(* Content-type: application/vnd.wolfram.mathematica *) (*** Wolfram Notebook File ***) (* http://www.wolfram.com/nb *) (* CreatedBy='Mathematica 13.2' *) (*CacheID: 234*) (* Internal cache information: NotebookFileLineBreakTest NotebookFileLineBreakTest NotebookDataPosition[ 158, 7] NotebookDataLength[ 192347, 3601] NotebookOptionsPosition[ 188345, 3533] NotebookOutlinePosition[ 188772, 3549] CellTagsIndexPosition[ 188729, 3546] WindowFrame->Normal*) (* Beginning of Notebook Content *) Notebook[{ Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzs3dlXHffd7/lndd+cy/4X+vLcnr45d736sq/OWuesXuswCxCjmCVAgAQS QkIDIBBIQhKTkEBCCA0IgcQgCRCDIH6cxMljx3EcJ07ixEkcxYkdyzZP99fs PLu361e7dk27alfx/qzXynoemV27hl/V/v3qW8P/nlf53wv/l3/5l385+J/k f/57bv3/VVOT2/A//jf5f/6fioMlRRUF+//vikMFRQU1/zXvf5V/fCP/c0/+ 9Lv/+/8lhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII IYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII IYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII IYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII IYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII IYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII IYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII IYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII IYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII IYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII IYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII IYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII IYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII IYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII IYQQQkjYs7397wAAAAAAAAAAABB+123cj++rFAAAAAAAAAAAIEX4XbdxP7JQ NTVdaWmVBQUtAAAAAAAAAAAAe1NaWuV/+2/5ftdt3M/29r//z/9ZWVPT1d// DAAAAAAAAAAAYG+qqen6z//5//C7buN+trf/vaCgRRbQ9/utAAAAAAAAAAAA /NLf/+y//Jf/0++6jfvZphIEAAAAAAAAAAD2PCpBAAAAAAAAAAAAYUUlCAAA AAAAAAAAIKyoBAEAAAAAAAAAAIQVlSAAAAAAAAAAAICwohIEAAAAAAAAAAAQ VlSCAAAAAAAAAAAAwopKEAAAAAAAAAAAQFhRCQIAAAAAAAAAAAgrKkEAAAAA AAAAAABhRSUIAAAAAAAAAAAgrKgEAQAAAAAAAAAAhBWVIAAAAAAAAAAAgLCi EgQAAAAAAAAAABBWVIIAAAAAAAAAAADCikoQAAAAAAAAAABAWFEJAgAAAAAA AAAACCsqQQAAAAAAAAAAAGFFJQgAAAAAAAAAACCsqAQBAAAAAAAAAACEFZUg AAAAAAAAAACAsKISBAAAAAAAAAAAEFZUggAAAAAAAAAAAMKKShAAAAAAAAAA AEBYUQkCAAAAAAAAAAAIKypBAAAAAAAAAAAAYUUlCAAAAAAAAAAAIKyoBAEA AAAAAAAAAIQVlSAAAAAAAAAAAICwohIEAAAAAAAAAAAQVlSCAAAAAAAAAAAA wopKEAAAAAAAAAAAQFhRCQIAAAAAAAAAAAgrKkEAAAAAAAAAAABhRSUIAAAA AAAAAAAgrKgEAQAAAAAAAAAAhBWVIAAAAAAAAAAAgLCiEgQAAAAAAAAAABBW VIIAAAAAAAAAAADCikoQAAAAAAAAAABAWFEJAgAAAAAAAAAACCsqQQAAAAAA AAAAAGFFJQgAAAAAAAAAACCsqAQBAAAAAAAAAACEFZUgAAAAAAAAAABCbG3t y8ePP7xz5+1bt7bu3v2R/N8bG1/5PlfwDJUghMmDB++OjLyIdfv2D3yfK8At tHAAgGpx8ff37v00am3tC99nCQAQeoxNACAoZIDQ3z9TVdWYnp6epqSior6z 8+bs7C99n08kG5UghMmJE5fUo5nvcwW4hRYOAFAdPnwq+ruQmZm1tval77ME AAg9xiYAEAi3bm3l5RWpBSBNjhzp8H1WkWxUghAm9EURbrRw+GHn+vXlpqZz 0thqa4/39d3ndgMgpcgumZmZFf1daGho932WAAB7AWMTAEh9g4MLCWtAkchf +j63SDYqQQgT+qIIN1o4PPbq1TdHjnRoWl1RUcWzZ3/0fd4ARIyNbcTuoQMD T32fJQDAXsDYBABS3IMH7+o+Dk43DPP3AipBzuy8fPn3ZNjcfON72wgi+qJ+ 0N8LXH80zebm17pftKfebUcLh8euXp3T7SI2NZ3zfd4ARLS29n1/BPcn32cJ AJAiFhY+GRpaam8fqKs7UVxcmZdXtG9ffm5uQVFReW1t6+nTQ9evr6yufm5v 4oxNgKBbW/siSWc145y94VSnp169+ubAgYMGpZ+MjMzo/11WVuv7DMMDVIKc kOOYcTnVdq5cmfW9bQQRfVHvxdsLsrP3ra//w8Uv6ui4oftF585d930leIYW Do/V17fp7nfp6Rl044PixYvPJid/eO3akwsXJjs6RuVY2ts7NTS09PDhe+4e pTVWVl7fufOvly8/6u6+LQdq+fbBwYUHD94N37Uuq6ufT06+ffXqXHf3hCxp Z+fYxYsPRkdXnjz59dbWTrK/XcZ3ubn7o/tmVVWj1SnQQpJEtv7s7C9HRp5f uHBXFvD8+VvSMMbHXy0u/t73eQMQenJsl2NOaWmNmZMP6enpR46ckyOw1W9h bILQkD6P9HyGh59LL0j6cvLDLf26K1dmb93aWlr6g++zlzxFReVmjhJuRfp7 vi/ynnLjxkvdDSFH75mZX8g4Ynv3nN7U1DsyCpBfDd9nGB6gEuQElaBUQ1/U ewZ7gfzouPUtW1s7+/eX6H4LlSDf5wohVlnZEG8HX1n5q++zBwMvXnzW23u3 vLzWoLORkZF5+PApOVZvbn7t1vfKgOLmzbVDh47FewpBTk5ua+vFx48/TN6y yzwMDS3JATOqo+OG69+yvv7lwMB8Tc3RtLS4z1vIyytqbx+Ym/tV8hb23r2f xH6jbHRaiO8tZGHhE+mc5OUVxluxxcWV3d0TS0ufJm8ZAexZcniXQ1BWVrbB 4T1eGhvPzM//zvx3MTZB0G1svBkZeV5f3xb7ykU1hYVlZ84Mzcz8wvcZdh2V oHDTvbCTE857HJUgJ6gEuWX3mskXUbYrCPRFvWewFzQ0nHbrW6am3on3Lalf CXKreW/TwmGFKw3v+PFe3f0uL6/Qg5sdYM/a2hcdHaOWTgHJGHBsbNP5V09P v19WZlRZiI0c0Gw/jiaezc0316+vqJdAywK6+kU7g4MLeXlF5tfwsWMXXrz4 LBmbW34EY79odvYjWoiPLUQ6Re3tA+npGWYWMCMj88yZ4dXVvyWjYQDYk3aG hpb27cs3f3hXk5mZvXsuwlQ3j7GJX1wcYO5Zm5tfS1M3uGxDN3V1J5J6sYr3 qAQlm4976/r6l2qntLq6yeQRHmFFJcgJKkFu6ey8Gbv40n21Nx36ot4z2Avk R2d5+S+ufMvJk1fifUvqV4Lcat7btHBY4UrDm55+P/bRwdFw53jKmpz8YWFh WbwDpnHa2i47ufVjYGDe/NtII5Gx55MnHztf6q2tbx8+fK+j40ZubkG8L3Jr Da+ufl5ff9LG6pV5u3fvJ65v8aKiCkuLSQuJ90XOv+XRo5/bWLeFhQdk3lxv GAD2mvX1L48c6bB6CIqXo0c7ZYIJv5SxiV9cHGDuTQ8evGvy2YlqpDOzW84I ybl0KkHJ5uPeeu/eT9VNMDz8zPd1An9RCXKCSpBb2tr6XTk20hf1nvFe4EpL 3tx8k5OTF+8rUr8S5Fbz3qaFwwq3Gt74+GbsxaXp6RldXeOhGfuEydbWtz09 dwyeVGYm9fVt9k71X778yN43Suuam/u1jW+U+Xz06OfXrj05frw34fWcblWC VlZeGz9OzTgZGZkTE2+5uNHn5n4VO/2zZ0doIX61kKmpd7KycuwtY2Zm9q1b Wy42DAB7zbNnfzLz8yRDqgMHDlVVNcoIoqCg1PiPq6uPyEDP+HsZm/jFxQHm XvPq1TednWMOu0MS6V1Iz8r3xXGOSlCy+bi3Dg4uqJsg3O+9ghlUgpzQPQde W9sqBzeHpqff971teKmp6Zwrx0b6ot4zrgRVVTU5/4rx8VcGX5H6lSC3mvc2 LRxWuNjwZDe/eXO9v39maGiJF1ukpo2NN83N5+MdJ9PT0w8cOFhf3ybKy2uN nxsjA1ur3x7vVaRpu/c7NDWdbW3tk6/OycnV/ZuCgtLl5ddWv1TzVDTjuFIJ 2tx8s/tWIJ0cOnSsr+/+2NjmvXs/lf+9enWupaVbty4g//jkiZ26hq6+vnux E5+a+jEtxJcW8vjxh/HKQDLzsuZlZtrbBxobT+fm7tf9s4yMzKmpd9xqGAD2 FDlClpRUxTu+yeGlpeX86Ojq8+d/1nxwbe2L27e35aiuewO4RH7ajL+asYlf XOzn7ymrq59Ln82gPyBdkerqpsOHT9XWtpaV1cbbNSLp6Bj1fYmcu3Jl1vyJ yoaGdnU9SCfH/BSkq+z7InvMx7314sUHmo2VlZXNJZ2gEuSE7jnwPVjjdu7g wRZXjo30Rb2X8M64+fnfOvwKg5NXaUGoBLnVvLdp4bDCxYaHFLex8VVd3Qnd I2R19ZHR0ZW1Nc0DXnZmZz/q6LgR79z19evL5r99YeET3RP4MtB+8ODd2L/c 3HwzPPx8//4S9Y933ytnbVTifSWou/u2OuWqqsZ4V++srPz11Kmreh9pcus1 W9XVTdHJyj7+6tU3tBDvW4isveLiCnWa5eV1d+78q2a2t7a+nZh4S5qN+vey BRcXuUoTgDVyhK+oOKx7ZEtPzzh9esjMW+qePfuj+mrI2trWhDeBMjbxC/18 G6SbceDAQd2dJTe34Pz5W7Ozv9T8asv+Jb/ahw+fitd/2GtXcVy5MquuBO4x Mebj3iqtWrOxpJvt+wqB76gEOUElyC2aX2QqQQGi7gWal1B3d084nH5mZlbs BDMzvzf91K8EudW8t2nhsMLFhodUFu8kvwxpb95cMz55vrj4h8rKBvWz+flF Zt4OEKE7Ou7ouBHviRkvXnym+6Wjo6uWFtzjStCzZ39Urwttajq3sfHG+IMD A0/V+dndNE43/fPnf46dZrxbdWghZuKkhZw9O6JOsK2tf3MzbtvY2trp7Z1S P1Vbe9ytKiGAPUKt4ERSUlI1M/OBpUlNTPwget9icXHl6urnCT/C2MQv9PPt 6eu7r2mxmZlZ3d0TCXs1Y2ObupfHVFQc9n2hvEQlyAYf91a1M+ziu1MRXFSC nKAS5Ja8vCJXjo30Rb2n7gWnTw9+/7emwsn9p8PDz2OnVl5ep7mmIvUrQW41 721aOKxwseEhleme55fjpPoQGF1yDC8r03mzgMm3vE1O/lD97KlTV40/tbLy urCwTPOpgoJSWRbzC+5xJaij44ZmmqWlNevr/zDz2TNnhjWfralpdr7pNY/+ vnlznRbifQtZWPgkPT1DM7WWlm4zPZ+rV+fUObF0vxWAPS7eszflIJ/wFT+6 5ud/V1JSlZOT9/Tpx2b+nrGJX+jn27XT2noxut7Ky2tnZz8y+dm7d3+Unq7z diHNDc7hRiXIBh/3VipB0EUlyAkqQW7RDKKpBAWIuhfcvr2teQPjw4fv2Z5+ fX1b7KQuXnxw4MCh2H9J/UqQW817mxYOK1xseEhxmlP9x45dSPg4l1hzc79W 73aRobGZz6rPWi8pqUp4m4y4d+8naUqGhpbMz/bz53+enf2lLvUCacejnp38 /GLNNG/d2jL58fX1L3NzCzQff/bsjw63e2Pj6ejUZAsanPSjhSSvhZw+PaSZ VEFB6draFyY/fuLERc3HCwvLLG0dAHvW6urnmhOMkVRVNVqqm2s8e/YnOQKb /GPGJn6hn2+b/MhGriw9erTT5CU9Ubp3AXd03PB9oTxDJcgGH/dWKkHQRSXI CSpBSVqNLlaCpCfs+wKGm7r5pqbe0Zz5aW8fsDfx3UfffK+oJN2M/PzvDXlS vBLkYvPepoXDNHcbHlJf9FT/7vHW8m2Yuq+zWVr61PhTMzO/UD81Pv7K5JfK AFzz2bKyQ66sDddHPU+ffqzuUPEebqZLfcfQ6OiKk1laX/8y9lmp9fUnaSHe t5DNzTc5OXmaSV279tT8FNbWvlCrhNevO2obAPaIzs6b6jFWBkrLy3/xbB4Y m/iCfr5DKyt/7em5Y6kjF7G09Ad1pzt4sMX3JfIMlSCr/N1bqQRBF5UgJ6gE uWJh4RO3jo1qX7S2ttX3BQw3dS8YG9vUPLImN3e/wdPyDfT3z8ROJ/I4Hc2V ySleCXKxeW/TwmGauw0PgbCx8dWlSw9tjGrFo0cfqP2ZGzdeGn+qo2NU85HS 0hrzbzmZnn5f/dKZmV84XxWuj3rGx19pJtjUdNbSFNSF7eoac3GWrl17Qgvx voXcufO2Zjo5Oblra2bfoBSh6eqkfdfbOep8GQGE24sXn2nezRrJxMRbXs4G YxNf0M/3keb5JGm75Vff58ozVIKs8ndvpRIEXVSCnKASFM/a2hejo6unTw82 NZ2tqzvR0tLd03Nn9wGqOsP/R49+7taxUe2L1te36f7lxsZXt25tyVwdO9bT 0HC6rq716NHOs2dHhoefO39gi2pra+fx4w/ld/PUqatNTeekhyxrprW1r6dn Urrr9h7jnLwtYom6FwwNLa2s/FVTrzH/FJ1YVVWNsRO5enVube1Lzdc5rAS9 evXNzMwHg4MLHR03ZLXI+pGt09h45vjx3u7u2+Pjr8y8LNWAi817mxYe35Mn H0vzkPUj207WSXNz15kzw9IULfVLZ2d/2d8/09p6UdbY7kTOd3SM3ry55rAN 6ApWw0sGaaJTU+9cuvRQ2syRIx3SRBsa2ltaznd1jd+48XJ5+bWTiUuDlNUr TUIOetLyZeKyQeX/kFZx7doT2dC8k11ZY9+qtzbIAcTwUzv795doPnLx4gNL 36uOpqUBOF8c10c9g4OLmglavddVdnnNFOQI6WSWND8HyTiwx6KF6Dp//pZm OnIMtzoR+YnMzt6nmc7CwidJ3aAAgq67eyJNidWrFJxLzbFJsrvZJs3P//ba tafSYThy5J+DoxMnLvb13ZMOsL2LJKM86+f7O8RL3hkMJ2TYoln5mZlZPs6P x3ypBHnWDpMxhPR3VJ7sSpD0V+UYLkfa5ubzhw+fiqwuObAPDMzPzf3as101 NY8VqYxKkBPeV4KkO1FRUa9h/kG+UdIv0kzk0KFjZj4oO1Tsp9QzIdLHk0F6 Tk6uumYkMv8rK68vXnzQ1TUmh/GWlvNyrCgtrdH8WXp6urqYseK95ljti8ov teZvpKvZ1tavDrpjv196p65cdLr9z1uPJwsLD8T/uu86D3LAtPeqQedbxOEC qnvBpUvT8u+yDmP/UX4arE756dPfaFrF8vJr9Y5se5UgGSBIt0FGIoYt4btk ZGTKzEvfw9zmTmLz3oMtPGHzFrIdq6uPGCxsff3JR48+MP6isbFNmb5BG5A1 70oXN4gNT7MVREtLj4OVsCNroLm5S/dC1tj5lQH7/fs/td4gX3d3TxQUlBqv XvmDzs6xhM+22lNkVKVZSydP9hv8/ezsR+qKtXr6Wo4emim48n4B10c9AwNP NRO08dOjeQiYevQ2b2vr29ipVVQcdr09qGghKhlvaqYjHWwb05EfBc10Ll16 6ME2BRBQ0p9US+2S3TNvns5JSo1NktTN1tB0jKXvrfmDzc2vh4aWystrDWZA xuZnzgyZHFwke4AZ50v9HOIl+wyGE2qbl0GNj/PjMY8rQck+mRbzRe4MIX3Z W2Pdvr3d3X1b9in59rq61ry8Qs23y9FP90sbG0+b/5aXL/9++fKjsjLt9Vqa FBVVyFp98eIz24sT6GNFKqMS5IT3laDZ2V+q32jjbguZSc1ETBamNT98VVVN 35+9j0pKquIdB3JzC7a3d3QXwWri/daov8vyoxD9r9I57O2dkl8KM1+Rnp7h cBguX9fXdy9hRzQ2R46cs3rYd75FHLZJdS84f/6W/Pv16yux/yir3erVGnLY j53C4cOntr976YD2ATVWT8fJj8W1a0/kV8n8dtlNekfHqGxT44kntXnvwRau Wd6ysu+9oFwm1dh4xuTCxnvP+LNnf5IempmJZGfnjo9v2l5dwW14Lr4LeHLy 7YQ9Rk2ams6aHlzs9PfPWGqQ0jDa2vqlDTjZEUKjpaVbs35kBGHw9+pIsLi4 wuqXPnz4nrpZZAzlcFlcrwTduPFSM8ETJy5anYimEiR7lu35uX//p7GT8uZu dFqISj2gTU392MZ01AYW76J6ANjevQ5KOTb+c6zksRQZmyS1m228yDLWjv2v cjw3PxtZWdmyfhI+tTXZA0wN74d43p/BcEIWVjNLBQWlPs6PxzyrBHlzMm2X m0NIj/dWlXrPmsmY7AnLdrl0aXrfvnzzU5YDXWfn2Pr6P2wsTqCPFamMSpAT VIJiDxdTU+/EK8VGEqkye3zGMnqmZWHhEzluWP2ivr779rbU4uLvKyoO21i0 vLzChw/fM/9FzreI63tBpFK/vv6l5pr/eKfi49Ec1UdGXsg/3r37I83Xma8E ySxJZ1t9NbP5NDWdNR4seF8JCncL1yzv7q/5P/+TtASrm3J4+Jlm+pOTb+fm 7jc/hfT0dBvFoKA3PFcqQWtrXx4/3mtvxqRnrm47jc3Nr5ubu2xP/+rVOTqK 6oZuaek2+PuWFu1dDDaKI9Kw1RNEzt9x4HolaHr6Z5oJlpbWWJrC5uYbGTbG TiFy1YQ9mvfv2Luq2SpaiEo+pZmOvW2xvPxaM52srByrJycB7B3t7dfSlIyN rXs/J76PTTzoZhsv8u6Twb7rQy4tfWry6jJN6utPGp8j9fLcsi9DPO/PYDih rp899XY/bypBnp1Mc30IGe5KkBzV7W2X3elXJHxOiyrQx4pURiXICSpBMlCN /PvMzAfqA+Q16eu7F28RrMZqJWhi4q2Esxcn6TbuM52eft9Jd1TWqu1+lI0t 4vpeEH33geYsUG3tcUvrMPaz0VuKxsY2NV9nrhK0MzLyQvcpClZz9uyIwRf5 VQkKawtXlzfyVG3pdKWnp1v9XmlFs7MfRSdubyLS5VtcNN83C0PDc14Jkimr 98VbjeFp852WlvMOp9/RMWp1RwiZ5mbtOjx58orB36vb9Nq1Jza+t7pae5rI +W+T65UgOfKo9Qg5EpqfgvqQcCfljNgrJQoLD3hTx6SFqNRLIhcXf29vltRr yOXA7sFmBRBAO+ojjKR3vbHxlfcz4+vYxKNudsJFfv78z2Nj63aX97scPNhi 8OYgz84t+zXE8/4Mhm3Ly6/V8WNn55iPs+QxDypBHp5Mc38IGeJKkGwXS7cC qZHBlNWraoN7rEhxVIKcoBIkkT7n6urnhYVlCXf8qal34i2C1Zg/YymD/f7+ x7rnezMzs2XcXVxcIccTg+/affy+hXMsBgcl6anKHF6+PC291sHBhfPnb9XV terOm/z2mbyz1fkWcX0vOHLkXOQ/Scv8/n9JN/9WDumQx36yubkr8u/Dw880 X2emEjQ39yvjVVFYeKC+vu348V5Zn8eO9ey+MiZujcDg3SXeV4LC3cLV5ZWV 09d3P963FxdXZmcbXRlSW9u6HecYGImMrGUixmss2hr3SMNzWAlaXPy98SOX 5b82NLTLtwhZG7o9TGlFBs9cUt/hEok0ifb2a9IUx8Y25XA0PPxcRmqVlQ3q X2Zn73v69DfmFyqU1Eu8envvxvvj3TtctG3V3m9KW9tlzXRkX3C4LMl4O6o6 VDx4sCXhE12iNK/2liZn7yEJ4smTj2Mndfr0UPJaRSznLcTGey23U7uFuHVP kGhsPK2Z1I0bL73ZsgCCRXpWaUqkK+XLzPg4NvGsm51wkeM9rTojI1O6nfJf 5QgvCyWLbzC3Bq/e8+bcsr9DPI/PYNh28eIDdZbu3bP8YtPgSnYlyMt2mIwh ZFgrQQ8fvmdw3Jbxe13diRMnLsoGOnz4VH5+Uby/lO1l6fbV4B4rUhyVICeo BEmePfuT+uh46XTJ8Xl+/rfLy3958uTjiYkfnD07sr7+5fbuO9Tk5yNWbe1x zcezsrI1f6OxtvaFydnLy9MeheR3obt7Ynb2o5gTODsycu/ouBHvIcbmjyGy NnQvTCotrZHNpHvKSH6k1LMcaaYf9ex8i7i+F0RPtm9uvtH8jkvfycw0ZUVp Npz8Ckf+U3//jObrTD4drrX1orqSq6uPDAzM69anZDWeOTOkO16orm6K9y1J bd57sIXrPntc8y/FxZVXr87FvohQWnhPz514fRXpxqgHwAMHDg4OLsa+e0LW mOwjmqc5RWP+5YMhaHhOKkHST4t3N5DMz5kzwzKKV3d/OUBVVTXG/rHBo0Lk OKZeNiYbTo4Vm5tf635EvlTzfvbR0RWTSxRW0qnOyMjUrEaD3oXsAuo2tffG JbW2a9DUTUpGJejBg3fVRTb5A7Sx8UZz5Dx9etD2nGjOQty9+6PkNYyYRaCF 6FCPb/beEyR2j/zfi/yQebBlAQTO2NiGeoA1Ochynb9jE2+62QkXWU1NzdGb N9c0l3zIL6n8bsp/ivepO3f+Vfcbkz3A3E6BIZ7HZzDsWVz8g/o0KqsXVQZd UitBXrbDJA0hPdhbjcluYvztubn7db90ePh5vGlKy1cP7JEcOnTszp23NQ/Y 3NrakXFTvJqULP6jRz83uTgBPVakPipBTlAJStvtdsb+vyUlVeavqIlQx+Mm Z8bM7Gly6tTVyEPGdMkRSfdydNOvdd6pq2tVP97ePmBwu3fE2Ni6eo7l9u0f +LJFLFH3gsrKhuh/PXnySux/OnDgkJlpTk6+HfupnJy86OMO1KZr8kSc/GTE vreovv6kmat3ZOdSt0ualcvGXGzeuptbk5C1cOPllcGjtPZ4XbUnT36dn1+s fkpzCXdWVs7Vq3PxHg9+795PdCtK5l/wEYKG56QSpFbuIqmtbTUeMkgHUkbx kZW/e5th3BGW9FrV6Q8OLiact8ePP4zUm4yfcLVHKLdwStJXVz+P9/fqK9tk 6CRbzcZX37jxUjMp2XMdLk4yKkHb3+0LOmec5LsS3hl0+fKj2I/I3r2w8Int 2Yg9iSS/j/GOge6ihehSXwmx+8R4O7OkXugiP+gebFkAgSO9UPXHyJurAlT+ jk186WYbL7L8mty+vW04hZ0rV2Z1Z0CG7SbfWOTuADMVhngen8GwQUYueu+m T0+1+Uy2ZFaCPG2Hng0h3d5brXHe45VhzsGDLeq6kmNvwjf5Tky8pfugv6Ki CpNlmiAeKwKBSpATVII0aWw8Y6Pw6k0lKCsr5+bNxPchjo+/Uj+bl1dk5koP +eFQP2v+jPHQ0JLms1VVia9NSsYWsUTdC2LPEktvXPNfzTw4RXO2LfZmeduV oO3/eDhPWVmtpTfjqCdn0gzv39fwrBIUyhZusLyFhWUzM78w/vjU1DvxPh5J aWn1kycfW51tSXl5rfmtFvSGZ7sSpJ6/jaStrd/kM7VmZz86fPiUwQmEbb1b 4M2/kkyG2zKc8eW5+qlGfVmq8dtvR0ZeaP5+//4Se1+tu59aenezKkmVIGmK uve4yfjIoLIj/0nz1ErzP1uqFy8+i72YuaXF6XPSTKKF6FJf2n78eK9bs9TU dNabjQsgWDQXpUdi8rnirvN3bLLtRzfbYJGli2vyYv54neTr103dpe7uADOl hnhpnpzBsGRra+f69eW8vEJ1Vi9ffuT77HkseZUgj9uhZ0PIoFeCdLe4HN5N 3rk5N6d/da7Jt7MF61gRIFSCnKASFJuamqP2Lkz1oBK0f3+J+Se36z4CdH7+ d8afkkOQeohraem2dLOw+pDhhDdOJmOLWGJcCZKOk+YZnglfyy4/qdnZ+2I/ Entxl9pazJ9Sk21kcPdHPLIO1fc45+YWmNyy3lSCwtrC4y1veXnt8vJfzHyj esF2NNXVR1ZX/5ZwCtKG1QaQlpZuXJ4IU8OzVwmSHVn39UDS6zb/ahUz1Pd0 JLw8CRpLS5+qD0IcGHhq8BH1FIqlt0fFmp39SG0nsY9qtCFJlaDt3bKO7qmA rKxsGauqhwXZEaqrm2L/sqSkysmYRTPOHR1dpYX42EIGBuY108nN3W+j63Xn zttpSoxrbQD2LPXxYhkZmX7NjI9jkwjvu9m6i5yZmT0y8sLSPKi/ROaP/C72 81NqiJfm1RkMk549++OVK7MHDhzUndW9+T76JFWCvG+Hng0hA10JkqGN9GzV LW6myh81Pf2+eseW/IuZg3xQjhWBQyXICd1K0LFjPbdubdkwMfFWwm9M2UqQ dJzsPf59O/mVoJKSKku/TbrvARwf3zT+lObZL2m71zKZOc8cSzrMmokkLHMk Y4tYou4F0q+O/QPNEwxkXGB8HvjmzbXYvy8sLIt9noyTSpBtFy7cVddwwntJ 4s2w65WgELdw3eUtL68z/72DgwvqFNK+6zk0mz8Z29l5U52CpSsP7UmRhmev EiSjcnXmZXc2X0EzKfapIJHYeyv9XnbyZL9mHebk5BpvqcgluLE5eLDF3rfL 4UttKuZfxaUreZWg7d2HhOsWgyJ7WVfXWPTCbPmxa24+H/sHWVk5MzMfOPn2 pqZz0anJGMrqQZgW4m4L0X0d0tDQkqWJyC+j7suRbdfOAIRbcbG2hCH9K79m xsexiUO2u9nqIssx/OHD96zOwNral7rdCTPPj3Wxn586Q7w0D89gqObmfj05 +fb4+Cv5Eb9wYfL48d54rzpN2+3O3bjx0pf59F2SKkHet0PPhpCBrgT19d1T N7eN+997eu6o0zFzG2YKHivCgUqQE7qVINsxc0BI2UrQ4OCC7dWY1DOW0r+y eoiYmfmFuoCXL08bfGRr61v1ogJ7z4ovL6+LnYj0QLzfIpboVYK+9+Py9OnH mj8wfpB17GmuNOWOYF8qQY8e/VxdwyavxE52JSjcLVz3LbSWLgVfXPy9urDy pZbqEeoLLyQjI3FfquiWFGl4NipBcW6kShsb23B9Lakvchobs3CREmTkq97u 0dk5ZvwptYE1Np62NwOrq5+rTUV6O04WKqmVoO3dO4NKS6vV2Y4kIyOzublr cvKHMlDS/LuZa34MbGx8FTturas7QQvxvYUcOHBIM6n9+0sM3qCkIb/I8QqL ZWUWHkMKYO9QX7vgY+HYr7GJc7a72U5eoKmhe3a0v38m4Qfd6uenzhAvEs/O YKjivd1eTVPTOSdvewy6ZFSCfGmHng0hg1sJevXqG+nTaj6emZn97Nkfrc7G +vo/dCeV8MROCh4rwoFKkBNUgiIpL6+19xbgCN+vXdeQw5S6jN3dtw0+oj7E Pjd3v713T6h3H7x48ZnHW8SShJUgUVX1vWfjnDhxMd7UVlc/19w6+vTpb4xb iweVINmU6kq+cOGumc8muxIU7hbufHllR1Av+JGuvqWJSG9HXWMXLz6wvR1N SpGGZ2MrPHjwrjrnu59y/7ikDhyOHbuQ7E0TIju1tcc1KzA7Ozdht1xtYFZ3 qyjdrtT09PtOlivZlaDIbKurziByIEr4AuWEpL8XO00ZjNNCfG8huvc/1tW1 mvmJHB1dVU9ExMxShb1ZAhBu6l2E1dWJ3y2bJH6NTZyz3c12sRKke2NpU9O5 hB90q5+fOkO8NG/PYKjMVIKam8/buPkrZJJRCfKlHXo2hAxuJWhyUufZxW1t l+3NSW/vlDq1hAWdFDxWhAOVICeoBEUyOmrqzYbxpFolSKgFa+M3msl/1fx9 e/s1e4swOrqqmZTxHTTJ2CKWmKkEac6TZGfvkw6/7tQ0z/JSxzW+VIJ0m0R7 +4CZD6ZgJUh3cVKzhbuyvOoF29ZPSO5kZmrLSdLPtL0dnWwp7xueja2gNpi0 73p6i8lYRbrvTbb6dKY9S325qqS3N/FpkLY27ePC3D3PH/t6OBuSXQmSwan0 o9Qqc7zIr97MzC+cf68MvmIn68r7eWkhDluINAb1QJ323XHysMFGf/z4Q83T 7NXHPfF0OAC61ANObW2rXzPj19jEFfa62S5Wgrb1Tkfn5RUl/JRb/fzUGeKl eXsGQ2WmEnT0aOf168uuP+k6WJJRCfKlHXo2hAxuJejUqavqKrL9hHzda2vr 608afyoFjxXhQCXICSpBkvz8YquvaNRIwUpQSUmVZjoyZYO/Lyur1fy97Ut/ 1TcjX7v2xOMtYomZStDKymvNnT7xnqx76NCx2D9Tbwr2qxKkPgiopaXHzAdT sxIUlBbuyvJWVx/RTMTGCUnZanu24dnYCpob8yXp6RlJGjdJU0zTS1fXmL1r yfaOpaVPc3JyNeutuLjSzHpTW0VYz/OrHj36ubJjpus2wti0tHQb3+Gb0NbW t7GPEfOgTEALMWli4q14272u7kR//8zk5A9nZj6Yn//t/fv/dvnytPq29/Ly 2okJ7aGMShAAXeqRubKywa+Z8Wts4gp73Wx3K0FHj3aqvx0JHzHqVj8/dYZ4 Hp/BUJl/Olx2dm5Hx43oSyH3mmRUgnxph54NIYNbCTpw4KDms7m5+53cjCM/ VZoJZmXlGO/4KXisCAcqQU5QCUpzY6CagpUg+ZRmOgZ90bW1L9SzQCsrf7W3 CGqtXPOiHA+2iCVmKkHb373952zs3zQ2nkm47BkZmerzZzyoBMkvi3Qhxsc3 L116eOrU1bq61sLCMnUTmzyplZqVoKC0cFeWt7a21d62i6VesLd3Gp7VrbC2 9qU624cOHXN3dcXY0VSQoyksPCCd//X1L5P21QEm7e3gwRZ1pd2587aZj6sN 7PDhU/bmJMWf/aUxOLioeWlOQUHpgwfvygzLnpKZmaXbFCOR0ZONDluU5qGL 3d0TtJDUaSFqv9p85IgqvZ17936i+XdZ/0ndxAACSr08qbzct9eK+TI2scHF bra7o28ZB6m/Cwl/41zp5wd9iOeuvr57MmaUMYXsTbs3iyW4yCc7O3dg4Gky Hnyd4lyvBPnXDj0aQga0EqTb/Tbz7EoDHR2j6jSNX/2ZgseKcKAS5ITu3tHe PiCN2Ya5uV8l/EYqQd7MnqW+6OPHH2r+OC+v0MVGZXyXuu/HRpOVoLGx9di/ ycjIVC920jxErqnprJnW4soJ+efP/3zjxsvTpwcrKxuMT+VFs3cqQT628NBX ggLR8KxuBbXBSKTj53x1xbO09GleXlG8NZaTk3vy5BUe662he+bB/JOf1QZm +9E0ul0p40GBjdlzpRJ04cJdzWSrqhqXl/8S/YOVldcXLz7YPbkUN7sDUjvn DTo7x2Kn8+jRB7SQlGohvb3a5mEmzc1da2vfnWoYH9/U/KeGhtNJ3cQAAqqo SPswyfz8Yr9mJpUrQUnqZrs7+tZ9Cuvt29vGn3Klnx/0IV5SbW5+/eTJx6Oj q6dOXS0sPBCvwbS29slf+j63XnK9EuRjO/RmCBnQStDMzAfqOunpueNkZkZG XqjTHBtbN/hI6h8rAopKkBO6g9MLFyaT941UgryZPUt90fHxV5o/Tk9Pl0Ww zfxXJ2mLWGKyErSx8UbzelP1KayaqzJ0fxTcrQQ9ffpxd/eEurnNZO9Ugnxs 4WGtBAWr4VndCmqDSUv+i3tmZz8y6MlHUlpa098/Y/saMzPkIGZjj3j27I9J XTkq3echFBVVrK19YXIKapmgqsrm66pl8KjOzNOnv3GygMk4z695jZ2kpqZZ 9/ERW1vf3ry5pj7pIpq2tn4bxSBpwNEpFBSUJvUyVFqIPXfuvG1cB4xNbm7B yMjz6HZUG5gHT0YCEETqEyalby4/Pb7MTApWgpLdzXZ39H3r1pY6GyMjL4w/ 5Uo/P+hDPM9sbe3cvfujw4dP6baZ5ubzLnbJUn8o4XolyN+TaR4MIQNaCVKv UJJcv77sZGY0zzeI5NKlaYOPBOtYESBUgpygEuTKnhj0StDw8HN1o7iYcFSC tpVXztXXt8X+1+fP/xx7X7C0gc3NN2Zai40T8q9efTM6ulpd3eRku+ydSpCP LTxklaCANjyrW0G3wUxOmnqilBNLS5/Gu8c/NpmZWceP996//2/JmAd7px0c PlvbqqdPf6O+YiAjI3N6+mfmJ3L58iNLrcKA+kBvSeyNNja4fp7/0aMPZFga O8GystpEr77auXlzTX0bdSRdXWOWZmB+/rexHzfzSmtaiJctJGpj46urV+fU J6vHJj+/qLf3rqb9qLcUWW0kAPaIlpbz6oFlYeETX2YmdSpBnnWz3R193737 I3U2rlyZNf6UK/38oA/xvDc+/kotNEj6+u679RWpP5RwvRLk78m07eQPIQNa CYozov+hk5mZn/+dOs0UfxdGWFEJcoJKkCt7YtArQbq/hi4mNJUgzTUA6enp sWdyNI+GO3XqqsnWYvWEvOwv6jtJ9ZJeXFzR2Hhapj84uKg+h2HvVIJ8bOFh qgQFt+FZ3Qq6Dcbh691N2tr6Vo4k+fnFJtZzWk3N0ampH7s7A6k/fFtZ+Wtx caU6D/39M5amMzz8TDOFwsIye7MkbUOdH4dvAnX3PP/m5teaG3xycvIWF39v 5rNra1+cPHlFd7tbehnupUvTsZ81+bIeWog3LUTX/Pxvh4aW5ItaWs7X158U LS09vb13p6be0b10v63tsmaWEp4JBLA3dXTcUI+KTt5D50SKVIK87Ga7O/rW /Y3zphIU9CGeL+bmfpWXV6iZ+YyMzPn537ky/dQfSrheCfL3ZFpEUoeQAa0E JWNEv3vttzbGJ1WCe6xIcVSCnKAS5MqeSCXIOJ2dNz3eIpaYrwRtb+9o+tvy gxv9r/X1bbH/6cGDd022FvMn5NfX/3HsWI/Bqs7KypahQW/v3bt3f6R5Bo7a JKgEuRWDFh6OSlDQG54rlaAk3YMTZ4V/2d//2NwJge9afqI7OyxI8eHb5uab gwdb1BloabH8XIs7d97WTCQzM9veXN28uaaZVG5ugcMldfc8/+DgomZqu+8I tjAFzaUOkRQWHjD/bPnYDZednat7z6xztBAfqWtefhH8nSUAqen69WX1QN3d PeHLzPheCfK+m+1BJSh2jKwrEJWgZA/x/DI19Y66sCdOXHRl4ik+lIjXbFK5 EmR8Mi1WkoaQYaoExTtHZ9LKymt1mmfPjhh8JNDHilRGJcgJKkGu7IlBrwQN DMxr/jgjI1O6kW4x3r6+HxutVIL+XQYpsX956NCxyL8vL7+OffBOUVFFvJNO titBq6t/q6xsSNOLbK+Wlp6xsU359TffJPZOJcjHFh6CSlAIGp7VraA2GIkM mux9u21bWztTUz9ube3LysrRXf/RlJZWLy66M4BK5eHb1ta3zc06z5OReTZo gfE8efKxOqnV1c9tzJj6GLGqqkaHC+vieX5Zb5ohoUxKWpfV6fT13VPXmMn3 Z8lPZOzTU3frMrSQVGkhLq38Hc27FCXev0EMQCDMzf1aPcAePNjiy8z4Wwny pZvt7uh7YuItdea9eU9Q0Id4Pjp6tFMz/5mZWa5cWpbKQ4kI1ytB/p5MU7k+ hAxoJUh9f2Xad/cE/cTJzOi++rOj44bBR4J+rEhZVIKcoBLkyp4Y9ErQ6Oiq 5o/lxyupr3JO9haxxFIlSPOmg7T/ONGh+aExuKrNXiXo1atvdJ/+Kluqo2PU zOsGUuGEvIubOygtPOiVoHA0PKtbYXR0RV3kGzdeetBgdK2tfTEw8NR4YFVS UmXvHHWAnD07oi74/v0l9s42b2x8pU7t0aMPbExK8wo5iYyvHS6si+f51Ss/ e3unbE1qR73po7y8zsxnR0a+96Ruh29rjYcWkoy1apJaOMvN3e/j/ABIZVtb 36ovK5G+pS89GR8rQX51s90dfav3HUsmJhI8P9aVfn7Qh3g+Um98loyPv/J9 xjzgeiXI35NpBtwaQga0EqRuF8nNm+tOZmZm5gN1mn199ww+EvRjRcqiEuQE lSBX9sSgV4Lu39e5p9uzKzl9PzZaqgSJmpqjsX8cefWA5tFw8/O/jfdxe5Wg ixcfqNuotLRmbu5XtpvE3qkE+djCg14JCkfDs7oV7t37ibrULr5K1bbp6Z+p l/BF09zc5fscJk9Pzx11kbOych4//tD2NNW3yVy/vmJjOuppHOf9KBfP83d1 jWsm9fDhe/YmpftWaDPHUtnro3+fnp6ejHN9tBB/K0HqBbE2fqcA7B0tLTrP Qxsefub9nPhYCfKrm+3u6FvtZkgS/vi60s8P+hDPRxsbX8U+zyQS4xffh4br lSB/T6aZ4XAIGdBK0P37/6YurNXXhmrcurWlTtP4FsigHytSFpUgJ6gEubIn Br0StLT0qbpR7tz51+Q1g2RvEUusVoIGBp7G/nFlZcPq6uexXamamqMGH7dR CZI5VC+ck2HCysprJ01i71SCfGzhga4EhabhWd0Ki4s69323tPR40GDMkM78 gQMH1TlM8/ZlRl6STru6sHLUnZh4y8lk1TFRR8eo1YlsbX2rPnvB+WuvXTzP 39h4WjMp20//kIXNzs61urAbG1/FriI5oLnYNrZpIY5biCsaG89o5ufy5Uc+ zg+AFKf7qiDjMVSS+FUJ8rGb7e7oW+1mSNbWEjyU1ZV+ftCHeP4qLCzTLMKx Y6ky2Ekq1ytB/p5MM8/2EDKglaBnz/6oLmlbW7+TmdF9Vvb9+z81+EgIjhWp iUqQEylSCUr4IFkVlSBjVvuieXmFmr/v6hpLXjNI9haxxGolaHX184yMzNi/ 1zQA4zdx26gE6T3jNN3qdc6pcELexc0dlBYe6EpQaBqe9a2wo47N8/OLUuE2 /wgZX2vuQ4zk+PFe3+fNdbqvbUpLdKQ1Q70Wt6am2epEdHs1z5//2eG8uXie X90HncxYdfURzdQSvhX69u0fxP59f/9j560iFi3EYQtxTvpRmZlZmvl5+vQ3 fs0PgNQnPRndd1hMT//M4znxa2ziYzfbxdH31ta36kviSkqqEn7QrX5+oId4 /rI9Rgs61ytB276eTLPE3hAyoJUgIeN3zWdLS2uczExDQ7tmgunp6caF7xAc K1ITlSAnvK8ELS7+Xv1G6QhZnQ6VIGNW+6JNTec0f+/wIOn9IttmtRK0/f0H 3WiS8BnXNipB6mXJjY1nnDeJPVUJ8quFB7oSFJqGZ2MrHD58Kk2J7WdqJYMc uKQNa+YwP7/Y9xlzV7yT/Bcu3HU+8enp9zWTTU/PsHq/zOXL05qJlJZWO5+3 pFaCXr36xvaMqYcj44dji5Mnr8T+vfQDna+fKFqI8xbi3LVrTzQzU15e69fM AAiKtrZ+9ehdV3fC49nwa2ziYzfbxdG3+i7ChAse4VY/P9BDPH+VlFRpFiHc D5qOSkYlyMeTaVbZGEIGtxKk+0y8hYVP7M3J+vqXWVnZVnf8EBwrUhOVICe8 rwQtL79Wv7G7+7bV6aR4JSgnJ9fepPzqiw4NLanb5cGDd5PXEpK6RSyxUQka H99UV1ckCbvfNipBxcUVmo9IB8Z5k7B9Qt5283ZxcwelhQe6EhSahmdjK+iO EdraLievqdig+2QV6aP6PmNu0X0HseTMmWFXpv/q1Te5ufs1Ex8dXbU0kYMH WzRTOH16yPm8uXieX/NuuzTDN9klpO7Rxvf4bG3txF6PV1Z2yJVtt00LSZlK kGxi9WxSKrxYDUCKe/z4Q91j+NjYhpez4dfYxMdutoujb3VSu1twM+EH3ern B3qI56PNza81DzmRnDp11fcZ80AyKkE+nkyzweoQ0t3TQVY56fGOjDxXl7S3 1+bVYqOjK+rUursnjD8V9GNFyqIS5IT3laCNja/Ub7R6+cHq6t/UQraPlSDd 9xTbm5RffdHl5ddqZ6Ch4XTyWkJSt4glNipBm5tv1IdHRTI+nqDra6MSZOO9 DGaahMkT8i42bxc3d1BaeKArQaFpeDa2wu6DhbXvUZUmtLT0afJai1ULC5+o qyil3k/qxOjoqvoq27R/Pr7Atcf0nTypvSC5vr7N/Md1b3O+d+8nzmfMxfP8 ssY0kxoYmLc3qa0tnQcn3rjx0uAj09M/i/1jF19GTAtxq4U43hDacXFmZpb8 5voyMwCCRfcxC3l5hS9efOZwypubX09NvWPmL/0am/jYzXZrkWUzZWZqr5CX 5TJzVZJb/fxAD/FsW17+i8MpSFdEXf/9/TOeLYKPklEJ8vFkmg1Wh5Dung6y ykmPd3X1c/UYlZ9fvLHxlfU52amqalTXw5Mnvzb+oO9nO8OKSpAT3leCtvWe 1ig749aW2WG79OvU86JpvlaCLl3SPvojze5D4P3qi4rW1j51KW7f3k5qY0jS FrHERiVItLcPqKsrN3f/5uYb4w/aqASlp2doPmL1muStrW/V4qnJE/IuNm8X N3dQWnigK0FqwzM+5atKkYZnbyvovgC3paU7qQ3GEt2TzMZPpwyKeCf5GxvP SB/AxS+6e/dH6reYf0r/2bMjms8WFJSa788YcPE8v4zrlfZ/2F6tRFPWicR4 BHT+/K3YP56efp8WkmotxAnpQUkfXjMnqXb7JICUNTf3a/X0qaS6usnWmbp/ kh+CpqZz0o+9c+fthH/s19jEx262W4ssR3t125m8r8TFfn5wh3j2XL++vG9f vsMO1YkTF9WVllLPwU6eZFSCtv07mWaD1SGku6eDrHLY41Wv6Uqzdev62Ni6 Op26utaEH/T9bGdYUQlywpdKUF3dCfVLTb4dMtKvUz+e5mslSPf+SjM9z+TN no3z5LOzH6nXwOflFTr/WfR+i1hirxIkPSV1o7e3DyT8oI1KUG5ugeYjlt6A ICOp5uYudW5NnpB3sXm7uLmD0sIDXQkKTcOztxV0zwBLZMaszoDxtZFbWzsy TRsnhycm3tLMW1ZWjot3Q/gl3kl+6Tw4OS8Ux456FqW29riZ1SiHDvVh0Qlf mmOSi+f5dS/8Gx1dsTGplpYezXQKCw8Yf+TAgUPRP96/v8SVIggtxN0WEmX9 FU47R45o++QZGZnuvgoKQLhpLhiIPaSvr//DxgRXV/8W7TlnZ++bmfmF8d/7 NTbxsZutLnJRUYXVH9A7d/5Vb7ulP3nysZmPu9jPD+4Qz97CSm8/bbfPb+Mm sojdC3u0a2z32uxvPVgE3yWpEuR9O/RsCOnu6SCrHPZ45Yiklt0zM7Pn5hLc yxPrxYvP8vK0tzNIJid/mPCzvp/tDCsqQU74Ugnq6hpXv9TMA+Kkf9LQ0K5+ NhIfK0G6Tzm295xVH8+Tb8e5sKekpOrZsz9ZnYG1NbPvqvD92GivEiS/leqD 8c1cSGOjElRRcVjzkcrKBpNLt7j4e7UxRGLyhLyLzdvFzR2UFh7oSlBoGp7t raB73UJGRubkpIWu79TUj2VsNTPzQbw/uHjxgUz28OFTVq+tam4+r5k3aSo2 1k9KuXHjpe5J/urqI+Z/VizRfbL3tWtPEn1wRzaZ5lPZ2ftWVv7qyly5e55f nVXpMj19+htLE1GHjZKeHqMe4/z872L/2JXnz9NCktFCtnePVJmZWdeuPTX9 kR3dm6M7Om4kYysACKuNjTfxeoxlZYes/lTJWKyo6Hvv38nPL1pcNDoT69fY xMdutu7LfcrL68yv7dnZX+bk5KkT2X1Aq6kpuDvADOgQz6qXL/+uOf8gv7kJ H0iisbT0aUFBqbq6Er7uJDSSVAna9rwdejaEdHdvtcp5j1dmVZ1/+aUwuV1k v6uqalKnYPJ50b6f7QwrKkFO+FIJ0n0qqWRwcMHgU0+fflxeXqv7wUh8rARt bHyl3tielZVt45jv73nylZW/qk/5SNt9mIn5F95tbn4tP6+5uQV37/7Iry1i id1K0L/LnhL7KfmVN3ORsI1KkO6Pl4kbE3aGh5/l5GifQR2NyRPyLjZvFzd3 UFp4oCtBoWl4treC/O6ozxZO2y0GXb06l3B/X139/PTpocjlYWVltboPrZKG F71OKTd3/9DQkskru8bHN9UZ250raysnpdy+/QPdp8SUl9fJgTpJX/rq1Tex 961EN7HMjMGnZOitzmdv75Rbc+Xuef7p6ffVuZXj3uzsL01P4WfqOR/pdxnX NS5ffhT79xMTb9FCUrOFzM5+FN2+Z84Mb2wkOK20tvblsWPaG8TSdjtCZl4P AQCxFhf/IF0g9ZCStnvl9vnzt1ZX/5ZwIsvLf5FOl+6lAsbn6/wam/jYzdat BKXt3hdw6dLDhD8B8muuWwbKzs4130t3d4AZ0CGeVep9uGm7Qwzzyyh/uX9/ iToR2QHN7GXhkLxKkJft0MshpLt7q1XOe7wyJNdt9sXFFQmHQtIwKisb1M/K 0XJ+/rdmvt33s51hRSXICV8qQVtb3+pehyA5fXpQfVXZ/PzvZPfXHHw6Okbz 8gpj/8XHSpCQHqa6ONXVTboF+qdPfxOvi+XvefLt3QciqbdP7iZdpmB8pdDy 8uu+vnvRjbt7XVPi3yPfj422K0ELC5/IX0Zdvjxt5lM2KkG6V2LLHtHXdz/e CxGmpt45eLBFbzv+/zFfTXCrebu4uYPSwgNdCQpNw3OyFXQHC5HU1BydnHxb t9e9tvZFf/+M5rkf6m+rjBfUX0OZN+miv3r1jcFcXb++oj51Sn4Ek3cy3APx TvJL1/3+/Z/KeOfWra2RkeeyRS5deigrs6PjhrRhk4wvlpN2q36vzMzly4/U DSEj5dZWnYerl5ZWm3+yihyIZHEMNDWd1Uw/L6/I+CPGW//MmSF1nqUVJTzn I2tAhoe6JdGBgXnjxTx06FjMd+UkPLlEC/GlhUjHW3MgKiqquH59Wfc4Lwe3 a9ee6A6oZYU8ehT35kcAMDA9/b5ucSH6C3LyZP/t29vqmyzkX+T4Jj093d+p 3WNjofH5Or/GJj52s+NVgiIpLDxw8eIDGeeqH3z69OPjx3vjfXBoaMnSGnNx gLkdzCGeVTKT8VZ+Q0O7jEoMhg9zc7/anWedUmmarWdfB1fyKkHbXrVD74eQ 7u6tlrhy7dPudtFp/HLI7ey8ubz8F/Uj0uOVI2G8srv5w53vZzvDikqQE75U grb/407GeCkvr21qOie7jIxzi4sr1D84ceKiHBXLy+ti/9HfStDo6KrusmRn 72tt7RsYeDo2tim/sF1d45E7m6amfpzU2bN9nnw7zoNQopGfpM7OMVkW6Yve u/dT+V/5QTl//tahQ8fUo6uZF1/6fmy0XQmyx0YlaGtL50l0keTnF589OyLN TzaEGB1d6ei4ob7UQBJ7Ri4S8yfk3WreLm7uoLTwQFeCQtPwHG6Fkyev6M5G JPv3l8jMyLB9cHDh2rUn3d23GxpOq33stN2u5uzsR7FT1j1dHJ1se/s1WbEz M7+QsYkMDWRIfv/+Ty9delhWpn97rJXHOqWceCf53UrCK7507+BI273HQTpF ExNvydHg5s31M2eGpLOh/tnuOfCfm19edX9M9jJubHxVU9Os+0FpbHKIm57+ WewDRra2vp2Z+eDSpenS0mrdT7W0dBufJ1ld/Tz2mHn0aCctJAVbiGxoTXc6 mtzc/S0tPXJwkx/Nq1fnurrGDh8+Fe9cq2RsbN2DYwWAsHrw4F3d46cmeXlF 0uurqmqU/9V9cUNsCgpKE74Mwq+xiY/dbONKUDQHDhyUPnBv710hndJ4z6OL 5OTJfqtrzMUBZkTghng2GBSDJDk5efX1J2WIJ/23gYF5GZjItpOVqXlkorLt rngw56kjqZWgbU/aofdDSNf3VvPcugteFjPeSpNeen19m4zi5W9kx5GOfVPT ucgLuXST8CxKLN/PdoYVlSAn/KoEra//o7CwLN6eZRw5skVeZqd5ALu/laBX r76Jd2jVjRxnkjp7Ts6Ti+Hh57pFc6uR/m28i5qSvUXMS/1KkJicfNv2VsjM zB4cXFhZea35d/Mn5N1q3i5u7qC08EBXgkLT8BxuBfnF0X3ss9UUFJQ+fvxh 7JSfPftTwms7TaalpcfMPZgpS31vsrtJeJ5fGpvBiwgTZmTkuaXl9b4StP3d BYSvq6oajSciG6KoqCI/v9i47NLYeDrhxX6a18uOjLyghaRmC5HRfZzrV81G fk+Hh58l9RABYC+Yn/+tbr3DXiorG8w8vMjHsYlf3Wx1kR3+yDY1nYucn7HE xQFmVLCGePbcvLmenR338YBWc+xYj/FdJOGT7ErQdvLbofdDyGTsrSa5+Dzk q1fnnK+x3VMoFgbdvp/tDCsqQU74VQna3r3sx8blnX1996L7naYU7m8laHv3 TWoGhWNNamqOJnX2HJ4nF/fv/1T36R/mI1ukv38mYdfC92NjICpBoqdnMs16 qqqanjz5ODIFTfu0VE1wpXm7uLmD0sKDXgkKR8NzYyvsXLr00MnJ0sbGMy9e fKZOWQbOMh5xOKBrbj6fsOae4sxcBuwkZl6Is7HxlY1T/TLQs3E3li+VoO3d 17uob4m1mjNnhsy0t6NHO6MfkbVk/EYhWoi/LWRq6h3bpwFzc/c7fwMUAESs r3+5+45Fp5FebuyNrgb8HZv40s1WF/nEiYtyJNd9xUnCtLdfs11KcGuAGStA QzzbFhf/UFt73Mky7ia9u3vCRgkv6DyoBG0nvx16P4RMxt5qhrtvxrx1a8vg SaTGyczMGhiw3KX3/WxnWFEJcsLHStD27tNxzR9M5EA6OfnD2I93do7F/oHv laDt3aG0yQNLenrG2toXyZs95+fJt3efdb/75k3LJz9lJXR23lSf5Oz9FjEj KJWg7e8Od4/N109zcwuuXXsS27vTXGVntZrgvHm7uLmD0sJDUAkKQcNzq9XN zPyiuvqIyfUQTWFhWcJHTKysvJbNYf4HMRr5iIwRAn03UEQqnOff3r3gTboW 5q/iy88vtncO3K9KUIQ0SHuD05KSqsnJt818xcbGm+zsfdEPHjp0jBaS4i3k xYvPYot3JtPUdM74FUsAYMOjRz/XPP3DfGpqjk5Pv2/+u3wfm3jfzVYXOfIp GRm1tw+YHxnJzJh5FLwxVwaYGkEZ4jk0Pv6qrOyQ1WWMpLKyYXr6Zx7PcIrw phK07Uk79HgImYy9NSF3K0Hbu+/HtNHjlbFMwmeN6kqFY0UoUQlywt9K0PZ3 94D/rqHhtPFOJ4cp3SOhHLti/ywVKkHbu1doHDlyLuGRpLy8Tvfllb73RXWX 6MyZYZMXix44cPDq1bn19S/NT9/3Y2OAKkFidvajuroTxlshL6/owoW76ov/ NBcz26gmOGzeLm7uoLTwcFSCgt7wXD3I7Ny587b8bJk5FSzfMjS0ZP5SK/mZ u3jxgckxnfzkdXSM6t5nFEQpcp4/Ymbmg4S3fmRn75M9yOQFDyp/K0Hbu5Wa wcHFysoGkxOX4c/o6Ir5i341L8K+fPkRLSQQLeT+/X/bXbQEx7eMjMzm5q6H D99zuFkBwMD09PunTl01f+KxsfHM7uUK1s5tpsLYxONudrxKUMSTJx8fO3bB uDglM9PdPZHwLfMmOR9gxptsig/x3LAzNfVOS8t5k/eGyPhFRjG3b/8gBFeR 2eZZJSgi2SfTtr0dQiZpbzXgeiUoQn5fpCubmZmVaFHS6+tPmrwQTlfKHCvC hkpQCDx9+pu+vnvSeSstrZZDk9i/v0R2kLa2y9evL7vVx/CS9KAuXJiUg0Zx cUVkiQoKSqurj5w8eWV4+PnS0qe+z6FVr159I90M6fI1NZ2VXyjp/kWXq6bm aGtrn/yk2quSwwYZL/T0fNfASktr8vIKZUMUFpbV1h4/e3bkzp23TT4Mwbbw Ne9tWrg5NLyo5eXXo6Mr7e0DsvjFxZWR+cnPL5JfrpaWbmkt0ad22PD06ceD gwvSbzx4sEXWcGTiQla7jPe7usYnJ3+Y7LWNhYVPLl2aPnq0M9rad3smh3d7 JitB7JnokpHv4ODiqVNXpbHJwCra2KRVS9uWFi47l42bPmSCsUMetwaDKSXE LWS3VSzID19lZYMcZiNNQo5Fhw4da2+/dvPmmu0KFwBYJV30Bw/e7eu7d/x4 rxyUoj9V+fnFJSVV0i86d+762NhmoI+6EZ51s9Uzk7IaNX+zvPyXgYGnzc1d MjP/8StwIDIzExNvJeOhxEnq5++RIZ5sEVlM2U2OHeuJ7Ca5uQX/MXyorqs7 IRtOfr4dPqoXtnnTDj0bQqbUqNwJ6dDeuPFShi3SxY2uschGkdU4NLTEne8p i0oQAAAAkBp2Yp8+JwNev+cHAAD8k1oJqq1t9X2uAAAwiUoQAAAAkAoePfp5 7Pmlrq4x32cJAABEUAkCAAQalSAAAAAgFXR33449v8TbZAAASB1qJaiyssH3 uQIAwCQqQQAAAEAqKCurjZ5cys8v2trau28lBgAg1fAGcwBAoFEJAgAAAHy3 uPj72JNLJ09e8X2WAABAFJUgAECgUQkCAAAAfNff/zj25NLt29u+zxIAAIii EgQACDQqQQAAAIDv6upao2eWsrKyNza+8n2WAABAFJUgAECgUQkCAAAA/LW6 +nl6ekb0zNKRI+d8nyUAABCLShAAINCoBAEAAAD+Gh1diT2zNDz83PdZAgAA sagEAQACjUoQAAAA4K+WlvMxJ5bSl5df+z5LAAAgFpUgAECgUQkCAAAAfLS5 +SY7Ozd6WungwRbfZwkAAGhQCQIABBqVIAAAAMBHz5//+cKFyai7d3/k+ywB AAANKkEAgECjEgQAAAAAAAAYoBIEAAg0KkEAAAAAAACAASpBAIBAoxIEAAAA AAAAGKASBAAINCpBAAAAAAAAAAAAYUUlCAAAAAAAAAAAIKyoBAEAAAAAAAAA AIQVlSAAAAAAAAAAAICwohIEAAAAAAAAAAAQVlSCAAAAAAAAAAAAwopKEAAA AAAAAAAAQFhRCQIAAAAAAAAAAAgrKkEAAAAAAAAAAABhRSUIAAAAAAAAAAAg rKgEAQAAAAAAAAAAhBWVIAAAAAAAAAAAgLCiEgQAAAAAAAAAABBWVIIAAAAA AAAAAADCikoQAAAAAAAAAABAWFEJAgAAAAAAAAAACCsqQQAAAAAAAAAAAGFF JQgAAAAAAAAAACCsqAQBAAAAAAAAAACEFZUgAAAAAAAAAACAsKISBAAAAAAA AAAAEFZUggAAAAAAAAAAAMKKShAAAAAAAAAAAEBYUQkCAAAAAAAAAAAIKypB AAAAAAAAAAAAYUUlCGF1/fry9PT7vs8GAAAAAAAAAAA+ohKEULp37ycZGZnZ 2fumpt7xfWYAAAAAAAAAAPALlSCEz/z8b3Nz96ftJisr5/btH/g+SwAAAAAA AAAA+IJKEEJmdfXz0tKatJhkZGTevLnm+4wBAAAAAAAAAOA9KkEIk83Nr+vq WtOUpKenP3nyse+zBwAAAAAAAACAx6gEBc6DB++OjLyIxdPPok6evKKWgSQX Lz7wfd4Ak6am3mEfBwAAAAAAAOAWKkGBc+LEJU2Zo6Ki3ve5SgWXL0/rloGu XJn1fd4A844c6WAfBwAAAAAAAOAWKkGBQyVI1+3b2+np6WoZaGBg3vd5Ayyh ErTn7Vy/vtzUdE62e23t8b6++2trX/g9SwAAAAAAAAgwKkGBQyVI9fjxh9nZ +zSrJT09fXBwwfd5A6yiErSXvXr1jdoAiooqnj37o+/zBgAAAAAAgICiEhQ4 VII0Xrz4rLDwgFoGGh1d9X3eABuoBO1lV6/Oqfc2Spqazvk+bwAAAAAAAAgo KkGBQyUo1sbGV1VVjZSBECZUgvay+vo23UpQenrGxsYb32cv1bx48dnk5A+v XXty4cJkR8doR8eN3t6poaGlhw/fW1//h++zBwAAAAAAkCKoBAUOlaAYOy0t 5zVrIyMj89atLb9nDLCPStBeVlnZoFsJkqys/NX32UsRL1581tt7t7y8Nt66 ivwWHD586saNl5ubX/s+wwAAAAAAAP6iEhQ4VIKizp+/pZ76u337B77PGOAE laC97PjxXt26Rl5e4dbWju+z57u1tS86OkazsrINakCaFBWVj41t+j7nAAAA AAAAPqISFDhUgiI2N78uL6+LXQ9ZWTkTE2/5PmOAQ1SC9rLp6fczMjLVcsbF iw98nzffTU7+sLCwzHwNKDZtbZe5OQgAAAAAAOxZVIICh0pQ1MrK6+jTgbKy cqam3vF9lgDnqATtcePjm/v25Ue3fnp6RlfX+Pb2nr4haGvr256eO7Iy7JWB Iqmvb6MYBAAAAAAA9iYqQYFDJSjWysrrysoGykAIEypBePny7zdvrvf3zwwN LS0tfer7/PhrY+NNc7P2lXAxlbL0AwcO1te3ifLy2tgimprjx3t9XxwAAAAA AADvUQkKHCpBGi9f/v3x4w99nw3ALVSCgKiNja/q6k7olnWqq4+Mjq6srX35 /Y/szM5+1NFxIysrR/dT168v+75QAAAAAAAAHqMSFDhUgoBwoxIERMQrA+Xm Fty8uWb8xLzFxT9UVjaon83PL1pf/9KV2QMAAAAAAAgKKkGBQyUICDcqQUCE biXo4MGW58//bObjL1/+vaysVi0GXbky6/uiAQAAAAAAeIlKUOBQCQLCjUoQ EKUpBh07dmFz82vzH5+b+3VGRqZmhyovr/V9uQAAAAAAALxEJShwqAQB4UYl CIgVLQa1tw8YPxFO16lTV9XbgpaWPvV9uQAAAAAAADxDJShwqAQB4Xb8eC/7 OBBrY+OrS5cebm19a+Ozjx59oFaCbtx46ftCAQAAAAAAeIZKUJJsbe08fvzh lSuzp05dbWo6V1vb2tR0trW1r6dncmLirZcv/257yilYCVpf//LWra3u7olj x3oaGk7X1bUePdp59uzI8PDzZ8/+6O+8eeDJk4+vXp2T7dLYeKa+vq25uevM meGhoaWlpT+Yn8js7C/7+2daWy/KCtydyPmOjtGbN9dWVz93fYaT1zhNWlv7 YnR09fTpQfneuroTLS3dPT13Hjx41+rV/gsLn0gb6+i4Iavr8OFTst4iDW9g YH5u7tc27h1InvX1f9y+/QNZTFnYyKxKO5FNPDq68uLFZ5o/Vm9hcL6P+77R 483VzMwHsvtIY5BtJ4eOyEaUPejatSeyU8gfuPhd4Wj2AV18H21tfZuTk6fZ p2Tl+z5jAAAAAAAAnqES5LqVlb/29EwWFh5QL0KOJjMz6+jRzt1zgJan71kl qKfnjkw5avexPNq/WVj4pK2tPysrJ/6ypjc0nJ6Z+YXVb5+f/23st0fcu/cT q9MZHFzQTOTQoWNuLf7ExFvV1UcMlr2+/uSjRx8Yf9HY2KZMP94kMjIyZYtb KioZSHbjTLjSNja+6uoaz8nJ1f1q2ehmvuXly79fvvyorOyQwVJIiooqursn 1DqLxx4//vDEiYuZmdlxW0l6xpEjHfJn0Y+cO3dd8zdO9vFkb3S7c/Vatk5B QanxRpQ/6Owcc/ggr8A1e80ERUtLT8oufiDU1rZqFvnkyX7f5woAAAAAAMAz VIJc9OrVN31997Kz9xmf24zNkSPnnj//s6Vv8awSpPmisrLvvWJ7c/NNd/eE +iZu3aSnZ1y69NDSt8/O/lKdzq1bW1aX4sKFSc1E9u3Ld774stUaG8+YXPah oSXdr3j27E/19W1mJpKdnTs+vhm4xllV1fT9bfpRSUlVvK/LzS1IeHOELMWl S9OyBc0vRVZWdmfn2Pr6PxzuDjasrn5+4sRFaQImZ1XW3srKa91Ga28f92aj W7fT3z9jaa5kJ2pr65f9JTXXgOvN3q2DvGcN4ObN9fr6k8XFFRUVh+V3IQXv MGpp6dYsaUvLed/nCgAAAAAAwDNUgtyyuPj7iorD5k+4RZOXV/jw4Xvmv8iv StDuGct//qf5+d8Z3MYSL319981/e6pVgmIX/+7dH8n/a2nZh4e1TXFy8u3c 3P3mp5Cenm67GORX4ywqKo/+p6mpd+LdExFJY+Np44kvLHxibyl256Qi4c1Z 7nrw4N38/GKr85mfXzQx8VZ//4zm323s455tdEs2N79ubu6yMVdp39VD9129 Omf+WWrBbfauHOQ9W/yTJ69oplBSUuX7vXgJV2lLS7fvcwUAAAAAAOAZKkGu mJ5+32ppIDZZWTnmz7z5VQmSbG6+2d59JFp2ttG5zfhJN//0oVSrBEUX/+rV ufR0s3d5RJOZmTU7+1F04vYmkp29b3HR8mPifGyc8tnIv8/MfKC+p0OTvr57 xkth6VYgNbIJHN5XZd7Y2LrJ2+V0U15ep/kXq/u4lxvdip2WlvO25yqSjo7R VFsDrjd75wd5zxb/+vVl3SkkLOx6rLlZ2/BOnrzi+1wBAAAAAAB4hkqQcwan +/bvLzlx4tLly9MjIy8GBxfOn79VV9eqWwLIzS0w+UweHytBS0t/kKXQnf/M zOyioori4grDdwbJrB42eUl/ClaCZPH7+u7rLpds6OLiSuMCWW1ta7xZiqSg oFQmYrwCm5u7gtU4Nza+Wl39vLCwzGChIpmaeifeZB8+fM9gtcgGras7ceLE Rfn2w4dP5ecXxftLWbqxsfVk7CyxJibeMiwDpZeW1jQ0tB85cq6m5qjJ+8Is 7eMeb3TzBgaexpur9vZrMj9jY5uyjw8PP+/sHKusbFD/Mjt739Onv0m1NeB6 s3d4kPdy8Q8dOhZv0Z49+2Oy9zXz1Nujenvv+j5XAAAAAAAAnqES5NCzZ3/a v79EPQlWWlpz69bW1ta36keeP/9zW9tl9SOH/z/27gNcqsJO/DemZzVm3d20 zZP2ZJ80TXNTdv/rrjGJyW5i+qaIIFJUQDooAgqKFEWKKCgoTcQCKCggCNLB y72XJqIiCFKlKb1zC/8T55fZYc7cudPuncvc930+T54Ic86cc+bMnbnzZWZu 7ZfKNeZxEnT77YPCrxbed9/EmTO3xOxpxfPPvzVw4GONGjVO+PJgkhf8Y6uD k6Dw7rds2f7hh2fFfg7SCy9sHzp0UlVji+eeeyO8PTfd1Hn06LmRL4iJFBzA AQPGNWx4TcKVhL9fvi6fnME2hL+ho2vXPuPGLQx2ZNGiA8ERmzhxRbC/RUXH E65z7tw9zZolHu506XLHpEmrS0rKYi9fWlrx7LPrbrttYMJFGje+dvr0N2vi /hJp9uwdVX0aWHBnGTZsaujV9Yrg7jNkyJPJ38GR+n289m/0FAtu3/A+Bif5 iBEziotPJ1xk1qytd9wxNPby48cvroNHIOenfTY/5Gt595O8U2/SpFU5PH+y admyk+HhbAaPJpIkSZIkSeduJkHZVXHLLb3Dr4D17z8q8kliSUr4+VFPPbWi 2ivN4yQoTr9+Dyf5ZvDp099M+CJhsNpUrr0OToJiNWrUePjwZ6t6BfuFF7Yl /I6YG29sG/ufjRs3efjhWXGDjGhTpryacKI0ZMiT59DJGRyl2P9s1arD1Kmv pX7zlZaWd+7cK7wXjRtfG/7qpbgmTlyZcLxy443tqpo6ZVlwUyZ8J8vVf30z 15AlSw4nWTbYpMGDn6hq/JfyfTwPN3qKjR27ILxho0fPrXbB559/q0OH7len +nFehXDaZ/FDvrZ3P8mb9SZOzNnJk2XBA0do6xomvz9KkiRJkiQVWCZB2TR6 9Nzwy18pv1BfOWbMvLhlO3ToUe1SdWES1LhxkwkTqv+UrSeeKAkv26zZjal8 QFxdngTdcEObGTM2JV/8mWfWVrV4ROvWHV94YXu6Z8jVf/0SmZtT2f46cnLG 6t79nnRHMCNHzgyvJzj9Unxn2axZiUdyAwaMS/dESqURI2Yk3PFBgyak+KGI 06atT/iRYinex/Nyo6dY+F1aN998Z4rLlpSUBWfCsmUn6+YRyPlpn/EP+drf /eACVe146u9erOl69hwct22dOt2e962SJEmSJEmqzUyCMq6o6Hj4ReZeve5L 8SXfSN273xO3hmo/uirvk6AWLVo9//xbKa4k4Vsk5szZWe2CdXYS1LbtzYsW HUhlDV279km4hkDHjrctWXKk2jWUllbceGO70NINk7wVa3kdOzmjOnW6vaq3 UFVVsJsJv0YnlSlktGnTNoTf6RD8SSonYVol/PSzQO/ew9I67AsX7m/VqkPc SlK5j+frRk+xuDfEBap9V1cGN0EBnPYJV1hnT4DRo19MuOOdO/fK7Y2bcfPm 7Q2/1W7UqNl53zBJkiRJkqTazCQo4x56aHrci0vNmt2Yysv7sT3//FtxK7n3 3keTL5LfSVCrVh3mzduT+kriPiUp4okniqtdsG5Ogtq2vSX1m7iq10g7deqZ +nsEBg2aEF7Ds8+uS75U3Tk5I5o2vX7+/HfTve2GDZsSXtWddz6Q7nqGDp0U Xk/fviPSXU/yEr4hqGXLTD6JLvz2mVTu4/m60VOsceNr49Y8Zcqrub0JCuO0 T7jCOnsClJZW9Ow5JG6RFi1azZ27O7c3bsYF9/S4zWvSpGm1w3RJkiRJkqQC yyQos0pLy8P/xP3hh2dlsKq2bW+JXUnr1p2SXz6Pk6BmzW5I94XNGTM2xb88 evXVDz00rdoF6+AkqFmzGxcvPpj6Vc+duzu8C8Htm9aLkE8/vSa8knHjFiRZ pE6dnBGjR7+Y7lWXlJSFv/i+UaNr589/J91VFRWdSLiqtG7N6qoIv5Hn6ky/ lT6DSVAeb/QUC3+hzOOPp/HermorjNO+qhXW5RMguOpRo+Z06NA9+NF6ww1t +vcftXDh/hzestk0a9a28BuCBg16PO8bJkmSJEmSVMuZBGVW+FtgmjZtkcrX WIQLv+kj+ctoeZwEZXBFRUUnwi+Q3nffU9UuWAcnQenufmlpRfh9ELfdNjCt lcyf/074OAwf/mySRerUyXn1e5+nFxyKdK968uTV4VX16fNQBnsR9MADz4TX lvEL9eGmTduQcMfT+mCuaBlMgvJ4o6dYeE5xxx335+r45/cI5PC0r2qFBXAC 5KOKm2++M25frr22aU5HwJIkSZIkSedGJkGZNWDAuLjXl/r3fySzVY0fvyRu VU8/vSbJ5c+tSVBQ+O0YwdGrdqkCmAQF3XRTl7iVpDsJWr68olGj+HHSoEET kixSp07OwPjxizO46n79Hg6vqtqPxauqhAO1rl37Zra2cEOGPBlef8bfRZLB JCiPN3qK3XHH0PAhGjNmXq5ugsI47ataYQGcALVfcHaFb5cHHng67xsmSZIk SZJU+5kEZVabNjfHvb701FMrMlvVzJlb4lb1yCMvJLn8OTcJCn9qVrDmFA5L IUyCOna8LW4l6U+CKoMNjltJ8m/uqFMnZ/PmLUtKyjK46ptu6hy3qqZNW2T8 Joug9u27xa2wceMmmW1buM6de10dr+GiRRm+9SCDSVAeb/QUC7YndIj+avDg xzN760rdOQI5PO2rWmEGJ8DEiXXrBKjl5s3b26RJ07gdadmyfU5ONkmSJEmS pHMuk6AMeumlY1df3TDuJabFiw9ltrbwuxWGDHkyyeXPuUlQsFTceurPJOjm m3vHrSSDSVD4Y7WSTIIK4+RcuvRo+Nbv0ePezPYi0sCB48PrDE6zbNYZqaSk LPwlOO3bd8t4helOgvJ7o6dcRZcud4RvgsANN9z0yCMvFBUdz3jlhXHaZ7zC c+QEqL2Cu2Si4ezVkyatzvu2SZIkSZIk5SWToAx6/vm34l5fatbshozXFn7R u3//UUkubxKUVvVtElQYJ+eMGRvDt/7QoZMy3pGgceMWhtf5+ONF2awz0rx5 e8Jr7tfv4YxXmO4kKL83ejoHam+zZjeGj1VEkyZN+/Yd+dxzb2Sw5sI47TNe 4blyAtRaCT+tMeNvGZMkSZIkSSqATIIy6IknSuJeYmrYsOF11zXPuLi1JR+U mASlVX2bBBXGyfnEE8XhW//RRxdlsKpozz67LrzOBx+cls06I02d+npu15zu JCi/N3pazZy5JckwKKJ1604jRsxI6y0thXHaZ7zCc+gEqIUSfg7hjTe2e+ml Y3nfNkmSJEmSpHxlEpRBY8cuSP5KZpZMgpabBMWU1iSoME7OhHsxefLLGawq 2pw5O8PrzMnHXgWnZXjNjz22NOMVpjsJyu+Nnm7z5u2t6mPiYjVq1PjOOx+Y OvX1jE+YWjsCeZ8EnVsnQI02e/aO8NcDXXNNo2nT1ud92yRJkiRJkvKYSVAG jRw5M48vu5kEpVV9mwQVxsmZcC+mTHktg1VFW7BgX3idSY5k6o0fvzi85gzO 1WjpToLye6NnUGlp+cMPz2revGUq196p0+3PPPNKBidMrR2BvE+CzrkToIZa vPhQy5btw9s/YsSMvG+bJEmSJElSfjMJyqCaftlt0KAJSa7dJCitTIJyq3ZO zoR78eyz6zJYVbTFiw+G1zlgwLhs1hkp4TcQFdIkKPmNnnFFRcdHjHi+VasO qWxDcGotXXq0bh6Bgp8E1dAJkNuKi0917twrvPG9eg1Zvrwi75snSZIkSZKU 30yCMmjUqDlxrzVdc02j224bmKuSv4ZsEpRW9W0SVBgn5+jRL4Zv/SlTXs1g VdHmzdsTXufAgY9ls85IEyYUhdeczbcapTsJyu+NnmWlpRXPPPNK797DGjdu Ej6MsVq37jh37p46eATyPgk6p0+AHJ1F5T17DgmfM8FxKyo6nvfNkyRJkiRJ ynsmQRk0fvyS8Mtutfavjk2C0qq+TYIK4+QM70VgwoSibLZtxoyN4XUOGzYl +71O+A31jzzyQsYrTHcSlN8bPVe99NKxUaNmh39cxGrVqsOSJYfr2hHI+ySo ME6AbBowYFz4bGnRotX8+e/kfdskSZIkSZLqQiZBGTR16mvhF51q7RUnk6C0 qm+ToMI4OadOfT28F1l+2Udw8oTXOW7cwuz3etq09eE1P/DAMxmvMN1JUH5v 9JwXHM/bbx8U3qOInj0H17UjkPdJUIGdAOk2dOik8O43btzk+effyvu2SZIk SZIk1ZFMgjJo3ry94dedJk1aVTs3mUlQWtW3SVBhnJzz578T3os+fUZks23D hk0Jr3Pq1Ney3+u5cxN87lzv3sMzXmG6k6D83ug11LRp62+6qXN4v9671V6v U0cg75OggjwBUmzEiBnhfW/YsOHEiSvzvm2SJEmSJEl1J5OgzGrW7Ia4l54G D368dm6y+jwJyuAdHPVtErS8UE7O5s1vjFtV69adstm2bt36x62wYcOGL72U g+8QKS0tb9To2riVt2nTJeMVpjsJyu+NXnMFt07Xrn2uDrnzzgfq1BHI+yQo v7ufx8JfkBQxatTsvG+bJEmSJElSncokKLN69Lg37qWnLF+mTr16MgmaO3d3 +PW90aNfTPfa6+EkqDBOzoSfD/bii7syW1tR0fHGjeOHNTm847Rte0toYxsu Xnwws7VlMAnK441eoy1dejTYkbhda968ZZ06AnVhElSoJ0CSqhoD3X//03nf NkmSJEmSpLqWSVBmjRkzL/wC1LPPrquFm6yeTIIWLToYPsL33fdUutdeDydB hXFyjhu3ILwXDzyQ4Wu848cvTnQ6TczVjvfvPyq8/rFj52e2tgwmQXm80Wu6 Rx9dFN61oqL4N3MVxmmf8QoL+ARI2OjRc8P7G7jnnrF53zZJkiRJkqQ6mElQ Zi1adPCaaxrFvQbVrdvdtXCT1ZNJ0LJlJ8Ov8iX8svgkLVlyJPyGgoKfBBXG yblkyeHwR641b94yODHSX1tFhw7dw6fTCy9sy9WOT5hQFF5/p049M1tbBpOg PN7oNd2LL+4KH9v589+pO0egLkyCCvgECDd+/JKGDRuGz4r3PjawIu+bJ0mS JEmSVAczCcq43r2HhV+Jeuqp5TV9k9WTSdDyRN8U07x5y9LSVF/oKy4+HR7E XF0PJkHLC+Xk7Nt3RHgvhg2bmu56Hn88wZjmllt653DHly49Gv70ucAzz6zN YFWtW3fM4DDm60av6RJ+UOSSJYfrzhGoC5OgPO5+LVfVGKh793uCn/l53zxJ kiRJkqS6mUlQxs2cueXqq+Nfj2rW7IZ58/bU6E1WfyZBt9xyV/jlvmnT1qey bHHx6fAXZ0TUh0lQYZycL7ywvWHDa+JW2KjRtbNmpfFenoUL9zdrFj9SDEye /HJu9/2OO4aGr+WmmzovW3Yq9ZUsXXq0U6fbw+tJ5TDm60ZPsdLSikcfXZT6 JDfaxIkr43aqceMmCd/6URinfcYrzOMJMGFCUdeufVu2bNeu3a333TcxOI1r 6IqqGgMFDxYZvVtQkiRJkiSpvmQSlE19+jwUfkmqVasO8+e/m+6qXnop/msv qqr+TIIGD34ifHhT+YC4ZctOduvWP7xsRH2YBC0vlJOzX7+Hw3tx443tUtyL pUuPdujQI7yGrl37ZLNVCZs2bUPC8+2uu4an+IlVCxbsa9fu1oQrSfEw5uVG T7Hhw58NNubWW/sFu5nWgj17Donbo+BuVaeOQB2ZBOVr9/v2HRm+xoUL92dz BBL22GNLE46BOna8LeenqyRJkiRJUoFlEpRNixcfat68ZfiFqeuvb536V3UX F58eOXJm06bXP/30mlQuH35tvHXrTjWxd3mfBE2Z8mr42AZGj34xyVKzZ29v 2/bmhAtG1JNJUF5Ozpy/JL5kyeEWLVqF96Jly3YzZ25Ovuy8eXvat+8WXrZx 4yZz5rydzVZVVffu9yQ85YLDUu0bFiZOXBn+OMR0D2NebvRUCq49+vaupk1b jBkzL8U3Bz3xRHF4dx5+eFadOgJ1ZxJU+7v/6KOLEp6x3bvn+CuKnnpqRfiL kAJt295Sc29BkiRJkiRJKphMgrLs6afXhD/A6j0N77rrwdmzdyRZdtGig8OG Tbn++taRBdq375bKewfC75S55ppGwapyvmt5nwSVlpZHD06cu+8eHf7K+Dlz dt5776NxrxYOHDi+WbMbYv+knkyClufj5KyJN6y9txcJ3ggQ3NCDBk1YtOhA eJGXXjo2fPizTZo0TbTvV48ZMy/LTaqq4Axs3LhJwitt3brj448vKykpCy81 bdr6225L/EmGGRzG2r/Rq23x4kPhO3KwR088UZzwgER79NHF4W9fCu6/yV/5 L4DTPpsV1vLud+lyR6Lr+qvwj+iMq2oM1KJFq6lTX3v22XVPPlk6btyCkSNn Pvjgc/ffP3ngwMeCH5Iplu6b1CRJkiRJks7FTIKyb8yYeVW9FBZ5MW3QoMcf fXTRM8+snTLlteB/H3108ZAhT3bpckf49e3HHlta7dWNG7cwfC3t2nV95JHZ Dz44rbS0PFf7lfdJ0PK/faJUVdq2vblHj3uDtfXoMaBly3bhC0Q+mKtt21ti /7D+TIJq/+SsoY8uDM7tqnbhmmsade3a5777ngouM2rUnPvvnxycElWNY9I6 dJk1duz8JAe8adPrgxNg8OAnHnjgmfvum3jnnQ/ceGOC8/ammzpncxhr+Uav tt69h1e1MS1atOrf/5Hx4xfPmLFp3rw9ixYdfPHFXVOnvvbgg8+1aZP4nX3B DV3XjkCdmgTV8u4HP06ruqJJk1bl5D5V1RgoV6p9d6EkSZIkSVIBZBKUk8aO XZDwbQvpatWqQ3Hx6eTX9eKLu5KsIYcfe1UXJkFFRSduuKFNZgezf/9HInOx W2/tF/vn9WoSVMsnZ819idXDD8/KfhfeO245eJNL8hJ+v1XqevS4NzjtGzVq HPuH6R7G2rzRq23+/Hc7d+6V/cYEevUamuIteE6f9tmvsNZ2P8nUdeLEFTm5 QzVten32O5KESZAkSZIkSaoPmQTlqqlTX0v4hSapu+665iNGzEj+cUmROna8 raqVPP54ca72qC5Mgpa/9w0jGfyD8GHDpkRfMY57S0J9mwTV5slZc5OgoCef LG3SpFlm29+oUeNRo6p/L0mOqhg6dFIGG9mw4TUPPPBM5Lxt1apDloexNn8i VVtpafnIkTOvvTbx5/WlqGfPIWmNpc7d0z4nK6yd3e/QoUdVi+fqnyUkedtR TpgESZIkSZKk+pBJUA5bsuTI3XePqeI7GpJp0qTZoEETliw5nOIVPfPM2qpW NXTopFztTh2ZBAVNnLgyyb88j9OiRavJk1+OXXzQoMdjL1APJ0HLa+vkrNFJ 0PK/vrvkndtvH5TuLnTpcsesWdtyuBmp9NRTy+O+oCq54EDNmLExunjXrn2y P4y19hMpxRYvPhicuqnfl6OCRUaMmJHB+7nO0dM+Vyushd0fPfrFhGvo3LlX rk4bkyBJkiRJkqTsMwnKeXPn7rnnnrEpfqDNTTd1fvjhWUVFx9O/4WYk/PCf DAYNVVV3JkFBc+bs7Nbt7uQHs3HjJglfvQyOVezF6uckKFJNn5w1PQmKNG3a hp49B8d9floiDbt27Tt58uqcb0CKLV16dOjQSdXOg1q16jB69Ny4b/jq23dk rg5j7fxESr3gHjp8+LNt2nRJZXuCe+vAgeMXLtyfzTWec6d9bldYo7tfWlrR s+eQuJW0aNFq7tzduTphTIIkSZIkSZKyzySohiopKXvmmbX33TexR48BN93U uVmzG6+7rnnQ9de37tTp9t69h40cOTPL9ynMnLl5wIBxbdveEll58+YtO3fu NWrUnLyfVDXX7Nk7hg2b0r37Pa1bd4wczxYtWrVr17VPn4cefXTR0qVH876F 50S1cHLWQkuWHH7ssaX9+j3cpcsdN9zQJnYX7rrrwTFj5i1YsC/vGxlUXHx6 0qRVAwc+1rVrn1atOkS3M9jse+99NLgh4mZANVQdvNFnz94+evSLwY0V/OCK 3oJBrVt36tat/+DBT0ye/HJx8akCPgK1Wc3tfnACB487HTp0D9YW3I79+4/K cnInSZIkSZKknGcSJEmSJEmSJEmSVKiZBEmSJEmSJEmSJBVqJkGSJEmSJEmS JEmFmkmQJEmSJEmSJElSoWYSJEmSJEmSJEmSVKiZBEmSJEmSJEmSJBVqJkGS JEmSJEmSJEmFmkmQJEmSJEmSJElSoWYSJEmSJEmSJEmSVKiZBEmSJEmSJEmS JBVqJkGSJEmSJEmSJEmFmkmQJEmSJEmSJElSoWYSJEmSJEmSJEmSVKiZBEmS JEmSJEmSJBVqJkGSJEmSJEmSJEmFmkmQJEmSJEmSJElSoWYSJEmSJEmSJEmS VKiZBEmSJEmSJEmSJBVqJkGSJEmSJEmSJEmFmkmQJEmSJEmSJElSoWYSJEmS JEmSJEmSVKiZBEmSJEmSJEmSJBVqJkGSJEmSJEmSJEmFmkmQJEmSJEmSJElS oWYSJEmSJEmSJEmSVKiZBEmSJEmSJEmSJBVqJkGSJEmSJEmSJEmFmkmQJEmS JEmSJElSoWYSJEmSJEmSJEmSVKiZBEmSJEmSJEmSJBVqJkGSJEmSJEmSJEmF mkmQJEmSJEmSJElSoWYSJEmSJEmSJEmSVKiZBEmSJEmSJEmSJBVqJkGSJEmS JEmSJEmFmkmQJEmSJEmSJElSoWYSJEmSJEmSJEmSVKiZBEmSJEmSJEmSJBVq JkGSJEmSJEmSJEmFmkmQJEmSJEmSJElSoWYSJEmSJEmSJEmSVKiZBEmSJEmS JEmSJBVqJkGSJEmSJEmSJEmFmkmQJEmSJEmSJElSoWYSJEmSJEmSJEmSVKiZ BEmSJEmSJEmSJBVqJkGSJEmSJEmSJEmFWgFPgi6//Ddf+cq3g72TJEmSJEmS JEmqn33lK9/+5Cc/m++5TY1YsmTJ9OnTFwAAAAAAANRX06dPf+qpp/I9tAEA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAA4P+cPrxv8zP3vHLPH9fc87+bJ/c7dXBPvrcIAAAAAACAHDh9 ZN/K235cdNPXoy3v9p+nDuzO93YBAAAAAAB1y+lD72x7flhOOr57U773pr7Y NuP+2DFQpM2T++Z7uwAAAAAAgLrl6PZ14ZlCZu1bMzffe1NfvPFIu/Dxf+3+ pvnerrro2NvrgzMz0v61C/O9OQAAAAAAUKtMgs5Fb03qEz7+b47vlu/tqluO 7nhj/ehOsYeopMsP8r1RAAAAAABQq0yCzkXHdr5Z3OG7sQd/WbtvHtmyNt/b VSec2r9r16In1g68OnyKmgQBAAAAAFDfmASdow5vWvXKPX+MHPmX+/324Pri fG9Rnu1eMnHjhB6r7/plklPUJAgAAAAAgPomPAnav3ZB2fHDGVRZXpbvval3 yk8eKz9xNN9bUSekMqw0CQIAAAAAoL4JT4IObSjJ90ZB2kyCAAAAAAAgzCSI wmASBAAAAAAAYSZBFIbgTA63ZcoAkyAAAAAAAOozkyAK2M75j5oEAQAAAABQ nxXqJKiy/PTx3ZuCfQk6snVt2dEDNXRFZccOHdnyyqENpcd3baw4dSLdxctP HD22443/t51bXqm57Uxw1ccPB4fo8KZVf73qzWtOH3onhyuvteOfnEkQAAAA AAD1XM1Ngk7u37nt+WGx7VszN8VlD21cEbfs6cPvprJg+clju5c89ep9TZa1 +2bcfq28/SebnrzjyJa1qe/C3mVTohsQ/P+4v923Zt6rgxsVtbk4ehXBlW6f 9VAqaz68ceWmp+5c3fsX4e+yWdnzp5sn9z2++63Uty2tY3t896YtUwas7v3L 8FUvv/WyDWO77F+74ExlRYpri5Pb4589kyAAAAAAAOq5GnxPUGXlq0Max665 uMN3T+57u/rlyk+v7Hll7IKvD7u++qUqynfOG1d6y7+HBxxxrXuo5cn9O1PZ g1fvuza61Mt9fhX98/Ljh994uG3Clb89d0zydR54bcmau/9Q7UYua3vJWxPv Kjt2MJVtC3pzfLdqd6fs2KE3H+te1OYb1V77qjt+vrd0WlrzoJo4/tkzCQIA AAAAoJ6r0U+HO75707J23zprCjCidbVL7Zw39qz5Uftvn3hnW/JFTh3YvXbg X6qdQUQr7fKDQxtKq92S2GnL8m7/GfnD00f2vdz3N1Wt+fDGlVWtrfzksTfH 3ZL6Rv71Srv+x4HXFle7balMgk7s3Ro3X6u2tYOuqfbIR9TQ8c+eSRAAAAAA APVcTX9P0PaZw+PWv3/twiSXLzt2qPTmH8Zeftvzw5JfxYm9W1f0+FFaM46i 996gdHjTquRrjp22LGv3zeBPKk6fSPKOnmVtLwkukHBVpw7uTTI/Slabi4/v 3pR826qdBJUdP7yq18+q2uaSzt+vchg08Orkh6hGj3/2TIIAAAAAAKjnanoS VFl+evVdZ30lzcqeP61qXBLYMmVA7IVX9fpZxemTSdZ/6uCe8BhixW1XbJvx wJGta8uOHawoO3XqwO53V8/eMKbzsraXnHWxHj9K8vFrZ0LTlvLjhzc+fvvZ 44zvvD6sRfCH60d3WnP3H165988J11N27FDC7+Up7vDdDeNu3rNsyqE3lx/d +ur+tQu2TR+68vafxF5m89P9U9m25JOgTU/dGXfVL/f51d6S54KjF7lAcJSO bFkbXPvyWy/7v83r+N1jO99MstqaPv7ZMwkCAAAAAKCeq+lJUODwppVx302z bfrQhJc8+e72uE+TO/D6kiRrrqwoD38o2ZapA6saHh1+6+UV3S+PvfCmJ+9I sv64acvupRNj30qzddqQsuOHYy9fUXYq0VZWvj7s+vAYaNNTd5Yd2Z/g4uVl b784urjjpX99S87ga4L/TGXbkkyCTh/et6zdN2Mv/Prw66s6ROUnj+14YWRx h+8GF9u7bEqSg3Om5o9/9kyCAAAAAACo52phEhTY9GSv2KtY1u5bJ/ZuCV9s w9gusRdbP6p98tXGfaNQKsOLE3s2x34Y2rK2l5zcv7OqC8dNWyLzkaL3vr7n 6NZXU9nxwO4lT8Vt5LJ233x35azkSx17e8PaQdecOrg3xW1LMgna89Lks6Yh nb9fdvRA8msPbp0dcx5JfpkzNX/8s2cSBAAAAABAPReeBL0+7Po3x3dLvcMb V1Z7LeXHD6/o/l9x1xJ3mSNb1p71on2nfz11YHeSdZYdPVDS5QdnvdVoxgOp 7PLuJRNjl9o6bUhVl4ybtvy/eVDHS4/v2pjKFZ157wt6Sm/+t/hxSclzKS6e ROqToLcm9j7rkuNuyf7az9TK8c+eSRAAAAAAAPVceBKUbtW+DSRi3+o5cQvu f3VR7AVeG3pd7N++PXdM8hVunzk89vKr7vjvqj5ILU5F2anSW/79/xbs9bOq LplwErS3+NlUriVixwsj4xbP1SAm9UnQ+tGdYi+5ZcqAnGxALRz/7JkEAQAA AABQz9XaJCiwbkTr2AVX9/5lZUV55K/2r10Y+1cv9/1N9K8Sq6yI+8aZd1fO TH2vNz11Z+yyJ9/dkfBi4UnQ2kENz1RWpno1wUb2+FHs4sUdvpPkA9/Skvok aOOEHrGXfH1Yixxcfa0c/+yZBAEAAAAAUM/V5iTo1IHdJZ2/l+CNP5UVL/f9 TeyfV/uJc4c2roi9fOnNP0zxDSkRe4ufPWuKseqFhBcLT4IOri9O/VriNjJo 0xO9Ul88udQnQW+/OCpuMw68tjjLa6+d4589kyAAAAAAAOq52pwEBXYtnHDW e2Q6Xnpq/65gDbF/uPGx7tWuZ9uM+1OcgyTe6x1vxC6+febwhBeLm7asufsP aV1L3EYGHdq4Iq01JJH6JOjE3i1Fbb5x1mHv8N09RU+n8eamkNo5/tkzCQIA AAAAoJ4LT4IObSipweurrFg78C+xV/f68OtX3HZF7LtLyo7sr3Y1rw9rEbuS XYueSGsrTu3fddZbdZ7slfBiqU9bUtnIks7fq+Yj79KR1ra9NfGu8AjvlQF/ fHf17LTeyxNVO8c/eyZBAAAAAADUc7U9CTpz5tjb65e1vaSqdxjtXjoplZWs 7HnlWW8qeX7YvjVzU++d0umxi28Y2yXhtWQ5CYrbyFfva5LW4smltW0Vp0++ MuCPCQ/48lsv2zJlwPFdm9K69to5/tkzCQIAAAAAoJ6r/UlQYOu0IQmnEq/c ++czlRWprCHJLCmD3hh5U8JryXISFLeRGyf0SGvx5NLdtrJjh14bel2Sg7B2 8DV7S56rOH0ylWuvneOfPZMgAAAAAADqubxMgspPHitu/+24613W9pKj219P ZfHK8rIcjiFqaBIU3sjNz9yT+uLVymTbKivenjumpPP3kxyK0pt/uO35YWXH DydbTW0d/+yZBAEAAAAAUM/lZRK0c97Y8DigpPP3Tx96J5XFy44fzu0kYuPj tye8omwmQeGN3Prc4NQXr1bG21Z2ZP+2Gfev6P5fSedB//buyllVrqG2jn/2 TIIAAAAAAKjnan8SdHL/zuKOlyacCKx/pH2KKwm/qWTz5H4Zt3/tgoTXkuWn w8Vt5KYne6W1eHJZbltlRfm+NfPWPdSqqM3FVQ1oNj/dv6rFa+f4Z88kCAAA AACAeq72J0HrRrRO8vaQfWvmprKSuFnSnqJnamJTs5y2FHf87llzrtEd6862 RZ3av2vb88NW9PhRwpvj7RdHJVyqdo5/9kyCAAAAAACo52p5ErRv9ZzY61r3 UMtNT/SK/ZMV3f8r+ZfURLzc51exS22ddl9NbG2W05bVvX8Zu/iau/9Qd7Yt TmX56T3Lpizv9p9xJ8Oytpcc370pfPnaOf7ZMwkCAAAAAKCeq81JUPmJI7Hv PVnW7lsn9m4pP344bgCx6Yme1a5q/agOsYu8Puz6mtjgLKctbzzS7qypSrtv lp88Vke2LaGyYwdfG3pd3PmQ8OaoneOfPZMgAAAAAADqudqcBL01qc9ZbyR5 dlDkz99dOStuGw6uL06+qp3zxsVevrjDd3I4ZInKctqyY84jcfv17qrZdWTb qlJx6sTL/X571ru0evwofLHaOf7ZMwkCAAAAAKCeq7VJ0JEta4vaXBy9ltJb /r38xJHo3752f7PYbVh1x88rTp9IsrYTezbHbfbupZNyvs1ZTluO7XgjbiNz +N6ZGpoEBfatmRe32RWnjsddpnaOf/ZMggAAAAAAqOdqZxJUWVG+pv/vYq9l 54LxsRc4vmvTsraXxF5gy9R7k69zzT3/G3v5lT1/WnH6ZG43O/tpy+rev4g7 vEe2vFJHtq0qJ97ZFrfNCb+5qRaOf/ZMggAAAAAAqOdqZxL09twxZ08Nrqws Px13mS1TBsReZlnbS45ufTXJOveWTovb8s3P3J3bzc5+2rJr4YS4jVzT/3cV ZadqeduSv8EqTvxbmdpcnHCDa+H4Z88kCAAAAACAeq4WJkEn971d3PG7sVex 7+UXwxcrP3lsxW1XnDU06ffbyoryqlYb/NXLfX8d+oyyiTnc8uwnQRWnT6zo fnncRv51PZUV1S576sDuTU/2quoIpL5tJ/fvXNH9v3YtevxMZWUq27xj9sOx a17d+xcJL1YLxz97JkEAAAAAANRztTAJWvdQy9j1v3Z/06ouuf+V+XEbs2P2 yCRrPrRxRex3D0Xa/vywJPOjiOACuxZOePW+JsknMjn5BLZ3lk+P28KgNx5p V57oI9f+tn2V7yyfUXrLv//1CLwwIpttqywvWzvwL5HLrHvwxhN7tyTf2pPv bi+9+Yexa94yZUBVF67p4589kyAAAAAAAOq58CTo9WHXvzm+W2aF1//uqtmx K1/W9pJjO99Msj3rH2kfe/ni9t8+vvutJJffPnN4eM7yct/fvLvqhYSfaXbq wO4dL4yMvvlo77IpSVaeq+/i2TC2S3gjl9962c5548qOHoi9ZGV52f61C9cO uubsI7Ap422L/8y9dt98a1KfE+9sS3jhQxtKV97+k7jLV3XhiBo9/inavfjJ nfMfTdgbI9ucdTp1+E5Vlzy6/fXstwQAAAAAAOqa8CQom+JWXn7iyIru/xV7 gc2T+ybfnlMH95R0/n7sIq8ObpTsY80qKzc9eUfCjSnueOlrQ6/b9GSvzc/c Hfzv+kfar77rqrjLrLjtiorTJ6tad64mQRWnTwR7kfigtbn45b6/Xjei9Rsj bwouU9L5e+HLBHuR2baVHz+8/Nb/SHSl31jT/3fBcXv7xdG7Fj2xc964zU/3 X9P/9+FLbp85vJp9q8njn6KSLj/I/tTdOf/RLDcDAAAAAADqoBqdBL018a7Y vy3t+v+VHTtU7SbtWvRE3Gp3L3kq2QKVldumD81sg5e1+9ahDaVVrThXk6BA xakTcZ+Sl2Ire14Z3EYZb9upA7tfH3Z9Zgdnw9ibU/r0tho7/ikyCQIAAAAA gKrU3CToyJZXitp8I/Zvdy+dmNI2VVZEv9omUknn7506sDv5QgffKFp1x8/T 2No239gwtsvJd7cnWWcOJ0Hv7VflznljSzr9a6pTkraXvDWpT/nJY9lv27ur Zq+683/SOTgXb31uSLVf9xOrJo5/ikyCAAAAAACgKjU0CaqsKF/T77exf7Wm /+9TeoPJe469vX5Z20tiF183onW1S1WWn97z0uSXz77ecKU3/3Dz0/1TmUHk eBL0nlMH926ZOnB510Qf2va3ijteunHCbSf2bM7ltlVW7Fs95/VhLYraXJx8 /LR+VPujO97IYNdyfvxTZBIEAAAAAAD1yvHdm3YvfnLj47e/dn+zVwb8ac3d f3ht6HUbxnbZPvPBg28sqywvy/cG/nVSdmhD6Y4XRmwYe/Or9127pv/v1w5q uG5E6y1T7923Zm7FqeM1d9VlR/a/u2p2cEVvjGzz6pDGa/r9du3Aq9c91Grz 5H7vLJ8e/G32V1H3jz8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAARJ0+fbqkpGTM mDHz5s3L97YAQPV27do1dOjQ55577uDBg/neFgAAAACou06fPn3nnXd+4hOf aPCeW265Jd9bBADVe+WVV97//vcHj1wf/OAHGzZsuHXr1nxvEQAAAADUOceO HbviiisaNGhw1VVXTZ8+/dChQ/neIgBIVVlZ2Zo1a3r16vWxj33swgsvLC4u zvcWAQAAAEDd0q5du/POO2/8+PH53hAAyNzWrVu//OUvf/aznz18+HC+twUA AAAA6oojR4585CMfadKkSb43BACyVVxc3KBBg5EjR+Z7QwAAAACgroi8aPbc c8/le0MAIAc+9alPtWjRIt9bAQAAAJADb7/9dklJyfz585cuXbp27dpjx47l e4s4Jy1YsKBBgwbB/+Z7QwAgB7797W//5je/yfdWAAAA1C8N/qZ9+/b53hYo BHv27OnRo8fnP//5Bmc777zzvvSlL+V76zj3mARRR2zcuPE//uM/grPxi1/8 4qxZs/K9OcC5yiSoNvldL7c8FAIAddChQ4dKS0vnz5+/YMGCFStW7Nu3L99b RB3ltwPIoeLi4n/8x39sULV8b2Bt8ACUW7maBLld2L1796pVqyLnQElJyc6d OysrK1NfPPLaV8QFF1ywf//+mttUcsh9v54I7s7bt28vKioKbuvFixe/+uqr p0+fzvdGJWYSVJv8rpdbHgoBgLrjwIEDAwYM+M53vnPeeefFvfz4uc99rnnz 5sHvBfnexjpqx44dw4cP//Of//zP//zPmzdvzvfmZCKzXfDbAREFcBfIu127 dl100UXR+9QnP/nJq6++OrhntWzZ8i9/+cull156/vnn53sba1DNPQDNmTNn 4sSJ1V4sy3N4w4YNQ4cOPXXqVCabWJOynAQVzBOD4P41NiT4w2oXzP6H2+TJ k+Oud968eRmsJ1+WLVt23XXXBTd3eDYd/FD63ve+16tXr1TWE7dsXg5CxqfB mazPhHPuNHDfrz/3/cGDB1955ZUXXnhh3A39oQ996Ec/+tGECRPSmvkmlNtn iefoJCjvT5X9rlcX5PGhML9nYN7P/1jBA1z37t3/7u/+zrkNQH0WPM9P/m/R I/K9mXXUkCFDooco789tMpPZLvjtgIgCuAvkXceOHaPHsGnTpsePH8/3FtWe mnsACn7x/NjHPhb87lntJbM8h48cORLswl133ZXBRtaobCZBhfTEIHIc4qRy WLL/4faFL3wh7nrPldcwd+3a9Ytf/KLaE+Db3/52Kmv74he/GF3kvPPOy8sj RcanwZmsz4Rz6zRw3z9Tn+771d7Ql1122d69e7O5itw+SzxHJ0F5f6rsd726 II8Phfk9A/N+/kcEv14NGDDgH/7hH2J/xDm3AaiHunXrFvec//zzz//qV78a PNP+/Oc/H/uvAfO9pXVUHXlukw2/HZCNArgL5N2nPvWpyAH8+te/XlZWlu/N qT01+gD05z//+dOf/nQqY7Xsz+H+/ft/+MMfrmvnf8aToAJ7YhA5DtOnTz8Q I5U7WvYnxqFDh2Kv9JJLLjknXsPcvXv3l7/85dgT4P3vf3/wJ8EJEOzCZz/7 2eg/pk1xEjRr1qwLLrigwXuvffXu3bumtz+hjE+DM1mfCefQaeC+H1F/7vvR 3bzwwgsvvvji4IYObu4PfOADsefAZZddls07g0yCztSBp8p+16sL8vhQWM8n QcGP/UceeSR49tIgxLkNQH1z9913xz4UXnXVVUuWLKmoqIhe4OjRo8GTliZN mnzkIx/J43bWZXl/bpM9vx2QjQK4C+TXxo0bowewT58++d6c2lOjD0BFRUXB OoOTM5ULZ38OR94W9Oc//zmDZWtOZpOgwntikPFELOc/3M6V1zB//etfR3f8 61//+tNPPx2eqG7btm3atGljxoxJcZ379u0LboJNmzblemNTlc1b5OrJS9nu +1H1575/zTXXTJgwYevWrbF/GDyijRgxIvYj46ZOnZrxVdSTu09yeX+q7He9 OiJfD4X1dhJUWVk5efLkr3zlK7GPbsFDmHMbgPpp1apV73vf+yIPgsH/CZ7z J7lwih+lXg/l/bl99vx2QDYK4C6QX5GZRcTTTz+d782pJTX9AHT55ZdfeOGF R44cSeXCOTmH77rrrmDxYL8yW7wmZPAqaEE+MfBqcFpiZ9Nf+9rXDh8+nO8t yg2ToOTc92PVz/t+nOnTp0cPQpMmTTJeT324+1Qr70+V/a5Xz9XbSdC4ceMa xPj4xz/et2/fmTNnOrcBqJ9+9KMfRR8Eg8fELNd27Nix1atXz58/f8mSJevW rTt9+nRVlywrK4t8VMLRo0cjf3Lq1Klly5YtWrQo7gWHbdu2BSt89dVXE64n +pELsf9esby8PLh8sFSwwnfeeSfLnUroyJEjsR/40K9fv+hhXLNmzYFEUvl4 ouCwRLY8cgCDHamJjc/hLiR8BnXixInIXpSUlASnROqbVJu7n8S+ffteeeWV YDMWLFgQ7MLOnTtr7rqid4SDBw/G/nlwPkePYa29BLd3796VK1dGdvzll1+O 3jcTqqG7QFpyfvTyeAbGfpFBxv/sNvWfwBF5P/1y+wAUJ3JIk/xyVxPn8Lvv vvvRj370qquuyu2+ZCODV0Hz9cQgiU2bNgVnY+Sn06pVq7Zs2ZLu3TP141DT P9zOidcwH3rooeheB/8/4/UkPFwR1X7SVPSSsU/wgtMpeHiK/HRKccgbK627 Q42eCXXzNHDfd98Pi3507RVXXJH6UgV890n92XLenyr7Xe9MDbxWkMFPtoSH OqJGHwrzewbm/fyP3ZKPf/zjwfV+6EMf6tSpU/Bc/czZv3mZBAFQfwRPIaKP gBdffHE2T8ZmzZr1s5/97IMf/GCDGBdccEHwRH3evHnhy0cffH/+858H/xk8 jfn85z8f+ZPgkTp4YnPmvSdpLVu2jK7txz/+cfg1yejfBvsS/OeePXuCh/K4 LwG88sorX3/99YSbncoTgISXufzyyxukKflzjK1bt95www2RZylRwX/+5S9/ qaF/Xp6TXYj+Va9evc6898tRcJNFvzugwXvPuBo1alTtMKX2dz/s0KFDAwYM +OY3vxne8Ysuuuh3v/tdeJFUbtzkl4neEYI7S+RPgufAvXv3/sxnPhNdMLhb Bcdh+/btsQvGPp0eO3Zs8l376U9/Gj2kcb+vBUd+8ODBv/zlL+PuNQ3e+/fA //Zv/zZu3LiEv6Tk9vzJ7ABmfPTC8n4GZjkJSvcncNyVZn8AM5DDB6CErrji ivPOO2/Dhg1VXSDnP8YjWrdu3eBvD0l1QbqToDw+MQhbu3Zt06ZNP/nJT4Zv i2C1//Iv/5L6pDL141BDJ0bUOfFqcPCYHt27iRMnZryeJAftwIEDKS67Zs2a 4D+Dn0JNmjQJnlTEngN//OMf4z7SKrm07g41eibUwdPAfd99P6FgsyM7m9Yk qMDuPpk9W67pM6paftc7k/VrBbEy/smW5IDX6ENhfs/AvJ//sbp27dq4ceMt W7ZE/8QkCID6qUuXLtFHwIz/zefBgwd/+9vfJn9YDx55T506FbtU9MH3sssu e/vtt+Oej33hC1+oqKjo1KlT3HqCZ55x1x79q+AJ2JIlSz7xiU8k3IDgSdpL L70U3vhUngAkvExun9s89thj559/flUL1tAXSub2t4M+ffqUlJR8+tOfTrjg 5z//+SS/IORl9+PMnDkz+o8eqxJeKsmRSfEysc9Cg/8Mfg356le/mvDaP/vZ z+7YsSO64K5du6Jf5pv8d/Ngqej3O7du3Trub1M5DX7yk5+E/8VjnZoEpXv0 4tSFMzDjSVBmP4HDV3omiwOYmZw8AFVlxYoVDf72Lw2qUkO/om7YsCE4Zxo1 apS7vclKupOgPD4xiBM8rLz//e9PvpJqX0KJ8mpwWgYNGhTdu+bNm2e8nmxu u+glly1bNmfOnIsuuijhej7zmc+kPgwyCUrCfd99P6yysjL6u1X4t7AkCuzu k9mz5by/Eu53vTNZv1YQkeVPtiRL1ehDoUlQEiZBANRP0X/l9b73vS/1X6li Bc94f/CDH0QfRj/ykY/8+te/btOmTYsWLb71rW/FPrIHT59i/61U9ME32Iam TZsG/ye4fPCsPnr5Z599NnhaGGxYkyZNov9A/cILLywrK4vdgOjlu3Xr9tGP fjT6n1/+8pe//vWvx/4iGawk/O7pVJ4AJLxM8P8vjxFcXfRiP/zhDy9P5IEH Hki4/rFjx0aX/cAHPnDllVcGB/DGG2/8zne+E3sAc/7RSTnZhegiDRs2DJ5C R/5/sOU33XRTs2bNYgd8//u//1undj/WuHHjoh+MHwh+VfnVr34VbEbbtm1/ 97vffelLX4r8eXjBjM+fqNhnoatXr/6nf/qnyP8Pfp+6/vrrg+MQ++6MP/3p T7HL/v73v4/8eXBPif03TnHuueee6BrC/+gu+iw9+M3ixz/+cXDPDbYz+GU/ 7vgHfx63YG7Pn+wnQekevai8nIGzZs1qf7Y//OEP0eu66qqr2icyZcqUuPVk /BM4hwcwY9k/ACUReVgJHkeSXCaHP8bj/M///M+HPvShyKdP5F26k6A8PjGI FfvpZMFD+WWXXRb8XApushtuuCG4s3z3u9+N/KPcmng1uOZOjIhz4tXg0tLS 2FvqwQcfzGw9cQfqC1/4QnSdqb/81bFjx+i/wf7+97/fqlWr4Jlh7D8sT/14 pnV3qNEzoQ6eBu777vthzz//fPQgLFy4MPUFC+zuc3lGz5Zr+oyqlt/1zmT9 WsGZXPxkizvItfZQmN8zMO/nf3ImQQDUQydPnow++fnGN76R2UqCZyDRx9A/ /elP+/bti/3bF198MfrSYoOz/4Vh9MH3/PPPDzbj05/+9P79+ysqKqJvNo+8 AtmnT5/gwgMGDIiuJO692w1C/vu//3vdunWRv928efO//uu/Rv/qrrvuitv+ VJ4ApHKZjL8DMdjUD3/4w5EFL7nkkjfeeCP2b4OjFP1kjOAorV+/PvU1pyvL bxGNCJ6gjhkzJvq3O3fu/NznPhf5q/e9733hLxeuC7tfXFwc+1vArbfeGv7I keCsa9u2bXjZ7M+f2Gehf//3fx85UHfeeWd04hncL6Jv0wj+as+ePdFlY389 D5/bUcGBjVzm0ksvDf/tH/7whw4dOhQVFcV++nTE9OnTo78xnXfeeck/H6zm voU2yWWyOXoR+ToDYz95KXXhI5DxT+DoDmZ5ADOWkwegqhw7duxjH/vYRRdd lPyfncfJ4VfZPvnkk8FKhg8fns1KciWtl77z+8Qg6vTp09EXl772ta9t2rQp 4WVWrVoV949DkvCt8en6yU9+EvvzJ3iwSPJZiymK/dGX+stfERdccMHkyZOj f7tt27boE4z/n717j4+iuh//v7mSAEm4REE0EosfKIhCqRBU7iAURUAo5mG4 FZCiKFj1QeRrK1hAKWihcq2XAmrkIsilVGOpkgAShYRLgz6QgITITUAgISyQ ++/8mEfPY9zNzs7OzO5swuv5hw/cPZf3nD2zO3PemRnxC6XzokXD06Cqtj/y nn3f3Q2770u7du2SH1lKSoqZpmr67mPJ0bLlM8pXN+a5nsONT2sFVVZ8s7mw 66fQ3hlo+/x3QSYIAHADEkdi8ucvOTnZQAviREy2MGDAgGrvKP7NN9/IO9k2 a9ZMllH/+Arvvvuu8rr6KS3iwEZ5buCGDRvkiy6nci5HR3/84x9d/hTn5MmT MTExyrstWrRwCU/PAYCeMoaPbYYNG6bUEofB1a6yikGWNwHz6bYMvjJ/diBO DcTBsEuBt956Sxb46KOPXN61ffPFbFH/Ndf777/vU3Xz88dlRxAb6/5Ehvfe e08W+Pjjj+Xr4mxUPl3rzjvvrLb3ffv2yboG1qVff/11WV3upNWyPRPk6+gp 7JqBlmSCzHwDK8wPoGHmf4A0KD8Z48eP96mWhaeoTqdTfB/27t3bTCNW8WkV 1N4DA2nXrl2ykQ8++MBAGO5YDfbViRMn1H/B67i+yjRw4MBPPvnEfS1UJ8PL X9HR0V999ZVLgbffflsWcD/AqBaZIE/Y993daPv+jh07NvyPOGIUO7tMDoqD JZ/+ssJd7d59dB4t274SfmOe6zl+zte1Aku+2VzY9VNIJkiNTBAA4Aa0bds2 +fM3YcIEAy088cQTSnVx/KZxc6o//OEPsiN5Iqb+8b311lvl+UWHDh3k6/IC 4c8++0y++M9//lPduProaMqUKdUG8PTTT8syR44cqba6LZmgEydOyJOsRYsW eSomD6FvueUWnS0bYP7sIC0tzb1AXl6eLPCXv/xF/VYwbH56eroMb+zYsb5W Nz9/XJbiq/1Dsu+//14WcLlzwowZM+Rb1d7dWt72X5w7FBYW+rqBYr+W7Wvf wTsYMkG+jp6NM1AMUcbPqQdw5syZGdU5fPiwuhEz38AKkwNohvkfIA2TJ08W za5bt86nWtaeonbt2lXsdFevXjXZjnn/+te/HNdvL6+nsL0HBtKOHTu0p6UB rAYbcPr06b59+zrcNG/e/I033nC/ftYrw8tf1e7OGgcYnpAJ8oR9392Ntu93 r+6hHpGRkRoHSPpZvvsMGDDAfFRW0Xm0bPtK+I15rqeO38BagSXfbC7s+ikk E6RGJggAcAMy//Mnb3Kr/VTu3Nxc2ZFytzeX3tUHLd1V92F2Op3uhV2epS5f 7927t6dbjn/88ceymMuDNvSMgJ4yxo5tli1bplQJDQ3VeKKE+k99NA5BTTJ5 djBq1KhqCxQVFcky/+///T/1W8Gw+b/73e+UlkNCQvQ/cloyP3/Uc1uchlTb gjhHkGXEuYP6rR9++EE+4ej3v/+9S8Xy8nL5nBdPH5C20tJSPdtYFQSZIAOj FwwzUNL4lvPEzDewe6cGBtAMv55/dezYUTTrcvsOr6w9RU1NTRXt7Nq1S2d5 0aPDF/oH7e9//7sor/P7zd4DA+nMmTPym61x48aff/65gUhcsBps2KZNm+6/ /373SRgXF/fKK68o127rZGz5y8ABhidkgjxh33d3o+371WaCHNcXwIcMGWLy tl3WDubDDz/cunVrk41YSOfRsu0r4TfmuZ5s2dhagSXfbC7s+imsoZkgPx0h kwkCANyA1D9/Tz31lK/VT548Katr/6F4RUWFfL6kvOOEp2VPeRoiTpeqDdVT JkjjF1x9y4t58+b5Wl1PGWPHNvKvjMRB5kXP0tLSZONff/21zsZ9FfiV/GDY fHEiqbRc7TN0vDI/f/Ss/5eVlcmrQo4ePery7oABA5TqcXFxLhcg/Pvf/5aN b9++Xf92qcdfzzZWBUEmyMDoBcMM9Glb1Ex+A+vvVHv6GWbyB0hbbGys+Ex9 rWXtCbKy+vHee+/pLC9m2nRfpKen62z5wQcf9HT3SHf2HhiojRw50qHSpUuX pUuXmnlSFavBJmVlZT3++OPyBjjSXXfd5fIX1BqMLX+ZPEJTIxPkCfu+uxtt 31++fLny+/Lcc8+NGzfugQcekI+nF+rWrbt582bDjVs7mEprOTk5Jtsxydej 5ZqeCaqh53p64ve0VmDhN5uaXT+FNTQT5KcjZDJBAIAb0FdffSV//oYPH+5r 9ezsbFl99erV2oV/+ctfKiX79u2rvBLITJDG37RbdXBl7NjmoYcecvho69at Ohv3VeDPDoJh8+Pi4pSWx4wZY6C6mc1XGLgSxMU///lP2YLLQ15GjBihvN6y ZUtPfwUnFBcXv//++8OGDWvVqlW9evU8DX4NzQRpCIYZKPm6LSa/gY11aiGT P0AalL+N1P7TzWpZe4L8+eefO7zdU9HfKioqZs2apWeGSPYeGKgVFhY+8MAD LjtgaGhor169Fi9e7OsFX1WsBlvk3Llzc+bMue2229Sfi/hfnQv1ZIKkYJsG 7Pvu2PfFZyF+RGT+Nzo6+uDBg8aasnYwxYFrYmJimzZtTpw4YbIpnzo1ebR8 A2aCguFIW0/8ntYKLPxmUyMTxN3hAACwRUFBgfz5+/Wvf+1rdZ+WEMW5j1Ky W7du2tX9nQmaNm2ar9X1lDF2bOPpPgwajC1f6BH4s4Ng2Hw98Zusrl3G/FJ8 WVmZvAVc//795evijFWeqM6dO9dT9TVr1jRp0kTP4Ne+TFAwzEDJ120x+Q1s rFMLmfwB8tryiBEjfK1o7SlqTk6Ow/Md6f0tNzc3NTW1ZcuWkZGRf/vb3/RX tPfAwIX4chPfXfHx8e57Yp06dSZNmuTTmnDtWw0WX/LLfaH/XoVeXbt2bdas WfIZ3w7duQMyQVKwJQXY990F7b4fYJ988okch8cff9xYI5YPZl5e3t133y0O dEeNGiW+38w3qM2So2XbV8I51/NUxtNagbXfbBKZIDJBAADYoqKiIjo6Wvn5 i4qKKi0t9al6VlaW/PXU/0cy8vmegcwEnT59WhZzubLbqoMrY8c28kHM4uRC 5/XOBp5lo1Pgzw6CYfPljS+MPR/Z/PyxZCn+T3/6k9JCWFiYmO3Ki/JOC+Hh 4Z7+WvvDDz90qCQkJAwfPvwPf/iDesz1bGNVzcwEBcMMlHzdFpPfwMY6tZDJ HyANyu3Ex48f72tFa09R9+3b53Wv8R9lW8S+//bbb/tU0d4Dg2qJGDZu3Dhs 2DARj+PnEhMT9d+xsPatBvvv2VI6ic9FNh4ZGel1OauKTJBKsCUF2PfdBe2+ H3i9e/dWxiE2NtZYC/5YBP7hhx/uvvtux/U7j1nSoCdWHS3bvhJ+Y57r6Ynf 01qBP77ZqsgEkQkCAMA+Xbt2lb+A4mzLp7oHDx6UdTWuOKi6/tj6OnXqKCXl Q+0DmQlS3/Li/fff97W6njLGjm0ee+wxpUqLFi10VvGfwJ8dBMPmyz/wU19N o5/5+WPJUvyxY8dCQkKURl5//XXlRXnyNWTIkGprFRcXy5vjhYaGLl26tNo7 yOnZxqqamQkKhhko+botJr+BjXVqLTM/QBqOHz/u8PxcXQ3+uCZo6tSpJtsx 5scff1y1atXgwYNFDGPHjhVzQH9dGw8MtF2+fHndunWPPvqofKC8cP/99+uM rfatBpeVleX7wsBttbwSP51ycPQ83IFMkBSESQH2fRdBu+8H3qRJk+RQGGvB 8sHcsmVLbGxs69atFy1atGfPHvMNemLh0bLtK+E35rmenvg9rRX46ZuNTBCZ IAAA7DJ79mz5C/jwww/7VLe0tFT+9aCnpWaF+uBKXr8fyEyQ+sDj22+/9an6 qVOnfO3i8OHDGqOhpjzBwXH9zOLChQs6a/mJsU0wc3QaDJt///33KzE0bNiw pKTE1+rm549VS/EPPvig0sg999yj9CsXTD755JNqq6xcuVJ2PXHiRE8t6/mI q/w2f7QH0OToBcMMlHzdFpPfwMY6tZaZHyANly9fNtagsTnsifKcIO3nCwfA Bx98IML485//rL+KjQcGOv33v/9t3ry5rK7z0gBLVoPNT4yqmrwa7E58LcvB +eKLL7yWrzWZIPMzIQinAfu+C/Z9ST538uabbzbWgrWDKQ4OY2JixKHvtWvX TDbllYVHy5bPKF/dmOd6vn406rUCP32zBUMmKPAz0Pb574JMEADgxnT8+HF5 dyxh06ZNPlXv0aOHUjEyMvLs2bOeio0fP14pFhISInpUXgxYJqiysrJ9+/ZK Gfe/R5J/vZOSkuJe98qVKx07dtRzkLBixQpZLDMz01MxF//5z39kLXF0pLOW nxjbBDNHp8Gw+X/84x9lDC7Xi+lhfv5YtRS/bt062c7BgweXLFmi/Pu2226r qKiotoo8OxNE9WrLiLM2PR9xldH5Y3IATY5eMMxAycC2mPkGNtyphUz+AGmI j49v27atr7WMzWFPli9fLtr56KOPdJY/ffp0d18sXLhQZ8uPPPJIw4YN9V8W ZOOBgX7Lli2TEep8urTh1WBrJ0ZVTV4Ndqe+JsjlL22qVaMzQdbOBDPTYPXq 1coVzeInMjs722QkEvu+i6Da9/30oevhdDpvuukmZRwGDx5srBFrB3PmzJmh oaHHjh0z2Y4eFh4tWz6jfHVjnut5jV97rcAf32x2/RTaOwMN9+6nI2QzmSDx JZyUlCQqNmjQYOrUqWVlZT5VBwDAXr///e/lj2BMTIz2iZJ8BIni7bfflnVH jhxZbZWdO3eGhYUpZdSnDwHLBC1atEiWmTdvnsu7rVq1Ut669dZbXRbKxP/+ 9re/dahoHCSIs1EDxxKlpaU333yzUqthw4aHDh3SWdEfjG2CmaPTYNh80am8 r5o4xdZ/23mF+flj1VK8GEx5p7sZM2b06tVL+bf6sacuxHm07Pqtt95yL3Dg wAFxQqRzVhibPyYH0OToBcMMlAxsi5lvYMOdWsvMD5CGrl27RkVF+XpeZmwO e6I8vWv//v06y/svE/Tuu++KSL777jv9wdt1YKCfkmhT7Nq1S08Vw6vB1k6M qhqSCar29kcuduzYIS8+Fd/heqrU6EyQtTPB8DQ4c+ZMZGSkjKRBgwb6vxu9 Yt9XC559368fujZxMDZ8+HDZ9fr16421Y+1g9u7du0OHDiYb0cnCo2XLZ5Sv bsxzPa/xa68V+OObza6fQntnoOHegy0TdOrUKXnHSEVqaqr+6gAA2O7ChQvq Oy2Ik3pxGvjf//5XXebSpUuffvrp6NGjxWmI+vWSkpI77rhD1h07dmxRUZG6 wKZNmxo3bqy8W6dOnYMHD8q3LM8E/fa3v71y5YpLeLNnz5bLFC1btrx69arL 5o8bN0628Morr8jXz5079/DDDzt+TuMgwel01q1bVykmDgVXrVrlXqbaRRL1 hdJirERFT3+5nZ+fr/GXSOYZ2wSTR6fBsPljxoyRMYizlZUrV7pfRCOm7qRJ k9zrmp8/Fi7FT5kyRWlHnJAq0z4kJETjDybXrl0ru77rrrvUj5AQ/37xxRfV yw7aH3GV0fljcgDNj14wzECFgW0x8w1suFNrmfkB0iBmr2ht9+7dPgVj+Gu8 Wn379o2NjfXpAT1+kp6eLrZox44d+qvYdWAgbd++XYQtuqg2vG+//VZ2IZrS eWNPw6vB1k6MqhqSCXr00UfFZyc2VvyIuG+d+D6cPn26vGeOoHPhpUZngqyd CYanQWZmpsuP46xZswy0Uy32fbXg2ff9+qEfOHDgxIkT7q+LY5733nvv7rvv lp326dPHp+1VC5LdxwALj5Ytn1G+ujHP9WTvxtYKLPlmc2HXT6G9M9De3ouL iy/+3ObNm+W4Pfnkky7vajQ1b948ly9kccDvp7ABAPATcQog/1xHiouLa9Wq lTjSvv322+VFEw6354RmZWWplwJiYmIGDx48efLkcePGtWnTRr4uDrFWr16t rmh5JkgQkfTq1eupp54SR0GPPfaYeqPE5ojNdN/23bt3q1u47777RPAjR44U P+jKKz179pT3p9I+uHruuefUTYlTpwkTJogqTzzxxCOPPCKOIUUB91riYLhf v37qiiLsRx999JlnnhF1xX/FhiQlJTVo0MB92y1nYBNkYWNHp8Gw+ZcuXbrn nnvUMTRr1mzYsGGTJk0Sk0GcNdx5553K6+51zc8fC5fiDx8+7Pi5Bx98UKO8 OCSWZy6O62dnv/vd78SY9+/fX57VRkRExMfH65n/VYbmj8kBND96wTADFca2 xfA3sJlOrWXmB8iTzz77TBSeM2eOr8EY+xp3V1paWr9+/QEDBvgagD8YWwW1 5cBAUhZJRBf/93//J76RlE9BfCenpKSI/VHd9bJly/w6DgqrJoaiRmSC5MGY 4/otcRITE9tdJ34uGzZs6DIxxHdmtbchFZ94u5+T164Kbdu2Vb/l/jBuWTJI MkFVls4Ew9Pg5MmT4eHh6jAeeughA+14wr6vFiT7vl8/dGVnr1evXvPmzZW9 UvxX3g5Ouvfee70uWWsLht3HAGuPlq2dUQbcgOd66q4NrBVUmf5mC6qfQntn oI29q49q9NBoasaMGS6FxUfvp7ABAPCf48eP9+nTx9jPYmZmpvs5o1rDhg3d j+v8kQnyRBykady+/oUXXvBUURwiXr16tXfv3sr/ev0zm27dummE4am6qCif xKrN30u1BjbB69Z5LRMMm3/hwgX3K1DcVVvX5Pyxdile3hRO4fUZJZs3b1Y/ FMBFQkLCtm3bxLm2RvxqxnYBMwNoyegFwwysMrEtxr6BTXZqLTM/QNW6du1a bGxsUlKSr5EY/hp3oVyGo3+h0q8Mr4IG/sBAUv+5rCd16tRZvHhxAMahyrqJ oahxmSANUVFR06ZN83QnRp8WXvxxgOHOZCbIwplgZhqob1cliJCMteMJ+74U PPu+/z50r/upOFCcMmWK+7USvgqS3ccAC4+WrZ1RBtyA53p6+tVeK6gy980W VD+F9s5AG3u3MBOUk5PjUviRRx7xU9gAAPjbf/7zn+TkZPe/9nRcvxx+8uTJ WVlZ1Va8cOHCyy+/rP6rGMXtt9/+0ksvnTt3zr2K5ZmgunXrqv9W0HH9zKVL ly5vv/12aWmpxlZXVlYuXbr0tttuU9f95S9/+cEHHygF9K+El5eXL1q0yH0c HNf/fOgvf/mLRt3t27c/9thjoli1RyNiJMUxRm5urnYA5vm6CfJdk0enwbD5 n3322ZAhQ9znf2Rk5H333Td79uxqa5mcP9YuxX/44Yeytfj4eD03TtmzZ89D Dz0k726tuPvuu8Vnffny5arr9wjy+vFJBnYBMwNo4ejZPgPNbIuBb2DznVrO 8A9QtZQbD/r65K8qc1/jUkpKSp06dQoLC33t3R9MLn0H8sBA2rx5c7du3ZS/ EHYnvh+mTJlSUFDg04aYHAdLJoaiRmSC/vWvfz3xxBPt27d3ufGRQhxfdezY ccaMGdXeVEoKquUvhclpUGXdTDAzDUQM6muZBw4caKwdbez7iiDZ9/33oY8c ObLaNEd4eHhSUtIbb7xh4Q27gmH3McbCo2ULZ5QxN9q5nuzC8FqBwvA3W7D9 FNo7A+3q3cJMkLBkyZKoqCilZOfOnQP24DYAAPyksrIyPz//yy+/zLjuwIED yiGuHuJkITs7W9TasWOHr2dqxqiPfESc4gBS9J6ZmfnNN9/oD1uRl5e3bdu2 nTt3mo/8/Pnzu3fvVgZw7969+husqKg4duyYMoaCOMs+cuSI+T/DM8DwJpgR DJsv5v/333//9ddfKzEcOnRI543oLZw/gSd2lpycHLG9u3btOnPmjPkGDcyf YBjAYJiBZgT+G9hyZn6A1MQu7DD3CFfD34HKo71HjRpluGtrmV/6rrLvwOCn n3769ttvt2/frvQrvqYuXLjge/j/P0vGocqKH8cakQmSxEd/6tSpPXv2yG/F gwcPeroIKPhZNQ2qTM8Ek9Ng6tSp8gD45ZdfNtyOV+z7ku37vv8+dLFHHz16 VGyUsnXiUxMHP3rWxg2zd/cxzNqjZVtOtWwPIPBH2hauFShqwcG2wt4ZaPv8 N0n8JIlZtH//fr8+2wsAALjz6W9gAAA3iI4dOzZq1Mjl6cAB8Morr4ifpJyc nAD364mFS981WvCMQ83KBNUytWYaqG+1tHfvXgsDq3340GsfvkWhH2sFAAAA tQlHdwAAd2vXrnUE/GE9JSUlTZo06dmzZyA71RY8q6D2Cp5xYA3TRrVjGnz+ +efydl7MJa/40GsfvkWhH2sFAAAAtQlHdwAAdxUVFa1bt77nnnsCed+GZcuW BcmSoxQ8q6D2Cp5xYA3TRjV3Gly9evXs2bNbt24dP368fFjJrbfeevLkSf8F WTvwodc+fItCP9YKAAAAahOO7gAA1Vq/fr34ddi0aVNguquoqGjRokWvXr0C 051OwbMKaq/gGQfWMG1Uc6fB9OnTHT/Xtm3bI0eO+C/CWoMPvfbhWxT6sVYA AABQm3B0BwDwpEuXLklJSYHpa9WqVSEhIdnZ2YHpTqfgWQW1V/CMA2uYNqq5 00CdFEhMTFyyZIlfn7Fem/Ch1z58i0I/1goAAABqE47uAACe5ObmhoWFpaen +7sj5WZ0EyZM8HdHvgqeVVB7KePgIjDD0rx5c5d+WcO0S82dBiLIuXPnrlix Yt++fYG842UtwIde+5AJgn6sFQAAANQm3f9n4cKFdscCAAg6e/bsOXTokL97 cTqdGRkZRUVF/u7IV2SCFKdPn17uRrwYgK7Xrl3r0u8XX3wRgH7hjmlwA+JD r33IBEE/1goAAAAAAMCNgEwQAKA2IRMEAAAAAAAAqJEJAgDUJmSCAAAAAAAA ADUyQQCA2qRt27ZDhgyxOwoAAAAAAAAgWOzZs8fhcHz44Yd2BwIAgFmVlZWN GzeeOHGi3YEAAAAAAAAAwaK0tLRhw4Z9+/a1OxAAAMzatGmTw+H4+OOP7Q4E AAAAAAAACCJz5sxxOBzPPfdcaWmp3bEAAGDQf/7zn9jY2LZt25aVldkdCwAA AAAAABBEKisrn3nmGYfDcccdd/z5z3/eunXr2bNn7Q4KAADvSkpKsrOz33rr rT59+ogfsjvvvPPw4cN2BwUAAAAAAAAEo3//+9+9evUKCwtzOBzPPvus3eEA AODdvn37HNfdfvvtM2fOdDqddkcEAAAAAAAABLXLly/n5OQcOnTI7kAAAPCu sLBw+/btR48etTsQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAECNMX36dMf/XLx40e5wAAAAAAAAAAAAYBkyQfCTysrK48ePZ2Vlbd26 dfv27d98801paandQfmsqKho7969YhMyMjJyc3MvX74cyOoAAAAAAAAAgOBx 8eLFl156qW7dukpW5dlnn9VTKy8v78033ywpKfF3eJ4EJhO0ZcuWNWvW6Cxs bCSrgmAwg5DhwTRj3rx5Dz74YGxsrOPnIiMje/TokZaWVllZWW3F7t27O3yk M6Rz587Fx8erK27YsEGjvNPpXLhw4a9+9auQkBB1rdDQ0HvvvXfRokXa08xk dQAAAAAAAABAULly5crcuXMbNWqkXvLVueReXFzcuHHjmTNn+jtITwKQCTpx 4kRMTMzixYu9ljQzklVBMJhBxeRgmuE1fdOlS5ezZ8+6V/RfJmjEiBEuFTUy QQcOHGjZsqV2v61btz58+LA/qgMAAAAAAAAAgkdZWdk777xz6623uq/06l9y nz17dp06dfLz8/0ZqUcByAQlJyc3bdr0ypUrGmUsGckquwczSFg1mIbJ7mJj Y++666527dq1atUqPDxcHUmXLl3crwzyUyYoPT3dvaKnTNDRo0fVVw/dcccd o0ePFuP2zDPP9OnTJzIyUr7VsmXLa9euWVsdAAAAAAAAABAkKisr165d6/KX /1FRUQaW3JUrWZKTk/0asCf+zgRlZWWJlufPn++pgIUjWWX3YNrO2sE0bPjw 4WlpaQUFBeoXxUfz97//XX3LOO37s2kYNGiQ0kL79u29Fhb9JiQkKOVbtWrl tfehQ4cqBSIjIz/44AOXdNWpU6fU6ap3333X2uoAAAAAAAAAgCCxYsUK9WJ7 XFzcq6+++umnnxpbcp85c6aosnfvXv8F7Im/M0Hdu3ePjY0tLi72VMDakayy dTBtZ/lgWm7z5s0ymNGjRxtoITc3V7awevVqr+UnTZqkFL7llls2bNignQk6 f/68vHbp+eefr7bBM2fOREREKGWGDRtmYXUAAAAAAAAAQPAoLi6Oi4tT/vL/ +eef/+mnn8SLGRkZxpbcRfXo6OgBAwb4LV6P9GSCrl69elHF/aZenigDoj0U 1o5kla2DaTvLB9MfmjRpogTTs2dPA9WTk5OV6i1atKioqNAunJWVFRISIlM/ 6qGoNhO0c+dOWeDjjz/21Gzbtm2VMt26dbOwOgAAAAAAAAAgqLz44osjR448 duyYfMXMkvvEiRNFrf3791sdphdeM0GHDx9WP3FGbLX+TFDPnj1DQkLy8vK0 i1k7klX2DWYwsHwwLdeuXTvDmaBvv/1WZnbefvtt7cLXrl1r06aNUli5YaDX TNC2bdtkgXfeecdTy4mJiUqZ/v37W1gdAAAAAAAAABDkzCy55+XlhYSEjBgx wk+xeaKdCXJJA7388sv6W87JyRFV+vXrZyAqk8kLuwYzOAVVJqiysvKmm25S gnnyySd9rf74448rdW+55ZaSkhLtwtOmTVMKx8fHnz17tkpHJuj06dOyQIcO Hart4ssvv5RlRBcWVgcAAAAAAAAABDmTS+79+/ePjIxU7ugVMBqZIJc00OzZ s31qecyYMaLWxo0bDURlPnlhy2AGp6DKBH3yyScymMzMTJ/qigkZFham1H3j jTe0Cx84cEA+jmflypXKi14zQVXXL2STZR599FGXR1x9//33zZs3V96tU6eO +torS6oDAAAAAAAAAIKZySX3VatWiYqLFy/2R2yeeMoEuaSBFixY4FOzTqcz JiamYcOGXi/cqJb55IUtgxmcgicTtGvXrvj4eCWSlJQUX6uPHj1aqSvmlUuG xUV5eXlSUpJSeNCgQfJ1PZmg/fv3R0dHy2KJiYlr1qyprKysqKhYsWKF6Fq+ tXTpUsurAwAAAAAAAACCmckld6fTGRER0bt3b3/E5km1mSB1Gig0NHTJkiW+ NrthwwZRd/z48caiMp+8sGUwg5NdmaAdO3Zs+J/FixcPHDhQXtEzbNgwX1OE 33//vazu9S6F8+fPV0rGxcWdOHFCvq4nEySkp6fXr1/fodL+Ovm/4eHhixYt 8lN1AAAAAAAAAEDQMr/k3rVr1+jo6KtXr1oemyfumSCXNFBaWpqBZidPniyq r1u3zlhUliQvAj+YwcmuTFD37t0dbiIjI40lQSZMmKC0UK9evXPnzmmUzM/P F2WUwv/4xz/Ub+nMBFVdf9RUly5d3OMXOnXqlJ2drR2tyeoAAAAAAAAAgOBk fsk9NTVV1N21a5flsXnikgk6fvx4YmKi8r8RERFr1qwx1mzHjh1FC+fPnzdW 3ZLkReAHMz8/v9rFf08Ck5cJqkyQ4/oVMUOGDDl06JD+pgoKCuRDf7xuQt++ fZWSDz74YGVlpfot/ZkgITMzU97LTq1du3Z6nnBksjoAAAAAAAAAIAiZX3Jf tmyZqPvee+9ZHpsn6kzQgQMHWrRoofw7IiJi06ZNhpuNjY1t3ry54eqWJC8C P5gXL16c7ov09PQARGVXJmj58uXKZj733HPjxo174IEHZDZHqFu37ubNm3U2 JS8IEi0UFBRolFyxYoVSsl69evn5+S7v6swEOZ3O8ePHq9M3jRs3dknovPDC C+Xl5f6oDgAAAAAAAAAIWuaX3D///HNRd8aMGZbH5ok6E5SQkCD/bfhqIKGo qEi00K9fP8MtWJK8CPxgBie7MkHuCgsLZ82aFRkZqQQTHR198OBBr7VOnToV FRWlVBkzZoxGyTNnzjRq1EgpuXDhQvcCejJBYvZ27txZvVOsW7eusrIyPT29 bdu26mzOiBEjXK45Ml8dAAAAAAAAABDMzC+55+TkiLpTpkyxPDZP1JmguXPn yn936tTJ6XQaa7OgoEBZ6DYclSXJi8APZnAKnkyQ4pNPPpHxPP74417Li5iV wiEhIdqZo+TkZKVkly5dKioq3AvoyQQ9+uijssxDDz1UWFgo3yorK5s2bZo6 m+PyHCLz1QEAAAAAAAAAwcz8kvu+ffsCvFzv8pygKVOmyP/t27dvSUmJgTaV x+WMHz/ecFSWJC8CP5jBKdgyQULv3r2VeGJjY7VLqi8IGjJkiEbJPXv2yM0c OHDgs9UZOnSoLDNgwAD5em5urtJIZmamLNC5c+dq5/+8efNkmebNm6uv6zFZ HQAAAAAAAAAQ5Ky6Jmjq1KmWx+aJSyaooqJi8ODB8pXHHnvMwNNMjh8/LuqO GjXKcFQWXhMUyMEMTkGYCZo0aZIMSbtkamqqLJmdna1RUr2ZvpLXBz399NPy xc8++8xTX+r7vH333XfydZPVAQAAAAAAAABBzqrnBL366quWx+aJSyao6vrz 7jt06CBfnDhxoq9tXr58WVR8+OGHDUdl4XOCAjmYwSkIM0EjRoxQ4rn55ps1 ip07d65evXpKyT59+mi3aUkmSF6sJJw/f95TX+qMz9atW+XrJqsDAAAAAAAA AIKc+SX35cuXi7offfSR5bF54p4Jqrp+S67bbrtNvj5t2jRfm42Pj2/btq3h qCxJXgR+ME+fPt3dFwsXLgxAVCYHMzs7OykpSdRt0KDB1KlTy8rKTMbjdDpv uukmJZ7BgwdrlHzxxRdl5F988YV2s/n5+dO9GT16tGwwOTlZvi4fP9SrVy9Z oKCgwFNf48aNk8VycnLk6yarAwAAAAAAAACCnPn8xZ/+9CdRd//+/ZbH5km1 maCq6w/ZkZdjCAsWLPCp2a5du0ZFRRnOGliSCQr8YNa+TNCpU6fi4uIcKqmp qWaCKS8vHz58uGxt/fr1nkqK2RgTE6MU69Spk5lOJfVQyOuA1MaOHSsLiF2j 2kYuXbrUtGlTpUx4eLj4X6uqAwAAAAAAAACCSnFx8cWf27x5s1wHfvLJJ13e 1dNm3759Y2NjDTyaxzBPmSBBbE5oaKh8d+XKlfqbVa7m2L17t57C/hjJKjsG MxhYO5jz5s1z/JwYUu0qBw4cOHHihPvrZ8+efe+99+6++27ZVJ8+fSorKz21 o56ZGgkjn3jNBH322WeyQFhY2IIFCyoqKtQFfvjhhx49esgyo0ePtrA6AAAA AAAAACCodO/e3eELrw2WlpbWr19/wIABAQhe0sgECX/729/kuxEREenp6Tqb VZbE58yZo6ew5SNZZdNgBgNrB3PGjBku5UNDQ/UEUK9evebNm7dt27Zdu3bi v/J2cNK9996rkYcSb8lrkVq3bu2STzHMayZIGDx4sDrOZs2apaSkPPvss08/ /XSPHj3Cw8PlWwkJCWfPnrW2OgAAAAAAAAAgeFiev0hPTxfFli1bFoDgJe1M kPDUU0/JAtHR0VlZWXqavXbtWmxsbFJSkp7C/sgE2TKYwcDawczJyXEp/8gj j5gMICIiYsqUKVevXtVoZNasWbL8ihUrfB4FD/RkgpxOZ0pKitdx69at27Fj xyyvDgAAAAAAAAAIHpbnL1JSUurUqVNYWBiA4CWvmaDy8vJ+/frJMg0bNszN zdXT8rhx40T5o0ePei3pj0yQLYMZDCwfzCVLlkRFRSmFO3fufPr0ae3yI0eO jIiIcO8oPDw8KSnpjTfe8HohTHFxcePGjZVaCQkJpaWlPmy/Jj2ZIEVmZmZy cnJsbKzLVtSvX3/QoEEbN27UuK+d+eoAAAAAAAAAgNrnzJkzkZGRo0aNsjsQ y3z99dcOhyM1NTXwXde+wbTXhQsXMjMz9+/frzN/UVZWdvTo0b1792Zcl52d feTIEQsTOgEjtldE/tVXX4mt2LlzZ15enk/3qTNZHQAAAAAAAABQm7zyyisO hyMnJ8fuQKzUsWPHRo0aXblyJcD91srBBAAAAAAAAAAANVRJSUmTJk169uxp dyAWW7t2rSPgD+uprYMJAAAAAAAAAABqqGXLljkcjoyMDLsDsVhFRUXr1q3v ueeeQD4VpbYOJgAAAAAAAAAAqIkqKipatGjRq1cvuwPxi/Xr1zscjk2bNgWm u9o9mAAAAAAAAAAAoMZZtWpVSEhIdna23YH4S5cuXZKSkgLTV60fTAAAAAAA AAAAUIMo90+bMGGC3YH4UW5ublhYWHp6ur87uhEGEwAAAAAAAAAA1CBOpzMj I6OoqMjuQPxrz549hw4d8ncvN8hgAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAALXS9OnTHf9z8eJFu8MBAAAAAAAAAAAIOpWVlceO Hdu2bdvWrVuzs7MLCwvtjkgvMkHQo6ioaO/evWJ6Z2Rk5ObmXr582e6IfFPT 4wcAAAAAAAAA2OXYsWOTJk1q0qSJQyU0NPT+++9ft26d1+p5eXlvvvlmSUlJ AEKtVmAyQVu2bFmzZo3OwiKMl156qW7dukpUzz77rM6Ktg9m7eN0OhcuXPir X/0qJCTEZYbfe++9ixYtCthoiw/3tddeGzRoUGJiYkxMjIxEe3qYid+hj3sA 3bt311lXsrwFAAAAAAAAAIB55eXlr776amRkpMby7NChQ51Op0YjxcXFjRs3 njlzZsDCdhGATNCJEydiYmIWL17steSVK1fmzp3bqFEj9RjqzwTZPpi1zIED B1q2bKmdgGjduvXhw4f9Gsbu3bt79uzpKQCN6WEyfu2KGgGQCQIAAAAAAACA WsDpdA4cOFC9EluvXr3W14WHh6tfHzZsmHZTs2fPrlOnTn5+fkACdxWATFBy cnLTpk2vXLmiUaasrOydd9659dZb3Ze49WeCquwezNrk6NGj8fHx8lO44447 Ro8eLT6LZ555pk+fPuoEaMuWLa9du+aPGCorK2fMmOFyOY/O6WE+fu/ZFw8B kAkCAAAAAAAAgFrgwoULd955p7IG26FDhy1btpSXlytvFRYWTpkyRb1Iu3Xr Vo2mlCtZkpOTAxK4K39ngrKyskTL8+fP91SgsrJy7dq1LtduREVFyX/7lAmy dzBrk6FDhyrjHxkZ+cEHH4iPSf3uqVOn1NmKd9991x8xPP300+pZkZSU9Prr r3/55ZcFBQVFRUX+jl++u2HDBiu36n8GDRqktN++fXu7WgAAAAAAAAAAaDh4 8GCDBg1eeumlsrIy93dfeOEFuZKckpKi3dTMmTNFsb179/onUi3+zgR17949 Nja2uLjYU4EVK1aoV/vj4uJeffXVTz/91FgmqMrWwaw1zp8/Ly9te/7556st c+bMmYiICKWM1wvfDEhLS5NzoE2bNjt37tRf15L4/ZoJys3Nle2vXr3alhYA AAAAAAAAAF79+OOPnt46e/ZsaGiosk7brFkz7XZ++umn6OjoAQMGWB2gd3oy QVevXr2o4nJ5hYaMjAyvqZzi4uK4uDjl2o3nn39eDIWsaCwTZONg1ho7d+6U 4//xxx97Kta2bVulTLdu3awNoKio6KabblIav/feewsLC32qbkn8fs0EJScn K423aNGioqLClhYAAAAAAAAAACa1atVKWaoNCwvzmj2ZOHGiKLl///7AxCZ5 zQQdPnxY/fieF198UX8mqGfPniEhIXl5edrFRJsjR448duyYfMVMJqjKvsGs NbZt2ybH/5133vFULDExUSnTv39/awP461//qrQcFRWlnhg6WRK//zJB3377 rXz40dtvv21LCwAAAAAAAAAA89q1a6cs1YaEhHgtnJeXJ4qNGDEiAIGpaWeC XNJAL7/8sv6Wc3JyRJV+/foZiMpkJsiuwaw1Tp8+Lce/Q4cOJSUl7mW+/PJL WWbatGnWBtCmTRul5cmTJxuobkn8/ssEPf7440rLt9xyS7WxBaAFAAAAAAAA AIB5CQkJymqt17vDKfr37x8ZGancHi1gNDJBLmmg2bNn+9TymDFjRK2NGzca iMpkJqjKpsGsTXr27Ck/gkcffdTlSU/ff/998+bNlXfr1Klj4LIdDUePHpVd Z2VlGWvEfPx+ygSJ3SosLExp+Y033rClBQAAAAAAAACAeUeOHJEryUOHDtVT ZdWqVaLw4sWL/R2bmqdMkEsaaMGCBT4163Q6Y2JiGjZsaOyCBfOZIFsGszbZ v39/dHS0/BQSExPXrFlTWVlZUVGxYsUK8cnKt5YuXWpt1ytXrlRaDg8PLy0t Fa+cPHly48aNb7zxxrRp02bOnCl6FOFp36XQfPyyQFpamtg1rl27ZsnWjR49 WmlWxOCSnwpYCwAAAAAAAAAA8yZPnuzrNQVOpzMiIqJ3797+jk2t2kyQOg0U Ghq6ZMkSX5sVmyzqjh8/3lhU5jNBtgxmLZOenl6/fn2HSvvr5P+Gh4cvWrTI 8n6nTp2qtH/HHXe89dZbHTp0cFSnVatW69at81/87j2GhYU1bdq0S5cuqamp xi5W+v777+XlPD7da9HCFgAAAAAAAAAA5u3evVuu1rZv376iokJnxa5du0ZH R1+9etWv4am5Z4Jc0kBpaWkGmlUSYdoL9RrMZ4Kq7BjM2icvL69Lly7VJmI6 deqUnZ3tj06HDx9ebY/VSk1N9VP8Xrvu3r37N99849OmTZgwQalbr169c+fO +VTXqhYAAAAAAAAAACb99NNP8hEkYWFhX3/9tf66qampotauXbv8F54Ll0zQ 8ePHExMTlf+NiIhYs2aNsWY7duwoWjh//ryx6pZkggI/mPn5+fpTGGY2LZAy MzPj4+Pdg2/Xrp14yx899unTR91RVFTUwIED58yZs3r16g0bNrz55psuBcTr /oi/u8p9990nyru3Exsbq3+CFRQUiH3KzEdvvgUAAAAAAAAAgElOpzMpKUku FL/++us+VV+2bJmo9d577/kpPHfqTNCBAwdatGih/DsiImLTpk2Gm42NjW3e vLnh6pZkggI/mBcvXpzui/T09IDFZoCYzOPHj1cnPho3buySCnnhhRfKy8ut 7bd79+5K4/Xr158/f/6lS5fcy6SlpYWEhCjFWrZsWe1ld/6I/+TJkwsWLGjW rJlsISEh4cqVK3rqyst5xM5VUFCgv1MLWwAAAAAAAAAAmOF0Onv27CmXiMeO HetrC59//rmoOGPGDH+EVy11JighIUH+2/DVQEJRUZFooV+/foZbsCQTFPjB rE3Eh9i5c2f13Fi3bl1lZWV6enrbtm3VyZQRI0aI1y3sWmaC2rVrp1Fs3Lhx MoacnJxAxv/jjz/KnKnw7rvveq1y6tSpqKgopfyYMWN86s6qFgAAAAAAAAAA ZrikgYYNG2bgWomcnBxRd8qUKf6IsFrqTNDcuXPlvzt16iS2yFibBQUFygK7 4agsyQQFfjCDQXFx8XJfeLq52aOPPio/goceeqiwsFC+VVZWNm3aNHUy5R// +IeFm/Cb3/xGabZly5YaxTIzM2UAS5cuDXD8q1evltVTUlK8lhfTWCkcEhJy 8OBBX7uzpAUAAAAAAAAAgGEuaaBHHnmkpKTEQDv79u0zk/swwOU5QVOmTJH/ 27dvX2NboTwuZ/z48YajsiQTFPjBDAaWPKtInWTp3LlztdNg3rx5skzz5s0t vCxo2LBhSrMNGjTQKHbu3DkZwCuvvBLg+E+fPi2rd+vWTbuw+nKeIUOG+NSR VS0AAAAAAAAAAAxzSQOlpKSUlZUZa0q5jGXq1KnWRqjBJRNUUVExePBg+cpj jz1m4Mqm48ePi7qjRo0yHJWF1wQFcjCDgZh7+b44f/68eyNPP/20HP/PPvvM U1/q26x99913Vm1CamqqbPbChQueionpKovNmjUrwPGfPXtWfyZIvUXZ2dk+ dWRVCwAAAAAAAAAAYwoLC7t27apOAxlInUjKo21effVVCyPU5pIJqrqe2OrQ oYN8ceLEib62efnyZVHx4YcfNhyVhc8JCuRg1hq9e/eW419tqkihTrhs3brV qt5Xrlwpm/300089FcvNzZXFXG7vFoD4t23bJuuOHj1ao+S5c+fq1aunlOzT p49PvVjVAgAAAAAAAADAmMLCwk6dOskF4bFjx5pJAwnLly8X7Xz00UdWReiV eyao6vqtqG677Tb5+rRp03xtNj4+vm3btoajsiQTFPjBPH36dHdfLFy4MGCx +aRXr15y/AsKCjwVGzdunCyWk5Oj0WB2dnZSUpLj+g3fpk6dqn3R3LFjx2Sz ogtPxebMmSOLHT161K/xu5O3sBM+/PBDjZIvvviiLPnFF1/41ItVLQAAAAAA AAAADHBJAz355JPmH5Xypz/9STS1f/9+SyLUo9pMUNX1h+zIyxCEBQsW+NRs 165do6KiDN8lz5JMUOAHs9ZkgsaOHSvHX8yQastcunSpadOmSpnw8HDxv55a O3XqVFxcnEMlNTVVO4D77rtPKRkREXHkyBH3AmfOnLn55puVMvfff79f43en TkIlJiZqPE5L7FMxMTFKSfF1ob8LC1sAAAAAAAAAABhQVFSkTgO1bdt2/fr1 GzSdOnXKa7N9+/aNjY01eWGRTzxlgoTNmzeHhobKd1euXKm/WeUqht27d+sp XFxcfPHnRNfqFJvLuzpjCPxg1hqfffaZHP+wsLAFCxZUVFSoC/zwww89evSQ ZbRvjzZv3jzHz4nPRTuAjRs3ysJ33XWXy0N8Dh061L59e1lg+/btlsdfUFBw 5swZlxevXbu2ZcuWfv36yYpiB8nIyNDYEPX+Jb4itLfaTy0AAAAAAAAAAAxQ X7Si04YNG7TbLC0trV+//oABAwKzCQqNTJDwt7/9Tb4bERGRnp6us1llKX7O nDl6Cnfv3t2nkdTTpi2DWZsMHjxYPebNmjVLSUl59tlnn3766R49eoSHh8u3 EhISzp49q9HUjBkzXD7B0NBQrwEkJyery4tJ8tRTT4nee/bsGRYWJt+aOXOm P+JX9ouYmJjmzZvffffd7dq1a9myZWRkpLpNsUesWrVKYxPEDiUvhmrdurVL NkoP8y0AAAAAAAAAAIzxRyYoPT1dFFu2bFlgNkGhnQkSnnrqKVkgOjo6KytL T7PXrl2LjY1NSkrSU9gfmSBbBrM2cTqdKSkpXj+Lbt26HTt2TLupnJwcl1qP PPKIngAGDRqk0XVUVNTixYv9FL96v6jWr3/9671792pvwqxZs2T5FStWeN1k f7QAAAAAAAAAADDGH5mglJSUOnXqFBYWBmYTFF4zQeXl5erbYTVs2DA3N1dP y+PGjRPljx496rWkPzJBtgxm7ZOZmZmcnBwbG+vyEdSvX3/QoEEbN27U+Wys JUuWREVFKXU7d+58+vRpPbVE46tXr05KSgoJCVH3npCQ8Pzzz+u53aLh+Bct WlS3bl33udesWbORI0eK3d/rhhcXFzdu3FgGXFpaqmeTrW0BAAAAAAAAABA8 zpw5ExkZOWrUKLsDsczXX3/tcDhSU1MD33XtG0x7VVZWHjly5KuvvsrIyNi5 c2deXp6B25RduHAhMzNz//79OpNHakVFRQcOHBC9ixjy8/N9rW44/tOnT+/d uzfjupycHD25JwAAAAAAAAAAqvXKK684HI6cnBy7A7FSx44dGzVqdOXKlQD3 WysHEwAAAAAAAAAA1FAlJSVNmjTp2bOn3YFYbO3atY6AP6yntg4mAAAAAAAA AACooZYtW+ZwODIyMuwOxGIVFRWtW7e+5557DNwQzLDaOpgAAAAAAAAAAKAm qqioaNGiRa9evewOxC/Wr1/vcDg2bdoUmO5q92ACAAAAAAAAAIAaZ9WqVSEh IdnZ2XYH4i9dunRJSkoKTF+1fjABAAAAAAAAAEANotw/bcKECXYH4ke5ublh YWHp6en+7uhGGEwAAAAAAAAAAFCDOJ3OjIyMoqIiuwPxrz179hw6dMjfvdwg gwkAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHCDmz59uuN/Ll68aHc4 AAAAAAAAAAAAwaWysvL48eNZWVlbt27dvn37N998U1paandQepEJqt3Ky8sP HjyYkZEhJmdubm5ZWdmNE4D5HVO0cOzYsW3btokWsrOzCwsLA9k7AAAAAAAA AMB28+bNe/DBB2NjYx0/FxkZ2aNHj7S0tMrKSu0W8vLy3nzzzZKSksAE7C4w maAtW7asWbNGZ2ERxksvvVS3bl0lqmeffVZnRdsHM6gcO3bs97//fYMGDdQz s379+qNGjfruu++COQCHPhoTw/yOKYKfNGlSkyZN1NVDQ0Pvv//+devWadc1 37sLMbFfe+21QYMGJSYmxsTE6BkBF+fOnYuPj1cHs2HDBp9iAAAAAAAAAIAb k9fF6i5dupw9e1ajheLi4saNG8+cOTNgMbsIQCboxIkTMTExixcv9lryypUr c+fObdSokc4Ffxe2D2bw+OCDD2QqzV2dOnXefffdoA3A627ldWJ4rauxY5aX l7/66quRkZEa1YcOHep0Ov3Ru4vdu3f37NnTwAi4GDFihEtdMkEAAAAAAAAA oIdcVo2Njb3rrrvatWvXqlWr8PBwl1Vf7UsAZs+eXadOnfz8/EBF/TMByAQl Jyc3bdr0ypUrGmXKysreeeedW2+91cxyd5Xdgxkk3n//ffUAduzY8cknnxw/ fnyLFi3Ur69cuTI4A/CU+NA/MQzvmE6nc+DAgepi9erVa32dS/Vhw4ZZ3rua eHfGjBkhISHGRkAtPT3dvS6ZIAAAAAAAAADQY/jw4WlpaQUFBeoXi4uL//73 v6vvDaW96KpcyZKcnOznYKvn70xQVlaWaHn+/PmeClRWVq5du7Zly5bqZeqo qChfl7sV9g5mMDh8+LAcvfj4+IyMDPlWRUXFvHnzZHIhJibm+PHjQRiA+WyF 4R3zwoULd955p/Juhw4dtmzZUl5errxVWFg4ZcoU9SzdunWrtb2rPf300+q+ kpKSXn/99S+//FI0W1RUpH8oRL8JCQlKI61atSITBAAAAAAAAABW2bx5s1x0 HT16tHbhmTNnimJ79+4NSGg/4+9MUPfu3WNjY4uLiz0VWLFihXrFOy4u7tVX X/3000+NZYKqbB3MYDB48GBl3MLCwnbv3u1e4JVXXpFj+9RTTwVhAH7NVnjd MQ8ePNigQYOXXnqprKzM/d0XXnhBVk9JSbG8d0VaWpos1qZNm507d/rakTRp 0iSlnVtuuUWMJ5kgAAAAAAAAALCQfNx8z549tUv+9NNP0dHRAwYMCExganoy QVevXr2oov959xkZGV5TOcXFxXFxcaJYZGTk888/L4ZCVjSWCbJxMG139OhR ecXNE088UW2Z0tLS22+/XSlTv379y5cvB1sA/s5WeN0xf/zxR091z549Gxoa qlRv1qyZP3ovKiq66aablDL33ntvYWGhgV4UWVlZ8uMQg6nercgEAQAAAAAA AIB57dq105kJEiZOnChK7t+/PwCBqXnNBB0+fFj9+J4XX3xRfyZIbHhISEhe Xp52MdHmyJEjjx07Jl8xkwmqsm8wbffaa6/Jcfvmm288FZsxY4afMgKWBODv bIVPO6Y7eY+1sLAw/fuC/t7/+te/KgWioqLUO4Wvrl271qZNG6Up5X6JZIIA AAAAAAAAwEKVlZXyD/uffPJJr+Xz8vJCQkJGjBgRgNjUtDNBLmmgl19+WX/L OTk5okq/fv0MRGUyE2TXYNquT58+yqDdcccdnsrk5uaqnxfz3HPPBVsAfs1W +LpjupOpHDHH/NG7TN9MnjzZQHjStGnTlHbi4+PPnj1bRSYIAAAAAAAAACz1 ySefyEXXzMxMPVX69+8fGRmp3B4tYDQyQS5poNmzZ/vU8pgxY0StjRs3GojK ZCaoyqbBtJ3MMowcObLaAkuXLo2KinKoGLsuxq8B+DVbYWDHdJGQkKBUN3B3 OK+9Hz16VBbIysoyEJ7iwIEDERERSjsrV65UXiQTBAAAAAAAAABW2bVrV3x8 vLLiqv+x8qtWrRLlFy9e7NfYXHjKBLmkgRYsWOBTs06nMyYmpmHDhiUlJQai Mp8JsmUw7XX27Fk5aDNmzHB599KlS8nJyQ43v/jFL4ItAPlWWlqamJPXrl2z KkJjO6bakSNHZHhDhw61vPeVK1cqBcLDw0tLS8UrJ0+e3Lhx4xtvvDFt2rSZ M2cuXbp0//792nelKy8vT0pKUtoZNGiQfJ1MEAAAAAAAAAAYs2PHjg3/s3jx 4oEDB4aFhSnLrcOGDdOfCnE6nREREb179/ZrtC6qzQSp00ChoaFLlizxtVkx FKLu+PHjjUVlPhNky2Da67///a8ctOXLl6vf2rdv35133infTUxM/NWvfqX8 u0GDBsEWgHu2SOxQTZs27dKlS2pqqv4rZazaMdUmT56sM5lirPepU6cqZe64 44633nqrQ4cO7qMhtGrVat26dZ66nj9/vlIsLi7uxIkT8nUyQQAAAAAAAABg TPfu3d2XaiMjIxctWuRrU127do2Ojr569ao/4qyWeybIJQ2UlpZmoFllwVxj sVqb+UxQlR2Daa8vv/xSDtr69evl6wsXLhSzUb41ePDgCxcuTJw4USZZgi2A anMfamKP++abb7zGY+GOqdi9e7fM5rRv376iosLy3ocPH+5186XU1FT3FvLz 8+vVq6cU+Mc//qF+i0wQAAAAAAAAABhT7ZKv4/r9nYYMGXLo0CH9TaWmpoqK u3bt8l+0LlwyQcePH09MTFT+NyIiYs2aNcaa7dixo2jh/PnzxqpbkgkK/GDm 5+frX8Y3s2nVcl/nLywsHDp0qHwxMjJy4cKFyo3FRNfy9WALoLvKfffd165d O3lTNSk2NtbrJ2vhjin89NNPzZs3V1oICwv7+uuv/dF7nz591IWjoqIGDhw4 Z86c1atXiyF98803XQqI111a6Nu3r/LWgw8+6HITOTJBAAAAAAAAAGDM8uXL p1/33HPPjRs37oEHHpDPahfq1q27efNmnU0tW7ZMVHnvvff8GrCaOhN04MCB Fi1aKP8Wm7Bp0ybDzcbGxjZv3txwdUsyQYEfzIsXL073RXp6uoW979ixQ73O v3v37l/84hfyFfHvnJwcWVgmYsLDw2tEACdPnlywYEGzZs1kgwkJCVeuXNGo YuGO6XQ65ZN3hNdff91rFWO9y/xR/fr158+ff+nSJfcyaWlpISEhSrGWLVuq L01asWKF8nq9evXy8/NdKpIJAgAAAAAAAACrFBYWzpo1S94RKzo6+uDBg3oq fv7556L8jBkz/B2hpM4EJSQkyH8bvhpIKCoqEi3069fPcAuWZIICP5j22rdv nxy0hx56SJ13GDZsmJiT6sJjxoxR3mrYsGENCuDHH3+UyUrh3Xff9SlCYzum 0+ns2bOn7HTs2LE+depT7zIT1K5dO42mxo0bJ+OR+bUzZ840atRIeXHhwoXu tcgEAQAAAAAAAIC1PvnkE7nu+vjjj+upkpOTIwpPmTLF37FJ6kzQ3Llz5b87 derkdDqNtVlQUCBaGDFihOGoLMkEBX4w7fXDDz843NSpU2fp0qXuhX/zm98o BVq3bl2zAli9erVsPCUlxUCcPu2YLmmgYcOGlZeXG+hUZ+9yWFq2bKnRSGZm pmxEDm9ycrLySpcuXap9hhGZIAAAAAAAAACwXO/evZV119jYWD3llasqrH18 jDaX5wRNmTJF/m/fvn1LSkoMtKk8Lmf8+PGGo7IkExT4wbRXWVlZeHi4Ogtz 5513ikGotrC8b9tvf/vbmhXA6dP/X3v3HhxVmSYOOCEkBJGgiOIwMjDLFC4j issoURdElIF15TZDITURoYSlvGutFkhZDloga6GuFxTcXRW1lgFvC1jWGJea JeAqKgSGCrOlIMNlcUBQIZkYrkn4nbFrz69tQqfT6UuIz/OHRfp83/u9/Z7T /vG9dc7ZE8a/8sork0s1wR9mTBto5MiRyf0iEl993LhxkUNnnHFGnAhffvll mNVDDz0UfLJ+/frwk1GjRt3dkOh3No0YMSL8vKKiovlfCgAAAADg++nOO+8M t14TGR+5jWXGjBnpTiwU0wmqq6sbM2ZM+Mn111+fxB0Qu3btCuZOnDgx6axS eE9QJouZdf369QvrVlJS0uBbZo5/t5mS2qfnZSCBffv2Nb8TlMgPM6YNFHyd Y8eOJbdc4qtPnz49PLR///6TRQh+quGwhx9++Ph3fzJN5f4gAAAAAICkTZgw IbLXes455yQyPvJqmzlz5qQ7sVBMJ+j4txvg/fv3Dz+87bbbmhrzm2++CSZe d911SWeVwvcEZbKYWXfHHXeEdYvzBpwXXnghHPb++++fWgmsXr06nDtp0qTk 8mz0h1lZWTlo0KBwoZKSkmY+FC7B1RcvXhwu+s4775wsQkVFRTjsxRdfPK4T BAAAAACQDTU1NWeffXZkr3XMmDGJTHnppZeCwa+//nq6cwud2AkK7N69+7zz zgs/nzlzZlPDdunSpW/fvklnlZJOUOaLuWfPnsFN8cwzz6Q2gZUrV4Z1u+OO OxocU19ff8EFF0TG/OhHP2rwhTLR1q1bV1xcnPPt88pmzJgR/76YdCQQI3x+ WuA3v/lNk+ZGNPrDrKysHDBgQLjK5MmTU9gGir/6jh07wnWnTJlysiBz584N h23btu34t49kfLAxkyZNCmeNHz8+/DxOzw4AAAAAgJOpra294YYbwn3XpUuX JjLrgQceCAZv3Lgx3emFGuwEHf/2JTsdOnQID82bN69JYQcNGlRYWJj007RS 0gnKfDGz3gmqr6/v06dPpG75+fkNvqPn2WefDWv75JNPxg+4e/fuTp065USZ Pn16JhOIEd0B6dmzZxJv7Wn0hxnTBrrllluCL9XUVZJePXD55ZeHBdy6deuJ A/bu3XvOOedExlxxxRWJrx79s3IfEAAAAABAozZt2vT555+f+Pm+ffteeeWV Cy+8MNx0HTp0aIKbycOGDSsqKkrhDQiNOlknKPD222+3adMmPLp48eLEw953 333BlLVr1yYyuLq6+sB3BUtHb8XHHE0wh8wXsyVYvnx5WLpu3bqtWbMmPFRX V7dgwYL8/PzI0b/+678+fPhw/GhPPPFEzncFJU1rAjt37ty7d2/Mh8GwFStW DB8+PIwcXJllZWUNJtCcH2ZVVVV0G6hv375Lly5dFtfu3btTtfqJBbzgggs+ /fTT6KObN2+++OKLwwHvvfdeg0VokE4QAAAAAECTDB48OCcnp0OHDj169Ojb t2+/fv2C/4bPfQpdcsklCTYvjh49evrpp48YMSLdmUeL0wkKPPXUU+HR/Pz8 0tLSBMO+++67wZS5c+cmMjhSycQlEjMrxWwh/uEf/iG6XFdeeeUdd9xx0003 /dVf/VX4YVFR0aZNmxoNNWvWrJjit2nTJq0JRC7Ijh07Bj+rCy+8MPhZ9e7d u6CgIDpgcCkuWbLkZKs354eZxNt2YloqKfnfwvjx46MLHsS89dZbb7/99iFD huTl5YWHZs+e3ei5ONm30wkCAAAAAGhUo/2L/Pz8adOmHTp0KMGApaWlwayF CxemNe0Y8TtBgVtvvTUc0L59++hbPOI4fPhwUVFRcXFxIoPT0QnKSjFbiNra 2qlTp8YpYI8ePdavX59IqPLy8pi5I0eOTGsC0Rdkg372s59t2LAhzurN+WGm qhOU3Oqhmpqa0aNHxwlSWFg4f/78Rk9EnG+nEwQAAAAA0Kgbb7wxfM5VtLZt 2xYXFz/++OP79u1rUsCSkpJ27dpVVlamKeEGNdoJqq2tjX4q15lnnllRUZFI 5ClTpuT83+vs40tHJygrxWxR/vM//3Po0KHRt5AEfvSjH82aNau6ujrxOAsW LCgsLIxMv+yyy/bs2ZPWBJ599tnTTjvtxJPerVu34BdXVlbW6IMWm/PDbH4n KFX/Wwi+5quvvhpMyc3NjY7TvXv3e+65J+aRdAnSCQIAAAAAaKpjx45t27Zt w4YNZd9at27d1q1bjx49mkSovXv3FhQUTJw4MeVJZstHH32Uk5Mzffr0zC/d +oqZtOrq6rVr1wYX5+rVq3fu3JlckP37969atWrjxo0Jvu6q+Qns2bMn/FmV l5c3tfGRwh9mElK7elVV1aZNm4I4H3744fbt21OaKQAAAAAAmfPQQw/l5OSU l5dnO5FUuvTSSzt37nzw4MEMr9sqiwkAAAAAAJyijhw50rVr1yFDhmQ7kRR7 4403cjL+sp7WWkwAAAAAAOAUtXDhwpycnLKysmwnkmJ1dXV9+vS56KKLkniq WNJaazEBAAAAAIBTUV1dXa9eva6++upsJ5IWS5cuzcnJeeuttzKzXOsuJgAA AAAAcMpZsmRJbm7uunXrsp1IugwcOLC4uDgza7X6YgIAAAAAAKeQyPPTbr75 5mwnkkYVFRV5eXmlpaXpXuj7UEwAAAAAAOAUUlNTU1ZWVlVVle1E0mv9+vWb N29O9yrfk2ICAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAECr9OCDD+b8nwMH DmQ7HQAAAAAAAFJGJ4hWr76+fseOHatXr165cuW6desqKyuznREAAAAAAKeq L7/8skuXLjlRli1bFmf8li1bnn766SNHjmQswxiZ6QStWLHitddeS3BwkMb9 999/2mmnRbK6++67E5yY9WK2bkF5/+mf/mn06NE9e/bs2LFjeNkkfoKSlvQl sWPHjjvvvLNr167RP8k2bdpcccUVb775ZgYSSG764MGDc5qoSfkAAAAAAJC0 CRMmxOzQxu8EVVdXn3XWWbNnz85YhjEy0An6/PPPO3bsOH/+/EZHHjx48NFH H+3cuXN0ARPfdc96MVurtWvXDhky5GQ9iLR2gpK+JGpra+fMmVNQUBCnezJ2 7Niampo0JdCc6TpBAAAAAAAtU2lp6Yk7tPE7QYFHHnmkXbt227dvz0iOsTLQ CRo/fvy555578ODBOGOOHTv2/PPP//CHPzyxgE1qNGS3mK1PfX39rFmzcnNz 4/Qg0tQJas4lUVNTM2rUqOgpHTp06POttm3bRn8+bty4dCTQzOk6QQAAAAAA LVB1dXX37t0ju7Lnn39+uEPbaCcocifL+PHjM5NnjHR3gtasWRNEfvLJJ082 oL6+/o033ujdu3f0tnZhYWGTdt1D2S1m63P77bdHn5fi4uLHHnvs/fff37lz Z1VVVZoWbf4lsX///p/85CeRwf3791+xYkVtbW3kUGVl5bRp06Ijr1y5MrUJ pPaSjmP06NGRgBdffHFKAgIAAAAAEMedd94Z2ZX9wQ9+sGzZssQ7QYHZs2cH Izds2JCBPGOkuxM0ePDgoqKi6urqkw14+eWXozfMO3XqNGfOnHfeeSfpbfMs FrOVWbRoUXgWfvrTn37wwQeZWTcll8Qnn3xyxhln3H///ceOHTvx6L333htG KykpSW0CKb+kG1RRUREGfPXVV5sfEAAAAACAONasWRM+QWvZsmVlZWVN6gR9 9dVX7du3HzFiRAZSjZFIJ+jQoUMHotTX1ycYPFKH+Pve1dXVnTp1CoYVFBTc c889QSnCicltm2exmK1JVVXV2WefHTkFl1xySWVlZcaWTtUl8cUXX5zs0L59 +9q0aROJ1q1bt9QmkPJLukHjx4+PROvVq1ddXV3zAwIAAAAAcDKHDx/+6U9/ GtmVjTyXrKmdoMBtt90WDN64cWOak43VaCfos88+i37XyX333Zd4J2jIkCG5 ublbtmyJPyyIeeONN+7YsSP8pJnb5tkqZmvyz//8z5H6FxYWRp+azEj5JXGi 8BGOeXl5J17SzUwg3fn/z//8T9h6/rd/+7dmRgMAAAAAIL6ZM2dGtmS7dOmy b9++40l1grZs2ZKbmzthwoQ0Jxsrficopg3061//OvHI5eXlwZThw4cnkVUz t82zVczWJGxu3nXXXdnO5S9S3gnq169fJFpwqWQggdTm/6tf/SoS6gc/+MGR I0eaGQ0AAAAAgDg2bdqUn58f2ZVdvHhx5MMkOkGBa6+9tqCgIPIsqYyJ0wmK aQM98sgjTYp80003BbOWL1+eRFbN3zbPSjFbjW3btoX1X7NmTbbT+YuUd4K6 d+8eiXbi0+HSkUAK8w9+mHl5eZFQjz/+eHNCAQAAAAAQX21tbXFxcWRLdvTo 0eHnyXWClixZEoyfP39+epJt2Mk6QTFtoHnz5jUpbE1NTceOHc8888zkblho /rZ5VorZaixevDhS/LZt2x49ejT45E9/+tPy5csff/zxmTNnzp49+7nnntu4 cWPizwlsvtR2grZu3RpGGzt2bAYSSGH+kyZNisQJfl/V1dXNCQUAAAAAQHxP PvlkZEu2U6dOn3/+efh5cp2gmpqa/Pz8a665Jj3JNqzBTlB0G6hNmzYLFixo atjgWwdzp06dmlxWzd82z0oxW40ZM2ZEiv/jH//4X//1X/v375/TkPPPP//N N9/MTEqp7QTdddddTf2FtpBO0B//+MfwhqAmPa0RAAAAAICm2r59e4cOHSJb si+++GL0oeQ6QYFBgwa1b9/+0KFDqU72pE7sBMW0gRYtWpRE2MhOe9JtgpRs m2e+mK3GDTfc0GDrp0HTp0/PQEop7AStXbs2bKZcfPHFdXV1GUggVfnffPPN kSDB/3y+/PLLpOMAAAAAANCoYcOGRbZkf/7zn8c8JivpTtD06dODKR9//HGq kz2pmE7Qrl27evbsGfkzPz//tddeSy7spZdeGkT4+uuvk5uekm3zzBdz+/bt iTdQmt/RSJ+hQ4dG51lYWDhq1Ki5c+e++uqrwfX89NNPxwwIPk93SqnqpHz1 1Vc9evSIxMnLy/voo48yk0BK8t+5c2f4VrIWe/EAAAAAALQOL7/8cmQ/tkOH Dtu3b485mnQnaOHChcGUV155JZW5xhXdCdq0aVOvXr3CNtBbb72VdNiioqIe PXokPT0l2+aZL+aBAwcebIrS0tKM5dYkgwcPjhT/9NNPf/LJJ//85z+fOGbR okW5ubmRYb17907wzpqkpeSSqKmpCV/sFXjssccylkBK8g9vCAp+njt37kwu CAAAAAAAjdq7d2/nzp0jW7LPPPPMiQOS7gT97ne/C6bMmjUrdck2IroT1L17 9/DfSd8NFKiqqgoiDB8+POkIKdk2z3wxW42wE9SvX784w6ZMmRKepvLy8rSm lJJXRw0ZMiQMMnny5Ewm0Pz8d+/eXVhYGIlw0003JREBAAAAAIAEjR8/PrIf O3DgwAZvhUi6E1ReXh5MmTZtWuqSbUR0J+jRRx8N/z1gwICamprkYu7cuTOI MGHChKSzSkknKPPFbAmqq6tfaooGn573d3/3d5Hi9+7dO85aq1atCk/Tc889 l7bv9BfNvCRi2kDjxo2rra3NZALNv6SDWZHpubm5n3zySRIRAAAAAABIxPr1 68Md3VGjRt3dkLFjx4ZjRowYEX5eUVERP/jvf//75vQ+khDznqBp06aFfw4b NuzIkSNJxIy8Lmfq1KlJZ5WSTlDmi9kSpORdRePGjYscPeOMM+Ks9eWXX4Zx HnroobR9p79oziUR0wYaOXJkEhd2djtB0TcE/fKXv2zqdAAAAAAAEhe9o9tU jd4fFLmNZcaMGZn5LsdP6ATV1dWNGTMm/OT6669v6q0TgV27dgVzJ06cmHRW KbwnKJPFbAmOHTu2vSm+/vrrE4NMnz49rP/+/ftPtlZwwYTDHn744XR+reQv iZg2UElJSVCiTCaQkunRZ2TdunVNnQ4AAAAAQOLS2gmKvNpmzpw5mfkux0/o BB3/due8f//+4Ye33XZbU2N+8803wcTrrrsu6axS+J6gTBaz1Vi8eHFY/3fe eedkwyoqKsJhL774YlpTSu6SqKysHDRoUHQbKInOZnMSSMn0L7/8skOHDpG5 Q4cOberSAAAAAAA0yfbt2x9szKRJk8Jd3/Hjx4efN/p2j5deeimY8vrrr2fm uxxvqBN0/NtHUZ133nnh5zNnzmxq2C5duvTt2zfprFLSCcp8Mffs2TO4KZ55 5pmM5dYkO3bsCOs/ZcqUkw2bO3duOGzbtm1xAq5bt664uDjn28fNzZgxI4m7 cpK4JCorKwcMGBDOmjx5ctJtoOQSSNX0++67L5z7X//1X01dGgAAAACAlIve 9W30PqBoDzzwQDBl48aN6cstRoOdoOPfvmQnvA0hMG/evCaFHTRoUGFhYXKP 4Tqeok5Q5ovZajpBgcsvvzxS//z8/K1bt544YO/eveecc05kzBVXXBEn1O7d uzt16pQTZfr06U3Np6mXREwb6JZbbqmvr2/qos1JIFXTg19lx44dIxODb9TU dQEAAAAASIekO0HDhg0rKipqzp0LTXWyTlDg7bffbtOmTXh08eLFiYeN3MWw du3aRAZXV1cf+K5g6eg9/JijCeaQ+WK2JsuXLw9PwQUXXPDpp59GH928efPF F18cDnjvvffihHriiSdyvis4L/FXb+YlUVVVFd0G6tu379KlS5fFtXv37hQm kMJLOvoXGnyL+HUDAAAAACAzkusEHT169PTTTx8xYkRac4sRpxMUeOqpp8Kj +fn5paWlCYZ99913gylz585NZPDgwYNzmiKRmFkpZiszfvz4sOZt2rQJTtOt t956++23DxkyJC8vLzw0e/bs+HFmzZoVcwaDaPGnNPOSSOJlXjG/02YmkKpL OvhJhrdT9enTp66uLn7dAAAAAADIjOQ6QaWlpcH4hQsXpjW3GPE7QYFbb701 HNC+ffs1a9YkEvbw4cNFRUXFxcWJDE5HJygrxWxlampqRo8eHedEFBYWzp8/ v9E45eXlMRNHjhwZf4pOUMTDDz8cjnn55ZcbLTUAAAAAAJmRXCeopKSkXbt2 lZWVac0tRqOdoNra2uHDh4djzjzzzIqKikQiT5kyJRi/bdu2RkemoxOUlWK2 PvX19a+++mpxcXFubm70Kejevfs999wT80S1OBYsWFBYWBiZe9lll+3Zsyf+ eJ2g498+Yu6ss86KDAgKfvTo0QSrDQAAAABAC7R3796CgoKJEydmO5GU+eij j3JycqZPn575pVtfMbOuqqpq06ZNZWVlH3744fbt25OIsH///lWrVm3cuLG+ vj7V2QEAAAAAQEv30EMP5eTklJeXZzuRVLr00ks7d+588ODBDK/bKosJAAAA AACcoo4cOdK1a9chQ4ZkO5EUe+ONN3Iy/rKe1lpMAAAAAADgFLVw4cKcnJyy srJsJ5JidXV1ffr0ueiiizL5QLDWWkwAAAAAAOBUVFdX16tXr6uvvjrbiaTF 0qVLc3Jy3nrrrcws17qLCQAAAAAAnHKWLFmSm5u7bt26bCeSLgMHDiwuLs7M Wq2+mAAAAAAAwCkk8vy0m2++OduJpFFFRUVeXl5paWm6F/o+FBMAAAAAADiF 1NTUlJWVVVVVZTuR9Fq/fv3mzZvTvcr3pJgAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAANAqPfjggzn/58CBA9lOBwAAAAAAgJTRCSK++vr6Xbt2rVmzZuXK le+9994f/vCHo0ePnlqrB0F27NixevXqIMi6desqKyubGqGqqmrDhg3B9LKy soqKim+++SaTqwMAAAAAkC05ibn77rtPFmHLli1PP/30kSNHMpl2tMx0glas WPHaa68lODhI4/777z/ttNMarV6MrBezlXniiSd+/vOfFxUVxVzPBQUFV111 1aJFi+rr61v46jt27Ljzzju7du0aHaFNmzZXXHHFm2++2ej0mpqaZ5555m/+ 5m9yc3NjIlxyySXPPvts/IutmasDAAAAAJB1ze8EVVdXn3XWWbNnz85k2tEy 0An6/PPPO3bsOH/+/EZHHjx48NFHH+3cuXOC1YuR9WK2Mo1e2AMHDty3b1/L XL22tnbOnDkFBQVxIowdO7ampuZkETZt2tS7d+/4OfTp0+ezzz5Lx+oAAAAA ALQEjW5WR8TvZTzyyCPt2rXbvn17prL+jgx0gsaPH3/uuecePHgwzphjx449 //zzP/zhD5tavRjZLWYrE56CoqKiCy64oF+/fueff37btm2jz87AgQPTdGdQ c1avqakZNWpU9MgOHTr0+VZMhHHjxjW4+rZt27p06RIO+/GPfzxp0qTgUrzj jjuGDh0a3eLp3bv34cOHU7s6AAAAAAAtRLiju2zZsqSDRO5kGT9+fAoTS1y6 O0Fr1qwJIj/55JMnG1BfX//GG2/E3HxRWFiYXCcou8VsZW644YZFixbt3Lkz +sOgwv/yL/8S/dC25lz8aVp9//79P/nJTyID+vfvv2LFitra2sihysrKadOm RV9sK1euPDHC2LFjI0cLCgr+/d//PabftHv37sGDB4cRXnjhhdSuDgAAAABA C5GqzfDZs2cHQTZs2JCqxBKX7k7Q4MGDi4qKqqurTzbg5Zdfjt4Y79Sp05w5 c955553kOkHHs1rM74+33347PEGTJk1qgat/8sknZ5xxxv3333/s2LETj957 771hhJKSkpijX3/9dXjzzj333NNg/L179+bn50fGnHhrT3NWBwAAAACg5UhV J+irr75q3779iBEjUpVY4hLpBB06dOhAlMSfBlZWVtZoK6e6urpTp06Rmy/u ueeeoBThxOQ6QVks5vdK165dIydoyJAhLXP1L7744mSH9u3b16ZNm0iEbt26 xRz94IMPwsvvP/7jP04WpG/fvpExV155ZQpXBwAAAACg5UhVJyhw2223BXE2 btyYksQS12gn6LPPPot+fc99992XeCdoyJAhubm5W7ZsiT8siHnjjTfu2LEj /KQ5naDj2Svm90q/fv2y2Alq/urnn39+JEJeXl7MJb169erw8nv++edPFqFn z56RMddee20KVwcAAAAAoOVIYSdoy5Ytubm5EyZMSEliiYvfCYppA/36179O PHJ5eXkwZfjw4Ulk1cxOULaK+f1RX19/9tlnR07QLbfcciquHvaSgksl5tCe PXvCy69///5Hjhw5cfr7778fjpk5c2YKVwcAAAAAoOVIYScocO211xYUFEQe j5YxcTpBMW2gRx55pEmRb7rppmDW8uXLk8iqmZ2g41kq5vfHb3/72/AErVq1 6lRcvXv37pEIDT6fbciQIeESv/jFL2JedPXHP/6xR48ekaPt2rWLvp0tJasD AAAAANBChHvFixYtOnDgwOHDh5sTbcmSJUGo+fPnpyq9RJysExTTBpo3b16T wtbU1HTs2PHMM89s8H6KRjW/E5SVYn5PfPzxx126dImcnZKSklNx9a1bt4YX 2NixY08csHHjxvbt24djevbs+dprr9XX19fV1b388svBhR0eeu6551K+OgAA AAAALUTOCfLy8s4999yBAwdOnz59zZo1TYpWU1OTn59/zTXXpCnbBjXYCYpu A7Vp02bBggVNDbts2bJg7tSpU5PLqvmdoKwUs1X67//+72X/Z/78+aNGjQou 8sipGTduXHKdvqyvftddd4UX2Mlu6CstLT399NOjf90Xfyv8s23bts8++2ya VgcAAAAAoCU4sRMUY/DgwX/4wx8SDzho0KD27dsfOnQofTnHOLETFNMGWrRo URJhI3vdb775ZnJZNb8TdDwbxWyVgmv4xAu7oKAguSZIS1h97dq1YTvp4osv rqurO9nILVu2DBw4sMGf9oABA9atW5fW1QEAAAAAyLrBUS6//PJ+/fqFz60K FRUVffzxxwkGnD59ejAl8fHNF9MJ2rVrV8+ePSN/5ufnv/baa8mFvfTSS4MI X3/9dXLTU9IJynwxt2/f3mDX4GSS/mqZ1GAvJufbO2J++ctfbt68+dRa/auv vgpf8ZOXl/fRRx/FH79q1aoTf9SB4MeexCuKmro6AAAAAAAt0J/+9Kd58+Z1 69Yt3DTu3r37wYMHE5m7cOHCYPwrr7yS7iRD0Z2gTZs29erVK/Lv/Pz8t956 K+mwRUVFPXr0SHp6SjpBmS/mgQMHHmyK0tLSjOWWtJdeeimS7T/+4z9OmTLl b//2b4NrIzw7p5122ttvv32qrF5TU1NcXBxOf+yxx+IPnjp1anT356yzzorp B9177721tbXpWB0AAAAAgBbuiy++CLsqgRdeeCGRWb/73e+CwbNmzUp3eqHo TlD37t3Dfyd9N1CgqqoqiDB8+PCkI6SkE5T5Yn5PVFZWPvzwwwUFBZET1L59 +08++aTlr15TUzNkyJDwupo8eXKcwcE1fNlll0X/NN588836+vrS0tK+fftG N4MmTJgQfJ7a1QEAAAAAOCW8+uqr4cZvSUlJIlPKy8uDwdOmTUt3bqHoTtCj jz4a/nvAgAE1NTXJxdy5c2dkhzzprFLSCcp8MVuC6urql5oi6afn/fa3vw3P 0a9+9avUfouUrx7TiBk3blz8e3l+8YtfhIP//u//vrKyMjx07NixmTNnRjeD XnzxxdSuDgAAAADAKWHPnj3h3u+VV16ZyJTf//73zel9JCHmPUHTpk0L/xw2 bNiRI0eSiBl5Xc7UqVOTziolnaDMF7MlyOS7iq655ppIkKKiohR+hZSvHtOI GTlyZPwLe9WqVeHgyy67rMHBTzzxRDimR48ecW4LaurqAAAAAACcKvbt29fU TlDkNpYZM2akO7dQTCeorq5uzJgx4SfXX399Ejcv7Nq1K5g7ceLEpLNK4T1B mSxmS3Ds2LHtTfH1118nvdadd94ZnqYUfoXUrh7TiCkpKQlKFD/y7bffHo5/ 9913TzYs+jFxn376aapWBwAAAADgVLF69epwB3jSpEmJTIm82mbOnDlpTu3/ i+kEHf9277p///7hh7fddltTY37zzTfBxOuuuy7prFL4nqBMFvP7ZsKECZFz dM4557TM1SsrKwcNGhTdiEmksxnebRSI0ymLbhitXLkyVasDAAAAAHCqGDdu XLgJ/Jvf/CaRKS+99FIw+PXXX093bqETO0GB3bt3n3feeeHnM2fObGrYLl26 9O3bN+msUtIJynwx9+zZM7gpnnnmmYzllnI1NTVnn3125ByNGTMm/uB169YV FxcHI88444wZM2Y0/76YRFavrKwcMGBAeCFNnjw5wUbM1VdfHc7auXPnyYZN mTIlHFZeXp6q1QEAAAAAOCXMnTs33ATu2bNngm8GeeCBB4LxGzduTHd6oQY7 Qce/fclOhw4dwkPz5s1rUthBgwYVFhYmveGfkk5Q5ov5/ekE1dbW3nDDDeE5 Wrp0aZzBu3fv7tSpU06U6dOnp3v1mEbMLbfcEudVPjEmT54cTgx+IA2O+fOf /3zuuedGxrRt2zb4M1WrAwAAAADQQuzcuXPv3r0xHx4+fHjFihXDhw8PN4Hb tGlTVlaWYMxhw4YVFRVl8t6Bk3WCAm+//XaQfHh08eLFiYe97777gilr165N ZHB1dfWB7wqWjt5FjzmaYA6ZL2ZrsmnTps8///zEz/ft2/fKK69ceOGF4Qka OnRo/DbHE088kfNdwXlJ6+pVVVXRjZi+ffsuXbp0WVy7d+8Op7/77rvh3Ly8 vHnz5tXV1UXH/9///d+rrroqHBPz7Mdmrg4AAAAAQAsR6aF07NixR48eF154 Yb9+/Xr37l1QUBC9452fn79kyZIEAx49evT0008fMWJEWtOOEacTFHjqqaei v0tpaWmCYSN76XPnzk1k8ODBg3OaIpGYWSlmaxI5KR06dAgu7759+waXd/Df 8IFsoUsuuaTR3tysWbNiZrVp0yatq0ffU5agZcuWRUcYM2ZM9NFu3bqVlJTc fffdt99++1VXXdW2bdvwUPfu3fft25fa1QEAAAAAaAmieygN+tnPfrZhw4bE A5aWlgazFi5cmL6cTxS/ExS49dZbwwHt27dfs2ZNImEPHz5cVFRUXFycyOB0 dIKyUszWpNGTkp+fP23atEOHDjUaqry8PGbuyJEj07p683sxNTU1JSUljc66 8sord+zYkfLVAQAAAABoCZ599tnTTjvtxE3dbt263XjjjWVlZU19M0hJSUm7 du0qKyvTlHCDGu0E1dbWRj/s7swzz6yoqEgk8pQpU4Lx27Zta3RkOjpBWSlm axJcw/n5+ScWv23btsXFxY8//njMjTDxLViwoLCwMBLhsssu27NnT1pXT1Uv ZtWqVePHjy8qKooZfPrpp48ePXr58uUN/sZ1ggAAAAAAWpM9e/Zs2LCh7Fvl 5eVJv+9j7969BQUFEydOTG16WfTRRx/l5ORMnz4980u3vmJmxbFjx7Zt2xZe 3uvWrdu6devRo0eTi7Z///5Vq1Zt3LgxwQ5paldvjiDhYOkPP/wwSOODDz7Y smVLzGuDAAAAAACgUQ899FBOTk55eXm2E0mlSy+9tHPnzgcPHszwuq2ymAAA AAAAwCnqyJEjXbt2HTJkSLYTSbE33ngjJ+Mv62mtxQQAAAAAAE5RCxcuzMnJ KSsry3YiKVZXV9enT5+LLrqoqa9Mao7WWkwAAAAAAOBUVFdX16tXr6uvvjrb iaTF0qVLc3Jy3nrrrcws17qLCQAAAAAAnHKWLFmSm5u7bt26bCeSLgMHDiwu Ls7MWq2+mAAAAAAAwCkk8vy0m2++OduJpFFFRUVeXl5paWm6F/o+FBMAAAAA ADiF1NTUlJWVVVVVZTuR9Fq/fv3mzZvTvcr3pJgAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADfW/8PwetyNg== "], {{0, 305.09415236207974`}, {666.5872237448782, 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->{240.00459999999998`, 240.00459999999998`}, SmoothingQuality->"High"], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSize->Automatic, ImageSizeRaw->{666.5872237448782, 305.09415236207974`}, PlotRange->{{0, 666.5872237448782}, {0, 305.09415236207974`}}]], "Input",Exp\ ressionUUID->"ec6246f3-aed7-455d-9d11-d9eba957b6d2"], Cell[BoxData[ RowBox[{ RowBox[{"c", "[", "t_", "]"}], ":=", RowBox[{"{", RowBox[{ RowBox[{ RowBox[{"Cos", "[", "t", "]"}], "-", RowBox[{"Sin", "[", "t", "]"}]}], ",", RowBox[{"Cos", "[", "t", "]"}], ",", RowBox[{"3", "t"}]}], "}"}]}]], "Input", CellChangeTimes->{{3.911354909385503*^9, 3.911354925719734*^9}}, CellLabel-> "In[133]:=",ExpressionUUID->"33511201-d594-4862-913d-95bce2d03185"], Cell["First we define our own version of the function norm", "Text", CellChangeTimes->{{3.911355082712869*^9, 3.9113550952494955`*^9}, { 3.9113804329714937`*^9, 3.911380433049288*^9}},ExpressionUUID->"9fa58fe8-f85e-4036-a110-\ da093dd6ca0d"], Cell[BoxData[ RowBox[{ RowBox[{"norm", "[", "u_", "]"}], ":=", SqrtBox[ RowBox[{"u", ".", "u"}]]}]], "Input", CellChangeTimes->{{3.911355097468933*^9, 3.91135510451937*^9}}, CellLabel-> "In[134]:=",ExpressionUUID->"75569eb6-d6ea-488b-948c-74cf9d17e9b5"], Cell[TextData[{ "We use the formula ", Cell[BoxData[ FormBox[ RowBox[{ RowBox[{"K", "(", "t", ")"}], "=", FractionBox[ RowBox[{"||", RowBox[{ RowBox[{"c", "'"}], RowBox[{ RowBox[{"(", "t", ")"}], "\[Cross]", RowBox[{"c", "''"}]}], RowBox[{"(", "t", ")"}]}], "||"}], RowBox[{"||", RowBox[{ RowBox[{"c", "'"}], RowBox[{"(", "t", ")"}]}], SuperscriptBox["||", "3"]}]]}], TraditionalForm]], FormatType->TraditionalForm,ExpressionUUID-> "d9e7f6ec-b8d1-4011-ab05-e908e856967a"] }], "Text", CellChangeTimes->{{3.9113550178597*^9, 3.911355071003481*^9}, { 3.9113804636913867`*^9, 3.9113804792285004`*^9}},ExpressionUUID->"c154f180-a5ec-4f71-9270-\ 6101c63f56f3"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ FractionBox[ RowBox[{"norm", "[", RowBox[{ RowBox[{ RowBox[{"c", "'"}], "[", "1", "]"}], "\[Cross]", RowBox[{ RowBox[{"c", "''"}], "[", "1", "]"}]}], "]"}], SuperscriptBox[ RowBox[{"norm", "[", RowBox[{ RowBox[{"c", "'"}], "[", "1", "]"}], "]"}], "3"]], "//", "N"}]], "Input", CellChangeTimes->{{3.9113551078312635`*^9, 3.9113551682632165`*^9}}, CellLabel-> "In[137]:=",ExpressionUUID->"bf7c240b-cfa6-4b82-a350-76267fcd8cf0"], Cell[BoxData["0.05323640846949796`"], "Output", CellChangeTimes->{{3.9113551396595373`*^9, 3.911355168798718*^9}}, CellLabel-> "Out[137]=",ExpressionUUID->"072afbba-4f2a-4f9b-b561-982fc6809788"] }, Open ]], Cell[TextData[{ "In the case the ask for the torsion we use ", Cell[BoxData[ FormBox[ RowBox[{ RowBox[{"\[Tau]", "(", "t", ")"}], "=", FractionBox[ RowBox[{ RowBox[{"<", RowBox[{ RowBox[{"c", "'"}], RowBox[{"(", "t", ")"}]}]}], ",", RowBox[{ RowBox[{"c", "''"}], RowBox[{"(", "t", ")"}]}], ",", RowBox[{ RowBox[{ RowBox[{"c", "'''"}], RowBox[{"(", "t", ")"}]}], ">"}]}], RowBox[{"||", RowBox[{ RowBox[{"c", "'"}], RowBox[{ RowBox[{"(", "t", ")"}], "\[Cross]", RowBox[{"c", "''"}]}], RowBox[{"(", "t", ")"}]}], SuperscriptBox["||", "2"]}]]}], TraditionalForm]], FormatType->TraditionalForm,ExpressionUUID-> "a662c889-63a9-4214-affc-1c25de53b23e"], " " }], "Text", CellChangeTimes->{{3.9113551827038403`*^9, 3.9113552501420107`*^9}},ExpressionUUID->"233f750f-5136-4c31-a258-\ 22a9992bf00b"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ FractionBox[ RowBox[{"Det", "[", RowBox[{"{", RowBox[{ RowBox[{ RowBox[{"c", "'"}], "[", "1", "]"}], ",", RowBox[{ RowBox[{"c", "''"}], "[", "1", "]"}], ",", RowBox[{ RowBox[{"c", "'''"}], "[", "1", "]"}]}], "}"}], "]"}], SuperscriptBox[ RowBox[{"norm", "[", RowBox[{ RowBox[{ RowBox[{"c", "'"}], "[", "1", "]"}], "\[Cross]", RowBox[{ RowBox[{"c", "''"}], "[", "1", "]"}]}], "]"}], "2"]], "//", "N"}]], "Input", CellChangeTimes->{{3.9113552551848917`*^9, 3.911355303072242*^9}}, CellLabel-> "In[140]:=",ExpressionUUID->"f40411c6-47bc-49ce-bb1d-8e79e27b23a3"], Cell[BoxData["0.6751187947983315`"], "Output", CellChangeTimes->{{3.911355292462956*^9, 3.911355303373683*^9}}, CellLabel-> "Out[140]=",ExpressionUUID->"a39a5e40-0e2c-442d-9505-d6ae64a81e61"] }, Open ]], Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzs3QmcTfX/+PGxk9C0UIn01TfZpaRFCVEiWVOWJGsLaSFaKGSrVN9I1BdJ ZIkkVHZqFvvM2BkGM8ZgZowx+3L/n5zf9/M/zr33zLnnnLvMeD0fHj1y7+fz Oe/zuedzzvm8neWOl17vPLB4UFDQW2XFfzr3fafFm2/2HdXlOvGXbkPfennQ 0AH92w59e8CgAW8+8FIJ8eFsUTa5dFDQP//vAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAU NtkXE4//PDlycreIyV2PL5mQdSHB3xEBAAAAAAAAgFZ2auLO91uGvFpb/tk+ 6pGs5DP+jgsAAAAAAADwouyUcydXTbPlT/qZaH+vzdXi5G//UWcylT/Hl3zs 77gCS25mWtaFBLGF52Vn+DsWAAAAAAAA2ODSqQPOaTFzfxIj1vl7ba4WB78d 6tz/+/7T199x+d+lk/tOrJga9enz4W/dd8WVqyMf3j9twOn1c7Mvnvd3jAAA AAAAADCJZGZhdGzxeOf+PzJvlL/j8qfU4xF7v3ihwK00dGj9Y4vG5lxK9ne8 AAAAAAAA8BjJzMIo7fSRsGH3aHJ0qTFR/o7LP/Jzs4//PDnktbrGt9Ud7z12 1XYXAAAAAABA4UUys5C6GL0rcnI3pef3TOh44VCYvyPyj6zkM5FTnjWxuYa9 cU/q8Qh/hw8AAAAAAAAPOCczk6I25qRfNPEnPzfH32tz1cnNTMvNuOTvKPwm PT5a81b3f/68VmfvF31i/5h5fueaxD1rE/5ecnzJhN1jn3LOZ24f2Sz7YqK/ VwIAAAAAAABGOSczUw6H+zsooGAZZ09sH/mwZus98PWg9DPRLssnRW7YPuoR 7YNG547wcdgAAAAAAAAwjWQmCqmzocuufGpogzNbF+lXyTh3Upv/fK1OxtkY 3wQMAAAAAAAAi0hmovA6s2WhstGGv9XkYvROI1WSIjdoNvgTK6Z6O04AAAAA AADYgmQmCrUzWxaGv31/WuxB41Uip3RTb/AREzp6LzwAAAAAAADYqKgmM/Nz s9PPRIt1EX9ST0TlXEr20oJy0lJSYyJTDm9Ljz+al5XhafXcjEtpsQf/L86Y SO/F6WLR6RdFF12M3vXPoo9HZKecs7Fxn/W/kJOa5FH5+E3zr7zTvG5eTpaX YgMAAAAAAICNvJfMzEw6fXLVNPWfxIh1BuumHN2hqZt98byRirmZaWe2/rT3 iz6hQ+tr1mvnB62iF36YGhNlfBXOhi6TAYj/13ybGLF+79ReIa/VVT22sf6p NTOMtHzx6M7onz5y+YrtnaMfP77k4/Qzx4zH5lHfpp+Jjlk2ZffYdi7f7n14 zttJURsd+XkGW9Owt/+95NKp/ZrYMhKO+zsoAAAAAAAAFMyLV2bm5+/9vLe6 5bBh92QmxhVcLzd75+jW6or7pw0ouFZe7un1c7eNeNA5R6d94fWMwZlJp42s wd4vXpC19ox/Wn6em37x4KwhLhuPWzdbv83kfVsjJnUpMMjQIfWOLRqXk3bB SGz/vJV73qgCVycnLeXID++GvFanwKXv+vCJs9t+9Sil6Y3+95Ls1ERNSJdO 7fdjPAAAAAAAADDIq7eZp5+JDh3a4IpE1jevFFjr9Po5V6RAX2+Yce6kfpWs 5DNRnz5XYBpN/tn29v0ph7cVGIk6Ybh91CPKh9mpiXs+fsZdyxePun0NTW5m 2pG5I4wH+c9C33k4ed+WAmMzkszMOHtCkyIu8E/UZz0L7HmFl/rfS/Ky0rXJ zBN7/RUMAAAAAAAAjPP2MzNPrZ6uaT8papNO+Zy0lG3Dm6rLn1w1TX8RGWdP 7HjvMY/SdCGXLxO9GL1Lv2V1wjB0aH3xSV52hs51laFD6okCLpvKunBWJwWq 9+e1uulnovVjKzCZmZN+cdeYNu5iDn+ridt85qfP63eRV/vfS7IuJGiCEavg l0gAAAAAAADgEW8nM/Nzs3ePu+LxjDtHP+4u4yfELJuiLrxrTJu87Eyd9rMu JDhn0na83+Lkb1/9896ZtAt5OVlZyWfO7/7j8Oy3QofUu6LYe4/p3MftcEoY 5qZfPPrjB1dm5Brtn9ZffHjov29GTOoS+Ul3l+3kpKW4fEZl2LB7Ds8dnhC6 LOXI9ksn9iZFbTy58sudH7RSlzm+dKKR2PSTmdE/faRZ9J7xT58NXyF6Tykg eik1JkosffvIZv8/vDfuSTt9RKdZb/e/lyTv/0uTzs3Py/V9GAAAAAAAAPCU D95mfjF6p+Y5jSdXfumyZOb5U5rb0pP3b9VpOT8v1/nu5pjln7rLf148tmfH u83VhaMXfqjTviZheOavReoM2IlfP89Jv6gu7/ql2Pn5+6cNcM5kRv/0kcvX cOfn5sSt/W/YG43/uTByak/xVyOx6SQzsy8mal7Hs3/6AHddlJuZFvv7zLBh 94hizu880obq5f73EhGhOobIKc/6PgYAAAAAAACY4INkphC9cMwV18INbZBx Nsa52OE5b6uLHfrudf1mNU/XNJJ/y0g4rr6rOnRIPZ2X0WgShkqKL+TyoyyN P2XxzNafNEGGDq1/fuca/VppcYejPuuZdeGswdh0kpkJfy9RlxSrn3MpWX/p 4teJ/fNb/TIO7/e/V+Tn7Rz9+BWp9YKeYwAAAAAAAIAA4ZzM3D9twJF5o4z/ 0XnljZSbfnHHu49qlqIpkxoTdUXO7c17s5LP6LSZcyk5/O37r8hK/faVkVU+ s3WRutaJXz93V1KTMPy/lOYbjdPjjxpZkOPywyq3DX9Am/ELX2Gwug7jycxj i8ZeUXLuCOtLd/ik/70hcfefmp/DZV4dAAAAAAAAAcg5menpnwIvxlM4J5GS 9m5WF9j35Yvqb+PWzdZvUPNqoV0fPunujmyNvJysbSMe/P8Vx7RxV9JlMvNs 2C9GlqKI/X2mprpduUTjycxD/31TXTJm2RRbAvBB/9svP2/PhI76SXUAAAAA AAAELJ8lM4UD37yirrh7bDv54pWkqE3qr/Z8/EwB72TJz9M8ffH8ztXG11rz QpzM87EuizknM6M+6+HIzze6GBHklS/HCRvWSOfOcY8YT2Yenf/elem7/jYs 3if9b7szfy3W/KAph7f5ZtEAAAAAAACwzpfJzKzkM+Fv3aeu+3+XX+bn7fn4 GfXnBd66nnJ0h7r8tuFNDV4WqDgb9ssVibhdv7ss5pzMvHAozPhSNEGKP9EL xhivrs94MjNu7XeaMJL3bbG4dN/0v72yUxPVV4SKPwe+HuSD5QIAAAAAAMAu vkxmCvGb5l9xpeIbjbOS4kUL6g+P/vBuge2c/O0/BlN5rtc69qC6+qnV010W 0yQMIyZ18WgpmiD/uQ7w6A6PWtBhPJmZcTZG8zb5sGH3JIQs9eASUye+6X97 Hfx2qHqhoUPrp8dH+2C5AAAAAAAAsItv3mb+/+XnRX36nHpx+6cP2PF+C/U1 fjmpSQU2s39af3Uj8ZsXeBRFVlL8FRdMLhzjspjxhKGRIMPfuq+Ae+c94VFs xxaNc85CR07pdn73Hx5dUSn5pv9tpHmle8g/Lx76wtsLBQAAAAAAgL18ncx0 ONLiDoUOqefuOs8zfy020sjO0a2vuLRv1bTEiHXG/5zbtlJd/fCct10uxWIy UxPk3i/6eFRdn0ex5WVnRk7p5rLDt49sFrNsiqfXKPqm/+1yKfZg2LB71Evc 83GHvJwsry4UAAAAAAAAtvN9MlM48evnLhNrkZ90d+TnGWlBJx1q4s/Bma+6 XIrFZKYmyKPz3/Oouj5PY8tJS9G8L17zJ2pqz7PhK/KyM40s3Tf9b4vs1MSd H7RSLy7sjXvS4g55b4kAAAAAAADwEr8kM3Mz08Jeb6hZbuiQepdO7TdSPT83 x8ZMmpeSmc5BHv95svHqBTITW35e3LrZ4W810emKbcObnlw1LSf9ol4zvup/ 6/Jzs/dO7aVZ3Nmw5V5aHAAAAAAAALzKL8nM0+vnOGe0wt9qkp1yzkj1nPSL 9ibTjv74gcsFWUlmOgd5YsVU49ULZDq2nNSkk7/9Z8e7j+qmNB84v3ON2xZ8 1f/WRS8YrV3W/Pe9tCwAAAAAAAB4m++TmZlJp8PeaOwyqXXo29cNNuJ8ad/x JRNM/0mK2uhyKRZvM9cEae9rbizGlp+Xmxix/sCMl0Neq+sux3h86UR31X3T /xadXDVNE2fklGcN3kcPAAAAAACAAOT7ZOaBb17RuUgvMWKdkUY06dCEkJ+9 EarFhGHYG1e8dObQf98InNikrKT4k6um7XjvMZc/R9za71zW8k3/WxG/ab5m XXZ+0Cr74nl/xwUAAAAAAADzfJzMTNz9p3pZB2YMjl4wRv3Jjncf1X9go2LP +KfVtU78+oU3orWYMNw9tp26esSkLoETm0Z+bnZC6LLtox7RbAyhQ+qln3Hx onPf9L9p57b/FvJaHXWE4W/fnx5/1N9xAQAAAAAAwBJfJjNzM1LVVwCGDm2Q cTYmN/2iJocWvWB0gU0d+m6Yusr+aQO8EbDFhOHBb4dekRgcWj83My1AYnMp J+2C8xvPXf4cvul/c5IiN4iu1vT8hYOh/o4LAAAAAAAAVvkymXls8fgrLuf7 5TPl8/M712hiuHAoTL+p0+vnqsuHDWtkY55QspgwjP3zW816nd/1R4DE5k5e VsaeCR2vuFb2vceci/mm/01wzmSGvFZX52VGAAAAAAAAKER8lsxMjYlSv2tm 24gHczNS5bf7/vOSOoZdHz6Rl52h01pGwnFN2Gf+Wmx7zBYThmmxBzVB2ngF o5eSmUJixHpN2HlZ6Zoyvul/T104FBY27B5tYFt/8ndcAAAAAAAAsIdvkpn5 ebkREzupl3J64zx1gfT46NAh9dQFYpZ/ot9mxOSu6vI7Rz9u+4uqrScMd499 StO9qTGRARKbOxnnTmpidvkUUx/0v0dcZjJPrf7ajyEBAAAAAADAXr5JZsat m31l4qt1fm62pkzMsinqMqFD6l06sVenzbPbftVEfvznSfaGbT1h6PxO7YiJ nfJysnwcm/5lrhraC0pfq+syYB/0v3EuM5nHf57sr3gAAAAAAADgDT5IZmYm xoW9cUWiKXHPWudiuZlpO95vcUXeb0LH/Lxcd82Kr/Z83MHpZudFNkZuPZmZ l52x493mmiD/aSc/r8C6WclnoheOcdcDxmPLTDq9491H4zf/6MjPNxJz7B+z 1C3vHvuUy2I+6H+DXGYyoxd+aHB9AQAAAAAAUFj4IJl5YMZgdfv7/tPXXcmk yA2aYGL/mKnTcsrRHerncP7fncWrpumkQBWiQPym+Xu/6KOfVLTlVu5z21dq IhR/Dn47NNfVvdv/iy//3Pbfto148J8e+P0bK7Hl5+ZEffqcUubA14Myzsbo R5t5/tS24U3VLccsm+KusLf734jU4xHhbzXRxHB4zvACYwAAAAAAAECh45zM 3D9twJF5o8z9cW7//K4/1I2HDqmXdvqITjyHvn1dXT7s9YbpZ47plD+1erpz qnDPx8+c3/W7y5ujs5LPxP4+U14CejZ0mU7jdj2X8vCct52D3D6y2en1c3Mu JatL5ufmJEVtivqs55U9EG06Nu3N+0PrH1s8PuPcSZeFUw5v2/lBK015d4UV Xu3/ArnMZO4a0+b8ztVn/loU+8fME79+cXzJhOgFY4xsvZdO7rMSDAAAAAAA ALzNOZlp5Y+m8dyM1B3vPqoucHzJx/rxZF1I0KSn9k7tpXe/cH5+9MIPXQYT 9kbjfV++GL1wzPGfJ4n/Hvr29d3j2mvK7Hi/hc5ra+xKZuZlZ4i1cN1pr9Xd 83GHA9+8cnDmq6JM+Fv3OZcRa2Euttz0i9tHPuxqoXUiJnYS/Ra39r/xmxec Xj/3+NKJERM7O5c8tXp6Aevmzf7Xd+nUfudMppU/iRHrzEUCAAAAAAAA3/Bq MvPYonHqb7e981BOWkqBIcVvXqBp9szWn/Qq5OefXPmluYBDhzZIObzNXcM2 vjE8LytDc7u9wT87R7cWv5Hp2LKSz+yfNsBc5xyeM9zQbeBe6399pzd8b+Om G0IyEwAAAAAAIOB5L5mZGhMZ8lod9bdG3w6Tnycf86j8CX/rvqzkM/qVLhwM 2fXhEx5E+1qdw3Pezjx/SqdNG5OZl9cr//T6OeFv3ms00Tek3rHF43Mz06zH dn7XH7s+autJ59Q9seJzjx476Y3+10cyEwAAAAAA4GrjpWRmfl5uxISO6q8i JnY2/raXtLhDoUPqqasf+OaVAmvl52Yn/L1kz5XLdf6zbXjT40snGkmj2ZzM vCzrwtmY5Z9uf8fV3d//+xP2RuOj89/PSDhuZ2z5eYm7/9w/rb/zK3s0GdRD 371+KfagiVWzvf/1kcwEAAAAAABAEZB+JvrMloVHf/xg339eipzybMSkLvu+ fPHwnLdPrf76wsHQ/Nwcfwf4T7I35fC22N+/OTxn+N4vXoiY2Dnqsx4Hvnkl ZvkniRHr8rLSvbfonNSk87v+EAs6OPO1vZ/3jpjQMerT5w/MePn4kgnntq8U 31pfROD3PwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAOAbcXFx4eHhGzZs+Ouvv6KiotLS0vwdEQAAAAAA sCTof15//XV/xwIANkhISHjvvfeqV68edKVixYrdcccd/o4OAAAAAOBCSkrK tm3bNmzYsHHjxh07diQmJvo7IgSoIpnMzMjI2Lt3r9j4lSuyjh07lpub6++g APhCWFjYDTfcEOSevwMEAAAAAPx/ycnJU6ZMadSoUbFixTTTt2rVqvXr12/L li3+jjFAxcbGTp8+vXv37rfeeuvx48f9HY4Z5lahKCUzxfb/+eefP/TQQyVL ltRs/2XKlGnRosWXX355/vx5f4cJuFAEdkGBID4+Pjg4WA78ypUrP//882Ln Nnjw4Oeee65x48bly5f3d4z2aNWqlVzN7777Tqek6JPrrrtOKXnTTTeZ2Af+ +eefixYtshBs4XD48GFxjMjKyvJ3IAAAAMBVZP78+fqXo3BRio7PP/9cdlEh zSSYW4WikczMz88Xq1+xYsUCt/9CvZooworALigQvPHGG7Ib+/btm56e7u+I vOXIkSNlypRR1jQ4OPjMmTPuSnbp0kX2iThP8HRBsbGxFSpUmD59urV4C4HU 1FRxEjVu3Dh/BwIAAABcLUaNGqVJ2pQvX75WrVoNGzasXr26+kJNf0caoIpA JuGqTWampaW1a9dOs/2Laf6//vUvsf3fdddd11xzTRFYTRRtRWAXFAiqVKmi 9GHt2rVzcnL8HY53jR8/Xm4z3bt3d1lm+fLlskybNm1MLEW0fPPNNxfhtLDa xIkTxbHD31EAAAAAV4VJkyap0zjt27ffunVrXl6eLHDp0qU1a9b06dOnbNmy fowzkBWBTMLVmczMzc0VM3T19t+1a9eNGzeq8xiiTFRUlJil1qpVq5CuJoq8 IrAL8rujR4/KPhw/fry/w/G67OzsOnXqyFVetWqVpkBycvKtt96qfFuuXLno 6GhPFxESEiLqio3TppADnXJxpr+jAAAAAIq+Xbt2FS9eXJmtiP/55ptvdArH x8f7LLDCpQhkEq7OZOaYMWPkKlSsWHHlypU6hfPz82NjY30WG2BcEdgF+Z2S eVMsXbrU3+H4wl9//SXvvKhevXpqaqr62wEDBsgOmTx5son2mzdvLvarmmaL tnHjxonTKn9HAQAAABRxjz32mJytfPzxxxZbS0tL271794YNG7Zu3XrgwIHs 7Gx3JXNycpIvu3TpkvJJVlZWaGjo5s2bL168qC558uRJ0eDevXtdtpP8P+pL SXNzc0V5UUs0eO7cOYsr5ZKYnSWrTJgwQXZjREREsitG7rMT3aJErnSgV1+i bcsquExmKm8DF2sRHh4uNgnjIfly9YXo6OhSpUop8ZcoUWLTpk0WGzx79uzO nTtF/Bs3btyzZ4/ctn1JrJTodiUGMaeOiYnxqBstVrdFYmJiZGSkEoMI5vTp 095bltwRXbhwQf252J/IbVizR/Iej7YfL+2CPGJ77/l4D6Ahul324fLly020 YPwIqAiEzW/QoEFyrYcNGyY/V/dGw4YNTdx0r7RQSP+dy7Tz58+3b9/e31EA AAAARZmYL8vZSt26da3MHNesWdOmTRuZGlJce+21zzzzzPr1653Ly4nSE088 If4qpmzVq1dXPqlUqZKYxDku5yQHDx4sW2vZsqXztE5+K9ZF/DUhIUFMna6/ /np1GK1bt96/f7/LsF3m4oyUad68eZCH9Od0J06cGDhwoFh3dRXx1+eee85L l3nYsgryqzFjxjguZ2PET6Z+yGTp0qV79epVYD7K96vvuHIW/+6775prREQ+ derUdu3aaba6oMuXOj/wwANz587Nz893V93I5mGkTFRUVN++fStXruz8q4lR eeedd+qnRCxWt0VKSsqUKVPq16/vHENwcHCnTp2cq1jvPbkjEjsr5ZP09PSx Y8fecsst6h4Q2+GpU6fUFdXJwzlz5uiv2uOPPy43aU163/T2Y+/4NdeBpnvP mV/2ABpWkpmeHgE1S7TegaYlJyfLJ4WKTW7btm2Oy/8gJUa95kNPtWjRolix YocPHy6wpMXt0DS7RrFzqMoJCQAAAABvePvtt+WZ/IwZM8w1cuHChY4dO7qf QP+jd+/eWVlZ6lpyEtesWbO4uDjNRP7222/Py8t78803Ne0MHjxYs3T5lZgw bt269aabbnIZgJgq/v33387Bm55D2ZvM/OGHH8qXL++uopgPiumt0d/DMHuT IePHjw8PD7/55ptdVqxevbpOPtMvq5+ZmSm2CmURZcuWTUxMNNeOkW5s1aqV u6vsdPrWeBnR+SVKlNCPITk52V37FqvbYvXq1TKj4o5zLeu9p85fib/u37+/ Vq1aLpdetWpV9UMG4uPjS5YsqXzVokULnVUTteSdvK+88ormW9PbT0AlMz3t PQ2/7AGcmUtmmjsCOi/RYaEDLVq0aJFcUKNGjXJyct555x35yZAhQ0y0uWPH jqD//WNlgeSyfJzMtGsUa4iSvXr1sitIAAAAABoNGzZUTs6LFy9uLlkhptj3 33+/nGWULVu2Q4cOr732Wv/+/Rs0aKCei4npnvr6IjmJEzH07dtX/I8o/8wz z8jyv/zyi5gRiMD69OkjL1OpWLGi5mY3WX7UqFHlypWTf61Zs2bt2rXVKRrR iPOTu0zPocT/N1cRi5PFmjZt2tyVr776ymX7c+bMkXXFxKp169aiAwcNGiQm leoOtP4QAA1bVkFW6dGjh8wNishfffXVl156SZ2j7tq1a0Ct/u+//y4b79mz p+l2ZE4pODi4ZcuWYssXHTt48GBN/OJzl9VNb4HSjBkzZAGxwTdr1kwsXZQc OHBgly5d7rnnHuVqMXcD3GJ1W8ydO1c+uVcoX778008/LTaDIUOGdOrU6Y47 7lA+d65ovffU2aTdu3ffeOONyv9Xr159wIABYjtUXyP37LPPqut27txZ+Vzs qWJiYtwFMHnyZNmC80WGprcfe8ev9WSmp70n+WsPsGbNmtevJDZ4ubj27du/ 7sqyZcvUjZg+AtrYgbZo27atXNALL7wgD5233XabuauylcO6OI4bKWxxO7TC llGsITqzdOnS58+ftzFOAAAAAIrMzEw5YalTp465Rvr06aOeamkub1u7dq2c nQVdefGnnMSVL19ehHHzzTcnJSXl5eXJO5SVSZzyStkpU6bIRjQ3jAc5efLJ Jw8cOKB8e/z48XvvvVd+NW7cOE38ds2hTL99Q4RapkwZpWK9evUOHjyo/lb0 krzzV/TSoUOHjLfsKYsvAFKUKlVq9uzZ8tvTp09Xq1ZN+ap48eLOL5Dy4+p/ 9NFHMuxvv/3WdDtdunQZNmxYSEiI+qmtipUrV8oEu5gpu7xR1OIWmJ2dLTPG d999t8vXDYsyYvbt8pF3FqvbIiwsTP2PDiNHjnROnohR7/LyMOvjV51Nuu66 65QNVWwbcn3FfkleLCe+SkhIkHVXrVol6zrvWySxYStlGjdu7Pytxe1H8t4L vHTKWOk9hR/3AOqXfxmn6QTTR0C5ghY70C4xMTHqx4NIK1asMNFaWlpahQoV goODXV6P6szidmiFLaNYY+HChaLk9OnTbYwTAAAAgEJMG+U5fPfu3U20sGvX LtlC+/btXT5yc+/evaVLl1bK3HrrrbKMehInfPfdd8rn6ifmVatWTXljxfLl y+WHoqK6fc3M67333tNc/RIXFydmVcq3NWvW1IRn1xzKdDKzW7duSi0xZ3c5 URWdLO+Dc77L3kbWk5mlSpUSk3dNgZkzZ8oCixcv1nzrx9Xv2bOnDEzzfDOx Cbl8f4qJV6h88sknzhu5msUtMDw8XH77ww8/eBSb9erWia5WX8A2b948j6pb H7+aHZHY2BYtWqQp8/3338sCP//8s/w8Ly9PPun3zjvvdLn03bt3y7omkhsF bj+S35OZnvaewo97AOvJTCtHQIX1DrSRemNTdO7c2VxTyiF7wIABBsvrbGMe lTHBG6M4LS1NHA1btWplY5wAAAAAFJs3b5bn54MGDTLRQv/+/eUUTOf+rGHD hskFyVSkehJXtWpVef1G48aN5efypkj1HcG//vqrunH1zGv48OEuA3j11Vdl maNHj7qs7pdkZmxsrLwmbdq0ae6Kyfn+LbfcYrBlE6wnM+fPn+9c4PDhw7LA pEmT1F/5d/VbtmwpA4uLi1N/lZycHOSGp/NoMS5kXZfP/bO4BW7dulV+a+Kx txarW7dmzRoZwEsvveRpdYu953DKJrnshOjoaFlAc7Oz+E3lVy6fyiufS1yu XDnNG6uNKHD7kQIhmelp7/l3DyB6aeOV1H04bty4ja4cOXJEtmDlCKiw2IH2 ysnJueeee+SyKlasqNkxGjd06FDRwtKlSw2Wt7gdWuSNUfzII4+IwhkZGfaG CgAAAEA9jTI3O7j99tuV6voP+Y+MjJQLUm4b1yxdneNSP0FOvjBU570M8vNW rVq5e2f0zz//LItpnnhm1xzKXCZh9uzZSpXixYvrPF9r1qxZsnGdKbNFFpMh L7zwgssCKSkpssyoUaPUX/l39dXvT9E8ENLGZGZ2drZ+XYtbYEJCgnza5A03 3LBu3TqPwrNY3boXX3xRWXqxYsVOnDjhaXXr41e9b3n11VddtqDeHsaMGaP+ 6uTJk7IDBw4cqKmYm5srn3noboDoK3D7kfyezDTRewG1A3R4/gIgK0dA5yWa 6EB7iQOoeq8oDsHODwYxqEmTJqIF429Vs7gdWuSNUTxixAhRPjw83GB5MWaD PGF7JwAAAACFhXoa9fLLL3taPS4uTlbXv1wkLy9PvhpG3s/ubtooJ1MNGzZ0 Gaq7ZKbOub36hvqpU6d6Wt1IGXOZBHlhj5gUu7uvWZg/f75sPCwszGDjnvJ9 MsS/q6+etp89e1b9VWpqqubNKZ5OIdXx69e1vgX27t07SKVZs2YzZsww/mw9 i9Utql27trJcI0+ic2a994zkr3JycuSFeceOHdN82759e6V6pUqVNBdi/fHH H7LxLVu2GF8v49uP5PdkponeC6gdoMHVkSweAY0vUX/zs5E6aazo1q2buaYq VqwoflPj5a0PZItsH8VKov777783WF5s52M8sWbNGs/WEAAAACgqQkND5Sm6 ibc5b9++XVb/6aef9AvffffdSsk2bdoon/gymalzZYtdcyhzmYSnnnoqyEMb Nmww2LinfJ8M8e/qP/nkk7JZ9a2jHq2CIjU1dd68eWLiX6tWrfLly7sL3kvJ zAsXLjz88MOaZRUvXrxly5bTp08v8OIoi9UtqlSpkrLEvn37mqhuvfc8vRjP 2a+//ipb0DzwsFevXsrnd911l7vrxh3Wth8pYJOZOgJqB+jwcHUsHgFNLNGr 4uLi5GBUM/g6cjXlanz9q1U1LG6H1lkfxRrr1q0LKujREAAAAABMOHHihDx7 v/feez2t7tEsrGHDhkrJRx99VL+6t5OZo0eP9rS6kTLmMgnqS/4M0jxyzUa+ T4b4d/XlVWHCb7/9pl9YZzXFzLdKlSpGgvdSMtNx+dqtKVOmqN+bLJUpU2bI kCH6OUmL1a0wsvoWq+uXsZ5NEr0n70Jt27at/Dw1NVVmJkX3uqtucfuRCmMy M6B2gA4PV8fiEdBEI17VsWNHGcljjz0m///WW29NSUnxqCnl1KJXr17Gq1jc Dq2zOIqd7dixI8j9c7wBAAAAmJaXl1euXDnlLL1s2bLZ2dkeVQ8JCZGTC+PX pbRv3175xJfJzPj4eFlMcz+gXXMoc5mENm3aKFWqVKli8OYyE88VNMj3yRD/ rr7mZR/6hd2two8//hikUq1atZ49ew4bNkwds34X2bUFOi4/X/GXX37p1q2b GM5BV6pRo0aBN6harG5OqVKllEWYeweZ9d6zJZv0/vvvKy2UKFFCPmZQ3hxd smRJd7ftW99+pMKYzAyoHaDDw9WxeAQ0sUTvWbp0qQyjZs2aGRkZnTp1kp94 OjaVxz8af5W5IwCSmQ4Lo9gl5QXoPNkSAAAA8IZHHnlEThA8vZvswIEDsq7+ FQu5ubllypRRSsqn6/symam+oX7evHmeVjdSxlwm4dlnn5XzR4NVvMf3yRD/ rr76LtECH9jochVSU1PljZnFixefMWOGy5sQ9bvIri1Q7dKlS0uXLu3UqZN8 q4Xw0EMPGalrvbpH5EWJ6quhjLPee7Zkk2JiYooVK6Y08sknnygfykxd586d XdayZfuRCmMyM6B2gA4PV8fiEdDEEr0kKSlJfW2w8hawU6dOVahQQX64efNm 4w2KukEevvHK+kC2ztwodke5MnPkyJFeiBQAAAC42k2cOFFOENq1a+dR3ezs bHlhp/55vjqXOGfOHOVDXyYz1dP8ffv2eVT99OnTni6iwAcwSuPHj1eqFC9e XMwoDdbyEnOrYGUS6t/Vz8vLq1atmoxNzD11CrtchQULFsjPX3nlFY/qGvzW YXgLdCkiIkK+cFnw9OpKi9WNeOihh5TGg4ODs7KyPK1uvffsyia1bt1aaaRB gwbKcmUqeNWqVS6r2LL9SF4av/odaLH3AmoH6PBwdSweAU0s0Uv69u0rY1Bn IL/66iv5+V133aV5LY6OS5cuBXl4RmF9INvCxCh2R3lmpv6boQAAAACYc+rU KXmbp7BixQqPqssna5UuXVrzPmi1AQMGKMWKFSsmlqh86LNkZn5+fqNGjZQy ztf/yAtmevTo4Vw3PT29SZMmRuZQc+fOlcU2bdrkrpjG2rVrZa3PP//cYC0v MbcKRjrHXRm/r/7YsWNlAPfdd59OMs3lKshUjLB06VKXFZOSkvS7yK4t0B3l pboKEy9PsVi9QO+9955sX3PVtBHWe8+ubJL6Rt0DBw58/fXXyv/fdttteXl5 LqvYsv1I5savxQ602Ht+3wNoeLo6Vo6A5pZoOyXnprjxxhvPnTsnvxLbrfrX F0PVeLOiqXr16hkvb+9u8KefflKuNRW1tm/fbjwME6PYnTlz5oiKixcvNlg+ Pj6+uSe++uorj+IBAAAAipiBAwfKs/cKFSrov15BPkhKMWvWLFm3d+/eLqv8 /fffJUqUUMp07NhRfu6zZOa0adNkmalTp2q+rVWrlvJV1apVc3Nz1V+Jv3bt 2jVIRWcOtWHDBuNTLSk7O7ty5cpKreDg4EOHDhms6A3mVsFIFXdl/L76qamp 8qUPQocOHcQnLku6XIVx48bJz2fOnOlcKyoqqmbNmvpdZNcW6I4yp1aEh4f7 uHqBxI8ub+2sUqWKpxd/Wu89u7JJYmOW9+qOHTu2ZcuWyv9r3jimZsv2I5kb vxY70GLv+X0PoOHp6lg5Appbor3S0tL+9a9/yQC+//57TYGIiAgZfMmSJcVf Dbb8yCOPlC1bNicnx2B5G3eDCQkJpUuXloWvu+46zXmLDhOj2B3lCZx79uwx WJ5kJgAAAOCRpKQk9Z2kxYsXHzhwoGbOcvHixdWrV/fp00fMEdSfZ2Vl3XHH HbLuSy+9pHnt6YoVK2644Qbl2zJlyhw4cEB+ZXsyU8x30tPTNeFNnDhR3ibm 8ka5fv36yRY+/PBD+fm5c+fatWsXdCWdOZSYFV5zzTVKMTH7W7hwoXMZlw/E U98cKvpKVNRM5aTjx4/rXPxjnblVMNI5OmX8vvpr1qxRPxlSjIWZM2dqXt69 f/9+l6uwZMkS+XndunXVtcT/v/POO+o5tbsusrgFbtmyRayCGKEu127fvn1y hIrudb701GJ1W6jvcq1cufKCBQucL4ISu44hQ4Y417U+fm3MJg0fPlxpp2bN mspGVaxYsZiYGHflbdl+JHPj12IHWu89v+8B1DxdHStHQHNLtNebb74pl96q VSuXRyi5VQddvnzd3a+jIbZeUX7btm0GI7HrQCxs2rRJU378+PEGw3B4Pord adOmTcWKFQ12FwAAAAAToqKi5OUxUqVKlWrVqtWwYcPq1avLS6cETd2QkBD5 3LCgy9d2duzYcejQoWJuUqdOHfm5mBRo3vdqezJTEJG0bNny5ZdfFpOdZ599 Vr1SYnXEajqvu5htqVt48MEHRfC9e/cW0xDlkxYtWsgb3PTnUG+88Ya6qfr1 6w8aNEhU6d+//9NPPy3mvKKAcy0x2XniiSfUFUXYnTp1eu2110Rd8V+xIk2b Nr3uuuuc1912JlZBFjaXzAyE1Z87d646nxl0efZapUqVepfJV7Q4r0JqaqrM VARdTsW8+OKLIua2bdvKNFSpUqVuvPFGnS6yuAUqb7sWAf/73/8Wy1V+ryFD hvTo0UP0m3rkzp4923npFqvb4uLFiw0aNFB3wq233tqtWzcRhuiKrl273nnn ncrnznWtj18bs0lHjhwJulLr1q11ytuy/aiZGL8WO9B67wXCHkAysTqmj4Cm l2iX7du3y/1e2bJl3T1nNS0tTZ2w/eyzz4w0/vvvv4vCkydPNhiMjQfiuLi4 kiVLqlt76qmnDIbh8HwUu5SdnX3ttddq3lwPAAAAwHanTp16/PHHgwxwrrtp 0ybnXKhacHCw8zTNG8lMd8SkUvPeH7W33nrLXUUxj87IyGjVqpXy1wKvjHr0 0Ud1wnBXXVTs1auXkRXx9mzXxCoUuHYFlgmE1d+8ebPMmOkbO3asuuLKlSvV T53VqFatmmj5mWee0e8iK1ugko3UV6ZMmenTp7tctMXqdklKSnK+/sqZy7oW x6+92SR5X6qiwCfm2bL9SOZ2QVY60JbeC4Q9gMLc6pg7AlpZonXZ2dnqf0GY MGGCTuE1a9bIkuXKlTPyLIjMzMyKFSs2bdrUeEh2HYgdVz7AQRCDwngYDs9H sTOlx7z3D0AAAAAA1NauXdu9e3cx83KeTdStW3fo0KEhISEuKyYlJX3wwQfq C1EU1atXf/fdd9XvFJBsT2Zec8016gvJgi5f1NSsWbNZs2aJiZvOWufn58+Y MeO2225T17377rt/+OEHpYDxZEJubu60adOc+yHo8hU7kyZN0qm7ZcsWMWUT xZzrKj359NNPR0ZG6gdgnaerIL81ncxU+H31s7Ky5s+f/8QTT6ivs5IbkpiV v//++y4D2Llz51NPPSWfLKeoX7++6KtLly6JAp06ddJffStb4MqVK8VUXblu zZloZPjw4SdOnHC31har2+v333/v3Lmz8/6ndOnSDz744MSJE13Wsjh+7c0m /fjjj7K1G2+80ciN+da3HzUTuyArHWhj7/l9D+CwsDomjoAWl2jRxx9/LJdb r149/UOk8Pzzz8vyjz/+uJFFKHeOG38Krr0HYnWqtkOHDgZjUJgYxRo9evQo U6bMhQsXPK0IAAAAwDQxpzh+/Phff/218bKoqChlTm3E2bNnt2/fLmpt3brV NzkQOekQExwRp5jtiqVv2rRp7969xsNWHD58ePPmzX///bf1yBMTE7dt26Z0 4K5du4w3mJeXFxMTo/ShEBIScvToUedHffqA6VWwIhBWPycn59ChQ2ITEgGI jeHIkSNGnnsmNrYdO3aIKuHh4QkJCeYWbWULPH/+/L59+7Zs2aJ0nQgmKSnJ Z9VtJPY/0dHRYWFhSiTitzCYTLBx/PqeLduPmonxGwgdGAh7ACt8fwQMWGII i+PyiBEjPK1oy3Y4cuRIeW7wwQcfmG7HnNKlS7/wwgs+XigAAACAQkSdzPR3 LAAA4B9NmjS5/vrrNS/m8w31gwt27drl46WLhe7YscPHCwUAAABQiJDMBAAg 0CxZsiTIH4+OXLdunXwU7TPPPOPjpWdlZbVo0cLHCwUAAABQuJDMBAAg0OTl 5dWuXbtBgwb5+fneXlZGRsbZs2c3bNgwYMAA+QTaqlWrxsXFeXvRGrNnz964 caOPFwoAAACgcCGZCQBAAFq2bJk4Oq9YscLbCxozZozm7Uv16tU7evSot5er kZeXV7NmTR8vFAAAAEChQzITAIDA1KxZs6ZNm3p7KepkZo0aNb7++mu/vDdq 4cKFxYoV8/1yAQAAABQuJDMBAAhMkZGRJUqUWLNmjVeXsnHjxilTpsydO3f3 7t0+uKvdJeW2+kGDBvll6QAAAAAKkeb/89VXX/k7FgAAcIWdO3ceOnTI31F4 XVpa2saNG1NSUvwdCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQBGX nZ0dHh4+e/bs9evXW2/t1KlTK1assN5OkbRp06Zhw4a1bdv20Ucf7d69+3// +9+srKwCa0VERGzdutUH4QH6GN06zp07N336dDGuH3vssdatW7/11lv79u0r sBajG37BWHaHgYwiIz4+/ssvvxQj/cKFC/6OBQAAwE7Z2dkfffTRTTfdFHTZ iBEjrLfZp0+ftm3bWm+niImNjRXToiAn9erVO3nypH7dn3/+uVy5comJib4J FXCH0e3OrFmzKlWqpBndxYsX//TTT/UrMrrhF4xllxjIKEoiIyNLlCghtuFS pUr16NHjxIkT/o4IAADABmlpaS1atBAnOe3bt1+5cmVKSor1Ns+fP1+mTJnl y5dbb6qIadSokehqMUt65513Fi9ePGfOnAceeECZKD344IP5+fk6dbOzsytX rlzgZArwKka3O7/88osylps3bz5r1qxly5aNHDmybNmyyodr1qzRqcvohu8x ll1iIKPoycnJiYiIGDNmTIUKFSpWrBgWFubviAAAAKwaOnRosWLF5s2bZ2Ob kyZNuvnmm8W5k41tFg0bN25s1qzZ6dOn5SdZWVlKhlPYsmWLfvW33377X//6 V15enpfDBNxidLuTm5vbrl27WbNmqT9cvHixMrpbtmypX53RDR9jLLvEQEYR duLEiZo1a1atWvXixYv+jgUAAMC81NTUsmXL9unTx8Y28/Pz77zzztdff93G Nou2L774QpklTZ48Wb/k/v37RbG1a9f6JjBAg9HtKdFjwcHBYtiWK1dOvySj G77EWPYIAxlFRlhYmNhEZ86c6e9AAAAAzFNOaex9/v/WrVtFm9zDYtyyZcuU ZObo0aMLLNygQYOePXv6ICrAGaPbBDFmlQGu/xwJB6MbPsRY9hQDGUVGlSpV +vfv7+8oAAAAzNu4caM4Mxf/tbHNfv363XHHHTY2WOR99913yhTpyy+/LLDw xIkTr7nmGu4Pgl8wuk247bbbxOgODg4usCSjGz7DWPYUAxlFRsOGDZ955hl/ RwEAAGCe7cnMzMzMihUrcueaR7p06aIkM/fu3Vtg4WPHjomS9j7jFDCC0W1C VFSUMrq7du1aYOFCMbqPHj368MMPizhr1Kih/zIUBCzGsqeK3kDG1Yxkpi8F /Q+7XFtwEgIAUNiezPz1119Fgxs2bLCrwSJvy5YtxYoVE53WqlUrg1UaNGjQ oUMHr0alSElJ2bZtm/g1xRayY8eOxMREHywUAYvR7an8/PzWrVsrs5itW7ca qeKb0X3mzJldu3YpQzs8PPz06dMF3jkrKZMIxbXXXpuUlOTVUOENjGWPBOxA dnCYhikkM32JZKa9OAkBAChsT2b26tWrUqVKnr4dNTk5+d13373mmmuutsN9 bGxs1apVlXcKHDhwwGCtUaNGlS1bNjU11UtRiZ9jypQpjRo1UrKsatWqVevX r1+Bb10PEPHx8XOciA+9vdwlS5ZoFrp+/XpvL9QHGN2e+vDDD5W17tu3r8Eq Xh3doaGhL774ohjFQU7Kly9/3333jRkzpsBGNBX9tW37a3Q7isQAZyx7JNAG soPDtB2KwEA2rZAmM8U58/Tp07t3737rrbceP368sARAMtNefjwJ8fsWqOPP P/9ctGiRv6PwusOHD3/55ZdZWVn+DsQQT38UsXYTJkwQO+caNWpUqFDB97sO EwEUrl8ERY+9yczc3FwxP+rYsaPxKunp6eKE/Prrr1cfmK6Sw/2FCxfq1aun rPKPP/5ovOJff/0lqvz888/eiGr+/Pk33HCDc65DwxuLtp2yeWvY+4RYl26/ /XbNQgvjrEGD0e0p+SzcRo0apaWlGazlpdEdHx//1FNPFTiuxQy3wKbEGY4s X6xYMX+dz/trdDsK/wBnLHskoAaygsO0LQr7QLaikCYzP//8c/lj+eXQYy6A q22f6W1+PAnx+xboTmxsbIUKFaZPn+7vQLwuNTVVHP7GjRvn70AK5tGPsm3b thYtWrg7mvtg12E6gEL0i6BIsjeZqbwdddq0aUYK5+TkfPvtt8p1ib4fs34n 5kTyRglxcPSorpiKli9fftCgQbZHNWrUKM1vIRZUq1Ytcd5bvXp19RUgti/a G5TNe+XKlckqnl6MZEJKSop6ifXq1SuMswYNRrdHli1bVrx4cbG+d95559mz Z41X9MboPnPmTM2aNdW/QokSJcQnYlyLjVP8TPJaOyPJzDVr1lx77bXKJGLs 2LE2xukRf41uR+Ef4Ixl4wJqICs4TNulsA9kK0hm+jKAq2qf6QN+PAnx+xbo Tvfu3W+++eb09HR/B+ILEydOLFOmTED1v0sGf5T8/HyxGTvfZOGz0y3rARSW XwRFkr3JzPfee0+0VuDt0mLULFmy5K677lIPk7Jly149h/u0tDT5zx+jR482 0ULr1q1r1Khhb1STJk1S/yLt27cXc968vDxZ4NKlS+IUok+fPuLHsnfRXmL7 UxTMKaSzBg1Gt3ErVqwoVaqUWNmqVauePHnS0+q2j+4OHTrI/q9du/bSpUud T65EnL/++uvs2bONNJiYmCiGVXR0tI1BeipARrejEA5wxrJBgTaQHRymvanQ DWQrCunK+j2VRDIzQPjrJMTvW6BLISEhQZ5fGFN4KZcCdu/e3d+B6DH+o7z6 6qvqw3rTpk0/+eSTv/7668SJEykpKT4I1XoAheIXQVFl72nkAw88cP311xf4 Iom5c+eqR02lSpU+/vjj1atXXyWH+6ysrDZt2ihr+s4775hr5KOPPhLVjx07 ZldUu3btUq4/EcT/fPPNNzqFffM8K+sCZJZUSGcNGoxug/7880+ZADly5IiJ Fuwd3UePHpWdf/fdd1+8eNGWZv0uQEa3oxAOcMayEYE2kB0cpr2s0A1kKwrp yvo9lUQy8yrn9y3QpebNm1esWNF7j2gOQOPGjRM/gTgm+jsQtwz+KPPnz5db VJ06df7++2/fhGd7AIH/i6CosvE0MjMzs3Tp0k8++WSBJcXQFtMisVxR/s03 3zx//rzjyucmFeHDfVZW1tNPP20xkyn8/vvvooUFCxbYFdhjjz0m+1/MWC22 lpaWtnv37g0bNmzduvXAgQPZ2dmethAdHR0eHq68oVXsG2NiYnJzcz1tJEBm SYV01qDG6DZo/fr15cqVs5IAcdg9umfMmCE7X/y/uUaS3TPyGnRZWH0JmdhL 7NmzR4xxMdJNnIQHyOh2FLYBzlg2IgAHsiPwDtMOO47UDGS/8O/Knj17dufO ncpmI44Cly5dcldS7IXUh5sJEybIIRAREeHykGTvnba2BOByn5mRkbF3717l CGj8YbyKnJwcpa4yeE2cHnvE5RFcLFSJITQ09Ny5cx41aGLnY+UkxMoZiN+3 wAIpu9AifDh2SZyQiGN0+/bt/R2IawZ/lJSUlJtuuknZnO67774LFy74Jjxv BBDgvwiKMBtPI5ULqo28DFd45513evfuLc57NZEE+BRJfRSbM2eOfuHHH39c KSnmg/JEpXPnzsqH999//3JXIiMjjUQSFxdnY0eJA7pcr7p161o5L1qzZk2b Nm2UC1qka6+9Vpw2G3nhYFRUVN++fStXrhzkRLR55513enRdmaebt5Et0MRW WgSmSIxuDZejOywsTEmAFC9e/JNPPnE5wI1EYu/oFr+aXC/Tb7p0Ho+SOHU3 Xl2c/Iu/njp1qk+fPqVLl1aP7m7dup04ccJ4SAEyuh2FbYAzltUK0UAOnMO0 w9YjNQPZHeuHJB2+X1mxe586dWq7du007xFTBtoDDzwwd+5c56xU8+bNnbcx ffbul2wJQH6l7HjPnj07ePBg+ajqoMv/YNSrV6/Tp08XGI/oxoEDByr/zCSJ vz733HPeuyZKLkjshcRfExISxDpqfsfWrVvv37+/wKZM73x0OrzAkxBZ0sQZ iN+3wAK1aNGiWLFihw8fLrCkkQi9tBbe2Ju98sorQf/bJgONwR/ls88+U9a0 bNmy6hMtn7E3gED+RVCE2ZjMnDZtmmhq6dKlViLxy4HAuPj4+JIlSypBij2V TsnY2Fj5KF0xuuXnNh4Eb7jhhocfftjS+vzP22+/LQMwfe3WhQsXOnbsqL92 Yl6clZXlroXx48eXKFFCvwUjmRMpQGZJgTZFMoHRreZudKvThu4YDMbG0S3P VYR+/fqZa8TikJSFQ0ND//zzz+DgYJdN3XLLLcbzmQEyuh2FbYAzlqXCNZAD 5DDtsPtIzUB2x/ohSYfvV9ZIUqhVq1aaqzT9nkqyN5kpxk54ePjNN9/ssmL1 6tX185k//PBD+fLl3S3Xe2/DkYtYv3791q1b5aVcGtdee63OPaoWdz46tYwn M02cgfh9C9S3Y8cOscQnnnjCSGEjEXppLbyxNzt8+LAo2atXLxvjtIXxH6VO nTrKmg4dOtQHgXk7gID9RVC02ZjMHDJkiGgqKirKSiReOhBkZmZ+/vnntvyr pby0UgxYnX/FmDx5slwd9XJtPAiKKdL1119vdX0uE6e1ytKLFy/uUbZQEuef 999/v1yLsmXLdujQ4bXXXuvfv3+DBg3UKyhOZlzeEqK+GVZMlJo1azZ48GDR GwMHDuzSpcs999yj/EsuyUy/COTR7bBvgFsc3TbmQGwc3du2bVMH8PXXX5to pPmVbr/9dtmgR8nMN954Q16S0aRJk5dffrlPnz7qi0yMj5QAGd2OwjbAGctS 4RrIgXCYdnjhSM1A1mHxkKTDj8nM4ODgli1biq1O/Dpi42nUqJF62xOfq2uJ MupDT82aNWXJpk2bNnflq6++sjFsWwKQVXr06KG8hlsQK/7qq6++9NJL6ksc u3bt6i6SOXPmyGIlS5Zs3bq1GLyDBg3SdKD1B1A4k42PGjVKuWpdIXqjdu3a 6n/auOWWW1zesm1952PlJESWNHEG4vctUF/fvn1FJL/88ouRwjJy3yczHd7Z m7Vt27Z06dLKM3ACh8Ef5dixY3JNQ0JCfBObtwMIzF8ERZuNyUxxYBXn2Pr/ oF9gJF7ahYojXZBNL3pbtWqVjHPcuHHuitWrV08p07hxY+sLdal3796ifet7 DDF5lKciderUMdeIOB+Q3fLss88mJiaqv127du2NN94oCzhfVZKdnS1P5+6+ +26XLygUZcRxLScnx3hUATJLCsApkqcCeXQ77BvgRW90K1q1ahWk0qVLFyN3 JOlQZ3s8SmYqxGxuyZIl8tuTJ09Wq1ZN+Uqc5cbGxhqJIUBGt6OwDXDGslSI BnIgHKYd3jlSM5B1eO+Q5PuVFcedYcOGiSmz+rmFipUrV8oUmTgEnDp1yl0j fn/9isUXAClKlSo1e/Zs+e3p06flEVDsnF2+uuvAgQNlypRRyoif++DBg+pv xfCRz3wQO4pDhw5ZWMWC4xeefPJJEZLyreiHe++9V39Dtb7z0fDoJEQTvJUz EL9vgWppaWkVKlQIDg42eECXkfslmemNvdnChQtFyenTp9saqSXGf5QFCxYo a1qyZEnlsbFxcXG//PLLp59+Onr0aNFFYiDs2bPHyHPpzfFGAAH4i6DIszGZ +e9///uWW26xGImXdqE2JjPFaVj16tWVOO+8806XZXbv3i3XxXsjWjmUh4aG WmxHnBTJaLt3726iBTFzkS20b9/e5bO89u7dKx9Qc+utt2rKhIeHyxZ++OEH k2viJEBmSQE4RfJUII9uh30DvOiNboU4OVdfThB0+Yy9Q4cO4tzSeV5pPDyF p8lMMWl1Xq9Zs2bJAosXLzYSQ4CMbkdhG+CMZUXhGsiBcJh2eOdIzUDW4b1D UqCt7CeffCLX4rvvvnNXzO+pJOvJzFKlSq1du1ZTYObMmfpHwG7duinfVq5c OSEhwbmAGODyNt7Bgwd7uFoexC+89957mvxGXFxchQoVlG/FyYZzbNZ3Phqm k5kWz0D8vgWqLV++XIQxYMAAg+WN7BiNlDHHG3uztLQ0MaBatWplb6hWGP9R Ro4cqazpHXfcIfYAjRs3DnKlVq1app8L5PsAAvAXQZH322+/Bdk0Zb722mvv vfde09ULSzJTGDt2rAzV5fNh5LOtxEHTe+8mU+72WrZsmcV2Nm/eLFdn0KBB Jlro37+/Ul2cSuncOzBs2DC5IM3MZevWrfIr008DcxYgs6RAmzWYEMij22Hr AC9io1uKj49v06aN81nK7bff/umnn3r0Xi2HtWSmy5Oiw4cPywKTJk0yEkOA jG5HYRvgjGVF4RrIgXCYdnjnSM1A1uelQ5JY2YB67azYJuVq6jz40e+pJOvJ zPnz5zsX0D8CxsbGyguzp02b5m4pMuFp5Z+rCox/+PDhLsu8+uqrsszRo0fV X9my89Ewncy0eAbi9y1QbejQoe7WyCUjO0YjZUzzxt7skUceEYUzMjLsDtYk 4z9Kz549gwwbMWKE7aF6KYBA+0VQ5H3zzTdiE/XoHbIupaeni3batm1ruoVC lMw8efJk8eLFlVAHDhyo+TY3N1ecSCjfvvDCC7Ys0aUlS5YE2TGhsN7z8tk1 +o87joyMlAsaP368+quEhATZpTfccMO6detMhOEsQGZJgTlFMi7AR7fD1gFe xEa3xooVKx566CHns5RKlSp9+OGH4oc22I7pZKa7TktJSZFlRo0aZSSGABnd jkI1wBnLUuEayIFwmHZ450jNQNbnpUNSu3btateubWuklmRnZxv51fyeSrKY zDR3BJw9e7byldgSdJ5Zob680N43I8tmW7Vq5e6e059//lkW0/zzjS07Hw1z yUzrZyB+3wLVmjRpIsLQ3LOvw3s7T4O8sTcbMWKEKB8eHm6wvPjVgjzhaT8Y /1HkS9sVyoNkJ0+e/NNPPy1fvvzLL7/UFBCfexSJvwLw9BcBLGrdurW7i709 cuLECY92Ps4KUTJTaN++vRJqpUqVNP/68Mcff8gV2bJli11LdLZhwwaxiI8+ +shiO+qef/nllz2tHhcXJ6vrP3g8Ly9PPvnc+UY55dliUrNmzcQE0OXdNMYF yCwpMKdIxgX46HbYPcCL0uh2KSQk5Pnnn5e3dEl169bVXFDhjulkpo0jK0BG t6NQDXDGslS4BnKAHKYdXjhSM5AL5I1DkpKT2bFjh93BeiZZxciv5vdUksVk prmNVl7ZePvttye7N3/+fNlIWFiY6XU0F7/6URhTp06Vn9u481Ezl8y0vtPw +xaoVrFiRbFJGC/vvZ2ncbbvzZRU//fff2+wvNhaxnhizZo1Hq2g8R9FvhNN bPZiu3J5h5QY1PLd7nfddZe5B0P5OABPfxHANLFBjh8/PsimVL/yLx0vvfSS 6RYKVzLz119/ldEuWrRI/VWvXr3kqPfeY3uFnTt3Bl1+do3FdkJDQ+W69OzZ 09Pq27dvl9UL3JbuvvtupWSbNm00X124cOHhhx8OulLx4sVbtmw5ffp04//y qBYgs6SAnSIZFOCj22H3AC9Ko1vHuXPnJk+efNttt6lHnPirkbwEyUy1QjTA GctS4RrIAXKYdnjhSM1ALpA3Dkmpqak1atSoU6eOwReu2UIsdN68ed26datV q1b58uWD3CCZqfnqqaeectdX7mzYsMH0OpqLX52OFmcI8nMbdz5qJDOVq0n1 L3bVsLEfTLN9b7Zu3bog3WdT+JJHP4rMJYpDj06xfv36yR6z95+fvBRAQP0i KKoiIyNHjBgh9hWlS5f+4osvbGlzz549Fnd9dk2RxMnSHCfiHFs0+/zzzzt/ Ze5C6JycHHkxvPqWPbF0eYY2ZcoU02thhPKcZOuHG+VaHYWJZ6mpf7jly5fr FxY7TKXko48+6vyt6FXRaep3GkplypQZMmSIpxOlAJkl2ThFcrl567DlOv/A Gd0OnwzwojS6C5SZmTl+/Hj54oAgY6kSkplqAZsDccZYlosuXAM5cA7TDruP 1AzkAnnpkHT48OH69euLFl544QUxvuyM2JVFixZVqVLFeZtxRjJT85XMORhn y3tdPYpfncwcPXq0/NzenY9EMlM5KPTq1ct4FRv7wTTb92Y7duwIcv8oVx/z 6Ed58sknlfW96667dIpt2rRJ/ij2PnvKSwEE1C+CokrZFYvZ66xZs+xq0/oZ u11TJG8/DUN6//33lRZKlCgRHx+vfCjv8hDda/EW6QLZNUvKy8srV66cEnbZ smWzs7M9qh4SEiI70/i/uuo8eV4E8Msvv3Tr1k0Eo/mxatSocezYMeOxBcgs ycYpks82b7XAGd0OX/VAkRndBokRJ3usdOnSBU4NSGaqBWwOxBljWflQGcuF aCAH2mHaYd+RmoFshJcOSSdPnqxfv37Q5fuX7QzXyY8//qjeQqpVq9azZ89h w4apb+c08qv5PZXkl2SmfHlflSpVDN4ba/1VCJ7GLzZLWUx9O7k3dj4Okpn/ O4Aaf5W5IzCSmQ6792Y+PlvW59GPIt/Ydd111+kUO3funPxRPvzwQ5si9WIA AfWLoKg6c+bMwoULO3bsGHT5drPc3Fzrbe7du9fipmvXFCknJ+e4k4iICNHs Bx984PyVufuXHZffvSifI/HJJ58oH8pTjs6dO5teBYOU3YU4G7Te1COPPCI7 X0xPPKp74MABWVf/X9PEllamTBmlpPOTn51dunRp6dKlnTp1ks+LFh566CHj sQXILMnGKZLLzVuH6c1bLXBGt8NXA7wojW6D2rZtK3+jAh+3RTJTLZBzIBqM ZeVDZSwXroEcmIdph+UjNQPZCG8ckv7888+KFSvWrl172rRpO3futDXeK6Sm plaqVEkJVWwkM2bMcHkPqZFfze+pJL8kM5999lnlq5o1a5qO3Aoj8asfhTFv 3jz5uZd2PiQzT506FeThE7C9t/P0iL17M+U6wJEjR3ohUo959KMoL8pRJCUl uSumvua5wBdjecRLAQTUL4Ii74cffgiy6R0Txy//Y4TBU1+XCtczMxWtW7dW Am7QoIH46+nTp+XJ/KpVq+xdljPlzEH9aBrTJk6cKDu/Xbt2HtXNzs6WV4zo H4PUpzoe3dMkprfyZYiCHy/5ED+xia00kKdIRgT46HZ4Z4AXmdFtkPhd5G+0 fv16/cJFMplpbnQ7CtUAZyw7VGO5cA3kAD9MO8weqRnIBtl7SBLVK1SoINrM zMz0QrBXWLBggfxFXnnlFXfFjPxq6lTSkSNHvBOvHnMBWDwCKi84CLqcCtbJ OXiPpz/Nvn375Ode2vkEQjLTL1ugdOnSpSAPjwXe23l6ysa9mfKERv13S/mM Rz+Kese4evVqd8UiIyNlsf/+97/2BeutAALqF8HV4Omnnw4ODrZ+cWZCQkKQ qefSS4Uxmbl06VIZ84EDB77++mvl/2+77TZ73zjmkrK7mDBhgvWmTp06VapU KbkuK1as8Kj6Y489plQsXbr02bNn3RUbMGCAUqxYsWJiiR4tQnk5msL4g809 nSXJfxTu0aOH87fp6elNmjQxsZUG+BSpQAE+uh3eGeBFZnQbpL4yUz0Tcakw JjO9NLod1gb4Tz/9pDxHTix9+/bt5hoxjrEsx3KhG8iBf5h2mDpSM5ANsveQ NG7cuOLFi8fExHgjVA2ZixPEWrgsk5SUZORXmzt3riy2adMmr4XslrkALB4B 165dK7+1fSJjRIHx5+fnN2rUSCnjfPmoN3Y+/kpm+n0LVLvxxhvr1atnvLy9 O08rOz0b92Zz5swRFRcvXmywfHx8fHNPfPXVVx7FY/xHEbtf2Qn9+vVzV2zy 5MmymP4/EXr6i9gegMLTX0RDRN60adOgyze/jxw5Micnx1w7uHp89913YoM5 ePCgxXbEgaxkyZLqZ/l6qjAmM7Ozs+XzzMeOHduyZUvl/9XPvvYesdcK8vza CXcGDhwo+79ChQr6Mwv5kBPFrFmzZN3evXu7rPL333+XKFFCKdOxY0dPw1P2 jQrjr4HwdJZUq1YtZRFVq1bVZPjFX7t27RqkcvUkMwN8dDu8M8CLzOg28lLI rVu3yn8WFxt/gVUKYzLTS6PbYWGAJyQkiMmdXKg4c9PsWm3HWJZjudANZEfA H6Ydpo7UDGSD7D0ktWrVqnHjxrYH6dK4ceNk58ycOdO5QFRUVM2aNY38ahs2 bPDq/qdA5gIwUkWnjPjpK1eurHwbHBx86NAh8ytgSoHxT5s2TZaZOnWq5ltv 7Hz8lcz0+xao9sgjj5QtW9Z4tsfGnafFnZ6NezPlCZx79uwxWN7byUyPfpQH H3xQWfFSpUodPXrUuYDoZzn29R/eYu4XsTEAydNfRO306dPymSSKESNGmGgH V5U1a9aITUXMZK03dcstt9x///0GC6empiZfaeXKlXLTHTx4sOZbi7F5KZkp DB8+XIlZnIkp2YBixYr55h+7xQ5WLO7333+3pbWkpCT1DWJiXcS8KSIiQl3m 4sWLq1ev7tOnj9hhqj/Pysq64447ZN2XXnopJSVFXWDFihU33HCD8m2ZMmUO HDigWfqWLVvEpijadxnbvn37ZPuiHbE4gyvl6SypX79+ci3UTzk+d+5cu3bt gq509SQzHYE9uh1eG+BFY3R36tRJDMmFCxeKyJ2zlMePHxeTAnkXmGDkzK0w JjO9NLodFga4+h2RCnsfiOQSY1kZy4VuIDv8fZh2eOdIzUA2zsZDki9PS5Ys WSJ7pm7duuqH34r/f+edd9RzcP1fLS0t7ZprrlGKlShRQhzXnMsY+fc708wF YGTV9Muo724Wg0ss190tdeKYrnP1ozly0V27dk1PT1d/JYb5xIkT5T+G3nXX XRkZGZrqtux8NPyVzPT7Fqgmxo4IY9u2bQbL27jztL7Ts2tv1qZNm4oVK9ry +g9bePSjqF++KfaNmkvLDh06JC94FsTxV6cpc7+IjQFIVn6RqVOnatZCNGWi HVxVPD2N1PHAAw9Uq1bNYOHmzZsHecJibN5LZh45ckQTauvWrW1fiksjR44U i9u7d69dDUZFRcl/f5EqVapUq1Ytcd5bvXp1+cRm518kJCREnQ+pUKFCx44d hw4dKg6dderUkZ+LA5bLtxkqpyWi/X//+99t27YdNGiQOIwOGTKkR48eTZs2 VS939uzZxtfI081bHH3U6/7ggw+KVejdu7fYlyqftGjRQt6FcVUlMwN5dDu8 NsCLxuhW/xxizlijRo2GlzVo0CA4OFizgp06dXK+x0eM4oZXkv+kLtSrV0/9 lcsn/MvCfkxmeml0/z/27jw8iipd/HiHrCxpVgFBJAgPiASCDhjQQIyguLAp Yi77ADKiID7ABXm8ikwCeFGEB9nGEQGHCHFgWC5XcRgkgBAlCcsEvCwiIQMB CUgSQhLI+js3def8ajpJd3V3VXXT+X7+mGfsfuvUW3XqVLpequpUuDHAs7Ky AgIC1Fk999xzLrTjFMayXqlq4Ut/piuM+UvNQNZOxz9JZv4syc/Pl6UqS2Ut 7re//e3UqVPFISTLmIGBgc2aNdPSa9OnT1fvga5duyrH4SuvvDJo0KB27dqJ AEM3x4UEZLDLfwFLS0sHDBigXq84D4i/12I3injxvy+//LIYg40aNRJfbdu2 Td9NVq9XnEOefPLJ1157TaxXrFR9OhInInGCqrYFN08+bv4IcX//q3n8CJS+ +eYbkcCiRYs0xut48nT/pKfL2ay4uLhBgwYDBw50dkHjONspsbGx6uNf/OgS g2vKlCmiL+S9ykJ8fLz9dlzuEb0SULjZI3FxcTZHhUjJtaZQe+hYzBw7dqw4 5MRhrCXYZ4qZgrw9XuHyayKc9dJLL4nzjPbbFLW4ePFi//79XeuRffv2Vb3I UmvcuHFNP7HU/8Zak+Dg4JUrVzq1OS4c3jNnzqwpAfGzraioqF+/fsp/1qpi pjeP7gojB7gPjG6N3RESEjJ37txqH41xqkOrHRf2v9Ueo+Ylo7vCvQGufgBT 6Nu3r2vtaMdY1itVLXzpz3SFMX+pGchO0etPksk/S3bu3Kl+46uNNm3a7N+/ X+SjpdcKCgrE7rVzBBr98K8LCWjJzWGMWO/o0aPtrFcytJhZk4ceesj+27bd Ofm4+SPEzldOxSg8fgRKt2/ftlqtkZGR2hfR8eTp/knP/bOZ8nipU3e5GM3Z ThGHkzz1VUv8ONf4J9W1HtExgQq3e0SZCV1t0KBBrjWF2kPHYuaCBQtEUz// /LOWYJMvkcQVuthGF95mr8UXX3wh82zWrJm+ly12dO/evVOnTka0/Le//S02 NrbqXVuWyrvQp02blpycXO2CN27cePfdd9X/zKq4//7733777WvXrtW0RvFD V5x1lX9TrurBBx+cNWtWZmamsxviwuFdXl6+evXq++67zyaBDRs2KAEaf2+r +UAx05tHd4WRA9wHRvd///d/v/LKK6JBm0f5FOICs2fPnnFxcZcuXaqpBZ8p ZhoxuivcG+ClpaXdunWTyQwePNi1drRjLFsqx7Lu7VfLl/5MVxjzl5qB7BS9 /iSZ/7PkyJEjzz33nPpWH0vlXW3/+Z//eevWrYrKN6Jo7DWxt1esWFH1GLZU 3vInGjR6W5xNQH7r/l/AAwcOvPzyy2It1Y5BMYrFtX96ero+21klt3r16qlv wLZU/oSIior64x//qOUfyFw++XhVMbPCC45ASXlyXMu0LAodT57un/TcP5uN HDkyODg4NzfX2QUN5UKnJCYm2jzdYKn8V54ZM2ZcvnxZYzsu94heCVTo0SOr Vq0KCQlREujVq5fRb5KHD9CxmLljxw7RlLhwdr8pOFRWVla/fn3XXtGvkTi5 ZWRkHDx4MKnSiRMnlB+cWmRnZ6empoqlvvvuO6cuba5fv/7jjz+KX2vKStPS 0sSPH5fS/1/uHN5nz57dv3//oUOHXCii2vCBYiaj20wGjW4xosVvEnFFqQyu 5OTkU6dO3b0TBXrJ6K5we4ArTyIrxIWe+/nYx1g2ja/+ma7Q9S81A9kjPPWz RByf4mgR3X348OGrV6+62dqvv/6akpKiHIRHjx7V5TC4KxIQ55YLFy4o41f5 a37u3LmqL6vUi7rQJ3owPT1drHTfvn0nT57UfsJRc+fk41U8fgT+8MMPFpcm SdHl5OnZk54y5c3YsWNNXq9DLndKXl6e+CMujqXvv/9e/GV3YdVu9oibCejV I+LnhDi9HD9+3LTXz+KupmMxMysrSzT1/vvvu98UHDp16pTY2wsXLvR0Il5N x8PbHT5QzGR0m4nRrYWXjO4Ktwe4+slBcTWkY2LVYiybhoGsBQPZI3zgZwlM oy5mejoX2OrZs2eTJk1sJmYyh2dPevPmzRPrTUtLM3m9WniqU+gR1EL6/oxs 1arVyJEjdWkK9iUkJHjJ739v5iVXSb5x1cDoNg2jWwsvGd0V7g3wPXv2yFfJ mXaWYCybg4GsBQPZI3zjZwnMQTHTm23evNniifdGevakd+fOnRYtWsTExJi8 Xo080in0CGonfX9GDhs2rF27dro0BfumTZvm7+/v2vMdtYeXXCX5xlUDo9s0 jG4tvGR0Vzg/wIuKirKzs/fu3Ttp0iT5BrnWrVtnZWUZl6QaY9kcDGQtGMge 4Rs/S2AOipnerKysrHPnzt26dTPhmVzvOemtXbvWS/5wVMu0TqFHAH1/Rq5Y sUK0dlf8kLvbiR+ivXv39nQW3s5LrpJ846qB0W0aRrcWXjK6K5wf4FWnhA4P Dz937pxxGdpgLJuDgawFA9kjfONnCcxBMdPLbd26VfTOjh07jF6Rl5z0ysrK 2rdv/+STT5q8XqeY0yn0CKDvz8jTp0+L1jZt2qRLa6jJr7/+6ufnN3fuXE8n 4u285CrJN64aGN3mYHRr5CWju8K9GkhYWNiqVauMm7ihWoxlEzCQNWIge4Rv /CyBOShmer+oqKjIyEij1+IlJz3x60X8eU1NTTV/1U4xoVPoEUD3n5Ft2rTx wpnFfMxf/vIX0WsHDx70dCLeTjm8bZhw0dS2bVublfrGVQOj2wSMbo08Nbor 3B7gIskPPvhg/fr1x44d89RcjYxlozGQNWIgewTFTGhHMdP7paen+/v779q1 y9C1eMNJT3mC+9VXX/XI2p1iQqfQI4DuxcypU6c2a9ZMHNh6NYiqfvvb3zZu 3Li0tNTTiXi7K1eurKtCfGj0ejdv3myz0m+//dbolZqA0W0CRrdGnhrdFT4x wBnLRmMga8RA9giKmdAu+p+WL1/u6VxQoyNHjpw5c8bTWRiuoKAgKSkpLy/P 04loUhs65e7qEfge3YuZ4qegaDA5OVmvBmFDXH42b958woQJnk4EtQ6j22iM bpiDsWwoBjK8HMVMAABwt9O9mFlaWtqkSZMZM2bo1SBsiMtP0WX/9V//5elE UOswuo3G6IY5GMuGYiDDy1HMBAAAdzsjXr0+ZcqUe++9l+fXDCIuPxs1anT7 9m1PJ4LaiNFtKEY3TMNYNg4DGV4uPDz8xRdf9HQWAAAArjty5IjFYvniiy90 bDM1NdW097fXNuLCs3Xr1pMnT/Z0IqilGN3GYXTDTIxlgzCQ4eXKy8ubNm36 +uuvezoRAAAA1xUXFzdu3Pjpp5/Wt9kuXbpMnDhR3zYh7N+/X1x+fv/9955O BLUXo9sgjG6YjLFsBAYyvNyOHTvEIfqXv/zF04kAAAC4ZdGiReJXzfTp04uL i/Vq8+OPP27QoMHNmzf1ahCKCRMmhIeHezoL1GqMboMwumEyxrIRGMjwZn/7 29+sVqs4REtKSjydCwAAgFvKy8unTp1qsVjatWv3+9//fu/evdnZ2W62KS6O QkND16xZo0uGUOTn54sLz08++cTTiaBWY3QbgdEN8zGWdcdAhhe6c+dOamqq OCz79+8vfu136NDhp59+8nRSAAAA+vjrX//65JNP+vv7i985b775pvsNTps2 7dFHH3W/HUifffZZ48aNCwoKPJ0IajtGt+4Y3fAIxrK+GMjwQseOHbNUuv/+ ++Pj4zk+AQCA77l161ZaWtqZM2fcb0r8WMrIyHC/HUjXr1//5ZdfPJ0FwOjW H6MbHsFY1hcDGV4oNzf3wIED58+f93QiAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADg blVeXn7x4sXk5OS9e/ceOHDg5MmTxcXFnk4KAAAAAACgIicn5+23365Xr56l 0ptvvqllqbNnzy5btuzOnTtGp+dZu3fv/vLLL7XHi92ycOHCIUOGhIWFhYaG Wv5J4151n2sJ1JLeNIdFG689JJYsWfLUU09ZrVabhIOCgp544omEhITy8nLt a3fq9BIdHa1x70lacrh27VqzZs3US23btk37JgAAAAAAAC9RWFj4wQcfNGnS xIUaS35+ftOmTePj441O0oMuXboUGhq6cuVKLcEpKSkxMTEerFy5k0Bt6E3T aKzCee0h4TDzqKio7Oxsh2t34fRiUDFz9OjRNktRzAQAAAAA4O5SUlLy6aef tm7dWnuJo6r3338/ODg4IyPDyEw9KTY2tmXLloWFhfbDysvL4+Li/Pz87JRc DK1c6ZKAz/emaTRW4bz2kJABVqu1S5cuERERnTp1CggIUC8bFRVl5/5Ml08v RhQzd+3aVXUpipkAAAAAANwtysvLN2/e3LFjR/WlfUhIiMZqg5pyO19sbKyh CXtKcnKy2BtLly51GDllyhT1zoyMjPzwww8PHjyYmZmZl5dnQqq6JODbvWkm b6iYuXNIjBo1KiEhQUSqPxSHxx/+8Af1s+fVbp2Opxc7hgwZorTWvXt3+5Ei 7TZt2ijBnTp18oauAQAAAAAATlm/fr26ztCwYcMFCxZ8/fXXrlUb4uPjxSJH jx41LmFPiY6Otlqt+fn59sMSEhLkrnvooYcOHTpkTnpGJODDvWkmj1fMjDsm d+7cKVseN25c1QB9Ty/VSk9Pl60lJibaD37jjTeUyHvvvVd0h8e7BgAAAAAA OCs/P79hw4aWyuk8ZsyYcf36dfFhUlKSa9UGsXjdunUHDhxoWL6eoewQh7si Ly/vnnvuUfZbjx49cnNzzUnPoAR8tTdN5tmKmdHHZIsWLZTGY2Jiqn6r7+ml WrGxsUpT7du3LysrsxOZnJwsH7QXfaFOg2ImAAAAAAB3kbfeemvMmDEXLlyQ n7hTbXj99dfFUsePH9c7TQe0JOzyRsXExPj5+Z09e9Z+2EcffaS0HxISot6f ptE9AU/1pi/xbMXM6GMyIiLCTjGzQu/Ti40ff/xR1if/+Mc/2om8ffv2Qw89 pEQqL0+gmAkAAAAAgM9wp9pw9uxZPz+/0aNHG5RbTYwrZqalpYlFBgwY4DBS VkumTZumvX0d6Z6Ap3rTl3i2YmboMVleXi5v+5w8ebLGpXQsZo4YMUJp5957 771z546dyLlz5yqRzZo1U+Zep5gJAAAAAIDPcLPa8OyzzwYFBSmPlJrGuGLm +PHjxSLbt2+3H3b+/HnZfnJysvb29WJQAh7pTV/iwYqZ0cfkV199Jdvft2+f xqX0Kmb+9NNP/v7+SjuLFy+2E3nixInAwEAlcuPGjVXToJgJAAAAAMBdzc1q w6ZNm8SCK1euNCK3mhhUzCwoKAgNDW3cuLH9+76EjRs3Ko0HBAQUFxeLT7Ky srZv37548eK5c+fGx8evXr36+PHj5eXl2jfKKQYl4JHe9CXyqEtISMjJybl9 +7Zpqzb0mDx8+HCzZs2U9keOHKl9Qb2KmePGjVMaEcPTzsxcpaWlkZGRSuSQ IUOqTYNiJgAAAAAAdzU3qw0FBQWBgYH9+vUzIreaGFTMVKY8njRpksPIOXPm KI23a9fuk08+eeSRRyzV6dSp05YtW7RulTMMSsAjvelLqnaBv79/y5Yto6Ki Zs+ebehNvDoeEt999922f1q5cuXgwYPlXZHDhw93WOpX06WY+fPPP8sE3n33 XTuRS5cuVcIaNmx46dKlatOgmAkAAAAAwF3N/WpDnz596tatW1RUpHtuNdGS sAsbNW3aNBGvpfo3atSoaitF1Zo9e7bWDdPMuATM701f4rAvoqOjT548acSq dTwkRJJVFwkKClqxYoWzWelSzHz11VeVFurXr3/t2rWawjIyMkSAEvnZZ5/V lAbFTAAAAAAA7mruVxtmz54tlj18+LDuudVES8IubFTPnj1F/K+//uowsn// /uo6T0hIyODBgxctWpSYmLht27Zly5bZBIjPtW6bNsYlYHJvZmRk2Cu6VeHm JDJGi1bp3bt3RESEfDpbslqtRuxeHQ+JaouZlsoH2F988cUzZ85oz8r900tm ZqZ8B6b9Fp5++mkl7KmnnrJ5mp5iJgAAAAAAPsP9asPatWvFsp9//rnuudVE S8IubJTVam3btq2WSFntadCgwdKlS2/evFk1JiEhwc/PTwnr2LFjWVmZxjQ8 m4DJvZmTk/OeM3bt2mVOYjrKysr6+OOPW7VqJY/JNm3aFBYW6rsWHQ+JdevW KXt7+vTpEydOfPzxx2U5UahXr97OnTs1ZuX+6UXelilyyMzMrCls/fr1Slj9 +vUzMjLspEExEwAAAACAu5r71YY9e/aIZePi4nTPrSZGFDPz8vJE8IABA7QE y8JRRESEnbCJEyfKNNLS0rS0rJFxCZjfm7XEL7/80r59e9kda9as0bd9Q4/J 3Nzc+fPnBwUFKQvWrVv31KlTWhZ08/Ry+fLlkJAQZfHx48fXFHb16tUmTZoo YcuXL7efBsVMAAAAAADuau4XM9PS0sSys2bN0j23mhhRzMzMzBTBo0eP1hL8 zDPPKI137NjRTti+fftkGqtXr9bSskbGJWB+b3qD/Pz8dc5w7TnxxMRE2R1O zQmuhQnH5FdffSWXHTFihJZF3Dy9iEWUZf38/OyUT2NjY5WwqKioam83pZgJ AAAAAIDPcL+YeezYMZeXdY0RxUzl5Y1apjIXhg8frjTeqFEjO2HXrl2Tacyb N09LyxoZl4D5vekNzHl155UrV2QLffv21XcTzDkm+/XrpyxrtVq1xLtzelHf lvniiy/WFHbkyBG5isGDB79ZnWHDhsmYgQMHys/T09OdSgkAAAAAAHicXndm zpkzR/fcaqIlYWc36uLFiyJ47NixWoKVWXIUN27cqCksJydHhs2fP19LyxoZ l4D5vekNSkpKMpyhZZaoqrKzs40rZppzTL7xxhtycS3x7pxe1FuUmpqqZRXO 4i5NAAAAAADuOnq9M3PBggW651YThwlfvnzZ2Y26deuWCH7++ee1BG/cuFG2 //XXX9cUlp6eLsM+++wzLS1rZFwC5vdm7bF//37ZHePGjdO3cXOOydGjRyvL Nm/eXEu8y6eXa9eu1a9fX1mwf//+GlfhLIqZAAAAAADcddwvZq5bt04s++c/ /1n33GoSHBysJFztiwcLCwt79uzpwkY1a9YsPDxcS+SFCxdk+xMnTqwpbNGi RTLs/Pnz9ttMTExs0aKFiBTJ27kPzbgEFCb35pUrV6KdUe30LncL+SS48MUX XziMF4dBZGSkpfLJ8Tlz5pSUlNgJNu6QkAoKCu655x5l2aFDh2pZxOXTy1tv vSUX/Pbbb+1EZmRkVDvxvdq4ceNka7GxsfJzjdMYAQAAAAAA7+F+MfOdd94R yx4/flz33GrSqVMnJeHWrVuXlpaqvxL/+dJLL6lvvtK+UX369AkJCbFfMpJ6 9+6ttB8YGHju3LmqAVevXm3evLkS89hjj9lvTQTLqaKV4tWVK1fMTEAyuTdr TzFTXUUMCwu7c+eO/fjLly83bNhQfSTPnj3b/iIGHRIKMbJGjRolk9m6dauW pVw7veTk5ISGhipLPfroo07l6TAN7sYEAAAAAOAukp+fn/Ovdu7cKS/zJ0+e bPOtljaffvppq9VqU1Q01MSJE2XO6klMrl279vzzz1v+lfYSinIzWEpKipbg 7du3y1V06dLl9OnT6m/PnDnTvXt3GXDgwAH7rannmFY4fJ+hvglI5vemz8jM zLx69arNh7dv3969e/eAAQNkX9SpUycpKclha0uWLLE5JBzOuePmIXHixIlL ly5VbTY7O/vzzz/v2rWrXLZ///7l5eVVI/U6vbz33ntyKY1VU/soZgIAAAAA cJeKjo62OMNhg8XFxQ0aNBg4cKAJyUspKSnqJHv37j1t2rQxY8ZYrVblk5iY GPmkufZi5jfffCPiFy1apDE+NjZWXaES+/a1116bMmWKWLu/v7/8Kj4+3mFT WVlZAQEB6o167rnnzExA4ZHe9BlKCS40NLRt27Zdu3aNiIjo2LGj+oZbS+U9 k5s2bdLSWlxcnM1gFF3scCl3Dgnl5FC/fn2Rf3h4uMhf/K98rlzq0aNHTXVI XU4vonF5S2rnzp3Lysq07C77KGYCAAAAAHCX0r2YuWvXLhG2du1aE5JXmzlz Zk05v/zyy0VFRf369VP+U3sx8/bt21arNTIyUmN8QUHBkCFD7Oy9kJCQlStX amwtPj5evayW2a71TaDCc73pG9T3E1brN7/5zdGjRzW2pkwrrzZo0CCHS7lz SDg8OQQGBs6aNUsMrprWrsvpZf78+TJg/fr1GneXfRQzAQAAAAC4S+lezBw5 cmRwcHBubq4JyauVl5evXr36vvvuU2f74IMPbtiwQQmQJR2nXgSqPMCufWIU kUZiYmJkZKSfn586kzZt2syYMePy5cvaV11aWtqtWzfZwuDBg01OoMJzvekb VqxYUa9evaqDqFWrVmPGjElKSqr20Ww7Vq1aFRISojTSq1cvh69RVbh8SIgk AwMDq+YfEBAgWlu8eHF2drb9Vbt/esnPz2/atKlMuLi4WMsmO0QxEwAAAAAA VPxz2pqxY8d6MIezZ8/u37//0KFDmZmZ7rf2ww8/WDTMtFJVXl7eiRMnkpKS vv/++4yMDNfWPmfOHFlyeffdd01OwBt60wdcuXLl6NGjSZXS0tKcrSfbuHHj xr59+44fP+5sIbTCpUOipKTk/PnzMv/U1NRz587pVVEEAAAAAADwrHnz5lks lrS0NE8noqeePXs2adKksLDQ/FWPHj1aFjO1P4+sF5/sTQAAAAAAAEC4c+dO ixYtYmJiPJ2IzjZv3mzxxHsj9+zZIx/yHTJkiMlr99XeBAAAAAAAAIS1a9da LJakpCRPJ6KzsrKyzp07d+vWzYUHe51VVFSUnZ29d+/eSZMmycmmW7dunZWV ZfSqbfhqbwIAAAAAAABlZWXt27d/8sknPZ2IIbZu3WqxWHbs2GH0iqrOfx0e Hn7u3Dmj12vDt3sTAAAAAAAAtdymTZv8/PxSU1M9nYhRoqKiIiMjjV6LupgZ Fha2atWqoqIio1dalc/3JgAAAAAAAGot5UHsV1991dOJGCg9Pd3f33/Xrl2G riUpKemDDz5Yv379sWPHTHiqvVq1oTcBAAAAAABQaxUUFCQlJeXl5Xk6EWMd OXLkzJkzns7CcLWkNwEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAABvUF5efuHChf379+/duzc1NTU3N9fTGQG+QIysixcvJicni5F1 4MCBkydPFhcXm59GaWnpqVOnkpKSRBrp6eklJSXal/XsycFLdiAAAAAAAPAS Fy5ceOONN1q0aGFRqVOnzmOPPbZlyxaHi589e3bZsmV37twxIVUP2r1795df fqk9XuyWhQsXDhkyJCwsLDQ0VO7YN99807gk3U+glvSmOZYsWfLUU09ZrVbL vwoKCnriiScSEhLKy8tNSEMM8N/97neNGjVS59CgQYOxY8eePn3a4bLunBxs OHtMurkDLdqYNiQBAAAAAICbSktLFyxYEBQUZOdKf9iwYQUFBXYayc/Pb9q0 aXx8vGlpm+/SpUuhoaErV67UEpySkhITE+PByok7CdSG3jSNwzJaVFRUdna2 oTls2LChXr16NSUQHBy8Zs2aahfU5eQguXZMurkDHS5uf+0AAAAAAMCrFBQU DB48WH1RX79+/c6VAgIC1J8PHz7cflPvv/9+cHBwRkaGKYl7QGxsbMuWLQsL C+2HlZeXx8XF+fn5eapyoksCPt+bppH73Gq1dunSJSIiolOnTjaDKyoqyrj7 M//0pz+p19WzZ8/JkydPmjSpffv26s83btxos6COJwd3jkk3d6CdNWpZOwAA AAAA8Co3btzo0KGDcjn/yCOP7N69u7S0VPkqNzd31qxZ6uv9vXv32mlKuZ0v NjbWlMTNlpycLPbA0qVLHUZOmTJFvdMiIyM//PDDgwcPZmZm5uXlmZCqLgn4 dm+aadSoUQkJCWLnqz8Uu/cPf/iD+tHpbdu2GbH2n376KSQkRFlFs2bNkpKS 5FdlZWVLliyRBcbQ0NCLFy+ql9Xx5ODOMenmDjR6DwMAAAAAAJOdOnWqUaNG b7/9drWzgcycOVNWA0aOHGm/qfj4eBF29OhRYzL1pOjoaKvVmp+fbz8sISFB 7q6HHnro0KFD5qRnRAI+3JteYufOnbKzxo0bZ8Qqhg4dqrTv7++fkpJSNWDe vHkyh9dee83mW11ODsYNCi07kGImAAAAAAC+55dffqnpq+zs7Dp16ijVgFat Wtlv5/r163Xr1h04cKDeCXpYUlKSRcODqHl5effcc4+yr3r06GH+XPD6JuCr velV5Kw6MTExujd+/vx5eePlK6+8Um1McXHx/fffr8Q0aNDg1q1bNgFunhyM HhQOdyDFTAAAAAAAaptOnTrJm7scvtnv9ddfF5HHjx83JzdJlizslBy1xFQr JibGz8/v7Nmz9sM++ugjpf2QkJALFy44tQpd6J6Ap3qz9oiIiDCumLlw4UJ5 zJ88ebKmsLi4OJcrfg5PDkYPCoc7kGImAAAAAAC1jSwX+Pn5OQw+e/asCBs9 erQJiakZV8xMS0sTiwwYMMBh5EMPPaS0P23aNO3t60j3BDzVm7VEeXm5vGtx 8uTJurffv39/pfF27drVFJOeni4LksL06dOdWoXDk4Ohg0LLDqSYCQAAAABA bdOmTRulGuDwMXPFs88+GxQUdP36daMTUzOumDl+/HixyPbt2+2HnT9/Xraf nJysvX29GJSAR3qzlvjqq69kl+3bt0/39mWhb8yYMdUGrF69Wk4PZP/+xprY PzkYPSi07ECKmQAAAAAA1Crnzp2T1YBhw4ZpWWTTpk0ieOXKlUbnpmZQMbOg oCA0NLRx48Z37tyxH7lx40al8YCAgOLiYvFJVlbW9u3bFy9ePHfu3Pj4+NWr Vx8/ftzhc/ouMygBj/RmbXD48OFmzZopXeZwai0XZGdnywM+Li7O5tubN2/G xsZaqnjggQe0r8LhycHQQaFxB8oMExIScnJybt++7cK6AAAAAADA3WLatGnO 3tpUUFAQGBjYr18/o3NTM6iYKTZZxE+aNMlh5Jw5c5TG27Vr98knnzzyyCNV K0VCp06dtmzZonWrnGFQAh7pTd/z3XffbfunlStXDh482N/fX+mR4cOHOyyV u+Dvf/+77PR169apvzp27FiHDh3kt2FhYQ8//LDy/xs1aqR9FQ5PDjoeky7v wKqrEwu2bNkyKipq9uzZHrmJGgAAAAAAGCQlJUVWDLp3715WVqZxwT59+tSt W7eoqMjQ9NRkpULfYqZSrtFS/Rs1alS1hZpqzZ49W+uGaWZcAub3pu+Jjo6u 2gtBQUErVqwwaI0HDx6UK9q6dav8fPny5WK98quhQ4feuHFDmenJUlno09i+ lpODjsekyzvQ4XpFy3ZmRwIAAAAAAHeL69evt23bVpY4fvjhB+3Lzp49Wyx1 +PBh49KzIUsT+hYze/bsKeJ//fVXh5FyshVFSEjI4MGDFy1alJiYuG3btmXL ltkEiM+1bps2xiVgcm9mZGQ4LECpOTs3vUdUW4uzVD5//eKLL545c0b3NSYl Jcm1KLdN5ubmDhs2TH4YFBS0fPly5RFvsQ/l51oa13hy0PGYdHkHRqv07t07 IiJCPpwuWa1WM09WAAAAAABAdwUFBZGRkfJi/8MPP3Rq8bVr14qlPv/8c4PS q0pLacuF8pfVam3btq2WSFlsadCgwdKlS2/evFk1JiEhwc/PTwnr2LGj9jtd PZuAyb2Zk5PznjN27dplTmLuWLdunZLt9OnTJ06c+PjjjwcGBsoDsl69ejt3 7tR3jd999526mJmSkvLAAw/IT8T/T0tLk8GymBkQEOCwZe0nBx2PSX13YFZW 1scff9yqVSvZQps2bQoLC7W3AAAAAAAAvEdBQUFMTIy8zJ8wYYKzLezZs8dS 3bQjxjGimJmXlyeCBwwYoCVY1m0iIiLshE2cOFGmoa4muc+4BMzvzdogNzd3 /vz58onvunXrnjp1Ssf2jx07Jjv6ueeeU5f+hg8fLtauDh4/frzyVePGje03 69TJwdBB4f4O/OWXX9q3by9XvWbNGqcWBwAAAAAA3sCmWDF8+PDS0lJnG0lL SxPLzpo1y4gMq2VEMTMzM1MEjx49WkvwM888ozTesWNHO2H79u2TaaxevVpL yxoZl4D5vekN8vPz1znDteeUv/rqK9kdI0aM0DH/f/zjH5YqgoODq+10efB0 7tzZTpvOnhxMGBRu7sDExES5uBFzygMAAAAAAEPZFCsGDRrk2jzLyl1hZr7M 0IhipvLyRi1TmQvDhw9XGrc/H/S1a9dkGvPmzdPSskbGJWB+b3oD017d2a9f P6UFq9WqY/4lJSUBAQHqDDt06CC6stpg+QT6Sy+9VFODLpwczBkU7uzAK1eu yFX37dvX2cUBAAAAAIAH2RQrRo4cWVJS4lpTyr18c+bM0TdDO7TUlJytO128 eFEEjx07VkuwMkuO4saNGzWF5eTkyLD58+draVkj4xIwvze9gTj4M5yhZZao ar3xxhuyR/TdhIiICPVwrvaVlRX/WtCr6WUCrp0czBkU7uzA7OxsipkAAAAA ANyNcnNz+/Tpoy5WuPB0uaS8ZXHBggU6Zmifw0Ll5cuXnS1m3rp1SwQ///zz WoI3btwo2//6669rCktPT5dhn332mZaWNTIuAfN7s1YZPXq00h3NmzfXt+Wp U6fKvrbzPsk1a9bIsIMHD1YNcPnkYM6gcGcH7t+/X6563Lhxzi4OAAAAAAA8 Ijc399FHH5UX9RMmTHCnkllROfWwaOfPf/6zXhk6FBwcLCstVb8tLCzs2bOn s8VMoVmzZuHh4VoiL1y4INufOHFiTWGLFi2SYefPn7ffZmJiYosWLUSkSD41 NdX8BBQm9+aVK1einbF8+XJzEjNCQUHBPffco3TH0KFDHcaLw0CZSbxRo0Zz 5syxf3vk3r17ZV9PnTq12pjy8vIuXbooMffff3/VycTdOTkYd0xKzu5AG/JB eOGLL75wdnEAAAAAAGA+m2LF5MmTy8vL3WzznXfeEU0dP35clwy16NSpk5J/ 69atbYot4j9feukli4r2YmafPn1CQkI0Pm7fu3dvpf3AwMBz585VDbh69Wrz 5s2VmMcee8x+ayJYztSsFK+uXLliZgKSyb1Ze4qZ4sgcNWqU7OKtW7faj798 +XLDhg3VR/Ls2bPtxIuB3LlzZ3lIVPvCzBUrVsjWli5davOt+ycHg45JhbM7 0Ia6iBoWFuba+4EBAAAAAICZ8vLy1MWK8PDwrVu3brPr8uXLDpt9+umnrVar m7d3OmXixIlyK9RziFy7du3555+3/Cvtxcy33npLxKekpGgJ3r59u1xFly5d Tp8+rf72zJkz3bt3lwEHDhyw35p6imeFw9cJ6puAZH5v+owTJ05cunSp6ufZ 2dmff/55165dZXf079/fYZ1wyZIlNoeEwylv1IdEq1atkpOT5VdlZWWrVq0K DAxUvn3wwQdv376tXlaXk4Obx6SbOzAzM/Pq1as2H4rN3L1794ABA+SyderU SUpKsr8nAQAAAACANxCX8BYnbdu2zX6bxcXFDRo0GDhwoDmboEhJSVEn2bt3 72nTpo0ZM8ZqtSqfxMTEyCfNtRczv/nmGxG/aNEijfGxsbHqCkl0dPRrr702 ZcoUsXZ/f3/5VXx8vMOmsrKybGajfu6558xMQOGR3vQZYv+LvV2/fv22bduG h4dHRESI/5WPRUs9evTIyclx2FpcXJzNgqKLHS71yiuvqBfp27fv1KlTx48f L2cwt1QWRU+cOGGzoF4nB3eOSTd34HvvvSe+DQ0NFYt37dpVLN6xY0f1Dc+W yltGN23a5HA3AgAAAAAAb2BEMXPXrl0ibO3ateZsgjRz5syacn755ZeLior6 9eun/Kf2Yubt27etVmtkZKTG+IKCgiFDhtjZeyEhIStXrtTYWnx8vHpZLbMt 65tAhed60zcotTg7AgMDZ82aJQ5OLa0p08qrDRo0yOFSpaWlkyZNspND27Zt jxw5UnVBvU4O7hyTbu5ApZhpx29+85ujR4863IcAAAAAAMBLGFHMHDlyZHBw cG5urjmbIJWXl69evfq+++5TZ/vggw9u2LBBCZAVFe3FzIp/PsCufV4SkUZi YmJkZKSfn586kzZt2syYMUPLQ/pSaWlpt27dZAuDBw82OYEKz/WmbxgzZox8 jlstICBAdNDixYuzs7OdanDVqlUhISFKI7169XL4GlXpr3/9a//+/dV3Qloq Z/yJi4vLz8+vdhEdTw4uH5Nu7sAVK1bUq1ev6uKtWrUSLYsNdP/9wAAAAAAA 4K6mTFszduxYD+Zw9uzZ/fv3Hzp0KDMz0/3WfvjhB4ujmVaqlZeXd+LEiaSk pO+//z4jI8O1tc+ZM0dWYN59912TE/CG3rzblZSUnD9//ujRo0mVUlNTz507 V1xc7HKDN27c2Ldv3/Hjx10oxOXn56ekpIg0xADRZXQ4y4Vj0v0deOXKFbl4 Wlqas/V8AAAAAADgw+bNm2exWNLS0jydiJ569uzZpEmTwsJC81c9evRoWcw0 /3lYn+xNAAAAAAAAQLhz506LFi1iYmI8nYjONm/ebPHEeyP37Nkjn7EdMmSI yWv31d4EAAAAAAAAhLVr11oslqSkJE8norOysrLOnTt369bNhDfsFRUVZWdn 7927d9KkSfINh61bt87KyjJ61TZ8tTcBAAAAAACAsrKy9u3bP/nkk55OxBBb t261WCw7duwwekVV518ODw8/d+6c0eu14du9CQAAAAAAgFpu06ZNfn5+qamp nk7EKFFRUZGRkUavRV3MDAsLW7VqVVFRkdErrcrnexMAAAAAAAC1lvIg9quv vurpRAyUnp7u7++/a9cuQ9eSlJT0wQcfrF+//tixYyY81V6t2tCbAAAAAAAA qLUKCgqSkpLy8vI8nYixjhw5cubMGU9nYbha0psAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAvMft27cPHDiwatWq3//+9zNnznzzzTcLCws9nRQAAAAAAAAA /H83btz493//98aNG1v+VU5OjqdTAwAAAAAAAID/c/LkybZt2yrVy+bNm7/w wgtTp05955133nvvvaKiIk9nBwAAAAAAAAD/68aNG/fdd5/FYrnnnns2btxY UlLi6YwAAAAAAAAAoBpTp061WCyNGjU6c+aMp3MBAAAAAAAAgOrl5eXVrVvX YrF89NFHns4FAAAAAAAAAGq0ZcsW5VWZV69e9XQuAAAAAAAAAFCj2bNnWyyW Nm3aZGRkfPTRRyNHjoyOju7bt++oUaPWr19/584dTycIAAAAAAAAAP9rxIgR FoslKCjIz8/PUkWXLl0uXbrk6RwBAAAAALVIeXn5xYsXk5OT9+7de+DAgZMn TxYXF3s6KcBHiPF14cKF/fv3i/GVmpqam5vr6YyclpeXd/ToUZF/UlJSenr6 rVu3zFy7+zvQs/mboLS09NSpU2LrxDaKDdR9qvFnnnlGqVs2bdp04sSJn376 6bZt29auXdu/f3/l88cff1zfNQIAAAAAUK0lS5Y89dRTVqvV5k6boKCgJ554 IiEhoby83H4LZ8+eXbZsmc8/Zrh79+4vv/xSe7zYLQsXLhwyZEhYWFhoaKjc sW+++aZxSbqfQC3pTdNcuHDhjTfeaNGihXpw1alT57HHHtuyZYvRa696B121 7BwSBQUFy5cvf/jhh23uxxOb0KNHjxUrVtR0qERHR2tcu1RtO27uQJfzN19O Ts7bb79dr149LeNUTeyi3/3ud40aNVJvYIMGDcaOHXv69Gm90lM6tEOHDoWF herPxR+IMWPGKCtNTk7Wa3UAAAAAANTEYYUhKioqOzvbTgv5+flNmzaNj483 LWfzXbp0KTQ0dOXKlVqCU1JSYmJiatqfJhQz3UmgNvSmOUpLSxcsWBAUFGRn cA0bNqygoMC4HByObvuHxIkTJzp27Gh/2c6dO//0009Vl3W/mOn+DnQnfzMV FhZ+8MEHTZo0USem8USxYcMGWf+sKjg4eM2aNdUueOrUqXF2vfvuu+p45Q7M 8PDwqk39/e9/V1a3ePFiFzYfAAAAAACnyMteq9XapUuXiIiITp06BQQEqK+I o6Ki7N+f+f7774ur5oyMDLOyNltsbGzLli1tbkmqSuyluLi4at8p52yNwjW6 JODzvWmCgoKCwYMHq3d7/fr1O1eyGVzDhw83Lg07h4HDQ+L8+fPNmjWTMe3a tRs3bpyInDp1av/+/dU1xo4dO96+fdtmcTeLme7vQDfzN0dJScmnn37aunVr jZ1i409/+pN6kZ49e06ePHnSpEnt27dXf75x48aqy168eHGpXQkJCer4f/u3 f7NU/pmo2lROTo6yorlz57q8KwAAAAAA0GjUqFHiojUzM1P9YX5+/h/+8Af1 s+fbtm2z04hyO19sbKzByXpGcnKy2APi6t5h5JQpU9Q1hMjIyA8//PDgwYNi 9+bl5ZmQqi4J+HZvmuPGjRsdOnRQeuGRRx7ZvXt3aWmp8lVubu6sWbPU3bR3 716D0tA4fqs1bNgwZdmgoKANGzbY/HPG5cuX1eXKmm7/c2jIkCFKC927d1d/ 7v4ONCd/l4l8Nm/ebHPjaEhIiPz/DouZP/30k4xv1qxZUlKS/KqsrGzJkiXy HzVCQ0MvXrzoZsILFixQWqt6I+vPP/+sfLVs2TI31wIAAAAAgDt27twpr6zH jRtnPzg+Pl6EHT161JTUTBUdHW21WvPz8+2HJSQkyN310EMPHTp0yJz0jEjA h3vTNKdOnWrUqNHbb79d7VQsM2fOlJ01cuRIg3JwuZj566+/yhsgZ8yYUW3M 1atXAwMDlRjX7i9NT0+XGSYmJtp8684ONCd/d6xfv15dxmzYsOGCBQu+/vpr 7cXMoUOHKpH+/v4pKSlVA+bNmydbe+2119xMOC0tTWnqP/7jP2y+Uk4Xwo8/ /ujmWgAAAAAAcJOcdyMmJsZ+5PXr1+vWrTtw4EBzEjNNUlKSlsJCXl7ePffc o+yrHj16mD9dtb4J+GpvmuyXX36p6avs7Ow6deoo/dWqVSuDEnC5mHno0CG5 7F/+8peawsLDw5WYvn37upBebGyssnj79u3LysqqBri8A83J3x35+fkNGza0 VN44OmPGDDHiKv55ttFSzDx//ry88fKVV16pNqa4uPj+++9XYho0aOD+BO69 evUSTQUGBn7xxRfKna6i19auXavUhF944QU32wcAAAAAwH0REREai5nC66+/ LiKPHz9uQmJqWi7/td/vZENsuJ+f39mzZ+2HffTRR0r7ISEhFy5ccGoVutA9 AU/1Zu3RqVMneWed/XfSuszlYub+/fvlsp9++mlNYWFhYUrMs88+62xuP/74 oyzH/fGPf3R28Qq7O9CE/N331ltvjRkzRj1atRczFy5cKCNPnjxZU1hcXJzL x0BVJ06ckO8ead68ufjrIN9KKv7/r7/+6mb7AAAAAAC4qby8XN7sN3nyZIfx Z8+e9fPzGz16tAm5qRlXzFSerBwwYIDDyIceekhpf9q0adrb15HuCXiqN2sP +S8FYj8btAqXC1lXrlyRyz7yyCN37typGnPw4EEZ48LMLyNGjFCWvffee6tt 3yE7O1D3/EULys2TGtm8hVgj7cVMZW5xS+XERjXFpKeny3qvMH36dBdSsvE/ //M/zzzzjLwnVmjSpMmcOXPcv+0TAAAAAAD3ffXVV/KKdd++fVoWefbZZ4OC gpy66nefccXM8ePHi0W2b99uP+z8+fOy/eTkZO3t68WgBDzSm7VHmzZtlC7z wsfMKyrvSZaLv/DCCzbvjP3555/btm2rfBscHOzszcA//fSTv7+/svjixYud zU1hfwfqm39sbGx4eHh2draWxN566y2r1Xr69Gnt26LQXsyU/8w0ZsyYagNW r16tnk7Iou3ueo3EOeH7778X2Z48ebLa9wMAAAAAAGC+w4cPy0cItU9QsmnT JhG/cuVKQ3OzYVAxs6CgIDQ0tHHjxg5vG9u4caPSeEBAQHFxsfgkKytr+/bt ixcvnjt3bnx8/OrVq48fP27Qo8TGJeCR3qwlzp07J4/JYcOGGbQWuYqEhISc nJzbt29rX1YcMHXr1pUthIWFffnll+IQKisrW79+vRgX8itxdDmb2Lhx45Rl RTsOp9aqlsMdqG/+3377rWhNSz3zrbfeUs6Zcvp17TQWM0UOMiwuLs7m25s3 b8qXkao98MADzuYDAAAAAIDX+u6777b908qVKwcPHizvmxo+fLj2h0ALCgoC AwP79etnaLY2DCpmil0h4idNmuQwcs6cOUrj7dq1++STTx555JGqlQShU6dO W7Zs0bpVzjAoAY/0Zi0xbdo02S/uv8ywJlWPATGuW7ZsGRUVNXv2bIc38e7a tatBgwbqxbtXkv8ZEBCwYsUKZ7P6+eef5enl3XffdW3TtOxAffPXUs90p5JZ obmY+fe//12GrVu3Tv3VsWPHOnToIL8NCwt7+OGHlf/fqFEjF1ICAAAAAMA7 RUdHV617BAUFuVCp6NOnj7jkLyoqMiLPamm5/NcSY0Oplmip/o0aNarq3qvJ 7NmztW6YZsYlYH5v1gYpKSmymte9e3fjHtR1eDCIgW9n+piKylenRkVFVbvs o48+mpqa6kJWr776qtJC/fr1r1275kIL2negvvnbr2e6Wcms0FzMVL/tc+vW rfLz5cuXi5O2/Gro0KE3btxQZvKyVNaxXcsKAAAAAAAvVG0x01J559KLL754 5swZ7U3Nnj1bLHj48GHjsrWh5fJfS4yNnj17ingtU/TKyTgUISEhgwcPXrRo UWJi4rZt25YtW2YTID7Xum3aGJeAyb2ZkZFR7XFYE2fnpvcG169fl29r9Pf3 /+GHH4xbV7RK79691dNPS1ar1X7/7tu3r+pSlsoJrDW+SlctMzMzMDDQne5z dgfqm39N9Uz3K5kVmouZ6jDlrtTc3Nxhw4bJD4OCgpYvX668VkK0Iz93OTEA AAAAALzNunXr3qs0ffr0iRMnPv7447LgINSrV2/nzp0am1q7dq1Y5PPPPzc0 YTUtl/8ulL+sVmvbtm21RMpScIMGDZYuXXrz5s2qMQkJCX5+fkpYx44d9b0Z z7gETO7NnJyc95yxa9cucxLTS0FBQWRkpDwaP/zwQ/NzyMrK+vjjj1u1aiXT aNOmTWFhYbXZTpo0SV0AbNq0qU1JcObMmU6V7+RtmeIM48KU307tQCPyr6iu nqlLJbNCczHzu+++k2Hbtm1LSUl54IEH5Cfi/6elpclgWcwMCAhwJzcAAAAA ALxcbm7u/Pnz5UOL4uL91KlTWhbcs2ePpbppKYyj5fJfS4xaXl6eCB4wYICW YFlLjIiIsBM2ceJEmYa62uA+4xIwvzd9WEFBgXqK7QkTJngwmV9++aV9+/Yy mTVr1tgEiCHQq1cvGdCmTZstW7aUl5fv2rUrPDxcXQ8cPXq0xrmlLl++LKfY Hj9+vLM5O7UDjchfUtcz9apkVmguZh47dkyGPffcc+p/eBo+fLg4dauDxX5W vmrcuLGb6QEAAAAA4P2++uoreZk8YsQILYukpaWJ4FmzZhmdm6Tl8l9LjFpm ZqZS5dAS/MwzzyiNd+zY0U7Yvn37ZBouTADtkQTM701vkJ+fv84ZWh7DtynE DR8+3P3al5sSExNlPiNHjrT59oUXXlBXzNQlspKSkrlz56rrgZ999pmWNcq7 BP38/DT+44jk7A40In81pZ6p3OqpSyWzQnMx8x//+IeliuDg4GoHtTw5dO7c 2f0MAQAAAADwfv369VOuha1Wq5Z45a4hM19mqOXyX0uMmvLyRi1TmQvDhw9X Grc/X/C1a9dkGvPmzdPSskbGJWB+b3oD3V/daVOIGzRo0J07d8zZFjuuXLki U+rbt6/6K3Xdu1evXtVmu2TJEhnTtm1bhzc3qm/LfPHFF51K1dkdaET+VY0Y McJSOY2R2JPOLlstjcXMkpKSgIAA9RHYoUMHMVSrDZZPoL/00ku6JAkAAAAA gJd744035CWzlnjlXr45c+YYnZik5fJfe91JcfHiRRE8duxYLcHKLDmKGzdu 1BSWk5Mjw+bPn6+lZY2MS8D83vQGJSUlGc6wP0uUTSFu5MiRon3TtsWO7Oxs mZVNMXPKlCnyq2+++aamFtTPa58+fdr+6tRHqVPTiLuwA43I34bydLlILDAw sKb5zZ2lsZgpREREqHdIta/JrfjXejUviwAAAAAA1BKjR49WroWbN2+uJV55 y+KCBQuMTkxyePl/+fJlZ4uZt27dEsHPP/+8luCNGzfK9r/++uuawtLT02WY C4+1eiQB83vTx+Tm5vbp00ddd/L40+XS/v37ZWLjxo1TfyXvxxbslGrVNcO9 e/faWde1a9fq16+vRPbv3197kq7tQN3zt6F+T+aOHTv0qmdqL2ZOnTpVRtp5 YH/NmjUy7ODBg26mBwAAAACA9ysoKLjnnnuUa+GhQ4dqWWTdunUi+M9//rPR uUnBwcGy0FH128LCwp49ezpbzBSaNWsWHh6uJfLChQuy/YkTJ9YUtmjRIhl2 /vx5+20mJia2aNFCRIrkHd7GZkQCCpN788qVK9HOWL58uTmJuSY3N/fRRx+V +3zChAnuVDLFYaBM5N2oUaM5c+a4f3unfDuB8MUXX6i/evLJJ+VXduYc1z6l lFL9U3z77bcaM3R5B+qev1rVGX/0qmdqL2bu3btXRk6dOrXamPLy8i5duigx 999/f1lZmTu5AQAAAADg/cSl+qhRo+Ql89atW7Us9c4774jg48ePG52e1KlT JyXD1q1b29Q6xH++9NJLFhXtxcw+ffqEhIRoLBn17t1baT8wMPDcuXNVA65e vdq8eXMl5rHHHrPfmgiW88grxSuHL+XTNwHJ5N70pWKmTSFu8uTJLryVUbp8 +XLDhg3VR/Ls2bPdSU9d2Q4LC7N5q+SECRPkt++99161Ldy8ebNly5ZKTEBA QE1POldUvt8gNDRUiRT7RGOG7uxAffNXq2nucl3qmdqLmWJXdO7cWQ75al+Y uWLFCtna0qVLXc4KAAAAAADvceLEiUuXLlX9XFyPf/755127dpXXwv3799dY SXj66aetVquZz9Kqb69Sz2tz7dq1559/3vKvtBczlapFSkqKluDt27fLVXTp 0sXm/Xtnzpzp3r27DDhw4ID91tTTlygcvuJS3wQk83vTN+Tl5akLceHh4Vu3 bt1m1+XLl+00qJ6tRuFwQq7MzMyrV6/afHj79u3du3cPGDBAtlOnTp2kpCSb sG+++UYG+Pv7f/zxxzb39f3jH/944oknZIzNU+o23nvvPRmp8d9E3NyB+uYv idOLpeZH3Z2tZ+bn5+f8q507d8qUJk+ebPOtzeLqId+qVavk5GT5ldjYVatW iWSUbx988EHR71pSAgAAAADAy0VHR1sqZ+Nt27atuAaPiIgQ/yufK5d69OhR 9VK6WsXFxQ0aNBg4cKDRmaulpKSos+3du/e0adPGjBljtVqVT2JiYuST5tqL mUo9ZNGiRRrjY2Nj1QUisW9fe+21KVOmiLX7+/vLr+Lj4x02lZWVZTNb8XPP PWdmAgqP9KZvUN9ip9G2bdvsNBgXF2cTL7rYfg5KCTE0NFSM7q5du4rR3bFj R/Udv5bKm/o2bdpU7eJDhw5VR7Zq1WrkyJFi+Igj6oknnlAfn23atLFTvhOn DnlPaefOnTU+7Oz+DtQrf+n999+3OHppp1P1TOX0q13VFl555RV1QN++fadO nTp+/Hg5g7mlsuh94sQJh8kAAAAAAHBXcHg1LS7MZ82aVVRUpLHBXbt2iaXW rl1raNpVzZw5s6ZNePnll0X+ckIQ7cXM27dvW63WyMhIjfEFBQVDhgyxszND QkJWrlypsbX4+HibMoXJCVR4rjd9gO7FTGVaebVBgwbZz0F9P2S1fvOb3xw9 erSmxcXhNHLkSIdpiyPzwoULdtKYP3++DF6/fr39nCX3d6Be+Su0VDIV2uuZ 7hczRTKTJk2ys0jbtm2PHDnicOsAAAAAALhbjBkzRj6KqBYQEBAZGbl48WJn 3/8mLvaDg4Nzc3MNSrgm5eXlq1evvu+++9Rb8eCDD27YsEEJkFU+7cXMin8+ wK5xrhwljcTERLHr/Pz81Jm0adNmxowZ9p8jtlFaWtqtWzfZwuDBg01OoMJz vekDdC9mCqtWrQoJCVGCe/Xq5fA1qitWrKhXr17VFbVq1UoMfJGhlhdH7Nu3 LzY2Vt7kLDVo0ECMqe3bt9tvJD8/v2nTpvIgLC4udrhGhV470M38pffff1/7 TPQ7duyIiopyOHDcL2Yq/vrXv/bv319997WlcsafuLg4sf+1JAwAAAAAwF2k pKTk/PnzR48eTaqUmpp67tw57TUHNWXamrFjx+qepHZnz57dv3//oUOH7Exh rN0PP/xgcWmmlby8vBMnToj9+f3332dkZLi29jlz5sjSxLvvvmtyAt7Qm7Bx 48aNffv2HT9+XPtUOFeuXJGjOy0tzdmCtkKsTpwWxLEkGhGDS4yyu2tq7Ls9 fy3y8/NTUlLEBooToC5nPwAAAAAAfJ4yO0ZaWpqnE9FTz549mzRpUlhYaP6q R48eLYuZdh4HNohP9iYAAAAAAAAg3Llzp0WLFjExMZ5ORGebN2+2eOK9kXv2 7JFvABgyZIjJa/fV3gQAAAAAAACEtWvXWiyWpKQkTyeis7Kyss6dO3fr1k37 g70uKyoqys7O3rt376RJk+Qb8Fq3bp2VlWX0qm34am8CAAAAAAAAZWVl7du3 f/LJJz2diCG2bt1qsVh27Nhh9IqqTj8dHh5+7tw5o9drw7d7EwAAAAAAALXc pk2b/Pz8UlNTPZ2IUaKioiIjI41ei7qYGRYWtmrVqqKiIqNXWpXP9yYAAAAA AABqLeVB7FdffdXTiRgoPT3d399/165dhq4lKSnpgw8+WL9+/bFjx0x4qr1a taE3AQAAAAAAUGsVFBQkJSXl5eV5OhFjHTly5MyZM57OwnC1pDcBAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAF4rLy/v6NGje/fu TUpKSk9Pv3Xrlqczco4H8y8vL7948WJycrJY+4EDB06ePFlcXOxsIzdv3jx+ /Lho4fDhw1euXDEiTwAAAAAAcPe6du1as2bNLCrbtm2zE3/27Nlly5bduXPH tAw9Yvfu3V9++aX2eLFbFi5cOGTIkLCwsNDQULkz33zzTeOSdD+BWtKbWhQU FCxfvvzhhx/28/NTD4c6der06NFjxYoVhu4lizZ2etOd/N1f+5IlS5566imr 1WqzSFBQ0BNPPJGQkFBeXm5/D4gAceaJjo4OCAhQt9C5c+e4uLi7rqQMAAD+ X3v3HxRVvf9xHOS3yGb+DuViw72QiuKYhhZGpGWmYmnEiCi3GMfK9E46EtOU ekVrTNOpRKtr/riSUnmVxjEaxytYqQmkjNYURPwogcQfQLSA/Op7pjN9vnsX 2D27e87ZFZ6PP+58232fz+e1n89hZ3x/z9kDAACgkcTERLP+g+VmZkNDw8CB A9PS0nRLqL/Lly8HBASkp6crKc7Ly4uJibGj/6MWRwL0ht1U4tKlS6GhoZZb eaNGjfrhhx80CmB5aqu76WB+B2dXMkJUVFRNTU13h1dVVU2dOtXC4SEhIdot PgAAAAAAuFVkZ2d37htYbmZKXnvtNR8fn7KyMl0yOkF8fPywYcMaGxstl3V0 dKxfv97sQjjl/R/HqRKgx++mVaWlpaYXJ995551JSUnSuj3//PPTp0/39vYW b4WGhjY3N2uRwcIOWt1Nx/M7MrvZCAaDYcyYMREREWFhYWbXWEZFRXV5fWZ1 dXVISIgoCwwMTExMlOZasGDB4MGDxesjRoyw0A4FAAAAAAA9XkNDQ1BQkNwo CAsLE00Dq81M+XK++Ph4fXLq7MyZM9IibNu2zWrlsmXLTHs1kZGRmzdv/vLL LysqKurr63WIqkqAnr2bSsyfP19eQG9v7/3795s13KqqqqKjo8Ui79q1S4sM yv/6OnM8vyOzyxYuXJiRkSGdeKYvSqfWO++8Y3rveZfjz5o1S363T58+Gzdu NL0dXvq/X3rpJXG4NIt98QAAAAAAQA+wfPlyuUVwxx13HDlyxKaGRlpamlR5 /vx5HXLqLDo62mAwNDQ0WC7LyMgQKzZ69OjTp0/rE0+LAD14N626fv26uIBw 5cqVXdZcuXLFy8tLromLi9Miht3tRFXyO97MtODo0aNi/KSkJLN3c3Nzxbtr 1qzpcoQnnnhCLnB3dy8uLlY9IQAAAAAAcH1nzpwRtycfOXIkJyfHpobGtWvX /Pz8Zs+erUNUPcnrYPXW7Pr6enED7MSJE+vq6vSJp1GAnrqbSpw+fVqc+f/5 z3+6KwsPD5dr7r//fi1i2N1OVCW/ps1MydChQ+XxY2JizN5KSkqS3xowYEB3 P+xQVFQkEr788staJAQAAAAAAK6subl59OjRcnNAvr/Y1mam5LnnnpOKCwsL NQ5rTuRU8gt+tv5kZUxMjJKrv9544w15fF9f3/LycpumUIXqAZy1m0536tQp cbb861//6q5s5MiRcs3MmTO1iGF3O1GV/Fo3MyMiIrprZv7lL3+R33r88cct jDB27Fi57O6779YiIQAAAAAAcGVr1qyROwODBg2Sn6lhRzOzuLjY3d09MTFR 47DmtGtmFhQUSIfMmDHDaqVoBa9YsUL5+CpSPYCzdtPpqqurxdkyYcIE0x9s FL788ktR092t0A6yu52oSn5Nm5kdHR3iKuJnnnnG7F1x/7vlM/nZZ5+Vyzw9 PVtaWlQPCQAAAAAAXNalS5dEA+HAgQPyi3Y0MyUzZ8709va+du2aZmG7oF0z 86mnnpIOycrKslxWWloqxj9z5ozy8dWiUQCn7KYriImJEev5+OOPm/1c6o8/ /hgcHCy/6+Pjo9GFuI60Ex3Pr2kz89ixY2L83Nxcs3fFj108//zzFgbZunWr GKSsrEz1kAAAAAAAwDW1tbVFRkbKPYG5c+eK1+1rZh48eFCqT09P1yZs1zRq ZhqNxoCAgNtvv73La9tMHThwwOwiscrKyqysrC1btqxZsyYtLW3nzp2FhYVm D5VWkUYBnLKbrkBaKz8/P3HOjBw58sMPP5RWr729fe/evdIpId6SFlajDGKK jIyM2tra5uZmPfM7Mrtl586dGzRokDx4QkJC5wJx0ea0adMsjGP6uKsLFy6o FQ8AAAAAALi4bdu2yQ2B22677fLly+J1+5qZRqPRy8vLchdCdRo1M+XnuS9Z ssRqZWpqqjz4nXfe+e67706YMMGtK2FhYYcOHVL6qWyhUQCn7KaLyM7O7tev n+nqjf+D+E9PT8/t27drF6Dz9nl4eAwbNiwqKiolJcXq9bcO5ndwduGLL744 8qf09PTY2FhpHHnAuLi4Lv/fBLNmzRIJf/rpp+5Glv88ZdKXlcI8AAAAAADg llZWVubv7y83BN5//33Tt+xrZkqmTp3q5+fX1NSkdthuiZzqNjNXrFgh1Svp /i1cuLDL5mGXUlJSlH4wxbQLoP9uuo7i4uKoqKgu1/Cee+7Jz8/XdHar+xgd Hf3NN99olN/x2WVSWedjvb29LfRRP/jgA1EpnX51dXWday5cuDBz5kxRdvLk SatJAAAAAABAD/Dwww/L3YCHHnrI7B5ku5uZKSkp0iHnzp1TO2y3RE51m5mT Jk2S6q9fv261cvr06aa9Gl9f39jY2E2bNmVmZkpL9+abb5oVSK8r/WzKaBdA 590sKyuz1kX7H7Y+m95Wubm54p5oUxEREZ1/7FFd0SamTJkizdg5icFgsLw1 dudXZfbfu2lmuv1x1eW8efOKioo6H9La2hoeHi4qhw0bJp2EBw8elM7kXbt2 LV++fMyYMWajcWUmAAAAAAC9wd69e+VWgL+/f+cnaNjdzNy9e7d0yL59+9TM apGS1pYd7S+DwRAcHKykUnRs+vXrt23btl9//bVzTUZGhniySWhoaHt7u8IY zg2g827W1tautUV2drZGSYxG45IlS0w7ZgMHDjTroa1ataqtrU2jAF2qrKx8 6623AgMDRYagoKDGxkZ98iufXdizZ4+8Uy+88EJycvJ9990nnjUm6du379Gj Rzsf9cMPPwwfPtxNsW+//Vb5pwAAAAAAALeiK1euDBgwQG4FvP32250L7G5m njhxQjpk/fr16oW1QuRUsZlZX18vFc+YMUNJseglRkREWChLTk4WMQoKCpSM rJB2AfTfTVcg7f7kyZNNW3aHDh3q6OjIzs42vWhQkpiYqN1znbrzyy+/hISE iAy7du3SM7/V2S2rq6vbsGGDt7e3fLifn993333X5SxLly417XwKBoPh73// +9q1a8UrXd6KDgAAAAAAepL4+Hi5DxAVFdXlRXp2NzMLCgqkQ1avXq1eWCu0 aGZWVFTIrR4lxY888og8eGhoqIWy3NxcEUPdp2BrF0D/3XQFjz/+uFioRx99 1LRX1traumbNGtPemtmPzeojMzNTBOj8THCt81ueXYljx46JERYsWNBdWW1t bVZW1ubNm9euXbthw4b33nvv7Nmz8qPV//nPf8qHjxgxwo4AAAAAAADgFvL1 11+LTkJsbOw/ujJ//nxRM3v2bPH6xYsXLQ9+4cIF5T1DVWjRzJR/vFHJo8wl cXFx8uD9+/e3UHb16lURY926dUpGVki7APrvptOZtnwnT57c5RO3t27dKmqC g4P1vzizurpaBLj//vtN39Ihv4XZlZs2bZo8gsFgsOPwOXPmyIc/+eST9gUA AAAAAAC3CtOrLm1l9SpN+Vq+1NRUfT7L79o0M3/++WepePHixUqK5afkyG7c uNFdWW1trSjbsGGDkpEV0i6A/rvpdMuWLROr9Nlnn3VXZnq/9vfff69nQklN TY2Y3aydqEN+C7Mrt3z5cjGIrcdev35d3Kj+73//274AAAAAAADgVqFpM1P+ lcWNGzfq81l+V9CorKqqslpj5rfffpOKZ82apaT4wIEDYvxPP/20u7KLFy+K MnXvTdYugP676XTiikE3i8+yN+0Znjx5Us+EklOnTonZk5KSTN/SIb+F2ZVL TEyURxgyZIitx65cuVI+tn///kaj0b4AAAAAAADgVlFWVmb1OdFJSUmiXxEf Hy9e7/JpHab27NkjHfLRRx/p81kkPj4+cs4uf76vsbFx0qRJtjYzJYMGDQoP D1dSWV5eLsZPTk7urmzTpk2irLS01PKYmZmZQ4cOlSql8Pn5+foHkOm8m9XV 1dG26PLZVQ568MEHxSpVVFR0V6b8aUrS9kVGRsqdt9TU1NbWVsdDih8WkHzw wQea5rdpdoWMRuPgwYPlER577DGbjt2/f7+7u7t87KuvvmrH7AAAAAAAoOex +wFAL7/8snRIYWGhdtnMhIWFyTmHDx/e1tZm+pb0n0888YSbCeXNzKlTp/r6 +ipsPU2ZMkUe38vLq6SkpHPBlStXhgwZItfce++9lkeTisVdtHITrLq6Ws8A gs676QrNzKefflqs/Nq1a7us+fXXX4cNGybXeHp6Sv/Z3WhVVVW33Xab6RmY kpLiYELTpvTIkSPNfhVT3fy2zq6E9Fe5cOFCMcjhw4cVHnj9+nXTm9MjIiLk hwEBAAAAAADY3cx8+OGHDQaDWVNRU6bXmJk+1+bq1auzZs1y+1/Km5kvvvii VJ+Xl6ekOCsrS0wxZswYsx8hLCoqGj9+vCj4/PPPLY9m+gwXmdWfuFQ3gKD/ bjrdZ599JhbKw8Pjrbfeam9vNy346aefHnjgAVFj+T5r00ftyJQ876aiouLK lStmLzY3Nx8/fnzGjBliqD59+kh/p6rnd2R22aVLly5fvtz59Zqamn379o0d O1YMMn369M6PH2pqaqr9U1lZ2enTp9PT0+fNm2fa4R8xYoSF604BAAAAAEBv Y18zs6WlpV+/frNnz9Y0m5m8vDzTZtGUKVNWrFixaNEig8EgvxITEyPuNFfe zJSbQps2bVJYHx8fb9rniY6OfvbZZ5ctWybN7uHhId5KS0uzOlRlZaWnp6fp h3r00Uf1DCBzym66gscee8x08QMDAxMSEqQzR1rMBx54wHRrgoKCampqLAy1 fv16t/8lbY3VAGvXrpUqAwICgoODx44dGxERERoaatrKc/vjEtyDBw9qkd/B 2SXSuSfV+Pv7SyOEh4dLI0j/K+4rFyZOnFhbW9vdx7dg8uTJXTZLAQAAAABA r2VfMzM7O1uq3717t6bZOlu1alV3fY8nn3yyqalJPBVFeTOzubnZYDBERkYq rDcajXPnzrXQgfH19U1PT1c4WlpamumxSp4ZrW6A3523m04nrWRCQoLlfpq8 KeXl5ZaHkh8Hb2rOnDlWA1jt5t19993nz5/XKL+Ds//+ZzPTAi8vr9WrV0t/ mLZ+/JCQkF27dvWqS4UBAAAAAIAS9jUzExISfHx86urqNM3WWUdHx86dO0eM GGHa97jrrrv2798vF4gun/Jm5u9/3sCu8Fk5cozMzMzIyEjxgBJZUFDQypUr q6qqlE/d1tY2btw4MUJsbKzOAX533m66iNzc3Pj4eHF9r9CvXz/pdMrKyup8 f3SXduzY4evrKx87efJkqz9/Ktm+fXvfvn07t/ICAwMXLVok/W0qmdru/I7P LpV5eXl1HsHT01M6Obds2WL5clbTZqafn9/f/va32bNnSy+ePXtW4ZoDAAAA AABYJT+2ZvHixU7MUFxcfOrUqdOnT6vye3pfffWVm11PbKmvr7906VJOTs7Z s2fLysrsmz01NVW0dF555RWdA7jCbrqCjo6OkpISaRmlxZTOK+kEM/sJSiVu 3LiRm5tbWFhoUy+uurr6/PnzOX8oKCiwtRctszu/g7O3traWlpaKEfLz86UY LS0tdnwEAAAAAAAALaxbt87Nza2goMDZQdQ0adKkAQMGNDY26j91YmKiaGZa vqtXCz1yNwEAAAAAAADJzZs3hw4dGhMT4+wgKvv444/dnPG7kSdOnBA36s6d O1fn2XvqbgIAAAAAAACS3bt3u7m55eTkODuIytrb20eNGjVu3Dgdfqyvqamp pqbm5MmTS5YsEc8fHz58eGVlpdZTm+mpuwkAAAAAAAC0t7eHhIQ8+OCDzg6i icOHD7u5uX3yySdaT9T5Ic7h4eElJSVaz2umZ+8mAAAAAAAAermDBw+6u7vn 5+c7O4hWoqKiIiMjtZ7FtJk5cuTIHTt2NDU1aT1pZz1+NwEAAAAAANBryTdi L1261NlBNHTx4kUPD4/s7GxNZ8nJyXn99df37t174cIFHe5q71Jv2E0AAAAA AAD0WkajMScnp76+3tlBtPX1118XFRU5O4XmesluAgAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAM7V0dFRXl5+6tSp kydP5ufn19XVOTuRDaTwP//885kzZ6Twn3/++TfffNPS0mLHIM5aAVXyAwAA AAAAF+GmzD/+8Y/uRiguLn7zzTdv3rypZ2z9HT9+/MMPP1ReLy3Lq6++Onfu 3JEjRwYEBChZSXXZF6CX7KZuysvLly9fPnToUNO/pj59+tx7772HDh3Sbt7o 6GiFf9pC50G2bt360EMPGQwGs0pvb+8HHnggIyOjo6PDahJHVkBh8u5OaVXy AwAAAAAAl+Jgu0DS0NAwcODAtLQ0PWPr7PLlywEBAenp6UqK8/LyYmJi7FhJ tTgSoDfspj7a2to2btzo7e1t4c9q/vz5RqNRi9lVaWZaPSQqKqqmpka7FVCY vLtT2sH8AAAAAADABTnYLpC99tprPj4+ZWVleqXWW3x8/LBhwxobGy2XdXR0 rF+/3t3d3e6VdJAqAXr8burAaDTGxsaaLru/v/+oP3h6epq+HhcXp0UAdZuZ BoNhzJgxERERYWFhZvmjoqK6vL5RlRVQmNxqM9OO/AAAAAAAwDWJf9QfOXLE 7kHky/ni4+NVDOY6zpw5I63Ptm3brFYuW7bMtE8SGRm5efPmL7/8sqKior6+ XoeoqgTo2bupjxs3bvz1r3+Vd2HChAnHjx9va2uT36qrq1u9erXpNp08edJZ OefOnStnGD9+fOd3Fy5cmJGRIZ08pi9Kp8c777xjeu92l18dqqyAg99OjuQH AAAAAACuSa1/0aelpUmDnD9/Xq1griM6OtpgMDQ0NFguy8jIEIs5evTo06dP 6xNPiwA9eDd189133/Xv3/+ll15qbW3t/O6qVavEZiUkJOgfT3Lx4kWRITMz 06Zjjx49Ko5NSkrqssbxFdCu36gkPwAAAAAAcEFqtQuuXbvm5+c3e/ZstYK5 iJycHDcFt2bX19cPHjxYXsmJEyfq/7hqdQP01N3U2S+//NLdWzU1NX369JH3 KzAwUM9UQnx8vBwgJCSkvb3d1sPFM31iYmK6q3FwBTS9eFJJfgAAAAAA4GpU bBc899xz0jiFhYWqBFNOfAQLLUclNV2KiYlxd3cvLi62XPbGG2/I4/v6+paX l9s0hSpUD+Cs3ew9wsLC5C3z8PDQ/2cbv/32W/Hbqu+9954dI0RERDjYDLS6 Apo2Mx3PDwAAAAAA9Kdiu6C4uNjd3T0xMVGVYMpp18wsKCiQDpkxY4bVytGj R8vjr1ixQvn4KlI9gLN2s/cQzTRpnfWffcGCBfLsd9xxx82bN209vKOjQ1wJ /Mwzz9iXweoKaNfMVCU/AAAAAADQn7rtgpkzZ3p7e1+7ds3xoZTTrpn51FNP SYdkZWVZListLRXjnzlzRvn4atEogFN2s/cICgqSt0z/28x/+OEHDw8PefYt W7bYMcKxY8fEKZebm2tfDKsroF0zU5X8AAAAAABAf+Jf9BkZGbW1tc3NzY6M dvDgQWmo9PR0teIpoVEz02g0BgQE3H777VavWztw4IA8uKenZ0tLi/RKZWVl VlbWli1b1qxZk5aWtnPnzsLCQu1uJdYogFN2s5coKSkR5+T8+fN1nj0pKUme Wjq9rT7ZqrNz584NGjRIHsHupxcpWQF1v50EVfIDAAAAAACncOvEw8Nj2LBh UVFRKSkptl7mZzQavby8pk2bplHaLmnUzDxy5IhUv2TJEquVqamp8uB33nnn u+++O2HChM6rKgkLCzt06JDST2ULjQI4ZTd7iRUrVoh90eIHIS348ccfxWWZ r7zyitX6L7744sif0tPTY2NjxeFxcXF23KIuU7ICqnw7aZQfAAAAAAA4RZdd L1PR0dHffPON8gGnTp3q5+fX1NSkXWYzShqVSmrMyM0WJd2/hQsXWl1GISUl RekHU0y7APrvZm+Ql5cn+mnjx4+340nijli6dKk8tb+//9WrV63WS98Anc8i b2/v7du3251B4QpYPZmVfDtpkR8AAAAAADhLtIkpU6ZERESIGzAFg8Fw7tw5 hQOmpKRIhyivd5zIqW4zc9KkSVL99evXrVZOnz7ddLl8fX1jY2M3bdqUmZl5 5MiRN99806xAel3pZ1NGuwA672ZZWZnV/pUpW59N7wquXbsWHBws5/fw8Pjq q6/0nL2iosLLy8um1euyGej2x28azJs3r6ioyNYMyldAlW8n1fMDAAAAAABX U1lZ+dZbbwUGBop/+AcFBTU2Nio5dvfu3VL9vn37tA4pKGlt2dH+MhgMwcHB SipFt6Rfv37btm379ddfO9dkZGS4u7vLZaGhoepejKddAJ13s7a2dq0tsrOz 9QmmFqPRGBkZKc7GzZs36xxAXJbp5eVVUVGh5JA9e/bIq/3CCy8kJyffd999 oh0q6du379GjR5UHcHwFbP12Ujc/AAAAAABwWb/88ktISIj4V/+uXbuUHHXi xAmpeP369VrHE7RoZtbX10vFM2bMUFIseokREREWypKTk0WMgoICJSMrpF0A /XezBzMajTExMWILnn76aZ0DVFVV+fr6yrM/9dRTdo9TV1e3YcMGb29veSg/ P7/vvvtOyYEqroB9304yu/MDAAAAAAAXl5mZKdoFCh/7W1BQIBWvXr1a62yC Fs3MiooKqTgxMVFJ8SOPPCIPHhoaaqEsNzdXxNi5c6eSkRXSLoD+u+kKGhoa 9thCyW34Zn28uLi4trY2HT6LKenkl2d3d3d3vH137Ngx8XEWLFhgtV71FbDj 28mUrfkBAAAAAIDrq66uFv/ev//++5UccuHCBeU9Q1Vo0cyUf7xRyaPMJXFx cfLg/fv3t1B29epVEWPdunVKRlZIuwD676YrUP2nO836eHPmzNH/Idqml2XO mzdPlTGnTZsmD2gwGCxXarECdnw7mVGeHwAAAAAA3BJqampsbRfI1/KlpqZq nU3Qopn5888/S8WLFy9WUiw/JUd248aN7spqa2tF2YYNG5SMrJB2AfTfTVfQ 2tpaZgvLT4ky6+MlJCRI4+v2WQTTkyQ/P1+VMZcvXy7GtFCm0QrY8e1kRmF+ AAAAAABwqzh16pT4x35SUpKSQ+RfWdy4caPG0f6f1UZlVVWVrc3M3377TSqe NWuWkuIDBw6I8T/99NPuyi5evCjK3n//fSUjK6RdAP13s4epq6ubOnWqaR9P /7vLf//jolx/f385w/Tp09UaNjExUR5zyJAh3dVotwJ2fDuZUZIfAAAAAADc QsT9y5IPPvhAySF79uyRij/66COtswk+Pj6iT9L53cbGxkmTJtnazJQMGjQo PDxcSWV5ebkYPzk5ubuyTZs2ibLS0lLLY2ZmZg4dOlSqlMJbvY5OiwAynXez uro62hZvv/22PsHsU1dXd88994g1f/rppx3p40mngfwc8P79+6emptp0ceOL L74oYvz3v/+1O4Mpo9E4ePBgeczHHnusyxp1V8CMHd9OppTkBwAAAAAAtxDT 3tfIkSMV/sbdyy+/LNUXFhZqHU8ICwuTQw4fPtysVSL95xNPPOFmQnkzc+rU qb6+vgpbRlOmTJHH9/LyKikp6Vxw5cqVIUOGyDX33nuv5dGkYvGoZbl5VV1d rWcAQefd7EnNTLM+3jPPPNPR0WH3aFVVVbfddpvpmZySkqLw2Nra2oCAAPko KZLdGUxJf1kLFy4UYQ4fPty5Rt0VMGPft5OgJD8AAAAAAHA1FRUVV65cMXux ubn5+PHjM2bMEP/S79OnT05OjsIxH374YYPBoOe9tMnJySKq6XNtrl69OmvW LLf/pbyZKV/MlpeXp6Q4KytLTDFmzJjvv//e9N2ioqLx48eLgs8//9zyaKaP HZdZ/YlLdQMI+u9mz1BfX2/axwsPDz98+PARi6qqqiwMuHXrVrNTQvkza9au XWtH1+7SpUuXL1/u/HpNTc2+ffvGjh0rxpw+fXrnLqXjK+Dgt5OD+QEAAAAA gAuSuxwBAQHBwcHSv+4jIiJCQ0NNrwl0++NKv4MHDyocsKWlpV+/frNnz9Y0 tpm8vDzTwFOmTFmxYsWiRYsMBoP8SkxMjLjTXHkz87PPPpPqN23apLA+Pj7e tMESHR397LPPLlu2TJrdw8NDvJWWlmZ1qMrKSk9PT9MP9eijj+oZQOaU3ewZ cnJy3Gx05MgRCwOuX7/erF7aYiVJamtrxSWdo0aNam9vV/gRpPNHOsTf31/6 cggPD5e+HKT/FfdlCxMnTpSm0GIFHPx2cjA/AAAAAABwQaaXbHXp7rvvPn/+ vPIBs7OzpaN2796tXeYurVq1qruP8OSTTzY1NU2bNk3+T+XNzObmZoPBEBkZ qbDeaDTOnTvXwmL6+vqmp6crHC0tLc30WCUPa1Y3wO/O280eQPVmpvxYeVNz 5sxRkmTDhg3ikL179yr/CHIz0AIvL6/Vq1dLf1warYCD304O5gcAAAAAAC5o +/btffv27fzP/MDAwEWLFuXk5Nh692VCQoKPj09dXZ1Ggbsj5dy5c+eIESNM P8Vdd921f/9+uUB0+ZQ3M3//8wZ2hc/KkWNkZmZGRka6u7ubJgkKClq5cqXl +4jNtLW1jRs3TowQGxurc4DfnbebPYDqzUzJjh07fH195eLJkydb/RlVSUND w8CBA8U50NLSovwjSN8AXl5enXN6enpKJ9iWLVtqamo0XQEHv50czA8AAAAA AFxWdXX1+fPnc/5QUFBga8tLkB9bs3jxYnXj2aS4uPjUqVOnT5+uqKhwfLSv vvrKzZYnrQj19fWXLl2S1vPs2bNlZWX2zZ6amio6MK+88orOAVxhN2Hmxo0b ubm5hYWF+vzGY2tra2lpqfhyyM/PLykpsakj6jhHvp1cIT8AAAAAAHBZ69at c3NzKygocHYQNU2aNGnAgAGNjY36T52YmCiamTbd7K+KHrmbAAAAAAAAgOTm zZtDhw6NiYlxdhCVffzxx27O+N3IEydOiJtk586dq/PsPXU3AQAAAAAAAMnu 3bvd3NxycnKcHURl7e3to0aNGjdunA439jY1NdXU1Jw8eXLJkiXi+ePDhw+v rKzUemozPXU3AQAAAAAAgPb29pCQkAcffNDZQTRx+PBhNze3Tz75ROuJOj++ OTw8vKSkROt5zfTs3QQAAAAAAEAvd/DgQXd39/z8fGcH0UpUVFRkZKTWs5g2 M0eOHLljx46mpiatJ+2sx+8mAAAAAAAAei35RuylS5c6O4iGLl686OHhkZ2d reksOTk5r7/++t69ey9cuKDP46o76w27CQAAAAAAgF7LaDTm5OTU19c7O4i2 vv7666KiImen0Fwv2U0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgBL/ B9yda+A= "], {{0, 200.9961475738382}, {532.489793945616, 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->{240.00459999999998`, 240.00459999999998`}, SmoothingQuality->"High"], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSize->Automatic, ImageSizeRaw->{532.489793945616, 200.9961475738382}, PlotRange->{{0, 532.489793945616}, {0, 200.9961475738382}}]], "Input",Expres\ sionUUID->"ea1feb88-60c4-44a9-9feb-3f354c89e880"], Cell[BoxData[ RowBox[{ RowBox[{"X", "[", RowBox[{"u_", ",", "v_"}], "]"}], ":=", RowBox[{"{", RowBox[{ RowBox[{ RowBox[{ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], RowBox[{"Cos", "[", "u", "]"}]}], "-", RowBox[{ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], RowBox[{"Sin", "[", "u", "]"}]}]}], ",", RowBox[{ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], RowBox[{"Cos", "[", "u", "]"}]}], ",", "v"}], "}"}]}]], "Input", CellChangeTimes->{{3.911355334411213*^9, 3.9113553680226073`*^9}}, CellLabel-> "In[141]:=",ExpressionUUID->"16bf7195-6550-4f78-abd4-a5ebab8826cc"], Cell["The unit normal vector is", "Text", CellChangeTimes->{{3.911355374750509*^9, 3.91135538173057*^9}},ExpressionUUID->"530daa1d-770f-4f27-be1a-\ 9d026ae661eb"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"n", "=", RowBox[{"(", RowBox[{ FractionBox[ RowBox[{ RowBox[{ SubscriptBox["\[PartialD]", "u"], RowBox[{"X", "[", RowBox[{"u", ",", "v"}], "]"}]}], "\[Cross]", RowBox[{ SubscriptBox["\[PartialD]", "v"], RowBox[{"X", "[", RowBox[{"u", ",", "v"}], "]"}]}]}], RowBox[{"norm", "[", RowBox[{ RowBox[{ SubscriptBox["\[PartialD]", "u"], RowBox[{"X", "[", RowBox[{"u", ",", "v"}], "]"}]}], "\[Cross]", RowBox[{ SubscriptBox["\[PartialD]", "v"], RowBox[{"X", "[", RowBox[{"u", ",", "v"}], "]"}]}]}], "]"}]], "//", "Simplify"}], ")"}]}]], "Input", CellChangeTimes->{{3.911355384560013*^9, 3.911355424652053*^9}}, CellLabel-> "In[143]:=",ExpressionUUID->"08a7fea8-95ce-45d9-b128-949f6069d847"], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"-", FractionBox[ RowBox[{ SqrtBox["2"], " ", RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], " ", RowBox[{"Sin", "[", "u", "]"}]}], SqrtBox[ RowBox[{ SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{"3", "+", RowBox[{"8", " ", SuperscriptBox["v", "2"]}], "-", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}], "+", RowBox[{"2", " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}]]]}], ",", FractionBox[ RowBox[{ SqrtBox["2"], " ", RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], " ", RowBox[{"(", RowBox[{ RowBox[{"Cos", "[", "u", "]"}], "+", RowBox[{"Sin", "[", "u", "]"}]}], ")"}]}], SqrtBox[ RowBox[{ SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{"3", "+", RowBox[{"8", " ", SuperscriptBox["v", "2"]}], "-", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}], "+", RowBox[{"2", " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}]]], ",", RowBox[{"-", FractionBox[ RowBox[{"2", " ", SqrtBox["2"], " ", RowBox[{"(", RowBox[{"v", "+", SuperscriptBox["v", "3"]}], ")"}]}], SqrtBox[ RowBox[{ SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{"3", "+", RowBox[{"8", " ", SuperscriptBox["v", "2"]}], "-", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}], "+", RowBox[{"2", " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}]]]}]}], "}"}]], "Output", CellChangeTimes->{{3.9113554139596868`*^9, 3.9113554253527193`*^9}}, CellLabel-> "Out[143]=",ExpressionUUID->"e6358eb4-7985-4768-9ae1-7deb9348b333"] }, Open ]], Cell["\<\ We write de derivatives of the unit normal vector as a combination of the \ tangent vectors\ \>", "Text", CellChangeTimes->{{3.911355431500002*^9, 3.9113554676995974`*^9}},ExpressionUUID->"79fa5935-105b-4600-ba8a-\ e045e1b80a72"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"coefficientsnu", "=", RowBox[{ RowBox[{ RowBox[{"Solve", "[", RowBox[{ RowBox[{ RowBox[{ SubscriptBox["\[PartialD]", "u"], "n"}], "==", RowBox[{ RowBox[{ SubscriptBox["\[Alpha]", "u"], RowBox[{ SubscriptBox["\[PartialD]", "u"], RowBox[{"X", "[", RowBox[{"u", ",", "v"}], "]"}]}]}], "+", RowBox[{ SubscriptBox["\[Beta]", "u"], RowBox[{ SubscriptBox["\[PartialD]", "v"], RowBox[{"X", "[", RowBox[{"u", ",", "v"}], "]"}]}]}]}]}], ",", RowBox[{"{", RowBox[{ SubscriptBox["\[Alpha]", "u"], ",", SubscriptBox["\[Beta]", "u"]}], "}"}]}], "]"}], "[", RowBox[{"[", "1", "]"}], "]"}], "//", "Simplify"}]}]], "Input", CellChangeTimes->{{3.911355469674904*^9, 3.911355520950759*^9}, { 3.9113555723608527`*^9, 3.9113555784016666`*^9}, {3.9113802823879194`*^9, 3.9113802844476814`*^9}}, CellLabel-> "In[184]:=",ExpressionUUID->"edcf63dd-8b2f-4268-8248-ccc5af3489ed"], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{ SubscriptBox["\[Alpha]", "u"], "\[Rule]", FractionBox[ RowBox[{"2", " ", SqrtBox["2"], " ", SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{"1", "+", RowBox[{"6", " ", SuperscriptBox["v", "2"]}], "+", RowBox[{"2", " ", SuperscriptBox["v", "2"], " ", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}]}], "-", RowBox[{"4", " ", SuperscriptBox["v", "2"], " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}], SuperscriptBox[ RowBox[{"(", RowBox[{ SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{"3", "+", RowBox[{"8", " ", SuperscriptBox["v", "2"]}], "-", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}], "+", RowBox[{"2", " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}], ")"}], RowBox[{"3", "/", "2"}]]]}], ",", RowBox[{ SubscriptBox["\[Beta]", "u"], "\[Rule]", FractionBox[ RowBox[{"2", " ", SqrtBox["2"], " ", "v", " ", SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "3"], " ", RowBox[{"(", RowBox[{ RowBox[{"2", " ", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}]}], "+", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}], ")"}]}], SuperscriptBox[ RowBox[{"(", RowBox[{ SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{"3", "+", RowBox[{"8", " ", SuperscriptBox["v", "2"]}], "-", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}], "+", RowBox[{"2", " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}], ")"}], RowBox[{"3", "/", "2"}]]]}]}], "}"}]], "Output", CellChangeTimes->{{3.911355513326336*^9, 3.911355522297586*^9}, 3.911355588759585*^9, 3.911355758455828*^9, 3.9113802867268567`*^9}, CellLabel-> "Out[184]=",ExpressionUUID->"f2352036-ff57-4736-be6a-8992d8dd6335"] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"coefficientesnv", "=", RowBox[{ RowBox[{ RowBox[{"Solve", "[", RowBox[{ RowBox[{ RowBox[{ SubscriptBox["\[PartialD]", "v"], "n"}], "==", RowBox[{ RowBox[{ SubscriptBox["\[Alpha]", "v"], RowBox[{ SubscriptBox["\[PartialD]", "u"], RowBox[{"X", "[", RowBox[{"u", ",", "v"}], "]"}]}]}], "+", RowBox[{ SubscriptBox["\[Beta]", "v"], RowBox[{ SubscriptBox["\[PartialD]", "v"], RowBox[{"X", "[", RowBox[{"u", ",", "v"}], "]"}]}]}]}]}], ",", RowBox[{"{", RowBox[{ SubscriptBox["\[Alpha]", "v"], ",", SubscriptBox["\[Beta]", "v"]}], "}"}]}], "]"}], "[", RowBox[{"[", "1", "]"}], "]"}], "//", "Simplify"}]}]], "Input", CellChangeTimes->{{3.911355538800262*^9, 3.911355538900625*^9}, { 3.9113555811645*^9, 3.911355586074061*^9}, {3.9113556740216217`*^9, 3.911355676833177*^9}, {3.9113557244511724`*^9, 3.91135572590122*^9}, { 3.911380290757612*^9, 3.911380291983873*^9}}, CellLabel-> "In[185]:=",ExpressionUUID->"9fe3e16f-63ec-41b4-a824-a11f23a0481a"], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{ SubscriptBox["\[Alpha]", "v"], "\[Rule]", RowBox[{"-", FractionBox[ RowBox[{"4", " ", SqrtBox["2"], " ", "v", " ", SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{ RowBox[{"2", " ", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}]}], "+", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}], ")"}]}], SuperscriptBox[ RowBox[{"(", RowBox[{ SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{"3", "+", RowBox[{"8", " ", SuperscriptBox["v", "2"]}], "-", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}], "+", RowBox[{"2", " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}], ")"}], RowBox[{"3", "/", "2"}]]]}]}], ",", RowBox[{ SubscriptBox["\[Beta]", "v"], "\[Rule]", FractionBox[ RowBox[{"2", " ", SqrtBox["2"], " ", SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "3"], " ", RowBox[{"(", RowBox[{ RowBox[{"-", "3"}], "+", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}], "-", RowBox[{"2", " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}], SuperscriptBox[ RowBox[{"(", RowBox[{ SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{"3", "+", RowBox[{"8", " ", SuperscriptBox["v", "2"]}], "-", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}], "+", RowBox[{"2", " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}], ")"}], RowBox[{"3", "/", "2"}]]]}]}], "}"}]], "Output", CellChangeTimes->{3.911355539768743*^9, 3.9113555903589683`*^9, 3.9113556782846403`*^9, 3.9113557262764096`*^9, 3.911355763643739*^9, 3.9113802938725595`*^9}, CellLabel-> "Out[185]=",ExpressionUUID->"dced1839-2900-4e4a-bddb-cd27a618ea1d"] }, Open ]], Cell["\<\ We use these coefficientes to obtain the Weingarten transformation\ \>", "Text", CellChangeTimes->{{3.9113555453931475`*^9, 3.911355561482232*^9}, { 3.9113810125896463`*^9, 3.9113810131030025`*^9}},ExpressionUUID->"80996b38-2c26-4407-81c5-\ 221837f1dc99"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"A", "=", RowBox[{"(", RowBox[{ RowBox[{"(", GridBox[{ { SubscriptBox["\[Alpha]", "u"], SubscriptBox["\[Beta]", "u"]}, { SubscriptBox["\[Alpha]", "v"], SubscriptBox["\[Beta]", "v"]} }], ")"}], "/.", RowBox[{"Join", "[", RowBox[{"coefficientsnu", ",", "coefficientesnv"}], "]"}]}], ")"}]}]], "Input", CellChangeTimes->{{3.9113555931826477`*^9, 3.9113556446344624`*^9}, { 3.911355704451085*^9, 3.9113557063032546`*^9}, {3.9113799253255186`*^9, 3.9113799337546263`*^9}, {3.9113802751585526`*^9, 3.91138030240105*^9}}, CellLabel-> "In[186]:=",ExpressionUUID->"047bc5c7-d48a-4254-9fdf-9aa0683394ea"], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ FractionBox[ RowBox[{"2", " ", SqrtBox["2"], " ", SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{"1", "+", RowBox[{"6", " ", SuperscriptBox["v", "2"]}], "+", RowBox[{"2", " ", SuperscriptBox["v", "2"], " ", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}]}], "-", RowBox[{"4", " ", SuperscriptBox["v", "2"], " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}], SuperscriptBox[ RowBox[{"(", RowBox[{ SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{"3", "+", RowBox[{"8", " ", SuperscriptBox["v", "2"]}], "-", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}], "+", RowBox[{"2", " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}], ")"}], RowBox[{"3", "/", "2"}]]], ",", FractionBox[ RowBox[{"2", " ", SqrtBox["2"], " ", "v", " ", SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "3"], " ", RowBox[{"(", RowBox[{ RowBox[{"2", " ", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}]}], "+", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}], ")"}]}], SuperscriptBox[ RowBox[{"(", RowBox[{ SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{"3", "+", RowBox[{"8", " ", SuperscriptBox["v", "2"]}], "-", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}], "+", RowBox[{"2", " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}], ")"}], RowBox[{"3", "/", "2"}]]]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"-", FractionBox[ RowBox[{"4", " ", SqrtBox["2"], " ", "v", " ", SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{ RowBox[{"2", " ", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}]}], "+", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}], ")"}]}], SuperscriptBox[ RowBox[{"(", RowBox[{ SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{"3", "+", RowBox[{"8", " ", SuperscriptBox["v", "2"]}], "-", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}], "+", RowBox[{"2", " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}], ")"}], RowBox[{"3", "/", "2"}]]]}], ",", FractionBox[ RowBox[{"2", " ", SqrtBox["2"], " ", SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "3"], " ", RowBox[{"(", RowBox[{ RowBox[{"-", "3"}], "+", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}], "-", RowBox[{"2", " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}], SuperscriptBox[ RowBox[{"(", RowBox[{ SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{"3", "+", RowBox[{"8", " ", SuperscriptBox["v", "2"]}], "-", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}], "+", RowBox[{"2", " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}], ")"}], RowBox[{"3", "/", "2"}]]]}], "}"}]}], "}"}]], "Output", CellChangeTimes->{3.911380302873602*^9}, CellLabel-> "Out[186]=",ExpressionUUID->"54a4c446-879d-46b7-b6ed-b195bc4cdd81"] }, Open ]], Cell["The Gauss curvature is ", "Text", CellChangeTimes->{{3.911355684171954*^9, 3.911355690400708*^9}, { 3.911380317692457*^9, 3.9113803187748566`*^9}},ExpressionUUID->"52a7556a-c4c8-4802-9ada-\ 442907eea8c7"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{"Det", "[", "A", "]"}], "//", "Simplify"}]], "Input", CellChangeTimes->{{3.9113556928193865`*^9, 3.9113556947012997`*^9}, { 3.911379941282405*^9, 3.911379944064125*^9}}, CellLabel-> "In[187]:=",ExpressionUUID->"97836029-4fb5-489f-9462-26d0aab7edfc"], Cell[BoxData[ RowBox[{"-", FractionBox["8", RowBox[{ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], " ", SuperscriptBox[ RowBox[{"(", RowBox[{"3", "+", RowBox[{"8", " ", SuperscriptBox["v", "2"]}], "-", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}], "+", RowBox[{"2", " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}], "2"]}]]}]], "Output", CellChangeTimes->{{3.9113556954388647`*^9, 3.911355732506424*^9}, { 3.9113799383754425`*^9, 3.911379944654378*^9}, 3.911380320674068*^9}, CellLabel-> "Out[187]=",ExpressionUUID->"b23e45be-9c93-42c8-a207-74af0ae6f2ab"] }, Open ]], Cell["At point (u,v)=(6,6):", "Text", CellChangeTimes->{{3.9113799522900467`*^9, 3.9113799678231716`*^9}},ExpressionUUID->"af8fd09b-210c-47e4-87bd-\ 4f3f474f442d"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"Det", "[", "A", "]"}], "/.", RowBox[{"{", RowBox[{ RowBox[{"u", "->", "6"}], ",", RowBox[{"v", "->", "6"}]}], "}"}]}], "//", "N"}]], "Input", CellChangeTimes->{{3.9113799704487195`*^9, 3.9113799849146643`*^9}}, CellLabel-> "In[188]:=",ExpressionUUID->"005bb264-a776-49cd-a779-bdf97da03dd3"], Cell[BoxData[ RowBox[{"-", "2.5872783610506233`*^-6"}]], "Output", CellChangeTimes->{{3.911379981282508*^9, 3.911379985417458*^9}, 3.91138032490567*^9}, CellLabel-> "Out[188]=",ExpressionUUID->"7b501ef3-bda5-4664-874f-3c93fa30b8ca"] }, Open ]], Cell["The mean curvature is ", "Text", CellChangeTimes->{{3.9113803270914774`*^9, 3.9113803316488543`*^9}},ExpressionUUID->"80954105-1861-4e8e-ac74-\ 1b6005a9b1d5"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ FractionBox["1", "2"], RowBox[{"Tr", "[", "A", "]"}]}], "//", "Simplify"}]], "Input", CellChangeTimes->{{3.9113803350493298`*^9, 3.911380354049876*^9}}, CellLabel-> "In[191]:=",ExpressionUUID->"3c81c9f2-7176-4a5b-b555-a754066c4c1b"], Cell[BoxData[ FractionBox[ RowBox[{ SqrtBox["2"], " ", SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{ RowBox[{"-", "2"}], "+", RowBox[{"3", " ", SuperscriptBox["v", "2"]}], "+", RowBox[{ RowBox[{"(", RowBox[{"1", "+", RowBox[{"3", " ", SuperscriptBox["v", "2"]}]}], ")"}], " ", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}]}], "-", RowBox[{"2", " ", RowBox[{"(", RowBox[{"1", "+", RowBox[{"3", " ", SuperscriptBox["v", "2"]}]}], ")"}], " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}], SuperscriptBox[ RowBox[{"(", RowBox[{ SuperscriptBox[ RowBox[{"(", RowBox[{"1", "+", SuperscriptBox["v", "2"]}], ")"}], "2"], " ", RowBox[{"(", RowBox[{"3", "+", RowBox[{"8", " ", SuperscriptBox["v", "2"]}], "-", RowBox[{"Cos", "[", RowBox[{"2", " ", "u"}], "]"}], "+", RowBox[{"2", " ", RowBox[{"Sin", "[", RowBox[{"2", " ", "u"}], "]"}]}]}], ")"}]}], ")"}], RowBox[{"3", "/", "2"}]]]], "Output", CellChangeTimes->{{3.9113803395162973`*^9, 3.9113803546953506`*^9}}, CellLabel-> "Out[191]=",ExpressionUUID->"9298f36b-59d4-488b-a1fa-661de71e8e77"] }, Open ]], Cell["At point (u,v)=(6,6):", "Text", CellChangeTimes->{{3.9113799522900467`*^9, 3.9113799678231716`*^9}},ExpressionUUID->"0ad5e9a3-1283-4431-ab54-\ 64430a74381f"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{ FractionBox["1", "2"], RowBox[{"Tr", "[", "A", "]"}]}], "/.", RowBox[{"{", RowBox[{ RowBox[{"u", "->", "6"}], ",", RowBox[{"v", "->", "6"}]}], "}"}]}], "//", "N"}]], "Input", CellChangeTimes->{3.9113803901211433`*^9}, CellLabel-> "In[192]:=",ExpressionUUID->"a1ba1f46-2e82-4f97-8023-b30447ccbe2c"], Cell[BoxData["0.0024492051410458147`"], "Output", CellChangeTimes->{3.911380390796452*^9}, CellLabel-> "Out[192]=",ExpressionUUID->"1faf29f8-f8a0-43ee-90e0-e577a871c5e2"] }, Open ]] }, WindowSize->{820.8, 479.7}, WindowMargins->{{Automatic, -4.7999999999999545`}, {Automatic, -4.8}}, FrontEndVersion->"13.2 para Microsoft Windows (64-bit) (November 18, 2022)", StyleDefinitions->"Default.nb", ExpressionUUID->"6f6890e7-be30-4747-9289-281e999359f7" ] (* End of Notebook Content *) (* Internal cache information *) (*CellTagsOutline CellTagsIndex->{} *) (*CellTagsIndex CellTagsIndex->{} *) (*NotebookFileOutline Notebook[{ Cell[558, 20, 93807, 1541, 308, "Input",ExpressionUUID->"ec6246f3-aed7-455d-9d11-d9eba957b6d2"], Cell[94368, 1563, 428, 12, 28, "Input",ExpressionUUID->"33511201-d594-4862-913d-95bce2d03185"], Cell[94799, 1577, 247, 4, 35, "Text",ExpressionUUID->"9fa58fe8-f85e-4036-a110-da093dd6ca0d"], Cell[95049, 1583, 266, 7, 31, "Input",ExpressionUUID->"75569eb6-d6ea-488b-948c-74cf9d17e9b5"], Cell[95318, 1592, 767, 25, 42, "Text",ExpressionUUID->"c154f180-a5ec-4f71-9270-6101c63f56f3"], Cell[CellGroupData[{ Cell[96110, 1621, 506, 15, 48, "Input",ExpressionUUID->"bf7c240b-cfa6-4b82-a350-76267fcd8cf0"], Cell[96619, 1638, 198, 3, 32, "Output",ExpressionUUID->"072afbba-4f2a-4f9b-b561-982fc6809788"] }, Open ]], Cell[96832, 1644, 962, 33, 42, "Text",ExpressionUUID->"233f750f-5136-4c31-a258-22a9992bf00b"], Cell[CellGroupData[{ Cell[97819, 1681, 687, 22, 48, "Input",ExpressionUUID->"f40411c6-47bc-49ce-bb1d-8e79e27b23a3"], Cell[98509, 1705, 195, 3, 32, "Output",ExpressionUUID->"a39a5e40-0e2c-442d-9505-d6ae64a81e61"] }, Open ]], Cell[98719, 1711, 67524, 1111, 213, "Input",ExpressionUUID->"ea1feb88-60c4-44a9-9feb-3f354c89e880"], Cell[166246, 2824, 752, 24, 31, "Input",ExpressionUUID->"16bf7195-6550-4f78-abd4-a5ebab8826cc"], Cell[167001, 2850, 166, 3, 35, "Text",ExpressionUUID->"530daa1d-770f-4f27-be1a-9d026ae661eb"], Cell[CellGroupData[{ Cell[167192, 2857, 873, 27, 48, "Input",ExpressionUUID->"08a7fea8-95ce-45d9-b128-949f6069d847"], Cell[168068, 2886, 2297, 75, 119, "Output",ExpressionUUID->"e6358eb4-7985-4768-9ae1-7deb9348b333"] }, Open ]], Cell[170380, 2964, 243, 6, 35, "Text",ExpressionUUID->"79fa5935-105b-4600-ba8a-e045e1b80a72"], Cell[CellGroupData[{ Cell[170648, 2974, 1088, 31, 28, "Input",ExpressionUUID->"edcf63dd-8b2f-4268-8248-ccc5af3489ed"], Cell[171739, 3007, 2554, 77, 111, "Output",ExpressionUUID->"f2352036-ff57-4736-be6a-8992d8dd6335"] }, Open ]], Cell[CellGroupData[{ Cell[174330, 3089, 1177, 32, 28, "Input",ExpressionUUID->"9fe3e16f-63ec-41b4-a824-a11f23a0481a"], Cell[175510, 3123, 2474, 75, 60, "Output",ExpressionUUID->"dced1839-2900-4e4a-bddb-cd27a618ea1d"] }, Open ]], Cell[177999, 3201, 271, 6, 35, "Text",ExpressionUUID->"80996b38-2c26-4407-81c5-221837f1dc99"], Cell[CellGroupData[{ Cell[178295, 3211, 706, 19, 44, "Input",ExpressionUUID->"047bc5c7-d48a-4254-9fdf-9aa0683394ea"], Cell[179004, 3232, 4597, 140, 111, "Output",ExpressionUUID->"54a4c446-879d-46b7-b6ed-b195bc4cdd81"] }, Open ]], Cell[183616, 3375, 216, 4, 35, "Text",ExpressionUUID->"52a7556a-c4c8-4802-9ada-442907eea8c7"], Cell[CellGroupData[{ Cell[183857, 3383, 289, 6, 28, "Input",ExpressionUUID->"97836029-4fb5-489f-9462-26d0aab7edfc"], Cell[184149, 3391, 709, 20, 55, "Output",ExpressionUUID->"b23e45be-9c93-42c8-a207-74af0ae6f2ab"] }, Open ]], Cell[184873, 3414, 167, 3, 35, "Text",ExpressionUUID->"af8fd09b-210c-47e4-87bd-4f3f474f442d"], Cell[CellGroupData[{ Cell[185065, 3421, 361, 10, 28, "Input",ExpressionUUID->"005bb264-a776-49cd-a779-bdf97da03dd3"], Cell[185429, 3433, 241, 5, 32, "Output",ExpressionUUID->"7b501ef3-bda5-4664-874f-3c93fa30b8ca"] }, Open ]], Cell[185685, 3441, 168, 3, 35, "Text",ExpressionUUID->"80954105-1861-4e8e-ac74-1b6005a9b1d5"], Cell[CellGroupData[{ Cell[185878, 3448, 278, 7, 45, "Input",ExpressionUUID->"3c81c9f2-7176-4a5b-b555-a754066c4c1b"], Cell[186159, 3457, 1409, 46, 60, "Output",ExpressionUUID->"9298f36b-59d4-488b-a1fa-661de71e8e77"] }, Open ]], Cell[187583, 3506, 167, 3, 35, "Text",ExpressionUUID->"0ad5e9a3-1283-4431-ab54-64430a74381f"], Cell[CellGroupData[{ Cell[187775, 3513, 377, 12, 45, "Input",ExpressionUUID->"a1ba1f46-2e82-4f97-8023-b30447ccbe2c"], Cell[188155, 3527, 174, 3, 32, "Output",ExpressionUUID->"1faf29f8-f8a0-43ee-90e0-e577a871c5e2"] }, Open ]] } ] *)