SKP_Public_Sharing_of_DEIP_CID.ipynb

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  "metadata": {
    "colab": {
      "name": " SKP_Public Sharing of DEIP_CID.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Swayamprakashpatel/DE_Interact.github.io/blob/main/SKP_Public_Sharing_of_DEIP_CID.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zjUGEEdt-SmR"
      },
      "source": [
        "**PROJECT DESCRIPTION**\n",
        "\n",
        "Determination of compatibility between drug and excipient is crucial step during any pharmaceutical formulation development. Presently only experiment based techniques are available to determine proabable incompatibility. \n",
        "\n",
        "This project is about development of artificial neural network (ANN) based model for prediction of incompatibility. This will save significant time and dependancy over costly tests like DSC and FTIR. This formal prediction allows formulators to choose excipient wisely. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W8-hk6nuT00w"
      },
      "source": [
        "**DRUG EXCIPIENT INTERACTION PREDICTION BY ARTIFICIAL NEURAL NETWORK**\n",
        "\n",
        "Accuracy of Training Data: 0.9834\n",
        "\n",
        "Accuracy of Validating Data: 0.9096\n",
        "\n",
        "Total Data: 1035"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zWKfVxDk9j3G"
      },
      "source": [
        "Developed by:\n",
        "**Dr Swayamprakash Patel**\n",
        "\n",
        "Asst. Professor\n",
        "\n",
        "Ramanbhai Patel College of Pharmacy\n",
        "\n",
        "Charotar University of Science and Technolgoy (CHARUSAT)\n",
        "\n",
        "CONTACT: swayamprakash.patel@gmail.com \n",
        "\n",
        "**PLEASE SEND YOUR SUGGESTION THROUGH BELOW LINK**\n",
        "\n",
        "https://forms.gle/9f6s2i4B2iBUYWCE9\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "![DE IMAGE 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DtIIgWX73vffjec2jj+EVn2R++Mibomm/8MYf3RL2v+SiuMx114+K8+bNxrnrW42yTzUggIby0EufJUMSlS7MeucrM12gyikexi3ObjyQXPiYTnruuXh6+nQU70XT62+8JZ4Kn3ywLppeFM/v2L0nXPzyvHD/nl3hpEcfCsfcdntcT19Rn2tAlXfXJNZAGqa3LE7mJU65bEYSD+iyEaJLH1kRPvHq5+lSGG7ZezKObUSnzofhM0s2xbHHI69sDCe8vDHNCeP3yhCDiILr5seB+rgXP4pjJrlVjXKu9h07H1416d3w0MlkOX74e0M1Ngn6TwvHzlY3MlSME+9ztK5Zq76Kl1d9eTAcPye5pY139L46cDq85pl34+dRUE3ZMcntbsR9ZRTcNy9qRI/VNp4jxV8F6nb1uQaEtw7i+WHzw6GTVifzQ16sNqAr5yTT4QvMBhTcsSSpM2pgb36yJ57HNLj9zTjg33P0bHi09o9NhNdNXhMG1yZ38YL7loaL1u8KBz+5Kjx6OgxHRRducOWL4Tuf7w+feuPLcPJbm8MPth2ObSG5CbHrcLXSYc+uiRscGlj8cDVqFGhy8Ttrdy6J77KhPhHS4/f0ov18NqofQgN/6s0v42294YWoR39gWfje1up6H0XdA2eGH0WNCa8+zXl3e+at8SzBC907KWk8x5dEHuWmW+L0ObPwmCAIZ05JbhycPo3nQ0G4c8++eH7MTdfHy6dOJ7fVMa914sTxOM3l2UmPpha9X33PA/kYNMtON6DKKxj4VHj80yHJfHBJ9P+58IXnno3mqw0IwvJjk5Jvo19xcdIgXElDWb8WHWAyP2DAwMr8TaP5cwaKuiDEBkRRTYgNiKIaFBsPRTUoNh6KalBsPBTVoNh4KKpBBXMmBiEhpDxsPIQ0CIdtFNWg2HgoqkEFv3wtCAkh5WHjIaRB2HgIaRA2HkIahI2HkAZh4yGkQdh4CGkQNh5CGqQtjecH378nHPrtCRUkXadJups/5xeX1dmiPrFx84BO883LsqS5eW5aVj6mel7b6Hyd55svAuzdY7rgby421wEmTry2xlbbuGkT/uyGuvwnHh9ek+bOC5Km85pJk3Q9r/P1si/dKqvTW0lbGw/m9cbLFCfeTZN5NJ6bR91Rkz5+7B9W5sVO5+t0lJc8befOl0nT+PJ1usa1t+ZlWc/rZeA7prjQXfuseXcZjUcfM0nX9n/443GVebHR+VYZPa/TBJ3mzmfla5ssO5nqa63VdLzxCMOueqiSJuUwLydS0vW8ttNIupzkn037do2d5AuSJvnazpoHVg8NkOaW0+h0TO//0xsraZIueW4ZbeN6Hm07adJ34ylsJE3yNVZZ7JeVru01YmPlN5Jmzcv0rp/8OJ53ERtrXpcX5FprNT3ieaw0X76V7uZru2f+YmglT9u5826aYNm5+TLV+e6yla6nOl3n6XxZBu4xnTlrQI2tIGlSzp13l9F4UJdVRuxx8cm82Oh8K006iTw7az4rX9vIvO6IJU9PZb7VdDzmwVSfLH1iXFsr3cpDDyXzOt+11eVdO53n5rtpmP5k3OjKvFtOo9Oz7K08AftndUiCW8eI793nzXeXLY/6/aEP1tnreSEvTeZd9PkSdHm3Lp3m2siyayfzbsfQStrSeAi5EGDjIaRB2HgIaRA2HkIahI2HkAbptY3nt7/9bYwvz50Wsc9KX/8Pf1lZ9tkXZdu2T2vqwHxe3f/yL5vN9Dx89fnSy+DWgWVBp/uAnRyL5f+j/pkNjjmm7vHqK/SZxiPLgj7gOh3Lm/736pplPbXSgTQenf/a3/5uzbI1decX/+p3CjUeSctatuxlXm+vthMkT+dby5i39lMvCzpf27nzelk3Huu8aBurzq+2/6/Kcm+jVzcea5qVZ9nqZV9jAFbj0ctSVpeTqVveajxvLv1OJV/S3GVMv975VV2+4CunlwWdj6mvgeh5YO2noO10vk4X0IlgqhsPpm59mIqNLGO6ZesHNctSpjfRKxsPDhZ6qX/+coV5ADG/4bNf1uXp6edfLDXTf7VkUF06kIvftdcnHVPBtdP4Gs/qNQ9W0q2pDN10GnpemXdxbTEVLDvZ91Xv3lMzBVn7qev0LWNezsnm/7MungK38bjnBedZNx5B1y/T3gZvGBDSIGw8hDQIGw8hDcLGQ0iDsPEQ0iBsPIQ0CBsPIQ3CxkNIg7DxENIgbDwt4KVfBuGixdXlIAjCO+8Owht+EIQjRyXLQPL+alp1XtIvuSQIn5hYWwdAmsz/58lB2K9fEP71nGo5TP/NpUmd2m7hy7aNLMt6SOOw8RDSIGw8hDQIGw8hDcLGQ0iDsPEQ0iBsPIQ0CBsPIQ3CxkNIgwQhRVENKZgzMQgJIeVh4yGkQThso6gGxcZDUQ0qmDNnTjpLUVRRod3EjWf48OGEkBJAbDyENAAbDyEN4m084cygBitv5PXfr0vzLRdJs+Y1SF/z6O9W5sVu3I3DapZB1rYJm56+tJLn2viWNVa6ttfb5fLaA79XU275w9+q5Ol0oNPdOl0bN93NL5uu8dn40jWWTTPnUqPT5TjqPLeslebm6WU3r2KT1Xhm/OT3K/O/fuqymjy3QsxP/uOrKuko6+YLkqbTcYB0vm++6AF3bfSyII3HtZF5oE+cXPCyrG11mqRnNR5dbs9zl5jpsqznyzQeK81Kz8sTdL62L1NW2zZ7Lt15qU93TFLu53cNqaSdmPqvKuna5uMn/21lXvIEnV7Jz2o8VmG97KaXzZdlaWg6Ty+DAy9cHKfLAdLIAZdlmcfUTde4ngeNX+wfH3N1JV3ssxqPa4v5vMaD9Vvpbj3YZ5kv03g0rq0vT6dpsF6dru1kXqe5WDbNnEudL+m6Pl+5M9MvqqS7++QiZaw8kNl49EWt0zXYGEnX+Tpt/n8YXEnX+VY5d9mdlwOEeclzD7igtw1IGUF7Ho3Yu+Xa5Xl887Ks5906XXud7qYt+Y8D6+qTZT3vA/ljRn6vMl+2rJvW7LnEVHd4RRqPzrdsVv75FZVl3/Uh5DYemZdKdGVWel6aLMtwyE2XZZnXID3vgOt0WdbzGuvguLaYlxNXxPPAY1npuozgy9fpOs9Kt9JakS77ocHwUttIurtsYeU3ey7deV/j0Wnu8pGf/evKsrYBDTceQkg2bDyENAgbDyENwsZDSIOw8RDSIGw8hDRIyxqPK0lz7cpStg4R5g8cOFCZd9m0aVOMlVcGXY9vXXlY5aD58+fHU8jNB63ah2YRyTK2yZW291HUzqXRcs0CNd14xowZE1fkpmth2T2o2mbkyJHpXCJ32apPdOLEiXhZp2FeNx4tvXzmzJl4KmlZtq5kv6HXXnstnatuu65T0PrNb36TziXy2fnSRFl5ojwbfQzzbCFfmgaaMWNGPL9y5cp4WST5IncZyrPB8ZM0y1bOv8i1/fjjj+P5RoFa5nl8aRB2BPNHjhyJlyFto+e//vrryrxMwa9//et4GXLzBBHm3cYjU31QJW3JkiXx1D3gd911VzzV3kWkl/U69LxM3XQ9L1MLkcxbeb5t/ulPf1rJk47LKi/SeTrfOl7uFJJyAiSNx5WkufZ6m7WNO83K00Jdbt7Pf/7zeIrlZoDaPmyD5IBYO4OpzE+ePDmeQq4d5KZhqrn//vvjdC1tC1kXAyR1yLxuMI8//nhNHnotLcn74Q9/WJmHpA4BQi8vkjRtI2naM2k7aM2aNfEU0nl6mzHNuxAnTJgQX7DwnEgTIJlax0um2qNIWQFyGw/mZV2yDMn+YDlvm33HT0+lA9ZCnuRDstwoUMtuGOCguCfBxb2YXHAyxo0bV1letGhRDObzympgq+spS9a6kIfey8qDd5R5SOcJOEZTpkwx8zS4oNzjmbddVrrGtfGdM3QWaFRuugvOl5VuYa2ryDa7oA7f8fPVp8+Fnm8GqGWNh1QRWXmkc4j0iAdy7RoBYuMhpAEqjYcQUo648cT/UxRVWmw8FNWg2HgoqkGx8VBUg2LjoagGxcZDUQ2KjYeiGhQbD0U1qF7feIJgSsN8uaf6EiHVnM69EsRAQTAuYnzEbSnj0vTaNExFtXk6PbGz8nq7urrxgDwFl0wLg4si20unh7uPnI3TLnpweTji+XXhsGfXhMF9y+K04N6l4bWT1yZpd74Vp2kFP3olnasquHdZOPTpd8ORU94Lr5y0Ohz85Ko0JwzHzvowvDrKu+GFdWH/CSvS1KjM3W+F10XrQbnTadrxaLPibYzYtNfuEF7/cHc6lygY/2Z48cNvh1dF670h2pfrnlsbDnhsZVx/OW2L/69tPHKhSyMa66RXG4qoNm98mmrVFc1ffk+a27t1wTae4OKp4ac7j6VLiXBxivS8CA3sva2H06VaWfYQ0o9KK4jk2pnrUQ1x8tLNNQ3Gst/4zfHMemvmo06gqIJBk8LzUYM5uja5TM7ufz6eysWudfJ8ki76ZM/hmmXMX/rQrHSpqqSupPFB8XL/e9Ol3q2+13hufzOMzlM48fUv6vMMdh1Ofi2qNW3FtrjxZMm8qC+bHm7YeTxdqgq2lj2E9FPY4FRYTvxbInM96gJ384MrX0znqoJNnd3lM9K5+jqKCo0n9jiLay+T5IK/PV1KxMbTC6UbwqyVyRDClVwo2lbjqsjFZNkEl80IP496eVcT3/gynpplojTVdupsrDKXRMMtUTBigWmjtefo2fCrA6e8dnnlfQoGPVozXBMlF3x24/nUaDz9H5qZLlWV1MXG0xa5jWDKsi3x1NV7Ww/V2N48bX2cLuW0ilxMsLFwG09w88vpnF1vXG7s63Ecg/njjiO0yugYCLpx6vqkHqv+/tPSObsuyJeepUueXhwOvydpOOCs8j7JBW83HhdRspwVC6n8Afelub1bfa7xQJW0KNh3JXlrNh0Mn4o8Aub3HTuX5iYqcjGZF2rkeTbschqPsgtufT0Mrqr9A8k1+VadVtqtr6ZztQqunlNnjzhMFFwxM3zmrfqv11jryFMw6Knww4XVxpN4n8SHJhd52cYzNiKv8dTm9Xb1qcYT3JT08sGAZ8ODo4Loopgcp4uCG15K7NKLRZfVCvpFaeqis2Re1FGZj7YfTZfC8Fh9OFV/cUfLJ9IgJ774B89OFlKZ69He5Ipq7AJp+3EvfpTOVWXWZ6TlKRj4VLj15drGkzQgW+6wraiSBlMdtvUl9UnPA6HxxA3oihE1NnJx1qRFuMIFte2Aug3myLwIo8azXt1t812oazYfSpeS5YMnkh4bcsv46hBl2eeVFVlpeQoGTQznTq1vPLj7ZomNpxdKX/zxvLoQzr79YNKAtE00bJJlzZ70GY4W6tIcSC/yYNi8Strd8z9N0kYtqqTFz3ounxHP/2j6+3E+tG5LFHelNgj6JdgHeJ4jitOcWEWjVUl3bhzgbiGWV3y+P01J70Cm9uPnJF4JQ7k4LfK2Xx30dxaurnjm5TAYMrau8Qjh3udSy0SnIoLhE5OFEgpGToxGFI+mS31LfaPxXJTcVtaNQWvrvpPhhp1Ha/ItqHKC97EajnB+X/Lc50JVn/E8lzyU3L7Fk3NJWxsNjwY9sTK8dMLb0XRVGFy/sJJnQZVTMOjp8OyrdsM59kbtpRME/kvJl6fTMS/0FfX6LXUbwKsf7I5cfXpjIIpBdB5eUdHLLkcwtqAKK7h2UhhceUd943EemkL6wr80mr737opw45ZtlfQ16z+osYHylnu7ev2W1jSA0a/G4/eRz6+LpzPfwcmJ0jGmj6ZXTlwdjdNnh2NmvF9Jc9EKrpsfxy6XPpo8VwnuWRo+9urnUY87q+4uF7R1f9L63t92JJy3ZkcYXDM3fu8MkjcWcAcOsdOgJ1dG27s4TgtufiUc/tzaKG56KV6G8HxoYGQDxe+uDZ0bHjwZL4azV20Pl2/YlyxEwl26k+pu++SlybMu/eYCZkdOWRfePO39uL4ln+yJ02978aO62+dlBO/jNh4qUZ9qPAvW7Ezm04YxQxpPvyh4jqZD0HiiC3XB2ujCRrqBKG4kUUPTF/2VT0VDv8Gzw+1RYI06X3n/m3goKHekg6HVi3DSm8lbBRCeKcE+fscsulC/2HOi5gXM2au+ShrqtfPi5bj+gbPCKcuTRvD2xn3xOvYfPx+OmvpeeOe8T8Jg3BvxviDQR8PAw9Udh5ItGTf7w7gxYRpcvyBOw7Os4LbX49d30FCheLsGzwq3RDFh5S0IzzMkn7DuYHC1AUGDB/YPh99wYzwvEo/x+WdRY03nj508ES6YNys8F/17Z83acPRN18d5Gq0zp6Pj5qT1ZvX6LbUaQB3O8C2LU7XPS6kC2nTYeTAcXeBXXnNtupRILvojR6IGqxrAS1HjgT767PNK4xkwYEA8/XLb9ng6YuiQmjJ9RX1viymql4iNh6IaFBsPRVEU1XHR+VAURVEdF50PRVEU1XHR+VAURVEdV+x85kwMCCGEkI5B50MIIaTjxM5n6bzRZiYhhBDSaha98Pt85kNRFEV1XnQ+FEVRVMcVO59fvhYQQgghHYPOhxBCSMeh8yGEENJx6HwIIYR0HDofQgghHYfOhxBCSMeh8yGEENJx6Hya5GfTvm2m9wQzZw0w00n30alz/YsF/c10UqXVfcCFcsz7lPP5wffvCYd+e4KX8WP/sGJr5WueeHx4nZ2UBXpdc35xWSVd21tkdQqWvUbsFvzNxXXp7jKYPuP3atJdbh51R8VWp1tpFq0uO+J795npReoDUtbFsnGvFd2gdbq77KOIndSfd50CsRXc/EmTvluTX/RcW3kusJvwZzdUljEv69F2Fnq73DwrXdIstJ1FUTurLVuUXa9rK2nDrnqoJt3FKuujiF3WMXeRY9EX6LPOx+cQiqahMfvsgLUun21RdHmLIva+fJ1uYdm2Ks0lrzPz1WmVcfMsLHufA/DZC0XzLMTOunYmTrzWtAU6Xdv5bHR6FlllLOeTZW/l6TSd5y77sOwaSbPaskVWfb50Nw2DGDetCL4yvnRfXl6aHIu+wAXhfH4ybnTNstj4yoKizken6by8NCmfhba3yvnSrQvQstVpmudf+P1SZbWti9hg9IbRuS4n+MpYeRaWvT5/ro1G7IVG8zR63ff/6Y015f5ozB/V2CIalDxJ0/ZZaSCrs/GVAa12Pn/zSr+aZUHX4WLZg6zrr5G27OKzs9Kz0nQkD/7TIyNqljVWPVnpvjydlnUs+gJdddsNiG1e2uTnvhOn4TaZTreQ8m4d4A9/PK4uTdtrXDuXu37y49jOrdMtKx2OO5q2sNadlWZh2ek0C9euyO0aK9/Ns7DsXecDsA3aVucJRfMs5PxZA5dn/mJojS3SfB22C2zLnGshK89yPkCXsfDd2vaVl3SLZux0mrRlnWYh58fK0+hb5zrdSrOQ7dHo/Kw8izLH3Fp3b4UvHCgwckHHD3RkZYELFKNa2KJjsGw6CToQbMtDD44y80n30KlzLetxIxFSRY6RduDNcCEd8z7jfLR3J4QQYmP1n70RRj6EEEI6Dp0PIYSQjkPnQwghpOPQ+fQylv+PH4fL/vsfmHmEENIt0Pk0wG9/+9saLBuXPNvX/vZ3S9XXLFnrWv8Pf1nJ1zZZZTrBtm2fZm6D5IGvtv+vOO2fNizMLOMC51/GvhWUWZ/YdnL7LPKOk7ud//IvmzPtG0Hqw3Vh5efR6u0h5aDzKYm+YK0GJcs63U2TxiLLeeUw/+bS71SWV6wcG/56469q8t2OWeZRzq3LWta4zgf83dpHKvNil7Xsdk56+7Wt8M7/vD1cvebByjJsli6/vma5qPP5/IullXnX+Xy986uaZZlHul4WdJqbv/hXv+PN1/vv5gnYZ52OZZlHuoXkg3/+ckXNss4vct4xxbnGcc6zXfv3T1aW9TGVfBed72LlY99/tWRQZRk2q969p2ZZ5oHed7c94djntReZ12m+NgQsZ6vzNZJPsqHzKYF1oQnoOLWNNH63rC/N7ZzdfJnX+Rpfw5EIQOdZyxrtfLCsO2xJQ8cry7DXYF/cxuo6L5mXZaA7G6tDLOp83I5X0Gn/8I9T67Yb+VYnI+WkA9PbKei6ZVmn+fLddEH230XbYNndXpn3nXe3jJB3Pl2n/ffr/3PNsovklcnXae426Hw4Kb1sOR+ZB1K/i5vva0OufSNtitRD51MQdxQuuE5D5qUDFDZ89stKnmuLebceNx9gBCdpbp7bcHRUpvnHj/9LnC/LUl6Dxm7lZ6UJEg24eXrZyrPKNBr56GMvabqMdWwkz1fm43/6r3Vp7jLQo3c330rT6e7y5v+zrsZO5+koEUh+0fOukbJWvj6fvqhR8jWSVzZfR39Wvk6XCMxyPljOai9uXThv1vWlbQBuj7t5vmWSDZ1PQeTCIoSQPKw+hNRC50MIIaTj0PkQQgjpOHQ+hBBCOg6dDyGEkI5D50MIIaTj0PkQQgjpOHQ+hBBCOg6dD+lVBEEQ/uVf1S4vWhyE/foF4Ytzk2WkYxnzN/ygaqdBmSuvqtr9m0urdn81rVq/xSWXJPZzFyT2WEb6t74VhAtfDsJvD0nS/3pOko75P/vzZIr1jhxVnccUZTCVtB/+OKlr7G21eVKH1Km3+ZbRtfuPqczLsqzPrQf7O+L6ZLtvG18tQ0hPQudDeg3Seeo0dLjfHVad152qnnenqAsdtjgfODR0wtd9P9/5wB4dtcy7edNmJPXfeXc1H47iZ9OD8ImJQfjnjyXr+M+Tg3D4/y+xAXBWyMe863zcOsRGO6c85yPzuh7ZXzof0tug8yGEENJx6HwIIYR0HDofQgghHYfOhxBCSMeh8yGEENJx6HwIIYR0HDofQgghHSd2PhRFURTVSdH5UBRFUR1X7HzmTAwIIYSQjkHnQwghpOPwthtFURTVcdH5UBRFUR1XctttzhxCCCGk7YhqnM/w4cMJIYSQtkDnQwghpOM05HzCmZFpBmNGfs8sB1xbywZk2WXlaRqxK5qn0y20LVjz6O9683X6uBuH1eRZNsCyAUVsNJuevrSmjItV5sjP/nWmjc7LokwZn63veBUB16muS3h8zNWmPfj62d8xy1i2Oj9vO7Wtla/RthZWmQmjh5q2wLK37DRWGR9WeVDGVrDKuFjlBNe2J9qly5npF+XaZ+UdeOHimnyNtlv0p4NMG0HbCrjes+yy8rL8AGja+cz4ye+b6b95pn9NGeB2WoJrB1wbXCRIf+2B36vLc8uCZtalLwad7itTJL2Zi7zRfbHyXVznI+lWmpVn2aBOAQ1D2+k8XUbb6HQLbduo89F14Pgi7adjv1OTru3dc3DXzd+tqwfoMjq9Xc7HynfR9no7dPoPf3Cdt4xOL4OuQ+rBeqx0q4xOL4NVh06b/MdXVdJ7sl26x8KyPzH1X9Xly7Lgcz6uHShiZ6VrezcP7SbP3qJp5+Mjqxw8sB6JWR5Z8orOu0hemXW5uHlFygjaFrgXuQ/rIpe8svvi5lnACegy7jLQ9m66u+ziDhYsG6BtLHy2jTgfXR6N27Jx0WWy8pb8x4Fmerucj4XPVqfjvLjofF3OQtv6yLL35el0F22XhS6Tdz33RLu0cKMfSdfbp9PhQGXecj6SB6zlLNui7HnukpqyZeto2vkg8nFD+rwyMvLVaT57t6yVrsu59mXWJXl6WZNVxkqX0TFodISl8xrZlzzc+mQdU/7kyjpbyw6NRtLcDgwgTZdz84UiNoK2bcT5/Pqpy2rq0Hk/v2uIGbX77N08fctOp7fL+Vj5Gm2rr0edLvjK6fQyZNXhy/Oll0HXIdepdW2CnmqXelnSLHts3/0/vqbGFmgb1/noiAzXct72+dIlz03T5O13Fi1xPlb6/P8w2EzPQux1GasOn41rl4WvjKT5TrhVxsfI679fsW/kItfpWfjq0uk+il48vlsMLm65RpyPha9TtyjikKxyLs2WsfI1Zc51mXrdZ1aWjUWZMlnPxYSPn/y3ZllN1rbq9DKUqaMn26VO0+k6D9vn2soxk2XtfNDnaFsf2hmvf/xy00Yjti4ddz6EdBNoiGhEK//8CjPfBQ9ScXsNZfQgqy8g+7r84W+Z+e0CHSbWC4o4LtIz4HqW86Qj5XZA50MIIaTj0PkQQgjpOHQ+hBBCOg6dDyGEkI5D50MIIaTj9Frnc+bMmXijXC1atKhio6XL9gSyvVZeK9DS6QcOHEhTw3DcuHE1eVloWfmdRkunjxkzpi6tXbjX3F133WXaAZ/uv/9+096HlpV/oaFl5W/atCnN9WvNmjVm2UYQWXmtRjRjxgwzv9volc5HKytdSy+7nQY6ZVc6X2vy5Mnx9Oc//3klf/78+XEa9Jvf/MZbVpSVr7cNjUQktnv27Knka7R0uuV8tPSyXreWtQw+/vjjNKWahs5VS9IFkTsPdH2QpGtbCMuvvfZauuSXVT7LYfzwhz9MrRLpPEs6X6MlaevXr487Rmy3tgVay5cv9+bp9DLHGQMykWvn1mM5x7x1WZRpU3rZOj+irPNjoa8RX4ethbadZ+M7P5j3HWdRlg3I2z+Ruy+//vWv05zQvL50/4RlEfoGvYxrVMpoSVqn6XXOBwdelDeC0cJJX7JkSboUxh0dbB5//PHKaMkt9/XXX9fVc+TIkXi0jfQTJ06kqfVloaw0rBcnW6dpYVk7H0jsLLR0ep7z8R0XLalLZC2PHDkyXarm63PlloFQRjobEdLEVqMlabpzybLV0naC3vaVK1fGaVpipyVpPjAI8UlstNw06eS0sNzIcbaWdT24/pH205/+NE0pty6Xsm2qyDWIbUG9WlK3jyznI5Lt0WlYj16GXBvf+dFyy0DWctnrT/ZFy7WBrGV9XYrzmTBhQppSX48s9wS9zvkArax0LdfG9fqQeH6RZSP1+NJ1Z59lp0f5uJ1j2bmRTxZaeelablrWPrsjMwgdFvL07Q7Mu4idyO0MgDVahnQ5SOx9zgeIrA7URTdInS5ylyFt56JlpWPQope1jYsWlnEsRe4xBu5xlnrc6xK2IrFx0TZ6HYKsy0WrTJsSFbGBJM1HEedj7RfQNrqci0iWs9q/z6bs9deM89FpcpyB3iYRnJLk9wS90vmALFk2bpp1gVvy1SP4lGWDNGtkrJ8pwKaM8wG+52CQttNy06zjIjZAO033FqBv/YgWxUYko0s33ZXcBtLKKufL0+kW+taFlkSLQEuXtfBJHA9wb2dpWfVIWpnjLMtuhwd05K6lO6Ui63LJk2vjltPrz5LY+MhyPoi4fRKbMudHlq3jLMqyKXP96X3xSfJdG0RWIn2cXTtI5/UEvdb5EGJhRU/WcwxCLgS0MOBzB7iujUjX0VPQ+RBCCOk4dD6EEEI6Tp3zoSiKoqhOSBwQnQ9FURTVcdH5UBRFUR0XnQ9FURTVcdH5UBRFUR0XnQ9FURTVcdH5UBRFUR0XnQ9FURTVcdH5UBTVYu0MjywOwsPv1HcvwSXjwiAYG3GrA9JuS60Svb1xR5Q2Os1z7ceEO4+dSi1rdc/st6J8lJN6hWTZUnDNw2m+S1TuxkmpFdVK0fm0QEEwpWcY8mK6BRTVMwqGPR1Pz0TOBjq+JgjPvVILdOzcueiavS2H6jf87HwbLTgly0aj9cwb60wbi6GTXklLUa0QnU8LZDqGDrFtvz36y1MwfEEYXBTV0W9quOi9XeH+4+fDQ1FVH2w7EgbXp3kpogMnzocPLfqsmnf7m+b6j5wOw6eXbKraXTw1fOfz/WlutoLLZ9SsM0vY3mueebe6HjB0bhiMfzMMrphZk+5q+4FT4RWPvVPJH/TkyvCbI2fT3Hq9uHp7xXb8nI/ifdQKbnsjzpv69tZ4+dT5MLxu8ppKmaKK7aNzkiWpsxTDq398rFUK7omO8aBnwj1LgvB87GQ+iDgXnn81cTpnI45FedDu46ej67XakWcpusxqbDcdPJ7mJHp/18GafC04MEnv/2D+4CwIbqnYa+cnSiKmtM7+96apVCtE59MCWU6hk5SRdO7vR04mTzdOfS+2dSUdWp6CS6fHdp/sOJqmZCu4+60wuCzZPjisopLtOeHxHZJvKSvPVTA+cTCugn7ZdRSuH+fminT/73gzTa2Xr764nJF34lzxbSiq3WfOhsHAp8NgxFXhuSjqiaOc1/3dSa3zuT1NtXXScT6uPtlz2Juvnc+lD81OU/0Kgh9V7K1bcsmttzSfzqelovNpgSyHINwz75PUytajr2w0y6ETfuvTveHBE1H99yxLOibLDgybl9aWLYyo0Qlt/OZYmpIvq9PydXKuxJF89k3tyNXS25/tq9R58cNvJ+u4Z2m8nCfZHl/cIvmWsvJcXfTg8mifpqdLVUkdY2d/mKaUF/Y1GJR0lsGgWXF9azYdipeLqsy+NKtg8KTI+UwMz6VRjnB610OpRa16xvnMSlP9ovPpOdH5tECmQ4jYc/RseN2ztX82O0/BNfNq67lkWhhcNz8MbnolDO5bFh5L/3THoZORLZyJWleWvjlypmWdU9F6JMr6vIDzceuTskWipqztiY9flHfYc3cyq6yr/hNWRB3uzHSpVuLYK9z6epqTr1c/3F23DVJP1A8XlpRpt7YejhzJoCjqufrGGscjhOdx+61WzTifYPwLIU4fLn0QDLq3Jl9Lp8OZSBmAO6XO3dLIRjuf28Idx8/GNrDHlafzgv73oAjVItH5tEA1zkIhCm5ZHF7z9LvVvAGzwi92239xUkvX5SKy0ixNeXtLyzonqedg5ADRUHUHeS7idJQAJ1nU+cBm12E091rJek6h0gyJ3eovD4YTX/+y8rznyqdWpxZ+SdkigvMv8pLHtdGAQ+qNiY5Dlnzrl/JFVda+UQVDIscz6Jnw5en1jidmcRCe3ftYap2oKeeTyfi0VKJgwH2GTS1arvPJYucx13VRzYjOpwXSDkDzqRq1T1jkub0W8cjLG1MrWzX20UheVJMOcm6/See0aW/kNZpQ0U5ObrtlRS94tiP1ZZElsdHu6+3P9pcqW0TBiAVhcP3CdKmY8KA/ax2Sl0cRlbFtRsHAJ2Pn89XLhuNRnH61tns5GHE4nWbJdT57o2WUE4rejMR6ZJ1C9Y9dJ3Jvu6FuvR7Xnmqd6HxaoDonkLJmU30zOzgqqGH8kKBa5t6l4YK1O5JbRaoeQRT0n27mg73H/GHCHXM/qnRQGwvcCvOpaCcnLxz4Xm4IhsyO9qXqTC3FxyNnfZLvvoEGSd6CtTvTlFpV8tfZ+VqwW7elvutDepZkHa7wogLeuMtSMPb1pHzOcTqZvlSQty2tkDifDxbYTsflyIpy3UzeM59WKu+ZD9U+0fm0QK4D2H4w6QUvxTOCaNnSodG1TsitQ3jurc2x/bx38YM728YlT9JJxVw3P34tWAuLwd1LkvwB9c84pGyeJKpZsbH+Nevg2iQiKKLg6jmZ65Q8Oe6uEBEWKQ8mp8dbS1499zkoKWs9d/O9CSfPoopI6ghGvpSm1Av7LtvRbgVXPhE7n0vH2c7GxxknEvLJfdW6nUp+jErn0xOi82mBrI4/7nhVelan8NHTQ2ts3/xoT5yedasuk1GL4vJF9NGOo5GTfDsMxrwaDnt2TaGXA7pZ8xBp4ZXv6HjcOuuDzEjS1btRpHvpI9GAIyqL3wJ16xOCr06cjZwPnvs8GYav244mj2Pxj1KzbzdT3S06nxZId/yiq55aXZNexx1LUku/cBvpw6+OhtPe3hpePSmqz3VoGeAHoRTVLgWDJsYOaOxDtnPJ49gadj0XungFtEBW5+9DXi548tUvkgfyhk2roKh2Khg0KeKZcI/8yLQAp9PP8PgUBEl+/wFXh8vfWlxZxnTH9i01y3p6/Y24fZbMv73mA5V/Ubjpiw01y8m0Wv7kiaOV5f17dsXTpx57OJ5Ckke1VjyqLZDV8RfiopxIBs9FML3p5fq8Itz9VrqFtVr1ZfIiBH77gptK1m9gjkRpeGVaJA/a9x07F//G6ED6pvjx9BUzRFo62jqI5ePnw8NR9Lbv+Ln4x7IQ1oV55Mt68co26gQQ0qVePI/Ca+moe19UX/wZoGgeUaGUl3LQh9uTN+tQDvugt+nddL+hVV8m7zEl+xLVl9piiof3OC7YRmjt5kPxMvKxTlkvtCf9JA+2B7fZtJ1+BoQ5WRfqhT2OJYTfYH2++3h4NNrnw5ENjg/2U9aDNAjlt+xLFvQ2QNhG0ebIRl4oETsc43YoGAgHNCnckvPm29koPwy/SgplCB399q+2hG+vWhPP44gue2d1+MoSfCwUz0YTxBYsffPV8IbU+UyfNi28fPCwOhvolbeWR/P94nlJg2T+5jHjw/17E+cz6dGHwxdffDGcNw+/uwvieaq1qp4BqmGZHX/KzVPfC5dv2BfPo+NY+fmByttqM97ZVrF75f1vkvlbMNpL0oZMXB0OfGJlYt9vargM9eA3LMiXaQ7HnWfg+q0vvGyw89CZyu9Q8DsZ+awNfsmP77/h9zoffpW8rSbfHMMnYCA817hl+vvxfPylBueLBFvVd9/gvIJRL4X3Lvg0XPz+7jgNHTGesUDYFvm2Wnx7Md0m1IH1whdJB4rvsInwMoJItu/1D3fH9ngt+uZp6+M0CMuf7ToeXvfc2jQlSouOdzyN8m6P9keEt8vQ2etndbNWflX3e6R9x86Hjy3+PF2K7C+dHju+4Jq5yfL46mdyJi/dks6F4cL3doXrtx4Ogxtrn89h3+Bsdxw8HV7//NrwvoXRqP2BZdH8utQiDEfPfD8Mrqx2hrh2oPiHrukXGPCVCDgwKBj3Rjxtl4KBT8W34IJhV8YfGHUdz9F329XNVAcW7dDZs07joVoqOp8WyOr0veCNJCsdIBK6eq6d1wQU1W59fjQaJMRR0DPhwplB7IROpW+3HdqfDDagjZ9WP0F0z2NPRtdnYjNzyhPxFEIaOH3yWLjvAD6lE4QvTJ9VSRcb6Lqhg8MF8zAQi6KrOO6slh9984jK/OnTJ8KTZ06FX2zZWk2Lv7QdhJ998UU8Fc2Y+1LN8ukzJ2qWqdaIR7QFsjr8XoUafVNUt+jIEfwU1K+TJ5Nbj9/s/ibcvSeJDvfs2R1+k87v3r0r3Ld/XzTdHe7duzeaJumwh/bsrTrNEyfzv0hClROdD0VRFNVx0flQFEVRHRedD0VRFNVx0flQFEVRHRedD0VRFNVx0flQFEVRHRedD0VRFNVx0flQFEVRHRedD0VRFNVx0flQFEVRHRedD0VRFNVxxc5nzsSAEEII6Qj/79n/JwyWL/xjM5MQQghpF3Q+hBBCOg6dDyGEkI7DZz6EEEI6CsS33SiKoqiOi86HoiiK6rjofCiKoqiOi86HoiiK6rhi5/PL1wJCCCGkI9D5EEII6Th0PoQQQjoOnQ8hhJCOQ+dDCCGk49D5EEII6Th0PoQQQjoOnQ8hhJCOQ+dDCCGk49D5EEII6Th0PoQQQjoOnQ8hhJCOQ+dDCCGk49D5EEII6Th0PoQQQjoOnU+T/GJB/xgrryfoTdtC2ksnzjWvp85zoRzzPud8hn57ghdt94Pv32PaCAv+5mKzTl2HL/2PxvxRTZ5G21lYZTSwuffum+vS3LKzf355bp2S79rklRNaXfb5F36/kv6zad/OtPUhti6WjZVmpf/hj8fVpFnc9ZMf15W1sNZhIXYanY/rN89GI/mN7ouUB/rcuGg7t43pPF+6RttYiF2jbdmiiJ3YuHY63c3T5OVrxLbRY+6ij0Vvp6ucDxC7vJMErDolzZdepHHrOlwse41lVzTNopmyciHrtKJlfXaS3mnn40vHck84nyceH16xBSO+d1+djc4Hbr4LbJp1PhjU6HQLsXXbmB6x63RJc9E2FnL9NdqWLZqxk7Si5a10F9g1c8wtxLa306edj5V2/5/eGKfpkzTnF5cVLi9pjdrq0b2Fr7yFttVIPvYrLx1RmluX2Flpf/NKv0oaOrIyZS20nba3nI/Gtc/DstdpIGt/hAl/dkMlb9Kk79blZ5XVWHboTK10ba+dh84vc66FRvZFp+FasNKlLqsjtOwlzcWysa6/VrRlF8uukTR9zPV2Spqgy2TllTnmeceit9MVzsdKt07SxInX1tlZZbPSZfn7Qx+ssS2KrtMFnYW2tUax/+mREWZd+qK10LZZadgvSZPRuWWn0yyy7LAPPeF8JM9d1pTpsF30+dPpVtr0Gb9XSdcdbpGyeedaaNb5aFsrz3I+kucuW1g21vXXaFt2yTs/Pxk3upJW9NqXtDyyypTJK3MsejsXhPOxyCrrS0cDlmW5deHaAUm3cG01rvOx7H15Ot3CstVpFq0uq+kp5+Oi6wDtcD4uw656yFuntexLy6OTzgd3HGQejlXmgVuPoG0sxK7RtuyS53z0NdmKqF+TVaZMXplj0du5IJyPbhjAvT2h87LS9Qj1D/7d3aYdsNKxPW6a2PnQtoJerx4l6nJAv5AAdB1WmkuZstrWxbXTyzrdJS/fxbLXafrcaXQdoJEO20Lb6RE1yLLFst5W2YYy51ropPNx8zW6Do1lK2i7VrRlF8sOx0jSxFFZdlZaHlllyuSVORa9na5wPrpxS+esT5KEp7IMZs4aUCmv0yXNl26luXnuMijrfLDN2lYjNnjAK2l4YC3puvOSdFkGYmelWVh2VpqFa2c9XHXLgLx8F8veTUPEodMkXdMO54Nlt6MQO9ze0ekWsCtzroVWOh/9QgRu8yDNdT5AljWS51LEBrSiLbtYdo2k6Zcsbh51R52toMsUzcs75nnHorfTp52PhdhZJ+mZvxhq2uo0i8nPfcdr++N//yd1aWJr4dq6WHZYdkfuzdbnS7Ow7HSaRdYbVTpNp2vy8l0s+7w0nS6U6bAtLDsrTa4pnebDKm8hdkIjzuehB0fVpFuIreV8dPTg5rkUsQGtbMvN2ElamfKWvZvX6DHPOxa9na5yPvodd+skueWznIpGbIrYjh/7h3X2GquMxrXBaMoq60vX6A5Hp2elWVh2Os0i77ckvvSi+S6WvZWWlQ7a6XwwKnXT3WVBd+K4liVd22usbW00isPdA52n0XaW8wHa3s3TFLEBZdqyTrOwyrqIjWun091zqdF2Ql5+I8e8SL/Wm+lzzqedILpAg8XDRkwtG00Z204g2+P+hoR0H5041+jEetP13RuBE8KAE8dJO4NGuZCOOZ0PIYSQjtOnnI8OLQkhhNhY/Wdvo085H0IIId0BnQ8hhJCOQ+dDCCGk49D5EEII6Th0PoQQQjoOnQ8hhJCOQ+dDCCGk49D59DJe+9vfDZf/j+qfayCEkG6EzqdBfvvb31aw8i3+5V8219hLeXE2sixs2/ZpZV7KZFHGNq9uydP5/7RhYWaZTpC1/vX/8JeV/GX//Q8q6VllLBb/6ndK2TeLbF/RdZaxbSd52yH5LpZto7h1ll0HbNEW9HKZ8qRx6HwaQC5QYdW71Q8/+oCDEXsrH0j+m0vb/2HAMs7nq+3/K07rS85H22SVsShr3yyyvqLr7PT2+cjbDp3/35Z9r7KMQZhr2yh6HWWRstr5kM5B59MActFidC3zkgfHIWk6LytNOyZh9ZoHTQehbcDXO7+qy/PZAozqkVfG+YiN5Xy0jS8vb1n41ZJB4dLl19elA18dGtf5iJ2eB7i1qW2AOHw3PescbPjsl/HyP3+5Il7e/H/WmXX848f/pa6sgH3Wy9rG1ylqe0HW7cuX8y5RnQbHLaus5FnXts53cfPdMnoZ4FhY6Zv+9+pKHVu2flCXDyTftyxIe9HXsmCV911T2hZt1con2dD5lER3jlh2L7h2Oh+drztQbJPOx7xuEFh2t7uo8/n8i6WVedf5oCHrZZmXBq7zspZ1usy7zsAtI8saq6P4u7WPVObFLmtZ5gUcp3/4x6nxPPbftckqb0W7Ol/S3TTgczzAtZN55OWddz0P4Dglci96Pl2kLhfLFrjXq6DTxCHKMq53vew6IeTpfD0P3PZS1PnIsttG3fo1OKZSnvih8ymJXGDolPWydBba+ehyWR0R8vSy5Lsdi5vvYuX7Rm5u3S6SlzXatdIFne/W6VsW0FHojrBIGaD31Rrhw8a3P8Dt4HTdkvbxP/3XeCrHz7XX83oZnaWVr9M0Ot/FtbHK+M47IiQ33XX0Fjpf1uEuu0i+Zu3fP1mXn1dG0FGirw697Oa5SL60XZ2m52U5L99dJtnQ+ZREX2AuyO+k85HbFILOx2hWlq3IqIzz0cuCTkNkgM5Oo/N9nbq7rNPkOZNr4y5rdIeLZcuBaafkbrO7r1KvTtN5mEpkiIjDKivLvv3RaUCP6LWNxs3Xy3nnXcAtQ0mXPJnPO59Sh7vs0ki+pLnrL7oNetnN87UXOp+egc6nBL/e+KvKxYXQWtAXnM/56HR54CrLZZ0PQGOU+RUrx9aVl3nfclnnY93Xxr14vezm62UN8nS+LLtpRUa6Gn1MJE2WrTQc96x1AHmuo+2s21BWWffa0Pmy7JbRy3+//j/X2AnaXuPmZS1bzrLZ8+nSSL6kuc5aIiZZdnHLu7ZZ7UVwy+tlfftZ8vOWSTZ0PiWQC0s6Hzcd+JyPa6eXizofbSPgNpCbZ9m69/7LOh+dpsvI6+NWnu6wtZ3ku8vAqg/IQ3tZ1mUEy/mArDRBHsgDKyrQZWTZdwwlTZDIT+f57LH8zv+83bQTJE93iBLl6HxJl3krH0hnDLLOp1XWzdc0mi/pgr7G9SAo6wUYd1nQdbn5elny9TXs5rlp7jLJhs6HEEJIx6HzKYEe2RBCiA+r/yC10PkQQgjpOHQ+hBBCOg6dDyGEkI5D50MIIaTj0PkQQgjpOHQ+hBBCOg6dDyGEkI5D50MIIaTj0PkQQgjpOHQ+hBBCOg6dDyGEkI5D50MIIaTj0PkQQgjpOHQ+hBBCOg6dDyGEkI5D50N6DTf8IAj/4MYg/KtpQTh3QTXt0SeCMAiSZUyn/5cg/Na3gnDsbUG48OUk7S//Kgjn/CKxve77VVvYvfTLIHxiYhBeeVWSJnX5kHxMsX6sR+f9+WNBePX/lSz365esW7bjltHV8pdckth9e0iyDc//rLoNUhbzixYn2439kjqw3bK+P/vzahlM73swyUcZOU6oA+vTdrItOk1PCelJ6HxIr0E6a3TKcBZI+zeXJtMR1yedPjpbdJ66c4YNQAeM8reNr9aHdHE+sP3rOdW6sayB05N0mcL5wLFgWUDanXdX69T2AOsAOg3bgDKYh7OS/UBdst3aHvuHKdKwD5jH9ul85OFYYYo6JE/XI/srywJsCOlJ6HxIrwKdMEbsOu27w4Lw3vuSeXScbuSDdHTC6IBHjqo6H+lotfORdEx9SDnUDeeg8+AE3Xrg5GTZzcMU2+pzPkC2W9fhOtefTU/2AfM//LHtfGQ7dD0yb00J6UnofAghhHQcOh9CCCEdh86HEEJIx6HzIYQQ0nHofAghhHQcOh9CCCEdh86HEEJIx6HzIYQQ0nHofAghhHQcOh9CCCEdh86HEEJIx6HzIYQQ0nHofAghhHQcOh9CCCEdh86HEEJIx6k4H4qiKIrqpOh8KIqiqI6LzoeiKIrquILNn74ZzpkYEEIIIR3h7958JAyWL/xjM5MQQghpF3Q+hBBCOk6w/u2/MDMIIYSQdvC3L97IFw4oiqKozovOh6Ioiuq46HwoiqKojovOh6Ioiuq46HwoiqKojqvifObMmUMIIYS0HajG+QwfPpwQQghpOXQ+hBBCOg6dDyGEkI5D50MIIaTj0PkQQgjpOA07n3BmZOrhzPSLzDKCtl30p4NybcDyh79VyZv/HwbX5euyGm3jW9eaR3+3xk7n6fRxNw7zlnH54Q+uq6kH6PwyeYK2KXLcsI2WjYsu4zJh9NBCZfLyLWb85PdL2fvq1+llOTH1X9XUVaROy953rLWNlS9sevrSwrbazkeZco+PubqwrcYt4wPn2Sp//4+vqbPNa1e6Dfqwygm/eaZ/pn2ZPEHbFO3PXMaM/F6N/YEXLq6z0flZeZqidmDJfxxYZ2+Vyco/8rN/7c1zaYvzEYqWK2uXlacpaude8L5OsYzzAWIrNJrn5he1a4XzASOv/36N/c/vGlJno/OBm2/R085H12Hh2vs6UY1bJitP027nY9lo3M7PsnHR9j6schp0stq+3c4HoIP02ev0vDw3v6ydi7bPcj6NDr6B25Z9di5Z9rpOna7tLVrifCQNo30rXaPzG7XLytMUtbMueMnTaVnOx7JHhyLpbp5Oz8tz84vaNeJ8rDSdbuUBd8SHfRe0nU7/6djvmHWW7WCs/DwQnVt16DS3Y9R5Et1jH3S6jtDdMjrdxT1Olo2g7RrpjCXddwzcMkXWYYFOSdeDThPpe567pCZdl/G1qzLo8rLtk//4qpp0n71Oz8tz84va+QZdOirLcj6yrM+f5GncKMRni4GHzsexQrp7V0CXQbTs5unlXz91WY29RUudT1Y60CNmbYuddG0lT1MkTyizLt9oS5cDZZ3PXTd/t5Lu5un0vLxGj1szzgfbbqVr+/WPX+610ZS16YTzySpvHVc36tF5uH3ky/Olu3TK+Vj5FrpMo85H1/Hxk//Wm6ePd7ucj5vus9fpeXmNtkuf89EdtuV8pD3rZZmXchrJc8myc50G0qzbspInfP3s79QsW/YuLXE+FtZzH51vLftsBVzAGFlaeVnlrWWNe8EL7rqynI+LO2oGlp1FVjlr2WcrF2seugw6QTQenaY7Dj2acsuKjUtZGwu3A9R5Or0oZctre7cjdfOLpLs06nwsihyr1x74vTp8ZSyKOCRt7+b5Bjd57UrX4UPbI5pwjy3w2Wehy7jlrGWfrQ9tr52PjjJ0XUWdD+5K4NmtXrbsgE4vgi5bto62OB9fp6dtsKzDRvdepLZ1QzyQFUa75bGctS59EtEIZd6ljPMBeh3AsrHIKoflosfNdx5cdBkLny2W9a0Va8AB3DIW2saiNzkf67jq/CLpLu1yPpjXeVl1SJ4vX9Os8/FtV1670nX4sMppytoLWeWwXLRdWmhboJ2PLq8HxPpYueUlXedZaVnpRdBODRS53Sa0xPm4y5Km0SNmGY1kNTg3XS9babps2XXpkygjQG0r6AbnNhJJd++fSjrwpWflNXPcrE7SQpeR+t2RMNCORtvqNLcMyMsH2qbZjq0IWeWR5t4yRaPKK2Pl+dJdihxHQds1eqx0mpvn5hdZh4Wuw337072rIOm+dlUGXV63GfecWvZF85ppl7jt5nba2hb4nI+AwXdR51Nm26wXT+RZnQ9d3sr30TLn46bJQysrz4fP3ipv2VhlfWh7y/m4D0VBEecDyqZn5el0Hz77RpyPlS9oOx/uiM8t5+YJ2qbTzkeP1twXCHxlfOlZeTrdJatzcNF2ZY9V1ojcl96o8ynaRrRTyCpTFF2+2WvJl6fTffjs5ZmPTgPaPs/5IE0fK13WehvORd+lwG1knafr8qVrithYtM356HR3xOxDj7Z1uq9udxk0si59En3bALKcjw+xd+vT6b68Zo8btlHSs9BlrHxB22WRVc7NE7SNjzL22taHVU5T1h6ULSN2rvOxKFonEFuA68OycdFlrHwXbe/DKuei7fuC82m2XfpeONARhut8sB/aFmk+56PtsihTBtGdttdoOyvfR0udj5suB1OnTfmTK2vsfQ8e3TR98OXtC9fGTSu6Lp/zAdq+rPPR9YCyeTqtkePWSufjjqbc/EbzBG3jo4y9tvVhPUsUfMfOshXK2gOxa6fzAeg8LDvBtbdsXNwyPqyygmvrtis3vwi6fDucj05rpF1q56OfEwFJd50PcO30sRIby86XlxUFu2g7l6J2Lg07H0K6BXRQeG0cDiDv/raAQQrs8ezCvU/em4HDxXaX2ddWIcfMHeCR3kWnzhOdDyGEkI5D50MIIaTj0PkQQgjpOHQ+hBBCOg6dDyGEkI5D50MIIaTj0PkQQgjpOHQ+hBBCOg6dDyGEkI5D50MIIaTj0PkQQgjpOL3a+fhk2ei0nmDMmDFt3w6tIul5iGbMmGHmdxqRuz1nzpyJ03Vau3Bl2YBx48alFvWy7H08/vjjaanO7F9fQGTlgSKyyjWClpXfSrSs/G6j1zqfPLl2umynee2119KtaO92aBVJz2PTpk0xP/3pT838TmNtj5a2bQfaEUyYMMG0EbTc5d/85jd19j7uv//+yn5b+RciIisPFJFVrhG0rPxWomXldxu90vlo/frXv66kHzhwIE2tnhwR5hctWpQu1Z88NG4t2Op8kTsvyOgbuuuuuyrp2vFoSb7eJujrr7+u5AER6pH9++EPf1hj49pCeekizPuOiwiRxscff5wu2TbYf0nDPojWrFlj2kOTJ0+Opz//+c/jvJEjR8bLIqzTKiuRjyWdvn79+rqykKRZ4HoS4ZhLunUesyJCkT4uJ06ciK8zXS9YsmRJap1I52VFPkWPs17W16ag69mzZ09dvmvjrstHmTaV1TZxHiFc/1gWuXYWWlb+8uXL09wwPj+WTdb50dLL+jhr+WwE3/UHtHQ6+gMtnSeIUKd7TWlZZazt7AS93vlY+ZosNWuDdHScPiHf6rSgsuvVjV/yXLTy0rPk2ridvXTq2lm4ZVxl5aMecUSW3LJFnQ/klnUdmsYn5DXqfLSszs0nqdvnfHzKy4fK1FPUxiVLrbbJQisrTwu3TPNssq5BUV4dkNhk2Vn5eWV0dO1KD5SRb7Vj7ZQlrdN0lfOxlnHgpWORMjLSgiRNC2VkNKCFZVyUItwyQZruuKQ+gNEenMr8+fMraSJ3GUIHLVGChVZeulaejdXQytYBuctHjhyJn4X5yrqIdKevJWm4JSZy7WTZRcuX5juPPnyaMmVKnA9nJJJo1u0YLOej5UvTwnWmR+/igLWK1OOzcWmkTVnLRW2y0NLp7nG2bIucH60ix7mV50I7jZUrV5o2+pr1tWMrTUtsOk3XOB95TmDdmgP6JGlJvsi99y6y0iAsZ3VaunPRknwtXc5CKy9dlHVcRFZnr+fRCPSyT66N1AnkhQxXaKxiI8pzPjodzrpM1Khvk2lhuazzETC4cIV0LbeMkOd8LLk2bjn39pXeZ5c8WWWEom2qyDUoyxj0iSQtCy1fuqWsshotN809zlCeTdb1p4Vl63hpO7dNufmQpLmRjkjye4Je73xk5AysTkYky1kXOIQRDu5pi1wb3fHpdCsNwrKv09KS0bDIspE0H1p56SJZzjouVmeP7RW5eRCctItrI+U0GMG5kjxREeej90eU9ZKAli+tqPPBcxORTteyli3ynE+Z4yxyOzxIbFy0fOuy0MprU7KcdQ3KMu4miCQtCy1fum+/tHRZjZablnWcRUVsIGsZt9ZEUkbbYd66faYlaTpdR1Q6v9P0SucD8uTaybJ7gevQ2i0DuWnoDCTNsrU6HnSWIjREn6PRDxslTUvSfOQJt+1cW1nOavi6s9dOB8LF7dpDOg23GeS4aYkNQIMXSZrIXS7ifNw8yM3XaGFZj7AxqEFamchHC+dc1wfBRh9z2SctLOc5H52Wd5xFzXR4kqbX5VK2Tcly1jUIua+vi00WWjodt7tEkoaoA9eh7FeR86Ml9YiKHGefjXX9abn1yPNELdfGkthYduKEe4pe63z0CMiVthPJsnuBay8vcu/r6nrcBmeVh+SiElxZaVpyj1lL1+cjS5adLLvHRdvozl6nQzrdzdOy8nU53y0ayC3rcz6QpOflWfgk+WWcj76GXGk7n+Sht+V8sspZ+W6avjZ9kvyiNpqybUqWrWtQp7kSmyy0svK05Hlulo2cHy23jBxnLZ+Na6dl5UuaHrBq6Zcm3AGjltgIWm5ep+m1zkeAE8IoBl5aH/CyYCQnD+0aAZ0EtgEdlJUPkI+LRd8qxDONVo8w0PixHXnb0y5wewvrxq0WeemiKI1stxxD/XwIlL1FA3DsUJc8CG4WPHNCfbi+rHxB9lu/fJJHM8dZg4EOrkvUpTteTSPrarZNCXJOdLtpJdjGvGPfyPlphEavP9k+uasiyCBWo1Umr9P0eudDiIsly46QCwERbs25URDy3duZUDODmVZB50P6HK4wmrTsCLkQ8CnLRuf1FHQ+hBBCOg6dDyGEkI5D50MIIaTjZDofQgghpN1AsfORBYqiKIrqhCqRD0VRFEV1SnQ+FEVRVMdF50NRFEV1XHQ+FEVRVMdF50NRFEV1XHQ+FEVRVMdF50NRFEV1XHQ+FEVRVMdF50NRFEV1XHQ+FEVRVMdF50NRFEV1XHQ+FEVRVMdF50NRFEV1XHQ+FEVRVMdF50NRFEV1XHQ+FEW1VIc+vCQ8+mrUtZxfn6YkmrFqYxgEYyJuNRgbHjp7PrVMFFx2u2kXM+bZ1KpeyTpuS+2ljMyPSa2q+mjXgSh9tLIRknIb9h5NLalWis6nSc1dvT26QKf0CBTV03po/lvx9MSXY+Pp8TeC8NwrCecjoq49Tn/lk63RNTsuYnwEHEM9h89UnY+VX6Vah6skz78ObIPWNZMWGDb1XPnY3LQE1SrR+TQpOh/qQlbw0IIwPPdeeOzdpCs5t7jqfMCpt5L0JIqwO3ZBnM+VExea+RYvvJ04N2jL4ZOmjYtWtqPSJM6Vap3ofJpUTzqf+xZsSLeiMb303q4wGDYvDC6K6tNEaWNmfpBaJQrufiu84rF3wmHPrglveOG9cMTz68IhT60Og/uWhfpmSXD7m+ElD78dXj1pdXh9ZAOujOwuenB5+N7Ww6mVX0G/qelctoI7loQXR+sZMnFVeN3ktfE23fDCunj7BjyxMuw/4e3wznmfpNa1Cu5ZGg6MbK6Nyo2c8l543XNrw0FPrgqDe5emFlU9+NJnYb+Hov15+t14HcMjW5QN7klG/FpXRvuM7a8cx4Gz4nJlhHJZ+nTn0XjfgweWhZc9+k44ONr/a55ZE454bl28LzjeOAZIv/SRFfH5wTlph4IbnwuDG54NT0UO5/jSpCs5+3Kt8zm2LMv5SCQEbg8PnU6upOptM3EMKCt2unzETZPiMtAbn++oz6+Uq5bXyrbF9lXzqdaKzqdJ9aTzAY0ITkA6yEFPrgw37T0ZHjkdht8cORs+89amaucZsWHnsbRUGK7dcigMLp1eyYPzOpfmaa3bGtldO79id8uM98MDJ2rv51uCBexvm/1hkpCj1V8eCIOLVWeP+R+9EgZjFlfTQNQBa2E9i+B4+1Vtln66N8k0tOPgmTAY/Wpie+m0cO3mQ2lOVXHeZdPDvceSI7Jh17GqIxoyO07L09NLkmO/41B0Mjy6e/4nSZ0laYeCAZPCYPCD8e01RDzQodW1zicM98XpiQNJO/MhD8VpPiXPW6TTvyNNraqaF3HT02lqGL75+c6avCLS9vPe35SmJtp96mxNPtVa0fk0qb7mfDCSjzukwdkdonTgX+w+nqYkGhWNruPyUUebpbMRZTu+YMSC0mXmvhuNdj1lPtoRRQlp3rb9p9LUqoIoGovzR76UpmQLtnuOYs9qFTuxCEtx/dcvTJeyVXGkl89IU+qFCNSKDlEGZVd/eTBNqQrprdYjSz8Kg0FPh488mTiZ86nziXXi9fDI+xdHMyeT5Ui6Ew+GPJCm2qp1PvW3u2oikqadT7WuA86pPRaNUsrWRxUXnU+T6mnnc8/8T9MtyRc6zrhzK9gZwQ4jeK2bpq1P6hgwM03xq8y6ILEHR/2D/xot+2xf5noqtxWvX5CmVHXNM+8meXcuSVOyBdtTTgC3Po0iEbVYGjvrwzC4Id+5YXfjbUnxKRi1KFy6oT5KE+eD7XGFaBWRbSsVDHgydj6nXtVRzo4k05DuxMs5n/pOv7XOB2/UJfYHnDD+yPnzlbyi9VHFRefTpIo4nw+/OhLf6sHAauOu43EHYtk1SlHJbaajZ9KEHAVD54bLo85dC7fQ4g6ywK2kvI5U68apiVN75JWNSblo3UW0etPBzPXg2ZQvH89vkH7JhLfTlGzB1vE98fMtpI/NuFWYF2VC8W3K0VG0GUVJqO+F5VvSnFoFQ15M52oVXDEzLvfR9vrXgq99dk34uRPBNis4HtxyE8cDDqfPdyzpTpzOh4LofJpUpvO5eFpqZQvPQYLLohGrVfa+ZeHOQ2fCNz7cnXRMlk1KEX2GZxBR5wSKavaqr8JbZ9W+eIDluJ6r56QpfpVZHxzjqvSWUZlyH0SOPcseL0nE+VEE5GrE84nzwcsGRWStAy87ZK2/qFAejg00Up84HzdSbYdW7DiUOJ9bLqtxPmcjfNKdeG91Pged2250Pu0VnU+TynI+y4zbI5bi5yOXTAunpqPdR1/5PAz6TzfrtHg0ihbyVLn9dNvraUpjumNu+sDb6MxdlelEtV1wXfKywuS3Nqcpfm38JookM9aDN86Q96l6cUKEt92Qhzfzisi7jnT9wIo88nRnekxF8uxn95H650s+ifP5PDoe7VYwdGLsfD6YW3U8Qni29uUOke7Eyzmf2t/lQJ1zPrXbTbVWdD5NKsv5QA+/lO8YRIdP4mJ36rnyxTC4cVEYjHk1vG1W9dbOxNe/rNqojssnefNq0fpdaUpjemDhhrgevByQp9iuyLYNnRsGd1RfB9bPP/KEFwl8ts9GzivO8zzAHzJxdZyPZz9F5NueKdGgQbYhJnIeq744kObmC1HfW+ptu8cWR4MP1BM5lKIS5/Pl7hNpSvsUDMLznmfCk6/XO59z+LKBId2Jl3U++86ej68J3C3e47yBpp3Pyx9tq8l75f3NcTkpK/Na2vkEVz9cYxvc8kw1L4Jqreh8mlSe8wnGl/+NRTA4cjhGfYIo+NHiujSfpGN8e2PtM5yyiqOytK6i5Ak27ivbRUf/eD1c1tN/workbTy8xRZFkkgbmuFYLsPvYCIb/C6miGDr0/FoM2t+4wOiZfcFBVfxrVejXqmjqCrOZ08HnM9ARD5PxW+41TmfiPDU8tSyKt2Jl73tJr/PMbm5+jufT/YcdvJVhKTStGqcTw3166RaKzqfJuV1PpdVR9vP4vcb6JiQftHU8PIJ76Q5fuHHk3V1pkh/Fty+pJLme0Atks7slfe/SVMaU8X5XDc/PBl5DNdpwFUg/cip6jqzNOCxlfGxcfWojP7VcbSk3+Cbt2ZHePe8TwutF+r3UPKq9VVN3nbTip3hJbVOKOtWXHDVnDC47Y10qSq8/o2yN059L03JVkedz+CnI+fzaPL7HgPr2U9NR17a+fhx99b+MaumqPMBtQ6Iaq3ofJpUXuQDxb9IN/JHTVmXWth6ELe4jHKimvTIqWWpMiq/p/5X/GVUeRtt+Pw0xa/YLiJLyF+6wY7GipTfcfB0nR32MU7L+L0MFDyQOJ8yb7sVFX6cK9uVVQ55x4y3D/Fj/7yyWvKqdbudz3E8hMfLBoMeNh2PcOyd2q6l+pWCqCO/ssyPTP08t6z2ZRjohbc/jPIk4rEin/GpZaJizufW1Jpqpeh8mlQR5wNZ+TGXZL8Rd+0za2rsd6c/cow7TpUOshQ/VynRmfn00KL0R6rXNv/CwaaooxSbTDJuXW7eW61DS27bPbzI/8wt/vQMyt78SpqSLXcdRSTbZr3wEH/+Js3PosiLK+J8vmiz89l5InL24nw8t92E8GR1oIMnYPguBN5nzHslwn3mg18uueQJRwHrwjqlDJbdGFQ7n8+PnY1ttH32TV+qGdH5NKmizgf39lcPD8KDo4Jwz8ggHDsk8NpCM1d+Fb+qrW2QBs1CnkoX5r3r/5Gf/qTOCfdeWQndJy8cXJP/OxxZn09xh5nxyvbB9HlIVh06wnAl6XgeYwnfnIttPF8ncGWtA995y3rDDL+HQrnHFn+RplSF9Klvb02X6nX9C+lvlApsX/zKfmSLt//aqe3HTiXOZ/CEXOcD5PM6ZeRGPu2Udj7u225Ue0Xn06SKOh/o3MrHYucj7IsIghdq7ONvk6k6hJnvJI5nWtRZWfkxebfe0JGBKCpoVHfMSV+1zvmdD/ybrM8n5OV9yUDqwHfkLMkXBqz14CObvjzoU/X5nTxJXa7wwVTrI6MiRFUot8758sAbH+0utF7ZPuvWnBY+dwS7T3a093c+O46nkc/gR8LQcDZ1xJ/dKff6eU85H/dHplR7RefTpEznM3BWmluv06+OrnFAB2IH5JR3+CJ9fXbAo++Y+Zosrfx8f6UzK/oFAfzQVWv0zPRHplfav7QX4S0vWZelYHjyHbc8xZ17Rj0r1D5ZCvonb70FN9jfV5Oy+HJDlmCDH6y6Cu58y7tuSG7/uYpfSrgi+5kUhC8axNuY9y2+9IOvRb4c3oyOnj+X3nZ7yvvCgUvy3bcvkwoKiM7nwhCdT5Oqcz79qx/cjJet5wmb3qxxQP0HDKutQ1CdVjBglm3j8NK6nWkJW9dNXpN0Zil4huMqvq2XjqTf/GhPmpoIryXHZaPOLkubPM9iREjHLbwikno276t+qFI07sUPM9cDSf4Tr9bf+uqvn7sYL1EsjI6n5FsKbkm/oG387gl/2gF5d8yt/s0ZaOUXB+L0bQdywr5Ir36QREi+9Yvk1XL9e6F2KRg4KXY+ZwvcdtOc2Vn/hWpLnXU+1bfjeNuts6LzaVKu8xHFI9E07cGF9R08dOjmxPm8PKw++pmQPihHZ+Lm5ZEnfOdLOjQf+ICoVnDf0moUIWBUP+bV1CLRSDynSJ8/VMCbdjcuCndFURR+TFqTN8r/0c2LH3q7+paeEEUAuAWFv2Wj/7xDDGxvernu+2v4ezY1diNfqnlFHK8z1+S7ZEQd8i22ClfPCYNrqn8j6cnXax2evEIdg+01XrMWBbe+XrUVlJPbfzyKQlBfGl1ViI4LXmFvl4KBT0XO55nwqPqrpUVApHQsjoKyFVw3IRoITEwY9Via2h4FI7EO8GR47HzOj7KolorOp0lp5/PIy4nD2H8c34RK0oQBxi2bWEe2RZ3Hc1XbwdXbWfFvN1QdRSkq/B5nAl6dHht1cncsiR9+p3/P64LUtgOnwpFTIucZOVT8YTn3o6pZwjl/4KUNybG8+6223/7qSQUDn4mdz+znbCej8T0XOrKOXc+FLl4BTUo7H4naL52wosYZVOg/vW5UriVvZTX71euX1jX3CR2KylJwfeJ8gqtuMh2Lxud8zhSIgKjuFq+AJqWdz33zq88w4tsfyiHUMGBW7ifu4aS+ikbiKzbuD2+f81EYjH/DrssDRbVL20/LSweTTMeSB26/JVc4dSGLzqdJ1b1wMHReOPOdbXHegePnEscxYmH9V6rTr0vjj3zF+XcvDQc/sTIcOml1/I2yYMzrYXDlnNoyJaCodioYCOfzdLhqvu1gfJyNPzya/GzA1acffRBdu0E46dnn4mXMW9N9+5Lfs508mQzg7rxjbLh3L75oHYSTX5gSHjyQvHRxw7VXx9OBl1+aTK+4LJ5CsB18ZZL/1GPJFxeQ5gKdOJX/YghVXsnRpRpWnfPJIop45LVpPGg2bVrEwrXZb71RVDMKRj2XRD+D7w7PWF+39hAe8v8+bM3K5DNH0unr6fCRP6pZBkePHIynVw3qH+7Ykfz5DSzv251c+9r+0LHjlWWo/xVDwsWLkp8bIH3ztu2VeT3FNw4OH6t/y5JqXtWzQTWkTOdz3fzwhWWbY0cQf0DT/cNxWbfmBs0Op+MHpYNn2/l5RHVTVDsVDEhuve1Wf0o7i6MfZHc3a1bik1FBOH/RK+G5c2fCPfsT5yI8NumZ2E6W4XxWrnwrvHLQpbHzkfS9u3dU5qFV771fswzp5Xsffqwyr6fJ/LnwyLFTcRrVWlXPBtWQSkU+JcDr2fH8RelrtE5+ESxt3nsyfpZ0OGpPB6MBHdh/Irn/Ln9eG29u4XNAIvkqM1Jgr/MgLOEr1joV9cv0UDpwhA3KI23/sXNxPYcwH60Ptx/3RWl4fVjXjz+TDaEsQF2yfZiiPq11W/A1r+TTPFiP/EkDfElhS/o7Iez/pug4yLYdPJnY4rNDmEL420oQ3gh8f1vyZ9CxfnffRWKPqWzXvmgqwn5hXQeiNNjIMYFWfZn87R+sG28bynrwOjhu+KA+aHX6l16xb0D0yY6jNcde6ouPL9bp2eZmFQxPbr0Fg+8wnY3m5Bv5XQ2cD4ROf8iAiyvzwo7tW8ITp46Hh44eDee+OCV2PmID5zP7RQzUIufzTXJbDvMvz0/eHpU6RP0HDImn69esiKeSp6cvvhiVPX8ufObZ58M5c7J/VE2VF51Pk8p1PuPeCB/D68z4kSicCF7FxRTg1+tih2X8ZkSW5TcsIxaG987/NMnDs6F7lsY//KzYZbDIeevN/aI1fkCJDlne0sOv7iGkQ3hlWH6Hg49gPr1kU80PNZGPjjKeHzAzDEYvThYiyY9t8RsbdOpSNyQ/UMUPRC9+OLnVsuSTvfHbfnjdOf46wvAF4fgXP4o7VXxlAcdiyttbwoserH6BOvjRy2Fw55J0KfkjbBD+yqr8gTj8GXIIv/95f9vhcOSU2j9RMHPVV+HN05OvG+BPcscf+7x3WfznwuGUsD0QOvHgqhfrvot30UNJh3nt5DXxD1ZvnJr8Pkr+JPjY2dU/AIg/h/4gXse+OrndEz/vi6JjaNCTUWQcXRsQjmU8Tb9CER8/OZ6Dkq9nvB9tKxTcmvzO6ubpyXp/lE7vXfBpPL042r6LHir21e5GhMgHDmjK88mXDCzHc9rzB+aoC1u8KppUpvMZMjt8Nxq5T35rU9zxoYNFB1N5FVv/0ThEOHAusowfdKYvHNwTOZ+KA7tvWdyhVuyy6Ff7DTc4O62n3vgi/hErnM+z0Tai80b0s/C9nfG3zCBxFPPX7ohH5tr5xB/1jPYRim8ppp0nVOks73kr3BtFNPF85IjjafqnDlD3Z+mHMGet2h6+8/n+OCrBVxfkoz7BzS9XPooK1Tif6FhovZ3+Lgf7PfTp1ZWIAcLHP+MfYzqfFZrz7vb4B7WyjZc+uiKctmJreEnkFPF3fvTHhfBJoVtnVz/jj8/tyNe9hz+3Nnwmcs74G0GQRCnXqT9UB+cTT6+eE+46nNR89dPVP3aH47cDjlZ9dgfHKHbG6eeMMAiBPhTnkzr1NZsPxX/VFccK51P+zHlwr/+7c63QS5/tSL54MPDp8Jhx+y15s+1cePasDHHCaF4f1Wyd5w8/u1Z0Pk3K63zgTK5bEAY3vhw+Csch6dFIFZ1wPK+czxzU4zofTG97I4l8blgUPvpyVE8UvRR2PhFacQccRSeDJ66Kb+k89urn4bKow0bHOyuKACCMrPE3e+K/zBnN44Oe8R9IS0fo+AYabg0hCoKCG15K/uQwvsA9/s3KbSvM3zHvk/Dl978JX/0g+QN2iFwg+bDp028mdaBbmhDtG94MXL5hb+SATked7rT48zuIrHYcSv5mDyRvCUKu84FzRYSy42DUgUdOT3db89ZEx3fw7Pgvo+JrC9eknf74OR/Gn8F5d9OB8LLI8cgXG8RJ4Tt9iCJwvFB+5Rf743QIkQtuf72wbGvlz4BjG9C1wpkHDzjbJw5k0OxwTTQogcPAH5PDnya/G+c4jTLhcGTb4XjwRYfgllfitKsjp3oq2pjnlm4Ohz37bvzHARGdIVKEUPYEzt3ItK57m/v7TUUUDMef1cYtuMfit9m088EfpcYtrPPnzsVTcA7fh4umo0ddk1QQCcvQwnlzw9Ono+g3Ammnz0TnPprOmPpseOoU0pP7lUj7Zse28J116xK707gKk/Sjx46Ew2+4OZ4/cSxxxrfcNDxqU4PCUyeOhSfSurdtxp+iT7Zp/SefhtOemxjPJ/X0S+fPhes++KiSTrVOPKJNqswzn/lr8CDUzhuM2y54HRu35Yz8Rnnjw9pvs1FUOxRc8WjqgKp/5yc8kjpE1XFXO3c4n6HxPCTpC+ch2q8+5zlz+kQ8nT71mUoatOHzdPCTpkm6zC96BQO8ZB7OR+bhfGTedT4yv2N7UrcIaVTrxaPapFr6wsH1L9npzRBFJBTVCQVDHgtHzcVnpM6ERz8dkCRGks5bOneZ9zkfkbaF89FC2vEowln0Bv5KcLUbc+dPRDba+ZyMnI/kbd3yZfjeu8vCIcOuD9/76JOKDV6dkchJbKnWi0e1SbXU+bQJiupGnTuXPKfT2rOv+lXv/QeTt/60zlduaNaW1bdoT59LbuGJDh6s3mqlWic6H4qiKKrjovOhKIqiOi46H4qiKKrjovOhKIqiOi46H4qiKIqiKIqiul4MfCiKoiiKoiiK6nox8KEoiqIoiqIoquvFwIeiKIqiKIqiqK4XAx+KoiiKoiiKorpeDHwoiqIoiqIoiup6MfChKIqiKIqiKKrrxcCHoiiKoiiKoqiuV03gc+TgznDvzg2EEEIIIYQQ0uf5/+/+5zTSSQOf9W//RThnYkAIIYQQQgghXcfrM0ckgc/yhX9sGhBCCCGEEEJIN8DAhxBCCCGEENL1xIHP5k/fNDMJIYQQQgghpK/zd28+UvtxA4qiKIqiKIqiqG4UAx+KoiiKoiiKorpeDHwoiqIoiqIoiup6MfChKIqiKIqiKKrrVQl8fvlaQAghhBBCCCFdB8TAhxBCCCGEENLVQAx8CCGEEEIIIV0NxMCHEEIIIYQQ0tVADHwIIYQQQgghXQ3EwIcQQgghhBDS1UAMfAghhBBCCCFdDcTAhxBCCCGEENLVQAx8CCGEEEIIIV0NxMCHEEIIIYQQ0tVADHwIIYQQQgghXQ3EwIcQQgghhBDS1UAMfAghhBBCCCFdDcTAhxBCCCGEENLVQAx8CCGEEEIIIV0NxMCHEEIIIYQQ0tVADHzaxA++f0849NsTCnPXT34cLvibi826hLJ1Cg89OMqsD1j2wLIFvm2Y84vLTHsB+TePusMs6/Kzad826yiDVW8ZpJ6/eaWfmQ9mzhpQs05hwp/dYNoDyx6MH/uHpr1m4sRrzbKCVQYUtcvimb8YWlePYNmDonZFKVIfrk/XTsB1ZZXRNlZ+I+g6i2DVAVw7X/vDderaCpY9kPw//PE4M78s6MPy1lkUqUdoR99nkdUO8/o4i2badTvOi69vQrpet0VWvyb8ZNxos6ymHddwGax68sjq/9pxbVr2ZWimvhHfuy9zfwWrLLBsNfCbVjkNxgpWWWDZN4JVN2j3dZ5H2T6LlANi4NMmGr3owbCrHmp5nYJbp2UDXDvBtw2+QQH2xbIvQlbnl4dVXxmK1vfE48Nr7HwDnT/4d3fX2AEMSCzbIrh1AcsOFLUrgnVtWnagqF1RytTn2oJuDnyAzxlbtkDyuznw0bh1uhQZkFnlXFrVrntD4FNkEOgD22/V2Y5ruAxWPUWx+r9O+uWitLK+51/4/br6suq0bIFlWwTXx1o2jaDr7PR1XhSrXtIcEAOfNuG76HEnBQMwNLQ8x1a0TivowF0by9YdgFs2QNtoim7DLxb0N+2AG9Bk2WI/tG1RrLqAZVsUOGWrzh//+z+J833H3HIclp2g77b9p0dGmDZA1wcsG9ConS9ovf9Pb6yxs2yAtiljVxSrLs2kSd+tse/2wEdwy1g2QPI7GfhIflna0fe5WGUsrLKCZS+Ubdc9HfhYdgLs8YYCntJgYGrZCG697biGy2DVA1y7ov1fb/DLeVh1AfSJAPv0/aEPmjZAfFyROl07X78L9Fhg+ozf8x5zPQ6w8huhSH3tuM5b2WeR8kAMfNpEmYt+9s8vN22BtitTJwZ9lm2nAh/LBmibdmKtG1i2ZcgK0iysOnx3hPG0yLIH6Bj/aMwfxU7EygdWnaBRO99rftgObWfZAG1Txq4oVl0u+g5tkcDHh1UOWLaNYNUNXDtf+3NBnyJlrHyg67XwDZLdgNLCKgcs2yK0o+/TWPY4htZATAcSmna1a5dmzouvLNK1nW9fXDuNb2CIc6ftOnkNW1j1ANeuaP/XjmvTsgHapgxWXaCMrfsqvmUDtE0ZOwHHHUEYrjXMWzYW1jqAZSv0xHXeqj6LNAbEwKdNlLnogWULtE1Rh+FDDwIFyw64dkKR/fIFctaAwbJzccsUwaqnKFmdnmCVc7HKAcsWWLZlsOoERe2KUrS+onZFsM6JZecDDvRCCnyA3E218oBbtwuOuVWumcCnCNa5bkffJ1ivp2LgJfluHrD6ccsOuHbN0sx58ZV1j7llA7SNhVUGaJtOXsMWVj1lcOvrpF8ugtV+LDvg2gHfINz135YN0Da+PrcdA3prPcCyFSx7YNlqrDJA27SzzyKNAzHwaRO+i77TgQ/uKLbiKYFQZL98nV0rAh8rX8C2lanXh+U4NNhXq5yLVRZYtsCyLYNVJyhql4ceDBapr6hdEYo6czhtvDJh5eF1DSvdrdfCKgcs20aw6gauna/9ZdXhw63bxTdI7iuBT17fB3y/69E29959c64NsGyAa9cszZwXX1n3mFs2QNtYWGWAtunkNWxh1VMEX//XjmvTKlMUq/1YdsC1A3gl07JtJPDxtS/3qVkrsNYDLFvBsgeWrcYqA7RNu/os0hwQA5824bvo2xH4SJ2+R/PA97qFZQssW2DZgkbtXPLKWPkCjk+ena6rEcq+d49BuFsHOn3LNqvDQ4codnDA1ld3dF2aRu2KYtUFGrUrilWXHvxZ+Ra6Th9WOWDZNoJVN3DtfH2A5Gf9dsRF12vRzADbKgcs2yK0o+8Dln0ZdF3tatcunQh88KTFssval6zXgLVdJ69hC6seYNkWoTf45TysuoBl6zv37vVl2QBtU8ZOo+3w29q8L94CXUZj2Qo9cZ0322eR5oAY+LSJvIte8N1NBO6P4ovWadkI2g7A2Vp2AA1T22on7aLtAD7RatkBKxgA1isnQNugQ/Kh67XqAbquslj1ARk0WL8HANZjfcsOyKsdQlbniH3WtpYN0DZl7Ipi1QUatSuKVZfrnIsEqtreh1UOWLaNYNUNXLu8QaNg2bi4ZVz6UuAjWDaCtsuzLUrRu+DNtGuXTgQ+wLID1pMP3wASuLadvIYtrHqAZVuETl6brl1RrLqAtsn6rTHQtsCyAa7d5Oe+Y9oB/TsugE85W3ZA21lYZYBlq7HKgHZd581cF6R5IAY+bcJ30RfFDTqy6nQbErDsBNc2K6DJI+s91CznXgar7jysesqgBzQ4vpYNwN0fvV7fl9+AtgNZnWge1t8QsuxAo3ZFseoCRe2KUmSQaQ3+8j5I4dpbWOWAZdsIVt3AtSs6aAR57doqo2lH4FMU91y3uu/z3WTJurPsu0nkbkOr27VLpwIfgJs2ln1RrDo7eQ1bWPUAy7YI7fDLVn4ZigbkRfC9lmbZAsu2mbFA0d+6WGWBZevSyeu82fEaaQ6IgU+b8F30WeT94aoyDQn4nkAAyz5r0O5S9lGs7zUQC9/fciiDVW8ZxHFkPTFw1ylk/T0Qa2CDAYtla5E1MLPsQaN2RbHqAkXtitJo4CNY9sCydbHKAcu2Eay6gWtXZtAI8NTYsgeWvaavBj4gq+/ztU+sw6pLY5UDlm2r2rVLJwMfIes6csGralYdQievYQurHmDZFqEdftnKK0OzgQ8CTvemnotVDli2mqy3TDR4G8Yq78OqA1i2PjpxnbdqvEYaA2LgQwghhBBCCOlqIAY+hBBCCCGEkK4GYuBDCCGEEEII6WogBj6EEEIIIYSQrgZi4EMIIYQQQgjpaiAGPoQQQgghhJCuBmLgQwghhBBCCOlqIAY+hBBCCCGEkK4GYuBDCCGEEEII6WogBj6EEEIIIYSQrgZi4EM6wlfb/1f429/+to5fLRlk2jfLa3/7u+HXO78y89xtWP4/flyTj7KujYC8bds+rUvX5Zuht9a9/h/+sq7slq0fmLb/tGFhna1l181047HOalN9Gff4tfIYtrPuvgj62maOh1s2i3f+5+1mHb0Bd1vRX2Tluz6qVfzDP06tWzdopx8ipCeBGPiQtvN3ax+p60Q1VplGKOpUXRvXqbj5GOzBQfzzlyvi/HY6hd5atzUYF1xbBj7dc6ybHaj2Bdz9a+U+trPuvkgnAx9h1bv3mHX1JO42or/Iym9l4OPW7a4btNMPEdKTQAx8SNtxO1DcvXbTrHJ4GoRgQ4N0PTBEQII05G3+P+tq6gRSbsNnv6zUK2nC0uXXV9I3/e/V3jpWrBwb26EuSROkbg3ukP/jx//FrA95VpmidfsGxzi2CDStMu0ajAM9uCgzGF+95sG66+Ff/mWz924tzlXW8fFdM4Kbh/Uv/tXvxOvEuvFk8s2l36kpc6EeaxyfvDYlNwQ0Vj0aHG+dv+y//0Gdjc4HVjuStm/h1pd1nt16QVZdAMdd2/jw1Y1txw0VLGObpA/y0cg1KCDfPe/g1xt/VXcuXMpeMxpcH1IGxwxtsx2BTxkb9zwWaf/g43/6r3V1FrkGsL9ynlH336//z3G6W5cbfLjbaV0fZf0L1o081x7bJ+sR26J+CDTbNsWvYirHCuCYu2UJaRaIgQ9pK7ozBHDgVrrlROCAXDsXcQhWnkY7FjdP7qa56S5wkrDLG9RiQOHm+0BHr8vm1e3mZVG27izyBuMAgwfY5g3GMdBw87PQg7O8gZN1zeh8N8+ijK3QjcfaytdIm3LT9aBLD341km+Vl9dfrcGmD3egZdm4ZNki3Vq/7HNR3PJ5uANcy8aHew1aNtb1AvR5b+aa8a03C102j6zy2A7rlWoEblnlXcTWugnmAzcIpBxAYGjZ+XCvKzdfP/Fp1L/4zr1GbPP6r3a3TU1vfmWR9D0gBj6kbVhOSPJw19rNc++0+QIfbaMpejfRtdFOJW/gDLKcguUsdVkAZwQ7DHD1ukGew8nCuvOm85up2xqM+85P3mDczZOAUrAGDZLXjsBH7sKW4UI41qDRNuVLFz7/Ymmc79Yvd3nz9gu4+XqA5eYB33m2bF0k0CyLVZcOFKwnarp8FnnXoJsHEIjmDSTdMmWumbz9cfPc/Dys8lmgn80rb10XeBLh2rk2br5c01YeghXJQ3Dr5qO/kHyrvPiJZv0LcMu76wZZ/Vc72qZuE1a+5BHSLBADH9IW1v79k2YHloeuwxrsZTntRgdp2jk0G/i46ZZTySKrbgAn7eYLcHhumi6bV3cWvsG4L89F6rHOURHkHOWdY2tQofPdPDdfc6Efa18dkqdxB8NuO8LxwittOg1PhvQykPrcdNxE0euzbECRPBfL1sLahjysenS+9XRFzjVo5hoEViBikdW+iiDl3XS3/yt6Pflwy7arvGVXBJQtso9uvnuc3Pyix7cIRerI6r/c9Fa3zbx8QpoBYuBDWo7lzMsg9VhBiB4UuOQNegXXRpwKsNapy4Isp2C92qPvZgE3HwMYySvjcHQeyLt7n1V3HlmDccHN12TZ+e7EW+A1KLd81p1WoMvn5Re1uxCONSjapkDWIFtsrOMi6Lpwh9jN1+3Iug5ws0Xy3TwgeS6WLe5q+/Lc8llY5REASn5W8OKm6zyQdw1qdB8H3CBUl3XTy1wz1mtY+tVHNw/o8nl0qnyRpxpZuGVx3Uue9baDzrfKy/lr1r9Y+VbgktV/tbtt5uUT0gwQAx/Scsp0XNYrBTLoKBv4ANde0I7FzWtl4APwI00334fr8LLqtpxxHv9t2fcK1Z1HkcE4kB8Ju7h2lo0P94e6lo2QdxfczXPzBR7r/PLuteuzdV+Vsmz0sROsoMuHW96y0fmaPNu8/Czccr5zJuiyzV6DVr4P+YF5I2Xda8Z6xTkLXTaPTpbP+xqpxm0LeTf/3H7KLa/zgPZRzfgXkPUbIbHJ67/a2Tbz8glpBoiBD+k64HQweMSTAEzdu52dBIMC3DHFdiDIa8X24I4a6pT68oLB3g7uBurj4w6UfWAAgDugKCM/iG81PNYJvaFNYZ242wz0HeS+Co4jrt+8zy234hrEeUZZ1IHjh/mibabRawbbCXv8bssNrPoaGLzLvuC3VZjXT+6ywPGTdmPlN0Mz/gVPDGGPABtBnnXzoSjd1jZJ9wIx8CGEEEIIIYR0NRADH9JSrMfUhBBCCCGNYo03CCkLxMCHEEIIIYQQ0tVADHwIIYQQQgghXQ3EwIcQQgghhBDS1UAMfAghhBBCCCFdDcTAhxBCCCGEENLVQAx8CCGEEEIIIV0NxMCHEEIIIYQQ0tVADHwIIYQQQgghXQ3EwIcQQgghhBDS1UAMfAghhBBCCCFdDcTAhxBCCCGEENLVQAx8CCGEEEIIIV0NxMCHEEIIIYQQ0tVADHwIIYQQQgghXQ3EwIcQQhz+zaVB+Nf/NZn/kzuC8NEn6m1AENSy8GXbrhluG5/UbeWBl36Z5D8xsVh6WWTfhCuvsu00f/lXtWX69bPt+hLf+layL1ZeEfLKL1pce8zAyFG2LShyfvOuHUIIuZCAGPgQQoji6v8rGbjfeXdturuM4MgaVMqAFPZ//lgyP3dBdeCL4AhTBANi+1fTqoNUdwCsB6+yTrHRdWiwvr+ek8y3IvC55JJkXvYHdd7wg+r63HW8OLeaBxAsyfZg/6WszGM/JB9BpgROctwkcEIa1qWPPabYPus4aHznwD3eWduBeTkfqMNniymuI8nX5QU5poJsB9L1NvnWIely7PVxknkGPoQQUgVi4EMIIQoMKN2BPAbaehnIIFSDwW6ZwMetB3Yy6JW7/UUDH2yz1IVASqfLNjcC6nBBugQvrj2Q7bz3viC87vvJvA7ugAzcAcroYyV2KIM8zH93WDVYwLIOEPRxwHHXQYEOFqxz4B5v33ZI2UfSfNj7bDHV24dtkvKy/XL+BbF1AyLfOtzza9Uv9ro+Qgi5UIEY+BBCSAfQA1NBBz1FAhQJKAT3KVQ3ovfXyi+DdQ4IIYRcGEAMfAghhBBCCCFdDcTAhxBCCCGEENLVQAx8CCGEEEIIIV0NxMCHEEIIIYQQ0tVADHwIIYQQQgghXQ3EwIcQQgghhBDS1UAMfAghhBBCCCFdDcTAhxBCCCGEENLVQAx8CCGEEEIIIV0NxMCHEEIIIYQQ0tVADHwIIYQQQgghXQ3EwIcQQgghhBDS1UAMfAghhBBCCCFdDcTAhxBCCCGEENLVQJXAh6IoiqIoiqIoqlvFwIeiKIqiKIqiqK4XAx+KoiiKoiiKorpelcDn9ZkjwjkTA0IIIYQQQgjpGhZOHhDHO3Hgs3zhH5tGhBBCCCGEENINMPAhhBBCCCGEdD0MfAghhBBCCCFdT+U3PlYmIYQQQgghhPR1/t+z/0818KEoiqIoiqIoiupWMfChKIqiKIqiKKrrxcCHoiiKoiiKoqiuV03gM2fOHEIIIYQQQgjp87iqftwgzXQLEEIIIYQQQkhfQ2IbEQMfQgghhBBCSNchsY3IG/gMHz6cEEIIIYQQQvoMEsvo2EbEwIcQQgghhBDSFUgso2MbEQMfQgghhBBCSFcgsYyObUQMfAghhBBCCCFdgcQyOrYRtTzwee2B3yvEoj8dFD4+5uqYkdd/36yrDNY6BMs+C6sOTdb2WvYaq0wWVh2CZZ/F5D++yqxnwuihpr1glQE//MF1pr1vPT6m/MmVpa+BrHVY9oJlD3z74sOqQ7Dss7DqANhHy74RZvzk9811WEi7tOrJY9yNw8w6wc/vGmKW0VjlGuX+H1/TtnVY9QLLFpS9vtoB2tmmpy8Nw5lRt59y4IWLW7p96Et+/dRl4dfP/k5lHVjnyj+/Ir42rDJZuMdRaGZ7s9qCZV8Uq76yWPXmAV+KY6yPuRx31Dlm5PfMcj7cbWoEq95WIvss+3rkZ/86XsY1btlnUdZfubSq7Vh1W6AfLTtuuhD8pUujY8z5/2GwWZ/GKgcsWw3qtsplYdUjWPZlQf+A/lm3J4B+vOgYBNektX0gb3wJrHKCZV8EiWV0bCOK9jCRzgRWRUXQB65RfAMWH+sfv9ysR2OV82GVd7HKnZj6r0xbjVXOR6v3a82jv2vWAeA4rDLAsge+gUzWeoqSN0jOWodlL1j2oMygrFPXG/bRsm8Et1MrS9EOyCqryesErTKNggFuu9Zh1QssW9DIoL9ZcM6sbckD14pVnwUco1VHEYoMyK1yoJnjmdUWLPuiWPWVxarXZc9zl5hli4DBoFWnxipXFqveZijiWy2KXMvN+qtWtW2r7jJYdQoXgr8sQ9YYEzeDrDIaX+Bn2WpQt1XOR6uPnWDVU4SsgPenY79jlgEfP/lvzTI4D5Y9QOBllSmKxDI6thFFa0ikM4FVURGsHWiEIh00uOvm75rlXcocRKu8i3UBWHYubhkf7divIh28Vc6yA77Or1lHImQNtnuqI+/k9YZ9tOwbodnAB+QNIqwyFlZZwbJvlAs18EFAYW1DWay6BdxBtco0glW/YNmDZo5nXw18cFfWKtMIVv2CZV8Wq95GKDIILUKjvqQIrWrbVt1lseoFF4K/LItvjFn0mnPLFQnOywQ+7Th2jd5A0ODJslW3YJUBZ6ZfVGO3/OFvmXag7FNqC4lldGwjitaSSGcCq6IiWDsByg5C3IPkwyqLx91WepFHbsAq675GAHQZbK/O8zUeXSYLq2yz+1W0g3c7BMsG+Dq/sp2sdWwBGoZlD3qqI7fKtuN6A9hHy74Rygz2sN2WHXBtBd+dKbddCFYdWWQ9vbDss7DqAK0YwFj1glYNjopgrR/gSYFlj5s4lj2w7IFlC7KcomUvWPbAsgXNHM9OBz6tOPdZgwTLXtB22O8ir5vqMppOXsMgawDqu/OM/bPsge81o0Z9Saux1g+s427ZgVbvo2UPil4LVtl2+ctWjTGt6846fvoYWDeCrDJlAh+3LGjm2DXSnnxPl3ETxrIXstaFfLxhZOUBt65GkVhGxzaiaE2JdCawKiqCtSPAuijx+MuyBUVed7PKAeT5BtNuHRa+cm6aXPjWRZ9VTx5WOYC8ZvYrq/OzkHJWHvB1fkU7Wbx762sgWa/egbL7kkeRjtwqB5DX6usNYB8t+0YoM9jLuivk2gLfnSnpTK28MnepQCcCn6JkXSuWPShyfbUC33UILPtGyHrVyrLXWGVA2UFLM8eztz7xaeS6asWdURdrPWVo1bVu1Q18gzShbJCY5UvkNyI+rPoaxVo/cI8nli074Bs3XQj+slVjTF/gY/UbUsa68W0dc6TrdflwywnIa/TYWWUA/Ldl3yxZN1Atyo4J8pBYBkhsI4rWmEhnAquiIlg7VBarXhffHWZtY+UDbWPhK+O78N00XJhZ9WTRzv3ydX5Zg8qsV1p8nV8znexvnulf6IeIne7IO329AeyjZd8IWYO9olj1AstWP13wvaJT9A4fYOBTDGvdAOffsm8Eq35Q5Hq1ygGf07NsQTPHs68FPlmvvFj2lp2FVRZYtmVoxbVedp81WL9VDlj2zfgSq75Gseovg4w7LC4Ef1kGq17BF/ggz01H8GQ9MYetdcyLBD7tOHZl20Qrsdbp0o4bOBLL6NhGFK01kc4EVkVFsHaqCFmvNbn4fhDlOl7cAbDs8gYBVhlfHu76uGlF6rFo9375Oj95/znr8aOFr/PzrQdgG4W89WW97pi1jkbI6sh74noDbv3NgG2w1lGErGNj2YNG7Xx0IvBBH4T15JF1x9mqF+QNFFqFtW6QNSgqi++JoO8HrBqrHPC9gmXZgmaOZ1ZbsOyLYtUH2nldlbF1scoCyxa0Yj/KYG0DsGw1lk8WLPtmfIlVX6NY9Rchz7+AC8Ff5lF0jJkV+GQ9QRKkr7WOeV7g085jZ9kDy7bVWMdUsOxbgcQyOrYRRWtOpDOBVVERrB0DOFFik/UjzbxXnIBVrixZj6kte8nLu/D1AMPKlzwLy74sWfvl6/zgrMQmy2m4+Do/33qAZQ8sW6C3TdPIOoBlDxoZ3Jeh7PUG3M6uGdox2CviBIpg1e2C68AqCyz7LKw6QCuCE6te0Iq6i5D1lTXLXrDsEeBYd+Kyfkfh2moa+e2YZQuaOZ6dDnzaeV0VGfRa5YBlCyxb0KlrWLC2AeS9EmOVAb7fuDXqS1qNtX7QiuN+IfjLVo0xswIf4Oa5iJ11zPMCH9e+EXzHzrIFeMJk2QPfh3Kyyli0q8/NQmIZHduIojUn0pnAqqgI1o4BfVGCvM+galuNZdsoVv0gz9bKF/LsdL7Gsm0Uq37g6/ys4MKyc/F1fmU6WbzWlvXkp6cDH8u2Uaz6gWULdGfbLK3ueHx3phqhyOCtE4EPfjgKp1EE311tq94ytGKQg5svVt0AbU2/053VjrKcdNbvfACOkdj6fpAr+No4sOzLYB3PrLZQFLdOYNmBVlxXwKpbsH7UnnfcXXvBsgWt2o8yWNshYHAr60F/lHXdA7duoVFf0mqs9YNW9AkXgr9s1RgzL/DJuuGnfZl1zLP6VNe2Gaz6gWUr6L/Zk/f7HF1nERj4KPIGT66974LDnUnXVlP2oOfZ+ToRdwBn2eh8oVP75dtu38ADv7ex7AVf55fVyZYh685M1jose8GyB9a+9OT1BrCPln0jtLrjseoBeb/PssoADJYse6ETgU8ZfNtr2ZahFYMcgHqs+ouy5D8ONOvVZP0OoyiNXi9FsY5nVlsoilsnsOzKktcOfF9JLEvW+bXsy5K3H2Vodp/hx6x6hUZ9Saux1g9a0SdcCP6yFWNMkBf4ADdf0DbWMfcFPp06diDrRnMRrDrzaPX4owgSy+jYRhStOZHOBFZFRbB2DFgXJfA9ShPELusC1vX5sMoBnJAitu2y6eR++Tq/rDuuWefH1yFndbJFKDLoandH3tPXG3A722ZoZcdj1QGsfXPBe9ZWWWDZCwx8GqeMo2v0Cz95N0k0Rdq3YJUvg3U8s9pCUdw6gWVXlqIBA+zKBAR4GlLkozHAKl+WVgY+QpmvROGaL/qD6UZ9Saux1g9a0SdcCP6y2TGmUCTwsY6nu62WjRX4dPLYabL+4KhLs19cy+pzLftWILEMkNhGFK05kc4EVkWEEEIIIYQQ0luRWEbHNiIGPoQQQgghhJCuQGIZHduIGPgQQgghhBBCugKJZXRsI2LgQwghhBBCCOkKJJbRsY2IgQ8hhBBCCCGkK5BYRsc2IgY+hBBCCCGEkK5AYhkd24gY+BBCCCGEEEK6AolldGwjYuBDCCGEEEII6QokltGxjYiBDyGEEEIIIaQrkFhGxzYiBj6EEEIIIYSQrkBiGR3biBj4EEIIIYQQQroCiWV0bCNi4JPB/fffHx+TLH399ddmWcGSZXchMmbMmPDMmTNmXm/HJ8sWHDhwILWo1bhx40z7ZrE0Y8YM0/ZCxFKR49OXr1kfJ06cSI9AvV577TWzTBE+/vjjtBa/8vrPVvD444+na6uVZUsuTCxZdj42bdqUlmpOa9asMevvSSxZdn0NS/SR3YPEMjq2ETHwMWhU8+fPL1SXa3Oh8MMf/tAcZFm2vR2fLFvAwKd3Yck6Pt10zVrkySqTR6Oy6moFDHxIHpYsOx8MfPoelugjuweJZXRsI2Lgo/A5SEjfmZwwYUJ819fSkSNHauq0pPMvJHD32JJl29vxybIFnQ58SGvopmvWZdGiRene1MqyLYql5cuXF7KDXDtCOoEly84HA5++hyUGPt2DxDI6thEx8FH4ZNmCkSNHxneDMTjCvGVjyZcuWrlyZV09AEFVGd111111dViyXulzX0FBsFdUuhzwDR4t6XJFXjXUcoNOAEfiqhWDWZ8sW1A08LHkSxdZ14sl3albwtMNXYfPDteCtvHdBHD1m9/8pqacYCnvmly/fn2ami/LmVnSdmWvWd/AR+oT0E9Ycu2KULQ/cK+PovvWyCAgT0uWLDGvsyzwJL2orG0u+8SnHdezL13k6/NBGVnlhWb3qyjt8lO+dJHvGOL11CwhMLdk1VUWX1sr0rbKXPfWMRSabT+WfOmioj6pkXqERvs/wZLvvDS7LpDV/pBn9VMYN0j5nvAxfRmJZXRsI2Lgk+Jzjnv27DHti9Ko3NfmLKExolNHPgbQltyGXFRi72tERSR1NBL4+Aa2uEstNr7fEEg+sAIfS3Ici+KTZQuaCXyKqMj1oq8FX1Cp67COnb577+vI0ZaQ//Of/zxNqRUGv1IHKKose10fAiRX2FZtY0kfn0auWet4FNlX300TH2UHlqKf/vSncfl2Bj4gy8H7ZA3cfH1aEel6igY+nb6eXbltOOu3V1lyn7C1ar+KYKmdfsqVewyL/M7MJ11PozQS+PgCtV//+tdxXwF814aup1Xtp1EV8UlF5NbTbP8nWHLPSzvXVUQ68AGd8jHdgMQyOrYRMfBJ8Q0E0dlY9kWx5D5NseRe8FngDrxvYO3W45O20VhyOyKfnXvn0OcEtE1RfHfpIG3nC3ysfSiDT5YtaCbwaeR6seR26tb5QJqvDr0duLNlSfI1lvLyIW2jyRtY42YF9iMrmLVkDUbKXrOWJM8agOtAvghF704XCWxb2R59lB146rKWrHaLoEkG8RZFAp92X89l23CZ7UHg4vYjQiv3q1ma9VOt6genTJlSY1PkBlqj+NpYVuBjCe3ZtbOeACAgknxLjbQfS606F2XraWX/Z0mfl1aty3fN6zqAJfcY+uwkrxU+pluQWEbHNiIGPgqfLFtBC08p3Nc4LLkDMqthuBd8o3dv3Hos6cGuiyXLzveERtuUHWgh6GxEug5f4KNtGsEnyxb4VMSukevFkuVsESS48pXX5Rq5q6+lBx+Wsq5J4HM2PrmvIFhqReADXOFYWel6kFIUS2VeudID5Eb2rQxwwr4BlXUNQ3kDN11HUYoEPu2+nsu2YSsfA11dRxFauV9FaKefalU/qPPL2pXF18bKBj5llFWPXk9RLLXqXJStx1Kj/Z8lfV4sNbIuBHeurDeJrLbqHkPBVSt9TLcgsYyObUQMfBSTJ0+Oj4El91UMPF70SdtZ0vkgr7EXvWtnNTC34Vgqe7cHd8iK2LmPYMsMtCzh/BSx0/ntCnx8rxpA7mPlrMfl2g5Ycm3yrhdgyedsi8gt08ofxlvKuibz8LVj7WQttSrwwXpcWW3TKpsHBqKWXLsi56fVgY9vne6rV4Il3EDJykd71nUA3MV2pYOEIoFPu69n1yavDTfTV+Jak/bTyv3Ko91+SueDRvtB9yaIr3/WNo3SqsDH8n15WGqk/VjS5UGj50Lng7x6Wtn/WdLnpVXr8t2k0+vy3Th2j6HQTh/TLUgso2MbEQMfBwxaG5VE3RpLrk1eY/cN3hFYwMH5HA7kbpOlvEFmI7JeESxyN1BsLWFfsK3Aujsi0utsV+ADGn3/F7KuFWDJtcm7XoClss5WZNkD3xM5edXMd3xc52sp65r0Ce9VwyEUeUXBknV8ylyzGp/TFDXzzrU1UCkit55WBz6gmd9UWE6+Uek6cC1Z0jagndezzgdF2jDuLjci94ZTq/Yrj3b7KZ0PihzDZvy5rqdRGgl8gE9oX75BMlS0njzl1aHzQZFzYUnngyL1tKr/s+Sel1atq8zHobTcfde008d0AxLL6NhGxMAng6y7+lruHSSNJdemSGP3OW+R3KGxpOuxlDXI1BQZ6KOjt8oKeY1V2+atD4NSy9lqh+1zxno9zZLliFxZT8s0llybIteLpUacbZHO03eMXfmuDUt516TvLpsl93UKYMl3fMpcsxpfEJDXRoqCQC9PWa86+AZllm1Zsga5rjC4sOoQig5eMXi2rteigY/QjuvZtSnShgVfIO8q77pqdr+K0E4/pfNBmWNovdILScBlya2jEXxtLK8vBnhtvqis8kKz7ceSa1PkXFjS+aDMOW22/7PkOy/NrkuD9ox9wvhGj1Us+fZdaLeP6ctILKNjGxEDH0IIaQMYSLhy72wTQgjpXvAzCUuunSXfa8ICfYwfiWV0bCNi4EMIIS0gT3RIhBBy4WEFKHnCb3bcevJEH1NFYhkd24gY+BBCSAvwia8dEEIIwUcqsl7hx6trWX/g2Sf6mHokltGxjYiBDyGEEEIIIaQrkFhGxzYiBj6EEEIIIYSQrkBiGR3biLyBDyGEEEIIIYT0VSS2ETHwIYQQQgghhHQdEtuIKoGPyC1ACCGEEEIIIX0NV3WBD0VRFEVRFEVRVLeJgQ9FURRFURRFUV0vBj4URVEURVEURXW9GPhQFEVRFEVRFNX1YuBDURRFURRFUVTXi4EPRVEURVEURVFdLwY+FEVRFEVRFEV1vRj4UBRFURRFURTV9WLgQ1EURVEURVFU14uBD0VRFEVRFEVRXS8GPhRFURRFURRFdb0Y+FAURVEURVEU1fVi4ENRFEVRFEVRVNeLgQ9FURRFURRFUV0vBj4URVEURfURnU+njcutodkam9+ictLrw3zWcqOSenx1tWIdFNUTYuBD9bje+Xx/GNy+5MLhzrfSPacoiqK0jh9Znc6J1oYnXo+GKi9HLA7CcxHnI05FnN7xUGpTq+D2F8IguD1iXMRtDTA+PHS2fmh/NiIYfE+UL/ViHW7ZPMZEjA0HPLkgqTRDZyKCIQ/G9knZO9NpGcYmlWUouP25yO6WCOzXHWm5oqDM+GT+9qlpjRTVe8XAh+pxzV29Peo0p1xQPLjws3TvKYqiLkwt+HBbeMXTL6dLYXhgdRAe+fCKdCnS4ZlxoHPuFT8H1tYOY4JLEYxIoNAo48LDZ2oDnw27Dxp2zXJreDqt39WpiCRIgh0Ci0aDuHFJhYb2nz4b5Y927JtlbLj3NJ8HUb1XDHyoHteFGPiALfvg2nqPPtlxNJy2Ylt434IN4e1zPgonvLwxXLhuZ7jjkM81Xxh6b+vhcM9R3OulekLHo0M/993t4d3zPw3vW7ghnL9mR7j/+Lk0t706FY3fVm86mC5RrdTCz74Kg4GTwmD87Hj5xIfRcCQKZI4trw5L9i+zgx3NmTdgn/RRu4+djvrWZoOeBDfwCS5B4NFo8JHBfTPTNdRqyUb4xVasz//EJ7gxOv5R8GWXa4LhT6ZroKjeJwY+VI/rQg18gqvnpkegs0JgEwydGwYXRdvQKP2mhsGweeHAJ1eGR1T8du+CT8Pg0ul2GR/9Im57Pa2hXsG18+xyWQyaFX7w1ZG0hsYVPLi8Uueuw3jxpDm98v6uMLh8Ru22Nkt0fJ5648t0DbbGzvogDC6OzplV3gfOywPL0hr8evSVjWHQf5pdh8X1C+NXhny6adr6MLisgWN0w8K0htYofq1p4Myk7hGN1z17VTTAd7e1XYxalK61d+ujvUfCYMATUTt9JuqLng5P73y48mQnCWSOJobn3k1eb3OCHeFMxLHPrklsI20/eirqWxH44AlJ+vqV4rHFa8MPdh+K+dBDnL/nUFpjopNRDOR/MhIFJ1dNCN/75mBdvR/sPhy+HxFc5nslLip70zPpWmr15uc7knyz3B3hgg82hx98k7MvUf5H+/z9YLJP9ccppt/t4Tub90T1HK6tM+K9nQfC4ObJqS220anjknvTNVBU7xMDH6rHdcEGPhH3zPs0PQrt1TNLNtUGJFHgMn7OR+GREg9z1mw+GA2y59cOtCI2fnM8tbB15VOra8tE627mTYhvjpyNHGvtQPv5ZVvS3Nbp9jmf1KwjGJLcmW61bpnxfu16IrKCLDx9igNM5xiA0TM/SK3yFdz8cm356PpolYJrqoH1sg370tRsBXe/Vd2WYXPDfceyn+qs3XwoDK5QAVIUiLdSwXW11zqeNjWiwU+uqtZz37LwqwOnw8OnwvBYdIpdcN6Dy2pvHCxa/41pizpg/+xbUduWoPbqOelae68wDE+CnqeTwGf41fHvdc5G4InP2Vej6Z6JiXGsQ+GBlUF4/uXktz3gbGR3aAmGL+8lJql2H/c98YkG50MeTK3KKwl8fE9G/K+SiTbsiYIfXxBz09OpVa3e/HxnlG+X6f9Qa/qiIPhRXd0Jt4YHjN84aR3LOib9GfhQvVcMfKge14Uc+IAdB9v3KtnMldtqB8gDZzX95AKvHgUD0jvhEZ/nBD43Tn2vun5w+Yw0pzHBHbtPlT7ant4hbpFWfXEgeeKh1hFz55LUonV646PddevBwLaIpq/YWle26AB9yEQ1IAe3vprmNC/UJfUWCa5rgowoOCijYORLSbkbW/e0A0+6KtujWLflcGpRXMHY9FjctzRNyZb7RBABXp4Wf/BNYh8FQM217vYrGDEpDAYj6AF3httfj4KZ9AmOcCIOasorCXyMgTg+QjDkgdSqvLIDn9tSK78+aXHgc+lDs1Kr5pQEPtbTqLHhgZy3SY+cPx/ZMfCh+p4Y+FA9rpYHPhgUY/CA17Gs/N7GtfPTI9FaYSCoB1C3z/0ozWleW/edjAZZSUCV90rZj6Y7TzQGNe+03bviG3YeS3NaI3nV6olXv4hfzdLrmvRm9mtlZRV/1VDVD06W+AlLcM/S2vJRwFZE1z67prZcgdfaiko/vcnblRteWFezHbNWfZXmFFP8lARB6rWteeITn3NsS9R/vL1xX+2Ng2g9u0v+3gtPolDXN4eLlXMDn6KvbMp12uyNjXbqntejcx0HPGBi+OBj0RDECXrAGTz1+eaJtFRxMfApJwY+1IUoBj5Uj6upwOeiqeFL7+1Ka8rXiagzX7PpYBiMeTV5PcSqswyXRoOUoXPDSx9ZEb9O9vqHu8Opy7fEd62Dy2faZQzund/aV97c13TumPtJmtM6ye9fpr69NU2xhdev9LYEV76Y5jQu9zcgn+3KfupURpVjF+0fFD9hcgajeU+5ymjd1kM1dYMyzwCtV+WKaMTza2vKXP7YO2lO86o8Mek/LU3x6+KH367ZjuCq8tfHF7tPhB/taP6p37voG3DDJNqO1V8mHzWY/Nbm2u27otwTy0de+Tyc/s62dClfwRXVp6ng0x3Fgvpt+0/FT/v24x2kXijsRTDoySToGfxUFIjcEp6MAhw36BHi4Ccs9pqkiIFPOTHwoS5EMfChelwNBT5R4HLxg2+Hzy1t/rcdGGTi62XxgMNdTxRYBYNnh88v3RxujQYW8AUYVtw975MwuPFlu0yDlL2T7FPwQPUH+UK7hkJb9p0MD5zIrn3cix/Wbk8UKDarusCnRYFIcNsbSZ1RQKuFL7vVfBzgkqmlfh+VpU+jAXul3pS8pyRa+rUyMHbWh2lOtq53Ap8rn1qV5jSvix9Kg5kCrzXGwYbajpgo+Lhu8prUojM6Gp1PeZJ466za30rVPVUr+TpeGbmBz8YWBvU9qeCxhUnAEwc+E8MnJyW/67GCHuFI/Mpb8X6xtwY+n/bBwOdgzmE/knVMGPhQvVgMfKgeVyOBz4fp6x/4MW/ej6DLCr9hEa3HgBev7eA3LcZ21IA7xaMXhzdPfS98YdmWcMHaneFL63aFL67aHo7FUw98GMAqJ7TglTd8NMB9OvHIKxvT3J7RPfM/rdkePFFpVm7g04onMPrVL+t1oSdfT1+BElrw5AratOdEbb0RRTX06Xdryl2UPqUqoqudskOfeTfNaV6XTEgDn6jdFBGersiTljoQcN72evx6ZTsVXJN+PTBqw5bcVx4nRtdDO+QGPnia1dd1NOqig0HqgwZD7g4P5AQ9wsGVxYcpPRP4jA/3nM6OEj7ZFV3fZtkIT+DzhverbuPD4M5i7SpP/sDntvDDaJuztO/0mcjO8+lwftWN6sVi4EP1uBoJfOSrYPuPn49/sN+aZyW28Bpa/OTH2I5cokFb/4ffTmuqaumne806n3q9ud+PLPtsX82gCSwt+FWtdunhRRtrtykaDN/w/Lr4s8V4TWvMrA/ipxS3zf4wfjoUE81jGem4+47X5fBboRunrg9veOG9ui+aNRv4vPL+N9HAO6lr9ZcH0tR61f3ovQUfBMDHLWrqjMgSnrD1f2RF7ROogTNLfyRjwBPv1Kzzmijwa5X6T4i2D/WW/D3XqujYo4zerjpwnm5+JX4K1yoFt7+Z1B31JWnXYsrdtiVRO2613MDnyygw7uua8eFXUbuflAQ9eOozclB4PuM1Nw0+fHDk3WJDlZ564lMFNggmblFT+WS0J0jwPfHZiCc+hn0M6kNQJPULelm2YXRaY72yAp8q1XoS8EdVsZ+ez2ADPvGherEY+FA9rtKBj/O7gfg3O0NerAxYPt15NHxgwYbwR9PeDydEg+5303f1m1X8Kswtr9jblEcU5LiKP3NbZzcl3Ivbow0KP7zXgyawbEPrB2dl9OgrzpOSNtBM4HMwCiTk79A8sfjzNNWv+Mfqat3jX2zuoxF4YqnrK8TQufEnrZv5IXvldbSUa1r4xKffQ+nrloMb/+wuArlBT670PwkSrpoTbmjidbA4kE7r2nko+3ji1c6agDNi897WPonqxsAnGPF0FFSmT3sGPBFOnBQNPQo+8RGSP2ya3Tf2fOBTlrFRO7ZfMT14Br+hQZBhlSuD/3PbxQKfsowNL36gNU+kKKodYuBD9bhKBz7RYMDVWvyNmfgVr5wnMxhEjXsz3H6guR9o3DYr/d2KtQ6LsfV/oDMYNNu2Hdb4q2BvxU+SojoUeX/cst3Cj7trtmn4gjSncbmv8zXzGx8MzuN6oqC2iPDFNXf9+KhFo4q/SqbqAq7mrtlRGwBE80X/Po5P+o+zAgQZrVJw+5Kk3gY+VOATAsTLHl3hD4SGzC71NTwIHx2Q8q9+UOwcxn9DSa/3kmnh8RZ+SK3mbxNF9PXAB0/jg0Hpb3sQ+AycEK58MQpmSgY+4Hj8wQP/hyJqAx/9mlhvDHzGhi+9vyldg60N+485ZWSf7N/+2LQr8LGe+IwLx89s3WfxKaodYuBD9bgaedUNd+ldxXfO8Slrw97L1XPjJ0SN6r4FG5IBilW3cNPLqXVVeEJl2qbMWlnuk76iU3DQzqAcd6h7UhNedl51a8NvfBr9nHUwYkG1HvkMOu644zddA2clUywD5AnOXX/w0Y5inx12hY9muHVZ4/f491sSpAnGtVVUda/t3d26v1GE7YrrbNEnpl3h+OB3PzXbDy6bXvjDENvxiqGcRwRT8blPzz/Ofc35V+cepK9FVmhhgBfXr+ru64HPvjNnawOfQY+EXy6Khh5GYFOIKGA6tXlMWnutdp1A4IMABa+VCeny4Hb9AVPB8ypbhXRbrns03H2qXIQO+2D4hLQOF2tdAHkIkMamtdQrO/DJW4fUH+3TgAfCT/c11v9RVKfFwIfqcTUS+GQFBsGoRandC9HAJnIWV9wQDYhuieafSNKcumKiAcYlxm9xsrT7yNmwH+6aY2Bk1jk1XLFxf2qd6J55BX8vFNk0+spbcKf6C/gpDy/6LM3tvB586bPa7WnBX9h3/45PI58y1sdp8fvfRAHwsTiA+mzXsXDjN8fDz3cfj39YDjCPtI1RHmzwieErn1pdsw1gx8Hyt/5Rt1sPAlif8On0Gvto0P7h9vKDDveJT6s+1gChrrjOm4s9RWv0gcn8tTtr9yFiRsFPR8eBDcpEgTiuHzn/8bmPz7+c++r5x7Uh59995RF/qLRZxQGdE/hg/X1ZO/EUZqAOfB4Ot6cBjBnYFABfgzvxJoYvO5OVtFl5gU9flT/wyf+qG0X1VTHwoXpcjQQ+CAyspz6iY5vXhAdvCsKDo2w2Xh9EA4wb7bpvqf+qE/6S/hWPvpP7pEZ47q3NaclE8eey+5d8GnVN4wECvnanB0/goR4Kfuq+6jZ0TprTuOKnM6rOon/kUTRG/W0hfH2vUQU31f6RWLz2dKLkgAFBS00dEbjeshSXcT7wUPYPkOJrfzXlI5r5fZkofu00fR0N5z5P8nd8puT8PSif3D/U+8Ly/HrwuqXYH2zwJzo4R+45aPZ1wfipnvs0Mwq2+rJ2Hj8bBT74sEEa+Ax+JPzipSae+DicWIZhTPNfdcwSAx+K6h4x8KF6XKUCnyh42LrvVFoyX6cX/8gMfDR3XBkFQdaToIJBToUoGHvrk9oPCdw595MwuDgaHFn2BXipmUH50Lk1A6iYK+fEX8LrpPB1tpptaPK1IMQEbuCzflvxwGfuu9H1Jq8qlQwWLNW9flZy/6y/Y3O44CWuB/Ax0XEp8/u1+FUuXf6aFvyNJXmScvHUzCdXouDe9JW7yD4v4LMUfyZe7YP+HL2l+AMlqe2aKEhrRni6pNcNJjTx+fg4mHKubfl0f1/VvtMIfCaqJz4TwmUvti7wAeGr8vGD1n3pT+tCDHzy/oApRfVVMfChelyFAp8Bs+oG7PEnaC+ZVujLVudWPREevNEOfMCWG6Lg56LIMVvrzuO+ZTU/bj58Mto23IW2bMsSDXz2NHEXfvTM+r/qH3P5jPijA40Id6XxKeo4MERd495Ic2z9aIazDUMa/9IXFA8O8fRM1bmu4KeN3/hkT+VpRKte7cKXwPAHTfX24G8/FdUbH+6uLRtRJjh1/x4PuOGFdWlutuI/nmr8Xmn45LWpRXENeAxfYEvriNrl3oJ/XwvXT2XdA2eGh0o8gQnGq7IReCUtS3pd+AJiK3TVpPpXHpd/1tiHJ/YdP1f5wqCAwLgvC11jMPjJauAzYFJ492P5f7y0UU7jAwgnW/d7Nai7Ax/rQwkMfKjuFQMfqseVGfhcNDVc9UXt31VJ/q5Ord31zxUcqB3fFR592A5+Zg/Dk5/aer2MWhR/2lZrIF6Fw6Dasm+GFnwM4InFX9QPzjV4ZSdaDwaGl0f7MeSpVcnfihn7enJH3RmMYbA8bs5Hlb+nZAm/3315/a7q7z0Uw55dU3f8iij+Q5fYJqe+YPj88M0oqPEJ2xL/IVpVBsFbK4Svq7l36WOi4/nmR/5twqGbveqr+idGIDoPZQa88df8JKAT4r9383L4wvItub+hwd8jqimrwQ/7o3qGTFwV3jrrw3Ds7I+SP/aKjwtg250f+j+/rPY1zzwFI1+qKR8TBeb4G0/4m0Wupr69Nf5ogrZH8JGl+PU75zjjmLVCg6PjousVBj65qtTfVlq/Lf1jyW5d0bX90nu7Uqu+qWAEnvikf8cH02uvbFvgA1A3AqDDqzHE2ZFsRBPCZVgNfBAo1AYLfVVJ4GPt063hgfz7iRTVJ8XAh+pxeQOfy2fWvSoTjI4GaJYtiIKku+d9kloW0Km94YkpUdCTPgm6ZtBAu15hzKs1f+cDTx7ijxu0I9hxWNTCgU/8d4/wtMy4028Cu9GLw3lrduS+uhT/XgOvT+E3RngNC0++8GpRdOziwXVUDwbRwQ0Lk0DrqshuYGQ/yP9HaOMfeyN4QgCGQfLNUX2oB3W69WG9+COTUSDy5kd7w+smRwP0wdEyymJbpNwt0RQfwUCZIdGAeOCsUj+wHx8FfXFAYG0X9hP7LPXDJtoH/L0X/LX/+GMY2G/kufsj2yb7hB/QY/sum577JOTQyfOJLYICfFVNHx98vQ7H5rIZZjAh+ubwmXAoniBhG61rwSLaRuxXM+Ok9VsPJx+LuDLaRmsdLlGgjt/T5D1Vwqt78bHG/uOYoP/AcflRdMxxfJEfXX+3zy3Rb0SKzy1e6cPrpO51rq/Ja6JzgYDL+EOuK784UN9WsF2oA9fQmKgeXAvY7uujunBe02tp8QffpLX0Db2EP8Y5qPZ1t60v2UFLO8DfDIpfhTvXmpsdFEX1XTHwoXpcvsAHX03Til+vMuxMrlsQfnWg+G+BoIMn0h/iD44GF6ij//TwyVe/qPk8bnxnHYMZDL7cdbYZfK6boiiqrwnPvYLBT6jAZ1I4YHwQnm/BU5/zL9vpPk5H6zwR/z2gxv/2FkVRfVcMfKgely/wOeLELatxh7SRgGPQ7PD2Fz8q/Pc9LH2y41h4B+7yj1iY3g1v/1OeOoyvzVEURfUF3bfk/bDyWevBeO3t4XBDB5/6nI04hSc/n49It4iiqAtRDHyoHpf3Vbd+U8MHFm5Irar6bNfxcMiTq5LfVcS/a0jBfL9paZpRn4A8vFJz37LM36gU1Wc7j4VjZnyQ/HAaT4PKfra6BPgsNkVRVF9UcDU+coAnPnjyE3HNiPBMFIy04smPD9SPvxl09G0Md9r72WuKonq/GPhQPa7Mjxu0iitmhs8v2xLuOlx9fW7Coo3J7yua+Nx0T1Dmq1cURVG9Rd+ciQYdA+WVNzz1ieZvvaStHzo48Xo0zDlb/u8rrVn5dtTf4oM3VZasSD6icerUsUqaSJbXf/JZeObMybr8USOGxcsbNyd/Z+rokUPx8tDrRsXLVw3qH15/4+hw5/atlbLCV7t2hft274jnRZIX6/yZ2uVIyXK/dCmR2GhEvmXNU48+VJeGP7uL6eFjdExU31D1KqeoHlLLAh88yRn/Zjjt7a3htv3Je3L4ItzI599LfjiML5dZ5foad7T2U60URVGd0hub91ZfeQMDnw6vuzN5Fc0KXBoBHzOIv+y2ZWy61vJas3J51N8GYb9+F8VTsGLt+3Ee5u986Ml4umzlu5W0wVddFU9fXojfiUpgEIYnT52I57dv/aKSJoEPGDL0uvDKOPC5JdyxY3MlXYD2fZMEPlbeTcOT9YIRNyX7nCxfFM+LkNav/xXhyZPJ9gDo5XkzKstDR9wUp0H79+yq2ECTHqsNfC7qf1mUysCH6luqXtEU1UNqKPDB62yXz0z+yjme2Mgrb5atweS3NiW/1THy8pi9Ktpe+aOJg2eHT7z6edjvATjJett2sbCJP2xKURTVk5rz/rY06Kl+4nrI7dFwxAhiyoIA6kj8WtuJZGUNas2qJPCBZKAPffXVl5VlnY7p7AUvVdK+3BL5GJWnuWzgVVHgc7AuXwc+ohuHXRkv700DH5GU2btnZ2VeOHZMnkjVP/HpP2BwupTo1MkjlXLC9t3Jhx/cwOepRx+uWU6UBD5HjpX7mBBF9ZTcK5iiOq5GAh/8/ZNn3twULsLfibl8Rjhtxbb4b3XMeXdHOH/NznDFxv3x/EvrdoWvf7A7vHPuJ/Gy8P62I/HvZWqCn6vmxEGN2Ly8/pvw1agsvhBXXfcLcbm1mw/V1Dcv4t0vk/WDj7YfjbYjSo9Y8tGeOO2TOC1aJ36bVKmvQaIgL++v3OPnS/g87jNLNoWb9p6M/6jlwy9vDHcePhN/QveO6JjgxT9sIz6XrP9CfPxp5BsWpkuJ4k84D5gZH2t8NvnFd7fH6d8cORvb4u/OrP7yQPw7p092HI32eXt4e1RmEP7OyRUzkr9ZM/7NuAyEcxDcuzRdSrY3to3WMWX5lmgwNCs+zlofRNuIYHdB+lsn/KFPPM37YveJZD/6TwsnRPsIfXM42q4HlsWfPMZ24Ljjk9IL30vOweL3k08CP/rK5/HfhsFTQnye+LYXP4zz5G8NvYjrM7KP/37MNVH+7A/jcqgXnxj+bNex8JIJb8fbgO3CNrzzxYFwwBMrw+eWRoMYfN543Ovx37cZ/tza8IVl0b5F9cnfZnnqjS/DBxd9Fs+Lvjp4Ov7U9eromL7z+f54u3B+TsZ/j2hJ/Anp6Su2hpOj+u+eX/0U84fbo+MTHY8v95yIjy3ODYTfn9274NP4OnjgpQ3hpY+sCPcejbZ/+ILwR9PXh/ui43jZoyui62Ju+Eq07/OjwHr0jPeTc4sgP2ob+JQ5PhDS76Hl4dsbkz/QiWOHv+fzUXS+cf5xfPYcPRuOnf1BfP5wrPEpdpwHS8E9S2vO8S78MVj8Ti7aP6wLWx/ctzT+BPuWfdH5ufLFeD/wJPcSfDr9uvnhQXWjecryreE9UX78Se7oGp/xTnStRsd/c3QeURZfbcSnoIc+vTrcmj4VvujB5eHN098PN0XH7Nm3ovMVXaP4gmIwKhrERsfwRLQRaM/oK3DOth2ItgOflo7ANuK44DPu+BtT0Ljo+sEflcU6r3km2o47kmt+097onETHZHq0TVr4eiXWhfO8JjoWOIZyTI7i1bDofD71+pfx9fVgdO4GT0xe27pz3ifxtfZG+rei7lu4oWV/lLUTWrbNefIzKJofcXl49uUgPO8EM0XAE55TeK3tfGs+Wf3emtVRX1sdImFeeO+TanvF8nXXj4qnS1a8Ez587x3x/O5dUX8XTb/YGPU90VR08kR0LUXL33zzTU065kePuyP8Zld0zUbzGmj/3tog5NJo/uJLB8Rpw0f+KE0Nw1HXJa/UgcsHDklTEyHt6mG1H3cQW9H4W26uLB/YW7uNzz8zsWIvnEsDHw1F9WbxCqV6XI2+6nZlNEhGUBM/+YmWH31lY5IXDVBueH5dMh8N5OSPMyKowcAIg7V4YCifrVaMiAamC6JB32ORzbRoYBn/HQ3HBoONIRPhFKeEc6PBPf6g5gMLPw0fjgavGCAOfiIavMdPhKZGA9wZ8eAOtvFf6G/wKZPJ3W+lR9AWtmVxdHwsYcCIAf6qKFCZggF9FDxWf/2UDmijASMGxaI4YLh8ejIAjQbi+IOhGPQhENFBUzzY7DclDkqgJ17F366ZEc+LjpwOk6AgGjw++foXaWq63sg2HqhGwRUCKFf4eIQMWiE5Dqjzva2H44AjGDAr/iqgfBwDg3m9DQj6cHywz6OmvpckRsJxweD4xdVfVf7A5VcHoiDk4qnhxm+Ox4PQRxd/Hqdv2HkseX0yGmDL31XBxzJwrenfYV304Nvx35zRwt/LQbB5TxS0YJtdod4V0UBY9BKOfVQvBuHXPLMmDg6wT+4f7oyDefzdpWHzK8EJ9gl/DFQrOTNRXnR9z40CRgiDfwQcSzfsjf9YqWhgdDwRMOI8T3l7SxxEYv9ugX20TVKXVvxUNBrkL4muEV/Qg21646OoTfSbGu5Rn66/MTofOO8IDmdFwfLome9X/n5UMGxuJcCAgjujIDAK6lxNXhoFl1Eb1EJfgKAYAbnUgWt85Avr4nlR5djc9ka87fh7O/hDtNh/+YOkaBfxNkZ9AW56jJ7xQbyNY2ZGAd9VL8Y2IvwB0mHPpq9DRcGXbitQHISrdrr8s33xcY0DrKi/WLulGhhi2/QHWeI/ehpdKzdPWx8H4H1Nh85FQd/AR6NrBU9+0gBo8O3h1iiQKfr0B5+nxscLTm+6Ja2VoijKLwY+VI+rdOCDL7LhDz9iPho0DcPd3Wiw95gEPtFg7hqkYV4Cn8gu/uOR0YAC6fHg3Ah8FsVPA/C+c7SMT1dnBT5RnTNRz8BZ4eWPrAgfeikKfKL8+MnDIPyV+KnxevpFA1+ktzzwiXh5ffLUwFL8RxZHLEiXaoWAAwEetGkPBrXTK4EP7nh/vvt4RBQcDnkx3h8ofpoRDdLipw5RoPRKOtjHYA5/70iE4FE/LbICH3lCgj9ciQHlunRwh4FpcOm08Fi0MTjOeMLjCtv6wbZqujxF2rr/ZHzHHMIf6cQ68dQGwnHCtmsFN0TX0NA58V17EY7LUvUX/RGUIMjC9YXACgGfBFM4fpUB+XUL4uMTH5voGtN/JDR4cHld4ANdHQUtWJ8lBFO4XkXx3f2bX47nr0S5US/F8+5Dv4cWbawO3KPADkFN/Mdqo/osBT9aHM5c+VU8P2rq+viaESEohvBERIQnILjuEfjET6KifbUCt/ipoFrnlihw0tuKgGduFDDgBsS10TnC9STnQQdzcWATrQ/HNV6+Zm4UiKjAJzofcu1o2YHPnPiplOjTnUfDyx99J1639fA0uPX18Prnk6AIxxRtF0EwVLuNb0XbOCXeRjwRwrVyQl1UaB9yjNzAZ83mg/E1gKepokcWR20gbbcIou6c92k8D137bNS20nMvivsi9eS0L2rglOgYVp78RAyYFA68LQlq8FU26/c/eCqE9BNrMIw5GW7+Ev1v9anDtp1J/7Tlyw016S9MT/6Y7OhR18TLWrOmJL/b0Vo4L/I3qrzknztX/aCATp8x9VkzPVHUL+m8iy6NUxfMg1+opg8bMSZOh3Q6OHT0WDj6puvjeejEseRVNem/Dx/cGy9v2Z74hlMnqh9iEI6ePBlu21J9ZQ+aMSU67tH8e58k15s8mZo2t+pDduyI2lVaBhw5fiK8bXTytCtR8vTny63J2wAU1RulWyRF9YgaeuKDv5oeDdriv7wuadGgQtvgFS8MinH3v5KOcpgiIIoGVJV0Df7S+sXT4ld0lm2InEj6RKkCgiddp6RFQUJNWpwerSMaWMXzw9KnQK5NM0SDLBkUWjp8KkxeQYvYdywZEuN1ItzBx2tPuHuMeYBXvfA0Z+u+6uMK2CBv56HTFbudUXmsE3l4FQzC6zp4Pef9bYfD42oUCWeMMrDddTjJQBl5YoN8BF6wQb6sA69Y4YkRyumnOyIMHvFEROehXpTVkjv0Uq8siyRwgXZE+xjvUwSmCPwgKSv7Gm9vNH8wCm7WbDoUI8cW6Yltsl0IEFDXl3tPxIGT6Hi046gHeb4/TItjkBzTI5XXGuNy6fbgPLg6EG0ynmYgAMS5F6Ec6loVBQ3yh4HxlAV1gW+OnIlf9ZJ9RUCC47/76Nl4Ga+viXCctDbuOh6fC3niAEu3Ln3ccRyQLvuEVxKxjFfB8JQD8wdU5CDBxvaoDtSFJ2/A93VDVJvUV73mUBbL7rmFcA0gMMGTHdnO/cfPVerA9Yy2gWVcA1KXPr7yMRURngahTgT3IuwH6gDYD9xcEOG1STnX+jqBcG7wCile7/siKqeDKjzVxLYA/cpfX1T89GfQQ2nwk37yevBD4byZUaDzam3QEwc88R8hTV47PXjoQNQf1g5nzp4/G+5Lf/+iheWl764LbzECn5lTnqhLWzgPPiZaZ7R9586dC8+dPx2eT7+i9tXO6m8tDx5OAvDpaeBzFrYRKCdC+vxXq6/7YvmSKwZH64A/qV2vCOkfbEieMsf2lw8MR99cDXwOp/sua8H88hV4yyDJP3XieGUewjzYurka+Jw9Ww3i1qeBD+affOqpeAodPrSvMp/oXHj8ZHIN90/Lguem1d7koqjeJn0VU1SPqNFX3XK54aVwJF55u2+Zne/j+oXx7ydwJxcfEYh/c2DZ9RbU72YoiqL6sj4/FA3UB0yMAp/0wwcDJ4Uz1n0QHn03CYDi3/JsvzW1TnTw0P6oL4zyZXn/3nDm7Fnh3t3wLbXDHCy/+c6q8JYbh9bl2YFP8sSnRlFQhbQt2/E0/Fx4zz33xMuPPf1cJfCxhPTFS1ekS2kQclH/8KW52YHPtddeFz7wwAPhJxuS3xbdkj7xOXrsaLh9W/IUBiH+Fuep11XDrg9Ppk98tm1PfnOEr7pBW9UTn36XXVGZR+Czd09y3DQH9++Op6KjRw6Eh49UX0VG3qhbbkuXKKr3ym5pFNVBtS3wuYBYtiH5PQdFUVQ3aNfpM2G/h2t/L5Wl3c7nno+dSJ62bfjkg5r0pydPidNH35Q88dHMnFL/4/2F8+tfdTsXng/PnztVl37qzOlwxrTJdenyYPncudN1eVBe4PPJhuprr9Dom2+o2MurboePJtN9h5LXKk+dSp70HDyUfDZbhHmwJQ18Dh7YE08/3Jh8avvd9e/H07Xvye8CkyDv1Llz4aYvagOro8erTzSxfNPo8ekSRfVeMfChelwMfFrAJdNqXtuiKIqiKIqiasXAh6IoiqIoiqKorhcDH4qiKIqiKIqiul4MfCiKoiiKoiiK6nox8KEoiqIoiqIoquvFwIeiKIqiKIqiqK4XAx+KoiiKoiiKorpeDHwoiqIoiqIoiup6MfChKIqiKIqiKKrrxcCHoiiKoiiKoqiuFwMfiqIoiqIoiqK6Xgx8KIqiKIqiKIrqejHwoSiKoiiKoiiq68XAh6IoiqIoiqKorhcDH4qiKIqiKIqiul4MfCiKoiiKoiiK6nox8KEoiqIoiqIoquvFwIeiKIqiKIqiqK4XAx+KoiiKoiiKorpeDHwoiqIoiqIoiup6MfChKIqiKIqiKKrrxcCHoiiKoiiKoqiuV03gs3fnBkIIIYQQQgjpCrTiwGfVqw+EcyYGhBBCCCGEENJV/O2LN1YDH8uAEEIIIYQQQroBBj6EEEIIIYSQroeBDyGEEEIIIaTrYeBDCCGEEEII6XoY+BBCCCGEEEK6HgY+hBBCCCGEkK6HgQ8hhBBCCCGk66kEPv/3kb2mASGEEEIIIYT0ZeQPmSbhD0VRFEVRFEVRVBeLgQ9FURRFURRFUV0vBj4URVEURVEURXW9GPhQFEVRFEVRFNX1YuBDURRFURRFUVTXi4EPRVEURVEURVFdLwY+FEVRFEVRFEV1vRj4UBRFURRFURTV9aoEPr98LSCEEEIIIYSQrkLEwIcQQgghhBDStYgY+BBCCCGEEEK6FhEDH0IIIYQQQkjXImLgQwghhBBCCOlaRAx8CCGEEEIIIV2LiIEPIYQQQgghpGsRMfAhhBBCCCGEdC0iBj6EEEIIIYSQrkXEwIcQQgghhBDStYgY+BBCCCGEEEK6FhEDH0IIIYQQQkjXImLgQwghhBBCCOlaRAx8CCGEEEIIIV2LiIEPIYQQQgghpGsRMfAhhBBCCCGEdC0iBj6EEEIIIYSQrkXEwIcQQgghhBDStYgY+BBCCCGEEEK6FhEDH0IIIYQQQkjXImLgQwghhBBCCOlaRAx8CCGEEEIIIV2LiIEPIYQQQgghpGsRMfAhhBBCCCGEdC0iBj6EEEIIIYSQrkXEwIcQQgghhBDStYgY+BBCCCGEEEK6FhEDH0IIIYQQQkjXImLgQwghhBBCCOlaRAx8CCGEEEIIIV2LiIEPIYQQQgghpGsRMfAhPcbPpn07xsq70OFxIaT76KZ2zT6KtIKZswaEv1jQ38zrDfA67x5EDHzaxNBvTyjMiO/dFz7zF0PNejRW2Tz+8MfjvJ2KZQ8sW8GyB5at5qEHR5nlXLC9VvmyWHWXQer5wffvMfOHXfVQzfo0lj2YOPFa0x4dv2WvuXnUHWZZYcKf3WCWQ7pra9ll8Udj/qiuDqGd63XRdfnOy9+80q/GTmPZA8nHtWfll+Wun/y4Zr15NHsMcSxcOwFO2yqjbaz8RmhlnbquRuvL6vt8WPUIln0WzbZry74RitSpbSxwHOGnrLIa7LNVXmOVa/YaLoNVVxZZ/R+wyuSRd21aZcqg6/L1lT7Qfy34m4tr6nAp02e5jB/7h2ZZjc9XtruPbvd1nkcjfRYpjoiBT5uwLuoiTJ/xe2Z9wLIviuVgLTvg2mkse2DZAjRiyz6POb+4zKyvKFadZShS1+TnvlNjB+69+2bT1hcoId2y9/GfHhlh1tPs4LkI1ro7sV5B15XlzLWdxrIFkt9XAx/w/Au/X2cLLuTAR8i7aSA88fhws7xglfHRinZt2TVCkTq1jcv3hz5olsnCqkew7EEz13AZrLqK4Ot7Ldui+K5Ny7YMuq6ygY+Q1W7K9FkCghmrTBZuHe3sozt1nRehaJ9FyiFi4NMmrIu5KD4HYNmW4f4/vbFQfdrGxbIHli2COMu2KFl37/Ow6iuDrgvnw7IB2g5YNsDaF8uuCBiguXW1YvBcBPduVKfWC3RdWc78J+NG19gKli2Q/L4c+ADXFjDwSXD7PgurnKboebXKFsFt15ZNIxSpU9toLNui+Ppvy1aw7HtL4AOsu/GWXRmsa9OyK4Ouq9HAB/hu2JXpswDajmVfBF1Pu/poy6YojVznRSjSZ5FyiBj4tAnrQi5DO+oERerTNi6WPXDt0BlYdmVx6y2KVVcZ3Pp8j7/xhEdscJcmz0Yoe0fYxa2vVYPnPFxH2Kn1Al1XnjMvM0CR/L4e+OAade0Z+FRx69QU7a+ssppWtmsrvxGK1KlthEbugLtY9Vp2QqPXcBmsuopiBQKWXVlaXaeuq5nAB+DVNF0fKNNnNXsDVD/9aEcf3RPXeVGseknjiBj4tAnrIgboxAGi+awG9+N//yeF63TtZv/8ctMOaDsrH2gbF8seuHZZAwBsn7bFsbDsgO9uSh5WXcCyLYpVH0Be1sDJrcfnyIHu5OEwfMfRHSC0YvCsbbJeS9B2rV5vUYo4c7eMZQMkv68HPmDSpO/W2Pe2wEfblMGqC7h2Rfs+lyK/OwBZr+C2ul1b+Y0g9WXVqW1A1u+T4Lewr7DD1HdTCFhPpy07TSPXcBmsuoC2Kdr/AcsGuHZlrk3LBrh2RfD1lfhdMY4t+pm8vs+ts1V9lv5tM14ltGyA2LS6j+6J69y1a7TPIuURMfBpE9ZFDFw7dPKWHXBtLRvg2hW1tfKBtnGx7EGjdoJ+WoJ5dKBugFQGvU6NZVsUdNJWnegQfQOZMk8efD+i9TkubdMKR9SIXTvWW4QigQ+cpC5j2QBtY+HbR3eA1iitOIYabd/MoNEqByxbF6scsGyLYNUFmrUVLHsMjtw0XHdWeeDaCs20awurDLBsXaxyoKid7zXSrI/XuLaWjYu274nApx12ZWwtG+DaFcF3nVlBvGUH3NeuivZZWIdlB7Rdnn3eByYa7aOtMqCd17lrV9aWNI6IgU+bsC5iUMbW/bKKZQO0TRlbKx9oGxfLHmgbvNpl2aDT0HbtxFo/sGzLUOaxuDvwFixbYNkCPE3CetG5+56AFXVEwLIDrp0vmNM27VhvEYoEPkAHz1Y+0PVa+PaxtwY+OG9iz8An2xb47vr6nuJadQDLFli2oEi7trDWASxbF6scaNROY9mDonaaVl3DFlZdwLUr0v8Bywa4dmVsLRvg2hWhTOBT9OlD0T7LunkArNfnBNxIRKCD827lW/i2p9HAx7IVLHvQqF1ZW9I4IgY+bcK6iIFli7sLlm3RH7tqGzhx/fTERdta+UDbuFj2QNv4ggNt026s9RfFqk9jlbGwymZ95c6yL0pRRwQsO6BtWu0AgWVXFLeuooEPkDJWHtD1WjTqVIvSjmMoX6DqbYFPEcrUp23K9H0a6xUWucPtpgPrvLerXVtY6wCWrYtVDmgb376gX9d2Lng92yrn/qkGy8aiFdewhVUX0DZlXj+ybIC2KXttWjZFcesqE/gAyxZom6J9lmUDygT6RWikj+6p61zbNNpnkcYQMfBpE9ZFDCxbNE7L1v2tgGVTBvdT2ZYN0DYulj0oawN8nZUGnbZbrghWXUWx6tMU+SwnOjSrrM+J/8G/u9u0L0pRRwQsu6JgkKjr6tR63brKBD5wLFnrd+t28e1jbw58AAYX3R74FMXt+zSWvQzMfIMSt452tWsLaz3AsnWxygFt49uXvN+u+X6r6V7Hlo2PZq9hC6uuorj9X7P1AevatOyK4tbVGwMfbdMKfNuT1Uf3puvcIqvPIo0h+v/Y+x+gXYY+zxO+rGUsq2nDaNrSmvbyauZ56aefZ59hDWNoy2jW0F5a8GpGs5o2hGCM04YRhGW8tGYs4WUtYRlxjDhG04bQrGUNLRh9tCUEcYI4cYQlBCMMUW9+67ryvvPO+1dVWf/u66rr/nzDx7nuql9mVWVlZeYvMysLx6cnrIwsLNuiBzD1HYUU4t6IsvhiuxDLXtS1EUWFVcgsOj7CChdihRGpTm5dUisiYdmlEvfUrdRx47iKKvOiSkmViLVdxHHHFF3jrDg+RXlKtGk0WuGEZRtjhUul6/isss+jUXUrjN+vDoyy/Z6+nmsL6zjCso2xwonQpqhjx8qPIalpYNn0lYctrLhSsUYqLLtUivKmZZtKHBeOjx1mGvk8lbIyC5rjhePTE1ZmFpatMrll28WDVFbxWvbCsvVY9qKujSgqrEJCx6fOyjPW/lTCeCyK5n6HFL3PVNSQqnp5s4rUikhYdilY35daieOKOK6iyrzJceK4Y4qusaxSrUPbNNR5FDl2RdMy4ngtrHDCso2xwqXSVXwpTocVLqXDKY67r+fawjqOsGxjrHAitClyNPTuZmgXU5SPU6Zs95WHLay4Uujy+3pVedMKk0ocF46PHWYa+byKlDILmuOF49MTVqYWlm1RD0JqISK7ssZ4vEiCp8m7OJa9qGsjigqQkK4dn9CmCSnT3DxW76CwbK0pFCFFXw33FKVlnIeEZVdG2YuoXRw3tkuhzPFRulv7iojjjim6xllyfLSvbK54TByvhRVOWLYxVjhh2aZgxSWURk3KPlGUT+LVJFPLScum7XNtYR1HWLYxVjgR2hSli58yWkTRkuBqYIZ2lk1fedjCiquMsvJPWGFEm7xp2QvLtoppOj5N2hhygONnsIqi8ykro6eRz0WbfAHt8MLx6QkrQwvLtqiwjx9ay0b4/UU9YyKMx1PkSBTNLS1rUIZ2RedR1GPmsQqvWXN8rDjLqBOHZesJ7bQYRlw4plZEwrITsV0K0zpumeMjyr4JERPGa1F0jbPm+BTttwjjLMIKJyzbGCucsGxTsOISfn/dsk8UNWBSiRuOlo0IbWJCO+u5tgjDhFi2MVY40dQuxLIX8ZL+lk1fedjCiktYtilYcQm/v0netGyFZVvFNB0fjXhadrGTEBIuNiLHKWXaV9My2gojLFuPZS9S8rnw+5vkC2iHF45PT1iZWbSxtWxEio16GEI7UTS/vajHQ0PAln3cI6YC1bKzziHEKrxmyfEp6qUpKtyFNeXNshNxwekpcoxDm9SKSFh2IrZLYVrHrXJ8RFmvWkgYxqLoGmfR8SlbjSokjLMIK5ywbGOscMKyTcGKS6TYFJU7lm0dwrKpLL42z7WFFUZYtjFWOJFqV9QTn9opJiybvvKwhRWXsGxTsOISKTZ186ZlW0Udx6dohdl4kY7UMqto5bSikdCifFQ1ctq0jLbCiL7yuUixqWorQTO8cHx6wsrMIrSpKuBDW2HZiNCmyJkRVi+LZSfiBnvRe0jCWsHMshNFqxwVOUth40LH0TUUEcZnxSVCmzpUfcm77F6qsAzjuuDCXzXtRFzgpn4wrW3jWcR2KUzruCmOj7BsYuIwMU0r1VTapmF8HlUdBCK0L8IKJyzbGCucsGxTsOISoU2dsq/onZy6hHH28VxbWGGEZRtjhROxXVla6jpD27Kyz3rHybLrKw9bWHEJyzYFKy4R2nRVL8d2KaQ6PkUdmyKerdFFuR93sJY5FVYahTQto1c6n4vQpm6+gHZ44fj0hJWR61DHmUi1E3VsU4njFGWOkielcot7VVOx4qpDanxh4SSnzrIRYVzCsqlDXCh3URHFdil0cdxUwrhSHR9VfJZdSBwmpmmlmkrbNLTOw7ILie0trHDCso2xwtUhNb5UOxHahdNp2hCnvWVTh/i5trDCCcs2xgon6tjWITXevvKwhRWXsGxTsOISqXaijm0KYVxFZWUdwvhEnTKrznuxRcRxxrQpo61wdakTb6qdiG2hHV44Pj1hZeJUilYCsmxFbFfW8xhXrJpTbtmlUvQ+kChzBFKRc2TFXYUVVx3CuIqmTFkfOrPsRFwhlPVuVWENg9epiCw7Edul0MVxUwnjSnV8RFUj1woTUnSNs+z4FE0x8cT2FlY4YdnGWOHqkBpfbJda9ln7y6aXpE5x6fq5trDCCss2xgonLNu2dUNRj7Vl21cetrDiEpZtClZcIrarUy9bNnUI42rr+MQzFkSdMksUTetMweoEjmlTRq9kPhexXZ18Ae3wwvHpCSsTp1Dk9AjLXtSxFbGthrEtuypSViQqe4GviqK58SlY8dXBx1M1xS2mbOg6rkCaTrcJ4/C0bTyL2C6FLo6bShhXHcdHWLYeyz6kTaWaQts0LDqPovn6wrKPscIJyzbGCleH1PhiuzJbof1Fz6i2x3GFWGFEbNflc21hhRWWbYwVTli2omx6TxllDTbLvq88bGHFJSzbFKy4RB1bkWqXQhhXG8enaLGNOmWWp8koa2oZ27aMXql8LurYCssemuGF49MTVgYuQ4VCVUPfCics27IH2arg6/Z6KP44jiLqOlZlo0ipWPHWoSoefSgzPF6IZS+KenSLlvyM0RxsK7xo23gWsV0KXRw3lTCuuo5PWR607EPaVqpVtE3DsvOw7IVlG2OFE5ZtjBWuDqnxxXaiquyztgsrrpCiqblF397o4rm2sOIQlm2MFU5YtiGpjWeN8lsjBCFWuL7ysIUVl7BsU7DiEpZtar1s7a9DeMwmjk/Rd+g8dcqsEN1nK5xFkdNl0VUZ3Xc+F5Zt3fYaNMMLxweWoaFbFSSq0FXZCzX0Nfph2dfBx+3jFfo7ZTh73lEaKC20Sl6YNkXLjgLA7DNPz7UaaDp31Qe6Dl2T/i6a7gNQhPKN8M+EnK1ZaeSTz+cTLxwfAAAAAACYW7xwfAAAAAAAYG7xwvEBAAAAAIC5xQvHp2Osl9MAAAAAAOpitTWhPl44PgAAAAAAMLd44fgAAAAAAMDc4oXjAwAAAAAAc4sXjg8AAAAAAMwtXjg+AAAAAAAwt3jh+AAAAAAAwNziheMDAAAAAABzixeODwAAAAAAzC1eOD4AAAAAADC3eOH4AAAAAADA3OKF4wMAAAAAAHOLF44PAAAAAADMLV44PgAAAAAAMLd44fgAAAAAAMDc4oXjAwAAAAAAc4sXjg9AAff/r7+9gLUfAAAAAGYfLxwfWBH+5m/+xsSy7YJ/9+eXZm+99fyy7dpWdQ5/+dKfLbPxaH+8rUvHaFbjjsOK2/7sbyXbWnbzTHz985DWRc/UkLnr3l9dln7aZtnWpc+4h0qcHnXy079/4cZl4Yt4482nzThmAZUF8fmG+1PqqC7403/9S9lfr3/b3Bcfv8t6CGBaeOH4QO+88OK/WlaQeiz7Nqz7d3+0EHcTx+fe+/dbtt/z8iv35jbx9llxTqpoE3cc1pNqa9nNM/H1Dzmtq56pIYPjs7LE6dGX4+P5s7t3NOOaJioL4vMM9/ft+KgT5T/+x9dL4w6PLbqshwCmhReOD/ROXIiG/MW//xdmmCbEcTdxfKz9/8erD+aNvwcfPjS3iffPinNSRZu447Ae6/5ZdrHNvBNf/1DTOo67TkN1COD4rCxxetTJT00cH2HFNU1UFpSdY1Ud1YaqY3timy7rIYBp4YXjA73yP9/368sK0RgrXBPieJs00uI4hGU3ROLr6qIxLjRloso23L8aiK9/qGkdx43jkw6Oz3Li9Gjr+Gib3x+OTIaEccwCqc5HH0zz2ADTxgvHB3rl7Xf+9yWFrKaLhX+LoukIGmkJefTxE5cM0ytuNST+7bp/ku+P49X8ZR/Wx6lpd35buK8oDhHaWeel6XF+X8hD/9uR+VzzMC5V9Gv/7ZmmvUiJW9esfWG8Hr2fVNS4im27aoyLKttwf4hGMWLbsDETE6eP7mfZfuH3KZ9Z+8IGk95j8fZitaZ16jPlf3tix0zPQGwT7hfxfj3n4X6l3Wv/56NLzuP1//BE4XNX5z7r/oXxivCe6hhxXKLonauQoriF4vDbyvK7iO1DyvKg0HlaDoPKoTidLeo+nx7dM+UV2aucVn7S9jiuLh0fEecTcd//8nfzfU2ef6F0istx1UHK27FtTDjNW8fTOVQ5H0V1VEzd+kXx6LkJ7YU/RliW+m2eometq2dThGmlOOKyBKAtXjg+0CthgSisxkBR5RfbWT16KkitCjHGx6ljWfvqxBFvjxu1OqfYxsJPnQuJbeK4w7nZZajiCMOJ2CaOu4w4bIwaYGW2YVwirOSKUIUah4tt4rwT7xd+n5X34jwVvuy7mtM69XnwjVvPn//FP1sST7xfhPtF2f54n0VoL+rcZ8vWOxJqeMX7wv1VWHFbDXOP1dBrkwdT7qGIw4mmz6eIG+NldO34qCEd2/hGeJ18IYruf4yVH6xjeaw8EIYtqqM8TesXyyYkvBfxPqv8im0s4jBWupSlVZEDBdAELxwf6A31PMUFmbZbvYhxWBHbWMiujtOyEo5PvL+MuGCP94dxq8Eb7y9DFWRq3FXEYS18A8DaF8ZV1viLiRt08f62jk+Mvx+rPa1Tnwddl7XdE+8Tjzx27ML+OHzY8Ay3V+HDiDr32bItS1u/L4WU84gJw7fJg7q+eL/yglX+aUQmPG6b51OjEJZdEfHzW0aV4/Nv1p66bL/w++vkC42UWfuLiEcALZsywrBFdZQn3ldGWL9Y+0PCexHvi8uveH8ZYbi2zwRAG7xwfKA34gLMV7BWpWJNu4htYvxUg1lyfOJ9QtP71ChRD5y134etG3fcYIn3hyMD1v64MisjDltEka2Px+qtVK+r369Gcbw/7AmP98UNp3i/8PtSKt2ieFZbWrd5Hvz2ovweOjdxD713itSgDrcLnZPuoeUQhOlf5z5btkXhtd2HS6EoHl1/UfqG06fifXXyYDxiE07h0tQvjcqoXFJ+CPNm18+n0LGskRjR1vGpIsxrdfKFwhXtE/G+8DhWh58oO/8w7jLHJ94uUusXa19IquPT97MZ48MCtMULxwd6wXJuyip1EYYvsvFzxS1iW6tSLatURLwv3m/ZlDkncY+oTxdVVqog48ZUHD61wayevbqVddvGuBpQ8bai3mIfj9UACo8j4v1Kq6J98T2O9wu/r6jSDcOnsFrS2tpvPVPxefg8XTZNy4dN3R42noTV+PL76txny9a6t/FzmoIVd10HuoiqPKgph/E+OSByYvw7LxZdP5/h9eic4/1WfiqiieMThq+TL2KbuGPOGtny+8rypoj3xfvL6qh4e936ReVQHEe43xPblNVxbZ9N5Uu/X3HF+/0+gLZ44fhAL1QVYCqYy/aLeL9lExLbWpVqWaUi4n3xfsvGVwpWb6m2xeHLiMMXNZhV8Rb1ooaEYeJ9bRvj2m5VchZl8aRQFD6+x/F+4fdZlW5KQ3O1prUVh/VMxe9CqIEah41HIHxDMtzmG0Apz5HSM7bx++rc56LGcEzcwEzBirvqvYtw5CCkizxoEaeLZZOCwlr3rWoKmJWfiqjj+FjxpuaL1DwR40e+rH1h/NaUvHB/UR3VRf1S9tyExDa+/Orj2Qyn41nOsd8H0BYvHB/ohbjwSsE3mMriCPfHxLZW5VdUqXjiffF+y8ZXCqmVShlxeB+3x+rl9az0KETZvpg6thZF4eN7HO8Xfp9V6YajkDGrPa2tOKxnyrKLR6jiEWClX/y86P4orpTnyLqXvgFW5z5btkWEU7pSsOL21+iJ94twf5s8aDUiLRSPD2PtT0FhU+5bvL8oP1lYjk9cZ5SRmi+s60jBP+Px9vgaq9KpqI5KSd8qUuOIbfy1pYS30rns2awKG+4HaIMXjg90TmqFaxHGU7U/Jra1KtWiSsUT74v3Wza+UkjpDRNxL2hIHN7Hbe0T4Spa1tQiv88KH8ZdRRxW+H11vtVk7dP0myrKwvt9VfutSlXbwvCe2E6strS24ihqqMZTq+LnTDZ6zyTcFo9g+LjiESQR3yc1WGMbv6/OfbZsiwgdhBSsuMOFHUS8v8oJqZMHQzRFWCNqsb3Hj0RZ+6w8EuOPE4eNy794f1F+sujD8bHyReykC+Vd67pDfFxVDqnuRdn+ojqqi/olxXERsY0vv/p4NsOwVfsB2uCF4wOdo3nsceGVSliIWvvD48TEtvF8etGn42Pti8/BelcjrLzifWUVTtwDHe8XZfu7aoyLqmk43s5qvITxVBGHFb6itxoUwoe1KtW40hak9SJxeOuZ8sS2Hk1zq7KJG7Dx/vi4ZY3L1PtcZCu0z3pJvWyEMKYs7qL9WvFS+7rIgx51RFU5IP64XT+foWNjLYwwi46PiO3K3i2NsaZ5h/cu3ifC8GV1VLy9bv2SOpUstqlTx9V9NsOwVfsB2uCF4wOdExdcQo21GOudhbAijPeJ8Dgxlr1/8drb9O34WC+cq2dWPYJF72ikxG319qlh5sO1iTuFOKxIsfGU2YUVp/X9kPA6y3qti/BhUxs+pHX5ceJnqsxWVDX6RBiPsBq5Onc9R9ZIRzgSknqfU2zjfSIMX4YVdxXeiW+bB8v2WfvDd4/ifXXyTNF5FTGrjo9VjpeVxSIMb+0vIwxbVke1rV+K8qTiKOugCMuvrp9Nvy9lP0AbvHB8oFNUCcYFV1nlFNuKlH0WVk+bx9v07fhY+8sIe+OssGHc8b4UUuOuIg4rYpuiBoEI7Yq+t2ERN4yqplFajpEPW6fhE9ulUBZ+iGktUp4pj9WjH9vF090sG4/ViLKQXRiuzn2usrWm7YQNxDKsuMtQwzUMb9lU4cNa5XAZ4XHb5BlrmlhIPBpg5bkiVtLxEbFtGfFonBwBy66IMGyTOqqIuH4pCx/ei3hfXH51+WzW2Q/QBi8cH+iUuNAScaUQYtlbKz554vAxVhjhK4AmlUq437KxGrUpFZ+VLrFNGLc1jcGj66qqNOJ9XTfGhdUjbNlaDcoYP/0mxur1FGp4W40jH65Ow4e0XsSyFymNqnh5bMtGDe3YxmOt/hhiOSF17nOKbbxfhPuLsOIucgzCRqenbR5UD3y8PyYczQlpk2dUflv2ejbj59O67iJW2vERKQ38ondqrClfQvusbR6lSdl+0bR+EUXlhvA28Xar/Orq2ayzH6ANXjg+MHeo0lalr0pRjSq9EG7ZrQSqMHQuqiQ05K/fll0dfJzqPS9rNA4B3RtdixpRPn3U4LNsQ3w4f48tmy4grcdM+5nSeer4miKrfzXyZ9kNBaWn0lLXUtYxJNrmQZ92uu8+/awPRlu0yTMadVK5pzDW/iGha9F11E0/NeR9+sVLmXdB0/pFzpoPp38Vj2WXwrw9mzC/eOH4AAAAAADA3OKF4wMAAAAAAHOLF44PdEo8PxcAAACgKVZbA6AuXjg+0ClWoQUAAADQBKutAVAXLxwfAAAAAACYW7xwfAAAAAAAYG7xwvEBAAAAAIC5xQvHBwAAAAAA5hYvHB8AAAAAAJhbvHB8AAAAAABgbvHC8QEAAAAAgLnFC8cHAAAAAADmFi8cHwAAAAAAmFu8cHwAAAAAAGBu8cLxAQAAAACAucULxwcAAAAAAOYWLxwfAAAAAACYW7xwfAAAAAAAYG7xwvEBAAAAAIC5xQvHBwAAAAAA5hYvHB8AAAAAAJhbvHB8AAAAAABgbvHC8QEAAAAAgLnFC8cHAAAAAADmFi8cHwAAAAAAmFu8cHwAAAAAAGBu8cLxAQAAAACAucULxwcAAAAAAOYWLxwfAAAAAACYW7xwfAAAAAAAYG7xwvEBAAAAAIC5xQvHBwAAAAAA5hYvHB8AAAAAAJhbvHB8AAAC/uiPR9nPfj7+fc31o2zzzZfbiN87fZSNRovs9t/Ydm1R3AccaO8Tv/zLY5t4+9/9ib29DocdsXh9nmtvsG1D4jBnnGXbDYWb/lW760gJv/9vLk0zYdl5Uu6v9pflHQCA1YIXjg8AwISbbxtlRx8zytZcO2407rLrePuWWy63CxunvtH6x5eN/953v/Hfe+09/ts3fC/6p+N/5Vj5OHzDVL91HB/XoYeN/w5t/sFR47+vnpyfjucdn9POGP8rx0TOm36LIsctBe/4KM7z/2j8W+fkz90f8/J/thjGX2t4Xb+25/hf3/D/r7ZeTCttl9OoOPWv/v47O4+yG29ZTAvZ+WvSufzj3xv/Ft5JVTroWn0a6ZyUhvqtuKx7oHBxelvnETou//ya8W/lE8tWcfrj+rQPw+vafb4K8eeh6/PpLnvrGNb99XlW6L5om37j+AAA4PgAACxDozhqWKrBGG5XQzT8O2z8httFOAKjf9WY9g1fNWD/yQXj395p8Q1XbVND1zeA/d/6V41Xf0w1ar1N6Ph4dDzFpwa2/vbn1YTQ8dHf/hje8fHE4fb+jaX7dZ46H12/Dxv+9vv12x8nTDft9+cSpkPoDMXpEFJ2D+L0LjuP8H5ov2Xrz0n5SPvD8CE+TT1Fjo91DP0Ot4fp5B0g/da/OD4AADg+AADLUKPVN6LVcFUD1DeOQzvf+PaNSjU8Dzp40WnyjVPfmA0bzr6xL6fJx+P/DcMoLv2t3zqOn1qn8/P2OrfY0fKOVNgw9vhtRfiwnirHR05EaB8TjkJ4h8DHGW6TrXdGPOF16Pr9v35baBunQ3jt+jd2XsJ7EKe3dR6x4+Id3rJz9tuURuGx/TXrnGXr8ecRknIM/Y6nXQqllf/XHwMAYLXiheMDABCw7S8t/VtOhhqs4TYRNzSFGs+xIxI2uuPRBm+jqWCxsxQeR41X7+xopMM7D2WOT7i9KaGT4tG5eMfBalT789R5/MF5i7+1z8cRjmT4OPRbaeDj9mG8oyDkgIUOYFE6lDk+RSM+si06j9DxCe0tW39+mmr33/698e86jo93Mj3aZqVLeL0+fjlk4bXpXxwfAAAcHwCAQvw7JHqn4p//C9tG/KMTxk6L8CMGougdH/9OjH+/RPiGsm8IFzk++u3fXwkbt0WOT+iA+LjqEjs+xx0/3l7m+Ai9D+SdD58GIrw27wD4hr6/pn3+H8udNv0Or8PH/U8vH/8rhyLV8YnvQZze1nmEjov/HTpQ8TmH7/jIAQrD13V8io4R31//DpbQNWqbfuP4AADg+AAArBhhwzfc7huqItxu4Ru6dcIMnXC0J067uhTdAwAAmH+8cHwAAAAAAGBu8cLxAQAAAACAucULxwcAAAAAAOYWLxwfAAAAAACYW7xwfAAAAAAAYG7xwvEBAAAAAIC5xQvHBwAAAAAA5hYvHB8AAAAAAJhbvHB8AAAAAABgbvHC8QEAAAAAgLnFC8cHAAAAAADmFi8cHwAAAAAAmFu8cHwAAAAAAGBu8cLxAQAAAACAucULxwcAAAAAAOYWLxwfAAAAAACYW7xwfAAAAAAAYG7xwvEBAAAAAIC5xQvHBwAAAAAA5hYvHB8AAAAAAJhbvHB8AAAAAABgbvHC8QEAAAAAgLnFC8cHAAAAAADmFi8cHwAAAAAAmFu8cHwAAAAAAGBu8cLxAQAAAACAucULxwcAAAAAAOYWLxwfAAAAAACYW7xwfAAAAAAAYG7xWvyFEEIIIYQQQnMqHB+EEEIIIYTQ3AvHByGEEEIIITT3wvFBCCGEEEIIzb1wfBBCCCGEEEJzLxwfhBBCCCGE0Nwrd3w++/ivsmvOGgEAAAAAAMwV/9fbTy86PpYBAAAAAADAPIDjAwAAAAAAcw+ODwAAAAAAzD04PgAAAAAAMPfg+AAAAAAAwNyD4wMAAAAAAHMPjg8AAAAAAMw9C47Pugf+0DQAAAAAAAAYMv96zU8WHR+EEEIIIYQQmmfh+CCEEEIIIYTmXjg+CCGEEEIIobkXjg9CCCGEEEJo7oXjgxBCCCGEEJp74fgghBBCCCGE5l44PgghhBBCCKG51xLH55prrgEAAAAAABg8sRYcH2snQgghhBBCCA1RsX+TOz5+o/eOAAAAAAAAhk4oHB8AAAAAAJhLQuH4AAAAAADAXBKq0PHZZ599AAAAAAAABkPoz4hQOD4AAAAAADAXhP6MCIXjAwAAAAAAc0Hoz4hQOD4AAAAAADAXhP6MCIXjAwAAAAAAc0Hoz4hQOD4AAAAAADAXhP6MCIXjAwAAAAAAc0Hoz4hQOD4AAAAAADAXhP6MCIXjAwAAAAAAc0Hoz4hQnTo+v/+L3bI/PeFvJyFbjxVXHQ7/yZ7mMcSf/M7OZpgirv/vdzLjCbHCCcs2RHFb4Yro8rqEFY+wbEOsMMKyFZZtGU3ygBWPUJpZ9uKKf/grZhhh2RfR5X35rd/c24xHWPZNseK30Pnrfhyw32+Y8VRhxemx7EMu+Pu7muGaYh2jLA+kovOsG7dlv5L84oBfzx7+ve2y187dOsuudMX+hL88e5vC62nCzf9oxyXH2PRP/8v870v+wS6mfRl9pacVn9CzaNmn0Ge+KuN3f3uP7PHTfmnZff3q0v8iv7c6LytcEdO6jjqc8vPd82v76/P/1sL16vqVv8vK/yKsa6iDFWdd6qR7k3aTFY8YWn2p67bisfDp1FVaecrqRss+xApTRpdpV4TK5rj82HDxFvkxUsvE+NxCLPsQ1RlWOKF6ywqTQujPiFDuKrtzfHSiYeLV5f6Tf9mMtwpVrlZ8HitMEbrhVhwhVjhh2YYobitcEV1el7DiEOt+f1vT3mOFEZatsGxTSc0DVlhRVsjFD3eIZV9El/dFBZsVh7Dsm2LFn8JfnbeVGV8RVhweFXBWGI8acFa4pljHKMsDqeg868Zt2a8Ed/8PO5jnY6GGpBVHFWogWfEVoca6FU9MX+lpxSeaNJo9feYriw8v3NKMowidnxVPzEpfRx1CR6eKOudgha+DFWdd2qS7HEErzhArnBhafbkSbUwrXEhRHOrYtuxDrHBldN0G9MihUOeIFadFlfNYVnfrnllhhM7DCiN0flaYVEJ/RoRyR5gdx0c0uVgrnpCUgsGT4vgUecGWbUhdx8eKI6TOdQkrDk+ZZ23ZC8tWWLZ1seINscKIlSjIrfAhde7LrDs+HivOGDnQVtgQK5ynrPBsgnWMPht2XeWvrrDOI4U6Ix91Ks8QpZUVX0hf6WnFJ4bg+PzOwb9mhk3FijNkpa6jDhpBso6TQkqPsRWuDlacdWmb7mWNS2GFEUOrL1eijWmFibHCpZSFVrgyrDhC6rYBRdM0VH6w4vNYYTyWvbBsPZZ9HUJ/RoRyR5gtx0fUGfnRcJ8VR0gdZyrF8bHOL8Xbr+P4dH1dwoojxAojLFth2QrLti5NH7K+C/Ku78tQHB/1PFnxhljhYsqmCeD4dId1DnWw4oyxwtWhqjzsKz2t+MSsOz5lZUUqVWXTSlxHHeqOJlpUOfJWmDpYcdali3QvK1stezG0+nIl2piWfUyX4Yroow3YNv3K0k35zwojrLK+7Pr03Mf2dQn9GRHKHWX2HB9hxW9hhbWwwlqkOD4iDqcMaNmF1HF8rPAWVtgirPAhKujqhLNshWXbBCtuj2Uv+i7IrbAWVliLoTg+worXk9oTrbn5VniB49MNKWVRFVXT3lLLySpUZ1jxi77S04pPzLrjY4VpQtMyMpWuHJ+yhlRdrPg9ln0drDjr0kW6Kw4rbmHZi6HVlyvRxrRsYyxn2rKLicOUYYW3sMJalE0rq4MVt0fv2VlhRDz6atmIus5cEaE/I0K5I62M42PZl1Welr2FFdai6v0Cj3VOVkMiDhfv/+7yzZZt68PxSb0uYYWPUQM2NVxs57FshVXIljV2Y9sQy17MSkGeel+m7fjEjb2yaSVlLy1XzUUOscILpZnukYUVj1AD3bIX1jG03YpHWPZ16DPuVKoqt/geljlJoV1IVYM0XMRFDYSq91HCuEP6Sk8rPtGX42PZ16Xs/RarM0EvLFu2QnVTbO+ZhTzsKStTdA1hXfUHh/6qaedp0sNfVpd0TZ10t2w8sa3HshVDqy9Xoo1p2cbEzn2qQxaGqcIKb5Gadlab1BPP6Ch7nqrasVYYj7fRu8PWfhHG1YbQnxGh3JGm5/ioUrRshWUfU/ROQdENtuKIsR4Uq3EeVpJWQ8AKk+r49HFdwgprkRoutvNYtqKokLVshWXrsexFnwV5H/dl1hyfMtuyVQkt+6LGV5N5yVY8om7jpM+G3Sw0Gssq+qIpP0WOiXrvLPsmxyib1lCUr/pKTys+McuOjxWvKFt8RI19bycHV39XPS+zkIc91jmIstFIy95j2QvLVtQtW9pQJ91Vflp2Irb1WLZiaPXlSrQxLVur4yEME19TURkZhimjj7SzwgmV/5Z90/Z5WceYnLSyeLuY4uYJ/RkRyh1teo5P2fxdyz7GCqcMU1TJ6oZY8YSkOj6hx29ddxvHJw4n2l6XsMJaxBWLZSNCmxDLVliFbFkPdWwbYtmLPgtyK1zb+zIkx6eoUCq6fu2ztjcZyrbiEXUbJ3027Gah0WgdW5RNMRSy+Ysz/+uk9Izj9ii8Ze+xwoii/NBXelrxiVl1fMpGbyz7NsxCHhZN2wZaMdAKIyx7YdmKumVLG+qke9m04tjWY9mKodWXK9HGtGytmRBlYYqOG4YpwwrbJu2aPk9yilQ+p44qecqmvBXRpF1QRujPiFDuiNNxfPTAWXae2D6m6OH3PY7WvqrKXxQ5PlYh4MNY3n5Tx6ev6xJW2CLCXnlrvwjjDrFsRVjIqpFR9jBWrcZjhWmDdYyQvu7LrDk+ZedTtAyxZSu0r2iKThxHFVYcom7jpKwyT8WKV8xCo9E6tginBbXFil/4Z6GIcAQixrLvKz2t+ERfjk8qVrxC5YhlLyz7NvR5HXUomx5p2YdYYYTqmzr2GolU+VJEVR1Vhzp5XQ1Ey07Eth7Ltg3WMUL6qi9Xoo1ZZBtv8+WFnI54X1k8VfSRdmVTZS37LrCOVYYVRxtCf0aEckdcGcenDilTYYrm//r91j4RxmFR5PhYD5EPE29X70BTx6ev6xJWuDKqwoVxh1i2dUj5zocVrg3WMUL6ui/Tdnw0VUaVbkqDx4pXWLa+h6hoWkbdHiQrDqHn0rIvos+GXZ3GS19YxxaWbVOs+IVlG1JUoQvLvq/0tOITs+r4FDV0dczYVo111cNVxOE8fV5HHax4RdWoorDCCX3Tqo59FUWOVBPK0l37RJnDI8rSxrJvg3WMkL7qS+VdK1xdytqYlr213ae31aFTFk8VfaSdZS+6HmUJsRzCIrp8ljyhPyNCuaPOluNT9hJiiBU2nKtYNNRW5VQVOT7aF29XA11eeLxdtk0dnziM6OK6hBVOFBW6/nytfSKO32PZppL6AFhh22AdI8QK08V9mbbjk0rRvHoNv1v2oY21X4Q2VVjhBY7PIiuRl+o6LyF1z6+v9LTiE7Pq+Fi2wtdLIVb9ZRGH8/R5HXWw4hXWNcdY4URRr7hlm8JKOT6pWPF6LPs2WMcIscJ0UV+uRBvTCqPt1j2y7H1dGW8X/hhlWOHapp1lL3RNln1XpHxAuy/nK/RnRCh35NlxfFIrHvVqWeHDRlCRt6lpaWFcMXUcH3n8Rd5+E8enz+sSVjhRtq9sdY84fo9lW5eqUR8rTBusY3j6vC9DcXysOIVlK0KbohcylWahXRlWeIHjs8hK5KU2x6gbtq/0tOITqfWPRZ/5yrIV6nSIbXF86oe1bFOYJcenbMVNYYVpg3UMT5/15Uq0Ma1w2q7zt7bH2/y9iLcLf4wi+ko7y14o31n2XWIdN8QK0wWhPyNCuSNP3/GRx1dnvqwVh2hqF1Lm+MjRiffFeG+/ieMT23ua2sVYYYT2VS2DaxHH77FshdJGD5rHsgkpyxOWfRusY3gse9HULmQlGqvCij+FsoKx6EXiuIGhBoJlV6fQtcKLLh0flV8pWPGKsrgt+z6wji0s26ZY8QvLNsRqOHgs+77S04pP9OX4WHnIwopXFE1xslZ069Pxsc7Zwoq3LkXX3Gaqm14Mr2Nfxaw4Pk0b822wjuGx7EVTuxDlLytMFXXamFb4on1FzlBVPEVYYURTO49lK4pmcnRJkaMmunyGYkJ/RoRyR18Zx8fbFBVoIiURyhJRhUdIUU9zWeFc5vgUNfRCvLdf1/Hp+7qEFUb4/dY5lxHGHWLZiqJGatGcVmHZC8tWlDWElXZWGGHZi77vy7Qdn/AdH4/ON+Vl+KL7ZsVp2QkrXgsrrOjS8bHs69Bn3KlYxxZVlb63U4+jtT8kjDeExQ3seC37OtSJu0/Hx7LvizYLOlhhRFFZYdmKumVLG8rSXftCVD5rpT8rniKseMXQ6kvts8IIb9O2jWmFK9sXkxKPRZ9p1/R58qvIqU5PaRMUER/T06bMrSL0Z0Qod/SVdXyEtd9TVUGnjLqkYMUtyhwfEe+L8XZ1HZ++r0tY9iLFxiIMF2LZiqJCtsyhtOyFZSu6Lsj7vi/TdnzaFDxWfHVJbVxYYUXdxkmTPJBKn3GnYh1bWNOiPEWjvUVhLFtRtbKQFUaE89dD+kpPKz4xq46P8rgVr7DsQ4rCWrZiFvKwKFq2V1j2Hr3nYIURlr2wbEXdsqUNfae7Fa8ou8Ym59R3fbkSbUwrjN9XdX3hCIq13++z6DPtrOW4PZa9x7KvM3rmseIRq8rxKZrH6AltYyz7Jsi7tuKfluMT2zal6LqEZS9Cm7Jeh5gwXIhlK4oK2bL8YNkLy1Z0XZBbtk0oui9DdXyqnuFU1HNlxR9jhRV1Gyd9NjBmodFYVnla9sKyFXqJ1rIvGxkuqhDLGrJFPYl9pacVn5hVx0dY8Yoip9Gj58MKZ9mKWcjDHuscRNk1W/Yey15YtqJu2dKGaeWfodWX02pj+n1Vs37C9LT2+30Wln0TitLOshVFHVZN3u8uwopDrCrHR1g2nqIX9VKmmqWih9o6RpXjU9awCOOs4/isxHUJy17EdmX3MSQO57FshVXINi2gLFvRZUG+EvdlqI6PFVdTrPhjrHCibuOkLA8orlTqxp2KFW9drHg9Ydla1jsuwjhjLHuPHCY/7U3PkHpBLTtPHLenr/S07OpgPTN95itRloZFnQdl52TZi76vow5ycKzz8ITHKfvIq1CdFsYdYtmLrq4jhSb3qg5WvKLsGuue00rUl323MYVlX7XfU2UX7g9ZibQrK0M0bT3sgCrr3CrrwC/CikesOsenzJsUsb0oexekCdYxqhyfsoZqWIjUcXxW4rqEZSss26I5pCFWOGHZNkHpYsUvLHvRZUG+EvdliI5PnVHBFMoaJR4rnKjbOCnLA3XoK24r3roULTFeB+vF+ZCupmU0fV5TseK17OpQ1/GpQxxviGXfFCt+sRLXUQcr7iZYcXsse1G3bGlDWbpb9nWx4hVDqy/7bmOKKltrv6fKLtwfMu02YB2seKuw4hGrzvERlp1HIwGp9kVDex4rjLAe+irHR8T7PaFNHccntvN0eV1l9patsGxDrDDCsm1C2TxSy150WZBbdqLL+zJEx6eo0au5v5a9p03lboURZffbos+GXRdxW/E2oW1FasUZk9I5UkaVc9VXelp2dZiW41P2DaU6lE0vXYnrqEMX19x1PdoHZelu2dfFilcMrb7su41ZFCbcXzQaovQK7SybcH+IZSu6zrtl7Y0Uij4CXIUVl1iVjk/RS7We0LZsSlRoZ2GFEVYFsNKOz0pdV5m9ZSvK5uULK4ywbOtSVCh5rDCiq4J8pe7LEB0fKx5RtdJQWSNmpRonfTbsuojbircpTZ0fK64iqqYjFZFSgfaVnpZdHabl+Ii2DZeqZ3SlrqMOVVMyy7Dii7HCibplSxvK0t2yr4sVrxhafdlnG9NTZVf0DMZpadmE+z0r2QYUOk8rTBVxO7gOVnyiSfsjldCfEaHc0afn+IiypQfDL+xa+4UezjA+izrLqKY4PpZTE59HquMT23i6vi5h2QnL1lN2fyx7YdmmosZa1bK4wgoruirILRvR9X0pa8jEtm2w4hd1C56yuciWfYwVTlSlqxVG1G2clOWBOvQVtxVvG6qme4RUrcpWRJ2GqZ7v1BWB+kpPy64O1jPTZ76yqDudMfV7GSt9HXXQCKF1LIs6PdNWeFG3bGlDWbpb9nWx4hVDqy/7bGN6LLu+bIrsRNdpF1OnY6xoAZpUrDjFXDg+AAAwm6hnUQ0HVage/d22UgtRQ8o6xko2IlcTajjovqqhH6e5HFIrzNCR46zrC69ZDUClQ6pTDQBjVIboeVJnin+etECNnifLfiiE/owIheMDAAAAAABzQejPiFA4PgAAAAAAMBeE/owIheMDAAAAAABzQejPiFA4PgAAAAAAMBeE/owIheMDAAAAAABzQejPiFA4PgAAAAAAMBeE/owIheMDAAAAAABzQejPiFA4PgAAAAAAMBeE/owIheMDAAAAAABzQejPiFA4PgAAAAAAMBeE/owIheMDAAAAAABzQejPiFA4PgAAAAAAMBeE/owIheMDAAAAAABzQejPiFA4PgAAAAAAMBeE/owIheMDAAAAAABzQejPiFA4PgAAAAAAMBeE/owIheMDAAAAAABzQejPiFA4PgAAAAAAMBeE/owIheMDAAAAAABzQejPiFA4PgAAAAAAMBeE/owIheMDAAAAAABzQejPiFA4PjX43d/93ewXv/iFuQ8AYMj81m/9VvY7v/M75r4u+P3f/31zOwAAQJeE/owIheNTwl/8xV/k6VKmP/mTPzHDitdee21itVSW7Wrk/vvvH2RjqEiWradIlm1blKaWLNvVSJv0GWqeLULlV5msMCnIgfrqq68msRTrggsuMMN3iaUrrrjCtIXVhyXV3ZZtEV3JinuazGsbRs+/JcsWhknoz4hQOD4GVY0BS1Y8OD42ajx64fjY9m3A8SmnSfoMPc9aaPS6Sla4KjZs2DAJnaZNmzaZ8XSFJRwf8FjC8RmD4wNDJfRnRCgcn4jHH388T4smiuPC8VnKn/7pn05SYFE4PrZ9G3B8yqmTPvOSZy2qHJS/+qu/MsOVoTBN1ORYqVjC8QGPJRyfMTg+MFRCf0aEwvEJOOWUU/J0aKq//uu/XhIfjs9SLOH42PZt0Ltoynsxlu1qpE76WJoXx8fSd999Z9qmYkkjOkozla9l04et+LrAutd/8Ad/YNrC6sOS8ohlW0RXsuKeJkoHS5btkMDxmX9Cf0aEwvEJKNMll1yyYHfzzTdPti5XGN+8FhpNsYTjY9vDbGBpnh2fNu/cKKyl2K6o0cEoDEwDSzg+Y3B8YKiE/owIheMz4fDDD8/TwFId+7/8y79csCkrNLSC0ocffjjZMlYYtogyp0vSqFPRykzWND5N5dG+cNqLzkvnF4fXNp2jJR+PRaqUpnFYbStKRy+lSRzOY0nb40Za2SIVMUWybD1FCm2s6/SNbKV9PDWpKL8ojCW/31LR9VuyVjYM34EJpRfcyxrTbfKkzlmjCZbKjluVPiJVyp9F1x7G57n++usne5fKsk1B8WmUJpamjhWVA6mywpZx9913T0IulUbYYltJ+V0o/XTPyxwfxaGyzZLyhnUMj6UqJ6tpfu7qGY4pSltJ52qFsWh6XXVpU0/1kYYabYyfE59ulnQOcRxNKJJla1GW71V2WmEsmj4/1r2QtE/3IrUN09dz0aT8E00cn6bH8hxwwAHL7kFYr1nyYYueW78/pOs6ZqiE/owIheMzoejdHk3NsOyFGl16QP0DHFNUaJRVYpIVlwqmOrJeGC5qZFqKwxY1MGM9/PDDy8KmKnZ8Uo/pZRU+lqy0VIEUhy2iSJatp0ihTVHloDQtUxiHUBhLfr9VAVpTnDTKaSm0KSpkLVn3p0meVAWSKuu6qtJHpMrnWUvWdCqr4lQaxnZV1JmWG4dNVRyuCl1vmdRAaDLFrI5Swxc5Pm3zc1fPsKfO+ZQ5c22vK5Uu6qmu07CowV8mnYMVV12KZNnGWGWFpar34+ooDttVG6bre9qm/BN1HJ+2xxJFdalX0zqpzzpm6IT+jAiF4zOhSGpkWfYpFBUaVbKcrSaKewKtRqZVKaj3LwxXV3FBnKrQ8WmaduFxRarq3OciWbaeIoU21jXLsa5SnF+qCtEixyGMQygfxApHhqoqLUuxc9skT9ZV3LhqWslY8tdjOelWo85SbFNFk1Unw/ydqvCYqdSR7r0VR0gTpcRhOQld5OeunmFRx1nx6uu6UmmiuJ7qMg2t8iVFOoc4riYUybINqau4zm4ajxSGb1oPx/eiy3vatvwTqY5PF8fSiE5ThfGsZB0zD4T+jAiF4zOhSJZtKk0LDSmMZ926dZOtSxVO8VIBYSmMJ7USCKcyWQ9bisKeiFSFla0lnYs/N9laiiv+VIVhqiiSZespUmjTVX5p2rBXuCobv69oKWRVZir4heU4SeEx6uZJqxJR76OPr6iSCfN0H45PUXqEcVp51qq4ylC6NpWPI1XhcVOpGvWxVNRjXZR/qhQ3pC3F5URX+bmrZ7iofEtRGE9X15VCV/VUV2komkrnEMfVhCJZtp6ikR5fPhc1xsNyUHTx/HR1L7qKp4vyT6Q4Pl0dq2kbSgrjWak6Zl4I/RkRCsdnQpEs21TKHnZl0KKMLIXxqMBT5aGKxfeGW0OXlsL9ZY1MP7877NEvevDVg+htRJFCmyK7uLEtdFxt15SnMA1jO6uiVfqENkVSYaDj6B7UndpRJMvWU6TQpsv8Yim00f2OpXnWZXGEvW9W5RynvbAU3vO6edKaMqB08/tTSEkfj6Xw/Ktsw7xlNfqK5tYXkdJILarU42NZKrq2OuiamyiMo6jsie91kaMV2liKHZ+u8nNXz3DRffbvA3is8w4brl1dVwqy76Ke6ioNi6YX6fy8TVE61y1TiiiSZSuK8n1sV9Vw7+r56epe9P1chDYp5V+K49PVsSzpufT7U69dWOq6jpkXQn9GhMLxmVAkyzaVooc9tCnK9KFNFaroihTaFTUyi+Z/FsUb2+nBshTbWWrS0NKc2/hlSK+w8S6KFNrUpUiWradIoU1X+UVpaim0EZb8Pit9q8JaBax1TapQ/P66ebKoQg+lY8YN25DU9BGWivKs1RMbjmZYCsOnYCm1cZkyBbXJ81hE1bz2WGGDvGh6Vhi/R+muxpO1T1iK84elJvm5q2fYknWf1egpu2eWmlxXF6TWU12loXe+YoU2wpLOIbZrQpEsW1En31vy+7p6frq6Fyv9XFgKy78Ux8dSk2NZip9BxWsptBErUcfMC6E/I0Lh+ExI8e5j1AAvGy1IediFpdjGo0ouZW6sVxi2qJEZ2oRYjd+wpyLEkgqyKpuUhpZGflKHi1Mcn7aVWpO8UqTQpqv8ojS1FNoIq3fIV4Cx4kZQW/l46uZJUZT+lmQrZykMn5o+wlLdhmbRPj3HYdgULFl2KY0+SynPYxNU0RdNhQrl7a1noajsqcJSiuNTRz6erp5hS+H+VNrKijMF5aOm9VRXaWjVGXE5Jiy1rSM8RbJsRVGHXqp8e6Sr56ere7HSz0VV+dfU8Qn3e6qOZSkM36VdrCZ1zLwQ+jMiFI7PhKJCuqwhEBasKlDVwxnu7+phF3UqkVBhHHUbmZbiXuMy27gXyVJR+ipsE6U4PnKkQpu6FN1XOcKWfdkoRWjXVX5RmloKbTyxlM+sqUop97KOfDxNHB8RL6NapTBsm/SRysoEy5nU9CSroo0dshQsWXZFTkZoY6ns2uqg/KK44s4PYeUvL29jNQD1fITxpGIJxydNVpxldFFPdZWGluNj5SFLTfNaTJEsW2FNTawj3wbp6vnp6l50FYZ1aKsAAP/0SURBVI+lcL+nqvzr0vGpOpalMHxdu77rmHkh9GdEKByfCUXDrCo8Lfuixmxo39XDbmV0SYVk6GxZCuOp28iUk2PJsrWUYlPU0CqSChk/z926nhTHJ2701KVoWDqcqhNSNO1A9zW06yq/1GnYWxWt1YMVh7Ok80/Fx9PU8fHoXhb1uIUK07pO+liqcg5iKc9ao1RW2CosWXZFPcehjaWqayvDUnivQ4oayH6/wlkK40jFUorjE+bXKnw8+m0pPJawVGd/Kpbicy/DirOIruopHddSaCMshftTnH9hqe61F1Eky1YUXbu/H1X4RYX021J8vCpS47EU7u8qHkvhfk9V+del41N1LEtxh5D1zq0U2oTE6rKOmRdCf0aEwvEJKFLcoC3rwQ8bDn0+7NbH5iyF++s2Movm6Md2RR+ri+0sWU5I6vxkq8Gb4vi0adx5ihSvrFOUhlI8z7er/FKnYa9zqFLqvGYrT1bR1vGxqErHOuljqcpxTum1bTrqaCl2oIvs4rxpqWvHR7Jsi0bs/P6qbzmFxFK5EF6HpRTHp0l+7rPM17MS2girE0bn4PdbanJdKVhqUk91lYbW+xBSaFNUl4dp2IYiWbYitS6toqvnp6t70edz0aT8a+r4NDmWJdURfn9Ze9LbxPRZx8wLoT8jQuH4BBQVlHUUxtfnwx7uF1U9qKJJI9NSOE+6aNpK7CwKS2oA+Xi8ndVzETs0wtK0HZ86iuPsKr/UadiLKllhinp3YzsV0LrHujYVxDq30OGrmycVT1zoh6tYeWKFFU2d9LFk5dmQFGfSCpdCUXqFDcwqp8Jjqc2zUZR/pTDeorwTj65bCsseUdXbKizFjk9X+bmrZ7jousLzLspnoYPU1XWlYCm2SamnukrDIhtJ11fWKaVziONqQpEsW4+leBq1X41N56lGtr9noY2lus9PV/eiq3i6Kv9SHJ+ujiX7pgrjCemzjpkXQn9GhMLxibBGEVIVVxRdPeyWVFGpoBNxQzBUGE/dRqYomtZVJSuuKnm7ovNUAa/rLRoRksIGbtEx4wqiCUXfi0mV9T5QV/lF12cptAlRuhbJ6uHyFElhiqaZSGEcdfNkUUNO2+WIFD0PYWVVJ32qZIURZYrzaF2aSA3OlHjaPhttFM9HL5pqW6Wwt1VYih2fIjupTn7u6hkusklRajx1risFS03qqS7T0HrPJ0U6hziuJhTJsvUUOYdqZMvBKbomlaVhPF08P13di2k/F3H5l+L4iCayytqyvF+mOJ6QMrWtY+aB0J8RoXB8DJoUllZF2tXDnlqAWQ9X+N2HJo6PKCqIixS/CO8pc1gk7wyUDf2GKipMwmNaatu48zT5WKMUftAvpKv8Uqdh7ymSZespe0m9SPEoSZM8WVdxL2ed9EnNszFlzmTb/Jf6fHhZo6XCUttzU3o0UdHogjX6WybrWi1Z5XUX+bnLBl5Kr24sq+zt4rpS6Kqe6jINU54V61nVOcRxNaFIlm1I3YZyPFrqafv8dHUvVvqehrLKhFTHp6uyVpTdU+23FMcR0mcdMw+E/owIheNTQNGDYSkstEO6fNirnDFVeFYjMuwFaur4iJRGftzAtCh7+DXV0NsVNU69fMVkycdRtL/LQqHuyE9Zg6Kr/FKUdqFNjDVkr3tl2YbUuX4rfNM8mdowCPO/p276pObZmCJZtk1I6aBRD7EVVljq4tmokydSyoyy77+EKrpWS5bjI9rm5y7LfFGnE0i2Vhyi7XWl0kU91XUaljmQmu5mlQe+fmlLkSzbmKKyMVbZMy7aPD9d3Yuu76loU/6lOj6etmWtR/W/Twv9G3acWQrDWhTJsl1thP6MCIXjk4AytFBGFfK0rRe++0aVl0YL/HmkPGhdo8o1TAtNlwhX7ElBD78K9fA6ihpc2q70lp163/2qNbOIzlXXEl6bKp2yxvE8oWvXPQrva9/PifJDeEx/3CY91mXUybMeS+oVt2zboPOI071o1HWl8c+EP7fUtLNQ+aewGoEO4+r6XnsU90rn5zLitNQ0tSbn0/d1zUI9ZaHGrj+nad7HuqhxHN53lUP6u2iUtIiVfn5WipUs/5ocy1rSP6aoY8KyDbHURx0zREJ/RoTC8QEA6JiiEVJVcJY9AADMH5Zix7toVC60iaGOKSf0Z0QoHB8AgJakygoLAADzSdE0OY0WaQSpyOmJ3xdKVRhmNRP6MyIUjg8AQEtUSVWp7tQUAAAYNk0WKZHieKhj6hH6MyIUjg8AQEvKlgWWrCVOAQBg/kldqMLLem+IOqYeoT8jQuH4AAC0pOjjx1q1jDnXAACrGy0MpYURylS02qSgjqlH6M+IUDg+AAAAAAAwF4T+jAiF4wMAAAAAAHNB6M+IUDg+AAAAAAAwF4T+jAiF4wMAAAAAAHNB6M+IUDg+AAAAAAAwF4T+jAhV6PgAAAAAAAAMmVA4PgAAAAAAMJeEwvEBAAAAAIC5JFTu+EjxDoQQQgghhBAaoizfZsHxkULvCAAAAAAAYIhYWuL4IIQQQgghhNA8CscHIYQQQgghNPfC8UEIIYQQQgjNvXB8EEIIIYQQQnMvHB+EEEIIIYTQ3AvHByGEEEIIITT3wvFBCCGEEEIIzb1wfBBCCCGEEEJzLxwfhBBCCCGE0NwLxwchhBBCCCE098LxQQghhBBCCM29cHwQQgghhBBCcy8cH4QQQgghhNDcC8cHIYQQQgghNPfC8UEIIYQQQgjNvXB8EEIIIYQQQnMvHB+EEEIIIYTQ3AvHByGEEEIIITT3wvFBCCGEEEIIzb1wfBBCCCGEEEJzLxwfhBBCCCGE0NwLxwchhBBCCCE098LxQQghhBBCCM29cHwQQgghhBBCcy8cH4QQQgghhNDcC8cHIYQQQgghNPfC8UEIIYQQQgjNvXB8EEIIIYQQQnMvHB+EEEIIIYTQ3AvHByGEEEKrQj9O8Ir/rqu24esqPp71dxeK40VoXoTjgxBCCKHZ1aY12cf3jLKvbhtln945yr596xC38evxPkMbvvs+Gx17eTYa/dxxqOOQBGQnDstG+5w6iWm5Nv2QZbufeb2z+6njFw4rrpjJOWxxdHbGbY9OYkrTGxu+zEb7nenC63j+HOP43Tkv2z7++/hr753EVKxvHT+76FZn/7MgfBnheRyRHX/To9n346gQmnnh+KCpa6czH8lGR969anjqrY2TK0cIIbSodyf/ev2Yff7MKPvxVodzeha4ZZRtvFfNl8/GZoFe3rDJNcblkBzhkEPQgL3PmMS2VDc89Zrbr8a+7A53HDX5XQeFPzx786tqV+HGp19xtnLejnToWPWv6bjr7pvEZuvFTz53dt6B03HseIpRGHFo9sS7n0xiRWh2heODpq7RXuo9u2T18PPbJleOEEKrV898vCkfbfDa8Njm7v+fj/+QPjozy5yT84NzfGK0fePa5U2Y8UiEb5TLOWngAO19+iS2Rb3+2VdunxwE7xzUiduw2+yo0lGSDd9/5+z86IrCCx0ziqeCY6+/fxLjcm34VsfQNdWP1+aIbOP3TJBDsy0cHzR1rTrHZ4vLsi+/m1w8QgitUo32OjP7yr9J8sPT+VS2LPMj4t9kG+9wf99uOz7iW+3PXh+bOz3y2npXxoaOT0MMx2e01xluXwMnqpDDs93OWDOJfbmOu+GR3GZs2/y4ZY7PNidfbYZpRck0QYRmQTg+aOpadY6P47FXP51cPUIIrT7d8dr72Winc7PPf9S4x4/ZhsfGzkyWvZPvz7LXsu9uGU9zix2eBeQofXLFxD7L9r/sXle+djB6ETk+n33/o9uuKWeGbRu2OHZyhOUav9fjbZte0+GFjo9SfTzaY4VrwxG874NmWjg+aOpajY7P6JA7Jlc/G9r0TZbd+cwH2Rm3v5Idde1z2THXP59deN8b2WOvrW4H7cPPv1/1aTBtPfXmxoV8ec5dr2ZrX1/+Xkdfevbdz7O3NriHA3UqjfGMdj0tG+18bvb2V5rs9nL2vXNkRPbNI7lN9t3T2Xehk2PwvXN8fli/OMIw2v9sV75ajfGa7H3aJMaxHn79Q7fdv9vTJUdmGwtmho0XGmjrxMnxeWAS41K9+8W3bn8fjs9RedwIzapwfNDUtSodny0uy76Y4nQ3TbU78JIns9HBt2SjHa7KRpu5cypic8dOV2ejA2/Otjn1wez6tesnsSzX6Oi7s9Hx92VbnfJgtuOZj2S7n/tYtvcFa7P9LnoiO8Adb7+Ln8j/3s1t3+HMh7MtTn4gGx1zb3bnsx9OYliqgy5d5/bfk41OvD/b5rSHsp3PeiTb47zHs30uXJvtf/GTDhef+63j6Hg67ui4e7MtXbxdaPTTW/M0eM41gLuQziu/nhPuz7Z2aak02PWcR7M9z9c1PZHtP0mnAy55Ik8zpZWubSd33br+LU95IP9X269fG78IXqzR0eNj5ml49jgN99XxXBrqePtetDY/h13cuWx3+sPZZi69FeaF9V9MYiiW0lv3XNej+7On4tZ1uLh1DXtd8Lg75qNu/0O57ZpHfY9+sa5+5O1s9Ivbs9G2VyzPj2LLy7LRHtflaXnu3a9NQnWrz90zMtrq8my041WTLc2kNMkXNtF9d+m02Un35/l0u9MfyvPsLi5tdI/3yvPA+Fnx90X5W/dpL+WD8x7L74/yzLbKC8pLuk9Kfz13R9yZvffZMObQPvLep67ccU7Kjmdlaz/4NPvyme0WHJkfP7lkYrU++9r9/X3BOz45t7smzOe3TOzd/TpQjk/ZVDftkzMh/HszIXp/x/2799LpWtc88abbHsYj2/Bvf8wwLk+Z83J49nHBOzHVjk/RNXh0LYdkJ1xnj/g88ErVtEDFUZZWxWGfeI9FDtDsCscHTV2r0vFxTGMk4Y2Pv85GR7lG0lau4Wg1KFNRg/SQ27Nz7nx1EvNYo4Nutu3L2OHK7J7nP5rEsFS/uOKpcSPXCleEHLVj75nE0Fyffe2uZ2vX8HVxbnFy8Tz5Ohodedf4/OJzbso27j64a93wdfkLxaPdrrHDl+Ea/C++l+D4HHiTHd5ii0tLHR8db/QTl4fqppHLj+s6Xi1RToePv80o02gv56CF59ojz7yzaXLU2dbowLNc/jrXcU52xl33Z1/eOZnS5hydb18/aGKVZV884JooBY6PRoO+zt/x2TA2dhrt5+JdaKhHTsPeZ2RPrt+QPf3BZznPGPh973zx1STGsfY5z+XJMK4lHJ6dfvfT2VOTsGF8z370eXbszY85m6LRosOzT77/YXKUpRpPrYuuYRJmdMLV2br3i6/D85Sz+XISX6wbn39rHJd5jMOcs32bi2PjsjifdnGuWavV5kJnL4zjcOf4LN4ThGZNOD5o6lqtjs/osDsnKbAyUi/8MofHNcpOv+3l7Ik3Nmbvbfwu2/hNlm36Nss++fLH7E3nJN3xzAd5j32h87HbtZPYF6U+5zc+/ia78cn3stGhty8Ps/ml2Uk3v5g9+ebG7NPiT3Es0Tsbvs1uWfdePtKhxvOyOH96S3b5g29lr330dWY3I+pLvesL8btjKm26kM7vrU++yS594M1stGdJo3j7K11D7kaXhnfkvfn5b5d2pu12V7h7WN441/mvc2l+5u2u0bLz1cvjcMc7/57Xcuej7mik7OUcHL7mWeeMjZ3FBQ6/M7vmsXeyt909LNMaZ5M7cpNwGt169p3Ps28Cn+7jL37IR7osR67rjoTR7tcuxL39GQ9PttaXmdY98fArs9/gfGPT59lohzMmjs+52S7H75J9r5Eb58jI+fn80aBZ8u1D+Xs+maa0TRwej8J896a+57Oo8YiP1Zg/PHvmk+ajtvucp2/chPEF7H/WxKpYo62PtsO686rv+BxR8gWjdI0dnzjucfyjbY6fWBVrdJQri3L75WmN44NmWTg+aOpatY6Pa0yvxHS3b13D0U/ZWuCAG7PHX09vKH7xfZYdctXTy52Ova+fWNhSUzeernSea1y3UT5tK4hPowN9zCiPG6y3PfX+ZE+30shKeJyDLls32bNcaiI98uqG8ahIECbHpfOnX5WP/Hg96pyE2ImSk9uFLrn/jcV4dyletSqUvm3lR9fEWXe+MtljS1epaZcLx3HIee9K1z3unKsgbjn+TZ/Vhetyz84tLg9tcPfoc5dhFV+MnL0lx3V5Qw5rbKfwiufF9750z/LiiNsdBVNGZ0l7X/3QZJrbeY4zsseuGzsx/ls9X9ypZsli0/7HT67MPtd+TYOT0+P+/cbx+Qu7TiwWlY8kmc7CodlzG6pHL4u0z7nFjs/hV5d/J0cabXOMGVZORrHjU7TM9OGdOD43PVfk+ByajQ46Z2JVrG1Ovc4IK3B80GwLxwdNXavW8XH0Pd3ta1enyjkJG1MHXvrkZG993eoabktGjfa7cbKnWPnIRXD8u1o2zvQuSRhfauO6jm58Yv3y6VZ7LB/d6kJ5/g+Oc+S1z032lGtZOjg2Pyl9Sl48ivfS+0WTYurp3c++XYhzxzPTRkrCkS85NKka7XPDQrin3+5umtfosDsW4vVc9uBbk73pyh3/SWfBgZcWO7ReepdryXF3u2ayp1ganfXP5LWPpb/3NQ05n82VByfnIz2547PrL7JNdwQjOXJqNPqTvZHbL+qD7Nu3j80+f8I5Rq/s7/5+Ybw50uKIz9KGuP59vs2IT4njc9SauyZWxZpFx+fmZ99eiG/ZMX7SzvFZi+ODZlg4PmjqWs2Oz+iYuyep0I+0IEHYkDpszTOTPc2lxQQW4tz3hsnWYsWOz90F7/OkSi/oh/Gp8du1it5bef2jLpocS6U0DI9x7PXPT/ZUa9n7IzVGJuIpaV1d25ffu7gncWrqW5XyhQwCJ7POedz3wscLYTUtrgsp/cx34FyeqKv8nbqJ45OyOpwWulhyTFc2piifBunstRLjLOuBt9392unMseOzwznZVoeOsh+jd3i0mMGPH106CVFPK+/4HJodvaa6DJ9Fx+eWZ99ZiC+OP8Xx2RrHBw1UOD5o6lrVjs/m/U132/b0yEE4srpnMlULK8HtXj0KEq8ad69rrLaRVsMK4xvtXz3qVEfvb/x+seEbjYqkjsbUkW+0ek686cXJnmpdev+bS8KK5xNXoIunIOq9oy6U9+pPnJEzbn95vLFEo4NuWTyPrS9f8k5PikY7jqckPvN2N47PgvOhqYDR1E45MnX0wIufjOPZNW1UUqu7hcdLGVGV8tUMnf2R11Q7mtPU6NSbcodnPOJzWvbEjc7RCUd8Jnz9dLOmyVQcn6vnzfE5orXjw1Q3NMvC8UFTV6eOzxauoaoGnebVW/tnkEdf7b6S0PsaSxptW1+Wr1LWlfTCeh7vNpdPthQrdnzUS99GetE8jE+jWl1qYbrRNldkV2k0IjyWc4T0zlSXkuMWHuPUW1+a7KlWvvJc5Jzd9OR7k73lih2fdz7t5k2p/MOIk7x3yQNvjjeWaMmIoDunusmr1QUVVotldCG/cMJ+F60dr8Lnz82xx3mPTazSlC9g4cLtdObk2zQV2uP8x5ccLzVv3/3ch7n99qc3X4Shb6n4GW176qLjs8th2Ze3LXd6xDf5ez71P4OJ45MuHB+0WoXjg6au1o7PdldmF9zzWvbBpu/z3mY1nFSV6P2W9a4x98CLH+cNgtFOa+zw0+b46pdj6yp+r+e4G+058U110X2vL8RdpdjxeeCldo6PvmMSxqcRgy7lz3eP88eN3NGuS1cQu/u5dlP1Yo32Xzqt7sw7yl/sj6X8H4a/6N7XJ3vKFYdb39E3YHLHZ8ux46NpbGXSh3PDRQ3kMNVdPe/km1/Kwz76avv35a56eNHRfX79F9ktT723MHqV4xz9OqmkaYsKV/btq1D6Xs/CscTBt072VCt3ZH+abr/SeuzdDe7ZGq/kNtrJOSj7b5UvWGA5PuMPmdZfQp6pbunC8UGrVTg+aOpq5fj85OZaU8XUuDvhhhfG02Os+OqgKSyKxzU2tOKZXojXyl/5t2f03ob2W+FiXANHHxTtSldG70yoMalVoLqWnw5mV9uLih2fB19q93E79Z6H8ekjrF1pzaOLDd+3J+9k6AOTfR1PClflEufeVW/Vuzh9b0j8sGns+Gg58y6k/OBHoapetpeTs8TxcdRdXU5n/czbm2o5JEVaWP0wmMIZLmstbnDPearufeGj7PgbX0h+/vLl2oNjjX5+22RPtc6/5/V8afpZ1b5X3z92evL3e87MzjvfNT8ihyfky+e3n4RM1+w6PkXLWeP4ILTSwvFBU1cjx0eNva0ubzV9Sy9Djxs6iQ6KHJndrs1Oufml7IV3v8g+/SrLv5R+qJZ5PtjFoxElTbWzwlbQ5fc3Rj+fNN4aNJ7qyK/Epe/+lClerrnttS5b+arDXu4FJyRYpluN1rhx/mZH78NI8QIUFyaO2HjF55b6EcvY8dGIaRfKHZ+JU5wy0hGfh/7e8GXH8wkTpG9KWQ7bTy9/aun5uTzSl/YJFw4Rh9w+2TN8jXY52ZXbk2luO5+YvXKb/X6P58u71Typ95zN7lS3bh2fLkqfPhc3wPFBsywcHzR1NXF8NH1ttMPVnfTyqmdfX7/PGxrxsZwjs/mJ92ePvfrpwhScp97emI2Ouy93gpJHdSrY7rSHxpG3VP7NnmhFqisfLp9u1FQaPUt52Tt2fB5pOSVp57OiEZifdeP4vPLBVwtTtG5et/SbPTpGeMyjOlzkIP4mj94LSZU+NrrkXS6XH1OnisUOx4efd+/4aBS0SrHjl+Oerdc/Wvr1/L4Vfhco7FD5zKXnkveoNtdoYA9DqE77XvjE4nHECn/kuC998u0P2Wh755Tk3+5xjs9uh47f7ymY6pbj9v24od5S9Ux1SxeOD1qtwvFBU1dtx2ebK8aNq/1u7HR54Te19OzPbssbHJudcF/2xOuf5e8JSXKOtjjpgcX3PazzasNmly4cq430MvmShpOjq9W6mmq009IPgcqJbKNlU886cnwWVsHbevnKYjc/+d7SYzqbrpq+o58Eq5o56jiqP7l03ZKwyr+pihc3+Kgjx0dJ5x0fpVuVbl4Xpa3HpXHdxQTaaLTzmvy4u53z6GTLohYW85igPNiH9r0ocnyOmA/H5/aX12cL7/eIvbfPP0JqOjwTvnf7v3i4XhNl5R2fw7Ijr75nYlWs0bbdOj79jvgckfQB061Pw/FBwxSOD5q6ajs++4+nmpx716vZ7kYjpa3Cd4YOv/qZbLSvVt1KGNnZZU3ea3zyTS9m1zz6TnbjE+9lN6xdn53jznOrk53TVDENrovpbsu+A+LowqFqo9HOSx2fx1t+tHXX2PHpYCpf3lifjIDscIa9MpZf7cujBnsXWrKcs2PNY+9M9pQrfz8mGrV59cP0UZJljs8XHTo+W48dn1sS0yhOgyXsuiY78prulxEPFS7W8dy7y7/wnztn4Xtzco57eK6WOT4dLkE/Te1/tSv/vNPj2OtI29mJ+VYfM/1h7SSWak3D8RkdduHEqljNprr93GFdy1HZZx3kvVsKP2DqHJ9dTplYFWt0rCt7loTz4Pig2RaOD5q66jo+258+nhaWrx6105pOFwYIdcZtL7vGR4nDo4aJGp57XDf+Fosabz+9LRsdcVe285mPZLc/tfwl7fy9Itka8e1yVtqSt2XyS/su4Bq305bvSfesfeOzyZ5m2vO8aMnfX7R3fM52zqmP7/WC6Xs7xosqNPigpaX8/bAg3tSX5+MpYqfckv79Hyn+gGm3js847lufWjplsEhyzvUh2vB8lrHLmuyAi5+chOhWCyvr7Xj1ZMtyxav7XZPooNbRMsfnqOppVEPQ6OfuWhYcn7Oz6y+zHZ0Yre72+WPpzZSVd3wU9yHZPa+sz0dhhEaCVSUJ/X32PeuiMCFljs/PHPG1CLftp+dnepPPHys8pv9blI0MFTs+Y86/9+lM3Shh3P73PS9ptEgjUsvDCRwfNMvC8UFTV13H5+w7Xp2EdGFdw0AfcexL+TsUeonfOI8kdr82e/jl5ZVA3miNbTe/tPU3YsajUy4uT+KHE/uUGqzhOWnK4E8ufTL76eXrsp9f+XS+It6hVz+Tj64dvmb872EObdO+Xzibn13xVHawsz/wkieXrYI2+kX7F8AX0m2f4i/lf+n8gvj9qZff/3Kyt7kWVhKbUDVKctmDby5baUzpVlfeOfF89EU3Qxih43Pb02mOj6SjL1u4wmL7K7P9L35iHKgD5e/4Td7hKXNmlFeXnIcrF7rWsnd8jq6eRjXryjuotjt10fHZ6eTs1ZttR8fim3zUJy1/T2XEZ+F4cgQ0SiOH5aeTf4X2HTL5N+bwCsfHCiOOcOhYIf644d8/y064zv5cQvFUN4+265r8tShO/a3f2lcUDscHzbZwfNDUVdfxuT/4AKbeX1EvrXqh+pKqpX3On6y2ZJxPJa4RGK8+98oHX7r4lo8mWU5SHY32WNogDpflnZZix6dzWo74PP76pwsLBFzx0FuTrbZ0rPDY+tJ+W8ULJ4z2viEbHXdvtvPZj7gG/pPZAa6RnzsEh96x7H0ppe1dz344iameYifu4yk7Pl4PuWdgWT622PWafDppW41OuG8cn8sDZatEKn36cHxDLXN8jhm+4/PpDz86Z/V0V05PFjbY+fjs45LV3CzG7/pU9wpNx/ERckREsTNgc2j28Xf2SOtopxMNe4+OZW1fzrHXPzCJcamqHR+LlOs8NHvuo3aj+gj1KRwfNHXVdXw0XSyUXg6/Llh+Vi+H/+KKp7PDrnom/yChlp3uQmsedRWFeoaNc6rimkeW9iTnvaDbXLHMbo+WDen4PRQ1Dqet0S7ROXVNS8dnoeHrGrVV70NdpW8khcd2+aFqOe8q+QU1ktFKgu6c73m+3YdUl6xU5uhqxEexeAeh7jd5QuXP264VTrNLiz3Of3wSor7y53DyHaRtE1ZWXDaVtGPHZJ/4Oz5z4Pg8/8mXzuE5c9Hx2fWI7Kuajk92m+PzGyYxFmt6jk9TjpgcYbl2OkuzAqww9Tj2evtDsM0cnxQOyZa/JYfQ7AjHB01ddR2f595dWoE9/dambLT9Vdnzbnv+LZ04jBYV2O+m7LyaH4a0lL+js71e6oyOUUE8kpB/sd5aCtudq93/l6ZlPeXbXTnZMz3FIz53PPNhPm1MK6fpWov6cbVd+zWaJ1uF0Td1NBISxtdmcQPF69912eFMe1GDWPG7Hmfd8cpkTzPFIz76CKWcmosfeDM76eYXF5da9zinwn9ctY1ix6er5awVi4/7zoajUaHyhQX2dmVEcK4xm59U/yv/0mm3vrQQh6a1Vkmja/lz64+95aXZJx1+c2ivOXR8bnheK7rp+z1yfNy/ex6cfVuxopvFl/eouVKe74fm+Bx0afHiFY+88f7Erp1j0o/jozDWqJPbfsBZkyMgNJvC8UFTV13HZ+3ry4fR8xfEt3INWMN+CTtenZ1z5+I7Qk2kDz3moxhW/AW8+fHSCvvINc+aduLRV5tPdxvtu/wF8bbvDbVV7Pi0XdxASxyH8bX5gOnhug+TeF58L23a0rL3UNz1tVH8js/1axdHL71G+0X39Sc3T/Y0V+z4vN/RB0xzx2fiHLQdlQqlbwKVTZs89+76HRujva4bh9+5eFGDWPFiHSfe+MJkT3vteX60cMccOD4n3/XMeKTHj/jsvWv2neHYVJEvb/3czpNYbS06PmFjfvx7thwfd067nTSJvVij/c8a2xZcUwor5/i4bZsfmS+AgNAsC8cHTV11HZ+rH1n+nZOFF5QNexPX4Hnm7bQv3FvKR2z0grkVd8xk+e1Q+cdPLVuHprs0lUY/ljScHO9+1ucbUNVa5vgYjmsd7RIv2X3QLZM99ZUvXKE49r4+n6KVojf1Xln40VDHvS80b+DHjo+m08XKjynHPrA75MqnJ3ubKXZ89EHaLqRROR/n/S8uvo/XhXSP8gUuwlEXz1aXlb6jEysfvZ3cx4vue2OytVp7xc7Jtld01rmwe7xi4Rw4PntfeW/g+JzjnJOtsx8bjPjog6ZymLKv7Jf1pUXHJ3wPZfzv8580n4DVueOz28kVY1djyYkYbe2Xwg6vyRptsTn2uhVyfLY4PNvUzWxZhHoVjg+auuo6PkUrOuXfyjHsC9nismyfFu8HfKEGnt6pseIOWPfmxkmIsY68pni0J8c1SJu2o6xVsVK+nt+nYsdHiwm00bJrbDj6seTbLGoA67s2Wp5c6L0PoWmN+Ta3T/s94fFFi+l2o4OXfsNGH6G1pNXtQjs1/tt8+yl2fN7qYPqc9Gq+cMc4zoc6+DaVpbuf/3DhPaKQOh9/XfJRUi3G4O+/7vmSex/df+O4TReYiLXbudFo5hys6jY6+6bA8XH/HrxZ/s6O6dxUoVGfu9RssfPV2PFRQ/zQiEOyFz9q3tFV7Ph4B0DHsPaHaGW3I7Ijr67/baYz71znnlcdR3F4/LVZx/LbD82Ob+X4lMW/ePw9z+1maX+EVkI4Pmjqquv4aDGDIuXvX1hhytjnhmxDw3n6mt+fN4yseB0HRt8dyXvu1cgybEMef63ZqMgF9y5+iHEB18CbpmLH57GWHzBd9j0dY0QtRfkqaWE8bXAO1BsF3/+pUuz4nH/P65M9yxV/80cN9PCDu6lSbo8dn1c/6GYVEC1o4ONsO7pXpssffGvRcfXsXbwceajPNGIbfceoFbt3s4jILvHHeefgOz6jk2+IHJ9R9l3NxQ1iPnvQbrpoMptKF+U6dTfpX/29tOupvoodn0Ozg666P4+/CH8+XbzwrwEVH5/+Da81xO8v63Yodnzc3z85Lw8rrPj9sfUboaEJxwdNXbUdH+c4FLkpmlqzsEz0Zq6S3eZQ55jsl422Ot797SreOC7P9lfWnvp2yf1vOKfpxvG0GyvO6Psy+bsPJVPcQvY6r9lIVP6tmahBqx5qbZ+W4nciHn1VVWZz7XDGw0vi0wdh60ormC2k0zF35wtmPL/+i+yF977IXnr/i3yZ4lecIyBeFu5vbdcL8C84O33dPx8RCM5j61MenMReT/mHb4N4zri9eLGEfEllrQYY2I+OaObYxvkkXi2xqU686cWFOJ9fXx1nm6yp7y6F16C8njLb5qDL1i2EufqRd8b33qH7+5K717rni/ff/T25/7KRre7RkuM64kVXmmjneDTzyPqjA7Om0XGuzAsdn5+Nsu9bOj4/3DLKvn5pr8kR+leZ43PUmmE6p6WOz4HnTKwQmj/h+KCpq7bj4yjrnVav6SE7j7K39x9lnx44ZoPjLff3XXuPXOPoWDNOvUNxX8W7Gloae+uTHxx/T8WKw7PHddlXUYsub+BathbOuWsqTf1a0nhyaKWwaalrx2e70yPHZ5/qZW5j7XjmYhwPvPTJZGs9bX7SA0vPwzkSer+lruL7dfLNL0722Do1WInMc8bt9e9vPOJx61P1v7ljSSus+Tjf+bQ8QbQUeP4sNZwqePR1zy25Bt2DqiXJpYXvITnntcmbTfmIWeT4jo5u3wAO82XOlEdru9DoaPf8RyM+P7R1fBw/Oufnx/eqFwjoQmWOz9FrhumcFjs+R7gyCccHza9wfNDU1cTxOeCi8i+3f3baotMT89EBo+y6PZ0DZI0AbXHZkg+kemlBBU2xy3vJ4zAxu16TbYxelxgdcodtW8KTbzSbSKDllZc0noRr5H45pTUOtGJWeC5t3kuR9L2VMD7ln7oa7Tj+dotGT5pKU/bil+xPvvmlyd50jQ5c6vgce/3zkz3FGh0WTdNz+VIf862j+Bs56jDoQuHUvappeHpO8qmf213RyAHRKG14DRrxqRpBuvYx1+CbTJH76RVPTbbWlz4yu+TYjvc2tnvIto9HMw9dOmo8RI2Ovy5wfBwHbdn8HZ8ILYudfXrV5Ej9qdzxGeqIj6vT8mvA8UGrSzg+aOqq6/hoxKXyjZwfv8g2HmU7Pp5n93XOzxanLj/GVpfnU170jZd86kmdpav3vn5Zr3/+gUrLtoI9G053k5Z94V8cO50XpeNzaev4bH1q5Pjsed1kT5rCUYJj3O82yvNueC7Ooaqr0YE3LYnj8DXPTPYUK19VcPLhzQX2uLbWohjLRgZ3at+AXDISkhDfp187ezk+zv6NaMn3FL320dfRNVQvS613wrx9m5XsXtEiDtHqfsffWO20lmmZU99i0YxZ0eik6B2f/XfozPHJbh87Pz9ubD5CnqL5dHwY8UGrUzg+aOqq4/gcdlV1o3BBX36QbTzMdno8r+5f4PyoUZnyXaCQ4+5dsqxt3pu97422bQrbNh+NONMa9XEcclW7JZCbaGF0ZcJDLzebWua11SkPLomv7ovl4XshbVcyy1foC8/F8cBL9ZZwHh2w1PH5WeIoRD4KGY046f2nVO1zYfSxTEfbD46G784oD1bpYy0OMlkl7bgb6n8P59rH3104njih4ps6+obUgrOyX/0pkrGWOb5bXrZsimsdbXVqlLdbLNU+Kxqd6RzsBcfnnGy0917Zd85hsRyZJmhpbMX344fnT47YvUodn6txfBAaknB80NSV6viccsvSaUR3P/dh9k3VfP7P3610fp7QyE/ZwgcJHHnN0pEDvceSv9ti2Nbh6beaL8EarxaWs3nxcuB19fTbm/LGc5Vix+fBhu/UeG2hZcuD+DRlK1XXPuYayn4lsA56079wjdz4+zoawakjLc4Qht//kvT7o+WOw7ByhO569oPJ3nLpJf14xEJTzj75stnHOLQQwMJ7Q+483q14v0fKPwas6aMKs/2VtRc6WLIktTumVk0skxaC8Pa3rGv/TtMx1z+/ePwJWia9qTaP83YHztm0tecV4Xd83L97HJZ/jNRyYpoi50cjP9++8YvJUbsVU90Qmh/h+KCpK8XxOemmpS983//Cx/kUmV3OemSypURff5RtOs52ejw77rCLedxKXGPr9qeXNjT1cnrt0aIC2rx38elXPy6bZrbAIbfnK4Q1kRzOfCnoSaNZDegyxVOy6o6IxBqduPjyfI5zMFMVTiu77eluXuYfHXHX0vNxjlWd9220OEMYXh/ITNVGTRXTcupBeDkfH32edm+18uCSsGK3a7JXP6y3tPWTb3625D4ffNm6yZ5yafGD0Pk6fM2zkz3VevbdTQujRTnH3zvZY0vTVxecLJdGmsraVlpIYdl3nVz6NdXo+PuWxrX7tZM9w9Vx+gbNguPj2PnI7IsOR3xivnhKzZp2I7mxVpfj4/7G8UFzLBwfNHVVOT7xt3Bec42yvLGh/Ztdmj2cNHXq2+zLi2ynR7x7gEZ9zl927FJ+dmv2/sbFPur8/YZjJy88W/ZNcI3aNnrzk6+XvwvicY1GjZ6kfFdH7zP89HLXgNnb3atgetXOZz9SObUnbpjfFjmKdbXs+zuJK+Dl3zgKzr2rxR7OuevVpecjTij+unys0Y6Rc3pcvXex9N0f63s28QIblt777Lt8gYclYYVzELY7/aHKbxPpGHuc9/hSB2T/G5NHbuRghfdEv1MWd3hCU9bCkUTnyGjFxTLJ2V+wd85zVwrfGfI88mqz99hGR969NK4WqzvOim7QlKqdznHlkJwfcVL2fgeruhVy2yj7/H41beovNFKkfc6bP8fnpue84xOD44PmWzg+aOoqdXxcIzeU2qrLPlLqGm7vb0prxf7w6JnZpwfZzs9oyxOWxluEc7ZOiVbvukejIB1MbbNo+32QDz//IW+MLmlQhajhqUakltt2Dfadznok20EfCdWqVXoBXqNGYeNUuO1PVKw6p0btPhesXd4o3+2a7Ia16ydW6dK3d86VkzF5GT7k51c+le8v0q1PvbesZ17fZGmrPD8eFTVWhbtmve/yWYnfoKlhy1bxEs7pOPmWF7MvaiyNHS+QkOMczj3Oe6zy+zx3P+/ybui4hOi+73ld7tAfeMmT+YjMwZc/lW11ygPjPOVHUDz73ZB9XsOhfPz1T5eG9/z01uwu90zF0jd18jQLj+vyg0ZzyrTsw6DH3pPVSN5C6dkc7RCNuAn3PK159J2JVbW0IMqFcszj0SOH8lGThR9mRc99ssmlh3N8cqfHscOZ2ZM3GA5Llzjn5wvnXH3/8YWTs2invRccH42OeMaOwlFXD3M565uffcud/yHB9Sxe0+ggHB80v8LxQVNXoePjGl0ffr6073hzTXOybHe5prSRuUQb38g+P8VyfI6z4w5xDf7XP1o8kEZ5tj/9oXED0bLvgJ2dE9KFjr/hhfEUvKhhVQvnHN1b8q2jfFRI7xZpZa/4/ZEQOUM6l31uyFey0gciLd2w9t1sdMw9ubM02rIkPqHj7XFt/t7LmZOPgFqjVEvs5US7+Os6QfnKcK5xvvBOSxFqoDunRKMykl6+z0cetMR3WfoIhc2djnuyByumB8rJLHRedJxdnFPurlNTRC3JOdK3p8zwiegbNHUH0TTdcJlj7NE902ih7p+mA8oBj9PM5ceiJaSfess1uJ0jbzomQum1343ZHjWmFkrvbfw+20Hf21G5VXYPdV06tnMa3/1suZt13t2vjZcl393l2diBjNFx1LFy6B3ZSRXfeZo1ffrDj648ONU5PZN3fJwTdNzphrPSMT86vndselTNnObvXUnHXX2ny4NnOM4KONNxenbp3d0sA7/SevS1d1zZ5K7hQHctBzj0b87p2QHnpE8fRmhowvFBU1eR43O+axiEeurNjePGkGGb4xqxH9d5MfvZqxcWPvhAU902P8eOV7hG+nl3LT2fm558zzXG+hnlWcL23X2nQj3LeaM9+oZLKa7RvPcFa/MphlXKG6pq1KvBrhEBOUE/v238Lskv3L9yFvTdmn1dQ1ZOihqz216eXfrAm5MYlmp0zN2u8XjVuHGoMPqW0s9cHHl8jjg+NfB1Ds5e15r/LadJCwjoXPx56Le2KV7npF183xuTI6Ypj1OjZNZ56Xp9/Don2bl9ebi9XLromrV9PxdOo2w+nBwihfXXpMa+juPO7+dXVK/Gp6lp+fVq6pWWUFd8+lejQbof7jyq3r25fu16d07u2JajaCHn4bh7K0dcyqRnNp+GqGuvcm49zhm68YnyUUNNmcvzjq79AHdN4f3RSKZW91N6bXtF/iHVVOXvuGlkRo7Iknw+uYdK8zx+d//kWG93RXbPc8s7C/L7rLxgPSs+Lxwc5AXlNeUll3eGptH2J7tz91PdHD/ZwnRWeuGWUfat3il662h3Ju1GzxFCwxeOD5q6TMdns0uXvTuSv0Qe28U4J0E9ven6MfvxyYtd4+MYOz7XyNr21IeWvC+h3tu8UeLO0QzTAy+/X+9l8xSpp/ymde/lq7zl07V0TYfekb/3I+dIq69pmhxafdJ3gta9tTGfcpd/W+awO8cO4+F3Zjuf/WjuqGiUru4IT5UUn0ZU1zz6drbfRZN8KUfA5UtNwdQS3m0/EopWXqMjnZO4MN3tPOfEHZh9c5trfjhMZyURjepk0bYitOT1p3c4+88ucmdEHkJotQrHB01dluOzw+nLv0eST5+K7Ey2uCw7LuEF6VhPakRJL85rWoniOfjW7PUPF6e1aRWofFpbRyu21WH3cx+bnAVCCA1LR9/+5OJojxyfnU7I3pfjc4vtpKQix+fHxDhC2w+f3HFyZgih1SYcHzR1WY7PJcbUo3wefGRXyt7XN/oOjqZIxS8T/+yyp8ZTqKzjrAQ7dDfdDSGEVlJPvbdh0ekRO5yWXXyZc0RWcMRH6J2fjfep2dN+YROE0DCF44OmLsvxOeyqZyZ7F5V/4T6yq2QL5yz99NZW7yBo0YSbnnhv/KK05vVPYcRHpLxjgxBCsyaVXPl7PjtNRn12duy/TT79zHJQ+uAbx5d3qsnzXn5OCKHVKRwfNHWZ7/hsfXk+8hJKX4LPX1aObVPQy9r73JCdeNMLrWd366OFWjr5lfe/zNa+/ll2y7r3stNvfTn7ySXrsi1Oun/8ErJ1Di3Z94K1kzNACKFhaXScK7sXprs5djk6++Jm20npmu81xU1T635YPoUaIbS6hOODpi7T8RH73ugcjKUrHGjBgzWPvDNeCUmjOfnqUxP022PF59EHGw+8ObvmsfTvbBRJ7/1c8+g72VYnPzheyWmHq/MFEczjtmXHqydHRQihYemWF9915aMWOJgsa73DWdntV40dEstZ6YLvXdzfOb6/3TV1Pr10ciYIodUsHB80dRU6PkIfq/zF7dkhVz6dj658NVlkTN/P0fS1i+9/I9tRHzTUsq+7XTv5AGCJ47PlZdk5d766ZLWyqx5+e7Js7C3Z6PC7ss1OuD/b7rSHsl3PfiTb89zHsl3OejT/W9vzleW0BLEcr+2vWtGV3UTVl/QRQmgWtenHH7PRdsH3fHY4Oxvts2O+upvltHSBHJ9vHV+/uu/kLNI1Go2WEK5v+fJLz+bbvD74YH3+9177Hpz//eLzz+R/X3vDzfnfkv4+8ZTTJ39l2bZbbZHtc+DY/vvvv1mIb6stNst/e7z0+6TTzsx/f/3150v2ffX1F0v+/vrLpfulL77cmG/zPPPC4vegfvzxuyX2b73xyhJb8fxLLy/bdtd9D2S33LQm22zLbSchEZp9LX0yEJqCSh2fjtjq5Aeyx1/7NHeYJC3Fu80pD46/42HYzyoHX/rk5AoQQmhYGp124+J7Pjuc4X4fn73W03s+mt6mkZ7P1jZr5sSNfOG112475n9v+mY8H/vtN19fYvPzg/bLf5953oX53999+/WS/VL497fffLnw228P8dv3PuAX+e8vP/90Ybt01qn6+PYoO+W0s/K/P9/4yZL90saNG/JtIb4+fHLtQ/nfZ55/Sf73qy8/v8z2yafHzl7IjbfemV1y/un5b4SGInIrmrp6c3yOuze785kPF94Vev7dz7Ndz350fLy+pqP1Dau7IYQGqsc+3JSNtj9z7PjkIz/nZDsd1X51NwvF+cX9auLU+DptIDXmn3v5lfz3e++8lP/tpd+bbzbKNtt6h/zvt998Ld/mbfzvM88fOz7PPfPkkv2S/3ubHXdf5vicdu4F+e+XXhqPHEn6d68DveMzdmK89NsjfT5xckJ5x8dLvx94bNyRtsPWWywJ75Vv22bPyV8/5n9/9d3S9MTxQUMTuRVNXZ05PjtenX/h/uGXP8m+/H68GtvZd7yaO0CjXa4ZrrMTwXQ3hNBQNdr9jMm7PhPnZ4cjso+ck6KlqS0Hpglyer65W82bd8cHbSDvCGy2+eYLv6V33x6P7qxd+8jCtrcCx+ed995b+O1HfPbba7fs4EOPzLcdd8oZ+Tb93mu/A/J/N25adEr0b8iOu4wdj3i7kL779qv893sfjI8rlY34fP3dd9lTT67Nf7/x7vp8n36vf++dZWH094Ljo6mK+jtAwvFBQxO5FU1djRwfLVBw5F3Zsdc9n92wdn1221PvZ5fd/2Z28KXr8q/L5+/7FC1yoPdytDCCtS+BI9c8m39Z/uYn1mc7n+kqvxV2qPS+E0IIDVE3vewa6DucNXF8HDudm/3kRNuBaYK+1fPNHa5p82O7FdziRv43340X2jn1pGPzvz/7bOxcaJXQ0PE56fSzF377ER/9/nTTF9kpJx6V//bbzr1M34YbZYcdNY7Tb48p237bTdcs/Na/t959v3N8Pl7Y5mVNdZNeeXk8qvTjj9/n/153y535dim3K3B8Ntts83zzJec7R9b9jdBQRG5FU1cjx2dr5/hs6yoNLX7gV3az7Cz2vG7ssFj7qtjuyuzM219ZXD3uqLuzNY+8PT4Xy74PmO6GEBqo9F7JaNfTx6M+fpW3nY7N1juHpfWUt1tG2dcaPdpw5fhgLaTG/HMvv5y98er4fZcnn3l+YXvIznvul7311qv579tv1juji/vOPP+i7OMP3l2yTfh4Lrz62uz1Vxbfp/HbTzv3/Pz3d9+MR3Mk/bv3gT/Pf/upbnlaun9jNpkjPkvfC/Laf69dloQNbfK/I8fnm++XfhCCER80NJFb0dTV2zs+ZTQdpdnisuy8u9W7F2zT6m4i3NYzqdPdNBt77RufZeve3JT5hcE/dUE3fpNlH3/xQ7bJGei3toWrFm34yr/2Otan7m9NHRTxPklxPfrqp9nbGxbnf8tOKIzCf/zF0nDxNXz69eIxdD6xtHS4P3dNZZR8/J98+WO+Xcude/lzzfdN4tR56l/t09koToXz57rJ/fZ/61/hj6l4Yr38wVfZ45PVBpWW/vwV78fO3h9L169vPy387a5V0un6MLF0ro+4NH1rgzv4RBsm1+KPJZtYL6z/InvC3fMgKfJrffLNjfl2fYdK8te7kEbudxyvfn/i9nvpb9l9GyTFax99lZ/nO5N7LxtRdo5eSpNQujZdo1d+Dybxhffhg03fL7k+L4XV9Yztled+yL507TSlf3hvdW6hnn5rU75qpN+usLLXsZSn/DV98uUP43OM08QR6vn1n+fPnT9H/evPPwzrpfN79LVP3TPhDCIpL+m+Kb5PJs+QTxeFk/JzdNvi65pV3fnGh67MPNs5PpNRH+f8bH3YeLTGdGgS0SpuX7/6k8lR2kmNeTk+/rf4+qtN+b9e5597av73W2+MHZ9PPh47OYcePV5s4IzzL8wO+dn+S8Lo90133JX/e+Ga6xa2eRv9u9X2O2Rr1lyd7b3nbku27/WTpY7Pt84J0b/PPv9Svv2+u2/L/9702UcL4bzid3y8tO30s8fvFD37zLolNvodOz4XXXyxO7c1OU8998yC4+O33X3ffWN7hGZUy58ChFZYtR2fg27JbnpyfXb5g29mW578QLbXeY9nT7pGwU1PvJdd89i72TPvfJ5Pf8t/v70pX7760vvfzP8W1z3+bvbIKxvGq7oF8R597XMLNkKNoVNufmnJaNJRzkaNKx+/51l3zBufGG+7yf17/4sfL8RxnftX9g+4bRohCo/ZlGOve26SesU67+7Xs9HPb8vPTQ0nfVhVja5Drno6X/b7hfe+yBt2owNvyg66bN2ShuTo2HvcuW+c/JVl7210FawLf7Q77pZKt72uW3CU9r/4yeygS9fljsx2pz+cbX7S/fn20WF3ZldrNGznq7NjXDgd9ymXHl6jn966pKEnp2m041XZyTe/OE6nX9w+2TOWGuz5cuJ7X+8afONtp9/2cnaSs88/bru/Voy6Om/oSvtetDa767mPsne0b7drs5NveWl8He64On+ZqaE4+tmtuc2R1z6bH1/38eYn38tGJ9yXp5e++bTzWY9kB1/uGgXuWvyxd3LbTr/9ZZf3Nua2ahTn53Dcvdl6d5ydzn4ku9TlUU3LVB4cHXK7u35ne8BN7hquy51SnYPO5xXnQIXa58K1+T1Rmm6l9D5xnKYvf/Blfu+Ou+H57DLFffy9eSPYS/ftKpfmD7v8vbM7vnSDux7d4ydcw155Qef45iffZOfe5Rz4rS7Lnnv383yVQ40knubSU9NGtz7lgex951yc4a5PS8BfdN8beVxvOydsS7dPjmd+Pw6+Jbvg3ted8/N1tt9FT+T3Lr8Xu12THXnNs9ltT7+f7Xn+Y9mbRoP+g03uXrjwoY6/8YX8ePo2lqTz3O3cx3IH5ieXPpnfHz1bVz78Vr4i4wk3vZDbeY2OuDNb48Jq3xFrnsnD3vfCR9mBl7iwLm/ovt7k2Mpdg6RyYLTP9Xl6vaQ0cGXLi85xvMWlgdLm/hc/yT53N0n3V8/S+s++y05x+Uj7rp18B+yl977Mdjnn0fz5kROk9FU4OSoaXX74lU/ye72Dlt13eTfsHJAOdvd5nwsez++18pnuoZc+uDw69I5snXNaxWi/G/M8vP4zl8ZaUt8dS/pC5+jOzzufQ9Bofzk8fsqbRn5Ozh5d03zUR+8IfbFOTZrljmUTqTH/0muv57/9YgHHHXd0/q/X+vXuGXR/v/7a2PH5zjsik6Wfz7tY5fUoO+SIYychsmz7bRYXErj8xvFy1w8/cGf+t+T3eY469oSF7T899Jj895eTpak/+nipg+NXj3t7/fL3dTYZjs+nny4N//33Ll+5v597dXzd+r3THgflvyX9HbLPQT/LrrrsvGXbEZplkUPR1FXX8dn+tIfGU9w2uyR7VA2XLS7LztcojGuYa1veaHWNOP2+Ye27br+mwrmwHtcQPVSN/zju0MZxjT6UGr8n5Lbf+7yrLDS1TXY7r3ENpxvyxmK+gIK2//S27Ow7xtPh1DgabeXO1TW69E2gJXG1Yfvy6W55Y98dV41Qr2Ovfz7vxc7TZ+vLJltd+jsH45IH3pz85SpVNaJcA390zGIDTNJ1PvrqhvH0Ctc41THuUVq46/SjCPpH9+bOZz/MG5iSGsFPOOdAUgNZyh0udx+OcI3jUGqwLjTE3X3z8XptqQ/FBg6RzucudyxJDf3Rvjc4h+uO/G811v2IghqcdzsnSModhs0vyVf7UwNejXtJDo+OL6kH/Z7nxvGODr19wUYLaDz+2mcLv693+Uu64J5JQ8E1Pve/+Im8IapRidyxcY3kDz//3jnkm/LzyZ0fd2369y53DDkHoR577dPcufHfrJI0xVIN/vy32/fqh2NH6XWXnjqWlB/L7XvEpYl0yQNjZ0XPwrrAidW9fss5FPlx3L3zzUTZabRIUtpoUEp/5w12d7/lGEjKR9Lo+Puckzt2ILxOvGn8bRA1yu+bnK/uYTwiIh3g0knpoHzk9dhrG7Idzhwfz48IeqfrQvevHFgvdWAofOj4ybGTVBY85MoGSel8zp2ufHD5UNL5yBmR9Pz635Js5ZznjoR7vv3ozL4XPpFtNnHo1Ymwu55ll8/lCEkX3T9Ja+ekyPnz+unlT+XHkI6+7vn8I8ehNDKkeDRa46X7cPO698b3Z8tLs4+dQ+2lY/tj3v/Sx/l+pdOl7p7Kfkh66/OvXDl2xsTxEWe7+7Z3trHB8tYaKfr8ITVnJg8DQggVCMcHTV2Nprq5RucZt72cf1hUfy84Pu533rCfTD27QY0jOS8H3eIash/lYc5yTsnR1zy3GJfH2anBfq5rJJ1268vZQy994ho/kePjyBv7Wlzh2Huyg9STfOidecNWDZxb1jkHaL8b84aqbHPHZ0vn+Ljz3aNLx8ehRm+RNBIx2ubyyV9LdaucNNewlCOgnm71GssZ8NKozu2uMa4Gu3qwveT4qKF/3A0v5CMm0vZqFDsnKdRoj+tcQ3Ht5C/3d+D4eG3r7tu5d7+aH0NTebxGu67J74GcBy1SESt2fJ5+e1N27wvjBvau5zyaNwpH212RnXjjC3lj0DcadU/ksHrJEb3jmQ/z0SL1oEu54+ManXKkNGrjpfur0Qs1RjXi4KfZ5YtouHQcnXjfgoOmUR314HspZu/4hNrsxPvzkR718MdSA1vpEEqOm29Qy7k5+85X80b1u58ubehpdErH08ibklUjo2rAh/fRSw18OQ5euvZz3XN0/j2v5aMYkpw12WkUT06h7tWCc+Psr3z47fx3LDk+Gr25xuX/+yb3J5ZWYBz97Lb8vnkpP8qpGx19d55vlGqXP/hWvu+S+53D6vKWVz5659Lfeg5Cx0fKHR+XT/XsapRYkhMuB1gjdbHkdCrd/OiwRny2mISTg6GRSj/6KCfxiofG56j8ofzrda3KHxePRqzy0SxXDoXKnx93P0ON9r8pX8Bkr/Mfz585L91PnWmYk/QsyNna/dzFNBySDl7jyu+F6W5j52e3Y9JHffwHSr+6U02ZxecbIYSKhOODpq5Gjo9WZZuMQKgnXL3rauwccNETeWPzgRc/yRv2asDJoVHjKXdWFHbry/OGRRxn7kRpGtTk7zUa8SlxfBYcILftVjk8Gv1xjZAHXENvl7MezbfnDkRPjs8JriFVpGOuf94dd7FRG+oWNeBd41gNzHflKLjGWOj47HL2o66h/kPe8DzmunHvvqT01rQmXctDL497yTXtabTT0tEn3c9fBCvPWY7P4WueyT7SMfJRk/GSqpIa/BfqGO4eadQn1tjxuW3yV5ZP/3nAOaiSRiak3LFz16epbqHjo/vlNTrBNeRdY3afCx+fbJlMCXP3UM6TprV56Z7KmVEj96L7xiM7Xppql4/oTRq0o0PuyKdkeRU5PvmIgttu9dLn0/wmI09eSnu9W5b/dg3lB136azqW7/330tXKcc0dsp/dmo9EqeGtEbZYchpjx0fOodLdjyg9/vqnC6NFOgfdb5/vlPflJFmS46Ppapoup/d/Ymmq2kMvb8idU91rP7qle/TqZNqf4tjROYaa0ipdHDk++aimO//4PSEpdnzkKCof6nyOWDMeZXxO0/5c2qjsiKURP+3TCIueETko3mF64KWP86l7+b11cW5x8v3Z5d7x2f7K/NthXrmz7JwrOZ4n3fTSMscnn0IXOz7OedVURjmvo/0WHR+Nvil/eidL+kAdLi4fvfjeeKRuaFKuHO3tFzmYjPrscGJ2w2XjUZyqd37yZatvd82Yb5eOTlv64Yfl97kL/fDD8vxXpSZhEELdCMcHTV11HZ+DLnGNUI3iuN/Xaj6/a7DlDTyN+DiH6O7nPlwYcfEjPnnD1TXc8jgKHB81usP3fqocn8OueiYfDdDoknqdc8fHNZbynlqF2/uGvKe3L8dH0+qKJGdA162GW6zrHnfXFU51+/lt2aWTqTpyQtTLrYbvobo+l7a+yaz0U498/hHYyYjESTe9mF/zklEbl/Z+ypEUOz7q+b7j2Q/HPfsa+XAOhZca/HpHy79TEWtHrcYXjDCpwakGvOQbppLev9H1L0x12/eGhaluUp4+rtF87WOL3/nQ6EQ40qIpRHnj1jk+GhnKnT6Xd/zUstue+iD/N3+nZOJAaMRnf+d8e+XhDcdHUh7y08dC5SNh7jjfBG0jjVDpfbX89zaXLzgmse59YXyNuUPm4shHRdy53fb0+FxD5e+MuDRauL8uH/uRHkl5Wo6Qz0OaHqc4/YhW/q7Swbfmv2PJafFT8yR/j7w0eqJj+XeL5AhJej/OO7xaNEFpp/eOJDmdo13H09Wkc+9y6XTQ0qljXrHjc4ZWYgycplc++DJ7f6NzGlxaXjzJ+6G0aEDoMO7pnmk/1e12l5Z+lEkOhxzfU28dv1yufOZ/S/koz2R6m0bKQsdHnQfn3OXOyzmpmoLqpTyoDp38XTd37/wHmCWNZsZOjvKR3oUaqt79Th0gwfLWYqdDs5dvcY6Pc2q+Kxj90Ts9udPz1djpKXrPxNr+3ffj1dK++j50QMbLOcdPahhW7H/gT/Ptl1+y9N2WDZtU5rlrCbaJsy+8JLeXbrlJsxIW933ymcrF5WG8Hn34gWX7vvtu/B7P+xvG+dtv94r/3mLyd4gU/pYLuvTvLNt+i6V/S95GbL3dTgvfEHrlzfH7bjdd5+q6KAxCsyZyKJq66jo+e573eLaDekOPuDNvKMnJCKe6LTgrrlF3o2vI6181LvKpTNs7h6jI8XHkjY3JqE+V45Pv+9ltecP4Pm2T4+P260X+653zsLlr1OfvGPXl+DjUOCxS7jy4693v4ifyXnpNyVKlftRkRECrTOV2+9+UjxLkvw++JW+sS3lD19lpNEjh5MDot6Z6qZd6oeffNT61MICkePYNGv55uB2vcukxHtXJe3idA+WVOyCuganpQVopTE6T3g3KHUl3bD/NyUvOiLZvdeqD2aFXO8cseDleI0FBGzHPV+rNz4/p8sZ50eiEridcYet4TeGbjGY98oo7L3dfc8fFNVz1ToekMHKq8t8H3pw3kA++/KmFKWv5VDOXzl5+VEILSYTKp1K56widg1ByKvRyvqTRsZ9ORqDUCFY4NYxj5dfpzlUv42tBhKMm9yR/Yd7lV11D/mL+cffm91DprNGIj774Pl/FTPdUIzCPv/ZpvlCBHJBznHOhUUsvOSHaJ8kR07MkZ09TSDXiptEQ3QM9C7JVXJom+PDLi+crx+GUwDnQO1lyZrXQhJwDP5ooKazui5SPZLn8rMUB8vd7nBMRvt/jpaasHEX//pX0syueyp3qR179JH+nR6Nq0uFrns2dq21Pf8g5i84JOf7evLMit3H50i86kU9NdPlBaaypslrB0Osod44aYZXkeOoclX/zUaP9bliII5++6Zx83bu8c+bI8VRO5WGNEipuLdaghS288vzkyqO3NnyTvTB5Hyh0fPJ35dz5a8W+Ieuu1z9wDnDo/DinYvd9so/l+BS886PRnu/ePTUPf8ctqkNG2RdffpV9OFk++vOvv1n4xs2nGzdmX37p0s/93n2fn2Q/mI7P2AFRmRVK25578ZXs22+/zb75Zpzh3lv/Vr593XMvZN9/58pJ91v4OB598qmxvcPru4nd8Sef7o7/bbb15j7M2Ol49sWXXZhv8nBej00cn2+/+Tq7+w6XF9zvL74YX8f7nyx3fJaey1ib539vnp/Ls0+NP1z6lYvP29338GPZhk/eX/h7rEVH6KMN47z+xKPjc9nojv/E465Mcb83uPS+5KLxd4t++HG8MMJr7yyO4CM0i1p8OhCakmpPdZPDofdoXMNHjcB8266uggs/SuoaXgde/GQ+9UgNmHybnBXXCM3Du4bRgm2IbJ2dvvOjVbC2PXX8DtES9nTnGztEe1w3jjvcJuSMKU7t26H7Ja9PCxqQltR41bQorVTlpUaZkGOjkQi96yAHSgshaLvvYdZogf7WfjW8xr/HjTiF1d9+pEcjPGp8vRaNRKz/dBLHZLumnulv37jwxxQ+Tr8Agt++6JqMpelKGnnSSIR30ny84ciKn+b2/sJ1LD23cLRFlhpF8cfU7/Xu3DRiNE6f8ejPu5+Nz1cruyn8Y64BrMUYJDlRstN+/86PHDUfZyg13q3toTTyladpcN7+PvgX/2M99+4X+TnFjtbrLo6HXtqQT+/y6emPr5XaNHVL5/6GQ/dKy3R7m/hY4fQ6TeHS8TRlz4+O6J4uxOXwcXnpPodx+vPwaft6dDzdAx1R90ROhK5NcRTJ5ykdW/Jh/XHyezt5r0vSaImm3WlUyjc59Q6PbPWvrtHfV/9MWOfopd/5EvJvbVp4lvScvPbhOA7FpXOQg+WlPKR7Ha/upxTVs/XgS5/kI3SaghrKPzNi6Dr8hged0xg4PjuemY323TX79I7lTo/e6xmv4DaWGty77LHv5C/3LBaMhjx4jzq1NGpSb8TnhZdfyaen/fDDON+ceerxS+KVfsxLkbHjs/bppyf2i7GtfVSzCRbDfLrBOb/ub++EvPLm0k4eyTs+0icfj52TTZs+y/99573xKK5+e5tnnx47NuKkM87Lt41HfLbKf69/941839ffjlefE7vudWB2+82LI1HSe++NnUex054H5tv0e4dd985/Sx9+PJ7S+fmm8feBTjzxxIXwCM2yyKVo6qrt+KSg9yTkGFn7ytjROURyViaLI1z50Fu2QzMrRO8HIITQULXZWTdPnJ6J86N3f/bbMdt0l3NWnMPj3/n57D41XRadRDW4jzjmxMlfi9L2A35+1OQv5zx89Ha+rc1Ut0effDrbZ7cd8t/SgfvuvrDPOz4hH3wyHsG85frx6JPXt99+mf+94bPx0tQhXt7xCfFT3WKkgw/YN9t+5z2yCyffF5LGIz6LbLntzvl2/T7jzPE3eHbaZvPs5ptvzH9LD913R/77uac0S2G8Tf8ed/IZ+e9YP9l7/L2hex5eHK1EaFa1+IQhNCX14vhsdmn+vs7+F62tPdKipa41WqR3WfK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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ebD6btha7vdZ",
        "outputId": "4feaeace-3889-4348-a562-b918454a8c64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#@title **INSERT PUBCHEM CID OF DRUG AND EXCIPIENT** *Run Code and wait to finish process*\n",
        "#%%capture\n",
        "\n",
        "import tensorflow as tf\n",
        "#import tensorflowjs as tfjs\n",
        "import pandas as pd \n",
        "import numpy as np \n",
        "import io\n",
        "#df = pd.read_csv('/content/drive/My Drive/DE.csv', low_memory=False,)\n",
        "url = 'https://github.com/Swayamprakashpatel/DETensorflow/blob/main/DEL.csv?raw=true'\n",
        "df = pd.read_csv(url, low_memory=False,) # Run for labeled data\n",
        " \n",
        "#df.fillna('&',inplace=True)\n",
        "df.drop('Drugs', axis = 1, inplace = True)\n",
        "df.drop('Excipients', axis = 1, inplace = True)\n",
        "print(df.shape)\n",
        "df.head()\n",
        "df.dtypes\n",
        "df.columns[df.isna().any()].tolist()\n",
        "dataset = df.values\n",
        "X = dataset[:,0:1762]\n",
        "#Y = dataset[:,1762:1763]\n",
        "# Run this for labeled data\n",
        "Y1 = dataset[:,1762:1763] \n",
        "#print(X, X.shape,X.dtype)\n",
        "#print(Y, Y.shape, Y.dtype)\n",
        " \n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "enc = OneHotEncoder(handle_unknown='ignore')\n",
        "enc.fit(Y1)\n",
        "OneHotEncoder(handle_unknown='ignore')\n",
        "enc.categories_\n",
        "Y = enc.transform(Y1).toarray()\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_val_and_test, Y_train, Y_val_and_test = train_test_split(X, Y, test_size=0.3,random_state = 42 )\n",
        "X_val, X_test, Y_val, Y_test = train_test_split(X_val_and_test, Y_val_and_test, test_size=0.5, random_state= 42)\n",
        "\n",
        " \n",
        "X_train = np.asarray(X_train).astype(np.int64)\n",
        "X_val = np.asarray(X_val).astype(np.int64)\n",
        "X_test = np.asarray(X_test).astype(np.int64)\n",
        "Y_train = np.asarray(Y_train).astype(np.int64)\n",
        "Y_val = np.asarray(Y_val).astype(np.int64)\n",
        "Y_test = np.asarray(Y_test).astype(np.int64)\n",
        " \n",
        "import tensorflow as tf\n",
        "import datetime\n",
        "log_dir = \"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
        "\n",
        "filepath = '/content/drive/MyDrive/Model_DE/Model.hdf5'\n",
        " \n",
        "checkpoint = [tf.keras.callbacks.ModelCheckpoint(filepath, monitor='val_loss', mode='min', save_best_only=True, Save_weights_only = False, verbose = 1), \n",
        "              tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=25, verbose =1), [tensorboard_callback]]\n",
        "\n",
        "output_nodes = Y.shape[1]\n",
        "print(output_nodes)\n",
        "model = tf.keras.Sequential([\n",
        "                             tf.keras.layers.Dense(1024, activation='relu', input_shape=(1762,)),\n",
        "                             tf.keras.layers.Dense(1024, activation='relu'),\n",
        "                             tf.keras.layers.Dense(1024, activation='relu'),\n",
        "                             tf.keras.layers.Dense(output_nodes, activation= 'softmax')])\n",
        "model.compile(optimizer=tf.keras.optimizers.RMSprop(), loss=tf.keras.losses.BinaryCrossentropy(from_logits = False), metrics=['accuracy'])\n",
        "hist = model.fit(X_train, Y_train, epochs= 2000, callbacks=[checkpoint],validation_data=(X_val, Y_val), batch_size= 2000)\n",
        "model.evaluate(X_test, Y_test)\n",
        " \n",
        "Y_train_predict = np.round(model.predict(X_train))\n",
        "Y_train_l = tf.argmax(Y_train, axis = 1)\n",
        "Y_train_predict_l = tf.argmax(Y_train_predict, axis =1)\n",
        "import sklearn.metrics as skm\n",
        "cm = skm.multilabel_confusion_matrix(Y_train_l, Y_train_predict_l)\n",
        "print(cm)\n",
        "print( skm.classification_report(Y_train_l, Y_train_predict_l))\n",
        " \n",
        "train_acc = max(hist.history['precision'])\n",
        "val_acc = max(hist.history['val_precision'])\n",
        "train_loss = min(hist.history['loss'])\n",
        "val_loss = min(hist.history['val_loss'])\n",
        "print('Training Precision is')\n",
        "print(train_acc)\n",
        "print('Validation Precision is')\n",
        "print(val_acc)\n",
        "print('Training loss is')\n",
        "print(train_loss)\n",
        "print('Validation loss is')\n",
        "print(val_loss)\n",
        " \n",
        "# Error Graph\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(hist.history['loss'])\n",
        "plt.plot(hist.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Val'], loc='upper right')\n",
        "#plt.show()\n",
        "#plt.figure(figsize=(5,5), dpi=300)\n",
        "#plt.gcf()\n",
        "plt.savefig('Loss.svg', DPI = 3500, Transperent = True)\n",
        " \n",
        "plt.plot(hist.history['precision'])\n",
        "plt.plot(hist.history['val_precision'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Precision')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Val'], loc='lower right')\n",
        "#plt.show()\n",
        "#plt.figure(figsize=(5,5), dpi=300)\n",
        "#plt.gcf()\n",
        "plt.savefig('Precision.svg', DPI = 3500, Transperent = True)\n",
        "\n",
        "\n",
        "plt.scatter(Y_train,model.predict(X_train), label = 'Train', c='blue')\n",
        "plt.title('Neural Network Predictor')\n",
        "plt.xlabel('Actual Interaction')\n",
        "plt.ylabel('Predicted Interaction')\n",
        "plt.scatter(y_test,model.predict(X_test),c='lightgreen', label='Test', alpha = 0.8)\n",
        "plt.legend(loc=4)\n",
        "#plt.show()\n",
        "plt.savefig('Act_vs_Pred.svg', DPI = 3500, Transperent = True)\n",
        "\n",
        "#@title Insert PubChem Fingerprint of Drug and Excipient\n",
        "!pip install pubchempy\n",
        "import pubchempy as pcp\n",
        "Drug_CID =  3878#@param {type:\"number\"}\n",
        "Excipient_CID =  104938#@param {type:\"number\"}\n",
        "\n",
        "\n",
        "CID_D = pcp.Compound.from_cid(Drug_CID)\n",
        "CID_E = pcp.Compound.from_cid(Excipient_CID)\n",
        "\n",
        "FPD = CID_D.cactvs_fingerprint\n",
        "FPE = CID_E.cactvs_fingerprint \n",
        "\n",
        "#from base64 import b64decode\n",
        " \n",
        "#def PCFP_BitString(pcfp_base64) :\n",
        " \n",
        "    #pcfp_bitstring = \"\".join( [\"{:08b}\".format(x) for x in b64decode( pcfp_base64 )] )[32:913]\n",
        "    #return pcfp_bitstring\n",
        " \n",
        "# *****copy paste PUBCHEM_CACTVS_SUBSKEYS*********\n",
        "# DRUG#\n",
        "#Chem1 = 'AAADccBwOAAAAAAAAAAAAAAAAAAAAAAAAAAwAAAAAAAAAAABAAAAGgAACAAADASAmAAyDoAABgCIAiDSCAACCAAkIAAIiAEGCMgMJzaENRqCe2Cl4BEIuYeIyCCOAAAAAAAIAAAAAAAAABAAAAAAAAAAAA=='\n",
        " \n",
        "#EXCIPIENT#\n",
        "#Chem2 = 'AAADceBwPAAAAAAAAAAAAAAAAAAAAAAAAAAkSAAAAAAAAAAAAAAAGgAACAAACBSwgAMACAAABgAAAAAAAAAAAAAAAAAAAAAAAAAREAIAAAAiQAAFAAAHAAHAYAwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=='\n",
        " \n",
        "#**************************************************\n",
        " \n",
        "# Do not edit below codes\n",
        "#Pubchem_Fingerprint_of_Drug = \"\" #@param {type:\"string\"}\n",
        "#Pubchem_Fingerprint_of_Excipient = \"\" #@param {type:\"string\"}\n",
        "\n",
        " \n",
        "#print(PCFP_BitString(Pubchem_Fingerprint_of_Drug))\n",
        "#print(PCFP_BitString(Pubchem_Fingerprint_of_Excipient))\n",
        "#dfChem1 = (PCFP_BitString(Pubchem_Fingerprint_of_Drug))\n",
        " \n",
        "#dfChem1 = (PCFP_BitString(Pubchem_Fingerprint_of_Drug))\n",
        "#dfChem2 = (PCFP_BitString(Pubchem_Fingerprint_of_Excipient))\n",
        "\n",
        "#********************************************************\n",
        "List1 = list(FPD)\n",
        "List2 = list(FPE)\n",
        "List = List1 + List2\n",
        " \n",
        "import pandas as pd\n",
        "t = pd.DataFrame(np.array(List).reshape(-1,len(List)))\n",
        " \n",
        "#@title RUN FOR PREDICTION\n",
        " \n",
        " \n",
        "dataset1 = t.values\n",
        "t.dtype = int\n",
        "X_Predict = (dataset1[:,0:1762].astype(int))\n",
        "print(X_Predict)\n",
        "print(X_Predict.shape)\n",
        "print(X_Predict.dtype)\n",
        " \n",
        "t1 = model.predict(X_Predict)\n",
        "print(t1)\n",
        " \n",
        "t = np.round(model.predict(X_Predict))\n",
        "print(t)\n",
        " \n",
        "Y_prediction = enc.inverse_transform(t)\n",
        "print(Y_prediction, Y_prediction.shape)\n",
        "\n",
        "%load_ext tensorboard\n",
        "%tensorboard --logdir logs"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4248, 1763)\n",
            "2\n",
            "Epoch 1/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 1.4804 - accuracy: 0.3966\n",
            "Epoch 1: val_loss improved from inf to 0.47278, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 3s 2s/step - loss: 1.4804 - accuracy: 0.3966 - val_loss: 0.4728 - val_accuracy: 0.8242\n",
            "Epoch 2/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.4284 - accuracy: 0.8396\n",
            "Epoch 2: val_loss improved from 0.47278 to 0.39441, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.4284 - accuracy: 0.8396 - val_loss: 0.3944 - val_accuracy: 0.8242\n",
            "Epoch 3/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.3641 - accuracy: 0.8396\n",
            "Epoch 3: val_loss improved from 0.39441 to 0.38460, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.3641 - accuracy: 0.8396 - val_loss: 0.3846 - val_accuracy: 0.8242\n",
            "Epoch 4/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.3682 - accuracy: 0.8412\n",
            "Epoch 4: val_loss did not improve from 0.38460\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.3682 - accuracy: 0.8412 - val_loss: 0.4068 - val_accuracy: 0.8242\n",
            "Epoch 5/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.3687 - accuracy: 0.8443\n",
            "Epoch 5: val_loss improved from 0.38460 to 0.33600, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.3687 - accuracy: 0.8443 - val_loss: 0.3360 - val_accuracy: 0.8414\n",
            "Epoch 6/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.3008 - accuracy: 0.8560\n",
            "Epoch 6: val_loss improved from 0.33600 to 0.31312, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.3008 - accuracy: 0.8560 - val_loss: 0.3131 - val_accuracy: 0.8493\n",
            "Epoch 7/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.3367 - accuracy: 0.8338\n",
            "Epoch 7: val_loss did not improve from 0.31312\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.3367 - accuracy: 0.8338 - val_loss: 0.5545 - val_accuracy: 0.8242\n",
            "Epoch 8/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.4478 - accuracy: 0.8500\n",
            "Epoch 8: val_loss improved from 0.31312 to 0.30058, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.4478 - accuracy: 0.8500 - val_loss: 0.3006 - val_accuracy: 0.8462\n",
            "Epoch 9/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.2679 - accuracy: 0.8648\n",
            "Epoch 9: val_loss improved from 0.30058 to 0.26942, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.2679 - accuracy: 0.8648 - val_loss: 0.2694 - val_accuracy: 0.8666\n",
            "Epoch 10/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.2335 - accuracy: 0.8823\n",
            "Epoch 10: val_loss improved from 0.26942 to 0.23119, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.2335 - accuracy: 0.8823 - val_loss: 0.2312 - val_accuracy: 0.8948\n",
            "Epoch 11/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1979 - accuracy: 0.9162\n",
            "Epoch 11: val_loss did not improve from 0.23119\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.1979 - accuracy: 0.9162 - val_loss: 0.7086 - val_accuracy: 0.8367\n",
            "Epoch 12/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.5617 - accuracy: 0.8480\n",
            "Epoch 12: val_loss did not improve from 0.23119\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.5617 - accuracy: 0.8480 - val_loss: 0.2860 - val_accuracy: 0.8524\n",
            "Epoch 13/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.2430 - accuracy: 0.8792\n",
            "Epoch 13: val_loss improved from 0.23119 to 0.22846, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.2430 - accuracy: 0.8792 - val_loss: 0.2285 - val_accuracy: 0.8870\n",
            "Epoch 14/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1866 - accuracy: 0.9166\n",
            "Epoch 14: val_loss improved from 0.22846 to 0.18934, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.1866 - accuracy: 0.9166 - val_loss: 0.1893 - val_accuracy: 0.9137\n",
            "Epoch 15/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1434 - accuracy: 0.9421\n",
            "Epoch 15: val_loss did not improve from 0.18934\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.1434 - accuracy: 0.9421 - val_loss: 0.2243 - val_accuracy: 0.9042\n",
            "Epoch 16/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.4713 - accuracy: 0.8147\n",
            "Epoch 16: val_loss did not improve from 0.18934\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.4713 - accuracy: 0.8147 - val_loss: 0.8861 - val_accuracy: 0.8367\n",
            "Epoch 17/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.6205 - accuracy: 0.8648\n",
            "Epoch 17: val_loss did not improve from 0.18934\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.6205 - accuracy: 0.8648 - val_loss: 0.2214 - val_accuracy: 0.8838\n",
            "Epoch 18/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1795 - accuracy: 0.9082\n",
            "Epoch 18: val_loss did not improve from 0.18934\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.1795 - accuracy: 0.9082 - val_loss: 0.1945 - val_accuracy: 0.9011\n",
            "Epoch 19/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1471 - accuracy: 0.9304\n",
            "Epoch 19: val_loss improved from 0.18934 to 0.16177, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.1471 - accuracy: 0.9304 - val_loss: 0.1618 - val_accuracy: 0.9341\n",
            "Epoch 20/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1094 - accuracy: 0.9556\n",
            "Epoch 20: val_loss improved from 0.16177 to 0.15556, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.1094 - accuracy: 0.9556 - val_loss: 0.1556 - val_accuracy: 0.9372\n",
            "Epoch 21/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1904 - accuracy: 0.9334\n",
            "Epoch 21: val_loss did not improve from 0.15556\n",
            "2/2 [==============================] - 2s 2s/step - loss: 0.1904 - accuracy: 0.9334 - val_loss: 0.2790 - val_accuracy: 0.8791\n",
            "Epoch 22/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1924 - accuracy: 0.9176\n",
            "Epoch 22: val_loss improved from 0.15556 to 0.15234, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.1924 - accuracy: 0.9176 - val_loss: 0.1523 - val_accuracy: 0.9419\n",
            "Epoch 23/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0998 - accuracy: 0.9650\n",
            "Epoch 23: val_loss improved from 0.15234 to 0.13064, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.0998 - accuracy: 0.9650 - val_loss: 0.1306 - val_accuracy: 0.9560\n",
            "Epoch 24/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0770 - accuracy: 0.9751\n",
            "Epoch 24: val_loss improved from 0.13064 to 0.12884, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.0770 - accuracy: 0.9751 - val_loss: 0.1288 - val_accuracy: 0.9576\n",
            "Epoch 25/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0920 - accuracy: 0.9620\n",
            "Epoch 25: val_loss did not improve from 0.12884\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.0920 - accuracy: 0.9620 - val_loss: 0.3650 - val_accuracy: 0.8493\n",
            "Epoch 26/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.2714 - accuracy: 0.8819\n",
            "Epoch 26: val_loss did not improve from 0.12884\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.2714 - accuracy: 0.8819 - val_loss: 0.1723 - val_accuracy: 0.9545\n",
            "Epoch 27/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1079 - accuracy: 0.9721\n",
            "Epoch 27: val_loss improved from 0.12884 to 0.12193, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 4s 2s/step - loss: 0.1079 - accuracy: 0.9721 - val_loss: 0.1219 - val_accuracy: 0.9513\n",
            "Epoch 28/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0648 - accuracy: 0.9768\n",
            "Epoch 28: val_loss improved from 0.12193 to 0.10737, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 4s 2s/step - loss: 0.0648 - accuracy: 0.9768 - val_loss: 0.1074 - val_accuracy: 0.9608\n",
            "Epoch 29/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0486 - accuracy: 0.9839\n",
            "Epoch 29: val_loss improved from 0.10737 to 0.10006, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.0486 - accuracy: 0.9839 - val_loss: 0.1001 - val_accuracy: 0.9608\n",
            "Epoch 30/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0390 - accuracy: 0.9869\n",
            "Epoch 30: val_loss did not improve from 0.10006\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.0390 - accuracy: 0.9869 - val_loss: 0.1513 - val_accuracy: 0.9309\n",
            "Epoch 31/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.2036 - accuracy: 0.9132\n",
            "Epoch 31: val_loss did not improve from 0.10006\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.2036 - accuracy: 0.9132 - val_loss: 1.0089 - val_accuracy: 0.8399\n",
            "Epoch 32/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.6493 - accuracy: 0.8927\n",
            "Epoch 32: val_loss did not improve from 0.10006\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.6493 - accuracy: 0.8927 - val_loss: 0.1387 - val_accuracy: 0.9419\n",
            "Epoch 33/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0872 - accuracy: 0.9724\n",
            "Epoch 33: val_loss did not improve from 0.10006\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.0872 - accuracy: 0.9724 - val_loss: 0.1113 - val_accuracy: 0.9623\n",
            "Epoch 34/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0549 - accuracy: 0.9832\n",
            "Epoch 34: val_loss did not improve from 0.10006\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.0549 - accuracy: 0.9832 - val_loss: 0.1019 - val_accuracy: 0.9623\n",
            "Epoch 35/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0543 - accuracy: 0.9802\n",
            "Epoch 35: val_loss did not improve from 0.10006\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.0543 - accuracy: 0.9802 - val_loss: 0.2005 - val_accuracy: 0.9089\n",
            "Epoch 36/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1116 - accuracy: 0.9485\n",
            "Epoch 36: val_loss did not improve from 0.10006\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.1116 - accuracy: 0.9485 - val_loss: 0.1029 - val_accuracy: 0.9576\n",
            "Epoch 37/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0500 - accuracy: 0.9808\n",
            "Epoch 37: val_loss improved from 0.10006 to 0.08831, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0500 - accuracy: 0.9808 - val_loss: 0.0883 - val_accuracy: 0.9670\n",
            "Epoch 38/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0342 - accuracy: 0.9879\n",
            "Epoch 38: val_loss improved from 0.08831 to 0.08508, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.0342 - accuracy: 0.9879 - val_loss: 0.0851 - val_accuracy: 0.9702\n",
            "Epoch 39/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0278 - accuracy: 0.9899\n",
            "Epoch 39: val_loss improved from 0.08508 to 0.08013, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0278 - accuracy: 0.9899 - val_loss: 0.0801 - val_accuracy: 0.9765\n",
            "Epoch 40/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0265 - accuracy: 0.9913\n",
            "Epoch 40: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0265 - accuracy: 0.9913 - val_loss: 0.0846 - val_accuracy: 0.9670\n",
            "Epoch 41/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0316 - accuracy: 0.9886\n",
            "Epoch 41: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0316 - accuracy: 0.9886 - val_loss: 0.1310 - val_accuracy: 0.9372\n",
            "Epoch 42/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1104 - accuracy: 0.9519\n",
            "Epoch 42: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.1104 - accuracy: 0.9519 - val_loss: 0.4860 - val_accuracy: 0.8666\n",
            "Epoch 43/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.3007 - accuracy: 0.9139\n",
            "Epoch 43: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.3007 - accuracy: 0.9139 - val_loss: 0.1050 - val_accuracy: 0.9608\n",
            "Epoch 44/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0537 - accuracy: 0.9835\n",
            "Epoch 44: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0537 - accuracy: 0.9835 - val_loss: 0.0875 - val_accuracy: 0.9702\n",
            "Epoch 45/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0349 - accuracy: 0.9886\n",
            "Epoch 45: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0349 - accuracy: 0.9886 - val_loss: 0.1071 - val_accuracy: 0.9623\n",
            "Epoch 46/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0345 - accuracy: 0.9882\n",
            "Epoch 46: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0345 - accuracy: 0.9882 - val_loss: 0.1224 - val_accuracy: 0.9560\n",
            "Epoch 47/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0327 - accuracy: 0.9889\n",
            "Epoch 47: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0327 - accuracy: 0.9889 - val_loss: 0.0833 - val_accuracy: 0.9749\n",
            "Epoch 48/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0221 - accuracy: 0.9923\n",
            "Epoch 48: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0221 - accuracy: 0.9923 - val_loss: 0.1067 - val_accuracy: 0.9655\n",
            "Epoch 49/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0237 - accuracy: 0.9923\n",
            "Epoch 49: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0237 - accuracy: 0.9923 - val_loss: 0.1092 - val_accuracy: 0.9639\n",
            "Epoch 50/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0239 - accuracy: 0.9913\n",
            "Epoch 50: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0239 - accuracy: 0.9913 - val_loss: 0.1414 - val_accuracy: 0.9592\n",
            "Epoch 51/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0325 - accuracy: 0.9876\n",
            "Epoch 51: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.0325 - accuracy: 0.9876 - val_loss: 0.1406 - val_accuracy: 0.9560\n",
            "Epoch 52/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0275 - accuracy: 0.9902\n",
            "Epoch 52: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.0275 - accuracy: 0.9902 - val_loss: 0.1219 - val_accuracy: 0.9639\n",
            "Epoch 53/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0225 - accuracy: 0.9916\n",
            "Epoch 53: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 4s 2s/step - loss: 0.0225 - accuracy: 0.9916 - val_loss: 0.1045 - val_accuracy: 0.9655\n",
            "Epoch 54/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0211 - accuracy: 0.9899\n",
            "Epoch 54: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.0211 - accuracy: 0.9899 - val_loss: 0.1102 - val_accuracy: 0.9608\n",
            "Epoch 55/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1205 - accuracy: 0.9526\n",
            "Epoch 55: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.1205 - accuracy: 0.9526 - val_loss: 1.5806 - val_accuracy: 0.8462\n",
            "Epoch 56/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 1.0126 - accuracy: 0.8934\n",
            "Epoch 56: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 3s 2s/step - loss: 1.0126 - accuracy: 0.8934 - val_loss: 0.1314 - val_accuracy: 0.9325\n",
            "Epoch 57/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0794 - accuracy: 0.9701\n",
            "Epoch 57: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0794 - accuracy: 0.9701 - val_loss: 0.1028 - val_accuracy: 0.9608\n",
            "Epoch 58/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0462 - accuracy: 0.9822\n",
            "Epoch 58: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0462 - accuracy: 0.9822 - val_loss: 0.0908 - val_accuracy: 0.9702\n",
            "Epoch 59/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0357 - accuracy: 0.9882\n",
            "Epoch 59: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0357 - accuracy: 0.9882 - val_loss: 0.0857 - val_accuracy: 0.9765\n",
            "Epoch 60/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0305 - accuracy: 0.9879\n",
            "Epoch 60: val_loss did not improve from 0.08013\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0305 - accuracy: 0.9879 - val_loss: 0.0820 - val_accuracy: 0.9749\n",
            "Epoch 61/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0268 - accuracy: 0.9892\n",
            "Epoch 61: val_loss improved from 0.08013 to 0.07945, saving model to /content/drive/MyDrive/Model_DE/Model.hdf5\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0268 - accuracy: 0.9892 - val_loss: 0.0795 - val_accuracy: 0.9812\n",
            "Epoch 62/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0212 - accuracy: 0.9926\n",
            "Epoch 62: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0212 - accuracy: 0.9926 - val_loss: 0.0872 - val_accuracy: 0.9717\n",
            "Epoch 63/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0172 - accuracy: 0.9943\n",
            "Epoch 63: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0172 - accuracy: 0.9943 - val_loss: 0.0834 - val_accuracy: 0.9796\n",
            "Epoch 64/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0235 - accuracy: 0.9896\n",
            "Epoch 64: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0235 - accuracy: 0.9896 - val_loss: 0.0837 - val_accuracy: 0.9639\n",
            "Epoch 65/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0290 - accuracy: 0.9872\n",
            "Epoch 65: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0290 - accuracy: 0.9872 - val_loss: 0.0826 - val_accuracy: 0.9780\n",
            "Epoch 66/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0214 - accuracy: 0.9909\n",
            "Epoch 66: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0214 - accuracy: 0.9909 - val_loss: 0.0840 - val_accuracy: 0.9733\n",
            "Epoch 67/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0162 - accuracy: 0.9929\n",
            "Epoch 67: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0162 - accuracy: 0.9929 - val_loss: 0.0868 - val_accuracy: 0.9749\n",
            "Epoch 68/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0189 - accuracy: 0.9929\n",
            "Epoch 68: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0189 - accuracy: 0.9929 - val_loss: 0.0795 - val_accuracy: 0.9780\n",
            "Epoch 69/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0184 - accuracy: 0.9926\n",
            "Epoch 69: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0184 - accuracy: 0.9926 - val_loss: 0.0866 - val_accuracy: 0.9812\n",
            "Epoch 70/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0157 - accuracy: 0.9933\n",
            "Epoch 70: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0157 - accuracy: 0.9933 - val_loss: 0.0897 - val_accuracy: 0.9765\n",
            "Epoch 71/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0228 - accuracy: 0.9906\n",
            "Epoch 71: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0228 - accuracy: 0.9906 - val_loss: 0.0879 - val_accuracy: 0.9733\n",
            "Epoch 72/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0283 - accuracy: 0.9879\n",
            "Epoch 72: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0283 - accuracy: 0.9879 - val_loss: 0.1696 - val_accuracy: 0.9513\n",
            "Epoch 73/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1036 - accuracy: 0.9590\n",
            "Epoch 73: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.1036 - accuracy: 0.9590 - val_loss: 0.3441 - val_accuracy: 0.8870\n",
            "Epoch 74/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.1963 - accuracy: 0.9273\n",
            "Epoch 74: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.1963 - accuracy: 0.9273 - val_loss: 0.0887 - val_accuracy: 0.9702\n",
            "Epoch 75/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0293 - accuracy: 0.9916\n",
            "Epoch 75: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0293 - accuracy: 0.9916 - val_loss: 0.0886 - val_accuracy: 0.9749\n",
            "Epoch 76/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0189 - accuracy: 0.9933\n",
            "Epoch 76: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0189 - accuracy: 0.9933 - val_loss: 0.1023 - val_accuracy: 0.9749\n",
            "Epoch 77/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0189 - accuracy: 0.9923\n",
            "Epoch 77: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0189 - accuracy: 0.9923 - val_loss: 0.1056 - val_accuracy: 0.9749\n",
            "Epoch 78/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0178 - accuracy: 0.9929\n",
            "Epoch 78: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0178 - accuracy: 0.9929 - val_loss: 0.1000 - val_accuracy: 0.9780\n",
            "Epoch 79/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0153 - accuracy: 0.9933\n",
            "Epoch 79: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0153 - accuracy: 0.9933 - val_loss: 0.0890 - val_accuracy: 0.9827\n",
            "Epoch 80/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0154 - accuracy: 0.9943\n",
            "Epoch 80: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0154 - accuracy: 0.9943 - val_loss: 0.0939 - val_accuracy: 0.9733\n",
            "Epoch 81/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0181 - accuracy: 0.9916\n",
            "Epoch 81: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0181 - accuracy: 0.9916 - val_loss: 0.0932 - val_accuracy: 0.9765\n",
            "Epoch 82/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0152 - accuracy: 0.9936\n",
            "Epoch 82: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0152 - accuracy: 0.9936 - val_loss: 0.0864 - val_accuracy: 0.9843\n",
            "Epoch 83/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0174 - accuracy: 0.9919\n",
            "Epoch 83: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0174 - accuracy: 0.9919 - val_loss: 0.0935 - val_accuracy: 0.9827\n",
            "Epoch 84/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0136 - accuracy: 0.9943\n",
            "Epoch 84: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0136 - accuracy: 0.9943 - val_loss: 0.1036 - val_accuracy: 0.9812\n",
            "Epoch 85/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0129 - accuracy: 0.9946\n",
            "Epoch 85: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0129 - accuracy: 0.9946 - val_loss: 0.1079 - val_accuracy: 0.9780\n",
            "Epoch 86/2000\n",
            "2/2 [==============================] - ETA: 0s - loss: 0.0150 - accuracy: 0.9933\n",
            "Epoch 86: val_loss did not improve from 0.07945\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0150 - accuracy: 0.9933 - val_loss: 0.1064 - val_accuracy: 0.9780\n",
            "Epoch 86: early stopping\n",
            "20/20 [==============================] - 0s 11ms/step - loss: 0.0759 - accuracy: 0.9875\n",
            "[[[ 477    0]\n",
            "  [  16 2480]]\n",
            "\n",
            " [[2480   16]\n",
            "  [   0  477]]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.99      1.00      2496\n",
            "           1       0.97      1.00      0.98       477\n",
            "\n",
            "    accuracy                           0.99      2973\n",
            "   macro avg       0.98      1.00      0.99      2973\n",
            "weighted avg       0.99      0.99      0.99      2973\n",
            "\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-aa4dd9f64ca0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mskm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_train_l\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train_predict_l\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m \u001b[0mtrain_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'precision'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     77\u001b[0m \u001b[0mval_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_precision'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m \u001b[0mtrain_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'precision'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJaAD-3J55XV",
        "cellView": "form",
        "outputId": "61005a12-8faa-491b-c15e-c5f30bc71eb6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#@title **SHOW RESULT** *Once above code executed then run this code to get result*\n",
        "\n",
        "print(Y_prediction)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['Compatible']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPO_iPsmjs8S",
        "outputId": "dde8fe36-e9bb-4288-fa2d-b3bf5de9b6bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Confusion Matrix Analysis on whole dataset\n",
        "\n",
        "X = np.asarray(X).astype(np.int64)\n",
        "Y_train_predict = np.round(model.predict(X))\n",
        "Y_train_l = np.round(Y)\n",
        "Y_train_predict_l = np.round(Y_train_predict)\n",
        "import sklearn.metrics as skm\n",
        "cm = skm.multilabel_confusion_matrix(Y_train_l, Y_train_predict_l)\n",
        "print(cm)\n",
        "print( skm.classification_report(Y_train_l, Y_train_predict_l))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[ 695    4]\n",
            "  [  34 3515]]\n",
            "\n",
            " [[3515   34]\n",
            "  [   4  695]]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.99      0.99      3549\n",
            "           1       0.95      0.99      0.97       699\n",
            "\n",
            "   micro avg       0.99      0.99      0.99      4248\n",
            "   macro avg       0.98      0.99      0.98      4248\n",
            "weighted avg       0.99      0.99      0.99      4248\n",
            " samples avg       0.99      0.99      0.99      4248\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6mZjU7IiEPZQ",
        "outputId": "21b8bd3b-b9d4-4cce-b14c-f284715232c8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "Y_train_l = tf.argmax(Y[:,:], axis = 1)\n",
        "print(Y_train_l)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor([1 1 1 ... 0 0 0], shape=(4248,), dtype=int64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEpELqWIBRgt",
        "outputId": "225c4498-9c63-42ae-a52a-b0c6312e6e95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "from sklearn.metrics import roc_curve,roc_auc_score\n",
        "\n",
        "fpr , tpr , thresholds = roc_curve ( Y_train_l, Y_train_predict_l)\n",
        "\n",
        "def plot_roc_curve(fpr,tpr): \n",
        "  plt.plot(fpr,tpr) \n",
        "  plt.axis([0,1,0,1]) \n",
        "  plt.xlabel('False Positive Rate') \n",
        "  plt.ylabel('True Positive Rate') \n",
        "  plt.show()    \n",
        "  \n",
        "plot_roc_curve (fpr,tpr) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d5us3RHzB-yV",
        "outputId": "8b8afb1f-5332-4ff7-c94f-090ccd284f8f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "auc_score=roc_auc_score(Y_train_l, Y_train_predict_l)\n",
        "print(auc_score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9876124205936025\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YdhKHmS8CBVy",
        "outputId": "5ebbdf8c-a559-4422-f5b0-6e4f2c9739e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 810
        }
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "import matplotlib.pyplot as plt\n",
        "true_value = Y\n",
        "predicted_value = model.predict(X[:,0:1762].astype(int))\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "plt.scatter(true_value, predicted_value, c='crimson')\n",
        "plt.yscale('linear')\n",
        "plt.xscale('linear')\n",
        "\n",
        "p1 = max(max(predicted_value), max(true_value))\n",
        "p2 = min(min(predicted_value), min(true_value))\n",
        "plt.plot([p1, p2], [p1, p2], 'blue')\n",
        "plt.xlabel('True Values', fontsize=15)\n",
        "plt.ylabel('Predictions', fontsize=15)\n",
        "plt.axis('equal')\n",
        "plt.title('Predicted vs Actual')\n",
        "plt.annotate(\"r-squared = {:.3f}\".format(r2_score(true_value, predicted_value)), (1,360), )\n",
        "plt.xticks( range(0,500,20) )\n",
        "plt.yticks( range(0,500,20) )\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-0a32cf48df48>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'linear'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mp1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredicted_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrue_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mp2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredicted_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrue_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mp1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'blue'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "bwpgB-U_HaSD"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}