(* Content-type: application/vnd.wolfram.mathematica *) (*** Wolfram Notebook File ***) (* http://www.wolfram.com/nb *) (* CreatedBy='Mathematica 11.2' *) (*CacheID: 234*) (* Internal cache information: NotebookFileLineBreakTest NotebookFileLineBreakTest NotebookDataPosition[ 158, 7] NotebookDataLength[ 196655, 3554] NotebookOptionsPosition[ 194371, 3506] NotebookOutlinePosition[ 194719, 3521] CellTagsIndexPosition[ 194676, 3518] WindowFrame->Normal*) (* Beginning of Notebook Content *) Notebook[{ Cell[BoxData[ RowBox[{ RowBox[{"Quit", "[", "]"}], ";"}]], "Input", CellChangeTimes->{{3.8295975129716744`*^9, 3.829597516114526*^9}},ExpressionUUID->"7f54ea3b-0e8c-485a-941b-\ eec13869228c"], Cell[CellGroupData[{ Cell["Integraci\[OAcute]n num\[EAcute]rica del problema 5 de la gu\[IAcute]a \ 4.", "Section", CellChangeTimes->{{3.829645546947966*^9, 3.8296455603808002`*^9}},ExpressionUUID->"b63e2b12-05c2-44b2-8dee-\ ecba43b579e6"], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzsnXdcE0vXx0mAhN57R0ARBEQEURQErBRBVMTee+/dj71X7L0rimLBgr2h oiiKgGJBekeqQCDJ+m7PJtlAUHyu977z/eP5PFeS7O7szJlzfnPmjOXoGSHj mTIyMnMU4P8JGbXAe/bsUYv6acD/MWD6nEkTpo8b23v63HETxs12Hy0L/2MZ /Nm78jIyyP//CQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAACA/xD8qsxXT96XQL/yXV7FtxexHyt+ 6buAv4u6opSkzBraP0E1ue8/5NX/0ctzClKSs2v/6CV+EV7Nj1r+P30TAADg H6G+JPX9tyowx/27gKpz3n/I5/7Tt9Fs8CrSEz+XgHno7+Z/4Sk1C1BNXlJK bt0/fRsAAADwd1NfkZ387FbEgU2Lpwzp2dZAQYahN/a2dLYTqivLeP/0+pm9 6xdMHOjbRpclw7SeG/ffcUv+f8Er/RwbfXr32rnj+nq0UJOVpb7KusLkh5eP 71w5a2SAq4kSQ95985fm9tZ43z89vXZq15o5Y4I7WqgyZVsvfvNX9SSo7PXe UZ0s1eVk5LXaLX3+L3CDAADA7wOVf312A57k5o/v72mjIcc0mngfxBZ/P5yC 9w+ijm1fMXOEv4sxPGd5bE//VysMP7Jf37lwaMuSqYO7O+ixZdh9Tlf+07cE EON/4Ck1D3VFKY+unNi5ataoQDczZaZcuzUpvH/6ngAAAICE93GDm4I8S0FJ WVVdQ0tHV9/QyMjQQE9XW0tDTVVZSYElL8uUkVXQMrZx6tgtdPaBx1l/epWd l7jCUVZGCKkFk/oXc6yYwt/9zwsmUNnj7VOnTJs5Z97CJctXrd2wedvO8PDt WzauX7Ny2eKF82bPmDp5zomkX2qC+gdTzNhsBSUVNbhr6BnAPUNfV1tTXVVZ kc1S6rw9TWTe5Wfv8VFisRXhrqSpjfYkAz0duB+pKCmwWQbjYprq00Nl12d7 e3i42mjJir1KfuapsV4endpZqGLvW6753QCo5MqMrvDlrTWwy/9dggk/P3pC awVyhGiPvgVCpuYDKrm/xNtUQ7ftyNNf/p53/l+AnxU12c1Aw7DTzOjcv9Nv /xdQfWchbBg72OrIY6MfCCb/Bnhfjgz3hOcscxUG+tr+7YIJ9/WWoC4eHR2N 8WkICCZ/I/8DT6l54GefGefl4eHSQg2/USCYUOEX3JzjYaSh7zrhYgZoFgDg n6E27/3jmEtHlvqZUlUKBb1Wbr5BAwcG+bhaa7OoAgTbrO/+lD8qmnCKPr99 GRsT3t+U0D6kzzCpKUhNiHtyfV1vXcb/F8GEn727q7xMQ8ja/XqkD3FrynJT omc6452AoR+w49m3oiqOBJvNr/tRnP5iT18j/N3JO0y9nJRTVsP9jZTx+mez WjAlvMrqq0O1GH/WDah7NNWM+ZcJJpUvV3XUbT3m9Lv8yqrC1Ls7R/Zd/QJk mDQb/LRtnfBYVHdMDOefvp3/ENxXC1rh+qPtwvi/ZTj9S+G+W+4gCwSTfxs/ ogap/xcEExyo4EB3FhBM/nb+B55S81B7a4w+AwgmIvCSVzvLYRGN2bTHv+nr 1RXlfwdNCwD8MtzXi1oLFBN5r11ZhFGFKlPOTHRWpQTgih03p/754VZ9vh+x gC69YIIBT+HdCJXnPy+YQN+P+QlJWmLId9z69femSN77lY6YtVYMiahq/PPV l8KwDiPbetHr32593oe17eQkvMr6pzPMmX/WDeDiKU9/jWDCz40INWIqBZ78 DgoX/BmgsitDUadNRsF1fTJwLZoPftahXmqoTVfvfTj7r3bc/374mTu7yAPB 5N9G/cMpJsz/jmDyk3NxgAIQTP52/geeUvPAfTnPRhYIJiJAhWdCUMFLRtkr /Jfded73xIvrRnQ0UrSc+RQssAEAvwr/23YPQZ4CVTBB4H7c2IEtiMDlXDf8 ecWEc3WI2i8KJj/LTgYQt/ufF0x+1lzoryDTEPLuW35TMOF/I1bcNYZfl2LB ve7OOD1Gs/UU3od1Lg0IJhZ/2A3g/WWCSeWNUYYMGVmbef/xfv2PAlUmXdy8 fM3hp3mgkZsXfsmrU+uWbzjzpvSvdtv/DfAzwz2BYPKv478nmIQqAsHkb+d/ 4Ck1D9yX81sCwUQcqOztuY3L152IK/rlVuF9XN9ernlCAgDg/zP87N1ekgWT n7zPm9zkyD8zDMbf/eMOGufaUPVfFUwqTgX+/xFMONdHaMrId1j5/Ftmdm5+ QVFJaVlFZWVFeWlJUWF+bnZWRkZR9W+mIhCeubS1MuruTzTCBJPmmJ2BYEKl 5towZKVBzmlVEnAnAID/vwDB5F8JEEwA/3uAYAIgozyFvuekSBQHAAD0CFfC EBNMflZHhAjSGJgtZv/5MzmAYCIldbfH6jFYPQ8X/bkNGkAw+WsEE2J/klyH zV+AOwEA/P8FCCb/SoBgAvjfAwQTAPT9WG+W9IniAMC/GX7m6REuraytpMHa xil4dxN23zcmmJSfDBTsyVEOOC4SnXMKE28e2nQ6obFRWF/65cW1k/u2b1y9 fPnqjTsOnLudkFVFf5d0ggmv5PWxhcMCPZ3btPPuO3rOuuNP6Y9ql1Yw4X5/ f3nrzGEhvXx8A4dMWrB+X9Sbon8+Jm4iqGDCDjxV8ecu8W8WTOoLX59bObJX h9bG6ipalm27Bo9adCg2rwnxRbMKJpycJwdmh3Zzd2ihr8pW1DCwtOsYMGbJ rksJRVI2U/1z7CAo+Y6/nVT5Cy1Tm/PmdsSBjQsnjxk/c9nmQ9eTSkUHL6cg 4dqeVYfiqmm/D/3IfH5+2+ozyfQNWZcXd3LpsB5urYzUVHSs2vn0G7v0+MvC PzEiocr0F9dP71k7b8KYiXNWbjtx90vVn9Mba9Lv7Jwa4tPB3kJXma2kZdTC oXPwhBUHopNLpb0mt/Tj3WObT8YLtSpUX11d12y+b13es+Mrxwd2aGWio8Jm K2ub2HbqO3n9+bclEiaR2rw3V/Zsv/JV6M98TvVv1XduIrzv7yJWjPTr0q6l saaigqquaUuXboNnbz71JONPn+X26/z4cmv3/KHd2rYw1FRiKajrWzj6DJm7 69bXH41/lZMXf+347i2bdhyKuPEysxqSQjDhlydf3jorrKuDhYGGIktR09Cq Xc8Riw48yKRvIKjqy40tE4O9Xe3MdZQVlLWNrRw9QyavPhyTWvFP10uqL055 cOnItmXTx46dtmj9nsiX+XSPXJ12e8+C4d2drYzw1nXwHjR7543PDY3u2owH B5eM6uVibaylzFZQ0zOz9wydsfXKh2Z6ZH7F10fnD+7YtGXP8Uv3k4rqpRFM 6gpenV4+vIebLWyatVs4w87O4iMvCv6OqgPckuTbZ/Zt3bR1/+krTz6X8ZpX MPmHeyCvJOH8hqn9OtuZ6akpsJQ0jWxc/ccuP/487+9o+9+gcU+Jk/v8+OIh 3VxbGqqp6Fq7+PYft/xkfDGN+a9Oi9k2ua+Pm705Np9ZOXTpO2nVwRspZb/w iqCq9KcXD+3cvHnX0cg77wrqpBFMuMUJ51eP7u3e2kRdWdPCySto5IL9T3Ka phn/lqPSnHCLXp9aMrRX57Y2RuoKsPkxt3XtMWze1rPPm/BA0o6a0uP+iGDC MJxwT/KP88s/RG+dFNTZwVxLRd3E3sN/6MztN9Po2wkA+GvhvlncWuTA3QZg 6I+/I/2Ia0QwqYud2YI8s0Yr5EwhPAz51YWf4mLOhS8c2tVaDY0o7ZckIPal 6t3BMZ7W2upGAfsEMV191p2Nw1z1xc9zYajY+M2/kCo2HkUFk7pvUbM66Yoe OKzadsrlTDGjJoVgws1/smOYkwZDRvQHHUafSq0R/XR9yccHZ7bMCvOy81qT yEU24r88MKmbnZEG4qabt+09edeTAty4Q+Vvj8/0czTRUlJUN2rZzitk5pHX EgMjqDr9wYE5A7ycbQzVlFT1rZw6B07a+SC7SeovKpgo9LsgdtPNR/MLJryy D9HbJvfp5NhCT0UJbicXrz7jNlz/SvsMvyyY8Euebwm2UlZ3HLLx4ovUrMyU JxdW+hnLIo/RZfnjEiln98YEk9qcZ8cXD/Z1aWWioaSsY+HQsUfYvCOvisUf uzpxT19LJTXHYduuvUrNysv+9Ob+qRWhDkgfVAq9JGWIV/9kuplkwQSqLU59 fGbD5EBn61FXkF7EK3i4cWgXWz1NQ4eg8ETy9pveMjXpd3eO62zEEh0utiPP ZfGh4vhz25dNGxbYyUodaSpWr6PU3+DnPDmyaeHEsF7tTZWRbqE+9JpYD+cX PlrT20xRy2XU1isvP2VlJD86u7ibPvykTH3fDXHllF/jVeUk3Ni/eJi3rfsy tKJwbdr1VQNcLfVUFNUM4QEXOGZVZHK5xJfLr/h4Zd2Qdjpywk8iq+U8O4YY p1Bd6ddnF7bOCHGzGXCCrjWgys+3wqf37exkZaCqpGZo4+wZMHr15VTxoBcq j9vQw4it7TZh7603n3PyMj++ijmysE9LFYYMw2BCIxsbq1Nv7Fk9e3RfLztd pN1l2yx9izwvN/fh9jFe1jqKaK9UMWnnN2HLddqQsL48/WXUzjmhHVsG7EMq rEIVCQcn9nA00tS18Vz2kDS5UHl8+ICWKmyznrP3RN5/+y0n/f39Ywv9LBEb ytR0mXT+C9E5kRe9benUIf7ulmrUF12dGrWsv4upBgt+vQyWprXHgLkHY+nE bH5t0YcHJ9dO8HNqNe4GnZnjfn8ftXGcv3sbC10VJQ0T2/bewRO33MkQ/yg3 69KkthpsC79lZ58kp+flpiU+vbRtvIch/F7lO20VPfK8ScCj6BM2iqxGRCEm 6cfHS0v7d4BttKKSlklrr+Frr30mLRVUkXRuQV9XawNVZS3Ydw+ZdeydpM7H y41Z7G3EVmsTturotdgPWdlf4qPDJ3TURcYzy7j7yoeFklY4atLvbB3pqkft s0w1a+/gzuhJchIEk7r0SzPcdVnwkNpw8gY8yrNSX1zaPNwZnfMULIO2xglP S9D3R8u99Fl6nacfuvP2K9xVU17eODCnVwslJKV01rOGI8aaBysDe/v3CQ7p Hxo2eHBYaP+Q4D4BfgELb+DXgEqi5wf6Bwb3GxA2eNDAASFBgX59Vj+Sbp6r z39xdE5va2WRuVrBtNfORIpB5hXcW97NREGl9YDlR648TcnM+fr6+u7JnfWR fipv6LPsbr546/KLn6z3N1dUsglafDDq8fuMnLS3MQdmehsjDS2r6zHverbY hFOR8erKrnlhHi17hX9DhlRl4pEpPdsaa+pYd154V1gw4Je+O7sowEaFeuMs PeeA3k5qyD/RCyb84qcbAi2UNNsO3xwVhxjCxxHLexoihlC365pY6ivjVmbG X909f1CXll03oKHlj09RS4LbIQNH3bgVPHDGr7+SKlEpknqgUZ694MWh6T5m 1IppDEUT937dW6EdU5JgAlV+ub1nZkiXttYGsJNjYN22i9/IFZEfKsXu7Ld6 IPYLdWXfXlzcPqu/uw1h8d6fnt3b0UxbWUnT1M7Nt9+UbTHfJEy1NR9Pjmmr ydL3mLTt7O34L9mZH56eWz3ATgV5NmXbwQcTRduS9yP33a2DS0f42HXEZ6Iv kfODXC20tMzdJkaSp6ZzK7PfXN+3aGhXW691yDZabn7s7vHdHJDQVlW/hUuf WQdfCJZL6nMebB/tbW+mpaxqaNuh58j1t7MkTxL1RW/Orxndq4OdOfl8U8Mf 5tA2VIOeEi//3ooeJoo6rmN3XHv1OSsj6cHpBd6IXZI17Lk1nvKmoNLY1T4G bN2OU/bfTvgCv6IPr24dmudvrQy/ItOpj5okK/HLUy4uD7FTZ1L6k7x2m97+ LlqST8mBSl/u6G+jotZm4LoLzz5mZn6Mvbgm0BTufwzNjovuN5Zl/ZuOCr+6 IPnusVVje7axm4wGV9zcR9tGdEYmBhW9Fk6dew9bdJzO85NIfdrZUXaqijbB ay48S8nIy/n67vGFjaPckDCH5bs/n3JrEKfk05OzG6f0aWc9LFLIz2jCqKn7 uhWrEq4/+ERi0tv4uNhH92/fvP22kLzlH8nHRztpKFkFLDn5MCkj6/PL6B1D bJEBr+wwISqL+jr4NYXYTO5oOwGJCeB58Oz84PbwPKigrGPWxnfMplvpZONB pQknZge2g519ZUT/HTD/bIpkCZtXmnxl84SAjg6WuCXrGjRh0y1JoxYAkMA/ JphAhZGDDIlZX7XzlmRkEHKiwpSFr4hElHXfHy/3wKo5I6ez4H5r1atNvvrY rSu2Hrb79rvs74WpD0/O66qLG0uGRseVz4VnW6pgohUwb76XHlOGFqX2K1+L BNqNCCZQZcKuPqbI47JtRx97mVlamhl3ZERLIhiUazH2ejE6nLnxO0K7d3S0 0lMm2h1+xvii+PCQFmyRu2C3mnqnjF+ZeGhwKyXRO1Trsu2DWKjNL3t7fEon fTnRD8sgxmnazUKpxfq6m6O0GaqDzn6OPbV6TC/X1mY6yrAD1LpDt5DRSw4/ zWmO1LtmFUzqch5uHeSgrW3j7tvdu6OTjZEa0fGYev6HvorPkb8mmHDTTg+2 VmAouS2Lo6rsnA9bvZATfBj6/c7kSDW1SRZMoIrks3O8TdSNnTy7d/Nsb2eh o0j0UcU2sx8Ki/s1L+a3lpNR6bYvgyf8G88XuyjpjJKmWWtzYo8t8MO7nrJR a8c2rVtZtzA3MXFeGFuTsM3fwUSV7E7s/pG13G+nBpoTjSvbcv5L7i+1DPT9 2QY/U7aybdimi48TM8sqi7++un12y8TOhvBvK/gd/w7xPkcsGDdqaJ92etg4 EfFDuG8PTRkzcnBPO3XMMIgJJnUfD/c1l2eoeW54S/UFqt+u7oCsXTJNhkbB 4wEqvT6ro7Uu2cZIf6grebIStSIMJtU8KLWZcZtG6ODmxizsrCuv0W7c7uhn KbnlFQUfn984sXZ4Oy0mbGRGXK/9yft0KMzZXIM0haweh0RGIrcgdtdIZx3N Fm4+3X09nFuaqBN2g6HlE54i5DJC5bfHmzMZOv3OFgjHpsV3p7RmNxYD8DN2 dKYKzIhgUl+ddKCfOc0x4grWofvfVQm+emqki6UmizDa8p13ZHCLYqY7kiZb bchV/B1UPl/mqgq7u2ERWcLZIsX3Z7dFl46Zhn1PpKN/I4Yb2Tzwi+YVP17e RUtUeUZsevtpVzLIEcN9tamnvbGgg6oMviJimjgZMev62WrqtOoEGwZ3R2tD 8sOyRv3PCCv4/LQ9vmoysvYLXgqb/rpPB/x12cK+ZhPgvtnq14Y6ikIiqoof LumkyUB7GPmQTP0e4e/h2+dmX5vaFo2oGIIGYFuGnfomZsb4eZdGtJCXUWg7 /6mwoFKXdmqgKTpu2PbT74mefAVVJZ2e4W2mYxe66mTMy89FVRV5qS9jjiwK slUlr0gnmHDTjvU1kmWodFr7RljGq0nZ44dV5FZ2XR5H9hio+PJQIybTaPhV 4Tvg518b1YJlj0l1kuGXf318dnlvU3yeZOj3Wnnx4avEdDIDjfv96+sHkduG 2MHWi6HmNu3kw7cZUuQM/Eg+MtRWmWXSc8mpe6+/FleWZb57cHHPXL8WikhE tfYD8fNQwbWxNiwZVpuZD4VloPqMiCEW6PtktZoUU0z9G/T9znR7RRk5m/E3 hQMtbu7VsZg3IGc54nI+1vH42efGurbQYhOtjsw43JJ7c5xViNegFHqRdOp5 +Y+2DG6ra+QxOTzqUWJWWdX3jMSnl7aPp0rONIJJ/efjAyxZ8FtbTY1Sf9a8 3+CBjFym0cDzefyfUP7FSW4ttMlbkXVckcjJv7OwI9JNmVRDyFB1WfRYTL9r ykDDqUu/viKolY5Fj3kHo2NT8iqqir4mPDi7dogLRXimEUx4RS/2jWmvq2np 6t3dx8O5lakGaSw1PLe8F+q0v9cD+WnHh7WzIH8esXjpnMzLk52Q1yPcJlqe G16Lr88lh3fTZjC0e+z+KGSWoIr4dV6oxsjQ9N6WhP+t4tbcTjZ6QjNR/Y/4 DV6kGWT57suDoMKoKR2sdBTI99RmaUJV6vGBLVgi96RoP/kGPEPAfunOAFSs o/yRqdVx+VPxkVLzNXpFoLWGnp2Hb7euHRys9EkXVd5ieFSB+MiS7ClxkvcF msgzNHy2vqc2y4/4Ze0RV0PWfHQ0PpfCM/AoUyZDPyxSeMBAhTfHt2S3WvBK 2nwMfvHzXSNd9Q3cxm2LfPA2o7SqNDPp2ZVdU7wpahyNYMLLiBjeUpGh6LIw lprMUvcp3BdxLRi6gccyG/TpftlRqY9d5dXakGxjGU3YWahMCO+DSjWUiQH+ Scsh52mHEM3dfNjcUUlG3mWV8ED4WZu83UdTIfBUOXZTieFBjqZqgjkp+Cyl /oiUo6b6682t47zMFGXEkXNc+R5tZ6goZpazGkPOZtz1fMoD8HLODkCjP1WP zWjkV/98jY+dkQrZFnArVefHzG6vJjJJyhr3OZQKf6HuW+RYeyQoovpoii1H RYqfkleXfW9TqJ2WTsuOWGxgqErGBgZBx/8TGxgB/zP+t4JJXWlO9vfqqvyU +3uH2xEagIrjlOg8rNtClZnv4h5dXtGNnCRkbacfWO2pSQ4Yls++XAhZXLo+ xhK/b3nnlYmCm4K+XxthQowhWYsJt6m7SiiCCfJbZr0XH495k1lalv3u/vFZ HtpUB1211yHh2LdBwQQqvDjIEL2qrMPSBDJk4Tyf25JoXbk2S9HYmJ9x/3D4 5sUD7EhDw9BxdLVS0bbr1n9QP982etTIRd7Gt7edCtvAuffAsCDPlprUOdpo wl0RgZRzZbAqkl3TbejkOfNnjQv1bEGVn5hmk+5JkaONUntxgCIyxYqHLMiV 1V2mXc3+3a2gpGAio9R+zMZtO8J37923n5Z9e3eH79i2cbw7dqyw+OyMdDQW 03DAOcEbq8m4sznEioV12lE3xJ77VwQTTtKmzqpI0tPCV6LKdPWd8cbIV2Rb znshzQCRKJjU3Z9oyFRwWR5P3jH/+9vT0ztiA4Llvvkzpd1rY8bqM2TknNeI 7ZOD8vf3sGp8cYafd3aEs4Nze3tDtGsz1Kw6eOH4TjibxeeU5edkfYnbE2yA Xp7d72DMXCdlRf3WbVvrIV6IXIdN6P00sWWgopuTWysy1LzWvxH12rjfwrsq W8wQnE8H5ePHeYv4IURDJq1yQqd9EcGkJn5Fe7gHy7db/V60ESqvDtVFV5vg kID7k1v8OeF13KOzU5zQqzBNg6cNadvSZ/rB+x/yf3A539NenJzhro2NO6bl tMfCsTQv48xAM3mmftChT6IPXpOwtK2Cyzok/KrOSX7z6tmtbcFoS4gJJlDJ sd5shk7AkW9kV+BkP9wZhq7CwL54aCQ1Xis/318Nsaa7xVwD3qcNHds2FoVy y3PTvqS+jZ7dDn1eWbtJ4Ut9bN3HbDx180VqYWV5TsqzG4fn+hrjVohp1O80 4S9yK4tys78lRo5vhdo0+c7rr2321mJrWTs5mKogy9Xm058gjQ2VxYy3gD+i 3OMgjXxY/2FzJ8z0qfjsRsXv2pLMr59TXhweZI41j++y/eNcWveavfvCvYT0 0srv394+vLR9tDORuafQZtYD3LWFKjLevX4Ve2W5NzZDiAomvE8b3eSY5iOu Ctq7+uuNNf5mcphBnPKA8tZ4KUglH4YOjXzLuTXapA/uazYddBR9fbW/HzZD yLsNGuZo2m7Y5qsJWRX1nJKP0cu66mAdTMl9WcSuECsj11Hbrr/LrqznFCdH ze+EPRtDI+iksGTDTz/YC/4b02RcDE21vcr7U9B9djJM45HRlFCAn399qpMq Q6Ht3MdiSYrcwifrexgwselFVDDhpmzuBEeKsq3mxYkL5lBx1FBs/UPWesZj LEaCCo/5wZM8q7f4wOW+Xdq2o1SHnUElV4Zhw0ZWQvBUdi5EhaHpd0S6iOLH yzWdNBisNtPuFIp8HiqKDNNX7h+Jj3B+1rFA2CNgGI6MpnnxP57MxGZ2hsGQ KPLxoIILYXArMLRDz9PYqtr4xY74EkHf09ir5FbBQyr9/eXJduiPyXVYfXVH Dx2WppWTg5kqkpxnNAl/C5ykfX1M5Zm6/vs/ibV+zZcLk5wwjUVMMKl9u9Zd GTF2yxJEu/WPm6NRuy5rt/h1/c+6wtQ38S/uHx+D+YOyNqEz+jvY9px77HFq YQ2vtuTL08MT2uFjUK71wldCv9akgYa2VMXztV66TFmLoRdzxCavisRDYdaY TCEumJSf7qPA0Oy17ytp1+tyn+weao+5k+p9z1Ba/nd7YE1uCtwmd/aEYYqu nNOwmf629kFLzz7/WlLLqylMvb9rqB3uY7HcNn4U+rXal0sc4WlV3mVtini3 5eccC8TGNavtcuzN8KtLcnMyUu8tcUftPtN61vkTA01ZamYOTlYaSDsq9rtQ jaSgFMNdJiVmngs2YVn4D+9madUDfk+fimrqf2S/ODS8FaYQME2HHTw7p4OR td+Ck08/F9fUV2Y+3R3agkX/Cn9y3y1rIytrM+kOGSVDlamXl3Y3wkafzbw4 MXdCkqf0I25xW/ge2B02fBR99LLIgehzy7dbi3ot0PfTQXDXZXU7ICbI8JJX u7ZfLV0Z+vrPx0NbsBmavtuTxDKKa9OvznLF5AoxwaT+w/aucFAu22rOM9Hv 1T6aik5IzBYznkqRidB0RwUq+5bw+uXTqMVd0OQwGbVukyd3tHEfs+NGYnYl l1uRlXBpaTcjTNRg6A66JM1mW+7rRbay8ICb9ljsZVVfGqg/EE86hiqz3r9+ 9ez6ul6Yzi0kmEg5avg5d8JXr920dUWIlSw6P3nN3EO47IduolaKn306BDYx DKPhV0VvHm6PtsijMfSGRCGzU21pXk7ml+fb/XQw4b3zkIF2Zh3G7LiZmFNZ X1uUdGleRzwKVPNed36Lv4VJp/G7YpJyq+prCxMjprfHmhBuposiIs+3bR7y TJPBF/MEscG3mA19LDFLbDT+zh/Mogf85/ifCibpqVs7UbMoGCrWfvPOJokm Ugl9T54lL6veutfI6dNGdm+pymD5Hy+F7zpptTPxCfmOW4X3EXDjF9qSj6TY dRfFgaAKJkpdtqYKGZWa5O2+6gKFQKHnISET3pBg8uPuRDPM5RU5mbXuwWRj Ur1ps0wQzPDTqSu9qh0WP8Y3dvIK7kxzoGxSYOj23JaAh5V1384MNCU1E/j3 3glPR5xLA5WUvPdkCJLhUsJ7CdZpmSZTHkqX3wiVHvcT2Sghgor7BrFItGkI BJMmQiOYwD+l4r75k8jUWhs3D+0IdLsrmy6Y8HMP90KcUvqjZKqjBqEHVsva LoyXYklEsmByZ7yhsZiLDuUdD0C7JouapoWPE4b+kMticykn8dyuGHG1nZ6G t+TAwdDhnpj0pKlr7bX8EZrryy99vtxdq9eRYqipLcPPjQiFQ0fMTRe/l2ez bNquoKTFE1ln9H4IVHCgO3pvQoIJ/1u4p4LEB4JdbjRip3pPAjvIbj3hWp7w l6oeTbPGoiPDidSexP20yxcOIdidd9Dt1uBcHarrs4ciGdRE9kevKyaYwO2r 7LTinUhr1L1biRk5zWHXBJM6L2WNsxyi3Ux5IKoCQhVxxw8+lq5Oc/0LrGwN 3Armoae+ig4PfnHsis6YMRSKCNH7ujkStSkMdW1j54lR6eh3az7s9Tdqh75/ Xuomd8RSKvQ+Sn8vVbfGYkaRaTaZEkiRR5kzlJ1n3SsW69EZUWNtMaMkb784 nnrHUO5eb7SlxASTj+td1X0oBhHjx4MplpgrPJuSj8OJHqaBvP8uO8RSOfgZ N3ac/82t59D3Y7hNZRr4702muuBQadQQPdxMM42DD6dSHwIqPN0XcxUVeh6m zkg/7k9GzBTTeOI92ow/fs6B7lgUyXbfRESG/OxTfeFLMS2nPqTfQE6cXScq mEDfsXuUaOCQmBmz5yo9DqKWB5+IZVvPjxMNOKCSp4eOPpeuPAH3PeZay8i1 pbEvUNGpIHWW+OihBap4OMNWHu7SI6/TbPTgp231MCZKwdc8mWmNJJnpj7lF 61FD+Uf9cI3CZS0uWNe9XmyP3KnGoEv0+0hKIvpjrxK2+6+pbsKd8frokFLT NnIaeyENfZ+1qYeCTR2WojuSa16vcIEHh4InumWHhtqYMfo0W3L4mXt9kT4g 1379R3HTXHW+H2paZR2Wk56EIN9LyWnmHZFF/7KbYzE/h2klXKK/SQMN+aG7 k23k4Pkk5KzYOMeoON0H9bbEBZOykwHK9otfi1gsbso6V3R0qQ28JLCLzdQD kTMDMVuo3nHFM+HvQAXn+utiwbjQwgU/91gA4mvKuW0U9Usw6uIXYSoZQyv0 PCXI4yYsQf+doaSlZ9v/4Hs0kK3PihrV0oR6jqTAgCs5Tr8tJP5xk9e6EN6b cru594WtN9xH0csyLWbGCo0Z+ML2WgHH8oSbBCq/Pgpb8WgjrsXTe0q8z1s6 Ii9P3jNcPDcDXR1Am8sNXWxBZRrEb575VNQgQaXPjx6OFU2Po6MuaROSLcVy pevl6AeeTEfFDxHBBCo4EYg4J7J22MZ/YWqjh6MTHdNqzgsprMuvOCoIgqRP WbMBJ0Wm4vrE1S5Y2KHSL0KKioLV5/shPoZyz0O5YuspX67siBLpi3j9EWHB pGmjhrh9hvHkByKtVHljFCKiMwzG3xGfoYjCeQzt4dHkmxeEfrKm/Y5/EZqA Ck4HEwvnchaDznyjXoyffbAnZo1V+p4to16G/3VLR9UuO0TdwJqns2yQYdDk PV+A/9/8bzNMeFWf7505GL5l09bdhyNuJ+RW087+UPGRXmS4Lmcz5koOZsyg kid7N1z+woVN+1J74qZFYhgEDlKAg/g+uwdF92jklJy653NsyMYg1ksJGhBM qq8NI7JTWL77qTMOuvJC/KJi/wuCLf5FeBCK/JzJ5AcUgwIVHfcnd+DIOq4Q 2lOdgp1ngiJWlpqf8/ho5Buhfbz1bxaTTSXD7ntOuhQT7KgwWd32A2et3H7k 7LljO1ZOC26jRs03YWiFXSxr/JcauAa5JUdr8IWiyuraep6EqRHi13Oqq4qv jNKXtCWnLjf+cbK488N9tQBdDJfvukd0qbvJggmUf7AHGs+xA07SPDc+76Me nhTzmkTBBCpPffgyUzwsKD3VB01AJFccEcpPB2GLUQbeCyLeFv+y4W9EMPlZ eSYI6/pKnjsox+jwvqdnlkNNbRl4kCGRiGA3j8iTlr29fJVasYEYsxL8kJKj vcT8EH76dswFURxwkWZtqP75bMzXVA6LIv7MS1mDDiy4M14WD3b4OXt90BaQ 77JT4ANWXBuO9EhJrcbPfx4RQ10O5kQP10CfRHRLDq8g4VGieOTAS17tjB1e tEmQV0QaVTkzv1WXk8t+MYpHvGPULdfsH0HvkvI+bemECRjKASeogVPd/Un4 3jghjwqqzE4vrkPuGlV0GqpnXHt/EpYEiNhYsjtzrg7BlLWWc5/T7/n78XCq Ja60TH1ItZiEFiG2Jac268XTj+I7m+sfT0OFZ7bfMcHDE6OAodp29L7HWTXN XQ+y+lxfrA912iamr9U9no6n13QVzxyqiRmDZgLAIY4g8+pn7S0sRIZ7kwSN DF8plEHDaCzQ5jydgdQNa+BIOklFX8sj+qFvR2VApIQ5hBx1Mkp+x5BbIrQX GZZV3w03PlX8ak4iP2sfGvXLyLaa/1LktvnfdnoqagSdpNkvQPNDX7Z5IK9A Y8gVWrWo7nNMxDNMLOXcwzaAsnz25kpo3ZLTwXjKIx4Tk7WzJVclr4gajC1g UDUKtOuZ4+v4syhxI1SVk17EQeaeY/6q4s4FFfqir/zsPV1Zkmd+ODLCksUU +52vFvwbvsow7ra4UgSHH+7oOxY9Qa8pAw02LZvckdtSD7sk6SBSyUVfeUXv Hr4TL2cOe1quqLFst1YQEjdTDyR2A9NmWcAuNGZKZZQGXBRUIco70A3pagy9 cZI8Zm7icgdMMdEcFCV4SNLFYxgOvUxptJqC9DxKryUNuMHom2JKQ/6B7qip YZpMvCc6AcLD1IP2Ff6sTo+N/Sz+zmtvjUYVIaX+50X7EK2nBDd6B/QBVMkd mkLfeTwNE93Uh0VzKLcjbxm49tqH8l94RdD386HIqKIzrDj0RV+h4mP+inhT 0Ah3xExGm2shzi84KthfCg5iUopQqEJSHhmKeg2ydkukOB+gLmYM+rIYGq6T jjzLbSwzpvZCP0yXpAomTRs1kgWT6ugRaLoIvVoLFeAZOVRJkQz92DSmjpgH ZRT9xJdiKqMGaeLpcsLNxMl5+YSm1nb9s9loBU1W94NSzR0AAMI/WPRVIqRt kaHPw4Xy9vsKBBUnfLcc5brErI7eMtXxa+xYYTIWQD6gNfIm9QOSBRNqGyr3 PZFbXlVTz4cgLqe6svTzTh/yVqmurdAzioQW1N9DCgVQW62OIgYp9DvfeDpZ 3a3ROuQX+kdKWeWoOili56nX34WTe79dnuwo2LDI0B0T8zvnTv7xU3J41fkv lqOWX66tWGJnkwUTYhFQRqHryjuxcW8SUz59y8orLCrMy8789vVT8u152OQq 7y3Jwxa6fJNOyYHqytJODUIvrzSQUsmVKrojpTpN2/UaPnfziVuvsyqb5Hc0 JphUnQ1mS362prUMsTLG6n1MmrWjX/JDaq8OwYa5eq+N9569fJP44fO37Pyi ogLkjr58Sro6DRtfWLoaSkP94Sea94JOr1RrUx09HJVJNVDHTwrqro/QxO2A FNWE+DVF7zagI0S2JdUG8r/t8RHstJNTt3QLGL1w+5m77yTIz/Rw3y5tgxXV lriHB8o/iLvdwtJx/YPJxgyJfRd2/3qIa0silJ8NxiN5ipQpeNFHJCw74wnH mFmkLqQTC2XiNUzo4FXlPJjriLxt4UquNU9mWJFzIUNBz75r6NRV+6NiP39v ljWo6ogQzDkNiRAL10nvUTksSrx2MZJVLGpxyaagD0uwB8U7Namtw90YVZzY gRL3F0kQTIiZsaEsRW7cPGy9gegwvI+b3AUThrymdcegcUvCz91PKmiaGgWV XgxDh5pwShJyyXfLHVgWUx9JlVbN/4bFZnLOjZ4tivwuJiXQSq7Yz33ZjLsZ KoMu11ICRXbACUlLCVDuXh9yr4lAWCdmHHrrA5WcDESGi2ieBhV6wYRzcyS2 kqPcbe3dWNgQpnwWMs03ZjnIiVgkwvlAVmpoGqnu7gQ0aJF3l+JANQkDjZ+2 tSP6Hlw30Cdf/GzyscL82uLkrWjLCtekbJ4eSFg8UWcMp/bSQCy1gKJA1FzG 8yobCHPrbo/TF1fPiEHL0EbqX0mENOA0iR+kKEFb45TwCKU7gZpbmXVjCiqr scSrONF6StUXQ7EKTFoBW5DZ9/2Hz+nZ+YXI7JuBzL6XJmJWAg/SeV+2dxFU 6JPXsHLvM3bRjrP33udL+4oqLw5EJg6GgeRjWugFE2LQwHPV0pjYuNcCzyUH GR4p9xe7yslIfu8i/LJgUnioB/oXzRHXae6fl7oBvQsp88MrY8aRFQlkGIoG Dr5h09ceuvoirYzuy5zI/uKCSdNGjUTBhNyEJOc49cLjF/HvklO/ZubCrZuf k5X+9fPHN1tQSVHIj5DcSj8lzIPk1XB1i2kmvM5ND+9H3pNFqC4JGyFpdoYC ACh/vWBC45WTa9boaHQVT3msONVHsPNHcUAk6U41JpjAQ7ITWRHIas5z6TJM ONeGUSqjSEZawYQwkWiridhqwuVFELZywtSVpMZeO7592dTBnhZkzStVqeKJ BuC8WmgnS3kc6avIivMnBJP675+fXty7fOLAPgEBgX0HT5wRjAYVsraLXot0 oiYLJuWnAkVr8uIdSY6lqKymqWNoae/a1a//6J3PpcjiaVQw4f/ISYg5sWnO iBD4SYIGjJo+sjPq5bCDzlAdSKjwzqIuNEV+5XXdxux/SXd8H22zSSmYsPtf oHHhmtQyZGYqu+856Q6Va7ofAuVTBFUhmPAdqcB3ZGTVpoO3/4Cx+8hNQY0I JkSeNMW1xffGIAmnjRxMQ9CoYMIt+/r88v5VUwYFId130PjZ/dug9ySS68bL ujytvZZ4uWq2kee0ExKPUxG5VEP+Nk79i7nWaMqFsKmkhA80DjcZasrT5EqQ H3qF+a9CAlTDLxqj+gK2q0kOKw5D0Khgwin++Oj8riXjQwMDAvr0Gzp5ir8V 5j+vFQoofiTsDrESL2LHVG3Vd+2tBg/7kIKGBBNkowH6RzrBBMrbh8aCQo4i ka7UoF5XezEU35Tjj8Tw8A9hA0ONxh3FkSCYVOJbJGh3xxP3SWaGkhNNfdq5 sY6Ufa6EZVAw9Z0TkSJ+ponEB3k0zQLriiOiKfav5uEUc4X26z9IZ+fwPVeU wvESIRZqad8H+XtXh6JBMR4pE1a0wSC/7u54bJVUvstOwcROzjjUXDKS+lhs zqfR/QWfoRNMhFJ1hV8AbpqNWrRx6+o/YMzul4Rhb0QwqY+did2LG92dSjfQ aq/iA72BOsqNCybc8rQXVw+unjoYMZbBYeNmDXRCUzqEcjCbpwc2IpgQexQp i9Wk6yu+b1oAIRzJMI0nC2JhykzU0EaQBg14g6foNnYCNVRbmHz/3I5FY/oH BgT26T98ysQemHfQWbyeMI2nxM/a5UW/1RqffXWNrBw6+ASEjj+Etww3I3KS sybNfGbSdeaZpEarOBO5NsLrCtI0SHVEXwmeiyxLgfBcvHr3G7XtqRRZw39I MJHinHfhn6uI2+xnLv5csuptBm5+IHKsML1g0qRRI1EwIbRVmtaVV1BS1dDW N7dt17ln3+Erb5JboBsUTGjnQfI2vm5BB5NEwQSOiB5H7l42AYkN+oQMmTQt 0EbEowMAGuXfKJiQKfUIcs6rxUpe1uKzLQq1znxjggmUu49MCFEJOi20BC5Z MKmNHEBqEgxN576jx+CMHTt+8ow5C5ev2Xrg/O1XX4prKeXIGhJMPjUkmMxu 0YBgwi97f3712ID2piqKBu36Ttt0+t6by1OILzA0R9z4nZyQn+g0Te7wkS5R USLNK5hUf72xbqCDllabfgv2x3woxZsTD0No9t03VTAhfU+GvsQc2ybQgGDC LXp1dIa3qaqJ5/iNES+ycU2fmPFpHEh++ZeHZ7bMDvNqqSnsqMi1nPFQmuW5 JggmkXTlHpvSMmTfbiC9XJim+yGkyya2TbuB+2qyYEKORKqBaZAGBJPajDtb hrXT0bANnLP7elIJftN41EDnA3C/f7hzYv20/p2wo3gFKLRb/lqK25FGMCFy xkVSnRsOHwiVRei8EVFISZj6G9IIJpzrmEwgkswvWTCBqlIvrwxpraHTNmzJ 4Xuf8YxvwvWiCyhqcl5d3rNkdC8nAwUhd4+hG3xKuiOwJNC8gglZ8UVEQhWC DM9VB1+ppWScKIRESFJ1Sf9XyD+HCvG0cfF9oBRqLw1E9Rlh37m+OOnWkTWT gzuYqwgHRcqdNiVLm7vDS8K2p8lo9DtH9A6o7NIgXfWAY9KeXkTM4LL2S2kq FgjBuTwI2xLP8jsusdZi/aOpWEUxpf6R1WjyJyayyHuL7QAlIaVCJLtNsBus oeNZSbsh23K+xIiQHJRCggmhHEtfveyXBZMmDDRitDYkXNVGYt2bTjDhZN3f PrK9rnpL/1nh1xKLCGN5dYiqWL/Fb/o3e2DTBRMy20hU7KZCzpxCXkAjMxHB nxBMoPLk84sDbNT1XYetPP4orRI/ygkPRekOYKITTNK2YouOsjbzaDfd0j9O SfLtY+umhHS0UBWezxRd17xr2Ksg3k5D5Si4cdisJNwgxFIPQ2vk7zrGP/8a wQSlOvP5xfCFI7q30RNWTphGgy9SfQ9JggmClKNGsmByaxSW3tZAxp0of0Qw +fH52ur+9prajgMWHbyTWoZbJrwwHxBMAE3h3yiYkHsLURNAsx27NnIAKZgI qfeNCSa8j0QOs4xiz0PCjphkwaQuZgy564VhKKUO3PyCCS/76qyOeuhcaxi0 NwmXgYmpQpKhaRqCyoUN7YOXiuYTTPi5V8YiJ4qouK+KFzb7zSeY/Kwh+pRC 4Knfqt2CXV6CYFKdsMVXhwnP/YMvCB8e0IBgQvlQ6efHEdtmh7oZ4j1VofsB aVSJ3xJMmtYy5CK31NW2fsEPKTvhjzWAatgl6dJYfiXDhMw4patFSYcEwQQq jJnupAw3n/OCWOHltAYEE8qvFn+4d2rjtOC2urjNUOt/vvHzXKQRTPARJy9S trDh8IHUQrAjlekhqrPIaFKqSUghmMDmpzcL2aAtUnhFgmDCy4gYasWSYah3 3fJeaNNGQ4IJCf9HdvzV/ctG+VqpYNadJlGtKTSvYEJqIRIiW+xDePY9XtQQ K00l0+CmFN7H9e1pir4S2w4aTE+vPh+CRrmS9mtwCpNuH183ObCNFuZyCNX7 awR+9n6shC1SzxB99/zcQz1V6U8IoocokiOjEnKukRFCaiEN7ZmsuzcBPRgI L8dEaiFM6ilfoj/8HJu/hSWMhgUTsuqDSr/zkla6iaBdOKqFDTemqimFREgl nf+iYNK0gVYbFabSmK9EjAfR+Q4qxg5dZjvMfiRctKwBwYTyoV/qgU0XTAS5 PQr+xyVtPSXPVRGq5vxPCSb1X471M5WDW6T3PuFzmJoomEBFePwvozHsmpQ7 wKnUFSXfPbFhapCjDh6XaA6OanCIk5V4FHoflbSXk3hm4QZBT5VsZMqRnr9J MBF8uyojLmrPomGeFvg2XpEWaEAwoTxZQ6NGomBClo+m2wQggWYXTHhZkSNs 4EdU7bzhrdBAB4IJ4Ff4NwomP8vPhaiQv6sUcq5S8vdlFHodEUQn+KSKPgqd YPIjahCW5yxScAtBsmBCnJCFPaY0m3v/gGDCzzrUSw3/PWoaKEUwaXB1UED1 5+vnH0vYbMPP2YOdRyHDtJz59LfUl+YSTKCyK0ORrcBM4wl3RX2fZhRMuG+w kBmO9Kc8/M3kfEmCCS9lfXvkftme4aKOGa1gws+Nv/UmX7y3Qd/vT7NFC2+K VsCi5/cEk6a1DBFlwI++KF4axYQTjW14Y3UXP3iQEgNSZ1jYq8emakmVZcVp umDysyyiH+5ueUgsNicEvWDy484EJDZj6A69KhoN0QomdZkv7r6nO7cw//Jw 7C1KY4GkEEzwJlH22SvcGRsJH2quY6XeZGTt6Y4ewJ4Br4ihHHhS8CRSCCa1 t8chpxQajLohbPJpBROo8GwIEsgwrWbFitgX2jgOKk158OwbTTWMmuQtXdE3 /XtbGptXMOFn7/HGJhDFXocllK2DSo75sdEONPUR+sVqvLKCjILPXgn7pbiv FqCGRsQ/530gqo1rDIyUoDYQ9RvJujg1abEPUmgOIuFmnRlg2HBJVZqHKbs0 CO1YrA4bU9HDmDa6KTksb+QUbSqkTMfQH36t4Rx7sn6PDNt3n6R0kfLTQYpo S024i7wzqPRMMC4qSbCjP8kzwxjaQ69ScnwaFkwE6QrGEg/BrIoIQVVr4aiW +5KoKmMpba7dLwgmTR1oZBKaZAtBJgmKCCY1D6ciDYVWnBfpOLSCSfP0wKYL JoJnRA4ulSAbELsXhc95/GcEE3425j7SZF81UTARVAaRrkxpbfrz+0k0mhI3 98Jg9Hgeoc1rNCA+KZbiqjP0ioRhXXttKOrYCzcI7/1KbM2jgZckPb/gqKA0 q2ACFSfefZFJ87mq1ys7oNZKKA+YXjBp0qiRKJgI0vVV+56Vrl5dcwsmUEnk QCTrj2k+9ZHoueZAMAH8An9YMKHsZpT3DG8uwYSfscuLTDUTPjsAe6YlZKUN Bc+dlIP4KJtnaAQTqOzyULSiKkOrx/6v4oVRJJ6SU/dgEnl4sIys9VQpNkI0 t2ACFRzqQdyevMc2wTNzHk8zJ77Q0IZsEqR0n7zlpHu0z1B5ZQgWDal2Df8i pWosgeYSTOpuYpl/NPMUodg1h2Dys+buBOwlM/QHXfzd9QhawURg88XrBNTc m4gdN0B1IKvO9tWlL/mIhz1SBiS/J5g0sWWqokfo4ufVDrooxRm4RDkB2h38 vLRd3liQIjTDVl4dhl2DaTLmZuPpFj9/STCBCoiDRWVtpj+SYp2bVjChuK+i J4bCNqI3Fu9SfYDcvd1a0CfY45uGlfqebY4ME87L+bayMnK2c5+LxGiNhA/Q 94j+Gg1LYriBYxqNjKa4ZY0KJoj7A1sfVZ/daSIdgVYw+YEXH6Q5t4o4vEw4 jns6w6YrzUGY5NaGJuWYi9O8ggkqGGAziFL3A/Qxfe29icjAlHda8RZ7D1DB 4V5YNpi888pEOqPLeb3EEY8hhP1z4vSUBs5VqrgUhpSgVuqyHZsa+F82e9jT K5a12LtWH3JFqnKt+JceT0eDdVmb2c9q6+Lmt1Lrtl9Kl0LkGWRUvHeJTfBC 8L9iB+ogpax30xcvrsNmVjn7xfHYKyOTYMSSsgR3gJ3bxrSe8YTaBxoWTAS5 KwztkDN04hhUdGEgVgteJKr9cXM0lofEMBp+VeLeIqFrNV0waepA+1lzcxR2 t0zzCbfpQlx++t5umGkVFkwEVxdrX8E5cpR+20w98BcEE2TsmeCKydhbtPvf oMIjyGBkaPgfow7ff0QwgUpPBqCGQUVwcBz5+bfLMF1HSsHkZ/nFMOwoKKbF pLuNucL8jJ1eLenVPHxPh0qopHO5cAQLlmq9DtEZBKjs+mgTfJ+oUINwHuHO MUOn/7nfKciH3u4vOCro3TWnYFJ3Z5xpb7ojf4itUsKHZNAKJk0bNYI9nJNE 7o+XtBp/L8pdd4lO2fQ0s2BCVP+nOWGAqAAOBBNAU/ijgkk6ccA4NpVvE40I JECtVQY73TRp0FDR2RDyeBCm+eT7VO8DGQr41xkGw65SlVKidB1qOy2HX6Ju e+Dl3ZjYGvmeStvZd2k89vKTVMFEuB5X/WvK4b1wlBa0561oraqa7I/plGJJ DT2jSNHXdImCSdAZ0soJbVNS8dr6oRap3pX3dHuwuaDsm4SAV4QflwdrMOQs wk6nic5inKSNnRCrz9Dy3pEs5E1n3940cejIhSffNVqii0RQYFe6xBd4KsC8 LJG61sSZt0zrmU+pcz2/5OmKznjkRieYYCumdKXViEMeRUrw1cUvtseza4xD z2bRTgCcyiqpFvKI8hBCr548+ILMOsep/XJ6iBV2aaoDiQTUavRHitTeHgs7 yopdd0lRB1+Q8y0pN4FoYkn9p2ktU5+4ypmFf7rfia+N2RNiyRuOXHYJu6tQ 5Zv1nsRZ12pDqcpRzZOZ1tiAlLUYeZkmCQf5TGUVJbmngf7wkyKYCNXw+/Fw agvsKvItx0XnNdbURAEO2LGmJL3h1RKYJuNvU0UXqOzVxm6YOCnkAyCl4nUG 0h7HWX5hgBrsagSdksL1I/xtpvHwK3TyxI/4lW5KDI1OK5+LjWgiPYS+6CtM 7dNZWOMztPqcyKXZYpC4wkkO9lF7HxJKzCEEEznnZQk0QQy/MHq0paysSd+j n0W7DFR6HD9WeNBlshOQpzXK2i9+Q/0Gr+DefDf8QFiq/1x3b6KpA618hHUO Wes5z4WuzClIvHVk9cSgjt3XSJPHRZQYpBdM8F1kDQomOkKOIrGwj2j00x6K dwioPHqkIUNGzmrCLTJUhgrP9ce3j6p6rH0jEohA3x/MclBVYKOLBoYThc+c gINYX7yuh9PS1zS1jLAju9ht5sfiPwvPSe2MRt+iWbrFzvNk6AyOolnHlAwZ Gan12HZglLHBsCtSiaGUZ8g+3Bvbk8tQ99zwpiGREyq5MBAXXS0m3qGJ6itj xpkyYfMyOlpggCtujDLCzr02GkY3rGqezLBmwn8MOy9sLAQzzmbaZYj6xNXt cJNpGhYhamDr0o4FGyqwsbogIl4W58U8W8wvkDUbEkkzGGFqKysFJg+ehEjB hO64MHHJoskDDbYuj6bhVejkW0+5I9pQ1e82ddVis+Xw+Y7S9oQFZRiOuk59 JVDFm2299HBliBJXNlMPbMTiCQQT6pINL3m9K+YwKnlsSRVvSn7Gbm8l2GR1 WPdWaJw1MhMRcBOwanK0x5wR51XRbr2jk+jJI8Hl2q8TOgK2Puf6NCfybHIx wYTeU/pxfzJ+MoGc1djr4kdAw0DVlVXoN3ifNnbQH3aNZl8UkrGlAs8h/SMa W4ThfdrqgenADIOgo6JuKzcrYpCZIjY84AYRqnhY/3aFEz6wDENOptNacU5l pVQxzy85Kj8p9aE0R9D5waRgItVmf070MB032sOnsIEtJ5yTV3sBF0yohbCa Nmr4mTu7oF1HJfSiyFtEDvTDpgyGRtdtSbQ5PNzKSspkT4ZFdGXJiSPGROZB /Fo0q41kAQHZVvPiqD/HL3q4uKM6zfnuAEDD/EnBROg0GwnSBx3UOttMc/r9 wNy0Q36C43LbznlEKIi8vKjheEqFvPW460JL2GQBBRx5Y68Jaw5cehB7P3Lv oiBrNuxGOY8/lULrR5EbTmXQuigiR45D3+9ObUUtuck28Ri+dPfpqOvXLhzb vW5GX0dttpC/Td2uxLScJXRcIHnQhIz4GeZUwYR6/iZUdNSPcrwDQ8mwlY2B IlKBQFGRvC/pjnIlUhzlTbvNO/W6sA75BsQpeX9hThc9ZHVMq9PSR0I/w886 0AOzi0xTkWMfG4DMh5RRlFyCkEJNVJgq3pGEEnLIwyxlFJ2mRmchTcwreXtu US8r007D+tihO1Ns5oku1hBtTFcfgshlEV2fgcqfLnLB94LJm/RYFpX8XfCr dblP9032auG6PEEaxYRY62RazRF4RVDRcX9sFYJp6B+egB53UpNxL3xUOxPb oDBP1H3HjrvAQLV9tt3kW6IBcmXccjcVhlqn9QlSViPF65nLuQh7S8R9EfOO xPqSTWyZmrcbu+Al2OWMuy2+mFImuCpU+fXu0dPPKKuh5NYXhprTmEOx6aV1 ELci8+2NrWG2KrpeU4djVYeEzltG4p27Mx3wAcG2CFwTnUq5Rm3mg/BxnSy7 bBRUJSX7A32RPlIwaSUkvUHlj+c64jlrCtbB62+l/aB4zKUp0Yci3wncAUIb oZ6lS+YawaOz9diLacgz8MuSL63o09LEbXhfdIQIH6IAf15Ouf2iJyKOPvT9 3ow2bIZ2j72fpLGyhGACo2w3OPwxpXo+VJ0WvaSroW778Udpj9whV9Joj/TA muXBjNZYlrSm96YEYXtam7TNW4Oh5DAtRiS9SFBgiqnjMeP0u1JK7nTp2+Nj nLTMui+JTqfxKcmQTaHfBUFz1z2YjB+wqOI2724e0ir1hfEn5/hYmHsN9cMC CupyW+2VwWpMw6AjonpM/dcjfQyYLOsJN4Ucd84N/MhW8R1C9JBlGQJPisX5 5CojO0T8qHhCMJFRFzkQh591KsQAy343H3wuQzgh+vv9GXYspo73lgQhN5b7 Mdwbk5FlmLqd55xLLMG6S03G/e2D7C18Vt3ajaaTMXTE8v7qU3d3w5YpWLYT r+cLvXt+/pURlrKyRn0OpgpOPo6bay2n7rHmleiW2cLr42zkmfpBx9KbmKTI /7ajCxaEysmxW82Pa/qeUF76yRBDrFcwtNwmHXlZSC3UkvP87NE7xBo1Pydi IJY5xzQJPZUmdC2oDB74bKaW5/p4oakLKrw6ygLL0DEIPPBR2PxWvVzmqsRQ 77D0qeiwEsw4a+lry0AV96a1YhGGpt/mu+mYoeF9T7yw0NfSbuiRyIVtsVMy RXIvoNKHc9vix7ayzPxWXk0pFdgHTvajPRO7WHZc8578N+LYdwnuGimYUCLP pg405K7yIweb4AURVB1H7X+WizVVXX7c0ckdzF2mXjiJblQUqSwvOCdbvuWI iC/ISOGXf7i8um8rY5ehIc5oXEndctBMPbARi0fWj/ESPhesNmGtOzYpKrWb L+wy/eSi/ZBlOfhcpsgvNjITkR/Dk0hoKwHXP52B+sC0Z/SQ9TyoaTrV10fi eZ8aXZY/LkL+vS7v+ZFpnU0tuw3uYSFhOUWip1R0a7IdMS+2CN5w81M5Jb8o /e720e6WXbehV0cCEDlV9+WiwjxUHDO5NYup63eo4VQw/PafLXTEZ3uWecDa G1+wgrVI51je26pl/z2XV6LhNNwgIvJS5fPlHXAtQ87Id1Hk+2JKaaG8Zwen +Vi1W/RKKkPzS44K6m9joQBV7qf8mRBMxA0yDZVngxVkzcLOimo/nA+7e2gz FexnPRSyPcRSGMuPEhk0cdSUne6jiM31PuueZpdXFCTHHDh4rwB9AZzkXd11 8dlGq8PU4y/zBE8Ie+lnFgbath53S6B9klOdUuglMd+VXIAXnQfRP+KCiZDQ T5ZRkVF2mXUrB3mz3OI3Z+Z3tzTrPDQAO0FTul3rAADKHxFMfqQ9uXzm0LbZ PUyoPy1r3H3WtkOnIq/FfpNUZwuqyHgX9/DC/M6CE62YlgO2X77/7FVipsgR cFDF651BZPqEvFYrr9CxYwf62utgSrme54Kb2aJpBUSdRjqYuh5zLqfRyaqV WYkvH0Yu7krmtMjIWg3eeyv2dWohZdxy0iInOasx6H9cVsNuxFlMdoaf8S38 jAu7CJ5RrvXoI3deJOfV8KuyEl/cOzXFmTx1R0bJdcbZ+y8SvpVhcxVVMGFo +y6PevQyObca/dn7U22EH05Oz2PayfjIsfpkRVotn2WXniTlNRJGE4tM+K+o 6JsYqmG/rGTZc9FVsZCFIvAo9TklReIvxP1R/OX2PBc8Y4dhELQnPqecI7n8 Iqc85/WBfsS2J3mnmTc+Ff3gEkf45UaEGhJtIqduYmmkytZyGXvgVUk9YWHV Ao5k11H8rpqSxG3d8AIUbstfFJC/hdxa/rMlLtheIfWeO9+X1FBvi1/0YFVP MzJLia1r49Klew9v11ZGqnIsI68ZZ1Mal34gbnVRwkYvzIlV6LT6VWE1cfma 1ytcCNmLoahnYa6jqGTptzw6rYaDH8EgazfnaQXuthCJIWxjj1Frj16+8yz+ 5eMbEeGzelmp6TgP2flCqhRsqL4q7yl2JD085fnvSfleKxwJccrTzo/Aig4y zYedSSmsrKNbMmpay0Dl8TuCLcleztaz8+gRGBzg28HeWEVWsf2qBGof+xG7 wIF61qusnBzqpZn1PZBU+WWLO1aT33barezKesoj83JvLfI2ItRChoJ+q/ae 3Xt0dbExUJZTMOs27+InIjRF+sN2oj+4r4yj9AccQjCRUeq66W1xNTUxreDB Cl9D8ipKxk6evfoE+3V1baWvyNTovhd396D6yuyY6diwYhiEHv9USjQzVHRt JLlpTk7N2NJEnaXuOCw8trCeWHpS9N2VxsHviDgbVcnCd9Lmk1fvvYh/+TD6 9NbJXc1UDdzHHX4nZQlMimCCXVjDumOvvqH9/dxttBVUbAdseZRH50YgveXx fGyZn6HRc3t8Tlktj66bcbOvTXdBA3OGqv3ANeefpeYW5iQ/itw5pbOBpl1Y +CvxhV1KRW5saOi38QroPzCkm7OZGlunw6Tj9Ccm8zllX88OwXfN20y8llFB 9E9+xtFAUlSX1zC1NFRh67pPPv6urD4FW8Vl6ISezccdZCR1B/WptRz7Ldx7 4ebjV/HP7lw6tDysrba6td+S61nC7QE7aFjHk3dY+qZRjRQeRd8iR2HOG9Ny 9MVv5RRzhDzBGcETRGdWCI8w0ouUbTPrbo5QH/9ZnXQgFDsIWVav06Rd1998 yy9IfxNzcs2gNlr6Haee/yxu66HvD5d20ia3kLLUja1tzLUVZJXbjI34yoGK sL3dcIw792FuldDF4GH7aoufCZbKYOI76+DtxIyC/K8vrx9d1tdGw8R7QXQW tSWqIvqhmyRUrHtN33Ym+sGL+LgHV09uHNfZSNW4y5RTKdLWe6XeQMExf2yc KnTeIVXhIHFqPp0Z46BK9As5TWs3X//gPj08nCw05OVaTLxN6Zk1H44Oboka aqaO+/id1+LT8gsyEm6fXj/USUvPbdKZVJpMKM6Xs6PtsaKmmi4jt1x++SWv MDPx3rnNo9vr6DiPPpIkMkKpM46Mmu+mlzllNXRDqj7t9FAbQS17RR2LltZG avKyej6rnxTziWolDC3/vcmiJrzg7rJuJuRKEVu3JWKau7ZvaagixzbxmX3+ YzVxK9VFr9d1xuvEdt2UUFQtaggJwYSh3ns3eZ2mDTSc6sRdASbkWo6sin6L li30leVY5sG7EyqhuhuYCME0Gx7xtYxDbh35fmtcC8JuyaoaWZpqsNXbDN7+ JL/+O55lxvbc+ok4jrAZeqCQxdMO3C86P/4UCCYMw9ATnwX3iny5+NGKrnrY wVQt/BedeJCcVZj36dmV/fN6mmtY+q1+UCDyW0KeSfsFD9JLxN4A8Z7Wd8E6 g6LnhjdFlBkJ7k8FcSvd8eoR3bYnloh0J0IwYWgF7Ev5Trg33M+7u2kQ75Cl ZWahr6xg6DnrbHJlPe4OMo1HXinmUq/TkKfEy74+31MwLyrq27rCs69XO2v4 HSta9Fx4+Ss2xRPrYMotuk/ZcurafeQVXTu1eYKniaqRx8Tj76VYSsOul3Vx nL0y0Z0YClrmNtbwTMrU9lh0N5/HJVbnVHy3vBXp1VDJk3X+FqQvwtKxbte5 Ww9vN1sjVXl5w85TT76X9vjzX3BUeLWln06EElXdpscIuzAI5NZ1ubYLHueJ eSYirYAnmsvptgtbeuBizJNXr2JjLu5f3M9eU8M2ePXdPMor4tdVZFweh2V6 Mc1GnE8rx7tuE0cNP+tgTzXKU8N2z2tTAvneqhL2hrVWIc2thoVTJ58evh3t zTRYclrtRuyOIwVRXm3Z55NhWIqebKspN7OobQE3VOrhECyLTLbN7Hsi8yAp mMjIuyyOzSfmLX72qb5k+COvbmIBxwY6bhMOvymtxwtDMTT7nsitk/IFA/7f w/u8o6ua+BHo9DCUHJdKsYubKLQvAdGkCQqcy4NU5eTZisqq6praunq62loa aqrKigosOTmNYeI5WvCs++3O7rlDurW11NdQlJdTUNMxbunmP3LBnpgvVTRj gPtmlZtF+yFLt6xfMGFgz46ONsbaSnJMObZmy+6Twh/mSnB7OdHDtYRuSlNd VUUJvid2O9ETjesL48+sHh/s0dpER1VBXkHd0MrBvfug+QfupwluhxMVpkzX LEiByh/4UoYY7ICTWGIBVTDBENRz42TErBvcubWJuoKCRouukw6+KoENYA1x vAF5nQbOxSAe49utLdP7ebQ21VNXkGcpaxlZtfUdPHtzxKsC+ibi59+c7WGo pmXXf++7xnYE1z+cZiZJoVPoskN01xY/e6+voiQVymA8Xoam5tuNdUM825hp Kasa2HqN2vGkAH1CQRIL2lCqg6I4yBqlAs1vydsveV0bv9hOnuZvCp47qbmo UNWnG7vmhnm1sdBTY7OUtU1bufqPW338QRpdlxOB92mTO5vmEqy2K7EEaF7J 66Mz/OGgXlVJ06Jd0IILH7FfpR78JMNgu6xJ4sGO8MvTW+YO7+Vma2GgqcRS 0DBu5eLlP3DKxisfKqSKJeqfz7OhG6gKXljRWc7lwaq0bc9yXv2eZgg3sWVq Mu7vmz+0mxP8aQWWsq6FfQefPsPnbLucJBZM8wqf7Z3R39fV1lhNHk1+Nuo8 6UBcIVewt4mA3Xk7NY6CypOvbJ81oLOdma4qm62ia2br3mfSulNPMohJn5+5 25uuP7DaraFuRCYFExxloWUiqPJT9LYZoZ72pjoqbLaqgZVjx+59xyzaG/MF dRvq7k8ypunyDKXAE5izwMm6u2Wkt4OFtrKKXkuPoZvuY/keZEyOwlQJPg3b gPrsJ8c2zBzc3aWlmZ66IktRy7S1q3fgkJk7YtKaEoCSW3IsBqxcM65H2xaG cA9iq+m38hy29tpX2kEsqbfIyBpOuEsno3MLXhxbPMzXEWl7eUUNffM2XqHT N55/U0xvfsgtOe0nbl0U1sXOHLY+LEVNE6feU/Y8LaD9TvWFAco0HZSpPQo/ JvLH5ysrBnrYmWooqRnZ+YzfF1eMvlRBVhp6Qf2xt+p+QhUp0XuWjQ/u7NDC WFuVzYIjOMdOPfqNWXr8ZaG42YMKj/qhXqXBkKhGMsZhg69Cc5NytgtfcX9W nutH+wRalPRsUjAh/qgz+ia1vWvSYnbM6OfRykhLCbPW7XqOWLD75qcG7FF9 buzhxSMCvFztLPRUFVX0rN0GbYtDh53wPlr4Ni1mCC90Q5Ufr2yaHORmbaAB Tw4q2siMO2bpwfvpYp2Gk/7g8NppA32cbZCJhKWkYw6P8KBhc/bcz2hC5RIR yqMGw7E5Q2vA+d+pI8UtfHlyxVh/V/gh0DFk6+LpN2jqutNx+WL9rDb97i7Y gtgaY61raOXcfdj88OsfG9p8ysl+uA+2g3Ym2soseSVNA0sn38FztsOGTbSU t4QZR+wVY8BNf23rtNDuHm1bwr+sqGFq7zv9PBZ4ErtGCBR89ghVc4AqPlzb MRs2UeaoadYxte0QMGHtiUffiMCGl7qxA93MxPbYRs0rIAQT8j5HYffZhIEm gP/9bcSa8X193R2tDDUUlbQsnAKWxuSiXyRPzcZgKA+4gJu3upwH20f7OFro KCvr2nQavP5OFmYsM/CdAdjnlfyPFUG/2wO5rxba0lk8Rb+j1Ow4QjAhnrLF bGpJDn7pu/Nrxvq5tNCHnSm2mq5p607BE1cdj80RcWcleSbws/Q5KVj74CWt acei+ZRSEDw7cN8sbUPXn9jumwTJJGRRVvINb0ULkECVKRcW9XO3NVZXUjdx 6DH1yJtSPvb5yYIKfQx5i+mP6iX1WxFPiV/2PmorbJlam+mqwAZV17x1x6Ap G87EZlIavy7z0dF1Mwb5tsPnM20zOzefPkNmhd+RuKwqkeqvMTtnhfXs7NzK VEdZUc24ddeJJz+i1yJr8NI1CMKPz7f2zB/c1cFCH+kn2iYtXXuPXXn03hdp tRLi7TTFUXkf0Z/O9suaT6eeH0gKJkQbd9tPv7MOASpNjApfPLZPJ3tLIy0V FlvV0Lpt5179x608m1BCfV5O9AhNmqiPoRwaWdP0UcMveLpjlKetsaYi/OFW vVY+EDXNnKzHR5aN6uFsZQh/RFETiSeGLdp3Pek7eU9lp4OUaNoCdcOgwiO9 FcX/JiNrTKmaQgomxB9Np2JpudVfo1cP6mJvqqmkatjae+zuZ4WYZXqzmOLR MTWHiyetAAAAAADw74VfV1VRDXIofxdpjhX+HyPFKTl/DVDRiUAVpPxc4LEG T3D4NwNx62pqautpE4j+QapvjNSVs5z++LdPtfhvAvHqa2tqONz/Sq9En6e2 7m/rhYB/Kfx6zv9meABHBQAAAAAAAOBfDRBMfgdeKpIoxjQccE7yYh/gj4Ac DqTsukFSjioAAAAAAAAAAAAAAAC/BRBMfoOquxPNmUyz4Vd+9yRKQFOB+62D VuAJurN1AQAAAAAAAAAAAAAA+H2AYPLL1Cetc1WQs5oQI8WJY4DmpfLWGFOz 8belrQQJAAAAAAAAAAAAAABAEwGCya9S+3iBl++kIwlNLAcI+H14abu8NZxW iJ+UCgAAAAAAAAAAAAAAQDPBTVhijwom9ksS/pL4k3N1iBommBwpBmIEQJT6 r/t6aWsHncwDnQMAAAAAAAAAAAAAAH8M4pxFpvXcuL9EMKm50B89PFDee28u iIkBCPVxK3r4zz/x8NWzy+uCzOXZzivf0Z9sDwAAAAAAAAAAAAAAwG8CcWvK C789WtqBjagTMuwOSx99Kyyv4f6DGgW/7kdpbkrEqBaIhCPD0Avel5BdUsn5 P/bOOi6qronj7C7dnSINAgpiI9higgVid3d3fOzu7kbERBRRDESxQFGwUFKQ FJGOZff63ty8C7vI86rPM98/Fdiz954zM+d35sxAI5T/Ouzn8+1YciQM/e77 3lf+7iEBAAAAAAAAAAAA/044b9e30lTX1NbVNzQ2bWDewNTYUF9XG/2XVuvf /iaBoipsgpm6hpaOnoGRiZk5f0g6A879+D0jAv4MkLywpT1cTDQ0jGzb+K++ /hlqvQIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD/LNUFn+OSCpHfPYofSW8Sf/zuUfxT sL9/iksp/kO+HTs/IT6l5A8ZjIxU5Wd/Z//uQfyr4RQmx30u+DtnB/Cvh1OU Gvc5n/u7hwEAAAAAAAD8q0GKU5+HBR7avHiyf6dGegoM7ZE3Kn/DKErSXty+ cHjLkimDOjvpK8qpDb76G0bxT4EUpTy7FXhw06JJAzs46MgzDMffqfp9gylM ehIacGDjwol+7e205Zmmk+//vsHICPtH2puH10/vXj3D39NaU8Fs6gNQTOoX pPRLzJ2gI1uXThvS1dlAUU7J92LF7x4TAFCUZrwMv3h027LpQ72aGCrJKfU5 V/y7hwQAAAAAAAD8q2FHrurq6dHGyUhRDuf3CCbsJ+t6oKNwNlYiRvHXCibc nFvzPEy1jVpOupzGof6x6uGKzp4erRsZKOBfjk4wqU48O8rVQNu88/IH+f/s kX5Z+OJOHuhg9MnB/EWCSemdRdjQHfTliaGDYFLvsF9s8m7n4d7ERJlYiCCY AH8Q1S+39UWnp4sZNT1pBBNuZshMdxNt49bTrmVA+kmtcFN3tSN9Py0M3SFX in73IAF6kMInG7pbaes5Dz76/u8MmACg7iCFMXsGOesbt9v6uvp3jwUAgP8L VXnZ33/zeud82txK/vcJJiTclJ0eCn+zYMJ5t9aN2M0zG86IFNnNc96ubSpP L5hU3ZtkwsB/T77V5k+cn/881W9WNmH9ZYIJSXXscmcWCCb/JNyMfR0VQDAB /kyQnMNeihIEk+rXKxqzCBNsM+8p2Ifa4HzeQnh+CXqJ/vBgSOL57VTkZv8Q V/+QrENdySMmrcFXS3/DuP5ykJLsnL/0RvK/FqQ0O0eqK+tIfsSilhpY2Kxg Ny/qLwtiAQCQFU7Bu2ubx3g2UG0wNeL3hnZI3rHuir9dMPn547S30t8smCC5 AQN0ceVDrcOeJJEIh/t1fycFesGEk7DVXYXIPhl8+R9OMSEH82V3O4W/UzDh pu7yVADB5B+lNLC/EggmwB9K5eWByhIEEyTrlI82boI1uh5MhRST2sAEEwUF 87YDhgwbMXL0mHETJk6eMm3GzNlz5s1fuHjJstXnXpf/7iH+l2HnvQpcPbSl kYrT0lfih2ql4RMbMLG5rtB4Oc1/AxKpSI88Nr+Pk7Zy5wOZoJj8GVR8jTqx qH9jHZV2u9NqM9w8tUTZ2mfjw5z/xxkjAAC/E27SNnf8HFe++YaPv3fJI9/+ DMHkzN8tmKAP8sfrwM0rN5x+nif2QpHMA5IEk58/q7MfH1u7ctvlt0X/H/fN /bKn/d8qmKSBYPJPA4IJ8AdTedlfRdKVnJ/cglcBm1ZuPBsNBWGlABNMlGzm PQNL+idSHbvMCc+XUpSwsS9NuL5j5ZrDDzL+Mif+m6m6NZo42NIcGgwO7s+g KnyiEf5OVAdeKqvxJ0m1RMGs04KLCZBYBQD/CZCcw12JzGLv0z9+81BAMPnH qVEw+X8DgglQAyCYAH8wNQomgCyAYPInw7sqrTks+N8XEf0+2JEzGmKpOSy7 BS8gM+fPgP10rg32TpiWs6NqsEa4WqJj0HrKydf/2laaAACIU3yuD6EPDLn2 m70hCCb/PCCY1A8gmPzzgGAC/MGAYFJvgGDyJ0MVk2KYTvnb3PQfTXXM4kZY 5o58s/Xv4TrHn0H1G6L6FKvJyjeSRazqjIgTBy68EE/gBoD/P0hF5svQc4d3 bt6y51jg9Qdx2cS+lVsQd+9Jusgelluc8ihg6+YrSfRzt/r7hzvH1++6k0Mr AyIlibd3z+jfztVKX13T1NG955AZW0M+/UIBJs73hMhrJ3etmj1h3LRF6/de eJJBF+lXpD04smxMj+a2ZrpqSsqahg2d2/vP2h78oZ4vQpQl394xtX/nVs4W BmpKqrqmNk3a9Z+y5kjoe2FNtOR8PyW8sNrYsP+zN6zKfR16Zv+2LTuPnL/x NKWEK4Vgwi38cGP7lL6eTSx01bUaOHv0Hj57563kmnPnaoGdF3fr7IHtW3Yc Dgh5klzMRaQQTEoTw/YtHN61qbWJjqqispaRpUvnYfP3hiVJyMyryow6On+Q l7uLjbGGsoqWsaVT615jl+y59Cq3bhaXW5j0JOTM3rXzJo6bsmDtrnMRqWXS Tp3fKphUZsWEnNq3bcuuoxdCX3wpQ+pLMOH+iL+ybZZ/+8YWRlrKiio6JjbN e4xZdjQyg/79IcWfbmye2KdjC8eGeqpKanpmtq4d/KavPx7+Sao6X8RH/oJg Ul3w8e6JDbslGKWflTmvQw6sOfyEfhfGLfoUunNav3YulnrqWmZObXsNm7n9 ZmKNKaHV+R8irp7YuXLWhPHTF2/YF/Qsk+a5IMUJN3fPH9bFFZ3TKooq2sZW TbsOX7A/PEXC4qr8+ujwXP+ubZpYG2koYT/u5O49btneK7F5db2DwP4WH3aO WIjXoxILOdIIJuVpDw7P8+/YzMZQQ8PIvlU3/8nrguJpKhP+BUhpruv5Q1Pv HVw4oneHDr0n7nsu8EGcH0lPbwWdPHXl8ecCoenN/v4xMvjc4b37jl24++Hv fNB1pDr/3Z2Ag9u3bD90LvjR5x+cXxRMkKKPIbvmDenkYmmsja83azevkYsO 3kuTUK2jIv3hgdkD0fVmZaSOrjcTK2d3n/HL91978+1fcML5VwsmFRmPjy8a 0qWFnbGGuoFti64DJ64+F5sv7tnRaDN02+R+nVo6WeirKaNux8al/YCpa4/d ThAJ/ND4N+bqvl0hKUJ/g1tZVl4txbtGylLuHZjj18HN2kBdw9ihdfdBUzde fl9jbMn98fnx9dN71sydOG7qwnW7AyKFJyEVMzCt5/63ChhX58dd3jC+l7uT ubaajoVL+z6jFhyIEN2B/MKff7XUEducK7TdkSLZkpanRx5dMLhzc1sjDQ1D u5Ze/pPWBr4p+Ccsb1Vu3J3Ao7u3bN51JODavdgMIqhESj5GRCaJRAFIWfqz izvXBryjFxU4RYkRZzdvCZZQvqkevbbU4RAnP/b8ipE9Pd3sTbVVlDUMGzo0 9xo+b1uAyI6SE7fKBRexWv5/+h4AwK+AFL8LmOlhKFwxnaXt0HXM7PEdTBUN R90owX6MnXLv8MaFE/293ExVGKglN5/+UNCSl3wI2bdmzpj+HZz0sQ24fNO1 78Qmf/mnwCktdFUsui04cS8uJT0p5tb+sU3U0J9WcRh5Pklmv1Cd9/Ls4j4O Ggzh+u6Kph02xwj8Me63Rxt7W6io2vVdeuRqZHza1+TXtw/P7mSGfWGWgceC mxnCFohTmvkm7MjyUV0cWy5+jv2dyrSw9YNa2xhpqGgY27m19x6z6kKceESN FESt7WysZOA+7dCd2MSvWV8+RIcdXdDbVk38WbG/7OmEPSSGnv/xuLevXz6P irx/51ZYbLaA/ar+Hn9184TebRpbGqirajdo1KJTv8nbwtPq7jkq0yP2TGhr IthJkKFu2a5/RyuWZMGk9N2psa7aqjbey85EvE1L//zixq5hjbCye2pNJl1N r4N1q/wauX9yOzMlwVGoWXgM6GTDkiyYcDJvL+1kqqTZePCaEyFRH9IzEmNu 7JnkboDl8Smaea2OEBFByt8dHmijpuE8dFvwi4/pWRmfYx8ErB3iqoNOFKX+ gbJKPdwfby+t9nfRYQlPM3mDVsseUl4Cqcz/9Oj85ml9mtmOuCTqOmoQTLhl 2fHhx1eN9XJuNv+x0ALgVuR9jDi7YXJvV5vReFIuJ/fJvvEdG5lqqagZWLm0 7T50wdEn2TVllZanhm8f3VJoXTM1bTv18zRnShBMOKVZ8XeOrRzT1anlYjyQ rky5tqR/ays93YYtxgem82ZnZVLQtJZ6inqtxm05d+v5p4z0hKeXNg9z1cIW ooqN7+6YQqHVgXy7v9TTQNG4/Zzj994kfc1Ke/fs5sHZXpboPGLZL4qWOjNW VDCpSg/fNLSNrTG2Lm3d2vcevSLgdYHQJ5d+uLF/7dyxAzo6G9AYJeT7q6Cd K2aM8Glrq429XMWuh7LFlnXZx7MT3HRUrXouPvUgPjU9MfrmnpHO2KZN1Wnc xVSasbNzo08v6G2nLmKVlMy6bI8V+PHqjJsL2xkrarkMW3fqxpOP6Rmfo6/v Gt9aD3s3SuY91j/6JhzMlMXt72+lqukyYkdIdAI6pz+9un92lX8TrMylqv8V 2bNBqrKiDk3r0EBoIaqat/XtYseSLJhUpYXMb2ekbNJu+sGw2OSMlNfhx2e0 xl67ouWAI++kLxKJVBUkP720Y7Zva7sBJ3LRZ16RErp+qGcjM20VFW1T+7aD lwe95QtpZZ+DVw9ytzfVUtM2b+LZZ+qB55KLVCClyfcOzvFr39TWRFNVw8im qWevkSuCaAoEyWCucdh5r4LWje3R2slCT01Vx9ypVRff6XsivsrmtLjfnu32 t1fle9kmK+OqMTU/fPsYD3MV3pRRth1yJpGNbh5iA1f4NSUbgRNWx6jT1lcy vO2S8BU+Pb379Bvg5z946NDB/n4D+vXx7umz/A6pNyA51+b59O7Tz3fg4CFD Bg0c0NenV98NNSVG879LWc67e6fWTujexGXWA8ySsNNDVw10t9XXaeA2/GSi oEWugy9j5zw7OrNzQ2XB6anSoI2vlwNuz+iKvlZ8S3h4buMU76a2Y2huMbC/ BM/1MFLUdhu54fTNpwnpGZ+eX9s+tgVe0EDJovfmJ9+Fp0hJ7C6fhirabmN2 34j5lJGdkfDy7ukVvs6a6M9rDg+R1g2jO5+rqwe3sdTVNHLx2x1DzOny5NDN E3w6d+k7aXtEVi0+FMm/sdC7l8gL7N3TZ10kaby5XwKne/fu09/XH3uBfgP6 evf23xEjhU2VVTApS7q5cYSnjZ6mgVPfTVGEna38cnfn1H5dOvuM23Qn4xf3 9agrfBt+YvW4bpQrrEq9vty3jY2ejnnz0Wf5u9vqzDvLu5ipGLSZtPfmy8T0 1Lj7Z+a100Nfi7xZ792xJfw/iHx/uLKDkaKh58yj4a9Rt/Pl/YvQw/N6WKti MsScJ9hHcHNfnNu+bNrQXq0sNXDD63MW66SMlHy4uGyAWwMtRfTPMpR07TwH LTz+NJv+C1amXJ3d1kDFrOOsw3deozYx9s7RKS2waaJk7X/iI1008/3NhRW+ jbWZwg5CwbDNqsc8E1oRv6YFNtOZFuOC4uJfxzyLenjvzq278WRheHZh6vMr u+YOdLfzPkj20Ea+BQyxamhpbWvfyKmxi0tjp0b2ttaW5lbDzlMzu/jyaOuG Vrb2jo2bNHF2tLexsrCfyj+wk9pyop9elBYdvHfBYE/7bjuxCvfsrw92jO3k 3FBXVUXL2LbVgIWnXvL9cGXanc0j2zs20FbTNHVy7zUenfMSZidS+HLfIAcN DUe/tReiPnz5kvDk6sb+lqjxY2i3WhAueNRRxxAdk0zDJ1liD16++ZJ7b+Ne RT99FHH39q3HibwTW3bGrcUdTVSMPKbuD32VlJ7y5t6p2W2xyFHBvM+BuF86 JhShPPHa4i4NhDt7MzWsO4yYPaWHpbJm33O4LMv9+ujE1iVThvRs2VAdG7iW sPGpTLpzcP388X5dXIwxW8m0nvtEbJrWl9fGkDocqk4NmtBEU9naZ/WFx+9S M78mxz2+vG28uxE6qRU67E0XcOBIccQMW1wwcV1w521cbPTTx9g7iRQ8SpN+ clYXp7+6cWDxsA4O7dZivhV9zNdX+rawMlBX0TS1b96xz4T1Vz5If0gHAIKw M+6s6W3ToNW4rYHh0Z/zSkrykt88Dt49sbUBtT1U6xdAlNkoe7pn8rjRg7vY E1sBkagSyb2zceLYkX4eDYkQXEwwQQoilrbWYTAthl0SFCi4ucGjLDBDoNJ8 9SsZ1IDyTwHjXTQUjDvNP3kHHXjxj4z4h9cOL+nnoI6GoY5LXlIfgXwPn4ku aHm7ibfyhFZJdeb18fa4tZK3GnWNkCqK7yxoa2eoQnkypuWsx5XfozZ0MUL/ hcEUdHAqjlNChf4eUnBzjDmTYTT4kvDHILm3JtorOVDbwvKUOzsndbJQkROH 5UiVQ69Mu73Bt5GOvkPbLl6d2rjYmmhQm16WqV9AuuyqcFnCxQXdrPTt+604 eetZQm5Jcc7nmPDTK/wEnba4YILk3Z7jpsmQt5twU1DJ4Xw9PxBvhavhsfWd LEFS+ecri3va6Nl4Lz0e+vRjTjE6ipd3z64eJChF0Agm3Kwro6wV5JSbLnws vA+vSj47yJzY3TnPvMePdytjljVRlFPtuCdZOBgtfrGqtbqW9MEuBjsteHYr XQW9NtMOhT77mF1UlPU+KuTEqsEu6EaVYToF3ShUx+3p62KuyZMllPqdLxH5 I7SCScXD5Z4ORqrUCxDImqiO2drD2UyD/xf9LlWUvTvqb43NViZTYCMu32DA yWSa6AMpeXtuVqeG+k7+a87cfoEu66KshBe3jy/p24gvLgoLJqX3FnvYG6oK zvyqstfbO+tT/6LQfs8XYhKwE4/6GDMZWu23xAn72tL4Xd30iTYV7ute8kIL JDdokBGDaT4uVDiC4Xy9MsJSsenat1LLboKCSeWP51u6GbPwdSnwRJTtJ1zn R1a1GCVO8qWlE8YM79vciHjWYoIJkn9vYQstBst69LVMgd/jZl0aivcoUGu9 IU5IdCr7cHq0s7qiadfFp+/GJKJWKT0u4urBhT526Dac5bIqjnpX3IygoRYK qM1b+lTY51cmnvQ1w5+5ssu8h/wHVv5soaO8nHrXg2nCRrXo6dLmqvpjZMtT q0y+vszbTg8Neo7dfPIhu7g4N/HV/XNrhzTV5S9EGsGkNHpLZ0MWw6jPsUSB j0MKHsx0xJ6fguOcyNpP/TkJRwY1tdDmSQDoM88qeLHFy4gpPLkZOh7rnqML iZt3fykerwr+p4KZ94F3YsuY8+3FkYmtjbQtm3fy6uLZrFFDHUoNYmi13Rgr 9PNSmmuC8qQbq3xstQ2dPLp07di6iY2RGvWcFCxHXpWQs0T73Xf17TBqw/mH 8a+vz2pGdHtpO2vnHC8rPYu2vhPnL1kwoX8zQ/JvKzVwa2VjaObauf/omYsX TRvsaUGKBwzdUTelV0y4BYkPzy3tZkauY6Zp7/VXI2Pi0wopi87OT4y5f3Hb YAd8Y+I+J+Dhm/Ra4kn249XtHU14DwE3a5XvD/Qypv5FvsXGBHKe1sGXVaXe XNXXQd+y24IjN6LeZxWV5CXFPji/flhzfb72KySYVMfu6N2kgZDBFJkcnLRz AxvIo+t1VbSwca5IONLHGH82qm6Lo/hrsfTRbHt5hlbPYxlCI0R+PFrgqmI8 6a406w0pjD04tJEqk0HNW8WWmz5WZNyY1VyT+heGdu8TX2v259X5CfdPze9o RL1AiwHbrj96GZ/OG2tV3qcXdwM39rfGjKFeh8VBj99+LZViTpKCycOvsZe3 TPPr2tLBVFNdz8q1vc+IubtvCyeQov7k5NgmGvxvIt94xevK7PDFbXV5a1K9 074ajuxrhMYVVlXE7+5myHM+7tvJznMVcXt7msozdLvteS/gfJDiZ4ubYt6R ZT3pNrlVR75dG27KZJqOvC6shHGzQ8ZYKzovf42tcPaDqWaCwjYmmHDy7i9x 1xGRu/F113ruzQwRZ1X8bH17AybTZMDpFMFg+Hv4FFx3Vmq88InQ8UlVypXp zbXl9dvOPBL2PCGnqCjz3ePrx1YMxIQ4psWsx2z0WSdc3zS6ramS2ADQ59Bm 66eUs6ObW+koUuNT8NzFayyCcCry4w/2MST+T95t8eOs4nK24EvhVpXlvlrf DrUkCvbDT7zKrSC+jtSWk5t+flwLa10l3jxouSmhKP5Qv4a4tiMQIau7zb+P Pnfkx7NNXfGpy+Bbb5ZBx03RYvkI3IzL4xxVGUqu8x4KvjB20sEe2Mtg6PY8 gs2vOofoSP6rs0v9XPXoOmmr9Asown+oLHaHlzGLod/rYIKABUEKH81rjHks ebvp9wrrY6/NyX28faCjmevQ9WfDnn/MLi7NT3v7NPTwrI5mlH6i0Ims9Vv9 +ui0cWOG9nAmOoCJCibFj3ZOGjvKv6MNocKLCyb14rXJX5I+HOJ83tVeTU7e dXmssKuq/Livu55yj+NEih7y/XXACn83fbp3otz7FDEPpJ2cyLfgGa1t9JWp eYbup15W5j5Y7omJqcJTQ73pvPsFoJkAsoEURu/oZaZo5H04QSz45OYFDsQm mvgRMPvpXGs8uKU7hkNNzvXhuLMRPczNuTrcnIk6dL/AXJGZSrWMYWj3Pyf6 fxKoeLOjkx5T3n789UyR7SJSEDq2oUrvUwXUx14cjG7uGXr+QTQ9Wytilrrg kTtDr/85bKtUnZ8Y+/J55IWZRDTLNPGZPqqZXYdph+6+zyqtrvqR+iJgrocB sfSYDSff41t95Pu5vupY+H9YLH7mvFvbsgW5LeRm39+/dv3m7WsG2uPBpYbH jP2HSI6GfsRjALzcF9Ni1HX+wyhLCl3XG3dK6MdOeyDTcTLy7e7CltoMBcdp d/LEhvb9+U6fBkSYKyqYcDPODTDGVIGR10VtC+ftmqbYYBiGw65KmbuOFEQs a6PLkLefGJojGlZxC2L29rcgjKaYYMJNPYI5TGaDCbdFdQiU4vvT8HJRckyz 0TfIoVTdn2LKkGM1XiF2HxLJP9HLQrpgF6f68zEfExbTfNC5FNHfKYma66iC xypIcXr8y+gnNzf0IAIVaQUTbkFy7MsXkRfntsJdnYBgghSlvXkZHRWypiu+ AuUU20+a296uxYit12O/FLKrS77GhaCzgfCsDO2+Z0RmHDf75nRXDYZy0/mR Yk6hOvfRxm7EBkFYMEHKv2d+TUt4sMoTHw3TYnrg+eGWShrmTZra6mJjp3Yo 7LgN2IBZzktfij9HJDfIn3gK8o3mPyWmKZJ1xEsZ//0isecbvahxu+3JUofZ PMHEsNe0sS3s2k3ef+dtZkk1uzAtJnBBe3KnyTQbFyYaAZSHjNClMUrUqPOO 98B9sIhgws0MGmSK2izDIWINnzkfN+Lnf0KGpfzV5nY66EKbcitb5EOQ/OAR Ziq8icFJ3N9FC53TllPvi+exIoW3J1oQc7rhhNvkM6u4Pd6IgYbA68SGj2Qf 6mZDa4npQX48XuWhx2TZjLmeJbYQ0R2enxWhZYgKJkjB3Wn26FdW73roi8jv IdnHeuCzRrXz/lq7Av5kF+V8TU+OPTcK9yJy8i5+I1qaO/utuRSdWlBV9SPp 7uZeZsSbVGwyN+DYSEcTl8EbrsSk/aiqKvgctrYruXNU7bhXZH9WFuSrwtDq sucTb16ys58cHOWihv+8hvcpgXUipbnGwW9Ys+ymhPPieKQ44dpyL1N8ICy7 Bc/rcLTOTdnhgT9oJYtuiwLjCngfV/F+Ryd8P81s0HfXkyz+GuNmBY+2IjqJ Ch/QSQGSe2mwMb4uWY2JnaLoD+Sf8VFl6PU9U8vunRokaiy+fH68uSsexDMM x565MsFeWc2scVN7PcwuKXodIZ6rzL4MKXq6voMBk2U5/PJXsaleFHd0sC1h 94QEE6T4S5ygwRQVTDgJuzpooB9nO/uR+HEqUnBjDNGhlWU19R65QstujNJH 11sr8RRxbvrezg5zxE9xxf9u7uVZA2cfvvvxW3n+vWmEu2eZt2zj4j5uy9nL gVuHOmKnJgz90aFSuHPu19N9iO+GblHp0taRzINdFBnGQy6J+XiJEG2FVdVU 1E2bdPQdO3nCsF7NTaiMHlWn0eepDRZScHOh37R9YW9zy35ELSAiJqZJ8zau rUZsOH05aNdoF3XsnzT9L9WxrAzufL58ilzfUYNwhZMCgkbbKKk3aOxqhzsf xV4n8cVX/HhBE9RSK3ts/yxqYb+fH6CFv/vWW3CtDsk92Qs1Soo9T4gFftWv lzd130QoehXf0pI+v39yaCAxBRS7rTo8pplTr/kHLt9/nVZQnJ8c++DS9lGu lMKl4rrwEV9VQ/LDJtqgM1mz13HRdcPNONgFf5bq3Y/wNDf2x4O9jFgsi2FB aaK+syhihp0KvkfmpobuWLthy/Yl3kQiqIHXwgNkhHj4WHhydXnWh1cxz8L3 D7YkjIGAYILDebeumQKxYeQfGwo+qW8neiqhriWMv/OXwXJWl+RlZqS+vT69 MTGh7fuObGdh773kXFRSfgW7OO3xvoHWxAplWU84eWaqm0mjfisCnyV/r2QX pkTs6NuQ8C4KbmvjhcZW/XkfZkyYNjMfirrEyqg5RElQi6kPyuoeole/vbB2 7cZt26e3wxNhWbYD1x+knmvAE+xEECmMmO2oiLmWPSmi0yvvtA8+yZU9dyb9 6s2c0rdHB1oraXfcHCsW0SI/bo3Ha9IyREVZ1C254UaT/sSv6uF0fLqICCb1 5rV/yhYOVb9Z2YQlxzCZfE8sSKwIGWE84AKhx3LeX1y/duPW7bM64uEZ02rA Wt47Ofs4Ex+V9JOTnfcpNuZ5xLlJjQkTZeU7c5CLQ9c5xyI+5pRxKr8nRZ2c 1pJUQll28578+4olAv8g3KygQSZMOfVuhzPolgulvcu33iLsnDA/i+vJ9IIJ ++k83LwJ703KI6Zj9p2hMyxYfItA1fyR0/ALKpRi5KXPlrgqyTF0/YO+i0cH SNahLrpUKdWql0udsbFqD7lC68uR/At+xArC5EieBafu1KEO1H7sVZFDhbKo uQ74fzIMxobxYh3yO7DsZj8WzdpDCp6eOBYlNNQai1qg1qellrgRK30wzYrG JNYCknt1OBoLMEzHhortV3Go4mKigklx6BgsjYRhPDGcJr2ZfMkMvZE3pElS RL6FjEb9AMNoeDC9wILkHPHC/ayoYFJ6fyo2cZhmk+/R2jfu18NehLau1IaM lHIOY38Ktd0XxUOlt0G7b0nlHVCq4je3RZ2kWrcjdLuIksD+2r1PCSgSBad6 E42PpBRMSDgfNjTHfY1YXQ4k92g3IvZAo4JDH4TD6uqE7R7E91bueULwRj03 42x/QzTysJoeQf9mqPVLW8OE83aNK+6WVXQNHPofeIMfNVdnhkxsZDzmVhX2 IoP8sSQSOjmKGBcarhHSl6b3SVx9IOcKq/Gyl6JvEMl7ePj0C9lrmKABl82I i1+EB1DxYrEzoX7qjggR2Rexn9EZJR6VN0Zo46tdSDApDZ+ERdEM/TGh4tss qnYcalquEq+7+PF8Z0XUKAy7RmPDuOl7OhiOukk8gOLbE7DghmkxI5JWu+Om 7etIbFyU2+3C5SRykTKMhl0T08Aq4wL33qY14TQg38MmWKGRjP6gSzS2E/+B k72IaSwsmLBjlzfG3quK92lx5ZmbspPc/XsdFr/TRE/FtSF4BIq+rg6bYoSs M7nm8f/U77rjtZDHKLw5Gk9vk1Nos004dC0N7K/qMP+ZyCSrTtjqTuxcfC/w LaAs5ro6dpmzrvfJLJGaB4U3x+BpGxIkiNrA4kBsRrquEc2wKgry08B3QjtF bqNz3q7FdWoFj5qu39NTHbvcGZ+v8i02ijeyR7JP9NJQarFewu14CVAnHXKK Ovp2PbdH46adk3t3lovuoMvE5JHRlyE/7k61k0fDhAHnJVQJKSKrpdPVMOEZ TGHBBCm8gU8Zps08+moQ3OSdHsShpWonYqjkhGaajrkptpjLY8/tv5sp2/Ov jl5ExAwKjec/Ju1d+cMZNobtVz2iOcihg9o2yim13yPuwTCToaLSTqbNHDd5 RzuTNtMD3gvUj6v6EjqvFakOKDVZ/FTMhfD2bSy7qXdJW1QVvchJv82iu2In ITJCbbvklHT07X12v8KVierssKnO+sOvV2KucmMLXI7rfFC80S7p9rHVsRNb HaSBZjkufC4qSCH5j4+eeCoUiBQH9CVmAEO9xcIIsVdSkRw02o7wPIquq16T s6jqxUL8rar1Py/e6pDX5Ual1wlCxKqIXYcdNWj0PklnI3+c8dbsG8Cf01Rc wLSd/5xuVVbeHIVHrmKCCRoSHe2uTuwYZ0SKi3FFV4boaXgdEpRcZbGc5Fe/ P9mUmCUaLRcLZYT8rIpZ4kRmmjG03Fc+EXrO5Y9mE7OY5bTsFf9rId/OD8C+ Dct+IU3rmsrb4/BTGKbFzEfUAq5DiE5A7WvEn1t1PHEMqNT9mPi5LS9MVuy4 T1pnSwtScHuiNUtOkdT1RKFKrIipXbwImV4w4bxfh69LYYtan15bpnCo8upg vNJC5wNi0j4nOWTX5Q9C3539eBZxTarN1kSxZyvz5Kx+Pt+WCB9UGk8LEzFK RXcnW5KnUTMi/1O1gYBfg4wi1Pqeo89NEjAswnEb6mfb4tWo6AUTKjYQ2ptU 3Z9CBJdOy2JprH/RWSIKkrgNE/r89ENeaoSbolVXOCn3Ah4Sy5Ta1yvQLUTy o68OJbJKBSs0U3tKOa2BF8U/A8k51l1FNGTnppLWR8HKZ33Ih8KarxnUXAW0 Iv3Z44/iVXDZkTNwEVmJPG6RBvZL3P4yTCfdlaCnSij6ShyxSYiv+W2R6c7g xKlGvZs8fhIp6r54SCj6WhE2Du/QrtjtqISjM/IkiR8p/SwJ8iVyNgzbzwt4 mVdHq0hpaWh0lkX3ydyMyIC7gnd+Ki76EqG8bIIJNXFoCplSrZTEfTtGaegY /NEI92KrfDwLO7mvoVRcjUVf0ZnfWp54eIMuCjzxitzUzFIE3U6f7YMr+1pD gyVcfeX9BTn1vmexeYq6ckJCUbLz2xqWWPwL0QZPMFHvFyButZBvp73V6PeE tEaJT1XYWPz4VkgwIZI6aLe0P/FkpZ7ECiCyPripeztiRkHT/zJNIhQ6gqTw 89SRSchI/NOUhPQ2oT+edaQbcWkPDc9wU1Z4ri9xoGbcadGF19/q6uk579c3 VyAkJUk6J33R1+qXS/A4jtlgWgTNh1OKkwxRSNWtMUQGI43ewFPQ5ZutE2tl gBp1PB5i6I4OFZq/3G9xD2LFN23cFMJfCZXgl8lcl6VGRX0Wn+4VYWMNsG+g 6hdUY/lfeshHJt96a6LIh1e/WIjlIyh0EIvMyy7iIkvN9QolwE3Z0x6fQizn 5aI+mPN5m7uynt8FGUuZcr/uJzYRDB2fUwISQlVeagalgsrkyziftrTBFpXW 4Cu0i+hnLV1yKIMpLJgUXxmCT06VfufpzwxQW76/E7FfVvY6jFl7JP9kL/wf mKZdl16M+/6r/UepJ6XkdYRvYLhFud9kyBXF5ARiyoq7ZCzlU9F4ZIiM1wXY FZXiE5+fzCKnJryvxqAEVdTZ8sNCpCQ3rx6KO6CL0pNM+B0QILCFq8xN/YpO Ic6H9bgjQRf+LRqhuereZFxJJQvp82I4RZv+m0I/FdUcpVReIaYVy2lJDH0C anH4pIbETsuK9LfsZ8TGjPcPwpRfHUzIFja4K0Zyz/TFUmDQ7ThtJMNNf3ju Xip/lLUJJpRkQRccFIWMNCIexVDRDGDk+wU/vQbjbgktHlksJwE7ag6heFrM eCh2hZraiDNt5z0VOx+iogFlgTAJKTjXF/fc9MEWT3sSSAisQ4hOjlySYFId hyVFYKcSE8PpplfYWDwgps2akJ7yR7OwYn0SZReeYEIUtxL4SvknetQkmHza 1FJMMKlPry1bOETNToZmswlHHn+tqO2KZw2CicyTk8huwULY0TfFHTM3bXc7 RXKuicuuAEAL8iNwgAY+/VfH07sSiYalDoIJ7yiK5TD+zMOnMa/fJiSlfc3J y83OTE9N/pzwer8PnouJXUisbeUiWYexDH85VqPFtVU24+3dlLxPix8BkH8u 80BnXpIvFU9xE7e2kZc8HvQr4hm2Qio5J3FnO34xPwVtmzZ9xi/Zdf5efHY5 TRqM7G1TOCVfH8x3wQ8Y20p9i4E6V6StZkkOhVYw4WUayLtMvxj5LObNu4Sk L5m56Bv7mp6a9Pnjq21dCYmLV3elpqGTN3hqSiWnF0xQi08I1xrDrktKoKMi C+wb4Ef4yLeggfq8C7NMtQZu3UfM3XwyNOZLsQxFaguD/DQJ/yhlL5nKS351 Ekwkd34hWynRXMnHfzF9bwcFOZGNI/sJEcgo+ZyVlKtVo2BCzXw5raHXaELg qjvjDcRFGmEo74ed6OIFBavfrm/Ov5CtoGvn0W/i8r1BEe9ya/Gj4kOvuUtO 9WtitrPsFoicU9VBMOEdd7KcJ59HV8BrdAWkZeYQKyD588fY3T2Jy0t4QMtN 39cRv0Nfu+RLZbuIbfgF4MSvdiF0K4Nxt7EfQnLO9eNfq2epmzfrMXL+1tNh L9NlmdOcBCKqQnfcEi0IrWCC5JFGgmk++OCDp9Gx8R8TU1EDnpeTmZGWnJgQ d3oYkfahM+qmlPaMVqSiKAvyxTf3ijTaMC82k6rxMVKR/3EPHjwL+yuZzDUd 1cXpodPwSaXYRaJxrYGqUPyEmMYmcj7iBo3mz1aFT8SC1joJJqhZDPQlasFY z34sZE/YaFytaDtX5hRlyqxhJQCkyQzlIcGXcZO342GChFsnODUKJrQGk/1o Jn7DjWE0gW4jhENtU3hNXLmZJ721+OtNo2HznqMWbDtzOzajpE6CL5HBgJrN WuMbySCZx3riW3CWk4jTrYicaaXYeHlsPZ2YVj0iLTg/O4JP5bUhajU/zDrD 18MHXqS7fhvYH3/1DMN+u+4/eREb/yExNSM7F7dBKYkJ8UHjiRtrygMvYXaB 83FLG36pOAUdW/e+E5btCbz/NodmhVdeGYRbgxr8JqWPyCl02o+lnCJZB4nI kWk5/GgEahPffkxMI2xiOm4Tj/sbMHhOH7sDiGkCUkvKtQkmkjMlUKqeE7kv cmrdDgulx3IzDnlpua2Jl0IBlGQ5iU9/QtzLp12qlLYgJKpRVBC5B/xbe9hf i5pNXjZ0Xxz6+PnLN+8/JadnodFm1tcvqUmfPkSuJLYPAhvquoToxGdJem5U BMo08dt3/wnh4jKyeS4uPmAULvHKqQ+lqSgtLeW3xmDJMpLjJ55g4rIqTrha meyCSX16bdnCIaEsUaxat1HjToNmrDsS/DSxgOZ71yiY0FLT5KSSjyTsTdjk 5SX6o2AAoIOaNQrt90jYw9anYIJu46xFSoKTK4mpoKSqoaVrZO7g5uHVb/jS a6I37cSouk8cJMg3pVM6hb/Co5nEoUBNPQir7k4kbncrtNudJp01ptQEEc2m Ou3SFDcd8e+p1KDj7ADhes7SCSaV3z4+DNq7bKK/j7d3H9/hU6f1tpGxgzzq BIjSgtSlQXHoBZOqu5OMxcueYSNmKSiramjrGVk0aubZvf/I1bfEKiGIgUY6 tUUjEgSTyhsjcQ1cTnuEpJ7HqAu+7E9eyulN6mLItwcrO5ooiA1eXr/56P1P c6U5L6RyDJlW0txXx8f6fxZM6F4cGsR1wf+thj4OUgkmgtmvAh9J3deQmN3+ k7gTh/8QXxGo+nxuTGNNsfnEULHwWngpQfrj+VoEE8qpM8VaP9RBMKEspOQV 0BBdAd36jVgRgoakVbfHEQlZLTfRZtoKUEGePDL0CS2EltLA/sSlHN5sQnLD l7QzEq+OpmDQatyhF9+kMghllwfhH61IFV2j/2RxwYS6DCIOU15JVV1L17CB nat71z5DZgfU9vUpahRMKq8PwxV0OsGk1sbH1UWpL0KOrp85rJ+3t0+/weNn D2mG2QdKe+L9mPTmmgCpyH13P3DXknF+Pt4+ffxGTpvcDXcv4ndnpIH8ijSp c+RJOs1LIn1VTXpXTZTdnUz2xxp/W2DRlYVPbKDivk20KkTt8MyaQe2yvzS+ rOL6cLwMRU0KlOyCCZmWU7P0XXjGh9B0VYjdNlYJKmxBW17lez6Khm0mHo35 LuPzL8PnrFTHCzVQdH0kmVQ4M1LAvBcGDzdU73Kg1tBJasquDcVfhLg0g89b TemOtmSGZ95J1Urkf5O3txV36aQNUlHX1DEws3Vp09l70JQTpB7ATg4c76Il 7naUzbvMu/Be6CaogGAiIQuJf22H3NZR8rxkm2hu37Rt1z5D511I5PCSnVn2 C6XrCvdLggnqx3cQt8wUmm8QuP7ASdjU2sDnlKTVJaXl/FmLYMKOmIZXhKEV TKpujcZTugUFkwry6YvBYCkqq6G+1tjSqUX7nr6jt/LKoNctRK/huVGHCRKm l5auQQNbF/cuPoNnnJY2+hYHnTONiThE0ualPgWT+vTasoVDGMUxO/tYKYv9 PEvTyW/T3XSh+SSVYCL15KxFMCETOGmzVwGAnrILAwjjL/FKRb0KJtRqq4c0 KMq1SbGTpc7DqTMBWqqjifUjp8jPkK+zNf5Znf/uzskN0wa4W2oIO1OVluve CJTYrFkwQUoSrq0e4Kit33TwsmP3PpMJ49SRhvQrnXdkU8MOiXfxW1gwCRtD 1s+TnJwjLbyDSDQQpr3dgkFVABEWTMov+pH7RsHrvSLwJC+NocH8HRS3KCky cMe8oR0b6QqHWfI2U+/WnrpcdY9QjBh6Y+jyf2n4EwQTXraN8oALknQIflha k2BCO/O5X/cTb7LGLVLZBWJDK/w3qnLjQo+tmdy3lbm6sONVb7fjo5S7iP+n YMKTDBUl5FCLfGfCnkoRD/Ouy6v4XZTY0Y+6sIId/gj0KChMjAjYNndwB3sd kTltPyui9oKLvKUuWSYXmHSCcgRvc1B/xzL/hGBS+fXhnnGtjTRteszaFfw6 l5yhZIhOE/ZLba4L3wUt9bbTMmo5YvWph8nkrTIqb5J2X1Ar5M6f5huSUTWd YEJ8k7oKJuhfJq46MXQHX6Z2HtjVQ12d/gHSFwvlIZXsL4Mvo3yAgrvkFMqK S4RHkFowQQpOESKvnPqQaxIzkiqvD9eUE/sq3MLPD85tmT2onZ228MZDwWl+ lEzXsIiRsRxk6KFOO8wnc4nraCZjb/Ea2mef6KVpOOyalNXXpYFXBYDVSKxu aGXwUHUs+PsHKgDUbN55O1rxrIEaYH97G3Z83dR+rS1E3I5a2y0CLf6kEUwq iapL1H1GKomB5toHDZS5YxhPlK7o/K8JJj+RvID+eEVmps2cKGoxYMVmbKY9 oDk7k9Vy1q9gQkUMNSUSi1D/ggmlLkhOuv91qLCa5SApPb4+BZP69NqyhUMU ZenPr+5bOrq7i5GwcsI09r8g4PVrEUxknJwgmAD1TtlFP+LYRWKHvPoUTHgZ r6zGK+pSIE8QdHuMn7LIKfc4XsvK5WkhNZ2H8Jr+CNzzq7tgIjDOvHd3T2+a 3tdFnwzFdYZe5V/arKkKaNqF4TaKcgytjtvihTZUsgsmvOz1GkZKJRwICya8 exXyLaUpUlIzVImaGvISePmtwoIJTwsR8x8CUBmgEgrk/KwuTHoctGv+4DZm pNVWIgtD1ARlVmuM3oX4EwQTbsa+DkTyqpvEOcKraC67YMJPfKhJgqRet0DK lhAVOXG3T6yb3NuZPN5nGI27Ld1d/v+nYFL9fIEdEW240f6OyF+gBA4VnzO1 FBji/ai80OGfMJUhI7SI6I3+KKq64HPkhR1z/VuZkJedyOILNUMWGq0pL5w3 q4TkCGxaENfS622jVN+CCVLwcHFLDYacotOMe8K/U4NgIvBDEs01O/Gkr7m8 HEOv58FPwm3EfkkwKSUWN80OjZx5il5iHXzQzSpeDraugglWaacDvoB5zQW4 Xw50VreYer8uJShqF0xk82W83KsackGoTETpM0woLaTG80uySKHErQW74NPD 89vn+LUwJtt+qvY6KYPERDriGipLSQknYVMrbAQMXf+LxPEOVn9GxX7+s/q8 IENVmKGLscqDMGmYYTLpVyo50FOzeeddniaLlMhKZe7bO6c2TPVpTPZOFypY L4VgwismP+QKnifLO5yQ6q1SRT8wsVqqVKBfFEwE8skMRwQTib1FIaMs3MVr jdbFctavYEJeT8SLlEh5u7HeBROstgVxGPRPTG0S3hGu5iD6Umf1KpjUp9eW LRwSg1uS/iL4wLJRnazVyS41gqVHahBM6jA5QTAB6h32w2lkKz1Jl+4lCyZk Q0QJYQ3qbBuKNqRAvp8mrobIqXQ/Vof73kIfT+o1cgwdvws1702QgoB+RPMp BXeJhwBlV4fgtSr0hl/nnRnVxRpXpD69/5ZmPNWZF4fiBW8F94+SQ00k9/wA XQZxMCDydGUXTARybCQ+AJ6yLiSYIJmkgCGn0f+81CVmJY0C7/NbQzgqcHwk LJhg1fiIYaj0OCbWAJQcKlmgj9lw+kP8iXGzX4bF0FwUQn5EzsUbjmGdQGuT 7ZDc4z2JG9CqXoek6rX5JwgmP8uuDSF2B8qdD0go5k6djNVFMKmmatOjyyVY Ql1GqhwbagCIvX5p0uOIjzQ5Pey0U/3x6vdSH1rUXTCJWYwLJhKsXfnVwcT+ XGDzjuQe606IEWrep2sdHhWvoNH8kCs1n/VyU6lWP2o+pyXkfVEl8ZnWZBod NzMm7FU2zZz+fn9GI9wWiSfPi0OVc5CowwgkvgvLEWXBw4i7cVIdp0pDPQsm WBMR7A1rDTgv2g+ANrKS1lxzM472wFYUTanUXxNMfpzujQsm/QNFUxXIm6Q0 mmTFpYGYG627YMJvC6fksQN7j1gPUlW3tW/rdIpRm2Aiqy/jVYlgOdNL3/zC TTIIJpzErWQVas0B5yVUWaZ6YPASxDhfo+/E5tJcdfh2ezK+cZDtHJrc7Es4 Y5IFJOt4L3XCLeHdDbH6M2rEu6w3eKZPqZP4yQIxbxl6dRItaqYW805W8JH2 2ZcnRz14T2OKq9MDBuKXuhU7H+ClO0shmJTdHI3GUswGE8PJBVtyyR83UnKK Ek4GBEGyDhFV3+TUe52QJgT+ZcEEddariTp06j2PZyFYs4RuDQeKF3aW1XIS n16vggmvQAbDcEyodNJtvQsmPytCR+vVEqX+MljDNfIIV+hSncBP1CCY9KTt I0n+HlknXajoaz16bdnCITQueXvvaSrNOEvfbPTA9mTClfckCiZ1mZwgmAD1 DqYlEF0dmdbTI+iyS6k7CeKCCdVgS3VAIE3M8nAmqUQKHuZyk7a3JUurtt70 4ddyTDi8xqXKbTa/rzH+wEpckR1UJF09Is+embazHvEtdR2sMTdtdwd7+kpO ZB6euv8l3nOWfPu79LK/OhkQinpuqli4DCudqu1L314Pg/1+YyvCEAp3yeHf f1TruDf5Fw1L0eVBhOFm2c8Ra+OJUZ2wrS2hqIm4A07C5lbExEEDRHrdouLe ZGyHo8Br94duK3T8L9O5IzJHUVLqgxD83q7yjRe/kHh3gs8fIZggOcd6EDqP gtvqOLqQtvLlMrKeaB0EE95VbLwAP21BGuTHBV9sh6nWaR9RmRKN/Fq70m+A yq9gNTUYeqNDpUvErbtgQhVON6Y5PULyLg8l000FN+/8S80qHttrLfCAfQLx 06rtdnyq0cLxOnNKjp3Lw8ZiewPFFuvJrWzJ+f4G9OnKpP4lGP1LpvT6cCIo RK3+AzrBi5O0uwNZEEg4fyP7WA/i3+WdF0fXvegdn/oVTHhREE1mVeEFonyz YGQlrblGCs544+tJffBVUfPJfr0Cn1R1Ekyo61E01oKqVCLang6dBcRtrhrk /9opuzcFP9BgOaLvsTJqto12z+N1vCZbm2Aisy8jSyJiJZQm3aHbuHJTD3Ql whbpBRN8L0EqJn3P0sf6JSEjsCRF5TZbyK3Sj9PeJrS9WKh6bLS9RyVCHjLV y+F1UcgoXDdQaLb2Lbs0fKK5vm+gjO2NfmI7mlcPYunlIyTvXD8svY1hNOii 2Hek+uNpjZBYJKvO1GLeke+BA4jSKkybGQ9ruxGFejIPZ7omtWiAEILXytEa xu/0VqtgguQFDNBhMLS6H+aFDtyvh7qSvtZ1RWxt75WbRPWuVmi6Irb2Zycg mDyj9cS1CiaYRNKVKLDaanNCxesVTR1pCjvLajnJT69XweRn1ZO5ZFih0/dM 7ZmSP+tHMBEeHJJ3xoeMlR3mPZEi4KsL5aGjiX4EDJMRwXTLj8qFFU+pplqA 0VZ4qoxd2ZRIJhEUTOrTa8sWDqFP2aorbXtJUvIWulfHE0xabxH+y3WanCCY APUPUnBtGBGcyCm5LngsegDMTj0/mMjKFDfIJRcGEIndZuPChIMWTtaNCXbk bV/hukbcrye9tYmPU2+z7iWtiswpKS6TxlYiOed9qb53rZc/rbEeRVHoGFOi ZYfpiGDxVuRYly9bJvqfg4MEUxKo9jpMC6mtMRoBtjaibdZJaFMMXb8L/M/n JXCo9BcWnXjd1lnOS18J2gFOzr2FrQh7LstKRwqujySyO+RUmi99KlLKECl8 srSFlrISrnBpCddV5abs70zEpgztjjve0pdXLC6WyrEghbfGEQlNcsquCx6J vDGk+MWqNtQoVEX0c+qUEn0itjMixPd4CNEeW95mUhjlf4rO9dGgLxtY+WAq Ogxljx2inTzpBx06lhy0UuOZd2uNSSsukoKJeLkVqvYHXekPKuGAptIdVe2i NsFE8MUhuYF+ZIsgDY/1r0TCSuT7gzlNNJSVJPbHq2XmY3Hf7vbkjrr5mjfi USKSdbI36sdUmi57Tk4NdL/iYk7b1Rqd7N2U0Lh8ZIjEe+MiH85/Vg9oAlQB wUQkR5p3MM1yWizSNrIy+ZSvKXm9XbjIDvfLke5Eug5D03PzG9qZzl8BSNbp PjrUg18bTVM0lP+9s075EMaQ5TA3StxkIN+vYrZZwX76PXKpYMdLmvR1iCru jEe31yod90q1aS+5N4UsXq/YePYD0Yit5OV6Tx1lJaKzmG+Q4DdGvl8bTpaB Vm4yL5LW6HJLi0ul3shTN5Po4z/i7omcYs8aBRP+CKtujyNbUggXc0CK3+zt bULE4oKRlbTmmtfpVL7FBqFjR/bXmzNcee3MZRdMqMtzvELVAl/+Bn4dS/zG FlWOg2kxI5K6yJ1x79Dp5zVNNjGozmkM7d67Dw43MRsbWnv1GwnfgTJrEipx ye7LSh/OIDZOcgqO08JF/XXZmy0ddZXI6SkuwggaTKHqNtx0IksI2zcsei6+ jsm2aoqOcx4WU0M/3FXL52wBzZcuuzkKnWlqXodrvdUp+I2JiKGOl0lEwE5d 8bsWBgMP7BmgbzU9QubdXVXCge76ik4Ln9EsgPIXS12wqycG3idopjU3iehk JOqnxX6uNCM6+MCyMT1a+x5IkjJaqcW8o+/3zsSG5PSwn3KbJpxD31xZcQn+ cdi5munYMJq4Bfl2sreKSMtdSjCRb7mWLtThZl8dYcGSt/A/kyywBUPygohG OKi/c1scRbsKuSXFpaQNLwgeYUJa0KbzImpL2+XdnNUbc4vuSVNpuzS6Kn+A P64OwwfIsp24fLh1+100iWmyWk4CqhsfbZFzdsRUM1IwEe/mxRdMBG8c8jvp MQx9jifRhh5VxcUCeWOyh+jU4Ag1R/wCN/Lj5mgz0jk6zbxPqyYipcXCbbJk nedlvO4x8nYTb4nV+86+OdGeKNQjJphQ9U+wzHrhqc/Nj5jnSl43Fy7tWJ9e W6ZwqCpsrHEz2rs7RPdmTK7nD5MdNduSrIgsrHDWaXJS/aFZjmIFmPD/rkGE AQCJVCce6k7qDgydVtPPvMojZjA79+X5BZ1sGrna4uq0uGDCPyGVN+26/Fp8 dimHW5H3+UnAwo4manaDpvbBrY5oDnP15+N9yI0JQ8ttwpGoDH53N+6Pd5dW DnC2H36F1gmKwc28NMyCRf2tsQejsgTrXmTHXDwaSiVFILnXx1gSJ+rGPoc/ CjvDkhcrWqoytFovFxGMOO/xPgUSyrXzrDHThn95FUulk9dos1JMk/h2e6qj ItOg11Ehc1oS5KtG2J72ayLTC4tyP4QfORyeyf1Zhe/pMdRbLbibhT1Bdm7M mXmdLS06DO9Vh2xgbsrx3qRXZ+i2nn76JVk0qSIjcv8oVwvPJTePDMHPbVQH iYRAle/2epG/yUR/89QLgafMyX8dsNinkeOEMOn2utwvp/sZkW9fu8XkE9E5 xCgqv0YdGtfMwn3B9eMj8cko3s+Hm352gDGRqW0xNDBNyDsi3+/PclJk6nfa Fsv7LezcU1HRYcIN0RsMpS/Xt9VkqLdaHS1lgInk357aiLqz7jhox70vAg0J 2d/eXDt67T1/elDROs2pOLrt8hCvE0NAeX5xw8/rSUNf8ZYnmIgk5VR/3NOJ 1CaZBp7zAuPyiWVYnnZ/5xBny85rwvbh57C00Tvn/bqaZj5G1fudHYnWA0qN Z9wRTlvnZl4e1pAp32DAiUTee8KOD1g6HTaLijfc7OAx1vIsE99zUm89qNs+ EmrO8gQT8RIdVeRFQTRwsOm/9XZCXjmXU5r1PuLolJa6mm6TJnYmbJ1IMVT2 x0O9qFmr02LK8WeZAmU9CuIuLOvr6DA6hFcqOj1wUANqjjefcORptsAYK7Oe Xzh2mwptOaknfIilJW896nK6kFtHvt2Z6qDANPTaHc/7NPzsVclpapjoiW/x 85Wt1BmabTfGSlcFBt3knh9owjfDR59lE3OnKuvp0YktLFrNuXJyLB5iKfUR OWvlZlwcbkXWmlVzHLr7fmopXwAu+Xxj41A36z4nJNwDE4dKIqEtQEuV9lTs KlbHQ0AwEdgzcz6T17Xl5G2GnU3AjAG3KCFkg6+jmdtQvxbKciJJwFKbayIR H3+l7VZG5nGIR3V8hqe5Vdeh3fAwr075ztTGk+Y6GllAWDze42VlMzRbTj50 9erxlUOaGTbovuV57TWsBcGeFWHV5OWVnJe9qvMlEQGzdpN2Tye7L0OyLw1t QHp2DZcxh56QK64q+/mJqa0tmk+/eGYSfpCj2POEWKwg2WBWJx7uQcQ6Cnbj gjOF11tu6ARbeZZxz/3veV8Cv/2v3GSWqEyOFEUtaabK0G6/LV4m4YO6C1dT szfp4cSTdy0Y8vIqLSQXQpJI2S1ijSs4TL4lbMDLP50egi5yhrb7yijaWVX9 cgl+ZUBETxWBfwebZb/gubSPim/eJVUbQnJuTHAgqzap2PltvZNYzB9+Wcrt 7aNbWXntxWUBrHCtvJbHumgR14nk3pxgp8A06nsyVeC5VfL6tDAN28+78PYH /89W5788NqqxrmXPlbe+iKeHnh/UUJ6cro1H7nuYxj/yQ4oTgtcNbmo74Cwl wmMfbUtaUHXnYbsj0gWMNjvv9ZWj1z/yswOIkinYZrHXrhdZRYVZcTcPHo/k TXpqLyl2Ki8EVSUYGx9VtkgYWS0nOVpSdqBt91J1Zzx+FkvbUYISTBQ67BPy FaXR69qSHY3kjTsuvPA6T2BDnfP8+GwvG9f5T3gPrA4hOgF1d0WOZTMy4F1+ cUHa86B9ga/x8JGbeXW0LRXwOQzacTdZQB0pTbq1ZURz656HBLZDdZjn3K+B A6l9kEaT0YeekFE15/vbqyt72du5NsKPXcQFE+6XvR0UybDOY27gy4wiNlL1 PTn68upeFmoNfaYNxGUA0Vt/9em1ZQiHyi4OVGU28D2TLBqHfTrUy4Cp5DA9 XFCPQqNOYkfJtBx6Ov5b8Y8v0Zf3BbwsrdvkRE0U0eCSPjeLEkwkFqMAAHqQ /EdrOhrwiocraJra2lsbqas17L48JLnovsSUP+7XAD8TgZrjqN/GTZCG28zQ zPLwCcRNU/UuO+LyywUXfdnbE6P4bd5Ymg1d2nTq1rVtEwtdJZZWkyHbInOk jzsrUy5Pa67N5P0tqxadevXt093TzVpXkdVgxDWBKLQy8fxYZ6KanE7z0duu vUjMyv0Sdy9w69gW+vpuY4+/Fc5b4JTnv93bk9hyKrRY9iS7hC2qA5PWWE7Z c8PLvDL8K1JlENSsvaZtOxty/1nM8wchZ7dOat9Aw9Rj8ql40d1i5klvoYZ3 DC3P9dHYQLhpJ3z0qf9R0Da3MlFXMmgz9dSbH2zKR+j7n8+WIcpFCp9t6GTI 6wKBvmYbeysDFZaK/fBTH8sRav/BajQt9EtRldB3LYk9MNhRnRqNvLala9vO 3bq4OzfUVpTXbTZq33PpBC58FMXRW72MeaOQ1zBBR2GoylK2HXzsXSnWGxhP W2LZTrqeVlQlPBHK3h72tyH+27DtlL03X6Vk56S+un1m3ZDGukbu04M+C+4W 2U+J9E5FE/dRa49fDY+KiX50K2jfvN52Wnoug3dEST/kn1hJg7BFnrzmkkx1 c7cOPfv27dm+ub2BMlO/z2lyr8etKkq7NoE4HGU2HBWUXFjJ+wacioKPR/sT uy6W8+zwjGL+fOJUfP9wyIe8Ouu64GEmb65xKwtTLo4mKk4wrcdfTRV9JnzB hOU487bgH0XdeMTytnq8BaqoZWZrZ6GnzFJrPP5CUiWSR/6evOv8iEzByY3P /B7EtJRvOi88Jb+UTbcikR/PNnU3JRofNfSaf+xu/Jfc7MRnN44t9bHRbth1 WdhXQUdUeLYP7tg07HvN3nX+5oPnMc/vXz+1cay7sYZ5x5mBCVLu9LFn9f6g ty45uoWRWeLrkroKrdhm9Yvc0mrB/8ZkJB2BBcdUUMBPafXar4n6Xkym8jOM B5769KNSKI57c2Q4vyMyS8vCxb0zarMaN9RRYmm7Dt8VJbTdrUi8MMlNi79e rFt27t2vb3fPplY6CiyLcTcFlLSSN/sHEF33WMbtpu+/FZuSk5MSE3Z6jb+T jonn7MvJgjsrqjCUkpnHmPUnroU/iXkRGXphz5weNpr6bsN2P5NQmoGe0tid PU15XT/k1Y2t7a0N1VhKVn6H4kp4QgbTauyVlEKhZ/GzKiloais9/mowc27d sZtXu6bW+iosdft+a+5kSBeBIOzijDuznIhqOEZ+J4WeObqWvtyYQlbrNhsa kCT8QviCCdN63JVUaoTIj7tTqeRGOZa6iZW5tpKG46BtEZlVPCWz7eYPlOIp vbmu/ryvqzb1RhV1G1oaqSmbtJ9z/l0xm9w7Ms1GB3+TNfSiMu7F+8ZR65ol Hu+xn5EX4ggYuu7zQ6V85IJwM490I2p7qXbeX/vlRAlfQNCsOU4N+fxN3FjU yZeVxe31bsBrAsVSN0Knp5GavKJFv32xxUhVKLHfYjYceUFoZggbTIGZQYAU v9rdpyFh90w7zjx0+3VqTk5ydOjJVb4O2qbt5wWnCkbdVfcm48kAyubtx288 FXz3Kbrebp7fNbOblaZB85H7o2VTqLAazkThWfr6AzLDTd7lScgGmr1P1qUm HFIUvbOPJWZ9FC16Ljx0NeJF9OPbgTtn97ZXZygYe866mCjJKlPdMmivRPGo fkGWiFTrsEdKMRE37z5kMmnjmbeSaOYT/qe/XJ/rYUitdIaqiWPLDt26tXez RWMJVetey0JSiBdZcsEX7zqqbttj5o6AGw+wFX79zOYJnqYaZu2mnX0vfCxT KdLYVtm4SUdvv0H9uzQ111AycJ9+lq7POPGbn85Paq5LrUrUJjbGbKKnqxXq cDUa+a6/+1UoMS39xjx3fZ4F1bBo1hGNJ3q0b2anr8Q08g3I5H9lTuKudkJj Yhh03/sOfy8IuyTr0SLiyjRD1+fg++8VkjQTzidCHcVq1dK3O5TRcqK/UF2a /XRFS2KJanbfHS8Y5WNDi1zIG9qhD6JDowQT3NcWCP4n8v3Jlj7WKtTXVdSz cfPo2q1zK0czTQUFo7ZTTr6h3kHdQnTqhT1b4CDU80reavjFdF5Dz5QrM9vw Aj6GmolTqw6Yi7MxUGWp2fqsvCV0YleHeY4tvpc7e5sJOmAbBxsTDRWT9nMv fPjxUkINE2y7dmOcleDI5eWJr9hozMWUMqooq2LrVc9zygSdQn15bQwpwyHq xiVLz3Xg0kOXwh5Fxzy5c+XIcv8mutr2PquEA0RsiDFLnYX6/rEaDj6fVi37 5ESqy/Jit3Qk1g3x7qtFpgavnYNG150iG1QAqA1O/qtzK0d1a2qhr6Gsaujo 6bfwbDxul2q+I1meeH3dmN7tXK1RO4Jbfoe+K68mlCC85msUTJ1RQocq7Mwn p9eM79nc1lRXVVFZ28TapdOQBXuD3+TVQezjfHsVuG5Snzb2pjrY3zKzb9au h/+UNScffxXTeiszIg7OH9zBqYGemqKCqo6xlWuXofN2XhM8SfiJJScc9lKR E0ehyQrBbBmeNSbBirlUfXl4YsOsIV2a2Tc01FJRVNFr6NSqc59hc/aEp9BX seLmPdk3vqNjA10VRVU9e6/ld/k7r9LPwasGeTiZa6tqmjp1nnjw+Td8VfMK VRPW0mi8DOm91TnPT68Y26dTq8bWxpoqagY2Lfw2PsalAySXTJymXpjheOHj ncr0yOMrxnRzszHRQb+XjqlN0y4jlhy8+fa77KaGkxdzdtU4YhRaKmr61s36 r4vAj7j4zR/ppw1Ww+32rlm+Hg7ovFFQVNM1tWnWfdSifbc+lYgGMtzcmMDt C0f3au1oaayjpqikZWrfrH0v/ykbr7wrrNPmgFv4/trW6b6ejtjkUdI0sW3q 0c1vwvLD91LwM7bKG6N0aDrUM9T8L5V9O+WtyhD/P1aDaQ8qs490o5tr8lZz 7l8doU3zW0yDcYJvnCeYkCi13S5QMYudGXVs6SjvDi2dLA01VNQNbVsN2fEc z2rkFx0lP9By1iM2N31/Z2XRD8S/hmqv43SX9ZGi91c2TfZpaWusraygpKHf wKG1z4SVxyK+iEXa5cl3j66d7t+pqa25gZayoqq+pXPrLv1GLjgodMBWI0je iV60z6rRYsE2nTzBhES1zxkBJQEpig9YMqy7e2MLXWX8cEy36dCt4WkV4ktA 3na+0LFUVcbjk6vG9UBtFm5nTGxcOw9btD8kPp++KlLey4A1E3xa2ZlgP63T wKFZ+56Dpq078zRTTOQsSwrdMWOAuz26shQU1fVMbZv3GL3kwO1E8Tmd8+Lc tvkje7RqhM5p3NY5NO/Qe9C0zcEfiuoyp1HLeX7NhH6dWzexMdFSUdWzcuu7 6l4O/nV4XY/J968x9JrQQuTkvbywaWqf1vbEajCybNzOb/aOS9FZUhojdC9q KtzCF4dlil0Qq7o1Vp92LfUP4BfMoQQT3n/6Etca2ZmReyZ0dbUyUFPTt20z aN2tNHzgvO4a5HzudjgT+SmLuUaK319c4tumkZmWqlaDJt2mH39VgD9zKvOc +LsKljNlKejJzby6aMy8PcGxOaI7aOT7rdUTlh29+5nmzSI54Uu6Wuto6Fk1 7z3jYOTXOu6+ke946VeGgeCtBBl+XYJZk2Mod9wjEizUyZdxv7++sG5i/y5t XGxMtFVUdS1dvZffziQOJqhG89TLH3ixDI05hmrQjIehOSy4UngwN7ZN69fG jlxvZnYte41ddig8WeyVc7Oentkyb0T3lg4WRtr4Km7UooP3kOlbQxKK61Lv pezWVFe3HuhnRUpVP7xWsFbCWHNfs/G3JZTelmZQSWG75wz0bGSGRmJK6qgF b+MzYcXR+6k1Vt1kP5zX3NVr5OJ999Ikz3befslu9iMpangiElyhHEPF61Am zQPjfn9zeeuM/u6NzPXVlZQ0DC2c2vafsSXwaYaAQ6lMfXBs/YxBnd3szA1x t2Ph3Lpz3xHz9t9Po0mNoQQThdbTdizy93S0MNRUUlDRNW/aa8ahp3m1FrDK iQ5YP9m7FWUTrRq395+78/LLbFqbyP0Rf3nT1P5tHcx01RSVtUztmnp295uE +s40EX9Y/fX+luEe9qbaykpqhs59tuC3z9lPF9jJ0zwr5S70Vd6RnDN9tNCl PjJE8kyR3nKyoxc3UhD/cIb6kKuVP9lRc61ph9Z5Pz+JkBJMKFS6Cr3isqTw A4uHd3axMsZemq6ZXfMeY1ceC/9EGcNfCNH5n/Hp6tL+za2MNJSUNM2ajzz+ XuS5c/JjL26e3g+bXujr1DC0dPbwnbkt6LmYuZVxngvALXx3af2E3i2sDTWV VfTs3PvOOhKdjydGSSr6Sjy8L+FbJ/Xt0MzOWB2Xm1Wtui0IiMNsOK+xAvWF +wUIlpj7Va8tNIjawyGk8G3w3mUT+no0tjbVQx+zhrGNq0d33/Erz76UsNGr SLq+wreFNfZONEzdhh3mN1WTenJmJO7wEAwMKJRabxa8NsYTTMjf1BoubStr AJCMFEWlCDjlRcUV9VmkHfg9INVV5eUVVZy6VQD8d43iPwHxqNn/vUeNsEsL Syr/c19bNhBOVQUsxH8pJcHDdOXt5j39e0NFhMP+D09PpCCgv7p8kxWv/8Sk crJ9FMtiTIjs1Wh/D1J0yQEAYf6JeV6zYCIAp6K4CNIjAOAPQWrBBAAAAACA vwOsDJl62+3S1L4G/kTYMYsbaXY9KEvh2f8blc8W2LPk5G2nhst0U/C3AoIJ ICv/yDyXWjABAOAPAgQTAAAAAPh3wY5e5Kg/4PzfcvwPiFLxcLqVyfBg2r7u vxmsTZseQ9Fp7qO6Nl/6HYBgAsjGPzTPQTABgL8REEwAAAAA4F9FYchIU6tp D2RuRgv8GSDfAv0MGknffeb/SdnjOfbyym7LXshW0+F3A4IJIBP/1DwHwQQA /kZAMAEAAACAfxHVn3Z4ajVf/x7C8b+Uqvi1LbTa7Ur+A6My5MfViW2951/8 9LfVxgHBBJCBf26eg2ACAH8jVNllBc+dqX+gawYAAAAAQHqqEvZ00THwC6Tr fAX8BVTEb/LQMBsV8vfUB/kbqLzsj/deUfI+8yfecwL+K1S/XNIIF0yarHzz JxZ0BgCAjqpbY/QwwUS+5aZPIHUCAAAAwF9H1eNlXXyWnIuMibqy1ttcQbnl hrcQi/9NlN2d23HAqguPox9dWOZlwlJrvxvK9dYvJWQzdcVuR0FKBH4f7Kg5 VlivepbDomgw0gDwx4NUVxQXZH8KmYqnhskxtLy2vfjyrbjiP9rADwAAAAD+ Tqoez7LEYnAchmHvwwl/YvELQCLldyaYMKgXyDLzO5MCW6l6g1NZ8j3z7bkR FvgSYZr4HX2TkV9SCYIU8H8F3XaVFOQk31/cQhFf58ruKyNTcgvLq2HbBQB/ LFV3Jpmra2hp6+obGpuYmZs3MDU21NfV1lTX7nYwA67mAAAAAMBfApJzc1H3 JsYamsZ27kPW3Uj6u6pxAj+56ZdndXEy1NAydWg3YsudtIrfPaB/EZUhowzR cFdHz8BIKNrV9Q8q+d1jA/47sJ8tdNJQ10S3XcRENDczMTLQ09HS0GqzEapN AQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AH8t3NL0uI+5nN89jF+GU5Qa9zmf+7uH8R+hIufdu4zK3z2KvwdOYXLc5wKk Pv5U1bcP8Wll9fGXAOCfglvyJS4h7290K0h5ZvyHLPbvHsbfQ3XB57ikwnqx bcC/iaqysv/yMuIUp7759O0PiUirvn38F0cNf81Mq8r78PZL+e8eRX3Dzk+I TykBDwD8S6nMjn9w9cTOlbNG9nQzVWEodjqQ+VfO9tKMl+EXj25bNn2oVxND JTmlPueKf/eQ/r2wv32IvH5mz9q5Y/u2sVBnyjdZ+T/2zjquye+L42yDMbob pEsQUMRCBREVgxBQELG726/9srtbsQMbE0VUTBSxAxMlREo6x+Lxt6e2Pduz MSbm777/VLbdvud87rnnPmf/7jL9yUBVWY+unti9Zs64fv4uBnQF5bCTtXJ+ Fbf0Y/Klo9uWzxgR2t5Wi0a1mnT377APAP9P1Hx9dv303nXzJw4I8DBmKNC7 7M7/S7aVuoLXN88e2LhoyuBeXuaqFMVWqz/8jVrPLwOqyEi5cmznqlmj+3Zy 0lOiaA+8CNRzAE5d5sU5vVyNGBSqqkXE4a9/iGbwK6jJeXr99J518yb07+pm pKxA73mg5LeVhVvy4R5iNYwM62DHsxqaTLzzz1kNf8FI4xS/v3vxyNZl00f0 9rbRpNFspz34B7oBKktPjj+6fcXMkeEd7bUVqaajb9T97jIBAD8FzvuY6I7e 7Zo3UaMowCj9pYIJ+/Ha4A7ebd3MGEg1gGDyM+Hmnhjj6+3d0laLijQ2DQgm 0mE9XNmLNzqbmeCjU37BpDJhZidv71YOeorINwHBBPAHwnm9LYK3rXhYqKLb yt8jmHAzDg718W7XwkoDXduAYFIPrNsL/dt7t2lqREfXNiCYAHBqnm/oYoDO I9i2bLf+E+zGsj/FDm1hqG3uM/taodyLAjc7bmwrY22TdpMv/nbfGCq6MbeT hbaBx+AjH/l2EPvllj4dvNu5mamgtZcumLBzElcPjxq778XPCTkovzwdthoc catBbsEEKkte3s1aW88lMibtT5rmDRtprHcHB7jpa5n7zrlR9Au3JajkwhSe 3dzKXpeGdsO/IZhUJ87iDa7WTvpKSK0oZIIJ6+3+/s30tS39F91unOBqAOB3 Un4sVO1vFkwwoPxdXehAMPlFMK+PNqP8CYIJVJmX/xeEAXK/bPVV+kHBBIOV +p8j7dcJJlBVXn7Fn9/AgD+M0sNBKn+XYIJTfXGgHuVPEEw4ZXkFf0H8POf9 qlaKQDAB8OFkH4+yNumy8mZGSVXZlycn5/YZE5vLWwRYt8aZY2Jki2Vv5Jxb bGz/4xkfTrMe/d7TGu6n9e0wX9FgWILI4Ie+7Q2g1yOY1L7Z3dsCnjyKHkte /8zFhv1olhPtRwQTKHenvzIqSmhFxlU1dvnkpKEjjRk/WA87H/be8PnXy22s +9Nsqf+OYILBfr6gGU2CYFJ3Zag+3uLrf0OLAwCNS93V4YaUv18w+c483YcB BJNfBPvJ3Ka03yuY1Gbf3jM9qKk2w+9vGLdVx3orN45gwvm4prXirxBManPu 7fuvt6uOSodNmWCjAzSMuvjBupS/UjBh3ZtsRf29gkl1xo0dk3s4aKr0PFj6 e0rQAKDCPd3oQDABYFSnLmypqkgmZnA+buygisgL+uHH5Q0x4WbHBGgiX6LV fc+X37sxQaXnoo0Qd5DhtUJM8GCe7acuTTCpSdsZZAZbUSoOEduf/Nz8P9z0 tW2UfijCpCpxJKpBKLnOe/JnRBU3fKRx0la2QrwEisngi2W/tLDo779d0VLx nxNMuFmbOihJEEw4r5e3RCxfivGA86V/lyUAAIjz7wgmfVWAYPKLYD/93YJJ 3WXUIVPQjDr3gxLEr6DxBBPuxzVtfoVgUpc4ErUFVfuc+gvOuQF/Fvj8/CsF E+vfK5jUYlsZxWhE4p9/Jxz6BgQTAA7nw9q2vJ1OuRep1MfJT963dMHqky9+ RB7gFqUeXr5g5dEnJb9fx4cqXp1es2Dpnru54mYQ81yUZMEEVktMaQp0c/// zrz/+QEb3PS1bX9MMOHZMO/Or1+weFfSlz9jTZJvpLFz78QsWbAuLu23xCX/ q4LJ5o6SBBPefvr19u7FC9adffMXRIIDAPUBBBNAg/n9ggnr9oQmsFtDs5/x 8M8475DK3yeYsO5PRaNHrSbf+3f2dsAvAggm8oOHMSu6L3r552dRAYIJgA/n /SoveHdS7XvmLzjG+LlIFkxqXu8INFU28p5w+GX5r1kcG0Mw+bP4K0fa/6Ng AgD8SwDBBNBgfr9ggl/KVWyxLO3Pdyr+QsGE/Xy+62++dQX4ewGCyQ+UABOD ldpvyPj9h+j1AQQTAJ/as/004LGgM+jS/73rJEEwgSoebxnaZ8Lu1KJfOLf/ PcHkrxxpQDABAH4dnJL3d84d2LRoyohhY2cu3Rx7N5vM+6r+dHXbfwO7NLc1 1VGlM7SMrJp16jd1U/wH8pCo+gWTmoxrO2cP6tbC1lRXla6saWjp6hsxecOF d/XmgmQVvLqBPK82fPiEOSt3nHmU33iLBLvo9dWjO9atXrfzyLk7H0o5sggm DawIq+Dhwdn9u3l72JloMhiaRpZOXgGDZm44/jBX1mqwS99fP7Bi/WUJ2dzr Cl9c2rlkx53Gvb7KLnwUu3xcqLdzEwNNhrKarqlDq8ARiw6l/EDjQ1WZyWf2 bFqzZsu+U4nP8piyCCbsohenlw/v0baphbaajqVbx6BBM7bfzG40i5r9ZI4z XAKldj+cSqph80UmWN9eXjmyfd3q9buOnr/3sYwji2BSk5m0a1pf3xa2hhoa Rg6tuvYdvfTEy1KxuskvmEAV7y5tmt6/s7uNiY4KXUXb2NrDP3rGtsTPpDdu OC8WuiGKlNeq9z/oN8q9gPwi2MVvbpzcu2XNqvU7D5+5mppZiTZ6zac7SW9E y1ib++Tc1sX7HpH3Ircy896xtUuPvyVvMVbB42OLBge0djbTUte19vANGTI7 5l5u49sYnPL0W0dWrzknwd1mFaUl7Fm25fo30vaHqj9f3z4l3Ke5jYG6hrFj 624RY1ecTqvnJJRT+u76sV3rV6/dfjDu5pti9t8jmHArPt0+GbNx9Zqt+09f f1lQJ4tg8pP7kXV3EpJFhe6/K+/H2q5hU1426gqexR/atnb1ht2xF+9/ruTK IJhAlemJWyeFdXS31lfXNHFqExA5btXZtw2a/uySt9f2Ld90VcJgYuY/u7B9 8a7kP+6shF34+PDc6ID2HvamWjwjwpBnRHQdMGNd7P0c0eHCKn5zdS9vVkpI 7sHM4y08i2JSxHuOVfT6Ssya2OeExofqqqvrZGngmuzbMTMi/TztjDQ0DO29 uvQdteTYc3kvu0DF+7vDY4GiO/iy3NNBNitXWinkWcEkfFVFxoNLR7YtmzFq 2Ohpi9YfvPZRdqtA6pWcn0zt19Tz+7esWb0x5vjlR19qoF8nmNTmPLl6fNeq WWOHjZw8f03MpVclP0V1bpSR9rOBar6knN23ec3qTXtOXHmcUwPJIJhwStPO rxkV6O1qqauubeHavteAKZuuZvycJ5TkhJn76MKBrWvhwRX/MKsa+kHB5Cc4 LID/dzhFT2Pn9m6qxX8/C4Vu7L30gdBGycm/vsDfnKHu3GfB3nN307Jy0h9f 2jq2vRHiWpr4zb+WJ7Z6SRVMuAU3F3ezYKg7hs2LOXvnNe/7nlzeMaGjCZLY 26jj7ISv5O5yXW5yzOSu1qrE0iqoWPbaTowHYBe/jFs1omcbVysDdVVtc6eW nUJGr03MlOZXs/IfxEz0a8IQ+l6KinmbsC6OipIFk4ZWhPnh4AAndVXH8BWn 77/Jys35+OzW8ZWDWurz2p8esJfc08Cpfhe/fem0YWGdXA1hJ5nmOu+Z0JdD Jc9ObVowcWCQt70O/OtKPluyhc0TqK4kPfnkukmhrexC9sD2MlT2/OCkbq4W uqqquk2atvYPn7DxmqQGqnq9b3AzLbpJx3EbjiU+Ts/JSrsTuyjUCe4HiobL wL2vG3pbFip/G7co3FWbJtTairou3Xp56Ut+JQcqe7w1wlFDwzl8yfF7b7Ky 3iXHrehtxVtSKdqtZiQKm7ycqq/Pr+yeN6izs9esFHh9ZGZeWRbR2tZIQ0XD 2L55x15DFh5/QZYqCipPHIWmZvScff3Viyep9+/cvJZw+S5mz0C1397dPrpy bGBzuyHn4Lbi5N9cFd3ByVDHpFnw5hf8IssxX+qhLvfeznE+5srCo1PVol1Y Z3uaZMGkLvPC9A5GDJMO43dcefrpy+dniXsntNbiNTDdKnT3a+JmKZ9gwv5y aWYHY7qWW/+lBy4mv83+8iH1/MbhrfXgJlS2CFh255uIiQxV3Jxghwgm7jOu vnrxNPX+XbiBb79H3RyIWfThzrHV44Nb2A04BQ8qbuGddYN8nI20jV16rhck hZN3ARGq79dTk3r26BUUEtYnol9UVGTf8NDgwJ49wtakYLXnfNg/olevoN5h fSP79YsI7x3cq2f0thcyBsXUZV1dEmgjvJbA/dWkXcSE8SGOagzfrVlcpBYP j66fNz66VxsbTbhNlAMPlwsXMftWzMr/RkV0bWEOP6ZL0Rt6Rcxu4BbdXxti q8brgFWnH7zL5k3Mk4t6wCn/KHodFtyW7YlDdkXW4wvb/ovq4NhlHeLLV746 OLF7c3MdfVvvGVdKa9MTdy6fMaKPv7sJg1cMqs3UZKEBAlW8Prdl0eTBIR2d kVcAFb1WigthzM9xk9sZqJj5Ttp19RlvID69GjOmpSbvy5Rt+u57S7rocL49 OjCtmw32ijCKsolXSDcXqa/kQNUZSbum9/Fpbm+iqaJuaOvu3T167lGy3Aas 8szUc1tmRHrb+2O1fntqVlBzS301FS0zJy+/3qNXX/wgTxYAbumrE3ODHTWE S65k4NazR3Ntya/kyN6P7IrsJxe3z+rv49hhCTIcaz6eXxDW0tpAXUXT1MHT N2jEsjNiehzSNCXnBhojW7L30mTe2vbw/p2ka1fikz+jCwG3puBN0qFlI7u7 uYy9Co8zds7VJbzW0dcx84jYLZDqGjzl64WZfXPziHYmdOG5om7VobevNU2y YFKbfmpCa30Vi85T9yQ+/5yd/jhh58jmsCPJsI86/KEe+7rqzcVtS6YODfV1 MYB/VeQtEaj4yYkN8ycMCGxnh2xQdP+dBIGJW1v49ubh5aN7utsORneBguSt w32dTLVU1Ays3dp1i5oRk5wncaWoyboVMzOiUwsH3t+rG9i4eQf0n334WQPe xmR9ih3SVEPFPmTpyeS0zNyc9Oe3T64a0sqAV1Z6Z7SovFl5fuviKUN6+5DO Sujbo2PIwtPWRgupYcA+bIjh0zm4g6Ou8AfrshJXDWxno6eCbI6all5B4zde laCPsb5cnuVromLkPXZb/JP07M/Prx+Y3E6HN/KULIK2vyB8ht+UHo4j4uGm ZOdeXx7V3sFAx9QtbDu/U6D83V0ku7HcmvyXl7dPD/dyHnGJfC2RwcrllQQe /qN6uGMlISLHCkYOt/ztueX9W+grEstC020+NQEfAwJbzb7PQbE1XLJgwpvC aTcOLuVN4aboFCb5H4cR8cjkzruzaUhHR8EQ/G9vSoE0o6T605XVAzwJxaZq 2fsFe5shF2xJBJN6thWsWqyyjJQzG6f2aWvfawd5rt2ajGubRrQ3pRMbjKLh NPgYwcL9zi56fnrF8O6tXXhrOM/od/byCx234UZ2g3xt+UYa3LzXDywZ3q2Z ++SbYtYTt+xd/MZxwd5uNjz7U9PEoYVP4LBl5z/IJS5Xfby0IspDT9hupmk7 +ge1NZH8Sg5U8WLPQFctNfvg+Uduv87Mfv/g/LpIe2W4DT3GXciRablmV3x5 cmnH7P4+Tp1WIq5W9Zsjk3u2aKKjZ9N28kWC48L8+uDQ/Gj/lk7mOqpqepau bbpETN39QOrgqslIXDfYy5AwuDTtOoW0t4BrRZr0tSr3RULM/MH+Tb1mkVS5 0R0WAKD244lRHlpKhh2m7E14+L6gvCzn1Z1zu+eGOfNMParddP4whPIvDLen K9BdJ98k7uqszOP9rZBBTncckyDi7UsWTKDC+NGODAUl53GJxM2AnX16sC3y fUp2Iy4ViHyq8sWufg6qyk16Ljh648mnooqSzGc3Tm2d0s2K55Yotl7zEZ+Q zMyE5WFOOvqO7Tp36dTGzc5EA5+HNNPwo9lky0NdxqWFwY76Vl1n7L54Ly23 vLIw/WlS7LL+wvsDiWDS4IqwXy33YijQW698Q5zi1c9Xd9Ri9D4udQmFiq6v HjV0UJ8OVqgfJiKYcDPPzh8xJDrEywR9kE4gmHDex0Q2t9RW4hvvnbbn1H4+ NdIVfvaZShWyIqj6fuueiznetS/W+epQKAY9dxHtUKgsZZE3bDPwdhf/zW9k 3Zeg4pTtw1obG3oOXXPixtPPxZWl2a+TL2yb6G+lItgDxAUT7pfTw5xVKcru 024VCzU2K31HAGyLUXS774ZDQiquzmhnb6iCVwp2/5nF95Z3NuL9C0W4rgoq zmPihU7doKInh+eEu+uJGDLon4YcLX66vmczc/5QUlAOP1XL/nw4whJvVprD TCzpiXzzRTLMT+fn9rLXs+4+a8+l5Dd5FRUFH5/cOLKkn4euYNskEUyqUlf7 GdIoRkF7Pgp1DVSSNNEZEdScp9wWHtByCCbcLyeiePVX8Zxzn3jMxvy4Pwyx oxQYbtNuYaYRVPzs6Py+zUUtRQRGzwMFzzcHu1loCho4JLaSnXW8vw1uKFGt p2BJT+RbQESpK0xLjJnQDnsOUIFm32/LxXtPXufwj/pq89+mXD2ysCe8aVON uy48k/w6VwYjByp5tGOQu5lz7/n7LiW/zimvKs1OS0nY91+AFa6fKHouR58k 5Lw7On34kOjA5gZoT4oIJuzHO0cPG9yvqxMyyUgEE/anI1F2DIpqq/kpwh3A fLPOBw4rphiFHZVqDnFzT41uZaOnTBEqGLvk5qyWmvi/MHofq6q4s2HUsEER nezU0H4gCibc3PilI4cODG1jjnaUuGBS8WBZRwMq1ST04GdhoaU4cQwi9im7 zkwmmi/snMTl4S76Fp0mbz9351VOeWXR5+e3Tq4e3NqIv4aRCSbc4sd7x7Q1 1rb09O3SuYOnUxNdvGIUjVZLHvH1QW7O8RFe1rqEWjO/XpnWCpYSCeshRav1 ggYFF3AL7m4c0MLQpO3ojadvPs8qqSzOfHnv7MbRwkoniWAiWz9C385NaG2r z8DLTXOe85hZkDSvPTyEiWubuse0G0JrD6cgZd/MYBeCPI2jFXWu/MGyzk1N 1fn/qzPoErPu3e4gU3wyKrovfoUWuWFTvn6q352c0dVa3yFk/v7LD94VVFbk f3iUeHB+uKu2oDrigglUdndhO10K1TwiNlN4SH2LH24Df47hPvehtLNTqODq Ct6YDfdugnaLiGDC+XRqDm8rDfY0QhtAIJiwH60JcDEj7gLVr2P6IosUlSok kSmah+7/JKqZcEueHRjf3kTLorkPPEKdLfX4nanWYt4D2Twozps1bVUVlDwX vyQuBrWvN/jpMAIPI490YLMyrJ0F6azkfDj+3wihhUcgmHDeLPcUXqHhD7Ir nmzqaUYyeFSdBxx4I9LQ1U/XdzGmUfR77Hgn1GtQ2Z1progAYz/+OqJeslNX dxNuSvWoc0zWx/2hFnxrzWXuU7gBkaPxIS3RdZqqpm9qbKinraHKUOuyI+vp 1r7eTQVfwusOMemifiuXrCTE72jwCiYB9teEWe0NlLRbjNh6MTnta1l5/tv7 8QeXDWyhS+UZMIMu1YrZavSuMWLbGJlgwhKfwtjoYKUsJ/wPr3a1lS92hMKG C4UwZJWaRBzJJHFref72wfEdzQ1c+y07cvXRx2+V5V/fplyOmdnLXp3/aYJg Itu2ws08PNjTWoeO/5tS+41ir+ZBxckre1goqzlFrj59+0VWacW39NSrsWtH t4eNXEaPA3xLsPbz5SUhDtoGzt7+sNFva6zOX7ss+p2QEIktijwjrfb2Al+h wUMxGknIp83Kvb0x2l1f1661Xxe/dh4O5lq4OUPR77bjfUPuJENlT/eOaW9q 4Ba9Ivba4/SiyrKcNw/id03rbqsm6AZxwQTKvzTeTZ2i5DQmoUCoGdhZB3vD /hlF02fjW2nFgArPjifsOm4LX7DK7s5vo4X/i3KvQ1h6XKjyzYmZ/k20TN06 +vv7eLlYG/ANcYbThGukJ5SVr45M6tREv2nfxYcSHn4orCzPffcwYe/sYCfB gQNRMKm6PsvbwVBV2MIXqXIjOywAAM9deL2liwGVZjv4zBfRGVaWONpahb9Q c7P3B/KWEAnvZVXdmeyArMUU4/5xBO9FkmDC/Xo0jPcfFMP+cSSHKtX3ZzRF Fh+KQd8TQltFRfICnj3L8JiaJHp+xc2PDdPX4j9pwnm/qpUi1XLQecGHq9Pj l/Zsooiu6+OSRBxLqPz+Mh9eS1hFn84R3S2g8hcxkXboEicmmDS8IqwH8Gvp VNup98U80srjYSbRF2Q6qKi9MtQAicIgCiZ4kYsPBSLCg1CESXXO6yepyQmb whG9VkGxxaApAY6uoQuPP/hUzORU579J3BjphEXtMLzXfyQ0Q3XyjKa8FqC3 WU1yg4KbubsbunAyWi57KYOfzU5HXANNn9XPxcxDZnb8jDbIIay4YML+sNWf 919U24m3RE0T5r0paBZRy7FJ1d/ZRR+fPk65fXxiC8Q9pZoEjh/Uwt5n3M5r ablV7LrSjIdHp3obULFdfvR1/rexXx1fsmTF2nXjOyAVotn1WbZjJ8quo8l5 XGZpXk72x5RtIcghrYJy2O6E6e5qKkbOHs6G8E8ptkb9IHnnCzlQ6d2F3nq8 eTrkfK7ors8te7oj3Bq1rUQFE6jk2jgH3vhT99+ZJRrjkbcnAOlsVb9tQhZK gwUTzsdtnXlNRbUae0PcWoTKEkZaUtFGHpGAqACctJPLlqxYs26SL3Kngmod uoTfwIfvfuXWlfEaOD11dzjqdykHbb8yy1NdxdDJo6mxCkWQVUa+BUQC3M87 /DUVROYL4Q8+rWurRG0y7LJsPiDzY+yQpqpqXnPuif99ZfI05L6ZglrEGYKX wc3e4oN0o4hggsF+OtcF/pyoYMJ8tbq9BuxYzEoVFcuqE0cirUhzmPFAml3A qSz8+iXj1YWJzRTRlWHB2W09DOnaNu5ulnDMC8VgWAL+eTwDhohgwq/d6Qik IUXPsouujIQ1LM0ee0W1G+6XHZ2RSarebbfgbLH6yfquJjSqadihDNFf4dlj hwY7o7KquGDCjItUo2j4rHvDX0XZ+Q92D/NAbqQrqHWL4ZvNzPy3Tx49uLF3 EDoVaU6Rk3q7OPeYefDO+8Iadu23D7d3DXfHjHvFZvOfyqgf1r2N6W1Jp+h1 2/JGXHT+FDehBWoCigkmMvcjq/D900cpN4+MQlxP3gwKmxjh5ug/Zc/Nt/nV HGZx+r3947x00HLT7Kcl8xui5snhJUtWrl03qjUy8WnO/ddgM2/n7hMPv0G1 Jbk5WR+T1wWgD4TpDDhwfqyTipqJi4ejPrz7KfluQzqvoVNeOtC3azO9tClK zuOuil0X4RSnbAg0R708UcEEKrwwxIqmQNEJPiR6s4ibubkjooFo9tqfW/+E rbkwAKmxiGDC/529AciXCQQTqDzz+ePUexcW+6NeFb3jqKkd7VsOWHP+aVYZ i12Z8+ICz9RADQaKdvAh4hBlXhygTVHzXvWS383swtS9Iz3Roabit110qSaD /Xi2E43XxBNuiw3L6jMRRhHEVJWVZyLJZiVew9wdnZHCCgST76zSnE8f3z4+ PQZZc3hLwuRtMzo4tR+9Ljbh4cdvFaXZr+5e2DmxgxHmgStaDTgj2JmgspuT nXnfqOq7+bPIz0GFBwMRP5/RfkM6/AF00D3Y1AuxZhTUI/demuSqqmrc1MMJ iaBFM+2wUhb7NHP3bGGLGgZKTdoEBiOE9NuYWlf05s71xEsHp3mjRoiYYCKT lYt16rkFndC5IyKYNHgFkwAn82hEEyWqUXDMe9FpXvN0ngcDVdFRW+3K+hB0 F5RVMIHKMp49fng3bk4HdDAJBBPkf4Rqp9pp9ERv+1aD1118ll3OYld8eXZ2 YTdz1Iqg6IYfEznH4eacG+2qTlHxnJ0sFqXHyru5xM8QlRcIESaybSs1uW94 y3Ditkgr9FlhUcEEKrw81lmFZyuueCJ67Yn9ebOvmsBQ4aQtba5ItRkeLyh8 1YcLiwLQBYRqM/meDM6xfCONW/whNSU56fjUVshxAlEwgfJ2daFTjHofyuJP htqsG+v7OKAP4ur3PyfDMon+TPbpYU1VKWqtFz4UCx5kfb22wEcf7QZRwYST uS+QN7+o5sMui3Yf++k81KwwHnxRWjE4Vd94nZl2eVpzZJDQ3Oac2RVkTNey dnOzQuJitQdi0TaIhUB3n/WA7yhxS17ETmmPlk2p5XLRrIDcvEvj3TV4bt30 22KmHLvgzoquxsjIIAomnOJ0goUvaq82rsMCAPCoebTAk2d4aoUcIbnZCn3b G6AVfhK16WvuTLaDD+eNhl0hPbOB8vb1UEc9Ds9lwlaHBMGE+WCGI7xk6g+8 QCrKQ4WHg7XQrdhtIRYAD5UkjrVXVKCajbhK8hnOu5WtLcckYTOK83aFlxbB E0SoShpnTWbtQ6XX4K+m6ITGSjjxLz8ShGaJEBFM5KhIBfpVmr0OiF0h57w7 s/HcR2lRa3zwtKTkgsn3uoRhyAIl7gDWnotCnQeKTodlD4l7EPfroWBdzJ4X zi3Bzd7VFf6QkvcG8pQezPvTMAXAoP/Z+l6gZ73d4MOz3xSbL35JLmqjkpKY YAJ9iw2Ft3tBFAehCAnDkJFGtRRs2nimDN7C6TA07guxYavvTXXEyjz0iojx wkoaa0Yh277xkmAX6yk6BnY+C24VIjcrSu4vaKOL3qiSe76Q/nHxlRHWPNdA P+JUMenohIr390CKIyKYsJ7Oc4VtFZVe4hG937mfN3ij/nkXoVwGDRVMKhJG IKEXlhNuk1oj3MytvmhIBaPDxk+CpsQTKSi2WfORbESVHuqFmhM6+jbtZ1/P RzSSsodLvPX9d8A+kDwLiDSqE0eZI9uyasAe8cwOnHerWitrdNtNGpgmRlXy TFc6z/Od/YisSfAWppiOEYkwrTkRhjQVuWDC/bLVF7nKSxBMuF/3BMADSXD6 T6hVXD/EUaI5zXpUfxuwbo5D2oCioWfSbHDsB2Qo1X48EG7pNDMV/zjPKm2h SLKE4t+B6SlE16zu4Uxkqqn1jhV/pBERt+FSqvTYh+1DlfdmuPAGtHrAHgmn glVx/ZDeJRFMTvdVt518R2TWcdI3dkQkFtWgwyJGGT7VeT/XYkYScZ5AxecH miLDguYoSwvyfv75MliOUG63VkKGkrobY8xIruQ0vB/ZKdPtUMVXxXXclXxi O5VfG22FqRZiLnXdpUE6kryw74IcewpKOvp2XVc+QJqL8y1penOd3sfguSbv lCcFKoiL5g06iunQeHJzHR/2ooIJLpBr9j0lHvyDuE7wh9SCj5AvmcLg2w25 YIIIHEh7iVzJ4ZU9piuqilBNAneKyGPsd+u80dMHRvd9xFj18/21rcYliZwU cDO2d1ZD/3yvhDwjwlSfCIPHs7AAiMP5eG5jHNFLYN2ZSDIrBSWKi0R+Wkgw weCNVlM0ro1uN/BUlugM4OTfmOGFRZwJ5DH2y8UecOsrd9sjPsD4/Un33SpQ F6DcHX5IU9J4G2rnpXeRjKXcojuzWuoEHuLbE9IvSkB5O/3RXZAomMhu5SL/ 8HV7J6R8RMGkoSuYBNjvt3TmeeLK7UknBvN8tIHfNiE9puZUuIqkqSolhwk3 c2N7pA5iKUv5teOZ0b33vifqSqy0la3RiSuySnIz9/XiWZM0+6n3yPO98DcF shwmMm0r35m8VYnE4uJ+Pd7XhKpAazrnMdlukzzF3oO/vXNeLfbQFtesKq6O RF88dPwvVdZYDvlGGn8giAgmuTs6q3suSxP5cebjuYiSRNEfekWmM1LOp12w lk1znvmQ/O/Zzxc0gwepqGBSdm4AbBpTzcfdJHmXF1sbeLZqQv3ZfPAUXBR1 XSOX6MPvkIlT9/lopLXdFMwe4PV3E2OxYyyo4Ggo4lootVtPGPvcL4fhIBeq 9fib5LF1nA+r0UYly2GCW/gi9mrjOiwAwHd4Hu/toYG6U6RGBSfjxpGb6GkH 8/poZN+k+0lK3QoVHQlB/XCiq00umPAmKXIKpdRhE5k/ClN6oo82+n0tliKS JOftylZ0eHkZHE86r2vfXj75gG831mY/uPtWPIMWz55H4iuUe+wXqjPn/eo2 8AKoFXmmkrw0kpK+ylMR5sWByD9QdNuM35+SJ2eiUvazea7SBJPro4zJBZO6 hGFoaIrznMfiH2SlzEQXEo3+5/kl42Zv9UGijc3GJknwotmPZzujC5D+wIvS g4rLzvaHCyDBeEO+jDTpK1RyJBi16/x2kJwb8q0Wuv8u3InC11oFrT4nxVdF KH9PNzQMp83adGIj1SOYfK84GoyGcat23CgkcHGKM7JgCV/++UICJ22ZJxw4 qzvggqSGJU/6ivcJvFGSdBvuCBB8qgYKJtUXBiKHrMo8q01CRXN3d8WiAXy3 CoZifYJJ7clwtIFV2q15J9TAJZmZcKZaeeaddPCTFgXljptEs5nC1rKScByS NFjPFrorSXlfid/ClqLGJfNMBOJikQsmuJVGEEygvN1dUZGFHxBLqBT2FhG8 bNV/ioU7jgpUm/FJgoUQqvqaUcAfVpz3K72kCSbJU23EXDPWA9S3p1qTviFd ExeJmP540B03a1snFdRsl+TtSk76ChW9SnosFobFD9+hOc0WWfXwWlMtxt4Q 31jwPHq8EX6QpIFFgAqPhGiRrrqCnyNN+ipHP+LWMcVw8CXxgcnN3NSBji2W IktQPYIJ7isoKGgGxAjVgvUtIxuW1+We8qSt8RhJrk0xHXVNUkZX8qSv+AJA Lp5/r0L1BNl0Lnbqf4gzLEEwqbsyFKmwmGCCH31I2CWq4ocYIQq+yMvpUMnr pEfiQiA+v2V74gLfxSnaXmP2Jn+tx9MhnZUCmBeiUflRTDBh3ZuC+kdGgy6Q LyCsF4vRM2eKTsQpZI9lv8AGpsi9BLzk2KvWFJPR1wXrGC75K6h33ibk4rCL MrIFZ+LS3Vh+dxDc2AZYucSSEASTBq5gkii/MBAeEkptRe0NFG7e/eMJwioG bio2UDDhT2FxwQSvHb3TNpJrmhXn+iNDnWY/Q2hW1SSNhV1qKYKD1KSvMm0r 3+tuoCYTcS7V3Z8Gt7uEWf4dKn129vxz/gCpyUy+917cRuLZwibwVzOCj8jq Gssz0r4LdhmRoc/Ke3L7lfjoYz9DV3UZnyurujoCjjeSYPDDSEj6WhkXhSz4 5MOO+2UrGtiq5C3DAwdoZBtivoy6KlgRoKrcjHysM6GKD7dSMsSLWH48DB6w wuGqPJh3J8FLE8+ckDRzpCZ9xS18or3auA4LAPAdNu729VBBZ5fURf67wDZT UOlzWtLWDPsBqHat3u+s0DpIJpjwXMAWqBUaEivJCYEtPvT7VMJPVgtmRr3+ pRQ4lTlJ092Q1A3t1glkTiTeXrI5gUAumMhTke/fyy4ONhHczFM1cfOPmrxi 74WUz2UNuMwov2Ai/d2iquOhmOUucBAqT4Yje7Oi+6KXkhqoNn4wGp9c30u8 VeejYV8H1tUlRUiSCya4q8Fb+GfF3015/Dzt/afs3ILCgtycrIz0929uL0B6 UdgHr8f951nKiDpEazr3iYgXVY9gUhkbgprKpI34A/NFHM471EMVVeaFIRVM oELM1aBaRO5Iup/69OXbjxk5+YWF+V+/ZH76+O7Fwf4mIoG7DRVMcFuIZ1fE S+pMzstFbtjtGKGdsj7BhHkqXIovIt+8kw43Y7MPMvKV8NwiOFVXhpsyvFZI eJtGBFbyFNgAoOgNvkzuAkpuYTkEk7rEkUZoaKnvosR7KU9epL3/jM6JL1mf 09+/vjoDPWmX6VV3PGaBaiml7+UQTPjHx1Sr6JibvIH46u3HTHQgZiMDcW9f 1PkzHA7nKIS+7uxMks6aQANfyYGYxe+390TMNeNR10QywGC1VhTtduyXrgxB 1jWlDpvqvypReixUA5lw429JakBywUSOfsTP1mjOc56QNBPr1njkaECxpejA rU8wweMmlHseIHEx5Z/yJOAqpbgWISgOqWDCD4Oh2Q3ezxtSz169TUeGVB48 pD68e74LiUVUoEi2mIVKIa9ggu8CZEkzhLwnKS0lqCaz5ENMiDYyv4dI3BiF qEgYYc5P8EJRMW7WOXLispjzDz6VyipjCpAimAg++E7C8sfN2ICGM9CcUS0S Dw6kmoRvvZGMbjtf8vjbzsujg9A7wQRFouRAT7qUQYk1knQ3lqw7GmDlipRE uHgNXMEkUX0R1Rq1B8j4Qra0qSpNMMGnsPijuGS1E8CzhNuhDv8IgcOPryQq occl7rXSBBOZthVyiwtfbOjd99cfKCYBTuWXq5ORVUaKjC2KHCMNhh8FSq4V EuBW5z9egrQ2zUXyRieAeQ2VfdRFbvIKQS6Y4NY0fK/uzJ0Hj5+/fvcp6ytv b8nLyc5I//A2dWUnNLjLbeGLek0c/PlImRZWYVhln48NQC6YKQtnaeSZS8h2 qIxlXSJBqmBCbk01qsMCAMDgwdO86fq0numKH2coqEXGSVzpmeej0TQA9G57 BHGJpO45riYrMMJOSHRl6i4PwW4II8f0NXGRyIm8UsfNsq56SKm+vb11Ysvc kX0De/UKCoseO66nLU10ktSex+yFzhLNNgmCiTwV+Q4L4/dWdLMQyfcNd4WO W9T627I9K/yzBBPm+f5IO9MFMTg8kwi9vKHYXPJZPe7Xwwdq0lxtzqvF7qjD NYXM4ULrRiqY1GL+pBgUGp2hpqGtZ2zVtGXH7mGD1/CzDdbj/uNlEb+xIKtg ohxO+iyN/PNFnOrTEahhJOX5JFLBhPNqiQdZalVecygqq6pr6Rqa27u39Q/q N/mowBhumGBSi52sUfSlOEa8wqGBvnACV/wfZRVMlIOPkqTblHPeSQcqOham g8ZezRaK/0UugmkFHpDt+Vrup/WIEUS1ljS8G1UwKTscqKxABkWRrqKmqaNv Yu3i5dsjfOim+/WHx/ClgxbLyKQDFDkEE95S5UKWaFQwEC0cPNr5B0VNOw5H a9VdQ8UDgqgtQv2CCac8M/Xi3uWTokN69QoMjhg+OaolMlT5V63Fak0umOAB 5Ure9R8Dsh+i4XnSLGBywUSOfqxHMMHLIh5fJatgItZSCPJPeRJ4vjWqzodK zHROLpjgUrd4e1GVlFU10CHl3SW4/8xTolk0xPlJgkl9DyJzKrIexe9bMXlA b2SEDpsUjTxdpqDW76ws7jRUnrKmh6X4qKFpuUasSSI+K9wYgonEsyT82g62 oPENAbLZrsKb7Qbmdm5tOwdGTjgoNDD5jry0ysvhxjbAyhUtiZCk0MAVTAL4 RTGK8chrMkhi33+9YIILQ8JCJ99flSLkySSYSN1WSC0uKH8XGimj3PtYQ4IA mIVpN49vmjO8D2L0Dxg7tjvy0BaeX04Gfo5gwir5eC9ux8IxkUG8Gd87atSU 0Kb1vAIs9NXp6KmuuOIvgFwwwSV/8bWSpoTazZbOnh26hQ5cdrX+bG+4YMIz zeu76AxVf31+9dDa6YND4fWtz+AJQ30Qx0PIsxCkT9KUnLqx4YJJozosAAAC vktKOw7D//RsPzThAr2HpDBcgRCtoBp+SrC6kbrnzEtopCFs70qco7j2qMCA fSaoaF8AMrMUPcgueYsDVb47uyjUWVvfI3LunusfytDP4DsCwYzENxGltpIt 9NpT4ahpRxBM5KgIv3xVmcknN80c4I8+aSi0BVsMOi9DHtBfKJhw3mDn+cKP JokC5e7sLItQjXvK0qwGoVVZSDCpPo6qAoTbQtL5BYIJman8A/NFDL7xI00p 5BdHWDDhW3nih8ySaZhgwr+ZpCJ8DVwEvnABP7uB/6PMggmpx/UD804a1ddG I11CtZpwC29HOCODmvlIsrRJEn4U8TAIAecEGlMwgW/go7l0hI8E5aUe6QBF HsEE927Fr76RUXuqD6O+lZ7vSZAIJnW5d7eNbGeiadN1wvq4J/nYkMNjOMS8 10YUTPClVbJaJjTwhQUTefrxpwsm4t4WjPxTXhx+rgUpYrCQfiPUcfg0ky3w px5+uWDCykveOaa9qaZV53FrTz/KxZYafKRJU9lFqc66f3rzrEFdXA2JygnV NOq0UOf+VMEEv66i3HknfFOWr9NLO94VQ7ojjyGHG9sAK1dKSRq4gkkAb0wF 1b4SA0+J/AmCCT8PnKpIJmEh+HuaVMFE2rZCanHhew18lClD8mb4Nsib0/OD HbUMPKPm70v6WI5+Ee5xk7/kTv5FjSyY1HxOWB3loafTNGTG9suvi7H1Gjvn kkkwwW1iBc1oidYv/rYV8Qt5UwA9matHvZYJvmkuzcrnFD05OMXfUsOs/fAV scnZ1WjX4cegwoIJ/zUuhpTwJWyXkF0waVSHBQBAYN1C92feakSWEoL4p5hv J8E4Q8BvChLvG5K653xXhjegUyQexWHpRrBrozWn+6LZxTQjTtc77TmZx6Nt 6QoULd+1LwlWHalgwj80MyGZkRh4nClRMJGnIuKFrci4f2bLrOgOTbAACqVW ZJmdRfiFggl/aVFQDTkqMXAOf+NDlSzDqFC58ev4dP9dkuJ58FNYomBSF4+m BZP2SdFC/R7B5AfmizhYej/yFxFQ+KY5QTDhX/qhWkj8pBgNE0z4jpE0Y4h5 YYAWakALOcA/Jpg0yrwjgf1sPpqHzXjwJfRn4URyDDex160lgnsPCsoBEnI3 NmqESc2pPmi2CEbg4fpTbNTDzxJM+FaRlIvKQvCTHOgOuCDJJa/FAv5EBROo FH3pUMlhtMiD3b9CMGHdn4re1hdPLStWDqL1Lkc//ibBRP4pT/JTRfu6ozar 5KTE/Ms3hI7jX/ppSCZHSfxSwQQqf7CkPW8fU7QdfqmAMKDkEUz4cCszU+K2 zR7Q0Qq1koj9/lMFk9qLA+F9meG7FZGu+FchKSajJGjGJPwswaQBVq6UkjRw BZMAbm7IfOj3Rwgm3M/rseN6yZfh+WHDjSuY8OMPZJK72J8O97VSUqDodNlM TMD8mwUTbt6lMS68jV215bwU4ts2DRFM+Cn6JOYxFUgSxC+EWxYVl9vJkKSk HuoXTGpebOpmSFWgmvWNJSaJJhNM+AlUpMSC4HEzsgsmjeqwAAAI8IDBkrsF H5ae2xvK290FPcFQ7ryDJFcUQtmRYBUFsfxtpO459G1/T9Q+lJK0sfJEOLz3 UwyHXobNZjxDEu8Hhl2RrphABbFIQmaq7RTRh8RIBRO+k0NzmSshblPw5CdB MJGnIt+5hc8TH34heQq44uG8lvDZKtVsLFmSTiK48CDB1sQzejaGYAJ7t/hL keNvSrBn8Fd76B03Sz3yg7ci7P2TiFMSHmllXh6CuExEwYSff5FiOCRetgjN 3ySY/MB8EQdPbyjF+8CvHoskfa0+1x+Nw2jAuVjDBBNuBnZErKAWeFDCGTFu exDvYP2YYCLXvJMFbsYWX+SLtUKQNbH29kQbzS676n0sUvAFWOQsz+YceIF0 mZIimMRFIoIJ+Y1t3HAlCCbsJ9j9ZKrFOEkzU2ZkFExQw02Cfld3YzQiAxI8 rMpTfdEEx3TJvSVUDCxpJDxiJDwEyY/IEBFM6lJmOsKF0wg8KLqj/QrBhD8d KEZD4iUcmFWfQW/YEa13OfrxNwkm8k95Enh7EXp3UVIaTKGABWLHlcX2Ro8X lP22S1phZYW3dTmiZw/zyYRR/DJwowgmrCdzXeDmI3mMq0GCCfTtxbUHWWS+ 3ONFrZF9hZCGAlPyJLglVSfC0IVHHsEEc6EEj/4JkgM0ILLxZwkmDbBypZWk YSuYJEqPh2Hf4i05H5kwf4Jg8r0CSwhB/oAcAh7w1ciCCf9QCb4l+0i6OQLl HgyEXwKmOf73UOQvf69gUh4/FL6xRjEeell0S2iIYPK99EgwuuKpi29uGPhB HfELuVl4Mjftvqd+9EGY+gQTzru1bRnIgr5OdEEnE0y+V59F339TYPhtl2Bl 4eFdDchh0pgOCwCAAr+Vie4lyi2XvpR6FsBNX++NeoD4OYIYdbcnwGNU0WXO I+ERSu6ew3HuWB597w3kWwe2x9Mcp99HfUDWg2nYM4qaXXdKtV2rTvdFVniS pyFwc59oRtZcHoK9I09I/SxU/4zt/uimIfJKjjwVqYsfZBJI9hYCfq1OJkMD f8mNNHkqVHx+EKYqN4Zg8r36yjB046I2GXOdVKuAcnf6K8MbTMiReqI/BHec 1TtvJ+3H8quj0OVO5FnhumTsAJeiE3xIpuOixhBMSIso3VT+gflCQtX5aNQA JWaZF6pG+iYfzHsgCCZQ3p4A9N8VXWalyuaGNfCVHM7rJc2xzuyxj7zja64M hT1Vestlr4TamC+YSLhXLF0wkWveyQRUdDwcyY6h7L3+I6fsXLSRycDzDbEy WHcnow+6UrSDDpINUn6Ihrhggr2JQJphkf1hfQekxsRnhWuujUInOsWo3+kf PCiRybLl3w/WiDgjthJA5dfGoFHnBA+Lm7PTH9O33Oc/rffUuQ4P0KKYRJ8l S/bH/XogEAs2Iwgm/NxHJJ5v+Zl+uj9bMBE8tUTRCTxA9iIyVHw22gR7t50w 8Bvejz8umJBfZ6tHMJF/ypMAYVly4VrMTCGbpqy0Fa3QlZTYcdzMzT7ov9M9 l7z8sSvo/PeGjElCIqDC01HGWGDjjwsmkncc3rddGIioRzIJJnWJIyy67yET rDD1nHAhBncOSE/qofxj4ahsJY9gUn1zvDVVge6xkD+vocJDgVinOk5LllGr /lmCSUOsXCklaeAKJrH8+3qgZiTNfuItGa5H/BGCCTdnR2dsqnktF30bF6Em ZSYm9zayYPK98uIgdGhSjPudlqp3lR9DFVSSrGZszFT+LYIJLtQrCb+ijf/I rga8iwXLPuhAZrRb+56sG6ruTkEvjol8oSADj0aXnT8oDtQjmPDzjZAE69fe noiEBRM8Cyh/TwA2sZovekE2sZiP52IZxGUXTBrVYQEAUKBvpyJRoUBB1XPW XYnZFuA/LToZga5cVKvRiSSaQkXCCJ6RS7MaelEkEho7Q1IiPmMGlZwbgJoh VIth8SSBBlU3xvL8DqrFgDi+QSxQLSg6fmufS0wqwH9UjeYy54nwDOPkX5/Z CnvLlWhGVt2agLriCkrO4xJF7dXq56t9dZWV0Wc5REQYOSrCjIvUJn/xBA11 UWq+VIaATSgPv4LnME3EI6zLPBqJZ9AXz3whoUdw+IIJ4Zib/XyRB7paq/lu Jslrxv20sT2DtyC3X/Oq/vRVn7b4onHDFIOeuz6IrIGcnDMDrVWV0aqJOD7s V8s80a2bYhi4N530l+oqKoSspQ+rW6NPuDZUMMHcJFpT8pgj/Ca/BMFE/vlC RuX1MagPrkB3nZwkOk0rHy9rr8PARmfYCWEDFXbP0MGpwGg27XYZ2S9xqyqq hIaBoMVITB+yiuYeQA52YOt46j3xrQkqjutvCF+RGH+d8POse6iuIOlCEv6s sHLwUVLDUp4FRDaqr41Cup5qNnD3uu7aTjMfNsw+rns8BzNPqOaRx7+IzBWo +NZ0D4aC+Bb/XSgxg2qXncSpCZWnLvFGVy44+Z6wDVf3aI4Len4EB8Fmky4c zIpKWfoSj7STnp2v9BCan5Qq9s4y++vZITZYvkei6AMVnkCfkVBQUGk+6145 6UCsrKjCV8iHs5qi30OzGXI+T2SVqn2zLcCAga4QQk+Ifxekn6To9j1JEFoq X+0INkM7RTyhhPRaCwkmMsQzc96sbI2mO6Wahh0WfVuRlXG4j5kKWnLRAPcG 9yMeiIM/TiICXzARCyqoi0cT5krQ5aECwZuk5GubnFOe9LtKzg80xQaG55z7 IgMDKkue01KLoYz0nBbxaREo70hvLJehqtf8FNJFglNZUS3D5OeHj9KaznpE nOzMTwfCTLGtVDyRgvRdQCCYCErODxfU6n2UsPBXv9kT3gQdoTIJJsyLA/Rb kYoY6HuZis0WCN3U5Ycg0tzmPyMuBbUfYoKNsJyQ4qlkcMGEajvmGskiC5Xf m+muTNHrtOaJ0FIAlV4abIZtWE0n3iC1K6GqikqCPXighyxJX1EPU8K7Q4Lu IBwbyG7lSilJA1cwSVTdHG+D9rKSw4iL4q+fi4Dn6iLTNvFEaSR50vhTWCxv cz3tzBdMCBmdodxDwbqobKHVcfVzEQkM+pY4vqkGA706QxZ2KNu2gq/dIkdU 8KvVdGxZDDuYLnEr5uZs80XvdngsekmwGfOuTmmBmpsNEkzkGGmkgknNyXBs ux+XJLxWcovvL/VFW1U2wYT35Xt7aKHdoOO34bWIvMwtiB/lqM5At2aR1605 HzZ2QE/OKLr+W96QTjBWRYUs50r4a7ywYEK26+DvQzL8thP8Duan44Md0I4k CCa8sXosXB+dWBrey56IeHVQcdKUZhoMZbF3yAVVI7dXG9NhAQAwoLxzgzET l6LhNnDrnRzh2zQFT07FXPyATQtuzvEIdBekmvc9/IkwcqHS29PdlKm6HVc8 EpUx8FNTsQM6KO9MtAWqh5qG7vtAmMRQefLs5gyKdrvFDwjZGtmf9gZhmztF v834A4+/CQ32mi/3juy/Dh/r1SWNxfQC9VYzruXCFWAVPDo0zc/K0ie6hz1i RookJIPyTkWZ0/CGGLIz+SsWDZKXsm9sa0vP8ScPjbJAHzgTPYRpcEVKDvRU pllHnxQ1imtfrvfToaq4zbwrU4pK1n0shZiCslXgistvC6o5nOq8N7f2jW+j r+E2bFQXZHEVT2SLb8ISgif4ggnBFeFZdKkLW2Ihga3m3iMaT7x+CTSkKtsN OiVbdHTNw/nN8XwtTbovvvgezc7FLX93YXFPO7uQTeeXITq1+AX1qtSl7dBN Q0HR2Hfm8WeFQnF4+Sl7J3exdZ+ezF/58ednKaZjyLLT8K/e2oreTOZnWqHZ Djz6uqiiJDPlxNZjz/AdDyrej9od5I+4IJWRb75I+K7YPiZoX1O0mo+IeZCH jrK63PsxI1tatppyZv9QRD1QDhLJfsH9cjLaGvXDFNScozbdyBCYdFDlh4sr oprbBO0TOvrgBy6RyfmkcDL2BaKmpKLNoNPZhP6Cvl0d66hENeyy6aXIboy/ GMAzJKIOvvxWUZqVenrr0cf85qg4EqQsvrsSvluOBUQmWCkzsYMaRUXVjptk ip0m/Pi3+OGYVayg4tB3Q1IW6rVxy9/Hrwp3sXV3MUQzy4oKJlDR4SBMSdRu MWrf/czSOohVlvn04upwe3WjzuOj0bsJIlnfoLK7sz3Vsdlk3nV+HD+nHI+6 r3d3jPWx8VrwVAazgOfNNRFPHSQK/24k79e6LTr/Kq+Kw60peHfv8LSORupO /cf0RFN0iFzT52bGRjTBdxrXgVtvZQpcWaji3bmlkR52oYdxhxQqujTMWpE/ bDffyq5B/of97cmRKR0s3YYfOTbZQfylSG7Gxg6olUSzjtj/Bh5MvEF+aVWE i5l7RJ9WSNtqiB6t4j6shLz/uGAi63P2lbenNsUScCrbhKxM+FSJlJxT+vrM 3C42TpG7zsxDLhYqNhdJmNHQfuSZrmjUAHliO/6LPWLRNqz76FudCorOo868 K64o/pwcu/VUGt4quNEvxXeVb8qTw/28tyfmiVJ0W48/+LgAXXZqv9zeNsjd sv3sS7v7IQk2VCNEdATOp4Oh+J6t6T50xx1skCDfWv4mbnGfZnYRx+t/+AGJ 9muCLq8M295rEt4V1nA5VblpN2PGeOlqNh810g8ZOWJnD/XsAnzBREgB4X7Z 1gkdHdQmYTGv4LEBVaVfWRvlZuYa3retJtwQUt6iF1ARG8KgNYmMFdXkmG+2 dtWjMlym3CSIVczro3Fdyr7P+mvvv/FqWPn19Y1dI1voaLccOwJ9UF1cEuLn KeXNWvchO+/nC0X6V747M7WdgVHbCUfTROUq7te4wXZYLntVx4j11z4JqSNV 6ZdXD/C06b5TKJKAf8jFkJJKWKBskaZ953VHd9LuaICVK1ySMGJJGraCSQIq 4239qKSqwLALWXHlk5DGwilJuxhzSkiRwEURJV/xw62aE2Fop4nnvearB2La Wz3tzH8+WUSdZL1e1wEzu2hGPv+dfIUuTVDVp6tr+zhbd11+eVNPeFiTpq2R bVvBXxYUCzmtebYK/21FM/85p9NKhYT4ivRr+44kw4JR7dUR6AEKRbPt7KR8 +G94BuH+yT6WVp36ByD7saLnclmviMk50kgEE36yVgWG6+hzGfCXcUpenJzX 0868zYCQZmh0hIzPtNQ9X9kWOzZRNOk850xaCVIdeAVZFepo0331pXXIiiOe tK72xXo/TF6m6rebfPhRvqCbON+eHJ7Zw8Fl7HUZYp7wh55pTqQyPVR8NAQt IcUoYN0jxE2ozbq5dbiXuUOvSF8jxIUSeRWS/XZzJ22sbAbtpx17UYR+cU3m jQ39XKz8Fl/ZisTzkMbSS7bwG9NhAQBwWJnnJrXSwQ5RFGiaVp6+PYKDAzq0 sNOjU00iTwotxjVv9kU5qKKTrs3ITRcefcrLz3x69ciKaHddw1Zjjr4T1Z5Z lTnXp2CZRwxCYt6W1BLWq9r3hwY6IbYIVc9r2Ppzqem5BVnPrsWuGtxCz8Bz xME0kliq6jcHB7tgZiVvI9G1b925Z0hgl3bultqKSg4TbiDSPzdzX6A+/pKW kraFtYm6skGbsQeel7Lw+aXfNzaPuKZUv9jSy1wJ/2aaupGNg42RmiLdMmTr 0woIP5ajNhl4PL2USZxmDaoI7osrGbaMWrD7TMLd1NS7V05u/y/EWVunadiK pHxZpzDnw46uukLvhfH8O+R9Dh3vebe+VZ9GswhSDHrz2h0vL9wjN6aiGjDF MHTPO5Ee+S4QTKjm/WOJFeUWXJ/TXh/5rIpd0LxDt9K+FHx9d+/sjqn+5tq2 gctvFcq++HByzo9108ALT1HWaWJvb6FNp+q0mXHlK4efrUS148onhdVsQlrH 4uTVQTYqeKXperbNvf27+rVyNtNUUjJqN2b/c/z0h1NT9GpLd3QpVmo5Nzmv kiViWPCzsDHaL39cWC3cFMwHMxwJjyMq8iUuLrPs04lBqIVNtRxwNK2goo60 5g2dL9Koerqhuym/QIrqxrzRaahGU7YO3/miUtBp1kPPfC4jjs669BNjW+nh jyJS1c1cWvt27dLBw0ZfhabuELL4qiCdDtJiAah1ouQ5+26uWIuRU/l8W6g1 Yr3RjDuM33b56ef8/M+Prhxc3Lepjkn7yac/kTheghN1fMbxzP9MpCjcuvKM uOE2mNoUeeBVfjmT1NCRZwGRAd4m7ImmotATiVSQlerXe/ra8t+toKoa2jja m2mp6Lcave9Z0XvJl54qb/G9bbRNFBUR1dOqz943lXjiQZrrlGs5hJ7hFiYt 7taE/0FlA3vPDl26dvJyNNVQpJv6TIpNq1eXg9jVBSmL2mCHMj7LH3wpqWGT V52beSDYiCooJEVRCbnQoNly2tW8GmydVNAK2PyqqIYwpd7HjvLUxT/IG4iu 8EBs726tx6BpOIUtu5ZD+Ou3e8KthZrQwNrB1kRDUdGs+5qUUgiXMShG4fuF FjGo/OYkJ3xUUdWMrZvoKKs7hq28/qWuGjvpU/Ra8qKan1WQXZXPr7VGlw0v iCWG4f+SXuDON8ViyyUJ7IzjQ5z4D6BTGHqW9nZmWko0/Y4Lkgq4/GwlWt02 vRT5OZn7kddbhU9X+6I/gi5eop2FCyYKGv6i1apGgq+EoDuOvIjG8XCZpR8O 9EU9D5rDiLj3hZXka5s8U14CUNmD5Z0M+a+2Kmma2jpYG6jQVByiD7ytgfAn 02lO4+KzyusI1ax9c2iYhza+jVA1LJq16dTV39vNSpdB02zad9VN0egkCcA2 u47wVqqkhPZ5x8X3iiuw+HuKcZ8D7/k7Im8X+HxyMHZT3mZ4XEa5aEPxBROa 88SELxXYhK28N8MVfxaPqmpk1USXoWYfsuxqFrMW844VWyx4UlVPuTmvUb1Z 0aBF5LxdpxPupKbeSzi9c06Yi462U8iSa7liw/gV3+UVriHVwG/5g5Ligz3R k2mzqCMfy4SbWCCYYJ2j6+jdPTQiPKCVrY6ypmv0lvuFEmYE6/OZiW0M8F6l qJk0beUDbzu2Bqo0NbvABZczBa+AMkvTj0ahpws02yEn3pJuqJzakvf7w9Hz MprLpMQcwh/Bm/LxgdimbDviXCaxO2S0cuGSxPZHT9to9qMviHxLw1YwiX2X n7Swswm+SlFUzdw7BgSF9PD1cjRSoWp32Z6OPejIqviSMBF1tinGfQ+8F7LV oLryrIuj7VFZ36zfkY9Chho8hQ9GYKk/nMZdzsZHHtrOeO0cxlwUnU1Cggmt 2dQbhD0G+nb9v9b8BlSga5nb2VvqMqjq7qNPf67jfsU+p9iCaDXItq3wzNLc O/+hxwEU3cAdacR1Fip7tDHEmoH/trJhU++ugSG9Ord2MVOnqbRc/BRZazif dgXwzWG6bhMrYzVlo/YTD78sZ73Ar9z1P5UvgzIh/0gjy2EC5Z/pj8d7Kyhq mlmbadJ1PAZvT/nGytnWCdW1uu3IZMpkZ3ALEqa15C94CnRtc3t73h5H1Wwx /lwWi5+thN5qwYP8KqLdXP5oSx9HNfyTijrWHu06d+vcxoVndivqtRy2M7W+ 2CukMx8t80Y7QsV70b0sks5kPlvWGt/8KAwDS0t9FRXLbnPPfazGY4hoDhOT SolnBcU357XTExpcZrzBxZtTaq7Dj6czoUJsHVV0n37zq/Cg5NQWv96KW/ie c+6JWPiN6bAAAAI4xc9PLB8d0tbRVFeVztAydWjePqDP6EX7bmeLmT21Gde2 TOnT3slMV1WJrqZrYtu8y4CZmy+9FY1RZN2ZZEVwODFUAmIIenht1o1t0yI6 NjXXU6MrqeqY2Hj4R0/feD6tTMpYZuU9ODB/aI+WtsbaKnQVXQvnlj49+01Y EZtaINA8qz6cWxjh3dRCW1XTtKnfyB0p35Apyk6ZYc83zRQUjYYTNEtu8bPj S0f27tzGzdZEW0VV18q917yEr8gH8cg6fClQ63OS6I3JXBGo6NmpjbOH9mrb 1NpEV11ZWdPEHm7uUUuOPy9u4OtWUMXr4/MGBLRrZqXHQGRfHbeIlVc+1wiF XmLQrCYl3Z5mR9IjFNXgI8L5VHDfG28ix/+EnyHhFj+NXTQ0oLm1oaayEkPT wMLZu/fYpYfu58pxrbfmU+KWqf14RotTE301hqapU8cR+14jjYrH/fFXUM9l xNPY6vTE7bOi/dysjbUYdFVdM3vPgKEL9iRikSq86n/d1UVFtKbwqtpsvvAN G75ggqEaekxwYFD9Pm5Ob09rIw1eF5l5DtybBh/7Mc9GaVBIvliB3nwJ6RuK ss4XGeB8exK7eESIX+tmtiZaKqp61s2DF15HjlIEb31ifaoRRTwc5hQ+Pr5y bFBrB3h0KmsaWbl2CJ+8/lSqoNu42dv8GCT1UnSYKUu4KNwl8esnhLZ1MNFR UaKr65naeQYMnr094WOlxIrWpp+fH9bSBm5gDdPm/Xch71kxLw7SoZKUQ4He bD7pg0PyLCD1AWdIYcB+0KQ7DUh/QgSO31k7NriNvbGWCkPH2qvH6M138uAK SM8Sw8m7s2V8qF9LR1NNJThVnLJ5x/F7UuHFS3RSMDptEz7whirfx2+ZHunj asWbmnQ1PQtHr54jlhxI+iS5A3DYzxe608nanKo1gPwJw6r3ZxcP7sHzFHje jwLsgDj3XnT+A+zNi6yTVMPhCcJLAzs/9eiy0b1a4QPR2rVj36kbTj/OI1s/ oLLXZ1aNDfNv625vpqOiot2kWbfp57OQNuO/K40NeNVeB7DLBKy8e9tGdW1u a6iupmfbus+iS5+RLuRbXejfq/ht/fxiaQuyWjM6EbIO4YIJXiHjUbK8+1v1 Pn7DpIiu3s0dLfRUVbTMm/qNi/2AFIR/KQP/uQ4bCVd96u1HQX4kIsqtVwln vuELJliNtYTikqDKtGPTe3lYGajzdnuLViOOfIQbtexoiCrZ2kZheK8jTczc 8CkvEXZ+ysH5Q4M6tXK1MdZUUTOwbRm+4i4Sysm/XiBhRMHWwOGlI3t62Zsh xouxdTOfyOmb4p4WNCjUGip/eXR2/25tXWEvkPcziroeUWsSM2vFC6BoN/1W 3ABtkpaiGgwTNif4ggneQe3QZmQX3N8xNqCFnZGGmp6NV9j88+mocF6KyRbY CO2wQUqibqjkRdzmOcOD2rlYm+qq05U1TOw82geEj1gU+7RIghEBlT479F9U 17YuPAcLOcrXbzFg3Q3YwuPijhuGkvMc/IYqLpjQHKOXLRrSmbfh6qjwGtnE udOQ1QmZ9YlinKKnJ1eND2nrZKHPm+0ahlYu3mET155IEYrwqD7ZR4100Cm3 Wsm/wAF9O9CLbGhiI7PmVF+y76Bo9icEk9Vj5UooCVVvSLzcK5jk/qt4f3H9 pL4dXSz0eSaghrGtW9suvYfBkweRRHm+pRmNpEaqgQeLmAnDDUl2SIpaaGy5 hClMMx556WSkOtn/mBKuOfAFEwxln81CYUB1X27vnjWwp0/LplaG6gx1I/s2 0ZsfIYFM+MUIHEX7GfdZsm0rrPsz7MkcBUZnYhLQmswbO2ZG+7vzlkUGXc3A yqW1X9DAaevPvhLEDkCV787M69PO2VxLVdPMxX/M7kfFyDfw04qhZTMbI/nx ph8caRKeFa7NTFg5wKeZpZ6aupFjh0HrbuUiaxPnLXYEgraKep8TMr36y8y6 uXNmdPeOns6WBrxuMHZoN2jHM+T5Hf5zuhhKTecQr5rXZibFzBvUxcPGBPac dEztmncZOHfn5bSSeo0lzutlnqSdSVGPFHltmlvy9OCUwJYOJpqqOpYevaYf S0PfBmKeixJ4FhRl9wWE1z1ZX+/tmTOol48Xb3BpqKgb2rXqtz4F6VtBmnGs A60m3WHJaq82psMCAAAaCsRh1dbU1nH+sERBELu6vEI2lfpvgsti1tQwWUAJ lhGIU/cnjs6/DSg/phuD3mqVeO7VH0fmtLpcZkV59Y8+mfqzgdg15RW1v29+ Yssx+y9cIdC17W8s+e8BYtfV/Iq1DWJVlVX+e1upEEgNRaMLSJHlWWEAgA8X nqQ8g+1fnj6S4ec8JUsf/UsLgtnN/1Y3oDsAC1i3AAAAAAD8ETBvT7DWCTrU wHSxstHAd4gAAADg9wAEEwBAZuC9HRZMJOSVAgAAAAAAAPhnqLw81KzJqESZ EvI2GCCYAACAvwIgmAAAMsN5uQi5463UZg3pJUYAAAAAAACAfwPo694euh6L yJ5MaQyAYAIAAP4KgGACAMgMlteUYjRClkRXAAAAAAAAAH8pNamzm2l33f3l Z50QAcEEAAD8FQDBBACQFahofw9l+AnOqLiy310WAAAAAAAAgJ9Fdep8TzWr MddlylcvF/iLAlTLiXeAYAIAAP5YWPemWCOCScsVPyMBNgDwD8G8McaMqkCz nXi75ncXBQAAAAAAAKBRKb8wtmPEstPJqbeOzPDRp2kFxGT/xAvI+FvWvz2T PgAAAEijLgm5Y6BAc1v4AggmAIBkoJLzA02oNMshF76BZ1wAAAAAAAD8W1Sc jdKFnQIERevoU19+jmsAcZhVZYUZyct91dGfcpt85UNeaTULmFcAAOCPAmJV l+Z/vDoNEXcVFNR9liVnFpbXsMFiBQAQYJdkf3zz4NQcPwO6afdNz6t/d3kA AAAAAAAAGhlO+tFRvo766tpmzr5DN97M+UlRH5y0FW201DW1dfUMjEzMzC0s zEyMDPR0tDQ0Xf4DTxACAIA/B3bq7GaaGlo6yGJlYWFuamyor6utqa7lsyEd PP8BAAjBSpnlbmzbKmTyznv5IAwLAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAPz/AFUX5JdDv7sUjQq36suLtwWcX/mT UE3uq7Svdb/yJwEAAAAAAAAAAAAAAAA0KlBt4cdH107vWTt3ZLCXmaqS5/I3 v1Rd+BnU5r64cWbf+gWTBnZvYapCUfLd+oX7k3+S9e3NrXMHNy+eOiSwlYUa VfFfaEYAAAAAAAAAAAAAAPzzcEtenl42sI2JqqqBdbN2AQMXnXxZ+rNdaJya k33U6Qw1DW1dfSMTE0MddVXbgWdy6/l1bs52fzVlFXVNHT1DY1MTYwM9HU11 FWXV9uvSG7fc3Owjw328vT2tNakKMP+Cp895uyuqo3e75k3UKEidfoFgws05 Nqq+ZqxIWdXDVkfPOXzHy9qfW5pfAlSWvLybtbaeS2RMGvN3FwYAAAAAAAAA AAAAAA2n+sOZGT4m2k2Dhg3u7etqxEAcWgWqnvfcm0W/6PYJt66yIP3Wqq76 qAOvQNHqsPp5TX2f4jDL895emdVaVUGB7jHpwuuvZbU/T8qouTzEgPKPCCY4 5SfC1H+RYIJTc3moIXkzQoX7umNjTz3sRPmvKc5PBMrd6a+M1kcrMq7qN5aE VZT3Ddx/AgAAAAAAAAAAAIAGUv10ja8uVc1r3n00NQdUmXZkpIc6qpmYDblU +gsTdpQdCsQ8TNiJtxt+qUCGH+d+Xt9OSdF98aufrGKwHky3o/5jgkld4ggj yq8VTKQ0Y83NcZZI+Imi438prF9TnJ9JVeJIc6Q+Sq7znrB/Rwm4ZW8vrhvp a6mqNzgeKCYAAAAAAAAAAAAA0BDYL5d6KitQ9AecrxT+13cbfTQQ1UKzX1z1 rysN81yUBlXH3Ay7KELR7bzpdb13GaBve7rRldpvzPjJHj875V8UTEb+asFE ajNWf7i4ccGi7dey/pEbLFXvzq9fsHhX0pffo1ZAuTv86PBMojWd83sUGwAA AAAAAAAAAAD4W2E/n+9K4/lTLvOeEv2p8mOharCnRe8aU/jrQkyYFwdqKzab Gx8b1YSGhpnQncZdre9aUMmBnnQlny3ZQDBpMH+aYAJoXMqPBCEhW3S/HV// raedAAAAAAAAAAAAAOAnU5cwDMnLod5zfz7Boao+EwHfyqE5zHz4C0+mUcFk wXN2Zcp8LzUsm4lB953vpd7PAIKJ3ADB5N+GGReJTCPl4KMVv7ssAAAAAAAA AAAAAPBXwXm7oqUikq2kyaDzQqEkzPvT7GgKFP2QQzm/6qkc5GdxwYRXsoxD oSboeyoKjGZTb0pJpVKPYFL18crWmdH+HjYmOqp0hpaRlZtf/+lbrqQ3NAvn P+Dpc8s+JB3ftWH12u0H426mFbFkEUzqclMOzRvQtZWjqaa6vm0Lv7Dh8w48 LCBR0WoyEjeND/Vr7WJloKasqmtq06x9yKiFuy6+LiF0nVzNyK0p/fatqLik tKy8orKquqaWWcdic7jwF3PZLGZNVUV5aXFJ5d+X96T2y929//Xr3NLeWEPd wK6lf5+Ri448LWq04cU8F4VkI1KNiJN8yQmqeHdp0/T+nd15M0SFrqJtbO3h Hz1jW+LnX3gZDwAAAAAAAAAAAAD+OLg5B4P0KGjCkA6L7qK+LSdzb089Ct12 wCmiCAFVvo1bFNnGSlfTyC1806MK5I9rPsWvGhHo1zl41LqbuT/o6QkJJvCj rLenu2OvplCMg/d9khTqIlkw4XxNmNPJVFnTNXLxvgv33mR/+fjo4uZRbQ1g h51u1mXRzYIGFFjU0698e2pWUHNLfTUVLTMnL7/eo1df/CBRhGEXPT+9Ynj3 1i68v1fVNnf28gsdt+FGNp7aAiq9PLtXj15BIaHhfSOjoiL7hoeGBPXsHrjw BvZSEPfrqUm9egaFhPWN7NcvIjw0OLBn6OoHsgsEnKKnh2d2t8WSw6AoG3sG BTRTk/xKDrfg1tLuTVR0PYesO/fwfXbm61uxc/yNeG1ANeq8MqVMoINAZSkr u5oq67Uatf3Kkw85uVlvUxP2zgpyUKfwem7UNUICDymCCas8M/Xs5ukR3g4B mz8LF4fzflUrRYV6oJiNTSI2CFTx4crmib3bu9saa6hqmtg379hr6JKz70g6 Caor/ZxyZuPUPm3sg3bBl1eYmVdXDujgbK6jqqJt6tC275zYF4L61qRfXBrl 7WiqpaZl7uodOHpLciHpOGKVZSBf2ta+1w7x5mV/vTqvs5mKQZtRW/7H3l3H ZXX+fxznpkNCERWZrdgtxuycXbNrOt2wW5x+pz9z1uyazrk5p86uzcbAjtkK FlgTEJSS5r73u7gP3CLcKG7iRbyef+yh5xxu3ufc55zHro9X/HHp3mOfa+6/ jqkb/ywaO7VafPmNKYVCH13cvcyte13nBrNvxf+mV3d2/K99lcIOOSxtnUpW a9j+61m7vML0VBRjfde0tNAWTFovv3bj6qVzp0+4H9r358WnurixT/5wq5vP 1LZCzxm/7D3t+fjJ3Qu7Fw2oYR//DZkVaD7TI+BjFkwBAAAAIEOJu7+2XWJX jhzl+q697LVvVDUb24oDtz1MWqHQBF9e2aOUpaEqsclt6jLbM/LJ3hFVbRK3 qOxarf1vHVLeKJgIMXdXtXRI+HjLKhNOh+jtZpJKwUT9bPsXRU0MzCu5nQx+ 4+eiH6zvWkA7R4pZ2eFHXqR1boekLf2ov/ePqW4rkhkaGiZpstvWmHw6xdCH SO9909s72zmUrt2kacOaFYrly5HY9jcu0H3z30rquBd3j/06vnHiF2H4SbvZ O09cvP4oOPGkYgLuXjjy+5xOxY3ja1t1xm06ce2JvjayntzP3Od0Le/gVG/Y sp0nrj8JDgv0uXpi2/f9P3U01SXXUzCJ9lzToZCJyqbe7CtJKwzhV6bXsNAm 7LUjYQ0jTfDBrwsZqnJ/vvHNYV2agMNDSpsVHXX6jTKGvoKJ+smmAS5Fcpkl 3knGNefdSxon9oKbs2mxLot2uZ88f/X2PZ+HibzPLWiWU6n32TVYcPN1ZSbW 79TSvpVz5yxavVHTxrUrO39im3iyqlyNltx6nSju3tqeVQrb6S6FuJEeBV1a 0Nwx/gYxNNQVmOK/21Pi/tMEnJisLWsk3Wns+Nnia5Gv86ofru9btUhO08QD TOosevjm5Y28trRFfmNVrmZLbiVZPFsTevabSvFRjIq6HngpfpnvtkHVi9rr rotRhSnXonwPfVMrZ4pbz7rqhBNJbvPox0eXDW36ZoEsgWHhESdjEi775h6F TAwsqk488+aTFXXv58+dtJ9uXmHM8Y+5ThYAAAAAZCzh15a1LWCia3sZ5m3/ 073IN47Q+G8b0XnkqsOeARGBR4Y4a6sNRgVcalao1X/u+m2b5vUoHd+IVuXu +2dkKr8jTZIXTMQvDjw0tHRCa9bQqfOGR3r+KV9vwUTts7q5aFYafvLVgbCU PxLqPqSYofKZffemsUGY2NI3KtVtRIeypVu6rfO48zwiNjLg7olVAyomlI2M y0+6/EZ9IO7WjMrGhkUH/Bmg+y2v7u6Z2vwT7TU0LDry1OtWvsZ34+dKfci4 0vSbes5U47+mubnKodNG37S2YSOuLm3pZGyYt91P91Ms1PLKa+OAcpb6CyYR F6dUE1+pSZXp15N3Ywnd3Usb0li03rVfVPDmTjbxlYaUnVTi7syuVenbK290 DdLbwyTKz/Ovi2cPr+pZzEhfwST60FeOJVPOpqN+vL6DdkSR+Bq7/p7kt2sC f25hpsrd+idv3Y9EPTm2uFspc6Vk0mXr62FCMSH+fz/xvrp5QAnlV5f7vE/N gmU6Ttl8zvtFVHTwg6Pz2xZQKlwmZYavX/tlWcdynadvveDzMjo66N7B7z5z VKYnNq89/+7rbyzi2W1xPoeWdytsqK9gEnpyXHmz5D+jJH+xsaNt/I+Y1Zjr FfdPtL+X+Bz3X/qXVh65El1GdCpf6rOxP5/w8o+Iiwy8d3KNaxW7hFuv9DcX dD2WAj1WTZ8xZ/6sXmW12c1cBi77IcGPu69r+4bF3Vve2FY8IYUHu6fsc6MJ PvC1ssSzYcGvDoSk2A8AAAAA2Yba/8DAkkaJNROrUl0XnwlIZbhK7IXxypEm 5caeVIbl/BNxfFixPPWmeLxrQZt3SFkwid94c1HjnAn/Up6jxtTzKeof+gom r9wHx7dVDZ0GHtE7eYP66aqmSq3ArGZ8yzQNElv6IkWVcUffPFPNi9198mt3 GpX85mLS+HE3plWy+2x18lpC6MGvC2qrLyXHX0hyePR5t4RrW+v7+yk666if rGpqZV5zXtryCuHnJlQUDXPLxise6e/5E7G3Ty49c5iovZfUM08txT/Bv7XV jvMwrjIjfoCItiQkLnWRIUeTN7w1Ied+WX3ieZrnMIk+OCCPSk/BJHxLp1xt fn355sGxD1Y114Y3MC0z8ugbVa/4laatKk65mqzWE311amVtXTBn7z0Rb+76 J+qPPnZKvdCuzvRzb3S4iPAYWSyhM4cqV4M5l964AcMOfPWJdqdxtVmeyc8n 6o8vtHfumwWTuNuzqsXXAPUuXaPxW9VUWyA0qb1QNywp5uQIpfJiYFlx5KE3 r6cmaN+Agkppo9joM8nOOP7R0N6xPXYlfwxCD3xVIP4JKTTshN41j9UPlzVQ BsSZ1130gIE5AAAAALKn6KdH5/con9OhavcBbZwtE4omxo5NZugtgKifLm8Q 3+w0a7r6dUcHdYh/wH/qXKKlt2AS34jc079YwjgWo0K9tv39ZvNNT8Ekcn9/ bdeD1FdF1vj/3DKhd0WSlunbJLb0DQsMdk95qrrpc81arQtKuiPi4elTd1JO nxl9xNUxPqF5u9+Ck37Mve9raReCNa447Uay9nf8rzB16L49rYOI1E9XN4uf ocS8xdrUlobWP+mr2mdhHW1hwaLzNj1fasyZ0UoBwarbjsj4Ms4y7f1gYFyw 5bSdN4PesarS2womMR7DtW3/5AWTuJc+t5MNQIq4/N2n1toahk2dudeSp4zz u3z8WkCKk467OT2+tmNgXGNu8q4d0YeVKxFfwUrepyahJCR2prw3/4m9+E0p bY3LOmVRItp9YP4UBZO42zOrGGuLL3336SlVRB8ZqL0xVLm/3J+4O/F3qBy/ Opi80iO+gPvf19R+AaafrUn2RadeMAnf00c7sMis1S8vU3lCnq1upi2MGZg2 WJbeS1ABAAAAQMYT4fXbgAo2Jk4t5yldSsLvbh9TO3fCP6hblBmwM+VslaEb 2pnFT4Uw8uSHXpUklYJJfMzLs2onjHoRLeTZV5I2G1MWTGIvTUhoxPbcndrq IHG3v6uqFGHs+vyR+hIir71jeZfo/f20LVCTuotT6c6R9JeHPTk4smx8xORd YzR+69raaBvnzuPOvdGejj4zpoRpqfHn9fYH0EPj/1Nzi5SdWN4MrbdgErm7 p3ZYiIFt8zlHTp//69rtu95PfJ8/93v25JH3vTs3dg9TRoiYisb2P/H9UZY3 SlgDOv7y2Bap3vrLbxZuOHz173A9F+KtBZOEnhTJCyYpTi1g/0BnbY3A0PHz 3/QN09JHHfH86ux6Jtprm+KSRB9xzafS830oFyRhdV7Tpqv9UlQXQn5rqy1x mbX9Lfn8NTFHBzulKJiEbuqgrUOo8rRf5H76/OXrt+/5PPH1f+7395OH3ve8 rm8eUESZPqTz1sQ6UOxfE7VXPH4OE3233uGE7DWTdwhKtWASc3aMtuilytX3 z9RuqLjrUytonxCVQ/8Dab3rAAAAACBL0Lw4NrFmTpXKrtnyu0kbRJF3N/Zx ViYOUTl0+C1Zh45/wjd1EC1Eo9IT/3pHZ4L3lnrBJL6/xNYeBRMGDRkX67/n dcs1ZcEk/nOUWkjvvanWQiK3dUkYlJOsT0gq3lEwiTk2RDs2w6T2Qh99jf2o 57eO/b544oDObVq3bvt578GDWxQx0tvZIWzfl9ouBoYFBrknqQuF/tnP0bLe 4rQPjog5rgQydpmd6hgevQUTje8PjV/PB5uUobGpRQ6bnLnzFytXo2GrzgNW XlJKZnGPdw6rlsswxfFm+esNW3c1OM1DctJWMIm9v6a1duSOgUnJoYff2t8m Nuj+mZ0/TBvSvV3r1m06dP96dKdy8SUAw0LDPZIP13lbwSR6X1/t4B+9BZOo bV0s3qNgon4w/1OTFFdKubpm8VfXwal4hZqNWncdtPZ64nPwjoJJzKmR2hqL cfXk91KqBZPIHd206w2rcr+lFvJqUwdlUI5Z+416JgICAAAAgKxK81KZd8Ok 6sybKbqKRFyaUlXpj6+nRb+xvdnbOy78W28rmMT/4nOTXRK6MqhyNlp4I6EN mLJgErGlU0JLr92GFKvWJIo+/LW2iRw/liIto4n+bcFEE3p726R2JW0dqvaY tPbovRBlp/rREm1nh5SN3H9iLiQMwHDovTsxveb5+nZ2ubtsSfOaPuJi7uqh bRMblf32cqo9TBImDXmjYKJ+vLS+0n2j8MhTae5EFPvi9qF1s4Z1+rSIjW4u HC3zKpMvJbm+/7VgEnZuUjWl0JXj0++upBygkiDy4aHve1fJbVeqzZhlf9wI TDiNhOKGYUF5BZM4r9kuyvieMv9La8XxwxdMlF5igkWnLalew+h9Sqcpg5xf pKkPFgAAAABkDZrAX9tYvqXdFrSpo3aOCEOnwcdi3vzBtS1M4xd4STHF5H/2 joKJaG36/Noxf0JPBtOSgw5op6lIWTDR1UJSaWEqB+3/MrdKuQCpFhSS+lcF k9gH67sUNjFQ5Wy65PYbVZm3FEz+Ud+b/6m2OWvbYYMyEYfae3E9q2IjPN5n mpjEyTAMbLvtSLkMiiJqd0/tl/zmkJygda2U1rR1t+0pZ1959y8OuH1k/Zxh 7Ss5JMw7Y9Np8+t5Wv5TwUTjt6uvtmOOgSpvu198UvlqNf4Hhle0MjAwrzz+ VLL1cuUXTDR/r2hkqnTueD1JyTt8+IKJrhaSyt2snNme3tqhWXrm0wEAAACA LEw0wspox4S4zLmjrzWUOLVnitZSQovPsMDQ4x+9YBK/3umJcRXNE7qZ5P5s hVe0JmXBRP1keUNlUIlF8zUpm7jKBwX+3NJM23geejxN7dZ/UTDRPFvXJn7R V6OS488nu1ZvK5iIFv/6dtqmqkWDZfEt7dirk8tbVp+T5tVxtBInFxVnOORY KpWW4PVtzFIWTEQDXFmUxcg55Vq+KUU/Onv4up7pgeN8d/bRzuH6xtwa/6Fg Eu25tKmyLo5JCdf9Ked0TfDqkGv8+i8qh167k6+HmwEKJrphUOLJmno9bd/o hy+YqH0WKfP6Gli1WZfKldT4rdYu2GNYdNTpD/2kAwAAAEAGFt8IVwZ+9Nun r1N++O5eObXDMoYdf7O1rfZeUNskfsUO1yMfeibINBRMhJi7q1s6JKwzbFZu pPuDn1OskhPnNae6UjGxbLrqqd7OCpFHBjrFD0iqOOVK2lqD/6JgErKpg3b0 iPnnm5P31Ii9NUNbztBbMPnnn7D9/bXrqxiXn3QlJuLYkMI52/7q/55rNsec HlVUW/dQ5em6Rd86ORq/DR2VfgbJlhUO3d1bucCGn/TfF5zyB9/8lL9XNCk6 5Ji+i5gwG6llh43/vYeJJsRjXAWlUmZVfdrF5NdTE3rup7m7H8S98UUkX/0o vnOUmeyCiebFpo7KnLqGxYYdT63vzxv+e8Gk+85kY2oSFwwS+1qu9dV7Z0Xs /zK+smNabeaNDz32DgAAAAAytJAtnZV1WR26bEn5b8zBe/vGD+iwqb84ef+T uBvTKhq/a0RB5P1d0wb07D9lq9f7jOmI2tXD2rjMuyeT1QQeGlo6YV5SVZ66 Dcoap1hsxn9jR6UzglHxYcdSzlipUc7PuJjr/lRWVU0h5mxCS7/KzHcUTBLb 6YkLMBsYV5p6Pek5xfoeHFVFmY0llYLJPzHnx5fU1rPs2y9d3i1PQddDaWpa vyH68qQKSjcCw8K9tyefuzfq7urWec3NtJfRpP4bBZN/IjxGFldmIjEq3Hen r96KU0RomPac4gtoubtu1zcraPCWzjYGBrbt1iep9bztMsZ4DC+k3Zdi3pxY 73UJY7FUDq3W3E95g0QfHVSo6oxb4qeidnbPoRR7vj6YNJQm6MKcJrmVOpCe gknCKC79BZM/Ewsmq96rYBLtPihxWeGk89qEHvy6oDKuzMR50AF9S3f/owkP DXt9BWIvTdAVTPQ8G68LJsk7iwWta63tQWT62Zrkj7jm2S/a3k/x/Z9Gn0r5 lGpe7OiZRyUSDj2SdNbeON9jCwf36jN2zcW0PjcAAAAAkPkE/9FPaYMa5m+1 +K8kkz1owm793L2osYFp0R4bU84TkdiqfcsKNHGec2oos2AYl55wMe3/PB2y vq2ZYcFhJ9LQ5SPq1uLGuRK6meht56ofr++YTxlYUqjHpodvTsPywn1EGVPD 3A2/v5z2eo44b20z16j8ZL2t1oSCSdJWa+TBr5S5VFQ2tSYc9YvfHON37ueR 9QsVbtizeVEjpaOFp95xGfHLHivVDmNj82QFl7TSvDwwsHjCkiwWJbsudH8Y rv2WYwOvbBrboHC5vr/8PlZZ3PjTBW/2xtAEHh5Z3iKhF0/hNjP2egW9Thn5 6OiSrz4tUneOtuIRd21KBWOrahM8gt5sQWteHBlRzkxl32zFnaTZk1zGFD2J EgsMxlVm3kpyUTRBJ9wqJUxBXLT/Xn2dZeI8Z1WzbrchJCGPUuwxLT1g24P4 7lHqoJvbp7R1/qR6nw4VtavkJJ+X53UnEpNa81MuRJQ4f27yupKyM6FgYtpi bfLaR/SB/tqeOskLQBq/vV+VNEv4Xkp0mnfwXujrjw33PjC/b/UiTZe+zpG4 CLBR6QmX3lYwqTzj5pv3UtTe3spyUZY1vjnsExwacMf9pxV/PlQOivNZ20bp SGRc9Ittj9/4YE3AwcElTQzzNF18PWn/Mo3/+vZKlUWVr+8fqc6nDAAAAACZ nSbwiFs1W6X9k6NE035uc3/4cemMcV82KWqhsijaZvrhp/pa6VF7etloB0Z0 25FawSSxURy/mk3vPamuwpFUXGTQ47ML44faGDkP2HLDNzTmXf+ArfHf27+Y caoFEyH8xqouxbSNWaM8nw5a+sdf3r5+Pn8d+HVG93K58tYauvlumudQ1cS+ 8js3pYbSqcW66cJrgREpekckFExU9m1+uP0iMqFN+mBVc11ZxzRXwcL5rMzy 1hm+/npIzLWEEVH5em7101sgUj9a3lCpEuT4bLX+YUVpEH33l27FzBPrSoYW DoWdi+W3NjHO13TW6ReaxNlKVLnbr9aFThD39/4JDfMnroCrMs9bslq9ps0a VC2Rz8rYvGCTcdvuKF9sYqPcsnDjQfN+3X3k7MXzx/b+Nn9wg4LW+Wp+teZq kl4emthw/3NTayqXMUfj+VcDIuI0un2v/M58W037+1S2zZfeSLzEMffWtFEm /TAwKfHVdq+niZ743Lt99eKZE0f2b/1hbJP8JoVHnNReSM3zPX0LJa5xbGzj VOQTW1PbCr2XnPKP8Vdm5TCwaLz0QZTuBtPEhD11H1NBey+luBKamNAnB4aV Vmaazdfll7tBkcmKEgkFE8OCfTY/CIpSv/7QZx7jKykfmqvNyltvXt7YR7tH 186TePuqLB1Lu9Rv1qxe5eJ5LI0si7b83x7vaN01e37puzrKV2jZYO7l5+Gx yR6NxIKJyrbFsptv/BpNwJYuuV+XFeOverWJJ1/3GAm7urxjEe1HG+WrO3T5 vsvefn7eF/evm9alTE7HOiO3PUg+kOfOnOoJmc2a/PCMPiYAAAAAsrJI70NL RnRuVL1MYQcrC2tH56oN2/Ya9t2Gs3+nWkwI3ze4YuXmX/7vhxOpt+LjHm79 umoeG4fKX/x6593dRSK2drF6o1WnNHWd3c6+42cjrsyuY6NMwpF8MV/dIQ8O LBrxee2S+XNZmpha5cpfrMpnX4xftu9OWJobe3E3ZlQxTZHOwMC84bJHSX5l YsEksTSRz/VQfJNXE+a1/dvOn5b+xNbSxqlsk0GrL77Q/lDMyYSJVZVzdRqU ckKY+KWEbeJb6V/sTT556XvRhNzeNW9opyafVizhlMvSwq5Auaajtvtof130 wQEOSa+8RbPVb8xnoQm+uWvhqM51yhR0sDYzy+FQsFTNtoO+W+/xMEnHnJgn Hj/PHtmjaVXngnlsLUwtchUo7dKwTc+Riw48SNp9J/bqlIr6LqNZre/vRl2c WMZEzz7zeot9grTDetLAuPJ0Xf+KqMeHv+/bsHxhe6sceZxr95rr/lR7uur7 39d8/XsMc7T/LSjm+PBCRik/zNCh//7o+DlYHPXsVFm2/uX1CJfEgoluZ9tf /U+PK2Gc8ucMzBuvSNpDRf3i6rZ5wzrUKlUgdw4zM+s8hcp82mHY3E1nniQ+ e3FeiT213mRWe8H9pLdeQsEkMXvufvt0N5PmxcXVg5qULWBvKb6ZYg3G/vEs +WMSfv/PBcM61nJ2zGlhYprDPn/xqs37Tlhx4J7eJ0QT6P6/BgVscjq3nX9e 3yAsAAAAAEBGERX44K9ju9YtX3vC7992wsi4QrZ2sTUu/c3FDz25bkqauJjI iIjo2Kx3DQEAAAAAQNYSe3VSeev6S5Kv9QIAAAAAAJBtRZ0eXSJP160vmCkC AAAAAABAS/Nye/d8xUeeTPPEtAAAAAAAAFlczO05NW1rzvXSu+QwAAAAAABA 9hN1a0F923zdtwcyHAcAAAAAAGRnkcfcGrX7dqPHxZNbJjd3MraoNc+T7iUA AAAAACBbizo6uIChQQJDx3Y/3YuRHQkAAAAAAEAq9d+7Rjctl9faxtG5ds9Z +7wjZAcCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAEBoaeufOneDgYNlBAAAAAAAA 5PP19R00aJCVlZWBgYGhoWG9evW2bNkiOxQAAAAAAIA0586dc3R07N27988/ /9yhQweVSmWgNW7cONnRAAAAAAAAJNi9e7epqWn79u11W06cOGFubq7UTP76 6y+J2QAAAAAAAD6+2NjYQoUKGRgYXLlyJen277//XimYDB06VFY2AAAAAAAA Kf7880+lMOLn55d0u/irsr1v376ysgEAAAAAAEixbt06pTCye/fupNuDgoKU 7cuXL5eVDQAAAAAAQIqTJ08qhZEqVapERUXpth88eFBstLOzCwkJkRgPAAAA AABkFv7+/qNGjWrVqtWKFSt0Gz08PKZMmbJhwwa1Wi0x2/vSaDTFixdXaiaf f/65El78t0KFCsbGxu7u7il/RJxpr169OnXqdP78eWVLbGzs2rVrJ0+efPHi xY+aHgAAAAAAZAxr1qyxs7MzSLRx48bo6OjRo0frtowZM0Z2xvdz5swZExMT JXzXrl3DwsKmTJliaGj4008/JTsyKCiof//+ujPNlStXQECAt7d3jRo1lC3i c06dOiXlLAAAAAAAgCyurq7dunXbtm3bhAkTlBJBnTp1mjZt2rp166+++srU 1FRssbKy0mg0spO+nzVr1ujKILa2tg4ODqdPn052TGBgYJUqVSZNmnTgwIGa NWsqBw8fPrxo0aL9+/dv1qyZsqVfv35STgEAAAAAAMgSFBSk/EGtVtva2iol gu+++07ZuGzZMvHXmTNnygv47/3xxx9WVlbKGYk/rF27NuUxutPXra1jZ2d3 6dKlf7QXpF69evb29rpxOgAAAAAAIBuqUKGCgYFBuXLldFvCw8O3b9+e8kiN RrNjx47Dhw+/1+cvWLDA4D9o27Zt2n9XbGzs/PnzLSwsChQooPuEdu3apTbj 6+3bt5Vjpk+frtt4/vz5O3fuvNc5AgAAAACALKZVq1YGBga9evV6+2G3b9+u VauWUl5Yv379x8n2Xi5fvly6dOkSJUqcPn06Li5u7ty55ubmSuC6detGRESk /JGwsDDlgL179378wAAAAAAAIMNq3769gYHBlClT3nLMtWvXSpUqdffu3R07 dhgaGpqamp45c+ajJUyL8+fP29raOjo6Pnr0SLfRw8NDNzynVatWKX8qKipK 2evp6fkRwwIAAAAAgIyubdu2BgYGc+bMSe2A6Ojo8uXL6+b0mDt3rjjexcXl YwV8txs3btjY2IhUJ0+eTLZLbFHmsBUuX76cbG9kZKSyK2mZBQAAAAAAoHnz 5gYGBpMmTUrtgNmzZ3fo0EH3V7VaXa5cOb3VCVm+/PJLkadYsWJ69/bu3Vup isyfPz/ZrpCQEGXX7du30z8mAAAAAADINBo1amRgYDBmzJjUDihatOiRI0eS btm4caP4kXHjxqV/ujTJkydPaoNuhK1btypVkQULFiTbFRAQoOxSlsgBAAAA AABQ1KlTx8DAYPjw4Xr3nj171tzcPNnGoKAgQ0PDxo0bp+XzP8IqORYWFuLI +vXr6927c+dO5aNSLv3z999/K7vEaablXAAAAAAAQDbh4uJiYGAwbNgwvXsn TpyYP3/+lNtr1apVtGjRdI6WVp988ok4BTMzs7CwsJR7J0yYoAzYiY2NTbbL 29tbKZhktDlsAQAAAACAXMWKFTMwMBg8eLDevQMGDChYsGDK7aNHjy5UqFD6 JkszEVKpe8yaNSvZrrCwMJFT7NqwYUPKH7x06ZLygxlnPhYAAAAAAJARKOvL DBgwQO/ebt265cmTJ+X2qVOnlitXLp2jpdWZM2cMDQ3FWRgZGa1fv16j0Sjb nz171qRJE5VKNWPGDN3GpPbv368UTNzd3T9uZAAAAAAAkHHFxsYqFYPUepj0 69dP7A0KCkq2fdKkSb169Ur/gGm1YcMG3fLB5cqV69q1a9OmTY2MjAoXLnzw 4MHUfmr9+vXieENDw+PHj3/MtAAAAAAAICOLi4tbu3btWxbVnT59uoGBwf79 +5NtHzRo0IoVK9I53fsJCAj49ddfe/fu3aJFi/bt27u5uV28ePHtP+Lt7b1h w4YXL158nIQAAAAAACBr2Lx5s4GBgaura9KNsbGx+fPnv3PnjqxUAAAAAAAA EgUHB5ubm5uaml6/fl23cefOnf369ZOYCgAAAAAAQK5evXoZGBg4Oztfv35d rVbv2bOnUKFCf//9t+xcAAAAAAAA0vj5+ZUuXVqZT1WlUhUvXvzatWuyQwEA AAAAAEgWEhKyevXqKVOm7Ny5MyIiQnYcAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAOlFo9EEBARcu3Zt//79P//886JFi6ZPnz5u3LiBAwf27Nmzbdu2DRs2 dHFxKVWqlJOTk62traWlpZmZmYmJiZGRkUqlUqZ1FX8WW8R2CwsLGxub/Pnz Ozs7V61atUGDBm3atOnRo4erq+vYsWOnTZu2cOHCtWvX7tu37+rVq8+fP1er 1bIvAAAAAAAAyKbUavXDhw+PHDmybt26WbNmDR06tGPHjjVr1ixYsKCpqamB PCYmJgUKFKhevXr79u0HDx48c+bMn3/++eDBg97e3nFxcbIvGwAAAAAAyDpC QkIuXLiwfv36//3vf506dSpfvryFhcVbqhZ2dnZlypRp0qRJ7969hw0bNnHi xFmzZi1btmzdunXbt28/dOjQ2bNnb968+ejRo5cvX4aHh0dGRkZHR8fGxqrV ao1GI/4r/iy2iO1ib1BQ0OPHj2/dunXu3LnDhw/v2LFDfI74NPGZIs/w4cP7 9OnTrFmzcuXK5cqV6y2pzMzMypYt27FjxwkTJohPEJ8mPln2pQUAAAAAAJlD dHT0X3/9tWrVqq+//rpevXp58+bVW3/Ily+f2NuzZ8+xY8cuWLDg999/9/Dw uH//fnh4uMTwkZGR3t7ep06d2rJly6JFi9zc3Hr16lW/fv38+fPrPQsHB4c6 deoMGDBg5cqVFy9ejIqKkhgeAAAAAABkHLGxsTdu3Fi7du2gQYNcXFzMzMyS VRXMzc3Lly/fqVOn//3vf7/++uv58+eDg4Nlp35voaGhly5d2rBhw+TJk7t0 6VKxYsWU/WRMTEyqVKni6uq6Zs2aq1evxsTEyE4NAAAAAAA+nmfPnm3cuHHE iBGffvqppaVlsrqBs7Nzjx495s+ff+DAAR8fn6w6k6o4r0ePHh06dGjRokW9 e/cuXbq0Muts0kpRzZo1hw4dun79+idPnsjOCwAAAAAAPryAgICtW7cOGjSo ZMmSySokRYoU6dy585w5c44ePZoZe498KKGhocePH//++++7detWvHjxZFdJ bPn6669///13f39/2UkBAAAAAMC/FxwcvGfPnpEjR1aoUCFp29/KyqpFixbT p08/cOBAYGCg7JgZ1MuXLw8fPjxr1qzWrVvb2NgkvYBly5YdNmzYzp07xTGy YwIAAAAAgHfTaDR//fXXpEmTXFxcDA0NdW18MzOzRo0azZgx4/Tp08zO8b5i Y2PPnz8/a9aspk2bJp38RKVSValSZcKECRcuXMiqY5cAAAAAAMi8YmJiDh8+ PGTIkE8++UTXnDc2Nq5du/akSZOOHj0aGRkpO2MWERUV5eHhMWXKlHr16pma muqutqOjo6ur64EDB6Kjo2VnBAAAAAAgWwsNDd2yZUv37t1tbW2TttwHDhz4 559/hoWFyQ6YxYWHhx88eHDo0KFJ61TW1tZdunTZuHFjUFCQ7IAAAAAAAGQj L1++XL16dfPmzZP2cChTpsyECRPOnz/P2JCPTxkJNXny5KTTxRgbGzdp0mTF ihVMFAMAAAAAQPqJjo7evXv3559/rquTqFSqOnXqzJs37+7du7LTIYG3t/fC hQsbNGigm0PGxMSkXbt227dvj4qKkp0OAAAAAIAsQqPRXLhwYciQIfb29ro6 SePGjdesWcMStxlZYGDgunXrWrRooauc2NnZubq6nj59WnynstMBAAAAAJBZ PXz4cObMmc7OzkkXtJ0zZ86TJ09kR8N78PX1XbBgQaVKlXTfY9GiRadMmXL/ /n3Z0QAAAAAAyDRiYmK2bNnSoEEDXfs6T548I0eOvHz5Mj0TMrXr16+PGzcu f/78um+2du3aGzZsYGEdAAAAAADewtfXd9q0aY6Ojkpr2tzcvFu3bvv27YuN jZUdDR9MXFzcoUOHevfubWlpqSuIffvtt0+fPpUdDQAAAACADESj0Zw5c6Z7 9+4mJiZKC7pUqVLLli0LDg6WHQ3pKCwsbPXq1eXLl1e+dCMjo06dOp04cYJ+ RAAAAACAbC4iIuKnn36qXLmy0mQ2NDTs0KHDkSNHaDJnH+K7PnHiROfOnY2M jJTboFy5cqtWrXr16pXsaAAAAAAAfGy+vr5ubm45c+ZU2si5c+eeMGHCo0eP ZOeCNE+fPp00aVKePHmUW8LGxmbUqFGM0wEAAAAAZBMPHz4cNGiQmZmZ0i52 cXFZt25dZGSk7FzIEKKiojZs2FCrVi3l9jA1Nf3qq68ePHggOxcAAAAAAOnF y8vriy++MDY2VtrCHTp0OHfunOxQyKAuXbrUpUsXlUqlTG/Ss2fPW7duyQ4F AAAAAMCHdPXq1c6dOyuNX0NDQ9H4vXnzpuxQyAS8vLz69u2btMh26dIl2aEA AAAAAPivzp4926pVK6W1a2Ji8tVXX92/f192KGQyPj4+gwcP1g3j+uyzzzw8 PGSHAgAAAADg37hy5UqzZs2UFq6FhcWIESOePHkiOxQysWfPno0dO9bKykq5 qRo2bHjx4kXZoQAAAAAASCsfH58ePXoorVpra+sJEyb4+/vLDoUsIjAwcPLk yXZ2dsoN1qVLF/osAQAAAAAyONGYHTlypKmpqbK+yahRowICAmSHQhb08uVL Nzc3ZZCOsbHxkCFDKMoBAAAAADKg8PDw7777ztraWjRgVSpVr169fHx8ZIdC Fvf48eN+/fopkwlbWVlNnTo1LCxMdigAAAAAAOLFxsb++OOPjo6Ougk5r1y5 IjsUspEbN260bt1auf3y5MmzcuXKmJgY2aEAAAAAANna0aNHS5curbRVq1Sp cuTIEdmJkE0dP368evXqyq3o7Ox88OBB2YkAAAAAANnRs2fPunXrprRPixYt umnTJrVaLTsUsjWNRrNt27YSJUoot2WnTp1YmAkAAAAA8NHExsYuXLgwR44c ok1qbm4+Y8aMqKgo2aGABNHR0bNnz7a0tFQmNpk3bx4jdAAAAAAA6e3UqVPl y5dX/gW/bdu2zOyKjOnRo0cdO3ZUbtQyZcocP35cdiIAAAAAQNb0/PnzL774 QmmBFi5ceO/evbITAe+wf//+YsWKKTdtz549fX19ZScCAAAAgA/JIJHsINmU Wq1euXKlnZ2d+ApMTU0nTZoUEREhOxSQJpGRkVOnTjUzMxN3r7W19dKlS+Pi 4mSHAgAAAIAPg4KJRN7e3vXr19ctGXz37l3ZiYD39uDBg1atWim3ce3ate/d uyc7EQAAAAB8ABRMpNBoNCtXrlQmz8yTJ8/WrVvFFtmhgH9J3L07d+7Mly+f uJ8tLCyWLFnCuk4AAAAAMjsKJh/fo0ePGjdurFz2rl27BgQEyE4EfAAvXrzo 2bOncmPXr1/f29tbdiIAAAAA+PcomHxMGo1mzZo1yqrB9vb2W7ZskZ0I+MB2 7Njh4OCgrDv8ww8/0HUKAAAAQCZFweSjefr0afPmzZWr3aFDBz8/P9mJgHTx /Pnzzp07K7d6kyZNHj9+LDsRAAAAALw3CiYfx/r1621sbMR1zpkz54YNG/hn d2R5mzdvtre3VxbQWbt2Lfc8AAAAgMyFgkl6CwsL6927t3KRW7du/ezZM9mJ gI/Ez8+vffv2ys3fvXv30NBQ2YkAAAAAIK0omKSr69evlyxZUlk6ZM2aNfwj O7Ibcc+vW7fOyspKPAXFixe/cuWK7EQAAAAAkCYUTNKJaCeuXr3a3NxcXNuy ZcveunVLdiJAGk9PzwoVKohnwczMbMWKFVQOAQAAAGR8FEzSQ0hISNeuXZUL 279///DwcNmJAMkiIiJcXV2Vh6JTp07BwcGyEwEAAADA21Aw+eAuX75crFgx ZVnV3377TXYcIAPZtGmTtbW1eDqKFCly8eJF2XEAAAAAIFUUTD4gjUazbNky U1NTcT0rVKjg5eUlOxGQ4dy9e7dy5criGTExMVm8eDHDcwAAAABkTBRMPpSI iIju3bsrF3PgwIHir7ITARlUZGTkkCFDlIela9eujFkDAAAAkAFRMPkgnj59 WrVqVWUYzu+//y47DpAJbN26NUeOHOKpqVKlypMnT2THAQAAAIA3UDD5786f P58vXz5xDQsXLnz9+nXZcYBM4+bNm0WLFhXPTp48ec6ePSs7DgAAAAC8RsHk P1q/fr2ZmZm4gPXq1Xv+/LnsOEAmExgY2LBhQ/EEmZqarlu3TnYcAAAAAEhA weRfi4uLGzdunHL1XF1do6OjZScCMqWYmBjdlCZjxowRT5bsRAAAAABAweRf CgkJadmypbhuRkZGy5cvlx0HyPR++OEHY2Nj8Uw1b948ODhYdhwAAAAA2R0F k3/h/v37pUqVEhctV65c7u7usuMAWcTx48ft7e3Fk+Xs7Hz37l3ZcQAAAABk axRM3tfFixdz584trliZMmXu378vOw6QpXh7e5crV048X/b29hcuXJAdBwAA AED2RcHkvRw6dMjKykpcrmbNmoWEhMiOA2RBoaGhLVq0EE+ZpaXlgQMHZMcB AAAAkE1RMEm7TZs2mZiYiGvVs2dPpngF0k9MTEyfPn3Es2ZsbPzbb7/JjgMA AAAgO6JgkkaLFi1SLtTo0aPVarXsOEAWp9FodKtQLViwQHYcAAAAANkOBZN3 Eg238ePHK1dp3rx5suMA2cj8+fOVR2/cuHHiSZQdBwAAAEA2QsHk7WJjY/v2 7assH7xu3TrZcYBsZ/369cpyw3369ImJiZEdBwAAAEB2QcHkLcLDw1u1aqVM Prlv3z7ZcYBsav/+/cpkyy1atHj16pXsOAAAAACyBQomqRHtsvr16yvLm547 d052HCBbO3/+vLKcd926damZAAAAAPgIKJjoJVpk9erVE5clf/78np6esuMA +MfLy8vJyUmpmYSFhcmOAwAAACCLo2CSkmiLiRaZUi25e/eu7DgAEty7d0+p mdSpU4eaCQAAAIB0RcEkGdEKE20xcUFEu4xqCZDR3Lt375NPPqFmAgAAACC9 UTBJKmm1RLTLZMcBoIeuZlK7du3Q0FDZcQAAAABkTRRMdETLS7S/xKUQbTGq JUBGdv/+fWomAAAAANIVBROFaHN9+umnSrVEtMVkxwHwDuI5LVCggHhmxZNL zQQAAADAB0fBRAgPD1f6loj2F9USILN48OCBrmYinmLZcQAAAABkKRRMYmJi WrRoQd8SIDPS1UzEUyyeZdlxAAAAAGQd2bxgolare/XqJU7f3t7e09NTdhwA 783Lyyt37tziKRbPsniiZccBAAAAkLFoNJrw8PB/0VjIzgUTcdFGjRolzt3K yur8+fOy4wD4ly5cuCCeYvEsiydaPNey4wAAAACQLzAwcNasWYULF1apVKKx YGxs3KhRoxUrVsTExHh6ek6ePPmdn5CdCyZz5swRJ25iYnLw4EHZWQD8J4cO HRLPsniixXMtOwsAAAAAyRYuXGhmZiYaCPnz5xd//uOPP9asWaMM53dwcBD/ NTU1feeHZNuCydq1a8VZq1SqjRs3ys4C4AP4/fffldKxeLplZwEAAAAgjWjm K4WO5s2bR0ZG6raHhoYWKlRI2ZUzZ853fk72LJjs3r3byMhInPWSJUtkZwHw wSxdulQ81+Lp3rNnj+wsAAAAACS4cOGC0vnc1tb22bNnyfZOnjxZqYHky5fv nR+VDQsmHh4eSs+cb7/9VnYWAB/YpEmTxNMtnvGTJ0/KzgIAAADgY+vbt69S 5ejdu3fKvXPnzlX2Ojk5vfOjslvBxNPT09bWVpzv119/zeSQQNYjnmvxdItn 3MbGhqWvAAAAgGwlNjY2Z86cSpXjp59+SnkABZPUvHjxolixYuJk27dvHxcX JzsOgHQhnu4OHTqIJ1087+Kplx0HAAAAwEdy+PBhXZXj2LFjKQ+gYKJXTExM o0aNxJlWqlTp1atXsuMASEfiGRdPunjexVMvnn3ZcQAAAAB8DIsWLdJVOfQO 0qdgotfgwYPFaebNm/fRo0eyswBId+JJF8+7eOqHDBkiOwsAAACAj2HmzJm6 Ksfx48dTHkDBJKUVK1YYaNdZPnPmjOwsAD4S8byLp148+ytXrpSdBQAAAEC6 mzJliq7KsXXr1pQHUDBJxt3d3dDQUJzjunXrZGcB8FGJp148+8bGxkePHpWd BQAAAED6Wrx4sa7KMWDAgJQHUDBJ6t69e3Z2duIE3dzcZGcBIIF49sUbQLwH xNtAdhYAAAAA6WjXrl26KkeBAgVSHkDBRCc4OLhkyZLi7Nq0acOyOED2JJ79 1q1bi/dAqVKlxDtBdhwAAAAA6cXLy8sgiWT9zMPCwmrUqPGWckoyWbhgolar W7RoIU6tXLlyoaGhsuMAkCYkJKRs2bLibdCyZUvxZpAdBwAAAEB6qV69uq7Q YW1tvX79+mfPnnl6ek6aNClXrly6XUWKFHnnR2Xhgsl3330nzktcEG9vb9lZ AEj24MED5fU4e/Zs2VkAAAAApJeNGzcapKJjx45DhgxR/uzs7PzOj8qqBRMP Dw9lotc///xTdhYAGcIff/wh3glGRkZ6F2QHAAAAkDUsWbJE/G+/rtxhY2Mz ePDga9eu/ZNkGZ0mTZq883OyZMHk+fPnjo6O4qTGjx8vOwuADESZADZ//vzi LSE7CwAAAID0EhAQsHXr1o0bNx4+fDg8PFy3ffjw4UoNZOzYse/8kKxXMFGr 1c2aNRNnVLt27djYWNlxAGQgMTExn376qXg/fPbZZ0xmAgAAAGQ3uhlOdu7c +c6Ds17BZObMmeJ07O3tnzx5IjsLgAzn8ePH4v0g3hKzZs2SnQUAAADAh3fy 5Em9/zwaGRlpYmKiTAabtNtJarJYweTEiRMqlUqczr59+2RnAZBB/fnnn8pk Jh4eHrKzAAAAAPiQtm3bJv5vf8mSJSl3rVmzRimAbNq0KS0flZUKJv7+/srU Jd98843sLAAytPHjxzOZCQAAAJDF3L1719raWvyvvqmp6Z49e5LuunPnjrKr f//+afy0LFMwUavVTZs2FSdSp04dpi4B8HYxMTG1a9cWb4xmzZoxmQkAAACQ NWzdulUZdSIYGxtPnTrV29v7wYMHM2bMsLe3NzExEVtEWyCNn5ZlCiYLFiwQ Z5E7d26mLgGQFrrJTBYtWiQ7CwAAAIAP4+TJk61btzZ4k6WlZdu2bW/cuPFe H5U1Ciaenp5mZmbiLHbv3i07C4BMY9euXeK9Id4eXl5esrMAAAAA+GDu3r27 dOnS6dOnz5s379ChQ1FRUf/iQ7JAwSQ2NtbFxUWcQt++fWVnAZDJ9OnTR7w9 atSowVA+AAAAAEllgYKJso5wgQIFgoODZWcBkMkEBQU5OTmxyjAAAACAZDJ7 weTatWvGxsYi/6FDh2RnAZApHThwQJkS6vr167KzAAAAAMgoMnXBJDo6ukKF CiL8oEGDZGcBkIm5urqKN0mlSpXEW0V2FgAAAAAZQqYumPzvf/8TyYsWLRoW FiY7C4BMLDQ0tHDhwuJ9MmnSJNlZAAAAAGQImbdgcuHCBUNDQ5VK5eHhITsL gEzv2LFj4k0o3ioXL16UnQUAAACAfJm0YBIZGVmqVCkRe8yYMbKzAMgiRowY Id4qpUuXFm8Y2VkAAAAASJZJCyaTJ08WmUuVKhURESE7C4AsIjw83NnZWbxb pk2bJjsLAAAAAMkyY8Hk7t27pqamIjODcQB8WMrAHPGGuX//vuwsAAAAAGTK dAUTjUbTrFkzEfiLL76QnQVAFtSrVy/xhmnRooV428jOAgAAAECaTFcw2bp1 q0hrZ2fn7+8vOwuALMjX19fW1la8Z3bs2CE7CwAAAABpMlfBJDQ0NH/+/CLt ypUrZWcBkGUtW7ZMvGc++eQTliwHAAAAsoObN2+m7GGeWsFEHPyxcr2HUaNG iajVq1ePi4uTnQVAliXeMFWrVhVvm3HjxsnOAgAAACDd7d27d/To0clqJnoL Ju7u7q6urh833btdu3bNUOvSpUuyswDI4i5cuKBSqYyMjDJm9RgAAADAB/Tq 1SsTE5NkNZOUBRN3d3dzc/NNmzbJyJgqtVpdq1YtkXPo0KGyswDIFgYNGiTe OXXr1mX2VwAAACDLa9Kkifj//6Q1k2QFE3d3dyMjI5VK9eLFC3kx9Vi7dq0I mTdv3uDgYNlZAGQLL1++dHBwEG+edevWyc4CAAAAIH0tWLBAKY/oaiZJCybu 7u7Kn2vWrCk76RvCwsLy5Mkjgv3222+yswDIRtatWyfePPny5Xv16pXsLAAA AADSkaenp65CotRMdH/VVUuEadOmyU76hilTpohUNWrUoGM8gI9JrVZXq1ZN vH9mzJghOwsAAACAdKTRaAoVKpS0ZmKgz8WLF2Unfc3X19fS0lKk8vDwkJ0F QLZz9OhR8f7JkSOHv7+/7CwAAAAA0tHAgQP1Fkl0HBwc1Gq17Jivubq6ilRt 27aVHQRANtWyZUvxFhoyZIjsIAAAAADS0Z49e95eMOnVq5fsjK95eXkpSwnf vn1bdhYA2dSNGzfEW8jIyOjevXuyswAAAABIL69evXp7wWTjxo2yM77Wvn17 Eenrr7+WHQRAttavXz/xLurUqZPsIAAAAADSkbK4cGoCAwNlB0xw6tQpkcfS 0vLZs2eyswDI1p48eWJubi7eSOfOnZOdBQAAAEB60S0unFLGWVBYo9HUqlVL RJo0aZLsLADwzzfffCPeSHXr1mW5LgAAACCrSrq4cDIZZ0HhHTt2GGhnoA0N DZWdBQD+CQ4Otre3F++lPXv2yM4CAAAAIF0kW1w4qQyyoHBcXFzJkiVFnmXL lsnOAgAJFi5cKN5LZcqUyVBLiQEAAAD4gPQuLpxxFhTesmWLyFOoUKGYmBjZ WQCZpk+fbvFWZ8+elZ0xG4mKiipQoIB4O+3YsUN2FgAAAADpQu/iwhlkQWGN RlO+fHmRZ+XKlbKzAJK5ubmlNoBOEI135tP4yJYuXSqufOXKlbnyAAAAQJak d3HhDLKgsFLMyZ8/f2RkpOwsgGRubm7m5uZt2rTp2LFj586du3Xr1rt37y+/ /HLgwIHDhg3bvHmz7IDZTkRERN68ecU7at++fbKzAAAAAEgXjRs3TlYwyQgL Cms0GhcXFxFm4cKFsrMA8rm5udWqVUt2Crxh7ty54h0lvhc6mQAAAABZ0vz5 85NWS2rUqCE7UbxDhw4ps6m8evVKdhZAPjc3t4yz2DcUoaGhuXLlEm+qY8eO yc4CAAAA4MO7fft20oLJ1KlTZSeKV7duXRFm1qxZsoMAGYKbm1sGKWYiqWnT pok3VaNGjWQHAQAAAPDhJVtcOCMsKHzixAmRxM7OLiQkRHYWZG7i9n7+/Lns FB+Am5tb9erV3+tH1Gp11jj3jOzly5fW1tbifcUqRQAAAECWpFtcOIMsKNy0 aVMR5v/+7/9kB0EmptFoVq9ebW9vL+6lSpUqBQUFiY3BwcF9+/bNnz9/vXr1 Hjx4IDvje3Bzc3NxcVH+HBsb+/DhQx8fn/DwcL0Hi6d4yZIltra24txr1qyp jGt78eJFjx49HB0dGzVq9OTJk48XPaubMGGCuM6tWrWSHQQAAADAh6dbXDgj LCh8/vx5kSRHjhyifSc7CzKrhw8f1qlTJ2fOnDY2Nsq9PXr06MuXLyftTFWl ShXZMd+Dm5ubCCwe1S5duuhOytzcvHfv3hcuXEh65L1791xcXOzt7cVDpBw2 bdq0s2fPOjk56c69bt26sk4k63n+/LmFhYW4quIGk50FAAAAwAemW1w4Iywo 3LlzZ5FENA9lB0EmNn78eHd3d41Gc/36dUNDQ3FH5cqVq2jRort27bp06VKF ChXElpw5c8bFxclOmlbiiVDGqY0ZM2blypVz5sxxdnZWHltjY+Pdu3frjhw9 evTJkyfFH86cOaMckDdv3uLFi//5558XL14sWbKk2OLk5CTvVLKgkSNHiqva s2dP2UEAAAAAfHiNGzdWqVTSFxR++vSpkdbjx4/lJkGWUbRoUaWqcOPGDWXL pUuXnJycbt68qff4iIiI9/r8FStWGPwHn332WVp+y/Tp00ePHh0aGqrbEhsb qwwGEUxMTPR2b1BGJJmZmd25c0fZcuLEiUKFCt29e/e9zhFv5+PjI96f4h7z 8/OTnQUAAADABzZ//vyMsAbHt99+K9p3n3/+uewgyDrq168vbqoGDRok3Zhy uWpvb+/Zs2dXqlSpd+/eHzFdWmk0mpQb1Wp17dq1lZrJgAEDUh5QuXJlsat5 8+ZJN7JUd3po27atuNTTp0+XHQQAAADAB3b79m3pCwpHRUU5ODiIRsfx48fl JkFWogzy0ltPUFy9erVatWq6Lh+urq4fM95/tH37diW2tbV1TExMsr3NmzcX u4YPHy4lW7Zy+PBhcakdHR1TfgsAAAAAMjWNRvPo0SO5GdavXy9aHOXLl9f7 j+nAv9OpU6e3/9N/VFSUv7//s2fPGjduLI4cM2bMx4z3HwUGBupKPb6+vsn2 fvbZZ2L7woULpWTLVsRbq1SpUuJqb968WXYWAAAAAFlN9erVRXNj9erVsoMg S+nYsaO4ryZPnvzOI+fNmyeO/Pbbbz9Cqg/IyspKKZj4+/sn26Ws0P39999L CZbdLFu2TFzt2rVryw4CAAAAIEu5cOGCsghIeHi47CzIUlq1apXGfiNr1qwR R06ZMuUjpPqAzMzMRGxDQ8OUg0Hq1avHxBofTWhoqLW1tbjgV65ckZ0FAAAA QNbRq1evTDcaApmC0sti8ODB7zxy9erVma688OTJE6V7SceOHVPurVmzptg1 ceLEjx8sexo+fLi44F9++aXsIAAAAACyCH9/fxMTE5VK9eDBA9lZkNUoq+QM HDjwnUcuX75cHDljxoz3+vyPsKzws2fPUtv1ww8/KJ9z6tSplHurVq0qdrm5 ub3XGeFfu3PnjrKOs/Ql2gEAAABkDaKJKloZbdq0kR0EWZCLi0sa175ZunTp vyiYpLfnz59/8skne/fuTbkrNjZWObvU1gQvXbq02Dt27Nh0zojXlIl258yZ IzsIAAAAgExPo9EULlxYNDEOHDggOwuyIOXu6t+//zuPXLx4cQYsmHz++eci VcGCBf38/JLtGjdunNhVrly5lOvjKJR1ullW+GPau3evuOYlSpRgtS8AAAAA /5GHh4doXzg5OcXFxcnOgixIWUSmZ8+e7zxy4cKFGbBgcujQIWVa16pVq96+ fVvZeOvWrc6dO4uN1atXf/nyZWo/a2RkJI756quvPlZYxHf7yZs3r7js586d k50FAAAAQObWv39/0bj45ptvZAdBFiRar2ZmZjVr1ty4ceM7D16wYEEGLJgI Dx48GDp0qJWVlUqlKlasmJOTk6GhYePGjTdv3hwdHZ3aT7169crU1LROnTrb tm37mGkxevRocSMNGjRIdhAAAAAAmVhERISyEOetW7dkZ0HWFBsbm8Yj586d mzELJoqgoKCDBw9u3bp1//79Pj4+afmRtJ87PqCrV6+KGylnzpxRUVGyswAA AADIrH7//XfRsqhWrZrsIMA/U6dOFXfjpEmTZAdBplehQgVxL+3YsUN2EAAA AACZVYsWLUSzYsmSJbKDAP9888034m4cP3687CDI9ObPny/upXbt2skOAgAA ACBT8vX1NTQ0NDY2DggIkJ0F+GfkyJGikSv+KzsIMj1ebgAAAAD+C/4RFhnK 1KlTe/TocebMGdlBkBUo3eeWLVsmOwgAAACAzEcZ5r99+3bZQQDgA9u0aZN4 v7m4uMgOAgAAACCTYSEJAFlYRESEjY2NeMt5enrKzgIAAAAgM5kwYYJoSgwc OFB2EABIF19++aV4y02ePFl2EAAAAACZSenSpUVT4vDhw7KD4N+Iiop6+PDh mTNntm3btnTp0m+++aZPnz5NmjQpU6aMk5NT3rx5c+XKZWNjY2FhYWJiYqBl ZGRkZmaWI0cOOzu73Llz58uXr2jRonXq1OnSpcuIESPmzp3722+/HT161MvL KyQkRKPRyD5F4L/at2+fuPMrVqwoOwgAAACATOPOnTuiHWFraxsTEyM7C95G rVaLL2vDhg0TJ07s27dvs2bNypUrlytXLoN0ZmlpWbx48Xr16nXr1m3UqFHL li07d+5cZGSk7OsBvIeoqKgcOXKI+9nHx0d2FgAAAACZw9y5c0UjomfPnrKD IDmNRnP//v3ff/99zJgx9evXt7a21lvQMDQ0dHJyqlatWtu2bQcOHDh9+vSf fvpp3759V69effz4sa+vb0BAQHBw8KtXr0SbMS4uTq1Wx8TEhIeHh4SEvHjx wt/f/+nTp15eXseOHduwYcO8efNGjRrVtWvXunXrFitWzMLCQu8vNTIyqlix Yv/+/X/44YdLly5FR0fLvlrAO3Tp0kXcuosWLZIdBAAAAEDmUKtWLdGI2Lp1 q+wgiK+QPHz4cNu2bePHj2/cuLGdnV2yMoWjo2ObNm2+/fbbH3/88Y8//rh8 +bKfn19cXFy6RgoODvb09HR3d1+/fv3s2bP79OlTpkwZlUqVNJiJiUnVqlVd XV3XrFlz9epVeishA9q4caO4V+vXry87CAAAAIBMwNfXV7QgzMzMQkNDZWfJ vvz8/H788cc2bdrY29snq5A4ODi0bNly8uTJe/fuffbsmeykr4WFhZ08eXLh woU9evRwdnZOFlvcUbVq1Zo+ffr169eZAgUZRHBwsImJiUqlCgwMlJ0FAAAA QEa3evVq0bxt1aqV7CDZkaen5+zZs2vWrJm01JArV65mzZpNnDhx586dT548 ySzVhpCQkGPHjs2bN69r167FihVLekaFCxceMWKE2BsbGys7JrI78XCJe/KX X36RHQQAAABARteiRQvRfPjxxx9lB8ku4uLiTp06NXbs2BIlSuhKCqampuKL WLVqlY+PT2apkLzdy5cvd+/e/eWXX+bOnVt3mjlz5uzVq9e2bdvCwsJkB0Q2 tWLFCnErtmvXTnYQAAAAABlaaGio0kHdz89PdpYsLjw8fPfu3f369ctuBYS3 F4h8fX1lB0T28vTpU3EHmpubi0dSdhYAAAAAGdeWLVtE26F27dqyg2RZGo3m +PHjnTt3Fg00XbmgSJEiI0eOzIZDVPQOQapVq9Yvv/zCIsX4aFxcXMSNt3v3 btlBAAAAAGRcvXr1Eg2HuXPnyg6SBYWFha1cubJs2bK6ykC1atVmzJhx48aN rDHo5r9QJrlt1aqVmZmZbuYWNzc3Hx8f2dGQ9c2cOVPccv369ZMdBAAAAEAG JZrtjo6OouEgmvCys2QpXl5eQ4cOtba2VkoBefLkmTRp0uPHj2XnyohevXq1 du3aypUrK9dKpVK1adPm0KFDarVadjRkWZcvXxY3W6FChWQHAQAAAJBBiXa9 smotHR4+iLi4uF27djVu3FjXpaR27dqbNm2Kjo6WHS2jE3fg2bNne/ToYWxs rFy6EiVKLF68ODg4WHY0ZEFqtTpnzpziNnvw4IHsLAAAAAAyouXLl4smQ9eu XWUHyfSeP38+a9asAgUKKI19CwuLAQMGXL16VXauzMfPz2/GjBlOTk7KlbS0 tHR1daUHFD64jh07sjoYAAAAgNQoTYZVq1bJDpKJ3bt3r0+fPiYmJkoDv1ix YgsWLHj58qXsXJlbbGzs9u3bGzZsqOurU69evSNHjsjOhaxDKRd369ZNdhAA AAAAGY6uU/r9+/dlZ8mU/Pz8Bg8ebGRkpLToW7VqtX//fmbe+LBu3bo1aNAg Kysr5SI3bdr08uXLskMhK/D09FTmF2JAIgAAAIBkrly5ItoLBQsWpL3wvkJD QydPnmxpaalMUtq3b19mQkhXISEh3333nY2NjVI26d69Oxcc/5Fuyutbt27J zgIAAAAgY5k/f75oLIjGvuwgmUl0dPSSJUty586ttNxbt27N9BofTWBg4OjR o5XRT8bGxsOGDXv+/LnsUMjEevbsKe6lpUuXyg4CAAAAIGNp2bKlaCz8+uuv soNkDmq1euPGjUWKFFFKJTVr1vTw8JAdKjt6+PBhnz59lG/Byspq6tSpYWFh skMhU/rpp5/EXdS+fXvZQQAAAABkIDExMcq8EE+fPpWdJRM4dOhQpUqVlEZ6 yZIld+3axTgmua5fv65U/JRpKJYvXy5uadmhkMn4+PiI+8fW1jYuLk52FgAA AAAZxdmzZ5W2v+wgGd2VK1caN26sNMwdHR3XrFkTGxsrOxQSHD9+vHr16rr1 ibZu3UohC+9F6TN26dIl2UEAAAAAZBTz5s0TzYSvv/5adpCMKyoqauLEiYaG huJC2djYzJ49Ozw8XHYoJKfRaLZt21aiRAmlbNKuXTs/Pz/ZoZBp9OvXT9w2 ixYtkh0EAAAAQEbRpUsX0UxYs2aN7CAZ1F9//VW2bFmlDT506NAXL17IToS3 iYmJWbFihbW1tfi+cuXKtWnTJrqaIC1Wrlwp7pmePXvKDgIAAAAgo1A6ol+7 dk12kAwnOjp60qRJSseSYsWKMbNrJvLo0aMmTZooZa6OHTv6+/vLToSM7tKl S+JucXZ2lh0EAAAAQIYQGBgo2ggWFhZMx5HMlStXKlSooLS4hw8fzhicTEej 0axatUqZ0Nje3n7Lli2yEyFDi46ONjU1FXdLSEiI7CwAAAAA5Dt48KBoIHz6 6aeyg2QgMTExU6ZMMTIyElemSJEix48fl50I/97Dhw8bNmyoFL46d+4cEBAg OxEyLhcXF3GfHD16VHYQAAAAAPLNmDFDNBBGjBghO0hGcfXq1YoVKyrt6yFD hrx69Up2IvxXarV6xYoVlpaW4jt1cHDYvn277ETIoAYNGiRukrlz58oOAgD/ z959x0dR7f8fd9MbIbTQQUAl0lG6hN4SpENoAhFEuhQpCqLSQarSRZBejfRe pYckBBUVAWmBEEJJgED6fn+fe+f+crmUkITdPZvM6/kHj2WzmXnPmTPzeJxP ds4BAADqNW/eXAYIK1asUB1EvYSEhLFjx9rZ2UmDFC1a9ODBg6oTwZQuXbpU u3ZtrRTWoUOHO3fuqE4Eq7NkyRLpHm3btlUdBAAAAIB6BQoUkAHCuXPnVAdR LDw8vGrVqtpouk+fPg8fPlSdCKaXnJw8e/ZsZ2dnOcv58uULDAxUnQjW5fff f9fqpaqDAAAAAFAsPDxcRgfu7u4ykFSdRaVTp07lz59fmqJQoUL79+9XHQfm dfHixRo1asjpdnBwWL58ueo4sCKJiYnao1vMdQMAAADo3ObNm2VoULduXdVB VFqxYoW2NIa3t3dkZKTqOLCEhISEXr16aV8oGjp0aFJSkupEsBY1a9aUXrFz 507VQQAAAACo9MUXX8jQYPjw4aqDqCHD5GHDhmmj5o8//jg+Pl51IljUvHnz tLWQmjRpEh0drToOrMKgQYOkS4wdO1Z1EAAAAAAqtWrVSoYGq1atUh1EARkg +/j4yOHLkFkGzqrjQI2DBw/mzJlTusFbb731999/q44D9ZYuXSr9wc/PT3UQ AAAAACqVKlVKhgYhISGqg1iaDI1lgCzHLoNlVsPRuUuXLpUuXVqbzGfXrl2q 40CxkydPSmcoX7686iAAAAAAlElKSrK3t5ehgd5WhJFBsQyN5cDLlCkjg2XV caDegwcPWrRoIV3CYDDMmDHDaDSqTgRloqKipCc4OzvrfCpsAAAAQM8uXrwo 44KCBQuqDmI5MhCePn26DIrlwFu2bKm3ShFSIaPj0aNHaxPadOvWLS4uTnUi KOPp6Snd4OrVq6qDAAAAAFBj+/btMiioV6+e6iAWkpiY6O/vr42IZWjMn4/x rPXr1zs7O0sPqVq16t27d1XHgRre3t7SB/bs2aM6CAAAAAA1ZsyYIYOCvn37 qg5iCYmJiX5+fto37Tds2KA6DqxXaGho4cKFtVks7ty5ozoOFOjZs6d0gNmz Z6sOAgAAAEANbVDw7bffqg5idgkJCW3btpWDdXNzO378uOo4sHbXr19/4403 pMOUK1fu9u3bquPA0qZNmyZnv1+/fqqDAAAAAFCjVq1aMijI8suCJCQktG7d Wo40W7ZsJ0+eVB0HmcP169fffPNNbWbgyMhI1XFgUVu3bpVTX79+fdVBAAAA AKiRN29eGRRcvnxZdRAzio+Pb9mypbZibGBgoOo4yExu3LihrT1NzURvzp8/ L+e9UKFCqoMAAAAAUCA6OlpGBE5OTll47tP4+Hhtrdjs2bMHBQWpjoPMJzw8 vGTJktKFSpcufevWLdVxYCGJiYnakusxMTGqswAAAACwtFOnTmlTNKgOYi7x 8fHNmjWTY/Tw8AgODlYdB5nVzZs3vby8pCO9/fbbERERquPAQuR0y0kPDQ1V HQQAAACApf30008yHGjRooXqIGYRFxf3/vvva9WSkJAQ1XGQuUVERGjDZ2om +tG0aVM545s3b1YdBAAAAIClfffddzIc6NOnj+ogphcXF+fr6ytHlyNHDv5A DJOIiIgoVaqUdCovL6+bN2+qjgOz0xYRmz9/vuogAAAAACxtxIgRMhwYN26c 6iAmFhcX5+PjI4eWM2fOM2fOqI6DrOPWrVulS5eWrlWyZElqJlneV199Jef6 iy++UB0EAAAAgKV16dJFhgOLFy9WHcSUjEajdly5cuX69ddfVcdBVhMZGVmm TBnpYJUrV378+LHqODCjhQsXyon+8MMPVQcBAAAAYGn169eX4cDOnTtVBzGl KVOmyEE5OzufPn1adRZkTbdu3SpSpIh0sw4dOhiNRtVxYC5bt26Vs9y4cWPV QQAAAABYmjaJZVb6GsbmzZtf+7eff/5ZdRZkZb/99purq6v0tAkTJqjOAnMJ CQmRU1y2bFnVQQAAAABYWvbs2WU4cOfOHdVBTIMxLCyJ6lyWFxERoT3cpzoI AAAAAIt69OiRjAUcHByyxjMFKU9JdOrUKWscEazf5MmTtee/WIkpS0pOTra1 tZVTHBcXpzoLAAAAAMu5cOGCDARef/111UFMQIYzNWrUkMOpUqVKbGys6jjQ C6PR+MEHH0jHK1SoUEREhOo4MD05s3J+L1++rDoIAAAAAMv55ZdfZCBQo0YN 1UFelQxau3XrJsdSsGBBVnqFhcXGxlatWlW6X7Vq1SjWZT1VqlSRk3vs2DHV QQAAAABYzpo1a2Qg0LZtW9VBXtXUqVNZFgcKRUREaN9D+OCDD3gcLItp2bKl nNn169erDgIAAADAcubOnSsDgd69e6sO8kq0dT9FQECA6izQr9DQUGdnZ+mH kydPVp0FptSzZ085rQsWLFAdBAAAAIDlaPNVDhs2THWQjPv999+1ZXHGjRun Ogv0LiAgQKvdbd68WXUWmMyQIUPknE6dOlV1EAAAAACWM3LkSBkIjB07VnWQ DHr8+PFbb70lh9ChQweeg4A1GD9+vHTI7NmzX79+XXUWmMZXX30l53T06NGq gwAAAACwnAEDBshAYObMmaqDZNDAgQMlv5eX1+PHj1VnAf7FaDT6+vpKt2zc uDFFvKxh2rRpckIHDRqkOggAAAAAy/H395eBwA8//KA6SEYcPHhQwtvY2AQF BanOAvxXeHh4jhw5pHN+//33qrPABBYuXChns0ePHqqDAAAAALCcNm3ayEBg 3bp1qoOk24MHD4oWLSrhv/zyS9VZgKetXbtWOqerq+ulS5dUZ8GrWr16tZxN Pz8/1UEAAAAAWE6jRo1kILBjxw7VQdLto48+kuQVK1ZMSEhQnQV4jnbt2kkX rVWrVnJysuoseCXaOlw+Pj6qgwAAAACwnOrVq8tA4MiRI6qDpM+OHTsktr29 /dmzZ1VnAZ7v9u3bnp6e0lFnzZqlOgteyaFDh+Q81qxZU3UQAAAAAJZTpkwZ GQicOXNGdZB0uHv3br58+ST2N998ozoLkJrNmzdLR3V0dDx37pzqLMi4kJAQ OY/ly5dXHQQAAACA5WjTgPzzzz+qg6RDx44dJXONGjWSkpJUZwFeolu3btJd K1eunJiYqDoLMuj8+fNyEosXL646CAAAAADLyZkzpwwEIiMjVQdJqw0bNkhg Z2fnCxcuqM4CvFx0dHShQoWk006YMEF1FmTQzZs35QzmyZNHdRAAAAAAluPs 7CwDgYcPH6oOkiYRERFahWfu3LmqswBptXfvXum0dnZ2mevZN6R48OCBtuaR 6iAAAAAALMfR0VEGAo8fP1Yd5OWMRmPz5s0lbf369Vl2BJlLnz59pOuWLVs2 Pj5edRak26NHj7QvtqkOAgAAAMBy7O3tZSAQFxenOsjLaSt7ZsuW7dq1a6qz AOkTExNTvHhxVszJpGJjY+XcOTg4qA4CAAAAwHJsbGxkIJCQkKA6yEskJia+ /fbbEnXGjBmqswAZsWXLFunAOXPmjI6OVp0F6RMfH689VKU6CAAAAADLee3f rP8Jl0WLFknO119/PVN8GQZ4ltFo9Pb2lm782Wefqc6C9ElMTJQTZ2NjozoI AAAAAMvRCiYylFMdJDUxMTH58uWTnGvXrlWdBci4wMBA6caOjo5hYWGqsyAd kpOTtVul6iAAAAAALCdTFEzGjRsnIStVqmT934QBUteuXTvpzP7+/qqDIB0o mAAAAAA6ZDAYZBSQlJSkOsgL3bp1y9XVVUIePHhQdRbgVV28eNHOzk7682+/ /aY6C9KKR3IAAAAAHbK1tbXySV/79u0rCZs2bao6CGAa/fv3ly7dpEkT1UGQ Vkz6CgAAAOiQg4ODNS8r/Pfff9va2hoMhrNnz6rOAphGZGSkm5ubXHf79u1T nQVpwrLCAAAAgA45OTnJQODx48eqgzxfq1atJF6PHj1UBwFMacKECdKxK1as yLQ8mcKjR4/kfMndUnUQAAAAAJajTQ/y4MED1UGe49ixY9og5caNG6qzAKYk A/D8+fNL9161apXqLHg5uUPKyXJxcVEdBAAAAIDleHp6ykAgPDxcdZCnGY3G 6tWrS7ZRo0apzgKY3uLFi6V7FylSxGofiEOKmzdvysnKkyeP6iAAAAAALKdE iRIyEPj7779VB3napk2bJFju3Lnv37+vOgtgeklJSaVKlZJOPmvWLNVZ8BLn z5+XM1W8eHHVQQAAAABYToUKFWQgEBwcrDrI09577z3Gksjafv75Z+nkxYoV s+Z1vSFCQkLkTJUvX151EAAAAACW4+3tLQOBgwcPqg7yP0JDQyWVm5ubdU6u AphEUlJS0aJFpatv3bpVdRak5tChQ3KaatasqToIAAAAAMvx9fWVgcCWLVtU B/kfH374oaT65JNPVAcBzOubb76Rrt6wYUPVQZCarVu3ymny8fFRHQQAAACA 5bRv397aluq4c+eOo6OjpDp//rzqLIB53b17V1va+9y5c6qz4IVWr14t58jP z091EAAAAACW89FHH8lAYMGCBaqD/NfkyZMlUpMmTVQHASyhR48e0uH79eun OgheaOHChXKO5EypDgIAAADAcgYPHiwDgalTp6oO8h+JiYmFCxeWSDt27FCd BbCEX3/9VTq8q6srC0JZrWnTpsk5GjRokOogAAAAACznyy+/lIGA/Ks6yH9o 64a88cYbycnJqrMAFqLNvTx79mzVQfB8X331lZyg0aNHqw4CAAAAwHK0v5wO HjxYdZD/qF27NqsJQ282bNgg3f6tt96iTmidrO2beAAAAAAswKqezf/tt98k jIuLS3R0tOosgOUkJiYWLFhQOv/u3btVZ8FzWOFcTwAAAADMTXsEplmzZqqD /EvPnj0lTJ8+fVQHASxtwoQJ0vmbNm2qOgieo0WLFnJ2fvrpJ9VBAAAAAFhO YGCgDATeffdd1UH+7969e87OzhLmzz//VJ0FsLTIyEgHBwfp///884/qLHha 5cqV5dScOHFCdRAAAAAAlhMWFiYDgfz586sO8p/ZVBo0aKA6CKBG165drWpC IaTQHpi6evWq6iAAAAAALCchIUEGAjY2NomJiWqTlCtXTpJs2rRJbQxAFe3r Xvnz52fqV6uSlJRka2srpyY+Pl51FgAAAAAW5enpKWOB69evK8xw6dIlyeDm 5hYXF6cwBqCQ0WgsVKiQXAiBgYGqs+C/wsPD5aTkzp1bdRAAAAAAllahQgUZ Dpw6dUphhhkzZkgGPz8/hRkA5fr37y8XwmeffaY6CP4rODhYTkq5cuVUBwEA AABgaU2bNlX+LIy3t7dkWLNmjcIMgHL79++XC8HLy0t1EPzXli1b5KQ0adJE dRAAAAAAlqYt5jtv3jxVASIjIw0Gg52d3f3791VlAKxBQkKCh4eHXI/nzp1T nQX/sWDBAjkj3bt3Vx0EAAAAgKV9/fXXMhz44osvVAVYsmSJBGjcuLGqAID1 +OCDD+RymDJliuog+I/Ro0fLGZF/VQcBAAAAYGnff/+9DAc+/PBDVQGaNWsm ARYsWKAqAGA9AgIC5HKoVq2a6iD4jx49enCDAgAAAPRp27ZtMhxo1KiRkr3H xMQ4OjpKgPDwcCUBAKvCFWFtfHx85HRs2bJFdRAAAAAAlnb27FkZDpQoUULJ 3rW/p1etWlXJ3gEr9P7778tFsXDhQtVB8C9vvfWWnI5ff/1VdRAAAAAAlhYX F2cwGGxsbOSF5feuzdgwefJky+8asE6LFy9mVh8rkZCQYGdnJ6fj8ePHqrMA AAAAUKB48eIyIjh79qyF98uaIMCzWDfKesitSW5QRYsWVR0EAAAAgBq+vr4y KAgICLDwfvft2yf79fLysvB+AStXs2ZNuTTWrl2rOojebdq0SeEUTwAAAACU Gzx4sAwKJk6caOH99uvXT/b72WefWXi/gJWbPn26XBodOnRQHUTvpkyZIidi wIABqoMAAAAAUGPhwoUyKOjWrZuF91umTBnZ75EjRyy8X8DK/fHHH3JpFC5c WHUQvevevbuciHnz5qkOAgAAAECNX375xfJL1cTExGiTzT569MiS+wWsX1JS kqurq1yVt27dUp1F19577z05C/v371cdBAAAAIAaERERMijw8PAwGo0W2+mR I0dkp+XKlbPYHoFMxNvbWy6Q7du3qw6ia7ly5ZKzcP36ddVBAAAAAKhhNBrd 3d0t/OfsmTNnyh579OhhsT0CmciQIUPkAvn6669VB9GvO3fuyClwdXW1ZCUZ AAAAgLWpUqWKDA0OHz5ssT127NhR9rhgwQKL7RHIRNasWSMXSNOmTVUH0a9j x47JKXjnnXdUBwEAAACgUpcuXWRosHDhQovt8Y033pA9hoSEWGyPQCZy8eJF uUA8PT35eoMqixcvllPQsWNH1UEAAAAAqDRt2jQZGvTq1csyu4uKipLdOTg4 xMfHW2aPQOZiNBo9PDzkMrl27ZrqLDrVv39/af9JkyapDgIAAABAJW2hnHff fdcyu9u3b5/srnLlypbZHZAZNWjQQC6Tn3/+WXUQnapWrZq0v9ysVAcBAAAA oNKDBw9kaGBvbx8XF2eB3U2aNEl217dvXwvsC8ikPvvsM7lM5F/VQfQoISHB yclJ2j8qKkp1FgAAAACKvf322zI6CAwMtMC+WrduLftaunSpBfYFZFI///yz XCb169dXHUSPQkNDpfHfeOMN1UEAAAAAqNe1a1cZIMydO9cC+ypcuLDs6+zZ sxbYF5BJhYWFyWWSPXt25n21vO+//14av0OHDqqDAAAAAFBv9uzZMkDw9/c3 945u3bolO3JxcUlKSjL3voDMy2g0enp6ysVy4cIF1Vl0p2fPntLyM2bMUB0E AAAAgHonT56UAUKZMmXMvaNt27bJjry9vc29IyCza9q0qVwsa9asUR1EdypW rCgtf+TIEdVBAAAAAKgXGxtra2trY2MTExNj1h1NnDhRRiKDBg0y616QWSQn J69fv378+PEnT55MefPWrVvbt28/f/68wmAZkJSUtG7dOjmWoKCglDcjIiLk WC5evJiBDY4aNUouFvnXdBnxcnIztLOzMxgM5r4ZAgAAAMgsKlSoIKOzw4cP m3Uvffr0kb18++23Zt0LMoXQ0NBKlSq99m+2trZHjx6VN9esWePu7q69mYlm Bg4ODn7nnXe02Pb29qdOnZI3V6xY4ebmpr25atWq9G5zwYIF8ovdu3c3Q168 0IkTJ6TZS5curToIAAAAAGthmcf2W7RoIXvZsGGDWfcC6/ftt9+WKVNm6NCh 2oMnom3btoMHDy5QoEDZsmW1d3LlyqU6ZppMnTq1XLlyw4cP9/Hx0ZJ37ty5 X79+BQsWlGPU3smXL196N7t161b5xSZNmpgjM17EYhM6AQAAAMgstIUh/Pz8 zLqXypUry16OHTtm1r3A+p06dUqb+Pfhw4fOzs5aVaFdu3YJCQny5ieffCL/ bdGiRaZYIyYwMDA5OVleREdHOzg4aMfSqVOnxMREebN3797y31atWqV3syEh IfKL5cqVM31ivNgHH3xgsSXDAAAAAGQK586dk2FCnjx5zDpELViwoOzl8uXL 5tsFMp2SJUtKryhcuLBWYRC3b98eNmzYU0spJScnb926ddasWcuWLUvX/BIz Zsx47RU0b9487fsqXry4/ErRokVTwoeHhw8fPlyrqKTLzZs3M9HXbLIGuftp 96jff/9ddRYAAAAA1kJGCgUKFDDrSEHGjDY2NrKLuLg4M+0CmVGjRo20R3JS +czx48fffffdlCKGp6fnn3/+abGEaVe3bl2J1759+1ffVFJSEteLhf3999/S 4Llz584UX20CAAAAYDHad9FnzZplpu1HRETwF3M8q2XLltIxPvvssxd9YO7c uTVq1NizZ094ePjmzZvz5csnn69ataolQ6ZRs2bNJNsXX3xhkq1pNcwrV66Y ZGt4qXnz5lngyUQAAAAAmc6PP/6Y3gcQ0uX06dOy/bJly5pp+8iktKmAp06d +qIP7Nq168n/rlmzRvueSUREhPnTpc/7779vwqqjtorQ8ePHTbI1vFTbtm2l wRcuXKg6CAAAAADrcuXKFRksuLu7p0wlYVrbtm2T7Tdu3NgcG0fm5evrKx1j zJgxaf8VOzs7+ZUbN26YL1XGSPeWYJMmTTLJ1rRS0k8//WSSrSF1ycnJuXLl kga/cOGC6iwAAAAArI42ZeWpU6fMsXFtIZ4PP/zQHBtH5lW/fn3pGMOGDUv7 rzg5OZUoUcJ8kTKsTp06ciyjR482ydb69OkjW/vuu+9MsjWk7syZM9LahQoV YgITAAAAAM/66KOPZMgwefJkc2z8yy+/lI2PGjXKHBtH5uXt7S0d45NPPknj 5y9cuCCfHzduXBo/b8lVcmrUqCG/Mnz48LT/SirGjx8vWxsxYoRJtobUaf2k W7duqoMAAAAAsEZr166VIUPDhg3NsXGtGjNv3jxzbByZV7Vq1aRjDBgwII2f nzZtWt68eR88eGDWVBlTuXJlOZahQ4eaZGtLliyRrXXp0sUkW0PqtPlnli9f rjoIAAAAAGukLWTj7OxsjpVMtakqNm/ebPItI1MrWbKkdIx+/fql5cMPHjzI nz//ihUrzJ0qY9544w05lsGDB5tka7t27ZKt1atXzyRbQyoSExOzZcsmrR0W FqY6CwAAAACVPnux3Llzy6jh0KFDJt9phQoVZMtBQUEm3zIyNW2mzZ49e6bl w4MGDWrZsqW5I2WYh4eHHEvfvn1NsrXffvtNtubl5WWSrSEVJ06ckKbOkSNH KvdG1RkBAAAAWEIqgwJtJdN0zcCZRoUKFZItX7582eRbRuaVnJxsMBikY3Tt 2vWlHz558mSpUqWs82Gc//v3txS0aU9MNbPxtWvXZGsFCxY0ydaQitGjR0tT V6xYkYIJAAAAoHOpDAo6deokA4c333zT5EtF5M2b1zqXgoVCCQkJH3/88Y8/ /hgZGZn6J69evVqiRIk///zTMsEyIC4uTo5l2bJlt2/fNskGb968KZeMXDgm 2RpSUbZsWWnq9u3bUzABAAAAdC5lCHDmGcHBwdmzZ5exg8lHpjlz5pTNvnRc DDwrKiqqTJkyAQEBqoNY1O3bt+WSkQtHdZAs7uLFi9LOrq6uQUFBz94SKZgA AAAAupJKwUQ0b95chg8TJ0407U7d3d1ls/fu3TPtZpHlxcfH165d2+Qd0vpF RUXJJSMXjuogWdz06dOlnZs0afLc+yEFEwAAAEBXUi+YzJw5U4YPVapUMe1O XVxcZLMPHz407WaRtSUmJrZq1crJyWng80RHR6sOaEYxMTHaqlWqg2Rx3t7e 0s7ffPMNBRMAAAAAqRdMTpw44ejoKCOI69evm3Cn9vb2ss3Y2FgTbhNZW1JS kp+f32svUKBAAdUBzSsuLk4OUy4c1UGyslu3btnY2NjZ2R09epSCCQAAAIDU Cyaibt26MlKbN2+eCXeqLYYiQ2ATbhNZ28OHD9e9mIxwVQc0L7lYtNKQ6iBZ 2eLFi6WF33vvvRfdDCmYAAAAALry0oLJ2LFjZRDRqFEjE+5UG/qZfPEdIKvS CiYGg0F1kKysWbNm0sijR4+mYAIAAADg/9JQMDl06JAM0+zs7KKioky1Uxsb GxmYJCQkmGqDQNbGIznm9vDhQ+3xw3379lEwAQAAAPB/aSiYiEqVKsk4YvXq 1abaqTYwefz4sak2CGRtTPpqbgEBAdLC5cqVS+VOSMEEAAAA0JW0FEyGDRsm Q4mWLVuaaqeurq6ywfv375tqg0DWxrLC5tahQwdp4YEDB1IwAQAAAKBJS8Fk 7969BoPB3t7+zp07Jtmph4eHjE3u3r1rkq0BWd7t27flksmZM6fqIFlTdHS0 k5OTtPDOnTspmAAAAADQpKVgImrUqCGjiblz55pkp7lz55atRUREmGRrQJZ3 8+ZNuWQ8PT1VB8mafvjhB2neSpUqpX4bpGACAAAA6EoaCyaTJk2SAUXVqlVN stP8+fPL1q5fv26SrQFZXlhYmFwyBQsWVB0ka6pVq5Y079ixYymYAAAAAEiR xoLJiRMnXFxcZExx7ty5V9/p66+/Lps6f/78q28K0IOzZ8/KJVOyZEnVQbKg S5cuSds6OjoeO3aMggkAAACAFGksmIgWLVrIsGLkyJGvvtNq1arJpg4fPvzq mwL0YM+ePXLJ1KtXT3WQLGjMmDHStj4+Pi+9B1IwAQAAAHQl7QWTxYsXy7Ci cOHCycnJr7jT1q1by6bWrl1rkkMAsrylS5fKJdO5c2fVQbIao9FYokQJadv5 8+dTMAEAAADwpLQXTE6fPl2gQAEZWRw4cOAVd9q/f3/ZzvTp001yCECWN3Hi RLlkhg0bpjpIVnPs2DFp2Ny5c4eEhFAwAQAAAPCktBdMRM+ePWVw4e/v/4o7 1aaQHTJkiEkOAbCY+/fvHzhwYPLkya1atSpatKi7u7u9vb02A4aHh0epUqXk 6pg/f76MvhMSEky43379+sleZs2aZcJtQvTq1Usatlu3bmm5AVIwAQAAAHQl XQWTzZs3y+DCzc0tJibmVXa6fPly2U6HDh1MdRSAWUVFRU2fPr106dKvpZmj o2ODBg3kkklKSnr1AK1atZJtrl+//tU3hRSxsbEeHh7SsD/99BMFEwAAAABP SVfBRJQrV07GF0uWLHmVne7bt082UqtWLVMdBWAmf/75Z+/evZ2dnbUyiJ2d XaVKlfr27bts2bI//vjj3r17MuhOSkp69OjR7du3T548+d1333Xu3PmNN95I qZwUL15c3nzw4MGrxNDmST569Kipjgti9erV2tpDabz7UTABAAAAdCW9BZOx Y8fKEOOdd94xGo0Z3qkMQmUjMqg04YEApnX58mVfX9+Uukf9+vU3b94cFxeX xl+/ffv2jBkzihYtqv16tmzZpkyZkuFvmxQpUkQ2cunSpYz9Op6rRo0a0qqj Ro2iYAIAAADgWektmAQGBmbPnl1GGceOHcvwTqOjo2ULLi4ur1J1AcxEuuUP P/zg5uYmvdTZ2blXr15//PFHxjaVmJgYEBBQs2ZNrWzi7e19+fLlDOSxs7OT X4+Njc1YDDwrJCREe8Dw+PHjFEwAAAAAPCu9BRPx4YcfykCjY8eOGd6pDAC1 Zxzu379vwmMBXl14eHjKF0tatWp169Ytk2x2x44defPm1Uboy5YtS1ep8Pbt 2/KLHh4eJkkCjb+/v7ZSc9pvfRRMAAAAAF3JQMFk586dBoPBzs5OhpYZ3m+J EiVktPLXX3+Z8FiAV3Ts2LEcOXJIz8yePfuqVatM+w2o27dva3O3iu7duycn J6fxF3/99Vf5lVKlSpkwjM5FRkY6OjpKq27ZsoWCCQAAAIDnykDBRNSrV0/G Gl999VWG91urVi3Zwr59+0x3KMArOXbsmKurq3TLhg0bXr9+3Ry7MBqNP/74 o/b1qh49eqSxZrJz5075fIMGDcwRSZ+0lc1r1qyZrvseBRMAAABAVzJWMFm0 aJEMN/LlyxcfH5+x/Xbo0EG2sHz5ctMeDpAxKdWSjh07JiYmmnVfBw8e1L7e 0K9fv7R8iWXx4sXy4a5du5o1lX7I+S1cuLA06dy5cymYAAAAAHiRjBVMQkND tWdqVq9enbH9DhkyRH598uTJpj0cIAMsWS3R7N69297eXvYoF8JLayba0lSM 000lICBA2rNIkSKnT5+mYAIAAADgRTJWMBFffPGFDDqqV6+esf3Onj1bft3f 39+0hwOkV3h4uDZvicWqJZqtW7dqa9+sXLky9U+2a9dOPrZ48WLLBMvy6tSp I+05bNiw9N70KJgAAAAAupLhgsnx48e1dVeDg4MzsF/5dfndMmXKmPyIgLQz Go0+Pj7avCWWrJZotEfbcuTIcfPmzVQ+VqxYMfnYb7/9ZrFgWdjvv/8ujenk 5HTkyBEKJgAAAABSkeGCiejcubMMPTp06JCB/T5+/NjGxsZgMMTExJj8oIA0 +uGHH7Q1ccw0y2vqjEZjo0aNtPWLX/Rgzt27d7UBvuXrOVmStiq6n59fBu54 FEwAAAAAXXmVgsnOnTvt7OxsbGz+/vvvDOy6XLlyMnI5cuSIyQ8KSIvLly9r U5esWrVKVYarV69qGdatW/fcD+zevftVnn3Dk+SMyy3LYDBs3bqVggkAAACA 1L1KwUS0atVKRnMffvhhBnbdo0cP+d2ZM2ea/KCAtNAexmndunValqoxn/nz 50sMT0/P2NjYZ386YcIE+emAAQMsHyzr6dOnjzSmr69vxm53FEwAAAAAXXnF gsmWLVsMBoOdnd2VK1fSu+uFCxdqM22a47iA1P3xxx/S/ZydnW/duqU2SXJy svZtqxUrVjz705YtW7IAt0ncuHHDwcFBGjMgIICCCQAAAICXesWCidD+TN+3 b9/07jokJER+8Y033jDHcQGp69Wrl3Q/+Vd1kH/5/vvvJUy1atWe/VGhQoXk R3/99ZflU2Ux2lLmDRo0yPC9joIJAAAAoCuvXjAJCAiQYYijo+ONGzfSteuE hATtD75RUVFmOjrgue7du+fs7Cx9748//lCd5V9iYmLc3d0lz+nTp598PyIi Qt50dXVNTk5WlS1riIyMdHFxkcZcu3YtBRMAAAAAafHqBRNRv359GYkMGTIk vXuvUqWK/OK+ffvMcWjAi0ybNk06nvRb1UH+65NPPpFIPXr0ePLNbdu2yZu1 atVSlSrLGDlypLRkzZo1X+VGR8EEAAAA0BWTFEzWrl0rgxEXF5fIyMh07b1v 377yi5MmTTLT0QHPVbp0ael4W7ZsUR3kv/78809tgeMnZ6D9+uuvM1aKxJOi oqK0L/AsW7aMggkAAACANDJJwUTUrFlTxiMjR45M196XLl2qLVNipqMDnhUd HS29zs7OLi4uTnWW/zIajXnz5pVgFy5cSHnT19dX3lmzZo3CYFnAuHHjpBmr VKnyinc5CiYAAACArpiqYLJs2TIZkri7u9+9ezfte9dWKilcuLD5DhB4yv79 +6XXVapUSXWQpzVt2lSbZEP7r9Fo9PT0fKqEgvR68OBBzpw5pRl/+OEHCiYA AAAA0s5UBROhTUgydOjQtO89KSlJm4lR+dKu0I/JkydnbF0nc9MewPn000+1 /4aFhT37kA7Sa/To0dKMFSpUCA0NpWACAAAAIO1MWDBZtWqVtlzOlStX0h6g Vq1a8lsBAQHmO0bgSa1atdKms1Ad5GnaFK+1a9fW/rtixQr5b8OGDZWGytzC w8O1kuzSpUtf/RZHwQQAAADQFRMWTESTJk1kbNKlS5e0B5g4caL8SteuXc13 jMCTihYtKl3uzz//VB3kadpXSvLkyaP9t02bNvLfb7/9Vm2qTO3jjz+WNqxb t65J7m8UTAAAAABdMW3BZPv27XZ2dgaDITQ0NI0BtMVBcuTIkZiYaNYjBTTa gin37t1THeRp9+/fl2Curq7yOjY2Vl7If9P1fS08Se4ttra2NjY2mzZtMsn9 jYIJAAAAoCumLZiIzp07yyivUaNGaQxgNBrffPNN+ZWDBw+a80CB/7C3t5f+ FhsbqzrI0xISEiSYDPDl9fbt2+V1+fLlVYfKxFq0aCFt2LZtW1Pd3CiYAAAA ALpi8oLJwYMHtb+M79mzJ40Zhg0bJp//5JNPzHqkgOa1f0tKSlId5GlawcTW 1lZe9+zZU15/+eWXqkNlVkeOHJEGdHJy2rdvn6lubhRMAAAAAF0xecFEfPLJ J9qyFMnJyWnJcPz4cfl8kSJFWA0EFuDo6Cj97dGjR6qDPO3BgwfaIzly4eTN m1dep/3RNjxJ7iTVqlWTBuzVq5cJ72wUTAAAAABdMUfB5OTJk56enjJaWbFi RVoyMDyEJXl4eEhnu337tuogTwsPD5dguXLlOnHiBCXEVxEQECANmDNnzmPH jpnwzkbBBAAAANAVcxRMxJgxY2TAUrRo0TTOFKEtZsEDCLCAUqVKSWc7efKk 6iBP27dvnwSrWrXqiBEj5MWAAQNUJ8qUEhIStGmRRo4cadrbGgUTAAAAQFfM VDAJCQkpUaKEjFnGjRuXlhg7d+6UD5crV87cxwv4+/tb53K9U6ZMkWB9+vQp WbKkvNi/f7/qRJnStGnTtO/nBAUFmfa2RsEEAAAA0BUzFUzEokWLtEkX//nn n5fGiIuLc3Nzk89funTJAkcNPVuwYIH0tE6dOqkO8jQ/Pz8JNmHCBPk3e/bs CQkJqhNlPmFhYdqk03PmzDH5PY2CCQAAAKAr5iuYCB8fHxm5+Pr6pmUqBm20 OHPmTAscNfTs9OnT0tNKlCihOsjT3njjDQk2aNAg+bdz586q42RKbdu2ldar X7++OW5oFEwAAAAAXTFrwWTfvn3a90Y2btz40iRr1qyRT9aqVcsCRw09S0hI 0BbKiYyMVJ3lv+7cuSOR7O3tteVdNmzYoDpR5qM92efk5LRr1y5z3NAomAAA AAC6YtaCifj8889lCFO4cOGYmJjUk0RHR9vZ2RkMBitcvgRZTMOGDaVbTp06 VXWQ/5o+fbpEqlOnjvzr4ODw4MED1YkymdjYWG3epMGDB5vpbkbBBAAAANAV cxdMQkJCvLy8ZBQzYsSIl4Zp3Lixdc7GiSxmy5YtWh0vMTFRdZZ/SU5O1p7H 6datm/z7/vvvq06U+Xz99dfSdMWLFzf5XK8pKJgAAAAAumLugolYuXKlDGTs 7OzOnj2bepiAgAD5pIwcZfxomcOHPqUUKNavX686y7/s3btXwhQsWLBo0aLy Yvv27aoTZTIXLlzQHrNasmSJ+W5lFEwAAAAAXbFAwURoMzHWqlUr9dlfExMT CxUqJJ/cuXOnxVoA+jRnzhzpadWqVVMd5F9atWqlTfQq/xYrVoyCYbrIXUX7 clqzZs3Meh+jYAIAAADoimUKJocPH86RI4eMaJYtW5Z6nkmTJsnHfHx8LHP4 0K2HDx9mz55dOtvmzZvVJjl16pTBYLC1ta1Vq5bkmT59uto8mc6GDRuk3bJl y3bgwAGz3scomAAAAAC6YpmCiRg3bpwManLlyhUREZFKntu3bzs4OMgnL1y4 YLFGgD7NmDFDelru3LlT75NmFRcX9/bbb0sMf39/bYWXe/fuqQqTGd25cydf vnzSdKNGjTL3TYyCCQAAAKArFiuYhIaGauulNm/ePPUHc7SR48CBAy3WCNCn 5OTkunXrSmfz9fVNvU+az8iRI7V5e3r37i0vPvroIyUxMq+OHTtKu1WsWDEk JMTcNzEKJgAAAICuWKxgInbt2uXq6vrSB3Nk4COfcXd3f/jwocXaAfoUFham PZgzf/58y+89ODjYxsZG9r579243Nzd5IZeJ5WNkXtrDOE5OTlu3brXAHYyC CQAAAKArliyYiLFjx8oAR4aoMlBNJVX16tVVjWGhN+vWrZPO5uDgYOGphq9c uaKtiTNw4MC5c+fKi5o1a1oyQGYXERGRO3duabeRI0da5vZFwQQAAADQFQsX TEJDQ7WZLRs3bpzKQxBr1qyRz5QqVUrVgxLQlb59+2orX2/cuNEye7x27drr r78uO61UqVJMTIyXl5e8XrdunWX2ngXInaFly5bSaFWrVpW7imVuXxRMAAAA AF2xcMFE7Nu3z93dXUY6CxcufFGqhIQEbSLH/fv3W7I1oE8y+h4wYID0Nxsb m7Vr15p7d2FhYcWLF5fdvfvuu1FRUdLJ5bV0eOn25t51lrFixQppNFdX1507 d1rs3kXBBAAAANAVyxdMxJQpU2Sw4+bmdunSpRcFGzNmjHymRYsWlmwN6JbR aBwxYoR0OYPBMH369KSkJDPtSPp/iRIlZEfvvPOOtiCO9k0J6fBm2mPWc/36 dW3mGWk0S964KJgAAAAAuqKkYCIaNmwo453atWsnJyc/N9jNmzft7Oxk9Hrl yhULtwn0yWg0anPsiOrVq//555+m3X5iYuL48eOlV8v2K1SocPfuXXnz6tWr 0snlTenwpt1dViWnqUmTJtKG3t7eFnsYR0PBBAAAANAVVQWTgwcP5siRQ0Y9 M2fOfFG2Tp06yQf69etnyQaBzm3atEl7HMze3n7ChAmmekzmzz//rFSpklaN 6dOnT8oKUNK95Z327dubZC96sHDhQmmxbNmy7d2718J3LQomAAAAgK6oKpiI mTNnaquTBAcHPzfbb7/9Jh+wtbW9cOGChZsFehYVFdW9e3etuFG8ePGpU6fe uXMnw1sLDAzs0qWLvb29bK1QoUIyzE/5kXRs6d4Gg+H33383RfCsTxrK2dlZ WnLSpEmWv2VRMAEAAAB0RWHBRPj5+Wlj0ujo6OfG69atm3ygXbt2Fm4WYO/e vdrUrFpZ74MPPjh27NiLniB71uPHj5ctW5byrRLRvXv3p/q5dGx539/f3wzx s6CHDx9qywk1b95cyf2KggkAAACgK2oLJoGBgSVLlpQRUNu2bZ+7gvC1a9cc HR3lAydPnrR840DnEhMTt2zZos2YocmWLVvt2rUHDx68atWqv/766969e7Gx sUlJSTExMXfu3Dl16tT8+fO7d+9evnx5W1tb7Vc8PDyGDh36zz//PLVx+bD8 VLp3WFiYkqPLXOT+0KVLF62+euLECSX3KwomAAAAgK6oLZgIGZC6uLjIOGjO nDnPTaitXeLt7f3cigpgAZcuXZJrpGDBgq+lmcFgqFSp0uLFix89evTsBqUz 165dWz42fPhwyx9OZrRkyRJpLicnp4CAAFU3KwomAAAAgK4oL5iIb775JpXJ TKKjo3PmzCkf2Lp1q+XbB3jSzZs3t23bNmbMmGbNmhUpUsTd3V36rTaQ9/Dw 8PLy6ty586xZs44cOZIyretzbd++XfvyibayMFKXMnXJ2LFjFd6pKJgAAAAA umINBZMzL5vMREag8tO33347MTHR8k0EmFZSUlLp0qWlS0+bNk11lkxA+dQl KSiYAAAAALpiJQWT1CcziYuLe/311+WnixYtUtJKgAktXbpUOnPhwoVjY2NV Z7F21jB1SQoKJgAAAICuWEnB5MzLJjNZs2aN/ChfvnwxMTGWbyXAVGJjYwsV KiSdefny5aqzZALWMHVJCgomAAAAgK5YT8HkzBOTmRw7duypnMnJye+++678 dPz48UoaCjAJrZOXK1cu7SsU61ZwcLA1TF2SgoIJAAAAoCtWVTARHTp0kPGR p6fn1atXn4p64MAB+ZGbm9utW7eUtBXwiu7evZs9e3bpxrt27VKdxdqFh4dr yxK1bNlS9W3pPyiYAAAAALpibQWT4ODgqlWryiipfPnyzz594+vrKz/q06eP krYCXtHgwYOlA9erV481slP3+PHjKlWqSFtVqFAhKChI9W3pPyiYAAAAALpi bQUTcfjw4SJFishYqU2bNk89tvD777/b2NjIj44ePaqqxYCMCQwMNBgM0nuf u3w2UhiNxs6dO0tD5c+f/8CBA6pvSP9FwQQAAADQFSssmIiNGze6ubnJiOnL L798KvDIkSO1JTOY/RWZyOPHj7V1oD799FPVWazd5MmTtYle169fr/pW9D8o mAAAAAC6Yp0FEzFv3jztz/Fr1659MnB8fHzZsmXl/b59+6pqNCC9hgwZIp3W y8uLpYRTt3nzZu3Cnzlzpuqb0NMomAAAAAC6YrUFEzFs2DDtD81BQUFPZpYf 2dnZyY/27t2rqt2AtDty5Ih0Vxsbm1OnTqnOYtV+++037atl/fv3V337eQ4K JgAAAICuWHPBJDQ0tFWrVjJ6KliwYHh4+JOxx48fL+8XKlQoOjpaVdMBaRET E1O8eHHprp9//rnqLFYtMjLy9ddfl4Zq0qSJXPuqbz/PQcEEAAAA0BVrLpiI oKCgihUryhiqUqVKDx8+TImdmJgo78j7/v7+ClsPeKl+/fpJRy1TpkxcXJzq LNbr8ePH7733njRU6dKlT548qfrG83wUTAAAAABdsfKCiThw4EDBggVlJNWw YcP4+PiU5H/99Zejo6O8v3XrVoUNCKRi//790kVtbW1DQ0NVZ7FeiYmJzZo1 k4by9PTcs2eP6lvOC1EwAQAAAHTF+gsmYuvWrTly5JDxVIcOHZ5caHjGjBny Zt68ee/cuaOwDYHnevDggbZA9pgxY1RnsV5Go7F79+7SSu7u7gEBAapvNqmh YAIAAADoSqYomIi1a9e6uLjIqGrAgAEywtLCJycne3t7y5vt27dX24zAs3r2 7Cmds2LFigkJCaqzWC+5+UgrOTo6Llu2TPVt5iUomAAAAAC6klkKJmLRokXa 4jjjx49Pyf/PP/9ohZR169YpbEbgKbt27ZJuaW9v//vvv6vOYr20L4nZ2trO mTNH9Q3m5SiYAAAAALqSiQomYtq0aQaDQUZY33//fcohLFiwQN7JmTNnWFiY wpYEUkRGRhYoUEC65aRJk1RnsV4rVqx47d8mTJig+taSJhRMAAAAAF3JXAUT 8cUXX8gIy8bGJiAgQDsEo9HYqFEjefOdd9559OiR2vYE4uPjtSfFqlatmpiY qDqOldqxY4f2hbFPP/1U9U0lrSiYAAAAALqS6Qomok+fPtqkB4cOHdKO4s6d O8WKFZM327VrlzLDCWB50v20qUvy589/48YN1XGs1IkTJ7Qn6fz9/VXfTtKB ggkAAACgK5mxYBIaGurn5yejrWzZsh07dkw7kLNnz7q5ubEiCdSaPXu2Vs07 deqU6ixWKiQkRFv0qnnz5nItq76dpAMFEwAAAEBXMmPBRMiYy8fHR8Zcbm5u KTWT7du3a1MibNiwQW2rQp/27dtnY2MjPXDVqlWqs1ip4OBgDw8PaaK6devK a9U3kvShYAIAAADoSiYtmAgZbT1bM5k2bZq84+TkFBISorZhoTfnz5/XSgEM qF/kyWpJUFCQ6ltIulEwAQAAAHQl8xZMzvxvzeTo0aP/9+8ZJPz9/eWdggUL hoeHq25d6EV0dHTJkiWl4zVr1iw5OVl1HGsUFBSUqaslZyiYAAAAADqTqQsm Z/63ZnLkyBE5ori4uBo1asg7VapUiY2NVd3AyPqSkpIaN24sXa506dIPHjxQ HccaZYFqyRkKJgAAAIDOZPaCyZkn5jNxdXXVaia3bt0qXLiwvNOpUycWzYG5 DRkyRDpbzpw5L126pDqLNTp16lT27NmlierVq5d5qyVnKJgAAAAAOpMFCiZn nlcz+fXXX7V1SydNmqS6jZGVLV26VLqZra1tyiLXeFKWqZacoWACAAAA6EzW KJic+XfNxNfXV6uZHDx4UA5t06ZN2qI5P/30k+pmRtZ0+PBhe3t76WMLFixQ ncUaHT9+XKuW1K9fP7NXS85QMAEAAAB0JssUTM78u2bStGlTGZ05ODhoRZKJ EyfKf+3s7DZt2qS6pZHVHDlyxNXVVTpY3759VWexRtu2bXN2ds4y1ZIzFEwA AAAAnclKBZMz/66Z+Pn5yRjNYDDMnz/faDRq80vY2tpu3LhRdWMj60iplnTq 1CkpKUl1HKuzdOlSueikfVq0aBEcHKz6xmAaFEwAAAAAXcliBRMRGhrav39/ 7WGcr776Kjk5+dNPP9VqJj///LPq9kZWQLUkFUajccqUKdoF2KNHD7keVd8S TIaCCQAAAKArWa9gohk9erSNjY0M2Xr37p2YmDh06FCtZsJ8JnhFVEtSkZyc rH2ny2AwDB8+XPVtwMQomAAAAAC6klULJmLmzJkODg4ydmvduvXjx4+HDRsm r21sbKiZIMOolqQiPj6+c+fO2qxBkydPVn0DMD0KJgAAAICuZOGCiViyZImb m5uM4OrUqRMVFTVixAitZrJhwwbVDY/Mh2pJKh4+fNi4cWNpHGdn5wULFqi+ 9M2CggkAAACgK1m7YCI2bNiQO3duGceVL18+LCxMjlSrmaxfv1512yMzoVqS ioiIiCpVqkjj5MiRY/Xq1aovenOhYAIAAADoSpYvmIgdO3YUKVJERnP58uU7 ceLE559/rtVM1q1bp7r5kTlQLUnF6dOnCxcuLI1ToECBzZs3q77czYiCCQAA AKAreiiYiEOHDlWqVEnGdI6OjsuXLx81apRWM1mzZo3qMwBrd/jwYaolL7Jh wwYXFxftG1z79+9XfaGbFwUTAAAAQFd0UjARQUFB7dq101Y7HTp0qFYzEePH jzcajarPA6zUsmXL7O3tqZY8Kzk5+auvvtIuoubNm586dUr1JW52FEwAAAAA XdFPwUQzcuRIW1tbGeL5+vp+/fXX2nDPz8/v0aNHqk8FrEtSUpK2GrW2ODXV kifFxMS0adNG+5qWtFJoaKjqK9sSKJgAAAAAuqK3golYtGiRu7u7jPXefvvt hQsXak9bVKxY8dq1a6rPBqzF/fv3fXx8pGPY2trOmzdPdRzrcuXKlfLly0vj uLm5zZ07V/UFbTkUTAAAAABd0WHBRGzbtq148eLaoh4//PBDsWLF5LWnp+fx 48dVnxCod+HCBS8vL617HDhwQHUc63L06NE8efJI4xQpUmTTpk2qL2WLomAC AAAA6Io+CyZCxn21atXSvkLw9ddf16lTR17b29svXbpU9TmBSvv378+RI4f2 BaSLFy+qjmNFjEbjnDlztBldqlWrdvjwYdUXsaVRMAEAAAB0RbcFE3H69Onu 3btrk1T4+Pj06NFDez148ODExETVZwYKzJ0718bGRvpA06ZN79+/rzqOFYmO jm7btq12gXTq1Ck4OFj15asABRMAAABAV/RcMNHMmjUrW7ZsMgwsVKjQsGHD tClhGzVqFBUVpfrkwHISEhJ69+6tFQSGDx/OFK9PCgoK0h5bc3Fx+eabb1Rf sspQMAEAAAB0hYKJ2LFjR9myZbXHcz7++ONcuXLJ6zfffPPcuXOqzw8s4c6d O9ozWQ4ODitWrFAdx4oYjcZvv/1WewzHy8try5Ytqi9WlSiYAAAAALpCwUQT FBTUrVs37QsGtWvXLlWqlLzIli3bkiVLZMyo+izBjPbt21e4cGE53Xnz5j15 8qTqOFbk3r17rVq10i6K9u3bnzp1SvVlqhgFEwAAAEBXKJg8afbs2dmzZ5fh Yb58+WrXrp0yvcmNGzdUnyiY3sOHD1Mew6lcuXJYWJjqRFYkMDDw9ddf19YO njZtmupL0ypQMAEAAAB0hYLJU3bv3l2+fHkZJxoMhjZt2mj1E/l3+fLlfNUk K/nll1+0eTns7OzGjx/PNL8pkpKSpk6dKs0ijVOqVKlt27apviitBQUTAAAA QFcomDwrODg4ZfWc8uXLe3t7a6+bN29+8+ZN1WcMr+rRo0cDBw5MOb+//vqr 6kRW5OLFizVr1kxZDYfHcJ5EwQQAAADQFQomL7JgwYK8efNqE4H6+flpK+nk yJFjzZo1fNUk8zp+/Pibb74pp9LGxmb06NHx8fGqE1mL5OTkOXPmuLi4SOPk ypXru+++U30JWh0KJgAAAICuUDBJxZEjR1q0aKH9tf3dd9997733tNetW7e+ deuW6lOH9ImNjR02bJjBYNCeNAkODladyIpcvny5Xr16KZP2HDp0SPXFZ40o mAAAAAC6QsHkpWbPnp07d24ZSDo5ObVv397V1VX7E/yGDRtUnz2kVXBw8Ntv v61NTTN8+PC4uDjViayF0WhcuHChm5ub9gUq5ndNBQUTAAAAQFcomKTF4cOH fX19tb+/V61atVq1atrrtm3bXrp0SfU5RGqioqKGDRtmY2Mj5+vNN988ceKE 6kRW5Nq1a40aNdI6c/369Q8cOKD6UrNqFEwAAAAAXaFgknYzZ87MkSOHDC1d XFz8/PycnZ3ltb29/cCBA2/fvq36TOJpcXFxM2bM0E6ZGDRo0KNHj1SHshbJ ycmLFi1yd3eXlpF/J0+eHBoaqvoKs3YUTAAAAABdoWCSLgcPHmzYsKE2AC9d unTjxo2119myZZswYQLjcSuRnJy8cuXKokWLamfH29s7MDBQdSgrEhoamvIt qVq1au3bt0/1hZU5UDABAAAAdIWCSQbMmDFDW0DHYDC0bt26bt262tgzX758 33//fWJiouqzqmt79uypUKGCdkZKlSq1bds2VjVKER0dPWDAAO0Bpdy5c0+a NIkvlqQdBRMAAABAVyiYZMyJEyf8/f1tbW21CWA//fTTd955RxuklyxZcuPG jQzSLe/06dMNGjTQzkKBAgWWLFmSlJSkOpS1kA65cuVKrdBnY2PTuXPno0eP qr6MMhkKJgAAAICuUDB5FT///HOlSpW0EXr16tWnTJlSrFixlP/KgFT16dWL S5cudezYUWt5bUaOx48fqw5lRf744486depo7VO+fPl169apvnQyJQomAAAA gK5QMHlFoaGhEydOzJUrl/aH+/79+0+dOlVbhlg0a9bs9OnTqk9yVnbjxo2B Awfa29trE/AOHjz4zp07qkNZkZiYmBEjRtjZ2Un7eHh4fP3119IhVV80mRUF EwAAAEBXKJiYxJEjRzp16qRNDZEnT56pU6eOHDnSxcVFK5vUqFFjzZo18fHx qs921mE0Gg8fPtyuXTvtqSjRuXPny5cvq85lRRITE7///vv8+fNrk+20adPm l19+UX2hZG4UTAAAAABdoWBiQuvWrStfvrw2fi9RosTChQsHDhyYLVs27Z28 efOOHj36xo0bqs955hYTE/P999+XLVtWa1UbG5s2bdqEhoaqzmVFjEbjxo0b vby8Uma+XblypeqLIyugYAIAAADoCgUT05KR+8yZM1MWtK1cufKOHTvmz58v g1btHVtb23bt2h0+fJhZYdPr4sWLQ4YMyZ49u9aSefLkGTVqVFhYmOpc1uXY sWPvvfee1kQFCxacMmUKz+CYCgUTAAAAQFcomJhDcHDw6NGjtYlNhI+Pz6+/ /nrw4ME2bdpoj+2IsmXLLly4MCYmRnUXsHbJyck7d+709fV97f+rWrXqypUr 4+LiVEezLn/99VerVq20JvLw8BgxYkRQUJDqSyFLoWACAAAA6AoFE/M5ceJE v379tJlMDAZDt27drl69GhYWNmrUqDx58qQs6TJo0KBz586p7gjW6M6dOzNn zixRooTWVg4ODl27dg0KClKdy+qEh4d//PHH2nQuTk5OH330EUsGmwMFEwAA AEBXKJiY24EDBzp06KAtUyJD/t69e1++fDkuLm7lypVVq1ZN+daEl5fXiBEj Tp48mZycrLpTKHbp0qWZM2fWqlXLYDBojVOoUKFJkyZFRkaqjmZ1wsLCBg4c 6OzsnDKdy969e1V3+SyLggkAAACgKxRMLGPr1q2NGjVKmcakW7du2rdKgoOD P/zww5R5OUS+fPk+/vjjHTt26OqRE6PRGBIS8sUXX6TM5qo1lDTaxo0bExMT VQe0OhcvXuzZs6e2nrKoW7euNJTqbp7FUTABAAAAdIWCiSUFBAT4+vpq05gY DIZ27dppy7skJCTs37+/f//+hQsXTikXuLq6tm3bduXKlffu3VPdTcxFDnzv 3r39+vUrVKhQyoG7ublJy6xevToqKkp1QGv0xx9/dO7cOaUXNWrUaP369aq7 ti5QMAEAAAB0hYKJ5W3btq1169baQzri/fffP3HihHY6jEbj6dOnv/zyy5Tl ibUvWtSrV++77747d+5c1nhgJyIiQsb4nTp1euqrNb169dq5c6euvlqTLiEh IdJzUnpF8+bNN23apLo76wgFEwAAAEBXKJiosnv37k6dOjk6Omrj33r16u3Z s+fJtYYvX7787bff1qlTJ2VhHZEtWzZ5Z+jQoevWrbt06VJmWZv47t27cnQT Jkxo2bLlk18mEW+//bZ0v8DAwKxRCzIHOcuHDh3y8fHRWsze3t7Pz2/Hjh2q u7DuUDABAAAAdIWCiVoHDhzo3r27tpKOKFmy5OzZs+/fv//kObp79+6KFSva tGlToECB1/5Xjhw5GjZs+Pnnn//888/Xrl2znvqJHMLBgwenTp3arl27YsWK PRXb1dW1du3a8tPz58+rTmrVYmJiFi5cmDKvi5OTU5cuXZjWVRUKJgAAAICu UDCxBkeOHOnfv3/KWsNubm7y37/++uvZ8xUeHr5ly5bRo0f7+Pjkzp37qUKE p6enr6/vyJEjFyxYIB8LDg6WzyclJZmv/xiNxnv37p09e3bPnj1Lly6dOHFi 586d33rrraeCyUi/evXqAwYMWL58uRwXXyZ5qQsXLgwePDjlkaVcuXL16tXr 0KFDqruqrlEwAQAAAHSFgon1CAoKmjp16rvvvptSZ2jQoMGmTZteVPEwGo3X rl0LCAiQ0yef9PDweO15DAZD/vz533nnnffff//jjz8eM2bMokWLtm/fHhoa evXq1fDw8MjIyKioqIcPH8bGxiYmJiYnJ8fHx8fExERHR9+5cyciIiIsLOyv v/46cODAypUrv/nmm4EDB7Zr1+69994rVqxYyiNFT7Gzs5MD6d279+LFi3/9 9VeWuUkjaXw5NSlP34jy5ctPmjRJ+obq7gkKJgAAAIC+UDCxQhs2bGjTpo2T k5M2ZC5atOiUKVMiIyNTP5VGo/Gff/5Zt27dF1980aNHDxl0y1g75Vsr5pMt W7a33nqrTp06nTp1Gjp06Pz584ODg5m4Nb3u3r07Y8aMEiVKaK1qb2/fvHnz NWvWqO6M+C8KJgAAAICuUDCxWocPHx46dGjKFKl2dnYygt6wYUNsbGy6TnFC QkJYWFhgYODGjRvnzp07cuRIf3//xo0bly1btmDBgnnz5s2VK5e7u7uLi4uD g4PBYND25ezsnC1btpw5c3p6esrHZCDv7e3dvn37wYMHT5s2bfXq1YcOHTp/ /vzDhw/N1DN1Ij4+Xs5L69atpfG1E50/f/5Bgwbx9I0VomACAAAA6AoFEyt3 +vTpOXPmeHt7p6yV4+Hh0atXr6NHj1rPFK9ILzl3J0+e7NevX65cubTTajAY qlevPnPmzJCQENWdDs9HwQQAAADQFQommcX+/fuHDRvm5eWV8ixMiRIlvvrq q4sXL6ruREiHK1eujB8//sl5ceU8Dh48eM+ePaq7GF6CggkAAACgKxRMMp2A gAB/f/8nJyepUaPGnDlzwsLCVPcmvFB4ePiCBQtq166dctZy5sz5wQcfrFu3 LjQ0VHWfQppQMAEAAAB0hYJJJhUSEiID8Pfffz9lbljx7rvvjhs37rfffuNp HSvx119/TZo0qWrVqinnyMHBoUmTJnPmzAkODlbdiZA+FEwAAAAAXaFgktkd P358woQJdevWfbJyUrx48SFDhvzyyy8vWpIY5pOcnCwnZfjw4SVLlnyyTlKr Vq0xY8YcPXpUdZdBBlEwAQAAAHSFgkmWcfLkyW+//bZFixY5cuRIGafnzp3b 39//559/joqKUt3XsrgHDx5s3br1448/zps3b0r7u7u7v//++zNmzDhx4oTq DoJXRcEEAAAA0BUKJllPSEjIjz/+2KVLl5QliYWNjU2lSpWGDx++a9eumJgY 1f0ui3j8+PG+fftGjhxZvXp1W1vblNYuUKBA586df/jhB567yUoomAAAAAC6 QsEkCwsNDQ0ICOjfv3+FChWeHM7b29vXrFnzyy+/PHToUFxcnOo+mMnEx8cf PXp07NixderUcXBweLIkVa5cud69ezOPa1ZFwQQAAADQFQomOnH8+PF58+Z1 69atdOnSBoMhZZjv5OTUoEGDr776atu2bREREar7o5WKjIzcuXPn2LFjmzRp 4urq+toTvLy8unbtOmfOnGPHjqk+yTAvCiYAAACArlAw0aEjR47MmjWrU6dO b7755mv/q1ChQq1atZowYcKePXvu3r2runsqExUVtX///smTJ7dt27Zo0aJP tVLx4sXbt28/Y8aMX375RfXJhOVQMAEAAAB0hYKJzh04cGDq1Kldu3atVKmS i4vLU5WBEiVKtG/fXj6wffv2ixcvZtU1d5KTky9fvrxz587p06c/t47k5ORU sWLFDz74YPLkyfv27VN90qAGBRMAAABAVyiYIMXp06c3btw4YcKEjh07li9f 3tHR8am6gbxTunTp1q1bf/7550uXLj1x4sS9e/dUd+F0i46ODgwMXL58+ahR o9q2bVu2bNknV2ROmealTJkyfn5+Y8eODQgICAkJUX1yoB4FEwAAAEBXKJjg RYKDg9evX//111/7+flVrlw5T548rz2Pp6dnzZo1O3bsOGTIkGnTpq1evfrQ oUPnz59XuxbPo0ePLl68ePjw4bVr186YMWPo0KGdOnWqVatWvnz5nnsUuXPn rlSpUps2bUaPHi2/EhQUpLr5YXUomAAAAAC6QsEEaXf8+PE1a9ZMmjSpV69e jRs3Llmy5LPfzXiSu7u7l5dXvXr1Onfu3K9fP+lmEyZM+O6773788ceffvpp 9+7dssHff//9ypUrd+/ejYmJefz4cVxcXEJCQlJSktFolH/ltbwj78tP7927 d/Xq1bNnz544cWLPnj0BAQFLly6dPXv2xIkTP//88/79+3fp0qVBgwalSpXy 8PBIJZWDg8Obb77ZsGHDjz76SPKsWrXq6NGjqpsWmQAFEwAAAEBXKJjgVYSG hu7evXvRokUTJ04cNGhQ586dGzZsWKFChYIFC9rb26dStTA3Ozu7AgUKlC9f vkGDBh07/r/27hhFeSgMw2ihiIW1vbW9vbW9MJ0LEOx0anENimsQW2txAbql /8K/gW8GZoLznlNIiDdfIMWFPE0+NpvN4XA4n8+32+35fHb92HhLggkAAEQR TPghr9fr8Xhcr9fT6bTf73e73Xq9Xq1Wy+VysVjM5/PZbDadTieTyXg8Ho1G w+FwMBj0+/1er/f/w8fttx23M+18+7etaSvb+nZVu7ZNaHPatDazTd5ut+0u x+Pxcrnc7/d2964fAH+NYAIAAFEEE4AKwQQAAKIIJgAVggkAAEQRTAAqBBMA AIgimABUCCYAABBFMAGoEEwAACCKYAJQIZgAAEAUwQSgQjABAIAogglAhWAC AABRBBOACsEEAACiCCYAFYIJAABEEUwAKgQTAACIIpgAVAgmAAAQRTABqBBM AAAgimACUCGYAABAFMEEoEIwAQCAKIIJQIVgAgAAUQQTgArBBAAAoggmABWC CQAARBFMACoEEwAAiCKYAFQIJgAAEEUwAagQTAAAIIpgAlAhmAAAQBTBBKBC MAEAgCiCCUCFYAIAAFEEE4AKwQQAAKIIJgAVggkAAEQRTAAqBBMAAIgimABU CCYAABBFMAGoEEwAACCKYAJQIZgAAEAUwQSgQjABAIAogglAhWACAABRBBOA CsEEAACiCCYAFYIJAABEEUwAKgQTAACIIpgAVAgmAAAQRTABqBBMAAAgimAC UCGYAABAFMEEoEIwAQCAKIIJQIVgAgAAUT4B+Iqut20AAOA3dP3mAfBmut62 AQCA39D1mwfAm+l62wYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAOD7/gEuqGvh "], {{0, 751}, {1467, 0}}, {0, 255}, ColorFunction->RGBColor], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSize->{1003., Automatic}, ImageSizeRaw->{1467, 751}, PlotRange->{{0, 1467}, {0, 751}}]], "Input",ExpressionUUID->"0cc23734-8fa9-\ 4d29-b540-be4bcb0f6088"], Cell[CellGroupData[{ Cell["\<\ Esta celda hace todo el trabajo de integraci\[OAcute]n y c\[AAcute]lculo de \ la soluci\[OAcute]n aproximada de peque\[NTilde]as oscilaciones. Pueden \ experimentar con cilindros de otros radios.\ \>", "Subsection", CellChangeTimes->{{3.829645633596376*^9, 3.8296456614512177`*^9}, { 3.829670458359045*^9, 3.829670466822683*^9}},ExpressionUUID->"c93191f8-aac1-4489-91e1-\ f3013967eb0d"], Cell[BoxData[ RowBox[{ RowBox[{"(*", RowBox[{ "Los", " ", "radios", " ", "de", " ", "los", " ", "cilindros", " ", "y", " ", "los", " ", "par\[AAcute]metros", " ", SubscriptBox["\[CapitalDelta]", "i"]}], "*)"}], "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"r0", " ", "=", "8"}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"r1", " ", "=", "5"}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"r2", " ", "=", "1"}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"\[CapitalDelta]1", " ", "=", " ", FractionBox[ RowBox[{"r0", "-", "r1"}], "r2"]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"\[CapitalDelta]2", " ", "=", " ", FractionBox[ RowBox[{"r1", "-", "r2"}], "r2"]}], ";"}], "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{"(*", RowBox[{ "Las", " ", "frecuencias", " ", "y", " ", "autovectores", " ", "del", " ", "problema", " ", "de", " ", "peque\[NTilde]as", " ", "oscilaciones"}], "*)"}], "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{ RowBox[{"M", " ", "=", " ", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"4", " ", SuperscriptBox["\[CapitalDelta]1", "2"]}], ",", " ", RowBox[{"2", " ", "\[CapitalDelta]1", " ", "\[CapitalDelta]2"}]}], "}"}], ",", " ", RowBox[{"{", RowBox[{ RowBox[{"2", "\[CapitalDelta]1", " ", "\[CapitalDelta]2"}], ",", " ", RowBox[{"2", SuperscriptBox["\[CapitalDelta]2", "2"]}]}], "}"}]}], "}"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"\[Omega]1", " ", "=", SqrtBox[ RowBox[{" ", FractionBox[ RowBox[{ "\[CapitalDelta]1", " ", "+", " ", "\[CapitalDelta]2", " ", "+", " ", SqrtBox[ RowBox[{ SuperscriptBox["\[CapitalDelta]1", "2"], "+", SuperscriptBox["\[CapitalDelta]2", "2"]}]]}], RowBox[{"2", "\[CapitalDelta]1", " ", "\[CapitalDelta]2"}]]}]]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"\[Omega]2", " ", "=", SqrtBox[ RowBox[{" ", FractionBox[ RowBox[{ "\[CapitalDelta]1", " ", "+", " ", "\[CapitalDelta]2", " ", "-", " ", SqrtBox[ RowBox[{ SuperscriptBox["\[CapitalDelta]1", "2"], "+", SuperscriptBox["\[CapitalDelta]2", "2"]}]]}], RowBox[{"2", "\[CapitalDelta]1", " ", "\[CapitalDelta]2"}]]}]]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"A1", " ", "=", " ", RowBox[{"{", RowBox[{"1", ",", " ", RowBox[{"-", FractionBox[ RowBox[{ RowBox[{"4", SuperscriptBox["\[Omega]1", "2"], "\[CapitalDelta]1"}], "-", "2"}], RowBox[{"2", SuperscriptBox["\[Omega]1", "2"], "\[CapitalDelta]2"}]]}]}], "}"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"A2", " ", "=", " ", RowBox[{"{", RowBox[{"1", ",", " ", RowBox[{"-", FractionBox[ RowBox[{ RowBox[{"4", SuperscriptBox["\[Omega]2", "2"], "\[CapitalDelta]1"}], "-", "2"}], RowBox[{"2", SuperscriptBox["\[Omega]2", "2"], "\[CapitalDelta]2"}]]}]}], "}"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"X1", " ", "=", " ", FractionBox["A1", SqrtBox[ RowBox[{"A1", " ", ".", " ", "M", " ", ".", " ", "A1"}]]]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"X2", " ", "=", " ", FractionBox["A2", SqrtBox[ RowBox[{"A2", " ", ".", " ", "M", " ", ".", " ", "A2"}]]]}], ";"}], "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{"(*", RowBox[{ "Integraci\[OAcute]n", " ", "num\[EAcute]rica", " ", "de", " ", "las", " ", "ecuaciones", " ", "de", " ", "movimiento", " ", "exactas"}], "*)"}], "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{"Module", "[", RowBox[{ RowBox[{"{", RowBox[{"x1", ",", " ", "x2", ",", " ", "t"}], "}"}], ",", " ", "\[IndentingNewLine]", RowBox[{ RowBox[{"sol", " ", "=", " ", RowBox[{ RowBox[{"NDSolve", "[", "\[IndentingNewLine]", RowBox[{ RowBox[{"{", "\[IndentingNewLine]", RowBox[{"(*", "Ecuaciones", "*)"}], "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{ RowBox[{"4", SuperscriptBox["\[CapitalDelta]1", "2"], RowBox[{ RowBox[{"\[Phi]1", "''"}], "[", "t", "]"}]}], " ", "+", " ", RowBox[{"\[CapitalDelta]1", " ", "\[CapitalDelta]2", " ", RowBox[{"(", RowBox[{ RowBox[{ RowBox[{ RowBox[{"\[Phi]2", "''"}], "[", "t", "]"}], " ", RowBox[{"(", RowBox[{"1", " ", "+", " ", RowBox[{"Cos", "[", RowBox[{ RowBox[{"\[Phi]1", "[", "t", "]"}], "-", " ", RowBox[{"\[Phi]2", "[", "t", "]"}]}], "]"}]}], ")"}]}], " ", "+", " ", RowBox[{ SuperscriptBox[ RowBox[{ RowBox[{"\[Phi]2", "'"}], "[", "t", "]"}], "2"], " ", RowBox[{"Sin", "[", RowBox[{ RowBox[{"\[Phi]1", "[", "t", "]"}], " ", "-", " ", RowBox[{"\[Phi]2", "[", "t", "]"}]}], "]"}]}]}], ")"}]}]}], " ", "\[Equal]", " ", RowBox[{ RowBox[{"-", " ", "2"}], " ", "\[CapitalDelta]1", " ", RowBox[{"Sin", "[", RowBox[{"\[Phi]1", "[", "t", "]"}], "]"}]}]}], ",", " ", "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"2", SuperscriptBox["\[CapitalDelta]2", "2"], RowBox[{ RowBox[{"\[Phi]2", "''"}], "[", "t", "]"}]}], " ", "+", " ", RowBox[{"\[CapitalDelta]1", " ", "\[CapitalDelta]2", RowBox[{"(", RowBox[{ RowBox[{ RowBox[{ RowBox[{"\[Phi]1", "''"}], "[", "t", "]"}], " ", RowBox[{"(", RowBox[{"1", " ", "+", " ", RowBox[{"Cos", "[", RowBox[{ RowBox[{"\[Phi]1", "[", "t", "]"}], " ", "-", " ", RowBox[{"\[Phi]2", "[", "t", "]"}]}], "]"}]}], ")"}]}], " ", "-", " ", RowBox[{ SuperscriptBox[ RowBox[{ RowBox[{"\[Phi]1", "'"}], "[", "t", "]"}], "2"], " ", RowBox[{"Sin", "[", RowBox[{ RowBox[{"\[Phi]1", "[", "t", "]"}], " ", "-", " ", RowBox[{"\[Phi]2", "[", "t", "]"}]}], "]"}]}]}], ")"}]}]}], " ", "\[Equal]", " ", RowBox[{ RowBox[{"-", " ", "\[CapitalDelta]2"}], " ", RowBox[{"Sin", "[", RowBox[{"\[Phi]2", "[", "t", "]"}], "]"}]}]}], ",", "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{"(*", RowBox[{"Condiciones", " ", "iniciales"}], "*)"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"\[Phi]1", "[", "0", "]"}], " ", "\[Equal]", " ", RowBox[{"(", RowBox[{"\[Phi]10", " ", "=", " ", "0"}], ")"}]}], ",", " ", "\[IndentingNewLine]", RowBox[{ RowBox[{"\[Phi]2", "[", "0", "]"}], " ", "\[Equal]", " ", RowBox[{"(", RowBox[{"\[Phi]20", " ", "=", " ", "0"}], ")"}]}], ",", "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"\[Phi]1", "'"}], "[", "0", "]"}], " ", "\[Equal]", RowBox[{"(", RowBox[{"\[Phi]1p0", " ", "=", RowBox[{"5", "/", "10"}]}], ")"}]}], ",", " ", "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"\[Phi]2", "'"}], "[", "0", "]"}], " ", "\[Equal]", " ", RowBox[{"(", RowBox[{"\[Phi]2p0", " ", "=", " ", RowBox[{"-", "1"}]}], ")"}]}]}], "\[IndentingNewLine]", "}"}], ",", " ", "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{"{", RowBox[{"\[Phi]1", ",", " ", "\[Phi]2"}], "}"}], ",", "\[IndentingNewLine]", RowBox[{"{", RowBox[{"t", ",", " ", "0", ",", " ", RowBox[{"tmax", " ", "=", " ", "400"}]}], "}"}], ",", " ", "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{"WorkingPrecision", "\[Rule]", "20"}], ",", " ", RowBox[{"MaxSteps", "\[Rule]", SuperscriptBox["10", "6"]}]}], "\[IndentingNewLine]", "]"}], "[", RowBox[{"[", "1", "]"}], "]"}]}], ";"}]}], "\[IndentingNewLine]", "]"}], "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"\[Phi]1sol", " ", "=", " ", RowBox[{ RowBox[{"sol", "[", RowBox[{"[", "1", "]"}], "]"}], "[", RowBox[{"[", "2", "]"}], "]"}]}], ",", " ", RowBox[{"\[Phi]2sol", " ", "=", " ", RowBox[{ RowBox[{"sol", "[", RowBox[{"[", "2", "]"}], "]"}], "[", RowBox[{"[", "2", "]"}], "]"}]}]}], "}"}], ";"}], "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{"(*", RowBox[{"La", " ", "energ\[IAcute]a", " ", RowBox[{"mec\[AAcute]nica", " ", "--"}], " ", "para", " ", "verificar", " ", "a", " ", "grandes", " ", "rasgos", " ", "que", " ", "la", " ", "integraci\[OAcute]n", " ", "num\[EAcute]rica", " ", "respete", " ", "su", " ", "conservaci\[OAcute]n"}], "*)"}], "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"T", "[", "t_", "]"}], ":=", " ", RowBox[{ RowBox[{"2", SuperscriptBox["\[CapitalDelta]1", "2"], SuperscriptBox[ RowBox[{ RowBox[{"\[Phi]1sol", "'"}], "[", "t", "]"}], "2"]}], "+", " ", RowBox[{ SuperscriptBox["\[CapitalDelta]2", "2"], " ", SuperscriptBox[ RowBox[{ RowBox[{"\[Phi]2sol", "'"}], "[", "t", "]"}], "2"]}], "+", " ", RowBox[{"\[CapitalDelta]1", " ", "\[CapitalDelta]2", " ", RowBox[{ RowBox[{"\[Phi]1sol", "'"}], "[", "t", "]"}], " ", RowBox[{ RowBox[{"\[Phi]2sol", "'"}], "[", "t", "]"}], " ", RowBox[{"(", " ", RowBox[{"1", " ", "+", " ", RowBox[{"Cos", "[", RowBox[{ RowBox[{"\[Phi]1sol", "[", "t", "]"}], " ", "-", " ", RowBox[{"\[Phi]2sol", "[", "t", "]"}]}], "]"}]}], ")"}]}], " ", "-", " ", RowBox[{"2", "\[CapitalDelta]1", " ", RowBox[{"Cos", "[", RowBox[{"\[Phi]1sol", "[", "t", "]"}], "]"}]}], " ", "-", " ", RowBox[{"\[CapitalDelta]2", " ", RowBox[{"Cos", "[", RowBox[{"\[Phi]2sol", "[", "t", "]"}], "]"}]}]}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"meanT", " ", "=", " ", RowBox[{ FractionBox["1", "tmax"], RowBox[{"NIntegrate", "[", RowBox[{ RowBox[{"T", "[", "t", "]"}], ",", " ", RowBox[{"{", RowBox[{"t", ",", " ", "0", ",", " ", "tmax"}], "}"}]}], "]"}]}]}], ";"}], "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{"(*", " ", RowBox[{ "La", " ", "soluci\[OAcute]n", " ", "de", " ", "peque\[NTilde]as", " ", "oscilaciones"}], " ", "*)"}], "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{ RowBox[{"\[Phi]0", " ", "=", " ", RowBox[{"{", RowBox[{"\[Phi]10", ",", " ", "\[Phi]20"}], "}"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{"\[Phi]p0", " ", "=", " ", RowBox[{"{", RowBox[{"\[Phi]1p0", ",", " ", "\[Phi]2p0"}], "}"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"\[Phi]osc", "[", "t_", "]"}], ":=", " ", RowBox[{ RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"X1", " ", ".", " ", "M", " ", ".", " ", "\[Phi]0"}], " ", RowBox[{"Cos", "[", RowBox[{"\[Omega]1", " ", "t"}], "]"}], " ", "X1"}], "+", " ", RowBox[{ FractionBox["1", "\[Omega]1"], RowBox[{"X1", ".", " ", "M", " ", ".", " ", "\[Phi]p0"}], " ", RowBox[{"Sin", "[", RowBox[{"\[Omega]1", " ", "t"}], "]"}], " ", "X1"}]}], ")"}], "+", " ", RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"X2", " ", ".", " ", "M", " ", ".", " ", "\[Phi]0"}], " ", RowBox[{"Cos", "[", RowBox[{"\[Omega]2", " ", "t"}], "]"}], " ", "X2"}], "+", " ", RowBox[{ FractionBox["1", "\[Omega]2"], RowBox[{"X2", ".", " ", "M", " ", ".", " ", "\[Phi]p0"}], " ", RowBox[{"Sin", "[", RowBox[{"\[Omega]2", " ", "t"}], "]"}], " ", "X2"}]}], ")"}]}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"\[Phi]1osc", "[", "t_", "]"}], ":=", " ", RowBox[{ RowBox[{"\[Phi]osc", "[", "t", "]"}], "[", RowBox[{"[", "1", "]"}], "]"}]}], ";"}], "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"\[Phi]2osc", "[", "t_", "]"}], ":=", " ", RowBox[{ RowBox[{"\[Phi]osc", "[", "t", "]"}], "[", RowBox[{"[", "2", "]"}], "]"}]}], ";"}], "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{"(*", RowBox[{ "Un", " ", "gr\[AAcute]fico", " ", "de", " ", "la", " ", "soluci\[OAcute]n", " ", "\"\\"", " ", "y", " ", "verificaci\[OAcute]n", " ", "de", " ", "que", " ", "la", " ", "energ\[IAcute]a", " ", "se", " ", "conserva", " ", "en", " ", "la", " ", "integraci\[OAcute]n", " ", "num\[EAcute]rica"}], " ", "*)"}], "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{"{", RowBox[{ RowBox[{"Plot", "[", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"\[Phi]1sol", "[", "t", "]"}], ",", " ", RowBox[{"\[Phi]2sol", "[", "t", "]"}]}], "}"}], ",", " ", RowBox[{"{", RowBox[{"t", ",", " ", "0", ",", " ", "200"}], "}"}], ",", " ", RowBox[{"ImageSize", "\[Rule]", "600"}]}], "]"}], ",", " ", RowBox[{"Plot", "[", RowBox[{ FractionBox[ RowBox[{ RowBox[{"T", "[", "t", "]"}], "-", "meanT"}], "meanT"], ",", " ", RowBox[{"{", RowBox[{"t", ",", " ", "0", ",", " ", "tmax"}], "}"}], ",", " ", RowBox[{"ImageSize", "\[Rule]", " ", "600"}], ",", " ", RowBox[{"PlotRange", "\[Rule]", " ", "All"}]}], "]"}]}], "}"}]}]}]], "Input", CellChangeTimes->{{3.8295717357479496`*^9, 3.8295721833506393`*^9}, { 3.82957229951915*^9, 3.8295724473536673`*^9}, {3.829572544669792*^9, 3.8295725546792355`*^9}, {3.829573427629888*^9, 3.829573445822575*^9}, { 3.829573492953061*^9, 3.829573510865293*^9}, {3.8295736014860177`*^9, 3.8295736057306676`*^9}, {3.829573670632476*^9, 3.829573673503298*^9}, { 3.8295740444336605`*^9, 3.8295740513979197`*^9}, {3.8295741604844418`*^9, 3.829574162307101*^9}, {3.829574212139044*^9, 3.829574214209008*^9}, { 3.8295743315660143`*^9, 3.829574344914747*^9}, {3.829574452669898*^9, 3.8295745197587624`*^9}, {3.8295746110917716`*^9, 3.829574612131223*^9}, { 3.8295747831860085`*^9, 3.8295748464770775`*^9}, 3.829575123622611*^9, { 3.8295752939727297`*^9, 3.8295752966140304`*^9}, {3.829575339904752*^9, 3.8295753488187943`*^9}, {3.8295754238626432`*^9, 3.8295754425603123`*^9}, {3.829575539536064*^9, 3.829575546672809*^9}, { 3.8295756495462875`*^9, 3.829575765242588*^9}, {3.829575818367502*^9, 3.8295758207322373`*^9}, {3.829575859880636*^9, 3.829575862938134*^9}, { 3.829575895766737*^9, 3.8295759175554914`*^9}, {3.8295759962140484`*^9, 3.829575997369525*^9}, {3.829576055286637*^9, 3.829576057810766*^9}, { 3.829576155262541*^9, 3.8295761594619117`*^9}, {3.8295762308795033`*^9, 3.829576242125456*^9}, {3.8295763227367573`*^9, 3.8295763273958693`*^9}, { 3.8295763926831436`*^9, 3.829576394797065*^9}, {3.8295764922660775`*^9, 3.829576492562626*^9}, {3.829576881397297*^9, 3.8295768917692904`*^9}, { 3.829576968219331*^9, 3.8295769712681*^9}, {3.8295798695799713`*^9, 3.8295798906010838`*^9}, {3.8295799884568624`*^9, 3.829579992064883*^9}, { 3.829580047018739*^9, 3.8295800491118927`*^9}, {3.8295865671732225`*^9, 3.8295865938618984`*^9}, {3.8295870012706223`*^9, 3.8295870074079685`*^9}, 3.8295870770246553`*^9, 3.829587160271923*^9, {3.8295872408418903`*^9, 3.8295872429987197`*^9}, {3.829587280813631*^9, 3.829587281536921*^9}, 3.829596055173258*^9, {3.8295975863663917`*^9, 3.8295976355905194`*^9}, { 3.829597719538785*^9, 3.829597735110322*^9}, 3.82959779653664*^9, { 3.8295978488962784`*^9, 3.8295978512041655`*^9}, 3.82959788153685*^9, { 3.8295979142319565`*^9, 3.829597915212565*^9}, 3.829597954890298*^9, { 3.8295980065142517`*^9, 3.8295980305876117`*^9}, {3.829598087827817*^9, 3.8295981080297637`*^9}, 3.8295981659253902`*^9, {3.8295982214958477`*^9, 3.829598222402072*^9}, {3.8295982970142565`*^9, 3.829598347084938*^9}, { 3.8295983770988317`*^9, 3.82959837720771*^9}, {3.829598417322132*^9, 3.8295985054975157`*^9}, {3.8295985613469987`*^9, 3.82959865943569*^9}, { 3.8295987103995295`*^9, 3.8295987863524075`*^9}, {3.8295988346292667`*^9, 3.8295990138213596`*^9}, {3.8295990757072134`*^9, 3.8295990758190603`*^9}, {3.8295991208372574`*^9, 3.8295991209460893`*^9}, {3.829599193393213*^9, 3.829599193486644*^9}, 3.8296406630228004`*^9, {3.829641125192794*^9, 3.829641184454321*^9}, 3.8296413568304024`*^9, 3.8296415157047887`*^9, {3.8296415881141696`*^9, 3.8296415888512077`*^9}, {3.829641637423376*^9, 3.8296416561102505`*^9}, { 3.8296418713851166`*^9, 3.829641898427003*^9}, {3.8296425187266407`*^9, 3.829642528179966*^9}, {3.829642568991843*^9, 3.8296429227770414`*^9}, { 3.8296430014740148`*^9, 3.829643064658807*^9}, {3.8296433840462275`*^9, 3.8296433867502003`*^9}, {3.8296439927735214`*^9, 3.8296440534787183`*^9}, {3.829644793600258*^9, 3.8296448680576715`*^9}, { 3.829645677175314*^9, 3.829645711357959*^9}, {3.8296457777467275`*^9, 3.8296457950044355`*^9}},ExpressionUUID->"57c00383-7b93-44cb-9b9f-\ 72306f7a6257"] }, Open ]], Cell[CellGroupData[{ Cell["\<\ Esta celda muestra una animaci\[OAcute]n del sistema para la soluci\[OAcute]n \ \[OpenCurlyDoubleQuote]exacta\[CloseCurlyDoubleQuote].\ \>", "Subsection", CellChangeTimes->{{3.8296457463244047`*^9, 3.829645771956212*^9}},ExpressionUUID->"63586b29-1d1d-4922-8dc5-\ ffe6945abf76"], Cell[BoxData[ RowBox[{ RowBox[{"(*", RowBox[{ "Se", " ", "incluyen", " ", "distintas", " ", "marcas", " ", "para", " ", "seguir", " ", "las", " ", "posiciones", " ", "y", " ", "orientaciones", " ", "de", " ", "los", " ", "cilindros"}], "*)"}], "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"mr", " ", "=", " ", "0.15"}], ";"}], "\[IndentingNewLine]", RowBox[{"DynamicModule", "[", RowBox[{ RowBox[{"{", "t", "}"}], ",", "\[IndentingNewLine]", RowBox[{"Manipulate", "[", "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"\[Phi]1t", " ", "=", " ", RowBox[{"\[Phi]1sol", "[", "t", "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"\[Phi]2t", " ", "=", " ", RowBox[{"\[Phi]2sol", "[", "t", "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"\[Alpha]1t", "=", " ", RowBox[{ RowBox[{"-", FractionBox["\[CapitalDelta]1", "r1"]}], "\[Phi]1t"}]}], ";", "\[IndentingNewLine]", RowBox[{"\[Alpha]2t", " ", "=", " ", RowBox[{ RowBox[{"-", FractionBox["1", "r2"]}], RowBox[{"(", RowBox[{ RowBox[{"\[CapitalDelta]1", " ", "\[Phi]1t"}], " ", "+", " ", RowBox[{"\[CapitalDelta]2", " ", "\[Phi]2t"}]}], ")"}]}]}], ";", "\[IndentingNewLine]", RowBox[{"r1t", " ", "=", " ", RowBox[{ RowBox[{"(", RowBox[{"r0", " ", "-", " ", "r1"}], ")"}], " ", RowBox[{"{", RowBox[{ RowBox[{"Sin", "[", "\[Phi]1t", "]"}], ",", " ", RowBox[{"-", RowBox[{"Cos", "[", "\[Phi]1t", "]"}]}]}], "}"}]}]}], ";", "\[IndentingNewLine]", RowBox[{"r2t", " ", "=", " ", RowBox[{"r1t", " ", "+", " ", RowBox[{ RowBox[{"(", RowBox[{"r1", " ", "-", " ", "r2"}], ")"}], " ", RowBox[{"{", RowBox[{ RowBox[{"Sin", "[", "\[Phi]2t", "]"}], ",", " ", RowBox[{"-", RowBox[{"Cos", "[", "\[Phi]2t", "]"}]}]}], "}"}]}]}]}], ";", "\[IndentingNewLine]", RowBox[{"marca11", " ", "=", " ", RowBox[{"r1t", " ", "+", " ", RowBox[{"(", RowBox[{"punto1", " ", "=", " ", RowBox[{ RowBox[{"(", RowBox[{"r1", "-", "mr"}], ")"}], " ", RowBox[{"{", RowBox[{ RowBox[{"Sin", "[", "\[Alpha]1t", "]"}], ",", " ", RowBox[{"-", RowBox[{"Cos", "[", "\[Alpha]1t", "]"}]}]}], "}"}]}]}], ")"}]}]}], ";", "\[IndentingNewLine]", RowBox[{"marca12", " ", "=", " ", RowBox[{"r1t", " ", "-", " ", "punto1"}]}], ";", "\[IndentingNewLine]", RowBox[{"marca13", " ", "=", " ", RowBox[{"r1t", " ", "-", " ", RowBox[{"(", RowBox[{"punto2", " ", "=", RowBox[{ RowBox[{"(", RowBox[{"r1", "-", "mr"}], ")"}], " ", RowBox[{"{", RowBox[{ RowBox[{"Cos", "[", "\[Alpha]1t", "]"}], ",", " ", RowBox[{"Sin", "[", "\[Alpha]1t", "]"}]}], "}"}]}]}], ")"}]}]}], ";", "\[IndentingNewLine]", RowBox[{"marca14", " ", "=", " ", RowBox[{"r1t", " ", "+", " ", "punto2"}]}], ";", "\[IndentingNewLine]", RowBox[{"marca21", " ", "=", " ", RowBox[{"r2t", " ", "+", " ", RowBox[{"(", RowBox[{"punto3", " ", "=", " ", RowBox[{ RowBox[{"(", RowBox[{"r2", "-", "mr"}], ")"}], " ", RowBox[{"{", RowBox[{ RowBox[{"Sin", "[", "\[Alpha]2t", "]"}], ",", " ", RowBox[{"-", RowBox[{"Cos", "[", "\[Alpha]2t", "]"}]}]}], "}"}]}]}], ")"}]}]}], ";", "\[IndentingNewLine]", RowBox[{"marca22", " ", "=", " ", RowBox[{"r2t", " ", "-", " ", "punto3"}]}], ";", "\[IndentingNewLine]", RowBox[{"marca23", " ", "=", " ", RowBox[{"r2t", " ", "+", " ", RowBox[{"(", RowBox[{"punto4", " ", "=", " ", RowBox[{ RowBox[{"(", RowBox[{"r2", "-", "mr"}], ")"}], " ", RowBox[{"{", RowBox[{ RowBox[{"Cos", "[", "\[Alpha]2t", "]"}], ",", " ", RowBox[{"Sin", "[", "\[Alpha]2t", "]"}]}], "}"}]}]}], ")"}]}]}], ";", "\[IndentingNewLine]", RowBox[{"marca24", " ", "=", " ", RowBox[{"r2t", " ", "-", " ", "punto4"}]}], ";", "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{"base", " ", "=", " ", RowBox[{"Graphics", "[", RowBox[{"{", RowBox[{"Red", ",", " ", "Thick", ",", " ", RowBox[{"Circle", "[", RowBox[{ RowBox[{"{", RowBox[{"0", ",", "0"}], "}"}], ",", " ", "r0"}], "]"}], ",", " ", RowBox[{"Disk", "[", RowBox[{ RowBox[{"{", RowBox[{"0", ",", "0"}], "}"}], ",", " ", "0.2"}], "]"}]}], "}"}], "]"}]}], ";", "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{"Show", "[", "\[IndentingNewLine]", RowBox[{"base", ",", "\[IndentingNewLine]", RowBox[{"Graphics", "[", RowBox[{"{", RowBox[{"Magenta", ",", "Thick", ",", " ", "Dashed", ",", " ", RowBox[{"Line", "[", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"0", ",", "0"}], "}"}], " ", ",", " ", "r1t"}], "}"}], "]"}], ",", " ", RowBox[{"Line", "[", RowBox[{"{", RowBox[{"r1t", ",", " ", "r2t"}], "}"}], "]"}]}], "}"}], "]"}], ",", " ", "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{"Graphics", "[", "\[IndentingNewLine]", RowBox[{"{", RowBox[{"Cyan", ",", " ", "Thick", ",", " ", RowBox[{"Circle", "[", RowBox[{"r1t", ",", " ", "r1"}], "]"}], ",", " ", "White", ",", " ", RowBox[{"Circle", "[", RowBox[{"r2t", ",", " ", "r2"}], "]"}], ",", " ", "\[IndentingNewLine]", "Cyan", ",", " ", RowBox[{"Line", "[", RowBox[{"{", RowBox[{"marca11", ",", " ", "marca12"}], "}"}], "]"}], ",", " ", RowBox[{"Line", "[", RowBox[{"{", RowBox[{"marca13", ",", " ", "marca14"}], "}"}], "]"}], ",", "\[IndentingNewLine]", "Pink", ",", " ", RowBox[{"Disk", "[", RowBox[{"marca11", ",", " ", "mr"}], "]"}], ",", " ", "Cyan", ",", " ", RowBox[{"Disk", "[", RowBox[{"marca12", ",", " ", "mr"}], "]"}], ",", " ", RowBox[{"Disk", "[", RowBox[{"marca13", ",", " ", "mr"}], "]"}], ",", " ", RowBox[{"Disk", "[", RowBox[{"marca14", ",", " ", "mr"}], "]"}], ",", "\[IndentingNewLine]", "White", ",", " ", RowBox[{"Line", "[", RowBox[{"{", RowBox[{"marca21", ",", " ", "marca22"}], "}"}], "]"}], ",", " ", RowBox[{"Line", "[", RowBox[{"{", RowBox[{"marca23", ",", " ", "marca24"}], "}"}], "]"}], ",", " ", "\[IndentingNewLine]", "Green", ",", " ", RowBox[{"Disk", "[", RowBox[{"marca21", ",", " ", "mr"}], "]"}], ",", " ", "White", ",", " ", RowBox[{"Disk", "[", RowBox[{"marca22", ",", " ", "mr"}], "]"}], ",", " ", RowBox[{"Disk", "[", RowBox[{"marca23", ",", " ", "mr"}], "]"}], ",", " ", RowBox[{"Disk", "[", RowBox[{"marca24", ",", " ", "mr"}], "]"}]}], "\[IndentingNewLine]", "}"}], "\[IndentingNewLine]", "]"}], ",", "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{"PlotRange", "\[Rule]", " ", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{ RowBox[{"-", "r0"}], "-", "2"}], ",", " ", RowBox[{"r0", "+", "2"}]}], "}"}], ",", " ", RowBox[{"{", RowBox[{ RowBox[{ RowBox[{"-", "r0"}], "-", "2"}], ",", " ", RowBox[{"r0", "+", "2"}]}], "}"}]}], "}"}]}], ",", " ", "\[IndentingNewLine]", RowBox[{"ImageSize", "\[Rule]", " ", "600"}], ",", "\[IndentingNewLine]", RowBox[{"Background", "\[Rule]", " ", "Black"}]}], "\[IndentingNewLine]", "]"}]}], ",", " ", "\[IndentingNewLine]", RowBox[{"{", RowBox[{ "t", ",", " ", "0", ",", " ", "tmax", ",", " ", "0.01", ",", " ", RowBox[{"Appearance", "\[Rule]", " ", "\"\\""}], ",", " ", RowBox[{"AnimationRate", "\[Rule]", " ", "5"}]}], "}"}]}], "\[IndentingNewLine]", "]"}]}], "\[IndentingNewLine]", "]"}]}]}]], "Input", CellChangeTimes->{{3.8295724902403603`*^9, 3.829572512388049*^9}, { 3.829572558673023*^9, 3.829572666410096*^9}, {3.829572696950465*^9, 3.829573081528413*^9}, {3.8295731162731*^9, 3.829573162994113*^9}, 3.829575555539193*^9, 3.8295765106060195`*^9, {3.8295866453834724`*^9, 3.8295867383128624`*^9}, {3.829586772736455*^9, 3.8295868721079016`*^9}, 3.829586907429874*^9, 3.8295960668327475`*^9, 3.829599571125992*^9, { 3.8295996176675873`*^9, 3.829599805706154*^9}, {3.8295998397935295`*^9, 3.829599945345705*^9}, {3.829639921538787*^9, 3.8296399257068105`*^9}, { 3.8296406115020094`*^9, 3.829640629777549*^9}, {3.8296414416224766`*^9, 3.829641442861165*^9}, {3.82964153741028*^9, 3.8296415379860086`*^9}, { 3.829641751512209*^9, 3.829641753162798*^9}, {3.8296431039030633`*^9, 3.8296431133674645`*^9}, {3.8296434304533105`*^9, 3.829643776521866*^9}, { 3.829644410649719*^9, 3.8296444579143963`*^9}, {3.8296444888008533`*^9, 3.829644489780199*^9}, {3.829644608681241*^9, 3.8296446239551787`*^9}, { 3.8296446689242516`*^9, 3.829644714269697*^9}, {3.829644894007022*^9, 3.8296451267156415`*^9}, {3.8296458027877345`*^9, 3.8296458821200914`*^9}, {3.829646072861659*^9, 3.829646112336958*^9}},ExpressionUUID->"4519cfd3-6058-4cab-8b64-\ dc38627c5c73"] }, Open ]], Cell[CellGroupData[{ Cell["De nuevo, el gr\[AAcute]fico de la soluci\[OAcute]n \ \[OpenCurlyDoubleQuote]exacta\[CloseCurlyDoubleQuote].", "Subsection", CellChangeTimes->{{3.8296460282344275`*^9, 3.8296460434084015`*^9}},ExpressionUUID->"82633925-8442-42a9-95b2-\ c714eee4af1f"], Cell[BoxData[ RowBox[{"Plot", "[", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"\[Phi]1sol", "[", "t", "]"}], ",", " ", RowBox[{"\[Phi]2sol", "[", "t", "]"}]}], "}"}], ",", " ", RowBox[{"{", RowBox[{"t", ",", " ", "0", ",", " ", "tmax"}], "}"}], ",", " ", RowBox[{"ImageSize", "\[Rule]", "600"}]}], "]"}]], "Input", CellChangeTimes->CompressedData[" 1:eJxTTMoPSmViYGAQAWIQrWbd/D2k441jj/fNXyA6IPoxUyiQ/jNNhxlEX/iu wwOiw87og2m198yCIJpjNqswiO7Y+V0MRLP/+gWma0T2uYLoEpFANxDdZC5w ACy+bR6Y3vS05DiInrXa8CSIvrzz5RMQfeP3ZzCt36+gHQakryy4DaZfvXtp CqKZNd6AaVbn4z3RQHrNrvNgem5a5XQQXRbsNQNE5wjcWwyiDYr+gGnb1beZ E4H0RsdHYNqu5gEHiM55/gJMq2seeFsGpIN+3wTTz//XO5UDaaGfjB4gmqtw 7ysQnc54DkyzvNWPrADSae6XokE0AB1PpUs= "],ExpressionUUID->"4a84ebbd-aee7-4a95-b531-71ca7a5821d2"] }, Open ]], Cell[CellGroupData[{ Cell["\<\ Comparaci\[OAcute]n entre la soluci\[OAcute]n \[OpenCurlyDoubleQuote]exacta\ \[CloseCurlyDoubleQuote] y la de peque\[NTilde]as oscilaciones.\ \>", "Subsection", CellChangeTimes->{{3.829646018480087*^9, 3.8296460405321627`*^9}},ExpressionUUID->"1ea3b3b2-397c-4246-8b4d-\ aa3ae007fa05"], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"Plot", "[", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"\[Phi]1sol", "[", "t", "]"}], ",", " ", RowBox[{"\[Phi]1osc", "[", "t", "]"}]}], "}"}], ",", " ", RowBox[{"{", RowBox[{"t", ",", " ", "0", ",", " ", "50"}], "}"}], ",", " ", RowBox[{"ImageSize", "\[Rule]", "600"}]}], "]"}], ",", " ", RowBox[{"Plot", "[", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"\[Phi]2sol", "[", "t", "]"}], ",", " ", RowBox[{"\[Phi]2osc", "[", "t", "]"}]}], "}"}], ",", " ", RowBox[{"{", RowBox[{"t", ",", " ", "0", ",", " ", "100"}], "}"}], ",", " ", RowBox[{"ImageSize", "\[Rule]", "600"}]}], "]"}]}], "}"}]], "Input", CellChangeTimes->{{3.8295870370752006`*^9, 3.8295870373613167`*^9}, { 3.829643151579488*^9, 3.829643185433663*^9}, {3.8296438499769754`*^9, 3.8296438598953495`*^9}, {3.829645149267709*^9, 3.829645173433631*^9}, { 3.829646014344696*^9, 3.829646016079403*^9}},ExpressionUUID->"7faccc3e-9c3c-4a2f-967f-\ a018391fc7cf"] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell["Precisi\[OAcute]n de la integraci\[OAcute]n", "Section", CellChangeTimes->{{3.829599213825012*^9, 3.829599218665546*^9}},ExpressionUUID->"83d39d8a-35ea-4e3c-b142-\ cbe397a85237"], Cell[CellGroupData[{ Cell["\<\ Integra num\[EAcute]ricamente el sistema variando la precisi\[OAcute]n \ \[OpenCurlyDoubleQuote]WorkingPrecision\[CloseCurlyDoubleQuote], para una \ condici\[OAcute]n inicial que da amplitudes de movimiento grandes. Puede tomar algunos minutos\ \>", "Subsection", CellChangeTimes->{ 3.829645968798711*^9},ExpressionUUID->"f1609241-0ef2-47e3-bb5f-\ 97d23b9dea56"], Cell[BoxData[ RowBox[{"Module", "[", RowBox[{ RowBox[{"{", RowBox[{ "r0", ",", " ", "r1", ",", " ", "r2", ",", " ", "\[CapitalDelta]1", ",", " ", "\[CapitalDelta]2", ",", "\[Phi]10", ",", " ", "\[Phi]20", ",", " ", "\[Phi]1p0", ",", " ", "\[Phi]2p0", ",", " ", "tmax", ",", " ", "\[Phi]1sol", ",", " ", "sol", ",", " ", "\[Phi]2sol"}], "}"}], ",", "\[IndentingNewLine]", RowBox[{ RowBox[{"r0", " ", "=", "8"}], ";", "\[IndentingNewLine]", RowBox[{"r1", " ", "=", "5"}], ";", "\[IndentingNewLine]", RowBox[{"r2", " ", "=", "1"}], ";", "\[IndentingNewLine]", RowBox[{"\[CapitalDelta]1", " ", "=", " ", FractionBox[ RowBox[{"r0", "-", "r1"}], "r2"]}], ";", "\[IndentingNewLine]", RowBox[{"\[CapitalDelta]2", " ", "=", " ", FractionBox[ RowBox[{"r1", "-", "r2"}], "r2"]}], ";", "\[IndentingNewLine]", "\[IndentingNewLine]", RowBox[{"Monitor", "[", "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"tplot", " ", "=", " ", RowBox[{"Table", "[", "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"Module", "[", RowBox[{ RowBox[{"{", RowBox[{"\[Phi]1", ",", " ", "\[Phi]2", ",", " ", "t"}], "}"}], ",", " ", "\[IndentingNewLine]", RowBox[{ RowBox[{"sol", " ", "=", " ", RowBox[{ RowBox[{"NDSolve", "[", "\[IndentingNewLine]", RowBox[{ RowBox[{"{", "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{ RowBox[{"4", SuperscriptBox["\[CapitalDelta]1", "2"], RowBox[{ RowBox[{"\[Phi]1", "''"}], "[", "t", "]"}]}], " ", "+", " ", RowBox[{"\[CapitalDelta]1", " ", "\[CapitalDelta]2", " ", RowBox[{"(", RowBox[{ RowBox[{ RowBox[{ RowBox[{"\[Phi]2", "''"}], "[", "t", "]"}], " ", RowBox[{"(", RowBox[{"1", " ", "+", " ", RowBox[{"Cos", "[", RowBox[{ RowBox[{"\[Phi]1", "[", "t", "]"}], "-", " ", RowBox[{"\[Phi]2", "[", "t", "]"}]}], "]"}]}], ")"}]}], " ", "+", " ", RowBox[{ SuperscriptBox[ RowBox[{ RowBox[{"\[Phi]2", "'"}], "[", "t", "]"}], "2"], " ", RowBox[{"Sin", "[", RowBox[{ RowBox[{"\[Phi]1", "[", "t", "]"}], " ", "-", " ", RowBox[{"\[Phi]2", "[", "t", "]"}]}], "]"}]}]}], ")"}]}]}], " ", "\[Equal]", " ", RowBox[{ RowBox[{"-", " ", "2"}], " ", "\[CapitalDelta]1", " ", RowBox[{"Sin", "[", RowBox[{"\[Phi]1", "[", "t", "]"}], "]"}]}]}], ",", " ", "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"2", SuperscriptBox["\[CapitalDelta]2", "2"], RowBox[{ RowBox[{"\[Phi]2", "''"}], "[", "t", "]"}]}], " ", "+", " ", RowBox[{"\[CapitalDelta]1", " ", "\[CapitalDelta]2", RowBox[{"(", RowBox[{ RowBox[{ RowBox[{ RowBox[{"\[Phi]1", "''"}], "[", "t", "]"}], " ", RowBox[{"(", RowBox[{"1", " ", "+", " ", RowBox[{"Cos", "[", RowBox[{ RowBox[{"\[Phi]1", "[", "t", "]"}], " ", "-", " ", RowBox[{"\[Phi]2", "[", "t", "]"}]}], "]"}]}], ")"}]}], " ", "-", " ", RowBox[{ SuperscriptBox[ RowBox[{ RowBox[{"\[Phi]1", "'"}], "[", "t", "]"}], "2"], " ", RowBox[{"Sin", "[", RowBox[{ RowBox[{"\[Phi]1", "[", "t", "]"}], " ", "-", " ", RowBox[{"\[Phi]2", "[", "t", "]"}]}], "]"}]}]}], ")"}]}]}], " ", "\[Equal]", " ", RowBox[{ RowBox[{"-", " ", "\[CapitalDelta]2"}], " ", RowBox[{"Sin", "[", RowBox[{"\[Phi]2", "[", "t", "]"}], "]"}]}]}], ",", "\[IndentingNewLine]", RowBox[{ RowBox[{"\[Phi]1", "[", "0", "]"}], " ", "\[Equal]", " ", RowBox[{"(", RowBox[{"\[Phi]10", " ", "=", " ", "0"}], ")"}]}], ",", " ", "\[IndentingNewLine]", RowBox[{ RowBox[{"\[Phi]2", "[", "0", "]"}], " ", "\[Equal]", " ", RowBox[{"(", RowBox[{"\[Phi]20", " ", "=", " ", "0"}], ")"}]}], ",", "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"\[Phi]1", "'"}], "[", "0", "]"}], " ", "\[Equal]", RowBox[{"(", RowBox[{"\[Phi]1p0", " ", "=", RowBox[{"12", "/", "10"}]}], ")"}]}], ",", " ", "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"\[Phi]2", "'"}], "[", "0", "]"}], " ", "\[Equal]", " ", RowBox[{"(", RowBox[{"\[Phi]2p0", " ", "=", " ", RowBox[{"-", "1"}]}], ")"}]}]}], "\[IndentingNewLine]", "}"}], ",", " ", "\[IndentingNewLine]", RowBox[{"{", RowBox[{"\[Phi]1", ",", " ", "\[Phi]2"}], "}"}], ",", "\[IndentingNewLine]", RowBox[{"{", RowBox[{"t", ",", " ", "0", ",", " ", RowBox[{"tmax", " ", "=", " ", "400"}]}], "}"}], "\[IndentingNewLine]", ",", " ", RowBox[{"WorkingPrecision", "\[Rule]", "n"}], ",", " ", RowBox[{"MaxSteps", "\[Rule]", SuperscriptBox["10", "8"]}]}], "\[IndentingNewLine]", "]"}], "[", RowBox[{"[", "1", "]"}], "]"}]}], ";"}]}], "\[IndentingNewLine]", "]"}], ";", "\[IndentingNewLine]", RowBox[{"{", RowBox[{ RowBox[{"\[Phi]1sol", " ", "=", " ", RowBox[{ RowBox[{"sol", "[", RowBox[{"[", "1", "]"}], "]"}], "[", RowBox[{"[", "2", "]"}], "]"}]}], ",", " ", RowBox[{"\[Phi]2sol", " ", "=", " ", RowBox[{ RowBox[{"sol", "[", RowBox[{"[", "2", "]"}], "]"}], "[", RowBox[{"[", "2", "]"}], "]"}]}]}], "}"}], ";", "\[IndentingNewLine]", RowBox[{"gtemp", " ", "=", " ", RowBox[{"Plot", "[", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"\[Phi]1sol", "[", "t", "]"}], ",", " ", RowBox[{"\[Phi]2sol", "[", "t", "]"}]}], "}"}], ",", " ", RowBox[{"{", RowBox[{"t", ",", " ", "0", ",", " ", "tmax"}], "}"}], ",", " ", RowBox[{"ImageSize", "\[Rule]", "600"}]}], "]"}]}]}], ",", "\[IndentingNewLine]", RowBox[{"{", RowBox[{"n", ",", " ", "20", ",", " ", "50"}], "}"}]}], "\[IndentingNewLine]", "]"}]}], ";"}], ",", " ", "\[IndentingNewLine]", RowBox[{"{", RowBox[{"n", ",", " ", RowBox[{"Show", "[", RowBox[{"gtemp", ",", " ", RowBox[{"ImageSize", "\[Rule]", " ", "600"}]}], "]"}]}], "}"}]}], "\[IndentingNewLine]", "]"}]}]}], "\[IndentingNewLine]", "]"}]], "Input", CellChangeTimes->{{3.8295717357479496`*^9, 3.8295721833506393`*^9}, { 3.82957229951915*^9, 3.8295724473536673`*^9}, {3.829572544669792*^9, 3.8295725546792355`*^9}, {3.829573427629888*^9, 3.829573445822575*^9}, { 3.829573492953061*^9, 3.829573510865293*^9}, {3.8295736014860177`*^9, 3.8295736057306676`*^9}, {3.829573670632476*^9, 3.829573673503298*^9}, { 3.8295740444336605`*^9, 3.8295740513979197`*^9}, {3.8295741604844418`*^9, 3.829574162307101*^9}, {3.829574212139044*^9, 3.829574214209008*^9}, { 3.8295743315660143`*^9, 3.829574344914747*^9}, {3.829574452669898*^9, 3.8295745197587624`*^9}, {3.8295746110917716`*^9, 3.829574612131223*^9}, { 3.8295747831860085`*^9, 3.8295748464770775`*^9}, 3.829575123622611*^9, { 3.8295752939727297`*^9, 3.8295752966140304`*^9}, {3.829575339904752*^9, 3.8295753488187943`*^9}, {3.8295754238626432`*^9, 3.8295754425603123`*^9}, {3.829575539536064*^9, 3.829575546672809*^9}, { 3.8295756495462875`*^9, 3.829575765242588*^9}, {3.829575818367502*^9, 3.8295758207322373`*^9}, {3.829575859880636*^9, 3.829575862938134*^9}, { 3.829575895766737*^9, 3.8295759175554914`*^9}, {3.8295759962140484`*^9, 3.829575997369525*^9}, {3.829576055286637*^9, 3.829576057810766*^9}, { 3.829576155262541*^9, 3.8295761594619117`*^9}, {3.8295762308795033`*^9, 3.829576242125456*^9}, {3.8295763227367573`*^9, 3.8295763273958693`*^9}, { 3.8295763926831436`*^9, 3.829576394797065*^9}, {3.8295764922660775`*^9, 3.829576492562626*^9}, {3.829576881397297*^9, 3.8295768917692904`*^9}, { 3.829576968219331*^9, 3.8295769712681*^9}, {3.8295798695799713`*^9, 3.8295798906010838`*^9}, {3.8295799884568624`*^9, 3.829579992064883*^9}, { 3.829580047018739*^9, 3.8295800491118927`*^9}, {3.8295865671732225`*^9, 3.8295865938618984`*^9}, {3.8295870012706223`*^9, 3.8295870074079685`*^9}, 3.8295870770246553`*^9, 3.829587160271923*^9, {3.8295872408418903`*^9, 3.8295872429987197`*^9}, {3.829587280813631*^9, 3.829587281536921*^9}, 3.829596055173258*^9, {3.8295975863663917`*^9, 3.8295976355905194`*^9}, { 3.829597719538785*^9, 3.829597735110322*^9}, 3.82959779653664*^9, { 3.8295978488962784`*^9, 3.8295978512041655`*^9}, 3.82959788153685*^9, { 3.8295979142319565`*^9, 3.829597915212565*^9}, 3.829597954890298*^9, { 3.8295980065142517`*^9, 3.8295980305876117`*^9}, {3.829598087827817*^9, 3.8295981080297637`*^9}, 3.8295981659253902`*^9, {3.8295982214958477`*^9, 3.829598222402072*^9}, {3.8295982970142565`*^9, 3.829598347084938*^9}, { 3.8295983770988317`*^9, 3.82959837720771*^9}, {3.829598417322132*^9, 3.8295985054975157`*^9}, {3.8295985613469987`*^9, 3.82959865943569*^9}, { 3.8295987103995295`*^9, 3.8295987863524075`*^9}, {3.8295988346292667`*^9, 3.8295990138213596`*^9}, {3.8295990757072134`*^9, 3.8295990758190603`*^9}, {3.8295991208372574`*^9, 3.8295991209460893`*^9}, {3.829599193393213*^9, 3.829599193486644*^9}, { 3.829599238264779*^9, 3.829599321543583*^9}, {3.8296432567283745`*^9, 3.829643362876795*^9}, {3.829643869037814*^9, 3.8296438903463583`*^9}, { 3.8296439239167376`*^9, 3.829643953525178*^9}, {3.8296440799117384`*^9, 3.8296441924461746`*^9}, {3.8296442415177217`*^9, 3.8296442497935658`*^9}, {3.829644363471789*^9, 3.829644372062296*^9}, { 3.8296452411345253`*^9, 3.8296453264435167`*^9}, {3.8296459595290866`*^9, 3.8296459638389163`*^9}},ExpressionUUID->"ef61eede-2dbe-45ec-8262-\ fe1ce6e37d57"] }, Open ]], Cell[CellGroupData[{ Cell["\<\ Este gr\[AAcute]fico muestra como cambia la soluci\[OAcute]n a medida que se \ aumenta la precisi\[OAcute]n -- Notar que a medida que aumenta la precisi\[OAcute]n el comportamiento a \ largos tiempos se va estabilizando.\ \>", "Subsection", CellChangeTimes->{{3.8296459872794247`*^9, 3.829645990764109*^9}},ExpressionUUID->"f72db277-ff9f-4cd9-abe8-\ e363c06618cc"], Cell[BoxData[ RowBox[{"Manipulate", "[", RowBox[{ RowBox[{"Show", "[", RowBox[{ RowBox[{"tplot", "[", RowBox[{"[", "i", "]"}], "]"}], ",", " ", RowBox[{"PlotRange", "\[Rule]", " ", RowBox[{"{", RowBox[{ RowBox[{"-", "25"}], ",", " ", "25"}], "}"}]}]}], "]"}], ",", " ", RowBox[{"{", RowBox[{"i", ",", " ", "1", ",", " ", RowBox[{"Length", "@", "tplot"}], ",", " ", "1", ",", " ", RowBox[{"Appearance", "\[Rule]", " ", "\"\\""}]}], "}"}]}], "]"}]], "Input", CellChangeTimes->{{3.8295994021586494`*^9, 3.8295994177773724`*^9}, { 3.8295994646999707`*^9, 3.8295994788079987`*^9}, {3.829644219494793*^9, 3.829644220994635*^9}, {3.829644266520382*^9, 3.829644343608214*^9}, { 3.8296455025905886`*^9, 3.829645508777566*^9}, {3.829645983116995*^9, 3.829645984410142*^9}},ExpressionUUID->"e21abe5b-37b6-4594-b3a0-\ 85652574e40e"] }, Open ]], Cell[CellGroupData[{ Cell["Todos los gr\[AAcute]ficos en uno solo.", "Subsection", CellChangeTimes->{{3.8296460006175327`*^9, 3.829646003607242*^9}},ExpressionUUID->"fc881f63-ef80-45e1-b30a-\ db0d4b21666a"], Cell[BoxData[ RowBox[{"Show", "[", RowBox[{"tplot", ",", " ", RowBox[{"PlotRange", "\[Rule]", " ", "All"}]}], "]"}]], "Input", CellChangeTimes->{{3.8296455164550357`*^9, 3.829645531285616*^9}, { 3.829645926478454*^9, 3.8296459296040373`*^9}, {3.8296459977309656`*^9, 3.829645998405452*^9}},ExpressionUUID->"b0d7293d-5275-412d-80e6-\ 0a4a3887dc07"] }, Open ]] }, Open ]] }, WindowSize->{1920, 997}, WindowMargins->{{-8, Automatic}, {Automatic, -8}}, FrontEndVersion->"11.2 for Microsoft Windows (64-bit) (September 10, 2017)", StyleDefinitions->"Default.nb" ] (* End of Notebook Content *) (* Internal cache information *) (*CellTagsOutline CellTagsIndex->{} *) (*CellTagsIndex CellTagsIndex->{} *) (*NotebookFileOutline Notebook[{ Cell[558, 20, 198, 5, 28, "Input",ExpressionUUID->"7f54ea3b-0e8c-485a-941b-eec13869228c"], Cell[CellGroupData[{ Cell[781, 29, 222, 4, 67, "Section",ExpressionUUID->"b63e2b12-05c2-44b2-8dee-ecba43b579e6"], Cell[1006, 35, 146306, 2403, 526, "Input",ExpressionUUID->"0cc23734-8fa9-4d29-b540-be4bcb0f6088"], Cell[CellGroupData[{ Cell[147337, 2442, 402, 8, 53, "Subsection",ExpressionUUID->"c93191f8-aac1-4489-91e1-f3013967eb0d"], Cell[147742, 2452, 18757, 429, 1558, "Input",ExpressionUUID->"57c00383-7b93-44cb-9b9f-72306f7a6257"] }, Open ]], Cell[CellGroupData[{ Cell[166536, 2886, 292, 6, 53, "Subsection",ExpressionUUID->"63586b29-1d1d-4922-8dc5-ffe6945abf76"], Cell[166831, 2894, 10686, 236, 899, "Input",ExpressionUUID->"4519cfd3-6058-4cab-8b64-dc38627c5c73"] }, Open ]], Cell[CellGroupData[{ Cell[177554, 3135, 260, 4, 53, "Subsection",ExpressionUUID->"82633925-8442-42a9-95b2-c714eee4af1f"], Cell[177817, 3141, 786, 17, 28, "Input",ExpressionUUID->"4a84ebbd-aee7-4a95-b531-71ca7a5821d2"] }, Open ]], Cell[CellGroupData[{ Cell[178640, 3163, 298, 6, 53, "Subsection",ExpressionUUID->"1ea3b3b2-397c-4246-8b4d-aa3ae007fa05"], Cell[178941, 3171, 1075, 26, 28, "Input",ExpressionUUID->"7faccc3e-9c3c-4a2f-967f-a018391fc7cf"] }, Open ]] }, Open ]], Cell[CellGroupData[{ Cell[180065, 3203, 188, 3, 67, "Section",ExpressionUUID->"83d39d8a-35ea-4e3c-b142-cbe397a85237"], Cell[CellGroupData[{ Cell[180278, 3210, 375, 8, 79, "Subsection",ExpressionUUID->"f1609241-0ef2-47e3-bb5f-97d23b9dea56"], Cell[180656, 3220, 11763, 229, 723, "Input",ExpressionUUID->"ef61eede-2dbe-45ec-8262-fe1ce6e37d57"] }, Open ]], Cell[CellGroupData[{ Cell[192456, 3454, 379, 8, 79, "Subsection",ExpressionUUID->"f72db277-ff9f-4cd9-abe8-e363c06618cc"], Cell[192838, 3464, 915, 21, 28, "Input",ExpressionUUID->"e21abe5b-37b6-4594-b3a0-85652574e40e"] }, Open ]], Cell[CellGroupData[{ Cell[193790, 3490, 189, 3, 53, "Subsection",ExpressionUUID->"fc881f63-ef80-45e1-b30a-db0d4b21666a"], Cell[193982, 3495, 361, 7, 28, "Input",ExpressionUUID->"b0d7293d-5275-412d-80e6-0a4a3887dc07"] }, Open ]] }, Open ]] } ] *)