Cap10_Segmentacao_Consumidores_Campanha_Marketing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Cap10-Segmentacao_Consumidores_Campanha_Marketing.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "fOOOJUmkPa6Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "b24599fc-6c94-43ba-9a30-d0156152a3c8"
      },
      "source": [
        "%load_ext rpy2.ipython"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/rpy2/robjects/pandas2ri.py:14: FutureWarning: pandas.core.index is deprecated and will be removed in a future version.  The public classes are available in the top-level namespace.\n",
            "  from pandas.core.index import Index as PandasIndex\n",
            "/usr/local/lib/python3.6/dist-packages/rpy2/robjects/pandas2ri.py:34: UserWarning: pandas >= 1.0 is not supported.\n",
            "  warnings.warn('pandas >= 1.0 is not supported.')\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xd_Q_ga_Rk1h",
        "colab_type": "text"
      },
      "source": [
        "# Lab - Segmentação de Consumidores Para Campanhas Customizadas de Marketing\n",
        "\n",
        "# Objetivo: Encontrar segmentos de consumidores para campanhas personalizadas de Marketing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8JYQIhaRiu-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Clustering com K-Means\n",
        "https://cran.r-project.org/web/views/Cluster.html\n",
        "\n",
        "Obs: Caso tenha problemas com a acentuação, consulte este link:\n",
        " https://support.rstudio.com/hc/en-us/articles/200532197-Character-Encoding"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOcYqhwnRioI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%R\n",
        "# Pacotes\n",
        "install.packages(\"factoextra\")\n",
        "install.packages(\"cluster\")\n",
        "install.packages(\"fpc\")\n",
        "install.packages(\"NbClust\")\n",
        "install.packages(\"clValid\")\n",
        "install.packages(\"clustertend\")\n",
        "library(factoextra)\n",
        "library(cluster)\n",
        "library(fpc)\n",
        "library(NbClust)\n",
        "library(clValid)\n",
        "library(magrittr)\n",
        "library(clustertend)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "06lJd8TfSCNF",
        "colab_type": "text"
      },
      "source": [
        "##Carregando os dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TymnO8GhRiku",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%R\n",
        "dados_clientes_v1 <- read.csv(\"dados_clientes.csv\")"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVxEMGauRih-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "b37d6709-5a09-44d9-ec87-dd9e838ed12b"
      },
      "source": [
        "%%R\n",
        "str(dados_clientes_v1)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'data.frame':\t200 obs. of  5 variables:\n",
            " $ IDCliente            : int  1 2 3 4 5 6 7 8 9 10 ...\n",
            " $ Sexo                 : Factor w/ 2 levels \"Feminino\",\"Masculino\": 2 2 1 1 1 1 1 1 2 1 ...\n",
            " $ Idade                : int  19 21 20 23 31 22 35 23 64 30 ...\n",
            " $ Salario_Mensal_Milhar: int  15 15 16 16 17 17 18 18 19 19 ...\n",
            " $ Pontuacao_Gasto      : int  39 81 6 77 40 76 6 94 3 72 ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_yNqGmcRifc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e26883e7-2375-4efc-aecd-7cc468096b28"
      },
      "source": [
        "%%R\n",
        "names(dados_clientes_v1)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1] \"IDCliente\"             \"Sexo\"                  \"Idade\"                \n",
            "[4] \"Salario_Mensal_Milhar\" \"Pontuacao_Gasto\"      \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_LqhP8zRicg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%R\n",
        "#View(dados_clientes_v1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vDcj6h04TPIN",
        "colab_type": "text"
      },
      "source": [
        "##Análise Exploratória"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a78zl1arRiZr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "a6803189-67e5-46d7-d3df-f10bfe27c736"
      },
      "source": [
        "%%R\n",
        "# Tabela de proporção do sexo dos clientes\n",
        "tabela_sexos = table(dados_clientes_v1$Sexo)\n",
        "tabela_sexos"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Feminino Masculino \n",
            "      112        88 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mA56wlJkf9Qg",
        "colab_type": "text"
      },
      "source": [
        "Checar valores missing através da tabela"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3T4nPDTARiXC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "26c34fa1-655e-4a5f-83d0-3fec60295d89"
      },
      "source": [
        "%%R\n",
        "table(dados_clientes_v1$Sexo, useNA = \"ifany\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " Feminino Masculino \n",
            "      112        88 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3TbHHh6TadX",
        "colab_type": "text"
      },
      "source": [
        "Buscando valores missing para variáveis relacionadas à **idade** - Função summary() é capaz de retornar **isna**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-TZaCkdTgaE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "6b79ae91-9d5f-4aff-880a-a5bcba70c60d"
      },
      "source": [
        "%%R\n",
        "summary(dados_clientes_v1$Idade)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
            "  18.00   28.75   36.00   38.85   49.00   70.00 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vKCIPbgUTiVd",
        "colab_type": "text"
      },
      "source": [
        "Buscando a média de idade"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sr2pna_KTkjq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "921519e7-cbcd-4741-c2cb-84285b79f3ec"
      },
      "source": [
        "%%R\n",
        "mean(dados_clientes_v1$Idade)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1] 38.85\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NFtET0rXTnbF",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Barplot de proporção do sexo dos clientes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8h0DE500Tujf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "d76cceff-33a7-4889-b420-c246059dd9f1"
      },
      "source": [
        "%%R\n",
        "barplot(tabela_sexos,\n",
        "        main = \"Proporção de Sexo dos Clientes\",\n",
        "        ylab = \"Total\",\n",
        "        xlab = \"Sexo\",\n",
        "        col = rainbow(2),\n",
        "        legend = rownames(tabela_sexos))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pe5ZrhEWTwuU",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Histograma com a distribuição de frequência das idades dos clientes\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rbcvhg-GT7GV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "a27c8383-52cc-40fb-eca9-5e75409dfac1"
      },
      "source": [
        "%%R\n",
        "hist(dados_clientes_v1$Idade,\n",
        "     col = \"blue\",\n",
        "     main = \"Histogramas de Idades\",\n",
        "     xlab = \"Idade\",\n",
        "     ylab = \"Frequência\",\n",
        "     labels = TRUE)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0gdgJZhT9c_",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Boxplot para análise descritiva da idade"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LSi9JDgxUEQL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "d195e516-4015-48fd-f9f5-19f38ceacff6"
      },
      "source": [
        "%%R\n",
        "boxplot(dados_clientes_v1$Idade, \n",
        "        col = 3, \n",
        "        main = \"Boxplot Para Análise Descritiva da Idade\")"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FQyUAob1UFMk",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Histograma com a distribuição de frequência do salário mensal\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "92Wf6Mk4UJpC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "a8dff082-05b4-48b1-e0ec-6f5528949f2f"
      },
      "source": [
        "%%R\n",
        "names(dados_clientes_v1)\n",
        "summary(dados_clientes_v1$Salario_Mensal_Milhar)\n",
        "hist(dados_clientes_v1$Salario_Mensal_Milhar,\n",
        "     col = \"#660033\",\n",
        "     main = \"Histograma de Salário Mensal\",\n",
        "     xlab = \"Salário Mensal\",\n",
        "     ylab = \"Frequência\",\n",
        "     labels = TRUE)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_YbAT3FxULRw",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "---\n",
        "##Análise da Pontuação dos Clientes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wb0smuPwUPIE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e64a3958-46fe-4c21-8c3e-e466895459ec"
      },
      "source": [
        "%%R\n",
        "summary(dados_clientes_v1$Pontuacao_Gasto)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
            "   1.00   34.75   50.00   50.20   73.00   99.00 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbUrVy0dUSqF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "f8a93f8a-d50a-4f90-ae4e-bff8901554bb"
      },
      "source": [
        "%%R\n",
        "boxplot(dados_clientes_v1$Pontuacao_Gasto,\n",
        "        horizontal = TRUE,\n",
        "        col = \"#990000\",\n",
        "        main = \"BoxPlot Para Análise Descritiva da Pontuação do Cliente\")"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yQq_G_hOUTb6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "ec4a573b-6a46-4404-8441-91794ec77a99"
      },
      "source": [
        "%%R\n",
        "hist(dados_clientes_v1$Pontuacao_Gasto,\n",
        "     main = \"Histograma de Pontuação de Gasto\",\n",
        "     xlab = \"Pontuação de Gasto\",\n",
        "     ylab = \"Frequência\",\n",
        "     col = \"#6600cc\",\n",
        "     labels = TRUE)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-eRQrn2gUWiY",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "##Pré-Processamento dos Dados - Versão_2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eNimCg-xUceI",
        "colab_type": "text"
      },
      "source": [
        "Pré-processando e analisando os dados independente do sexo\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zU_OLJH9Uew5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "935c494d-ba23-44fe-dc1f-a1a5080c7c91"
      },
      "source": [
        "%%R\n",
        "dados_clientes_v2 <- dados_clientes_v1[,-c(1,2)]\n",
        "head(dados_clientes_v2)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Idade Salario_Mensal_Milhar Pontuacao_Gasto\n",
            "1    19                    15              39\n",
            "2    21                    15              81\n",
            "3    20                    16               6\n",
            "4    23                    16              77\n",
            "5    31                    17              40\n",
            "6    22                    17              76\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iA2lfoKwUhp1",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Padronizando as variáveis - K-Means requer dados normalizados numa **distribuição normal**, média igual a 0 e desvio padrão igual a 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oXKE2TNEUkqV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "861cf2f3-1186-4104-cb61-ebfa57e9ff7e"
      },
      "source": [
        "%%R\n",
        "# Estestes dados representam os mesmos dados do head() acima\n",
        "dados_clientes_v2_scaled = scale(dados_clientes_v2)\n",
        "head(dados_clientes_v2_scaled)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "          Idade Salario_Mensal_Milhar Pontuacao_Gasto\n",
            "[1,] -1.4210029             -1.734646      -0.4337131\n",
            "[2,] -1.2778288             -1.734646       1.1927111\n",
            "[3,] -1.3494159             -1.696572      -1.7116178\n",
            "[4,] -1.1346547             -1.696572       1.0378135\n",
            "[5,] -0.5619583             -1.658498      -0.3949887\n",
            "[6,] -1.2062418             -1.658498       0.9990891\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2DQccummUuE3",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "## Avaliando a Tendência de Cluster - **Número ideal de Clusters**\n",
        "Estatística Hopkins para o conjunto de dados, métrica que verifica se o conjunto é **\"clusterizável**\"\n",
        "- Valores > .5 significam que o dataset não é \"clusterizável\"\n",
        "- Valores < .5 significam que o dataset é \"clusterizável\"\n",
        "\n",
        "<font color = 'red'>Quanto mais próximo de zero, melhor. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAXOmkXHUwgv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%R\n",
        "set.seed(123)\n",
        "?hopkins\n",
        "hopkins(dados_clientes_v2_scaled, n = nrow(dados_clientes_v2_scaled)-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t2RiOolGUzNX",
        "colab_type": "text"
      },
      "source": [
        "Valor = 0.3137406 indica que o dataset é \"clusterizável\".\n",
        "\n",
        "---\n",
        "\n",
        "## K-means - Determinando o Número Ideal de Clusters - NbClust()\n",
        "\n",
        "Pacote NbClust: 30 índices para determinar o número de clusters em um conjunto de dados.\n",
        "-  Se index = 'all' - Executa 30 índices para determinar o número ideal de clusters.\n",
        "-  Se index = \"silhouette\" - Usa uma medida para estimar a diferença entre clusters.\n",
        "\n",
        "Um valor de silhueta mais alto é preferido para determinar o número ideal de clusters\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaJ6QpyCWl_5",
        "colab_type": "text"
      },
      "source": [
        "#**Opção 1**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i3NrDZh5U2AU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%R\n",
        "# Opção 1:\n",
        "?NbClust\n",
        "num_clusters_opt1 <- NbClust(dados_clientes_v2_scaled,  \n",
        "                             distance = \"euclidean\",\n",
        "                             min.nc = 2, # testar de 2\n",
        "                             max.nc = 15, # a 15 clusters\n",
        "                             method = \"kmeans\",\n",
        "                             index = \"silhouette\")"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KQqaV0U22Tz7",
        "colab_type": "text"
      },
      "source": [
        "Best.nc - Melhor número de clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1jUw141U4DH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "95969830-a5a1-4330-ec59-7c88ce4197c2"
      },
      "source": [
        "%%R\n",
        "num_clusters_opt1$Best.nc"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number_clusters     Value_Index \n",
            "         6.0000          0.4274 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fwLuVIUSU5Wu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "2319bb0d-5dea-4f44-b56d-7b3254eea8e8"
      },
      "source": [
        "%%R\n",
        "num_clusters_opt1$All.index"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     2      3      4      5      6      7      8      9     10     11     12 \n",
            "0.3355 0.3578 0.3498 0.4166 0.4274 0.3870 0.4121 0.4153 0.4261 0.4187 0.4046 \n",
            "    13     14     15 \n",
            "0.3750 0.3651 0.3550 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HhBLTNEsWhmo",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "##**Opção 2**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9h4wHB-WsRh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4e4c7151-202a-4c12-bc6d-74f63aa4891b"
      },
      "source": [
        "%%R\n",
        "# Opção 2:\n",
        "num_clusters_opt2 <- NbClust(dados_clientes_v2_scaled,  \n",
        "                             distance = \"euclidean\", \n",
        "                             min.nc = 2, \n",
        "                             max.nc = 15, \n",
        "                             method = \"kmeans\",\n",
        "                             index = \"all\")"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*** : The Hubert index is a graphical method of determining the number of clusters.\n",
            "                In the plot of Hubert index, we seek a significant knee that corresponds to a \n",
            "                significant increase of the value of the measure i.e the significant peak in Hubert\n",
            "                index second differences plot. \n",
            " \n",
            "*** : The D index is a graphical method of determining the number of clusters. \n",
            "                In the plot of D index, we seek a significant knee (the significant peak in Dindex\n",
            "                second differences plot) that corresponds to a significant increase of the value of\n",
            "                the measure. \n",
            " \n",
            "******************************************************************* \n",
            "* Among all indices:                                                \n",
            "* 4 proposed 2 as the best number of clusters \n",
            "* 3 proposed 3 as the best number of clusters \n",
            "* 2 proposed 4 as the best number of clusters \n",
            "* 4 proposed 5 as the best number of clusters \n",
            "* 4 proposed 6 as the best number of clusters \n",
            "* 1 proposed 9 as the best number of clusters \n",
            "* 1 proposed 10 as the best number of clusters \n",
            "* 2 proposed 12 as the best number of clusters \n",
            "* 1 proposed 13 as the best number of clusters \n",
            "* 1 proposed 15 as the best number of clusters \n",
            "\n",
            "                   ***** Conclusion *****                            \n",
            " \n",
            "* According to the majority rule, the best number of clusters is  2 \n",
            " \n",
            " \n",
            "******************************************************************* \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rrv-C3epWt9h",
        "colab_type": "text"
      },
      "source": [
        "Um método recomenda 6 clusters e o outro recomenda 2. Usaremos 4 clusters.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kggW-vauXe-M",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "##Criação do Modelo K-Means e Análise de Cluster"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JdCqbSBOW2Tl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "c7f65e27-5d13-45f1-ae80-982d5ead7d3f"
      },
      "source": [
        "%%R\n",
        "?kmeans\n",
        "modelo <- kmeans(dados_clientes_v2_scaled, 4)\n",
        "print(modelo)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "K-means clustering with 4 clusters of sizes 38, 40, 65, 57\n",
            "\n",
            "Cluster means:\n",
            "        Idade Salario_Mensal_Milhar Pontuacao_Gasto\n",
            "1  0.03711223             0.9876366      -1.1857814\n",
            "2 -0.42773261             0.9724070       1.2130414\n",
            "3  1.08344244            -0.4893373      -0.3961802\n",
            "4 -0.96008279            -0.7827991       0.3910484\n",
            "\n",
            "Clustering vector:\n",
            "  [1] 4 4 4 4 4 4 3 4 3 4 3 4 3 4 3 4 4 4 3 4 4 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3\n",
            " [38] 4 3 4 3 4 3 4 3 4 3 4 4 4 3 4 4 3 3 3 3 3 4 3 3 4 3 3 3 4 3 3 4 4 3 3 3 3\n",
            " [75] 3 4 3 3 4 3 3 4 3 3 4 3 3 4 4 3 3 4 3 3 4 4 3 4 3 4 4 3 3 4 3 4 3 3 3 3 3\n",
            "[112] 4 1 4 4 4 3 3 3 3 4 1 2 2 1 2 1 2 3 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2\n",
            "[149] 1 2 1 2 1 2 1 2 1 2 1 2 3 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1\n",
            "[186] 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2\n",
            "\n",
            "Within cluster sum of squares by cluster:\n",
            "[1] 44.01863 23.91544 74.83280 61.43215\n",
            " (between_SS / total_SS =  65.8 %)\n",
            "\n",
            "Available components:\n",
            "\n",
            "[1] \"cluster\"      \"centers\"      \"totss\"        \"withinss\"     \"tot.withinss\"\n",
            "[6] \"betweenss\"    \"size\"         \"iter\"         \"ifault\"      \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nyU50UyWXlkd",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Verificando o tamanho dos clusters\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "asb9bVfjW2QQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4bdd4c56-719f-457b-ae2c-bd66fa1a636d"
      },
      "source": [
        "%%R\n",
        "modelo$size"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1] 38 40 65 57\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-XdDNbgtXqNa",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Verificando o centro|centroides dos clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQeI-uNxW2NY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "54d87507-5a32-4828-f7f2-67828c2b56eb"
      },
      "source": [
        "%%R\n",
        "# Verificando o centro dos clusters\n",
        "modelo$centers"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        Idade Salario_Mensal_Milhar Pontuacao_Gasto\n",
            "1  0.03711223             0.9876366      -1.1857814\n",
            "2 -0.42773261             0.9724070       1.2130414\n",
            "3  1.08344244            -0.4893373      -0.3961802\n",
            "4 -0.96008279            -0.7827991       0.3910484\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iyVL8aDFXotv",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Aplicando o ID dos clusters ao DataFrame original"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ms9ssX-RW79k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "966e7e2e-02aa-4275-c513-9f462893447c"
      },
      "source": [
        "%%R\n",
        "# Aplicando o ID dos clusters ao dataframe original\n",
        "modelo$cluster"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  [1] 4 4 4 4 4 4 3 4 3 4 3 4 3 4 3 4 4 4 3 4 4 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3\n",
            " [38] 4 3 4 3 4 3 4 3 4 3 4 4 4 3 4 4 3 3 3 3 3 4 3 3 4 3 3 3 4 3 3 4 4 3 3 3 3\n",
            " [75] 3 4 3 3 4 3 3 4 3 3 4 3 3 4 4 3 3 4 3 3 4 4 3 4 3 4 4 3 3 4 3 4 3 3 3 3 3\n",
            "[112] 4 1 4 4 4 3 3 3 3 4 1 2 2 1 2 1 2 3 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2\n",
            "[149] 1 2 1 2 1 2 1 2 1 2 1 2 3 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1\n",
            "[186] 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LWM80WleW76p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "e8594555-598a-4670-b37e-6c72fd18b86b"
      },
      "source": [
        "%%R\n",
        "dados_clientes_v1$Cluster <- modelo$cluster\n",
        "#View(dados_clientes_v1)\n",
        "head(dados_clientes_v1)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  IDCliente      Sexo Idade Salario_Mensal_Milhar Pontuacao_Gasto Cluster\n",
            "1         1 Masculino    19                    15              39       4\n",
            "2         2 Masculino    21                    15              81       4\n",
            "3         3  Feminino    20                    16               6       4\n",
            "4         4  Feminino    23                    16              77       4\n",
            "5         5  Feminino    31                    17              40       4\n",
            "6         6  Feminino    22                    17              76       4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XeWetV5CXyA7",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Média de idade por Cluster\n",
        "  - Função aggregate nos permite criar subsets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uVT5xQHLW74B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "4abbbc81-95df-400d-cc79-2e525989e1bd"
      },
      "source": [
        "%%R\n",
        "# Média de idade por cluster\n",
        "?aggregate\n",
        "aggregate(data = dados_clientes_v1, Idade ~ Cluster, mean)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Cluster    Idade\n",
            "1       1 39.36842\n",
            "2       2 32.87500\n",
            "3       3 53.98462\n",
            "4       4 25.43860\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GOOYSSDZXz24",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Média de salário por Cluster"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ac5iezwIXMh6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "d7cca0d5-e7c8-4586-f689-bfd734faa256"
      },
      "source": [
        "%%R\n",
        "# Média de salário por cluster\n",
        "aggregate(data = dados_clientes_v1, Salario_Mensal_Milhar ~ Cluster, mean)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Cluster Salario_Mensal_Milhar\n",
            "1       1              86.50000\n",
            "2       2              86.10000\n",
            "3       3              47.70769\n",
            "4       4              40.00000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GRedJVFiX3Rq",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Média de pontuação de gasto por Cluster\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpm6IMhkXMgB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "d4c15cdb-e90c-4554-a032-c42b20cbd8fa"
      },
      "source": [
        "%%R\n",
        "# Média de pontuação de gasto por cluster\n",
        "aggregate(data = dados_clientes_v1, Pontuacao_Gasto ~ Cluster, mean)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Cluster Pontuacao_Gasto\n",
            "1       1        19.57895\n",
            "2       2        81.52500\n",
            "3       3        39.96923\n",
            "4       4        60.29825\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ezohujd3X6x_",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Média de idade e salário por Cluster"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R9mcNimDXMc0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "6e8d5cc1-da59-490d-c9eb-dd6faa63aff2"
      },
      "source": [
        "%%R\n",
        "# Média de idade e salário por cluster\n",
        "aggregate(data = dados_clientes_v1, cbind(Idade, Salario_Mensal_Milhar) ~ Cluster, mean)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Cluster    Idade Salario_Mensal_Milhar\n",
            "1       1 39.36842              86.50000\n",
            "2       2 32.87500              86.10000\n",
            "3       3 53.98462              47.70769\n",
            "4       4 25.43860              40.00000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0MQe3VmhX91k",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Total de pessoas por sexo e por Cluster"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KICC1pPZXMaR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "4bc9d803-6f45-4edd-cbff-834ff19f9c15"
      },
      "source": [
        "%%R\n",
        "# Total de pessoas por sexo e por cluster\n",
        "with(dados_clientes_v1, table(Sexo, Cluster))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "           Cluster\n",
            "Sexo         1  2  3  4\n",
            "  Feminino  19 22 37 34\n",
            "  Masculino 19 18 28 23\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zA3JjTcjYBP8",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "#**Plotagem do Modelo Normalizado**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "75teEmuQXMXo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "1ab0d35b-5045-4110-a778-cc0a3143f00d"
      },
      "source": [
        "%%R\n",
        "# Plot do Modelo\n",
        "plot(dados_clientes_v2_scaled, col = modelo$cluster, pch = 15) "
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UO7B_AloYCv4",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Melhorando a visualização"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5i2iJ-0LXMVF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%R\n",
        "# Melhorando a Visualização\n",
        "?eclust\n",
        "cluster_viz <- eclust(dados_clientes_v2_scaled, \"kmeans\", k = 4, nstart = 25, graph = FALSE)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPGASPUoYEzB",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        "Visualizando Clusters K-Means dados Normalizados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRDIEpYTXacz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "f5e0d8ee-54a7-47dc-89e4-a4d71f858b32"
      },
      "source": [
        "%%R\n",
        "# Visualizando Clusters K-Means\n",
        "?fviz_cluster\n",
        "fviz_cluster(cluster_viz, geom = \"point\", ellipse.type = \"norm\")"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tBvgTHt3YLzX",
        "colab_type": "text"
      },
      "source": [
        "Outra opção de visualização"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUJCdNb7XaZJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "3e71c1d2-ae19-4f91-d4f7-2647476851a3"
      },
      "source": [
        "%%R\n",
        "# Outra opção de visualização com conjunto de dados Original\n",
        "?clusplot\n",
        "par(c(1,1))\n",
        "clusplot(dados_clientes_v1, dados_clientes_v1$Cluster, color=TRUE, shade=TRUE, labels=5, lines=0)\n"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ky2Pl5THzOgh2kBCWKEWlo0RHSQhJSAhLiAmKNfeVtiqIoAkEQgV0Nj2UHnrv9z0mYkaVRuUwcwRAAAA6C3WOK4sTFxJX76guLiQuLiw+qN0gOpt+Pvs8AjRSdGQOMyeXlVvEKmIDO52ZrimmKS8mr9RBidw/rA9E0ATCPw0LscJKwoKLgt/R3iWUJbQXbJ/Pyu8u3N26h/XgtoMlhSVxs52pO4e0G3JQ+iDnsTtTd45tP7ZykM6mm0iYfCn+sjttd3xp/Oj2oyd3mLxQfKGQgFDTfbBWARE0gfAv8p3+3S3LLZ2RnsvKHS06Wr6DvF1PO9m2sraptqWo9JD0Ic7GO1N3MhCjsoirCh7pfQQAJrSfsKrrKgBYnLD4Us6lU1mn1Duqr+22trdI78b/fK0EImgC4V+BzqY/K3x2//f9MHpYMSru36b/hX4Xugp3pRo0iJ0rB6VEpK7IXGED+9HvRysTV/YS6WXZ3XKE6IhG+IitDTLVm0Bo5dDYNI88j3nx83RidL7Rv63rtk69o7pGRw3vAd5NYGcqKAiCMzrP8B/oX4JK5sTN2ZW6q4Rd0vCftnVBBE0gtE4YiOGe424cb6wXq5fCSDkkfejV4Fe2PWy98rzoiH5Y+jBn48a2M4VNqk1vkd4RwyMGth2oHq0eUhzSMJ+2lUJSHARCayO8JPx89nnPPM8h7Yac6XtmYNuB1K5dqbua184sxDogfQAAFksu1uiksTRx6ZQOU3b33F2/T9xqIYImEFoJJewSz3zPKzlXpEWkAWCBxAIuEfOPnTHSItIjRUf6/fZDgOx62tXlM7d2SIqDQGjx/Cr7tSNlh1q0Wg4z53b/2/3a9Osu0p3P7YyDwiD8aegnGptmn2Zf64/9D0AETSC0YD4Wf1wQv2Bd0rqpHae+GfLGsrula6ZrZRHzp52p4EHpgxmMjLPZZ2v9+Vs7JMXRGoiKgnHjgEaDu3fB0LC5r4bQJHj/9j6ddbqPSJ/dPXcPazcMB3mKmM/tjHHr4zYsfFjfNn11OunU8Bv4FyCCbvEwGGBqCm3bAo0GI0c299UQGhkEyCffZ1PyJtm2stdlrvcS6UXtarl2BoDdabs1O2nuTdurKqYqKij61+/hH4GkOFo8u3dDZiZoaICoKAwgyx60XhAg73xvlSiVIxlH9DvrBwwKaDV2xsETfU6s6bbGOcP5r1/FvwMRdMsmMBBcXODqVUhMhOHDQZD8PFspjwoeTYua9rH44+T2kyeLTT7R5wTn3urtXMIoyS7KxkG+tTMOLpJYFFgUmM5Ir8FX8k9A/oduweTnw6JFsHUrKCtDeDiMIFNnWyMfaB+0Y7T9C/w9B3gKgRAIQE2GyZxBh/sOs0/ORgjxuZ0BQAAEtkttt021rcEX809ActAtmLVrQUwMzM3h61coLAQZGaDTQZSk71oLCWUJNik2ggKCZ/qekWkjU/MkBmcwPjv+Xdw7lcEqs5/MHjhiID/bGfOs4NnTwqd0Np1koqF5R9A5OTnN2HtLJz8fbt2C8HDo1QvGjQMAsLeHO3ea+7IIDUERu8gm1WZl4kpLKctrMtfqbGcA2HZ724E5BxhjGR/ef7Dvas/Zkg/tbJ1qLSIgsqHbBr/fflV+O/8STSHoiIgINTW1Pn36mJiYpKamUnFpaekm6L21IiwMT5+Wv0xMAAB8fEBPr7kvi1A/EKAbuTc0ojVGi45+MujJxPYToTY3ALmCAREB4h3Efdr4sIXZB7QOHHpcMXzmTzsjhJylneeKz/XO967ua/pnaApBr1ixYsqUKX5+fmPGjFFRUYmJiWmCTls9YmKgoVH+KimBHj3AwAC6dGnuyyLUg/CScJ0YnfCS8OeDns8Xn48fZVJnO7PYrF2+uzrId8B5Z9PJpsGxwYk5icDfdgYAmTYyyYxkNrD/+o21epoiBx0aGhoYGCgoKDh27NhRo0bp6em9fPmyR48e1R91586d06dPcwXj4uJGjRq1bt26RrvYFgnJbLQsMgoypDpJcUbobLphnCFCyK2v2+C2g6l4ne0MAGcDz7Yb0E6kgwjOOwsICDjNdrLxtpGZIcPPdsYMazcsoiSCmoDzz9IUgu7Vq9fbt2+VlJQAQEdHZ/fu3VpaWn5+f8kxzZkzZ86cOVxBS0vLtLS0xrpQPqOkBBISICcHcnMhJwciIiA3F4SEIDsbcnKAxQIAEBAAJhOSkmDSJJCUBElJkJAo/6+EBLRpAyNGQPv2zf1JCBxkFGTI7ZJ7ZPFoUv9JOPKi8MXihMWj2o96MPABZ8v62BkAToadLCgsEEgS0ARNKhheED63ZO7RAUc5W/KbnQEgsTTxbv7dYT2IoBsfJyenGTNmXLhwwcjICABMTExERUWVlZXLysqaoPcWRF4eXLsGhYXw7RukpYGoKMjIQNeuICEBpaXg4wNOTjB0aLl/hYUBAGg0MDKCc+dgzJhyj+P/RkeDhwe0aweiokCjQY8eMHIkjBkDCgrQvXtzf85/GxtvG5d5LtZ3rZ9tflbALtiWsu0d7d3MLjOrKm3OK85jspjdOnaDWs5G0ZmtU7lmYyyqeIRgUm5SH4k+fGhn61RrSWFJFmLBP09TCNrQ0HDixIkCAgKcEVVVVV9f3ybonc/5/RuePYPnz+H7dyguBjodjhyBpUuhZ8+KNhERsHQp+PtD//5/HIvtvGULaGgAQIV5EQILC9DTg93/X2U3IwPCwuDrV9i/H3JzQUUF1NVBU5PIuqkJ+RVSVFK0VGlpYk7ijpc7AnsFyrSR0eqkdVj6cGBU4G7f3QFbAwQEBDida37DvKi0yGe9T8POFQxLCVNwUli4amEnyU78ZmeE0J5eew6k/9H436SJ6qB79+Z+TKSEhMTSpUubpnc+JCEB7t6FR49ASAg0NGDtWvj5E65ehdu3oV27P1piO9+48Rc7U2A7S0pW2BkApKRASgry80FKCgIDITERnj+HVasgPx+mTQNdXZgwodE+KoGD7V7bTy08RWPTUkak+F71NTEzEW4jfFj6MIvNsvWxlespd/PDzZ99flLOfRv7VkhQSLqLtMkrk14DezXgTG7ru9bTZ09/4v8kziqu+pZVBRvPzs7SznmsvBwWKcMlE1WalsxMuHULvL1BSgpmz4a7d0FMDADg9u1GtDPGywuuXCnvYsgQGDIE1q2D0lK4fBl0dUFWFrS0wMQEBg/mPpDQUNz8cFN+gHxuh1yzaLOtUltz5HPev37/dtFbADgfdF5vlJ65uvlgp8GzF80+1v8YALAR2/qutfty9+N5x31v+MZPiec8m02STZlgWd3sfOfLnZyOOcpDlHtk9HgY9nDGyBlVtawq2Kh2BgAxQTEam1b1d/mvQKZ6NwUIwZMnICcH8+eDuDjcuwe3bsG8eRV2dndvOjtzkpAAly/Du3fw6hVMnAh2dqChAZcvA53eMB+cQEFn0N1euAmNE9qbvvfOgDshxSEDRgzolNMpPju+gF7g/tZ9k8YmxyzHUZNGdQkrL5a8/OayxjCNcyXnoB1sU9x28uVJ6mzm4ebHjxzf3mk7Zxc87Wz1yyo3P5dTr6XMUov7FpNUJh2UPrjXYK/DfYdSZinwk50BIC4rLiMmo5rv8x+BjKAbFzodLl+Ga9egpAR0dMDVlbtBM9o5MhKWLKnoQlcXdHUhJwfOn4e+fWHpUjA3hz596vrJCX/i/cM7NC806UKSlIjUeMZ4NrB7i/QuYZTc+3YvISdhu872fZn76Ih+X/u+9lHthJwEyQ6SZwLPTDOdxkTMw9KHmT2ZaofVTCebdhXruit119OnT3dp77L1tj1rVr7IfVVj58CXgcwkJmsoS0hQCAd1vHRGjB1xbMAxABBvL2462fRkwMns4dnV2xkhpH5E3WaGzYvOLxrbzgCw+dbm+Mz4fJX8Lu3/6dp+MoJuLAoKwNER1NUBIRg9GmbN4ms7U7RrBy9ewKVLoKYGq1eDmRmEh9fykxMq8bTgqZuo20u7l0l7kqYvm26yyiR9T/on20/f93yfMXJGRFrEp+6fcDZZQEDAeY6z9R3r/Q/295nUhynMxEkMYUFhO307O1+7Xam7YhJjlLooWelY5dByPiV8gqrtnJWb1T6r/Zxxcy68voCDFlEWsTGxD/Qq6vnWTl175N2R/ML86sfOtz/fHtR90FKvpQwWo7HtfO/bvSTRJMnJkg73HWr3Rbc6yAi64aHRwM0N7t2DdesgMBA2b4aePWHXLu5mfGhnGg1mz4aNG0FXFwBARwc+f4bdu0FEBNatA2XlWn0NBAAANrAd0hy+078/Gvios1Dnyrf19j3Y9ynzU/iV8EFtB1EFy+/i3kn1kWKymb9Df3NWMSeyEvVZ+ulv0m+svAEAh4wOrbq6Sn6+fCnwzjvnBOUcmHNgVO9Rai5q8ybMO5R/KDYutq9IX21XbaplbGksAjStaFrlwyk70xn0I0+OKC1QGiw6uF90P+D4u6rB7VzGLFvjs2b6gunCosJRvlGR6ZFDegypwTfdSkEtCgsLi/nz5zf3VVQJk4nOnkXy8ujyZcRkIjYbmZujvXt5tPT0RHp6iE7njv/8ieTlUVwcd7yoCOnooKdPueNsNtq4Ee3Zw6OL27d5dxERUWUXWlro/n0eXRgbo5Ej0dKlKCmJR0eEqshl5urH6B9OP8xGbISQbYrtluQtXG2sf1mvj1mfS8vlfG2N3Vq5JT789MvTB/wPUEFFd0X9x/pcLW1SbLYlb3vx88XKKytxxPuL9+QLk7clb+PZ8q9Bez97vYd625O3M1gM5QPKWYVZ5RefYr09eTv3J+IV3JGywyrZqibBaR7TdH10nxY8dUhz+JHyw+A/A8TfLF++PCYmppFOTgTdYAQFIWVl5Oxc7sRWY2eqi6AgNHUqsrNDxcU8eiRwEVocqhCh8LLwJd7kaefaBn8X/1Z0UixhlODgzpSdm+I2Tdo/iVZKo1pivTJZzCnOU9Ly06ig7EHZiLQIznPW0M7Jecl99vXZllQefBr+dMONDahx7Gwead5nTx8Gi3Em68y1nGsIoQ03NjwMe4j4mEYVNMlBNwC5ubB8OZw+DR4eYGUF7doBQrBpE3Tr1iIzG1V1MWUKvHgBAwaAmhq4u//9a/mXuZt/1zzJ3KO/h6qYKtR70jYVvPHhRi4tV++4nuYRzQFOA65dufbD+0ceLc/3a/mcLyo1cSbwzKyxs3p07kEF/Uz9rO5YUeesec2G9lVtZTXlg73LgxpyGr9yfq38urJhMxsAsCNlR/CL4MWjFr+MfPko/FHBr4JnP58pD1Le7rWdwWJU8U23ckgOur7cuQMuLrBnD2j+P1XYKu2MERAAMzNgs8HBAe7fh2PH/pjx+M9Sxiw7/er0RvWNAIAA7UvbF1Ea4T/QHy85z+Xcw48PG082Pl18mjOYX5zfpX2Xvyp7jeqaNaprAMA21XYMGlNNvXN4anhkeuSj749iS2PZwB7UdlAIhAgICOQV54m3F6+5ndf+WBsVEdWT0VPzbUUq/FvOt26B3X5s/MHZ0ibVhk6nu8r+cSu8VnYWFRQ1H2WeXpD+OfHz95zv4yXGfxb4DABmCmZMNlNESKTaH0IrpZFG5o0EX6U48vKQqSnasAEVFVUEW19mo6ou3rxBioro2jUebf41Dj06JGst+/znczqLbhpv6pDmgJPOqFK+4kvil/EO40e6jeQMJucld9nYZc3XNTVMd+xM2bk1eStXsIb5iloFa57EsE6xnvdqnoyVTHFZRf6r5pkNq2Qr+1R7apPJZqpEqqAWAslBV8A/gn7zBsnLo8eP/wj+O3bG0Olo82Y0fz7KzeXR+B8hoyBD0UkxJS9lkuMktUg1j1wPaldlvWoe0VwXtk7umFxgVCAVND1nOsN3Rn/n/lxnbjI7rwlbo35XnStYKztv/bVV+YDywUcH994r/9dfZzsjhL7Qvqz/tR61EEgOmr9ACA4dAmdn8PMDLa0/4vXJbCTkJLBR+QrlNBoYzqUvXZ/GJ5mNqmeDkVgAACAASURBVLpo1w5cXEBbG+TkIDiYu/0/gs1dG/uZ9qXtSzP7Zo5NGDtPfB6OV85XeHz0KO5aLCou+tTs6U7vnfjH/S7uXWhJqNx4Of1++g9CK8qT67PORm2Dzx4/KwgviEyPrPhQtZwr2De67+xxs7dobXn+8/mv3F+1zWzY9bSjIgjQ1pStg9oNAgKQFEctycpCc+agvXsRi/VHvP5j5wtBF5ZcXMJis4qKkOb0ovG2WgERAVxd8M/YmauLjx/RjBnoyBEeB7ZuviR+mXt67hfaF4UIhe9F3+Ud5bMLsxGvwW9xWbG0nbR5jDne3Htv76U3l9hsdr99/Vb+WIkQyqXlTt4/GVdoNGVmY17gvPXX13MWtNVq7Lw9eXsuLXeK85QyZhlCKDgmeLjb8DqPndmIvSlp08DvA/OYeaiFQFIcFTSxoI8/P55ZkEltHvPx7tj/83//cTdrqMzGmVdn5p401pheMHGXju9XX64u+NbOuAsWC+3ahUxM/q0iPA0Xjevx16dETkktS0UI3ft2b+PNjTz1qnJdZfqD6dQmvYw+ef/k6T7Tla4rUcH/Xvzn8sSlKe285dcWqq4ZF7TV1s4IofXX11OVcDtSdgw7NiwoOoizZa3svCN5h1a0Fmo5EEFX0MSC/pz4WdFJEReT2l+5Layre+ost6UaNu8sZ+QmYd7TO8Sbqws+tzPF3btIWRmFh/M4T+vj1odbI51HjvQbeen9Jc+PnvglsV1iachSrpabIjaJWYqdennqzKsz1GvI4SFilmKcJcxMFrPP3j5rItZwHd54dwVdn7oef34cR3JpudL20lsSK02lqdbOnENvLOLkvGSVgyosNoszyHV4VXa2T7W/mnP1WMYx1HJoVEGTMrvqGNd3nJuJ25xTc0a1W3rhuc+VRV4m8/9I8dY87/w65vX4fuNFRURx3tnldFKZaDFAxRxWGg0M5xaLTXgwbaih50dP/VH6eHUbfss7V9+FhgYcOABz58KNGzBqFPfZWhmRwpGM3gxTYdOM3PJ1154UPBk+frh1L2uqzaPvjx4IPoB2cNHkIuexHnkeHcU79srrZfCfARWMLY0tY5SNzxzP8e+isfLODMTY0WXHzM8zA7YG4OCh/ENyI+WkI6Whb0XLvyajb364mf47XfOIZkxpDAAMbDtwCSzJKcr5nPh5oszEmuedLZMtxYXE7XraGcQanO7L/TDSf5dGEn8j0Sw56Hn7DwgsFj9/K4ErXquxs0+Iz4xjM0LCiuXlUVBIgryjfGR6JEIoMj0yPDW8qAhpTqdN3KVz+9Nt/zD/M6/OGJ81ZrKYLWjsTHVx7x5KTkbKyigggMc5Ww1Xc67Oip1Vwi6hIpVTE7m0XClbqUEug7iOrfkEwkatqFt/ff2iC4vwcH6673RtX+0TASd6be2F0+ioceYKVjN2Rgill6XPiJmBWhRkBN2crHLy8nkXeHj+w4vxJrq/7+CpWVD7mg2DMQbpqcJq++Z5nXDZ5r/4/OLzg6UGA0CHth3mnJwr8u6s3Az/6YqrLr65uERpySqVVWXMsrtfvF9fNWopY2fchbk56OkBAPj5wbx5UFAAM2dW/lJbPFdyrtz/fd+zv6eIQPnsCZ5FFzo3dRTVFLsld3v847H2cO1qWjZlzQYVNJ5knJqfCgA38262adfGVMIUAI7OP9pOpB00wipI8LexMwC457ovklgEBIpGEn8j0cQj6JWOt9vq6/nepyOE3se9V3JWwrdT6px3PuhzSXyj+OfEz1S8qAhN1U0atkPhc+LnWSdmXX93Hcdb6NiZk+JipKeHjh7lcf4WjXuOu1GsURm7jIrwHPyu+bpGxlkGIZRZkKngpICLHPhk7MxJHe4KctJQY2eEEAuxFCIUOP8oaRGQm4QVNKWgL11CnUf6YTtjviR+ic6IrrOdX39NlHeUPxlwUu+4Hp5wRc1GicuMk9gk4ezvjNu3AjvjLjZsQMOHo5s3efTSQvHI9TCMNSxll1KRqpw74OCAsOQwvOnyxMXtuRuxM2eEy84Iofv5961TrFFLgwi6giYTtJcXkpREDyutolVPO39P+Y4Quv/tvt5xvay8YmznUkbprBOz3J67KTgphCaHYnUusXnOeZKQXyHZhdkty86bNiF7e1RSgmbORHfu8OirxeGX76cXo1d93hkHDV8Ybrq5iYowWIw+e/usi1pXueX4C+NzinI4g/+mnRFCOtE6yWXJqKVBBF1B0wg6IAB16YL8/Ljj9amoO/78eHhqRfXZzWDvCQaPKTvjzEZSbpKCk4KpeeiePWjvvb2WHpa48evo1yoHVa7czG9xdsbQ6UhHBz15wqPHFsTLwpcaURo0VkVVXFV2tvxlOXz3cM+Pnk/Dn+KXaZDpxMsTzW+Yc7Wc9XyWopMiXr0T88/a+QPtw5KEJagFQgRdQRMI+vt31KkT8vTkjjfSOhtvY9/e/XKX6mKZedKMbeV1qXa+dha3LN7EvFE+oHzxenYLtTOmoACpqqKPH3n02yL4Wvx1SuQUzult1eQrikqKDvgfcPZ3xi81DzVlD2Vnf+crwVc4W1okWsg7yucU5ei76Ycmh6J/2M5sxFaOVI6gR6AWCBF0BY0t6Kws1KULOn+eO95cqyAtu7xMeqt0S7cz5vx5JCXF41T8T0JpgnyEfEpZChVpkNX3D/gfOPXyFEIoOiN6+tHp/GDnMmbZmqtr8P3Mqlo2hp2N441Hh49GLRMi6AoaVdBsNpozBzXeTG5USzvjsbPBvu39jS3odJScl5ySV+GI8J+skdM+txQ7e3khXV309StSVET5+Twa8C25zFylSKVwekV6qkHsjNfAY7AYODj5wuRZz2ZxtWz6sbPrU1clZyWXJy5VtWwMO29K2jT4x+CokijUMiGCrqBRBX3gAHJw4A42r53x2HnnHTuLWxZRGVHyjvKJOYkIofCfrG5Gi/d5na7cBd/aGXfx5AkyMOBeaopvKWOX6UTrBBQEUJF62pkaJi+7vOzJjydUcGPMRs5nWaEGtXMpo/Rc4Dn0NzvnFOVMcZ5CL6MrOSttjNjYqHZ+/vO500MnHFwcv3h14mrUYiGCrqDxBB0UhGbObPg16ihqu76zzV0bzszGAf8DsZmxEWkR8o7yT97FdDNabOd5vHIX/Gzn0y9Pv4l5gxA6cgTZ2iKfEB8q+c63rEpcdSn7ErVZHzuz2eyNERuxnb/++tp7W28qQ63ioeLs76x6UPXw48O4ccOOnQ8+OihjJbPo3aLqU8yrr65+9P0RQsjktcnoU9wJhwa0M4PFUHJWUj2ouvjbYrtUO+VI5bSyNNRiIYKuoJEEnZuL5OVRZuYfQf5cff/Bm3CRJd02XbGr3AU/2xkhlEvLVT2o+jLyJUJo0pz7Qy01i0qKeBzDN7hlunEmhes5dtZ/oN95a2dc/55dmI2rO0yCTOYGzn0a/tTG2+bet3tRGVGooe2Mcynrfqzr7dCbWsAIw2nn7ynf55ycQwVnn5z9KeET1bJhMxvHnh1zfea68MtCuSNyZ7POOqU5oZYMEXQFjSToBQu47cmfdsaZjQ2XbOUd5eOyKnrifztjsKM3X3HuMk9v7MTiX794HMUnvCx8qRejx2Qz8WY97bwtfltPu54nA07a+1V8U1S6IyItQsZKxtbbFiFkk2JTt1uFz38+59ly2eVlCwIXbE/evvfe3ouvL1JxrnSH7nHdyPRIKhibGavuos5ms1FD2xknUswTze1T7ZdeWSr3SK7FTR3kggi6gsYQ9M2baNOmPyL8bGec2cC5DuzolmJnzBGfW8JLJC8/exwSgrS0EJPJ49hmJ7ksWSFCIZdZ/iCv+uedJ1+e7PHRg81mTz00NSE7Af1ZUafvpv8t6ZuWq9aGHxvqZufbn2532dhlxZcVXC2/JH6Rc5XDzsUrUP8u/o0q2dknxGeE3YhpHtNUPVSp6sCRdiPvfrnb4HcF111bNytgFg4uilg0Yv8IqmikhUIEXUGDCzojAykoIFrF5IOmsHMBvXDuhtec6sS5P1StOoepvXLwOklFfqT82Htvb8uy8+kH98Xn6737lqZ6UNUnxOfA4RLn8sntiMVmcdaoNCNl7DL1KPWQ4hC8WX87rwxbqXZYDY9G38a+NTlnwmnnJz+e4Aksyz8tH3p0KNfhNbEzNu/yT8srP9VQxlFmZehKatPzo+eOOzsq3ypMyk0yDjKeHzifmlmDX+t/rm9YO39P+S57WBYHXxW+Mok34VyQuoVCBF1Bgwt64UL08mXFZtOMnddvLBm0YbpPiA+O7PbdvcVzC2pp62zU2s4P73WZP+NHZHkStptlt4n7JmroFIeGIhabNd5hPLUUSfOyLXnbuaxz+H2D1GzMOTnnY3zFFJ2R/41c8GYBfl/GLFNwUsguzMYtl11exvmcsxrmnffe26v3QG9b8rZtt7fd+Vwxp37W41myB2W5nCu5Q3JV2CquczZZRZ2Ms8zwo8OtvKy23N4ifVba3NN8s8fmbpbdCksKUYuFLDfaWDx7Bm3agKpq+WY9n/rKCY0GRkawZQvwfOprd8m236zv4HsyX5O+FtALXOe78v8KopxdWFqCuDjY2QEXd+7ApUvg5cWji5MH+7454zVssCgASIpJeq72XHFlhbCigYGRd0cTJUkxCSsdK+7TNTmPCx6nMFLwapw1Xxe0msVCpxdO9xDzmCAzgQpO0ZgS7hfOVmALCgi6vXAznWzqWuCKlxXNnJ059/TcF1teCAkK1XAF0ZT8lPPfzs9bPG+f1L4S3ZLpx6bPGDmjnUg7m1Sbtu3brhy58nPiZ85PN2LsiC3dtnCes7FXEOUM6uvqL+64GABcM13XDVmn3UkbAEzlTdu3aQ8EnjSS+BuJBhxBl5UhRUWUlVW+2fR55+Ky4kE2g/Td9FFrHzvjLmJjueOvIz53WCwnsqLjgE2qPE7X5GQyMhUiFPKZ+ehvw2TvL96ljFKuICdUEsPmro3qQVUNFw0NF43+jv37OfbTcNGYvH9yeGp4ZkHmAOsBC14uwIUc+DXv9Lwzr87UvGZjxPERxsHGCCF1F/Uzr86cDzq//8F+PpzJzRl8WfhyXtw81FogKY4KGlDQrq7Iza38fbPcFdztu9v8pvmMYzO2nbj7D9q5qAhparF6WQxuv66DuIlmZGzzP2t2duzsoMIg9Dc7h6eG99ra69CjQ1W15DlpGweZLObEfRODY4IRQgnZCbgIetrhaWYXzKi7c/MC5tXQzmbvzPo79Y/NjD0ZcHLxxcVj94z9lPBJylZq3U/uZfP4x855zDyFCIVsRjZqLRBBV9BQgs7NRYqKiMFAqIHsnP47HXHYuaikiDOtxtPOeLG66x7F3RfN8HjPPWWDf+z8If4DnpGM7bx5VzKezchJ3ezcf8voaYenfU78LLNVTmqJJq4Rbi6u5FzBDvprilnvuN7XX1+VnJUsIi1qZWeE0JlXZ8xvmE89NJXFZuHgj5QfWq5a8o7y9DI6quWkbcNnhqvcV624vEJqs9SSi0u0XLW67+w++tTot7FvuVrWys7+Yf4Lzy/kCnK1rJudEULz4+Y/Lag0fmnJEEFX0FCC3r4deXsj1HBjZ5u7Nnbe+7Gd84vzsXfwrsrqzC7MdnzgiP6vztyC4p3eOznPxj92Rgi5B7vPOz2vlFG2aROy3JVIPU2Rog521tJCSnZG6i7qOPIh/kOXFUOXuVWaaN9UpJalKkUqlbBL/mpnv69+eKHnha8Xjjw5kqtl9Xb+Xfwbz+fee2+v/gN9HNQ9rvs95fuFoAv77u+r3s5UUgX9qVfHB47UTG7Zg7J4zXGeLasPYhGXMcsUnRSNThnh25UNa+czWWd2pOxArQsi6AoaRNCpqWjaNMRm19rOXp+9bgd8pLz2JfGLx0cPvKuwkN1vgfnio3uxnZ+FP8Px1vFslAtBFwetmbvJNk7eUZ56Sgimbna+dw/hKYUUL7+FDRqR3VxrdBjFGr0tevtXO5cySuUd5bMLs3GQqzyjejsjhCw9LHHpjlWiVU+7nr+Lf/t+9cV/SLHYrL77+q7+wb0kBWVnJos5du9Yv69+6E+9pv9OVzmowmQxcTA8NXz60ekIoQJ6QX5xfh0yG4cfHz4RcALPP9yetL0B7RxSHKIZrclgM1Drggi6ggYR9KZN6PHjmto5OiM6s6B8Dvibz5kdjRVd/NwRQiG/QhScFHDpLs5sPHnCXu2+ur91/1ZmZ5zZ0NpySNJCMuRXCOeuOtu5MhYWSE0Nubvz2NXY+Ob7bvi1oSbFc4ceHToZcJIKxmXFUQXOf7UzXlOUCnp89Nh2exteDxohZJNiY/rW1OyCGefhnAPqM6/O7LizQ95Rfvuv7Zx6XXxx8cvIl5zO3XBjw4PQB0suLpE7KldbO3OusTfVY6q2jzZXyzrbOZeZKx8h3xIfmPJXiKArqL+gMzLKh881HDuH/ApRclbKKMj4+RNNUqBP2a8pt0vO/a27vKP8H3Z+ysouyp52aNqsE7Mc7jug1mVnnNnY92Df3NNzC0sKv/76ijjs/C7uXeUuamVnKytkb4+KipC8PCot5dGg8aCxaPIR8tuStv3VzhkFGUNthy79sHThh4WfEj7h19zTc299uPVXOyOE8Kr8nMFBNoP23tuLOERscs4kKDoI7+W0M5Ub0fHVmeoxlTpnVmFWzy09ZRxlcHEIfsk7yqseVB1+fPjIEyO5/kyxTrGu/gbgyisrH39/jINbE7dSvz8wdbYzk83Uj9HnXBSwNdGoghZACDV3pV8tsLS0TEtLu3XrVp3PYGsLEybAixd/r3f+nPh5YPeBnUU7h/wKWX5hAyvwRHt1y41aq3p06mFwwkBnhI7qYNUlk9YbGYHFZubZmPmxWbEuc13UhqptvLWxe0ep7Ke2DViM3Iz1zoKdfr0VnX9+8fnhvYZfDr5879u9/OJ8JTGrL/e0vLzg8vvTwbHB7svcObu4fh0GDKhRFwCwYweIipaXVB87Bu3awerV3G0aj91pu8OKw2Tbyf61tDkyPXLZ42VMxJzWcRpny5jOMf3l+h+SPpScl+wd4m2uZg7/L4I+JH0Itwn5FTL18FTJnpJsYA9qOwgAWGxWYFTgIKlBor1F2Yg9ucNkAEj7nVZYUhiwNYCr3nmL5xblQcofpD4wWcy3N956rvHs2bkn3lW5ihkhJHtAVltf276P/bwz83BVNdSgtPlr0td99/d5rfWign7f/F5Gvjwy7wjUst6ZK7gjZUdPkZ6bum+q5Q+nZbBixQpra2tZWdlGOXsjib+RqOcImkZDCgpow4YajZ1fRb6aemhqfnH+z59ITv2J8Crhbbe34cxGaFLoFOcp2kd05YyOP37CND1nqu6ijlerQQixWOzRa8yX2QRW7qLFjZ3t7dGhR4c4n6boHuy+85R/NzOtOx/vnQs8t+DMAmrV+TqPnSnodCQvj8qaam2GhNKE/mH9G2R9Z4SQyTmTsXvHhqeG12RAjRCKz45fF7ZuZdjK2MxY6pWWn8Z1qzA6I1rnqA6Vmnjx88Xyy8vxLp75Cv0H+krXlPB7Z3/nM6/OoJoVz810m6nkrDTUdehQ16FzT8/FL7H1Ymn5aZXHzqjGA2r3HPflCctR64WkOCqop6BPn0ZTp9birmBgVOAEe5XRKhHi5l1XXlk5es/oCfsmpOanIoTi0zPEzZQm79adsG8CfnARpjVlNqqpd87OLxq+e7jKAZUGtDPm8GF06RKPxo3ByPCRi+IXcQXrZufgmGCzC2bhqeGyB2RrYmdU44q6mW4zl39Zzilio1NGH+I/8LTz1oStvfb0otZxLWWUKjkrWcRa1KS0mVZKs4i12BSzKZeWy/mqj52DCoO0orVK2U2bt2paiKArqKege/ZEu3bxiFdTUTdEy0d4lcjW21sRQpffXJ7oMDE1PxXnnT3upWoe0ZR3kqdWe/lH7Eyno3OB54xOGmke0bz37R5qODsjhK5eRb16ITabx66GJaQ4RD1KnStYNzuz2CzVg6qJOYk7U3aOOzcOfyc8W1LU0M6R6ZFilmKcKWYNF43xDuPHnBrD8waggrvCjfc3OIOzn82eeGEiV8v6zEbhGeRp58iSSMUIxdY0J4UnRNAV1EfQr18jbe6b0ghVa+cJSrmDrUesuLJC5aAKvlvyIf6D6+MTOjoIZzZOBpxkspiLLy4+/vx409h5uXnqTruKde5ZbFZsZmydu6iznXFmo6ikSMtV6/TDew1lZ9zF5s3I35/H3obFIMbgJ/0nZ6RudkYI4SpmHMyl5U7eP5l6eFU9HwVb20nbWq5a6i7qlMplHGVkHGVUD6qyOX7jNY2d08vS5SPkY0sq/bNodRBBV1AfQS9bhkJCuIPV23ny3vIngARFB2FHU3MFrbyszgeVP/2byWKaXTAzWP+oCcbOy3cGqbuoF9ALEEIsNmvxxcUXgi40pZ3zi/MtblkwWeULOX8OLZAyXNeAdi4uRomJSF+fR4MG5CPto1n8HzVtdbZzAb1g0v5J2xO2U8ETASfwRPCa2LmopKiAXtAgduYK1lzEDW7n38zfqpGqH2kf0T8AEXQFdRY0jYaUlbmD1c8VPOx7BT9DDxMQEXA56Ca1zgaVe0Xl6mTb2fNYfL4xMhtPfjxRd1HPo+XhkTt/rrNRZztjZs/mcdoGZHbs7Ah6BLVZZzsjhKzvWsu5ymn5ap15dQa/Tgac7LW1l2WUZU3GzqvcVw0/Nrw12ZnOoutE6zz5/QT9G5DlRhuAe/fAwOCPSA1WEDXjjE/sPfWARcUKosKC5V/d/8vdBHbvFuLqtJEq6jSHaSKEhtkNs9Sw7Jlr3owriNanoq5yF7dvg6hoeWTlSjh/HhwduVs2CBElEUICQkPaDcGbtV1BlCqewyR1T+oj1GdOlzlUREhQaKzKWKYQ84j0kazCrG4du+F45cVCvyZ9DcwOFBIR0qP98WXxXAK05sGaLxZawxVEeQZ5VtQxEMM4wXhF1xWanTSBUH8aSfyNRJ1H0LNnI84n4DXjk6vqf1cQZzbWXV830kpdR/93Kxs7Y1gsNHFi+WpWDc7qxNXBRcH4fX3Gzn8NBscES1pIZhdmoypSzP2d+q8IXZH+O131oCr1RNeWO3ZmsBnz4+a75zTHfNDmg6Q4KqiboAsLkZpaxWZt7RyTmjrZIJCyM5PF9PrshZrVzjizMXn2k2mH1PFT5mrYRWV1lpSVctmZWpTnukeRxsxUzk8RnRGNGt/OuAtp6fIFrRqWPGaeaqQqft+wdqaX0X+k/KCCuLTjQtCFddfW8bSz4RPDyZcn4/f7H+zHtzRarp2ZbKZpvOnZrLPoH6PVCjo9Pb22h9RN0F5e6MCB8vd1GDtrTi8at1PDP8wfIcRkMReeX3gy4CRPO0dlRCEOO2cWZObR8qi9DVJRd+fznf9e/Ed18fj7Y84nRdVh7Ky67pjq5l3/32RvvLnxSvAVhJCXF1KfmTxpn3xMZvk/vqPPjq69trYJ7BwZieTl0ePHaBF3jXIDcDzz+MXsi6gRxs57/PZIWElYJFjgzYuvL+KZ3IMPD17yeQnX4dsTt+P1kvBmCaNE0UlR9ZbqlkTu3luKnRfGLzyZeRL9e7RaQbdt27a2h9RN0EuXovBwhOqR2aCV0rRdtX1CfMwumLk9d6tq7GzrbWvktAt3kZKXouCkgJWN+Lve2fqu9Xav7djOe/z2II7MRlRGlLyjfHRGtOtT16WXlob/ZNXNzo4PHDlH+mcDz/7nHlONnXEXyso8PmY9UY5ULmIVNbidk3KT+u7rq3NPB3+BhSWFk/ZPopXSbFJsVoat1Dmqw9nYJsVG6ZrSpTeXOINKV5XabWh3wP8AZ7BF2LmMXWYcZ3wq6xT6J2nxgs6qgqYRNJuNFBURqnfeuYBe0Gtrr9Xuq6tX50Bj6823tqflpyk6KX5K+ITj1ahTU5PbzpHpkd5ffKguCksKTwZUDEwaqWZjwr4JcrvksFxu32bLGG98G12+nnVURlQ/q35zTs2ps50RQq8iX6kcVMml5SKEXJ+6TrNbOkOXVb2dEUL79iE/Px6nrTOhxaFLE5Y2Rt55+LHhxsHGbDZ76qGpCdkJO+7s8PzoSWU2tt7eevdL+TMZcHCK85Rxe8eNdxiPXz129Wizrs0ou1FTD07FU1VRC7FzCbtkduxs/EfJv0mLF7SAgIAQL6q/Renl5aVRib59+6pxppNrQGgoWrWqvnbGlc4uT1y0XbX11/tVP7DdeHOj9FbpmtiZ59i5pKxUdrXhHKtrCKGikiJtV228CjBqNDuz2eyV7is7mXdadGHR7dvsfsbr7X32UY1dn7oanTYavUt+jEp0fTIb2NH7H+6voZ0RQt+/o2XLeJy5ztik2JjEmTS4nZe8WzLcbTh+/zb2rYGbgbqLuk1yRd65gF6g6KRIL6NXVe+sfkf92LNjCKHX0a+XXV6GWoidi1hFM2Jm3My9if5hWrygt27das8rB9k0I+ijR5GlZQPYGWc21m2kDdxQYUwKSp147Lz00tLtXttRXTMbu+1LDU8YXgi60DR2xpmNjIIMyQ29xZYM4bLzssvLwn+yxqhEj7WTxzcJ/9oFqiLvvOHGhi7re2jqZ9fEzhgFhYac9t07rPfWJG691tPOhk8M+zj04XwM2HiH8ZI7JLnmZ4+yH2UcaMzTzhujNyo5K5Uxy9eImnt67uIPi/nfzjmMnGlR0x7kP0D/Ni1e0AwGw8DAIKTSNL6mEfSkSUhbG5WUcMdrXlEXlxXnHuxOZTYKSwpdnrhwHsJlZzx2tr5rveLs9jrnnXOLcqUspTZ7bubqgov6V9S5PnXFC1jfvs3ubbKs44ZOu3zK7xlGpkeuu7aOymxEZUStcl/11y5QFXZ2feqqZr9M3vDlFOfyXAdFVXYuLEQyMigggEcXdSCiJEIxQpErWE87Gz0zarO+zbwz8zhXF1r5ZWXvvb2psjlMNXMF115bi29BY9aHcHTSSgAAIABJREFUr+/n2I/95+8lfrNzYmmiUqTS68LX6J+nxQu6KrKza72KSh0EPWRIveyMqeEtu80em/Fi9gihiAjUy2CHz5sPlbv4q51xZuPu57uGJwyvvbvWqHMF8ZRIKrORUZCh6Kz4Pu49Zxf1r9k49uyYmv0ynNl4Fv5s2uFpeLY6qtrONBrS1ka2tsjRkUcvdeBYxrEbuX8sJFRPO+/4taPXnl6bPTd3teg659QcvD6nnKvcENchc0/PTc6reHpINXYOSw4bvHOw50dP/DJ8Ymjw2EDtsNrNDze5WnL33nx2/lr8VSFC4Qf9ByK0YkHXgdoKOiEBmZpyB/l8NkphSaGGS3lVXwmjZIL1zHFGHo09G2WA8dZ998rL9dJ/pys6KUamR9bNzjvtaJR8qRNanfThzDsHxwSn5achDjuz2ey119ZSD/bOLyjru2CDlzc9Px/p6KAGYVbsrPSyisrO+s9GUb6pjJ/WeuTJEbfnbqhOqyDFZMZQc8Sn+07X9tXG76nfkfxmZ//f/sqRyillKYiAECKC5qS2gr59Gx09+keEz+2MEIrLinvx8wXVxQz9kktB12rVRR3mCsakJXEG82h5X8IK6zZ2Dk8NVz6gjKfPIYSOPz8+w2HjXyvqEEL3v93XPKJZVFKUX1AmZTLH4sRlHFdVbYAl/NmIPSVyCrVZfzuvi1inelAVLxrFYDGUDyhbRFtUY+dfOb+ov65a7myUE5knZsbMLGAWoGqIikJqaggAnTtXXbPWAhF0BbUV9M6d6NWrik3+t3P9u+CHmdzBMcHKB5SzCrNOvzzdb/MoZYOQ4mK09NLSzILMUy9PPf/5vIxZZnRs+QSlHK4u7n+7P+2gRncTA8rOCCELC/T+PY8ea0VsSSy1fF2DzORefHHxq8iKf1vGQcbjzo3jask5djY8YajgpMBgMVqonRlsxvpf6zclbWKyeSwKVoGvL2rXDklJIQD07l11LVsLRNAV1FbQM2ei3P/fjuI3O9/9cvdhqD/Vxcf4j2cDK6bJNradrbysTl9LpOzs+MDxW9I3zi7qmXcOjgkeZDNI3naOun6y/H7FTwmfPid+lrWWNTxhWFxWrH1wjqzOlcpd5BeUdVmk1NdyOPVMEISQhwc6fpxHp7XiTt4d1wxX1EB2/hD/QclZiXp07LKPy8w+mI13GP895TvVktPOz8Kfrb221uWJi9ZdrZZo52xG9vTo6TWaxu3sjB4+RMuXIwEBVFT09/YtHyLoCmoraGqJUX6zM0KoqITWb7W2yQ4/hNCXxC+KTorUDIXbt6sb2DbI2Pn4lYguZvI/k+MQQna+dha3LDi7qP9dwTOvzoy20pQwm5KUlYWLW2zu2ugc1ZF3lJ/qqFuVnXFmg8p1lDJK/3vxX1wcmjWrvM2xZ8eopahrhUOaw+PfjxtqFaSAiAArLyv8UrisMPnSZPyeWp+W0844AZJVmGWVZNV7b28q+YPhfzuHFIfIR8gHFQahmjNxIho4sBbtWzJE0BXUStClpUhDAyG+tDPuwtaepu2qffTp0Sa2M85sfEuIkHeU33RzU2PYecou4xm6zBc/3mA3HXp0qKtF1zcxbxQc1NstGvTuW1qlLthSJkYWJ67gzbtf7s44NoPBYljftZ64zqp/f4QQsrhlYedrx+MiasCShCUbfm1oEDtzUpO7gseeHTv67Ch27pMfT8xvmFO7GtzOaflp0zynNaCd3XPc1aLUksuSUc1hsVD79sjQsBaHtGSIoCuolaAjI9HKlfxrZ9xFcExwl41d8PpEqAntjLvYcH1Dzy0947LiUMPZ+VPCJ3X75VTNxuvo1yoHVYzPGidmJ3bZINl75h6/4M+KToq4igNDoyFtbfbhK++5zsNgMXbsQIrmtv3mbdnqYWXvx+vz1AzZHzV9lmvD2jmnKGeK8xSrJCvKufpu+qHJoahxxs6jToyS2iVFzWLF1M3OdBZ9deLq9b/W1/qRrxERCADZ2dXuqBYLEXQFtRL048fIwqJR7Mxms6k/tLHXomO5ly6+EXx3pOE1ys5B0UGuT125usCZjdisWDxjsIntjDMbEWkR8o7yz97H1cHOC6x8LgRdoCKvIl+5PHHhWqPuzKsziy8uZrKY0w/Nk9E9NtZOITw1/H3ceyVnJVyNh+udea65sWMHsrJCCKEeKxVH7+KeY1IrJv2cxBVpAjsjhNZfX6/to70qbFVsZix+PQh9oOWq1Rh2XvJpidxRucScRHUXdWqeS93sHFUSpRypzFU2XlM8PREAunOnLse2QIigK6iVoPftQwMHosRE7nj9x85ff33VPa5bXFaM1Xnusd+Si3+sJ1lUhDS1y5R2z8FD49fRryfvn+xwz4HqooBesPrqaiVnpfTf6QihwpLCUVYak2c/aTI777u/z9LDEscfvAkXM5EP/spd2frXsTO1+Cr6/2MbL9/M4aqoC/kVwmQxIyPRaJWI2FiU/jv90ptL/mH+sZmxpYxSGg0p6oWuO366cheUna28rGbY2s9xtN3iyZ2gqDnUGtCYprEzQkjRXXH0qdGr3FdxvsadGVfDZUVrkXdO3iHjJBOfHY8Q2um9Ez/bu252vppzVSVSJbIkEtWWx4+RkxPS0UEAaONGdPBgrc/QAiGCrqBWgt60CXl4cAcbKrNx/9v9qY56kxSLLz59qHlEs7CkkLMLnNkoY5bNOTnH1scW1wXvuLNj/BqrPXsQfhj2fy/+yyjIoLrQ1isMiuCuJmu8iroP8R84u3j5OS6rMKsmXaA/MxvY0Zs9N/O0M+4o/CeTGp4n5SZFZUThvxhoNDRFL3zY9imck+4wlJ1x3vnRI7RvH7K+a23lxS2mGjI1air1vsns3JQVdfr++rt9d+NNWilNwUlhc9zm2tr5N/O3WbzZxqSNdFadlnnV0kIAFa9/4z4hEXQFtRL0unUoLOyPSMPmnYdq3h+9e5K6izpPO2NeRb3qatH1xIsTuAvlzTs2e2zWctW6963CfE2c2ajcRT3zzq8iX3Xf3H3R4YM8Z6Psu32uq9HCqGgmQig+O17eUT4mM4ZWStM4rD1szonq7VzKKMWLJkdHo+XLEULofND5ulVxUIJulXbekrBF0UmRszZR74He1OtTuVpWb+fAwkD5CPmHvx8iQm0gD42tIzk50LVrxSaNBkZGFU99peB6JCsn1T/1dbuDoFswQ0RIREhQiOqC86mvwbHBtt62YfZhG25s8PESmyJl5m9lO9J+5Ph+4/VG6VFdXL5c5SNZG+6pr8jDk92uXcVjbZlsZmy0cP2f+vo65vUu313OE77verB526aToqLrcDyjIGPttbX2U6/fd1mxeRts9p/FQqw8Wt6FxRdku8kWF0PxK6dfvdXdDI5Id5Hm7MLaGhACZ2cAgDbCbZYoLgGAPn0gMREAYPmU5dzXVDMQQlDFU19rHqz81Neqgk381Fexb2JSnaSuv7+Og3fz74qASFRIVPL05N7ivXGwmqe+WklZWaVYxZbG+sn6dRPuBnWAxYLcXMjNhbZtoXNnEBevy0kIlWjNgs7Lq/h30uB2XuXgfzPMNdAq8FXkq3mn53mu8WSXiXLa+WPCR+u71j7rfcTbS/T4eSNcaEF/TYHZp64dNz7+JubNjjs7nOc4N5WdYa9b+Nxz27zWerVv0x4AHn1/dOrp9UzPq/W08/v497betsv7+Hpe6/LT49Kq64sk3nZcpLAIAKQ6Sen0X67iYPD6knfHbpqnDzoUlBQEbgsc1mtYcTFoz/+ZP2xjyNYPG65vkOwgqT9aH5+Q086cPHgA379zB2sFAtRa7ews7fyh7MPwXsNx8Hru9U6ineaKzzWbb9ahbQccrMbOMzrP0IjRWNV1FddlcJOeDmFh8O0b/PgBnTpBbi7k5EBxMQAAQiAoCJKSICEBUVGQmAj9+lUcKCEBkpLQtWv5fwcPhkmTQLg1m6chaaSReSNRqxSHqmr5mwavqHv47pvOUR1aKQ0H73y+s+jcUq6KurisuFxaLtVFHi1P3lH+0fdHeK+lh+V85z11zmyY3zCn8tcsFnv82m2Wtr+4D/gzs/EwrDxX/uj7IwUH9YmKBfWvqEvOS3a/lU9lNhgsRmR6+Z0lvM7G+cf3lQ8oj7EfM3rP6D1+exacWfC7kKGk933odgWc2SgsKdQ8ovn4+2PEkdng4s4dpKuLpkzhsavmjAwfaZFkwRVs6ZmNetY770zZuS15m0GMAe+VjzIykLc32rIFaWoiJSVkZISsrdGwYcjVFX39in79QjQa9yGVn4vBZqOsLBQZiYKD0e3bSE4OzZqFpk1Dqqpo2TJ08SKKjuY+SUuD5KArqJWgp05FqHHqnVlsFl6lE1NUhDS0Sv+yvjMt911cxdIEt2+jSbP965x3pmYesljs0Ws3qG1xqPwpKuedH4Y9HLd33KS9UxvEzuhvT32NjUUJ2Qmy1rIdN3T8FP8JIXTi2bkexguOXfuWkldhhMKSwjcxb6q3c3ExUlev15JJG5M2viv6Y2mIf9zOi+MXK0QoXMv5cx2uwkLk64vWr0dKSsjAAB06hN68Kf8B5+ejadPQ8+eoKqp6ahHmf+ydeVzM+R/HP8LP7mKx1rWudezag2U3R/ehgxA5CiUVK/eRUIjI0aEcuYqUdYaKiJD7LFI50qXSfd93TfP6/fGd5p5qamYq5vXoj+bz/Xw/n+mY57zn/XkfXNGUdDpiYnDqFExMoKAAU1O4uqKwUODirVhSQLMkLKBbSTYK7xbNPxWkGP3HcrNG0hnAnQ93ft8y5vv56u+jS7jmi4nOcnvlLC5ZuD1y09qvlZ1fOmkSVh0++TSOO2m4QToDmDMHWVl85jRSZ/LOuOe4Mx9+zXRekrRk9MfRpp9Nc2rq4nZyc7F+PaZOhYYG9u5FaChqOboNiJjOvDp+HL/9Bm1t6Ori9GkUFQmc2fokBTRLQgFaWfmLpTOA2lr6r8vmdls6iJkjzpQgOlOejVN3b/KNCxQtnXNLcuX2yjGbml986dfbSK/+bBQusdMZwOLFzfo0HFcZZ/LZhPr+q6UzjU6bGDtx4LuBD4sfAkBFBS5ehK4u/vwT48Yhgzv/niFx0/nWLWhpoaQEADIycPgw1NSwYAFHIcpWLCmgWRIK0L174+5d7sEvhs6UZ+NN0hv2Oh4QQOfItEiFXZrjFEooz8a18GvGp4zr3wLNoDOAmtqa7OJsapx6hf53mc/rv5F0BrBuHXiapgkn+Wh5OugSo/P4U+PnPuH+X21BOj8teaoco2ycaFxNr0ZkJFatgoICnJzg4gIDA9Rwp8IyJEk6sysyEmvWQF4eGzbw+VdrTZICmqWmHRIy9WXQmU7HmGVrNdYzOkFRpS8LygogON75Y1TtBPkadr9zVU1VPVtAAJ13BezadPQqE517b+69EnqFulR/56rm2M7MyS9f8pnceK1JWWP62VQydDYLNRvqMFTRQZH9rKKl6Py56vP8hPkLExemVaXi5k3o6GD+fDx5AgCnTrVGOlOqqMDkyfj3X0yYgD17Wq3fQwpolppwSMjUF0PntWuxZAtHq8OojKjiimKxZqNQunC5vLfxFO9gPwBOt53Mz5hTNR/ETWcAFhZ8Pg8JpesF18dFjeMaFJNn42eHnxNyErb7b2fWrmsROhfSCq1SrTRiNUKKX+LcOSgqwsoKqXWZQa2ZzlVVmDEDFy4AQE0Nzp6FggIcHVuhNS0FNEtNtqC/JDo3mCuYnJccnRHN3GKCHO38gweN3AINeTbyisqnHJqy0HOhJOkcHY0BA0RQfkc1RjW3hlWOWUx01g3UtblqA2DGkRldVnZJykuSPJ0raitcslwUohV8831w7hwUFLBnD0eYRFuhM1M0Gjw9ISeH//4DZ8vzlpUU0CwJa0FTf8evis4A0gvT5e3l36W+o+isu3++x1OPxmyBxvmd99zaM2jTIN83vpAInakt1qzBo0d87hJKvgW+W9K2UN+Lic7rP6+Xt5cvqSy59/HeinMrNvtt7re9nyTpXE2vds9xl4uWc8txqwkMgJIS7Oy4Udjm6MxUeTlMTTFsGMLCBK4vWUkBzZJQgNbVRVHRV0HnPwwO5hWxsgYuhFx4nfh67A7lUWrhegcXHH14tDFboHF0pjwbZVVlUw5NORLgJxk6f/oEGxs8FaanB1/RQVePVf9c9Vl8MRu2/rZnXpxhdlHZnLr5u7XfBX4IZJ8pJjpvS9vmkesxIXrC/qz9FZHhmDYNq1cjJ4drZhumM4DHj6GigogIzJgBS8vW4PGQApoloQC9cCESElojncPfl8rJ0dm3oILemmw7X3vDqqjn8dRjrvvcyKiaf5RSe67pveXqFt6fosl0Pnz/8MrzKynPxtvI8u5zdXidJ+KgM4CNG/HqFe+tQutN2ZtfI3/dnLaZa1wkdM4tye22utuS/5Yo2Cso2CuMcRsz+vhoBQeFnyx/Ys4UB50tUyynxE2Ri5ZzyXIpK87Ghg3Q0cHHj3x+/i+Azsw2oxcuQF4eL14InC8RSQHNklCAtrTEvHmtjs7R0Rg++dy8I0uYJdXXea9ze+TWTM8Glcl99OFRis4T5Gh6BxfY37JXdlRmdoNtfkRdRmEGu985KrYyrzSPfb6Y6AxgxQp8+MA9p2lambzyYNZB9hFRxTvT6fSEnIQ3n9/I7pJd+nYps07/vBPzHsU8ghjonFeTpxqjOuD9gKPZRytqK3DrFuTk+JTZpfQl0ZlSZiamT8fOnaA1pcahSCQFNEtCAXrxYhgb8xlvDZ6N/Xf3Lz69uJZea+VjteP6DpFUEF3nva7P+j6v3xVQdKY8GxmFGRSjxRTvzC7x0RmAsTGf3gtNE41O0/2ke7eIERQi8myUFedWyP8nP/fJ3KCPQdTXmRdn/rH7xyrVSoR0jqmMWZG8QjVG9WDWwRp6DbKzYWqKxYtRUMD/x/7y6EyJToeLC37/HZmZAm8Xp6SAZkkoQF+8iOPHuQdbA50p7b+7/68df9letxUJnU89PWXgZuB260a3eVqXHj/xfObJvJRakLrj6t42TWcA06aJMhC2iFakGqP6vPS5OHIFtXy1mH2+mV8KZxQsP3N3UWkCnWl02vXC61M/TTVIMHhS8oRx2c8PXbvi7FmBP/CXSmcA1dXQ18e2bZCTQwh3ywsJSApoloQC9PPn2MzpaWw9dAZg7Ws96cAkE4/FWtq1zaTzyScn57nPi4yqkZOD+23/yQcnl1ezsNcitvPMozOT8pIAWFtj/aayGUdmcDlDhKIz+KUdNVO5NbkjPoyYnzCfa7zVZnKnVKfYpdvJRcttS9+WUp3CuFZTg7Vr8eOP9UWJf8F0ptFgZITTpwEgKwuamrh4UeBS4pEU0CwJBej0dLDPbW10tvKxKi3FiDkuWnaLa+kctWmE7Y1yLvgcRWcKnXcj7zLLxTWGzhXVFUXlLOvUxwda07OaaTtTvWiXbUxYv6ls8sHJ18KvsV8Vls6lpRg2jM+P0EwV04qnf5runOlMB+M8oBXSeX3K+lnxs6Z8mjLj04xrhddq6GycTUuDqmrI3wN2uy07EHSAvUYgS18JnSlVVmLBAuzdK3BBMUgKaJaEAjSdDhUVxvetis4nnpyw9Wd5Nvbd3kc1/GZuIeHOVbFZsYoOilTpDB8f/KV/cpGnOdf8Jng2zDdG91s2XtVJtZl0prYYOZLPFs1XLWp3Zeya9mlaclVyq6JzJb3yRuGNCdETfn7/84GsAxnVPJVMHj+GrOwh7f7z7bXvfbzn88ZHyUHpTdIbjjlfFZ0p0enYtAlr1kgsmUUKaJaEAjQAFRXQ6a2LzuxbtJ6+glTRJY/zGX/NOTX7qAF7BQk01e+8flOZiqPKiK0jEnJYP3/T6HzuHGbP5rOLqBRaFvpH5B9qMWpltRxF6BsEcUV1BdVIW1R0LqYVX86/bJhoqByjbJduF10Rzf8ZHzsGLa0K+fFKW0czP35lFGZMdZ3KmvMV0pmplSuhosJdNFU8kgKaJWEBbWyMo0eldOaQIL+zk+eb70yHTz04XVR01jmocy38GuXroBjdNDpfv45Xr2DJfcAmYtWi1jPXUy5azinTqYBWgMaZyTuu7xhlO8oqxaqZdF70eZFzpvPkuMlacVpOmU5xlYIrq9JoWLMG//4LRcWPt72Xn1vOflFtX10Bmq+Zzi9eQFkZ9vYwNZUAo6WAZklYQK9ahfHjUVnJPS6lM+8Wf805peGsJW8vn1HI+jTd5JiNaa7T/CP8qZHItEi5vXJnLxU2jc4ALlzAsWN89hK5qunVZ/LOqMWq/R31t1GiEYfDl4fOKfkpKk4qWn5aE69M5FqnQTrX0GvCy8OPZB8ZFz1u4PuB5knmVwqu5NMEIAkory5/m/I2MzMRs2dj714oKSE4uLy6XNVJlRlQn1OSM/ngZEBKZ2VG/uSBA1i6VNy+DimgWRIW0HfvYts27kEpnXm3YHo2KF8HxejmRNRxHVidupA5ZSqtaXQGYGtbH0/EoQfFD7akbVGMUZybMNc9xz2yItI61ZrLTJ5/Yr7JK5ONyRsVHRTZ39X4n/WlrjdNND2de3ptylqNWA2VGJVVyavO5Z1LrU5FQ/IL81OwV1j33/IZhr1MtyrXKCsimNG+y/6W/b///RuSEHLnwx1VJ9UXn15I6cyR3W5nB2trgfNFISmgWRIW0IWFmDKFY+SrpbO1tcAtVGa8NzxhzPRshCSELD69uKXinfluYWCAdO6+MRJSUlWSV66XUaLRsA/D1GLVjBON16as3Zm+c33o+oGHBhomGr4ue33+/fkF/y14X/7+ScmTmfEz9eL1DmYdtE23XZOyxjjRWCtOa2zU2BEfRqxLWeeR6xFaFlpFr2r8E8gsylR3Vq9KS4GyMvz9j+oOO+DB0f32YfTDTT6b7G7YxWfHS+nMp/bIypU4ckTgXc2WFNAsCQtoAEpKrI84XzadLS5uUp6SyNxiz809zCRvQehsbdkogrZQVuazsuRVQ69JqkoKLw+/V3TvT4c/l3xcsjtj9/qU9aafTQfuG2gQYmCTZrMoadGp3FN+BX4Pix9GlEd8rvosFI55dT3i+n6fHVBQwN27UFIqefZwmus0/lOldOalM3Xv9Onw9ORzSRSSApqlJgDa3JxRw+HLpnNpKZSmxozYKBefHQ9gu/92i0sW1KW2TueSEkyezGfxFpTHU489N/ewj8Rnx2u4aNDF4O589MLXdsZAPHhA+Z1TC1INTxrymSelM186A6ithaEhfvkF0QJCYponKaBZagKgz53D0aNfPp2pLWIyY+T2yq2+sJqdzn+vWs70CNfSa9dcXJOSn9JW6AzgyRNxexGF1vpL6zVdNLm+tPZrZRaJoBxEaWXpFr8tqk6q2ge0z949VqEkr7x5VLjm3wgOLqksMXAzuBvJkzQopXM9dDYxgasr4uOhqCjwSTZDUkCz1ARAp6VBW/uroDOllRdW9rPsR9nRFDojkiPk7eVTC1LpdPqys8sO3jvYhugMQFNTrC7EVid9N33vV94AynMzl+n/dOq4RZL6OGNHHVUnVU0XzathV7lvkNK5QTpTCgjAggUCl2qqpIBmqQmABtC/P//s4S+PzpRng7Kjl22MZ6KTYrSxh3Gbo/PmzfjlF2Rl8bn0RSq7OHv2sdkAUFmJyZNrfC5rGHZnxmzwkZTOjaQzJUtLeHkJXLBJkgKapaYBesMGRgtjpr5IOu8K2DXl0BSqTNKSjVE/LZO7Gn41NisWAJ1O1z+uP8hq0IlzaW2Lzhs3QkmJz6UvVdEZ0cvOLgOdDkNDuLlBSUlt2z8CZ0vpLBSdqWelrMznH70ZkgKapaYB+tEjbGQrsfBF0hnA68TXfmF+U12nbrAut7LC+ZDzSo5KheWFlGfDIdDB2TOih4l8fAZ3SZ1WS+dNmxAWhhUr+Fz9UkWrpcnbyxdv3QQbGygpvfA/uej0Iv5TpXQWls6U3r2DmpoIMwylgGapaYCm0aCgwPj+S6UzU/rrA4Ytnxb0MUjFSSW/LB/A0rNLDwQdoLYIjgtXsFdgr/zZmukMwNERV3mcro1VAWAI9AB+AJYAAlDW2vTwqI3iol5bZg5c4TJbw0UjtySXzyQpnZtGZwDh4Rg0qL4JQqrlAZ2by+9fpCXUNEAD+PdfRER8+XSm0Olwy6Hn2p7phYzUjojkCHa/c1xWXGllKXWpldMZgJZWM+r06wMdgE3ATIAAHg3f0fIKC4OycqnihBf+J9+mvOUftyelc3PoLC+PpCQoKiKDp0BgkyQ5QEdFRamrqw8YMGD+/PlpaawPwp06dRLT9sKqyYAOCsKGDV8FnR/HPFZxUvF+5a21X4sCcZs7FWTSOS+veRHQXsBJAEAYQAB+f+LWpfx8jB2L8eOlp4L8JRI6U2R7+hQLFwqcKYwkB2hFRcVt27aFhYU5OjoOGzYsLo5RUusLADSNhl69cPs29/gXSWfKsxHwNkBrv9b5S6VtlM4A/vuv2QF25UAkYAh0At42bylxi07HlCkYNUpKZ/4SIZ0pGRmJpCO45ADdtWvX2jrfeWBg4IgRIzIyMvBFABqApSUePOAY+cLoDMDtkVtheSFzfPOxAJUZHxpP59LSVkRnADNmcLygmqLjAAEGACJ4JYpZu3Zh8GApnflL5HQGkJQEDY3m17qTHKBHjBjx7Nkz5sPz58+PGjUqMTHxywD0x48wMmI9/PLozHeLtkvn/HxoafGZKZw+AqeAH4EJzV5KrHr4EN2712fQSeksWjpT2rQJ167xGRdGkgO0n5/f999/f+XKFfaRAQMGtGvXTkzbC6vmABqApibjryylM+8WTaPzFt/tz+JYb+oOgQ6ed+42n84A3N1x4gSfyY3VI8AXAKpp1emq6fgfIN6ywM1QZSX69oW/v8AJbZPOyXnJl19fvue9v0ZVuTXSGUBuLkaMaGbInUSjOFJSUlJTOQrU5uXleYqtEJSwaiagL1yAo6OUzny2aLLtXFCHfxKxAAAgAElEQVRWoOqk+jD6IQCn205zD5vLydGbT2cAEyeioIDPeGM1BWgPbECFRUVNhxpMafiOFpOVFf77T+DVtkbnjMKMgrICr+dekw9OPnJi3fZZg1X2KvAPFmxZOtPpWLECEyfCx0fgCo1Qy4fZtR41E9BVVZgwAatWiYbOCRmZ7OisrKksKCvA10RnShSjl59bLkI6HzqEsWP5jAuhJGAK0A30H+hPZZ9CBFWMxKPXrzFjhsCrbYrOH9M/qjqpznOfN/ng5F4WvfLvBlCejccxj1eeX8l9e4vTedUq7NqF3FyoqgpcpBH6QgBdWVkZExMTHh4eFxdXI+gfriE1E9AAzMzAd4Em2M6/zt43y4GRoVheXT7l0JSH0Q+bSWe7G3aezzyZWww12Lrh+HnepVoPnSnZXbfrtbbfHxoPRULngAAMGYKgID6XmqD8svyZR2eKZi2Rq6YGSkpITuZ/tU3RGYCKk8rn3M8AHsc8NtqrtVS/H+XZoNPp6s7qzGkFZQWrz69SNeutvuH3E08EuLEkQ2dKa9c2p2GPpAGdzPPvEhIS0pw90tPTZ8+e3alTpz59+gwZMqRXr17fffedqalpviC3lGA1H9D5+VBW5j65bbJnY9u1besvrafo7Bfm13zbmVZLMzxpePzR8dJSDNW3menAp1Vqa6Pzvtv7DFyXjFXIH79T9VHMI/ZLTaPz5MlQVORzqT7VApnAe+AecA44gJWrV2oaaWqu1lS1VO29srfmDk3NvZqazprXIwQjSfJydsahQ/wvtTU6pxWkGZ1knMLHBVwwMeijZs+oopJVnKV3VI85U9d1WqCxOk6frqZVr/Ne5/bIjXt9SdIZQHw89PT4T26EJA3oP//8k/1hSUlJ9+7dm7OHpqamtbV1YSEr/CsrK2v58uUz6vlkB1RXV+fzaPny5QYGBs15MgA2buRIV2mm33mz3+Yh1kNEQmdKtFqawXHDH4102gSdD947aHhkBeXZyC3JVXZUDk8Opy41jc7TpsHZGR71ZP0VA48AZ8AYUAf6AEqAGqAPrAJ2Am7AVcAQMANuIv9k/sw1M7EGmIs0jbSNOhvxGzAZMAf+A0RZNkdIpadDRQU0Gp9LbY3OAPJK82YdmwUwYjbmHJo+wmZEaWVpXFaczkEd5jt3Wm7yfLOfmZ4NWi1Nw0WDYyEJ05mSnh4+fxa4Zr2SHKC9vLz69OnTrl27TmySkZHRal64U48ePWg8/4VVVVU9e/as5y5/f399Hv3yyy8aGhr13NUYZWZCra45fTPpXF5drnNQx8DdwOKShQj9zkP1tw60+P34o+Ncl1obnQHcDY5j9zsXlBVkF2ejGXQuLoacHM+vKxO4ACwClAAdYCtwFYgDZgDn+CyFLUBdeSx2F0e8Z/yiBYtQWZfAcgJYCCgCk4EjgOiLuderhQvx9Cmf8TZIZ0rTD09/cOkgVFToeXmOgY7a+7WnuU4zPmUcnFAX3E2jfVwwbfk2jiboavvUWA9ahM4Abt7Eli0Cl61XErWg6XS6np5eDpvYLd+m6bfffnvCVe4TCAoKGjVqlLBLNd/FQYkKfxQJna+GXaXTMWHFdnkLC96NmkRnm5kOlkxfB/NSK6SzSOKdKVF0Li/H2bOwtwcA0IEQYA3QD5gBuAKRbDdUATMbpjPYAX0R8TPjF53iKQ73AhgH2AEawGzgOsDPqBWxXr3C/Pl8xtssnQHk3L2+yKCvyl4FVSdVW39bZhtihmg0GBnRvDzl7eWpZFcAdyPvrjhXV66wpehMPbdx4wT+2utVmz8kvHXrVs+ePTU0NFavXr1p06aVK1dOnDixV69eQcIfA4kK0Lm5+O235kbU2frbUnSmbOdt17ZdC+cIem8CnUfM3j/bkQGwalq1vpv+jbc38NXQmao7WHofWAvIAf8CfwLPeW5oNJ0BlFeXr/NeF+8ZHz8z/tH7R/pu+vHZ8dRXeXU5XgDKAJMJCcA2YAJgAzQzibF+aWvz+Sdry3RufDbK80/PFewVVl9YvcBjwTTXaUXlRUCL0hlAVBT6968vFF2wJA3omzdvysnJDRo0qD+bmrlNYWHhpUuXdu/evWXLlj179vj6+hYXFzdhHVEBGsCKFTh2jHuwxeOdvf04Pq/QamkllSVfCZ2RinB9pA4CVgDPgExAEeA9nxaGzpSsd1ubG5mbe5nPc583wmaE+Rlz6uul/0sOOjOVAPwCjAesAXFUcgwMxNq13INfB50pVdOq36W+S8xNZDxuWTpTr8nAQJiaCpwjWJIG9G+//XblypWIiIj3bBLT9sJKhICuqIC8PNjfJlqczq083plrC5HQ2etKkvLMV5XXgZmonYLNv6KEKlWdJTI64yIwHagEgPjseFb9ey7bmalkQB54D9CBa4Ai4ARU8Vu5aaLToarK3cXra6Izt1oDnePjL4RcUDH8XtVRZarr1IjkCIHzeSRpQDfzSFCsEiGgAfj4wLIuUEJKZy5JgM6BflimHOA4zQkbgXgcOlT3MqyXzmecz/Bpa90IOoMd0A3SmSka4AbIAYIJI5xu3OD+dUjp3NJ0vvfx3vwT8ytsNuPOnZT8FCUHJfamFvVL0oDeunXrvXv3xLRfMyVaQAOYOhXv3zeRzltty9hLxwHIKMyQ0plL/OlchERd5HXCNbsAp+tOAPLyoKCA6uqGbWfX+64+bzhzcxtHZzAB3Xg6M348QB2YCKwASvltJJTU1TnM59ZN50K9KfGnDtJqBRybfhF0BmB+xjwmMwavX2PlSgCu91193/gKvJFTkgb06NGjZWRkunXrJkIftKgkckDHxWHCBMjJ8QmCbNB2js6IVnJQyipmvNhc77vq7FolpTO7VlmlctO5ENiJoiFI64yKGAS8DXC67QRg5UrcvNkozwY3oBtNZwAV1RXB/sFC03kyQJ0e+QIKzasr/eABVq9mPWzFdKZVlC9d8POkTX+ZeZkp2CsEfeQ50v9S6AzAxNMkKS8JdDrVHO/EkxPng/lk8PKVpAEdHh7+nkdi2l5YiRzQABYtgq0t92AjPRtvkt4o2CtkFGa4P3ZX2jZ/ylSalM5Mrbcq+mUFWy5SBbAPkEeEaYXKtCkTd2tqumiO2Tnmly2/jLfV7G2iqe2o/Un9E18602fS408yQi9s/W2PPjxKfZ9ondh4OgPCeDYYPx4bnSklAeqA4E/8DWj6dCQmlleXF5QVtGY6o6pq/7yRrk4m1KPSylJmIwiGviA6A7j46qKVjxUAGBtXRH3QcNFILUgVeDunWibMrrq6Oj09XUy7NlniAHRNDdTUUNc9BhDS7/wm6c2wzcPGb9WT0pldmzdj7cbCGUdmAEAtcAaQA07g6SFE9UD2x+L8svz8snzvV96213YqTsyPfJlfpFwkyHZOOZXCDL1QcFDQ2q9lfsbcfLX50pVLiyt4woHER2dKFcAfwHLh65dGR5cYzDQ8aah9QFtv099apn3i0qOZF0MSQqa6TlXbpzbl0JRn4bdb3O881fpvZu9KAPa37G9/qOtI1IrpnF+at3PZBON1Y+xv2TMC+Lgk4DW53X+7sqPywi2KcmuHULGtjZSkAZ2VlaWnp9exY0cqw3vVqlXPn/NGoraMxAFoAB8/QkODkXMr7Kmg2yO3vzdP7rFQPjGL+/3sa6bzpk0oLC+ccWQGXgBqwE6gDE8OI6oHKtiWCngbMHWz04ndQsRsMFwcwng2ANHRmQ6sAPYAdsBiIVNa1qxZuW/mtfBrlO0cmx7FLCH0OfezqpMq1ec3KzVuolGPWP8zAteRyKng7GOz2WuE2ly1eRr3FGjVdC4ozVc07+e/xSglP+Xy68sqTiplVWUcMwS9JgEAVTVVyUsX0CcI19xB0oBWV1d3dnYuKSkZPHgwgFevXo1tbuVHkUlMgAZw5AhsbZtCZ+XthlOm0l7GvVGwV2A20sZXTOdZ630nrXV3f+x+0O/g6MWj3Re6u19zd3/sfnrfOy46AzgeEDBKz0moiDrX+64+lj4tTGdKR4H5QCOzz8rKoKiotk+N3bNheNIwrSANwKF7hxinUoWFUFcPPOdgf8ue/zqSitnwC/NbdHpRVU0VgMi0SGVH5YrqitZMZ9Dph5epnLOewxw4/uj46edsT6ZeOgPA1q3YsEHYSl2SBvTQoUOpbyhAAxg+fLiYthdW4gM0nY7p0yEry7+xLF86hyWFaez4d8rUWurFEpIQYn7GnLr01dJ582bMs3hw+dXlyw6XT+ucHmc17vLry5dfX9656/LdAfFcdK6qgopGsKfWWaHinV0tXH3W8auwLmE6U3IHFgCN6cjh5YVDh9TWj2D3O+u76VOlS3YH7A76GMT0Oz//9HyLH7/SEJKNqPN67qXipDLReaKBm0FCTkIrpzNWrdpgqRT6OZQ59jD64Y7rda/bRtIZgJ4esrMFTuORpAH9119/vX37FnWAjoqKGjlypJi2F1biAzSApCSMHImMDI5BabwzlxqOqIsFtICdKCxk+KB5PRuU7FYhc7jQ2SixlrGpBam2/rZhSWGs8RahMyV9YBq/cS5paeHQoa3Gvx+9f5jx1D69mHJoCvN7Y7d59Dq/85L/ljyIesC9gjTeuaFTwbMvzzrfcWYOb/ffzqi+0Hg6A9i9m4+ZJliSBvSNGzd++OEHLS2tzp07z5gxo1evXgHs1TlbVGIFNIBXr6ClhepqxkMpnblUP52tNgL7gYnARwAMH7QgOt+7gJheTc8VtPa1fhn/kvGgBel8ApgHrAdc+F1lKjERY8fCwKC6snyTzyZlR2V1Z3XDk4aZRXWNXgoLD+gNV90yevWF1erO6o6BjtwrSOnciJgNWi1txpEZm/02+77xtbxsOf/EfDqdLhydAQQEwMFB4GQetUAUR2pqqru7u6Oj45kzZzIzW1GzIHEDGoCXF5YvB6R0FpLO9ksBLcCJdW5WXFGsYjmTL52fX8Wbb1H2kGehRmdyswB9EZiOgNAAxhEWUxKjcw1QC8wC6rG65s+HomKDEXUllSXvU9/ziUuR0rnREXV0Ov3Ohzuu910ZH0GEpTOAxEQYGwucz6M2X81OhJIAoAFYWODQISmdOVQ/nb2nAkrc1HtyGK97FfHS+eMjvGyPFwd5FmqIzmVVZaGfQ6kvU09Tr+deoSdCQ2eFvo1/e+zhMe9X3qz5kqQzpSJAAUjiNxOAnBxKBeQgiiHeuaSy5OzLs673Xd+mvH11/qCeUW9VeyUTT5OEHJ5kWcF0rqypBNoYnbnVBDqjrlhKo/UlVLMTlSQDaBoNY8fCyIjPJSmdubTDEm/+BNZxexgEeTY+vUBIR/jwJn+z0ZlOp1PBAwzV2c4xmTFWPlbUl9xeOWMbY6v5VlaXrGyu2hx9cJQFaMnTmdI7QB6o5pkcFwdDQ36riIXOSXlJcnvljj486vvGV8dm7HCT71JTYwC8S32n6KBI9TVmSACdz748K28vP+nAJOUtf93SGfl10ZmSiorAu3gkrWbHkmQADaC0FOrqePGCY1BKZy65LsbnnwCeEwpBdI5/iVf/wyHedz5O2zk4IXjbtW2MB4IriL6c/ZL5rsCyoFuKzgDuAb8CvA3j9+3D5ct81hFPrqCJpwmjGNutW1v1h4y1+yenhPHrOP7o+IWQOhwLoPPzT88N3Ayqaqrw+HGpqqK2k3pcVhx49QXTGcC0achrrcWSvp5qdvUrJweKinj3jvFQSmcuvZBFei+ApyF1PXQO/QZbNHkW4qTzs7hn96LuMYLMBGejWBtaXw+9Pn7PeNldsrK7ZAdtGjTUeqistazsAlnl3cpcRawkQefHgCqQB0zi2UhLC7zVz8WWyc3oIHXrFrS0lnkuXnl+JbMfoO8bX9f7rkB9ng1rX+vghGCmZ8Pnjc/hurATlr5sOickoG9f/t3I+ElazY4lSQIaQEoK5OQQHS2lM6eqEDsE+d0Ankzaeuj85lusGs9zTsbjd15xbsX54PNb/LbUnytofZktioOyoM95t5jtTNGZ8gTEARpsWeCFhZg0iXu+OOtszD42O9XvLHUqeD74/J/b/0zOY7yLznOfF54cXv+poJWPVbDvcabfmcV0pr5sOn/+DDk5rF+PRoeuSavZsSRhQANITMTvv0NNrel0DnwfmFHICK62tsbSjZ+exHJ3aGxLdM5ARm+k9wZ4lqqfzuZjeHbnpPPD6IdBH4OmH55u7Ws9f+X8IIsgt8du67zXcbhN6yLqYjJj2CtFHDt1zHuKd8vTmdJOwKvu+2vXuGO2xFwFKfSCq4pxj+fv733K/mR3w+6P7X/MPjbb1t9Wa7/W/rv7G4zZeHblyDyDXtU5WQDKqsomHZgUmxXLuvw10DkyEqdP49QpgdM4Ja1mx5LkAQ3gzRtMmIAPHzgGG287MyveWVvDfEOc3F65T9kcf862ROcw5PyAxP5C0/nfv3iivDjpXEuvdbnr4hDoIL9Xft6qeerL1B0CHZb8t0Rzv+bn3LpSsILjnY/pHvO+7801vO3MtmqFaknTGUA5IFvXK2v1arx+zbokkRp1MQnhm3w2LT69+Hzw+Vp6bVJe0uOYx9nF2Y2MqPsv6Ji8vfyUQ1MUHRQD3rIZkl8JnQHcvAlHnlB0AWqZMLuioqK4uLimdQ4Un1oE0ABSUqCoiJC6rAphPRvhyeH9l8sbWwYrOShxHbm0JTr7I7MX4gYLTeclo1HE5QzhORXUd9OnvoYtHTZh2YTft/2u76Y//fB0Vqpuvdkor8JfRaZFcownY+q8qaVhPMFtZcBkHD10lHu+qOgMIBYYDlgDAJSVGVW40PIVRIWKdy6v5vwzfz10BvDiBaytBU7mlKQBHRUVNX78eBkZmS5dusjIyKiqqiYmJoppe2HVUoAGkJ0NNTXcutVEv/Oc9Te7rur6PI6jLmBbovMhJPVHzBCh6bzsnwZsZwA1tTWMTttW8QuWL7C/ab/49OKwpLD/nv9n5WuVX5affzY/blZcQX4BuFRvzMbUPVPZ3SAAy3beenXrs7hnrHHR0lkOiAUUgc+FmMJI5m5bdObWV0VnAO/fU61VGiNJA1pFReXYsWMVFRUAysvLXVxcWk9cRwsCGkBJCSZPxpAh4H3Dqp/OyzYmKjkoXY+4Lm8vT1UvQxuicy2wHlHDETVUCDpPtzd83QnrFFDJZfM2lI2y7OwyBXsFyoJWdVL9c/uf+lv19Y31ZXfKutzlzKduKKJuqisnoNk8GxyAFjmdqd/GRWBhHHbuBKR05k/nlPyUi68u3vI/UqkwoRXRGUBCAszMBN7CqRaIg25wpKXUsoAGUFUFc3OsXcsRjdAYOlOejfDkcIrRbYbO1YARVkzafXtkLJPO7o/dqW8E0Tn2GWR11XZMYX24Z6gRmdwrzq0ISwpLyU8B8CDqwY69OyjPxvFHxy++usiaL4DOPnd8ZI1kZbfIyu6S7bam2987/6bi8FacXkGfTM/3yacaBVhetgx8H5hflp9fml+2okz0dAZAAwalwv+ulM586Xwh5IL2AW23s1v3TB+kaDeOfweTFqEzgLQ0gblFPJI0oEeOHBnH1l8kNjZ21KhRYtpeWLU4oCm5uUFHh1GSsMGIuiuhV9j9zmFJYTZut9sGncsAXbwYC5NJFmGxrNJxVKStIDq/DUJwB4z4V437QuPqbNj62zJPBR8cfbBj/g7K78wB6MZlo7As6DJgMt6de6fpokl9DbEeMnbXWE0XTc0lmnpr9WrpPAVDm0lnSqNPYk+25OnMyNIGN52jMqLcH7tfCb3C8i+3EJ3Lq8sV7BWq37ymPBsRyRELPBZwT2opOgPIzISBgcAbOSVpQF++fLlbt266urpmZma6urrdu3f39+c98G4ZtRJAA3j+HHJyuH//y413LgI08UgeUUNhcd6Cvban2j41vnQuKi+6dDbep3u8p038hL0TGG7l7Pis4qzGV0Fi6SL85/hr7dNyCHRwCHSYeXTm/JPzHQIdHNwdnGY4labxnP7xRNQxAM0vZmPr1a3PYp+JxbPBLtnp+CZDknR2vuMsby+vfUBbw0UjOOYpO52PPjw68+jMc8HnDt47KG8vn1qQ2oJ+57CkMIsjC9j9zoz8GqZakM4AsrIwZw7/SzwSK6A7EB7p6+tPmDAhKCgoJydHWVn5+PHj/fv35532lUtBgfj7E01NMmzc+yvZLjr0U+1l2hNCXn9+7f7YvVe0B0AcHLjv8vUlXl7kyhXy7bcc47GxxMSEnD9Phg7lGC8rI7Nnk5Uria4u91KbNxO+W/j5EU9PgVucO0eGDeMYLy8ns2bxbJFPaubUXGqX0qeI/O8RKXpQlFaQ1u3bboSQzp06F6WRXv+Rn1+TbziX2nPgccL9gHYrSY9fScq7FMfbjtT4b71+szhhQWYTYkR8w3z1xuhRvytCCNlKSA0hTtw/BfEm5CIZ7TZ6RsyMEX1GEEIyijI6d+osWyZLrpJ2tu3+1+d/HPNTCJlLyAlCRnKuU06IISHLCZnOs8V+QgYSsoVn/CQhDwg5SwjXi+MJIdsJuUpID87xOEIWEnKOEM7fBkkpJu82knEdSeeJPHsQQgi5coWcOUOuXCHffMPnKv8/DJsCA8mBA8TPj3TpQg1cen0pJT/lmdUzmXYyOXlpsyxHX5/t0GP+fEJIQXmBzxuf++vvt2vXjhCi/IuyjdsSr8Aycu0a6dGDz+K1tcTEhGhpERMT/ru/fEmsrIifH/nxRz5X6XSyaBGRlSWrV/O9u39awecnt4jPe/LTT4SQksqSTh06sS4DZM0a0qcPsbHhv3tMDDE15fOCYcrGhlRVkX37+F9NSiLz5pFTp8gff/CfcOAAiYvjf0nCEkTu7OzsmJiYHEFvjy2k1mNBU6LT4eyMP2d5TNs/t6a2hgp5XrUpvW3bzrmACo5rhRtMMTf3NDc/Yz5qx6iRtiP7b+jff0P/3ssHfm/Sg+rJremiSaWZFRVhlQESfkJ13cEbyyDitJ11D+uyymkK01fw+KPjF89fFCpX8OLTi7TJNL7xzluXbn227RnPBdHZzoXAuBJoO+AVsIzPPuLwbMxzn8dIiaqqwowZzg5GzOanz+Kebb26le3Heaxm1L1lYzZMjxgcCDpQUFYQlxWne1g38H0g42rL2s4AnJxgaAgTE4ETOCVpF8fjx49//fVXGRmZzp07E0L++OOPF1xFg1pOrQ3QlN6+xS/TPP5Yry1vL/8l0FkZd1Q5YjYsLjFcHJRnQ8WO49Pow4fQkUX+HxzV9xmA5vFssADNj87BCcFJXkl8452Pex6/OPWiqHIFn25/mlWcxX1JhHRWB7Y/wuHDAKAIVHFOEM+p4Fz3uZlFmUy/s8tdl+sRjEVS8lP03fTrfpzHGepyegen8l9cUhF1NbU1xx4em3l0pomnCVtETSugs7k5Pn7EMr7vq3wkaUCPGTPm1q1bNTU1ACoqKs6cOcNsTtjiap2ABhAS/+aHZcM6601fvpJPUfY2Q+d8QAW31bgj6ihAM/3OTOu4sBDLlmHZLFSP5+6NorZPja/fmQFoAbazvb194JxAvtkoJ3RPXH7AUxOuRTK5KdVP5/uAvT1u3QIAW+Am2wSxxWxcCLmw/sJa+owZuHAhtyRXyUEpr5RVks3My8zptlPqbd9Q7X80HFXZK5mw1Bg6Kym1eLwznU73C/Oz8rE6EHQgv4ztbyMSOtPpePWqvnU41QJdvblGfvnlFzFtL6xaJ6CZydw2Zz36Gs7daFXD/vJpM3QuAtRwV4VPvLPFJYtTTmHMU0EK0BcvQk4Od88BijBzMuMySB9/eMyk89Wwq8zwiV4WvdRXqGsu09R00VxzcQ3HNhdhP88+MCwQXHoBKKMmq4ZWyxm1x0Zn41NsLTBaA50BLF/OYMF7YHHdBDFH1DnO+0Pe8hedgzrqzupcCKbV0k6dslpg0Hu1l/n7VH7FG9pONoqZl9l2/+0v4196v/JmHHhCdHQGEBRU3zPhlKQBvWrVqsBAxouETqdfunTJ0tJSTNsLq1YI6NisWEUHRSae3B+7a+5YOmECTp0CjdZ26FwOaOG+EoPOge8D2UvmuzkGs/dGWXHSTlsb1tYoSwQUgRAYnTTiiGMVHLOhu1K3eAO/+gEXgemwv27P8kVSalxEHcvl3UroDEBXl5HhTgN+BmolF+/MnaXN+HG+kFzBtylvF51exLzy/NPztRfXipLOALy9ceyYwMmckjSg//zzT0JI9+7dBw8e3KVLlw4dOgwfPnxYncT0PBqpVgjoiuoKrurDmUWZ5eXYuxcjR0JWti3QuRqYgcdyLNuZ4c0EwBnvnJgIExPo6yMuDshi0BngBHS9EXW6y3W5eu7FZsVqbtLUXKipuU9z+Jbhf+/8m7K1px+eXvmsspGnggxAtx46o64rBw0wAlQBh6CvMBuFIZFmcl8JvXLkwRHmxYrqCrWVg/3XzXyd+Jr/7cLSGYCrK65cETifU5IG9KtXr8IFS0zPo5FqhYCuRykp2LgRysrw80NtXSZEq6MzHTDGS1kOzwYT0Ew6JyVh+XJMnoyX1OfmLOSp5IXeZHQI1Dmoc+v9rdDPoaFxoR/nfKwn3pkjioMSW8yG/S02C7oh2zm3JJcZaj1h74T4pPj4qfHxF+KLyjkrM7UInQGoqoIGGANHgE3P7stZZeXwdDegJKWzMKeC71Pfm3qaMh9arJ3ws/n3LnddVl1YNdV1KvenhybQGcCWLXj+XMAN3JJ0HPS4ceMyMzMzMjJqa2uZg2PHjpVY5N8XowEDiJMTyc0lBw8SZ2diZkYmTCDm5q0p3pkQYkUiwkj3CvLbB0I4l3p6hPTaTqrPk2W7SE4OsbIiKiqEEEKyCZlFEjYn+FT4kFBCCEnKSwp4F9C1Q1dynfSS7fW70e/cT7cu3nnU1VH/68AWwuxNyEVCLhPSiXP+S0KsCPEjhCvKli3e2fuh97vUd4zhvBRHa0ciRwiNaEZq6o/VZ8wHIasI6S/OeOciQmYSYkMIV7gzZLwJJgkAACAASURBVIgZIRMI6X2FBPjeHTy4c0l67x8Hcj8N4eOdOVRdTQwMyNy5ZP58/rc/eUK2bRNXvHNtLVm8uJ54ZxIRQVasID4+VLwzt5oU7zyy/8iO7Tta+1pPHzP91dEdXuURH50S+3XvRwjxfu3tctfFZmrdag3GO+/bRz59Im5upF07jvGUFDJgAP9bJCxeZq9YsaJLly7Dhw8fwSYxvT8Iq7ZlQbOrpASHDqFfPyxbxm3btmSNuiOIHsawnW9/uM08yuuzvs9fa1Tkpmn2NdT8xXT5mzdst7B5NgDQ6fSH0Q+NThqlZqc2IVeQK6KOYUEL31dQzVittXg2KFXR0PcujrD8zlazrII/BfM887ZsO9NoLVijLuBtgM1apSUr/3K8zSrcXFZVNuVQXfnAptnOlCZO5KkjI1At0FGlQpCnrKXVdgFNiU7H7duYPRuTJuHMGZSUMOi81+uR2yM35rTItEi7G3Zip/N1JP/E8mzQamlUIaHg8PzfTWb69IzZuTY/KiG/oprtn4GTzgAqayonHZhk5G6UOju1mXQGcD74/Ovrr5vQuUrNiqf0RwvSmQbMq8YoN/ZTQStzq+A7nICW0rnZ8c7PPz238mG9SGKzYs28zIDm0Rmtu6v3zJkzK7lrRLYWtXVAM5WVBVdXqKtj4EBYWSEvn2biaUJl5UVnRMvtlVu2MUG8dA5H1o8c9Z1jY+HgAFVVbJ2GWXpz06MyeZ40N51BAdplkpGxUaonv2pkwtAZaHpPbp5KDi1KZ2PAvhgKCmF6ypr7JlIfSn5e+fO4deOo72ccmUGv53MTJSmdG5GNUlNbo+GicfPdTVot7XPuZ+0D2q8TXzeXzrGx+PtvgffySNKAvn///siRI5cvX27JJjFtL6y+GEAzFRuL/fuhrQ3tSTTZ9SZL3GwkQecM5PyAuMEoy8WdO7CwgKIijI3h54fKEEAecw+yojgY4kdnAJVllZNMJn1w/8AdpAwR09nljEtJJSet2GI2HkY/ZI23OJ2PALGxGD6cvRi21XKr4El1FrTUdhZdrmB+Wb61r/VE54nz3OeFJIQ0l85paRg1Cgt4SusJlqQPCVetWvX333/37Nmzffv2vFelEq1++YVYWBALC1Jc3P70tU3Wz9S6pqzITxhiYkKCgshff5E+fRgzm38qmFmU2btr388xpL0cKQKxHkJq5hElJTJ3Ltm3j7RvT8hbQpYTcoUMezXs245s22QTMouQ/YSMZww43nZMyEkgdFJ7rzayW6Trt67kPCGE6P2tpzNSh5AGqiCRy6S6ffX/CNtpYUOngq9fvDasNOzSqe6grJyQ2awqSGoj1BjjIGQVCfkxJFk7WZ/ocywl7lPBWsI4FVxJSNL/iKIi6VR39OlMSDUhMtQzl54KirIKUo/vetjPsmc8aPKpIKWsLKKvT2bOJD//zP92yYuX2aqqqmJ6N2i+vjwLminKsxGXFWfiaeIY4BoQgJ07MWcOFBWhrY2ZMzFiBA4dgrc37t1DeDiSklBayt92LijAu3cYPx47d+LcObi6wtYWS5agh9FEVVV87Ifkngh+iOpqzmcQAcgDvI4KfrZzVnFWfFp8/Oz4qBNRKo4qzHA3Rv3lhmznjMKMhacWssYb4dlgVQJCw/HO1yOuO99x5rgkMduZUlISFtb9gPuAJbDysQrWDpbazi1fZ0OQ7ZyZCQUFhIZi7VpwHIs3IElb0OPHjw8ODpaTk5P8u8VXq7jsOFMv00tLLw36YdApk1Nmp816/HJy+/Yl1NXycvL6NSktJSUlJDeXxMaSvDySn0/y88mHD6RvX7J4MampIR07Mlbr3p20b09+/pl07EjKy0n//mTUKPLTTyTWn/7oO0JSCIknAznNcKbtTLgqy/LYzpR6d+rd26Q3mU2q5lZ9e+Tbob3Y7J2GbGfSidRW1NbS64I4GxFRxyFO25kl9oi6t5yXJGk7U+rQgdBohBDiTEgsIe5k6NOhPbt0J9PMiIXUduYncVcQbdB2njWLuLoSWVmyaRMZOZLPnBYRL7NHjx4tIyPTs2fPwWwS0/uDsPpSLejUglRmr0IAtFpa6OdQke+itk0NwwFe40wY2xngyBWkojhYlxrnd04tSDU6aQQ0YDsH3ghkj/xTcVTRdNLUXKC5xG4J93xOvzOHBS1h25lSbi5mzaJsZ1AWW1kZhlzCvqc899dJUrZzZFpkwNuAxNxEjglS2zk0FACqqyGkC0HSFvTp06c7dOAzLpX41L87h+HaXqa97GBZkaz8JunNm6Q3hBBSQtKT0k8cPEHCCCFk0A+DJo+cTIjQtjOpJmQeoarvU0/1p+51FlNDtnNIekhJZQkhJLc0N7Mo857vPeJBiBMZ2XFkX9KXNb/Odp48cvJkMpkam39i/oHpB/qa9RVkO5f/VD6n85ya/TWEkJySnPLq8tsfbpM0QvKJ+wH3oR047TLx2c6UvvuOROqQnoS4E9Kuzu882YkM+Ivnt0MIkZDtjIUL//1vMa2WNqr/qFPPTo3sP9Juhh0hUtu5znYmhLx7R8aM4b9OS4gPiMeMGUMIKS4uzs7O7tOnT9euXSX+rKQSmb7p+E2P73oQEOJKOg7p2KMP4wXc5ZsuhDSXzoSQDjIdPE09CWmUZ+Nd6rv8snxCSHFlcX5O/ptnb8i/hBSS3iW9+3arA7Qgz0YtIYvr82x8t+W7W+QWNXbj7Y3YrFjLJEuSLlnPBlNHvyXFfTnovHIlafcXieKZSZpIZwC+Yb4hiSE/5VaYXg7vcS2gQc/GxZALQ38cunXqVurKv//9+zL+pXw2kdKZQWdCyNOnRFGR/1ItIl6jOioqavz48TIyMl26dJGRkVFVVU1MTBSTAS+svlQXh9i1B3DiCRZuhmeDW0JG1KXeSzUyMBIq3nme4byMSxnc4wIi6q5HXHe2c24BzwYlyrOhStVvYjsVPABo80xuqmfD6KSR3Q27EF+3S1N/l7Mbm16Yzud2zlPBVRdWsRcavfH2hvOJ1VLPBsegnh4yeTIA6pVYXRwyvMheunSpqalpWVlZSUlJaWnp9OnTzc3NJf/OIZXI9IKQEEI2cA42zXaexbKdWWqE7cyhl4TsJURFuFPBaUrTus3sxjFeT52NO4TEtZDtXHcqSAg4IuoCCblKCOdP0GTPRujn0K7fdN32ver4QxcMzj53nOvifMeZ+3aeU8HeXXtnFGUwr6e9edrH/67UdmYN0mgkN5cV2doaxMvs3377rcGRlpLUghZahYAckAUAGi4ajMGm2c5n+a3fpGyU1Pi6Q0KmRNobJcEw4dWnV9zjjzFv7ryq7CrucZHbzpTFpqYGbW2G7XwL0AJyOS3oZpwKXgi54O6xgXkqWFJZMtWVs4UVv4i6xNxEJQelj+kfAby4dkLR6PvCFAHW61doOwN4/hzCJ+VJOpNw5MiRcXFxzIexsbGjRo0S0/bCSgpooWUM3GZ8W1BWALQ8nZGDWnptfDbbi1NSnat09ulwl6MUE50BjB0LLy+gjs4Uh1XrrjYvZiPsqse/+n2ZMRv3o+5vvML2lxAc7/wx/ePCUwvVt/2zYna/1Ph3/FcXhs50Ov3G2xs7ru/weu7F+N02gs50Obmg+2fcHrm9+MSv32mL0JnaNyhI4LICJGlAX758uVu3brq6umZmZrq6ut27d/f3532JtIykgBZOFwHOrlKtgc7ckmBfQZ2DnIAWH50B2NjgyRMOOqMO0KKIqFvkbmTta/007qnXcy8FewVWyzHJZqMs8Fhgc9XmQdSD44+OKzkoFZYVNEjnavkJ0x01N/ttvhByYeX5lYtPL+aY0FJ0BqCkhCqeD1gNSdKABpCUlOTh4WFvb+/p6Zmayq8IjiiUKaQzHlJAC6U0QJGzu2BronNlTWVMZowE6Jx3Ny9UOzT0XWjo51AFe4Xnn55TTQY+v/4sRjoDOHUK64M46AxAVZTxzjff3dx2bZvbIzdWD4RG0Pml9hhHn+0eTz24OgExbheGzk/jnrI3lvR747tr2YQGPRsnffdSdcEoWV62DPpYZ7e2IJ3j4mBoKHBlwZI0oKl+3pRKS0vFtDGATp06CXuLFNBCSBdgd8O2JjoDiEiOWHtirQRs5yCdIPOT5uZnzM3PmA/cOHDR6UXmZ8zND5s7T3MWI50BOL3HkDjutKBReS2byb1j5mCz4/Nvvrvp+cxTbq9cUl4Sx+2NoHNFUsLugN1q+9QmHZhkdtrs3Mu6sB46PX2l2bw1gt2hdX7nledXfkj7wBy+9f6WY6Aj0KJ0BqCvD09PgVcFS3KALikp0dTUdHNjFSY2NzefPXt2lfBmP7tyBEgKaDHKC9jC9jAC4erhkqQzq+esAM9GxKuItTPXSsazwRTDxSFWzwalW4BaNXTncw7eQo+IFqRzuvqEKfs0mQPhyeGM6skQwnY29TT1eOpRS68try6fdXSW1n4tgOF3vrXFeLv/dv63s50KOgY6Xg27yrxy8N5B71feLUxnNzf06YMm2aOSA/SKFSumTJlSwvYPVFFRMWnSJBsbm+bs0a5du/b8xDeGpH5JAd0opQNKbJZsBCAPtd08Je3FaTsv8FiQU5JTj985Qi1i7ZG13ONi7vqqc1Cn/GO5JOhMeTaUldkGb0F+CX4QQBCJVEG6H+xnd8OOfZgRGt9oOpdWluoc1GEO19BqfrT40fn2vogV8y5uNpC3l88p4efa5ozZyC7OlreXv/PhTmZR5pXQK6pOquVbrVqYznp6MDEROKFeSS7V++rVqw8fPuzCFpL5zTffHDp0SEdHZ9euXU2O5LO0tOzSpYutrS3X+DfffFPPXXl5eZ8/f+YazMrKqqmpafIz+Vq0hhCnuuhjZrzzRc45DcY7L+BZVph4Z1otjRZCIzu5qyDtvrm7NLuUXCfZutnvC95b+1oTQtq1a2eqYDri+xENV0HiUkNVkCyDLF0MXFjjZYT8S8g5MvPOTJ9lPu1l6grqNifemSvKNpCQA4T4EdKFkK5dSUkJ6dqVBAaS3T6EdoL8yi8mV1JVkIa2K3WP8GYOx+fE9+vWT6h45/z8lB+7sv6cHdp3+LPfn33873p9UzpYXT9Q6US3b7tx384T79yra6+rK64efnDY67nXqAGjbhSO/7a6VqLxzuxydychIWTYMDJlCv8JLSqO/+vc3NwBPK0SBw8enJ6e3pw97O3t58yZExERMUaYJPf379/fvn2bazAuLq5nz57NeTJfvvwJ6UeIPCGkGdkozaMzIYTkEnKRT406jR4aZYfKiAVJ6JxQ+KFQ8w9NQkg70q7f//qJnM6kBwlLDmONx5Ft97Z98983ZBgpqyqjg96etCdEbHQmhPz9N3nzhlRUkN0+pOYEudyemPEsJcEadT+TH3t07mF73dZgrEF6YbrdDTs3o2NCZaMM/GFgfHZ8QXlBj+96EELCk8LKPkWd+9/3ZNCw3//3XddveMpCCMhG6fN9n916uwkhxMaGVKOF6Xz8OFFXF/gcWlbs5vSvv/764MEDLhs7ICBATIkqubm5wt4idXE0oGJADqCO9CPgO8nXysvKysfKysdq0KZB1DdWZ6y8dbzF4dnIKMwI+hgU9DEoyCdI3VD90v1L1MOQhLrN2GI2IpIj1l6sc3E05NkwP2POMUyn39l3pzGeDVZ2O6ffWWu/VjWtGhCPZ4Op69dhagqFRRhHQyL1hDhvkXh9ZzqdfiX0yqoLq9ZfWu94097NTD7RZYfA2/llozyLe6booLj16tZ13usGL+m6fr1iRXVFRXWFrb8ttwO6dWajMOXmBjMz1Nbi8mXs3StwWkOSnA/axcVl2LBhr16xzv7v3bvXp0+fQ4cOiXDLQ4cONTk4RAroBrQe8AHA8DunRqVSIWWhn0Nld8mGfg4NDQsN1QhNvp/MfaMo/M7hyeEOgQ5z9szZMHvDaJvRW69udQh0cAh0OPnkJMAd78wCdCP8zlxVRKrcqrTNtBvjd2bcGAvIIftdNvO3MWHvhOCE4NAPoaGTQ+OuxXGvIxI6A8jMRF81jK9l0BlsiSpoyer7Lz69UHZQ+s9M6eIuU00XzesR/BoICM4VLKsqexb7NHSFgfKqwezjKk5svVbbCp0BaGsLW3+DXZLzQVtYWJSUlKipqfXq1atv377JycmlpaWbNm1as2aNCG12Ozs7AwODzp07i3BNqQgh5AMhsYS4sDwb/fv371/n3ej6TVfZb2XJajF6NsYMHDMmdYxNqI3OVp3UN6mr1FfVU6OuY/uOHTt0bFT1fS6dJOQRIeMFeja8PnpdCLlAjb1Neau1W4tEEjKN9A/t3/d7xvPJKs7yfeErc1WGaJGfu/88nAxnrdN8zwZTBX1I2RFyAeRnnqVatPr+tms2ftEDfvxHgaxePaOmQsNFQ3c0Zw+BeutsfNfxW0XXS9V9//i2ayH7+Lcdv62iVXXq0Kk11tlgF+XZ8PAgMjLk3TvSu3frqr/BLl5m5+Tk+Pv7nzlz5uHDhwUFBSJ/T+jZs2dGBk9ZssZJakHXp0lAjMB4Z7U9apKJd956fuuzuGccHaqSAXmkBnM/p5qSmnps57LdZfll+dSXkoMS4/vj+ZXzK6sqqrQPcNaFE1CjTs1OjW/MhpaTVvXEanF5NihFAXKA6Q6wfSRlWNAt3blKbXFf9piNOcfncERfNLrOhtZ+rZT8FGo4tSCVEXLXhmxnAGZmeP1a4ORGSNIF+3/88cfp06fzjotK27dvl9aYFr0uEzKGkAqBp4Jd33cV76kgs3PVM87xOtt50b1FN8fd7CBT9y9XTjrod6jHdj78z+E3Z99QYzFZMUvPLo2MjCwuL+4/sv8ArwFvU98auBtQV026mkw9MZV/jbooATXq3hKyWQyngkxFE2JGyEVCIseSu3fJuHGEEFJFSKdW0bkK3btULTen/oB00NML03t2rjt7F6ZG3X6D/XPd52r8rkEIeRD9wG2BW1uynQkhaWkkI4OMHct/cmuQmMAvJkktaP6qAOSA5y2WKxgREKE5X1PTQVPTRXOI9ZCxu8YqOihquGhMd5peq1BL+Z1Z53IQOt5ZbZ8aTiBnfk58Rnx8dnx0erSyozKjU21AfIVahaD6zmp2PNHfhYA6tGzYngwlkdvOidRPWoaJExnjHwBVemvo+ur9ynvWsVkRyREf0z8uPr34QNABxgTha9RVVFc8jnn8OOZxRXVFE2znWnot64GEbWcA69bh9m2B8xunFqjF0WolBTR/7QVsJUFn97nuN0JvcI9zZqNsvbr1WdwzgPtUkAVo4bNR1NarscdsVNXUuTgaqr5/6fUljvG6mI3w5HCOcTHRmdL06cjIQDYwho6fnrc4nRlzP71YfWH1srPL7kbeZUyoo3NReVFIQgjTd8GQqCuIPv/0fKLzxInOExUdFD2feSIpSdJ0zsyEurrA+Y2WpF0cUrUx5RByhZBOhPiIJt7ZpNDksPPh78n3HOPehFwkVSurqtpVcYzX25M7Yk/Eq/xX5AkhhKQWpHo89WhPa0/cSL9Z/XSnc55K1X8qWNDE6vsGxIA1zhbvPIawheSLw7PxM9ugnh45d4dcW0hqPYniuFbSk1t+mLz8MHnWhDrPxvmUhyfPnxw/ZHxibmKXTl08TDzay7QXefX9zKLMzX6br664+kPnH2pqa/49Nn/Ajn1ap3wk5Nmg5OhINm7kP7/ViE9HFanamNYSUikyOpMaUjayrKaWM12T6XfmQqQgOmcz/M7f/fFdj+96UF8d23fs0b5Hj2M9emj26Kba6N4oJwl5QDat3cS+tYyMzMCqgY3vjWLkYSTGbBSm+NKZEKKoR/bIkvbWRPFPoiOgaaxk6cwuALevH3Pepudnt+jz/6rPvjx73/K+0xynK8uu/DP4n+OPjnPROTI98tLrS4xOxJSE9zs/in1kNMHoh84/EEI6pmVs9v3kN/0vidI5JYW8e0d0dPjf0moktaDbuMIICSQkkhCuE53m5Aoe57zjQvV73/fEmZBMkpKfUllTSb04271vN2bfGBk/Ge5cwR80hlgPoSLqfiW//trnV2r85KOTs+1nd1zWsQm5gjodOF5IHZ518Aj0aHznqvTc9Bajcw4hi3uQH1aR+cokT45wJ+oSQlqYzoZOOj8Hf1CytH9fkWZ3VG+R8iJmBrzheMOlZ5euuhrDpLPFJYu8sjz5ofIeTz32B+0/u/isTGxcE04FabU0xi7JyWTu3A4uDrRPvvxvFwedCSG2tmTnTv63tCZJAd3GdYCQABHQOX9zfmJ1IllDSBIpKC94m/K223fdCCFd73ftdqPblflXyDtCCAlPDu/yTZe80jySRmQey4y4OKLzj5zx7ClE3Vqdf1/Bt4QsFXEmN4fq6Sv4oeXoPIuQfYSkzyZRUSSZkDk8e7UcnQkht28cGxL8Ye/RV+Snn6YSkl2Sfe/jvTUTGXkP+WV53d9GkQFjKDo/inlEo9POLDpDXXW87XjhmssCJ78mxGyo/6ZufMp4Xl/FzsZmdI+T+yOOzRgzg8/tYqJzRAQpLm5d3bsFSArotqxIQsoJ4fo3a5LtHF0VfV3xOgklhJC0gjT/CP9v//ctiSJ9I/uu817n0MmBmnj4weGfuv80u2o2OUSObjya2TFzGDsO6+362v6f9u10OV9m4qRzcUVxbmkuKSFkGan4pyJhVALJIYSQrt907dW11/mX5ycfn9xzQk9J0FmOkOppxNmZdN1CBnEuKmo6v/78+vCDw3mleUrDldaqr/pu+er662x88NinZOnAjKhbKL9Q56BOWHLYP4P+KSjLt7SdbNVLk+l3fv35tc5I1keZqV3/tPUyOT99ZIXvoh86/2Crazt64GiO9QVH1PXv3t/6n0WTbMf30hufdcPcYKzBtL+mcU8SE50JIVZW5OhR/ne1MkkB3ZZlR8h2zpGmejYU9isoEAVq4FP2p+2623sG9CSv+cU7xxJyjBA/kvgwsbCcLZFMMJ29FnopLVS6pHeJFQRNGkvnmMyYEX1HMMaFsZ0fxT66GXqT3CVEg6TkpjjedqTGf+v7m8VEi2cnn8mOke25kqfwljjoTAj53/+IqioJKCDf/sCaI2o6v4h/sfPGziOGRwb2GOgbemXe6j/9/9nQrt4qSEPttn6oSGeWcfuU/Wmx0mLX+65JeUnfxiVa9FJX3HWCeUe/bv2S85MZD2Jinuxd/f7P71+s9v2xy4/J+ckLPBZcXnqZlTtaf7xzcrL2lqPap4ILf/6p27fd2vEiWHx09vYmsrJk+HB+t7U+iSk6REyShtmx9B7Q5xwRUUTd7GOzc8/k8o13dvVw9ZniQ0XUWV62DP1cF9VUb+eqLY5bnn96zjHe6PrOk//f3nnGRXV0YfyhSZOOogJKCIIRsb+xYUOxx4JojD2aGDUYo2g0atTEJPbYjRVbbCBIVGyosSGxgqKRJhZUkI70tvN+uLBsowm7exfO/+eHu+fOnXt27uzjcO7MmY0DcvJzGKt4Rp0kIlmQxFJ5FDI2gU1fMP1p3FPJS6o/o44jgTFnxoLFjVGvWIPQ0o9ymFE3ZueY4rlxhYVs0qQZC5wfvS5jW9iSGXX5hfl91vfZe2NvRHyEzz2friu7pmalljWj7n3O+26rul1+ejn3yaPbvVtZz2184v4J4dlDwYeKk66witYKKn5GnZD0dNa584cl5i8LuU6zo1kcKsvvgOiUpxrMIBoL+MpeKzji8Iief/aUOaOurMgGZgDNxe2KiTuX/1ZQwlXIbewsJNsaBsl4+hSQV9w5MTNRNL9zs15D3qS+kXG5yFpBLQ2tM7POZOdnr7uwLiYxJuC7AGNdo7Jm1BnoGPjO8D177a9hK3ocHN2uX5tBtualoWdDXcOM3Ayg4rEzPv9ccWsFJVi4EIsWQXUSAVGIQzWJBHIB4ZStGs3vvDhisckxE5krueM3x/944kfOEJUQdSPqhiEMEQbT4abHWx0XKy+aBemkiP0D1Pkm8HMNqPONiBu/rPkFZkAewh+FP3z9UL+ePoCGhg0Ppx2WrzrnA18D7Vth7UJMniynt4LtmrYLfHxhwB/e6NCBeXhc3th/UhepSqRWcuto6Xi4eBSfZSx21pQN2pEvTC3anVkxu89sQ12x6fAWcWnr9j/F4fuwtQ14FHDg1oF2TdsBYIwd/vfwj4N+5LU637iB1FR89pmMU7xFTiNzOUEhDt/7vokZiWwKY/+WmJS066unt+e92/fKimw8/OxhB88OHVZ06LCiQyPPRg5LHLjj/tP6C36V9aerSGQj+Fkwl6d01blV9nPtfx3x6yq/VavOrTp462Bp+arsK3gt4prEWsHph0RCHHKNbDDG8hhzY2w4Y5cZ69KFdenC0qS20+ao3lrBtIxkl6mNFy5y3XJ5y8CNA3de2ylZoqKV3LHfTu7yrfWNqBvJmcl+D/x6rumZWyDSG6TWCi77e1nf9X2/O/pd99Xdd1zdwd/IBmPsxQtma8vevSvz8g+FlnqXQgLtccQj7EEY61/yWUnqzBjz3Ot5z+VeZXZ9XeS3KCg6qPJx59iU2OLE/8cD/zfhfwHBAdzHBy8fFJeo9q6vpQKtGHU+yNgwxpIZ8/cvc++7mljJXbR509WIqz73fF4lS6X8rkSejWWevUtXfjO25vya0t1dy1jJnZad9uj1o8zcTF6rc0ICa9yYrVxZ5uXVgJZ6E+IcBmYDUMjOVeWs5D4DLC477lyN+c5WJlZWJla4DmzH+iHrXTq46GiJbF/5wXFnxcyoEyUf+AIYDkwAdgOmwLBh2LsXERFwcBArWUPzndVnzeop8/LK5aiLtc7+uEFps9o1tCuetlH2WkEjXSMnSydeRzYSE9GrFzp0wMKFsi/nM3ISfjlBI2iPAx5hLmFMoMyxMzdn4/LZy0kZ4puWlZEFaZHfoqAZQcKxs9jUgor25C6dxcFR7bEzx4FbBxJWJSho7MyVGVFiDw1lI0eKO1mTWZBkUOkcdVuvbN1xdYfwgsINswAAIABJREFUzPRD04Oig6qb31npY+dPP2VOTixD+onWDBTiKKVuCvSt6Ft91/fl/lnPsO48u3PflX37ftF39O+jJYsqSp0rE9koRsAuTL/w5udSdSid9FaROjPGhm0dlleQV2yvIXVmjBWsLdjw9QYFqTNjLEA8tjNlCvvnnxIn+aLOjLG8grwhm4f8cOKHA7cOfLnvy++Pfa/y6tytG/v0U/bggewCNQEJdCl1U6BLETCPkR5hd8OUO3aukjpLx52LBXoXSxqTlJIuNRFYfL5zadbmmlNntpZlf509cONASbuc1Jkx9hNjl0Q+xsczZ2dWUMArdS6xCf4J/2df0L4HLx+ovDo7O7MvvmC7d8suUEOQQJdS1wX6AvOY7hHmEqa66sxKsu+zMWzPtT1//fuX2LlqrEYRo6L8ztl5UgItP3VmjPUv2WpdyObNbNUqvqmzGLVAnZcvZ19/XWYNNQS9JCRK2AxoAfOV9Faw/NUo5b4V/Cf8nwtPLnBnYp7HLMxcCDc8DHnY1LTpuE7jii+p8dUo5bwVLBS3y+OtoJBCIAeQ2OVt5kw0b47p0+WUBalKO1fJoJo7Vyn9raCbG778EkeOICBAdg0qAgm06vAECEabjW3MuohnkOC9OgNobtG8iBUBwFmczz3fd35faKCAFTQ0aFhcQiHqPDdybliLMGyAgAkexj50/cMVAJLh8cRjmN8wuagzgBCgvZQzGho4fRpz52LePBkqQ+pcTXX29MTq1fD3h7a2jDIqhJxG5nKiToc4FjO2RcqoOpENxhjbxVLHpNoutOUWoYzcPvLznZ+vOrdq1fZVq4evfh/3XrJ8TUc2hG8Fs/NLQhxyjWxw9GfsT1l2xtjq1WztWkkjRTaqGdkIDGTOzuX5UKNQDLqUuivQRYx1Zkx8m1OVU2c2huXm5Lb5uQ238GTu8bkLfRcGHg8MHBgYeCewdLYGh9zUmQkFWgHqvISxjxhLlXWKMVZUxPr3Z/fvl1pInaupzlevMldXdu1amZXUNCTQpdRdgb7MmMTvQgXVmZtRN2jTIM62+/ruQ/sOKeatoMSMuuz87IGLBypCnWczJrWxuBivX7OuXYsXf5M6V1Odr19nQ4cyP78yK5EDJNCl1F2BniQulOWqc7xnvIyVvvxQZ8ZYQVHx0e49uw8NOaR4dWaMFQYUbnXbKnd19mQskLGfZJ0VJTCQubmxoCBS5zIvr4w637zJRo1iB2U+DDlCAl1KHRXoLMZ6iHysaOx89M7RP6+KRz15o86lXGP7Ptt39J+jknb5q7OCIhuejDHG5jIWJKuAZPklrGlTUmfZVFKdx4xhe/aUWYncIIEupY4K9FHG1pUcVyKyISnQvFRn1pMVJBUUFhWK2WuZOjPGujFWKKuMBAIBGzuWHTki4xSpc2UiG+7ubO/eMiuRJyTQpdRRgR7BWCxjrLJxZzGBrro6X+179dFVqZ04alqdlRLZULQ6RzA2QVYZmeTmMlfX0iXgHKTOFarzlSts8GB2+HCZlcgZWqhSt0kHMgCrCuY7v89/v7LLSubLADyNf5pbkPsi6QWeAv9hrtfchtoNxcqXO9/50bePmhg0cYJTqb0mctQVo8jVKArIUSfBT0AOsK7k499SLVYO2to4fhxDh2LzZrRrB9B850rMd168GCtWYO5cDJHac7Z2ICfhlxN1cQR9mLFNFY+dBQLBg5cP7r24d+/Fvd/P/v6j34/3dt2753bvftR94Uu5YiqKbGy+vFl0rzmFjZ1j7seIpeRn8h07CwSCU6Gn5DV25nBhrKq73719y7p0YY8f09i54rGzjw/r0oXdulVmJQqBQhyl1EWBdmfsWdVm1B29c/TPdX9+cNxZTKDloM7PEsRFoSSy8W/MvwtOLCi1yzmyUVBU0PeXvnJU51jG3GSVrBBO+4YOZVtlfknGGKmzM9u2jXXpwqKiyqxEUVCIow6TAyQA86q4kvsWcLdqK7ljx8e693Ev8i+CPxIyErQ0tFaeXQkB9J/pn/n6jMFQ8UQS1YtsfLX+qyueV4rtopGN5yLlFRDZiAD+k09kg+MEMFKqZGVo2hReXhg+HMuWyS5AkY2uXREQgIAA2fs61iJIoPnNRSAR+LpqeTZ0g3TzPPKqlGfDepf17Va3OcOWK1uaGDcZ2WKknOLOjLFiO6fOh5QUd54KDJabOgM4BZySMlaSTz7B1asYPRo//wwX8Vao4+o8fDhMTSEQ4O+/oVn75av2f0PVZgMwscpZkIZ5D6tuFqQ8Bb0VZAeZ20W3zFOZANJz0xPfJ95/dh8Pga5Ybbe6vWiSoRpS5wW+Cx789wD/gQ1mIYkhxfmSgGk9po1qNqrG1HkDkCT1v0KVaNwYp09jzBi8fYvxJT2g0upcKCgsEhRpa4r3A1VX54EDUViIuXMx8sP+NlE9SKD5TQGwQMoo7xx1+cC2GlbnU69OPb39lLO9Snm1+q/V8AY80IV1OTnzJGe//fz2yeCTq/askuvYebnD8twNudiDQqvCUX+O8p7uzdmNMo1qTJ29gZ3ASil7VTE2xqlTmD4dT59ixQo8elQZdc6aP2fW/ikvkl9oa2rX06i3bdw2KxMrQPXVuWtXGBrCz69M/2sjJNA8JhxwkNId+WcQtd9pbzbCrGbHzk3Sm+QV5HFmXaZre8gWs4BGaGDQoLR8JuAn98iG7te6ukd1YYNCQaGWhpaJngkAJAIja06d/wKMgRqZ91WvHry8sGED+vRBdjb8/dG4sYxiImPnBUc8hrQe4tbeDUBobOi0g9POzj6r2uocEYFu3fDZZ9i1C1pasmuorcjp5aOcqFuzODYzJrGyTNlZkCbslVp3UfUZdb2+6MWk372nsX8H/rtg/QJJu9xWoxQUFfRd35exGpqzwXGcsc8YC2Rsjqyz1WHfPtaxo+wpZeJzNkq3fGSMMea23S059LYKz9k4cIB9+ik7JHMOEy+Q6ywOWf9fETzhivhYkgfZ92NTYsVOfdhqlE8AO3F7OjACTb9t2sO1h5hdnqtR1NXUP2n8SU2+FeTGzj7AIWCK1NlqMnkyAgKwciV+/hmFIvvBVPRWkGVkqH37rUqOnaOjMXgwIiJw7VppFL6uISfhlxN1aARdxJizyMdqj529bno9ePmgmnk2xEZnH7aSO4q5/uEqZq/6fOdLyy8d+/qY8ldyi8KNnXMZS2XMRVaBGkEgYNu2sR492MOHxR+l5jvPPjr76J3iFFR3r54Y/Lmx6o2dMzPZ4sWsXz/2+HGZ1/IGmgddJ3kEtCk5rvbY+fbz27ef37YussbCKo+dkxslp3+djkQAyCnIiUmM4W5tfsvc8JBh1cbOhwA7CN8KAh84oy71TWri54nKX8ktRDh21gZ2ypp1U1OoqWHmTAwYgFmz0KYNUlNhaSkxdv7d7fc5x+fsvLZTKzff6FH47iXnlDV2fvLFgJfzvmxta2El83KZY+eiIhw4gN278d13WLFCds11CRJovnITcAZQM5GNp3FPExMScfBDIhtHOh95fP4xZ4tNiV19fjXCgTfoN7ffSE3x2U6VUGcA+tr6xfYPnu88DXgvbueJOjPgOBAoVaZmsbVFQAC2bcPRo1i9GoyJCplePb2dE3YKwp8WTflS66+7SlHnori3k2a3rTe4Swvd3B2HZ3b9uOvCgQvFSkirM2Pw98f69RgyBFeuQFe3gkaoI8hpZC4n6lCIYwxjL2sgspGalZqSlbLVb+uQMUNO/n0yJSslJSslM7ckQ0QVV3L3WtvrgyMbklQ9spG6KjXl65SUzJT9QftXn1vNfZeUrBRBgED5kQ2OK4zNlVVMTmRksEWLmIuLZA48Za/k3jX44w1epQ3xxa4vQl+FcsfXI6/v/XVi0JTBpZGNoiLm68u6d2c//shSpDsQ36EQR50kFthZ3bFzRHyExxEP5OJt+NukhklvH77dFr0NgF49vRMzTmjlaVV5NUoccL/qbwUPyX4rWKWx8/vV76dHTxd0FOAvvE59nVuQe+/lPQCIx4bbGyz9LJU8dubYKut5yY/69fHbb3jzBitWYPVqLFiAXr34MKMu2M3xp888hLZhbYcFxwQ7WTmN3T228aukDnEFR/s47dz/5f4xf6r99Rf270ffvjh5EmZmMiqs25BA85IEIKm66pyYkTjJa1JhbiFikG2RnV+Un5GXoaampqOlc3Ta0Q9R591AqnLUGetg+Mzw2K5jXNz5xP0T8enxHi4eOAf48iCywfEMEEgtW1cAlpbYsQMvXmDtWixahORknD2r3DkbDV/4xKXHfWT+EWd+m/bWxtzG+55367e5i942xZ4941+8WLpxwqnj7Yd95oGLF1G/OmsuazVyGpnLiboS4vBirJ8s+4fOd94XtM9tu1vgf4HF9iz2evDrD8hRd+XxFUl7VSIbf4f+XSM56nzu+Wy5vIUXczZEmcXYP7IKK5Lnz9n8+axzZ7ZiBXv9Wmg+//j8wI0De85oOnNm23fv38m+tubmbITHhfdc0zM6IZoxdiPqRvfV3TNzM39Y0u/2xIHs4EE2YAAbOfL64fU/nVzy4d+UN1CIo+7xFpgrZazOfOdbIvZsYCQmtJ1wZegVsfKVWCvYW7O3mL2KY+cN5zYMvTi0ZtYK/gec4s3YGUAK8AjYLFVYwdjYYM0aFBbi1CnMnInCQowefcXRZOed/UfetTTWd7z+9Wdjdo258P0FLQ3xJXk1Ot/ZoZHDpjGbfvL/Kf59vGMTx+PjvfS/nW0deT/aqOGnyck4cAANG0bd9GrKDGu8AWobchJ+OVFXRtDujMWLW6q3VnBf0L7FJxe/Tn0tfCsosd6s5neukvVWsNe4XjWS3/ml78sng57wZOz85M2T5Mxk9gtjx2WVVy7x8WzbtrFjG8e2bc769eNeGy44sSD4mXhbyGm+83//sY0b2eDBzMWFffNNyqO7XVd2Pf/4fGJGYsCjgG6rur3PeV+tb8cPaARd94gHLEQ+VnutoL2Fvb2FvWU9y9K4s+jAsMZ3rior7tylZvJsNN3RlD9jZ98Hvl2zuvbZ1AfvpMorHQsLzJyZWnDG7JcN+PcefvoJL182sM9LepmLgUawt4eGRk2OnbOy8PgxgoMRFIQ3b9CiBfr1w/79MDcHYAL42/hvurxp1/VdrSxbnfI4ZaBjIKNCQgRlCnRycrIZvbeVJg/QEflYEyu5u37cFdm4MvZK9KBoGAPX8Tbt7a7ruwCA4ZPjn3S36i4ndd4QuCE4Ihi3gN54kvpk9M7RnH2A44ApXaZUoM47eLCvoBCZkQ0AccAOYBGgIXUJP3Bu7nwi4c6EcRMwblyRoOjsb87j9W2wcSMiIyEQIDwckyfj7l3ExMDMDGZmMDWFmVmxHEurc04OkpORkoLkZDx/jt9+Q58++P13vHsHfX20aoXOnbFpk8yUew0MGvw6/FcFfnWVRxECHR4ePnPmzKioqO7du69bt65JyZOztLTMzc1VgAMqRhTQvOS4RvNsGH5haNKyWFOLE7kxwAuGDQ3lN3ae0W7G5DWT4Qn0wNAtQ3dO2MnZDbUMK1ZnifQMPFTnB8AZwBAoY1tXPuDZz3Pc7nGXnl5qZtbseuT1GQO+t/jf58XnOIGOjkZyMsLDkZyMpKRi/QWQno7ERLRtiz59UFRUrNG6uqUirq2Nb79Fu3Zo1QoNGpTpAfGhKEKgv/rqKxcXl/Xr1wcGBvbo0eP8+fN2dnYVX1ZniQQcANR8FqSOQzt2REfOtv3q9lEdRsEDsJZvZENntI7OEh0usqGpoVmc3rPCyAa/1flZ4rM159cgCQjCA6sHt3Vuex/zBqCprrlxzEbJ92/KRltT+8SME1EJUfHp8Z79PI10jUrPqaujZcsygxscGRnQ04MGX/9AqNUoQqAfPXp0/fp1dXX1du3atW7desiQIVevXm3UqJECbq2SRAOO8s9RB0XFnT9gzga/1RlAU9OmC6wXYC/wB7au2Nres31Xh64ANNQ1+KbOQpo3bN68YfOKy0ljQJFipaEIgW7SpElwcHC3bt0ADBgwYOnSpf369Tt1qoL92qKiokJDQ6WNGrX+f/IYQAO4Lt8MopqRmrBRtDo7NnGsBeoMQOuhlu1iW/gCh2DS1qSxWWPbBnVomw9CYShCoFeuXDlo0KC9e/e6u7sDGDt2rK6ubvfu3fPz88u5Kjs7OzU1VcKYm5uro6Mjs3zt4V/gHXACkBiK1Wh+Z7+mfoofO2/9fGstUGc8AGYBvoAucBJYKHUhQdQUcpq+J0FsbOxrkXVNjLHk5GQvL6+q1lMn5kE7MZYvZazpvVFkoLwsSMXznaW31ODbWkHG2H3GujIWxxhjbDFjPmzTpU2S04qJuoRc50ErYaHKpk2bMjMzKy4nizoh0D2kLKTOovBEnV8z1lvWhltEHaO2bXn1yy+/ZGRkKP6+qkEOIJEIVw47V0miQm8Fn/IgstGo5PKfpaZpE0SNQnsS8oxEoKHIR1JnUZ4CU/ihziFABtBd6nKCqFGUsJJw6dKlBjRxpyySAOHiSlJnUfijzgAWAtulLieImkYJI+jvvvtOX1+/4nJ1k7QSTay6OieOSvx3zL+kzoCc1dkbaK+MvM9E3YNCHDzjPWD0gWPnqNFRpw1Pi9lJnSWovjpnARuBxVI1EIQcIIHmGRnAuw+NbDiL20mdJai+OgNYAXwv5SRByAcSaJ4RBZykuHMJfFPnx0AYMFqqEoKQD5QPmmfoAYuqoM6CkYJJnSflxeVhJ5Izk+PS46ISogDgAZY0XtJ6UWvJ+kmdRamSOguAOfRukFAoJNA8QxswEreUO3ZWn6G+vd/2QkEhgDvP71x8cnHJ4CX4AerW6kZLJCoidRanSuoMYBfQSyQTLEHIHxJonsHE1z5UIrJhgOI5iwY6BjpaOiY/mFBko5SaUudQYK/41o4EIX9IoHmGGsBKjj8g7nwBaErqXEJNqfMboA+wQyp9FUHIGXpJyDM0gUIAH6LOxn8YNzFuQupcTE2pczzgAgwHRklVRRByhkbQPKMekPeBczZaWrVsuUhqawxSZ1E+QJ2HAXr0bpBQDjSC5hl6wBuaUSeCctV5NKAN7JW6hCAUAo2geUYhsA04AziK20mdRVGYOncBDIH2UrURhEKgETTPMAWGkzoD4IE6zwYe0IYphDKhETTPaCYyi4OD1FkUhanzemAOcBio7VtgEnyGRtA8owGQIPKRZ+p8MuRkyvuU2q/OW4E9wPdAM6kKCUKBkEDzjIYiAs0zdQZw6cml+FnxtV+d/wM0AHepCglCsVCIg2dolcyD5p86owi4ArSu7epcD/gTuCBVIUEoHBJo/qEGZAHu/FPnLwFL4AupW9cmdbYFBgAHAR2pOglC4ZBA84+GwBBgDl/U+dazW8v8l+E/wAzhReEhB0L0tfUBmNc3PzrtaK1SZydgLLAQsJWqkyCUAQk0/2BAf76oM4CuNl0DHwZycedvD3/7be9vWzYpWa/IW3X2rro6twbWAC2AIVJ1EoSSIIHmH25AirhF6ZEN1Xor6C0VoKiMOp8HbgM+UnUShPKgWRz8oxXwWOQjqbME8lDncOA3YD/9IAh+QSNo/tECCC855pk6t7ZqbaJvUtvUORmYAhxGSWJtguALJND8QxNgQCGgwS91BvBNz29qmzrnAWOAtcBHUtUShLIhgeYlTkAosI9f6gzUusgGA6YAXwHdpKolCB5AITde0rVknTGpM4c81BnAD0Ab4HOpagmCH5BA85L2QB7wg5Sd1FmUaqrzRiBfViMTBG+gEAcvsQe0gSLxVGqkzqJUU53/Au4Ch6SqJQg+QSNovtIJuC3ykdRZlGqq8ynAG9hH3Z/gO9RD+cpg4EzJMamzKNVU50vANuAYUE+qZoLgGSTQfKU7cB0AqbM41VTna8DvgA+gJ1UzQfAPikHzFS3AAVgORJA6l1BNdb4BLAdOAoZSNRMELyGB5jEuwDrgntSuS6TOolRSnYWNZixVM0HwFQpx8JgxsrISkzqLUkl1vgwsk9VoBMFvSKB5jAbgCpwTsZA6i1JJdT4DrAb+JnUmVA8SaH7zFbCr5Fh56hzuHb5o/yKVVOcjwA6KOxOqCsWg+U1TQBcIBxKUOXbOPZibMyZH9dR5CxAE+Eol7ycIFYEEmvfMBeYBmUqNbGwBQsXtPFdnBiwC3gNH6K9EQoUhgeY9nYDngLcS1PnJ8Se5h3KxCZHpkQnvE+6/vM/ZW6S30P9Gn7/qnA9MBVoCK6WqJQiVggRaFdgBbAR2i1gUMnb2OeGTOy4X/yEhIyEyIdLnng8AJGPC0QmORx15qs7JwFhgsqzdxwlC1SCBVgW6A+uAp8AnABQX2Vjut5yLO4fGhh64dWDVyFV8j2w8BaYCaym/M1FLoPicivA7sAAAzagTt4uq8xlgGnCE1JmoPdAIWkVwBD4C/gQOKkmdU3iszk7ACuAxcE7KbYJQZUigVYdfAHsgUAnqbJ9m//Xxr3mqztaAG9AZOAaoSVVLEKoMhThUByNgN7BN3KiQyIbeNL2W+1vyUZ3zgEHALGABqTNRCyGBVimGAgUieaLrctx5C3AJWAr4SX19gqgtKEGgGWPx8fGMMcXfujawCVgJxFZBnfML84uPao06LweWADlAANBYqk6CqC0oQqAjIyNdXV2bNWv2/fffJycnt2nTxtLSsnHjxkFBQQq4e23DANgOjAOGVUqd07LTJnlNAmqROnP/LS0BFtNfgEQtRxEd/JtvvmnXrt3x48fT0tJGjBjx5Zdf5uXlrV69eu7cuQq4ey2kDdAX0AV6i9tlRTaKBEUFRQW1RJ1HAIZANBAIdJKqkCBqHYqYxRESEnLp0iUNDQ0HBwdTU9OLFy9qampOnDhxzpw5Crh77WQp8AuwHPi5xFJO3DkSyFJ9dXYBtIDfKOJM1CEUIdCmpqbh4eGOjo4mJibz5s3T0dEB8OzZMxMTStBbDZYC04HNwHcy1Pnui7vpOekA3h97n5CZcGnOJUQAgGMTx8ZGjQGVUud7QH9gELAD0C+3TQiidqEIgf7ll1969ux5+PDh/v37r127FsDly5cnTpw4b968cq66c+fOP//8I2G8d++eoSFl9i1hOzAZ2ALclRw7h70OS8xMxHVkJ2anNkm9/6o4z5Gpvmljo8Yqo875wB/AKWAXMLLcpiCI2ogiBHr8+PH/+9//tLVLk/KamZnt3bt3wIAB5VzVuHHjDh06SBiTk5N1dXXl4qUqog7sA1wBE8nIxhTnKVgHJCF5ffKTw08WDFhQek5V1Pk0sBKYDNykl4FEHUVBKwkdHByEx5s3b546dWrbtm3Lv8Ta2tra2lrCmJKSkpSUVPP+qS4awGVgPvAdsEFke1nhW8Fs8fIqoc4hwGLAAQigfaqIOo0SRia//PJLRkaG4u9ba1ED1gGfAMOBFACqPKMuGhgHrAI2AxtInYm6DuXiqC3MAFoCg4EOgADYWbz02VTfdG6/uQImUD+vzmt1jgF+BRKB5YBkZIsg6ihKEOilS5caGBgo/r61n56AP9APGA4UAloAoKam5nXTq1FII9tdtjxV50RgPpAO/Ah0qfKXJohajBIE+rvvvlP8TesKFkAosBvoA6wu0bvXYLcYH9X5O+ArwBCYD7Sr6lcliNoPhThqHWrANGAIMB/4E+gLPAa280mdrwJTADPgPrAdaFr170gQdQMS6NpJlGbUP9/8gxhgD8JbhB+7dazBywYA6mvXH9tprHLUuRC4BBwAbgNzgK+lhJsgCHFIoGsnevX0TPRM0Ar4A9r+2kb+RiY6JugPg9YGilbnIuA64AvcB/oAywEHqfIEQciCBLp2YmlsOarjKO74wpMLA2cP/Dj+Y+wENgIJwBH5q/M2wB84B0QB3YEJwBbKqU8QVYMEus7gBGwFMgBfYCvwI9AD6A50AXJrQp0ZEA1sA84AjYFZgAuwiMbLBPHhkEDXftTV1NXVSpasGACTgclAHnALuAFsAEKBTsA54D7QBGgINALMgKmy1DkDWAikAJ8DfwIJQCIQDWQBdkAj4CDwKfUsgqgB6GdU+1k/er2BjtTEc22gd0lG6UIgGkgA3gKvgLtAHPAUSAOSAS9AB8gBigBNQA9IAfoAL4EGgAPQEPiI8swRRM1DAl37kaHOEmgCLYAWZZxlQDbpL0EoAcoSRlSEGqkzQSgHEmiCIAieQgJNEATBU0igCYIgeAoJNEEQBE8hgSYIguApJNAEQRA8hQSaIAiCp5BAEwRB8BQSaIIgCJ5CAk0QBMFTVCwXh5GR0e+//37y5Ek51f/ixYuEhARNTd41S05Ojo6Ojpoa7xIqZ2dn6+npKdsLSQQCQUFBgba2trIdkSQ/P19dXZ2fHUxXV1fZXsiAnx2sqKjI0NDQwcEBQGxsrBybjhEibN261dvbW9leyMDNzS05OVnZXsigV69eynZBBvfu3fP09FS2FzJYvXp1QECAsr2QQf/+/XNzc5XthQz42cGuXbu2dOlSBdyIQhwEQRA8hQSaIAiCp5BAEwRB8BQSaIIgCJ5CAk0QBMFTSKDF0NDQ0NDQULYXMlBXV1dX5+PD4uGMMfC4uXjbwTQ0NHg4iRN1voOpMcYUcBtVITc3V0NDQ0tLS9mOSJKRkWFgUNHWgsqAn44xxrKysurXr69sRyTJycmpV68eDzWan88RfHVMIBDk5OTo68t9LzgSaIIgCJ7Cxz8DCYIgCJBAEwRB8BYSaIIgCJ5CAk0QBMFTSKAJgiB4Cgk0QRAETyGBJgiC4Ckk0GIcO3bM3t7e2NjYxcUlOjpa2e6UcurUqU8++cTY2LhXr16RkZHKdqeUwsLCH374QV1dPSkpSdm+FHP+/HknJyczM7MBAwbEx8cr251ieNhQHLztWrz9MXJcvXpVTU0tPDxcrnchgS4lMjLSw8PD19c3OTm5Y8eOM2fOVLZHxbx+/XrixIl79uxJSUlxdnaePn26sj0qxd3dXU9Pjz/rqtMTLcmeAAAR3klEQVTT0ydMmLBr165379517Nhx1qxZyvaoGL41FAdvuxZvf4wceXl5c+bMsbCwkPudFLApgKrw/Pnzc+fOccfBwcFNmzZVrj9CYmNjhfu8PHjwwNLSUrn+iBISEsIY09DQSExMVLYvjDHm7e3dv39/7jgtLU1bW5snG4XwraE4eNu1ePtj5Fi2bNnSpUsdHR2fPn0q1xvxMRGJsrCxsbGxsQHw/v37nTt3Dh06VNkeFWNlZTVq1CjuODAw0NnZWbn+iNK2bVtluyBGZGSkvb09d2xkZGRsbPzy5UuhRYnwraE4eNu1ePtjBBAZGenr63v37l1fX19534tff3Dxgfnz5xsZGT179uznn39Wti+SXLhwYceOHevWrVO2I/wlOztbdAdPPT297OxsJfqjKvCza/HzxzhjxowNGzbo6Ogo4F51XaA3b95sbm5ubm6+efNmzrJ27dqsrKxhw4b17t2bKS+TlLRjR44cmT179sWLF62srJTllUzHeIW+vn5WVpbwY2ZmJg9z2vENnnQtaXjyYxTlwIEDjRs37tu3r4LuJ9cACv/JzMyMi4uLi4vLzMwMDQ29fPkyZy8oKFBXV4+Li+ODY4wxf39/JycnJfpTlmMc/Amt+vr69ujRgzt+/fq1np5eQUGBcl0ShT8NJYQ/XUsUXv0YRRk+fLi5ubmFhYWFhYWmpqa5ufnp06fld7u6LtCinDt3ztLSMjo6mjG2f/9+CwsLgUCgbKcYYywlJcXKyur58+fKdqRM+KM779+/Nzc3v3z5ckFBwYwZMyZOnKhsj8TgT0Nx8LZr8fbHKIoCXhKSQIuxZs2aZs2aGRsbt2/f/tq1a8p2pxgvLy81NTVtEZKSkpTtFGOMJSUlcf4A4A7i4+OV7RS7ePFiy5YtTU1NhwwZQg1VPrztWoyvP0ZRFCDQlLCfIAiCp9T1l4QEQRC8hQSaIAiCp5BAEwRB8BQSaIIgCJ5CAk0QBMFTSKAJgiB4Cgk0QRAETyGBJgiC4Ckk0ARBEDyFBJogCIKnkEATBEHwFBJogiAInkICTRAEwVNIoAmCIHgKCTRBEARPIYEmCILgKSTQBEEQPIUEmiAIgqeQQBMEQfAUEmiCRzDG/vjjjxYtWmhra1tZWc2ePTsrK0vZTlXM/v37JSyFhYU//PCDurp6UlKSMjwiagkk0ASPWLBgwZYtW7Zs2ZKUlHThwoWIiIjhw4cr26kKYIwtXLhQwuju7q6np6euTr8vonrIdc9wgqg8iYmJ9erVu337ttCSkZHx559/5ufnM8a8vb0dHR1btGjRs2dPbq/7kJCQNm3azJ8/v2fPni1btrx8+bKbm1vr1q09PDwYY3fu3GnVqpWHh0fv3r3btWt3/fp1rs6y6lm0aJGrq6uDg8P58+e5kqdPn3ZycrK1te3Tp09CQkJZJYcPH66mpubo6Pjq1Suh5yEhIYwxDQ2NxMREhTQeUTshgSb4wt9//92sWTOZp16+fGloaBgREcEY27p1a6dOnRhjYWFh6urqQUFBjLGxY8c6Ojrm5ubm5OTUr18/Li4uJCQEwNmzZxljZ8+etbe3L7+eixcvMsZ8fHy6dOnCGHv9+rWpqenDhw8ZY+vWrRs+fHhZJRMTE7W1tWW6TQJNVBP6E4zgC+np6Y0aNZJ5KjAwsGfPnvb29gCmTp169+7d9+/fAzAxMenatSsAW1vbHj16aGtr6+joNGrUKC4uDoCxsfHAgQMBuLq6RkVFJSYmllWPgYGBq6srgObNm7958wZAQEBAhw4dWrduDeCbb74JCAgoKCiQWZIg5Iemsh0giGKsra1jY2NlnkpMTDQ3N+eOdXR09PT0EhISANSvX58zamho6OnpCY+Lioo0NTWNjIw4i6ampp6eXmpqamXqKSoqApCWlnbr1i0bGxvOrq+vz73uky5JEPKDRtAEX2jbtm1GRsaVK1eEltzc3OnTp2dlZVlYWCQnJwuN2dnZFhYWFVaYnJzMGOMuycrKMjU1rXw9TZo06du374sSUlNTGzduXN1vSBBVhASa4AvGxsY//PDDuHHjzpw5k5GRERkZ6ebmlpSUpK+v7+rqev369ejoaAA7duzo1q2bgYFBhRXm5uaeOHECgI+PT8uWLc3NzStfj6ura3BwcGRkJIC7d+/OmjWrrLtoaWkVFhaqxHRAQuWgEAfBIxYvXqyvr+/p6fnixYtGjRqNHz9+2bJlAKysrLy8vNzc3PLz862traXnHcvExsbm9u3bS5YsUVNT27t3b5XqsbCw8PLycnd3z8nJMTIy2rhxY1kljYyMXFxcrKyszp8/36lTJwDJycmWlpYAioqKrKysALx8+bIyQ36CkECN+xuQIGoZoaGh7u7u3GCZIFQUCnEQBEHwFBJogiAInkIhDoIgCJ5CI2iCIAieQgJNEATBU1RPoPv376+pqampqammpqZZQlBQEDefSR5UclJXrUGu3zc0NNTOzq6ss/fu3SvnrCivXr3q3bt3/fr127RpExoayhm5REh2dnaurq4xMTESlxw7dsze3t7Y2NjFxUU4u0NmPRyZmZkjR478/PPPt2/fLjQOGjTo7t27lfEQwIwZM8zMzPz9/YUWrm0fP34sv+5aYRtW//mWfwuZj1h4ifCsaCV17SdWBZScC6QaWFpaBgcHc8dhYWGWlpbyuItAILCwsJBHzfxE3t83JCTk448/LutsQUFBJbMLde/efe3atfn5+fv27Zs0aRJj7PXr18bGxjExMYyx9evX9+nTR7R8RESEmZnZo0ePCgsL58+f7+rqWlY9Qnbs2PHHH38wxtq1a8dl1PP19Z02bVrlv6ypqSmXmIlD2Lby666sojaskedb/i1kPmLhJcKzQktd+4lVidoj0DY2Nj///LONjU2zZs3KyRiZl5c3fvx4W1tbGxubsWPHZmdnyywmRJhMEkBUVBRj7NixY1paWllZWYyxdevWzZo1i8lKYinKoUOHPvroI0tLy3HjxuXm5sosz7fkmdHR0dINJUS6tnXr1g0dOpQ76+rqum3bNpk+i/56d+/e3bx5cxsbm549e3K5Ou/evcudLetbcMTExFhZWRUVFYkar1271qpVK2F/kPjNP3/+/Ny5c9xxcHBw06ZNy6pHyJw5c/z8/BhjLi4uL1++zMrKat++fVJSkszC0g9i+PDh6urqDg4Op0+f5soI2/bs2bOV7K5CKt8Bym9D6eSoHTt29PX15Y79/Py4DH/Sj+bBgwft2rUbP3583759hbeQWTIkJMTOzm7u3LnW1tZ2dnZXrlyR8Io7EFqELjVs2NDHx4er9tSpU23btpXZ1HWK2iPQ2traBw4cYIzt3Lmza9eurIyMkT4+Pq6urgKBoKioyNPT8+bNmzKLCREmk5wwYcLBgwcZYzNnzuzcufM///zDGBs+fLi/v7/MJJZCnj9/bmZmFhMTU1hYOGTIkJUrV6pE8kzphhJ+I5m1FRYWtm/f/sKFC/7+/p07dy4qKpLps/D3mZCQoK2t/fz5c8bY1KlTv/nmGybyo5Xpm5CTJ0/27t37q6++atasmYuLCydMaWlpFhYW9+/fFwgEK1asGD9+vMxuk56ePnnyZO6/PZn1CJk3b563tzdjzNnZ+c2bN4sWLdq1a9e8efMGDRrE9TQhZXUAIyOj2NhY6b5U+e4qpPIdoPw2lE6OumrVqokTJ3LHEydOXL9+vcxHExYWpq+vf/ToUdFbyCwZEhKiqanp5eXFGNu/f3/z5s1ZuQItdGn9+vUjRozgPJkyZcrKlStlPsE6Re0RaAMDA+44NDSUGx/t3LlT+JdsRkaGlpZWfn7+jRs3LC0tz5w5k5OTw52SWUx4F2Hv8fLymjFjBmOsffv2u3fv/vXXXxljjRo1SktL27Nnz2effcaVz8nJUVdXT09PF9awa9cu4dAyKysrNzdXZvmwsDAzMzPOuGTJEu5ejDE7O7sHDx6EhIQYGxtzloKCAjU1tYSEhLLqMTIyqkxTyCwp/L7SDSWkrBa7f/9+q1atWrRo8ejRI8aYTJ9FR9DCUfnhw4e5CkXFRdo3Ifv27dPV1b18+bJAIFi/fr2TkxNn/+uvvzQ1NU1MTKysrJ49e8akmDdvHoDu3btzeZTKqofD29t7/vz5mZmZbdq0iYiIcHZ2DgwMnDRpUmFhYYcOHd6/fy8sWVYHKEegK9ldhddWvgOU34bSAv3s2TNzc/PCwsKCggIzMzNuFCz9aMLCwnR1dbm/NkRH0NIlQ0JC9PX1uZJ5eXlqamqJiYmVEeg3b97o6uqmpaUVFRU1aNBA5hOsa6jeS8KyMDQ05A6kM0ba2Ni0atWKyxjp7Oy8YcOGtWvXWlhYTJkyJSsrS2Yx6fp79+4dHBycmppar149FxeXoKCg8PDwpk2bGhkZlZXEkiM1NdXExIQ71tPT09bW/oDkmQBqJHmmxHcsJ3mmdEMJT5VVW/v27Q0NDRs0aODk5MSVlPZZWAljbN26dZ9++mmnTp2WL18uEAgkGrwc34yMjBwcHFxcXNTU1GbPnh0REZGcnBwWFrZkyZLo6OiUlJQNGzYMGjRIus61a9dmZWUNGzasd+/ejDGZ9QgLjxgxIjMzc8SIEatWrZo7d+6GDRtCQ0M7duyooaHx8ccfP336VFiy/A4gk0p2V4lvLdGYFd63MslRbW1traysbt26df36dQcHB2tr67IejYmJicQmXmWVNDMz40rWq1dP4rmXQ5MmTTp16uTn53fz5k0bGxtbW9vKXFW7qT0CLU1ZGSNHjRp19erVmJiYV69ebd++vZKJJW1sbDIyMs6fP9+lSxdbW9vnz5/fuHGjT58+AMpPYmlubp6YmMgdp6amxsbGqkryTImGqrC2gIAATU3NvLy8gICAsnwWVuLn5+fj4xMYGMjlM6rQGVE++ugjCa3X1NS8fPly165dmzVrBsDd3f358+dv374Vlnn48CGXyFRPT2/27NmPHz9+9+6dzHqEHzU1Nbdv337x4sWcnBwrK6uOHTsKBAJOd9TU1DQ0NIQlP+yBSlDhM6pOBygfd3f3U6dO+fv7jx49GmU/GjU1NYkLyyqZmprKuZqfn5+VlWVmZlZJT8aOHevj43PixInPP//8A75I7aM2C7TMjJGbN29evnw5Y8zU1NTa2lpNTa38xJKiySR79OixadOmbt26AWjRooWXlxcn0OUnsezfv39QUNCTJ0+KioqmTZvG/SXI/+SZ0g1Vfm1ZWVmzZ8/eunXr1q1bPTw8uBaT9llYSVxcXLNmzYyMjNLS0v7666/MzMwKW0BI27ZtjYyM9u3bxxjbunVrmzZtjIyMHB0db926xQlWYGCggYEBtz/LkSNH3r17FxcXN3HixGfPngE4fPhwgwYNLCwsZNYjca+cnJzffvvt999/B9CyZcuIiAgAMTExH3/8sWiDVOZBlJ+YtMJnVJ0OUL4Po0aNunTp0pkzZ9zd3VGVR1NWyaysLF9fXwDHjx93dHQU/Y+5fJfc3d1v3rzp4+MzatQolDy78r9RLUfxUZWaoqxpdqLHZ86ccXJysrOz69Chw40bNxhjCQkJgwcPtra2trGxGTVqVEZGhsxiori6uhobG//7778HDx4E8PbtW8bYmjVrtLW1hfFZPz8/JycnBweHvn37SsfOvL29raysuPyZ3CwO6fJhYWHCHfmWLVvm6enJHTs4ONy9e5d7M+7p6Wlvb+/g4CB8ZSeznko2RVklue97+vRp6YYSIl2bp6fn7NmzubNTp079/vvvZfos+pKwU6dOnOe3bt2ysLCYN2+eaPxUpm9CoqOj27dvb2xs3K1bt/DwcM7422+/2dnZ2dnZdezY8dq1a5zRwsKC83DNmjXNmjUzNjZu37698KzMekRZsmTJ7t27uePCwsKRI0e6uLj89ttvEsVkdgCJGLSwbffs2VPJZySk8h2gwjYU9mfR+lu3bu3s7Mwdy3w0ojWIviSULnnnzh0nJydPT08HBwehq+XEoCVc+uyzz7i3pqLPrs5CuThUA1VMnqmKPvOWutOY06dPb9269cyZM5XtCC+ozSEOgiBUi//+++/cuXPjxo1TtiN8gQSaIAhesGjRogEDBmzbtk36TUCdhUIcBEEQPOX/4hD5LibCqRAAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}