/BFVVxWI75oAqc586/LYiStZW5ou8MJPFYjoXgSk15HJJFC0zRiagoF+p50p0nEwWbScWhTZlx3o9uMy2KkOxxH0zR8URWjHuLJFAa9Dl9UpdZmIhBVsZkMRNUU0URqQH2ybcyW54skMOoVIokU9O1fURQiiSReuxmjknn6EIgk0TQIxZPoFQgnUugUCMaTGPUQSaQw9dXDZFBIptIogJrSUBRIJjM34B6LoW+9zH67wwmcFgNzptmxmvQFbUxkyo6nMBkUApHMMfNHk5ljGUrQ4DITVVMFx7x4m7PnNXvOOkIx9DqFjkD8w/LcZqLJVObYRFWclqG3L8xMXOr6yR7zvf4YTW4Le/0fnotKXN9CTCaVvO4HC1vvn1F7R3eED3ojBGMqb+3Rs3h2DTE1ndff9IRUmt0W3tob5L9e2UkgpmI3GWh0W3BaDXl9TiCSxGRU8EUSOM0GNuzoxahXcPf1kxE1hV5Hpo9xmvBFEzQ4Lbk2l+pT5kyz8+aewIBjNbvORpPbSk8kgcmgK+sYvtcR4umtHfgjSbqCAebWOwjEksPuu4YzNKDeZS5az45gnP/Z+IEMqxFCiClCwsvHuXIzpo5GVt3CMhpcZlae0EqDM/NkocFpYeWSVmbXOji4wYXTYsCgV2jyWFl5QiuNbgsGvUKNw8QB02yYDAppTcNm0rHi+NZcMrU6R6acOocZg16Hy2pk/nQ3zR4r86e7cVsN6PVQ77Jw6UlzmFFjLVqfbBvrHBbOOKyRWbV25k93YzPpsBj1GA0KXoeRGruJgxudHNDg5NKT5lDvNmPQg9tm5LDpbjx2I26bEbfNxPzpbhrdFg5udNLgyvw7w2tjXqMLo15Bp2RuuA9rdrNwppeVJ7TithrpjWRuuL904hxavPYBbXRbjcxv9tDoyZRZ7zZn/nWZObjBRZ3TxDSXmRk11vxjXqLN2fOaPWcNTgv1Tgt1zkxb65wmpjksTPdYWbmklQaXuaztCzMTl7p+su3LfHnMlLvihNZhJ1ObqNmghdgflbruhwpbLwwND8SSWE0G0prGP3f6qLEbB/Q3R8+u4b9e2ZkL+w7EkuzojuAwG/L6nHq3mYPrXdTYTEQSKfQ6haNmeon3/fGzxmak3mXO9DEOCw1OS16bS/UpLX35JQqPVbbfH2428XhS48QD6zDqdbR1hXBbDcPqu4Y7NKDYuT7pwDqee6dThtUIIcQUIt9sJ4ByM6aORlbdYmUcN8db9Pf9yV5+6rx6jpjhGlH28sL69K/3BUfNGDJ7+bL5DaOavfzCRdOLZi8frI1DZS8vdcyLnddiYaNDlTfU9uVcP/3btz/Zy0czG7QQE0UlsqAXZvYuDFvvCMTYG4jhMhtIpbXccJJFM2sIxlXOWzCd2XV2Dqy309YZxmMzsr0rTHc4znSPLS/se+lB9bR4bQP6z55Qgvte2TEgnPq8hc1YTYYBfVQ2CWUkkeSwZhc3nj8/r//PHqvCDOuFx7CwvELZkH6vw0itw8Q0h4m9gRiXnNjKIU3uso/1SIYGFJ7r/R1eILM9CDE1DDc0/v0fnVOhmojRIL31BFFuxtTRyKpbWMZgv7f0267OYcm78WpwWVjQUlNyP4Xr99e/3KHqU7i8wWUpOkXZYPttKbFuf6Xa0+IdOFXYYPsqR7ltHqvXSxlp+0Zr/0JMZKOdBb1/Zu9iWcof/r89vNOXFNKoU/qetJqIqCmaPVbqXeZcBvFdvRF6wwmcViPBqEooluTgRlcu7Ht2nQ1fJDkgzHpWibDvepcl19ahZsk447DGvH6l3JD5wUK1nRYDakrjiS0dmPSZfB8LWzzMqi3ed5cy0qEBhed6pMMLZLYHIYSYmCS8XAghhJiACkOds5m9HRZ90Szl4USKo2Z60esys0q4LYa84SSRRJK7X9hOZyCOP6IST6YJx5M4zAYCMZXdvkgu7NtmMhQNswYGDfseapaMUiHxQ4XMlxWqrWXycoDS96827GNeiaFc5ZYhsz0IIcTEVdVHS88++yw/+clP2LhxI3v37uXPf/4z559/fu71lStXcs899+Rts2zZMh599NExrunEUc2ws+y+hwoPLwxDz4YFZsPCm9wW3DZTbv3CcrNPKUqFtZcKsS4V/m4z6oioaZrcFuxmY678UvspDKUuXF7sHJS7beGxLDfcu3CfQ10H5R6z4Z770bruJHxSiIEK3xe5kGmbCTWVxmsz4Y8lOOOQRlLah33XvkAsL5zZZTGyxx/hgiOnYzToc+tlpmxUcdkM7A1mbg6T6UzfGI6n+PTiFk6dV0+L154rM2/f0QTBWLJo2HdXKEYqDT2hBHv9UZrdtryM6XajATWdxusw4o98mNV8qFDsbMh8NgN74ev9Q++NBh2nHtyAooCmgT+WGLBeYZ9TKhN8sXD34RjJ8AKZ7UEIISauqvbS4XCYBQsW8MUvfpELLrig6Dpnnnkmd911V+53s3l42ZGnkmqGnWX3/cYHft5qD2Ax6qh3Wlh2WCP+qJqrk9tq5LE32+kIxoipaVq8NhQgnEiyrSOEpikoisbhMzwcP6c2F3aYLVdNZZ681NhMtHhtHDWzhtd29g4oz27WE46naHJbmVVrG7Bfg07hg94IakojpWVCPnQKOPrC+8xGPXE1RY3dREvNh/vJtmPlklZOnVefC8XMLi9s7xmHNbKzJ5K3Tqlts8uzx3JHd4S9/ihNHguz+pIJFTuf5YRq9t8uu9+hjlm5189oX3cSPinEQMXeF80eC2oyzZNb92HU6VDTaWZ5rTy+pT0zE0K/9fqHM/dEEpgNOp59tytvvWwG8c5AHC2t4UuoGBWFbR1hrGY92zpDhOOpkvteMMOdC5EuzJS+1x/FbtYTiCYJxlTebg+yeJaXqJrCoFNY39aNxaDLhX0XzrAwVMj8W3sCLJ5dQypN3uuFofc9kUTRcor1OcNdPlzDHV4gsz0IIcTEVdXw8rPOOosbb7yRT3ziEyXXMZvNNDY25n5qakqPEQaIx+MEAoG8n6mgmmFn2X3v9kfYui9AKJ4kmdLoCMa58/k2dvsjTHNY2O2PcOdzbXQE4yRTGoFYks27/XzQG+bN3X4iiRSJdIqommbzHj9tnSHufmE7O3pCbN0XwB9V6QkniKpp/DGVvf4od7+4nX2BWF95Kpt3+2n3x9jaHqQ7lKAzFGNHT4g7n2ujMxgjmYLecJxtnWESycwNN0AaSGrgiyaJJJJ0BeNE1RT+qMpeX2Y/HcE49c7Msb3nxe280x7g7he257L6dofj3PlcG7t90dw5eOifH/Db59py65TaNrt8V084L7twb0SlMxCnIxgvej4HhGqGYn031PGi10FX3+vdoQTJFLlzsC+Qf8w6QrGyrp/Rvu4kfFKIgQZ9X2TipAGNVFrj/e7okCHfDrMeDYVQPJW3ns1kYOWSVmodJnQ6Bb1OwWo2oGoaNqOeJpet5L4zlAF1zvZl3aEEW9uD+CIqNpOeVFrjtZ09OMx6ZtXa0esUioV9lwrFhvyQeUWBf+704bDo814vN/S+8Jh1hWLDWj4WfZTM9iCEEBPXuO+pn376aerr66mpqeHUU0/lxhtvpLa2tuT6a9eu5frrrx/DGo4P1Qw7y+7bajSQSGrU2k2kNfBYDbR1xbEaDVhNeqzGTAhfo8tMd1jFY83MVWrU60hpYDfpSaQ07GYdajJNMp25kZ6uWEkkNZzmzPZ2s4F0GswGHYlUGptJTyiewmM10RGM4zDr6QimmFFjIpnS0Ck6IokkDS47PWEVm9GAP5aZMzadHtgevU5HKp3GatSR1sBszOzHYzXkZe/d0vc0ucmdSRBUazfTGQxgNepz52DLXh++qMp0j3XQbbPL2zrDBGIqHquJtnQ4l13YYzXij6oDzmfhefdYTQRiPjxWY9HroN0fJxBTqXWYMhnerUY6gzHspvxj5rGaiu6v1LkfretOwifFeDBWwxvK3U+p90W7P45Rr3DqIfUoKASjCV7Y1p17/3ttJvb4IuzoiuB1mPjU4hmZEO9wnPte2cU0hwk1ncZp0bPHH6MjEOPUefXU2Az85rntNLrNpNKw+QM/JoMeRSFX5vtdEYx6hRMPrCORTGMy6IgmUiUzpXtsRnq6EtQ7DWjA4lkeosk0Sw+exj/e6mDuNCeKAvFUiq5Qgo5AjNl1mafHxUKx3+8K5cLK3RZTLmT+5Ll1xNQUO7rCRY/ZeQumYzXp+5UTzgt3z673flfm6Xyd04yaTuOw6Nnji/J+V2TQPqrS147M9iCEEBPTuO6tzzzzTC644AJaW1vZtm0b3/nOdzjrrLNYv349er2+6DZr1qxh9erVud8DgQAtLeXkpp7Yqhl2lt33bn8Ek0GhO5zAYzXiiyaxmQ1E1STRRIqomvlS4osm0TQNX1TFqFdQU2n0Cpm5XfUK4XgKm1mPQafgshhJa2lMhkymXZ2Sed1u1hNPpjHpdUQSqb7yEhj1mac3JoNCdyhBg9tMWktjMxkIRFU0DSJqEoXiN9wAqb4Xomoau1lHXM3sxxdN4rImc9l7D21y4bIYc+PQu8NxbCYDUTVFNJGiPRCjyW2l3R/PrVNq2+zyOdPsvLknQEcohl6nsNcfY5rDhC+q0uAyDzifhefdF03gshjxRVWcfcv7XwfZ8NHuUAJNA19UxaDXEU7kHzNfNEGD0zLk9TPa152ET4pqG6vhDcPZT6n3Rfb93BPK3ASG+0LFfdFMErQNO3qIqynaOsPM8FqZ5bUzr8nF67t8bNrZS29YxWExEIol8dqNPGQzcUSLh9d3+egKxdkXiHNkixsUBTWdpisY5/92+9ApCk9v7aAzEGNnb3RAeHlhpnQ1lWZfIEYkkeaN3T5cFiNqKs1RM2uYXWfPtCGSQK+DDe/3otfBQ//cw7kLmvOylPfPfl4srDyaSPHTx98hnEhiNxlodFtIpck7ZvUuc145j7y+h3f2hXLh7sm0hqaleXprB/+3y093OI7TbCAYT1JrN+O2GlGT6ZLh7mNx7chsD6WlUimuu+46/vCHP9De3k5zczMrV67ke9/7HkpfZIamaVx77bXceeed+Hw+lixZwm233caBBx5Y5doLISazcZ29fPny5Xz84x/n8MMP5/zzz+fhhx/m1Vdf5emnny65jdlsxuVy5f1MBdUMO8vue4bHxsENLpwWAwa9QoPLzKUnzmGGx0ZnKMYMj41LT5pDg8uMQa/gthqYP91Ni9fOYdPd2M16TDo9NpOO+c1uDqh3sHJJK7NrHRzc4MJjM+K1m7CZdLgtRpo9Vlae0Eqj29JXnpH50900eSwc3OikzmlimsPC7FoHl540h3qXBYMevA4zB0yzYzIo6PuiIXWAQck8nbebDdQ5zdhMetxWI801mf00uMx0BGO57L0HNbpYuaQVr91IRzBGncPMpSfNYUaNNXcOzj9yBpecNCe3Tqlts8tb+sZuNzgt1DsteO3GvOzCheez8Lw3OC2sXJKpa7HroM6Reb3OacKgJ3cOGt35x6zBaSnr+hnt607CJ0U1jdXwhuHup9T7os5hKfr+r7EZeW1nD6m0htWkJxBL0hmIs9ufyS/RGYpjNepJJNPs9UdJpFJYjXo6w5nhJ76omgvZfn13gMOaXRza5OSNPT4AjpzpIRRPsaMnQiqt0T+8PNu2XNg3Cp3BeCZU3aBkbnxyUeharm0Oi55/7vShKHDUTC/hRGrQITWFYeU6RWN7V5hALEm900IglmRHdwSzURk0i3oonuLImR4AXtvZg9mgZELvE0msRh1qKs1efww1lcZm0hFKJFEUBYe5vDB1GRoztn784x9z22238ctf/pK33nqLH//4x9x0003ccsstuXVuuukmfvGLX3D77bfz8ssvY7fbWbZsGbFYrIo1F0JMdhPqm+ycOXOoq6vjvffe47TTTqt2dcadaoad9d/3UNnLl81vGFb28uPmeEcle3nhfkcje/mp8+o5YoZr0Ozlc+sdA9YptW3hsSwnm3ix8549ZsW267/f0chePtrXnYRPimopd3jD/oYQD7Wf4WTM7r8822edfGAdXeEEVoOO//vAzzSHmWRaw2o09A0F0mMxGjiwwUFbV5g5dXb0Oh1GnY7ucJzDp7uZXmPLhWxfvKQV0PjtC+/nwrk/8EWIqmmWHFCL12EmkUzRGUrwfkFYt06Bp99JsrDFzXudEcyGTPTQETPcxJJpdnSH8drNnHFIA13BOM1uG26bEX8swR5fLC/MvNix0+uhrTPMgfUOXt7eM2DIztKD6mnx2gbM7BBNpPLKcduM7PHFcuHuTqMJs1FP6zQ72zsjzKmzYTJkhvDE1BTnLWzGajLkyi3MED+WQ2NktocPvfjii5x33nmcc845AMyePZv/+q//4pVXXgEyT7lvvvlmvve973HeeecB8Pvf/56GhgYeeughli9fXrW6CyEmtwnVO3/wwQd0d3fT1NRU7aqMW9UMO+u/75YSyyHztLXYFCsNLgsHNQ6MTChV7lDlDbVeg8vCgpbBE/OVs5/C5cXOQbnbDlbGYArXH2r7co/ZSPc/3soTohzlDG8YjRDioTJyjyST9hu7/by1NzNzRK3djN2spyuUoCsYJ55KM7vWTlRN4rIYiaqZnBbdocwNuC+awGE28s+dPvyRJOvbujm2FZJpjWaPlXpXZtaQJpeFnpCKP6LyclsPgUiCDTt6OajByXsdIfQ6eMpsQE1pubaFEylqHWYURYfNmKmTzaRjw/u9pNJp2jpCzKix0eS2YDLo6Ykk8McSJcPM+x+7QCzB8+92kdY0OoNx1GR6wJCd2XW2XF/X/xhajfq8MPGekEqz25ILd+8IxYgnU2zvDBNPptnWGeaAafbc0Jt6lyWvj6rW0BiZ7SHfCSecwB133ME777zDQQcdxP/93//x/PPP87Of/QyA7du3097ezumnn57bxu12c+yxx7J+/fqiN93xeJx4PJ77PZugV1VVVFWtcIvyZfc31vsdbfvTDrNeG3qlMWTWaXn/VtP+XBeT5dqCsW9Lufup6rfaUCjEe++9l/t9+/btbNq0Ca/Xi9fr5frrr+fCCy+ksbGRbdu28a1vfYu5c+eybNmyKtZaCCHEZJQNdV63pX3QkOTeiJq7sVq3pZ1mz+xh/5Gs2H6AouV7bDMGXb7HH2Vre5BQPIlRZ6InlOD9bpVpTjNeh4nOUJxoIkm9w8Jp8xrZ2h4gmU6T1jLh3cG+aQ7NRj0nHljHts4Qr+3s4YQD6vLaf8ZhjTzyxh5efK8bg15hyYHTeLcjyPPvdtHgsnDUTC+JlNaXJdyQCXl3mTn5oGlsbQ+gJjPjuwPRJEa9DqOxL/Q9FMNq1mFQFMwGHa++3zsgzDx7jLPH7qFNH/DiG90k0xpz6xwE4pls6plEmh8O2cnecBc7d8XCxLMh+4+8sYdoIoXRqMNrNxNMqEQSKWpspkGH+pS6diphtK7HyeTb3/42gUCAefPmodfrSaVS/OAHP+Ciiy4CoL09k82/oaEhb7uGhobca4VKJeh9/PHHsdlso9yC8qxbt64q+x1tI2nHTcdUoCKj4PuLSyQKGkN/+9vf9ruMyXJtwdi1JRKJlLVeVXvlDRs2cMopp+R+zyZAW7FiBbfddhuvv/4699xzDz6fj+bmZs444wy+//3vT4i5ussN99rf9UZrPyMJT8tukw0LnzPNTovXPuD1bMhyYXh4YWhzdt+F4eKFy7Ohle+0B9iyN0CNzUhmkKBGb0Sltc5Oo9ua219hOVn/t6uXTbt8zGt0MrvOMaB+qXQaX0QlribZG4jn1isMxS7VvkZ3JmHPYOetcNvsPrMh9oWvD3VMSh27kV4Xpdbb3+tRiPFqsOENo5ldv9h+SoUoZ2cdyC53WjJzZr+9JzMLgtWgJ5FK52aOcNkMdEfiLJzhodFtzWUEP29hM7PrHLnhJ9n+ZY8vwr0v72SmN/NUuNljYWdPhJMPrMNpybzfs0Nlzlswna5gHLfFhIaGoclJMKZyxAwXbqsRTQN/LJHLEp7tuw6sb8o8yQ4luO+VHbgsRt7YHcBhUQjHVeymTJbzpQfX44+q1NktmI06QrEk73UE2bo3SHONFaclU4/jWmt5fHM7s5x27BYDZpOennCcVaccgMdmzvWBXaFYLsla0WzmBWHi2XOTbWe2HnE1TVc4k/18dp297HNaSTLbw0B//OMfuffee7nvvvs47LDD2LRpE1deeSXNzc2sWLFiRGWWStB7xhlnjHneIFVVWbduHR/96EcxGo1juu/RtD/tmH/dYxWq1ciYdRrfX5zm6g064mll6A0qaPN1I38oOVmuLRj7tpQ7PXVVe+WlS5eiaaXDMR57bHy9scpVbrjX/q43WvsZSXhadpv1bd288UEmm63LauTSk+Zw0XGzcq/v6Amz1xdD02BXbwQ1lSYcz0z7ZdTraPHaUIAmj4VZXjtuq5HH3mynIxgnpqY4pMnJ4dM9ueXZOtbYTDzzTgfhRIpkSkOvQErL3AAbdAozauwk02kUBTQNDmlycfh0d65t1zy0mT9u2Ima0lCAZo8VnU4hmUoTiifRKwrhRJKUpqGmMrf0egVm19lo9tiwm/V9WdQNhONJNGBXz4ftq7GbqLWbmFVrZ5rTXPS85Y5N37aRRJJAVMWg12PQwQH1DuwmQ+7YfdiW4sfkqJk1vLazl45gjJiaHtDm0br+5jW5eLtvyrORXI9CjHelhjeMdghx4X6GylLeHogRjKk8/24XKS3NBz1RptdYsZr0mPQ6usMJaqwmApFM9u6kpmHsW97szoREF+73vY4Qz2ztYldPZhqw7Hs5kcxkAp9RY2NWrS33fq53mYnEkzz5dgdqMk1ETWPQwaNv7qPBacFoUFgww029y8xLbT3c/cL2XJ+wckkrx83x0uS29vXxSXa0RzAbdazf1s1RMz3MrrNh0ut4flsnwViS3b4oBh28+n4vhzW7OHy6h3lNLl7b0UMglmSPrxeXzUgqpTGz1sq8Jhe+SJL//b+97OjOTPvV5LHQ7LYWzTpeGCaeVe8y0+S20htRcVgsmWPYL9R+uNdOJchsDwP927/9G9/+9rdzYeKHH344O3bsYO3ataxYsYLGxkxEyb59+/KGKu7bt4+FCxcWLdNsNhd92GM0Gqt2c1LNfY+mkbQjnqrujW0p8bRS9bqNxjUxWa4tGLu2lLuPcZ29fCIqNyvt/q7XFYqNyn5Gkq03u83OnjCbd/uJJ9PoFIVALMlvnm/jnfYAj/fdOHcE4nQG4mze48MfSdATThBJpOgJJ/BHVDbv9tMRzKyzozvMnc+10RmM525+t7YH2dYR5M7n2ugOx6l3Wtjri/LI63uIJJIkUxoakNQy+XOTaUgkNbZ1hugNxwlEkgRimXL2+KOs29LOy21duRtunZLZdmdvlJ5QnO5wPFO/iEosmbnhBnL72NYZYVdvmK3tQTqCMba2B9kXiLF5tx9/OEFPSCWmpvBFEuzojvByWw82k37Aecsdm2Cczbv9+CKZbRNJjZiaJJpI8+ZuP7t7I2ze4yMQVfFH1Vxb2jr7jkkoQb3TQkcwzj0vbmefP0oyRe7YZds8WtdfRyiT3bgjGB/R9SjERFbp7PpDZSk3GxSef7cLNZVmTp2DcCLFju4IHpuRgxudOC0G9Hqod5v50klzmO6xDlrPwkzgmgbPv9dFMp3G0i/0uyMUy72fI4kk7/f9gTGRSpNKp0mmMj894XhfNnOF7lCcu1/YTm9Epd6Z6RPueXE7kUSSMw5rpMaWGVtu1Ovw2k3odQq5tOaKksms7ouSTGXC4EPxFFvbg+zoDnP3i5kM69P6boCD0SR6ncLM2swT6MffbKcjFKMjGKM3otIZiNMbUYuGk5c6dxNhJoWJUMexFolE0Onyv9rq9XrSfVOBtra20tjYyBNPPJF7PRAI8PLLL3P88cePaV2FEFPL1O2ZK6TccK/9Xa8w3HCk+xlJeFp2Gx0KakrDYTagKAous4FQXGVL31NQj9VIWzoT6rjLBy6LgYgax2bSE1VT2E16/DEVj9VAMg06JTPdTKPLTHdYpdZuIhxPkUxnvhzO9NqwmQyYjTpSGpj1OpLpNIXBEnodqGkwGXSoaQ2v1UQilcZq0OOPqrzyfg/JtIbJoCOtgV5Lk9JAp1NIpxTMegU1lUIPpPqVqwPSff/Gkxr1TiMf+GJ4rCbUlJZpXzKeecKU1rAYFdKaRiKpDThv2WPjthr4oDeN3awnGEtiMigk0xpmo0JU1TDoFdQUeG0GAvEkXpuRRDJNMpV/TDxWAzt70tjMBkLxVObYJVK5No/W9eexmgjEfHisxhFdj0JMdKMZQlw4HKMrFCOmpjj3iKYBw0Pm1jtYenA9T23toMFlxWUxYjYm2BeIc9xsL58/bnbJmSOyYeTZUOtsuYXv70ym8Q4WtHjY3hnBY9MRU1N4rKZcP9IZzNxYH1jvYE9fpE53KM7sWhs6nY5Dm530RhO83NZNdzhOg8ual0283R9n/nQ35y1spiucwGMxoKFgMihEEina/XGMeoX5zS72+mN4rEb8sSQui56omiaeStMdijOn1k6N1YR3tolgLMniWTXodEq/PtZEWzpMk9tCTE3jsRqJJQdmHS/3XGePYTbUvthsFiM97/tDZnvI97GPfYwf/OAHzJw5k8MOO4x//vOf/OxnP+OLX/wiAIqicOWVV3LjjTdy4IEH0traytVXX01zczPnn39+dSsvhJjUpnbvXAHlhnvt73r9ww33Zz8jCU/LbuOLJDDqFULxTCijL6ritho4tMmVmeolGEevg0AkiVEP0USy78Y6hV6nEE6kMOh1+KJJmt2GXFIffzSJpml0R1RcFgMGXeYv+t3hOBajnriaRq+Amhp4ww2ZL0YKkEimMej0+KKZm9xoMsV0h5WjZnow6DJPUnR9YekA6bSGTtGI9y1IFZSbJlNuGjAZFHxRFZNeRziRwqhXiMaT6NARSSSxmvQkUxpmgw6TQRlw3rLHxh9NYtDriKlpFBQSSQ2dDuJqJmQ+mdIw6jNPrnUKfW0xYND3HZNQAotRjy+axKzXEYkn0TSF7kgik6W4r82jdf35oplyfVEVZ9/y4VyPQkwGoxFCXDgco3C4yMolrZw6rz5vm9l1NmrtZjqDcWKJJNs6w5gMCuvbemj02Jhb7xgwc8QeXyxvOEuT25oLF2/2WPLet9lM48l0Gr0O9vpjTHOachm7nRYDeh24LEa6Qwn0Oh09kQQGg45gPIlJr+PRze0kU2le3tZNVE3TFUwwt95BIJbM9RUA9S4LJr2O9W09uTwYC1s8NLrNqCmNzXsCxNUUwZiKQVHYF8jM9f3iu13odAqbPvBh0uuIqmmmOUwkUhoNdtOHfWwohl6nZNrgMOGLqjS4zCXDyQc719ljOJxzVe55H41hODLbw4duueUWrr76ar7yla/Q0dFBc3Mzl19+Oddcc01unW9961uEw2Euu+wyfD4fJ554Io8++igWy+jN6CGEEIUkvHyUlRvutb/rZcMN93c/IwlPy24zq9bO/OluLEYdaS2TGO1LJ87hoEYXZxzW2PcFx0y928z8Zg8euwmv3YTdrMdrN+GxG5k/3U2Dy8w0l5nZdXYuPWkO9S4zBr0Op8XAwY1O5jY4ufSkOdQ5zHQEYzTXWDnniGbsZgMGvYICGJTMDbFBl7khPmCaA6/DjMtmwG3NlDPdY+WjhzZy7Jw6Pr14Jka9QlrLbDuzxkqt05ybasdrM2IxKBj1mTZn93HANBszvXYObnTS4LJwcKOTRreF+dPdeBwmvI7ME2CPzcTsOhvHHuAlkkgNOG+5Y+MyM3+6mxp7ZluTQcFiNGAz6zhsupsZXhvzmz24bZkvetm2HFDfd0ycJjqCmQzBK05opdFjxaAnd+yybR6t66/BaWHlklYaXOYRXY9CiIHDNvb4o3nDRbKh2F2hWN52dY7M+89tNbC9O4LBoLBk7jQSKW3Q4SHZ4Sy9ETUvXBzIf3+7zKw8oZUZHhv1LjNeu5FpDgsNTkvu/ZytQ53ThEGfidaZ3+ymxmaiI5B5Cm41Gogl0xj1oFOgrSuE22rIyyYOgKahKABK379abrnJoKPJY8HQF9Gk1ylYDXoUnUK9y4xOpxBT07itBqa5zDS4zPl9rNNCvdOSaUO/14fbJxWeq92+aN5wp1LnqpyyZBjO6HM6ndx8883s2LGDaDTKtm3buPHGGzGZTLl1FEXhhhtuoL29nVgsxj/+8Q8OOuigKtZaCDEVyDfiCig33Gt/1xut/YwkPC27zQVHzSiavbxYWF652cuXzW8omoE7u3w0spffcP58Llw0vSrZywc7NsPNXl54TIaTvXx/rp9s9uORXI9i//3oRz9izZo1fP3rX+fmm28GIBaL8c1vfpP777+feDzOsmXL+NWvfjVgahxRfYVh3QadQjCWZLpnYCh2YejyqfPqqbEaufO5bcypc9JcYyWaSGVupgNxrKYP33/Z/WSHs2RCrT8MF+8IxHBaDHxq8Yy8Prgwu3n/93MkkeSwZhffPfuQ3JzYbpuJd/cFWfu3t/DYTHQE41hNesKJFB85sI54Ks1Fx8ykucaWC88OxpIYDTpOPGAaiVQKBQV/LMHbewIk0hrHzfGS1qA7FOOV7b0snOFhtz/WN3wnzeHT3QTiSS5Y2IzRoM8L8y7Wxw7VJ5UKFy88V1ajPm9oz2DnaqjzLsNwhBBi6pBevkLKDffa3/VGaz8jCU/LbtPgsnBQ48BpM4qV2TJgrYHqHJaiX14Klx/U6Cq633L3t6ClhgUtNcOu33D3V+55G2rfxV4vPCaljl0pI71+9vd6FCP36quv8utf/5ojjjgib/k3vvENHnnkER588EHcbjdXXHEFF1xwAS+88EKVaipK6T9sQ6+DTbt8JFNp3u0IgkYmf0O/UOz+3usI8ey7nXSHVTpD3SzWvCTTGpqW5i+bdhNVU7mw5Wz4eHY4S/9wcYOi46F/7iGW/HD9hiLZzQv3/fib7XlZwWd57ZxxWCPzmpzUOsx0hxJoGrkhLopOwWMy8cw7XXn7avZYUJNpNu7oRE1qdIZiOMwG3t0XyiXcdJgNBGIq6bTGtq4wFqOezlCcaU4TYTUzpKaw3Gyo9nD6oiff7hiQZT0bLl44xCaqpvKGO+31x0qeq8HOuwzDEUKIqUXCy4UQYoIIhUJcdNFF3HnnndTUfPgHI7/fz29/+1t+9rOfceqpp7Jo0SLuuusuXnzxRV566aWiZcXjcQKBQN6PGBvZYRsOi55/7vRh1Os4YW4tRoOOtu4QbkuRUGw+DE8OxVMcOdMDwGs7ezAbFDQUQvFUXtgykDecJRsuXmMzoWka4URq2LNWFGYF7wjGWbelHZvJkBd2nh3iMs1pLr0vRSGV1ugJx1FTaSLxFL6oSiSRJJ5MsTcQJZlO0+Ayo9crRBNJ3BbDiNtQTFffjAyFWdaz4eKFQ2xm1Fjzhjt57cai52qw8y7DcIQQYuqRnn6MlZu1dH8yowLs6gnT1hnOC/kut15AWWHhpdqQDfvOhnkXhvcVZtQtXF5YbuHy7O/ZsHaLMZOIzGMzotfpcvUrrHdh/dv9UbZ3hbEZdUTUNE1uC3azMRfiXVheYTh5qf33P2fZ85ANwSwWgt4/y/BQrw8ntL3/dqXOVeF+y91OVMeqVas455xzOP3007nxxhtzyzdu3Iiqqpx++um5ZfPmzWPmzJmsX7+e4447bkBZa9eu5frrrx+Teo93o329l+qz+v/utBg4eW4dO7vDzKxxMM1lZl6Ti509ET7XF4q9qyecN6wkmsgPT3bbjOzxxVh68DT+8VYH0xwDw5aLhVpHEynuf3Vn0fWHmrWiWFbwbGbzU+fVc8QMV94Qly27/fznS+9zYL0Lq0mP12Zijy8zJ7hRr7B4Vg0bdvRi1ivs8sXwWA2EYklm1drY3ZsZtqTXKblw8n85ugWvw1yyDTu6IqS00sOXCs9FNtt5k9tSMly82NCZwqE95ZJhOEIIMTVJbz+Gys1aOlioWznufWkHdz7Xlhs7d+lJc7jouFll1UtNpkFRCMWS7PVH0dDY1RMlmdIIxVXsZgNGvY5DmpwcPt0zoA03r3uHu1/cTqxvgusDpjmodZhzGXPnNbl4e29gQCbd7PLCY1N4zLLrrW/r4o0P/CRSGgk1hcGggAYuqxGbyUCtw0R3KJGb79ts0BNPpnL1N+gUPuiNoKa0zHRhSibsw2LUk0yn0el0pNNp3FYjVpOBlhobigJ2k4FwIkk4kWRbR4hEMp3Jkq7PzC9b6zBzwDQHK5e0srcv4U4gmiStpZleYyUcT6Gm0oTjSWpsJlq8NpYd1og/qvLGB37eag/k6lxjN9FS8+Hr2ZBOu1lPOJ5pSzieHHCOCrcrdb1lj212vxajjnqnZcjtRHXcf//9vPbaa7z66qsDXmtvb8dkMuHxePKWNzQ00N7eXrS8NWvWsHr16tzvgUCAlpb9HWAx8Yx2NulSfVbh7zt6wnzQEyUYS7KzJ8riWZkw8RqrkWff7eLt9iBv7Q2gKKBpcEiji4MbnagpLRee3BNSaXZbmF1nHzRsuTDUOpJIjnjWilJZwbPb9h/icvO6d7jrxe2E4yme39bNEdPdKIqCTlF4emsHncE4bZ1hukNxYskUZn1mJgaTQSEcS2E16zKZ010WwmqSZreFWXX23M1zYRt6wzFufGQL3eEEMTVFi9eKgjLo50w223l2fHqpcPHCYzjcoTyDlSWEEGLyk15/jPTPWpr9grBuSzvNntl5H779Q92yXwDueXE7R8xwlfUBv6snzJ3PtRGKJ6m1m+gOJ/jN822cfFBd0Sfe/evltZl4cus+UunM3NudgTjtwSiaphBPpkhrEE9mpqja2h7EazfnteGd9gB3v7idqJpGryjEkmm2tgdpnZa5KTUaFF5q66bFa8tl0jXoFQwGeGl7d+apvMeeOzYe24y8Y7bLF+alF7qZ5jCxebefaCJFIqWhAUlVQwF6wgniaor2vifQ8WSKdDo75ZZCXE1j0EMonqb/bGNpLTMVWCiRInP7nEIHdIcTONQ0/miCeocZVdPQKwq7eqKk02nUdCbfbjKpoevbv90U5fan32OPP0okkUJRIBpPsnVvELtZTyKlkdZAF1MxBWLc+Vwbi2a52bovQCCiEk0mSWsKuqiKSRflN8+1cWyrl45gnO5Qgg96k5gMOhK9GgadQmcolneO9JEk+3Qx7ny+jSVza5lT6xxwvWXP+25/hK37AoTiSQw6I52BOL95ro0lB9bRWusoeZ2KsbVr1y6+/vWvs27dulGb1sZsNmM2Dz0OdTIrt18eaXm7eiO8tH17rm/L9mHZPjAQS2I36UikNV7b2cPRs71EVYXOUJyt7UECsWQuw/fWjgBepxGrQY/DYig6e8C6Le1lhS1nw5zLXb9wm2g8TVrTBs0Knv08SCTTOMx6gvEk/9yZSVx50kHTCCWS7OgOo1MUvHYzneEYNpOeOocJp8VAJJHCZjQQUZMDMqgXa4PZoLC9O0JMTZFMpQnEkmze7afRac37/JkzzUFLjS13rlecMJuVS1q558Xtww4XF0IIIcol36LHSLlZS8sJdRtMW2eYSCJzw201Gqi1Q28kQVtnuOhNd/96qak0Rp2OdDpFPJXGZTOwywcei56omsJq1BFVUzjNBhJJDatRnwsrtJkMbNkbIJFKYzfrUZMaZoNGLKlhNmTmrLYa9ARiKlajnlS/TLpWo4FANBO22P/YZI9F9ph5rCYCMR81NhNqCsxGHYlUCj2ZObWNfXNumww6omoSi1FPVE1jNiqE4yksJh3xZBqjXo9GGoMCySLzfGfDEQ16SKYzT79DiRQ2s4HOYAKPTU9a0zAZdajx9If710Nay/zBoisUJxxPUWMzEYonsRoN+OOZm+VYMondbCCdBrtJT28kQTKdmaPbYTEQDqawm/WkNbCZDfT6oii6zNjHWoeJtk6VeqeRD3wxPFYTe/zZc5QpN6Vp2Mx6en0JrEZD0este96txsy5rLWbSGvgshno7opjNeglu+44snHjRjo6OjjqqKNyy1KpFM8++yy//OUveeyxx0gkEvh8vryn3fv27aOxsbEKNZ4YOgIx9gZiNHtGJ5t0YT/vsRrz+jZ7XwKu1jp7Xh+4qC90OhsmrkNPIpUJ3e6JJKi1GomrGgZdpg87Z+40jAZdyYzdQw0n0evIy1qe7fOy0VHFZEPizz2iqeSsEPDh0KhNO3tIpNI4zAbMBj2KAoFokuYaM3olM9VjVE2x5IA6vHZzJroqEOHcw5vR6ZQBQ3Ky+8vWMVufbBt29YR5/r2uTJ8Yj+KyGOgKJXDZDHmfPx6rccC57h8Wnx2i0xWKyY23EEKIUSPfosdIuVlLyw11K2XONHtfZtUEtfbMk1qnxcCcacXHdfevl9dmQk1nngCb9br/z96fR8lxn+f96Kf2qt5nX4DBRpAACW6iKImLKMuSTF5HOpFjHTu5khPRsZmbeEkc5Zz4p9z4RHZ8LDv5JXZy4yWifEU5to+vc2JLiWSbomVL4iaJuwCCBEkMgAFm65nu6b32qvtHdTeme7pnegaDjarnHJxBV32X97v0W11Vz/u8rFRsFCl6+ysKYLoBoiBQtT2GEgqm67M3ZbTHcMtUBlUSqds+kgC2FyIBtucjSwJmU2HWdP0OJV3T9cgYCiXTIa0p7blpzUVrzkpmpIYri9ENbsMOgOiGF8ANox+PjhfZabk+ohBiu9GbIqv5Bt71AwR633BD9OMTwPNBEMFy/WZ/HqoiYDfbd9yu/n3QFIGa7TGa0nD8gJLpNH9cekhctC2ih0cpdRKqjCyGqLJAqe4iCmH7fMP2SKoyYRAiiQL5ih2VM11USaRm++vWKGo3pco07Oghgel6mI6/Yb+11n2+3ECVBQp1J7pBaET9mZ7fs16Mq4MPfvCDHD9+vOPYT/7kT3L06FF+8Rd/kZmZGRRF4etf/zof+9jHADh16hRzc3Pce++9V8Pkax5v5Wt85ZUF3liq8NpChbsPDOEHXNJ+7/bzJdNt+zbbDfjOmQLlhsfLF0pokshaI2grca+niS+UTVRJpGS6iEDJdDFUiZfPlwiDkNmVOnuHjbZy+CCK3W3l8QFDewapu17xHDpDo6Rm/u2aHQmbNZwAUYBX5iq8cr5Kw/HRJIHnz61x00Sa0ys1KqbLc2fXSKhSR6jLIDYfGI1iv9/MV3G8ANsLUCWRSsNjakhuX39KpktaVzb4ttGUzvcuVC4ptCtGjBgxYsToh/iX9BXCoHS+0ZR+SVS3meEkjzxwiM8/NctaI7rh/un3HuorprberrLpcMfeLBDljBUEGM2oXFgzcT2ZuuOSUGVUWeTIZJq9Q0bHGG6azPDwfQf54rNnMB0fVRK4YSzFaFpjLKWzJ2fwgaMTnFqq4AVNemJKZ28uwQePTnJqqbIpZXIirfO+G8c5tVQhX7M5MV9G8jbGdCc1meGkSrHu4HoBdcdDlSQc32/bP5EZPKY7ocnszSUQRdp0x4yhMLtSw3Y7Y7qHkypTOYNP3hfFdH/+qVnKDY+EJjOdM2g4Po4X0HA8srrCZFbnwVsmqVguDTvk9eUKCU+i7nhkDYXJnNE+HyIQhCEJNWqn9bZnIqt1rFEmITOZu9hur/22ft2PTLi8vlxBlgTGM9qm9WJcHaTTaW699daOY8lkkpGRkfbxn/qpn+JTn/oUw8PDZDIZfv7nf5577723p4ja9ztaNPC643PXvmGeP1fkpbkS9x0euaT93u3nJzIa77tpjOMXSjxzehVJFHjvjaOcXqlhuT5ZQ+mgTq/3eUcm07y+FCnKB2GIJklIgoCiilQsj5WKjaHIA9Hh28rjVXug0J717fWra2hiR9leoVFjSY2S5VB3PHRZIKHp+EGA4/n4YYgiSThewNNvrjCUUqnbHpYXoEkSKxWbR5+a5T0Hh8hXnC1t/rG793JgOKKNQ8R6GkoojKTUDdefXr7tUkO7YsSIESNGjM0Q/5K+ghhUtXQ91W0n6uWfuGc/77tpdGD18m67YOfq5b/wQzfxd26b3FS9/J5Dwz3Vy1vH17fba87uOTTMj96197pQL2+tw7WoXr5+bmP18usfv/mbv4koinzsYx/Dtm0eeughfud3fudqm3XFMMiebZUxHb+DBh69XW7w0Tv2cGC0v78cpI9ePuvQaILVusN0Tierq+wfTbBQsvj4u2cYTmobfF4usZcHbhxr+yJVEviLV5fJaBLH5ytkDbVDOTxfsTBUeYNfaLWbr9gslk0yujJQaM96ev1F1XKF2QDGUhp12yOpyB3hRd2hUWNpjSAM+IUP3YzQfIj5+0+fI6NJLFVtBARM1+fwRIo3l2scHk3xbLVAzlBBuBjqEgbQcHzG0mpEE2/anFRk3CBgOKVQbrgslW3GMjo/ftc+HN/HCwNWa05bCb41J7fvzXTMTQv9QrvOrtZ7lt/OnriU/RQjRowYMd4e2JaXn52d5eDBgwiCcLnsedtjUNXSS1FGheiN9yCpwvrZ1W3jdjSNb5rMcNNkZuC+tnu89Xkio2/az1Z2T2R07pgZ2qTE5hio/x7r0Mum9WPc6nzfvnZQr1+/sbru7qFQKDAyMgJEgmiPPvoopmnyd//u3+WBBx7Ycbvf+MY3Oj7rus5v//Zv89u//duXYu51iUEUyNeXMRQJ1wsuqoA3HKZzBuOZ/mE821E57/7+jGd0pppq46ootVXH948kN3zP2lTuZraCqZzOVMaIQndcf4NyuCwKfOmlBRYrJoslq51hYT0V+/iFEm8s13D9AFkU2rT2fqE96+n1bdXyqo3t+Zwq1NEkkWf9gLv2DbXLrg+Nyugyb+VrqLLI6dUGd+0f5oWzRSoNh5VqFObTUil/a7mGEIa8tVpr5un2GE+qVBoCiiRycrFKxXSxvYCDowlM10MWBZ6dLaDLIo4fcudMrt3/WiOakxfPlRAFgW+9ucpte3Mb6OjrafHd9rfedGuyyN++voIf5jfNALET5fvdVs2PESNGjBjXNsTtFL7xxhtZWVlpf/77f//vs7y8vOtGxYgRI8b1juPHj3PgwAHGx8c5evQoL7/8Mu9617v4zd/8TT73uc/xgz/4g3zpS1+62mZe91ivGD6W0llruDxxcomG4/UtU7N9BEEgpUkDhVEM0sdmaNHOh5PKpv21qdw1i3zVYq3hslKxKZkuAiFDCZXxtM5wUmEsozGUVAjDkJLpkq9E2Q1O5SsU6jYrNYuFstmmTL9jXw5JFJq0drkd2vPwfQeZSOt97WrZPpRQMB0PWRIYTqlIogDrckC0QqOyhszsag1FErn/8Cg1x+Oxp89Qd3zuv3EMWRQpW9GN81RWQ1VEQkFAESWmcjqyKNBwPXJJhVumm7m9kxqyLNBwInHK/SPJZv8C0TuAsG1nSpN4ca4IwDv25VhruDz2zBnyNWvTtWvZP5xUyFctsobM/mEDxw971ruUPXGp+ylGjBgxYlx/2NarrDDsVJ76i7/4Cz772c/uqkHfr9iKZrZdGlpLQbZFF+7+3E1nXk+J7nfufLHO7Eq9TaPupm1307Jbnyumzdmiye17shydyrZp2f0o19308O2OoVWumxa+Vb/9+u93vB/tvJvaCfCd2VW+e7bYnoMWtb1Fad/K9lb5oYQCXFT2bfXRr17rfGvtuut176urRXd8O9Is//W//tfcdttt/NEf/RH/43/8Dz7ykY/w4Q9/mEcffRSAn//5n+fXf/3X+ZEf+ZGra+h1jkEyQ/Qr89E7pzto2f3UuwfNPrEZ1tPO+/V3kcqtMhvUGUtHVG5ViqjYH7t5snnDG9VfWDP5w++cZTih4Qe0sxtMZVTqjo8sQsVyUSQBSRR4574cq3WHT3RRrm8cT264DsDF7+V0TufBYxOcX2tEPktVCEMoW06b2p7WZT5wdJyprMbvP3WGfcMJRlM682sNCjWbW6ez3DSRJq3JfPONPO85NEzW0HA8n++eLXLPoWHSukLJtDlfNPl775jm+XMlsrqKIIDtBqzWLd5/0zh//doy+4aSOL6PgNC0w+bweIoHb5lgbq3BvuEEYymdfMXakkLfwvrQLkkQ+PIr84ylojUfTqgslBrkKzYHRuVL2hO7rZofI0aMGDGufcTe/RrAVjSz7dLQ1ivIZnSFu/YN8eLcWvtzSxH2+IUyry1V0BWR8XT0lH/fcIKvvbrE8fkSry1W0RWJ8bTGgZEk33pzhbVGlAc7ocqRuI8sYXs+kiRSt1xEIRL70hWpmS81bKuEC8Bo8qJCugDsGTKo21G5mu2S1GQUSWQkpVKoOW3b+o6hy85WuXzVwnIDZoYTCEDd8TidrxGGIAiwJ2dQd3xcP6Bue+1xtPofTams1pz2+W67BAHCEGaGEggCJDW5WU6ibvsd6r7/n6+/yVe/t4DfnIecIWO6Pn4QEoYwktI4Mpnua3uhbnM6X8Nrir5pioAuS9y2N8e9h0bIGgqPv7p0sV7TppYNjhfwpZfnqZguQRi263UrAA+iYnw58HalWT733HP8zd/8Dbfffjt33HEHn/vc5/iZn/kZRDEiGP38z/98LHK2CxgkM0S/MuMZnYWSteX+GzT7xFZIqPKm/bWp3DULy/U5t1RFFGGu2GAspTKa0vjw7dMcHk/xN6/n+f0nZ5ldrQNRrLUfhAiEvJmvossStuNTsz2+8r0F/CDEcgMmMzp7c6vcPhNRrtfT2Neroa//Xrp+iOv5nC82OLvaaCu9h2HIl15awGoqgz94bJL9I0kOjaZYa7g0nDrfPVOkYnl8+0yB9wBuEJIxFF5dqKLLdSwvQBZF6k4krvb82RKSCC/NlXH9kGLDYTKjU6hHIQAHRhO4XsAL51ZwvZCVmsVIUuXLL89z294cxy+UuFA0mSs0uHv/MKbrb0mhX49WaFfD8dprLosCz58rIgoCX355ng/fPs10Tt/RnrgcqvkxYsSIEePax7bo5YIgbIjnjuO7Lw1b0cy2S0Nbr8A6ntZZqVoRta5qM57WKdRtHn1yltMrFU4tV6jZHp4fUqg5/H+fnOVLL11gvmRyaqkanQsCFssmf/r8HKWGg+MFOF7IWsOlbnsU6jYNx6dYc3C8ENMNsL2QkulheWFHWq4QWKlHSraOF9CwfU4tVllrOBTqFqYbUKw7rNUdXp2vUDYdfB+Wy9EYVrrHkK922LlcidRml8sWng8Vy+PEQpn5Up1X58uRcrgf0HB8Ti1VWavbFOsODcePxmH7FOsOpYbDiYUypYbTPl+sO5TqNq/OVyg1HMqmS9l0ObFQYrlicWqpSr4a/S3UHFZqFvmaxe8/eZqvfG8BL4weMoTAWnNu/CBKW1ao21xYa3TYXrVcTsyXubDW4NRiFac5lyFguSEN2+PEfJk3lso8+uQs+aqF54dRvYUS+arFSs1idqXGF585Q9l0EYQo3dmJ+TKzK9UOymW+ag9EwbzS+/96RrFYbOfITqVSJJNJhoYuaggMDQ1RrVavlnlvGwxC3e5XBhho/w1KD98KW+33NpXbUDHdoEmhppm1QGLNdHji5BLni/Xmg1WPgyMJgjAkX7FIahKGKqPIIsMpDYToTbfnhzhegOuHmG6UD/uxZ86wUDY7aOz5qs0TJ5dYrVltO7MJhVfOl3htqcpt0zkEAV6aK6HJAmEYUnf8jrEAEc1bl3hproQkCtx3eARJFHhxrkhKkzro4ZIocGAk0S4vCHDXvmEcP3qAkNJ7hAAIAn4QUqzbeEE0Nys1u4NKD/DiXJGhhLIlhX6zfdVNV6/Zfsc4t7MnulXzW3OZ0qU4S0SMGDFivM2xbXr5ww8/jKZFYjOWZfFP/+k/JZnsFIr6sz/7s92z8G2OrShq26WwdSuwZgyFc8UGOSOi7Y4kNVaqFbxAwPFCRpIqQUjzDa7NYsViyFA7zmmygBeE6IqI7Uf5pG0vRJVELC9AkSP6tSJFuaplIcqZLQJBjzFLCCAI6KpAzfajdtwAQxExXZ+EImG6PmlNAQESmoRTCshuGAMddiZVCdsPSGgSNdsnZyisVC1kUcJvnfdCdEWgavuosojleehKlFtc10RsL0BXRBqOj960I6FGfzVFoe46pFSZmuOT0WWKDbf5g89hPK1zYc1i71CkspszVJ6+sEIQgiwCCARB2JwDCIXoCxg2b8i7bV+uRjm+A0ARwV43maoiNn9AhzQcj8lMkkLdbdfLGUrEMghCbD9gNKlheQEpTcZ0g+ihxDrKZc5QBqZg7iZ2g7Z7LSN+SHllMEhmiF5llivWwPtv0OwTm6G134dTCm4QkNIlFkpmm7Lc6uejd06zWncwZJFXLpRJalLbp5RNl9mVeoefF0SBxbLJR++Y4vXlGtlERP9erVicL5rszUVicTlRxPZ8wjD6/huyhB+EkSK545FQJMqm276OTGZ03CBAlaIb5NG0xntvGGNurcY9h0Z46q1VsoloLGldYqFsca5QZzip8eDNE6xWbaazCbIJhfGUxuxqnXfsy+GdXePwWLrNGCpbDu+/aZxyw22XNx0/CgG4Yw+GejFk5lyhjuMH3L1/qGNuDFlqq6yPZ3SyCYWFksWDxyYYTmp9Vcu32lettWgpz5cbbptmvt090e3vBlXNjxEjRowY1z+29avhk5/8ZMfnn/iJn9hVY74fsRVtcbu0xm4F1orpokoiJdMjY0RvphOqjCxGN8+FenSDVqg5DCVUpjLRG4v2uYSC7YXIooDlBhCGOM3X144fIAjg+iEC4PmRDa23271uuAF8QuQwxHJCxGY7ohC9JRcFgYbrIwoCVdtFkyUazRvz8oYx0GFn3fHRJJGGHf2oLJkusiziBT6SEKWdiYSEQiTA8aL+LDcah+UESFL0WRTBatrRqme7HpIgUHM8RFGgansoUtRuNMfNeas5TGQ1SqbD4fEUL86V8AIQ14kO+YAYgkd0Qx7CBtsVSSAkenjhdk2m4wYkdQlDEUioMiXTbQsqKVJUfzonI4sCmiRStb3mWDx0RUKW6KBclkx3WxTM3cJu0XavVWz2kNK27atp2tsOgyjud5fZ7v67VFX/tC7j+iFfP5nH8wNWajYjSa1NWW7RzFtq5/mqHT3UqzqMpVVKpsNEWufQWLLt5yUR3lqugSDwpVcWySUUXlus4gU+S2Ub0/F4c6UePdhrPlQ8uVBFV0RMz8d2fU4Vq2iSyLf9gLv2XVQCX6pYDKeU6K2zELJas3nlQqQK/u3ZAitVm+fPruEFAatVh4QmMZuvsXcowVRWR5Ulig2Hc8U6T725ih8GFGoOe4YM/ICOOT8wmmAqa1BsOKiyuC4EQGtT8s8VGlxYa1CxXGRRRJWF9tyYTYp7yXRJ6wrFmosmiTx+YrmD/t6tWr4V1ivPVxreBpr54fHUwHuie78NopofI0aMGDHeHtjWr4cvfOELl8uO71u0KGxPnFzqSVHb6nw3WgqsX3zmDPmqxXhG56FjU7x0PooVHk1pfPzdk1Qsl4Yd8vpyBVkSGE2rfPK+KKb7iZNLHJlM8/pSFVkUmcho3HtolCffWqFYdwjDSCk2JESVJBzfRxQHj+n2woAgBFGB6ZxBw/FxvZC645JQZVRZZDipUqw7SFKUmuuHb+szBtfvsPOHb43KLVci9dm9QwlEAXIJjdmVGkHQ7Ddr0HD9iObueO1xtPofSaoU6k77fLddLezNJRBF2iJkCVWKcsqmdCbSOj90yyQNJ2jHdAt0xnSLIYwkNWaGE/zdO/a0bc8YStt2Q5U2xHQbisSte7IcmcryroOjfO3k0sV6TZvGUjoHRhPsHUrw5VfmKTdcdEXk1j1ZbhhP83duz3BqqcJKzWIio/G+m8ban3dKn93t/X89Y5CHlP/oH/2jK2VOjB64KvsvDAnCkGLdaVKjxTZtfDp3oH1j37LLdD2CMOzwKTPDSR6+/yCPfus0J+bLIMDhsSQNx2OhZDKc0ChbLkGTet1wAxwvUmw3FAlVFjkwkiRnKJhuECmSJ9U25Xt9/+WGy50zOVw/4Ph8CWjSrB2Pc4U6XhCNxfECIKQkimiKhaGJyIKAJAg89eYqrh9weDxFxfI4V2gwleuke4+m9J5rAXQoules6I28F4DpBG0l9j05gw8cnWj7sJQuUWm41B2/fXO/fo4HRWsuvvq9BZ45vXpx/E2a+Xbaezv7uxgxYsSIsTliT38NYCuK2nYpbOsVWHeiXt7qK1Yv3x318v/y/3wHH3/PzGVVL3/o1olN1cs/9s49PdXL7zk03LGvuj9fCewGbfdaRPyQ8spgO8r3vcpeyv7brup+1fJQZJF7Do7wwtwaSVXGCwKSisxC2SJfsTgwmtpgV6+MCB84Oo4qwa985TWmsjpDCY2VauQ/Do8nWShLyKLAuUKDfcMKF0o2B4YNBFHknQeGCIKQ9x8Zp2y6jKY1NFkiJKTccKla3oZ5Obda5/NPn2G66bOWyyYV0+W26TSCICCLcGHNIpdU2lR4y/W5aybHk2+tMJExyOgKSc0jX41UyGeGE1uuRSsEoFvR/Z37s5iuz8ffvZ/h1EWfdlF9HL78ykJbfbxf6MAga9iLZt6iv283FGY3/N3bMdtDjBgxrjwO/F9fbf9fk0L+w7vh1s88ju33DoU7++sfvlKmvS0Re+trBFvRFrdLa2wpsPb7vL69mU36Wn9uZjjJzPClx51NZHRumsx0HOu2odfxQcfQXW4n/W7HrkHwnkOjvOfQaIctd8wMbSi3me29ym9Vr21vn7Xr3leXSp/dKa5WvzGub2xH+X6zsjvZfztR3W+rk7do4zWbpCry7GwBRRL40ksLfOSO6YHtOjqViWjKdRfXC3hrpYbnB7yZr5FQJUoND1URaLiRnkPd9ZlIKzRsn4mM1qZ0rzVcUpmNYSWt/t/K1/jayWXeXKrx7dMFVFlgteriBQELZQtFFHC8AEEUeGOpyg1jyTYV/ubpTFOHw0YWBRbLF+nkvXxWvxCA9YrumiLiNqnw+0cT7fLr10SXJVw/3DR0YDtruJ5mrorSJYXCXIq/e7tme4gRI0aMtzu2pV4eI0aMGDFiXAvYjvL9bqvk77S9Fr14IqMxntHI6jINJ1Ipv2vfMHXH35ZdrXCirCFzptBAkUXuOzyCoUbhPUNJhSPjGSYyOkcm04ymNMYyGhMZrYPSvZkCd3uspoOhiDhewELJwvUDZBH8IFIwD8MQQxFR5EiIciihdlDhh5MK+WrUxyfvO7jpQ8Jec9ZSdFckkeGk0qbC91uTuuP3Vz/fwRruloL9peDtnO0hRowYMd7uiF8tvU3Qj252OWlo3W23PndTIbuPd9PBu8+3aOAtmvp66jvAG0sVTi5WODiaZDJrbKCBt8r3K9dNg++mdnfTzfv9bVG5W9TvVj8t+w+NRW+We9G6+/XVanN9GzPDyb7U+u657m63e+660d3ubu+JGDEuF7ajfL9V2UH9Z+uz6fgd7Q0n1Lai9YFRedPv1Xp6cbFm88ffndug2L2VXevxgaPjTGV0Pv/UaaYyCVK6TN1xWSxbfOLd+5geSmzwvS0/03A20si7xz+7UqdYd0gqUaqu6axOzfYYSio0nICcIVFsuBweTyEKcOe+HJ4fdihyrw95Wt/3ep/VzwetNt9wv+/GUVbrDmMpFVWWsD2f1ardnvO2MnxCxfUjZfjVms/Hbp5gOKVtGNtOMidsNVeX2/e93bM9xIgRI8bbGbGXfhugH93sctLQuts+OpXh9cUK5woNFssmUzmd/cPJDcdDQs4XTXRFYjyt8dCxScqm2z5fd7xINCwI8fyA0VSk1Pvw/Qf5wNFxfuuJN3jsmTNYbiSVvnfIwAvA9QPqtsdQUmVmKMFQQuWbb+SxPB/Ci+VM16fccNqCb6MpjUNjKe7aN8SLc2ucX2uwVnfaQnCqHL3Zaf3Vmj/2kpqMIonNOEYzEoYTYDytUbEipfAIIRD1ddveHPceGiFrKDz+6lK7L02RsN2LbSY1ifk1E1EQyBgK77txjLOFenuuu+esNdfd7bbmojV33fib1/PNXL9Ru/3K7XRPxLTHGJcT21Ee36zsoP6z5csqlouhSLhewFLFQhaFDkXr0ZTG4+vq9fpetejFaV3uqdi9mV29sH80Qc5Q+c7ZAq4fsFK1GUmqfPON1TZdvdVet99otdt909YqX6jbvDxXwnJ9inWHsuVFObJrLpoiUm9md1hYa2BoMq+cL3PXvtwGRe7RlE6p4fF/Xllsj6nls/rN1XoflVRlJrM6VVlCFoMNKuLTOR3XC/ibU8sdczCSUPnIHdMbVMt3mjmhFzX8Svm+t3u2hxgxYsR4OyOml1/n6Ec3W61Zl42G1t1nvmrz2DNnWCib5KsWaw2XlYrNfMnsOJ6v2pyYL1O1oljAlarF55+cZa5QJ1+1WC5bvDpfoeF4WK6P64cU6jbLFYsvPnOG78yu8tgzZ9qpxVw/5PRKg2LVpFhzsNyAsulyoVjnq99boG57iIKI7TXL1W1WqzaOF6Unc7yo/YVSgy8+c4bFkkm54WG60Q+2hhP9yKxZHsW6Q8PxWK1dPF6s25xeaWB7IWEYYnshc2sWDccnCEPWGi6lhkdAgO0FnJgv88ZShUefnGW5bFJuuJhuwGrVxnQCinWHtZrNqcVqe4zlhsOfPj/HUtliPK1TqDkdc9aa63PFWtRuxaJsutheNBcr1WjuVmtWxxqu1iwee/oMaw2X8XS0P3qVu9R9GNMeY1wubIfu268sMJD/bPm4fM1iLKVTsyMlcE0WeHGuCESK1vmaxaNPzlKoOQN9r7Zr16bfJ0HAD0OKtUgV3WimElw/npYCeMtv5Kt2z3bXf59Hkzq2F1A0HcLmw8WkKqFIQvR/TWIooaApcocCeje6fcRC2dx0rrp9VMXymCvWEYWwY85bKuINx9t0DrrHuFt08Svp+64FinuMGDFixNgZYk99naMf3WypbF82Glp3nzlDoWK6GLKEH4RMZXUsN8BQpI7jOUPmwhpMpBUEore4hXodQRTwg5CkJuE34wIbTpRHPAijH3hl0+W7Z4s4fkBSk3C9EEUKsbwQSZKxA4+UJuMHgCDih5DWJIJQQJPB8qIfiwCKBI5Ps30BSRSx/QBNEQmI+q/6AVozNtFQRUwnQJUlzMBHV0RsL0AWxWZ+bQgRUMQQJwBNFgjDiz87w1AgpUmYboDZTEE2lDMIas7FvhQB2wvQZJmq45NSRERRiD7bHilNIqHKjKRUVpas9py15loUxKjdhEEQQEqTcYOQjKFQNl2WynYHdbO1P6ayOglVZiqrk28qH++EZh7THmPsFJdCy92OEvRmythb+c+Wj8sZake59x8Zo2x5bUXr2dUaDcdjT85AFATGUhrFhr3p92oQu1r09XOr9Q6qdGvuijUbx/O5e98Qr1woN1XRQ3Lrvv8VyyWpSDRcn7G0iufTPt/9PV1P1a7Z0VtmCPGCkENjCXwEbhpPUqy7fPDoGE/PFhlLqsiShCoLNByffMXGUC+OKV+xWKxY5HSZtYaD5wVULY+JrBbNVTpKx9iaq34+6p37h2m4wQYV8aWyjSIJ3HtwhOfPrW2Yg257+s39ThTpr6Tve7tme4gRI0aMtztib32dox/dbDKrXTYaWnefJdMlYyiYXkQzXCxbjKVUTNfvOF4yPRQJSqbLUFKlYkY/5sIgRBIF6raPJAhYbgCA44WockRfnMzqvPvAML8vnWmWA9cPEQDf9xCJ6ic0CcIASQDT8VFkCduLboCDZr7wJjMdxwvRFPCDAE0Ssd0gaqdZwPYCBAEsJ/rr+NFxy42Ej7wg6t/xQRZDvMhsbC8kobaI5SAIITXbQ1ckDFmMftjZHiJQb47VdkNEUcD2fESIcueKIg3PRxYFarZPw/Eo1JyOOWvNdRAGzR+MPqIINdvDUKOHHuMZnclsJ9WztT9aKdRaisLd5Xa6J2LaY4xBsBu03O0oQfdTxt7Kf7Z8XMl0SGsXVb4PjCY7FK1N10ORRN7MVzEUGcvzmRlKbPm92syuFn3ddgNmV15j77DRGbpTrHOhaFK1PGRJQBYFVmo2YymVkukykdGYzGq4fsh359ZYrdrYXsCB0UT7fPf3NK3Lbaq2KAi8la9iuj6iKHChaLJnyODVhQDPD/jTF+Yp1h3qtsdYSkOWRA6MJPjyy/PRNaBJzf/e+RLfOV1gqWIhSQKuF9U/cb5M1lDxw5CZ4Ytz1c9H3TyV5q18bYOKeKt8vma1leFbcyCLdNjTT7n+UhTpr6Tvi7M9bI75+Xl+8Rd/kb/8y7+k0Whw+PBhvvCFL3D33XcDEIYh/+7f/TseffRRSqUS999/P7/7u7/LjTfeeJUtjxHj0rA+BViMaw8xvfw6Rz+62SCqtLvV50RG4+H7DrInZzCe1hlOKoxlNPYOGR3HJzIat+7JkjEUZFFkPKPz0w8cYv9okvG0zmRO59ieDElNRlci+uJIUmMyq/PJ+w7ynkOjPHzfQRKqSBCGKJLADWMJRjIGwykVQxXJGgozI0k+fPs0KV0mCAM0uVkupTGa1lBlAUMRUeWo/T1DCT5530GmhwyyCZmEKjKW1khqEsNJlbQR0SaTmsxo6uLxkZTGDWMJNFloU033DekkNQlREBhOKOQSCiIiuiJy654sR6YyPPLAISZzBtmEQkIVGU1rJDSR4aTKcFrjyFS6PcZcUuXH797HVC56yzOaVjvmrDXXB0ZSUbtZnayhoCvRXIxn9J5KwS3V450qCg+6D+MfhjH64VoISRjUf7Z83ERa37TceErnlukMiizi+QGyJHBgJLHt70HLrpQm8eJcsc0AqlheR+jOfDkSb6tYHglVBCF6KJg15A6V8oQqQxg9qBtOaciygOkEDG32PW3qUvhBFB4TIpDSZEJgvmTi+gGGKlO1XEzbx/WjEBnXDzhbqLNmOh3U/KWKScPxcP0Q0/bw/RBREAgFqNoekihwYMRo29LPR80049D7rdlEWu/wjUMJhZDoweVm++xSFelj33dtYG1tjfvvvx9FUfjLv/xLTp48yX/6T/+JoaGLKTf/w3/4D/zX//pf+b3f+z2+853vkEwmeeihh7CsnYVXxYgRI8YgiK8KbwP0o5tdThpar7bvOTTcU1G7+/ilqJf/wg/dxN+5bfK6Vy9/6NaJy6Je3t3uZqrk6xWFd0O9PKY9xtgOrpWQhO34z5YvW09FTusyP3b3XvwATMfDdH1u3ZPF8QJEAUqmR75icWB08Df4rXYfvGWC1bqDIYu8cqHMWErDC0IIQ5bKJjNDBn5AM8zE5517slRsj4+/e4bhpNb2D/mKhSKLfODIBIIANctjsdLgwZujN7ndlOp8xcLxAx64cZRi3WF+zUIQo36CMGSu0ODmyTRLFQdZFCjWHQ6NpgiBoxNpXpkvtan4LWr+3qwBCExkNIp1h7QWxVvfOJ5EEETu2JvFD0NOLVZR5Mgv91I9X61ZHXPeb81aYzcdjz957jxjqc332aXsxyvt++IsEf3xG7/xG8zMzPCFL3yhfezgwYPt/4dhyG/91m/xb//tv+WjH/0oAH/wB3/AxMQEX/rSl/gH/+AfbGjTtm1s225/rlQqALiui+u6l2soPdHq70r3u9u4lHFoUrjb5lwSNDHs+Hu9YpBxXC/77kp/TwbtRwjD8PreJVugUqmQzWYpl8tkMpmrbU6MGDGuImJ/0B9Xem4ajsdjT59lreF20HI/ed+B6+JGohcVeTqnt8ckifD82TUkEe49NNpWEd9Ou4YiMVdscGa1HtHC/YCcoVAyXeq2T1KTmEjrhMBYWmXfSIKJtM4n7zvAQslqt6PLElU7yqqwXm39vhtGuG1vrq3Kvp4K/uzsKn4Ax/Zk+D8vL1CzPTRZjHQ2NIkPHB3nzGqdM6v1ph6FyMHRFIfHU5xfa0QPDHNJzq81mF2tMZ3V+e6ZIqXmg0/HCwjCkIwuM5VNIEsCNcvD8nwSqsx4WmurmW+lvL4ZBt1n18t+vJJZIq5Hf3nLLbfw0EMPceHCBb75zW+yZ88efuZnfoZHHnkEgNnZWW644QZeeukl7rzzzna9H/iBH+DOO+/kv/yX/7Khzc985jP88i//8objf/zHf0wikbhsY4kRI8b1gUajwcc//vEtfeW1cyWJESNGjBjfN2jRcp84uXTd0XLXU5FbN2hPnFzik/cd4MFjk3z1+ALPvFVAEOCufcPUnUhhezq3+Q1cd7vnS3XOFRoADKdUlsoW82smCU3i8HiSxbJFvmpycDTFWEpnIq1vUD9v2ScQoikiz525qPy9Zjo89vQZDo2lmBlKcL5U59tPF5ppFId5/lyR750vIYkCkiQgCJFORVaXyRoKphOgyFFoTNX2MB2P8bTGB2+e4NRSpU3Nf99NY5xaqlBueJxcLFOxAmRJZDSlYnk++YrFaFqLmALNFI2Fus0XnznDjePJDcrrsihgKPJAczroPrse9mO/fbfVHHw/YXZ2lt/93d/lU5/6FP/m3/wbnnvuOf75P//nqKrKJz/5SZaWlgCYmJjoqDcxMdE+141Pf/rTfOpTn2p/rlQqzMzM8OCDD17xhxGu6/LEE0/wQz/0QyiKckX73k1cyjhu/czjl8mqnUETQ/793QG/9LyIHWzM3HC9YJBxnPjMQ1fYqp3hSn9PWuyXrXBVvfS3vvUt/uN//I+88MILLC4u8ud//uf8yI/8SPv81RC7uNq0rX604Uu1a6v66+nLQAedbytaeD8bW232o0y3cL5YZ3al3qZ7d9O/CzWLs4UGRyfTHBhNtenckhBSbHgcGEkwktLb/XTTzbup1t12ddtbt10WyxYJRaThBm3aePc4W/10z9Nma9Rdt5vW3W1bv3L91rWbhr7VWPu1u9X5GDF2A9drSMJmVOTD4ykevHmCuWKDfcMJxlI65YbLQimKvz4w2t+fb8wMoeIFAfcdGiVjKMyt1Xn81WVuHE8xljbIGgpLZYt/+J597BtNtb+vZ1frLJZNprOJTrX1m8Ypmx5jKRVVlijWbAp1h0NjIcW6jecFLFcsjk6mmRlOoMsSr1woUnd8Do+lcPwAVRIxPZ8bx5OcXzO59/AwQ4ZGzYn85oO3THDzdHZD6EqLmj+br/I/vjPHzLDBkKFRqNs8c7rALVMpnjm9xkhSJQhhJKlRNl1mV+pUrEg5fjaotzM2IIQcny9zarHCO/YPb7peg6qUX+v78VoJybiWEQQBd999N7/2a78GwDve8Q5OnDjB7/3e7/HJT35yR21qmoambRRDVBTlqt34Xs2+dxOKonDjL31tm7WuzRtbOxCw/WvTtu1gs3Fcb3vuSn1PBu3jqnrper3OHXfcwT/+x/+YH/3RH91wviV28cUvfpGDBw/yS7/0Szz00EOcPHkSXd/9G4ErSdvarP9zxTqLJYuprMH+kURbpXandm01rr95Pc9jT5+hYrm4fgAh+CFYrs/MsIGAQAicLzbQFZHxtM5DxyYpm24HJXG9jVlD4fFXl8hXLSw3SvM1v2YiClGqsEceOMQn7tnPH337HI8+Octaw8F2fXRFxnIjGmQQhkhilLYLQBJgKKk0b1pDmmLh0ZsXQyaXUBlJqRRqkZhP3fbQFAnb9RlKqswMJbhr3xAvzq2Rr9pYrs/NU2lu25Nr23tqqUqhZhMQqZ1LAiiSwLE9WT54dKI9zuPzZV5brCAIEIYwM5RAEGivWa81aq3D8fkSry1W0RWpg0LZWofWnLXG0przVrl+69oaQ+vzxbFG7d08leG2PdkN5brbXb8fep2PEWM3cT0qMW+mWP1Wvsbjry5zodjg3GqDG8aTzK7UEQWBL788v4HOvd5XbMwM4ZDRFRquz0hKQ0Box0KHIZxeqSMAf/DtOfaPJNvXi+MXSryxXOP1pSp37x/GC8Km2noCVRL51hureIFPvuLQcHzOFepIooDp+IgCfOV7HnfO5Fip2fhByFrd4UKxgSwJBAGMpFT+f8/Nc6HU4JXzJW7fm2W15iAKAl87ucxixe45xoWSxXfPrlGoOaxUbe7eP4zrh4ykVARRQJUFCnWHXEKhULcZTWkcGkvy6kKFfM1qZ2ywXY8X59YIw5AT82X+X++7gU/cs3/TNRtUpfxa3o9xloitMTU1xS233NJx7Oabb+Z//a//BcDkZMQEWV5eZmpqql1meXm5g24eI0aMGLuNq6pe/sM//MP86q/+Kn/v7/29Dee6xS5uv/12/uAP/oCFhQW+9KUv9W3Ttm0qlUrHv0FwtZV0W/3nqzb5is1aw2WlZjFfbvDYM2fI16wd2bXVuFZrFo89fYa1hkvWUDi32uBsoYHl+lQtlxPzZRZLJifmy1QsF9+HlYrN55+cZaFsRuq0zTbyVZuxlM58yeTRJ2fJVy08P6TccDi1WMV0A0RBoGJ5fP6pWb4zu8qjT85SMV0cL8DxQkqmi+WFmG6A7YXUHZ+QKP2WF8JKLTofrBtjSCRWVKjZvDpfoVS3KdYcTMdjtWpjugFl02WxFM3lcsXC8wNqtseppSqnVyo8+uQs86UGhbqNF15ML+aHUWqxV+fLnFos89gzZ5gr1Dm1VKVieZQbLhXT4cRCiXzVYqVmka9ZG9aotQ7zJZNTS1VqtocXBG0K5RtLFR57+gyFmoPnh1RMl1fnK5TNaM4LNYcvPnOG1ZrVc11bc16o24yndVaqUbvLZQvPpz3W2Xw1KldzGE9H+2F9u+v3Q6/zMWLE6K9YDRGtu+743LVvmCAMeebNAn4YdtC5W76y2x9vyAzRfNg2kdFYqVnsHTJ45IFDDCVUzhQaSKLAWFqj3sxV3bperJkO79iXA+DFuSIpTepQMQ9DKNbddtouPwipmR5+GKJIEo7r8fRbq7h+wG1NgTMviOoFwErNoWZ5HBpJ4foBz7xVwPWDTce4WotizGu232HbUELh4fsOcmAkxZHJNGldRhZFRlNah1p5S5XcUEQWShZ+GDKW0mg4Pp9/apbzxfpAa3e1r/WXglgpfWvcf//9nDp1quPYG2+8wf790UOZgwcPMjk5yde//vX2+Uqlwne+8x3uvffeK2prjBgxvr9wzXrqM2fOsLS0xIc+9KH2sWw2y3ve8x6effbZngqTAJ/97Gd7Cl5shatN22r1nzMUZtep0RqKTMV020qw27Vrq3EtlW0qlstUVo/UdkWBMAzxg5CcobBcddAUCc8PGEvrIEAmIVNYtTFkqU2BrFglcoaCoUoYikTD8ZjMJCnUXQxVpmx5pBQRURTIaDI12+W7Z4s0HI+UJmN6AYoEtg+qAG4IohDd9MJFMlEr93Uv9T9JjHJka4qC6TXttn0MJRL/0RQJxw9IqBI122ckqVK3fbxAoOF4DCVUwjB6ErX+pl4WIztML7oZ3pMzcJqCRsWGQ1aTKTQ8coaC54fkDJWy6XasUWsdDEXC8cINFMqTzbdCIymVxbJFSpOp2R5pTQYherNUNl2WyjajKX3DurbmfN9wlJ4oa8icKwYktHVjdXzckI5yU9koFVmr3fX7odf5GDEuFVc7hGe9DYOEhPSrn9ZlPnh0vCNkZj2tO5tQEIBvvpHn3oMj7BlKkK9aHb5yOKF20M5hMNX0u/YN8fmnTpPRFU4uVklqMp4foIgiharN0Yk0uYTKAzeOslJz+Oid0xwYTXF2tY7jB7xzf45XLpRxvYBTy1WyhkIxcEioEmEI4xmDxYrFsekMaU1BlyX0lMRIUgUh5PRKnUxCZiyj4xHwxlKNY1MZ9g0nN4yxdc1p+ZbhlIKAwD0Hh1msWDx4bIKbp7LtMXaHIXUrxH93tsBn/s9JhhIqKU1GU0TWGg6zK/WOsKV+uNrX+kvFtU6Bv9r4l//yX3Lffffxa7/2a/z4j/843/3ud/nc5z7H5z73OQAEQeAXfuEX+NVf/VVuvPHGNotyenq6I7wxRowYMXYb16y33onYBfQXvNgKV5u21eo/X7WRRFgsW4ylVUzXI2MolEyHtKZs266txjWZ1cjoCovl6HgQhISAJAqUTDe6EXZ9ZEmkZDqMJDQqDY+kKmN6PqbjtymQJdMlrSuYbqQ+G1EgQ0zHQwRMN0AWRUqmS9aQefeBYf7XC/NUTBfCEDdikeOE0Y11sO7Oev1Ndj+5fT8AURCwXQ8RAbvZoNmkt9uujypFdPUwDCk0XDK6jCyGJFQZLwgQ1t3ot+AFoMpgyBE1PgxCVCkah0iUY1aRoGS6TOdkSqbDRFrvWKPWOsyXzJ4UylumMmR0hULNIQxDak2l4artockShZrDaFptx9x3r2trzgt1G12RKJsemiTSsH3CEAqNaI0UIXpbEtHWpfa6t9pdvx+msvqG8zFiXAqudgjPehu6w3gGteVimEgUYrI+5Ga1ZnfQuk3XZyStUXe9Db7S9oK2iviXX57nw7dP96U4d3/eP5ogZ6i8dL7Eas3GdgMmMhovzbkUag5f+d4i4xkVWZS4cybHeEbnrXyNr35vgTeWa7h+gOsFLJVNvCBiAoVhGOXLFoRIM0MSeHWhwp0zOSzPp2Z5VC0HPxQQEKg0PCTB5Ey+ThCEvLpQIW0oWJ7fcT1oXXMmsxquH/L1k3m8wGe16jKcVHn8xDKKJLXp53/9Wp6K5fLqQqVnaNWd+3Kk9cjPSiIU6g5pXW6naNwKV/tavxu4linwVxvvete7+PM//3M+/elP8yu/8iscPHiQ3/qt3+ITn/hEu8y//tf/mnq9zj/5J/+EUqnEe9/7Xv7qr/7qsoQtxogRI0YLV5VefjmgaRqZTKbj3yC42rStVv8TGY3xjMZwUmEspbM3l+Dh+w4ykdZ3ZNdW4xpNRfTF4aRC2XTZP5rk4GgCXZHIGAq37skyPWRwazMWWJJgPKvx0w8cYk/O2JQCOZHRkSWBXFLlyFSahCoShFEu6p9+7yHec2iURx44RDahoMoiqiyQMxR0WcBQRDRZIKlKCEQ34bIAY6no/PqNKwA5Q2Y0rXFsT4ahlMZwSiWhRccSqkjWUJgeiuZyMqsjSyJpXebIZJrD4xkeeeAQe4cSjCQ1ZCF6yw5RTLcqRzHdR6ezPHzfQfaPJjkymSZryGQTCtmEyq3TOSYyeoeCcPeP5gePTbJ3yOhJobxpMsPD9x9kNK0iSwLZhMKxPZlITE6C0bTKJ+872H7b3L2urTkfTWnkqxbjmajdyZyOLNEe6w0T6ahcWiVfbaXEudju+v3Q63yMGDvFtUDr7RfG0yskZLP6C+WLYSKtkJtHn5olX7N6Uqdb/rvlK4cSCi/OXVQRr9n+9udCEJBEgeGkgiQK5Cs2ggDjaQ0/CCnWo7hvCNt2r6d2r1RtVFliOmcgSQKiKCAJ0QNXSRSYzhmIosD3LpSRBJBFAUEQEYGRpEo6IXOm0EBRRO4/PIoii7w0V2IooXZcDzquOeup7X6AoYqULKeDft7aH/mq3TO0aiSl8cgDh0jrMmuN6Ib7p997aKC33HD1r/UxLj8+8pGPcPz4cSzL4rXXXmunC2tBEAR+5Vd+haWlJSzL4q//+q+56aabrpK1MWLE+H7BNXuVuRpiF1ebtrW+/27aYze9cKft9qr/gaPjHUqzsDP18m4bH7p1Ykv18k/cs5/33TR6TaiXt+zdSr28Nc7tqpevX4dequTr12EQ9fJe69oaw6Bj7dVu936Ib7hj7AauFK13M/p6vzCepCKzULbIVywOjPZ/290OE5GjUJVWmEg75EaR2TecJJtQWChZbVp39/dpKqtxfi1SNx9N6ZjNmOzuueg3lnzFxvF83nvjKJossVw2eeZ0gaMTac4UGxxNqVhuwN37h/DDcAO1+469Weq2x7sPDpE1NEqmzXdni9w4nuSt1QY5XQZR5I49WeZLJg3H45Zpo91/2XT4h+/ZzzfeyLNvKMVYRosU2ssNHrx5kuGUyo3jyQ7/mK/YKLLIPYeGef7cWpsSn1RlFkomZ1cbXcrtSt/QqvXXje5sGFvtC0mkg64eU7RjxIgRI8aVwDV7pVkvdtG6yW6JXfyzf/bPLlu/V5u21a//S7Vrq/qjKb3j5qrfjdZ6ov5WFMjuNicyOjdNbmQezAxv9aMpyw+s+zSR0bljZmiT8p12dqPbrq2Od2P9OLcOXNhe3V42bNbHVnO+07EOOhcxYgyKK0Hr3Yq+3iuMJ6mKPDtbQJEEvvTSAh+5Y7ovzbxVf6FsokoihbrDkKFSaXgkNBmzSSMv1lyms3qb1r3epqNTGb53vsT5YoOzqw3uPjCEH7BhLvqNZT1N/PWlKofGkryxVKPccPnGGyvt0JwDownqjs9ERuuidgfkKza2F/D111YYz6gIRCykF+bKVC2PM35AVpdxvYCbp9KMJDWKdacj5OTOfTnOr5msNVxSjkyx4aDJIl87GTEBFksWSVWm7nhMZQ2mczquF2CFkFAlVqo2CVXk2dMFFEnkG6fyEeW9rdzubhpatfV1Y+O+6BVSMJGJ/VyMGDFixLj8uKr08lqtxssvv8zLL78MROJpL7/8MnNzcx1iF//7f/9vjh8/zj/6R/8oFruIESNGjOsQl5vWOwh9vTuMJ6vLNJwASRS4a98wdWdzmner/p7cxTCRVsjNI+89xN5coqeaeTdlumQ53LVvGEGAl+ZKpHSpYy76jaVbAdwPQ555q0BIyFhai+jkTdq56QQMdVO7CSnWHbwgQBbB8yMaepQxwcMPQiQBQkJMLyQIwFAkfuKe/RtCTlqq4q31TGkSIQJrDZd8xaZQcziVr1Co26zULNZMB0EQGEoo0dwbF+f+Hfty2F6IIAikNCmi4me0Swqt6t4XOw0piBEjRowYMXYDV/VN9/PPP88P/uAPtj+3BNA++clP8thjj11VsYsrpbDb3U+LDtyP1ruVXd3nL1WldzO8sVTh5GKFW6Yy3DSZaffVj3bdOr6wVudUvsaR8RTTQ8kN9O8WWnPROt9Nue6mTPebm/PFegcNsVWuRVNv0cdbn6eyOklNadPcp7I62YTaU1W3V3+9jm81N91/t1q/7uO9qPvr+70UleZYJTfGbuFyhvAMSl9fb0OxZvPH351rq433o3n3G0N3+Ed3OEe+YvelTI+ndXRZYm6txvsOj5LWI//vB2A6vcfSpoknVAQB7t43xNNvrXLnTI7ZlTpjGZ267XH7niyW5/O+w2NYrs8r59coWS43T6YJghBNlriwZrJ/JIEXhOwfNliumhwYTrBai26OTdfnzpkssiRy1/4h3nVwaMO1af1cmI7Hnzx3nrQmM9vM5T274rI3p7WzOliuz4PHJvADcD2fP3t5gdGUiiZFqully2mfb/XTCudp+bCGM1g4Qst3mY7fM6SgV5aJQdrr54tjHxkjRowYMTbDVb1CvP/97ycM+2lRXxS7+JVf+ZUraNWVU9jt7idrKDy+7vPD9x/kA0fHB7arF43x9cUK5woNFssmUzmd/c23E5c6nt964g0ee+YMjh+gSiJ/9449TGZ1jl8o89pSBUGAMISZ4UQzxVfI+aJJoeZQtS++WUiqIookMpRUmRlKtMf8N6/neezpM5wvNlhrOCQ1GUUSGU2pzR+FUfs3T6W5bU+up8rt4fEUf/Ttczz65Gz7h9qP3LkHVRb5m9eXOTFfJiBERGAoqbJWd/CDKEe8ocm4XqTaLotCu19REMkYF9vpRf3sXiOgqXZc4rXFajvlWWtukppE3fY7qJj7RxIX16+LEtl9vG57nF6ptW175IFDfOKe/RcplTtc/2tBaTrG2w+XK4RnO/T1lg1pXWYqa1BsOKiyODDlvVeYSK/v23TW6EuZtt1Ivdx2A/7vr71BxpCp2z5TWYOprI7rhxvGMpnVcL2Avzm1jCKKWJ6PoUq4QfTGeKVqM5ZScYMoa8T//bVTLJRMig0nyrEdgqGICAJRBoaGy0RGQxAEEqpMzfbbmSsyuowbhEwYSvuGsteD4NZcNByvg7qfrzioikChbjOR0SmZDrIg8viJZSzPR5clGrbHU4sVFFHEDQL2Dyfa59f7nIWStS1ftN53GYoU0drdoCMzSK8sE1u11+1L+113YsSIESNGjG7Ej2W7sJ7W1/qx88TJJaYwwYLPAAB2rElEQVRzB3Zd7Gd9P2cKNf70+fMkVZk9QwaLZYsvPnOG2/dm2qJlm9nVff58qc63ny4wM2KQr1qsNVxkUcBQ5EsezxtLFR575gy2F5DSZKqWy58+P8eHbh7n1HKViuUBIQRwwiwzmlRYrTv4fkjV9jvaqjsBqhTlB9flaMx7czqPPX2GfNWibLlYro/tBmiKyHLZRFdkBAEEAU4tVcnoCt8+U4je8uaS7bkJgnEefXKWmu0xklRZqTl88ZkzvOeGYU7Ml3H8EFEUcP2QxbKNGFlNSPTWTAC8wEcIaQqziaQMgbLp8NizZ/h/3DLJjROZdn+5xN4Na/TV4wuEAazUbE4trZubEE7MlxlLaXhBgCZJXAgaGIqELAkossC3ZwvMDCfalMhex5fLFueKdUIEUrpAxXL5/FOzvOvAEH/9Wp58zdrR+l+p70GMGLuFFvX7iZNLA9ORd1KnF9oU5h7fN1nspEy/76Yxjl8o8czpVfwwxFCim9/FshmVlwQMTUQWBFK6vNEuoZlagRBJFNg3kmA8peN5EIQhYxmNlCZxfL5B3fYoWy62GxASIiBghSGp5k30UFJhLKNxYCTJ7XtzfO3kEssVu53tYO+QMfB8rJ9L0/UIwpCEkqDheoyldIYSKpWGS93xo2vUWoNzhToBoBDihyFnCw1SusLMUGJTv7qZL+rluwRBYMiQMV0tmqM+WSa2s7ayKPa87sQ+MkaMGDFi9EJ8ZejClVLY7e7HkCUajse+4QQJVWYqq5OvRnTC0ZS+pV3d53OGSsUqYShp/CBsUuoCcoayLUpdL5xcrOD40Q23Jkv4akjFcqnZPo4XkjMUinWHjC5TbLhoioTrRylnekEShUgBuGnbyeabg5yhsFS2STTfAOtKlGM7qUrUXJ8RQ8H2QkRB6Klye3KxQsOJbrgNRSZnhCxVTMqmSxCCJgt4AcgiuEGUHsxv5QgnOh4CiiLgOiGaIiAJIklNZLVuI4hCT+rn+jU6W6xDGGIoUs+5SWkS+arPWFpmfs1rUzENWWq/pfHXUSK7jyc0CT+EtC4hCSIpXaZmr59Dldmgvu31v1LfgxgxdhOHx1PkEnu3pby/G5T3i6rond+3hCZRtTw+dmyC4aTWpiYPJWTm1hqMJFReX6oiSyLFFYfxtETd8Ukq0ff6o3fswVAvhqGcWqxQNh1u35PBDyOfVbY83nfjKNNDiXa588U6T7212vahSU2iZnuMplX8IOS+gyNYfsBPvGcfh8bTfTNOrJ+PVphOK9ymF726F/W+1dbCmskffucs+4ZSGKpEQpMw3YD7bxhhOKVRNV2ePh3ZvN7nvL5Q4fRqjX3DiYF8UT/f9eAtE/ghG8KUzhfrG8Y6yNoaitRXXT32kTFixIgRoxvxlaELV0Jht1c/pueTUGUKNQddkdoKsa0UXlvZ1X2+ZDpkdAXT9ZBEIaLUpVRKZkQnvJTx3DKVQZVEak2aeMPx2m9zVDmiJooi1CwPRRKwXR9FAt/vHUrgByGiABXTZTyjc8tUpklTtBBFaNgeIkJEDxSg7viIApRMl6yhEIRhT5XbW6Yy0ZzWHUaSUXlNinJ2iwLRDbso4AdNO8KLb7oBvGaMteuGCIDthihyQNX2UWWRMAgxHb+D+tm9RqMptf2mu2Nu7GhuaraPKgtUGl4HFdNs0itN1++gRHYfb9h+NCe2T0qHkumQNaKxL5QisaCdrP+V+h7EiLGb2GlIxKVS3tuq6Ou+bwlV5Nm3CqiywOMnlrl9JtcOC7lQNCOVcEnA9QKWKzam63F8vknr9gLu2jfEeEZrU6uPz5d4daFCqRGxf1paF0lNYrFk8VMPHGqHI0kiHT60bvuICNRtH00WeOZMAYBSw+2o1y9rQStMp2J6BGHAbXuz3HtotC+9upsOnjUU/uL4IrOrdb4tFLl9bzZ6SNlweP7cGu8+ONymlJdMl3TT9xQbNv/tb09zrljn24LAe28cJaOrm/qiXr4rDAO+dnIZ0/U7QrnyVQvLDbh5MsNte7M990uvtR1LqVFbm6irx4gRI0aMGOtxVdXLr0VcboXdfv3syRk88sAhRtNqh0Js6wfQVnZ1n59I6zx8/0H25hKMp3WGmzTCiYx2yeO5aTLDw/cdRFdE6o6HoUr8+N37OLYnx5GJDFlDJmsoZJMKt+7JMj2U4NY9WUbSGmmts9+kKpLUovLjGZ1P3ncwav/+g0xkdLJ69NZjOKUylFQ5tidLLqmQTShkDIUjk2kOjCZ7qtzeNJnhkQcOkdZl1hoOWUPmk/cd5I69Q9y6J4sqCYhCiCYLTGU1VFmIaINC9ENLkwV0RSJlyNwwliChiYSBQC6h8vC9B9k/muzobzSlb1ijD982zUfumGbvUKR2nDVksnpk/617skzldI5MphnPahwZzzCa0hhL6ezJGdH6DRmMZ7Ro/Xocn8zpHJvOklAj27KGwk+/9xA3TWYilea0vqP1v1LfgxgxdguDqJdfLrRV0Zvft6whYzoBshSpopcsh8eePsN8yWyKq3kkVBE/DFmp2kgSaIoUMcfX0cdbY5ovmZxaqlKzPGzPxw9C6k6AH4QEIRTrUejMas0CopvnTh8qMpxSSWkSbhDFdh8aSVGxvI56vXC+WI9uuC0XQQixvYAT82VO56s89swZ8jWrp8J6ax0WyiaPPjlLqeFycCSB6wc881aBIAy5/8YxJFHgpbkSQwm1abPGSs1CkwXOrdapOz6HRlJ4QaTUrinCpr6o23e1VNVrts9YSme+3ODRJ2fJV208P6Rme5zKV5gvN3rul+61bfnSvUPGrqirx4gRI0aM7w/EV4ceuJwKu1v106L39aJGbmVXr/Pdyq+7NZ5f+KGb+Du3TV429fIPHB3n9r2ZbamXt8a6foyfuGc/77tpdIN6+Y/etfeyqJf3W6N+lMut1Mv7rV/38V7q5ett2cn6X6nvQYwYu4GrHRJxeDyFJk8xu1JHlUT+4sRiWxV9vtSgULc5OJrsCBe5YSyF5fi8Y2aIM4U6mixiewG3781ieQGvL1ZYLJsoooDjhaS0KNQmoYrU7YCUJiEIAllDpmy67XAkiHzojeNJZlfq5BIKkihybrXGf/mbtxhLa6Q1BUkWWK05nF2t982W8cK5Neq2R0qVqdouhhqFynhBxE5SJZFi3UZTBBbKFq/MlZhdRwdfHzo1nIxE215fqnJsKsO+kSRTGYPVusVH79jDgdFk27edLzZ46q1VhhIKoiBweCxFqeHw/pvGOTye6pvdopvm3lJVH0s1Q7mUiBI/mdEo1F1Gkip128dQ5L6hN5v50l7XnRgxYsSIEaMb8RWiDy6Xwu5W/fSj9w1qV/f5yzmOmyajm+1efc30qTMD3Lony4MDtL/ZXPRqv99YZ4Y702i1yk1kdO6YGWof7/7cjYmM3ne8W9kxyNz0wqB9dNu2Vf1L7T9GjGsNVzskopVxoWK5JFWZyYxGseFQsVy+c6ZAueHx8oUSmiSy1ghIqiInFio0bI/XlyvN4y4JVeT5s2v4QcCbS1Uajt98+R1Ss31EQWhrWVheSFKLhB7HM1o7HAl6Z7M4uVil0nBZWDNJaBIN2yelyfzt6yvkElrPbBgX1hrUHZ9i3QGit+RJTYq0MPyA//PKIn4QYLoBaV3mb19fjoQvZYn33jiKLAkdoVMl00FTRF5dqnC20MANAu7Ym2U8E9ne8jmSCK4X8OK5NUQRggAOjCY4MJq4mN1ircFa3SGlKciS0M5m0aKJr1dVb4dyudFNdcn0CMOQQsMlY8iYrsfeXKLvftmOv48RI0aMGDG6EdPLY8SIESPGdY+rGRKxWrN47OkzrDVcxtM6FcvjXNFEFEJenCsiiVE8siqJWK5PSpVoOAGqLHL/jWMokojpRm+uTSdAEMBQZeqOT0IVkSSBhBq9xR1JaiQ1ieGUQkIVm6E5Wkc4UjfVPt+0r9TUzQiJNC5CYDyjU3f8Dmr1+voTWR1VFAihnTVCFkWySQVC8IMAxwvw/IBCzaZhRyFHju/zdFPMbX3o1FBC5dbpSBfkooJGb5FNhNYpAQQIESjWIqr+SsWm3PAw3YBC3aZiupxaqjJfMjvG0r0v9uYSPPLAISYyGrIkRErt4xn25hIxPTxGjBgxYlw2xFeXLdCLQnw1+u1nx1b2DWr/ZuVaNL5uOvegffejULeo1nXbZbFstWne3e23yvVTI+6mGbbKtdR2W5TrVrnW+e5x9bNzq78te3up+wI952grJeCdhgPsdL2v1j7vZ0+MGDvBlQ6JaO3b88U6FSsKRVmffeKd+4douAHTOZ2srrJ/NMFCyeKhm8d5/ORym36+fzjJQrnBPQeH+erxRcbTBrOrdbKGguX63D6Zpthw+Ng79pA21Lav6w6BaTi9s1kkFYlC3WHvsIGhStwyleFcsc6hsRSqJG3IarC+frFhk9RkEqrESErDUCTqjsfRiQwvnitxYDjBhZKJpogUalG2CkkQ2JtLUDJt7pzJ8aFbJtuhU5IIX355gWxCQUAgJKTc2EjrXirbKJLIO/cPISDgBQFrDZeXz69FbAJdwisHJNQoq0VKk3G8EFkUWGjGzh8Y7R/200+pPfZFMWLEiBHjciC+omyCnSrh7na//RRit7JvUPs3K9ei8bVVXqcy3LYn29em7jbPFRoslk2SmkTd9klqMnXbIwzh/FqDquVSbrgIgoAkwrE9WT54dKLd/vH5Mq8tVtAVkfGmOFxLaXe9fS2a4VBSZWYowYGRJN96c6X9I/R9N45xtlBv23vXviFenFsjX7WxXJ+ZYQMBoW1fspmmrNvu7s9TOZ39w0kcL+BLL89TMV2CMOS2vTlumkhDGKLIYsccXVQCbpbdk+PeG0baY27NWavtQffdTtd7q7W83Lha37MYb09cKbrv+n0rCUJb2Xoqq7ezT9w8leGtfJ1izUUVJYo1l+msztHpDN+br1BsOKiySLHhYDoe/+M7c5wrNIAyYymNkEhs8vlzayiSwHfOrPGRO6bX+f8VzhXrLJYsprIG+0cSPHhskumc3qZUSyJ890yRQtXm66/lEQXwfVBliarlMpGWNmQ1WE/VT+kSbhCJusmSSL5mMzOU4M6ZHF985ixvFhrYno/XzAKxWLbRZIELJRNFEvn8U2cQRZEPHB1nNKW36d7FmrtpGEArG0Sx7pLWJE6v1FFlgdcWq7h+wPyajeMFuH4kWFezPRKqxMsXSuiyxJdfnufDt0+3fckgoVyxL4oRI0aMGJcLMb28D66WEm4/WmC+am+qENtt36D2b1auRZks1Bw8P0pzdWqpylyh3tOm7r7zNYt81aJQczi1VGWlanFqqcpS2eTEQom1uk2x7uKF4AUhjhfy6kKFNxbLPPbMGeYK9Uix1/bwfSjUOhV6V9fNTdl0sb2Asukyv9bgT5+fo2K6DCVUyqbLnz4/x3LFYjytk6/afPGZMyyXLTw/oGq5nJgvs1yJ7MtXLU4tV1ip2Bc/L1XJV6PPreOFus1KxWZ2JVLxLZsuoihgewHHL5R47myB782Xyepqe47eWKo0lYA9BIFICXihxOmVCo89c4aFskm+arHWcFmp2OSr9kD7bqfr3W9/XQnF5+3YHSPGtYTufev4IQdHEmQNuSP7xEzzoVk35b0704EmC5wpNKjbPgdHEgRhSL5iYSgiDSdAEiMV9BYNvOX/81WbfMWO/EUtShH4xMklAB48NklKl3hproSAwHimeYMZCiiSQEKTGEqoPbMarKdkV02PsbRGUpXxgxBZEjgwEomkQUQQD7qyQdpeSBiGZPWo/nq/PWgYQEuBPWvInCk0kGWB+w+PESIghKBKIoYmIUsCmiyR1CUSqowqibxjX46a7W/Ll8S+KEaMGDFiXE7Eb7r74Gop4Xb3mzNUKlaJnKF02LFUtje1b1D7Nyu3Uo36GEmpLJatSOXV8RFEgYrlbrCpu++coTIb1BlJqcyuuIylFeZLFjlDZaFsYygi4CMBoQCqJBAEIQ0/pGK67MkZOH7ASFIlCGEkpXYo9LbmIGfILJUhpcm4zR+FXhAp/RqKTFrzqVouCTX6UZYzZOaKAUlNomr75AyF5apDQpVYqTmMp3UurFmMpWXm1zzG0woXShbjaZULa150vGSxN2ngBeD54PgBo0kNyw1IaTIN18fzQ1KqiCDQnqOTixUajkfOUKjZHilNwHQDvECgYroYsoQfhE1142AD7fNS9+ug++tKKT5fbcXpGNcudiM05nKh176VRPip996AH3ZmYjg8niKX2LshK8X646WGzVNvrbbp6boqsVyx+Yf37OO7Z9faNHTT8VmpWZxdjdgwGV3pUEPPGWrbXxweT/HgzZPMFeqkNYXXlqocmUhjewHHptNULI9PvHsf00OJdjhLixnUsm86d4DZlTohIQlVomr5pHUJ1w95bbEKwKHRBGdW6wiEOH7khyumy3hKI6nLbb99drXRDstJ6zI/dvfejhCa7vAfiBTYpzI6n3/qNPuGUoxlNPIVC1kW+fDtU6iyhOf7rNYdfvjYJN94c7VN5W/NVbcv6Rf2ZDp+T190brWxYU3XI6ajx4ixEQf+r69uel6TQv7Du+HWzzxOX02HGDHeZoivEH1wtZRwu/stmQ4ZXaFkuqSbx4eTSpt618++Qe3frJwkQkZXKNQcwhAKjciWMAh72tTdd75mIYkC+YqNKgvt9DI120ORwGnyEX1ACMHxQlRFJCEJZIyoH1USKdQdhgyVQs1hNK22FXpbc5Cv2ohi9CbeUCU8P4rra32u2h6yKNBwfBqOR8n00CSRuu0ThiEl00WRoOH4qJJIyXQjexseqixEnyWRkuldPC6JFOo2E2kdWYreulRtD0kUqNsemiwiSwJuEBCGtOfolqlMUznXRSCk4fjoiogshmQMBdPz2zTVsZS6gfY56L4ZdL377a8rpfh8tRWnY1yb2I3QmMuJfvt2/2hiw41XPxs3o6evVB1GUyp37hvi/JrVpqEvVSzCMOAbp/K8sVyLqNWiwFojYCytUjIdJtI6aV2O2j+5xJvLNVZqDoQhATCR0Xj5fBlFEvjmG6vcPpPrG16SUGUOjSUBgWdPF9ux5AdGErxwrkih5lCxPTwvwA2in85rDRdRgDXTRZZECjWHhCbyjVN5VqrOhtCZiYzeofye0ZWOMKL9owluGEuz1nBJOZHvzBgKThAwkTBYqljcMJbijn05Ti3X2lT+Xr6kHfbUpOS3woimsgbTOR3XCzrWdK1u8atfPUm9SYnvDm+K6egxYsSIEWNQxPTyPrhaSrjd/U4045gnMtqm9MRu+wa1f7NyLXrfaFpFlqIfmkcm0+wfTfa0qbvvibTOeFpnNK1yZDLNeEbnyGSaqZzBrdM5hlMaw0kFWQBZFFBlgWPTGY5MZ3n4voPsH01yZDIdPQCQYDStdij0tuybyGhkDQVdiZR89w4n+PG795FNKKw1HHIJhR+/ex+TTXGjiabS72ROR5ZEMobCrXuyTGYj+yYyOkcmMoxntYufJ9NMZKLPreOjKY2xjMYN42kevu8guYRCEIToishte3O8+8AId+zNUrac9hzdNJnhkQcOkTVkwhB0ReTW6RyHxzM8fN9B9uQMxtM6w0mlJ+1z0H0z6Hr3219X6o3N1VScjnFtYjdCYy43Bt23/WzsDg8alJ6e0iRCopRh79iXQxIFLNcna8iMpXQm0jo/dMskQNS+6WCoEkEY8b8FIF+xEYjo6iXLGSy8JAybacsEgjDkbKFOzfEYy2gIRPRygZayOSQ1GVkSMV2fjCGzbzhJzfF6hs6cL9Y7lN/XGu6mdPSJjMbD9x1kIq1v65rYDntqUvILNYdT+UoUJlSzWDMdBEEgpUkdlP+K5fW0K6ajx4gRI0aM7SD+ZbsJrrQS7mb93nNoeIMdW9k3qP2blfvA0XFu35vpqfLay6Zebe5UvbzV/mbq5evtu9rq5R97556B1Ms/cc9+3nfTaE/18taYd6JefinrvdlaXm5cre9ZjN3BbtNru6nbwwmVhVKjrUbd6q8fHThfsTBU+ZLs6UV17kYv2nj3XGwYS0phoWRxdrXecTylyhTr8FP3H0AURXIJBUkUOV+sd1CxTcfjT547z1gqqpdNRO19/N0zDCe1tt/IV+x2iI8mSxybzlC3fQ6PJXj5Qpl7Dg2zZyhBvmr1DC/JV2wMNRpH1fJQZJF37s9RrLtossDx+QqKKJJQZW6cSDJXaDCZ0Vmtu8wMGQTA/iGN1ZrLj75zD28s15EEaLg+Y2kVz4eEJrFQtnit+ZZ9LKUhCgJjKY1iw26HEbXm+lKvifmKxWLFIq3L+AHtsKe9OQ3PD0kqEhXb52PHJhhOary5XOFvT60wkdE6FOlbdsWhMTFixIgRYzuIrwxb4Eop4W7Vbz87trJvUPs3K9dL5XW3+p4Z0KbNyvWzb2Y4utnuV65fvZ2iu78Wes3BZmUvZb/tdL2v1j6/VvqPsTNcDnrteuq2LAo8f66IKAh8+eV5btt7kQptKNIGOnAYhnzppQUsz9+xPZtRnTcbe68sAN0q4s+fXUMS4W81GdcPWapYVC2XJ99cwfECnju7xlhao1BzEAQIQ7h5Ks1te3Ib2pvM6G019P0jSRZKVtue1txYzXCVlarDWEoFUWAkqVF3fEzH7xleEoYBX355PnpLrSu876ZRTq/U+M5skSCI3ngPJVVemiuxUDIpmS6EIaZnklQkzhcbOEHIq/NlJFHgbLHB/uEkpuuzWrWxvYCJjM6zbxVQZQFDFnG9gFcXy+iyhOX5zAwl2mFELVzKNfGtfI2vvLLAG0uVKIxJFqhZPqoiUKjbpDWZZ2eLKJLA4yeWuX0mxwtni1QaDqtVmxvGklRtvx3a1b1P49CYGDFixIixFWJ6eYwYMWLE2DYuF722RSdOaRIvzhUBeMe+HGtmJxW6ZvsddOCULhGGIXXH37E9rYwI/ajO/cbeLwsAdKmIC3DXvmEcP0QgRBRDnn5zBdcPMBSJquXx6nyZcsOhbLpUrChjxHzJ7Givm0INdNjTmpshQ+0IV9mbS3SEk3SHl7To6zX74hx+5ZVFTsyXcf0o/aHrhxTqDq4X0HB8whA0WUJozksYBjRsjyAM0WSRhu1z/EIJzw8YTmlIosBy1UIUL84FAkiigNdM/3VgZGNs/E7RWqu643PXvmFkScB0AoaSCkfGMwwlVBqO31aIb1Hu647P/TeOITdp5llD7ghvikNjYsSIESPGdhBfHS4TdotyOQjNsVe/g9KTuynW68v3G0M3bbu7XKvNbrp3P9XY7vb7jaGfPd3H+5UbdC5b42tRv8sNh8Wy1aZ89qvfq/3uYzvdF4OOfaeIFXhjbBeXk157eDzFR++cZrXutNWo+1GhP3rnNIYqYzo+f/LcXJt6vRN7WhkRWiri3ZTifmPfLAvA4fEUH71jD6tVm9GkjqaI2G7Aat3i3XuHeerNAhlNYqlqk9SkZr5pmbrrkzNkTDdAEYUOVfJuCvVyxepJY29Rpbt97u17Mx2+tUXV7qavD6cUnj9TxHYD9gwZqJJIoxmbPT1ksGY6aLKEHwRkVJkLZZMbx1O8Ml9FV0RAIKGIFBo+RybS3DCeZrli8vTpVW7fmyVrKIgCKJLIR26bQpFFVFnEdPy+quP9fFQ/X9sdhpDRFRbKDT7+7v0Mp1SKNYc//u65tkJ8a58llGgdR5Mai5UGP/GeA0wPGT0V3ge5/sSIESNGjO9vxFeEy4DdolwOSnPs7vdcobFBIbZX/63281ULyw24eTLDbXuzPHjs4puT7jH80bfP8eiTs+0fHj9y5x5UWWyXyxoKj7+6xPm1Bmt1h6GkysxQgoeOTVI23YuqsZpM3fY22NhvDL2om90KwP0onofHUwPPZWt8FdMlCENGUyqrNQfPD/GCgNGUxqGx1Ib6vdoHOo615mC7+2IQ9eNL2WexAm+MneBy02vHMzpTTQq1Kkp9lfbHM3r7ZudS7WllRGipiC+WrQ5Kcb+xb5UFYDyjoUoiT51ewfVCVmpRCsaEKkEYcnq1juMFWK6PKEQ3mEEIy83MDy+dL/GuA0Pt9rop1OvtWU9jf/zEMh+5Y7qvv5jI6B3trZ/DVjueH+AHAatVm4mMRtVyUWWRlCYhCQLLVRshhEXfQhAE3lypE4YhZdNFlgQajoAsiYjNBwevXChTMT3+6sQyE2kdUQRZEjrUyPupjvfzUd3+d72v7Q5DKDYcpnNGW2k+rctMZY22QnzJdJBFkW+fKaDLEm4QsH/Y4FtvrrQp990K7621iH1pjBgxYsToh5hevsvYLcrloDTH7n7zNaunQmx3/632C80byprtcSpfYb7c4KvHF/jKKwsbxvDGUoVHn5ylZnsMJVSqlstjz55hrlBnLKWzUDZ59MlZlssm5YaL7QWUTZflisWjT81yrli7qBq7VI1UY9fZ2FL07R7DfMnksWfOkK9ZmyoA56t2z3JbqeO2cL5Yj264LQ9BiPK2nl5pULc8LM9v0iptlitWR/1ea/Xot07zuW+ebh8r1G0efXKW+ZK5rX0xqPrxTvdZrMAbY6e43PTa7Srt74Y9rYwIw0mlQ0W8m9myoywAgoAfhBTrNl4Q5b2uOz4IIIsiqiyhyBIjSY20riCJAookMJXVUOXorfFWc9VNY687/rb8Ra927r1hlFv35hCEkGLdIWMoPHzvQQ6OpUhoMhLg+AGCAFlDIVI4j5TMRUFAkURu35NlLK21wwXGMhpBGM0FwP6RJEOGuqnqeD/bu/1voebw+SdnWSibPcMQtto3QwmV/SMJJFEAQvwg5GzBvKbV9GPEiBEjxrWP+E33LmO3KJeD0hy7+80ZKrNBRI223ICcobRpiev7b7U/klJZLEdvXeq2j6HIrDZzuh4YSXWM4eRihYbjMZJUMRSZnBGwWLEQRAFDlTBkiYbjMZQzCGoOqaZYUEKVWCs5iIJ4UTV2tc7epIEX0LaxZVP3GAxFomJGx9fb0yp/keKp9Cw3u1IfaC5nV+o0HI+coVCzvYgG6vsoioDjgipHPyYTqtS2dzSl91yrs4U6AAdGkiRUmZGkxkq1gqFI29oX/fZT99h3us9iBd7rA5/97Gf5sz/7M15//XUMw+C+++7jN37jNzhy5Ei7jGVZ/Kt/9a/4kz/5E2zb5qGHHuJ3fud3mJiYuGx2XW7l+e0q7W/Xnl5U4PUZEbZSLx/UtnzFxvF83nlgiO+dL5PUJDw/JAxCXD/kgZtGMGQZP4ge7H3o5gmeni0wndVJagohIeWG26Eq3mvsLRp7iypdthwWShavL1ZYrFhM55pq6brEWys1zq3WuXk62zEPvdoZTWnsH07w7oPD3DKVIZtQmc1XeWOpys2TKZ6dXSOpSgAMJRXOFU3ef9MoaV1FEkLKlsc7ZnKULY+MJnF8vsJYSqNuR3MSBGE7TKA7TMn1fRbLJtPZRE8f1e1/R1IqK0sWhiz1DENoUez70cRbYQqHJ1IICFRNh6dPF3qGDqxfg9iXXpv49V//dT796U/zL/7Fv+C3fuu3gKvjK2PEiBEjvhLsMnaLcjkozbG733zNQhIFFssWYymVkukykdE29N9qv1BzCMOQQsMlY8iYbpSTNAzYMIZbpjIkVJlC3WEkCSUzohmGQYjp+JieH1EUbQ8RqNkehirRcHwSmkwQBkgi5CsOqiRSqNtMpPW2jS2buscQ5XpVKJkOae0idbNV/iLF0+1Z7tBYcqC5PDQW3SCXTBeBENsNEADXjYR+Wqq3DcdnMqu36/daq5GkShjSPlao2yRUGdONVIMH3Rf99lP32He6z2IF3usD3/zmN/nZn/1Z3vWud+F5Hv/m3/wbHnzwQU6ePEkyGang/8t/+S/56le/yv/8n/+TbDbLz/3cz/GjP/qjPP3005fVtsutPL9dpf1B7dmMCjxoZoNBbHsrX+Or31vgjeUarh8gN9XEEULeWK5SsVwWSiZZQ6FieeiySLHusmfIwHJDcobYU1W8F3V5PKO1qdJly+H5s2s4ns+bS1Xqjs9rCyLjaY1XLpQIwpClksXfuW1qQ9jLdE7voFwvVSxmhhMcHk/x16/lOT5f4tWFCmZTgMwPA5YqHposULM9dEVEVyUs1+epN1fxw4ALRZM9QwaSSIeiesP2mcho7TAB6Ax9ajg+uiKRNarcvX8YLwg7fFS3/y3UHJKqjOl1+trxjN6h8N6PJt6i2Bdr0Q10vTnf/UIHWoh96bWH5557jv/+3/87t99+e8fxq+UrY8SI8f2NmF6+y9gtyuWgNMfufifSeoda7URG69l/q/3RtIosCaR1mSPjGfbmEnz4tmk+csf0hjHcNJnhkQcOkdZl1hoXaYb7R5Os1Cz25AweeeAQkzmDbEJBV0SyhsJkVueR9x7iwEiK8YzGaFrlyGSa0ZTWYeNoSu85hr1DBg/fd5CJtN5hT6t8m+KZ0XqWmxlODjSXM8NJHnngEFlDJgyjPLI3jCVIGTK6LKFIUbqdyazeUb/XWj3yvhv4Jz9wQ/vYaErjkQcOsXfI2Na+6Lefuse+030WK/BeH/irv/orHn74YY4dO8Ydd9zBY489xtzcHC+88AIA5XKZ3//93+c//+f/zAc+8AHe+c538oUvfIFnnnmGb3/72z3btG2bSqXS8e/7BVeKCtzqp2b7vGNfDkkUsFwfQxFZqdgEAWR1BdcPWCpbeC0Vc9tlrlhHk4W+quKD0MPDEHQlorEnVBHb83nqrVVcP+TQSIpSw+XRp2aZLzd6qq6v9wvvvXGMb72xynzJ5NRSlYbjY6iRiJpp+0giqLKIqojcMp0hpco89eYqrh9waDRF3fGZK9ZJqfKm16jO0CdoOD6m4+H6AS/OFUlpUkf5bv87mlb56QcOsSfX6WuBbVHstxU60KNe7EuvLmq1Gp/4xCd49NFHGRoaah/fia+MESNGjN1AfDW4DNgtyuWgNMde/Q6iXr6+/V7q5b3G8Il79vO+m0Y3VS9/6NaJHauXbzaGXtTNQSmeg87l+vFtR728X/vdx3aibNtvP+3WPrvcFOEYu49yuQzA8PAwAC+88AKu6/KhD32oXebo0aPs27ePZ599lnvuuWdDG5/97Gf55V/+5Stj8DWGflTgfMXqoDgPin4ZF7r7ySYiVfF37x/i9751mpyhkq/aIEQU9CFDQVOiuO6K5XLPoWFyiUh9/MsvL0ShOg2HtC51hA1108MfvHmSuUIdXZU4OV8la8i4QcjB4QQX1hocGk0wltGRJIGVJQuCkGLDJtVsN1+xSOsyHzw6zmLZ4tBYEj+AxbJJSIjpBqQ0GT8ImM4luFAy+dDRMUbTelt9/NhUhr89lWcqm2IooZLUItXz9x8ZZ2Y40fca1Q4zSigslEyGEgqmG3BsOoPtBTx4bKLjDX/D8Tg2neH//Xdubtva67p0drXGYsViNKXi+gEpXWKhHK35gdFOxsB2wxo2qxfj6uBnf/Zn+fCHP8yHPvQhfvVXf7V9fCe+0rZtbNtuf249oHRdF9d1L+MoNqLV35Xud7vQpHDz82LY8fd6xttlLIOM41rfdy1c6e/JoP3EV4TLhN2iXA5Kc9xpv5u136+tmeHoR02/cv3aHNS2fuUGPd6v3KBz2T2+iYzOTZOZLev1ar/72E73xXbnZLfaj3HtIQgCfuEXfoH777+fW2+9FYClpSVUVSWXy3WUnZiYYGlpqWc7n/70p/nUpz7V/lypVJiZmblsdl9L6EUFDsOQL720gOX1p2/3QjvjQjMzw1TWYP9Iok3TXt9PseYyndW5c1+OkaRGoeZQs1yWqzZhCPmazRhQkAUMRebZ00X8MESXJWZXapxcqBKEIaIg8J4bhknrcs8MDscvlDg+X2GhZOEHAX4QktBkzkgCrhcyu1pHEkRKZvRD4a9fX0EWBBDglj0ZvvTSAqeWK7y2WEVXJJKaxMxQgtmVOvmKRcl0cLwQWRIo1CP9DkWWGE5o7bk8sVCmYQe8vljh8HiKiuUxnFQ4MJrY1A9PZjVkUeDN5RqOH+D4ARld4tWFCpos8viJZRRJ6sx20WPuD4+nOlTFv/LKAq/MrVGoO6R1harlMpJU+VJCbSu8r8d2wxq2Wy7G5cOf/Mmf8OKLL/Lcc89tOLcTX9nvAeXXvvY1EonErti8XTzxxBNXpd9B8R/ePVi5f393cHkNuYJ4u4xls3H8xV/8xRW05NJxpb4njUZjoHLxlSFGjBgxriP87M/+LCdOnOCpp566pHY0TUPTemtEvN3RogI/cXIpom/rEpWGS93x2zfhT5xcYjp3YNMbqHbWiKpNvmKz1ojSZBmayBMnl/jkfQc6+ukOeXn0m6c5U6ghiQIJRcJ0fcqWx77hBHuHjIgplNGZXa1xYqGC5weRNoTjM1eoU6jZbcr0ZEbnfKnOt58uMJ3TadgefhAQhJECd8P2UHSFPTmdquMzW6gxM5zA9mSqto8siZiux8n5ChlV5tRSlZrtIQoCxbrNYtliIqXhhwFe8zdZGIYIiGR1hVxC6ZjLIBR5742jPPXmKrOrNW4YS20aIrV+bfaPJFkq2yCE+AF4QaSEvl6RPZfYu+nct9autUYl08VQZdyKzULZRJdFjKaGxyBrHeP6wPnz5/kX/+Jf8MQTT6Drg7+w2Az9HlA++OCDZDJbP5DfTbiuyxNPPMEP/dAPoSjKFe17O7j1M49vel4TQ/793QG/9LyIHfTPzHA94O0ylkHGceIzD11hq3aGK/09GTQ8L77CXGPYCf14N+sP0uZ2+2ip0LYo7JvRyzcbQz87HM+nbHob2r9c9vWbn83K73RdWrZ1U9qvxX0S4/Lj537u5/jKV77Ct771Lfbu3ds+Pjk5ieM4lEqljjc4y8vLTE5OXgVLrxx2upfXU4GLNZs/fu4805nNFaq7ka9YLDap2H5AM+OCT85QO2jaH7l9qsNHNRyPDxwdRxXhV776GmNpjbSmULM88jWTn7hnH68uVhlLRbR0Q5HwmrHRmhKphJdNh9eaquRjKRU3CEgqEhXLZS8GCALjaY1i3WUkIVO2XPaPJkmoEj8wmabQcHj/jWP8/tNnmM4lEAUB2/M4vVLHCyLhyJGkiuMFqJKIF0SCkjNDSepOlWFDQRAF9uZ0TC/g/TdFtPGW+nc2oTCSUhlLqSxWLH7qvQe5eSq75TourDUQBPjI7ZPIkkTDcfnObJF37hsiayjYvs9CuanIXjbJ6Ap+AGNplbrjo4piB228RfFPNOfthtEEZ4sNDo2lkASxb4aPGNcnXnjhBfL5PHfddVf7mO/7fOtb3+K//bf/xuOPP75tX9nvAaWiKFftxvdq9j0IbH+wm087EAYue63j7TKWzcZx4y99bVttnf31D++GSTvGlfqeDNpHfIW5hrCZmu6VqD9Im0enMry+WBm4j/UqtJYbMDOcQACSmkTd9jfQAfuNoZ8dx+dLvLZYJcqoCjNDCQSBDe1u3z6Zuu0xldPZP5zs206b3lhosFg2e5bf6bq0bGvVe/j+g3zg6Pg1uU9iXF6EYcjP//zP8+d//ud84xvf4ODBgx3n3/nOd6IoCl//+tf52Mc+BsCpU6eYm5vj3nvvvRomXxFc6l5OqDILJYvHX13mjaUKry1UuPvAEH7AlsrTLcryG0uVdmaDtUbAWFqlZDrIgsiXXlpgsWyxWDZ7+ryj0xkmM5GoV1KNboynsgZ37hvi/JrVpqWbbvQmena11n7TPZrWeP7sGi/PrbFWdxlNq4iCgCyKBIQQhuSb2SmKjQBVFqlbHhlDxgkCDo+luHNfDukZgVPLVXRZwnQ9FElEFqMUiYW6Q0ZXcPwAWRIhhPOlOn4QUGg4jKU0KrbHaEpr08Ybjofrh3z9ZL6t63HnTI79I8lN5/Jrry5x/EKZVxfLmI6PJku898ZRdFXEUCVemFsjCENWqjZJVeLNppibLAl4fsByxUYUBM4XGoymLtLGp3M6rhfw0vkSq1Uby/PRZJFSw+nInhGrjL898MEPfpDjx493HPvJn/xJjh49yi/+4i8yMzPzfekrY8SIcfURq5dfI7hUNd3Locbb3Wa+qSqbr9oD9dGtQluxPE7Ml1kuW5xaqlKoOZF4US2icq7WrJ5j6D6er9o89swZzhVrnFqqUrE8ypZLxXQ5sVBipWp1tDuofVXLjeyrrLOvYpOv2j3baVNLaxb5qsVaw91Qfqfr0rJtreEyno7qffGZM5wv1q+5fRLj8uNnf/Zn+cM//EP++I//mHQ6zdLSEktLS5imCUA2m+Wnfuqn+NSnPsXf/u3f8sILL/CTP/mT3HvvvT2Fgd4O2I293Gqj7vjctW8YQYCX5kqkdGlT5enuerIkYDoBWUNmLKUzlFAJw5CS5ZCvWhRqTk+fl1DlnpkVZpoP7lpK2ONpjVunM6iShONFaccgpO74GIqE6wcU6w6CAAdGDEZTGglNRpZEEqqMKosMJ1VG0ipjKZ2JtM4P3TLJSErjwGiyfeOqyCK37MmwbyTJkck0aT2qu284ybsPDOOHoCoS01mjSUf3GUqoG2njYYggAAjNv/2FeVpzOV9ucGq5EqmiK9E4n35rFU0Soxt2AYo1B9cPMN2g+WZaJAhD8hUbQYz68YMQQ5HatPGG44EgIIkCw0kVpUkrHzLUTTN8xLg+kU6nufXWWzv+JZNJRkZGuPXWW78vfWWMGDGuDcRXmWsE/dR0B6W8XWr9QdrMGSoVq0TOGIyC2VKhHUmpkfq3obBStUhqEvmqz94hFc8P21TMVvnuMXQfzxkKFdNlT87A8UJyhkKx7pDRZYqNKFf3+na3Y99y1SGhSqzUHPYOGXgBfemHrfnJGSqzQb1JLQ06ygM7WpeWbVPZKHftVFYnX7WYXalfc/skxuXH7/7u7wLw/ve/v+P4F77wBR5++GEAfvM3fxNRFPnYxz6Gbds89NBD/M7v/M4VtvTKYZC9vBX1vLuNjK6wUG7w0Tv2cGC0/5vZfvU+/u79DKfUNsU6rUjMBnVGUiqzKy5TGYW67ZNU5LaP6JX5YLVmYbk+H7l9ClWWKNZsVms2h0aTWF6AKMCLcyUMRUJXZI7tyVK3Xe4+MIwfhNxzaITFsskPHh1DFkVEAUqmx8ffPcNwUmuHwuQrNmNpjY/cPsVa3SGtyzg+PHhsgn/w7n3t0J3JrEbN8vn9p2YZTWtoskTNdlksmfz0e2/g5ulMx9wossgHjkwgCBCGULacvj6mNZeGIrcp7UEIM8MJSqbDO/cP8+LcGpMZnefPrSELAueKDUZSKgA3jKdoOD7vmMlyttAgqbX8v9K+riiSwAduHkdAwPZ8Vqs2H3/3PoZTF+ditWZtK6woxvWL7zdfGSNGjGsD8RXlGkEvNd2t6I27WX+QNktmRDUsmS7p5vHN+pjMamR0hULNIQyhZLrIkkjd9iPqYs1hIqtRMiOaX6t89xi6j5fM6MY6CANUWaBkuogi1GwPRYKK6TKVk9vtbsc+RRJoOD6qJEb2ZbS+9MPW/ORrFpIosFiOYiu7y+9kXVq2LZYtprI6i+Wo3qGxJK8uVK6pfRLj8iMMt05Fous6v/3bv81v//ZvXwGLrj622suDUM+72yg2HKZzBuOZzQXm+tXbP5po602s9w35io0AvLkS0bif9QPu2jfUtnV9loPusJKHjk2yWrN55XyZQt1mLKUhihGN3HSj/NgrVbsZ0+whCwLfni1wYc1irmhy9/5hnCBkOquzfyTJQslqz4uhSJzOVzm5WG2/QZ7KaIysU/Ru6dknVI+prMFawyWVUbDdkMPjafaPJnrOTbHhDORjWuXny402pT1nKJQaLqNpjZun0ryVr5GvWSRUiXzFbl8/0k1Vc9PxeX2phiaL0Vys88MtX1qsRQ9JClZrrS7ORSs8qF/YU4zrG9/4xjc6Pn+/+coYMWJcG4jp5dcIWmq6LTphS+V20Cftl1p/kDYn0joP33+QiYw2UB+jqaj8aFpFliBryNy6J8tkTufIZJrRLqrjaErvOYbu4xMZjYfvO8iBkRRHJtNkDZmsrpBNKNw6nWM8o3e0O6h9GUOJ7Muus28T+mFrfibSOuNpneGksqH8TtelZdtWtNNrYZ/EiHE1sNleHpR6vtPvw1b1un3DUELBUEUUWWQ4pSKJLRWKTnSHlRRqDp9/cpbFkomhRGJmxboDwIGRBONpjfGMFvmelM6QoRIiYHsh79iXA+DFuSIpLaLLAxtCdU4uVnG8AMcL8IIoB/dKbWNIzaBztd05bZXfm0twZCJDWpeRJYHRtNrh81pzOZpWOTKZZiip0HB8FEnkvsMjqLKI5TYp/uv8cL/rSmsuWuFB/UIA4tCbGDFixIixG4h/aV9DWK+muxNq26XWH7TNew4ND9zHeurkIOrg/cawmR2Xol6+Xfs2m59+5Xe6Lr1op5fS3qXaEyPGtYZ+e3k7YRTb+T6sp6tvVq/hRMd+7O69+AEsrDX4w+/OMZpSkQURVZIwPY98xcZQL/qO88VGR1hJLqGwVLUIQtAUiRvHU5RMlztmsmiyxEfvnMZQ5Xb9Yt3mj797npwuEyLwzn05VusODx6b4PB4iuWK1TEvrXjwAyMJVmsOkihiuVFMdaS+HtnXGt/6Mbf87mrN2pAGrNfcbJbloVe7/Xxee6w1hz/+7rk23X08rbFQtviJ9+xjOpfY8vpxdrXGYsUio0n4QdgOAegOe4pDb2LEiBEjxm4gvpJcY2i9Hb1a9Qdpc7t9rKdO7qS/QeyY2VB6cGzXvq3s2mmZXuhn27W4T2LEuBrotZe3G0YxyPehH129u16vTAvHL5R4c6nKt+sOYykdRRbYP2zw5ZfnWSibLJYsprIGY2kVuRmqktFl3lyu4nkBry9XsN2A+TUTQRAo1hzuPTzCeEZv9/9WvsbjJ5b5zukCSxULSRLw/ZDJrM7jJ5ZRJInpnN4xL6brk1BlaraPJArNECIZ0/UxEPnyy/OYrr9hvN+eLfbMrNBvTgfJ8rCVP+9eo0jkTeLJN1bx/Ojt/EhS41tvrPLh26eZyOh9669Xnnf9EEUSqFm9w57i0JsYMWLEiLEbiOnlMWLEiBHjbYXdDqMYlK6+IeNDM9PCSs3GUKUmPdzG8QLOFhoRtbliR5kPahY1x2P/aJKUJjG7WkOVJe4/PIooCMyXTEIgbcj4Qchcod7uv9XvSs1upuwKMO3ob8P2Wa1HdHGgY172Dhk88sAhxjMasiSS1mWOTKYZT2uECNRsf8N4+2VWWK1Zm87dZlkedowwJAgjyr0XhCRUkTXT2bTdbuV5SRQwHZ+hpNIz7Cl+OBkjRowYMXYD1/TV5DOf+Qy//Mu/3HHsyJEjvP7661fJov7YSiV3t+rtdj+DtLdV3XLDYbFscWgsycxwf8Xf1aYS+VY08O7+trKxX/lummK3vVNZnWxC3UAn77arX/tblbuUdbnWcL3YGSNGCzuljQ+ict6Prr4x40OUacGQJTRZ4th0hrrtc3gsycsXyiiCgB/QzHzgk1Qk/AB++r0H+ctXl5jK6iRVhaGEwvligxvH02QTajNFmM1S2WY0pa9TAJcAgcmMRqHuMpxU8YIQWRRYKFvkKxaHx1PkEns7fPG7Dgx1+ETT8fiT584zlro43rlinefOFJFEgYrlRoJugsBYSqPYuGhL91zmKzaLZZOMoeAHYc8sDzu5lpmOjyKL3HNwhBfm1kiqMl4QbKCFd9uzlfJ8L3p7jBgxYsSIcam45n89Hzt2jL/+679uf5bla8/kQVRyd6PebvczSHtb1X12tsDxCyVEQSBjKDzywCE+cc/+DTa0FHnzVRvL9ZkZSiAIbKAZ9qJmvr5Y6Wtjv/LH50u8tlhFV6JYv4eOTVI2XZ6dXeX4hTIhIBClm0mqMklVpu54JDWZuu217eruv/W5mya5lZ27uZ5XGteLnTFidONSaOPrMShdfWPGhyjTgulF9O2VqkNCFTk+X6HScHjpfBldEVlrBCRVkWdniyiSQEaTCUP49mwRRRRpuB6aIlG1PDKGwkrVbmd2WN/vfMlElQXKpossRTfHSVXi5fMlNFnkSy8tcPtMLvJhxTqLJavt+9YrdnfT0L9ztsAbS1VemlsjocnULJfFsoUuS1he5M8ns1pfav0byzVcP8ovHrEANmZ5GATr2zcUCdcL8EOI0jxGCu7raeG91rZ7bOuV59cru8e+LkaMGDFi7CaueXq5LMtMTk62/42Ojm5a3rZtKpVKx7/LiUFph5dab7f7Wa1ZW7a3Vd25Yp0T82VsL0AUBCqWx+efmuV8sd5hQ4uOWKjbeH5AxXI5sVBipWJ30Ay7bco36+Wrdk8bN1A5m+XPFeqcWqpSsz28IGClavPok7OcWixzYr6M6UZKvaYb8Op8mYWSyal8hZWqfVG9tmIzX2509N+iii6UzQ6aZHe5QdZmp+t5pXG92Bkjxk6w2yrnGzI+NDMt7MkZjKd1soaM6QRoisj9N46hyiKmE5DSJBpOdGN+175h6o7PuUIdPwyBEEUSuXU6y1BXNoPWm9i2AviQwZHJNBlDIWsopDUZQ5VRJJG79g1Tshwee/oM8yWTfMWOFLvzFQp1u0OxGy7S0OeKdd5YqqJKIpNZg6rlUrE8EEI8P0CWBA6MRGnDelHr10yHd+zLIYlCT3Xx7bCC1rdfs30EQWAooXQouLdo4d32tNZ2/dh6qZnHvi5GjBgxYlwOXHuvjbvw5ptvMj09ja7r3HvvvXz2s59l3759fct/9rOf3UBJv5zYjkrupdTb7X6WyvaW7W1VV0TA9UNSmowgRG9narbL7Eq9g2beKj+S1FgsW+QMlXzVJpOQ8QLaNMNum3KGSsUqkTOUnjZupHJG5ffkDBwvZCSpEoRRqrJC3cb0QlwfkpqE6QYYskDd8dEVkVLDYzwlc6EUxTl6ARiKTMVy2/2vp4qup0l2lxtkbXa6nlca14udMb7/sBshD5dD5XyzTAstxe3pbIJsQmH/cJKFcoOHjk3y+KtL7eMXig1Mx+f+w6MMpzQcz2el5vCjd06jyFKbFt5wLtrZSwHc9X3+7MX5drv5qkXFKiELAg3HJ5dQKBdc9ua0DYrdrfaeO1Pkpbk1JrMGCVVmJKmxUrX54JEJhpJq88GB3/bfwykFNwhIaBIV0yVnqNEDh4TCQslqj2Er+vZWtPDhVNTex45NMJzUNoT7dCu1t9Y2X7E7lOW3Kh/7uhgxYsSIsRu4pq8k73nPe3jsscc4cuQIi4uL/PIv/zIPPPAAJ06cIJ1O96zz6U9/mk996lPtz5VKhZmZS9G23hzbVcndab3d7mcyq23Z3lZ1Sw0nUn21PZKqTMl0yRoyh8Y647pb5Qt1mzAMKZkOigSVhsdUTm7TDLttipR0FUqmS7p5fL2NG6mcUfkgDCMV2roT/ag0ox9NhiygSFC3fUQBTDdEEsByA1RFoGx6qJIYqddmNEzX6+i/myq6WLYYS6kbyg2yNjtdzyuN68XOGN9f2K2Qh8uhct6rXOtzWpeZyhoUGw6qLLapzUen0nzvQpliw6FiuXznTIGK6fH8uTVumkjzVr6GJMI331C5fSbHt95Y6Tn2bgXwhuMxlS22+yuZDrIo8vKFEmt1B8vz0SSRQt1moulD1yt2J1SZY3sy5BJqO+a7ULdJqNGD1uGE1nFdcP2Qr5/Mo0oCphvRyUumQ1pTKNZcNEnkm2+sYnn+puu2FS1cEuH5s2tIIjx+YpmP3DE9UEhAGAYbFNlbKuexr4sRI0aMGJcT1zS9/Id/+If5sR/7MW6//XYeeugh/uIv/oJSqcSf/umf9q2jaRqZTKbj3+XETlVyt1tvt/sZTelbtrdV3f0jSW7dk0VXRIIwJGvI/PR7D20QUxtN6Tx8/0FGU5FCbtZQuHU6x3hW66AZdts0kY7qTWS0njZuoHI2yx8YTXJkMk1al5FFkfGMxiMPHOLodJZb92RJqCKqLJJQRY7tybJnyODIeIbxjHZRvTajsTeX6Oi/myo6nFR6lhtkbXZbXfly4XqxM8b3D3Yz5OFK7++tfGpKk3hxLhIru+/GEQQBnnpzlTCkgx4+aChLd39DCZX9IwlUWWQ4qaLIIoYqM5Tor9jd8t/DTWr7aCryp3uHjI1zFoYIAoDQpp0PJVRWahYpXSIMQ+rORkX09ei3vhDRwlO6xEtzJQSBNhV/kJCAlCb1VWTfbG1iXxcjRowYMXYD19XVJJfLcdNNN/HWW29dbVM6sB2V3Eupt9v9DNLeVnV/9K69A6mXf+DoOLfvzWypXr4ZNbOXjZuV76Vevt7eQdXLu/tvfd6q3E7X5VrD9WJnjO8P7HbIw5XY3+szHvSiNq/WLCzX5303jbJad5jO6WR1layh8I1Ted65P8fMcIL5UoNC3ea2PdmIYp1QWSg1yFdsDoz2p7u31MolQeDLr8xzeCKFgEDNdlksmfzEew4wPWT0Hf8g/vvsah3HD3jvDWNoikgYQtly+OgdezBUCdPx+ZPn5joU0QdRgF9f7vB4io/esYfVqt2mzJuOP1BIQC9F9u56sa+LESNGjBiXC9fVFaVWq3H69Gn+4T/8h1fblA0YlHZ4qfV2u59B2tuq7kRG56bJrRkFoyl9oBQs/aiZOyk/06PcoPZu156drM1O1/NK43qxM8bbH5eDBnw593eLKt1SC1+vEv7/b+/Og9yqz3zhf4+2c7Sr1d3qxXa729h4CWCMHYxtksp4HNspZgKBeifDy1uxfRlmJrGhEidTGacqZJuKM5e5QFWKZSo3LFV5M2SoCkkGMg6O2YJtErzlAoYG493davWmfTvS+d0/1JJbW2+WWmr5+6nqcuvoLL/nHOmRT+s5z2lzKLk7O2Q6jRvQ7lQwElYRjKZw5OwoogkNR86NIhhP4cOBEALRFA6fHsa1bUl8PBiGTpLw6xOXcNsNxSXW47c/vuP3SFiFQSfhyLkR6CQJb3w0iNtu6MyVWpfSYlPgj6bwX3/uzyv7bnMoOOUL46X/04cPB8L4QISwZqEbKU3Abc00OcveumsmHeAL5/M45LwS/aleEjDV7TPXERFRNdR1efk3vvENvP766zh79iwOHTqEL3zhC9Dr9bj77rtrPTQiIqqBuVQGnC2V9oUS8AUTmTsejOsSfmEkgmcOnsFoVIXHriAYT+HccBQ6nciVmd+6JHPHjoOnBgEg73FaE1jV5UI4UbrEulzHb9kg4dj5EQCYcPmJ1lV4N4twIo1VXS4AwLHzI7DJ+gkvB5pqB/jJLiuq1iVdRERElVTXnzYXL17E3XffjeHhYbS2tuLWW2/FW2+9hdbW1loPrWZm2rG3Ep1+K7GOK9luufLvwnFVex8NjXVwL9d9dzrbr9U+JbpStXzt1nsZcDZH6HXI3dngtIaxOx6kc13C3+8PYjiSadxoMRnQajdhIJjAEo8Nw+EkutwWtNgUOBQDXv9wEGsWNqG7xYZmqxGvfTiEW65pxnyXpWyJdblS7VsWueENxnPrzy5/bjiCtIaSuS27rmx3cpuiR58/hrND0bxtZLuU335jJ7pb8r95n0kH+Gy+HwrHc3l/ti7pIiIiqpS6/sR57rnnaj2EujLTjr2V6PRbqW7B01VYmmk1GRBJpnIlmss6HPigP5gbV+HjSu+j8aWgDsWI7Rt6sHGZZ9rrme68RPWkHl679VoGXKpc3K4Yodchc8cDuynTRVzS4cjZUQSjSQyFEuhwKej3xyEB+MWfLiKeSuPsUBSLPTZ8OBBCNJnC22dHodNJiKfSaLaaEE2kM7frmkapthAChz8ewYWRKM4ORbGmuwlpDRiJJvAvL76PyFgZdmFusyuGXHfyVFrDYDiBZqsMp9kINaXltjESVtHpVOApU6o+1eNmMRnQ54+XLc1f7LHNyiVdRERElVDX5eV02Uw79lai028luwVPR2Fp5nA4iV5fEMORBAbDcfQFYnmdfH3h+LQ6+043vqGx9WdLQUejKp49dAZD4fi01jPdeYnqCV+75RXmiGA8hfMjEdhkPTwOOXPHA5uCJosJQggISNiwpBV6nYRTvgh0OqDVLiOe0mAx6pHSNLx5agiSBGxY3AqDXsLx8340WUxTumNCUQfvsQ7iybTATV1uSBJw/Lwfegk4NxRBMJ4qmdtyhIAmBEYiSaQ0AYtJh3AyBUmSYJP1FS3bnqw0n683IiKaS/jn3jliph17K9Hpt9Ldgqcqu91saWazzYTTgyrmu2Sk0gJmgz73vNmkh8tsQjDuzz2u9D7yBhIIxlV0OBVYTAZ0OBX4Qpky0habMq39VKt9SnSl+Nq9rLDEvlyO+MxSDxa4LblS6Wwnb6diQrPVBPMndDj48RBWLXDh7EgMVpMeqTQwz2XASDiB5R12rOhwYaHbir5AFLevnIfuFmtRR/FocuIO3iPhJH7+p3PotClwWoxwKEb0BaK4qcuFt84Mo9UmQydJaLXJGIkmcrkNyBx3o0GH1V1uHDk3kvtW3WU2Ia6mcfuNnTCbDEVl21O9/KdwemH+LyzNvxpfb0S10v3PL9V6CERzHj+x5oiZduytRKffanQLns52faHMtZG+YBImo4ThSAJtDgWxVBoOxQh/TIVdMcIfS+Y9rvQ+anfKcCjG3O3G+gOZ+dqd8rTWM915ieoJX7sZpUrsy+WI7hZL3jXS0WQKakrDK70DMOp0iKfSMOp16B0IYyScRCKlwWzU4dj5zO+/Ot6PkbCKeU0WdLrM8Djky5feDEfRH4ihw6VgodtastQ/V6p90pvpMO693GG802XG8k4H9JKE9/oDUAx6xFNpLGiy5HIbMFZentJw/IIfw+EE+gIx9DRb4I8l0WbPlJMXngQX7qNyl/+U2pedLiUv/48vzW+zK1fd642IiOY2lpfPEbXs2Fqrrq/Z7bY5ZHgcMlrsJiz1ONBik9FqUzDPZc4rsWyzK1MquZxpfC22zPrdViN8ocx829b35P4zPZ39xE66NFfxtVu+xN5iMkyYI/JI0tgvIjdJJ0lw22QISUNfII60JtBqk5EWAgdPDUEnCXx2RTsAZEqvw3H4QvFM6XUwAV8oMWEX83IdxpttMrpbrDDoJaTSGgx6Cd3NluJjKknQ6yS4rSYY9BKiSQ1NFlPJ41+4j8pd/pPtfl64LwHk5f9saX6bXbnqXm9ERDT38VNrDqllx9ZadX0t1cW2sHv5LYvceeMqfDzd7Uy03MZlnlxJZ6kOv9PZT+ykS3PV1frazZZAx5LlS+xL5YhSpdNGvYSNyz2QICEUS+Lgx8O4pacZDrMRJy6M4pVeH1qsJjTbFChGPUajSVzX6cBijw0DwfhY6bUJp7XIWOl15jrwPn8MvmAC3S3lu5gXdhg/OxSBxaTHF1bNgxCAyaBDLJnOK+HOjvlTS1qQTAlIkoA/quZK3Qv30Ugkgf5gHJ2uzDatJgOGIwlcP88Js0kPt8WEPn+0qPv5+H1ZKv/X4s4d1VwfERFdHfiJMcfMtPNqJTq21qrr62TbLXy+2vuoxaaU/uZqBttnJ12aq6621+74EmjFoIeaFmVL7MfniIlKp0fCmZPNiJq5VCaqpjESTeLY+VGk0wL9wTgS6TRCcQ0mvYR3+4JY3hm+XHodjkOvk9AfiMNi0uOtM8Mw6nX49YlLuO2GzlyZeeElAeM7jJ/yhfHS/+nDhwNhaEJgzUI3wvF00SUD2e7lb3w4BJNeQjItcOMCFzwOuWgfnRuO4uJo5mT6/T49rvFY8dFAGIFoCodPD+PatiQ+HgxDJ0l4rdeX1/28cF9eyeus0l3266FrPxERzU0sLyciIppAYal0JJmGBAGbMnHH7nJl6ADySvSzl8bYZD0OfjQISZLQ1WyGJAFDYRV6HbBhcQs0IeWXXtsVeOwKnIoBsWQKep2EVV0uhBPpvDLzcpcEAJiw7LzoZFeIsap4aezfy6XxuW7jYyXvwXgq04E9LXDoo2FAAm5d0gIAOHhqEGlNYFWXC4mUqGr380p12WfXfiIiuhJXz9cUVDUzLbebalfbao9jLrqaYiWaSKXzSCmF5dluW6Y8+67lbXDb5LLb8gUT6A/E0Om0TFg6nV3epANePjmALrsMm2xEk1nGx4MhbFrWhiUdDggIBKJq0fIj4QR+/vYFdLoUOBUTYsl05vZawQTMpsz6F3tscFnm53U7942VqbttRkjIlI4PhpP49JIWxNU0hsLxou7lt17TimQ6DZNej1gqhXNDEaQFoJdQsuR9eYcFb58dwbqeZsxrsqDZasRrHw5hdXcTXGYThAAC8WSu+3m2jLxUJ/YrOWZX2mU/uz63zQhV0+C2GXPHgjmYiIgmw08KuiIzLbebalfbao9jLrqaYiWaSKXzSDnjy7P1OuDI2VHodcDv3hvAX63sRJuj+HKT8WXb47uFlyudPuUL460zIwhGVfT5Y3AqRqhpDbLRgI+HIugPxnMl3YXL2xUDOsbKxk06PbzBOITQ8OsTlxAbK13P7pvx3c47HGYMhhI4cnY0VzLutprwby9/iEgyBYdixPYNPdi4zJPrXn703CCMOh1UTUOTxYh/eel9RJIpWE0GtDsV2M2GXMl7q80EVRNotsuIqCnEkmlE1DTMRh2Onh3NlOlrGlbOd8LjUDId1iuU2yrdZT9bXn/gpC+vvJ5d1ImIaCpYXk4zNtNyu6l2tZ1q2d7VVPZ3NcVKNJFK55GJZMuzbYoex8/7IUnATV1uRJLpGXULL1eGHk6k0Tp2jXQooUKvl+BQ9NDrdChV0l04vmz5uE3WQ0DKrG/cvrkUiOZ1Ox+MxHFuOAIhAEBCStPwbl8A/qgKjz2zD589dAZD4XhmQ+M6rifTGt7rC+bmDcZTODcchU02wGNXMt3GHTLmN5mxfX0P2uyZb5qbzCYsbLZCr5PGxSJVPLdVpcv+BOX1REREE+GfaGnGZlq+V7icy2xCMO6Hy2ycURlgpcsI69nVFCvReIVl44Wl21eaR0ptY7zFHhs2L2/H+eEIuppsaHXIuTLuwm2ML0UeX7a9eUUb7IohVzpd2A3dZTaiySKjaaEJoXgKi9wWvNMfxLJOG1yKKVfSXSqm8eXjeh3w6xN9aLWNdQ436jEcSWJBU6aTeqtdRiotYJQkxFQN6xc3o9kioz8Yw3+/049mmwkWkwEdTgW+UBzeQAKtdsCol3DrkhYkUxr8kST2+wdy87baZQwEY7hxngvtnzDnStgL7yoRS6bw3NsXsLDZgmRKwGSQEE2m4Q0kKp7bKtllP1tev3FpGyQJubJ45t76snfvXvzyl7/EBx98ALPZjPXr1+Nf//VfsXTp0tw88XgcX//61/Hcc88hkUhgy5YtePzxx9HW1lbDkRNRo+MnBc3YTMv3Cpfzx5JwKEb4YyrsY9OnUwZY6TLCenY1xUqUVaqM/J2L/rzS7VgqdUV5ZLLLNk75wnj5pBcXR+M4PxIrWS6eVaoUubvZgpdPDhSVe4/vhh5XM03ThsIqdBA4dHoYgZiKPn8c85sU2BUjVs53loypXHd1vQ7405kRDIfjOPBBApIEaBrQ5pBx/EISwVgSR86MYu0iN1KaBovJgOFwEopRj/5AZh+2O2VYTNny8lEYdTpEkikY9ToMh5NIawKnfGFIEvC/D57BwmYLFrqtY/fZzpTeZ0vho8lUyS7o7U65KrmtUl32s7l3JJpk7q1jr7/+Onbu3IlPfvKTSKVS+Na3voXNmzfj5MmTsFozt7b72te+hpdeegnPP/88nE4ndu3ahTvvvBMHDx6s8eiJqJHx04JmLFu+t/+kd1rle4XLtdkVfHqJB73e4IzKAGc6jrnoaoqVCMgvI293KLjgj+Ctg8NY1GrDqi4Xjp4bxbHzI1h/TQu2b5hZHinchjcYx/6TXnS6unMniuPLxcdvs+w2xpUia0LD2eEIbGYDFriseTEsaLLAG4xDgkCT1YiYKiOupnFmMAINAk6LEf5YCv2BOBSjHoBUtKlS45cgIBt0ePvsKADAYzdjIBSHJACdLtPkrbPJjA2LW/HxYBjHz/uxfnEz7vvUIrx80gtfKLMPt63vyd1rfHx5ucmgw4p5DoSjKZweCsOgk+C2mxBJpDEYTMBsNOTtw3L7RpIEAFH3ua3ex0cZ+/bty3v8zDPPwOPx4OjRo/j0pz+NQCCAn/70p/j5z3+OjRs3AgCefvppLF++HG+99RZuueWWonUmEgkkEonc42AwCABQVRWqqlYxmmLZ7c32dmV9ZS+lkHUi79+5rFFiqUYcs/06LdzubG1/qtvhpwVdkZmW75VaLlt+OJMywEqWEda7qylWookuR/E4FDgtmU7it9/Yie4W24zyyGSXbRQ+X7jNQr5gAsm0hluvaYVs1CEYU3Ho9BBcZtOEpfCbl7cjLQQGAjE8vP9DmE0GDATj8NhMiKkaPtHhgEEvlS1nLxz/Z5Z6EIipsMtGvNsXRItNRiSZwmKPFSfO+3FLTzPmuzPfSvcForh95Tx0t1ix5bo2eAMJtDvl/O7legkbl3sgQcp1Ur/1mmb86s99cFtM+MAbgkEvIZJIwyLrEYgVd/eeqEy73nNbvY+PigUCAQCA2+0GABw9ehSqqmLTpk25eZYtW4auri4cPny45En33r178b3vfa9o+ssvvwyLxVKlkU9s//79s7q9/3lzddb7gzVadVZcA40SSyXj+O1vf1uxdc3EbL1PotHolObjJwZdsZmW7xUud6VlgJUqI5wLrqZY6eo22eUoI2EVnU4FnoIy5ivZRmHpcOHzhdscL69ruRgrfVfTcJiN8MeSsMvGkpfUCKHh5ZNexNQ09JKERErD2aEAVE1DKg2YjTq81xfEmm53yXL2UuPvbrGgw2mGL5S5znswnECr3QRJktBskxFV04gl0xiJJtHpMsMz1sStxabkTrYLtzESVvO2sazTgUWXgvCF40ik0uj1RiEbdDh8SsPqhU1lx1quTLvec1u9j48u0zQNX/3qV7FhwwZcd911AACv1wuTyQSXy5U3b1tbG7xeb8n17NmzB7t37849DgaDWLBgATZv3gyHw1G18Zeiqir279+Pz372szAajbO23eu++7uKrk/WCfxgjYZvH9EhoRVX78wljRJLPcTx7ne3VGQ9s/0+yVa/TIafHEREVLcqfTnKVLZRuM6plhZPVIa+fX1PbsyFMdhkPYJxgXAijXaHgtNDYQTjKoQkQQAQEiAkaayyvHz38sLxtdiU3PSYmoImBFptCua5zNi4rG1a+3Cybbz0Th9iyTQMBgluqwyDfnx38qnva6JK2blzJ9599128+eabV7QeWZYhy3LRdKPROKsnvrXcdiJdnZOwhCZVbd2zrVFiqWUclX5Nz9b7ZKrb4KfcVaZcd96JuvbWq2qNeS7uC6K5airvt0pfjlJuG9nu3ya9hLiaxlA4nvvGd6LS4stdyNMTlqEXjvmG+Q54AwmoqTR+efxSrhO72agHACxrs2EwnIBep0NC1bByvgsGvS5Xsj1+35Ub3/i4ynUUzz4uPBaFjyfaxu0r52EolECLVYFs1E3Y3Xv8evQ6IK0h19F9upivqZRdu3bhxRdfxBtvvIH58+fnpre3tyOZTMLv9+d92z0wMID29vYajJSIrhb8hLqKlOvOO1nX3npUrTHPxX1BNFdN5/1W6ctRyo3lnUt+vN8fgmLUw2OXsX1DDzYu85Td5vgYzEY91JRWtgx9/PLZ5c6NRHBxJIZQXL3ciV1Nw2IyIKZqkA16+GMqHIoBKU3Aac6UYpfbdxONLztfYUfxUvON765euP5S+93jkNHhNGM0qsKmTN7d22IyoM8fv6J8y3xNhYQQuP/++/HCCy/gtddeQ09PT97zq1evhtFoxIEDB3DXXXcBAHp7e3H+/HmsW7euFkMmoquErtYDoNkxvrttq03BaFTF/pNeDIXjJadHk6laD7mscrFc6ZirtV4iKlZP77fsWC75Y+j1hhBOpJDSNAxHEnj20BkMheMTLpeNIZxIQ5Ik2GT9lMrQfaEEfMEEgvEULCY90prAsfMj8Nhl3PepRfA4ZBj0OtgVA5a22zG/yYzPrsh8GzeVfTfVfVw4ny+UwDOHzsAXjk/52GTLxt1W45TKxq/0+NfT64fqx86dO/Gzn/0MP//5z2G32+H1euH1ehGLxQAATqcT9957L3bv3o1XX30VR48exY4dO7Bu3bqSTdSIiCqF33RfJcp1t/UGEhN27a1Hk3Uarrf1ElGxenq/ZcdiNuqRTAm4LEakNQGX2YhATIU3kChqLFYqBrctU05+1yfa4LbKRWXa2VLqbBm6y2zEaQ3ocCqIq2msXuhAMJHG5hVtcNtkfPraFgRiqdz9rLPdxAeC8ZL7zheMw2wy5LZbOD6bosepwTDODUWwvNNZNg6X2YhgTM11W5/qsSlVfl6u/Lvc8S+MYbrLM19f3Z544gkAwGc+85m86U8//TS2b98OAHjkkUeg0+lw1113IZFIYMuWLXj88cdneaREdLXhJ9NVolx323anPGHX3no0WafhelsvERWrp/dbdiyX/DFIELg0GodskBBJpNDltqLdWdxEafxy3mAceh1w5Owo9Drgd+8O4K9WdqLNoVwuIx+Ooj8QQ4dLQafTDDWlIa5q0OuA/kAcrXYTImoasl7CyycHMh3Py5R5d7qUon0nhMCvjvchnkqXnC8YT+LNj4agCQGvP457P7UoVzZf3CFezeu2Pp1jM1HJ+vjy71LHvzCGciXu5ZZnviYhJr/HsKIoeOyxx/DYY4/NwoiIiDJYXn6VKFf6l+08O9WSwHow3TLGWq+XiIrV0/stOxaPQ4bZZIBBL8Fk0MNo0KG72Vx2TNnlbIoex8/7IUnATV1uRJLpvMt3fOE4fKE4RqMqBoMJjEZVSJKEJosRHocMt9WIVpuCJrMJAhLCiXSmzDscxzMHz8AXSuSVUAPI23c2RQ8hBCLJdMn5ZKOEQx8NI6UJLGq2IRhP5ZXNFx6LNoeM7et70GZXZnxsJiv/LtxmYQyTlbjX0+uHiIhoMvx0uopM1Hm2XFfeelWtMc/FfUE0V9X6/VbY/TvbgdupmCAgYNLrEUulJixZHr9cp9MCp8WIWDKNwXAcZ4ci6A/G4ZAz12tnysg1uMxGxFNp3H5jJ8wmw7iy8xSee/sCWm3ZMm8TgnE/XGZjUQn1+K7keknCr/98CU6LEaqmwW0zIhBVc/N95loPXn3fhzaHGQ6zEdZkCr5QPK9sfvz6smXs2e7q2ceT7cPpln+PP/6xZBrPvX1+XOyTl7jX+vVDREQ0VfyEusqU6zxb6U7As6FaY56L+4JorqrV+61U6XOnS8l14J5OyXK2c/dINAmTQZcrlX71g0F86A1CTQsY9dLYt74m+GMq2hwyPA6l6NZj+WXeSTgUI/wxFXYlv8y7sGv6YCiBI2dHc9d/37jAlRt3d4sFzTYZg+EEDHoJ/YHLlxeV2x8TlXZPtA+nW/6dPf7FsU+txJ35moiI5gJ+UhER0VVlfOlz9oRw/0kvtq3vxuZPtGP/Se+0Spazpc7Z5WyKHsGoimRa4KYuN46cG0EsmYbTbESrQ0abQy653sL1tNkVfHqJB73eYN54AOSN/4I/gnPDUWhCANBBkgSAy9e2ttgUbN/Qg2cPnYEvlFnPtvU9uW+vC/fHhdEo3jpzBotarVjgsub2T6erOzfmcvswO09hLJPty6LYHTI+fW1rUew8wSYq1v3PL9V6CEQ0CX56Uc2UK0skIprM0NjdFwpLn6eSVyYqfZ5JyXI0mZn3/1kzP9edfHyptGLQ4/xoGP/f2m50NpknXG+p7d+yyJ33+OxQGP3BODpdl8vQU5qG9Yta4DAbIQQQiCfzSrE3LvOULRefSffy6ZaPT2VfTiV2IiKiuYifYFQTE5UlEhFN5JUPfHjm4Jlc/ti+oQcbl3mmnFcmK32eTslyuTL17PoNOglHzo1AJ0l446NB3HZDpqv5RAq3X9gR/MU/9+FDbxDv9wWxprsp1+07qqbRbJPLlmK32JSS12bPpHv5dMvHp2qi2ImIiOYqdi+nWTdZV1sionKGxjp6j0ZVeOyZ/PHsoTO4MBKZcl6pVOfrcrkMyHQNt8l6HDs/AgBY1eVCOJG+olyX3V4kmcZNXW5IEnD8vB9NFhO2b+hBm0OeUTwz6V7O7uFERERTx09HmnVTKUskIirFG0ggGFfR4cw0IetwKvCF4jg9GCnKK/2BGE4PRrCo1VqUW0p1656uycrUN3+iDRdGo+hyW9BiU3JdzX3BBMymVK5rebZ0urA0vvBx4fYcihF9gShuXzkP3S3WKyrFnklpN7uHExERTQ0/IWnWTbUskYioULtThkMxoj8QR4dTyXXiXtRqxXt9wVxeed8bhC8YxwvHL6LZKheVmlfiEpeJctkpXxi/e3cAF0aiODsUxZruJqQ1QAgNvz5xCX2BGPr9cXQ4zVjYbCnqFl6qe/j4svV2h4KRaBKdLjM8jkwX8istxZ5JaTfLv4mIiCbH8nKadSxLJKKZynbidluNeZ24F7itubzSH4jBF4yjzaGg02kpKjWv1CUu5XIZgJJl4LJRgkDm1mG+YAKjURWD4Tj6AjE8c/AMfKEEWm0KfGMl9NnHhWXrzJ1ERERzCz+pqSZYlkhEM1WuE3c2r5wejOCF4xfR6bSUvIRlsktcpnNnhVK5bCAYL1kG/plrPfj9+wOwywac1oAOp4K4mobZoEcwrsJlNua6kQfj/tzj7Ph8wURel/R6zp1T3YeVuosF74ZBRET1jJ9MVDMsSySimSrXidtiMmBRqxXNVrnsJSyTlYVPt+y8MJcVrj9bBt7dYoFDMcIXSkCvA/oDcbTaTYiNdR/3x1TYlUzX8PGPvcF4riw9pqZz45qsC3qtTHUfVuouFrwbBhER1TuWlxMRUUOZ7BKWycrCq1V23mJTxk6WZXgcMtxWI1ptCua5zHndx9vsSt5jm6yHgIRwIl33d3yYaul+pUr8eTcMIiKaC+bE14yPPfYYHnroIXi9XqxcuRI//vGPcfPNN9d6WNQgWJZIjaRW+bLa76OhcHzCTuOFz0/WnXx8WXgylUYglsK5oSiCcRVumxGqpsGm6NHnj+HcUBRumynXbdwbiOHMUAQrOhy4tt2Riz37vF6X+bb7r27oQCCWgtNsgMmgRzSZytvu+PnTGnDDfEde2Xi2e3jfaBT//5/Oo8udXy5/bjiCtIZcfOWOQXZ6Ns7s/Nl9lh1fdrnsdJNeQjItSu6/UjGnNSCWnNrdKXzBOPqDcXS6MvO5LSb0+aPwBRPobikee7nXVSUvFSAiIqqWuv8E+sUvfoHdu3fjySefxNq1a/Hoo49iy5Yt6O3thcfjqfXwaI5jWSI1klrly2q/j175wIdnDp7JrX/7hh5sXOaZ8Pkut2XSMVlMBrx1eiS3rNVkgEXWYzSiIpXWMBhOwCYbcHowAodiRCSZwnA4gY8HwwAAxajH51fOQ7tTwbmRCPr9cVhNBkSSKVhlAyKJVO5xh0vBwrFmb4s9ttwJYKl9ly0bz47vp384jdNDYbwl6XDrkhbYFSNGogn8y4vvI5JMwaEYseUT7QjE1KJ4s+t/51IA7/cHoRh18NgV3NTVhGPnR+ELJRBX01jeYcf181xwmo343XteXBiNYjSSRJPVhAVNlrx9nl1nLuaxWDtcCjocZqhpMeHdKU75wnjxz3340BvE+31BXOOx4vRgBDpJwq9PXMJtN3TmjX2iY1jpSwWIiKg2uv/5pSnPe/ZHt1VxJNVR9+XlDz/8MO677z7s2LEDK1aswJNPPgmLxYKnnnqq5PyJRALBYDDvh6gUliVSo5lOvqxUrqz2+2horJP3aFSFx55Z/7OHzmAoHC/7/FNvnsYLxy5OOqbCZf2xJE72BRFX0xiJJKGmNUQTKYxEkugdCOLSaAS93hDUtIBOkhBTNfznkfPo9frhCyYwHE6i1xfEYCiBXm8IvlAcvQNBDIeTGAwm4AslptVFPTu+YDyFRS02qGkNB08NQdM0nBuKIBhPwWNXMBxJ4Cd/OI1L/ljeeobCcbz8nhd9gRh6vSGEEymk04A3EMezh85gIBBHKq0hnEih1xvCx74QfvKH0xgIxBCIppBIaQjEVAyGErl9nh2zL5TIxBzJxJqN0R9TIUHApuhLlvZnl892dteEwKGPhpEWAqu6XAgn0nljn+wYVvtSASIiokqo65PuZDKJo0ePYtOmTblpOp0OmzZtwuHDh0sus3fvXjidztzPggULZmu4NMeUKksMxFSE4vxPGc09082XlcqV1X4feQMJBOMqOpwKLCYDOpyZ9XsDibLPj0ZVeIOxScdUuGyzVYaa1rC83Y4OlxlLWu0QkgSn2YBkSsCg1yENwGTQQa/TwWzUIaUJxFKZ0upmmwlJVWTmT2twmY1IpgSabSakNMBlNuaNY7J9N358rXYFn5jnhMNswLXtNqQ0kTfuaDIFs1Gft57s8maDHsm0hmarCZAAm6xHIq3BKushSRKarSYkUwIpLXNSbJEN0CBgkw3QBOA0G3Lry47ZZTZmYrbKmXWPi9Gg1+H2lfOwY0MPtq3vzvt2eXzMC9wW3NLTDIfZgHU9zehyW4vGPpXX1WKPDdvWd+dtj/mdiIjqSV2fdA8NDSGdTqOtrS1veltbG7xeb8ll9uzZg0AgkPu5cOHCbAyV5qDxZYmxZBreYBxOc34ZJNFcMd18WalcWe33UbtThkMxoj+Q+Za1P5BZf7tTLvt8k8WIdod50jEVLjscScBiMkCnk2Ax6eGPqTDpdQjEUjAZJKTSGvQAkikNaU1DTNVg0EkwGzLXNA+HkzAZpcz8el1meYOE4XASBh3gj6l545hs3xWObzCUQItNxqoFTSXHHVPTeevJLh9LpWHS6zAcSQICCCfSkPU6RBJpCCEwHEnCZJBg0GW+OY4mUtBBQjiRgk7C2HXpmfVlx+yPqZmYI4nMugti9DhktDmUouuoC2OOqmk022VE1FTJsU/1dWUxGfK2x/xORET1pK5PumdClmU4HI68H6JSJutwTNTIKpUrq/0+arFlOnm7rUb4Qpn1b1vfk2vsVer5/3HrInzhpvmTjqlw2RabjPs+tQjdLVZ4HDJa7CYsbbfD45CxtM2B+W4rlrbbYdRL0ISAxaTD36zpwrIO1+X5PY7M/O12tDkULG1zoMVuQqtDRptDnlIX9ezz5WK/tt1Rctzzm8wlu6XPc5mxtN0Ou2KAXg90uBRsW9+DdpcCg14Hu2LA0nY7FrfZcd+nFqHdZYbTYoBi1OVOoLP7PDvmbAf2Flsm1nIxTvZ6aXPI2L6+B212peTYZ/q6Yn4nIqJ6UtefPi0tLdDr9RgYGMibPjAwgPb29hqNihrJ+E7C7G5Lc1kt82W130cbl3lww3xH2U7k5Z6fyphKLVuuM/d0upeX+rfUOCbbd+Vim2jc49dTqkv7ZN3Lt1zXNmH38ok6sE/l+JeKOdupvdzYZ/K6Yn4nIqJ6UdefQCaTCatXr8aBAwdwxx13AAA0TcOBAwewa9eu2g6OGobFxP+M0dxX63xZ7fdRi00peauwiZ6f6pgKl51suTaHgpULmqa9nXImW75c7FMd9/jp46/cn+p6ZzLmyRQuP5WxV2I7REREtVD3n0S7d+/Gtm3bsGbNGtx888149NFHEYlEsGPHjloPjYiorjBfEhERUaOb6PZisl7gf94MXPfd3yGRlgDUxy3G6v6k+4tf/CIGBwfx4IMPwuv14sYbb8S+ffuKmgUREV3tmC+JiOrPdO4/PJFSJxNENDfU/Uk3AOzatYvl5EREU8B8SURERFRf5sRJNxERERFRpTz22GN46KGH4PV6sXLlSvz4xz/GzTffXOthEVEVTLfapBrl6A13yzAiIiIionJ+8YtfYPfu3fjOd76DY8eOYeXKldiyZQt8Pl+th0ZEDYon3URERER01Xj44Ydx3333YceOHVixYgWefPJJWCwWPPXUU7UeGhE1qIYvLxdCAACCwWCNR0JEtZbNA9m8QJcxVxLReI2aL5PJJI4ePYo9e/bkpul0OmzatAmHDx8umj+RSCCRSOQeBwIBAMDIyAhUVZ3SNg2pyBWOemw9mkA0qsGg6pDW5m4jtUaJA2icWBolDqAysQwPD0953lAoBGDyXNnwJ93ZHbFgwYJJ5iSiq0UoFILT6az1MOoKcyURldJo+XJoaAjpdLrorg5tbW344IMPiubfu3cvvve97xVN7+npqdoYJ/L/1mSrldcocQCNE0ujxAFceSwt/2v6y0yWKxv+pLuzsxMXLlyA3W6HJM3eX26CwSAWLFiACxcuwOFwzNp2q6FRYmmUOIDGiWW24xBCIBQKobOzs+rbmmuYK69co8TSKHEAjOVKMF9m7NmzB7t378491jQNIyMjaG5untVcCTTO67lR4gAaJ5ZGiQOo31zZ8CfdOp0O8+fPr9n2HQ7HnH/xZjVKLI0SB9A4scxmHI30jU0lMVdWTqPE0ihxAIxlphoxX7a0tECv12NgYCBv+sDAANrb24vml2UZsiznTXO5XNUc4qQa5fXcKHEAjRNLo8QB1F+uZCM1IiIiIroqmEwmrF69GgcOHMhN0zQNBw4cwLp162o4MiJqZA3/TTcRERERUdbu3buxbds2rFmzBjfffDMeffRRRCIR7Nixo9ZDI6IGxZPuKpFlGd/5zneKSpLmokaJpVHiABonlkaJg2aukV4DjRJLo8QBMBYq7Ytf/CIGBwfx4IMPwuv14sYbb8S+ffuKmqvVm0Z5DTRKHEDjxNIocQD1G4skGu1eEERERERERER1gtd0ExEREREREVUJT7qJiIiIiIiIqoQn3URERERERERVwpNuIiIiIiIioirhSfc0vPHGG/jrv/5rdHZ2QpIk/OpXv8o9p6oqvvnNb+L666+H1WpFZ2cnvvSlL6Gvry9vHd3d3ZAkKe/nRz/60SxHMnEsALB9+/aicW7dujVvnpGREdxzzz1wOBxwuVy49957EQ6HZzGKjMliKYwj+/PQQw/l5qmH47J371588pOfhN1uh8fjwR133IHe3t68eeLxOHbu3Inm5mbYbDbcddddGBgYyJvn/PnzuO2222CxWODxePBP//RPSKVSdRPHyMgI7r//fixduhRmsxldXV144IEHEAgE8tZT6pg999xzsxYHXZlGyZfMlcyV1cR8SY2SK4HGyZfMlcyV1cCT7mmIRCJYuXIlHnvssaLnotEojh07hm9/+9s4duwYfvnLX6K3txef//zni+b9/ve/j/7+/tzP/fffPxvDzzNRLFlbt27NG+d//Md/5D1/zz334L333sP+/fvx4osv4o033sDf//3fV3voRSaLZXwM/f39eOqppyBJEu666668+Wp9XF5//XXs3LkTb731Fvbv3w9VVbF582ZEIpHcPF/72tfwX//1X3j++efx+uuvo6+vD3feeWfu+XQ6jdtuuw3JZBKHDh3Cs88+i2eeeQYPPvhg3cTR19eHvr4+/Nu//RveffddPPPMM9i3bx/uvffeonU9/fTTecfkjjvumLU46Mo0Sr5krmSurGUszJeNr1FyJdA4+ZK5krmyKgTNCADxwgsvTDjPn/70JwFAnDt3Ljdt4cKF4pFHHqnu4KapVCzbtm0Tt99+e9llTp48KQCIt99+Ozftv//7v4UkSeLSpUtVGunkpnJcbr/9drFx48a8afV4XHw+nwAgXn/9dSGEEH6/XxiNRvH888/n5nn//fcFAHH48GEhhBC//e1vhU6nE16vNzfPE088IRwOh0gkErMbwJjCOEr5z//8T2EymYSqqrlpUzmWNDc0Sr5krqy/YyJE4+RKIZgvr3aNkiuFaJx8yVzJXFkp/Ka7igKBACRJgsvlypv+ox/9CM3NzVi1ahUeeuihWS/RmKrXXnsNHo8HS5cuxZe//GUMDw/nnjt8+DBcLhfWrFmTm7Zp0ybodDr88Y9/rMVwp2RgYAAvvfRSyb981dtxyZbEuN1uAMDRo0ehqio2bdqUm2fZsmXo6urC4cOHAWSOy/XXX4+2trbcPFu2bEEwGMR77703i6O/rDCOcvM4HA4YDIa86Tt37kRLSwtuvvlmPPXUU8jkS2pEczlfMlcyV1YK8yVNZi7nSqDx8iVzZW3MxVxpmHwWmol4PI5vfvObuPvuu+FwOHLTH3jgAdx0001wu904dOgQ9uzZg/7+fjz88MM1HG2xrVu34s4770RPTw8+/vhjfOtb38LnPvc5HD58GHq9Hl6vFx6PJ28Zg8EAt9sNr9dbo1FP7tlnn4Xdbs8rnQHq77homoavfvWr2LBhA6677joAgNfrhclkKvqgbWtry+1zr9eblxizz2efm22l4ig0NDSEH/zgB0XlY9///vexceNGWCwWvPzyy/jKV76CcDiMBx54YDaGTrNoLudL5krmykphvqTJzOVcCTRmvmSunH1zNVfypLsKVFXF3/zN30AIgSeeeCLvud27d+d+v+GGG2AymfAP//AP2Lt3L2RZnu2hlvW3f/u3ud+vv/563HDDDbjmmmvw2muv4S//8i9rOLIr89RTT+Gee+6Boih50+vtuOzcuRPvvvsu3nzzzVnfdiVNFkcwGMRtt92GFStW4Lvf/W7ec9/+9rdzv69atQqRSAQPPfQQ/xPZYOZ6vmSuZK6sFOZLmshcz5VAY+ZL5srZN1dzJcvLKyybFM+dO4f9+/fn/SWylLVr1yKVSuHs2bOzM8AZWrRoEVpaWnDq1CkAQHt7O3w+X948qVQKIyMjaG9vr8UQJ/WHP/wBvb29+Lu/+7tJ563lcdm1axdefPFFvPrqq5g/f35uent7O5LJJPx+f978AwMDuX3e3t5e1HUy+3i2j0u5OLJCoRC2bt0Ku92OF154AUajccL1rV27FhcvXkQikajWkGmWNWK+ZK6cPY2SKwHmS5pYI+ZKYO7nS+ZK5srp4El3BWWT4kcffYTf//73aG5unnSZEydOQKfTFZXT1JuLFy9ieHgYHR0dAIB169bB7/fj6NGjuXleeeUVaJqGtWvX1mqYE/rpT3+K1atXY+XKlZPOW4vjIoTArl278MILL+CVV15BT09P3vOrV6+G0WjEgQMHctN6e3tx/vx5rFu3DkDmuLzzzjt5H1rZD+gVK1bURRxA5q+Qmzdvhslkwm9+85uivxCXcuLECTQ1NdXVX+1p5ho1XzJXVl+j5EqA+ZIm16i5Epj7+ZK5krlyWmrSvm2OCoVC4vjx4+L48eMCgHj44YfF8ePHxblz50QymRSf//znxfz588WJEydEf39/7ifb3e/QoUPikUceESdOnBAff/yx+NnPfiZaW1vFl770pbqKJRQKiW984xvi8OHD4syZM+L3v/+9uOmmm8SSJUtEPB7PrWPr1q1i1apV4o9//KN48803xZIlS8Tdd99dV7FkBQIBYbFYxBNPPFG0fL0cly9/+cvC6XSK1157Le/1E41Gc/P84z/+o+jq6hKvvPKKOHLkiFi3bp1Yt25d7vlUKiWuu+46sXnzZnHixAmxb98+0draKvbs2VM3cQQCAbF27Vpx/fXXi1OnTuXNk0qlhBBC/OY3vxE/+clPxDvvvCM++ugj8fjjjwuLxSIefPDBWYuDrkyj5Evmysvq5Zg0Sq6cSizMl42vUXLlZLHMpXzJXMlcWQ086Z6GV199VQAo+tm2bZs4c+ZMyecAiFdffVUIIcTRo0fF2rVrhdPpFIqiiOXLl4sf/vCHecmmHmKJRqNi8+bNorW1VRiNRrFw4UJx33335d0uQAghhoeHxd133y1sNptwOBxix44dIhQK1VUsWf/+7/8uzGaz8Pv9RcvXy3Ep9/p5+umnc/PEYjHxla98RTQ1NQmLxSK+8IUviP7+/rz1nD17Vnzuc58TZrNZtLS0iK9//et5t0uodRzljhcAcebMGSFE5hYhN954o7DZbMJqtYqVK1eKJ598UqTT6VmLg65Mo+RL5srL6uWYNEqunEoszJeNr1Fy5WSxzKV8yVzJXFkN0lggRERERERERFRhvKabiIiIiIiIqEp40k1ERERERERUJTzpJiIiIiIiIqoSnnQTERERERERVQlPuomIiIiIiIiqhCfdRERERERERFXCk24iIiIiIiKiKuFJNxEREREREVGV8KSbiIiIiIiIqEp40k0Nbfv27ZAkqehn69attR4aEVHdYK4kIiKqHkOtB0BUbVu3bsXTTz+dN02W5RqNhoioPjFXEhERVQdPuqnhybKM9vb2Wg+DiKiuMVcSERFVB8vLiYiIiIiIiKqEJ93U8F588UXYbLa8nx/+8Ie1HhYRUV1hriQiIqoOlpdTw/uLv/gLPPHEE3nT3G53jUZDRFSfmCuJiIiqgyfd1PCsVisWL15c62EQEdU15koiIqLqYHk5ERERERERUZXwm25qeIlEAl6vN2+awWBAS0tLjUZERFR/mCuJiIiqgyfd1PD27duHjo6OvGlLly7FBx98UKMRERHVH+ZKIiKi6pCEEKLWgyAiIiIiIiJqRLymm4iIiIiIiKhKeNJNREREREREVCU86SYiIiIiIiKqEp50ExEREREREVUJT7qJiIiIiIiIqoQn3URERERERERVwpNuIiIiIiIioirhSTcRERERERFRlfCkm4iIiIiIiKhKeNJNREREREREVCU86SYiIiIiIiKqkv8LXHGgSruDdHQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# sample size\n",
        "N = 1000\n",
        "\n",
        "# define the mean mu and standard deviation sigma of the demand for each class\n",
        "mu = demand.mean()\n",
        "sigma = {\"F\": 4, \"B\": 16, \"E\": 20}\n",
        "display(pd.DataFrame({\"mu\": mu, \"sigma\": sigma}))\n",
        "\n",
        "# correlation matrix\n",
        "P = np.array([[1, 0.6, 0.2], [0.6, 1, 0.4], [0.2, 0.4, 1]])\n",
        "\n",
        "# build covariance matrix from covariances and correlations\n",
        "s = np.array(list(sigma.values()))\n",
        "S = np.diag(s) @ P @ np.diag(s)\n",
        "\n",
        "# create samples\n",
        "np.random.seed(1)\n",
        "samples = np.random.multivariate_normal(list(mu), S, N).round()\n",
        "\n",
        "# truncate to integers and non-negative values\n",
        "classes = demand.columns\n",
        "demand_saa = pd.DataFrame(samples, columns=classes)\n",
        "demand_saa[demand_saa < 0] = 0\n",
        "\n",
        "df = pd.DataFrame(mu, columns=[\"mu\"])\n",
        "df[\"sample means\"] = demand_saa.mean()\n",
        "display(df)\n",
        "\n",
        "df = pd.DataFrame(pd.Series(sigma), columns=[\"sigma\"])\n",
        "df[\"sample std dev\"] = demand_saa.std()\n",
        "display(df)\n",
        "\n",
        "print(\"\\nModel Covariance\")\n",
        "df = pd.DataFrame(S, index=classes, columns=classes)\n",
        "display(df)\n",
        "\n",
        "print(\"\\nSample Covariance\")\n",
        "display(pd.DataFrame(demand_saa.cov()))\n",
        "\n",
        "fig, ax = plt.subplots(3, 3, figsize=(10, 10))\n",
        "for i, ci in enumerate(classes):\n",
        "    for j, cj in enumerate(classes):\n",
        "        if i == j:\n",
        "            demand_saa[ci].hist(ax=ax[i, i], bins=20)\n",
        "            ax[i, i].set_title(f\"Histogram {ci}\")\n",
        "        else:\n",
        "            ax[i, j].plot(demand_saa[ci], demand_saa[cj], \".\", alpha=0.4)\n",
        "            ax[i, j].set_xlabel(ci)\n",
        "            ax[i, j].set_ylabel(cj)\n",
        "            ax[i, j].set_title(f\"Convariance: {ci} vs {cj}\")\n",
        "fig.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "2c9fdfc2-7f45-4a08-ab45-6d8715443743",
      "metadata": {
        "id": "2c9fdfc2-7f45-4a08-ab45-6d8715443743",
        "outputId": "7af979cd-cc28-4885-fe23-dccbee660dca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 210891\n",
            "2103 simplex iterations\n",
            "1 branching nodes\n",
            " \n",
            "\n",
            "Seat Allocation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                       F     B      E  TOTAL\n",
              "seat allocation                     11.0  20.0  148.0  179.0\n",
              "economy equivalent seat allocation  22.0  30.0  148.0  200.0"
            ],
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              "      <th>seat allocation</th>\n",
              "      <td>11.0</td>\n",
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              "      <th>economy equivalent seat allocation</th>\n",
              "      <td>22.0</td>\n",
              "      <td>30.0</td>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8e7da5b9-ce9f-4373-88a8-7603aaea9129 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8e7da5b9-ce9f-4373-88a8-7603aaea9129');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ddccddfa-9af4-4f76-a604-fae07bf0a83f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ddccddfa-9af4-4f76-a604-fae07bf0a83f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ddccddfa-9af4-4f76-a604-fae07bf0a83f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Mean Tickets Sold\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "F      9.868\n",
              "B     17.021\n",
              "E    147.245\n",
              "dtype: float64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Mean Seats not Sold\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "F    1.132\n",
              "B    2.979\n",
              "E    0.755\n",
              "dtype: float64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Mean Spillage (Unfulfilled Demand)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "F     2.190\n",
              "B    11.334\n",
              "E    28.191\n",
              "dtype: float64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Expected Revenue (in units of economy ticket price): 210.89\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x300 with 3 Axes>"
            ],
            "image/png": 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D9T//8z9atGiR2rVrp4kTJ57zX3i8zb/+9S+tX79eb731ln2b2WxWXV2dKioqHK6WKisrk9lstrfZtWuXw7GaVudratOSkJAQhYSENNtuMpmcOwAQGOQ1gyVOz+ZFyHb+GhoaFBAQoMDAwGbvNQEBrlx4ufH/neO/52npve5cND2vef8D7NnCw8M1ZcoUPf744woNDW1WK75/jIcffliNjY362c9+1qxWdOnS5QdrxaZNm9SvX79Wa8WePXuUlZXVYtbWasWaNWtUUFCgwMBA5eTk6Pnnn9cjjzxirxUzZsxQbm6u2rVr55A9MDBQXbt21dChQzVu3LhmtaKpTUvfz4CAgBZ/9vz1/zMAkPhc4QufK9o0KPXdd9+pX79+GjBggNasWaMOHTro888/1yWXXGJvM2/ePL3wwgt6/fXXlZycrCeffFIZGRk6ePCg/S8zo0aN0pEjR1RYWCibzab7779fY8eO1bJly5ybDoDPevLJJ9WuXTtNmzZNhw8fVmJioh588MEW206cOFGNjY265ZZbtHbtWmVkZGjVqlWaNWuWnnnmGZlMJqWkpDj8deR3v/udcnNz9eqrryoxMVGfffaZjh49qtGjR6usrEyXXnqpRowY0eLtYJKUmpqq8PBwPfroo/r6668VEhKiq666Sn/4wx907733SpJ+9KMfafXq1Zo8ebKuv/56xcbGasyYMZo6dWqruQsKCvTrX/9aN910kxISEvTUU0/pySefvIDvpOcUFBQoPj5emZmZ9m29e/eWyWTShg0b7IW5pKREpaWlslgskk7PpTJnzhyVl5crPj5e0unbnaKiopSamur+IAC8FrXC92sFALgatcLLa4XRBlOmTDFuvPHGVvc3NjYaZrPZ+O1vf2vfVlFRYYSEhBh/+tOfDMMwjIMHDxqSjN27d9vbrFmzxggICDC+/fbbc+pHZWWlIcn4z3/+05bu+4S6ujrj7bffNurq6jzdFacj24U7deqUcfDgQePUqVMuPc+ZGhoajO+++85oaGhw63ndwdez/dDPxH/+8x9DklFZWen2fjU0NBhJSUnGlClTmu178MEHjaSkJGPjxo3Gnj17DIvFYlgsFvv++vp647rrrjOGDBli7N2711i7dq3RoUMHIy8vr019cFWteHPHv5x6vPPB+6lvolb4Ll/P5q21whvwucI3+WO2pt8vqBW+y9ezeapWtOma6nfffVd9+vTRnXfeqfj4ePXs2VOvvvqqff+hQ4dktVodVlWKjo5W3759VVRUJEkqKipSTEyM/XY/SUpPT1dgYKB27tx53oNrAADvsX79epWWlupXv/pVs33z58/X8OHDlZWVpf79+8tsNjvc4hcUFKRVq1YpKChIFotF99xzj0aPHq1Zs2a5MwIAAAAAF2vT7Xv//Oc/tWjRIuXm5uqJJ57Q7t279fDDDys4OFjZ2dn2VZFaWjXp+6sqNd2OYe9Eu3aKjY1tdVUlty3z7QVYCts3ecMy365kfG8pbHee1x18PZu3LvM9ZMgQ+/f2TKGhoVq4cKEWLlzY6vO7dOmi1atXu6p7AAAAALxAmwalGhsb1adPH82dO1eS1LNnT+3fv1+LFy9Wdna2SzoouW+Zb2/CUti+yZPLfLtD05LY/shXs7HMNwAAAABf1aZBqcTExGaTzDYtTSj9d1WksrIyJSYm2tuUlZXZl9s1m80qLy93OEZ9fb2OHTvW6qpK7lrm2xuwFLZv8oZlvl3JMAyHpbD9ia9nY5lvAAAAAL6qTYNS/fr1U0lJicO2zz77TF26dJEkJScny2w2a8OGDfZBqKqqKu3cuVPjxo2TdHpVpYqKChUXF6t3796SpI0bN6qxsVF9+/Zt8bxuW+bbi5DNN3lymW9XarqtrbWlpn2Zr2djmW8AAAAAvqpNg1KTJk3ST37yE82dO1e/+MUvtGvXLr3yyit65ZVXJJ3+UDdx4kQ99dRTuuqqq5ScnKwnn3xSHTt21O233y7p9JVVQ4cO1QMPPKDFixfLZrNp/PjxGjlypDp27Oj0gAAAAAAAAPA+bRqUuuGGG7Ry5Url5eVp1qxZSk5O1oIFCzRq1Ch7m8cee0wnTpzQ2LFjVVFRoRtvvFFr1651uK3kzTff1Pjx4zVo0CAFBgYqKytLL7zwgvNSAQAAAAAAwKu1aVBKkoYPH67hw4e3uj8gIECzZs36waW7Y2NjtWzZsraeGgAAAAAAAH7C9yZQAQAvct9999lvTwYAoCXUCgDA2VystYJBKQBucd999ykgIMA+KXdCQoIGDx6s1157zT7ZOADg4katAACcDbXCv7T59j0AXmpPgeuObRgKrqmRQkOlgIDT2/rc3+bDDB06VAUFBWpoaFBZWZnWrl2rRx55RH/961/17rvvql073pIAwKWoFQCAs6FWwI24UgqA24SEhMhsNutHP/qRevXqpSeeeELvvPOO1qxZo6VLl0qSKioq9Otf/1odOnRQVFSUBg4cqI8//th+jBkzZqhHjx567bXXlJSUpIiICD300ENqaGjQvHnzZDabFR8frzlz5jic+7nnnlO3bt3Uvn17de7cWQ899JCqq6vt+5cuXaqYmBh98MEH6tq1qyIiIjR06FAdOXLE3qahoUG5ubmKiYlRXFycHnvsMRmG4dpvGgBcZKgVAICzoVb4DwalAHjUwIEDdf311+utt96SJN15550qLy/XmjVrVFxcrF69emnQoEE6duyY/Tlffvml1qxZo7Vr1+pPf/qTlixZoszMTH3zzTfasmWLnnnmGU2dOlU7d+60PycwMFAvvPCCDhw4oNdff10bN27UY4895tCXkydP6tlnn9X//u//auvWrSotLdVvfvMb+/7f/e53Wrp0qV577TV9+OGHOnbsmFauXOni7xAAgFoBADgbaoVv4po2AB6XkpKiTz75RB9++KF27dql8vJyhYSESJKeffZZvf322/rrX/+qsWPHSpIaGxv12muvKTIyUqmpqRowYIBKSkq0evVqBQYG6pprrtEzzzyjTZs2qW/fvpKkiRMn2s932WWX6amnntKDDz6ol156yb7dZrNp8eLFuuKKKyRJ48ePd1hJdMGCBcrLy9OIESMkSYsXL9YHH3zg0u8NAOA0agUA4GyoFb6HQSkAHmcYhgICAvTxxx+rurpacXFxDvtPnTqlL7/80v74sssuU2RkpP1xQkKCgoKCFBgY6LCtvLzc/nj9+vXKz8/XP/7xD1VVVam+vl41NTU6efKkvU14eLi9cEhSYmKi/RiVlZU6cuSIvRhJUrt27dSnT5+L9lJbAHAnagUA4GyoFb6HQSkAHvfpp58qOTlZ1dXVSkxM1ObNm5u1iYmJsX9tMpkc9jWtvHHmtqbVN7766isNHz5c48aN05w5cxQbG6sPP/xQY8aMUV1dnb3otHSMi7EwAIA3olYAAM6GWuF7mFMKgEdt3LhR+/btU1ZWlnr16iWr1ap27drpyiuvdPh36aWXnvc5iouL1djYqN/97ndKS0vT1VdfrcOHD7fpGNHR0UpMTHS4n7y+vl7FxcXn3S8AwLmhVvifhoYGPfnkk0pOTlZYWJiuuOIKzZ492+FDm2EYmjZtmhITExUWFqb09HR9/vnnDsc5duyYRo0apaioKMXExGjMmDEOEw4DuHhQK3wTV0oBcJva2lpZrVaHpVvz8/M1fPhwjR49WoGBgbJYLLr99ts1b948+5v8+++/rzvuuEN9+vQ5r/NeeeWVstlsevHFF3Xrrbdq27ZtWrx4cZuP88gjj+jpp5/WVVddpZSUFD333HOqqKg4rz4BAFpGrbg4PPPMM1q0aJFef/11XXvttdqzZ4/uv/9+RUdH6+GHH5YkzZs3Ty+88IJef/11JScn68knn1RGRoYOHjyo0NBQSdKoUaN05MgRFRYWymaz6f7779fYsWO1bNkyT8YD4GLUCv/BlVIA3Gbt2rVKTEzUZZddpqFDh2rTpk164YUX9M477ygoKEgBAQFavXq1+vfvr/vvv19XX321Ro4cqX/9619KSEg47/Nef/31eu655/TMM8/ouuuu05tvvqn8/Pw2H+fRRx/Vvffeq+zsbFksFkVGRuqOO+44734BAJqjVlwctm/frttuu02ZmZm67LLL9POf/1xDhgzRrl27JJ2+SmrBggWaOnWqbrvtNnXv3l1//OMfdfjwYb399tuSTt+ms3btWv3hD39Q3759deONN+rFF1/U8uXL23zlAgDfQq3wHwGGD97YWFVVpejoaP3nP/9pNnGZr7PZbFq9erVuueWWZveh+jqyXbiamhodOnRIycnJ9r8QukNjY6OqqqoUFRXlMOmfP/D1bD/0M3H06FFdeumlqqysVFRUlId66DmuqhXLdpbq7r5JTjve+eD91DdRK3yXr2fzxloxd+5cvfLKK1q3bp2uvvpqffzxxxoyZIiee+45jRo1Sv/85z91xRVX6KOPPlKPHj3sz7vpppvUo0cPPf/883rttdf06KOP6rvvvrPvr6+vV2hoqFasWNHiB7za2lrV1tbaH1dVValz5846cuSIX36uKCws1ODBg/3y/dTfsq3Y843u7NPJbdlqamr09ddf67LLLnNrrTAMQ8ePH1dkZKQCAgLcdl538PVsNTU1+uqrr9S5c+cWa0ViYqJLagW37wEAAABwq8cff1xVVVVKSUlRUFCQGhoaNGfOHI0aNUqSZLVaJanZFQ0JCQn2fVarVfHx8Q7727Vrp9jYWHubM+Xn52vmzJnNtm/atEnh4eEXnMsbFRYWeroLLuNP2dpLWr36E/tjV2dr166dzGazqqurVVdX59JzteT48eNuP6e7+Gq2uro6nTp1Slu3blV9fb3Dvu+vLOhsDEoBAAAAcKu//OUvevPNN7Vs2TJde+212rt3ryZOnKiOHTsqOzvbZefNy8tTbm6u/XHTlVIDBgzgSikf4o/ZPHWlVEREBFdKOYmvZ6upqVFYWJj69+/f4pVSrsKgFAAAAAC3mjx5sh5//HGNHDlSktStWzf961//Un5+vrKzs2U2myVJZWVlSkxMtD+vrKzMfjuf2WxWeXm5w3Hr6+t17Ngx+/PPFBISopCQkGbbTSaT3wxunIlsPiIwyCGLq7M1NDQoICBAgYGBbr0tubGxUZLs5/Ynvp4tMDBQAQEBLf7sufJn0fe+UwAAr/ftt9/qnnvuUVxcnMLCwtStWzft2bPHvp9lvgHg4nby5MlmH9qCgoLsH+qSk5NlNpu1YcMG+/6qqirt3LlTFotFkmSxWFRRUeGwjPrGjRvV2Niovn37uiEFAOBCMSgFAHCq7777Tv369ZPJZNKaNWt08OBB/e53v9Mll1xib9O0zPfixYu1c+dOtW/fXhkZGaqpqbG3GTVqlA4cOKDCwkKtWrVKW7du1dixYz0RCQDgZLfeeqvmzJmj999/X1999ZVWrlyp5557zj45eUBAgCZOnKinnnpK7777rvbt26fRo0erY8eOuv322yVJXbt21dChQ/XAAw9o165d2rZtm8aPH6+RI0eqY8eOHkwHADhX3L4H+CAfXDQTLuKNPwvPPPOMOnfurIKCAvu25ORk+9dnLvMtSX/84x+VkJCgt99+WyNHjrQv871792716dNHkvTiiy/qlltu0bPPPsuHDeAceOP7AzzDG38WXnzxRT355JN66KGHVF5ero4dO+p//ud/NG3aNHubxx57TCdOnNDYsWNVUVGhG2+8UWvXrnWY6+TNN9/U+PHjNWjQIAUGBiorK0svvPCCJyIBPskb3x/gGZ76WWBQCvAhTffynjx5UmFhYR7uDbxB00oY3jSfwrvvvquMjAzdeeed2rJli370ox/poYce0gMPPCBJOnTokKxWq9LT0+3PiY6OVt++fVVUVKSRI0eqqKhIMTEx9gEpSUpPT1dgYKB27tx5zst8S6cnQ7XZbM4L2Njg3OOdh6bze7ofrkA25zAMQ9XV1S3OnePKczb9t+kWLH/h69mqq6vtGc78+fPU/2uRkZFasGCBFixY0GqbgIAAzZo1S7NmzWq1TWxsrJYtW+aCHgL+jc8VOJOnPlcwKAX4kKCgIMXExNgn9QwPD3fLyg6NjY2qq6tTTU2NT07a90N8NZthGDp58qTKy8sVExOjoKAgT3fJ7p///KcWLVqk3NxcPfHEE9q9e7cefvhhBQcHKzs72+eX+T5zyWZP8qelsM9EtgsTGRmp2tpa1dTUKDg42K2rALlyhR5P87VshmGorq5O//nPf/Tdd981m7tPcu0y3wC8F58rnM9Xs3n6cwWDUoCPaVpN5szVZlzJMAydOnVKYWFhPrm86Q/x9WwxMTGtrjDkKY2NjerTp4/mzp0rSerZs6f279+vxYsX+8Uy301LNnuSPy6F3YRszmEYhsrLy+1XDLqDYRiqqalRaGioT76f/hBfz9ahQwdde+21Lfbd1wbaADgPnyucy9ezeepzBYNSgI8JCAhQYmKi4uPj3XbJvc1m09atW9W/f3+//JDoq9lMJpNXXSHVJDExUampqQ7bunbtqr/97W+S5PvLfJ+xZLMn+dVS2Gcg24Xr1KmTGhrcd7upL7+fno0vZztbrfC1PACch88VzuXL2Tz5uYJBKcBHBQUFue2NIygoSPX19QoNDfW5N9iz8edsntKvXz+VlJQ4bPvss8/UpUsXSY7LfDcNQjUt8z1u3DhJjst89+7dWxLLfAPng1rhHP6cDQCoFc7hz9lciUEpAIBTTZo0ST/5yU80d+5c/eIXv9CuXbv0yiuv6JVXXpHkuMz3VVddpeTkZD355JOtLvO9ePFi2Ww2lvkGAAAA/AyDUgAAp7rhhhu0cuVK5eXladasWUpOTtaCBQs0atQoexuW+QYAAADAoBQAwOmGDx+u4cOHt7qfZb4BAAAAtGmdwhkzZiggIMDhX0pKin1/TU2NcnJyFBcXp4iICGVlZamsrMzhGKWlpcrMzFR4eLji4+M1efJk1dfXOycNAAAAAAAAfEKbr5S69tprtX79+v8eoN1/DzFp0iS9//77WrFihaKjozV+/HiNGDFC27ZtkyQ1NDQoMzNTZrNZ27dv15EjRzR69GiZTCb70uEAAAAAAADwf20elGrXrl2Ly3FXVlZqyZIlWrZsmQYOHChJKigoUNeuXbVjxw6lpaVp3bp1OnjwoNavX6+EhAT16NFDs2fP1pQpUzRjxgwFBwdfeCIAAAAAAAB4vTYPSn3++efq2LGjQkNDZbFYlJ+fr6SkJBUXF8tmsyk9Pd3eNiUlRUlJSSoqKlJaWpqKiorUrVs3JSQk2NtkZGRo3LhxOnDggHr27NniOWtra1VbW2t/XFVVJUmy2Wyy2WxtjeDVmvL4Wy6JbL7Mn/NdDNkAAAAAwBu1aVCqb9++Wrp0qa655hodOXJEM2fO1E9/+lPt379fVqtVwcHBiomJcXhOQkKCrFarJMlqtToMSDXtb9rXmvz8fM2cObPZ9k2bNik8PLwtEXxGYWGhp7vgMmTzXf6czx+znTx50tNdAAAAAIBWtWlQatiwYfavu3fvrr59+6pLly76y1/+orCwMKd3rkleXp5yc3Ptj6uqqtS5c2cNGDBAcXFxLjuvJ9hsNhUWFmrw4MEymUye7o5Tkc13+XM+f8529OhRT3cBAAAAAFrV5tv3vi8mJkZXX321vvjiCw0ePFh1dXWqqKhwuFqqrKzMPgeV2WzWrl27HI7RtDpfS/NUNQkJCVFISEiz7SaTye8+RDYhm2/y52ySf+fzx2z+lgcAAACAfwm8kCdXV1fryy+/VGJionr37i2TyaQNGzbY95eUlKi0tFQWi0WSZLFYtG/fPpWXl9vbFBYWKioqSqmpqRfSFQAAAAAAAPiQNl0p9Zvf/Ea33nqrunTposOHD2v69OkKCgrSXXfdpejoaI0ZM0a5ubmKjY1VVFSUJkyYIIvForS0NEnSkCFDlJqaqnvvvVfz5s2T1WrV1KlTlZOT0+KVUAAAAAAAAPBPbRqU+uabb3TXXXfp6NGj6tChg2688Ubt2LFDHTp0kCTNnz9fgYGBysrKUm1trTIyMvTyyy/bnx8UFKRVq1Zp3Lhxslgsat++vbKzszVr1iznpgIAAAAAAIBXa9Og1PLly39wf2hoqBYuXKiFCxe22qZLly5avXp1W04LAAAAAAAAP3NBc0oBAAAAAAAA54NBKQAAAAAAALgdg1IAAAAAAABwOwalAAAAAAAA4HYMSgEAAAAAAMDtGJQCAAAAAACA2zEoBQAAAAAAALdjUAoAAAAAAABux6AUAAAAAAAA3I5BKQAAAAAAALgdg1IAAKeaMWOGAgICHP6lpKTY99fU1CgnJ0dxcXGKiIhQVlaWysrKHI5RWlqqzMxMhYeHKz4+XpMnT1Z9fb27owAAAABwoXae7gAAwP9ce+21Wr9+vf1xu3b/LTeTJk3S+++/rxUrVig6Olrjx4/XiBEjtG3bNklSQ0ODMjMzZTabtX37dh05ckSjR4+WyWTS3Llz3Z4FAAAAgGswKAUAcLp27drJbDY3215ZWaklS5Zo2bJlGjhwoCSpoKBAXbt21Y4dO5SWlqZ169bp4MGDWr9+vRISEtSjRw/Nnj1bU6ZM0YwZMxQcHOzuOAAAAABcgNv3AABO9/nnn6tjx466/PLLNWrUKJWWlkqSiouLZbPZlJ6ebm+bkpKipKQkFRUVSZKKiorUrVs3JSQk2NtkZGSoqqpKBw4ccG8QAAAAAC7DlVIAAKfq27evli5dqmuuuUZHjhzRzJkz9dOf/lT79++X1WpVcHCwYmJiHJ6TkJAgq9UqSbJarQ4DUk37m/a1pra2VrW1tfbHVVVVkiSbzSabzeaMaKc1Njj3eOeh6fye7ocrkM13+XO+iyEbAACewKAUAMCphg0bZv+6e/fu6tu3r7p06aK//OUvCgsLc9l58/PzNXPmzGbbN23apPDwcKedp72k1as/cdrxLkRhYaGnu+AyZPNd/pzPH7OdPHnS010AAFzEGJQCALhUTEyMrr76an3xxRcaPHiw6urqVFFR4XC1VFlZmX0OKrPZrF27djkco2l1vpbmqWqSl5en3Nxc++Oqqip17txZAwYMUFxcnNPyrNjzje7s08lpxzsfNptNhYWFGjx4sEwmk0f74mxk813+nM+fsx09etTTXQAAXMQYlAIAuFR1dbW+/PJL3Xvvverdu7dMJpM2bNigrKwsSVJJSYlKS0tlsVgkSRaLRXPmzFF5ebni4+Mlnb46ISoqSqmpqa2eJyQkRCEhIc22m0wm536IDAzymg+lTs/mRcjmu/w5nz9m87c8AADfwqAUAMCpfvOb3+jWW29Vly5ddPjwYU2fPl1BQUG66667FB0drTFjxig3N1exsbGKiorShAkTZLFYlJaWJkkaMmSIUlNTde+992revHmyWq2aOnWqcnJyWhx0AgAAAOCbGJQCADjVN998o7vuuktHjx5Vhw4ddOONN2rHjh3q0KGDJGn+/PkKDAxUVlaWamtrlZGRoZdfftn+/KCgIK1atUrjxo2TxWJR+/btlZ2drVmzZnkqEgAAAAAXYFAKAOBUy5cv/8H9oaGhWrhwoRYuXNhqmy5dumj16tXO7hoAAAAALxLo6Q4AAAAAuPh8++23uueeexQXF6ewsDB169ZNe/bsse83DEPTpk1TYmKiwsLClJ6ers8//9zhGMeOHdOoUaMUFRWlmJgYjRkzRtXV1e6OAgA4TwxKAQAAAHCr7777Tv369ZPJZNKaNWt08OBB/e53v9Mll1xibzNv3jy98MILWrx4sXbu3Kn27dsrIyNDNTU19jajRo3SgQMHVFhYqFWrVmnr1q0aO3asJyIBAM4Dt+8BAAAAcKtnnnlGnTt3VkFBgX1bcnKy/WvDMLRgwQJNnTpVt912myTpj3/8oxISEvT2229r5MiR+vTTT7V27Vrt3r1bffr0kSS9+OKLuuWWW/Tss8+qY8eO7g0FAGgzBqUAAAAAuNW7776rjIwM3XnnndqyZYt+9KMf6aGHHtIDDzwgSTp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          },
          "metadata": {}
        }
      ],
      "source": [
        "model_ssa = airline_stochastic(demand_saa)\n",
        "seats_saa = airline_solve(model_ssa)\n",
        "seat_report_saa(seats_saa, demand_saa)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d20a5abd-2b54-436d-a171-b8f1fa3048dd",
      "metadata": {
        "id": "d20a5abd-2b54-436d-a171-b8f1fa3048dd"
      },
      "source": [
        "## Model 6. Tackling chance constraints using SAA in the case of correlated demand\n",
        "\n",
        "The linear counterparts of the chance constraints used above in Model 3 were derived under the assumption of independent normal distributions of demand for first-class and business travel. That assumption no longer holds for the case where demand scenarios are sampled from correlated distributions.\n",
        "\n",
        "This final model replaces the chance constraints by approximating them using two linear constraints that explicitly track unsatisfied demand. In doing so, we introduce two new sets of integer variables $y_s$ and $w_s$ and a big-M constant and approximate the true multivariate distribution with the empirical one obtained from the sample.\n",
        "\n",
        "The first stage remains unchanged and so does the objective value of the second stage. The adjusted second-stage constraints are:\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "    t_c & \\leq s_c & \\forall c\\in C \\\\\n",
        "    t_c & \\leq z_{c, s} & \\forall (c, s) \\in C \\times S \\\\\n",
        "    s_F + M y_s & \\geq z_{F, s} & \\forall s \\in  S\\\\\n",
        "    s_F + s_B + M w_s & \\geq z_{F, s} + z_{B,s} & \\forall s \\in  S \\\\\n",
        "    \\frac{1}{N} \\sum_{s\\in S} y_s & \\leq 1 - 0.98 \\\\\n",
        "    \\frac{1}{N} \\sum_{s\\in S} z_s & \\leq 1 - 0.95\n",
        "\\end{align*}\n",
        "$$\n",
        "\n",
        "where $y_s$ and $w_s$ are binary variables indicating those scenarios which do not satisfy the requirements of the airline's loyalty programs for first-class and business-class passengers.\n",
        "\n",
        "The following cell implements this new model. Note that the running time for the cell can be up to a few minutes for a large number of scenarios."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "aa07016d",
      "metadata": {
        "id": "aa07016d",
        "outputId": "9fcee4c4-028b-48e8-da76-3ccd015141f9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting airline_final.mod\n"
          ]
        }
      ],
      "source": [
        "%%writefile airline_final.mod\n",
        "\n",
        "param capacity;\n",
        "\n",
        "set CLASSES;\n",
        "set SCENARIOS;\n",
        "\n",
        "param nSCENARIOS := card(SCENARIOS);\n",
        "param bigM;\n",
        "\n",
        "param demand{CLASSES, SCENARIOS};\n",
        "param seat_factor{CLASSES};\n",
        "param revenue_factor{CLASSES};\n",
        "\n",
        "param first_class_id symbolic;\n",
        "param business_class_id symbolic;\n",
        "\n",
        "param first_class_demand_cover_prop;\n",
        "param business_class_demand_cover_prop;\n",
        "\n",
        "# first stage variables and constraints\n",
        "var seats{CLASSES} integer >= 0;\n",
        "\n",
        "s.t. plane_seats: sum{c in CLASSES}(seats[c] * seat_factor[c]) <= capacity;\n",
        "\n",
        "# second stage variable and constraints\n",
        "var tickets{CLASSES, SCENARIOS} integer >= 0;\n",
        "var first_class {SCENARIOS} binary;\n",
        "var business_class {SCENARIOS} binary;\n",
        "\n",
        "s.t. demand_limits {c in CLASSES, s in SCENARIOS}: tickets[c, s] <= demand[c, s];\n",
        "s.t. seat_limits {c in CLASSES, s in SCENARIOS}: tickets[c, s] <= seats[c];\n",
        "\n",
        "s.t. first_class_loyality {s in SCENARIOS}:\n",
        "    seats[first_class_id] + bigM * first_class[s] >= demand[first_class_id, s];\n",
        "s.t. first_class_loyality_rate:\n",
        "    sum{s in SCENARIOS} first_class[s] <= (1 - first_class_demand_cover_prop) * nSCENARIOS;\n",
        "s.t. business_class_loyality {s in SCENARIOS}:\n",
        "    seats[first_class_id] + seats[business_class_id] + bigM * business_class[s] >=\n",
        "        demand[business_class_id, s] + demand[first_class_id, s];\n",
        "s.t. business_class_loyality_rate:\n",
        "    sum{s in SCENARIOS} business_class[s] <= (1 - business_class_demand_cover_prop) * nSCENARIOS;\n",
        "\n",
        "# objective\n",
        "maximize revenue: sum{c in CLASSES, s in SCENARIOS}(tickets[c, s] * revenue_factor[c]);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "f10836c0-a503-4b06-b450-a9731e1fd55d",
      "metadata": {
        "id": "f10836c0-a503-4b06-b450-a9731e1fd55d",
        "outputId": "0b5134c5-b3ed-4bca-db6d-3afbb22bfddf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "HiGHS 1.5.3: \b\b\b\b\b\b\b\b\b\b\b\b\bHiGHS 1.5.3: optimal solution; objective 170908\n",
            "3591 simplex iterations\n",
            "1 branching nodes\n",
            " \n",
            "\n",
            "Seat Allocation\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                       F     B     E  TOTAL\n",
              "seat allocation                     20.0  54.0  79.0  153.0\n",
              "economy equivalent seat allocation  40.0  81.0  79.0  200.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-181c11f5-3783-4ad9-9cdc-fd9e5f1a46bf\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>F</th>\n",
              "      <th>B</th>\n",
              "      <th>E</th>\n",
              "      <th>TOTAL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>seat allocation</th>\n",
              "      <td>20.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>153.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>economy equivalent seat allocation</th>\n",
              "      <td>40.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-181c11f5-3783-4ad9-9cdc-fd9e5f1a46bf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-181c11f5-3783-4ad9-9cdc-fd9e5f1a46bf button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-181c11f5-3783-4ad9-9cdc-fd9e5f1a46bf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-13ef32ad-01fd-42ac-923b-48cc746b73b0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-13ef32ad-01fd-42ac-923b-48cc746b73b0')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-13ef32ad-01fd-42ac-923b-48cc746b73b0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Mean Tickets Sold\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "F    12.018\n",
              "B    27.927\n",
              "E    79.000\n",
              "dtype: float64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Mean Seats not Sold\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "F     7.982\n",
              "B    26.073\n",
              "E     0.000\n",
              "dtype: float64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Mean Spillage (Unfulfilled Demand)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "F     0.040\n",
              "B     0.428\n",
              "E    96.436\n",
              "dtype: float64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Expected Revenue (in units of economy ticket price): 170.91\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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6j4iIKFLZDAsLU0REhF/84EoKuXgjE9/IxRuZ+EYuvpGLNzLxzV9zKcpLHnmDEAAAAKyhbAIAAMAayiYAAACsoWwCAADAGsomAAAArKFsAgAAwBrKJgAAAKyhbAIAAMAayiYAAACsoWwCAADAGsomAAAArKFsAgAAwJqgsp4AAFwJGo15r9T3+cPknqW+TwA4H2c2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGBNscpmXl6ekpOTFRsbq9DQUDVu3FjPPvusjDHuMcYYjRs3TnXr1lVoaKji4uK0b9++Ep84AAAAyr9ilc0pU6Zo9uzZeuWVV7R7925NmTJFU6dO1YwZM9xjpk6dqpdffllz5szRli1bVLlyZcXHxysnJ6fEJw8AAIDyLag4gzdt2qTevXurZ8+ekqRGjRrprbfe0meffSbpt7Oa06dP19/+9jf17t1bkvSvf/1LkZGRWr58uQYOHFjC0wcAAEB5Vqyy2bFjR82dO1d79+7Vtddeqy+//FIbN27UtGnTJEkHDhxQWlqa4uLi3LepWrWq2rVrp9TUVJ9lMzc3V7m5ue7rWVlZkiSXyyWXy3XR+RSsL2zclYZcvJGJb+TizVYmzkBT+KASVpLHwH3FN3LxRia++VsuxTkOh/n9Cy4LkZ+fr6eeekpTp05VYGCg8vLy9Pzzz2vs2LGSfjvz2alTJx09elR169Z1365///5yOBxatGiR1zbHjx+vCRMmeC1fuHChwsLCinwgAAAAKB3Z2dkaNGiQMjMzFRERcdGxxTqzuXjxYi1YsEALFy5U8+bN9cUXXygpKUnR0dFKSEi4pMmOHTtWI0eOdF/PyspSTEyMunfvXujkXS6XUlJSdOuttyo4OPiS9u+PyMUbmfhGLt5sZdJi/OoS21ZR7RwfX2Lb4r7iG7l4IxPf/C2Xgmeii6JYZXP06NEaM2aM++nwli1b6uDBg5o0aZISEhIUFRUlSUpPT/c4s5menq5WrVr53KbT6ZTT6fRaHhwcXOQfRnHGXknIxRuZ+EYu3ko6k9w8R4ltq6hs/Ey5r/hGLt7IxDd/yaU4x1Csd6NnZ2crIMDzJoGBgcrPz5ckxcbGKioqSmvWrHGvz8rK0pYtW9ShQ4fi7AoAAAB+oFhnNu+44w49//zzatCggZo3b64dO3Zo2rRpevDBByVJDodDSUlJeu6553TNNdcoNjZWycnJio6OVp8+fWzMHwAAAOVYscrmjBkzlJycrL/85S86fvy4oqOj9cgjj2jcuHHuMU888YTOnDmjhx9+WBkZGercubNWrVqlkJCQEp88AAAAyrdilc3w8HBNnz5d06dPv+AYh8OhiRMnauLEiZc7NwAAAFRwfDc6AAAArKFsAgAAwBrKJgAAAKyhbAIAAMCaYr1BCABsaTTmvVLd3w+Te5bq/gDgSsWZTQAAAFhD2QQAAIA1lE0AAABYQ9kEAACANZRNAAAAWEPZBAAAgDWUTQAAAFhD2QQAAIA1lE0AAABYwzcIAQAqJL51CqgYOLMJAAAAayibAAAAsIan0QFckS70FKwz0GhqW6nF+NXKzXOU8qxKVkk+zVyUXHiaGYAvnNkEAACANZRNAAAAWEPZBAAAgDWUTQAAAFhD2QQAAIA1lE0AAABYQ9kEAACANZRNAAAAWEPZBAAAgDWUTQAAAFhD2QQAAIA1lE0AAABYQ9kEAACANZRNAAAAWEPZBAAAgDWUTQAAAFhD2QQAAIA1lE0AAABYQ9kEAACANZRNAAAAWBNU1hMAUD41GvNeWU8BAOAHOLMJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwpthl88iRI7r33ntVs2ZNhYaGqmXLlvr888/d640xGjdunOrWravQ0FDFxcVp3759JTppAAAAVAzFKps///yzOnXqpODgYH3wwQf65ptv9OKLL6p69eruMVOnTtXLL7+sOXPmaMuWLapcubLi4+OVk5NT4pMHAABA+RZUnMFTpkxRTEyM5s2b514WGxvr/rsxRtOnT9ff/vY39e7dW5L0r3/9S5GRkVq+fLkGDhxYQtMGAABARVCsM5srVqxQmzZtdPfdd6tOnTq68cYb9eqrr7rXHzhwQGlpaYqLi3Mvq1q1qtq1a6fU1NSSmzUAAAAqhGKd2fz+++81e/ZsjRw5Uk899ZS2bt2q4cOHq1KlSkpISFBaWpokKTIy0uN2kZGR7nXny83NVW5urvt6VlaWJMnlcsnlcl10PgXrCxt3pSEXb2Ti28VycQaa0p5OueAMMB5/4jdFyaW0f79K+z7q6/h4bPFGJr75Wy7FOQ6HMabIv62VKlVSmzZttGnTJvey4cOHa+vWrUpNTdWmTZvUqVMnHT16VHXr1nWP6d+/vxwOhxYtWuS1zfHjx2vChAleyxcuXKiwsLAiHwgAAABKR3Z2tgYNGqTMzExFRERcdGyxzmzWrVtXzZo181jWtGlTLV26VJIUFRUlSUpPT/com+np6WrVqpXPbY4dO1YjR450X8/KylJMTIy6d+9e6ORdLpdSUlJ06623Kjg4uDiH4tfIxRuZ+HaxXFqMX11GsypbzgCjZ9vkK/nzAOXmO8p6OuVGUXLZOT6+VOdU2vdRX8fHY4s3MvHN33IpeCa6KIpVNjt16qQ9e/Z4LNu7d68aNmwo6bc3C0VFRWnNmjXucpmVlaUtW7boz3/+s89tOp1OOZ1Or+XBwcFF/mEUZ+yVhFy8kYlvvnLJzbuyi1ZuvuOKz8CXi+VS2r9bpf3zudjx8djijUx885dcinMMxSqbI0aMUMeOHfW///u/6t+/vz777DPNnTtXc+fOlSQ5HA4lJSXpueee0zXXXKPY2FglJycrOjpaffr0KdZBAAAAoOIrVtm86aabtGzZMo0dO1YTJ05UbGyspk+frsGDB7vHPPHEEzpz5owefvhhZWRkqHPnzlq1apVCQkJKfPIAAAAo34pVNiXp9ttv1+23337B9Q6HQxMnTtTEiRMva2IAAACo+IpdNgGUjUZj3ivxbToDjaa2/e2NFrw+EQBgQ7G/Gx0AAAAoKsomAAAArKFsAgAAwBrKJgAAAKyhbAIAAMAayiYAAACs4aOPAAAlwsbHcwGo+DizCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGuCynoCAADAW6Mx75Xq/n6Y3LNU94crB2c2AQAAYA1lEwAAANbwNDoAAEXg62ltZ6DR1LZSi/GrlZvnKINZAeUfZzYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYE1QWU8AAACUvUZj3iuR7TgDjaa2lVqMX63cPMdFx/4wuWeJ7BPlG2c2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWXFbZnDx5shwOh5KSktzLcnJylJiYqJo1a6pKlSrq16+f0tPTL3eeAAAAqIAu+d3oW7du1T//+U9df/31HstHjBih9957T0uWLFHVqlU1bNgw9e3bV59++ullTxYoL0rqXZsAAPi7Szqz+csvv2jw4MF69dVXVb16dffyzMxMvfbaa5o2bZpuueUWtW7dWvPmzdOmTZu0efPmEps0AAAAKoZLKpuJiYnq2bOn4uLiPJZv27ZNLpfLY3mTJk3UoEEDpaamXt5MAQAAUOEU+2n0//znP9q+fbu2bt3qtS4tLU2VKlVStWrVPJZHRkYqLS3N5/Zyc3OVm5vrvp6VlSVJcrlccrlcF51LwfrCxl1pyMVbSWfiDDQlsp2y5gwwHn+CTC6EXHwjF2/FyeRK+nfK3/5tLs5xFKtsHj58WI8//rhSUlIUEhJS7In5MmnSJE2YMMFr+YcffqiwsLAibSMlJaVE5uJvyMVbSWUytW2JbKbceLZNfllPodwhE9/IxTdy8VaUTN5///1SmEn54i//NmdnZxd5rMMYU+T/ji1fvlx33nmnAgMD3cvy8vLkcDgUEBCg1atXKy4uTj///LPH2c2GDRsqKSlJI0aM8NqmrzObMTExOnHihCIiIi46H5fLpZSUFN16660KDg4u6mH4vfKQS4vxq0t1fzvHx190fUlnUtrHZ4szwOjZNvlK/jxAufkX/1q5KwWZ+EYuvpGLt+JkUthjtz8pD/82l6SsrCzVqlVLmZmZhfa1Yp3Z7Natm77++muPZUOGDFGTJk305JNPKiYmRsHBwVqzZo369esnSdqzZ48OHTqkDh06+Nym0+mU0+n0Wh4cHFzkH0Zxxl5JyjKXwr4Pt6SV9n2ltI/Pttx8h98d0+UiE9/IxTdy8VaUTK7Ef7v9pbMU5xiKVTbDw8PVokULj2WVK1dWzZo13cuHDh2qkSNHqkaNGoqIiNBjjz2mDh06qH379sXZFQAAAPzAJX/O5oW89NJLCggIUL9+/ZSbm6v4+HjNmjWrpHcDAACACuCyy+b69es9roeEhGjmzJmaOXPm5W4aAAAAFRzfjQ4AAABrSvxpdAAAgKIo7a/+/WFyz1LdH37DmU0AAABYQ9kEAACANTyNDgAArgg8bV82OLMJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwho8+AgAAsOD3H7XkDDSa2lZqMX61cvMcVvZXXj9qiTObAAAAsIayCQAAAGt4Gh1+obBvhSiNpy8AAIA3zmwCAADAGsomAAAArKFsAgAAwBpes3mFKOw1jQAAADZwZhMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANYElfUErlSNxrxnbdvOQKOpbaUW41crN89hbT8AAACF4cwmAAAArKFsAgAAwBrKJgAAAKyhbAIAAMAayiYAAACsoWwCAADAGsomAAAArKFsAgAAwBrKJgAAAKyhbAIAAMAayiYAAACsoWwCAADAGsomAAAArKFsAgAAwBrKJgAAAKwpVtmcNGmSbrrpJoWHh6tOnTrq06eP9uzZ4zEmJydHiYmJqlmzpqpUqaJ+/fopPT29RCcNAACAiqFYZXPDhg1KTEzU5s2blZKSIpfLpe7du+vMmTPuMSNGjNB///tfLVmyRBs2bNDRo0fVt2/fEp84AAAAyr+g4gxetWqVx/X58+erTp062rZtm26++WZlZmbqtdde08KFC3XLLbdIkubNm6emTZtq8+bNat++fcnNHAAAAOVescrm+TIzMyVJNWrUkCRt27ZNLpdLcXFx7jFNmjRRgwYNlJqa6rNs5ubmKjc31309KytLkuRyueRyuS66/4L1hY0rj5yBxt62A4zHnyCTCyEXb2TiG7n4Ri7eyMS30silNPtQcfblMMZc0lHn5+erV69eysjI0MaNGyVJCxcu1JAhQzzKoyS1bdtWf/zjHzVlyhSv7YwfP14TJkzwWr5w4UKFhYVdytQAAABgUXZ2tgYNGqTMzExFRERcdOwln9lMTEzUzp073UXzUo0dO1YjR450X8/KylJMTIy6d+9e6ORdLpdSUlJ06623Kjg4+LLmUdpajF9tbdvOAKNn2+Qr+fMA5eY7rO2nIiET38jFG5n4Ri6+kYs3MvGtNHLZOT7eynZ9KXgmuiguqWwOGzZMK1eu1Mcff6z69eu7l0dFRencuXPKyMhQtWrV3MvT09MVFRXlc1tOp1NOp9NreXBwcJELZHHGlhe5efZ/AXPzHaWyn4qETHwjF29k4hu5+EYu3sjEN5u5lGYXKs6+ivVudGOMhg0bpmXLlmnt2rWKjY31WN+6dWsFBwdrzZo17mV79uzRoUOH1KFDh+LsCgAAAH6gWGc2ExMTtXDhQr377rsKDw9XWlqaJKlq1aoKDQ1V1apVNXToUI0cOVI1atRQRESEHnvsMXXo0IF3ogMAAFyBilU2Z8+eLUnq2rWrx/J58+bpgQcekCS99NJLCggIUL9+/ZSbm6v4+HjNmjWrRCYLAACAiqVYZbMob1wPCQnRzJkzNXPmzEueFAAAAPwD340OAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrgsp6AuVFozHvlfUUAAAA/A5nNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgDWUTAAAA1lA2AQAAYA1lEwAAANZQNgEAAGANZRMAAADWUDYBAABgjbWyOXPmTDVq1EghISFq166dPvvsM1u7AgAAQDllpWwuWrRII0eO1DPPPKPt27frhhtuUHx8vI4fP25jdwAAACinrJTNadOm6aGHHtKQIUPUrFkzzZkzR2FhYXr99ddt7A4AAADlVFBJb/DcuXPatm2bxo4d614WEBCguLg4paameo3Pzc1Vbm6u+3pmZqYk6dSpU3K5XBfdl8vlUnZ2tk6ePKng4ODLmnfQr2cu6/blSVC+UXZ2voJcAcrLd5T1dMoFMvGNXLyRiW/k4hu5eCMT30ojl5MnT1rZri+nT5+WJBljCh9sStiRI0eMJLNp0yaP5aNHjzZt27b1Gv/MM88YSVy4cOHChQsXLlwq2OXw4cOFdsMSP7NZXGPHjtXIkSPd1/Pz83Xq1CnVrFlTDsfFm39WVpZiYmJ0+PBhRURE2J5qhUEu3sjEN3LxRia+kYtv5OKNTHzzt1yMMTp9+rSio6MLHVviZbNWrVoKDAxUenq6x/L09HRFRUV5jXc6nXI6nR7LqlWrVqx9RkRE+MUPrqSRizcy8Y1cvJGJb+TiG7l4IxPf/CmXqlWrFmlcib9BqFKlSmrdurXWrFnjXpafn681a9aoQ4cOJb07AAAAlGNWnkYfOXKkEhIS1KZNG7Vt21bTp0/XmTNnNGTIEBu7AwAAQDllpWwOGDBAP/30k8aNG6e0tDS1atVKq1atUmRkZInux+l06plnnvF6Gv5KRy7eyMQ3cvFGJr6Ri2/k4o1MfLuSc3EYU5T3rAMAAADFx3ejAwAAwBrKJgAAAKyhbAIAAMAayiYAAACsKfdlMy8vT8nJyYqNjVVoaKgaN26sZ5991uO7OI0xGjdunOrWravQ0FDFxcVp3759ZTjrkvfxxx/rjjvuUHR0tBwOh5YvX+6xvigZnDp1SoMHD1ZERISqVaumoUOH6pdffinFoyh5F8vF5XLpySefVMuWLVW5cmVFR0fr/vvv19GjRz224W+5FHZf+b1HH31UDodD06dP91jub5lIRctl9+7d6tWrl6pWrarKlSvrpptu0qFDh9zrc3JylJiYqJo1a6pKlSrq16+f1xdYVCSFZfLLL79o2LBhql+/vkJDQ9WsWTPNmTPHY4y/ZSJJkyZN0k033aTw8HDVqVNHffr00Z49ezzGFOW4Dx06pJ49eyosLEx16tTR6NGj9euvv5bmoZSYwjI5deqUHnvsMV133XUKDQ1VgwYNNHz4cGVmZnpsx58ykYp2XylgjFGPHj18/q75Wy7nK/dlc8qUKZo9e7ZeeeUV7d69W1OmTNHUqVM1Y8YM95ipU6fq5Zdf1pw5c7RlyxZVrlxZ8fHxysnJKcOZl6wzZ87ohhtu0MyZM32uL0oGgwcP1q5du5SSkqKVK1fq448/1sMPP1xah2DFxXLJzs7W9u3blZycrO3bt+udd97Rnj171KtXL49x/pZLYfeVAsuWLdPmzZt9ftWYv2UiFZ7L/v371blzZzVp0kTr16/XV199peTkZIWEhLjHjBgxQv/973+1ZMkSbdiwQUePHlXfvn1L6xBKXGGZjBw5UqtWrdKbb76p3bt3KykpScOGDdOKFSvcY/wtE0nasGGDEhMTtXnzZqWkpMjlcql79+46c+aMe0xhx52Xl6eePXvq3Llz2rRpk9544w3Nnz9f48aNK4tDumyFZXL06FEdPXpUf//737Vz507Nnz9fq1at0tChQ93b8LdMpKLdVwpMnz7d59dw+2MuXgr99vQy1rNnT/Pggw96LOvbt68ZPHiwMcaY/Px8ExUVZV544QX3+oyMDON0Os1bb71VqnMtLZLMsmXL3NeLksE333xjJJmtW7e6x3zwwQfG4XCYI0eOlNrcbTo/F18+++wzI8kcPHjQGOP/uVwokx9//NHUq1fP7Ny50zRs2NC89NJL7nX+nokxvnMZMGCAuffeey94m4yMDBMcHGyWLFniXrZ7924jyaSmptqaaqnxlUnz5s3NxIkTPZb9v//3/8zTTz9tjPH/TAocP37cSDIbNmwwxhTtuN9//30TEBBg0tLS3GNmz55tIiIiTG5ubukegAXnZ+LL4sWLTaVKlYzL5TLG+H8mxlw4lx07dph69eqZY8eOef2uXQm5lPszmx07dtSaNWu0d+9eSdKXX36pjRs3qkePHpKkAwcOKC0tTXFxce7bVK1aVe3atVNqamqZzLm0FSWD1NRUVatWTW3atHGPiYuLU0BAgLZs2VLqcy4rmZmZcjgcqlatmqQrM5f8/Hzdd999Gj16tJo3b+61/krN5L333tO1116r+Ph41alTR+3atfN4qmvbtm1yuVwev2dNmjRRgwYN/PaxpmPHjlqxYoWOHDkiY4zWrVunvXv3qnv37pKunEwKngquUaOGpKIdd2pqqlq2bOnxZSbx8fHKysrSrl27SnH2dpyfyYXGREREKCjot++P8fdMJN+5ZGdna9CgQZo5c6aioqK8bnMl5FLuy+aYMWM0cOBANWnSRMHBwbrxxhuVlJSkwYMHS5LS0tIkyevbiSIjI93r/F1RMkhLS1OdOnU81gcFBalGjRpXTE45OTl68skndc899ygiIkLSlZnLlClTFBQUpOHDh/tcfyVmcvz4cf3yyy+aPHmybrvtNn344Ye688471bdvX23YsEHSb7lUqlTJ/R+VAv78WDNjxgw1a9ZM9evXV6VKlXTbbbdp5syZuvnmmyVdGZnk5+crKSlJnTp1UosWLSQV7bjT0tJ8PiYXrKvIfGVyvhMnTujZZ5/1ePmNP2ciXTiXESNGqGPHjurdu7fP2/l7LpKlr6ssSYsXL9aCBQu0cOFCNW/eXF988YWSkpIUHR2thISEsp4eKgiXy6X+/fvLGKPZs2eX9XTKzLZt2/SPf/xD27dv9/naoStVfn6+JKl3794aMWKEJKlVq1batGmT5syZoy5dupTl9MrMjBkztHnzZq1YsUINGzbUxx9/rMTEREVHR3uc1fNniYmJ2rlzpzZu3FjWUyk3CsskKytLPXv2VLNmzTR+/PjSnVwZ8pXLihUrtHbtWu3YsaMMZ1b2yv2ZzdGjR7vPbrZs2VL33XefRowYoUmTJkmS+5T0+e8CTE9P93m62h8VJYOoqCgdP37cY/2vv/6qU6dO+X1OBUXz4MGDSklJcZ/VlK68XD755BMdP35cDRo0UFBQkIKCgnTw4EGNGjVKjRo1knTlZSJJtWrVUlBQkJo1a+axvGnTpu53o0dFRencuXPKyMjwGOOvjzVnz57VU089pWnTpumOO+7Q9ddfr2HDhmnAgAH6+9//Lsn/Mxk2bJhWrlypdevWqX79+u7lRTnuqKgon4/JBesqqgtlUuD06dO67bbbFB4ermXLlik4ONi9zl8zkS6cy9q1a7V//35Vq1bN/ZgrSf369VPXrl0l+XcuBcp92czOzlZAgOc0AwMD3WciYmNjFRUVpTVr1rjXZ2VlacuWLerQoUOpzrWsFCWDDh06KCMjQ9u2bXOPWbt2rfLz89WuXbtSn3NpKSia+/bt00cffaSaNWt6rL/Scrnvvvv01Vdf6YsvvnBfoqOjNXr0aK1evVrSlZeJJFWqVEk33XST10eW7N27Vw0bNpQktW7dWsHBwR6/Z3v27NGhQ4f88rHG5XLJ5XJd9PHXXzMxxmjYsGFatmyZ1q5dq9jYWI/1RTnuDh066Ouvv/b4j1vBf3bP/09NRVBYJtJv/+50795dlSpV0ooVKzw+yUHyv0ykwnMZM2aM12OuJL300kuaN2+eJP/MxUuZvj2pCBISEky9evXMypUrzYEDB8w777xjatWqZZ544gn3mMmTJ5tq1aqZd99913z11Vemd+/eJjY21pw9e7YMZ16yTp8+bXbs2GF27NhhJJlp06aZHTt2uN9VXZQMbrvtNnPjjTeaLVu2mI0bN5prrrnG3HPPPWV1SCXiYrmcO3fO9OrVy9SvX9988cUX5tixY+7L79/h52+5FHZfOd/570Y3xv8yMabwXN555x0THBxs5s6da/bt22dmzJhhAgMDzSeffOLexqOPPmoaNGhg1q5daz7//HPToUMH06FDh7I6pMtWWCZdunQxzZs3N+vWrTPff/+9mTdvngkJCTGzZs1yb8PfMjHGmD//+c+matWqZv369R6PG9nZ2e4xhR33r7/+alq0aGG6d+9uvvjiC7Nq1SpTu3ZtM3bs2LI4pMtWWCaZmZmmXbt2pmXLlua7777zGPPrr78aY/wvE2OKdl85n857N7o/5nK+cl82s7KyzOOPP24aNGhgQkJCzFVXXWWefvppj7KQn59vkpOTTWRkpHE6naZbt25mz549ZTjrkrdu3TojyeuSkJBgjClaBidPnjT33HOPqVKliomIiDBDhgwxp0+fLoOjKTkXy+XAgQM+10ky69atc2/D33Ip7L5yPl9l098yMaZoubz22mvm6quvNiEhIeaGG24wy5cv99jG2bNnzV/+8hdTvXp1ExYWZu68805z7NixUj6SklNYJseOHTMPPPCAiY6ONiEhIea6664zL774osnPz3dvw98yMcZc8HFj3rx57jFFOe4ffvjB9OjRw4SGhppatWqZUaNGuT8GqKIpLJML3ZckmQMHDri340+ZGFO0+4qv25z/MWP+lsv5HMb87qt4AAAAgBJU7l+zCQAAgIqLsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGsomwAAALCGsgkAAABrKJsAAACwhrIJAAAAayibAAAAsIayCQAAAGv+Pyig/MjvU0L9AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x300 with 3 Axes>"
            ],
            "image/png": 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