(* Content-type: application/vnd.wolfram.mathematica *) (*** Wolfram Notebook File ***) (* http://www.wolfram.com/nb *) (* CreatedBy='Mathematica 11.3' *) (*CacheID: 234*) (* Internal cache information: NotebookFileLineBreakTest NotebookFileLineBreakTest NotebookDataPosition[ 158, 7] NotebookDataLength[ 203242, 3548] NotebookOptionsPosition[ 199624, 3485] NotebookOutlinePosition[ 200060, 3502] CellTagsIndexPosition[ 200017, 3499] WindowFrame->Normal*) (* Beginning of Notebook Content *) Notebook[{ Cell[TextData[Cell[BoxData[ GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJzs3QdgFNXaN/BNNpveCyWhhJCQQKQjioBeEQSlCoKiVEWxgjQRpCldqoBI R5ogSCeE3kJvgSSk997LJtk27Zs5Z2YS0cu9+inyXv+/y42bsrOzs+ecmXme U5q8M37g+9YajWaSvfhl4OipL06cOHraIHfxm8HjJn04dtx7Y14ZN/m9se9N fPYdrfhDWyuNplz8U+mxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/2w8zz/0AAAAAAAAAP6Zfn1jiFtF AAAAAPiLcBz3H38CIPxWJgv3KQAAAAAAACDg9hAAAAAAHgv1spNmsnAVCr+p dsEQiwrKCQAAAAAAADzU4xG3igAAAADwl1LHZLEs+/fuCTyx1HQnLSS4SQEA AAAAAAA1jYV8FgAAAAA8HphmEP4jzDQIAAAAAAAAqoyMjCtXrly4cOHy5csX L168dOmS+OASAAAAAMBfIDk5WUB6Av6Th0oIsp8AAAAAAAD/cOKN4apVq3SE ra2tnZ2d+FWr1doCAAAAAPwFpk6d+ndfAsP/GZGRkb169erZs+err75KH/QC AAAAAACAf6RXXnklJCREo9FYE+IDKysr+gAAAAAA4E83ZcoUlmXp6BssngWP lpiYOHny5M8//3wyIT6YAgAAAAAAAP9I4l3hiy++aGVl9XcHNgAAAADgH2HS pEk0VVF75VZMPwgAAAAAAAAAj7Zs2bK/O6oBAAAAAP8UkydP/ruvfwEAAAAA AADg/x7kswAAAADgsUE+CwAAAAAAAAD+AOSzAAAAAOCxQT4LAAAAAAAAAP4A 5LMAAAAA4LFBPgsAAAAAAAAA/gDkswAAAADgsUE+CwAAAAAAAAD+AOSzAAAA AOCxQT4LAAAAAAAAAP4A5LMAAAAA4LFBPgsAAAAAAAAA/gDkswAAAADgsUE+ C/4bvPwfVhA48oiT/vHSF0Ywkx/wDPk7TmAFjnvEhjhefsAL9BkWsnXpKSz9 OXk2K/z7jfyJeLLL9L2I3/GstHs83S2W7pf0Hc9z9A9B9m+OBU8OJG+RC4z4 EXOsenjpE5UDLCgHlxQKXvoPS3/I/qV7DvC/hrRRAq2VUqtMW+Caxo0VWza5 yvFCrcr4WxsSLLQy8qRB5gWlUnPkh7xSc0nrTV6AV58qNd28/DPy0oy8U9Lu MPRZnFK75Ufys6UWQG1hpdZX3A2O/JwzyycI8i540qb8OUftjyFnOJ6cDJUd 5nj6Y9qCcYyFnt0ecZz/NsoR5vna3yoHlZNO3rz8acitsfRf+bfqufsx7CZD Tw+kRDG0PEgvLZZsuuuMcvawyHsuF3m6dxZ59+VPgWPkZ3HyOyZXMnJJZJUv nKAUdPJ76QU4syCXf1o8a22EU6pGzVlMPPHRg8YrJzleLiUsI+8qr+wiRw4r /SAsAq/UCp61kIJUU+9YadfFX7O8/A5ZeZcZuQ7SneHps+jVFKm5SpWnP5Su behB4OUCq1RkpQxLlYtRf1HrYNbg6WbJn7PKDtSczWuOi7x/pJZbarUhbE0p AkJuY7maj4IeYdL8cjUXnWq1VJ7GKk9mHsNeqlf+PLkY5pUPVPx85SrDcNIF M6f8/MlCmw7hocZBOpfw5BCaBOntWdSartwHSC2A/GHU3BfQBywt8xxHDj9L 669am+RXrXVqZOVqK7UAYo2wsLTu87WeIv2KIdVU/WDVusPJJxm5DskbFF+X l2632JomiF4/c+TP5Je3KHvBqY2PfKnA1+ygwJvVWzJyHlBq+u9E81lWVlZ/ d2wDAAAA4HfDNcyT76HPaMqUKYIS/lIvbgEeohQNGtWSIyKMIN94k7shhtxb yTmpf78hGujjeDlrwXFK6Ea5+7Mot4r/NmfyZ1KjqfIuMMojTg6FKfekvPDI 9/WP89ufjdKGMDTowcphGUGN19TcQdPYOK9G/qRjzggkK4q8IcAfwahhfBJg 5Gmok9ZBTuD/u1M8w8uheBpAs9QO09GqqqT/Sa8D8tDMi+22FA8kEVgzr8TQ 5PgmJ/dPYJQnig9ZOUZn4Wp+yPEsJ8f9LBa1S4PakkhviBH+xo4FLD2ivFkO M3LKXtOMj5LEklP4nOXv2s/fSz0F1/5Hg65MTaaUNODi5/WI/ip/2g7R5Asn HXBafFlGDhnzwi8TInRnLHKxlPaNkz4O+fJCPaEr5ybpzy2/COdy5IXIhml0 mlfOViw5KdErFnqYGDVerFweiJ8yQ/9ePSo8wyjpOIHE+QV6BUV+IO5Zre4d Soydpo856Z2KzzSTXAarJEnlay453cbQ+sATnJKmFmsfSZlxcnaD49VESe0K q2SiOfnFyeta5Boql+FHxbGVHVdD6OTv6bbEnRT3gVX2Rw6eM/TqimGFX0TU gZKTiaRI10r88covOTm3q2ZVGKlVkdpGpRgxj6M9VFpgUmWUKkmTp5ySk6NN xBPb8Ys010rTQZsyTuBrnRTlDJaZnsSkH8tvVD6ZKg2McsClBJ68PVLpLOJn ItdZ0sLwcncOJT0pdflja75VKgKjpIDpp6zWX6XJqcmBW3jxs2dZ2seD3n7x tU6OnEWpcSaSoaOtnEWuj3Lr8Ys2Xj4svJLsl/urcLQDwB/Lky5ZssTa2pqG Gmi0AXEhAAAAeGLVvlyh1zBPJvXKSv1W3NtHXGX94Quwh17o/3+Dfx2tVit+ nTRpUs31Pi9f2iOxBQ8jsR4aSVLuszi5Z6DaS5SR+/fVHo3z6+3Q3sLKPZpZ GY0lR5xIl0WmVq/Bv5xyB0dfkJFjR3JUgeWVjtrk90/kffqThFPiGoISRad3 9DVjrtS4qHJbT6IM8sgOQY0kAMB/Tx4sIo9zpHmiml9ySm9zUkEfEadSI/6C /EBsis1kC8qWBF7NJvDKQBnyRIuarRJq+j/I0UJ17IwcUiOxQqXPOR3SojTC UtSRYVmSKONon/+auJ8g5yn+tnZYbsd4i9J9XRkXw5mVlk1OoPB/rHf741XT 0vLKGV16ZCIZK5J/lAdby+WHvuXHcF7mlTOJRWAYNaxvEdRhVkqyVU4/kcFT HCktdCiZdAnB8uqwa7pVmnPhaf3glACvPApGHgpD8kHSeYuVU2JyxFiOYMuB fLkDmDSIW87ZspxBuUagowjlOH9N5kt+AV4ddMPTuDfDyykkTqmc5DjLySZG uVLi1L0VavUAob+QskjKcLqatJfyZgVeOfuKO8YxtU6vnFl+KH7iTLmeNxm5 2u3GLz4RmrXiGHVknMBZ5Gw1T8an8EqyQ21GSLKP7CD5XGgNZv/u8ZVPFOWy h5YxUmgZnmZD1DFEahaSlwcQ0QyX/FzG/DjamZpcrVwSyN4xaspG3tUnOZ8l CGpppbcz8nlHvudl1UvQmstRkphWOy3Qz4XWuFoJYumkwPJ0kgplUCQd60Wq kpz/qvkcydlQ/JSlcceMoI7hkiorabWkXVMe8/JYLYG1KKk4TmmN5bHSUoHh 6XAqNeVGX+IX71d5ITLyivQbUV6X/tciHwrpOzoy+o+cZsUtxMTEbNiwYePG jevWrVu/fj19sAEAAADgCfPhhx96eHg8lMN6klNaj/ZQvum/Tz9ZKf6Cnfrz qfnHOnXqDB06dOHChVevXlUv6YVaKS2AX5D7McphHJ4tM5bEJFw8f/dyxOWr ERHXb5+/l52hZ43k3u3R97M8zyrd1wXOXGIsvhdzI+LS1StXrly7dPnejZzK 4mo6Rc/jKYjyPFe8PrMg9WZEhPR2Ll++ffN+Rny1YHwMHdH/d5BenZWpGYnX L166fIW4LLl0LeJSxNXzV66IB/aq+IU8uHz5ys1r16KvJaTEZ5ZmGyxykSEh Pkw4CPD7KYM4pKpkNJXEZd27dCYiJaGoupL8Voq5k2j2I2qXOn0Yr45OVTpz c+bS6rKE/Kx7SdFX70ScvHjqSFj48dPHzxw/e+3s7ftX41Kjcypyyg1q9KzW GC6GI5PCkcgZw1vKqsuTsxPvJty+fOPSmaNHjx4+fuTY8cPi1o4fv3D8yu3r D5Jis0syzXS8rxLb5wSarfhlxu1xo5HcWp02OJavLM+JjLt65vixS6ej0hP1 BmUsmuXJ7/9Q62LPRIfQkGn4Cqvy47PiLkZdPX7qQW56qVGNoKrzWP71eybN 0MfJ+SOO400CX6YvSspKiUyIvCIWvzMnwsPCwo4cPxl27s6ZyNibyfmx+fpi C0fjzwKJ1rJKjeCU6cAsgokTzIaS3KLM+4n3L0ReOnn2RNih48eOHQ87cfjE ybCIY5ciL0QnRGYUZesrK5XRxTRKrMwBp07iJ1ayYmP+g5SkS3fDr929lR5X zSnJL4GGv0lEvMqkzyzMfJAWc+n29TMRJ46dOHY0POxE+JkLJ27cuxKb/iCz KKfKaJDftDTAiU62SV6XEeThaWausqA8Kykj5lb07VPXr4hv+8iRY0cPnz19 4uq1iDuxd9IKUiuMegurzIRJclx0OApP0nycmrGUr6vom5LyZQaxsl2+c/Wn beGR11OLy/haZ1/+4c9afroUvecEY2F5QUJGwqXoW8fPnxGr75ljYSfFOnzi zLVT16Mvx6fFF5pKzcr8bIIyzkQqQYa/tOz8H0IvlUmeT2w5KyzFGTmpdxOi Lty+fPB82E9HDu7dsfvHH/fs3rvrp917D/8Yfv7QufNnrt25fD8uKi0vocRU aGJYnnkso0AZQc1li1VIqDTqk9Mvnzh74ca1+MJccS8Ei5ymfCJnk1Rua5UE HF/TgHNctaEsrTDjXkrcrXs3jl8MP3Hi+EmxJIeFR5y9du/6g5TYtPJsPSee jEh7oIyhU4dv1Qw7Jg0NZ2KMRXqxvsdfv3/t9JWTJ8VT24kjR46fPhd2PfLK /YyorJL8ymoLR/O7PBmtyZL8GOkrwlqUPRO/1RsrMioy7qc8uBB9+8y5E9KO HT8cHnb0wtkr9y5FJ0ZlFeVUGgw0B8Ww6lhpJTklbVnK0xlzynLj0hJuRd4+ feP0idPhx06GHT954tzJazcjohLuJmUlllbrWcZSq9OIPI0p+7vrKT3MLMvW /hYAAADgCXT06NH69evrdLr/K4OzHko8/WYS6vempf6vpLF+LSAgYPXq1Tk5 OcIvk1kCLkHhtyhBUKWbvjE6++qqlQMHj+z9et9+vQYMGjPwg52b7uYlmfia EVy/vSGpv6J8t8NZzEV308InfPbO4IH9+vbv9/qANyZ8eDD6XE6V0sH4r6dM clidtDd8ywcD+vXvN6B//z5jR0/bujKxOt/IqrfnPP+oZcH+qWofEWkqqqqY jXtWjuk/oHe/fn36i//EY9mvb79+A8Rv+0kGkK99+vTt36f/m/0Hvjvsi2VT fz73Y0xWRrnRwFqUJQIA4HeqmaiK4/nM1Gvfbp3c85nX1y49nxzHKQNgBaGm 5/9vb0Pu9k9CYTRwxwqWMra6pCTlavT5Dfs2jFs4seeH/Vv1aB8U1LRZQNBT IS+89uzw6QOmrp2+89SBmLSEckMpCcXLA2jkLdKhNBaDpbgi80rU6RU/rhw9 +4PnX+sRKm6lefPmQc1ahAS2Cwnp3u718f1mrJ68/dSOqOzEcqbcLI37pV0g yNCbv3nQk7JAGJ2EioR4Dbfu7P1s3ptPP9X2tZe+OfZ9TAXHm2n64wm+kKo1 2x75Vtpnk56pLK8szs6MC795dPHmr/tOGNyy29pTu+NK1HQIHdgkD0b4a/eP l6fFEwuisdJUkVaacv5K+De7vhm58OOX3+nb6tk2TQObNw1o2zG0x7DOYz5/ Y976rw9dOJ5ckKWvrjKTMVYMHQihTLclvkfWxLMlhsKopCtrD234eNGEHqN6 PdW1VXBISEhQcLNmwaHNWnVu++qonp9/M+H7A5uvPYgsMuhZhqNzS3LyKCeW q2YZg8VQrS9OTY0/dvPY3K2zB0zsMPSjKfs25pqNyigkaRwTbzaZiksyb8dc 3XFg+8TlU/t+MrBTnzZBbVsEBoW0DenUu93bE/pNXf3FhuM/3EhJLNIXmQSG YeiEgbygRrV5xlRRVfwgNernU7tnbvh6yBfvdB748lPNA5sENwtt1rFHmwFj e3y6cOyqXevO37ueZSioYMmKlRaamlRW2iLjqGh2q1Z3IylLWF1UFn/g6ra3 pgxo4Ntjxgf7Hlw3cjVxcSWfpRYUEq028bzBZCjMTT558+C8jfPemjLyuZee DmndLDgwICgguEVI51efHTZx0PxN0w7cPZWWn1dtIml01ki3QBcrAkJpsQ0M W6ovfBAXsWXv6pFfffSvEf0De7St7+/h6GirtXfU2djb2ddv6tH25ZAevTu9 9WmPSd+8u3L38sPXIvLLC5jHMdzNJKcl5bG4rJCUF712e/+WvZ4bP2rhuaOl 0mBG2hdCGXX7pFFndGRqBhWaS4zV+dWFMdFXthxZO+7bKX0/Ghr6QtuQkMCg ZoGtm7Xs+fQbE/vOWDlpx8kfb2ZGFZtLDSaTQGbFFmhGjI49lEewMoLJYimv KotKi9pz7sfpa2cNGD+4Q+82wU89FShV1U49W77xaZ/PVn6xMXzXzeSY4nK9 hTOxjDzcUkk30+4fFq6KMZZW5kfFXt0Vtmny2s/7fvR2uxefbt4yuHlQ0zYB oX06vT6x37Sl47aEbbmdHJNbVWZgzYIyJkuQRxGLDRdr1lcXpaXf33P+wJz1 i4ZNG911cKeWHUODmgWFNm3zYmi/d3p+Ou/TFXu/PR19M7siv5ozMySTzslt xf/PDVjtYAJCCgAAAPAEOnTokK+vr5rP0jzZKa2HPDT34P9nWupPSY391ej8 iuq81k2bNl2zZk16ero0E1Gtq01cecJvolPo1EyBUn0h6fDYj9y8m7l7urm5 uLr4OXsPemXthT05RuUO/d9tSBlCIG2uoChy84HxjYMbeni7uru61nH1/Ve7 1Vd/TK4icSwL96gN/Unvi6WTKBorIhdtmd7Gw8XV1d3NzbVNYM+ZY+5Wpxlq r54Fv/ZQPsusvzJ5wfvNXdwc3UVuLh5u7i5uzh5unh5u3m6uXm5uHuKPXd1d XNxc3dy93Tz9vOv5NWzyXJPO41797vy+pLJidW1rAPivkVaXrEXI0nRVatq5 L9e84e/aZvGk8OS7dGCGsqbQI3LGvKDOsiZ/ZS05lrz9x78fMmlQy87BjRr7 1atT19PHxcnV3l7n5OCoc3ZycnLx9PGq18CncUiTl4a9PGvb2tiytKpqM3k+ qyyqwvEGofJW3OFJK8Y892KQr399L18vB3E7djpH8Z+tzsrZ3kFn72Lr4eXs 4+vn37HJM6P6zD+0/V5eOivPgUZi6XSEyN/XQCiT70n7w5kEobDk4vQV73YI DHqu7gc/LL+aEadnlHlULU9wMyaPsyYY6Y1UJ6ff2X5666crJ3Qb3Klpu8Z+ DevX8azXrMmS05sTSS99eSJYXn36X76HclQ2LevmmoPze3/wcmj7Zg0bNvSq 4+Xs5e7goLOxt7VzcHBydPR08azjXb+BX/AzIS991n/1qR8S86ulfigmMi8e Z1GmueP0sVk3F278otuAzv6N/L2869p6eehcxCJs46CztbK112kd3R1cPZzr +Ls3atLwxbe7zdw143phqt4sD++S1i2qNpQm5EVuO/3Dx6vGPT+4W2CgeKB8 PTwdXu02/tCGEq5SYOhaVAJXKhQeOL/tvdlD2/UMbhDo5+lX19XHw97TwVF8 NRudnaO9i6Ont5NPXZ/GoQFd+nacsnbR9aLoMgtLl8fi6WSbepaJjD40/fvx XQZ3bRrYsK5Y9Xw93dyd7O1snGzt7XQOjnau3q4+fp71Gzdo17PViHnvh8Vc yamyMByrzBsojdLglPnP5Iwk+ejZUlZ/Nz584vKR7V8M8a7jqtW6twv6ZN20 63lmXpnX8aF8Fp0s2phXkXb4/PqBn/cL7dzMx8/Pw9fd1s1ZRz4OO3sHGzsn Vwf3ei51GvsFPN1q6Izxu2+fKLBUKil1tRkCCUkV8xV3E64s2jjxuZ7tGgY0 rO8X3L5xjzefe3vUi68P6PZyjx7dXunZo9eQN155a0TXQb2bvdTet0WTBj6+ bfyC3hq6/dbRfMtjaA+VBKs8FzfHx2ZfmbeqlWNLzzdemnT4h2LWTBtFVp2M 8klD81m1M22V+rRjl3YOmzG8dce2foEN6jfy9nHzcRLPafaOYkF2ttU5ig2L a726Pg0CmoT27zp16+Jr6Q8MSmcPQfrs1IG40n9NiakRS3fO6T7s+YAWjf3q 13USWyp3nYPOzsbGVuto7+Tg5enoU7eOf4smXXs/N2XrzAuJkWW8PG0jWTfL SKYOFRsuccfu/vTezNGtu7bxb9SwfgNfp3reDva2DnbimdJJJ+6Ys7uXu0+9 ug1b+gf1fm7yhrlnEuKNrKCMDGXpmy27lnxq5nfj/zWoXZOWjev41vf2cXf0 crIXN+Roayftj4eXUx1fD7+g+qHPhIyY89nPd0/lm0x0fkR5+eDfXVEfx9KK AAAAAH+GI0eONGjQgK7BpGZJ/uaczW/5d1m2/5iB+mMTCdYe/1V7O79rI38d dU8CAgLWrl2bkZGhdqB6aKIAgF/iauae4XhOfyZh3/B3tTY+UqkSmwGdjaZh 3Xe/m34xo4CuwP5vKCuwS1NhsOXnbu0c/XErKy9HrVQyrZ20zu2bLo3YnlhR s1DFX46uxmzU3567bkKgVqojUv3wfXH68NvmpCpGnpyH55/Im/S/Xe27V/FI GksiPp09vKFGSp5rSUtjo7HW/Lr5k3LrGukvbDU6jdZB4+Lv3en9wasvHUyr FHgBSXWA34Uu4aMuNM+xqYnhU1cNaOQa/NXEIw8iOUb+E+E/pOb52tFIOj2Y Pkp/d9zng0Nae+mcNFqxmdZai5VWrNikXltppDZTrPDSD2w0no3s277WadyG ZRFp96tMghoR5xhz6d34w5/NHBHSvrGdo8bOxlonPk3cgpZeoIlnAGkL1lLz K/7c2tbGtqFrm0Evzt65+mZ+GXmH8rpUf+lx/I9ISJ8uFMma881le/ZP6Tao W6+WI1fNuZB0vazKQBYnIeODuMc0xPiPUPJZJO/GWSpK7i/6dtbLvTs3ebqZ Qz1bGzsr8dNxsbFvGbDg1Ja4KrKYi3IBwP+HIvQn7R8dGc1x3LW7P7w3u4tb EweNnZaencXiIRYV8dpeKjAaqfxYS99p7TXujeo/Pbzn/L3rH1RUW3iaUaXT aLFVd1Ivzv9+7DM9A23cXcRCa0PmlbCiBVj8r9ZGKo3kC9m6s49jcM+2I1cu iUiOrGBYWquE7LSb3+2a8XzProHt/d3cXcnTpZugVzt9uG9TgbQcFU8Xz6nO Yx7M+eadtk/76lyl4i3+jY1W+kd3VnyiVOJJDdJZOdV1CurVdvSq2WH371SZ GXkgjHgMivnqI2ETn38r1MXH0Ya8Z+mpZB91GnKClR7SGmTrZd/wuQYvfzFi +42j2dVG+VOWpjRj1Q1K74EMwTBm50TtPfb98LEDmrVtqHOzkeqvVuNq3+WT /usiIhnexPPKIj2CIH/hSAuTl3f7+73zBwzs7BnopXWW3oKOtAfiTtiop3pr aT9tNRonG9/WDfpOGbLh7InC6jJpyAfLc8wTXC8eP0bQ34o//MU3Y1/4V4eO rV4d1Wfcwqnf/vD9/lN7T186cuLE6bDwU8eOHzt25MyZQxdP7Tl5eMvh7Us2 Lp361cSPPx0xYWZ45JmCx9Ii1h44LxXyoqqMC9dXzFg5e8fa8PibFl5dIkrJ Wj5p5AUmla8cJxRmR8zaOLl5qK9GrFo6K7nsig0fuWZVGhYrsW7YajVedkE9 23/07ZyTcSlmwVBrBk3ypay65HrisUnzxrTvEupRx0WsS9L9DKnnpHpqreRT m7VU38W2xaVJ91aDZ32859rxnDLSTLFyVxOp7ajIuzH3h6lBLRvaOtqJfy+2 VDqyc9YaepIVN2lFIxt21po6LsHPP/X+8snHE2KrLVWCWTnFM0LWzmNLe77c zMVLaytVTCsbesq2kjdFKqv02MZG62Dl167JgOnvbog4U2jUy9Mk8H+4nmIl bgAAAHjyHT58uH79+ra2tuqSTH9i2uWv8NBkgxqS6hL339nZ2dvbu1GjRiEh Ia1aterYsWOXLl1eeuml3r17v/baa0OGDBk6dOjbb789fPjwESNGjCTEB8OG DXvrrbfefPPNwYMH9+/f/+WXX+7atWuHDh1atGjRuHHjevXqubi40MFrT8iR qf0xiV+bNGmyZs2azMxMAdec8N/g5SUk5CBY5cX4/SNH6Kzqam1o3kKKRnb4 6PVVEZequEdON8IpU/eY9A9W7JvdsYOdtaO0AfH2zEnr0iZg6bkdyWXyqzye eUukPaouu/vV2gn+NnJ1aVb/+Umjb5pTqs0183TxAtZ1+pVf5bMufzJ7WD0S qRbvl21tXeq6NWzk49vIl/Dz85O+1vdt0qh+04YeDXy0ts7WUvRAK/7fz+v1 5aOPxRZLobfHsiIEwP8UTl6TXvqXnnhy8rLXGpJ8VvxtVmDlNYD+Q+d5JelF vyGPyu5WXhz4emhdLykU52rl2cC5cZBv81aBrdoENw8JfapZyxZ+gQ107vYa W5IIsLLz0dTp9vSCw+tiy0wkryOF1s3JFbHfbhrWqK2fll58SS2tSx3bJi3q NW8TGNK8eWizp5r7tw7x8a9r62ZPY/a2Go2H09Pvdfvm8k2DoGfpNE90ob2/ sxM4XcZLPNim0uiCs2MmDezbf9TS8WFxySazmcQjlZ4YzBMZ1xXUAXrKymhG xlxcfv6zaWOebtncv3lTJx+dzlb6AOx0Dq0aLzy1Ka5aUCfBe4xvipxzjQJ/ NmL+62PqkZOEg5O9TyOvgOYNW7QLCGnWokXIU80DWrfwDqxr5+4o/tqOZLd8 nJ75oNvy6zdKjRXSKAoyvZh4sZG28ei857vW0blIJc/GWmNrpfOwq9+0TnCr oBatxJLcIrRpq5B6Teu7eEhZWynYa62rZ+36fMv5RzfElhvpkp5sWtKJr9eO CW7VukmrRm51XLUkMiz+ba/OH+/bUCQwytgI3phkvDP6027+nlLG1lHj4ePc KKB+SOuAlq1Dmoe2bNG0bQvfUD9nb0crms+V/lO3S+svti26lW+k05dJfX6y DJXbd/Vr2sOd5IydPO0bBPsEtGsa2iokOKhF88DWof6hAR4N3G1sdVKSTzqL anx9Bi7+5HBimolVEpCcRR4VT0PMLG9KTY9cv/ebvqOftnV10ErP0tnpXHzd Qp9pM3zBhzsvRxqFajqrWk0+i5PzWXmHji9/5Y12tnI/FZ2LtWdjz1btWnZ5 4dkX/9Xtped79Oj6YueWrZv5NPC0JaVIo/MI9uw19d2wpOsFJkZZRAsoizmn 9O7cVR91eqH9M/V6zhyzPXx3XFFyJcPWuvZlpOGBnDpLntT5qjq7LO16zMVD V9JS0ysfx3WpMk0lR6er5aWpMSv1pVl5+YVFeouJq/mzJ/PTpfNLCHTxLI6O nC1ODxs7b6ATSTo52zjXtQ8M8Wv2VNOQ0GYtg5q3CGrX3LdlA/s6TnZSCkkr thd2Vk16tvh42/pcQ7aZJ4s7MuTjMDMl15POfrV0SL3Q+lZaG1tbcjekc/a2 9wuoH9oyKKRl6FPBbVo0aNnQtZ6rja012ZzYCtm1CRi07IODkfcNvInuHhns xQj6vPApS0Y4SBXTWqdzqefSKMgvpE3T5s2Dg8WGKrB9y4ZBDR19nHU2GtqV xNa6cc+gUevWpOlT5IVopbdpSlq2dUpLX42059YOHvb1gusEtfBv2T6oRcvQ Fs3ah/q3bupV301rZ03u3cSGzCPEt8/MN89nxlfyFnKH9gdKFcZnAQAAwP8V aj7r8aZl/n+JO+zt7d2kSZNWrVo999xzvXv3fuutt8aOHTtx4sRZs2bNnz9/ 5cqV69at27Fjx88//3zkyJHw8PDTp0+fPXv2woULFy9ejCAuXbp0/vx58ecn T54MCws7cOCA+Pfr169fsWLF3Llzp0yZ8sknn7z99tuvvPJKhw4dAgICPDw8 ak/M+Ld46NXp+lk0nyX8apprZLjg3yBxMPH/FWfj948crdXWEwuTlNISb4rs rByebf3+pq+TzFX8Q+ttMYKy7Ijcb0+6Lc5OPjpm5mueNQN5NPYalzaBSy/u Tqgi6z7QtaelZZdZtWe4PJ+JlO8gi0Qoy9PL3UfpMAXl1p+j/5c7J1vo7TiZ OYgV5Af0T1nBVHr76/WTGlnJ/b2b1v3XF6NuVqVWGuQ3wSsLSdM+zrwy5RTP C/JdLZmKh86vxSlHSQ4EKR0maQ9WecZFC8ORrA2nDolkyBSL8ndyNEkaCye9 C7WDtLJleel55aUFZQpHeudukXaGlTNwyvaVaR4FwUxfQfp7Rp1ZqGY78q/o sy1yX01WDmr9qns+V+uBsejquFmjfG001lJIUFPfvXWfoLEz3xoxevjwt4eP GCV+fXv4WyNGjPrgw6HTJnb/oLtnsI/GTvwf6SaqCX77pTnhl1ihnLwyS9Yi IUdYWpmLrlLNyIsDMMrRVI4VQ/uSmsh+0g6mNIJhUjqbMspiB/Kq5bySNeNJ QVIOAiMvA0FWP6fTNKmjEujiJuqBJHtoIRN2CQJvqXX81XEBnLKfHC8/XS7V 8mtzFroAtzzPG0+LvVx+GHnqRU4JApIXUj+gWhOfqU8hak10xvzi05J7E0ul nuXldZQ4Gn6X91kJgtKhNRxfqwBzmCiqBl8Tb1TKYc3oJ1JI6SgegYaRLerK I+QLo4x9oEXFolYiUipqUgZyFeTlkkA/KloIacNFl6eniQlOaWDJxlISz3y+ vF+AW/BX44/G3+F+UQwehVMaUvpFfFbZff35gYPa1qln72nr95Tny28/9+H8 MYvWfvnd2gXzFq5bPXPj4qEz3gl+ydfWy95aZ60hV4TW1t2/GLYrKkWuY4yl 5GLcnj596jl5kE7v4l9pdZ4ubfq0em/e6G83f7Vw4TdLZ6/9dsK6Bb0mD/Bv 62+jdRD/TCe1CrYdmvVaMS2FyzSycpskMI+6PCEVnKMTo9H3wMlHiZOaPbXR MDBKbSB/xZNqyyn12kQ/OUbepNiWcqyygpISsisoTD12bs5LI+Z+N/dcegap /WTNMQurviRfu8JI07GSrdNGWGkulN1m5I+UNjl0Git6xmFqKrFS60kRMtec d6TVmkjXfKlqc7+nV714fjFzrKkq5aeju79dM/+D7TOCO9RzcZIyEY7Wdq2b LDi1Kb5KUNuB/0hp99RTFd1bQZm+ijZ/8uRzysUA/WtWPl8zgtxk09EPJ84v 6D/W19bG2dU1sEvj/u93n/rNJ6u2frl46TeL561dPXHT0tem9m/UqYmdlNOh SRan0LodF0xN0CcY6CAj8dXzko68M2MQGU2uISODreo51vlX0xEzBi5cNm3l ukWLFm1ePX3tgoHTR7boGeLgaWclD93S2Vh3+3r8wehY+dgX5ccfv7Zv7rqN s1Z8GPJKS51yQd2788f7NhUwyqgoga3OFO6MGfOyv6/OVVcnxP35N595d86I ed9+se77eYuXfbdq9oalIxd/HNq3qUtdJzo2RBq+YdXpo1e/ux3HcwY6wyGb adZv3/FaaI+6jnbeDdxa9ggdPnngnJWffbth/sJ5y1fM3rDs02XTuozp7BLs oSW1RepcZOXY99nP9m3Il9arYmqKDceQcyRrKjZlrdk5rUP/po7klsDO2lZn 7dnEvdN7L608tfFBQaZFmVmaV1K39LH4oXHF+SeHT33N31XqDCgeIFtrz+Z1 e37a67ujqy88uBAdF5cQk5l8OfLcon0Lur3/vJObvXSkbcQmwSvQeczOFbey igW1hfmnkU/QtJER5OJUypZcurq4Tf92rep1mzXkXE5ipUVuc3j+0XdhD/+W r3XWYJXV8Ug14mr6l1nkacPp2n+CcmnIK1dV5MqYJX8mqJeg6pUGWTZP6RFh kSej4+Q591i64BKtuRydnZReDpHns8qFI52vVRqOVOuqnk64J89My8u5M/m5 dHpMXr0O4eV1GeVSTdsI+Y3XXMXJPTtYepFT08yq06GLTylKDXt39pte9g6u rk7N6nQa2v7Dr4YvWj992eq5i5YuXzJj66oRC8e26NHGw0MrFWPpLsWqjkvr kW0OpN8pMrDkLoC8clHO1fm7xjXwslHGLIunQQc3h9DuzUfNGrxs05zlK1at mbVh6dgV49sNau8c4G5tZ03aKfF/jk83H7zmi6Ty/Gpy2UkPnFCZfnLyilHu WnsnJ8fmXk+/1fajWW8t3vzFkhXzvlm0YtGcnavGLBjXrncHR3dbnQ0d5Kzx dg5+vfmPqefzKug7FXfPEr9y25SOje1dbe29PVv2Dh46uf+MbyetXffVipXL l87ZtGb86jnPj+7i+ZS7NOCaZJ6ttU06eX56fG9mRbHyUQEAAAD8rzp8+DBd P+svzsM87Dcn9PvNP6g906D4WNxVBweHRo0avfrqqxMnTly3bt25c+fS09Or qqp4Re03+LtyOg9lgsSv1dXVCQkJhw4dmjJlSpcuXfz8/Ozs7LRa7X8/nO0v zX8FBASsWbMmIyPjTywS8A/wcD6rDimkZFoN8f/e7i+N6703Ns9gNgs1EVSl 0yYnp0WkOqIXyvcfm/HSkCZkjh+J+NXRyrVt4NKLuxIqeHozKqelzMpNK6Pc 7crbpb/myA05mQ5Ijt9zalRNMNaqyCaWbkSaIp6kPaTbXhOJGFv0N7/eOKGh NA2R1FU6pF7XyaNumZOreCWLYeY4OSsnZxlIN0j5dpvcOMs3o9L+kIQCz9W6 Cxd4dUYRgSZT1P2nAVI15sAoUzKqOSs1dcWp+RTeLHW6ZkiQSg1f8JxZvdNX Zx7jldgII6hTwdT0oeVodJWTd6n2XtWEe3ka4pASNvIfMbXzGr/IZ5VEfDpr lC8tFGIz49/78/dOxN9JzEyNT0xOiktITI5LSRIfpCanpKfdSrrx1dJhrbo4 0+6hWo17r7ajdm4tFwpZmoqqFVGhny4N9dO3KB9chiefC0N+RdOc8lpBJDfE KXk6En3h6CfFyyFl+vZZOfJT849VHpB4MhmLQZOVJOFgEmgog6fHXD7aShiB oQErlu4vx8uHl1cOJ90hukKKUoyVIs0oR7wm88iry50IjEUpeLXLP32rFjVC xQtszdboP5NUQ6TipwyyUHZBENSAjzJko2bLjHTwaMJLmXvqd5wR//dxSm6R RPN4+rnzNAnCsGQFCvEYWni5eMjzlgpyIaAPxDrFCzWHVc6F82qtJIFHC6m3 DJkoTK6+tPkSahKOXE26gH6OXGrSickrXmv0e/NZDClIajkRSxZfelt/9b3h 079+e+H21UevX41KjU3JS8rJSy3Mz87OzMvPLMhJyIw/dn33mxO6NwyU8lXS MBlN/aEvTz9+QM9USSNWjKaUPZdXtwx01zlKUy/pNJ7+bm/OnbzvxtmkrMSc gqzs/KKcnNy8zIKs6Kz7Px1Y/Pqbz9qRNkG8Bgr06jx+0JXC6AqzfHHFmR/1 XpTKK0VulcinHB1VQrOkGbQISnWgrYFcO1ilgshRUKVbgZo6ljcuVUcLW1Gd FZkk7niZNELBItDEk3Kao4dTTuDIH37t3gAWniPBYZa8LYPa60DKVdU6IdQE qnmxWJmVbh7SF1bJMcv7pnyGLP/fZ7RoU2MRTKWlBTnZsQfj93fv4e/iIjXg 9lqH5iSfpRf++3yWes6q2TjZM1bdabEkm+je04y9/CnQVD6pRCw5pZKNVAt8 xJUV8+f2/uLdtYe3n78jFr97WRmphYXZOeLupmXnZhXmJiXd235w6ZARnXQO Wi0Z2tfQI2jMC8fSLxeQ2bcEg8DGXv++94fP2dDhDBpNo7qdxvbfcvZEbEZS bnFqfl5mVmpuQWZRTkJW1LEr+0aM6+rdzMlG6pJhY6Np/OEb312/UE3Psxaz ocJUmJKXF3ltQ89RLzopF9QP57OE6jTD3U8mz546ZPbmZYfPn7uffi8tOyEn Jyu3JDMvPbcgtTAvPjc54s6hT2a83rSthw1JD2k0dfq1G/vTrjJLOS15XIGl 4sDutz76fMzyKTuPH7ufcCMx40F2XkZ2QVZOZk5OXlFefm7qtbQby1aPf/qF YHovZq2xDm00ZPGnt0rKjeT8qKQQpQJVnZafsObHzzr0bOHobaslQ7o8Hdr0 7zx987Lzsbfyi4uMRpZTzkG8UFNwpS8VHHP96oLn32hnT4PoOtt2gW8s+CTs QWR+SX5VeYWBra42Gi0V1ZXZJWknbu3/dOrzHk3dyaWhtp5V8MS3d10/U86Q evcP7RchX3TQ4ymV+KzKlFWr+jVu23nICzOOHK8wV7A8+8im+t/dltZqBBiS Uqff0sxUTc8fQWnBlGsbeVlFlizkRa5YSFqcVlb5rli9nLDQqw0zvQqWt0N+ yyqXmoLSgYrkyOg1Kj1TkatnC7kv4NQXlh4zNT1qyBWceLkv57Zo5zU5tU0a ZLKrtHmUr0uly1rattDzL2nNa139ckpTQg86o95KsFxZ0umvNywe0XvR5vlH rp25k3IvNSM+NzcrK0uspjnZ6cX5aXmJ1yJ2vD9jUMOGGmsHaYSotVXjzm6z Lx5MKalULke50rBTq14f4W9na2srTQOocbLz6ur/8eqZhyLOx2bH54pVNTs3 P0fcYFbqxZjLq9d8/myfUHsyo6GttbWrpsWAp745dy7PrKc7Kl6tCWVZFxb8 sOjNXnPWLzh8+fTdmOjknKTsouycvMy8rOzs7NK8lJyEi5d2f/bNMH9/sYkS K7FGp6vfwX3qqT3xxZXqlXzC6t2bxnUdt/SL3SePXIm+mZCampGbWiA2QUU5 uRn5hSk5mZHR55dumtLl5bpaV9oZwKWFc8el827kxhqf1GkjAQAAAP4kf1c+ S/NbK1I9NJneQz8Uibvas2fPuXPnirt9586dtLS0/Pz88vJyo3j7VmvFqEcP ln9EwuvXdxniZsWNl5SUiK9148aNAwcOiK/evXt3Dw+PX+/kb74pzV+Z0kI+ C/6Qh/NZvlY2ypQ7pIdf456B7/20J78iR7rplTtJKsE1tc+mxWLJLL/z4ewB zUJs1byztTTph0uHwKUXd9TKZwnKDalAXphlLRZ9WXFRXkleQa74r7CkpNxo spjlERAMjcBbWN5sUW6w5aFYZFMMfXXOYKwqKi4tycvLE78WVQtGzlxxZ+73 k/21dE+sGzZ4YdrIm4bkKp42CTWZINq9Vro1rzJV5BXlZecXZOeXlpaVVlUr g4BogI6Ou6HPsggWzlRVXV5YlJefk19SpmcMrIlsWbw351i2SnwX1aZau6qk PKTZuqqKygpzcwoKCwoK8guKCksMJil5Y1EC3ZwyXkF8LWkQAa9mT2olRaTw K1NSUfQgMSY9tqAs11SzOrwcMFXjvRwvqFk01syUpeUkp8QmF2YbaP95gf9l NuuhfFb5xfGzR9TXkOn9raxbBQxePj6yusikHEA1jyS98QqhOvzM1y+9GUgO ulgM7P/V8s3Na4uEIot6tC3qZ0dXU7EYSsTDmJ+fW5RbmJMvloTKKiNrYkmK TYoMs6xS3Dh1lJy0gAiJS/NVjLGotDwpIyEuJvp+zL3I6Ki4uNTs1KKKgioL uYNXgjlyFkkKlFjkokvGV0jj74ycobhIfPHCAnEfpAe5eYWVpmqLlDJkBYZX j4mc7zDxTFV5bk5+UXZxfkFhcVFRldkoJSQsHJ08jUZdxB1nyyvKi8QTU15x vvhBF5UbqswcDanXinFxNB4oFgGW1YslsKQgNzensLCgTF9prDKzSpaDlg9p h2mulTzfwnMmEtfkLJbS6pL0wpQHMVHRsfcfxN6Pvx+TlCwW52oD6SlMQ0Nc TX6E1j8EOGpwSkabRvcsUhmrLtYXRkfFpCfmV5dWSYdQTmGzSlMmyIkS+UlS fK68qig5KTk1OatST0KFpE7Tj0Awc3w1X1SZl5SReP3G5XNnT5w/eebi+bsP bqXlJpdUMUo/eNJxXo740bGrApeeFDZ52Wt+vzefpQ4foMkFaeumfEt22JGb t8OTCtL1rDIKTOCVasLyYttSxBcdOvHV84PaONKu3la2/2o/atvibKNRqobV ZXEbj81t0MCNjsT0tKnTvcXqiL0peoOcyDDRnDKpZtlFETM3fdDQTT4r1HF9 eljno3mRFeq40UcHw03yQVY+Jt5SVpJRVJKp17O8PNaUM5vZyoq8lMz4hMjY yJiY2Li04jyxAZfGqnI0kEufy9EDS5ohtjonLzM1MbmgwCSNaCKHyGLhOXk0 Hv00aUPMGoTKrIy0zMSMgjwDI8d7pQ2ZWENyVlFpRqXRQoPDnMVUnVuWmZDw ICr6bkx0fHJMbnmxwURaErX8iPtjEFvLqsKM9KS46OgH0VHxCRnFuZWsPLSW NHVqe1W7u8J/R/qgaRaDL7tefLJfn6YuntLFp5O1fYvfnc+SD7p6tuRJjpAl fQAYMuzWUFWclpkWez866n5UzL3UjMTSMnLk5ZablUug8nJcZtbNq1cP3r6Y U5FvsdATk9JRhFMGoaQX3Pxm7+dNG9s6kBsSZ8eGz/utu3c8u4IcwwqBizg/ t/vwYCv5xOTSs93725fkGQqlVpcmIOmwXPHbrKqcnYc+a/5qM2sy0tBO4/jW q1+f3Vcqx8TJxyn+bVHCj6+O7WGvXFD/Kp/FVDP5Z89GRh6Lzksqr2AYtbGQ mgOasWPYKkvJyYgV3cc8J21HmuPQ5pngPqu+TDPnmulHX8mbEx4cvXztcsbd okp9TS1WMhScNBKMsSTHHXnny9fq+khVT3x7bm6vTOh1JLvUyMv5LJI84JjM 4ujNh+d36dPcy9vJRmPtqnVo6tPzkyHLj267k59g4M30kCujPtVeQcowumyL ftvO4UHdfKy14lWfjVbbcmyf1eePlZEhOryZlnPSvYTlmGJ9fti1ue16NXcU D6NW46rR9ei48Pj3adWkx84/Lp9Vax5XJVct/sAUWxYzbVbLeqG9J7+2IypP /EHtcbrCfx6lpWKVSzZamxlDuaEspzg3NuFBzN2o+7ejb0fHREZGxcVnFueU mczkY5IvFViSNuLlUV2sUGUxFOlLM+MLDZUWOkut1IfHUplRmJaTlVZexEiN nvgJcpZqfd6D+PSslFKTUagpmeIlusAW5KfmleSWVkiVi/ZBsvDmyvK82ITE mNio+2JLF5uRk62vLBXMyqhUnlfWf2Tlg8TySg8fTspbMayxsqo4oyDxgfim omIi79+7fff+zbj0gtT8iiKDySB3VBDkL/KUrywdcl5zzOk7kjpnmcuTrt69 deJoXF5ypblavc6n0yZIl08m8W8Mmbsjvh/wpreVjzR7pp1N3RCbfrvWRBVl S39vkmZNj/xy5ZjgIPHMJk00obNu2Nn/zSWzIxLvFlVUyB83mbBBenmjYEhI uTBrzaft2nta2dtIi+pZeXdw67lq8f3CeDq3uTRpcHVJ2qWY2yfDYgviy8U2 gg63N9H9V86AVVV5h25sHjisnlV9O7K6lru/Vc9N39zOTVdHtZVGPYi/9uON 5DvFZWUm8V0xtPVS+lRIY+K4iluJYRMW93erKy3yZ6PR+jv6vDc6LONyuTLE GAAAAOB/1N+Yz1LVzgFJd6m1Hru6ugYFBXXv3n3kyJHjx49fsWLFwYMH4+Li SkpKLBbpelX8WlFRUVBQkJKSEhkZeePGjYiIiDNnzojva9++fbt27dq2bdvm zZvXr1//PbGWEB9s2LBh06ZNW7duFf9m7969+/fvDw8Pv3Tp0q1bt+Lj43Nz c8vLyw0GQ+07EbPZLL5QTEyMuA8LFix4//33e/fu3bx5cx8fHzs7u9pv56HH yGfBE+bhfJaXlZW1eBNn6+Bgr7Wxsbaya1633dQR1/LulrNyf05lIhPl+eKP 9MaSG3dWPtO3o4u9VqOzs7HV0VUknHQuHZouO789UboRlGe84Q0GU2F+2t2k B+ejbpy4dO7o4b3bt237buOGdRtFm37Ysu/MoXPXLt1LuplRUVhtlCYVZJUJ RuTbdnIny5jNXJm+MDot7vK9C0cP/7h946a1GzdvWLtt16ajp0/cun/9p0kL 3vPXyKPFmtZ7YfrwW+UpBou6zjWZe46pNhdWFtxLfHDx1qWjx/fs2rBl6/qN 67Zs3Lll39FDF+6cj02NzaqS+kWTeK8UImBpt1JDZXbkg1Nbd27dtH7j3p9O R1+vMprMFfrCuNTIE1fO79px7v6d3JIKMvqAZfIK8qOT4s7FRJ6+efr48X27 t2/bLL7XzRu3bNq8fdPOgz+dvH46Mv5WWm5KCVspSO/QTKLKSiCTYZTBGsrt Lyf1ii9NKLi2ftXCZasPXgtLK7PQ+dD4WmMxajpjcyQvUymY0lIurNu9cfOq PZGXq3i9haV/8Mu0/y/zWRHj5oxsQJfW1mqaN319+YSoaj2dwkiQn036j4q7 XM1z164sG/BeqC1ZJltj7dqr1ejt6/WWAkbpWE7CjBauqrKquDg7MvHuuYun 9/28bbPYCG/YKB7K7du2/rwr/OKxOzG3UvIy9GYTz5KhMTSorYyuYljOmFaa dT/p5qnLR3f8uHXZyoWzvpg1Y+asaTNmLJi1cO3Szbt+OHw6/FrM5fiMxJIK KVwt5ZtolIdshDdaDIWGnOiUBzfFz/38/m1bNmxcv2HTlg3ffy9+Mpu2bd53 4GD4qXMXb16Kun81OjutsFzPy8fWzBYY8q7f2PHDni2bNmzauPmH3Vuv58aV mPhfTDgm9Sm2FF+7fXbPnk2bxDf3/fpdeyJiLudXkQ/RRCavo5EWTjAWi8Um Jfbq/Ssnw3/+ccvmdeJ5asvG7Vt3HNx75vrlqJSonOK8KlatcOqwLJLrKK3K j0q9f+7KqT0/7/x27bezZs2ZPf3L2TOmz58xZ+k3G3ds3B++/+zNizGZkUXG IjoOhmdrKjEo1PE7vHx0pGE1poKbsZdXLZ+9akXY9XNZ1fLozZo4mzzNmhpz 4/hSoTwyZv/KtZt+Xn80Joo1WeRV2C1m3igYcoqT78Sc37tvw4IV88ZMGPf2 iBEjhg/7ZNTk2ZO+2bBi++HDt9Nu5+mLDXIpUtacoynI9JTwL5YMbPhH5htk 5LQ1HRNJhxnSzUupK3nwqSCPTpUzKhaBLUo+OnRi3zqkS4BOY9U6aOiqKbHG Sum5xvKoLYdm+fl4SLMuW2sauPgNG3Qs9XyhEjBj6fShDD1ExqRVh+e1r6fR 2Oq0Gitvj3bDnzmUF1VGUnXkjVj4f5tbkaaxkof2kGaKqRSqr9/ZeSRs3+UL ZUaDMa80Jyb5/vmr53bv3ThvyTdfTP9yzpwvv5r27eblB04euxZ/J1tfYLQY lRi+nC2S6oCZT//59E+bl6w//lMRU2gmAz15MvyBFYzyB01GD/AW1lxoTvhh 17qt3/5883yxoOfp8GGGMxaZ0n768ciFHZfjckyGsoKE9DtnLh3fsmPNzNlz v545a87MuSsWbPrph2O3z8TmZBVXGKRxeEZTWXpBypWYiJ/2b1u8YtmsmV/O mPblokViqxwecfpBZnKppYI1yKOSaelif28+iyLNUdHN4hMDewe5S9FNjZMy 3+DvyGcpE4UJ6nmTBlFZtqqqOCMvPiLywo5DPy5bunTOjFlfzvly9pxv1izZ cfCnC1HnE7PSKi16rpqO/mVJKFoJRDM0vi0oszEKyhBdZQSchS8Mv7e7b3dH W6m7mI1WW7+N01cX9yWWkmB4lcCeOT2n+7Cm0t2KlPNpPPxfM06dNHFVvDJJ rzw9pJR7NRrv31z97OtP21nRXhY2Q3rPPrlHT6P99HUNvFAU9+MrH73srFxQ /yqfJaetpT42TE1PDjmoTmL1dNB3Zf6ZCXPfrqMl64NprAL9u331wd2qVNp4 SKWLdOPg1GkzOXnUoRSl5+iUvOIHzqQu3zXh6ZYaKzqq0eaF0e1/iC82koG5 cl1gqnLDz20a/F4HBwcHaWoxjVuQ9zOf9tt57XBqhd7C1ZrSWRkvw8uDiOWp es1pTO6SZd2btJemECQr+7y1curJpHxOzk7RXhlKdw7OaEjUXxs9rptfIzp6 3yq48fgfvr5V+geK5v8A+bDISVh5RmLBcKf01oTP/d2e6j9t8JEkw/9X+kAq CRZjbkXe7fvXw88e37l/x/ebvps/d+bM2TO//mrmrLnTZ06fvuCrb7d8dyDs 8LWYOynFqeVVVep5gadDg6UBVUzh7ftX9s398V5UiamQKagqjU+7E3buwJr1 K7dt2nDrSrVQIRW7SkvhvcRjc9dtObbnWlYmWU5KHubOmYSqyzd2HT2x/9aF CpPRlFucF5Ny//SNE3v2b16wZPHMGV9OE3dn1sot3+6/dOxm3L38qlwj7UfB qQPUlc4U4nsy8aZCQ2FM+oOIyye2/Lx98aaFC77++vPpM6Z9PXOuuKGvvlm/ dMPuLQfDj126czmpKLVcOdmSnWHpTLJki1K+Th3WLE1lwNCV5UijzchdHWrO a/KZmuHvJ0V8uaqzbSPSJmqcguxDV8y9nhcj1cwKns+M2zHg024u0tykYqWy 9nbtMq7HltjEatYkj50lyXJS36VdEt9i5e17Bz+Z+ayVu4O0bJ6VprG938hB x+IuFLE1lwd8TTmh8xKokwyzgjJGmI3Ourti9Qva5i6kUXNqog1dMu1CcpTc nUNpaqSPRm6LTAI9vsokjdLXyrLYLcfn+jauryVzrfvovAb03h0bVmCoScAC AAAA/C/62+cbVP06k9WiRYuBAwd+8cUXO3bsuHHjRmpqakVFhcViMZlM2dnZ sbGxV65cOX369O7du9etW7dkyZIZM2ZMmDBhzJgxQ4cOHTBgQK9evV544YVn n322ffv2bdq0admy5VNPPdVCIX4r/rBjx47PP/98jx49Xn311SFDhowdO3by 5Mlz585du3atuNmwsDDxdVNSUsrKysxmc+3ZCMvLy2NiYvbv3z9r1qx33323 S5cu9erV02q1td/Fb77NPxfyWfCHPJzPaih1oXdysqsXFOzp5KTVaFzs/Xo0 XH79QGpVFelyKaiDFNQexUxaZdJ33w/wbV3fysrRTds4IMDd1l1qSFxsnNsH LD23PbFSza1wQkFRyoFz8wfMHtXi9Wc9WtbT2mvo+sXW0iIYUtLEx7bRs76D ZvVfeXz7g/xEIyeHkUmAxkxmLxL/y1pKTXmnb+15f8H7zXuE2DpZidVNS+Is dlZ2wa6d3+n9VqfBL7uJN5hSPFbb3Pf56cNu61P1ZGiPPNKnSjDnVmYev/rD yEUjG3cNctDRrA1ZEUaj89IEvuT/ztdjd98Ky9KbzDQjQ7tki3ezCelnZ67s 6+AhdVZuEzhgxefRVblZJ+78/OH8YYEdg909Bq/98kxyDifedBvZggMXt30w d1THIS/4tHW38qItm4as2067zbu39+r1WffFe1ZFZOUzNO4kvY4y0wurhE3U HtYigyk//MH+vgPqePv2nzpsX2wBR4ZaSKuTMcq0Whb1U5ZCcEwmk7d999vN uzzXq+24/fsr2ELpVybu4RvdX+SziiI+/npkY2trGxKfCw4ctGTiPX2FxaxM hsbUmhTQIJRs2/vpM308bemYDm3A0K7TT5/luHI58mMR6JIMlXEFkZuOfdt3 Sj+vtr7SVC1kzijps5fuwTWNfF78oPO8XYsvJqeZaO7GzJN3IEX9WJPAFZYl bD20ts+4V7xDnTQOJPQgNrA2JHcp7aqVTqPzdmr1aov3103aLy2VInWTJkMz SBzRwpizy1L2X980cvE7wb2aOtQjqx9oremgRLK6t5W1xraefb1Ovj0Gd3j/ +2mHYx8w0rxa0lswXss9++knfrb+GrIEg7e/w5cH18UUq7NsyfFJc6kp5vOZ gwPqSjFJcZMN/UYs+SiiSBoEQsfy0IgIX1WecSrqyNjl7wX1DtT6SmuLk2Mg lQwvB+923kO/HrLt4sHEQqMULRXk5WNo1WPKStOPXts1fN6bfs/Vd7TX6MQn ywMMxMokzT0lHg93XdMXGo+eO+KnG+EpenVsGvW7xmj8b1MiZjQ5S2NHrDHv eOT6F9q5+NR/a9WU0ymlZLSfMrudXCPJk+UpgVhjQknsgm9eqtu2w6AXZp46 zvLl8kSkYqkvrXiw5tDiLm93dHbWauytraUQtJWOTIFmY6Wx1XqHOHefMmDn jf2ZerNFjiYr+yZWnJSkE18se63x781n/WbqTRDkUZ/yAADSmZ6RZ+BUs+GG 0oufzBxWX9pNsWhpWgYPWT4l2lghDZy0MAn7Ln7b0tdLS5YX8XOqN+zVn+LO 5jOkgintibwAPWN4sHzfzNZ1rcnCitZN63Yc3yeiNL6MU/sIMY94M+o6YvRb SwGXs2LV62+82X/BB3fzMtIOXN057Ou3Azs2sHK2kdb4s7OystaRtEWdEO8e n/VYe+VIblUxT3tDyG9Nmj+Wqco/9d4nr3Wp3+HjoVfLYvW8ekbj5fihvP6J NPixODJzf9c+zzzf7PWVE6Iqyy1yTwNWn1Bw6JXBQz7o9tmBn/MjL+z7YMmw wC4NraylZVd00jlEapKsNY3+FfLJ5s8vJeeLrYL5QdqNZfvmdBna2q6hs05K FNK+H1bO2qAX/cesnnAhLabILMcmGUZQhnP+XnIwuvha8ckBrwS5OJP1s2wd W/7efBYrx1w5ZbFFjiFziFkqridemvH9hBZ9Q219nUnXF621jVZqdnRuflYt B7Sd8uPCi3nigeXUzBxH53OVZw+jU4fVLKNDTiV0yLf0B/pryUdHj3Z1bKQh wwPrPmU/5+zPiRXyoor83RurX/mwo9j2k3NNw9e7TTty0CQlIlmWzIHG8cpq m+WmyltXljwzqL2N1Kpb22kavj9k5aVwo1mgE+VxZMitUBL3U++PXnZULqgf zmfJSxSpa/eoMwbz8iBsGuvmeJP+1oLvJjSVzpZScQ9o+MKs9+5UJVeZ1O4o agSbPMlMPyxWUNJkdILHoq1Hp7/0ilSWySmpy8j222KLjerKa+IuZSQfG/n1 QE8a/LaybuT13ISXt9+/X15dycnJdbIkEifPlEuyhMoAOJJ3NiSzqbPmPtO4 rXh4xcNiVa/O5N2LbhaSGVXNfM1SpPJKczyTz5UsXzfoqc72NuRE6eE0csnE szmcvLjRP8u/y2eV3Zo0JcC91Wtfvh2WSD8syyPzCL85YkuZF5CzpB+8tf3l Qe2d63qIxclWrGLk1pJceWiliemk+8q6TV26vtdp3oFVt9MTTWSQOKP2q5FW 5Su6M3/LrN4O7b9bejf7bt75+MOfLRxUt0UdJwfN0wEvrp6fbckwi216dnH0 hv3D7UPbfTho0bmLZOZu0hSaBUu2kLfk255vDn592bi7qXlphyO2vTV7qF/H RjbO0rSaYhNmK63zJpbUOqF+vSb2XnP+dHF5ASt3C5En8VMOgoUvM2Sfu//z B8veDX6pqbOdDWmrraXpqcUrN/FcYy21IjYaR2edf9cmMw+suZNv4uXEECfP VSgI6ij4WicsOisj94vzCVmflORnGSUTxAuZBfe/2/e6LtBbI92UOwTrQpbO vpodRc7RguXCianPvBakIbP+ibvVqdU7m+YlGPTS9I1K7ZQnhZa+Ja9UUhK9 4fiXjRt50qktnHUenYNWXv45WS8IyhB4mkWWp2dUR0pKbaE8+4D0/jLy4rce GO7arj65ERIvMAMWTD0VF8XTKw0LT6dsoE9UT4okhafO7ij+q8o6GLm+dWAD O9KD1lvr2qfXzuij+QZkswAAAOB/2xMyPqv2t+Klu6Oj43PPPbdu3bqCggKB TPpHZ50SvxqNxoSEhO3bt48dO7Zjx47+/v7Ozs50hJeWoA+sa7GxsbEjxLcp 9SuuRf1LLYl/09/S3bC1tfX29u7WrdvcuXMjIiLKysoYhmEJQbn1oHsl/uT0 6dNDhw61t7dXn04f/Ob0g38i5LPgD3k4n+UjRSjru/p1fXHy4Jea+wba2mpc A2xfXDHnfHqsmdwqK3fKyv0RZyw4k3yk37AgOz+dvV1Ae5+Ro95s7hPoLJZ7 F22tfJbSWzgx6+K81Z282zdy8nK1d5YWarcmVY4s2CXlpHRWTs52dQO9m7Vp 9sbUT3dcuVhiMHA0yGyRgzNV95Mjvlo7vmOX1g0a13fwdLIi0w7J8xxaW9na uHl5N/Co66HT0gXlNU38nv982O2q9Er5PtIisIzhXvypeWs+6dQupFFAHXtv RynrQO5hdXSFeY29h4NfwwYd+z/9yerJV7Li9IyJlzuT80Ja2rlp3/ez8nYV N9/Q7emhnRatWDKu25BnfP3rOzi6u+oGrP78bFKKdJwMTOyCb99v36G+p4+X nbO0dbqb5IvUsVqnsXK292zgEtgusOfYPusjDqeVVdDMiDRUi6lZLEY54HJI 0JxSHLl46cterVoO7jrp5M/lXCUr39gqq1KpUzyRlV3K7xReGT62Y1BQ7/F9 t93LlJYhkZIj/yGfdfXjucMbaGiaxSo0cPCK8fcNJUxNt1J6QBnBaNDfyTk6 4rOX6jfQkvyUlbdzvzmjf0oqlub0o7f/BoGtNuccurxl1OTXQ1sF+/h6aZ3t SdtIt29NFuvRuFh5+nr6PxfQ/cNX1kUcTqosY+l8jFI4kdNHZV2Zveyd1j3a +DTysXUiC3/YSAfVRiuv+yYFk6VwolNdl6fee3HGidMMUyEosQjxeBafubtv 0lej2rcO9mzoZ+vlZiPFhZRPw1ZDo/dSXMXaxl3nLW7kk6HLrp4VyzmJj/L/ j723gIti7dvHt+ju7g4lpZSwEAwUxVbsDuzuALtbDDAPBraiqCAmXdLdubDL 9s7s/OeemV04nnjP8Tnv8/5/z7PfD67LMjM7cd/3zP29vtd1MTMb3y9doSNn ScIal6YBbcura/ldRG4FIjKWEJ8lyo/aGWFiAAgs6K4Z684+vugLA88nQphC jgBpqPmw58LKYaMc9SyNldUVqPIUGn4QcuBmIUNS0KTpmBk6jOw/e/fKZyWl LCEHS+Zi6ZmmpuwTd3aEjHLStdShKSuC00i0dtCfaGTsPSirkFenWTjo7Hp4 MbcLFwwlqqbFdkLSQCQ8FOIXMWLI/FT3YfNmH1U73yWhx1NS2HwcxyIUlCRG aUQ+U8hveV3wy4hhNubmk/bMf1jdBf4AEnqCzqz6lKjdM9wDvdych80YvP7Q +nPXjl2/GX/1yo2bsTevHjh7cNaWBX4hXpYOfpOCd987UdTNwsHTXjSgsvzV hhNhxj+BZ/VeZRyvgXkInuoWiTEbWCQBTTB9U6D1CiPtdYmz141VwqsNSBR/ jxmxB+ogNkb9ELa/KksMD9WR0wL9TYGk7maw7sHpjI46wMzCi+aFWBocQri5 +XfmbhqtIodTZWUGWIw8sKWG08AlYIG+FlS/fwDYUAZjgloIvwlp3X92dPDo /sGeCxfMCvUMGujjHjLBb+HqSVuio3Yf3rN3/7EDaw5umbA60nO4v71hv7BB 0c/jc+gdhPaVAK+vR2AmIzlyWbiP1oD1EV/5hd0iAvTH/yoimGpYYpgHdeZ2 PhoY4jHIZtKxNbmArotVGsAQq7L10fDxI0PsfCJmLRvo7e3iMDw8YMG66dtj Nsccijm68/Cu2buXD54xxMS+v59D+Lp58XdObp44L3xgv8GjXWYvGLdp34o9 Mbtidh49vO7QxrHLxrkH9vc0Hrh16dOCd3QefmnwZDck+tuIAV4DIer43JE0 bqS1mjoY3+So8vZ/n5+Fo0I4QAZhjnB0fsfLD2dnRI3ycHT26T952ditB6KO xx65GHf76smLp9ce2Bq+bEq/Ac4urjN2L7mTm8cF8r+95AKcnyUCnlsignyG GcnhGCJMLMZrf5F/c2yIHFUD23Ml00EG5zKfVnVh6AC6fHvtk9k7JqgRI56C l3nYkSWfm6u4EJ/oyzgdGBayyjqztx4KM3TVwPAlsiJp6L4VD0uKIfGNDqMq ieDWsjujFw37QzwLIXqH2DQTQ4sI0BEWiAnO6GftzUkrYyJVMQYTOvK724w4 tr5K2MAjku+gS8N4HUgfWWDsniHCi1HA5oVQxfG4KE93cHeToZEUVYcuC35U 08kTEPCZoFPQeObmcv8RujJk9JZFVqR6zQve//BJeXV52aes9NR3Ke/fpX7+ VFRZ1sTsAicaEkqM+TDFSLAr3HJhzca9A0xdQe9WIpFMtdfcjs5sEqON4ssP i6+OqBvm3H00b8AYNQp265OXnRa94FUNIBv3Vs78t8Qf6Q3Sv2/d46zVP2TF yCs51URFEYz8cf/93cGPgLhECKc07tVxD0sdM92BE71mrJ0btXFFzIEtew/s 2rl/785tJw6tO7B5/NIprqEB5jpOvv0its6//ullN79HhN+SYMwyk9eTd+jq zlBFpy0xt3Ye3Dhh8pBAzykrw7cc23T+6a1XRd95UA/4zpbOkmsJM5U8XZeN jUlJxm1oCcHYFqhp94mRIWH9QgYtnDs/xMPP19Nh9PjAhesjtsSs3L3/wIEd 0YfWHto0buV0t5CB7vqukwJjkm4V0wGkRTQMIc6UgqHKpk/Hr20K9OtvbusZ 6Dp+xog1e1dGn9i2Z9funXui9247fnjNjo3zNy8ZPi/cwl5NS2/x+TUpTdgm xLIMYkFgsZeixFwK4xpjjRwiBpNeiqLkEwxB+l77Yc9RH4qZCiYPqNOPMiL+ RG5rLeiUrUj3letT7AOx4VIW7cI2s0IPv3/Jw3Ro8Vsx7oqJW5SKcD1qrqDh TeHNgAADBSVcm1fVVGndw3PZjSxYvMcwpgwAC3E0rldumnhIx3j6cEld1pFT g6gOatiTm4Y1edjlQ+mNtYh4ztHHmBUW8WFJCQYkEdRFB6KezqL4d9vNbHRk McFTEyWVudOfVL7rFt/cpCENaUhDGtKQhjT+Q+P/EM/6Afqh0WjongQHB2/b ti0uLu7Dhw9NTU18Pp/FYpWXl798+TI2Nnbr1q3Tpk0LCAhwc3NzcXHx8PBA 34eEhEydOnXp0qWbNm3au3fvkSNHzp49e/Xq1Rs3bty9e/fevXsPHjxIxOIB Fugnd+7cQb/i8uXLp06dio6O3r59+/r16xcsWBARETFs2DBXV1djY2NVVVUF BQV1dXULCwtPT89x48atXLkS3Ti6ekFBAbpXxOQDi87OzoyMDHQPZ86caWtr ix+R5ABxsOx/I6R4ljR+Kn7Es/QAzcVc3X7KmLvXVk/2DDWioTMrkvbY0FPv 79SAImPxjIiYH4mQ9rb8c483WvXTkpUlm6kPnD708tFNXrqequhsSoWk6mZ1 9P2NUjYiFukSidKr366PsaGYKGBmy7LKJANbNX0TLX0jIz1VW3N5VUUwDyNh eSqyvo/ttINRr8uqYSEXFgoI463G5o+Hrqz1DjKlKMpQiB5FIePkGpBqIcvI ijlfIINKVSCRbIwDN85MZ1SxcNtuGEYaGz7sP73CxQ8ze8JKnKkkBSMVc2sd Cz1LfWUTNRoZq7Amy+nJW4farLx1Or21koe7EqA/lTXvdp4eI6MB7GUUaHqm Gr6+bjYquqpkkIBStJSdfGFHWkMLKN3tFhRs2DPZEpCAwDHJ07QMFY1sdHT1 9Uw0zAxpehpk3I2dQpInqTvKB2ybc7/gfStXgNeDiiQuV3gdNCw+++gnXezW pA97PcJcvO3Cjy5O72riiqe4CLEu3KsTxWRV3f98uN8gy4HuSy9uy+nk4BNt gMrDfVN2P+BZHR9X7I40xrNzJLKj5cRjq3I5nViKASvy5gHZk66atpJnqfei tkeY9jdTBJdARl/OcXzw4WcXyzpwBytsT3oEjM9f4mavH2Nsr0vCro4MWV5d Rt9Wf8BABy9nX0edfoYKSthsnkSRoSiaaPlHTYlNe1jFwk8CArW15Z5/stnd 15Sqjgm70miqNEMbdUdvM1c/Z0cnf08bV0d1a0NVExWAb9E0w30WP3oIiTqx nKEQ7oZ7cnITlmybYu9qiBEoyFgjJClRZfXkzCyMTHUM1NGNYjJWAFGigXah PTtkT+prIaGKJuLk1KatWK6rYIaXJ+hqK2x6FJfXzMOr7CHCmQsRMOHc9dsn WGmTcIDJRH/uweUfO8AZw8p6hZzS7rKT16M8g61UVLEvQg+ZpGaAtg0tAx0L UxUdNaoMSRZUYZPUKWaDzSNP7s1uLGZi3BCoW9T17GVM6BwPDXUyrmwlQ9Uw VbXrb+Y5wNa9v0c/C79+hs7WKmgzVkavnbyq3Oq4o1+b+Fg2Ba8tJ+gG0iAC 7lMnTyTNEFFjT/m9pA1mAa6DXRZf2VfM7sGcZRAxRwPB0+FEn6tvyzr7aKW5 peFgn023T5dxiJPLr6j+dvTGcmdfD3+vqesXXnpyN7ckvaq1prW7va2xs6O5 q+V7Y9XH9DenHhwePSvAxWvI4pDopMetnA4IkpReI0h1RdLG4+P+tt6gSOLz Iyll783F4iODkCBYwAih7Af+xoaEHz4cD5rtrUTG+75mRNCGZ7cx2SWAp7Lz 2rIOHhth5KSD6TLJaVHdxoesPrnz/vtfvmYkpyV//Jj85eur9Pc3HsfO2xBh 52VKw5B8JQXHmUN2PEpi8rshXG4OwoDqP8FWhBgIABFjoLAFaTlyYWxAgIat 0YBhrmOmj1q/f/XFWxffFrzMKc38XlhYXlBVkVWYk5T55Ghs9JhIb1u9gesj r3x90yEU9R65EBYwOt5ELh/vre29YspXenEXzoTCGWqwhBeHhYDfntv6OGiE j7/thENr89hM7GwJED6fWdL6NHjiMFs1M2eHUWP8Zu9befrBueSc11kV30rK S6pzgKbup/gXsXPWjHH3tLXRHzE2YMzIUXOipu+LP/7iy6OMiq8FxUWl+ZVV 2eU5d1Lil++b6+6g2s91463ozCb6rzDWv82jxFPpcPsXoDdoqaoJhipFqsLf 5mfh2xJJSBAwX9T9rejh1NXj/L19w/yWHtv26FNiRlFGVWN5e0tbe3lrfVZJ VuLHRzuOLfYdETTCLTJm9afaSg4sEBc/QCKYSAwLJZ0IM4IR9d7cYJjdWXYu ab+LpQJga5DIxkqWUwIfVnxoFUhIwYzCY/HbBrhqgCGSStJWNwuxmX1gw51X N1KyX6elp354m/nt1beUGy9urju90MXfSE0DpKcVyGrujtsTT+e0d0EEUQIr DkHvYu0Vt8MWDP9jPIsgPaHjO6+3joMA4Ih+yhMxEUFm/pXw1QEKJFAjQSGr h7rNvXW1W0QnDNEIvx+c6YZZhALEgWhpBCqGNsKulrRle6eba4KHGSqZbGcy Pnrp53ZADAQr83jM/NoXUxYPMTGUoWFMHQ1ljwjfeWs3blq1etnMubOmTYqY PGHytCmLli9YtWfr4fhTTz+/yGsqpnMYfS4oIqgQNu87GGjpQQXFO2SyikLk 2S1vajvwBcAdt1dBFyCq4OgePFrhM04DJzJT5Kbsn/ei6gfI778qekdR/OJC 9T3V565PNPUbFD5gw6NbzdwuPhh7/4R/KuxtS7/iamGFEDxOyYXnMS4qigMc o86teZj+7ktuVklFTmFZYUFJyffcirKM4pzX6S8vPjy9eN1098Ge/m4TNs9M LMxgclkAcYfwW3x39r6LWz1kdUf4h4eETJ8btjx6973UuzmF2Y2dzQwB+lwN GiFc31p88eFMJVe3pWGHUt4gmBIC0U+bkaaYw6Fugdp2ugNCPUKmhq8/vPbq 46vvMl7klWQWlRSVF5YU51dnJX17GXN1+/hpXraG/htnXct4zeBhjEVsZEV6 EEFbR+7Ja5tHhg/2NA+eOXXP9ZjENw++FnwprMgvKi0sLPxelFdfnluUnZb3 /uTj4yPCaGTj2cfXp7RgvEdJTRckcSHsSzoWWw0SxR9CiS0eDhxjws64BKKA /jrjVmSkHlVXDmNgmfqobUx5WE0H+qPcerhu6/6h5i6EXASFNHzTrLiCIpHY CxAoHPIhwmERFvsAwwj9a9ObmXOMlY3wyYSiGmXapX1pdS3grwKclSp+ysbn QRAuaYxfemyHBRD9bUnizAWGMqaymNKAUX/5tS+vF3fQReLbN0yAyxIOmojY FHFrxz6ubfoQfWmygqkKMOEiKzuqOR/Y9aU5l/DS+y/tp9KQhjSkIQ1pSOO/ If4P8ay+6oJaWlrDhw/fsWPHvXv38vLy2trauFwuk8n8+vXrhQsXVqxYER4e HhAQ4O7u7unpOWrUqKVLl+7fv//ixYvXr19PTExMTk7+9OlTdnZ2UVFRZWVl bW1tS0tLR0dHd3c3g8FAt8MSB/oe/YROp7e3tzc3N6NLlpaW5ufnZ2Vlffz4 8eXLlwkJCbGxscePH9++ffvixYtHjx7t7OyM7p6ysrKFhYW3t/eIESMWLFiA LpCamop+heRM8vn8pqamt2/fRkdHh4aG6unpAUEhqd6gNP7/GD/iWQYAWDHS 7D97YkrhlQPhC4KUqBQ5KslIZ37sltRapggn5ohw2wuMKpVZ8Gjldl95NXkK RWWgy9S9uz7dPRCkPwio0yuQVFytj324VcIGttKEVEhZ/afjZwbZD/F08xrg 4T50ZMC89RPmzJ05K3LB9Kk7V4wIH+rgYaJmDIRI0KFBlmIdbhv19DmTTScQ jR6Y8fJFzOCp/WRB/h+wqZTkdMwN+vXv5+M30C9o0EBvL59+Hh52dqYqemoU rNtRKGQr/YBts9JZ5UB1g4+ImvmMR4/XuY2ykyMBhTcaWcFY03KQ45BpI1dt nr1yzpq5oXPH9/ex19BWkQECbgpKJKOJoec+/NLAEuFWIEhJzbudx8PlNRUw L3WAKchQqDqKRpYm/b1cB08fsu/F7fzOLiC5xYVL959aHezn6Nnfy9fbb8yg yXPDFq+fNXXqkoWzouaFzh3n7GmnraNIwyzkZakkC+N1N6OzOvE0EQY4CGHi XAuJWm6Q/QOQCcyu6no/c3WIvfmgOb6X8vK6GFyxsgqC4PlQiKjnFJTWpMWc H6Vi7bZ4/InUZ91CHNcTV3z+IZ7V/mHVjlnGJEzhhgz8s46sy+thoHN6WCDg s5hN2RWFqe+STt7eF7FshJa+MolKk1XUtNFyCvfe/Mulb43VRD6WhyAcAbuk O3f7/snGbrrYXYZCpakb6wwY7TVzy4xD53cd2XFmz4ztC/0HuuvYaMkBKRsg nqehGhGz4lFZjUDIh0UC+GvB7WVbPcgqgJGlIKNirubo32/Ohqlbjq7ee3TH rrVnjq7at2PGhoVjl00YGOhj7+y1bNL6Fy9gYHkDCnc5Rd3fo49NMnLTxZKN 6HxfSU/B3Nbec4jP0DljFy6ftXTqgsmDBvrYuJgqaihSSNiVlTGYHbo7ORkC JxGk+Fjpde+WrtJVMqdikj+aljJbHl0p6MSdFSDirANnK37+it2THIxIuNuI if6sg0vTuogrKGJzap/lnnEfZaGkCMQSqVSKtrJlP5vQycNnr58WOWv94hC0 Bfo66hmok5UB8UxDwTDE5kBaQiUTeIrxGziFa/eNsbbGyGgkmqaaqb9t6KJx Gw5HHT63+WD0wb0rTu2du3PNuIXhASO9XDw9na32PLyY046VCuPpZIHEbF0a iATaE/vL4MkjSCTkdOW3PA2d6WVjFbxq9P2KOjYwrSB8OYiqfIKoIeR+KXi0 aquvnm6/1bOvfU3tEeEpJ07DzY9nx4wy07YeH73gXs5HukhcQ47gImY45U4I 0eHOJy8PBE3x9TLy3jDzc1NGF04WwjOlFWU/pTeI7YIAJjBxgZgdg+cBCecs EUHnwWvpMXMffhOv6cS5Gf399XCklSbju35abG4+JCS4XqIuLv3Tt0tDZg7R M1ZRBMqWMjSKkZvh0FlBy9bMnL1o8aJ5UUvGrp3rOdpV3kJNBnAeqcpy+u42 S85teNfARMTihn31G38vBJLMHYyeLC4CtyBNx0+H2flqOKn5R824+u5eSWct T+IKBotBOhiB21qKr73c6djPYIDTsou7croFhCAVcFpBL2z7uznLw720PaMm fmUX9fCxondxVpDYNSFRqkQvbLk3eLifj9Wk46vz2V08kBcFCePuso4nYycF 6MvaDLOLijuQWVvUI+QTiUvxMAzzIebblDNjZvdXIslbaUzfszgxK7Wdj2Da ekRrw69R14fvj+ctsJQxDdk0Jy6nXAB4dOBqiH4qDYmNRKKur+1vxoZYKmGE A2WSvPPfxrNEuEgsro4KCwVVHUWxcdPNvX3GuK+5euI7qwXrK9iLAO8UEMKB Be3MrO2nZw/wHTDaelvys1oOUwSJOSo4Lwnn3bERIp0rkfAGH0NQWeWTrcfG KyvKUIDyl1w/A7/dazI6c9lwbyK3J60gce2u4QbmmjLKFDKFpEhSstDwn+Ib uWri8jXzFsxev3L66vl+0/x1bOVJMrJkCkWRquduMGjN4meFaV0Y+oQQJ1aE oO2qreRm2KIQifvtj3iWUGx6BYkpmWJAC+s6IgybEnbxGq/fWuM71BQTvkVv hi5zhx1OzRDCPQRoBWNPQTAhoCYSXyYx0ool4NlCQfbnUyPm+Cjisr0kxeGe y385U8dn49wQYSer+fXHHQ5DbLGpE+AWa8nr6mnraumCRx0gPojT1ihAQ1dT TtdDI2xeyI7rh5NyM9tYbAghLBShGqjj/OXxNoPUZch4NVDA2mlX0j/2oGcY L0IRiO2HEMzVq1PATbiz2CNMA7vpkEmyU/cuSqrmw6L/SpYvLLbkg8WtCA06 r/vbt1OBkf5eDkHLw+9+TvpeU9TMaKN3tfcJdFbb3t7Z0UHv7Ozq4fK4Qkgi AdwLaQHkl88ujnsd46kkP9j/0ItrFULiZiOunwCPJIBUyuaxims+rTs5wzHI xVdvTvz5opYK0Nnw4YvXlRV9bQPaVrRUbScN3pFwNqcRCE3DvfqrWLR0FV9O mKHg7LYk/PDb9xDW0vDD5NeLmg+dDLHx1nTTDNwwI/7j/fLGeg5G1USIHoHv DwDF8mOfbbOx1/Z2jYrf/50uPhR0X5sFnakpu/0mD/SzHLJ+0r3cd/U9dGwW IYGG8fEBvfcgjJTKJ/MXy1JN5pxYl1zHl9DfEOIbJQOiGM+CxScGvMOwV8ny QolCIPbo083IO5e4rZ8DhSSHkaAUXSOcb1R+bmfxgYdYubB4UZS3qTWZkJ6W mXgo6mVlF3hS5uGOV9gl4BNHRdDBhAinqCV3w1ZLbScKpg+goEMKObP5VXkl QtDEIIxfjKvvQsRJww9FIH7w6GEUXXu136u/DFkF3HA1FK2H218rfVfPFuD+ X4ikIoA4fAz95mHnCxs5QF2KgN+d9PnatDl2FC1MFFTGxFtn9tNblfR63EHs z+pGpCENaUhDGtKQhjT+347/W/8s4H5Co2lqaoaEhMTHxzc3N+N7JRQKcWxo 9erVLi4uilgYGBj4+PgsWrQoISGhpKSExWJJjgIWC3/8VpP8b4VkdXSDTCaz srIyMTFx+/bto0ePtre319LSQvcWJ1uZm5svWbLk+fPnra2tfP6vZCXq6+vv 378/ffp0KysreXn5/9XTKMWzpPFT8SOepQ+QCzOtfgvD3zW+fbPq4FwzbUwa j+a2fNzpL6kQJhUkEkKEGYSAX33m5Z5BLhSSLFmG5LVg/LHb+XXnLw0x9lEG /Cyyiofl0bQbZV3Et4EC7xZ6zefUo4+fp5Z+rWmrY/DQPs7nwXwBmL5DfGZ7 6eUn+0Km2WEjA1Be8zDxPbK7oquUi01Y+VWszKVbgs3sQCYfoz3q+1vMOBCV VJjaRG/lsdm8Tm5zVunXSw9jguf4q2DicWg/tcX0BhkVQFoFhpjZ3K8zFrkC Z3NC38117vADT36pY/fwIIGAyed3Mhqf598YMddFU4cQbtNQm3tmbWoLizhn pRXJO0+Gyesq4MMXlURRI1mNcl15fv2bwuwODoPDB1wukLTkIS3v3318HfsG 2FvXMrrZfAF6sFyhQMBn8zjNrOonBfFDp9tp6uKC/eiZDNmzOLG8HiS7hHia Fvr19RKfSeA+L+i8lrDCe1Q/b90JNy7UdtfARM4Dp8/hSWyQIul6lnll6nhF Fb2p57anVDbAuMcIQqT1fjVY/grP6kxbsWuWMQ7Gk0nONhFHovJYDDC553VX PcqIHxk1QtvWCCi4AoVXdCFNd4PQjSHxX962MOhYVgTCpGtAnq7+ed5Fx4GW ykpgY+hJVZEbtmVa/NeUNgEf6oGEXA76055R92nJnjBTd218YJMjaQz3WfXg QivEQqf1Xb8k7x82niorCxKYOppeC/2vZH1s7+4UQnwRj8cXgpbJ4/OETA63 uaP41cdrj5Nu5nyDEOwOwWbWPq847zrQjqaI33fINBmXqQN3/XI6o66eI0Av mUAA8dhNPfSXz7b2D3fEdhL90Z07avfbNxCW40ODld6QsnSFnqI5Zr9A1jKR 2/QktrD9h5weLOyC89bsiLDWJePbMdabfXjZRzqeHIVFhY1p+074y1mr4iJs uiSVYd77n17Ma+viwRwBn8ejsxpf5zxctNFX3lITtFGakinJN2bL68pCgZDL KG5+PGKKny7YMFWOpDZ+yK7Hp8u6GUIBT8AXwpAQRk8GR4D+xmihV33MTbt4 KKngfXUHjINtEJ6R+TPbImkQIWyEm0+dnmjr5xZsu+F5CgPpQAjBLuIhAcKV ASGkOvb5/qE+KuZqUb+cTG9k4RiZiFn/dt3RuU40tTFDLqUn1PAIFlQvj1IM gIJXPuv73ssL3fub+mhHf3pUzejptY4qr3y5+eg4w7+NZ/1BiLB9FucJCVsd BMHziWxBZ2r1vYBwNyVVwJSUo6Ctd+XlTZ/bMNBWgK+OoOMk69n7s6FzBiqr gcFBFrRGWRkKVUaWQqJRAUsWU31G+zAVSKLJORvPvbL9fXWpEMJOmlCCISJ/ lhHvOxwhGD/r0LkxzgO9x9lfLCjpYvfq3orzltgbzCexI6MzNXKGjblNxI7p L+qFCGFvhzlosehvZi0f76s7YPnkL+zvjD5DrBjSEmcOhXx6fktiQKjXIOuJ R9fmsbv54lp5Znnb45AJAQPNRu9dmMls5gjxkntEzMTBhmAABFS/XntouhVV YejwK1kPGvgw8sOgC8BEWNjUVnztXqSys+PUkJ2vHrIgAcQn/FZ+BtDCVun8 gusNqoGxVMLPYv11PItQjySyqUJ+x6vq+xNG6VjaRh6NelvfI+IJcSqJhE0m wjfNReD8rNjJUSFOSv1idn9pzRIQp0bc0oSSMyDeE5iQWET/1nYpcc/gCRpU 4KqI3p5thtkvf/Ccye0QivsN6IB8bnta2bN564eqW2jihCEKhSxHocpRZGWA hyKNRKNRZMhiJysdZ4MxO6c8rc6jc/h4M4bF+XiQAO8qvj1y0Yg/5GeJlTkF CG4shYVYchC7yohQwKxseTJ+7lBtVfzGQTLVmXpgzvtatpj3B/dZHhHzAAkA Ay8tQb+BWy2s331orI2fEpVIq3tHTYz9miNA8Ew4T1jR8T3m+GD1/urAnw1T FcNUi0m4rRUF5wPLECsDgyOqDEWWaq4WunlifPZ3GOFg6XQY6RQKUpO2D4iw w/npMrKKvjbzTq1K72SKJCgDAXxj7PAOuPHwhSmOgxTJGPtdTmZGzMLnFfw+ J0QaMNTSU77/yhLXQGN9mnGI/aqDi+OeX3n0MuH+vfsPH6DzwQf3Hj54cA/9 NeFh0pMXOSkVLXVsIVvQR+lUUucg4nOKr7844KEiH+B/8M1VoOz4O19I/Air ih/N2zrWUVV/4rC7Fa+bBdgYCP7UnR19Zb0rSSHIPubllYJ2rrAXO5aAIwjS 2F505cFMeRf3JeEHP7yF4d4KA0ET0hxzMsTe129S/9i8Wib6pA73WZEY5XBs FmlPaXkTOdVU33rq3gUvm4QIn4ejLaySnqyla1yMnQIWDz37qUAo5BDFGn34 aZJd6kmteDpvnryMxZzj697V84m/IH9pGAQup3gNgIhAoPCAMJFnflbe7Tkb g5WxPo424f6GQbtXV3JrWBg5t6dU9HlipLe+FvawC+Dg+afXva0RiEW/EYn3 qAgSc3ixb4AqGeXHz/lpuKqA53cSTY3isXPRo4KC3wgL4u9xDhlGNAM4FOYO XJR3b+n+UCVZCkgrUGUddPx2L8nvKmDx8YGVuFmLT4SIwKckTQXfEw796/bz yyx1QJGeLJmkougZ6X6zLK+Diz3uI9J+Kg1pSEMa0pCGNP6D49+AZ/3WSUoC ZqGvGhoaQUFBSUlJHR0dEjipra3tl19+CQgIUFNTQ1dXUlJycHBYsmTJ06dP W1paJA5W+MJ/jmH97mJ/tO5vf4VhmMPhVFVVxcXFhYWFGRgY0Gg0/KDU1dUD AwNv3bqFm3zhmBq+BT6f39jYuHz5ciMjI8l56MtH+6fOrRTPksZPxY94FpZ+ N1ZzXjjpQ3d2yc3nB0aMMgCeRiQVP+f5Z/cX9bD4vdIfCNxZ/2xVzDQDDZBC MdaddnDt89Sm5vMXhpv4gpmdCknF1fJIyo1ShvjrhDDaJzhdPVWd7QxGD4sH YTpP+LwbE/UQwpyyildbzszRVABaOhQS2VrLcUXk586sLnS+xuZ3F9QkBE3z 0gQzUjKNQrW0nnZwxcPC7C4OOs8GhfMiHiJk8nsqq5LWHphnQsW0dEgkC+PA zZEZzGpg0czm1CeVXvceYq2IUaKUyRQftx33T2bX13PFk2shT8hv4Dc8eLDB a7QDnlai0Qatn3I1p0gkwiRiSqvfbj8VRtUGCTB0CmugYjl52Oknl3Jqi+kc FgzhHAyitJ7X0tnV1tDO6OIhbCKXJxIR9a9CiFfJrjsbO9MxSA9zcSKTyA4r J5z9lA6LBe4g+IfrJTmZCMIVCXPzr09fHeKsZLd4+quyL50IQlAwgFw/D8/v Ipyu7Ji4dR4WsgN9jybHlwEhGLyoHpNDQfokc/t+xa/xLPKv8Sykq7Pg0pNt lq5mVCU5Esamo6HzcIrzMJsFhxfezEhtFnQCMgeMe64JkGp6xunb05SttKgg A0fVlzGYMvZcyi+VzHb8u3CtM5jBp6d8uzxu3mAtBRKWDafamk84tOxra7cA Qui/vNoXMg44waA7Y2k8aveMj+0NbNCAiEQ2MdnnIiIBxGljNLR31XM7iBxA Q2vOmRtT1S21SFS0RVO0qJqhww7dv1jQXN0t4CD4iuiuMhD+p5QYvylusthA TSHpzcHxLC5+VsR4lhn5p/AsLHfN63yecXNqhBpJG8vB0ow9lRYlHEuvq2Zx +eJyZUTY3FnxMOOE5zBHBQV0G1RNksa8SZe+vaYL+cySlifB4wGehTZMTbJB 1LS4rNdcAOrhpbtCovQaVNrzBUxmZ1ltB7MTy2r0IhciwD/6O2PFf2PAcA8i yPx0bNTsQC/NgP2bs1rze3jilBRCmOkAULit6d2Gg7P6meqGB1xLf4aVVQtB Vj8798r0qOHOKp47N36oy+JAfV0ssNp2gdiwBsOJmK/en5o+17u/XOiV499q q8SioyJRRfnLzcfH/23/rD8OLHcqlFDSBEQpO/rKzm1I3X54tK69LkiGU2R0 SXaR4VfTH7USNfBCwlwKQnpSv12dtmaYlgHmjUXGOCnAAJFo80CKEHOjQ0dI dbLaIPNVNw5+qqwVCvAxBgfrxSqyf3gF+r7pi2c5XCosZ/Ik2lMSbA7/HXzO zOvJWrrCwdwxfNOkx1VcsaQkplvI7Hwd+U/gWcE4nrU4m9XK+7UVoRjPQpCe zq+7Ly3zkFEdMfxKVmITTsqDxciX5Og6mVX30zbruzpMCFj96DYdwm0Hfz3+ //X4Ezzrb+kN4k+zELafPZ0lcW8O+NqpDPLf+fhUBRcSs3sxWgnekCVGM231 H9afXjhI3WDh9EffU7r4YlYBfsB9PN1wrihheMVBhEW5t6etDdHRJ+EQjZnB iM2Tn1RX8gWEOBuxCYGA/rHh/eq9Y7Wc9WgYfRg34pTBHDDBTQZXIAZ+mugn Rh4mMw/OTWkoZgkw7xmh+CEfU/xD6KW3R/2J3qBEvVckYbVIWDmYKRifVdxV cOjMLBMvU4oMiUah0EhmE4OPJt1uEJvxiX6AJuE+P/hG0OPr7Km+/z6630gb ecyLB90fZ6uouOiMlm6YyKILmd8aXs+fb65kQCPjpqNYcYiMrKa5mkuAuf8Y 7+DBI4LdRw6y8LGX01bFTiJOH9d2Nw3fteB9bQUd1zpji+CW2qfTN44x0sFA MRJVT8HUw2LU2lnH7u67nXgh4f6du/FPnlxKvH3w1pkNh7dNXDHO3tdMVYOC oWQUXa1FZzem1UOINE3eJ9ChhVNS+fHInR3BE/yMze0djfq7O7j6ubu6ebi5 u7m5uLm5ubu7u7i5uri6ew7w8w0YNiRyw6yT98+nVVQIBHwIhxqhv4xnIUQT EnEZpSd/2RrkqeVqvTcptrALo9gJ0IEd4Fnr3KnKwwee/3qvjguJMeG/i2f5 DZzserWgHmg/i341gok3go0Q3zo/L1piZWQ/cdv0xzX4oCAUcbkNSd/jfYda ubvOObLpC52F9WIC/kb6PH/2wbPmytF+As/CHOhEBI0LjFoisdOWgC9oafu2 5excJw9NMkZjpCn3jwzc/eYNC2FiEtBQT6Hw26RpXrqaxCSdIjPnxIb3deJH sr7IG9z3viOCq7uqT50P0PVSw7qbjArJauPCO3lfgcof4HX+TmpCJDka9HR2 dWbtu7DS3UuThJXfyZGdwvttTXpHh+hC3EVXMoD0SuH2AcpAa+FDPfzWe8/3 DZ7qpordi9EO6ms7/+yOsp5GngjqU+srDWlIQxrSkIY0pPEfGf8eftYPSBYJ c8tCn/AXL1589erVz58/t7a24pysV69e7dixY/LkyQMGDDA3N/fz81u4cOGZ M2eSk5MLCws7OjpwMhT6kCYQCLq7u9FViouLP336lJSUlJiYePv27Tgsrl+/ jr7evHnz/v37z549e/36dVpaWlZWVnl5eUNDA7odHo/XF4Hq+9r3DR5sNhtd C139+fPnFy5cWLt27YgRI+zs7AwMDFxdXSdOnBgTE5OamsrhcCTroofz7du3 Q4cOoUuiZ1hOTo5MiKBR+p6HfzGkeJY0fip+xLP0QKs00+i/OCKlK6M5L//+ sl1DaXKyFCpZVy1oSfCdqjIOHxIJQY+B6Qj71at9Iyf3o1IoqiT5oV67Hpwt L2A2nrs63MhHFZ1SqVCUXTH/rB7x3FlAFFcTqBAXgVksRgurraa+tq6iuqGW wWhnMGozjifusJCjYVXAZBMtmwVhb9u+AY2gLnbTm297rYIc5LDySTWS4vjg I+9vN/AhsRCLOK8J07N2XFxlSsJLj8k2hgFbIjNYlT3A8Ku79M7bjTqOpuAL yFRDqvKSGY8K33dzsfm1RIaNg8AN+ZdHLQ2SJZNkQXLMIjL0wLsXAnymX179 dsepMKoW4BpRyIpuGp4HNue0FHJxjEBcCinOYonw0QDMqlk8Vnsnvaaluqi6 vqm6ua2Z0cLhf3i12X2KLZWC5X7J+vPHHHyfDGrcxSH61fXq8x79LiY9fVfs igHW+oPsj7y7UQzyhpA43Y1ljHmIsPR7wsItEx0UnTYtSi79Sufj54qY+P8c niXqaC+8/niHlY2VnrmhqrGmLI0GdP/JxlZq/hGBM9evuJR0q6C+hi/OcgrT a15u3G5PNlbCsC91R5khlw9n1VcIBIjYqQE/WxDCYmZvOTnPxgRPkpNVVIYs HXKvnM4VIR13k6NHhlFklUAVq6HWoMWhdzI+1bU1tbXTW5vau9taOzs6uns6 2VwurhCFiMSpaQHCySh7tXqvk4y+IkivUzTsaUPO7/tSW8QTiDNC+JSfhYgy Ph4OmO4uSwzN/yiehdMahdXXk4/52JEoioAgqKzkFOFyp/5zGw8W8QV4qS3Y GUTYXcZ4O3bqQB11kKxVJJOGeUUnXapiCLuK658FjfNVA72ApkZRnRSy//G1 ktZGOru5s7Wlrbmjrb2Lzupi8rlCgVjgq7ctgXpjSJqE/EuB9Vxma+ryI9Nd zY0nD7pd9KqRx8eT9ljzwiTs6Agn9cuJsYuGeqj5H96R01DMxhPUPBHnQdK2 oVM8vVQiH9yq6G6GiUJ8zAaEwBzFFioY/09UXfp0/eFwC1nDrSufFeEJMWw/ qitebDoWbvSP4VkiXCdN4s8lJCwR+RU1n0/HbxngqyOjIYu2XCVZHS+zJQnn MlsaIESMBqG9tJPX8bnw9vq981yDHZTUSIRAE5kiR5Y3lLOwNLPUM9FRVCE+ Rgc1OZJKP+3Bs8P23T79rbFJJCLACaw9Cv8sUfmHeJbj5e8VPfwf8Cx8ixD+ O6eIXbh2q4NRv7Co8YnlHHGJO45n/UP8LALPWpLDbuNLhK0k28EvH6s7Ozpu rQ9NJWT4lawnTTiS0meHif9ZrNoXOQcNPO3H+S17eLUVN1n5aamBfwjPkiwJ 9qK96fPh68vstIwWRlxJf8HAbuVitAeRyBLiRyZCekoOJ2wO0FcIGnj168Na Dr4ZsWtWH9chXIUPBFvQnVX/eUPMZAdPYwqZJA9ofdaTg3YkXq1js/kCRExl RNfgd2V8f7PnwsqAsW4qxqq4gC3QH6aoa8lpG2voqlkayqli9xp5HOfStFDz mjxw1YmYdxV5nWI9PexEwSL0cb2j4vboRcOUxQ/Uv/HPQnqvGO7mIz5kEWa8 1tCRe/V5zKBgOyVtWUw3WMFJf+a5fcmVFTzC70ZIPBUIEUlRAd6LgaKjABMl 6+qpffwlfvpCL1lzVfQuJUtSMqG5rZtzO+dNOxfPmwPXraanRXGDfTTlFMF9 RUlGyUJ/8MzgOWsW7ziy5cL1Q/H3rybcvZdw7uGNvdePLd02P2DMACNjBSqG eynJW44w3Pr+STWTDsyVwIOKsCo2MTpknIMiYIUAwrs8Vclcw3NE/1Fjh4wO Gxk6YvTYgLEj3EN8LDwdlMwV0RsnbliqTKIMcNny+EQJ4y+xZv6LQoD2W17P 96a8288vrNq9ZObcaUPDRw4J9B3o7+vn6+vtg774DfLzGzRooNfgQNdgf3M3 L3f74RP9lpzeklqb3cXDyZP4HeGv4VkIMTaykzOuzpxnrKu76OrBb410BB/p eN3ZMVfXe1OVRwTHFzwhwHQs/j6e5XatsJEj4e5JBFOxjWCtHOLm0LNWbrA2 sJ+wIeJRNcFARBiMkvjkbYZ2NpMG73p6AytggkTiJ0+4z/j2D+BZYhs6YoQB 7pCgu3HreiouJmz1HuusqozNu6maHjbzT6z/1MZACBFdqKcQ/jY9coCeNm6/ iz73zzmx7n01X1y/B/UpP5A8P+F3bUbtufOBBgNUsaIOmjrJZOPCGznp2OH1 isagN31JcQIO4aO/C9o5NTcfbx8c4a6iQgZ1WiR1N8s5R1e9b+oS8rE8AnZ7 hIineaSP0ScxjIgwDdKOd5lx45cNM7BSkiNR0Z3XVR2ybvLlr7lcERsnmMIS iV9pSEMa0pCGNKQhjf/A+N/Gs37gIqHPk+h3mZiYjBo16ujRo58+fWpubhYI BAwG48OHDwcOHJgwYYKDg4OlpaWvr++qVauuXLmSkpJSXV0NQRCQohYKe3p6 0F+Tk5OvXbsWHR29ZcuWlStXzpo1a9KkSeHh4aGhoUOxGDJkCPoaHBw8evRo dJvoX2fMmDF//nx04Y0bN+7atevUqVM3b95MSkrKzMysr6+n0+k/yAb+bnC5 3JaWFnSV+/fvx8TETJ06Fd1VHR0dV1fXBQsWxMbGlpeX46gWAvIUrOLi4oSE hD179vj5+amoqJB+jWT960QtKZ4ljZ+K39EbJJGMtZznR7xvzWZ0tuWfebjJ 0kBRDowLlgGWix/cb+M2QRggwmvuKd96cJKDmwJJhqpBdlo7/0Z2GrOKVX/5 2jB9HM+iqrhZH0u5XcpCJDNfkAvlwoLW7tba+orMvG/JSU/uv7xzKe7CxYMn blx++Oz2s/eP4zceWW4mC9Sr0G810rRbOC657iudL0RaGTUJz5cY+VhgnUdW l2S/ZdG9/C88kFHCEz54Vk2ITvAyt15ZawHcN0DfsjUM2DQrk1vOQeeW9S2F FxNnyVobABMlkrwJyWBZ5OVXt79lZOV+y8jMzc7O/Zb9NSv7W2Z20qNjIyL8 5XBJQpLqWP+ohHgmhhZBFdVvd50Kk8N4YiSysq9R0OWzFcwGrKIewqeuWMkn li4W8Hh0RmdtU/X3vIw3H94mvPgl7u7lQ6cvXb9wO/F+0pM3Hy6eXmofbEsi XC8MFoYf/PAeAum2Xo2m3uv160k9gO6evr84PsJWTyvi3NbXlRV8WJIlwL2n Efovj3eMGO/vo7b8SXx5V3OvXYOY1NBX0fDP8CzHPvwsTnfl88zr6Ig7cfGs IbPGDnCzt3AwUFZRlEFHNoqsHMl5XPDh53Fl9B58S8y3+XfmLVAl6VNBeTfZ coDC+pTE2g6GJFkqwsv6Mc5tS/yjHYNHyFIAMZBMIftMcjuTU8kSCJiP3x4d O1mBJEcG2B/FfJBBZMyKawmXHjy9ez8xIeHug3v37z168fxt+pe88oJmegtb yAYSThiUyE7NvjN3uRJJCwhQUahWfqo7vj6r7u4i5BxhomAYYSNw+odDgdPc MMUm9Fj0543c8/t4Funv41nYgfLY+cfubbNG2w4VtEE9LZsJ/pdTb6Rl52bn 5GRkZmWmZ2blZeVkZ35+mHlzzDh3VXVw+tGT4eqw9cHhkg6IXdr5avzMQCNQ zEtGO4mFyZj14UfvXHz0Aj0P9x88vp9w75cHD395nZqSV5pX01zRze8RgsOE CF6MWLcLliY4/ueAREJew+VHe0NCzZx0liZczKa3icEgESY2CMFdcO2xC3N8 Ar1G2exOed3OZOC8J5gn7Lh8a9nAkf39NXanvW1gs/HkP0LktfCzL8SbPmHa 1dmYtvfqIls52XmT4zOTOoUwjCcFqypfbjo2zuQf1BvEYWRCUA50PDCUsCov PooeOd5Ohoqxz2WU7XX8N81IbsjoAIlBkBPDmhCPmV2Vtm5niKa9BvYkgw6Q asYG5rbm/fzdhswYvnDF7KXjl0wYNMbdycXJzMpYVxOTW0ZHW6rDONfNj+61 CQF5ExZgmksw8mel47+PZ/l5jcXxLCExOvbxWBERAyDMLWB8X7fNVsdlDMCz uL2Egr+AZ/VmUP8qntXKgyV6g+Lt4Neb3ZUVHbfOl6IUHHw1+2mLoM+3iHOv 4Bd2T21SxgEdb5sxg5Y9jm0DemEigh71E1f4n8SzxLUZ6AP3ttNTDdUt508/ 9yyhoLT0e1FxSWVJccH3799LiooLS4q/FxcVfy9B3xR9ryx7u/fqhoFmqu62 R97GlbGxG01vJlx8loUQJMAcYnh8XllDxul7S4xcTeVlAa9KjqRqabk4dvu7 hjb8/g5GLRjjFDfUf919ZZlrf3mgq4WOgbJKavLGdhbO/fsNDw+YtnBC5LgV M33Dhrj3czC1MVLT16ShW6SSVEnyLsbLbu77VF8r9qPE+VkipK3k1sjFw1XE D9S/wbPEO4+dDXEJBM6OgDtZ9Ylvz46bZi+jDHylZGgKtvJOyyIeFrxt4WO+ OQQSByF9LrpI8gI+ESB8uDsr9/bSXRF6amTsMUBWTdE+rP+B5AdljG6EAMLQ YFXfTTvlbKKBQ1SG8vqTgq98vVve3cojgGkxIQVG+B2dZdefHA6ZaEzSlpUF zDVVewXv49u/1OURuW10eKltSjt8c7WPr7WaoTJZVgbcWdGvxx24sLQ+FUio UdCbMRl90JPHnDRB/ZLSpLFnPz/owMtipDxfIohyKADXo1PV7p6m7xWZT1Kf XLty8cKlCxfROH/x3PkLly5dir1+5dyN60fvxy45FBUU5mNh7uSvOefmsa81 5Sy+GCr663gW+EYR9L38+cYD/jq6Y45uflVaieCNlcPIPnRlrS9FMTQ4Pu9x Ux8S6U/hWQ1sGCYokuKSDGIjYPQTcvM7slZtslB3iNg45WkND8IfNpqa884n LNSxcl8x/fy3ZDbeF2DidEF9aK3/Mp6F38uwFz4OIGO31i5m2cP0K0Hh9sqY rLccSUZLdfCmyBvp77qFWJUdBq71FEFfp8/yMdDC5QbJJNnZp1a/rSYMvHoZ taK+oBLG9SxnVZ48PVDPVRnAYKB3OO9YfDc7ExML7LOgQHyXhzEtRC4iYHTX vs6/FTLLXUsf9CsqkNEetGFy7KdkBhcimKiwkIDpRAR3D5ZUKAmxZdi8jo9l yRu2h2j2AzLdNDJZRUZrXODu55eLe8DyuDzyXzyB0pCGNKQhDWlIQxr/b8a/ gZ8lQXCoVKqmpqaTk1NkZGRiYmJ3d7dAIGAymS0tLWlpaQsXLrSzs9PT07O2 th49evTRo0cbGho4HA4Mwz09Pa2trbW1tYWFhe/fvz958uSMGTNcXFx0dXVV VVW1tLR0dHTQFY2MjExMTExNTc3NzS2wMDMzwz9BX9HDRJfR1tZGl0dXRP/q 4eExYcKETZs2xcbGPnz48OPHj8XFxVVVVTiBC/1SPh+UeP0gb4gHbrCVmpoa FRUVEBCAfqOBgUG/fv1Onz6N7iR6aBLyl1AoZLPZ6OH4+Pioqanh9lv/lOSg FM+Sxk/Fj3iWIciQm2g5LZrwvimnmws1v8q7ERaqStIFLhS2av3WLkztTGdw EIQHdWWV3PAf66MqT5YnqVrJL084kdHahTSyG2KvDNP3BpkhJaqKm9XR1Ful DInJNRoQr5JRevnpmYj1EXYD7VQ11JXUVFQVFOQU1JTQXqygpqKhqiCrgJUK gxSomZrD3Alvm7918iGkhVX/4OkiI19zCoAIFHVJocd2vK+sRfrWkCNYnord nbnj8horEkkGy8tYGAZumJXOKQeq/XUN+WfuTKXZ6pBpJArmQKGopKaqpKau qa2mqa6hpaKCvqhpKqmqq2jJUxRAP5UBuSPKCJ9Z98+0I0DiSlSJ41laylhW ScVb2//iycrO2t55o0BSwo3ALW3F9z/Ezdg31ybYSt1aQ0VZVUVZRV4RPVRl DTUNdRUNdQV5miwF5IVBZzacNzYm5b3Y+Rufjfa5Xj/OSWFRVU3q7kuT9PRM poadep/YCYmtZLjopF4EdbOy1uyd4uPpPdvrblUelkeWJNbEecUfaF+SN3+M ZwGPbATmtjM4TA6jndmSU5F+5NY6+1B7OVn8KKjKsr4rx576XCAUcNGL3v0i K27aNDIJmEGhdwBbP+UjGe8bOAKEIFOI8OQ6nhnjvHl/cuISXVk5fHRzCnPa lfq1h8+AM4oSV+72JCnKUjF5KTmygrKCqpyCirq2uoaykrqKig56brX0zHX9 IgfuuHf6S3U5B2TbwBnpepF+I3I6iaQHEnbKFIcQk8tFn5p7xD4uMExU86Ir ZKUdDpruDuhm4Lj15v6DeBZ2RTns/GO3tjpgWydhEjPyYOfVlNHmp6Gpq6qp pamhpqWhibYNdRVZGoVKocpgPAMz8zXxu7M6EW4bv3jtnjFW9hgjBiQf5VRo cspoM1ZVV1NRU0VPgroqepMx0u432mP5+TXJ9XlMrNYXEULiRkPk2qXx54H3 DmFB/tM1+0YaK3lsXv64LJ0LCwlYEA2hgF3d/jJ84TAfq+Frx79rrOLi51iI CBlQ27lr832GOgzTP5H5pYWLu/CIqSkwLHlLZMrQFTkdOUfvrHeWpY0dcTbt QROgkGCbKy9/ten4P4dnQWJOjQgjFGAIUAfMfvnmYNBkLyVQSwAU3HRovgsG n8vKYwpZYocUTHStsyPn7P3NpsZaFEzOVYairC87cuuk8y8TvlfVdXfTGRxG Tzeb2d7RklWYGnN7+8BJ3gpYb6JQaAbqnouDXjSld/cgYpnQP1VC+p/xLPyv vXgWcS+AYX5Bd8HGzfZa/cPWSfAsoB0NIMp/H54lQgBFQoJnPW7iIT/sKpEp ZffUvcw8ZOhjFTZo+cPLHfC/hjb/U3qD4lMBhuiS8qTNx8LUVXR8dQLH9B85 ytfbx8PbC33x9R3o5+vt5eXl5+Pr4+Pn4ePu5zNwQoBFiJuGhpKL5eEP18uY v85+i7EtnKMBSjc6mnOOJ25z8lCiquHeT2oWsv671j0r+cji4E/R6JIguQ3R Ec6r5zuHTnWSk8WtnCjGii7T/Q++uZDTUNRM72EwGUwWg9nR1VpQmXn11YUx c31UrdWB1aUsSYmsExEanXy/HepFlEToVluKboUuDf5Dflafx34hfr2wERR4 CSGspE/nRy4L0pKnyqFP9RSSprzTBLvD75Pqe5iQGAkU4S5d4sPHjl2I8HHZ UhhmI6LG2pfLDk+wNgdEKYwbYhFoMffCuTp+NQ+deQgQGGeTIT1l8R9OOupr KGD7aatuumHR56aMHqzlQZLN4//xIWFze/6lF1FaVnpYTyVryyotnPiw6E0X 3l8gdP/53LauysS0axM2jzJ01KFQSDicRgWQFhWzbgRPOOpKRkboNhTImBmZ sj7JN3r9y+Jc6F8diP7TQogRasQDAQSzhRCXL+RxeT1cEHz0HzqZ5bJ5PPCv W8Blsplf8h8s2TfOXEZpuNfxpCvVhGne38OzwBfX16XtPT9BU99l6+I7BRlC mHgSzjoYu9qPpjxqRHyeBEzHX38az5I8PUpKCQjVXHYhPXPNJis1+/Gbpj6u 5okEfMAMqqnLPHV9ho51yJYlCbk5uIGXCJZMCkS9vlT/Kp4F4bK9YHCBxOUN PG7j08/XJ852ltFToZFocjIUPZJaiP/FD3eqmFx8qxhyBLOKhbmzF/oaG5Jw QUISdcqx5S8qmWKjScnA1bfNgzW5+R2ZG3c7aNjKY2vJacqMOr0luapasiBo DgLc6woWmwxCCI/d8jT7zrSlLnImOJBO1SSpDht06PX1ym4I99UieJzoDggg YiQhEDsRQRAXwNyC8rfrj0zVM9KkggInihJZw0lt5e0jn+qa+OIHW1GvzKQ0 pCENaUhDGtKQxn9k/Hv0BkkYqqWkpBQREZGQkNDY2Mjj8dAntO7u7uzs7GPH jvn7+xsYGOAmWS9evKiqqkIf/nEsqbW1NSUl5fjx4ytXrhw+fLilpaWqqqq2 tra1tTU6jx4yZMiECRMWLly4YcOGffv2oZs6ffr0OSwuXLhw/vx59NcTJ04c OHBg69atOJMrPDx82LBhLi4upqammpqaKioqysrKurq66AYDAgJmzJixa9eu W7duff78ubq6ms1m/yBALZHFhrFgMBgFBQWXL18eP368urq6jY0NegjJycld XV2Q2DwWXayhoSE+Ph79XvS7+tqH/YshxbOk8VPxW34W2hrN1J2XhX/oyOjm I/zitqyDJwerOmvKUEgqsnpDbA99vVfF4EB1jIobt2eY+BqhK2gr6ozyuJLz pJ4JIQ2MpnOXRxj5qqAzQkWAZx1Jiy+lS9Q/hPSPpW92HprrNspd205XWUce t1nB7MtxDyaAMQGZIAoZVF5TSYbKtrPC3rant6EbaGPW33+6yMDXHEORlA1I 487s/1BdBwhHwB9ZgJcxg1kfn5G1+8JqSxLBebIwDFw361tPZQ86D2xozDtz a5qcnQ5m5EfD/JexTCsGLtBwOAZzgcGM5MmEDweZPMJrzu0zbQjG6Cmveb/t dBhVSxH7o+oA7cCrp2rpFSBXiuuo4BpaAhFU3/h5/6VVQWPcTZxN5NXAsZHE lc9kvAoUYGVkWfxDcOAGC8bFpCT1tdn543koDDLrPHZ5QvpJb18DV8cVF7Z+ ae6TZGLCrIys84FTgoPdIs7tKGPUcyBgrQWJ86giSVrjV01C/OYP9QbR6TY+ 0UYAgYWPCFgwvbQxf++xRa5DjShYjTeFpBnoMuPK6QZuC7qPXW+y4qZNIZF0 8FSBtZ/SkawPtWyYyOdDeF6PmL8LU76cnbbKkEzDxI3I9hNsN79J5QiYSHtr /vXXux399ShqNBIO/9Ek1w7XiKHKkuTlSCq6MvYB7guPbnqUVwUJBOihdL3M ipsymUTSB2tpKPePcLhdkdnKRQgZKCKxifGzMnE8S6I3+Ed41s/4Z4HEAoue eyBuqz3e3ki4HBa48KAUHjsoCs4IJBO2RPif5UlkM5NVN/flMhGYgTDfpRwN m+ehJI+tCDoRboEibl1YuyVTFXTkTfyshi6fefdLcg0d7pO2l1bV/5UgOoiQ 2V5w+vmO/mZ6IQMPPb1cyUawCnCs8dJ5zUmp2+1DA8I8lt08Wc/rxHWB0AsN sZC2szfnug91GKJ7MvNrC5uPEJQuEPCvUot4Ug/tCPT8k093uCrIhQaeenej gSfOopUWv9p4bJzZP8fPgvukuGAhu7q9KO7JkaERg3RNNPB2p6fst2zs0Rf3 Ktq7RSIBLvVG1L7nlz1bfzBATkUePC7SVO00B62clfDteTm9sYfQFyV4CjBD 2F1Vn3323r6hQWokTBaOJmPqr7U66V5NdzsYH7GzIfpJvcFyJu+3eJYEhxLx 83oKN2901HUK29AXz5Lws5aF+/yDeoO/65+FfZ2gOzvm+jpvqlJo8NXsxAaW +O+So8NHQVZP3cusA+a+1mMHLb9/qYPXxzjrJ3DnfwbPwpsfwWAVlZQnbz0+ VkNd3VHbK8hsYICzs7N9PwdHZwdHR2dHR0cn9K09+h79zQ59cXexd3N2sLIb E3g5/XEVQygGkHot/LCxF0JYCFzTlHokdtXgMVaKmlgymarjpDs8ata9rHeN rE5IfI0xhUCIXS+s3XMswtlRHgz5VIqckvf8kTFPH1V1VrP4LKL34TBZj4BV 1Vb24uPZodN81YGXFJlGkbG3mnt+bWYbUV0Ddgntia3fb49cMlxJ/ED9W71B uPd0SK4dv72n8l7qufDZIYYWGjTsUUBd3n2K/65710q72tCnJ5wthbPKxHKd +G0X7/HgPwGD1f6h8NHCbeMtBxgqKGMIHcXC32HpqR3vaks5EFeioQzu9TC7 PP7DUTsDdQXstmKjYbplbXbr9x4Yr1XodV/CRMaE6Cmoe5F7zsnTXB7geVQN efmw4XG5j1r4GJMLXYaH/gh5rYy6TwVp91/cOnw6JmrLyimzZo+bOGnKpMkz JkeuXrTlwKbd8zfPs3NWoimA+7oSRc/LZMvbuIKOTsCFlzBXpIGIJMpwot7W gl1wsdBfn/IhnJEOI1xW+a3XJ4YNlNM2WXpue1oj6/fwrGvVf4ZnYSNbXe2H 3RcnaRo7bJx/M/cTwUDkAjxrjY+MyujguNx/BM+SjLHiTgyJoVQYYRV2Zazd aKPuFL5p2sNKIdCSRT9uaM4+FjdDw2rqgRXPCivwHRBiKqOE5q14LPoH8Kze UwsgJCGLXnnrzalxy4P1jBUBxZ1CMVRznOq798mVkpZ6DtEHcfNKhFchql67 xd/ciUQlHrqGbp1zq7ASl1wgdAxgWPSriwhOMuNTS8rs+cbqRlgFHklBhzT1 8p53NS0EG0uCZ2GVG2B1tOuxmBX3ki9OXRuqZ6GA9ihgKatsN8l1z7PY3JY6 FlgWwkQDCOwPkVyQ3pI2EcyD6N/yEqP2zXFwMqAqyqBPknIyJoFGM4/uTKnI 6YJ4YGjjiSVexQO5NKQhDWlIQxrSkMZ/Yvx78Cwqldq/f/+tW7d+/PixpaWF x+MJBIKampro6Ojhw4e7urqis+AVK1b88ssveXl57e3t6AJcLhdd4MqVKwsW LBg0aJCTkxO6hcGDB0dGRu7bty8uLu7Jkydv375FN5iRkZGbm1tYWFhaWlpR UVFVVYWuWIcF+qa6uhr9BP28uLg4Pz8/Kyvry5cvnz9/Rtd9+vTp5cuXN23a FBERgdt16erqGhkZ2dnZubu7+/v7jxo1at26dY8ePUI3JQGn+gYOdaG7ii6Q mpp66tSpIUOGoLsaFBS0dOnStLQ0FguTXBOJ0OOtr69/+fLltGnT0BOO5cnJ /zqkJcWzpPFT8SOeZQBak6ma09Lw1K70biGEdHbXv0zb6xBkJy9HkSWrW8mP OLL1XUlu0/vyF5FzHRVMFckkZVu1ATuiPtRkMrgipIbZfP7aUGM/VXRKqEhW 8bA8mhZf2klMAEU19d+O3FnnPkCDrEGlkCjyZDVDdbdhniPCBgePDgkdOmbM 8JHDPIe6GblYyFLwImGygbrt3LHv2r91oNtoYdYnPFui72cGio0pCgYkv2Pb XpUVQiIxIwImckQiAcCz1liQCDjA0iho46x0ViULnSM2NuWeuTVF3kYH6zkU FSrNXM3W2cSpv7WlFRqW1hYWltaWlua2FjZm1qbW1rboj7mlhZXDvIm7nj5k Ij0YnlX7btupMRQtRSpAUdQ89AfFnqumV4mE4lwoNiRA9ayGu4/Xe0fYKykA 4huVpGapZj/IcejIwLFhIaHDRg8PHBXiHTzYwtNERRuQkQAmQTVYEH4g9Tk6 6YUhfAb9Z3pTeFKu+2vdx+UrvM36hW2ccjWvDEb4MITuACxo5taeuzDdJnD4 rCEx7z5yhUyJ3Qlx/UVIHy8ASZMQv/lj/yyM5MVDIAk0Bg6Y/+nzmVFLvMj4 gZAo9lbBB5Z9h+o4MEJPzoibOplM0QWF3ySyrY/KkZy0hh4xlUwowpMb+JSb l/Tl5MQFBmQKTpFyDrfb/T6LxWeJYLizsD7j8Ml5gRP9zFwtVPVVKYDoisM3 FBp2rWVImBQTlSInYzzOLSr+YjPcxkWQ7tc5cZNm0NAdINEoaopOo63iyjNb hbh3CZGfwPUGRRlph4Omeshix0AiG8wbtffdP4ZngRDQc47f3GZDIUZ9GVkV ExUnL2NrO0tr0AJtLc2sbCwtLWztLdEPLGxsbMytrcys7W1shw0+9DS2hIUV 63Z0ZJ1J2B82McjG017dXEVWBRy1jPgHa/dUrMCeJEdWtNOMjN35tq4aO1RI DB9Isxv/Y0hqsQUdb7/fnznR0sx+7oG1b+oZkoIWbkFXzr79wXY+YWsnX/xW IEA4BB0DXYIvajtzc7bLUEd/3RM5X1vYeO4XT/0BzgWCEE5aYsoGurmu3OOJ W13lZUYPOf3hRgNHbLVUVv5y8z+oN4gFXsAv4DFLar6cvLdv1BR3JUNltN3I UdRN1Fynjzz0Kja3qV0kEGfhcLcgGOlO+nphyjI9kiYF43CYB+kvS7xT395G iEhhHAURnq0EVokwJ7PsedQuN1kjBQzm1vWUG3z6VH57NcD8hNjygj/dyd43 /yOeJVkY7ACvkJW/eaODltOYNf8G/6zf4lkwQWjjd2dHX1vrSVMeGRyX+7iJ I/57b/k+9trTU/cyPdrI23ac/4rE2HaBEPpJ66zebf4DeJbkoNBTWlb2Zsvx cF11w7F+S3cvPHpqz55d6M/uPfv27t6zd+9+9IE8et/+vXt37923c8+efbv2 7d2z59CeA1cvpdcU0zniY4YlFpWAsgR3cTq+VaTtOrPCK9RRFewnhULWsjMe vSryYtq7NjbaqAA2LPGjRIQQq7onZeKKIfrqYICWIVHM9GafXfe+uQfhCSGO JIFMEEbQX3l0dt6q6Cm29sTTgLJq2JbxT6tEiMTwC122rejOqMXBCuIH6t/w s8SbFV8sAdxT1pQd//Jo+JxATVMdGlDzk1dWcp4YuP328fQ2Og8RAoIMZhMn gcKI24w4I43+yuvoLHvy8e6SreMNnXTkqGQaSU6BpD/AecGpzc/y89l8/EvF XnPgjs6uuJl23FFXHVRBkCkWahbr539sS+8CvRMihBCxbxRBBD2w+V3Ng6DB ZlpyoFBCnaYwdFhsZmKTgKBCgxMrACuCVTicruq6mm/Fma/fvXny6vGrl89f P36d9ir90ZMri6MnmJrJUtCbK1nFRs0/au7LGvTuCYkwXzApz7c3hBL5U6EE 2MKvNhH4Yv8fe+8BFkWWtY93oskZupuck4ASBJFkQMQIoiLmnCNgjphzBgyY dcyIWUEBBbNkVJLk3OSGpulc/7q3qlpmd2dn5/t2n+f/+7bPM8P0dLh169aN 5z3ve/AOgG+9uj+WJa1eY0AxDt+z6mlRwz/KnwXwrD+eDiCeVVyZuj1uNMvA f/eqB/nZ+H4GnXwOXFo7CJ18RlzNf1wv/DXt/K/xLALQIjju6F1z8zoy1663 1nWauHnqwzIRxuSS1DVkH7s+Q91i1K4VCd8LERzUFuF41t/Nvf87vUEiO2Ev n1vS8v3Og8Mjl/qzrNHzCLpfU2Bpuk4ZtvFGbHFLM1gP8JxSONVa2Ih0nD0/ 0XaoIowyRbfiVjPHHkh9zBX3yTomu2sZzM3n1T37cWOoH0tTHQwxdIBYqay+ d/JLQzciwiEkokvAFzwRt7St+MHzE6HLAw1sNWH0CN1Yw2miT8T10wVtZVyg IoHRP7ErYEA2IsWaCjvjCJHehq66N5m31uyYbjvIhAZmQpIGzczPce7hNUk/ v3V041v9Pmn//rz15CY3uclNbnKTm9z+n7X/KJ4lw2usra0jIyPz8vIwWhZ6 3ba2tvj4eF9fX2VlZRaLNW/evJcvX3Z3g6QrGCcrPT193759w4cPNzc3t7W1 HTNmzMKFCw8dOvTs2bPy8nIOh/MPAaa+9kcJtbH3gb9FIkGrgdYKbYRz585F RUWhV7Gzs9PS0sIahEqlWlhYhIaGHjhwIC0tDa2VUCiU/bZvUehf9KO6ujr0 aO/q6kqn09FWjY6Ozs/P75vFlcvl3r17NzAwUFFR8d/SwnI8S27/I/tbPIsJ 2B6muk6Lpqa1Z3cA7o+4s7TjzaR5Q3R0qXQSRZOiNjko5vnFV5df7XOw1aIq kugUC3+zZU/uNXU3g1NXVXfjpSsBxhg/C+JZ6Td+toLriAVC8dfPcZOWOKPn NwWYzNxCw2Nu4Ikn+5+8vpvyLiU16ePHe2mPj98/GrYpTB/j5pBIptp28ya+ bvrUJpQg9dy6R89WMoeY00CgNV2bZLBx8c2cDCwKEsajEuAIvyMzOh7wszA9 TztD/y0zM3mlPWIRUtdcEHt3uqo9C2hzkBW0SPpTPCP3zNyxfU3k6rWro1ZF RqyLWLMyKmLdmvUREasj10RFRERERq2K2vpb7PPifCGGMZVXp0afClHQVaMD AEXD1cj30vGq9ioYeA2DKsFBWdyR1fRp7hJHTXPIAiORNBX7T/PfcH7rw5QH Hz49Tkl6l/zwQ1Lc47tLd0009TWlYwmbSMxFE/e/fQVTZhOOB+SPzvK4VIuk obfxbuJCmwD/MM+1966289skQjA7cb6AwFEHm4FhB2e/AgG+Ity/0lf9/29K /tfwLKiWhldMjHkn+Ii0seThoujxGmQySL9DJVmbDN84J6u3pleMdCblXA+f rgD4WRQSlWzjrrLna3ItB+aIgUl88NM7DEJuT3h1cHy4PkDF0Gek4D5+wPHP JV2CTvCZUMSvZxdff3lx0d5VATNHO/bvZ+fgYGfbz9HRxsHBwczSTIulRcVp TWRLfd/1IRntlV08tIdnX501W4GsB3qEAsk2QPtw7utqrhDKAwETC6HnhItI MzMODwl3UcBXLtb8MTtT/l14FmzuLm5BzO0d1nglKUxFk+EW60/MidgSGbF6 TUTUmjVo91sdGbl2dcSaNZEREWvWoZ0wKmJ9xKqDR5JLPzQLxXj8Mbut4vnn uxFHN45aMto1wNXGzsHe1t7O3t7ewd7awQroz6grUAD3kUInGS2ddC79BY// yxmD8XPk9icmEWJ9UlDWUXDyfJC+y6iV40+kf+wVAY6WFOmtf1l0b/xoYxeX 5ec2fm3CRpYUz3TBk7Sd+W2Rd4DTUO0TXz/X92KOP+g9E0oJsAyPbMdGsrSl +dO+88vslJWmBMdnPW4l2CZIadmzzSdCTf+N+bNAJcUiQVdldebpm5sGjXWg k2G6HJK6uUb/MPcjqQk/WxrFffyuuP9TKOY8fH1g7CQSFdIwqVTXsTanM/O7 QINIcbYj9JpiaT/Aq7qa7KOXw5QsdWBWLuV+iv1OH8isKRLDTd6f6CD9id5g 34B8HC8AExL8X15hd/4moDcYEvUP82etnuj9n+ZnQf8nn5N74Oo6D7LqqBFX ch7V98gegcyxDI3XXZ2cc9DAwzbEe0XipVYgMCdG+rKZ/pL9u/QGiZsCjVdR lrrt1BRTbdvolQ++fYV5IhGci4dnP5TgX8WeqRDBb0EswPq6SIT9C9AW8AMu l539I2Xb+QUGziwFOoUK6NEquowR6ydfTn/ZISXUNvFklPjY6SysfxIY4q0O XbgKJPIg1633ThUJJLi7F4hnEh1PAhFkjrj53J0F3n5wtgX5rUYvH3u3HP2I B4a2EKTAQtglt4P/HM+C3Rqk+uLV1uddSzzoN92FpAKAIhpZkaltPtZx2+ML uXUNIjFO0gEObSlkYkqk0l+sHTwJF7+9s+Z52rU564N0VQAehv6roWriypx6 HNArurFmB2AbMW+AkcWrvP8uxpWpR1YE8QomKsYLpyRVvmuVSolnjuFnGDcQ HQj85tf194cMt1QFEs4kJlV1DBC9rJeICE87Xj5CSP5KZZsDIJeHcJuqv+6I X+rsBVNrkSmKivZjnbY+fdDYxca7t3wZ+RuDAJ8E07sTwRmU3wfKBAZnSFyS Gv2OECloeBd92JZiO3bn0vuFpYj4r+JZcKLKLHy+ev8ghl5Y3M5XlVXwKmhf BXjWOk+qWlDQje9PG3GOKlaJ/xme1Td/FoKFOSAEVN2b2561doOVvl3o5mlP Snvwqb2uLif25gwda+eI6We+vgHLqYiQ2EV+N///7/EsMP5BkBK6rtVln392 yGeChTqLhvZddNgzVCwn+G65e6S4iy8Vi/Ca95nfpF2I8MXTte6hZjQSTIRF ovu5Lb66r6qrB6szIvsrq4wEEXW2FF5L2mbM0qfDQC4Nuoa72aHUayWdYmLe gEFfMPpFIhRxSyt/XE0+4RNmr2pMgztcupaixfBBG28eym/uwZYFTGMcgqJS 2SYbIcjFgBna1lHx9GPCvPUBWvYg3Rc6NFXJOk4Wc06uflZcKZLiZGRCShEX QJAPVLnJTW5yk5vc5PZ/1/5zeBaFAkLoVVVVmUzm4sWLU1NTsTA1DodTWVmZ lpY2cuRIKysrOzu70NDQjIwMLGVVU1NTcXHxw4cPo6KinJycLC0thwwZEhkZ +erVK/RXGOCF/AFW9Q/fBNtcEXrOFIPzrlgsFArRv7J3+moJ1tfXJycnb9my JSAgwN7e3sDAQFdXF60/jUZDb2Hq1Kn37t37/v17bW0tlvmrL5gls7y8vI0b N5qYmKirq/v6+h4/fryxsZHL5WLoG3pd9DZ37Njh4uKira2Nlvy/bGQ5niW3 v2REdwXdHvRITlpxwrx5VBoL9CYjXaeFoensrx18KNrTKG47d3l+/yD03ERR IlEZ+rPWh21ZFj2aqqdMpZGMdAYv9Lpf+rOzB5ycJJXdjRcujTAeDAThFWlq 7ubH3/1WyoXHQBHSciVhpX8QkNYDzBuy55TB+95kd/Hreru5PYJeAZ/f2y3k tTTlnHqww1YVjgkFkoGKw9zJr5uy2tDiO7ob3qXvMQ20xzQAVcgKIQEH0282 QMETEOyNILiEvqjjy874SDMSRQFiBtZGQ9fPyRRUcHoRpJ1dcitpnaaTIR0w wJRMSea71icVvGvt6ujgtLa3dHa0tLVy2jvb2to62tvbOzvaOls6mjvRP13d PAmW0AqRVlalbj8dogT0BgHryl3fP/50ZXelRCLze4kQsbD+VdF1NzcLRej4 VaEpeTnvfBKf19zSw+0WCHj8Xr6gW8Cr7eWmv9riPtGZIGkaLAw9lJEiIrxM f+q6lkqkEgGP+7Py3ohZQzysAnfP/thW341ONJyu8lufD9n3tw3x35IQU83/ k3L+1kQguDdj1e65BlhKDQqpv/WUIxH53DZhn5r9AkjQd2tLHiyMDlbF5PJI ZDuLoTvnZXaVoa3O+1DwaNkaPZI+HYo4GvZXmvf0ehG7TubSx70rfHBarzx5 a623J5VCB8qTNIWhc72vFzVyhThmJOKDpGDc5p4WdktzQ11NY31jQ319Y11j VX1l7teExbunm6ljipEkDaX+YTbXS/NbxAj/y4/HS6IMaUwMRjJyUZ53/0px C1tCRElLsBq0I0j2hwO+gJ+F5QphLRizKz0J89qgfbD7S23aqjV62ubAPUgh 6xsrbnxx4QeGZxFOdahQJ8pfs2OKjTEJ06Ax0Jt3ZMX7VlCMRMIrv5B0cCCD TIKcPGP9AfNHv2p4V9nMaWttbm9rb2/taG/t7ERfdaL9sLW1ndPW3tre1slp beOJBLg6IvSYCYX8nvaOzqa25vqGuvr6hib0L7u+oariS1HmiUvzTf0sIfJL VqZQA3w2PYypE0BOBB7DLOdn/QsmxIEZpJvb9Dbv5IAg11HOs8/vr+hqBZ6t zo68+IebrUzN5oSc+pDQiklCEQ5wCU/adScxcnjIQB+trekv6ro5CD5eMIeg GOeqiHE+lxRA9tVpW07PMadpLZ5zJ/81B5uf0QFeUZG08VioiYbdrsjHP7Il Epn/Gna5X2p7YhlQgjmaZdJ/xD8YyAJfCwTteXXpiw6H2w8yU1CjYXxEQ+VB S0ccS0puEjbzRYSvG09ADyLqpSJpa0LawVGhJJIyNsoHBNkez8xq7xX09W9C 4VUCpKup/Xr4cgjNTIeM7gbJ9P70fkf2ZtUXY9/DaFzYPCAl6FZSsfjvnW9S iGc1Hjsz3snXc4JT/I9yjmxO6yt5Rvyw9zvn+6at9gyn4MiJD0sFUozACzu/ qKMtZc6KUE8D94hJn7t/dArwxgHPgY81HQakoHcgaitoTvQf5TXYOuzk2vye DtyvLBJ0lzQ9GTV5iH9fPAvpsxUk9AYBP+vi+oGKqmOCruQ/buDhO0aiMxD/ BfmzMg8YeltP9F2WcLGFSIMIfJsi/L7EMtcr1s5/OKPjrdj6sTVp4hhrbYBn kVXIyv3N96VeLuIiMh+yRCwrQ4h1DVAszGMoJW4HfBuKZSHN9R/33VhtqWu0 ct617Fe9fOzxIVA3EoOxwAsJjnOh7wiwT4grSAgcFspnCcWtyZ+vzNo41lBT jUqnKdBIVLKWDW342jkJX5PY7VwiGxQcgxIiYEAi7ixsfBo4zlsNJqDVIJGc XDc9OlLUgfl9RQSIJoOI0WGINJ2+PM/NlwypwSQSdWxk8L1ygUTKx0ePAEHY RTfHLR0J5mOwoJNG+6x4cLEJ6f3FpYL6fRjFr7u8+dOGC0tdA2xUVRQgj4PM UO43yWHni4Syzjq+QNpnFCAYg4kYr7juHBACrO+tvfhgl3fYQF1tJRgaQVIh WYywWHRmb25rfpdAJJElACLc/liPYqcV3x8xRF9ZG60rVVOJOdw+LudhOZcP IQ2IpouRX9nueL1VibknHB3NlIGMIZVJUgsNvVHwtLEHkfxC0jGkjFiCAVca fihEekpbfkSfWThgmImCGtjDkCh0D4uwU6sy65t6pbLUk//FRN+/naMkONMN vBZLcDAXwYOsZKCMSCpLPoVgPN/Syk/bj1qTrUO3LL1fXAp+hfYffk/RhZQD gzXoQ/wPvb1a0UOIDyAE5ohDuFhvR7pAxM5ic4buoisHP9c1wi0Z2gE6sw9d WjtIQX1s4LWCJw3ETCLDcfCZE+JZM1Wc3Zajm883OGEJTl+CWqTxwMmRDhDP KqjrEfe5kb899kp5BW2Zkeutde1CN057VC7AUd3mlsIrj6P0bW0Xhh5+k0jI KWIVIOhsEtjnwEhBut//fDJ/nrKC1ZyYP8KzpPC2MXwHzkJCbMUDJaBLh7ii MmVL7FJHPzUlFUUQFkVRYijaThx8+PnFguZmgZhYLkWwiSAqBcrpRsSV+ZfG LByiBGhW6O6OaqwzcsO4B6X1PCmxsEIgmMCY0KuLhV+LnkXtGqysrQhPJWSG iu7s0fd+pKDzPF5RKDeJ0ZAFVXUZey6vcPDUVtamk+joEKbrkazGu+99HJfD bhHidCpYPJg58WxZ+I3zQVNIhGKE11Z8/smRkbPclHTVaAogSERNQd1Nb1H8 rtTK711cnNaFdUIEkXWbv5QzUW5yk5vc5CY3ucnt/y37T+BZFApGjiDp6ur6 +voeOHAgMzOzu7sbS5j14MGDJUuWuLm5OTk5RUREPH36tKqqisvlNjU1vX79 euvWrcOGDfPy8goODkZf379/Pz8/v7m5ube3VyQS/e0umkCU+go6yAz9Pp/P 7+npQS/aDq21tZXNZrdAa2tr43A4aLEynhf6fezLxcXFL1++jImJiYqK8vf3 Z7FYVCpVRUXF1NTU29sbrfzNmzfROqOFI3+3sRcIBOjPL1++PHHiREtLSxcX F7SQjIwM9OqyrFu1tbWJiYnz5s1jMBhUSOCQtdhfNTmeJbf/keGpHJDOlOKE eXMpFEPQA830+y2b/JaT3YGp5/Ug/E8fYsYs8FSjkOloN6WZ2bLsbVxZFAUa haToZhN+dNO3rhYc46jqbjp/MYAJ8Sz0kDXQ/PibmyUccB0JH2k+e3uZ9zDc ZUSiDls84mpuBzyTi3GXKUib3lYc92KfA0FdNNW0WxSc2vypQyCVcoWt+SU3 B0300IeghBJaVdPZxyNfltRhmeLx4xpQvuvI3X9pjQUBEdky/TfPzeouB+GP vYKa5MLLA4baa4HwbqoBVT0s6NyH+zU8WVIpwi+Mzggi4sQtay3sMlVVqdtP hSjogIBuCknTnel/IaaCU0XE0mNOSEHdyx+XXByMoeeNrEtTHTY4/lNCPTyg E5IyiLRXiuSnH/ae7EzBaawGCyccepsilso8eP/k+RGeJJFE3Mgr2n50ppen z1SX4+/fN/G6xeU1b3edDDY0GbF16a3sTN5fzXIB8Cz2u4jo2UaAigRq1892 0uGovK5OoRj6ygREZDfml+CJBe/fxIxZ6gaQSJh9ytkscO/qH/xavkgsKKx7 t/P0MIqVJsQpVW3I/Tavfv0jqwtzmIoJrx1finTUJC/bMZmlCf1nZLKB/tgt oS+busX4Y5GlJ8JANDHmRAJFiADoUB7/ZJe/L9pLaRQ62sdcR5nEfMtu5Eqk hVXv950cSrXUBJwvkqqFgmPU3Gd5GZxePBoWIw4g3VLk+4fD/jNcFEgwfxfV YP6Y3a+ThWKhFMbYCzKb3q5YzlIHudloZJI6k7b4ZuzXhnbCSS0CfqxeKa+6 M3n2mtHG2lgHJJsbzzm8/F0HBi5I2pKzrk+ZqEXSoytQSHrK1iGWRz48quRw JbinBaIBQug4Eco89nicvviXp0IiC7GW4p1TjBNkekWcnO9XhoUN1oZpxVDz cltz62g5v6/n/4+Yy3L7ZUT7glh3Xinn4/LVIzzdAtZMeFRb0yPi937Ku79o +3BDk+CT25N//hQJ8TBs/Hd8RJDx7tj4xf5uyiPPxnxvqgCPF4sJx4AAiRi+ IETJJNLezKwbi7b6Wyi7HtqeUpnPl+DYpbS8/OWmY6Emmg47Ip+UZUl+0XaE eI4vESAayqTNcNSAwLMQIj6ccFmLJE3sorvJ5xasCDT1MFDVppMUKMokZSej 4K3z418nlDWzJb/fTklFWCOAEnrffr02ZzVLUYMCITBjb91Z8edK2spw8T+Y +AMBbEcMBJB0ZxQkzIu0oxkogugCqm4/hcBLp743lmK+RNzbhjvhxDhs8zsR M7wioH+zkabjZ4MdMX5WOQdcUiolAtpxIxytPQWd3zZieNakxJ98DA6AmAIi 7Gx7M2/9JC8Dl8WB79vyOwT4D6V9CsGyGyF8Qee35gS/sQN9raccWVfA68S+ i4j4XcXsR0GT/P1Mx+7ui2f16TsYBgHwrAvr3BWURwVcyv1HeoO/8Kycw0aD bCf6rnoY3ybhiwniwy/QSSQG/CDsB6I/hxHaPkA8SxPDs2jKAywPJl8o6sDu T4TjjRIC0peJ5QITEwQ7bG6B+nV87vdrKfs8jPTGDD/4+lqtgEi/KCDgG7wc bC7C8Q4p3r/xJFzwomJueduP87f2jJ8/wtSGQYW8BnUls2HWs/dvfpL7phZy vaW4Qx1DZyT4AoG2U2nPx5krAs21gSYYuoazdKYeXfm8qlYiFf8ijoiIgSgU 81vZH1fuDDUxImNxYyoawdvCXlRLpAT4i+5MQP6s4CUjVEj45iTIZ8X9S81S GSUGhw2k7M7Kpx9/WxQ1oZ+3mYY2iLdQIqlY641YOfnIk4Si5mp0349TjWWx DTJKDoKDpFJJT8un4uQ9sSs8Rg/QMFTHgtm01d3D/bZdOfah8GeXsAt7JOgA wXz2OJ0N6ya5dRkrVllrmwFvOEVB3VJ1xpl9b6sqezEx0F4Zkgf+T1RenbE7 drKKpRaFSqaQqKYKrHXLnpW95yG/uEISYiGRSGVQKSLhtjWk5D7ccCDcwd9a XQ+Qx9DHZMkaH73kbtaXVoQnxe9KQDT0f6X9g72ZiBgzEIcVElMZphdHJCwk Wg8HBLvfFT2at0yDZB52YOWz0ib84Ym5JddfHvRUURo8+FDK1Roh8VCF4r/N WAakJnsq4h/tHjmI4WizM/n6dzCjwS8BftbFKE+q6ugRV/OfNIhkIAs26KW4 3F9Da9HlxFmqzm7LQg+np4nAdIx/JKxDGg6dCurn7RPucjm/lisi2P2/u3e8 D/R+68iKAnqDkzeFPy0TSDA8hssve5Zz0nmAjb97xK0DJR0EFI9N/0JiM4xg 41fak4bxs6znHfsjPEtC0AoxRAuCWHCxEzT11iWnX5m/fob9QDM1FSz7LdPB YOzq4Phnd360VnOhtDVOgMLvEUKO2DPqaPkQsW+GhRGFRAc7XlWSiZ/ljJPR XxpKOb0CKbYeS4TEDIyIaxs/7D+/wtlNl6REhclPtV11Avbvzqn/jgusEtcS sIXNyR9ur9g409nDQkmLBIOlDK0NAlaMPv38ekFTeU8vJlEgm+rhQYaA+6SA CCmW9Ig7ssqTt55aOXRCf32WGglyQ1majpM81106kV6c3cbnSxCcQNeX14tl yv3vHadyk5vc5CY3ucnt/779e/GsvgmhjIyM5s+f//r169LS0s7OTolEgv69 cOFCaGiok5OTj49PbGzsly9fmpqauFxucnLyunXrgoOD0U93795969attLS0 b9++oZ/yeLy/34/hOSygoT9vaGj48eNHenp6YmLilStXYmJi9u3bt3379qio qGXLlqHVmD179vTp08PCwqZMmRIeHj5t2rRZs2YtWLBg9erVu3btQr+fkJDw 9evXxsZGtEA+n9/W1lZRUZGXl5eamhoXF4f+3NjYWE1NTVtb28TEZNCgQWhp V69era2tlWkPIsS+saenp66u7sOHD0uWLHF2dra0tBwxYgR6R2w2G/uOUChE L4TWds6cOebm5lhzyYCtv5RUS45nye2vGhH7B089XWkliXMXUGlQHs0Ew7Ny 2kUwjhodY50tGWuPzDDSJ9EUIeIKDm5YjiSL8GHRr5K6JF0g+FoolVRz6s9f CsTwLHWamqvZ8eRbpZ0wZlWAtJ1NXOk7HHZwkMXIf8GQM5kVIqQH8+VKQPpi SfvPktfrYxcaK9Cw1FcmurZLg9+wP3IgUYJb05m7dMMYKys6NlIU6P0neq07 d/pTyefK+p8tLQ1t9S01P+urvuY9Wb53ljEgFlEpVJIda9jmmVncUiD1JES6 89ozF0T4GFmqkcCJlWLFnLxn+Y30lOK6n53dDT18nhgLiefyBJ28zk5OW01z dWlNe3unUOberqxK3YrhWSBHkYYHwy/+dHlnBcEvgMdkPr82qeiKi7O5Osxj pEdV9nE//ul6aY9AQoSei7r4nSUtJecvrXAKtFKhYjOnwcLQw+kpMrXBf+ov ItzaAMpBeKlpB4JmBQ7WnHju1I+m4tqkDxdnhpvbOm18dDSrETgxpX8pPhPy s96t3jnHkNAbtDUbHz03taKwtLa6rLauqrKs4md5dU1VVUV1VRm78mNx+vZD ixz9dUHfINPQ5+9nP/XMkSZBs0AMPIE/br3awnJjKSqRSIpUbZKqj9v22wc/ VJdwugWY/pqYJ+wuavp+5eZ6j9FOwLUIitH0c1524WBRD/pQRIhA1FXbWZfz sbQ2p76ttqu3ky8iwuBhgK+ks6fs/LMdQ/3JkDxCplNdxpvG5+W29SJiNrvo 5psolrMRll5Ki6rq5rji/PZnBe+qW6vbuRxuG4fTyK7NLPt+5tJK11F2SkBy Bn28rAXB0elvYI4fIWAg5Dd/Wr/dXs0R9k+qmi7Fd+vCm5mvGzpauzq7u9mc 1uLK4tfZSYfiotxGOKqrU6GkDMmUNfsEyJ+Fg7bFFR/2xQ2jWquj3ZxC0jFX Dty64OqbO98rC5s5zd1CHqYGhfaT3i4ut7W9vaapubK0qRskVIGiW1KkmVNe UF6Qk1nHrmjhtINoedBLIPlKIhZ39bakZ58PCPPSAA8OHQNkH8+1945VCDCu hbQP6CK3f2YEYgioGcIOpOP2nZWeoUMn2G5+ndrKa6y+8fLghNFmgxyPpF0v aeNhXBKJ7FfoM6kqe7L0wGQHDbNFcxJ/vGrtFeFuOBy+IbQ2McBGJGy8kLgz OMTRQ3XR48sFTS041ID2hYqfLzefDDVVt4mOfFKaTWAlxNiXYQe/HikOxEOH GkzAgUfci8Wdoo6i6szzd/aNn+ejxySRFUAvVyer22j4rh579Mn5d9/yYbJR dFyXV5ZWVpZXlldUV9dWtnZ18KH8GVJclLT+8FCqLh1MCzRlMxXHqaMOPziV kZdRWlbR2NrQ1dXZxenqauqqL2+uSMp7vOXUUhdXZZIawKcVqRZ+GuteJP7s bMUriM2WQuCSFOEgi4z40/dBQCdxE9J0JG6cg69XqOPFwnKuUPz778gWNWCQ n7XNgekcEjUpsbwHa3ExjJ0Q93Rnrdk3dZCd0xSXuxUf2L29MmgY88FKIBMN ovaC9oL6O36BHl62k09EFvTI8CwRt7T5MeBnmY4H/KxGwR/mz+LkHri80VNR dcyIy3n/QG+wD56VdYjlYR3svSoxvhUnPRHkODFEuH/BGn/SbbElo+3D7/QG lfob739xDviTiapi0xECRQaksJNIMHaalCCSILhvXoJOvyJJU3Lp/WljLZzd Fp1cn1bZAD3pGNYjywAnBKElRM3FhFYeTlrhS6Tt3TXvvyUdid/gFdpfw0AV E11VoBl7WU7ePPNC2tPvP7+Xl1WUlVdX1lSina+mrAbthjXs6jaRgA+L5TeJ yvecDHdyVQX7CCpFie44yiPi3KHUgtSSuu81LehM3Mlp7e5BZ9KqlrJPPz+c ubnWK8hJA+JGNBLVwWxpXMTXVmy3DkkhPJg/K2RpoDoJx7NG+ay4C/UGiRAZ KQ/h/azNufboxPQVQfoWyopgeaEoKigaawxaMHznzcNp3wrRQVNZXQWS9Zaj /1SXl5WX11c0tjf3wl6NNoGkVyhic0peptxYET3DYTCDokVTArevyqJYjh64 Onbjw89ppRV1FRXoLaMjrqKyrroSlFbW1FTLleniVnYUXbgcauRuAFnQyhok 6+CgPXfOfa0u6ebyxcTMI+4RdZbVZZ+9t3/UWCuSOhyqJO1+KiMvxmY3lWBa d1IZY0MWtyMSS3r4bSXVmb89OTt3w0RzZ1WyElAI1qAomWsOWjHp4odndZ3Y Vu93PV1uwCQ4sCvTWpViAP3fS4aCFEhSiQCdXqQIT1h+M+P0yEFklvWaO7s/ NcHQLHQLKuAWXUre76GmMtj38Kur1b1SgpeJ4fKYAcIQOuaE5WVPlu+b7cG0 Ch91J+d1HR/BsrhKeVj+LAX1sSOv5z9tFBO4xi9+FiymrqXwUsIMZSfXpROP AH7WL71BUSPScOj0KEcf36muVwrqecT00AeUk92UlFcA8CxLHYfgTeFPKoWQ AwUahP22MSk4zMHeccL2afe/VQt7+VCaFIJJIqIwHPUTdr8pebJgvgrdZN7J P+ZnYVg/jrrDVUMo7PhWm3Xjxempi0fo2TCoJLAHVVBQN9cKmO+3//axDwW5 xeUVVeiIqqoAy1s5+qe8oqaqrhM9vGDTFKhS462nB0aGGgFpBxC8R2YomY+0 WxO398mXtJK6knpOG6+nldPc2lLCLv9Qknbq/Dq/8a4gYg2IMJBUlftNGrD3 /Ut2VwsEyeCuDIzE2rybKbHhq4L1bZkUiMLTKGos3SHTh+y6cTjj25diOGNU gZTfaMXQaa+6or6qpqO1V9KLdSf0UfZWt5S/yrq/ft9Max9jJQ0SpN6TVJWs x/VfEhv1JPPTj4pi9Laqf1ZWlFXVgIkInY5qKpvYXGGv+O86oNzkJje5yU1u cpPb/y37d+FZMhSGQqGgpaFlzp49++nTp1iELZ/PR/eQd+7cmThx4rBhw2bM mHH06NHCwsKCgoK3b98mJCRERkaGhISEhYUdOHAAfbOnB5z9ZTiRSCTi8Xgd HR319fXFxcWZmZkZGRnJycmPHj26d+/euXPn0F9t2LBh8eLF4eHhwcHBI0eO 9Pf39/Pz8/Hx8fT0dHd3d3Nzc3V1dXFx6d+/fz9oTk5OjtDQT9Fvjh8/ftmy ZYcPH0Yr8/r166ysLLTCLS0taM0bGxtTUlLQ8tGSBwwYYGJiQqfTtbS0goKC Tp069fHjx/Ly8q6urr6tinkVnj17Nn/+fLQpaDTa1KlTnzx5wuFwZLHP6D0+ ePAgNDRUT0+P9Hso8F+HtOR4ltz+khGnGwLP6kwpSpg9l0LRB73OhOG0aGJ6 K8yfBWJ2QWBy1fUHO32HqZKpVDIm5Qak4EhKGuOi594rqUdg1nZweqvvro+/ OsLIWwPtu5pUdVeLY69vlHaA64h7xZ13EjYNmaBNgSAQiWQe4Lzy2uXC2oKm hkZ2Q3NDaWvNl6+vd8dv85zqporFUlNITB3bBRNSmz62QceApF3Ufu/xRq8Q cxoQtQMeHW26VYDNgqNL4+6dfPL0ZuL9xKsx9y9sPLXWfYqHGg0CMSSyOdN/ /exMbjlXDJRJpHWSjkdPItzHWFHo6BUoFLKStd7wBSP2XD/4MvVZ9vcvRTVV 1WU1Fd++Z77PTX/2MeXa48vxd75+z2wlMkYh5VWpW09PoOmqQm+c+kCG38WY Ck7Zr9TYCMgT3fi29J6/l7WyMmwziqINa/6pLc+//WA3Ntc2s1ur2RVZee/O PDs1ZOYgFWttYryzFkw4mP4v8rOw/BVQ00YkQdiNSRFH5w7WMZkT+jTv0ctd 19b5mTEnjb+V97Kx+0/K+QcG8Ky2d2u2zjUBqQTIFDLFguG7YOjBa8diL8fG nUXn29Nn4mLPXThz+tTVCwdun1+5Z5bxIAsVNYhDgQO70+yh+9LeigQwOw2f 3/Sx5knwVFctJhASRM//SiSnkMFbLx9Kyyuub6tg1zfUfCn+dPFJtNMIS7oO toiQVKiuKyfFvPnci/BhRK649nXug1VzD55c/9u9i6kf07+XllRW1tbXNTS3 dDSW1Ze+f3t95qYwE1WYNYpCYmh6zvJ8VJvfzkOfSm/ju5o73kFOGtp09KFT STQqydDPftL2eRcTrqe+f5We/Dbp2vMba+Ojfad5aoE83SCElkZizRu/802a EOnBKAaSms6f8TeC9d0YMN0QSZmk4oN2v5V33r56m/buw8PXDw/c3hOydJS6 mSoJ737gv6Z6804s/8SG8lNoXTjcykc5sQ5DbDTo6DUo6HhiKvvN942+uPfB mxfvs/NKf/6ori4vKyrJTs/5+DQ95Vbio+uH06t+NPWCGGaEK5WW5V7fd3Z7 RGR8wvnkjy+zi3PLyytrG+qbG5oba+oqM/PS95+aZ+VlClqRSlIkKYYE7H5+ nS36HerxVxl7/30mxRx7UnyIIdLiwutT1oUNMx5xaFtx/dsXq08s8De0Xjbr dfWndgylFv36Poiv5rb/OJ2wdbCVbn+bTYkns9kdvcCBLlOnxFzBYoAx9iK9 TfVJS/ZMdTbvH+pyqzCV3YWx7aBPrLzk+cYTE0w07Q6seVqYCbyduDteKiHS 2eNpMnCdQVkcvgw3gZfrFXXnNH8+ErfM3t9aVRkIXiqA7knWV7IY7bDuwvqz N05cunzuzNmzcfExZ8/FnY09G3fm7JnYc2d++y0p72uLlA9urJmdd/bRZjMr XRq6HACnH0mDZDys38zo8OM3Tt57eedtRlIGOhruvkk8efNkaOQ4M3dDGoUM Z2IKQ8VjjveDhjy2RCzpxaErQUd7B6ezqbtRKJUA1hFoH5gZqw8TH+u34kak 6ciZ8f28PUMc4r//7OL/TnOP6Nb4X943zvdNW+z0HcdHTEr8ycU/wqLrhaKa k5dX+PraDNPYnP6grJ0jlvJlPAEprAT69ES9op76tp8vPxyx9+3nZT3lSNQ3 rgzPEhD8LJNxO5fn8QCehfmwifQ0CP6Uezuy911cP5CuMnrE5bzEhj54lowj A/70dFW/yD5k6GUX6rsy8WITRreRChGRmEgMSdD64E8wWb9/br/Ds5Qpis6W +5Oul/CRX4gV2tZSYQe7rb2zpUvYDa+D87+kBB8K575BAqywtDn38KmxLPfh c8buepTQ2MMWiYg1T0QEe+A+fGzrAEkHIlwHDOELOZ9zHq7cNdXcAu15ICKG AsML1Kk+c4dsO7fnWkJM3MkTZ8+fPRdz+szZ2DMxMedOx8XEnr1yL/ZLU0kn rLm0CxEkPd/iP90K7hEo6FKiTDMcoDth1dij13feTvrtxatXaenvPj5NfRh7 /8SsIwstPPSV0MmYBn3OSozw4buf3GqUEJsfCYZnAb3BQFViQz3GZ8X9C01C PGciwhfzqtrzTt/YNHC8Ix1sXsiAm0sh6dC0fUyXnVxx5nbspd/OxKF1Pn/2 PLooxp8+G3/uzPnzZ25fe/L+TQu/G+sMghY+O/HNiaCFgzWZVBgOBMFkKnOA YujO2cevH7lyFb3ZM+fi0HGHjr4zsedjz8ShxcY8fH23mMdDRwNAAbr4nVk/ rvmGemgogfUF3Qipkl3DBm68dvTtj5zKxsrmuqb6srrKrNz001e3DJ7kroIF BlHpmor9x1sd+vS2vpcrJXBSKASNPhssHELay+HUvS9M23El0j3IWUUVO4Mo KJLVzJn95/qcz3pV1d0JCII4NxDDJKR/2g//Www0pFDE44slOIDbh0AtkeAD GWLluAYd2no8XkXZqy1nF9ipqo8ZHpNxq7YHWzukCI9bfD51v6ea8lDPfann KntwpTuREG7JRfhTkArQztldfeP+Zu+QQYMMxsYe/d72TSiAETHod3oxPIum NibgSs7DBpzujUmX9pmCaluKLibOVHJyXRp6OB3Ds7DHKhEBftbJkQ6DfKa4 XM7H9AZleNbvk/tJpLz8jqyIDVbaThOjpz7+KcD0WsGEWtpVcfDUWDsvjwkD 1v52rbK1pgdtKYB8Q3ETONWIhVih0q6M6gfzFqhQjOf/od4gIqOPgQ+EoCRR dd2H4zfXe4xikbVoWLARXJgMh7kt2Lv4zPUzZy7Hx51Gh9apMxfPoqP07Om4 +DPnL92Ju174sbGzVRbkI6mqSNl2doaxjiYJZPQDZxVFioorY+K6cXtvHrz7 5F7G+7T0R6+eH7l9fOqWqUbOpsoKJDqULyAp0vubTti18FN7FV9MwHPo9FLX 8Dn2zlav0eYkTToIsKMBKIpKMhruNPPw4nPX0PGOHt7Pnr2Czhtn4uLPnj2L 1u3M+atxN798qOO1IBj7WMgpv5N6buJSP7qKClTTBkWgy7edXsjG4MO/Hbty 5eS5+OPn49CpMz7uAlpgHFrM2WvxF5If/+RU8uTULLnJTW5yk5vc5PZ/3P7t eBaNRkMLnDZt2qtXrzgcmDhCKq2oqIiJifH39/fy8jp8+HBOTk5zc/OnT5+O HTs2efJk9M2xY8ei73/9+pXL5fZlPGEwVk1NTXZ2dmpq6o0bN/bv379s2bLQ 0FA/Pz9nZ2dra2tzc3P0iiwWy9TUdMCAAUOGDBk1atT06dMXL14cFRW1ZcuW 6OjoPXv27Nu3D/0t+mLHjh2bN2+OjIxcsGABVk7//v1NTEyYTKa+vr6ZmdnQ oUOXL18eFxeHNk5RUVF3d7dAIEDv5d27d+h2ce7cuba2tmpqaoqKiuil0TtF v4neUU9PT99sXKh1dXU9f/58ypQpaLFoDWfNmoWWIEsBJhaL0S9cvXo1MDCQ 0Fj7y8KDcjxLbn/JZHgW5jRDOKkliXPnUaksAEWY6TgtCs5gZ3ZIMEUOGDSZ m5+4cocPHQT6Yk56sgKJ4tpv442DBR0CRJbnpbqr/uyVAGMvEOmsStNwszqe caOEA68lRMTZ2VfnRHnQYWChAkVBhWbkoD9s+qApM8ZNnTx90rD5Y+wH9GOY 6GgALXroq6FQjLUc5oemsT+1YiGGfKm0pvZF1JEp5iYyrEBFTUGDqa9nyDAz NjU1NmMwjZgMHR26ijJ6FSqckGxM/bfOzuwq75biAeSS5rq0Vftn2fRTApRI tBSqmrKavrGRqY2pmbWllamltYW1rYWVmbmZKXjTnDku5FT6tWqoBwLzZ1Wk bjkVQtVVAUgP5GddjKvqLpNK+hzwRaKub12flkZ5sOxoFIjgKdK0TE08Qlwm zg4aPXL6nCGzR/cfbmtorquqpqqohPmV0UOq4ZLQQxmvxYSW3588R4mYOOwD ymfFlZf7g4dq2dpvO7d286SoyV7qQ0/tyq4rBQypv5rmAuBZre9W7J1jpEAh wQzZihRlbUWWmQFD28DAiMVgGrAYLKa+oQGTZaDHZKobaamoAzF/4Eggk/oZ hR9anlzTiUDZLLFYKGkSNt26F+E62oZKoQCHA1lZTVnPUMfG0Sxg2uSpwyaO cBhsom2qp6KiDHIekEgaZIqL2drrR7LbOqG3QSIR9ZTcfhvrjj4lfQNDI3Nb K3M7Sysvl5EzAmaHL57iMdrR3NlYR1ONSgXCmAokuot10IHVpbx6oI4kEvHr eTUXLix09DbGeheJTlVRUmOpGBoYmRubmJmamzBMDHUNtBQ1VClKVIyFSKaw Fozem/JKivAgVxFBOrkNbz7scx7mCCi0YKKmKSpoMbQMzM0sjE3MDcwMmfoM DQNFKpjCyTQ6GfprKQYGc04ufc+GmpbAlyjureWW7T81zd7DEFsvSWQVXU09 pq6xgYmpuZWluaW1lY2VtRm6rFmg7xiYuAw0OZD+oKyVB3xIAgQp+nxuStRw PR2mkYGhsbmFrZXtIHv3EI9Zi8KnjZg01NLGlKGrpaqmCLS8SFQ1BY/tS+/m fwHphog8KXIHx79kwj6oAUix0V2wL35doJtlqP/NxP3bgpZPDjJccPt2DbdJ KJbloJH5MMHw7Pyc/2TNeg9tE8/Zww89vVjegcl09k1Xg4kZdnyLjlk8INDV z3LyiT0/OeUCiQwdQJCKshcbT4aaqjrsWvO0OA9DTqQIJtSJSVPiUpx4uhZc 30qKKZWJZdmQ2hsLLjxcbextq4FOOBjGBKmXJCpNWUHPQNvIhMHSZehrGxmZ oFspAyY6uI3RHZExc6j7krO7Cnu4YL4Rijrflb+cv8JN0QymJ6HTFMkURYq6 roahITopGJtbWaBzp5GxiTHLmKWpq0ZTRkckWRmMOLMg1xWXDtdymkDsPmBc AgZU5a3Hx7dELD+5KaexrBtzlmLAq+DXc4BeVIBnsY+eDXb0GTTB8WIhhmf9 ctX1wbPALA3xrG0OBk7BkSEPS6D+E5TXwzKn8N6mn5u61Mda2W7N5Nuf3jZ1 AfIaJr4HsCyQx6Wz/sOP5E3nI91HGGjpKjpbh53og2dJhN0lGD/LLJjQG0QI 2svvKiPk5B64uM6TrjI68Gruw8Y+eoO/x7Ng/ixjT5tQnxX3L7JlSwlEguBD hLXCUikJ4et/FqIA3LOt75v78LNoygOM97+4WNQjRGS4NqerIeXHzWnLV8ZE 38391IuDU1DPFRPNEyF4oj2s3YTirpyqBxNXBTi7e07zO/Di+s/2ih6REKaS AVM0lsEGz4kG6Had3F4BVBwEhQg4Xe9Xbpllo69OoYNoABpc9ACmQ1FTV9TW YaHLioEeOtsZGDCZDCMDlp6BgYEei2ns4mN1NOVhZZtACjiyUlFTw5t1J2db GYGgBZgik0pRVNXT1DPUNjRjmTHR3buxqYGRsZ4xU1dPn6oCxPLoANGjmWhN PrU1uaIcsjqwMQGJ4ezS2yB/FhkaiTTWZ3nihSZxL4IRq5o7G+6nb3IM6qei pAzDGIA/WRGEClCUlLVY2kymroEBOH0w9dH6Gxqgq5Meuj6ymIPdJu+NzOus 4QIomd/0vvL+qCA3XWUYBoNlEaXDrRQ6drT19RiGRiymCcPQxJChD1qAqW/E ZBmyDEzGLwy6862Bz8eZcrwGUfmBMwv6+TIARR1fSXX19SzsTH2nDp8+Zkqo +6j+Fo6m+kxdRW1lOgyroJK0fGymnthZ2lMtEBAsRpijB+9FIqm0oTY79tHO 4aGODDOGopoCZOCjGxx9V6OwrQsfZhU293QJsAYR43Bo357+327g0UiEHE7l p6KyyuLW7h44z0OYRhbqQKgHg3YHCdzEosraj1tiFzkPtxygNOHiwS+VRXxs 7gZjrav4wov9rmoqg/uvu3M8r7lThKVclDFz0cfAF3GKa3PO390ycIybLWvw Yt+LWZ/aeN0IDlajG2ZO7kEMzwq8lofrDRJxFH3xLEJvcPnEw+9keBYY0aI6 pOng6VGOg/2mul8G+bMkeL7G3/GzMM4XhmdttNazC90a9rgQ28EKQW8RiPnf Sq+HrQk0M3EIsI+6sedNdX4bH0JIWMI7HF8WiXk9dQlfz4+bokw2nPtP8CzZ ZAtrIu0StZ6/tSJgvI4KuiLBaDnUFEEKVLomTZehZWRmgJ4QmDrGLHRwGmMb V4a+IdPE3dgz5mR2TYlEQjCdxZLWtz+eLlvlrW6ihY9TdH9G11TR0jPRMza1 NLG2MDE0NTMwYujqa5KUaFS4iKJXNaCNiJp2+dO3LnSvKMbUy8VIF9J+7eG6 MRO11dUUyXSoa4GtuRQFNQV1LR0WA53z0KHOYIC5Dl18WejEgS6i5u6mPkf2 p1QUo7MUWp6kIPfyxDVBuhqqNNzHgteMTFfVVtc2ZJig23EGui1H9+P6hujU iS7EDCbD3sx21vS7ealsHvY0/81dXm5yk5vc5CY3ucnt/zf279UbpFKpGDOr rKyMx+OJ0c0dtMOHD7u7u9va2t64cQP9qLy8/LfffhsyZAh6/PTw8Ni2bdvX r1+xJFl9OVlcLjcnJ+fUqVOTJ09Gf2tqago2fgyGlpaWjo6OlZWVp6fnqFGj li9fjpZ/8+bN1NTUwsLCysrKmpqa2traxsZGtEw2m43+bWlpaSUMfY2+iX7a 0NBQX1+PVqagoCA5OfnQoUPBwcFGRkbq6upo+ehWE71iWFjYnTt32tvbEShs iP4Wrf/Dhw/RymtqaqLthlYGrdX06dNfvXoly/AF1MlhWq6urq7v379v3LjR wcFBV1c3ICAgNzcXLQe7R/Q7HA4HLd/JyUlJSUkGZsn5WXL7D5tMb/BNceL8 2VQKCxy4TLX7LQ/NaM/tlGWdRv9tac6OSVhrYKIGoAgayNzAIFksm3L1w8uO X+paiKSK23Tm0ghDb030sKVGVXexOvrhRkk3kaejuS0v9uluz8FGJA0aTOyj SCWpG6ozDbQMtQ0YmqY6JEVFZRrLTMvR2kZZQRmMA3NNmznBbxo/txNSfoiw pzkl88Ga6FAzF20SpNFAug0VSiCCVyT8uAdfwyFkY+K3ZUYWr4RLxJKLBdK2 jKzE1UfmOg81VNBShnGYJJhQiwRxCpAACg5DUIQKieTrfeB1fHUP0WhVlUT+ LPA1DXc9n3MxFZyfiMwXAV+JW8QtT1/u85/pqsmiA2wEiJho6CszTHV0tEyM 1PR1yJo0JRLd1tpOh2lAo1Mg+sCE/CwocPen508ibh+EzoP/dn/8nrB6u7em yYixbgPd/YcH2+59/7qR24Z5Uv7acRbgWS3vVkfPMsEakwbj0gn+LZbdio5x 5KC3kIKJUFKoShS6hZb/5plX371sEgCXCA6gCCT80tp3++M2eI50UFaCka/g WK+gTVUzYhhoaWtSVCCwA/PO6ygbBlrPjd2WUvSlTSTCmhWR8MruvjvmoKtJ h9Ht0JNJViLpMNSNmSaGyiy4fIFEIWQqScFIf8iaSTHpHzkIB0+u0C2SFJYn rTs939XXFBZAgY4+vLfgRsMcDuBN6DpgzBuz6+Ur0L4SiO32Srsr276u3jnD 1pkJvg4vR4JuCgXYMGSymo6ClZ/LnEWBo5wHG2NkRBODmSeXfWgWY+qRwHfS LeZ9L3q57dSqwcOdFOjgZuhQ6opGhW2AF0ai4bUxsFTakXavpAU2Qw8i/fYp ZsyyQfAnWJIIkhpZxUDF2FTPUI2hhd8FuEVVQyX7YM+DSVeK2jnAwYIHRP91 fPO/0QhmCp5bBlC02p6kn5+9xNnCdFK490Cv4aMX+V778b2bz4cNi2FY+PDH PIpSTkf1ky9XwhaPGeg9PNx/8aH1N15ffvs1Obsw60de8ffP37Jfvnt26O7R uZsn2g32HOI5J3rxvZJCrpQrRn4BI9Kq8pebjgN+VnTkk+JcMXGV3ryfqRfO R2xYE/3odmZ1LZ5bTtInk9Mv5ydkyrAbs0/eDqNa6ZEwEAt2WrSr0GAnpmHj jySLpoFIAZi1SI424afX5vcCXVkgTdcsaErNOBO+ZqyRjT7s/FhsAaXPBhKE omMjAxteahSr4Y5LYze++FEiRHolWIui1WuvfbZ4+6xgz1GH1//k/uRCtbpf mQph9X9l8WoSNx6NGwfyZzld+FbRJfgVmEHc5i+2CMSzttqzHMdHTUj4ycdJ xERTSNmNOcfvb/UbaGjlPGrRhM1nt91+den1x6Q3D1OSrjy7efDW8eVbFk0I Dx00PNA7cKyNN2uwb9iRqDwguoiVIYR6gyB/1ridy7O5Tb0yiPJX18GkFDm5 By+u8wT5sy5n//P8WVkHDQbaBPusSrjUgggwyrOgsqM48fbGVdu2XY9NLiyS SPm/HLl/Zu2fW15NGm2lBaJL0LVW2dn2QMqlYj6WrRKdzIRIWUNB3PVJLO+w 3cvu5OdJEFmWK1kHlmIxILKqirq6Gx9kXFi4cZLPALfAoWERc3df2Hsn6erL L4lPnj569jT5WWL6mztJ9+MfXtocF3si8lJWXiu/B3Ohi9rYSdOWTTAkuhnu mMUwJBJFCV9zYY+BBFoStqzQDG2ou9LulnQQcpRCPvst2ANMNnMDewAwBZPx aZxGxaZNGpzZsW4JylVRYnrqhUUvu1eQWsOFWWaE+H1K+VKkpfT2uIWBst47 xnfFw0vNWAIzdDQ1tlbee7ZAz8sE7gcgDEzG1gq4FcK6v2whoOI9Hh1ctsbD Ns3K7a7uhvuWhtSKq64uNuimAcBwxO5eAR8kFAXINcNKBk2Db1/IVKrv9P5X C9kCgUSCha/0IrzC74+3npjv6G9GgpK2mJOcStbQUzfQNNRT0lIgYwsAbAS6 gqmv4+KTax9++94j5eCcbvwRw+0QYCxKkbaWrCOJ662tFUkq4A7oZEWmksMo 7w3ntj/Pe8fmdQsxxByfEnHWUR9puv9ug2EqvIrSR0t3b5g9aeXOVccu7rv5 7PLTpIS3n55/+f4571t21ufcnK8F+VnfspKz3lx5cWfXle3hy0PdB/t7D5i0 dsGD0g+NnRyMlAo5pL3FV5IPOqqpMDUGDPectnL+rrhN5xOOXr5+9kL8+fjY u7dP3rm44fiW8AXBrkOdrKyDlow6+OJeWW+7ACaiEmE7PkFH1sGLUZ50tTEj r+U9bRJigLsUIZLm4eO6vvnHxYQZys4DlqKbzzdiWdZFBMOzTo508PINx/As YjbGVxlp3/mZV9CeGbnWUrtf2KZJT8ux4SPChqy4u7vq8ssToctG9jO09hs4 Zn7ouoPr4m6dSLx7If7KpYvnbt6IvXN5S+ze+dsWjgr2N7EiU7QW/qHeIKY0 iF0eHQ5ACph94tIkp6EkYtDjJwBACYavaTA4ifgMDD900FKpyg5ku9itH2sL EKjEKBEBCqiwtQedin9bvGWatYeFEg2bj/AFDqNmyoY/SOJLomgo6LoYjds4 90pGQnlrF4Iz2IATQyyUsE9fnz9wOFYZbK7Afg+PA2D/TOwfcZyKDA8jSoYk i30bX1cVgLvtRZDcz8eClrnTsFWVhG27cdV3CjZbkLE8BbLKglcMmmbosNtF z9iiv67PIDe5yU1ucpOb3OT2/5L9G/EsLMQxICAgISEBEwxEt7sdHR13794d PXr0oEGDNm7cmJeX9/TpU/TFkCFDjI2NQ0JCTp8+nZubKyM3YUhQc3Nzenr6 oUOHwsPD3dzcWCwWtudTV1d3cnKaMGFCZGRkTEzMgwcPkpKSMjMzS0pK6uvr 0WvJEKV/aDKwqe+b6OUEAkFbW1txcfGrV68w+Mzc3BwLwmcymYGBgQcOHCgt LeXxgEcCvQSbzb5///60adNkUoFWVlZLlixBS0C/05elhV4LfScnJ2fq1KkY 8nXixImqqqq+1UBvf9WqVfr6+rJm/NfbXI5nye1/ZDieJelMKUyYO0dBwRD4 Uwx1HZdNftue0y4SY6deKGklrH+R/duYIFMVTaCPRqGpWSgEn977prycyBsC z8fV3fXx8YHGMG+PGlWjv9WRjN+KO4kLiaQdOVWfD8auCQgbauHqwDDWI2tT qeBQp6xNM7LSsnV19AkLWLh+3vbVm8cYD2LR1ammOrYLQlPrvrQJZBHvIklH V116/pOog/NGTB7iNMhe25xJ11MiKZLBEQ7k1dI2UNY11jC2Yzi4WA30cRky e0LUuWMlggaBBJfcR4CME7fx/beUg/Hrxk8f4+7rZNLfQsOMRVelk8jEeZWk pqmgY6ihb69vOzn43Js79RALAGnnKytStp8KVtRVocL8We5GQ8/HlbWXI7hD G0FwIRSRoI6df+rm3vD5I/q52mnYMzXVlCFXi6KurGWuZTfAcvBov8k7Nm1b sHy6p5epInoLVIOFoQfTXosk/wo/C3+GACSRwhjX5qbcM482mjpaG+ppDrAb GRX+puZnJwF5/XU8i/NudfRcc+hTJON5z6DYCYb6UUkKhL9akULRoOmbqFjZ mbgFugRHTb+Y8vBnU/uvVGEYpCSUikqLP526viN4csAAXyczGxMlI03iBE5R VlDWVzCxM3Ye5DRs9pgVZ3a+qSho5/RgnVAEk57XvCy4PmGkn3k/S01TpqKm KgmGx1IgrAPlp1R1VFmO+k7+/YMWTT398ur3Vr5USuTYglJmHZ/yn+87vSYw wMvS1UbfmqWipw59lFQtRS0zJSsnUzcXBxN1phbRB1iLQnelpwnxXN0wK043 0pX28XrUrikOXrY6plpUujKJpKRC1jZXNbM3dxniMm5+8I64/W9eXD4xOWoY 2jI0MonJnHVy5fsmBAtvx3TPxCJhZ9bPt3Hxe8ZPCfQY6mLuaKNpxqRp0zHo ECaAoOopahtqmJhr+4y1P/bh+c92iK2g/bCk6O6KQzPsBthrmBlQmSpkOhlf u4nnpKKga67p4GU1asHILVficmuLORiS1TcJhdz+xKCzToz1YTzGXlhRmbL3 7FRdUyOGBnOo2/RjG3K6mgWImIBUMA09Aq2A/mdBPaf+6atT09YE+/h4+jlN nhk0f9mMqE0RW7Zu3bRoc0To8plDQoJcbJ28Bs6JXn77/csGIZFZCqsFOgNX FidvPj7RXMdud9STH3liCe7Y70x8f2ridHV9TcfoFfe+ZYuxYSbEro6568VS Ea6aCAgp9Y25MffDNWz0cM1XCpkOsQB8CNJIcFiTsREBv4J54ciOdlNj1uX1 cPlYuwjFwhZe3Ys3l5dGzxsybLCts522pT5NWRHCsFAKFt/DKGspG9kYOHvZ DwkJ3HB2f0rxx9ZebMICbSXlSYWZ6Qd9wkeNtZ917Vor0iAW/p2uVB+MSNQk aToWF+Ls6znB6cK3cg5fgstE9ZXOJH7eU9D5beNWe5ZT8NopiRW9MlIJBk6i z7Urt/rdgVNzPcZ52jt4DnEaOydg9qLp80Jmho+YOMZj7Eh3V+9hnuPmhUbt OX5l6lKPEaGTjm741sPh41iboPtn0+MxYX5+5mN3rsrlsXuFBAwnqwWkbEj5 ndmHLq0fpKg6auTlrMcN3F+slt/hWTxubdLXgywP2xDflfcutUrguipEuO8r UleuZiiZm88dfeBtqhg4Nwk/8j/tuugNtn1sTp402lpbE+L+FCUny/2v4gs7 Me4VpDVkfH+0eJGRlv2SmO2pVR0470KA34BU1paY61oE0HAIb3EqH7y9smzD pEHD+w/wDBjrERo2bPKCCZPDQqdMnBoWPH/+qJkhQdMnDRoxf6H7rnepjT0g eyMgarCbU2avGG8EVfKw2QoPjSCkWSEqJOPGUqDoMIlCN7BT3pWWWNghlD1B Ka+L/fbb43VHFgyd7u/g4qhvw1LUoQK6E5RvhcEVaKl0bQV9S6aNi/XgccMX 7F2emJ/SwG2Rivsy4wA8hLQU3wpZEqSBuYXJ5NHoI7jA5gnwbU1je82dp0sM PI3BnEwlUTCEjAwrTyXJfOgKCtCnrIAxzsB3bAwBnsWt6kILEvbWpZRcG+hm q0YibhetH86zwCMisMWUgoNi0HMOEDrfaW5Xihr4fCIyBCzNve0fCpJ3xawd Gupr52Kvb65FVQcavsD7jjedii6dYaVnO9Daf+yIyNPbX+a+b+XhmSpl3Uci e7jo4+F31yRkXBwbaKakqaKlZuhhOnzm6OgbMZ/Kv3FEWN8WYWgINiVKYbo1 udggbhDP4paX3A9bP8XOqb9zv8H+DoGhvuGzx8xfOW3llpVrgRhIVFTEhg0r N0XMWL0ADJBhPo79vEd7L9iz4s6H9y3CNhHGpsWeTS+v8MKrfQ6qSprKplaG zo4Og736DR3vOSxgsLevj++gsaMHBg23GeBqa+Ls7RKwYsrRR9d+NPeKIVkX UufEUgHQcs05dGntIEX1sSOv5T2p/xV0gUj6TkH1zUVXEmaruQ5cMfFQeppI JnYNFi9p/cGTQXYYP6uWKztZE/ysPnOemFfQnhW10YbpNGHdlMdVfHyzjX1X iEibG/Ovvj41b9EwBz8nS1uXAdZ+I1zGjx3o5z18iOfIEV7jhjn6eli59Lc0 t1RhkCgqc0/9E34WxOQEuGKtkIs0xlyZ7uoHwzPwnJBwhqHBEAsIN9NIxJsy xIusZEOyPRb9seaHBAe3cUhfzBF2ZHx9uOXYytHj/e0GOWiaMRT0lKkyrBmM XZqOgo6lhvUA66FTAubvWnvzc1JVU5tYgCB99tsisagl9tYCryEK1F/neSJQ Dgu9w4XV0cFOxalbYP1VMiNbHNz4uiJXgjY4V4Lkfz4euGigKtzrEnMGmYwH ksA9MBXD2yi/7o9E0qNpjB3+27cXDWKJPHxJbnKTm9zkJje5/Z+2fxeeRaVS dXR0XFxc9u/fX1VVhWW8Ki4u/u2330aNGmVtbR0cHHzz5s1bt27NnTvX0dHR 1NQ0JCTk+vXr9fX1mMBgV1dXZWXl169f0SrFxMQsXLiwf//+mIqgk5OTn5/f mDFj0N/u3r37/v37+fn5bW1tsrvAklJhlK7W1la0zIqKisLCwpycnC9fvnz4 8CEjIyM9Pf0tYehr9E30o+zs7B8/fqBfRn+CFtjd3d3S0vLs2bMNGzaglxs8 eLC5uTl6X66urnv37k1JSSktLe3s7EQg8erBgwezZs2ysbHR0tJSUVGxs7Pb s2cPWqysYjJgSywWnzhxYuDAgerq6hMmTHj+/Dn6c1y0WyJhs9mPHj3y9vZG yyHJ8Sy5/YdNSojOgP7X/abi5co1+vr99Vl6THtbn4i5n7q+dYjg4ZrAQQQ/ 2gqOnRppNthCX5dhpu843OnE1wc/uwSY/BE8vCGSup6m67cm9h9toa+vZ8m0 HO5+LuteWTfmGZNgmTUE7YK2d7kP18VsG7kkxGGkua4F05zpPNw+fP2Y7Rd3 PMt5U9PD4RQ1vJ2zLdjJw9DR1mvDnPSGbJCeBheDwo6wEqRZVJeR9XR7fPSY FZMdxziwbFn6erpGLKaN0bApLiNnB03fNH3n2c3X7p95m5da3loLclAjsvBU EX467pHwCyrfn31ydunhFUMWjrdws2Wx9PQZ6C3qGegNHOIQMNt//MqJEaf3 vSv91I61G3oT1fXv9l+axbAxZTD0mHqWQQ7jb1yo6gT4NNCBEhBqY3gMLJ+d VvF6z8XtIzeEOw5zMTVmmrKYgy1HLg3cFLf63vunNWJ2b2FD2q7zy20tDJhM x3WzYz6lS5FfZIJ/+hCxDAXgOUmAl1LUlFqZOGqcmZqG3kiPRbcvtwk7QMOJ fgFt/6oBp1n75/UHl7mgjaENRFj19RnaDAa4ZaY+S1dfD20oJkNbi6HHYJjq GztZBS4NWHl4ycXX8XnNNbweCZ60GzjYQWtLJJiXXiLpEbcVNnw6mXB8avQy r4n+BuYMtGhdhtEAY6dgp0WbJh64ffhVflo7XyAGeRH6OF6EIm5xS+GlxEvL jq4duzTUboSTlrGBlq62jo62pq6Olo4ui+k+xHZqZPD+uye/1P1oxZ0LEikm NoZnsRcLazuqUz4/2HR+R0hEmH2ghy5Tn8Ew8rIcNtt7XfT8+FNb5/UPcgCY HXA4sBYE7U5JlUi6CQcvdAZKhJy8ivdHru8YsWCkqau1rq6tk0nQXO9l+2af eXjxY8VPnkQo7Wh6f/BCpB1oKEZ/21VnN35qxXoF6CEyUqOEL+aU1+dcfHFl 2dENQUvH2Q2x0bNiGhho6TNZJroWPvZBM/znrhy5I25xUnk2myvF1NKknOaS xPR7G2K2j42a5jLaydhaj8nQVdPR0oX/mOiYuFqMWjJ8x/XoF98zOwVczIMp IdIq4ck75IjWv2LwuYuh0B5oNhG38t77eK9BZvr6HouDj6e87MXehybFOsj/ x957wEdtbH+8N7YfNsa410uxqQZMtem9914SAiH0TqgJnRBKAIdeAqTQAgRC CJBg03uxMcW9997t3fXuequ076xG0mrXawO59/3/7x/OF3+MLI1mzoykGWl+ c2bYRawYBVdHqkaF9GnM7a9/WN17Sq96Lf/t4exUt46jnYND3bpOzo7NOjcY vnDQrusnovITZQpGBaNo7n5j1KjMpMdbjn/Wql6X79bdTo1jUtOvsCf5K/To 59Pc69dvu335tfg35Plmp4PS6bhnhzb8l1cQ+8PVufU7N/eApxgeOlc3R09X Fzc3DwdPDzdXBzcPV1c3L3gaPV2d9DWbi6e7q1t9F3dP915dZh/alKiooPjK hFldUVlYmhYUdmnF4fUD549o2rWlu4urg7OzKzyN+vvQo55Tu2FtPlk+dsfJ 7UHJT4plpWqNhrWK0i/5R+Wpyo6d+rRF/+Eze+5/mkDr9C7rWn4mK24lLPIn vB+qC3Slh09+0mX4oM86nUvNlGmEehY3TklLAusUsZVJm3YENOkxbevnf2XJ eG9jsiAUU59QigxZytnr349ZMa5x13pubo51wXhXx4YuTXs3m7Rp/N7fDz2J jygvUhUcPjZp5rxZR9bFK6VqthrQyNPKb46dOWxYuyn7V8QoS/RzugqeKCIN MQ4ssqi9F7/p7+b18diL8UFFCoOeRQnnJtTrWbEHWwwMmDH0yz/PsYNJoGl+ nfXgixWNPNv4zp+488F9LTNFo77Tmta+tT4Xh1U8nvppx8ZNoGF0b+DRoI// wcfnU6TcDaHRpp++ta+/30cdOu26cyRZxkylS7q79fctLZC/mUuiZN2baWb5 Nll6ScIvN459vmVW64Fdnd1cnaEednJygo26cPs06N1ozMLB206vv5sVV6HU kOeCEpc8XvzVFD9nT/v6Lq5wg7m7/VvfzrrrG1y4CZ3d3TzgrtH/5+7h4uii n7TP2dPFw711F4/vHl9NFTGDCjSsVAu1qEZSUfTozc1vT28fv/Ljpr19PBu4 udg7O7k5OTo4ODi5NnBv1rXx6MVDv/zhy2svr6WXq9T6oQC8b4p+lIS+EpTr dOUpv3/85SR4pMEkF1fXKcPWB52GBogtjRJxzuXby1oM83eExs/VxYu0g67Q 7rm6QRvo5eYMj4yz27+94C83F2d9c6aPx8W9U7vR3y6LUDEapkpZ9Cj9t36D OjV0dXdydXfRN3n6B9DLywX+c4dtDw8vfUPrAZE66yPQl4kLROs5ctbw88ml Cn2rwThGsZdJq63QloVEBn11/Jv+swb6dHBx84THDi6Cs6ujawO3dn19Jy0b seXEpuDI0KKKMjXndMMshAYtGBMb8xRTXEutySmNOXxuqkfXdvA+9u2sPyNe SHUiaLiZMRjMc6UxTIRMk1WYdLp3mhr5H49ez6JUZdL4kzd+XLR9Se/5Ixu1 8YZL6ejs6AQXxb5uHbgn6zjo70wXB0cnl8ZevsN9Z22f8dPtiwlF+WpDw8Hc m/q3L3HqmZs7A+xs/HwmrBz51dy1n7Ue7OvW1MMBnjJoPhxcXB2b92g+/qtx 3138Lqw4WiLjHUTZKk4/gEEtjd57YVM/139PHnMp6UYRu+aUwTdLP6gAEs4t TzkbNN+tV9+vphzUv3yyV1RfERToCvednNRp4NDZPX5NzlfwqwBoBO5dLNrK WGnU2s3tG/X8bOucW7mVnPJu0N5puVwUkRu6+/zOCcvGNu7f2hWaCntXZ3hm XTwbOnv3azl++cTV09Yt8B9mU8trerX+WbRwsIf+V4W25OAvcwePcLf3cPu3 o6v+TZWpVRq4Ozu4eHjqH1Zo2Vy99C+uLi7/dnV1dmfmG2zY1aXrgd1h2Sla Tpij2JEgzAAxhajwQfrtb89sG7dsom/31vpaTj8tjEtdF6eGLg39m46e3+/r Hzdci7hVWCmjOCGSnVSaGYBHV1Blp64uHzIGnmMX/buzs7sXvDbDj5Ob/gGH CsTZHR55dy8XF2htvVw9neF90d3JrV4HT//9255mROmlRai0IsJ+nLh0YH19 TQH1o7unm4unvvl2hToUcquPASJ0J9tu5GXdw9mteb3GH0/8Pf5hIa2pcrEQ BEEQBEH+Sfy39Cx40+7Tp8+JEycyMjJIzCkpKbt27WratGmtWrUaN248ceLE TZs2tWvXztPTs0WLFnPmzHn69KlIJCJiVnl5eVhY2Pfffz958mQ4xc7OztbW FkJ269Zt9uzZhw4dunfvXlxcXFlZmVrNTnPBDw9TqVRKpRIOETns1q1b586d 27dv34YNGxYsWDBlypQxY8YMHTp0wIABYGGvXr3gN2yPGDFi0qRJYAYEO3Dg wJkzZx4+fJiYmCiRSBQKhVgshu0rV67MmzfPz88P7IGPEThl//79oaGhkBzY LJVK37x5s379+h49esCrspWVVe3atRcuXPjo0SMw0jAVA7MBIZcuXQoB4K14 5cqVERERGo2GfCAQf7Rt27YFBAS874VAPQt5X/hhlsysderKssLMuIjE6Nio 6PjYlKw8sUxOuu6ZYExfiFpTUVKakZAWHR4VERmRmJFQWCFSqDWGKUf0X160 QlSSHp8cGx8dGRMZl5heWCxSa9mvY9JPSasptVRWkV+cn5KRGpcUFx0fHRMR nxqflpOWX1IgqpCr1BStlEvyCzKiE6KTEpKzskVyGWsspSPyDaOSaFUVMklh SUF6VnpCUnxUXFR4VHRsTGx8XFJyYmpGamZOZl5hQamoVCJTKLVqDUWZ+aDT aDUKhaxMVJxbkA3nxCXER0fHhEdGR0FUsXFJ8clpKekQT1GpRF6p4bsbFEpZ UX52QnxMOGQzJjYpPqOwVKrRcCOfTYqX1sgqK4qInYkJMTFwTnRyYkp2al5x TrlYpFJqaEVlRUFJVlxMdGJMfFp2kURmmPLFrNnCi0jpSH8UxfTkisNKn8yY 7/Pvpr2XjTwVla7RVcKXtZbiBv2+H+rKkrzspJjoN1FREbGxsTExcVFRUTGQ g2gooKhojsiomFgoOSir7MKcknKRTKnipzyjdcZDuJluHy1T5iXZeZnJaclx sTERUfro4mPiU5Iz8jILykrEUplarTXcWuwl11GVaoVYVJqTn5OclR6XHP/q Zcjduzd/v/zHX5ev3w56HPo8OjEuM18fg7RSVr1eQ6nEcklhcX5adkYiXBF9 HmIS4xIzkrLTMsueBH/T/ZP27OSB//KcNWzLvbtaXSVnArnRNdpKVUVpWV5y ZmpMXGxkVFx8TEp6clZRdklFiUyjYkQrlby4JDspKSoiOio+MbskW1q134/1 1VLISiTlBUW56ZkpCfBExMZExehLNzYqLjEuOS0xMwdulXxxhVzN3wtKjVJS UaY/JS89Pi3+dWTogyd3rl679teVKzeu337+9E1CZFpeSn5xgURagd2NfxfB U0z+1tGUSqMUybNCnt388+bjN2EZZTK9PqPV6jQ1yQs0VAKV8IyXFsSmxT0I e3D5z7M//nLgwO7Dv/58+e710OjQ5KyUQvbB4R9VThRT6agKaWlqatTDayHp 8UWiSjLXkl4tKSrPeBl+46/rTxLicyVSylBpcD+s3sN20OkV4VK4J+ElijzF 70h4TFJyVkGpTKHipCauU1StUokqRPmFeekZqTGJsS9Cn94Munbh96t/Xb5x 68bTsNDY5ITMgoz8UqjSKrSMrqwvQyXrJCIKr3g6bkandn4Tt35+pwgeWF4Z 5M0mGWKuA7RTcl1lcdqbByGPwkMKi2UqQ9UnmHtVTSxU6aR0RXLyw1tPwxJi CsTsuHl+wUEyjIhWqlRlFaWpeamvYkPu3/3z2tUrQUF3Qh5AbZCWmZMvKpFR cq1Mp8hOCw+LfpGVIFNrWDFYo9PK1aURb54+eRSam1ahkAq8Fmjeu4lM2SVL zot7fvP669fZqny1kmhqxvcHBfeVSimVpT148uTlo9jSfBWZ4w9SEctK4+Ju /XXvwasXaeVl+tZATdOcMF3t/caUGlWpkBZmpERDy6hvp6KT44pkEpWG1q9c Rql1RfmP1hya08LWfv7nv0U+Eqkog4bFzZZZLVAMKrWiXFyalZ8ZmxT3MvTp raA/L/5+7frvf90Kvv/ieURSTHouNOhF4kqFlrdUXSkthnqbaVD0FX5N91xk lP6HaVyi4pMgqjKlSYWub0rU6gqIs6QgNS89Njkm7E3Ig3s3r1+5/MeVP2/d fPDiaWR8VGo2nJtfLpEqNfrV1tjcCV/L9YsZycQFORnwTMCrTXRUbEpmbllF Je/HpKYqS6H5TomPjo2MYGtmeHpiq32C4qP1LVpEbDK8GBRKGF0MbjxVhaQ8 Iz0F2s4IOB1+IqMj9C1PVGx18UBaETGxsanpueXiSo3girOuhhpKLZdJIPvp 6ckRcRFPQ25fv/nX1SvXb9x+9iIsNjEuIyu9oDRfVKmA1xytmRkrBDUD8b3S UqK4nGffn7r//HZsSqpIKqUE0gb2h78dJfN+kgfvJxkpkfERz0MeXL959dzN 33/66eDOA7t37dl7KHD/Tz/9duOPh29uhSdB3ZhdJJJUqtXCK8vcnFpaJU06 eWtXZ0frnp02X977JiE/PSU1Niz8+b07wVev/nntz3tPHocnxKYXZBSWlcoq KzW08M2TG0ZB0aLUgpiXQTdfvM6SFSvYdoHm02LDV6orMrJjb94NTYjOLGEW 6iJRwf9SnSorO/TBs5DokBypQl+5KhmfX7Z6oIVjZOD1rCw37mHQk5dxb4oZ Jz6Ka0ApthaA9yJ4ea7MS89JfR0bcff5Dahzf/8z6HbQk1ev41LjkwrTUq89 vzhlunWtxrMOfHU/Tz9ygKlLSY0qfCHUGYyEJ7S8NCc5MTYuNhoeYqO3U+G2 CVFRKfHJOfCuSBYL5IZGUGwtodNo1HJ5RVFpYVp2amRSTOjzx8FB1/6AV6zg u6Eh4Umxabmp+YUFIgk8X/xktib3A6WsKM3NSoplHnfGmBqe9yjyKgqv2dEJ cUnZhSK5mh2OIof3w4xU+DqJYCpEfRXE/FRTd0JMEElkfGxCem6JRCpwuEMQ BEEQBPlH8jf0LH6ZJ37bzs6uX79+r1+/Li4u5vWmffv2dezY0dra2tLSEuJ3 dnb28vJydHScNm3a5cuXs7KyZDKZlkEkEgUGBg4cOLBhw4ZOTk5wiqen57Bh w06dOgVxQsiSkpKKigqlUinUiQjwZ2JiIoRctGhRnz592rRp06xZMx8fn/r1 9auZu7m5QYoODg7wu06dOnUFkJ36wUweHhAYTmnRogXkYtmyZXfv3s3Pz6+s rATDMjMzz58//8knn9jY2MApEBIMu3fvXnl5OSQtlUohZGho6KpVq4ikBYW5 ZMmShIQEjUYjNFIul//xxx89evSwtbWFSBYuXAhhIEe8Z1lBQcGuXbv8/f15 /yyyUbO7FupZyPsheHr0cqp+YDbTxUVpmcHfOr7vhOs6o5gfLUVxY7QNvXZk CjVmZCLNzNNOGeaMYoPRpEuSMkgTjCag4xwHGD8GfU+jlpeEiA5C60c7aviJ bihuFa3q4hF8yxP/IBIhRdfcH8PYTJtOMs9nk6RBHHNoMlsRKRDOwU3Nuu3o hLOMGtmpHyDJ2cl4U2m5nlUj3YdmzeaCvmUEPiM46itPbsCtVJJ6Nfxg377e 3QOWHNvwsqiSfP5TdI3ZrwG1Tie4mjRtdh5XvvNZK5jLqMbkmAunVfEXTsed QmLQCGIwTpHrqGenstFolXJ5RVl5cUlpmUgsVyoF86rUZACrsmn4+4dLroLS hT8LHPBZh1ps9es1c+TWR/c0+kmp+MvHoKEpFX830kYZ5/1KNMQnir+m1RQF qykzP1o+FT4p/g7k+sq1ghPZR4DWVCpkInFpUVmZSFKhUGrIKbT5Xhbk/RA+ hhqartRp5FIoaJlCpCKuqe+oF1P6sdaURCEtLMnPzklLS07LzykWlyu0cqMq l/H0EQo6etlGoZaWlUo1Co2ar3b0XYuqClm5uEiqlTP1pJamqr4a6Qy6sN6X VkcphYfeBZp7KrX838JamlRXjC6gVcmkovLSkqKycrGkUqWiuYqRqNo0qT/J yACpPPtmxL5W3doM6LLo1PZkCYmbX6OH62kXPg76x0QjLpaLFSXkgaK15NHV GmoSJgn9YwepSKmSIrFcxTqnaCla6ELFxkzKRKFTyGTl5XBVxVJlBeeUrNaR 2bsqVRJRpZySc+bpHSj0qVTIxBKZVCPR8r6oWqOrxjZ5agoqqWK9slxp7HQm uDH09QmtEJVXyEUqDVcI+iEiao1ULSktE8vFSra51bBDBGpoHShyF5FWmy9A NlqaqRsUL14envLFAL+P+h3f/jwnUUVuCa76omuK37hdU8EdpVRIxaKS0tJi qIclMpVSQ24Y2miRHUOVy9/A1cVvfA9zt1/VbLL1MNugKLQKqUxcVsaYIZYp iBn8CwAbsyBrXELMFMS0QpgcP5qCdJiz7SBnV3VNocEugdmGJl5Xqb/KBjWS qppZ44JgNGO+QWRea4zEUPYqM28XWpWiAr5ISkvEUgk8iDTbNU8Lsm8K8dfh JmBkzJJry5LzxJUiJWWygic2JG+HLUnuvZRSq+USeVmxqDQnKz0pNTklOSUz KTUnt1BSLFFImDmljV4C+WiY+00ef+LGt53r1u7Ra/fdU9lSUiHAC49EXAq1 a1lFZYWKfSjUXDWrRzD+iXkprISarahUAslpdewDKfQL5c5SUNIieGQY2VfH v7cwgSs1snKJSCbWsK83XAIawY1Nmi14iJTKsgK5XCrVu1GynQDkzVmvqNKc 8zLjG6gfFCQSS0oLS+GOldOM3KzTlFyN+2XckP/HpdHiH9c9LyA+9TqaW9qP 09mrtGt6V3f+K+Bdmzbmw0PYrpHnXfAWz7+ewRsVI1aWl5aWSUQVqkrmFYuU fHXPF/stRTGinEFEr8EcHc1V1xqafeqZby79yyHz+fOO0wYa/Grf/WsCQRAE QRDk/y7vq2fx8op+BVJuu0WLFuvXr5dKpaRTFzaePn06cuTIJk2a+Pn5tWvX ztHREQLb2Nh069btzJkzhYX69UQgcFlZ2bNnz3bs2NG1a1dnZ2dLS0tXV9cx Y8Z8++23QUFB2dnZvDqmM/76k0gk0dHRly9f3r9//+zZs3v37g1pQSru7u6N Gzdu27Ztv379JkyYMH369AULFixdunT58uXLli1bvHjxnDlzpk2bNnbs2D59 +rRq1crLy6t27dq8clS3bl2IZ+LEiQcPHnz58iVR3HJycn799deBAwe6uLhA MCiuWbNmRUZGkszCb7lcDsUIcVpZWUGxdOjQYfPmzenp6WS9Ld7sxMREyClY CMHAwl27dhUXF5NDJCpIEeysVasWWYnMpNjNCluoZyHvCcV/9BmrD4KOJv1H Gj+YkzINqf90VXHKDuniYycp0vFd+rSO71Jh+naq9zQi2hDNJqvR8v3wFFm8 Wsd+R5PZRd6ja+U9vm0NpzATtpg7kZkHhhEAKb5Ditaygx8pIzHCqDu6agcd l19axw2HJ/29bI+jXv6j2M/QmszXcB2PTL+lquJFxF+rvx3VsF6n5Z/89Cio SEHz+mANXl41wH2o0/Rber/1YiPbT0jxdxePsSLGTRdDC083+MKQSdpIYIMS yZlhJl2K8SYwlDXptdFWcYIQnGXaqcB0dOt/ZDpdeMjOvp+3+8iCLKPmMWXE lsf3tGRRGa5DgGI7WEz7MdhrodX3stOkK5nvaGK0DPOY6Weg+OmcTLoUuWeN /+EvLNN8CJcfIp08ak7/Qv42pAON39Zo+f3cA8tXkmahTX84/ZRz5xFUv8be VeQeow3x6MhZzJxvFHeE5qsULpzhFKHEX2NdUnMJ6Hv5SK3GP4zkOasSo/42 pI2M4Z5HmutUY0ZFULml0ccuTrRr0WPe6APP7iiUBv2H6bllTjaMAeCKgjXA 8Fixf1J8NUMLSo8c4mpVo8W5+IqT4rtohbUc2/ZRZHY6HTdJF1mJhGJmReSC qrmroqaFehbNlgatM6rxTJYyEfgA8o2smjKM3NCSU9g4tez5+klka1piUcOX BW2otBh1RUXTxPer+ORvXw0e3K6Px9YX1zIkEhKEW2Os5vvZbAPM+WjzzTPp BzZt9k3uzJqh2Wqcfceu7hTBtTB6dszUklwwqmorRAtzTFoE9s6rIelq7OaN Eeh3NNu+828Ib+9h5h80nVCJM9eWs5FpaKHuzL1mVGu64RGiSANCVy030zoK eTu0oKYi0hDFzAMrqDApgULLvToyV1nL3hTqysSfb+zsZG/bq3fgk5/TK9k7 lja+Ivr/jOsBYUtjdOH0tZ9WY6jr2JucnSGP32M4jXusSP3JOjaqafYNmWIG oQnTEEyRrVe69dKt/panGCVNwzZSzBwLpC5iRsixpUCxSrpUEnPsxvZerWy6 t99ybV+8SMPFxyr4bHVseB5p7vHnGo73uk76m95wTarc5TTXknJVCMUu8K0T mFH994ihqPlI3mqiQFzmgpqp5ykd911kNl1KwxeKofZAPQtBEARBkH8uf8M/ y0Rbsba2Hj169PXr14n2JJfLo6KiFi1a1LZt244dO86cObN3794uLi42Nja+ vr47duxISkoiXZ25ublXrlxZsmRJq1at4ChZfmvGjBnXrl3LyckRKlk8IpEo PT09JCTk4sWLmzZtGjNmTEBAQPPmzSEtSGXkyJGzZs1as2ZNYGDg6dOng4OD nz59Gh4eHh8fn5CQAL/BsFevXj158gSs/emnn7Zs2bJgwYKxY8f26tXL39/f x8fH0dHRwsICjOnUqdPy5csvXboUGxsrlUrz8vJOnjzZp08fMBKOQont378/ Li6ONzIjI+P48eOQwdq1a0MAPz+/77//Pi0tTas19GBUVFSA5R06dKhTpw4k BLG9fv0aiksYAFJs2LAhxGCiHlYH6lnIfwLptFBz37Pclzj7vUYJB3/yX4/c X+yGwAGK1gqcbgS9QNwnldGYZF6l4r8c+U4bHTu6UDDwntezzChN7FLOAg3F EE8NWdeZdgDoTMILoxJ2LAstYFx1tLypJrmutuvMdBQoZeiHNKRI16hEMReN VutHsapLxa92/rKiW3ungOarfz8aXlSqD6ChuN73/25/lHE5G2eE+yQXpGv+ EtBVu+aEd47gwphor9WojUZbNeSXvyJV9kM9HP4ksCfrn2Whn29w9Jantymi RVEUnxuK1vD5ZSVJwyMiLA1hB1F10JxsIdQNaeM7gb0ZqrhcCXrgDV3lgpsW OyL/PoK+R6GkRZbd4XuL+XuiBn2BEtzJwprQIP4y6fG/SOqsFkyzworWWD7i +xLVROEyfsoM9pgMXfg7yjYtuM9pzmQuckMqgtuc1aKN9S+Ke5b12/LQpODF K+rZ+k4/sPpubglTGQg63zWGrFKG9kVDxFmaiFQGewzSHteI0JwzAE08gwyu E8LHgtJxfsF85c/08aqNLgox29CuUXz5s0qTfj8jiBtfAn76R5rvbuUaWaPS NfypEQz/MJxCs82fRnDaO3VOCjozyZ/M2jz6f8ry0EXffNbTt9Osobfy35TL mSAavhRqqLR5m0mZC2sk4c3wtpb33eFvPDOxsXUml1OhfyIteJHgG3rhEyRs 94nkwJ1odLcI4xfWvTUqXIKqQVDzs/emUbv/Fv8skzj1dzX7YqY1U7xGAyz4 l6ia3n9og3MKF7/OSEZE3hfTlz2drurQGmYcjkZwmxkeFnKiltZIU07d2BXg WLtP7x2PTmZWCl4vTDy4+an4BHDVCGW4CYmoZGixdKSiYzVswQsdF567Bygt Pz6B4mowg0s7/xfzAJLouWVGad5gRtqmKQ18K8so5n1VHxVb3RFXJ51Woah4 GX95+dYRjWu5zxx/OvRmiYqNgaKNss+V4X/crgnK3ygWo0pDUEvwdYuWm8ei WoWKb7yNm5G3mqQjr9CCVwLD95FxVVHt48lZqTW6tf5G+SAIgiAIgvwf4W/o WZaWlvy2hYWFu7v75s2bRSKRjnmRy8rKOnLkSN26ddu2bTtv3rwtW7Z4eHhY WVn5+PjMnTs3IyNDpdIPeC8rKzt79mzPnj0hNojE1tZ24MCB+/fvz8vL02g0 /Nsj0YPgT6VSCUm8fv36wIEDffv2rV+/vpOTU7169dq1azdnzpyTJ0++fPmy oKAAgul05gdVGn9xsygUisLCwujo6IsXLy5btqxjx45gCXGzgjJxdXXdsGHD q1ev1Gq1XC4/evRov379iD+Xt7f3t99+W1paStKC3+np6WvWrGnevDnxRINS PXPmjFgs5pODMEVFRZ9//jkYD0Xn7Ox8/PhxKBByiIz+grQmT54MWTMREFHP Qv4rmH0KyJch/4Vo8rnEfZwZBn6TGfH5r0ijTlrSCyecP1An/J4y/YTnJl1h p4piv9Z5YwT6FG2wzeRrjqreH6dmPUvQ00XT/GRfb4tJ0KFEGR8wmnLQjJ2C 7ghKw6wOQAmGTxp3brO21YC+g5dWUhWxKXe+OvKZ37CAPvU+PrD1SV54uUat Y68I+9WtqTkmM1SVjWr+8x2jEvQPaE2KqMo3uwlVOn+M+2AM4cxIZVXjqYpM p33z8Luen3Wy/de//h9o5D7ymj168+1bNKNnMZ1BwvnQjDLDeXNoyEhsmp9f y+B4WG3hVL/f0Gdl1CVStRy0Rr4hgk6ev+eZhwgwuj+Z/wx1GjzCWrLceg3V jEDsNqokzfR8EtWKNjrR5IerHs0NLKcEzxQlFBre/VGtNhPGuhtJzvytWM3z yPgaqdkpa4uCX12aP8a3/5hDDy+kSEiPpUZHCYpIcOua8Q81Lm5axw6NIB25 nGjICmSciKblZq/iy4pZC4ZfMEUYH9fpy9fzNLdGl1bDREOmkFJrjcNz+pWO m2fU0JvIj8cwV7YkDD8vlH5tLzJVID/DL03csnR6909ddW0db72ORKLSkakR 2TLUMoWsotQFGTfmbV742cD5Jw4U6vKUZL5GQaVanaE6Qyb55tjg4GM8r6+w /qnhVdys/cKKXUde5rXCE41uuerrc+4iGv8IbTB+BTITD8U4o7zX48N66hmc I9iYjEcjCFzA3i1Swf9Gz53g7cE0n6Q9qi5KSmdSLVDs7LUCwYLWcT5B2JC8 HdP6kB+TYPrSYu4+FOra8ITLks/c2tXR3rpXj133TmbL2bO56pEWDJQS3hvG l5N9OlkvVB2jwhiGZZk+QULpkzWb2E4L1Cua9dXljGEDa2nBG5Hed4uIMqy9 Go1KUxbyIOTmmVsvbyWlx5dXlCl0WlKFUipKVSYrjsgMO/HnoXHzRvq19uvl suzaiciSHP1AUX7yVb5CFnq51eB8+A5wVZO5xqW6H2H5mNmuEn2VnVrTGsaQ qlHDaojFbPw1VB2CAzV8FiEIgiAIgvxz+Bt6llDMsrS0XL169cuXL/UruTCy TnBw8JAhQ6ytradPnz537txGjRpZWVl5eXktWrQoKSlJoVAQzevUqVPdu3e3 t7f/6KOPbG1tly5dCifm5eXxHk9857BUKo2JiTl48OBnn33WrVu3pk2b+vj4 TJ48ee/evffv34+MjExLSyssLIRgYINQCzPZEP4p1JjglIoK/ezzqampISEh Z86cGTp0qLu7OxgGljds2BAsf/z4MQQuKCi4du3ajBkz4FDt2rWHDx9+6dIl PrbKysrMzMyZM2dCZqFwbGxsFi9eDBEK04JUICP+/v7/YhYdgyKCAMJu8IyM jCNHjnh7e5s4wVUnaaGehfwNjHQTdiEGdgf5lDV00NGGL0oTHwETSYJmxw+T Ac+UcWCTSLkbnjaNh6SmNfTyGXWRaUlw5oEzkT/eZyZCPul326njfA2qQhmc KEz2EzsFMfB2apm+BeOx0+akHPLxXlMOqMKS+N/vHpi9eGKnHn0Hd5/5zcrr MS9L5WUkHX0HlbL6PL0VvsfM7BxZVUK/ZZ0y/nTOl8E4NK+lCnXM91HQSG// O4wtp02uDt89K9Fpw59812tagKUV07j9y3PWqC137mjI1Dn6oIKpnNhoeKcA wz0syJdQja25QEw6Mcz0C7PdL1y0ZvtpjTtt3+VyIO8NLzwIhCoznuSG4Kyc wU1rafpOou/joszNJ8bVxYJK2LRXje/lI88ULahXjasOioyQf+c1OKrab7yT SbGaM4y7SY3mUGIEIEqjlcRlxgVf+DHoWkxBikRLMWq7lkxFSPMajuF0QxFx 7lq8bVxmtexFoThhl6bYifu4wRUmoy9o/nyae8oIhnWF2Li53lqK3U/ruIVO KB1FG5WPoK3UsS5nOs4GitbVIC9wISkyhy27/BRZf4Wi+ARqrNnMXQthvcTE oaRocVlS0JM7Ny89SEpQ6qRsARrkUT6z1UVp0PPfQ+gx35qYgavizLg2G0fF /ph2a5t2PhvqT1pn0ijruKdSR7RC3gTDu4cZkeKd7K8ynocrVT7Fd4lG8Cev g1Qj39Fc61lTX7fwELnnmRc2tbC6MLsIETYk/wHC8uSrfeFdKQhK3AMVsvjj N3d2qmvTt8fuB0TPYj11dUYvLcJbQeh4Thv0VK2WnyOXl9pNRC2Kveg6dvFB w72qZU/Q/9YKmye2KiaBKXYeVorz9+JqGw2Z3ZSWU+l7zny3YPTIKeOmL5qx dMWSVevWrPlq3dpN69dvXLt2+ZdfTFswucvAIf7th0zqufTwN0/T40WVzLgg /RSJJDbDODDBeJL/qF0zd0tzV0e4y9D4mr4Jm28W+X0GxZn9wK/xhczkoPGr I19vGE3BbRbhl8h7CfEIgiAIgiD/R/lP9Cw7O7vu3bvfuHFDIpHAO1tlZeWz Z8+WLVvWtm3bSZMmrVy5cvjw4VZWVnXq1Jk6dWpwcLBKpRKJRDdv3tywYUPf vn1dXFxat249bty4devWPX36tKysjJhE3gBlMll8fHxQUNC+ffsgTgg2aNCg sWPHLlq0KDAwEPbHxMRkZmampqZGRUWFhITcvXsXdv7JcO3aNfh969atx48f h4eHp6WlFRUVyeVyrVbLToIt0Jj4ooCdYGFWVtaVK1fApNGjR7dp06Zu3bot W7acN2/ew4cPxWIxxAOR9+/f39HRsVGjRjNmzIiOjgZT+Uh++eWXgQMHksXF OnfufODAASgWjYad/0ehUEARDRkyhAhekP0zZ85ImBUM+Fy/evWqX79+UGg1 K1moZyH/IYaPJuFcZ4KvIMFOw2cUpfdKUFXtoaJNRA3aEG11n1V8tLwlFL/O ANcvVGUg8f8O1Qglpntq7lDS8aVk7OxQzZdszQbBt35y8oPNhyc1bdtpUJdF 368Lio6W631fyaxB+utEelBp7d8ovuqWaHmrbSb2v3+f59sTFUqZtND/he/1 fY/+Da6vQCfR0VEvfvx82Sjvlo28mzRq3qTz2jmHw15omEG1fA8n0Q44lxBj sww/tGAFinfqxa0OusqPYNOop5bix+oLWzQcoPu3qX4otY695Vg/PB3p66sm MCcWmO+FMrlpacO8qWYDCrqsOfHUJJigYiFvFCYd+NXZ+S6Y7eiuKhDwoyMM HaacfxOrUmn5+WO1gun+iKZD0TpOchL2/AsC8bPUCjQIimZXueL0LIHmYmhQ eOGGX87G+KoI+4GNcm2ISr9fP2kAe5bGbPPE+zewggjbnUm9zbeK1/I4vxgy S5hOx2kxWor3AK0WfgVD4R6toS5iN9jS1ujYdpdvxf9mc2t+3kuBW8q7NQrV HTJXyGZLntWnDA9C1fqT72E2MrX6jFVJqAb7heN4hPq1oH4WBq5+nEOVhMyZ IUiravVCG/Vum56uP4GdyNH4ATQqK5354kLeDs3Nh/kezm1ssVfKk8/ePzyi WaOJYw7dv5RTqTOjYxgEVvOPhpa0UFp2tIWhWaCMrqbw2nP1JtGS9B6sfBCK beP4+pb7T3iD6nS8VypTLfJ6ljb7/O/bP53cs333Ns1a+jVo3qR+I08vN3dP D8/6XvWbebRs6du+k//YRR9v/3Xvq9x8lY7x42UrQ5I86x4rzKCpcvR+CF1H 3/0ON/ogqnLI9Pl6H7hK2/gBrzbwO0VodhtBEARBEOQfxvvqWUReIZ5ZZM69 tLQ0HbOy1ZMnTxYuXNilS5cBAwacPXt2zpw5rVq1cnJy6t+//6+//lpYWCiX y4ODg6dNm1avXj1ra+sOHTqsXr365s2bmZmZRPEhb3RisTgxMfH27dvbt2+f NGkSnD548OD58+fv2LHjwoULr169SkpKev78+V9//fXjjz/u27cP9kPIrQzf fPPN5s2bN23atHHjxg0bNqxfvx7+3Lt3708//QQ5jYyMLC4uJj5cNbzvKZXK 9PR0CA/n9unTx9XVFQyeN28enA6H8vLyfv755/bt29vZ2TVr1mzbtm1RUVEK hYK8EoNtkG7dunWhoOzt7adOnRoWFka80uCoWq2G06GUXFxcoBhtbW2XLVsW EREh/OwtKysD+9u2bUtKG4oa9Szkv4ZxBwjXbWIkQgl6IzVmBjzrdKbf1/wK I/xxM8+XRld11h1msRIzFvLjPKuOtWb/qzpPlPmvWjP9PIIuStPR10ZVwVs+ k2neFtIPYNybx0/EJIycnwLLHKb+btWHZA9qdeWlyX89P75s2+UXweniIrZI WLOYHmSK61P9m5j2Xlfv/VR1/sAqAd7pm9rku57tYTavJPL9tKyp/NjdmvpL uR+jjmjWiSM3/+mFP37+ZsOWLdCGfLPnrzNPU+OZtIyFIdIVL1hLzqgb4+/P zUQbb9Ncf5HRvUpXCSfoxienCOdqQ/4eVZ4a03ubK3CKpumaytn8Uyzo5zft P9YZDnHPlLEHmEAmMHOrUzrjuog2/nl/KE5rY2PjomVFJWHPqnAdMcroeWQe Mf1vFbtTf6dq2Zh18PKn5bNFOl355AQTJzJ+nfp/au4h09DEocrQlUqKVFjR CqpTZuI6vYKmP0fDL1kofMT0kQt7bvnqnY+Ez59AJOLCcMHIY2rsiVN9jylb a9N8IdOGlbmYqBjvIU6U4+clNEt1V5k5WW3IBWOXhlFmGac5/gLoY65BdeNu OXOtM9d0m1ZZZix7hyaJ5io3k2a0SpdvzXe1SUVabf1sZBvrWKGjjGRP+h3s N5Sw4ZFjdExhG2Fo4GpoLo0UVRPbTApfoMOSQDR3Z1azrh9FczMSc8WhHx6j runmQWrEuIjMXCNeMRFcejPvS8xVUavznyYEfT33853bbkQ9E/H1lZnARn/Q hopKa3wr0mQJLe4C08wqXWQdKFKvsvUBzd0DfI2t15I4704mHm5aWFMly2in fg87iJMxXq2tzCxJvPkq6NDFn1fvXTNjzdTJkz+ZNOmT2dOX7Vj2w2/7/oy6 ny7KU2ppbmJYil3Ul+SIYmdmEORZWNVUN49Bzc0yY7Fx211lDkNz7SZXC9VQ 9fAFUtVRuloENwPvj1814hrHBwqTRhAEQRAE+UD4e3oW2ejcuXNSUlJlZSW8 ZSUkJAwfPtzFxaVLly7ffPPNuXPnunXrZm1tHRAQ8PjxY7K6Vk5OzujRo4mU 4+rqeuTIkbS0tKpa0vPnz1euXNm6dWsLC4s6depMnz79/v375KhMJouNjT14 8OCAAQOcnZ0tLS1r1arVvn17CLN79+4//vjj9u3bDx8+vHv37oULF7Zu3Tpu 3Dhvb28IAyl6eXlNmjTpxo0b+fn5JLbqVC1+Q6PRQDyQFlkS64cffsjNzYWj YAakCBkhK3/t3bs3MzOTPxfMgEIgiTZu3HjJkiXE9YyPFjIOxUKKsV27drt2 7RKmC4lCHqdMmcJfFJO5B1HPQpAPHEb+V6mlRSWFOTmFJWWVpDeqmv4r5O1o oDxl0vLC3KzMDGiXMjJzCotFchWl1dJ/X6JCEARBEAR5K8yrhppWlorL8uMS 07KLpFIl/Z8MSfr/C1q5qlIkLsstyk/LSk9Ijo+Ji4mNi4mPS81OyS7MKxOL 5UoVJZDDEARBEARBEOQdeBc9q6qYAnuaNm26YsUKmUxGUVRRUdGlS5cgHisr q7Fjx54+fXrgwIHu7u4dO3Y8cOAAHNVoNMnJybt27fL29oYwjRo1+uKLLxIS EpRKpdDTv6Cg4PLly0OHDm3QoIGnp2e7du327NkTFhZWWlqqUqmio6P3798/ cuRIX1/fli1bjh8/fufOnefOnXv69GlcXFxWVlZJSUlZWZlIJCovLy8uLs7I yAgPDw8ODt6yZQsEhlPc3NyI+HX+/HkIU3X6warziuTm5m7btq1Vq1aWlpaD Bw/+/fffySpdsDFs2DDiPDVixIg//viDz8jr16+/+uorOzs7sswWFMKjR4/E YjEf+c2bNz/55BNSqg4ODrNmzQJj+DkJiV62adMmV1dXs4WPehaCfOBw42H1 awfoF3IycUZA3huKHdBOsw4aWm5eufdemg1BEARBEOR9YPykyDyQlIbSasja f/+A1zribcq4iRkcqrjFDfnF5ugqo0wRBEEQBEEQpAb+nn8WnDJ79ux79+5R DC9fvly8eLG9vX23bt3Wrl27e/duDw+PJk2arFixIikpiUzf9/3333fv3t3N zW3gwIFbt259+vSpVCrl568uKCh48uTJ3r17J02a5O/v379//7lz58Ipr169 CgkJuXbtGhxasmTJJ598Mnr06BkzZmzbtg0sj4+PLy0tVasNs/GYKFNgm1gs hmB//fVXYGAgRN6yZUtfX99hw4YdOHDgzZs3ZWVlJkto6QRv1GQDcrdhw4Zm zZo1aNBg3rx59+/f12q1mZmZmzdvrlevnoWFBZTG6tWrCwsLyST5EOeVK1c6 dOhgZ2cHR6EoVq5cGR4ezieRmpoKJeDg4GBpaQkBhg4d+vr1a7lcLrT/2LFj 7dq1q1nMQj0LQT5QmGU9DIs0MXNbofLyH2IyDRTO3YIgCIIgyP8A7BzR7Mx9 GiP1558FNz2rYCGw/22TEARBEARBkP9z1KBnVTfNnaWl5dixY69cuUIkGLFY fOTIkU4M+/fvP3z48CeffGJtbT1x4sSrV6/qmOWzL1y4MHLkyNq1a3ft2vWn n34S6i9qtbqgoABimz9/Phzt0aPH8uXLf/nll9DQ0OTkZNi/cePGUaNGdejQ oVevXhDm5MmT0dHRIpHIRIcCICp+AXThb0JpaemTJ0/WrFkzaNCgZs2a+fr6 rl69Wiir8QinOgfj4XdsbOyCBQugoHx8fJYtW0ZEtGvXrg0dOhRKA0oJLLx7 965KpSKnZ2RkfPXVV40bN4biqlWrVosWLaAESHEBCoUCSgZyBKUEAWDjxIkT JSUlQoNv3rz58ccfo56FIIg5hCu/sCudYYfAfwUySKOGxcIQBEEQBEH+i9BV lp8zXrvz/zz8kCHBGrimi8GhtIUgCIIgCIK8I++rZ1lZWdWtW/fSpUvl5eUk hps3b06ZMqVJkyY//fQT8cNycHCAYHv37i0oKNBoNGKxeO7cuY6Ojh4eHj/+ +COE0TFvrUQ2KiwsvHbtWocOHSDA4MGDb9++XVZWplKp0tLS9uzZ06xZMxsb G19f3w0bNrx580YikUCE5HVXq2Xf8/k/JQy8bMQnwWtbcC7E/OzZs0WLFtWq VQtyvWTJklevXgnj0enMzDpYUVEBwQICAiwsLMDU58+fQ6aioqI2bdpkaWkJ xQKHAgMDlUolCQ82PH78uH///hCeFOPOnTszMjL4OMPDw+fPn09W4PLx8Vm6 dGlubq4wRYgcsmxyOVDPQhBEV6WO4nb+zxvyD8F8LwqWJ4IgCIIg/wOw45P0 7va8wz2l+wdIWkaiFdmjzyPH/5pdCIIgCIIgyP9l3ne+QRcXlyFDhrx8+ZJo Q3l5eV988UWnTp0mT54cExNz7NgxOAoRzpkzJzQ0tKysLDY2du3atW3atGnd uvWaNWvi4+OVSiVZuKq8vPzy5csLFy7s2bNn//79N2/efOfOnZycnMePHx89 enTWrFl+fn5w1owZM06dOpWYmEjELKCioiIyMvL69euQ3I4dO1avXg3JTZky ZQLDp59+Onfu3P3794ORxPeKn3uQZFksFgcHB0+cOLFu3bodOnTYuXOnXC43 Ub748iH7IVGwdteuXR07dmzSpMmePXsyMzOLiop+/fVXJycnS0tLyDIYDBaS syB8cXHxtGnTIAlSbosXL37x4gUfbVZW1g8//NCgQYOPPvrI1dV12LBhqamp Qo+z/Pz848ePw3XhVUXUsxAEIfDTDPKOWjod6i//CUYjhP93TUEQBEEQ5MMC 3ugo03GV/4DXkereqWjzszqjXzyCIAiCIAjyTlSnZ1U3012jRo2+++474mpU UlICp3dn2Lx585UrVyZMmNC4ceOePXvevn27tLQ0JiZm69atLVu2bNq06bx5 816+fCmTyUi6crn84sWLZEGrjh077tu3LyoqKj09HSJZvHhxjx49WrRoERAQ sGnTpjt37uTl5ekYaUkkEr148WLv3r0QZuzYsRCsc+fOvXv3Hjx48KhRo4YO HdqpUyc4EWyAOOfOnXvhwgV+hSyhVxdEePbsWV9fXzc3t9GjRz9//txk+Srh Njkdfr9582bp0qXt27cHsyFrSqUyJCQE8u7g4GBnZ9e/f//U1NTKykpyokaj AeObNGlCym3cuHFk9kWir0kkkmfPnoEBVlZWderUadOmzatXrxQKBT9cDbah bBs0aGBtbV3DrIOoZyHIBwgn0wtWekIV5j+gqk8ulieCIAiCIP9TsB+bun/o G53Jm1U18wz8AzOOIAiCIAiC/H/Be/lnWVhY+Pv7P3v2TCQSwTtnRkbGmjVr fHx8Pv744/Pnz0+dOrU+w8KFC8VisVQq/eWXX3x9fSHy/v37nzp1ik+0srIy Li5u/PjxDRs2hAi//vrrgoKCioqKu3fvjhkzxsnJCRLy8/PbuHEjPwsfJCeT yR4+fLh8+XJ7e/vatWvDb0i6c+fOM2fO3L1799mzZ8+cObNly5bPP/+8a9eu bm5uEGbs2LEhISFKpdLERUur1ebk5IwaNcrDwwMsBAPy8vLMvkULfaaAy5cv Q5xt2rR5/vw5HMrMzPzyyy+9vb0/+uijFi1a3L59m5QMMfjQoUOdOnUiRdez Z88ffvhBGC0YADttbW0hs2DGlStXSktL+QBwemho6ODBgx0dHWu4IqhnIciH jEDYwvWz/lP0Jcm6vAm83syE+x+0CUEQBEGQfzrCdziaH6dE/3P8lcwtm8X8 GGccQRAEQRAEQd6Ft+pZvHMQbECwAQMG5Ofnq9VqODc2NnbIkCH16tVbsGDB 1atXYcPS0rJz584HDx6Uy+XZ2dkbNmwgy0uR2fZ4oSc1NXXPnj1eXl5t2rT5 +uuvy8vLIcLQ0NBly5YRfcfd3X3q1KlpaWkKhYLYCWeFhITMnz/fzc0NAtjb 2w8bNuzIkSNhYWGFhYWVDCqVSiaTFRUVPX78ePLkyRB/y5Yt169fzy/1peO6 f4mH1OHDh/39/V1cXCCqhIQEsKG6gWEURZFD4eHhGzduBPNu374NyUFap0+f bt++PeTRx8fn0KFDwkWy4FDfvn3BWjgKYaBYdIIpDcFsKD0nJyc4Ctk5duwY nCuc8DAuLm7hwoUeHh7/qt5dDvUsBEEQBEEQBEEQBEEQBEEQBEH+8dQ836BQ RoHtdu3abd++XaFQUBRVUFBw4cIFb2/vUaNGwc49e/bY2tpCPBMnTgwJCdFo NNevX58wYYK1tXWrVq1OnTpVXFwMySmVynv37i1fvtzf39/Pz2/btm3R0dEy mezOnTvz589v1qyZvb39mDFjdu/e/ezZM5JQWVkZbK9fv37EiBHNmzdv2rTp +PHjv/vuu7t372ZkZFRUVEBawhypVCqRSAQ27NixA0L279//4sWLubm5Jp5W YAkkPWXKFAcHB4gzKCgIEhIGMMxeLtiAXFy6dMnd3f2HH37IyckByyGSIUOG QN7d3NzmzJkTGRlJAkNywcHBUAKk9CAJyKwwNrLAlpeXFxRsnTp1lixZ8vr1 a6EBZI2txo0b80tooZ6FIAiCIAiCIAiCIAiCIAiCIMgHyLvPNwhhpk2b9vz5 cx2zONTDhw/nzJnj7Oy8e/fuEydOfP755zY2Nj4+Pps3by4uLoYAgYGB/v7+ DRo02LRpU0REhFarVSgU8fHxK1eubNq0qb29/YIFC0JCQsrLy6OioubNm9e4 cWM3N7fBgwefPXs2JSWFuIBBPPfv31+6dGmTJk0g/rZt2y5fvvyvv/5KT0+v rKwUSlRC1yrYD6cnJyfv3bu3T58+EyZMuH79Oi9X8T5Qcrl89erV7u7uTk5O R48eNXGPMoGkBfY8efKkVatW69evf/HiBWRKLBZDRqAM7ezsIK2nT5/y+hrk bvbs2cQ/y9PTc926dcIIwZ5ly5ZBruGotbX18OHDb968KQwAMUMhd+rUiVwd s6oW6lkIgiAIgiAIgiAIgiAIgiAIgvzjqUHPMnHRcnd3/+6775RKpY7RYgID A5s3b96sWbP79++fO3cuICAAIpkwYQJEqNVqy8vLZ8yY0bhx4xEjRsTHxxNP K7FYfPToUQhZu3ZtPz+/4ODgrKysiIiI7du3e3l52djYdO3a9eeffy4pKdEx yhGklZycvGLFCqIW1a9f/8svv4TYiOVCt6nqVph9+fLlwoULHR0dFy9eHBYW JgxA9CnIha+vr729/ZYtW+Li4mooKD7ayMjI/v37Q+6uXbtG9uzcubNdu3Zg f5MmTSBTKpWKBE5ISFi1ahUpQLBh6dKlUDK8tVAakPE2bdrAUSsrq5YtW549 e5bXwiAMxJOZmTl06FDIe3UiI+pZCIIgCIIgCIIgCIIgCIIgCIL843l3/6yu XbueP39exyhB169fHzt2rJub2/jx4588eRIYGAjbnp6ex48fz83NLSsrO3fu XLdu3fr37//rr7/KZDI4hczvN3jwYHd397Zt2+7atSs/P//+/fuLFi2CE62t rf39/Xfs2FFUVEQ8s+RyeWxs7LRp07y9veGUESNGnDx5Mjk5GeIxUa+qulPx ezIyMo4ePero6Ni5c2fY4JfB4tm/f3+bNm3s7Oy2bNkCyfEBqhPIIEB8fPyn n346bNiwU6dOkUMnTpzo27evlZUVJHT58mWy5hccysrK2rhxIyk9SGL+/PnE p4ycVVFR8f3333fq1Omjjz6ytLSEQjhy5IhYLOZT1Gq1EokESsDDwwPnG0QQ BEEQBEEQBEEQBEEQBEEQ5IPlHfUsS0vL6dOnP3jwQKPRKBSK5cuXN2rUyNvb +5tvvjl79uyMGTM8PDwmTZoUEhJCdKhRo0Y1b978iy++yMvLU6lUkFB0dPTX X3/doEGDgICAjRs3JiQkpKamrl69ulmzZpA6RAVH4+LiiJgFJCcnQ7AWLVo0 bNhwwoQJjx49ys7OlkqlOnMrW5kFjubm5p45c8bZ2ZlMewjGE7cs/sS9e/dC Eg4ODvv3709JSakhTn4/GDZv3rzu3bsfPnyYxHb16tXRo0d/9NFHtWrVIvod CVlcXLx9+3YyT6CNjc3s2bMlEglvAAQ7d+5cz549iRMc2ACBwWBhilB0K1eu JHMS/st4OTPUsxAEQRAEQRAEQRAEQRAEQRAE+UCoTs/iJxskG1ZWVuvWrYuO jlapVMXFxcOGDbOxsWnevPnZs2e3bt3at29fHx+fo0ePZmdnl5eX3759u169 ei1btty9ezdxR9JoNFevXu3YsaOdnd306dPv378vk8nu3r07dOhQiByiGjFi xK1bt3ir5HI5hOnSpYutrW337t2PHDkitNlEcqr6J+9jlZeX98svvzg7O3t5 eYH9vFjGB9u5c2ezZs0gwOnTpyGwSeFU9dKC38nJyUuWLAHbAgMDyfSAYPmk SZMsLCygGM+dO1dRUUFOEYvFUAKkbGvVqjVr1qyysjKtVqvjVu+6fPlyv379 SDnXrl17zZo1aWlpwuSgtLds2eLn51edzoh6FoIgCIIgCIIgCIIgCIIgCIIg /3je0T+rVq1aBw8ezMzMlEql4eHhHTt2/Oijj1q3bn379u0vvvgCNtq3b//4 8WOJRJKbm/vzzz/b29v36tXr1KlTJJXi4uI9e/bY2Ng4ODgcPny4rKxMLBZ/ //33HTp0sLKygp0bN26MiYnRcTJTQUHB8ePHbW1tIZ45c+a8fv1ax8y/R2Lj ZSbi66Sr3mMrNTUVzHZ1dQULAwMDhStnkQg3bdrk7e1dv3794ODg0tJSnbkJ DE1ULYhzxYoV/v7+27ZtI6uJPXjwYPLkyVAgkBfePwtCVlRUHDhwAHJB/LMg IyKRiM+FXC6/fv36oEGDiJ4FlwCiTUxMFCatVquh3AICAiwsLFDPQhAEQRAE QRAEQRAEQRAEQRDkw6QGPYuf3c7Kyqpu3bpXrlwRiUTFxcUXLlzw8/OztLTs 0qVLZGTkxx9/7O3tPXz48JKSEo1GEx4evmrVKltb22nTpt29e1fHKDthYWEL Fy60t7fv0aNHcHAwBIN4pkyZAknXrl27devWd+7cgciJSUql8ty5cwMHDoR0 BwwYcP78ed7jSVdFujI7PSB/6NWrV8uWLXN3d1+yZElISIgwANggFosXLVpU v359SAsyQsSpGiBCWEpKCpzVoUOH7du3k8W8nj59+tlnnxHR6tdff62srNRx MwoePnwYcg1lBQUyf/58uVzO62UQ7N69e4MHDyblDGHAVCLq8ajV6oMHD0I5 4/pZCIIgCIIgCIIgCIIgCIIgCIJ8sLyLf5a9vX23bt1CQ0NVKlVGRsbXX3/d qFEjBweH4cOHx8bGDho0yMfHZ9asWTKZjKbpu3fvjhw50traet26dVFRUUS7 uXDhAgR2cnL64osvwsPDFQoFHOrcubONjY2vry+EhGiJ4xJFUQkJCUuWLAGr PD09jx07Bod4j6rqMCtywe+rV68OHjx4zpw5N27cKCsrI2FIbEqlEnI0duzY Vq1abdy4MTc3V5hKDatoJSYmzpw5MyAgIDAwkNj86NGjqVOnQhmCwZAiWS+M 6FlHjhxxdHS0tLSEMoS8azQaPmY4+vjxYzLjIlEPly9fHhMTI0xarVZDCXTv 3r26S4N6FoIgCIIgCIIgCIIgCIIgCIIg/3jeRc9q2LDhokWLUlJSIHx8fPzn n3/u4eHh7e09ffr0kJCQ7t27N2/efPXq1QqFQqvV/v777wEBAdbW1nv37s3P z9cxys6+ffs6dOjg5eX1448/ZmVlwf6LFy/6+Pg4OjqOGjUKIiGOSzpGZoIY Bg8eDId69uwZFRWlq15d4qEYyDa/8lRERMSuXbtGjx4dHBxcXFxsInWVl5cf OnSoW7duffv2vXHjhkQiqTl+fjsmJmbkyJFw4pEjR8j+O3fufPzxx7a2tq1a tbp9+zZZpYufb7Bu3bqWlpaurq5QRMKMQHE9efJk2LBhvBPcihUrEhIShPnV aDTff/89lDC/kBnqWQiCIAiCIAiCIAiCIAiCIAiCfGhUp2cJBZR27dqdP3+e SELh4eG9e/d2cHBo3br1ihUrgoKCOnbs2L59+x07digUCrVa/fPPPzds2NDd 3Z1fSYqiqLVr19arV8/Hx+fBgwfl5eWxsbHr1q1zcXFp1arV1q1bKysriTAE 8UMk69evb9GiRYMGDZYuXZqZmVmzmAUnVl08S6VS5eXlQaKffvrpnj17qmpV Wq02Kytr/PjxTZo0mT59Opik0Wh0b1s8S8fNYQj5HTJkCJQJOXrlypVRo0Y5 OTkNHTo0JCSEF7/EYjGkbm1tDSXp7e29ZcsWYTxg5JMnT0aMGEHKuXbt2mBw amqqTiC6gVX79+/v1KkT+mchCIIgCIIgCIIgCIIgCIIgCPLB8lb/LGtr6wED BsTFxREXqhcvXjRt2tTW1rZ3796BgYHnzp1r27Ztly5dDh48qFAoIMzevXvd 3Nz8/f3v3bunZZBIJHPmzPH09OzcuXNCQgIECwkJmTx5Mpmx8I8//uA1Kdgo Ly+fPXt2gwYN2rRp88MPPxQVFb1LLkxULThr+/btkMSuXbvy8/PBBv4oCQkm PXr0qEWLFmA8hCQBalCy+O2KioqgoCB3d/cZM2ZABsnOM2fO9O/fH3bOnz8/ OjqaP6u0tHTnzp1ErvLz89u9e7dOoFWpVCqwAUoAjlpYWDg6OoIleXl5wkTV avWOHTvat2/Pi4yoZyEIgiAIgiAIgiAIgiAIgiAI8qHxVj3LxcXl008/lUgk RJx68OCBu7u7tbX1qFGjfmZo3bp1165djx49qlQqS0pKtmzZ4ubmNmbMmJCQ EB2zAlRmZuaECRPq1as3cuTInJwcmqYfP348YsQIOzu7zz///OHDh7wxFEWV lpZOmTLF1dW1ffv2ly5dgj/fKzsQOdgQHBw8evToXbt2vX79GuIkq1wJ9Skw 49SpU15eXv369YMN/lzhBvnNn0v+zMrKOnDgAGR/7dq14eHhxBXr0KFDXbp0 qV+//vbt28msjCR8Xl4elAYpxo4dOx45ckRoamVl5f3798n6WVZWVlCqBw8e FIlEQjNUKtWGDRtatWpldrJB1LMQBEEQBEEQBEEQBEEQBEEQBPkQqEHPIhpK o0aNVq1apdFoaJoWi8WXL1+uW7euhYXFxx9//Pvvvx85cqR169bdunU7duyY UqnMzMxcu3atp6fnokWLXr9+rWOm/gsPDx86dGiDBg0+++wzsqLWgwcPhgwZ UqdOnQULFoSFhfHOWbAhEommTp3q5eXl7+9/7dq1kpIS3butnwUWFhcXR0RE XL16ddeuXXPnzn3+/DlRowi8SgXExMR89dVX7u7uM2fOfPz4sVDAEiLcA0lA Xu7fvz9v3jxra+ujR49mZ2dDANi/efPmli1b+vj4nDhxIicnhz8lNTV13bp1 pCT79u17+vRpYYSVlZW3bt0aPHgwBLC0tITTf/rpJ4VCITQGUly2bBlcApxv EEEQBEEQBEEQBEEQBEEQBEGQD5a3+md16dKFdyzKyck5evSopaUl7J87d+79 +/cDAwObNWvWvXv3H374QaVSxcXFLV26FCLcsWNHUlISnCKVSu/evdu3b98m TZrAobKyMtj56NGjkSNH2tnZQSQvXrwQ2gPhV61aBXG2a9fu7Nmzb51vkNd9 8vPzL168OHv27OHDh0NCkZGRFRUVOnPTBmq12ps3b3bu3LlevXo7d+4sKCgQ HjWrnZGdxcXF3377bcuWLSGDd+7cUSqVROODRL28vFq1anXv3r3y8nL+rPDw 8CVLlpBinDhxYlBQkDByuVx+5cqVfv36wVEo/27dul2+fJlfe4skKpFIpk6d 6u7u/i/jFc1Qz0IQBEEQBEEQBEEQBEEQBEEQ5MOhZj3LwsJi2LBhf/zxh46R VxISErZv3w47rayslixZ8uTJk61btzZt2pToWUqlMjIycvHixd7e3idPnszL yyNyz5UrVyBAkyZNVqxYUVxcDDvDwsJmzJjh4OAwYcIEMEC4dpVKpbp+/Tok 6uPjs3Tp0oyMjLdmITc398yZM59++umgQYMOHjz47NkzOEsmkxFtqKo+FR8f v3HjRi8vr+nTpz969AhSrBqnia5E9pw/f3748OEeHh5Dhgx5/fo17IdUkpOT 4U97e/suXbqkpKQoFAr+xMePH8+cOZOU5KJFi8gEjHyEcO65c+d69uwJR21t bSdOnPjgwQOhDZWVlZARyJSdnR25FuifhSAIgiAIgiAIgiAIgiAIgiDIB8hb 5xv89NNPHz16RAJHR0dv3LjRwsICwq9YsSIkJGTdunWNGzfu3r37sWPHFAoF BFi8eHGTJk3++usvMlWgWCz+9ddfIUDDhg0XLFhQXFwMO2NjYzds2ODu7u7v 779//34ymSEvP2VnZ+/YsQNOCQgIgHNzc3OrThsol8szMjIeP3587tw5iGr6 9OlTp06Fs8LDw+GQrspiWGQD4gEDDhw4MHDgQF9f399++413zqoZsLCsrGzu 3Ln169f38fH56quvkpOTYX95efm1a9c6duzo6Og4cuRIotbxiULZjhs3jhTj +vXrExMThfaIRKKjR4/CuRDAzs5u7dq1ERERQvUNiu7Zs2cQAErbrHMW6lkI giAIgiAIgiAIgiAIgiAIgnwI1KxnAfPmzQsPDyeBo6Oj169fb2FhYWNjs2rV qtDQ0LVr1zZq1Khz585HjhxRKBRkvsHmzZvfv39fJBKR9bAuXbrUtWtXLy+v KVOmkPkDc3NzT506Vb9+fQ8Pj1mzZmVmZqrVap3AKyokJGTlypXu7u7Tpk37 8ccfX758mZGRkZ2dDSGTkpJevXp18+bNQ4cOLV68eOTIkcOHD1+4cOEvv/xS WlpKnK2qmzMQjLly5crQoUPB5kmTJkGckKJQLDMLhJFIJGFhYZ06dbKysmrV qtUff/xBMgImbdu2rWnTpp6enmCqTCbjfc3grBMnTvTv3x/KEIp33759hYWF JnLV9u3b27VrBwGcnZ2hACF3OsG0hwUFBRcuXGjRooVZ1znUsxAEQRAEQRAE QRAEQRAEQRAE+UCoeb5BKyurlStXpqWlkcDR0dEbNmywsLCwtbX96quvwsLC 1qxZ06hRo44dOx44cEChUKSkpKxatapFixahoaEVFRVkBairV692797d2dl5 +PDhubm5OmaqvVevXvn6+kJUAQEBp0+fLikpEXo2abXaR48e+fv7W1tbN2jQ YOTIkdu3bz906NDOnTsXLlzYrVs3d3d3OOTp6UlmLCRuXyYTDPIREiDOhISE nj17Ojo6Qszff/89WczLLLwsRX5nZWXt3bsXcgoF0qtXr8zMTKVSqWOmLhw/ fjwY06xZMygo4hpGAGPgFEgI8ujm5nbu3DnhVIQ6xj8LypAUgpeXV1BQkFgs Ftqfnp4eGBjYsGHDf1WzeBbqWQiCIAiCIAiCIAiCIAiCIAiCfAjU7J8F+9et W5efn08Cx8fHk/WzbGxsVq5cGRoaSuYbDAgI2L17d2VlZXZ29vr161u0aPH8 +XOizshkstu3b/fr169u3bp9+vRJSUnRaDQURWVkZPTu3Rt2uru7jx07Nikp CfaTVHgJ6eDBg23atLGzs3NxcWnSpEnz5s0bMzRr1mzkyJHffPNNcHBwVFRU SUkJiVN4etVtsO3kyZMNGzaETA0bNiw8PJxoUsLVu4QId0IqvG61evVqhUIB yanVaigBPz+/2rVrg507duyAEhCeu2HDBh8fHzjavXt3KAQTua20tHTRokXe 3t5QmJC1ly9fEnt4YmJiIAAkiv5ZCIIgCIIgCIIgCIIgCIIgCIJ8yNSsZ9nY 2GzevLm8vJyoMBkZGYcPH7aysoLwS5cuff78+ddff924ceO2bdtu27ZNoVCU lpbChp+f3927d4n3k1wuDwsLGzZsmLW1dbt27fj1rYqLi2fOnAlJ16pVq1Gj RqdPn+ZFGX6FrPj4+CNHjqxYsWLWrFnTpk2D8PPmzYM/IYnLly/HxMRIpVJe xtIxXl386bBf6GBVVFR09erVyZMng7WDBg2CaCUSCZmcsGYgnszMzOPHj8OJ YP+yZcsePXpEIodi+e233xwdHaFA+vbte/bsWaVSSRYCA0tEIhEY7OLi4unp uWrVqujoaKE9pATAHg8PD4ihV69eSUlJQkkOtp8+fQrR2tvbo56FIAiCIAiC IAiCIAiCIAiCIMiHTM16lq2t7c6dO3m3o5KSkvPnz9vY2FhZWS1YsODRo0ff ffdd8+bNW7ZsuX79epVKBSEPHDjQoUOHS5cukRWmYGdmZuakSZOsra29vb15 nUsqlR46dKhLly6QEEQ4btw4sEQikfByD9GM5HJ5bm5uQkJCZGRkTExMampq aWkp8eQSzisoVLV4yE6lUgkGgD2zZ89u2rTpxIkTf/nlF6F2Vp1/FjlaUVHx 22+/gXmOjo6LFy++d+8eP21gfHz8t99+C/mCLEAGw8LCyCpgAJTD48ePBw0a 5ODgAKVx69Ytkms+Ia1WC1b17t3bzs7Oy8vrs88+M9GkIJWrV6+6ubmR+Mnl QD0LQRAEQRAEQRAEQRAEQRAEQZAPkJr1rLp16+7evZvXj+RyeVBQkL29vZWV 1fTp02/dunXs2DE/P7+mTZuuWLGCzML3888/d+nS5dChQxkZGSQJ2L9gwQJH R0dPT89z587l5eXBTrVaHRcXN3r0aCLT1KpVa9asWU+ePNFxOpQQikGr1fK+ V/zcfUKnJxPPLAAsLygo2LBhAxgJBvTr1y82NhbsEUpgNYhZkGJ4ePhnn30G 53bs2PHVq1fCOQPv3bsHhWDFsHjxYkiIPzc/P3/z5s0tWrRo0KDBnDlziouL TSYbrKioePbsGQSAc5s3b/7NN9+QSR35AIWFhcePH69OxkI9C0EQBEEQBEEQ BEEQBEEQBEGQD4ea9SwHBwehngUbDx48cHFxsbS0/Pjjj69evfrzzz+3adPG 29t7yZIlMpkMgp07d6579+5ff/11fHw8n8rKlSvd3d09PDwOHjyYlpam41yf AgMDfX19IS0LC4v69evPmzcvMTGRd4CquhKWUKsyOVQV2J+Tk3Py5MnWrVvX qVOnXbt2O3bsKCkpEbqA8SHNRqtWqw8fPtylS5dGjRqtXr2aWM6f9fvvv/fp 0weKArIPpcSbrVQqIyMjhw4d6uzs3LVr159++kkqlZrEnJ+fDwUFWYZC9vf3 P3HiRGlpqTBASkoKFI6JZ1ZVbQv1LARBEARBEARBEARBEARBEARB/vFUp2cR 6aRu3bp79uwh60wR0ef58+f16tWzsbEZNmzYyZMnf/311w4dOnh5eU2fPp34 Pf3222/9+vWbMmVKWFgYLxLt2LHD19fXycnpyy+/jIiI4FOH2DZv3tyjRw9H R0ewAcJs3Ljxzp07iYmJEomE6Gi6avQmHpNDRIcqLS0NDQ09ePDgqFGjIIMd O3ZcvXr169evyRJXZmMQ7oekCwsLHz58OH78eD8/v3Hjxt29e1ckEvEhIX4o GR8fH2tr608//TQoKIjXuTIyMo4fP+7t7e3g4DB58mTILyRqklB4ePjy5ctd XFwsLCz69OkDCUmlUl6zg9+vXr1aunRp1SuCehaCIAiCIAiCIAiCIAiCIAiC IB8aNftn1alTR7h+FvDy5UtfX9/atWv36NEjMDDw5s2bHTt2dHJyGj9+vFQq pSgqKChoxIgRAQEBwcHBKpWKSFEnTpwYMGAAxDZ69OiHDx+SqIjw9P+2d9/R OWX9//9vua5fehUpFimSILpo0WuiM3rvhGGih4nOkFsdceu9G8Mw/MIYgqhD CEKQRBBJJCG9SE/Id6+cNWed+wpmxszc48Pz8cdZJ+faZ+/3Pvz3WnvvyMhI 8WurVq3Mzc1FGdbW1v3791+xYsXp06fj4uLkJEhJeXKWxsPCwkJRxrNnz/z8 /CZOnChqq1ixoqhn1apVt2/fFr8qV3i9NSaTNjOMiYkRX2bUqFEODg7u7u4b N27Mzs6WNzwU18uXLw8ZMsTY2NjR0XHLli3yboFiRqdOnerWrZu2tradnd38 +fOlr6ccSDw5duyY+ETiM4pvMmDAgOjoaFGbsrATJ06IT/qeJIs8CwAAAAAA AAAAfCbevz5LT09v8eLFmZmZcvugoCBXV1cTE5NatWp5eXndunWrefPmurq6 bdu2TUhIKCoqOn/+fJ8+fYyMjLZv3y7vsxcQEDBkyBAxSo0aNY4fPy6ayR3m 5eVlZGQcPnxY9CAGValU+vr6hoaGderUmTt37oMHD6RIq/ShWhKNzQNFVxcv XvT29nZychKdVKhQoW/fvo8fPxaVSKvMit+2P6H0rlxVbm7u5s2bGzZsKCqp Vq2auBdP5F+l0MrT09POzk58umnTpoki5a4SExN9fHzEd9PS0mrRosXOnTuV i7+kTh49ejR//nzx0cR8nZ2dxRcWHWqEa1u3bq1fv/77F2eRZwEAAAAAAAAA gM/B+/MsHR2dWbNmvXz5svjXRObu3bvt27c3MzOrWLHiyJEjnzx50q1bN11d 3bp16968eTMzMzMoKGjixIkGBgbffPON+LW4JC169OjR7NmzRTMTE5MlS5ZE RkZqbO4XHx9/5MiRKVOmNG7cWLRRq9WGhoZVq1bt3bu3l5fXunXrRJ3nz5+/ ffu26DMuLi4xMTEhIUG8FR0dHRIScvHiRfH6ypUrPTw83N3dq1ev7ujo2LVr Vx8fn4CAgJycnLfuMajxUFq6JaZw4MAB8W7ZsmXFl5k/f77oX5lJpaenX7ly pVWrVuXKlWvbtu0vv/winijXbQ0bNkz6huPGjRN/Fv93glZUVHTo0KEuXbpo aWmJaY4YMeLcuXPKMkSDV69eLVq0SIz+npVZ5FkAAAAAAAAAAOAz8f79BsVz T0/P8PBwqfGbN28ePnzYv39/a2trY2Pjbt26PX/+fNSoUeJPJyen7du3R0dH h4WF+fj4GBkZeXh4XL16VXoxPT19z549VapU0dfX79Sp08GDB/Pz8zWWXKWl pQUFBW3dunXChAlubm6Ojo7a2tqGhoY2Njaurq69evUaMmTIV1995e3tvWjR Ip8Sixcvnj9//pQpU0aMGNG9e3cXFxcxRN26dTt37jx79uxDhw6FhIRkZ2cr Byq9V6EUYwniRtR/4MCBHj16VK5cuVq1aqLnwMDAjIwMZWPRZt68efb29mIs Ly8vZZglrmvWrGnUqJFKpTIxMVm9enVsbKy0JkvaxlCa5owZM2xtbUUbKysr 0T4hIUFZoWh/+/btoUOH6ujoKLNFzs8CAAAAAAAAAACfp/fkWYJKpRo9evSt W7eKf81rnjx54unpWbFixTJlyjRq1Ojx48fz5893cXGxtraePHnyw4cPY2Nj d+zYYWxs3LFjx++//15+8caNG+PGjTM0NDQ3Nx8+fPjTp08LCwuLS20YKB6+ fPny+PHjorfatWtbWlrq6emJMt6zRklLS0tHR8fExMTGxkYMunDhwjNnzihD KFnpJ0ppaWlHjx5t0KCBkZGRk5PT9OnT4+PjpSPA5PLE9dq1a2K+YoJubm6H Dx8uKCiQXi8qKsrJyfHw8BATFDXXqlVLOkGsWLFZYl5eXlhYWI8ePcTXEzW7 urqK7y8XIDUTHW7YsKFJkyYaa+XENMmzAAAAAAAAAADAZ+j9eZbQr1+/s2fP Fv8aBj19+nTRokX29vbiJxcXl6CgoB07dnTo0MHExKRjx47Xr19PSEj44Ycf TE1Nxa8bNmyQX4yPjz948KCNjY1ara5fv/7atWvT0tKktVEaJYmHWVlZUVFR p06dmjRpUqNGjczNzd+6Okl6KO1M2L9//82bN9+/fz8lJUU+7koOksQo0hPl cPKv0s3FixfHjh2rra0tip86dWpoaGjpRWTPnz/fvn27sbGxlpbWyJEjHz58 +OZXmZmZoofmzZurVCoLCwv5XC3l6q3s7Gx/f383NzdRtoGBwaBBg65evaps UFwSq3355ZfiQ2msyWJ9FgAAAAAAAAAA+Dy9J8+SFgS1b9/+4MGDcuby4sWL 7du3V69eXfzk7Ozs5+d3/vz5iRMnGhoa2tvb79+///nz55cuXapataqtre3U qVNFe2mrvfz8/NDQ0C5dulhYWJibm7dt2/ann35KSkqSyigdMxUUFIhfg4OD RYXbtm1bvnz5okWLvL29p02bNmXKlOnTp8+aNWv+/PnLli3buHHjkSNHrly5 Eh0dnZ2dLffzrvVZGs8zMjJEYWKOI0eOrFatmrW19ZgxY86ePSt3JSdWT58+ 3bRpU4cOHVQqlZub2+7du9PT06UGWVlZt2/fHjRoUPny5UUPX3zxRUBAQEpK SvF/Z2riiY+PT82aNcW3LVeu3Lfffvv48WNlbWLQe/futWjRwsDA4F0JI3kW AAAAAAAAAAD4rPzm+qz69esvXbpUavzmzZvU1NRz5865uLiInxwdHVevXn3/ /n1xFZ3o6OjMnDnz5s2bERER/fr1s7GxcXd3P3r0aFZWlvyuaCm9a2pqOn78 +F9++aWwsFB5ppXG9oNCfn6+6OHly5fR0dHh4eEhISHBwcF3794NCwuLjIx8 8eJFenq6tK2f3Enxu8MsjZ+SkpL8/f1nzZrVsWNHJyen2rVrT5w4UUxQCtrk lqJIMcr69evbtWtnaWkppubr6yumKRUpmj179uw///mP+Aj6+vqtW7feuXNn WlqadCaXPJYoUhTcs2dPCwsLQ0PDhg0bXrhwQWNfxPj4+H379jk4OPyeMIs8 CwAAAAAAAAAAfA5+M8+ysrIaO3ZsTk6OFM2Im4iIiJYtW6pUKvHThAkTHj58 eOjQoQYNGohO2rRpc/DgwZcvX27ZsqV+/fqi58GDB0dGRkp5k7jev3+/b9++ BgYGorGlpeWqVatiYmIyMzPlU6iK/zuQUmY97z/96j2rsUp3IuYiJpKamvrT Tz8NGzZMzEVHR6dSpUpTpkxJSkqSDvZSysjIuHnzpphjmTJlxKT69+8fEhIi /yqKP3ny5BdffKFWq8uWLfv111/LEZ6yk5SUlJ9//tne3l6MVb169RkzZogP pTycSxDfZ+rUqeLLkGcBAAAAAAAAAABI3pNnSQc26ejodO7cOSwsLCcnR7Qv KipKT08fMmSIhYWFmZmZu7v7zZs3L126NHLkSNGJePj111+/fPkyMjKyd+/e pqamlSpV8vHxCQ8PlyKb3NzcI0eODBw40MTERFtbu3Llyh4eHuJJcnKynCIp N+jTeKL01rhK+avGW8r1WYmJiaJmb2/vpk2bWltbGxoaNmnSZPXq1Xfv3pV3 F5Q7Ee7duzdixIjy5csbGRk1b9783Llzqamp8uFfoaGhs2fPFr9qaWl16tTp 4MGD4ivJS7ekQcXszp8/L2V5NjY2Y8eOffDggfgayuLFW6KNKOl3bjZIngUA AAAAAAAAAD4H71+fVaZMGS0trbp16+7du/fly5fSK69fv169enWDBg10dXUr Vap06tSphw8frly5Uvypo6MzePDgW7duZWRkLFiwwMnJSU9PT7x+8ODBtLQ0 6fW4uDg/P79p06ZJQZKdnV379u03bNjw5MmToqIiubDSqdZb/3wX5evKUCk6 OvrEiRPz5s3r0qVL1apVK1as2Lx586VLlx47dkzMIjMzs/QoQUFBs2fPFjMt W7Zsx44dd+3alZSUJC0oy8nJEW/NmjWrZs2a4huK6c+fPz8sLKx0DTExMd9+ +2358uXFBxk6dOiZM2eys7NL17Zu3ToxilqtJs8CAAAAAAAAAACQvCvPkhZn qVQqcbW1tZ05c+bTp0+Lf81oLl68OGjQIB0dHX19/V27dj169Ej0Y2FhIdq3 bdv2xx9/zMnJ2bt3r5ubm9TPnDlzQkND5dezsrJu3rwpfjUxMRG/itHbtWu3 bdu2uLg4adFT6bOu3roOS3oor5NSPi/+7yVa4iY5OfnGjRsrV64cMGBAlSpV tLS0RP3NmjVbunTpixcvCgsL5cZyrCZuEhMTfX1969SpI6bWtGnTzZs3S5sE isbilaioKNGhs7Oztra2np5e9erVxXcQsytd6uXLl0eNGiUGtbS03LJlS2pq qvyTXP/Zs2cHDx4sfTHyLAAAAAAAAAAAAMn78yzpWq5cuU6dOt27d09OfGJi Yry9vc3NzcWvXl5eN2/evHPnTqNGjQwNDevVq7dixYrs7OxLly6NHj1aS0tL dNKvX7+ff/5ZvC5FReKalpYmmjVs2FBXV1c0UKvVHTt23LlzZ2pqam5ubkFB gTKlUq7bUiodYyk3IRRviX5Eb5mZmcnJyf7+/h4eHmIuYjhtbW0TE5PKlSuL GqKiojRelG/y8vICAgJ69+6tUqlE+3Xr1kmhniQjI0NMSnSir68vvoONjc3c uXPDw8OLS8VwohIxtTZt2oiWLVu2PH36tEbBxSVLvUQxNWrUIM8CAAAAAAAA AABQ+s3zswRtbe3y5cufPHkyOzu7uCSFKSwsPHbsWLdu3bS0tFxdXbdu3RoT E7N9+3ZxX6FChf79+7948SIhIWHt2rX29vaijZOT09dff52SklJUVCQlOKKH J0+erF+/XnQiXlGr1aampnXq1Bk9evSePXuCgoLi4uKkGEs+iOpd67NKL+YS L+bl5Yn+z58/v2nTJk9Pz65du7q4uFhaWhobGzs4OIhBlyxZEhAQIMrOz88v /VlEV6LaS5cuDRo0SJTn6Og4Z86ce/fuiW6lBqKqy5cvi2r19fVF8eL7iJah oaFZWVkaOyWK/sUo48aNE6OXK1du2bJlERERGtMRb/3www8dO3YUvZFnAQAA AAAAAAAAKP3m+VnSVUdHZ9OmTXJu8ubNm2fPnv3nP/8xNTUtV67c1KlTHz9+ /PTp09mzZ9erV6927dqHDh2Kioq6ffv2nDlzKlasaGxs3Lx5c9E+NTVVynFe v35dUFAg3jp16pSvr2+LFi1MTEz09PRE41atWvXt23fKlCnbt28PCgpKSkp6 a+SkQXT46tUrUdXly5e///77RYsWffnllz169HB1dXVwcLCwsHB0dOzUqdOC BQu2bdsmnfkl2surseR+pOVUovLVq1eL121tbRs0aDBjxow7d+5IcZ7k5s2b 4qHoU/pEbdu23b9/f05OjrKf4l/DrG+++aZu3boVKlTo3Lnz9evX5VO6JKKN KHvgwIHiH+L3J1nkWQAAAAAAAAAA4DPx/jxLplKppk2bduvWrWJFUnP9+vV6 9eoZGhr26NHj3LlzRUVFAQEBo0aNEh0OGDDgypUrKSkpwcHBQ4cOtbe3NzMz a9as2aVLlzIyMpQFiN7S0tLWrFnTs2fPatWqlStXTltbu0yZMqampo0bN/by 8tq2bdvp06dFb4GBgbdv37579+69EuJGdC6e3Lhx4+rVq/7+/ocOHVqxYsVX X33l7u4uRhRlVKxYsXr16qIf8cTT0/PAgQMvXrwoKCjQKEC+F1MQ5YmeFyxY UKdOHR0dHScnp5kzZwYFBcltcnNzIyMj58yZU7t2bS0tLT09vapVq86fPz86 OlrjJC/R2+PHj3fu3FmzZk0xr9atW2/evDk9PV3jnyAxMfHkyZOOjo7iO4sO ybMAAAAAAAAAAACUfuf6LC0tra5dux4/frxYEQA9ffp04sSJNjY29erVW7ly ZW5ubk5Ozo4dO2rVqmVsbLxixYro6Gjx8MGDB4MHDzY3Nzc1NZ00adKtW7fk HfkkosP8/PzAwMA1a9Z06dKlQoUK+vr6qhJidNGVGKJ+/fqtWrXq1q3bgAED hg0bNmLECHEdOHBgr1692rVrJwqws7MT/evq6oq5GBgY2Nraivbjx4/fuHHj lStXoqKiXr16JY2lHF0jfkpPT7927ZqHh4eTk5O2traFhcX8+fODg4OV7UVX Pj4+lStXFrWJgcTN0qVL79+/r7EdohglIyNj9+7dNWrUkHKxBQsWZGdnS82U J4KFhIRMnTpVjKXx2cmzAAAAAAAAAAAAin9fniXdODo6btiwQQpipOOfUlJS xOv169e3sLDo06dPamqq+PXGjRsTJkwwNDTs27evn5/f69evs7Oz9+3b16FD B5VKZWdn5+vrGxcXJ4dK8vFYWVlZ8fHxDx48OHHixIoVKwYMGFChQgVRmFqt 1tbWNjAwMDY2Njc3F2NZlhA35cqVk2IyMZx0MFbnzp0nT568efPmCxcu3L17 NyIiQoyVkZGRk5MjH90l0Th4S5QRFRUlXqxXr54Y19raukuXLt999114eLgc QglhYWGificnJ319fR0dnYYNGx4/fjwyMlIUL3clD+Hv7z9q1CgjIyPxAUeP Hi1KkhdwyVfx5Oeff65Vq5bo8PcuyiLPAgAAAAAAAAAAn5N35VmllwgZGBh4 e3vHxsZKL7558yY3NzcqKqpbt27Gxsb169cPCAhITk6Oj4/fvXu3mZlZ1apV fXx8cnJyRMsHDx7MmTPHxMRErVZ37Nhx69ataWlp8hol+UQt6T49PT08PPzn n39euXLlmDFjWrdu7eTkJDrU1dWVq9LS0tLX1y9XrpwoXgzduXPn8ePHi/bH jx+/detWTEyMGFdjFZis9PosUYkoe9u2be7u7mIIc3PzgQMHHjp06OXLl/Lm hKLlw4cPv/322+bNm0s1VK5ceerUqQkJCYWFhcX/veRKsmbNmgYNGohva21t vX79etGy9HFd0dHRok/xbVUq1e9flkWeBQAAAAAAAAAAPh+/eX6WcolWly5d Tpw4kZ+fL68zys3NXbhwYZ06dSpUqODl5XXr1q3ExMSbN2926NDB1tZ28ODB 165dS05OTktL8/Pz69ixo5WVlZmZWffu3Y8ePRoTEyO6Kv7vpVKyvLy81NRU 0dWOHTtEz/369ROvN2/evEmJFi1adO7ceeDAgePGjVu2bNmPP/4YGhqakZEh xU/vWoelfCilWpmZmdHR0YGBgbt27eratauYhY2NjZimKE9eclVUVJSTk/P4 8ePly5e7urqqVCotLa1y5coNGDDg2LFjUj9yRiZ6Fu1TUlIePHgwZMgQ8RGc nJyGDh36yy+/aOR3guhWDNSjRw+NY7PYbxAAAAAAAAAAAED2njxLmapI9xUr Vhw5cuSrV6+Uuw4+fvx4xowZZcuWNTc3X7JkSVhYWEZGxoULFzp06CB67tSp k7+/f1paWlZW1s2bNzt37mxlZaWjo+Ps7Lx///7o6GjlxoPKDQDlm9clsrOz k5OTnz9/HlUiNjY2JSUlNzdXTqZKL33SWJ+ljJzkh3fv3l2+fHnjxo2NjY31 9PREVZMmTRJ1pqamKl988uTJvHnz7O3tpU+hra3dp08fOfPSKDs9Pf369esD Bw4U0xcdio8THh4ulaqsJC8vLyYmZvTo0dKGhFKkVabE7wyzyLMAAAAAAAAA AMDn4PfkWXLIIpo1bdr07NmzaWlpcg/Z2dmHDx92c3PT19evXr36ihUr0tPT U1JSxI1obGFh4erq+t1334lXkpOT/f39O3fuLFoaGho2aNBg27ZtL1++1FhC VXpFVVFRUWFhYX5+fl5eXm5urriKe/FEjtWK351kafRc/OtOiZGRkf/+97+7 du3q6OhoYmKiq6vbs2fPXbt2hYeHi/qlLQQFcSPmO3bsWHt7ez09PfERrKys +vfvf/r06YSEBOVA0r34Gj/++GOHDh1EM2dnZy8vr6dPn0qndylnJJ0XJoZr 2bJlmV+VjhHJswAAAAAAAAAAAH5zv0GNYMve3v7f//7306dP5R7evHlz//79 hQsXGhoaamtr9+vX7/r16/n5+b/88svYsWN1Snh4eFy+fLmwsDAtLW3v3r39 +/evVKmSiYlJ+/btly9ffuHCBek4LWWSVfrmrd66nWBxqZBL/vPVq1cRERHH jx+fOXNmw4YNzczMrKysmjdvPm7cuMOHD8fFxSk7EbMICAj46quvRLXSRyhf vnyPHj38/PwSExOVPcvB1p07dzw9PY2NjbW0tPr27XvixIm3Bm3iRowlvo+t re2HJVnkWQAAAAAAAAAA4DPxe/IsJVNTU3d395s3b0pHX0kyMjL8/f3r1Klj ZGTk4uKyYsWKV69epaam7t69u0GDBnp6erVq1Zo/f35sbKwUaQUEBIwfP97e 3t7Q0NDBwWHUqFFnzpyJi4tTnn6lsVtg8buDrbdGYBopkiCKfPz48alTp5Yv X/7FF1+Iiejo6NjZ2fXq1WvLli2PHj3Kzc2V24tKUlJSgoKCxo0bJyoUE1ep VKLxgAED9u3bJ20eqBGZSZsi+vr6NmnSRK1W29jYrF+/PiYmRp6LsiTxccRH qFevnqhB9PxHYyzyLAAAAAAAAAAA8Pn4o3mWtra2gYHBoUOHMjIyihXZU1xc 3IYNG2rUqGFiYtKxY0fxZ1FRUXx8/Pfff+/o6Kijo+Pi4rJkyZKUlBTRvqCg QDSYNGmSvb19mTJlRIetW7fes2dPUlKSRmhVevnVu9ZwvbWlfJ+bm3v58uWJ EyeWL19eT0/v/ythbm4+ffr0wMBAaetC6QAv6a3ExMQjR460b9++bNmyKpVK rVYbGxv7+PiEhIRIQZ4yzJLeysnJEb926dJFV1fXyspq9uzZDx48EM81dhqU PtetW7eGDx8uCpCOzZJ8wEIt8iwAAAAAAAAAAPDJ+6N5lqBWqz08PC5duqQM jPLz86OioqZOners7Ozo6Ojr6/v06dO8vLwnT54sX768Xr16BgYGjRs3Pnbs mLSnX05OzpUrV4YNG2ZtbV2mTBkzMzNXV1dPT8+ffvopISGhsLBQue6p+Ld2 HXxrA2nBVGRk5N69e6dPn965c2c7OztppmK4Jk2abNiwISQkRArmlG+JClev Xt2mTRvRTGovPtHo0aOvXbuWlZVVeuGYNP2HDx+OHDnS1tZWjDJkyJDbt2+/ evVKzvuUcdvz58/Xrl0rmilXZn3AZoPkWQAAAAAAAAAA4HPwR/MsKXZxdnae MWPGnTt3CgsL5a5ev34dEBDg4eFRuXLlFi1a7N27Nz4+Pj8/PywsbMqUKeKh oaHhoEGD9u3bJ56kpKSkpqb6+fl5eXl16dLFwcHBzMysUqVKffr08fHx2bNn j/jp4sWLISEh0dHRSUlJ2dnZeXl50hlbyvqlvQRFGbm5uenp6WLEx48f37t3 79KlSydPntyyZcvXX3/t5ubm5ORkYWFhZWXVsGFDd3f3CRMmbNu27fnz58pd E0X/sbGxV65cWb16datWrXR1daXkq0GDBpMnTz579mxaWpo8qHwj6hHX0NDQ VatW2ZSQjs3Kyckp/bWlbQ/3798vpqxSqaTFWWVKfECYRZ4FAAAAAAAAAAA+ Bx+wPktLS0ulUtWsWXP27NkJCQnSsVNSzCTuT5482bNnT11d3X79+h07dkyK dS5evOjp6WltbW1kZNS+ffvFixefO3fu5cuXWVlZ4nr27NnJkye7urqam5ur 1WpTU1MHB4cWLVoMHz58wYIFW7du/fHHHy9cuBAYGHj//v2IiIjIyMioEtJN eHh4cHDwlStXTpw4sWfPntWrV8+dO3fo0KEtW7a0t7c3MDAQ3VaoUKFRo0bi ofjVz89PvJKfny/FUuJaVFSUl5f36NGjw4cPjx49Woyup6cnKrGwsHB3d/f1 9b13755oWXrnQOlhdHT0+vXrGzZsqKOj07Zt23379pU+CEy6ilHu3LkzYMAA UdWHBVjkWQAAAAAAAAAA4HPzAXmWRK1W165dW7yemJgodSWfPHX48GFLS8uy Zct6eHiEhoYWFBTk5+cHBgZ6eXlZWFjo6uoaGxtXqVJl9+7dMTExr1+/zs3N TU1NPXbs2IgRI8zMzETP0pFVhiWMjIzMzc2trKyqVq3apEmT9u3b9+jRo3// /gMGDOjTp0+3bt3atGnj4uLi4OAgOjc1NTUxMRFvGRgYiEnp6emJ2fXu3dvX 1/fKlSvJycnZ2dliOHk/Q+laVFQUERExa9asatWqSS/+q2SPwSFDhly+fDkt Le1d+x+K56LyuXPn1q1bV8yrTJkyq1atioqKKlas4VISjZcsWVKrVi0xu399 6B6D5FkAAAAAAAAAAOCz8gF5lpzCWFpa9unT5/Lly8pTpfLy8mJiYtauXdu6 deu6desOHTp0x44dT548SU5ODgoK8vb2btCggampqa6ubrNmzaZOnbpv377A wMCkpKS4uDhxs3PnzpkzZw4bNqx9+/bVqlWzsLAwNDSUtubT0dExNjY2NzcX 45YvYWVlJRqULVtWtBEdGhkZiYfVq1dv0qTJF1988eWXXy5btuzgwYNXr159 9uxZenq6tD2gVKe4LygoCAsLEw1mzZrVq1cvZ2dnPT09fX39Ro0aeXp67t69 WxScmpoqv6I8A0sQMwoICJgwYYKYpphRlSpV5s6de/v2bfEFlF9YDs5SUlL8 /f3btWsnGv+rZJnbnwyzyLMAAAAAAAAAAMDn4M/kWWq12srKaubMmdeuXVPu xVdQUBAXF7dy5coWLVqULVu2bdu269ate/jwYV5e3r179zZt2jR8+PB69eqZ mJhUrlzZzc1t/PjxmzdvDgwMTEhISE1NvXv3rr+///79+1etWjVr1iwPD4+e PXuKZs2aNXNxcalZs6azs3PVqlVr1KhRt27dhg0btm7dunPnzgMHDpw0adK8 efPEuNu2bTt+/LioKjIyMiMjo/jXUEki7rOysiIiIg4fPjx79uyuXbva2dnp 6ekZGxvXrl171KhRokJRTEpKitxeGWlJ18zMzDNnzowdO9bS0lJbW7tKlSqe np4hISE5OTnKTyG/KOZ+4cKF0aNHi/Yqler9K7N+/7ot8iwAAAAAAAAAAPDJ +6N5lkbUIv4Ury9cuDApKUm5g5+4RkREzJ8/X1qL1KJFi02bNmVmZornRUVF 4eHha9asqV+/vrGxsZaWllqttre3nzx5sr+/f2JiYn5+vnIVVUJCQnBw8MmT J/fu3evr6/vNN994e3vPmjVr8eLFK1as2Lhx45EjR3755ZcnT568evVKI3gq LpVkFRQUpKWl3b17V3RVq1YtIyMjUZ5KpTI0NBT1iIJjYmIKCwvl5WZymCXf FJUQJXl6epqZmYkvoK2t3b9//4sXL0qVa+xMKBHdLlmyRLT8sNyKPAsAAAAA AAAAAHy2/mSeJejo6HTv3v3s2bP5+flSn3IAdPHixVGjRhmU6NGjx+nTp7Oz s4tLFivFx8fv2rWrdevWarVa9CmuJiYmrq6uPj4+wcHBopkySBLtxZPMzMyM jIy0tLSUlJTk5OTU1FRxn56enpWVlZubK2dJyhRJGS2J+8LCwidPnuzZs2f4 8OF2dnaici0tLTF3IyMjNze3HTt2REZGFhUVKYM5jX6k4mNjY8eMGWNjYyNe F8XXqlVr9erVohJle/mYLend8+fPDx48uEyJP5lhkWcBAAAAAAAAAIDPygfs NyiTohlxdXBwGDt2bFBQUGZmptStlOMkJSVdvnzZx8enefPm1apV69q165Yt W+7fv19YIjo6Woy+cuXKcePGtW3bVnRSrly5GjVq9OnTZ/LkyUuXLt21a9fp 06dDQkKeP3+ekZGRl5cnh02/hxSEvXz58u7du2fPnt2zZ8/ChQsHDhzYtGlT Ozs7Q0NDR0dHNzc3MZaoSjSIioqSIjlllCZ3VVwSiokZXbp0afr06VWrVjUx MalUqdLw4cO3b98eGhoqZqRRm/RnQUGB6HnevHnVq1f/C5MsCXkWAAAAAAAA AAD45P2ZPEumq6vr6Og4efJkPz+/58+fK/vPz89/9uzZmjVr3N3dTUxMWrVq tXDhwlOnTkVHRxcWFmZlZcXGxt64cWPv3r1z587t06dPnTp1RD3W1tZOTk5u bm5DhgyZPn368uXLt23bdvjw4TNnzly7du3Bgweiz7i4uKSkpJSUlNTU1PT0 dHFNTExMSEgQBYSHh9++ffvcuXOHDh1avXr1lClThg0b1q5du0qVKolSzczM qlWr1qtXrzlz5nz33Xe3bt1KS0uTD70qvbxLjqWCg4NFGWPGjLG1tRX9VKlS Zfz48RcvXhSDil/lV5QpmJhgWFiYr69vo0aNDAwMSu/W+OfiLPIsAAAAAAAA AADw6fuwPKv0vnnSn507dz58+HBeXl5xyTlT0hDSGVibNm2qXLmynp6eiYlJ mzZttm/fHhsbK7cpLtnHLyIiYv369R07dnRwcDA1NZX2AxQ9q1Qq8aKNjU2T Jk0GDBgwe/Zs0ey7777z8/M7ffr02bNnL1y4cP78+Z9++uno0aM7d+5cuXLl hAkTxCh2dna6urpSeaIrIyMja2vrli1bLliw4Pbt21Kdb13w9bqE/Gtubq6o dtq0aVWrVpXma2BgMHr06Dt37ijTK+Ur0k14eLivr6+lpaV0cpa8ou1Pxlgy 8iwAAAAAAAAAAPDJ++D1WXIoo6WlJT80NjYePnz43bt3CwsLixWLlV6/fh0Z Gblv376aNWsaGhoaGBhUq1Ztw4YN4qFyPVRubm5iYuK9e/f8/PwWLlzYtm1b KysrlUolehZXbW1tPT09MYSFhYWo2cbGxt7e3tHRsXLlys7OzlWqVKlUqZKd nV3FihWtra3NzMzEKLq6unJ55cuX79279549e+7cuSNGyc/Pl9M0uUiNbQbl Bs+ePVu8eLEYRdQgOhTX0aNHnzlzRjoOTJnKKfvMzMzcvXt3jRo1dHR0Sn9A 5Xf7YORZAAAAAAAAAADgk/dH86z3rC2SAprq1at7eXldv349NTVVDoYKCwvz 8/OjoqLWrl3bq1cvBwcHExOTJk2aTJ8+fe/evVeuXImPj5e37MvLy0tMTAwL Czt58uTGjRsXLlw4adKkoUOH9u7d293dvXHjxjVr1hQ9VKhQwcLCwszMrGzZ subm5uJG9GlsbCzura2tRYNatWo1a9ase/fu48aNmz179oYNG86fPx8XFyed w1X839sJSjQOzBLXyMjI48ePT5s2TfSmr69vamrq4uIyfvz406dPJyQkyI3l REx6IvrPzc0V37Zfv37irbemV+RZAAAAAAAAAAAAv8cH7zf4L8Wug8rtB3V0 dBwcHKZMmXLu3LnU1NRiRUhUWFgYHx9/9OjRGTNmdO3a1dbW1snJqVWrVuPG jdu8eXNgYGBSUpJykZS4z8rKEq/cu3fv4sWLfn5+e/fuXbdunY+Pz9y5c6dP nz5+/PixY8eOGTNm+PDhw4YNGzVqlIeHh3g4ceJEb2/vZcuWbd269fjx46Ln Z8+eZWZmFhUVvXV3QYnyp/z8/JcvX4oXV61a1bNnTzs7O/GJqlWr1rt376VL lwYHB7969UqjK/nPgoKC5ORkf3//ESNGiBdLf7e/EHkWAAAAAAAAAAD45H3w foNvpQy2vvzyy4sXL2oshpLk5OSEhYX17dvX1tZWT09PR0fH0tJy9OjRJ0+e TEpKEr/m5uZKOxZKNJZQaQRJr1+/LigoyM/Pl18p3UbDW3+VHop+srOzo6Ki fvjhhwEDBojC1Gq1vr5++fLlFy9efO/evbf2oMzsUlJSTp8+3aZNG1NT0788 wNJAngUAAAAAAAAAAD55f22epVSxYsWBAwceOnQoKioqJydHHlGKfrKzs+/e vbt9+/Zx48Y1b97c2tq6fPnyLi4ugwcPXrZs2ZEjR27fvp2ampqXlye9pTxm S9mPfC8v7PqdSmdeBQUFcXFxly5dWrFiRe/evZ2dnU1NTXV1dStVqiQmcuDA gYiICFGPxp6EGkJDQ1euXNmoUSMTExO1Wv2Xf1UN5FkAAAAAAAAAAOCT9xfm WRprkdRqtZ2dXZs2bTw9PS9cuJCeni6N+OZXBQUFz549u3r16pEjRxYsWNCu XTvRvly5cvXr13d3dx80aNDMmTP37t1769atxMRE5bvF712BpcybfjPhEg1y c3Pj4uLEKDt37pw+fXqPHj3q1q1rbm6upaUlPouoasmSJQEBAS9evMjPz1ce uSVHbNKfop9Hjx6JiTRu3FhXV/cvOR7rN5FnAQAAAAAAAACAT97fsd+gFGxJ gY64NzAwmDhx4s2bN+UkSxpazoPETXR09L59+4YOHVq9enVjY2O1Wi1KEjct W7acPn36gQMHxOuPHj2KjY3NysoqPYs/tMGguC8sLMzOzk5KShJ9BgQEbN++ XYzSrFkzc3NzaSLSFogNGjTYvHlzZGSkRnRVXGq9WG5ubkRExOrVq11cXORl WXLA9/ftOkieBQAAAAAAAAAAPnl/VZ6lEdloaWkpnzg7Oy9ZsiQrK0s+S6v0 KqrXr18/fvzY19e3SZMm5ubm2traarVa9KOrq1uxYsUOHTpMnTr1wIEDoaGh r169ysvLKygoKCwslI7NEu8WKYgnynvpT7mxuGZkZERERIi5e3t7N2vWTAyh UqmkUsWNGNfKyqp79+5Hjx5NTk7W+GLKgqWr6Dw2NnbXrl1ly5aVUjytEtJn kXv+O5BnAQAAAAAAAACAT97fsT7rX6XiLQMDA2dn5zFjxojhYmNj5dGV2/cJ +fn58fHx165d8/PzW7t27bRp03r37t2oUSNHR0dra2sbG5sqVao0adKke/fu Q4cOnTRp0uzZs318fFavXr1ly5Y9e/YcPHjw6NGjx0scO3ZM3IsnO3fu3Lhx 47fffrto0aJZs2aNGzduyJAhoocWLVrUrFmzYsWKxsbGUuSkVqstLS0bNmzo 4eGxb9++4ODg5ORkKSwrVqRXcuXSlonp6emXLl2SthnU1taWk6z/DfIsAAAA AAAAAADwyfur8ixliCOvUZL+lLItaZlVp06d1q5de+vWrby8PGmtlkz8+fr1 a2kzwPT09Ojo6Bs3bpw6dWrv3r2+vr5ff/31uHHjunfv7urqWqVKFTs7O0dH x+rVq9euXbtBgwbNmzdv27Zt+/btRf9dSoibDh06uLm5tW7dulmzZg0bNqxV q5azs7N40cLCwtDQUNoVUFyNjY3t7e2bNGkybNiwxYsX79mzJzAwMDY2tqCg 4K1fTE7fMjIygoOD582bN3DgQNG56LP0N3lruvcXIs8CAAAAAAAAAACfvL92 fZa8yZ70pzLHke9dXFxmzpx59uzZx48fp6enS6mW8jgqjbOuikvWbb18+fLB gwei2mXLlnl4eHTt2rVp06aiq9q1a1erVq1SpUoVKlSwtLQsW7asqampiYmJ sbGxUQlxI/40MzMrV66clZWVaGZjY+Pk5CRlYdJqrylTpqxfv/7q1atJSUnF pQ7J0lhEJh7m5eXFxsZeuHBhzpw55ubmarVaeWSYcr5/X5IlIc8CAAAAAAAA AACfvL82zyqtdKCjUql0dXUdHBxmzJhx/fr17OxsqRJleKSkjLqKfz2vKiUl JTw8PDAw8OTJkzt37vzmm2/Gjx/fv3//Tp06tWzZsmnTpg0aNHBxcalXr56r q2uLFi3c3d179uw5ZMiQr776ytvbe9WqVfv27Tt79uyDBw9EV9K6MOWpXhLl IV/yw9zc3Ojo6HXr1nXp0sXc3Px/k1u9C3kWAAAAAAAAAAD45P3deVZpZcqU UalUenp6lSpVat269dSpU0UNycnJxe9epaUktSksLMzOzk5PT09JSYmPj3/6 9GlYWFhISMidO3eCgoJu3rx548aNwBLiXjy5detWcHDw/fv3RbOIiIioqKgX L16kpqaKTgoKCsRYGkOXXpMl/SnGOnr0aI8ePerUqVO2bFnp4C3l1P5nn1FC ngUAAAAAAAAAAD55//s8S0lHR8fBwaFv37579+599uxZcUlypIyWNBZnyZQr p96VfP15ylEyMjIuXry4ZMmSL774wtjYWP5i8k6D/8gSLfIsAAAAAAAAAADw yfun8ixpZZN04JS2tnaHDh22bt0aEhISFxeXlZUlrZmSi1SulnrrxoAaPizt UjaWx8rOzk5ISAgNDT1z5szo0aOdnJz+9Y4Dwv4R5FkAAAAAAAAAAOCT98+u z5JIOxBaW1u7ubktXrw4ICAgISFBjrT+2uVXb0243nVy1uvXrx88eLB9+/bh w4c7ODjo6elJAVzp+qWbt/76tyLPAgAAAAAAAAAAn7x/Ns9Srm9Sq9Xm5uZV qlRp3rx5v379Fi9eHBgYmJWVVVySLv3m0VpvFETj37OGS6ONHJ/l5eWFhYUd OHBg5syZnTt3rlWrlrW1tb6+vkqlUq7M+scXZ/2LPAsAAAAAAAAAAHwG/jd5 Vuno561hkBwS6evr161b18vL69ChQ0FBQdHR0a9evSosLPzgFVtydPWed7Oy suLj40NCQk6dOrV48eJu3bo5ODhoa2trRFfvirT+kYSLPAsAAAAAAAAAAHzy Pob9BiUaYZCenp6tra2bm9uUKVO2bNly/vz54ODgqKiotLS0vLy89yy/ev9P Gk8KCgpEh/Hx8U+fPr169er+/fvnz5/fr1+/qlWrGhkZvavIMr/6H32adyPP AgAAAAAAAAAAn7yPJ8/SIB2qpaOjY2hoaGpqamlpWa9evS+//PLAgQMRERFZ WVnK3OpdOxC+9Ua+CjExMeILeHt7d+3atUaNGubm5kZGRnp6emq1+mOIq34T eRYAAAAAAAAAAPjkfZx51luzJAMDAzs7O1dX1169es2YMWPHjh3+/v7Soq3M zMzCwkKNqWmEXKJBVlZWbGxsaGjo5cuX9+zZs2jRosGDB7ds2bJy5cqWlpa6 urr/+5n+SeRZAAAAAAAAAADgk/dx5llvJYVcWlpaurq69vb2rVq1GjBggKen 54IFC9atW7dr167vvvvuyJEjP/7447Fjx/7/EsePHxd//vDDD4cOHdqzZ8/G jRsXL17s5eU1YsSIdu3aValSxdjY+F1LsVifBQAAAAAAAAAA8DH42PKs0inS W0+qUj4RxRsaGlpYWNjY2FSuXLl69eq1a9d2cXGpW7euuKlWrZqjo6Otra2V lZWJiYm2tvZHcvTVX4I8CwAAAAAAAAAAfPI+tjzrXaQESplDyU8kKpVKzEKt Vourrq6utra2jo6OuBFXdQnRQEtLS/nuv0pWe5UeSzwsPdzHiTwLAAAAAAAA AAB88j7mPKuMgvzkX++OmX4zfpK70mj5rrDs40eeBQAAAAAAAAAAPnkfW571 riCp9EKq0mnXH6XRp0ZX/yciLfIsAAAAAAAAAADwyfvY8qzf9OdjprcuzvoL +/9fIs8CAAAAAAAAAACfvOPHj1esWPH/UJ4FZeLm4OCwYcMG8iwAAAAAAAAA APAJO3HihJWVlVqtViYmpTf3w8dG+jeyt7dft25dTEzMP/3/CAAAAAAAAAAA 4O/i5+dXoUIF5fqs/1sb7n1utLS0lP9Ajo6OGzZsiIqK+qf/HwEAAAAAAAAA APxdjh07Zm1tra2tLa/JIs/6P4TzswAAAAAAAAAAwCfvxIkTFSpUkPcblFIt Iq2PlvRPI/8DOTk5rV27lv0GAQAAAAAAAADAJywiImLDhg3ffvvtyhLLli1b vnz5CnyslpeQ7sU/1pYtW+7cuZOZmflP/z8CAAAAAAAAAAD427158+afLgF/ jPxP9vr163+2EgAAAAAAAAAAgL+VnIZI+QjhyEdOjrGkG4JIAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAPgc/D/jd9XL "], {{0, 157.2}, {1372.8, 0}}, {0, 255}, ColorFunction->RGBColor, ImageResolution->{120., 120.}, SmoothingQuality->"High"], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSize->{564.827999999999, Automatic}, ImageSizeRaw->{1372.8, 157.2}, PlotRange->{{0, 1372.8}, {0, 157.2}}]], "Input", TextAlignment-> Center,ExpressionUUID->"200b3f5f-9d2c-4fe9-a2ae-72ef043ce7cb"]], "Text", CellChangeTimes->{3.8567883069589844`*^9}, TextAlignment->Center,ExpressionUUID->"cedd8c58-a069-4888-8c97-885cce7c46fb"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"Style", "[", RowBox[{ RowBox[{"DateString", "[", "]"}], ",", "15", ",", RowBox[{"ColorData", "[", RowBox[{"59", ",", "1"}], "]"}], ",", "Bold", ",", RowBox[{"FontFamily", "\[Rule]", "\"\\""}]}], "]"}]], "Input", CellChangeTimes->{{3.624363777037792*^9, 3.624363829937866*^9}, { 3.624363909067977*^9, 3.6243639142379837`*^9}, {3.6243639753680696`*^9, 3.6243639868780856`*^9}, 3.6276667393883543`*^9, 3.6283947451893377`*^9, 3.63283258409818*^9, 3.632832972322857*^9, 3.6333551239066377`*^9, 3.6348164204455366`*^9, {3.6591282803184257`*^9, 3.6591282819185143`*^9}, 3.661808523288576*^9}, CellLabel->"In[1]:=",ExpressionUUID->"c80193b7-45ab-4793-ae3f-a206acdcab7a"], Cell[BoxData[ StyleBox["\<\"Sun 13 Mar 2022 20:24:17\"\>", StripOnInput->False, LineColor->RGBColor[0.151827, 0.281636, 0.714931], FrontFaceColor->RGBColor[0.151827, 0.281636, 0.714931], BackFaceColor->RGBColor[0.151827, 0.281636, 0.714931], GraphicsColor->RGBColor[0.151827, 0.281636, 0.714931], FontFamily->"Helvetica", FontSize->15, FontWeight->Bold, FontColor->RGBColor[0.151827, 0.281636, 0.714931]]], "Output", CellChangeTimes->{ 3.624690246606372*^9, 3.6247232142268844`*^9, 3.6247287088459845`*^9, 3.624733047823346*^9, 3.624773259428534*^9, 3.624782591907127*^9, 3.624790136996372*^9, 3.6247940690459127`*^9, 3.6248079094853525`*^9, 3.6248492781496477`*^9, 3.624855047814946*^9, 3.625222650557405*^9, 3.625231674853086*^9, 3.6263313489646573`*^9, {3.6263629675626497`*^9, 3.626362988491663*^9}, 3.6263712452448177`*^9, 3.626371520369991*^9, 3.626373050462963*^9, 3.626373448622181*^9, 3.626408587927809*^9, 3.626411661074218*^9, 3.62641315038021*^9, 3.626419994705942*^9, 3.6264247807612467`*^9, 3.6264253034660106`*^9, 3.626425607818455*^9, 3.626433177280367*^9, 3.6264420208375893`*^9, 3.6264467465459876`*^9, 3.626456219269533*^9, 3.626518620296234*^9, 3.62652142844014*^9, 3.6265340315709095`*^9, 3.6265444196144085`*^9, 3.6265897076976676`*^9, 3.626593134810811*^9, 3.6267942747048287`*^9, 3.6268420196118155`*^9, 3.6268745943050585`*^9, 3.6268876562781577`*^9, 3.6269281944543295`*^9, 3.626928815591268*^9, 3.626929617188311*^9, 3.6269300342955685`*^9, 3.6270290651040273`*^9, 3.6271229552802134`*^9, 3.6271408644818726`*^9, 3.6271827766636887`*^9, 3.627193996038499*^9, 3.627201945916085*^9, 3.627213294162159*^9, 3.627225768701268*^9, 3.6272318033264503`*^9, 3.6272774076909733`*^9, 3.627282061466107*^9, 3.627315819932926*^9, 3.627331710252996*^9, 3.6273729553309526`*^9, 3.627377139803295*^9, 3.6274040777874565`*^9, 3.627416874398264*^9, 3.627417275906307*^9, 3.6274416645206003`*^9, 3.627463060746663*^9, 3.627463316056035*^9, 3.6274639284388943`*^9, 3.6274754482262583`*^9, 3.62747907549642*^9, 3.6274879017781315`*^9, 3.6274916785505085`*^9, 3.6274939725287075`*^9, 3.627499029077035*^9, 3.6275488243213673`*^9, 3.627565453032778*^9, 3.6275665172712774`*^9, 3.6275693408833227`*^9, 3.627569635019826*^9, 3.6275699243352413`*^9, 3.627570017545372*^9, 3.627570415013996*^9, 3.6275717845908823`*^9, 3.6275759257740245`*^9, 3.6275798926854844`*^9, 3.6275801182048526`*^9, 3.6275813204387856`*^9, 3.627624245411259*^9, 3.627661325492343*^9, {3.6276667393883543`*^9, 3.627666747499366*^9}, 3.6276678870624804`*^9, 3.62773698386275*^9, 3.627746982254857*^9, 3.62774738682349*^9, 3.6277509003194957`*^9, 3.627751290191368*^9, 3.6277544499298525`*^9, 3.6277547506054325`*^9, 3.62779510772033*^9, 3.627809514287154*^9, 3.6278140962737083`*^9, 3.627841286840105*^9, 3.6278792795491486`*^9, 3.6278981095516043`*^9, 3.6279262603374944`*^9, 3.627926452296832*^9, 3.627926541614189*^9, 3.62792799996815*^9, 3.6279826975592613`*^9, 3.627991742246234*^9, 3.627992143551547*^9, 3.627993680207081*^9, 3.628017028937646*^9, 3.6280546872422266`*^9, 3.6280603353572483`*^9, 3.628072572737534*^9, 3.628074018331874*^9, 3.6280742085742073`*^9, 3.6280806118301945`*^9, 3.6280906720630116`*^9, 3.6280926649508348`*^9, 3.628100460170232*^9, 3.628102833157997*^9, 3.6281029454322248`*^9, 3.6281035881971416`*^9, 3.6281375734231677`*^9, 3.628181626647048*^9, 3.6281880253998976`*^9, 3.628226503626051*^9, 3.6282586565767508`*^9, 3.6282648634549885`*^9, 3.6283150645249715`*^9, 3.628329200550356*^9, 3.628334215208299*^9, 3.6283514608004537`*^9, 3.6283622040115848`*^9, 3.6283947451893377`*^9, 3.6283955127104125`*^9, 3.6284045551224594`*^9, 3.6293880860610523`*^9, 3.6327475922689033`*^9, 3.63275811227643*^9, 3.6327596450336637`*^9, 3.6328096625802097`*^9, 3.6328243568826213`*^9, 3.6328248799063573`*^9, 3.632830704530515*^9, 3.632832144698387*^9, {3.63283258409818*^9, 3.632832586395003*^9}, 3.632832755584462*^9, 3.632832939852034*^9, 3.632832972322857*^9, 3.632833007166625*^9, 3.6328332610762587`*^9, 3.6328333638701973`*^9, 3.632833711379533*^9, 3.6328339162952023`*^9, 3.6328346723106337`*^9, 3.6329014485915203`*^9, 3.6329939536336937`*^9, 3.6329955764861927`*^9, 3.63301335349563*^9, 3.633015934900961*^9, 3.6330163439942884`*^9, 3.633066532821421*^9, 3.6330724752293386`*^9, 3.633072932379979*^9, 3.6330731606043267`*^9, 3.6330786070231333`*^9, 3.633080383781625*^9, 3.633081943392832*^9, 3.633085598164918*^9, 3.633091185731144*^9, 3.6330933553053737`*^9, 3.6330936906589355`*^9, 3.6330977774221926`*^9, 3.6330981309972534`*^9, 3.6330992270808334`*^9, 3.633100853416046*^9, 3.633103641774427*^9, 3.633103862070203*^9, 3.6331083690792933`*^9, 3.633110349025402*^9, 3.633157082289732*^9, 3.6331608116366*^9, 3.633160950316794*^9, 3.633168254210478*^9, 3.633169118823697*^9, 3.6331694739761953`*^9, 3.633170206781225*^9, 3.63317419313419*^9, 3.6331798795779676`*^9, 3.6331900064620643`*^9, 3.633190249870801*^9, 3.633237705148573*^9, 3.6332548777948685`*^9, 3.633266271152281*^9, 3.6332805022159653`*^9, 3.63335101772985*^9, 3.6333526016555786`*^9, 3.6333548756536427`*^9, 3.6333551239066377`*^9, 3.6333555846924667`*^9, 3.633357840967106*^9, 3.633359446094453*^9, 3.6333660949492435`*^9, 3.6333695354931917`*^9, 3.633417649325833*^9, 3.6334181834905834`*^9, 3.6334456457223415`*^9, 3.633451266743796*^9, 3.633490849942999*^9, 3.633493051256154*^9, 3.633606661426292*^9, 3.633670534913086*^9, 3.6337568863295608`*^9, 3.6337570208438005`*^9, 3.633845739015579*^9, 3.633845983703948*^9, 3.633857105466902*^9, 3.6338583095217695`*^9, 3.633858487783966*^9, 3.6338593717075233`*^9, 3.6338597258577795`*^9, 3.63385989160026*^9, 3.633863502781498*^9, 3.6338635679991393`*^9, 3.633927911313442*^9, 3.633944462696988*^9, 3.6339465668934216`*^9, 3.633948327880703*^9, 3.6339547548712645`*^9, 3.6339550835996113`*^9, 3.6339568842921095`*^9, 3.634296543703839*^9, 3.6342982929752927`*^9, 3.6343107510683417`*^9, 3.6343152773914175`*^9, 3.6343617544482474`*^9, 3.634389144975466*^9, 3.634401172718356*^9, 3.634429709788642*^9, 3.6344454730479145`*^9, 3.63445216627279*^9, 3.6344540801191063`*^9, 3.6344542537853546`*^9, 3.6344568045809355`*^9, 3.6344594138186035`*^9, 3.634460633733334*^9, 3.6344831368804145`*^9, 3.6344878462619777`*^9, 3.634488759269484*^9, 3.634488981864042*^9, 3.634489389058013*^9, 3.634489652364867*^9, 3.63449070304314*^9, 3.634537057569696*^9, 3.634544704347421*^9, 3.6345556617715683`*^9, 3.6345648206349754`*^9, 3.6345649540205007`*^9, 3.634606404958544*^9, 3.6346245610786567`*^9, 3.634651596531582*^9, 3.634694759595237*^9, 3.6347042957928977`*^9, 3.634735330543215*^9, 3.6347484531718683`*^9, 3.6347868719508963`*^9, 3.634804520446327*^9, {3.6348164204455366`*^9, 3.6348164281579647`*^9}, 3.6348168319793797`*^9, 3.6348349727579055`*^9, 3.6348352567096677`*^9, 3.6348779575015316`*^9, 3.6348984633228335`*^9, 3.634920662801611*^9, 3.6349856899099426`*^9, 3.635006653512184*^9, 3.635088237854039*^9, 3.635143506900036*^9, 3.6351513494288635`*^9, 3.6351602614012876`*^9, 3.635171768060941*^9, 3.6352263517127733`*^9, 3.6352418074559627`*^9, 3.635273733925703*^9, 3.6353108551943026`*^9, 3.6353382277239056`*^9, 3.6353402486643543`*^9, 3.6354338918005123`*^9, 3.6356129380971355`*^9, 3.635652414686679*^9, 3.635655357453823*^9, 3.635665846710067*^9, 3.6367054798278847`*^9, 3.636705943318534*^9, 3.636706182472876*^9, 3.6367063225010777`*^9, 3.636798740200516*^9, 3.6368097615008607`*^9, 3.636809918596149*^9, 3.6368234427204275`*^9, 3.6368610270577693`*^9, 3.6368713063670263`*^9, 3.6368833404590163`*^9, 3.636891712602128*^9, 3.63690722253533*^9, 3.636907610126249*^9, 3.6369089916239357`*^9, 3.6369092377204866`*^9, 3.6369795894238524`*^9, 3.6369800987449684`*^9, 3.6369872353960953`*^9, 3.6370300540181546`*^9, 3.637043043372732*^9, 3.637067764286369*^9, 3.637072265883547*^9, 3.637075978459358*^9, 3.6371310478956485`*^9, 3.6371311791598606`*^9, 3.637134220013728*^9, 3.637141685969936*^9, 3.637154624734623*^9, 3.637243945500225*^9, 3.6372903627126837`*^9, 3.6372906719975805`*^9, 3.6372912657118645`*^9, 3.6373268777244277`*^9, 3.6373371220850573`*^9, 3.6373427018903704`*^9, 3.6374233344333067`*^9, 3.637470421056179*^9, 3.637481806164936*^9, 3.6375021895628476`*^9, 3.6375605803726816`*^9, 3.637570629034377*^9, 3.6375714738665695`*^9, 3.63758942785167*^9, 3.6375901126821623`*^9, 3.6375917531932473`*^9, 3.637639881527644*^9, 3.6376480645419664`*^9, 3.6376514062217054`*^9, 3.6376515407620735`*^9, 3.6376721966369877`*^9, 3.637675866906809*^9, 3.6377626400154552`*^9, 3.6378173699107523`*^9, 3.6378365319849124`*^9, 3.6378380727910223`*^9, 3.6378527980835953`*^9, 3.637935639341964*^9, 3.6385871910570107`*^9, 3.6385872235098715`*^9, 3.638681130155402*^9, 3.6390490437544813`*^9, 3.639320520138667*^9, 3.640168352762763*^9, 3.640169562582554*^9, 3.640170044651168*^9, 3.6401766151307316`*^9, 3.6401775371966352`*^9, 3.6401853657526464`*^9, 3.6401859838890133`*^9, 3.640186080842367*^9, 3.6401866961033335`*^9, 3.6407656315931005`*^9, 3.6427856171347494`*^9, 3.64321394940527*^9, 3.643523315105192*^9, 3.6435239498300843`*^9, 3.6435247015581455`*^9, 3.6437223786040387`*^9, 3.643724357478019*^9, 3.6441609662612543`*^9, 3.6441648904553704`*^9, 3.6441690807014713`*^9, 3.6471034002201505`*^9, 3.648202345635458*^9, 3.6482027840521154`*^9, 3.648203904949717*^9, 3.648635996187812*^9, 3.648636385928562*^9, 3.6514857578192463`*^9, 3.65151477115307*^9, {3.6515151253420525`*^9, 3.6515151380507708`*^9}, 3.6515155740523663`*^9, 3.6515167231481895`*^9, 3.651571645050321*^9, 3.6516677247677035`*^9, 3.6516762690975766`*^9, 3.6516903688888397`*^9, 3.6517488270467196`*^9, 3.651782031436618*^9, 3.6518312099364543`*^9, 3.652010536835266*^9, 3.652097821366135*^9, 3.653142169688303*^9, 3.6532176349646053`*^9, 3.6532186653994923`*^9, 3.653242783965487*^9, 3.654285620274969*^9, 3.6542862500829325`*^9, 3.654287922898259*^9, 3.654368914190504*^9, 3.654377011669202*^9, 3.6543791182243137`*^9, 3.654445058424417*^9, 3.6544614752581215`*^9, 3.6545252679291267`*^9, 3.654537189028146*^9, 3.6546854147241*^9, 3.6548592996602716`*^9, 3.6548810585381203`*^9, 3.6549872653117623`*^9, 3.6550473021018906`*^9, 3.655067016566227*^9, 3.6581518913675776`*^9, 3.658152332270917*^9, 3.6581534830084486`*^9, 3.6586033265321026`*^9, 3.658661426451205*^9, 3.6590025118992825`*^9, {3.6590029932675977`*^9, 3.659003005417797*^9}, 3.6590354623481016`*^9, 3.659090941332369*^9, 3.659128282498549*^9, 3.659129040215064*^9, 3.659439238155571*^9, 3.6615195350949593`*^9, 3.661519592014273*^9, 3.6615264368464994`*^9, 3.6615273707158732`*^9, 3.6615277022001734`*^9, 3.661528987234847*^9, 3.661533302405304*^9, 3.661542204544524*^9, 3.661542621802521*^9, 3.6615437537632513`*^9, 3.6615440073357067`*^9, 3.6615444469467177`*^9, 3.661544708035095*^9, 3.661545008218809*^9, 3.6615458059223633`*^9, 3.6615476513386016`*^9, 3.66154898778817*^9, 3.6615536340654664`*^9, 3.6615974342143345`*^9, 3.661622058772508*^9, 3.661631289839349*^9, 3.6616314242303643`*^9, 3.661682958427944*^9, 3.6616936283562093`*^9, 3.6617162711942883`*^9, 3.6617777309771767`*^9, 3.661802551763891*^9, 3.6618085727404556`*^9, 3.661948749157985*^9, 3.661958030011758*^9, 3.66197085526651*^9, 3.661977563052079*^9, 3.661977958812107*^9, 3.6619793603597116`*^9, 3.662034614475441*^9, 3.662148721104062*^9, 3.662154102749016*^9, 3.6621560812690964`*^9, 3.6622080036606493`*^9, 3.6622221624729633`*^9, 3.662311565259903*^9, 3.6623125145070477`*^9, 3.6624112117470922`*^9, 3.6624119048181577`*^9, 3.662420585555168*^9, 3.6626651167273273`*^9, 3.662673488649074*^9, 3.662673987085095*^9, 3.662674492384516*^9, 3.6627471558717127`*^9, 3.662753370553383*^9, 3.746303854228786*^9, 3.7463618873910503`*^9, 3.7464485814958763`*^9, 3.7468723173664923`*^9, 3.746892153376624*^9, 3.746960485584292*^9, 3.7469814750505857`*^9, 3.7469820075626264`*^9, 3.747048733055873*^9, 3.74707349178995*^9, 3.7475792228126445`*^9, 3.7478608332814274`*^9, 3.747936126599375*^9, 3.748193714473692*^9, 3.7606960962655067`*^9, 3.766142462824398*^9, 3.7661491218868303`*^9, 3.7689364207061925`*^9, 3.769120150266882*^9, 3.769193190760233*^9, 3.769202245548669*^9, 3.769254453403333*^9, 3.7692753289463577`*^9, 3.782765540457901*^9, 3.7851043743040805`*^9, 3.7854503420680637`*^9, 3.785503947282214*^9, 3.7940737073355923`*^9, 3.7946705931968975`*^9, 3.7946820665322304`*^9, 3.7948379359181895`*^9, 3.7948527270080895`*^9, 3.7949163805519714`*^9, 3.7949408208839674`*^9, 3.7949436378268337`*^9, 3.7949436843971663`*^9, 3.7950037647127676`*^9, 3.7950898362582765`*^9, 3.795187575687792*^9, 3.795281261182495*^9, { 3.7952823675983515`*^9, 3.7952823895283556`*^9}, 3.795282848048464*^9, 3.7952846972007427`*^9, 3.7952857582714133`*^9, 3.79529449257148*^9, 3.7952948559516335`*^9, 3.795295285866824*^9, 3.7953911750835533`*^9, 3.79545450611907*^9, 3.7954633358191843`*^9, 3.79546713017157*^9, 3.795474812093571*^9, 3.795479254585565*^9, 3.796502708528337*^9, 3.7966744669426546`*^9, 3.7966764058009377`*^9, 3.796679248750098*^9, 3.796683929096917*^9, 3.7966839843463755`*^9, 3.7966840321777935`*^9, 3.7967468702027903`*^9, 3.796754408716819*^9, 3.7967717643901243`*^9, 3.797265345174561*^9, 3.797266061703961*^9, 3.7972729377215567`*^9, 3.797277178634888*^9, 3.79727731937062*^9, 3.7972785695252657`*^9, 3.7972806637110777`*^9, 3.845328360345699*^9, 3.8453861062954645`*^9, 3.8454067074773874`*^9, 3.845411500500553*^9, 3.845499902735919*^9, 3.845500294948691*^9, 3.845500334137039*^9, 3.8455018803590713`*^9, 3.854651726939639*^9, 3.8562128465767136`*^9, 3.8562134590503035`*^9}, CellLabel->"Out[1]=",ExpressionUUID->"f134eb7f-05a0-4ead-b912-09a18a098987"] }, Open ]], Cell[BoxData[ RowBox[{"Import", "[", "\"\\"", "]"}]], "Input", CellChangeTimes->{{3.8567882526268663`*^9, 3.856788261240925*^9}}, CellLabel->"In[61]:=",ExpressionUUID->"36d6f4e7-1c87-4b6e-8e3f-9302f85c6674"], Cell[TextData[{ StyleBox["\n", FontWeight->"Bold"], StyleBox["Mathematica", FontSize->24, FontWeight->"Bold", FontSlant->"Italic", FontColor->RGBColor[0.5019607843137255, 0., 0.]], StyleBox[" notebook", FontSize->24, FontWeight->"Bold", FontColor->RGBColor[0.5019607843137255, 0., 0.]] }], "Text", CellChangeTimes->{{3.5504332603062*^9, 3.5504332633792*^9}, { 3.5504333382841997`*^9, 3.5504333866102*^9}, {3.5504335609122*^9, 3.5504335779372*^9}, {3.5504340356392*^9, 3.5504340366462*^9}, { 3.5504344248332*^9, 3.5504344730081997`*^9}, 3.5504352845532*^9, 3.5511887173428*^9, {3.5572466927492*^9, 3.5572467342402*^9}, { 3.5572506522362003`*^9, 3.5572506579862003`*^9}, {3.5723570003498*^9, 3.5723570146538*^9}, {3.5762657320113115`*^9, 3.5762658232479362`*^9}, 3.57632069334723*^9, {3.5785171414270935`*^9, 3.578517174413866*^9}, { 3.578518165486311*^9, 3.578518165822355*^9}, {3.619930609987628*^9, 3.619930640707671*^9}, {3.6242846691753197`*^9, 3.624284686862855*^9}, { 3.6244646351460104`*^9, 3.6244646401147213`*^9}, 3.6276667393883543`*^9, 3.6283947451893377`*^9, 3.63283258409818*^9, 3.632832972322857*^9, 3.6333551239066377`*^9, 3.6348164204455366`*^9, {3.6353103984629855`*^9, 3.6353104030392485`*^9}, {3.637134806970595*^9, 3.6371348105906*^9}, 3.63713484627065*^9, {3.637836243658865*^9, 3.637836248634158*^9}, { 3.6401700506905007`*^9, 3.6401700707866106`*^9}, {3.64018598798024*^9, 3.6401859880852456`*^9}, {3.647102207891888*^9, 3.6471022596518073`*^9}, { 3.6471031975487185`*^9, 3.6471032213310595`*^9}, {3.6471046078591795`*^9, 3.647104609299262*^9}, {3.647115793997024*^9, 3.6471158764446754`*^9}, { 3.6471160289242773`*^9, 3.6471160543797154`*^9}, {3.6514849485245914`*^9, 3.651484980828414*^9}, {3.661977617100213*^9, 3.6619776627968783`*^9}, { 3.662410978965165*^9, 3.6624109963245907`*^9}, {3.7463037700163264`*^9, 3.746303772687351*^9}, {3.7478608028268003`*^9, 3.7478608029068003`*^9}, { 3.7666058892426424`*^9, 3.7666059126539044`*^9}, {3.766605999673442*^9, 3.7666060702433653`*^9}, {3.8453281779436235`*^9, 3.845328188347512*^9}, { 3.8453282558665*^9, 3.845328294036148*^9}, {3.8454069731376457`*^9, 3.8454069830880604`*^9}, {3.8454070289546504`*^9, 3.8454070489070435`*^9}, {3.8454116185571156`*^9, 3.845411625729253*^9}, { 3.8454996628666973`*^9, 3.845499669706202*^9}}, TextAlignment->Center, LineSpacing->{1.5, 3},ExpressionUUID->"84e856ff-9974-4529-8a72-069222b6cded"], Cell[TextData[{ StyleBox["\n", FontFamily->"Times New Roman"], StyleBox["Dr. Martin Ricker\n", FontFamily->"Times New Roman", FontSize->18, FontWeight->"Bold"], StyleBox["Instituto de Biolog\[IAcute]a, Universidad Nacional \ Aut\[OAcute]noma de M\[EAcute]xico (UNAM), Mexico City", FontFamily->"Times New Roman", FontSize->12], StyleBox["\n", FontFamily->"Times New Roman", FontSize->18], StyleBox["mricker@ib.unam.mx, martin_tuxtlas@yahoo.com.mx\n", FontFamily->"Times New Roman"] }], "Text", CellChangeTimes->{{3.5504332603062*^9, 3.5504332633792*^9}, { 3.5504333382841997`*^9, 3.5504333866102*^9}, {3.5504335609122*^9, 3.5504335779372*^9}, {3.5504336152172003`*^9, 3.5504336473292*^9}, { 3.5504337156012*^9, 3.5504337170112*^9}, 3.5504337833962*^9, { 3.5504338461602*^9, 3.5504338938742*^9}, {3.5504340065172*^9, 3.5504340099681997`*^9}, {3.5504346934402*^9, 3.5504346944002*^9}, { 3.5572501510372*^9, 3.5572502442392*^9}, 3.5572506628422003`*^9, { 3.5723568631088*^9, 3.5723568915327997`*^9}, {3.5734709481212683`*^9, 3.573470987886074*^9}, {3.576266023481459*^9, 3.5762660264808426`*^9}, { 3.5849083786578503`*^9, 3.5849083874419565`*^9}, 3.619930660787699*^9, { 3.624376043545732*^9, 3.6243760978739223`*^9}, {3.6247901309763637`*^9, 3.624790132216365*^9}, 3.6276667393883543`*^9, 3.6283947451893377`*^9, 3.63283258409818*^9, 3.632832972322857*^9, 3.6333551239066377`*^9, 3.6348164204455366`*^9, {3.7666060829232264`*^9, 3.7666060884633045`*^9}, { 3.7666061402734246`*^9, 3.7666061436131625`*^9}, 3.797265350751593*^9, 3.8454070638119397`*^9, {3.845407311004424*^9, 3.8454073402000437`*^9}}, TextAlignment->Center, TextJustification->1, FontSize->16,ExpressionUUID->"06446736-44e9-4cd5-beb4-de7c4ca26425"], Cell[TextData[StyleBox["The first function \ (\[OpenCurlyDoubleQuote]confidenceIntervalsForProportionsFunction\ \[CloseCurlyDoubleQuote]) calculates simultaneous 95% confidence intervals \ for several proportions. Each proportion is calculated for one cohort with an \ initial and a final number of individuals:", FontSize->18, FontColor->RGBColor[0.5019607843137255, 0.25098039215686274`, 0.]]], "Text", CellChangeTimes->{{3.5504332603062*^9, 3.5504332633792*^9}, { 3.5504333382841997`*^9, 3.5504333866102*^9}, {3.5504335609122*^9, 3.5504335779372*^9}, {3.5504340356392*^9, 3.5504340366462*^9}, { 3.5504344248332*^9, 3.5504344730081997`*^9}, 3.5504352845532*^9, 3.5511887173428*^9, {3.5572466927492*^9, 3.5572467342402*^9}, { 3.5572506522362003`*^9, 3.5572506579862003`*^9}, {3.5723570003498*^9, 3.5723570146538*^9}, {3.5762657320113115`*^9, 3.5762658232479362`*^9}, 3.57632069334723*^9, {3.5785171414270935`*^9, 3.578517174413866*^9}, { 3.578518165486311*^9, 3.578518165822355*^9}, {3.619930609987628*^9, 3.619930640707671*^9}, {3.6242846691753197`*^9, 3.624284686862855*^9}, { 3.6244646351460104`*^9, 3.6244646401147213`*^9}, 3.6276667393883543`*^9, 3.6283947451893377`*^9, 3.63283258409818*^9, 3.632832972322857*^9, 3.6333551239066377`*^9, 3.6348164204455366`*^9, {3.6353103984629855`*^9, 3.6353104030392485`*^9}, {3.637134806970595*^9, 3.6371348105906*^9}, 3.63713484627065*^9, {3.637836243658865*^9, 3.637836248634158*^9}, { 3.6401700506905007`*^9, 3.6401700707866106`*^9}, {3.64018598798024*^9, 3.6401859880852456`*^9}, {3.647102207891888*^9, 3.6471022596518073`*^9}, { 3.6471031975487185`*^9, 3.6471032213310595`*^9}, {3.6471046078591795`*^9, 3.647104609299262*^9}, {3.647115793997024*^9, 3.6471158764446754`*^9}, { 3.6471160289242773`*^9, 3.6471160543797154`*^9}, {3.6514849485245914`*^9, 3.651484980828414*^9}, {3.661977617100213*^9, 3.6619776627968783`*^9}, { 3.662410978965165*^9, 3.6624109963245907`*^9}, {3.7463037700163264`*^9, 3.746303772687351*^9}, {3.74765852582615*^9, 3.7476585333261337`*^9}, { 3.747658567529183*^9, 3.7476585681385574`*^9}, {3.74765862279745*^9, 3.747658669342142*^9}, {3.766606665412409*^9, 3.766606737762308*^9}, { 3.7666067994921026`*^9, 3.766606816892201*^9}, {3.766606886222011*^9, 3.766606900411637*^9}, {3.766607182781154*^9, 3.766607193601019*^9}, { 3.7972654280324497`*^9, 3.7972654386704035`*^9}, {3.7972655308934584`*^9, 3.797265534127198*^9}, {3.8453283880929685`*^9, 3.8453285096118307`*^9}, { 3.8453285913546557`*^9, 3.845328659362878*^9}, {3.845328743855522*^9, 3.8453287487082205`*^9}, {3.8453291314206295`*^9, 3.8453292262995577`*^9}, {3.8454070816358213`*^9, 3.8454071163316426`*^9}, 3.84540907404557*^9, {3.8454999753970504`*^9, 3.845500038917721*^9}, 3.845500571758481*^9, {3.8455006071423078`*^9, 3.8455006255693064`*^9}, { 3.8455007354694633`*^9, 3.845500759647338*^9}, {3.856211250473535*^9, 3.8562112556718903`*^9}, {3.856212751039124*^9, 3.856212766387054*^9}, { 3.856212912930011*^9, 3.85621292105375*^9}}, TextAlignment->Left, LineSpacing->{1, 3},ExpressionUUID->"8271e16b-edbc-4fda-863c-2d6c0380800c"], Cell[TextData[{ StyleBox["The input consists of two numbers for each of one to several \ cohorts: the initial and the final number of individuals. The initial number \ of individuals may vary among cohorts. The method used for calculating \ confidence intervals for proportions is explained in Fleiss et al. (2003: \ 25). The Bonferroni method is used to adjust confidence intervals for \ simultaneous inspection. It is explained, for example, in Sokal & Rohlf \ (2012: 239).\n\nFleiss, J.L., Levin, B., & Paik, M.C. (2003). ", FontFamily->"Times New Roman", FontSize->12], StyleBox["Statistical Methods for Rates and Proportions", FontFamily->"Times New Roman", FontSize->12, FontSlant->"Italic"], StyleBox[" (3rd edition). Hoboken, New Jersey: John Wiley & Sons, 760 pages.\ \n\nSokal, R.R., & Rohlf, F.J. (2012). Biometry (4th edition). New York, USA: \ W.H. Freeman and Company. 937 pages.\n", FontFamily->"Times New Roman", FontSize->12] }], "Text", CellChangeTimes->{{3.5504332603062*^9, 3.5504332633792*^9}, { 3.5504333382841997`*^9, 3.5504333866102*^9}, {3.5504335609122*^9, 3.5504335779372*^9}, {3.5504336152172003`*^9, 3.5504336473292*^9}, { 3.5504337156012*^9, 3.5504337170112*^9}, 3.5504337833962*^9, { 3.5504338461602*^9, 3.5504338938742*^9}, {3.5504340065172*^9, 3.5504340099681997`*^9}, {3.5504346934402*^9, 3.5504346944002*^9}, { 3.5572501510372*^9, 3.5572502442392*^9}, 3.5572506628422003`*^9, { 3.5723568631088*^9, 3.5723568915327997`*^9}, {3.5734709481212683`*^9, 3.573470987886074*^9}, {3.576266023481459*^9, 3.5762660264808426`*^9}, { 3.5849083786578503`*^9, 3.5849083874419565`*^9}, 3.619930660787699*^9, { 3.624376043545732*^9, 3.6243760978739223`*^9}, {3.6247901309763637`*^9, 3.624790132216365*^9}, 3.6276667393883543`*^9, 3.6283947451893377`*^9, 3.63283258409818*^9, 3.632832972322857*^9, 3.6333551239066377`*^9, 3.6348164204455366`*^9, {3.7666060829232264`*^9, 3.7666060884633045`*^9}, { 3.7666061402734246`*^9, 3.7666061436131625`*^9}, 3.797265350751593*^9, { 3.8453861320318937`*^9, 3.8453861632565055`*^9}, {3.8453861960350494`*^9, 3.845386294409191*^9}, {3.8453863304049053`*^9, 3.845386334330849*^9}, { 3.8453863751597576`*^9, 3.8453864562989798`*^9}, {3.845386533016405*^9, 3.845386709034373*^9}, {3.8453975617379704`*^9, 3.845397566450004*^9}, { 3.845406742300975*^9, 3.8454067506504354`*^9}, {3.8454071754399033`*^9, 3.845407235108447*^9}, {3.8454093441784506`*^9, 3.8454093467461147`*^9}, { 3.84550055070208*^9, 3.845500579689122*^9}, {3.8455006370463877`*^9, 3.8455006753893995`*^9}, {3.8455007098383417`*^9, 3.8455007237923517`*^9}, {3.845500780479319*^9, 3.8455008479275656`*^9}, 3.8455008823685246`*^9, {3.8562112982431555`*^9, 3.856211313935365*^9}, { 3.856212552850913*^9, 3.8562125562311087`*^9}, {3.8562126641862636`*^9, 3.8562126667532473`*^9}, 3.8562127960615263`*^9}, LineSpacing->{1, 3}, FontSize->16,ExpressionUUID->"0008737f-036f-4678-9c9b-a31065e6c628"], Cell[BoxData[ RowBox[{ RowBox[{ "confidenceIntervalsForProportionsFunction", "=", "\[IndentingNewLine]", RowBox[{"Function", "[", RowBox[{"groupedZeroOneData", ",", "\[IndentingNewLine]", RowBox[{ RowBox[{"nCohorts", "=", RowBox[{"Length", "[", "groupedZeroOneData", "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"If", "[", RowBox[{ RowBox[{"nCohorts", "==", "1"}], ",", RowBox[{ "Print", "[", "\"\<95% confidence interval for 1 cohort:\>\"", "]"}]}], "]"}], ";", "\[IndentingNewLine]", RowBox[{"If", "[", RowBox[{ RowBox[{"nCohorts", ">", "1"}], ",", RowBox[{"Print", "[", RowBox[{ "\"\\"", ",", "nCohorts", ",", "\"\< cohorts:\>\""}], "]"}]}], "]"}], ";", "\[IndentingNewLine]", RowBox[{ "Print", "[", "\"\\"", "]"}], ";", "\[IndentingNewLine]", RowBox[{"Table", "[", "\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"x0", "=", RowBox[{"groupedZeroOneData", "[", RowBox[{"[", RowBox[{"i", ",", "1"}], "]"}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"nn", "=", RowBox[{"groupedZeroOneData", "[", RowBox[{"[", RowBox[{"i", ",", "2"}], "]"}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"alphaAdj", "=", RowBox[{"0.05", "/", "nCohorts"}]}], ";", "\[IndentingNewLine]", RowBox[{"If", "[", RowBox[{ RowBox[{"x0", "\[Equal]", "0"}], ",", RowBox[{"PL", "=", "0"}], ",", "\[IndentingNewLine]", RowBox[{ RowBox[{"FLvalue", "=", RowBox[{"InverseCDF", "[", RowBox[{ RowBox[{"FRatioDistribution", "[", RowBox[{ RowBox[{"2", "*", RowBox[{"(", RowBox[{"nn", "-", "x0", "+", "1"}], ")"}]}], ",", RowBox[{"2", "*", "x0"}]}], "]"}], ",", RowBox[{"1", "-", "alphaAdj"}]}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"PL", "=", RowBox[{"x0", "/", RowBox[{"(", RowBox[{"x0", "+", RowBox[{ RowBox[{"(", RowBox[{"nn", "-", "x0", "+", "1"}], ")"}], "*", "FLvalue"}]}], ")"}]}]}]}]}], "]"}], ";", "\[IndentingNewLine]", RowBox[{"If", "[", RowBox[{ RowBox[{"x0", "\[Equal]", "nn"}], ",", RowBox[{"PU", "=", "1"}], ",", "\[IndentingNewLine]", RowBox[{ RowBox[{"FUvalue", "=", RowBox[{"InverseCDF", "[", RowBox[{ RowBox[{"FRatioDistribution", "[", RowBox[{ RowBox[{"2", "*", RowBox[{"(", RowBox[{"x0", "+", "1"}], ")"}]}], ",", RowBox[{"2", "*", RowBox[{"(", RowBox[{"nn", "-", "x0"}], ")"}]}]}], "]"}], ",", RowBox[{"1", "-", "alphaAdj"}]}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"PU", "=", RowBox[{ RowBox[{"(", RowBox[{"x0", "+", "1"}], ")"}], "*", RowBox[{"FUvalue", "/", RowBox[{"(", RowBox[{ RowBox[{"(", RowBox[{"nn", "-", "x0"}], ")"}], "+", RowBox[{ RowBox[{"(", RowBox[{"x0", "+", "1"}], ")"}], "*", "FUvalue"}]}], ")"}]}]}]}]}]}], "]"}], ";", "\[IndentingNewLine]", RowBox[{"{", RowBox[{"PL", ",", RowBox[{"N", "[", RowBox[{"x0", "/", "nn"}], "]"}], ",", "PU", ",", "nn"}], "}"}]}], ",", RowBox[{"{", RowBox[{"i", ",", "1", ",", "nCohorts"}], "}"}]}], "]"}]}]}], "]"}]}], ";"}]], "Input", CellChangeTimes->{{3.7567382245881095`*^9, 3.7567382246002536`*^9}, 3.756738262022267*^9, {3.7567384541679153`*^9, 3.7567384622419767`*^9}, { 3.8249180678194*^9, 3.824918072874754*^9}, {3.8453287888450117`*^9, 3.845328874812406*^9}, {3.845328975373369*^9, 3.8453289754141407`*^9}, { 3.845386158336094*^9, 3.8453861600332813`*^9}, {3.845500188212738*^9, 3.8455002400637665`*^9}, {3.8455003133010416`*^9, 3.8455003149840746`*^9}, {3.8455010241774273`*^9, 3.8455010862647867`*^9}, {3.8455012219568405`*^9, 3.8455012227767677`*^9}, {3.8455016360062943`*^9, 3.8455016484332643`*^9}, 3.8546516788114862`*^9, {3.8562113514632545`*^9, 3.8562114104900837`*^9}, { 3.856212485832651*^9, 3.8562124925263824`*^9}, {3.8562128143515277`*^9, 3.856212815774148*^9}}, CellLabel->"In[2]:=",ExpressionUUID->"441d689b-7e5e-4160-8447-b69179304833"], Cell[TextData[StyleBox["\nFollowing, in the first example, there are 4 \ cohorts. In the second example, there is only 1 cohort.\n", FontFamily->"Times New Roman", FontSize->12]], "Text", CellChangeTimes->{{3.5504332603062*^9, 3.5504332633792*^9}, { 3.5504333382841997`*^9, 3.5504333866102*^9}, {3.5504335609122*^9, 3.5504335779372*^9}, {3.5504336152172003`*^9, 3.5504336473292*^9}, { 3.5504337156012*^9, 3.5504337170112*^9}, 3.5504337833962*^9, { 3.5504338461602*^9, 3.5504338938742*^9}, {3.5504340065172*^9, 3.5504340099681997`*^9}, {3.5504346934402*^9, 3.5504346944002*^9}, { 3.5572501510372*^9, 3.5572502442392*^9}, 3.5572506628422003`*^9, { 3.5723568631088*^9, 3.5723568915327997`*^9}, {3.5734709481212683`*^9, 3.573470987886074*^9}, {3.576266023481459*^9, 3.5762660264808426`*^9}, { 3.5849083786578503`*^9, 3.5849083874419565`*^9}, 3.619930660787699*^9, { 3.624376043545732*^9, 3.6243760978739223`*^9}, {3.6247901309763637`*^9, 3.624790132216365*^9}, 3.6276667393883543`*^9, 3.6283947451893377`*^9, 3.63283258409818*^9, 3.632832972322857*^9, 3.6333551239066377`*^9, 3.6348164204455366`*^9, {3.7666060829232264`*^9, 3.7666060884633045`*^9}, { 3.7666061402734246`*^9, 3.7666061436131625`*^9}, 3.797265350751593*^9, { 3.8453861320318937`*^9, 3.8453861632565055`*^9}, {3.8453861960350494`*^9, 3.845386294409191*^9}, {3.8453863304049053`*^9, 3.845386334330849*^9}, { 3.8453863751597576`*^9, 3.8453864562989798`*^9}, {3.845386533016405*^9, 3.845386709034373*^9}, {3.8453975617379704`*^9, 3.845397566450004*^9}, { 3.845406742300975*^9, 3.8454067506504354`*^9}, {3.8454068862960663`*^9, 3.8454069166064*^9}, {3.845407127262068*^9, 3.8454071524757657`*^9}, { 3.8454072470136642`*^9, 3.8454072841257696`*^9}, {3.845409553328089*^9, 3.8454095539003897`*^9}, {3.8455000804867673`*^9, 3.8455000912278566`*^9}, {3.845500141358058*^9, 3.8455001445499754`*^9}, { 3.845501398983124*^9, 3.8455014360147943`*^9}, 3.856212833986143*^9}, LineSpacing->{1, 3}, FontSize->16,ExpressionUUID->"dced7fde-cd14-47d9-86af-7f5c53f55a23"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"confidenceIntervalsForProportionsFunction", "[", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"7", ",", "60"}], "}"}], ",", RowBox[{"{", RowBox[{"11", ",", "60"}], "}"}], ",", RowBox[{"{", RowBox[{"4", ",", "61"}], "}"}], ",", RowBox[{"{", RowBox[{"6", ",", "61"}], "}"}]}], "}"}], "]"}]], "Input", CellChangeTimes->{{3.756738336277936*^9, 3.756738342882082*^9}, { 3.845328887659314*^9, 3.845328909499669*^9}, {3.845406906125908*^9, 3.845406938074546*^9}, {3.85621285957812*^9, 3.856212863986117*^9}}, CellLabel->"In[3]:=",ExpressionUUID->"daad61ff-5548-4ea5-9b27-b52430a843ce"], Cell[CellGroupData[{ Cell[BoxData[ InterpretationBox[ RowBox[{"\<\"Simultaneous 95% confidence intervals for \"\>", "\[InvisibleSpace]", "4", "\[InvisibleSpace]", "\<\" cohorts:\"\>"}], SequenceForm["Simultaneous 95% confidence intervals for ", 4, " cohorts:"], Editable->False]], "Print", CellChangeTimes->{3.7567383436581573`*^9, 3.756738464808059*^9, 3.8249180884244146`*^9, 3.8454115006825085`*^9, 3.845499902971429*^9, 3.8455002952139454`*^9, 3.845500334299431*^9, 3.845501099259598*^9, 3.845501657260359*^9, 3.8455018806320972`*^9, 3.8562128793420677`*^9, 3.8562134593000307`*^9}, CellLabel-> "During evaluation of \ In[3]:=",ExpressionUUID->"8d2c13b7-3038-4b22-81e6-8bdb3505dcb7"], Cell[BoxData["\<\"Lower limit, Proportion, Upper limit, Initial number\"\>"], \ "Print", CellChangeTimes->{3.7567383436581573`*^9, 3.756738464808059*^9, 3.8249180884244146`*^9, 3.8454115006825085`*^9, 3.845499902971429*^9, 3.8455002952139454`*^9, 3.845500334299431*^9, 3.845501099259598*^9, 3.845501657260359*^9, 3.8455018806320972`*^9, 3.8562128793420677`*^9, 3.8562134593040295`*^9}, CellLabel-> "During evaluation of \ In[3]:=",ExpressionUUID->"c913289a-3102-4171-97a4-66c3cb345729"] }, Open ]], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ "0.04187967223536748`", ",", "0.11666666666666667`", ",", "0.24207735789234694`", ",", "60"}], "}"}], ",", RowBox[{"{", RowBox[{ "0.0859581823034846`", ",", "0.18333333333333332`", ",", "0.321928451307146`", ",", "60"}], "}"}], ",", RowBox[{"{", RowBox[{ "0.014686326407957863`", ",", "0.06557377049180328`", ",", "0.17413993569971684`", ",", "61"}], "}"}], ",", RowBox[{"{", RowBox[{ "0.03158040387680183`", ",", "0.09836065573770492`", ",", "0.21762583224969068`", ",", "61"}], "}"}]}], "}"}]], "Output", CellChangeTimes->{3.7567383440483823`*^9, 3.756738464817938*^9, 3.8249180884344826`*^9, 3.845411500753613*^9, 3.8454999031015344`*^9, 3.845500295251882*^9, 3.8455003343178434`*^9, 3.8455010992756257`*^9, 3.8455016572788773`*^9, 3.8455018806610217`*^9, 3.856212879419984*^9, 3.856213459324029*^9}, CellLabel->"Out[3]=",ExpressionUUID->"7098a074-4344-4056-9751-592611b25800"] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"\[IndentingNewLine]", RowBox[{"confidenceIntervalsForProportionsFunction", "[", RowBox[{"{", RowBox[{"{", RowBox[{"7", ",", "60"}], "}"}], "}"}], "]"}]}]], "Input", CellChangeTimes->{{3.7567386757915907`*^9, 3.7567386763578453`*^9}, { 3.8453289173143454`*^9, 3.8453290079089975`*^9}, 3.8454069254413085`*^9, { 3.8562128898461223`*^9, 3.856212895099546*^9}}, CellLabel->"In[4]:=",ExpressionUUID->"1585bbd8-9a66-457e-8102-e096681b753d"], Cell[CellGroupData[{ Cell[BoxData["\<\"95% confidence interval for 1 cohort:\"\>"], "Print", CellChangeTimes->{3.8562128958797646`*^9, 3.856213459356147*^9}, CellLabel-> "During evaluation of \ In[4]:=",ExpressionUUID->"8b4a5d30-4670-41da-b2fe-0d0d9bb9d580"], Cell[BoxData["\<\"Lower limit, Proportion, Upper limit, Initial number\"\>"], \ "Print", CellChangeTimes->{3.8562128958797646`*^9, 3.856213459363649*^9}, CellLabel-> "During evaluation of \ In[4]:=",ExpressionUUID->"4c1e8c3e-f930-49c5-aedf-8ba022d06580"] }, Open ]], Cell[BoxData[ RowBox[{"{", RowBox[{"{", RowBox[{ "0.05605488163549999`", ",", "0.11666666666666667`", ",", "0.20799093719079725`", ",", "60"}], "}"}], "}"}]], "Output", CellChangeTimes->{ 3.7567386779178247`*^9, 3.8454115008038883`*^9, 3.8454999031480045`*^9, 3.8455002953090963`*^9, 3.8455003343746924`*^9, 3.845501104800581*^9, 3.845501235239741*^9, 3.845501667351284*^9, 3.8455018807193885`*^9, { 3.8562128872941*^9, 3.856212895896942*^9}, 3.8562134593796515`*^9}, CellLabel->"Out[4]=",ExpressionUUID->"b15a32bd-26c4-4c64-a68c-73f21dd39ca4"] }, Open ]], Cell[BoxData[ RowBox[{"\[IndentingNewLine]", "\[IndentingNewLine]"}]], "Input", CellChangeTimes->{{3.7567387275681925`*^9, 3.756738727747855*^9}}, CellLabel->"In[5]:=",ExpressionUUID->"d64c8c96-ce21-4be8-9f32-2d6bb864e4e5"], Cell[TextData[StyleBox["The second function \ (\[OpenCurlyDoubleQuote]confidenceIntervalsForMediansFunction\ \[CloseCurlyDoubleQuote]) calculates simultaneous 95% confidence intervals \ for medians of several statistical groups:", FontSize->18, FontColor->RGBColor[0.5019607843137255, 0.25098039215686274`, 0.]]], "Text", CellChangeTimes->{{3.5504332603062*^9, 3.5504332633792*^9}, { 3.5504333382841997`*^9, 3.5504333866102*^9}, {3.5504335609122*^9, 3.5504335779372*^9}, {3.5504340356392*^9, 3.5504340366462*^9}, { 3.5504344248332*^9, 3.5504344730081997`*^9}, 3.5504352845532*^9, 3.5511887173428*^9, {3.5572466927492*^9, 3.5572467342402*^9}, { 3.5572506522362003`*^9, 3.5572506579862003`*^9}, {3.5723570003498*^9, 3.5723570146538*^9}, {3.5762657320113115`*^9, 3.5762658232479362`*^9}, 3.57632069334723*^9, {3.5785171414270935`*^9, 3.578517174413866*^9}, { 3.578518165486311*^9, 3.578518165822355*^9}, {3.619930609987628*^9, 3.619930640707671*^9}, {3.6242846691753197`*^9, 3.624284686862855*^9}, { 3.6244646351460104`*^9, 3.6244646401147213`*^9}, 3.6276667393883543`*^9, 3.6283947451893377`*^9, 3.63283258409818*^9, 3.632832972322857*^9, 3.6333551239066377`*^9, 3.6348164204455366`*^9, {3.6353103984629855`*^9, 3.6353104030392485`*^9}, {3.637134806970595*^9, 3.6371348105906*^9}, 3.63713484627065*^9, {3.637836243658865*^9, 3.637836248634158*^9}, { 3.6401700506905007`*^9, 3.6401700707866106`*^9}, {3.64018598798024*^9, 3.6401859880852456`*^9}, {3.647102207891888*^9, 3.6471022596518073`*^9}, { 3.6471031975487185`*^9, 3.6471032213310595`*^9}, {3.6471046078591795`*^9, 3.647104609299262*^9}, {3.647115793997024*^9, 3.6471158764446754`*^9}, { 3.6471160289242773`*^9, 3.6471160543797154`*^9}, {3.6514849485245914`*^9, 3.651484980828414*^9}, {3.661977617100213*^9, 3.6619776627968783`*^9}, { 3.662410978965165*^9, 3.6624109963245907`*^9}, {3.7463037700163264`*^9, 3.746303772687351*^9}, {3.74765852582615*^9, 3.7476585333261337`*^9}, { 3.747658567529183*^9, 3.7476585681385574`*^9}, {3.74765862279745*^9, 3.747658669342142*^9}, {3.766606665412409*^9, 3.766606737762308*^9}, { 3.7666067994921026`*^9, 3.766606816892201*^9}, {3.766606886222011*^9, 3.766606900411637*^9}, {3.766607182781154*^9, 3.766607193601019*^9}, { 3.7972654280324497`*^9, 3.7972654386704035`*^9}, {3.7972655308934584`*^9, 3.797265534127198*^9}, {3.8453283880929685`*^9, 3.8453285096118307`*^9}, { 3.8453285913546557`*^9, 3.845328659362878*^9}, {3.845328743855522*^9, 3.8453287487082205`*^9}, {3.8453291314206295`*^9, 3.8453292262995577`*^9}, {3.8454070816358213`*^9, 3.8454071163316426`*^9}, {3.8454074845962663`*^9, 3.845407541962059*^9}, { 3.8454093761992707`*^9, 3.845409378512434*^9}, {3.856212933891436*^9, 3.856212956713872*^9}}, TextAlignment->Left, LineSpacing->{1, 3},ExpressionUUID->"020f1f17-b9d0-4569-9c6f-38162d85fbad"], Cell[TextData[StyleBox["The input consists of data for one to several \ groups, for each of which the median is calculated. The method to calculate \ confidence intervals for medians is described in Higgins (2004: 13-14). The \ groups can have different numbers of data points. The Bonferroni method is \ used to adjust confidence intervals for simultaneous inspection. It is \ explained, for example, in Sokal & Rohlf (2012: 239). It is explained, for \ example, in Sokal & Rohlf (2012: 239).\n\nHiggins, J.J. (2004). Introduction \ to Modern Nonparametric Statistics. Pacific Grove, California, USA: \ Brooks/Cole - Thomson Learning. 366 pages.\n\nSokal, R.R., & Rohlf, F.J. \ (2012). Biometry (4th edition). New York, USA: W.H. Freeman and Company. 937 \ pages.\n", FontFamily->"Times New Roman", FontSize->12]], "Text", CellChangeTimes->{{3.5504332603062*^9, 3.5504332633792*^9}, { 3.5504333382841997`*^9, 3.5504333866102*^9}, {3.5504335609122*^9, 3.5504335779372*^9}, {3.5504336152172003`*^9, 3.5504336473292*^9}, { 3.5504337156012*^9, 3.5504337170112*^9}, 3.5504337833962*^9, { 3.5504338461602*^9, 3.5504338938742*^9}, {3.5504340065172*^9, 3.5504340099681997`*^9}, {3.5504346934402*^9, 3.5504346944002*^9}, { 3.5572501510372*^9, 3.5572502442392*^9}, 3.5572506628422003`*^9, { 3.5723568631088*^9, 3.5723568915327997`*^9}, {3.5734709481212683`*^9, 3.573470987886074*^9}, {3.576266023481459*^9, 3.5762660264808426`*^9}, { 3.5849083786578503`*^9, 3.5849083874419565`*^9}, 3.619930660787699*^9, { 3.624376043545732*^9, 3.6243760978739223`*^9}, {3.6247901309763637`*^9, 3.624790132216365*^9}, 3.6276667393883543`*^9, 3.6283947451893377`*^9, 3.63283258409818*^9, 3.632832972322857*^9, 3.6333551239066377`*^9, 3.6348164204455366`*^9, {3.7666060829232264`*^9, 3.7666060884633045`*^9}, { 3.7666061402734246`*^9, 3.7666061436131625`*^9}, 3.797265350751593*^9, { 3.8453861320318937`*^9, 3.8453861632565055`*^9}, {3.8453861960350494`*^9, 3.845386294409191*^9}, {3.8453863304049053`*^9, 3.845386334330849*^9}, { 3.8453863751597576`*^9, 3.8453864562989798`*^9}, {3.845386533016405*^9, 3.845386709034373*^9}, {3.8453975617379704`*^9, 3.845397566450004*^9}, { 3.845406742300975*^9, 3.8454067506504354`*^9}, {3.8454068862960663`*^9, 3.8454069166064*^9}, {3.845407127262068*^9, 3.8454071524757657`*^9}, { 3.8454072470136642`*^9, 3.8454072841257696`*^9}, {3.8454077594894*^9, 3.845407764186693*^9}, 3.845408912145059*^9, {3.8454090270148478`*^9, 3.8454091832356205`*^9}, {3.8454092784052095`*^9, 3.845409366175048*^9}, { 3.8454094704330196`*^9, 3.845409480679222*^9}, {3.8454101670025845`*^9, 3.845410251938655*^9}, 3.845410286622778*^9, {3.8455004099870963`*^9, 3.8455004446945066`*^9}, 3.845500586553108*^9, {3.8455009012058554`*^9, 3.8455009206296*^9}, {3.8562129842138863`*^9, 3.8562129986059837`*^9}}, LineSpacing->{1, 3}, FontSize->16,ExpressionUUID->"b851013f-d67a-4ec9-8660-0373bfc35e8e"], Cell[BoxData[ RowBox[{ RowBox[{ RowBox[{"confidenceIntervalsForMediansFunction", "=", RowBox[{"Function", "[", RowBox[{"groupedData", ",", RowBox[{ RowBox[{"nGroups", "=", RowBox[{"Length", "[", "groupedData", "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"alphaAdj", "=", RowBox[{"0.05", "/", "nGroups"}]}], ";", "\[IndentingNewLine]", RowBox[{"zz", "=", RowBox[{"InverseCDF", "[", RowBox[{ RowBox[{"NormalDistribution", "[", "]"}], ",", RowBox[{"1", "-", "alphaAdj"}]}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"positionsWithNormalDistr", "=", RowBox[{"Table", "[", RowBox[{ RowBox[{ RowBox[{"nn", "=", RowBox[{"Length", "[", RowBox[{"groupedData", "[", RowBox[{"[", "i", "]"}], "]"}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"lowerPos0", "=", RowBox[{"Round", "[", RowBox[{ RowBox[{"(", RowBox[{"nn", "-", RowBox[{ RowBox[{"Sqrt", "[", "nn", "]"}], "*", "zz"}]}], ")"}], "/", "2"}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"upperPos0", "=", RowBox[{"Round", "[", RowBox[{ RowBox[{"(", RowBox[{"2", "+", "nn", "+", RowBox[{ RowBox[{"Sqrt", "[", "nn", "]"}], "*", "zz"}]}], ")"}], "/", "2"}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"If", "[", RowBox[{ RowBox[{"lowerPos0", "<", "1"}], ",", RowBox[{"lowerPos", "=", "1"}], ",", RowBox[{"lowerPos", "=", "lowerPos0"}]}], "]"}], ";", "\[IndentingNewLine]", RowBox[{"If", "[", RowBox[{ RowBox[{"upperPos0", ">", "nn"}], ",", RowBox[{"upperPos", "=", "nn"}], ",", RowBox[{"upperPos", "=", "upperPos0"}]}], "]"}], ";", "\[IndentingNewLine]", RowBox[{"{", RowBox[{"lowerPos", ",", "upperPos", ",", "nn"}], "}"}]}], ",", RowBox[{"{", RowBox[{"i", ",", "1", ",", "nGroups"}], "}"}]}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"solution", "=", RowBox[{"Table", "[", RowBox[{ RowBox[{ RowBox[{"possiblePositions", "=", RowBox[{"{", RowBox[{ RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "3"}], "]"}], "]"}], ",", RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", RowBox[{"1", ";;", "2"}]}], "]"}], "]"}], ",", RowBox[{"If", "[", RowBox[{ RowBox[{ RowBox[{ RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "1"}], "]"}], "]"}], "-", "1"}], "\[GreaterEqual]", "1"}], ",", RowBox[{"{", RowBox[{ RowBox[{ RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "1"}], "]"}], "]"}], "-", "1"}], ",", RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "2"}], "]"}], "]"}]}], "}"}], ",", RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", "i", "]"}], "]"}]}], "]"}], ",", RowBox[{"{", RowBox[{ RowBox[{ RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "1"}], "]"}], "]"}], "+", "1"}], ",", RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "2"}], "]"}], "]"}]}], "}"}], ",", RowBox[{"{", RowBox[{ RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "1"}], "]"}], "]"}], ",", RowBox[{ RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "2"}], "]"}], "]"}], "-", "1"}]}], "}"}], ",", RowBox[{"If", "[", RowBox[{ RowBox[{ RowBox[{ RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "2"}], "]"}], "]"}], "+", "1"}], "\[LessEqual]", RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "3"}], "]"}], "]"}]}], ",", RowBox[{"{", RowBox[{ RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "1"}], "]"}], "]"}], ",", RowBox[{ RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "2"}], "]"}], "]"}], "+", "1"}]}], "}"}], ",", RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", "i", "]"}], "]"}]}], "]"}], ",", RowBox[{"{", RowBox[{ RowBox[{ RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "1"}], "]"}], "]"}], "+", "1"}], ",", RowBox[{ RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "2"}], "]"}], "]"}], "-", "1"}]}], "}"}], ",", RowBox[{"If", "[", RowBox[{ RowBox[{ RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "1"}], "]"}], "]"}], "-", "1"}], "\[GreaterEqual]", "1"}], ")"}], "&&", RowBox[{"(", RowBox[{ RowBox[{ RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "2"}], "]"}], "]"}], "+", "1"}], "\[LessEqual]", RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "3"}], "]"}], "]"}]}], ")"}]}], ",", RowBox[{"{", RowBox[{ RowBox[{ RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "1"}], "]"}], "]"}], "-", "1"}], ",", RowBox[{ RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", RowBox[{"i", ",", "2"}], "]"}], "]"}], "+", "1"}]}], "}"}], ",", RowBox[{"positionsWithNormalDistr", "[", RowBox[{"[", "i", "]"}], "]"}]}], "]"}]}], "}"}]}], ";", "\[IndentingNewLine]", RowBox[{"nn", "=", RowBox[{"possiblePositions", "[", RowBox[{"[", "1", "]"}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"alphasCalc", "=", RowBox[{"Table", "[", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"k1", "=", RowBox[{"possiblePositions", "[", RowBox[{"[", RowBox[{"j", ",", "1"}], "]"}], "]"}]}], ",", RowBox[{"k2", "=", RowBox[{"possiblePositions", "[", RowBox[{"[", RowBox[{"j", ",", "2"}], "]"}], "]"}]}], ",", RowBox[{"N", "[", RowBox[{"1", "-", RowBox[{"Total", "[", RowBox[{ RowBox[{"Table", "[", RowBox[{ RowBox[{ RowBox[{"nn", "!"}], "/", RowBox[{"(", RowBox[{ RowBox[{"l", "!"}], "*", RowBox[{ RowBox[{"(", RowBox[{"nn", "-", "l"}], ")"}], "!"}]}], ")"}]}], ",", RowBox[{"{", RowBox[{"l", ",", "k1", ",", "k2"}], "}"}]}], "]"}], "/", RowBox[{"2", "^", "nn"}]}], "]"}]}], "]"}]}], "}"}], ",", RowBox[{"{", RowBox[{"j", ",", "2", ",", RowBox[{"Length", "[", "possiblePositions", "]"}]}], "}"}]}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{ RowBox[{"Sort", "[", RowBox[{ RowBox[{"Transpose", "[", RowBox[{"{", RowBox[{ RowBox[{ RowBox[{"Transpose", "[", "alphasCalc", "]"}], "[", RowBox[{"[", "1", "]"}], "]"}], ",", RowBox[{ RowBox[{"Transpose", "[", "alphasCalc", "]"}], "[", RowBox[{"[", "2", "]"}], "]"}], ",", RowBox[{"(", RowBox[{"alphaAdj", "-", RowBox[{ RowBox[{"Transpose", "[", "alphasCalc", "]"}], "[", RowBox[{"[", "3", "]"}], "]"}]}], ")"}]}], "}"}], "]"}], ",", RowBox[{ RowBox[{ RowBox[{"Abs", "[", RowBox[{"#1", "[", RowBox[{"[", RowBox[{"-", "1"}], "]"}], "]"}], "]"}], "<", RowBox[{"Abs", "[", RowBox[{"#2", "[", RowBox[{"[", RowBox[{"-", "1"}], "]"}], "]"}], "]"}]}], "&"}]}], "]"}], "[", RowBox[{"[", "1", "]"}], "]"}]}], ",", RowBox[{"{", RowBox[{"i", ",", "1", ",", "nGroups"}], "}"}]}], "]"}]}], ";", "\[IndentingNewLine]", RowBox[{"If", "[", RowBox[{ RowBox[{"nGroups", "==", "1"}], ",", RowBox[{ "Print", "[", "\"\<95% confidence interval for 1 group:\>\"", "]"}]}], "]"}], ";", "\[IndentingNewLine]", RowBox[{"If", "[", RowBox[{ RowBox[{"nGroups", ">", "1"}], ",", RowBox[{"Print", "[", RowBox[{ "\"\\"", ",", "nGroups", ",", "\"\< groups:\>\""}], "]"}]}], "]"}], ";", "\[IndentingNewLine]", RowBox[{"Print", "[", RowBox[{ "\"\\"", ",", RowBox[{ RowBox[{"Subscript", "[", RowBox[{"\[Alpha]", ",", "\"\\""}], "]"}], "-", RowBox[{"Subscript", "[", RowBox[{"\[Alpha]", ",", "\"\\""}], "]"}]}]}], "]"}], ";", "\[IndentingNewLine]", RowBox[{"Table", "[", RowBox[{ RowBox[{"{", RowBox[{ RowBox[{"groupedData", "[", RowBox[{"[", RowBox[{"i", ",", RowBox[{"solution", "[", RowBox[{"[", RowBox[{"i", ",", "1"}], "]"}], "]"}]}], "]"}], "]"}], ",", RowBox[{"N", "[", RowBox[{"Median", "[", RowBox[{"groupedData", "[", RowBox[{"[", "i", "]"}], "]"}], "]"}], "]"}], ",", RowBox[{"groupedData", "[", RowBox[{"[", RowBox[{"i", ",", RowBox[{"solution", "[", RowBox[{"[", RowBox[{"i", ",", "2"}], "]"}], "]"}]}], "]"}], "]"}], ",", RowBox[{"Length", "[", RowBox[{"groupedData", "[", RowBox[{"[", "i", "]"}], "]"}], "]"}], ",", RowBox[{"solution", "[", RowBox[{"[", RowBox[{"i", ",", "3"}], "]"}], "]"}]}], "}"}], ",", RowBox[{"{", RowBox[{"i", ",", "1", ",", "nGroups"}], "}"}]}], "]"}]}]}], "]"}]}], ";"}], "\n"}]], "Input", CellChangeTimes->{{3.756739754127652*^9, 3.756739788227398*^9}, { 3.8453293300556684`*^9, 3.845329336931655*^9}, {3.8454075458175488`*^9, 3.8454075498293347`*^9}, {3.845501129154803*^9, 3.8455012024858017`*^9}, { 3.8455016803990383`*^9, 3.845501684738142*^9}, {3.856213017349969*^9, 3.8562130269859943`*^9}, {3.8562130707128663`*^9, 3.856213074587777*^9}, 3.856213177085865*^9, 3.8562132579261255`*^9}, CellLabel->"In[6]:=",ExpressionUUID->"12f21f5d-9bb4-46db-a3e3-f4e3ef1af320"], Cell[TextData[StyleBox["\nIn the first example, there are 4 different-sized \ groups. The output consists of the lower and upper confidence limits, the \ number of data points in the group, and the difference between the adjusted \ and calculated alpha (where alpha is the significance level). The upper and \ lower confidence limits correspond to actual data values in the sample. \ According to equation (2) of the manuscript, one has to find the two \ positions \[OpenCurlyDoubleQuote]k1\[CloseCurlyDoubleQuote] and \ \[OpenCurlyDoubleQuote]k2\[CloseCurlyDoubleQuote] in the ordered sample that \ lead to the desired alpha, and the corresponding data values are the \ resulting limits. Since the positions in the ordered sample are integer \ numbers, and the number of data in the group is limited, it is generally not \ possible to get the desired exact alpha. In the second example, there is only \ 1 group.\n", FontFamily->"Times New Roman", FontSize->12]], "Text", CellChangeTimes->{{3.5504332603062*^9, 3.5504332633792*^9}, { 3.5504333382841997`*^9, 3.5504333866102*^9}, {3.5504335609122*^9, 3.5504335779372*^9}, {3.5504336152172003`*^9, 3.5504336473292*^9}, { 3.5504337156012*^9, 3.5504337170112*^9}, 3.5504337833962*^9, { 3.5504338461602*^9, 3.5504338938742*^9}, {3.5504340065172*^9, 3.5504340099681997`*^9}, {3.5504346934402*^9, 3.5504346944002*^9}, { 3.5572501510372*^9, 3.5572502442392*^9}, 3.5572506628422003`*^9, { 3.5723568631088*^9, 3.5723568915327997`*^9}, {3.5734709481212683`*^9, 3.573470987886074*^9}, {3.576266023481459*^9, 3.5762660264808426`*^9}, { 3.5849083786578503`*^9, 3.5849083874419565`*^9}, 3.619930660787699*^9, { 3.624376043545732*^9, 3.6243760978739223`*^9}, {3.6247901309763637`*^9, 3.624790132216365*^9}, 3.6276667393883543`*^9, 3.6283947451893377`*^9, 3.63283258409818*^9, 3.632832972322857*^9, 3.6333551239066377`*^9, 3.6348164204455366`*^9, {3.7666060829232264`*^9, 3.7666060884633045`*^9}, { 3.7666061402734246`*^9, 3.7666061436131625`*^9}, 3.797265350751593*^9, { 3.8453861320318937`*^9, 3.8453861632565055`*^9}, {3.8453861960350494`*^9, 3.845386294409191*^9}, {3.8453863304049053`*^9, 3.845386334330849*^9}, { 3.8453863751597576`*^9, 3.8453864562989798`*^9}, {3.845386533016405*^9, 3.845386709034373*^9}, {3.8453975617379704`*^9, 3.845397566450004*^9}, { 3.845406742300975*^9, 3.8454067506504354`*^9}, {3.8454068862960663`*^9, 3.8454069166064*^9}, {3.845407127262068*^9, 3.8454071524757657`*^9}, { 3.8454072470136642`*^9, 3.8454072841257696`*^9}, {3.845409531311681*^9, 3.845409534999429*^9}, {3.84540956538445*^9, 3.8454097230966034`*^9}, 3.845410301079278*^9, {3.8454104333039227`*^9, 3.845410641399618*^9}, { 3.8454113328592763`*^9, 3.8454114546037087`*^9}, {3.84550094312257*^9, 3.845500946852503*^9}, {3.845501289465647*^9, 3.845501295880823*^9}, { 3.84550132593773*^9, 3.845501342791087*^9}, {3.8455014737748723`*^9, 3.845501546809185*^9}, {3.8455018063273907`*^9, 3.845501824943598*^9}, { 3.8562131302298856`*^9, 3.8562131371180906`*^9}, {3.856779773713485*^9, 3.856779776863544*^9}}, LineSpacing->{1, 3}, FontSize->16,ExpressionUUID->"792a56c1-c791-47bb-a135-c37980234aa9"], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"\[IndentingNewLine]", RowBox[{ RowBox[{ RowBox[{"data", "=", RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{ "0.5", ",", "1", ",", "2", ",", "2.5", ",", "3", ",", "3", ",", "3", ",", "3.5", ",", "5.5", ",", "6", ",", "7", ",", "7", ",", "7.5", ",", "7.5", ",", "8.5", ",", "9.5", ",", "10", ",", "10", ",", "10.5", ",", "12", ",", "12", ",", "18", ",", "18", ",", "19", ",", "24", ",", "25", ",", "30"}], "}"}], ",", RowBox[{"{", RowBox[{ "2.5", ",", "5", ",", "6.5", ",", "7.5", ",", "7.5", ",", "8.5", ",", "10.5", ",", "11", ",", "12", ",", "13.5", ",", "14", ",", "17", ",", "21", ",", "22", ",", "22", ",", "23.5", ",", "23.5"}], "}"}], ",", RowBox[{"{", RowBox[{ "1.5", ",", "3.5", ",", "4.5", ",", "7.5", ",", "8", ",", "9.5", ",", "12.5", ",", "16", ",", "16", ",", "18", ",", "43.5"}], "}"}], ",", RowBox[{"{", RowBox[{"5.5", ",", "9", ",", "18.5"}], "}"}]}], "}"}]}], ";"}], "\[IndentingNewLine]", "\n", RowBox[{ "confidenceIntervalsForMediansFunction", "[", "data", "]"}]}]}]], "Input", CellChangeTimes->{{3.756738857132081*^9, 3.7567388627080235`*^9}, { 3.8562131618598785`*^9, 3.856213165274541*^9}}, CellLabel->"In[7]:=",ExpressionUUID->"8ce965ee-15d1-4aaf-9649-badf021ce4ab"], Cell[CellGroupData[{ Cell[BoxData[ InterpretationBox[ RowBox[{"\<\"Simultaneous 95% confidence intervals for \"\>", "\[InvisibleSpace]", "4", "\[InvisibleSpace]", "\<\" groups:\"\>"}], SequenceForm["Simultaneous 95% confidence intervals for ", 4, " groups:"], Editable->False]], "Print", CellChangeTimes->{ 3.7567388634264903`*^9, {3.756739098697937*^9, 3.756739125768401*^9}, 3.7567392087077255`*^9, 3.756739767777281*^9, 3.845411500937021*^9, 3.845499903282997*^9, 3.8455002954237385`*^9, 3.8455003344819193`*^9, { 3.8455012618800936`*^9, 3.8455012662562065`*^9}, 3.845501689274446*^9, 3.8455018808224077`*^9, {3.856213183182264*^9, 3.856213203962285*^9}, 3.8562134622034273`*^9}, CellLabel-> "During evaluation of \ In[7]:=",ExpressionUUID->"ac176987-ae49-4946-bf57-eea2e29845eb"], Cell[BoxData[ InterpretationBox[ RowBox[{"\<\"Lower limit, Median, Upper limit, Initial number, \"\>", "\[InvisibleSpace]", RowBox[{ SubscriptBox["\[Alpha]", "\<\"adj.\"\>"], "-", SubscriptBox["\[Alpha]", "\<\"calc.\"\>"]}]}], SequenceForm[ "Lower limit, Median, Upper limit, Initial number, ", Subscript[$CellContext`\[Alpha], "adj."] - Subscript[$CellContext`\[Alpha], "calc."]], Editable->False]], "Print", CellChangeTimes->{ 3.7567388634264903`*^9, {3.756739098697937*^9, 3.756739125768401*^9}, 3.7567392087077255`*^9, 3.756739767777281*^9, 3.845411500937021*^9, 3.845499903282997*^9, 3.8455002954237385`*^9, 3.8455003344819193`*^9, { 3.8455012618800936`*^9, 3.8455012662562065`*^9}, 3.845501689274446*^9, 3.8455018808224077`*^9, {3.856213183182264*^9, 3.856213203962285*^9}, 3.856213462215426*^9}, CellLabel-> "During evaluation of \ In[7]:=",ExpressionUUID->"dce02c68-c9dc-4dbc-a40f-fe9640917da6"] }, Open ]], Cell[BoxData[ RowBox[{"{", RowBox[{ RowBox[{"{", RowBox[{"3.5`", ",", "7.5`", ",", "12", ",", "27", ",", RowBox[{"-", "0.000040951371192931435`"}]}], "}"}], ",", RowBox[{"{", RowBox[{"7.5`", ",", "12.`", ",", "21", ",", "17", ",", RowBox[{"-", "0.0002258300781249993`"}]}], "}"}], ",", RowBox[{"{", RowBox[{ "3.5`", ",", "9.5`", ",", "16", ",", "11", ",", "0.0007812500000000007`"}], "}"}], ",", RowBox[{"{", RowBox[{"5.5`", ",", "9.`", ",", "18.5`", ",", "3", ",", RowBox[{"-", "0.1125`"}]}], "}"}]}], "}"}]], "Output", CellChangeTimes->{ 3.756738863437272*^9, {3.7567390987563562`*^9, 3.756739125781994*^9}, 3.756739208721736*^9, 3.7567397677972145`*^9, 3.845411500957035*^9, 3.845499903310956*^9, 3.8455002954517*^9, 3.845500334502864*^9, { 3.845501261898046*^9, 3.8455012662728214`*^9}, 3.845501689290493*^9, 3.8455018808493347`*^9, {3.856213183237206*^9, 3.856213203983241*^9}, 3.856213462231428*^9}, CellLabel->"Out[8]=",ExpressionUUID->"35578830-66af-4eac-a950-e23576a2176f"] }, Open ]], Cell[CellGroupData[{ Cell[BoxData[ RowBox[{"\[IndentingNewLine]", RowBox[{"confidenceIntervalsForMediansFunction", "[", RowBox[{"{", RowBox[{"{", RowBox[{ "1.5", ",", "3.5", ",", "4.5", ",", "7.5", ",", "8", ",", "9.5", ",", "12.5", ",", "16", ",", "16", ",", "18", ",", "43.5"}], "}"}], "}"}], "]"}]}]], "Input", CellChangeTimes->{{3.7567389416712723`*^9, 3.7567389510479484`*^9}, { 3.756738986687824*^9, 3.756738989637557*^9}, {3.856213208257841*^9, 3.8562132108177648`*^9}}, CellLabel->"In[9]:=",ExpressionUUID->"7953fa3f-d3a1-4b0c-a167-8ce39adb92f4"], Cell[CellGroupData[{ Cell[BoxData["\<\"95% confidence interval for 1 group:\"\>"], "Print", CellChangeTimes->{{3.756738944968115*^9, 3.756738951607839*^9}, 3.756738990477639*^9, 3.7567391380676293`*^9, 3.7567392119696054`*^9, 3.756739821267434*^9, 3.8454115010019956`*^9, 3.84549990335227*^9, 3.8455002955017385`*^9, 3.845500334551734*^9, 3.8455012705023565`*^9, 3.8455016927236257`*^9, 3.845501880884945*^9, 3.856213214183552*^9, 3.856213462279426*^9}, CellLabel-> "During evaluation of \ In[9]:=",ExpressionUUID->"9f68eeae-cfc6-4d9b-9cb0-a9fd977e63f8"], Cell[BoxData[ InterpretationBox[ RowBox[{"\<\"Lower limit, Median, Upper limit, Initial number, \"\>", "\[InvisibleSpace]", RowBox[{ SubscriptBox["\[Alpha]", "\<\"adj.\"\>"], "-", SubscriptBox["\[Alpha]", "\<\"calc.\"\>"]}]}], SequenceForm[ "Lower limit, Median, Upper limit, Initial number, ", Subscript[$CellContext`\[Alpha], "adj."] - Subscript[$CellContext`\[Alpha], "calc."]], Editable->False]], "Print", CellChangeTimes->{{3.756738944968115*^9, 3.756738951607839*^9}, 3.756738990477639*^9, 3.7567391380676293`*^9, 3.7567392119696054`*^9, 3.756739821267434*^9, 3.8454115010019956`*^9, 3.84549990335227*^9, 3.8455002955017385`*^9, 3.845500334551734*^9, 3.8455012705023565`*^9, 3.8455016927236257`*^9, 3.845501880884945*^9, 3.856213214183552*^9, 3.856213462291426*^9}, CellLabel-> "During evaluation of \ In[9]:=",ExpressionUUID->"750e97f5-65ff-41dc-8f39-0ff77228335c"] }, Open ]], Cell[BoxData[ RowBox[{"{", RowBox[{"{", RowBox[{ "4.5`", ",", "9.5`", ",", "16", ",", "11", ",", "0.011425781250000003`"}], "}"}], "}"}]], "Output", CellChangeTimes->{{3.7567389449779806`*^9, 3.756738951617796*^9}, 3.7567389904811454`*^9, 3.756739138071637*^9, 3.75673921197761*^9, 3.7567398212714367`*^9, 3.8454115010099874`*^9, 3.845499903361517*^9, 3.8455002955137234`*^9, 3.845500334560709*^9, 3.8455012705176673`*^9, 3.845501692738719*^9, 3.8455018809078836`*^9, 3.856213214199587*^9, 3.856213462315427*^9}, CellLabel->"Out[9]=",ExpressionUUID->"adbf6d7e-bcd0-4b19-8cb1-497495c36df5"] }, Open ]] }, WindowSize->{840., 574.8}, WindowMargins->{{16.8, Automatic}, {Automatic, 0}}, Magnification:>1.25 Inherited, FrontEndVersion->"13.0 for Microsoft Windows (64-bit) (December 2, 2021)", StyleDefinitions->"Default.nb", ExpressionUUID->"2af8fdd7-8580-44fe-b292-7b48111377be" ] (* End of Notebook Content *) (* Internal cache information *) (*CellTagsOutline CellTagsIndex->{} *) (*CellTagsIndex CellTagsIndex->{} *) (*NotebookFileOutline Notebook[{ Cell[558, 20, 131840, 2166, 99, "Text",ExpressionUUID->"cedd8c58-a069-4888-8c97-885cce7c46fb"], Cell[CellGroupData[{ Cell[132423, 2190, 739, 13, 35, "Input",ExpressionUUID->"c80193b7-45ab-4793-ae3f-a206acdcab7a"], Cell[133165, 2205, 14352, 201, 42, "Output",ExpressionUUID->"f134eb7f-05a0-4ead-b912-09a18a098987"] }, Open ]], Cell[147532, 2409, 223, 3, 35, "Input",ExpressionUUID->"36d6f4e7-1c87-4b6e-8e3f-9302f85c6674"], Cell[147758, 2414, 2521, 43, 106, "Text",ExpressionUUID->"84e856ff-9974-4529-8a72-069222b6cded"], Cell[150282, 2459, 1790, 35, 146, "Text",ExpressionUUID->"06446736-44e9-4cd5-beb4-de7c4ca26425"], Cell[152075, 2496, 3201, 45, 150, "Text",ExpressionUUID->"8271e16b-edbc-4fda-863c-2d6c0380800c"], Cell[155279, 2543, 3015, 48, 232, "Text",ExpressionUUID->"0008737f-036f-4678-9c9b-a31065e6c628"], Cell[158297, 2593, 5035, 119, 440, "Input",ExpressionUUID->"441d689b-7e5e-4160-8447-b69179304833"], Cell[163335, 2714, 2100, 30, 80, "Text",ExpressionUUID->"dced7fde-cd14-47d9-86af-7f5c53f55a23"], Cell[CellGroupData[{ Cell[165460, 2748, 657, 15, 60, "Input",ExpressionUUID->"daad61ff-5548-4ea5-9b27-b52430a843ce"], Cell[CellGroupData[{ Cell[166142, 2767, 693, 13, 27, "Print",ExpressionUUID->"8d2c13b7-3038-4b22-81e6-8bdb3505dcb7"], Cell[166838, 2782, 502, 9, 27, "Print",ExpressionUUID->"c913289a-3102-4171-97a4-66c3cb345729"] }, Open ]], Cell[167355, 2794, 1031, 24, 88, "Output",ExpressionUUID->"7098a074-4344-4056-9751-592611b25800"] }, Open ]], Cell[CellGroupData[{ Cell[168423, 2823, 484, 9, 60, "Input",ExpressionUUID->"1585bbd8-9a66-457e-8102-e096681b753d"], Cell[CellGroupData[{ Cell[168932, 2836, 241, 4, 27, "Print",ExpressionUUID->"8b4a5d30-4670-41da-b2fe-0d0d9bb9d580"], Cell[169176, 2842, 258, 5, 27, "Print",ExpressionUUID->"4c1e8c3e-f930-49c5-aedf-8ba022d06580"] }, Open ]], Cell[169449, 2850, 575, 11, 40, "Output",ExpressionUUID->"b15a32bd-26c4-4c64-a68c-73f21dd39ca4"] }, Open ]], Cell[170039, 2864, 226, 3, 83, "Input",ExpressionUUID->"d64c8c96-ce21-4be8-9f32-2d6bb864e4e5"], Cell[170268, 2869, 2924, 42, 116, "Text",ExpressionUUID->"020f1f17-b9d0-4569-9c6f-38162d85fbad"], Cell[173195, 2913, 2983, 42, 276, "Text",ExpressionUUID->"b851013f-d67a-4ec9-8660-0373bfc35e8e"], Cell[176181, 2957, 13117, 308, 1225, "Input",ExpressionUUID->"12f21f5d-9bb4-46db-a3e3-f4e3ef1af320"], Cell[189301, 3267, 3230, 46, 232, "Text",ExpressionUUID->"792a56c1-c791-47bb-a135-c37980234aa9"], Cell[CellGroupData[{ Cell[192556, 3317, 1409, 30, 202, "Input",ExpressionUUID->"8ce965ee-15d1-4aaf-9649-badf021ce4ab"], Cell[CellGroupData[{ Cell[193990, 3351, 798, 15, 27, "Print",ExpressionUUID->"ac176987-ae49-4946-bf57-eea2e29845eb"], Cell[194791, 3368, 962, 21, 30, "Print",ExpressionUUID->"dce02c68-c9dc-4dbc-a40f-fe9640917da6"] }, Open ]], Cell[195768, 3392, 1072, 23, 65, "Output",ExpressionUUID->"35578830-66af-4eac-a950-e23576a2176f"] }, Open ]], Cell[CellGroupData[{ Cell[196877, 3420, 575, 12, 83, "Input",ExpressionUUID->"7953fa3f-d3a1-4b0c-a167-8ce39adb92f4"], Cell[CellGroupData[{ Cell[197477, 3436, 557, 9, 27, "Print",ExpressionUUID->"9f68eeae-cfc6-4d9b-9cb0-a9fd977e63f8"], Cell[198037, 3447, 930, 20, 30, "Print",ExpressionUUID->"750e97f5-65ff-41dc-8f39-0ff77228335c"] }, Open ]], Cell[198982, 3470, 626, 12, 40, "Output",ExpressionUUID->"adbf6d7e-bcd0-4b19-8cb1-497495c36df5"] }, Open ]] } ] *)